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Imaging of Neurodegenerative Disorders
 9781604068542, 9781604068559, 2014034741

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TPS 23 x 31 - 2 | 12.09.15 - 13:03

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Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

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Imaging of Neurodegenerative Disorders Best Evidence Recommendations 2nd Edition

Sangam G. Kanekar, MD Associate Professor of Radiology and Neurology Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania Seung-Ho Shin, MD Assistant Professor of Otology and Neurotology Department of Otolaryngology–Head and Neck Surgery Cha University Seongnam, Republic of Korea

1874 illustrations

Thieme New York • Stuttgart • Delhi • Rio de Janeiro

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Thieme Medical Publishers, Inc. 333 Seventh Avenue New York, New York 10001 Executive Editor: William Lamsback Managing Editor: J. Owen Zurhellen IV Director, Editorial Services: Mary Jo Casey International Production Director: Andreas Schabert Vice President, Editorial and E-Product Development: Vera Spillner International Marketing Director: Fiona Henderson International Sales Director: Louisa Turrell Director of Sales, North America: Mike Roseman Senior Vice President and Chief Operating Officer: Sarah Vanderbilt President: Brian D. Scanlan Library of Congress Cataloging-in-Publication Data Imaging of neurodegenerative disorders / [edited by] Sangam G. Kanekar. p. ; cm. Includes bibliographical references and index. ISBN 978-1-60406-854-2 (hardcover : alk. paper) – ISBN 978-160406-855-9 (ebook) I. Kanekar, Sangam G., editor. [DNLM: 1. Neurodegenerative Diseases–diagnosis. 2. Neuroimaging– methods. WL 358.5] RC376.5 616.8'307548–dc23 2014034741

Important note: Medicine is an ever-changing science undergoing continual development. Research and clinical experience are continually expanding our knowledge, in particular our knowledge of proper treatment and drug therapy. Insofar as this book mentions any dosage or application, readers may rest assured that the authors, editors, and publishers have made every effort to ensure that such references are in accordance with the state of knowledge at the time of production of the book. Nevertheless, this does not involve, imply, or express any guarantee or responsibility on the part of the publishers in respect to any dosage instructions and forms of applications stated in the book. Every user is requested to examine carefully the manufacturers’ leaflets accompanying each drug and to check, if necessary in consultation with a physician or specialist, whether the dosage schedules mentioned therein or the contraindications stated by the manufacturers differ from the statements made in the present book. Such examination is particularly important with drugs that are either rarely used or have been newly released on the market. Every dosage schedule or every form of application used is entirely at the user’s own risk and responsibility. The authors and publishers request every user to report to the publishers any discrepancies or inaccuracies noticed. If errors in this work are found after publication, errata will be posted at www.thieme.com on the product description page. Some of the product names, patents, and registered designs referred to in this book are in fact registered trademarks or proprietary names even though specific reference to this fact is not always made in the text. Therefore, the appearance of a name without designation as proprietary is not to be construed as a representation by the publisher that it is in the public domain.

Copyright © 2016 by Thieme Medical Publishers, Inc.

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This book, including all parts thereof, is legally protected by copyright. Any use, exploitation, or commercialization outside the narrow limits set by copyright legislation without the publisher’s consent is illegal and liable to prosecution. This applies in particular to photostat reproduction, copying, mimeographing or duplication of any kind, translating, preparation of microfilms, and electronic data processing and storage.

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

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I dedicate this book to “MahaSaraswati” and to my parents Gurudas and the late Meerabai Kanekar

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

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Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Contents Foreword 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi Foreword 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix Part I. Introduction Chapter 1: Overview of Neurodegenerative Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Sangam G. Kanekar and Maya L. Lichtenstein

Part II. Imaging Techniques Chapter 2: Structural Imaging of Dementia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Sangam G. Kanekar and Vijay Mittal

Chapter 3: Magnetic Resonance Spectroscopy in Neurodegenerative Disorders . . . . . . . . . . . . . . . . . . . . . . . . 24 Tushar Chandra, Suyash Mohan, Sanjeev Chawla, and Harish Poptani

Chapter 4: SPECT and PET Imaging of Neurotransmitters in Dementia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Mateen Moghbel, Andrew Newberg, Mijail Serruya, and Abass Alavi

Chapter 5: Diffusion Tensor Imaging in Neurodegenerative Disorders. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Dhiraj Baruah, Suyash Mohan, and Sumei Wang

Chapter 6: Functional Imaging of the Brain. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Leslie Hartman and Aaron S. Field

Chapter 7: Role of Noninvasive Angiogram and Perfusion in the Evaluation of Neurodegenerative Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Sangam G. Kanekar and Puneet Devgun

Part III. Normal Aging Chapter 8: Imaging of the Normal Aging Brain. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Ruth A. Wood, Ludovico Minati, and Dennis Chan

Chapter 9: Iron Accumulation and Iron Imaging in the Human Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Stefan Ropele and Christian Langkammer

Part IV. Alzheimer’s Disease Chapter 10: Mild Cognitive Impairment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Kei Yamada and Koji Sakai

vii Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Contents

Chapter 11: Overview of Alzheimer’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Leonardo Cruz de Souza and Marie Sarazin

Chapter 12: Genetics, Neuropathology, and Biomarkers in Alzheimer’s Disease . . . . . . . . . . . . . . . . . . . . . . 119 Maria Martinez-Lage Alvarez and Rashmi Tondon

Chapter 13: Imaging of Alzheimer’s Disease: Part 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 Donald G. McLaren, Guofan Xu, and Vivek Prabhakaran

Chapter 14: Imaging of Alzheimer’s Disease: Part 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Christian La, Wolfgang Gaggl, and Vivek Prabhakaran

Chapter 15: Magnetic Resonance Imaging and Histopathological Correlation in Alzheimer’s Disease. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Mark D. Meadowcroft and Qing X. Yang

Part V. Non-Alzheimer’s Cortical Dementia Chapter 16: Dementia with Lewy Body Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 Aristides A. Capizzano and Toshio Moritani

Chapter 17: Frontotemporal Lobar Degeneration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Aristides A. Capizzano and Toshio Moritani

Part VI. Dementia with Extrapyramidal Syndromes Chapter 18: Parkinson’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Jennifer G. Goldman, John W. Ebersole, Douglas Merkitch, and Glenn T. Stebbins

Chapter 19: Atypical Parkinsonian Syndromes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 Nicola Pavese and David J. Brooks

Chapter 20: Secondary Parkinsonism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 Thyagarajan Subramanian, Kala Venkiteswaran, and Elisabeth Lucassen

Part VII. Vascular Dementia Chapter 21: Vascular Dementia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 A.M. Barrett and Vahid Behravan

Chapter 22: Neuroimaging of Vascular Dementias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Amit Agarwal and Sangam G. Kanekar

Chapter 23: Imaging of Specific Hereditary Microangiopathies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 Kenneth Lury and Mauricio Castillo

Chapter 24: Vasculitis and Dementia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 Sampson K. Kyere, Olaguoke Akinwande, Dheeraj Gandhi, and Gaurav Jindal

viii Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Contents

Part VIII. Infection and Inflammatory Conditions Associated with Dementia Chapter 25: Human Immunodeficiency Virus (HIV) Dementia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226 Toshio Moritani, Aristides Capizzano, and Sangam G. Kanekar

Chapter 26: Non-Human Immunodeficiency Virus (HIV) Infectious Dementia . . . . . . . . . . . . . . . . . . . . . . . . 232 Krishan K. Jain, Jitendra K. Saini, and Rakesh K. Gupta

Chapter 27: Prion Disease. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 Toshio Moritani, Aristides Capizzano, Girish Bathla, and Yoshimitsu Ohgiya

Chapter 28: Immune-Mediated Dementias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Sangam G. Kanekar, Vinod Maller, and Amit Agarwal

Part IX. Normal Pressure Hydrocephalus Chapter 29: Normal Pressure Hydrocephalus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 Ritu Shah, Fathima Fijula Palot Manzil, and Surjith Vattoth

Part X. Tumor-Related Cognitive Dysfunction Chapter 30: Brain Tumors and Cognitive Dysfunction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Sangam G. Kanekar and Hazem Matta

Chapter 31: Paraneoplastic Syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Toshio Moritani, Aristides A. Capizzano, and Yoshimitsu Ohgiya

Part XI. Trauma Chapter 32: Posttraumatic Cognitive Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 Inga Koerte, Alexander Lin, Marc Muehlmann, Boris-Stephan Rauchmann, Kyle Cooper, Michael Mayinger, Robert A. Stern, and Martha E. Shenton

Part XII. Endocrine and Toxins-Related Dementia Chapter 33: Endocrine-, Metabolic-, Toxin-, and Drug-Related Dementia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296 Sangam G. Kanekar and Brian S. Bentley

Part XIII. Inborn Errors of Metabolism Chapter 34: Inborn Errors of Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 Sangam G. Kanekar and Dejan Samardzic

Part XIV. Cerebellar Degeneration and Dysfunction Chapter 35: Normal Anatomy and Pathways of Cerebellum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318 Sangam G. Kanekar and Jeffrey D. Poot

ix Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Contents

Chapter 36: Imaging of Cerebellar Degeneration and Cerebellar Ataxia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328 Sangam G. Kanekar and Kyaw Tun

Part XV. Motor Neuron Disorders Chapter 37: Overview of Motor Neuron Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 Divisha Raheja and Zachary Simmons

Chapter 38: Neuroimaging of Motor Neuron Disorders. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 Divisha Raheja and Zachary Simmons

Part XVI. Clinical Approach and Treatment Chapter 39: Reversible versus Nonreversible Dementia: Practical Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 362 Sol De Jesus and Sangam G. Kanekar

Chapter 40: Advances in the Treatment of Dementia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Madhav Thambisetty, Néstor Gálvez-Jiménez, and Thyagarajan Subramanian

Chapter 41: Imaging of Deep Brain Stimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 Falgun H. Chokshi

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386

x Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

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Foreword 1 Modern advances in extending the human lifespan are, in part, accountable for the steady rise in neurodegenerative disease worldwide. While there are many tomes written on Alzheimer’s disease and related dementias, until this one I hadn’t encountered a book that broadly emphasizes the full range of neurodegenerative syndromes, including chronic head injury, vascular causes, viral syndromes (e.g., HIV encephalopathy), prion disease, paraneoplastic syndromes, and toxin and drug-related conditions. This thoughtful and comprehensive approach has produced a unique and highly useful body of work. Over the past several decades, the imaging tools at our disposal for evaluating neurodegenerative disorders have dramatically evolved. A half-century ago, there was no effective way to visualize the brain through an intact skull. Now, the coordinated use of structural and functional modalities permits diagnostic and prognostic assessments and provides specific biomarkers poised to monitor the success of therapeutic strategies that as yet remain largely rudimentary. Sangam G. Kanekar’s Imaging of Neurodegenerative Disorders clearly fills an important gap in the literature as it uniquely offers a broader lens through to consider neurodegenerative conditions. The introductory chapter, written by Dr. Kanekar and Maya L. Lichtenstein, provides the context and rationale for the work and its organizational structure. Imaging technology has contributed to discriminating

among underlying etiologies for what was previously an array of poorly understood and overlapping signs and symptoms that affected a person’s mood, memory and personality. The integration of genetic, epidemiologic and underlying neuropathologic information is critical as well. Part II addresses imaging techniques relevant to the disorders in the book, such as diffusion tensor imaging with MRI and amyloid PET imaging. The book is grounded in the fundamental dictum that neuroimaging evaluation of the brain requires a thorough understanding of how the brain’s appearance and physiology change with normal aging. Dr. Kanekar has done a stellar job of gathering a large multidisciplinary group of experts from 21 institutions around the globe to contribute to this book. Dr. Kanekar is Associate Professor of Radiology and Neurology at Penn State College of Medicine and a prolific writer and editor. The enormous value of this book should be appreciated by clinicians, students, and neuroscientists alike. Carolyn Cidis Meltzer, MD, FACR William P. Timmie Professor and Chair of Radiology and Imaging Sciences Emory University School of Medicine Atlanta, Georgia

xi Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

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Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

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Foreword 2 I have to admit that when Sangam G. Kanekar asked me to write this Foreword for his book I felt some unease about doing it. Wouldn’t it be strange to have a person who coauthored a chapter in this book praise it here? But after reading the materials that form the rest of the book, all uneasiness disappeared and I am glad to be writing this short message of introduction. Imaging neurodegenerative disorders seems to be one of the most difficult clinical neuroradiological tasks yet one that has enormous importance for patients and their families. We are beginning to move beyond the limited information offered by anatomical/ structural imaging and embracing newer techniques that shed light into the physiology of these disorders, provide guidance in achieving a correct diagnosis and may even help monitor the effects of therapies. Here 41 chapters authored by 82 experts worldwide explore, explain, try to make sense of, and teach us about these devastating conditions. This book is thus, a veritable “what’s what” by “who’s who.” A few years ago, my mother who had had multiple myeloma for many years (basically and fortunately asymptom-

atic) developed a rapidly progressive dementia characterized by bizarre behavior. For her physicians, family and friends the situation became a true “casse-tete.” No explanation for it was ever found. I re-tell this painful episode because many if not most of us will be confronted with a loved one facing and battling a neurodegenerative disorder. Their diagnosis is slippery, their treatment is generally non-existing, and the pain and cost they result in, are enormous. Dr. Kanekar and all of the authors in this book are to be congratulated for producing an excellent and readable oeuvre that I hope and expect will help neuroradiologists, neurologists, and many others who deal with these terrible diseases to understand them better. Mauricio Castillo, MD, FACR Professor of Radiology Chief, Division of Neuroradiology University of North Carolina Chapel Hill, North Carolina

xiii Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

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Preface The World Health Organization estimated in 2010 that there were 36.5 million people worldwide living with dementia, with global cost for care of 604 billion dollars per year. A new case of dementia is diagnosed approximately every 4 seconds. With the continuing increase of the elderly population and a corresponding increase in neurodegenerative disorders, it is important for all physicians to be familiar with the various types of dementia. The diagnosis of dementia historically involved clinical suspicion alone, with, when available, confirmation via postmortem neuropathological analysis. The advancement in neuroimaging has afforded significant insight into progressive neurodegenerative disorders and their mimics. Distinguishing between preventable, potentially reversible, and irreversible (progressive) etiologies has serious implications for future planning in regard to the patient's medical, social, and economic spheres. In recent years, numerous new developments have occurred in neuroimaging. Besides improvement in structural imaging with thinner sections, 3D volume, and higher-resolution imaging, molecular and cellular imaging

have made a big impact on how we look at the brain and its function. MR spectroscopy, DTI, perfusion imaging, fMRI, and PET scans have further increased our understanding of the pathological processes of the brain, neurodegenerative diseases in particular. However, in spite of the wealth of new concepts that have evolved from these resources, there have been no dedicated textbooks on the imaging of neurodegenerative diseases until now. Imaging of Neurodegenerative Disorders covers the application of these fascinating techniques, along with basic structural imaging in the diagnosis of various neurodegerative disorders. This book has many contributors who have brought fresh insights and expertise that encompass more disease entities. We attempt, at least in part, to fill the gap of knowledge that exists in the imaging and understanding of neurodegenerative diseases. The author expects you will find this book enjoyable and educational, and hopes it guides you toward a better understanding of neurodegenerative diseases. Sangam G. Kanekar, MD

xv Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

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Acknowledgments Working on this book with so many outstanding contributors has been an enjoyable and immensely informative experience. I thank them, the staff at Thieme Publishers Inc., and my family for their overwhelming support.

xvii Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

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Contributors Amit K. Agarwal, MD Assistant Professor of Radiology Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania

David J. Brooks, MD, DSc, FRCP FMedSci Hartnett Professor of Neurology Department of Medicine Imperial College London London, United Kingdom

Olaguoke Akinwande, MD Fellow Department of Radiology Johns Hopkins University Baltimore, Maryland

Aristides A. Capizzano, MD Assistant Professor of Radiology University of Iowa Hospitals and Clinics Iowa City, Iowa

Abass Alavi, MD, PhD(Hon), DSc(Hon) Professor of Radiology and Neurology Director of Research Education Department of Radiology University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania Girish Bathla, FRCR, DMRD, MMeD Department of Radiology University of Iowa Hospitals and Clinics Iowa City, Iowa A. M. Barrett, MD Director, Stroke Rehabilitation Research Kessler Foundation Chief, Neurorehabilitation Program Innovation Kessler Institute of Rehabilitation Professor, Physical Medicine and Rehabilitation Rutgers-New Jersey Medical School West Orange, New Jersey Dhiraj Baruah, MD, PDCC Assistant Professor of Radiology Medical College of Wisconsin Milwaukee, Wisconsin Vahid Behravan, MD Private practice Kensington, Maryland Brian S. Bentley, DO Chief Resident Department of Radiology Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania

Mauricio Castillo, MD, FACR Professor of Radiology Chief, Division of Neuroradiology University of North Carolina Chapel Hill, North Carolina Dennis Chan, MD, PhD, FRCP University Lecturer and Honorary Consultant in Clinical Neurosciences University of Cambridge Cambridge, United Kingdom Tushar Chandra, MD Pediatric Neuroradiologist Department of medical Imaging Nemours Children's Hospital Orlando, Florida Sanjeev Chawla, PhD Senior Research Investigator Department of Radiology University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania Falgun H. Chokshi, MD, MS, DABR Department of Radiology and Imaging Sciences Emory University School of Medicine Atlanta, Georgia Jeffrey Kyle Cooper, BA Harvard Medical School Boston, Massachusetts Sol De Jesus, MD Adjunct Clinical Post-Doctoral Associate Center for Movement Disorders and Neurorestoration University of Florida Gainesville, Florida

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Contributors

Leonardo Cruz de Souza, MD, PhD Neurologist, Faculty of Medicine Federal University of Minas Gerais Belo Horizonte, Brazil

Rakesh K. Gupta, MD Director and Head, Department of Radiology and Imaging Fortis Memorial Research Institute Gurgaon, Haryana, India

Puneet S. Devgun, DO Department of Radiology Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania

Leslie Hartman, MD Department of Radiology School of Medicine and Public Health University of Wisconsin Madison, Wisconsin Staff Radiologist Regional Diagnostic Radiology St. Cloud, Minnesota

John W. Ebersole, MD Resident Department of Radiology Rush University Medical Center Chicago, Illinois Aaron S. Field, MD, PhD Professor of Radiology and Biomedical Engineering Chief of Neuroradiology School of Medicine and Public Health University of Wisconsin Madison, Wisconsin Wolfgang Gaggl, PhD Department of Radiology School of Medicine and Public Health University of Wisconsin Madison, Wisconsin Néstor Gálvez-Jiménez, MD, MSc, MS(HSA), FACP Professor of Medicine (Neurology-Florida) Cleveland Clinic Lerner College of Medicine Chairman, Department of Neurology Director, Neurosciences Center Chief, Movement Disorders Program Cleveland Clinic Weston, Florida Clinical Professor and Associate Chair of Neurology Herbert Wertheim College of Medicine Florida International University Miami, Florida Dheeraj Gandhi, MD Director, Division of Interventional Neuroradiology Professor of Radiology, Neurology, and Neurosurgery University of Maryland School of Medicine Baltimore, Maryland Jennifer G. Goldman, MD, MS Associate Professor Department of Neurological Sciences Rush University Medical Center Chicago, Illinois

Krishan K. Jain, MD, PDCC(Neuroradiology) Consultant Department of Radiology and Imaging Fortis Memorial Research Institute Gurgaon, Haryana, India Gaurav Jindal, MD Assistant Professor of Radiology Division of Interventional Neuroradiology University of Maryland Medical Center Baltimore, Maryland Sangam G. Kanekar, MD Associate Professor of Radiology and Neurology Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania Inga Katharina Koerte, MD Professor of Neurobiological Research Department of Child and Adolescent Psychiatry, Psychosomatic, and Psychotherapy Ludwig-Maximilian-University Munich, Germany and Psychiatry Neuroimaging Laboratory Department of Psychiatry Brigham and Women's Hospital Harvard Medical School Boston, Massachusetts Sampson K. Kyere, MD, PhD Resident Department of Radiology University of Maryland Medical Center Baltimore, Maryland

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Contributors

Christian La, BA Department of Radiology School of Medicine and Public Health University of Wisconsin Madison, Wisconsin

Michael Mayinger Department of Child and Adolescent Psychiatry, Psychosomatic, and Psychotherapy Ludwig-Maximilian-University Munich, Germany

Christian Langkammer, PhD Department of Neurology Medical University of Graz Graz, Austria

Donald G. McLaren, PhD Clinical Imaging Scientist Biospective, Inc. Montreal, Canada

Maya Lichtenstein, MD Clinical Fellow in Behavioral Neurology Clinic for Alzheimer's Disease and Related Disorders University of British Columbia Vancouver, British Columbia, Canada

Mark D. Meadowcroft, PhD Assistant Professor of Neurosurgery and Radiology Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania

Alexander P. Lin, PhD Director, Center for Clinical Spectroscopy Brigham and Women's Hospital Assistant Professor of Radiology Harvard Medical School Boston, Massachusetts

Douglas V. Merkitch, BA Research Assistant Department of Neurological Sciences Rush University Medical Center Chicago, Illinois

Elisabeth B. Lucassen, MD Assistant Professor of Neurology Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania Kenneth M. Lury, MD Assistant Professor of Radiology - Retired Division of Neuroradiology University of North Carolina School of Medicine Chapel Hill, North Carolina Vinod G. Maller, MD Fellow in Interventional Radiology University of Tennessee Health Science Center Memphis, Tennessee Maria Martinez-Lage Alvarez, MD Assistant Professor of Pathology and Laboratory Medicine University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania Hazem M. Matta, DO Department of Radiology Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania

Ludovico Minati, PhD Researcher Fondazione IRCCS Istituto Neurologico Carlo Besta Milan, Italy Vijay K. Mittal, MD Department of Radiology Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania Mateen C. Moghbel, BS Stanford University School of Medicine Stanford, California Suyash Mohan, MD, PDCC Assistant Professor of Radiology Division of Neuroradiology Perelman School of Medicine at University of Pennsylvania Philadelphia, Pennsylvania Toshio Moritani, MD, PhD Department of Radiology University of Iowa Hospitals and Clinics Iowa City, Iowa

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Contributors

Marc Mühlmann, MD Institute for Clincal Radiology Ludwig-Maximilian-University Munich, Germany

Boris-Stephan Rauchmann Institute for Clinical Radiology Ludwig-Maximilian-University Munich, Germany

Andrew Newberg, MD Department of Radiology and Emergency Medicine Thomas Jefferson University Philadelphia, Pennsylvania

Stefan Ropele, PhD Associate Professor of Medical Physics Department of Neurology Medical University of Graz Graz, Austria

Yoshimitsu Ohgiya, MD Associate Professor of Radiology Showa University School of Medicine Tokyo, Japan Fathima Fijula Palot Manzil, MBBS, DMRT, ABNM certified Nuclear Medicine/Clinical Imaging Hamad Medical Corporation Doha, Qatar Nicola Pavese, MD, PhD Clinical Senior Lecturer and Consultant in Neurology Neurology Imaging Unit (NIU) Imperial College London Division of Brain Sciences Hammersmith Campus London, United Kingdom Jeffrey D. Poot, DO Diagnostic Radiology Resident Department of Radiology Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania Harish Poptani, PhD Research Associate Professor Department of Radiology and Radiation Oncology University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania Vivek Prabhakaran, MD, PhD Assistant Professor of Radiology and Neurology School of Medicine and Public Health University of Wisconsin Madison, Wisconsin Divisha Raheja, MD Assistant Professor of Neurology Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania

Jitender Saini, MD, MBBS Associate Professor Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore, India Koji Sakai, PhD Associate Professor Advanced MR Imaging Research Laboratory Department of Radiology Graduate School of Medical Science Kyoto Prefectural University of Medicine Kyoto, Japan Dejan Samardzic, MD Department of Radiology Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania Marie Sarazin, MD, PhD Professor of Neurology Unité de Neurologie de la Mémoire et du langage Centre Hospitalier Sainte Anne Université Paris Descartes, Sorbonne Paris Cité Paris, France Mijail Serruya, MD, PhD Assistant Professor of Neurology Kimmel Medical College Thomas Jefferson University Philadelphia, Pennsylvania Ritu Shah, MD Radiology Associates of Florida Tampa, Florida Martha E. Shenton, PhD Professor, Departments of Psychiatry and Radiology Director, Psychiatry Neuroimaging Laboratory Brigham and Women's Hospital Harvard Medical School VA Healthcare System Boston, Massachusetts

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Contributors

Zachary Simmons, MD Professor of Neurology and Humanities Director, Neuromuscular Program and ALS Center Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania Glenn T. Stebbins, PhD Professor Department of Neurological Sciences Rush University Medical Center Chicago, Illinois Robert A. Stern, PhD Professor of Neurology, Neurosurgery, and Anatomy and Neurobiology Clinical Core Director, BU Alzheimer's Disease Center Clinical Research Director, BU CTE Center Boston University School of Medicine Boston, Massachusetts Thyagarajan Subramanian, MD Professor of Neurology and Neural and Behavioral Sciences Director, Central PA APDA Informational Center and Movement Disorders Program Penn State University College of Medicine Hershey, Pennsylvania Rashmi Tondon, MD Surgical Pathology Fellow Department of Pathology and Laboratory Medicine University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania Madhav Thambisetty, MD, PhD Clinical Investigator and Chief Unit of Clinical and Translational Neuroscience Laboratory of Behavioral Neuroscience National Institute on Aging National Institutes of Health Baltimore, Maryland Kyaw Nyan Tun, DO Neuroradiology Fellow Department of Radiology Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania

Surjith Vattoth, MD, DNB, FRCR, DABR Senior Consultant Neuroradiologist Hamad Medical Corporation Doha, Qatar Kala Venkiteswaran, PhD Assistant Professor Departments of Neurology and Neural and Behavioral Sciences Milton S. Hershey Medical Center Penn State University College of Medicine Hershey, Pennsylvania Sumei Wang, MD Department of Radiology University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania Ruth A. Wood, BM BCh, MRCP(UK) MRC Clinical Research Training Fellow Sainsbury Wellcome Centre for Neural Circuits and Behaviour University College London London, United Kingdom Guofan Xu, MD Department of Radiology University of Wisconsin Hospital and Clinics Madison, Wisconsin Kei Yamada, MD, PhD Professor and Chairman Department of Radiology Kyoto Prefectural University of Medicine Kyoto, Japan Qing X. Yang, PhD Professor of Radiology, Biogengineering, Engineering Sciences, and Neurosurgery Center for NMR Research Department of Radiology Penn State University College of Medicine Hershey, Pennsylvania

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Part I Introduction

I

1

Overview of Neurodegenerative Diseases

2

Introduction

1 Overview of Neurodegenerative Diseases Sangam G. Kanekar and Maya L. Lichtenstein

1.1 History Neurodegenerative diseases comprise a broad swath of different neurologic diseases, all of which have one thing in common: the pathology is ultimately the loss of neurons in the central nervous system. Onset can be acute but is more often chronic, and the symptoms tend to get progressively worse over time. The diseases are difficult to talk about broadly because they manifest with such a myriad of signs and symptoms. The most common neurodegenerative disease is the dementing disease of Alzheimer’s. There are many other dementing diseases as well that affect different parts of the brain, causing different symptoms. Dementia is commonly understood as the loss of function of at least two cognitive domains that is severe enough to cause loss of daily function in social or occupational spheres.1 Some neurodegenerative diseases are primarily motor disorders, such as Parkinson’s disease and amyotrophic lateral sclerosis (ALS). The underlying cause of the neuronal loss that ties these diseases together is different in each case. Some diseases are caused primarily by proteins, for example through abnormal accumulation or misfolding. These protein accumulations disrupt the normal function of the cells and ultimately lead to cell death. Examples of these “proteinopathies” include tau, amyloid, TDP-43, and α-synuclein. Some overlap is seen between diseases and pathologies, but overall pathology is usually distinctive enough for a definitive diagnosis. Other neurodegenerative diseases are caused by inflammation or infection, toxins, or vitamin deficiencies. Some diseases are primarily genetic, caused by deletions or trinucleotide repetitions. This is where we are now in understanding these diseases. The latter part of the 20th century up to the present has provided an enormous amount of understanding of these various diseases, but much work remains to be done toward a better understanding to be able ultimately to treat the people who have these diseases more effectively. One of the biggest advances in understanding these diseases has been the role of neuroimaging. Although there was an American Society of Neuroradiology made up of 14 neuroradiologists in 1962, before the 1970s, this field was not widely recognized.2 Skull films had been done since the advent of the roentgenogram in the early 20th century, but they could really only be used to detect skull fractures or calcifications in the head. In the early 20th century, the pneumoencephalogram was developed by Walter Dandy, but it was uncomfortable and dangerous for the patient. In the mid-20th century, angiography was developed and used by radiologists and neurosurgeons to look at the blood vessels in the brain by using contrast material injected into the arteries. Angiography has become safer in the last few decades but initially carried substantial risks. These methods were invasive and did not provide a good image of the brain itself. Angiography, for example, was used not only to look at blood vessels in the brain but also to detect masses by visualizing the vessels to determine whether any had shifted from their usual locations. In 1971, the first computed tomography (CT) scan was introduced by Godfrey Hounsfield in a South London hospital for a woman in whom a frontal brain tumor was suspected

(▶ Fig. 1.1). Since that time, the technology continues to improve, with the CT scan being used as the underlying modality for positron emission tomography (PET), single-photon emission computed tomography (SPECT), and noninvasive angiography. However, because of the same underlying technology, there are limitations to what can be seen in the brain using a CT scan. Magnetic resonance imaging (MRI) was developed in the 1980s and has emerged as the gold standard for looking at brain structures, having increased sensitivity for brain structures compared to the CT scan. Structures as small as 1 mm can be detected on MRI, and quantitative measurements can be made reliably. In addition to structural imaging, MRI can be used for functional imaging. Innovations in the field of neuroimaging have provided ways to see neurodegenerative diseases in vivo in a way that is not possible at autopsy. Imaging also has provided a new lens for understanding and monitoring the progression of these diseases, not just clinically, as in the past. We have come a long way since the ancient Egyptians, who believed that dementia was the end result of aging, and since the Middle Ages, when health fell into the realm of the church

Fig. 1.1 Reproduction of first clinical head computed tomography scan in South London, 1971; suspected frontal brain tumor, later confirmed at biopsy, in woman. (Brain scan from Atkinson Morley Hospital, as appears in Beckmann EC. CT scanning: the early days. Br J Radiol 2006;79(937):5-8.)

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Overview of Neurodegenerative Diseases

Fig. 1.2 Binswanger’s disease. (a) Axial computed tomography scan shows diffuse hypodensity in the cerebral white matter (white arrows). (b) Axial fluid-attenuated inversion recovery (FLAIR) image shows generalized prominence of convexity sulci. Diffuse hyperintensity is seen in the cerebral white matter (white arrows), indicating small vessel disease.

and senility was considered a “fading away from spirituality.”3 The term senility, which is defined by Merriam-Webster as “the physical and mental infirmity of old age,” continues to be used casually as a synonym for dementia, although dementia is a pathology and not necessarily part of normal aging. Until relatively recently, in fact, Alzheimer’s was termed presenile dementia to set it apart from the regular dementia that any old person was expected to develop along with aging. Until the late 19th century, it was primarily through careful clinical description that individual diseases were able to be split out from the “black box” of any era’s belief about what caused any progressive debilitation. James Parkinson wrote his essay on the “shaking palsy” in 1817, and some of his descriptions were observations of people walking around his neighborhood. Earlier descriptions in the literature of “rest tremor” and “festination” can be found,4 but it was through Parkinson’s more detailed accounts that later investigators, importantly JeanMartin Charcot, launched their research. Charcot was much more descriptive, naming rigidity as being a cardinal feature of the disease. He made differentiation between the features of resting versus action tremor and other features, such as posture, gait, lack of actual weakness, and rigidity, which we see with what we now call Parkinson’s disease.5 Once that archetype of the disease was confidently described, Charcot’s team could find variants of that archetype, including descriptions of what today would be called Parkinson-plus syndromes. Without any ancillary tests, these kinds of clinical observations were how diseases were described and defined. There was some gross central nervous system pathology, but no stains for neurons until the late 19th century, when Camillo Golgi discovered a silver stain for neurons.6 This was used by him and Santiago Ramon y Cajal for the first time to see and describe neurons, axons, dendrites, and other parts of the central nervous system, thus opening the field of neuropathology. To give an example of the field around the turn of the 20th century, in 1894, Otto Binswanger described a case of progressive dementia with stroke symptoms, which he called encephalitis subcorticalis chronica progressiva.7 His study of that patient’s brain was thereby the first to state that white matter atrophy caused by vascular insufficiency can result in dementia.

The disease was described without use of histopathology, using only gross pathology. The disease was later called Binswanger’s disease by Alzheimer, a term that is sometimes still used to refer to someone with severe vascular dementia (▶ Fig. 1.2). Alois Alzheimer described a patient he saw in 1901 with short-term memory loss and behavioral disorders and was able to examine her brain after her death in 1906. He was able to identify amyloid plaques with Nissl stain, and possibly Mann stain, and presented his findings at a conference. This was one of the first clinicopathologic neurologic cases, and Emil Kraepelin named the disease after Alzheimer.8 Around the same time, Arnold Pick also described the disease later named after him on both clinical and pathological grounds: a patient with speech and behavioral problems, progressing to dementia, who had argyrophilic spherical inclusions (Pick bodies) and globose neurons on pathology.9 During this period, most dementia was considered to be due to syphilis, although there had not yet been any pathological proof of this (this proof came in 1913 with Hideyo Noguchi’s contribution), and later Binswanger’s disease. Alzheimer’s and Pick’s disease were considered interesting outliers of dementing illnesses. In the last 30 years, as Alzheimer’s has become recognized as the most prevalent and most studied cause of dementia, and many patients with other dementing illnesses are often generically labeled as having Alzheimer’s disease.10 The term became the late 20th century’s version of neurosyphilis as a catch-all diagnosis. The study of neurodegenerative diseases in the early 20th century did not differ much from that of the hundred years preceding it. There was no good way to differentiate some diseases in vivo and no treatment or cure for them. Dementias were classified in the back of psychiatric manuals as organic brain diseases, and no special attention was given to them. Demented people, regardless of the underlying cause or disease, were often treated much the same as psychotic patients and were placed in institutions. The latter 20th century showed witnessed the advent of more refined diagnostic tools in the form of neuroimaging and neuropsychology, as well as discoveries in molecular and cellular pathways and genetics. For example, there was the discovery of dopamine pathways and the development of levodopa in the 1960s to treat patients with

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Introduction

Fig. 1.3 Progressive supranuclear palsy. Sagittal T1-weighted image shows moderate atrophy of the midbrain, often called “bird’s-beak” appearance (white arrow). Fig. 1.4 Huntington disease. Axial T2-weighted image shows atrophy of the caudate nuclei bilaterally (arrows) with dilatation of the frontal horns of the lateral ventricles.

Parkinson’s disease.11 Oliver Sacks wrote the book Awakenings about treating the institutionalized patients with levadopa and how they came to life with the medication. The drug also helped to differentiate idiopathic Parkinson’s disease from other, less typical presentations. Since the 19th century, there were descriptions from Charcot about atypical Parkinson-type patients, but only in 1964 was there a distinct clinical and pathological entity of what is now called progressive supranuclear palsy, described by Steele-Richardson-Olszewski, differentiating this syndrome from Parkinson’s disease (▶ Fig. 1.3).12 This description was not immediately accepted by all of the neurologic community, and the disease was considered by some to be more of a “subspecies” of Parkinson’s disease, not its own disease species. The atypically presenting progressive supranuclear palsy patients were grouped together with typical Parkinson’s disease patients for the initial drug trials of levodopa. The drug had different effects on the two populations: it worked well for the symptoms of the idiopathic Parkinson’s diseases patients, and it worked poorly or not at all for the progressive supranuclear palsy patients.13 The logic of this finding ultimately allowed for wider acceptance of progressive supranuclear palsy as a separate disease from Parkinson’s. This method of drug trial and error is still used in patients to differentiate types of underlying disease pathology that clinically may look similar. Although Huntington’s disease had been described clinically and pathologically in 1872, and was known to be inherited in an autosomal dominant fashion, it was only in 1983 that the Huntington gene was mapped to human chromosome 4p; it was the first autosomal dominant disease to be mapped. It was

a full 10 years later that the pathogenic mutation was identified as a CAG-repeat expansion (▶ Fig. 1.4).14 This mapping and identification allowed for a whole new way of studying and understanding the disease and held promise as a way for definitively diagnosing other diseases. Genetic testing for some diseases still provides some of the only other definitive diagnoses besides pathology. For example, genetics has allowed precise delineations of the myriad groups of disorders called the spinocerebellar ataxias. Because they are caused by different genes, they can be classified as different entities, and can be studied and their courses and prognoses further elucidated with some confidence. This has helped enormously in allowing clinicians’ observations to have more definitive validation or a way to be “checked” against an objective test during the patient’s life. Because of the advent of these diverse modalities (i.e., imaging, blood and fluid assays, genetics, pharmacology, immunology) and the overwhelming amount of research in these fields over the course of the 20th, and now the 21st, century, there have also been more consensus committees, standardizing test results, diagnostic criteria, and the definition of disease, so that researchers and clinicians can speak in the same language about these diseases. These tasks remains difficult because the neurodegenerative diseases are often quite heterogeneous. Although adjuvant testing has helped to make up diagnostic criteria, many of these modalities are still in the research phase only, and the diagnoses for these diseases remain largely clinicalpathological. The role of neuroimaging in helping to differentiate and define diseases in vivo is evidenced by the fact that imaging correlates are used for diagnostic criteria of many neurodegenerative diseases.

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Overview of Neurodegenerative Diseases The aim of this book is to cover each disease as it is currently understood and to show what it might look like using various techniques of neuroimaging. It is of enormous importance that we start to look for not just what is there in imaging, but what is missing, as well to be able to see and quantify patterns of atrophy, where and why there is signal change, increased or decreased uptake, which tests are most helpful, which tests are available in research or clinically, and what tests can help differentiate between diseases. Clinically, neuroimaging is crucial for ruling out tumor, stroke, bleeding, normal-pressure hydrocephalus, or other potentially treatable or reversible causes of dementia. In the past it was thought that primary causes of dementia and other neurodegenerative diseases were progressive and relentless, and besides being used as a modality for “ruling out” other diseases, neuroimaging did not have a role in these diseases. Increasingly, however, the role of imaging has expanded and is more acknowledged. In the clinical realm, as well as for research and knowledge for its own sake, imaging helps enormously in affirming suspicion, differentiating entities that have clinical overlap, and charting progression of neurodegenerative diseases. It is hoped that someday these modalities can be used to chart the effects of treatments as well. This book can be used by radiologists, neuroradiologists, neurologists, other internal medicine doctors as well as by anyone seeking to understand these diseases through the lens of the image, which is increasingly becoming more sophisticated.

1.2 Epidemiology Recognition and diagnosis of neurodegenerative diseases are crucial because for these diseases the greatest risk factor is age, and we have an increasingly older population. As people are surviving infections, heart attacks, cancer, accidents, and other hazards of being alive, they are living longer. Along with living longer, there is an increased risk of developing a neurodegenerative disease. This is especially true for in many low- to middle-income nations, with a major expected rise in the prevalence of dementing illnesses in their populations in the coming years as their populations grow and more people live longer (▶ Fig. 1.5).15 The difficulty in assessing the prevalence of dementia is twofold, as identified in a 2013 meta-analysis: dementia is difficult to diagnose: it often requires multi-domain specialties; batteries of tests; and, to be definite, genetic testing or autopsy, any or all of which are not always performed. Another problem has been in the study designs themselves and in misapplication of study designs involving two or more phases, which leads to underestimation of prevalence and overprecision.15 Another issue is that not all neurodegenerative diseases are classified primarily as dementias, and so diseases like ALS, Parkinson’s disease, and secondary causes of neurodegenerative disease, such as alcohol or vasculitis, are often not included in those studies because they are grouped differently. Alzheimer’s disease is the most prevalent neurodegenerative disease and accounts for 60 to 80% of all dementias. It is estimated that in 2010 there were 36.5 million people worldwide living with dementia. A case of dementia is diagnosed every 4 seconds.16 The World Health Organization estimated that the annual global cost for dementia in 2010 was 604 billion

Fig. 1.5 The growth in numbers of people with dementia in highincome (HIC) and low- and middle-income countries (LMIC). (Used with permission from Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, and Ferri CP. The global prevalence of dementia. Alzheimer Dementia 2013;9:70.)

dollars.15 Medical complications from neurodegenerative diseases are common, and these patients are hospitalized more often and for longer periods than other people in their age groups. These diseases impose an enormous burden on the economy as well as on social and family structures. As the diseases progress, families and caregivers must often give up their jobs to take care of the patients, and the patients often need an increased level of care in nursing homes and other assistedliving facilities.15 These costs are expected to continue to grow along with the aging population; the number of people living with dementia is predicted to double every 20 years to 65.7 million in 2030.15 Although there are variable rates of disease based on epidemiologic studies, there is not a race, country, gender, or socioeconomic class that is not at risk for developing neurodegenerative diseases. The most common neurodegenerative disease in any age group is Alzheimer’s disease, but the proportions are different when the age for the patient is younger than 65. In one British population study, younger patients with Alzheimer’s disease accounted for only 34% of young-onset dementia. In patients younger than 65, other causes (such as those from metabolic, toxic, or systemic illnesses) are more common; but even among young-onset dementias, it seems that Alzheimer’s, followed by vascular dementia, FTD, and dementia with Lewy bodies, is the most prevalent, as is true for patients older than 65, although Lewy body dementia is the second most common cause of dementia in patients older than 65. Although there is a paucity of epidemiology relating to young-onset dementias, a British study showed an overall prevalence of 54 per 100,000 in patients age 30 to 65, and a Japanese study showed a similar overall prevalence of 43 per 100,000 in people age 18 to 65. Still, the overwhelming burden of disease is carried by the older population, with an estimated prevalence of 1 to 2% at age 65, 10 to 15% by age 80, and as high as 40% in 90-year-olds.1

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Introduction

1.3 Clinical Approach Neurodegenerative diseases are defined by loss of neurons, which can occur anywhere in the central nervous system: cortical and subcortical areas, brainstem, cerebellum, and spinal cord. It is the “where” of the neuronal loss that gives us the clinical presentation: both what the patient and the family notice and what we see from history and examination. The “where” can also be seen on imaging, either by injury to the area or atrophy of the area, with concurrent loss of function. The approach to the patient with a neurodegenerative disease involves first and foremost the suspicion that this may be the cause of the patient’s complaints. The chief complaints are quite variable, and the presentations are as well: although varyingly acute, subacute, or chronic, they tend to be progressive, but some relapse and remit, and some seem to plateau. Because we deal with loss of neurons in the central nervous system, we expect the complaints to pertain chiefly to one or more cognitive functions or a motor function; included in these overbroad categories are dysfunctions that we know as clinicians to be caused by damage to a particular part of the brain, but patients do not necessarily know that memory and concentration are

two different things anatomically, or that being unsteady is not always at all a sign of being weak (▶ Table 1.1). Often there are many complaints, and they accrue over the years. Often there are disturbances that were overlooked or ignored or not considered relevant by the patient or the family when the symptoms first appeared; so a careful history must be obtained, as well as a full medical and family history, and a social history that includes exposures and prior level of function. While examining the patient, the clinician will tend to concentrate on the area of chief complaint, but a full general, neurologic, and psychiatric examination also must be obtained. It is helpful for the clinician to know that many diseases may manifest with a nonspecific memory complaint or cognitive slowing. It is important to look for other neurologic signs that may help to narrow the differential diagnosis. For example, a dementia with ataxia may lead the clinician to think of spinocerebellar ataxia, paraneoplastic diseases, alcoholic dementia, multiple sclerosis, prion disease, and others (▶ Fig. 1.6).17 Such an approach will help guide further testing. In general, if the patient is older than 65 and the clinical suspicion is a primary neurodegenerative disease, such as Alzheimer’s disease, the diagnosis is clinical; but some basic

Table 1.1 Cognitive and motor complaints, with examples and differential diagnosis Complaints

Examples of symptoms

Examples of possible syndromes

Behavior/personality

Inappropriate behavior, decline in social behavior, emotional blunting, decline in grooming, mental rigidity, hallucinations

FTD, DLB, CBS, HD, PDD, CJD, vitamin deficiency, toxins, VaD, AD

Executive skills

Problems with cooking, multi-tasking, using computer, keeping up with bills and finances, judgment

FTD, later-stage AD, PDD, ALS

Visual-spatial

Trouble recognizing faces, getting lost, seeing things properly, judging distances

PCA, AD

Memory

Repeating questions, forgetting appointments, no recall of events or shows/movies

AD, PD, DLB, PDD, VaD, vitamin deficiencies

Attention

Does not attend to things said, walks into rooms but doesn’t remember why/what was wanted, easily distractible

TBI, PDD, DLB, CJD, NPH

Speech/language

Speech apraxia, phoneme &/or syntax errors, poor naming, impaired comprehension, hesitant speech, severe wordfinding difficulties

PPA [PNFA, SD, LPA], CBS, PSP

Praxis

Have trouble doing things on command, have trouble completing multi-step tasks in the right order, have trouble using tools, using the hands or legs properly

CBS, AD, PD, HD, PCA

Motor

Examples of symptoms

Examples of possible syndromes

Unsteadiness/ataxia

Coordination trouble with any or all of following: speech, arms, legs, gait, trunk, eye movements

Vitamin deficiencies, heavy metals, toxins, SCA, HD, NPH, PD, PSP, DLB, VaD, MSA

Abnormal movements

Flinging limb movements, tremor, jerking of limb or body (myoclonus), abnormal posturing

HD, CBS, PD, CJD, SCA, heavy metal, vitamin deficiency, toxins

Less movement/hypokinesis

Masked face, decreased arm swing, less spontaneous movement; slow moving or talking, smaller steps.

PD, PDD, DLB, PSP, CBS, VaD, heavy metals

Weakness/falls

Trouble going up or down stairs, trouble getting up from chairs, falling backward, difficulty lifting arms and holding onto objects

VaD, PSP, PD, NPH, ALS, SCA, HD, MSA, vitamin deficiencies, toxins

Bulbar problems

Dysphagia for solids or liquids, tongue weakness, decreased gag or cough reflex, changed (hoarse or quiet) voice, inappropriate emotionality

VaD, ALS, MSA

Cognitive

Abbreviations: AD, Alzheimer’s dementia; ALS, amyotrophic lateral sclerosis; CBD, corticobasal degeneration; CBS cortical basal syndrome, CJD, Creutzfelt-Jakob disease; DLB, dementia with Lewy bodies; FTD, frontotemporal dementia; HD, Huntington’s disease; LPA, logopenic aphasia; MSA, multisystem atrophy; NPH, normal pressure hydrocephalus; PCA, posterior cortical atrophy; PD, Parkinson’s disease; PDD, Parkinson’s disease with dementia; PNFA, progressive nonfluent aphasia; PSP, progressive supranuclear palsy; SCA, spinocerebellar ataxia; SD, semantic dementia; VaD, vascular disease.

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Overview of Neurodegenerative Diseases show quantitative measures of atrophy in various parts of the brain even before a patient has clinical symptoms, such as in mild cognitive impairment. Potentially, imaging may be able to show objective efficacy of treatments.

1.4 Pathology

Fig. 1.6 Spinocerebellar atrophy. Axial T2-weighted image shows prominence of cerebellar folia suggestive of cerebellar atrophy. Pons is normal in morphology and signal intensity.

laboratory studies must be obtained, including complete blood cell count, complete metabolic panel, vitamin B12, and thyroidstimulating hormone.18 Also required is an imaging study, either head CT or MRI of the brain. If the patient is younger or there is doubt about the diagnosis, further testing should be obtained (▶ Fig. 1.7). As may be indicated, appropriate laboratory, electroencephalographic, electromyographic, sleep, and imaging studies should be obtained to confirm or rule out other possible causes of complaint. Throughout life, however, the accumulation of clinical data tends to trump any test we do, and we often wait for families to allow pathological evidence or genetic testing for confirmation of our clinical suspicion. The hope is that we can use already developed tools more wisely, and further hope is that there continue to be more innovative approaches to diagnose more definitively and earlier in the course of disease. As research moves toward identifying diseases at earlier stages for treatment, we must develop new tools to identify correctly the patients and patient groups for study. An enormously powerful tool has been and continues to be neuroimaging; it can act as a surrogate for pathology, both in the gross pathological sense in quantitative measures but also increasingly as a histopathological marker of disease in vivo. It has great advantages as well, even over pathology: images can be made showing function in different parts of the brain or highlighting loss of function; and images can be taken longitudinally, showing changes over time (▶ Fig. 1.8). Studies can

It would be a lot simpler if, given a thorough history and examination, the astute clinician could always know what disease is causing the symptoms or, short of that, could obtain the one test that makes the diagnosis. Neurodegenerative diseases are devastating for patients and families, and often the not knowing adds to the difficulty. There is, of course, clinical overlap, and as the disease progresses, the signs and symptoms overlap even more, as more of the brain becomes engorged by disease and areas are damaged, connections are lost. A strange homogeneity and heterogeneity can exist pathologically as well. The loss of neurons causing disease can be caused by many different processes, such as abnormal protein accumulation, vascular damage, inflammation, vitamin deficiencies, toxins, infections, or some combination thereof. These entities cause the pathology that make the disease. Pathologically, as diseases progress, there is an end pattern of neuronal and synaptic loss, as well as laminar spongiosis and astrocytosis,19 and the causative agent is not always evident. Grossly, there is often atrophy in regions typical for each disease. For example, atrophy is present in frontal and/or temporal regions in frontotemporal degeneration; this can be seen in imaging during life, as well as at autopsy (▶ Fig. 1.9). Early in the disease, different brain regions are more affected than others, depending on the clinical subtype; for example, a substantial decrease in weight and volume in the dominant frontal lobe is expected in progressive nonfluent aphasia. As the neurodegenerative disease progresses, however, often an increase in atrophy occurs more globally, although usually the initial area remains the most affected. Many of the neurodegenerative diseases are defined pathologically by the type and distribution of protein accumulation. Diseases that clinically resemble one another may have a totally different underlying pathology. An example is the “Parkinsonplus” disease progressive supranuclear palsy, which clinically can resemble idiopathic Parkinson’s disease in its extrapyramidal rigidity, bradykinesia, and gait impairment. Although often progressive supranuclear palsy also involves dementia, bulbar palsy, and the characteristic supranuclear ophthalmoplegia, these are not always present, or at least present initially.14,15 It is certainly a disease that clinically can be confused with Parkinson’s disease.20 At autopsy, however, the pathology of Parkinson’s disease shows loss of dopaminergic cells in the midbrain and Lewy body deposits made of α-synuclein. Although in progressive supranuclear palsy there is also loss of midbrain neurons and loss of substantia nigra pigment seen at autopsy, this disease also has characteristic tau pathology (▶ Fig. 1.10). In contrast, some diseases have a quite different clinical presentation, such as in patients with ALS or FTD, who can have the same pathology in TDP-43; these patients may also share the same genetic mutation in C9ORF72.21 By no means, however, do all patients with FTD or ALS exhibit that pathology; for example, some FTDs show tau protein deposition, and other familial forms of ALS have aggregates of superoxide dismutase in cell bodies.22 The difference in protein pathology and

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Introduction

Fig. 1.7 Flowchart for the assessment and investigation of young-onset dementia. This algorithm provides an overview of the diagnostic approach to patients with young-onset dementia; it is only a general guide. In amnestic young-onset dementia, first-line genetic testing is for amyloid precursor protein (APP), presenilin-1 (PSEN1), presenilin-2 (PSEN2), and prion. In behavioral cases, first-line testing is for MAPT (particularly if symmetrical atrophy on magnetic resonance imaging [MRI]) and granulin (GRN, particularly if asymmetric pattern of atrophy). Aβ, amyloid β; CADASIL, cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy; CJD, Creutzfeldt-Jakob disease; EEG, electroencephalograph; FDG, fluorodeoxyglucose; FTLD, frontotemporal lobar degeneration; SPECT, single-photon emission computed tomography. VGKC, voltage-gated potassium channel. (Used with permission from Rossor MN, Fox NC, Mummery CJ, Schott JM, Warren JD. The diagnosis of young-onset dementia. Lancet Neurol 2010; 9:802.)

Fig. 1.8 Alzheimer’s disease. Coronal T1weighted imaging shows severe atrophy of the bilateral hippocampi (arrows). There is mild generalized atrophy of the frontotemporal lobes. Coronal positron emission tomography image shows decreased uptake in the medial temporal lobes, indicating hypometabolism typical for Alzheimer’s disease.

distribution allows for definitive diagnoses to be made in many of the primary neurodegenerative diseases and has helped to differentiate and redefine some of these diseases (▶ Fig. 1.11). Pathology remains the gold standard for definitive diagnosis in many primary neurodegenerative diseases. Not all neuro-

degenerative diseases are caused by abnormal protein accumulation and deposits, however, and not all diseases carry a readable “protein signature” pathologically. Some are caused by infection, inflammation, or neuronal death by direct toxic injury or less directly from ischemia or hypoxia, and other

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Overview of Neurodegenerative Diseases

Fig. 1.9 Frontotemporal dementia. (a) Coronal computed tomography image shows severe atrophy of the frontal and temporal lobes bilaterally (arrows) with marked dilatation of the cerebrospinal fluid spaces. (b) Sagittal T1WI shows selective atrophy of the frontal lobe with normal parietal and occipital lobes. (c) Gross pathology of patient with pathologically proven frontotemporal dementia. Arrows indicate frontal lobe atrophy (a,b). (parts used by permission from Jennifer W. Baccon, MD, PhD, Penn State Hershey Medical Center.)

diseases have unknown causes or are caused by deleterious gain or loss of genetic function. There is a broad range of what can be seen pathologically with these diseases.

1.5 Genetics

Fig. 1.10 Immunohistochemical stain for tau. (Pathology slide used by permission from Jennifer W. Baccon, MD, PhD, Penn State Hershey Medical Center)

Although it was known that there seemed to be a hereditary component to some neurodegenerative diseases, such as Huntington disease, which for the vast majority of cases seemed to be inherited in an autosomal dominant fashion, it was not until the 1980s and 1990s that the ability to map genes became possible. Huntington disease, as mentioned earlier herein, was the first disease gene discovered. Since then, the discovery of genes that either cause, predispose to, or protect from disease has provided a whole new way of understanding and looking at diseases. It is important for the nongeneticist to understand a few fundamentals of the genetics behind disease. It is not always as simple as a defect in gene X causing disease Y, but this is the case in some diseases with a high degree of penetrance. In diseases like Huntington and the spinocerebellar ataxias, a gene

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Introduction

Fig. 1.11 The overlap between clinical and pathological descriptions of neurodegenerative diseases: Some proteinopathies and clinical entities.

mutation causes disease (▶ Table 1.2). Other genes that behave like this are the APP gene on chromosome 21 or the presenilin 1 gene on chromosome 14, both of which cause rare forms of familial Alzheimer’s disease, less than 5% of all Alzheimer’s disease. These genes are also inherited in an autosomal dominant fashion. Some genes that are known to cause disease may not cause disease in each carrier, or they may cause a modified disease. This is the case of the progranulin mutation, which causes FTD, in which the chance of developing the disease increases as the carrier of the mutation ages, but it is not 100% penetrant. Then there are genes that have been discovered that seem to confer a risk for developing disease, such as apolipoprotein (Apo) E4 and Alzheimer’s disease. Each person has two copies of ApoE, and it comes in three forms: E2, E3, and E4. A person with a copy of ApoE4 is considered at increased risk for developing Alzheimer’s disease. A person with two copies of ApoE4 is considered to have an even greater risk and is likely to develop the disease at an earlier age14; but this is only a risk factor, just as traumatic brain injury, insulin resistance, cerebral vascular disease, and smoking may be risk factors: none of these risks guarantees the development of Alzheimer’s disease. The person who has two copies of ApoE4 and is suffering from dementia might not be suffering from Alzheimer’s disease, and the reverse is also true: a person suffering from Alzheimer’s dis-

ease does not necessarily have even one copy of the ApoE4 gene. For a small subset of patients with neurodegenerative diseases, however, genetic testing can provide a definitive diagnosis, and genes likely play a much greater role than we now understand in who develops disease and why.

1.6 Summary Although it can be quite useful to have constructs in mind, it is important not to be too rigid about how we classify neurodegenerative disorders. The definitions of these diseases and how we understand them shift like tidal sands as we learn more about each disease individually and then step back and refit our new knowledge into the greater patterns. Of course, it also depends on through which lens we are looking at them. Clinicians, geneticists, pathologists, radiologists, molecular biologists, and chemists all have different ways of sorting and understanding these diseases. It is only through the effort of each part of this multidisciplinary approach that we can hope to gain a better understanding of these diseases. In doing so, we will be able to better care for and communicate with the increasing number of patients who suffer from these illnesses. The aim of this book is to develop a better understanding of neurodegenerative diseases through neuroimaging. Chapters

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Overview of Neurodegenerative Diseases Table 1.2 Overview of genetics of some established neurodegenerative diseases Disease

Gene

Protein

Chromosome

Inheritance

Alzheimer’s

APP

A-β precursor

21

Dominant

APOE

Apolipoprotein E

19

Risk factor

PSEN1

Presenilin 1

14

Dominant

PSEN2

Presenilin 2

1

Dominant

SNCA

α-synuclein

4

Dominant

PRKN

Parkin

6

Recessive

DJ1

DJ-1

1

Recessive

PINK1

PTEN-induced putative kinase 1

1

Recessive

LRRK2

Leucine-rich repeat kinase 2; dardarin

12

Dominant

MAPT

Microtubule-associated protein tau

17

Dominant

PRG

Progranulin

17

Dominant

VCP

Valosin-containing protein

9

Dominant

FTD and MND

C9ORF72

C9ORF72-encoded protein (unknown)

9

Dominant

ALS

SOD1

Superoxide dismutase 1

21

Dominant and Recessive

Huntington

ALS2

Alsin

2

Recessive

Parkinson’s

FTD FTD with IBM and early Paget disease

spinocerebellar

HTT

Huntingtin

4

Dominant

ataxias

ATXN I, II, and III,

Ataxin 1, 2, and 3,

6, 12, 14, respectively

Dominant

Wilson’s

ATP7B

P-type ATPase

13

Recessive

Prion

PRNP

Prion protein

20

Dominant and Risk factor

CADASIL

NOTCH3

Neurogenic locus notch homolog protein 3

19

Dominant

CARASIL

HTRA1

HTRA serine protease

10

Recessive

Abbreviations: ALS, amyotrophic lateral sclerosis; ATPase, adenosine triphosphatase; CADASIL, cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy; CARASIL, cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy; FTD, frontotemporal dementia; IBM, inclusion body myositis; MND, motor neuron disease.

are arranged by disease, and with a brief discussion of each disease as it is now understood are the images themselves. Along with the images are discussions of what tests to order, what to look for, what is expected to be seen in each disorder, and new clinical and research modalities.

References [1] McKhann GM, Knopman DS, Chertkow H et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 263–269 [2] Leeds NE, Kieffer SA. Evolution of diagnostic neuroradiology from 1904 to 1999. Radiology 2000; 217: 309–318 [3] Albert ML, Mildworf B. The concept of dementia. J Neurolinguist 1989; 4: 301–308 [4] Pearce JMS. Aspects of the history of Parkinson’s disease. J Neurol Neurosurg Psychiatry 1989; 52 Suppl: 6–10 [5] Goetz CG. The history of Parkinson’s disease: early clinical descriptions and neurological therapies. Cold Spring Harb Perspect Med 2011; 1: a008862 [6] Henry JM. Neurons and Nobel Prizes: a centennial history of neuropathology. Neurosurgery 1998; 42: 143–156 [7] Mast H, Tatemichi TK, Mohr JP. Chronic brain ischemia: the contributions of Otto Binswanger and Alois Alzheimer to the mechanisms of vascular dementia. J Neurol Sci 1995; 132: 4–10 [8] Graeber MB, Kösel S, Egensperger R et al. Rediscovery of the case described by Alois Alzheimer in 1911: historical, histological and molecular genetic analysis. Neurogenetics 1997; 1: 73–80 [9] Pan XD, Chen XC. Clinic, neuropathology and molecular genetics of frontotemporal dementia: a mini-review. Transl Neurodegener 2013; 2: 8

[10] Snowden JS, Neary D, Mann DM. Frontotemporal dementia. Br J Psychiatry 2002; 180: 140–143 [11] Hornykiewicz O. A brief history of levodopa. J Neurol 2010; 257 Suppl 2: S249–S252 [12] Colosimo C, Bak TH, Bologna M, Berardelli A. Fifty years of progressive supranuclear palsy. J Neurol Neurosurg Psychiatry 2014; 85: 938–944 [13] Daroff RB. Progressive supranuclear palsy: a brief personalized history. Yale J Biol Med 1987; 60: 119–122 [14] Bertram L, Tanzi RE. The genetic epidemiology of neurodegenerative disease. J Clin Invest 2005; 115: 1449–1457 [15] Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement 2013; 9: 63–75, e2 [16] World Health Organization. Dementia: a public health priority. http://apps. who.int/iris/bitstream/10665/75263/1/9789241564458_eng.pdf. 2012 [17] Rossor MN, Fox NC, Mummery CJ, Schott JM, Warren JD. The diagnosis of young-onset dementia. Lancet Neurol 2010; 9: 793–806 [18] Galasko D. The diagnostic evaluation of a patient with dementia. Continuum (Minneap Minn) 2013; 19 2 Dementia: 397–410 [19] Duyckaerts C. Neuropathologic classification of dementias: introduction. In: Duyckaerts C, Litvan I, eds. Handbook of Clinical Neurology. Vol 89 (3rd Series) Dementias. New York, NY: Elsevier; 2008; 147–159 [20] Bower JH, Dickson DW, Taylor L, Maraganore DM, Rocca WA. Clinical correlates of the pathology underlying parkinsonism: a population perspective. Mov Disord 2002; 17: 910–916 [21] Hsiung GY, DeJesus-Hernandez M, Feldman HH et al. Clinical and pathological features of familial frontotemporal dementia caused by C9ORF72 mutation on chromosome 9p. Brain 2012; 135: 709–722 [22] Mackenzie IR, Bigio EH, Ince PG et al. Pathological TDP-43 distinguishes sporadic amyotrophic lateral sclerosis from amyotrophic lateral sclerosis with SOD1 mutations. Ann Neurol 2007; 61: 427–434

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Part II

2

Structural Imaging of Dementia

14

3

Magnetic Resonance Spectroscopy in Neurodegenerative Disorders

24

SPECT and PET Imaging of Neurotransmitters in Dementia

34

Diffusion Tensor Imaging in Neurodegenerative Disorders

42

6

Functional Imaging of the Brain

51

7

Role of Noninvasive Angiogram and Perfusion in the Evaluation of Neurodegenerative Disorders

60

Imaging Techniques 4 5

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Imaging Techniques

2 Structural Imaging of Dementia Sangam G. Kanekar and Vijay Mittal Dementia is derived from Latin as “away from the mind,” and it encompasses a vast spectrum of diseases, which can be divided into reversible and irreversible causes. Diagnosis remains a challenging task to the clinician because patients with different diseases can have similar signs and symptoms. Because therapies have become more specific with advances in research, accurate diagnosis is paramount. Fortunately, cross-sectional imaging has evolved and has proved an invaluable tool in diagnostic work-up. When combined with clinical signs and symptoms, structural imaging establishes the cause of dementia and allows focused treatments. The two most compelling arguments against routine imaging of dementia include cost and case management. Rough estimates of diagnostic imaging tests in dementia may range from $350 to $700 million per year in the near future.1,2,3 Use of imaging may decline if it becomes a “rule-out” tool rather than a diagnostic tool. Additionally, many findings are equivocal and therefore would not significantly change patient treatment. For example, findings of cortical atrophy on head computed tomography (CT) are problematic because the degree of atrophy has overlap with absent disease state.4 Lastly, cellular and functional imaging with fluorodeoxyglucose F18, single-photon emission computed tomography (SPECT) perfusion studies with technetium-hexamethylpropylenamine oxime, and magnetic resonance imaging functional studies (fMRI)—including perfusion MRI, blood oxygenation level–dependent fMRI, and MRI spectroscopy—are extremely resource limited and expensive. The high expense and limited availability of these functional and cellular imaging tests have resulted in their being used less often, even though they are the most sensitive tools in early diagnosis of many diseases, such as the parkinsonian syndromes.5 Structural imaging is more than an exclusionary tool, however, and provides valuable diagnostic information as well. Despite the argument about equivocal imaging, CT separates normal subjects from those with true dementia with more than 89% accuracy,6 or a specificity of more than 95%.7 The identification of pseudodementia, or dementia as a symptom of depression, can indicate an easily treatable condition. Also, MRI has emerged as a more specific and sensitive modality that can provide excellent diagnostic yield. It quantifies gray and white matter structures8; for example, it has been able to quantify hippocampal volume, which is highly accurate in diagnosing Alzheimer’s disease (AD),9,10 in addition to correlating with clinical progression.4 Although routine clinical scanning of patients may not provide immediate benefit, our longitudinal knowledge of various pathological processes and early alterations in brain anatomy on imaging can enable us to identify abnormalities much earlier during the clinical setting, which may benefit future at-risk patients who could undergo directed therapy. Unfortunately, postmortem examination does not allow such treatment. An example is our evolution of knowledge to distinguish AD, normal pressure hydrocephalus (NPH), and microvascular disease, which have different clinical outcomes and treatments.4 Our understanding of microvascular disease, so-called unidentified

bright objects, has expanded, and we now understand that these areas of demyelination correlate with clinical signs, such as delayed reaction time or falls. Additionally, chronic microvascular disease can be differentiated from more acute subcortical infarcts by MRI.11

2.1 Imaging Modalities A decade ago, the primary role of imaging in a suspected case of dementia or neurodegenerative disease was limited mainly to the exclusion of treatable (reversible) causes of dementia, such as tumor, subdural hematoma, infection, and stroke. With advances in technology, identifying the minute brain details at the structural and functional level has changed the role of neuroimaging (▶ Fig. 2.1). Although imaging still plays a greater role in distinguishing reversible causes from irreversible causes of dementia, this role is relatively small because reversible causes constitute only around 1% of the causes of dementia. Today, neuroimaging helps in differentiating and classifying various irreversible causes of dementia. This is even more important because concordant advances have been made in pharmaceutical, behavioral, and cognitive therapies to treat and prevent various types of dementia. Most of the functional techniques are new and not widely available or understood. Additionally, their role in the diagnosis of neurodegenerative diseases is still not well established for clinical practice. Structural imaging is more readily available and easy to interpret. The development of diagnostic clinical criteria has improved diagnostic accuracy, but these criteria are still far from perfect. For example, the accuracy of the criteria for diagnosis of AD is limited and depends on the expertise of the clinical center, with specificity ranging between 76 and 88% and sensitivity between 53 and 65%. With newer structural and functional imaging techniques, the diagnosis of many dementias can be suspected or established in the early stages and helps clinicians tailor treatment as well as understand the heritance and prognosis of the disease, which facilitates discussion with patients and relatives.

2.1.1 Computed Tomography Versus Magnetic Resonance Imaging Computed tomography is fast and relatively inexpensive compared with MRI, and its clinical utility revolves around exclusion of disease rather than diagnosis. Whereas CT relies on volume changes, MRI adds soft tissue information and thus allows radiologists to assess the disease characteristics accurately.12 Besides the basic T1- and T2-weighted images in axial, sagittal, and coronal planes, gradient-echo T2* imaging and volumetric MRI using three-dimensional (3D) T1-weighted sequences play an important role in the evaluation of neurodegenerative diseases. Molecular and cellular imaging techniques, such as diffusion tensor imaging, iron-quantifying techniques, spectroscopy, and perfusion may be added to improve the sensitivity and specificity of the diagnosis.

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Structural Imaging of Dementia

Fig. 2.1 Coronal T2 magnetic resonance imaging through the hippocampus and entorhinal cortex. (a) Head. (b) Body. (c) Hippocampal tail. Images show: (1), hippocampus; (2), amygdala; (3), temporal horn; (red arrow), subiculum; (4), parahippocampal gyrus; (yellow arrows), entorhinal cortex; (5), fornix.

T1- and T2-weighted imaging is used to assess the gross anatomy of the brain and to exclude the presence of subdural hematoma, mass effect, hydrocephalus, or other anomalies. In addition, T2-weighted sequences are sensitive to changes in tissue properties, including tissue damage, resulting from changes in the transverse magnetization or T2 decay.12 This property of T2 is helpful in evaluating neurodegenerative diseases, which are mostly characterized by cell loss, astrogliosis, microglial proliferation, and increased deposition of iron or other paramagnetic substances. Nonheme iron in ferritin and hemosiderin is seen as signal loss on T2-weighted imaging; this loss results from shortening of T2. The sensitivity for signal changes resulting from iron deposition in the brain can be increased by using T2*weighted gradient-echo sequences or susceptibility-weighted imaging.12 By using an inversion pulse, the contrast of T1weighted images can be improved, as in a magnetizationprepared rapid acquisition with gradient-echo sequence of high-resolution 3D data sets. This sequence is helpful in volumetric analysis of the brain. Simply put, structural imaging using MRI plays a vital role in evaluation of neurodegenerative diseases, demonstrating superior ability to distinguish various degenerative diseases from each other and from age-related changes. As a prognostic tool, it estimates the future likelihood of clinical progression based on the current extent and severity of disease. Finally, it also acts as an indicator of the disease progression over time, derived from serial measurements.

2.2 Voxel-Based Methods One of the primary findings in most neurodegenerative diseases on pathology is selective atrophy of a specified anatomical structure early in the disease. These changes have been well studied using histopathology. Efforts are continuously being made to use imaging to quantify early neuron loss in specific

locations, which would provide the likely diagnosis. Different techniques exist, ranging from simple quantitative measures of diameter, area, and volume to the most advanced voxel-based morphometry (VBM) and voxel-based relaxometry (VBR), both of which require high-quality 3D sampling of the entire brain.13 The goal of VBM and VBR is to provide superior gray/white matter differentiation, to define cortical and deep gray matter structures, and to outline the cerebrospinal fluid (CSF)-filled spaces (▶ Fig. 2.2). The sequence and the slice partitions depend on the institution and capabilities of the particular scanner. The sophistication of image-processing techniques with 3D volume acquisition has allowed accurate characterization of brain shape (deformation-based morphometry) and brain tissue composition (voxel-based morphometry) after macroscopic differences in shape have been discounted. Using these techniques, information about overall shape (deformation fields) and residual anatomical differences inherent in the data (normalized images) can be partitioned. VBM is based on coregistration of high-resolution 3D datasets, which are normalized to a study-specific template for detection of volume differences between two or more groups.13,14 Normalization is based on intracranial volume and has proven to reduce interindividual variations and account for gender differences. VBM involves a voxel-wise comparison of the local concentration of gray matter, white matter, and CSF between two subjects. The procedure involves spatially normalizing the images, smoothing, correcting interindividual variations in gyral anatomy, and then voxelwise analyzing the data. VBM has shown to be more sensitive than the normal two-dimensional measurements of structures. Although manual segmentation is time consuming and has limited intraobserver and interobserver reliability, it remains the gold standard in quantitative AD imaging studies. In contrast, VBM has the advantage of being automatic, not requiring expert-dependent manual delineation of structural boundaries, and having no intraobserver and interobserver limitations.

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Imaging Techniques

Fig. 2.2 Voxel-based morphometry in mild cognitive impairment (MCI) and Alzheimer’s disease (AD). In MCI, the gray matter loss is predominately seen in the medial basal and lateral temporal lobes. In AD, loss of gray matter is more extensive and involves the medial temporal lobe, basal temporal lobe, lateral temporal and parietal neocortex, posterior cingulate, temporal parietal association neocortex, and prefrontal cortex. L, left; R, right.

2.3 Structural Imaging in Aging Differentiating normal age-related physiologic changes from early neurodegenerative disease is challenging clinically as well as on imaging. With increasing age, normal structural changes may overlap with the spectrum of neurodegenerative diseases on various imaging modalities. Common imaging and pathological findings in aging include brain atrophy, white matter lesions, cerebral microbleeds, silent brain infarcts, and enlarged perivascular spaces. Various cross-sectional imaging studies have shown smaller brain volumes with increasing age, especially in a person older than 55 years. Brain volume is expressed as a percentage of intracranial volume. A mean rate of brain volume loss of 0.4 to 0.5% per year has been described as normal.15 Hippocampal volume is shown to decline approximately 1.4 to 1.6% per year in normal aging compared with AD, which shows volume loss of 4.7% per year.16 T2-weighted hyperintense white matter changes are the most common finding in aging. These changes are due mainly to hypoxic/ischemic injury. T2*-weighted gradient-echo technique allows easy identification of microbleeds. The prevalence of microbleeds has been estimated to be more than 20% in persons aged 60 years and older, increasing to nearly 40% in those older than 80 years.17 In the aging population, microbleeds are lobar in location. These lobar microbleeds correlate well with worse cognitive function. Lastly, dilated perivascular spaces, which can be seen at all ages, become more prominent with aging and are associated with the presence of silent brain

infarcts and hyperintense white matter changes. Dilated perivascular spaces are thought to be associated with cognitive deficits, independent of white matter changes and infarcts.

2.4 Role of Structural Imaging in Irreversible Dimentia 2.4.1 Mild Cognitive Impairment Mild cognitive impairment (MCI) is defined as a mild but definite decline from previous cognitive ability, confirmed by a reliable observer and substantiated by deficits on neurocognitive testing. According to Petersen and colleagues,18 the criteria for amnestic MCI require (1) memory complaints, (2) difficulties with normal activities of daily living, (3) normal general (nonmemory) cognitive function decline, (4) abnormal memory scores, and (5) no dementia. Early identification is crucial because 50 to 75% of elderly patients with MCI are at increased risk for developing of AD. Compared with normal controls, significant atrophy was identified in the hippocampus and entorhinal cortex of patients with MCI but not in the parahippocampal gyrus, fusiform gyri, and temporal gyri. Patients with MCI have less severe hippocampal atrophy (-12 to -14%) than those with AD (-22 to -23%), as well as less entorhinal cortex volume losses (-21%) than those with AD (-38%).19 The VBM method applied to MCI and normal groups confirmed atrophy of the hippocampus, medial temporal lobe, parahippocampal gyrus, and amygdala but also revealed differences in volumes.

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Structural Imaging of Dementia

2.4.2 Alzheimer’s Disease The main pathological features in AD are neuronal loss with gliosis in the temporal cortex, neurofibrillary tangles (NFTs) formed by tau protein aggregates, granulovacuolar degeneration of neurons, senile plaques, and amyloid angiopathy formed mainly by β-amyloid deposits. NFTs and neuropil threads first appear in the transentorhinal and entorhinal areas (parahippocampal gyrus), increasing in density during the course of the disease. These changes progress to involve the hippocampus, limbic system, temporal and parietal cortices, and finally the entire neocortex.20 Historically, AD has been a clinical diagnosis that uses neuropsychiatric tests. Over the past decade, however, neuroimaging has become a more direct diagnostic tool in which specific changes may suggest the diagnosis of AD. The findings in AD can be largely classified according to the stage of the disease. In the earliest transentorhinal stage, volume changes are confined primarily to transentorhinal and entorhinal regions, with mild involvement of the hippocampus. During the limbic stage, the imaging and pathologic changes involve larger parts of the hippocampal formation, subcortical structures (thalamus, amygdala), and basal forebrain (▶ Fig. 2.3). In the later stages, there is widespread cortical atrophy. These changes can be characterized using nonvolumetric assessment or may be quantified using newer techniques of volumetric measurements. Early atrophic changes in the medial temporal lobe, which include the height of the hippocampal formation and the sizes of the choroidal fissure and the temporal horn, can suggest the presence of AD. The diagnostic accuracy of the visual rating was reported at 95%, which was higher than the 85% accuracy of the hippocampal volumetry in differentiating AD patients from control subjects.20 Sensitivity and specificity in distinguishing patients with AD from healthy controls are in the range of 85 and 88%, respectively. The entorhinal cortex is affected earlier than the hippocampus by NFTs and has a greater potential as an early marker, but it is a more challenging region to assess on imaging. Volumetric analysis techniques have shown the reduction in entorhinal cortex volume in AD to be approximately 35 to 40% compared with healthy controls.19 Some authorities believe that the diag-

nostic accuracy of the entorhinal cortex volume alone is close to 100% and superior to the hippocampus; however, this is debatable. In AD, hippocampal neuronal atrophy strongly correlates with NFT pathology. The percentage reduction in total number of hippocampal neurons correlates with the percentage of neurons with NFTs. Based on histologic volumetry, a difference of 30% between healthy controls and age-matched AD subjects was found, which correlated well with MRI volumetric studies. As the name suggests, various parts of the limbic system show NFT and atrophic changes in the limbic stage before spreading to the neocortex. There is 20 to 33% volume reduction with prolonged T2 relaxation time in the amygdala.19 T2 changes are thought to be due to increased free water content in the tissue. Changes of atrophy may also be seen in the parahippocampal gyrus (left > right) and in the temporal gyri. Longitudinal studies have shown rapid enlargement of the ventricular size and evolution of brain atrophy in individuals with dementia compared with controls (▶ Fig. 2.4). Using an automated body substance isolation method applied to serial MRIs acquired 1 year apart, Fox and colleagues21 described median amygdala volume loss of 12.3 mL per year in the AD group and 0.3 mL in controls. On fluorodeoxyglucose-positron emission tomography (FDG-PET) scan, hypometabolism is seen in the medial temporal and parietal lobes (▶ Fig. 2.5).

2.4.3 Non-Alzheimer's Dementia Frontotemporal Degeneration Frontotemporal degeneration (FTD) is a common cause of dementia, especially in patients younger than 70 years. It typically presents between ages 45 and 65 years. Clinically, FTD can be classified into three types: frontotemporal dementia, semantic dementia, and nonfluent aphasia.22 FTD is characterized pathologically by extensive loss of pyramidal neurons in the frontotemporal cortex, severe gliosis within the gray and white matter, spongiosis, and the presence of argyrophilic intraneuronal inclusion bodies (Pick bodies). Imaging does play a role in differentiating FTD from other neurodegenerative changes. There is preferential atrophy of the frontal and anterior temporal lobes, which helps distinguish it from AD.22 The three

Fig. 2.3 Alzheimer’s disease. (a) Coronal computed tomography scan image shows severe atrophy of amygdala and marked bilateral atrophy of the hippocampal formations with dilatation of the temporal horns. There is dilatation of the lateral ventricles. (b) Axial fluid-attenuated inversion recovery (FLAIR) image of the same patients shows severe atrophy of the amygdala and head and body of the hippocampi bilaterally (arrows).

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Imaging Techniques

Fig. 2.4 Serial changes in the hippocampus in patient with Alzheimer’s disease. Serial coronal magnetic resonance images of a patient with Alzheimer’s disease in (a) 2004, (b) 2006, and (c) 2008 show progressive hippocampal atrophy (arrows) with dilatation of the temporal horn. (d) Axial fluid-attenuated inversion recovery (FLAIR) image from the 2008 study shows severe atrophy of amygdala (arrows) and marked bilateral atrophy of the hippocampal formations (arrowheads) with dilatation of the temporal horns.

Fig. 2.5 Magnetic resonance imaging and positron emission tomography (PET) correlation in Alzheimer’s disease. (a) Coronal T1-weighted image shows moderate atrophy of the hippocampi in a 71-year-old man with memory loss, classic for Alzheimer’s disease. (b) Coronal and (c) axial fluorodeoxyglucose (FDG)-PET images of the brain show bilateral low uptake in the temporal (arrows in coronal) and parietal lobes (arrows in axial).

different types of FTD may show different appearance on MRI: (1) frontotemporal dementia is characterized clinically by behavioral disturbances, antisocial behavior, and disinhibition owing to primary involvement of the frontal lobes. There is atrophy primarily affecting the frontal lobes and anterior portions of the temporal lobes in the late stages of the disease (▶ Fig. 2.6). (2) Semantic dementia typically manifests with progressive anomia resulting from loss of long-term memory of language comprehension and object recognition. Unlike in AD patients, short-term memory is usually intact. Structural imaging shows atrophy of the frontal and temporal lobes, more pronounced in the temporal lobes, and often asymmetric, affecting the left temporal lobe more. (3) Nonfluent progressive aphasia is characterized by the preservation of verbal comprehension with severe disruption of conversational speech, speech dysfluency, and phonologic errors. MRI in these patients shows atrophy in the perisylvian regions of the frontal and temporal lobes. Severe thinning of the cortical gyri, giving a “knife-blade” appearance, is seen, especially in the anterior portion the superior temporal gyrus (▶ Fig. 2.7).

Lewy Body Dementia Lewy body dementia is a neurodegenerative disease with the histopathological hallmark of the intraneuronal aggregation of

α-synuclein protein inclusions (Lewy bodies). Patients have fluctuations in cognition, visual hallucinations, depression, and nighttime agitation. Antidopaminergic and anticholinergic neuroleptics may cause irreversible extrapyramidal symptoms in Lewy body dementia, making diagnosis crucial. Volumetric studies have shown that gray matter structures may be more affected than white matter structures. Conventional CT and MRI findings, although nonspecific, include atrophy of the putamen and cortical atrophy, predominantly in the occipital lobe.23,24

Corticobasal Degeneration Corticobasal degeneration (CBD) manifests in late adulthood. Patients with CBD have asymmetric limb apraxia, rigidity, or akinesia. Severe depression and cognitive decline, leading to dementia, may also be seen. Clinically, CBD is difficult to differentiate from FTD and progressive supranuclear palsy (PSP). No specific imaging appearances are identified, but progressive atrophy of the parietal lobes and caudate nuclei favor CBD (▶ Fig. 2.8). The cerebral hemispheres are often asymmetric and contralateral to the clinically affected side.25 Putaminal hypointensity, as well as hyperintense signal changes in the motor cortex or subcortical white matter on T2-weighted images, may also be seen in CBD.24 The asymmetric cerebral atrophy seen in

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Structural Imaging of Dementia

Fig. 2.6 Frontotemporal dementia in patient with behavioral disturbances. Axial (a) computed tomography and (b) T2-weighted magnetic resonance images show mild frontotemporal atrophy with sparing of the occipitoparietal lobes. Sagittal (c) single-photon emission computed tomography images show hypometabolism in the frontal lobe (arrow) with normal uptake in the rest of the cerebral parenchyma.

Fig. 2.7 Frontotemporal dementia in a 61-year old patient with nonfluent aphasia. Axial (a) T2-weighted and (b) T1-weighted images show severe atrophy of the anterior temporal lobes bilaterally left more than right. There is severe thinning of the superior temporal gyrus, giving a “knife-blade” (black arrows in T2 and white arrows in FLAIR images) appearance.

CBD is believed to distinguish it from AD. However, none of these structural MRI abnormalities seems to be of diagnostic relevance for CBD.

Huntington Disease Huntington disease is an autosomal dominant neurodegenerative disorder that typically manifests with chorea and dementia. The classic signs of Huntington disease include chorea (diffuse, involuntary, rapid, irregular, jerky movements) and a gradual loss of thought processing and acquired intellectual

abilities (dementia). The neurodegeneration associated with Huntington disease affects primarily the basal ganglia (especially the caudate nucleus) and the cerebral cortex. The characteristic imaging finding in Huntington disease is marked atrophy of the caudate nuclei and corpus striatum (▶ Fig. 2.9).24 The larger bicaudate and bifrontal ratios in Huntington disease patients are due to caudate atrophy and ventricular enlargement, respectively. Diffuse cerebral volume loss also may be seen and can be more pronounced in the frontal lobes than elsewhere. Preferential gray-matter atrophy also is described in the opercular cortex, hypothalamus, and right paracentral

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Imaging Techniques

Fig. 2.8 Corticobasal degeneration in 65-year-old man. (a) Axial and (b) coronal T1-weighted images show symmetric atrophy of the parietal lobes (arrows).

Studies have indicated that putamen atrophy occurs first and faster in Huntington disease than does caudate atrophy. Caudate atrophy is more prominent in the late stage of the disease.

Parkinsonian Disorders Brain MRI techniques have more easily demarcated the lines between the various parkinsonian disorders rather than the clinical symptoms, which overlap too much and have different prognoses and management.5 The parkinsonian diseases include idiopathic Parkinson’s disease, multiple-system atrophy (MSA), PSP, CBD, and manganese-induced parkinsonism.

Idiopathic Parkinson’s Disease

Fig. 2.9 Huntington disease. Axial T2-weighted images reveal atrophy of the head of the caudate nuclei (white arrows), with enlargement of the frontal horns of the lateral ventricles. The right putamen is slightly atrophic (arrowheads).

lobule. Patients who have the juvenile form of Huntington disease may also demonstrate hyperintense T2 signal in the caudate nuclei and putamina.25 Simmons et al26 showed that putamenatrophy (~50.1%) exceeded caudate changes (~27.7%), and volumetric measurement of the putamen was a more sensitive indicator of brain abnormalities in patients with mild Huntington disease than were measures of caudate atrophy. Data have also suggested that putamen volume measured with MRI is a preferable marker of preclinical Huntington disease.

Idiopathic Parkinson’s disease (IPD) is a movement disorder that is clinically characterized by resting tremor, rigidity, bradykinesia, and postural instability resulting from loss of dopaminergic neurons in the substantia nigra (SN) pars compacta.27,28 Pathological characteristics include the loss of pigmented dopaminergic neurons of the pars compacta of the SN and loss of pigmented cells of the locus ceruleus and dorsal motor nucleus of the vagus. In addition, reactive astrocytosis and intraneuronal aggregations of Lewy bodies are seen in the pars compacta. About 40 to 70% of patients with PD have dementia, which is mainly subcortical, resulting from dopaminergic insufficiency. The dementia in these patients is characterized predominantly by attention deficits and impairment in executive functions, whereas memory impairment may be secondary. On histology, PD patients show higher concentrations of Lewy bodies in the transrhinal and entorhinal cortices, the hippocampi, and the amygdala than do PD patients without dementia. Although conventional MRI is usually normal in early PD, it excludes the other possible causes of secondary parkinsonism (including vascular disorders, hydrocephalus, and neoplasms). On higher magnetic fields (3 tesla [T]) right-left asymmetry of the pars compacta may be a feature in early stages of the disease, especially in patients who have hemi-parkinsonism symptoms. Narrowing of the pars compacta of the SN may be seen in patients who have long-standing PD. The normal width of the pars compacta has been reported to be 4 mm, whereas in PD,

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Structural Imaging of Dementia the average width is 2.7 mm.29,30 Advanced cases of PD may show distinct abnormalities of the SN, including signal increase on T2-weighted MRIs or smudging of the hypointensity in the SN toward the red nucleus. Reduction or absence of normal hypointensity on T2-weighted images is seen in the pars reticulata of the SN as a result of selective neuronal loss. Segmented inversion recovery ratio imaging may demonstrate a significant decrease SN:midbrain ratio. Mild, nonspecific cortical and subcortical volume loss is observed in some patients.

Multiple-System Atrophy Multiple-system atrophy, characterized by autonomic dysfunction, pyramidal tract dysfunction, and cerebellar ataxia,31,32 is due to neuronal loss and gliosis of the nigrostriatal tract in the MSA-parkinsonism type (MSA-P) and olivopontocerebellar tract in the MSA-cerebellar type (MSA-C).5 Pathology demonstrates glial cytoplasmic inclusion bodies. MSA is often confused with PD. Structural MRI findings that point toward MSA-P include atrophy and signal alteration in the putamen. Putaminal hypointensities and putaminal rim hyperintensities (“slit-like” margin) on T2-weighted imaging correspond to neuronal loss, iron deposition, and gliosis. Putaminal hyperintense rim helps to differentiate MSA from IPD, but it does not help in differentiating MSA from PSP and CBD. On 3.0 T, a hyperintense putaminal rim on T2-weighted imaging is thought to be nonspecific and may be a normal finding in elderly patients. Putaminal hypointensity is not unique to MSA but may rarely be seen in IPD because iron accumulation occurs in both.33,34 Specificity of these findings for differentiating MSA-P from PD and healthy controls is considered high, whereas sensitivity, especially in the early disease stages, seems insufficient. T2-weighted gradient-echo putaminal hypointensity and fluid-attenuated inversion recovery (FLAIR) putaminal rim hyperintensity constitute the most accurate method for differentiating MSA from IPD.35 The MSA cerebellar type (MSA-C) can involve early childhood or old age. Its first sign is ataxia, first in the legs then in the arms and hands, and finally it shows bulbar manifestation.5 The primary degeneration involves pontine nuclei, with subsequent progressive antegrade degeneration of the pontocerebellar tracts and the cerebellar cortex hemispheric greater than vermian. Later in the disease, the inferior olive loses its normal

bulge because of neuronal loss and gliosis. MRI shows atrophy of the pons with flattening of the inferior part (loss of normal pregnant belly of pons) (▶ Fig. 2.10a).36 Atrophy of the cerebellar cortex (hemispheric greater than vermian), MCP, and inferior olives is also seen. Degeneration of pontine neurons and transverse pontocerebellar fibers with normal signal intensity in the surrounding parenchyma give a classic “hot-cross bun” sign of the pons on axial T2-weighted images (▶ Fig. 2.10b).37 The average MCP width was significantly smaller in patients (cutoff value of 8 mm) with MSA than in those with PD or in control subjects.38

Progressive Supranuclear Palsy Progressive supranuclear palsy occurs in late adulthood and is characterized by vertical gaze palsy, slow vertical saccades, postural instability, and frequent falls. Dementia is mild and is seen in the late stages of the disease. PSP is histologically characterized by tau-positive NFTs and glial and neuronal loss, mainly in the basal ganglia and brainstem.5 It is important to differentiate PSP from other forms of movement disorders because PSP patients typically do not respond well to dopamine replacement therapy. Structural MRI findings that point toward PSP include symmetric progressive atrophy of the midbrain, superior cerebellar peduncles, thalami, and caudate nuclei.39,40 There is associated enlargement of the third ventricle and tegmental atrophy, with increase signal intensity in the midbrain. On sagittal images, the superior contour of the midbrain may have a flattened or concave profile, a finding believed highly specific for PSP. A reduced anteroposterior midbrain diameter of less than 14 mm has been proposed to optimally separate PSP from other types of neurodegenerative parkinsonism and healthy controls.36,41 Another indirect sign of midbrain atrophy in patients with PSP is the “penguin silhouette” or “hummingbird” sign, corresponding to the shape of the midbrain tegmentum (the bird’s head) and pons (the bird’s body) on midsagittal MRI (▶ Fig. 2.11).42 Visual assessment of atrophy of the superior cerebellar peduncle (SCP) can distinguish PSP patients from controls and from patients with other parkinsonian disorders, including MSA and PD, with a sensitivity of 74% and a specificity of 94%. Ratios and indices of the pons and midbrain are also used to distinguish PD, MSA, and PSP from each other. Calculation of the ratio

Fig. 2.10 Multiple-system atrophy (MSA)-cerebellar type (MSA-C). Olivopontocerebellar degeneration. (a) Sagittal T1-weighted and (b) T2-weighted images show atrophy of the pons (white wide arrow), the middle cerebellar peduncles, and the cerebellar hemispheres (white thin arrow). Axial T2-weighted image shows classic cruciform pattern of the pontine fibers called “hot-cross bun sign” (black arrow).

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Imaging Techniques

Fig. 2.11 Serial changes in the midbrain in a patient with progressive supranuclear palsy (PSP). (a) Sagittal T1-weighted image in 2009 shows mild atrophy in the midbrain, early changes of PSP. (b) Magnetic resonance imaging done in 2010 shows concave profile (white arrow) of the superior surface of the midbrain. In 2011 (c) midsagittal image shows atrophy of midbrain, dilatation of the third ventricle, and widening of the interpeduncular fossa, giving a ‘‘hummingbird’’ appearance (arrow).

between pontine and midbrain areas has been demonstrated to discriminate between PSP patients and patients with PD, MSA-P, or healthy controls. Quattrone and colleagues43 have proposed an index termed the MRI parkinsonism index (MRPI), which is calculated by multiplying the pontine:midbrain area ratio by the ratio of the MCP:SCP width (MCP/superior cerebellar puduncle). The MRPI is shown to be significantly larger in patients with PSP than in healthy controls or in PD and MSA-P patients. Atrophic changes are also seen in the inferior olives and frontal and temporal lobes. Atrophy of the frontal lobes is particularly seen in the orbitofrontal and medial cortex, which may help in distinguishing PSP from PD. The degree of atrophy seen in the frontal lobes correlates well with the level of behavioral disturbance seen clinically, as does the degree of atrophy in the caudate nuclei and brainstem with the severity of motor function impairment.

2.5 Reversible Dementia Imaging plays an important role in diagnosing and differentiating the various causes of reversible or preventable dementia. Clinical history, examinations, various laboratory tests, and imaging can easily pinpoint the diagnosis of reversible dementia. Medications, nutritional abnormalities, endocrine dysfunction, infection and inflammatory conditions, vascular problems, and toxins are a few of the common causes of reversible dementia. Space-occupying lesions, such as subdural hematoma, large intra-axial or extra-axial masses, and NPH, are well-known causes of reversible dementia and can be diagnosed easily by imaging. Imaging findings of these pathologies are discussed in detail in subsequent chapters. Cognitive decline and dementia resulting from medications, nutritional abnormalities, or endocrine dysfunction are predominantly suspected or diagnosed based on clinical history, examination, and laboratory analysis. Imaging plays a role of exclusion in the diagnosis of these conditions. Infection and inflammatory conditions, such as human immunodeficiency (HIV) dementia, Creutzfeldt-Jakob disease

(CJD), progressive multifocal leukoencephalopathy (PML), Lyme disease, and multiple sclerosis, may be diagnosed with a combination of imaging and blood and CSF examinations. HIV dementia (AIDS dementia complex) is caused by direct infection of the macrophages and microglia of the central nervous system by the HIV retrovirus. With the advent of highly active antiretroviral therapy, there has been a significant decrease in the incidence of HIV dementia. Gray as well as white matter may be affected with HIV infection, leading to generalized cortical atrophy and diffuse bilateral white-matter abnormalities. These abnormalities are seen most commonly in the peritrigonal and subinsular white matter, although they can progress to a more confluent and diffuse pattern of leukoencephalopathy. PML is seen mostly in the setting of HIV or in patients undergoing immunosuppressive therapy or who have hematologic malignancies. PML is caused by reactivation of the Jamestown Canyon virus. The diagnosis of PML is confirmed by detection of JCV DNA by polymerase chain reaction in CSF. However, imaging, especially MRI, certainly leads to the diagnosis of PML from the pattern and distribution of the abnormality. The diagnostic hallmark of PML is the presence of multiple foci of demyelination found initially sparsely distributed in the subcortical white matter but also in the cortex and deep gray structures. These lesions are frequently bilateral and multiple with involvement of the subcortical U fiber. Mass effect and hemorrhage are unusual. Demyelination is predominantly seen involving the parietal, occipital, and frontal lobes. Lesions lack enhancement and restricted diffusion. CJD is caused by a protease-resistant prion protein. Clinically, it presents with triad of myoclonus, progressive dementia, and periodic sharp-wave patterns on electroencephalography. The disease is characterized histopathologically by neuronal destruction, gemistocytic astrocytosis, spongiform changes, and prion deposition. On MRI, signal abnormalities are seen, most commonly in the gray-matter structures, including the cerebral cortex, basal ganglia, and thalami. Diffusion-weighted imaging may show restricted diffusion with bright signal on T2-weighted images in these areas. The variant form of CJD has a characteristic appearance on conventional MRI sequences (called the pulvinar sign), sym-

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Structural Imaging of Dementia metric high signal on T2-weighted images, and FLAIR sequences in the posterior thalami. Intracranial space-occupying lesions causing dementia are discussed in detail in Chapters 30 and 39. Cross-sectional imaging by either CT or MRI is sensitive and diagnostic of these conditions. NPH shows the classic imaging appearance of ventricular dilatation out of proportion to the convexity sulci, which becomes significant with the appropriate clinical setting: the triad of dementia, recent-onset gait apraxia, and urinary incontinence. Dilation of the temporal horns occurs without the hippocampal atrophy seen in AD. Additionally, the parahippocampal fissure is spared in NPH compared with hydrocephalus of other causes. Radioisotope cisternography demonstrates decreased CSF flow with delayed transit of the radiotracer to the subarachnoid space over the cerebral convexities. NPH can be identified on routine imaging, and patients achieve substantial benefit from CSF shunting. Vascular dementia is the term used to define the cognitive impairment resulting from cerebrovascular disease and ischemic or hemorrhagic brain injury. Pathophysiology, causes, criteria, and imaging findings are discussed in detail in Chapters 21 and 22.

References [1] George AE, de Leon MJ, Golomb J, Kluger A, Convit A. Imaging the brain in dementia: expensive and futile? AJNR Am J Neuroradiol 1997; 18: 1847– 1850 [2] Schoenberg BS. Epidemiology of Alzheimer’s disease and other dementing illnesses. J Chronic Dis 1986; 39: 1095–1104 [3] Clark RF, Goate AM. Molecular genetics of Alzheimer’s disease. Arch Neurol 1993; 50: 1164–1172 [4] Golomb J, de Leon MJ, Kluger A, George AE, Tarshish C, Ferris SH. Hippocampal atrophy in normal aging: an association with recent memory impairment. Arch Neurol 1993; 50: 967–973 [5] Sitburana O, Ondo WG. Brain magnetic resonance imaging (MRI) in parkinsonian disorders. Parkinsonism Relat Disord 2009; 15: 165–174 [6] Le May M. CT changes in dementing diseases. AJNR Am J Neuroradiol 1986; 7: 841–853 [7] George AE, de Leon MJ, Stylopoulos LA et al. CT diagnostic features of Alzheimer’s disease: importance of the choroidal/hippocampal fissure complex. AJNR Am J Neuroradiol 1990; 11: 101–107 [8] Rusinek H, de Leon MJ, George AE et al. Alzheimer’s disease: measuring loss of cerebral gray matter with MR imaging. Radiology 1991; 178: 109–114 [9] Jack CR, Jr, Petersen RC, O’Brien PC, Tangalos EG. MR-based hippocampal volumetry in the diagnosis of Alzheimer’s disease. Neurology 1992; 42: 183– 188 [10] De Leon MJ, George AE, Golomb J et al. Frequency of hippocampal formation atrophy in normal aging and Alzheimer’s disease. Neurobiol Aging 1997; 18: 1–11 [11] Ebisu T, Tanaka C, Umeda M et al. Hemorrhagic and nonhemorrhagic stroke: diagnosis with diffusion-weighted and T2-weighted echo-planar MR imaging. Radiology 1997; 203: 823–828 [12] Vernooij MW, Smits M. Structural neuroimaging in aging and Alzheimer’s disease. Neuroimaging Clin N Am 2012; 22: 33–55, vii–viii [13] Ashburner J, Friston KJ. Voxel-based morphometry—the methods. Neuroimage 2000; 11: 805–821 [14] Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005; 26: 839– 851 [15] Enzinger C, Fazekas F, Matthews PM et al. Risk factors for progression of brain atrophy in aging: six-year follow-up of normal subjects. Neurology 2005; 64: 1704–1711 [16] Barnes J, Bartlett JW, van de Pol LA et al. A meta-analysis of hippocampal atrophy rates in Alzheimer’s disease. Neurobiol Aging 2009; 30: 1711–1723 [17] Vernooij MW, van der Lugt A, Ikram MA et al. Prevalence and risk factors of cerebral microbleeds: the Rotterdam Scan Study. Neurology 2008; 70: 1208– 1214

[18] Petersen RC, Smith GE, Waring SC. Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 1999; 56: 303–308 [19] Glodzik-Sobanska L, Rusinek H, Mosconi L, et al. The role of quantitative structural imaging in the early diagnosis of Alzheimer’s disease. Neuroimaging Clin N Am 2005; 15: 803–826, x [20] Ohm TG, Müller H, Braak H, Bohl J. Close-meshed prevalence rates of different stages as a tool to uncover the rate of Alzheimer’s disease-related neurofibrillary changes. Neuroscience 1995; 64: 209–217 [21] Fox NC, Freeborough PA, Rossor MN. Visualisation and quantification of rates of atrophy in Alzheimer’s disease. Lancet 1996; 348: 94–97 [22] Tartaglia MC. Frontotemporal lobar degeneration: new understanding brings new approaches. Neuroimaging Clin N Am 2012; 22: 83–97, viii [23] Keyserling H, Mukundan S, Jr. The role of conventional MR and CT in the work-up of dementia patients. Magn Reson Imaging Clin N Am 2006; 14: 169–182 [24] Gallucci M, Limbucci N, Catalucci A, Caulo M. Neurodegenerative diseases. Radiol Clin North Am 2008; 46: 799–817, vii [25] Tokumaru AM, O’uchi T, Kuru Y, Maki T, Murayama S, Horichi Y. Corticobasal degeneration: MR with histopathologic comparison. AJNR Am J Neuroradiol 1996; 17: 1849–1852 [26] Simmons JT, Pastakia B, Chase TN, Shults CW. Magnetic resonance imaging in Huntington disease. AJNR Am J Neuroradiol 1986; 7: 25–28 [27] Hughes AJ, Daniel SE, Kilford L, Lees AJ. Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry 1992; 55: 181–184 [28] Dexter DT, Wells FR, Lees AJ et al. Increased nigral iron content and alterations in other metal ions occurring in brain in Parkinson’s disease. J Neurochem 1989; 52: 1830–1836 [29] Friehs GM, Ojakangas CL, Pachatz P, Schröttner O, Ott E, Pendl G. Thalamotomy and caudatotomy with the Gamma Knife as a treatment for parkinsonism with a comment on lesion sizes. Stereotact Funct Neurosurg 1995; 64 Suppl 1: 209–221 [30] Braffman BH, Grossman RI, Goldberg HI et al. MR imaging of Parkinson’s disease with spin-echo and gradient echo sequences. AJNR Am J Neuroradiol 1988; 9: 1093–1099 [31] Quinn N. Multiple system atrophy—the nature of the beast. J Neurol Neurosurg Psychiatry 1989 Suppl: 78–89 [32] Geser F, Seppi K, Stampfer-Kountchev M et al. EMSA-SG. The European Multiple System Atrophy-Study Group (EMSA-SG). J Neural Transm 2005; 112: 1677–1686 [33] Kraft E, Schwarz J, Trenkwalder C, Vogl T, Pfluger T, Oertel WH. The combination of hypointense and hyperintense signal changes on T2-weighted magnetic resonance imaging sequences: a specific marker of multiple system atrophy? Arch Neurol 1999; 56: 225–228 [34] Lee EA, Cho HI, Kim SS, Lee WY. Comparison of magnetic resonance imaging in subtypes of multiple system atrophy. Parkinsonism Relat Disord 2004; 10: 363–368 [35] von Lewinski F, Werner C, Jörn T, Mohr A, Sixel-Döring F, Trenkwalder C. T2*weighted MRI in diagnosis of multiple system atrophy: a practical approach for clinicians. J Neurol 2007; 254: 1184–1188 [36] Schrag A, Good CD, Miszkiel K et al. Differentiation of atypical parkinsonian syndromes with routine MRI. Neurology 2000; 54: 697–702 [37] Abe K, Hikita T, Yokoe M, Mihara M, Sakoda S. The “cross” signs in patients with multiple system atrophy: a quantitative study. J Neuroimaging 2006; 16: 73–77 [38] Nicoletti G, Fera F, Condino F et al. MR imaging of middle cerebellar peduncle width: differentiation of multiple system atrophy from Parkinson’s disease. Radiology 2006; 239: 825–830 [39] Savoiardo M, Girotti F, Strada L, Ciceri E. Magnetic resonance imaging in progressive supranuclear palsy and other parkinsonian disorders. J Neural Transm Suppl 1994; 42: 93–110 [40] Aiba I, Hashizume Y, Yoshida M, Okuda S, Murakami N, Ujihira N. Relationship between brainstem MRI and pathological findings in progressive supranuclear palsy—study in autopsy cases. J Neurol Sci 1997; 152: 210–217 [41] Asato R, Akiguchi I, Masunaga S, Hashimoto N. Magnetic resonance imaging distinguishes progressive supranuclear palsy from multiple system atrophy. J Neural Transm 2000; 107: 1427–1436 [42] Oba H, Yagishita A, Terada H et al. New and reliable MRI diagnosis for progressive supranuclear palsy. Neurology 2005; 64: 2050–2055 [43] Quattrone A, Nicoletti G, Messina D et al. MR imaging index for differentiation of progressive supranuclear palsy from Parkinson’s disease and the Parkinson variant of multiple system atrophy. Radiology 2008; 246: 214–221

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Imaging Techniques

3 Magnetic Resonance Spectroscopy in Neurodegenerative Disorders Tushar Chandra, Suyash Mohan, Sanjeev Chawla, and Harish Poptani Magnetic resonance spectroscopy (MRS) has evolved as a useful technique to complement the anatomical information obtained from magnetic resonance imaging (MRI) with quantitative information on the chemical composition of brain in vivo. The fundamental theory of nuclear magnetic resonance (NMR) is the same for both MRI and MRS. MRI relies on obtaining anatomical information from hydrogen protons of water, whereas MRS provides information about the chemical environment of hydrogen protons of other brain metabolites. MRS has long been used in chemistry to characterize the synthesis and purity of chemical compounds; however, it has taken a long time for MRS to evolve to the extent that it can be relevant to diagnostic evaluation of patients and helpful in clinical decision-making. In current clinical practice, MRS is a useful noninvasive diagnostic tool to provide information about the metabolites in the brain and how they are affected in disease processes. Although the sensitivity of MRS to differentiate disease processes remains limited, it is a useful technique for complementing critical chemical information with the anatomical information obtained from MRI, and in many cases it can clinch the diagnosis. The goal of this chapter is to familiarize readers with basic concepts of MRS and to analyze the role MRS plays in evaluation of the plethora of neurodegenerative disorders that affect the brain. This requires a thorough understanding of the basic principles that govern the technique, knowledge about the neuron-specific markers, and, most importantly, knowing how to implement the technique according to clinical requirements.

3.1 Basic Principles and Technique Many fundamental physics concepts need to be understood before we can really look at how MRS gives us neurometabolic information. The first of these is the concept of nuclear magnetism.

3.1.1 Nuclear Magnetism The basic concept of electromagnetism is that a charged particle has a magnetic field around it. This concept applies to biological tissues as well. Atoms possessing an even number of protons and neutrons are not magnetic and therefore cannot be used with this technique. The nuclei that can be used in MRS studies include hydrogen (H1), phosphorus (P31), C13, F19, and Na23; however, only H1 and P31 exist in biological tissues in high enough concentrations to obtain a spectrum. Proton spectroscopy that is based on H1, the most abundant nuclei in the body, has been most widely used to date.

3.1.2 Chemical Shift The protons in various biological tissues are in a state of rotation (or precession) around an axis and get aligned to the direction of the externally applied magnetic field. On application of a

radiofrequency pulse that matches the frequency of the external magnetic field, there is resonance. MRI uses this phenomenon to generate signals from protons in vivo. The frequency of precession of an atom is given by the Larmor frequency, which is described by the following equation: W ¼ γBo

ð3:1Þ strength.1

For where γ = gyromagnetic ratio, Bo = magnetic field hydrogen (H1) nucleus at 1.5 tesla (T), the Larmor frequency is 63.5 MHz, whereas for phosphorus (P31), it is 25 MHz. Selectively applying radiofrequency pulses to match the Larmor frequency of a given nucleus allows for specific observation of different nuclei in MRS. The magnetic field experienced by a nucleus depends not only on the external magnetic field but also on the small magnetic fields that are generated by the electron clouds that surround the nucleus. These electron clouds shield the nucleus from the external magnetic field and result in a slightly different magnetic field actually experienced by the nucleus. As different nuclei in biological tissues have different microenvironments (because of the electron cloud), the shielding effect is different. Hence, based on the local chemical environment, the magnetic field experienced by the nuclei differs; the difference in local magnetic field is quite small and is called the chemical shift. This chemical shift can be expressed in terms of 1 Hz per million Hertz Hz, or simply parts per million (ppm). The chemical shift specific for a given metabolite is independent of the external magnetic field strength and can help in identifying the compound on the MR spectrum. In the spectrum, the frequency characterized by chemical shift in parts per million is depicted on the x-axis, and the amplitude is depicted on the y-axis. The quantification of metabolite concentration can be made by the area under the peak.

3.1.3 Data Acquisition Acquiring data is similar to MRI with a few additional steps. Shimming is the first step required to produce MRS data. Shimming refers to the process of creating a homogeneous magnetic field. The inhomogeneity can be minimized by tuning various field gradients in the x-, y-, and z-axis. This is usually done automatically but may also be done manually. The second step is water and fat suppression. The concentration of water protons is about ten thousand times the concentration of other metabolites in a biological tissue.2,3,4,5 The predominant spectrum, therefore, is of water; unless it is suppressed, the other metabolite cannot be observed to the extent of obtaining meaningful data. This can be done by adding water-suppressing pulses. Chemical shift selective water suppression is the most commonly applied technique for this purpose. Additionally, frequency-selective fat-suppression pulse is used to suppress the lipid and fat signal from the skull and marrow. Most of the lipid inside the brain is in the membranebound form and is not visible on in vivo MRS.

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Magnetic Resonance Spectroscopy in Neurodegenerative Disorders

3.2 Techniques Typically, MRS is performed after obtaining anatomical information by MRI. A suitable volume of interest is selected for placement of a voxel to obtain a spectrum from the region of interest. Each of the many different MRS techniques that can be performed has its merits and limitations. MRS can be done using a single-voxel or multivoxel technique using short or long echo time (TE). Knowledge of these techniques and choosing the appropriate technique in a given clinical scenario are vital for successful implementation of MRS.

3.2.1 Single-Voxel Spectroscopy Versus Chemical Shift Imaging As the name suggests, single-voxel spectroscopy provides data from a single voxel at a time. The region of interest is selected based on the clinical question being addressed. It is highly accurate (with minimal partial volume) and provides good field homogeneity. The other technique is multivoxel spectroscopy, also known as chemical shift imaging or magnetic resonance spectroscopic imaging. This technique allows evaluation of a larger area of interest that encompasses multiple voxels and can be used with either a two-dimensional or three-dimensional technique. The tradeoff is longer acquisition time and a slightly less accurate voxel localization as the data from each voxel “bleed” into the neighboring voxel.

3.2.2 Short Versus Long Echo Time In general, clinical MRS data vary with the choice of the TE used in the pulse sequence. Short TE techniques generally use a TE of approximately 20 to 40 ms, which permits detection of additional metabolites that have a relatively short T2 compared with sequences with long TE. Furthermore, because of the lower TE value used, the signal-to-noise ratio (SNR) in these techniques is higher compared with long TE techniques;

however, the spectra can be “crowded” by the larger number of metabolite peaks. Thus, several metabolite peaks can appear as overlapping signals on short TE techniques. Magnetic resonance spectroscopy can be performed with intermediate and long TE as well, in the range of 135 to 288 ms, which results in lesser metabolite peaks and a “cleaner” spectrum. However, the SNR is worse compared with that with short TE techniques. An advantage of long TE techniques is that the lactate peak is inverted below the baseline in the form of a doublet at 135 to 144 ms, and this can help in separating the lipid spectrum from lactate, which remains over the baseline.

3.3 Metabolite Peaks The following are the major metabolite peaks observed in proton MRS (▶ Fig. 3.1): ● N-acetyl aspartate (NAA): NAA is the largest metabolite peak and resonates at 2.02 ppm. It is the marker of neuronal and axonal integrity. NAA is decreased in any pathological condition that results in neuronal loss; hence, a decrease in NAA is seen in almost all neurodegenerative disorders. An increase in NAA is observed in Canavan’s disease (an autosomal recessive leukodystrophy), however, because it is caused by a deficiency of the enzyme aspartoacylase, which leads to elevation of NAA in the brain and urine. ● Creatine (Cr): Creatine and phosphocreatine resonate at 3.0 ppm. Cr is a marker for brain energy metabolism and is thought to be stable; it is used as an internal reference for other brain metabolites. ● Choline (Cho): Choline-containing compounds (free choline, phosphocholine, and glycerophosphocholine) resonate at 3.2 ppm. Cho is a constituent of cell membrane and is a marker for membrane turnover. The Cho level is increased in conditions with rapid cell membrane turnover or an increased number of cells. Cho levels are high in tumors and demyelinating conditions. ● Lipids: Lipids (or free fatty acids) resonate from 0.9 to 1.5 ppm. Lipids are markers of severe cell stress and tissue

Fig. 3.1 Axial T1-weighted image (a) from a normal healthy subject demonstrating voxel position from right frontal lobe. Proton magnetic resonance spectra acquired with positron-resolved spectroscopy sequence using short echo time (TE; 30 ms); (b) long TE (135 ms); (c) displaying characteristic resonances: N-acetyl aspartate (NAA) (2.02 parts per million [ppm]), creatine (Cr, 3.02 ppm), choline (Cho, 3.22 ppm), glutamate (Glx, 2.35 ppm), and myo-inositol (mI, 3.56 ppm) from the voxel shown in (a). Note the spectra acquired with TE = 30 ms. (b) Broader resonances along with appreciable baseline distortion, mainly because of contamination of signals from shorter T2 components such as macromolecules. Also because of shorter T2 value of Cr than that of Cho, a higher Cho:Cr ratio is observed at longer TE spectra (c) compared with shorter TE spectra (b).

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Imaging Techniques







damage, such as the liberation of membrane lipids that is seen in the necrotic brain tumors. Lactate: Lactate is seen as an inverted doublet at 1.3 ppm on MRS performed at a TE of 135 to 144 ms. At low TE values (20 to 40 ms) and higher TE values (270 ms), lactate is seen as a doublet peak above the baseline that overlaps with the lipid peak at short TE spectra. Lactate is not normally detectable in the brain spectra, and the presence of lactate signifies lack of oxidative phosphorylation and onset of anaerobic glycolysis. Increased lactate levels are seen in ischemia, hypoxia, brain tumors, and mitochondrial disorders. Myo-inositol (mI): mI resonates at 3.56 ppm and is seen when using a short TE; it is an osmolyte and astrocytic marker. An increase in mI is seen in Alzheimer’s disease (AD) and frontotemporal dementias (FTDs). Glutamine and glutamate (Glx): These metabolites resonate from 2.2 to 2.4 ppm. Increased levels are noted in metabolic conditions resulting in hyperammonemia, such as hepatic encephalopathy.

3.4 Normal Aging The normal process of aging induces many microstructural changes in the brain that involve both the cortex as well as the white matter. The volume of the brain decreases by approximately 5% every decade after the age of 40.6 Structurally, in addition to the volume loss, there is increased iron deposition and increased white matter hyperintensities. In terms of chemical composition, there is decreased brain water content and increased cerebrospinal fluid (CSF).7 Within the brain, however, not all structures are affected equally by aging. The earliest affected area is the prefrontal cortex, followed by the striatum, temporal lobe, cerebellar vermis, cerebellar hemispheres, and hippocampus. The occipital cortex is the least affected.8 Individual variations in neurometabolite levels correlate significantly with cognitive function in the elderly. Maintenance of creatine level is important in the pathophysiology of normal aging. Proton MRS studies have shown higher Cr levels in healthy aging brains compared with healthy young brains.9,10 Creatine is a sum of phosphocreatine and creatine. Phosphocreatine is converted to adenosine triphosphate by creatine kinase, an enzyme that decreases with aging. Therefore, it is logical that Cr concentration increases with age. Furthermore, increased Cr level may be a marker of decreased brain energy metabolism and may be related to age-related mild cognitive impairment or even frank dementia.11,12 Kadota et al have also demonstrated a steady and almost linear decrease in the white matter NAA:Cr ratio starting in the third decade and continuing into old age.13 No correlation has been found between NAA or Cho levels and the process of aging.

3.5 Alzheimer’s Disease Alzheimer’s disease is the leading cause of dementia in the elderly. Typically, there is progressive dementia that most profoundly affects the declarative memory, especially early in the disease process. The disease is diagnosed based on clinical criteria that require exclusion of other causes of dementia and demonstration of progressive loss in more than one

domain. Clinical diagnosis of AD is currently made by the Diagnostic and Statistical Manual of Mental Disorders, 4th edition text revision and the National Institute of Neurological and Communicative Disorders and Stroke Alzheimer's Criteria. The definitive diagnosis of AD, however, can be made only at autopsy. Pathological changes develop first in the hippocampus and the entorhinal cortex and include a combination of neuronal loss, amyloid deposition, glial proliferation, decreased synaptic density, and vascular changes with formation of senile plaques and NFTs.14,15,16 The role of imaging in AD cases is to diagnose the condition before the onset of overt symptoms to provide a therapeutic window for drug treatment. Anatomical changes in the brain develop late in the disease process, and findings on MRI can be nonspecific. Although hippocampal atrophy is the hallmark of AD, it can be seen in various other forms of neurodegeneration. The temporal evolution of neuropathological changes in AD is thought to follow a distinct pattern. The earliest changes of AD in the preclinical stage develop in the entorhinal cortex and hippocampus. Subsequently, there is involvement of the neocortex and development of overt dementia. Many studies have correlated the neuropathological findings to the development of dementia.17,18 MRS also mirrors these findings, with abnormal spectra from the posterior cingulate gyrus and hippocampus in early AD. For a long time, MRS has been used in neurodegenerative disorders. Klunk et al were probably first to demonstrate decreased NAA levels on spectra from perchloric extracts in patients with AD.19 The primary neurometabolites affected in AD are NAA and mI. Because NAA is found in all neurons, a decrease in NAA is expected in any condition that involves neuronal loss. It is a marker of neuronal viability and functionality. Increased levels of mI reflect glial proliferation or increased glial size.20 Elevation of mI in AD is thought to represent glial activation and microglial proliferation.21 The role of other neurometabolites in the diagnosis of AD is not certain. Creatine levels are used as an internal reference to calculate ratios. Regarding Cho, results have been conflicting among various studies, and no clear consensus of authorities has been established as to whether it is increased, decreased, or unchanged in AD. The hallmark of spectroscopic alterations in AD is elevation of mI:Cr and a decrease in NAA:Cr ratios in various anatomical regions within the brain.19–23 It has also been found that mI:Cr is elevated in mild cognitive impairment and mild AD, even in the absence of a decrease in NAA:Cr.21,24 Therefore, the initial change in the progression of AD is elevation of mI:Cr, and a decrease in NAA:Cr develops later. Furthermore, the decrease in NAA:Cr correlates with dementia severity and cognitive symptoms, indicating that decreased NAA is the marker to quantitatively assess disease severity.25,26 MRS has not been widely used to assess treatment response, although a few single-site trials have shown improvement in NAA:Cr ratios after therapy. No multicentric data have reliably demonstrated improvement in NAA:Cr or mI:Cr ratios after drug therapy.

3.6 Dementia with Lewy Bodies Dementia with Lewy bodies (DLB) is the second most common cause of dementia, after AD, and it frequently coexists with AD.

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Magnetic Resonance Spectroscopy in Neurodegenerative Disorders The classic clinical picture is a triad of fluctuating cognitive impairment, recurrent visual hallucinations, and parkinsonism. The symptoms overlap with both AD and Parkinson’s disease. DLB is pathologically characterized by the finding of Lewy bodies in the cortex. Lewy bodies are seen in the substantia nigra in Parkinson’s disease. In patients with DLB, loss of cholinergic neurons is thought to account for degeneration of cognitive function, and the death of dopaminergic neurons appears to be responsible for degeneration of motor control. The most important spectroscopic discriminating feature of DLB from other forms of dementias is the finding of a normal NAA:Cr ratio in the posterior cingulate gyrus. Patients with AD, FTD, or vascular dementia have decreased NAA:Cr ratios in this region.27 Molina et al demonstrated significantly lower mean NAA:Cr, Glx:Cr, and Cho:Cr ratios in the white matter in patients with DLB compared with controls.28 The spectra obtained from the gray matter were normal, suggesting involvement of white matter in DLB, a finding subsequently confirmed by diffusion tensor imaging.29,30 Kantarcki et al demonstrated increased Cho:Cr in the posterior cingulate gyrus in patients with DLB. The Cho level was also elevated in DLB as well as in AD.27 Xuan et al showed that patients with DLB had significantly lower NAA:Cr ratios in the bilateral hippocampi, whereas the Cho:Cr ratio did not differ from the control group.31 However, AD can coexist in many patients with DLB, and thus the hippocampal spectrum in these patients may reflect pathological changes as a result of AD rather than of DLB.

3.7 Frontotemporal Dementia Frontotemporal dementia is a progressive neurodegenerative disorder characterized by tau- or ubiquitin-positive spherical cortical inclusions, gliosis, and microvacuolar degeneration predominantly involving the frontal and anterior temporal lobes.32,33,34,35 FTD accounts for nearly 20% of presenile dementia cases. The disease has three major variants: the behavioral variant, semantic dementia, and progressive nonfluent aphasia. On the basis of cognitive neuropsychological evidence, the ventromedial prefrontal cortex is a major locus of dysfunction early in the course of the behavioral variant of FTD.36 Proton MRS studies have demonstrated a decrease in NAA levels and an increase in Cho and mI from many sites, including the anterior and posterior cingulate cortex, medial frontal cortex, and temporal cortex. This pattern is similar to the findings observed in patients with AD, and there is considerable overlap in the neurometabolite abnormalities observed in these conditions. Chawla et al demonstrated similar findings in spectra obtained from the dorsolateral prefrontral cortex as well as the motor cortex in these patients (▶ Fig. 3.2, ▶ Fig. 3.3).37 They suggested a possible association between FTD and motor neuron disease (MND) in view of the similar metabolic alterations in the motor cortex from this subset of patients. This is supported by the fact that some FTD patients with clinically normal motor examination demonstrate abnormal electromyography of the tongue and extremity muscles, as seen in MND.

Fig. 3.2 Proton magnetic resonance spectroscopic imaging grids overlaid over axial T2weighted images demonstrating the location of voxels from dorsolateral prefrontal cortex region from a frontotemporal dementia (FTD) patient (a) and from a healthy controls (b). Corresponding spectra (echo time [TE] = 30 ms) from the voxels demonstrating various metabolites. Note the reduced N-acetyl aspartate (NAA) and elevated resonances of choline (Cho) and myoinositol (mI) in FTD patients compared with controls.

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Imaging Techniques

Fig. 3.3 Proton magnetic resonance spectroscopic imaging grids overlaid over axial T2weighted images demonstrating the location of voxels from motor cortex region from a frontotemporal dementia (FTD) patient (a) and from healthy controls (b). Corresponding spectra (echo time [TE] = 30 ms) from the voxels demonstrating various metabolites. Please notice reduced Nacetyl aspartate (NAA) and elevated resonances of choline (Cho) and myo-inositol (mI) in FTD patients compared with controls.

3.8 Creutzfeldt-Jakob Disease Creutzfeldt-Jakob disease (CJD) is an incurable and invariably fatal neurodegenerative disease caused by infection with agents called prions. Prions are misfolded proteins, and they cause the properly folded proteins in their host to become misfolded, leading to rapid neurodegeneration. Clinical presentation is rapidly progressive dementia and myoclonus. Apart from the clinical signs and symptoms, diagnosis can be made by demonstrating characteristic triphasic spikes on electroencephalography and 14–3-3 protein in CSF analysis. The disease has four subtypes: sporadic, variant, iatrogenic, and familial. It is important to differentiate the variant form because it is transmitted by cattle infected by bovine spongiform encephalopathy virus. Variant CJD has a pathognomic “pulvinar” sign on MRI, defined as high T2 signal in the pulvinar thalami, which is higher than that in the basal ganglia. The other subtypes of the disease demonstrate high T2 signal and restricted diffusion in the striatum, thalamus, and cortex. The characteristic histopathological features are spongiform degeneration of the neurons, astrocytic gliosis, amyloid plaque formation, and neuronal loss. Spongiform degeneration is seen in the cortex, putamen, caudate nucleus, thalamus, and hippocampus. Spongiform change or vacuolization restricts free diffusion of protons, leading to hyperintensity of lesions on diffusion-weighted imaging (DWI).38 The characteristic findings are restricted diffusion in the basal ganglia and the cortex. On DWI, changes are detected earlier during the disease course compared with T2 and fluid-attenuated inversion recovery (FLAIR)

sequences.39,40 The disease can be followed up with serial MRI using DWI.41,42 As with all other forms of neurodegeneration, MRS demonstrates decreased NAA from the involved regions in patients with this disease. Various authorities have noted that the decrease in NAA occurs relatively late during the course of disease.43 In cases of sporadic CJD, involvement of basal ganglia has been noted to correlate with rapid progression.44 Kim et al demonstrated that basal ganglia involvement was strongly associated with lower NAA:Cr ratios and shorter disease duration. Therefore, NAA:Cr ratios of the affected brain at the early stage of sporadic CJD can be a useful parameter in predicting the clinical course.45

3.9 Huntington Disease Huntington disease (HD) is a genetic neurodegenerative disorder that affects muscle coordination and leads to cognitive decline and psychiatric symptoms. It is the most common genetic cause of involuntary writhing movements called chorea. The disease is caused by expansion of a CAG triplet repeat stretch within the HD or IT15 gene located on the short arm of chromosome 4, which encodes a protein called huntingtin. This expansion results in synthesis of an abnormal protein that causes neuronal degeneration and brain atrophy. Striatal atrophy is considered the hallmark of pathological findings in HD.46 MRI demonstrates atrophy in the caudate nucleus and putamen, much earlier than clinical manifestations of the disease.47

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Magnetic Resonance Spectroscopy in Neurodegenerative Disorders Several investigators have shown MRS to demonstrate alterations in NAA and Cr levels in the striatum.48,49 A decrease in NAA corresponds to neuronal loss, and decreased Cr is consistent with impaired energy metabolism seen in this disease. Sanchez et al demonstrated decreased Cr and NAA in the striatum in patients with HD.50 This was also confirmed by several other investigators, including a study by Bogaard et al, in which a high-field 7 T magnet was used.51 Bogaard et al also demonstrated a relationship between the differences in NAA and Cr levels and clinical measures of disease severity. Therefore MRS potentially could be used to monitor the disease process. Another postulated mechanism of development of HD is the theory of abnormal excitotoxicity of neurons, which states that abnormal activation of neurons leads to cell death.52 This event is caused by an increase in glutamate levels, which is thought to be an excitotoxic neurometabolite. Taylor et al demonstrated increased glutamate:Cr levels in HD,53 supporting this hypothesis; however, Bogaard et al51 found decreased glutamate levels in the striatum in patients with HD, a finding that can be explained by a decrease in the number of viable neurons to the extent that glutamate is lowered along with the neuron count.

3.10 Parkinson’s Disease and Related Disorders Parkinson’s disease is a progressive neurodegenerative disorder characterized by bradykinesia, rigidity, tremor, gait disorders, and cognitive dysfunction. Dementia can occur late in course of the disease. The disease is diagnosed by history and the clinical examination. The pathological hallmark of Parkinson’s disease is selective loss of dopaminergic neurons in the pars compacta of substantia nigra. As the disease progresses, there is involvement of the basal forebrain and the neocortex. Another important pathological feature is the presence of Lewy bodies. Magnetic resonance spectroscopy is a powerful tool for quantification of brain metabolites that gives us insight into the pathophysiology of these disorders. Both proton and phosphorus spectroscopy have been used by several authors for Parkinson’s disease and related disorders. Mitochondrial dysfunction in the neostriatal dopaminergic neurons has been implicated in the disease pathogenesis, and MRS can target this condition. Early studies showed no significant reduction in NAA in the striatum,54 putamen, and globus pallidus.55 Hattingen et al performed combined phosphorus and proton MRS in the neostriatal region in 16 patients with early and 13 patients with advanced Parkinson’s disease and in 19 age-matched controls. They found bilateral reduction of high-energy phosphates such as adenosine triphosphate and phosphocreatine with normal levels of low-energy metabolites, such as adenosine diphosphate and inorganic phosphate.56 They concluded that mitochondrial dysfunction is an early and persistent event in the pathophysiology of dopaminergic degeneration in Parkinson’s disease. Recently, Zhou et al performed proton MRS in the substantia nigra in patients with Parkinson’s disease and found significantly lower NAA:Cr, NAA:Cho, NAA:(Cho + Cr) levels in Parkinson’s disease patients compared with healthy controls. They also observed significantly lower NAA:Cr, NAA:Cho, NAA:

(Cho + Cr) in patients with severe Parkinson’s disease compared with patients with mild Parkinson’s disease.57 Clinically, diagnosis of Parkinson’s disease can be quite challenging, and the differential diagnosis includes multisystem atrophy (MSA), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD). In MSA, the middle cerebellar peduncle and pontine nuclei are severely involved, whereas in PSP, the dentate nuclei and superior cerebellar peduncles are afflicted. In CBD, there is severe involvement of the thalamus and pontocerebellar locations. The specific diagnosis of these diseases is difficult and calls for quantitative biomarkers. MRS studies focusing on differentiating these disorders are sparse and do not provide consistent results. Further multicenter trials and prospective studies are required to evaluate the role of MRS in discriminating these disorders.

3.11 Amyotrophic Lateral Sclerosis Amyotrophic lateral sclerosis (ALS), or Lou Gehrig disease, is a progressive neurodegenerative MND that involves the motor cortex, corticospinal tract, upper brainstem, and spinal cord anterior horn cells.58 The disease is uniformly fatal and involves both the upper motor neurons and lower motor neurons. The precise cause of this devastating neurodegenerative disorder is not yet known. The pathogenesis of this disease involves loss of neuronal integrity in the corticospinal tracts. Because NAA is a surrogate marker for neuronal integrity and viability, MRS can be helpful in providing critical information that might not be available on conventional MRI sequences (▶ Fig. 3.4). Jones et al59 performed MRS on ALS and reported reduction of NAA and NAA:Cho ratios in motor cortex and adjacent cortex Many studies have shown decreased NAA:Cr ratios in areas of the brain that contribute significantly to corticospinal tracts in patients with ALS.60,61,62,63 In addition to decreased NAA, recent focus has been on the role of glutamate (glu) in the pathogenesis of ALS. The levels of glu have been found to be elevated in the plasma and CSF of patients with ALS.64,65 Glutamate is a neurometabolite that takes part in synaptic transmission. In patients with ALS, there is decreased reuptake of glu by postsynaptic receptors, which leads to increased activation of excitatory amino acid receptors, causing increased calcium ion uptake by the neurons. This is lethal for the cell and can cause activation of catabolic enzymes such as protein kinases and phospholipases that can lead to neuronal death.66 Glu and glutamine (gln) levels are thought to be relatively constant in the brain, and these metabolites appear as overlapping multiple peaks at 2.35 and 3.75 ppm. The combined peak from glu and gln is generally also referred to as Glx. Han at el demonstrated increased glu:Cr and Glx:Cr ratios in spectra obtained from the posterior limb of the internal capsule in patients with ALS.67 Therefore, for clinical evaluation of ALS, glu:Cr, Glx:Cr, and NAA:Cr ratios are ideal indexes. The ability of MRS to provide qualitative information that can be monitored for disease progression over time makes it an ideal modality for evaluation of these patients.

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Imaging Techniques

Fig. 3.4 Axial T1-weighted images demonstrating region of interests from precentral gyrus (pre-CG, red), postcentral gyrus (post-CG, green), and posterior limb of internal capsule (IC, yellow) from a representative amyotrophic laterial sclerosis (ALS) patient. Occipital region (OR, orange) may be considered as an internal control as this region has been reported to be relatively spared from atrophy and abnormal glucose metabolism in ALS patients. Proton magnetic resonance spectra from these regions displaying different metabolites. Compared with occipital region, reduced NAA and elevated choline (Cho) resonances are discernible from other locations.

Fig. 3.5 Axial T2 fluid-attenuated inversion recovery (FLAIR) image demonstrating hyperintense multiple sclerosis lesions in periventricular white matter regions. A representative voxel encompassing multiple sclerosis plaque is shown, along with corresponding spectrum (echo time [TE] = 30 ms) displaying various metabolites. Please note the diminished signal from N-acetyl aspartate (NAA) and elevated signals from choline (Cho) and myo-inositol (mI). Glx, glutamate.

3.12 Multiple Sclerosis Multiple sclerosis (MS) is the most common autoimmune neurodegenerative/complex inflammatory disorder, especially in young adults. Most patients with MS follow a relapsing-remitting course characterized by relapses of variable severity followed by remissions of varying duration. An increasing body of evidence suggest sthat MS is characterized by demyelination, axonal loss, inflammation, gliosis, and edema. Contrast-enhancing acute MS lesions typically show elevations in Cho and lactate and lipid levels during the first 6 to 10 weeks after their appearance. The NAA concentration in the acute phase of lesion development is highly variable, ranging from almost no change to significant decreases. Creatine, which is generally higher in glial cells than in neurons, usually remains stable; however, significant increases68 or decreases69 have been observed in MS. These changes may be related to varying amounts of neuronal and oligodendroglial loss and astrocytic proliferation rather than altered energy metabolism. Increases have also been reported in mI levels, likely a result of microglial proliferation and in Glx levels secondary to active inflammatory infiltrates (▶ Fig. 3.5).

Acute MS plaques usually progress to chronic plaques that appear hypointense on T1-weighted images, also commonly referred to as “black holes.” These lesions harbor varying degrees of neuronal and axonal loss as inflammatory process decreases, edema resolves, and reparative mechanisms such as remyelination become active. These pathological changes can be seen as alterations in the metabolite pattern. There is a progressive return of lactate levels to normal levels within weeks, whereas Cho and lipid levels decrease for some months but do not always return to normal values. A moderate increase in Cr may also be observed secondary to gliosis and remyelination.70 NAA may further decrease, indicating progressive neuronal or axonal damage or show partial recovery over several months without reaching normality. Several mechanisms have been proposed to explain this behavior, such as resolution of edema and inflammation, an increase in the diameter of previously shrunken axons secondary to remyelination, and reversible metabolic changes in neuronal mitochondria.71 It is now widely accepted that normal-appearing white matter (NAWM) and normal-appearing gray matter (NAGM) regions, which appear normal both macroscopically and

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Magnetic Resonance Spectroscopy in Neurodegenerative Disorders on conventional MRI, are actually not normal. Several studies72,73,74 have also reported abnormal metabolite pattern from NAWM and NAGM regions in MS patients compared with normal subjects. To investigate the course of metabolism from MS plaques at different stages of evolutionary development, several longitudinal studies have been performed.70,75 A reduction in NAA:Cr ratio was reported by most of these studies during the course of the disease. A few investigators found a subsequent recovery of NAA:Cr over time, leading to the suggestion that axonal loss is not the only mechanism of reduction in the NAA:Cr ratio. An increase in the Cho:Cr and its subsequent normalization has also been reported. A small number of studies have reported that Cr concentration does not remain stable over time.70 In a study performed by Narayana et al,76 NAA levels reached their minimum value when lesion volume reached its maximum. In another serial study, increased Cho and lipid levels were observed from NAWM regions that subsequently went on to develop MRI visible lesions.77 Using whole-brain MRS, Gonen et al observed lower global NAA in MS patients compared with controls.78 This difference was greater among older than among younger subjects. Another study observed a 3.5 times faster decrease in global NAA levels compared with atrophy in MS patients, implying that neuronal cell injury precedes atrophy and that degenerating axons may leave behind their empty myelin sheaths. This study suggests that NAA is a more sensitive indicator of disease progression than either lesion load or atrophy in MS.79

3.13 Human Immunodeficiency Virus Infection Involvement of the central nervous system is a common feature of human immunodeficiency virus (HIV) infection, and in particular subcortical gray matter regions carry a heavy HIV load. Neurons have not been appreciably infected by HIV owing to a lack of CD4 + cell surface receptors. However, an inflammatory response involving microglial cells and perivascular macrophages leads to neuronal dysfunction and ultimately neuronal loss. In the initial phase, brain inflammation caused by the HIV is clinically asymptomatic and turns to mild-to-advanced HIVassociated neurocognitive impairments (HNCIs) and finally in about 20% of patients to dementia or encephalopathy in the course of HIV infection.80 Several 1 H MRS studies81,82,83 have reported reduced NAA suggestive of axonal loss along with increased Cho secondary to infiltration by inflammatory cells and increased mI related to gliosis from patients with HNCIs. Furthermore, abnormal metabolite pattern has also been observed, even from neurologically asymptomatic HIV patients who do not show any abnormalities on conventional MRI, suggesting a higher sensitivity of 1 H MRS in the detection of early brain damage induced by HIV (▶ Fig. 3.6). In a cohort of HIV-positive patients treated with highly active antiretroviral therapy (HAART), Roc et al84 observed elevated levels of lipids and lactate from lenticular nuclei, suggesting that HIV-induced oxidative stress and inflammation occur even after initiation of HAART. Taken

Fig. 3.6 T1-weighted images (first column) and corresponding spectra from a representative control subject (a), human immunodeficiency virus (HIV) + subsyndromic (b), HIV + symptomatic (c) patients are shown. Proton magnetic resonance spectroscopic imaging grid is centered over subcortical gray matter region for each of the subjects. Spectra shown on the right were taken from voxels overlapping the lenticular nuclei (blue squares). The x-axis for all the spectra ranged from 0.2 to 4.3 parts per million (ppm). Spectra acquired at echo time (TE) = 135 ms (second column) and at TE = 30 m (third column) display resonances of N-acetyl aspartate (NAA, 2.02 ppm), creatine (Cr, 3.02 ppm), choline (Cho, 3.22 ppm), lactate (Lac), and lipid (Lip) at 1.33 ppm. Note that the peak of lactate is inverted below the baseline from spectra acquired at TE = 135 ms (second column).

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Imaging Techniques together, these studies suggest that quantitative 1 H MRS may play a role in the objective assessment of the presence, magnitude, and progression of brain involvement in HIV infection.

3.14 Summary and Future Perspectives Magnetic resonance spectroscopy offers a noninvasive means of assessing in vivo brain function and dysfunction, both in normal aging as well as in a plethora of neurodegenerative disorders. Studies obtained at higher field strengths have resulted in sampling of smaller tissue volumes, greater SNR, and higher metabolic spatial resolution. Despite these significant technical advancements in the acquisition and analysis of proton MRS, translation of MRS in clinical practice is still not seamless, mainly because of the lack of normative data and an insufficient understanding of the pathologic basis of proton MRS metabolite changes. We believe further advances in these areas would expand the impact of proton MRS as a biomarker for the early detection of neurodegenerative diseases and in monitoring the potential neuroprotective effects of newer experimental therapy in this era of personalized medicine.

References [1] Miller BL, Chang L, Booth R et al. In vivo 1 H MRS choline: correlation with in vitro chemistry/histology. Life Sci 1996; 58: 1929–1935 [2] Rubaek Danielsen E, Ross B, eds. Magnetic Resonance Spectroscopy: Diagnosis of Neurological Disease. New York: Marcel Dekker. 1999 [3] Burtscher IM, Holtås S. Proton MR spectroscopy in clinical routine. J Magn Reson Imaging 2001; 13: 560–567 [4] Valk J, Barkhof F, Scheltens P. Magnetic resonance in dementia. Berlin: Heidelberg, Springer-Verlag. 2002 [5] Castillo M, Kwock L, Mukherji SK. Clinical applications of proton MR spectroscopy. AJNR Am J Neuroradiol 1996; 17: 1–15 [6] Svennerholm L, Boström K, Jungbjer B. Changes in weight and compositions of major membrane components of human brain during the span of adult human life of Swedes. Acta Neuropathol 1997; 94: 345–352 [7] Chang L, Ernst T, Poland RE, Jenden DJ. In vivo proton magnetic resonance spectroscopy of the normal aging human brain. Life Sci 1996; 58: 2049–2056 [8] Raz N. The aging brain: Structural changes and their implications for cognitive ageing. In: Dixon R, Bäckman L, Nilsson L, eds. New Frontiers in Cognitive Aging. Oxford: Oxford UP. 2004;115–134 [9] Leary SM, Brex PA, MacManus DG et al. A (1)H magnetic resonance spectroscopy study of aging in parietal white matter: implications for trials in multiple sclerosis. Magn Reson Imaging 2000; 18: 455–459 [10] Schuff N, Amend DL, Meyerhoff DJ et al. Alzheimer’s disease: quantitative H-1 MR spectroscopic imaging of frontoparietal brain. Radiology 1998; 207: 91– 102 [11] Wyss M, Kaddurah-Daouk R. Creatine and creatinine metabolism. Physiol Rev 2000; 80: 1107–1213 [12] Ferguson KJ, MacLullich AM, Marshall I et al. Magnetic resonance spectroscopy and cognitive function in healthy elderly men. Brain 2002; 125: 2743– 2749 [13] Kadota T, Horinouchi T, Kuroda C. Development and aging of the cerebrum: assessment with proton MR spectroscopy. AJNR Am J Neuroradiol 2001; 22: 128–135 [14] Giannakopoulos P, Herrmann FR, Bussière T et al. Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer’s disease. Neurology 2003; 60: 1495–1500 [15] Gómez-Isla T, Hollister R, West H et al. Neuronal loss correlates with but exceeds neurofibrillary tangles in Alzheimer’s disease. Ann Neurol 1997; 41: 17–24

[16] Huesgen CT, Burger PC, Crain BJ, Johnson GA. In vitro MR microscopy of the hippocampus in Alzheimer’s disease. Neurology 1993; 43: 145–152 [17] Klunk WE, Panchalingam K, Moossy J, McClure RJ, Pettegrew JW. N-acetyl-Laspartate and other amino acid metabolites in Alzheimer’s disease brain: a preliminary proton nuclear magnetic resonance study. Neurology 1992; 42: 1578–1585 [18] Soares DP, Law M. Magnetic resonance spectroscopy of the brain: review of metabolites and clinical applications. Clin Radiol 2009; 64: 12–21 [19] Moats RA, Ernst T, Shonk TK, Ross BD. Abnormal cerebral metabolite concentrations in patients with probable Alzheimer’s disease. Magn Reson Med 1994; 32: 110–115 [20] Pfefferbaum A, Adalsteinsson E, Spielman D, Sullivan EV, Lim KO. In vivo spectroscopic quantification of the N-acetyl moiety, creatine, and choline from large volumes of brain gray and white matter: effects of normal aging. Magn Reson Med 1999; 41: 276–284 [21] Kantarci K, Jack CR, Jr, Xu YC et al. Regional metabolic patterns in mild cognitive impairment and Alzheimer’s disease: a 1 H MRS study. Neurology 2000; 55: 210–217 [22] Rose SE, de Zubicaray GI, Wang D et al. A 1 H MRS study of probable Alzheimer’s disease and normal aging: implications for longitudinal monitoring of dementia progression. Magn Reson Imaging 1999; 17: 291–299 [23] Rai GS, McConnell JR, Waldman A, Grant D, Chaudry M. Brain proton spectroscopy in dementia: an aid to clinical diagnosis. Lancet 1999; 353: 1063– 1064 [24] Huang W, Alexander GE, Chang L et al. Brain metabolite concentration and dementia severity in Alzheimer’s disease: a (1)H MRS study. Neurology 2001; 57: 626–632 [25] Kwo-On-Yuen PF, Newmark RD, Budinger TF, Kaye JA, Ball MJ, Jagust WJ. Brain N-acetyl-L-aspartic acid in Alzheimer’s disease: a proton magnetic resonance spectroscopy study. Brain Res 1994; 667: 167–174 [26] Chantal S, Braun CM, Bouchard RW, Labelle M, Boulanger Y. Similar 1 H magnetic resonance spectroscopic metabolic pattern in the medial temporal lobes of patients with mild cognitive impairment and Alzheimer’s disease. Brain Res 2004; 1003: 26–35 [27] Kantarci K, Petersen RC, Boeve BF et al. 1 H MR spectroscopy in common dementias. Neurology 2004; 63: 1393–1398 [28] Molina JA, García-Segura JM, Benito-León J et al. Proton magnetic resonance spectroscopy in dementia with Lewy bodies. Eur Neurol 2002; 48: 158–163 [29] Firbank MJ, Blamire AM, Krishnan MS et al. Diffusion tensor imaging in dementia with Lewy bodies and Alzheimer’s disease. Psychiatry Res 2007; 155: 135–145 [30] Kantarci K, Avula R, Senjem ML et al. Dementia with Lewy bodies and Alzheimer’s disease: neurodegenerative patterns characterized by DTI. Neurology 2010; 74: 1814–1821 [31] Xuan X, Ding M, Gong X. Proton magnetic resonance spectroscopy detects a relative decrease of N-acetylaspartate in the hippocampus of patients with dementia with Lewy bodies. J Neuroimaging 2008; 18: 137–141 [32] Grossman M. Frontotemporal dementia: a review. J Int Neuropsychol Soc 2002; 8: 566–583 [33] Forman MS, Farmer J, Johnson JK et al. Frontotemporal dementia: clinicopathological correlations. Ann Neurol 2006; 59: 952–962 [34] Jackson M, Lowe J. The new neuropathology of degenerative frontotemporal dementias. Acta Neuropathol 1996; 91: 127–134 [35] Mann DM. Dementia of frontal type and dementias with subcortical gliosis. Brain Pathol 1998; 8: 325–338 [36] Rahman S, Sahakian BJ, Hodges JR, Rogers RD, Robbins TW. Specific cognitive deficits in mild frontal variant frontotemporal dementia. Brain 1999; 122: 1469–1493 [37] Chawla S, Wang S, Moore P et al. Quantitative proton magnetic resonance spectroscopy detects abnormalities in dorsolateral prefrontal cortex and motor cortex of patients with frontotemporal lobar degeneration. J Neurol 2010; 257: 114–121 [38] Mittal S, Farmer P, Kalina P, Kingsley PB, Halperin J. Correlation of diffusionweighted magnetic resonance imaging with neuropathology in CreutzfeldtJakob disease. Arch Neurol 2002; 59: 128–134 [39] Kropp S, Finkenstaedt M, Zerr I, Schröter A, Poser S. [Diffusion-weighted MRI in patients with Creutzfeldt-Jakob disease] [in German] Nervenarzt 2000; 71: 91–95 [40] Shiga Y, Miyazawa K, Sato S et al. Diffusion-weighted MRI abnormalities as an early diagnostic marker for Creutzfeldt-Jakob disease. Neurology 2004; 63: 443–449 [41] Ukisu R, Kushihashi T, Kitanosono T et al. Serial diffusion-weighted MRI of Creutzfeldt-Jakob disease. AJR Am J Roentgenol 2005; 184: 560–566

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| 12.09.15 - 10:48

Magnetic Resonance Spectroscopy in Neurodegenerative Disorders [42] Murata T, Shiga Y, Higano S, Takahashi S, Mugikura S. Conspicuity and evolution of lesions in Creutzfeldt-Jakob disease at diffusion-weighted imaging. AJNR Am J Neuroradiol 2002; 23: 1164–1172 [43] Bruhn H, Weber T, Thorwirth V, Frahm J. In-vivo monitoring of neuronal loss in Creutzfeldt-Jakob disease by proton magnetic resonance spectroscopy. Lancet 1991; 337: 1610–1611 [44] Yi SH, Park KC, Yoon SS, Kim EJ, Shin WC. Relationship between clinical course and Diffusion-weighted MRI findings in sporadic Creutzfeldt-Jakob disease. Neurol Sci 2008; 29: 251–255 [45] Kim JH, Choi BS, Jung C, Chang Y, Kim S. Diffusion-weighted imaging and magnetic resonance spectroscopy of sporadic Creutzfeldt-Jakob disease: correlation with clinical course. Neuroradiology 2011; 53: 939–945 [46] Roos RAC. Neuropathology of Huntington’s chorea. In: Vinken PJ, Bruyn GW, Klawans HL, eds. Handbook of Clinical Neurology: Extrapyramidal Disorders. Amsterdam: Elsevier. 1986 [47] van den Bogaard SJ, Dumas EM, Acharya TP et al. TRACK-HD Investigator Group. Early atrophy of pallidum and accumbens nucleus in Huntington’s disease. J Neurol 2011; 258: 412–420 [48] Gómez-Ansón B, Alegret M, Muñoz E, Sainz A, Monte GC, Tolosa E. Decreased frontal choline and neuropsychological performance in preclinical Huntington disease. Neurology 2007; 68: 906–910 [49] Jenkins BG, Koroshetz WJ, Beal MF, Rosen BR. Evidence for impairment of energy metabolism in vivo in Huntington’s disease using localized 1 H NMR spectroscopy. Neurology 1993; 43: 2689–2695 [50] Sánchez-Pernaute R, García-Segura JM, del Barrio Alba A, Viaño J, de Yébenes JG. Clinical correlation of striatal 1 H MRS changes in Huntington’s disease. Neurology 1999; 53: 806–812 [51] van den Bogaard SJ, Dumas EM, Teeuwisse WM et al. Exploratory 7-Tesla magnetic resonance spectroscopy in Huntington’s disease provides in vivo evidence for impaired energy metabolism. J Neurol 2011; 258: 2230–2239 [52] Roze E, Saudou F, Caboche J. Pathophysiology of Huntington’s disease: from huntingtin functions to potential treatments. Curr Opin Neurol 2008; 21: 497–503 [53] Taylor-Robinson SD, Weeks RA, Bryant DJ et al. Proton magnetic resonance spectroscopy in Huntington’s disease: evidence in favour of the glutamate excitotoxic theory. Mov Disord 1996; 11: 167–173 [54] Holshouser BA, Komu M, Möller HE et al. Localized proton NMR spectroscopy in the striatum of patients with idiopathic Parkinson’s disease: a multicenter pilot study. Magn Reson Med 1995; 33: 589–594 [55] Davie CA, Wenning GK, Barker GJ et al. Differentiation of multiple system atrophy from idiopathic Parkinson’s disease using proton magnetic resonance spectroscopy. Ann Neurol 1995; 37: 204–210 [56] Hattingen E, Magerkurth J, Pilatus U et al. Phosphorus and proton magnetic resonance spectroscopy demonstrates mitochondrial dysfunction in early and advanced Parkinson’s disease. Brain 2009; 132: 3285–3297 [57] Zhou B, Yuan F, He Z, Tan C. Application of proton magnetic resonance spectroscopy on substantia nigra metabolites in Parkinson’s disease. Brain Imaging Behav 2014; 8: 97–101 [58] Turner MR, Kiernan MC, Leigh PN, Talbot K. Biomarkers in amyotrophic lateral sclerosis. Lancet Neurol 2009; 8: 94–109 [59] Jones AP, Gunawardena WJ, Coutinho CM, Gatt JA, Shaw IC, Mitchell JD. Preliminary results of proton magnetic resonance spectroscopy in motor neurone disease (amytrophic lateral sclerosis). J Neurol Sci 1995; 129 Suppl: 85–89 [60] Pioro EP. MR spectroscopy in amyotrophic lateral sclerosis/motor neuron disease. J Neurol Sci 1997; 152 Suppl 1: S49–S53 [61] Cwik VA, Hanstock CC, Allen PS, Martin WR. Estimation of brainstem neuronal loss in amyotrophic lateral sclerosis with in vivo proton magnetic resonance spectroscopy. Neurology 1998; 50: 72–77 [62] Block W, Karitzky J, Träber F et al. Proton magnetic resonance spectroscopy of the primary motor cortex in patients with motor neuron disease: subgroup analysis and follow-up measurements. Arch Neurol 1998; 55: 931–936 [63] Gredal O, Pakkenberg H, Karlsborg M, Pakkenberg B. Unchanged total number of neurons in motor cortex and neocortex in amyotrophic lateral sclerosis: a stereological study. J Neurosci Methods 2000; 95: 171–176

[64] Rooney WD, Miller RG, Gelinas D, Schuff N, Maudsley AA, Weiner MW. Decreased N-acetylaspartate in motor cortex and corticospinal tract in ALS. Neurology 1998; 50: 1800–1805 [65] Rule RR, Suhy J, Schuff N, Gelinas DF, Miller RG, Weiner MW. Reduced NAA in motor and non-motor brain regions in amyotrophic lateral sclerosis: a cross-sectional and longitudinal study. Amyotroph Lateral Scler Other Motor Neuron Disord 2004; 5: 141–149 [66] Heath PR, Shaw PJ. Update on the glutamatergic neurotransmitter system and the role of excitotoxicity in amyotrophic lateral sclerosis. Muscle Nerve 2002; 26: 438–458 [67] Han J, Ma L. Study of the features of proton MR spectroscopy ((1)H-MRS) on amyotrophic lateral sclerosis. J Magn Reson Imaging 2010; 31: 305–308 [68] Srinivasan R, Sailasuta N, Hurd R, Nelson S, Pelletier D. Evidence of elevated glutamate in multiple sclerosis using magnetic resonance spectroscopy at 3 T. Brain 2005; 128: 1016–1025 [69] Caramanos Z, Narayanan S, Arnold DL. 1H-MRS quantification of tNA and tCr in patients with multiple sclerosis: a meta-analytic review. Brain 2005; 128: 2483–2506 [70] Mader I, Roser W, Kappos L et al. Serial proton MR spectroscopy of contrastenhancing multiple sclerosis plaques: absolute metabolic values over 2 years during a clinical pharmacological study. AJNR Am J Neuroradiol 2000; 21: 1220–1227 [71] Arnold DL, De Stefano N, Narayanan S, Matthews PM. Proton MR spectroscopy in multiple sclerosis. Neuroimaging Clin N Am 2000; 10: 789–798, ix–x [72] Sastre-Garriga J, Ingle GT, Chard DT et al. Metabolite changes in normalappearing gray and white matter are linked with disability in early primary progressive multiple sclerosis. Arch Neurol 2005; 62: 569–573 [73] Adalsteinsson E, Langer-Gould A, Homer RJ et al. Gray matter N-acetyl aspartate deficits in secondary progressive but not relapsing-remitting multiple sclerosis. AJNR Am J Neuroradiol 2003; 24: 1941–1945 [74] Inglese M, Li BS, Rusinek H, Babb JS, Grossman RI, Gonen O. Diffusely elevated cerebral choline and creatine in relapsing-remitting multiple sclerosis. Magn Reson Med 2003; 50: 190–195 [75] De Stefano N, Matthews PM, Fu L et al. Axonal damage correlates with disability in patients with relapsing-remitting multiple sclerosis. Results of a longitudinal magnetic resonance spectroscopy study. Brain 1998; 121: 1469– 1477 [76] Narayana PA, Doyle TJ, Lai D, Wolinsky JS. Serial proton magnetic resonance spectroscopic imaging, contrast-enhanced magnetic resonance imaging, and quantitative lesion volumetry in multiple sclerosis. Ann Neurol 1998; 43: 56–71 [77] Tartaglia MC, Narayanan S, De Stefano N et al. Choline is increased in prelesional normal appearing white matter in multiple sclerosis. J Neurol 2002; 249: 1382–1390 [78] Gonen O, Catalaa I, Babb JS et al. Total brain N-acetylaspartate: a new measure of disease load in MS. Neurology 2000; 54: 15–19 [79] Ge Y, Gonen O, Inglese M, Babb JS, Markowitz CE, Grossman RI. Neuronal cell injury precedes brain atrophy in multiple sclerosis. Neurology 2004; 62: 624–627 [80] Navia BA, Jordan BD, Price RW. The AIDS dementia complex: I. Clinical features. Ann Neurol 1986; 19: 517–524 [81] Chang L, Ernst T, Leonido-Yee M et al. Highly active antiretroviral therapy reverses brain metabolite abnormalities in mild HIV dementia. Neurology 1999; 53: 782–789 [82] Meyerhoff DJ, Bloomer C, Cardenas V, Norman D, Weiner MW, Fein G. Elevated subcortical choline metabolites in cognitively and clinically asymptomatic HIV + patients. Neurology 1999; 52: 995–1003 [83] Suwanwelaa N, Phanuphak P, Phanthumchinda K et al. Magnetic resonance spectroscopy of the brain in neurologically asymptomatic HIV-infected patients. Magn Reson Imaging 2000; 18: 859–865 [84] Roc AC, Ances BM, Chawla S et al. Detection of human immunodeficiency virus induced inflammation and oxidative stress in lenticular nuclei with magnetic resonance spectroscopy despite antiretroviral therapy. Arch Neurol 2007; 64: 1249–1257

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Imaging Techniques

4 SPECT and PET Imaging of Neurotransmitters in Dementia Mateen Moghbel, Andrew Newberg, Mijail Serruya, and Abass Alavi Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) have contributed substantially to uncovering the basis of various neuropsychiatric disorders. Our understanding of the pathophysiology and treatment of these complex diseases has been informed by studies on cerebral metabolism, blood flow, and neurotransmitters that have been carried out using these functional imaging modalities. As novel radiotracers are developed and innovative applications are devised, PET and SPECT will continue to provide tremendous insight into the causes, diagnosis, and treatment of neurologic and psychiatric diseases. Perhaps the most common disorders studied with PET and SPECT are those that result in dementia symptoms. Thus, PET and SPECT have been used extensively in the study of Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia (FTD), dementia with Lewy bodies (DLB), and other related disorders. Although much of the focus has been on the evaluation of cerebral blood flow and glucose metabolism, a wide array of studies have explored various neurotransmitter systems in these disorders. This chapter reviews the current literature regarding neurotransmitter imaging with PET and SPECT in the evaluation of dementia. Fluorine 18 (18F)-labeled glucose is the radioligand most commonly used in clinical brain PET. Glucose is radiolabeled with 18F by substituting the hydroxyl group with 18F to create the radioligand 2-deoxy-2-fluorodeoxyglucose ([18F]FDG). [18F]FDG is taken up by brain cells in the same way as unlabeled glucose, but after phosphorylation to [18F]FDG-6-phosphate, it cannot continue glycolysis and becomes trapped in the brain cell. The PET scanner detects the amount of labeled glucose taken up by the brain because the 18F isotope, as well as the other PET isotopes, undergoes radioactive decay to emit a positron and neutrino, the process of positive beta decay. The emitted positron travels through tissues before colliding with an electron in its path, causing both particles to be annihilated. The nuclei of positron emitters are generally rich in protons and consequently attempt to maintain stability by gaining neutrons and losing excess protons. This can be accomplished in one of two isobaric decay processes: positron emission or electron capture. The mass number in both the parent and daughter nuclei remains the same in either process. A PET scanner then detects the photons that are released by the annihilation in coincidence, forming a PET image. The resulting image is a map of the distribution of the annihilations occurring within the organ of interest. The map illustrates the particular tissues in which the tracer has become concentrated. The result is a detailed evaluation of the pattern of cerebral metabolism (▶ Fig. 4.1). A nuclear medicine physician can then analyze these results in the context of the patient’s diagnosis and treatment plan. For brain imaging, FDG is the most common tracer for clinical purposes, but there are many experimental tracers that have been used to evaluate different neurotransmitter systems in patients with neurodegenerative disorders. These tracers bind to specific receptors in the brain, and the amount of radioactivity detected in specific structures is correlated with the receptor availability.

Fig. 4.1 Normal fluorodeoxyglucose (FDG) positron emission tomography scan from a healthy control without any neuropsychiatric disorders. The scan reveals relatively uniform metabolism in all cortical and subcortical structures.

For SPECT imaging, the two most common tracers are hexamethylpropyleneamine (HMPAO) and ethyl cysteine dimer, both of which are used for evaluating cerebral blood flow. The basic SPECT imaging technique requires injection of a gammaemitting radioisotope combined with a particular molecule that follows some type of neurophysiologic process, including binding to neurotransmitter receptors. Subsequently, because of the gamma emission of the isotope, the ligand concentration is visualized by a gamma camera. Tomography enables the localization of radioactivity and, hence, the location of the tracer concentration. Although SPECT imaging requires longer acquisition times, has poorer spatial resolution, and has greater susceptibility to artifacts, technical advancements in the instruments of SPECT have begun to markedly improve these limitations. Most clinical SPECT systems that are used to perform patient studies still utilize scintillation cameras with NaI(Tl) (thallium-activated sodium iodide) detectors. These systems consist of one or more scintillation camera heads attached to a gantry that revolves around the patient to collect projection views. The most common configuration has two scintillation cameras that are fixed at either 90 or 180 degrees or have the capability to be positioned at selected orientations. The projection information required for SPECT is acquired by gamma-ray detectors, and much of the quality of the projection depends on the properties of these detectors. Over the years, numerous tracers have been developed for both PET and SPECT imaging for neurologic applications. After their injection into a subject, many of these experimental tracers function by binding to receptors for neurotransmitters, such as serotonin and dopamine. These tracers for both SPECT and PET imaging of neurotransmitters in dementia are particularly useful for the study of neurodegenerative disorders. This chapter reviews some of the major applications and findings of

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SPECT and PET Imaging of Neurotransmitters in Dementia PET imaging in the evaluation of neurodegenerative disorders that result in dementia.

4.1 Alzheimer’s Disease The criteria for the diagnosis of AD were originally defined by the Working Group of the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) in 1984.1 The criteria for the diagnosis of AD include progressive, chronic cognitive deficits in the middle-aged and elderly patients without any identifiable underlying cause. Although patients in the advanced stages of dementia can often be accurately diagnosed, it is a challenge to differentiate between AD and other forms of dementia in the earlier stages.2,3 With the aid of functional imaging modalities like PET, the diagnostic and etiologic questions that continue to surround AD may be answered in years to come. Most PET studies of AD have focused on glucose metabolism and have found that whole-brain glucose metabolism (CMRGlc) is reduced in AD patients; the bilateral parietal and temporal lobes are especially affected.4,5,6,7,8,9,10 This parietal hypometabolism (▶ Fig. 4.2) is often considered the “typical” presentation of AD and may be particularly pronounced in patients under the age of 65 years.11,12,13 Based on a large number of studies, this pattern of parietal hypometabolism carries a general sensitivity and specificity of approximately 85 and 60%, respectively. However, the pattern is not pathognomonic for AD and might also be observed in patients with PD, bilateral parietal subdural hematomas, bilateral parietal stroke, and bilateral parietal radiation therapy ports.14 It has also been reported that the magnitude and extent of hypometabolism correlate with the severity of the dementia symptoms. Patients with moderate dementia have been found to have significant hypometabolism in the left midfrontal lobes, bilateral parietal lobes, and the superior temporal regions. In more advanced cases of AD, the same regions have an even greater reduction in metabolism.

Fig. 4.2 Fluorodeoxyglucose (FDG) positron emission tomography scan of an Alzheimer’s disease patient shows moderately decreased metabolism in the bilateral temporoparietal regions.

Another application of PET is the measurement of changes in various neurotransmitter systems that are associated with AD. It has been reported in the literature that the neocortex, hippocampus, and amygdala of AD patients demonstrate significantly reduced acetylcholinesterase activity, which suggests that cholinergic innervation to the basal forebrain has been lost in these patients.15 The regions that were most affected were the temporal and parietal cortices. A study by Kuhl et al showed that the onset of AD before 65 years of age correlated with reduced binding of iodobenzovesamicol (an in vivo marker of the vesicular acetylcholine transporter) throughout the cerebral cortex and hippocampus. However, when onset of disease occurred after age 65, binding reductions were limited to the temporal cortex and hippocampus.16 A small PET study of nine AD patients, eight patients with mild cognitive impairment (MCI), and seven age-matched healthy controls showed a significant reduction in 2-[18F]FA85380 BP(ND), a marker of nicotinic acetylcholine receptor activity, in typical AD-affected brain regions.17 The 2-[18F]FA85380 BP(ND) correlated with the severity of cognitive impairment, and only MCI patients who subsequently converted to AD had a reduction in 2-[18F]FA-85380 BP(ND). Thus, the nicotinic receptors in dementia may not only reflect the degree of impairment, but may also predict the clinical course of disease. A related SPECT study investigated in vivo changes in the α4β2-nicotinic acetylcholine receptor in 16 AD patients and 16 controls.18 Subjects also underwent perfusion imaging with 99mTc-hexamethylenepropyleneamineoxime SPECT. The results showed significant bilateral reductions in nicotinic receptor binding in the frontal lobe, striatum, right medial temporal lobe, and pons in patients with AD compared with controls. However, unlike the PET study already mentioned, no significant correlations were made with clinical or cognitive measures. Although this was a small sample size, both 123I-5IA85380 and 99mTc-HMPAO SPECT imaging demonstrated similar diagnostic performance in correctly classifying controls and patients with AD. A study of 27 patients with mild AD underwent PET scanning with 15O-water for regional cerebral blood flow and (S)(-)[11C] nicotine for the assessment of nicotine binding.19 Mean cortical [11C]nicotine binding significantly correlated with the results of attention tests such as the Digit Symbol test and Trail Making Test A, but [11C]nicotine binding was not significantly correlated with the results of tests of episodic memory or visuospatial ability. No correlations were observed between cerebral blood flow and cognition. Thus, the cortical nicotinic receptors appear to be related to the cognitive function of attention in patients with AD. A number of other neurotransmitter systems have also been evaluated in patients with AD. The serotonin and dopamine systems have been of particular interest. For example, an early study of nine AD patients using [18F]setoperone PET revealed markedly decreased 5-hydroxytrypamine (5HT)2 binding in the temporal, frontal, parietal, and occipital cortices in patients with AD relative to control values.20 Another study on serotonin-2A receptors in AD patients demonstrated a temporal pattern of reduced receptor density in early stages of the disease, followed by a plateauing effect as the disease progresses.21 However, studies using [11C]DASB PET have shown that this decrease in neocortical serotonin 2A receptor binding that has

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Imaging Techniques been observed in early AD is not the result of a primary loss of serotonergic neurons or their projections.22 In yet another study, hippocampal dopamine D2 receptors density was shown to be reduced, correlating with impairments in memory in AD patients.23 Other studies have revealed decreases in postsynaptic serotonin receptor binding in AD patients. A study of nine AD patients and 26 controls using 123I-5-I-R91150 SPECT to evaluate the 5-HT2A receptors demonstrated an age-related decline of neocortical binding potential (11.6% per decade).24 Furthermore, AD patients had a significant regional reduction in the 5-HT2A binding in the orbitofrontal, prefrontal, lateral frontal, cingulate, sensorimotor, parietal inferior, and occipital regions. One study using [18F]deuteroaltanserin PET showed a significant decrease in the binding potential in 5-HT2A receptors in the anterior cingulate in AD patients, but this decrease did not correlate with behavioral measures such as depressive and psychotic symptoms.25 Another study using [18F]altanserin and [11C]DASB PET of early AD patients and controls demonstrated a decrease of roughly 30% in cortical 5-HT2A receptor binding in patients with MCI compared with healthy controls.22 In AD patients, decreases were marked in [18F]altanserin binding but largely insignificant in [11C]DASB binding. The only exception was in the mesial temporal cortex, where a 33% reduction was observed in [11C]DASB binding. A [18F]altanserin PET study of MCI patients and healthy agematched controls for 2 years reported that 8 of the 14 MCI patients had progressed to probable AD by the end of the follow-up period.21 In patients as well as controls, no significant changes were detected in 5-HT2A receptor binding over the 2-year period. Thus, despite the marked decreases in cortical 5-HT2A receptor binding that are seen in early MCI, further reductions have not been associated with progression from MCI to AD. One study of the 5-HT1A binding in 10 AD patients and 10 controls revealed significantly decreased 5-HT1A binding potential in the right medial temporal lobe, but not in the other regions such as the frontal, lateral temporal, parietal, and cerebellar cortices.26 Another PET study of the 5-HT1A in AD, MCI, and controls showed that significantly decreased receptor densities in both hippocampi and the raphe nuclei in AD patients.27 The authors also reported a strong correlation between 5-HT1A receptor decreases in the hippocampus and worsening MiniMental State Examination (MMSE) scores. Additionally, decreased 5-HT1A receptor measures correlated with decreased cerebral glucose metabolism as measured by FDG-PET. A separate study of 5-HT1A receptor density in the hippocampus using a voxel-based analysis revealed decreased whole-brain binding in AD brains but increased whole-brain binding in the brain of patients with amnestic MCI.28 More specifically, they noted a significant decrease of binding potential in the hippocampus and parahippocampal gyri of AD patients, whereas there was a significant increase of binding potential in the inferior occipital gyrus in amnestic MCI patients. The authors suggest that this difference in serotonergic receptor labeling may help distinguish amnestic MCI patients from mild AD patients. Of note, PET imaging has been useful for evaluating medication for the potential treatment of AD. For example, one study used 11C-labeled WAY-100635 PET to evaluate the binding of

lecozotan, a 5-hydroxytryptamine-1A (5-HT1A) antagonist under development as therapy for AD.29 The results demonstrated that lecozotan binds to 5-HT1A receptors in the brain with a maximum observed receptor occupancy of 50 to 60% after a single 5-mg dose in elderly subjects and AD patients. Such studies can help to further identify and evaluate treatment interventions for AD and other dementing illnesses. Another PET study assessed 5-HT4 binding and cortical Aβ burden using [11C]SB207145 and [11C]PIB, respectively.30 No significant difference in 5-HT4 receptor binding was seen between patients and healthy subjects when the diagnosis of AD was made using clinical criteria. However, when patients were assessed based on their Aβ burden, those who had positive findings on Pittsburgh compound B (PIB) studies showed a 13% increase in 5-HT4 receptor binding. In summary, this study found a positive correlation between 5-HT4 receptor binding and Aβ burden in AD patients, as well as a negative correlation between 5-HT4 receptor binding and MMSE scores. The authors indicated that the data suggest that cerebral 5-HT4 receptor upregulation begins before the onset of clinical symptoms and progresses while dementia is still in its early stages. They speculated that this may be a compensatory effect in response to decreased levels of interstitial 5-HT. Such a compensatory effect might help to improve cognitive function transiently, increase acetylcholine release, or counteract Aβ accumulation. The dopaminergic system has also been evaluated in AD patients. For example, one study31 investigated the relationship between striatal DA (D2) receptor availability using [11C]raclopride PET and compared the imaging results to measures of cognition (sustained visual attention, spatial planning, word recognition) and motor (speed and dexterity) function in 24 patients with mild to moderate AD. In this study, higher D2 binding was associated with increased motor speed and, paradoxically, poorer attentional performance. The authors argued that these findings suggest that the use of DA (D2) receptor agonists as an adjunctive treatment in AD may have dissociable effects on cognitive function. A study of 27 MCI patients were evaluated using PET with [11C]dihydotetrabenazine to measure striatal dopamine terminal integrity and [11C]PIB to measure cerebral amyloid burden.32 The results showed that 11 subjects were initially classified clinically as amnestic MCI, 7 as multidomain MCI, and 9 as nonamnestic MCI. At a mean follow-up of 3 years, 18 subjects converted to dementia with significant cerebral amyloid deposition or nigrostriatal denervation as a strong predictor of conversion to dementia. As with most of the other studies described in this chapter, there was only moderate concordance between the clinical classifications and PET-based classification of dementia subtypes. The benzodiazepine/γ-aminobutyric (GABA) receptor system has also been evaluated in AD patients. For example, a small study33 of six early AD patients and six controls evaluated GABA binding using [11C]flumazenil PET and found decreased binding in the inferomedial temporal cortex, hippocampus, retrosplenial cortex, and posterior perisylvian regions. In addition, [11C] flumazenil hippocampal binding correlated with memory performance. Interestingly, the authors report that [11C]flumazenil binding was decreased, particularly in the brain regions with the greatest degree of neuronal loss in postmortem studies of early AD. The authors suggest that despite the

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SPECT and PET Imaging of Neurotransmitters in Dementia small sample size of their study, [11C]flumazenil binding could be a useful marker of neuronal loss in early AD. However, a SPECT study using [123I]Iomazenil and [99mTc] HMPAO in 16 patients with amnestic MCI and 14 elderly control subjects revealed no significant difference in GABA binding.34 Furthermore, hypoperfusion of the precuneus and posterior cingulate cortex suggested that GABA receptors are preserved in early dementia and that functional changes precede neuronal or synaptic loss in neocortical posterior regions. Clearly, future studies are needed to better evaluate the use of GABA receptor imaging in MCI and AD. Perhaps one of the most important potential roles for PET or SPECT imaging is in the evaluation of therapeutic interventions for AD. The relatively recent development of several pharmaceuticals for AD provides an important area for PET imaging. Patients can be imaged before therapy to determine who might be the best candidates for therapy. Patients can also be followed up longitudinally to determine the effectiveness of the pharmaceutical intervention. Also, PET imaging can be useful in the physiologic evaluation of various pharmacologic interventions. A PET study by Kuhl et al aimed to elucidate the pharmacologic mechanism by investigating the effects of donepezil on acetylcholinesterase activity.35 It was reported that donepezil hydrochloride inhibits cerebral cortical acetylcholinesterase activity in AD patients; on average, acetylcholinesterase activity was decreased by 27%. This finding suggests that the clinical trials of donepezil are not reflecting the actual degree of pharmacologic activity and that further investigation of the effects of this drug are warranted. A PET study of the use of tacrine in patients with AD demonstrated improvement of nicotinic receptors (measured as [11C] nicotine binding), cerebral blood flow, and cognitive tests (Trail Making Test and block design test) that preceded improvements in glucose metabolism.36 These improvements were observed in both short- and long-terms treatment regimens. Propentofylline (PPF) has been explored as a potential pharmacologic intervention in patients with both vascular dementia and AD because of the elaboration of inflammatory cytokines and neurotoxic free radicals, decreased secretion of nerve growth factor by astrocytes, excess release of glutamate with associated neurotoxicity, and loss of cholinergic neurons in these two types of dementia. A phase II study using PET showed significant improvements in cerebral glucose metabolism in patients with both vascular dementia and AD after treatment with PPF. Patients treated with a placebo had significant decreases in cerebral metabolism during the same period.37 Thus, PET and SPECT imaging have been used extensively in patients with AD, both in its early stages as well as in later stages in which treatment is attempted. It is likely that as more research is performed to understand the pathophysiology and management of AD, receptor studies with PET or SPECT will play an important role.

4.2 Frontotemporal Dementia Frontotemporal dementia is a clinical neurologic disorder that results from the degeneration of the frontal and temporal anterior lobes of the brain. The classification of FTD has remained controversial for years, but the current definition includes Pick

Fig. 4.3 Fluorodeoxyglucose (FDG) positron emission tomography scan of a patient with frontal lobe dementia showing hypometabolism in the bilateral frontal lobes and the anterior temporal lobes. The remainder of the cortex in this patient has preserved metabolism.

disease, primary progressive aphasia, and semantic dementia as defining characteristics. The two clinical patterns that the symptoms of FTD fall into are behavioral changes and aphasia. Identification of the regions of the brain that are affected by FTD has been aided by FDG-PET, allowing for improved accuracy in diagnosis. Several studies have demonstrated hypometabolism and deficits in perfusion, primarily in the frontal lobes of FTD patients (▶ Fig. 4.3). Diehl et al38 reported an association between FTD and metabolism in the frontal lobe. Grimmer et al39 showed that FTD patients have substantial deficits in the metabolism of the frontal cortices, as well as the caudate nuclei and the thalami. A focal loss of serotonin receptors has been identified by PET studies as a critical aspect of the pathophysiology of FTD, a finding that is in line with postmortem reports. A study of 5-HT1 receptor distribution in FTD patients demonstrated marked reductions in bilateral [11C]WAY-100635 binding in the frontal, medial, and lateral temporal regions.40 Similarly, a study of 5-HT2 receptor distribution in FTD using [11C]MDL 100,907 PET demonstrated substantial decreases in binding in the orbitofrontal, frontal medial, and cingulate cortices.41 A PET study of four patients with frontotemporal lobar dementia (FTLD) using [11C]WAY-100635 demonstrated that the FTLD patients had significantly decreased serotonin 5-HT1A binding potential compared with controls in the frontal, temporal, and occipital regions.40 The FTLD patients had binding potential values that were 50 to 69% that of controls and suggest that profound 5-HT1A binding potential decreases may be present and contribute to the symptoms in these patients.

4.3 Parkinson’s Disease PD is a neurologic disorder with a clinical triad of bradykinesia, tremor, and rigidity resulting from neuronal loss in the substantia nigra and locus ceruleus. The destruction of pigmented neurons in these regions leads to reduced production and storage of dopamine, as well as dysfunction of the nigrostriatal system. PD can also manifest with cognitive impairment in as many as 30% of patients.

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Imaging Techniques

Fig. 4.4 A fluorodopa positron emission tomography scan of a patient with Parkinson’s disease (right scan) reveals markedly reduced uptake in the putamen and only mild uptake in the caudate nuclei compared with a healthy control subject with robust uptake throughout the basal ganglia (left scan).

Multiple PET studies in the literature report hypermetabolism in the basal ganglia in the early stages of PD.42,43 Patients have also displayed mild and diffuse cortical hypometabolism that correlates with the severity of their bradykinesia. There is evidence that hemi-parkinsonism is related to hypermetabolism in the contralateral basal ganglia. The PET radiotracers targeting the dopaminergic system presynaptically and postsynaptically—through [18F]fluorodopa and 18F-N-methylspiperone, respectively—are most suitable for the diagnosis, management, and follow-up of PD and other movement disorders. The pathophysiology of PD is dependent on the progressive deterioration of dopaminergic neurons in the substantia nigra; therefore, the uptake of [18F]fluorodopa is consistently reduced in the striatum (▶ Fig. 4.4) and abnormal in extrastriatal regions to varying degrees.44 Whole-brain PET imaging has demonstrated additional differences in [18F]fluorodopa uptake between PD patients and healthy controls in extrastriatal regions, which may underlie the impaired cognition associated with PD. Studies have shown marked decreases in [18F]fluorodopa uptake in the frontal cortex,45 while others have reported lower uptake in the midbrain and anterior cingulate.46 On the other hand, some studies have demonstrated that despite decreased [18F]fluorodopa uptake in the striatum, patients with early stage PD manifest substantially higher bilateral uptake in dorsolateral prefrontal regions.47,48 A study using FP-CIT (DaTscan) SPECT of seven patients with PD without dementia, 17 with PD plus dementia, and 18 healthy controls revealed no difference in dopamine transporter (DAT) binding in the striatum between PD patients with and without dementia.49 Although this sample is small, the results suggest that DAT binding is not associated with dementia symptoms in PD patients. A [99mC]raclopride study investigated the possibility that frontal lobe dysfunction is responsible for cognitive impairment in PD, either as a direct result of hindered transmission in the mesocortical dopaminergic system or as an indirect result of altered dopaminergic function in the substantia nigra.50 This study involved a spatial working memory task (SWT) as well as a visuomotor control task (VMT). In controls, raclopride binding in the dorsal caudate was lower in SWTs than in VMTs, a finding that is consistent with the

heightened release of endogenous dopamine during executive functions. However, this difference in binding in the dorsal cauadate was not observed in PD patients. Both patients and controls demonstrated reduced racolpride binding in the anterior cingulate cortex during SWTs. Furthermore, dopamine release in the dorsal caudate was markedly decreased in PD patients, but it remained steady in the medial prefrontal cortex. The results of this study suggested that executive deficits in the early stages of PD are related to reduced nigrostriatal dopaminergic function resulting in abnormal processing in the corticobasal ganglia circuit. However, dopaminergic transmission appears well preserved in the mesocortices of patients with early PD. This study demonstrates not only a deficit of dopaminergic function in PD patients but also specific effects that directly relate to cognitive impairment in these patients. Additional studies have investigated the relationship between PD and the serotonin system. Politis et al used [11C]DASB PET to demonstrate that a progressive nonlinear reduction in serotonin transporter binding occurs in PD but is not significantly correlated with the severity of the condition.51 In contrast, another [11C]DASB PET study showed increased serotonin transporter binding in similar patients.52 As with the other disorders, it appears that neurotransmitter studies with PET and SPECT imaging will be highly useful in both clinical and research applications in patients with PD. It is also interesting to note that neurotransmitters other than dopamine, the primary target of PD, may also be of significance in understanding the pathophysiology of the disorder, specifically when it results in dementia.

4.4 Dementia With Lewy Bodies Dementia with Lewy bodies (DLB) is a disorder that results in cognitive impairment but is found to have Lewy bodies on histopathological evaluation, differentiating the disorder from other dementing illnesses. The dopamine receptor system is primarily affected in DLB, and it thus has been a primary focus of neuroimaging studies. For example, in a multicenter study53 using DaTscan SPECT in 326 patients with a clinical diagnosis of probable (n=94) or possible (n=57) DLB or non-DLB dementia (n=147), established by a consensus panel, the authors reported a mean sensitivity of 78% for detecting clinically probable DLB and a specificity of 90% for excluding non-DLB dementia, which was predominantly due to AD. In this study, the positive predictive value was 82%, and the negative predictive value was 88%. There was also relatively high inter-rater reliability in reading the scans. A smaller study compared DaTscan and 99mTc-exametazime blood flow SPECT in 33 controls, 33 AD patients, and 28 DLB patients.54 Agreement between raters in categorizing scans was found to be “moderate” (mean kappa = 0.53) for 99mTc-exametazime and “excellent” (mean kappa = 0.88) for 123I-FP-CIT. In AD and DLB patients, the consensus rating was in line with the clinical diagnosis in 56% of cases using 99mTc-exametazime and 84% using 123I-FP-CIT. Receiver operator characteristic analysis revealed superior diagnostic accuracy with 123I-FP-CIT (sensitivity 79%, specificity 88%) compared with occipital 99mTc-exametazime (sensitivity 64%, specificity 64%). Thus, this study showed that DaTscan SPECT had significantly greater diagnostic

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SPECT and PET Imaging of Neurotransmitters in Dementia accuracy compared with 99mTc-exametazime in the differentiation of DLB from AD. A similar earlier study evaluated DaTscan in 164 older subjects (33 healthy older control subjects, 34 with AD, 23 with DLB, 38 with PD, and 36 with PD plus dementia).55 The results revealed a significant reduction in DAT binding in subjects with DLB compared with subjects with AD and controls but decreased binding similar to that seen in PD. Interestingly, the DLB patients had a flatter rostrocaudal (caudate-putamen) gradient compared with PD patients consistent with the pathophysiologic progression of the two disorders. The greatest loss in all three striatal regions was seen in those who had PD and dementia. Perhaps the largest analysis to date is a systematic metaanalysis of studies in the literature that assess the accuracy of presynaptic dopaminergic imaging with 123I-FP-CIT (DaTscan) in the diagnosis of patients with DLB.56 The meta-analysis included studies in which DaTscan was performed in cases of diagnostic uncertainty and studies in which patients already had established diagnoses of DLB or non-DLB dementia or controls. Four studies with a total of 419 subjects were deemed suitable by the authors for the meta-analysis. The metaanalysis demonstrated a pooled sensitivity of DaTscan in differentiating DLB versus no DLB was 86.5% and a specificity of 93.6%. The authors concluded that DaTscan provided high diagnostic accuracy for the diagnosis of DLB, especially in terms of specificity. Another study57 compared FDG PET and 123I-β-CIT SPECT for differentiating DLB from AD and found that the most sensitive indicator (88%) was hypometabolism in the lateral occipital cortex, whereas the most specific sign (100%) was preservation of the mid or posterior cingulate gyrus. However β-CIT achieved 100% accuracy and greater effect size than did [18F]FDG-PET. Although the dopamine system has been the primary focus in DLB, other neurotransmitters are likely also affected. To investigate in vivo differences in the distribution of α4β2 subtypes of nicotinic acetylcholine receptors, one study used the ligand 123I-5-Iodo-3-[2(S)-2-azetidinylmethoxy] pyridine (5IA85380) SPECT in 15 patients with DLB and 16 controls.18 Compared with controls, there were significant reductions in α4β2 nicotinic acetylcholine receptors in the frontal, striatal, temporal, and cingulate regions in DLB patients. Also, there was increased uptake of 123I-5IA-85380 in the occipital cortex in DLB patients relative to controls. This increase was particularly noted to be associated with DLB subjects with a recent history of visual hallucinations. The authors suggested that these findings indicate a link between cholinergic changes in occipital lobe and visual hallucinations in DLB. In a related study, PET imaging with N-[11C]-methyl-4piperidyl acetate to measure brain acetylcholinesterase activity was performed in 18 patients with PD, 21 patients with PD with dementia (PDD) or DLB, and 26 healthy controls.58 The PDD/ DLB group consisted of 10 patients with PDD and 11 patients with DLB. Among the PD patients, acetylcholinesterase activity was significantly reduced in the cerebral cortex, especially in the medial occipital cortex, but it was even lower in patients with PDD/DLB. However, there was no significant difference in regional AChE activity deficits between early PD and advanced PD groups or between DLB and PDD groups.

Thus, neurotransmitter imaging using both PET and SPECT might be helpful in the clinical evaluation and research investigation of patients with DLB.

4.5 Studies Comparing Different Dementias In this final section, we review several studies, in addition to those described already, that specifically compare patients across multiple types of dementia. Such studies are particularly important for differentiating the disorders from one another and also helping to determine the most useful imaging studies for that purpose. A study of 27 subjects with neurodegenerative dementia associated with parkinsonism evaluated the use of FDG-PET and DaTscan SPECT.59 The subjects were placed in groups according to their clinical diagnoses of probable AD (five subjects), corticobasal degeneration (six subjects), DLB (eight subjects), FTD (four subjects), or PD with dementia (four subjects). Using discriminant analysis of the two scans, the authors reported that 85% of the patients were correctly classified using FDG-PET alone. When DATscan was evaluated alone, 59% were correctly classified, but the combination of both DAT and normalized FDG uptake yielded 100% accurate classifications. The authors concluded that an automated analysis approach combining FDG uptake and DAT binding may be the most effective approach for classifying individual patients with dementia and parkinsonism. To assess several different physiologic parameters in dementia, one study used PET with FDG, [18F]fluorodopa, and N-11Cmethyl-4-piperidyl acetate (MP4A) to measure cholinergic function in eight patients with PDD, six patients with DLB, and nine patients with PD without dementia, all compared with age-matched controls.60 The results found that patients with DLB and PDD share the same profile of dopaminergic and cholinergic deficits in the brain. The authors argued that the two disorders may represent two sides of the “same coin” in a continuum of DLBs. The authors also suggested that cholinergic deficits, in addition to motor symptoms, are crucial for the development of dementia. An interesting study of the serotonin transporter binding in the midbrain of 53 patients with PD (15), DLB (15), PSP (8), and essential tremor (15) were evaluated with FP-CIT SPECT imaging.61 Patients with PD demonstrated a moderately lower serotonin level than patients with essential tremors and controls. However, patients with PSP and DLB showed substantially lowered to undetectable levels of serotonin, respectively. The authors suggested that their findings indicate that the neurodegenerative process affects serotoninergic neurons in parkinsonian syndromes, with much more severe involvement in DLB than in PD patients, despite a comparable loss of striatal DATs. An assessment of several different types of dementia patients demonstrated the problem in comparing clinical and imaging findings for diagnosis. In this study,62 75 subjects with mild dementia underwent a conventional clinical evaluation followed by PET imaging with [11C]-dihydrotetrabenazine and [11C]PIB. Based on clinical evaluation, 36 subjects were classified as having AD, 25 as having FTD, and 14 as having DLB. Based on PET imaging, 47 subjects were classified as having AD,

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Imaging Techniques 15 as having DLB, and 13 as having FTD. This study found that clinical consensus and neuroimaging classifications were in limited agreement in all types of dementia, with discordance of classifications occurring in approximately 35% of subjects. This study did not compare the clinical and PET findings with postmortem diagnosis, which complicates the ability to understand the findings. However, it appears that both clinical and PET findings may be helpful in more accurately classifying dementia patients.

[14]

[15]

[16]

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4.6 Conclusion Overall, neurotransmitter imaging with PET and SPECT has been a powerful tool for evaluating patients with neurologic disorders associated with dementia. PET and SPECT imaging have shed light on the causes and pathophysiology of numerous disease processes. In the clinical settings, these functional imaging modalities prove valuable in initial diagnoses and evaluation of diseases. In the years to come, the development of radiopharmaceuticals targeting specific disorders, as well as the neurotransmitter systems they involve, will expand the scope of applications for these modalities, both clinically and in research. Moreover, functional imaging will continue to improve its abilities to assess the suitability of medical and surgical interventions for patients, to determine prognosis, and to evaluate the response to treatment. Thus, PET and SPECT imaging will continue to be a critical asset for studying the brain in patients with dementia.

References [1] McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 1984; 34: 939–944 [2] Tierney MC, Fisher RH, Lewis AJ et al. The NINCDS-ADRDA Work Group criteria for the clinical diagnosis of probable Alzheimer’s disease: a clinicopathologic study of 57 cases. Neurology 1988; 38: 359–364 [3] Joachim CL, Morris JH, Selkoe DJ. Clinically diagnosed Alzheimer’s disease: autopsy results in 150 cases. Ann Neurol 1988; 24: 50–56 [4] Heiss WD, Kessler J, Szelies B, Grond M, Fink G, Herholz K. Positron emission tomography in the differential diagnosis of organic dementias. J Neural Transm Suppl 1991; 33: 13–19 [5] Jamieson DG, Chawluk JB, Alavi A, Hurtig HI, Rosen M, Bais S, Reivich M. 1987The effect of disease severity on local cerebral glucose metabolism in Alzheimer’s disease J Cerebr Blood Flow Metab; 7 Suppl 1: 410 [6] Kumar A, Schapiro MB, Grady C et al. High-resolution PET studies in Alzheimer’s disease. Neuropsychopharmacology 1991; 4: 35–46 [7] Faulstich ME, Sullivan DC. Positron emission tomography in neuropsychiatry. Invest Radiol 1991; 26: 184–194 [8] Bonte FJ, Hom J, Tintner R, Weiner MF. Single photon tomography in Alzheimer’s disease and the dementias. Semin Nucl Med 1990; 20: 342–352 [9] Friedland RP, Jagust WJ, Huesman RH et al. Regional cerebral glucose transport and utilization in Alzheimer’s disease. Neurology 1989; 39: 1427–1434 [10] Rapoport SI, Horwitz B, Grady CL, Haxby JV, DeCarli C, Schapiro MB. Abnormal brain glucose metabolism in Alzheimer’s disease, as measured by position emission tomography. Adv Exp Med Biol 1991; 291: 231–248 [11] Ichimiya A, Herholz K, Mielke R, Kessler J, Slansky I, Heiss WD. Difference of regional cerebral metabolic pattern between presenile and senile dementia of the Alzheimer type: a factor analytic study. J Neurol Sci 1994; 123: 11–17 [12] Frackowiak RS, Pozzilli C, Legg NJ et al. Regional cerebral oxygen supply and utilization in dementia: a clinical and physiological study with oxygen-15 and positron tomography. Brain 1981; 104: 753–778 [13] Foster NL, Mann U, Mohr E, Sunderland T, Katz D, Chase TN (1989, July). FOCAL CEREBRAL GLUCOSE HYPOMETABOLISM IN DEFINITE ALZHEIMERS-

[18]

[19]

[20]

[21]

[22]

[23]

[24]

[25]

[26]

[27] [28]

[29]

[30] [31]

[32]

[33] [34]

[35]

[36]

DISEASE. In ANNALS OF NEUROLOGY (Vol. 26, No. 1, pp. 132-133). 34 BEACON STREET, BOSTON, MA 02108-1493: LITTLE BROWN CO Mazziotta JC, Frackowiak RS, Phelps ME. The use of positron emission tomography in the clinical assessment of dementia. Semin Nucl Med 1992; 22: 233–246 Shinotoh H, Namba H, Fukushi K et al. Brain acetylcholinesterase activity in Alzheimer’s disease measured by positron emission tomography. Alzheimer Dis Assoc Disord 2000; 14 Suppl 1: S114–S118 Kuhl DE, Minoshima S, Fessler JA et al. In vivo mapping of cholinergic terminals in normal aging, Alzheimer’s disease, and Parkinson’s disease. Ann Neurol 1996; 40: 399–410 Kendziorra K, Wolf H, Meyer PM et al. Decreased cerebral α 4 2* nicotinic acetylcholine receptor availability in patients with mild cognitive impairment and Alzheimer’s disease assessed with positron emission tomography. Eur J Nucl Med Mol Imaging 2011; 38: 515–525 O’Brien JT, Colloby SJ, Pakrasi S et al. Alpha4beta2 nicotinic receptor status in Alzheimer’s disease using 123I-5IA-85380 single-photon-emission computed tomography. J Neurol Neurosurg Psychiatry 2007; 78: 356–362 Kadir A, Almkvist O, Wall A, Långström B, Nordberg A. PET imaging of cortical 11C-nicotine binding correlates with the cognitive function of attention in Alzheimer’s disease. Psychopharmacology (Berl) 2006; 188: 509–520 Blin J, Baron JC, Dubois B et al. Loss of brain 5-HT2 receptors in Alzheimer’s disease: in vivo assessment with positron emission tomography and [18F] setoperone. Brain 1993; 116: 497–510 Marner L, Knudsen GM, Madsen K, Holm S, Baaré W, Hasselbalch SG. The reduction of baseline serotonin 2A receptors in mild cognitive impairment is stable at two-year follow-up. J Alzheimers Dis 2011; 23: 453–459 Marner L, Frokjaer VG, Kalbitzer J et al. Loss of serotonin 2A receptors exceeds loss of serotonergic projections in early Alzheimer’s disease: a combined [11C]DASB and [18F]altanserin-PET study. Neurobiol Aging 2012; 33: 479–487 Kemppainen N, Laine M, Laakso MP et al. Hippocampal dopamine D2 receptors correlate with memory functions in Alzheimer’s disease. Eur J Neurosci 2003; 18: 149–154 Versijpt J, Van Laere KJ, Dumont F et al. Imaging of the 5-HT2A system: age-, gender-, and Alzheimer’s disease-related findings. Neurobiol Aging 2003; 24: 553–561 Santhosh L, Estok KM, Vogel RS et al. Regional distribution and behavioral correlates of 5-HT2A receptors in Alzheimer’s disease with [18F]deuteroaltanserin and PET. Psychiatry Res 2009; 173: 212–217 Lanctôt KL, Hussey DF, Herrmann N et al. A positron emission tomography study of 5-hydroxytryptamine-1A receptors in Alzheimer’s disease. Am J Geriatr Psychiatry 2007; 15: 888–898 Kepe V, Barrio JR, Huang SC et al. Serotonin 1A receptors in the living brain of Alzheimer’s disease patients. Proc Natl Acad Sci U S A 2006; 103: 702–707 Truchot L, Costes N, Zimmer L et al. A distinct [18F]MPPF PET profile in amnestic mild cognitive impairment compared to mild Alzheimer’s disease. Neuroimage 2008; 40: 1251–1256 Raje S, Patat AA, Parks V et al. A positron emission tomography study to assess binding of lecozotan, a novel 5-hydroxytryptamine-1A silent antagonist, to brain 5-HT1A receptors in healthy young and elderly subjects, and in patients with Alzheimer’s disease. Clin Pharmacol Ther 2008; 83: 86–96 Madsen K, Neumann WJ, Holst K et al. Cerebral serotonin 4 receptors and amyloid-β in early Alzheimer’s disease. J Alzheimers Dis 2011; 26: 457–466 Reeves S, Mehta M, Howard R, Grasby P, Brown R. The dopaminergic basis of cognitive and motor performance in Alzheimer’s disease. Neurobiol Dis 2010; 37: 477–482 Albin RL, Burke JF, Koeppe RA, Giordani B, Gilman S, Frey KA. Assessing mild cognitive impairment with amyloid and dopamine terminal molecular imaging. J Nucl Med 2013; 54: 887–893 Pascual B, Prieto E, Arbizu J et al. Decreased carbon-11-flumazenil binding in early Alzheimer’s disease. Brain 2012; 135: 2817–2825 Pappatà S, Varrone A, Vicidomini C et al. SPECT imaging of GABA(A)/benzodiazepine receptors and cerebral perfusion in mild cognitive impairment. Eur J Nucl Med Mol Imaging 2010; 37: 1156–1163 Kuhl DE, Minoshima S, Frey KA, Foster NL, Kilbourn MR, Koeppe RA. Limited donepezil inhibition of acetylcholinesterase measured with positron emission tomography in living Alzheimer cerebral cortex. Ann Neurol 2000; 48: 391–395 Nordberg A, Amberla K, Shigeta M et al. Long-term tacrine treatment in three mild Alzheimer patients: effects on nicotinic receptors, cerebral blood flow, glucose metabolism, EEG, and cognitive abilities. Alzheimer Dis Assoc Disord 1998; 12: 228–237

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SPECT and PET Imaging of Neurotransmitters in Dementia [37] Mielke R, Möller HJ, Erkinjuntti T, Rosenkranz B, Rother M, Kittner B. Propentofylline in the treatment of vascular dementia and Alzheimer-type dementia: overview of phase I and phase II clinical trials. Alzheimer Dis Assoc Disord 1998; 12 Suppl 2: S29–S35 [38] Diehl J, Grimmer T, Drzezga A, Riemenschneider M, Förstl H, Kurz A. Cerebral metabolic patterns at early stages of frontotemporal dementia and semantic dementia: a PET study. Neurobiol Aging 2004; 25: 1051–1056 [39] Grimmer T, Diehl J, Drzezga A, Förstl H, Kurz A. Region-specific decline of cerebral glucose metabolism in patients with frontotemporal dementia: a prospective 18F-FDG-PET study. Dement Geriatr Cogn Disord 2004; 18: 32– 36 [40] Lanctôt KL, Herrmann N, Ganjavi H et al. Serotonin-1A receptors in frontotemporal dementia compared with controls. Psychiatry Res 2007; 156: 247– 250 [41] Franceschi M, Anchisi D, Pelati O et al. Glucose metabolism and serotonin receptors in the frontotemporal lobe degeneration. Ann Neurol 2005; 57: 216–225 [42] Rougemont D, Baron JC, Collard P, Bustany P, Comar D, Agid Y. Local cerebral glucose utilisation in treated and untreated patients with Parkinson’s disease. J Neurol Neurosurg Psychiatry 1984; 47: 824–830 [43] Eidelberg D, Moeller JR, Dhawan V et al. The metabolic anatomy of Parkinson’s disease: complementary [18F]fluorodeoxyglucose and [18F]fluorodopa positron emission tomographic studies. Mov Disord 1990; 5: 203–213 [44] Heiss WD, Hilker R. The sensitivity of 18-fluorodopa positron emission tomography and magnetic resonance imaging in Parkinson’s disease. Eur J Neurol 2004; 11: 5–12 [45] Kaasinen V, Rinne JO. Functional imaging studies of dopamine system and cognition in normal aging and Parkinson’s disease. Neurosci Biobehav Rev 2002; 26: 785–793 [46] Ito K, Nagano-Saito A, Kato T et al. Striatal and extrastriatal dysfunction in Parkinson’s disease with dementia: a 6-[18F]fluoro-L-dopa PET study. Brain 2002; 125: 1358–1365 [47] Rakshi JS, Uema T, Ito K et al. Frontal, midbrain and striatal dopaminergic function in early and advanced Parkinson’s disease A 3D [18F]dopa-PET study. Brain 1999; 122: 1637–1650 [48] Brück A, Aalto S, Nurmi E, Bergman J, Rinne JO. Cortical 6-[18F]fluoro-L-dopa uptake and frontal cognitive functions in early Parkinson’s disease. Neurobiol Aging 2005; 26: 891–898 [49] Song IU, Chung YA, Oh JK, Chung SW (2014). An FP-CIT PET comparison of the difference in dopaminergic neuronal loss in subtypes of early Parkinson’s disease. Acta Radiologica, 55(3), 366-371

[50] Sawamoto N, Piccini P, Hotton G, Pavese N, Thielemans K, Brooks DJ. Cognitive deficits and striato-frontal dopamine release in Parkinson’s disease. Brain 2008; 131: 1294–1302 [51] Politis M, Wu K, Loane C et al. Staging of serotonergic dysfunction in Parkinson’s disease: an in vivo 11C-DASB PET study. Neurobiol Dis 2010; 40: 216– 221 [52] Boileau I, Warsh JJ, Guttman M et al. Elevated serotonin transporter binding in depressed patients with Parkinson’s disease: a preliminary PET study with [11C]DASB. Mov Disord 2008; 23: 1776–1780 [53] McKeith I, O’Brien J, Walker Z et al. DLB Study Group. Sensitivity and specificity of dopamine transporter imaging with 123I-FP-CIT SPECT in dementia with Lewy bodies: a phase III, multicentre study. Lancet Neurol 2007; 6: 305–313 [54] Colloby SJ, Firbank MJ, Pakrasi S et al. A comparison of 99mTc-exametazime and 123I-FP-CIT SPECT imaging in the differential diagnosis of Alzheimer’s disease and dementia with Lewy bodies. Int Psychogeriatr 2008; 20: 1124–1140 [55] O’Brien JT, Colloby S, Fenwick J et al. Dopamine transporter loss visualized with FP-CIT SPECT in the differential diagnosis of dementia with Lewy bodies. Arch Neurol 2004; 61: 919–925 [56] Papathanasiou ND, Boutsiadis A, Dickson J, Bomanji JB. Diagnostic accuracy of ¹²³I-FP-CIT (DaTSCAN) in dementia with Lewy bodies: a meta-analysis of published studies. Parkinsonism Relat Disord 2012; 18: 225–229 [57] Lim SM, Katsifis A, Villemagne VL et al. The 18F-FDG-PET cingulate island sign and comparison to 123I-β-CIT SPECT for diagnosis of dementia with Lewy bodies. J Nucl Med 2009; 50: 1638–1645 [58] Shimada H, Hirano S, Shinotoh H et al. Mapping of brain acetylcholinesterase alterations in Lewy body disease by PET. Neurology 2009; 73: 273–278 [59] Garibotto V, Montandon ML, Viaud CT et al. Regions of interest-based discriminant analysis of DaTscan SPECT and FDG-PET for the classification of dementia. Clin Nucl Med 2013; 38: e112–e117 [60] Klein JC, Eggers C, Kalbe E et al. Neurotransmitter changes in dementia with Lewy bodies and Parkinson’s disease dementia in vivo. Neurology 2010; 74: 885–892 [61] Roselli F, Pisciotta NM, Pennelli M et al. Midbrain SERT in degenerative parkinsonisms: a 123I-FP-CIT SPECT study. Mov Disord 2010; 25: 1853– 1859 [62] Burke JF, Albin RL, Koeppe RA et al. Assessment of mild dementia with amyloid and dopamine terminal positron emission tomography. Brain 2011; 134: 1647–1657

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5 Diffusion Tensor Imaging in Neurodegenerative Disorders Dhiraj Baruah, Suyash Mohan, and Sumei Wang Neurodegeneration results in deterioration of neurons in the brain and spinal cord. Neurodegenerative disorder is defined as a progressive condition of the nervous system associated with destruction or loss of selective neurons associated with functions like movement and cognition, ultimately leading to death.1 These disorders may be hereditary or sporadic. People suffering with these conditions place a significant amount of physical and emotional burden on their family and caregivers. The increasing prevalence of neurodegenerative diseases is a major health problem that uses a significant amount of health-related expenditures.2 Depending on loss of function, neurodegenerative disorders are classified mainly into two categories: affecting cognition (such as Alzheimer’s disease [AD]) and affecting movement (such as Parkinson’s disease [PD]). Although significant progress has been made in recent years for understanding the pathophysiology of these diseases, treatment of these conditions is still symptomatic rather than effecting a cure. Conventional imaging techniques, including magnetic resonance imaging (MRI), are limited in understanding these conditions and usually show abnormalities only in advanced stages of the disease. Understanding the parenchymal changes in the brain at a microstructural level helps to understand the different disorders in this group and might help in developing curative or preventive treatments in the future. One of these advanced imaging techniques is diffusion tensor imaging (DTI), which evaluates the microstructural changes in the brain.3,4 Our aim in this chapter is to describe the recent advances in understanding changes of white matter (WM) in patients with some common neurodegenerative disorders. Before going to the disorders, we first look at some basic facts about DTI.

5.1 Diffusion Tensor Imaging: Basic Concepts Diffusion tensor imaging uses the property of random motion (Brownian motion) of water molecules in vivo. Water diffusion in WM is constrained by the physical boundaries, including the axon sheath, leading the movement to be greater along the z (long) axis of the fiber than across it. This asymmetric property of water diffusion in WM is known as anisotropy. Color maps can be generated by using this information to localize WM tracts (▶ Fig. 5.1). Conventional DWI gives information in only one direction. DTI helps to quantify this property at the voxel level by using a tensor. Tensor is a mathematical concept that not only allows quantification of molecular motion in each direction but also gives an average magnitude of water diffusion.5 The two commonly used indices are mean diffusivity (MD) and fractional anisotropy (FA), which can be calculated using the following equations: MD ¼

ð !1 þ ! 2 þ ! 3 Þ 3

Fig. 5.1 Diffusion tensor imaging (DTI)-based color map of a healthy subject. Colors indicate directions as follows: red, left-right; green, anterior-posterior; blue, superior-inferior. White line delineates manually segmented corticospinal tract (CST) (a) Reconstructed CSTs (green) are overlaid on color maps (b) (Reprinted with permission from Wang S, Poptani H, Bilello M, Wu X, Woo JH, Elman LB et al. AJNR Am J Neuroradiol. 2006 Jun-Jul;27(6):1234-8.)

v ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u 3 ð! % %!Þ2 þ ð! % %!Þ2 þ ð! % %!Þ2 t 1 2 3 FA ¼ 2 !1 2 þ ! 2 2 þ ! 3 2

Where λ1 , λ2 , and λ3 are three eigenvalues of the diffusion tensor, and λ denotes the mean of the three eigenvalues. MD is a measure of the directionally averaged magnitude of diffusion and is related to the integrity of the local brain tissue. FA represents the degree of anisotropy in the diffusion and reflects the degree of alignment of cellular structure. DTI indirectly assesses the integrity of tissue and could be useful in characterizing neurodegenerative disorders.

5.2 Aging Brain It is important to understand normal age-related microstructural changes of WM before exploring neurodegenerative diseases, which is usually not possible with conventional MRI. Degenerative WM changes with normal aging include a decrease in myelin density and alterations in myelin structure.6,7 Conventional MRI is helpful in evaluating volumetric changes of the aging brain. Microstructural disruption of WM in the aging brain can be detected using DTI. In a study of 38 participants, Salat et al8 have shown significant age-related decline of FA in frontal WM, the posterior limb of the internal capsule, and the genu of the corpus callosum, with preservation of temporal and posterior WM. Other studies have also shown more anterior WM changes associated with the aging brain compared with posterior WM.9 The subtle and probably preclinical changes with aging seen using DTI may enable monitoring of WM recovery in normal aging, trauma, and disease.10

5.3 Alzheimer’s Disease Alzheimer’s disease is the most common form of dementia and has been defined pathologically by the presence of intracellular

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Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Diffusion Tensor Imaging in Neurodegenerative Disorders neurofibrillary tangles and extracellular neuritic plaques. There is an accelerated loss of cortical neurons compared with age-matched nondemented persons.11 Many authorities have documented that changes in WM are also more pronounced in patients with AD than in age-matched nondemented persons.12,13 These WM changes are the focus of evaluation using DTI. Studies have reported changes seen using DTI, including decreased FA, increased MD, and decreased lattice index.14,15,16,17,18,19,20

Slight impairment of cognitive function without a full-blown picture of dementia is defined as mild cognitive impairment (MCI). Patients with MCI carry a higher risk of developing AD in later life (10 to 15% conversion rate).21 Researchers have generated in vivo quantitative DTI markers to identify patients with high risk (▶ Fig. 5.2).22,23,24 Most of the changes in MCI and AD patients are posteriorly located (involving the hippocampus, pallidum, thalamus, and caudate), whereas changes in the normal aging brain are commonly seen anteriorly (involving frontal WM).20,25,26,27

Fig. 5.2 Results from group comparisons of fractional anisotropy (FA) and mean diffusivity (MD). The anatomical underlay is the MNI (Montreal Neurological Institute) space registered target FA image. Maps are referenced to a standard human white matter atlas (Mori et al., 2005). The group at high risk for AD showed decreased FA (shown in red) compared with the low-risk group in a number of regions, prominently including the fornix and inferior longitudinal fasciculus (ILF) in the temporal lobe and anterior portions of the inferior fronto-occipital fasciculus (IFOF)/uncinate fasciculus (UNC) in the frontal lobe. There were no regions in which the low-risk group showed decreased FA compared with the high-risk group. Within regions of decreased FA, there were only two regions of increased MD in the high-risk group (shown in orange): the genu and the right IFOF/ILF. CING, cingulum; L, left; R, right. (Reprinted with permission from Gold BT, Powell DK, Andersen AH, Smith CD.Neuroimage. 2010 Oct 1;52(4):1487-94.)

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Imaging Techniques

5.4 Dementias Other than Alzheimer’s Disease 5.4.1 Dementia with Lewy Bodies Dementia with Lewy bodies (DLB), also known as Lewy body variant of AD, is the second most common form of dementia in elderly patients after AD.28 The pathophysiology of DLB is likely due to neuronal synaptic dysfunction rather than to neuronal loss. Clinical differentiation between AD and DLB is not always possible. Three main clinical features described with DLB are impairment in cognitive function, visual hallucinations, and spontaneous parkinsonism.28 Posterior predominance of changes in DTI are more common in DLB than are frontal changes29; WM in the region of parieto-occipital and temporal lobes is commonly involved. As already stated, changes of FA in the posterior WM are also common in AD; however, posterior to anterior preferential involvement occurs in a significantly greater extent with DLB compared with AD. Researchers have shown significant association of decreased FA with DLB involving occipital areas (precuneus) and inferior longitudinal fasciculus (ILF).30,31 Although preferential FA changes were seen posteriorly in DLB, an increase in MD was rather diffuse than regional.29 Rosie et al found reduced FA in the left thalamic WM in DLB compared with that in AD patients.29

5.4.2 Frontotemporal Dementia Frontotemporal dementia (FTD) is a neurodegenerative condition that is characterized by involvement of the frontal and anterior temporal lobes.32 Depending on the predominant involvement of frontal or temporal lobe, FTD is divided into two main categories: frontal lobe variant and temporal lobe variant.33 Patients with the frontal variant of FTD usually have gradually worsening change in personality and behavior. Patients with the temporal variant of FTD have gradually worsening fluent aphasia.34 The first observation of decreased FA was shown in a postmortem brain by Larsson et al.35 In 36 patients with FTD, Borroni et al found significant involvement of the superior longitudinal fasciculus (SLF) with frontal variant FTD and bilateral ILF involvement in temporal variant FTD.33 Elise et al have shown decreased FA and increased radial diffusivity in frontotemporal WM and reduced connectivity between frontoinsula and anterior mid-cingulate cortex on resting state functional MRI in presymptomatic FTD patients years before symptom onset.36

5.5 Human Prion Disease 5.5.1 Creutzfeldt-Jakob Disease Creutzfeldt-Jakob disease (CJD) was first described in the 1920s by German neurologists Hans Gerhard Creutzfeldt and Alfons Maria Jakob. Its pathogenesis is not completely clear; however, researchers have shown that CJD is transmissible to nonhuman primates and other animals on filtration of the inoculum, indicating that the agent is small and “replicating.”37 Patients with this rapidly progressive fatal neurodegenerative disease typically have progressive dementia, generalized myoclonus, and

mutism. Cerebrospinal fluid examination for 14–3-3 protein is a highly sensitive and specific marker for CJD in the appropriate scenario.38 In a blinded study, Steinhoff et al have shown high diagnostic value of electroencephalography (EEG), with sensitivity and specificity of periodic sharp wave complexes of 67 and 86%, respectively, for the diagnosis of CJD.39 Although these laboratory examinations are helpful for diagnosis of CJD, they do not have reliable markers to assess progression of the disease. Another option for diagnosis is brain biopsy; however, this procedure is invasive and risky. Among the routine MRI sequences, DWI is accepted as the most helpful imaging modality for the diagnosis of CJD, with studies showing benefit of DWI in early stages with or without changes in EEG (▶ Fig. 5.3).40,41 Using 3-tesla (T) MRI in three patients with CJD, Fujita et al have shown significant lower MD values than those found in control patients in the striatum, caudate nucleus, putamen, globus pallidus, and thalamus; however, they found no significant abnormality of FA compared with the control group.42

5.6 Parkinson's and Related Movement Disorders Parkinson’s disease (PD) is a common chronic, progressive neurologic disease with classic findings including resting tremor, rigidity, bradykinesia, and postural instability. Among all the clinical symptoms, resting tremor is the most characteristic of PD.43 If tremor is absent, evaluation of conditions that can show signs of parkinsonism, including multiple system atrophy, progressive supranuclear palsy, and striatonigral degeneration, should be considered.44 Characteristic pathologic finding for the diagnosis of PD is loss of dopaminergic neurons in the pars compacta of the substantia nigra. The usefulness of positron emission tomography and single-photon emission computed tomography for the diagnosis of PD is described in the literature.45 Routine MRI is usually unremarkable in patients with PD, even in advanced stage, but it is useful for ruling out secondary causes of parkinsonism-type symptoms.46,47 Predominant frontal lobe atrophy has been consistently found in patients with PD without dementia, but GM volume reduction is seen in the parietal and temporal lobes more specifically associated with PD with dementia.48,49,50,51 Measurement of MD and FA using DTI helps differentiate PD patients from control groups and those with progressive supranuclear palsy.52,53,54 In patients with PD, decreased FA is seen in the frontal lobes, premotor areas, and cingulum.53,54 The thalamus, globus pallidus, putamen, and caudate nucleus are commonly involved in atypical parkinsonisms as opposed to PD.55,56 Increased diffusivity (increased MD) in the substantia nigra may be due to significant loss of dopaminergic neurons, leading to decreased cellular matrix.57 In PD patients without dementia, correlation of executive impairment with reduced FA in the parietal WM has been seen.58 FA and MD are not significantly changed in the corticospinal tract (CST) of PD patients.59,60 Although changes in the DTI parameters in the genu of corpus callosum have been described, the splenium is not involved.61 Prodoehl et al have shown that DTI of the basal ganglia and

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Diffusion Tensor Imaging in Neurodegenerative Disorders

Fig. 5.3 Sporadic Creutzfeldt-Jakob disease in a 34-year-old man who had abnormal leg movement and slow thinking. (a) Axial T2-weighted magnetic resonance image shows an area of subtle abnormal signal hyperintensity in the right putamen and caudate nuclei. (b) Axial diffusion-weighted images show bilateral areas of abnormal high signal intensity at the putamen and caudate nuclei, particularly in the right. (c) Axial apparent diffusion coefficient (ADC) map from diffusion-weighted imaging demonstrates reduced ADC value.

cerebellum is useful in classifying PD and atypical parkinsonism accurately and also in distinguishing them from nonmovement disorders.62

5.7 Huntington’s Disease Huntington’s disease (HD) is a devastating late-onset autosomal dominant trinucleotide CAG repeat neurodegenerative disorder that involves chromosome 4, leading to abnormal elongation of the polyglutamine stretch of huntingtin, which becomes increasingly toxic. Although this mutation and resultant elongated huntingtin are seen in the tissues of almost all organs of HD patients, pathological changes are seen only in the brain. The main structure involved is striatum, including caudate and putamen. Symptoms of HD depend on the length of CAG repeat, with longer CAG expansion causing earlier disease onset.63 Clinical pictures include the triad of progressive cognitive, psychiatric, and motor symptoms. Among the motor signs, chorea is considered the classic manifestation of HD. Although pathological changes in HD, including neuronal loss, are described as occurring predominantly in the striatum, significant changes are also described in brain structures other than striatum.64,65 Studies have shown a valuable role of MRI in characterizing changes in the brain of presymptomatic and symptomatic HD patients.66,67,68,69 Many cross-sectional volumetric studies have shown decreased WM volume in patients with HD.70,71,72 The interpretation of DTI parameters is complex in HD patients because multiple parenchymal changes occur, including demyelination, axon damage, neuronal loss, and gliosis.73 Decreased FA on DTI is observed in presymptomatic patients and in patients at early stage compared with control subjects.74,75,76 As decreasing FA is consistently shown in patients with HD, DTI may become an important biomarker for the early detection of HD.77,78,79

5.8 Motor Neuron Disease 5.8.1 Amyotrophic Lateral Sclerosis Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder and the most common motor neuron disease. ALS has a rapidly progressive course, usually leading to death in 2 to 3 years.80,81 Because ALS is a disease of the motor system, patients have muscle weakness and paralysis; however, cognitive and behavioral symptoms are also described with this motor neuron disease.82 Pathologically, ALS causes damage of upper motor neurons in the cerebral cortex and lower motor neurons in the brainstem and spinal cord. There are different genetic subtypes.83 The familial form of ALS usually has autosomal dominant inheritance. Genetic mutations are described in familial ALS patients involving multiple locations.84 Electromyography can help identify the involvement of lower motor neurons. However, neurologic examination is the only way to detect the upper motor neuron involvement but is subjective and unreliable. Among imaging techniques, advanced MRI, including DTI, has the potential to be used as an objective diagnostic or prognostic marker of ALS. DTI color-based map can show thinning of the CST (▶ Fig. 5.4). Reduced FA along the CST is the predominant abnormality shown in most DTI studies.85,86,87,88,89,90,91,92,93,94,95 The CST damage observed in ALS patients possibly correlates with the rate of disease progression,96 although some disagreement remains regarding whether the CST damage correlates with disease severity.97,98,99,100 Verstraete et al have shown disconnection of motor systems in patients with ALS and concluded that the disease progresses along structural brain connections rather than only through the involvement of CST.101 Moreover, ALS is a multisystem disorder. Decreased FA has also been described in areas other than motor

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Imaging Techniques tracts in patients with ALS by different voxel-based DTI studies. Kassubek et al have shown widespread WM involvement in ALS patients compared with control subjects, including CST, adjacent subcortical WM, and corpus callosum (▶ Fig. 5.5).93,94,102 Also, Agosto et al have shown that subtle involvement of the right uncinate fasciculus may precede the appearance of behavioral symptoms in patients with ALS.96

5.9 Multiple Sclerosis Fig. 5.4 Diffusion tensor imaging–based color maps of a healthy subject (a) and an amyotrophic lateral sclerosis (ALS) patient (b). The left corticospinal tract (arrows) appears thinner in the ALS patient (b). (Reprinted with permission from Wang S, Poptani H, Bilello M, Wu X, Woo JH, Elman LB et al. AJNR Am J Neuroradiol. 2006 Jun-Jul;27 (6):1234-8.)

Multiple sclerosis (MS) is a chronic inflammatory immunemediated demyelinating and neurodegenerative disease.103,104 MS is the most common cause of nontraumatic disability in young and middle-aged adults.105 The exact causes and pathogenesis of MS are not clear; however, experimental models suggest autoimmunity as the basis of WM injury.106 Demyelinated plaque is the predominant pathologic hallmark of MS, with a

Fig. 5.5 Comparison of fractional anisotropy (FA) maps based on diffusion tensor imaging (DTI) data of 20 patients with amyotrophic lateral sclerosis (ALS) and 20 age- and gender-matched controls. Upper panel: Group-averaged FA maps of controls (left) and patients with ALS (right) in coronal (large) and axial/sagittal view. FA display threshold is 0.2. Lower left: Comparison between the ALS group and controls by whole-brain–based statistical voxel-wise comparison at group level at P < 0.05 after correction for multiple comparisons. The areas with decreased FA in ALS are displayed, with the significance of the alterations coded by temperature of the color bar. Right: Fiber tracking of the corticospinal tract (CST) in group-averaged DTI data sets. The underlying FA values were averaged and statistically compared. Differences between group-averaged ALS FA maps and group-averaged control FA maps were highly significant, as indicated. (Reprinted with permission from Kassubek J, Ludolph AC, Müller HP. Ther Adv Neurol Disord. 2012 Mar;5(2):119-27.)

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Diffusion Tensor Imaging in Neurodegenerative Disorders

Fig. 5.6 Axial fluid-attenuated inversion recovery (FLAIR) image (a), mean diffusivity (MD) map (b), fractional anisotropy (FA) map (c), and diffusion tensor imaging (DTI)-based color map (d) from the brain of a patient with multiple sclerosis. The lesions demonstrate increased MD value and reduced FA value.

special predilection for periventricular WM, corpus callosum, optic nerves, and spinal cord.106 Most patients with MS have a relapsing-remitting clinical picture with episodic onset of symptoms followed by residual deficits or full recovery. Complete recovery is common in the early stage of the disease.107 A secondary progressive course after many episodes of relapse and recovery is more common than a primary progressive course from the onset.108 Multiple sclerosis is diagnosed clinically. Because of its sensitivity in detection and characterization of demyelinating areas, however, MRI is integrated in the diagnostic criteria for MS.109 MRI with conventional sequences is used routinely for diagnosis and for monitoring treatment response and disease progression. Association of conventional MRI with clinical status is limited, and DTI gives better information about WM damage compared with conventional imaging; FA and MD values are more useful in the assessment of MS patients (▶ Fig. 5.6).110 DTI shows reduced FA and increased MD in MS lesions; studies have shown a reduction of FA in enhancing compared with nonenhancing lesions.111,112,113 Although DTI shows WM damage in MS more accurately than does conventional MRI, these changes do not always correlate with clinical disability.114 Genova et al have shown a relationship between executive functioning and processing speed with changes in FA in WM regions in patients with MS.115 Moreover, the difference in WM tract disruption can be directly visualized using diffusion tensor tractography (▶ Fig. 5.7).

Besides involvement of WM in the brain, involvement of the spinal cord leading to weakness and loss of proprioception frequently causes significant disability in patients with MS.116,117,118 In MS, spinal cord involvement is seen by conventional MRI in 80% of patients and in 99% of patients at autopsy.116,118 Conventional T2-weighted and contrast-enhanced MRI sequences are routinely used for diagnosis, progression, and monitoring of disease response with new treatment modalities. However, advanced imaging markers, including DTI, are more valuable in showing disease extent. More precise DTI methods like tract-specific DTI may become helpful in assessing changes with new neuroprotection and neural repair treatments, particularly in patients with progressive MS, in whom the criteria of enhancement are always useful.119

5.10 Summary Although neurodegenerative disorders are diagnosed mainly clinically, imaging may be helpful in providing adjunctive information. Standard MRI has limited usefulness for evaluating these diseases, but microstructural information provided by DTI may impact early diagnosis and better understanding of the pathogenesis of neurodegenerative diseases, thus impacting the formulation of new treatment modalities.

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Imaging Techniques

Fig. 5.7 Diffusion tensor tractography of corpus callosum (CC) in the same patient as in ▶ Fig. 5.6. Region of interest (ROI) is placed at the midsagittal level. Note that the fibers of CC are disrupted in the location of the lesions.

References [1] Wang S, Woo JH, Melhem ER. DTI in neurodegenerative disorders. In: Holodny AI, ed. Functional Neuroimaging: A Clinical Approach. New York: Informa Healthcare; 2008 [2] Forman MS, Trojanowski JQ, Lee VM. Neurodegenerative diseases: a decade of discoveries paves the way for therapeutic breakthroughs. Nat Med 2004; 10: 1055–1063 [3] Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B 1996; 111: 209–219 [4] Beaulieu C. The basis of anisotropic water diffusion in the nervous system: a technical review. NMR Biomed 2002; 15: 435–455 [5] Basser PJ. Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed 1995; 8: 333–344 [6] Meier-Ruge W, Ulrich J, Brühlmann M, Meier E. Age-related white matter atrophy in the human brain. Ann N Y Acad Sci 1992; 673: 260–269 [7] Marner L, Nyengaard JR, Tang Y, Pakkenberg B. Marked loss of myelinated nerve fibers in the human brain with age. J Comp Neurol 2003; 462: 144–152 [8] Salat DH, Tuch DS, Greve DN et al. Age-related alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiol Aging 2005; 26: 1215–1227 [9] Ardekani S, Kumar A, Bartzokis G, Sinha U. Exploratory voxel-based analysis of diffusion indices and hemispheric asymmetry in normal aging. Magn Reson Imaging 2007; 25: 154–167 [10] Sullivan EV, Pfefferbaum A. Neuroradiological characterization of normal adult ageing. Br J Radiol 2007; 80: S99–S108 [11] Hauw JJ, Duyckaerts C, Delaere P, Lamy C, Henry P. Alzheimer’s disease: neuropathological and etiological data. Biomed Pharmacother 1989; 43: 469–482 [12] Brun A, Englund E. A white matter disorder in dementia of the Alzheimer type: a pathoanatomical study. Ann Neurol 1986; 19: 253–262 [13] Englund E. Neuropathology of white matter changes in Alzheimer’s disease and vascular dementia. Dement Geriatr Cogn Disord 1998; 9 Suppl 1: 6–12 [14] Bozzali M, Falini A, Franceschi M et al. White matter damage in Alzheimer’s disease assessed in vivo using diffusion tensor magnetic resonance imaging. J Neurol Neurosurg Psychiatry 2002; 72: 742–746 [15] Hanyu H, Sakurai H, Iwamoto T, Takasaki M, Shindo H, Abe K. Diffusionweighted MR imaging of the hippocampus and temporal white matter in Alzheimer’s disease. J Neurol Sci 1998; 156: 195–200

[16] Kantarci K, Jack CR, Jr, Xu YC et al. Mild cognitive impairment and Alzheimer’s disease: regional diffusivity of water. Radiology 2001; 219: 101–107 [17] Ramani A, Jensen JH, Helpern JA. Quantitative MR imaging in Alzheimer’s disease. Radiology 2006; 241: 26–44 [18] Sandson TA, Felician O, Edelman RR, Warach S. Diffusion-weighted magnetic resonance imaging in Alzheimer’s disease. Dement Geriatr Cogn Disord 1999; 10: 166–171 [19] Sugihara S, Kinoshita T, Matsusue E, Fujii S, Ogawa T. Usefulness of diffusion tensor imaging of white matter in Alzheimer’s disease and vascular dementia. Acta Radiol 2004; 45: 658–663 [20] Rose SE, Chen F, Chalk JB et al. Loss of connectivity in Alzheimer’s disease: an evaluation of white matter tract integrity with colour coded MR diffusion tensor imaging. J Neurol Neurosurg Psychiatry 2000; 69: 528–530 [21] Petersen RC, Doody R, Kurz A et al. Current concepts in mild cognitive impairment. Arch Neurol 2001; 58: 1985–1992 [22] deToledo-Morrell L, Stoub TR, Bulgakova M et al. MRI-derived entorhinal volume is a good predictor of conversion from MCI to AD. Neurobiol Aging 2004; 25: 1197–1203 [23] Gold BT, Powell DK, Andersen AH, Smith CD. Alterations in multiple measures of white matter integrity in normal women at high risk for Alzheimer’s disease. Neuroimage 2010; 52: 1487–1494 [24] Dickerson BC, Goncharova I, Sullivan MP et al. MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer’s disease. Neurobiol Aging 2001; 22: 747–754 [25] Yoshiura T, Mihara F, Kuwabara Y et al. MR relative cerebral blood flow mapping of Alzheimer’s disease: correlation with Tc-99 m HMPAO SPECT. Acad Radiol 2002; 9: 1383–1387 [26] Head D, Buckner RL, Shimony JS et al. Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: evidence from diffusion tensor imaging. Cereb Cortex 2004; 14: 410–423 [27] Sydykova D, Stahl R, Dietrich O et al. Fiber connections between the cerebral cortex and the corpus callosum in Alzheimer’s disease: a diffusion tensor imaging and voxel-based morphometry study. Cereb Cortex 2007; 17: 2276– 2282 [28] Hansen L, Salmon D, Galasko D et al. The Lewy body variant of Alzheimer’s disease: a clinical and pathologic entity. Neurology 1990; 40: 1–8 [29] Watson R, Blamire AM, Colloby SJ et al. Characterizing dementia with Lewy bodies by means of diffusion tensor imaging. Neurology 2012; 79: 906–914

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| 12.09.15 - 10:49

Diffusion Tensor Imaging in Neurodegenerative Disorders [30] Bozzali M, Falini A, Cercignani M et al. Brain tissue damage in dementia with Lewy bodies: an in vivo diffusion tensor MRI study. Brain 2005; 128: 1595– 1604 [31] Lee JE, Park HJ, Park B et al. A comparative analysis of cognitive profiles and white-matter alterations using voxel-based diffusion tensor imaging between patients with Parkinson’s disease dementia and dementia with Lewy bodies. J Neurol Neurosurg Psychiatry 2010; 81: 320–326 [32] Hodges JR, Davies RR, Xuereb JH et al. Clinicopathological correlates in frontotemporal dementia. Ann Neurol 2004; 56: 399–406 [33] Borroni B, Brambati SM, Agosti C et al. Evidence of white matter changes on diffusion tensor imaging in frontotemporal dementia. Arch Neurol 2007; 64: 246–251 [34] Neary D, Snowden JS, Gustafson L et al. Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology 1998; 51: 1546–1554 [35] Larsson EM, Englund E, Sjöbeck M, Lätt J, Brockstedt S. MRI with diffusion tensor imaging post-mortem at 3.0 T in a patient with frontotemporal dementia. Dement Geriatr Cogn Disord 2004; 17: 316–319 [36] Dopper EGP, Rombouts SARB, Jiskoot LC et al. Structural and functional brain connectivity in presymptomatic familial frontotemporal dementia. Neurology 2013; 80: 814–823 [37] Gibbs CJ, Jr, Gajdusek DC, Asher DM et al. Creutzfeldt-Jakob disease (spongiform encephalopathy): transmission to the chimpanzee. Science 1968; 161: 388–389 [38] Lemstra AW, van Meegen MT, Vreyling JP et al. 14–3-3 testing in diagnosing Creutzfeldt-Jakob disease: a prospective study in 112 patients. Neurology 2000; 55: 514–516 [39] Steinhoff BJ, Räcker S, Herrendorf G et al. Accuracy and reliability of periodic sharp wave complexes in Creutzfeldt-Jakob disease. Arch Neurol 1996; 53: 162–166 [40] Shiga Y, Miyazawa K, Sato S et al. Diffusion-weighted MRI abnormalities as an early diagnostic marker for Creutzfeldt-Jakob disease. Neurology 2004; 63: 443–449 [41] Cohen OS, Hoffmann C, Lee H, Chapman J, Fulbright RK, Prohovnik I. MRI detection of the cerebellar syndrome in Creutzfeldt-Jakob disease. Cerebellum 2009; 8: 373–381 [42] Fujita K, Nakane S, Harada M, Izumi Y, Kaji R. Diffusion tensor imaging in patients with Creutzfeldt-Jakob disease. J Neurol Neurosurg Psychiatry 2008; 79: 1304–1306 [43] Hoehn MM, Yahr MD. Parkinsonism: onset, progression and mortality. Neurology 1967; 17: 427–442 [44] Lang AE, Lozano AM. Parkinson’s disease: first of two parts. N Engl J Med 1998; 339: 1044–1053 [45] Ravina B, Eidelberg D, Ahlskog JE et al. The role of radiotracer imaging in Parkinson’s disease. Neurology 2005; 64: 208–215 [46] Schrag A, Good CD, Miszkiel K et al. Differentiation of atypical parkinsonian syndromes with routine MRI. Neurology 2000; 54: 697–702 [47] Savoiardo M, Grisoli M. Role of CT and MRI in diagnosis and research. In: Atypical Parkinsonian Disorders: Clinical and Research Aspects. Litvan I, ed. Totowa, NJ: Humana Press; 2005 [48] Double KL, Halliday GM, McRitchie DA, Reid WG, Hely MA, Morris JG. Regional brain atrophy in idiopathic parkinson’s disease and diffuse Lewy body disease. Dementia 1996; 7: 304–313 [49] Burton EJ, McKeith IG, Burn DJ, Williams ED, O’Brien JT. Cerebral atrophy in Parkinson’s disease with and without dementia: a comparison with Alzheimer’s disease, dementia with Lewy bodies and controls. Brain 2004; 127: 791–800 [50] Nagano-Saito A, Washimi Y, Arahata Y et al. Cerebral atrophy and its relation to cognitive impairment in Parkinson’s disease. Neurology 2005; 64: 224– 229 [51] Beyer MK, Janvin CC, Larsen JP, Aarsland D. A magnetic resonance imaging study of patients with Parkinson’s disease with mild cognitive impairment and dementia using voxel-based morphometry. J Neurol Neurosurg Psychiatry 2007; 78: 254–259 [52] Yoshikawa K, Nakata Y, Yamada K, Nakagawa M. Early pathological changes in the parkinsonian brain demonstrated by diffusion tensor MRI. J Neurol Neurosurg Psychiatry 2004; 75: 481–484 [53] Seppi K, Schocke MF, Esterhammer R et al. Diffusion-weighted imaging discriminates progressive supranuclear palsy from PD, but not from the parkinson variant of multiple system atrophy. Neurology 2003; 60: 922– 927 [54] Schocke MF, Seppi K, Esterhammer R et al. Trace of diffusion tensor differentiates the Parkinson variant of multiple system atrophy and Parkinson’s disease. Neuroimage 2004; 21: 1443–1451

[55] Karagulle Kendi AT, Lehericy S, Luciana M, Ugurbil K, Tuite P. Altered diffusion in the frontal lobe in Parkinson’s disease. AJNR Am J Neuroradiol 2008; 29: 501–505 [56] Nicoletti G, Lodi R, Condino F et al. Apparent diffusion coefficient measurements of the middle cerebellar peduncle differentiate the Parkinson variant of MSA from Parkinson’s disease and progressive supranuclear palsy. Brain 2006; 129: 2679–2687 [57] Braak H, Del Tredici K, Rüb U, de Vos RA, Jansen Steur EN, Braak E. Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol Aging 2003; 24: 197–211 [58] Matsui H, Nishinaka K, Oda M et al. Wisconsin Card Sorting Test in Parkinson’s disease: diffusion tensor imaging. Acta Neurol Scand 2007; 116: 108– 112 [59] Eusebio A, Azulay JP, Witjas T, Rico A, Attarian S. Assessment of cortico-spinal tract impairment in multiple system atrophy using transcranial magnetic stimulation. Clin Neurophysiol 2007; 118: 815–823 [60] Nilsson C, Markenroth Bloch K, Brockstedt S, Lätt J, Widner H, Larsson EM. Tracking the neurodegeneration of parkinsonian disorders: a pilot study. Neuroradiology 2007; 49: 111–119 [61] Gattellaro G, Minati L, Grisoli M et al. White matter involvement in idiopathic Parkinson’s disease: a diffusion tensor imaging study. AJNR Am J Neuroradiol 2009; 30: 1222–1226 [62] Prodoehl J, Li H, Planetta PJ et al. Diffusion tensor imaging of Parkinson’s disease, atypical parkinsonism, and essential tremor. Mov Disord 2013; 28: 1816–1822 [63] Vonsattel JP, Keller C, Cortes Ramirez EP. Huntington’s disease: neuropathology. Handb Clin Neurol 2011; 100: 83–100 [64] Rosas HD, Koroshetz WJ, Chen YI et al. Evidence for more widespread cerebral pathology in early HD: an MRI-based morphometric analysis. Neurology 2003; 60: 1615–1620 [65] Ross CA. Huntington’s disease: new paths to pathogenesis. Cell 2004; 118: 4–7 [66] Fennema-Notestine C, Archibald SL, Jacobson MW et al. In vivo evidence of cerebellar atrophy and cerebral white matter loss in Huntington disease. Neurology 2004; 63: 989–995 [67] Tai YF, Pavese N, Gerhard A et al. Microglial activation in presymptomatic Huntington’s disease gene carriers. Brain 2007; 130: 1759–1766 [68] Thieben MJ, Duggins AJ, Good CD et al. The distribution of structural neuropathology in pre-clinical Huntington’s disease. Brain 2002; 125: 1815–1828 [69] Vymazal J, Klempír J, Jech R et al. MR relaxometry in Huntington’s disease: correlation between imaging, genetic and clinical parameters. J Neurol Sci 2007; 263: 20–25 [70] Aylward EH, Anderson NB, Bylsma FW et al. Frontal lobe volume in patients with Huntington’s disease. Neurology 1998; 50: 252–258 [71] Beglinger LJ, Nopoulos PC, Jorge RE et al. White matter volume and cognitive dysfunction in early Huntington’s disease. Cogn Behav Neurol 2005; 18: 102–107 [72] Ciarmiello A, Cannella M, Lastoria S et al. Brain white-matter volume loss and glucose hypometabolism precede the clinical symptoms of Huntington’s disease. J Nucl Med 2006; 47: 215–222 [73] Van Camp N, Blockx I, Camón L et al. A complementary diffusion tensor imaging (DTI)-histological study in a model of Huntington’s disease. Neurobiol Aging 2012; 33: 945–959 [74] Klöppel S, Draganski B, Golding CV et al. White matter connections reflect changes in voluntary-guided saccades in pre-symptomatic Huntington’s disease. Brain 2008; 131: 196–204 [75] Rosas HD, Tuch DS, Hevelone ND et al. Diffusion tensor imaging in presymptomatic and early Huntington’s disease: selective white matter pathology and its relationship to clinical measures. Mov Disord 2006; 21: 1317– 1325 [76] Reading SA, Yassa MA, Bakker A et al. Regional white matter change in presymptomatic Huntington’s disease: a diffusion tensor imaging study. Psychiatry Res 2005; 140: 55–62 [77] Bohanna I, Georgiou-Karistianis N, Hannan AJ, Egan GF. Magnetic resonance imaging as an approach towards identifying neuropathological biomarkers for Huntington’s disease. Brain Res Brain Res Rev 2008; 58: 209–225 [78] Aylward EH. Change in MRI striatal volumes as a biomarker in preclinical Huntington’s disease. Brain Res Bull 2007; 72: 152–158 [79] Weaver KE, Richards TL, Liang O, Laurino MY, Samii A, Aylward EH. Longitudinal diffusion tensor imaging in Huntington’s disease. Exp Neurol 2009; 216: 525–529 [80] Rowland LP, Shneider NA. Amyotrophic lateral sclerosis. N Engl J Med 2001; 344: 1688–1700

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| 12.09.15 - 10:49

Imaging Techniques [81] del Aguila MA, Longstreth WT, Jr, McGuire V, Koepsell TD, van Belle G. Prognosis in amyotrophic lateral sclerosis: a population-based study. Neurology 2003; 60: 813–819 [82] Phukan J, Pender NP, Hardiman O. Cognitive impairment in amyotrophic lateral sclerosis. Lancet Neurol 2007; 6: 994–1003 [83] Ferraiuolo L, Kirby J, Grierson AJ, Sendtner M, Shaw PJ. Molecular pathways of motor neuron injury in amyotrophic lateral sclerosis. Nat Rev Neurol 2011; 7: 616–630 [84] Ticozzi N, Tiloca C, Morelli C et al. Genetics of familial amyotrophic lateral sclerosis. Arch Ital Biol 2011; 149: 65–82 [85] Cosottini M, Giannelli M, Siciliano G et al. Diffusion-tensor MR imaging of corticospinal tract in amyotrophic lateral sclerosis and progressive muscular atrophy. Radiology 2005; 237: 258–264 [86] Toosy AT, Werring DJ, Orrell RW et al. Diffusion tensor imaging detects corticospinal tract involvement at multiple levels in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry 2003; 74: 1250–1257 [87] Abe O, Yamada H, Masutani Y et al. Amyotrophic lateral sclerosis: diffusion tensor tractography and voxel-based analysis. NMR Biomed 2004; 17: 411–416 [88] Iwata NK, Aoki S, Okabe S et al. Evaluation of corticospinal tracts in ALS with diffusion tensor MRI and brainstem stimulation. Neurology 2008; 70: 528–532 [89] Schimrigk SK, Bellenberg B, Schlüter M et al. Diffusion tensor imaging-based fractional anisotropy quantification in the corticospinal tract of patients with amyotrophic lateral sclerosis using a probabilistic mixture model. AJNR Am J Neuroradiol 2007; 28: 724–730 [90] Senda J, Ito M, Watanabe H et al. Correlation between pyramidal tract degeneration and widespread white matter involvement in amyotrophic lateral sclerosis: a study with tractography and diffusion-tensor imaging. Amyotroph Lateral Scler 2009; 10: 288–294 [91] Wang S, Poptani H, Woo JH et al. Amyotrophic lateral sclerosis: diffusion-tensor and chemical shift MR imaging at 3.0 T. Radiology 2006; 239: 831–838 [92] Agosta F, Pagani E, Rocca MA et al. Voxel-based morphometry study of brain volumetry and diffusivity in amyotrophic lateral sclerosis patients with mild disability. Hum Brain Mapp 2007; 28: 1430–1438 [93] Ciccarelli O, Behrens TE, Johansen-Berg H et al. Investigation of white matter pathology in ALS and PLS using tract-based spatial statistics. Hum Brain Mapp 2009; 30: 615–624 [94] Sage CA, Peeters RR, Görner A, Robberecht W, Sunaert S. Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis. Neuroimage 2007; 34: 486–499 [95] Hong YH, Sung JJ, Kim SM et al. Diffusion tensor tractography-based analysis of the pyramidal tract in patients with amyotrophic lateral sclerosis. J Neuroimaging 2008; 18: 282–287 [96] Agosta F, Pagani E, Petrolini M et al. Assessment of white matter tract damage in patients with amyotrophic lateral sclerosis: a diffusion tensor MR imaging tractography study. AJNR Am J Neuroradiol 2010; 31: 1457–1461 [97] Blain CR, Williams VC, Johnston C et al. A longitudinal study of diffusion tensor MRI in ALS. Amyotroph Lateral Scler 2007; 8: 348–355 [98] Mitsumoto H, Ulug AM, Pullman SL et al. Quantitative objective markers for upper and lower motor neuron dysfunction in ALS. Neurology 2007; 68: 1402–1410 [99] Senda J, Kato S, Kaga T et al. Progressive and widespread brain damage in ALS: MRI voxel-based morphometry and diffusion tensor imaging study. Amyotroph Lateral Scler 2011; 12: 59–69 [100] van der Graaff MM, Sage CA, Caan MW et al. Upper and extra-motoneuron involvement in early motoneuron disease: a diffusion tensor imaging study. Brain 2011; 134: 1211–1228

[101] Verstraete E, Veldink JH, van den Berg LH, van den Heuvel MP. Structural brain network imaging shows expanding disconnection of the motor system in amyotrophic lateral sclerosis. Hum Brain Mapp 2014; 35: 1351– 1361 [102] Sach M, Winkler G, Glauche V et al. Diffusion tensor MRI of early upper motor neuron involvement in amyotrophic lateral sclerosis. Brain 2004; 127: 340– 350 [103] Fox RJ, Bethoux F, Goldman MD, Cohen JA. Multiple sclerosis: advances in understanding, diagnosing, and treating the underlying disease. Cleve Clin J Med 2006; 73: 91–102 [104] Frohman EM, Havrdova E, Lublin F et al. Most patients with multiple sclerosis or a clinically isolated demyelinating syndrome should be treated at the time of diagnosis. Arch Neurol 2006; 63: 614–619 [105] Rodriguez M, Siva A, Ward J, Stolp-Smith K, O’Brien P, Kurland L. Impairment, disability, and handicap in multiple sclerosis: a population-based study in Olmsted County, Minnesota. Neurology 1994; 44: 28–33 [106] Lassmann H. Pathology of multiple sclerosis. In: Compston A, Ebers G, Lassmann H, McDonald WI, Matthews B, Wekerle H, eds. McAlpine’s Multiple Sclerosis. London: Churchill Livingstone; 1998 [107] Weinshenker BG, Bass B, Rice GP et al. The natural history of multiple sclerosis: a geographically based study. I. Clinical course and disability. Brain 1989; 112: 133–146 [108] Lublin FD, Reingold SC National Multiple Sclerosis Society (USA) Advisory Committee on Clinical Trials of New Agents in Multiple Sclerosis. Defining the clinical course of multiple sclerosis: results of an international survey. Neurology 1996; 46: 907–911 [109] McDonald WI, Compston A, Edan G et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the Diagnosis of Multiple Sclerosis. Ann Neurol 2001; 50: 121–127 [110] Schmierer K, Wheeler-Kingshott CA, Boulby PA et al. Diffusion tensor imaging of post mortem multiple sclerosis brain. Neuroimage 2007; 35: 467–477 [111] Filippi M, Cercignani M, Inglese M, Horsfield MA, Comi G. Diffusion tensor magnetic resonance imaging in multiple sclerosis. Neurology 2001; 56: 304– 311 [112] Castriota-Scanderbeg A, Fasano F, Hagberg G, Nocentini U, Filippi M, Caltagirone C. Coefficient D(av) is more sensitive than fractional anisotropy in monitoring progression of irreversible tissue damage in focal nonactive multiple sclerosis lesions. AJNR Am J Neuroradiol 2003; 24: 663–670 [113] Werring DJ, Clark CA, Barker GJ, Thompson AJ, Miller DH. Diffusion tensor imaging of lesions and normal-appearing white matter in multiple sclerosis. Neurology 1999; 52: 1626–1632 [114] Temel S, Keklikoğlu HD, Vural G, Deniz O, Ercan K. Diffusion tensor magnetic resonance imaging in patients with multiple sclerosis and its relationship with disability. Neuroradiol J 2013; 26: 3–17 [115] Genova HM, DeLuca J, Chiaravalloti N, Wylie G. The relationship between executive functioning, processing speed, and white matter integrity in multiple sclerosis. J Clin Exp Neuropsychol 2013; 35: 631–641 [116] Bot JC, Barkhof F, Polman CH et al. Spinal cord abnormalities in recently diagnosed MS patients: added value of spinal MRI examination. Neurology 2004; 62: 226–233 [117] Lycklama G, Thompson A, Filippi M et al. Spinal-cord MRI in multiple sclerosis. Lancet Neurol 2003; 2: 555–562 [118] Ikuta F, Zimmerman HM. Distribution of plaques in seventy autopsy cases of multiple sclerosis in the United States. Neurology 1976; 26: 26–28 [119] Naismith RT, Xu J, Klawiter EC et al. Spinal cord tract diffusion tensor imaging reveals disability substrate in demyelinating disease. Neurology 2013; 80: 2201–2209

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Functional Imaging of the Brain

6 Functional Imaging of the Brain Leslie Hartman and Aaron S. Field Since its discovery as a viable technique to noninvasively image the functioning brain, functional magnetic resonance imaging (fMRI) has become an increasingly popular tool for researchers, clinical neuroscientists, neuroradiologists, and others. This chapter provides an overview of the physics and technical aspects of fMRI, introduces fMRI paradigms and resting-state fMRI, reviews the advantages and disadvantages of fMRI, discusses the role of fMRI in neurodegenerative disorders, and briefly touches on the future of fMRI, especially as it relates to neurodegenerative disorders.

6.1 Overview of the Physiology and Physics Underlying Functional MRI An understanding of how fMR images are created is critical to appropriate interpretation. A complete discussion of the physiology and physics is beyond the scope of this introductory chapter, but excellent resources are available for a more detailed explanation, including Huettel and McCarthy (2008)1 and Jezzard et al (2001).2 The most common method for fMRI is based on the blood-oxygen-level–dependent (BOLD) contrast, the fMRI technique that is the focus of this chapter. Understanding the physiology behind fMRI dates back to 1890 and the experiments of Roy and Sherrington at Cambridge University, where the idea that regional cerebral blood flow could reflect neuronal activity was first investigated.3 It is this basic principle that makes fMRI possible. The BOLD effect is a

complex biophysical phenomenon. The origin of BOLD fMRI signal change lies in the different magnetic properties of oxygenated hemoglobin (O-Hb), which is diamagnetic, and deoxygenated hemoglobin (D-Hb), which is slightly paramagnetic relative to brain tissue.4 Vessels that contain oxygenated arterial blood cause little or no distortion to the magnetic field in their vicinity, whereas field inhomogeneities resulting from the presence of D-Hb lead to shortening of the T2* relaxation time and thus a reduction of signal on any MRIs sensitive to magnetic susceptibility effects (▶ Fig. 6.1). As a result, the MRI signal increases with an increase in the ratio of O-Hb to D-Hb.5 In an fMRI experiment, a series of images is rapidly acquired as the subject performs a task that shifts brain activity between two or more well-defined states. Via neurovascular coupling, as the neuronal activity in a region of brain tissue increases with a specific task, there is an increase in the supply of oxygenated blood, a decrease in concentration of D-Hb, and thus an increase in MRI signal in any regions of brain associated with the task (the BOLD signal, ▶ Fig. 6.1).6 Alternatively, so-called resting-state fMRI can be performed with a subject resting in the MRI scanner with eyes closed but without sleeping; in this case, BOLD signal fluctuations that demonstrate synchrony between regions are thought to reflect functional connectivity. This technique is increasingly popular and is discussed in a later section in this chapter. Additionally, many fMRI studies have demonstrated decreased task-related activity in certain brain regions, a phenomenon typically explained on the basis of a socalled default mode of resting brain activity.7,8,9,10,11 The most common MRI sequence used in BOLD fMRI is a T2* gradient-echo sequence using single-shot echo planar imaging

Fig. 6.1 Illustration of neurovascular coupling and resultant changes in the blood-oxygen-level–dependent (BOLD) signal. In the resting or basal state, there is a greater proportion of deoxyhemoglobin in capillaries and venules, which cause microscopic-field inhomogeneities that lead to decreased signal in the gradient echo magnetic resonance (MR) image (a). In the activated state, there is an increase in the flow but only a modest increase in oxygen consumption, which leads to a decrease in the concentration of deoxyhemoglobin (b). The signal difference in the gradient echo planar imaging (EPI) has been exaggerated for illustration purposes. The actual signal change is on the order of 1 to 5% at 1.5 T and 2–10% at 3.0 T and requires statistical analysis to detect. The deoxyhemoglobin is labeled as D-Hb (blue ovals) while the oxyhemoglobin is labeled as O-Hb (red ovals).

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Imaging Techniques

Fig. 6.2 Example of the distortion and loss of signal in the anteroinferior temporal regions resulting from susceptibility-related field inhomogeneities in these regions (e.g., air in the frontal sinuses, mastoid cavities). Unfortunately, blood-oxygen-level–dependent (BOLD) functional magnetic resonance imaging (fMRI) relies on such susceptibility effects to detect regional changes in deoxyhemoglobin concentrations.

(EPI), which allows whole-brain data collection in a few seconds or less, as is necessary to capture brain function in near “real time.”12 This high speed comes at the expense of spatial resolution, which is substantially lower than for a conventional MRI scan, typically only 3 to 5 mm.13,14 Another drawback to EPI is distortion and signal loss in the frontotemporal regions secondary to the sensitivity of EPI to magnetic susceptibility differences (▶ Fig. 6.2).

6.2 Blocked Paradigm Designs and Postprocessing When designing a paradigm for BOLD fMRI, tasks are chosen that can be performed in the scanner and that are expected to activate the region of interest. For example, if the concern is related to language, typically tasks that use the Broca and Wernicke areas are chosen, targeting expressive and receptive language, respectively. The most commonly used paradigms are “blocked” designs, in which the subject repeatedly performs a task for a specified time, resting for a similar time between repetitions (i.e., alternating “task” and “control” blocks) (▶ Fig. 6.3). This repetition is required for statistical processing to detect the small signal changes that character-

ize the BOLD response, on the order of 1 to 5% at 1.5 tesla (T) or 2 to 10% at 3.0 T, against a background of physiologic noise.15,16 Postprocessing of the data typically includes head motion correction and both spatial and temporal filtering of the data. Most commonly, the blocked time course of the task paradigm is convolved with a model of the hemodynamic response to generate an expected time course for BOLD signal in activated regions. Statistical tests based on the correlation or regression are then performed to identify voxels in which the signal changes over time correlate with the timing of the alternating task and control blocks, accounting for the hemodynamic response timing. This results in a voxel-wise statistical map (e.g., t-statistic),17 to which a significance threshold is applied to determine which voxels will be considered “activated” (▶ Fig. 6.4). Those activated voxels are typically overlaid in color onto a higher-spatial-resolution anatomical brain image (▶ Fig. 6.5). The specifics of these postprocessing techniques are beyond the scope of this introductory chapter, but several excellent resources are available, including Jezzard et al (2001)2 and Huettel and McCarthy (2008).1 Several software packages are also freely available with tools for analysis of fMRI data (e.g., SPM at http://www.fil.ion.ucl.ac.uk/spm, Brain Voyager at http://www.brainvoyager.com/, AFNI at http://afni.nimh.nih.gov/afni).

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Functional Imaging of the Brain

Fig. 6.3 Schematic example of a block-design functional magnetic resonance imaging (fMRI) experiment. In this case, finger tapping is used. Each block in a typical task-based fMRI experiment is approximately 20 to 30 seconds in duration. The “off” block is with the subject at rest; the “on” block is with the subject tapping his or her finger.

Fig. 6.4 Convolving the block design with the hemodynamic response function (HRF) yields the theoretically expected time course of signal change in an “activated” voxel. This “reference waveform” is then compared (statistically, e.g., correlation or regression analysis) to the bloodoxygen-level–dependent (BOLD) signal variations voxel by voxel; those voxels for which the similarity between the actual and expected signal changes is above the statistical threshold are considered to be activated (typically after a voxel-clustering procedure to exclude spurious “positives”).

6.3 Resting-State Functional Magnetic Resonance Imaging and Functional Connectivity Interest has been increasing about using fMRI to learn how different brain regions interact with one another, how these inter-

actions relate to observable behaviors, and how they may be compromised in various neurodegenerative or psychiatric disorders. Resting-state fMRI (RS-fMRI) is used to study these interactions, commonly known as functional connectivity, which is defined as the temporal dependency between spatially remote neurophysiologic events18,19; in RS-fMRI, synchronous neuronal activity between regions of the brain is sought

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Imaging Techniques

Fig. 6.5 Example of overlaying thresholded functional magnetic resonance imaging (fMRI) statistical maps onto anatomical MRI. This is a resting-state fMRI obtained in a 5-year-old child under general anesthesia. The case illustrates the ability to obtain meaningful fMRI data in individuals who cannot cooperate for a task-based fMRI. (Courtesy of Dr. V. Prabhakaran, University of Wisconsin–Madison.)

through the BOLD signal at rest and is presumed to reflect functional connectivity.20,21,22,23 Low-frequency oscillations (~0.01 to 0.1 Hz) have been identified in the resting state (RS) that help to identify these functional networks.20,22,24 A precise neuronal basis for these low-frequency oscillations has not been elucidated but is presumed to exist because most RS patterns tend to occur between regions that overlap in function and neuroanatomy.22,25,26,27 In addition, studies have shown a strong association between spontaneous BOLD fluctuations and simultaneously measured fluctuations in neuronal spiking.10 Other studies have illustrated an indirect association between the amplitude of RS-fMRI correlations and electrophysiologic recordings of neuronal firing.28 Commonly used methods to assess functional connectivity maps for a specific region of interest in RS-fMRI is the seed voxel approach or independent component analysis.29,30,31,32,33,34 RS-fMRI is also appealing in that it requires minimal cooperation and motivation from subjects and is ideal in those who cannot fully engage enough to perform task-based fMRI studies (e.g., sedated or comatose patients). There are at least seven commonly reported, functionally linked RS networks (▶ Fig. 6.6), including the default-mode network (DMN), primary visual and extrastriatal visual networks, executive control network, bilateral lateralized frontoparietal networks, primary sensorimotor network, and auditory-phonologic network.25,26,29,34,35,36,37,38,39 One of the most studied networks is the DMN, which has been shown to have an elevated level of neuronal activity at rest normally.40,41 Connectivity and activity of the DMN have been linked to human cognitive processes, including monitoring the world around us, integration of emotional and cognitive processing, and mind-wandering with stimulus-independent thoughts.21,40,42 These findings have piqued investigators’ interest in these networks, especially the DMN, in neurologic and psychiatric brain disorders. Some of the psychiatric disorders that have been studied with fMRI include depression, schizophrenia, autism, posttraumatic syndromes, attention-deficit hyperactivity disorder, and dyslexia.43,44,45,46,47,48,49 The use of fMRI in neurodegenerative disorders is discussed in the next section of this chapter.

6.4 Functional Magnetic Resonance Imaging in Neurodegenerative Disorders The most common clinical application of fMRI is to localize the eloquent cortex during neurosurgical planning for intractable epilepsy or tumor resections, for example, to lateralize language before temporal lobectomy for epilepsy.50 However, many fMRI studies have sought to identify changes in brain activity in neurodegenerative disorders, particularly in Alzheimer’s disease (AD) and mild cognitive impairment (MCI) in preclinical stages. fMRI has demonstrated potential in diagnosing early AD in apolipoprotein-E4 gene carriers without clinical symptoms and in predicting the development of AD in individuals with MCI.51,52,53 fMRI studies demonstrating a reduction in corticocortical connectivity in AD are supported by electrophysiologic studies, including electroencephalography (EEG) and magnetoencephalography-based studies.54 Altered levels of functional connectivity on RS-fMRI in neurodegenerative disease have been reported in the DMN and other RS networks. Some of the neurodegenerative diseases studied with RS-fMRI include AD, frontotemporal dementia, dementia, multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), dementia with Lewy bodies, and Huntington’s disease.55–66 Together, these studies suggest that neurodegenerative diseases target interconnected cortical networks rather than single regions within the brain.57 An example of the strength of RS functional connectivity between certain regions of the brain in association with verbal episodic memory tasks in patients with MS is illustrated in ▶ Fig. 6.7. In addition to RS-fMRI studies, fMRI studies can demonstrate alterations in brain activity with specific tasks in ALS, AD, PD, Huntington’s disease, semantic dementia in the frontotemporal lobar degeneration spectrum, and human immunodeficiency virus positivity.52,67,68,69,70,71,72,73,74,75 For example, Tessitore et al demonstrated reduced activity in brain regions encompassing the primary motor and premotor cortex and the right parietal association cortex but heightened activity in the

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Functional Imaging of the Brain

Fig. 6.6 The most consistently reported resting-state (RS) networks with major components detailed. This is a composite from multiple RS functional magnetic resonance imaging (fMRI) studies that used different groups of subjects and acquisition protocols. Networks include the default mode network (DMN) consisting of precuneus/posterior cingulate, medial frontal, and inferior parietal cortical regions and medial temporal lobe (a); primary visual (orange) and extrastriatal visual (gold) networks comprising retinotropic occipital cortex and temporo-occipital regions (b); executive control network composed of the superior and middle prefrontal cortices, anterior cingulate, and ventrolateral prefrontal cortex (c); left and right lateralized network, including inferior and medial frontal gyri, precuneus, inferior parietal, and angular gyrus (d), primary sensorimotor network (e), and auditoryphonological network consisting of superior temporal, insular, and postcentral cortex (f).

anterior putamen in an area involved in motor execution in individuals with sporadic ALS.68 Various pharmacologic agents cause changes in the physiology of the central nervous system, and this is the basis of pharmacologic fMRI (Ph-fMRI). Over the past decade, there has been increasing interest in the application of Ph-fMRI to evaluate changes in brain activity with pharmacologic agents in those

with and without neurodegenerative disease. fMRI signal changes have been reported with administration of D-amphetamine, dextroamphetamine, haloperidol, and dopamine via methylphenidate, among others.76,77,78,79 Ph-fMRI has also been used to study changes in various neurodegenerative states in response to a pharmacologic agent or by using the pharmacologic agent as a stimulus. Examples include the use of levodopa

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Imaging Techniques

Fig. 6.7 The strength of resting-state functional connectivity between the left middle frontal gyrus (posterior Brodmann area 9) and the right middle temporal gyrus is associated with peak performance on a verbal episodic memory task in patients with multiple sclerosis (peak r = 0.56, P = 1 × 10-4, corrected for multiple comparisons) (a). (b) Illustrates the strength of resting-state functional connectivity between the left middle frontal gyrus (posterior Brodmann area 9) and the anterior left middle frontal gyrus and shows an inverse association with performance on a verbal episodic memory task in patients with multiple sclerosis (r = –0.63, P = 1 × 10-5, corrected for multiple comparisons). (Courtesy of Drs. M. Phillips and M. Lowe, Cleveland Clinic.)

in hemiparkinson syndrome and PD and of rivastigmine in AD, MS, and MCI.80,81,82,83,84,85,86 A study by Mattay et al in 2002 on the use of dopaminergic modulation in evaluating cortical function in subjects with PD showed promising results, with evidence supporting that the hypodopaminergic state is associated with decreased efficiency of prefrontal cortical information processing and that dopaminergic therapy improves the physiologic efficiency of this region.87 A review article by Jenkins on Ph-fMRI provides a good overview of its potential uses and factors to consider when designing a Ph-fMRI study and evaluating the data.88

6.5 Advantages and Limitations Functional MRI is a powerful, noninvasive method to identify and evaluate brain responses to cognitive tasks and stimuli, to promote understanding of complex networks and the interactions of various brain regions, and to assess RS networks in “normal” subjects and those with neurologic and psychiatric disorders. The use of fMRI in presurgical planning is now commonplace in the clinic. For example, in presurgical planning for intractable epilepsy or brain tumors, fMRI can lateralize language noninvasively, has a high correspondence to Wada testing (intracarotid sodium amobarbital procedure), and provides detailed information about the language network that the Wada test cannot provide.89,90 The advantages of fMRI have made the technique increasingly popular over the last several years, particularly given its now wide availability as a “turnkey” add-on to MRI scanners, including pulse sequences, task paradigms, devices for presentation of stimuli to subjects in the scanner, and devices to record the subject’s responses.91 The main advantages of fMRI in brain mapping are its noninvasiveness and relatively high spatial resolution on the order of a few millimeters, which is superior to the spatial resolution in positron emission tomography (PET).92 In addition, fMRI has a relatively high temporal resolution compared with PET, lacks ionizing radiation, and does not need external contrast agents

or tracers. BOLD sensitivity for signal change via neurovascular coupling, and thus brain activation with a specific task or at rest, is directly proportional to magnetic field strength. Therefore, a higher field strength (i.e., 3.0 T and greater) will be more sensitive than a 1.5-T scanner.16 Thus, as MRI technology continues to evolve and higher field strengths become more commonly used, BOLD sensitivity for signal change will also continue to improve. Functional MRI has several inherent disadvantages and pitfalls that are important to understand. Some of these can be mitigated with specific techniques. One must remember that BOLD fMRI detects only changes, rather than the absolute level, of brain activity and that it does so indirectly through neurovascular coupling, presuming a stable relationship between neural activation and resulting changes in absolute D-Hb concentration. Unfortunately, this coupling may be variable across individuals or across brain regions within an individual. It may be compromised by pharmacologic modulations, such as medications used during anesthesia; it may change with aging; and it may be altered by the pathology under study, such as a vascular tumor.93,94,95,96 There is often reduced signal intensity and geometric distortion in the frontal and temporal regions or in the vicinity of surgical changes, blood products, and so forth, secondary to magnetic susceptibility effects, potentially leading to false-negatives. Parallel acquisition techniques have been developed that not only reduce image acquisition time but also reduce susceptibility artifacts, improving signal detection in basal frontal and mesial temporal regions and benefitting studies involving memory, emotion, and executive function.97 Decreasing voxel size (e.g., through decreasing slice thickness in EPI acquisitions) can decrease distortion in hippocampus and amygdala.98,99 An obvious limitation of fMRI is that MRI is contraindicated in some individuals (e.g., those with cardiac pacemaker, implanted metal, claustrophobia). BOLD fMRI is highly sensitive to head motion, including task-related head motion or motion associated with cardiac or respiratory cycles, which can cause both false-positives and false-negatives. Task-related fMRI relies on the subject to understand and to complete the required tasks, which can also lead to false-negatives if the subject is unable to cooperate sufficiently. Lastly, fMRI acquisition generates loud noise, up to 120 dB, which can interfere with paradigms involving auditory processing.100,101,102 One can minimize this effect by directly reducing the source of the noise or using the hemodynamic delay of BOLD response and inserting silent periods in the acquisition process.103,104

6.6 The Future of fMRI in Neurodegenerative Disorders The wealth of information fMRI provides about structurefunction relationships in the brain may go beyond the immediate implications of identifying brain regions responsible for executing certain functions. Functional brain networks evaluated with fMRI, including RS networks like the DMN, are emerging as potential neuroimaging biomarkers for various neurodegenerative disorders and improved early diagnosis. They are providing new insights into functional effects on the brain in different diseases and may have implications in the

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Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Functional Imaging of the Brain development and assessment of pharmacologic therapy in neurodegenerative disorders. Whereas many fMRI studies have been conducted on AD, MCI, and Huntington’s disease, further fMRI research is still needed, such as in frontotemporal lobar degeneration spectrum, dementia with Lewy bodies, and PD. Several recent studies have elucidated an RS network involving the basal ganglia, promising new applications of fMRI to study neurodegenerative changes involving the basal ganglia during disease progression or pharmacologic therapy.105,106,107 Ph-fMRI methods and results will need to be considered carefully with each potential application, as there may be many confounding factors; pharmacologic effects may be direct or indirect, shortand long-term pharmacologic responses may be different, and results in study populations may not apply in individual subjects. Although Ph-fMRI is promising, further data are still needed to make its use clinically relevant. Functional MRI is well-suited to identifying imaging biomarkers for longitudinal, disease-monitoring studies because it is safe and easily repeatable. Whether fMRI methods will prove successful in reliably detecting preclinical stages of various neurodegenerative diseases and monitoring meaningful changes are questions awaiting future studies. With ongoing advances in fMRI acquisition and analysis techniques, fMRI is likely to become an increasingly powerful tool for understanding and evaluating neurodegenerative diseases and their progressive impact on brain networks. With ultrahigh-field fMRI on the horizon, spatial resolution will likely improve to submillimeter level, and it may ultimately be possible to identify the specific cortical layer(s) altered by neurodegenerative diseases.108,109 Finally, the increasing combination of fMRI with other advanced MRI techniques (e.g., diffusion tensor imaging) and with EEG is likely to yield new insights into the structural and functional changes of neurodegenerative disorders beyond what is possible by fMRI alone.

References [1] Huettel SA, McCarthy G. Functional Magnetic Resonance Imaging. 2nd ed. Sunderland, MA: Sinauer Associates; 2008 [2] Jezzard P, Matthews PM, Smith SM. Functional Magnetic Resonance Imaging: An Introduction to Methods. 1st ed. New York: Oxford University Press; 2001 [3] Roy CS, Sherrington CS. On the regulation of the blood-supply of the brain. J Physiol 1890; 11: 85–158, 17 [4] Pauling L, Coryell CD. The magnetic properties and structure of hemoglobin, oxyhemoglobin and carbonmonoxyhemoglobin. Proc Natl Acad Sci U S A 1936; 22: 210–216 [5] Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A 1990; 87: 9868–9872 [6] Gore JC. Principles and practice of functional MRI of the human brain. J Clin Invest 2003; 112: 4–9 [7] Amedi A, Malach R, Pascual-Leone A. Negative BOLD differentiates visual imagery and perception. Neuron 2005; 48: 859–872 [8] Nilsson J, Ferrier IN, Coventry K, Bester A, Finkelmeyer A. Negative BOLD response in the hippocampus during short-term spatial memory retrieval. J Cogn Neurosci 2013; 25: 1358–1371 [9] Lin P, Hasson U, Jovicich J, Robinson S. A neuronal basis for task-negative responses in the human brain. Cereb Cortex 2011; 21: 821–830 [10] Shmuel A, Yacoub E, Pfeuffer J et al. Sustained negative BOLD, blood flow and oxygen consumption response and its coupling to the positive response in the human brain. Neuron 2002; 36: 1195–1210 [11] Smith AT, Singh KD, Greenlee MW. Attentional suppression of activity in the human visual cortex. Neuroreport 2000; 11: 271–277

[12] Mansfield P. Multi-planar image-formation using nmr spin echoes. J Phys C Solid State phys 1977; 10: L55–L58 [13] Rees G, Friston K, Koch C. A direct quantitative relationship between the functional properties of human and macaque V5. Nat Neurosci 2000; 3: 716–723 [14] Kim DS, Ronen I, Olman C, Kim SG, Ugurbil K, Toth LJ. Spatial relationship between neuronal activity and BOLD functional MRI. Neuroimage 2004; 21: 876–885 [15] Purdon PL, Weisskoff RM. Effect of temporal autocorrelation due to physiological noise and stimulus paradigm on voxel-level false-positive rates in fMRI. Hum Brain Mapp 1998; 6: 239–249 [16] Krüger G, Kastrup A, Glover GH. Neuroimaging at 1.5 T and 3.0 T: comparison of oxygenation-sensitive magnetic resonance imaging. Magn Reson Med 2001; 45: 595–604 [17] Worsley KJ. Statistical analysis of activation images. In: Jezzard P, Matthews PM, Smith SM, eds. Functional Magnetic Resonance Imaging: An Introduction to Methods. 1st ed. New York: Oxford University Press; 2001;251–270 [18] Aertsen AM, Gerstein GL, Habib MK, Palm G. Dynamics of neuronal firing correlation: modulation of “effective connectivity.” J Neurophysiol 1989; 61: 900–917 [19] Friston KJ, Frith CD, Liddle PF, Frackowiak RS. Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab 1993; 13: 5–14 [20] Biswal BB, Van Kylen J, Hyde JS. Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps. NMR Biomed 1997; 10: 165–170 [21] Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A 2003; 100: 253–258 [22] Lowe MJ, Dzemidzic M, Lurito JT, Mathews VP, Phillips MD. Correlations in low-frequency BOLD fluctuations reflect cortico-cortical connections. Neuroimage 2000; 12: 582–587 [23] Biswal BB, Mennes M, Zuo XN et al. Toward discovery science of human brain function. Proc Natl Acad Sci U S A 2010; 107: 4734–4739 [24] Cordes D, Haughton VM, Arfanakis K et al. Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR Am J Neuroradiol 2001; 22: 1326–1333 [25] Damoiseaux JS, Rombouts SA, Barkhof F et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A 2006; 103: 13848– 13853 [26] De Luca M, Smith S, De Stefano N, Federico A, Matthews PM. Blood oxygenation level dependent contrast resting state networks are relevant to functional activity in the neocortical sensorimotor system. Exp Brain Res 2005; 167: 587–594 [27] Salvador R, Suckling J, Coleman MR, Pickard JD, Menon D, Bullmore E. Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb Cortex 2005; 15: 1332–1342 [28] Nir Y, Mukamel R, Dinstein I et al. Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex. Nat Neurosci 2008; 11: 1100–1108 [29] Beckmann CF, DeLuca M, Devlin JT, Smith SM. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci 2005; 360: 1001–1013 [30] Buckner RL, Vincent JL. Unrest at rest: default activity and spontaneous network correlations. Neuroimage 2007; 37: 1091–1099 [31] Calhoun VD, Adali T, Pearlson GD, Pekar JJ. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 2001; 14: 140–151 [32] van de Ven VG, Formisano E, Prvulovic D, Roeder CH, Linden DE. Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest. Hum Brain Mapp 2004; 22: 165–178 [33] De Luca M, Beckmann CF, De Stefano N, Matthews PM, Smith SM. fMRI resting state networks define distinct modes of long-distance interactions in the human brain. Neuroimage 2006; 29: 1359–1367 [34] Cordes D, Haughton VM, Arfanakis K et al. Mapping functionally related regions of brain with functional connectivity MR imaging. AJNR Am J Neuroradiol 2000; 21: 1636–1644 [35] Koyama MS, Kelly C, Shehzad Z, Penesetti D, Castellanos FX, Milham MP. Reading networks at rest. Cereb Cortex 2010; 20: 2549–2559 [36] Rosazza C, Minati L. Resting-state brain networks: literature review and clinical applications. Neurol Sci 2011; 32: 773–785 [37] Seeley WW, Menon V, Schatzberg AF et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 2007; 27: 2349–2356

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Imaging Techniques [38] van den Heuvel M, Mandl R, Hulshoff Pol H. Normalized cut group clustering of resting-state fMRI data. PLoS ONE 2008; 3: e2001 [39] Voss HU, Schiff ND. MRI of neuronal network structure, function, and plasticity. Prog Brain Res 2009; 175: 483–496 [40] Gusnard DA, Raichle ME, Raichle ME. Searching for a baseline: functional imaging and the resting human brain. Nat Rev Neurosci 2001; 2: 685–694 [41] Raichle ME, Snyder AZ. A default mode of brain function: a brief history of an evolving idea. Neuroimage 2007; 37: 1083–1090, discussion 1097– 1099 [42] Mason MF, Norton MI, Van Horn JD, Wegner DM, Grafton ST, Macrae CN. Wandering minds: the default network and stimulus-independent thought. Science 2007; 315: 393–395 [43] Chen CH, Ridler K, Suckling J et al. Brain imaging correlates of depressive symptom severity and predictors of symptom improvement after antidepressant treatment. Biol Psychiatry 2007; 62: 407–414 [44] Greicius MD, Flores BH, Menon V et al. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry 2007; 62: 429–437 [45] Whitfield-Gabrieli S, Thermenos HW, Milanovic S et al. Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci U S A 2009; 106: 1279–1284 [46] Just MA, Cherkassky VL, Keller TA, Kana RK, Minshew NJ. Functional and anatomical cortical underconnectivity in autism: evidence from an fMRI study of an executive function task and corpus callosum morphometry. Cereb Cortex 2007; 17: 951–961 [47] Lanius RA, Bluhm RL, Coupland NJ et al. Default mode network connectivity as a predictor of post-traumatic stress disorder symptom severity in acutely traumatized subjects. Acta Psychiatr Scand 2010; 121: 33–40 [48] Konrad K, Eickhoff SB. Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder. Hum Brain Mapp 2010; 31: 904–916 [49] Pekkola J, Laasonen M, Ojanen V et al. Perception of matching and conflicting audiovisual speech in dyslexic and fluent readers: an fMRI study at 3 T. Neuroimage 2006; 29: 797–807 [50] Adcock JE, Wise RG, Oxbury JM, Oxbury SM, Matthews PM. Quantitative fMRI assessment of the differences in lateralization of language-related brain activation in patients with temporal lobe epilepsy. Neuroimage 2003; 18: 423– 438 [51] Wierenga CE, Stricker NH, McCauley A et al. Increased functional brain response during word retrieval in cognitively intact older adults at genetic risk for Alzheimer’s disease. Neuroimage 2010; 51: 1222–1233 [52] Dickerson BC, Sperling RA. Functional abnormalities of the medial temporal lobe memory system in mild cognitive impairment and Alzheimer’s disease: insights from functional MRI studies. Neuropsychologia 2008; 46: 1624– 1635 [53] Rombouts SA, Barkhof F, Goekoop R, Stam CJ, Scheltens P. Altered resting state networks in mild cognitive impairment and mild Alzheimer’s disease: an fMRI study. Hum Brain Mapp 2005; 26: 231–239 [54] Pievani M, de Haan W, Wu T, Seeley WW, Frisoni GB. Functional network disruption in the degenerative dementias. Lancet Neurol 2011; 10: 829–843 [55] Zhang HY, Wang SJ, Liu B et al. Resting brain connectivity: changes during the progress of Alzheimer’s disease. Radiology 2010; 256: 598–606 [56] Zhou J, Greicius MD, Gennatas ED et al. Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer’s disease. Brain 2010; 133: 1352–1367 [57] Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD. Neurodegenerative diseases target large-scale human brain networks. Neuron 2009; 62: 42–52 [58] Whitwell JL, Josephs KA, Avula R et al. Altered functional connectivity in asymptomatic MAPT subjects: a comparison to bvFTD. Neurology 2011; 77: 866–874 [59] Rombouts SA, Damoiseaux JS, Goekoop R et al. Model-free group analysis shows altered BOLD FMRI networks in dementia. Hum Brain Mapp 2009; 30: 256–266 [60] Lowe MJ, Beall EB, Sakaie KE et al. Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity. Hum Brain Mapp 2008; 29: 818–827 [61] Mohammadi B, Kollewe K, Samii A, Krampfl K, Dengler R, Münte TF. Changes of resting state brain networks in amyotrophic lateral sclerosis. Exp Neurol 2009; 217: 147–153 [62] Wu T, Long X, Wang L et al. Functional connectivity of cortical motor areas in the resting state in Parkinson’s disease. Hum Brain Mapp 2011; 32: 1443– 1457

[63] Helmich RC, Derikx LC, Bakker M, Scheeringa R, Bloem BR, Toni I. Spatial remapping of cortico-striatal connectivity in Parkinson’s disease. Cereb Cortex 2010; 20: 1175–1186 [64] Baudrexel S, Witte T, Seifried C et al. Resting state fMRI reveals increased subthalamic nucleus-motor cortex connectivity in Parkinson’s disease. Neuroimage 2011; 55: 1728–1738 [65] Galvin JE, Price JL, Yan Z, Morris JC, Sheline YI. Resting bold fMRI differentiates dementia with Lewy bodies vs Alzheimer’s disease. Neurology 2011; 76: 1797–1803 [66] Wolf RC, Sambataro F, Vasic N et al. Default-mode network changes in preclinical Huntington’s disease. Exp Neurol 2012; 237: 191–198 [67] Chang L, Tomasi D, Yakupov R et al. Adaptation of the attention network in human immunodeficiency virus brain injury. Ann Neurol 2004; 56: 259–272 [68] Tessitore A, Esposito F, Monsurrò MR et al. Subcortical motor plasticity in patients with sporadic ALS: an fMRI study. Brain Res Bull 2006; 69: 489–494 [69] Stanton BR, Williams VC, Leigh PN et al. Altered cortical activation during a motor task in ALS. Evidence for involvement of central pathways. J Neurol 2007; 254: 1260–1267 [70] Johnson SC, Schmitz TW, Moritz CH et al. Activation of brain regions vulnerable to Alzheimer’s disease: the effect of mild cognitive impairment. Neurobiol Aging 2006; 27: 1604–1612 [71] Wu T, Wang L, Hallett M, Chen Y, Li K, Chan P. Effective connectivity of brain networks during self-initiated movement in Parkinson’s disease. Neuroimage 2011; 55: 204–215 [72] Gray MA, Egan GF, Ando A et al. Prefrontal activity in Huntington’s disease reflects cognitive and neuropsychiatric disturbances: the IMAGE-HD study. Exp Neurol 2013; 239: 218–228 [73] Wolf RC, Vasic N, Schönfeldt-Lecuona C, Landwehrmeyer GB, Ecker D. Dorsolateral prefrontal cortex dysfunction in presymptomatic Huntington’s disease: evidence from event-related fMRI. Brain 2007; 130: 2845–2857 [74] Goll JC, Ridgway GR, Crutch SJ, Theunissen FE, Warren JD. Nonverbal sound processing in semantic dementia: a functional MRI study. Neuroimage 2012; 61: 170–180 [75] Maguire EA, Kumaran D, Hassabis D, Kopelman MD. Autobiographical memory in semantic dementia: a longitudinal fMRI study. Neuropsychologia 2010; 48: 123–136 [76] Chen YC, Galpern WR, Brownell AL et al. Detection of dopaminergic neurotransmitter activity using pharmacologic MRI: correlation with PET, microdialysis, and behavioral data. Magn Reson Med 1997; 38: 389–398 [77] Brassen S, Tost H, Hoehn F, Weber-Fahr W, Klein S, Braus DF. Haloperidol challenge in healthy male humans: a functional magnetic resonance imaging study. Neurosci Lett 2003; 340: 193–196 [78] Hariri AR, Mattay VS, Tessitore A, Fera F, Smith WG, Weinberger DR. Dextroamphetamine modulates the response of the human amygdala. Neuropsychopharmacology 2002; 27: 1036–1040 [79] Honey GD, Suckling J, Zelaya F et al. Dopaminergic drug effects on physiological connectivity in a human cortico-striato-thalamic system. Brain 2003; 126: 1767–1781 [80] Buhmann C, Glauche V, Stürenburg HJ, Oechsner M, Weiller C, Büchel C. Pharmacologically modulated fMRI: cortical responsiveness to levodopa in drugnaive hemiparkinsonian patients. Brain 2003; 126: 451–461 [81] Haslinger B, Erhard P, Kämpfe N et al. Event-related functional magnetic resonance imaging in Parkinson’s disease before and after levodopa. Brain 2001; 124: 558–570 [82] Mattis PJ, Tang CC, Ma Y, Dhawan V, Eidelberg D. Network correlates of the cognitive response to levodopa in Parkinson’s disease. Neurology 2011; 77: 858–865 [83] Kwak Y, Peltier S, Bohnen NI, Müller ML, Dayalu P, Seidler RD. Altered resting state cortico-striatal connectivity in mild to moderate stage Parkinson’s disease. Front Syst Neurosci 2010; 4: 143 [84] Rombouts SA, Barkhof F, Van Meel CS, Scheltens P. Alterations in brain activation during cholinergic enhancement with rivastigmine in Alzheimer’s disease. J Neurol Neurosurg Psychiatry 2002; 73: 665–671 [85] Parry AM, Scott RB, Palace J, Smith S, Matthews PM. Potentially adaptive functional changes in cognitive processing for patients with multiple sclerosis and their acute modulation by rivastigmine. Brain 2003; 126: 2750–2760 [86] Goekoop R, Rombouts SA, Jonker C et al. Challenging the cholinergic system in mild cognitive impairment: a pharmacological fMRI study. Neuroimage 2004; 23: 1450–1459 [87] Mattay VS, Tessitore A, Callicott JH et al. Dopaminergic modulation of cortical function in patients with Parkinson’s disease. Ann Neurol 2002; 51: 156–164 [88] Jenkins BG. Pharmacologic magnetic resonance imaging (phMRI): imaging drug action in the brain. Neuroimage 2012; 62: 1072–1085

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Functional Imaging of the Brain [89] Binder JR, Swanson SJ, Hammeke TA et al. Determination of language dominance using functional MRI: a comparison with the Wada test. Neurology 1996; 46: 978–984 [90] Woermann FG, Jokeit H, Luerding R et al. Language lateralization by Wada test and fMRI in 100 patients with epilepsy. Neurology 2003; 61: 699–701 [91] Matthews PM, Jezzard P. Functional magnetic resonance imaging. J Neurol Neurosurg Psychiatry 2004; 75: 6–12 [92] Connelly A, Jackson GD, Frackowiak RS, Belliveau JW, Vargha-Khadem F, Gadian DG. Functional mapping of activated human primary cortex with a clinical MR imaging system. Radiology 1993; 188: 125–130 [93] Martin E, Thiel T, Joeri P et al. Effect of pentobarbital on visual processing in man. Hum Brain Mapp 2000; 10: 132–139 [94] Ross MH, Yurgelun-Todd DA, Renshaw PF et al. Age-related reduction in functional MRI response to photic stimulation. Neurology 1997; 48: 173–176 [95] Hesselmann V, Zaro Weber O, Wedekind C et al. Age related signal decrease in functional magnetic resonance imaging during motor stimulation in humans. Neurosci Lett 2001; 308: 141–144 [96] Fujiwara N, Sakatani K, Katayama Y et al. Evoked-cerebral blood oxygenation changes in false-negative activations in BOLD contrast functional MRI of patients with brain tumors. Neuroimage 2004; 21: 1464–1471 [97] Golay X, de Zwart JA, Ho YC, Sitoh YY. Parallel imaging techniques in functional MRI. Top Magn Reson Imaging 2004; 15: 255–265 [98] Merboldt KD, Finsterbusch J, Frahm J. Reducing inhomogeneity artifacts in functional MRI of human brain activation-thin sections vs gradient compensation. J Magn Reson 2000; 145: 184–191 [99] Merboldt KD, Fransson P, Bruhn H, Frahm J. Functional MRI of the human amygdala? Neuroimage 2001; 14: 253–257

[100] Mansfield P, Glover PM, Beaumont J. Sound generation in gradient coil structures for MRI. Magn Reson Med 1998; 39: 539–550 [101] Anderson AW, Marois R, Colson ER et al. Neonatal auditory activation detected by functional magnetic resonance imaging. Magn Reson Imaging 2001; 19: 1–5 [102] Bandettini PA, Jesmanowicz A, Van Kylen J, Birn RM, Hyde JS. Functional MRI of brain activation induced by scanner acoustic noise. Magn Reson Med 1998; 39: 410–416 [103] Amaro E, Jr, Williams SC, Shergill SS et al. Acoustic noise and functional magnetic resonance imaging: current strategies and future prospects. J Magn Reson Imaging 2002; 16: 497–510 [104] Mansfield P, Chapman BL, Bowtell R, Glover P, Coxon R, Harvey PR. Active acoustic screening: reduction of noise in gradient coils by Lorentz force balancing. Magn Reson Med 1995; 33: 276–281 [105] Damoiseaux JS, Beckmann CF, Arigita EJS et al. Reduced resting-state brain activity in the “default network” in normal aging. Cereb Cortex 2008; 18: 1856–1864 [106] Di Martino A, Scheres A, Margulies DS et al. Functional connectivity of human striatum: a resting state fMRI study. Cereb Cortex 2008; 18: 2735– 2747 [107] Robinson S, Basso G, Soldati N et al. A resting state network in the motor control circuit of the basal ganglia. BMC Neurosci 2009; 10: 137 [108] Koopmans PJ, Barth M, Norris DG. Layer-specific BOLD activation in human V1. Hum Brain Mapp 2010; 31: 1297–1304 [109] Yacoub E, Harel N, Ugurbil K. High-field fMRI unveils orientation columns in humans. Proc Natl Acad Sci U S A 2008; 105: 10607–10612

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7 Role of Noninvasive Angiogram and Perfusion in Evaluation of Neurodegenerative Disorders Sangam G. Kanekar and Puneet Devgun Computed tomography (CT) and magnetic resonance (MR) angiograms are well-established noninvasive techniques in the evaluation of the intracranial and extracranial vessels. Today, accuracy in the evaluation of extracranial and medium-sized intracranial vessel pathologies is comparable to the conventional angiogram. Cerebral perfusion is another noninvasive technique commonly used in the evaluation of stroke that gives information regarding the structural as well as the molecular functioning of the brain tissue. CT and MR perfusion both provide insight into capillary-level hemodynamics and cerebral perfusion. Compared with CT perfusion (CTP), magnetic resonance perfusion (MRP) does not require radiation. Today cerebrovascular disease (CVD) is thought to be the second most common cause of dementia. There is also ongoing debate as to whether Alzheimer’s disease (AD) and vascular dementia combined are more common than AD alone. It has been suggested that CVD may play an important role in determining the presence and severity of clinical symptoms of AD. Clinicians believe that the prevalence of AD with CVD is grossly underestimated. The prevalence of vascular dementia rises from 0 to 2% in the 60- to 69-year-old age group, up to 16% (3 to 6% for men) age 80 to 89.1 In addition, risk factors for vascular dementia are the same as those for CVD, stroke, and white matter lesions, which include arterial hypertension, atrial fibrillation, myocardial infarction, coronary artery disease, diabetes, generalized atherosclerosis, lipid abnormalities, smoking, family history, and specific genetic features. All this suggests the importance of vascular imaging in evaluation of a patient with dementia. Single-photon emission computed tomography (SPECT) and positron emission tomography (PET) have been widely used in the evaluation of dementia patients to estimate cerebral perfusion. Over the last decade, CT and MR angiogram and perfusion techniques have also been successfully used in the evaluation of the neurodegenerative disorders, especially AD and vascular dementia. In this chapter, we discuss the principles, techniques, and applications of these noninvasive angiography and perfusion techniques.

7.1 Noninvasive Angiography 7.1.1 Computed Tomography Angiography Computed tomography angiography (CTA) is a noninvasive imaging technique typically used to evaluate large cervical and intracranial arteries. CTA is a thin-section volumetric CT examination performed with intravenous contrast medium used to enhance the carotid, vertebral arteries, and the circle of Willis. CTA typically involves a volumetric helical acquisition that extends from the aortic arch to the circle of Willis (▶ Fig. 7.1). Many different scan parameters must be balanced to produce a diagnostic CTA, including contrast administration, reformatting, and reconstruction parameters. Ideal CTA imaging requires

Fig. 7.1 Normal neck computed tomography angiography using a bone subtracted technique reveals normal carotid and vertebral arteries in the neck.

intravenous iodinated contrast opacification of the arterial tree with no venous enhancement. The contrast opacification is dependent on the type and timing of contrast, and the optimal arterial opacification is dependent on the volume, rate, and administration of contrast. Various contrast timing strategies are available for optimal arterial opacification, including fixed delay, bolus tracking, and test bolus (GE Healthcare ).2,3,4 Fixed

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Role of Noninvasive Angiogram and Perfusion in Evaluation delay, the simplest of the timing strategies, uses a fixed delay from the time of contrast injection to imaging. A slightly more complex strategy is bolus tracking, where scanning starts once a pretest HU (hounsfield) opacification is reached in a vessel of interest (typically the ascending aorta). A disadvantage of this technique is the inherent lag between the time to start scanning to actually acquiring images. An alternative to bolus tracking is a test bolus, in which 10 mL of contrast is injected while a region of interest (ROI) is set in the proximal internal carotid artery. Using a low-level radiation scan, the ROI is scanned continuously to determine the predelay, which corresponds to 50% of the maximal test vessel opacification. Postprocessing of the data is extremely important for correct interpretation of the images. Various techniques have been developed for postprocessing, including maximum intensity projection (MIP), multiplanar volume reformat (MPR), curved reformat (CR), shaded surface display, or volume rendering, where it is possible to evaluate the vessel in its entirety.5,6,7,8 MIP constructs a two-dimensional (2D) image by displaying only pixels with a maximum CT attenuation (▶ Fig. 7.2). Because this technique relies on detecting the highest pixel on a given ray, it is sensitive to overlap from adjacent bony and opacified venous structures. MPR, unlike MIP, constructs a 2D image from the mean of CT attenuation compared with the maximum. In the CR technique, the vessel is traced along its course, with the user selecting the pixels to display on consecutive axial images. It is mostly useful for long, tortuous vessels, such as the carotid or vertebral arteries (▶ Fig. 7.3). CR is a time-consuming technique and is also subject to interpretative error. Compared with other techniques, CTA has both advantages and disadvantages. With advances in technology and multidetector row CT (MDCT), examination can be completed in a shorter time. The vessel from the arch of the aorta to the intracranial arteries can be scanned in less than 15 seconds using a 64-slice MDCT. Given the speed of the examination, CTA is less prone to motion artifact and can provide true anatomical representation of stenosis, lumen diameters, and calcifications. Unlike MR, there is no restriction on patients with pacemakers, ventilators, or monitoring devices or on claustrophobic patients. The greatest disadvantages of CTA are the radiation dose and iodinated contrast.

7.1.2 Magnetic Resonance Angiography Like CTA, magnetic resonance angiography (MRA) is a noninvasive imaging technique used to depict the extracranial and intracranial circulation. Neck and intracranial MRA can be obtained using various imaging techniques, which include time of flight (TOF), multiple overlapping thin slab acquisition (MOTSA), phase-contrast (PC), and contrast-enhanced MRA. TOF can be obtained as either a 2D or 3D TOF.9,10

Time of Flight Time-of-flight MRA is a gradient-echo sequence that depicts vascular flow by repeatedly applying a radiofrequency (RF) pulse to a volume of tissue, followed by dephasing and rephasing. TOF uses the difference in longitudinal magnetization between unsaturated (high signal) and saturated spins. Stationary tissues in this volume become saturated by the repeated RF pulses and demonstrate low signal, whereas blood flowing in vessels carries unsaturated spins and has relatively high signal intensity.9,10 2D TOF MRA is typically performed in the neck, using a large flip angle. Blood flowing perpendicular to the multiple thin slices is well imaged and produces a bright signal compared with the stationary tissue. 3D TOF MRA is performed in the head (mostly for the circle of Willis) and uses a smaller flip angle, which reduces saturation artifacts (▶ Fig. 7.4). The smaller flip angle and the addition of magnetization transfer decrease the background saturation. 3D TOF, compared with 2D TOF, has better spatial resolution and better signal-to-noise resolution, but it covers only a small volume. MOTSA is a hybrid technique between 2D and 3D TOF and has higher spatial resolution than 2D TOF while covering a larger area than 3D TOF MRA with less saturation artifact.

Contrast-Enhanced Magnetic Resonance Angiography Contrast-enhanced (CE) MRA is performed with a rapid, short repetition time (TR) gradient-echo sequence (10 ms) after an intravenous bolus of gadolinium. Contrast-enhanced MRA, like CTA, requires a balance of diagnostic techniques of contrast

Fig. 7.2 Normal head computed tomography angiography (CTA). Axial (a) and coronal (b) reformatted maximum intensity projection images from CTA demonstrate normal course and caliber of intracranial vessels.

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Fig. 7.4 Maximum intensity projection images of the three-dimensional time-of-flight magnetic resonance angiograms of the head show normal course and caliber of intracranial vessels around the circle of Willis.

One of the disadvantages of CE MRA and CTA is that the data must be acquired during a narrow window of contrast enhancement, which relies on proper contrast bolus and image acquisition. TOF and MRA can both be postprocessed into MIP images. MIP produces 3D images by a set of parallel rays drawn along the highest intensity in the source images. Multiple different projections are taken to construct a rotating 3D image.

Phase Contrast

Fig. 7.3 Curved reformatted image of neck computed tomography angiography shows the course of the entire neck portion of the internal carotid artery in a single image. Large ulcerative plaque (white arrow) is seen in the distal portion of the common carotid artery.

administration, reformatting, and reconstruction. Injected gadolinium shortens the T1 to less than 10 ms so that opacified vessels are hyperintense.9 The timing of CE MRA can be a test bolus or automatic bolus technique, as described earlier. CE MRA can cover a much larger area of anatomy in a much shorter time and is less susceptible to motion artifact (▶ Fig. 7.5). CE MRA is obtained with intravenous contrast, which produces shortened T1, giving an anatomical representation of the vessel, whereas TOF anatomy is inferred from physiology (velocity dependent).

Phase-contrast MRA is a gradient-echo sequence that depicts blood flow by quantifying the difference in the transverse magnetization between stationary and moving tissue. After an RF pulse, a pair of symmetric but opposed phase-encoding gradients are applied in one direction within the image voxel.9 The first gradient dephases, and the second rephases the transverse magnetizations. Stationary tissues have no net change in phase because they experience equal but opposite magnetic-field environments during the dephasing and then rephasing gradients. Moving blood experiences different magnetic fields as each gradient is applied. The net phase shift, either positive or negative, determines the direction of flow and the amount of phase shift, which is directly proportional to the velocity of the blood flow. Phase-contrast (PC) MRA capitalizes on the change in transverse magnetization (phase shift) that occurs when flowing protons encounter changes in gradient strength, produced by bipolar gradient pulses. By applying a bipolar gradient echo to the tissue, a phase shift is induced in moving spins but not in stationary tissue. PC MRA demonstrates flow directionality and

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7.1.3 Perfusion Computed Tomography Perfusion Computed tomography perfusion expands the role of CT by providing insight into capillary-level hemodynamics and the brain parenchyma. Cerebral CTP is a functional imaging technique that reflects cerebral microcirculation and reveals changes in cerebral microcirculation and metabolism that cannot be detected by conventional CT or MRI scans. The general principles underlying the computation of perfusion parameters, such as cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT), are the same for both MR and CT. CTP and MR perfusion-weighted imaging (PWI) both attempt to evaluate the capillary-level hemodynamics using different techniques. CTP relies on direct visualization of the contrast material, whereas MR PWI techniques rely on the indirect T2* effect induced in adjacent tissues. A basic principle in CTP is monitoring the first pass of an iodinated contrast agent through the cerebral circulation. This is accomplished by continuous cine imaging for 45 seconds over the same volume of tissue during the rapid administration of a small, high-flow contrast material. A transient hyperattenuation caused within the brain tissue is directly proportional to the amount of contrast material in the vessels and blood for that region. This provides insight into the delivery of blood to the brain parenchyma. The generic term cerebral perfusion refers to tissue-level blood flow in the brain. This principle is used to generate time-attenuation curves for an arterial ROI, a venous ROI, and each pixel (▶ Fig. 7.6). This flow can be described using a variety of parameters, which primarily include CBF, CBV, and MTT (▶ Fig. 7.7). CBV is defined as the total volume of blood in a given unit of volume of the brain, including blood in the tissues, as well as the blood in the large capacitance vessels, such as arteries, arterioles, capillaries, venules, and veins. CBF is defined as the volume of blood moving through a given unit volume of brain per unit time. MTT is defined as the average of the transit time of blood though a given brain region. The transit time of blood varies depending on the distance traveled between arterial inflow and venous outflow. MTT is related to both CBV and CBF according to the central volume principle, which states MTT = CBV/ CVF.11,12

Magnetic Resonance Perfusion

Fig. 7.5 Contrast-enhanced neck magnetic resonance angiography shows normal course and caliber of common carotid arteries, carotid bifurcation, and internal and external carotid arteries.

shows slowly moving blood. PC MRA can be obtained after intravenous gadolinium because PC MRA does not rely on T1 values to generate the MRA image. A major disadvantage of PC MRA is that it is a much longer sequence to acquire and therefore more susceptible to motion artifact.

Perfusion MRI techniques include dynamic susceptibility contrast (DSC) and arterial spin labeling (ASL). Because of the lack of radiation exposure, these techniques are usually preferred in the evaluation of neurodegenerative diseases. In DSC, images are acquired dynamically before, during, and after the injection of a bolus of a gadolinium. These images are used to track the bolus and the blood in which it is dissolved as it passes through the microvasculature of the brain. In higher concentrations, gadolinium ions confined within a cerebral blood vessel create a magnetic susceptibility effect that results in substantial loss of signal on T2*-weighted images, a signal loss that extends over a distance comparable in magnitude to the diameter of the blood vessel.13 This allows for signal changes affecting

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Fig. 7.6 Time-density curves (TDCs) generated from this artery (A) and vein (V) show the arrival, peak, and passage of the contrast bolus over time. These TDCs serve as the arterial input function and the venous output for the subsequent deconvolution step to formulate color-coded computed tomography perfusion maps.

Fig. 7.7 Computed tomography perfusion colored maps calculated using deconvolution techniques show normal cerebral blood flow (a) and cerebral blood volume (b).

all of the spins in an image voxel. The technique requires a pulse sequence capable of repeatedly acquiring T2*-weighted images rapidly enough that the concentration of gadolinium within each tissue voxel can be sampled with sufficient temporal resolution, preferably every 1.5 seconds or less. The sequence also needs to be multislice to cover most of the brain tissue. This is accomplished through a fast imaging technique, usually echo-planar imaging (EPI), with which interleaved images of many tissue slices can be obtained within a single TR. Both EPI spin-echo and EPI gradient-echo sequences have been used successfully for PWI.14,15 DSC provides higher spatial resolution, requires shorter scanning time, and can measure CBF, CBV, MTT, and TTP (▶ Fig. 7.8). One of the limitations of the DSC MRI perfusion is use of gadolinium contrast, which can

have potential increased risk in patients with impaired renal function. In ASL, a preimaging RF pulse is used to magnetically label the hydrogen nuclei within water molecules in arterial blood before they flow into the imaged portion of the brain. Compared with a baseline image acquired without labeling, the labeling pulse attenuates the signal arising from each voxel in the brain to a degree dependent on the rate at which the labeled spins flow into the voxel.16,17 This allows for the measurement of regional CBF (▶ Fig. 7.9). Major advantages of ALS include lower cost and lack of adverse reactions to contrast material. One of the major limitations is that it allows only measurement of regional CBF and maps produced by ASL are noisier.

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Fig. 7.8 Colored maps generated from the magnetic resonance dynamic susceptibility contrast perfusion show normal cerebral blood flow (a), cerebral blood volume (b), and mean transit time (c).

Fig. 7.9 Normal arterial spin labeling. Multisection cerebral blood flow color maps representing units of mL/100 g tissue per minute.

7.2 Application in Neurodegenerative Imaging 7.2.1 Changes in Perfusion Parameters in the Normal Aging Brain In clinical practice, perfusion imaging is rarely performed to understand normal aging; however, brain and vascular changes may be appreciated while scanning the patient for other neurologic disorders, such as acute stroke, using CT or MRP. Various SPECT studies using technetium 99m-radiolabeled hexamethylpropyleneamine oxime (HMPAO) have shown age-related decreases in the regional CBF compared with normal subjects.18 These changes are predominantly seen in the cingulate gyrus, frontal lobe, parietal lobe, and temporal lobe. On CT and MRP, these changes are difficult to appreciate by “eyeballing” and require a quantitative analysis, which is more of a research interest than of clinical significance (▶ Fig. 7.10).

7.2.2 Alzheimer’s Disease Neuropathological studies suggest that evidence of AD may be present in the brain years or even decades before onset of clinical symptoms. Research to identify these changes is ongoing. One of the parameters under investigation to understand these neuropathological changes is by evaluation of the blood circulation. Fluorodeoxyglucose PET, which measures glucose metabolism, and HMPAO SPECT, which measures CBF, have been widely used in the evaluation of cerebral metabolism or blood flow. Today, CT or CE MRP can calculate the relative CBF, CBV, MTT, and TTP (time to peak). These techniques, which are predominantly used in stroke and tumor imaging, are gaining early application in the evaluation of dementia, especially AD and vascular dementia. Although the SPECT and PET literature is more robust compared with CTP and MRP, the results are comparable. ASL, as described above under magnetic perfusion, is another noninvasive MR technique used in evaluation of AD, MCI, and vascular dementia.

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Fig. 7.11 Magnetic resonance perfusion (MRP) in vascular dementia. (a) Cerebral blood flow (CBF) map of MRP shows diffuse decrease in the CBF in the supratentorial white matter in a 60-year-old man with subcortical dementia. (b) MRP perfusion in a 43-year-old man with frontal dementia after a head injury shows an increase in the mean transit time in the bilateral frontal lobes.

Fig. 7.10 Quantitative analysis of computed tomography angiography. Cerebral blood flow map from computed tomography perfusion study shows quantitative analysis of the cerebral blood flow using multiple regions of interest in the white and gray matter.

Perfusion defects largely depend on the stage of the AD.19,20 In mild to moderate AD, the greatest hypoperfusion or decrease in the CBF is seen in the parietal lobes and cingulate gyri; a smaller effect may be seen in the frontal lobes. In the early stages of the disease, these deficits are asymmetric. In the later stages, characteristic hypoperfusion and hypometabolism, located mainly in the temporal, parietal, and posterior cingulate cortices, are mostly bilateral and symmetrical.21,22 It is presumed that the localized hypoperfusion in the posterior cingulate gyrus is due to hypoactivity of the posterior cingulate gyrus, caused by neuronal damage, whereas in the medial temporal lobe structures, it is due to loss of tight neuronal connections. On MRP, decreased CBV is most pronounced in the temporoparietal region.23 Primary sensorimotor and primary visual cortices, as well as the striatum, thalamus, and cerebellum, are spared in AD patients. CBV decline in the frontal and parietal lobes was primarily in white matter, which was well correlated with structural damage as seen on DTI. It is documented by SPECT studies that frontal lobe deficit (at baseline) was more predictive of future cognitive decline than was that in the temporoparietal regions. The MTTs and TTPs of the aforementioned ROIs were significantly lower in the healthy control group than in the AD group. The results further demonstrate that the pathological basis of AD is neuron injury due to impaired microcirculation. As the changes in hemodynamic parameters indirectly reflect physiologic and metabolic conditions of brain tissue, the CTP scan can provide evidence for the early diagnosis of AD. Although the major pathological change of AD is cerebral neurodegeneration, there is evidence of vascular risk factors and CVD in patients with AD, which indicates that vascular-related risk factors may play important roles in the development and progression of AD.

7.2.3 Vascular Parkinsonism The imaging modality of choice for patients with parkinsonian syndromes continues to be MRI. Imaging is primarily done to differentiate between PD and secondary parkinsonism, which also includes vascular parkinsonism (VP). Signal changes in the nigral and subcortical regions are nonspecific and nondiscriminatory. Putaminal hypointensity seen on MRI is regarded as due to postsynaptic striatal dysfunction. Frontal lobe atrophy is related to the L-dopa-nonresponsive, predominantly axial parkinsonian syndrome Patients with VP may show vascular impairment in more than one vascular territory, such as periventricular and subcortical white matter, the basal ganglia, and the brainstem. These may be in the form of lacunar or territorial infarcts. It is well documented that dementia occurs more commonly in patients with VP than in those with PD. Although MRI is quite sensitive in detecting these abnormalities, perfusion studies, along with MRA, may be useful in identifying associated changes in the focal or generalized vascular abnormalities.

7.2.4 Vascular Dementia Both PET and SPECT have limited roles in the evaluation of vascular dementia compared with degenerative dementia because vascular dementia is diagnosed with MRI in most cases. CT or MRP may show a typical perfusion defect in the cortical and subcortical structures and cerebellum, depending on the localization of the ischemic change. On the PET/SPECT, AD can be differentiated from the pattern of defect seen. In AD, blood flow reduction tends to be posterior predominant, whereas in vascular dementia, it shows anterior predominance (▶ Fig. 7.11).24

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Fig. 7.12 A 21-year-old man with sickle cell disease with early signs of subcortical dementia shows (a) a diffuse decrease in the cerebral blood volume (CBV) and (b) an increase in the mean transit time in the cerebral white matter bilaterally, left more than right. (c) Intracranial time-of-flight magnetic resonance imaging shows severe narrowing of the distal internal carotid and middle cerebral arteries bilaterally, findings suggestive of moyamoya.

Although this defect is seen mostly in the white matter, the overlapping cortex also shows the CBF reduction secondary to disconnection between the deep cerebrum and the cortex. Sometimes these defects may be in the remote region away from the site of infarction and are believed to be caused by functional cortical-subcortical disconnections.

7.2.5 Sickle Cell Disease Cognitive decline and dementia symptoms are not uncommon in sickle cell disease (SCD). These changes are mostly related to damage to the brain parenchyma from the various vascular insults, which include vascular endothelial damage and occlusion, cerebral ischemia, silent strokes, white matter (subcortical) changes, and intracranial hemorrhage.25 A prevalence of overt stroke in SCD is 250 times higher than in the general population.26 Overt stroke produces focal neurologic deficits that are easily diagnosed by MRI. Silent strokes occur in approximately 22% of children with SCD and can herald subsequent overt stroke.27 Mechanisms responsible for cerebral ischemia in SCD are complex and seem related to impaired blood flow. Blood flow abnormalities can be caused by narrowing or occlusion of cerebral vessels, increased viscosity, adherence of red blood cells to the vascular endothelium, and exhaustion of autoregulatory vasodilation. Detection of abnormalities of blood flow before clinical progression to stroke could be important information in helping or halting progression. Transcranial Doppler scanning is commonly used in assessment of this abnormality in the CBF. Measurement of the middle cerebral artery or the terminal portion of the internal carotid artery velocity is used as one of the major Doppler criteria. A velocity greater than 200 cm/s in these vessels is more prone to overt stroke, which can be prevented by using periodic red blood cell transfusion.28 Unfortunately, Doppler has its own limitations. This technique is operator dependent, and overt strokes are seen with a velocity less than 200 cm/s.29 MRP has been used to evaluate the cerebral vascular dynamics, such as CBF and CBV, in SCD patients (▶ Fig. 7.12). MRP showed asymmetrical, elevated baseline CBF in SCD patients related to large cerebral artery stenosis, low hematocrit levels, and resultant

vasodilation.30,31 In the nonstroke groups, CBF abnormalities were more prevalent than transcranial Doppler velocity.

7.2.6 Perfusion Changes in Normal Pressure Hydrocephalus Diagnosis of normal pressure hydrocephalus (NPH) is clinical, supplemented with various imaging techniques. The imaging tests fail to answer the most important question in patients with NPH, which is, which patients will benefit from ventricular shunting. Vascular imaging plays a limited role in the diagnosis of NPH. However, perfusion analysis in NPH patients using PET has shown decreased regional CBV and regional CBF.32 These parameters showed significant improvement after shunting, suggesting vascular compromise within the brain parenchyma, possibly resulting from a mass effect and raised intracranial pressure. It is also demonstrated that MRP can improve the prediction of the outcome after shunt placement in patients with NPH.

References [1] O’Brien JT, Erkinjuntti T, Reisberg B et al. Vascular cognitive impairment. Lancet Neurol 2003; 2: 89–98 [2] Kopka L, Funke M, Fischer U, Vosshenrich R, Oestmann JW, Grabbe E. Parenchymal liver enhancement with bolus-triggered helical CT: preliminary clinical results. Radiology 1995; 195: 282–284 [3] Puskás Z, Schuierer G. [Determination of blood circulation time for optimizing contrast medium administration in CT angiography] [in German] Radiologe 1996; 36: 750–757 [4] Bae KT, Heiken JP, Brink JA. Aortic and hepatic contrast medium enhancement at CT. Part II. Effect of reduced cardiac output in a porcine model. Radiology 1998; 207: 657–662 [5] Napel S, Marks MP, Rubin GD et al. CT angiography with spiral CT and maximum intensity projection. Radiology 1992; 185: 607–610 [6] Rubin GD, Dake MD, Napel S et al. Spiral CT of renal artery stenosis: comparison of three-dimensional rendering techniques. Radiology 1994; 190: 181– 189 [7] Vieco PT, Morin EE, III, Gross CE. CT angiography in the examination of patients with aneurysm clips. AJNR Am J Neuroradiol 1996; 17: 455–457 [8] Kuszyk BS, Heath DG, Ney DR et al. CT angiography with volume rendering: imaging findings. AJR Am J Roentgenol 1995; 165: 445–448 [9] Jewells V, Castillo M. MR angiography of the extracranial circulation. Magn Reson Imaging Clin N Am 2003; 11: 585–597, vivi

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Imaging Techniques [10] Sohn CH, Sevick RJ, Frayne R. Contrast-enhanced MR angiography of the intracranial circulation. Magn Reson Imaging Clin N Am 2003; 11: 599–614 [11] Meier P, Zierler KL. On the theory of the indicator-dilution method for measurement of blood flow and volume. J Appl Physiol 1954; 6: 731–744 [12] Roberts GW, Larson KB, Spaeth EE. The interpretation of mean transit time measurements for multiphase tissue systems. J Theor Biol 1973; 39: 447–475 [13] Rosen BR, Belliveau JW, Vevea JM, Brady TJ. Perfusion imaging with NMR contrast agents. Magn Reson Med 1990; 14: 249–265 [14] Speck O, Chang L, DeSilva NM, Ernst T. Perfusion MRI of the human brain with dynamic susceptibility contrast: gradient-echo versus spin-echo techniques. J Magn Reson Imaging 2000; 12: 381–387 [15] Heiland S, Kreibich W, Reith W et al. Comparison of echo-planar sequences for perfusion-weighted MRI: which is best? Neuroradiology 1998; 40: 216– 221 [16] Williams DS, Detre JA, Leigh JS, Koretsky AP. Magnetic resonance imaging of perfusion using spin inversion of arterial water. Proc Natl Acad Sci U S A 1992; 89: 212–216 [17] Edelman RR, Siewert B, Darby DG et al. Qualitative mapping of cerebral blood flow and functional localization with echo-planar MR imaging and signal targeting with alternating radio frequency. Radiology 1994; 192: 513–520 [18] Takahashi K, Yamaguchi S, Kobayashi S, Yamamoto Y. Effects of aging on regional cerebral blood flow assessed by using technetium Tc 99 m hexamethylpropyleneamine oxime single-photon emission tomography with 3D stereotactic surface projection analysis. AJNR Am J Neuroradiol 2005; 26: 2005–2009 [19] Jagust WJ. Neuroimaging in dementia. Neurol Clin 2000; 18: 885–902 [20] Petrella JR, Coleman RE, Doraiswamy PM. Neuroimaging and early diagnosis of Alzheimer’s disease: a look to the future. Radiology 2003; 226: 315–336 [21] Bradley KM, O’Sullivan VT, Soper ND et al. Cerebral perfusion SPET correlated with Braak pathological stage in Alzheimer’s disease. Brain 2002; 125: 1772–1781

[22] Lee YC, Liu RS, Liao YC et al. Statistical parametric mapping of brain SPECT perfusion abnormalities in patients with Alzheimer’s disease. Eur Neurol 2003; 49: 142–145 [23] Yoshiura T, Hiwatashi A, Noguchi T et al. Arterial spin labelling at 3-T MR imaging for detection of individuals with Alzheimer’s disease. Eur Radiol 2009; 19: 2819–2825 [24] Yoshikawa T, Murase K, Oku N et al. Heterogeneity of cerebral blood flow in Alzheimer’s disease and vascular dementia. AJNR Am J Neuroradiol 2003; 24: 1341–1347 [25] Behpour AM, Shah PS, Mikulis DJ, Kassner A. Cerebral blood flow abnormalities in children with sickle cell disease: a systematic review. Pediatr Neurol 2013; 48: 188–199 [26] Earley CJ, Kittner SJ, Feeser BR et al. Stroke in children and sickle-cell disease: Baltimore-Washington Cooperative Young Stroke Study. Neurology 1998; 51: 169–176 [27] Pegelow CH, Macklin EA, Moser FG et al. Longitudinal changes in brain magnetic resonance imaging findings in children with sickle cell disease. Blood 2002; 99: 3014–3018 [28] Adams RJ, McKie VC, Brambilla D et al. Stroke prevention trial in sickle cell anemia. Control Clin Trials 1998; 19: 110–129 [29] Adams RJ, Brambilla DJ, Granger S et al. STOP Study. Stroke and conversion to high risk in children screened with transcranial Doppler ultrasound during the STOP study. Blood 2004; 103: 3689–3694 [30] Stockman JA, Nigro MA, Mishkin MM, Oski FA. Occlusion of large cerebral vessels in sickle-cell anemia. N Engl J Med 1972; 287: 846–849 [31] Gerald B, Sebes JI, Langston JW. Cerebral infarction secondary to sickle cell disease: arteriographic findings. AJR Am J Roentgenol 1980; 134: 1209–1212 [32] Walter C, Hertel F, Naumann E, Mörsdorf M. Alteration of cerebral perfusion in patients with idiopathic normal pressure hydrocephalus measured by 3D perfusion weighted magnetic resonance imaging. J Neurol 2005; 252: 1465–1471

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Part III Normal Aging

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Imaging of the Normal Aging Brain

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Iron Accumulation and Iron Imaging in the Human Brain

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Normal Aging

8 Imaging of the Normal Aging Brain Ruth A. Wood, Ludovico Minati, and Dennis Chan Elderly adults represent a significant and rapidly expanding proportion of the population. Some estimates state that by 2030 there will be 72 million individuals over the age of 65 years in the United States of America, constituting 19% of the population.1 Given the high prevalence of brain disorders in this population, attaining a greater understanding of the changes that occur in normal brain aging is of critical importance. Furthermore, an appreciation of such changes is a prerequisite for the identification of abnormalities associated with underlying pathology. As the prevalence of many neurological diseases increases with age, it can be difficult to differentiate between the effects of aging and those of prodromal age-related disease. In addition, several operational challenges exist when establishing the effects of aging on the brain. First, there is a potential ascertainment bias in the selection of individuals considered to represent “normal aging”; without exhaustive screening and follow-up, there is a risk that cohorts contain individuals with clinically silent disease. Second, most studies on normal aging are cross-sectional, with limited longitudinal data on the progression of age-related changes. Third, the brain changes that occur during aging reflect a complex interaction between alterations of disparate physiological variables, most notably structure, function, perfusion, and metabolism. Because each of these variables is evaluated using different imaging modalities, careful implementation of multimodal imaging is required to obtain a comprehensive view of aging of the brain, whereas most studies published to date have described changes as observed through single techniques applied in isolation. This chapter provides an overview of the main brain changes associated with normal aging and the imaging modalities currently used to study them. It does not intend to be a metaanalysis; the focus is on conceptual comprehensiveness rather than exhaustive comparison of published studies.

8.1 Brain Structure 8.1.1 Gray Matter Volume Autopsy studies consistently demonstrate an age-related reduction in brain weight and volume, with concomitant enlargement of ventricular cerebrospinal fluid (CSF) spaces.2 These gross pathological changes correspond on a histopathological level to neuronal loss in the neocortex, hippocampus, and cerebellum and neuronal shrinkage and loss of myelinated fibers, particularly in subcortical regions.2 Brain-volume loss observed at autopsy correlates with atrophy as determined using in vivo structural brain imaging; in addition to the use of crosssectional imaging to identify changes at a single time point, longitudinal imaging studies permit determination of change in rates of atrophy. Most of these studies use magnetic resonance imaging (MRI) techniques for their superior resolution and gray matter (GM):white matter (WM) contrast compared with computed tomography (CT).

The changes in brain volume over the human life span do not follow a linear trajectory. Brain volume increases in early life, followed by a plateau during which the CSF:brain volume ratio remains approximately constant.3 Brain volume then declines after the fifth decade, with progressive ventriculomegaly, sulcal expansion, and enlargement of pericerebellar subarachnoid spaces (▶ Fig. 8.1).3 Several trends emerge on more detailed scrutiny, although there is a degree of interstudy variability regarding the precise time course and spatial distribution of age-related volume changes. First, the rate of brain atrophy accelerates with age.4 Second, WM and GM are affected differently (▶ Fig. 8.2). Loss of GM occurs at an early age, and both linear and nonlinear patterns of correlation with age have been described (▶ Fig. 8.3),5 whereas WM volume peaks in the fifth decade before declining in older age.5 With regard to the anatomical distribution of volume loss, the regions of the cortex are affected in a non-uniform manner. Overall, there is an anteroposterior gradient of volume loss, with the earliest age-related atrophy occurring in the prefrontal cortex.6 Age-associated atrophy has also been demonstrated in the hippocampus, amygdala, striatum, and cerebellum (▶ Fig. 8.4).2 Conflicting reports exist regarding age-related atrophy within the thalamus, and some regions within the basal ganglia and brainstem appear relatively unaffected.7 Although these observations are useful for understanding the aging process, it is important to keep in mind that they are the result of analyses conducted over large groups; at the singlecase level, there is substantial individual variability in the extent and localization of atrophy, ranging from near absence of atrophy in comparison to a typical young brain, to moderate atrophy without any accompanying cognitive deficit. Detailed robust understanding of the regional distribution of volume loss in normal aging is of particular relevance in view of the patterns of atrophy associated with different neurodegenerative disorders. For example, atrophy of the medial temporal lobe is a typical early feature of Alzheimer’s disease, but this region does not exhibit significant age-related atrophy in the normal aging population.8 In frontotemporal dementia, there is asymmetrical atrophy predominantly affecting the frontal and temporal lobes, which also contrasts with the symmetrical pattern of atrophy in normal aging.9 However, in any comparison of change between aging and neurodegenerative diseases, it is crucial to bear in mind the fact that atrophy can be absent or minimal in the preclinical and early clinical phases of most neurodegenerative disorders, making differentiation of early-stage disease from normal aging at the individual level quite difficult. Although the association between the integrity of specific cognitive functions and regional brain atrophy in disease is widely recognized, weaker and less consistent correlations are observed between cognition and age-related atrophy.10,11 For example, reduced GM volume in the medial temporal lobe, prefrontal cortex, and posterior parietal cortex appears to be associated with lower scores on the Mini-Mental State Examination11; furthermore, there is evidence that reduced

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Imaging of the Normal Aging Brain

Fig. 8.1 Axial and coronal T1-weighted magnetic resonance imaging of two representative young (a,b) and elderly (c,d) healthy brains demonstrating global volume loss and sulcal enlargement.

Fig. 8.2 Scatterplots demonstrating the complex effect of age on white matter, gray matter, and cerebrospinal fluid volumes (CSF). (Reproduced with permission from Sowell ER, Peterson BS, Thompson PM, et al. Mapping cortical change across the human life span. Nat Neurosci. 2003;6:309–15. Copyright Nature Publishing Group, Inc.)

hippocampal volume is associated with poor performance on tests of episodic memory.10

Cortical Thickness The development of automated techniques to obtain quantitative MRI measures has permitted the study of the effects of aging on structural parameters, such as cortical thickness (▶ Fig. 8.5), with regional changes in cortical thickness detectable over time intervals as short as 1 year. In the normal aging population, annual reductions in cortical thickness of 0.5 to

1.0% have been reported for most brain regions.12 In line with measurements of regional brain volume, the most prominent age-related reduction in cortical thickness is observed in the prefrontal cortex.13 Cortical thinning with age is also consistently found in parietal and insular regions, and in these regions, thickness measures appear more sensitive than volume measurements to age-related changes.13 Changes in cortical thickness occur in different brain regions at different times during the life span; between young adulthood and middle age, cortical thinning occurs mainly in the prefrontal cortex and at the parietal-temporal-occipital junction, whereas in the oldest

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Fig. 8.3 Peak age maps showing the nonlinear effect of age on gray matter (GM). Color maps show the mean age at which peak GM density is reached for each point on the lateral, medial, and top surfaces of the brain. Shown in black are regions where the partial correlation coefficient for the nonlinear age effect did not reach statistical significance; age effects in these regions tended to decrease linearly with age rather than quadratically. (Reproduced with permission from Sowell ER, Peterson BS, Thompson PM, et al. Mapping cortical change across the human life span. Nat Neurosci 2003;6:309–315. Copyright Nature Publishing Group, Inc.)

Fig. 8.4 Coronal T1-weighted magnetic resonance imaging of two representative young (a) and elderly (b) healthy brains, demonstrating hippocampal atrophy, ventricular enlargement, and increased prominence of the sylvian fissures.

Fig. 8.5 Illustration of the cortical thickness measurement principle. (a) Original magnetic resonance imaging scan; (b) extraction of the cortical gray matter (GM) boundaries (yellow: white matter to GM boundary, red: cerebrospinal fluid to GM boundary); (c) thickness measurement (blue segment).

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Imaging of the Normal Aging Brain elderly, changes are most prominent in the primary sensory and motor cortices.14 The relationship between cognitive function and cortical thickness is less well established than that with cortical volumes, although some data suggest that cortical thickness correlates with performance on tests of reaction time, verbal working memory, and episodic memory.15

8.1.2 Iron Deposition in the Extrapyramidal Nuclei Iron accumulates in substantial quantities in the brain with normal aging, and its magnetic properties are readily detectable using MRI. MRI signal intensity changes in GM nuclei, particularly on gradient-echo sequences, are well described in association with normal aging and neurodegenerative disease and correlate with non-heme iron deposition as determined at autopsy (▶ Fig. 8.6).16 Iron deposits are not present at birth, and in children under the age of 10 years, the basal ganglia are hyperintense on MRI, becoming hypointense by approximately 25 years of age.16 In healthy adults, the highest iron concentrations are found in the globus pallidus, red nucleus, and pars reticularis of the substantia nigra.17 Deposition also occurs, albeit at a slower rate, in the cerebellum, dentate nucleus, and neostriatum.17

8.1.3 White Matter

Fig. 8.6 Axial gradient-echo MRI demonstrating hypointensity of the putamina (as indicated by white arrows) in an elderly brain due to iron accumulation.

Macrostructural Lesions Changes in WM are a common finding in cognitively intact elderly individuals and on T2-weighted MRI scans are visualized as hyperintense areas. These WM hyperintensities (WMHs) are classified as periventricular or deep, depending on the lesion’s proximity to the lateral ventricle (▶ Fig. 8.7). The prevalence of WMHs increases with age, and by the fifth decade, they are detectable in nearly all healthy individuals.18 Postmortem studies have identified distinct histopathological correlates for the various WMH subtypes. Periventricular WMHs are subdivided into “caps” surrounding the frontal horns of the lateral ventricles, pencil-thin lining, and halos. Pencil-thin lining and halos represent areas of demyelination

and subependymal gliosis (▶ Fig. 8.8).19,20 Periventricular caps are associated with myelin pallor, arteriosclerosis, and astrogliosis.20 Deep WMHs can be punctate, early confluent, or confluent. Confluent and early confluent WMHs represent a continuum of ischemic lesions; histology shows incomplete parenchymal destruction with axonal loss and astrogliosis.19 By comparison, punctate WMHs are considered more benign and correlate with regions of reduced myelination and widening of Virchow-Robin perivascular spaces (▶ Fig. 8.9).19 The relationship between WMH density and cognition in normal aging remains unclear. One meta-analysis has uncovered limited evidence that WMHs in healthy individuals are

Fig. 8.7 Axial T2-weighted (a) and coronal fluid-attenuated inversion recovery (FLAIR) (b) magnetic resonance imaging demonstrating, respectively, confluent and periventricular white matter hyperintensities, as sometimes observed in the brains of elderly patients.

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Normal Aging

Fig. 8.8 Apologies, the legend is correct but does in fact refer to a different MR image which is attached. Axial T2-weighted image showing an example of nonspecific gliosis in the left frontal WM as indicated by the yellow arrow.

associated with poor performance on tasks assessing declarative memory and executive function,21 whereas more recent work suggests that periventricular WMHs correlate most strongly with deficits in multiple cognitive domains.22

Microstructure Diffusion-based MRI techniques, such as diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion spectrum imaging (DSI), measure aspects of the diffusion of water molecules within brain tissue to probe tissue microarchitecture. The diffusion properties can be quantified using various parameters. Mean diffusivity (MD) is a measure of the rate at which water molecules diffuse: high MD indicates that biological barriers are sparse and indicates low tissue density. Fractional anisotropy (FA) measures the directional coherency with which diffusion occurs. In normal WM, water molecules tend to diffuse in parallel to the myelinated axon bundles, and disruption of these myelinated bundles allows water molecules to diffuse in other planes more readily, measured as a reduction in FA. Diffusion studies can identify WM changes before the development of WMHs. In normal aging, the most consistent findings are reduced FA and increased MD; axonal degeneration, myelin breakdown, and glial scarring appear to be the main histopathological correlates of these changes.23 Brain regions that exhibit reduced FA with aging include the internal capsule, corpus callosum, and centrum semiovale.23 According to some

Fig. 8.9 Axial T2-weighted MRI demonstrating diffusely enlarged Virchow-Robin perivascular spaces in subcortical WM. An example of an enlarged Virchow Robin space is indicated by the white arrow.

investigations, there is evidence of an anteroposterior gradient of reduced FA with aging, paralleling the changes observed using GM volumetry.24 There is limited additional evidence of a superoinferior gradient, with more marked age-related reduction of FA in dorsal fiber tracts, such as the superior longitudinal fasciculus.25 Aging also correlates with MD changes, although the amplitude of these changes is smaller in comparison to changes in FA. The most consistent findings, namely, of an anteroposterior gradient of MD and of increased MD in the corpus callosum, parallel the changes observed in FA.23 There is evidence to indicate that, within the normal aging population, DTI measures are associated with cognitive function. For example, FA in the body of the corpus callosum correlates with performance in motor skill tests, and diffusivity measures in frontal regions correlate with performance on tests of verbal fluency.23

8.1.4 Cerebrovascular Changes Microbleeds Cerebral microbleeds (CMBs) appear as small, round, homogeneous foci of signal hypointensity on gradient-echo T2*weighted MRI (▶ Fig. 8.10). Histopathology reveals focal deposits of perivascular hemosiderin, suggesting that these regions represent previous microhemorrhages.26 In normal aging, CMBs do not have a specific distribution pattern and can be found in cortical and subcortical regions.27

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Imaging of the Normal Aging Brain this is not supported by the results of studies combining quantitative perfusion with detailed structural neuroimaging, indicating that regional CBF reduction is not always coupled to regional brain atrophy.31,32

Cerebrovascular Reactivity

Fig. 8.10 Axial gradient-echo T2-weighted MRI showing a case of multiple cerebral microbleeds. One such microbleed is indicated by the white arrow.

During the aging process, there is loss of elasticity, progressive fibrosis, and atherosclerosis within the cerebral vasculature.33 Macroscopic cerebrovascular reactivity (CVR), a measure of elasticity in large blood vessels, can be measured noninvasively using transcranial Doppler whereas CT, PET, and MRI-based techniques are used to assess microvascular reactivity. As age advances, macrovascular CVR declines; however, it is uncertain whether this represents a truly physiological component of aging or a reflection of underlying neurodegenerative and cerebrovascular disease.34 A reduction in microvascular CVR has also been demonstrated in association with aging in a range of studies using carbon dioxide, breath holding, and acetazolamide challenges to assess the vasodilatory capacity of small cerebral vessels.35 A progressive attenuation of both vasodilator and vasoconstrictor responses is seen with advancing age, indicating impaired vessel elasticity. This reduction in CVR is more pronounced in the presence of risk factors for cardiovascular disease, for example, diabetes, smoking, and hypertension.35

8.2 Metabolism It is uncertain whether CMBs are a feature of normal aging or a marker of small-vessel disease; in one study, 6.4% of healthy participants from an elderly population had at least one CMB.28 The prevalence of CMBs increases with advancing age; however, CMBs are also associated with risk factors for cerebrovascular disease, such as diabetes.27 In addition, there are correlations between the presence of CMBs and lacunar infarcts and confluent WMHs, pointing to an association with vascular disease.28 The presence of CMBs correlates with cognitive impairment in patients with cerebrovascular disease, but few studies have explored this relationship in healthy elderly adults. A single study, however, has reported an association between CMBs and subjective memory impairment.29

Perfusion Single-photon emission computed tomography (SPECT), MRI, and positron emission tomography (PET) studies have all shown that increasing age is associated with a decline in global cerebral blood flow (CBF).30,31,32 However, changes in CBF are not uniform across the brain; the most marked age effects are found in the prefrontal cortex, although decreased perfusion is also detectable in the parietal lobe, inferior temporal regions, motor cortex, and basal ganglia.30,31 Areas with relatively preserved CBF include the occipital lobe and the posterior superior temporal lobe.30,31 The cause of age-related CBF reduction remains uncertain. The pattern of change is reminiscent of the anteroposterior gradient of atrophy and as a consequence led to the proposal that the reduction in CBF occurs secondary to volume loss, although

Positron emission tomography detects gamma radiation emitted by a positron-emitting radionuclide, or tracer, linked to a biologically active molecule. Molecules like fluorodeoxyglucose (FDG), a glucose analog, and oxygen can therefore be used to estimate differing aspects of brain metabolic activity. Positron emission tomography studies using labeled oxygen have demonstrated an age-related decline in the global cerebral metabolic rate of oxygen (CMRO2), with one study finding a decrease in CMRO2 of 0.5% per year in certain brain regions.36 Some key trends are apparent. First, age effects on the CMRO2 are more prominent in GM than in WM.37 Second, the most marked age effects are seen in supratentorial regions, specifically in the frontal, temporosylvian, and parieto-occipital cortices.37 In view of the correspondence with the pattern of atrophy observed in aging, it has been suggested that these PET findings occur secondary to volume loss and do not reflect true hypometabolism.38 Although a variety of techniques aiming to correct for the presence of atrophy now exist, many PET studies of aging predate the development of such techniques, and therefore this issue remains contentious.38 FDG-PET studies of the normal aging population have produced somewhat contradictory results.39 Although some research suggests that the aging brain becomes globally hypometabolic, this finding is not replicated in all studies.39,40 With regard to the cortical distribution of such changes, FDG hypometabolism is predominantly observed in the frontal lobe, particularly after the age of 60 years (▶ Fig. 8.11).41,42 Hypometabolism has also been reported in the temporal, parietal, and somatosensory cortices, but changes are small in magnitude compared with those observed in frontal regions.39,42

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Fig. 8.11 Significant areas of negative correlation between age and glucose metabolism as determined by FDG-PET in a group of males. Significant areas (p < 0.05) are overlaid on a T1-weighted MRI image. Areas with significant negative correlation in this study included (1) left superior temporal gyrus, (2) right superior temporal gyrus, (3) medial frontal gyrus, and (4) caudate/left subcallosal gyrus. The color scale denotes t value. Reproduced with permission from Shen X, Liu H, Hu Z, Hu H, Shi P. The relationship between cerebral glucose metabolism and age: report of a large brain PET data set. PLoS One. 2012; 7:e51517. Copyright Shen et al.

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Imaging of the Normal Aging Brain

8.3 Brain Function Imaging techniques used to investigate brain function at rest or in response to stimuli include PET, functional MRI (fMRI), and SPECT. In healthy adults, a transient increase in blood flow occurs locally in engaged brain regions after activation in response to task performance or internal processes. This causes an increase in the oxyhemoglobin/deoxyhemoglobin ratio and, since these two forms of hemoglobin have differing magnetic properties, a blood oxygen-level dependent (BOLD) fMRI signal is generated.43 Task-related fMRI, during which changes in the BOLD signal, as an indirect measure of brain activation, are measured during engagement in specific tasks, can be used to investigate structure-function relationships. With advancing age, task-related activations tend to become weaker and more diffuse, regardless of which task is performed.23 More specifically, a reduction in functional hemispheric lateralization is seen in the prefrontal

cortex during tests of perception, episodic memory, and inhibitory control,23 although the explanation for this altered lateralization remains unclear. In some studies, reduced functional hemispheric lateralization was associated with superior performance on episodic, semantic, and working memory tasks, indicating a potential compensatory mechanism.44 Reduced functional hemispheric lateralization may also represent agerelated loss of cortical inhibition.44 In contrast to task-related fMRI, task-free or resting-state fMRI provides information on intrinsic brain connectivity in the absence of engagement in explicit tasks.45 In normal adults, a specific network of brain regions, termed the default-mode network, can be prominently identified on task-free fMRI, and this encompasses the precuneus, posterior cingulate cortex, medial prefrontal cortex, and medial temporal lobe.45 In the healthy elderly population, a consistent finding is decreased functional connectivity across this network, independent of changes in brain volume (▶ Fig. 8.12).23

Fig. 8.12 Whole-brain analyses of functional connectivity using resting-state fMRI representing coherent activity in the default-mode network, comprising correlations between the posterior cingulate cortex/retrosplenial cortex and both the medial prefrontal cortex and the bilateral lateral parietal cortex, and associated decline in old age. The z value refers to the coordinate of the MRI scan along the ventral-dorsal axis according to the Talairach atlas. Reproduced with permission from Andrews-Hanna JR, Snyder AZ, Vincent JL et al. Disruption of large-scale brain systems in advanced aging. Neuron. 2007; 565:924-935. Copyright Elsevier, Inc.

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Normal Aging Table 8.1 Take-home points of the signs and symptoms generally associated with advancing age Reduced total brain weight and volume and enlargement of CSF spaces Reduced brain weight and volume2 Increased prevalence of white matter hyperintensities18 Reduced fractional anisotropy and increased mean diffusivity in the white matter23 Darkening of basal ganglia on T2* magnetic resonance imaging due to iron deposition23 Increased prevalence of cerebral microbleeds27 Reduced cerebral blood flow, globally but particularly in the prefrontal cortex30,31,32 Declining rate of cerebral oxygen and glucose metabolism, especially in frontal regions41,42 Decreased hemispheric lateralization of activation during performance of active tasks23 Reduced integrity of the default-mode network at rest23

8.4 Conclusions The physiological processes associated with normal aging result in multidimensional alterations in brain structure and function, ranging from directly observable atrophy to disruption of functional connectivity as measured using complex analytical methods (▶ Table 8.1). The regional age-associated volume loss is distinct from that observed in neurodegenerative dementias. The development of lesions like WMHs and CMBs reflects a progressive accumulation of focal pathology, whereas hypoperfusion and hypometabolism are indicative of more widespread degenerative processes. As brain imaging techniques and associated methods of analysis become increasingly sophisticated, their continued application to the study of normal aging will be a critical first step toward improved understanding of the imaging changes that accompany the diseases of later life.

8.5 Acknowledgments The authors are grateful to Ludovico D’Incerti, MD (Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy), Paolo Vitali, MD (Scientific Department, Fondazione IRCCS Istituto Neurologico Mondino, Pavia, Italy), Kuven Moodley, MRCP (Brighton & Sussex Medical School, Falmer, UK), and Daniela Perani, MD (Department of Clinical Neuroscience, Università Vita-Salute e Ospedale San Raffaele, Milan, Italy), for insightful advice on previous drafts and provision of part of the figures.

References [1] Administration on Aging, Department of Health and Human Services, United States of America Government. Available at: http://www.aoa.gov/Aging_Statistics. Accessed May 8, 2013 [2] Raz N, Rodrigue KM. Differential aging of the brain: patterns, cognitive correlates and modifiers. Neurosci Biobehav Rev 2006; 30: 730–748 [3] Hedman AM, van Haren NE, Schnack HG, Kahn RS, Hulshoff Pol HE. Human brain changes across the life span: a review of 56 longitudinal magnetic resonance imaging studies. Hum Brain Mapp 2012; 33: 1987–2002

[4] Takao H, Hayashi N, Ohtomo K. A longitudinal study of brain volume changes in normal aging. Eur J Radiol 2012; 81: 2801–2804 [5] Ge Y, Grossman RI, Babb JS, Rabin ML, Mannon LJ, Kolson DL. Agerelated total gray matter and white matter changes in normal adult brain. Part I: volumetric MR imaging analysis. AJNR Am J Neuroradiol 2002; 23: 1327–1333 [6] Jernigan TL, Archibald SL, Fennema-Notestine C et al. Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiol Aging 2001; 22: 581–594 [7] Raz N. The aging brain observed in vivo: Differential changes and their modifiers. In: Cabeza R, Nyberg L, Park DC, eds. Cognitive Neuroscience of Aging: Linking Cognitive and Cerebral Aging. New York: Oxford University Press; 2004:17–55 [8] Jobst KA, Smith AD, Szatmari M et al. Detection in life of confirmed Alzheimer’s disease using a simple measurement of medial temporal lobe atrophy by computed tomography. Lancet 1992; 340: 1179–1183 [9] Whitwell JL, Jack CR, Jr. Comparisons between Alzheimer’s disease, frontotemporal lobar degeneration, and normal aging with brain mapping. Top Magn Reson Imaging 2005; 16: 409–425 [10] Mungas D, Harvey D, Reed BR et al. Longitudinal volumetric MRI change and rate of cognitive decline. Neurology 2005; 65: 565–571 [11] Tisserand DJ, van Boxtel MP, Pruessner JC, Hofman P, Evans AC, Jolles J. A voxel-based morphometric study to determine individual differences in gray matter density associated with age and cognitive change over time. Cereb Cortex 2004; 14: 966–973 [12] Fjell AM, Walhovd KB. Structural brain changes in aging: courses, causes and cognitive consequences. Rev Neurosci 2010; 21: 187–221 [13] Long X, Liao W, Jiang C, Liang D, Qiu B, Zhang L. Healthy aging: an automatic analysis of global and regional morphological alterations of human brain. Acad Radiol 2012; 19: 785–793 [14] McGinnis SM, Brickhouse M, Pascual B, Dickerson BC. Age-related changes in the thickness of cortical zones in humans. Brain Topogr 2011; 24: 279–291 [15] Gautam P, Cherbuin N, Sachdev PS, Wen W, Anstey KJ. Relationships between cognitive function and frontal grey matter volumes and thickness in middle aged and early old-aged adults: the PATH Through Life Study. Neuroimage 2011; 55: 845–855 [16] Aquino D, Bizzi A, Grisoli M et al. Age-related iron deposition in the basal ganglia: quantitative analysis in healthy subjects. Radiology 2009; 252: 165– 172 [17] Drayer B, Burger P, Darwin R, Riederer S, Herfkens R, Johnson GA. MRI of brain iron. AJR Am J Roentgenol 1986; 147: 103–110 [18] Wen W, Sachdev P. The topography of white matter hyperintensities on brain MRI in healthy 60- to 64-year-old individuals. Neuroimage 2004; 22: 144– 154 [19] Fazekas F, Kleinert R, Offenbacher H et al. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology 1993; 43: 1683–1689 [20] Chimowitz MI, Estes ML, Furlan AJ, Awad IA. Further observations on the pathology of subcortical lesions identified on magnetic resonance imaging. Arch Neurol 1992; 49: 747–752 [21] Gunning-Dixon FM, Raz N. The cognitive correlates of white matter abnormalities in normal aging: a quantitative review. Neuropsychology 2000; 14: 224–232 [22] Bolandzadeh N, Davis JC, Tam R, Handy TC, Liu-Ambrose T. The association between cognitive function and white matter lesion location in older adults: a systematic review. BMC Neurol 2012; 12: 126 [23] Minati L, Grisoli M, Bruzzone MG. MR spectroscopy, functional MRI, and diffusion-tensor imaging in the aging brain: a conceptual review. J Geriatr Psychiatry Neurol 2007; 20: 3–21 [24] O’Sullivan M, Jones DK, Summers PE, Morris RG, Williams SC, Markus HS. Evidence for cortical “disconnection” as a mechanism of age-related cognitive decline. Neurology 2001; 57: 632–638 [25] Sullivan EV, Rohlfing T, Pfefferbaum A. Longitudinal study of callosal microstructure in the normal adult aging brain using quantitative DTI fiber tracking. Dev Neuropsychol 2010; 35: 233–256 [26] Fazekas F, Kleinert R, Roob G et al. Histopathologic analysis of foci of signal loss on gradient-echo T2*-weighted MR images in patients with spontaneous intracerebral hemorrhage: evidence of microangiopathy-related microbleeds. AJNR Am J Neuroradiol 1999; 20: 637–642 [27] Loitfelder M, Seiler S, Schwingenschuh P, Schmidt R. Cerebral microbleeds: a review. Panminerva Med 2012; 54: 149–160 [28] Roob G, Schmidt R, Kapeller P, Lechner A, Hartung HP, Fazekas F. MRI evidence of past cerebral microbleeds in a healthy elderly population. Neurology 1999; 52: 991–994

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Imaging of the Normal Aging Brain [29] van Norden AG, van Uden IW, de Laat KF et al. Cerebral microbleeds are related to subjective cognitive failures: the RUN DMC study. Neurobiol Aging 2013; 34: 2225–2230 [30] Meyer JS, Terayama Y, Takashima S. Cerebral circulation in the elderly. Cerebrovasc Brain Metab Rev 1993; 5: 122–146 [31] Chen JJ, Rosas HD, Salat DH. Age-associated reductions in cerebral blood flow are independent from regional atrophy. Neuroimage 2011; 55: 468–478 [32] van Es AC, van der Grond J, ten Dam VH et al. PROSPER Study Group. Associations between total cerebral blood flow and age related changes of the brain. PLoS ONE 2010; 5: e9825 [33] Hegedüs K, Molnár P. Age-related changes in reticulin fibers and other connective tissue elements in the intima of the major intracranial arteries Clin Neuropathol 1989; 8: 92–97 [34] Keage HA, Churches OF, Kohler M et al. Cerebrovascular function in aging and dementia: a systematic review of transcranial Doppler studies. Dement Geriatr Cogn Dis Extra 2012; 2: 258–270 [35] Naritomi H, Meyer JS, Sakai F, Yamaguchi F, Shaw T. Effects of advancing age on regional cerebral blood flow: studies in normal subjects and subjects with risk factors for atherothrombotic stroke. Arch Neurol 1979; 36: 410–416 [36] Leenders KL, Perani D, Lammertsma AA et al. Cerebral blood flow, blood volume and oxygen utilization: normal values and effect of age. Brain 1990; 113: 27–47

[37] Pantano P, Baron JC, Lebrun-Grandié P, Duquesnoy N, Bousser MG, Comar D. Regional cerebral blood flow and oxygen consumption in human aging. Stroke 1984; 15: 635–641 [38] Fazio F, Perani D. Importance of partial-volume correction in brain PET studies. J Nucl Med 2000; 41: 1849–1850 [39] Meltzer CC, Becker JT, Price JC, Moses-Kolko E. Positron emission tomography imaging of the aging brain. Neuroimaging Clin N Am 2003; 13: 759–767 [40] Kuhl DE, Metter EJ, Riege WH, Phelps ME. Effects of human aging on patterns of local cerebral glucose utilization determined by the [18F]fluorodeoxyglucose method. J Cereb Blood Flow Metab 1982; 2: 163–171 [41] Kalpouzos G, Chételat G, Baron JC et al. Voxel-based mapping of brain gray matter volume and glucose metabolism profiles in normal aging. Neurobiol Aging 2009; 30: 112–124 [42] Herholz K, Salmon E, Perani D et al. Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage 2002; 17: 302–316 [43] Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A 1990; 87: 9868–9872 [44] Cabeza R. Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychol Aging 2002; 17: 85–100 [45] Rosazza C, Minati L. Resting-state brain networks: literature review and clinical applications. Neurol Sci 2011; 32: 773–785

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Normal Aging

9 Iron Accumulation and Iron Imaging in the Human Brain Stefan Ropele and Christian Langkammer

9.1 Iron Accumulation in the Normal Aging Brain Iron is an abundant trace element that is essential to the human body and has manifold functions, including blood oxygenation, conversion of blood sugar to energy, and myelin production.1 Although more than 60% of the total body iron is bound to hemoglobin, the most frequently found iron compartment in the brain is (intracellular) ferritin. Ferritin is a storage protein that keeps iron available in a nontoxic and soluble form. Each ferritin shell can host up to 5,000 iron ions. Early work using histologic Perls’ staining demonstrated that iron is not equally distributed across different brain structures; highest concentrations are found in deep gray matter nuclei.2 Iron accumulation in the brain is a nonlinear process. As revealed by a chemical brain analysis by Hallgren and Sourander in 1958,3 iron accumulates in the first four decades of life and plateaus afterward; no iron is present at birth (▶ Fig. 9.1). The reason for the accumulates is unclear, but it seems that iron transfer to the brain is largely one-way traffic. Throughout the brain, the highest iron concentrations can be found in the globus pallidus, red nucleus, substantia nigra, putamen, dentate nucleus, caudate nucleus, and thalamus (descending from 250 to 50 mg/kg). In contrast, cortical areas and white matter have significantly lower iron concentrations.3,4,5 Remarkably, in human brain tissue, there seems to be a baseline of iron levels of 30 mg/kg, which underlines an essential, multifaceted role of iron and seems to reflect a minimum requirement for normal brain metabolism. Given these rather large differences of concentrations, surprisingly little is known about why iron accumulates preferably in the basal ganglia structures. Moreover, the exact mechanism of iron transfer between neurons and glia is poorly understood. Loading of intracellular ferritin may involve mitochondrial catabolism, whereas the export of ferritin from the cells to oligodendrocytes is thought to act through the mediation of ferritin receptors.

9.2 Abnormal Iron Accumulation Although iron is an essential cofactor for many proteins and functions in the brain, iron overload is assumed to exert toxic effects as free or unbound iron ions serve as pro-oxidants.6 Ferric iron (Fe3 + ) reacts with superoxide and generates Fe2 + (Haber-Weiss reaction); ferrous iron (Fe2 + ) triggers the conversion of reactive oxygen species to hydroxyl radicals (Fenton reaction). Hydroxyl radicals are highly reactive oxygen species that induce oxidative stress, which may interfere with cellular signaling and lead to neuronal damage. Therefore, iron is often discussed in the context of triggering or mediating an inflammatory or neurodegenerative cascade in many neurologic diseases. Increased iron levels in deep gray matter are a frequent but unspecific finding in several neurologic disorders and are frequently observed in a variety of neurodegenerative and inflammatory diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease, multiple sclerosis,

Fig. 9.1 The dynamics of iron accumulation in different structures of the brain according to the study of Hallgren and Sourander. The globus pallidus shows the highest iron content and the highest rate of accumulation. The only region that does not show a plateauing effect is the thalamus, where the iron content decreases after the fourth decade of life (not shown). (From Hallgren B, Sourander P. The effect of age on the non-haemin iron in the human brain. J Neurochem 1958; 3 (1): 41–51.)

and amyotrophic lateral sclerosis (ALS).7,8,9,10 Besides deep gray nuclei, histologic studies in AD have found abnormally increased levels of iron in the proximity of neuritic plaques and neurofibrillary tangles.11,12 In deep gray matter and neuritic plaques, the role of iron in this context is not entirely clear, but abnormally high concentrations of iron can yield oxidative stress and induce neuronal vulnerability.13 Consequently, iron accumulation might additionally increase the toxicity of exogenous or endogenous toxins.

9.3 In Vivo Iron Assessment by Quantitative Magnetic Resonance Imaging Single iron ions can be barely detected by magnetic resonance imaging (MRI), despite its high sensitivity for the underlying magnetic properties of tissues. However, the iron core of ferritin represents a highly organized structure similar to the mineral ferrihydrite (5Fe2O3 · 9H2O) and exhibits a strong paramagnetic effect that makes it detectable by MRI. This has opened a new window for the in vivo assessment of iron levels and for studying the pathological role of iron in the brain. At the very beginning of clinical MRI, it was observed that the basal ganglia of healthy subjects often appeared hypointense on T2-weighted spin-echo sequences (▶ Fig. 9.2). The susceptibility affects scale with field strengths that make the detection of iron at high and ultrahigh field strengths more sensitive (▶ Fig. 9.3). Using iron staining in a histologic correlation study, it could be confirmed that the presence of iron was

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Iron Accumulation and Iron Imaging in the Human Brain

Fig. 9.2 Spectrum of hypointensities that can be typically observed on T2-weighted sequences in normal aging subjects as a consequence of iron accumulation. Appearance of the dendtate nucleus (arrow, a), red nucleus and substantia nigra (arrow, b), globu spallidus, putamen, and caudate nucleus (arrow, c) in a 68-year-old healthy woman on T2-weighted spin-echo (top rows) and corresponding fluid-attenuated inversion recovery (FLAIR, bottom rows) sequences.

responsible for this observation.14 Iron detection in these early studies was done using Perl’s staining, which has a moderate sensitivity for iron in white matter. More sophisticated approaches are based on diaminobenzidine-enhanced staining (▶ Fig. 9.4). Subsequent studies with visual rating of iron deposition in the basal ganglia followed but were limited by the intrinsically low sensitivity and a rater bias of this approach. Nowadays, quantitative MRI techniques provide sensitive measures of iron content on a continuous scale, and these measurements are highly reproducible and comparable among subjects and scanners. The following sections of this chapter present a brief overview of proposed MR methods for iron mapping along

with their advantages and limitations; a summary is provided in ▶ Table 9.1. The longitudinal relaxation time T1 (also often represented by its inverse R1 = 1/T1) is only moderately affected by brain iron,15 which can be explained by a weak dipolar interaction. In contrast, the magnetic field perturbations introduced by the iron accelerate spin dephasing and, therefore, loss of transversal magnetization. In the case of a spin-echo sequence, where these field inhomogeneities are compensated by refocusing radiofrequency pulses, some irreversible dephasing effects remain because of the stochastic nature of diffusion. This effect can be considered a series of arbitrary oriented jumps through these

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Normal Aging

Fig. 9.3 Ultrahigh-field imaging allows better depiction of iron-loaded gray matter structures because of its higher sensitivity for variation of the magnetic susceptibility and its higher spatial resolution. Appearance of the dentate nucleus (arrows) in a formalin-fixed brain sample at 3 T (left) and 7 T (right). Images were acquired with a spoiled T2*weighted gradient-echo sequence. Fig. 9.4 Total nonheme iron (ferric and ferrous) can be stained with diaminobenzidine (DAB)-enhanced Perl’s (Turnbull) blue staining.14 Whereas cortical iron content is comparable to the iron content in white matter (see also ▶ Fig. 9.1), substantial variations can be observed in subcortical regions.

Table 9.1 Most relevant magnetic resonance (MR) imaging techniques proposed for the assessment of brain iron MR method

Advantages

Limitations

T1 relaxometry

Robust against susceptibility artifacts

Low sensitivity for iron Time consuming

R2 relaxometry

Sequence is readily available on clinical systems Moderate sensitivity for iron Robust against susceptibility artifacts

Moderate acquisition speed In multislice acquisition or with fast spin-echo readout, the observed R2 may also be affected by magnetization transfer effects

R2* relaxometry

Sequence is readily available on clinical systems Fast 3-dimensional whole-brain acquisition (< 10 min) High sensitivity for iron

Calcifications cannot be separated from clustered iron deposits Sensitive to macroscopic susceptibility artifacts

Phase imaging

Sequence is readily available on clinical systems High sensitivity for iron Calcifications can be distinguished from iron deposits

Phase unwrapping and filtering needed Not a linear measure for iron (does not reflect only local susceptibility)

Susceptibility-weighted imaging

Good sensitivity for iron Calcifications can be distinguished from iron deposits Enhanced tissue contrast

Same as for phase imaging Not quantitative (depends on postprocessing parameters)

Quantitative susceptibility mapping

High sensitivity for iron Linear measure for iron Calcifications can be distinguished from iron deposits

Extensive image postprocessing

field variations as a consequence of Brownian motion. Consequently, iron accelerates signal loss in spin-echo (T2) and gradient-echo sequences (T2*), which in turn result in increased transverse relaxation rates R2 and R2* (R2 = 1/T2 and R2* = 1/ R2*), respectively. Both R2 and R2* relaxation rates have been proven to be sensitive and linear measures for brain iron and can be assessed using conventional MR sequences readily available on clinical scanners.16,17 R2 can be measured using a spinecho sequence and R2* using a gradient-echo sequence, both with a minimum of two acquired echoes. R2* has a higher sensitivity for iron than R2, allows faster acquisition of the entire brain, and can also be acquired rapidly at ultrahigh field strengths (7 tesla [T]), where specific absorption rate restrictions are an issue. Given these advantages, R2* should be the preferred measure for brain iron in a clinical setup.5 Neverthe-

less, R2* is more prone to artifacts in the proximity of the sinus and cavities, which in turn renders R2 an interesting measure at these specific locations.18 Another related measure is R2', which separates reversible contributions (associated with microstructure) from total signal dephasing (R2* = R2 + R2'). R2’ is also sensitive for iron but requires a dedicated MRI sequence or, alternatively, the acquisition of both a gradient echo and a spin echo, which is time consuming in a clinical setup. Other studies have demonstrated field-dependent R2 rate increase as a means to measure brain iron, but the application of this technique in a clinical environment is complicated because it requires scanning of the subjects at a minimum of two different field strengths.19 Another interesting approach for iron detection is MR phase imaging.20 Phase values represent shifts in the MR frequency induced by iron (as well as other paramagnetic and

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Iron Accumulation and Iron Imaging in the Human Brain diamagnetic compounds) and have been shown to scale with iron content.21 Owing to the fact that the MR signal represents the complex transverse magnetization, the raw-phase data obtained have 2π ambiguities, which subsequently have to be eliminated by so-called phase unwrapping algorithms.20 Spatial varying low-frequency field components can be removed by high-pass filtering.22 However, high-pass filtering substantially reduces the sensitivity for iron in a nonlinear manner, and this is why recent studies suggest that the phase-based iron content can be compared in specific brain structures only.23 Susceptibility-weighted imaging (SWI) is a related imaging modality that combines filtered phase and magnitude images to obtain images whose contrast provides a high sensitivity for iron, veins, and other paramagnetic inclusions.22,24 Because of its sensitivity for even small veins, SWI can provide valuable information for the radiologic assessment; however, SWI is not quantitative and remains a nonlinear measure for iron content.23 The latest technique to overcome this issue is quantitative susceptibility mapping (QSM). QSM provides absolute values of the magnetic susceptibility, an intrinsic physical property of matter, rendering its results comparable when different scanners and field strengths are used. Although the clinical impact of QSM has not yet been fully explored, it is evident that it is a proportional and highly sensitive measure for iron.25,26,27 QSM is mathematically and computationally challenging, and the development of fast algorithms is the focus of current research.28 For practical reasons, the impact of other approaches for imaging brain iron, such as magnetic-field correlation and direct saturation imaging, remains unclear and the subject of further investigations.29,30 Although iron mapping in gray matter can be accomplished reliably, iron mapping in white matter remains challenging because myelin content and neuronal fiber orientation significantly affect the bulk susceptibility. Diamagnetic myelin counteracts the observed susceptibility of paramagnetic iron; in contrast, it additively impacts relaxation rates.25,31,32 Current research focuses on disentangling these effects and providing precise individual assessment of iron and myelin content. The orientation of white matter fiber bundles with respect to the main magnetic field of the scanner also impacts the effective transverse relaxation rate R2*, gradient-echo phase, and magnetic susceptibility.33,34 This circumstance can significantly limit comparative studies that are based on regional assessment of iron in white matter. So far, only a limited number of MRI studies have focused on the in vivo measurement of cortical iron. Because of the thinness of the cortical layer and its relatively low iron concentration, a quite sensitive sequence is required. Unfortunately, especially MR images of the cortex are affected by artifacts of the tissueliquor interface, which makes the application of postprocessing and correction methods essential. Therefore, current in vivo MRI studies focusing on cortical pathology are based mainly on histogram techniques or on an atlas-based group analyses.

9.4 Iron Mapping in Patients with Alzheimer’s Disease In vivo MRI studies have measured iron accumulation in the gray matter of patients with AD and related its extent to disease

severity and progression. So far, findings have been related mostly to deep gray matter because iron mapping in the cortex suffers from the previously mentioned limitations. Visual rating of T2-weighted images demonstrated lower signal intensities (as indicative for higher iron concentration) in the putamen and red nucleus of AD patients than in healthy controls.35 In addition, R2 and R2* relaxometry demonstrated increased iron in the hippocampus,36,37 the temporal cortex,38 and the pulvinar nucleus.39 An R2* map showing increased iron deposition in the basal ganglia of an AD patient, and how this is related to a healthy control is depicted in ▶ Fig. 9.5. Extending these findings, MR phase imaging revealed higher iron concentrations in the hippocampus, parietal cortex, putamen, caudate nucleus, and dentate nucleus.40,41 Further evidence of increased iron levels in deep gray matter comes from magnetic field dependency studies, where iron levels in the caudate nucleus, putamen, and globus pallidus were elevated in AD patients.42,43 The affinity of iron to amyloid plaques was used to study plaque load and evolution of plaques in postmortem brains, as well as in transgenic animal models. It has been speculated that iron might be involved in the formation of amyloid because of the formation of reactive oxygen species; alternatively, iron might be secondarily involved in the removal of amyloid.12,44 Although the idea of depicting amyloid plaques with methods other than positron emission tomography is intriguing, MRI of these plaques is challenging because plaque size is below the resolution of clinical MRI. Nonetheless, the plaque-attached iron causes perturbations of the magnetic field that are strong enough to affect bulk tissue susceptibility and MR relaxation times.36 So far, single amyloid plaques have been detected by T2*-weighted imaging in human brain tissue only ex vivo.45 Unfortunately, the required signal-to-noise ratio and the spatial resolution (40 µm isotropic) cannot be achieved in vivo within a clinically feasible scan time, although much hope was put on ultrahigh field scanners (7 T and greater), which intrinsically provide a higher signal and a greater sensitivity for susceptibility changes. Consequently, a better strategy might be to use a histogrambased technique for the detection of macroscopic susceptibility, changes that may reduce the need for detecting neuritic plaques on an individual basis.

9.5 Iron Mapping in Animal Models of Alzheimer’s Disease Small-bore systems usually operate at ultrahigh field strengths, allowing noninvasive study of the development of pathologic features in animal models at extremely high image resolution. Whereas animal models allow histologic or histochemical validation of MRI findings at any time point, validation studies in patients with AD are obviously limited to the time point of death. This limitation hinders investigation of the disease at an early stage and also the investigation of individual therapeutic interventions. Transgenic animal models have been used mostly to refine single-plaque imaging for in vivo applications.46,47 So far, only a single serial MRI study on the dynamics of plaque formation and development has been performed in a

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Normal Aging

Fig. 9.5 Iron mapping with R2* relaxometry is a linear, sensitive, and quantitative approach in the sense that it is reproducible and comparable among subjects. Top row: Fluid-attenuated inversion recovery (FLAIR) image (left) and corresponding R2* map (right) of a 62-year-old Alzheimer's patient. In the R2* map, a higher signal corresponds to a higher iron concentration. Lower row: FLAIR image and R2* map of a 58-year-old healthy control. Note that the windowing in both maps is identical.

transgenic mouse model at 12, 14, 16, and 18 months.48 Disease progression was reflected by an increase in the number and size of plaques. This progression was also paralleled by an increase of the R2 relaxation rate in the hippocampus and cortex of AD mice, whereas R2 in control mice remained unchanged. The association between plaque development and diffuse iron accumulation in gray matter needs further investigation but might offer a new way to assess plaque load and development indirectly. In line with this finding, a study revealed elevated iron levels in the cortex in a presenilin amyloid precursor protein mouse model at an early stage of 24 weeks.49 In contrast to histochemical studies, this study did not find elevated iron levels in neuritic plaques using X-ray fluorescence microscopy. In this context, only neuritic plaques with iron attached seem to be detectable by MRI in a mutant APP mouse model, whereas ironnegative plaques remain invisible for T2*-weighed MRI, which highlights the pathologic role of iron in at least a certain percentage of neuritic plaques.50

9.6 Iron Mapping in Patients with Parkinson’s Disease The concentration of iron in the substantia nigra (SN) is among the highest in all anatomical structures.3 This feature can be used to precisely locate the SN and adjacent regions for deep brain stimulation.51 Postmortem studies in patients with PD and related disorders, such as progressive supranuclear palsy (PSP) and multiple system atrophy (MSA), consistently demonstrated elevated levels of iron in the SN.52,53,54 Although these studies additionally revealed higher iron levels in the basal ganglia of patients with PSP and MSA, the iron concentration was conversely found to be lower in PD. In contrast, other studies suggested higher iron concentrations also in the basal ganglia of PD patients. Histologic investigation revealed higher iron levels in the putamen, and levels were less pronounced in the SN and the caudate nucleus55; related postmortem work found increased iron levels in the lateral segment of the globus pallidus.56

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Fig. 9.6 In Parkinson’s disease, the substantia nigra is the structure with the highest disease-related iron accumulation. Although this is not obvious in conventional magnetic resonance imaging, susceptibilityweighted techniques allow a better depiction of iron-accumulating structures. (a) Fluid-attenuated inversion recovery (FLAIR) image. (b) Corresponding R2* map.

In vivo MRI studies found higher R2* levels in the SN57,58,59,60 but also in the basal ganglia.61 ▶ Fig. 9.6 shows an example of the appearance of the SN on a R2* map from a patient with PD. An increase in the SN was also found using SWI62 or phase mapping.63 Another study found higher R2* rates in the lateral SN pars compacta but additionally found a correlation between the lateralized motor score from the clinically more affected side and the contralateral R2* rate in the SN.64 Additionally, increased iron levels were shown in the SN, even in patients with early untreated PD.65 Patients who developed PD along with an existing dementia showed more iron in the SN than patients with AD alone, a finding that might be relevant for differential diagnosis.66 Furthermore, MSA and PD were differentiated by using phase values from the inner region of the putamen and the pulvinar thalamus.67 Higher R2’ rates were found in the basal ganglia in patients with PSP compared with those with PD, and stepwise discriminant analysis allowed patients with PSP to be distinguished from patients with PD and healthy controls.68 In a recent longitudinal study, patients with PD showed an increase in R2* in the SN and the putamen during a 3-year period,69 whereas these regions did not shown any change in controls. Additionally, the variation in R2* was correlated with worsening of motor symptoms of PD. These results suggest longitudinal iron mapping as a tool for assessing neurodegeneration and monitoring treatments effects in PD.

9.7 Iron Mapping in Patients with Motor Neuron Diseases In amyotrophic lateral sclerosis (ALS), the focus of MRI research is mainly on the pyramidal tract of the central nervous system, starting from the spinal cord, to the corticospinal tract (CST), and extending to the motor cortex. Early work revealed a high prevalence of T2-shortening (R2 rate increase) in the precentral cortex in ALS patients, whereas this observation is rarely made in normal subjects.70 Consequently, studies on iron deposition

Fig. 9.7 Tract-based spatial statistics demonstrates significant reduction in fractional anisotropy (FA) (left column) and significantly increased R2* (right column) in amyotrophic lateral sclerosis patients compared with age- and sex-matched controls (significant voxels at p < 0.05 are shown in red). The regions with decreased FA and those with increased R2* in the mesencephalic part of the corticospinal tract are closely localized. (Used with permission from Langkammer C, Enzinger C, Quasthoff S, et al. Mapping of iron deposition in conjunction with assessment of nerve fiber tract integrity in amyotrophic lateral sclerosis. J Magn Reson Imaging 2010;31(6): 1339–1345.)

in ALS patients focusing on the precentral gyrus confirmed this pattern of cortical hypointensities on T2 and T2*-weighted MRI.71,72 Besides this cortical hallmark, degeneration of the CST is consistently found in ALS patients.73 Patients with ALS showed a trend for increased R2* rates along the mesencephalic CST and in the caudate nucleus compared with controls.74 Complementary diffusion tensor imaging revealed lower fractional anisotropy closely localized in the region of the CST, where R2* was also increased, suggesting iron mapping as a potential biomarker paralleling neurodegeneration (▶ Fig. 9.7). The source of T2 and T2* changes in ALS is yet not clear. An interesting study compared T2 and T2*-weighted MRI and found that the cortical hypointensities in ALS are present more often on T2- than on T2*-weighted images, which argues against elevated iron levels and suggests that other factors are more dominant.75 The observation that the occurrence of hypointensities in the precentral gyrus is related to normal aging also argues for microstructural tissue changes.76 On the other hand, a study that combined in vivo and postmortem MRI with subsequent histology demonstrated that cortical T2* hypointensites are due to abnormally high iron deposition in deeper layers of the motor cortex in ALS.8 Additionally, histologic staining of iron revealed its accumulation in microglial cells. Although the source of the signal variations has not been fully resolved, it seems that the extent and dynamics of the signal change are closely related to the disease state and progression.

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Normal Aging A longitudinal analysis of T2*-weighted images found more pronounced hypointensities in the precentral gyrus at the 6-month follow-up examination after the first evaluation,71 with the extent of the hypointensities correlated with disability as determined by the ALS functional rating scale.

9.8 Conclusion and Outlook Iron, the most prevalent trace metal, accumulates in the human brain in the process of normal aging. However, abnormally increased iron levels in the basal ganglia are consistently found in pathogenesis, sharing a neurodegenerative and probably also an inflammatory component. Nevertheless, current knowledge about pathologically relevant iron deposition still comes mainly from histologic studies, but it is anticipated that MRI can aid in investigation of the role of iron (i.e., to clarify whether iron accumulation is secondary and reflects accumulated neurodegeneration or can also trigger, or at least mediate, the neurodegenerative cascade). Iron has a strong paramagnetic effect in perturbing the magnetic fields, thus rendering it detectable by MRI. Several MRI techniques and novel developments allow not only detection but also quantitative assessment of iron concentrations in the brain in vivo. Owing to the noninvasive nature of MRI, quantitative MRI techniques are especially promising in longitudinal studies for monitoring disease progression and possible treatment effects. New insight may be expected from the application of novel quantitative MR techniques and from moving to ultrahigh-field-strength MRI systems.

References [1] Andrews PA. Disorders of iron metabolism. N Engl J Med 2000; 342: 1293, author reply 1294 [2] Spatz H. Über den Eisennachweis im Gehirn, besonders in Zentren des extrapyramidal-motorischen ysstems. Zentralbl Gesamte Neurol psychiatry 1922; 77: 261–390 [3] Hallgren B, Sourander P. The effect of age on the non-haemin iron in the human brain. J Neurochem 1958; 3: 41–51 [4] Höck A, Demmel U, Schicha H, Kasperek K, Feinendegen LE. Trace element concentration in human brain. Activation analysis of cobalt, iron, rubidium, selenium, zinc, chromium, silver, cesium, antimony and scandium. Brain 1975; 98: 49–64 [5] Langkammer C, Krebs N, Goessler W et al. Quantitative MR imaging of brain iron: a postmortem validation study. Radiology 2010; 257: 455–462 [6] Smith MA, Harris PL, Sayre LM, Perry G. Iron accumulation in Alzheimer’s disease is a source of redox-generated free radicals. Proc Natl Acad Sci U S A 1997; 94: 9866–9868 [7] Griffiths PD, Dobson BR, Jones GR, Clarke DT. Iron in the basal ganglia in Parkinson’s disease. An in vitro study using extended X-ray absorption fine structure and cryo-electron microscopy. Brain 1999; 122: 667–673 [8] Kwan JY, Jeong SY, Van Gelderen P et al. Iron accumulation in deep cortical layers accounts for MRI signal abnormalities in ALS: correlating 7 tesla MRI and pathology. PLoS ONE 2012; 7: e35241 [9] Ropele S, de Graaf W, Khalil M et al. MRI assessment of iron deposition in multiple sclerosis. J Magn Reson Imaging 2011; 34: 13–21 [10] Bartzokis G, Cummings J, Perlman S, Hance DB, Mintz J. Increased basal ganglia iron levels in Huntington disease. Arch Neurol 1999; 56: 569–574 [11] Zecca L, Youdim MB, Riederer P, Connor JR, Crichton RR. Iron, brain ageing and neurodegenerative disorders. Nat Rev Neurosci 2004; 5: 863–873 [12] Grundke-Iqbal I, Fleming J, Tung YC, Lassmann H, Iqbal K, Joshi JG. Ferritin is a component of the neuritic (senile) plaque in Alzheimer dementia. Acta Neuropathol 1990; 81: 105–110 [13] Gerlach M, Ben-Shachar D, Riederer P, Youdim MB. Altered brain metabolism of iron as a cause of neurodegenerative diseases? J Neurochem 1994; 63: 793–807

[14] Drayer B, Burger P, Darwin R, Riederer S, Herfkens R, Johnson GA. MRI of brain iron. AJR Am J Roentgenol 1986; 147: 103–110 [15] Ogg RJ, Steen RG. Age-related changes in brain T1 are correlated with iron concentration. Magn Reson Med 1998; 40: 749–753 [16] Haacke EM, Cheng NY, House MJ et al. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging 2005; 23: 1–25 [17] Aquino D, Bizzi A, Grisoli M et al. Age-related iron deposition in the basal ganglia: quantitative analysis in healthy subjects. Radiology 2009; 252: 165– 172 [18] Chamberlain R, Reyes D, Curran GL et al. Comparison of amyloid plaque contrast generated by T2-weighted, T2*-weighted, and susceptibility-weighted imaging methods in transgenic mouse models of Alzheimer’s disease. Magn Reson Med 2009; 61: 1158–1164 [19] Bartzokis G, Aravagiri M, Oldendorf WH, Mintz J, Marder SR. Field dependent transverse relaxation rate increase may be a specific measure of tissue iron stores. Magn Reson Med 1993; 29: 459–464 [20] Rauscher A, Barth M, Reichenbach JR, Stollberger R, Moser E. Automated unwrapping of MR phase images applied to BOLD MR-venography at 3 Tesla. J Magn Reson Imaging 2003; 18: 175–180 [21] Ogg RJ, Langston JW, Haacke EM, Steen RG, Taylor JS. The correlation between phase shifts in gradient-echo MR images and regional brain iron concentration. Magn Reson Imaging 1999; 17: 1141–1148 [22] Reichenbach JR, Haacke EM. High-resolution BOLD venographic imaging: a window into brain function. NMR Biomed 2001; 14: 453–467 [23] Walsh AJ, Wilman AH. Susceptibility phase imaging with comparison to R2 mapping of iron-rich deep grey matter. Neuroimage 2011; 57: 452–461 [24] Haacke EM, Xu Y, Cheng YC, Reichenbach JR. Susceptibility weighted imaging (SWI). Magn Reson Med 2004; 52: 612–618 [25] Schweser F, Deistung A, Lehr BW, Reichenbach JR. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain iron metabolism? Neuroimage 2011; 54: 2789–2807 [26] Liu J, Liu T, de Rochefort L et al. Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map. Neuroimage 2012; 59: 2560– 2568 [27] Langkammer C, Schweser F, Krebs N et al. Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. Neuroimage 2012; 62: 1593–1599 [28] Schweser F, Deistung A, Sommer K, Reichenbach JR. Toward online reconstruction of quantitative susceptibility maps: superfast dipole inversion. Magn Reson Med 2013; 69: 1582–1594 [29] Smith SA, Bulte JW, van Zijl PC. Direct saturation MRI: theory and application to imaging brain iron. Magn Reson Med 2009; 62: 384–393 [30] Jensen JH, Chandra R, Ramani A et al. Magnetic field correlation imaging. Magn Reson Med 2006; 55: 1350–1361 [31] Lee J, Shmueli K, Kang B-T et al. The contribution of myelin to magnetic susceptibility-weighted contrasts in high-field MRI of the brain. Neuroimage 2012; 59: 3967–3975 [32] Langkammer C, Krebs N, Goessler W et al. Susceptibility induced gray-white matter MRI contrast in the human brain. Neuroimage 2012; 59: 1413–1419 [33] Denk C, Hernandez Torres E, MacKay A, Rauscher A. The influence of white matter fibre orientation on MR signal phase and decay. NMR Biomed 2011; 24: 246–252 [34] Li X, Vikram DS, Lim IAL, Jones CK, Farrell JA, van Zijl PC. Mapping magnetic susceptibility anisotropies of white matter in vivo in the human brain at 7 T. Neuroimage 2012; 62: 314–330 [35] Parsey RV, Krishnan KR. Quantitative analysis of T2 signal intensities in Alzheimer’s disease. Psychiatry Res 1998; 82: 181–185 [36] Schenck JF, Zimmerman EA. High-field magnetic resonance imaging of brain iron: birth of a biomarker? NMR Biomed 2004; 17: 433–445 [37] Antharam V, Collingwood JF, Bullivant J-P et al. High field magnetic resonance microscopy of the human hippocampus in Alzheimer’s disease: quantitative imaging and correlation with iron. Neuroimage 2012; 59: 1249–1260 [38] House MJ, St Pierre TG, Foster JK, Martins RN, Clarnette R. Quantitative MR imaging R2 relaxometry in elderly participants reporting memory loss. AJNR Am J Neuroradiol 2006; 27: 430–439 [39] Moon W-J, Kim H-J, Roh HG, Choi JW, Han S-H. Fluid-attenuated inversion recovery hypointensity of the pulvinar nucleus of patients with Alzheimer’s disease: its possible association with iron accumulation as evidenced by the T2 map. Korean J Radiol 2012; 13: 674–683 [40] Zhu WZ, Zhong WD, Wang W et al. Quantitative MR phase-corrected imaging to investigate increased brain iron deposition of patients with Alzheimer’s disease. Radiology 2009; 253: 497–504

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Iron Accumulation and Iron Imaging in the Human Brain [41] Ding B, Chen K-M, Ling H-W et al. Correlation of iron in the hippocampus with MMSE in patients with Alzheimer’s disease. J Magn Reson Imaging 2009; 29: 793–798 [42] Bartzokis G, Sultzer D, Mintz J et al. In vivo evaluation of brain iron in Alzheimer’s disease and normal subjects using MRI. Biol Psychiatry 1994; 35: 480–487 [43] Bartzokis G, Sultzer D, Cummings J et al. In vivo evaluation of brain iron in Alzheimer’s disease using magnetic resonance imaging. Arch Gen Psychiatry 2000; 57: 47–53 [44] Connor JR, Menzies SL, St Martin SM, Mufson EJ. A histochemical study of iron, transferrin, and ferritin in Alzheimer’s diseased brains. J Neurosci Res 1992; 31: 75–83 [45] Benveniste H, Einstein G, Kim KR, Hulette C, Johnson GA. Detection of neuritic plaques in Alzheimer’s disease by magnetic resonance microscopy. Proc Natl Acad Sci U S A 1999; 96: 14079–14084 [46] Jack CR, Jr, Garwood M, Wengenack TM et al. In vivo visualization of Alzheimer’s amyloid plaques by magnetic resonance imaging in transgenic mice without a contrast agent. Magn Reson Med 2004; 52: 1263–1271 [47] Lee S-P, Falangola MF, Nixon RA, Duff K, Helpern JA. Visualization of betaamyloid plaques in a transgenic mouse model of Alzheimer’s disease using MR microscopy without contrast reagents. Magn Reson Med 2004; 52: 538– 544 [48] Braakman N, Matysik J, van Duinen SG et al. Longitudinal assessment of Alzheimer’s beta-amyloid plaque development in transgenic mice monitored by in vivo magnetic resonance microimaging. J Magn Reson Imaging 2006; 24: 530–536 [49] Leskovjan AC, Kretlow A, Lanzirotti A, Barrea R, Vogt S, Miller LM. Increased brain iron coincides with early plaque formation in a mouse model of Alzheimer’s disease. Neuroimage 2011; 55: 32–38 [50] Vanhoutte G, Dewachter I, Borghgraef P, Van Leuven F, Van der Linden A. Noninvasive in vivo MRI detection of neuritic plaques associated with iron in APP [V717I] transgenic mice, a model for Alzheimer’s disease. Magn Reson Med 2005; 53: 607–613 [51] Deistung A, Schäfer A, Schweser F, Biedermann U, Turner R, Reichenbach JR. Toward in vivo histology: a comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2*-imaging at ultra-high magnetic field strength. Neuroimage 2013; 65: 299–314 [52] Dexter DT, Wells FR, Lees AJ et al. Increased nigral iron content and alterations in other metal ions occurring in brain in Parkinson’s disease. J Neurochem 1989; 52: 1830–1836 [53] Dexter DT, Carayon A, Javoy-Agid F et al. Alterations in the levels of iron, ferritin and other trace metals in Parkinson’s disease and other neurodegenerative diseases affecting the basal ganglia. Brain 1991; 114: 1953–1975 [54] Berg D, Hochstrasser H. Iron metabolism in Parkinsonian syndromes. Mov Disord 2006; 21: 1299–1310 [55] Drayer BP, Olanow W, Burger P, Johnson GA, Herfkens R, Riederer S. Parkinson plus syndrome: diagnosis using high field MR imaging of brain iron. Radiology 1986; 159: 493–498 [56] Griffiths PD, Crossman AR. Distribution of iron in the basal ganglia and neocortex in postmortem tissue in Parkinson’s disease and Alzheimer’s disease. Dementia 1993; 4: 61–65 [57] Péran P, Cherubini A, Assogna F et al. Magnetic resonance imaging markers of Parkinson’s disease nigrostriatal signature. Brain 2010; 133: 3423–3433 [58] Gorell JM, Ordidge RJ, Brown GG, Deniau JC, Buderer NM, Helpern JA. Increased iron-related MRI contrast in the substantia nigra in Parkinson’s disease. Neurology 1995; 45: 1138–1143

[59] Baudrexel S, Nürnberger L, Rüb U et al. Quantitative mapping of T1 and T2* discloses nigral and brainstem pathology in early Parkinson’s disease. Neuroimage 2010; 51: 512–520 [60] Du G, Lewis MM, Styner M et al. Combined R2* and diffusion tensor imaging changes in the substantia nigra in Parkinson’s disease. Mov Disord 2011; 26: 1627–1632 [61] Ye FQ, Allen PS, Martin WR. Basal ganglia iron content in Parkinson’s disease measured with magnetic resonance. Mov Disord 1996; 11: 243–249 [62] Zhang J, Zhang Y, Wang J et al. Characterizing iron deposition in Parkinson’s disease using susceptibility-weighted imaging: an in vivo MR study. Brain Res 2010; 1330: 124–130 [63] Jin L, Wang J, Zhao L et al. Decreased serum ceruloplasmin levels characteristically aggravate nigral iron deposition in Parkinson’s disease. Brain 2011; 134: 50–58 [64] Martin WRW, Wieler M, Gee M. Midbrain iron content in early Parkinson’s disease: a potential biomarker of disease status. Neurology 2008; 70: 1411– 1417 [65] Martin WRW. Quantitative estimation of regional brain iron with magnetic resonance imaging. Parkinsonism Relat Disord 2009; 15 Suppl 3: S215–S218 [66] Brar S, Henderson D, Schenck J, Zimmerman EA. Iron accumulation in the substantia nigra of patients with Alzheimer’s disease and parkinsonism. Arch Neurol 2009; 66: 371–374 [67] Wang Y, Butros SR, Shuai X et al. Different iron-deposition patterns of multiple system atrophy with predominant parkinsonism and idiopathetic Parkinson’s diseases demonstrated by phase-corrected susceptibilityweighted imaging. AJNR Am J Neuroradiol 2012; 33: 266–273 [68] Boelmans K, Holst B, Hackius M et al. Brain iron deposition fingerprints in Parkinson’s disease and progressive supranuclear palsy. Mov Disord 2012; 27: 421–427 [69] Ulla M, Bonny JM, Ouchchane L, Rieu I, Claise B, Durif F. Is R2* a new MRI biomarker for the progression of Parkinson’s disease? A longitudinal follow-up. PLoS ONE 2013; 8: e57904 [70] Oba H, Araki T, Ohtomo K et al. Amyotrophic lateral sclerosis: T2 shortening in motor cortex at MR imaging. Radiology 1993; 189: 843–846 [71] Ignjatović A, Stević Z, Lavrnić S, Daković M, Bačić G. Brain iron MRI: a biomarker for amyotrophic lateral sclerosis. J Magn Reson Imaging 2013; 38: 1472–1479 [72] Imon Y, Yamaguchi S, Yamamura Y et al. Low intensity areas observed on T2weighted magnetic resonance imaging of the cerebral cortex in various neurological diseases. J Neurol Sci 1995; 134 Suppl: 27–32 [73] Ellis CM, Suckling J, Amaro E, Jr et al. Volumetric analysis reveals corticospinal tract degeneration and extramotor involvement in ALS. Neurology 2001; 57: 1571–1578 [74] Langkammer C, Enzinger C, Quasthoff S et al. Mapping of iron deposition in conjunction with assessment of nerve fiber tract integrity in amyotrophic lateral sclerosis. J Magn Reson Imaging 2010; 31: 1339–1345 [75] Hecht MJ, Fellner C, Schmid A, Neundörfer B, Fellner FA. Cortical T2 signal shortening in amyotrophic lateral sclerosis is not due to iron deposits. Neuroradiology 2005; 47: 805–808 [76] Ngai S, Tang YM, Du L, Stuckey S. Hyperintensity of the precentral gyral subcortical white matter and hypointensity of the precentral gyrus on fluidattenuated inversion recovery: variation with age and implications for the diagnosis of amyotrophic lateral sclerosis. AJNR Am J Neuroradiol 2007; 28: 250–254

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Part IV Alzheimer’s Disease

10 Mild Cognitive Impairment

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11 Overview of Alzheimer’s Disease

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12 Genetics, Neuropathology, and Biomarkers in Alzheimer’s Disease

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13 Imaging of Alzheimer’s Disease: Part 1

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14 Imaging of Alzheimer’s Disease: Part 2

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15 Magnetic Resonance Imaging and Histopathological Correlation in Alzheimer’s Disease

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Alzheimer’s Disease

10 Mild Cognitive Impairment Kei Yamada and Koji Sakai

10.1 Outline of Mild Cognitive Impairment Mild cognitive impairment (MCI), by definition, is a state of cognitive decline in which cognitive deficits are noted but not significant enough to meet the diagnostic criteria for dementia; MCI is recognized as an intermediate state between normal cognition and dementia (▶ Fig. 10.1). The original concept of MCI was proposed by Petersen et al,1 who emphasized mainly memory impairment and the status of MCI as a precursor for Alzheimer’s disease (AD). After several years, MCI was recognized as a concept of heterogeneous clinical presentation, cause, and prevalence2,3,4 and was expanded to be adapted to other cognitive domains, thereby extending the early detection of other dementias in their prodromal stages.5,6,7

10.2 Diagnostic Concept and Its Evolution The conceptual topics of MCI and MCI-related staging systems are listed in ▶ Table 10.1. As an early concept, in 1837, Prichard identified four stages of dementia: (1) impairment of recent memory with intact remote memories, (2) loss of reason, (3) incomprehension, and (4) loss of instinctive actions.8 Later, in 1962, Kral described an entity that distinguished relatively unimpaired and impaired by the terms benign senescent forgetfulness and malignant senescent forgetfulness.9 By the early 1980s, several staging systems for progressive aging and dementia associated with AD were published: Limited Cognitive Disturbance,10 Clinical Dementia Rating (CDR)11 0.5 (“questionable dementia”), and Global Deterioration Scale for

Assessment of Primary Degenerative Dementia (GDS).12 The third stage of GDS was initially termed mild cognitive decline and subsequently was retermed mild cognitive impairment by Reisberg and colleagues.13,14 After the late 1980s, several diagnostic criteria to describe cognitive decline by aging and as a precursor of dementia were proposed: age-associated memory impairment,15, aging-associated cognitive decline,16 mild cognitive disorder,17 and mild neurocognitive disorder.18 Later, in 1992, Zaudig proposed his own concept and definition for MCI.19 More detailed information regarding the history of MCI evolution can be found in the writings of Reisberg and colleagues.20 The current MCI concept, which has been generally accepted and referred to, was proposed by Petersen and colleagues.5,21 This concept divides MCI into four subtypes21: amnestic singledomain MCI, amnestic multidomain MCI, nonamnestic singledomain MCI, and nonamnestic multidomain MCI. Although concerns over ambiguity and difficulty in establishing diagnosis remain in the current diagnostic guidelines provided by the National Institute on Aging—Alzheimer’s Association workgroups,22,23 the term mild cognitive impairment has already been recognized as the expression of a clinical stage between normal cognitive decline and dementia.

10.3 Epidemiology 10.3.1 Mild Cognitive Impairment Findings from epidemiologic studies have yet to provide unified information regarding the clinical aspects of MCI. Although several findings of MCI have been reported, because of differing diagnostic criteria, measuring instruments, definitions of severity, and different samples based on study population

Fig. 10.1 Mild cognitive impairment as an intermediate stage in the longitudinal course of Alzheimer’s disease. (Reprinted with permission from Smith GE, Bondi MW, Mild Cognitive Impairment and Dementia, Definitions, Diagnosis, and Treatment, New York: Oxford University Press; 2013:6. Original: Petersen, 2004.)

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Mild Cognitive Impairment Table 10.1 Topics in mild cognitive impairment (MCI): concepts and criteria.



Year

Study

Topic



1837

Prichard8

Four stages of cognitive impairment

1962

Kral9

Benign/malignant senescent forgetfulness

1982

Gurland et al10

Limited cognitive disturbance

1982

Hughes et al11

Clinical Dementia Rating (CDR)

1982

Reisberg et al12

Global Deterioration Scale for Assessment of Primary Degenerative Dementia (GDS), (GDS 3 = mild cognitive impairment)

1986

Crook et al15

Age-associated Memory impairment (AAMI)

1992

Zaudig1,9

Zaudig’s MCI based on Diagnostic and Statistical Manual IIIR/ International Classification of Disease-10

1994

American Psychiatric Association174

Mild neurocognitive disorder (MND)

1995

Levy16

Aging-associated cognitive decline (AACD)

1995

Christensen et al17

Mild cognitive disorder (MCD)

1995

Petersen174

Petersen’s MCI (CDR = 0.5)

1995

Ebly et al173

Cognitive impairment no dementia (CIND)

2004

Petersen7

Four MCI categories

2011

Albert et al21

New criteria for MCI and biomarkers

and clinical reports, we have not established complete epidemiologic findings for MCI. Currently, several nationwide epidemiologic studies associated with the Alzheimer’s Disease Neuroimaging Initiative (ADNI)24 have been ongoing in countries around the world. However, as of today, their findings, particularly regarding the clinical aspects of MCI, have not been summarized to be shared with physicians worldwide. In the following sections of this chapter, findings from epidemiologic studies are summarized. In general, epidemiology serves a triple role in public health: descriptive, analytical, and interventional. The relationships between these roles and MCI are as follows25:



Descriptive epidemiology: The monitoring of MCI prevalence and incidence across time Analytical epidemiology: The determination of risk factors and their patterns of interaction, permitting the construction of hypothetical etiologic models of the disease process Interventional epidemiology: The designation of potential intervention points for the reduction of morbidity and mortality, which may guide more targeted clinical research.

10.3.2 Descriptive Epidemiology How widespread are both explicit and implicit MCI in the general population? The reported incidence rates for MCI vary in the literature. A variety of population-based cohort studies have reported incidence rates within their elderly populations (i.e., 65 to 75 years) to be between 14 and 111 per 1,000 patientyears26,27,28,29,30,31,32; amnestic MCI appears to occur more commonly than nonamnestic MCI.31

10.3.3 Analytical Epidemiology Various studies had reported that gender, race, and lower education are inconsistently associated with various MCIs.31,33,34,55, 36,37 In a community-based study (participants between 70 and 89 years),31,33 MCI was more common in men (odds ratio [OR] = 1.5). In addition, elevated blood pressure, diabetes with or without symptomatic cerebrovascular disease, obesity,34,35,36, 37,38,39,40,41 cardiac disease,42 and apolipoprotein E epsilon 4 genotype43,44 were all found to be associated with higher risk of MCI or certain subtypes of MCI. Compared with normal subjects, MCI groups were generally seen to manifest left medial temporal lobe atrophy and smaller medical temporal lobe volumes.45,46 Artero and colleagues have suggested that white matter lesions, particularly in periventricular areas, are associated with MCI.47 Tervo and colleagues48 examined a range of demographic, vascular, and genetic factors and found the most significant risk factors to be age (OR 1.08), apolipoprotein E4 (Apo-E4) allele (OR 2.04), and medicated hypertension (OR 1.86). ▶ Fig. 10.2 shows the theoretical pathways to MCI,25 which incorporate most of the known risk factors for dementia. There are, however, insufficient population data at present to permit either a statistical calculation of transition probabilities in relation to individual risk factors or a maximum likelihood

Fig. 10.2 Hypothetical etiologic model of mild cognitive impairment (black) and possible treatments (blue). (Reprinted with permission from Fig. 2 in Ritchie K. Mild cognitive impairment: an epidemiological perspective, Dialogues Clin Neurosci 2004;6(4): 401–408. © Les Laboratoires Servier)

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Alzheimer’s Disease calculation to assess the overall predictive value of possible competing hypothetical general models of MCI.

10.3.4 Interventional Epidemiology Currently, there is no clearly specified treatment for MCI. Nevertheless, it may be possible to reduce overall risk by many kinds of simple risk factor management,49 for instance, management of cardiovascular and cerebrovascular risk factors, such as high blood pressure, from early adult life onward to reduce the risk of infarcts and white matter lesion accumulation; depression control; and the provision of adequate learning opportunities from a younger age.

10.4 Clinical Features 10.4.1 Symptoms Patients with MCI, especially the amnestic subtypes, are known for their memory complaints; this represents a change over baseline. Subjective memory complaints have been demonstrated to predict cognitive decline, even when patients appear to be unimpaired on testing.50,51 Mood and behavioral symptoms are more common in patients with MCI than in normal subjects with intact cognition.52,53,54,55 A population-based study found that apathy, agitation, anxiety, irritability, depression, and delusions were significantly more common in patients with MCI compared with those with normal cognition.54 The correlation between depression and cognitive impairment is complicated. Cognitive impairment may be an initial symptom of depression, so called pseudodementia. A number of population-based studies have found an association between various measures of depression and the presence of MCI.52,56,57 However, follow-up studies have yielded mixed results.52,56,61 Overall, depression is more likely to be an early manifestation of cognitive decline rather than an independent risk factor for MCI, although some studies have found disparate results.62,63

10.4.2 Subtypes The four subtypes of MCI are based on the presence of memory impairment and the number of cognitively impaired domains: (1) amnestic MCI, single domain; (2) amnestic MCI, multidomain; (3) nonamnestic MCI, single domain; and (4) nonamnestic MCI multidomain.21 Within MCI are several types of progression to degenerative dementia other than AD, including vascular dementia and dementia with mental and physical causes; also, some MCI patients retain their MCI state for several years and then return to healthy condition.7 Amnestic MCI is often thought of as a precursor to AD.46 Autopsy studies of brains from MCI patients64,65 have not revealed any consistent findings regarding the neuropathological and clinical features of MCI. Therefore, it is important that MCI be recognized as “a group of patients without dementia that exhibits cognitive decline at an abnormal age, but has no difficulty during daily life.”

10.4.3 Underlying Diseases Diseases that affect cognitive functions, such as intracranial disease, mental disorder, systematic internal disease, and medical poisoning, may all be possible underlying diseases of MCI; known disorders or conditions that can be fitted into such a category include AD, limbic neurofibrillary tangle dementia (LNTD), dementia with Lewy bodies (DLB), frontotemporal lobar degeneration (FTLD), depression, and others (▶ Table 10.2).

10.4.4 Differential Diagnosis After excluding physiologic factors (organic factors), other possible diagnoses should be considered, including depression, as well as other psychosocial factors (e.g., forfeiture of social role, loss of spouse or family member, illness). Aged patients suffering from depression may display decreased cognitive performance and lowered physical activity as a result of decreased attention and slower psychomotor activity, thus appearing to have dementia (hence the term pseudodementia).66 These

Table 10.2 Underlying diseases of mild cognitive impairment Dementia

Underlying disease and conditions

Dementia with degenerative disease

Frontotemporal lobar degeneration (frontotemporal dementia, semantic dementia), dementia with Lewy body, limbic system neurofibril degenerative dementia, progressive supranuclear palsy, corticobasal degeneration

Dementia with cerebrovascular disease

Cerebral infarction, bleeding cerebrally, multiple infarction dementia, Binswanger’s disease

Dementia with endocrine disease

Hypothyroidism, hypoparathyroidism, reiterate hypoglycemic attack

Dementia with trophopathy and metabolic disorder

Wernicke encephalopathy, vitamin B12 deficiency, chronic metabolic disorder (liver failure, kidney failure), hyponatremia

Dementia with hypoxic encephalopathy

Heart/lung disease, carbon monoxide poisoning

Dementia with tumor

Brain tumor (primary, metastasis), meningitis carcinomatosa, remote effect of cancer

Dementia with infectious disease

Meningitis, encephalitis, brain tumor, neurosyphilis, progressive multifocal leukoencephalopathy, AIDS

Dementia with abnormal metal metabolism

Aluminum (dialysis encephalopathy), Copper (Wilson disease)

Dementia with medical poisoning

Antineoplastic drug, antipsychotic drug, sleeping drug, anticholinergic drug, L-DOPA, cimetidine, β-blocking agents, digitalis and preparations, steroid hormone, antituberculosis drug, hypoglycemic drug, alcohol, etc.

Others

Normal pressure hydrocephalus, chronic subdural hematoma, cerebral contusion, melancholy epilepsy (hippocampal sclerosis), etc.

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Mild Cognitive Impairment symptoms should be viewed separately because they may be relieved by antidepressants. On the other hand, if patients with MCI begin to develop depression, their risk of progressing to AD will be 2.6 times greater than those without depression.67 For those cases, long-term observation is important. Apart from depression, other mental disorders, such as delirium, epilepsy, and chemical-induced forgetfulness (e.g., benzodiazepinederived medicine), should also be differentiated from MCI.

10.5 Neuropathology Neuropathological studies suggest that MCI represents an early clinical expression of age-related neurodegenerative disease. Several autopsy studies have found that MCI patients have AD pathology that is intermediate in severity between normal and more advanced AD.44,64,68,69,70,71,72,73,74 Some studies also found that pathologies consistent with other dementing processes (DLB, cerebrovascular disease) are overrepresented in MCI patients.44,46,65,75,76 Therefore, having a broad understanding of the knowledge and up-to-date information on AD pathology, as well as other dementing processes, is crucial to a further understanding of MCI. A large autopsy study conducted by Schneider et al76 found that, of 134 subjects who died having a final antemortem diagnosis of MCI, slightly more than half met the pathological criteria for AD. The subjects who met the pathologic criteria for “definite” AD were roughly equally divided between amnestic and nonamnestic MCI subtypes, along with another 20% with mixed pathologies (▶ Table 10.3). Statistics indicate that MCI is a pathologically heterogeneous disorder; whether MCI was diagnosed in its amnestic or nonamnestic form, many subjects exhibit mixed pathologies. These neuropathological findings suggest that MCI is more than a state of uncertainty for clini-

Table 10.3 Number and percentage of amnestic or nonamnestic MCI patients with no pathology, one type of pathology, or mixed pathology at autopsy Presence of pathology

Amnestic MCI (n = 75)

Nonamnestic MCI (n = 59)

One pathology

41 (54.7%)

32 (54.2%)

AD diagnosis

27 (36.0%)

20 (33.9%)

NIA: high

6 (8%)

4 (6.8%)

NIA: intermediate

21 (28.0%)

16 (27.1%)

Infarcts

10 (13.3%)

11 (18.6%)

Lewy bodies

4 (5.3%)

1 (1.7%)

Mixed pathology

17 (22.7%)

9 (15.3%)

AD + infarcts

15 (20.0%)

8 (13.6%)

AD + Lewy bodies

2 (2.7%)

1 (1.7%)

AD + infarcts + Lewy bodies

0

0

Infarcts + Lewy bodies

0

0

No AD, infarcts, or Lewy bodies

17 (22.7%)

18 (30.5%)

Abbreviations: AD, Alzheimer’s disease; MCI, mild cognitive impairment; NIA, National Institute on Aging. Source: Table 3 in Schneider J, Arvanitakis Z, Leurgans S, et al. The neuropathology of probable Alzheimer’s disease and mild cognitive impairment. Ann Neurol 2009;66:200–208.

cians: the clinical syndrome of MCI may also reflect a transitional neuropathological process.

10.5.1 Risk Factors The maximum risk factors for AD are recognized as aging and Apo-E4. Some studies report that most MCI patients were Apo E4 positive.77 The risk factors for angiopathy were also significant in MCI patients.78 Cholesterolemia might be associated with MCI and AD, and the latter has received much attention lately.79 Steenland et al reported that late-life depression was also strong risk factor for normal subjects progressing to MCI.80

10.5.2 Biomarkers The annual incidence rate of AD among MCI patients is quite high, roughly estimated at around 12%,81 and a certain number of autopsies of MCI patients have shown pathological features similar to those of AD.81 Therefore, it is of pivotal importance to diagnostically predict whether or not MCI patients will proceed to AD; as a matter of fact, biomarkers aimed to distinguish possible AD patients among MCI patients are still being explored continuously to this day. In 1998, the Ronald and Nancy Reagan Research Institute of the Alzheimer’s Association and National Institute on Aging Working Group proposed a guideline for biomarkers of AD82 as follows: (1) biomarkers are able to detect a fundamental feature of Alzheimer's neuropathology; (2) biomarkers should be validated in neuropathologically confirmed AD cases; (3) biomarkers should have preciseness (i.e., able to detect AD early in its course and distinguish it from other dementias); (4) biomarkers should be reliable; (5) biomarkers should be noninvasive; (6) biomarkers should be simple to perform; and (7) biomarkers should be inexpensive. At the moment, no biomarker that has met all these criteria is clinically available. As diagnostic biomarkers for AD, Aβ42, Aβ40 in cerebrospinal fluid (CSF), and total tau and phosphate tau have all been recognized with clinical evidence,83 and it is expected that these biomarkers will soon be applied to MCI.

10.5.3 Current Diagnostic Guidelines The current diagnostic guidelines for MCI, proposed by the National Institute on Aging—Alzheimer’s Association task force22 are as follows: ● Concern regarding a change in cognition reported by patient or informant or observed by clinician ● Objective evidence of impairment in one or more cognitive domain, typically including memory ● Preservation of independence in functional abilities ● Not demented However, the pathology of AD is a continuum of brain change, which began a long time before MCI symptoms appear; therefore, the clear criteria that divide MCI and AD are unnatural. Nevertheless, effective treatment in the early stage of MCI requires effective criteria to divide MCI from AD. Because the current criteria for MCI are still ambiguous in the categorical distinction between MCI and dementia, overlap between MCI and mild AD can cause confusion among clinicians.23

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Fig. 10.3 An example of a diagnostic algorithm for mild cognitive impairment. AD, Alzheimer’s disease; CT, computed tomography; MRI, magnetic resonance imaging; PET, positron emission tomography; SPECT, single-photon emission computed tomography. (Reprinted with permission from Mizukami K, How do we deal with mild cognitive impairment [in Japanese]. Seishin Shinkeigaku Zasshi. 2009;111(1):26-30.)

10.5.4 Diagnostic Algorithm An example of an MCI diagnostic algorithm is as follows (▶ Fig. 10.3)84: ● Report (background, medical or surgical history, family history, medication) by the patient or a knowledgeable informant or observation by the clinician (refer to rating measures such as the CDR11 and Functional Assessment Staging of Alzheimer’s Disease [FAST]85 ● Neuropsychiatric test (refer for tests for dementia) ● Physical and neurologic checkup ● Blood and urine tests ● Neuroimaging (computed tomography [CT], magnetic resonance imaging [MRI], positron emission tomography [PET]/ single-photon emission computed tomography [SPECT]) ● Electroencephalography and other tests In addition, the following should be excluded: (1) mental disorders, such as depression, schizophrenia, and delusional disorder; (2) the causes of symptomatic psychosis; and (3) side effects of medicinal treatment. The ADNI items81 are helpful for tangible inspection.

10.6 Neuroimaging 10.6.1 Outline Although the role of neuroimaging in the evaluation of MCI is yet to be clearly defined, the modalities have provided valuable

information about both healthy elderly and AD patients. Therefore, these neuroimaging studies are anticipated to provide valuable information for clarifying MCI pathology. The main neuroimaging modalities are SPECT, PET, and MRI. The main role of neuroimaging is to undertake risk validations for MCI, as well as to rule out other types of neurodegenerative dementia. The methods of neuroimaging used to validate MCI originated from those used to validate dementia. In the following subsections, validation methods and neuroimaging studies regarding MCI are summarized.

10.6.2 Nuclear Medical Imaging: Single-Photon Emission Computed Tomography and Positron Emission Tomography Both PET and SPECT provide mapping of the accumulation of radiopharmaceutical agents at certain regions of the body. These modalities can validate blood flow and brain metabolism. The spatial resolutions, however, of PET and SPECT (3 to 6 mm) are relatively lower than those of CT and MRI (CT, 0.5 to 1.5 mm). On the other hand, the main function of CT and MRI is to depict the structure of the brain. Therefore, these neuroimaging modalities have been combined to depict the anatomy and function simultaneously. This subsection outlines neuroimaging studies on MCI using both single modality and mixed modalities of nuclear medicine.

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Validation of Perfusion by Single-Photon Emission Computed Tomography In AD pathology, from normal elderly to AD, MCI is thought to represent an intermediate stage of the decline in blood flow. Representative features of SPECT of MCI are the decline of blood flow around the association area of the posterior cerebral cortex (posterior cingulate gyrus, precuneus, parietotemporal lobe), and hippocampus compared with healthy elderly subjects (▶ Fig. 10.4).86 Because of the pathological variability of MCI, the patterns of blood flow are also diverse. In general, in the converter group, which shows relatively short-term progress to AD, the decline of blood flow at the region from posterior cingulate gyrus to the parietotemporal lobe is prominent compared with that seen in nonconverter groups (▶ Fig. 10.5).87,88,89,90 Furthermore, the converter group shows a significant decline of blood flow at the hippocampus.91,92 (▶ Fig. 10.6). These hypoperfusions are noted with neurobiological knowledge of AD, specifically, that the entorhinal cortex is the earliest site to be compromised, even at a preclinical stage.93 A longitudinal follow-up study94 showed perfusion decline in an MCI group of patients in a small region of the middle and posterior cingulate and the frontal, temporal, and parietal regions. In contrast to the MCI group, the AD group showed a decline in perfusion in all cerebral lobes (▶ Fig. 10.7). Evaluation of SPECT scans using quantitative (voxel-based statistical) analysis can potentially differentiate MCI likely to progress to AD from stable MCI with an accuracy of approximately 73% before the appearance of clinical signs of significant cognitive impairment.95 Furthermore, perfusion SPECT can differentiate MCI of the AD type from other types of dementia with a sensitivity of 84% and a specificity of 89%.92,96,97 MCI patients who converted to AD showed hypoperfusion in the parahippocampal and inferior temporal gyri bilaterally.

Basal Metabolism Based on findings and experiences with fluorodeoxyglucose (FDG) PET, which provides a measure glucose metabolism, it

has been suggested that MCI exists on a continuum between normal elderly people and patients with AD. However, it is difficult to detect the hypometabolic patterns on FDG PET images by visual inspection. Therefore, statistically analyzed images have frequently been used. As shown in ▶ Fig. 10.8, bilateral parietal and posterior cingulate metabolism is decreased in patients with MCI compared with healthy elderly subjects.98 In keeping with the distribution of early neurofibrillary pathology of AD, the decline in glucose metabolism involves the limbic and paralimbic cortex, as well as the temporal and parietal association cortex, in MCI. Longitudinal studies99 have indicated that FDG PET may predict the progression of MCI patients toward AD (▶ Fig. 10.9). Regional reductions in glucose metabolism included foci in the bilateral paracampal/hippocampal cortex, right inferior prefrontal cortex, left anterior insular cortex, left middle temporal cortex, bilateral inferior parietal cortex, and posterior cingulate cortex in a group of MCI patients who developed AD (▶ Fig. 10.9a). MCI patients who did not develop AD showed only subtle abnormalities in the bilateral inferior frontal gyrus and bilateral temporal gyrus (▶ Fig. 10.9b). Compared with stable MCI patients, significantly lower metabolism of the bilateral posterior cingulate cortex and right precuneus was found in the converter MCI group (▶ Fig. 10.9c).

Amyloid Imaging A certain amount of cerebral β-amyloid (Aβ) burden has been recognized to be the primary cause of brain deterioration and cognitive decline in AD pathology.100 Therefore, amyloid imaging has rapidly become accepted as one of the central biomarkers in the study of AD progression. Among the amyloid ligands, carbon-11-labeled Pittsburgh Compound B (11C-PIB) is the most commonly studied and used tracer to date, and it appears to bind to brain fibrillar Aβ deposits with high sensitivity.101,102 As shown in ▶ Fig. 10.10a, 11C-PIB PET imaging shows a clear difference in 11C-PIB uptake among nonconverters to AD.103 The MCI patients showed intermediate uptake and retention between AD and nonconverters and a similar topographic

Fig. 10.4 Mild cognitive impairment versus healthy control on blood flow by single-photon emission computed tomography. (Caffarra P, Ghetti C, Concari L, Venneri A, Differential patterns of hypoperfusion in subtypes of mild cognitive impairment. The Open Neuroimaging Journal 2008;2: 20–28.)

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Fig. 10.5 Mild cognitive impairment (MCI) in patients who converted to Alzheimer disease compared with nonconverted MCI. Rendered brain regions indicating hypoperfusion examined by single-photon emission computed tomography. (Reprinted with permission from Park KW, Yoon HJ, Kang DY, Kim BC, Kim SY, Kim JW, Regional cerebral blood flow differences in patients with mild cognitive impairment between those who did and did not develop Alzheimer’s disease. Psychiatry Res Neuroimag 2012;203:201–206.)

Fig. 10.6 Mild cognitive impairment (MCI) in patients who converted to Alzheimer disease compared with nonconverted MCI. Significant decline of blood flow at hippocampus examined by single-photon emission computed tomography. (Reprinted with permission from Habert MO, Horna JF, Sarazin M, et al. Brain perfusion SPECT with an automated quantitative tool can identify prodromal Alzheimer’s disease among patients with mild cognitive impairment. Neurobiol Aging 2011;32:15–23.)

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Fig. 10.7 Regional changes in brain perfusion between baseline and 2-year follow-up in mild cognitive impairment (a) and Alzheimer’s disease (b) groups. (Reprinted with permission from Fig. 2 in Alegret M, Cuberas-Borrós G, VinyesJunqué G, et al. A two-year follow-up of cognitive deficits and brain perfusion in mild cognitive impairment and mild Alzheimer’s disease. J Alzheimer Dis 2012;30(1):109–120. doi:10.3233/JAD-2012-111850.)

Fig. 10.8 Mild cognitive impairment versus healthy control in glucose metabolism by fluorodeoxyglucose (FDG)-positron emission tomography. (Reprinted with permission from Ishi K, PET Approaches for Diagnosis of Dementia, AJNR Am J Neuroradiol 2014;http://dx.doi.org/ 10.3174/ajnr.A3695Au: Please update with volume and page numbers.)

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Fig. 10.9 Regional changes in brain metabolism between baseline and 1-year follow-up in Alzheimer’s disease (a), and healthy volunteers (b), and MCI groups (c). (Reprinted with permission from Fig. 1, Drzezga A, Lautenschlager N, Siebner H, Cerebral metabolic changes accompanying conversion of mild cognitive impairment into Alzheimer’s disease: a PET follow-up study. Eur J Nucl Med Mol Imaging 2003;30(8):1104–1113.)

Fig. 10.10 (a) Differences in 18F-florbetabenfluorodeoxyencephalography (18F-FDG) and carbon-11-labeled Pittsburgh Compound B (11CPIB) in a normal subject (upper row), a subject with mild cognitive impairment (MCI) (middle row) and a subject with dementia due to Alzheimer’s disease (bottom row). (b) Amyloid imaging with 18F-florbetaben in a healthy control, a participant classified as MCI, one subject with Alzheimer’s disease (AD) and one with frontotemporal lobar degeneration (FTLD). (Reprinted with permission from Jimenez Bonilla JF, Carril Carril JM. Molecular neuroimaging in degenerative dementias. Rev Esp Med Nucl Imag Mol 2013;32(5):301–309.) (Reprinted with permission from Villemagne VL, Rowe CG. Amyloid imaging. Int Psychogeriatrics 2011;23(Suppl 2): S41–S49.)

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Mild Cognitive Impairment pattern in the posterior cingulate gyrus, anterior cingulate, and frontal cortex. Although 11C-PIB shows usability for AD diagnosis, the radioactive decay half-life of 11C is relatively short (approximately 20 minutes) and limits its facility. To overcome this limitation, 18F-labeled Aβ imaging tracers such as 18F-florbetaben have also been studied (18F has approximately 110 minutes of radioactive decay half-life (▶ Fig. 10.10b).104 Cortical retention of 18F-florbetaben in the frontal, posterior cingulate/precuneus, and lateral temporal areas was noted, with relative sparing of occipital and sensorimotor cortex in MCI and AD subjects. In contrast, no cortical 18F-florbetaben retention was seen in healthy controls or FTLD subjects. As individuals progress to MCI and dementia, clinical decline and neurodegeneration accelerate and appear to proceed independently of amyloid accumulation.105 This supposition largely concurs with the model of dynamic changes proposed by Jack et al106 wherein AD biomarkers are most informative in the preclinical period (▶ Fig. 10.11). In addition, findings indicate that a substantial proportion of cognitively intact elderly patients also have a significant level of Aβ plaque burden,107 further suggesting that Aβ may be necessary, but not sufficient, for AD progression.108 The lack of specificity of Aβ to predict cognitive decline, as well as its weak association with clinical symptoms and disease severity, has

lessened the enthusiasm for cerebral Aβ (e.g., amyloid imaging) to become a stand-alone biomarker of AD.

10.6.3 Magnetic Resonance Imaging Several MRI techniques and methods have been implemented in the diagnosis and research of MCI, including MR volumetry, structural analysis, H1 (proton) magnetic resonance spectroscopy (MRS) for metabolite evaluation, diffusion-weighted MRI for the evaluation of structure and constitution, and functional MRI (fMRI) for assessing activated brain regions in MCI patients. The following subsections summarize the validation methods based on MRI methods and MRI-based studies on MCI.

MRI Volumetry Magnetic resonance imaging volumetry is defined as the accumulation of voxels or subvoxels within the region of interest (ROI) on MRI. When voxels are used for assessing morphometric change of ROI, this procedure is called voxel-based morphometry (VBM). By using volumetry and VBM, volume losses in MCI patients can be observed, including regions at the hippocampus, entorhinal cortex, and amygdala. Among VBM studies, Chételat et al109 reported that significant volume loss

Fig. 10.11 (a) Dynamic biomarkers of the Alzheimer's pathological cascade. (b) Positron emission tomography amyloid imaging with Pittsburgh compound B in (a) normal without atrophy on magnetic resonance imaging (MRI), (b) normal with atrophy on MRI, and (c) Alzheimer’s disease patient. (Reprinted with permission from Figs. 1 and 2 in Jack C Jr, Knopman D, Jagust W, et al, Hypothetical model of dynamic biomarkers of the Alzheimer pathological cascade, Lancet Neurol 2010;9(1):119— 128.)

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Fig. 10.12 Atrophy on (a) mild cognitive impairment (MCI) compared with healthy control and (b) Alzheimer’s disease compared with MCI. (Reprinted with permission from Karas GB, Scheltens P, Rombouts SA, et al. Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. Neuroimage 2004;23:708–716.)

was observed in amnestic MCI patients compared with healthy elderly subjects; these losses were seen in the hippocampus, posterior cingulate gyrus, and subcallosal area; on the other hand, compared with AD patients, the volume of gray matter at the posterior association region of the cerebellum was spared. Karas et al110 also reported that, compared with healthy elderly subjects, amnestic MCI patients exhibited significant volume loss at regions of the interior temporal lobe, insula, and thalamus; compared with AD patients, volumes of the parietal lobe, as well as the anterior and posterior cingular gyrus, were spared (▶ Fig. 10.12 and ▶ Table 10.4). With these results, it was thought that amnestic MCI patients might exhibit significant brain atrophy at certain regions, although not as widely as in AD patients, especially at the interior temporal lobe region, including the hippocampus.109,110,111 Furthermore, BellMcGinty et al112 revealed that the brain atrophy patterns differ in amnestic MCI and multiple cognitive domain MCI (▶ Fig. 10.13). In the amnestic group, significant volume losses were noted at the left entorhinal cortex and inferior parietal lobe; in multiple cognitive domain MCI subjects, on the other hand, significant volume losses were noted at the right inferior frontal gyrus, right middle temporal gyrus, and bilateral superior temporal gyrus. These results suggest that amnestic MCI, which exhibits memory impairment as the primary feature, may be associated with medial temporal lobe atrophy, and multiple cognitive domain MCI may be associated with extensive lesions within the cerebral cortex. Longitudinal observations also have provided valuable information and knowledge about the development of MCI. Whitwell et al113 observed prospectively 33 amnestic MCI patients

Table 10.4 Gray matter difference in anatomical regions of normal controls versus patients with mild cognitive impairment and Alzheimer’s disease Label

Mean percentage difference NCLR vs. MCI L

MCI vs. AD R

L

R

Lobes Frontal

4.5

3.1

11.1

9.4

Temporal

1.7

0.8

10.9

11.2

Parietal

6.3

7.2

13.1

12.4

Occipital

0.5

-0.2

12.9

11.2 7.6

Medial temporal lobe, basal ganglia, and insula Amygdala

3.3

4.1

10.7

Hippocampus

4.9

5.9

7.9

5.5

Thalamus

13.4

12.4

14.1

14.2

Caudate head

4.6

4.1

10.5

10.6

Insula

4.6

3.2

6.9

8.2

Superior temporal cortex

7.2

6.4

8.5

10.7

Cortical association areas and cingulate Parietal association

2.3

3.0

18.7

16

Retrosplenial cingulate

3.1

3.5

7.3

5.9

Anterior cingulate

–0.2

1.2

9.2

8.1

Abbreviations: AD, Alzheimer’s disease; L, left; R, right; MCI, mild cognitive impairment; NCLR, normal controls. Source: Table 4 in Karas KB, Scheltens P, Rombouts SA, et al. Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. Neuroimage 2004;23:708–716.

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Fig. 10.13 Different atrophy patterns in amnestic mild cognitive impairment and multiple cognitive domain mild cognitive impairment. (Reprinted with permission from Bell-McGinty S, Lopez OL, Meltzer C, et al, Differential cortical atrophy in subgroups of mild cognitive impairment. Arch Neurol 2005;62:1393–1399.)

for several years until they progressed to AD, and then the investigators analyzed the patients’ longitudinal structural MRI data by VBM (▶ Fig. 10.14). At the time point of 3 years before onset of AD, significant—but not severe—volume loss was observed at the left-side–dominant amygdala, head of hippocampi, entorhinal cortex, and fusiform gyrus; by the time point of 1 year before onset of AD, significant volume loss developed at the bilateral whole hippocampi, as well as in regions from

the posterior temporal lobe to the parietal lobe. By the time AD was diagnosed, severe volume losses involving the entire region of the temporal lobe, as well as wide-area volume loss from the temporoparietal lobe to the frontal lobe, were observed. In these studies, compared with AD patients, wide-area atrophy in the cerebral cortex was not observed among MCI patients. However, significant atrophy was noted in the hippocampal region compared with healthy subjects. Furthermore,

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Fig. 10.14 Regional changes in brain atrophy between baseline and 9 to 18 months’ follow-up in patients with amnestic mild cognitive impairment. L, left; R, right. (Reprinted with permission from Whitwell JL, Przybelski SA, Weigand SD, et al. 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer’s disease. Brain 2007;130:1777–1786.)

atrophy at the hippocampal region had already begun at the early stages of MCI, and those with severe atrophy frequently progressed to AD within a short time.

Blood Flow Analysis by Arterial Spin Labeling Neuronal activity is tightly coupled with cerebral blood flow (CBF). Therefore, one of the most reliable approaches to assess

the disease progression of MCI or AD is to measure CBF. MRbased CBF measurements have been developed to investigate hemodynamic alterations in MCI or AD, including arterial spin labeling (ASL)114 and dynamic contrast techniques.115 ASL is a noninvasive MRI technique that allows measurement of CBF without using any contrast agents. Several researchers have reported the usefulness of ASL for revealing CBF abnormalities in patients with MCI.

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Fig. 10.15 Regional perfusion abnormalities between patients with mild cognitive impairment (MCI) or Alzheimer’s disease (AD) patients and healthy elderly controls (HC) using pulsed arterial spin labeling (PASL). L, left; R, right. (Reprinted with permission from Fig. 1 in Alexopoulos P, Sorg C, Förschler A, et al. Perfusion abnormalities in mild cognitive impairment and mild dementia in Alzheimer’s disease measured by pulsed arterial spin labeling MRI. Eur Arch Psychiatry Clin Neurosci. 2012;262(1):69–77.)

Fig. 10.16 Regional perfusion differences between (a) control, (b) mild cognitive impairment patient, and (c) Alzheimer’s disease patient on cerebral blood flow. (Reprinted with permission from Zhang Q, Stafford RB, Wang Z, et al. Microvascular perfusion based on arterial spin labeled perfusion MRI as a measure of vascular risk in Alzheimer’s disease, J Alzheimers Dis 2012;32(3):677–687. doi:10.3233/JAD-2012120964.)

Comparison studies of the regional perfusion abnormalities between MCI or AD patients and healthy elderly controls using pulsed ASL (PASL) have been performed116,117 and showed significant differences between healthy elderly controls and MCI and AD patients (▶ Fig. 10.15, ▶ Fig. 10.16). Lower CBF in

patients with MCI compared with healthy controls was found in the right and left superior parietal gyrus, right and left angular gyrus, left inferior parietal gyrus, left and right middle temporal gyrus, and middle occipital gyrus. Patients with AD showed lower CBF than healthy controls in the right angular gyrus, left

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Alzheimer’s Disease and right superior parietal gyrus, left and right inferior parietal lobe, right middle occipital gyrus, left precuneus, and caudate. They concluded that PASL may be a valuable instrument for investigating perfusion changes in the transition from normal aging to dementia. Furthermore, the cross-validation studies of ASL and PET for assessing CBF on AD pathology have shown good agreement among these two modalities.118,119 Although clinical evidence of ASL on AD pathology has not been established, ASL is expected to become one of the primary measurement techniques for AD pathology because of its noninvasiveness. 1H

Magnetic Resonance Spectroscopy

Proton MR spectroscopy (1H MRS) is an analytical imaging technique that is sensitive to the changes in the chemical environment in the brain at the cellular level. With 1H MRS, major proton-containing metabolites in the brain, including N-acetyl aspartate (NAA), myo-inositol (MI), choline (Cho), and creatine (Cr), are quantitatively measured during a common data acquisition period as the mean values in the certain size of voxel of interest. Therefore, 1H MRS may have an important role in the clinical evaluation and monitoring of dementia in early stages of AD pathology.120 Several investigations have aimed to distinguish the behavior of brain metabolites in AD pathology from that in normal controls. The NAA metabolite is a marker for neuronal integrity, and it decreases in a variety of neurologic disorders, including MCI and AD.121,122 The MI spectrographic peak consists of glial metabolites that are responsible for osmoregulation.123 Elevated MI levels correlate with glial proliferation in inflammatory central nervous system demyelination124 and are higher in the 1H MRS spectra of patients with MCI and AD than in cognitively normal elderly.121 The greatest amount of Cho in the brain is bound in membrane phospholipids that are precursors of Cho and acetylcholine synthesis. It has been postulated that eleva-

tion of the Cho peak is the consequence of membrane phosphatidylcholine catabolism to provide free Cho for the chronically deficient acetylcholine production in AD. The result is decreased NAA, whereas MI and Cho are increased in MCI relative to normal values (▶ Fig. 10.17).121 Furthermore, Kantarci et al125 showed that NAA:MI and hippocampal volume:total intracranial volume ratios showed an independent effect and found that low levels of the neuronal integrity marker NAA and high levels of the glial metabolite MI increased the risk for MCI. Therefore, the joint effects of these two independent parameters can be predictors of MCI in cognitively normal older adults. From these mentioned studies, the observation of brain metabolites in MCI patients by using 1H MRS may provide a new differential diagnosis method based on the biochemical activity of brain. However, the values found using this method can be easily affected by the quantification method used and by the physical and chemical environment of the brain, such as temperature and hydrogen ion content (pH), and no fundamental solutions have been found. Therefore, further investigation and evaluation are required for stable application in MCI and AD pathologies.

Diffusion Tensor Magnetic Resonance Imaging Diffusion tensor imaging (DTI) is an MRI technique that allows for investigation of the microstructural integrity of white matter.126 Based on changes in translational diffusion (mean diffusivity [MD], apparent diffusion coefficient [ADC]), and directional diffusion (fractional anisotropy [FA]), structural changes in the white matter can be assessed. Furthermore, the combination of increased MD and ADC and decreased FA shows damage to white matter. Measures of MD/ADC and FA from DTI can quantify the alterations in water diffusivity resulting from microscopic structural

Fig. 10.17 Brain metabolites in controls (C), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) patients. Cr, creatine; MI, myo-inositol; NAA, N-acetyl aspartate. (Reprinted with permission from Figs. 3 and 5 in Kantarci K, Jack C Jr, Xu Y, et al. Regional metabolic patterns in mild cognitive impairment and Alzheimer’s disease: a 1H MRS study, Neurology 2000;55:210–217.)

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Mild Cognitive Impairment guished from normal elderly subjects using noninvasive imaging techniques.132

Functional Magnetic Resonance Imaging

Fig. 10.18 Mean fractional anisotropy (FA) and mean diffusivity (MD) values for the genu and splenium in normal control (NC) participants and mild cognitive impairment (MCI) patients. ADC, apparent diffusion coefficient. (Reprinted with permission from Fig. 1 in Delano-Wood L, Bondi MW, Jak AJ, et al, Stroke risk modifies regional white matter differences in mild cognitive impairment. Neurobiol Aging 2010;31 (10):1721–1731.)

changes. Several researchers have revealed the difference between normal controls and MCI patients using these water diffusion–derived measures. The changes in the white matter of study participants (normal controls and MCI patients) by FA showed significant regional reductions in participants with MCI.127,128,129,130 Delano-Wood et al found that the FA value of the splenium was significantly lower in MCI patients than in normal controls, despite finding no differences in gross morphometry or hippocampal volumes (▶ Fig. 10.18).128 Furthermore, they found that MCI patients demonstrated considerably diminished white matter integrity in the posterior cingulum (PC) (▶ Fig. 10.19).129 Stebbins et al131 summarized in their review that the brain lobe where MD increased and FA decreased was identified in MCI patients and that the pattern of white matter integrity disruption tends to follow an anterior to posterior gradient, with greater damage noted in posterior regions in AD and MCI patients. As mentioned, DTI-derived measures, such as FA and ADC, have already shown their usefulness, and advances in their application should provide new insights into AD pathologies. In addition, more evolutionary techniques may bring highly accurate early identification methods for MCI patients as distin-

Functional MRI (fMRI) is a noninvasive technique used to investigate the neural underpinnings of higher cognitive functions by measuring regional hemodynamic changes. These hemodynamic changes are thought to be linked to underlying cellular activity.133,134 The blood-oxygen-level-dependent (BOLD) signal changes detected by fMRI are thought to represent integrated synaptic activity by measuring changes in blood flow, blood volume, and the blood oxyhemoglobin:deoxyhemoglobin ratio underlying such synaptic activity.135 The pattern of activated and deactivated brain regions modeled in block or event-related design paradigms allows for the identification of brain regions and networks whose activity is modulated by the experimental task. By creating contrasts between disparate behavioral or cognitive conditions (e.g., fixation versus memory encoding), the dynamic nature of the BOLD signal, coupled with the relatively static hemodynamic response to activity, allows for inference of which regions are selectively activated or inactivated by a task (i.e, task-positive or task-negative brain regions).136 Enriching our understanding of task-positive and tasknegative brain networks are so-called task-free or resting state or functional connectivity MRI (fcMRI) studies, in which statistical correlations in BOLD signal dynamics while the brain is at rest have enabled identification of several large-scale neural networks composed of widely anatomically separated brain regions.137 In the following subsections, task-based and task-free fMRI studies are summarized.

Task-Activated Studies Most studies in task-based fMRI studies have focused on interrogating memory functions, given the early occurrence of known AD pathologic changes in the medial temporal lobes and the reliance on these structures for learning and memory. Nevertheless, it should be noted that the specific abnormality found in a task-based fMRI study of any patient group depends greatly on the task used in the study.138 By using taskbased fMRI, greater medial temporal lobe activation in MCI patients compared with controls has been demonstrated (▶ Fig. 10.20).139 As mentioned, this method, which requires cognitive tasking, allows us to observe the current status of brain functions and its relation to the function of associated regions. Therefore, if the task is successfully conducted, the results bring useful information about brain function and CBF.

Task-Free Studies Resting-state functional MRI (rs-fMRI) is an imaging method that reflects synaptic activity through changes in blood flow and the oxyhemoglobin:deoxyhemoglobin ratio.140 By measuring functional connectivity between spatially distinct brain regions, rs-fMRI can be used to evaluate brain function.141,142 Several networks encompassing brain regions that display functional connectivity during the resting state, so-called resting

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Alzheimer’s Disease

Fig. 10.19 (a,b) Posterior cingulum (PC) fractional anisotropy hemispheric differences in normal controls (NC) and mild cognitive impairment (MCI) group. (Reprinted with permission from Figs. 1 and 2 in Delano-Wood L, Stricker N, Sorg S, et al. Posterior cingulum white matter disruption and its associations with verbal memory and stroke risk in mild cognitive impairment. J Alzheimer Dis 2012;29:589–603.)

Fig. 10.20 A phase of compensatory hyperactivation appears to occur in the medial temporal lobe (MTL) in very mild cognitive impairment (MCI) preceding Alzheimer’s disease (AD) dementia. (Reprinted with permission from Dickerson BC, Salat DH, Greve DN, Chua EF, Rand-Giovannetti E, Rentz DM, et al. (2005). Increased hippocampal activation in mild cognitive impairment compared with normal aging and AD. Neurology 2005;65:404–411.)

state networks.143,144,145 One network, referred to as the default mode network (DMN), consists of the bilateral parietal cortex, precuneus, posterior cingulate cortex, anterior cingulate cortex, medial prefrontal cortex, hippocampus, and thalamus. The network is active during episodic and autobiographical memory retrieval but shows decreased activity during performance of cognitive tasks that demand attention to external stimuli.136,146,147 The use of resting-state analyses to identify alterations in functional connectivity that distinguish normal aging from MCI and AD has gained momentum in recent years, although relatively few studies have thus far been completed

(▶ Fig. 10.21).148 Several groups have reported finding decreased connectivity within the posterior DMN (especially the posterior cingulate cortex) in subjects with amnestic MCI compared with controls (▶ Table 10.5).149,150,151,152 As mentioned, this method, which does not required cognitive tasks, allows us to apply a wide variety of studies for observing default network connectivity in the brain. This possibility was reinforced by the recent National Institute on Aging— Alzheimer’s Association Workgroup definition of preclinical AD by Sperling et al,153 who offered the possibility that fMRI measurement of default network connectivity holds promise as a possible preclinical marker of AD. Further studies may reveal

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Mild Cognitive Impairment

Fig. 10.21 Mean z-values from default-mode network (DMN) z-maps reveals alterations in functional connectivity in continuous MCI (cMCI) and Alzheimer’s disease (AD). HC, healthy controls. (Reprinted with permission from Fig. 4 in Binnewijzend MAA, Schoonheim MM, SanzArigita E, et al. Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment, Neurobiol Aging 2012;33:2018–2028.)

the differences in default network connectivity among normal, MCI, and AD subjects.

10.6.4 Image Analysis Information and knowledge about MCI and AD have accumulated through the use of neuroimaging modalities like PET, SPECT, and MRI. Amid this accumulation of knowledge, image analysis techniques have played many important roles.

Traditionally, visual assessment has been used on a daily clinical basis, but this method cannot produce quantitative evaluations of pathological conditions. Therefore, manually placed ROIs and statistics within the ROI have been applied to quantitative analysis. However, manual ROI placement is greatly labor intense because it requires considerable time for placing the ROIs along the slices. Routine assessment by this manual ROI placement method is not feasible; therefore, several semiautomated and fully automated ROI analysis methods have been devised.154,155,156 In general, medical image analysis techniques can be roughly divided into morphometric analysis (morphometry) and photometric analysis (photometry). Recently, whole-brain image registration techniques, which provide precise anatomical compensation by linear or nonlinear transformation to the standardized template brain image, have become commonly available.154,155,156 Furthermore, automated segmentation of gray matter, white matter, and cerebrospinal fluid from the whole brain is also now possible by using software like Statistical Parametric Mapping (SPM). Furthermore, MRIstudio can provide registration among variable subjects and percolated brain template for automated brain regional analysis in both morphometry and photometry.157,158 By using such software, we can obtain the volume changes and morphologic changes in subjects quantitatively. These procedures are classified as morphometry.159 In addition, software programs that can provide computational analysis for not only general ROI averaging but also automated statistical analysis after transformation to standardized brain template (MRIstudio) are also available. Several research methods have already used such software programs to reveal regionally specific DTI in MCI and AD160 and neuropsychiatric symptoms in MCI and AD.161 These procedures are classified as photometry. Both morphometry and photometry are based on voxel-based analysis (VBA), which is strongly affected by registration inaccuracy, moving artifacts, and imaging artifacts. These errors cause drawbacks in VBA and may lead to difficulty in comparing the results from different image scanners. To compensate for these drawbacks, large ROIs, including a large numbers of voxels, may be one solution to maintaining statistical power.

Table 10.5 Comparison of major findings in studies of resting-state default mode network activity in aMCI groups compared with normal controls Present study

Sorg et al1,54

Posterior cingulate cortex/precuneus

–B

–L

Inferior parietal lobe

+L

Medial temporal lobe (hippocampus, entorhinal cortex, perirhinal cortex, parahippocampal gyrus)

–L

Fusiform gyrus

–L

Lateral perifrontal cortex

–B

Medial prefrontal cortex

+B

Middle cingulate cortex

+B

Medial temporal gyrus

–L

Angular gyrus

–R

Subjects

Putamen

Bai149

Qi et al151,6

–B

–B

+R

–R, + l

+R

–L

*

+L

–R

+B +L +B

Abbreviations: –, decreased activities in aMCI; + , increased activities in aMCI, amnestic mild cognitive impairment; B, bilateral; L, left; R, right Source: Table 4 in Jin M, Pelak VS, Cordes D. Aberrant default mode network in subjects with amnestic mild cognitive impairment using resting-state functional MRI. Magn Reson Imaging 2012;30(1):48–61.

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Alzheimer’s Disease Image analysis methods are under development, and many software programs are available both commercially and freely. Nevertheless, database communization for obtaining strong statistical evidence by image analysis has not been realized, and differences among the different centers and different image scanners remain unresolved.

10.7 Clinical Trials 10.7.1 Medication for Mild Cognitive Impairment At present, no medicines or nonpharmacologic therapeutic methods are available for MCI. Therefore, one of the most prospective therapeutic methods might be existing medicines. Several clinical trials that use cholinesterase inhibitors, such as donepezil hydrochloride, galanthamine, and rivastigmine, have been reported. In 2004, Salloway et al made the first report regarding treatment outcomes with donepezil hydrochloride for MCI.162 A significant difference was found between the medicine and placebo; the improvement ratio was 32.6 and 24.3%, respectively, with no serious side effects reported. In 2005, Petersen et al163 also reported on the effect of donepezil hydrochloride for treatment of MCI. This article concluded that within a year, this medicine can prevent progression to dementia, and after a second year, no additional positive effects to control progression are noted. On the other hand, a clinical trial for MCI with galantamine treatment reported that this agent demonstrated significant effects in preventing MCI from progressing to dementia.164

10.7.2 Follow-Up Pharmacologic Follow-Up Some authorities believe that current AD medications, namely, cholinesterase inhibitors (ChEIs), could impact the outcome for MCI patients, especially those with the amnestic subtype. On the other hand, Raschetti et al concluded from their review that “the use of cholinesterase inhibitors in MCI was not associated with any delay in the onset of AD or dementia.” Moreover, the safety profile from this review showed that risks associated with ChEls are not negligible.165 Furthermore, the British Association for Psychopharmacology166 concluded that the medications approved for AD do not demonstrate efficacy in delaying or preventing dementia in MCI patients.

Nonpharmacologic Follow-Up One of the most representative nonpharmacologic follow-ups is thought to be memory-cognitive rehabilitation and buildup training. Computer training programs have shown modest benefit on cognition and mood in patients with MCI,167 as well as short story recall, abstract reasoning, and behavioral problems.168 Belleville et al169 reported that through their mixed training, which included education, computer-based training, and memory compensatory training, patients demonstrated improvements in list recall memory, face-name association performance, as well as improvements in subjective memory report and sense of well-being. On the other hand, the memory

compensatory strategy, using external aids, also showed preliminary evidence that it improved amnestic MCI.170 Physical activity interventions are also being explored as a way to minimize cognitive decline in MCI. Recently, combination training with memory compensation, decision making, physical fitness, talking with others, and educational programs have been carried out, and the evaluated data showed a positive impact on patient functional outcomes. Multiple aspects of nonpharmacologic follow-up might be of benefit for MCI patients.

10.7.3 Prophylactic Follow-Up Several prophylactic follow-ups have reported benefits in MCI prevention. One follow-up reported that individuals with habits involving intellectual activities, such as reading newspapers or magazines, playing games, doing puzzles, or visiting museums, showed a 33% decrease in the risk for dementia.171 Another follow-up reported an eightfold difference in the incidence rate between people who are single and meet other people less than once a week compared with those who live with family members and have interactions with others more than once a week.172

References [1] Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 1999; 56: 303–308 [2] Voisin T, Touchon J, Vellas B. Mild cognitive impairment: a nosological entity? Curr Opin Neurol 2003; 16 Suppl 2: S43–S45 [3] Ganguli M, Dodge HH, Shen C, DeKosky ST. Mild cognitive impairment, amnestic type: an epidemiologic study. Neurology 2004; 63: 115–121 [4] Petersen RC. Conceptual overview. In: Petersen RC, ed. Mild Cognitive Impairment: Aging to Alzheimer’s Disease. New York: Oxford University Press; 2003:1 [5] Petersen RC, Stevens JC, Ganguli M, Tangalos EG, Cummings JL, DeKosky ST Report of the Quality Standards Subcommittee of the American Academy of Neurology. Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review). Neurology 2001; 56: 1133–1142 [6] Salmon D, Hodges JR. Introduction: mild cognitive impairment—cognitive, behavioral, and biological factors. Neurocase 2005; 11: 1–2 [7] Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004; 256: 183–194 [8] Prichard JC. A Treatise on Insanity. Philadelphia: Haswell, Barrington, and Haswell; 1837 [9] Kral VA. Senescent forgetfulness: benign and malignant. Can Med Assoc J 1962; 86: 257–260 [10] Gurland BJ, Dean LL, Copeland J, Gurland R, Golden R. Criteria for the diagnosis of dementia in the community elderly. Gerontologist 1982; 22: 180–186 [11] Hughes CP, Berg L, Danziger WL, Coben LA, Martin RL. A new clinical scale for the staging of dementia. Br J Psychiatry 1982; 140: 566–572 [12] Reisberg B, Ferris SH, de Leon MJ, Crook T. The Global Deterioration Scale for assessment of primary degenerative dementia. Am J Psychiatry 1982; 139: 1136–1139 [13] Flicker C, Ferris SH, Reisberg B. Mild cognitive impairment in the elderly: predictors of dementia. Neurology 1991; 41: 1006–1009 [14] Reisberg B, Ferris SH, de Leon MJ et al. Stage-specific behavioral cognitive and in vivo changes in community residing subjects with age-associated memory impairment and primary degenerative dementia of the Alzheimer’s type. Drug Dev Res 1988; 15: 101–114 [15] Crook T, Bartus RT, Ferris SH et al. Age-associated memory impairment; proposed diagnostic criteria and measures of clinical changes: report of National Institute of Mental Health Work Group. Dev Neuropsychology 1986; 2: 261– 276 [16] Levy R. Aging-associated cognitive decline: Working Party of the International Psychogeriatric Association in collaboration with the World Health Organization. Int Psychogeriatr 1994; 6: 63–68

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Mild Cognitive Impairment [17] Christensen H, Henderson AS, Jorm AF, Mackinnon AJ, Scott R, Korten AE. ICD-10 mild cognitive disorder: epidemiological evidence on its validity. Psychol Med 1995; 25: 105–120 [18] American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th ed. Washington D.C.: American Psychiatric Association; 1994 [19] Zaudig M. A new systematic method of measurement and diagnosis of “mild cognitive impairment” and dementia according to ICD-10 and DSM-III-R criteria. Int Psychogeriatr 1992; 4 Suppl 2: 203–219 [20] Reisberg B, Ferris SH, Kluger A, Franssen E, Wegiel J, de Leon MJ. Mild cognitive impairment (MCI): a historical perspective. Int Psychogeriatr 2008; 20: 18–31 [21] Petersen RC, Morris JC. Mild cognitive impairment as a clinical entity and treatment target. Arch Neurol 2005; 62: 1160–1163, discussion 1167 [22] Albert MS, DeKosky ST, Dickson D et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 270–279 [23] Morris JC. Revised criteria for mild cognitive impairment may compromise the diagnosis of Alzheimer’s disease dementia. Arch Neurol 2012; 69: 700–708 [24] World Wide Alzheimer’s Disease Neuroimaging Initiative. Alzheimer’s Association 2014. Available at: http://www.alz.org/research/funding/partnerships/ ww-adni_europe.asp [25] Ritchie K. Mild cognitive impairment: an epidemiological perspective. Dialogues Clin Neurosci 2004; 6: 401–408 [26] Manly JJ, Tang MX, Schupf N, Stern Y, Vonsattel JP, Mayeux R. Frequency and course of mild cognitive impairment in a multiethnic community. Ann Neurol 2008; 63: 494–506 [27] Caracciolo B, Palmer K, Monastero R, Winblad B, Bäckman L, Fratiglioni L. Occurrence of cognitive impairment and dementia in the community: a 9year-long prospective study. Neurology 2008; 70: 1778–1785 [28] Luck T, Luppa M, Briel S et al. Mild cognitive impairment: incidence and risk factors: results of the Leipzig Longitudinal Study of the Aged. J Am Geriatr Soc 2010; 58: 1903–1910 [29] Luck T, Luppa M, Briel S, Riedel-Heller SG. Incidence of mild cognitive impairment: a systematic review. Dement Geriatr Cogn Disord 2010; 29: 164–175 [30] Plassman BL, Langa KM, McCammon RJ et al. Incidence of dementia and cognitive impairment, not dementia in the United States. Ann Neurol 2011; 70: 418–426 [31] Roberts RO, Geda YE, Knopman DS et al. The incidence of MCI differs by subtype and is higher in men: the Mayo Clinic Study of Aging. Neurology 2012; 78: 342–351 [32] Lopez OL, Becker JT, Chang YF et al. Incidence of mild cognitive impairment in the Pittsburgh Cardiovascular Health Study-Cognition Study. Neurology 2012; 79: 1599–1606 [33] Petersen RC, Roberts RO, Knopman DS et al. The Mayo Clinic Study of Aging. Prevalence of mild cognitive impairment is higher in men. Neurology 2010; 75: 889–897 [34] Manly JJ, Bell-McGinty S, Tang MX, Schupf N, Stern Y, Mayeux R. Implementing diagnostic criteria and estimating frequency of mild cognitive impairment in an urban community. Arch Neurol 2005; 62: 1739–1746 [35] Kryscio RJ, Schmitt FA, Salazar JC, Mendiondo MS, Markesbery WR. Risk factors for transitions from normal to mild cognitive impairment and dementia. Neurology 2006; 66: 828–832 [36] Das SK, Bose P, Biswas A et al. An epidemiologic study of mild cognitive impairment in Kolkata, India. Neurology 2007; 68: 2019–2026 [37] Tyas SL, Salazar JC, Snowdon DA et al. Transitions to mild cognitive impairments, dementia, and death: findings from the Nun Study. Am J Epidemiol 2007; 165: 1231–1238 [38] Tschanz JT, Welsh-Bohmer KA, Lyketsos CG et al. Cache County Investigators. Conversion to dementia from mild cognitive disorder: the Cache County Study. Neurology 2006; 67: 229–234 [39] Luchsinger JA, Reitz C, Patel B, Tang MX, Manly JJ, Mayeux R. Relation of diabetes to mild cognitive impairment. Arch Neurol 2007; 64: 570–575 [40] Reitz C, Tang MX, Manly J, Mayeux R, Luchsinger JA. Hypertension and the risk of mild cognitive impairment. Arch Neurol 2007; 64: 1734–1740 [41] Roberts RO, Geda YE, Knopman DS et al. Association of duration and severity of diabetes mellitus with mild cognitive impairment. Arch Neurol 2008; 65: 1066–1073 [42] Roberts RO, Geda YE, Knopman DS et al. Cardiac disease associated with increased risk of nonamnestic cognitive impairment: stronger effect on women. JAMA Neurol 2013; 70: 374–382 [43] Schultz MR, Lyons MJ, Franz CE et al. Apolipoprotein E genotype and memory in the sixth decade of life. Neurology 2008; 70: 1771–1777

[44] Boyle PA, Buchman AS, Wilson RS, Kelly JF, Bennett DA. The APOE epsilon4 allele is associated with incident mild cognitive impairment among community-dwelling older persons. Neuroepidemiology 2010; 34: 43–49 [45] Wolf H, Grunwald M, Ecke GM et al. The prognosis of mild cognitive impairment in the elderly. J Neural Transm Suppl 1998; 54: 31–50 [46] Morris JC, Storandt M, Miller JP et al. Mild cognitive impairment represents early-stage Alzheimer’s disease. Arch Neurol 2001; 58: 397–405 [47] Artero S, Tiemeier H, Prins ND, Sabatier R, Breteler MM, Ritchie K. Neuroanatomical localisation and clinical correlates of white matter lesions in the elderly. J Neurol Neurosurg Psychiatry 2004; 75: 1304–1308 [48] Tervo S, Kivipelto M, Hänninen T et al. Incidence and risk factors for mild cognitive impairment: a population-based three-year follow-up study of cognitively healthy elderly subjects. Dement Geriatr Cogn Disord 2004; 17: 196–203 [49] Gauthier S. Pharmacotherapy of mild cognitive impairment. Dialogues Clin Neurosci 2004; 6: 391–395 [50] van Norden AG, Fick WF, de Laat KF et al. Subjective cognitive failures and hippocampal volume in elderly with white matter lesions. Neurology 2008; 71: 1152–1159 [51] Scheef L, Spottke A, Daerr M et al. Glucose metabolism, gray matter structure, and memory decline in subjective memory impairment. Neurology 2012; 79: 1332–1339 [52] Palmer K, Berger AK, Monastero R, Winblad B, Bäckman L, Fratiglioni L. Predictors of progression from mild cognitive impairment to Alzheimer disease. Neurology 2007; 68: 1596–1602 [53] Feldman H, Scheltens P, Scarpini E et al. Behavioral symptoms in mild cognitive impairment. Neurology 2004; 62: 1199–1201 [54] Geda YE, Roberts RO, Knopman DS et al. Prevalence of neuropsychiatric symptoms in mild cognitive impairment and normal cognitive aging: population-based study. Arch Gen Psychiatry 2008; 65: 1193–1198 [55] Okura T, Plassman BL, Steffens DC, Llewellyn DJ, Potter GG, Langa KM. Prevalence of neuropsychiatric symptoms and their association with functional limitations in older adults in the United States: the aging, demographics, and memory study. J Am Geriatr Soc 2010; 58: 330–337 [56] Vinkers DJ, Gussekloo J, Stek ML, Westendorp RG, van der Mast RC. Temporal relation between depression and cognitive impairment in old age: prospective population based study. BMJ 2004; 329: 881 [57] Wilson RS, Schneider JA, Boyle PA, Arnold SE, Tang Y, Bennett DA. Chronic distress and incidence of mild cognitive impairment. Neurology 2007; 68: 2085–2092 [58] Geda YE, Knopman DS, Mrazek DA et al. Depression, apolipoprotein E genotype, and the incidence of mild cognitive impairment: a prospective cohort study. Arch Neurol 2006; 63: 435–440 [59] Goveas JS, Espeland MA, Woods NF, Wassertheil-Smoller S, Kotchen JM. Depressive symptoms and incidence of mild cognitive impairment and probable dementia in elderly women: the Women’s Health Initiative Memory Study. J Am Geriatr Soc 2011; 59: 57–66 [60] Caracciolo B, Bäckman L, Monastero R, Winblad B, Fratiglioni L. The symptom of low mood in the prodromal stage of mild cognitive impairment and dementia: a cohort study of a community dwelling elderly population. J Neurol Neurosurg Psychiatry 2011; 82: 788–793 [61] Visser PJ, Scheltens P, Verhey FR. Do MCI criteria in drug trials accurately identify subjects with predementia Alzheimer’s disease? J Neurol Neurosurg Psychiatry 2005; 76: 1348–1354 [62] Wilson RS, Arnold SE, Beck TL, Bienias JL, Bennett DA. Change in depressive symptoms during the prodromal phase of Alzheimer’s disease. Arch Gen Psychiatry 2008; 65: 439–445 [63] Wilson RS, Hoganson GM, Rajan KB, Barnes LL, Mendes de Leon CF, Evans DA. Temporal course of depressive symptoms during the development of Alzheimer’s disease. Neurology 2010; 75: 21–26 [64] Petersen RC, Parisi JE, Dickson DW et al. Neuropathologic features of amnestic mild cognitive impairment. Arch Neurol 2006; 63: 665–672 [65] Jicha GA, Parisi JE, Dickson DW et al. Neuropathologic outcome of mild cognitive impairment following progression to clinical dementia. Arch Neurol 2006; 63: 674–681 [66] Butters MA, Whyte EM, Nebes RD et al. The nature and determinants of neuropsychological functioning in late-life depression. Arch Gen Psychiatry 2004; 61: 587–595 [67] Modrego PJ, Ferrández J. Depression in patients with mild cognitive impairment increases the risk of developing dementia of Alzheimer type: a prospective cohort study. Arch Neurol 2004; 61: 1290–1293 [68] Petersen RC, Roberts RO, Knopman DS et al. Mild cognitive impairment: ten years later. Arch Neurol 2009; 66: 1447–1455

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Alzheimer’s Disease [69] Bennett DA, Schneider JA, Bienias JL, Evans DA, Wilson RS. Mild cognitive impairment is related to Alzheimer’s disease pathology and cerebral infarctions. Neurology 2005; 64: 834–841 [70] Sabbagh MN, Shah F, Reid RT et al. Pathologic and nicotinic receptor binding differences between mild cognitive impairment, Alzheimer’s disease, and normal aging. Arch Neurol 2006; 63: 1771–1776 [71] Davis KL, Mohs RC, Marin D et al. Cholinergic markers in elderly patients with early signs of Alzheimer’s disease. JAMA 1999; 281: 1401–1406 [72] DeKosky ST, Ikonomovic MD, Styren SD et al. Upregulation of choline acetyltransferase activity in hippocampus and frontal cortex of elderly subjects with mild cognitive impairment. Ann Neurol 2002; 51: 145–155 [73] Markesbery WR, Schmitt FA, Kryscio RJ, Davis DG, Smith CD, Wekstein DR. Neuropathologic substrate of mild cognitive impairment. Arch Neurol 2006; 63: 38–46 [74] Haroutunian V, Hoffman LB, Beeri MS. Is there a neuropathology difference between mild cognitive impairment and dementia? Dialogues Clin Neurosci 2009; 11: 171–179 [75] Guillozet AL, Weintraub S, Mash DC, Mesulam MM. Neurofibrillary tangles, amyloid, and memory in aging and mild cognitive impairment. Arch Neurol 2003; 60: 729–736 [76] Schneider JA, Arvanitakis Z, Leurgans SE, Bennett DA. The neuropathology of probable Alzheimer’s disease and mild cognitive impairment. Ann Neurol 2009; 66: 200–208 [77] Bartrés-Faz D, Junqué C, López-Alomar A et al. Neuropsychological and genetic differences between age-associated memory impairment and mild cognitive impairment entities. J Am Geriatr Soc 2001; 49: 985–990 [78] Kivipelto M, Helkala EL, Hänninen T et al. Midlife vascular risk factors and late-life mild cognitive impairment: a population-based study. Neurology 2001; 56: 1683–1689 [79] Zambón D, Quintana M, Mata P et al. Higher incidence of mild cognitive impairment in familial hypercholesterolemia. Am J Med 2010; 123: 267–274 [80] Steenland K, Karnes C, Seals R, Carnevale C, Hermida A, Levey A. Late-life depression as a risk factor for mild cognitive impairment or Alzheimer’s disease in 30 US Alzheimer’s disease centers. J Alzheimers Dis 2012; 31: 265– 275 [81] Alzheimer’s Disease Neuroimaging Initiative. ADNI procedures, protocols, and grants. 2013. Available at: http://www.adni-info.org/Scientists/ADNIStudyProcedures.aspx [82] The Ronald and Nancy Reagan Research Institute of the Alzheimer’s Association and the National Institute on Aging Working Group. Consensus report of the Working Group on: “Molecular and Biochemical Markers of Alzheimer’s Disease”. Neurobiol Aging 1998; 19: 109–116 [83] Bateman RJ, Munsell LY, Morris JC, Swarm R, Yarasheski KE, Holtzman DM. Human amyloid-β synthesis and clearance rates as measured in cerebrospinal fluid in vivo. Nat Med 2006; 12: 856–861 [84] Mizukami K. [How do we deal with mild cognitive impairment?] [in Japanese] Seishin Shinkeigaku Zasshi 2009; 111: 26–30[Article in Japanese] [85] Reisberg B. Dementia: a systematic approach to identifying reversible causes. Geriatrics 1986; 41: 30–46 [86] Caffarra P, Ghetti C, Concari L, Venneri A. Differential patterns of hypoperfusion in subtypes of mild cognitive impairment. Open Neuroimaging J 2008; 2: 20–28 [87] Hirao K, Ohnishi T, Hirata Y et al. The prediction of rapid conversion to Alzheimer’s disease in mild cognitive impairment using regional cerebral blood flow SPECT. Neuroimage 2005; 28: 1014–1021 [88] Chételat G, Desgranges B, de la Sayette V, Viader F, Eustache F, Baron JC. Mild cognitive impairment: can FDG-PET predict who is to rapidly convert to Alzheimer’s disease? Neurology 2003; 60: 1374–1377 [89] Mosconi L, Perani D, Sorbi S et al. MCI conversion to dementia and the APOE genotype: a prediction study with FDG-PET. Neurology 2004; 63: 2332–2340 [90] Park KW, Yoon HJ, Kang DY, Kim BC, Kim S, Kim JW. Regional cerebral blood flow differences in patients with mild cognitive impairment between those who did and did not develop Alzheimer’s disease. Psychiatry Res 2012; 203: 201–206 [91] Habert MO, Horn JF, Sarazin M et al. Brain perfusion SPECT with an automated quantitative tool can identify prodromal Alzheimer’s disease among patients with mild cognitive impairment. Neurobiol Aging 2011; 32: 15–23 [92] Caroli A, Testa C, Geroldi C et al. Cerebral perfusion correlates of conversion to Alzheimer’s disease in amnestic mild cognitive impairment. J Neurol 2007; 254: 1698–1707 [93] Pennanen C, Kivipelto M, Tuomainen S et al. Hippocampus and entorhinal cortex in mild cognitive impairment and early AD. Neurobiol Aging 2004; 25: 303–310

[94] Alegret M, Cuberas-Borrós G, Vinyes-Junqué G et al. A two-year follow-up of cognitive deficits and brain perfusion in mild cognitive impairment and mild Alzheimer’s disease. J Alzheimers Dis 2012; 30: 109–120 [95] Matsuda H. Role of neuroimaging in Alzheimer’s disease, with emphasis on brain perfusion SPECT. J Nucl Med 2007; 48: 1289–1300 [96] Honda N, Machida K, Matsumoto T et al. Three-dimensional stereotactic surface projection of brain perfusion SPECT improves diagnosis of Alzheimer’s disease. Ann Nucl Med 2003; 17: 641–648 [97] Elgh E, Sundström T, Näsman B, Ahlström R, Nyberg L. Memory functions and rCBF (99m)Tc-HMPAO SPET: developing diagnostics in Alzheimer’s disease. Eur J Nucl Med Mol Imaging 2002; 29: 1140–1148 [98] Ishi K. PET approaches for diagnosis of dementia. AJNR Am J Neuroradiol 2014 [99] Drzezga A, Lautenschlager N, Siebner H et al. Cerebral metabolic changes accompanying conversion of mild cognitive impairment into Alzheimer’s disease: a PET follow-up study. Eur J Nucl Med Mol Imaging 2003; 30: 1104– 1113 [100] Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 2002; 297: 353–356 [101] Hatashita S, Yamasaki H. Diagnosed mild cognitive impairment due to Alzheimer’s disease with PET biomarkers of beta amyloid and neuronal dysfunction. PLoS ONE 2013; 8: e66877 [102] Laforce R, Jr, Rabinovici GD. Amyloid imaging in the differential diagnosis of dementia: review and potential clinical applications. Alzheimers Res Ther 2011; 3: 31 [103] Jiménez Bonilla JF, Carril Carril JM. Molecular neuroimaging in degenerative dementias. Rev Esp Med Nucl Imagen Mol 2013; 32: 301–309 [104] Villemagne VL, Rowe CC. Amyloid imaging. Int Psychogeriatr 2011; 23 Suppl 2: S41–S49 [105] Rabinovici GD, Jagust WJ. Amyloid imaging in aging and dementia: testing the amyloid hypothesis in vivo. Behav Neurol 2009; 21: 117–128 [106] Jack CR, Jr, Knopman DS, Jagust WJ et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 2010; 9: 119–128 [107] Sonnen JA, Santa Cruz K, Hemmy LS et al. Ecology of the aging human brain. Arch Neurol 2011; 68: 1049–1056 [108] Zaccai J, Brayne C, McKeith I, Matthews F, Ince PG MRC Cognitive Function, Ageing Neuropathology Study. Patterns and stages of alpha-synucleinopathy: relevance in a population-based cohort. Neurology 2008; 70: 1042–1048 [109] Chételat G, Desgranges B, De La Sayette V, Viader F, Eustache F, Baron JC. Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment. Neuroreport 2002; 13: 1939–1943 [110] Karas GB, Scheltens P, Rombouts SA et al. Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. Neuroimage 2004; 23: 708–716 [111] Pennanen C, Testa C, Laakso MP et al. A voxel based morphometry study on mild cognitive impairment. J Neurol Neurosurg Psychiatry 2005; 76: 11–14 [112] Bell-McGinty S, Lopez OL, Meltzer CC et al. Differential cortical atrophy in subgroups of mild cognitive impairment. Arch Neurol 2005; 62: 1393– 1397 [113] Whitwell JL, Przybelski SA, Weigand SD et al. 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer’s disease. Brain 2007; 130: 1777–1786 [114] Detre JA, Leigh JS, Williams DS, Koretsky AP. Perfusion imaging. Magn Reson Med 1992; 23: 37–45 [115] Liu HL, Pu Y, Liu Y et al. Cerebral blood flow measurement by dynamic contrast MRI using singular value decomposition with an adaptive threshold. Magn Reson Med 1999; 42: 167–172 [116] Alexopoulos P, Sorg C, Förschler A et al. Perfusion abnormalities in mild cognitive impairment and mild dementia in Alzheimer’s disease measured by pulsed arterial spin labeling MRI. Eur Arch Psychiatry Clin Neurosci 2012; 262: 69–77 [117] Zhang Q, Stafford RB, Wang Z, Arnold SE, Wolk DA, Detre JA. Microvascular perfusion based on arterial spin labeled perfusion MRI as a measure of vascular risk in Alzheimer’s disease. J Alzheimers Dis 2012; 32: 677–687 [118] Xu G, Rowley HA, Wu G et al. Reliability and precision of pseudo-continuous arterial spin labeling perfusion MRI on 3.0 T and comparison with 15O-water PET in elderly subjects at risk for Alzheimer’s disease. NMR Biomed 2010; 23: 286–293 [119] Chen Y, Wolk DA, Reddin JS et al. Voxel-level comparison of arterial spinlabeled perfusion MRI and FDG-PET in Alzheimer’s disease. Neurology 2011; 77: 1977–1985

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Mild Cognitive Impairment [120] Kantarci K, Petersen R, Boeve B et al. Annual decrease in N-acetylaspartate/ creatine ratio correlates with the progression of Alzheimer’s disease. Neurology 2004; 62: 300–305 [121] Kantarci K, Jack CR, Jr, Xu YC et al. Regional metabolic patterns in mild cognitive impairment and Alzheimer’s disease: a 1 H MRS study. Neurology 2000; 55: 210–217 [122] Klunk WE, Engler H, Nordberg A et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol 2004; 55: 306–319 [123] Urenjak J, Williams SR, Gadian DG, Noble M. Proton nuclear magnetic resonance spectroscopy unambiguously identifies different neural cell types. J Neurosci 1993; 13: 981–989 [124] Bitsch A, Bruhn H, Vougioukas V et al. Inflammatory CNS demyelination: histopathologic correlation with in vivo quantitative proton MR spectroscopy. AJNR Am J Neuroradiol 1999; 20: 1619–1627 [125] Kantarci K, Weigand SD, Przybelski SA et al. MRI and MRS predictors of mild cognitive impairment in a population-based sample. Neurology 2013; 81: 126–133 [126] Le Bihan D. Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci 2003; 4: 469–480 [127] Medina D, DeToledo-Morrell L, Urresta F et al. White matter changes in mild cognitive impairment and AD: a diffusion tensor imaging study. Neurobiol Aging 2006; 27: 663–672 [128] Delano-Wood L, Bondi MW, Jak AJ et al. Stroke risk modifies regional white matter differences in mild cognitive impairment. Neurobiol Aging 2010; 31: 1721–1731 [129] Delano-Wood L, Stricker NH, Sorg SF et al. Posterior cingulum white matter disruption and its associations with verbal memory and stroke risk in mild cognitive impairment. J Alzheimers Dis 2012; 29: 589–603 [130] Bosch B, Arenaza-Urquijo EM, Rami L et al. Multiple DTI index analysis in normal aging, amnestic MCI and AD: relationship with neuropsychological performance. Neurobiol Aging 2012; 33: 61–74 [131] Stebbins GT, Murphy CM. Diffusion tensor imaging in Alzheimer’s disease and mild cognitive impairment. Behav Neurol 2009; 21: 39–49 [132] O’Dwyer L, Lamberton F, Bokde ALW et al. Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment. PLoS ONE 2012; 7: e32441 [133] Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A. Neurophysiological investigation of the basis of the fMRI signal. Nature 2001; 412: 150–157 [134] Shmuel A, Augath M, Oeltermann A, Logothetis NK. Negative functional MRI response correlates with decreases in neuronal activity in monkey visual area V1. Nat Neurosci 2006; 9: 569–577 [135] Gusnard DA, Raichle ME, Raichle ME. Searching for a baseline: functional imaging and the resting human brain. Nat Rev Neurosci 2001; 2: 685–694 [136] Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A 2003; 100: 253–258 [137] Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 2005; 102: 9673–9678 [138] Dickerson BC, Sperling RA. Functional abnormalities of the medial temporal lobe memory system in mild cognitive impairment and Alzheimer’s disease: insights from functional MRI studies. Neuropsychologia 2008; 46: 1624– 1635 [139] Dickerson BC, Salat DH, Greve DN et al. Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD. Neurology 2005; 65: 404–411 [140] Schölvinck ML, Maier A, Ye FQ, Duyn JH, Leopold DA. Neural basis of global resting-state fMRI activity. Proc Natl Acad Sci U S A 2010; 107: 10238– 10243 [141] Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995; 34: 537–541 [142] Cordes D, Haughton VM, Arfanakis K et al. Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR Am J Neuroradiol 2001; 22: 1326–1333 [143] Beckmann CF, DeLuca M, Devlin JT, Smith SM. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci 2005; 360: 1001–1013 [144] Damoiseaux JS, Beckmann CF, Arigita EJ et al. Reduced resting-state brain activity in the “default network” in normal aging. Cereb Cortex 2008; 18: 1856–1864

[145] Damoiseaux JS, Rombouts SA, Barkhof F et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A 2006; 103: 13848– 13853 [146] Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A 2004; 101: 4637–4642 [147] Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A 2001; 98: 676–682 [148] Binnewijzend MAA, Schoonheim MM, Sanz-Arigita E et al. Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging 2012; 33: 2018–2028 [149] Sorg C, Riedl V, Mühlau M et al. Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. Proc Natl Acad Sci U S A 2007; 104: 18760–18765 [150] Bai F, Zhang Z, Yu H et al. Default-mode network activity distinguishes amnestic type mild cognitive impairment from healthy aging: a combined structural and resting-state functional MRI study. Neurosci Lett 2008; 438: 111–115 [151] Jin M, Pelak VS, Cordes D. Aberrant default mode network in subjects with amnestic mild cognitive impairment using resting-state functional MRI. Magn Reson Imaging 2012; 30: 48–61 [152] Qi Z, Wu X, Wang Z et al. Impairment and compensation coexist in amnestic MCI default mode network. Neuroimage 2010; 50: 48–55 [153] Sperling RA, Aisen P, Beckett L et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging—Alzheimer’s Association Research Roundtable workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 280–292 [154] Mapping SP. Wellcome Trust Centre for Neuroimaging. 2013. Available at: http://www.fil.ion.ucl.ac.uk/spm [155] Studio MRI. An Image Processing Program. 17 May 2007. Available at: https:// www.mristudio.org [156] FSL. FMRIB Software Library v5.0. Analysis Group, FMRIB, Oxford UK. 2014. Available at: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki [157] Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 1999; 45: 265–269 [158] Jiang H, van Zijl PCM, Kim J, Pearlson GD, Mori S. DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking. Comput Methods Programs Biomed 2006; 81: 106–116 [159] Miller MI, Priebe CE, Qiu A et al. Morphometry BIRN. Collaborative computational anatomy: an MRI morphometry study of the human brain via diffeomorphic metric mapping. Hum Brain Mapp 2009; 30: 2132–2141 [160] Mielke MM, Kozauer NA, Chan KCG et al. Regionally-specific diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease. Neuroimage 2009; 46: 47–55 [161] Tighe SK, Oishi K, Mori S et al. Diffusion tensor imaging of neuropsychiatric symptoms in mild cognitive impairment and Alzheimer’s dementia. J Neuropsychiatry Clin Neurosci 2012; 24: 484–488 [162] Salloway S, Ferris S, Kluger A et al. Donepezil 401 Study Group. Efficacy of donepezil in mild cognitive impairment: a randomized placebo-controlled trial. Neurology 2004; 63: 651–657 [163] Petersen RC, Thomas RG, Grundman M et al. Alzheimer’s Disease Cooperative Study Group. Vitamin E and donepezil for the treatment of mild cognitive impairment. N Engl J Med 2005; 352: 2379–2388 [164] Petersen RC. Mild cognitive impairment clinical trials. Nat Rev Drug Discov 2003; 2: 646–653 [165] Raschetti R, Albanese E, Vanacore N, Maggini M. Cholinesterase inhibitors in mild cognitive impairment: a systematic review of randomised trials. PLoS Med 2007; 4: e338 [166] O’Brien JT, Burns A BAP Dementia Consensus Group. Clinical practice with anti-dementia drugs: a revised (second) consensus statement from the British Association for Psychopharmacology. J Psychopharmacol 2011; 25: 997–1019 [167] Talassi E, Guerreschi M, Feriani M, Fedi V, Bianchetti A, Trabucchi M. Effectiveness of a cognitive rehabilitation program in mild dementia (MD) and mild cognitive impairment (MCI): a case control study. Arch Gerontol Geriatr 2007; 44 Suppl 1: 391–399 [168] Rozzini L, Costardi D, Chilovi BV, Franzoni S, Trabucchi M, Padovani A. Efficacy of cognitive rehabilitation in patients with mild cognitive impairment treated with cholinesterase inhibitors. Int J Geriatr Psychiatry 2007; 22: 356–360

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Alzheimer’s Disease [169] Belleville S, Gilbert B, Fontaine F, Gagnon L, Ménard E, Gauthier S. Improvement of episodic memory in persons with mild cognitive impairment and healthy older adults: evidence from a cognitive intervention program. Dement Geriatr Cogn Disord 2006; 22: 486–499 [170] Greenaway M, Smith G, Lepore S et al. Compensating for memory loss in amnestic mild cognitive impairment. Alzheimers Dementia 2006; 2 Suppl 1: S571 [171] Wilson RS, Mendes De Leon CF, Barnes LL et al. Participation in cognitively stimulating activities and risk of incident Alzheimer’s disease. JAMA 2002; 287: 742–748

[172] Fratiglioni L, Wang HX, Ericsson K, Maytan M, Winblad B. Influence of social network on occurrence of dementia: a community-based longitudinal study. Lancet 2000; 355: 1315–1319 [173] Ebly EM, Hogan DB, Parhad IM. Cognitive impairment in the nondemented elderly: results from the Canadian Study of Health and Aging. Arch Neurol 1995; 52: 612–619 [174] Petersen RC, Smith GE, Ivnik RJ et al. Apolipoprotein E status as a predictor of the development of Alzheimer’s disease in memory-impaired individuals. JAMA 1995; 273: 1274–1278

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Overview of Alzheimer’s Disease

11 Overview of Alzheimer’s Disease Leonardo Cruz de Souza and Marie Sarazin Since Alois Alzheimer’s first description of Alzheimer’s disease (AD) in 1906, the disease that was later named after him has became a major public health concern. To date, it is estimated that 24 million people worldwide have dementia, most of whom are thought to have AD. The main risk factor for developing AD is age.1 Epidemiologic data show that the percentage of patients with AD doubles every 5 years from age 65 onward.1 Thus, the percentage of patients with AD would be of 1% for the population aged 60 years, 5% for the population aged 65 years, and about 30% for the population aged 85 years.1,2 It should be noted, however, that although AD mainly affects the elderly, it also affects an important group of young patients.2 Considering that the incidence of AD and other dementias increases with age, the prevalence of dementias is estimated to grow in the following decades worldwide as a result of the increasing longevity of populations.3 The number of people with dementia worldwide is predicted to double every 20 years, to more than 65 million in 2030 and more than 115 million in 2050.4 Besides its dramatic impact on patients and their families, AD represents an economic strain for health care systems and communities worldwide, and it is expected that the cost of caring for people with dementia in United States will grow to almost $190 billion by 2015.3

11.1 Neuropathology Alzheimer’s disease is associated with different neuropathological findings, such as neuronal death and a decreased number of synapses. Its main pathological hallmarks, however, are the presence of senile plaques and neurofibrillary tangles, which reflect amyloid and tau pathology, respectively. Extracellular amyloid plaques are formed by β-amyloid protein peptides (Aβ), which are fragments formed by the cleavage of the amyloid precursor protein (APP).5 APP is a transmembrane glycoprotein that can be processed by αand γ-secretases, generating a nonamyloidogenic product, or by β- and γ-secretases, generating Aβ peptides, which are amyloidogenic and are prone to form plaques. However, there is no direct correlation between the number and topography of cortical senile plaques and the cognitive deficits in AD patients. The amount of senile plaques is not correlated with the severity of the disease, and amyloid deposition seems to remain stable during progression of the disease.6,7,8 Recent longitudinal imaging studies indicate that cerebral Aβ deposition precedes the clinical symptoms of AD by a decade or longer.9 Intracellular neurofibrillary tangles are formed as a result of hyperphosphorylation and oligomerization of tau, a microtubule-associated protein that is present mainly in the axons of neurons. The progression of tau pathology in the brain is closely correlated to clinical symptoms and to the severity of the disease10 as established by Braak and Braak.11 In the early stages of AD (Braak stages I, II, and III), neurofibrillary degeneration can be identified in areas critical for episodic memory,

such as medial temporal regions (hippocampal formations, parahippocampal gyrus, and entorhinal cortex); consequently, episodic memory deficit is the initial symptom for most of AD patients. As the condition progresses, deficits occur in instrumental functions (language, praxis, visuospatial abilities), which are consistent with the extension of lesions into the neocortical associative areas (Braak stage V).

11.2 Genetics In most cases, AD is considered a disease with multiple causes and results from the interactions between genetic and environmental factors.12 The role of genetic factors in the incidence and pathogenesis of AD is complex. AD can be divided into two types according to genetic factors13: (1) familial AD, with mendelian transmission, usually early onset (before age 60 years); and (2) sporadic AD, usually with onset in older age (over 60 years), without an autosomal dominant pattern of transmission. This dichotomy should be taken with caution, however, because cases of early onset AD without evidence of autosomal dominant transmission do occur13; conversely, the importance of genetic factors in sporadic forms of the disease has been established14 by the involvement of sortilin-related receptor (SORL1).15,16,17 Familial AD with autosomal dominant transmission is rare and accounts for less than 5% of AD cases.17 Genetic studies indicate that these forms are related to three possible mutations17,18: (1) the gene for APP, located on chromosome 21; (2) the gene for presenilin 1 (PSEN1), located on chromosome 14; and (3) the gene for presenilin 2 (PSEN2), located on chromosome 1. These changes have virtually 100% penetrance by the age of 60 years. PSEN1 gene mutations are the most frequent (75% of cases among all cases of familial AD).17,18 By contrast, in a whole-genome sequencing study of Icelandic people, a recent work identified a coding variant in APP that protects against AD and cognitive decline. The mutation leads to reduced production of Aβ by BACE1.19 The genetics of sporadic forms of the disease is much more complex because in most cases there is no obvious familial aggregation.18 The risk of developing AD increases from 4 to 10 times in normal subjects with a first-degree relative affected by the disease compared with patients without a family history.20 The SORL1 gene has been noted to be involved in sporadic forms of the disease.15,16,17 The SORL1 gene is located on chromosome 11 and is involved in intracellular trafficking of the amyloid precursor. Deletion of this gene increases the production of toxic β-amyloid peptide.15,21 However, this mutation is not present in all cases of sporadic AD.16 Among the genetic factors modulating susceptibility to AD, the polymorphism of apolipoprotein E (ApoE) is the most important. ApoE has an essential role in the regulation of lipid metabolism and is implicated in the transport, distribution, endocytosis, and catabolism of lipid particles.22 It also has a role in the mechanisms of neuronal plasticity by participating in synaptogenesis and the stability of synaptic connections.22 The mechanisms by which ApoE is involved in the

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Alzheimer’s Disease pathophysiology of AD are not entirely clear, but ApoE seems to have an important role in the metabolism and the accumulation of Aβ.22,23 The ApoE gene is located on chromosome 19 and has three alleles (ε2, ε3, and ε4). The ε3 allele is present in 50 to 90% of individuals, the ε4 allele in 5 to 35%, and the ε2 allele in 1 to 5%.23 The risk of developing AD associated with ApoE ε4 is dose dependent: individuals carrying one ε4 allele have three times more risk of developing the disease, whereas ε4 homozygotes have 12 times more risk of developing AD than do those with ε3 homozygotes.22,23 On the other hand, the ε2 allele is associated with a reduced risk of developing AD.22,23,24,25 Recent publications showed that, in addition to autosomal dominant frontotemporal lobar degeneration, mutations in the progranulin gene may be a risk factor for AD clinical phenotypes and neuropathology.26

11.3 Clinical Features 11.3.1 Episodic Memory Deficits The most prominent feature of AD is the decline in cognitive function, with an early impairment of anterograde episodic memory.17 The initial amnestic deficits with progressive worsening that remains predominant during the course of the disease is the most frequent phenotype of AD.2 Amnestic symptoms are characterized by memory impairment of recent events, unusual repeated omissions, and difficulty learning new information. Initially, amnestic symptoms are not associated with a loss of autonomy, and the patient remains independent for activities of daily living. The investigation of amnestic symptoms requires formal neuropsychological testing to quantify and qualify the nature of the memory deficit. In fact, memory disorders are commonly observed in patients with neuropsychiatric disorders other than AD, such as as Parkinson’s disease, vascular dementia, depression, or even iatrogenic conditions. Moreover, subjective memory complaints are also common in the elderly. The appropriate neuropsychological evaluation can distinguish genuine memory impairment (e.g., failure of information storage and new memory formation) from attention or retrieval disorders (such as normal aging or depression). More particularly, neuropsychological tests that provide encoding specificity are of great interest and improve the accuracy of the AD diagnosis. The Free and Cued Selective Reminding Test (FCSRT) is a neuropsychological tool in which target materials are encoded along with semantic cues. These cues are used to control for effective encoding and subsequently are presented to optimize retrieval.27 The FCSRT can identify the so-called amnestic syndrome of the medial temporal type (or of the hippocampal type), which is defined by (1) poor free recall (as in any memory disorder) and (2) decreased total recall resulting from an insufficient effect of cueing. The low performance of total recall despite retrieval facilitation indicates poor storage of information and seems specific of a hippocampal memory disorder. The amnestic syndrome of the medial temporal type differs from functional and subcorticofrontal memory disorders, characterized by a low free recall performance with normalization (or a quasi-normalization) of the performance in total recall because of good efficacy of cueing.28 This subcortical-frontal profile of

memory impairment can be observed in depression,29 vascular dementia,30 and subcortical dementia.28 The identification of an amnestic syndrome of the medial temporal type by the FCSRT can successfully differentiate patients with AD from healthy controls, even when the disease is in an initial stage. Moreover, the FCSRT, by isolating an amnestic syndrome of the hippocampal type in subjects with mild cognitive impairment (MCI), is able to distinguish patients at an early stage of AD from those with “nonconverter” MCI with a high sensitivity (80%) and specificity (90%).31 Performance of the FCSRT has been well correlated with the left medial temporal lobe volume assessed both by voxel based morphometry analysis and automatic volumetric method in a series of AD patients.32 These findings support considering the measure of episodic memory by the FCSRT as a useful clinical marker of medial temporal damage. A multicenter German study comprising 185 MCI patients investigated whether the performance of the FCSRT predicts Alzheimer’s pathology.33 In this study, three different memory tests (the FCSRT, the Word List Learning Task from the Consortium to Establish a Registry for AD, and the Logical Memory Paragraph Recall Test from the Wechsler Memory Scale— Revised) were compared for their ability to predict a cerebrospinal fluid (CSF) biomarker profile indicative of AD, defined by Aβ42/tau ratio.34 Their results showed that among the three memory tests, the cued recall deficits identified by the FCSRT were by far more predictive of a CSF biomarker profile indicative of AD pathophysiology. It should be noted, however, that the amnestic syndrome of hippocampal type may also be observed in other conditions, such as hippocampal sclerosis or behavioral variant frontotemporal dementia.35

11.3.2 Other Cognitive Deficits Besides episodic memory deficits, temporal-spatial disorientation (disorientation in nonfamiliar places and difficulty in establishing a chronological order to recent events) is also present at the initial stages of AD. Patients often show difficulty orienting themselves in familiar places during intermediate stages and progress to severe disorientation in their personal residence as the disease progresses. The progression of cognitive deficits follows extension of the underlying pathological lesions (more specifically of tau pathology) through the neocortical associative areas. Patients may develop language disorders, visuospatial and recognition deficits, and difficulties in executing the more complex tasks of daily living, leading to loss of autonomy and dementia.17 Patients progress from loss of higher-level activities of daily living, such as financial transactions and the use of public transportation, to abnormalities in the more basic activities of daily living. At severe stages of the disease, patients require continued assistance for basic activities of daily living. Aphasia may appear as the condition progresses, characterized by decreased verbal comprehension and naming difficulty. As AD advances, all aspects of language (oral production, comprehension, reading, and writing), can be impaired, resulting in mutism or incomprehensible language in severe cases. Gestural apraxia refers to an inability to perform learned skilled movements, which cannot be attributed to an alteration of judgment or to sensitive motor deficits. It is usually measured by asking

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Overview of Alzheimer’s Disease the patient to perform pantomimes of tool uses (e.g., asking the patient to imitate how to use a hammer) or symbolic gestures (asking the patient to perform a military salute) or to imitate meaningless gestures. Difficulty using objects, as well as dressing apraxia, is observed during moderate to severe AD.36 Patients with AD commonly show visuospatial dysfunction in the moderate stages of AD. Deficits arise first during complex tasks, which require perceptual analysis and spatial planning. Impairment in constructional ability can be easily tested by drawing and copying tasks. In typical AD, a visuoconstructive deficit predicts the development of severe dependency.37 Visual agnosia and complex visual processing dysfunction are observed in advanced stages of the disease. Patients may show impaired recognition of objects or faces.

11.3.3 Severity of the Disease Different stages of severity are described in AD, from mild to moderate and severe (dementia) stages. According to the recently proposed AD criteria,38 the terms prodromal AD or MCI due to AD refer to early stage of the disease, which precedes the appearance of dementia.39,40 In the prodromal or MCI stage, the patient can live alone. In mild stages of AD, patients require limited home care. In moderate stages, patients need supervision and regular assistance in most activities. In severe stages, residential health care may be required. The Mini-Mental State Examination (MMSE) assesses global cognitive efficiency and is generally used to evaluate dementia severity. Although the MMSE is not a specific neuropsychological test for AD diagnosis, it is easy and quick to administer and can track the overall progression of cognitive decline. Longitudinal studies have shown that the mean annual rate of progression of cognitive impairment using MMSE is approximately 2 to 6 points. The Clinical Dementia Rating Scale, based on an overall evaluation of the patient’s condition, offers incremental stages of severity.41 Functional decline increases with disease progression.

11.3.4 Atypical Clinical Presentation Neuropathological studies have long recognized that AD can manifest as atypical or variant syndromes without predominant amnestic features.42,43,44,45 The most common variant AD phenotypes are posterior cortical atrophy (PCA), logopenic-variant primary progressive aphasia (lgPPA), and frontal variant AD. The new criteria for AD grouped these focal variants into atypical AD. Atypical AD is more frequent in early onset of AD. In PCA, visuospatial deficit is the initial symptom, and then patients develop features of Bálint syndrome (ocular apraxia, optic ataxia, and simultanagnosia), Gerstmann syndrome (acalculia, agraphia, finger agnosia, and left-right disorientation), visual agnosia, and transcortical sensory aphasia, whereas episodic memory is preserved or only mildly impaired.46 Magnetic resonance imaging (MRI) and functional imaging have shown parieto-occipital localization of atrophy and hypometabolism.47 In lgPPA, language deficit is the initial symptom, characterized by frequent pauses that disrupt the flow of conversation and the generation of phonologic errors, associated with deficit in sentence repetition. In contrast with other forms of PPA, in lgPPA, patients lack motor speech disorders or show agram-

matism as in nonfluent PPA and have less severe semantic impairments than do those with semantic variant PPA.48 Neuroimaging showed asymmetric involvement of the temporoparietal junction, which is more severe in the dominant hemisphere. The frontal variant of AD is defined by a predominant dysexecutive syndrome, which is frequently associated with frontal behavioral symptoms. These clinical features can lead to misdiagnosis of frontotemporal dementia.49

11.3.5 Neuropsychiatric Features Behavioral and neuropsychiatric symptoms associated with AD include depressive mood, apathy, agitation, and aggressivity, as well as psychotic symptoms, such as delusions and hallucinations.50 These manifestations tend to fluctuate over time, resulting in individual differences. The prevalence of psychosis and behavioral disturbance increases as the disease progresses and may indicate a poor prognosis.37,51 The most frequent behavioral disorder in AD is apathy, which has been found in 25 to 75% of cases.50,52 The prevalence of apathy also increases with the severity of dementia. It is noteworthy that apathy should not be confounded with depression, and apathy may be present without concomitant depression. Delusions are observed more often than hallucinations, and their frequency is estimated at 20 to 70%.52 Paranoid delusions are probably the most common type, but misidentification phenomena and Capgras delusion may also be observed. Hallucinations, commonly visual, are rare in the early stages, but they become more prevalent as the disease progresses.52 Symptoms of psychosis or agitation are associated with distress for the patient, an increased burden on caregivers, more rapid cognitive decline, and an increased likelihood of institutionalization.53

11.4 Alzheimer’s Disease Diagnosis Until recently, the diagnosis of AD has been based on the National Institute of Neurological and Communicative Disorders and Stroke (NINCDS)—Alzheimer’s Disease and Related Disorders Association (ADRDA) criteria, which referred to a clinical dementia entity that typically manifests with a characteristic progressive amnestic disorder with subsequent appearance of other cognitive and neuropsychiatric changes that impair social function and activities of daily living.54 In the NINCDS– ADRDA criteria, biological investigation (blood and CSF) and neuroimaging examination (computed tomography scan or MRI) were proposed to exclude other causes of the dementia syndrome (vascular lesions, tumor, infectious or inflammatory processes). Advances in establishing the biomarkers of AD, which provide in vivo information about the pathophysiologic process associated with AD, have stimulated the proposal of new diagnostic criteria.38,39,55,56 According to these frameworks, the diagnosis of AD is based on core clinical criteria, with the support of biomarkers based on imaging and CSF measures. This combined clinical and biological approach may improve accuracy of the diagnosis. These new diagnostic frameworks permit an earlier diagnosis of AD, before the development of dementia. Following this new perspective of a clinical-biological diagnosis approach, a consideration of preclinical stages of AD was proposed, according to which the pathophysiological process of the

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Alzheimer’s Disease disease precedes the clinical manifestations.57 Whatever the stage of the disease, the new AD criteria integrated in the clinical approach the use of biomarkers, such as neuroimaging and biological tools.

11.4.1 Structural Brain Imaging Brain MRI has an important role in the investigation of patients with suspected AD, that is, to detect the treatable causes of cognitive dysfunction, such as normal pressure hydrocephalus and subdural hematoma. Moreover, it has also been increasingly recognized that brain MRI is important in identifying the specific patterns of anatomic damage associated with AD. Atrophy of medial temporal regions, mainly the entorhinal cortex and hippocampus, is observed in early AD. There is a progression of hippocampal atrophy in AD through its different stages: hippocampal volumes of AD patients are 10 to 12% smaller than those of age-matched controls in early (prodromal) stages, 15 to 30% reduced in mild stages, and 30 to 40% smaller in moderate stages of the disease.58 Measurement of medial temporal lobe atrophy can distinguish AD from agematched controls with an overall accuracy greater than 85%.59 Similarly, hippocampal measures provide sensitivity and specificity of approximately 75 to 80% to predict conversion to AD in patients with MCI.58,59,60 Qualitative visual scales or volumetric measurements with specific software can be used to assess hippocampal atrophy.61,62 Whereas quantitative methods are restricted mainly to research centers, visual rating scales, which assess medial temporal atrophy on coronal T1-weighted MRI, are of value in clinical settings.63 In summary, hippocampal volume and medial temporal atrophy by volumetric measures or visual rating are the best validated markers of early AD. Accordingly, in the new AD criteria, loss of hippocampal volume is considered a marker of neuronal injury indirectly caused by the tau pathology. It should be emphasized, however, that medial temporal atrophy is not specific for AD and may be observed in other clinical situations, such as frontotemporal dementia and even depression and normal aging.58,64 On the other hand, the rate of hippocampal atrophy may be a better indicator of AD pathology, as the progression of hippocampal loss is approximately two to four times faster in AD patients than in healthy controls.65,66

11.4.2 Cerebrospinal Fluid Biomarkers Biomarkers can be defined as “an objective measure of a biological or pathogenic process that can be used to evaluate disease risk or prognosis, to guide clinical diagnosis or to monitor therapeutic interventions.”67 In the context of AD, the development of biomarkers, especially the CSF biomarkers, opened the possibility of identifying in vivo a specific underlying pathophysiologic mechanism and thus leading to a redefinition of clinical diagnosis of the disease.68 The main biomarkers used in diagnosis of AD are β-amyloid peptide (Aβ42), total tau, and the isoforms of phosphorylated tau (P-tau181 and P-tau231). A series of clinicopathological studies demonstrated that these biomarkers reflect the core pathological hallmarks of AD, with CSF levels of Aβ42 reflecting the extracellular deposits of Aβ peptide and CSF levels of tau and P-tau being correlated with the amount neurofibrillary

tangles.69,70,71 CSF biomarkers may thus be considered surrogate markers of the pathophysiologic process of AD.67 Patients with AD typically exhibit a decrease in CSF Aβ42 and an increase in CSF tau and P-tau compared with healthy controls.67,72,73 Each of these biomarkers differentiates AD patients from age-matched controls with 80 to 90% sensitivity and specificity, but accuracy is increased by using the combined analysis of two or more of the three main AD CSF markers (total tau, P-tau, and Aβ42). The CSF markers can also identify with high accuracy an AD pathophysiology among patients with MCI,67,74 which may be referred to as prodromal AD39 or as MCI due to AD.40 The CSF biomarkers have also been increasingly used in the differential diagnosis between AD and other dementias.75,76,77,78 The combined analysis of CSF biomarkers can differentiate AD from behavioral or semantic manifestations of frontotemporal lobe degenerations with high accuracy and may also identify an AD underlying mechanism in patients with atypical presentations, such as lgPPA or PCA.77,79

11.4.3 Single-Photon Emission Computed Tomography and Fluorodeoxyglucose-Positron Emission Tomography Imaging Functional neuroimaging techniques include the measure of blood flow (technetium 99m [99mTc]-hexamethylpropyleneamine [HMPAO] or 133Xe) with single-photon emission CT (SPECT) and positron emission tomography (PET) with fluorodeoxyglucose ([18F]-FDG). In AD, abnormalities in SPECT or [18F]-FDG-PET reflect general damage to neurons and synapses, mainly resulting from tau pathology. The 99mTc-HMPAO SPECT is a useful neuroimaging technique for distinguishing AD from frontotemporal dementia, but a systematic review reported a clinical accuracy for patients with AD versus controls of only 74%.80 On the other hand, in a group with amnestic MCI, an automated quantitative tool for brain perfusion SPECT images using the mean activity in right and left parietal cortex and hippocampus was able to distinguish patients at an early stage of AD from patients with stable MCI (sensitivity, specificity, and accuracy of 82, 90, and 89%, respectively).81 Positron emission tomography with measures of glucose metabolism has shown good accuracy in distinguishing AD patients from both normal controls and patients with non-AD dementias. A reduction in glucose metabolism in bilateral temporoparietal regions and in the posterior cingulate cortex is the most common finding in AD.38,40,82 However, one study showed that within different imaging markers, the largest variability of likelihood ratio for AD diagnosis was of [18F]-FDG-PET.83

Amyloid Imaging The development of amyloid markers in molecular neuroimaging enabled the in vivo assessment of amyloid load, a key feature in the pathophysiology of AD. The most extensively studied amyloid marker is the carbon-11-labeled Pittsburgh compound B (11C-PiB), for which a high level of correlation has been demonstrated between in vivo 11C-PIB uptake and postmortem measures of insoluble (fibrillar) Aβ peptide deposits and plaque load.84

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Overview of Alzheimer’s Disease A multitude of studies during the last decade showed that AD patients typically have 50 to 90% greater 11C-PIB cortical retention than age-matched controls and thus discriminate AD from aged-matched controls with 80 to 100% sensitivity.85 The specificity varies according to the age of the population. For instance, a high cortical 11C-PIB retention may be observed in less than 10% of asymptomatic subjects younger than 70 years but is found in up to 40% of asymptomatic subjects at the age of 80 years.86,87 A high 11C-PIB cortical uptake may also be found in cerebral amyloid angiopathy and in dementia with Lewy bodies.86,87Amyloid imaging by PIB-PET can also identify AD pathology in atypical clinical presentation without initial amnesia, such as PCA and lgPPA.7,88 Molecular amyloid imaging is restricted mainly to research centers, but progress in the field will likely increase the availability of amyloid markers for clinical practice in the following years, especially in light of new amyloid markers that have been studied, such as 18F-florbetapir (18FAV-45) and florbetaben (18FBAY94–9172).89

11.5 Conclusion New proposals for the diagnosis of AD have incorporated biological markers for identifying an underlying pathophysiological process. This approach allows establishment of a clinical diagnosis of AD before the dementia stage, in contrast to previous diagnostic criteria published in 1984.54 According to these new frameworks, diagnosis of the disease is possible at an early stage, when the cognitive symptoms are still mild and the autonomy is preserved. The core clinical criteria remain the main landmark of the diagnosis of AD in clinical practice, but evidence provided by biomarkers such as neuroimaging, CSF markers, and amyloid imaging is expected to increase the specificity of the diagnosis. A recent meta-analysis showed that diagnostic accuracy of imaging AD biomarkers is at least as dependent on how the biomarker is measured as on the biomarker itself.83 Extensive work on biomarker standardization is needed before widespread adoption of these recommendations at any stage of the disease. Despite these limitations, biomarkers to improve the accuracy of the clinical diagnosis will be an essential requisite for new disease-modifying treatments that will tap into specific pathophysiologic targets.

References [1] Querfurth HW, LaFerla FM. Alzheimer’s disease. N Engl J Med 2010; 362: 329–344 [2] Cummings JL. Alzheimer’s disease. N Engl J Med 2004; 351: 56–67 [3] Middleton LE, Yaffe K. Promising strategies for the prevention of dementia. Arch Neurol 2009; 66: 1210–1215 [4] Abbott A. Dementia: a problem for our age. Nature 2011; 475: S2–S4 [5] Selkoe DJ. Toward a comprehensive theory for Alzheimer’s disease. Hypothesis: Alzheimer’s disease is caused by the cerebral accumulation and cytotoxicity of amyloid beta-protein. Ann N Y Acad Sci 2000; 924: 17–25 [6] Jack CR, Jr, Knopman DS, Jagust WJ et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 2010; 9: 119–128 [7] Cruz de Souza L, Corlier F, Habert MO et al. Similar amyloid-β burden in posterior cortical atrophy and Alzheimer’s disease. Brain 2011; 134: 2036–2043 [8] Villemagne VL, Pike KE, Chételat G et al. Longitudinal assessment of Aβ and cognition in aging and Alzheimer’s disease. Ann Neurol 2011; 69: 181–192

[9] Sperling RA, Karlawish J, Johnson KA. Preclinical Alzheimer’s disease: the challenges ahead. Nat Rev Neurol 2013; 9: 54–58 [10] Hyman BT. Amyloid-dependent and amyloid-independent stages of Alzheimer’s disease. Arch Neurol 2011; 68: 1062–1064 [11] Braak H, Braak E. Staging of Alzheimer’s disease-related neurofibrillary changes. Neurobiol Aging 1995; 16: 271–284 [12] Lee JH, Cheng R, Honig LS, Vonsattel JP, Clark L, Mayeux R. Association between genetic variants in SORL1 and autopsy-confirmed Alzheimer’s disease. Neurology 2008; 70: 887–889 [13] Bertram L, Lill CM, Tanzi RE. The genetics of Alzheimer’s disease: back to the future. Neuron 2010; 68: 270–281 [14] Gatz M, Reynolds CA, Fratiglioni L et al. Role of genes and environments for explaining Alzheimer’s disease. Arch Gen Psychiatry 2006; 63: 168–174 [15] Rogaeva E, Meng Y, Lee JH et al. The neuronal sortilin-related receptor SORL1 is genetically associated with Alzheimer’s disease. Nat Genet 2007; 39: 168–177 [16] Lee JH, Barral S, Reitz C. The neuronal sortilin-related receptor gene SORL1 and late-onset alzheimer’s disease. Curr Neurol Neurosci Rep 2008; 8: 384–391 [17] Ballard C, Gauthier S, Corbett A, Brayne C, Aarsland D, Jones E. Alzheimer’s disease. Lancet 2011; 377: 1019–1031 [18] Bertram L, Tanzi RE. Thirty years of Alzheimer’s disease genetics: the implications of systematic meta-analyses. Nat Rev Neurosci 2008; 9: 768–778 [19] Jonsson T, Atwal JK, Steinberg S et al. A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline. Nature 2012; 488: 96–99 [20] Mosconi L, Brys M, Switalski R et al. Maternal family history of Alzheimer’s disease predisposes to reduced brain glucose metabolism. Proc Natl Acad Sci U S A 2007; 104: 19067–19072 [21] Mayeux R, Hyslop PS. Alzheimer’s disease: advances in trafficking. Lancet Neurol 2008; 7: 2–3 [22] Kim J, Basak JM, Holtzman DM. The role of apolipoprotein E in Alzheimer’s disease. Neuron 2009; 63: 287–303 [23] Verghese PB, Castellano JM, Holtzman DM. Apolipoprotein E in Alzheimer’s disease and other neurological disorders. Lancet Neurol 2011; 10: 241–252 [24] Berlau DJ, Corrada MM, Head E, Kawas CH. APOE epsilon2 is associated with intact cognition but increased Alzheimer pathology in the oldest old. Neurology 2009; 72: 829–834 [25] Chiang GC, Insel PS, Tosun D et al. Alzheimer’s Disease Neuroimaging Initiative. Hippocampal atrophy rates and CSF biomarkers in elderly APOE2 normal subjects. Neurology 2010; 75: 1976–1981 [26] Perry DC, Lehmann M, Yokoyama JS et al. Progranulin mutations as risk factors for Alzheimer’s disease. JAMA Neurol 2013; 70: 774–778 [27] Grober E, Buschke H, Crystal H, Bang S, Dresner R. Screening for dementia by memory testing. Neurology 1988; 38: 900–903 [28] Pillon B, Blin J, Vidailhet M et al. The neuropsychological pattern of corticobasal degeneration: comparison with progressive supranuclear palsy and Alzheimer’s disease. Neurology 1995; 45: 1477–1483 [29] Fossati P, Coyette F, Ergis AM, Allilaire JF. Influence of age and executive functioning on verbal memory of inpatients with depression. J Affect Disord 2002; 68: 261–271 [30] Traykov L, Baudic S, Raoux N et al. Patterns of memory impairment and perseverative behavior discriminate early Alzheimer’s disease from subcortical vascular dementia. J Neurol Sci 2005; 229–230: 75–79 [31] Sarazin M, Berr C, De Rotrou J et al. Amnestic syndrome of the medial temporal type identifies prodromal AD: a longitudinal study. Neurology 2007; 69: 1859–1867 [32] Sarazin M, Chauviré V, Gerardin E et al. The amnestic syndrome of hippocampal type in Alzheimer’s disease: an MRI study. J Alzheimers Dis 2010; 22: 285–294 [33] Wagner M, Wolf S, Reischies FM et al. Biomarker validation of a cued recall memory deficit in prodromal Alzheimer’s disease. Neurology 2012; 78: 379– 386 [34] Visser PJ, Verhey F, Knol DL et al. Prevalence and prognostic value of CSF markers of Alzheimer’s disease pathology in patients with subjective cognitive impairment or mild cognitive impairment in the DESCRIPA study: a prospective cohort study. Lancet Neurol 2009; 8: 619–627 [35] Bertoux M, Cruz de Souza L, Corlier F et al. Two distinct amnesic profiles in behavioral variant frontotemporal dementia. Biol Psychiatry 2014; 75: 582–588 [36] Thomas-Anterion C, Laurent B. [Neuropsychological markers for the diagnosis of Alzheimer’s disease] [in French] Rev Neurol (Paris) 2006; 162: 913–920 [37] Sarazin M, Stern Y, Berr C et al. Neuropsychological predictors of dependency in patients with Alzheimer’s disease. Neurology 2005; 64: 1027–1031 [38] Dubois B, Feldman HH, Jacova C et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol 2007; 6: 734–746

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Alzheimer’s Disease [39] Dubois B, Feldman HH, Jacova C et al. Revising the definition of Alzheimer’s disease: a new lexicon. Lancet Neurol 2010; 9: 1118–1127 [40] Albert MS, DeKosky ST, Dickson D et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 270–279 [41] Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993; 43: 2412–2414 [42] Renner JA, Burns JM, Hou CE, McKeel DW, Jr, Storandt M, Morris JC. Progressive posterior cortical dysfunction: a clinicopathologic series. Neurology 2004; 63: 1175–1180 [43] Tang-Wai DF, Graff-Radford NR, Boeve BF et al. Clinical, genetic, and neuropathologic characteristics of posterior cortical atrophy. Neurology 2004; 63: 1168–1174 [44] Alladi S, Xuereb J, Bak T et al. Focal cortical presentations of Alzheimer’s disease. Brain 2007; 130: 2636–2645 [45] Murray ME, Graff-Radford NR, Ross OA, Petersen RC, Duara R, Dickson DW. Neuropathologically defined subtypes of Alzheimer’s disease with distinct clinical characteristics: a retrospective study. Lancet Neurol 2011; 10: 785–796 [46] McMonagle P, Deering F, Berliner Y, Kertesz A. The cognitive profile of posterior cortical atrophy. Neurology 2006; 66: 331–338 [47] Kas A, Cruz de Souza L, Samri D et al. Neural correlates of cognitive impairment in posterior cortical atrophy. Brain 2011; 134: 1464–1478 [48] Gorno-Tempini ML, Hillis AE, Weintraub S et al. Classification of primary progressive aphasia and its variants. Neurology 2011; 76: 1006–1014 [49] Taylor KI, Probst A, Miserez AR, Monsch AU, Tolnay M. Clinical course of neuropathologically confirmed frontal-variant Alzheimer’s disease. Nat Clin Pract Neurol 2008; 4: 226–232 [50] Weiner MF, Hynan LS, Bret ME, White C, III. Early behavioral symptoms and course of Alzheimer’s disease. Acta Psychiatr Scand 2005; 111: 367–371 [51] Scarmeas N, Brandt J, Albert M et al. Delusions and hallucinations are associated with worse outcome in Alzheimer’s disease. Arch Neurol 2005; 62: 1601–1608 [52] Hodges JR. Alzheimer’s centennial legacy: origins, landmarks and the current status of knowledge concerning cognitive aspects. Brain 2006; 129: 2811– 2822 [53] Scarmeas N, Brandt J, Blacker D et al. Disruptive behavior as a predictor in Alzheimer’s disease. Arch Neurol 2007; 64: 1755–1761 [54] McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 1984; 34: 939–944 [55] DeKosky ST, Carrillo MC, Phelps C et al. Revision of the criteria for Alzheimer’s disease: A symposium. Alzheimers Dement 2011; 7: e1–e12 [56] Jack CR, Jr, Albert MS, Knopman DS et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 257–262 [57] Sperling RA, Aisen PS, Beckett LA et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 280–292 [58] Frisoni GB, Fox NC, Jack CR, Jr, Scheltens P, Thompson PM. The clinical use of structural MRI in Alzheimer’s disease. Nat Rev Neurol 2010; 6: 67–77 [59] Chincarini A, Bosco P, Calvini P et al. Alzheimer’s Disease Neuroimaging Initiative. Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer’s disease. Neuroimage 2011; 58: 469–480 [60] Costafreda SG, Dinov ID, Tu Z et al. Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment. Neuroimage 2011; 56: 212–219 [61] Duara R, Loewenstein DA, Potter E et al. Medial temporal lobe atrophy on MRI scans and the diagnosis of Alzheimer’s disease. Neurology 2008; 71: 1986–1992 [62] Cuingnet R, Gerardin E, Tessieras J et al. Alzheimer’s Disease Neuroimaging Initiative. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 2011; 56: 766–781 [63] Boutet C, Chupin M, Colliot O et al. Alzheimer’s Disease Neuroimaging Initiative. Is radiological evaluation as good as computer-based volumetry to assess hippocampal atrophy in Alzheimer’s disease? Neuroradiology 2012; 54: 1321–1330 [64] Cruz de Souza L, Chupin M, Bertoux M et al. Is hippocampal volume a good marker to differentiate Alzheimer’s disease from frontotemporal dementia? J Alzheimers Dis 2013; 36: 57–66

[65] den Heijer T, van der Lijn F, Koudstaal PJ et al. A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline. Brain 2010; 133: 1163–1172 [66] Lo RY, Hubbard AE, Shaw LM et al. Alzheimer’s Disease Neuroimaging Initiative. Longitudinal change of biomarkers in cognitive decline. Arch Neurol 2011; 68: 1257–1266 [67] Blennow K, Hampel H, Weiner M, Zetterberg H. Cerebrospinal fluid and plasma biomarkers in Alzheimer’s disease. Nat Rev Neurol 2010; 6: 131–144 [68] Sarazin M, Cruz de Souza L, Lehéricy S, Dubois B. Clinical and research diagnostic criteria for Alzheimer’s disease. Neuroimaging Clin N Am 2012; 22: 23–32, viii [69] Buerger K, Ewers M, Pirttilä T et al. CSF phosphorylated tau protein correlates with neocortical neurofibrillary pathology in Alzheimer’s disease. Brain 2006; 129: 3035–3041 [70] Tapiola T, Alafuzoff I, Herukka SK et al. Cerebrospinal fluid beta-amyloid 42 and tau proteins as biomarkers of Alzheimer-type pathologic changes in the brain. Arch Neurol 2009; 66: 382–389 [71] Seppälä TT, Nerg O, Koivisto AM et al. CSF biomarkers for Alzheimer’s disease correlate with cortical brain biopsy findings. Neurology 2012; 78: 1568–1575 [72] De Meyer G, Shapiro F, Vanderstichele H et al. Alzheimer’s Disease Neuroimaging Initiative. Diagnosis-independent Alzheimer’s disease biomarker signature in cognitively normal elderly people. Arch Neurol 2010; 67: 949–956 [73] Forlenza OV, Diniz BS, Gattaz WF. Diagnosis and biomarkers of predementia in Alzheimer’s disease. BMC Med 2010; 8: 89 [74] Hansson O, Zetterberg H, Buchhave P, Londos E, Blennow K, Minthon L. Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol 2006; 5: 228–234 [75] Bian H, Van Swieten JC, Leight S et al. CSF biomarkers in frontotemporal lobar degeneration with known pathology. Neurology 2008; 70: 1827–1835 [76] Bibl M, Mollenhauer B, Lewczuk P et al. Cerebrospinal fluid tau, P-tau 181 and amyloid-β 38/40/42 in frontotemporal dementias and primary progressive aphasias. Dement Geriatr Cogn Disord 2011; 31: 37–44 [77] Cruz de Souza L, Lamari F, Belliard S et al. Cerebrospinal fluid biomarkers in the differential diagnosis of Alzheimer’s disease from other cortical dementias. J Neurol Neurosurg Psychiatry 2011; 82: 240–246 [78] Schoonenboom NS, Reesink FE, Verwey NA et al. Cerebrospinal fluid markers for differential dementia diagnosis in a large memory clinic cohort. Neurology 2012; 78: 47–54 [79] Kas A, Uspenskaya O, Lamari F et al. Distinct brain perfusion pattern associated with CSF biomarkers profile in primary progressive aphasia. J Neurol Neurosurg Psychiatry 2012; 83: 695–698 [80] Dougall NJ, Bruggink S, Ebmeier KP. Systematic review of the diagnostic accuracy of 99mTc-HMPAO-SPECT in dementia. Am J Geriatr Psychiatry 2004; 12: 554–570 [81] Habert MO, Horn JF, Sarazin M et al. Brain perfusion SPECT with an automated quantitative tool can identify prodromal Alzheimer’s disease among patients with mild cognitive impairment. Neurobiol Aging 2011; 32: 15– 23 [82] McKhann GM, Knopman DS, Chertkow H et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 263–269 [83] Frisoni GB, Bocchetta M, Chételat G et al. ISTAART’s NeuroImaging Professional Interest Area. Imaging markers for Alzheimer’s disease: which vs how. Neurology 2013; 81: 487–500 [84] Ikonomovic MD, Klunk WE, Abrahamson EE et al. Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer’s disease. Brain 2008; 131: 1630–1645 [85] Noble JM, Scarmeas N. Application of pet imaging to diagnosis of Alzheimer’s disease and mild cognitive impairment. Int Rev Neurobiol 2009; 84: 133–149 [86] Herholz K, Ebmeier K. Clinical amyloid imaging in Alzheimer’s disease. Lancet Neurol 2011; 10: 667–670 [87] Villemagne VL, Rowe CC. Amyloid imaging. Int Psychogeriatr 2011; 23 Suppl 2: S41–S49 [88] Leyton CE, Villemagne VL, Savage S et al. Subtypes of progressive aphasia: application of the International Consensus Criteria and validation using β-amyloid imaging. Brain 2011; 134: 3030–3043 [89] Fleisher AS, Chen K, Liu X et al. Using positron emission tomography and florbetapir F18 to image cortical amyloid in patients with mild cognitive impairment or dementia due to Alzheimer’s disease. Arch Neurol 2011; 68: 1404–1411

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Genetics, Neuropathology, and Biomarkers in Alzheimer’s Disease

12 Genetics, Neuropathology, and Biomarkers in Alzheimer’s Disease Maria Martinez-Lage Alvarez and Rashmi Tondon Alzheimer’s disease (AD) is an adult-onset, slowly progressive neurodegenerative disorder that initially affects memory and later involves other cognitive and basic neurologic functions. AD is the most common form of dementia, particularly in elderly adults. It is estimated that 5.2 million Americans have AD in 2014, including approximately 200,000 individuals younger than age 65.1 The pathological hallmarks of the disease found in the brain are extracellular senile plaques, composed of βamyloid (Aβ), and intracellular neurofibrillary tangles (NFTs), composed of phosphorylated tau protein, which can also be seen in the form of neuropil threads and neurites. AD can be clinically considered as either late or early onset, depending on whether it manifests before or after age 65 years. This chapter describes the genetic aspects, neuropathological findings, and nonimaging biomarkers of AD.

12.1 Genetics of Alzheimer’s Disease 12.1.1 Late-Onset Alzheimer’s Disease More than 95% of patients with AD have onset after the age of 65, and it is well established that the risk of developing the disease increases exponentially with age: 11% of the population age 65 years and older suffer from AD, and the prevalence is 32% for those age 85 years and older.1 The causes of AD in this sporadic (as opposed to inherited) population is undoubtedly multifactorial, likely resulting from factors like cerebrovascular disease, type 2 diabetes, or obesity. Certain genetic susceptibility loci have been known for more than two decades, whereas numerous new candidates for genetic risk factors have been discovered in more recent years thanks to improved technology and increased access to genomic studies of well-selected samples.

12.1.2 Apolipoprotein E The association of the apolipoprotein E (ApoE) ε4 allele with late-onset AD in non-Hispanic whites of European ancestry has been well known for more than a decade. ApoE is a plasma protein involved in the transport of cholesterol that exists as three isoforms determined by three alleles (ε2, ε3, and ε4). A single ApoE ε4 allele conveys a twofold to threefold increase in risk of developing AD, whereas having two copies is associated with a fivefold increase, demonstrating an additive risk association.2 The ε2 allele is considered protective, also in an additive manner, so that a homozygous ε2/ε2 genotype confers a lower risk than just one ε2 allele. Not only a higher risk of developing the disease, but also an earlier age of onset, has been associated with the ε4 allele; however, the presence of ε4 is neither sufficient nor necessary to develop AD.

Sortilin-Related Receptor Encoding a protein that participates in vesicle trafficking between the cell surface and Golgi apparatus, the sortilinrelated receptor (SORL1) gene was chosen as a potential candidate for AD susceptibility in an association study.3 Despite initial contradicting results from replication cohorts, sufficient evidence now supports the association of specific variants in this gene with a higher risk of developing AD, at least in the white population.4

Additional Genes Discovered in Genome-Wide Association Studies With the advance of genome-wide technologies, it has been possible to move away from candidate gene approaches and toward unbiased assessments of the whole genome, eliminating the need to preselect candidates and opening the possibilities of detecting either novel genes or pathways not suspected to participate in a particular disorder. The effort has been tremendous in AD in the last several years, with collaborative studies analyzing more than 10,000 patients and more than 10,000 controls.2 Several genes have been identified with this method and replicated in diverse cohorts, making it worthwhile to mention them here. Of note, none of these other genes is similar in effect to ApoE, which still remains the major genetic risk factor for late-onset AD. The most salient genes identified in these studies are listed in ▶ Table 12.1, along with their relative odds ratio values.2 Of note, these genes and their related encoded proteins can be clustered in a few functional and metabolic pathways, including lipid metabolism, innate and adaptive immunity, cell adhesion, and endocytosis, all of which are likely involved in the development of the neuropathologic substrates of AD.

12.1.3 Early-Onset Alzheimer’s Disease The identification of families with autosomal dominant inheritance patterns contributed to the discovery in the late 1980s and early 1990s of three genes responsible for most early-onset AD (approximately 1% of all cases of AD). Amyloid precursor protein (APP) and presenilins 1 and 2 (PSEN1 and PSEN2 ) are involved in the processing of the Aβ molecule.5 Most mutations in these genes are autosomal dominant, albeit not always fully penetrant. These are considered causative genes because individuals carrying mutations will inevitably develop the disease (except with incomplete penetrance), and detection of the mutation in an individual is diagnostic of AD.

Amyloid Precursor Protein Located on chromosome 21q21.3, APP encodes for the amyloid precursor protein, a 110- to 130-kDa ubiquitously expressed

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Alzheimer’s Disease Table 12.1 Molecular genetic classification of Alzheimer’s disease Gene symbol

Gene name

Chromosomal location

Key information

Definitive disease-causing genes (causative mutations of early onset AD) APP

Amyloid precursor protein

21q21.3

Autosomal dominant, 25 pathogenic mutations, 16% of early-onset AD

PSEN1

Presenilin-1

14q24

Autosomal dominant, 185 pathogenic mutations, 66% of early-onset AD

PSEN2

Presenilin-2

1q31

Autosomal dominant,12 pathogenic mutations

Genes with increased susceptibility (risk variants for late-onset AD) ApoE

Apolipoprotein E

19q13.32

ε2q13.32rot–15 ε5q13.32rotein –3

SORL1

Sortilin-related receptor

11q24.1

OR 1.08

ABCA7

Adenosing triphosphate-binding cassette, subfamily A, member 7

19p13.3

OR 1.2

BIN1

Bridging integrator 1

2q14.3

OR 1.2

CD33 (SIGLEC6)

CD33 antigen/sialic-acid binding immunoglobulin-like lectin 6

19q13.41

OR 0.9

CD2AP

CD2-associated protein

6p12.3

OR 1.1

CLU

Clusterin

8p21.1

OR 0.8

CR1

Complement component receptor 1

1q32.2

OR 1.2

EPHA1

Ephrin receptor EphA1

7q34-q35

OR 0.9

MS4A4E/MS4A6A

Membrane-spanning 4-domains, subfamily A, members 6E, 4A

11q12.2

OR 0.9

PICALM

Phosphatidylinositol-binding clathrin assembly protein

11q14.2

OR 0.8

Abbreviations: OR, odds ratio (note that an OR > 1 implies higher relative risk of disease, whereas an OR < 1 implies lower relative risk for the disease; the greater the number, the largest the size effect for that genotype). Source: OMIM (http://omim.org/entry/104300), Schellenberg GD, Montine TJ. The genetics and neuropathology of Alzheimer’s disease. Acta Neuropathologica 2012;124(3):305–323.

transmembrane protein that contains an internal 39–43 amino acid sequence coding for Aβ peptides. Cleavage by β- and γ-secretases results in the formation of peptides Aβ1-40 and Aβ1-42, the major component of the hallmark senile plaques of AD.6 Up to 25 point mutations have been identified as pathogenic to date7 (see http://www.molgen.vib-ua.be/ADMutations). All point mutations are clustered in a 54 amino acid segment that lies within or adjacent to the sequence encoding for Aβ peptides.2 In addition to being responsible for approximately 16% of cases of early-onset AD,8 mutations in the APP gene can cause autosomal dominant cerebral amyloid angiopathy and syndromes in which the two overlap. The London mutation (V717I) is the most common APP mutation and results in increased levels of Aβ1–42 by interfering with the activity of γ-secretase. The Swedish mutation involves two different codons (K670 M and N671K) and increases total levels of Aβ production. The excess of Aβ is therefore considered sufficient to cause the disease, and this has been largely supported by the observation of a high prevalence of AD neuropathological changes and increased incidence of dementia in patients with trisomy 21 (Down syndrome), who carry an extra copy of the APP gene.2 Furthermore, several gene duplication events not associated with trisomy have been recognized as pathogenic events in AD.7 Lastly, the Arctic mutation, E693G, rather than altering the total amount of Aβ or interfering with γ-secretase activity, creates a mutant peptide that is more prone to aggregation than is wild-type Aβ.9

Presenilins 1 and 2 Mutations in presenilin 1 (PSEN1), located in chromosome 14q24.3, are responsible for the highest percentage of autosomal dominant early-onset AD, accounting for up to 66% of all cases.8 At least 185 pathogenic mutations have been identified to date7 (see http://www.molgen.vib-ua.be/AD mutations), all with complete penetrance by age 60 to 65 years. As is the case for APP, there is significant heterogeneity in the phenotypic characteristics of individuals with mutations in PSEN1 in terms of age of onset (as early as the late 20s), rate of progression, and severity of the disease.10 PSEN1 is the catalytic component of γ-secretase, a protein complex responsible for the cleavage of a number of membrane proteins, including APP. Normal γ-secretase activity yields mainly Aβ1–40, with smaller amounts of Aβ1–42. PSEN1 mutations alter the secretase activity,11 leading to increased ratio of Aβ1–42 to Aβ1–40, thus facilitating the deposition of amyloidogenic species. Presenilin 2 (PSEN2) is a highly homologous protein located in 1q31-q42, which also participates in the γ-secretase complex as the catalytic domain in the absence of PSEN1. PSEN2 mutations are less common than PSEN1 variants, with only 13 pathogenic mutations known to date.7 Compared with those with PSEN1, patients with PSEN2 mutations tend to have a higher age of onset (accounting for the small number of late-onset AD cases caused by an inherited causative mutation), longer course of the disease, and more variable penetrance.2

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Genetics, Neuropathology, and Biomarkers in Alzheimer’s Disease

12.2 Neuropathology of Alzheimer’s Disease The hallmark pathological features of AD include the accumulation of the protein fragment Aβ in extracellular “senile” plaques and other deposit and the intracellular buildup of phosphorylated protein tau in the form of NFTs and neuritic threads. Other changes that are characteristic of AD include cerebral amyloid angiopathy (CAA) caused by deposition of Aβ in and around vessels, loss of synapses, neuron loss, glial activation, and ultimately brain atrophy. ▶ Fig. 12.1 illustrates these pathological features of AD. Amyloid precursor protein, the product of the APP gene, can be processed in two divergent pathways. When the fulllength protein is cleaved by α- and γ-secretases, it results in a C-terminal fragment that is nonamyloidogenic. Conversely, when cleaved via the β- and γ-secretases, several species of Aβ fragments can occur, with Aβ1–40 the most common and Aβ1–42 being prone to aggregation (amyloidogenic). Aβ1–42 molecules form toxic oligomers, which then aggregate as extracellular insoluble fibrils with β-pleated sheet conformation, giving rise to the typical amyloid senile plaques. Aβ deposits are morphologically variable, ranging from the so-called neuritic plaques in which they are at the center of a cluster of tau-positive dystrophic neurites, to diffuse (non-neuritic) plaques and diffuse

deposits known as amyloid lakes. The “amyloid cascade hypothesis” suggests that the accumulation of Aβ fibrils in plaques is the primary pathological event in the disease and that it leads to the formation of the other pathological features, such as NFTs, synaptic loss, neuronal degeneration, and death.12 Tau protein is associated with microtubules and is thought to participate in regulating their stability in neuronal axons. For reasons not entirely understood, tau protein becomes aberrantly hyperphosphorylated, dissociates from microtubules, and aggregates into paired helical filaments, insoluble fibrils that then are deposited as the characteristic NFTs and neuropil threads with β-pleated sheet conformation. Studies have demonstrated that in at least some individuals, tau pathology appears well before β-amyloid deposits are seen, which cannot be explained in the light of the amyloid cascade hypothesis and suggests that tau pathology can be an initiating event in the disease. The progression of AD pathology, including amyloid plaques and NFTs, follows a specific spatial and anatomical pattern, starting in the limbic cortex (entorhinal cortex and hippocampus) and extending toward the neocortical surface, some subcortical nuclei, and in some cases the brainstem. With respect to NFTs, the staging system described by Braak and Braak13 is still recommended because it reflects this pattern of progression with robust reliability. It proposes six stages, but there is

Fig. 12.1 Histopathological hallmarks of Alzheimer’s disease (AD). Photomicrographs of immunohistochemical stains demonstrate the typical findings of AD and comorbid pathologies. β-amyloid immunohistochemistry highlights abundant senile plaques in the hippocampus (a, 500x) and frontal cortex c, 100x) in this case of advanced AD. Phosphorylated tau immunohistochemistry demonstrates neurofibrillary tangles as well as neuropil threads and neurites in the hippocampus (b, 500x; d, 200x) in the same case. Note that phosphorylated tau also labels a number of neuritic senile plaques as there are tau-positive neurites in the center of these amyloid plaques. Coexisting pathology was present in this case, as is commonly seen in AD. Lewy bodies and Lewy neurites are seen with α-synuclein immunostaining in the amygdala (e, 200x); whereas an antibody for phosphorylated TDP-43 also demonstrates inclusions in the same region (e, 200x).

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Alzheimer’s Disease increased inter-rater agreement when reduced to four stages14: (1) no NFT; (2) in Braak stages I/II, NFTs are predominantly in the entorhinal cortex and closely related areas; (3) in Braak stages III/IV, NFTs are more abundant in the hippocampus and amygdala and there is slight involvement of association cortex; (4) in Braak stages V/VI, NFTs are widely distributed throughout the neocortical areas, with eventual involvement of the primary motor and sensory areas. In addition, many but not all cases of AD will demonstrate additional coexisting non-AD-type pathology in the brain, such as Lewy body disease (LBD), vascular brain injury, hippocampal sclerosis, and TDP-43 pathology.14 Lewy bodies, composed largely of α-synuclein and characteristic of Parkinson’s disease and LBD, are commonly seen in brains with moderate to severe AD changes, not only in sporadic cases but also in some patients with PSEN1 and PSEN2 mutations.15 Cerebrovascular disease and vascular brain injury, together with CAA, are commonly identified in brains with AD changes and should be acknowledged. TDP-43 is the major protein present in the pathological inclusions of frontotemporal lobar degeneration not caused by tau pathology and in amyotrophic lateral sclerosis,16 and it is increasingly recognized as present in the limbic structures of brains with AD pathology, with or without coexisting hippocampal sclerosis. Currently, a definitive diagnosis of AD still relies on postmortem examination of the brain to detect the typical senile plaques and NFTs, which are known to correlate with the presence of clinical symptoms of AD. The guidelines for neuropathologic evaluation and assessment of AD were reviewed in a seminal consensus paper from the National Institute on Aging and the Alzheimer’s Association in 2012, 25 years after the prior consensus.14 The main change to the diagnostic criteria was the recognition of AD as a dynamic entity, with a prodromal asymptomatic phase during which pathology has started to accumulate but has not caused major symptoms, thus allowing for the diagnosis of AD neuropathologic changes in the absence of a clinical history of dementia and bringing to the neuropathology arena the concept of early and preclinical AD. The guidelines recommend documentation of the AD pathologic features as stated herein, as well as documentation of the comorbidities in the autopsy report, including LBD, vascular pathology, and TDP-43 pathology. The “ABC” staging protocol is recommended, based on the data-driven documentation of the relative amounts of each of the three morphologic characteristics of AD: A, Aβ/amyloid plaque score (based on Thal phases17: A0 to A3); B, NFT score (based on Braak stage13: B0, B1 = I/II, B2 = III/IV, B3 = V/VI); and C, neuritic plaque score (based on Consortium to Establish a Registry for Alzheimer’s Disease [CERAD] criteria18: C0 to C3). The combination of A, B, and C scores provides, for each case, a descriptor of “not,” “low,” “intermediate,” and “high” for AD neuropathologic change (entirely independent of clinical symptoms).14 At autopsy, patients with disease-causing mutations (APP, PSEN1, and PSEN2 ) tend to have greater amounts of neocortical senile plaques than patients who had “sporadic” AD, with no difference in the amounts of tau pathology. Some mutations in these genes also result in differences in the morphology of the amyloid pathology compared with sporadic cases, such as large dense plaques in the APP Flemish mutation,19 ringlike plaques in the APP Arctic mutation,20 and cotton-wool plaques in PSEN1 mutations.2

12.3 Nonimaging Biomarkers in the Diagnosis of Alzheimer’s Disease Revised clinical criteria and guidelines for diagnosing AD were proposed and published in 201121 and recommended the consideration of AD as a slowly progressive disease that begins well before clinical symptoms emerge. In addition to imaging biomarkers largely discussed elsewhere, enormous efforts have been dedicated in the last two decades to the discovery of other biological biomarkers for the diagnosis of AD. As discussed earlier, a definitive diagnosis has classically been considered attainable only postmortem with the examination of the affected brain. This, however, is insufficient when we take into account the need to establish a degree of certainty both at the research level (e.g., to identify and monitor possible diseasemodifying agents), as well as at the clinical level when approaching an individual patient.

12.3.1 Plasma Plasma biomarker discovery started off with the idea of detecting β-amyloid in plasma. With the hypothesis that there is always an equilibrium state of Aβ production and deposition in the brain and that there is correlation with plasma levels, studies were able to determine that there is an increase in plasma levels of Aβ, at least in patients with familial AD.22 Plasma level results in the general population in sporadic AD are, however, controversial and limited by complicated technological and methods problems with this test.

12.3.2 Cerebrospinal Fluid Cerebrospinal fluid (CSF) has the potential to reflect reliably the state of chemical and cellular homeostasis in the brain, given its direct contact with the cerebral extracellular space. As such, CSF biomarkers have been incorporated into the revised research diagnostic criteria for AD since 2007,23 although they are still not largely available in community clinical practice. Levels of Aβ1–42, total tau, and phospho-tau can be used to aid in the diagnosis of AD.24 The increase in the total concentrations of tau in CSF is directly related to axonal degeneration in the cortex, whereas levels of phospho-tau are associated with NFTs. In this setting, total tau levels can increase in any process that involves cortical degeneration, such as stroke, trauma, and other neurodegenerative diseases,25 but phospho-tau is a more precise measurement associated with the underlying pathology of AD. In addition to confirmation of diagnosis in patients manifesting full-blown symptoms, the importance of CSF AD biomarkers lies in their ability to contribute to early diagnosis, identify patients in prodromal phases (including mild cognitive impairment) that will go on to develop the disease, and select and monitor subjects for clinical trials. Because of analytical issues with the technology used (enzyme-linked immunosorbent assay and other immunoassay methods) across institutions and geographic locations, definite standardization is under way to establish homogeneity of results and improve the yield and quality of data obtained from these assays.25 Of note, CSF biomarkers are to be used in conjunction with clinical, genetic, and neuroimaging data to provide the most accurate diagnostic information.

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Genetics, Neuropathology, and Biomarkers in Alzheimer’s Disease

References [1] Alzheimer’s Association. 2014 Alzheimer’s disease facts and figures. Alzheimers Dement 2014; 10: e47–92 [2] Schellenberg GD, Montine TJ. The genetics and neuropathology of Alzheimer’s disease. Acta Neuropathol 2012; 124: 305–323 [3] Rogaeva E, Meng Y, Lee JH et al. The neuronal sortilin-related receptor SORL1 is genetically associated with Alzheimer’s disease. Nat Genet 2007; 39: 168–177 [4] Reitz C, Cheng R, Rogaeva E et al. Genetic and Environmental Risk in Alzheimer Disease 1 Consortium. Meta-analysis of the association between variants in SORL1 and Alzheimer’s disease. Arch Neurol 2011; 68: 99–106 [5] Reitz C, Mayeux R. Alzheimer’s disease: epidemiology, diagnostic criteria, risk factors and biomarkers. Biochem Pharmacol 2014; 88: 640–651 [6] O’Brien RJ, Wong PC. Amyloid precursor protein processing and Alzheimer’s disease. Annu Rev Neurosci 2011; 34: 185–204 [7] Cruts M, Theuns J, Van Broeckhoven C. Locus-specific mutation databases for neurodegenerative brain diseases. Hum Mutat 2012; 33: 1340–1344 [8] Raux G, Guyant-Maréchal L, Martin C et al. Molecular diagnosis of autosomal dominant early onset Alzheimer’s disease: an update. J Med Genet 2005; 42: 793–795 [9] Nilsberth C, Westlind-Danielsson A, Eckman CB et al. The ‘Arctic’ APP mutation (E693G) causes Alzheimer’s disease by enhanced Abeta protofibril formation. Nat Neurosci 2001; 4: 887–893 [10] Ridge PG, Ebbert MT, Kauwe JS. Genetics of Alzheimer’s disease. Biomed Res Int 2013; 2013: 254954 [11] Chau D-M, Crump CJ, Villa JC, Scheinberg DA, Li Y-M. Familial Alzheimer’s disease presenilin-1 mutations alter the active site conformation of γ-secretase. J Biol Chem 2012; 287: 17288–17296 [12] Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 2002; 297: 353–356 [13] Braak H, Braak E. Neuropathological staging of Alzheimer-related changes. Acta Neuropathol 1991; 82: 239–259 [14] Hyman BT, Phelps CH, Beach TG et al. National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimers Dement 2012; 8: 1–13

[15] Leverenz JB, Fishel MA, Peskind ER et al. Lewy body pathology in familial Alzheimer disease: evidence for disease- and mutation-specific pathologic phenotype. Arch Neurol 2006; 63: 370–376 [16] Neumann M, Sampathu DM, Kwong LK et al. Ubiquitinated TDP-43 in frontotemporal lobar degeneration and amyotrophic lateral sclerosis. Science 2006; 314: 130–133 [17] Thal DR, Rüb U, Orantes M, Braak H. Phases of Aβ-deposition in the human brain and its relevance for the development of AD. Neurology 2002; 58: 1791–1800 [18] Mirra SS, Heyman A, McKeel D et al. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease. Neurology 1991; 41: 479–486 [19] Kumar-Singh S, Cras P, Wang R et al. Dense-core senile plaques in the Flemish variant of Alzheimer’s disease are vasocentric. Am J Pathol 2002; 161: 507–520 [20] Basun H, Bogdanovic N, Ingelsson M et al. Clinical and neuropathological features of the arctic APP gene mutation causing early-onset alzheimer disease. Arch Neurol 2008; 65: 499–505 [21] McKhann GM, Knopman DS, Chertkow H et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 263–269 [22] Scheuner D, Eckman C, Jensen M et al. Secreted amyloid β-protein similar to that in the senile plaques of Alzheimer’s disease is increased in vivo by the presenilin 1 and 2 and APP mutations linked to familial Alzheimer’s disease. Nat Med 1996; 2: 864–870 [23] Dubois B, Feldman HH, Jacova C et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol 2007; 6: 734–746 [24] Hansson O, Zetterberg H, Buchhave P et al. Prediction of Alzheimer’s disease using the CSF Abeta42/Abeta40 ratio in patients with mild cognitive impairment. Dement Geriatr Cogn Disord 2007; 23: 316–320 [25] Kang JH, Korecka M, Toledo JB, Trojanowski JQ, Shaw LM. Clinical utility and analytical challenges in measurement of cerebrospinal fluid amyloid-β (1–42) and τ proteins as Alzheimer’s disease biomarkers. Clin Chem 2013; 59: 903–916

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Alzheimer’s Disease

13 Imaging of Alzheimer’s Disease: Part 1 Donald G. McLaren, Guofan Xu, and Vivek Prabhakaran Alzheimer’s disease (AD), the most common dementia, is a progressive, devastating, nonreversible, and ultimately fatal neurodegenerative disorder that leads to loss of memory and of the ability to function independently.1 It is expected that by 2050 more than 16 million Americans and 135 million people worldwide will suffer from AD. These numbers, coupled with the fact that current treatments only slow the clinical progression of AD, necessitate the utilization and improvement of biomarkers for understanding the disease processes, improving the diagnostic accuracy, and improving treatment outcomes. Although the spatial progression of AD pathology has been understood for a number of years,2,3 the development of biomarkers to measure the spatial progression has only partially mirrored the histologic work. In 2011, the National Institute on Aging/Alzheimer’s Association Workgroup released revised criteria for the diagnosis of AD,4 which called for research on the use of abnormal levels of biomarkers in future diagnostic criteria. The workgroup concluded that advancements in biomarkers would enhance the pathophysiologic specificity of the diagnosis. Thus, this chapter focuses on structural (i.e., magnetic resonance imaging [MRI]) and metabolic or molecular imaging of AD (i.e., single photon emission tomography [SPECT] and positron emission tomography [PET]), and Chapter 14 focuses on functional neuroimaging and brain connectivity in AD (e.g., perfusion, functional MRI, and diffusion tensor imaging). It is important keep in mind that use of neuroimaging and neuroimaging biomarkers is not a replacement for neuropsychological or neurologic assessment; rather, it complements them.

13.1 Magnetic Resonance Imaging Magnetic resonance imaging is a noninvasive imaging technique that can measure brain structure and function (see

Chapter 14). The image is typically formed by detecting the radiofrequency signal emitted by hydrogen atoms after applying a radiofrequency pulse. Different types of images are acquired by modifying the time and strength of the pulse as well as the time before detecting the emitted signal. The most common types of brain MRI are T1-weighted images, T2-weighted images, and T2*-weighted images. Some MRI scans have specific clinical uses. For example, susceptibility-weighted imaging (SWI) can be used to detect cerebral microhemorrhages.

13.1.1 T1-Weighted Imaging T1-weighted scans provide a clear picture of the gray and white matter in the brain, which appear as gray and white on the image, respectively (▶ Fig. 13.1). T1-weighted imaging is commonly used to assess whether a patient has normal or abnormal brain structure. In cases of cognitive impairment, it can be used to rule out strokes and tumors as well as to identify areas of atrophy in the brain. Atrophy, or brain volume loss, in the hippocampus, medial and lateral temporal lobes, lateral parietal lobes, and precuneus is typical in AD patients (▶ Fig. 13.2).5 A similar pattern, albeit to a lesser degree and more limited to the temporal lobes, is found in patients with mild cognitive impairment (MCI).5 Recently, researchers have focused on individuals with preclinical AD. In 2011, the National Institute on Aging and Alzheimer’s Association Workgroup came up with three stages characterizing preclinical AD.6 Preclinical stage 1, asymptomatic amyloidosis, includes patients with high PET amyloid tracer retention or low cerebrospinal fluid (CSF) β-amyloid (Aβ)1–42. Preclinical stage 2, asymptomatic amyloidosis plus neurodegeneration, includes patients who meet the definition for stage 1 but also have neuronal dysfunction on fluorodeoxyglucose (FDG)-PET, high CSF tau/p-tau, or cortical thinning or hippo-

Fig. 13.1 Axial, coronal, and sagittal slices of a T1-weighted magnetic resonance imaging scan showing gray matter (gray areas) and white matter (white areas). Blue crosshairs are located in the right hippocampus.

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Imaging of Alzheimer’s Disease: Part 1

Fig. 13.2 Graphical representation of the cortical signature of Alzheimer’s disease (AD < controls p < 0.0001). Nodes with significant cortical thinning observed between individuals with AD and healthy controls. Data shown are on the pial surface of the FreeSurfer average brain. (Used, with permission, from Gross AL, Manly JJ, Pa J, et al; Alzheimer’s Disease Neuroimaging Initiative. Cortical signatures of cognition and their relationship to Alzheimer’s disease. Brain Imaging Behav 2012;6(4):584–598.)

campal atrophy on structural MRI. Preclinical stage 3, asymptomatic amyloidosis plus neurodegeneration plus subtle cognitive decline, includes patients who met the definition for stage 2 but also show subtle cognitive decline from baseline or poor performance on challenging cognitive tests and did not meet the criteria for MCI. Researchers have reported the prevalence of each of these stages in cognitively normal older adults7,8: 16% had preclinical AD stage 1, 12% had preclinical AD stage 2, and 2% had preclinical AD stage 3. Interestingly, an additional 23% of the sample had evidence of abnormal hippocampal volume or hypometabolism as measured by FDG-PET. These patients were labeled as having suspected non-AD pathology, indicating that there are other pathways to hypoperfusion and hippocampal volume loss. This research indicates that more research is needed into the earliest structural changes in AD. Because of the low cost of MRI scans, large-scale research studies (e.g., Alzheimer’s Disease Neuroimaging Initiative, or ADNI) routinely collect high-resolution T1-weighted images to conduct voxel-based morphometry (VBM) and cortical thickness studies.9 VBM studies10 are conducted to investigate how gray matter volume changes with clinical status as well as with cognition. For example, researchers found significant correlations between gray matter volume in the left supramarginal gyrus, anterior cerebellum, and left superior temporal gyrus and the Free and Cued Selective Reminding Test.11 Cortical thickness studies have also investigated the relationship of cortical thinning to clinical status5,12,13 (▶ Fig. 13.2) and the relationship between cortical thickness and cognition.14,15 In the research setting, cortical thickness can be estimated using Freesurfer (http://freesurfer.net)16; clinicians may prefer the commercial and Food and Drug Administration (FDA)-approved Neuroquant package from CorTech Labs (http://www.cortechs. net) for diagnostic imaging. Large longitudinal data sets, such as ADNI, have also enabled more advanced analysis approaches of atrophy across the AD spectrum. One recent study investigated the covariation of atrophy across brain regions in patients with MCI (▶ Fig. 13.3).17

This study found two patterns of atrophy related to AD biomarkers. The first pattern revealed coordinated atrophy across posterior nodes of the default-mode network, and the second pattern largely represented atrophy in the medial temporal lobe. The research indicates that there are likely to be independent, yet simultaneous disease processes causing atrophy in patients with MCI. Future longitudinal studies will likely investigate the pathophysiological basis of these distinct patterns of atrophy.

13.1.2 White Matter Hyperintensities White matter hyperintensities (WMHs) are lesions in deep white matter that are thought to reflect small-vessel disease (▶ Fig. 13.4). Current thinking is that lesions result from chronic hypoperfusion and disrupted blood-brain barrier integrity.18 WMHs appear bright on T2-weighted fluid attenuated inversion recovery (FLAIR) scans and can now be quantified with imaging software. The association between WMH and AD has been mixed, with some studies concluding that there is a relationship19,20 and other studies concluding that there is not a relationship.21 The differences between studies likely reflect the analysis performed. In the aforementioned study showing a relationship, individuals with AD were compared with healthy controls, whereas the other study investigated whether WMHs were predictive of future AD. Yet another study revealed that individuals with high cognitive reserve may be able to cope with a greater WMH burden than those with low cognitive reserve. The implication is that cognitive reserve may be able to delay the onset of AD symptoms.22 These studies indicate that more research is needed in tracking the progression of WMHs during normal aging as well as throughout the AD pathophysiological process. Compared with the relationship between WMH and AD, the relationship between WMH and cognition is more established. A number of studies have reported that increased WMH burden is associated with lower cognitive performance.23,24,25 Although the progression of WMHs in the course of

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Alzheimer’s Disease

Fig. 13.3 Visual depiction of the coevolution of atrophy factors related to Alzheimer’s disease (AD) biomarkers. Top row: Factor 1: Covariation of atrophy in the posterior default-mode network and hippocampus. Bottom row: Factor 3: Covariation of atrophy in medial temporal cortices. Note: Factors not related to AD biomarkers are not shown. (Modified, with permission, from Carmichael O, McLaren DG, Tommet D, Mungas D, Jones RN; for the Alzheimer’s Disease Neuroimaging Initiative. Coevolution of brain structures in amnestic mild cognitive impairment. Neuroimage 2012;66C:449–456.)

Fig. 13.4 T2 fluid-attenuated inversion recovery (FLAIR) images provide evaluation for white matter disease. White matter hyperintensities are lesions in the deep white matter that are thought to reflect small-vessel disease and indicate areas of gliosis.

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Imaging of Alzheimer’s Disease: Part 1 the development of AD is unknown, the risk factors for WMH are modifiable. Thus, there could be potential value in screening for WMHs clinically. Future research will likely investigate the potential value of routine WMH scans.

13.1.3 Amyloid-Related Imaging Abnormalities Although the assessment of amyloid-related imaging abnormalities (ARIA) is not useful in the diagnosis of dementia, it is critical in the development of anti-amyloid therapeutics and is potentially important in patients’ prescribed anti-amyloid therapies. ARIAs have arisen through the advent of AD therapeutics aimed at lowering Aβ burden. Specifically, clinical trials of amyloid-lowering therapeutics revealed MRI signal changes that represent ARIA-edema or effusions (ARIA-E) and ARIAhemosiderin deposition (ARIA-H). The observance of these abnormalities has led to new recommendations on MRI in clinical trials of anti-amyloid therapeutics.26 The finding of ARIA-E was an unexpected transient MRI signal abnormality in several individuals undergoing anti-amyloid therapy (the highest bapineuzumab dose group [5 mg/kg], 3 of 10 patients developed ARIA-E) and was initially labeled vasogenic edema based on MRI characteristics. Because of the scarcity of histopathological evidence that the MRI signal changes were in fact vasogenic edema, the MRI signal changes are now referred to as ARIA-E. ARIA-E is most often characterized as increased MRI signal on T2-weighted FLAIR scans in the parenchyma, leptomeninges, or both. A low incidence of spontaneous ARIA-E has also been noted. ARIA-H is a MRI signal abnormality that is thought to represent hemosiderin deposits, including microhemorrhages and superficial siderosis. Microhemorrhages are round, focal, lowintensity lesions in the parenchyma that are detected by T2* GRE. SWI, which is essentially a T2*-gradient-echo (GRE) sequence with added susceptibility weighting, is more sensitive at detecting microhemorrhages. Not surprisingly, the size and number of microhemorrhages detected are related to the sequence resolution, sensitivity, and scanner strength. Thus, criteria for determining abnormal microhemorrhages need to be adjusted accordingly. Superficial siderois is characterized as curvilinear low intensities adjacent to the brain surface. The prevalence of microhemorrhages increases with age. The prevalence of microhemorrhages in persons over age 80 has been reported to be greater than 35% and is higher in those with hypertension and AD.26 Patients with microhemorrhages at baseline are more likely to have them during the clinical trials of anti-amyloid therapeutics.26 Work is ongoing to understand the natural progression in the increase in microhemorrhages with age and clinical status. An Alzheimer’s Association Research Roundtable Workgroup recommends T2* GRE (due to its availability) with a slice thickness of 5 mm or less, echo time of at least 20 ms on at least a 1.5 T scanner to identify ARIA-H, and a T2-weighted FLAIR sequence to identify ARIA-E.26 The following are additional recommendations: more frequent scanning in phase I and early phase II clinical trials to ascertain the rates of abnormalities; considering the pharmacodynamics effects in determining the postdose time of scanning; and having short rescan intervals in individuals who develop ARIA during treatment. ARIA-E should

be interpreted for the severity and relevance of symptoms. Individuals with more than four microhemorrhages using the aforementioned sequences should be excluded from clinical trials of anti-amyloid therapies. The workgroup also recommended discontinuation from the study of individuals whose incident ARIA-H is related to significant clinical symptoms. Ongoing and future work is aimed at standardizing and reporting ARIA.27

13.2 Single-Photon Emission Computed Tomography/Positron Emission Tomography Imaging Nuclear medicine techniques, such SPECT and PET, for imaging the central nervous system are largely different from the anatomical imaging methods like computed tomography (CT) and MRI. Nuclear medicine imaging relies on radiotracers that provide specific molecular information about pathophysiological brain processes. Nuclear medicine imaging of dementia can be divided into two major approaches: SPECT and PET. Radiotracers have been developed for measuring regional cerebral blood flow (rCBF), regional cerebral glucose metabolism, cerebral amyloid deposition, neurofibrillary tangles, dopamine transporter density, and many more.

13.3 Single-Photon Emission Computed Tomography Brain imaging with SPECT uses lipophilic radiopharmaceuticals that cross the blood-brain barrier to measure brain perfusion. The most commonly used radiotracers include technetium99m-hexamethylpropyleneamine oxime [HMPAO] and 99mTcethylene l-cysteinate dimer [ECD]. Both these agents are injected intravenously using doses of 10 to 20 mCi (370 to 740 MBq) and are retained in brain tissue with a rapid first-pass uptake. Uptake of these radiotracers is localized in active brain tissue and reflects rCBF. SPECT images are obtained 15 to 20 minutes after the tracer injection. The resolution of the SPECT perfusion image is about 1 cm. Although high-resolution imaging obtained with dedicated multidetector cameras could provide greater anatomical details, the primary purpose of SPECT imaging is to evaluate relative rCBF rather than the specificity of anatomical structures. The brain is a well perfused and regulated organ based on tight neural vascular coupling mechanisms. The rCBF reflects underlying normal physiological or pathophysiologic processes. External sensory stimuli, such as touch, sound, smell, and vision, as well as patient’s motion and cognitive activities, could all affect rCBF. In dementia patients, focal pathological processes can result in substantial neuronal loss leading to perfusion deficits. Specific patterns of deficits in rCBF can help diagnosis and differentiate dementias. As the normal distribution of perfusion agents is proportional to regional blood flow, there is approximately fourfold greater uptake in the cortical gray matter compared with white matter. Normal brain perfusion is symmetric and greater in the strip of cortex along the convexity of the frontal, parietal, temporal, and occipital lobes. Activity, and consequently uptake, is also high in the regions corresponding to subcortical gray matter, including

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Alzheimer’s Disease the basal ganglia and the thalamus. The cortical white matter has significantly lower activity and correspondingly low uptake. The border between the white matter and ventricles may be indistinct. Visual interpretation of the cerebral perfusion images usually is performed by comparing both hemispheres for symmetry or by scrutinizing the continuity in the rims of cortical gray matter. The local perfusion is measured as increased, similar, or decreased relative to the perfusion in the identical area in the contralateral hemisphere. In AD patients, the most common finding using SPECT brain perfusion imaging is symmetric bilateral posterior temporal and parietal perfusion defects. This pattern of decreases has a positive predictive value of greater than 80%.28 Although the pattern has a high positive predictive value, the pattern is not pathognomonic for AD. Other pathophysiological processes can alter focal brain perfusion to produce a similar pattern of changes in perfusion. The pattern of deficits observed in AD has also been reported in vascular dementia, Parkinson’s disease, and various encephalopathies. Furthermore, about 30% of AD patients manifest with asymmetric decreased cortical perfusion, depending on the stage of their dementia. In those cases, unilateral temporal or parietal hypoperfusion could be seen. Frontal lobe hypoperfusion has also been seen, but with less predictive value. The negative predictive value of a normal SPECT perfusion scan is high for mid-tolate stage of disease.28 Clinical use of SPECT perfusion in the diagnosis of dementia is limited by its relatively low resolution, lack of anatomical specificity, and nonspecific perfusion deficit pattern among milder AD patients. However, it is a great neuroimaging research tool for dementia imaging because of its lower cost and ease of access relative to PET. Numerous research studies have been using SPECT brain perfusion to characterize various dementias, as well as the normal aging process and the relationship of perfusion to cognitive change.29 These research projects are usually facilitated by computer-aided fusion of SPECT images with corresponding CT or MRI images. A control cohort is then compiled based on high-quality “normal” brains. Finally, various advanced imaging analysis techniques, such as voxel-based analyses, three-dimensional stereotactic surface–based projection, and tomographic z-score mapping, greatly enhance both the sensitivity and specificity of SPECT perfusion imaging in dementia characterization. Additionally, partial volume correction based on MRI anatomical imaging has been reported to further improve the specificity and sensitivity in dementia characterization.29

13.4 Positron Emission Tomography Positron emission tomography uses positron-emitting radiopharmaceuticals to provide spatially specific information about brain metabolism or specific molecular targets (e.g., amyloid). 2-Deoxy-2-(18F)fluoro-D-glucose, or FDG, a glucose analog that reflects glucose metabolism, is currently the most widely used PET tracer for dementia imaging in the clinic. However, there are many other emerging tracers, such as amyloid tracers, carbon-11-labeled Pittsburgh compound B (PIB), or 18F-labeled AβPET radiopharmaceuticals, to enable in vivo detection of human

brain amyloid deposition, and tau tracers, 18F-labeled T807, and 18F-labeled T808 to enable in vivo detection of hyperphoshporylated tau proteins. These newer tracers show great potential for dementia characterization. Because amyloid deposition occurs decades before symptoms, amyloid tracers could potentially be used for early diagnoses and to guide potential therapy for amyloid-related dementia.30

13.5 Positron Emission Tomography: Fluorodeoxyglucose The normal distribution of FDG in the brain is similar to those SPECT perfusion agents that have the highest uptake in cortical gray matter, basal ganglia, and thalami. This normal metabolic imaging pattern changes with aging and shows significant intersubject variations. Relatively decreased uptake has been reported to be associated with normal aging. However, the uptake within the thalamus, basal ganglia, occipital cortex, and cerebellum is usually unchanged with normal aging. The posterior cingulate cortex, lateral temporal lobe, posterior parietal lobes, and anterior frontal lobes generally have higher uptake related to their high resting metabolism. The regions collectively make up the default-mode network.31 Metabolic imaging by FDG-PET has shown usefulness in certain discrete clinical settings to evaluate the cause of dementia, including AD, frontotemporal dementia, dementia with Lewy bodies, Parkinson’s disease, multi-infarct dementia, and Huntington disease (▶ Fig. 13.5). AD patients have reduction in both glucose metabolism and CBF in the parietotemporal association cortex. The parietotemporal involvement is usually bilateral, although asymmetry of perfusion or metabolism reduction is commonly seen. These deficits then spread to the frontal lobes as disease progresses. The primary motor, sensory, and visual cortices are typically spared until very late stage of dementia. These findings have been widely recognized as a diagnostic pattern for AD (▶ Fig. 13.5a). As with SPECT, the FDG diagnostic pattern typical in AD is not pathognomic, although it is highly predictive.32

13.5.1 Positron Emission Tomography: Amyloid and Tau Imaging Many other non-FDG PET tracers show great success in characterizing AD by in vivo imaging of amyloid deposition. Among many of these emerging tracers, N-methyl [11C]2-(4 methylaminophenyl)-6-hydroxy-benzothiasole (Pittsburgh compound B), named 11C-PIB, is the most successful amyloid tracer in the field of dementia neuroimaging research.33,34 It has revealed high in vivo retention that correlates with cerebral pathological changes of Alzheimer's patients (▶ Fig. 13.6). Despite its great success of in vivo amyloid imaging, the clinical use of 11C-PIB is limited by its relatively short half-life and the limited availability of the tracer. Another promising agent, 18F-florbetapir, also known as AV45 or Amyvid, has shown similar capability of in vivo mapping of beta amyloid density in the brain.35 Amyvid was recently approved by the FDA for clinical use and was selected as the amyloid imaging tracer in the anti-amyloid treatment in asymptomatic AD (A4) trial for its wide availability.

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Imaging of Alzheimer’s Disease: Part 1

Fig. 13.5 (a) Alzheimer’s disease (AD). A 58-year-old woman with complaints of forgetfulness and a family history of AD. Fluorodeoxyglucose (FDG) positron emission tomography (PET) shows significant hypometabolism in the bilateral parietotemporal association cortices, as well as the bilateral frontal lobes. Of note, the motor and visual cortices are spared. (b) Dementia with Lewy bodies. A 58-year-old woman with progressive cognitive decline over 2 years. FDG-PET shows significant hypometabolism in the bilateral parietotemporal association cortices, right greater than left, with metabolism deficits in the bilateral visual cortices. Of note, the bilateral frontal lobes are spared. (c) Frontotemporal dementia. A 66-year-old woman with progressive cognitive decline and memory loss for 2 years with speech difficulty. Significant FDG hypometabolism in the bilateral frontal lobes, left greater than the right. Mild hypometabolism is also seen within the bilateral temporal lobes. There is sparing of the parietal lobes and posterior cingulate cortices.

Although amyloid plaques are one of the defining pathological features of AD, normal elderly people without dementia and other patients with clinical syndromes other than dementia could have elevated levels of amyloid deposition in the brain.36 Whereas a positive amyloid scan indicates a significant amyloid burden, a negative scan carries no prognosis of future amyloid burden. Therefore, the clinical utility of amyloid PET imaging requires careful consideration to ensure its role in the proper clinical context. It is particularly important with the consideration of cost-effective use of limited health care resources. The current diagnostic guideline from the Amyloid Imaging Task Force does not advocate the use of such neuroimaging biomarker tests for routine diagnostic purposes.37,38 There are several reasons for the limitation, including current clinical core criteria, which provide good diagnostic accuracy and utility in most patients; more work needs to ensure the appropriate criteria of biomarker use, limited standardization of biomarkers, and limited access to the biomarkers. Presently, the use of these advanced amyloid imaging markers may be useful only in the

following circumstances: investigational studies, clinical trials, and as optional clinical tools where available and when determined appropriate by the clinician (e.g., differentiate frontotemporal dementia from AD). Another investigational PET tracer, fluoroethyl methyl amino-2 napthyl ethylidene malononitrile (18F-FDDNP) appears to bind to senile plaques and neurofibrillary tangles. Thus, for imaging amyloid, the other aforementioned tracers are preferred. Recently, novel tracers (e.g. F18-T807 and F18-T808) have been developed that are thought to bind to hyperphosphorylated tau proteins (PHF-tau), such as neurofibrillary tangles.39,40 The tau specificity is based on co-localization of tracer uptake with immunoreactive PHF-tau pathology, but not amyloid pathology.41 Recent research in humans has shown increased tracer uptake in patients with AD.39,40 Researchers are now investigating whether this tracer can map the temporal progression of tau from the entorhinal cortex to other cortical areas, as well as the clinical significance of hyperphosphorylated tau burden.

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Alzheimer’s Disease

Fig. 13.6 Amyloid image with [11C] Pittsburgh Compound B PET scan. (a,b) Unambiguous positive amyloid binding in the cortex relative to white matter and the cerebellum. (c,d) An indeterminate classification where gray matter binding was present in the cortex of at least three lobes resembling an Alzheimer’s disease (AD) pattern but less intense and convincing than an overtly positive scan. (e,f) No cortical amyloid burden or only nonspecific white matter uptake; nonsignificant patchy or diffuse cortical gray matter binding not resembling an AD pattern (significant uptake in the basal ganglia was common and not considered in the visual rating). (Images courtesy of Dr. Sterling Johnson.)

13.6 Early Diagnosis of Alzheimer’s Disease Given their great sensitivity for pathophysiological abnormality in dementia patients, both SPECT perfusion and FDG-PET have been used in early AD patients or people at high risk for developing AD. It is crucial to recognize their brain perfusion and metabolic abnormalities to facilitate possible intervention or disease-modifying therapy. Individuals with amnestic MCI, patients who have memory problems but do not meet the criteria for AD, are most likely to convert to AD in the future. Diagnosis typically includes the following criteria: patient has memory concerns, objective memory impairment for age, normal general cognitive function, capability of normal daily activity,

and not demented. Many studies have demonstrated that amnestic MCI patients have a reduction in both glucose metabolism and CBF in the posterior cingulate cortex and precuneus. Because these brain regions have a high level of perfusion as well as metabolism in normal patients, it is quite difficult to visualize these subtle decreases in the very early stage of disease onset. However, statistical analysis reveals lower metabolism in amnestic MCI patients relative to controls in these areas. Furthermore, reduction in metabolism and perfusion in these areas could predict a cognitive decline in asymptomatic patients (i.e., preclinical AD stage 2). The glucose hypometabolism seen on PET reflects projections from dysfunctional neurons in other brain regions, such as the hippocampus within the mesial temporal lobe. Amnestic MCI patients could potentially benefit more from early intervention and disease-modify-

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Imaging of Alzheimer’s Disease: Part 1 ing therapies than could mild AD patients. The idea that AD therapies may work best early in the disease process is the premise of the A4 clinical trial. Researchers will give solanezumab, a monoclonal antibody-targeting amyloid, or placebo to elderly adults with amyloid (e.g., preclinical AD). The hope is that earlier treatment will be disease modifying.

13.7 Conclusions Structural, metabolic, and molecular imaging research over the past several decades has advanced our understanding of AD pathophysiological processes, yet imaging biomarkers should not replace clinical neurologic assessments. These advances have led to an influential conceptual model for the progression of AD (biomarker) pathology.42 However, the earliest neuroimaging biomarkers are costly, and their initiation is unknown, making it difficult to identify the earliest rise in AD risk. Future multimodal imaging research, coupled with more sensitive markers of AD pathology, will aid in identifying those at an increased risk earlier in the pathophysiological cascade. Key to this identification will be the ability to separate brain changes of normal aging from those attributable to AD-related pathology. To differentiate AD from normal or exaggerated aging, numerous studies have investigated the relationship between aging and neuroimaging findings. For example, in FDG-PET studies, the reductions most common with age were observed in the dorsolateral and medial frontal areas and the perisylvian insular cortices rather than in the default-mode network.29 The growing neuroimaging literature is focusing on investigating brain changes in regions affected by AD. In one study, thinner cortices in areas of atrophy in AD were associated with increased risk of conversion from normal to AD.43 Other studies have gone further to create criteria for defining abnormal biomarkers to help identify individuals in the various stages of preclinical AD.7,8,9 Future studies will use the preclinical AD stages to look for other subtle changes related to AD pathology. Finally, studies that investigate networks of atrophy and cortical thinning may have more potential to differentiate AD-related brain changes from those associated with normal aging based on the co-occurring change in spatially disparate regions.17

References [1] Blennow K, de Leon MJ, Zetterberg H. Alzheimer’s disease. Lancet 2006; 368: 387–403 [2] Braak H, Braak E. Neuropathological staging of Alzheimer-related changes. Acta Neuropathol 1991; 82: 239–259 [3] Braak H, Braak E. Staging of Alzheimer-related cortical destruction. Int Psychogeriatr 1997; 9 Suppl 1: 257–261, discussion 269–272 [4] McKhann GM, Knopman DS, Chertkow H et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 263–269 [5] Dickerson BC, Bakkour A, Salat DH et al. The cortical signature of Alzheimer’s disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloidpositive individuals. Cereb Cortex 2009; 19: 497–510 [6] Sperling RA, Aisen PS, Beckett LA et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 280–292 [7] Jack CR, Jr, Knopman DS, Weigand SD et al. An operational approach to National Institute on Aging-Alzheimer’s Association criteria for preclinical Alzheimer’s disease. Ann Neurol 2012; 71: 765–775

[8] Knopman DS, Jack CR, Jr, Wiste HJ et al. Short-term clinical outcomes for stages of NIA-AA preclinical Alzheimer’s disease. Neurology 2012; 78: 1576– 1582 [9] Mueller SG, Weiner MW, Thal LJ et al. Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimers Dement 2005; 1: 55–66 [10] Ashburner J, Friston KJ. Why voxel-based morphometry should be used. Neuroimage 2001; 14: 1238–1243 [11] Rami L, Solé-Padullés C, Fortea J et al. Applying the new research diagnostic criteria: MRI findings and neuropsychological correlations of prodromal AD. Int J Geriatr Psychiatry 2012; 27: 127–134 [12] Julkunen V, Niskanen E, Koikkalainen J et al. Differences in cortical thickness in healthy controls, subjects with mild cognitive impairment, and Alzheimer’s disease patients: a longitudinal study. J Alzheimers Dis 2010; 21: 1141–1151 [13] Im K, Lee JM, Seo SW et al. Variations in cortical thickness with dementia severity in Alzheimer’s disease. Neurosci Lett 2008; 436: 227–231 [14] Gross AL, Manly JJ, Pa J et al. Alzheimer’s Disease Neuroimaging Initiative. Cortical signatures of cognition and their relationship to Alzheimer’s disease. Brain Imaging Behav 2012; 6: 584–598 [15] Ahn H-J, Seo SW, Chin J et al. The cortical neuroanatomy of neuropsychological deficits in mild cognitive impairment and Alzheimer’s disease: a surface-based morphometric analysis. Neuropsychologia 2011; 49: 3931– 3945 [16] Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 2000; 97: 11050–11055 [17] Carmichael O, McLaren DG, Tommet D, Mungas D, Jones RN for the Alzheimer’s Disease Neuroimaging Initiative. Coevolution of brain structures in amnestic mild cognitive impairment. Neuroimage 2012; 66C: 449–456 [18] Debette S, Markus HS. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and metaanalysis. BMJ 2010; 341: c3666 [19] Holland CM, Smith EE, Csapo I et al. Spatial distribution of white-matter hyperintensities in Alzheimer’s disease, cerebral amyloid angiopathy, and healthy aging. Stroke 2008; 39: 1127–1133 [20] Provenzano FA, Muraskin J, Tosto G et al. Alzheimer’s Disease Neuroimaging Initiative. White matter hyperintensities and cerebral amyloidosis: necessary and sufficient for clinical expression of Alzheimer’s disease? JAMA Neurol 2013; 70: 455–461 [21] Weinstein G, Beiser AS, Decarli C, Au R, Wolf PA, Seshadri S. Brain imaging and cognitive predictors of stroke and Alzheimer’s disease in the Framingham Heart Study. Stroke 2013; 44: 2787–2794 [22] Brickman AM, Siedlecki KL, Muraskin J, et al. White matter hyperintensities and cognition: Testing the reserve hypothesis. NBA. 2009:1–11 [23] Brickman AM, Honig LS, Scarmeas N et al. Measuring cerebral atrophy and white matter hyperintensity burden to predict the rate of cognitive decline in Alzheimer’s disease. Arch Neurol 2008; 65: 1202–1208 [24] Hedden T, Mormino EC, Amariglio RE et al. Cognitive profile of amyloid burden and white matter hyperintensities in cognitively normal older adults. J Neurosci 2012; 32: 16233–16242 [25] Oosterman JM, Sergeant JA, Weinstein HC, Scherder EJA. Timed executive functions and white matter in aging with and without cardiovascular risk factors. Rev Neurosci 2004; 15: 439–462 [26] Sperling RA, Jack CR, Jr, Black SE et al. Amyloid-related imaging abnormalities in amyloid-modifying therapeutic trials: recommendations from the Alzheimer’s Association Research Roundtable Workgroup. Alzheimers Dement 2011; 7: 367–385 [27] Barkhof F, Daams M, Scheltens P et al. An MRI rating scale for amyloid-related imaging abnormalities with edema or effusion. AJNR Am J Neuroradiol 2013; 34: 1550–1555 [28] Mettler F, Guiberteau M. Essentials of nuclear medicine imaging. Essentials of nuclear medicine imaging. 6th ed. Philadelphia: Elsevier/Saunders; 2012:71– 97 [29] Matsuda H. Role of neuroimaging in Alzheimer’s disease, with emphasis on brain perfusion SPECT. J Nucl Med 2007; 48: 1289–1300 [30] Rowe CC, Villemagne VL. Brain amyloid imaging. J Nucl Med Technol 2013; 41: 11–18 [31] Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A 2001; 98: 676–682 [32] Murray AD. Imaging approaches for dementia. AJNR Am J Neuroradiol 2012; 33: 1836–1844 [33] Klunk WE, Engler H, Nordberg A et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol 2004; 55: 306–319

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Alzheimer’s Disease [34] Klunk WE, Lopresti BJ, Ikonomovic MD et al. Binding of the positron emission tomography tracer Pittsburgh compound-B reflects the amount of amyloidbeta in Alzheimer’s disease brain but not in transgenic mouse brain. J Neurosci 2005; 25: 10598–10606 [35] Wong DF, Rosenberg PB, Zhou Y et al. In vivo imaging of amyloid deposition in Alzheimer’s disease using the radioligand 18F-AV-45 (florbetapir [corrected] F 18). J Nucl Med 2010; 51: 913–920 [36] Mintun MA, Larossa GN, Sheline YI et al. [11C]PIB in a nondemented population: potential antecedent marker of Alzheimer’s disease. Neurology 2006; 67: 446–452 [37] Johnson KA, Minoshima S, Bohnen NI et al. Alzheimer’s Association. Society of Nuclear Medicine and Molecular Imaging. Amyloid Imaging Task Force. Appropriate use criteria for amyloid PET: A report of the Amyloid Imaging Task Force, the Society of Nuclear Medicine and Molecular Imaging, and the Alzheimer’s Association. Alzheimers Dement 2013; 9: e-1–e-16 [38] Johnson KA, Minoshima S, Bohnen NI et al. Amyloid Imaging Task Force of the Alzheimer’s Association and Society for Nuclear Medicine and Molecular Imaging. Update on appropriate use criteria for amyloid PET imaging: demen-

[39]

[40]

[41]

[42]

[43]

tia experts, mild cognitive impairment, and education. Alzheimers Dement 2013; 9: e106–e109 Chien DT, Bahri S, Szardenings AK et al. Early clinical PET imaging results with the novel PHF-tau radioligand [F-18]-T807. J Alzheimers Dis 2013; 34: 457–468 Chien DT, Szardenings AK, Bahri S et al. Early clinical PET imaging results with the novel PHF-tau radioligand [F18]-T808. J Alzheimers Dis 2014; 38: 171– 184 Xia CF, Arteaga J, Chen G et al. [(18)F]T807, a novel tau positron emission tomography imaging agent for Alzheimer’s disease. Alzheimers Dement 2013; 9: 666–676 Jack CR, Jr, Knopman DS, Jagust WJ et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol 2013; 12: 207–216 Dickerson BC, Stoub TR, Shah RC et al. Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults. Neurology 2011; 76: 1395–1402

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Imaging of Alzheimer’s Disease: Part 2

14 Imaging of Alzheimer’s Disease: Part 2 Christian La, Wolfgang Gaggl, and Vivek Prabhakaran As an ongoing effort toward the identification of various biomarkers and early detection of Alzheimer’s disease (AD), the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was conceived to help researchers and clinicians develop new treatments and increase the safety and efficacy of drug development. With a primary focus on structural magnetic resonance imaging (MRI) of the brain, data generated from ADNI-1 have improved the understanding of relationships between imaging and chemical biomarkers of AD with the acquisition of a three-dimensional T1-weighted magnetization-prepared rapid acquisition with gradient echo (MP-RAGE) and a dual fast spin-echo (proton density/T2-weighted) sequence. As a continuation to the initiative, ADNI-GO and ADNI-2 expanded on the ADNI imaging core protocol with inclusion of resting-state functional MRI (fMRI), T2 fluid-attenuated inversion recovery (FLAIR), diffusion tensor imaging (DTI), and arterial spin-label perfusion imaging. In this chapter, we provide an overview of the research involving perfusion imaging, FLAIR MRI, DTI, and magnetic resonance spectroscopy (MRS) in the AD population (▶ Table 14.1).

14.1 Perfusion-Weighted Imaging Perfusion-weighted imaging (PWI) is an MRI sequence that is sensitive to the flow of blood in the capillaries and capillary beds, a technique that is getting more attention in the investigation of AD and other neurodegenerative diseases. For the past two decades, single-photon emission computed tomography (SPECT) and positron emission tomography (PET) have served as the mainstream imaging for perfusion and metabolism and remain highly effective. Although the risk of radioactivity is rather minimal, preparation of the necessary isotopes and radioactive tracer remains a potential challenge in these nuclear medicine techniques. Only a few large-scale hospitals and research institutes have the resources to support such a

Table 14.1 Magnetic resonance imaging (MRI) acquisition protocol for ADNI-1 and ADNI-GO/2 ADNI-1: (1.5-Tesla scanner)

ADNI-GO/2 (3-Tesla scanner)



Localizer



Localizer



MP-RAGE



Sagittal MP-RAGE/IR-SPGR



MP-RAGE (repeat)



Accelerated sagittal MP-RAGE/IR-SPGR



B1 calibration: head coil



Resting-state fMRI (Philips Systems only): eyes open



B1 calibration: body coil



Axial T2-FLAIR



T2 dual echo



Axial T2



Axial ASL perfusion (Siemens systems only) – eyes open



Axial DTI scan (GE systems)

Abbreviations: ADNI, Alzheimer’s Disease Neuroimaging Initiative; ASL, arterial spin labeling; DTI, diffusion tensor imaging; FLAIR, fluidattenuated inversion recovery; fMRI, functional magnetic resonance imaging; MP-RAGE, magnetization-prepared rapid acquisition with gradient echo. Source: (http://adni.loni.usc.edu/methods/mri-analysis/mri-acquisition/).

system. In contrast, PWI provides a survey of perfusion that is free of such radioactive isotopes. This method is comparatively easy to implement and is available for most commercial scanners used at hospitals and medical centers. Perfusion-weighted MRI can be categorized into two general classes, depending on the method of obtaining contrast. Also called bolus-tracking MRI, dynamic-susceptibility contrast (DSC) is currently the most widely used approach. With the tracking of a bolus injection of paramagnetic, gadolinium-based contrast (GBC) agents, relative measures of regional cerebral blood flow (rCBF), regional cerebral blood volume (rCBV), mean transit time (MTT), and time-to-peak (TTP) can be assessed and recorded. However, administration of GBC agents has been associated with nephrogenic systemic fibrosis in patients with significant renal insufficiency, limiting its current application. Arterial spin labeling (ASL) technique, on the other hand, makes use of endogenous arterial blood as the tracer for the quantification of blood flow. In contrast to DSC MR perfusion, which provides relative perfusion measurements, ASL MRI allows absolute quantification of perfusion as expressed in terms of milliliters per 100 g per minute. The quantitative values obtained from ASL MRI offer a reliable whole-brain CBF measurement that is comparable to the traditional 15O-water PET perfusion imaging method,1 without the radioactivity. Its ease of acquisition, noninvasive nature, and high reproducibility over time also make it an attractive and potentially cost-effective alternative to PET. Perfusion imaging methods have been successful in the detection of AD-related perfusion deficiencies. The pathology of AD is that of a slowly progressing neurodegenerative disorder commonly characterized by decreased rCBF. It has been previously reported that patients with AD symptoms consistently show patterns of cerebral hypoperfusion, and although in such individuals a global decrease in blood flow is regularly demonstrated compared with healthy controls, CBF reduction may be more pronounced in certain regions than in others. Indeed, individuals with AD have shown a more pronounced reduction of CBF in the following regions: the precuneus, the posterior cingulate, and the lateral parietal cortices, with such findings demonstrated by DSC perfusion2 and ASL perfusion (▶ Fig. 14.1).3 These perfusion abnormalities have been recorded as early as in patients with mild cognitive impairments (MCI) and in patients in the early preclinical phases of AD, with effects persisting well into the later stages of the disease. During the preclinical stages of asymptomatic individuals, the apolipoprotein (Apo)E4 allele and family history are wellrecognized risk factors for the onset of AD. Carriers of ApoE4 allele and the presence of family history, in particular maternal family history, increase the likelihood of AD-related hypoperfusion as tested in the asymptomatic population. Previously, the gender of the AD-affected parent has been suggested to influence the risk of disease progression.4 Along with CBF deficiencies, patients even in early stages of the disease often exhibit changes in their cortical structure. Brain-volume losses are particularly prominent in the mesotemporal structures.5 The extent of atrophy in MCI patients is

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Alzheimer’s Disease

Fig. 14.1 Cerebral perfusion reduction in Alzheimer’s disease (AD) patients compared with healthy elderly controls. Statistical t-map of cerebral blood flow difference in AD patients and healthy elderly controls by way of arterial spin labeling perfusion magnetic resonance imaging, with activation representing areas of reduced cerebral perfusion in patients at p < 0.005 uncorrected. (Image courtesy of Ozioma C. Okonwo and the Wisconsin Alzheimer’s Disease Research Center.)

more limited. Patients with amnestic MCI who eventually progress to AD have demonstrated greater susceptibility to gray matter loss in the medial and inferior temporal lobes, temporoparietal, posterior cingulate, precuneus, anterior cingulate, and some regions of the frontal lobes compared with clinically stable MCI patients.6 Despite this fact, after adjusting for brain atrophy and gray matter volume, the previously described effects of regional hypoperfusion in the posterior cingulate, the precuneus, the inferior parietal, and the lateral prefrontal cortices persist and cannot be explained solely by brain atrophy.3 Such hypoperfusion patterns are in line with numerous fluorodeoxyglucose (FDG) PET studies.7 CBF and cerebral metabolism are generally believed to be tightly coupled. A study combining FDG-PET and ASL-MR perfusion imaging in the same patients demonstrated a high degree of overlap between the two modalities of perfusion MRI and PET.8 In that study, implementation of concurrent FDG-PET and ASL-MRI demonstrated not only similar regional abnormalities in AD between the two modalities, but the two modalities also provided comparable sensitivity and specificity for the detection of AD as reviewed by expert readers.8 The agreement in hypoperfusion and hypometabolism patterns suggests a possible sensitivity of ASL CBF toward the neurometabolic alterations among individuals with risk factors for AD.3,8 Nonetheless, despite the strong correlation between the two measures of cerebral perfusion and cerebral metabolism, some regions exhibited differential observations. Whereas the reduction of CBF is somewhat consistent in the population of AD, some studies have also reported opposite findings. Such signs of hyperperfusion are discordant with the hypometabolism reported for the same regions by a number of FDG-PET studies.9 Although the explanation for this decoupling remains unclear,

it has been suggested that this increase of perfusion might be the direct or indirect result of local inflammatory response or compensatory activity in the face of neurodegeneration.10

14.2 Functional Magnetic Resonance Imaging A different modality that has been successfully used in the AD population, and that has been added to ADNI-2, is fMRI. Given the impairments in memory associated with progression of AD, numerous fMRI studies have focused on the functional changes in such processes. Memory issues are one of the first observable symptoms of AD, but not all memory systems are equally impaired. Episodic memory is generally the first and most affected of the memory systems. Not surprisingly, areas of the medial temporal lobe that are critical to episodic memory have been reported to sustain heavy neuronal loss, as previously stated.5 In AD, multiple regions pertaining to functional networks subserving episodic memory sustain alterations in cortical activation patterns compared with age-matched healthy controls. Decreased activity from episodic memory task-fMRI has been reported in the hippocampal formation in AD patients, such as during picture encoding11 and verbal retrieval.12 Conversely, lateral prefrontal activity has been shown to increase during verbal retrieval, suggesting the notion of a compensatory mechanism.12 Despite those promising results, investigations using taskevoked fMRI in the AD population are heavily confounded by individual differences and in their abilities to perform the task. Alternatively, resting-state fMRI (rs-fMRI), also called tasknegative or task-free fMRI, provides an investigation of the

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Imaging of Alzheimer’s Disease: Part 2 brain network free of the confounding factors associated with a task. In this context, rest refers to a constant condition without imposed stimuli. By surveying the existing spontaneous lowfrequency fluctuation (SLFF) of blood-oxygen-level–dependent (BOLD) signal during rest as originally illustrated by Biswal et al,13 valuable information can be extracted. A wide array of consistent, segregated functional networks, or rs networks (RSNs), can be assessed using such a technique. Functional connectivity analysis provides an assessment of functional networks and allows for an appraisal of network integrity of specific RSNs in clinical populations compared with healthy normal persons. A survey of rs intrinsic activity might be as significant as, if not more significant than, evoked activity in terms of overall brain function. Of particular interest is the default-mode network (DMN). Comprising primarily the precuneus/posterior cingulate, inferolateral parietal, and medial prefrontal cortices, this network is thought to have a role in introspection and selfreflective thinking. In normal aging, mild cognitive impairment, AD, and various other neurologic disorders, the DMN experiences disruption, especially in terms of functional connectivity (▶ Fig. 14.2).14,15 Greicius et al14 demonstrated abnormality within the DMN in AD patients, with a decrease in DMN coactivity in the posterior cingulate and hippocampus from a study of 13 mild AD patients. These findings of reduced DMN connectivity have been replicated on multiple occasions in AD patients,16,17 in MCI patients,15 as well as in cognitively healthy older controls harboring amyloid plaques18,19 and in healthy older carriers of the ApoE4 allele.19 The study from Sorg et al15 additionally revealed that functional connectivity between both hippocampi in the medial temporal lobes and the posterior cingulate of the DMN was present in healthy controls but absent in patients, represent-

ing the effects of ongoing early neurodegeneration, possibly reflecting the later reduced integrity of the communicating fiber tract.20 Analyses of rs-fMRI have yielded consistent findings, primarily with a loss of intranetwork connectivity in large-scale networks in AD and MCI, including the DMN, dorsal attention network, salience network, and sensorimotor network.21 Within the DMN, patients suffer from disruption of functional connectivity spanning from the posterior to the anterior portions of the network.22 Although with age the anterior DMN shows increases in frontal lobe connectivity and the posterior DMN shows declines in connectivity throughout, with onset of AD pathology hastening these patterns of age-associated changes, particularly in the posterior regions.16 Furthermore, functional connectivity between regions separated by greater physical distance was markedly attenuated with increasing disease severity, with such loss associated with less efficient global and nodal network topology.23 In addition to intranetwork connectivity deficiency, internetwork connectivity was also consistently disrupted.21 Studies have found decreased DMN connectivity to be associated with increased prefrontal connectivity24 and increased salience network connectivity,17 suggesting that the pathology of AD is associated with an alteration in large-scale functional brain networks, which extends well beyond the DMN. Several task-fMRI studies have demonstrated decreased ability to deactivate regions irrelevant for task performance in AD and MCI populations compared with the normal elderly population.25 Therefore, the ability to decrease brain activity and disengage the DMN during executive tasks is also associated with brain health. Together, these findings suggest that AD pathology is associated with widespread disruption of both intranetwork and internetwork correlations.

Fig. 14.2 Resting-state functional connectivity reduction in Alzheimer’s disease (AD) patients compared with healthy elderly controls. Statistical t-map of functional connectivity difference in AD patients and healthy elderly controls by way of seed-based approach with a seed placed in the posterior cingulate cortex (PCC) MNI [2–54 26] overlaid on an averaged anatomical brain image. Activation clusters represent areas of reduced functional connectivity with the PCC, a principal component of the default-mode network at p < 0.005, uncorrected. (Image courtesy of Sterling C. Johnson and the Wisconsin Alzheimer’s Disease Research Center.)

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Alzheimer’s Disease A family history of the presence of the ApoE4 allele and amyloid aggregation, two well-accepted risk factors for AD, have been studied using such methods. Fleisher et al26 demonstrated that the high-risk population exhibited distinct characteristics compared with low-risk population. In that study, groups were defined by family history of dementia and whether or not participants were carriers of the ApoE4 allele. The two risk groups were distinguished by their activity over nine regions, including regions of the prefrontal, orbital frontal, temporal, and parietal lobes.26 In another study of cognitively normal individuals, a family history of late-onset AD was associated with reduced resting state functional connectivity between particular nodes of the DMN, namely, the posterior cingulate and medial temporal cortex,27 where amyloid deposition has been previously observed in individuals with a family history of AD, but not in those without such a history.28 As suggested by Wang et al,27 it is possible that the decreased functional connectivity may be related to amyloid deposition in an age-dependent fashion in individuals with a family history of AD. Although much work has been dedicated toward solving the increasingly complex puzzle that constitutes AD, the relationship between functional-structural connectivity and metabolic measures remains to be better understood. Previously, amyloid deposition and aerobic glycolysis were demonstrated to be correlated for both individuals with AD and for cognitively normal amyloid-positive participants, suggesting a possible association between regional aerobic glycolysis and the later development of AD pathology.29 Additionally, regions of normally high aerobic glycolysis in healthy individuals coincides with regions of the DMN, where decreased metabolic activity, concurrent with increased amyloid deposition, is found to have occurred in AD patients. Together, this distinct pattern offers the suggestion of a particular susceptibility of these regions the pathophysiology of AD.29 From rs-fMRI, it has been demonstrated that those regions are also associated with a reduction of functional connectivity with onset of the disease. Moreover, several studies have provided evidence of decrease connectivity within the DMN in cognitively normal elderly individuals with elevated brain amyloid.18 This discovery in cognitively normal elderly individuals supports the concept that rs-fMRI may have the ability to detect early manifestations of amyloid (Aβ) toxicity, before any appearance of clinical symptoms.

14.3 Diffusion Tensor Imaging in Alzheimer’s Disease The changes demonstrated in volumetric studies, fMRI and rs-fMRI, as described previously, are attributed to gray matter loss and neuronal degeneration and with it the altered connectivity of the functional network of the brain. More recently, white matter changes have been investigated in subjects with AD and MCI, including decreased myelin density and myelin basic protein, as well as oligodendrocyte loss. Additionally, wallerian degeneration is a mechanism causing axonal loss following neuronal degeneration. A promising MRI modality for studying structural changes in white matter is DTI. Changes in diffusivity in the direction of the fiber bundles (longitudinal or axial diffusivity) are attrib-

uted primarily to axonal loss through wallerian degeneration, whereas changes in diffusivity in the direction orthogonal to the fiber bundles (transverse or radial diffusivity) have been associated with damage of cell walls and myelin sheaths. Loss of white matter fibers has been reported by Wang et al,30 who showed a decreased fractional anisotropy (FA) and increased mean diffusivity (MD) in multiple areas of the brain correlated to functional impairments as assessed using the Mini-Mental State Examination and the AD Assessment Scale. Li et al31 combined volumetric analysis with DTI by comparing mild AD patients with normal aging controls and demonstrated hippocampal atrophy at the mild AD stage. A study by Solodkin et al32 suggests that DTI in the parahippocampus could be used as a biomarker of disease progression in the white matter pathology of AD. They classified MCI and AD cases using discriminant analysis and noted that the MCI cases identified as AD in their analysis either met the diagnostic criteria for AD or showed significant cognitive decline 1 year later. With this evidence demonstrating white matter disruption to be an important part in the pathogenesis of AD, Shu et al33 performed a systematic study of white matter changes and compared DTI at 7 T and histologic images in a APP/PS1 mouse model compared with wild-type controls. Abnormalities of FA or axial diffusivity agreed with ultrastructural findings demonstrating histopathological changes of AD. Methods that are investigating DTI as a marker of AD use either region-of-interest (ROI) or voxel-wise approaches. ROI approaches can rely either on manually drawn ROIs that have the advantage of being anatomically accurate in the individual subject or on template warping, which allows efficient processing of many subjects in group studies. Tract-based analysis poses an efficient alternative to manual ROI drawing for the individual subject. Voxel-wise approaches always rely on warping of the subject anatomy to a specified template, but because of individual brain differences and imperfections of nonlinear mapping algorithms, this method is typically used for large group studies. Whether it is more effective to use whole-brain assessments or to focus on specific brain areas that have been shown to be altered by AD remains an open question. A concern regarding methods is the effect of anatomical normalization during template mapping and its effects on the scalar measures of DTI, but this has been found to be much smaller than averaging or blurring in ROI studies. Several studies have documented the changes of DTI markers with AD in the hippocampus,34 the medial temporal lobe,34 the parahippocampal white matter and perforant path,35 entorhinal cortex,36 and posterior cingulum.37 Degeneration generally seems to follow the pattern of retrogenesis, where areas of late myelination during development are affected early by the disease, and areas of early myelination are affected later in the progression of the disease. Compared with healthy aging, DTI changes to posterior brain structures are generally found earlier in the disease than are changes to the frontal areas.38 The DTI studies that have investigated the loss of memory, the most prevalent symptom defining AD, have focused on the area around the hippocampus and parahippocampal white matter,35 with the perforant pathway transmitting inputs into the entorhinal cortex to the hippocampus. Multiple studies found DTI changes in the perforant pathway for both AD and MCI39 compared with healthy aging, with changes in AD

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Imaging of Alzheimer’s Disease: Part 2

Fig. 14.3 Patient diagnosed with Alzheimer's disease (right image) compared with age-matched normal subject (left image). White matter atrophy can be observed in the splenium of the corpus callosum (yellow arrows). (Image courtesy of S. C. Johnson, University of Wisconsin-Madison.)

generally more pronounced than in MCI. Solodkin et al32 demonstrated the use of DTI for in vivo assessment of parahippocampal white matter to identify patients with MCI at risk of converting to AD.32 Furthermore, a study by Bendlin et al4 suggested that changes to white matter structure can be detected many years before detection of cognitive changes associated with AD by looking at asymptomatic patients with a family history of AD. They found that a parental family history of AD is correlated with a lowered FA value in brain areas that have been identified to be affected by the disease, including the hippocampus, corpus callosum, cingulum, uncinate fasciculus, tapetum, and neighboring white matter structures. Although reports disagree about the structures affected by AD and MCI compared with normal aging, such as the frontal lobes and parietal lobes, most studies found consistent structural changes in several brain areas using DTI metrics (typically FA and MD), reflecting the development of pathological changes with AD (▶ Fig. 14.3). Differences between studies may be caused by different sensitivities of the analysis method that is used (ROI, tract, or voxel based) and the DTI sequence parameters chosen, such as image resolution, motion robustness, eddy current compensation, and the use of parallel imaging methods as well as technological advances in scanner hardware and DTI pulse sequences.40

14.4 Proton Magnetic Resonance Spectroscopy Magnetic resonance spectroscopy (MRS) allows quantification of biochemical metabolites by means of MRI. Changes from normative levels provide biomarkers, for disease processes, complementing other diagnostic imaging methods. Metabolites that are generally quantified include N-acetyl aspartate (NAA), choline (Cho), creatine (Cr), and myo-inositol (mI). Creatine can be used as an internal control because it is generally unchanged in AD. The American Academy of Neurology does not recommend using MRS for routine clinical imaging in AD diagnosis because of lack of evidence, but as a research tool, MRS can provide valuable information for studying the disease and its progression.

Klunk et al demonstrated that, compared to control subjects, a decrease in NAA in postmortem brain samples of AD patients correlated with the presence of plaques and neurofibrillary tangles.41 Several studies have shown MRS to be able to distinguish between AD patients and healthy controls. Whereas Klunk et al41 showed a decrease in NAA in AD patients, several investigators have demonstrated that NAA levels can improve in AD patients after acetylcholinesterase treatment.42 NAA has also been shown to correlate with psychiatric components of AD, where AD patients with psychosis had significantly reduced NAA levels compared with healthy controls.43 Not only does MRS hold promise of providing insight into the availability of selected metabolites as disease biomarkers, but it also provides a wide chemical spectrum of metabolites that can be used as a chemical fingerprint of the patient’s disease state.44 Together with other metrics, such as measuring the hippocampal volume42 and modalities like amyloid PET imaging,45 MRS can be used to provide complementary data in clinical diagnosis. There is evidence that amyloid plaques start accumulating before behavioral symptoms of neurodegeneration can be seen: in cognitively normal older adults, the Cho:Cr and mI:Cr ratios correlated with amyloid PET imaging.45 Additionally, MRS can provide a measure of glial activity by elevation of mI in AD.46 Although there is ample evidence that adding MRS to the diagnosis and treatment of AD provides a more complete picture and allows better disease monitoring and a more tailored treatment regimen, the technique has not been widely adopted for routine clinical care of AD patients. Graff-Radford and Kantarci44 state that the primary reasons are both the lack of standardization and normative data across sites and an insufficient understanding of the pathological basis of the changes observed using MRS. A practical approach would be to combine MRS with other clinical imaging techniques (e.g., volumetric MRI, DTI, functional connectivity MRI, FDG-PET).

References [1] Xu G, Rowley HA, Wu G, et al. Reliability and precision of pseudo-continuous arterial spin labeling perfusion MRI on 3.0 T and comparison with 15O-water PET in elderly subjects at risk for Alzheimer’s disease. NMR Biomed 2010; 23: 286–293

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Alzheimer’s Disease [2] Luckhaus C, Flüb MO, Wittsack H-J et al. Detection of changed regional cerebral blood flow in mild cognitive impairment and early Alzheimer’s dementia by perfusion-weighted magnetic resonance imaging. Neuroimage 2008; 40: 495–503 [3] Johnson NA, Jahng G-H, Weiner MW et al. Pattern of cerebral hypoperfusion in Alzheimer’s disease and mild cognitive impairment measured with arterial spin-labeling MR imaging: initial experience. Radiology 2005; 234: 851–859 [4] Bendlin BB, Ries ML, Canu E et al. White matter is altered with parental family history of Alzheimer’s disease. Alzheimers Dement 2010; 6: 394–403 [5] Devanand DP, Pradhaban G, Liu X et al. Hippocampal and entorhinal atrophy in mild cognitive impairment: prediction of Alzheimer’s disease. Neurology 2007; 68: 828–836 [6] Whitwell JL, Przybelski SA, Weigand SD et al. 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer’s disease. Brain 2007; 130: 1777–1786 [7] Landau SM, Harvey D, Madison CM et al. Alzheimer’s Disease Neuroimaging Initiative. Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol Aging 2011; 32: 1207–1218 [8] Musiek ES, Chen Y, Korczykowski M et al. Direct comparison of fluorodeoxyglucose positron emission tomography and arterial spin labeling magnetic resonance imaging in Alzheimer’s disease. Alzheimers Dement 2012; 8: 51– 59 [9] De Santi S, de Leon MJ, Rusinek H et al. Hippocampal formation glucose metabolism and volume losses in MCI and AD. Neurobiol Aging 2001; 22: 529–539 [10] Hu WT, Wang Z, Lee VM, Trojanowski JQ, Detre JA, Grossman M. Distinct cerebral perfusion patterns in FTLD and AD. Neurology 2010; 75: 881–888 [11] Rombouts SA, Barkhof F, Veltman DJ et al. Functional MR imaging in Alzheimer’s disease during memory encoding. AJNR Am J Neuroradiol 2000; 21: 1869–1875 [12] Becker JT, Mintun MA, Aleva K, Wiseman MB, Nichols T, DeKosky ST. Compensatory reallocation of brain resources supporting verbal episodic memory in Alzheimer’s disease. Neurology 1996; 46: 692–700 [13] Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995; 34: 537–541 [14] Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A 2004; 101: 4637–4642 [15] Sorg C, Riedl V, Mühlau M et al. Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. Proc Natl Acad Sci U S A 2007; 104: 18760–18765 [16] Jones DT, Machulda MM, Vemuri P et al. Age-related changes in the default mode network are more advanced in Alzheimer’s disease. Neurology 2011; 77: 1524–1531 [17] Zhou J, Greicius MD, Gennatas ED et al. Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer’s disease. Brain 2010; 133: 1352–1367 [18] Hedden T, Van Dijk KRA, Becker JA et al. Disruption of functional connectivity in clinically normal older adults harboring amyloid burden. J Neurosci 2009; 29: 12686–12694 [19] Sheline YI, Morris JC, Snyder AZ et al. APOE4 allele disrupts resting state fMRI connectivity in the absence of amyloid plaques or decreased CSF A β42. J Neurosci 2010; 30: 17035–17040 [20] Zhang Y, Schuff N, Jahng GH et al. Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer’s disease. Neurology 2007; 68: 13–19 [21] Brier MR, Thomas JB, Snyder AZ et al. Loss of intranetwork and internetwork resting state functional connections with Alzheimer’s disease progression. J Neurosci 2012; 32: 8890–8899 [22] Bai F, Zhang Z, Yu H et al. Default-mode network activity distinguishes amnestic type mild cognitive impairment from healthy aging: a combined structural and resting-state functional MRI study. Neurosci Lett 2008; 438: 111–115 [23] Liu Y, Yu C, Zhang X et al. Impaired long distance functional connectivity and weighted network architecture in Alzheimer’s disease. Cereb Cortex 2014; 24: 1422–1435 [24] Agosta F, Pievani M, Geroldi C, Copetti M, Frisoni GB, Filippi M. Resting state fMRI in Alzheimer’s disease: beyond the default mode network. Neurobiol Aging 2012; 33: 1564–1578

[25] Lustig C, Snyder AZ, Bhakta M et al. Functional deactivations: change with age and dementia of the Alzheimer type. Proc Natl Acad Sci U S A 2003; 100: 14504–14509 [26] Fleisher AS, Sherzai A, Taylor C, Langbaum JBS, Chen K, Buxton RB. Restingstate BOLD networks versus task-associated functional MRI for distinguishing Alzheimer’s disease risk groups. Neuroimage 2009; 47: 1678–1690 [27] Wang L, Roe CM, Snyder AZ et al. Alzheimer’s disease family history impacts resting state functional connectivity. Ann Neurol 2012; 72: 571–577 [28] Xiong C, Roe CM, Buckles V et al. Role of family history for Alzheimer biomarker abnormalities in the adult children study. Arch Neurol 2011; 68: 1313–1319 [29] Buckner RL, Snyder AZ, Shannon BJ et al. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 2005; 25: 7709–7717 [30] Wang JH, Lv PY, Wang HB et al. Diffusion tensor imaging measures of normal appearing white matter in patients who are aging, or have amnestic mild cognitive impairment, or Alzheimer’s disease. J Clin Neurosci 2013; 20: 1089–1094 [31] Li YD, Dong HB, Xie GM, Zhang LJ. Discriminative analysis of mild Alzheimer’s disease and normal aging using volume of hippocampal subfields and hippocampal mean diffusivity: an in vivo magnetic resonance imaging study. Am J Alzheimers Dis Other Demen 2013; 28: 627–633 [32] Solodkin A, Chen EE, Van Hoesen GW et al. In vivo parahippocampal white matter pathology as a biomarker of disease progression to Alzheimer’s disease. J Comp Neurol 2013; 521: 4300–4317 [33] Shu X, Qin YY, Zhang S et al. Voxel-based diffusion tensor imaging of an APP/PS1 mouse model of Alzheimer’s disease. Mol Neurobiol 2013; 48: 78–83 [34] Kantarci K, Jack CR, Jr, Xu YC et al. Mild cognitive impairment and Alzheimer’s disease: regional diffusivity of water. Radiology 2001; 219: 101–107 [35] Kalus P, Slotboom J, Gallinat J et al. Examining the gateway to the limbic system with diffusion tensor imaging: the perforant pathway in dementia. Neuroimage 2006; 30: 713–720 [36] Rose SE, McMahon KL, Janke AL et al. Diffusion indices on magnetic resonance imaging and neuropsychological performance in amnestic mild cognitive impairment. J Neurol Neurosurg Psychiatry 2006; 77: 1122–1128 [37] Yoshiura T, Mihara F, Ogomori K, Tanaka A, Kaneko K, Masuda K. Diffusion tensor imaging in posterior cingulate gyrus: correlation with cognitive decline in Alzheimer’s disease. Neuroreport 2002; 13: 2299–2302 [38] Head D, Buckner RL, Shimony JS et al. Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: evidence from diffusion tensor imaging. Cereb Cortex 2004; 14: 410–423 [39] Stahl R, Dietrich O, Teipel SJ, Hampel H, Reiser MF, Schoenberg SO. White matter damage in Alzheimer’s disease and mild cognitive impairment: assessment with diffusion-tensor MR imaging and parallel imaging techniques. Radiology 2007; 243: 483–492 [40] Stebbins GT, Murphy CM. Diffusion tensor imaging in Alzheimer’s disease and mild cognitive impairment. Behav Neurol 2009; 21: 39–49 [41] Klunk WE, Panchalingam K, Moossy J, McClure RJ, Pettegrew JW. N-Acetyl-Laspartate and other amino acid metabolites in Alzheimer’s disease brain: a preliminary proton nuclear magnetic resonance study. Neurology 1992; 42: 1578–1585 [42] Krishnan KR, Charles HC, Doraiswamy PM et al. Randomized, placebocontrolled trial of the effects of donepezil on neuronal markers and hippocampal volumes in Alzheimer’s disease. Am J Psychiatry 2003; 160: 2003–2011 [43] Sweet RA, Panchalingam K, Pettegrew JW et al. Psychosis in Alzheimer disease: postmortem magnetic resonance spectroscopy evidence of excess neuronal and membrane phospholipid pathology. Neurobiol Aging 2002; 23: 547–553 [44] Graff-Radford J, Kantarci K. Magnetic resonance spectroscopy in Alzheimer’s disease. Neuropsychiatr Dis Treat 2013; 9: 687–696 [45] Kantarci K, Lowe V, Przybelski SA et al. Magnetic resonance spectroscopy, β-amyloid load, and cognition in a population-based sample of cognitively normal older adults. Neurology 2011; 77: 951–958 [46] Hattori N, Abe K, Sakoda S, Sawada T. Proton MR spectroscopic study at 3 Tesla on glutamate/glutamine in Alzheimer’s disease. Neuroreport 2002; 13: 183–186

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Magnetic Resonance Imaging and Histopathological Correlation

15 Magnetic Resonance Imaging and Histopathological Correlation in Alzheimer’s Disease Mark D. Meadowcroft and Qing X. Yang Magnetic resonance imaging (MRI) presents a unique opportunity to noninvasively image neurodegenerative diseases and their progression. Although MRI has been extremely valuable in clinical diagnosis and treatment, currently, how MRI parameters relate to the specific pathological changes in the Alzheimer’s disease (AD) brain have not been established and validated. A gap exists between MRI contrast/metrics and the alterations in micro- and macrostructural histologic patterns of disease pathology, which results in a fundamental concern when using MRI findings in the clinical interpretation of disease processes without direct knowledge of the relationship between image contrast and disease pathology. Whereas many in vivo studies have ostensibly reported correlations of MRI contrast and metrics to AD stage, the exact anatomic-pathologic underpinnings and correlates of MRI findings remain unclear. Advances in MRI have allowed researchers to push the boundaries of resolution constraints by using microscopic magnetic resonance imaging (µMRI). Numerous microimaging studies have been performed within the literature base, with most techniques comprising the placement of whole-tissue samples within volume or under surface radiofrequency coils, which presents difficulty in the coregistration of MR slice selection with actual tissue sections cut on a cryostat or vibratome. This obstacle can be overcome by direct imaging of tissue sample slices followed by histologic staining of the tissue sections, resulting in a one-to-one comparison between MRI and microscope images.1,2 The formation of beta-amyloid (Aβ) plaques remains a major neuropathological hallmark and cardinal feature of Alzheimer’s pathology. The ability to distinguish Aβ plaques with MRI has been demonstrated ex vivo with human AD tissue samples and in vivo with transgenic mice that produce amyloid plaques. These data have shown Aβ plaques as hypointensities on T2- and, to a more pronounced degree, T2*- weighted images.

The signal dropout in T2-/T2*-weighted MRIs has been attributed to the iron deposition associated with amyloid plaques in human tissue samples (▶ Fig. 15.1). Homeostatic misregulation of iron is known to occur in the AD brain. The increased concentration of iron in the brain tissue of AD patients has been well demonstrated,3,4,5 and a close association of iron with amyloid plaques in AD tissue has been established.6,7 Aβ amyloid fibrils have a high affinity for iron, on the order of eight magnitudes greater than transferrin for iron.8 The association with iron aids in the formation of Aβ plaque masses as the incorporation of the Aβ fibrils into plaque assemblies is accelerated in an iron-enriched environment.9,10 Focal iron deposition in the form of hemosiderin, derived from ferritin protein breakdown or cerebral microbleeds, and diffuse iron are found throughout the Alzheimer’s brain parenchyma. Microscopic MRI of tissue samples from late-stage AD tissue (Braak VI) demonstrates focal hypointensities within the gray matter in gradient-echo (GRE) images (▶ Fig. 15.2). Costaining of the same MRI tissues samples for fibrillar amyloid, with thioflavin-S, and iron, with Perls’ stain, demonstrates that these hypointensities correspond to amyloid plaques and/or focal iron deposition. Amyloid plaques that are high in iron content exhibit a greater signal dropout on the GRE images than Aβ plaques that have minimal iron association (▶ Fig. 15.3). Larger plaques exhibit a greater signal dropout than smaller plaques. It is apparent that the amount of signal dropout on MRI is associated with the quantity of iron present at that location and the morphology of the plaques. A similar trend in transverse relaxation is found when imaging amyloid plaques in a transgenic mouse model that harbors human mutations in amyloid precursor protein (APP) and presenilin-1 (PS1). The mice produce plaques throughout the brain in response to increased production of Aβ at approximately 9 months of age. GRE images of animal

Fig. 15.1 Iron associated with β-amyloid plaques in Alzheimer’s disease at 400x magnification. The amyloid protein is a metalloprotein with a high affinity for iron. Iron is found throughout the diffuse plaque regions as well as the highly fibrillar core. Iron within microglial cells can be viewed around the periphery of amyloid plaque mass. (a) Perls’ stain; (b) thioflavin-S amyloid stain.

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Alzheimer’s Disease

Fig. 15.2 Gradient-echo microscopic magnetic resonance images 60-µm-thick in Alzheimer’s (a) and age-matched control (b) tissue sections from the entorhinal cortex (Brodmann area 28/ 34). Cortical gray matter and subcortical white matter are clearly visible on the images at the same resolution. Alzheimer’s tissue exhibits punctate hypointensities within the cortical gray matter, which are not found within the gray matter of control tissue.

plaques demonstrate the same relationship of hypointensities with the plaques (▶ Fig. 15.4). When staining is done for fibrillar amyloid and iron, a negligible amount of ferric iron is observed in plaque mass in the mouse tissue. Transverse MR relaxation measurements from regions of interest consisting of individual plaques and surrounding tissue demonstrate that plaques in both AD and APP/PS1 tissue have faster relaxation rates than the surrounding tissue. In comparison of plaques in AD with plaques in APP/PS1 tissue, the human plaques have shorter relaxation times (T2*) and increased relaxation rates (R2*) (▶ Fig. 15.5). Although relaxation does not differ between control tissue and regions without plaques in AD and APP tissue, there is a significant relaxation difference between AD and APP/PS1 Aβ plaques. Histologic staining of the same tissue sections show that less iron is associated with the amyloid plaques in the APP/PS1 mouse model (▶ Fig. 15.6). The decrease in iron within the transgenic mouse plaques is congruent with the reduced R2 in plaques compared with AD plaques. The difference in the R2 between the AD and APP/PS1 plaques of approximately 22% is hypothesized to be due to the synergistic role that both Aβ plaques and iron have in transverse relaxation. The increased rate of relaxation in the AD plaques is predominantly a result of the summation in relaxation attributable to higher iron content in, and morphology of, the plaques. Whereas both human and transgenic mouse plaques are composed of aggregation of Aβ protein fibrils, the morphology of the transgenic plaques is quite dissimilar to that found in Alzheimer’s tissue. APP/PS1 plaques are larger, globular shaped, and have a greater core density, with smaller extent in the surrounding diffuse halo region. Conversely, Alzheimer’s plaques are generally smaller, with a smaller core and a larger diffuse region. The size of the plaques plays a role in the ability to visualize them on MRI, with larger plaques more easily distinguishable. The minimal size of discernable transgenic and AD plaques is approximately 40 µm in diameter. Of note, though, plaque diameter alone does not confer the ability to visualize Aβ plaques. Alzheimer’s plaques of similar size without iron are marginally discernable on MRI, whereas APP/PS1 plaques of the same size without iron are visible as hypointense spots.

The composition and morphology of the Aβ plaques are important for the interpretation of the associated changes in image contrast and parametric measurements. Immunohistologic staining reveals that the core of Alzheimer’s plaques is composed primarily of the 42 amino acid Aβ variant (Aβ42), whereas the coronal region is composed of Aβ40. Transgenic mouse plaques stain solely for Aβ40 in both the core and coronal regions (▶ Fig. 15.7). The composition of the Aβ protein contains numerous hydrophobic amino-acid residues, for which Aβ42 contains two additional hydrophobic amino-acid residues compared with Aβ40.11 The increased hydrophobic nature of the Aβ42 protein is hypothesized to result in its increased amyloidogenic properties. In general, proteins centralize hydrophobic side-chains in the middle of the protein during thermal folding. The central core composition of the Alzheimer’s Aβ plaques is congruent with this notion. Conversely, the transgenic plaque cores and coronal regions are composed of less hydrophobic Aβ40. The hypointense image contrast of transgenic mouse plaques is generated by the reduced mobile water content due to the aggregation of hydrophobic Aβ protein in the plaques. In addition to mobile proton (water) content, image contrast of Alzheimer’s plaques is also dependent on the amount of iron colocalized with the plaques as a result of magnetic susceptibility inhomogeneities induced by iron within the plaques. To tease apart the synergistic effect of contrast enhancement due to iron content and plaque morphology, AD tissue samples were subjected to iron chelation with deferoxamine mesylate salt (DFO) overnight to reduce Aβ plaque iron load. The binding affinity of DFO for Fe3 + is greater than that of Aβ, with a binding constant specific for Fe3 + (not Fe2 + ) on the order of 1030. Thioflavin-S and Perls’ staining for plaques and iron (respectively) indicate that DFO chelation reduces the amount of iron associated with the Aβ plaques (▶ Fig. 15.8). MRI of the AD tissue samples treated with DFO demonstrates that AD plaques can be discerned without iron load in the plaques (▶ Fig. 15.9), congruent with the transgenic plaques’ case, which have low iron load as well. It is also noted that MRI signal decrease in AD plaques treated with DFO is visibly less than that in untreated ones. The corresponding R2* rate is higher in the plaques with iron than in

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Magnetic Resonance Imaging and Histopathological Correlation

Fig. 15.3 T2* -weighted gradient-echo magnetic resonance imaging (MRI) (a) and histologic images of thioflavin-S staining for β-amyloid plaques (b) and Perls’ iron stain (c) of the same 60-µm-thick tissue sample from the entorhinal cortex of an Alzheimer’s disease patient. Selected hypointensities in the MRI correspond to the location of amyloid plaques (red arrows) and/or focal regions of high iron (blue arrows). The figure illustrates that the size of the β-amyloid plaque and the amount of focal iron associated with the amyloid mass are responsible for the hypointensities on the T2*-weighted images. Large plaques are more readily visible on the images, as are plaques containing a high amount of iron. β-amyloid plaques of smaller diameter and those with minimal associated iron are still visible to a reduced degree.

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Alzheimer’s Disease

Fig. 15.4 T2*-weighted image (a) and histologic thioflavin-S β-amyloid (b) and Perls’ iron (c) stains of the same 60-µm-thick slice from amyloid precursor protein(APP)/ presenilin-1 (PS1) mouse brain at –2.92 mm Bregma. Select hypointensities on the magnetic resonance imaging (MRI), which correspond to the location of β-amyloid plaques, are highlighted with red arrows. The figure illustrates that the hypointensities seen in the T2*weighted image are in the same region as large β-amyloid plaques approximately 50 to 60 µm in diameter. Unlike the Alzheimer’s tissue, iron deposition is not present at the plaque locations. Similar to Alzheimer’s tissue, plaque diameter is an important consideration for visibility on MRI visibility, as larger plaques are more readily visible on T2*-weighted images.

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Magnetic Resonance Imaging and Histopathological Correlation

Fig. 15.5 Transverse relaxation R2* rates from regions of interest (ROIs) with plaques and without plaques and in control tissue in human (a) and mouse (b). The R2* rate of plaque ROIs in the Alzheimer’s disease (AD) tissue is significantly greater than both regions without plaques and control tissue sections. A similar trend is found in the mouse data. The increased R2* rate for the plaque ROIs in the AD compared with the amyloid precursor protein (APP)/presenilin-1 (PS1) mouse is hypothesized to be due to higher iron in the AD plaques.

plaques without iron, similar to the comparison of AD with transgenic plaques in ▶ Fig. 15.5. The ability to discern Aβ plaques on MRI has generally been attributed to the iron within the plaques.12,13 Microscopic MRI has provided evidence that iron is not the only cause of Aβ plaque-associated hypointensities. Our data have shown that there is a synergistic dual relaxation mechanism in play between the amount of iron integrated within the plaques and the size or morphology of the plaques. The relaxation data

support the hypothesis that plaque size and morphology play a dominant role in their imaging, as most of the relaxation remains after iron chelation. It is interesting to note that the R2* rate in the plaques stripped of iron is still quite high compared with that of the surrounding gray matter, similar again to the transgenic mouse plaques. The plaques in both chelated and unchelated conditions have R2* values that are significantly greater than those in surrounding gray matter. The percentage of reduction in R2* after iron chelation was 14.6% for Aβ plaques, 17.4% for white matter, and 2.0% for gray matter. White matter is known to have large amounts of iron associated with oligodendrocytes, and iron is required for myelination. The similar reduction in R2* for white matter and Aβ plaques is indicative of the similar iron decrease in these tissue types. Gray matter tissues have less iron, and chelation resulted in only a minor change in R2* rate in these regions. The mechanism for T2 relaxation in Aβ plaques is multifaceted, with iron loading in the plaques accounting for a portion of the apparent transverse relaxation. Iron is well known to perturb the local magnetic field, causing the MR signal from rapid diffusing water molecules in and near the plaques to dephase during each echo time.14 However, the morphology and composition of the plaques themselves are also synergistically involved as major contributors. Several plausible mechanisms can lead to increased T2 relaxation rate in the transgenic mouse and human plaques without appreciable iron content. The Aβ plaque morphology revealed by the transmission electron microscopy (TEM) in ▶ Fig. 15.10 provides evidence for a plausible relaxation mechanism. The transgenic mouse plaques are densely packed globular aggregates, whereas Alzheimer’s plaques appear as loosely connected patches with numerous infiltrated gaps or channels within the plaque mass. The highly compacted plaques behave similarly to a polymer-like solid. In such cases, water molecules are either repelled from the hydrophobic moieties and/or bound to hydrophilic regions of the plaques. Hydrogen bonding of water molecules to the hydrophilic regions would result in a first-order cross-relaxation via proton-proton magnetization exchange, leading to rapid T2 relaxation. Such an effect, termed plaque dehydration, could be a significant contributor to the hypointense T2 contrast in the plaques. In addition, the magnetic susceptibility differences between the highly compact Aβ protein mass and surrounding tissue could induce static magnetic field inhomogeneity in concert with iron but to a lesser degree. The gaps and channels in the human AD plaques allow water molecules to diffuse in and out the plaques, leading to increased water molecule interaction with the macromolecular environment, which, in turn, would increase proton T2 relaxation. Our data suggest that plaque dehydration appears to be a dominant factor over iron loading in the shortening T2 relaxation in the Aβ plaques. The mechanism of water dehydration can be validated by using magnetization transfer (MT) contrast imaging. When applied to AD patients, the magnetization transfer ratio (MTR) (i.e. ratio of image contrast with and without RF frequency offset) has been reported to be decreased in the whole brain.15 Regional measures of MTR are reduced in the hippocampus, amygdala, and

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Alzheimer’s Disease

Fig. 15.6 Alzheimer’s (left) and amyloid precursor protein (APP)/presenilin-1 (PS1) transgenic (right) thioflavin-S (a,b) and Perls’ iron (c,d) stains of β-amyloid plaques at 100x magnification. The thioflavin-S stain and Perls’ stain illustrate a close relationship between β-amyloid plaques and focal iron deposition in Alzheimer’s disease (AD). The relationship between plaques and iron is not seen in the APP/PS1 animal. Differences in plaque morphology between the AD and APP/PS1 plaques are evident. The human AD plaques have a dense core of fibrillar amyloid protein with a halo of amyloid protein. APP/PS1 plaques exhibit a larger and denser thioflavin-S-positive core with a smaller halo region around them. Compared with the human AD plaques, the APP/PS1 images show a reduction in focal iron within the plaques that is diffusely found throughout the plaque.

temporal lobe of AD and mild cognitive impairment (MCI) patients compared with controls.16 A longitudinal decline in global brain MTR in AD patients over a period of 6 and 12 months has also been reported.17 The basis for the decrease in MTR has been speculated as due to microstructural changes of the gray and white matter. Specifically, it has been hypothesized that neurodegeneration, inflammation, gliosis, and increased interstitial fluid reduce the MT ratio. When transitioned to the preclinical space, it is interesting to note that studies using the amyloid-generating transgenic mouse model present data that are contradictory to the MTRs seen in AD. A longitudinal increase in global and regional brain MTR within the APP/PS1 model compared with controls has been reported.18,19 The mechanism for the increased MTR in the APP/PSI animal models has been hypothesized to be associated with Aβ-plaque load. To better understand the cause of the paradoxical human and animal model data, it is necessary to understand the physical mechanism of the MT technique. MT imaging relays information on the exchange between free and bound water molecules.20 The transverse relaxation for protons (water) in the direct vicinity of macromolecules or cellular structures is very short (> 1 ms) compared with free diffused water, as they are rotationally (or irrotationally) bound to the macromolecules via hydrogen bonds. As a result, through dipolar coupling or chemical exchange mechanisms, the macromolecular pool is able to influence the relaxation of the free protons. Saturation of the protein-bound macromolecular pool via a radiofrequency

pulse with given frequency offset causes the net magnetization of the free water pool to decrease, resulting in an increase in the MTR. To understand proton magnetization transfer in the direct vicinity of Aβ plaques, experiments using off-resonant saturation of amyloid-bound protons in 60-µm slices of Alzheimer’s tissue were undertaken. An optimal predetermined offresonant pulse at 15 kHz (50 parts per million [ppm]) was used to saturate the amyloid-bound proton pool. Amyloid plaques are again visible with thioflavin stains and correspond to the location of hypointensities on GRE images. When plaque location is overlaid on the MTR data set, it is apparent that voxels containing amyloid plaques have increased MTR compared with the surrounding gray matter (▶ Fig. 15.11). The increased MTR in white matter verifies the MTR calculation, as white matter is known to have an increased MTR compared to that of gray matter. Regions of interest in the plaques have significantly higher MTR than in surrounding gray matter. Following known trends on MTR values in gray and white matter, the white matter has a significantly greater MTR than gray matter in the data set. The decrease in MTR in AD plaques follows the same trend as previously published amyloid-generating mouse model data. These transgenic animals produce Aβ plaques with a moderate gliotic inflammatory response. Data from individual plaque measurements support the hypothesis that the increase in MTR for the transgenic animals is in fact due to amyloid load. The

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Magnetic Resonance Imaging and Histopathological Correlation

Fig. 15.7 β-Amyloids Aβ40 and Aβ42 immunohistologic and thioflavin-S (Thio-S) stains of amyloid plaques at 200x magnification in Alzheimer’s (top) and amyloid precursor protein (APP)/ presenilin-1 (PS1) (bottom) tissue samples. Alzheimer’s plaques contain both 40 and 42 amino-acid β-amyloid fragments. APP/PS1 transgenic plaques contain only the 40-amino-acid variant while staining negatively for Aβ42. The figure illustrates the morphologic and compositional differences between Alzheimer’s and transgenic plaques.

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Alzheimer’s Disease

Fig. 15.8 Alzheimer’s tissue samples untreated (left) and treated (right) with deferoxamine (DFO) stained with thioflavin-S (top) and Perls’ iron (bottom) stains at 200x magnification. The β-amyloid plaques in the DFO iron-chelated samples are stripped of iron compared with untreated plaques.

Fig. 15.9 T2*-weighted (a,d) and histologic stains for Perls’ iron (b,e) and thioflavin-S (c,f) in deferoxaminemesylate salt (DFO) untreated (left) and treated (right) Alzheimer’s entorhinal cortex tissue samples. Large plaques and those with high iron content are readily visible on magnetic resonance images of the DFO-untreated samples. Amyloid plaques in DFO-treated samples with little to no associated iron retain their discernibility on the gradient echo data sets. Chelated Alzheimer’s plaques without iron generate hypointensities on T2*-weighted images similar to amyloid precursor protein (APP)/presenilin-1 (PS1) plaques without iron. The data illustrate the ability to visualize plaques without iron and support the synergistic hypothesis that plaques are able to induce transverse proton relaxation as a result of both their association with iron and their morphology.

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Fig. 15.10 Transmission electron microscope images of an Alzheimer’s disease (AD, left) and APP/ presenilin-1 (PS1) transgenic (right) plaque at 4,600x (top) and 22,500x (bottom) magnification. The ultrastructural composition of AD and APP/ PS1 plaques are evident in the images. The Alzheimer’s plaques exhibit a reduced density compared with the transgenic plaques, even in the condensed core of the amyloid mass (bottom). The reduced density of the Alzheimer’s plaques allows the infiltration of water (protons) into the core, which is hypothesized to aid in the increased transverse relaxation associated with Alzheimer’s plaques.

plaques in the AD tissue also have an increased MTR; however, in vivo, this is hypothesized to be overshadowed by the dominant partial-volume decrease in MTR resulting from an increase in regional interstitial fluid. Magnetic resonance imaging of AD and corresponding histologic analysis of individual Aβ plaques from the same tissue

sample have allowed the establishment of specific relationships between image metrics and disease pathology. Such relationships will provide a foundation for clinical interpretation of MRI findings in AD and other neurodegenerative diseases, that is, the ability of MRI to determine Aβ plaque load to aid in the diagnostic determination of AD severity.

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Alzheimer’s Disease

Fig. 15.11 T2*-weighted (a), Perls’ iron stain (b), thioflavin-S stain (c), and magnetization transfer ratio (MTR) images of the same Alzheimer’s entorhinal cortex tissue sample. Hypointensities on the gradient-echo magnetic resonance image correspond to thioflavin and iron-positive amyloid plaques (arrows). Selection of these plaques on the magnetization transfer image shows that these plaques cause an increase in magnetization transfer, notwithstanding the noise present in the image. This is hypothesized to be due to the macromolecular interaction of protons in the vicinity of the amyloid mass. In addition, the known increase in MTR associated with white matter tracts can be seen on the image set.

References [1] Meadowcroft MD, Connor JR, Smith MB, Yang QX. MRI and histological analysis of beta-amyloid plaques in both human Alzheimer’s disease and APP/PS1 transgenic mice. J Magn Reson Imaging 2009; 29: 997–1007 [2] Meadowcroft MD, Zhang S, Liu W et al. Direct magnetic resonance imaging of histological tissue samples at 3.0 T. Magn Reson Med 2007; 57: 835–841 [3] Connor JR, Snyder BS, Beard JL, Fine RE, Mufson EJ. Regional distribution of iron and iron-regulatory proteins in the brain in aging and Alzheimer’s disease. J Neurosci Res 1992; 31: 327–335 [4] Connor JR, Menzies SL, St Martin SM, Mufson EJ. A histochemical study of iron, transferrin, and ferritin in Alzheimer’s diseased brains. J Neurosci Res 1992; 31: 75–83 [5] Lovell MA, Robertson JD, Teesdale WJ, Campbell JL, Markesbery WR. Copper, iron and zinc in Alzheimer’s disease senile plaques. J Neurol Sci 1998; 158: 47–52 [6] Collingwood J, Dobson J. Mapping and characterization of iron compounds in Alzheimer’s tissue. J Alzheimers Dis 2006; 10: 215–222 [7] Collingwood JF, Chong RK, Kasama T et al. Three-dimensional tomographic imaging and characterization of iron compounds within Alzheimer’s plaque core material. J Alzheimers Dis 2008; 14: 235–245 [8] Jiang D, Li X, Williams R et al. Ternary complexes of iron, amyloid-beta, and nitrilotriacetic acid: binding affinities, redox properties, and relevance to iron-induced oxidative stress in Alzheimer’s disease. Biochemistry 2009; 48: 7939–7947 [9] Collingwood JF, Mikhaylova A, Davidson M et al. In situ characterization and mapping of iron compounds in Alzheimer’s disease tissue. J Alzheimers Dis 2005; 7: 267–272 [10] Bush AI. The metallobiology of Alzheimer’s disease. Trends Neurosci 2003; 26: 207–214

[11] Yan Y, Liu J, McCallum SA, Yang D, Wang C. Methyl dynamics of the amyloidbeta peptides Abeta40 and Abeta42. Biochem Biophys Res Commun 2007; 362: 410–414 [12] Falangola MF, Lee SP, Nixon RA, Duff K, Helpern JA. Histological co-localization of iron in Abeta plaques of PS/APP transgenic mice. Neurochem Res 2005; 30: 201–205 [13] Jack CR, Jr, Garwood M, Wengenack TM et al. In vivo visualization of Alzheimer’s amyloid plaques by magnetic resonance imaging in transgenic mice without a contrast agent. Magn Reson Med 2004; 52: 1263–1271 [14] Chavhan GB, Babyn PS, Thomas B, Shroff MM, Haacke EM. Principles, techniques, and applications of T2*-based MR imaging and its special applications. Radiographics 2009; 29: 1433–1449 [15] Kabani NJ, Sled JG, Chertkow H. Magnetization transfer ratio in mild cognitive impairment and dementia of Alzheimer’s type. Neuroimage 2002; 15: 604–610 [16] Mascalchi M, Ginestroni A, Bessi V et al. Regional analysis of the magnetization transfer ratio of the brain in mild Alzheimer’s disease and amnestic mild cognitive impairment. AJNR Am J Neuroradiol 2013; 34: 2098–2104 [17] Ropele S, Schmidt R, Enzinger C, Windisch M, Martinez NP, Fazekas F. Longitudinal magnetization transfer imaging in mild to severe Alzheimer’s disease. AJNR Am J Neuroradiol 2012; 33: 570–575 [18] Pérez-Torres CJ, Reynolds JO, Pautler RG. Use of magnetization transfer contrast MRI to detect early molecular pathology in Alzheimer’s disease. Magn Reson Med 2014; 71: 333–338 [19] Bigot C, Vanhoutte G, Verhoye M, Van der Linden A. Magnetization transfer contrast imaging reveals amyloid pathology in Alzheimer’s disease transgenic mice. Neuroimage 2014; 87: 111–119 [20] Henkelman RM, Stanisz GJ, Graham SJ. Magnetization transfer in MRI: a review. NMR Biomed 2001; 14: 57–64

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Part V

16 Dementia with Lewy Body Disease

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17 Frontotemporal Lobar Degeneration

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Non-Alzheimer’s Cortical Dementia

16 Dementia with Lewy Body Disease Aristides A. Capizzano and Toshio Moritani

16.1 History Dementia with Lewy bodies (DLB) is the most recently recognized of the major neurodegenerative dementias. Lewy bodies (LBs) are proteinaceous cytoplasmic neuronal inclusions (▶ Fig. 16.1) originally described by Friedrich Lewy in Parkinson’s disease (PD).1 The two morphologically and molecularly distinct types of LBs are the classic brainstem and cortical LBs, both of which are immunoreactive to the presynaptic protein α-synuclein (▶ Fig. 16.2).2 Therefore, from a molecular standpoint, DLB is counted among the α-synucleinopathies, together with PD and multiple-system atrophy (MSA). A report of two cases from 1961 described two elderly men with progressive dementia and flexion contractures, who on neuropathological examination revealed extensive LBs along the neuraxis as the only pathological alteration.3 More than 30 other clinical dementia cases in which LBs with or without senile plaques and neurofibrillary tangles were the main pathological findings were reported by Japanese investigators over the following two decades.4 The overlap between LBs and Alzheimer’s disease (AD)-like neuropathology led to the consideration of DLB as a variant of AD.5

Fig. 16.1 (a) Hematoxylin and eosin stain, 600x. Cortical Lewy body (arrow) in patient with Lewy body dementia. (b) Same Lewy body at 1,000x. (Courtesy Dr. Patricia Kirby, University of Iowa.)

By the late 1980s, there was increased recognition of a syndrome affecting as much as 20% of the demented elderly population with confusion, hallucinations, and behavioral disturbances in which cortical and subcortical LBs, with variable plaque formation, heralded the pathological picture.6 This syndrome of “senile dementia of Lewy body type” was then considered within the spectrum of LB diseases, between the polar types of PD and “diffuse Lewy body disease.” Improved neuropathological techniques to label LBs, such as anti-ubiquitin immunohistochemistry, have been instrumental in advancing

Fig. 16.2 (a,b) α-Synuclein immunohistochemistry at 1,000x. Cortical Lewy bodies. (Courtesy Dr. Patricia Kirby, University of Iowa.)

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Dementia with Lewy Body Disease the understanding of what is now termed dementia with Lewy bodies, which accounts for approximately 15% of cases of lateonset dementia, being the second most prevalent degenerative dementia after AD.

16.2 Clinical Features The characteristic initial symptoms and signs of DLB have been operationalized in consensus diagnostic criteria originally published in 19967 and revised in 2005.8 The essential central feature is dementia, with memory deficits not necessarily occurring in the early stages but common with progression, and particularly prominent deficits in attention, executive function, and visuospatial skills. Core features are fluctuating cognition, recurrent visual hallucinations, and spontaneous parkinsonism, and these distinguish DLB from AD. Suggestive diagnostic features are rapid eye movement sleep behavior disorder, severe neuroleptic sensitivity, and reduced dopamine transporter uptake in the basal ganglia on single-photon emission computed tomography (SPECT) or positron emission tomography (PET) imaging. Probable DLB is diagnosed with two core features or one core and at least one suggestive feature; possible DLB is diagnosed with either one core or one or more suggestive features.8 Supportive features of the diagnosis, which are commonly present but do not have proven diagnostic specificity, are repeated falls, transient unexplained loss of consciousness, severe autonomic dysfunction, systematized delusions, depression, nonvisual hallucinations, relative preservation of medial temporal lobe volume on structural imaging, generalized low SPECT/PET uptake with reduced occipital activity, low 123I-metaiodobenzylguanidine (MIBG) myocardial uptake, and prominent slow-wave activity on electroencephalography with temporal lobe transient sharp waves. A diagnosis of DLB is less likely in the presence of clinical or imaging signs of cerebrovascular disease, of any other illness sufficient to account at least in part for the clinical picture, or if parkinsonism appears only at a stage of severe dementia.8 Men are more susceptible than women to DLB. The parkinsonian signs are commonly bilateral, with rigidity, bradykinesia, amimia, and slow shuffling gait. Resting tremor is less common.9 These symptoms show modest response to levodopa treatment. Visual hallucinations are the best clinical discriminator with AD, seen in up to 80% of DLB patients; they are recurrent and vivid and typically involve animals or people. Depression is commonly associated with DLB. In terms of imaging, supportive features for DLB diagnosis are lack of medial temporal atrophy (as typically seen in AD), low SPECT/ PET perfusion in the occipital lobe, and low MIBG myocardial scintigraphy. Main differential diagnostic considerations of DLB are PD with dementia (PDD) and AD. Diagnostic criteria indicate that dementia should occur before or concurrently with parkinsonism to diagnose DLB,8 whereas PDD is diagnosed when parkinsonism is present for 12 months or longer before the onset of dementia.10 The arbitrariness of the distinction between DLB and PDD strongly suggests that both clinical phenotypes lie along the same pathological continuum.

16.3 Genetics DLB was long considered a sporadic disorder with late onset, and twin investigations of DLB did not support a major genetic cause for this disease.11 However, DLB and core clinical features thereof aggregate in families.12 A systematic review suggested a genetic overlap between familial cases of DLB and PDD.13 Further support for a genetic predisposition to DLB comes from families with combined features of dementia and parkinsonism inherited in a mendelian manner.14 The first locus for DLB was mapped on chromosome 2q35-q36 in an autosomal dominant family with autopsy-confirmed DLB,15 but in-depth molecular genetic follow-up investigations did not reveal a simple pathogenic or gene dosage mutation that cosegregated with DLB, suggesting that the mutation responsible for DLB in this family is complex.16 Because the current understanding of the genetics of DLB is unclear and a major DLB gene has not yet been uncovered, it has been suggested that mutations underlying DLB are biologically more complex than expected for monogenic disorders.14

16.4 Neuropathology Macroscopically, the degree of cortical atrophy is variable in DLB. Given the clinical importance of distinguishing DLB from AD, some studies compared pathological and imaging changes in the hippocampal formation between the two diseases. Medial temporal lobe (including the hippocampus and parahippocampal gyrus) area measurements in fixed brains were significantly larger in DLB than in AD or mixed AD/DLB cases.17 Accordingly, neuron counts in the perforant pathway connecting the entorhinal cortex with the dentate gyrus are significantly depleted in AD compared with DLB and controls, although high variability was reported.18 An important macroscopic feature of DLB brains is pallor of the substantia nigra and locus ceruleus, as in PD, reflecting a loss of neuromelanin. Preliminary results in PD have shown signal loss seen on in vivo heavily T1-weighted MRI in the substantia nigra compared with controls, likely reflecting a loss of paramagnetic neuromelanin.19 Microscopically, the presence of LBs is the only histopathological requirement for the diagnosis of DLB.7,20 Classic brainstem and cortical LBs are best demonstrated by using immunostaining for α-synuclein21 (▶ Fig. 16.2), but they contain a variety of other molecular components, such as ubiquitin, neurofilaments, parkin, components of the ubiquitin-proteasome system, molecular chaperones, and lipids.2 LBs likely represent a cellular response to the accumulation of abnormal proteins and undergo several phases during their formation.2 Cortical LB progression starts in the amygdala, spreads to the limbic cortex, and finally spreads to the neocortex.22 Apart from LBs, other histopathological features of DLB are Lewy-related neurites, AD-type pathology (plaques and tangles), spongiform changes, and synapse loss.7 In accord with the National Institute on Aging Reagan Criteria for diagnosis of AD,23 DLB diagnosis is related directly to the burden of LB pathology and inversely to AD pathology.8 Subtypes of DLB have been recognized in relationship to the burden

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Non-Alzheimer’s Cortical Dementia of AD-type pathology.24 The “pure” form of DLB contains LBs in only the brainstem and cerebral cortex; LBs associated with senile plaques but a low Braak tangle stage define the “common” form; finally, LBs in conjunction with senile plaques and NFTs sufficient to diagnose AD are seen in the “AD form” of DLB.

16.5 Neuroimaging 16.5.1 Structural Magnetic Resonance Imaging In contradistinction to AD, where brain atrophy has been extensively reported from neuroimaging studies, atrophic changes are less conspicuous and are distributed differently in imaging studies of DLB (▶ Fig. 16.3). Using voxel-based morphometry (VBM), a pattern of volume loss involving the dorsal midbrain, hypothalamus, and substantia innominata with sparing of the hippocampus and temporoparietal cortex has been proposed in DLB (▶ Fig. 16.4, ▶ Fig. 16.5).25 Furthermore, patients with a high probability of DLB on postmortem neuropathological assessment were found to have a low volume of dorsal mesopontine gray matter with normal hippocampal volumes on antemortem MRI.26 Moreover, a high-resolution study of the medial temporal lobes with submillimiter pixel resolution27

showed that AD patients had a thinner subiculum, smaller CA1 area, and effacement of the hippocampal striation compared with DLB, features that could therefore distinguish between the two disorders. On the other hand, other studies found hippocampal volume loss in DLB. A shape analysis of hippocampal volumes in DLB showed a differential atrophy pattern involving mostly the anterior aspect of the CA1 sector in DLB compared with AD28; hippocampal volume deficit was between 10 and 20% in DLB vs. controls (▶ Fig. 16.6). More recently, using the same hippocampal radial distance technique as in the previously mentioned study, DLB had left-predominant hippocampal atrophy centered at CA1 and the subiculum compared with controls, whereas no significant differences were detected between DLB and AD, although the latter may be secondary to the smaller sample size of DLB subjects.29 White matter T2 signal hyperintensities (WMSHs) are a wellknown feature in the elderly and in patients with neurodegenerative or vascular dementia; they correlate with age and history of hypertension and denote myelin loss, gliosis, and periventricular interstitial fluid accumulation. Comparison of DLB and AD showed that the results of WMSH load were inconsistent even among quantitative studies, probably reflecting differences in methods or subject inclusion criteria. Greater WMSHs were seen in AD than in DLB, and the latter showed WMSHs similar to those of controls30; also, similar WMSH loads

Fig. 16.3 A 75-year-old man with executive visuospatial dysfunction and visual hallucinations, rapid eye movement (REM) behavior disorder, and gait instability raising concern for dementia with Lewy bodies. (a,b) Axial T1-weighted images showing bilateral mild frontotemporal volume loss. (c) Sagittal T1-weighted with mild expansion of the sylvian fissure. (d) Axial fluid-attenuated inversion recovery with mild posterior periventricular leukoaraiosis.

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Dementia with Lewy Body Disease

Fig. 16.4 Voxel-based morphometry-derived patterns of gray matter loss in Lewy body dementia (DLB) (left side) vs. controls (right side) and Alzheimer’s disease vs. controls (right side) corrected for multiple comparisons, p < 0.05. Gray matter loss in DLB is focused on the substantia innominata, dorsal midbrain, and hypothalamus. A, anterior; P, posterior. (Used with permission from Whitwell JL, Weigand SD, Shiung MM, et al. Focal atrophy in dementia with Lewy bodies on MRI: a distinct pattern from Alzheimer’s disease. Brain 2007; 130(Pt 3):708–719.)

in AD and DLB have been reported.31 The frequent coexistence of DLB with AD should also be considered (▶ Fig. 16.7). Apart from the preceding differences between DLB and AD, an important comparison is the differential atrophy pattern between DLB and PDD, the main clinical phenotypes of LB pathology. Using VBM, DLB had gray matter reduction in the right superior frontal, premotor, and inferior frontal regions compared with PDD.32 Furthermore, frontal gray matter deficits in DLB correlated with attentional deficits, whereas right hippocampal and amygdala volumes correlated with visual memory performance. In a pathologically confirmed cohort of DLB and PDD, amygdala volumes in MRI in vivo inversely correlated with the density of Lewy body neuropathology in the amygdala.33

16.5.2 Diffusion Tensor Imaging Using tract-based spatial statistics, a voxel-wise approach to diffusion tensor imaging (DTI) data in the style of VBM, DLB displayed lower fractional anisotropy (FA) of parieto-occipital white matter voxels compared with controls, with significantly fewer changes in frontal regions. AD subjects, on the other hand, had more diffuse reductions in FA on both sides of the central sulcus. Mean diffusivity (MD) changes were

widespread in both conditions. The DTI changes in DLB correlated with episodic memory, letter fluency, and parkinsonian signs.34 A previous study demonstrated increased MD in the amygdala and reduced FA in the inferior longitudinal fasciculus in DLB, which correlated with parkinsonism and visual hallucinations, respectively, with a different pattern of DTI changes in AD patients who had involvement of temporoparietal regions and associated white matter tracts.35 Also, DLB (but not AD) patients had reduced FA compared with controls in the bilateral inferior occipitofrontal and left inferior longitudinal fasciculi, encompassing visual association areas, with both demented groups showing lower FA in the uncinate fasciculus bilaterally.36

16.5.3 Dopaminergic Imaging Dopaminergic function in DLB using either SPECT or PET has become a suggestive feature of the diagnosis under the consensus criteria.7,8 DLB and PDD patients display severely reduced dopaminergic uptake in the caudate and putamen compared with controls and AD patients. [123I]N-ω-fluoropropyl-2βcarbomethoxy-3β-(4-iodophenyl)nortropane (FP-CIT) SPECT has 80 to 90% sensitivity and specificity for the diagnosis of DLB and PDD,37 which becomes clinically significant for the

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Non-Alzheimer’s Cortical Dementia

Fig. 16.5 Three-dimensional surface renders showing voxel-based morphometry-derived patterns of gray matter loss in Lewy body dementia (DLB) vs. controls (left) and Alzheimer’s disease (AD) vs. controls (right) corrected for multiple comparisons; p < 0.05. DLB shows much less cortical atrophy than AD. (Used with permission from Whitwell JL, Weigand SD, Shiung MM, et al. Focal atrophy in dementia with Lewy bodies on MRI: a distinct pattern from Alzheimer’s disease. Brain 2007;130(Pt 3):708–719.)

differential diagnosis of DLB and AD. The specificity of FP-CIT scans to distinguish DLB from FTD, however, is significantly less because one-third of FTD cases also have reduced striatal dopamine uptake.38 The reduced dopaminergic uptake in the basal ganglia in DLB correlates with neuronal depletion in the substantia nigra but not with the pathological burden of α-synuclein, tau, or amyloid deposition, suggesting that disruption of the nigrostriatal pathway is responsible for the FP-CIT scan abnormalities in DLB.39

16.5.4 Perfusion and Metabolism Imaging Occipital hypoperfusion and hypometabolism in DLB have been reported using SPECT and fluorodeoxyglucose (FDG) PET, respectively.40 In distinguishing DLB from AD, however, FP-CIT displayed better diagnostic accuracy than technetium 99mexametazime SPECT.41 Accordingly, low SPECT/PET uptake in the occipital lobes is considered only among the supportive features of the diagnosis of DLB.8 FDG PET was shown to be more sensitive than SPECT-iodoamphetamine to the occipital and parietal changes in DLB, which may result from improved spatial resolution with PET over SPECT and also from higher metabolic than perfusion deficits in DLB.42 Furthermore, psychotic symptom clusters in DLB correlate with the anatomical distri-

bution of perfusion deficits, with visual hallucinations correlated with parieto-occipital hypoperfusion.43 Clinical fluctuations, a core characteristic of DLB, correlate with brain perfusion changes on hexamethylpropyleneamine (HMPAO) SPECT,44 and reduced hallucinations in response to the acetylcholinesterase inhibitor donepezil correlate with improved occipital blood flow.45

16.5.5 Management of Lewy Body Dementia No disease-modifying treatment is available at this point for patients with DLB. Nonpharmacologic interventions include education, reassurance, orientation and memory prompts, attentional cues, and targeted behavioral interventions.46 Parkinsonism is treated with the lowest effective dose of levodopa because higher doses are associated with worsened confusion and hallucinations. The percentage of patients with more than 10% motor improvement is lower among DLB patients than for those with PD and PDD.47 Cholinesterase inhibitors are effective and relatively safe for treating neuropsychiatric and cognitive symptoms in DLB.48 The role of these agents in DLB may become important given the high risk of severe sensitivity reactions and cerebrovascular events with neuroleptics in these patients. However, current evidence supporting the efficacy of

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Dementia with Lewy Body Disease

Fig. 16.6 A 71-year old woman with Lewy body dementia (DLB) with parkinsonism, delirium, and orthostatic hypotension. Axial fluid-attenuated inversion recovery (FLAIR) (upper row) and coronal inversion recovery (IR) T1 weighted images (lower row) displaying bilateral temporal and hippocampal atrophy without significant FLAIR hyperintensity. R, right; S, superior. (Courtesy of Dr. Kei Yamada, Kyoto Prefectural University of Medicine, Japan.)

Fig. 16.7 A 74-year-old man with a clinical picture consistent with mixed Lewy body dementia (DLB)/Alzheimer’s disease (AD) dementia. (a) Axial fluid-attenuated inversion recovery showing confluent leukoaraiosis. Oblique coronal T1-weighted (b) and T2weighted (c,d) images perpendicular to the temporal horn display severe hippocampal atrophy and confluent leukoaraiosis.

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Non-Alzheimer’s Cortical Dementia cholinesterase inhibitors or the N-methyl-D-aspartate receptor antagonist memantine in DLB remains inconclusive.49

References [1] [2] [3]

[4] [5] [6]

[7]

[8]

[9] [10]

[11]

[12] [13]

[14] [15]

[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]

Lewy F. Paralysis Agitans: I. Pathologische Anatomie. In: Handbuch Der Neurologie, Vol 3. Berlin: Julius Springer; 1912:920–933 Jellinger KA. Formation and development of Lewy pathology: a critical update. J Neurol 2009; 256 Suppl 3: 270–279 Okazaki H, Lipkin LE, Aronson SM. Diffuse intracytoplasmic ganglionic inclusions (Lewy type) associated with progressive dementia and quadriparesis in flexion. J Neuropathol Exp Neurol 1961; 20: 237–244 Kosaka K. Diffuse Lewy body disease in Japan. J Neurol 1990; 237: 197–204 Hansen L, Salmon D, Galasko D et al. The Lewy body variant of Alzheimer’s disease: a clinical and pathologic entity. Neurology 1990; 40: 1–8 Perry RH, Irving D, Blessed G, Fairbairn A, Perry EK. Senile dementia of Lewy body type: a clinically and neuropathologically distinct form of Lewy body dementia in the elderly. J Neurol Sci 1990; 95: 119–139 McKeith IG, Galasko D, Kosaka K et al. Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): report of the consortium on DLB international workshop. Neurology 1996; 47: 1113–1124 McKeith IG, Dickson DW, Lowe J et al. Consortium on DLB. Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium. Neurology 2005; 65: 1863–1872 McKeith I. Clinical aspects of dementia with Lewy bodies. In: Aminoff M, Boller F, Swaab D, eds. 3rd Series. New York: Elsevier; 2008;307–311 Emre M, Aarsland D, Brown R et al. Clinical diagnostic criteria for dementia associated with Parkinson’s disease. Mov Disord 2007; 22: 1689–1707, quiz 1837 Wang CS, Burke JR, Steffens DC, Hulette CM, Breitner JCS, Plassman BL. Twin pairs discordant for neuropathologically confirmed Lewy body dementia. J Neurol Neurosurg Psychiatry 2009; 80: 562–565 Nervi A, Reitz C, Tang MX et al. Familial aggregation of dementia with Lewy bodies. Arch Neurol 2011; 68: 90–93 Kurz MW, Schlitter AM, Larsen JP, Ballard C, Aarsland D. Familial occurrence of dementia and parkinsonism: a systematic review. Dement Geriatr Cogn Disord 2006; 22: 288–295 Meeus B, Theuns J, Van Broeckhoven C. The genetics of dementia with Lewy bodies: what are we missing? Arch Neurol 2012; 69: 1113–1118 Bogaerts V, Engelborghs S, Kumar-Singh S et al. A novel locus for dementia with Lewy bodies: a clinically and genetically heterogeneous disorder. Brain 2007; 130: 2277–2291 Meeus B, Nuytemans K, Crosiers D et al. Comprehensive genetic and mutation analysis of familial dementia with Lewy bodies linked to 2q35-q36. J Alzheimers Dis 2010; 20: 197–205 Lippa CF, Johnson R, Smith TW. The medial temporal lobe in dementia with Lewy bodies: a comparative study with Alzheimer’s disease. Ann Neurol 1998; 43: 102–106 Lippa CF, Pulaski-Salo D, Dickson DW, Smith TW. Alzheimer’s disease, Lewy body disease and aging: a comparative study of the perforant pathway. J Neurol Sci 1997; 147: 161–166 Schwarz ST, Rittman T, Gontu V, Morgan PS, Bajaj N, Auer DP. T1-weighted MRI shows stage-dependent substantia nigra signal loss in Parkinson’s disease. Mov Disord 2011; 26: 1633–1638 Tolnay M, Probst A. International Winter Meeting on Neuropathology and Genetics, of Dementia. In: Neuropathology and Genetics of Dementia. Tolnay M, Probst A, eds. New York; London: Kluwer Academic/Plenum Publishers; 2001 Lowe J. Neuropathology of dementia with Lewy bodies. In: Handbook of Clinical Neurology: Dementias. Aminoff M, Boller F, Swaab D, eds. 3rd Series. New York: Elsevier; 2008;321–330 Marui W, Iseki E, Nakai T et al. Progression and staging of Lewy pathology in brains from patients with dementia with Lewy bodies. J Neurol Sci 2002; 195: 153–159 Consensus recommendations for the postmortem diagnosis of Alzheimer’s disease. The National Institute on Aging, and Reagan Institute working group on diagnostic criteria for the neuropathological assessment of Alzheimer’s disease. Neurobiol Aging 1997; 18 Suppl: S1–S2 Marui W, Iseki E, Kato M, Akatsu H, Kosaka K. Pathological entity of dementia with Lewy bodies and its differentiation from Alzheimer’s disease. Acta Neuropathol 2004; 108: 121–128

[25] Whitwell JL, Weigand SD, Shiung MM et al. Focal atrophy in dementia with Lewy bodies on MRI: a distinct pattern from Alzheimer’s disease. Brain 2007; 130: 708–719 [26] Kantarci K, Ferman TJ, Boeve BF et al. Focal atrophy on MRI and neuropathologic classification of dementia with Lewy bodies. Neurology 2012; 79: 553–560 [27] Firbank MJ, Blamire AM, Teodorczuk A et al. High resolution imaging of the medial temporal lobe in Alzheimer’s disease and dementia with Lewy bodies. J Alzheimers Dis 2010; 21: 1129–1140 [28] Sabattoli F, Boccardi M, Galluzzi S, Treves A, Thompson PM, Frisoni GB. Hippocampal shape differences in dementia with Lewy bodies. Neuroimage 2008; 41: 699–705 [29] Chow N, Aarsland D, Honarpisheh H et al. Comparing hippocampal atrophy in Alzheimer’s dementia and dementia with lewy bodies. Dement Geriatr Cogn Disord 2012; 34: 44–50 [30] Burton EJ, McKeith IG, Burn DJ, Firbank MJ, O’Brien JT. Progression of white matter hyperintensities in Alzheimer’s disease, dementia with lewy bodies, and Parkinson’s disease dementia: a comparison with normal aging. Am J Geriatr Psychiatry 2006; 14: 842–849 [31] Oppedal K, Aarsland D, Firbank MJ et al. White matter hyperintensities in mild lewy body dementia. Dement Geriatr Cogn Dis Extra 2012; 2: 481–495 [32] Sanchez-Castaneda C, Rene R, Ramirez-Ruiz B et al. Correlations between gray matter reductions and cognitive deficits in dementia with Lewy Bodies and Parkinson’s disease with dementia. Mov Disord 2009; 24: 1740–1746 [33] Burton EJ, Mukaetova-Ladinska EB, Perry RH, Jaros E, Barber R, O’Brien JT. Neuropathological correlates of volumetric MRI in autopsy-confirmed Lewy body dementia. Neurobiol Aging 2012; 33: 1228–1236 [34] Watson R, Blamire AM, Colloby SJ et al. Characterizing dementia with Lewy bodies by means of diffusion tensor imaging. Neurology 2012; 79: 906–914 [35] Kantarci K, Avula R, Senjem ML et al. Dementia with Lewy bodies and Alzheimer’s disease: neurodegenerative patterns characterized by DTI. Neurology 2010; 74: 1814–1821 [36] Kiuchi K, Morikawa M, Taoka T et al. White matter changes in dementia with Lewy bodies and Alzheimer’s disease: a tractography-based study. J Psychiatr Res 2011; 45: 1095–1100 [37] McKeith I, O’Brien J, Walker Z et al. DLB Study Group. Sensitivity and specificity of dopamine transporter imaging with 123I-FP-CIT SPECT in dementia with Lewy bodies: a phase III, multicentre study. Lancet Neurol 2007; 6: 305–313 [38] Morgan S, Kemp P, Booij J et al. Differentiation of frontotemporal dementia from dementia with Lewy bodies using FP-CIT SPECT. J Neurol Neurosurg Psychiatry 2012; 83: 1063–1070 [39] Colloby SJ, McParland S, O’Brien JT, Attems J. Neuropathological correlates of dopaminergic imaging in Alzheimer’s disease and Lewy body dementias. Brain 2012; 135: 2798–2808 [40] Taylor JP, O’Brien J. Neuroimaging of dementia with Lewy bodies. Neuroimaging Clin N Am 2012; 22: 67–81, viiiviii [41] Colloby SJ, Firbank MJ, Pakrasi S et al. A comparison of 99mTc-exametazime and 123I-FP-CIT SPECT imaging in the differential diagnosis of Alzheimer’s disease and dementia with Lewy bodies. Int Psychogeriatr 2008; 20: 1124– 1140 [42] Ishii K, Hosaka K, Mori T, Mori E. Comparison of FDG-PET and IMP-SPECT in patients with dementia with Lewy bodies. Ann Nucl Med 2004; 18: 447–451 [43] Nagahama Y, Okina T, Suzuki N, Matsuda M. Neural correlates of psychotic symptoms in dementia with Lewy bodies. Brain 2010; 133: 557–567 [44] O’Brien JT, Firbank MJ, Mosimann UP, Burn DJ, McKeith IG. Change in perfusion, hallucinations and fluctuations in consciousness in dementia with Lewy bodies. Psychiatry Res 2005; 139: 79–88 [45] Mori T, Ikeda M, Fukuhara R, Nestor PJ, Tanabe H. Correlation of visual hallucinations with occipital rCBF changes by donepezil in DLB. Neurology 2006; 66: 935–937 [46] McKeith I. Clinical aspects of dementia with Lewy bodies. In: Handbook of Clinical Neurology: Dementias. Aminoff M, Boller F, Swaab D, eds. 3rd Series. New York: Elsevier; 2008:307–311 [47] Bonelli SB, Ransmayr G, Steffelbauer M, Lukas T, Lampl C, Deibl M. L-dopa responsiveness in dementia with Lewy bodies, Parkinson’s disease with and without dementia. Neurology 2004; 63: 376–378 [48] Aarsland D, Mosimann UP, McKeith IG. Role of cholinesterase inhibitors in Parkinson’s disease and dementia with Lewy bodies. J Geriatr Psychiatry Neurol 2004; 17: 164–171 [49] Schwarz S, Froelich L, Burns A. Pharmacological treatment of dementia. Curr Opin Psychiatry 2012; 25: 542–550

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Frontotemporal Lobar Degeneration

17 Frontotemporal Lobar Degeneration Aristides A. Capizzano and Toshio Moritani Frontotemporal lobar degeneration (FTLD) includes different neurodegenerative disorders clinically characterized by progressive behavioral changes and language disturbances. The average age at clinical onset is 50 to 60 years, and the incidence is roughly equal between men and women. Prevalence under 65 years has been reported at 15 per 100,000 inhabitants1 and is thought to approach that of presenile Alzheimer’s disease (AD). Twenty percent of FTLD patients are older than 65 at onset.2 Survival is shorter than for AD, with functional and cognitive decline occurring significantly more rapidly in FTLD than in AD, although there is heterogeneity depending on the particular FTLD syndrome involved.3

17.1 Clinical Features 17.1.1 Frontotemporal Dementia Syndromes Frontotemporal dementia (FTD) syndromes encompass three clinical syndromes: behavioral variant frontotemporal dementia (bvFTD), semantic dementia (SD), and progressive nonfluent aphasia (PNFA).4 These differentiate in clinical and anatomical terms but not in pathological substrate; although one syndrome predominates early in the disease process, with progression of brain atrophy, there is increasing clinical overlap.5 Behavioral variant FTD is characterized by profound personality change with disinhibition and/or apathy, blunted affect, loss of insight and empathy to others, and pressured speech. These are the characteristics of bvFTD that frequently lead to first consultation at a psychiatric clinic. Anatomically, it may involve dorsomedial or ventromedial and orbitofrontal prefrontal areas. Cognitive deficits are less severe than the personality changes and involve executive tasks, with impairment in working memory, attention, set shift, verbal fluency, response inhibition, and abstract reasoning. Memory complaints are variable, and there is preservation of declarative verbal and visual memory in contradistinction with AD. Semantic dementia or semantic variant of primary progressive aphasia (svPPA) is a disorder of progressive loss of knowledge about words and concepts associated with anterior temporal atrophy; the clinical manifestation depends on the side of the brain preferentially involved. Left-predominant cases manifest as fluent, anomic aphasia. Patients complain of word-finding difficulties and trouble with naming; speech remains fluent with intact syntax and prosody. The loss of knowledge extends beyond language, with a lack of ability to put objects in their proper context. Episodic memory and executive and spatial functions are preserved. Subjects with right-predominant temporal lobe atrophy show a behavioral syndrome with flat affect, loss of insight, and alterations of social conduct. Progressive nonfluent aphasia, or agrammatic variant of primary progressive aphasia (agPPA), is a disorder of expressive language and speech production related to left perisylvian atrophy. Naming and repetition are impaired, but comprehension of

single words is preserved. Reading and writing are affected with errors, and speech apraxia is commonly present. PNFA may resemble logopenic aphasia (▶ Fig. 17.1), in which brain atrophy involves more posterior brain regions and is thought to be associated with AD.6 Apart from the three classic syndromes outlined in the preceding, there is a strong association between FTLD and amyotrophic lateral sclerosis (ALS): half of ALS patients have cognitive impairment of the frontal type; 15% meet the criteria for FTD.7 Conversely, in a prospective study, up to 50% of FTD patients had clinical features of ALS, and 14% met the criteria for definite ALS.8 Apart from the clinical overlap, FTLD and ALS share pathological and genetic features that suggest that both entities constitute manifestations of the same disease process.9 Finally, there is also clinical overlap with corticobasal degeneration (CBD) and progressive supranuclear palsy (PSP), both of which are also tauopathies, covered in Chapter 18 in this volume.

17.1.2 Inclusion and Exclusion Criteria Consensus criteria for the core FTD syndromes have been widely used in research and clinical practice.4 For bvFTD, five core diagnostic features are required to fulfill the diagnostic criteria: insidious onset and gradual progression, early decline in social interpersonal conduct, early impairment in regulation of personal conduct, early emotional blunting, and early loss of insight.4 However, limitations of these criteria, such as ambiguity of some descriptors and arbitrary distinction of core and supportive features, have led to more recently revised guidelines for diagnosis of bvFTD.10 The International Consensus Diagnostic Criteria for possible bvFTD require three of the following symptoms: early behavioral disinhibition; early apathy or inertia; early loss of sympathy or empathy; early perseverative, stereotyped, or compulsive behavior; hyperorality; and a neuropsychological profile of executive deficits with sparing of memory and visuospatial functions. Probable bvFTD is diagnosed when, apart from meeting the criteria for possible bvFTD, the following are present: significant functional decline and frontal and/or anterior temporal atrophy (on magnetic resonance imaging [MRI] or computed tomography [CT]) or hypometabolism/hypoperfusion (on positron emission tomography [PET] or single-photon emission computed tomography [SPECT]). Exclusionary criteria for bvFTD are that deficits are better accounted for by nondegenerative disorders, that behavioral disturbance is better accounted for by a psychiatric diagnosis, or that biomarkers are strongly indicative of AD or other neurodegenerative process.10

17.2 Genetics Between 35 and 50% of FTLD patients have a family history of dementia, which supports a strong genetic role in this disease, usually involving autosomal dominant inheritance.11 Approximately 50% of familial cases are associated with mutations in the tau or progranulin (GRN) genes, with less than 5% of muta-

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Non-Alzheimer’s Cortical Dementia

Fig. 17.1 A 73-year-old woman with primary progressive aphasia, logopenic variant. Coronal T2-weighted images perpendicular to the temporal lobe axis (a-d) in rostrocaudal order displaying mild to moderate left temporopolar and left hippocampal atrophy.

tions occurring in the valosin-containing protein, charged multivesicular body protein 2B, transactive response (TAR)-DNA binding protein (TARDP), and fused in sarcoma (FUS) genes, which illustrate the heterogeneity of FTLD.12 More than 40 mutations have been recognized in the tau gene in families with FTD and parkinsonism associated with chromosome 17q (FTDP-17), which correlate with tau neuropathology.13 The GRN gene, also on chromosome 17, like tau, has been involved in more than 60 mutations in familial FTD. GRN encodes progranulin, a growth factor abundantly expressed in specific neuronal populations. Neuropathologically, GRN mutations result in tau-inegative, ubiquitin- and TDP-43-positive inclusions with characteristic intranuclear neuronal inclusions.14 More recently, the expansion of a noncoding hexanucleotide repeat in the C9ORF72 gene on chromosome 9p was shown to be the most common genetic abnormality in familial FTD (11.7%) and familial ALS (23.5%).15 Different patterns of gray matter atrophy were identified using voxel-based morphometry (VBM) among patients with C9ORF72, tau, and progranulin mutations and sporadic FTD.16

atrophy leads to bvFTD, anterior temporal atrophy correlates with SD, and left perisylvian atrophy correlates with PNFA. Because the sequence of atrophy in FTLD is predictable, a staging system has been proposed: Initial atrophy occurs in the orbital and superior medial frontal cortices and hippocampus (stage 1), progressing to involve other anterior frontal regions, temporal cortices, and basal ganglia (stage 2), and then becoming diffuse with white matter loss and ventricular dilatation (stage 3) until marked atrophy is observed in all areas, including marked basal ganglia flattening resulting in concavity of the lateral ventricles (stage 4).17 Based on the types of intracellular inclusions and immunohistochemistry, three subtypes of FTLD are recognized18: (1) tau-positive pathology with or without inclusions, (2) taunegative ubiquitin-positive inclusions, and (3) tau-negative, ubiquitin-negative pathology.

17.3 Neuropathology

Named after Arnold Pick, who in 1892 reported the case of a 71-year-old man with behavioral changes and progressive aphasia with focal left temporal atrophy, Pick disease is characterized by Pick bodies: round/oval argyrophilic cytoplasmic neuronal inclusions (▶ Fig. 17.2). These are found in the hippocampus, amygdala, and frontal and temporal isocortex and are readily detected with tau immunohistochemistry and stain

Although FTLD is pathologically heterogeneous, the different subtypes share common features. An early recognized feature is gross circumscribed atrophy of the frontal or anterior temporal lobes. The early pattern of atrophy, as depicted by clinical imaging, determines the specific clinical syndrome. Thus, prefrontal

17.3.1 Frontotemporal Lobe Degeneration-Tau: Pick’s Disease and Other Tauopathies

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Frontotemporal Lobar Degeneration

Fig. 17.2 Tau immunohistochemistry at X600. Pick bodies in the dentate gyrus. (Courtesy Dr. Patricia Kirby, University of Iowa.)

variably for ubiquitin. The predominant tau isoform in Pick's disease is 3-repeat (3R).19

17.3.2 FTLD with Ubiquitin and Transactive-Response-43 Positive Inclusions Transactive-response DNA-binding protein of 43k Da (TDP-43) is an RNA and DNA binding protein with neuronal and glial nuclear localization that is normally involved in gene transcription regulation. TDP-43 is the main component of inclusions seen in ALS/motor neuron disease and FTLD-U, in which the protein is detected in the cytoplasm.20 There is a wide range in the morphology and distribution of ubiquitin and TDP-43positive inclusions, with significant overlap between FTD and ALS, suggesting that these syndromes are strongly related.21

17.3.3 Dementia Lacking Distinctive Histology The term dementia lacking distinctive histology (DLDH) describes dementia cases with evident atrophic brain changes, but with both tau and ubiquitin immunohistochemistry being negative,22 and represents a minority of cases. Apart from FTLD-T, FTLD-U, and DLDH, which together encompass the vast majority of FTLD cases, other rare subtypes have been reported. More importantly, patients with the clinical syndromes of FTLD may present lack of FTLD neuropathology but rather have pathological findings of AD, vascular dementia, dementia with Lewy bodies (DLB), prion disease, or even normal brains on autopsy.23

17.4 Neuroimaging 17.4.1 Structural Imaging In contradistinction to the subtle structural imaging findings of LBD, FTLD cases demonstrate characteristic atrophic patterns

on clinical CT and MRI studies that correlate with the clinical syndrome.24 In bvFTD, there is an anteroposterior gradient of atrophy involving the frontal and temporal lobes, with sparing of the parietal and occipital lobes. Although commonly bilateral, the volume loss is often asymmetrical (▶ Fig. 17.3, ▶ Fig. 17.4). A recent meta-analysis of VBM studies in bvFTD demonstrated significant gray matter loss in prefrontal regions compared with controls, with the most significant changes in the medial frontal lobes and also volume reductions in the insula and striatum.25 The earliest site of involvement is the orbitofrontal cortex, which shows sulcal widening before the mesiofrontal regions. The dorsolateral prefrontal cortex becomes involved later in the course of the disease (▶ Fig. 17.5). Hippocampal and amygdalar atrophy are also seen with bvFTD.26 Anterior mesiotemporal atrophy involving the amygdala and hippocampal head predominates in FTLD and correlates with temporopolar atrophy. In the early phase of the disease, structural imaging is often normal, but most patients progress to show frontotemporal atrophy later. On 1-year follow-up, limbic and paralimbic regions, particularly the anterior cingulate cortex, exhibit progressing gray matter atrophy in bvFTD.27 Furthermore, baseline determination of the site of predominant brain atrophy predicts functional decline in bvFTD, with frontal and frontotemporal predominant atrophy subtypes having faster decline compared with the temporal dominant and temporofrontal parietal subtypes.28 Behavioral deficits, such as disinhibition and apathy, are associated with right frontotemporal atrophy in patients with dementia.29 On the other hand, it has been recognized that there is a subgroup of bvFTD that does not display brain atrophy on MRI and has a significantly more benign course of the disease.30 Yet another perplexing observation is the occurrence of bvFTD symptoms in patients displaying brain sagging from intracranial hypotension,31 a potentially reversible condition, for which the name of frontotemporal brain sagging syndrome was proposed (▶ Fig. 17.6). Very interestingly, the latter two groups of patients are almost exclusively males, suggesting a strong gender effect on the vulnerability for the clinical phenotype of bvFTD. Semantic dementia shows consistent left anterior temporal lobe atrophy, also involving inferior and mesial temporal lobe regions, with a an anteroposterior gradient (predominant atrophy seen anteriorly) that distinguishes SD from AD.32 The rate of volume loss over time is also more accelerated in FTD (predominantly frontal atrophy) and SD (predominantly temporal atrophy) compared with AD.33 Progressive nonfluent aphasia leads to cortical thinning and atrophy of the left inferior frontal lobe, including the Broca area, superior temporal lobe, and insula (▶ Fig. 17.7).6,34 Therefore, the patterns of cortical thinning differ between both variants of PPA, with more frontal and parietal atrophy in PNFA and bilateral temporal cortical atrophy in SD.34 PPA patients who show aphasia of speech, a motor speech disorder characterized by slow speaking rate, abnormal prosody, and distorted sound substitutions, additions, and repetitions have predominant atrophy in premotor and supplementary motor cortices, whereas the anterior perisylvian region correlates with nonfluent aphasia.35

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Fig. 17.3 A 55-year-old man with apraxia, impaired visual reasoning, acalculia, deficient executive functioning, and defective speeded visual processing. He had less pronounced deficits in the areas of verbal memory, associative fluency, and verbal comprehension of complex instructions. Symptoms progressed over 7 years. (a) Right sagittal T1, (b) axial T2, (c,d) coronal T2-weighted magnetic resonance imaging.

Fig. 17.4 Same patient’s magnetic resonance imaging as ▶ Fig. 17.3. (a) Right sagittal T1 and (b) axial T2 at the corresponding levels of (a) and (b) on ▶ Fig. 17.3, respectively, but 7 years earlier, showing interval marked progression of right-predominant frontotemporal and parietal atrophy.

17.4.2 Diffusion Tensor Imaging and Functional Magnetic Resonance Imaging Diffusion tensor imaging (DTI) studies in bvFTD showed bilateral involvement of white matter tracts connecting the frontal lobes, such as the anterior cingulum, superior longitudinal fasciculus, and genu of the corpus callosum.36,37 PPA patients displayed more focal white matter involvement than bvFTD,

patients with differential involvement in the three clinical subtypes of PPA (nonfluent, semantic, and logopenic variants).37 White matter disorganization in FTLD likely results from axonal degeneration secondary to neuronal body death, as supported by the correlation between white matter changes and cortical atrophy. Semantic dementia patients displayed abnormal white matter on DTI analysis involving the uncinate and inferior

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Fig. 17.5 A 60-year-old man with severe mixed dementia with 10 years of evolution: Alzheimer’s disease early onset, behavioral variant frontotemporal dementia. (a-d) axial computed tomography (CT), (e,f) coronal CT images with prominent bilateral anterior prefrontal cortical atrophy and marked bilateral mesial temporal atrophy (left > right) and enlargement of the third ventricle.

longitudinal fasciculi, whereas nonfluent patients had damage of the superior longitudinal fasciculus,38 which corresponds to the topography of cortical atrophy in these disorders. White matter tract degeneration in PNFA involves primarily the left superior longitudinal fasciculus and its subcomponent the arcuate fasciculus, which projects to the inferior frontal lobe, with sparing of ventral tracts.38 Resting-state fMRI studies evaluating functional connectivity described reduced connectivity in the salience network (including the anterior cingulate and frontoinsular regions) in bvFTD, with increased connectivity in the default network, features which may distinguish FTLD from AD.39

17.4.3 Nuclear Medicine Nuclear medicine clinical imaging studies in FTLD demonstrate abnormal brain perfusion and glucose metabolism using SPECT and PET, respectively. A recent meta-analysis of brain perfusion SPECT studies for differentiating AD from FTD reported pooled weighted sensitivity of 79.7% and pooled

weighted specificity of 79.9%.40 A pattern of bilaterally reduced frontal cerebral blood flow in the absence of parietal hypoperfusion is characteristic in pathologically confirmed FTLD.41 However, there is substantial heterogeneity in the reported SPECT results. Reduced frontal and anterior temporal glucose metabolism in FTD compared with controls has been reported using FDG-PET (▶ Fig. 17.8), with involvement also of the medial temporal lobes, striatum, and thalamus.42 The regions of hypometabolism in FTD correspond to those with cortical atrophy as determined with MRI using VBM analysis, whereas less congruent and asymmetric changes are seen in the temporal lobes.43 Using Pittsburgh compound-B (PiB-PET), which labels amyloid deposits as those present in AD but not FTD neuropathology, PET imaging had 89.5% sensitivity and 83% specificity in distinguishing FTD from AD, with an overall similar diagnostic accuracy compared with FDG-PET.44 PNFA shows reduced glucose metabolism in left frontal regions, whereas in SD the left anterior temporal lobe is hypometabolic.45 The two latter subtypes of FTLD also show less amyloid tracer uptake

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Non-Alzheimer’s Cortical Dementia

Fig. 17.6 Frontotemporal brain sagging syndrome. A 48-year-old man with clinical diagnosis of behavior variant frontotemporal dementia and severe brain sagging on magnetic resonance imaging (MRI). (a) Sagittal T1- (b-d) axial T2weighted MRI. Neuropathological evaluation at autopsy was negative for frontotemporal lobar dementia.

Fig. 17.7 An 84-year-old woman with progressive nonfluent aphasia. Axial fluid-attenuated inversion recovery (FLAIR) (upper row) and coronal IR T1weighted images (lower row) with bilateral (right greater than left) perisylvian and hippocampal atrophy and FLAIR hyperintensity. (Courtesy Dr. Kei Yamada, Kyoto Prefectural University of Medicine, Japan.)

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Frontotemporal Lobar Degeneration

Fig. 17.8 Sagittal reconstructions of fluorodeoxyglucose (FDG) positron emission tomography with classical findings in frontotemporal lobar dementia of marked frontal hypometabolism with preservation of occipitoparietal glucose uptake. (Courtesy Dr. Yusuf Menda, University of Iowa.)

than the logopenic variant of PPA, which is a recently described variant of AD.

References [1] Harvey RJ, Skelton-Robinson M, Rossor MN. The prevalence and causes of dementia in people under the age of 65 years. J Neurol Neurosurg Psychiatry 2003; 74: 1206–1209 [2] Rosso SM, Donker Kaat L, Baks T et al. Frontotemporal dementia in the Netherlands: patient characteristics and prevalence estimates from a populationbased study. Brain 2003; 126: 2016–2022 [3] Roberson ED, Hesse JH, Rose KD et al. Frontotemporal dementia progresses to death faster than Alzheimer’s disease. Neurology 2005; 65: 719–725 [4] Neary D, Snowden JS, Gustafson L et al. Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology 1998; 51: 1546–1554 [5] Rabinovici G, Rascovsky K, Miller B. Frontotemporal lobar degeneration: Clinical and pathological overview. In: Aminoff M, Boller F, Swaab D, eds. Handbook of Clinical Neurology: Dementias. 3rd Series. New York: Elsevier; 2008:343–364 [6] Gorno-Tempini ML, Dronkers NF, Rankin KP et al. Cognition and anatomy in three variants of primary progressive aphasia. Ann Neurol 2004; 55: 335– 346 [7] Ringholz GM, Appel SH, Bradshaw M, Cooke NA, Mosnik DM, Schulz PE. Prevalence and patterns of cognitive impairment in sporadic ALS. Neurology 2005; 65: 586–590 [8] Lomen-Hoerth C, Anderson T, Miller B. The overlap of amyotrophic lateral sclerosis and frontotemporal dementia. Neurology 2002; 59: 1077– 1079 [9] Morris HR, Waite AJ, Williams NM, Neal JW, Blake DJ. Recent advances in the genetics of the ALS-FTLD complex. Curr Neurol Neurosci Rep 2012; 12: 243– 250 [10] Rascovsky K, Hodges JR, Knopman D et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 2011; 134: 2456–2477 [11] Chow TW, Miller BL, Hayashi VN, Geschwind DH. Inheritance of frontotemporal dementia. Arch Neurol 1999; 56: 817–822 [12] Seelaar H, Rohrer JD, Pijnenburg YAL, Fox NC, van Swieten JC. Clinical, genetic and pathological heterogeneity of frontotemporal dementia: a review. J Neurol Neurosurg Psychiatry 2011; 82: 476–486 [13] Hutton M, Lendon CL, Rizzu P et al. Association of missense and 5’-splice-site mutations in tau with the inherited dementia FTDP-17. Nature 1998; 393: 702–705 [14] Mackenzie IR, Baker M, Pickering-Brown S et al. The neuropathology of frontotemporal lobar degeneration caused by mutations in the progranulin gene. Brain 2006; 129: 3081–3090 [15] DeJesus-Hernandez M, Mackenzie IR, Boeve BF et al. Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron 2011; 72: 245–256 [16] Whitwell JL, Weigand SD, Boeve BF et al. Neuroimaging signatures of frontotemporal dementia genetics: C9ORF72, tau, progranulin and sporadics. Brain 2012; 135: 794–806

[17] Broe M, Hodges JR, Schofield E, Shepherd CE, Kril JJ, Halliday GM. Staging disease severity in pathologically confirmed cases of frontotemporal dementia. Neurology 2003; 60: 1005–1011 [18] Cairns NJ, Bigio EH, Mackenzie IRA et al. Consortium for Frontotemporal Lobar Degeneration. Neuropathologic diagnostic and nosologic criteria for frontotemporal lobar degeneration: consensus of the Consortium for Frontotemporal Lobar Degeneration. Acta Neuropathol 2007; 114: 5–22 [19] Muñoz DG, Dickson DW, Bergeron C, Mackenzie IRA, Delacourte A, Zhukareva V. The neuropathology and biochemistry of frontotemporal dementia. Ann Neurol 2003; 54 Suppl 5: S24–S28 [20] Neumann M, Sampathu DM, Kwong LK et al. Ubiquitinated TDP-43 in frontotemporal lobar degeneration and amyotrophic lateral sclerosis. Science 2006; 314: 130–133 [21] Mackenzie IRA, Feldman HH. Ubiquitin immunohistochemistry suggests classic motor neuron disease, motor neuron disease with dementia, and frontotemporal dementia of the motor neuron disease type represent a clinicopathologic spectrum. J Neuropathol Exp Neurol 2005; 64: 730–739 [22] McKhann GM, Albert MS, Grossman M, Miller B, Dickson D, Trojanowski JQ Work Group on Frontotemporal Dementia and Pick’s Disease. Clinical and pathological diagnosis of frontotemporal dementia: report of the Work Group on Frontotemporal Dementia and Pick’s Disease. Arch Neurol 2001; 58: 1803–1809 [23] Forman MS, Farmer J, Johnson JK et al. Frontotemporal dementia: clinicopathological correlations. Ann Neurol 2006; 59: 952–962 [24] Lu PH, Mendez MF, Lee GJ et al. Patterns of brain atrophy in clinical variants of frontotemporal lobar degeneration. Dement Geriatr Cogn Disord 2013; 35: 34–50 [25] Pan PL, Song W, Yang J et al. Gray matter atrophy in behavioral variant frontotemporal dementia: a meta-analysis of voxel-based morphometry studies. Dement Geriatr Cogn Disord 2012; 33: 141–148 [26] Muñoz-Ruiz MA, Hartikainen P, Koikkalainen J et al. Structural MRI in frontotemporal dementia: comparisons between hippocampal volumetry, tensorbased morphometry and voxel-based morphometry. PLoS ONE 2012; 7: e52531–e52531 [27] Brambati SM, Renda NC, Rankin KP et al. A tensor based morphometry study of longitudinal gray matter contraction in FTD. Neuroimage 2007; 35: 998– 1003 [28] Josephs KA, Jr, Whitwell JL, Weigand SD et al. Predicting functional decline in behavioural variant frontotemporal dementia. Brain 2011; 134: 432–448 [29] Rosen HJ, Allison SC, Schauer GF, Gorno-Tempini ML, Weiner MW, Miller BL. Neuroanatomical correlates of behavioural disorders in dementia. Brain 2005; 128: 2612–2625 [30] Davies RR, Kipps CM, Mitchell J, Kril JJ, Halliday GM, Hodges JR. Progression in frontotemporal dementia: identifying a benign behavioral variant by magnetic resonance imaging. Arch Neurol 2006; 63: 1627–1631 [31] Wicklund MR, Mokri B, Drubach DA, Boeve BF, Parisi JE, Josephs KA. Frontotemporal brain sagging syndrome: an SIH-like presentation mimicking FTD. Neurology 2011; 76: 1377–1382 [32] Chan D, Fox NC, Scahill RI et al. Patterns of temporal lobe atrophy in semantic dementia and Alzheimer’s disease. Ann Neurol 2001; 49: 433–442 [33] Krueger CE, Dean DL, Rosen HJ et al. Longitudinal rates of lobar atrophy in frontotemporal dementia, semantic dementia, and Alzheimer’s disease. Alzheimer Dis Assoc Disord 2010; 24: 43–48 [34] Rohrer JD, Warren JD, Modat M et al. Patterns of cortical thinning in the language variants of frontotemporal lobar degeneration. Neurology 2009; 72: 1562–1569 [35] Josephs KA, Duffy JR, Strand EA et al. Clinicopathological and imaging correlates of progressive aphasia and apraxia of speech. Brain 2006; 129: 1385– 1398 [36] Whitwell JL, Avula R, Senjem ML et al. Gray and white matter water diffusion in the syndromic variants of frontotemporal dementia. Neurology 2010; 74: 1279–1287 [37] Agosta F, Scola E, Canu E et al. White matter damage in frontotemporal lobar degeneration spectrum. Cereb Cortex 2012; 22: 2705–2714 [38] Galantucci S, Tartaglia MC, Wilson SM et al. White matter damage in primary progressive aphasias: a diffusion tensor tractography study. Brain 2011; 134: 3011–3029 [39] Zhou J, Greicius MD, Gennatas ED et al. Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer’s disease. Brain 2010; 133: 1352–1367 [40] Yeo JM, Lim X, Khan Z, Pal S. Systematic review of the diagnostic utility of SPECT imaging in dementia. Eur Arch Psychiatry Clin Neurosci 2013; 263: 539–552

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Non-Alzheimer’s Cortical Dementia [41] McNeill R, Sare GM, Manoharan M et al. Accuracy of single-photon emission computed tomography in differentiating frontotemporal dementia from Alzheimer’s disease. J Neurol Neurosurg Psychiatry 2007; 78: 350–355 [42] Ishii K. PET approaches for diagnosis of dementia. AJNR Am J Neuroradiol 2013[epub ahead of print] [43] Kanda T, Ishii K, Uemura T et al. Comparison of grey matter and metabolic reductions in frontotemporal dementia using FDG-PET and voxel-

based morphometric MR studies. Eur J Nucl Med Mol Imaging 2008; 35: 2227–2234 [44] Rabinovici GD, Rosen HJ, Alkalay A et al. Amyloid vs FDG-PET in the differential diagnosis of AD and FTLD. Neurology 2011; 77: 2034–2042 [45] Rabinovici GD, Jagust WJ, Furst AJ et al. Abeta amyloid and glucose metabolism in three variants of primary progressive aphasia. Ann Neurol 2008; 64: 388–401

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Part VI Dementia with Extrapyramidal Syndromes

18 Parkinson’s Disease

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19 Atypical Parkinsonian Syndromes

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20 Secondary Parkinsonism

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Dementia with Extrapyramidal Syndromes

18 Parkinson’s Disease Jennifer G. Goldman, John W. Ebersole, Doug Merkitch, and Glenn T. Stebbins Parkinson’s disease (PD) is a chronic and progressive neurodegenerative disease that affects about 1 to 2% of the population older than 60 years. About four million people over the age of 50 have PD, and rates are expected to double by 2030.1 PD symptoms include its cardinal motor features, including bradykinesia, resting tremor, rigidity, and gait or postural impairment. In addition, nonmotor features are well recognized in cognitive, behavioral, mood, sleep, autonomic, vision, and pain systems. These motor and nonmotor features can occur throughout the stages of PD, extending from premotor or early PD to moderate and advanced PD, and they affect daily function, independence, and the quality of life of patients and caregivers. At present, however, no curative or neuroprotective therapies for PD have been established, but imaging and other biomarkers may play a role in the development of these therapeutics. The diagnosis of PD has been largely clinically based, but more recently techniques like dopamine transporter imaging, transcranial ultrasound (TCS), diffusion tensor imaging (DTI), and others provide a way to detect brain changes associated with PD or parkinsonian disorders. Structural, functional, metabolic, and neurochemical imaging techniques may advance our understanding of the underlying neurochemistry and neuropathology of PD. PD is accompanied by neurodegeneration and neurotransmitter changes in the brainstem, striatal, subcortical, and cortical regions, which affect norepinephrine, serotonin, dopamine, acetylcholine, and glutamate, among others. Lewy bodies with α-synuclein staining and depigmentation of substantia nigra neurons are the neuropathological hallmarks of PD. A stepwise staging system of neurodegeneration has been proposed, beginning with Lewy-related changes in the autonomic and olfactory systems and subsequently involving the brainstem and cortex.2,3 The field awaits in vivo imaging of αsynuclein in PD patients, although research is ongoing. This chapter discusses neuroimaging in PD as related to the diagnosis of PD, its motor features and complications, and nonmotor issues that can occur not only in the premotor phase of PD but also in more advanced PD.

18.1 Imaging in the Diagnosis of Parkinson’s Disease 18.1.1 Early Diagnosis The diagnosis of PD has largely relied on clinical criteria demonstrating the classic motor features of rest tremor, bradykinesia, rigidity, and gait impairment.4 However, evidence suggests that dopaminergic degeneration in the substantia nigra precedes symptom onset, with clinical symptoms not appearing until approximately 80% of striatal and 50% of nigral dopaminergic neurons are lost.5 Further, degeneration of dopaminergic neurons progresses most rapidly during the presymptomatic phase and the first years after symptom onset.6,7,8 Thus, early diagnosis is critical for allowing early intervention and developing possible neuroprotective measures. Imaging techniques that

may aid in the presymptomatic or early diagnosis of PD include molecular imaging of dopamine transport proteins using single-photon emission computed tomography (SPECT) and positron emission tomography (PET), TCS, and magnetic resonance imaging (MRI) with techniques such as DTI. SPECT imaging can measure the density of transmembrane dopamine transporters (DATs) and thereby reflect presynaptic dopaminergic neuron integrity in vivo. [123I]FP-CIT (ioflupane I-123, 123I-labeled-2β-carbomethoxy-3β-(4-iodoDaTSCAN) and phenyl-nortropane ([123I]-β-CIT), among others, are radiopharmaceuticals used for SPECT brain imaging.9 DAT imaging ligands differ in their kinetics and dopamine transporter affinity; [123I]FP-CIT has a rapid time to peak (2 to 3 hours), whereas [123I]-β-CIT has a longer time to peak (8 to 18 hours), although both have a prolonged washout phase. Studies are interpreted based on the signal shape and intensity in the striatal regions; normal studies demonstrate two symmetric, crescent-shaped regions of uptake, with distinct margins relative to surrounding brain tissue (▶ Fig. 18.1), whereas abnormal studies reveal either symmetric or asymmetric decreased or absent activity in the putamen greater than caudate, such as in PD.10 SPECT imaging has been proposed as a highly sensitive indicator of early PD.11 [123I]-β-CIT SPECT imaging had 92% sensitivity and 100% specificity in diagnosing PD compared with the gold standard of clinical diagnosis by a movement disorder specialist.12 SPECT may have a role in ruling out PD in clinically uncertain cases.12, 13 In cases of suspected PD, DAT binding will be reduced in 90%.14 In several studies, [123I]FP-CIT has differentiated with high sensitivity and specificity, PD from essential tremor (ET), a neurologic condition characterized by postural or action tremors in the hands or tremors affecting the head, neck, or voice that can mimic some PD signs.15 In 2011, the United States Food and Drug Administration approved DaTSCAN using the [123I]FPCIT ligand for use in suspected parkinsonian syndromes, based on two multicenter, phase III studies.13,16 In early parkinsonian patients with or without tremor (designated possible and probable PD), compared with patients with non-PD tremor and healthy controls, the DaTSCAN had 79% sensitivity and 97% specificity, whereas clinical diagnosis in early PD had 98% sensitivity but 67% specificity. Positron emission tomography is an imaging modality that has been studied as an in vivo technique to measure dopaminergic function, cerebral blood flow, and metabolic changes. Besides dopaminergic function, there is a growing interest in radiotracers for serotonin, acetylcholine, and opioids, as well as for measuring amyloid and microglia activity in PD studies. PET can measure the ability of striatal dopaminergic neurons to take up radiolabeled levodopa or measure dopamine turnover and dopa decarboxylase activity using a variety of 11C or 18F ligands. The typical finding in PET scans in PD is asymmetric decreased uptake of the radiopharmaceutical in the putamen. The order of this decreased uptake on PET in PD occurs from rostral to caudal, with relative preservation of the caudate and reduced uptake progressing from the anterior to posterior putamen, in opposite direction from what is seen in normal aging.11 By the time motor symptoms develop, there is a 50% reduction in the

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Parkinson’s Disease

Fig. 18.1 [123I]FP-CIT (Ioflupane I-123, DaTSCAN) single-photon emission computed tomography scan of a patient with parkinsonian features. Regions of increased presynaptic dopamine transporter receptor binding in the caudate and putamen are indicated by increased signal. Note the relatively blurred margins and slightly asymmetric uptake, with caudate regions demonstrating increase uptake compared with the putamen. The color scale indicates the magnitude of ligand uptake, with lowest appearing in dark green/black and the highest in bright orange/white. The right side of the image represents the left side of the brain.

uptake of 6-[18F]-fluoro-L-dopa (18F-dopa), suggesting that PET may be useful in presymptomatic diagnosis and monitoring progression of PD.17 PET scans, however, have several limitations, including the low availability of cyclotrons, regional specificity resulting from limited spatial resolution and partial volume effects, longer scanning times, and radioactive ligands, despite safety monitoring and low doses. To acquire a truly quantitative measure of ligand uptake, monitoring of arterial trace elements is also required. Transcranial ultrasound permits the visualization of the echogenicity of the substantia nigra at the level of the mesencephalic brainstem. The typical TCS finding in PD is bilateral increased echogenicity (i.e., hyperechogenicity) of the lateral midbrain, which is present in 90% to 96% of clinically diagnosed PD cases.18,19,20 The degree of echogenicity, however, does not correlate with severity of PD motor symptoms.18 Echogenicity of the substantia nigra may represent iron deposition and an increased susceptibility to PD, although the exact reason is unknown and other iron-rich brain regions do not exhibit hyperechogenicity.21 These findings, coupled with its high sensitivity, low cost, and noninvasive and readily available use, make TCS a promising potential screening tool for early PD. Drawbacks, however, include its dependence on user experience, obtaining an adequate bone window, lack of well-established cutoffs for hyperechogenicity or hypoechogenicity, and a fairly high rate of false-positives, particularly with ET, in which up to 16% of patients can have positive findings.22

Structural MRI in general plays a limited role in the early diagnosis of PD, aside from ruling out alternative diagnoses, such as hydrocephalus or vascular disease. Diffusion-weighted imaging, which measures the ability of water to diffuse freely in brain tissue, is highly sensitive to changes in striatal structure. Experimentation with T2 relaxometry has shown reduced T2 relaxation times in the substantia nigra in PD, possibly indicating tissue destruction detectable on MRI. DTI, which provides a measure of directional diffusion and may be considered a proxy for tissue integrity (▶ Fig. 18.2), has demonstrated differences in fractional ansiotropy (FA) in different cortical and subcortical regions (▶ Fig. 18.3). Decreased FA has been found in the substantia nigra in all 14 newly diagnosed PD cases, compared with healthy controls, suggesting high sensitivity and specificity in this study.24 The reduced FA in the substantia nigra regions occurred particularly caudally, consistent with postmortem dopaminergic cell loss location. Magnetic resonance spectroscopy, which detects differences in the neurochemical profiles of brain structures, revealed significantly higher concentrations of γ-aminobutryic acid (GABA) in the pons and putamen of PD patients compared with healthy controls,25 suggesting involvement of the lower brainstem structures, similar to Braak PD staging, as well as basal ganglia in early PD.

18.1.2 Differential Diagnosis Distinguishing idiopathic PD from other parkinsonian syndromes can be difficult, especially early in the disease, when

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Dementia with Extrapyramidal Syndromes

Fig. 18.2 In diffusion tensor imaging, the application of at least six noncollinear gradients creates a 3 × 3 matrix that can be described by a mathematical construct called a tensor. From the diffusion tensor in each voxel, the three eigenvalues (λ1, λ2, and λ3) define the magnitude of the diffusion system and the three associated eigenvectors that describe the direction of the diffusion system. Based on the ratio of the three eigenvalues, the intravoxel direction of hydrogen diffusion can be determined and is termed fractional anisotropy (FA). Cerebrospinal fluid has extremely low FA values because hydrogen is free to diffuse in any direction. Gray matter has low FA because cellular structures (e.g., cell membrane, organelles) impede the free diffusion of hydrogen, but these structures do not promote organized, directional diffusion. Highly organized white matter tracts have high FA because hydrogen diffusion is directionally constrained by the tract’s cellular organization. In the figure, changes in FA across the life span can be seen as a decrease in the intensity in major white matter pathways.

Fig. 18.3 Decreases in fractional anisotropy (FA) in 20 patients with Parkinson’s disease compared with 20 age- and gender-matched normal control subjects. Significant decreases in FA were found in bilateral frontal forceps, superior longitudinal fasciculi, and the anterior and posterior limb of the internal capsule (regions in black circles). Additional regions of decreased FA were noted in the brainstem (not shown). Differences were analyzed using a two-sample t-test statistic. Significance thresholds were set for p < 0.05, corrected for multiple comparisons. Voxels evidencing significant differences between groups are displayed on representative axial sections on a canonical brain image. The color scale indicates the magnitude of t values, with lowest appearing in dark red and the highest in bright yellow/white. The left side of the images represents the left side of the brain.

some symptoms might not yet be present or at their fullest extent. Clinical differentiation can be unclear, leading to misdiagnosis in up to 24% of cases.26 Other diagnoses may include atypical parkinsonian syndromes (e.g., multisystem atrophy [MSA], progressive supranuclear palsy [PSP], and corticobasal degeneration [CBD]), ET, vascular parkinsonism, and druginduced parkinsonism. Imaging studies can aid in the differen-

tial diagnosis of these conditions and subsequently directing patient management. In MRI studies, patients with MSA-parkinsonian type may exhibit putaminal hypointensities and a hyperintense rim along the lateral putamen on T1 images on 1.5 Tesla MRI. MRI T2 images may reveal a “hot cross bun sign” in addition to cerebellar atrophy in MSA cerebellar-type patients, which is thought to

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Parkinson’s Disease indicate a loss of myelinated transverse pontocerebellar fibers. PSP patients can have midbrain and frontal lobe atrophy, as well as the “hummingbird sign” on sagittal MRI sequences as a result of midbrain atrophy and third ventricle widening.27 CBD patients may exhibit asymmetric atrophy of posterior frontal and parietal lobes on structural MRI. In several studies, apparent diffusion coefficient measurements on MRI scans may differentiate MSA or PSP from PD with high sensitivities and positive predictive values.28,29,30 TCS also can help differentiate PD from atypical parkinsonian syndromes, with 91% sensitivity and 82% specificity and greater hyperechogenicity in atypical parkinsonism compared with PD.31 Dopamine transporter SPECT imaging does not readily distinguish among parkinsonian syndromes, although more symmetric loss of the putamen and caudate may suggest an atypical parkinsonian syndrome. Approximately 10% of patients with clinically diagnosed with early PD, however, will have scans without evidence of dopaminergic deficiency (SWEDDs) on SPECT scan.32 Most SWEDD patients are unlikely to have PD at follow-up and in some cases have ET or dystonia.13 DAT SPECT scans, however, can distinguish PD from other neurologic diagnoses, such as ET, with 95% sensitivity and specificity.33 Vascular parkinsonism, which manifests clinically as a “lower body” parkinsonism with prominent gait disorder and postural instability, can be accompanied by white matter ischemic changes or lacunar lesions in the basal ganglia on structural MRI, but a definitive diagnosis of vascular parkinsonism is only made at autopsy. Mean [123I]FP-CIT uptake in the basal ganglia was significantly decreased in vascular parkinsonism compared with healthy controls, and preservation of symmetrical uptake may help discriminate it from PD.34,35 Drug-induced parkinsonism attributable to dopamine-blocking medications (e.g., for nausea or psychiatric reasons) can clinically mimic PD. [123I]FP-CIT SPECT can differentiate these entities by showing integrity of nigrostriatal neurons in drug-induced parkinsonism versus degeneration of these neurons and reduced uptake in PD.36

18.2 Imaging and Parkinson’s Disease Motor Features 18.2.1 Motor Hallmarks In addition to the clinical examination and rating scales used to assess the classic motor symptoms of PD, imaging techniques provide a complementary approach to understanding the structural, functional, and metabolic alterations in the PD brain and how these changes relate to pathophysiology, motor phenotype, and disease progression. This section highlights several different imaging techniques used to examine the motor features of PD (▶ Table 18.1).

Tremor Rest tremor, one of the cardinal motor features associated with PD, occurs in about 70% of patients. Although nigrostriatal degeneration of dopaminergic neurons is a pathological hallmark of PD, tremor does not correlate with the severity of striatal dopaminergic deficit.37,38 Thus, nondopaminergic mechanisms and circuitry extending beyond the striatum contribute to PD tremor. Studies suggest that PD tremor is mediated by an interaction of the basal ganglia, cerebellar, and thalamic circuits.39 Using combined surface electromyography and whole-head magnetoencephalography, tremor-related oscillatory activity was found within a cerebral network, with abnormal coupling in a cerebello-diencephalic-cortical loop and cortical motor and sensory areas contralateral to the tremor hand.40 PD patients with tremor, compared with PD patients without tremor and healthy controls, demonstrated increased imagery-related activity in the somatosensory area to a motor imagery task during functional MRI scanning. This increased activity was independent from tremor-related activity identified in the motor cortex, cerebellum, and thalamic ventral intermediate nucleus (Vim), which is often a target for deep brain stimulation in tremor patients. In structural MRI studies using

Table 18.1 Imaging and Parkinson’s disease motor severity Modality/analysis

subjects

Brain regions correlated with motor rating scales

[18F] F-DOPA PET

32 PD, assessed at baseline and mean 18 + /– 6 months

Reduced putamen > caudate uptake inversely correlated with UPDRS Putamen with most rapid mean rate of progression (4.7% of normal mean per year)17

[18F] DOPA PET

27 nondemented PD 10 controls

Reduced putamen > caudate uptake inversely correlated with Hoehn and Yahr stage and UPDRS63

[123I] CIT SPECT

12 PD

Reduced putamen uptake inversely correlated with UPDRS, especially bradykinesia subscore145

MRI: FreeSurfer software

142 PD

Decreased cortical thickness in parietotemporal regions inversely correlated with UPDRS48

MRI-VBM gray matter

Meta-analysis of PD studies 498 PD 375 controls

Decreased gray matter volume in the left inferior frontal/ orbitofrontal gyrus inversely correlated with Hoehn and Yahr stage146

MRI-DTI region of interest approach (caudate, putamen, globus pallidus, thalamus, substantia nigra), measuring FA

151 PD 78 controls

Reduced FA in the substantia nigra inversely correlated with Hoehn and Yahr stage147

Abbreviations: CIT, carbomethoxy-iodophenyl-nortropan FA, fractional anisotropy; FDOPA, fluorodopa; MRI, magnetic resonance imaging; PD, Parkinson’s diseases; PET, positron emission tomography; SPECT, single-photon emission computed tomography; UPDRS, Unified PD Rating Scale; VBM, voxel-based morphometry.

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Fig. 18.4 Voxel-based morphometry (VBM) processing allows for comparison of individual images in a standardized coordinate system. In the process, images are first segmented into gray matter, white matter, cerebrospinal fluid (CSF), and nonbrain compartments. Then the gray matter segment is spatially normalized to a standard gray matter template using a 12-parameter affine normalization and nonlinear adjustments with 7X7X7 basis functions. The transformation parameters obtained from the gray matter normalization are then applied to the whole-brain T1-weighted volumes. Individual normalized whole-brain volumes are then segmented into gray matter, white matter, CSF, and nonbrain partitions. To correct for possible volume changes during normalization, the normalized gray and white matter segments are modulated to maintain the original non-normalized volume per voxel in the normalized gray and white matter segments. In the modulation step, voxel values are multiplied by the Jacobian determinants derived from the normalization of the T1-weighted images. The segmented, normalized, and modulated segments are then smoothed with a gaussian kernel. The smoothing step compensates for interindividual variability and conforms the data more closely to gaussian random field theory, which provides for corrected statistical inference.143,144

voxel-based morphometry (VBM) techniques (▶ Fig. 18.4), PD patients with unilateral rest tremor demonstrated increased gray matter in the Vim nucleus contralateral to the side that is most affected, although no comparison group was included.41 Compared with PD patients without rest tremor, PD patients with rest tremor exhibited decreased gray matter in the posterior right quadrangular lobe and decline of the cerebellum.42 Using [18F]-fluorodeoxyglucose (FDG) PET, a tremor-related metabolic pattern characterized by increased activity in the cerebellum/dentate nucleus, primary motor cortex, and to some degree the striatum was detected. This pattern correlated significantly with clinical ratings of tremor but not akinesiarigidity scores. Expression of the tremor-related metabolic pattern was suppressed to a greater degree by Vim rather than by subthalamic deep brain stimulation, thereby supporting the selective involvement of cerebellothalamocortical pathways in PD tremor.43 Several imaging studies suggest serotonergic

dysfunction in PD tremor. Serotonin (5HT) receptor 1A binding potential as measured by 11C-WAY 100635 PET, which is a selective antagonist for 5HT1A receptors, in the midbrain raphe was significantly reduced in PD patients compared with healthy controls. In addition, the 5HT1A binding correlated significantly with tremor rating scores but not bradykinesia or rigidity scores.44 A PET study using 11C-3amino-4-[2-[(di(methyl)amino)methyl]phenyl]sulfanylbenzonitrile (11C-DASB), a marker of serotonin transporter binding, revealed reduced tracer uptake in the raphe nuclei, caudate, putamen, thalamus, and motor circuitry regions in tremordominant PD patients, compared with akinetic-rigid PD patients and healthy controls, suggesting potential contributions of presynaptic 5HT terminal dysfunction to PD tremor, although because this reduction correlated primarily with action and postural tremor, further study is needed regarding rest tremor.45

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Parkinson’s Disease

Bradykinesia and Rigidity Bradykinesia and rigidity have been investigated in several imaging studies, either independently or in combination. Using [18F]FDG-PET, a pattern of glucose metabolism differentiated PD patients from healthy controls and MSA (striatonigral degeneration) patients.46 This PD-related motor pattern, with increased metabolic activity in the globus pallidum, putamen, and thalamus and decreased activity in the lateral frontal, paracentral, interior parietal and parieto-occipital areas, significantly correlated with Unified PD Rating Scale (UPDRS) scores of bradykinesia and rigidity but not tremor ratings. The PDrelated motor pattern has undergone further validation using both oxygen-15 water (H2O15) and [18F]FDG-PET scans and demonstrates high within-subject and test-retest reproducibility in early and advanced stage PD patients.47 Distinct metabolic patterns such as these may have use in the diagnosis or therapeutic monitoring. Structural MRI scans analyzed for cortical thickness revealed correlations between decreased cortical thickness in the parietotemporal sensory association areas and longer PD duration and increased motor deficits on the UPDRS, particularly bradykinesia and axial motor function48; cortical thickness in this PD sample, however, did not correlate with tremor scores. Although additional study is needed, these cortical regions overlap with those exhibiting decreased metabolic activity in other studies and suggest cerebral cortical dysfunction in advancing PD.

Comparisons of Tremor-Dominant and Akinetic-Rigid Parkinson’s Disease Phenotypes Some, but not all, imaging studies have identified significant differences in the neurochemistry and neural circuitry underlying tremor-dominant versus postural instability-gait impairment or akinetic-rigid motor phenotypes of PD. Tremordominant patients are frequently younger at onset and have a slower rate of disease progression and less cognitive decline. Using [123I]FP-CIT scans, nigrostriatal dopaminergic system impairment, even at early stages of PD, with reduced uptake in putamen and caudate regions is associated more with akineticrigid than tremor-dominant symptomology.49 Other [123I]FPCIT studies, however, failed to find a significant difference in striatal dopamine transporter uptake between tremor-dominant and non-tremor-dominant PD subgroups.37 These studies suggest that nondopaminergic mechanisms may contribute to differences in PD motor phenotype and that further refinement of optimal methods for classification (visual morphology, semiquantitative, other) is needed. Because structural MRI scans of PD patients with different motor phenotypes have not revealed robust differences, studies have explored differences in blood-oxygen-leveldependent (BOLD) function on functional MRI (fMRI). BOLD activation was reduced in bilateral dorsolateral prefrontal cortex, contralateral lingual gyrus, caudate, globus pallidum interna and externa, and ispilateral thalamus in a nontremor dominant PD group compared with the tremor-dominant PD group. No significant differences were seen in gray or white matter volume between these groups as detected using

VBM.50 In another study using fMRI with regions of interest examining the striatal-thalamo-cortical and cerebellothalamo-cortical circuits, akinetic-rigid PD patients showed more activity during a finger-tapping task in multiple cortical and subcortical regions, as well as striatal-thalamocortical and cerebello-thalamo-cortical circuits, compared with tremor-dominant PD patients; in contrast, tremordominant patients had greater activity in the vermis, contralateral cerebellar hemisphere, and ipsilateral thalamus.51 Thus, different imaging modalities may differentiate the PD subtypes and identify specific neurobiological substrates for the different motor phenotypes.

Gait Impairment Gait and balance issues in PD are associated with morbidity, mortality, and disability and in advanced PD may respond poorly to dopaminergic treatments. Falls, start hesitation, and freezing of gait also can occur in advancing PD. Whereas imaging during actual gait is often impossible because most scanners require that subjects be immobile and supine, novel methods, such as motor imagery, virtual reality, or foot pedals, have been developed to simulate gait-related brain activity. Studies using SPECT reveal that although PD patients and healthy controls have similar patterns of gait-related brain activation (e.g., cortical motor regions for foot and trunk, brainstem, and cerebellum), PD patients show significantly less regional cerebral blood flow activation in the right supplemental motor area, left precuneus, and right cerebellar hemisphere. In addition, PD patients had increased activation in the lateral premotor area when visual cues are added to improve PD gait.52 Using fMRI to measure gait-related activation during mental imagery, the PD group had differences in the mean gait activation pattern (i.e., hypoactivity within the parieto-occipital regions, left hippocampus, midline/lateral cerebellum, and pedunculopontine nucleus locomotor area) compared with healthy controls; activation levels in the right posterior parietal cortex correlated with severity of gait measures.53 Another fMRI study investigating gait-related activation during mental imagery and video stimuli of gait initiation, stepping over an obstacle, and gait termination found that PD patients had greater activation of visuomotor areas during the latter two mental imagery scenarios compared with healthy controls.54 Other studies suggest that gait disturbances in PD invoke nondopaminergic and extrastriatal systems, including the cholinergic system, which is involved in locomotion and cognition. Comparing acetylcholinesterase hydrolysis rates in PD patients with a history of falls to PD patients without a history of falls, thalamic acetylcholinesterase activity was reduced in the “fallers” as measured by [11C]PMP-PET; in contrast, DTZB-PET scans did not reveal significant nigrostriatal dopaminergic differences between the groups.55 Freezing of gait (FOG) is an intriguing, although not well understood, episodic gait phenomenon that occurs in moderate-advanced PD and is characterized by the inability to initiate and produce effective stepping or gait patterns.56 Studies have examined metabolic, functional, and structural imaging correlates of FOG, comparing PD patients who have FOG (FOG +) with

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Dementia with Extrapyramidal Syndromes those who do not FOG (FOG–). Using [18F]-6-fluoro-levodopa (FDOPA) and [18F]FDG in a small sample of PD patients, trends toward differences in striatal tracer uptake in FOG + compared with FOG– patients were noted. In FOG + patients, caudate uptake of FDOPA and FDG was reduced, whereas FDOPA decrease in the putamen was associated with FDG increases. Computer-based virtual reality paradigms have been developed to simulate FOG in the MRI environment. Processing cognitive and environmental information obtained during bipedal motor activity (i.e., using foot pedals) has been shown to induce FOG-like symptoms in PD patients who are prone to FOG episodes in the “off” state.57 By using this task during fMRI, increased BOLD signal was found in the bilateral dorsolateral prefrontal cortex and posterior parietal cortices, with a concurrent decrease in BOLD signal in bilateral sensorimotor cortices during the contrasts of the motor arrests and simulated “walking.” In addition, this was also associated with significantly decreased BOLD signal in various basal ganglia and thalamic nuclei during periods of motor arrest compared with “walking.”58 Several studies have examined structural gray and white matter differences associated with FOG in PD. Using VBM, greater gray matter atrophy in frontal and parietal cortices has been found in FOG + patients than in FOG– patients, suggesting contributions of executive dysfunction and/or altered perceptual judgment.59 Another VBM study demonstrated a predominantly posterior pattern of gray matter atrophy (i.e., cuneus, precuneus, lingual gyrus, and posterior cingulate cortex), which may implicate visuoperceptive and discrimination dysfunction in FOG.60 Using probabilistic tractography DTI analyses to examine the white matter connectivity of the pedunculopontine nucleus in a small sample of PD patients, there was decreased connectivity of the pedunculopontine nucleus and cerebellum and increased connectivity with the pons in FOG + patients compared to FOG– patients.61

18.3 Imaging and Parkinson’s Disease Nonmotor Features and Complications 18.3.1 Nonmotor Features In addition to the motor features described above, nonmotor features are now recognized to accompany PD, even from early stages to more advanced disease. Early nonmotor features include decreased or loss of sense of smell (i.e., hyposmia or anosmia), depression, anxiety, constipation, cognitive impairment, and sleep disturbances, including dream enactment with loss of normal muscle atonia during rapid eye movement (REM) sleep (i.e., REM behavior disorder, or RBD). In moderate to advanced PD, nonmotor symptoms may include depression, anxiety, fatigue, apathy, sleep disturbances, cognitive impairment or dementia, and hallucinations or psychosis. Imaging studies with structural, functional, metabolic, and other techniques have been used to identify neurobiological substrates of various nonmotor features and to develop related biomarkers.

This section highlights imaging studies examining select olfaction, sleep, cognitive, and behavioral issues.

Premotor Symptoms In recent years, the concept of a prodromal or premotor phase of PD before the onset of its classic motor features has emerged and is characterized primarily by several nonmotor features that have been associated with increased risk of developing PD.62,63,64,65 These symptoms include hyposmia or anosmia, constipation, depression, anxiety, and RBD and may be due to PD-related neurochemical and neuropathological changes in the olfactory system, gastrointestinal mucosa, and brainstem.2,3 Hyposmia or anosmia has been studied as a possible biomarker for the development of PD in epidemiologic and imaging studies, in some cases in first-degree relatives of PD patients.66,67 In a prospective study of 361 asymptomatic firstdegree relatives of PD patients, DaT-SPECT scanning was combined with olfactory testing. After 5 years, 5 of 40 hyposmic relatives were diagnosed by clinical criteria with PD, and all these individuals had abnormal DaT scans at baseline.67,68 In addition, several studies have focused on olfaction in already clinically diagnosed PD patients. [18F]FDG PET was used in a study of 69 Japanese, nondemented PD patients who were also evaluated for hyposmia. It found olfactory dysfunction was clinically related to cognitive dysfunction, but also to abnormal brain glucose metabolism in the piriform cortex and amygdala, regions involved in olfaction, memory, and emotion.69 Interestingly, activation in similar brain regions was abnormal in a small fMRI study of eight PD patients compared with controls, in which subjects rated olfactory stimuli as pleasant or unpleasant during fMRI. In PD patients, both pleasant and unpleasant smells were associated with decreased activation in the amygdalohippocampal complex, whereas in controls pleasant smells were associated with increased activity in the striatum and left inferior frontal gyrus and unpleasant smells with decreased activation of the ventral striatum.70 Neurochemical deficits associated with hyposmia in PD have been examined by PET scans using ligands to measure cholinergic and monoaminergic activity. In a study of nondemented PD patients who underwent [11C]-methyl-4-piperidinyl proprionate acetylcholinesterase PET and [11C] dihydrotetrabenazine vesicular monoamine transporter type 2 PET along with olfactory testing, smell identification scores correlated positively with acetylcholinesterease activity in the hippocampus, amygdala, and neocortex and with monoaminergic activity in the striatum.71 Some studies have focused on white matter microstructural integrity of the olfactory structures. In a voxel-wise analysis, increased diffusivity was found in the olfactory tracts in PD patients compared with controls in one study.72 In another study, using tract-based spatial statistics, reduced FA was found in white matter adjacent to the gyrus rectus or in primary olfactory areas in PD patients with hyposmia or anosmia.73 Rapid eye movement behavior disorder symptoms may precede PD or other synucleinopathies for up to 5 to 50 years before motor parkinsonism is seen.74,75 Similar to the olfaction imaging studies, some studies have focused on identifying brain changes associated with idiopathic RBD or development of PD,

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Parkinson’s Disease whereas others have examined already diagnosed PD patients who have RBD symptoms. In a structural MRI VBM study comparing 20 idiopathic RBD patients with controls, those with RBD had significantly decreased gray matter volume in the anterior lobes of the cerebellum bilaterally, tegmental part of the pons, and left parahippocampal gyrus.76 Brainstem white matter changes also have been found in DTI studies of idiopathic RBD patients compared with healthy controls, with decreased FA in the midbrain tegmentum and rostral pons and increased mean diffusivity (MD) in the pontine reticular formation. Interestingly, this study also detected increased gray matter density in bilateral hippocampi in RBD patients, which requires further study.77 A functional connectivity study of the substantia nigra revealed different correlations using voxelwise analyses between the left substantia nigra and left putamen as well as in the right cuneus/precuneus and superior occipital gyrus in the RBD patients compared with PD patients and healthy controls.78 RBD patients who underwent serial dopamine transporter imaging with [123I]FP-CIT demonstrated reduced mean binding in striatal regions at baseline and after 3 years. At 3 years, three patients were diagnosed with PD; these patients also had the lowest dopamine transporter uptake at baseline and about a mean 24 to 33% reduction in striatal uptake at 3 years' follow-up.79 Using technetium 99m ethylene cysteinate dimer (ECD) SPECT, 20 idiopathic RBD patients were examined at baseline, and 10 of these patients developed PD or dementia with Lewy bodies after 3 years; those who converted to PD or dementia with Lewy bodies had increased regional cerebral blood flow in the hippocampus at baseline compared with those who did not convert.80 In some cases, RBD has been associated with mild cognitive impairment, and perfusion changes may relate to incipient or mild cognitive deficits.81 PET studies using ligands for acetylcholine, serotonin, and monoamines demonstrate that nondemented PD patients who have RBD symptoms have decreased neocortical, limbic, and thalamic cholinergic innervation compared with those without RBD symptoms, but no differences in brainstem or striatal serotonin transporter binding were seen.82

Cognitive and Behavioral Symptoms Cognitive and behavioral symptoms are important contributors to PD patients’ overall function and well-being, quality of life, and outcomes. These symptoms include mild cognitive impairment and dementia; depression, anxiety, apathy, or other mood disorders; and hallucinations and delusions. In recent years, imaging techniques have been used to identify specific neurobiological substrates of these issues and biomarkers suggestive of underlying neuropathology or disease progression. Cognitive decline and dementia in PD occur in about 80% of patients as the disease progresses.83,84 Cognitive deficits that are mild but do not impair one’s ability to carry out activities of daily living have been termed mild cognitive impairment (PDMCI).85,86 About 40% of PD patients develop dementia,87 and these patients typically have more advanced disease, older age in general, older age at PD onset, and sometimes greater posterior cortical neuropsychological deficits (i.e., impaired semantic fluency, visuospatial abilities).88 Many structural MRI studies of

PD dementia have focused on gray matter atrophy of the mesial temporal lobe and were based largely on manual volumetry, visual rating scales, or semiautomated techniques (whole-brain or region of interest VBM) used in the AD field.89–99 In summary, these studies highlight that hippocampal atrophy occurs in PD dementia (PDD) and, in some studies, in nondemented PD patients; hippocampal or mesial temporal atrophy in PDD, however, was less severe than in AD. Some volumetric MRI studies note greater atrophy of the anterior cingulate gyrus, amygdala, or entorhinal cortex in PDD compared with cognitively normal or nondemented PD.93,95,98,100,101,102 Structural MRI VBM studies examining PD-MCI patients have yielded variable results, with some finding no gray matter differences between PD-MCI patients and controls103,104,105 but others identifying greater gray matter atrophy in temporal (e.g., hippocampal), parietal, and frontal (e.g., prefrontal and orbitofrontal) lobe regions in PD-MCI patients with impaired verbal memory, decision-making, and reaction time tests.103–109 A small number of studies have evaluated white matter changes, either as hyperintensities on T2-weighted or fluid-attenuated inversion recovery sequences using visual rating scales or semiautomated segmentation protocols or altered microstructural integrity with DTI measuring FA and MD. In some, but not all studies, PDD was associated with increased deep and periventricular white matter hyperintensities compared with cognitively normal or PD-MCI patients.110,111 DTI studies using tract-based spatial statistics found reduced FA in the superior longitudinal, inferior longitudinal, fronto-occipital, and uncinate fasciculi, cingulum, and corpus callosum in patients with PDD compared with those with normal cognition; further studies are needed to determine whether there are differences in PD-MCI.105 Using [18-F]FDG-PET and a VBM modeling approach in two studies, a PD-related cognitive pattern characterized by decreased metabolism in frontal and relatively, association areas and relative increased metabolism in the cerebellum correlated with performance on tests of memory and executive function and demonstrated progressively worse metabolic changes across the PD cognitive spectrum (from cognitively normal to single-domain PD-MCI to multiple-domain impairment PD-MCI).112,113 Because some PDD patients have comorbid AD pathology at autopsy, there has been interest in amyloid imaging with the PET tracer Pittsburgh Compound B (PIB). Results of PIB studies in PDD, however, have been somewhat variable, although several revealed increased PIB uptake or higher amyloid burden, generally to a lesser degree than that found in AD and dementia with Lewy bodies.114,115,116 Mood disorders like depression occur in about 45% of PD patients117 and may be present in premotor or early phases as well as in more advanced PD. Intrinsic neurochemical alterations and neurodegenerative changes in the brainstem, frontal/ limbic cortical regions, and subcortical structures likely contribute. Structural MRI studies of depression, in some cases accompanied by apathy or anxiety, yield mixed results regarding morphologic changes. Using VBM, one study found greater gray matter atrophy in the left orbitofrontal cortex, bilateral rectal gyrus, and right superior temporal pole,118 but another did not detect gray matter differences in PD depression.119 White matter hyperintensities as measured on T2-weighted MRI by a

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Dementia with Extrapyramidal Syndromes visual rating scale were significantly increased in periventricular regions in depressed PD patients compared with nondepressed PD patients and healthy controls of similar age, sex, and cerebrovascular risk factors, but they did not show significant differences in other brain regions.120 In VBM studies, white matter loss in the right frontal lobe, anterior cingulate, and inferior orbitofrontal region have been detected in depressed PD patients.119 In addition, reduced FA in bilateral mediodorsal thalamic regions has been found in DTI studies of depressed PD patients compared with nondepressed PD patients.121 Restingstate fMRI studies suggest abnormal activity in the prefrontallimbic network in depressed PD patients. In two studies, PD patients with depression, compared with those who did not have depression and with healthy controls, had reduced amplitudes of low-frequency fluctuations in the left orbitofrontal areas122 and dorsolateral prefrontal cortex, ventromedial prefrontal cortex, and rostral anterior cingulate cortex.123 Abnormalities in the serotonin system are well recognized to be associated with depression. In a PET study using [18-F]MPPF, a selective 5HT1A receptor antagonist, to investigate the postsynaptic serotonergic system, depressed PD patients had reduced tracer uptake in the left hippocampus, right insula, left superior temporal cortex, and orbitofrontal cortex compared with nondepressed PD patients, thereby implicating limbic serotonergic dysfunction.124 In a study using a different serotonin ligand, [11C]DASB, depressed PD patients exhibited increased binding in the dorsolateral and prefrontal cortex compared with healthy controls.125 Psychosis in PD ranges from mild illusions to formed hallucinations to delusions.126,127 Hallucinations occur in about onethird of PD patients treated with chronic dopaminergic therapy and are most often visual. These hallucinations may be due to medications but also may be due to disease-related factors, such as age, akinetic-rigid motor phenotype, cognitive impairment or impaired attention, depression, sleep disturbances, and visual problems. Structural MRI studies of visual hallucinations in PD have examined regional and global brain atrophy patterns. VBM studies comparing PD hallucinators with nonhallucinating PD patients and healthy controls demonstrate gray matter atrophy in the hippocampal, limbic, paralimbic, frontal, and neocortical regions.94,128,129,130 These studies support regional neuroanatomical changes and strong links between hallucinations and cognitive impairment but also pose potential confounds because these brain regions are implicated in cognitive impairment or dementia. Other VBM studies suggest thalamic or pedunculopontine atrophy in PD hallucinators.128,131 Given the predominant visual hallucinatory phenotype in PD, however, other VBM studies have found greater gray matter atrophy in regions associated with visual processing in PD hallucinators compared with nonhallucinators, including the left lingual gyrus and bilateral superior parietal lobes132 and when carefully controlling for effects of cognitive status, bilateral cunei, fusiform, middle occipital, precentral, cingulate gyri, inferior parietal lobules, right lingual gyrus, and left paracentral gyrus.133 fMRI studies in PD hallucinators demonstrate altered cortical activation patterns compared with those of PD nonhallucinators. Using visual stimulation fMRI paradigms (i.e., stroboscopic and kinematic), PD hallucinators had significantly greater frontal and subcortical activation to both visual stimula-

tion paradigms and decreased cerebral activation in occipital, parietal, and temporal-parietal regions compared with nonhallucinators,134 thereby suggesting a disruption in normal visual processing mechanisms in the hallucinators (▶ Fig. 18.5). Another study using complex visual stimuli (e.g., face recognition task) revealed significant reductions in right prefrontal areas, including the inferior, superior, and middle frontal gyrus and anterior cingulate gyrus in PD hallucinators to the face stimulus compared with nonhallucinating PD and healthy controls.135 Further evidence for impaired visual processing in hallucinating PD comes from an fMRI study in which several seconds before an image recognition task, the nondemented, hallucinating PD patients showed reduced activation of the lateral occipital cortex and extrastriate temporal visual cortices compared with nonhallucinating PD patients and healthy controls.136 One issue with these studies is that the actual hallucinatory event is not captured during imaging. Contrasts are developed between individuals who have hallucinations by self-report and those who deny hallucinations. A recent study was able to capture BOLD activation during visual hallucinations in a single patient with PD. Increased activation during visual hallucinations was noted in the frontal lobes, insula, cingulate, thalamus, and brainstem, whereas decreased activation was found in the fusiform, inferior occipital lobe, superior temporal lobe, and middle frontal lobe (▶ Fig. 18.6).137 In a restingstate fMRI study, PD patients with misperceptions had decreased functional connectivity between the ventral and dorsal attention networks, thereby implicating the role of attention in generating hallucinations.130 Decreased perfusion or glucose metabolism in predominantly posterior brain regions, frequently involved in visual processing, has been reported in PD hallucinators by using SPECT or PET and, in some studies, increased frontal perfusion or metabolism. Using technetium 99m-hexamethylpropyleneamine oxime (HMPAO) SPECT, hallucinating PD patients had decreased cerebral blood flow to temporal-occipital lobe regions138 and reduced perfusion in bilateral parieto-occipital regions in PD patients with visual hallucinations compared with those without hallucinations.139 Another [123I]IMP SPECT study, however, found hypoperfusion in the right fusiform gyrus but also hyperperfusion in the right superior and middle temporal gyri in PD hallucinators when covarying for Mini-Mental State Examination score and PD duration.140 Similarly, decreased metabolism in temporaloccipital-parietal regions and also increased metabolic rates in frontal regions, especially the left superior frontal gyrus, have been identified in PD hallucinators compared with nonhallucinators using [18F]FDG-PET.141,142

18.4 Conclusion Neuroimaging in PD has grown tremendously over the years and has advanced our understanding of the neurobiological substrates of PD-related motor and nonmotor features. Neuroimaging biomarkers will be relevant and important for diagnosing premotor PD and, as classically defined, PD; monitoring disease progression; and measuring the effects of treatments for both PD-related motor and nonmotor symptoms.

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Parkinson’s Disease

Fig. 18.5 Representative regions of significant functional magnetic resonance imaging activation during stroboscopic versus no visual stimulation in nonhallucinating Parkinson’s disease (PD) patients (top panel) and hallucinating PD patients (second panel). Note the decreased occipital lobe activation in the hallucinating patients. The two bottom panels display activation differences during apparent kinematic versus stationary visual stimulation in nonhallucinating PD patients (third panel) and hallucinating PD patients (bottom panel). Note the decreased activation in MT/V5 region and increased frontal lobe activation in the hallucinating patients. Significance thresholds were set for p < 0.001 (uncorrected for multiple comparisons) for both analyses. Voxels evidencing significant activation are displayed on representative axial sections (z = z plane Talairach coordinates) on a canonical brain image. The color scale indicates the magnitude of t values, with the lowest appearing in dark red and the highest in bright yellow/white. The left side of the images represents the left side of the brain.

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Dementia with Extrapyramidal Syndromes

Fig. 18.6 This figure displays representative regions of significant functional magnetic resonance imaging activation in a single patient with Parkinson’s disease who experienced visual hallucinations in the scanner. This individual had frequent and brief hallucinations of African tribesmen and chimpanzees. During the scan, the patient reported 16 hallucinations interspersed with periods of no hallucinations. Voxels evidencing significant differences in activation during the hallucinations are displayed on representative sagittal, axial, coronal sections on a canonical brain image. The color scale indicates the magnitude of t values, with the lowest appearing in dark red and the highest in bright yellow/white. The left side of the images represents the left side of the brain.

References [1] Dorsey ER, Constantinescu R, Thompson JP et al. Projected number of people with Parkinson’s disease in the most populous nations, 2005 through 2030. Neurology 2007; 68: 384–386 [2] Braak H, Del Tredici K, Rüb U, de Vos RA, Jansen Steur EN, Braak E. Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol Aging 2003; 24: 197–211 [3] Braak H, Ghebremedhin E, Rüb U, Bratzke H, Del Tredici K. Stages in the development of Parkinson’s disease-related pathology. Cell Tissue Res 2004; 318: 121–134 [4] Hughes AJ, Daniel SE, Blankson S, Lees AJ. A clinicopathologic study of 100 cases of Parkinson’s disease. Arch Neurol 1993; 50: 140–148 [5] Fearnley JM, Lees AJ. Ageing and Parkinson’s disease: substantia nigra regional selectivity. Brain 1991; 114: 2283–2301 [6] Hilker R, Schweitzer K, Coburger S et al. Nonlinear progression of Parkinson’s disease as determined by serial positron emission tomographic imaging of striatal fluorodopa F 18 activity. Arch Neurol 2005; 62: 378–382 [7] Lee CS, Schulzer M, de la Fuente-Fernández R et al. Lack of regional selectivity during the progression of Parkinson’s disease: implications for pathogenesis. Arch Neurol 2004; 61: 1920–1925 [8] Nandhagopal R, Kuramoto L, Schulzer M et al. Longitudinal progression of sporadic Parkinson’s disease: a multi-tracer positron emission tomography study. Brain 2009; 132: 2970–2979 [9] Jaber M, Jones S, Giros B, Caron MG. The dopamine transporter: a crucial component regulating dopamine transmission. Mov Disord 1997; 12: 629– 633 [10] Hauser RA, Grosset DG. [123I]FP-CIT (DaTscan) SPECT brain imaging in patients with suspected parkinsonian syndromes. J Neuroimaging 2012; 22: 225–230 [11] Godau J, Hussl A, Lolekha P, Stoessl AJ, Seppi K. Neuroimaging: current role in detecting pre-motor Parkinson’s disease. Mov Disord 2012; 27: 634–643 [12] Jennings DL, Seibyl JP, Oakes D, Eberly S, Murphy J, Marek K. (123I) beta-CIT and single-photon emission computed tomographic imaging vs clinical evaluation in Parkinsonian syndrome: unmasking an early diagnosis. Arch Neurol 2004; 61: 1224–1229

[13] Marshall VL, Reininger CB, Marquardt M et al. Parkinson’s disease is overdiagnosed clinically at baseline in diagnostically uncertain cases: a 3-year European multicenter study with repeat [123I]FP-CIT SPECT. Mov Disord 2009; 24: 500–508 [14] Benamer HT, Patterson J, Wyper DJ, Hadley DM, Macphee GJ, Grosset DG. Correlation of Parkinson’s disease severity and duration with 123I-FP-CIT SPECT striatal uptake. Mov Disord 2000; 15: 692–698 [15] Cummings JL, Henchcliffe C, Schaier S, Simuni T, Waxman A, Kemp P. The role of dopaminergic imaging in patients with symptoms of dopaminergic system neurodegeneration. Brain 2011; 134: 3146–3166 [16] Benamer TS, Patterson J, Grosset DG et al. Accurate differentiation of parkinsonism and essential tremor using visual assessment of [123I]-FP-CIT SPECT imaging: the [123I]-FP-CIT study group. Mov Disord 2000; 15: 503–510 [17] Morrish PK, Rakshi JS, Bailey DL, Sawle GV, Brooks DJ. Measuring the rate of progression and estimating the preclinical period of Parkinson’s disease with [18F]dopa PET. J Neurol Neurosurg Psychiatry 1998; 64: 314–319 [18] Berg D, Merz B, Reiners K, Naumann M, Becker G. Five-year follow-up study of hyperechogenicity of the substantia nigra in Parkinson’s disease. Mov Disord 2005; 20: 383–385 [19] Berg D, Siefker C, Becker G. Echogenicity of the substantia nigra in Parkinson’s disease and its relation to clinical findings. J Neurol 2001; 248: 684–689 [20] Walter U, Niehaus L, Probst T, Benecke R, Meyer BU, Dressler D. Brain parenchyma sonography discriminates Parkinson’s disease and atypical parkinsonian syndromes. Neurology 2003; 60: 74–77 [21] Berg D, Roggendorf W, Schröder U et al. Echogenicity of the substantia nigra: association with increased iron content and marker for susceptibility to nigrostriatal injury. Arch Neurol 2002; 59: 999–1005 [22] Stockner H, Sojer M, K KS et al. Midbrain sonography in patients with essential tremor. Mov Disord 2007; 22: 414–417 [23] Behnke S, Schroeder U, Dillmann U et al. Hyperechogenicity of the substantia nigra in healthy controls is related to MRI changes and to neuronal loss as determined by F-Dopa PET. Neuroimage 2009; 47: 1237–1243 [24] Vaillancourt DE, Spraker MB, Prodoehl J et al. High-resolution diffusion tensor imaging in the substantia nigra of de novo Parkinson’s disease. Neurology 2009; 72: 1378–1384

176

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

| 12.09.15 - 10:52

Parkinson’s Disease [25] Emir UE, Tuite PJ, Öz G. Elevated pontine and putamenal GABA levels in mildmoderate Parkinson’s disease detected by 7 tesla proton MRS. PLoS ONE 2012; 7: e30918 [26] Hughes AJ, Daniel SE, Ben-Shlomo Y, Lees AJ. The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain 2002; 125: 861–870 [27] Morelli M, Arabia G, Salsone M et al. Accuracy of magnetic resonance parkinsonism index for differentiation of progressive supranuclear palsy from probable or possible Parkinson’s disease. Mov Disord 2011; 26: 527–533 [28] Nicoletti G, Lodi R, Condino F et al. Apparent diffusion coefficient measurements of the middle cerebellar peduncle differentiate the Parkinson variant of MSA from Parkinson’s disease and progressive supranuclear palsy. Brain 2006; 129: 2679–2687 [29] Schocke MF, Seppi K, Esterhammer R et al. Diffusion-weighted MRI differentiates the Parkinson variant of multiple system atrophy from PD. Neurology 2002; 58: 575–580 [30] Seppi K, Schocke MF, Esterhammer R et al. Diffusion-weighted imaging discriminates progressive supranuclear palsy from PD, but not from the parkinson variant of multiple system atrophy. Neurology 2003; 60: 922–927 [31] Gaenslen A, Unmuth B, Godau J et al. The specificity and sensitivity of transcranial ultrasound in the differential diagnosis of Parkinson’s disease: a prospective blinded study. Lancet Neurol 2008; 7: 417–424 [32] Schwingenschuh P, Ruge D, Edwards MJ et al. Distinguishing SWEDDs patients with asymmetric resting tremor from Parkinson’s disease: a clinical and electrophysiological study. Mov Disord 2010; 25: 560–569 [33] Asenbaum S, Pirker W, Angelberger P, Bencsits G, Pruckmayer M, Brücke T. [123I]beta-CIT and SPECT in essential tremor and Parkinson’s disease. J Neural Transm 1998; 105: 1213–1228 [34] Zijlmans J, Evans A, Fontes F et al. [123I] FP-CIT spect study in vascular parkinsonism and Parkinson’s disease. Mov Disord 2007; 22: 1278–1285 [35] Benítez-Rivero S, Marín-Oyaga VA, García-Solís D et al. Clinical features and 123I-FP-CIT SPECT imaging in vascular parkinsonism and Parkinson’s disease. J Neurol Neurosurg Psychiatry 2013; 84: 122–129 [36] Diaz-Corrales FJ, Sanz-Viedma S, Garcia-Solis D, Escobar-Delgado T, Mir P. Clinical features and 123I-FP-CIT SPECT imaging in drug-induced parkinsonism and Parkinson’s disease. Eur J Nucl Med Mol Imaging 2010; 37: 556–564 [37] Song IU, Chung YA, Oh JK, Chung SW. An FP-CIT PET comparison of the difference in dopaminergic neuronal loss in subtypes of early Parkinson’s disease. Acta Radiol 2014; 55: 366–371 [38] Zaidel A, Arkadir D, Israel Z, Bergman H. Akineto-rigid vs. tremor syndromes in Parkinsonism. Curr Opin Neurol 2009; 22: 387–393 [39] Helmich RC, Bloem BR, Toni I. Motor imagery evokes increased somatosensory activity in Parkinson’s disease patients with tremor. Hum Brain Mapp 2012; 33: 1763–1779 [40] Timmermann L, Gross J, Dirks M, Volkmann J, Freund HJ, Schnitzler A. The cerebral oscillatory network of parkinsonian resting tremor. Brain 2003; 126: 199–212 [41] Kassubek J, Juengling FD, Hellwig B, Spreer J, Lücking CH. Thalamic gray matter changes in unilateral Parkinsonian resting tremor: a voxel-based morphometric analysis of 3-dimensional magnetic resonance imaging. Neurosci Lett 2002; 323: 29–32 [42] Benninger DH, Thees S, Kollias SS, Bassetti CL, Waldvogel D. Morphological differences in Parkinson’s disease with and without rest tremor. J Neurol 2009; 256: 256–263 [43] Mure H, Hirano S, Tang CC et al. Parkinson’s disease tremor-related metabolic network: characterization, progression, and treatment effects. Neuroimage 2011; 54: 1244–1253 [44] Doder M, Rabiner EA, Turjanski N, Lees AJ, Brooks DJ 11C-WAY 100635 PET study. Tremor in Parkinson’s disease and serotonergic dysfunction: an 11CWAY 100635 PET study. Neurology 2003; 60: 601–605 [45] Loane C, Wu K, Bain P, Brooks DJ, Piccini P, Politis M. Serotonergic loss in motor circuitries correlates with severity of action-postural tremor in PD. Neurology 2013; 80: 1850–1855 [46] Eidelberg D, Moeller JR, Dhawan V et al. The metabolic topography of parkinsonism. J Cereb Blood Flow Metab 1994; 14: 783–801 [47] Ma Y, Tang C, Spetsieris PG, Dhawan V, Eidelberg D. Abnormal metabolic network activity in Parkinson’s disease: test-retest reproducibility. J Cereb Blood Flow Metab 2007; 27: 597–605 [48] Lyoo CH, Ryu YH, Lee MS. Cerebral cortical areas in which thickness correlates with severity of motor deficits of Parkinson’s disease. J Neurol 2011; 258: 1871–1876 [49] Schillaci O, Chiaravalloti A, Pierantozzi M et al. Different patterns of nigrostriatal degeneration in tremor type versus the akinetic-rigid and mixed types

[50]

[51]

[52] [53]

[54]

[55] [56] [57]

[58]

[59] [60]

[61]

[62] [63]

[64]

[65]

[66]

[67]

[68]

[69] [70] [71]

[72]

[73]

[74]

[75]

of Parkinson’s disease at the early stages: molecular imaging with 123I-FPCIT SPECT. Int J Mol Med 2011; 28: 881–886 Prodoehl J, Planetta PJ, Kurani AS, Comella CL, Corcos DM, Vaillancourt DE. Differences in brain activation between tremor- and nontremor-dominant Parkinson’s disease. JAMA Neurol 2013; 70: 100–106 Lewis MM, Du G, Sen S et al. Differential involvement of striato- and cerebello-thalamo-cortical pathways in tremor- and akinetic/rigid-predominant Parkinson’s disease. Neuroscience 2011; 177: 230–239 Shibasaki H, Fukuyama H, Hanakawa T. Neural control mechanisms for normal versus parkinsonian gait. Prog Brain Res 2004; 143: 199–205 Crémers J, D’Ostilio K, Stamatakis J, Delvaux V, Garraux G. Brain activation pattern related to gait disturbances in Parkinson’s disease. Mov Disord 2012; 27: 1498–1505 Wai YY, Wang JJ, Weng YH et al. Cortical involvement in a gait-related imagery task: comparison between Parkinson’s disease and normal aging. Parkinsonism Relat Disord 2012; 18: 537–542 Bohnen NI, Müller ML, Koeppe RA et al. History of falls in Parkinson’s disease is associated with reduced cholinergic activity. Neurology 2009; 73: 1670–1676 Giladi N, Treves TA, Simon ES et al. Freezing of gait in patients with advanced Parkinson’s disease. J Neural Transm 2001; 108: 53–61 Shine JM, Ward PB, Naismith SL, Pearson M, Lewis SJ. Utilising functional MRI (fMRI) to explore the freezing phenomenon in Parkinson’s disease. J Clin Neurosci 2011; 18: 807–810 Shine JM, Matar E, Ward PB et al. Exploring the cortical and subcortical functional magnetic resonance imaging changes associated with freezing in Parkinson’s disease. Brain 2013; 136: 1204–1215 Kostic VS, Agosta F, Pievani M et al. Pattern of brain tissue loss associated with freezing of gait in Parkinson’s disease. Neurology 2012; 78: 409–416 Tessitore A, Amboni M, Cirillo G et al. Regional gray matter atrophy in patients with Parkinson’s disease and freezing of gait. AJNR Am J Neuroradiol 2012; 33: 1804–1809 Schweder PM, Hansen PC, Green AL, Quaghebeur G, Stein J, Aziz TZ. Connectivity of the pedunculopontine nucleus in parkinsonian freezing of gait. Neuroreport 2010; 21: 914–916 Abbott RD, Petrovitch H, White LR et al. Frequency of bowel movements and the future risk of Parkinson’s disease. Neurology 2001; 57: 456–462 Postuma RB, Gagnon JF, Vendette M, Fantini ML, Massicotte-Marquez J, Montplaisir J. Quantifying the risk of neurodegenerative disease in idiopathic REM sleep behavior disorder. Neurology 2009; 72: 1296–1300 Postuma RB, Lang AE, Gagnon JF, Pelletier A, Montplaisir JY. How does parkinsonism start? Prodromal parkinsonism motor changes in idiopathic REM sleep behaviour disorder. Brain 2012; 135: 1860–1870 Ross GW, Abbott RD, Petrovitch H, Tanner CM, White LR. Pre-motor features of Parkinson’s disease: the Honolulu-Asia Aging Study experience. Parkinsonism Relat Disord 2012; 18 Suppl 1: S199–S202 Siderowf A, Jennings D, Eberly S et al. PARS Investigators. Impaired olfaction and other prodromal features in the Parkinson At-Risk Syndrome Study. Mov Disord 2012; 27: 406–412 Berendse HW, Ponsen MM. Diagnosing premotor Parkinson’s disease using a two-step approach combining olfactory testing and DAT SPECT imaging. Parkinsonism Relat Disord 2009; 15 Suppl 3: S26–S30 Ponsen MM, Stoffers D, Wolters ECh, Booij J, Berendse HW. Olfactory testing combined with dopamine transporter imaging as a method to detect prodromal Parkinson’s disease. J Neurol Neurosurg Psychiatry 2010; 81: 396–399 Baba T, Takeda A, Kikuchi A et al. Association of olfactory dysfunction and brain. Metabolism in Parkinson’s disease. Mov Disord 2011; 26: 621–628 Hummel T, Fliessbach K, Abele M et al. Olfactory fMRI in patients with Parkinson’s disease. Front Integr Neurosci 2010; 4: 125 Bohnen NI, Müller ML, Kotagal V et al. Olfactory dysfunction, central cholinergic integrity and cognitive impairment in Parkinson’s disease. Brain 2010; 133: 1747–1754 Scherfler C, Schocke MF, Seppi K et al. Voxel-wise analysis of diffusion weighted imaging reveals disruption of the olfactory tract in Parkinson’s disease. Brain 2006; 129: 538–542 Ibarretxe-Bilbao N, Junque C, Marti MJ et al. Olfactory impairment in Parkinson’s disease and white matter abnormalities in central olfactory areas: A voxel-based diffusion tensor imaging study. Mov Disord 2010; 25: 1888–1894 Claassen DO, Josephs KA, Ahlskog JE, Silber MH, Tippmann-Peikert M, Boeve BF. REM sleep behavior disorder preceding other aspects of synucleinopathies by up to half a century. Neurology 2010; 75: 494–499 Hawkes CH. The prodromal phase of sporadic Parkinson’s disease: does it exist and if so how long is it? Mov Disord 2008; 23: 1799–1807

177

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

| 12.09.15 - 10:52

Dementia with Extrapyramidal Syndromes [76] Hanyu H, Inoue Y, Sakurai H et al. Voxel-based magnetic resonance imaging study of structural brain changes in patients with idiopathic REM sleep behavior disorder. Parkinsonism Relat Disord 2012; 18: 136– 139 [77] Scherfler C, Frauscher B, Schocke M et al. SINBAR (Sleep Innsbruck Barcelona) Group. White and gray matter abnormalities in idiopathic rapid eye movement sleep behavior disorder: a diffusion-tensor imaging and voxel-based morphometry study. Ann Neurol 2011; 69: 400–407 [78] Ellmore TM, Castriotta RJ, Hendley KL et al. Altered nigrostriatal and nigrocortical functional connectivity in rapid eye movement sleep behavior disorder. Sleep 2013; 36: 1885–1892 [79] Iranzo A, Valldeoriola F, Lomeña F et al. Serial dopamine transporter imaging of nigrostriatal function in patients with idiopathic rapid-eye-movement sleep behaviour disorder: a prospective study. Lancet Neurol 2011; 10: 797– 805 [80] Dang-Vu TT, Gagnon JF, Vendette M, Soucy JP, Postuma RB, Montplaisir J. Hippocampal perfusion predicts impending neurodegeneration in REM sleep behavior disorder. Neurology 2012; 79: 2302–2306 [81] Vendette M, Montplaisir J, Gosselin N et al. Brain perfusion anomalies in rapid eye movement sleep behavior disorder with mild cognitive impairment. Mov Disord 2012; 27: 1255–1261 [82] Kotagal V, Albin RL, Müller ML et al. Symptoms of rapid eye movement sleep behavior disorder are associated with cholinergic denervation in Parkinson’s disease. Ann Neurol 2012; 71: 560–568 [83] Aarsland D, Andersen K, Larsen JP, Lolk A, Kragh-Sørensen P. Prevalence and characteristics of dementia in Parkinson’s disease: an 8-year prospective study. Arch Neurol 2003; 60: 387–392 [84] Hely MA, Reid WG, Adena MA, Halliday GM, Morris JG. The Sydney multicenter study of Parkinson’s disease: the inevitability of dementia at 20 years. Mov Disord 2008; 23: 837–844 [85] Litvan I, Aarsland D, Adler CH et al. MDS Task Force on mild cognitive impairment in Parkinson’s disease: critical review of PD-MCI. Mov Disord 2011; 26: 1814–1824 [86] Litvan I, Goldman JG, Tröster AI et al. Diagnostic criteria for mild cognitive impairment in Parkinson’s disease: Movement Disorder Society Task Force guidelines. Mov Disord 2012; 27: 349–356 [87] Emre M, Aarsland D, Brown R et al. Clinical diagnostic criteria for dementia associated with Parkinson’s disease. Mov Disord 2007; 22: 1689–1707, quiz 1837 [88] Williams-Gray CH, Evans JR, Goris A et al. The distinct cognitive syndromes of Parkinson’s disease: 5 year follow-up of the CamPaIGN cohort. Brain 2009; 132: 2958–2969 [89] Beyer MK, Larsen JP, Aarsland D. Gray matter atrophy in Parkinson’s disease with dementia and dementia with Lewy bodies. Neurology 2007; 69: 747– 754 [90] Burton EJ, McKeith IG, Burn DJ, Williams ED, O’Brien JT. Cerebral atrophy in Parkinson’s disease with and without dementia: a comparison with Alzheimer’s disease, dementia with Lewy bodies and controls. Brain 2004; 127: 791–800 [91] Camicioli R, Moore MM, Kinney A, Corbridge E, Glassberg K, Kaye JA. Parkinson’s disease is associated with hippocampal atrophy. Mov Disord 2003; 18: 784–790 [92] Duncan GW, Firbank MJ, O’Brien JT, Burn DJ. Magnetic resonance imaging: a biomarker for cognitive impairment in Parkinson’s disease? Mov Disord 2013; 28: 425–438 [93] Goldman JG, Stebbins GT, Bernard B, Stoub TR, Goetz CG, deToledo-Morrell L. Entorhinal cortex atrophy differentiates Parkinson’s disease patients with and without dementia. Mov Disord 2012; 27: 727–734 [94] Ibarretxe-Bilbao N, Ramírez-Ruiz B, Tolosa E et al. Hippocampal head atrophy predominance in Parkinson’s disease with hallucinations and with dementia. J Neurol 2008; 255: 1324–1331 [95] Junqué C, Ramírez-Ruiz B, Tolosa E et al. Amygdalar and hippocampal MRI volumetric reductions in Parkinson’s disease with dementia. Mov Disord 2005; 20: 540–544 [96] Laakso MP, Partanen K, Riekkinen P et al. Hippocampal volumes in Alzheimer’s disease, Parkinson’s disease with and without dementia, and in vascular dementia: An MRI study. Neurology 1996; 46: 678–681 [97] Nagano-Saito A, Washimi Y, Arahata Y et al. Cerebral atrophy and its relation to cognitive impairment in Parkinson’s disease. Neurology 2005; 64: 224–229 [98] Summerfield C, Junqué C, Tolosa E et al. Structural brain changes in Parkinson’s disease with dementia: a voxel-based morphometry study. Arch Neurol 2005; 62: 281–285

[99] Tam CW, Burton EJ, McKeith IG, Burn DJ, O’Brien JT. Temporal lobe atrophy on MRI in Parkinson’s disease with dementia: a comparison with Alzheimer’s disease and dementia with Lewy bodies. Neurology 2005; 64: 861–865 [100] Beyer MK, Janvin CC, Larsen JP, Aarsland D. A magnetic resonance imaging study of patients with Parkinson’s disease with mild cognitive impairment and dementia using voxel-based morphometry. J Neurol Neurosurg Psychiatry 2007; 78: 254–259 [101] Bouchard TP, Malykhin N, Martin WR et al. Age and dementia-associated atrophy predominates in the hippocampal head and amygdala in Parkinson’s disease. Neurobiol Aging 2008; 29: 1027–1039 [102] Kenny ER, Burton EJ, O’Brien JT. A volumetric magnetic resonance imaging study of entorhinal cortex volume in dementia with lewy bodies. A comparison with Alzheimer’s disease and Parkinson’s disease with and without dementia. Dement Geriatr Cogn Disord 2008; 26: 218–225 [103] Apostolova LG, Beyer M, Green AE et al. Hippocampal, caudate, and ventricular changes in Parkinson’s disease with and without dementia. Mov Disord 2010; 25: 687–695 [104] Dalaker TO, Zivadinov R, Larsen JP et al. Gray matter correlations of cognition in incident Parkinson’s disease. Mov Disord 2010; 25: 629–633 [105] Hattori T, Orimo S, Aoki S et al. Cognitive status correlates with white matter alteration in Parkinson’s disease. Hum Brain Mapp 2012; 33: 727–739 [106] Brück A, Kurki T, Kaasinen V, Vahlberg T, Rinne JO. Hippocampal and prefrontal atrophy in patients with early non-demented Parkinson’s disease is related to cognitive impairment. J Neurol Neurosurg Psychiatry 2004; 75: 1467–1469 [107] Melzer TR, Watts R, MacAskill MR et al. Grey matter atrophy in cognitively impaired Parkinson’s disease. J Neurol Neurosurg Psychiatry 2012; 83: 188– 194 [108] Song SK, Lee JE, Park HJ, Sohn YH, Lee JD, Lee PH. The pattern of cortical atrophy in patients with Parkinson’s disease according to cognitive status. Mov Disord 2011; 26: 289–296 [109] Weintraub D, Doshi J, Koka D et al. Neurodegeneration across stages of cognitive decline in Parkinson’s disease. Arch Neurol 2011; 68: 1562–1568 [110] Beyer MK, Aarsland D, Greve OJ, Larsen JP. Visual rating of white matter hyperintensities in Parkinson’s disease. Mov Disord 2006; 21: 223–229 [111] Shin J, Choi S, Lee JE, Lee HS, Sohn YH, Lee PH. Subcortical white matter hyperintensities within the cholinergic pathways of Parkinson’s disease patients according to cognitive status. J Neurol Neurosurg Psychiatry 2012; 83: 315–321 [112] Huang C, Mattis P, Perrine K, Brown N, Dhawan V, Eidelberg D. Metabolic abnormalities associated with mild cognitive impairment in Parkinson’s disease. Neurology 2008; 70: 1470–1477 [113] Huang C, Mattis P, Tang C, Perrine K, Carbon M, Eidelberg D. Metabolic brain networks associated with cognitive function in Parkinson’s disease. Neuroimage 2007; 34: 714–723 [114] Gomperts SN, Locascio JJ, Marquie M et al. Brain amyloid and cognition in Lewy body diseases. Mov Disord 2012; 27: 965–973 [115] Edison P, Rowe CC, Rinne JO et al. Amyloid load in Parkinson’s disease dementia and Lewy body dementia measured with [11C]PIB positron emission tomography. J Neurol Neurosurg Psychiatry 2008; 79: 1331–1338 [116] Foster ER, Campbell MC, Burack MA et al. Amyloid imaging of Lewy bodyassociated disorders. Mov Disord 2010; 25: 2516–2523 [117] Cummings JL, Masterman DL. Depression in patients with Parkinson’s disease. Int J Geriatr Psychiatry 1999; 14: 711–718 [118] Feldmann A, Illes Z, Kosztolanyi P et al. Morphometric changes of gray matter in Parkinson’s disease with depression: a voxel-based morphometry study. Mov Disord 2008; 23: 42–46 [119] Kostić VS, Agosta F, Petrović I et al. Regional patterns of brain tissue loss associated with depression in Parkinson’s disease. Neurology 2010; 75: 857–863 [120] Petrovic IN, Stefanova E, Kozic D et al. White matter lesions and depression in patients with Parkinson’s disease. J Neurol Sci 2012; 322: 132–136 [121] Li W, Liu J, Skidmore F, Liu Y, Tian J, Li K. White matter microstructure changes in the thalamus in Parkinson’s disease with depression: a diffusion tensor MR imaging study. AJNR Am J Neuroradiol 2010; 31: 1861–1866 [122] Luo C, Chen Q, Song W et al. Resting-state fMRI study on drug-naive patients with Parkinson’s disease and with depression. J Neurol Neurosurg Psychiatry 2014 [123] Wen X, Wu X, Liu J, Li K, Yao L. Abnormal baseline brain activity in nondepressed Parkinson’s disease and depressed Parkinson’s disease: a restingstate functional magnetic resonance imaging study. PLoS ONE 2013; 8: e63691

178

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

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Parkinson’s Disease [124] Ballanger B, Klinger H, Eche J et al. Role of serotonergic 1A receptor dysfunction in depression associated with Parkinson’s disease. Mov Disord 2012; 27: 84–89 [125] Boileau I, Warsh JJ, Guttman M et al. Elevated serotonin transporter binding in depressed patients with Parkinson’s disease: a preliminary PET study with [11C]DASB. Mov Disord 2008; 23: 1776–1780 [126] Fénelon G, Alves G. Epidemiology of psychosis in Parkinson’s disease. J Neurol Sci 2010; 289: 12–17 [127] Ravina B, Marder K, Fernandez HH et al. Diagnostic criteria for psychosis in Parkinson’s disease: report of an NINDS, NIMH work group. Mov Disord 2007; 22: 1061–1068 [128] Shin S, Lee JE, Hong JY, Sunwoo MK, Sohn YH, Lee PH. Neuroanatomical substrates of visual hallucinations in patients with non-demented Parkinson’s disease. J Neurol Neurosurg Psychiatry 2012; 83: 1155–1161 [129] Ibarretxe-Bilbao N, Ramirez-Ruiz B, Junque C et al. Differential progression of brain atrophy in Parkinson’s disease with and without visual hallucinations. J Neurol Neurosurg Psychiatry 2010; 81: 650–657 [130] Shine JM, Halliday GM, Gilat M et al. The role of dysfunctional attentional control networks in visual misperceptions in Parkinson’s disease. Hum Brain Mapp 2014 [131] Janzen J, van ’t Ent D, Lemstra AW, Berendse HW, Barkhof F, Foncke EM. The pedunculopontine nucleus is related to visual hallucinations in Parkinson’s disease: preliminary results of a voxel-based morphometry study. J Neurol 2012; 259: 147–154 [132] Ramírez-Ruiz B, Martí MJ, Tolosa E et al. Cerebral atrophy in Parkinson’s disease patients with visual hallucinations. Eur J Neurol 2007; 14: 750–756 [133] Goldman JG, Stebbins GT, Dinh V et al. Visuoperceptive region atrophy independent of cognitive status in patients with Parkinson’s disease with hallucinations. Brain 2014; 137: 849–859 [134] Stebbins GT, Goetz CG, Carrillo MC et al. Altered cortical visual processing in PD with hallucinations: an fMRI study. Neurology 2004; 63: 1409–1416 [135] Ramírez-Ruiz B, Martí MJ, Tolosa E et al. Brain response to complex visual stimuli in Parkinson’s patients with hallucinations: a functional magnetic resonance imaging study. Mov Disord 2008; 23: 2335–2343

[136] Meppelink AM, de Jong BM, Renken R, Leenders KL, Cornelissen FW, van Laar T. Impaired visual processing preceding image recognition in Parkinson’s disease patients with visual hallucinations. Brain 2009; 132: 2980–2993 [137] Goetz CG, Vaughan CL, Goldman JG, Stebbins GT. I finally see what you see: Parkinson’s disease visual hallucinations captured with functional neuroimaging. Mov Disord 2014; 29: 115–117 [138] Okada K, Suyama N, Oguro H, Yamaguchi S, Kobayashi S. Medication-induced hallucination and cerebral blood flow in Parkinson’s disease. J Neurol 1999; 246: 365–368 [139] Matsui H, Nishinaka K, Oda M et al. Hypoperfusion of the visual pathway in parkinsonian patients with visual hallucinations. Mov Disord 2006; 21: 2140–2144 [140] Oishi N, Udaka F, Kameyama M, Sawamoto N, Hashikawa K, Fukuyama H. Regional cerebral blood flow in Parkinson’s disease with nonpsychotic visual hallucinations. Neurology 2005; 65: 1708–1715 [141] Boecker H, Ceballos-Baumann AO, Volk D, Conrad B, Forstl H, Haussermann P. Metabolic alterations in patients with Parkinson’s disease and visual hallucinations. Arch Neurol 2007; 64: 984–988 [142] Nagano-Saito A, Washimi Y, Arahata Y et al. Visual hallucination in Parkinson’s disease with FDG PET. Mov Disord 2004; 19: 801–806 [143] Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 2001; 14: 21–36 [144] Good CD, Scahill RI, Fox NC et al. Automatic differentiation of anatomical patterns in the human brain: validation with studies of degenerative dementias. Neuroimage 2002; 17: 29–46 [145] Shinotoh H, Uchida Y, Ito H, Harrori T. Relationship between striatal [123I] beta-CIT binding and four major clinical signs in Parkinson’s disease. Ann Nucl Med 2000; 14: 199–203 [146] Pan PL, Song W, Shang HF. Voxel-wise meta-analysis of gray matter abnormalities in idiopathic Parkinson’s disease. Eur J Neurol 2012; 19: 199–206 [147] Chan LL, Rumpel H, Yap K et al. Case control study of diffusion tensor imaging in Parkinson’s disease. J Neurol Neurosurg Psychiatry 2007; 78: 1383–1386

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19 Atypical Parkinsonian Syndromes Nicola Pavese and David J. Brooks In this chapter, we discuss the different contributions of structural and functional imaging to the diagnosis and management of atypical parkinsonian disorders. We focus mainly on the most common clinical conditions: multiple-system atrophy (MSA), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD).

19.1 Multiple-System Atrophy Multiple-system atrophy is a progressive neurodegenerative disorder characterized clinically by varying combinations of parkinsonism, cerebellar dysfunction, autonomic failure, and corticospinal tract dysfunction.1 Based on the predominant symptoms, MSA is classified into two subtypes, MSA with predominant parkinsonism (MSA-P) and MSA with cerebellar features (MSA-C). The pathological hallmark of the disease is neuronal loss and gliosis in the striatal, nigral, olivo-ponto-cerebellar network, and the lateral columns of the spinal cord, with the presence of intracytoplasmic and intranuclear argyrophilic fibrillary inclusions containing α-synuclein in both oligodendrocytes and neurons.2 Despite the different pathology, there is a significant clinical overlap between MSA and Parkinson’s disease (PD), particularly in the early stages of the disease. Three levels of diagnostic certainty—possible, probable, and definite—have been proposed for MSA, and abnormalities in structural and functional imaging are indicated as features supporting (“red flags”) a diagnosis of possible MSA.1

19.1.1 Structural Imaging Magnetic resonance imaging (MRI) with conventional sequences and, more sensitively, diffusion-weighted (DWI) and diffusion tensor imaging (DTI) have proved to have a role for discriminating MSA from typical PD and other atypical parkinsonian syndromes.3,4 Atrophy of the putamen, the presence of a “slit” hyperintensity of the lateral margin of the putamen in T2weighted MRI images (so called “slit sign”), and putaminal hypointesity are specific features of established MSA but are present in only around half of the cases (▶ Fig. 19.1). Other

typical features (“red flags”) of MSA include atrophy of several subtentorial structures, such as the pons, the middle cerebellar peduncles, and the cerebellum, with dilatation of the fourth ventricle. In MSA-C, the severe loss of neurons and myelinated fibers at the basis pontis and the gliosis of the middle part of the reticular formation result in a characteristic cruciform hyperintensitity in the pons on T2-weighted MRI sequences, which is known as the “hot cross bun sign” (▶ Fig. 19.1). The hot cross bun sign can also be seen in patients with MSA-P. Horimoto and colleagues5 performed a longitudinal MRI study to determine the exact time when the hot cross bun sign and slit sign appeared in a cohort of MSA patients. They graded the development of hot cross bun sign into six progressive stages and the slit sign into four stages. The hot cross bun sign was seen (MRI shows cross, stage IV) earlier in MSA-C than in MSAP, often before 5 years of symptomatic disease duration. Conversely, MSA-P showed earlier bilateral putamen changes (stage II) than MSA-C, generally before 3 years of symptoms (stage I). Despite being highly specific for MSA (specificity > 90%), these abnormalities seen on T2-weighted MRI have not proved sensitive enough to be of diagnostic value (sensitivity up to 50 to 60%, with higher values of sensitivity for the basal ganglia abnormalities than for the subtentorial ones).3,4 In contrast, DWI and DTI are more sensitive to changes in putamen structure and are potentially useful for discriminating MSA from idiopathic PD. DWI MRI has been reported to detect raised water-proton apparent diffusion coefficients in the putamen in up to 100% of patients with clinically probable MSA, whereas apparent diffusion coefficients in the putamen are normal in PD.6,7,8 An altered water diffusion signal in the middle cerebral peduncle has been reported to be useful to discriminate MSA from PSP.8 A possible limitation of these studies is that they have all involved well-established atypical cases, whereas it remains to be established whether DWI MRI is also valuable to discriminate early cases where there is clinical diagnostic uncertainty. Voxel-based morphometry (VBM) is an MRI technique that localizes significant changes in gray and white matter density in disease. Compared with controls, MSA patients show significant reductions in the gray matter of the cerebellum and

Fig. 19.1 Details of T2 magnetic resonance imaging showing the “slit” sign (red arrow) and the “hot cross bun” sign (blue arrow). Both signs are visible on axial images.

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Atypical Parkinsonian Syndromes cerebral cortex and in the white matter of the cerebellar peduncles and brainstem. White matter loss along the corpus callosum has also been reported in MSA patients.9 Finally, transcranial sonography of brain parenchyma, which shows atypical lateral midbrain hyperechogenicity in more than 90% of patients with idiopathic PD, is normal in most MSA cases. However, MSA patients can show increased echogenicity of the lentiform nucleus, which is absent in typical PD. It has been reported that a combination of normal midbrain signal combined with lentiform nucleus hyperechogenicity separated atypical from typical PD with a sensitivity of 59% and specificity of 100% and a positive predictive value of 100%.10

19.1.2 Functional Imaging In vivo functional neuroimaging investigations in MSA have focused on dopaminergic dysfunction in MSA-P and the changes in brain regional glucose metabolism and cerebral blood flow that occur in patients with MSA-P and MSA-C subtypes. Both presynaptic11,12 and postsynaptic striatal dopamine deficits13,14 have been reported in MSA-P patients. Regional cerebral [18F]flurodopa (18F-dopa) uptake, expressed as an influx constant, Ki, reflects the functional integrity of monoaminergic terminals.15 In the striatum, where dopamine innervation from the midbrain substantia nigra is the major monoaminergic component, 18F-dopa uptake reflects the integrity of dopaminergic nigrostriatal terminals and correlates well with striatal dopamine levels and also with nigrostriatal cell counts in postmortem and animal studies.16,17 MSA patients show an asymmetrically reduced striatal uptake on 18F-dopa positron emission tomography (PET) that resembles the pattern observed in patients with idiopathic PD with relatively preserved head of caudate function. The caudate nucleus can be more severely affected in MSA than in PD, leading to a more homogeneous reduction in tracer uptake within the striatal structures (▶ Fig. 19.2).11,13,18 There is, however, considerable overlap of individual levels of putamen 18F-dopa uptake in MSA and PD patients. Therefore, 18F-dopa PET is not useful in clinical practice for discriminating MSA from typical PD.19 Similar

findings have been observed in a number of PET and SPECT studies imaging the dopamine transporter (DAT), another commonly used marker for nigrostriatal dopaminergic terminals nerve in the striatum. A recent study with 18F-FP-CIT PET reported that MSA patients showed a more prominent and earlier DAT loss in the ventral putamen compared with PD patients.20 Briefly, both MSA and PD groups showed similar anteroposterior gradients of putaminal DAT loss. However, the MSA group did not show the typical ventrodorsal gradient of putaminal DAT loss described in PD. In fact, there was a relatively even DAT loss from the ventral putamen to the posterior putamen, which could reflect the loss of striatal dopaminergic terminals that precedes nigral involvement in MSA.21 These authors suggested that the assessment of the ventrodorsal gradient could be useful for differentiating PD from MSA, even in the early stage. The availability of postsynaptic D2 receptors can be evaluated using PET and SPECT benzamide tracers such as 11C-raclopride and 123I-IBZM. Both 11C-raclopride and 123I-IBZM binding is reduced in MSA patients compared with that in normal subjects and untreated PD patients, suggesting that degeneration of striatal D2 receptors occurs in this condition.22,23 Unfortunately, this finding is not sensitive enough to be used in clinical practice to differentiate MSA from PD because there is an overlap of their D2 binding ranges. 18F-2-fluoro-2-deoxyglucose (FDG) PET studies of MSA patients have shown significant bilateral hypometabolism in both caudate and putamen nuclei. Further reductions have been reported in the cerebellum and in the frontal cortex.24,25,26 The same areas showed a reduction in regional cerebral blood flow with perfusion SPECT.27,28 Hypometabolism and hypoperfusion in the cerebellum and pons are particularly prominent in MSA-C patients.29 Eckert and colleagues have reported that FDG-PET has 96% sensitivity and 99% specificity for the diagnosis of MSA versus PD when computer-assisted methods are applied.30 This finding has been confirmed in subsequent studies.31,32 Finally, network analysis of metabolic changes across the brain by spatial covariance analysis of FDG-PET scans has identified an MSA-related pattern (MSARP) characterized by

Fig. 19.2 18F-dopa positron emission tomography images in a healthy control (HC), a patient with Parkinson’s disease (PD), and a patient with multiple-system atrophy (MSA).

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Dementia with Extrapyramidal Syndromes covarying metabolic reductions in the putamen and the cerebellum. MSARP values correlate with clinical ratings of motor disability and with disease duration.33 It has been suggested that the MSARP may be a useful biomarker in trials of novel neuroprotective therapies for MSA. Despite the widespread subcortical neurodegeneration reported in postmortem studies in MSA patients, the involvement of extrastriatal monoaminergic and cholinergic pathways has not been extensively investigated with functional neuroimaging in vivo. Using SPECT with 123I-β-CIT, a tropane derivative with high affinity for all monoamine transporters, Scherfler et al reported decreased uptake in midbrain and pontine regions in patients with MSA-P but not in PD patients.34 In a recent study, 18F-dopa PET was used to explore changes in brain monoaminergic function in both striatal and extrastriatal areas in MSA-P. Findings in MSA-P patients were compared with those seen in idiopathic PD patients matched for disease duration and healthy controls. The results of the study suggest the presence of a more widespread monoaminergic dysfunction in MSA than in PD with similar disease duration. The MSA patients showed significantly decreased 18F-dopa uptake in putamen, caudate nucleus, ventral striatum, globus pallidus externa, and red nucleus compared with that in controls, whereas PD patients showed decreased 18F-dopa uptake only in the putamen, caudate nucleus, and ventral striatum. Additionally, in contrast to PD, no evidence was seen of early compensatory pallidal increases in regional 18F-dopa uptake in MSA patients. Interestingly, MSA cases with orthostatic hypotension had lower 18F-dopa uptake in the locus coeruleus than patients without this symptom.35 Cholinergic pathways in MSA-P patients have been investigated with 11C-PMP PET, a marker of acetylcholinesterase (AChE) activity. Whereas cerebral cortical cholinergic activity was decreased to a similar level in MSA-P, PD, and PSP compared with normal controls, thalamic and pontine cholinergic activity was significantly lower in MSA-P and PSP patients than in those with PD. Interestingly, decreased AChE activity in the brainstem and cerebellum of all three disorders correlated with disturbances of balance and gait. The authors suggest that the earlier cholinergic reductions may account for the greater gait disturbances in the early stages of MSA-P and PSP than in PD.36 Cholinesterase activity has also been evaluated with PET in a small group of patients with MSA-C,37 and these cases also showed a reduction of AChE activity in the thalamus and cerebellum. Taken together, these findings suggest that pharmacologic boosting of the cholinergic system could have a role in the treatment of these conditions. The role of neuroinflammation and microglia activation in the pathogenesis of MSA has been investigated with 11C-(R)PK11195 PET, a selective in vivo marker of activated microglia. Activation of microglia in response to acute and chronic brain insults occurs in order to remodel connections and clear damaged tissue in the affected areas. However, there is cumulative evidence suggesting that, in conditions characterized by extensive chronic microglial activation, cytokines and other neurotoxic factors are released by these cells, which may promote further neurodegeneration by causing death of surrounding healthy neurons.38 Gerhard and colleagues39 have reported increased 11C-(R)-PK11195 binding in both basal ganglia (putamen, pallidum) and extrastriatal regions (dorsolateral pre-

frontal cortex, pons, and substantia nigra) in MSA patients compared with normal controls, suggesting that neuroinflammatory responses by activated microglia occur in MSA and may contribute to the neurodegenerative process. A prospective 48-week, randomized, double-blind, multinational clinical trial was recently conducted to investigate the efficacy of the antibiotic minocycline, a suppressant of microglial activation, as a drug treatment of MSA-P patients.40 In a small subgroup of patients, 11C-(R)-PK11195-PET was performed to assess the effect of minocycline on activated microglia. This study failed to show a clinical effect of minocycline on symptom severity as assessed by clinical motor function. In the PET subgroup, however, the three patients treated with minocycline showed a 30% reduction in microglial activation compared with the two cases treated with placebo. These findings warrant further investigations. Finally, MIBG SPECT and 18F-dopamine PET studies have reported that patients with idiopathic PD show a significant loss of adrenergic innervation of the heart. This loss is not seen in patients with MSA as the loss of sympathetic function is presynaptic rather than postsynaptic. However, up to 50% of early PD cases (Hoehn and Yahr stage I) still show normal tracer binding,41,42 so cardiac sympathetic imaging is not a sensitive discriminator of MSA from PD.

19.2 Progressive Supranuclear Palsy Progressive supranuclear palsy is another cause of parkinsonism, accounting for around 5% of cases. The disease usually develops after the sixth decade of life and is characterized by a combination of symmetric parkinsonism targeting the trunk and neck, which are held extended rather than flexed, supranuclear vertical gaze palsy, dementia of subcortical type, and pseudobulbar signs, including dysphagia, dysarthria, and emotional incontinence. Bradykinesia, rigidity affecting axial muscles more than the limbs, postural instability, and gait disturbances are the most common parkinsonian symptoms. Pathological changes in PSP consist of decreased pigment in the substantia nigra and locus coeruleus and loss of neurons in the basal ganglia, brainstem and ocular nuclei, cerebellar nuclei, and frontal cortex. Neurofibrillary 4-repeat tau tangles are present in affected structures and frontal and midbrain atrophy; third ventricular widening are common in advanced cases.

19.2.1 Structural Imaging Conventional T1- and T2-weighted MRI sequences detect characteristic structural changes in established PSP patients.3,4 The most common MRI finding in PSP is atrophy of the midbrain and superior cerebellar peduncle with dilatation of the third ventricle. Other commonly observed findings include atrophy of the basal ganglia, frontal and temporal cortices, and increased T2 signal in the midbrain. The selective atrophy of the midbrain, along with the dilatation of the third ventricle and a relatively preserved pontine profile, creates a peculiar visual effect on midsagittal T2 MRI images, which recall the silhouette of a bird where the head of the bird is represented by the atrophied midbrain and the body by the pons, known as

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Atypical Parkinsonian Syndromes

Fig. 19.3 Details of T2 magnetic resonance imaging showing “the penguin” sign (red arrow) and the “Mickey mouse” sign (blue arrow). The penguin sign is visible on midsagittal images, whereas the Mickey mouse sign is visible on axial images.

“the penguin” or “the hummingbird” sign and has been reported to be highly specific for PSP (▶ Fig. 19.3).43 On axial T2, the reduction of the anterior–posterior diameter of the midbrain with selective atrophy of the midbrain tegmentum, along with the thinning of cerebral peduncle, can form the so-called “morning glory” or “Mickey mouse” sign (▶ Fig. 19.3). Several planimetric measurements of pons, midbrain, middle, and superior cerebellar peduncles have been proposed to differentiate PSP from idiopathic PD and MSA. The midbrain-to-pontine ratio (m:p ratio) and the more complex MR parkinsonism index (MRPI)44 have been shown to have 80 to 100% diagnostic accuracy when used to differentiate PSP from controls, MSA, and idiopathic PD patients, with MRPI being more accurate to differentiate PSP from MSA-P and the m:p ratio more sensitive to differentiate PSP from PD.45 DWI MRI shows raised water-proton apparent diffusion coefficients in the superior rather than the middle cerebellar peduncles, caudate, putamen, globus pallidus, thalamus, pons, prefrontal white matter, and precentral white matter in PSP patients compared with controls and PD patients.7,8 Finally, in PSP patients, VBM has detected pronounced loss in the gray matter of the frontotemporal cortex, including the prefrontal and insular cortices and in the white matter of the central midbrain region and the cerebral peduncles.

19.2.2 Functional Imaging Perfusion SPECT studies in PSP patients reveals hypoperfusion in the frontal cortex and the midbrain.18,28,46 FDG-PET studies also show areas of reduced glucose metabolism in the frontal cortex, midbrain, and striatum.47,48 Overall, these findings parallel those observed in MSA. However, when computer-assisted methods are applied, FDG-PET has been reported to have 85% sensitivity and 99% specificity for discriminating PSP from other parkinsonisms.30 18F-dopa PET studies reveal a uniform symmetric reduction of dopamine storage in the caudate and the anterior and posterior putamen, in contrast to PD and MSA, where reductions are asymmetric and target putamen.11 Using a voxel-based statistical parametric mapping, Tai and colleagues49 have detected reduced 18F-dopa uptake in the orbitofrontal cortex in patients with familial PSP. Striatal D2 binding measured with 11Craclopride PET and 123I-IBZM SPECT is reduced in PSP compared

with healthy controls and PD patients as a result of degeneration of D2 receptors.22,50 Cholinergic function has been investigated in PSP with 11CMP4A PET, a marker of AChE activity. PSP patients showed a severe reduction of thalamic 11C-MP4A uptake,51 which is likely to reflect reduced input from the degenerating pedunculopontine nucleus and other brainstem cholinergic nuclei, which are the main sources of thalamic cholinergic input. The peduncolopontine nucleus is involved in posture and gait control, eye movements, and attention. Therefore, its dysfunction may contribute to the locomotor and cognitive impairment observed in PSP patients. Finally, Gerhard and colleagues have reported widespread increases of activated microglia in basal ganglia, midbrain, frontal lobe, and cerebellum of PSP patients.52

19.3 Corticobasal Degeneration Corticobasal degeneration is a progressive neurodegenerative disease that involves the basal ganglia and cerebral cortex. Clinically, CBD is characterized by a progressive asymmetric akinetic-rigid syndrome with apraxia, limb dystonia, myoclonus, and other features indicative of cortical dysfunction, such as cortical sensory loss, alien limb phenomena, and mirror movements. The neuropathological hallmarks of CBD include mild atrophy of the cortical gyri with swollen, achromatic neurons scattered throughout the cerebrum, particularly in the posterior frontal and inferior parietal areas, and severe neuronal loss within the substantia nigra. Abnormal tau accumulation in both neurons and glial cells is extensive in gray and white matter of the cortex, basal ganglia, diencephalon, and the rostral part of the brainstem. Abnormal tau accumulation within astrocytes forms pathognomonic astrocytic plaques.53

19.3.1 Structural Imaging The most common MRI finding in CBD is asymmetric cortical atrophy, although symmetric atrophy has also been reported. The cortical atrophy typically targets the parietal lobe, the paracentral regions, and the frontal lobe (anterior middle and posterior inferior frontal lobe). Atrophy of the ipsilateral cerebral peduncle is often present in these patients. A subtle hyperintensity of the white matter adjacent to the areas of cortical

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Dementia with Extrapyramidal Syndromes atrophy is detected on fluid-attenuated inversion recovery (FLAIR) image, likely to reflect demyelination secondary to axonal loss or dysfunction. Conversely, basal ganglia structures generally show normal volume and MRI signal in these patients.54 Using VBM, Josephs and colleagues55 analyzed antemortem MRI images of patients with subsequent autopsy-confirmed CBD. All the MRI had been performed at the first neurologic evaluation. CBD patients were divided into two groups: patients with clinically dominant dementia syndrome and pathologically confirmed CBD (D-CBD) and patients with clinically dominant extrapyramidal features and pathologically confirmed CBD (E-CBD). They found a characteristic pattern of posterior frontal atrophy in these patients regardless of the clinical syndrome, suggesting that this finding could be a useful biomarker of CBD pathology. The middle corpus callosum and the basal ganglia, in particular the pallidum, were heavily affected, whereas there was no evidence of brainstem atrophy. The E-CBD and D-CBD subgroups differed from each other in terms of the patterns of atrophy. The D-CBD group showed more cortical gray matter atrophy yet practically no white matter atrophy, compared with the E-CBD subgroup, which had both moderate cortical gray matter and white matter atrophy. Finally, in CBD, DTI has shown an increased water diffusion coefficient in the motor thalamus, the superior mesenteric artery (SMA), and the precentral and postcentral gyri contralateral to affected limbs. FA was decreased in the precentral gyrus, SMA, postcentral gyrus, and cingulum.

19.3.2 Functional Imaging Striatal 18F-dopa uptake is asymmetrically reduced in CBD patients, targeting the caudate and putamen similarly.56 Asymmetrically decreased striatal DAT binding has also been reported in CBD patients. Postsynaptic striatal D2 receptor availability may be reduced or may be preserved.57 Perfusion SPECT studies have revealed asymmetric hypoperfusion in the basal ganglia and the frontoparietal cortex. Similarly, FDG-PET studies have shown a characteristic pattern of reduced glucose metabolism in striatum, thalamus, and inferior parietal cortex contralateral to the most affected side. FDG-PET has been reported to have 91% sensitivity and 99% specificity for the diagnosis of CBD compared with other parkinsonisms when computer-assisted methodologies are applied.30 Evidence of microglial activation involvement in the pathogenesis of CBD has been reported.52 Finally, an fMRI study has shown decreased activation of the parietal lobe contralateral to the more affected arm in patients with early CBD, when movements, simple or complex, were performed with the hand. This finding suggests that altered higher cortical motor organization is present early in the disease.58

References [1] Gilman S, Wenning GK, Low PA et al. Second consensus statement on the diagnosis of multiple system atrophy. Neurology 2008; 71: 670–676 [2] Wenning GK, Stefanova N, Jellinger KA, Poewe W, Schlossmacher MG. Multiple system atrophy: a primary oligodendrogliopathy. Ann Neurol 2008; 64: 239–246

[3] Seppi K, Poewe W. Brain magnetic resonance imaging techniques in the diagnosis of parkinsonian syndromes. Neuroimaging Clin N Am 2010; 20: 29–55 [4] Massey LA, Micallef C, Paviour DC et al. Conventional magnetic resonance imaging in confirmed progressive supranuclear palsy and multiple system atrophy. Mov Disord 2012; 27: 1754–1762 [5] Horimoto Y, Aiba I, Yasuda T et al. Longitudinal MRI study of multiple system atrophy - when do the findings appear, and what is the course? J Neurol 2002; 249: 847–854 [6] Schocke MF, Seppi K, Esterhammer R et al. Diffusion-weighted MRI differentiates the Parkinson variant of multiple system atrophy from PD. Neurology 2002; 58: 575–580 [7] Seppi K, Schocke MF, Esterhammer R et al. Diffusion-weighted imaging discriminates progressive supranuclear palsy from PD, but not from the parkinson variant of multiple system atrophy. Neurology 2003; 60: 922–927 [8] Nicoletti G, Lodi R, Condino F et al. Apparent diffusion coefficient measurements of the middle cerebellar peduncle differentiate the Parkinson variant of MSA from Parkinson’s disease and progressive supranuclear palsy. Brain 2006; 129: 2679–2687 [9] Minnerop M, Lüders E, Specht K et al. Callosal tissue loss in multiple system atrophy—a one-year follow-up study. Mov Disord 2010; 25: 2613–2620 [10] Walter U, Dressler D, Probst T et al. Transcranial brain sonography findings in discriminating between parkinsonism and idiopathic Parkinson’s disease. Arch Neurol 2007; 64: 1635–1640 [11] Brooks DJ, Ibanez V, Sawle GV et al. Differing patterns of striatal 18F-dopa uptake in Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy. Ann Neurol 1990; 28: 547–555 [12] Varrone A, Marek KL, Jennings D, Innis RB, Seibyl JP. [123I]β-CIT SPECT imaging demonstrates reduced density of striatal dopamine transporters in Parkinson’s disease and multiple system atrophy. Mov Disord 2001; 16: 1023–1032 [13] Antonini A, Leenders KL, Vontobel P et al. Complementary PET studies of striatal neuronal function in the differential diagnosis between multiple system atrophy and Parkinson’s disease. Brain 1997; 120: 2187–2195 [14] Schulz JB, Klockgether T, Petersen D et al. Multiple system atrophy: natural history, MRI morphology, and dopamine receptor imaging with 123IBZMSPECT. J Neurol Neurosurg Psychiatry 1994; 57: 1047–1056 [15] Moore RY, Whone AL, McGowan S, Brooks DJ. Monoamine neuron innervation of the normal human brain: an 18F-DOPA PET study. Brain Res 2003; 982: 137–145 [16] Snow BJ, Tooyama I, McGeer EG et al. Human positron emission tomographic [18F]fluorodopa studies correlate with dopamine cell counts and levels. Ann Neurol 1993; 34: 324–330 [17] Pate BD, Kawamata T, Yamada T et al. Correlation of striatal fluorodopa uptake in the MPTP monkey with dopaminergic indices. Ann Neurol 1993; 34: 331–338 [18] Pirker W, Djamshidian S, Asenbaum S et al. Progression of dopaminergic degeneration in Parkinson’s disease and atypical parkinsonism: a longitudinal beta-CIT SPECT study. Mov Disord 2002; 17: 45–53 [19] Burn DJ, Sawle GV, Brooks DJ. Differential diagnosis of Parkinson’s disease, multiple system atrophy, and Steele-Richardson-Olszewski syndrome: discriminant analysis of striatal 18F-dopa PET data. J Neurol Neurosurg Psychiatry 1994; 57: 278–284 [20] Oh M, Kim JS, Kim JY et al. Subregional patterns of preferential striatal dopamine transporter loss differ in Parkinson’s disease, progressive supranuclear palsy, and multiple-system atrophy. J Nucl Med 2012; 53: 399–406 [21] Goto S, Matsumoto S, Ushio Y, Hirano A. Subregional loss of putaminal efferents to the basal ganglia output nuclei may cause parkinsonism in striatonigral degeneration. Neurology 1996; 47: 1032–1036 [22] Brooks DJ, Ibanez V, Sawle GV et al. Striatal D2 receptor status in Parkinson’s disease, striatonigral degeneration, and progressive supranuclear palsy, measured with 11C-raclopride and positron emission tomography. Ann Neurol 1992; 31: 184–192 [23] Plotkin M, Amthauer H, Klaffke S et al. Combined 123I-FP-CIT and 123I-IBZM SPECT for the diagnosis of parkinsonian syndromes: study on 72 patients. J Neural Transm 2005; 112: 677–692 [24] Otsuka M, Ichiya Y, Kuwabara Y et al. Glucose metabolism in the cortical and subcortical brain structures in multiple system atrophy and Parkinson’s disease: a positron emission tomographic study. J Neurol Sci 1996; 144: 77–83 [25] Taniwaki T, Nakagawa M, Yamada T et al. Cerebral metabolic changes in early multiple system atrophy: a PET study. J Neurol Sci 2002; 200: 79–84 [26] Juh R, Pae C-U, Lee C-U et al. Voxel based comparison of glucose metabolism in the differential diagnosis of the multiple system atrophy using statistical parametric mapping. Neurosci Res 2005; 52: 211–219

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Atypical Parkinsonian Syndromes [27] Cilia R, Marotta G, Benti R, Pezzoli G, Antonini A. Brain SPECT imaging in multiple system atrophy. J Neural Transm 2005; 112: 1635–1645 [28] Van Laere K, Casteels C, De Ceuninck L et al. Dual-tracer dopamine transporter and perfusion SPECT in differential diagnosis of parkinsonism using template-based discriminant analysis. J Nucl Med 2006; 47: 384–392 [29] Shinotoh H. Neuroimaging of PD, PSP, CBD and MSA-PET and SPECT studies. J Neurol 2006; 253 Suppl 3: iii30–iii34 [30] Eckert T, Barnes A, Dhawan V et al. FDG PET in the differential diagnosis of parkinsonian disorders. Neuroimage 2005; 26: 912–921 [31] Kwon KY, Choi CG, Kim JS, Lee MC, Chung SJ. Diagnostic value of brain MRI and 18F-FDG PET in the differentiation of Parkinsonian-type multiple system atrophy from Parkinson’s disease. Eur J Neurol 2008; 15: 1043–1049 [32] Hellwig S, Amtage F, Kreft A et al. [¹⁸F]FDG-PET is superior to [¹²³I]IBZMSPECT for the differential diagnosis of parkinsonism. Neurology 2012; 79: 1314–1322 [33] Poston KL, Tang CC, Eckert T et al. Network correlates of disease severity in multiple system atrophy. Neurology 2012; 78: 1237–1244 [34] Scherfler C, Seppi K, Donnemiller E et al. Voxel-wise analysis of [123I]beta-CIT SPECT differentiates the Parkinson variant of multiple system atrophy from idiopathic Parkinson’s disease. Brain 2005; 128: 1605–1612 [35] Lewis SJ, Pavese N, Rivero-Bosch M et al. Brain monoamine systems in multiple system atrophy: a positron emission tomography study. Neurobiol Dis 2012; 46: 130–136 [36] Gilman S, Koeppe RA, Nan B et al. Cerebral cortical and subcortical cholinergic deficits in parkinsonian syndromes. Neurology 2010; 74: 1416–1423 [37] Hirano S, Shinotoh H, Arai K et al. PET study of brain acetylcholinesterase in cerebellar degenerative disorders. Mov Disord 2008; 23: 1154–1160 [38] Smith JA, Das A, Ray SK, Banik NL. Role of pro-inflammatory cytokines released from microglia in neurodegenerative diseases. Brain Res Bull 2012; 87: 10–20 [39] Gerhard A, Banati RB, Goerres GB et al. [11C](R)-PK11195 PET imaging of microglial activation in multiple system atrophy. Neurology 2003; 61: 686– 689 [40] Dodel R, Spottke A, Gerhard A et al. Minocycline 1-year therapy in multiplesystem-atrophy: effect on clinical symptoms and [11C] (R)-PK11195 PET (MEMSA-trial). Mov Disord 2010; 25: 97–107 [41] Goldstein DS, Holmes CS, Dendi R, Bruce SR, Li ST. Orthostatic hypotension from sympathetic denervation in Parkinson’s disease. Neurology 2002; 58: 1247–1255 [42] Takatsu H, Nishida H, Matsuo H et al. Cardiac sympathetic denervation from the early stage of Parkinson’s disease: clinical and experimental studies with radiolabeled MIBG. J Nucl Med 2000; 41: 71–77 [43] Kato N, Arai K, Hattori T. Study of the rostral midbrain atrophy in progressive supranuclear palsy. J Neurol Sci 2003; 210: 57–60

[44] Quattrone A, Nicoletti G, Messina D et al. MR imaging index for differentiation of progressive supranuclear palsy from Parkinson’s disease and the Parkinson variant of multiple system atrophy. Radiology 2008; 246: 214–221 [45] Hussl A, Mahlknecht P, Scherfler C et al. Diagnostic accuracy of the magnetic resonance Parkinsonism index and the midbrain-to-pontine area ratio to differentiate progressive supranuclear palsy from Parkinson’s disease and the Parkinson variant of multiple system atrophy. Mov Disord 2010; 25: 2444– 2449 [46] Johnson KA, Sperling RA, Holman BL, Nagel JS, Growdon JH. Cerebral perfusion in progressive supranuclear palsy. J Nucl Med 1992; 33: 704–709 [47] Karbe H, Grond M, Huber M, Herholz K, Kessler J, Heiss WD. Subcortical damage and cortical dysfunction in progressive supranuclear palsy demonstrated by positron emission tomography. J Neurol 1992; 239: 98–102 [48] Piccini P, de Yebenez J, Lees AJ et al. Familial progressive supranuclear palsy: detection of subclinical cases using 18F-dopa and 18fluorodeoxyglucose positron emission tomography. Arch Neurol 2001; 58: 1846–1851 [49] Tai YF, Ahsan RL, de Yébenes JG, Pavese N, Brooks DJ, Piccini P. Characterization of dopaminergic dysfunction in familial progressive supranuclear palsy: an 18F-dopa PET study. J Neural Transm 2007; 114: 337–340 [50] Schwarz J, Tatsch K, Arnold G et al. 123I-iodobenzamide-SPECT in 83 patients with de novo parkinsonism. Neurology 1993; 43 Suppl 6: S17–S20 [51] Shinotoh H, Namba H, Yamaguchi M et al. Positron emission tomographic measurement of acetylcholinesterase activity reveals differential loss of ascending cholinergic systems in Parkinson’s disease and progressive supranuclear palsy. Ann Neurol 1999; 46: 62–69 [52] Gerhard A, Trender-Gerhard I, Turkheimer F, Quinn NP, Bhatia KP, Brooks DJ. In vivo imaging of microglial activation with [11C](R)-PK11195 PET in progressive supranuclear palsy. Mov Disord 2006; 21: 89–93 [53] Kouri N, Whitwell JL, Josephs KA, Rademakers R, Dickson DW. Corticobasal degeneration: a pathologically distinct 4 R tauopathy. Nat Rev Neurol 2011; 7: 263–272 [54] Koyama M, Yagishita A, Nakata Y, Hayashi M, Bandoh M, Mizutani T. Imaging of corticobasal degeneration syndrome. Neuroradiology 2007; 49: 905–912 [55] Josephs KA, Whitwell JL, Dickson DW et al. Voxel-based morphometry in autopsy proven PSP and CBD. Neurobiol Aging 2008; 29: 280–289 [56] Sawle GV, Brooks DJ, Marsden CD, Frackowiak RS. Corticobasal degeneration. A unique pattern of regional cortical oxygen hypometabolism and striatal fluorodopa uptake demonstrated by positron emission tomography. Brain 1991; 114 Pt 1B: 541–556 [57] Klaffke S, Kuhn AA, Plotkin M et al. Dopamine transporters, D2 receptors, and glucose metabolism in corticobasal degeneration. Mov Disord 2006; 21: 1724–1727 [58] Ukmar M, Moretti R, Torre P, Antonello RM, Longo R, Bava A. Corticobasal degeneration: structural and functional MRI and single-photon emission computed tomography. Neuroradiology 2003; 45: 708–712

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Dementia with Extrapyramidal Syndromes

20 Secondary Parkinsonism Thyagarajan Subramanian, Kala Venkiteswaran, and Elisabeth Lucassen In this chapter, we consider a limited set of disorders that manifest with clinical parkinsonism and have known secondary causes; the common clinical manifestation is secondary parkinsonism. One simplistic but useful way to classify secondary parkinsonism is based on the location of pathology in the central nervous system. This simplistic classification can help organize a large variety of disorders. The first group of conditions comprises those that cause secondary parkinsonism as a result of lesions primarily at the level of the substantia nigra pars compacta (SNpc) neurons in the midbrain (group 1 disorders). Examples of this type of secondary parkinsonism include focal vascular malformations or ischemic infarcts in the midbrain that cause hemiparkinsonism with hemiparesis. Another example is the hemiparkinsonism-hemiatrophy syndrome, in which a developmental defect appears at the level of the SNpc and its immediate surrounding region. The second group of conditions (group 2 disorders) manifest as secondary parkinsonism and result from lesions primarily at the level of the striatum (caudate and the putamen) or its connections to the remainder of the basal ganglia connectome. Examples in this category include drug-induced parkinsonism, vascular parkinsonism, Wilson’s disease, parkinsonism seen in Huntington’s disease (HD), dentatorubral-pallidoluysian atrophy (DRPLA), pantothenate kinase–associated neurodegeneration (PKAN, Hallervorden-Spatz syndrome), other associated disorders included in the classification of disorders under neurodegeneration with brain iron accumulation (NBIA) category, and toxin-induced parkinsonism. The final group (group 3 disorders) consists of secondary parkinsonisms that potentially involve the SNpc, their striatal targets, other basal ganglia nuclei, and diffuse pathology in the central nervous system (CNS). Examples include postinfectious parkinsonism and frontotemporal dementia with parkinsonism. Because some of these disorders are discussed in detail elsewhere in this book, this chapter focuses on the underlying common pathology for secondary parkinsonism and more detailed discussion of four key examples: HD, DRPLA, Wilson’s disease, and vascular parkinsonism.

20.1 Pathology of Secondary Parkinsonism One of the key pathological mediators of secondary parkinsonism is the dopaminergic nigrostriatal pathway and its targets in the striatum. To understand this pathology, a brief review of the basal ganglia connectome is necessary. The dopaminergic neurons in SNpc are located in the midbrain adjacent to the crux cerebri, the red nucleus, and the cerebral aqueduct. The long axons derived from these cell bodies terminate in the caudate nucleus and the striatum primarily, but they also have minor connections to the globus pallidus internal segment (GPi), globus pallidus external segment (GPe), thalamus, substantia nigra pars reticulata (SNr), and into the subthalamic nucleus (STN). These minor connections represent only 20% of the dopamine synthesized by the nigrostriatal pathway. Although

most of these connections are unilateral, there is evidence for interhemispheric connectivity in the nigrostriatal pathway that may be of considerable importance.1 The vast majority of dopamine secreted by the nigrostriatal pathway is used in the striatum to act on the D1-type and D2-type receptors located on the medium spiny neurons. These medium spiny neurons then send their axonal connections to either the GPi/SNr via the direct pathway or to the GPe via the indirect pathway. The GPe neurons project to the STN, which in turn projects to the GPi and SNr. The output of the GPi and SNr both project to the motor thalamus and from there to the primary motor cortex and the supplementary motor cortex. These direct and indirect pathways not only mediate motor systems, but also modulate various aspects of emotions, eye movements, and cognition and complications associated with various forms of parkinsonism.2 In secondary parkinsonism, lesions at the level of the midbrain location of the cell bodies of the substantia nigra (group 1 disease, as defined earlier in this chapter) usually cause damage to adjacent pyramidal tracts, most frequently manifesting clinically as unilateral parkinsonism. Examples of this type of pathology are cavernomas that bleed, causing focal neuronal injury in the midbrain.3,4,5 Other examples include traumatic injuries and inadvertent injury during neurosurgical manipulations of the midbrain.6,7,8,9,10 An uncommon and rare disorder that is considered genetic or developmental in origin is the hemiatrophy-hemiparkinsonism (HA-HP) syndrome.11,12 Here the pathology is associated with developmental atrophy of the contralateral midbrain and in many cases the entire hemisphere. Although HA-HP is classically thought to be due to pathology that implicates the SNpc and its immediate surroundings, a recent report has suggested putaminal pathology that would place this condition in group 2 disorders (striatal pathology). Injury to the SNpc cell bodies has also been noted in postencephalitic parkinsonism, especially with neurotropic viruses like Japanese B, West Nile, Coxsackie, and polio infection-related encephalitis.13–17 In most of these patients, however, there is more diffuse pathology outside the SNpc. The exclusive involvement of the SNpc is rare, but when it happens, it can be dramatic. The neurotoxin 1-methyl-4-phenyl-1,2,3,6tetrahydropyridine (MPTP) specifically causes degeneration of the SNpc and produces pathology in the nigrostriatal pathway that is similar in many ways to idiopathic PD.18 However, MPTP-induced parkinsonism produces pathology that is quite symmetric and lacks the classic α-synuclein-positive Lewy bodies as intracytoplasmic inclusions that are obligate in PD.19,20 Dementia with Lewy bodies is another example where the pathology is primarily presynaptic (i.e., in the SNpc and its axons).21 This entity is discussed in Chapter 16. Secondary parkinsonism with pathology that afflicts the striatum is much more common (group 2 disorders). Perhaps the most common condition that presents itself as symmetric parkinsonism is drug-induced parkinsonism.22 Here the D1-type and D2-type receptors are variably blocked, resulting in parkinsonism; pathological studies in such patients are scant.23 In general, pathological studies in such patients do not provide

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Secondary Parkinsonism any specific clues except for the primary pathology for which the dopamine antagonists were used (e.g., schizophrenia) or that they have vascular lesions that predispose them to parkinsonism. The second most common disease that presents with symmetric parkinsonism is vascular parkinsonism.24 Here the most frequent pathology is related to multiple lacunar infarcts bilaterally involving most frequently the lenticular-striate arteries.25 Frequently, these lacunar infarcts involve the internal capsule in addition to the striatum, resulting in symmetric parkinsonism and pyramidal tract signs. It is not uncommon to find ischemic changes in the subcortical white matter. Underlying poorly treated hypertension, diabetes, and hypercholesterolemia-related pathology is frequently seen. Overall, atrophy of the brain and evidence of vascular lesions elsewhere in the brain, such as the pons and the cerebellum, are also frequent.24,25 Manganese toxicity, NBIA, and Wilson’s disease are characterized by the deposition of metals in the basal ganglia and are seen clinically as mostly symmetric parkinsonism.26,27,28,29 Manganese accumulates in the striatum and in the globus pallidus in patients environmentally or occupationally exposed to large quantities of manganese (e.g., in manganese miners). This situation can also occur in the setting of chronic liver failure resulting in a more subtle manganese deposition but causes a parkinsonian syndrome that is quite similar to other secondary parkinsonisms. Accumulation of iron in the globus pallidus is characteristic in NBIA to generate the “eye of the tiger” imaging finding on MRI along with other very characteristic imaging findings.26,27 Wilson’s disease pathology causes copper accumulation in the brain and in the eyes and in many other organs.28 The pathophysiological reasons for why heavy metals have a selective affinity to the basal ganglia have not been completely established. It is thought that the relative abundance of enzymes that use heavy metals in the basal ganglia may be a putative reason for the basal ganglia to be at risk for heavy metal deposition. It is also unclear as to why this leads to parkinsonism. The best worked out molecular pathology is in the case of Wilson’s disease, where there is mutation in the Wilson’s disease protein ATP7B gene. This is an autosomal recessive genetic disorder, and it occurs when the child inherits both copies of the mutation and results in deficiency of ceruloplasmin and release of free copper into the serum, resulting in its widespread deposition in the body but particularly in the kidneys, eyes, and in brain. Classic Wilson’s disease pathology shows clear-cut serum deficiency of ceruloplasmin, paradoxically low serum copper (thought to be due to its deposition in tissues), excess secretion of copper in the urine, and deposition of copper into the cornea (KF ring) and into the basal ganglia.30 Huntington’s disease and the closely related DRPLA cause unique pathology characterized by progressive degeneration of neurons primarily in the caudate nuclei and putamen of the basal ganglia. Many other diseases can appear as HD-like secondary parkinsonism. These include ataxia with oculomotor apraxia, certain spinocerebellar ataxias, PLA2G6-associated neurodegeneration, Wilson’s disease (as discussed earlier), and PKAN form of NBIA. However, the pathology that causes parkinsonism in all these conditions appear to be centered at the level of the striatum. Most of these disorders also have associated pathology in other parts of the CNS.31 The review of all pathol-

ogy and genetics is beyond the scope of this chapter, so here we focus primarily on HD pathology. In addition to striatal pathology, HD is associated with degeneration of the temporal and the frontal lobes of the cerebral cortex, a part of the brain responsible for integrating higher mental functioning, movements, and sensations. The degenerative changes in HD primarily affect the striatal medium-sized spiny neurons that project into the GP and SNr. These spiny neurons secrete γ-aminobutyric acid as the primary neurotransmitter. One theory suggests that selective loss of these specialized cells involved in the “indirect pathway” of the basal ganglia and the relative sparing of the cells involved in the “direct pathway” of the basal ganglia result in decreased inhibition (i.e., increased activity) of the thalamus. Therefore, the thalamus increases its output to regions of the cerebral cortex involved in movement, which may be the cause of the disorganized, excessive (hyperkinetic) movement patterns of chorea. However, as disease progresses, more and more medium-sized spiny neurons degenerate, and medium spiny neurons involved in both the direct and indirect pathways are equally involved in advanced HD; in such patients, chorea no longer occurs and is replaced by severe secondary parkinsonism. It is reported that even in advanced HD, the SNpc remains relatively spared. Huntington’s disease is due to a mutation in a gene that is transmitted as an autosomal dominant trait. In addition, although HD usually occurs in certain families, the disorder may sometimes occur as the result of a spontaneous (sporadic) change in the gene for HD. The gene responsible for HD is known as IT15 and is located on chromosome 4. This gene regulates, controls, or encodes the production of a protein known as huntingtin. Mutations of the IT15 gene result in abnormally long CAG trinucleotide repeats. These expanded CAG sequences result in the production of abnormal huntingtin protein (polyglutamine). The length of the expanded CAG repeats is thought to have some relation to the age at symptom onset. For example, those with a large number of repeats tend to develop symptoms at an earlier age.32 Patients with CAG repeat lengths that are larger (usually > 60) manifest with symptoms in childhood or adolescence. This form of HD is called juvenile HD (Westphal variant).33 Most individuals with juvenile HD experience an age of onset that is much younger than that of their affected parents. They also often face a much more rapid progression of the disease. This occurrence is described as genetic anticipation, where a disease increases in severity in successive generations. Genetic anticipation occurs in many other genetic disorders and is not unique to HD. The molecular pathology of HD has been the focus of much research. There is clearly some commonality between other degenerative disorders in that misfolding of the huntingtin protein seems crucial for the neurodegeneration to occur in HD.34 Group 3 disorders have more widespread pathology that often involves both the SNpc cell bodies and their striatal targets. Examples include postinfectious parkinsonism reported from West Nile encephalitis. Here diffuse pathology has been noted in patients who exhibited parkinsonism, which often involves multiple nuclei in the basal ganglia.35,36 Postinfectious parkinsonism has also been reported with dengue fever, Japanese B encephalitis, and in an epidemic termed encephalitis lethargica that occurred between 1915 and 1926. The validity

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Dementia with Extrapyramidal Syndromes of this later entity has been recently questioned, and the pathology in these disorders has been quite variable.14,37,38 Therefore, modern examples of postencephalitic parkinsonism that primarily show imaging abnormalities in the midbrain and basal ganglia should be distinguished from encephalitis lethargica.

20.1.1 Specific Examples of Secondary Parkinsonism Huntington’s Disease Huntington’s disease is a hereditary progressive neurodegenerative disorder characterized by the development of emotional, behavioral, and psychiatric abnormalities; loss of previously acquired intellectual or cognitive functioning; and movement abnormalities.39 The classic signs of HD include the development of chorea–or involuntary, rapid, irregular, jerky dancelike movements that simultaneously afflict both proximal and distal muscles. This movement disorder may affect the face, arms, legs, or trunk. Patients also have gradual loss of thought processing and acquired intellectual abilities (dementia). There may be impairment of memory, abstract thinking, and judgment; disorientation; increased agitation; and personality changes. Although symptoms typically become evident during the fourth or fifth decades of life, age at onset is variable and ranges from early childhood to late adulthood. HD is transmitted as an autosomal dominant trait and is due to gene mutations on chromosome 4 (4p16.3). See details of pathology in an earlier section of this book. The clinical course of HD can last 15 to 20 years. In the early stages, the chorea is focal and segmental but progresses to involve multiple body parts. The chorea typically peaks within 10 years and is gradually replaced by bradykinesia, rigidity, and dystonia. In a very small percentage of cases, HD may present with a parkinsonian syndrome rather than with chorea (Westphal variant).33 The latter cases typically have an early onset (e.g., < 20 years). The behavioral and cognitive disturbances characteristic of HD most often account for the brunt of the patient’s disability and most of the hardship to the family. Approximately one-third develop dysthymia or an affective disorder; one-third an intermittent explosive disorder; and the remaining third substance-abuse problems, sexual dysfunction, antisocial personality traits, or schizophreniform symptoms. Depression with suicidal tendencies is not uncommon. Even the minority who may not manifest behavioral problems ultimately succumb to dementia. Thus, secondary parkinsonism in HD is a late feature in adults patients and is an early feature in juvenile HD. This is a matter of major imaging importance when patients are referred for neuroimaging with a putative diagnosis of HD. The diagnosis of HD is confirmed by genetic testing. Imaging abnormalities typically involve early loss of volume in the CNS and atrophy of the caudate head in early disease (▶ Fig. 20.1). As the disease advances, there is further degeneration and atrophy of the entire striatum and adjacent basal ganglia structures. The midbrain is relatively preserved in HD. In more advanced HD, degenerative changes in the cerebellum and more advanced cortical atrophy, especially in the prefrontal lobes, are noted.

Fig. 20.1 Coronal magnetic resonance imaging showing atrophy of the caudate and putamen in adult Huntington’s disease. There is also accompanying cortical atrophy. The loss of signals from the caudate head resulting in change of the shape and size of the lateral ventricles is classic for this diagnosis.

Treatment of HD involves a multidisciplinary team that can provide social, medical, neuropsychiatric, and genetic guidance to patients and families throughout the course of the illness. Although dopamine blockers are moderately effective for chorea, they may aggravate bradykinesia and dystonia. Tetrabenazine, a short-acting agent that can provide relief without high risk of causing parkinsonism, is often used to treat chorea in HD. Treatment of concomitant depression and substance abuse, if any, is critical in HD patients. From an imaging perspective, these matters need to be considered when interpreting imaging findings in HD. See Chapter 40 on advances in the treatment of dementia for details.

Dentatorubral-Pallidoluysian Atrophy Dentatorubral-pallidoluysian atrophy is a rare subtype of type I autosomal dominant cerebellar ataxia. It is characterized by involuntary movements, ataxia, epilepsy, mental disorders, cognitive decline, and prominent genetic anticipation.40 The disease is found most commonly in Japan, where the prevalence is estimated to be 1 in 208,000. Age of onset ranges from 1 to 60 years. The clinical symptoms are variable depending on the age of onset of the disease; myoclonus, epilepsy, and mental retardation are the main symptoms in juvenile onset, whereas cerebellar ataxia, choreoathetosis, and dementia are seen in adult onset, which is quite similar to onset in some adult HD patients. Clinical features are significantly correlated with the size of CAG repeats. Head magnetic resonance imaging (MRI) shows atrophy of cerebellum, brainstem, cerebrum, and high signal in periventricular white matter.41 T1-weighted MRI frequently shows cerebral atrophy, predominantly in the frontotemporal region, with dilatation of the lateral ventricles and atrophy of the cerebellum, pons, and midbrain accompanied by dilatation of the fourth ventricle and the aqueduct (▶ Fig. 20.2).

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Secondary Parkinsonism

Fig. 20.2 Magnetic resonance images of patient with adult-onset DRPLA. (a–f). The T2-weighted axial images obtained from a 60-year-old patient show high-signal-intensity lesions in the middle and upper pons, midbrain tegmentum, and cerebral white matter, in addition to a left pallidal highsignal-intensity spot resulting from an old lacunar infarction (d). (f) A T1-weighted midsagittal image shows atrophy of the brainstem and cerebellum. (Used with permission from Sunami Y, Koide R, Arai N, Yamada M, Mizutani T, Oyanagi K. Radiologic and neuropathologic findings in patients in a family with dentatorubral-pallidoluysian atrophy. AJNR Am J Neuroradiol. 2011 Jan;32(1):109-14.)

Diffuse high signal intensities throughout the periventricular white matter and centrum semiovale on T2-weighted MRI, mimicking leukoaraiosis or leukodystrophy, seem to be characteristic findings in DRPLA. Fluid-attenuated inversion recovery (FLAIR) images are useful for demonstrating the pathological changes in white matter more clearly than conventional T2weighted images in DRPLA. Axial midbrain images show the signal difference between the red nucleus and the surrounding fasciculi, which has been described as a characteristic of this disease. Neuropathologically, a combined degeneration of the dentatorubral and pallidoluysian systems is a characteristic feature of DRPLA.42 Neuropathological findings in DRPLA include thickening of the skull bone, atrophy of the brain, degeneration of the dentate nucleus and its afferent fibers, degeneration of the GP-STN nucleus system, atrophy of the tegmentum of the brainstem especially in the pons, degeneration of the striatum, degeneration of the superior colliculus, degeneration of the gracile nucleus, degeneration of the pyramidal tract, mild degeneration of the cerebellar cortex, mild degeneration of the cerebral cortex, and degeneration of the cerebral white matter. In juvenile type manifesting with progressive myoclonus epilepsy syndromes, degeneration of the GP is more severe than that of the dentate nucleus. In adult patients with cerebellar

ataxia and choreoathetoid movements without myoclonus or epilepsy, degeneration of the dentate nucleus is more severe than that of the GP.43 One clear distinction of DRPLA from other entities that have similar pathology is the preservation of SNpc in this disease.44 The pathological basis of the diffuse white matter changes seen in the subcortical white matter is unclear. Histopathological investigation has disclosed diffuse decrease of myelin sheaths and axons without gliosis and without evidence of microvascular pathology. These findings suggest that the primary genetic defect may be the basis for the MRI-detected pathology in the white mater. These prominent white matter changes seen on FLAIR imaging may be quite useful because this is usually not seen in HD patients or in other forms of spinocerebellar atrophies that come in the differential diagnosis of DRPLA. An unstable expansion of the trinucleotide (CAG) repeats in the DRPLA gene on the short arm of chromosome 12 (ATN1 gene; 12p13.31) has been identified as causative. DRPLA progresses rather rapidly. The mean disease duration is about 13 years. Recurrent seizures and dysphagia with frequent fluid and food aspiration lead to bronchopneumonia and subsequent death. However, some patients can reach 60 years of age or older.

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Dementia with Extrapyramidal Syndromes

Wilson’s Disease Wilson’s disease is a rare genetic disorder of copper metabolism that leads to an excessive accumulation of copper in certain tissues and organs, including the liver, brain, kidneys, or corneas of the eyes. Without prompt, appropriate treatment, the disorder may result in progressive liver disease, degenerative changes of the brain, psychiatric abnormalities, and other symptoms. Neurologic findings may include resting, action, or postural tremor; somewhat characteristic wing-beating tremor or flapping tremor where the patient has proximal tremor at the shoulders that mimics the beating of wings of a bird; choreoathetosis; sustained muscle contractions (e.g., risus sardonicus, a forced facial grimace); dystonia; dysarthria; and dysphagia. Some patients may also experience increasing irritability, anxiety, severe depression, or other psychiatric symptoms. Diagnosis is via genetic testing and blood and urine chemistries to document copper abnormalities as detailed in the preceding pathology section. Imaging changes in Wilson disease are well described. MRI often shows T2 and FLAIR hyperintense lesions involving thalami, midbrain, and pons. These lesions are typically hypointense on T1-weighted sequence and showed no evidence of restricted diffusion. Occasionally, hyperintense signal on T2/FLAIR images are seen in the striatum. Involvement of the midbrain gives the appearance of the “face of the giant panda” as dorsal pontine signal abnormalities resemble the face of a cub panda. Face of the giant panda and her cub constitute the “double panda sign” (▶ Fig. 20.3, ▶ Fig. 20.4), which is characteristic for this disease.45

Fig. 20.3 T2-weighted axial MRI demonstrating the “face of the giant panda” in the midbrain (arrow). (Used with permission from Jacobs DA, Markowitz CE, Liebeskind DS, Galetta SL. The “double panda sign” in Wilson’s disease. Neurology 2003;61(7):969.)

These findings are attributed to neurodegeneration and the deposition of copper into the striatum and the GP. The “face of the giant panda” sign consists of high signal intensity in the tegmentum, preservation of signal intensity of the lateral portion of the pars reticulata of the SN and red nucleus, and hypointensity of the superior colliculus. The “face of panda cub” is sometimes identified in the dorsal pons. “Eyes of the panda” are formed from the relative hypointensity of the central tegmental tracts, in contrast with the hyperintensity of the aqueduct opening into the fourth ventricle (“nose and mouth of the panda”) bounded inferiorly by the superior medullary velum. The panda’s “cheeks” are formed from the superior cerebellar peduncles.46 Prompt initiation of treatment as a child is often curative. The standard treatment of Wilson’s disease has evolved and is discussed in Chapter 40 in greater detail. In selected patients, liver transplantation is needed, and when successful can cure the patient.

Vascular Parkinsonism Vascular parkinsonism frequently presents as what is classically described as lower body parkinsonism.47 The findings are that patients have disproportionate amount of parkinsonian signs and symptoms in the lower body (below the umbilicus). Symptom onset is insidious and can sometimes be noted as mild cognitive deficits. Most frequent manifestation is with slowness in walking and difficulty climbing stairs. On examination, these patients have increased tone in the lower extremities and

Fig. 20.4 T2-weighted axial MRI reveals the “face of the miniature panda” in the pontine tegmentum (arrow). (Used with permission from Jacobs DA, Markowitz CE, Liebeskind DS, Galetta SL. The “double panda sign” in Wilson’s disease. Neurology 2003;61(7):969.)

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Secondary Parkinsonism

Fig. 20.5 Magnetic resonance imaging from a patient showing vascular parkinsonism. (a) T1-weighted image showing multiple lacunar strokes bilaterally in the putamen. (b) Corresponding T2-weighted image. (Used with permission from Fujimoto KI. Vascular parkinsonism. J Neurol 2006;253 [Suppl 3]:III/ 16–III/21.)

bradykinesia seen with foot tapping and stomping with their heels. Gait is also slow, and patients have difficulty turning; some patients have a positive retropulsion test. Deep tendon jerks may be exaggerated, and a positive Babinski sign is not uncommon. Some patients have parkinsonism in the upper body as well, but this is disproportionately mild. These patients usually have other comorbid diabetes, hypertension, cardiovascular disorders or hypercholesterolemia. Other well-known vascular risk factors, like family history of strokes or cardiovascular disease, are also common, along with a history of nicotine abuse in some patients. Brain imaging frequently demonstrates lacunar infarcts in the basal ganglia (▶ Fig. 20.5), corona radiata, thalamus, or pons.48 In many patients, these lesions are silent without any known previous symptoms; others have clinical history of transient ischemic attacks or clearly documented strokes with subsequent near complete recovery. There is also frequent report of brain volume loss and atrophy in this entity. Clinical diagnosis of concomitant Binswanger’s disease is not uncommon. Interestingly, imaging studies in a large series of patients diagnosed as having vascular parkinsonism failed to provide specific criteria for imaging abnormalities.25 Instead, a large variety of multifocal small vascular lesions were noted. It is important to note, from an imaging perspective, that such patients are exceedingly unlikely to have imaging findings exclusive to the SNpc which is much more characteristic of idiopathic PD. However, midbrain atrophy can be frequently seen in vascular parkinsonism and is thought to be secondary to wallerian degeneration and atrophy of the crux cerbri.49 Treatment for vascular parkinsonism is primarily symptomatic. Modest clinical improvement is noted with large doses of L-dopa given multiple times every day. Often the dose needed to get clinical benefit is in the range of 1.5 to 2 g/day of L-dopa.50 Interestingly, such patients generally do not exhibit motor fluctuations that are noted in idiopathic PD patients, and it is rare for such patients to develop drug-induced dyskinesias.24 This is an important clinical distinction, and from an imaging perspective, the risk of involuntary movements interfering with imaging quality is low. Distinction between vascular parkinsonism and idiopathic PD on clinical grounds is usually not difficult. However, on occasion, this distinction can be problematic in certain subtypes of idiopathic PD patients. In such patients, a dopamine transporter single-photon emission

computed tomography (SPECT) scan may be useful.51 The differential diagnosis of vascular parkinsonism always includes normal pressure hydrocephalus (NPH), and imaging studies can often be helpful, as the distinctive features of NPH, where there is more or less uniform enlargement of the entire ventricular system and accompanying brain cortical atrophy, are lacking in vascular parkinsonism. Instead, in vascular parkinsonism, there is an apparent enlargement of the lateral ventricles resulting from the loss of gray matter volume in the caudate and the putamen secondary to multiple lacunar infarcts. In some cases, however, this distinction is difficult to make on the basis of structural imaging alone. A large-volume lumbar puncture and removal of cerebrospinal fluid to determine whether the patient has tangible improvement in parkinsonism and gait is often performed as a diagnostic test. In itself, however, this test or any improvement in parkinsonism as a result of this test is not definitive.52,53 In such patients, diagnostic uncertainty remains, and often a pragmatic treatment approach that combines shunting with pharmacotherapy is required.

References [1] Lieu CA, Subramanian T. The interhemispheric connections of the striatum: Implications for Parkinson’s disease and drug-induced dyskinesias. Brain Res Bull 2012; 87: 1–9 [2] Lieu CA, Shivkumar V, Gilmour TP, et al. Pathophysiology of drug-induced dyskinesias. In: Parkinson’s Disease Book 3 [Internet], 2011; InTech Publishers. http://www.intechopen.com/books/symptoms-of-parkinson-s-disease [3] Ghaemi K, Krauss JK, Nakamura M. Hemiparkinsonism due to a pontomesencephalic cavernoma: improvement after resection: case report. J Neurosurg Pediatr 2009; 4: 143–146 [4] Li ST, Zhong J. Surgery for mesencephalic cavernoma: case report. Surg Neurol 2007; 67: 413–417, discussion 417–418 [5] Vhora S, Kobayashi S, Okudera H. Pineal cavernous angioma presenting with Parkinsonism. J Clin Neurosci 2001; 8: 263–266 [6] Matsuda W, Matsumura A, Komatsu Y, Yanaka K, Nose T. Awakenings from persistent vegetative state: report of three cases with parkinsonism and brain stem lesions on MRI. J Neurol Neurosurg Psychiatry 2003; 74: 1571–1573 [7] Pérez Errazquin F, Gomez Heredia MJ. [Levodopa-responsive parkinsonismdystonia due to a traumatic injury of the substantia nigra] [in Spanish] Neurologia 2012; 27: 181–183 [8] Bhatt M, Desai J, Mankodi A, Elias M, Wadia N. Posttraumatic akinetic-rigid syndrome resembling Parkinson’s disease: a report on three patients. Mov Disord 2000; 15: 313–317 [9] Nayernouri T. Posttraumatic parkinsonism. Surg Neurol 1985; 24: 263–264

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Dementia with Extrapyramidal Syndromes [10] Krauss JK, Trankle R, Raabe A. Tremor and dystonia after penetrating diencephalic-mesencephalic trauma. Parkinsonism Relat Disord 1997; 3: 117–119 [11] Silvers DS, Menkes DL. Hemibody mirror movements in hemiparkinsonismhemiatrophy syndrome. J Neurol Sci 2009; 287: 260–263 [12] Wijemanne S, Jankovic J. Hemiparkinsonism-hemiatrophy syndrome. Neurology 2007; 69: 1585–1594 [13] Sridam N, Phanthumchinda K. Encephalitis lethargica like illness: case report and literature review. J Med Assoc Thai 2006; 89: 1521–1527 [14] Vilensky JA, Gilman S, McCall S. A historical analysis of the relationship between encephalitis lethargica and postencephalitic parkinsonism: a complex rather than a direct relationship. Mov Disord 2010; 25: 1116–1123 [15] Hayase Y, Tobita K. Influenza virus and neurological diseases. Psychiatry Clin Neurosci 1997; 51: 181–184 [16] Toovey S. Influenza-associated central nervous system dysfunction: a literature review. Travel Med Infect Dis 2008; 6: 114–124 [17] Misra UK, Kalita J. Overview: Japanese encephalitis. Prog Neurobiol 2010; 91: 108–120 [18] Centers for Disease Control. Street-drug contaminant causing parkinsonism. Morbidity Mortal Week Rep. 1984; 33: 351–352 [19] Langston JW, Ballard P, Tetrud JW, Irwin I. Chronic Parkinsonism in humans due to a product of meperidine-analog synthesis. Science 1983; 219: 979–980 [20] Vingerhoets FJ, Snow BJ, Tetrud JW, Langston JW, Schulzer M, Calne DB. Positron emission tomographic evidence for progression of human MPTPinduced dopaminergic lesions. Ann Neurol 1994; 36: 765–770 [21] Yasuda T, Nakata Y, Choong CJ, Mochizuki H. Neurodegenerative changes initiated by presynaptic dysfunction. Transl Neurodegener 2013; 2: 16 [22] Bondon-Guitton E, Perez-Lloret S, Bagheri H, Brefel C, Rascol O, Montastruc JL. Drug-induced parkinsonism: a review of 17 years’ experience in a regional pharmacovigilance center in France. Mov Disord 2011; 26: 2226–2231 [23] Bower JH, Dickson DW, Taylor L, Maraganore DM, Rocca WA. Clinical correlates of the pathology underlying parkinsonism: a population perspective. Mov Disord 2002; 17: 910–916 [24] Kalra S, Grosset DG, Benamer HT. Differentiating vascular parkinsonism from idiopathic Parkinson’s disease: a systematic review. Mov Disord 2010; 25: 149–156 [25] Zijlmans JC, Daniel SE, Hughes AJ, Révész T, Lees AJ. Clinicopathological investigation of vascular parkinsonism, including clinical criteria for diagnosis. Mov Disord 2004; 19: 630–640 [26] Kimura Y, Sato N, Sugai K et al. MRI, MR spectroscopy, and diffusion tensor imaging findings in patient with static encephalopathy of childhood with neurodegeneration in adulthood (SENDA). Brain Dev 2013; 35: 458–461 [27] Kruer MC, Boddaert N, Schneider SA et al. Neuroimaging features of neurodegeneration with brain iron accumulation. AJNR Am J Neuroradiol 2012; 33: 407–414 [28] Kim TJ, Kim IO, Kim WS et al. MR imaging of the brain in Wilson disease of childhood: findings before and after treatment with clinical correlation. AJNR Am J Neuroradiol 2006; 27: 1373–1378 [29] Racette BA, Aschner M, Guilarte TR, Dydak U, Criswell SR, Zheng W. Pathophysiology of manganese-associated neurotoxicity. Neurotoxicology 2012; 33: 881–886 [30] Ala A, Walker AP, Ashkan K, Dooley JS, Schilsky ML. Wilson’s disease. Lancet 2007; 369: 397–408 [31] Martino D, Stamelou M, Bhatia KP. The differential diagnosis of Huntington’s disease-like syndromes: ‘red flags’ for the clinician. J Neurol Neurosurg Psychiatry 2013; 84: 650–656

[32] Snell RG, MacMillan JC, Cheadle JP et al. Relationship between trinucleotide repeat expansion and phenotypic variation in Huntington’s disease. Nat Genet 1993; 4: 393–397 [33] Douglas I, Evans S, Rawlins MD, Smeeth L, Tabrizi SJ, Wexler NS. Juvenile Huntington’s disease: a population-based study using the General Practice Research Database. BMJ Open 2013; 3 [34] Labbadia J, Morimoto RI. Huntington’s disease: underlying molecular mechanisms and emerging concepts. Trends Biochem Sci 2013; 38: 378–385 [35] Petersen LR, Brault AC, Nasci RS. West Nile virus: review of the literature. JAMA 2013; 310: 308–315 [36] Sejvar JJ, Haddad MB, Tierney BC et al. Neurologic manifestations and outcome of West Nile virus infection. JAMA 2003; 290: 511–515 [37] Vilensky JA, Gilman S, McCall S. Does the historical literature on encephalitis lethargica support a simple (direct) relationship with postencephalitic Parkinsonism? Mov Disord 2010; 25: 1124–1130 [38] Anderson LL, Vilensky JA, Duvoisin RC. Review: neuropathology of acute phase encephalitis lethargica: a review of cases from the epidemic period. Neuropathol Appl Neurobiol 2009; 35: 462–472 [39] Finkbeiner S. Huntington’s Disease. Cold Spring Harb Perspect Biol 2011; 3: a007476 [40] Wardle M, Morris HR, Robertson NP. Clinical and genetic characteristics of non-Asian dentatorubral-pallidoluysian atrophy: a systematic review. Mov Disord 2009; 24: 1636–1640 [41] Yoshii F, Tomiyasu H, Shinohara Y. Fluid attenuation inversion recovery (FLAIR) images of dentatorubropallidoluysian atrophy: case report. J Neurol Neurosurg Psychiatry 1998; 65: 396–399 [42] Takeda S, Takahashi H. Neuropathology of dentatorubropallidoluysian atrophy. Neuropathology 2007; 16: 48–55 [43] Takahashi H, Yamada M, Takeda S. [Neuropathology of dentatorubral-pallidoluysian atrophy and Machado-Joseph disease] [in Japanese] No To Shinkei 1995; 47: 947–953 [44] Wong JC, Armstrong MJ, Lang AE, Hazrati LN. Clinicopathological review of pallidonigroluysian atrophy. Mov Disord 2013; 28: 274–281 [45] Singh P, Ahluwalia A, Saggar K, Grewal CS. Wilson’s disease: MRI features. J Pediatr Neurosci 2011; 6: 27–28 [46] Jacobs DA, Markowitz CE, Liebeskind DS, Galetta SL. The “double panda sign” in Wilson’s disease. Neurology 2003; 61: 969 [47] Demirkiran M, Bozdemir H, Sarica Y. Vascular parkinsonism: a distinct, heterogeneous clinical entity. Acta Neurol Scand 2001; 104: 63–67 [48] Zijlmans JC, Thijssen HO, Vogels OJ et al. MRI in patients with suspected vascular parkinsonism. Neurology 1995; 45: 2183–2188 [49] Choi SM, Kim BC, Nam TS et al. Midbrain atrophy in vascular Parkinsonism. Eur Neurol 2011; 65: 296–301 [50] Zijlmans JC, Katzenschlager R, Daniel SE, Lees AJ. The L-dopa response in vascular parkinsonism. J Neurol Neurosurg Psychiatry 2004; 75: 545–547 [51] Gerschlager W, Bencsits G, Pirker W et al. [123I]beta-CIT SPECT distinguishes vascular parkinsonism from Parkinson’s disease. Mov Disord 2002; 17: 518– 523 [52] Ondo WG, Chan LL, Levy JK. Vascular parkinsonism: clinical correlates predicting motor improvement after lumbar puncture. Mov Disord 2002; 17: 91–97 [53] Akiguchi I, Ishii M, Watanabe Y et al. Shunt-responsive parkinsonism and reversible white matter lesions in patients with idiopathic NPH. J Neurol 2008; 255: 1392–1399

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Part VII Vascular Dementia

21 Vascular Dementia

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22 Neuroimaging of Vascular Dementias

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23 Imaging of Specific Hereditary Microangiopathies

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24 Vasculitis and Dementia

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Vascular Dementia

21 Vascular Dementia A.M. Barrett and Vahid Behravan A spectrum of ischemia-related cognitive problems has been identified, including vascular dementia, vascular cognitive impairment,1 multi-infarct dementia,2 and subcortical vascular dementia.3,4 The syndrome of clinical stroke or subclinical vascular brain injury and functionally relevant memory and cognitive impairment5 may have first been introduced to the field by Binswanger and Alzheimer in the 19th century.6 In contrast to dementia caused by neurodegenerative conditions affecting the brain, such as Alzheimer’s disease, frontotemporal dementia and variants (primarily cortical), Parkinson-plus syndromes, Huntington’s disease, and other neurodegenerative disorders primarily affecting the subcortical systems, vascular and ischemic-related dementia can be considered a “secondary” form of dementia.7 These forms can be distinguished from primary neurodegenerative disorders by the mechanism of cognitive impairment: not only is there direct damage to cortical and subcortical cells and white matter circuitry, but there is indirect damage via abnormal cellular homeostasis (for example, the brain in vascular dementia may be affected by the common cooccurrence of diabetes and hyperglycemia, and the mechanics of brain perfusion may be altered related to co-occurring cardiac problems). Brain imaging is a critical part of the diagnosis of vascular dementia because its constellation of cognitive symptoms can overlap with that associated with cortical dementia like Alzheimer’s disease. Although vascular dementia can commonly be distinguished from Alzheimer’s disease by its early

deficits in motivation, initiation, and organization of thinking (subcortical deficits, sparing crystallized knowledge), one or more strokes in the posterior cortical parietal regions, either directly affecting the cortex or damaging its white matter input from the thalamus or other cortical regions, may mimic cortical neurodegenerative disease (▶ Fig. 21.1).8

21.1 Diagnostic Criteria Although different definitions of vascular dementia have been provided by Alzheimer’s Disease Diagnostic and Treatment Centers (ADDTC),9 International Statistical Classification of Diseases, 10th Revision (ICD-1010), National Institute of Neurological Disorders and Stroke-Association Internationale pour la Recherche et l’Enseignement en Neurosciences (NINDS-AIREN11), and the Hachinski ischemic score,12,13 an excellent general set of diagnostic criteria are provided by the American Psychiatric Association Diagnostic and Statistical Manual (DSM, 4th edition).14 The DSM specifies vascular dementia as (1) memory impairment (impaired ability to learn new information or to recall previously learned information), required to make the diagnosis of a dementia and a prominent early symptom; and (2) one (or more) cognitive disturbances, including aphasia (language disturbance), apraxia (dysfunctional skilled learned purposive movement despite intact strength), agnosia (failure to recognize or identify faces, objects, pantomimes or other domain-specific

Fig. 21.1 Schematic coronal slice representing different pathologic syndromes in vascular dementia. Gray areas mark brain regions affected by ischemia and infarction. Multi-infarct dementia is characterized by small- and large-vessel lesions all over the gray matter (left side); strategic infarct dementia is characterized by fewer lesions in critical regions for memory function: hippocampal formation or paramedian thalamus. In subcortical vascular encephalopathy, multiple periventricular confluent white matter lesions may co-occur with small-vessel lesions. Amy, amygdala; Bgl, basal ganglia; CAI, hippocampus CA1 region; Cing, cingulate gyrus; ER, entorhinal cortex; F, frontal neocortex; Hypoth, hypothalamus; NBM, basal nucleus of Meynert; T, temporal neocortex; Thal, thalamus. (Used with permission from Thal DR, Grinberg LT, Attems J. Vascular dementia: different forms of vessel disorders contribute to the development of dementia in the elderly brain. Exp Gerontol 2012;47(11):816 –824.)

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Vascular Dementia Table 21.1 Pathology related to progressive cognitive impairment in vascular dementia Pathologic disorder

Cause

Useful references

Large artery and watershed infarction

Embolic, hypoperfusion

Wright, 201315; Leys et al, 200540

Small artery infarctions or lacunes, counted as part of NINDS-AIREN criteria

Thrombosis affecting caudate, putamen, globus pallidus, thalamus, internal capsule, cerebellum, brainstem

Arvanitakis et al, 201141; Roman et al, 199311

Chronic subcortical ischemia affecting > 25% periventricular white matter

Cerebral vessel disease, atherosclerosis/ lipohyalinosis

Chui et al, 20009; Thal et al, 20125

Cerebral microbleeds

Cerebral vascular abnormality

Kirsch et al, 200942; Van der Flier and Cordonnier, 201236

Hippocampal atrophy and sclerosis

Neurodegeneration, presumably from metabolic insufficiency or chronic ischemia

Gorelick et al, 201143; Zarow et al, 200844

Abbreviations: NINDS-AIREN, National Institute of Neurological Disorders and Stroke-Association Internationale pour la Recherche et l’Enseignement en Neurosciences.

information despite intact sensory function), and disturbance in executive functioning (e.g., planning, organizing, sequencing, abstracting). Anomia (pathologic difficulty producing the names of words or objects) is sufficient to meet criteria for aphasia. The cognitive deficits must cause significant impairment in social or occupational functioning and represent a significant decline from a previous level of functioning. The upcoming fifth edition of the DSM is stated to propose that diagnosis of a major neurocognitive disorder of vascular origin (dementia) can involve memory; however, a minor neurocognitive disorder, which allows the person to continue functional activities but only while using compensatory measures, can be defined by a single cognitive deficit, such as executive dysfunction. In contrast to other types of dementia, vascular dementia is defined by focal neurologic signs and symptoms (e.g., exaggeration of deep tendon reflexes, extensor plantar response, pseudobulbar palsy, gait abnormalities, weakness of an extremity) or laboratory evidence indicative of cerebrovascular disease (e.g., multiple infarctions involving cortex and underlying white matter) that are judged to be causally related to the disturbance.8,15 Lastly, in vascular dementia, cognitive deficits and

neurologic signs may not occur exclusively during the course of the delirium.

21.2 Pathophysiology Five different but interrelated pathologic sets of changes (▶ Table 21.1) contribute to the evolution and progression of cognitive and functional deficit in this disorder.5,15,16 These changes include large-vessel embolic strokes, either of cardiac or artery-to-artery origin, and large watershed infarcts caused by brain hypoperfusion; small-vessel lacunar strokes (▶ Fig. 21.2), chronic periventricular white matter ischemia (▶ Fig. 21.2), cerebral microbleeds (▶ Fig. 21.2, ▶ Fig. 21.3), and medial temporal atrophy. Some current reviews that report the co-occurrence of medial temporal and hippocampal atrophy with pathologic evidence of vascular dementia have been criticized on the basis that subjects with medial temporal atrophy may have had undetected Alzheimer’s disease in addition to cerebrovascular pathology. Even if medial temporal atrophy has independent causes compared with other radiologic signs of vascular dementia, it is independently predictive of progression of cognitive symptoms over time.17

Fig. 21.2 (a) Brain magnetic resonance imaging illustrating periventricular white matter abnormality (areas of increased signal intensity, black arrows) on fluid-attenuated inversion recovery imaging. (b) Microbleeds (areas of decreased signal intensity, white arrows) are seen on T2*weighted imaging. (Used with permission from Van der Flier WM, Cordonnier C. Microbleeds in vascular dementia: clinical aspects. Exp Gerontol 2012;47(11): 853–857.)

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Vascular Dementia

Fig. 21.3 (a) Brain magnetic resonance imaging illustrating lacunar strokes (areas of decreased signal intensity, white arrows) on fluid-attenuated inversion recovery imaging. (b) Microbleeds (areas of decreased signal intensity, white arrows) are seen on T2*-weighted imaging. (Used with permission from Van der Flier WM, Cordonnier C. Microbleeds in vascular dementia: clinical aspects. Exp Gerontol 2012;47(11):853–857.)

21.3 Genetics Vascular dementia genetics have codeveloped along with the genetics of Alzheimer’s disease and other primary neurodegenerative diseases, as the distinction between these conditions has been clarified (▶ Table 21.2). Genetic risk factors in Alzheimer’s disease have been widely studied, and these same genes, as well as different genes, have been studied in relation to the risk of development of vascular dementia. Traditionally, to find a genetic link for a certain condition, researchers need large families or ethnicities affected with the disease. When we consider the major pathologic conditions associated with vascular dementia, genetic predictors are relevant to all three types: multi-infarct dementia, small-vessel and strategic infarct-type dementias, and subcortical arteriosclerotic leukoencephalopathy (Binswanger encephalopathy). Some of the genetic associations with vascular dementia, however, may primarily reflect the genetics of Alzheimer’s disease; Alzheimer’s disease and vascular dementia co-occur in up to half of the people diagnosed with Alzheimer’s disease.15

Table 21.2 Conditions associated with subcortical, stroke-related and vascular dementia, and genetic associations. See text for details. Condition

Gene

Location

Alzheimer’s disease and vascular dementia

Apolipoprotein E

Chromosome 19

CADASIL

NOTCH 3

Chromosome 19

CARASIL

HTRA1

Chromosome 10

CRV, HERNS, HVR

TREX1

Chromosome 3

HDLS

CFR1R

Chromosome 5

FBD

BRI

Chromosome 13

Hereditary cerebral amyloid angiopathy

APP, CST3,ITM2B

Chromosome 21, 20, 13

Abbreviations: CADASIL, Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy; CARASIL, cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy; CRV, cerebroretinal vasculopathy; FBD, familial British dementia; HDLS, hereditary diffuse leukoencephalopathy with neuroaxonal spheroids; HERNS, hereditary endotheliopathy with retinopathy, nephopathy, and stroke; HVR, hereditary vascular retinopathy.

An extensively studied gene in Alzheimer’s disease is apolipoprotein E (ApoE), a cholesterol carrier that supports injury repair in the brain. Polymorphic alleles of ApoE are the main genetic determinants of Alzheimer’s disease risk. The Apo-E 4 allele, which is most relevant to increased risk of Alzheimer's disease, is also considered to increase the risk of vascular dementia.18,19,20 Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is considered the most common heritable cause of stroke and vascular dementia in adults. It is a familial form of vascular dementia21,22 caused by mutations in the NOTCH3 gene located on chromosome 19 and is clinically characterized by migraines with aura, recurrent ischemic strokes, cognitive and behavior impairments, and dementia. Cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy (CARASIL, also known as Maeda syndrome), is also a heritable small-vessel disease clinically characterized by nonhypertensive leukoencephalopathy associated with alopecia and spondylosis.23 Diagnosis relies on brain MRI findings and molecular genetic testing of HTRA1 , a gene located on chromosome 10. Other causes of hereditary cerebral vasculopathy that can lead to strokes, and potentially cognitive impairment and dementia, are less common. These causes include but are not limited to hereditary vascular retinopathy (HVR); cerebroretinal vasculopathy (CRV); and hereditary endotheliopathy with retinopathy, nephropathy, and stroke (HERNS).24 HERNS is characterized by retinal capillary obliteration and CNS vasculopathy. These patients commonly have dementia and, like patients with CADASIL, they can suffer migraine headaches. HERNS is linked to chromosome 3p21, and transmission is autosomal dominant. Hereditary cerebral amyloid angiopathy is a condition that can cause a progressive dementia, stroke, and other neurologic problems. Many different types are associated with kindred of different nationalities. The Dutch type of hereditary cerebral amyloid angiopathy is the most common form. Flemish, Italian, Icelandic, and Arctic types of hereditary cerebral amyloid angiopathy and two other types, known as familial British dementia (FBD) and familial Danish dementia, are known to be associated with dementia.25,26,27,28 FBD with amyloid angiopathy is an

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Vascular Dementia

Fig. 21.4 Axial (a) DWI and (b) ADC images in a patient with hereditary leukoencephalopathy (HDLS) reveals increased signal intensity in both diffusion-weighted imaging (arrows in a) and on apparent diffusion coefficient mapping (arrows in b). (Used with permission from Boissé L, Islam O, Woulfe J, Ludwin SK, Brunet DG. Neurological picture: hereditary diffuse leukoencephalopathy with neuroaxonal spheroids: novel imaging findings. J Neurol Neurosurg Psychiatry 2010;81 (3):313–314.)

autosomal dominant condition characterized by vascular dementia, progressive spastic paraparesis, and cerebellar ataxia; onset is in the sixth decade of life. A point mutation in the BRI gene on chromosome 13 is the genetic abnormality.29 Other rare reported causes of vascular dementia with genetic associations include Sneddon's syndrome (livedo reticularis with cerebrovascular disease), which causes a progressive arteriopathy. Sneddon's syndrome occurs sporadically, but familial cases have been reported, inherited in an autosomal dominant (with incomplete penetrance) or autosomal recessive fashion.30 Dementia has also been reported in other hereditary leukoencephalothies, such as hereditary diffuse leukoencephalopathy with neuroaxonal spheroids (HDLS)31 or vanishing white matter disease.32 This is relevant because neuroradiologic abnormalities in leukodystrophies of heritable or metabolic origin may be mistaken for ischemic changes (▶ Fig. 21.4).33

21.4 Classification Before computerized protocols were widely available to calculate the volume of brain lesions and the extent of white matter abnormality, visual rating methods were commonly used to stage ischemic injury in vascular dementia. The Fazekas scale34 rates white matter hyperintensities both generally and in periventricular regions as a marker of small-vessel disease. Scheltens et al35 suggested further modifications to quantify basal ganglia hyperintensities; however, these methods did not correlate well with computerized volumetric assessments with confirmed objective reliability and validity. Even computerized volume measurements for ischemic lesions did not correlate well with progression of cognitive and functional deficits, and therefore the value of these techniques has been questioned; because of evidence of threshold values definitely associated with cognitive deficits, however, and a stronger relation to gait and motor functional problems in small- and large-vessel ischemia, a clinical role for these techniques still seems possible.15 Clear predictive associations with clinical diagnosis of vascular dementia or progression of symptoms are not yet available for microbleeds. However, counting microbleed sites might be useful in the future to predict cognitive functional progression.36

Visual rating is still a widely used method for classification of medial temporal atrophy, despite the availability of computerized techniques. One method is a 1 to 5 scale recommended by Scheltens and colleagues.37 In Alzheimer’s disease, visual inspection for hippocampal atrophy had greater than 80% sensitivity and greater than 90% specificity for diagnosis.16

21.5 Additional NeuroanatomicBehavioral Considerations Barrett and colleagues38 and Cramer et al39 pointed out that modality-specific outcomes accurately assess the natural evolution of specific poststroke conditions, such as aphasia, spatial neglect, and limb apraxia, which are themselves tremendously disabling. A strategic infarct can affect critical regions for memory and cognitive function, such as the left temporal-parietal junction. When people with preexisting aphasia, spatial neglect, or other focal cognitive syndromes develop an additional, stepwise loss of functional ability, such as loss of ability to dress independently, clinicians commonly obtain a brain image to determine whether a new large-vessel stroke contributes to that process. Given the potential relevance of assessing the volume of white matter lesion burden, counting lacunar lesions, assessing the volume of microbleeds, and assessing hippocampal atrophy in people with vascular dementia, we can expect that they may also assist with predicting progress of functional disability and the need for more aggressive management and treatment of risk factors after large-artery stroke. This means that, in the future, clinicians may routinely examine the risk of future vascular dementia in patients with a new or chronic large-artery stroke by evaluating neuroradiologic-neuropathologic data, making new therapies for vascular dementia more widely available as they are identified.

21.6 Acknowledgments This work was supported by the Kessler Foundation, the National Institutes of Health, and the Department of Education/ National Institute of Disability and Rehabilitation Research (Grants K24 HD062647, H133 G120203, PI: Barrett). Study

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Vascular Dementia contents do not necessarily represent the policy of the Department of Education, and one should not assume endorsement by the federal government.

References [1] Hachinski V, Iadecola C, Petersen RC et al. National Institute of Neurological Disorders and Stroke-Canadian Stroke Network vascular cognitive impairment harmonization standards. Stroke 2006; 37: 2220–2241 [2] Hachinski VC, Lassen NA, Marshall J. Multi-infarct dementia: a cause of mental deterioration in the elderly. Lancet 1974; 2: 207–210 [3] Tomimoto H. Subcortical vascular dementia. Neurosci Res 2011; 71: 193–199 [4] Jellinger KA. Pathology and pathogenesis of vascular cognitive impairment—a critical update. Front Aging Neurosci 2013; 5: 17 [5] Thal DR, Grinberg LT, Attems J. Vascular dementia: different forms of vessel disorders contribute to the development of dementia in the elderly brain. Exp Gerontol 2012; 47: 816–824 [6] Libon DJ, Price CC, Davis Garrett K, Giovannetti T. From Binswanger’s disease to leuokoaraiosis: what we have learned about subcortical vascular dementia. Clin Neuropsychol 2004; 18: 83–100 [7] Emre M. Classification and diagnosis of dementia: a mechanism-based approach. Eur J Neurol 2009; 16: 168–173 [8] Barrett AM. Is it Alzheimer’s disease or something else? 10 disorders that may feature impaired memory and cognition. Postgrad Med 2005; 117: 47– 53 [9] Chui HC, Mack W, Jackson JE et al. Clinical criteria for the diagnosis of vascular dementia: a multicenter study of comparability and interrater reliability. Arch Neurol 2000; 57: 191–196 [10] World Health Organization (WHO). International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10), Geneva: WHO; 1992 [11] Román GC, Tatemichi TK, Erkinjuntti T et al. Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology 1993; 43: 250–260 [12] Hachinski VC, Bowler JV. Vascular dementia. Neurology 1993; 43: 2159– 2160, author reply 2160–2161 [13] Pantoni L, Inzitari D. Hachinski’s ischemic score and the diagnosis of vascular dementia: a review. Ital J Neurol Sci 1993; 14: 539–546 [14] American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders Revised 4th ed. Washington, DC. 2000 [15] Wright CB. Etiology, Clinical Manifestations and Diagnosis of Vascular Dementia. Waltham, MA: UptoDate Inc; 2013 [16] Román G, Pascual B. Contribution of neuroimaging to the diagnosis of Alzheimer’s disease and vascular dementia. Arch Med Res 2012; 43: 671–676 [17] Mungas D, Jagust WJ, Reed BR et al. MRI predictors of cognition in subcortical ischemic vascular disease and Alzheimer’s disease. Neurology 2001; 57: 2229–2235 [18] Baum L, Lam LC, Kwok T et al. Apolipoprotein E epsilon4 allele is associated with vascular dementia. Dement Geriatr Cogn Disord 2006; 22: 301–305 [19] McGuinness B, Carson R, Barrett SL, Craig D, Passmore AP. Apolipoprotein epsilon4 and neuropsychological performance in Alzheimer’s disease and vascular dementia. Neurosci Lett 2010; 483: 62–66 [20] Chuang YF, Hayden KM, Norton MC et al. Association between APOE epsilon4 allele and vascular dementia: the Cache County study. Dement Geriatr Cogn Disord 2010; 29: 248–253 [21] Tournier-Lasserve E, Joutel A, Melki J et al. Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy maps to chromosome 19q12. Nat Genet 1993; 3: 256–259 [22] Peters N, Opherk C, Zacherle S, Capell A, Gempel P, Dichgans M. CADASILassociated Notch3 mutations have differential effects both on ligand binding and ligand-induced Notch3 receptor signaling through RBP-Jk. Exp Cell Res 2004; 299: 454–464 [23] Fukutake T. Cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy (CARASIL): from discovery to gene identification. J Stroke Cerebrovasc Dis 2011; 20: 85–93

[24] Ophoff RA, DeYoung J, Service SK et al. Hereditary vascular retinopathy, cerebroretinal vasculopathy, and hereditary endotheliopathy with retinopathy, nephropathy, and stroke map to a single locus on chromosome 3p21.1-p21.3. Am J Hum Genet 2001; 69: 447–453 [25] Maat-Schieman M, Roos R, van Duinen S. Hereditary cerebral hemorrhage with amyloidosis-Dutch type. Neuropathology 2005; 25: 288–297 [26] Maia LF, Mackenzie IR, Feldman HH. Clinical phenotypes of cerebral amyloid angiopathy. J Neurol Sci 2007; 257: 23–30 [27] Palsdottir A, Snorradottir AO, Thorsteinsson L. Hereditary cystatin C amyloid angiopathy: genetic, clinical, and pathological aspects. Brain Pathol 2006; 16: 55–59 [28] Mead S, James-Galton M, Revesz T et al. Familial British dementia with amyloid angiopathy: early clinical, neuropsychological and imaging findings. Brain 2000; 123: 975–991 [29] Revesz T, Ghiso J, Lashley T et al. Cerebral amyloid angiopathies: a pathologic, biochemical, and genetic view. J Neuropathol Exp Neurol 2003; 62: 885–898 [30] Mascarenhas R, Santo G, Gonçalo M, Ferro MA, Tellechea O, Figueiredo A. Familial Sneddon’s syndrome. Eur J Dermatol 2003; 13: 283–287 [31] Keegan BM, Giannini C, Parisi JE, Lucchinetti CF, Boeve BF, Josephs KA. Sporadic adult-onset leukoencephalopathy with neuroaxonal spheroids mimicking cerebral MS. Neurology 2008; 70: 1128–1133 [32] Gascon-Bayarri J, Campdelacreu J, Sánchez-Castañeda C et al. Leukoencephalopathy with vanishing white matter presenting with presenile dementia. J Neurol Neurosurg Psychiatry 2009; 80: 810–811 [33] Boissé L, Islam O, Woulfe J, Ludwin SK, Brunet DG. Neurological picture: hereditary diffuse leukoencephalopathy with neuroaxonal spheroids: novel imaging findings. J Neurol Neurosurg Psychiatry 2010; 81: 313–314 [34] Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol 1987; 149: 351–356 [35] Scheltens P, Barkhof F, Leys D et al. A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging. J Neurol Sci 1993; 114: 7–12 [36] Van der Flier WM, Cordonnier C. Microbleeds in vascular dementia: clinical aspects. Exp Gerontol 2012; 47: 853–857 [37] Scheltens P, Leys D, Barkhof F et al. Atrophy of medial temporal lobes on MRI in “probable” Alzheimer’s disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry 1992; 55: 967– 972 [38] Barrett AM, Levy CE, Gonzalez Rothi LJ. Treatment innovation in rehabilitation of cognitive and motor deficits after stroke and brain injury: physiological adjunctive treatments. Am J Phys Med Rehabil 2007; 86: 423–425 [39] Cramer SC, Koroshetz WJ, Finklestein SP. The case for modality-specific outcome measures in clinical trials of stroke recovery-promoting agents. Stroke 2007; 38: 1393–1395 [40] Leys D, Hénon H, Mackowiak-Cordoliani MA, Pasquier F. Poststroke dementia. Lancet Neurol 2005; 4: 752–759 [41] Arvanitakis Z, Leurgans SE, Barnes LL, Bennett DA, Schneider JA. Microinfarct pathology, dementia, and cognitive systems. Stroke 2011; 42: 722–727 [42] Kirsch W, McAuley G, Holshouser B et al. Serial susceptibility weighted MRI measures brain iron and microbleeds in dementia. J Alzheimers Dis 2009; 17: 599–609 [43] Gorelick PB, Scuteri A, Black SE et al. American Heart Association Stroke Council, Council on Epidemiology and Prevention, Council on Cardiovascular Nursing, Council on Cardiovascular Radiology and Intervention, and Council on Cardiovascular Surgery and Anesthesia. Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the american heart association/american stroke association. Stroke 2011; 42: 2672–2713 [44] Zarow C, Sitzer TE, Chui HC. Understanding hippocampal sclerosis in the elderly: epidemiology, characterization, and diagnostic issues. Curr Neurol Neurosci Rep 2008; 8: 363–370

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Neuroimaging of Vascular Dementias

22 Neuroimaging of Vascular Dementias Amit Agarwal and Sangam G. Kanekar Vascular dementia (VaD) is the second most common cause of dementia, following Alzheimer’s disease (AD), and it remains a diagnostic challenge in clinical settings all over the world.1,2 As a clinical syndrome, VaD relates to different vascular mechanisms and changes in the brain and has different causes and clinical manifestations.3 VaD is not limited to multi-infarct dementia (MID), as initially proposed by Hachinski and colleagues in 1974.4 The pathophysiology of VaD indeed incorporates interactions between vascular causes, such as cerebrovascular diseases (CVD) and vascular risk factors; changes in the brain, such as infarcts, white matter lesions, and atrophy; host factors, such as age, education, and sex; and cognition factors, such as impairments in executive functions and psychomotor speed.1 The term vascular cognitive impairment (VCI) has been proposed as an “umbrella” term to recognize the broad spectrum of cognitive and, indeed, behavioral, changes associated with vascular pathology. The prevalence of multi-ischemic dementia in Western countries has been estimated to be 7 to 10%, whereas epidemiologic data collected in Japan showed that 48.5% of individuals older than the age of 65 years had vascular dementia.5,6 Clearly, vascular dementia is common among the elderly population; however, this type of dementia represents a diagnostic challenge because of its various clinical manifestations and different vascular causes. This challenge is illustrated by the number of clinical diagnostic criteria published and used over the past 30 years. At least eight different clinical diagnostic criteria sets for vascular dementia or multi-ischemic dementia have been used in clinical and research settings, including the original Hachinski Ischemic Scale; the criteria proposed by the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV); that proposed by the California Alzheimer’s Disease Diagnostic and Treatment Centers (CAD–DTC); the criteria of the National Institute of Neurological Disorders and Stroke-

Association Internationale pour la Recherche et l’Enseignement en Neurosciences (NINDS–AIREN).6 These criteria, which have been validated by some neuropathological studies and a centralized imaging rater, have provided increased consistency in the diagnosis of VaD. Neuroimaging is required for confirmation of CVD in VaD and provides information about the topography and severity of the vascular lesions.7 The pathophysiology, diagnostic criteria, genetics, and classification of VaDs are covered in depth in Chapter 21. This chapter examines the role of conventional and advanced neuroimaging in VaD.

22.1 Clinical Criteria Vascular dementia is a broad term that encompasses all instances of dementia associated not only with ischemic CVD but also with hemorrhagic lesions, hypoxic–ischemic cerebral lesions, such as those due to cardiac arrest, and senile leukoencephalopathic lesions. It excludes patients with pure asphyxia or respiratory failure (hypoxemic anoxia) and carbon monoxide or cyanide poisoning (histotoxic anoxia).8 The clinical presentation of VaD varies greatly depending on the causes and location of cerebral damage. Large-vessel disease leads commonly to multiple cortical infarcts and a multifocal cortical dementia syndrome, whereas small-vessel disease, usually resulting from hypertension and diabetes, causes periventricular white matter ischemia and lacunar strokes characterized clinically by subcortical dementia with frontal lobe deficits, executive dysfunction, slow information processing, impaired memory, inattention, depressive mood changes, slowing of motor function and parkinsonian features (▶ Fig. 22.1).9,10 In the absence of biological markers to diagnose AD or VaD, clinicians must rely on clinical criteria to identify and describe dementia in these patients.

Fig. 22.1 Clinical presentation of vascular dementia varies depending on the type and location of cerebral vascular lesions. (Adapted from Román GC. Defining dementia. Acta Neurol Scand Suppl 2002;178:6–9.)

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Vascular Dementia Table 22.1 Operational definitions for the radiologic part of the NINDS-AIREN criteria (2003) Topography

Definition

Large-vessel stroke

Large-vessel stroke is an infarction defined as a parenchymal defect in an arterial territory involving the cortical gray matter ACA: Only bilateral ACA infarcts are sufficient to meet the NINDS-AIREN criteria PCA: Infarcts in the PCA territory can be included only when they involve the following regions: ● Paramedian thalamic infarct ● Inferior medial temporal lobe lesions Association areas: MCA infarcts need to involve the following regions: Parietotemporal lobe (e.g., angular gyrus) Temporo-occipital cortex: Watershed carotid territories (between the MCA and PCA or the MCA and ACA)

Small-vessel disease

Multiple basal ganglia and frontal white matter lacunes Extensive periventricular white matter lesions (leukoaraiosis) Bilateral thalamic lesions

Severity

Large-vessel disease of the dominant hemisphere Bilateral large-vessel hemispheric strokes Leukoencephalopathy involving at least 25% of the total white matter

Fulfillment of radiologic criteria for probable VaD

Large-vessel disease: Both the topography and severity criteria should be met (a lesion must be scored in at least one subsection of both topography and severity) Small-vessel disease: For white matter lesions, both the topography and severity criteria should be met

ACA, anterior cerebral artery; PCA, posterior cerebral artery; MCA, middle cerebral artery; PCA, posterior cerebral artery; NINDS-AIREN, National Institute of Neurological Disorders and Stroke-Association Internationale pour la Recherche et l’Enseignement en Neurosciences; VaD, vascular dementia.

Although their sensitivity and specificity vary, all clinical criteria (DSM-IV, NINDS, CAD-DTC) identify patients with VaD. Two tools have been used successfully, with high specificity, for the diagnosis of VaD: the criteria developed by the NINDSAIREN8 and those of the CAD-DTC.11 Although largely based on AD-like criteria, both sets of VaD criteria have been validated neuropathologically and have been shown to exclude successfully most patients with AD (NINDS-AIREN, 91%; CAD-DTC, 87%). Both sets of criteria outline the three critical elements necessary for the diagnosis of VaD, including dementia, CVD, and a reasonable relationship between the two.12 Finally, these two sets of VaD criteria rely on neuroimaging by computed tomography (CT) or magnetic resonance imaging (MRI) for confirmation of cerebrovascular lesions (although neither requires imaged lesions to correlate with cognitive or functional deficits).13

22.2 Role of Imaging The traditional view has been that CT and MRI are performed to exclude other abnormalities that are potentially amenable to surgical treatment, such as a tumor, hematoma, or hydrocephalus.14 However, in the recent practice parameter on the diagnosis of dementia, structural neuroimaging in the routine initial evaluation of patients with dementia is recommended as a guideline. Functional imaging provides insight into the operational aspects of the brain, and because it appears that brain pathology in dementia begins long before there is clinical evidence of disease, functional imaging is attractive for the early detection of dementia. Single-photon emission computed tomography (SPECT), positron emission tomography (PET), and functional MRI (fMRI) are becoming increasingly relevant to the study of dementia.15 The role of these imaging

techniques is discussed in detail in a previous chapter (Chapter 3). Absence of vascular lesions on brain CT or MRI rules out probable VaD and represents the most important element to distinguish AD from VaD. There is no pathognomonic brain CT or MRI of VaD. Thus, correlation with the clinical evidence is mandatory.7 The NINDS-AIREN criteria provide a list of possible vascular lesions considered relevant for the pathogenesis, although without clearly defining their imaging criteria in terms of topography and severity of lesions. To enhance their clinical implementation, operational definitions for the radiologic part of the NINDS-AIREN criteria were subsequently defined (▶ Table 22.1).16 To be considered evidence in favor of VaD (probable VaD), the radiologic findings should fulfill the minimum standards of the NINDS-AIREN criteria for both severity and topography (large vessel and small vessel). Imaging appearance of vascular dementia can be broadly divided into (1) large-vessel vascular dementia; (2) small-vessel vascular dementia and (3) microhemorrhage and dementia (▶ Table 22.2). Microhemorrhage and dementia are discussed in Chapter 23.

22.2.1 Large-Vessel Disease Large-vessel VaD is broad term encompassing poststroke dementia, multi-infarct dementia, or strategic infarct dementia. The risk factors for VaD are believed to be the same as those for stroke in general. The cause and pathology of large-vessel VaD may be described considering either the type of brain lesion or the underlying type of vessel abnormality. Brain lesions mostly include large-vessel cortical–subcortical or subcortical infarcts (e.g., watershed infarcts) and hemorrhages. Vessel abnormalities encompass atherosclerosis and embolic sources. Vasculitis is another cause of large-vessel disease.

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Neuroimaging of Vascular Dementias Table 22.2 Neuroimaging classification of vascular dementia Large-vessel vascular dementia

Multi-infarct dementia (multiple large complete infarcts involving cortical and subcortical areas) Watershed infarction Strategic single-infarct dementia Hypoperfusion and ischemic encephalopathy

Small-vessel vascular dementia

Subcortical vascular dementia Lacunes Perivascular spaces Silent cerebral infarcts

Microhemorrhage and dementia

CADASIL CARASIL HERNS CAA

Abbreviations: CAA, cerebral amyloid angiopathy; CADASIL, cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy; CARASIL, cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy; HERNS, hereditary endotheliopathy retinopathy and stroke.

Multi-Infarct Dementia (Poststroke Dementia) Classic MID is characterized by multiple large and small infarcts involving the areas of major cerebral arteries: (sub)territorial lesions of variable size resulting from atherosclerosis of extracranial and intracranial vessels giving rise to local thromboembolism or hypoperfusion and cardiac sources of cerebral emboli. Occlusion or stenosis of extracranial arteries and the major intracranial arteries can lead to MID. The thesis underlying the concept of “multi-infarct dementia” is that multiple lesions have a synergistic effect on mental functions, resulting in dementia, irrespective of specific location or volume. MID accounts for about 15% of VaD cases; the dominant hemisphere is more frequently involved. Medium-sized arteries in the lep-

tomeninges and proximal perforating arteries can be involved. MID cannot be linked to a specific vessel disorder. Its relation to age-related vessel disorders varies, and a combination of vascular lesions is frequently seen. Atherosclerosis of cerebral arteries shows a trend to more severe lesions in the circle of Willis in VaD cases than in cognitively normal controls, suggesting that atherosclerosis-related thrombosis and embolic events are important for this type of VaD. This trend is supported by the finding that the likelihood of dementia is increased in the presence of high-grade internal carotid artery (ICA) atherosclerosis. The damage can be worse depending on the presence of hypertension and related CVD.17 Cross-sectional imaging with CT and MRI, along with CT angiography and MR angiography, have become the imaging modalities to evaluate the patient with vascular dementia.7 These modalities are quite sensitive in confirming the size and location of symptomatic as well as asymptomatic (silent) strokes. MRI is also helpful in the diagnosis of microbleeds and anoxic or hypoxic brain injury and in identifying the changes of gliosis and encephalomalacia. The parenchymal perfusion status also may be obtained using CT or MR perfusion. Diffusionweighted imaging (DWI)-apparent diffusion coefficient has an established role in the diagnosis of hyperacute infarction. In the subacute-chronic stage of infarction, imaging is characterized by local brain atrophy, gliosis, cavity formation, and ex vacuo dilatation of the ipsilateral ventricle. Encephalomalacia and gliosis are seen on T2 and fluid-attenuated inversion recovery (FLAIR) images as a loss of parenchymal tissue with hyperintensity in the infarcted and subjacent tissue with prominence of cerebrospinal fluid (CSF) space. The corticosubcortical occipitotemporal infarct shown in ▶ Fig. 22.2 is typical of cortical VaD. A lesion like this, a cerebral artery infarction that extends from the occipital to the medial temporal region, would be expected to produce symptoms of amnestic memory disorder. Calcification and deposition of blood products (hemosiderin) may be seen on T2 and gradient echo (GRE) sequences (▶ Fig. 22.3a,b). Corticospinal tract degeneration (i.e., wallerian

Fig. 22.2 Corticosubcortical occipitotemporal infarcts in large-vessel vascular dementia. Axial fluid-attenuated inversion recovery (a,b) and coronal T2 (c) magnetic resonance images show large chronic left temporo-occipital infarct with encephalomalacia and surrounding hyperintensity resulting from gliosis. There is involvement of the left hippocampus (c, arrow) with dilation of temporal horn. Also seen is smaller right occipital cortical infarct.

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Fig. 22.3 Large right middle cerebral artery territory infarct in a 62-year-old man with dementia. Axial T2 spin-echo (SE) (a) and susceptibilityweighted images (SWI) (b) show areas of signal dropout in the region of infarct, consistent with hemosiderin (blood products). Fluid-attenuated inversion recovery image (c) in the same patient depicts wallerian degeneration, with signal changes and atrophy of the ipsilateral brainstem and cerebral peduncle (arrow).

degeneration) is also seen with hemispheric infarction (▶ Fig. 22.3c). Cortical laminar necrosis, neuronal ischemia accompanied by gliosis, and layered deposition of fat-laden macrophages may be seen in the infarcted region. Gray matter is more vulnerable to hypoxia than white matter. On MR, laminar necrosis is seen as hyperintensity in the cortex on T1-weighted and FLAIR images (▶ Fig. 22.4). These changes are visible 2 weeks after infarction and are most prominent at 1 to 3 months.

Watershed Infarcts Watershed or border-zone infarcts are ischemic lesions that occur in characteristic locations at the junction between two main arterial territories. These lesions constitute approximately 10% of all brain infarcts and are well described in the literature. Their pathophysiology has not yet been fully elucidated, but a

commonly accepted hypothesis holds that decreased perfusion in the distal regions of the vascular territories leaves them vulnerable to infarction. Two types of border-zone infarcts are recognized: external (cortical) and internal (subcortical) (▶ Fig. 22.5).18 In acute events, DWI is quite sensitive for the diagnosis of both cortical watershed infarct and internal watershed infarct. Classically, cortical watershed infarcts appear as fan or wedge-shaped hyperintensities extending from the lateral margins of the lateral ventricle toward the cortex, whereas internal watershed infarcts are seen as hyperintensities running parallel to the lateral ventricles, either confluent or focal, and may be unilateral or bilateral. Anterior cortical watershed infarcts are those located between the cortical supply of the anterior cerebral artery (ACA) and middle cerebral artery (MCA) (▶ Fig. 22.6a). Posterior cortical watershed infarcts are those located between the cortical supply of the MCA and the posterior cerebral artery (PCA) (▶ Fig. 22.6b). Internal watershed

Fig. 22.4 Laminar necrosis. Axial diffusionweighted (a) image shows acute left occipital lobe infarct. T1-weighted image (b) from 6 weeks of follow-up magnetic resonance imaging reveals hyperintensity along the gyri within the infarcted area suggestive of cortical laminar necrosis.

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Fig. 22.5 Color overlays on axial T2-weighted magnetic resonance images of normal cerebrum show probable locations of external (blue) and internal (red) border zone infarcts.

Fig. 22.6 Watershed infarct in a patient presenting with dementia. Axial T2 magnetic resonance (MR) image (a) shows cortical watershed infarct in the right ACA/MCA (thin arrow) and middle and posterior cerebral artery territories (thick arrow). MR angiogram of the neck (b) reveals complete occlusion of the left inferior cerebral artery from its origin with poor reconstitution (arrows).

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Vascular Dementia

Fig. 22.7 Diffusion-weighted and apparent diffusion coefficient magnetic resonance images, obtained in a 52-year-old woman with cognitive decline, progressive weakness, and numbness, show multiple internal border zone infarcts in a rosary-like pattern along the left centrum semiovale.

Fig. 22.8 Strategic subcortical infarct. Axial T2-weighted (a) and fluid-attenuated inversion recovery (b) magnetic resonance images show chronic infarction of left caudate nucleus (arrow) with ex vacuo dilatation of the left lateral ventricle.

infarcts are those located between the ACA, MCA, and PCA, and the area supplied by the Heubner, the lenticulostriate, and the anterior choroidal arteries. It can be difficult to recognize this watershed infarct from centrum ovale infarcts within the territory fed by the medullary branches of the MCA. The presence of multiple rosary-like lesions highly favors the diagnosis of watershed infarct (▶ Fig. 22.7). Watershed infarcts involving more than one of the border zone areas in a single hemisphere are mostly related to severe ICA stenosis or occlusion. Bilateral watershed infarcts are typically related to a profound global reduction in perfusion pressure (hypoxia, hypovolemia) or to diffuse cerebral vasculopathy.

Strategic Single-Infarct Dementia Strategic infarct dementia is characterized by focal, ischemic lesions in areas that control or participate in cognition and behavior or higher cortical functions. Strategic cortical sites include the hippocampal formation, angular gyrus, and cingulate gyrus; subcortical sites leading to impairment are the thalamus, fornix, basal forebrain, caudate, globus pallidus, and the genu or anterior limb of the internal capsule.19 The mechanism by which the strategic single infarct leads to dementia is not

completely understood, but it is thought to be due to interruption of frontal-subcortical circuits.20 The general organization of these circuits includes the frontal lobes, striatum, globus pallidus/substantia nigra, and thalamus. Neuroimaging has also helped to prove that single strategic lesions can cause dementia by disrupting communication between specific areas of the brain. Cognitive decline and the clinical symptoms largely depend on the strategic area involved. Caudate nucleus infarctions can lead to abulia, restlessness, hyperactivity, language deficits, and poor memory (▶ Fig. 22.8). The cognitive disorders seen most often in caudate infarcts are decreased problem-solving ability, impaired recent and remote memory with preservation of recognition memory, and decreased attention. Ischemic stroke or a subarachnoid hemorrhage from ruptured aneurysms involving the mesial temporal lobe and thalamus may cause memory and other cognitive deficits resulting from interruption of the cholinergic projections to the cholinergic nuclei in the basal forebrain. These patients may have severe anterograde amnesia for verbal or visuospatial material, along with severe apathy, lack of initiative and spontaneity, and executive dysfunction. A thalamic stroke produces a peculiar form of thalamic VaD. These patients show a depressed level of

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Fig. 22.9 Strategic single-infarct dementia. The left-thalamic infarct shown on the axial T2 (a) and T1 sagittal image (b) is of sufficient size to disrupt the Papez circuit (frontal subcortical and eloquent behavior-related circuits). The white arrows depict the normal Papez circuit from the hippocampus (orange box) to the thalamus and to the cingulate gyrus.

Fig. 22.10 Dementia from hypoperfusion in a 59-year-old man. Single-photon emission computed tomography images after 30 minutes of acetazolamide (Diamox) injection show global hypoperfusion in the supratentorial brain. The patient returned 2 days later for a baseline study that showed no significant perfusion defect.

consciousness, impairments in attention, motivation, initiative, executive functions, and memory, as well as dramatic verbal and motor slowness and apathy. Thalamic lesions may cause thalamic amnesia as a result of damage to the mammillothalamic tract; even small or unilateral damage to this structure may affect memory, executive functioning, and attention. The left-thalamic lesion shown in ▶ Fig. 22.9 is of sufficient size to disrupt the Papez circuit (frontal subcortical and eloquent behavior-related circuits). PCA infarcts may cause damage to the hippocampus, isthmus, entorhinal and perirhinal cortex, and parahippocampal gyrus. Therefore, the patient may manifest with amnesia.

Hypoperfusion and Ischemic Encephalopathy Cerebrovascular disease is recognized as a common cause of cognitive impairment and dementia, alone or coexisting with other neurodegenerative diseases, mostly AD.21 Diseases of the large arteries and the heart can lead to cerebral hypoperfusion and have been associated with the development of dementia after stroke. Although age is one of the most important risk factors for VaD, other common cardiovascular risk factors are also involved. By investigating these risk factors, a high proportion

of these cognitive disorders can be prevented or delayed. Hypoperfusion may affect both gray and white matter. Hypoperfusion affecting white matter may lead to leukoaraiosis and incomplete infarction, which comprise zones of partial neuronal or axonal loss with demyelination, increased perivascular spaces, reactive astrocytosis, and gliosis. Hypoperfusion can also produce hippocampal neuronal loss or severe white matter changes leading to hippocampal sclerosis.22 Brain MRI is becoming the method of choice to investigate cerebral vascular pathologies. However, evaluation of the hemodynamic status of the brain requires physiologic imaging. In addition, inadequate perfusion of the brain may be caused by multiple pathophysiologic mechanisms, including cardiac disease, resulting in decreased ejection fraction, atherosclerosis, or other vasculopathy and the status of collateral circulation in the brain. Identifying patients who are at increased risk for hemodynamic stroke is important because they may benefit from flow augmentation procedures, such as carotid endarterectomy, external carotid-internal carotid bypass, or even angioplasty. The cerebral hemodynamic status can be determined by measuring CBF before and after vasodilatory challenge, which can be done with either hypercapnia or acetazolamide (▶ Fig. 22.10).23 Despite this, functional imaging, such as CT and MRI perfusion

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Vascular Dementia studies, SPECT, and PET, performed in baseline conditions and/or after an acetazolamide challenge, is underused in the evaluation of VaD.21

22.2.2 Small-Vessel Disease The prototypical presentation of poststroke dementia with sudden onset or stepwise decline is often associated with overt stroke events, verified on imaging, but this is only the “tip of the iceberg.” An important concept that has been emerging in the field of VaD is that the CVD contributing to cognitive decline may be otherwise clinically silent. This insidious form of VaD arises from covert small-vessel pathology, which is prevalent with aging, as revealed by large population imaging studies conducted over the past 15 years. Small-vessel disease may be a final common pathway that expresses the adverse effects of vascular risk factors on the brain and may be one of the reasons that AD, stroke, and VaD share common vascular risk factors, such as aging, hypertension, elevated cholesterol, diabetes, and previous strokes. According to the NINDS-AIREN diagnostic criteria, smallvessel VaDs are classified into two types: subcortical and cortical forms.8,16 The subcortical form is a classic subcortical VaD, whereas the cortical form is seen mostly in cerebral amyloid angiopathy (CAA).

Subcortical Vascular Dementia Subcortical vascular dementia (SCVD) is the most common subtype of small-vessel VaD and constitutes approximately 50% of VaD cases.24 SCVD is attributed to small-vessel disease and is characterized by focal and diffuse ischemic white matter lesions (WMLs), lacunar infarcts, and incomplete ischemic injury. All of these disease conditions may coexist. In neuropathology literature, these lesions are described under various synonyms, such as subcortical arteriosclerotic encephalopathy (SAE) or Binswanger’s disease, diffuse white matter disease, white matter lesions, leukoaraiosis, periventricular arteriosclerotic (leuko) encephalopathy or leukomalacia, subcortical vascular encephalopathy, and periventricular lucency. With the advent of MRI, diffuse or focal white matter lesions are detected with higher sensitivity.

There are two main pathophysiologic pathways involved in SCVD. In the first, critical stenosis and hypoperfusion of multiple medullary arterioles cause widespread incomplete infarction of deep white matter with a clinical picture of Binswanger’s disease. In the second, occlusion of the arteriolar lumen resulting from arteriolosclerosis leads to the formation of lacunes, which results in a lacunar state (état lacunaire). In practice, the two clinical pathways can overlap; lacunes and white matter lesions are often seen together, which is not surprising, given their common origins. In addition, a combination of small-vessel and large-vessel CVD in the same patient is not unusual. In more than half of these cases, cortical and basal ganglia microinfarctions may be present, even though these lesions are not apparent on MRI. Neuroimaging plays an important role in diagnosis of SCVD, especially because it is a slow progressive disease. MRI can show changes before the symptoms are evident clinically. The most commonly seen abnormality on MRI is a diffuse hyperintensity on T2-weighted imaging, primarily in the centrum semiovale and around the ventricles.24 Confluent areas of hyperintensities (leukoaraiosis) may also be seen commonly in occipital, periventricular, and sometimes frontal white matter. On T1-weighted imaging, corresponding areas are not hypointense. For the diagnosis of the Binswanger’s disease, it is important to have associated clinical cognitive decline from a previously higher level of functioning in memory and two or more cognitive domains, in addition to the white matter changes on neuroimaging (▶ Fig. 22.11).25 The decline must at least be severe enough to interfere with activities of daily living. Without clinical findings, such findings on imaging are to be termed as leukoaraiosis. The second most common imaging finding seen with small-vessel disease is focal white matter lesions, found in 22% of subjects under age 40 and in 27 to 60% of those over age 65 years, whereas in patients with AD and VaD, they are detected by MR in almost 100% of patients.26 The white matter lesions can be categorized into periventricular white matter lesions, which are attached to the ventricular system, and deep white matter lesions, which are located in subcortical white matter (▶ Fig. 22.12). Lacunae are common small, deep infarcts of the brain smaller than 2 cm in diameter, are single or multiple, and are clinically silent or less frequently symptomatic. Lacunae result from Fig. 22.11 Binswanger’s disease in an 83-year-old man with dementia. Axial fluid-attenuated inversion recovery magnetic resonance images show extensive symmetrical hyperintensity involving periventricular and lobar white matter. These lesions have a rather sharp outer border and show relative sparing of the U-fibers. This diffuse involvement was considered > 25% of the total white matter. There was marked worsening of white matter changes and cognitive decline within a span of 5 years (2002 and 2005).

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Fig. 22.12 Periventricular and subcortical white matter lesions in a woman with mild cognitive decline. Axial fluid-attenuated inversion recovery (a) magnetic resonance images show multiple focal hyperintensities in the periventricular and subcortical white matter. On T1-weighted images (b), corresponding areas are not hypointense.

small-vessel disease with lumen occlusion secondary to arteriolosclerosis resulting from microatheroma and lipohyalinosis, or embolism, usually in patients with arterial hypertension. Lacunae are located in a territory supplied by the deep perforators such as lenticulostriate, thalamoperforating, and long medullary arterioles (▶ Fig. 22.13 a, b).27 Therefore, lacunae can be located in the basal ganglia, the upper two-thirds of the putamen, the internal capsule, the thalamus, the paramedian, and lateral regions of the brainstem (pontine base), corona radiata, and centrum semiovale.7 Lacunae must be distinguished from dilated periventricular spaces (état criblé). Lacunae are round, oval, or slitlike, small cavitated ischemic brain infarcts measuring up to 2 cm in maximum diameter, and resulting in a lacunar state or état lacunaire. Lacunar infarcts are hypodense on CT scans, hypointense on T1-weighted MRI, and hyperintense on T2-weighted MRI. In late stages, lacunae may be hypointense on FLAIR with an irregular rim of hyperintensity around. A common differential diagnosis includes Virchow-Robin spaces, which follow CSF signal on all MRI sequences (▶ Fig. 22.13). In practice, the two forms of subcortical ischemic VaD, lacunae and deep white matter lesions, are often seen together, presumably because of their common origin.

Perivascular Spaces The perivascular spaces (PVSs) of the brain, also known as Virchow-Robin spaces (VRS), are pial-lined interstitial fluid -filled structures that accompany penetrating arteries and arterioles for a variable distance as they descend into the cerebral substance. Recent studies have shown that the PVSs are surprisingly complex entities with significant variability in both ultrastructure and possible function. Prominent VRS may occur in all age groups and are regarded as incidental findings without much clinical significance. However, when they are prominent in elderly patients, they indicate the shrinkage of the surrounding white matter. On histopathology, there is no evidence of necrosis, macrophages, or tissue debris in the VRS. Occasionally, the PVSs may become strikingly enlarged, causing mass effect and assuming bizarre configurations that may be mistaken for a more ominous disease, such as a cystic neoplasm. Typical PVSs

Fig. 22.13 Lacunar infarcts versus perivascular spaces. Axial T2 and fluid-attenuated inversion recovery (FLAIR) images in lacunar infarct (a, b) and prominent perivascular space (c,d) in the basal ganglia. Enlarged perivascular spaces are isointense to the cerebrospinal fluid space on all three sequences with no hyperintense rim on FLAIR images. On the other hand, chronic lacunar infarct has a hypointense center with a hyperintense rim (arrow) on FLAIR images, an appearance that reflects gliosis.

occur in many locations. The most common site is along the lenticulostriate arteries, just above the anterior perforated substance and adjacent to the anterior commissure. Less commonly, PVSs occur along arteries that have penetrated the cerebral cortex and extended into the white matter. Other areas where prominent PVSs can be identified include the subinsular

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Vascular Dementia region, dentate nuclei, and cerebellum. PVSs have typical MRI features (▶ Fig. 22.13c,d). They are round or oval with a welldefined, smooth margin; occur along the path of penetrating arteries; are isointense relative to CSF; and demonstrate no enhancement after contrast medium administration. When PVSs become enlarged, they are known as giant PVSs, cavernous dilatation, or expanding lacunae.28

22.2.3 Mixed Dementia Vascular dementia secondary to CVD has been traditionally distinguished from AD, which is a purely neurodegenerative form of dementia. However, CVDs such as lacunae and white matter lesions are common in patients with AD, whereas certain pathological changes of AD, including senile plaques and tangles, are observed in elderly patients with VaD. These findings indicate that mixed vascular-degenerative dementia (MD) is the most common cause of dementia in the elderly. In the treatment and prevention of dementia, the accurate diagnosis of each individual type of dementia is vital. However, recognizing the distinction between these diseases can be difficult in clinical practice.29 Although little is known about specific risk factors for MD, these most likely include the presence of risk factors for both AD and VaD, which have been extensively studied. Vascular risk factors appear to be particularly important. Hypertension is one of the most potent risk factors for the development of VaD, but it may also increase AD risk.

Clinical criteria reflect the diversity of the MD concept. Most of these criteria remain controversial and have not been well validated by neuropathological studies. Advances in neuroimaging techniques have led to a better understanding of the features indicative of AD and VaD and may lead to improved criteria. For example, in patients with VaD, CT and MRI findings include infarctions, white matter irregular periventricular hyperintensities, and hyperintense foci in the basal ganglia and thalamus. Hippocampal atrophy is most consistent with AD, although recent data suggest that it may also occur in VaD or in mixed ischemic and degenerative pathology. SPECT studies have shown that VaD patients usually exhibit a diffuse and asymmetric decrease of CBF in the cerebral cortex and subcortical nuclei, with a loss of cerebral vasomotor responsiveness. In contrast, AD patients exhibit reduced CBF in temporal and parietal areas and show preserved ability to vasodilate and increase CBF in response to various stimuli. PET reveals multiple focal metabolic defects in VaD as opposed to reduced regional metabolic rates in the temporal lobes in early AD (▶ Fig. 22.14). However, structural and functional neuroimaging characteristics are often unable to distinguish AD from VaD, especially in advanced cases. Unfortunately, features that might be particularly suggestive of MD have not been well described, and further studies are needed to better define the potential role of neuroimaging in establishing or corroborating this diagnosis.30

Fig. 22.14 Glucose metabolism in a normal control, in a patient with vascular dementia, and in a patient with Alzheimer’s disease. The severity of dementia was comparable, and the pattern of pathologic changes differentiated these two cases: patchy metabolic defects in vascular dementia (VaD) in the frontal lobe, basal ganglia, and thalamus; hypometabolism in bilateral parietotemporal cortex and to a lesser degree in the frontal association areas in Alzheimer's disease (AD).

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22.3 Conclusion Currently, CVD is viewed as a distinctly secondary cause of dementia that is of uncertain importance. Although it is commonly cited as the second leading cause of dementia, a lack of well-validated diagnostic criteria, which in turn reflects important gaps in fundamental knowledge about disease mechanisms, makes accurate epidemiology difficult. Distinguishing VaD from AD and other forms of dementia can be challenging because these disorders share many features. Understanding of VaD has evolved substantially in recent years, based on preclinical, neuropathologic, neuroimaging, physiologic, and epidemiologic studies. There is a need for prospective, quantitative, clinical-pathological-neuroimaging studies to improve knowledge of the pathological basis of neuroimaging change and the complex interplay between vascular and AD pathologies in the evolution of clinical VaD and AD. Among the tools available to help clinicians diagnose and monitor VaD progression, neuroimaging is especially useful for confirming the diagnosis and identifying specific VaD subtypes.

References [1] Erkinjuntti T. Diagnosis and management of vascular cognitive impairment and dementia. J Neural Transm Suppl 2002; 63: 91–109 [2] Bowler JV. Criteria for vascular dementia: replacing dogma with data. Arch Neurol 2000; 57: 170–171 [3] Erkinjuntti T, Inzitari D, Pantoni L et al. Limitations of clinical criteria for the diagnosis of vascular dementia in clinical trials: is a focus on subcortical vascular dementia a solution? Ann N Y Acad Sci 2000; 903: 262–272 [4] Hachinski VC, Lassen NA, Marshall J. Multi-infarct dementia: a cause of mental deterioration in the elderly. Lancet 1974; 2: 207–210 [5] Yanagihara T. Vascular dementia in Japan. Ann N Y Acad Sci 2002; 977: 24–28 [6] Wiederkehr S, Simard M, Fortin C, van Reekum R. Validity of the clinical diagnostic criteria for vascular dementia: a critical review. Part II. J Neuropsychiatry Clin Neurosci 2008; 20: 162–177 [7] Guermazi A, Miaux Y, Rovira-Cañellas A et al. Neuroradiological findings in vascular dementia. Neuroradiology 2007; 49: 1–22 [8] Román GC, Tatemichi TK, Erkinjuntti T et al. Vascular dementia: diagnostic criteria for research studies: report of the NINDS-AIREN International Workshop. Neurology 1993; 43: 250–260 [9] Román GC, Royall DR. Executive control function: a rational basis for the diagnosis of vascular dementia. Alzheimer Dis Assoc Disord 1999; 13 Suppl 3: S69–S80

[10] Kurz AF. What is vascular dementia? Int J Clin Pract Suppl 2001; 120: 5–8 [11] Chui HC, Victoroff JI, Margolin D, Jagust W, Shankle R, Katzman R. Criteria for the diagnosis of ischemic vascular dementia proposed by the State of California Alzheimer’s Disease Diagnostic and Treatment Centers. Neurology 1992; 42: 473–480 [12] Román GC. Defining dementia: clinical criteria for the diagnosis of vascular dementia. Acta Neurol Scand Suppl 2002; 178: 6–9 [13] Erkinjuntti T, Román G, Gauthier S, Feldman H, Rockwood K. Emerging therapies for vascular dementia and vascular cognitive impairment. Stroke 2004; 35: 1010–1017 [14] van der Flier WM, Scheltens P. Use of laboratory and imaging investigations in dementia. J Neurol Neurosurg Psychiatry 2005; 76 Suppl 5: v45–v52 [15] Tartaglia MC, Rosen HJ, Miller BL. Neuroimaging in dementia. Neurotherapeutics 2011; 8: 82–92 [16] van Straaten EC, Scheltens P, Knol DL et al. Operational definitions for the NINDS-AIREN criteria for vascular dementia: an interobserver study. Stroke 2003; 34: 1907–1912 [17] Jellinger KA. Pathology and pathogenesis of vascular cognitive impairment—a critical update. Front Aging Neurosci 2013; 5: 17 [18] Mangla R, Kolar B, Almast J, Ekholm SE. Border zone infarcts: pathophysiologic and imaging characteristics. Radiographics 2011; 31: 1201–1214 [19] Ferro JM. Hyperacute cognitive stroke syndromes. J Neurol 2001; 248: 841– 849 [20] Desmond DW. Vascular dementia: a construct in evolution. Cerebrovasc Brain Metab Rev 1996; 8: 296–325 [21] Farid K, Petras S, Ducasse V et al. Brain perfusion SPECT imaging and acetazolamide challenge in vascular cognitive impairment. Nucl Med Commun 2012; 33: 571–580 [22] Jellinger KA. The enigma of vascular cognitive disorder and vascular dementia. Acta Neuropathol 2007; 113: 349–388 [23] Ozgur HT, Kent Walsh T, Masaryk A et al. Correlation of cerebrovascular reserve as measured by acetazolamide-challenged SPECT with angiographic flow patterns and intra- or extracranial arterial stenosis. AJNR Am J Neuroradiol 2001; 22: 928–936 [24] Tomimoto H. Subcortical vascular dementia. Neurosci Res 2011; 71: 193– 199 [25] Bennett DA, Wilson RS, Gilley DW, Fox JH. Clinical diagnosis of Binswanger’s disease. J Neurol Neurosurg Psychiatry 1990; 53: 961–965 [26] Vernooij MW, Smits M. Structural neuroimaging in aging and Alzheimer’s disease. Neuroimaging Clin N Am 2012;22(1):33–55 [27] Leys D, Pasquier F, Lucas C, Pruvo JP. [Magnetic resonance imaging in vascular dementia] [in French] J Mal Vasc 1995; 20: 194–202 [28] Salzman KL, Osborn AG, House P et al. Giant tumefactive perivascular spaces. AJNR Am J Neuroradiol 2005; 26: 298–305 [29] Hanyu H. [Diagnosis and treatment of mixed dementia] Brain Nerve 2012; 64: 1047–1055 [30] Zekry D, Hauw JJ, Gold G. Mixed dementia: epidemiology, diagnosis, and treatment. J Am Geriatr Soc 2002; 50: 1431–1438

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Vascular Dementia

23 Imaging of Specific Hereditary Microangiopathies Kenneth Lury and Mauricio Castillo The hereditary cerebral microangiopathies (HCM)s encompass a diverse group of diseases that have as common manifestations recurring stroke symptoms, cerebral infarctions, and diffuse white matter lesions, leading to a broad spectrum of neurologic problems and deficits. The cerebral blood vessels most often affected by HCM are the lenticulostriate arteries, pontine branches arising from the basilar artery, and thalamo-perforating arteries that arise from the P1 and P2 segments of the posterior cerebral arteries, tip of the basilar artery, and posterior communicating arteries. Penetrating arteries arising from leptomeningeal branches may also be affected.1 In recent years, advances in molecular genetics have identified several monogenic conditions responsible for HCM. A diagnosis of hereditary cerebral small-vessel disease has to be considered in cerebrovascular disorders presenting in youth and adulthood.2 In this chapter, we address the clinical and imaging findings of the most common causes of HCM.

23.1 Fabry's Disease Fabry's disease is an X-linked inherited disorder of glycosphingolipid metabolism that results from deficient or absent lysosomal α-galactosidase A (α-Gal A) leading to a progressive accumulation of globotriaosylceramide (GB3).1,2 These lipid deposits occur preferentially in the endothelial and smooth muscle cells, resulting in vascular dysfunction, tissue ischemia, and eventual vessel occlusion.3 The disease has no ethnic predilection, and its reported annual incidence is about 1:40,000 to 100,000.1,2 The classic form of the disease involves males with no detectable α-Gal A activity. Clinical presentations include angiokeratomas, acroparesthesia, hypohidrosis, and childhood or adolescent cataracts in addition to progressive vascular disease of the heart, kidneys, and central nervous system. With advancing age, progressive damage to other vital organs may lead to their failure.2 Elevated GB3 levels in the blood allow a definitive diagnosis in men. In women, however, the diagnosis can be made only by gene sequencing.1 Enzyme replacement therapy

has been shown to normalize cerebral-vessel compliance, but the influence of this treatment on clinical outcomes has not been demonstrated.3 End-stage renal disease and life-threatening cardiovascular or cerebrovascular complications decrease life expectancy of untreated male and female patients by 20 and 10 years, respectively.2 Fabry's disease should be considered in the differential diagnosis in young patients with cryptogenic stroke(s) especially affecting the vertebral basilar system.3 Imaging findings are otherwise nonspecific.

23.2 CADASIL CADASIL, or cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, is caused by dominant mutations in the NOTCH3 gene on chromosome 19p 13.2.2 CADASIL is the most common cause of inherited cerebral infarction(s) and vascular cognitive impairment in adults; it is characterized by five main symptoms: migraine with aura, mood disturbances, apathy, cognitive impairment, and subcortical ischemic strokes. The last, along with transient ischemic attacks (TIAs), are the most frequent manifestation of CADASIL and occur in 60 to 85% of patients, generally at a mean age of 49 years (range, 20 to 70 years). Territorial infarcts are rare. In most patients, there is an absence of conventional vascular risk factors as well as absence of involvement of other organs. At present, there is no treatment for CADASIL, either for the disease or for its symptoms. The usual preventive measures for noncardiologic embolic ischemic stroke, including the use of antiplatelet drugs rather than anticoagulants (because of the increased risk of intracerebral hemorrhage) and treatment of vascular risk factors, may offer some benefit.1,4 Although white matter abnormalities resulting from CADASIL may be indistinguishable from those found in other disorders, such as multiple sclerosis, subcortical cerebrovascular diseases, and leukodystrophies, the high frequency of early involvement of the anterior aspects of the temporal lobes and the late involvement of the external capsules are strongly suggestive of the diagnosis at different stages (▶ Fig. 23.1). Other MRI

Fig. 23.1 CADASIL (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy). (a) Axial T2-weighted image obtained at level of the basal ganglia demonstrates extensive high signal intensity in the white matter, including the external capsule bilaterally (thick arrows) (b). Axial T2-weighted image obtained inferior to (a) shows abnormal signal in the white matter of the anterior and inferior white matter of the temporal lobes (thin arrows).

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Imaging of Specific Hereditary Microangiopathies findings include dilated perivascular spaces that are sometimes prominent and diffuse (état criblé), diffuse white matter abnormalities, infarctions, and atrophy.2

23.3 CARASIL CARASIL, or cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy, is an entity clinically similar to CADASIL that is caused by mutations in the HtrA serine protease 1 (HTRA1) gene (chromosome 10q 26.3). Serine proteases regulate cell growth by controlling the availability of insulin-like growth factors. CARASIL's micropathological expression is distinguished from the CADASIL phenotype by absence of depositions of granular osmiophilic material in the tunica media of small arteries and arterioles in the skin as seen in CADASIL. In addition to the typical clinical features found in CADASIL, early alopecia and spondylosis are frequently present in CARASIL. This disease is rare and has been seen only in Japanese or Chinese families.1,5 The most characteristic brain magnetic resonance imaging (MRI) findings in patients with CARASIL are white matter lesions, more often in the periventricular and deep regions than in the superficial white matter (U-fibers) and multiple lacunar infarctions in the basal ganglia and thalamus.5

23.4 Amyloid Angiopathy Cerebral amyloid angiopathy (CAA) is an important cause of spontaneous cortical and subcortical intracranial hemorrhages (ICHs), as well as nontraumatic convexity subarachnoid hemorrhages in normotensive elderly individuals. Microscopically, there is deposition of amyloid in the media and adventitia of small and medium-sized blood vessels of the cerebral cortex, subcortical regions, and leptomeninges, with sparing of similarly sized vessels in the deep white matter. Amyloid deposition engenders fibrinoid necrosis, focal vessel wall fragmentation, and microaneurysms, all of which predispose patients to repeated episodes of blood vessel leakages or frank hemorrhages. Additionally, at sites of fibrinoid necrosis, there may be luminal narrowing that can lead to ischemia. CAA is not associated

with presence of systemic amyloidosis, and both sporadic and hereditary CAA forms may occur. Hereditary CAA is rare and generally demonstrates an autosomal dominant transmission.6 Amino acid substitutions at four sites in the β-amyloid precursor protein, all situated within the β-amyloid peptide sequence itself, have been shown to cause heritable forms of CAA.7 Hereditary forms of CAA display a broader range of clinical manifestations than the sporadic types, which are more common in the elderly and increase in both prevalence and severity with advancing age. Many patients with CAA are asymptomatic. When symptomatic, typical presentations include acute ICH, TIAs, and eventual dementia. The most common clinical presentation of CAA is the development of a sudden neurologic deficit secondary to acute ICH or subarachnoid hemorrhage, but these symptoms are not specific for CAA. Dementia may be seen before symptomatic ICH in 25 to 40% of patients and may be slowly progressive, similar to that seen in patients with Alzheimer’s disease. On imaging studies, CAA-related macrohemorrhages typically exhibit irregular borders and may be associated with subarachnoid hemorrhage (particularly in the cerebral convexities), subdural hematomas, and less commonly, intraventricular hemorrhage. Subarachnoid and subdural hemorrhages may be due to direct extension from the cortical-subcortical hemorrhage or may be primary in nature. Typically, MRI obtained with blood-sensitive sequences (gradient-recalled echoes or susceptibility-weighted images) demonstrate microhemorrhages (< 5 mm), which are often clinically silent. (▶ Fig. 23.2) These microbleeds are preferentially located on the surface of the brain and the cortex and may coexist with larger acute ICHs (> 5 mm in diameter), which may be of varying ages and involve any lobe of the cerebral hemispheres but preferentially the parietal lobes and posterior regions. These larger bleeds occur in a distinctive corticalsubcortical distribution, generally sparing the basal ganglia and brainstem. The larger bleeds may show fluid levels and occasionally occur in “mirror-like” locations synchronously or metachronously. (▶ Fig. 23.3) These findings are often accompanied by leukoencephalopathy and diffuse cerebral atrophy (▶ Fig. 23.4).6 Superficial siderosis may be seen as a consequence of subarachnoid bleeds and when secondary to CAA it

Fig. 23.2 Cerebral amyloid. (a) Axial T2* (gradient echo) at the level of the corona radiata demonstrates multiple areas of punctate subcortical hemorrhage, as well as blood in the subarachnoid spaces and surface of brain compatible with superficial hemosiderosis. (b) Axial T2* (gradient echo) at level of basal ganglia in the same patient demonstrates multiple punctate hemorrhages predominantly in cortical and subcortical regions and, to a lesser degree, in the basal ganglia.

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Fig. 23.3 Cerebral amyloid. Axial fluid-attenuated inversion recovery at the level of centra semiovale demonstrates “fluid/fluid” level in an acute left parietal lobe hemorrhage as well as a subacute left frontal hemorrhage. Note also the subarachnoid spaces are high signal, indicative of acute hemorrhage.

Fig. 23.4 Cerebral amyloid. Computed tomography scan at level of the lateral ventricles shows a right frontal subarachnoid hemorrhage as well as white matter hypodensities, especially posteriorly.

generally affects the cerebral convexities (▶ Fig. 23.5). Whenever an elderly adult presents with nontraumatic and spontaneous subarachnoid hemorrhage in the cerebral convexities, CAA should be included in the differential diagnosis. Currently, there is no treatment to halt or reverse CAA. Thus, attention is directed to prevention of adverse outcomes associated with the natural history of CAA. Patients with a new diagnosis of CAA should have a risk:benefit evaluation before continuation of anticoagulation therapy for other disorders. Whereas hypertension is the most common cause of nontraumatic hemorrhage in adults, the typical cortical-subcortical location of CAA-related hemorrhages is helpful in distinguishing them from the more common deep gray matter, cerebellar, and brainstem location of hypertensive hemorrhages.6 Occasionally, CAA may present in atypical ways. A case series of five patients with “tumefactive cerebral amyloid angiopathy” reported MRI findings that are difficult to distinguish from low-grade gliomas. These lesions were nonenhancing, nonhemorrhagic, poorly marginated, infiltrative and masslike, with cortical swelling and variable adjacent leptomeningeal enhancement (▶ Fig. 23.6).8

23.5 Sickle Cell Disease Sickle cell disease (SCD) is caused by a point mutation in the βglobin gene resulting in the mutant protein hemoglobin S (HbS) in which the sixth amino acid changes from glutamic acid to valine. The most common and severe form of SCD, homozygous

Fig. 23.5 Cerebral amyloid. Axial T2-weighted image at level of cerebral convexities shows superficial siderosis along the convexities (arrows).

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Fig. 23.6 Cerebral infiltrative amyloid. (a) Axial noncontrast T1-weighted image at the level of cerebral convexities shows an area of low intensity and mass effect in the left frontal region. (b) Left parasagittal T1-weighted image again shows the lesion in the left frontal region. (c) Axial postgadolinium T1-weighted image at same level as (a) demonstrates leptomeningeal enhancement (arrow).

Fig. 23.7 Sickle cell disease. (a) Magnetic resonance angiography through the circle of Willis shows severe bilateral stenoses involving the anterior and middle cerebral arteries (blue arrows) and diminished flow-related enhancement in the right posterior cerebral artery (white arrow). (b) Axial fluid-attenuated inversion recovery of the same patient at level of cerebral convexities demonstrates abnormal signal corresponding to infarction in vascular region (right parietal lobe) supplied by the right posterior cerebral artery. Note the high signal in the subarachnoid space compatible with the “ivy sign” from slow flow through collateral circulation.

HbSS, is referred to as sickle cell anemia (SCA), in which deoxygenated HbS molecules form intracellular red blood cell polymers that damage the red cell membranes and increase their rigidity.9 SCD occurs most commonly in people of African, Mediterranean, Indian, and Middle Eastern ancestry. In North America, SCD is more frequent in African Americans, Africans, and Hispanic patients from the Caribbean and Central and South America. SCD affects about 50,000 people in the United States. Among newborn infants, SCD occurs in approximately: 1:400 African Americans, 1:36,000 Hispanics, and 1:80,000 whites. SCD is diagnosed by hemoglobin electrophoresis that identifies the abnormal hemoglobin.10 The most common histopathology abnormality related to cerebrovascular disease is damage to the endothelium of mid- to large-sized arteries, particularly at branch points, producing intimal proliferation, fibrin deposition, and thrombus formation.9 The most common neurologic lesions in SCA are silent cerebral infarctions.11 Moyamoya, meaning a “hazy puff of smoke” in Japanese, is a common manifestation of cerebral SCD. Moyamoya is defined as a chronic, occlusive cerebrovascular disease with bilateral stenosis or occlusion of the terminal portions of the internal carotid arteries and/or the proximal portions of the anterior cerebral arteries and middle cerebral arteries. The “puff of

smoke” appearance is the result of small lenticulostriate arteries arising from the internal carotid artery terminus and proximal anterior and middle cerebral arteries, forming a network of collateral circulation to bypass the narrowed or occlusive segments, moyamoya is diagnosed by MR angiography or conventional digital subtraction catheter angiography. The development of Moyamoya-like changes is a grave prognostic finding in patients with SCD and strokes (▶ Fig. 23.7, ▶ Fig. 23.8).9

23.6 Homocystinuria Homocystinuria is an inherited autosomal recessive disorder in which the body is unable to process amino acids properly. Homocystinuria has multiple forms that are distinguished by their signs, symptoms, and genetic causes. Mutations in the CBS gene cause the most common form. The CBS gene is responsible for cystathionine β-synthase, an enzyme that converts homocysteine to cystathionine. Mutations in this gene disrupt the function of cystathionine β-synthase, preventing homocysteine from being used properly. As a result, this amino acid and its toxic byproduct substances accumulate. The most common form of homocystinuria affects 1:200,000 to 335,000 people worldwide. It is more common in Ireland (1:65,000), Germany

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Fig. 23.8 Sickle cell disease and moyamoya phenomenon. (a) Source image for time-of-flight magnetic resonance angiography of the circle of Willis shows tangles of collateral blood vessels in the sylvian regions bilaterally (arrows). (b) Digital subtraction catheter angiogram of an injection in the internal carotid artery (left posterior oblique projection) shows that some of right middle cerebral artery territory is supplied by dysplastic collaterals compatible with moyamoya vessels.

Fig. 23.9 Homocystinuria axial T2-weighted imaging through the orbits shows bilateral lens dislocation.

(1:17,800), Norway (1:6,400), and Qatar (1:1,800) and is characterized by nearsightedness, dislocation of the ocular lens, an increased risk of blood clotting, arterial dissections, and osteoporosis (▶ Fig. 23.9). Less common manifestations of homocystinuria include intellectual disabilities, failure to thrive, seizures, movement disorders, and megaloblastic anemia. Signs and symptoms of homocystinuria typically develop within the first year of life, although some people with a mild form of the disease might not develop these features until later in life.12 Intrauterine diagnosis of homocystinuria involves culturing amniotic cells or chorionic villi to test for cystathionine synthase. Although no cure exists for homocystinuria, vitamin B6 therapy can help about 50% of patients.13

23.7 Antiphospholipid Syndrome Antiphospholipid syndrome (APS) is an autoimmune condition defined by arterial and venous thrombosis with persistently positive antiphospholipid antibodies (aPLs). In addition to peripheral arterial and venous thromboses affecting any size blood vessels, a variety of clinical manifestations have been reported: skin disease; cardiac, pulmonary, and renal involvement; hematologic manifestations; and a wide spectrum of

neurologic disorders. Cerebral involvement in APS is common and is characterized by cerebral infarctions, epilepsy, dementia, cognitive deficits, headaches, psychiatric disorders, chorea, multiple sclerosis–like disease, transverse myelitis, and ocular symptoms, any of which may be the initial features or may appear later. Headaches are a common neurologic manifestation. Symptoms may result from thrombosis or direct injury to the brain. Secondary APS is seen in patients with other connective tissue diseases, most often systemic lupus erythematosus. Genetic and environmental factors are involved in the causes of APS, and infections, autoimmune, and other inflammatory diseases and drugs and neoplasms may also induce production of aPLs. Strokes and TIAs are common as a result of arterial thromboses. Almost 20% of female stroke patients younger than 45 years of age have associated APS. The spectrum of neuroradiologic findings in patients with APS is largely a consequence of multiple arterial or venous thromboses. Infarcts of various sizes and focal T2/fluidattenuated inversion recovery (FLAIR)/diffusion-weighted imaging (DWI) hyperintense lesions in white matter are the most common abnormalities. Venous occlusions on magnetic resonance venogram and computed tomography venogram are typical.14

23.8 Mitochondrial Encephalomyopathy Mitochondrial encephalomyopathy with lactic acidosis and strokelike episodes (MELAS) is most commonly associated with the m.3243A > G mutation in MT-TL1, the tRNA gene responsible for leucine. This condition is inherited in a maternal pattern that applies to all genes contained in mitochondrial DNA, as only egg cells contribute mitochondria to the developing embryo.15 Typically, clinical episodes begin with severe “migraine-like” headaches associated with nausea, vomiting, and sometimes seizures, followed by hemiparesis, hemianopia, and/or cortical blindness. The infarctions are often parietooccipital and do not conform to vascular territories. Proton MR spectroscopy often demonstrates lactic acid in the basal ganglia and elsewhere (▶ Fig. 23.10). Frequent comorbidities include dementia, ataxia, deafness, muscle weakness, cardiomyopathy,

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Imaging of Specific Hereditary Microangiopathies

23.11 Aicardi Goutières Syndrome Aicardi Goutières syndrome (AGS) is an inherited encephalopathy (mostly autosomal recessive) associated with mutations of four different genes. It affects newborns and usually results in severe mental and physical handicaps. There are two forms of the syndrome. The early-onset form demonstrates jittery behavior and poor feeding ability mimicking congenital viral infection. Those with later-onset type have symptoms after the first weeks or months of normal development, starting with progressive decline in head growth, weak or stiffened muscles (spasticity), and leading to moderate to severe mental and developmental retardation.19 MRI shows three features: cerebral calcifications, white matter abnormalities, and cerebral atrophy. Calcifications are typically bilateral and located in the basal ganglia and cerebellar dentate nuclei and are best visualized by CT. In 50 to 70% of cases, calcifications extend to the white matter, particularly the periventricular areas.20

References Fig. 23.10 Mitochondrial encephalomyopathy with lactic acidosis and strokelike episodes (MELAS), P (Proton)-magnetic resonance spectroscopy voxel in the basal ganglia shows inverted peak at 1.3 parts per million corresponding to lactate (arrow).

and diabetes.15,16 The underlying pathogenesis for the infarctions is not clear.1

23.9 Retinal Vasculopathy + Leukoencephalopathy Retinal vasculopathy + leukoencephalopathy (AD-RVLC) is inherited in an autosomal dominant pattern and is caused by mutations in the 3‘–5‘ DNA exonuclease TREX1. The main clinical manifestations are retinopathy, nephropathy, and recurrent strokes. Characteristic electron microscopic findings are described in biopsies from the brain, kidney, or skin showing multilayered basal membranes. MRI shows subcortical contrast-enhancing lesions. In contrast to CADASIL, there is no temporal lobe disease preponderance.1

23.10 COL4A1 COL4A1-related brain small-vessel disease is very rare. It is inherited in an autosomal dominant pattern, involving mutations in the COL4A1 gene, which encodes for one component of a protein called type IV collagen, the main component of basement membranes. COL4A1-related brain small-vessel disease is characterized by weakening of the blood vessels in the brain. Stroke is often the first symptom and typically occurs in midadulthood. Hemorrhagic infarction is more common than ischemic infarction, although either type can occur. MRI may also show a diffuse leukoencephalopathy associated with dilated perivascular spaces.17,18

[1] Ringelstein EB, Kleffner I, Dittrich R, Kuhlenbäumer G, Ritter MA. Hereditary and non-hereditary microangiopathies in the young. An up-date. J Neurol Sci 2010; 299: 81–85 [2] Federico A, Di Donato I, Bianchi S, Di Palma C, Taglia I, Dotti MT. Hereditary cerebral small vessel diseases: a review. J Neurol Sci 2012; 322: 25–30 [3] Fellgiebel A, Müller MJ, Ginsberg L. CNS manifestations of Fabry’s disease. Lancet Neurol 2006; 5: 791–795 [4] Chabriat H, Joutel A, Dichgans M, Tournier-Lasserve E, Bousser MG. CADASIL. Lancet Neurol 2009; 8: 643–653 [5] Fukutake T. Cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy (CARASIL): from discovery to gene identification. J Stroke Cerebrovasc Dis 2011; 20: 85–93 [6] Chao CP, Kotsenas AL, Broderick DF. Cerebral amyloid angiopathy: CT and MR imaging findings. Radiographics 2006; 26: 1517–1531 [7] Zhang-Nunes SX, Maat-Schieman ML, van Duinen SG, Roos RA, Frosch MP, Greenberg SM. The cerebral beta-amyloid angiopathies: hereditary and sporadic. Brain Pathol 2006; 16: 30–39 [8] Kotsenas AL, Morris JM, Wald JT, Parisi JE, Campeau NG. Tumefactive cerebral amyloid angiopathy mimicking CNS neoplasm. AJR Am J Roentgenol 2013; 200: 50–56 [9] Kassim AA, DeBaun MR. Sickle cell disease, vasculopathy, and therapeutics. Annu Rev Med 2013; 64: 451–466 [10] About Sickle Cell Disease [11] DeBaun MR, Armstrong FD, McKinstry RC, Ware RE, Vichinsky E, Kirkham FJ. Silent cerebral infarcts: a review on a prevalent and progressive cause of neurologic injury in sickle cell anemia. Blood 2012; 119: 4587–4596 [12] Homocystinuria. http://ghr.nlm.nih.gov/condition/homocystinuria accessed 1/29/13 [13] Homocystinuria [14] Mayer M, Cerovec M, Rados M, Cikes N. Antiphospholipid syndrome and central nervous system. Clin Neurol Neurosurg 2010; 112: 602–608 [15] Mitochondrial encephalomyopathy, lactic acidosis-and-stroke-like-episodes. Available at: , 2013 [16] Rahman S, Hanna MG. Diagnosis and therapy in neuromuscular disorders: diagnosis and new treatments in mitochondrial diseases. J Neurol Neurosurg Psychiatry 2009; 80: 943–953 [17] Gould DB, Phalan FC, van Mil SE et al. Role of COL4A1 in small-vessel disease and hemorrhagic stroke. N Engl J Med 2006; 354: 1489–1496 [18] COL4A1-related brain small-vessel disease. Available at: http://ghr.nlm.nih. gov/condition/col4a1-related-brain-small-vessel-disease. Accessed February 27, 2013 [19] NINDS Aicardi-Goutieres Syndrome Disorder Information Page. http://www .ninds.nih.gov/disorders/aicardi_goutieres/aicardi-goutieres.htm. Accessed February 27, 2013 [20] Orcesi S, La Piana R, Fazzi E. Aicardi-Goutieres syndrome. Br Med Bull 2009; 89: 183–201

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Vascular Dementia

24 Vasculitis and Dementia Sampson K. Kyere, Olaguoke Akinwande, Dheeraj Gandhi, and Gaurav Jindal Vasculitis comprises a heterogeneous group of disorders marked by inflammation and necrosis of the vessel wall. Manifestations of vasculitis in the central nervous system (CNS) are rare compared with those of other organ systems.1 Vasculitisrelated dementia is usually of rapid progression evolving over weeks to months compared with degenerative dementia, which may take years to develop. Among the vasculitides, the pattern of cognitive decline is indistinguishable. Many of these conditions are treatable; therefore, a high degree of suspicion and early diagnostic evaluation of patients with signs of dementia are critical. In this chapter, we discuss the vasculitides, which may manifest with clinical dementia and the imaging findings associated with these disorders. Many of the imaging findings, both on cross-sectional imaging and digital subtraction angiography (DSA), overlap from one disorder to the next. Empiric treatment can be started if clinical suspicion warrants therapy. Biopsy also can aid in the diagnosis when equivocal.

24.1 Primary Central Nervous System Vasculitis 24.1.1 Primary Angiitis of the Central Nervous System Causes and Histopathology Primary angiitis of the central nervous system (PACNS) is an uncommon, heterogeneous group of vasculitides of the brain and spinal cord of unknown cause and without systemic manifestations.2 PACNS usually affects middle-aged patients at a mean of 42 years, but it can affect a wide age range of patients, from age 3 to the elderly. There is a slight male predominance. Histologically, PACNS typically features a varying degree of mononuclear inflammation and necrosis of the media and adventitia of small and medium leptomeningeal and parenchymal arteries.3 Granulomatous vasculitis is the most common form (58%) of PACNS, demonstrating wellformed granulomas with multinucleated cells within vessel walls. Lymphocytic vasculitis is the second most common pattern (28%) and is marked predominantly by lymphocytic inflammation. Necrotizing vasculitis is the least common (14%) pattern, characterized by transmural necrosis associated with intracranial hemorrhage.2,3 The histologic patterns remain stable over time, indicating that they do not represent different phases.2

Clinical and Laboratory Features Clinical presentations of PACNS are diverse but often marked by headache, altered cognition, focal weakness, or stroke. Spinal cord involvement is seen in 5% of cases.4 There is generally a lack of systemic constitutional symptoms, such as weight loss and fever. The course can be relapsing-remitting or slowly progressive resulting in subcortical dementia. In fact, about 30% of

cases of PACNS have cognitive impairment at manifestation. Because of the potential devastating sequelae of the disease, prompt diagnosis and initiation of treatment are critical. Brain biopsy is the gold standard diagnostic tool for PACNS with a specimen consisting of dura, leptomeninges, cortex, and white matter. Sensitivity of biopsy increases to 80% if targeted to a region of radiographic abnormality.5 Because there are no systemic manifestations, blood tests are usually normal. Cerebrospinal fluid (CSF) analysis will show a mildly increased leukocyte count and total protein in 80 to 90% of patients without evidence of infection or malignancy. Electroencephalographic findings are nonspecific. Treatment consisting of cyclophosphamide in combination with corticosteroids can achieve a favorable response in most patients.6 Digital subtraction angiography offers the highest spatial resolution of the available imaging modalities and is the gold standard imaging technique for diagnosis in conjunction with both a high clinical suspicion and laboratory correlation in the absence of a brain biopsy.7 Common to most CNS vasculitides, DSA will show nonspecific alternating stenoses and dilatations primarily affecting leptomeningeal arteries or intracranial vessels (▶ Fig. 24.1). DSA also may be normal (▶ Fig. 24.2). Microaneurysms are rarely seen. Magnetic resonance angiography (MRA), although less invasive than DSA, is far less sensitive in the detection of abnormalities in small distal intracranial vessels. Findings on brain magnetic resonance imaging (MRI) include cortical and subcortical infarctions, parenchymal and leptomeningeal enhancement, intracranial hemorrhage, and patchy punctiform areas of subcortical enhancement (▶ Fig. 24.1, ▶ Fig. 24.2). Although these findings can be nonspecific, the sensitivity of MRI exceeds CT, approaching 100% for the diagnosis of PACNS with a high clinical suspicion.2,8 The findings on MRI can mimic those of demyelinating disease, such as multiple sclerosis (▶ Fig. 24.3).

24.2 Secondary Vasculitis 24.2.1 Systemic Lupus Erythematosus Cause and Histopathology Systemic lupus erythematosus (SLE) is a multisystem autoimmune disorder that demonstrates neuropsychiatric symptoms in up to 75% of cases.9 All age groups are affected, with a strong female predominance. There is a high prevalence in African American women. Focal neurologic symptoms are caused by cytokine-mediated effects on vascular endothelium, leading to complement activation and occlusive vasculopathy.10 Circulating immune complexes composed of antiphospholipid antibodies may cause thrombosis. In addition, neuronal dysfunction is mediated by circulating antineuronal, antiribosomal P-protein, and anticytokine antibodies. Histologically, this results in nonspecific hyalinization, endothelial proliferation, and perivascular gliosis. Subtle cerebral edema may also be seen.

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Vasculitis and Dementia

Fig. 24.1 A 44-year-old woman with rapid-onset dementia. (a) Axial fluid-attenuated inversion recovery magnetic resonance image (MRI) of the brain demonstrates lacunar infarction in the left basal ganglia (thin arrow) and widespread bilateral deep and periventricular white matter signal abnormality (thick arrows). (b) Axial diffusion-weighted MRI through the level of the thalami demonstrates foci of diffusion restriction (thin arrows) within the bilateral thalami consistent with acute infarctions. (c) Anteroposterior digital subtraction angiography (DSA) image, (d) lateral oblique DSA image and (e) lateral DSA image of the brain, right internal carotid artery injection, demonstrate multifocal luminal irregularity of multiple small and medium branches of the right middle and right anterior cerebral arteries (arrows). The imaging findings are consistent with primary angiitis of the central nervous system.

Clinical and Laboratory Features Neurologic complications worsen the prognosis of SLE and manifest as migraines, seizures, or stroke, as well as psychosis and mood disorders. Cognitive decline affects a minority of patients but is much more common than dementia and has an insidious onset. Dementia, although rare, is virtually always preceded by clinical and laboratory manifestations of SLE. Stroke is seen in 3 to 15% of cases and is predominantly attributable to cardiac embolism or an antibody-mediated hypercoagulable state.11 Neuropsychiatric SLE can be diagnosed using a combination of CSF analysis for oligoclonal bands and elevated antineuronal antibodies, as well as serum analysis for antiribosomal protein P antibodies. In acute cases, MRI shows focal infarcts and swelling of the basal ganglia. Chronic neuropsychiatric SLE demonstrates generalized cerebral atrophy and nonspecific white matter

lesions.12 However, it is important to remember that a negative MRI does not exclude cerebral SLE. Single-photon emission computed tomography (SPECT) and positron emission tomography (PET) are more sensitive than MRI in mild cases of SLE, showing reduced perfusion and hypometabolism in the parieto-occipital regions.13,14 DSA or MRA/computed tomography angiography (CTA) rarely detects cerebral lupus vasculitis. Treatment remains a challenge because of the broad spectrum of disease manifestations and usually requires a combination of cyclophosphamide, steroids, and anticoagulation.9

24.2.2 Behçet’s Disease Cause and Histopathology Behçet’s disease is an inflammatory disorder that was classically described in 1937 as the clinical triad of oral aphthous

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Vascular Dementia

Fig. 24.2 Patient is a 36-year-old pregnant white woman with confusion, memory loss, unsteady gait, headaches, and intermittent numbness in the left foot. The patient’s symptoms improved after Solu Medrol (methylprednisolone sodium succinate) treatment given after labor induction. Brain biopsy demonstrated minimal inflammation in a few small vessels consistent with vasculitis status post steroid treatment. The patient was started on prednisone and cyclophosphamide (Cytoxan). (a). Axial fluidattenuated inversion recovery magnetic resonance image of the brain demonstrates multifocal white matter foci of abnormal signal (white arrows). (b) Diffusion-weighted axial image demonstrates multifocal diffusion restriction compatible with small infarctions (white arrows). (c) Contrast-enhanced T1-weighted axial image demonstrates multiple leptomeningeal and parenchymal enhancing foci (white arrows). (d) Lateral view digital subtraction angiography image of the brain shows no vascular abnormalities.

ulcers, genital ulceration, and uveitis.15 The median age of incidence is about 40 years. About 5 to 10% of patients develop CNS involvement, which typically manifests about 5 years after onset of the aforementioned classic symptoms. CNS involvement has a 4:1 male predilection and is thought to be secondary to immune-mediated small-vessel vasculitis.15

mass (▶ Fig. 24.4). Lesions may be solitary or multifocal and are usually isointense to hypointense on T1-weighted imaging, with some lesions showing nodular or patchy enhancement. Empirical treatment with corticosteroids for acute episodes and immunosuppressive therapy for long-term therapy have been shown to be effective in improving symptomatology, although there are limited reports on the efficacy of such treatments.15

Clinical and Laboratory Features Initial symptoms of patients with CNS involvement usually include pyramidal signs, ataxia, and hemiparesis. Symptoms tend to relapse and remit with increased frequency correlating with disease severity or prognosis.16 Up to 10% of patients with neurologic involvement manifest with dementia.15,17 Cognitive decline is usually associated with other deficits and rarely in isolation.17 Patients are also prone to developing venous sinus thrombosis.15 CSF analysis shows a pleocytosis and/or elevated protein content and may show an elevated IgG index or oligoclonal band.16 Computed tomography (CT) imaging of the brain is usually unrevealing. MRI of the brain typically shows a prominent T2 hyperintense focus in the midbrain (most commonly) (▶ Fig. 24.4), pons, basal ganglia, thalami, and white matter.18,19 Expansion of the associated brain parenchyma may mimic a

24.2.3 Sjögren’s Syndrome Cause and Histopathology Sjögren’s syndrome is a chronic autoimmune disease that occurs in about 2 to 3% of adults, characterized as primary if occurring in isolation or secondary in patients with a preexisting connective tissue disorder. It is characterized by lymphocytic infiltration and destruction of exocrine glands causing xerostomia (dry mouth) and keratoconjunctivitis sicca (dry eyes). Predominant nervous system involvement is a peripheral neuropathy secondary to small-vessel vasculitis, whereas involvement of the CNS is less common. The prevalence of CNS involvement is controversial, as is the mechanism that has been reported to be immune mediated because of cryoglobulinemia and anti-Ro/SSA antibodies.20,21,22,23

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Vasculitis and Dementia

Fig. 24.3 A 35-year-old white man with a 4-week history of confusion, urinary incontinence, and generalized weakness. (a) Sagittal T1-weighted magnetic resonance image (MRI) through the midline brain demonstrates foci of abnormal signal intensity within the corpus callosum (white arrows), similar to findings that can be seen in the setting of demyelinating disease, such as multiple sclerosis. (b) Axial FLAIR MRI of the brain demonstrates multiple foci of abnormal signal intensity (white arrows), predominantly within the white matter. (c) Axial diffusion weighted MR image of the brain demonstrates restricted diffusion in the left frontal lobe (white arrow). (d) Lateral view DSA image of the brain and (e) lateral magnified view demonstrate no angiographically visible arterial abnormalities. Brain biopsy demonstrated findings of vasculitis. The patient improved on prednisone and Cytoxan.

Clinical and Laboratory Features The reported prevalence of CNS symptoms in Sjögren’s syndrome varies widely, ranging from 2.5 to 60.0% as a result of variability in the inclusion of psychiatric symptoms.24,25 Cases of dementia resulting from CNS involvement are rare; however, severe focal or multifocal deficits are seen in up to 10% of patients.24 Symptoms include generalized cognitive deficits, psychiatric abnormalities (most commonly depression), and migraine. Chronic encephalopathy, recurrent meningoencephalitis, subarachnoid hemorrhage, and transverse myelitis are also seen. The disease course can be relapsing and remitting, mimicking multiple sclerosis. CSF analysis shows an increased immunoglobulin G (IgG) index, the presence of one or more oligoclonal bands, and elevated lymphocyte count.25 Computed tomography imaging of the brain is insensitive, whereas MRI demonstrates multiple scattered areas of hyperat-

tenuation in the subcortical and periventricular white matter on fluid-attenuated inversion recovery (FLAIR) and T2-weighted images.26 This finding is seen in patients with and without CNS impairment and may represent manifestations of ischemia or demyelination. Some patients also have brain atrophy on brain imaging. Cerebral angiography is typically performed to exclude other etiologies and is often normal, although findings of small vessel vasculitis may be seen. Empirical treatment is usually with corticosteroids and cyclophosphamide.27

24.2.4 Susac’s Syndrome Cause and Histopathology Susac’s syndrome was first described in 1977 as the clinical triad of encephalopathy, branch retinal artery occlusions, and hearing loss.28,29 It is more common in females and occurs in a

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Vascular Dementia

Fig. 24.4 A 37-year-old white man with progressive left hemiparesis and headache. (a) Axial fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) through the pons and (b) midbrain demonstrate a right brainstem hemorrhage with expansile signal abnormality involving the pons, midbrain, and right cerebral peduncle as well as right middle cerebellar peduncle (not shown). (c) Coronal T1-weighted contrast-enhanced MRI through the brainstem demonstrates enhancement of the right cerebral peduncle and right midbrain. The patient had confirmed oral aphthous ulcers as well as genital ulceration. Cerebrospinal fluid analysis demonstrated pleocytosis and an elevated protein count. The clinical and imaging findings are consistent with Behçet’s disease.

wide age range from the midteens to the early postmenopausal years. The pathogenesis is unknown, but it is thought to be autoimmune mediated. This condition is characterized by a microangiopathy affecting the precapillary arterioles of the brain, retina, and inner ear.

Clinical and Laboratory Features Patients most commonly present with headaches, but others present with memory loss, altered mental status, and dementia. Patients can present with multiple cerebral infarcts causing cognitive decline and focal neurologic defects that may progress to dementia. Brain MRI often shows white matter lesions with a predilection for the corpus callosum.30 For this reason, it was previously thought to represent multiple sclerosis; however, lesions in Susac's syndrome tend to involve the central portion of the corpus callosum rather than the undersurface as seen with multiple sclerosis. The corpus callosal lesions in Susac's syndrome are usually small and multifocal. Basal ganglia and thalamus involvement is variable. Enhancement and restricted diffusion may occur during the acute phase. Cortical lesions are not typically seen despite findings of microinfarctions seen on histology. Leptomeningeal enhancement may occur in a small subset of patients. Branch retinal artery occlusions are usually not seen on imaging but may be appreciated on funduscopic examination. Cerebral angiography is usually normal, likely because the precapillary arterioles are beyond the resolution of DSA. Susac's syndrome is usually self-limited, with most patients recovering without significant residual clinical deficits. Patients who do have spontaneous improvement are usually treated with immunosuppressants.

24.2.5 Wegener's Granulomatosis Cause and Histopathology Wegener's granulomatosis is an idiopathic granulomatous vasculitis that affects small and medium-sized vessels in various organs. It affects men more than women and typically occurs in patients between 40 and 65 years of age.31 CNS involvement has been reported to occur as a result of vasculitis; granulomatous lesions within the brain, meninges, or cranial nerves; or contiguous spread from concomitant skull base disease.32,33

Clinical and Laboratory Features Wegener's granulomatosis most commonly manifests with renal dysfunction (progressive glomerulonephritis), hemoptysis (resulting from pulmonary involvement), and rhinologic symptoms. Neurologic involvement is seen in 3 to 9% of patients.32 Neurologic involvement may manifest as headache, stroke, seizures, delirium, or dementia. It tends to cause a cerebritis but may also cause peripheral and cranial neuropathies.31 The c-antineutrophil cytoplasmic antibody (c-ANCA) is highly specific (> 90%), with varying reported ranges of sensitivity (40 to 90%). C-reactive protein and erythrocyte sedimentation rate are usually elevated. Although MRI and CT studies are nonspecific, they may help localize suspected lesions when the diagnosis is suggested, particularly if there is meningeal, orbital, or paranasal mucosal involvement.34,35 Cerebral angiography is generally unrevealing. Treatment is with systemic corticosteroids and cyclophosphamide.

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Vasculitis and Dementia

24.2.6 Polyarteritis Nodosa Cause and Histopathology Polyarteritis nodosa is a multisystem disease characterized by hyaline-like necrosis of the media and internal elastic lamina of small and medium-sized arteries. It most commonly involves the kidneys, gastrointestinal tract, and skin, but it can affect any organ except the lungs and spleen. Polyarteritis nodosa occasionally causes CNS involvement resulting in cognitive decline. It tends to involve the small branches of the major cerebral arteries but occasionally may involve larger ones like the middle and anterior cerebral arteries.

Clinical and Laboratory Features Involvement of the CNS is seen in about 5% of patients and often manifests as headaches, seizures, confusion, and focal neurologic deficits resulting from multiple small infarcts. Reversible encephalopathy is a characteristic finding in patients with CNS involvement.36 MRI demonstrates nonspecific subcortical and white matter hyperintensities on FLAIR and T2-weighted images, some of which can be attributed to infarcts.37 Lesions have a predilection for the brainstem and basal ganglia, consistent with disease propensity for small vessels. Cerebral angiography is nonspecific and may show findings compatible with arteritis, including occlusion of small arteries and alternating aneurysms resulting in a “beaded” appearance. Some cases may be angiographically occult, as lesions affecting the small vessels can be below the resolution of DSA. Treatment is with corticosteroids and cyclophosphamide.

24.2.7 Giant Cell Arteritis Cause and Histopathology Giant cell arteritis is a chronic granulomatous panarteritis of medium- to large-sized vessels with a predilection for the cranial vessels. In addition, small vessels, particularly those supplying the optic nerve, can be affected. It is rare in patients younger than age 50 and often is associated with polymyalgia rheumatica.

Clinical and Laboratory Features Patients may manifest clinically with new-onset headache or tenderness of the temporal artery. The most common neurologic manifestation is visual loss, seen in 15 to 20% of patients; 80 to 90% is due to ischemic optic neuritis and the remainder to occlusion of the retinal artery.32 Additional CNS involvement manifests as ischemia in the carotid and vertebral artery territories as a result of extradural involvement of the intracranial branches of the carotid and vertebral arteries.32,38 Multi-infarct

dementia occurs in 3 to 6% of patients as a result of multiple vessel involvement, stenosis, and thromboemboli.38 Laboratory findings will show elevated erythrocyte sedimentation rate and C-reactive protein. The gold standard for diagnosis is a temporal artery biopsy demonstrating evidence of vascultis with mononucleated infiltrate or giant cells; however, the presence of skip lesions can lead to false-negatives.39 MRI has demonstrated a diagnostic sensitivity of 80.6% and specificity of 97.0% for the evaluation of mural inflammation of the superficial cranial arteries, thereby potentially guiding biopsy.40 MRI also can evaluate for intracranial disease. Treatment is long-term high-dose steroids to prevent progression of visual impairment.

24.2.8 Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy Cause and Histopathology Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is an autosomal dominant vascular dementia that is linked to a gene on chromosome 19. There is disproportionate cortical hypometabolism. Histologically, an angiopathy of small and middle-sized arteries is characteristic, without atherosclerosis or amyloid deposition.41

Clinical and Laboratory Features Initial signs usually include recurrent transient ischemic attacks (TIAs) or strokes in multiple vascular territories and eventual dementia. Presenile dementia and migraines develop in the third to fourth decades of life. The disease has a manifestation similar to migraines and also may include auras. Depression, psychosis, pseudobulbar palsy, and focal neurologic defects are also seen.41,42 Diagnosis requires identification of the mutated gene.43 CT is nonspecific, demonstrating white matter regions of low attenuation. MRI demonstrates widespread confluent white matter hyperintensities (▶ Fig. 24.5). Focal hyperintense lesions are also seen in the basal ganglia, thalamus, and pons. Although the subcortical white matter can be diffusely involved, the frontal (93%) and temporal (86%) lobes and subinsular white matter (93%) are classic (▶ Fig. 24.5).42 There is relative sparing of the occipital and orbitofrontal subcortical white matter and cortex. Cerebral microhemorrhages have been reported to occur in 25 to 70% of cases without a characteristic distribution.44 Nonspecific angiographic findings mimicking that of primary intracranial vasculitis in the setting of CADASIL have been reported in the literature.45 Typically, the disease has a variable but progressive course leading to death between 50 and 70 years of age.43

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Vascular Dementia

Fig. 24.5 A 64-year-old woman with acute right arm and leg numbness. History of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). (a) Axial fluid-attenuated image recovery (FLAIR) image of the brain shows confluent diffuse white matter signal abnormality with extension into the temporal lobes along the temporal horns (arrows in b). (c) Diffusion-weighted magnetic resonance image of the superior aspects of the brain show diffusion abnormality (arrow) within the posterior right frontal lobe consistent with focal acute infarction.

References [1] Moore PM. Neurological manifestation of vasculitis: update on immunopathogenic mechanisms and clinical features. Ann Neurol 1995; 37 Suppl 1: S131–S141 [2] Salvarani C, Brown RD, Jr, Hunder GG. Adult primary central nervous system vasculitis. Lancet 2012; 380: 767–777 [3] Miller DV, Salvarani C, Hunder GG et al. Biopsy findings in primary angiitis of the central nervous system. Am J Surg Pathol 2009; 33: 35–43 [4] Salvarani C, Brown RD, Jr, Calamia KT et al. Primary CNS vasculitis with spinal cord involvement. Neurology 2008; 70: 2394–2400 [5] Alrawi A, Trobe JD, Blaivas M, Musch DC. Brain biopsy in primary angiitis of the central nervous system. Neurology 1999; 53: 858–860 [6] Cupps TR, Moore PM, Fauci AS. Isolated angiitis of the central nervous system. Prospective diagnostic and therapeutic experience. Am J Med 1983; 74: 97–105 [7] Salvarani C, Brown RD, Jr, Calamia KT et al. Primary central nervous system vasculitis: analysis of 101 patients. Ann Neurol 2007; 62: 442–451 [8] Pomper MG, Miller TJ, Stone JH, Tidmore WC, Hellmann DB. CNS vasculitis in autoimmune disease: MR imaging findings and correlation with angiography. AJNR Am J Neuroradiol 1999; 20: 75–85 [9] Popescu A, Kao AH. Neuropsychiatric systemic lupus erythematosus. Curr Neuropharmacol 2011; 9: 449–457 [10] Belmont HM, Abramson SB, Lie JT. Pathology and pathogenesis of vascular injury in systemic lupus erythematosus. Interactions of inflammatory cells and activated endothelium. Arthritis Rheum 1996; 39: 9–22 [11] Futrell N, Millikan C. Frequency, etiology, and prevention of stroke in patients with systemic lupus erythematosus. Stroke 1989; 20: 583–591 [12] Appenzeller S, Vasconcelos Faria A, Li LM, Costallat LT, Cendes F. Quantitative magnetic resonance imaging analyses and clinical significance of hyperintense white matter lesions in systemic lupus erythematosus patients. Ann Neurol 2008; 64: 635–643 [13] Chen JJ, Yen RF, Kao A, Lin CC, Lee CC. Abnormal regional cerebral blood flow found by technetium-99 m ethyl cysteinate dimer brain single photon emission computed tomography in systemic lupus erythematosus patients with normal brain MRI findings. Clin Rheumatol 2002; 21: 516–519 [14] Kao CH, Ho YJ, Lan JL, Changlai SP, Liao KK, Chieng PU. Discrepancy between regional cerebral blood flow and glucose metabolism of the brain in systemic lupus erythematosus patients with normal brain magnetic resonance imaging findings. Arthritis Rheum 1999; 42: 61–68 [15] Siva A, Saip S. The spectrum of nervous system involvement in Behçet’s syndrome and its differential diagnosis. J Neurol 2009; 256: 513–529

[16] Akman-Demir G, Serdaroglu P, Tasçi B The Neuro-Behçet Study Group. Clinical patterns of neurological involvement in Behçet’s disease: evaluation of 200 patients. Brain 1999; 122: 2171–2182 [17] Farah S, Al-Shubaili A, Montaser A et al. Behçet’s syndrome: a report of 41 patients with emphasis on neurological manifestations. J Neurol Neurosurg Psychiatry 1998; 64: 382–384 [18] Koçer N, Islak C, Siva A et al. CNS involvement in neuro-Behçet syndrome: an MR study. AJNR Am J Neuroradiol 1999; 20: 1015–1024 [19] Lee SH, Yoon PH, Park SJ, Kim DI. MRI findings in neuro-Behçet’s disease. Clin Radiol 2001; 56: 485–494 [20] Alexander GE, Provost TT, Stevens MB, Alexander EL. Sjögren’s syndrome: central nervous system manifestations. Neurology 1981; 31: 1391–1396 [21] Alexander EL, Provost TT, Stevens MB, Alexander GE. Neurologic complications of primary Sjögren’s syndrome. Medicine (Baltimore) 1982; 61: 247–257 [22] Alexander EL. Neurologic disease in Sjögren’s syndrome: mononuclear inflammatory vasculopathy affecting central/peripheral nervous system and muscle: a clinical review and update of immunopathogenesis. Rheum Dis Clin North Am 1993; 19: 869–908 [23] Delalande S, de Seze J, Fauchais AL et al. Neurologic manifestations in primary Sjögren’s syndrome: a study of 82 patients. Medicine (Baltimore) 2004; 83: 280–291 [24] Segal B, Carpenter A, Walk D. Involvement of nervous system pathways in primary Sjögren’s syndrome. Rheum Dis Clin North Am 2008; 34: 885–906, viiiviii [25] Soliotis FC, Mavragani CP, Moutsopoulos HM. Central nervous system involvement in Sjogren’s syndrome. Ann Rheum Dis 2004; 63: 616–620 [26] Tzarouchi LC, Tsifetaki N, Konitsiotis S et al. CNS involvement in primary Sjögren’s Syndrome: assessment of gray and white matter changes with MRI and voxel-based morphometry. AJR Am J Roentgenol 2011; 197: 1207–1212 [27] Caselli RJ, Scheithauer BW, Bowles CA et al. The treatable dementia of Sjögren’s syndrome. Ann Neurol 1991; 30: 98–101 [28] Susac JO. Susac’s syndrome: the triad of microangiopathy of the brain and retina with hearing loss in young women. Neurology 1994; 44: 591–593 [29] Susac JO, Hardman JM, Selhorst JB. Microangiopathy of the brain and retina. Neurology 1979; 29: 313–316 [30] Susac JO, Murtagh FR, Egan RA et al. MRI findings in Susac’s syndrome. Neurology 2003; 61: 1783–1787 [31] Nishino H, Rubino FA, DeRemee RA, Swanson JW, Parisi JE. Neurological involvement in Wegener’s granulomatosis: an analysis of 324 consecutive patients at the Mayo Clinic. Ann Neurol 1993; 33: 4–9 [32] Alba MA, Espígol-Frigolé G, Prieto-González S et al. Central nervous system vasculitis: still more questions than answers. Curr Neuropharmacol 2011; 9: 437–448

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Vasculitis and Dementia [33] Seror R, Mahr A, Ramanoelina J, Pagnoux C, Cohen P, Guillevin L. Central nervous system involvement in Wegener granulomatosis. Medicine (Baltimore) 2006; 85: 54–65 [34] Provenzale JM, Allen NB. Wegener granulomatosis: CT and MR findings. AJNR Am J Neuroradiol 1996; 17: 785–792 [35] Murphy JM, Gomez-Anson B, Gillard JH et al. Wegener granulomatosis: MR imaging findings in brain and meninges. Radiology 1999; 213: 794– 799 [36] Rosenberg MR, Parshley M, Gibson S, Wernick R. Central nervous system polyarteritis nodosa. West J Med 1990; 153: 553–556 [37] Provenzale JM, Allen NB. Neuroradiologic findings in polyarteritis nodosa. AJNR Am J Neuroradiol 1996; 17: 1119–1126 [38] Gonzalez-Gay MA, Vazquez-Rodriguez TR, Gomez-Acebo I et al. Strokes at time of disease diagnosis in a series of 287 patients with biopsy-proven giant cell arteritis. Medicine (Baltimore) 2009; 88: 227–235 [39] Klein RG, Campbell RJ, Hunder GG, Carney JA. Skip lesions in temporal arteritis. Mayo Clin Proc 1976; 51: 504–510

[40] Bley TA, Uhl M, Carew J et al. Diagnostic value of high-resolution MR imaging in giant cell arteritis. AJNR Am J Neuroradiol 2007; 28: 1722–1727 [41] Auer DP, Pütz B, Gössl C, Elbel G, Gasser T, Dichgans M. Differential lesion patterns in CADASIL and sporadic subcortical arteriosclerotic encephalopathy: MR imaging study with statistical parametric group comparison. Radiology 2001; 218: 443–451 [42] Yousry TA, Seelos K, Mayer M et al. Characteristic MR lesion pattern and correlation of T1 and T2 lesion volume with neurologic and neuropsychological findings in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). AJNR Am J Neuroradiol 1999; 20: 91–100 [43] Bohlega S, Al Shubili A, Edris A et al. CADASIL in Arabs: clinical and genetic findings. BMC Med Genet 2007; 8: 67 [44] Blitstein MK, Tung GA. MRI of cerebral microhemorrhages. AJR Am J Roentgenol 2007; 189: 720–725 [45] Engelter ST, Rueegg S, Kirsch EC et al. CADASIL mimicking primary angiitis of the central nervous system. Arch Neurol 2002; 59: 1480–1483

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Part VIII Infection and Inflammatory Conditions Associated with Dementia

25 Human Immunodeficiency Virus (HIV) Dementia

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26 Non-Human Immunodeficiency Virus (HIV) Infectious Dementia

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27 Prion Disease

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28 Immune-Mediated Dementias

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Infection and Inflammatory Conditions Associated with Dementia

25 Human Immunodeficiency Virus (HIV) Dementia Toshio Moritani, Aristides Capizzano, and Sangam G. Kanekar

25.1 Epidemiology

25.2 Clinical Findings

Initial case reports from New York and San Francisco of what would later become known as acquired immunodeficiency syndrome (AIDS) first appeared in 1981.1 Human immunodeficiency virus type 1 (HIV-1) was first described as the putative cause for AIDS in 1983.2 The HIV/AIDS pandemic has become a global tragedy. Since the first report, almost 60 million people have been infected by HIV worldwide. Of these, about 25 million have already died. It is also estimated that about 2.5 million people become newly infected with HIV-1, and 2.1 million people die of AIDS-related diseases every year.3 More than 95% of AIDS cases occur in developing countries. The epidemic is growing most rapidly in China, India, Eastern Europe, and the sub-Saharan African countries. Sub-Saharan Africa is home to two-thirds of the 33.3 million people living with HIV/AIDS worldwide.4 Most HIV-positive individuals worldwide are infected as a result of unprotected sexual intercourse; 70% acquired the infection through heterosexual intercourse. Genetic epidemiologic studies have shown polymorphisms in genes such as cytokines and their receptors and several human leucocyte antigen alleles that influence HIV progression to AIDS.5–9 White individuals deficient in chemokine receptor type 5, one of the main chemokine receptors for HIV entry into macrophages, are resistant to HIV infection. Human immunodeficiency virus is a single-stranded ribonucleic acid (RNA) retrovirus that is lymphotropic and neurotropic. The central nervous system (CNS) is a primary target for HIV. HIV enters the brain transiently in the early stage of infection, but productive infection is rarely detectable before immunosuppression has developed. In 10% of all HIV-infected and AIDS patients, neurologic complaints are the initial clinical manifestation, and an additional 30% to 60% of all patients will develop neurologic symptoms during the course of the infection,10 40 to 50% have an active neurologic disease, and more than 90% develop CNS involvement by the time of death. The introduction of highly active antiretroviral therapy (HAART) has resulted in profound declines in morbidity and mortality with improved immunologic status.5,6,11 Although the prevalence of opportunistic infections decreased markedly, the effects of HAART on neurologic function have remained uncertain, and HIV-associated neurocognitive disorders (HAND) remain frequent, although typically with milder symptoms.5–9 On the other hand, antiretrovirus drug–induced toxic neuropathy has increased.12 Nearly 50% of HIV patients in the United States demonstrate neuropsychological testing performance below expectations compared with age-, education-, gender-, and ethnicity-matched normative groups. HAND occurs in all groups at risk of HIV infection, including children; 85% of AIDS in children is vertically transmitted from an infected mother.13 The onset of neurologic disease in children is between 2 month and 5 years. The prevalence of CNS disease in HIV-infected children ranges from 20 to 60%.

Human immunodeficiency virus involves the brain directly and leads to a subcortical dementia. The terms AIDS dementia complex, HIV encephalopathy, and HIV-associated dementia (HAD) have been used interchangeably to describe a clinical triad of cognitive, motor, and behavioral changes, typically in advanced stages of HIV infection. HIV encephalitis is the term related to neuropathlogical findings in a subgroup of the patients. Recently, nosologic and diagnostic criteria have been refined and updated. In 2007, a U.S. National Institute of Mental Health and National Institute of Neurological Disease and Stroke panel proposed the term HIV-associated neurocognitive disorder (HAND) for the entire spectrum of neurologic disease associated with HIV, recognizing three research diagnostic categories: (1) HAD as the most severe form of injury, (2) minor neurocognitive disorder as a milder form of impairment that still impacts daily activities of living, and (3) asymptomatic neurocognitive impairment for individuals with impairments on neuropsychological testing that do not interfere with everyday functioning.5,11,14 Typically, HAND presents as a subcortical dementia with cognitive, behavioral, and motor decline over weeks or months and cannot be explained by another preexisting neurologic disease, severe substance abuse, or another cause of dementia.11,15,16,17 The neurologic signs of HAND progress in more than 50% of patients not receiving any form of antiretroviral therapy.18 HAND symptoms may include asymptomatic neurocognitive impairment, minor cognitive disorders, or a more severe form with profound motor and behavioral and psychosocial abnormalities that disrupt work or other activities of daily living.19 Disorientation, mood disturbances, psychomotor slowing, and a decrease in attention, memory, and visuoconstructive coordination are part of the clinical picture. Motor slowing and impaired movements are due to predominant dopaminergic dysfunction. Myoclonus and tremor are rare but can occur. Cortical symptoms occur when dementia is advanced. Urinary urgency, nonspecific headache, depressive symptoms, psychosis, delirium, and seizures can also occur. HAART has decreased the severity of neurologic signs of HAND.20,21 Recently, HAD only afflicts 1 to 2% of subjects with AIDS. On the other hand, the prevalence of HAND has increased due to the longer survival and increasing age of patients.22,23 Clinical features of congenital HIV infection include microcephaly, developmental delay, and progressive loss of developmental milestones. Cerebrospinal fluid (CSF) analysis reveals minor mononuclear pleocytosis in 18% of asymptomatic patients and 40% of symptomatic patients. CSF analysis can demonstrate that HIV enters the CNS soon after exposure, even before antibodies are detectable in blood.24,25 The diagnosis of HIV infection is made through the detection of HIV antibody using enzyme-linked immunosorbent assay and Western blot, which is usually detectable within 4 weeks of inoculation. Polymerase chain reaction–based tests measure the load of HIV-replicating

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Human Immunodeficiency Virus (HIV) Dementia virus in blood, which is useful both for testing for HIV before seroconversion and as quantitative estimate of viral load. CD4 lymphocyte count is used to stage HIV infection.

25.3 Neuropathology In AIDS autopsy cases, HIV encephalitis (HIVE) is the productive infection of the CNS by the HIV virus and affects predominantly the white matter, basal ganglia, and brainstem and is identified in 20 to 26% in AIDS autopsy cases. Its neuropathological hallmarks are perivascular inflammation, microglial nodules, and multinucleated giant cells.26 HIVE can involve both white matter (leukoencephalopathy) and gray matter (diffuse poliodystrophy) and is characterized histopathologically by diffuse myelin breakdown, astrogliosis, and multinucleated giant cells with little inflammation. Frequently, individual brains show an overlap of encephalopathy and encephalitis.27,28,29 Pathological findings after HAART show neuronal loss with apoptosis, astrocytosis, myelin pallor, and at least some activated microglia and perivascular macrophages, although the hallmarks of HIV encephalitis are typically absent.30,31 Immunocytochemical studies show a preponderance of HIV infection in the basal ganglia, brainstem, and deep white matter.27,32,33 Initial infection occurs in the deep brain regions from perivascular trafficking of monocytes and macrophages. HIV gains access to the CNS from the bloodstream. Circulating monocytes carry the virus across the blood-brain barrier. HIVinfected and -activated monocytes differentiate into HIV-infected and -activated macrophages and microglia. These activated cells release several potent neurotoxins, including viral gene products, such as transactivating protein of transcription (tat) and viral envelope glycoprotein gp120, and induce secretion of proinflammatory cytokines. These toxic substances induce glutamateinduced excitotoxicity and mitochondrial dysfunction, which lead to the final stage of neuronal damage, consisting of neuronal apoptosis.5,18,34,35 The infected astrocytes, macrophages, and microglia cells serve as lifelong hosts for HIV, which causes accelerated neurodegeneration and decreased synaptic function.5

Increased numbers of activated macrophages and activated astrocyte and macrophage-derived products most strongly correlate with dementia severity.36 The development and worsening of HAND are associated with the inflammatory response, possibly independently of viral replication.18,30,37,38,39 The severity of HAND pathology includes loss of both synaptic connections and neuronal differentiation.20 The density of apoptotic astrocytes and HIV-DNA-containing astrocytes correlates with rapid progression of dementia. HIV promotes neuronal apoptosis more prominently in children.40,41

25.4 Neuroimaging Computed tomography (CT) usually demonstrates cerebral atrophy. Magnetic resonance imaging (MRI) has been shown to be a sensitive diagnostic tool in the investigation and management of HIV-related CNS disorders, not only HIVE but also opportunistic infections. T2-weighted and fluid-attenuated inversion recovery (FLAIR) images show patchy or diffuse periventricular white matter ill-defined hyperintensities, with relative sparing of the subcortical white matter and posterior fossa structures. There is no mass effect or contrast enhancement.42 Another typical finding is cerebral atrophy with enlarged cerebral sulci and lateral ventricles. The degree of cerebral atrophy as quantified by MRI is correlated with the severity of dementia.43 In some cases, brainstem, basal ganglia, and corpus callosum are involved with minimal edema and mass effect, which may represent the presence of HIV. During the course of infection in patients with HAND, MRI shows high signal patchy or diffuse changes in white matter.44,45 There is a significant correlation between MRI changes and cognitive impairment in HIV infection.24 The presence and progression of hyperintensity on T2-weighted MRI reflecting cerebral infection with HIV are significantly related to impaired immune state as measured by CD4 + cell count.24 Diffusion-weighted imaging (DWI) demonstrates high signal with increased apparent diffusion coefficient (ADC), which represents T2 shine through (▶ Fig. 25.1). Diffusion tensor imaging

Fig. 25.1 Human immunodeficiency virus encephalopathy. A 45-year-old man presented with forgetfulness, memory problems, and behavioral changes 1 year after antiretroviral treatment. (a) Fluid-attenuated inversion recovery image demonstrates diffuse symmetric hyperintensity in the periventricular and deep white matter (b,c) Diffusion-weighted image shows no restricted diffusion in the areas associated with increased apparent diffusion coefficient.

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Infection and Inflammatory Conditions Associated with Dementia

Fig. 25.2 Human immunodeficiency virus encephalopathy. A 49-year-old woman presented with memory problems and behavioral changes, and dementia. (a) T2-weighted image demonstrates white matter hyperintensity with ventriculomegaly and diffuse brain atrophy. (b) Magnetic resonance spectroscopy shows decreased N-acetyl aspartate (NAA) and increased choline and myoinositol (Minos) in the white matter abnormality.

Fig. 25.3 Progressive multifocal leukoencephalopathy. A 42-year-old man had confusion and mental decline with human immunodeficiency virus (HIV) for 12 years. (a) Fluid-attenuated inversion recovery image shows asymmetric multiple hyperintensity lesions in the white matter extending into the U fibers. (b) Diffusionweighted imaging demonstrates a core of increased diffusion surrounded by an area of relatively reduced diffusion consistent with progressive multifocal leukoencephalopathy.

(DTI)-derived variables, such as fractional anisotropy and ADC, are correlated with neuropathologic changes, dementia severity, and motor-speed losses in studies of HIV-associated cognitive impairment.46,47,48 However, DTI may be not helpful in identifying patients with early HIV infection.49 Proton magnetic resonance spectroscopy (1H-MRS) provides a sensitive and noninvasive in vivo method to detect inflammatory and neuronal changes in the brain. 1H-MRS abnormalities have been reported in all stages of HIV patients, including reduced levels in the ratio of N-acetyl aspartate (NAA, a marker of neuronal integrity) to creatine (NAA/Cr), and elevations in the choline (Cho, a marker of cell membrane damage) to creatine (Cho/Cr), and myoinositol (mI, a glial cell marker) to creatine (mI:Cr) metabolic ratios (▶ Fig. 25.2).50–61 MRS changes are correlated with the severity of dementia in HIV patients. MRS is useful for evaluation of antiretroviral treatment effects. Patients with HIV have a small but definite increased incidence of stroke (6 to 12%) mainly due to opportunistic varicella zoster virus infection or HIV vasculopathy.62 HIV vasculopathy was found in 5.5% of an autopsy cohort with HIV-infected patients.63 MRI with DWI is useful to detect infarctions. HIV infection is associated with many other opportunistic CNS infections, including viral infections (▶ Fig. 25.3, ▶ Fig. 25.4) as well as bacterial, fungal, and parasitic infections. Multiple

coexistent CNS infections sometimes make the interpretation of MRI findings complicated. HAART can result in clinical deterioration by a paradoxical activation of an inflammatory response known as immune reconstitution inflammatory syndrome (IRIS),63,64,65,66 which can be responsive to steroid therapy. The most common abnormalities of congenital HIV CNS infection are brain atrophy and white matter disease (▶ Fig. 25.5).67,68 Intracranial calcifications can be detected in 33% of HIV-infected children, which is usually not seen before 10 months of age. HIV-associated vasculitis is most commonly seen in pediatric HIV patients. An annual risk of cerebrovascular accident in HIV-positive children is 1.3%.69

25.5 Therapy The cornerstone of treatment of HAND is HAART. The introduction of aggressive treatment with HAART has been shown to improve immune recovery, delay progression to AIDS, and reduce mortality among HIV-infected patients.4,5,70 With millions of replicative cycles occurring daily and a high error rate in RNA transcription of HIV, drug-resistant variants remain in the CNS unless HIV replication is suppressed completely. HIVinfected patients must continue treatment with antiretroviral therapy for their entire lives because the virus reemerges

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Human Immunodeficiency Virus (HIV) Dementia

Fig. 25.4 Varicella zoster virus meningoencephalitis. A 44-year-old man had altered mental status with advanced acquired immunodefficiency syndrome (AIDS). (a) Fluid-attenuated inversion recovery image shows multiple periventricular curvilinear and white matter round hyperintensity lesions. (b) Postcontrast T1-weighted image demonstrates minimal enhancement in these lesions. (c) Diffusion-weighted imaging reveals restricted diffusion in these lesions.

able CNS pharmacokinetics. CNS penetration effectiveness index ranks ART regimens according to how effectively they penetrate the CNS.4,72 Drug availability, drug-drug interactions, and comorbidities are other factors to determine the ART regimens. Adjuvant therapy to improve cognitive function has been investigated, including paroxetine, fluconazole, and rivastigmine.4

References

Fig. 25.5 Congenital human immunodeficiency virus infection in a 9year-old boy. T2-weighted image demonstrates characteristic white matter hyperintensity with diffuse brain atrophy consistent with human immunodeficiency virus encephalopathy.

when the drugs are withdrawn. The complexity of the interaction between the drug, the blood-brain barrier, and the blood-CSF barrier makes it difficult to predict which agents will cross adequately into the CNS. To date, no studies have demonstrated the superiority of a specific combination ART regimen for the prevention or treatment of HAND.71 It is reasonable to use combination ART regimens that include agents with favor-

[1] Gottlieb MS, Schroff R, Schanker HM et al. Pneumocystis carinii pneumonia and mucosal candidiasis in previously healthy homosexual men: evidence of a new acquired cellular immunodeficiency. N Engl J Med 1981; 305: 1425– 1431 [2] Barré-Sinoussi F, Chermann JC, Rey F et al. Isolation of a T-lymphotropic retrovirus from a patient at risk for acquired immune deficiency syndrome (AIDS). Science 1983; 220: 868–871 [3] Xia C, Luo D, Yu X, Jiang S, Liu S. HIV-associated dementia in the era of highly active antiretroviral therapy (HAART). Microbes Infect 2011; 13: 419–425 [4] Alkali NH, Bwala SA, Nyandaiti YW, Danesi MA. NeuroAIDS in sub-Saharan Africa: a clinical review. Ann Afr Med 2013; 12: 1–10 [5] Avdoshina V, Bachis A, Mocchetti I. Synaptic dysfunction in human immunodeficiency virus type-1-positive subjects: inflammation or impaired neuronal plasticity? J Intern Med 2013; 273: 454–465 [6] Palella FJ, Jr, Delaney KM, Moorman AC et al. HIV Outpatient Study Investigators. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. N Engl J Med 1998; 338: 853–860 [7] Ledergerber B, Egger M, Opravil M et al. Clinical progression and virological failure on highly active antiretroviral therapy in HIV-1 patients: a prospective cohort study. Swiss HIV Cohort Study. Lancet 1999; 353: 863–868 [8] Sacktor N, McDermott MP, Marder K et al. HIV-associated cognitive impairment before and after the advent of combination therapy. J Neurovirol 2002; 8: 136–142 [9] Tozzi V, Balestra P, Lorenzini P et al. Prevalence and risk factors for human immunodeficiency virus-associated neurocognitive impairment, 1996 to 2002: results from an urban observational cohort. J Neurovirol 2005; 11: 265–273 [10] Price RW, Brew BJ. The AIDS dementia complex. J Infect Dis 1988; 158: 1079– 1083 [11] Antinori A, Arendt G, Becker JT et al. Updated research nosology for HIVassociated neurocognitive disorders. Neurology 2007; 69: 1789–1799

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Infection and Inflammatory Conditions Associated with Dementia [12] Keswani SC, Pardo CA, Cherry CL, Hoke A, McArthur JC. HIV-associated sensory neuropathies. AIDS 2002; 16: 2105–2117 [13] Lo CP, Chen CY. Neuroimaging of viral infections in infants and young children. Neuroimaging Clin N Am 2008; 18: 119–132, viii [14] Valcour V, Sithinamsuwan P, Letendre S, Ances B. Pathogenesis of HIV in the central nervous system. Curr HIV/AIDS Rep 2011; 8: 54–61 [15] Steinbrink F, Evers S, Buerke B et al. German Competence Network HIV/AIDS. Cognitive impairment in HIV infection is associated with MRI and CSF pattern of neurodegeneration. Eur J Neurol 2013; 20: 420–428 [16] McArthur JC, Hoover DR, Bacellar H et al. Dementia in AIDS patients: incidence and risk factors. Multicenter AIDS Cohort Study. Neurology 1993; 43: 2245–2252 [17] McArthur JC. HIV dementia: an evolving disease. J Neuroimmunol 2004; 157: 3–10 [18] McArthur JC, Steiner J, Sacktor N, Nath A. Human immunodeficiency virusassociated neurocognitive disorders: mind the gap. Ann Neurol 2010; 67: 699–714 [19] González-Scarano F, Martín-García J. The neuropathogenesis of AIDS. Nat Rev Immunol 2005; 5: 69–81 [20] Ellis R, Langford D, Masliah E. HIV and antiretroviral therapy in the brain: neuronal injury and repair. Nat Rev Neurosci 2007; 8: 33–44 [21] Joska JA, Gouse H, Paul RH, Stein DJ, Flisher AJ. Does highly active antiretroviral therapy improve neurocognitive function? A systematic review. J Neurovirol 2010; 16: 101–114 [22] Robertson KR, Smurzynski M, Parsons TD et al. The prevalence and incidence of neurocognitive impairment in the HAART era. AIDS 2007; 21: 1915–1921 [23] Husstedt IW, Frohne L, Böckenholt S et al. Impact of highly active antiretroviral therapy on cognitive processing in HIV infection: cross-sectional and longitudinal studies of event-related potentials. AIDS Res Hum Retroviruses 2002; 18: 485–490 [24] Hanning U, Husstedt IW, Niederstadt TU, Evers S, Heindel W, Kloska SP. Cerebral signal intensity abnormalities on T2-weighted MR images in HIV patients with highly active antiretroviral therapy: relationship with clinical parameters and interval changes. Acad Radiol 2011; 18: 1144–1150 [25] Davis LE, Hjelle BL, Miller VE et al. Early viral brain invasion in iatrogenic human immunodeficiency virus infection. Neurology 1992; 42: 1736–1739 [26] Hoffmann C, Tabrizian S, Wolf E et al. Survival of AIDS patients with primary central nervous system lymphoma is dramatically improved by HAARTinduced immune recovery. AIDS 2001; 15: 2119–2127 [27] Kure K, Llena JF, Lyman WD et al. Human immunodeficiency virus-1 infection of the nervous system: an autopsy study of 268 adult, pediatric, and fetal brains. Hum Pathol 1991; 22: 700–710 [28] Budka H, Wiley CA, Kleihues P et al. HIV-associated disease of the nervous system: review of nomenclature and proposal for neuropathology-based terminology. Brain Pathol 1991; 1: 143–152 [29] Shankar SK, Mahadevan A, Kovoor JM. Neuropathology of viral infections of the central nervous system. Neuroimaging Clin N Am 2008; 18: 19–39, vii [30] Gannon P, Khan MZ, Kolson DL. Current understanding of HIV-associated neurocognitive disorders pathogenesis. Curr Opin Neurol 2011; 24: 275–283 [31] Gras G, Chrétien F, Vallat-Decouvelaere AV et al. Regulated expression of sodium-dependent glutamate transporters and synthetase: a neuroprotective role for activated microglia and macrophages in HIV infection? Brain Pathol 2003; 13: 211–222 [32] Takahashi K, Wesselingh SL, Griffin DE, McArthur JC, Johnson RT, Glass JD. Localization of HIV-1 in human brain using polymerase chain reaction/in situ hybridization and immunocytochemistry. Ann Neurol 1996; 39: 705–711 [33] Brew BJ, Rosenblum M, Cronin K, Price RW. AIDS dementia complex and HIV-1 brain infection: clinical-virological correlations. Ann Neurol 1995; 38: 563–570 [34] Yadav A, Collman RG. CNS inflammation and macrophage/microglial biology, associated with HIV-1 infection. J Neuroimmune Pharmacol 2009; 4: 430–447 [35] Pelle M-T, Bazille C, Gray F. Neuropathology and HIV dementia In: Aminoff M, Boller F, Swaab D, eds Handbook of Clinical Neurology: Dementias. 3rd Series. New York: Elsevier; 2008:343–364 [36] McClernon DR, Lanier R, Gartner S et al. HIV in the brain: RNA levels and patterns of zidovudine resistance. Neurology 2001; 57: 1396–1401 [37] Grovit-Ferbas K, Harris-White ME. Thinking about HIV: the intersection of virus, neuroinflammation and cognitive dysfunction. Immunol Res 2010; 48: 40–58 [38] Kraft-Terry SD, Buch SJ, Fox HS, Gendelman HE. A coat of many colors: neuroimmune crosstalk in human immunodeficiency virus infection. Neuron 2009; 64: 133–145

[39] Kaul M, Lipton SA. Mechanisms of neuroimmunity and neurodegeneration associated with HIV-1 infection and AIDS. J Neuroimmune Pharmacol 2006; 1: 138–151 [40] Garden GA, Budd SL, Tsai E et al. Caspase cascades in human immunodeficiency virus-associated neurodegeneration. J Neurosci 2002; 22: 4015– 4024 [41] Gelbard HA, Epstein LG. HIV-1 encephalopathy in children. Curr Opin Pediatr 1995; 7: 655–662 [42] Flowers CH, Mafee MF, Crowell R et al. Encephalopathy in AIDS patients: evaluation with MR imaging. AJNR Am J Neuroradiol 1990; 11: 1235–1245 [43] Dal Pan GJ, McArthur JH, Aylward E et al. Patterns of cerebral atrophy in HIV1-infected individuals: results of a quantitative MRI analysis. Neurology 1992; 42: 2125–2130 [44] Olsen WL, Longo FM, Mills CM, Norman D. White matter disease in AIDS: findings at MR imaging. Radiology 1988; 169: 445–448 [45] Jernigan TL, Archibald S, Hesselink JR et al. The HNRC Group. Magnetic resonance imaging morphometric analysis of cerebral volume loss in human immunodeficiency virus infection. Arch Neurol 1993; 50: 250–255 [46] Ragin AB, Storey P, Cohen BA, Epstein LG, Edelman RR. Whole brain diffusion tensor imaging in HIV-associated cognitive impairment. AJNR Am J Neuroradiol 2004; 25: 195–200 [47] Filippi CG, Ulug AM, Ryan E, Ferrando SJ, van Gorp W. Diffusion tensor imaging of patients with HIV and normal-appearing white matter on MR images of the brain. AJNR Am J Neuroradiol 2001; 22: 277–283 [48] Wu Y, Storey P, Cohen BA, Epstein LG, Edelman RR, Ragin AB. Diffusion alterations in corpus callosum of patients with HIV. AJNR Am J Neuroradiol 2006; 27: 656–660 [49] Thurnher MM, Castillo M, Stadler A, Rieger A, Schmid B, Sundgren PC. Diffusion-tensor MR imaging of the brain in human immunodeficiency viruspositive patients. AJNR Am J Neuroradiol 2005; 26: 2275–2281 [50] Paley M, Cozzone PJ, Alonso J et al. A multicenter proton magnetic resonance spectroscopy study of neurological complications of AIDS. AIDS Res Hum Retroviruses 1996; 12: 213–222 [51] Chang L, Ernst T, Leonido-Yee M, Walot I, Singer E. Cerebral metabolite abnormalities correlate with clinical severity of HIV-1 cognitive motor complex. Neurology 1999; 52: 100–108 [52] Chang L, Ernst T, Witt MD et al. Persistent brain abnormalities in antiretroviral-naive HIV patients 3 months after HAART. Antivir Ther 2003; 8: 17–26 [53] Chang L, Lee PL, Yiannoutsos CT et al. HIV MRS Consortium. A multicenter in vivo proton-MRS study of HIV-associated dementia and its relationship to age. Neuroimage 2004; 23: 1336–1347 [54] Lee PL, Yiannoutsos CT, Ernst T et al. HIV MRS Consortium. A multi-center 1 H MRS study of the AIDS dementia complex: validation and preliminary analysis. J Magn Reson Imaging 2003; 17: 625–633 [55] Yiannoutsos CT, Ernst T, Chang L et al. Regional patterns of brain metabolites in AIDS dementia complex. Neuroimage 2004; 23: 928–935 [56] Valcour VG, Sacktor NC, Paul RH et al. Insulin resistance is associated with cognition among HIV-1-infected patients: the Hawaii Aging With HIV cohort. J Acquir Immune Defic Syndr 2006; 43: 405–410 [57] Salvan AM, Vion-Dury J, Confort-Gouny S, Nicoli F, Lamoureux S, Cozzone PJ. Brain proton magnetic resonance spectroscopy in HIV-related encephalopathy: identification of evolving metabolic patterns in relation to dementia and therapy. AIDS Res Hum Retroviruses 1997; 13: 1055–1066 [58] López-Villegas D, Lenkinski RE, Frank I. Biochemical changes in the frontal lobe of HIV-infected individuals detected by magnetic resonance spectroscopy. Proc Natl Acad Sci U S A 1997; 94: 9854–9859 [59] Meyerhoff DJ, Bloomer C, Cardenas V, Norman D, Weiner MW, Fein G. Elevated subcortical choline metabolites in cognitively and clinically asymptomatic HIV + patients. Neurology 1999; 52: 995–1003 [60] Mohamed MA, Lentz MR, Lee V et al. Factor analysis of proton MR spectroscopic imaging data in HIV infection: metabolite-derived factors help identify infection and dementia. Radiology 2010; 254: 577–586 [61] Winston A, Duncombe C, Li PC et al. Altair Study Group. Two patterns of cerebral metabolite abnormalities are detected on proton magnetic resonance spectroscopy in HIV-infected subjects commencing antiretroviral therapy. Neuroradiology 2012; 54: 1331–1339 [62] Nagel MA, Mahalingam R, Cohrs RJ, Gilden D. Virus vasculopathy and stroke: an under-recognized cause and treatment target. Infect Disord Drug Targets 2010; 10: 105–111 [63] Connor MD, Lammie GA, Bell JE, Warlow CP, Simmonds P, Brettle RD. Cerebral infarction in adult AIDS patients: observations from the Edinburgh HIV Autopsy Cohort. Stroke 2000; 31: 2117–2126

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Human Immunodeficiency Virus (HIV) Dementia [64] Shelburne SA, Visnegarwala F, Darcourt J et al. Incidence and risk factors for immune reconstitution inflammatory syndrome during highly active antiretroviral therapy. AIDS 2005; 19: 399–406 [65] Venkataramana A, Pardo CA, McArthur JC et al. Immune reconstitution inflammatory syndrome in the CNS of HIV-infected patients. Neurology 2006; 67: 383–388 [66] Shelburne SA, III, Darcourt J, White AC, Jr et al. The role of immune reconstitution inflammatory syndrome in AIDS-related Cryptococcus neoformans disease in the era of highly active antiretroviral therapy. Clin Infect Dis 2005; 40: 1049–1052 [67] Safriel YI, Haller JO, Lefton DR, Obedian R. Imaging of the brain in the HIVpositive child. Pediatr Radiol 2000; 30: 725–732

[68] Shah SS, Zimmerman RA, Rorke LB, Vezina LG. Cerebrovascular complications of HIV in children. AJNR Am J Neuroradiol 1996; 17: 1913–1917 [69] Park YD, Belman AL, Kim TS et al. Stroke in pediatric acquired immunodeficiency syndrome. Ann Neurol 1990; 28: 303–311 [70] Lucas S. Causes of death in the HAART era. Curr Opin Infect Dis 2012; 25: 36– 41 [71] Nabha L, Duong L, Timpone J. HIV-associated neurocognitive disorders: perspective on management strategies. Drugs 2013; 73: 893–905 [72] Cysique LA, Vaida F, Letendre S et al. Dynamics of cognitive change in impaired HIV-positive patients initiating antiretroviral therapy. Neurology 2009; 73: 342–348

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26 Non-Human Immunodeficiency Virus (HIV) Infectious Dementia Krishan K. Jain, Jitendra K. Saini, and Rakesh K. Gupta Dementia is characterized by loss of cognitive abilities, decline in memory encoding and retrieval, and impairment of normal executive function in decision making, which interfere with an individual’s activities of daily living.1,2 Dementia disorders usually affect elderly individuals but may affect individuals younger than 65 years.2 In elderly patients, primary dementias like Alzheimer’s disease constitute the most common cause of memory impairments and cognitive deficits.3 Early onset and rapidly progressive dementias (RPDs) include a diverse range of conditions, from reversible to intransigent and rapidly progressive.1 Reversible dementias account for approximately 1.5% of all dementias.4 Depression, vitamin B12 deficiency, and hypothyroidism are the commonly listed reversible causes of dementia.3 Central nervous system (CNS) infections can sometimes manifest with memory impairment that clinically mimics primary dementias.3 Infection represents an infrequent but important cause of RPD, as timely identification and appropriate treatment may produce a favorable outcome in selected instances.5 This chapter reviews neuroimaging, with an emphasis on magnetic resonance imaging (MRI), in dementias related to infectious causes, excluding acquired immunodeficiency syndrome (AIDS)/human immunodeficiency virus (HIV) and prion disease.

along with behavioral disturbances and personality changes.6 Hokkanen et al reported findings of subcortical cognitive impairment, along with mood changes and behavioral disinhibition in patients with herpes zoster encephalitis (HZE).9 On MRI, HSE lesions are hyperintense on T2-weighted images and hypointense on T1-weighted imaging, predominantly involving bilateral inferior and medial aspects of the temporal lobes, and could be seen extending up to the insula. Unilateral temporal lobe involvement is also not uncommon.7,10,11 Diffusion-weighted imaging (DWI) is more sensitive than conventional T2-weighted imaging or fluid-attenuated inversion recovery (FLAIR) imaging for early detection of HSE.7,11 With progression of disease, gyriform enhancement may be observed.11 Few other rare conditions can also involve bilateral temporal lobes in similar fashion to paraneoplastic limbic encephalitis, Japanese encephalitis (JE), and neurosyphilis.7 In chronic cases, cerebral atrophy (▶ Fig. 26.1) is seen, along with neurologic sequelae manifesting as anterograde memory loss, anosmia, and dysphasia.11,12 Early diagnosis is critical so that acyclovir therapy can be started to improve the cognitive outcome and reduce mortality rates.13

26.1 Viral Infections

Much less is known about non-HSV encephalitides, in which both mild and severe cognitive defects have been observed.14 In North America, West Nile virus (WNV) has become the most important cause of epidemic viral encephalitis. Polyomaviruses, including JC and BK viruses, frequently manifest progressive multifocal neurologic deficits or meningoencephalitis, but these do cause RPD in the immunocompromised population.15 JE is rare in Western countries; however, its neurologic, cognitive, and psychiatric sequelae constitute a major public health

26.1.1 Herpes Virus Infections Herpes simplex virus type 1 (HSV-1) remains the most common identifiable cause of acute viral encephalitis.6,7 Patients with herpes simplex encephalitis (HSE) have fever, headache, seizures, focal neurologic signs, impaired consciousness, and altered mental status.8 Loss of memory is frequently prominent,

26.1.2 Nonherpetic Viruses

Fig. 26.1 A 35-year-old man with chronic herpetic encephalitis with memory loss. Increased signal intensity in bilateral medial temporal lobes on axial T2-weighted (a), coronal T2-weighted (b), and axial fluid-attenuated inversion recovery (FLAIR) (c) images with features of cerebral atrophy and no focus of abnormal susceptibility on gradient image (d).

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Non-Human Immunodeficiency Virus (HIV) Infectious Dementia problem in several countries in east, southeast, and south Asia, where it is endemic.16,17 Imaging studies may be normal or may reveal T2 hyperintense signal in regions of the brain infected by the virus. Distributions of signal change can suggest specific viruses. Involvement of the thalamus or basal ganglia frequently occurs with eastern equine encephalitis, JE, and WNV.5,17

evaluate for potential complications, to rule out meningitis mimics, and to increase intracranial pressure before lumbar puncture.25

26.2.2 Spirochetes Neurosyphilis

Subacute Sclerosing Panencephalitis Viral encephalitis is sometimes insidious, manifesting with more gradual behavioral and mental status changes.15 Subacute sclerosing panencephalitis (SSPE) is an example of chronic viral encephalitis leading to dementia.18 It is a rare progressive neurologic disorder usually occurring in childhood and adolescence, caused by persistent defective measles virus. Diagnosis is achieved by typical clinical findings, increased measles antibody titer in cerebrospinal fluid (CSF) and serum, and characteristic waveform in electroencephalography.19,20 Early-stage conventional MRI reveals no abnormalities, but widespread periventricular hyperintensities are noted on T2-weighted imaging in late stage (▶ Fig. 26.2).20 It can be rapidly progressive, and diffuse cortical atrophy may be seen in relatively advanced cases.21 Diffusion tensor imaging can detect early white matter damage, even when conventional MRI reveals no abnormalities.22

26.2 Bacterial Infections 26.2.1 Bacterial Meningitis Meningitis is inflammation of the dura, the leptomeninges, and the adjacent subarachnoid space. Signs and symptoms include headache, fever, neck stiffness, photophobia, vomiting, and altered consciousness. The diagnosis is usually based on a combination of clinical signs and symptoms along with appropriate CSF findings.7,23 Survivors of bacterial meningitis often complain about neurologic and neuropsychological consequences. The pattern of neuropsychological impairment resembles features observed in subcortical cognitive impairment.24 The role of imaging is primarily to confirm suspected meningitis, to

Syphilis is a sexually transmitted disease caused by a spirochete, Treponema pallidum, and it can affect most organs.26 CNS involvement occurs in 5 to 30% of syphilis patients.27 Symptomatic neurosyphilis can be divided into early and late forms. Early neurosyphilis manifests as meningitis and meningovascular disease or stroke, whereas late neurosyphilis involves the meninges and brain or spinal cord parenchyma and manifests clinically as tabes dorsalis and general paresis.28,29 General paresis usually develops between 10 and 25 years after original infection; the invading spirochetes progressively destruct the neurons, resulting in impairments in memory, intellect, affect, and judgment.5,30 Imaging features are as varied as the clinical manifestations. Imaging studies may be normal in a large number of patients. Cortical cerebral atrophy is the most commonly reported finding.26,27,31 Russouw et al demonstrated correlation between atrophy and cognitive impairment, as well as between frontal lesions and overall psychiatric morbidity.32 Other radiologic findings in the general paresis stage include mesiotemporal signal changes (▶ Fig. 26.3) and hydrocephalus. Periventricular white matter changes with ventricular prominence can mimic the imaging features of normal pressure hydrocephalus.5,33

Lyme Disease (Neuroborreliosis) Lyme disease is a multisystem illness caused by spirochete Borrelia burgdorferi. CNS manifestations are rare.1,34 Lyme disease can appear as RPD and is often accompanied by cranial nerve palsies, meningitis, polyradiculopathy, depression, or psychosis.15 MRI abnormalities are seen in fewer than half of the patients. Foci of high T2 signal may be visualized in the cerebral white matter and brainstem.7 These lesions may mimic a demyelinating process. Other nonspecific MRI findings include

Fig. 26.2 A 9-year-old child with subacute sclerosing panencephalitis with cognitive decline and myoclonic jerks. Axial fluid-attenuated inversion recovery (FLAIR) images (a,b) showing subtle signal abnormalities In temporal and periventricular white matter. Diagnosis was confirmed with presence of measles antibody titer in cerebrospinal fluid and characteristic waveform on electroencephalography.

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Fig. 26.3 Imaging findings in patients with neurosyphilis whose initial symptoms were memory impairment and cognitive deficits. Increased signal intensity in bilateral medial temporal lobes on axial T2-weighted images (a), axial fluid-attenuated image recovery (FLAIR) (b), and coronal T2weighted (c) images and low signal on axial T1-weighted image (d). Post-contrast T1-weighted image (e) shows no enhancement of the temporal lesions. Axial FLAIR image (f) of another patient shows a similar pattern of medial temporal lobe signal changes.

focal or diffuse leptomeningeal enhancement involving the cranial nerves, surface of the spinal cord, cauda equina, and spinal nerve roots.35,36

26.2.3 Central Nervous System Tuberculosis Tuberculosis of the CNS is commonly caused by Mycobacterium tuberculosis and constitutes 1% of all tuberculosis cases and 10% to 15% of extrapulmonary tuberculosis cases.37 Sundar et al, in their series of 76 patients younger than 65 years of age, found that 26 of 76 (34.21%) had a reversible cause of dementia with infection present in 11 of those patients. Of these 11 patients, 5 had CNS tuberculosis.38 In two separate case reports, CNS tuberculosis and disseminated tuberculosis were identified as the cause of dementia.3,39 In a recently published study on tubercular meningitis, at the end of 1 year, neurologic sequelae were observed in 78.5% of patients: cognitive impairment in

55%, motor deficit in 40%, optic atrophy in 37%, and other cranial nerve palsy in 23%.40 Infection of the CNS manifests either in diffuse form as leptomeningitis or in localized parenchymal involvement as tuberculoma, abscess, and focal cerebritis. Tubercular meningitis is the most common of this type of infection, with a predilection for the meninges covering the base of the brain. The common imaging triad comprises thick enhancement in the basal cisterns, hydrocephalus (▶ Fig. 26.4), and infarctions.7 Diagnosis is usually established via demonstration of acid-fast bacilli on smear or culture of CSF.5 CNS tuberculomas are commonly seen near the corticomedullary junction or in the periventricular location. The imaging appearance depends on the pathological stage of the granuloma.41,42 A cellular component of tuberculomas will show an increased signal on T1-weighted imaging, and a greater number of parenchymal lesions can be seen on magnetization transfer sequence compared with conventional spin-echo sequences.43

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Non-Human Immunodeficiency Virus (HIV) Infectious Dementia

Fig. 26.4 Tubercular meningitis with cognitive decline. Axial T1-weighted (a) and T2-weighted (b) images show isointense to hyperintense lesions in the effaced basal cisterns with mild ventricular dilatation. Post-contrast T1-weighted image (c) shows enhancement of the exudates along basal cisterns and left sylvian fissure along with few ring-enhancing lesions.

26.2.4 Other Bacterial Infections Bartonella henselae is a gram-negative bacteria that is the causative agent of cat scratch disease. Neurologic manifestations are rare, usually acute encephalitis. Sometimes RPD is seen, especially in immunocompromised persons.5,15,44 Structural imaging studies in Bartonella encephalitis may be normal, or it may reveal focal diffusion or T2 abnormalities.5 Infection with M. pneumoniae and neoaurum has been related to a number of neurologic syndromes, including RPD. Neuroimaging can be normal or abnormal in patients with M. pneumoniae encephalitis. Multiple regions can be affected, including the thalamus, striatum, subcortical white matter, brainstem, and cerebellum.15,45,46 Whipple disease is a rare bacterial infection caused by grampositive, acid-fast negative and periodic acid Schiff–positive bacillus Tropheryma whippelii. It often begins as a malabsorption syndrome, but 5% of cases begin as a neurologic syndrome with dementia, movement disorder, or psychiatric signs. Imaging with computed tomography or MRI may reveal focal areas of signal change.47

vary with the stage of cyst development. Vesicular stage is typified by a smooth, thin-walled T2 hyperintense cyst with small eccentric mural nodules. In the colloidal vesicular stage, cyst degeneration with pericystic edema and cyst wall enhancement is present.7,51 In the granular nodular stage, lesions shrink in size, cyst wall thickens, and scolex may become mineralized; edema and enhancement are persistent. In the nodular calcified stage, small calcified nodules without mass effect and enhancement are seen (▶ Fig. 26.5).7,42 Susceptibility-weighted imaging can demonstrate scolex in fully calcified lesions. Volumetric T2-weighted images are valuable in the demonstration of scolex in a cysticercal cyst.52,53 In around 10 to 15% of affected individuals, the parasitic cysts are located in the subarachnoid spaces and have a multiloculated appearance. The scolex is rarely visible in this condition.54 In large parenchymal and subarachnoid cysticercal cysts, magnetic resonance spectroscopy shows the presence of succinate, acetate, and lactate, along with amino acids, and may help establish the diagnosis when the scolex is not demonstrable on MRI.55

26.3.2 Other Parasitic Infections

26.3 Parasitic Infections 26.3.1 Neurocysticercosis Globally, neurocysticercosis (NCC) is one of the most common CNS parasitic infections; it is caused by the larvae of Taenia solium.42,48 Cognitive decline related to NCC is poorly characterized and underdiagnosed. It should be considered one of the differential diagnoses in cases of dementia, especially in endemic areas.49 Brain parenchyma is the most commonly involved location in patients with NCC. On the basis of imaging and histopathology, four stages of NCC have been described, and these are grouped as vesicular or cystic, necrotic colloidal, granular nodular, and fibrocalcified stage.50 Imaging findings

Two parasitic infections, trypanosomiasis and malaria, must be considered as the cause of RPD in returning travelers or in those who are living in endemic areas, like tropical African regions.5,15 Infection with Trypanosoma spp. causes sleeping sickness with profound neurologic consequences. Alterations in the sleep/wake cycle with progressive mental deterioration can be mistaken for neurodegenerative causes of RPD.56 Malaria infection caused by Plasmodia falciparum typically manifests with relapsing fevers and other systemic signs, but it sometimes leads to cerebral involvement. Acute losses of consciousness or convulsions are typical features of cerebral malaria. RPD is another manifestation, usually accompanied by other signs of organ involvement, including anemia, jaundice, and severe hyperpyrexia15,57,58

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Fig. 26.5 Imaging findings in a follow-up case of neurocysticercosis involving a 40-year-old man with neuropsychiatric manifestations and decreased cognitive abilities. Axial T2-weighted, fluid-attenuated inversion recovery (FLAIR), T1-weighted, and gradient images (a–d) show multiple small hypointense calcified lesions in both cerebral hemispheres with features of diffuse cerebral atrophy. A few of these lesions show minimal peripheral enhancement on post-contrast T1-weighted image (e).

Cerebral toxoplasmosis occurs primarily in immunodeficient patients, although there are rare case reports of dementia caused by Toxoplasma infection in immunocompetent individuals.59,60 On imaging, the most common finding is single or multiple ring-enhancing lesions, some with a characteristic ‘‘eccentric target’’ appearance.61

26.4 Fungal Infections 26.4.1 Central Nervous System Cryptococcosis Cryptococcus neoformans infection may occur in immunocompetent individuals but is much more common in immunocompromised patients.7,62,63 Most patients with CNS cryptococcosis have clinical features of subacute meningitis or meningoencephalitis; however, rapidly progressive neurologic dysfunction and altered mental status may be the initial

manifestation.15,63,64 Some reports describe C. neoformans as causing rapidly progressive cognitive dysfunction, and in one case it was misdiagnosed as Alzheimer’s disease.65 Sometimes CNS cryptococcosis may initially be seen with cognitive or behavioral symptoms.66,67,68 Infection of the CNS can be either meningeal or parenchymal. Meningeal infection spreads along the base of the skull and may involve the adjacent brain parenchyma, giving rise to cryptococcomas, or it may extend along the perivascular spaces, which become dilated to form pseudocysts. These pseudocysts show CSF intensity on both T1- and T2-weighted imaging, which fails to enhance. Demonstration of clusters of these cysts in the basal ganglia and thalami strongly suggests cryptococcal infection.7,62,63,69 Cryptococcomas are usually seen as solid nodules and show hypointense signal on T1-weighted imaging and hyperintense signal on T2-weighted imaging. On DWI, cryptococcoma will show hypointensity in the central cavity and mimic a necrotic brain tumor.63,70

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Non-Human Immunodeficiency Virus (HIV) Infectious Dementia

26.5 Conclusion Infections of the CNS should be included in the differential diagnosis of primary dementia, especially in the presence of other clinical features related to infection. Both the clinician and radiologist alike should be open to the possibility of alternate diagnoses in the setting of rapidly progressive presenile dementias, particularly because several diseases within this group benefit from urgent and specific treatment. Neuroimaging can play an important role in cases with suspicion of infectious dementia because, with the help of certain imaging features, diagnosis of CNS infection can be established noninvasively, at least in a good number of cases.

26.6 References [1] Degnan AJ, Levy LM. Neuroimaging of rapidly progressive dementias, part 2: prion, inflammatory, neoplastic, and other etiologies. AJNR Am J Neuroradiol 2014; 35: 424–431 [2] Fadil H, Borazanci A, Ait Ben Haddou E et al. Early onset dementia. Int Rev Neurobiol 2009; 84: 245–262 [3] Sethi NK, Sethi PK, Torgovnick J, Arsura E. Central nervous system tuberculosis masquerading as primary dementia: a case report. Neurol Neurochir Pol 2011; 45: 510–513 [4] Sellal F, Becker H. [Potentially reversible dementia] [in French] Presse Med 2007; 36: 289–298 [5] McGinnis SM. Infectious causes of rapidly progressive dementia. Semin Neurol 2011; 31: 266–285 [6] Wang T, Rumbaugh JA, Nath A. Viruses and the brain: from inflammation to dementia. Clin Sci (Lond) 2006; 110: 393–407 [7] Rumboldt Z, Thurnher MM, Gupta RK. Central nervous system infections. Semin Roentgenol 2007; 42: 62–91 [8] Tyler KL. Herpes simplex virus infections of the central nervous system: encephalitis and meningitis, including Mollaret’s. Herpes 2004; 11 Suppl 2: 57A–64A [9] Hokkanen L, Launes J, Poutiainen E et al. Subcortical type cognitive impairment in herpes zoster encephalitis. J Neurol 1997; 244: 239–245 [10] Demaerel P, Wilms G, Robberecht W et al. MRI of herpes simplex encephalitis. Neuroradiology 1992; 34: 490–493 [11] Gupta RK, Soni N, Kumar S, Khandelwal N. Imaging of central nervous system viral diseases. J Magn Reson Imaging 2012; 35: 477–491 [12] Armien AG, Hu S, Little MR et al. Chronic cortical and subcortical pathology with associated neurological deficits ensuing experimental herpes encephalitis. Brain Pathol 2010; 20: 738–750 [13] Akyldz BN, Gümüş H, Kumandaş S, Coşkun A, Karakukuçu M, Yklmaz A. Diffusion-weighted magnetic resonance is better than polymerase chain reaction for early diagnosis of herpes simplex encephalitis: a case report. Pediatr Emerg Care 2008; 24: 377–379 [14] Hokkanen L, Launes J. Cognitive outcome in acute sporadic encephalitis. Neuropsychol Rev 2000; 10: 151–167 [15] Geschwind MD, Shu H, Haman A, Sejvar JJ, Miller BL. Rapidly progressive dementia. Ann Neurol 2008; 64: 97–108 [16] Monnet FP. Behavioural disturbances following Japanese B encephalitis. Eur Psychiatry 2003; 18: 269–273 [17] Gupta RK, Jain KK, Kumar S. Imaging of nonspecific (nonherpetic) acute viral infections. Neuroimaging Clin N Am 2008; 18: 41–52, vii [18] Barkhof F, Fox NC, Bastois-Leite AJ, Scheltens P. Infections. In: Neuroimaging in Dementia. Berlin, Heiderlberg: Springer-Verlag 2011; 178–184 [19] Garg RK. Subacute sclerosing panencephalitis. Postgrad Med J 2002; 78: 63– 70 [20] Alkan A, Sarac K, Kutlu R et al. Early- and late-state subacute sclerosing panencephalitis: chemical shift imaging and single-voxel MR spectroscopy. AJNR Am J Neuroradiol 2003; 24: 501–506 [21] Duda EE, Huttenlocher PR, Patronas NJ. CT of subacute sclerosing panencephalitis. AJNR Am J Neuroradiol 1980; 1: 35–38 [22] Trivedi R, Gupta RK, Agarawal A et al. Assessment of white matter damage in subacute sclerosing panencephalitis using quantitative diffusion tensor MR imaging. AJNR Am J Neuroradiol 2006; 27: 1712–1716

[23] Kanamalla US, Ibarra RA, Jinkins JR. Imaging of cranial meningitis and ventriculitis. Neuroimaging Clin N Am 2000; 10: 309–331 [24] Schmidt H, Heimann B, Djukic M et al. Neuropsychological sequelae of bacterial and viral meningitis. Brain 2006; 129: 333–345 [25] Mohan S, Jain KK, Arabi M, Shah GV. Imaging of meningitis and ventriculitis. Neuroimaging Clin N Am 2012; 22: 557–583 [26] Brightbill TC, Ihmeidan IH, Post MJ, Berger JR, Katz DA. Neurosyphilis in HIV-positive and HIV-negative patients: neuroimaging findings. AJNR Am J Neuroradiol 1995; 16: 703–711 [27] Nagappa M, Sinha S, Taly AB et al. Neurosyphilis: MRI features and their phenotypic correlation in a cohort of 35 patients from a tertiary care university hospital. Neuroradiology 2013; 55: 379–388 [28] Golden MR, Marra CM, Holmes KK. Update on syphilis: resurgence of an old problem. JAMA 2003; 290: 1510–1514 [29] Zetola NM, Engelman J, Jensen TP, Klausner JD. Syphilis in the United States: an update for clinicians with an emphasis on HIV coinfection. Mayo Clin Proc 2007; 82: 1091–1102 [30] Luo W, Ouyang Z, Xu H, Chen J, Ding M, Zhang B. The clinical analysis of general paresis with 5 cases. J Neuropsychiatry Clin Neurosci 2008; 20: 490– 493 [31] Zifko U, Wimberger D, Lindner K, Zier G, Grisold W, Schindler E. MRI in patients with general paresis. Neuroradiology 1996; 38: 120–123 [32] Russouw HG, Roberts MC, Emsley RA, Truter R. Psychiatric manifestations and magnetic resonance imaging in HIV-negative neurosyphilis. Biol Psychiatry 1997; 41: 467–473 [33] Fadil H, Gonzalez-Toledo E, Kelley BJ, Kelley RE. Neuroimaging findings in neurosyphilis. J Neuroimaging 2006; 16: 286–289 [34] Fernandez RE, Rothberg M, Ferencz G, Wujack D. Lyme disease of the CNS: MR imaging findings in 14 cases. AJNR Am J Neuroradiol 1990; 11: 479–481 [35] Vanzieleghem B, Lemmerling M, Carton D et al. Lyme disease in a child presenting with bilateral facial nerve palsy: MRI findings and review of the literature. Neuroradiology 1998; 40: 739–742 [36] Hattingen E, Weidauer S, Kieslich M, Boda V, Zanella FE. MR imaging in neuroborreliosis of the cervical spinal cord. Eur Radiol 2004; 14: 2072–2075 [37] Gupta RK, Kumar S. Central nervous system tuberculosis. Neuroimaging Clin N Am 2011; 21: 795–814, vii–viii [38] Sundar U, Sharma A, Yeolekar ME. Presenile dementia—etiology, clinical profile and treatment response at four month follow up. J Assoc Physicians India 2004; 52: 953–958 [39] Kesav P, Vishnu VY, Lal V, Prabhakar S. Disseminated tuberculosis presenting as rapidly progressive dementia. QJM 2014 [40] Kalita J, Misra UK, Ranjan P. Predictors of long-term neurological sequelae of tuberculous meningitis: a multivariate analysis. Eur J Neurol 2007; 14: 33–37 [41] Trivedi R, Saksena S, Gupta RK. Magnetic resonance imaging in central nervous system tuberculosis. Indian J Radiol Imaging 2009; 19: 256–265 [42] Gupta RK, Lufkin RB. MR imaging and spectroscopy of central nervous system infection. In: Tuberculosis and Other Non-Tuberculous Bacterial Granulomatous Infection, New York: Kluwer Academic/Plenum Publishers; 2001:95– 145 [43] Gupta RK, Kathuria MK, Pradhan S. Magnetization transfer MR imaging in CNS tuberculosis. AJNR Am J Neuroradiol 1999; 20: 867–875 [44] Revol A, Vighetto A, Jouvet A, Aimard G, Trillet M. Encephalitis in cat scratch disease with persistent dementia. J Neurol Neurosurg Psychiatry 1992; 55: 133–135 [45] Smith R, Eviatar L. Neurologic manifestations of Mycoplasma pneumoniae infections: diverse spectrum of diseases: a report of six cases and review of the literature. Clin Pediatr (Phila) 2000; 39: 195–201 [46] Daxboeck F. Mycoplasma pneumoniae central nervous system infections. Curr Opin Neurol 2006; 19: 374–378 [47] Durand DV, Lecomte C, Cathébras P, Rousset H, Godeau P. Whipple disease. Clinical review of 52 cases: the SNFMI Research Group on Whipple Disease. Société Nationale Française de Médecine Interne. Medicine (Baltimore) 1997; 76: 170–184 [48] Garcia HH, Del Brutto OH. Taenia solium cysticercosis. Infect Dis Clin North Am 2000; 14: 97–119, ix [49] Anand KS, Dhikav V. An unusual cause of dementia. JIACM 2010; 11: 300– 301 [50] Dumas JL, Visy JM, Belin C, Gaston A, Goldlust D, Dumas M. Parenchymal neurocysticercosis: follow-up and staging by MRI. Neuroradiology 1997; 39: 12–18 [51] Castillo M. Imaging of neurocysticercosis. Semin Roentgenol 2004; 39: 465– 473

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Infection and Inflammatory Conditions Associated with Dementia [52] Chawla S, Gupta RK, Kumar R et al. Demonstration of scolex in calcified cysticercus lesion using gradient echo with or without corrected phase imaging and its clinical implications. Clin Radiol 2002; 57: 826–834 [53] Gupta RK, Kumar R, Chawla S, Pradhan S. Demonstration of scolex within calcified cysticercus cyst: its possible role in the pathogenesis of perilesional edema. Epilepsia 2002; 43: 1502–1508 [54] Amaral L, Maschietto M, Maschietto R et al. Unusual manifestations of neurocysticercosis in MR imaging: analysis of 172 cases. Arq Neuropsiquiatr 2003; 61 3A: 533–541 [55] Mishra AM, Gupta RK, Jaggi RS et al. Role of diffusion-weighted imaging and in vivo proton magnetic resonance spectroscopy in the differential diagnosis of ring enhancing intracranial cystic mass lesions. J Comput Assist Tomogr 2004; 28: 540–546 [56] Kennedy PG. Human African trypanosomiasis-neurological aspects. J Neurol 2006; 253: 411–416 [57] Varney NR, Roberts RJ, Springer JA, Connell SK, Wood PS. Neuropsychiatric sequelae of cerebral malaria in Vietnam veterans. J Nerv Ment Dis 1997; 185: 695–703 [58] Newton CR, Hien TT, White N. Cerebral malaria. J Neurol Neurosurg Psychiatry 2000; 69: 433–441 [59] Bach MC, Armstrong RM. Acute toxoplasmic encephalitis in a normal adult. Arch Neurol 1983; 40: 596–597 [60] Habek M, Ozretić D, Zarković K, Djaković V, Mubrin Z. Unusual cause of dementia in an immunocompetent host: toxoplasmic encephalitis. Neurol Sci 2009; 30: 45–49 [61] Kumar GG, Mahadevan A, Guruprasad AS et al. Eccentric target sign in cerebral toxoplasmosis: neuropathological correlate to the imaging feature. J Magn Reson Imaging 2010; 31: 1469–1472

[62] Tien RD, Chu PK, Hesselink JR, Duberg A, Wiley C. Intracranial cryptococcosis in immunocompromised patients: CT and MR findings in 29 cases. AJNR Am J Neuroradiol 1991; 12: 283–289 [63] Jain KK, Mittal SK, Kumar S, Gupta RK. Imaging features of central nervous system fungal infections. Neurol India 2007; 55: 241–250 [64] Kathuria MK, Gupta RK. Fungal infections. In: Gupta RK, Lufkin RB, eds. MR Imaging and Spectroscopy of Central Nervous System Infections. New York: Kluwer Press; 2001:177–203 [65] Hoffmann M, Muniz J, Carroll E, De Villasante J. Cryptococcal meningitis misdiagnosed as Alzheimer’s disease: complete neurological and cognitive recovery with treatment. J Alzheimers Dis 2009; 16: 517–520 [66] Ala TA, Doss RC, Sullivan CJ. Reversible dementia: a case of cryptococcal meningitis masquerading as Alzheimer’s disease. J Alzheimers Dis 2004; 6: 503–508 [67] Prakash PY, Sugandhi RP. Neuropsychiatric manifestation of confusional psychosis due to Cryptococcus neoformans var. grubii in an apparently immunocompetent host: a case report. Cases J 2009; 2: 9084 [68] Sa’adah MA, Araj GF, Diab SM, Nazzal M. Cryptococcal meningitis and confusional psychosis: a case report and literature review. Trop Geogr Med 1995; 47: 224–226 [69] Saigal G, Post MJD, Lolayekar S, Murtaza A. Unusual presentation of central nervous system cryptococcal infection in an immunocompetent patient. AJNR Am J Neuroradiol 2005; 26: 2522–2526 [70] Ho TL, Lee HJ, Lee KW, Chen WL. Diffusion-weighted and conventional magnetic resonance imaging in cerebral cryptococcoma. Acta Radiol 2005; 46: 411–414

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Part IX

29 Normal Pressure Hydrocephalus

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Normal Pressure Hydrocephalus

29 Normal Pressure Hydrocephalus Ritu Shah, Fathima Fijula Palot Manzil, and Surjith Vattoth Normal pressure hydrocephalus (NPH) is a syndrome characterized by ventricular enlargement and a classic clinical triad of gait disturbance, urinary incontinence, and dementia. The condition is also sometimes described as idiopathic adult hydrocephalus syndrome, emphasizing the fact that the intracranial pressure in NPH is not always normal. When not otherwise specified, the term NPH usually refers to the idiopathic condition and contrasts with the symptomatic or secondary forms of hydrocephalus, which may develop in a setting of trauma, hemorrhage, mass lesions, infection, or aqueductal stenosis. A prompt and accurate diagnosis of NPH is critical in the treatment of these patients because it is one of the few causes of dementia that is potentially reversible.

29.1 Epidemiology The incidence of NPH has been estimated in population studies to be about 5.5 per 100,000 persons in the general population or up to 1.4 to 1.5% of elderly adults. The prevalence is 21.9 per 100,000 in the general population and increases with age, rising from 3.3 per 100,000 in the age group 50 to 59 years, to 181.7 per 100,000 at 70 to 79 years of age.

29.2 Clinical Features 29.2.1 Gait Disturbance The gait in NPH is often described as “shuffling,” “wide-based,” or “magnetic.” In the classic “magnetic apraxia” of gait, the patient has difficulty with initiation and changes in trajectory of gait and appears to be literally “stuck” to the floor. The degree of severity of magnetic apraxia can vary, with some patients only having subtle findings, particularly on turns or transfers, whereas others may be so severe that they cannot even obtain the upright position. Many patients show disequilibrium and slowness of gait because of short steps and gait apraxia or slowness of both the upper and lower extremities, which can improve with shunting. Appendicular tremor is seen in in 40% patients with NPH and usually does not respond to shunting.1

impaired executive functions. These deficits are often mistaken for the consequence of old age. There is a gradual decline in active retrieval from memory (immediate and delayed recall) with relatively preserved memory storage (recognition). Furthermore, there is a decline in executive functions and complex information processing. Sometimes deficits in visuospatial perception and visual construction skills are noted.3 Whereas most patients show improvement with shunting, some patients may have persistent global cognitive deficits, which often correlate with the presence of vascular risk factors.

29.3 Challenges in Diagnosis The diagnosis of NPH requires careful exclusion of other causes of dementia, which can manifest with overlapping signs and symptoms.4 The presence of an asymmetric resting tremor, lead-pipe rigidity, or visual hallucinations may suggest dementia with Lewy bodies, which causes similar cognitive deficits. Depression with pseudodementia is in the differential diagnosis as well. Early presence of cortical deficits such as aphasia, apraxia, or agnosia, should raise suspicion for dementia with cortical pathology, such as Alzheimer’s disease, multi-infarct dementia, or frontotemporal dementia. In patients with progressive dementia who have a normal gait, causes other than NPH should be carefully evaluated. Some patients with NPH can also have Alzheimer’s disease as a comorbidity; both conditions are associated with hypertension and advanced age. Although shunting can result in improvement in gait disturbances in patients with NPH, the procedure needs careful evaluation for risks and benefits in patients with advanced dementia.

29.4 Neuroimaging Imaging of the brain is an essential component of the evaluation when the diagnosis of NPH is considered. Magnetic resonance imaging (MRI) of the brain is the preferred radiologic examination for the diagnosis of NPH, although computed tomography (CT) scanning is useful if MRI is unavailable. Both radiologic techniques require clinical correlation.

29.2.2 Urinary Incontinence

29.4.1 Ventriculomegaly

Most patients have urinary frequency, urgency, or frank incontinence resulting from detrusor overactivity. In a study on 42 patients with probable NPH,2 lower urinary tract symptoms were seen in 93% of the patients, with storage symptoms (93%) more common than voiding symptoms (71%). Urinary urgency (overactive bladder)/frequency (64%) is seen more frequently than urinary incontinence (57%).

Hydrocephalus is a key finding on CT or MRI scans, where ventriculomegaly is disproportionate to the severity of sulcal atrophy. This “ventriculosulcal disproportion” helps differentiate NPH from ex vacuo ventriculomegly. In NPH, ventriculomegaly is prominent in all three horns of the lateral ventricles and in the third ventricle with relative sparing of the fourth ventricle (▶ Fig. 29.1).5 On MRI, the temporal horns of the lateral ventricles may show dilatation out of proportion to hippocampal atrophy (▶ Fig. 29.2). Evan’s index (maximal ventricular width divided by the largest biparietal distance between the inner tables of the skull) of 0.3 or greater is one criterion for probable NPH (▶ Fig. 29.3).

29.2.3 Dementia The dementia of NPH is characterized by subcortical cognitive deficits, involving psychomotor slowing, impaired recall, and

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Fig. 29.1 Coronal reformatted computed tomography scan shows dilatation of the third and lateral ventricles out of proportion to sulcal atrophy (ventriculosulcal disproportion). Also note bilateral sylvian fissure dilatation (arrows) seen in normal pressure hydrocephalus.

Fig. 29.3 Axial postcontrast T1-weighted image showing an Evan’s index greater than 0.3, a criterion for probable normal pressure hydrocephalus. Evan’s index is calculated by dividing the maximal width of frontal horns (a) by the largest biparietal distance between the inner tables of the skull (b).

29.4.2 Periventricular White Matter Changes Some patients may show frontal and occipital periventricular hypoattenuating areas on CT or contiguous T2/fluid-attenuated

Fig. 29.2 Coronal T1-weighted magnetic resonance imaging through the hippocampi shows dilatation of temporal horns of the lateral ventricles out of proportion to hippocampal atrophy.

Fig. 29.4 Axial fluid-attenuated inversion recovery (FLAIR) image shows bilateral anterior and posterior periventricular hyperintensities indicative of transependymal edema resulting from elevated cerebrospinal fluid pressures.

inversion recovery (FLAIR) hyperintensities on MRI indicative of transependymal edema resulting from elevated cerebrospinal fluid (CSF) pressures (▶ Fig. 29.4).5,6 However, periventricular leukoencephalopathy of microangiopathic disease can also produce an identical appearance.

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Fig. 29.6 Sagittal T1-weighted magnetic resonance imaging shows upward bowing and thinning of the corpus callosum (vertical arrow) with large lateral ventricles (horizontal arrow). Note that the fourth ventricle is relatively normal (arrowhead). Fig. 29.5 Axial T2-weighted magnetic resonance imaging through the midbrain shows a hypointense cerebrospinal fluid flow void in the cerebral aqueduct (arrow).

Cerebrospinal Fluid Flow Void Sign The presence of a flow void at the aqueduct on axial T1- and T2-weighted images in patients with NPH has been termed the CSF flow void sign (▶ Fig. 29.5). Studies have shown conflicting results in the usefulness of this sign in predicting shunt-responsive NPH. Bradley et al7 investigated the predictive value of CSF voids for shunt responsiveness and found a significant correlation; but in a later study,8 they did not find a statistically significant relationship between responsiveness to CSF shunting and aqueductal flow void score.

Corpus Callosal Thinning Corpus callosal thinning is seen as upward bowing of the corpus callosum on sagittal T1-weighted images (▶ Fig. 29.6).9 The callosal angle on coronal MRI of the posterior commissure perpendicular to the anteroposterior commissure plane may also have some diagnostic significance, although it is not accepted as a reliable criterion.10 The callosal angle was significantly smaller in NPH (mean ± standard deviation, 66 ± 14 degrees) (▶ Fig. 29.7) than in Alzheimer’s disease (104 ± 15 degrees) and normal controls (112 ± 11 degrees) (▶ Fig. 29.8). Using a threshold of 90 degrees, an accuracy of 93%, a sensitivity of 97%, and a specificity of 88% were observed for discrimination of NPH from Alzheimer’s disease.

Cingulate Sulcus Sign Adachi et al11 noted that on paramedian sagittal images, the cingulate sulcus appeared to be narrow and tight posteriorly in patients with NPH (▶ Fig. 29.9). The cingulate sign was seen in all 10 NPH patients in their series, but never in Alzheimer’s disease or progressive supranuclear palsy (PSP), which had similar

Fig. 29.7 Coronal T1-weighted imaging through the corpus callosum at the level of the posterior commissure, obtained perpendicular to the anterior commissure-posterior commissure. Line shows a relatively narrow callosal angle in a patient with NPH.

appearance of the anterior and posterior aspects of the cingulate sulcus (▶ Fig. 29.10).

29.4.3 Brainstem Changes Normal pressure hydrocephalus may be associated with the “upper midbrain profile sign”; Adachi et al11 recorded this abnormality in 7 of 10 patients with NPH compared with 5 of

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Fig. 29.8 (a) Sagittal T1-weighted magnetic resonance imaging shows the plane of coronal image acquisition to calculate the callosal angle at the level of posterior commissure (vertical dotted line), which is perpendicular to the anterior commissure-posterior commissure line (horizontal dotted line). (b) Coronal T1-weighted magnetic resonance image obtained as shown in (a) demonstrates the wider normal callosal angle in a control subject.

Fig. 29.9 Parasagittal section of a sagittal T1-weighted magnetic resonance imaging sequence shows that the cingulate sulcus is narrow and tight posteriorly in a patient with normal pressure hydrocephalus (arrowheads), the so-called cingulate sulcus sign. Compare this with the normal-appearing cingulate sulcus in ▶ Fig. 29.10.

Fig. 29.10 Parasagittal section of a sagittal T1 weighted magnetic resonance imaging sequence shows the normal appearance of the posterior aspect of the cingulate sulcus somewhat similar to its anterior aspect (arrows) in a patient with progressive supranuclear palsy. Normal subjects also show the same pattern.

11 with Alzheimer’s disease and 3 of 5 with PSP. The upper midbrain profile sign was an abnormal appearance of the superior profile of the midbrain on the midsagittal T1-weighted images.12 The profile was considered normal when it was convex, as represented by an imaginary curve connecting a point immediately posterior to the mammillary body and one located at the upper orifice of the aqueduct. The profile was considered abnormal when it was flat or concave, as identified by using the same imaginary line or curve (▶ Fig. 29.11). This is also called “hummingbird” sign and is considered a characteristic sign of PSP, but it could also sometimes be seen in other conditions, as described earlier.

Cine Phase-Contrast Magnetic Resonance Quantification of Cerebrospinal Fuid Flow Cine phase-contrast magnetic resonance quantification of CSF flow can measure the stroke volume (= mean volume of CSF passing through the cerebral aqueduct in systole – volume passing during diastole).8,13 Stroke volumes greater than 42 microliters may predict the likelihood of response to shunting.

Fig. 29.11 Sagittal T1-weighted magnetic resonance imaging shows flattening/concavity of the normally convex superior profile of the midbrain (arrow), also known as “hummingbird” sign” in a patient with normal pressure hydrocephalus.

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Normal Pressure Hydrocephalus

Fig. 29.12 Sagittal cine phase-contrast magnetic resonance imaging (cerebrospinal fluid flow study) shows prominent bidirectional flow in the aqueduct (arrow).

In one study,8 18 of 42 patients were selected for ventriculoperitoneal shunting based on results of flow studies, 12 of 12 patients with stroke volume > greater than 42 microliters improved versus 3 of 6 with stroke volumes less than 42 microliters. CSF stroke volume often increases after onset of symptoms, plateaus after 18 to 20 months, and then declines. When sophisticated software for CSF flow quantification is not available, visual assessment of the CSF flow study should be carefully carried out to evaluate for prominent bidirectional flow in the aqueduct, which is seen in NPH (▶ Fig. 29.12). In another CSF hydrodynamics study,14 14 shunt-responsive patients were compared with 6 nonresponders. Using a threshold mean CSF velocity through the aqueduct of greater than 26 millimeters per second as a predictor of responsiveness, the investigators found a sensitivity of 50%, specificity of 83.3%, positive predictive value of 87.5%, and accuracy of 70%. Scollato et al15 described changes in aqueductal stroke volume in 65 shunted patients with NPH who underwent clinical evaluation and stroke volume measurements 7 to 30 days before, and 1, 3, 6, and 12 months after, surgery. Aqueductal stroke volume decreased in all patients in whom the ventriculoperitoneal shunt worked properly, and the rate of reduction in stroke volume correlated with clinical improvement. Postoperative rise in stroke volume indicated shunt malfunction. A precipitous drop of stroke volume after shunt may be the consequence of increased drainage and herald the occurrence of a subdural fluid collection.

the periventricular and deep white matter in the frontal and occipital regions. After shunt surgery, ADC values were reduced in NPH in the frontal periventricular white matter. Increased diffusion in Binswanger’s disease may reflect irreversible breakdown of axonal integrity caused by the subcortical ischemic vascular disease. Several investigators have evaluated changes in the ADC during the cardiac cycle.17,18,19 Patients with NPH show pronounced ADC changes over the cardiac cycle compared with either healthy controls and/or patients with ex vacuo ventricular dilatation, indicating altered biomechanical properties of intracranial tissues. Demura et al18 measured regional fractional anisotropy and ADCs in several white matter regions before, and 24 hours after, a CSF tap test. ADC values were significantly decreased in the frontal periventricular region and the body of the corpus callosum in patients who showed improved neurologic status after the spinal tap, whereas no significant change was shown in the negative group. Fractional anisotropy values were significantly increased in the body of the corpus callosum in both responders and nonresponders. These findings indicate that changes in water dynamics in white matter may have a role in the mechanism causing symptoms of NPH.

Cerebral Perfusion Measurements Using by Dynamic Susceptibility-Contrast Magnetic Resonance Imaging Dynamic susceptibility-contrast (DSC) MRI perfusion is a potentially useful diagnostic tool and a possible predictor of shunt response in NPH. In a recent study,20 DSC MRI was used to measure absolute perfusion values in cortical, subcortical, and periventricular regions and along the periventricular and paraventricular profiles in 21 cases of NPH and 16 age-matched healthy controls. Relative cerebral blood flow (rCBF), calculated with the occipital cortex as internal reference, was measured in the two groups. NPH was associated with decreased rCBF in the basal medial frontal cortex, hippocampus, lentiform nucleus, periventricular white matter (PVWM), central gray matter, and global parenchyma compared with controls. NPH patients with higher preoperative rCBF in the PVWM performed better in clinical tests. Shunt responders had higher rCBF in the basal medial frontal cortex than did nonresponders.

29.4.5 Nuclear Imaging Techniques Nuclear imaging techniques, such as isotope cisternography and CT cisternography, have been used in NPH to evaluate CSF dynamics, such as reversal of flow (▶ Fig. 29.13). These tests have generally not been found useful and have been replaced by other modalities.

29.4.4 Diffusion-Weighted Imaging

Single-Photon Emission Computed Tomography

Diffusion-weighted imaging (DWI) can be useful in differentiating NPH from subcortical leukoencephalopathy/Binswanger’s disease.16 Patients with Binswanger’s disease had higher apparent diffusion coefficient (ADC) values than those with NPH in

Single-photon emission computed tomography (SPECT) with technetium 99 m hexamethylpropyleneamine oxime (HMPAO) typically shows decreased rCBF in frontoparietal areas and the subcortical white matter. A combination of clinical assessment

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Normal Pressure Hydrocephalus

Fig. 29.13 (a) Normal indium-11-labeled diethylenetriamine pentaacetic acid (DTPA) cisternogram planar images obtained after injection into lumbar subarachnoid space. Images at 4 hours (top row) demonstrate radioactivity in the cervical spinal subarachnoid space, basal cisterns, and mild activity in the sylvian fissure and interhemispheric cisterns in a trident pattern. Images at 24 hours (bottom row) show normal ascent of activity over the cerebral convexities with relative clearing of the spinal canal and basal subarachnoid cisterns. Transient slight ventricular activity at 4 hours, disappearing at 24 hours, may be normal-variant flow pattern, but any major persistent ventricular activity is abnormal. (b) Indium-11-labeled DTPA cisternogram planar images obtained after injection into lumbar subarachnoid space in normal pressure hydrocephalus (NPH). Images at 4 hours (top row) demonstrate central heart-shaped radioactivity reflux into the lateral ventricles. Images at 24 hours (middle row) show some tracer ascending into the sylvian and interhemispheric fissures, with only faint visualization of convexity activity. Images at 48 hours (bottom row) also show stagnant radioactivity within the lateral ventricles and basal cisterns without normal significant concentration over the superior convexity/superior sagittal sinus, which would be expected by 24 hours in a normal case. ANT, anterior; L, left; LAT, lateral; R, right.

with SPECT (with perfusion-weighted MRI) can be helpful in the preoperative selection of patients for shunting procedures with suspected NPH syndrome. Sasaki et al21 found decreased blood flow in frontal areas and around the corpus callosum, findings consistent with the deficits noted on clinical assessment. In another study, Kristensen et al22 described rCBF hypoperfusion in the caudal frontal and temporal gray matter and the subcortical white matter. Removal of CSF was not accompanied by improvement in rCBF, indicating that the study might not provide additional information in preoperative evaluation. More recently,23 acetazolamide SPECT was found useful in identifying patients who are unable to increase rCBF after acetazolamide administration and, therefore, have a low capacity for vasodilation in the brain as a result of compression and stretching by ventriculomegaly. In this study, an increase of less than 20% in preoperative acetazolamide SPECT predicted improve-

ment in cognitive impairment after surgery with 100% sensitivity and 60% specificity.

Positron Emission Tomography [18F]-flutemetamol PET scanning in patients with NPH may be useful in establishing the total burden of β-amyloid and, therefore, the likely benefit (or lack thereof) of invasive surgical treatment of NPH, such as ventriculoperitoneal shunting in dementia. In one study, PET was used to measure cerebral retention of Pittsburgh compound B to localize or estimate β-amyloid accumulation in patients with NPH with cognitive impairment.24 Regional cerebral metabolic rate of glucose is a promising research tool to investigate regional disturbances in metabolism before and after ventricular shunt placement in idiopathic NPH.

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Normal Pressure Hydrocephalus

29.5 Management

29.6 Conclusions

Patients exhibiting a progressive decline in gait, ventricular enlargement disproportionate to the degree of atrophy, and in whom no other explanation of the gait disturbance is identified should be considered candidates for CSF shunting, even if they lack substantial dementia or urinary incontinence. Nevertheless, the criteria for selecting patients for shunt placement are unclear, and to date, the “gold standard” for diagnosis remains clinical improvement with CSF shunting. Patients with probable NPH25 are 40 years of age or older with insidious (nonacute) progression of symptoms over 3 months or longer and have CSF opening pressures of 70 to 245 mm H2O. MRI or CT must show an Evan’s index (maximal ventricular width divided by the largest biparietal distance between the inner tables of the skull) of 0.3, temporal horn enlargement, periventricular signal changes, periventricular edema, or an aqueductal/fourth ventricular flow void. Although a callosal angle of 40 degrees or greater was included in the guidelines, it is not a widely recognized criterion. Clinically, patients must demonstrate gait dysfunction plus either urinary or cognitive dysfunction. Abnormal urinary urgency or frequency is sufficient to document urinary bladder dysfunction. To meet the criteria for cognitive dysfunction, there must be impairments of two or more domains, such as psychomotor speed, fine motor speed or accuracy, attention, short-term recall, executive function, or behavioral or personality changes. A spinal tap to remove 30 milliliters or more of CSF is an accepted method to establish the diagnosis of NPH and for predicting the response to shunting. In some patients who do not show a significant response to a spinal tap, external lumbar drainage (ELD) can be useful in predicting the outcome of surgical intervention.26 For ELD, a spinal catheter is inserted into the lumbar spine and CSF is drained at a rate of 10 to 15 milliliters per hour for 72 hours. The response to ELD can be assessed based on gait analysis or walking speed using a timed 10-meter walk before and after ELD. Nearly 85 to 90% patients who had a positive test showed improvement in walking speed, whereas a third with a negative response to ELD improved after shunting. Overall, ELD provided sensitivity, specificity, positive predictive value, and negative predictive value of 95, 64, 90, and 78%, respectively. It is important to remember that a positive tap or ELD test has sufficient positive predictive value to recommend shunting, whereas those with a negative test should undergo a careful analysis of risk versus an estimated 20% chance of benefit. Cerebrospinal fluid shunting procedures, including ventriculoperitoneal, ventriculopleural, or ventriculoatrial shunting, can lead to significant clinical improvement in NPH symptoms in approximately 60% of patients. In a systematic review,27 data from 64 published studies on the effect of shunt procedures in idiopathic NPH were reviewed: 27 studies reported positive outcome for 1,028 of 1,446 patients 3 months after shunt; 42 studies reported positive outcome 1 year after shunt for 1,343 of 1,805 patients; 10 studies reported an improvement in 415 of 640 patients on long-term outcome (i.e., at least 3 years after the surgery). Total mortality was 1%. Common complications include subdural hemorrhage or effusion (6.3%), intracerebral hemorrhage or stroke (0.4%), infection (3%), and new-onset seizures (0.7%). A total of 1,401 patients had a shunt revision rate of 16% (range, 5 to 53%) as reported in 26 studies.

Despite major advances in clinical and diagnostic modalities, NPH remains a challenge for diagnosis and clinical management. Radiology has an important place in the diagnosis and clinical monitoring of these patients. Although the radiologic features are often not specific for NPH, a diligent and careful approach can be useful in patients with suggestive clinical signs and symptoms.

29.7 Acknowledgment The authors thank Dr. Eva Dubowsky for providing images of the indium-11-labeled DTPA cisternogram.

References [1] Bugalho P, Alves L, Miguel R. Gait dysfunction in Parkinson’s disease and normal pressure hydrocephalus: a comparative study. J Neural Transm 2013; 120: 1201–1207 [2] Sakakibara R, Kanda T, Sekido T et al. Mechanism of bladder dysfunction in idiopathic normal pressure hydrocephalus. Neurourol Urodyn 2008; 27: 507–510 [3] Chaudhry P, Kharkar S, Heidler-Gary J et al. Characteristics and reversibility of dementia in normal pressure hydrocephalus. Behav Neurol 2007; 18: 149– 158 [4] Sorbi S, Hort J, Erkinjuntti T et al. EFNS Scientist Panel on Dementia and Cognitive Neurology. EFNS-ENS Guidelines on the diagnosis and management of disorders associated with dementia. Eur J Neurol 2012; 19: 1159–1179 [5] Inatomi Y, Yonehara T, Hashimoto Y, Hirano T, Uchino M. Correlation between ventricular enlargement and white matter changes. J Neurol Sci 2008; 269: 12–17 [6] Tullberg M, Jensen C, Ekholm S, Wikkelsø C. Normal pressure hydrocephalus: vascular white matter changes on MR images must not exclude patients from shunt surgery. AJNR Am J Neuroradiol 2001; 22: 1665–1673 [7] Bradley WG, Jr, Whittemore AR, Kortman KE et al. Marked cerebrospinal fluid void: indicator of successful shunt in patients with suspected normalpressure hydrocephalus. Radiology 1991; 178: 459–466 [8] Bradley WG, Jr, Scalzo D, Queralt J, Nitz WN, Atkinson DJ, Wong P. Normalpressure hydrocephalus: evaluation with cerebrospinal fluid flow measurements at MR imaging. Radiology 1996; 198: 523–529 [9] Lee WJ, Wang SJ, Hsu LC, Lirng JF, Wu CH, Fuh JL. Brain MRI as a predictor of CSF tap test response in patients with idiopathic normal pressure hydrocephalus. J Neurol 2010; 257: 1675–1681 [10] Ishii K, Kanda T, Harada A et al. Clinical impact of the callosal angle in the diagnosis of idiopathic normal pressure hydrocephalus. Eur Radiol 2008; 18: 2678–2683 [11] Adachi M, Kawanami T, Ohshima F, Kato T. Upper midbrain profile sign and cingulate sulcus sign: MRI findings on sagittal images in idiopathic normalpressure hydrocephalus, Alzheimer’s disease, and progressive supranuclear palsy. Radiat Med 2006; 24: 568–572 [12] Righini A, Antonini A, De Notaris R et al. MR imaging of the superior profile of the midbrain: differential diagnosis between progressive supranuclear palsy and Parkinson’s disease. AJNR Am J Neuroradiol 2004; 25: 927–932 [13] Bradley WG. Cerebrospinal fluid dynamics and shunt responsiveness in patients with normal-pressure hydrocephalus Mayo Clinic proceedings 2002; 77: 507–508 [14] Witthiwej T, Sathira-ankul P, Chawalparit O, Chotinaiwattarakul W, Tisavipat N, Charnchaowanish P. MRI study of intracranial hydrodynamics and ventriculoperitoneal shunt responsiveness in patient with normal pressure hydrocephalus. J Med Assoc Thai 2012; 95: 1556–1562 [15] Scollato A, Tenenbaum R, Bahl G, Celerini M, Salani B, Di Lorenzo N. Changes in aqueductal CSF stroke volume and progression of symptoms in patients with unshunted idiopathic normal pressure hydrocephalus. AJNR Am J Neuroradiol 2008; 29: 192–197 [16] Tullberg M, Hultin L, Ekholm S, Månsson JE, Fredman P, Wikkelsø C. White matter changes in normal pressure hydrocephalus and Binswanger’s disease: specificity, predictive value and correlations to axonal degeneration and demyelination. Acta Neurol Scand 2002; 105: 417–426

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Normal Pressure Hydrocephalus [17] Ohno N, Miyati T, Mase M et al. Idiopathic normal-pressure hydrocephalus: temporal changes in ADC during cardiac cycle. Radiology 2011; 261: 560–565 [18] Demura K, Mase M, Miyati T et al. Changes of fractional anisotropy and apparent diffusion coefficient in patients with idiopathic normal pressure hydrocephalus. Acta Neurochir Suppl (Wien) 2012; 113: 29–32 [19] Osawa T, Mase M, Miyati T et al. Delta-ADC (apparent diffusion coefficient) analysis in patients with idiopathic normal pressure hydrocephalus. Acta Neurochir Suppl (Wien) 2012; 114: 197–200 [20] Ziegelitz D, Starck G, Kristiansen D, et al. Cerebral perfusion measured by dynamic susceptibility contrast MRI is reduced in patients with idiopathic normal pressure hydrocephalus. J Mag Reson Imag 2014; 39: 1533–1542. [21] Sasaki H, Ishii K, Kono AK et al. Cerebral perfusion pattern of idiopathic normal pressure hydrocephalus studied by SPECT and statistical brain mapping. Ann Nucl Med 2007; 21: 39–45 [22] Kristensen B, Malm J, Fagerland M et al. Regional cerebral blood flow, white matter abnormalities, and cerebrospinal fluid hydrodynamics in patients with idiopathic adult hydrocephalus syndrome. J Neurol Neurosurg Psychiatry 1996; 60: 282–288

[23] Yamada SM, Masahira N, Kawanishi Y, Fujimoto Y, Shimizu K. Preoperative acetazolamide SPECT is useful for predicting outcome of shunt operation in idiopathic normal pressure hydrocephalus patients. Clin Nucl Med 2013; 38: 671–676 [24] Kondo M, Tokuda T, Itsukage M et al. Distribution of amyloid burden differs between idiopathic normal pressure hydrocephalus and Alzheimer’s disease. Neuroradiol J 2013; 26: 41–46 [25] Relkin N, Marmarou A, Klinge P, Bergsneider M, Black PM. Diagnosing idiopathic normal-pressure hydrocephalus. Neurosurgery 2005; 57 Suppl: S4– S16, discussion ii–v [26] Panagiotopoulos V, Konstantinou D, Kalogeropoulos A, Maraziotis T. The predictive value of external continuous lumbar drainage, with cerebrospinal fluid outflow controlled by medium pressure valve, in normal pressure hydrocephalus. Acta Neurochir (Wien) 2005; 147: 953–958, discussion 958 [27] Toma AK, Papadopoulos MC, Stapleton S, Kitchen ND, Watkins LD. Systematic review of the outcome of shunt surgery in idiopathic normal-pressure hydrocephalus. Acta Neurochir (Wien) 2013; 155: 1977–1980

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Part X

30 Brain Tumors and Cognitive Dysfunction 266 31 Paraneoplastic Syndrome

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Tumor-Related Cognitive Dysfunction

30 Brain Tumors and Cognitive Dysfunction Sangam G. Kanekar and Hazem Matta Surgery, radiotherapy, and chemotherapy play important roles in the treatment of nervous system cancers (CNS) and extracerebral malignancies. These therapies have become the mainstay in treating primary or metastatic CNS and non-CNS malignancies; and with the overall improvement in survival of oncology patients, a wider spectrum of injuries involving structures of both the CNS and peripheral nervous system (PNS) are identified on routine follow-up studies. CNS neurotoxicity of anticancer treatments has received growing interest in recent years among oncologists, radiologists, and other supporting staff so that treatment-induced morbidity and mortality rates can be significantly reduced with early diagnosis and by modifying treatment regimens. Despite continuous improvements in cancer treatment, CNS toxicity remains an important issue. Up to two-thirds of cancer patients experience some form of cognitive impairment during or after treatment with cancer therapy. For 35% of these patients, this deficit may persist for months or years after treatment. With more than 11 million cancer survivors in the United States, up to 3.9 million individuals may be living with long-lasting cognitive difficulties from cancer and cancer treatments. Today various clinical as well as imaging tools are deployed to diagnose the signs and symptoms of neurotoxicity resulting from cancer therapy, so that the necessary dose reduction or change in the protocol may be done to avoid the long-lasting effect on the patient’s brain cells. Structural and newer functional imaging techniques help in identifying the changes in brain volume, metabolic status, and CNS activity after treatment. Today positron emission tomography (PET) and magnetic resonance (MR), especially MR perfusion and MR spectroscopy, allow assessment of the metabolic activity and hence functioning of the brain cells. Cognitive decline and dementia resulting from intracranial tumors could be due to either direct tumor-related effects of the primary or secondary tumor or, most commonly, to treatment-related neurotoxicity.

30.1 Direct Tumor-Related Cognitive Effects The brain resides in the confines of the calvarium; therefore, it is susceptible to the slightest changes in pressure or volume. Symptoms depend on the histopathological characteristics of the tumor and the acuity of the disease process. For example, mass-forming tumors, such as meningiomas and metastasis, typically cause compression and mass effect and manifest with headaches and seizure,1 ultimately leading to intracranial hypertension and hydrocephalus. Alternatively, infiltrative tumors, including gliobastoma multiforme (GBM) and primary CNS lymphoma, behave more insidiously, often manifesting with slow cognitive decline despite having little or no mass effect. The extent of this cognitive impairment can be diffuse and severe, attributable to marked underestimation of parenchymal microinvasion on current imaging techniques. The sequelae of tumor progression can involve multiple domains or

functions, depending on which neural networks are affected. Psychomotor slowness, executive dysfunction, memory impairment, and personality or behavior changes are the most commonly reported in infiltrative-type tumors.2

30.1.1 Cognitive Decline in Various Tumors Meningiomas Meningioma is one of the most commonly encountered CNS tumors, especially in the elderly population. It is a mesenchymal tumor, which almost always manifests as an extra-axial space-occupying mass. The vast majority of meningiomas are benign and slow growing and may manifest with seizures or focal neurologic deficits related to local brain compression. Cognitive decline as a primary clinical symptom is not uncommon and largely depends on the site and size of the meningioma. Patients with frontal meningiomas predominately show serious impairment in verbal fluency tasks and compromised executive functioning, found more often in left-sided meningioma. Patients with skull-base meningiomas have worse neurocognitive functioning than those with convexity meningiomas, especially for information-processing speed and psychomotor speed.3,4 Neuroimaging findings are classic and seen as an extra-axial intensely enhancing mass with significant compression of the underlying brain (▶ Fig. 30.1).

Glioblastoma Multiforme and Low-Grade Gliomas Glioblastoma multiforme is a World Health Organization grade IV astrocytoma that is notorious for its rapid infiltration and poor prognosis. Therapy-related neurotoxicity is seldom an issue in the management of high-grade glioma patients as the mean survival is 12 to 16 months.2 Cognitive dysfunction is often the trigger for diagnostic workup and can in fact be used to predict survival.5 Parenchymal microinvasion is the key characteristic of this tumor that contributes to the progressive cognitive decline. It is challenging to estimate the entire tumor burden on routine magnetic resonance imaging (MRI), including postcontrast T1-weighted imaging (▶ Fig. 30.2a,b). Today, with newer imaging techniques, such as MR spectroscopy, MR perfusion, and diffusion tensor imaging (DTI), an attempt is made to map the infiltration and extent of the tumor (▶ Fig. 30.2c). A significant decline in the Mini-Mental State Examination (MMSE) scores is seen for patients with tumor progression compared with patients without tumor progression. Conversely, areas of infiltrative tumors can have some preserved function resulting from the plasticity and compensation mechanism found in the brain of tumor patients; this preservation is facilitated by the slow and gradual onset of the tumor. Further decline in cognitive function is commonly seen after surgery because of the resection of the partially functional tissue, especially in eloquent areas.

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Fig. 30.1 A 51-year-old woman with dementia and frontal lobe symptoms. (a) Axial T2 and (b) sagittal postcontrast T1-weighted images show intensely enhancing extra-axial mass (star) causing severe compression and mass effect (arrows) on the frontal lobes bilaterally. Diffuse hyperintensity seen on T2-weighted imaging in the frontal white matter suggestive of edema (arrowhead).

Fig. 30.2 Infiltrative glioma in 64-year-old man with seizure and early symptoms of dementia. Axial (a) T2-and (b) postcontrast T1-weighted images show large necrotic mass in the right temporoparietal lobe (star) with surrounding perilesional hyperintensity (arrowheads). (c) Colored fractional anistropy map shows diffuse infiltration and disruption of the frontal and temporal lobe white matter fibers (arrows) away from the enhancing lesion.

Although low-grade gliomas are also infiltrative by nature, they have mass effect with a high incidence of focal symptoms, especially seizures. Unfortunately, controlled and uncontrolled seizures are associated with cognitive dysfunction. This effect may be further compounded by deleterious effects of surgery, radiation, and chemotherapy. Fifty percent of these patients are reported to have cognitive dysfunction in several domains, such as information-processing speed, psychomotor function, attention, executive functioning, and verbal working memory.6

Metastatic Disease The bulk of intracranial metastatic disease originates from lung carcinoma, breast cancer, and melanoma. Most patients initially have headaches nausea or vomiting, and seizures. In addition, cognitive dysfunction is common in patients with brain metastasis, which causes emotional difficulties, significantly decreasing quality of life. In a phase III study, up to 91% of 401 patients had impairment of one or more neurocognitive domains before

treatment, including memory, fine motor speed, executive function, and global neurocognitive impairment testing.7 The severity of deficit correlated with the overall tumor volume, not the number of lesions, and was predictive of survival. Whole-brain radiation (WBRT) is the mainstay treatment, increasing median survival by 3 to 6 months.8,9 Most patients are treated with a short course of large-fraction radiation therapy (RT) (e.g., 30 Gy in 10 fractions). Large, daily RT fraction sizes have been reported to increase the risk of neurocognitive deficits. In the vast majority of patients, survival after diagnosis of brain metastasis is short, and patients do not survive long enough to develop cognitive deficits from RT. The progressive disease and large fraction size are associated with higher incidence of dementia and cognitive decline. Given that the prevalence of brain metastasis far exceeds that of primary CNS tumors and prolonged life expectancy, treatment-related dementia poses a newly added entity that was not previously considered in the treatment of these cancer survivors.

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Lymphoma Similarly to GBM, primary CNS lymphoma is diffusely infiltrative; unlike other CNS tumors, however, lymphoma is potentially curable with possible reversibility of associated symptoms posttreatment.10 Cognitive dysfunction in primary CNS lymphoma patients is due to multiple factors, including the effects of the tumor itself, given its infiltrative and multifocal pattern, age, and the delayed effects of WBRT and high-dose methotrexate-based chemotherapy (HD-MTX). Cognitive domains most likely to be impaired include attention, executive functions, memory, naming, and psychomotor speed. CNS lymphoma has a predisposition for perivascular infiltration, which may lead to a break in the blood–brain barrier, which may further potentiate chemotherapy (CT) and RT toxicity.10,11

30.2 Tumor Therapy-Related Cognitive Effects Both RT and chemotherapy are neurotoxic and cause either derangement of the cell function or cell death by various mechanisms. Combined therapy may potentiate the effect and cause severe leukoencephalopathy and brain atrophy.

30.2.1 Radiotherapy and Dementia Sheline12 was first to classify the RT-induced side effects according to their time of appearance after irradiation into acute disorders (days to weeks), early delayed complications (1 to 6 months), and late-delayed complications (longer than 6 months). The exact mechanisms underlying different types of RT-induced CNS effects are not clear. The mechanism of RTinduced damage to the CNS appears to be complex and likely to include a combination of vascular injury, demyelination, and neuronal damage.13 The vascular injury is thought to be partly responsible for the abnormal vasculature, thrombosis, and fibrinoid necrosis eventually leading to radiation necrosis. RT also causes cell depletion, especially oligodendrocytes, which in turn lead to demyelination and white matter necrosis. Other cells, such as neurons, astrocytes, and microglia, are also damaged. RT toxicity is also thought to be due to cytokines and microglial proliferation.14 Other factors that increase the risk of RT-induced toxicity are older age, concurrent diseases (such as diabetes, hypertension), vascular disease, adjuvant chemotherapy, and genetic predisposition.

Mechanism of Radiation’s Effect on Brain Understanding the pathophysiology and early diagnosis of these complications on imaging are of vital importance to prevent further damage and morbidity. This understanding also helps the clinician in modifying the regimen and the researcher to seek preventable or curable therapies.

Photon-Cell Interaction In diagnostic radiography, where the range of radiation is around 80 to 120 kiloelectron-volts (keV), photoelectric effect

Fig. 30.3 Illustration of radiation-induced direct and indirect effects on cellular DNA. In the direct effect, photons directly damage the DNA structure, producing a base change and/or damage to the sugar-phosphate backbone. In the indirect effects, photons cause an ionization reaction within the intracellular water, leading to a break in the oxygen-hydrogen bond, producing hydrogen (H) and hydroxyls (OH). Hydroxyl radicals react with cellular DNA to cause base lesions and other damage.

predominates. This effect rate is proportional to the cube of the atomical number of the target material. In RT, the range of photon energy is between 1 and 20 MeV.15 At this range of energy, Compton electrons cause most of the collateral radiation tissue damage. The occurrence of this effect is relatively independent of atomic number. The biological damage is the result of radiation interactions with the atoms forming the cells by a process called ionization. This ionization of atoms affects the molecules, which in turn affect the cells, tissues, and ultimately the organs and their function. The biological effects of RT are due to two mechanisms: direct and indirect effects.15,16 Direct effects refer to the absorption of radiation by DNA, producing lesions, such as base changes or damage to the sugarphosphate backbone (▶ Fig. 30.3). This may result in single- and double-strand breaks. However, for any given cell, water makes up the most of the cell’s volume, and DNA forms a small part of the cell. Therefore, the probability of the radiation interacting with the DNA molecule is quite small. Much of the radiation (photons) interaction (ionization) takes place with the cell water, leading to a break in the water molecule bond, producing hydrogen (H) and hydroxyls (OH). Hydroxyl radicals react with cellular DNA to cause base lesions and other damage (▶ Fig. 30.3), referred to as the indirect effect; indirect effect is thought to cause about 70% of mammalian cell killing by X-rays. Double-strand breaks are the most toxic of radiation lesions. For example, 1 Gy (100 rad) of radiation produces about a thousand ionization tracks within a cell, resulting in about a thousand single-strand breaks and about 40 double-strand breaks.15,16 Repair of this double-strand break is critical for cell survival. Cells have tremendous ability to repair damage. After RT, an exposed cell may show three different effects: it may be completely repaired to function normally, it may become mutated and pass this on to the daughter cells, or, if the damage is severe, it may die.

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Pathophysiology of Radiation Therapy’s Effect on Brain and Clinical Stages In the human body, different cell systems have different sensitivity to RT. Cells that are actively reproducing are more sensitive to RT than those that are not.15 Blood cells that are constantly regenerating are therefore most sensitive; nerve and muscle cells are slowest to regenerate and therefore are least sensitive to radiation. Although the vascular endothelial cells and oligodendrocytes have been regarded as direct primary targets of radiation, the overall effect is thought to be multifactorial and attributed to more than one cell lineage.17 Acute changes are predominately thought to be due to RT effect on the blood–brain barrier (BBB), blood–spinal barrier, and the parenchymal CNS cells.18 Maintenance of the BBB is essential for brain homeostasis and cellular anatomic specificity. RT causes disruption of the BBB through the acid sphingomyelinase pathway, leading to apoptosis of endothelial cells.19 BBB disruption also occurs through intercellular adhesion molecule-1 (ICAM-1)20 and tumor necrosis factor α (TNF-α) (▶ Fig. 30.4a).21 Thus, the acute post-RT symptoms are thought to be due to vasogenic edema resulting from damage to the capillary endothelium, leading to cerebral edema and raised intracranial pressure. In the delayed phase, there is also breakdown in the BBB, which is thought to be due to upregulation of hypoxia-inducible factor 1α (HIF1α) from hypoxia and ICAM-1 from vascular endothelial growth factor (VEGF) stimulation.18,22 In this phase, there is significant decrease in the mature oligodendrocytes and neural stem cells (▶ Fig. 30.4b). Neuropathology reveals demyelination, astrogliosis, multifocal coagulative necrosis, and cavitations.23 Vascular changes of endothelial proliferation leading to secondary ischemic changes account for most of the mentioned changes in the brain. These changes predominate in the periventricular white matter and the centrum semiovale. Later stages may show deposition of iron salts and calcium in the vessel walls as well as in the deep gray matter nuclei and subcortical tissue.

Neuroimaging Inflammation, demyelination, and breakdown of the BBB are the underlying pathogeneses hindering the short- and longterm outcomes of cancer survivors who under go brain RT. Traditional MRI can easily identify inflammation and edema in the acute phase of postradiation insult; however, it is ineffective in distinguishing between radiation-related necrosis from tumor progression. Conventional MRI and CT are also unsuccessful at demonstrating the periventricular white matter lesions that are pathologically evident in the first 12 to 18 months after completion of RT.24,25 Therefore, using new techniques in neuroimaging, such as DTI, which is the most sensitive in identifying white matter changes well before structural abnormalities manifest, can help redirect RT and salvage areas susceptible to damage. Conventional CT and MRI may be used to calculate the atrophy index in post-RT patients. However, atrophy calculated by this atrophy index often does not correlate with negative changes on cognitive testing.26 Therefore, full assessments of patients require a combination of imaging modalities and multiple types of cognitive testing. Cognitive impairment is a continuous spectrum that can range from mild dysfunction to severe dementia. As the survival of cancer patients has increased, there is more and more awareness of this dysfunction. It is thought to be due to the consequences of complex interactions between preexisting cognitive abnormalities, brain tumor growth, concomitant treatments with chemotherapy, antiepileptic or psychotropic drugs, paraneoplastic encephalomyelitis, and endocrine dysfunction.27,28 Several factors, such as old age (i.e., older than 60 years), large radiation doses, large irradiated brain volume, and combined treatment with chemotherapy and RT have been linked to the increased risk of leukoencephalopathy. For example, for patients treated with WBRT (40 Gray [Gy] + 14 Gy boost) and a combination of intravenous and intrathecal MTX for CNS lymphoma, the incidence of severe progressive cognitive impairment is up to 83% in patients over the age of 60 years. Even the timing of the MTX chemotherapy is important.28,29,30

Fig. 30.4 Illustration of radiation-induced changes in cerebral vascular and cellular compartments in acute (a) early delayed (b) and late delayed (c) stages of radiation-induced complications. In acute stages, radiation therapy (RT) causes increased blood–brain barrier (BBB) permeability and apoptosis of oligoprogenitor cells, mediated through the acid sphingomyelinase pathway and overexpression of intercellular adhesion molecule-1 (ICAM-1) and tumor necrosis factor α (TNFα). In the early delayed phase (b), there is peak production of TNFα by microglial cells and astrocytes, leading to vasogenic edema. In the late delayed phase (c), there is breakdown in the BBB due to upregulation of hypoxia-inducible factor 1α (HIF1α) due to hypoxia and ICAM-1 from vascular endothelial growth factor (VEGF) stimulation.

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Tumor-Related Cognitive Dysfunction The cognitive dysfunction rate is higher whenever MTX is prescribed during or after RT, and therefore this drug is given before radiation.

Radiation-Induced Mild to Moderate Cognitive Impairment The incidence of mild to moderate cognitive dysfunction is greater than that for RT-induced dementia. Unfortunately, clear-cut definition of this condition is not achieved by the neurologic examinations. Clinical features include impairment of attention and short-term memory, with preservation of intellectual functions. Hippocampal dysfunction is one of the prominent features. This deficit is significant in children, especially when RT is given before 7 years of age. CT scan may show abnormal, periventricular hypodensities with or without ventricular enlargement. MRI is more sensitive and shows focal patchy or diffuse T2 hyperintensities in the white matter (▶ Fig. 30.5).22,31,32 There may be concomitant dilatation of the ventricles. The subcortical U-fibers may be affected, but the corpus callosum is usually spared. Degree of the MR hyperintensities grossly correlates with neuropsychological examinations. The course of the disease is difficult to predict: some patients deteriorate slowly, whereas most apparently remain stable. No therapy has proven beneficial for prevention, but symptomatic relief is advocated with use of methylphenidate.33 Research has shown free-radical scavengers, such as amifostin or angiotensin-converting enzyme inhibitor (ACEi)34 to have a protective effect; administration of erythropoietin prevents cognitive impairment.35

Radiation-Induced Dementia and Diffuse Late Atrophy In the largest of the series, the incidence of RT-induced dementia was shown to be 12.3%.45 The clinical picture is characterized by a “subcortical dementia” pattern resulting from diffuse white matter injury, which occurs in 69% of patients within 2 years of RT.27,36 Patients have progressive memory and attention deficits, intellectual loss, gait abnormalities, emotional lability, apathy, and fatigue. In the later stages, patients may develop gait ataxia, incontinence, and sometimes a picture of

Fig. 30.5 Mild to moderate cognitive dysfunction in a 57-year-old woman who was treated with whole-brain radiation therapy for brain metastasis from renal cell carcinoma. Patient had short-term memory loss. Axial T2-weighted imaging shows diffuse hyperintensity in the cerebral white matter bilaterally (arrows).

akinetic mutism with features of seizures, pyramidal or extrapyramidal signs, or tremor. Neuropathology shows spongiosis of the white matter but no vascular changes, which is a hallmark of radiation necrosis. Neuroimaging always shows diffuse white matter T2 hyperintensity associated with cortical and subcortical atrophy, as well as ventricular enlargement (▶ Fig. 30.6).37 These changes are much more diffuse in the case of WBRT. MR spectroscopy shows decrease in the peak of

Fig. 30.6 Radiation-induced subcortical dementia. An 11-year-old boy treated with whole-brain radiation therapy for midbrain grade III glioma (arrows) with leptomeningeal spread showing signs and symptoms of subcortical dementia. Axial T2-weighted (a) and fluid-attenuated inversion recovery (FLAIR) (b) images show diffuse cerebral atrophy and white matter hyperintensity (arrowheads) suggestive of leukoencephalopathy.

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Brain Tumors and Cognitive Dysfunction

Fig. 30.7 A 49-year-old man treated with intensity-modulated radiation for left frontal lobe infiltrative oligoastrocytoma. The patient had frontal dementia 6 years after completion of treatment. Axial fluid-attenuated inversion recovery (a) image from magnetic resonance imaging (MRI) dated June 28, 2006, shows an infiltrative right frontal lobe glioma (arrowhead). Axial T2-weighted imaging (b) and axial diffuse tensor imaging map (c) from MRI dated January 11, 2012, shows severe leukoencephalopathic changes and loss of white matter tracts in the bilateral frontal lobes compared with the rest of the brain.

N-acetyl aspartate (NAA), choline, and creatine, implying axonal and membrane damage. Hyperintensity changes may be localized to one or two lobes or to a specific region when intensitymodulated RT is given. For example, radiation injury to the frontal lobes is more prominent after treatment of olfactory neuroblastoma, which manifests with frontal lobe dementia (▶ Fig. 30.7), whereas pediatric patients treated for medulloblastoma may have ataxia from radiation-induced cerebellar atrophy (▶ Fig. 30.8). In association with the white matter changes, cerebral parenchyma may also show diffuse mild to moderate cortical atrophy (▶ Fig. 30.9). The pathogenesis of the cerebral atrophy is not clear. No specific treatment is currently available for radiation-induced dementia. The occurrence of communicating dilatation of the ventricles is thought to be due to combination of radiation-induced arachnoiditis or obliteration of pacchionian granulations and/or simply loss and softening of cerebral white matter. It can be treated with ventriculoperitoneal shunt, similar to normal pressure hydrocephalus.38

30.2.2 Chemotherapy and Dementia Most chemotherapy drugs have an effect on CNS cells. It is important to identify chemotherapy-induced neurotoxicity early, either to discontinue or to change the treatment regimen so as to decrease neurotoxicity. Chemotherapy-related complications can be divided into early and delayed complicatios. Common complications seen with chemotherapy include acute encephalopathy, seizure, headaches, aseptic meningitis, acute cerebellar syndrome, vasculopathy, neuropathy, visual loss, myelopathy, posterior reversible leukoencephalopathy syndrome (PRES), and dementia.30,31 Almost all groups of chemotherapy

Fig. 30.8 Radiation-induced ataxia in a 7-year-old boy treated with surgery and radiation therapy (RT) for fourth ventricular medulloblastoma. Three years after RT, the patient had cerebellar ataxia. Axial fluid-attenuated inversion recovery image through the posterior fossa shows bilateral cerebellar atrophy (arrows) and white matter gliosis (arrowheads).

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Fig. 30.9 Radiation-induced cortical atrophy. A 10-year-old boy previously treated with radiation therapy for pineal tumor showed cognitive dysfunction and parietal lobe syndrome. Contrast-enhanced sagittal T1-weighted imaging (a) and axial fluid-attenuated inversion recovery image (b) show severe cortical atrophy of the bilateral frontoparietal lobes (arrows and arrowhead).

drugs can cause neurotoxicity. Broadly, these drugs are classified according to their class and mechanism of action into the following groups: anti-metabolite (methotrexate, 5-fluorouracil (5-FU)), alkylating agents (ifosfamide, cisplatin, carboplatin, carmustine and lomustine); microtubule inhibitor (vincristine, vindesine, vinblastine, and vinorelbine); amino acid degrader (L-asparaginase); immunomodulatory agent (thalidomide); anti-inflammatory agent (corticosteroids) and hormonal agent (tamoxifen).39 The true incidence of cognitive impairment is difficult to predict since there is lack of pre-chemotherapy assessments in most studies making it difficult to establish the causal relationship between cognitive dysfunction and chemotherapy. It is shown that the incidence and severity of cognitive dysfunction largely depend on the chemotherapy regimen used, and the dose and duration of chemotherapy and associated therapy.40 Most studies report an incidence between 15 and 50% in patients who received chemotherapy. Chemotherapy-induced cognitive impairment mainly involves memory and concentration.41 In contrast to RT, chemotherapy induced cognitive dysfunction appears to be transient and resolves slowly over a period of time.

Pathogenesis and Mechanisms of Chemotherapy-Induced Neurotoxicity The exact etiology of chemotherapy-induced neurotoxicity and cognitive impairment in cancer patients is unknown but is believed to be multifactorial. Acute toxic effects of chemotherapy are mediated through excitatory mechanisms and apoptotic cell death.13,18 Cognitive dysfunction is thought to be due to combination of direct neurotoxic effects of the agent; oxidative damage; indirect effects, such as chemotherapy-induced hormonal changes; immune dysregulation with release of cytokines; anemia, and to some extent genetic predisposition. Chemotherapy agents are hypothesized to affect the microglia, oligodendrocytes, and neuronal axons, causing demyelination or alterations in water content. Free radicals released from oxidative stress cause damage to cerebral blood vessels, while activation of the immune system causes increased levels of proinflammatory cytokines (interleukin IL-1, IL-6, and tumor necrosis factor-alpha [TNFα]) that cross the blood–brain barrier and are associated with cognitive impairment and/or fatigue. Associated anemia in cancer patients causes decreased cerebral oxygenation, which leads to worsening of visual memory and executive function tasks.

Specific Chemotherapy-Related Complications Discussing the CNS side effects of all the groups of chemotherapy drugs is beyond the scope of this chapter. Methotrexate (MTX) is a dihydrofolate reductase inhibitor. Its toxic effect mainly depends on route of administration, dose, and the use of other treatment modalities, such as radiation. High-dose systemic MTX is associated with CNS complications like encephalopathy and subacute stroke-like syndrome, which is characterized by transient focal neurologic deficits, confusion, and seizures. These symptoms may resolve completely after 2 to 3 days. MTX may affect the white matter, leading to leukoencephalopathy, an effect further enhanced by associated RT. CSF analysis is mostly unremarkable, and EEG may show diffuse nonspecific slowing typical of encephalopathy. MRI shows bilateral increased signal intensities mostly involving the supratentorial white matter, particularly in the centrum semiovale.23, 42,43 These areas may show restricted diffusion on DWI-ADC (▶ Fig. 30.10). Rarely, there may be florid areas of demyelination that are seen as multiple patchy/conglomerated hyperintensities on T2 weighted images. MTX-induced chronic leukoencephalopathy may manifest after months to years following highto moderate doses of MTX and presents with hemiparesis, quadriparesis, profound dementia, and coma. Less severe, but permanent, deficits include a mild to moderate dementia (▶ Fig. 30.11).23 5-Fluorouracil (5-FU), a fluorinated pyrimidine, and cytosine arabinoside (cytarabine, ara-C), a pyrimidine analog, both disrupt DNA synthesis. In conventional doses, neurotoxicity is rare with these drugs. In higher doses, 5-FU crosses the BBB and is found in the highest concentration in the cerebellum, where it is toxic to Purkinje and granule cells, leading to acute cerebellar syndrome.44 It has an acute onset, with ataxia, dysmetria, dysarthria, and nystagmus. Overall, the risk of cognitive impairment is significantly greater in patients with high doses of chemotherapy compared with the risk in control groups.

30.2.3 Combined Radiotherapy and Chemotherapy and Dementia The toxic effects of combined RT and chemotherapy are greater than those of single-modality treatment. These combined effects largely depend on therapeutic factors of chemotherapy

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Fig. 30.10 Methotrexate (MTX)-induced acute demyelination of the cerebral white matter in a 21-year-old man treated with intrathecal MTX for leukemia. Axial diffusion-weighted imaging (DWI) (a) and apparent diffusion coefficient (b) images show bilateral areas of restricted diffusion in the centrum semiovale bilaterally resulting from acute demyelination (arrows).

Fig. 30.11 Gradually worsening atrophy and leukoencephalopathy in a 5-year-old boy treated with intrathecal and systemic methotrexate for leukemia. One year after completion of therapy (May 11, 2010), a noncontrast computed tomography scan of the brain (a) showed mild prominence of the convexity sulci and ventricular system, advanced for the age of the patient. One year later, the patient developed symptoms of fatigue and weakness. Axial T2-weighted magnetic resonance imaging (MRI) performed on March 22, 2011 (b) shows mild atrophy and leukoencephalopathic changes in the bilateral cerebral parenchyma. Two years after completion of treatment, the patient showed signs of early dementia. Axial T2-weighted MRI dated June 3, 2012 (c) showed severe diffuse atrophy with leukoencephalopathy and dilatation of the lateral ventricles.

and RT, which include the following18: (1) agent-type, dose, and schedule; (2) RT dose, fractionation rate, treatment time, treatment volume, and dose distribution; (3) time between chemotherapy and RT; (4) chemical and biological dose-response modifiers (e.g., sensitizers, protectors, and immunotherapy). Some chemotherapeutic agents, such as bis-chloroethyl nitrosourea (BCNU), methotrexate, and cisplatin, are radiosensitizers. On the other hand, RT may induce changes in the permeability of the BBB and thus increase the delivery and concentration and in turn toxicity of chemotherapy drugs. The most striking toxic effect of combined therapy seen on white matter is disseminated necrotizing leukoencephalopathy. Clinically, patients may have progressive subcortical dementia, ataxia, and pyramidal and extrapyramidal syndrome, eventually leading to death. Neuropathology shows combined myelin and axonal loss, spongiosis, white-matter gliosis, areas of

necrosis, and fibrotic thickening of small blood vessels in the deep white matter. MRI shows large confluent areas of hyperintensity on T2-weighted imaging, predominately involving the white matter. In the acute stage, these regions show restricted diffusion on DWI-ADC due to cytotoxic edema (▶ Fig. 30.12).37 Associated areas of necrosis may be seen. In the late stages, diffuse cortical-subcortical brain atrophy is present, with ex vacuo dilatation of ventricles.

30.2.4 Effect of Radiation on a Pediatric Brain The long-term survival and high rate of potential cure in the pediatric population makes awareness of the side effects of RT of great clinical significance. The clinical and imaging features

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Fig. 30.12 Disseminated necrotizing leukoencephalopathy. A 63-year-old woman was treated with radiation therapy and methotrexate for brain metastasis from breast cancer and developed acute-onset dementia and pyramidal signs. Axial diffusion-weighted imaging (a), and apparent diffusion coefficient (b), show diffuse atrophy of the cerebral parenchyma with large areas of acute demyelination (arrows) in the supratentorial white matter.

of white matter injury secondary to chemotherapy and RT are not significantly different from those in adults. As in adults, the changes are divided into acute and early delayed and late delayed. Besides diffuse or patchy white matter areas of demyelination, multiple foci of hemorrhagic lesions may be seen within the radiation portal. In the chronic stage, occult vascular malformation or cavernoma may develop. Young children are more susceptible than adults to RT-induced vascular changes, especially around the circle of Willis. Clinically, these patients may show growth retardation, cognitive impairment, strokes, and developmental delay. In addition, endocrine dysfunctions, such as growth hormone deficiency and hypothyroidism, may be noted. Overall, unmyelinated and immature white matter shows greater toxic effect of chemotherapy and RT therapy than in older children.

References [1] Davies E, Clarke C. Early symptoms of brain tumours. J Neurol Neurosurg Psychiatry 2004; 75: 1205–1206 [2] Omuro AM, Delattre JY. Brain tumors and dementia. Handb Clin Neurol 2008; 89: 877–886 [3] Tucha O, Smely C, Lange KW. Effects of surgery on cognitive functioning of elderly patients with intracranial meningioma. Br J Neurosurg 2001; 15: 184–188 [4] Tucha O, Smely C, Preier M, Lange KW. Cognitive deficits before treatment among patients with brain tumors. Neurosurgery 2000; 47: 324–333, discussion 333–334 [5] Meyers CA, Hess KR, Yung WK, Levin VA. Cognitive function as a predictor of survival in patients with recurrent malignant glioma. J Clin Oncol 2000; 18: 646–650 [6] Klein M, Engelberts NH, van der Ploeg HM et al. Epilepsy in low-grade gliomas: the impact on cognitive function and quality of life. Ann Neurol 2003; 54: 514–520 [7] Gaspar L, Scott C, Rotman M et al. Recursive partitioning analysis (RPA) of prognostic factors in three Radiation Therapy Oncology Group (RTOG) brain metastases trials. Int J Radiat Oncol Biol Phys 1997; 37: 745–751 [8] Zimm S, Wampler GL, Stablein D, Hazra T, Young HF. Intracerebral metastases in solid-tumor patients: natural history and results of treatment. Cancer 1981; 48: 384–394 [9] Khuntia D, Brown P, Li J, Mehta MP. Whole-brain radiotherapy in the management of brain metastasis. J Clin Oncol 2006; 24: 1295–1304 [10] DeAngelis LM. Primary central nervous system lymphoma: a curable brain tumor. J Clin Oncol 2003; 21: 4471–4473 [11] Lai R, Rosenblum MK, DeAngelis LM. Primary CNS lymphoma: a whole-brain disease? Neurology 2002; 59: 1557–1562

[12] Sheline GE. Radiation therapy of brain tumors. Cancer 1977; 39 Suppl: 873– 881 [13] Grimm SA, Deangelis LA. Neurological complications of chemotherapy and radiation therapy. In: Aminoff MJ, ed. Neurology and General Medicine. 4th ed. Elsevier. 2008;523–545 [14] Swennen MH, Bromberg JE, Witkamp TD, Terhaard CH, Postma TJ, Taphoorn MJ. Delayed radiation toxicity after focal or whole brain radiotherapy for lowgrade glioma. J Neurooncol 2004; 66: 333–339 [15] Busch DB. Radiation and chemotherapy injury: pathophysiology, diagnosis, and treatment. Crit Rev Oncol Hematol 1993; 15: 49–89 [16] Little JB. Radiation-induced genomic instability. Int J Radiat Biol 1998; 74: 663–671 [17] Cross NE, Glantz MJ. Neurologic complications of radiation therapy. Neurol Clin 2003; 21: 249–277 [18] Soussain C, Ricard D, Fike JR, Mazeron JJ, Psimaras D, Delattre JY. CNS complications of radiotherapy and chemotherapy. Lancet 2009; 374: 1639–1651 [19] Peña LA, Fuks Z, Kolesnick RN. Radiation-induced apoptosis of endothelial cells in the murine central nervous system: protection by fibroblast growth factor and sphingomyelinase deficiency. Cancer Res 2000; 60: 321–327 [20] Nordal RA, Wong CS. Intercellular adhesion molecule-1 and blood-spinal cord barrier disruption in central nervous system radiation injury. J Neuropathol Exp Neurol 2004; 63: 474–483 [21] Daigle JL, Hong JH, Chiang CS, McBride WH. The role of tumor necrosis factor signaling pathways in the response of murine brain to irradiation. Cancer Res 2001; 61: 8859–8865 [22] Nordal RA, Nagy A, Pintilie M, Wong CS. Hypoxia and hypoxia-inducible factor-1 target genes in central nervous system radiation injury: a role for vascular endothelial growth factor. Clin Cancer Res 2004; 10: 3342–3353 [23] Ball WS, Jr, Prenger EC, Ballard ET. Neurotoxicity of radio/chemotherapy in children: pathologic and MR correlation. AJNR Am J Neuroradiol 1992; 13: 761–776 [24] Constine LS, Konski A, Ekholm S, McDonald S, Rubin P. Adverse effects of brain irradiation correlated with MR and CT imaging. Int J Radiat Oncol Biol Phys 1988; 15: 319–330 [25] Packer RJ, Zimmerman RA, Bilaniuk LT. Magnetic resonance imaging in the evaluation of treatment-related central nervous system damage. Cancer 1986; 58: 635–640 [26] Shibamoto Y, Baba F, Oda K et al. Incidence of brain atrophy and decline in Mini-Mental State Examination score after whole-brain radiotherapy in patients with brain metastases: a prospective study. Int J Radiat Oncol Biol Phys 2008; 72: 1168–1173 [27] Omuro AMP, Martin-uverneuil N, Delattre J. Complications of radiotherapy to the central nervous system. In: Handbook of Clinical Neurology, 3rd ed. New York: Elsevier; 2012:887–901 [28] Klein M, Heimans JJ, Aaronson NK et al. Effect of radiotherapy and other treatment-related factors on mid-term to long-term cognitive sequelae in low-grade gliomas: a comparative study. Lancet 2002; 360: 1361–1368 [29] DeAngelis LM, Yahalom J, Thaler HT, Kher U. Combined modality therapy for primary CNS lymphoma. J Clin Oncol 1992; 10: 635–643 [30] Abrey LE, Yahalom J, DeAngelis LM. Treatment for primary CNS lymphoma: the next step. J Clin Oncol 2000; 18: 3144–3150

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Brain Tumors and Cognitive Dysfunction [31] Postma TJ, Klein M, Verstappen CC et al. Radiotherapy-induced cerebral abnormalities in patients with low-grade glioma. Neurology 2002; 59: 121–123 [32] Robain O, Dulac O, Dommergues JP et al. Necrotising leukoencephalopathy complicating treatment of childhood leukaemia. J Neurol Neurosurg Psychiatry 1984; 47: 65–72 [33] Meyers CA, Weitzner MA, Valentine AD, Levin VA. Methylphenidate therapy improves cognition, mood, and function of brain tumor patients. J Clin Oncol 1998; 16: 2522–2527 [34] Shaw EG, Rosdhal R, D’Agostino RB, Jr et al. Phase II study of donepezil in irradiated brain tumor patients: effect on cognitive function, mood, and quality of life. J Clin Oncol 2006; 24: 1415–1420 [35] Senzer N. Rationale for a phase III study of erythropoietin as a neurocognitive protectant in patients with lung cancer receiving prophylactic cranial irradiation. Semin Oncol 2002; 29 Suppl 19: 47–52 [36] Armstrong CL, Corn BW, Ruffer JE, Pruitt AA, Mollman JE, Phillips PC. Radiotherapeutic effects on brain function: double dissociation of memory systems. Neuropsychiatry Neuropsychol Behav Neurol 2000; 13: 101–111 [37] Atlas SW, Grossman RI, Packer RJ et al. Magnetic resonance imaging diagnosis of disseminated necrotizing leukoencephalopathy. J Comput Tomogr 1987; 11: 39–43 [38] Perrini P, Scollato A, Cioffi F, Mouchaty H, Conti R, Di Lorenzo N. Radiation leukoencephalopathy associated with moderate hydrocephalus: intracranial

[39] [40]

[41]

[42]

[43]

[44] [45]

pressure monitoring and results of ventriculoperitoneal shunting. Neurol Sci 2002; 23: 237–241 Plotkin SR, Wen PY. Neurologic complications of cancer therapy. Neurol Clin 2003; 21: 279–318, x Muldoon LL, Soussain C, Jahnke K et al. Chemotherapy delivery issues in central nervous system malignancy: a reality check. J Clin Oncol 2007; 25: 2295–2305 Matsuda T, Takayama T, Tashiro M, Nakamura Y, Ohashi Y, Shimozuma K. Mild cognitive impairment after adjuvant chemotherapy in breast cancer patients—evaluation of appropriate research design and methodology to measure symptoms. Breast Cancer 2005; 12: 279–287 Lövblad K, Kelkar P, Ozdoba C, Ramelli G, Remonda L, Schroth G. Pure methotrexate encephalopathy presenting with seizures: CT and MRI features. Pediatr Radiol 1998; 28: 86–91 Chen CY, Zimmerman RA, Faro S, Bilaniuk LT, Chou TY, Molloy PT. Childhood leukemia: central nervous system abnormalities during and after treatment. AJNR Am J Neuroradiol 1996; 17: 295–310 Riehl JL, Brown WJ. Acute cerebellar syndrome secondary to 5-fluorouracil therapy. Neurology 1964; 14: 961–967 Crossen JR, Garwood D, Glatstein E et al. Neurobehavioral sequelae of cranial irradiation in adults: a review of radiation-induced encephalopathy J Clin Oncol 1994; 12: 627–42

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31 Paraneoplastic Syndrome Toshio Moritani, Aristides A. Capizzano, and Yoshimitsu Ohgiya Paraneoplastic neurologic syndrome (PNS) occurs in less than 1% of cancer patients based on serologic tests without further criteria.1,2 PNS is associated with central and peripheral nervous disorders and syndromes, which include limbic encephalitis, cerebellar degeneration, brainstem encephalitis, striatal encephalitis, opsoclonus-myoclonus syndrome, myelitis, motor neuron disease, stiff-person syndrome, Lambert-Eaton syndrome, neuromyotonia, and Guillain-Barré syndrome. Paraneoplastic and nonparaneoplastic encephalopathies are included in the category of autoimmune-mediated encephalopathies and are associated with various specific antibodies.1,3,4,5,6,7 The two main types of specific antibodies are (1) antibodies to intracellular antigens: Hu (ANNA-1, antineuronal nuclear antibody type 1), Ri (ANNA-2), ANNA-3, AGNA (anti-antiglial/neuronal nuclear antibody), Yo (PCA-1, Purkinje cell cytoplasmic antigen type 1), PCA-2, Ma1, Ma2, CV2/CRMP-5 (collapsing response mediator protein type 5), ZIC4 (zinc finger transcription factor), Tr, amphiphysin, and glutamic acid decarboxylase (GAD) and (2) antibodies to cell surface antigens: N-methyl-D-aspartate receptor (NMDAR), voltage-gated potassium channel (VGKC), leucine-rich, glioma-inactivated 1 (LGI1), contactin-associated protein 2 (CASPARS2), α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR), P/Q and N-type calcium channel, neuromyelitis optica (NMO) immunoglobulin G, glycine receptor, acetylcholine receptor, γ-aminobutyric acid B1 receptor (GABABR), and metabotropic glutamate receptor-5. The common paraneoplastic and nonparaneoplastic encephalopathies are summarized in ▶ Table 31.1. They can also be classified according to the distribution of the lesion, usually based on the pattern of magnetic resonance imaging (MRI) findings. Early correct diagnosis is important for the appropriate treatment for either paraneoplastic or nonparaneoplastic autoimmune-mediated encephalopathies.

31.1 Clinical Features Paraneoplastic or nonparaneoplastic autoimmune-mediated encephalopathy is associated with a heterogeneous spectrum of clinical presentations that includes cognitive impairment, behavioral and personality changes, movement disorder, and seizures.1,3,4,5,6,7 Cognitive impairment is sometimes accompanied by tremor, myoclonus, ataxia, and sleep disturbance. It is often associated with some component of delirium. These disorders can evolve in chronic or rapidly progressive fashion. New-onset epilepsy associated with PNS is often antiepileptic drug resistant. Encephalopathy is usually progressive, may be fluctuating, or may evolve over days to several months. Neurologic symptoms often precede the diagnosis of underlying tumor in approximately 60% of patients.8 Cognitive and mood changes, particularly depression, are common in cancer patients and should be considered in the differential diagnosis. Electroencephalography (EEG) typically shows diffuse slowing of electrical activity with or without spikes indicative of heightened cortical irritability.9 Cerebrospinal fluid (CSF) findings are essential to rule out infection and neoplasm. CSF findings of immune-mediated encephalopathy include pleocytosis in early stage and elevated protein concentrations and oligoclonal bands later in the illness. Fewer than 5% of the patients have completely normal CSF.10 The detection of a neural-specific antibody in serum or CSF raises the possibility of a paraneoplastic cause. However, antibody testing does not replace clinical evaluation.5 The detection of certain paraneoplastic or nonparaneoplastic antibodies requires nonroutine laboratory analysis, frequently requiring referral to specialized centers. Patients may have more than one pathogenic antibody. All paraneoplastic and nonparaneoplastic encephalopathies can occur in the absence of, or with low titers of, known antibodies. If the paraneoplastic panel is

Table 31.1 Paraneoplastic and nonparaneoplastic immune-mediated encephalopathies Antibody (antigen)

Neoplasms (%)

Common MRI patterns / Clinical syndrome

NMDAR (S)

Ovarian teratoma, testicular carcinoma (9–56%)

Normal, LE, SE, BE, cerebellitis/psychosis, memory deficits, hypoventilation

VGKC (S)

Thymoma, SCLC, prostate cancer (5–30%)

LE, SE/Morvan, neuromytonia, SIADH

AMPAR (S)

SCLC, breast (70%)

LE/agitation

GABABR (S)

SCLC (47%)

LE/epilepsy

GAD (I)

SCLC, thymoma, pancreatic/renal cell carcinoma (8%)

LE, CD/stiff-person syndrome, ataxia

Hu (I)

SCLC (98%), thymoma, neuroblastoma

LE, CD, BE, SE/sensory neuropathy, ataxia, brainstem dysfunction

Ma-2 (I)

Testicular germ cell tumor, non-SCLC, breast (96%)

LE, CD, BE/narcolepsy, hyperthermia, endocrine dysfunction

CV2/CRMP-5 (I)

SCLC, thymoma (96%)

SE, LE, BE,CD/chorea, uveitis, optic neuritis

Tr (I)

Hodgkin lymphoma

CD, BE, LE

Ri (I)

Breast, SCLC, gynecologic (97%)

BE, CD/opsoclonus-myoclonus

Yo (I)

Ovary, breast (98%)

CD/pancerebellar syndrome

Abbreviations: BE, brainstem encephalitis; CD, cerebellar degeneration; I, intracellular antigen; LE, limbic encephalitis; MRI, magnetic resonance imaging; S, cell surface antigen; SCLC, small cell lung cancer; SE, striatal encephalitis; SIADH, inappropriate antidiuretic hormone secretion syndrome.

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Paraneoplastic Syndrome unrevealing for any specific antibody, the diagnosis is inferred when all other putative conditions are excluded.3

31.2 Pathophysiology and Pathology Tumor-targeted immune responses are initiated by onconeuronal proteins, which are expressed by neural-type tissue within a neoplasm (such as teratoma) at the plasma membrane, nucleus, cytoplasm, or nucleolus. These antigens are also expressed in neurons or glia (coincidental targets), which leads to paraneoplastic neurologic syndrome by means of immune cross-reaction.11Neuropathologic features are dominant lymphocyte T-cell infiltration, neuronal loss, activated microglia, neuronophagia, and reactive astrocytosis. Because intracellular antigen-targeting antibodies are targeted by specific cytotoxic T cells, the histopathology is characterized by CD4 and CD8 lymphocyte T-cell infiltration.12,13 On the other hand, such inflammation is less severe in patients with antibodies to cell surface or synaptic antigens, which are characterized by lymphocyte B and plasma cell infiltration, leading to antibody and complement deposition.14 Thus, steroids, intravenous immunoglobulin (IVIG), or plasmapheresis would be more effective in treating patients with antibodies to cell surface or synaptic antigens than in patients with intracellular antigen-directed antibodies.

31.3 Imaging Brain MRI is the primary neuroimaging tool for the diagnostic evaluation of paraneoplastic or nonparaneoplastic encephalopathies.3 MRI findings and clinical evaluations are helpful to narrow the range of tests of specific antibodies that should be ordered. MRI can be normal in any immune-mediated encephalopathy. The common or classic pattern of MRI findings of paraneoplastic encephalopathy includes limbic encephalitis, cerebellar degeneration, brainstem encephalitis, striatal encephalitis, and myelitis. However, other types have atypical or multifocal distributions. Often PNS predates malignancy symptoms, so cost-effective screening is important.4 Recommendations for screening for tumors are different between whole-body screening for classic PNS and more localized tumor search for surface antibody syndrome.4,15 Screening for systemic malignant tumors, such as small cell lung cancer or lymphoma, should be done by contrast-enhanced computed tomography (CT) of the chest, abdomen, and pelvis and/or fluorine-18-fluorodeoxyglucose (FDG) positron emission (PET)/PET-CT.16,17 If the brain MRI pattern is suggestive of specific localized tumor, mammography or scrotal/pelvic ultrasound should be obtained.4 For ovarian teratoma, CT/MRI of pelvis/abdomen or transvaginal ultrasound has been used.

31.4 Treatment Early treatment is critical for paraneoplastic or nonparaneoplastic autoimmune-mediated encephalopathy.4,7,18 Vigorous antiepileptic medications and adequate support measures, including sedation and adequate ventilation, are essential for patients with seizures. Removal of tumors and appropriate

oncologic treatment may result in improvement of PNS. Intravenous high-dose steroids and IVIG are recommended as the first-line therapy. Plasma exchange is an additional therapeutic option. Second-line therapy usually begins after 10 days if there is no initial response. Rituximab, monoclonal antibody against CD20 protein, is recommended at treatment start. This may be combined with cyclophosphamide in adult patients. Autoimmune-mediated encephalopathy with cell surface antigens generally tends to be more responsive to treatment than that with intracellular antigens. Treatment of patients with these disorders should be based on a comprehensive clinical assessment, not only on antibody titers.5

31.5 Limbic Encephalitis Limbic encephalitis is initially described as a paraneoplastic syndrome characterized by an acute or subacute onset of confusion, temporal lobe seizures, short-term memory loss, and psychiatric symptoms.19,20,21,22 Limbic encephalitis is relatively frequent among autoimmune encephalitis.3,20 The most common associated tumors are small cell lung cancer, breast cancer, ovarian tumor including teratoma, testicular tumor, and thymic tumor. It may be rarely associated with colon cancer, pancreatic cancer, renal cell cancer, esophageal cancer, bladder cancer, prostate cancer, neuroblastoma, melanoma, and Hodgkin and non-Hodgkin lymphoma.23 It is commonly associated with antibodies to intracellular antigens (anti-Ma2, Hu, CV2/CRMP-5, GAD) and neuronal surface antibodies (NMDAR, VGKC, AMPAR, GABABR) with or without tumor association.1,3,4,5,6,7,20,23 Magnetic resonance imaging contributes to the diagnosis by showing T2 and fluid-attenuated inversion recovery (FLAIR) high signal and swelling in the medial temporal lobe, which is more often bilateral than unilateral (▶ Fig. 31.1, ▶ Fig. 31.2). Significant atrophy is visible approximately 1 year after symptom onset.24 Other limbic structures, such as insular, frontal, orbital surface of the temporal lobe, anterior cingulate, and pyriform cortex, are also involved. Diffusion-weighted imaging (DWI) shows hyperintensity in the medial temporal lobe, usually with slightly increased apparent diffusion coefficient (ADC) but possibly with decreased ADC in an acute phase.18,23,25,26,27 Abnormal enhancement can be seen but is rare in limbic encephalitis. Differential diagnosis includes viral encephalitis, especially herpes encephalitis, because of its predilection for limbic structures, postictal changes from status epilepticus, gliomatosis cerebri, and infiltrative lymphoma (lymphomatosis).

31.6 Cerebellar Degeneration Cerebellar dysfunction is one of the common paraneoplastic presentations. Anti-Yo (Purkinje cell cytoplasmic antibody type 1, PCA-1) antibody is most commonly associated with paraneoplastic cerebellar degeneration with ovarian and breast carcinoma. Anti-Hu, anti-Cv2/CRMP-5, anti-Ri, and anti-VGCC (P/Q type and N-type voltage-gated calcium channel) can also be associated with paraneoplastic cerebellar degeneration with small cell lung cancer, breast cancer, and gynecologic malignancies.28,29,30,31 MRI shows global volume loss in both the vermis and cerebellar hemispheres, with relative sparing of the brainstem (▶ Fig. 31.3). A superior cerebellar hyperintense sign on FLAIR image has been reported.32 Differential diagnosis of

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Fig. 31.1 Anti-N-methyl-D-aspartate receptor (NMDAR) antibody limbic encephalitis with ovarian teratoma. A 23-year-old woman had myoclonic jerking, fever, and body aches. (a) Fluid-attenuated inversion recovery (FLAIR) image shows bilateral symmetric hyperintensity in the amygdala and hippocampi consistent with limbic encephalitis. (b) Diffusion-weighted imaging demonstrates restricted diffusion in these lesions associated with slightly decreased apparent diffusion coefficient (not shown). (c) Postcontrast computed tomography reveals a fat-contained, calcified mass consistent with an ovarian teratoma.

Fig. 31.2 Anti–voltage-gated potassium channel (VGKC) antibody limbic encephalitis (nonparaneoplastic). A 48-year-old man was having memory problems and psychiatric symptoms. (a) Fluid-attenuated inversion recovery (FLAIR) image shows bilateral symmetric hyperintensity in the amygdala and hippocampi consistent with limbic encephalitis. (b) Diffusion-weighted imaging demonstrates no restricted diffusion in these lesions.

Fig. 31.3 Anti-Yo antibody paraneoplastic cerebellar degeneration with ovarian carcinoma. A 63-year-old woman had progressive ataxia. (a) T2weighted image demonstrates diffuse cerebellar atrophy. (b) Postcontrast computed tomography reveals a heterogeneously enhancing mass (arrow) in the left ovary.

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Paraneoplastic Syndrome cerebellar degeneration includes alcohol and antiepileptic toxicity (predominant cerebellar vermis atrophy), and primary degenerative disorders, such as multiple system atrophy and spinocerebellar ataxia (concomitant brainstem atrophy).

31.7 Striatal Encephalitis Striatal encephalitis is an uncommon manifestation of paraneoplastic encephalopathy. The pattern of striatal encephalitis is frequently seen in anti-CV2/CRMP5 encephalitis associated with small cell lung cancer and thymoma.33,34,35 However, the pattern can be seen in other paraneoplastic encephalopathies, including anti-VGKC, anti-NMDAR, and anti-Hu antibodies.36,37,38 MRI shows T2 hyperintensity in the bilateral caudate nuclei and putamina, frequently associated with limbic encephalitis and cerebellar degeneration. Reduced diffusion is usually not seen in striatal encephalitis. Differential diagnosis includes infectious encephalitis, toxic and metabolic diseases, vasculitis, demyelinating disease, Sydenham chorea, Huntington’s disease, Wilson’s disease, and Creutzfeldt-Jakob disease.

31.8 Brainstem Encephalitis The pattern of brainstem encephalitis is commonly seen antiMa2 in young men with testicular cancer.39,40,41 Brainstem encephalitis can be combined with limbic and diencephalic encephalitis. Anti-Ri and anti-Hu, anti-Tr, and anti-NMDAR are less common causes of paraneoplastic brainstem encephalitis.3, 4,5,6,7,40,41,42 MRI shows FLAIR and T2 hyperintensity with or without enhancing nodules in the brainstem (▶ Fig. 31.4). Differential diagnosis includes infectious brainstem encephalitis, tumor infiltration, vasculitis, demyelinating disease, and chronic lymphocytic inflammation with pontine perivascular enhancement response to steroids (CLIPPERS).

31.9 Special Antibodies and Paraneoplastic Encephalitis Anti-NMDAR encephalitis is the most common autoimmunemediated encephalopathy and was first described in 2007.43 Before this description, there were a few case reports of reversible encephalopathy associated with ovarian teratoma.43,44 One of the first cases reported as being Bickerstaff encephalitis may be anti-NMDAR encephalitis.41 Anti-NMDA receptor antibody is a cell-surface antibody and is often associated with an ovarian teratoma or occult ovarian teratoma found after oophorectomy.38 The antibody is rarely seen in association with small cell lung cancer, neuroblastoma, or Hodgkin disease3,4,14; 80% of patients with anti-NMDAR encephalitis are women, and the presence of an ovarian teratoma is more likely if the patient is older than 18 years. In children, irritability, hyperactivity, seizures, and memory change may be the first notable symptoms.45 On the other hand, in adults, prodromal headache, psychiatric symptoms, fever, and gastrointestinal or upper respiratory symptoms are seen in 70% of patients.46,47 Motor or complex seizures develop at early stages of the disease, and their frequency decreases with disease evolution. Short-term

memory loss and disintegration of language, followed by decreased responsiveness with hypoventilation requiring ventilator support, commonly ensue.14 The antibody targets NR1/ NR2 heteromers of NMDAR, an inotropic glutamate receptor expressed in the neuropil of the hippocampus and throughout the brain.38 CSF usually shows moderate lymphocytic pleocytosis and oligoclonal bands (> 50%).14 A characteristic EEG pattern, “extreme delta brush,” may help in the diagnosis and follow-up of this disorder48; 75% of patients have complete or near-complete recovery after tumor resection and immunomodulatory therapy,14,49,50 and patients in whom a tumor is not detected have worse therapy response and more frequently require second-line immunotherapy (cyclophosphamide or rituximab or both) compared with patients with tumors. Brain MRI is normal in approximately half of patients with anti-NMDAR encephalitis. The pattern of MR findings includes limbic encephalitis, cerebellitis, brainstem encephalitis, and striatal encephalitis (▶ Fig. 31.1); however, atypical MRI findings are common, with transient FLAIR, T2, and diffusion abnormalities outside the medial temporal lobe, sometimes with cortical enhancement.51, 52 FDG-PET can demonstrate increased glucose metabolism in the frontotemporo-occipital area.53,54 Antibody to VGKC was initially described with Isaacs syndrome (neuromyotonia) and Morvan’s syndrome (neuromyotonia, dysautonomia, insomnia, and delirium).55,56,57,58,59 Other clinical presentations are peripheral nerve hyperexcitability, rapid-eye-movement sleep behavior disorder, and epilepsy. Hyponatremia from inappropriate antidiuretic hormone secretion syndrome (SIADH) occurs in 60% of patients.4 CNS features may be not indistinguishable from other limbic encephalitis. Recent evidence shows that the antibody is not directly binding to the Kv1.1 subunit of the potassium ion channel, but LGI1, CASPR2, or unknown protein (VGKC complex) is associated with the channel.3,4,5 Although it is typically considered nonparaneoplastic, 5 to 30% of cases are with associated tumor (small cell lung cancer, thymoma, prostatic cancer). VGKC may have a more chronic course than other cell surface antigen encephaloitides. Brain MRI typically shows the pattern of limbic encephalitis (▶ Fig. 31.2), rarely coexistent with striatal encephalitis.60 Patients with the antibody to LGI1 or CASPR2 usually have a good response to immunomodulatory treatment.5,60,61,62,63 Anti-AMPAR (Glu R1 and GluR2) encephalitis is rare and is discovered in patients with limbic encephalitis.3,64 Patients have confusion, memory disturbance, and psychiatric symptoms, with or without seizures in women, with a median age of 60 years; 70% of the patients had tumors of the lung, breast, or thymus. Anti-GABABR encephalitis usually shows a pattern of limbic encephalitis with subacute onset of partial complex or tonicclonic seizures,3,65 and 47% had tumors, especially small cell lung cancer. Like other cell surface antigen–mediated syndromes, anti-AMPAR and anti-GABABR encephalitis respond to immunotherapy. Antibodies against glycine receptors have been reported in patients with progressive rigidity, muscle spasm gaze palsies, and encephalomyelitis. Anti-GAD antibodies are implicated in the pathogenesis of type 1 diabetes mellitus and most commonly are associated with stiff-person syndrome, nonparaneoplastic, or epilepsydominated limbic encephalitis.66,67,68,69,70,71

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Tumor-Related Cognitive Dysfunction

Fig. 31.4 Paraneoplastic brainstem encephalitis with Hodgkin’s lymphoma. A 13-year-old girl had weight loss, ataxia, and blurred vision. (a) Fluidattenuated inversion recovery (FLAIR) image shows hyperintense lesions in the brainstem. (b) Postcontrast computed tomography reveals enlarged mediastinal lymph nodes. (c) Whole-body positron emission tomography image demonstrates abnormal uptake in the mediastinal lymph nodes.

Anti-Hu antibody, also known as ANNA-1, was first described in 1985 associated with sensory neuropathy in patients with small cell lung carcinoma.72 Anti-Hu antibodies can be seen in patients with extrapulmonary small cell carcinoma, thymoma, or neuroblastoma. Paraneoplastic sensory neuropathy is seen in approximately 60%.72,73,74,75,76,77 Ataxia, tremor, limbic, or brainstem encephalopathy syndrome,

myelopathy, and dysautonomia (intestinal pseudoobstruction) are other manifestations seen in 10 to 20% of patients. Anti-Hu encephalitis associated with intracellular antigens usually has poor response to tumor and immunomodulatory therapy.78,79,80,81 Early definitive diagnosis is important because the early stage of the disease is more responsive to treatment.

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Paraneoplastic Syndrome Anti CV2/CRMP-5 is associated with small cell lung carcinoma and thymoma. Limbic encephalopathy, cerebellar or brainstem syndrome, and neuropathy have been described.33,34,35,82,83,84 Movement disorder (chorea) and ocular syndromes (uveitis, optic neuritis) are distinctive features. Striatal encephalitis is common. It may manifest with limbic or brainstem encephalitis or cerebellar degeneration. Anti-Tr antibody is associated with Hodgkin’s lymphoma. Limbic encephalopathy and cerebellar or brainstem syndrome have been described.6,23 Anti-Ma2 (Ta) encephalitis usually occurs in young men with testicular germ cell tumors. MRI is abnormal in 75% and shows the pattern of limbic, diencephalic, or brainstem encephalitis. Nodular parenchymal enhancement can be seen.38,83,84 Anti-Ri antibody is associated with breast cancer, small cell lung cancer, or gynecologic malignancies. Opsoclonus–myoclonus syndrome, brainstem encephalitis, and paraneoplastic cerebellar degeneration have been described.4 Anti-Yo antibody is the most common cause of paraneoplastic cerebellar degeneration. The anti-Yo antibody targets intracellular antigens in the Purkinje cells of the cerebellar cortex. The cerebellar degeneration occurs in women and is associated with ovarian or breast malignancy.2,85,86 Patients clinically present subacute pan-cerebellar symptoms, including ataxia, nystagmus, and dysarthria. MRI shows diffuse cerebellar atrophy without brainstem atrophy (▶ Fig. 31.3). Despite tumor and immunomodulatory therapy patients often remains disabled.

31.10 Conclusions Paraneoplastic and nonparaneoplastic encephalopathies are autoimmune-mediated encephalopathies associated with various specific antibodies. Brain MRI is the primary neuroimaging tool for the diagnostic evaluation. The pattern of MRI findings is associated with specific antibodies.

References [1] Dalmau J, Rosenfeld MR. Paraneoplastic syndromes of the CNS. Lancet Neurol 2008; 7: 327–340 [2] Pittock SJ, Kryzer TJ, Lennon VA. Paraneoplastic antibodies coexist and predict cancer, not neurological syndrome. Ann Neurol 2004; 56: 715–719 [3] Saket RR, Geschwind MD, Josephson SA et al. Autoimmune-mediated encephalopathy: classification, evaluation, and MR imaging patterns of disease. Neurographics. 2011; 16: 2–16 [4] Pruitt AA. Immune-mediated encephalopathies with an emphasis on paraneoplastic encephalopathies. Semin Neurol 2011; 31: 158–168 [5] Rosenfeld MR, Titulaer MJ, Dalmau J. Paraneoplastic syndromes and autoimmune encephalitis: five new things. Neurol Clin Pract 2012; 2: 215–223 [6] Irani S, Lang B. Autoantibody-mediated disorders of the central nervous system. Autoimmunity 2008; 41: 55–65 [7] McKeon A. Paraneoplastic and other autoimmune disorders of the central nervous system. Neurohospitalist 2013; 3: 53–64 [8] Dalmau J, Gonzalez RG, Lerwill MF. Case records of the Massachusetts General Hospital. Case 4–2007: a 56-year-old woman with rapidly progressive vertigo and ataxia. N Engl J Med 2007; 356: 612–620 [9] Vernino S, Geschwind M, Boeve B. Autoimmune encephalopathies. Neurologist 2007; 13: 140–147 [10] Psimaras D, Carpentier AF, Rossi C; PNS Euronetwork. Cerebrospinal fluid study in paraneoplastic syndromes. J Neurol Neurosurg Psychiatry 2010; 81: 42–45 [11] Lancaster E, Martinez-Hernandez E, Dalmau J. Encephalitis and antibodies to synaptic and neuronal cell surface proteins. Neurology 2011; 77: 179–189

[12] McKeon A, Pittock SJ. Paraneoplastic encephalomyelopathies: pathology and mechanisms. Acta Neuropathol 2011; 122: 381–400 [13] Thuerl C, Müller K, Laubenberger J, Volk B, Langer M. MR imaging of autopsyproved paraneoplastic limbic encephalitis in non-Hodgkin lymphoma. AJNR Am J Neuroradiol 2003; 24: 507–511 [14] Dalmau J, Lancaster E, Martinez-Hernandez E, Rosenfeld MR, Balice-Gordon R. Clinical experience and laboratory investigations in patients with antiNMDAR encephalitis. Lancet Neurol 2011; 10: 63–74 [15] Titulaer MJ, Soffietti R, Dalmau J et al. European Federation of Neurological Societies. Screening for tumours in paraneoplastic syndromes: report of an EFNS task force. Eur J Neurol 2011; 18: 19–e3 [16] Sioka C, Fotopoulos A, Kyritsis AP. Paraneoplastic neurological syndromes and the role of PET imaging. Oncology 2010; 78: 150–156 [17] McKeon A, Apiwattanakul M, Lachance DH et al. Positron emission tomography-computed tomography in paraneoplastic neurologic disorders: systematic analysis and review. Arch Neurol 2010; 67: 322–329 [18] Tüzün E, Dalmau J. Limbic encephalitis and variants: classification, diagnosis and treatment. Neurologist 2007; 13: 261–271 [19] Graus F, Saiz A. Limbic encephalitis: an expanding concept. Neurology 2008; 70: 500–501 [20] Anderson NE, Barber PA. Limbic encephalitis: a review. J Clin Neurosci 2008; 15: 961–971 [21] Mochizuki Y, Mizutani T, Isozaki E, Ohtake T, Takahashi Y. Acute limbic encephalitis: a new entity? Neurosci Lett 2006; 394: 5–8 [22] Asaoka K, Shoji H, Nishizaka S et al. Non-herpetic acute limbic encephalitis: cerebrospinal fluid cytokines and magnetic resonance imaging findings. Intern Med 2004; 43: 42–48 [23] Gultekin SH, Rosenfeld MR, Voltz R, Eichen J, Posner JB, Dalmau J. Paraneoplastic limbic encephalitis: neurological symptoms, immunological findings and tumour association in 50 patients. Brain 2000; 123: 1481–1494 [24] Urbach H, Soeder BM, Jeub M, Klockgether T, Meyer B, Bien CG. Serial MRI of limbic encephalitis. Neuroradiology 2006; 48: 380–386 [25] Sener RN. MRI and diffusion MRI in nonparaneoplastic limbic encephalitis. Comput Med Imaging Graph 2002; 26: 339–342 [26] Lawn ND, Westmoreland BF, Kiely MJ, Lennon VA, Vernino S. Clinical, magnetic resonance imaging, and electroencephalographic findings in paraneoplastic limbic encephalitis. Mayo Clin Proc 2003; 78: 1363–1368 [27] Ances BM, Vitaliani R, Taylor RA et al. Treatment-responsive limbic encephalitis identified by neuropil antibodies: MRI and PET correlates. Brain 2005; 128: 1764–1777 [28] Shams’ili S, Grefkens J, de Leeuw B et al. Paraneoplastic cerebellar degeneration associated with antineuronal antibodies: analysis of 50 patients. Brain 2003; 126: 1409–1418 [29] Peterson K, Rosenblum MK, Kotanides H, Posner JB. Paraneoplastic cerebellar degeneration. I: a clinical analysis of 55 anti-Yo antibody-positive patients. Neurology 1992; 42: 1931–1937 [30] Honnorat J, Cartalat-Carel S, Ricard D et al. Onco-neural antibodies and tumour type determine survival and neurological symptoms in paraneoplastic neurological syndromes with Hu or CV2/CRMP5 antibodies. J Neurol Neurosurg Psychiatry 2009; 80: 412–416 [31] Ogawa E, Sakakibara R, Kawashima K et al. VGCC antibody-positive paraneoplastic cerebellar degeneration presenting with positioning vertigo. Neurol Sci 2011; 32: 1209–1212 [32] Aragão MdeM, Pedroso JL, Albuquerque MV, Dutra LA, Barsottini OG. Superior cerebellar hyperintense sign on FLAIR-weighted magnetic resonance imaging in paraneoplastic cerebellar degeneration. Arq Neuropsiquiatr 2012; 70: 967 [33] Yu Z, Kryzer TJ, Griesmann GE, Kim K, Benarroch EE, Lennon VA. CRMP-5 neuronal autoantibody: marker of lung cancer and thymoma-related autoimmunity. Ann Neurol 2001; 49: 146–154 [34] Rogemond V, Honnorat J. Anti-CV2 autoantibodies and paraneoplastic neurological syndromes. Clin Rev Allergy Immunol 2000; 19: 51–59 [35] Vernino S, Tuite P, Adler CH et al. Paraneoplastic chorea associated with CRMP-5 neuronal antibody and lung carcinoma. Ann Neurol 2002; 51: 625– 630 [36] Hiraga A, Kuwabara S, Hayakawa S et al. Voltage-gated potassium channel antibody-associated encephalitis with basal ganglia lesions. Neurology 2006; 66: 1780–1781 [37] Heckmann JG, Lang CJ, Druschky A, Claus D, Bartels O, Neundörfer B. Chorea resulting from paraneoplastic encephalitis. Mov Disord 1997; 12: 464–466 [38] Dalmau J, Gleichman AJ, Hughes EG et al. Anti-NMDA-receptor encephalitis: case series and analysis of the effects of antibodies. Lancet Neurol 2008; 7: 1091–1098

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Tumor-Related Cognitive Dysfunction [39] Dalmau J, Graus F, Villarejo A et al. Clinical analysis of anti-Ma2-associated encephalitis. Brain 2004; 127: 1831–1844 [40] Moragas M, Martínez-Yélamos S, Majós C, Fernández-Viladrich P, Rubio F, Arbizu T. Rhombencephalitis: a series of 97 patients. Medicine (Baltimore) 2011; 90: 256–261 [41] Blaes F. Paraneoplastic brain stem encephalitis. Curr Treat Options Neurol 2013; 15: 201–209 [42] Merwick A, Dalmau J, Delanty N. Insights into antibody-associated encephalitis—Bickerstaff’s 1950’s papers revisited. J Neurol Sci 2013; 334: 167–168 [Epub ahead of print] [43] Dalmau J, Tüzün E, Wu HY et al. Paraneoplastic anti-N-methyl-D-aspartate receptor encephalitis associated with ovarian teratoma. Ann Neurol 2007; 61: 25–36 [44] Nokura K, Yamamoto H, Okawara Y, Koga H, Osawa H, Sakai K. Reversible limbic encephalitis caused by ovarian teratoma. Acta Neurol Scand 1997; 95: 367–373 [45] Armangue T, Titulaer MJ, Málaga I et al. Spanish Anti-N-methyl-Aspartate Receptor (NMDAR) Encephalitis Work Group. Pediatric anti-N-methylaspartate receptor encephalitis-clinical analysis and novel findings in a series of 20 patients. J Pediatr 2013; 162: 850–856, e2 [46] Vitaliani R, Mason W, Ances B, Zwerdling T, Jiang Z, Dalmau J. Paraneoplastic encephalitis, psychiatric symptoms, and hypoventilation in ovarian teratoma. Ann Neurol 2005; 58: 594–604 [47] Kayser MS, Titulaer MJ, Gresa-Arribas N, Dalmau J. Frequency and characteristics of isolated psychiatric episodes in anti–N-methyl-D-aspartate receptor encephalitis. JAMA Neurol 2013; 70: 1133–1139[Epub ahead of print] [48] Schmitt SE, Pargeon K, Frechette ES, Hirsch LJ, Dalmau J, Friedman D. Extreme delta brush: a unique EEG pattern in adults with anti-NMDA receptor encephalitis. Neurology 2012; 79: 1094–1100 [49] Iizuka T, Sakai F, Ide T et al. Anti-NMDA receptor encephalitis in Japan: longterm outcome without tumor removal. Neurology 2008; 70: 504–511 [50] Ishiura H, Matsuda S, Higashihara M et al. Response of anti-NMDA receptor encephalitis without tumor to immunotherapy including rituximab. Neurology 2008; 71: 1921–1923 [51] Chan SH, Wong VC, Fung CW, Dale RC, Vincent A. Anti-NMDA receptor encephalitis with atypical brain changes on MRI. Pediatr Neurol 2010; 43: 274–278 [52] Greiner H, Leach JL, Lee KH, Krueger DA. Anti-NMDA receptor encephalitis presenting with imaging findings and clinical features mimicking Rasmussen syndrome. Seizure 2011; 20: 266–270 [53] Ochoa-Figueroa MA, Cárdenas-Negro C, Allende-Riera A, Uña-Gorospe J, Cabello García D, Desequera-Rahola M. [Changes in cerebral metabolism detected by 18F-FDG PET-CT in a case of anti-NMDA receptor encephalitis] [in Spanish] Rev Esp Med Nucl Imagen Mol 2012; 31: 219–222 [54] Maqbool M, Oleske DA, Huq AH, Salman BA, Khodabakhsh K, Chugani HT. Novel FDG-PET findings in anti-NMDA receptor encephalitis: a case based report. J Child Neurol 2011; 26: 1325–1328 [55] Hart IK, Waters C, Vincent A et al. Autoantibodies detected to expressed K + channels are implicated in neuromyotonia. Ann Neurol 1997; 41: 238–246 [56] Kleopa KA, Elman LB, Lang B, Vincent A, Scherer SS. Neuromyotonia and limbic encephalitis sera target mature Shaker-type K + channels: subunit specificity correlates with clinical manifestations. Brain 2006; 129: 1570–1584 [57] Barber PA, Anderson NE, Vincent A. Morvan’s syndrome associated with voltage-gated K + channel antibodies. Neurology 2000; 54: 771–772 [58] Irani SR, Alexander S, Waters P et al. Antibodies to Kv1 potassium channelcomplex proteins leucine-rich, glioma inactivated 1 protein and contactinassociated protein-2 in limbic encephalitis, Morvan’s syndrome and acquired neuromyotonia. Brain 2010; 133: 2734–2748 [59] Vernino S, Lennon VA. Ion channel and striational antibodies define a continuum of autoimmune neuromuscular hyperexcitability. Muscle Nerve 2002; 26: 702–707 [60] Geschwind MD, Tan KM, Lennon VA et al. Voltage-gated potassium channel autoimmunity mimicking Creutzfeldt-Jakob disease. Arch Neurol 2008; 65: 1341–1346 [61] Thieben MJ, Lennon VA, Boeve BF, Aksamit AJ, Keegan M, Vernino S. Potentially reversible autoimmune limbic encephalitis with neuronal potassium channel antibody. Neurology 2004; 62: 1177–1182 [62] Lai M, Huijbers MG, Lancaster E et al. Investigation of LGI1 as the antigen in limbic encephalitis previously attributed to potassium channels: a case series. Lancet Neurol 2010; 9: 776–785

[63] Vincent A, Buckley C, Schott JM et al. Potassium channel antibody-associated encephalopathy: a potentially immunotherapy-responsive form of limbic encephalitis. Brain 2004; 127: 701–712 [64] Lai M, Hughes EG, Peng X et al. AMPA receptor antibodies in limbic encephalitis alter synaptic receptor location. Ann Neurol 2009; 65: 424–434 [65] Lancaster E, Lai M, Peng X et al. Antibodies to the GABAB receptor in limbic encephalitis with seizures: case series and characterisation of the antigen. Lancet Neurol 2010; 9: 67–76 [66] Baekkeskov S, Aanstoot HJ, Christgau S et al. Identification of the 64K autoantigen in insulin-dependent diabetes as the GABA-synthesizing enzyme glutamic acid decarboxylase. Nature 1990; 347: 151–156 [67] Solimena M, De Camilli P. Autoimmunity to glutamic acid decarboxylase (GAD) in stiff-man syndrome and insulin-dependent diabetes mellitus. Trends Neurosci 1991; 14: 452–457 [68] Honnorat J, Saiz A, Giometto B et al. Cerebellar ataxia with anti-glutamic acid decarboxylase antibodies: study of 14 patients. Arch Neurol 2001; 58: 225– 230 [69] Alexopoulos H, Dalakas MC. A critical update on the immunopathogenesis of stiff person syndrome. Eur J Clin Invest 2010; 40: 1018–1025 [70] Malter MP, Helmstaedter C, Urbach H, Vincent A, Bien CG. Antibodies to glutamic acid decarboxylase define a form of limbic encephalitis. Ann Neurol 2010; 67: 470–478 [71] Peltola J, Kulmala P, Isojärvi J et al. Autoantibodies to glutamic acid decarboxylase in patients with therapy-resistant epilepsy. Neurology 2000; 55: 46–50 [72] Graus F, Cordon-Cardo C, Posner JB. Neuronal antinuclear antibody in sensory neuronopathy from lung cancer. Neurology 1985; 35: 538–543 [73] Graus F, Elkon KB, Cordon-Cardo C, Posner JB. Sensory neuronopathy and small cell lung cancer: antineuronal antibody that also reacts with the tumor. Am J Med 1986; 80: 45–52 [74] Dalmau J, Graus F, Rosenblum MK, Posner JB. Anti-Hu–associated paraneoplastic encephalomyelitis/sensory neuronopathy: a clinical study of 71 patients. Medicine (Baltimore) 1992; 71: 59–72 [75] Graus F, Keime-Guibert F, Reñe R et al. Anti-Hu-associated paraneoplastic encephalomyelitis: analysis of 200 patients. Brain 2001; 124: 1138–1148 [76] Vernino S, Eggenberger ER, Rogers LR, Lennon VA. Paraneoplastic neurological autoimmunity associated with ANNA-1 autoantibody and thymoma. Neurology 2002; 59: 929–932 [77] Sillevis Smitt P, Grefkens J, de Leeuw B et al. Survival and outcome in 73 antiHu positive patients with paraneoplastic encephalomyelitis/sensory neuronopathy. J Neurol 2002; 249: 745–753 [78] Voltz R, Dalmau J, Posner JB, Rosenfeld MR. T-cell receptor analysis in anti-Hu associated paraneoplastic encephalomyelitis. Neurology 1998; 51: 1146– 1150 [79] Benyahia B, Liblau R, Merle-Béral H, Tourani JM, Dalmau J, Delattre JY. Cellmediated autoimmunity in paraneoplastic neurological syndromes with antiHu antibodies. Ann Neurol 1999; 45: 162–167 [80] Tanaka M, Maruyama Y, Sugie M, Motizuki H, Kamakura K, Tanaka K. Cytotoxic T cell activity against peptides of Hu protein in anti-Hu syndrome. J Neurol Sci 2002; 201: 9–12 [81] Voltz RD, Posner JB, Dalmau J, Graus F. Paraneoplastic encephalomyelitis: an update of the effects of the anti-Hu immune response on the nervous system and tumour. J Neurol Neurosurg Psychiatry 1997; 63: 133–136 [82] Samii A, Dahlen DD, Spence AM, Maronian NC, Kraus EE, Lennon VA. Paraneoplastic movement disorder in a patient with non-Hodgkin’s lymphoma and CRMP-5 autoantibody. Mov Disord 2003; 18: 1556–1558 [83] Rosenfeld MR, Eichen JG, Wade DF, Posner JB, Dalmau J. Molecular and clinical diversity in paraneoplastic immunity to Ma proteins. Ann Neurol 2001; 50: 339–348 [84] Mathew RM, Vandenberghe R, Garcia-Merino A et al. Orchiectomy for suspected microscopic tumor in patients with anti-Ma2-associated encephalitis. Neurology 2007; 68: 900–905 [85] Rojas I, Graus F, Keime-Guibert F et al. Long-term clinical outcome of paraneoplastic cerebellar degeneration and anti-Yo antibodies. Neurology 2000; 55: 713–715 [86] Hammack JE, Kimmel DW, O’Neill BP, Lennon VA. Paraneoplastic cerebellar degeneration: a clinical comparison of patients with and without Purkinje cell cytoplasmic antibodies. Mayo Clin Proc 1990; 65: 1423–1431

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Part XI

32 Posttraumatic Cognitive Disorders

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Trauma

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Trauma

32 Posttraumatic Cognitive Disorders Inga K. Koerte, Alexander Lin, Marc Muehlmann, Boris-Stephan Rauchmann, Kyle Cooper, Michael Mayinger, Robert A. Stern, and Martha E. Shenton Mild traumatic brain injury, or mTBI, also referred to as concussion, is usually caused by a single event, such as a bump or blow to the head that leads to temporary alterations in the brain. Common causes include sports injuries, vehicle accidents, and falls.1 In addition to a direct impact to the head, explosions are also known to result in mTBI.2 It is estimated that at least 6 per 1,000 people experience mTBI every year.3 However, this number is likely to be higher due to variations in the definition of mTBI and, more importantly, to underreporting of head injuries that occurs because many patients are not seen in medical settings after head trauma, or they are seen by private physicians.4 Based on current knowledge, the four postinjury phases of mTBI are (1) the acute phase, less than 24 hours after injury; (2) the early subacute phase from day 1 to 13; (3) the late subacute phase from day 14 to 20; and (4) the chronic phase after day 20. About 50% of all patients suffer from chronic symptoms lasting for up to several months postinjury in mTBI. Symptoms include irritability, personality change, insomnia, anxiety, and depression. These symptoms are typically most prominent immediately postinjury and resolve over weeks, although cognitive and behavioral sequelae may persist for months.5,6,7,8 Neuropsychological evaluation also demonstrates cognitive deficits in attention, working memory, processing speed, and reaction time.9 Further, cognitive deficits are generally mild and are likely to resolve by themselves within months after the trauma.10,11 However, for a significant number of patients (15 to 20%), the so called “miserable minority,” prolonged postconcussive symptoms are disabling and persist longer than several months. Moreover, a small number of these patients will develop a neurodegenerative disease years or decades later in life, such as chronic traumatic encephalopathy (CTE). To date, the mechanisms leading to CTE are not understood. However, it is assumed that repetitive brain trauma (RBT) may be a necessary condition for developing CTE. Further, although CTE currently can only be diagnosed postmortem, longitudinal studies using advanced neuroimaging may shed light on the underlying morphologic, pathophysiologic, and biochemical changes that occur in the different stages of mTBI and RBT.

32.1 Pathophysiology Research over the last two decades has dramatically improved our understanding of the underlying pathomechanisms in mTBI. Although the precise pathomechanisms that tie frequent mTBI to neuropathological changes are not completely understood, they likely involve a series of multifocal mild axonal injuries set in motion by the initial trauma. More specifically, during mTBI, the brain undergoes shear deformation that produces a stretch of axons, resulting in alterations in axonal membrane permeability and ionic shifts, including a massive influx of calcium into the [...] cell, resulting in an accumulation in the [...] mitochondria, which impairs oxidative metabolism, leading to energy failure and the breakdown of microtubules.12 Additive effects may include a decrease in total cerebral blood flow,

activation of N-methyl-D-aspartate receptors, and a decrease in γ-aminobutyric acid and other inhibitory neurotransmitters.13,14,15 Trauma-induced metabolic changes, however, may return to baseline within a relatively short period. Nonetheless, some mTBI patients with persistent neurobehavioral and neurocognitive deficits demonstrate brain abnormalities that are revealed by advanced neuroimaging techniques.7,16,17,18 These advances go beyond conventional computed tomography (CT) and magnetic resonance imaging (MRI) and are reviewed below.

32.2 Imaging 32.2.1 Conventional Computed Tomography and Magnetic Resonance Imaging Conventional CT and MRI are widely used to evaluate the acute effects of TBI and to rule out skull fracture, intracranial hemorrhage, and brain edema. Therefore, many patients evaluated for mTBI will have undergone a CT scan or MRI or both as part of their acute evaluation. In mTBI, about 10% of the CT scans and about 30% of the conventional MRIs will show abnormalities, such as subarachnoid hemorrhage, subdural hemorrhage, or brain contusions.19,20,21 In the acute setting, conventional CT and MRI are often used to rule out severe complications of TBI, although both modalities have been demonstrated to lack sensitivity to detect subtle structural changes known to occur in mTBI. Moreover, these methods fail to accurately predict long-term outcome.22,23 In what follows, we review advanced neuroimaging techniques that are currently being used in research studies to evaluate subtle differences in the brains of patients who do not show abnormalities using conventional MRI sequences. We begin with high-resolution structural MRI, followed by a discussion of susceptibility-weighted imaging (SWI), diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS), functional and functional connectivity MRI, positron emission tomography (PET), and single-photon emission computer tomography (SPECT).

32.2.2 High-Resolution Structural MR Imaging To date, almost fully automated software tools, such as SPM and FreeSurfer,24 are available to quantify human brain volume. Region-of-interest (ROI) or voxel-based methods (VBM) also enable the characterization of specific white or gray matter structures, and they make possible comparisons between the groups. Consequently, quantitative analyses of brain volume and cortical thickness, based on high-resolution structural MRI, provide useful information for the diagnosis and prognosis of neurodegenerative diseases, such as mild cognitive impairment (MCI),25,26,27 Alzheimer’s disease (AD),27,28,29 and Parkinson’s

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Posttraumatic Cognitive Disorders disease.30,31 Brain volume and cortical thickness are also known to correlate with cognitive performance.25,26,27 Moreover, a pronounced decrease in cortical thickness with age is thought to be a risk factor for the development of neurodegenerative diseases.28,29,30,31,32,33 Recent literature, in fact, suggests that the quantitative analysis of the brain’s structure, such as cortical thickness, may also be helpful in both diagnostic assessment and prognosis of mTBI.34 Studies by Lewén et al35 using animal models to investigate the effects of mTBI on cortical thickness in induced brain trauma in a murine model showed a significant increase in cortical thickness at the region of impact (frontoparietal cortex) at day 1, followed by a decrease in cortical thickness of 15 to 20% after 21 days. In another study,36 mice were treated with fluid percussion to induce brain trauma. Cortical thinning was found in the frontal and occipital regions of the ipsilateral hemisphere compared with a control group 17 days postinjury. These findings suggest a temporary swelling of the cortex directly after the trauma, followed by a decrease in cortical thickness in the chronic stage. This cortical thinning is likely due to wallerian degeneration and reactive astrogliosis. Merkley et al37 also investigated changes in cortical thickness after TBI as a result of traffic accidents in children (age range, 9 to 16 years). In this study, widespread significant cortical thinning was found in the frontal, parietal, temporal, and occipital lobes at a mean postinjury scan interval of about 3 years. These investigators further report a correlation between memory performance and “key regions” that have been reported to subserve working memory function.37 In a separate study, Wilde et al38 compared the cortex of 40 children with moderate to severe TBI with a control group who had orthopedic injury only. Cortical thinning was found in the frontal lobes and in the right temporal lobe in children with TBI compared with the control group. Work by Tremblay et al34 also suggests a link between a pronounced decrease in cortical thickness with age and a history of concussion in athletes participating in contact sports. The underlying mechanism is not understood, although shear injury of the axons with consecutive wallerian degeneration may play a role. High-resolution structural MRI may thus be a useful technique in assessment of the more chronic stages after mTBI and RBT. Future research is needed, however, to identify early changes in mTBI that may help to provide an accurate prognosis for developing neurodegenerative diseases such as CTE, as well as provide a window of opportunity for possible preventative treatment to halt the course of progressive symptoms, which are evident in a small, albeit important, cohort of patients afflicted with mTBI.

32.2.3 Susceptibility-Weighted Imaging Susceptibility-weighted imaging (SWI) has proven an effective method for the study of brain microstructural changes and microhemorrhages that may result from acute, subacute, and chronic mTBI.39 For example, several studies have shown the positive predictive effects of SWI in pediatric patients with mTBIs of varying severity levels.40,41 These studies have also shown that a greater number and volume of microhemorrhages detected through SWI correlate with worse neuropsychological and clinical outcomes in pediatric populations. Evidence also

supports the use of SWI as an effective tool in predicting longterm neurologic outcomes after TBI.42 Few studies have been conducted, however, to determine whether SWI is useful to detect microstructural changes in adult patients with acute and subacute mTBI. Existing studies have used SWI to determine the presence of microhemorrhages in these patients. For example, Toth et al43 studied brain microhemorrhages in 14 patients with mTBI using DTI and SWI 72 hours and 1 month postinjury. SWI failed to detect microhemorrhages in any subjects, although significant changes in mean diffusivity (MD) and fractional anisotropy (FA) were detected using DTI.43 Similar studies in RBT have shown surprisingly few microhemorrhages in amateur boxers (3 of 42 studied), even though findings showed statistically significant differences from controls, where controls showed no microhemorrhages,44 and 2 of 21 boxers in another study showed microhemorrhages.45 However, in a more recent prospective study on the incidence of mTBIs in male and female hockey players, a new method for quantifying information from SWI was used to detect microstructural changes in the brain during the acute and subacute phases of mTBI,46 as shown in ▶ Fig. 32.1a-c. During the season, five male and six female college-age hockey players who sustained a medically diagnosed concussion were tested using SWI 72 hours, 2 weeks, and 2 months postinjury. In all subjects, microhemorrhages were found. Furthermore, as noted earlier, this study used a novel measure of hypointensity burden (HIB), which showed significantly higher HIB in male hockey players compared with females, as well as between the preseason scan and 2 weeks postinjury (▶ Fig. 32.1d). This may be a result of both acute damage on impact and then secondary damage in the chronic stages of injury. Therefore, SWI may be a useful modality for detecting subtle microstructural brain changes and microhemorrhages associated with postconcussive symptoms. However, to date, SWI has not been used to study patients with postconcussive symptoms. Future research is needed in this area, as early identification of these changes may help in determining patient prognosis and propensity for developing postconcussive syndrome (see review by Shenton et al16).

32.2.4 Magnetic Resonance-Diffusion Tensor Imaging Diffusion tensor imaging quantifies the diffusion of water molecules in the tissue investigated. DTI is a sensitive technique to evaluate the brain’s white matter microstructure after mTBI (for reviews see Niogi et al18 and Shenton et al16). Sensitivity of DTI for detecting subtle alterations after mTBI, compared with conventional CT or MRI, has been demonstrated in several studies.47,48 Most studies using DTI to investigate brain alterations in mTBI and RBT have found alterations in the white matter microstructure. These alterations are thought to play a role in persistent cognitive and behavioral symptoms observed in postconcussive syndrome. Fractional anisotropy and MD, common parameters derived from DTI, have been shown to be sensitive for detecting traumatic axonal injury (TAI).49,50 A study by Niogi et al51 reported that 10 of 11 patients with postconcussive syndrome had a normal 3 T MRI but showed decreased FA compared with a group

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Fig. 32.1 Representative (a) axial, (b) sagittal, and (c) coronal susceptibility-weighted images of a hockey player; hypointensity clusters that fall below intensity and size thresholds are overlaid in white, demonstrating exclusion of obvious blood vessels and sulci. (d) Hypointensity burden (HIB; = [total number of voxels in accepted clusters]/[total number of brain voxels] X [volume in mm3 of one voxel]) at various time points before season (BOS), 72 hours after the start of the season, 2 weeks, 2 months, and at end of season (EOS) for both male (squares) and female (circles) patients; statistically significant difference is noted at 2 weeks. (Modified, with permission, from Helmer KG, Pasternak O, Fredman E, et al. Hockey Concussion Education Project, Part 1. Susceptibility-weighted imaging study in male and female ice hockey players over a single season. J Neurosurg 2014;120 (4):864–872.)

of 26 normal controls. Interestingly, the subjects’ reaction time correlated significantly with the number of DTI lesions at least 1 month postinjury.51 In prolonged postconcussive syndrome, a decrease in FA and an increase in MD were demonstrated in the white matter tracts. Such alterations are most likely due to TAI49,50 and are most frequently found in the anterior corona radiata, the uncinate fasciculus, and the superior longitudinal fasciculus. These are all fiber tracts that represent association pathways within the same hemisphere and are most likely involved in cognitive functions like attention and memory.51 Further, commissural fibers of the corpus callosum have also been reported to be affected.50,52,53,54,55,56 The development of multitensor tractography overcomes the problem of crossing fibers and enables the tracking of fibers in the periphery until the gray/white matter border that is particularly vulnerable to TAI. ▶ Fig. 32.2 shows the fiber tracts of the corpus callosum using multitensor tractography.

More recently, DTI has been used to evaluate white matter microstructure in active professional soccer players without a history of symptomatic concussion. Soccer players are at high risk for exposure to repetitive subconcussive brain trauma resulting from heading the ball with the unprotected head. Twelve male athletes trained for a career as professional soccer players since childhood were compared with athletes of noncontact sports.57 DTI revealed increased radial diffusivity in soccer players (▶ Fig. 32.3). This finding is similar to the alterations seen in the chronic stages of mTBI and in RBT, suggesting that frequent subconcussive brain trauma may affect the brain’s microstructure.57 This hypothesis was further supported by a study of ice-hockey players who showed increased mean, radial, and axial diffusivity measure over the course of one play season.58 Three of the investigated athletes sustained a concussion, and they showed the most pronounced changes.

Fig. 32.2 Fiber tracts of the corpus callosum using multitensor tractography (left: coronal; right: sagittal).

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Fig. 32.3 Group comparison of diffusion tensor imaging data in 12 professional soccer players compared with eight competitive swimmers. The voxels highlighted in red demonstrate significantly increased radial diffusivity values for the soccer group compared with swimmers, indicating reduced axon diameter or myelin sheath. (Modified, with permission, from Koerte IK, Ertl-Wagner B, Reiser M, Zafonte R, Shenton ME. White matter integrity in the brains of professional soccer players without a symptomatic concussion. JAMA 2012;308(18):1859–1861.)

A strong correlation between parameters of cognitive function and alteration in white matter tracts has also been demonstrated in postconcussive syndrome.59 Specifically, in a sample of 43 mTBI patients, FA was decreased in the left anterior corona radiata compared with 23 healthy controls. Further, the decrease in FA was significantly correlated with less attentional control. Additionally, reduced FA in the uncinate fasciculus was correlated with memory performance.59 Of further note, Strain et al60 found that FA in the frontal lobe was negatively correlated with measures of depression. This supports the use of DTI as a biomarker of behavioral disturbance in RBT. Finally, DTI baseline evaluation in the acute phase after mTBI may be a predictor of long-term outcome in cognitive and behavioral function. For example, one study reported that acutely following TBI, decreased FA as well as increased MD predicted executive function at 6 months’ follow-up.61 Other advances on the horizon involve a free-water method derived from DTI.62 This method is able to separate free water that is extracellular (FW) from water that is surrounding tissue (FAt), where the former (i.e., FW) may suggest neuroinflammatory processes that precede neurodegeneration and the latter (FAt) may suggest neurodegeneration. This method has recently been applied to ice-hockey players, who are known to be at high risk for RBT.63 This study investigated the longitudinal course of concussion by comparing DTI in concussed ice-hockey players, before and 72 hours after a concussion. The alterations found suggest decreased extracellular space (increased FA and

decreased RD) in white matter after acute concussion. This finding might be explained by neuroinflammation and other possible alterations that occur due to concussions. The distinction between neuroinflammation and neurodegeneration might be important for identifying early stages of CTE after mTBI and RBT. In summary, DTI is the only in vivo tool for investigating microstructural changes in white matter and is thus an important new probe for understanding diffuse axonal injuries in mTBI, the most common injuries observed in these patients. Finally, combining DTI results with other imaging information will likely also be most helpful in future studies.

32.2.5 Magnetic Resonance Spectroscopy Several studies have examined mTBI in the acute and subacute phases. These studies have generally demonstrated specific biochemical changes in N-acetyl aspartate (NAA, a putative neuronal marker), glutamatine and glutamine (Glx, excitatory neurotransmitter), creatinine (Cr, a marker of brain energetics), choline (Cho, marker of membrane turnover), and myoinositol (mI, a marker of glial proliferation).64 A large proportion of these studies showed consistent reductions in NAA, reflective of neuronal injury.65–70 However, findings from a study of acute to subacute mTBI in children failed to show reductions in NAA,

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Trauma suggesting that children may be protected from some neurologic alterations associated with TBI in adults or that other underlying biological changes predominate.71 Other studies have also demonstrated a reduction in Glx in brain gray matter along with increases in Glx and Cr in white matter. These findings suggest disturbances in neurotransmitter function in gray matter regions and altered energetics within white matter during the acute to subacute stages of mTBI. Evidence from these prospective studies also suggests that NAA, Cr, and Glx all return to baseline levels after adequate recovery without further TBIs.67,69 Evidence also suggests that NAA returns to normal levels but may be slowed by a second episode of TBI.70 Furthermore, Chamard et al72 observed a significantly greater decrease in NAA/Cr over the course of a hockey season for female hockey players compared with male players, suggesting a possible difference in the effects of traumatic brain injury between sexes. Some evidence also supports the existence of abnormal levels of the glial marker mI in the acute to subacute stages of mTBI,73 although this finding has been less consistent across studies. More recent MRS studies have also begun to focus on longterm outcomes in the chronic phase of mTBI. These studies suggest that alterations in brain metabolism may become manifest several years after the initial episode of TBI. A primary finding of long-term studies of mTBI is a reduction in NAA across different brain regions, including the white matter, splenium, and centrum semiovale, suggesting neuronal injury and loss.65,70,74,75,76 Other findings from these studies suggest an elevation of Cho, reflective of tissue injury or glial proliferation.65,66 It is likely that elevation of Cho in the acute phase is due to forces that cause injury to cell membranes and myelin, leading to an elevation in the amount of free Cho in the brain. On the other hand, an elevation of Cho in the chronic phase likely reflects a proliferation of glia, which is supported by increases in mI.77 These chronic findings seem to contradict the return of metabolites to baseline values after an acute episode of mTBI. It is thus possible that these results are reflective of postconcussive syndrome, whereby between 15 and 30% of individuals who suffer from mTBI develop symptoms that last longer than 3 months. However, most studies to date have not acquired adequate neuropsychological information to validate this possibility.64 Finally, Sarmento et al78 demonstrated larger reductions in NAA in individuals with chronic posttraumatic headaches compared with those with acute headaches, suggesting possible long-term neuronal loss. However, future prospective studies are needed to explore this possibility. Several studies have documented reductions in NAA across brain regions in subjects exposed to RBT. A study of Iraq and Afghanistan war veterans exposed to RBT injuries showed reductions in hippocampal NAA:Cr and NAA:Cho in subjects with memory dysfunction compared with controls.79 Another study of retired boxers with parkinsonian syndrome demonstrated NAA reduction in the lentiform nucleus compared with controls and Parkinson’s disease patients, suggesting neuronal loss resulting from RBT.80 MRS has also been used to examine ice hockey and professional football players with histories of multiple concussions. These athletes exhibited increased mI in the left medial temporal lobe that correlated with episodic memory loss as well as an elevation in Cho in the prefrontal

cortex.34 Evidence also suggests that exposure to RBT can extend the time needed to fully recover NAA to baseline levels.70 Taken together, these results suggest that mTBI may lead to an extended period of brain vulnerability, which may be exacerbated by RBT. A pilot study of RBT used a novel approach called twodimensional correlated spectroscopy (2D-COSY), which obtains a second spectral dimension to measure additional metabolites not seen in studies using conventional MRS due to spectral overlap, as shown in ▶ Fig. 32.4. Among the changes observed in this study were statistically significant increases in Cho and Glx, consistent with excitotoxicity and axonal injury, as well as an increase in mI. 2D-COSY also showed changes in threonine, aspartate, and glutathione in the brains of these athletes, findings not observed using conventional MRS studies of RBT.81

32.2.6 Functional and Functional Connectivity Magnetic Resonance Imaging Functional magnetic resonance imaging (fMRI) is an advanced MR neuroimaging that uses BOLD (blood-oxygen-level-dependent) contrast to distinguish between active and inactive brain regions. Compared with the previously discussed modalities, fMRI has been less frequently used in the evaluation of both the acute and chronic phase of mTBI. So far, it has been applied in research and, to lesser extent, in clinical settings. Most fMRI studies in the field of TBI investigated the patterns of activation while subjects performed simple auditory-verbal, visual-verbal, or motor tests. Few studies applied resting-state fMRI, which has become an important area of research during the last decade.82,83,84,85,86,87 The resting-state activity of the brain, measured by means of fMRI, provides a measure of brain activation in the state of relaxed consciousness.88,89,90 In the resting awake state, the brain still uses 16% of the total body energy for neuronal firing and cycling of neurotransmitters.91 In this state, temporal synchronicity of neuronal activation patterns of spatially separated brain regions is detectable. The obtained data can be figured as a network of nodes and links, where the nodes are determined by voxels and the links are reflected by the degree of correlation in their activity. There is evidence that the integrity and strength of spontaneous functional connectivity in several networks are of behavioral and cognitive relevance (▶ Fig. 32.5)92,93,94,95,96 Between 1999 and 2006, McAllister et al performed five fMRI studies of mTBI patients and controls using auditory-verbal and visual-verbal N-back tasks. Although the analysis of the task performance revealed no group differences, the mTBI patients reported more neuropsychological symptoms. The observed activation was, however, shown to differ depending on the difficulty of the task variation. For example, mTBI patients showed increased activation while performing tasks of moderate difficulty and decreased activation when the tasks were more complex. Although four of five studies were performed with subjects who suffered from mTBI approximately 1 month before the scan, the remaining study investigated changes after the postconcussive symptoms were no longer present, at around 1 year postinjury. Although the symptoms had ceased, increased activation patterns in the right frontal lobe could still

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Fig. 32.4 Representative in vivo two-dimensional correlated spectroscopy of a human subject. Major metabolites are labeled at the diagonal; however, multiple resonances that represent cross-peaks of metabolites with additional resonances can also be observed. Color is scaled to the ratio to creatinine (Cr). Cho, choline; Glx, glutamine; mI, myoinositol; NAA, N-acetyl aspartate.

Fig. 32.5 Schematic presentation of the default mode network and its connections to dorsolateral prefrontal cortex “D.” The strength of the connectivity between cortical regions by means of correlation coefficient (r) is reflected by color. In general, an r > 0.3 is considered a threshold of valid connection; LLP, left lateral parietal; MPFC, medial prefrontal cortex; PCC, posterior cingulate cortex; RLP, right lateral parietal. (Modified, with permission, from Slobounov et al. Brain Imaging Behav 2012.)

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Trauma be observed. Therefore, it was concluded that persistent alterations in brain activation could be found even when postconcussive symptoms had abated. In another study, Smits et al97 reported significantly lower Nback task performance in patients with postconcussive syndrome. Additionally, positive correlations between postconcussive symptoms and atypical higher activation patterns were observed outside the normal activation circuit. One explanation may be that previously damaged brain areas need back up from brain areas that were previously not involved or that were involved to a lesser extent. Additionally, Gosselin et al98 reported a variety of brain regions that showed both increased and decreased brain activation in mTBI patients with more postconcussive symptoms, and in 2011 Matthews et al investigated mTBI patients 3 years postinjury and reported greater activation in patients with major depressive disorder in the fearful emotional face-matching task.99 In this same year, Matthews et al published a second study that compared mTBI patients who had suffered loss of consciousness with patients who had only altered consciousness. The time between the trauma and the investigation was again around 3 years. Participants in this second study were presented with a stop and go signal test. The fMRI analysis revealed decreased left frontal activation in the group of patients who were reported to have lost consciousness.100 Moreover, activation in the left frontal area correlated with the reported symptoms, which indicates that this area is a neural correlate of the impaired self-awareness after loss of consciousness.100 Initial studies using resting-state fMRI found activity disruptions within the default-mode network (DMN) in both patients with (blast-related) TBI101,102 and mTBI.103,104 A recently published study by Palacios et al102reported that increased amplitudes of low fluctuation are related to better neurocognitive performance in patients after TBI with chronic and severe axonal injury and that the loss of functional integrity in certain brain areas can lead to compensatory increases in nodes of the default-mode network (DMN). Dysfunction of the DMN in patients with mTBI compared to healthy controls was observed by Zhou et al and further investigated by Sours et al, who hypothesized that disruptions between the DMN and the taskpositive network (TPN) might account for the memory dysfunctions observed in patients with mTBI. The long-term effects of mTBI have been investigated by Monti et al, who compared younger and older mTBI patients with their respective age- and gender-matched controls. In accordance with previous findings, decreased bilateral hippocampal values were observed, whereas fMRI analysis showed reduced activity of the posterior parietal cortex during a hippocampal memory task. These findings may indicate the (functional) long-term significance of a previous mTBI.105

32.2.7 Positron Emission Tomography Most positron emission tomography (PET) studies have focused on the chronic stages of mTBI as opposed to acute stage or more severe injury.106,107,108,109 Using 2-deoxy-2-(18F)-fluoro-Dglucose (FDG), studies performed in a resting state106,107 or with performance stimuli108,110 have demonstrated hypometabolism in the frontal and temporal lobes, which in some cases correlated with neuropsychological examinations but not with MRI or CT changes. Other studies have also shown hyper-

metabolism. This discrepancy is likely the result of differences in individual subjects, including type, rate, and time of injury, as well as differences in protocols.108,110 Two FDG-PET studies have focused on the effects of RBT in boxers111 and soldiers suffering from multiple blast exposures.112 Both groups showed hypometabolism in the cerebellum, whereas boxers showed additional changes in the posterior cingulate and frontal lobes, and the blast victims also showed changes in the medial temporal lobe and pons. In veterans, there is also the strong comorbidity of posttraumatic stress disorder (PTSD). Mendez et al113 excluded military subjects with PTSD and examined blast versus blunt head trauma. Their results showed hypometabolism in the frontotemporal regions but hypermetabolism in the caudate in soldiers with blast trauma but not blunt head trauma. With such a small number of studies, it is difficult to draw conclusions regarding patterns of glucose metabolism. More recent studies show great promise by focusing on different aspects of RBT. The presence of tau aggregates has also been well-documented in postmortem studies of CTE.114,115 In a preliminary study, Small et al used 2-(1-)6-[(2-[18F] fluoroethyl) (methyl)amino]-2-naphthyl) ethylidene) malononitrile (FDDNP) for PET imaging in five retired National Football League players with history of cognitive and mood symptoms.116 FDDNP binds to both tau neurofibrillary tangles and amyloid plaque in brain tissue117 and therefore is not specific to tau, whereas tauspecific PET ligands have been developed118,119,120,121 that show great promise in characterizing tau aggregates in vivo, as opposed to postmortem. These latter, more specific tau ligands will be important in determining whether or not those with RBT are characterized more by tau pathology, which would follow the postmortem work of McKee et al114,115 in National Football League players and military postmortem findings of tau rather than amyloid plaque in brain tissue. The PET ligands can also explore physiologic changes, such as neuroinflammation. By targeting peripheral benzodiazepine receptors found on activated microglia from neuroinflammation, 11C-PK11195 has been used to examine inflammation in chronic mTBI122 up to 17 years postinjury.123 However, 11C -PK11195 has low binding specificity, such that other probes may prove to be more effective.124 Further, combining this measure with a measure of free water and MRS measures sensitive to neuroinflammation, such as glutathione, described previously in this chapter, would help to specify further the role of neuroinflammation in RBT and mTBI.

32.2.8 Single-Photon Emission Computed Tomography Single-photon emission computed tomography has been used to examine regional cerebral blood flow (rCBF) in several studies of mTBI. Two studies have shown rCBF defects in the acute stage after injury including reduced perfusion of SPECT tracers in the frontal lobes of patients with mTBI. In the subacute stage, similar patterns have been observed of reduced rCBF in the frontal lobes127 as well as parietal lobe.128 In the chronic stages of mTBI, many SPECT studies focus on comparison with other imaging modalities, such as MRI and CT.128,129,130,131,132 In all studies, SPECT showed greater sensitivity to detect abnormal changes compared with both imaging

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Posttraumatic Cognitive Disorders methods. This is not surprising given the known lack of sensitivity of conventional MRI and CT. Other studies have compared SPECT findings with neuropsychological measures,130,131,133,134, 135 which have shown more mixed results, with some studies showing expected correlations with frontal brain regions and others not. This may be due to the fact that SPECT is acquired in the resting state, as opposed to the active state when patients are engaged in complex tasks and that rCBF patterns do not correspond with resting and activated brain states.134 With regard to RBT, studies are limited to two by the same group who studied 100 retired National Football League players,136 including a subset of 30 players who underwent evaluation for potential treatments.137 The result of their 100-subject study revealed hypoperfusion in the prefrontal poles, temporal poles, occipital lobes, anterior and posterior cingulate gyri, and hippocampus. Importantly, however, SPECT studies are limited by the fact that the method relies on comparison with “normal” brain regions. Given the diffuse changes that occur in mTBI, however, there may not be unaffected regions for comparison.138 Second, the observed SPECT changes are not specific to mTBI and have also been observed in chronic pain, in drug and alcohol abuse, and in headaches, many of which are comorbid issues, particularly in professional athletes.139 As a result, SPECT findings are not considered diagnostic of mTBI, although the absence of SPECT abnormalities is considered prognostic for good recovery.140

32.3 Summary and Future Directions Today, advanced multimodal neuroimaging techniques can provide a diagnosis of brain injury in mTBI and RBT that goes beyond self-report and other measures, and may elucidate further the mechanisms and neurophysiology underlying mTBI. This is an important first step. More studies using multimodal imaging techniques are needed, however, as no single imaging modality captures the kind of injury extant in mTBI. There is also a need to understand mTBI and RBT in general, and to be cognizant that they are heterogeneous disorders. We need to develop individual profiles of injury. An example is the recent work by Bouix et al,141 who observed increases in gray matter in chronic mTBI using individual profiles of injury for patients, based on developing a normative atlas and comparing individual patients to the atlas to develop the profile of injury. Further, future research should also include multimodal imaging to acquire different information from different modalities in the same patient to have a more complete picture of each patient’s brain alterations. Moreover, based on their enhanced sensitivity, many of these newer and more advanced imaging techniques can be used to monitor treatment efficacy, and they may also serve as endpoints for new trials of medication aimed at neuroplasticity or reduced neuroinflammation in TBI. This is an exciting new era of discovery that has been long overdue in diagnosing mTBI on the basis of objective radiologic evidence, which sets the stage for a more complete understanding of the pathophysiological mechanisms underlying mTBI and RBT, with the hope that this understanding, including following the trajectory of injury and recovery, will lead to more efficacious

treatments and to the monitoring of treatments using more sensitive imaging techniques.

32.4 Acknowledgment The authors of this chapter wish to acknowledge the Else Kröner-Fresenius Stiftung, Germany (IK, MM). This work was also partially funded by grants from The Department of Defense (W81XWH-10-1-0835: APL; W81XWH-07-CC-CSDoD: MES; W81XWH-13-2-0063: MES), the National Institutes of Health (R01-NS078337: APL, MES; R01-NS078337), and a VA Merit Award (MES).

References [1] Ropper AH, Gorson KC. Clinical practice. Concussion. N Engl J Med 2007; 356: 166–172 [2] Mac Donald CL, Johnson AM, Cooper D et al. Detection of blast-related traumatic brain injury in U.S. military personnel. N Engl J Med 2011; 364: 2091– 2100 [3] Cassidy JD, Carroll LJ, Peloso PM et al. WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. Incidence, risk factors and prevention of mild traumatic brain injury: results of the WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. J Rehabil Med 2004 Suppl: 28–60 [4] Langlois JA, Rutland-Brown W, Wald MM. The epidemiology and impact of traumatic brain injury: a brief overview. J Head Trauma Rehabil 2006; 21: 375–378 [5] Ponsford J, Cameron P, Fitzgerald M, Grant M, Mikocka-Walus A. Long-term outcomes after uncomplicated mild traumatic brain injury: a comparison with trauma controls. J Neurotrauma 2011; 28: 937–946 [6] Kurca E, Sivák S, Kucera P. Impaired cognitive functions in mild traumatic brain injury patients with normal and pathologic magnetic resonance imaging. Neuroradiology 2006; 48: 661–669 [7] Konrad C, Geburek AJ, Rist F et al. Long-term cognitive and emotional consequences of mild traumatic brain injury. Psychol Med 2011; 41: 1197– 1211 [8] Maruta J, Lee SW, Jacobs EF, Ghajar J. A unified science of concussion. Ann N Y Acad Sci 2010; 1208: 58–66 [9] Baandrup L, Jensen R. Chronic post-traumatic headache: clinical analysis in relation to the International Headache Classification 2nd edition. Cephalalgia 2005;25:132–138 [10] Belanger HG, Curtiss G, Demery JA, Lebowitz BK, Vanderploeg RD. Factors moderating neuropsychological outcomes following mild traumatic brain injury: a meta-analysis. J Int Neuropsychol Soc 2005; 11: 215–227 [11] Schretlen DJ, Shapiro AM. A quantitative review of the effects of traumatic brain injury on cognitive functioning. Int Rev Psychiatry 2003; 15: 341–349 [12] Bigler ED, Maxwell WL. Neuropathology of mild traumatic brain injury: relationship to neuroimaging findings. Brain Imaging Behav 2012; 6: 108– 136 [13] Giza CC, Hovda DA. The neurometabolic cascade of concussion. J Athl Train 2001; 36: 228–235 [14] Binder LI, Guillozet-Bongaarts AL, Garcia-Sierra F, Berry RW. Tau, tangles, and Alzheimer’s disease. Biochim Biophys Acta 2005; 1739: 216–223 [15] Serbest G, Burkhardt MF, Siman R, Raghupathi R, Saatman KE. Temporal profiles of cytoskeletal protein loss following traumatic axonal injury in mice. Neurochem Res 2007; 32: 2006–2014 [16] Shenton ME, Hamoda HM, Schneiderman JS et al. A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury. Brain Imaging Behav 2012; 6: 137–192 [17] Jantzen KJ. Functional magnetic resonance imaging of mild traumatic brain injury. J Head Trauma Rehabil 2010; 25: 256–266 [18] Niogi SN, Mukherjee P. Diffusion tensor imaging of mild traumatic brain injury. J Head Trauma Rehabil 2010; 25: 241–255 [19] Borg J, Holm L, Peloso PM et al. WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. Non-surgical intervention and cost for mild traumatic

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[20]

[21]

[22] [23] [24]

[25]

[26]

[27]

[28]

[29]

[30] [31] [32] [33]

[34] [35]

[36]

[37]

[38]

[39] [40]

[41]

[42]

[43]

[44]

brain injury: results of the WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. J Rehabil Med 2004 Suppl: 76–83 Hesselink JR, Dowd CF, Healy ME, Hajek P, Baker LL, Luerssen TG. MR imaging of brain contusions: a comparative study with CT. AJR Am J Roentgenol 1988; 150: 1133–1142 Mittl RL, Grossman RI, Hiehle JF et al. Prevalence of MR evidence of diffuse axonal injury in patients with mild head injury and normal head CT findings. AJNR Am J Neuroradiol 1994; 15: 1583–1589 Le TH, Gean AD. Neuroimaging of traumatic brain injury. Mt Sinai J Med 2009; 76: 145–162 Iverson GL. Outcome from mild traumatic brain injury. Curr Opin Psychiatry 2005; 18: 301–317 Khan AR, Wang L, Beg MF. FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using large deformation diffeomorphic metric mapping. Neuroimage 2008; 41: 735–746 Chang YL, Jacobson MW, Fennema-Notestine C et al. Alzheimer’s Disease Neuroimaging Initiative. Level of executive function influences verbal memory in amnestic mild cognitive impairment and predicts prefrontal and posterior cingulate thickness. Cereb Cortex 2010; 20: 1305–1313 Seo SW, Ahn J, Yoon U et al. Cortical thinning in vascular mild cognitive impairment and vascular dementia of subcortical type. J Neuroimaging 2010; 20: 37–45 Singh V, Chertkow H, Lerch JP, Evans AC, Dorr AE, Kabani NJ. Spatial patterns of cortical thinning in mild cognitive impairment and Alzheimer’s disease. Brain 2006; 129: 2885–2893 Dickerson BC, Wolk DA; Alzheimer’s Disease Neuroimaging Initiative. MRI cortical thickness biomarker predicts AD-like CSF and cognitive decline in normal adults. Neurology 2012; 78: 84–90 Park H, Yang JJ, Seo J, Lee JM; ADNI. Dimensionality reduced cortical features and their use in predicting longitudinal changes in Alzheimer’s disease. Neurosci Lett 2013; 550: 17–22 Jubault T, Gagnon JF, Karama S et al. Patterns of cortical thickness and surface area in early Parkinson’s disease. Neuroimage 2011; 55: 462–467 Ibarretxe-Bilbao N, Junque C, Segura B et al. Progression of cortical thinning in early Parkinson’s disease. Mov Disord 2012; 27: 1746–1753 Agosta F, Valsasina P, Riva N et al. The cortical signature of amyotrophic lateral sclerosis. PLoS ONE 2012; 7: e42816 Verstraete E, Veldink JH, Hendrikse J, Schelhaas HJ, van den Heuvel MP, van den Berg LH. Structural MRI reveals cortical thinning in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry 2012; 83: 383–388 Tremblay S, De Beaumont L, Henry LC et al. Sports concussions and aging: a neuroimaging investigation. Cereb Cortex 2013; 23: 1159–1166 Lewén A, Li GL, Nilsson P, Olsson Y, Hillered L. Traumatic brain injury in rat produces changes of beta-amyloid precursor protein immunoreactivity. Neuroreport 1995; 6: 357–360 Fineman I, Giza CC, Nahed BV, Lee SM, Hovda DA. Inhibition of neocortical plasticity during development by a moderate concussive brain injury. J Neurotrauma 2000; 17: 739–749 Merkley TL, Bigler ED, Wilde EA, McCauley SR, Hunter JV, Levin HS. Diffuse changes in cortical thickness in pediatric moderate-to-severe traumatic brain injury. J Neurotrauma 2008; 25: 1343–1345 Wilde EA, McCauley SR, Kelly TM et al. The neurological outcome scale for traumatic brain injury (NOS-TBI): I. construct validity. J Neurotrauma 2010; 27: 983–989 Barnes SR, Haacke EM. Susceptibility-weighted imaging: clinical angiographic applications. Magn Reson Imaging Clin N Am 2009; 17: 47–61 Ashwal S, Babikian T, Gardner-Nichols J, Freier MC, Tong KA, Holshouser BA. Susceptibility-weighted imaging and proton magnetic resonance spectroscopy in assessment of outcome after pediatric traumatic brain injury. Arch Phys Med Rehabil 2006; 87 Suppl 2: S50–S58 Beauchamp MH, Beare R, Ditchfield M et al. Susceptibility-weighted imaging and its relationship to outcome after pediatric traumatic brain injury. Cortex 2013; 49: 591–598 Colbert CA, Holshouser BA, Aaen GS et al. Value of cerebral microhemorrhages detected with susceptibility-weighted MR Imaging for prediction of long-term outcome in children with nonaccidental trauma. Radiology 2010; 256: 898–905 Toth A, Kovacs N, Perlaki G et al. Multi-modal magnetic resonance imaging in the acute and sub-acute phase of mild traumatic brain injury: can we see the difference? J Neurotrauma 2013; 30: 2–10 Hähnel S, Stippich C, Weber I et al. Prevalence of cerebral microhemorrhages in amateur boxers as detected by 3 T MR imaging. AJNR Am J Neuroradiol 2008; 29: 388–391

[45] Hasiloglu ZI, Albayram S, Selcuk H et al. Cerebral microhemorrhages detected by susceptibility-weighted imaging in amateur boxers. AJNR Am J Neuroradiol 2011; 32: 99–102 [46] Helmer KG, Pasternak O, Fredman E et al. Hockey Concussion Education Project, Part 1. Susceptibility-weighted imaging study in male and female ice hockey players over a single season. J Neurosurg 2014; 120: 864–872 [47] Arfanakis K, Cordes D, Haughton VM, Carew JD, Meyerand ME. Independent component analysis applied to diffusion tensor MRI. Magn Reson Med 2002; 47: 354–363 [48] Huang MX, Theilmann RJ, Robb A et al. Integrated imaging approach with MEG and DTI to detect mild traumatic brain injury in military and civilian patients. J Neurotrauma 2009; 26: 1213–1226 [49] Inglese M, Makani S, Johnson G et al. Diffuse axonal injury in mild traumatic brain injury: a diffusion tensor imaging study. J Neurosurg 2005; 103: 298– 303 [50] Lipton ML, Gellella E, Lo C et al. Multifocal white matter ultrastructural abnormalities in mild traumatic brain injury with cognitive disability: a voxel-wise analysis of diffusion tensor imaging. J Neurotrauma 2008; 25: 1335–1342 [51] Niogi SN, Mukherjee P, Ghajar J et al. Extent of microstructural white matter injury in postconcussive syndrome correlates with impaired cognitive reaction time: a 3 T diffusion tensor imaging study of mild traumatic brain injury. AJNR Am J Neuroradiol 2008; 29: 967–973 [52] Salmond CH, Menon DK, Chatfield DA, Pickard JD, Sahakian BJ. Changes over time in cognitive and structural profiles of head injury survivors. Neuropsychologia 2006; 44: 1995–1998 [53] Kraus MF, Susmaras T, Caughlin BP, Walker CJ, Sweeney JA, Little DM. White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study. Brain 2007; 130: 2508–2519 [54] Rutgers DR, Fillard P, Paradot G, Tadié M, Lasjaunias P, Ducreux D. Diffusion tensor imaging characteristics of the corpus callosum in mild, moderate, and severe traumatic brain injury. AJNR Am J Neuroradiol 2008; 29: 1730–1735 [55] Little AS, Liu S, Beeman S et al. Brain retraction and thickness of cerebral neocortex: an automated technique for detecting retraction-induced anatomic changes using magnetic resonance imaging. Neurosurgery 2010; 67 Suppl Operative: ons277–ons282, discussion ons282 [56] Mayer AR, Ling J, Mannell MV et al. A prospective diffusion tensor imaging study in mild traumatic brain injury. Neurology 2010; 74: 643–650 [57] Koerte IK, Ertl-Wagner B, Reiser M, Zafonte R, Shenton ME. White matter integrity in the brains of professional soccer players without a symptomatic concussion. JAMA 2012; 308: 1859–1861 [58] Koerte IK, Kaufmann D, Hartl E et al. A prospective study of physicianobserved concussion during a varsity university hockey season: white matter integrity in ice hockey players. Part 3 of 4. Neurosurg Focus 2012; 33: E3–, 1–7 [59] Niogi SN, Mukherjee P, Ghajar J et al. Structural dissociation of attentional control and memory in adults with and without mild traumatic brain injury. Brain 2008; 131: 3209–3221 [60] Strain J, Didehbani N, Cullum CM et al. Depressive symptoms and white matter dysfunction in retired NFL players with concussion history. Neurology 2013; 81: 25–32 [61] Miles L, Grossman RI, Johnson G, Babb JS, Diller L, Inglese M. Short-term DTI predictors of cognitive dysfunction in mild traumatic brain injury. Brain Inj 2008; 22: 115–122 [62] Pasternak O, Sochen N, Gur Y, Intrator N, Assaf Y. Free water elimination and mapping from diffusion MRI. Magn Reson Med 2009; 62: 717–730 [63] Pasternak O, Koerte IK, Bouix S et al. Hockey Concussion Education Project, Part 2. Microstructural white matter alterations in acutely concussed ice hockey players: a longitudinal free-water MRI study. J Neurosurg 2014; 120: 873–881 [64] Lin AP, Liao HJ, Merugumala SK, Prabhu SP, Meehan WP, III, Ross BD. Metabolic imaging of mild traumatic brain injury. Brain Imaging Behav 2012; 6: 208–223 [65] Garnett MR, Blamire AM, Corkill RG, Cadoux-Hudson TA, Rajagopalan B, Styles P. Early proton magnetic resonance spectroscopy in normal-appearing brain correlates with outcome in patients following traumatic brain injury. Brain 2000; 123: 2046–2054 [66] Govindaraju V, Gauger GE, Manley GT, Ebel A, Meeker M, Maudsley AA. Volumetric proton spectroscopic imaging of mild traumatic brain injury. AJNR Am J Neuroradiol 2004; 25: 730–737 [67] Henry LC, Tremblay S, Leclerc S et al. Metabolic changes in concussed American football players during the acute and chronic post-injury phases. BMC Neurol 2011; 11: 105

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| 12.09.15 - 10:54

Posttraumatic Cognitive Disorders [68] Sivák S, , Bittšanský M, Grossmann J et al. Clinical correlations of proton magnetic resonance spectroscopy findings in acute phase after mild traumatic brain injury. Brain Inj 2014; 28: 1–346 [69] Vagnozzi R, Signoretti S, Cristofori L et al. Assessment of metabolic brain damage and recovery following mild traumatic brain injury: a multicentre, proton magnetic resonance spectroscopic study in concussed patients. Brain 2010; 133: 3232–3242 [70] Vagnozzi R, Signoretti S, Tavazzi B et al. Temporal window of metabolic brain vulnerability to concussion: a pilot 1H-magnetic resonance spectroscopic study in concussed athletes—part III. Neurosurgery 2008; 62: 1286–1296 [71] Maugans TA, Farley C, Altaye M, Leach J, Cecil KM. Pediatric sports-related concussion produces cerebral blood flow alterations. Pediatrics 2012; 129: 28–37 [72] Chamard E, Lassonde M, Henry L et al. Neurometabolic and microstructural alterations following a sports-related concussion in female athletes. Brain Inj 2013; 27: 1038–1046 [73] Kierans AS, Kirov II, Gonen O et al. Myoinositol and glutamate complex neurometabolite abnormality after mild traumatic brain injury. Neurology 2014; 82: 521–528 [74] Cecil KM, Hills EC, Sandel ME et al. Proton magnetic resonance spectroscopy for detection of axonal injury in the splenium of the corpus callosum of brain-injured patients. J Neurosurg 1998; 88: 795–801 [75] Cimatti M. Assessment of metabolic cerebral damage using proton magnetic resonance spectroscopy in mild traumatic brain injury. J Neurosurg Sci 2006; 50: 83–88 [76] Cohen BA, Inglese M, Rusinek H, Babb JS, Grossman RI, Gonen O. Proton MR spectroscopy and MRI-volumetry in mild traumatic brain injury. AJNR Am J Neuroradiol 2007; 28: 907–913 [77] Ashwal S, Holshouser B, Tong K et al. Proton spectroscopy detected myoinositol in children with traumatic brain injury. Pediatr Res 2004; 56: 630– 638 [78] Sarmento E, Moreira P, Brito C, Souza J, Jevoux C, Bigal M. Proton spectroscopy in patients with post-traumatic headache attributed to mild head injury. Headache 2009; 49: 1345–1352 [79] Hetherington HP, Hamid H, Kulas J et al. MRSI of the medial temporal lobe at 7 T in explosive blast mild traumatic brain injury. Magn Reson Med 2014; 71: 1358–1367 [80] Davie CA, Pirtosek Z, Barker GJ, Kingsley DP, Miller PH, Lees AJ. Magnetic resonance spectroscopic study of parkinsonism related to boxing. J Neurol Neurosurg Psychiatry 1995; 58: 688–691 [81] Lin AP et al. Changes in the neurochemistry of athletes with repetitive brain trauma: preliminary results using localized correlated spectroscopy. Alzheimers Res Ther 2015; 7: 13 [82] Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci USA 2003; 100: 253–258 [83] Damoiseaux JS, Rombouts SA, Barkhof F et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A 2006; 103: 13848– 13853 [84] Biswal BB, Mennes M, Zuo XN et al. Toward discovery science of human brain function. Proc Natl Acad Sci U S A 2010; 107: 4734–4739 [85] Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 2008; 1124: 1– 38 [86] Buckner RL, Vincent JL. Unrest at rest: default activity and spontaneous network correlations. Neuroimage 2007; 37: 1091–1096, discussion 1097–1099 [87] Filippini N, MacIntosh BJ, Hough MG et al. Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc Natl Acad Sci U S A 2009; 106: 7209–7214 [88] Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A 2001; 98: 676–682 [89] Raichle ME, Snyder AZ. A default mode of brain function: a brief history of an evolving idea. Neuroimage 2007; 37: 1083–1090, discussion 1097–1099 [90] Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 2007; 8: 700– 711 [91] Shulman RG, Rothman DL, Behar KL, Hyder F. Energetic basis of brain activity: implications for neuroimaging. Trends Neurosci 2004; 27: 489–495 [92] Hampson M, Driesen NR, Skudlarski P, Gore JC, Constable RT. Brain connectivity related to working memory performance. J Neurosci 2006; 26: 13338–13343 [93] He BJ, Snyder AZ, Vincent JL, Epstein A, Shulman GL, Corbetta M. Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits in spatial neglect. Neuron 2007; 53: 905–918

[94] Ferrarelli F, Massimini M, Sarasso S et al. Breakdown in cortical effective connectivity during midazolam-induced loss of consciousness. Proc Natl Acad Sci U S A 2010; 107: 2681–2686 [95] Kelly AM, Uddin LQ, Biswal BB, Castellanos FX, Milham MP. Competition between functional brain networks mediates behavioral variability. Neuroimage 2008; 39: 527–537 [96] Wirth M, Jann K, Dierks T, Federspiel A, Wiest R, Horn H. Semantic memory involvement in the default mode network: a functional neuroimaging study using independent component analysis. Neuroimage 2011; 54: 3057–3066 [97] Smits M, Dippel DW, Houston GC et al. Postconcussion syndrome after minor head injury: brain activation of working memory and attention. Hum Brain Mapp 2009; 30: 2789–2803 [98] Gosselin N, Bottari C, Chen JK et al. Electrophysiology and functional MRI in post-acute mild traumatic brain injury. J Neurotrauma 2011; 28: 329–341 [99] Matthews SC, Strigo IA, Simmons AN, O’Connell RM, Reinhardt LE, Moseley SA. A multimodal imaging study in U.S. veterans of Operations Iraqi and Enduring Freedom with and without major depression after blast-related concussion. Neuroimage 2011; 54 Suppl 1: S69–S75 [100] Matthews S, Simmons A, Strigo I. The effects of loss versus alteration of consciousness on inhibition-related brain activity among individuals with a history of blast-related concussion. Psychiatry Res 2011; 191: 76–79 [101] Han K, Mac Donald CL, Johnson AM et al. Disrupted modular organization of resting-state cortical functional connectivity in U.S. military personnel following concussive ‘mild’ blast-related traumatic brain injury. Neuroimage 2014; 84: 76–96 [102] Palacios EM, Sala-Llonch R, Junque C et al. Resting-state functional magnetic resonance imaging activity and connectivity and cognitive outcome in traumatic brain injury. JAMA Neurol 2013; 70: 845–851 [103] Sours C, Zhuo J, Janowich J, Aarabi B, Shanmuganathan K, Gullapalli RP. Default mode network interference in mild traumatic brain injury: a pilot resting state study. Brain Res 2013; 1537: 201–215 [104] Zhou Y, Milham MP, Lui YW et al. Default-mode network disruption in mild traumatic brain injury. Radiology 2012; 265: 882–892 [105] Monti JM, Voss MW, Pence A, McAuley E, Kramer AF, Cohen NJ. History of mild traumatic brain injury is associated with deficits in relational memory, reduced hippocampal volume, and less neural activity later in life. Front Aging Neurosci 2013; 5: 41 [106] Kato T, Nakayama N, Yasokawa Y, Okumura A, Shinoda J, Iwama T. Statistical image analysis of cerebral glucose metabolism in patients with cognitive impairment following diffuse traumatic brain injury. J Neurotrauma 2007; 24: 919–926 [107] García-Panach J, Lull N, Lull JJ et al. A voxel-based analysis of FDG-PET in traumatic brain injury: regional metabolism and relationship between the thalamus and cortical areas. J Neurotrauma 2011; 28: 1707–1717 [108] Gross H, Kling A, Henry G, Herndon C, Lavretsky H. Local cerebral glucose metabolism in patients with long-term behavioral and cognitive deficits following mild traumatic brain injury. J Neuropsychiatry Clin Neurosci 1996; 8: 324–334 [109] Chen SH, Kareken DA, Fastenau PS, Trexler LE, Hutchins GD. A study of persistent post-concussion symptoms in mild head trauma using positron emission tomography. J Neurol Neurosurg Psychiatry 2003; 74: 326–332 [110] Humayun MS, Presty SK, Lafrance ND et al. Local cerebral glucose abnormalities in mild closed head injured patients with cognitive impairments. Nucl Med Commun 1989; 10: 335–344 [111] Provenzano FA, Jordan B, Tikofsky RS, Saxena C, Van Heertum RL, Ichise M. F18 FDG PET imaging of chronic traumatic brain injury in boxers: a statistical parametric analysis. Nucl Med Commun 2010; 31: 952–957 [112] Peskind ER, Petrie EC, Cross DJ et al. Cerebrocerebellar hypometabolism associated with repetitive blast exposure mild traumatic brain injury in 12 Iraq war veterans with persistent post-concussive symptoms. Neuroimage 2011; 54 Suppl 1: S76–S82 [113] Mendez MF, Owens EM, Reza Berenji G, Peppers DC, Liang LJ, Licht EA. Mild traumatic brain injury from primary blast vs. blunt forces: post-concussion consequences and functional neuroimaging. NeuroRehabilitation 2013; 32: 397–407 [114] McKee AC, Stern RA, Nowinski CJ et al. The spectrum of disease in chronic traumatic encephalopathy. Brain 2013; 136: 43–64 [115] McKee AC, Cantu RC, Nowinski CJ et al. Chronic traumatic encephalopathy in athletes: progressive tauopathy after repetitive head injury. J Neuropathol Exp Neurol 2009; 68: 709–735 [116] Small GW, Kepe V, Siddarth P et al. PET scanning of brain tau in retired National Football League players: preliminary findings. Am J Geriatr Psychiatry 2013; 21: 138–144

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| 12.09.15 - 10:54

Trauma [117] Shoghi-Jadid K, Small GW, Agdeppa ED et al. Localization of neurofibrillary tangles and beta-amyloid plaques in the brains of living patients with Alzheimer’s disease. Am J Geriatr Psychiatry 2002; 10: 24–35 [118] Zhang W, Arteaga J, Cashion DK et al. A highly selective and specific PET tracer for imaging of tau pathologies. J Alzheimers Dis 2012; 31: 601–612 [119] Shao X, Carpenter GM, Desmond TJ et al. Evaluation of [11C]N-methyl lansoprazole as a radiopharmaceutical for PET imaging of tau neurofibrillary tangles. ACS Med Chem Lett 2012; 3: 936–941 [120] Shoup TM, Yokell DL, Rice PA et al. A concise radiosynthesis of the tau radiopharmaceutical, [18F]T807. J Labelled Comp Radiopharm 2013; 56: 736– 740 [121] Chien DT, Bahri S, Szardenings AK et al. Early clinical PET imaging results with the novel PHF-tau radioligand [F-18]-T807. J Alzheimers Dis 2013; 34: 457–468 [122] Folkersma H, Boellaard R, Yaqub M et al. Widespread and prolonged increase in (R)-11C-PK11195 binding after traumatic brain injury. J Nucl Med 2011; 52: 1235–1239 [123] Ramlackhansingh AF, Brooks DJ, Greenwood RJ et al. Inflammation after trauma: microglial activation and traumatic brain injury. Ann Neurol 2011; 70: 374–383 [124] Yanamoto K, Yamasaki T, Kumata K et al. Evaluation of N-benzyl-N-[11C] methyl-2-(7-methyl-8-oxo-2-phenyl-7,8-dihydro-9H-purin-9-yl)acetamide ([11C]DAC) as a novel translocator protein (18 kDa) radioligand in kainic acidlesioned rat. Synapse 2009; 63: 961–971 [125] Abu-Judeh HH, Singh M, Masdeu JC, Abdel-Dayem HM. Discordance between FDG uptake and technetium-99m-HMPAO brain perfusion in acute traumatic brain injury. J Nucl Med 1998; 39: 1357–1359 [126] Gowda NK, Agrawal D, Bal C et al. Technetium Tc-99 m ethyl cysteinate dimer brain single-photon emission CT in mild traumatic brain injury: a prospective study. AJNR Am J Neuroradiol 2006; 27: 447–451 [127] Audenaert K, Jansen HM, Otte A et al. Imaging of mild traumatic brain injury using 57Co and 99mTc HMPAO SPECT as compared to other diagnostic procedures. Med Sci Monit 2003; 9: MT112–MT117 [128] Hofman PA, Stapert SZ, van Kroonenburgh MJ, Jolles J, de Kruijk J, Wilmink JT. MR imaging, single-photon emission CT, and neurocognitive performance after mild traumatic brain injury. AJNR Am J Neuroradiol 2001; 22: 441–449 [129] Gray BG, Ichise M, Chung DG, Kirsh JC, Franks W. Technetium-99m-HMPAO SPECT in the evaluation of patients with a remote history of traumatic brain

[130]

[131]

[132]

[133] [134]

[135]

[136]

[137]

[138]

[139]

[140] [141]

injury: a comparison with x-ray computed tomography. J Nucl Med 1992; 33: 52–58 Ichise M, Chung DG, Wang P, Wortzman G, Gray BG, Franks W. Technetium99m-HMPAO SPECT, CT and MRI in the evaluation of patients with chronic traumatic brain injury: a correlation with neuropsychological performance. J Nucl Med 1994; 35: 217–226 Kant R, Smith-Seemiller L, Isaac G, Duffy J. Tc-HMPAO SPECT in persistent post-concussion syndrome after mild head injury: comparison with MRI/CT. Brain Inj 1997; 11: 115–124 Lewine JD, Davis JT, Bigler ED et al. Objective documentation of traumatic brain injury subsequent to mild head trauma: multimodal brain imaging with MEG, SPECT, and MRI. J Head Trauma Rehabil 2007; 22: 141–155 Bonne O, Gilboa A, Louzoun Y et al. Cerebral blood flow in chronic symptomatic mild traumatic brain injury. Psychiatry Res 2003; 124: 141–152 Umile EM, Plotkin RC, Sandel ME. Functional assessment of mild traumatic brain injury using SPECT and neuropsychological testing. Brain Inj 1998; 12: 577–594 Umile EM, Sandel ME, Alavi A, Terry CM, Plotkin RC. Dynamic imaging in mild traumatic brain injury: support for the theory of medial temporal vulnerability. Arch Phys Med Rehabil 2002; 83: 1506–1513 Amen DG, Newberg A, Thatcher R et al. Impact of playing American professional football on long-term brain function. J Neuropsychiatry Clin Neurosci 2011; 23: 98–106 Amen DG, Wu JC, Taylor D, Willeumier K. Reversing brain damage in former NFL players: implications for traumatic brain injury and substance abuse rehabilitation. J Psychoactive Drugs 2011; 43: 1–5 Belanger HG, Vanderploeg RD, Curtiss G, Warden DL. Recent neuroimaging techniques in mild traumatic brain injury. J Neuropsychiatry Clin Neurosci 2007; 19: 5–20 Wortzel HS, Filley CM, Anderson CA, Oster T, Arciniegas DB. Forensic applications of cerebral single photon emission computed tomography in mild traumatic brain injury. J Am Acad Psychiatry Law 2008; 36: 310–322 Jacobs A, Put E, Ingels M, Put T, Bossuyt A. One-year follow-up of technetium99m-HMPAO SPECT in mild head injury. J Nucl Med 1996; 37: 1605–1609 Bouix S, Pasternak O, Rathi Y, Pelavin PE, Zafonte R, Shenton ME. Increased gray matter diffusion anisotropy in patients with persistent post-concussive symptoms following mild traumatic brain injury. PLoS ONE 2013; 8: e66205

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Part XII

33 Endocrine-, Metabolic-, Toxin-, and Drug-Related Dementia

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Endocrine and Toxins-Related Dementia

33 Endocrine-, Metabolic-, Toxin-, and Drug-Related Dementia Sangam G. Kanekar and Brian S. Bentley The economic burden of dementia is enormous. Establishing a precise cause of dementia whenever possible allows a more focused treatment plan and an accurate assessment of prognosis. Reversible or preventable causes form around 15 to 20% of dementia cases. The exact incidence of dementia from endocrine-, metabolic-, nutritional-, toxin-, and drug-related causes is difficult to estimate. Increasingly, attention has been directed to these causes because these conditions are either preventable or reversible with appropriate treatment. Several endocrine disorders and nutritional deficiencies can masquerade as dementia and need to be investigated, especially in young patients with rapidly progressive dementias. Metabolic derangements resulting from hepatic or renal failure can lead to neurotoxicity and cognitive decline. Several toxins, such as arsenic, mercury, aluminum, lithium, or lead, can also lead to cognitive decline. Other silent causes of dementia are chronic medications, particularly medicines for central nervous system (CNS) disorders (anticholinergic, antiepileptic, antiparkinson), especially in elderly patients. In this chapter, we discuss the clinical and imaging findings of various common and uncommon causes of dementia attributable to endocrine-, metabolic-, nutritional-, toxin-, and drug-related causes (▶ Table 33.1).

Table 33.1 Common causes of irreversible dementia due to endocrine disorders, metabolic disorders, nutritional deficiencies and toxins Endocrine dysfunction

Hypothyroidism Hyperthyroidism Hashimoto encephalopathy Hypo- and hyperparathyroididsm Pituitary insufficiency Cushing’s disease Addison’s disease Hypoglycemia type 2 diabetes mellitus

Metabolic

Uremic encephalopathy Dialysis disequilibrium syndrome Dialysis dementia Hepatic encephalopathy Hepatic or portal systemic encephalopathy Electrolyte imbalance Porphyria

Nutritional deficiencies

Wernicke-Korsakoff syndrome Pellagra, caused by niacin deficiency Vitamin B12 deficiency

Toxins

33.1 Endocrine Dysfunction Various endocrinal abnormalities can cause derangement in the functioning of the CNS. These functional derangements could be due to either direct effects of hormones on the CNS or indirect effects through various electrolytes or immunemediated processes.

33.1.1 Thyroid Hormones and Cognition Disorders Acute as well as chronic dysfunction in the thyroid gland may lead to cognitive decline and dementia. Hyperthyroidism as well as hypothyroidism can affect brain functioning at the transmitter level, leading to various neuropsychiatric and cognitive manifestations.1 Hyperthyroidism is more frequent in women and may manifest with myopathy, peripheral neuropathy, movement disorders, seizures, ophthalmoplegia, along with attention, memory, and visuospatial deficits. Hypothyroidism can cause peripheral neuropathy, myopathy, ataxia, cerebellar symptoms, myxedema, depression, and a dementia syndrome with a frontal-subcortical pattern. Deficits in attention, recent memory, and abstract thinking may be present. Thyroid hormones (TH) [T3 and T4] are essential for the development, maturation, and maintenance of the brain cells and cholinergic functions. THs essentially modulate all metabolic pathways through alterations in oxygen consumption and changes in protein, lipid, carbohydrates, and vitamin metabolism. Astrocytes display TH receptors and possess a dependency on THs for glucose transport and expression of specific structural proteins. THs exert great influence on the selected brain regions, notably hippocampal (CA3 and CA2 areas of hippocampus), cortical, basal forebrain, and cerebellar areas, where they have influence on neurotransmitters (acetylcholine and cholinergic) functions and nerve growth factor.1 Deficiency in THs is shown to have a deleterious effect on synaptic connectivity and decreases myelination. Low levels of TH are thought to increase amyloid precursor protein expression, which in turn increases the A- β production. Hypothyroidism has been considered a reversible cause of secondary dementia in the elderly and therefore thyroid function tests is a must in the workup for patients with dementia.1 Computed tomography (CT) and magnetic resonance imaging (MRI) of the brain are usually unremarkable. Single-photon emission computed tomography (SPECT)/positron emission tomography (PET) may show frontotemporoparietal cortical hypometabolism (▶ Fig. 33.1). Definitive diagnosis is by hormonal assay and clinical examination.

Alcoholic-related dementia Heavy metal poisoning Carbon monoxide poisoning Drugs and medications

33.1.2 Hashimoto’s Encephalopathy Hashimoto’s encephalopathy (HE) is an uncommon neurologic syndrome associated with Hashimoto thyroiditis. The

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Endocrine-, Metabolic-, Toxin-, and Drug-Related Dementia

Fig. 33.1 Hypothyroidism. Sagittal view of fluorodeoxyglucose-positron emission tomography scan shows hypometabolism in the frontal lobes with normal uptake in the rest of the lobes of the cerebral parenchyma.

Fig. 33.2 Cushing’s syndrome with memory loss. Coronal T1-weighted image shows moderate atrophy of hippocampi bilaterally (arrows).

pathophysiology of HE is not entirely clear; the cause has been proposed to be autoimmune because of its association with other immunologic disorders. It is proposed that immune complexes deposit on vessels, causing cerebral microvasculature disruption and microscopic brain damage. HE is characterized by various neuropsychological symptoms, including cognition and/or consciousness deterioration, personality changes, seizures, and myoclonus. More recently, HE has gained growing attention as a cause of treatable dementias. HE has two patterns: acute encephalopathy (25%) manifests with focal neurologic deficits and a variable degree of cognitive dysfunction and consciousness impairment, whereas diffuse progressive pattern (75%) shows slow cognitive decline, dementia, and confusion.2 In approximately 50% of patients with HE, MRI may be normal. In the remaining patients, MRI manifestations can vary from ischemic lesions, demyelination, and vasogenic edema to atrophy. The most commonly reported findings are generalized cerebral atrophy, diffuse increased signal on T2-weighted and fluid-attenuated inversion recovery (FLAIR) images in subcortical white matter and dural enhancement.3 White matter hyperintensity and atrophy may also be seen in the cerebellum. Rarely, the mesial temporal lobes and basal ganglia may be involved. Diffusion-weighted imaging (DWI) may show reversible areas of restricted diffusion in these regions. On magnetic resonance spectroscopy (MRS), affected areas may demonstrate reduction of N-acetyl-aspartate (NAA) and increased choline (Cho). Imaging findings may resolve with steroids or other immunosuppressant treatments. SPECT may show nonspecific patterns of reduced blood flow involving multiple regions in the brain, and PET scans may demonstrate some nonspecific hypometabolism. Diagnosis of Hashimoto’s thyroiditis is con-

firmed by the presence of elevated serums levels of antithyroid antibodies.

33.1.3 Cushing’s Syndrome Cushing’s syndrome is an endocrine disorder characterized by sustained hypercortisolemia, either from an endogenous overproduction of cortisol or treatment with exogenous steroids. In Cushing’s syndrome, the most prominent pathological change seen in the brain is atrophy.4,5 A combination of water loss as a result of alterations in vascular permeability and diuresis of sodium and water and the catabolic effects of cortisol are believed to be responsible for the reduction in brain mass. Glucocorticoids affect both the structure as well as the neurotransmitter system of the brain, which are believed to suppress the myelin content in the brain, modulate neurotransmitter systems, affect serotonin biosynthesis, increase the uptake of norepinephrine, and regulate the plasticity and circuitry of many brain regions.4 Clinical presentation may include difficulty in concentration, memory difficulties, and short- and long-term logical memory deficits, as well as possible associated attention, language, visuospatial, and reasoning deficits. Like Cushing’s disease, patients with Addison’s syndrome can have a dementia syndrome with irritability, psychosis, apathy, fatigue, and depression. Besides generalized atrophy on imaging, research studies have also documented hippocampal changes on MRI. In the chronic stages, MRI findings may correlate with the pathological findings of hippocampal sclerosis characterized by neuronal loss and gliosis in the CA-1 and subiculum of the hippocampus (▶ Fig. 33.2). MRS may show decreased NAA:Cho

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Endocrine and Toxins-Related Dementia and NAA:creatinine (Cr) levels, suggestive of neuronal changes in the temporal lobe.

33.1.4 Parathyroidism Cognitive disturbances are seen in both hypoparathyroidism and hyperparathyroidism and are most often due to derangements in free calcium.5 Patients with hyperparathyroidism may have generalized weakness, fatigue, irritability, lethargy, depressed mood, and memory deficits. Hypoparathyroidism (idiopathic or acquired) and pseudohypoparathyroidism (a familial disease) are rare and mostly manifest with tetanus, seizures, extrapyramidal signs, dystonia, ataxia, and dementia.5 The exact biochemical mechanism responsible for the development of dementia in parathyroid dysfunction is unknown but presumed to be due to influence of calcium metabolism on higher cortical functioning. In addition, increased cerebrospinal spinal fluid concentrations of both total and ionized calcium are thought to impair blood–brain barrier (BBB) function. CT, MRI, and plain radiography of the skull reveal calcium deposits in the basal ganglia and occasionally in the thalamus and cerebellum (▶ Fig. 33.3). Functional MRI has demonstrated a significant reduction in the regional cerebral blood flow in cingulate cortex, superior and inferior frontal cortex bilaterally, anterior temporal cortex, precentral gyrus, postcentral gyrus, and parietal cortex. Final diagnosis is by biochemical analysis.

33.1.5 Hypoglycemia and Type 2 Diabetes Mellitus It is well established that the patients with diabetes mellitus are at increased risk of dementia.5 However, the exact cause and mechanism leading to this are still debatable. Hypoglycemia is a frequent phenomenon in patients treated for diabetes mellitus. These frequent hypoglycemic events are thought to impair nutrient delivery to the brain, downregulate different markers of neuronal plasticity, and increase the amount of neurotoxic glutamate. Severe hypoglycemia can result in permanent neurologic sequelae, including neuronal cell death, which may further accelerate the process of dementia.5 This damage

Fig. 33.3 Hyperparathyroidism. Axial computed tomography image shows dense bilateral calcification of the basal ganglia, cerebellum, and left cerebral subcortical white matter (black arrows).

particularly impacts the neuronal receptors in the CA-1, subiculum dentate, and granule cell areas of the hippocampus; regions that are critical for learning and memory. In addition, associated cerebrovascular disease and hyperinsulinemia may further compound the cognitive decline. Magnetic resonance imaging is quite sensitive in identifying the pathologic changes due to hypoglycemia. In acute stages, DWI shows areas of restricted diffusion involving the cerebral cortex, hippocampi, and deep gray matter nuclei bilaterally with corresponding apparent diffusion coefficient changes (▶ Fig. 33.4). In the later stages, these areas may show increased

Fig. 33.4 Acute hypoglycemia. (a,b) Axial diffusion-weighted images show areas of restricted diffusion (arrows) in the left frontal, parietal, and temporal lobes, gyri, and subjacent white matter.

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Endocrine-, Metabolic-, Toxin-, and Drug-Related Dementia signal intensity on T2 and FLAIR images, with mild to moderate changes of atrophy.

33.2 Metabolic Disorders 33.2.1 Uremic Encephalopathy Epidemiologic data have shown the prevalence of chronic kidney disease increasing rapidly in the elderly population, with more than 25% of people over the age of 60 having stage 3 disease (National Institutes of Health, 2012).6 The rising rate of renal disease in elderly patients who may already have a primary CNS impairment makes the diagnosis of uremic encephalopathy critical. Imaging findings, along with corresponding biochemical changes, may be helpful in differentiating uremic encephalopathy from other neurodegenerative disorders. The role of CT is somewhat limited when evaluating uremic encephalopathy. Imaging typically demonstrates cerebral atrophy with secondary ventricular dilation in the setting of chronic uremia. MRI is the preferred imaging modality when assessing a patient with acute uremia, and two patterns are typically seen: cortical edema or basal ganglia edema.7 Edematous cortex and subjacent white matter show hyperintensity on T2 and FLAIR images with corresponding reversible hypointensity on T1-weighted images (▶ Fig. 33.5). This appearance on MRI is somewhat similar to that seen in posterior reversible encephalopathy syndrome (PRES) and may be related. In the basal ganglia pattern, the hyperintensity attributable to vasogenic edema is seen predominately involving bilateral corpus striatum and globus pallidus,8 which resolves after dialysis and is characteristic of uremic encephalopathy. Although the exact pathophysiology of both edema patterns is unclear, proposed mechanisms involve a combination of impaired cellular metabolism from uremic toxins, such as parathyroid hormone, along with vascular territories that are vulnerable to ischemia.

33.2.2 Dialysis Disequilibrium Syndrome Dialysis disequilibrium syndrome (DDS) is another metabolic disorder encountered in patients with impaired kidney func-

tion who develop cerebral edema. Patients with markedly elevated blood urea nitrogen (BUN) levels who subsequently undergo renal replacement therapy with hemodialysis can have a rapid decrease in urea, resulting in reduced plasma osmolality and increased osmotic gradient across cell membranes. An intracellular shift of water occurs and results in reversible cerebral edema seen on imaging. An alternative mechanism proposes that a reduction in intracellular pH causes increased brain osmolality and edema. Typically, DDS arises in patients at the end of, or shortly after, dialysis who complain of headache, dizziness, or vomiting. Imaging findings of DDS can range from diffuse cerebral edema to bilateral patchy T2-weighted and FLAIR white matter signal hyperintensity indicating increased brain water content.9 Incidence of DDS has improved with newer dialysis regimens; however, recognized risk factors remain older age, first dialysis treatment, preexisting neurologic disease, severe metabolic acidosis, and BUN levels > 175 mg/dl. It is important to note that DDS is separate from dialysis-associated osmotic demyelination syndrome, which may occur after rapid electrolyte shifts causing myelinolysis classically in the central pons, although extrapontine lesions are not atypical.

33.2.3 Dialysis Dementia As patients with renal disease are living longer with dialysis, there is10 corresponding concern for increased rates of chronic dialysis-related comorbidities. Dialysis dementia was initially caused by accumulation of the dialysate phosphate binder, aluminum hydroxide, in the cortical gray matter.11 As dialysis improved and newer binding agents were developed, the rates of dialysis dementia have decreased. Cortical atrophy is the common imaging finding seen in dialysis dementia patients (▶ Fig. 33.6). Studies suggest that chronic dialysis patients experiencing cognitive impairment or movement disorders have deranged cerebral metabolite concentrations. Significant elevations of myoinositol (Myo) peak and increased Myo:Cr ratio are seen in the cortical gray matter on MRS. DTI studies in these patients have demonstrated decreasing fractional anisotropy in the white matter, possibly due to microstructural distortion.12

Fig. 33.5 Uremic encephalopathy. (a,b) Axial T2weighted images show edematous gyri with mild effacement of the sulci resulting from gyral edema (arrowheads). Similar changes are also seen along the cerebellar folia (arrow).

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Endocrine and Toxins-Related Dementia

Fig. 33.6 Dialysis dementia in 47-year-old man. (a) Axial T2-weighted image shows generalized prominence of convexity sulci advanced for the age of the patient. (b) Axial computed tomography image (bone window) shows diffuse thickening of the calvarium with sclerosis.

33.2.4 Hepatic Encephalopathy Patients with advanced liver disease can experience a reversible syndrome of brain dysfunction that is characterized by neuropsychiatric symptoms and is termed hepatic encephalopathy (HepE). HepE is thought to be due to an increase in blood ammonia levels and its associated neurotoxicity.13 Final diagnosis is mostly by clinical and laboratory abnormalities. The most frequent imaging abnormality associated with HepE is bilateral symmetric T1-weighted hyperintensities in the basal ganglia, most commonly in the globus pallidus, secondary to manganese deposition and its paramagnetic effect (▶ Fig. 33.7). Studies using proton MRS have demonstrated an association between the severity of HepE and the increased magnitude of the glutamine/glutamate peak,14 which reverses after treatment with liver transplantation. As with other metabolic neurodegenerative diseases, HepE patients are susceptible to diffuse cerebral edema and subtle shifts in the brain water content of white matter. On MRI, besides FLAIR and DWI, magnetization transfer (MT) is quite sensitive and shows mild decreases in MT ratios, especially in the parietal and frontal lobes.15 T2 hyperintensity may also be seen along the white matter corticospinal tracts, indicative of mild edema.

33.3 Nutritional Deficiencies 33.3.1 Wernicke-Korsakoff Syndrome Nutritional deficiencies can frequently result in neurologic sequelae. Wernicke-Korsakoff syndrome (WKS) is a relatively common illness caused by low thiamine (vitamin B1) intake, which is often associated with alcoholism. It may also be seen with malignancy, total parenteral nutrition, abdominal surgery, hyperemesis gravidarum, hemodialysis, or any situation that predisposes an individual to a chronically malnourished state. Generically, WKS encompasses two different syndromes: an acute presentation of confusion and ataxia, referred to as Wernicke encephalopathy (WE), and a chronic dementia with confabulation and psychosis known as Korsakoff syndrome (KS). Korsakoff psychosis (KP) is frequently confused with alcoholic dementia. KP is not a dementia but rather a pure amnesia with

Fig. 33.7 Hepatic encephalopathy. Axial T1-weighted imaging shows typical diffuse hyperintensity signal in the lentiform nucleus (arrows).

severely impaired short-term recall but excellent long-term memory and other intellectual functions. In a thiamine-deficient state, there is inability to regulate the osmotic gradients that disrupt the BBB, resulting in cytotoxic edema and, eventually, permanent neuronal loss in the areas with the highest metabolic demands. The classic triad of WE ataxia, global confusion, and ophthalmoplegia - may not be present in most patients. The most common initial symptom is nonspecific mental status changes.

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Fig. 33.8 Alcoholic encephalopathy. Axial (a) fluid-attenuated inversion recovery and (b) T2-weighted images show symmetric hyperintensity in the medial thalami (arrowhead) and mamillary bodies (arrow).

In the acute setting of WE, typical imaging abnormalities are related to focally edematous lesions in the medial thalami, mammillary bodies, tectal plate, and periventricular and periaqueductal gray matter, as noted by bilateral symmetric T2weighted and FLAIR signal hyperintensity (▶ Fig. 33.8).16 These areas may also show restricted diffusion in the acute stage of WE. The thalamus and mammillary bodies frequently display enhancement on postcontrast T1-weighted imaging; this is more often associated with alcohol-induced WE as opposed to nonalcoholic WE.17 Cerebellum may show mild atrophy with hyperintensity in the dentate nuclei. Persistent thiamine deficiency results in disease progression leading to diffuse cerebral atrophy, enlargement of the ventricles, and atrophy of the mammillary bodies. Quantitative MRI may demonstrate significant regional volume deficits in KS patients involving the mammillary bodies, medial thalamus, and genu of the corpus callosum. PET and fMRI may be useful in defining the memory functions, which are disrupted in KS. 18Ffluorodeoxyglucose (FDG) PET may demonstrate significant hypometabolism in the diencephalic gray matter, suggesting interruption of the diencephalic-limbic circuit. MRS studies have revealed a low NAA:Cr ratio and an elevated lactate peak in the thalamus and cerebellum in the setting of KS.18

33.3.2 Vitamin B12 Deficiency Deficiency of vitamin B12 (cobalamin) is seen secondary to pernicious anemia, malabsoprtion, or a congenital defect in B12 metabolism and may manifest with neurologic symptoms, including progressive motor and sensory deficits, ataxia, and cognitive impairment. The most commonly recognized manifestation of vitamin B12 deficiency is subacute combined degeneration (SCD). SCD has a gradual onset of progressive extremity weakness, numbness, and paresthesias resulting from degeneration of the posterior and lateral columns of the cord. Pathology shows swelling of the myelin sheaths, demyelination, wallerian degeneration, and gliosis. MRI of the cervical and thoracic spinal cord may demonstrate cord expansion early in the disease process, along with high-signal lesions on T2-weighted sequences in the dorsal and lateral columns (▶ Fig. 33.9). Brain

changes in B12 deficiency have not been studied extensively; however, multifocal white matter T2-weighted signal hyperintensities and atrophy have been identified.19 Newborns of mothers with pernicious anemia can have severe diffuse cerebral atrophy with moderate ventricular enlargement, which is often reversible following appropriate replacement therapy.20

33.4 Toxins 33.4.1 Alcohol-Related Dementia The current Diagnostic and Statistical Manual IV21 criteria for alcohol-related dementia (ARD) specify the persistence of cognitive and functional decline after the cessation of alcohol consumption, with all other causes of dementia excluded. The exact incidence and prevalence of ARD vary in the literature. Prevalence studies in nursing homes have reported ARD to account for 10 to 24% of all dementias. The clinical manifestations of alcohol dementia are similar to other types of dementia, which include memory problems, language impairment, and inability to perform complex motor tasks. ARD has a younger age of onset (< 60 years of age), is less progressive than Alzheimer’s disease, and is even potentially partially reversible. Autopsy evaluations demonstrate some degree of brain pathology in up to 78% of alcoholics. Direct neurotoxic effect of alcohol is through glutamate excitotoxicity, oxidative stress, and the disruption of neurogenesis.22,23 This effect is particularly seen in binge drinking and with frequent withdrawals, which enhances the neuronal injury through increased vulnerability of upregulated Nmethyl-D-aspartate (NMDA) receptors to glutamate-induced excitotoxicity. Other mechanisms attributed to the effects of alcohol on the brain include mitochondrial damage, apoptosis, and hyperhomocysteinemia leading to arterial thrombosis and strokes. In addition, hepatic encephalopathy leads to accumulation of ammonia and manganese in the brain, which leads interference with neurotransmitter activity and neuroprotective functions. The neurotoxic effect of alcohol is particularly seen in the hippocampus, hypothalamus, and cerebellum,

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Fig. 33.9 A 51-year-old vegetarian man with progressive extremity weakness and mild cognitive decline. (a) Sagittal and (b) axial T2-weighted images through the cervical spine shows hyperintensity in the posterior column (arrowheads). (c) Axial T2 image through brain shows mild atrophy of the cerebral parenchyma.

leading to impairment in memory and learning. Cholinergic neurotransmission in the basal forebrain, which plays a key role in attention, learning, and memory, also appears to be impacted by alcohol. In addition to the direct toxicity of chronic longterm deficiency of thiamine (vitamin B1), it causes development of profound memory impairment, Korsakoff syndrome. Neuropathological studies show that loss of brain tissue largely accounts for reduction in the white matter volume.22,23 Neuronal loss is documented in cerebral cortex, especially the superior frontal cortex, Brodmann area 8, hypothalamus (supraoptic and paraventricular nuclei), and cerebellum. The frontal lobes are particularly more susceptible to damage compared with the rest of the brain.22,23 Neuroimaging in alcoholic dementia demonstrates typical bilateral frontal atrophy. Early changes of atrophy are seen in the frontal gyrus, which proceeds to the posterior part and eventually leads to widening of sylvian fissures bilaterally (▶ Fig. 33.10). There is a marked decrease in the frontal lobe white matter. In addition, there may be thinning of corpus callosum with scattered white matter hyperintensities from associated hyperlipidemia. Marchiafava-Bignami disease (MBD) is a rare disorder that results in progressive demyelination and necrosis of the corpus collosum, which is generally associated with chronic alcohol abuse. MBD is most prevalent in men between 40 and 60 years of age. The main pathologic change is in the corpus callosum, which shows demyelination accompanied by infiltration of macrophages, leading to thinning of the corpus callosum, cavity formation, and ultimately necrosis. Clinically, MBD may initially manifest either in acute form, which is often fatal, or in the chronic form, which lasts for several months or years and is

characterized by variable degrees of mental confusion, dementia, and impairment of gait. Computed tomography is less sensitive and may show diffuse periventricular low-density and focal areas of low density in the genu and splenium of the corpus callosum. MRI of the brain shows areas of T1 hypointensity and T2/FLAIR hyperintensity in the corpus callosum and adjacent white matter. As the disease progresses, these areas show severe atrophy with areas of cavitations. MRS may show decreases in the NAA:Cr ratio during the early stages of disease as a result of secondary axonal injury after myelin degradation and prominent lactate and lipid peaks in the subacute-chronic stages from necrosis of axons and oligodendrocytes.

33.4.2 Heavy Metal Poisoning Exposure to large doses of heavy metals such as lead, arsenic, mercury, manganese, thallium, aluminum, toluene, bismuth, and lithium may lead to irreversible neurologic damage and dementia. This exposure may be acute or chronic. Their effect on the brain cells largely depends on permeability through the BBB.24 The diagnosis of heavy metal poisoning is mainly by clinical and biochemical analysis and by exclusion. Neuroimaging in these cases plays a limited role.

Lead Chronic lead exposure is seen in both developed and underdeveloped nations. Cognitive decline is seen mostly with chronic exposure and may occur long after cessation of the exposure.

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Fig. 33.10 Frontal lobes and cerebellar atrophy in a 47-year-old alcoholic patient. (a) Axial T2-weighted image shows prominence of frontal convexity sulci (black arrowheads) and decreased white matter (white arrows). (b) Coronal T1-weighted image reveals moderate prominence of cerebellar folia (arrows) suggestive of cerebellar atrophy.

Exposure to lead may be via drinking water (lead pipes), workers inhaling larger amounts of lead fumes (e.g., at battery refinery plants), or from household paint, mainly seen in children. Lead mimics calcium in biochemical processes, crosses the BBB, and concentrates in gray matter. Lead interferes with calcium-mediated signal transduction in neurotransmitters, as it binds to synaptotagmin, a Ca2 + -binding membrane protein widely expressed in the central and peripheral nervous system.24 This protein has a much higher binding affinity for Pb2 + ion than for Ca2 + .

Mercury Mercury in its organic and inorganic form is neurotoxic and may cause neurochemical changes similar to Alzheimer’s disease. One of the major sources of organic mercury (methyl mercury) is through the consumption of contaminated fish, and dental amalgams are a common source of inorganic mercury. Other sources of mercury include volcanoes, mercury mines, smelters, power plants, cement factories, and crematoria. Methylmercury is slowly demethylated to inorganic mercury, most of which is excreted through the bile and then into feces. Part of inorganic mercury will cross through the BBB and gets deposited in the brain, leading to decreased performance in areas of motor function and memory, and disruption of attention and verbal memory observed in adults on exposure to low mercury levels.24

Manganese Manganese toxicity is most commonly seen in welders, from inhalation of the toxic fumes. The other source for the general public is the manganese emitted by motor vehicles. Manganese may be transported across the BBB via diffusion, active transport, or divalent metal transport. Acute excessive manganese exposure can cause manganese madness syndrome, characterized by hallucinations, violent acts, and irritability.24 Workers in the welding industry are also at risk of Parkinson’s disease as a result of deposition of manganese in the deep gray matter nuclei. Most of these individuals show T1 hyperintensity on MRI in the basal ganglia and other brain regions reflecting manganese accumulation. Long-term exposure may cause impaired

manual dexterity and speed, short-term memory, and visual identification.

33.4.3 Carbon Monoxide Exposure Carbon monoxide poisoning is not uncommon. Most of the time, carbon monoxide poisoning is mild and goes unrecognized. Carbon monoxide has an affinity to hemoglobin 200 to 250 times higher than that of oxygen, effectively displacing oxygen from heme-binding sites and shifting the oxygen dissociation curve to the left. The severity of symptoms largely depends on the level of carboxyhemoglobin in the blood. In the acute stage, the patient has headache, mental status changes, dyspnea, syncope, lactic acidosis, hypotension, coma, and seizures. Up to 30% of patients with carbon monoxide poisoning exhibit some degree of cognitive decline, ranging from subtle impairments to dementia.25 Patients may have deficits in attention, concentration, executive function, visuospatial skills, verbal fluency, speed of information processing, and memory. Movement disorders, such as bradykinesia, masked facies, and rigidity are also seen. The neurotoxicity of carbon monoxide is thought to be secondary to a massive release of excitatory amino acids, particularly glutamate, which causes excessive calcium influx, free radical–mediated injury, and inhibition of antioxidant defenses. It also causes brain lipid peroxidation, which leads to the degradation of unsaturated fatty acids, reversible demyelination of CNS lipids, and vascular endothelial damage leading to neuronal cell death.25 In the acute stage, CT shows symmetric hypodensity in the medial portions of globus pallidus, with corresponding hyperintense signal on T2 and FLAIR images (▶ Fig. 33.11a). The caudate nucleus, putamen, and thalamus may show similar changes but less so than the globus pallidus. On DWI, these regions may show restricted diffusion from cytotoxic edema and acute tissue necrosis. Involvement of the brainstem and cerebellum may be a reflection of more severe poisoning. Late stages show atrophy of the affected parts, predominately the globus pallidus, and diffuse brain atrophy, with corresponding movement disorders and cognitive decline (▶ Fig. 33.11b).

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Fig. 33.11 Atrophy of globus pallidi after carbon monoxide poisoning. Axial (a) T2-weighted imaging and (b) diffusion-weighted images reveal increased signal intensity in the bilateral globus pallidi (arrows) in a 31-year-old woman with carbon monoxide poisoning. Follow-up scan after 2 years done for movement disorder shows severe atrophy (arrowheads) of the globus pallidi bilaterally on T1-weighted imaging (c).

33.4.4 Illicit Drugs and Medications Most cases if medication toxicity result in delirium, but cognitive decline or dementia is not uncommon, especially in elderly patients with long-term use of medications. It has been suggested that medication toxicity could account for up to 12% of all dementia cases.26 Psychoactive drugs are reported to be among the most common cases of druginduced cognitive impairment. Many commonly prescribed drugs for elderly patients have significant anticholinergic effects, leading to severe cognitive decline. Neuroimaging has a limited role in the diagnosis of dementia from drugs. Diagnosis in these cases is mostly by exclusion and clinical history and examination.

References [1] Smith JW, Evans AT, Costall B, Smythe JW. Thyroid hormones, brain function and cognition: a brief review. Neurosci Biobehav Rev 2002; 26: 45–60 [2] Mocellin R, Walterfang M, Velakoulis D. Hashimoto’s encephalopathy: epidemiology, pathogenesis and management. CNS Drugs 2007; 21: 799–811 [3] Song YM, Seo DW, Chang GY. MR findings in Hashimoto encephalopathy. AJNR Am J Neuroradiol 2004; 25: 807–808 [4] Belanoff JK, Gross K, Yager A, Schatzberg AF. Corticosteroids and cognition. J Psychiatr Res 2001; 35: 127–145 [5] Geffken GR, Ward HE, Staab JP, Carmichael SL, Evans DL. Psychiatric morbidity in endocrine disorders. Psychiatr Clin North Am 1998; 21: 473–489 [6] National Institutes of Health. Kidney Disease Statistics for the United States. National Kidney and Urologic Disease Information Clearinghouse. NIH Publication No. 12–3895. Available at: http://kidney.niddk.nih.gov/kudiseases/ pubs/kustats/KU_Diseases_Stats_508.pdf. Published June 2012. Accessed September 30, 2013 [7] Kang E, Jeon SJ, Choi SS. Uremic encephalopathy with atypical magnetic resonance features on diffusion-weighted images. Korean J Radiol 2012; 13: 808–811 [8] Yoon CH, Seok JI, Lee DK, An GS. Bilateral basal ganglia and unilateral cortical involvement in a diabetic uremic patient. Clin Neurol Neurosurg 2009; 111: 477–479 [9] Chen CL, Lai PH, Chou KJ, Lee PT, Chung HM, Fang HC. A preliminary report of brain edema in patients with uremia at first hemodialysis: evaluation by diffusion-weighted MR imaging. AJNR Am J Neuroradiol 2007; 28: 68–71

[10] Okechukwu CN, Lopes AA, Stack AG, Feng S, Wolfe RA, Port FK. Impact of years of dialysis therapy on mortality risk and the characteristics of longer term dialysis survivors. Am J Kidney Dis 2002; 39: 533–538 [11] Rizzo MA, Frediani F, Granata A, Ravasi B, Cusi D, Gallieni M. Neurological complications of hemodialysis: state of the art. J Nephrol 2012; 25: 170–182 [12] Hsieh TJ, Chang JM, Chuang HY et al. End-stage renal disease: in vivo diffusion-tensor imaging of silent white matter damage. Radiology 2009; 252: 518–525 [13] Albrecht J, Norenberg MD. Glutamine: a Trojan horse in ammonia neurotoxicity. Hepatology 2006; 44: 788–794 [14] Rovira A, Alonso J, Córdoba J. MR imaging findings in hepatic encephalopathy. AJNR Am J Neuroradiol 2008; 29: 1612–1621 [15] Miese F, Kircheis G, Wittsack HJ et al. 1H-MR spectroscopy, magnetization transfer, and diffusion-weighted imaging in alcoholic and nonalcoholic patients with cirrhosis with hepatic encephalopathy. AJNR Am J Neuroradiol 2006; 27: 1019–1026 [16] Zuccoli G, Pipitone N. Neuroimaging findings in acute Wernicke’s encephalopathy: review of the literature. AJR Am J Roentgenol 2009; 192: 501–508 [17] Zuccoli G, Santa Cruz D, Bertolini M et al. MR imaging findings in 56 patients with Wernicke encephalopathy: nonalcoholics may differ from alcoholics. AJNR Am J Neuroradiol 2009; 30: 171–176 [18] Jung YC, Chanraud S, Sullivan EV. Neuroimaging of Wernicke’s encephalopathy and Korsakoff’s syndrome. Neuropsychol Rev 2012; 22: 170–180 [19] Kalita J, Misra UK. Vitamin B12 deficiency neurological syndromes: correlation of clinical, MRI and cognitive evoked potential. J Neurol 2008; 255: 353– 359 [20] Korenke GC, Hunneman DH, Eber S, Hanefeld F. Severe encephalopathy with epilepsy in an infant caused by subclinical maternal pernicious anaemia: case report and review of the literature. Eur J Pediatr 2004; 163: 196–201 [21] American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th ed., Text Revison. Washington, DC: American Psychiatric Association. 2000 [22] Namura I. Alcoholic brain damage and dementia viewed by MRI, with special consideration on frontal atrophy and white matter damage in dyslipidemic patients. Psychogeriatrics 2006; 6: 119–127 [23] Mukamal KJ, Kuller LH, Fitzpatrick AL, Longstreth WT, Jr, Mittleman MA, Siscovick DS. Prospective study of alcohol consumption and risk of dementia in older adults. JAMA 2003; 289: 1405–1413 [24] Charleta L, Chapronb Y, Faller PC, Kirscha R,, Stoned AT, Baveyee PC. Neurodegenerative diseases and exposure to the environmental metals Mn, Pb, and Hg. Coord Chem Rev 2012; 3: 2147–2163 [25] Choi IS. Delayed neurologic sequelae in carbon monoxide intoxication. Arch Neurol 1983; 40: 433–435 [26] Starr JM, Whalley LJ. Drug-induced dementia. Incidence, management and prevention. Drug Saf 1994; 11: 310–317

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Part XIII

34 Inborn Errors of Metabolism

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Inborn Errors of Metabolism

34 Inborn Errors of Metabolism Sangam G. Kanekar and Dejan Samardzic Inborn errors of metabolism comprise a large and heterogeneous group of disorders. Many of these disorders affect the central nervous system (CNS) and cause white and gray matter damage and dysfunction. Although most of these disorders manifest in childhood, late-onset forms are common. The exact prevalence of these diseases causing cognitive decline is underestimated because many are unrecognized or misdiagnosed.1 More than 750 inherited metabolic disorders are described in the literature, making it impossible to encompass them all in a single chapter. We focus on the more common inborn errors of metabolism that may manifest with cognitive decline or dementia. We discuss the clinical features, enzyme deficiency, pathogenesis, and imaging findings of the most clinically relevant inborn disorders. Given the complexity of metabolic diseases, definitive classification has not been established and has traditionally been based on the specific organelle involved (▶ Table 34.1). Although these diseases have little in common with respect to pathogenesis, what they do share is progressive cognitive decline at a premature age. Imaging is often one of the first tests performed in patients who have inborn error of metabolism, and, therefore, radiologists are uniquely positioned to narrow the diagnostic possibilities and provide a guide for further biochemical workup. A combination of clinical features, imaging findings, and biochemical analysis of metabolites provides the final diagnosis, which can be confirmed via genetic testing.

34.1 Lysosomal Storage Disease Lysosomal disorders are a group of inherited metabolic disorders characterized by accumulation of nonmetabolized macromolecules leading to cellular dysfunction.2,3 Most of them are autosomal recessive, except for Fabry disease and mucopolysaccharidosis type II, which are X-linked. The initial clinical symptoms of various subtypes depend on the extent of CNS versus visceral involvement and the specific enzymatic defect. The more common ones are described below.

34.1.1 Metachromatic Leukodystrophy (Arylsulfatase A Deficiency) Metachromatic leukodystrophy (MLD) is most often due to deficiency in the lysosomal enzyme arylsulfatase A encoded by the ARSA gene on chromosome 22q13.4,5,6,7 Deficiency of this enzyme impairs desulfation of sulfatide, a precursor to cerebroside, and leads to accumulation of metachromatic sulfatides throughout the nervous system.4,8,9,10 This accumulation leads to a severe reduction in cerebroside content, leading to demyelination. Demyelination starts and is most intense in the periventricular area, extending into the internal capsule, cerebral peduncles, pyramidal tracts in the pons, and pyramids in the medulla, producing a white, chalky appearance of the white matter on gross examination.8 The name of this disorder comes from the deposition of metachromatically staining sulfatides in the white matter.11

The incidence of MLD is as high as 1 in 40,000.11,12,13 Depending on the age of onset, MLD is divided into four subtypes: congenital (rare), late infantile (40%, 6 months to 3 years), juvenile (40%, 4 to 16 years), and adult (20%, 16 to 30 years).11 Disease onset can be as late as the seventh decade of life.10 Clinical symptoms vary widely within each subtype, ranging from nonspecific symptoms, such as delusions, hallucinations, and disorganized behavior, to a gradual decline in the intellectual abilities and development of severe dementia.4,8 Progressive spastic paraparesis, cerebellar ataxia, extrapyramidal symptoms, and demyelinating polyneuropathy are secondary features.1,2,14,15 Computed tomography scan shows nonspecific, symmetric, diffuse hypodensity of cerebral white matter.8 Magnetic resonance imaging (MRI) is more sensitive than CT and shows symmetric confluent areas of hyperintensity on T2-weighted imaging within the periventricular white matter with sparing of the subcortical U-fibers early in the disease.4,10,11,16,17 (▶ Fig. 34.1). In later stages, there is involvement of the posterior limb of the internal capsule, pyramidal tracts, and cerebellar white matter with prominent atrophy of the corpus callosum.4,8 In the adult form of MLD, there is predominant involvement of the frontal white matter with diffuse cerebral atrophy.8 Proton magnetic resonance spectroscopy (MRS) reveals decreased N-acetyl aspartate (NAA), high cholinereflecting axonal damage and myelin breakdown, elevated myoinositol-reflecting gliosis, and occasionally elevated lactate.4,11 These changes might be seen before the abnormality is apparent on conventional MRI. Diagnosis of MLD is suggested by the presence of high urinary excretion of sulfatides and is confirmed by molecular analysis of the ARSA gene.10 Hematopoietic stem cell transplantation has been successful in treating the adult-onset form of the disease and remains the mainstay of treatment.7,18,19 Enzyme replacement has been attempted with less success.4

34.1.2 Globoid Cell Leukodystrophy (Krabbe’s Disease) Krabbe’s disease (KD) is due to deficiency of galactocerebroside β-galactosidase enzyme, which is coded on chromosome 14q31 in the GALC gene.20,21 Deficiency of this enzyme leads to accumulation of galactosylceramide and its metabolites within multinucleated macrophages, forming characteristic “globoid” cells.4,9 A combination of direct destruction by these reactive macrophages and toxic effects of accumulated galactosylceramide metabolites, chiefly psychosine, results in diffuse demyelination affecting both the central and peripheral nervous systems.4,9,10,20 The incidence of KD is around 1 in 100,000, with 10% appearing in relatively mild form in adulthood.10 The infantile (6 months to 3 years) and juvenile (4 to 10 years) forms are rapidly progressive and lethal. Clinical symptoms include intermittent fevers of unknown origin, irritability, organomegaly, and hypertonicity of the lower extremities resulting from

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Inborn Errors of Metabolism Table 34.1 Organelle-based classification of inborn errors of metabolism Lysosomal storage disorders

Defects in genes encoding myelin proteins

Metachromatic leukodystrophy

Pelizaeus-Merzbacher disease

Multiple sulfatase deficiency

18q syndrome

Krabbe’s disease Gangliosidosis Fabry’s disease Fucosidosis Mucopolysaccharidoses Neuronal ceroid-lipofuscinoses Peroxisomal disorders

Disorders of amino acid and organic acid metabolism

Peroxisome biogenesis defects

Phenylketonuria

Bifunctional protein deficiency

Glutaricaciduria type 1

Acyl-CoA oxidase deficiency

Propionic acidemia

X-linked adrenoleukodystrophy

Nonketotic hyperglycinemia

Adrenomyeloneuropathy

Maple syrup urine disease

Refsum’s disease

3-Hydroxy-3-methylglutaryl-CoA lyase deficiency Canavan’s disease Hydroxyglutaricaciduria Hyperhomocysteinemias Urea cycle defects

Mitochondrial dysfunction with leukoencephalopathy Mitochondrial myopathy encephalopathy, lactic acidosis, and strokelike episodes

Miscellaneous Sulfite oxidase deficiency

Myoclonic epilepsy with ragged red fibers

Galactosemia

Kearns-Sayre syndrome

Wilson’s disease

Leigh’s disease

Menkes’ disease

Carboxylase deficiency

Fragile X-associated tremor/ataxia syndrome

Cerebrotendinous xanthomatosis

Hypomelanosis of Ito Incontinentia pigmenti Alexander’s disease Megalencephalic leukoencephalopathy with subcortical cysts Congenital muscular dystrophies Vanishing white matter disease Leukoencephalopathy with calcifications and cysts Hypomyelination with atrophy of the basal ganglia/cerebellum Dentatorubral-pallidoluysian atrophy Amyloid angiopathy Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) Cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy (CARASIL) Adult autosomal dominant leukoencephalopathies

Nuclear DNA repair defects Cockayne’s syndrome Trichothiodystrophy with photosensitivity

pyramidal tract degeneration. Later symptoms include dementia, cerebellar ataxia, peripheral neuropathy, and loss of vision.20 Computed tomography reveals characteristic symmetric hyperattenuation in the bilateral thalami, corona radiata, and cerebellar dentate nuclei.20,21 This hyperdensity correlates to a high concentration of globoid cells, proliferating glia, and microcalcification seen histologically.8 Later stages of the dis-

ease demonstrate symmetric hypodensity in the deep white matter on CT.22 On MRI, characteristic hyperintensity is seen along the corticospinal tracts on T2 and fluid-attenuated inversion recovery (FLAIR)-weighted imaging. These changes might be symmetric, asymmetric, or even unilateral in distribution. Hyperintensity may also involve the periventricular and parieto-occipital white matter, with relative sparing of the

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Fig. 34.1 Metachromatic leukodystrophy. Axial (a) T2 and (a) fluid-attenuated inversion recovery images show diffuse hyperintensity in the cerebral white matter with sparring of U-fibers.

Fig. 34.2 Krabbe’s disease. Axial (a) fluidattenuated inversion recovery and (b) T2 images reveal hyperintensity in the bilateral periventricular and frontal lobes white matter. Hyperintensities near the peritrigonal regions are called flame-shaped lesions.

subcortical U-fibers, although these may be involved in later stages (▶ Fig. 34.2).7,8,20 Deep gray matter, cerebellar white matter, and generalized atrophy may be seen in chronic stages of the disease.22,23 Enlargement and enhancement of multiple cranial nerves, especially the optic nerve, have been reported and are thought to be due to a combination of myelin breakdown and inflammatory response. MRS shows elevated levels of myoinositol and choline, with a moderate increase in total creatine (Cr) (both Cr and phosphocreatine) and decreased total NAA.22, 23 Occasionally, there is a lactate peak. Diffusion tensor imaging (DTI) is useful in quantitative evaluation of white matter abnormalities and is more sensitive than T2-weighted imaging. Definitive diagnosis is established by demonstrating reduced galactosylceramidase activity in peripheral blood leukocytes or cultured fibroblasts and can be confirmed via gene testing.4,7,8,20 Cerebrospinal fluid (CSF) analysis is nonspecific.8,20 Treatment for the adolescent-adult form of KD is mainly supportive.1,10 Stem cell bone marrow transplantation has been effective in the treatment of infantile KD but only if given in the presymptomatic stage.24

34.1.3 Fabry’s Disease Fabry’s disease (FD) is an X-linked multisystem disorder that results from a deficiency of α-galactosidase A enzyme, which is responsible for the hydrolysis of terminal α-galactosyl residues from glycolipids and glycoproteins.10,25 Deficiency of this enzyme leads to the accumulation of neutral glycosphingolipids in the vascular endothelium, smooth muscles, and neurons. This lipid deposition in the vascular endothelium leads to thickening and obstruction of vessel walls resulting in infarcts. It also accumulates in the brain parenchyma predominantly in the amygdala, the leptomeninges, and the choroid stroma. Besides the CNS, lipid deposition occurs in the epithelial cells of the cornea, in renal glomeruli and tubuli, and in cardiac muscle fibers. Early manifestations of FD include episodic extremity pain and a telangiectatic scaly maculopapular rash.25 Neurologically, patients have transient ischemic attacks or strokes in smallartery territories or in the vertebrobasilar circulation.8,10 Repeated episodes of small-vessel infarcts lead to the

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Inborn Errors of Metabolism dominantly involving periventricular white matter and basal ganglia. Definitive diagnosis of FD is established by demonstration of a deficiency of α-galactosidase A activity in plasma, leukocytes, urine, cultured skin fibroblasts, or hair roots. Enzyme replacement therapy is used to slow the progression of disease; treatment is otherwise supportive.10

34.1.4 Mucopolysaccharidoses

Fig. 34.3 Fabry’s disease. Axial T1-weighted image shows hyperintensity in the thalami bilaterally resulting from calcification.

development of early dementia. As the disease progresses, various cardiovascular symptoms and congestive heart failure dominate. The most characteristic finding on neuroimaging is hyperintensity of the pulvinar nuclei (pulvinar sign) on T1weighted imaging (▶ Fig. 34.3).26 Corresponding areas demonstrate low signal on gradient-recalled echo or susceptibility-weighted imaging (SWI) and hyperdensity on CT consistent with calcification. Similar findings can also be seen in the deep gray matter nuclei but are less specific. In addition, widespread hyperintensities are seen on T2- and FLAIRweighted images as a result of small-vessel involvement pre-

Mucopolysaccharidoses (MPS) are heritable lysosomal storage disorders caused by the deficiency of specific lysosomal enzymes involved in the degradation of mucopolysaccharides (glycosaminoglycans). The MPS are classified into six groups: MPS I (Hurler), MPS II (Hunter), MPS III (Sanfilippo), MPS IV (Morquio), MPS VI (Maroteaux Lamy), and MPS VII (Sly). MPS V and MPS VIII are no longer used.27,28 All MPS are inherited by an autosomal recessive transmission, except Hunter’s disease, which is an X-linked disease. Detailed discussion of MPS is beyond the scope of this chapter. Clinical signs and symptoms vary widely with the type of MPS and severity of the deficiency of the enzyme. Definitive diagnosis of the MPS is established by enzyme assays. Neuroimaging changes seen in MPS are due mainly to increases in perivascular connective tissue and neuronal storage of mucopolysaccharides. In the early stages, imaging may be normal, but in later stages, T2-weighted images may reveal multiple small, sharply defined CSF intensity lesions dispersed throughout the white matter but most prominent in the parietal and occipital lobes and in the corpus callosum (▶ Fig. 34.4). These cystic areas have a radial orientation from the subependymal region toward the cortex and represent dilated perivascular spaces filled with MPS and CSF on pathology. Other imaging features include cortical atrophy (▶ Fig. 34.5) and multifocal hyperintense areas on T2 and FLAIR images.29,30,31 These areas can become extensive and confluent and might resemble leukodystrophy. Besides white matter changes, compression of the medulla or cervical cord may be identified on sagittal T1- and T2-weighted images, believed to be due to atlantoaxial subluxation or diffuse thickening of the cervical dura caused by deposition of collagen and MPS. It is important to diagnose this complication to avoid myelopathic changes in the adjacent cord.

Fig. 34.4 Mucopolysaccharidoses (MPS). (a) Axial T2 and (b) sagittal T1 weighted images show multiple well-defined CSF intensity focal areas (fat arrows) in the cerebral parenchyma and corpus callosum. Axial T2-weighted image also shows bilateral frontal lobe atrophy (arrows).

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Fig. 34.6 Neuronal ceroid-lipofuscinosis (NCL). Coronal T1-weighted image shows gross prominence of convexity sulci, sylvian fissures, and severe thinning of the cortex. There is moderate dilation of the ventricular system.

Fig. 34.5 Mucopolysaccharidoses (Sanfilippo). Axial computed tomography scan image of a 10-year-old child shows generalized prominence of convexity sulci and dilation of the ventricular system, advanced for the age of the patient.

34.1.5 Neuronal Ceroid-Lipofuscinosis Neuronal ceroid-lipofuscinosis (NCL) is one of the most common neurodegenerative disorders of childhood, with an incidence rate of 1 in 25,000.8 NCL is due to impairment of palmitoyl protein thioesterase1 (PPT1), which leads to accumulation of ceroid lipopigment in lysosomes of neurons. This accumulation leads to neurotoxicity and neuronal death. Although NCL is a lysosomal storage disorder, unlike the classic lysosomal storage diseases, it accumulates proteins and not lipids, thus making NCL a proteinosis rather than a lipidosis. On a molecular basis and according to the genetic defects, nine subtypes of NCL have been identified. At present, at least six genes (CLN 1, 2, 3, 5, 6, and 8) have been implicated in relation to these nine types of NCL.8 The first symptom of an affected child is failure of vision.8 Other symptoms may include muscular hypotonia, microcephaly, ataxia, choreoathetosis, epilepsy, irritability, and cognitive decline. The pathologic hallmark of NCL is neuronal lipofuscin storage and neuronal loss, leading to severe atrophy of the brain with moderate to severe loss of myelin and significant astrogliosis. Both CT and MR of the brain show severe brain atrophy, cerebral more severe than cerebellar8 (▶ Fig. 34.6). The periventricular white matter and posterior limb of the internal capsule may also show hyperintensity on T2-weighted images. These changes are thought to be due to a combination of delayed and

disturbed myelination, progressively severe gliosis, and some myelin loss. In the early stage of the disease, strikingly low signal is seen in the thalamus and globus pallidus, which in the later stages demonstrate severe atrophy. Positron emission tomography scans show a severe reduction in fluorodeoxyglucose uptake in the cortical and subcortical regions bilaterally. Similar changes might also be seen in the cerebellum.

34.2 Mitochondrial Dysfunction Mitochondrial disorders are the most common inborn errors of metabolism, with an estimated incidence of 1 per 10,000 live births. These disorders are due to dysfunction of pyruvic acid metabolism, the citric acid cycle, or mitochondrial respiratory chain. Thus, tissues and organs that are highly dependent on aerobic metabolism (brain and muscle) are preferentially involved. Mitochondrial disorders might be caused by defects in either nuclear DNA (nDNA) or mitochondrial DNA (mtDNA).32 nDNA defects might be inherited in an autosomal recessive or autosomal dominant manner, whereas mtDNA defects are propagated by maternal inheritance. Clinically, mitochondrial diseases are multiple-organ disorders with prominent brain and muscle dysfunction. Features include ptosis, external ophthalmoplegia, proximal myopathy and exercise intolerance, cardiomyopathy, sensorineural deafness, optic atrophy, pigmentary retinopathy, and diabetes melli-

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Inborn Errors of Metabolism tus. The CNS findings are often fluctuating encephalopathy, seizures, dementia, migraine, strokelike episodes, ataxia, and spasticity. Chorea and dementia might also be prominent features. Imaging hallmarks of these disorders include deep gray matter involvement and diffusely elevated lactate peak on MRS, the latter of which is not seen with nonmitochondrial causes of dementia.22,23 Most are treated symptomatically. The characteristic histologic finding is accumulation of abnormal mitochondria that can be seen in appropriately stained muscle biopsy specimens as ragged red fibers.9,15

34.2.1 Leigh’s Disease (Subacute Necrotizing Encephalomyelopathy) Most prominent inborn errors in Leigh’s disease include pyruvate dehydrogenase complex deficiency and defects in the mitochondrial electron transport chain, namely, complexes I, II, IV, and V.7,8,22 Inheritance is most commonly autosomal recessive, although maternal and X-linked transmissions have been identified. It is two to three times more common in males.22 Although the onset of symptoms is most often during infancy, both juvenile and adult forms also exist. The adult form may manifest with dementia, seizures, neuropathy, ataxia, and ophthalmoplegia.33 Imaging appearance largely varies with the stage of the disease. In the acute stage, lentiform, caudate, subthalamic and dentate nuclei, substantia nigra, tegmentum of pons, cerebral peduncles, periaqueductal gray, red nucleus, medulla, and other brainstem structures may show symmetric edematous changes with hyperintensity on T2 or FLAIR imaging (▶ Fig. 34.7). Rarely, edematous changes may be asymmetric. Diffusion-weighted imaging (DWI) demonstrates restricted diffusion (representing cytotoxic edema) in the involved regions. As the disease progresses, deep gray matter nuclei might become atrophic with cystic degeneration (▶ Fig. 34.8). In severe and advanced cases, there may be involvement of cerebral and cerebellar white matter. MRS typically demonstrates an abnormal lactate peak with a decrease in the NAA:Cr and an increase in the choline:Cr ratios predominantly in the basal ganglia and is useful in differentiating other nonmitochondrial processes.22 Increased levels of lactate in the blood and CSF, typical MRI

Fig. 34.7 Acute stage of Leigh’s disease. Axial T2-weighted imaging shows bilateral symmetric hyperintensity (arrowheads) in the putamen and caudate head (corpus striatum).

findings, and enzyme assays on fibroblasts all help to establish the diagnosis.7,15

34.2.2 Kearns-Sayre (Chronic Progressive External Ophthalmoplegia) Kearns-Sayre syndrome is due to macrodeletion of mitochondrial DNA-encoding components of the electron transport chain.15 It is sporadic and characterized chiefly by external

Fig. 34.8 Chronic stage of Leigh’s disease. (a) Axial T2- and (b) coronal T1-weighted images reveal symmetric atrophy of the putamen with cystic degeneration (arrowheads) in a 12-year-old boy who had movement disorders.

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Fig. 34.9 Mitochondrial myopathy, encephalopathy, lactic acidosis, and strokelike episodes (MELAS). (a) Axial fluid-attenuated inversion recovery (FLAIR) image shows areas of gliosis and encephalomalacia in the bilateral temporal lobes from chronic infarctions (arrows). Coronal T1-weighted imaging (b,c) shows atrophy of the right hippocampus (arrowhead) and temporal lobe (arrow).

ophthalmoplegia and retinitis pigmentosa.8,9,22 A minority of patients have onset in their second or third decade of life with dementia.9,23 Other symptoms include cardiac conduction defects, ataxia, endocrine dysfunction, paresis, neuropathy, pyramidal symptoms, and elevated CSF protein.22,23 Computed tomographic scan often reveals calcium deposits in the globus pallidus and caudate nucleus with diffuse hypodensity of the cerebral white matter and progressive atrophy.8 On MRI, T2-weighted images show bilateral symmetric hyperintensity in the globus pallidus, caudate nucleus, substantia nigra, and thalamus.23 DWI shows high signal in the involved areas, as is usual in vacuolating myelinopathies.23 Histologically, these findings correspond to spongiosis, gliosis, and perivascular calcifications in the involved areas.8 Patchy hyperintense areas are also seen in the subcortical U-fibers, with relative sparing of the periventricular white matter.7,8,23 As in most mitochondrial disorders, proton MRS shows increased lactate and decreased NAA in the affected white matter. The combination of subcortical white matter disease combined with deep gray matter involvement (globi pallidi and thalami) is characteristic for Kearns-Sayre syndrome. Diagnosis, however, is based on clinical symptoms and age of onset. Some require increased CSF protein, cerebellar ataxia, and heart block as criteria for diagnosis.22 DNA analysis on leukocytes is confirmatory.7

34.2.3 Mitochrondrial Myopathy, Encephalopathy, Lactic Acidosis, and Strokelike Epislodes Mitochondrial myopathy, encephalopathy, lactic acidosis, and strokelike episodes (MELAS) is a mitochondrial disorder resulting from decreased levels in all mitochondrially encoded proteins most often secondary to defective leucine tRNA.34 Defective oxidative phosphorylation results in metabolic strokes that are caused by an area of brain exceeding its respiratory availability rather than by thromboembolic disease.7 Accord-

ingly, angiographic studies do not demonstrate significant vascular occlusions. Most patients have onset in the second decade of life. Signs and symptoms include visual symptoms, throbbing headache, nausea, vomiting, seizures, and multiple strokelike events.8,15 Parietal and occipital lobes are common targets in MELAS, which clinically manifests with hemiparesis and hemianopsia or cortical blindness. As a result of multifocal infarctions and loss of gray and subjacent white matter, these patients may develop significant cognitive impairment or dementia. The strokelike events in MELAS predominantly involve the gray matter, basal ganglia, and, less likely, the underlying white matter (▶ Fig. 34.9). For unknown reasons, the occipital and posterior temporal lobes are preferentially involved. The affected area demonstrates swelling and hyperintensity on T2 and FLAIR images with restricted diffusion on DWI.34 These ischemic lesions are small, often multiple, asymmetric, and importantly do not follow a vascular territorial distribution.8,9 Infarctions might also involve the thalamus, basal ganglia, and brainstem. MRI shows “migrating infarcts” with new lesions appearing next to resolving ones. Over time, this leads to progressive atrophy with enlargement of the ventricular system and subarachnoid spaces. In addition, there might be diffuse cerebellar involvement and calcification of the globus pallidus and caudate nucleus. Muscle biopsy may show cyclooxygenasepositive ragged red fibers.15 The definite diagnosis is reached by molecular testing demonstrating mutations in the mitochondrial tRNA leucine gene (MTTL1).

34.2.4 Myclonic Epilepsy with Ragged Red Fibers Myoclonic epilepsy with ragged red fibers (MERRF) is an extremely rare syndromic mitochondrial disorder that is most commonly caused by a mutation in mitochondrial gene A8344G, which accounts for more than 80% of the cases.7,8 This encodes for mitochondrial transfer RNA, resulting in defective

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Inborn Errors of Metabolism protein synthesis. The classic features of MERRF include myoclonus, epileptic seizures, ataxia, and ragged red fibers on muscle biopsy seen as late as 40 years of age.9 Less consistent clinical features include dementia, hearing loss, lactic acidosis, short stature, exercise intolerance, cardiac defects, eye abnormalities, and speech impairment. Most of the MERRF cases are maternally inherited because of a mutation within the mitochondria.34 Imaging patterns of MERRF have not been as established as with other mitochondrial disorders. Basal ganglia calcification and atrophy, particularly in the globus pallidus, may be seen on CT and MRI. Biochemical aberrations may include elevations of serum pyruvate and lactate and reduced activities of complexes I and IV.7 Diagnosis is established via genetic testing.

34.3 Peroxisomal Defects 34.3.1 Adrenomyeloneuropathy (Adult-Onset Adrenoleukodystrophy) Adrenomyeloneuropathy (AMN) is an X-linked recessive disorder resulting in accumulation of very long chain fatty acids (VLCFAs) throughout the body. AMN is thought to represent the adult-onset form of X-linked adrenoleukodystrophy.4 Both arise from mutations in the gene ABCD1 on chromosome Xq28, which encodes ALDP, a peroxisomal adenosine triphosphatebinding cassette protein that functions in transmembrane transport of VLCFAs during oxidation.35 A defect in this step is characterized by elevation of VLCFA, predominantly in the CNS white matter, peripheral nerves, adrenal cortex, and testes. Within the nervous system, elevated VLCFA levels result in inflammatory demyelination.22 Predominately, AMN affects men in the third or fourth decade of life with early-onset dementia, progressive spastic

paraparesis, and urinary incontinence.35 Adrenal insufficiency, cerebellar ataxia, and peripheral neuropathy (sensory and autonomic) may also be seen.8 Pathologic studies in AMN demonstrate bilateral, usually symmetric, long-tract degeneration in the spinal cord, with the most prominent involvement of the lumbar corticospinal and the cervical dorsal tracts with or without cerebral demyelination.22 If neurologic involvement is confined to the spinal cord and peripheral nerves, MRI of the brain can be normal.8 When the brain is involved, however, the abnormality is limited mainly to cerebellar white matter and brainstem corticospinal tracts. There is no inflammatory zone in AMN; therefore, there is no enhancement on postcontrast scans. Rarely, imaging findings in the brain are somewhat similar to milder forms of adrenoleukodystrophy, with symmetric T2 hyperintensity in the pyramidal tracts, posterior limb of the internal capsule, cerebellar white matter, and splenium or genu of the corpus callosum (▶ Fig. 34.10).36 Diagnosis of AMN is established by demonstrating low NAA in the urine or via enzyme assay on fibroblasts.7

34.4 Disorders of Neurotransmitter Metabolism 34.4.1 Fragile X-Associated Tremor/ Ataxia Syndrome Fragile X–associated tremor/ataxia syndrome (FXTAS) is an Xlinked dominant disorder closely related to fragile X syndrome and resulting in progressive cognitive and cerebellar dysfunction.4 In fragile X syndrome, full trinucleotide repeat mutation of the FMR1 gene on chromosome X involving more than 200 CGG repeats causes defective or absent production of fragile X mental retardation protein (FMRP), an enzyme that has

Fig. 34.10 Adrenoleukodystrophy (ALD). Axial (a) T2 and (b) fluid-attenuated inversion recovery (FLAIR) images show bilateral symmetric hyperintensity involving the corticospinal tracts (back arrowheads) and occipital and temporal white matter bilaterally (black arrows). (c) Postcontrast T1-weighted image shows lead enhancement in the area of active demyelination (arrows). Also note enhancement of the corticospinal tracts (black arrowheads).

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Inborn Errors of Metabolism key roles in synapse function.37,38 In FXTAS, there is a smaller number of involved trinucleotides in the premutation range of 50 to 200 CGG repeats.37 This offset in the number of trinucleotide repeats causes excess production of FMR1 mRNA, which exerts gradual neurotoxic effects by sequestering and perturbing the function of nuclear proteins.37 Unlike fragile X syndrome, patients with FXTAS develop symptoms later in life. Given its variable penetrance and underdiagnosis, the number of affected individuals is difficult to estimate. The disease usually manifests between the ages 50 and 80 and is more common in men.4,37 Progressive cognitive and cerebellar dysfunction result in early dementia, intention tremors, and ataxia. Less characteristic features include autonomic dysfunction, including hypertension, bowel and bladder dysfunction, and impotence.39 Parkinsonian features, peripheral neuropathy, and personality changes may be seen. Magnetic resonance imaging plays an important role in the diagnosis of FXTAS. The most characteristic findings are symmetric hyperintense lesions on FLAIR-/T2-weighted images in the middle cerebellar peduncles (MCP), known as the MCP sign.37 Signal abnormalities may also be seen in the cerebellar and deep periventricular white matter. In addition, there may be associated cerebral and cerebellar atrophy. Diagnosis is established from combination of imaging findings and clinical features. Treatment of FXTAS is mainly supportive.

cysteinemia), defects in AA transport (cystinuria, Lowe’s syndrome, Hartnup’s disease, iminoglycinuria), and unknown errors of metabolism.40 The final end-product of AA oxidation is ammonia, which in higher concentration is neurotoxic to the cells as well as to the neurotrasmitters. One of the major functions of the urea cycle is to detoxify this ammonia produced from catabolism of AA. Any defect in the urea cycle will cause hyperammonemia or elevated plasma glutamine, which leads to severe nerve cell damage. Damage to the brain cells and its functions leading to various neurologic symptoms, including cognitive decline, largely depends on the type and the level of concentration of the toxic substrate accumulated.40 These defects may be in the urea cycle or in the degradation of the specific amino acid–like glycine in nonketotic hyperglycinemia and homocysteine in hyperhomocysteinemias. Maple syrup urine disease is caused by a deficiency of the branched chain 2-keto acid dehydrogenase, leading to abnormal oxidative decarboxylation of the branched chain AAs (BCAAs) leucine, isoleucine, and valine. The enzyme defect results in marked increases in the branched chain 2-keto acids in brain cells, causing neurotoxicity (▶ Fig. 34.11)

34.6 Miscellaneous Disorders

34.5 Amino Acid Disorders

34.6.1 Vanishing White Matter Disease (Childhood Ataxia with Central Nervous System Hypomyelination)

Aminoaciduria is defined as high levels of amino acids in the urine, which may be due to either inborn errors of metabolism or chronic liver failure or renal disorders.40 Inborn errors of metabolism related to amino acids are rare. They might be due to either inherited deficiency or altered function of an enzyme or transport system that mediates the disposition of a particular amino acid (AA). They can be classified according to disorders of AA metabolism (phenylketonuria, histidinemia, hyperprolinemia, tyrosinemia, nonketotic hyperglycinemia, and hyperhomo-

Vanishing white matter disease (VWMD) is an autosomal recessive disorder that arises from defects in translation initiation factor eIF2B, which consists of five nonidentical subunits encoded by different genes (EIF2B1, EIF2B2, EIF2B3, EIF2B4, and EIF2B5) located on different chromosomes (12q24.3, 14q24, 1p34.1, 2p23.3, and 3q27, respectively).41 Mutations in any of these genes cause dysfunction of eIF2B, inadequate protein synthesis, and cell death. On pathology, the cerebral white matter shows myelin pallor, thin myelin sheaths, vacuolation, myelin

Fig. 34.11 Aminoaciduria (maple syrup urine disease). (a,b) Axial T2-weighted images show extensive bilateral edematous changes involving thalamus, globus pallidus, caudate nuclei (arrows), and frontal lobe white matter. (c) Coronal T1-weighted imaging shows diffuse hypointensity in the temporal lobe white matter (arrowheads) with mild bilateral hippocampal atrophy.

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Inborn Errors of Metabolism

Fig. 34.12 Vanishing white matter disease. (a) Axial T2- and (b) coronal T1-weighted images show diffuse hyperintensity involving the supratentorial white matter with severe loss of white matter.

loss, cystic change, and rarely active demyelination. Gray matter is unaffected. The incidence of VWMD is around 1 in 40,000.40 Depending on the age of onset, VWMD is divided into three forms: classic form (2 to 6 years of age), severe infantile form (3 to 9 months), and late-onset form (10 to 21 years) of age.40,41 These patients are completely normal until adulthood, when they show psychiatric symptoms and slowly develop signs and symptoms of dementia.40 Behavioral changes may precede cognitive decline by several years. On MRI, T2- and FLAIR-weighted images show symmetric and diffuse abnormal areas of hyperintensity on T2 or FLAIR images (▶ Fig. 34.12).40 As the disease progresses, there is liquefaction of the affected white matter, which is replaced by fluidcontaining cysts. Patchy areas of normal white matter might be seen in a background of diffusely abnormal white matter, which gives a “stripelike” appearance on sagittal images following the corona radiata.23 In the late stages, dilation of the ventricles occurs as a result of diffuse loss of white matter. MRS shows a decrease in the heights and finally the disappearance of all the major peaks, namely, NAA, Cr, and choline. Phosphorus MRS shows a reduction in nucleoside triphosphate and inorganic phosphate with elevated phosphocreatine, suggesting a change in the energy state of the residual cells in the cerebral white matter.42

References [1] Sedel F. Inborn errors of metabolism in adults: a diagnostic approach to neurological and psychiatric presentations. In: Saudubray JM, Berghe G, Walter JH, eds. Inborn Metabolic Diseases: Diagnosis and Treatment. Berlin, Germany: Springer; 2012:56–74 [2] Wraith JE. Lysosomal disorders. Semin Neonatol 2002; 7: 75–83 [3] Valk J, van der Knaap MS. Lysosomes and lysosomal disorders. In: Magnetic Resonance of Myelination, and Myelin Disorders. Berlin, Germany: Springer; 2005:66–73 [4] Barkhof F, Fox NC, Basto-Leite AJ, Scheltens P. Disorders mainly affecting white matter. In: Neuroimaging in Dementia. New York: Springer; 2011:177–242 [5] van der Knaap MS, Valk J, de Neeling N, Nauta JJ. Pattern recognition in magnetic resonance imaging of white matter disorders in children and young adults. Neuroradiology 1991; 33: 478–493 [6] Inglese M, Nusbaum AO, Pastores GM, Gianutsos J, Kolodny EH, Gonen O. MR imaging and proton spectroscopy of neuronal injury in late-onset GM2 gangliosidosis. AJNR Am J Neuroradiol 2005; 26: 2037–2042

[7] Brodsky MC. Neuro-ophthalmologic manifestations of neurodegenerative disease in childhood. In: Pediatric Neuro-Ophthalmology. 2nd ed. New York: Springer; 2010:465–501 [8] Kanekar S, Gustas C. Metabolic disorders of the brain: part I. Semin Ultrasound CT MR 2011; 32: 590–614 [9] Kovnar EH. Manifestations of metabolic, toxic, and degenerative diseases In: Holmes GL, Moshe SL, Jones Jr HR., eds. Clinical Neurophysiology of Infancy, Childhood, and Adolescence. Philadelphia: Elsevier; 2006:327–352 [10] Sedel F, Tourbah A, Fontaine B et al. Leukoencephalopathies associated with inborn errors of metabolism in adults. J Inherit Metab Dis 2008; 31: 295–307 [11] Valk J, van der Knaap MS. Metachromatic leukodystrophy. In: Magnetic Resonance of Myelination, and Myelin Disorders. Berlin, Germany: Springer; 2005:74–81 [12] Becker LE. Lysosomes, peroxisomes and mitochondria: function and disorder. AJNR Am J Neuroradiol 1992; 13: 609–620 [13] Kendall BE. Disorders of lysosomes, peroxisomes, and mitochondria. AJNR Am J Neuroradiol 1992; 13: 621–653 [14] Schoenberg MR, Scott JG. Cognitive decline in childhood or young adulthood. In: The Little Black Book of Neuropsychology: A Syndrome-Based Approach. New York, NY: Springer; 2011:839–861 [15] Granata T. Metabolic and degenerative disorders. In: Stefan H, Theodore WH, eds. Handbook of Clinical Neurology, Vol. 108 (3rd series). Epilepsy, part II. China: Elsevier; 2012:485–511 [16] Faerber EN, Melvin J, Smergel EM. MRI appearances of metachromatic leukodystrophy. Pediatr Radiol 1999; 29: 669–672 [17] Sener RN. Metachromatic leukodystrophy: diffusion MR imaging findings. AJNR Am J Neuroradiol 2002; 23: 1424–1426 [18] Kidd D, Nelson J, Jones F et al. Long-term stabilization after bone marrow transplantation in juvenile metachromatic leukodystrophy. Arch Neurol 1998; 55: 98–99 [19] van Karnebeek CD, Stockler S. Treatable inborn errors of metabolism causing intellectual disability: a systematic literature review. Mol Genet Metab 2012; 105: 368–381 [20] Bajaj NPS, Waldman A, Orrell R, Wood NW, Bhatia KP. Familial adult onset of Krabbe’s disease resembling hereditary spastic paraplegia with normal neuroimaging. J Neurol Neurosurg Psychiatry 2002; 72: 635–638 [21] Valk J, van der Knaap MS. Globoid cell leukodystrophy (Krabbe disease). In: Magnetic resonance of myelination and myelin disorders. Berlin, Germany: Springer; 2005:87–95 [22] Phelan JA, Lowe LH, Glasier CM. Pediatric neurodegenerative white matter processes: leukodystrophies and beyond. Pediatr Radiol 2008; 38: 729–749 [23] Barkhof F, Valk J, Fox NC, Scheltens P. Disorders primarily affecting white matter. In: Magnetic Resonance in Dementia. Germany: Springer;2002:139– 227 [24] Krivit W, Shapiro EG, Peters C et al. Hematopoietic stem-cell transplantation in globoid-cell leukodystrophy. N Engl J Med 1998; 338: 1119–1126 [25] Valk J, van der Knaap MS. Fabry disease. In: Magnetic Resonance of Myelination, and Myelin Disorders. Berlin, Germany: Springer; 2005:112–118

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Inborn Errors of Metabolism [26] Moore DF, Ye F, Schiffmann R, Butman JA. Increased signal intensity in the pulvinar on T1-weighted images: a pathognomonic MR imaging sign of Fabry disease. AJNR Am J Neuroradiol 2003; 24: 1096–1101 [27] Neufeld E, Musner J. The mucopolysaccharidoses. In: Scriver C, Beudet A, Sly W et al, eds. The Metabolic and Molecular Bases on Inherited Disease, 8th ed. New York: McGraw-Hill; 2001:3421–3452 [28] Valk J, van der Knaap MS. Mucopolysaccharidoses. In: Magnetic Resonance of Myelination, and Myelin Disorders. Berlin, Germany: Springer; 2005:112– 118 [29] Barone R, Parano E, Trifiletti RR, Fiumara A, Pavone P. White matter changes mimicking a leukodystrophy in a patient with mucopolysaccharidosis: characterization by MRI. J Neurol Sci 2002; 195: 171–175 [30] Murata R, Nakajima S, Tanaka A et al. MR imaging of the brain in patients with mucopolysaccharidosis. AJNR Am J Neuroradiol 1989; 10: 1165–1170 [31] Parsons VJ, Hughes DG, Wraith JE. Magnetic resonance imaging of the brain, neck and cervical spine in mild Hunter’s syndrome (mucopolysaccharidoses type II). Clin Radiol 1996; 51: 719–723 [32] DiMauro S, Moraes CT. Mitochondrial encephalomyopathies. Arch Neurol 1993; 50: 1197–1208 [33] Dermaut B, Seneca S, Dom L et al. Progressive myoclonic epilepsy as an adultonset manifestation of Leigh syndrome due to m.14487T > C. J Neurol Neurosurg Psychiatry 2010; 81: 90–93

[34] Barkovich AJ, Good WV, Koch TK, Berg BO. Mitochondrial disorders: analysis of their clinical and imaging characteristics. AJNR Am J Neuroradiol 1993; 14: 1119–1137 [35] Moser HW. Adrenoleukodystrophy: phenotype, genetics, pathogenesis and therapy. Brain 1997; 120: 1485–1508 [36] Barkovich AJ, Ferriero DM, Bass N, Boyer R. Involvement of the pontomedullary corticospinal tracts: a useful finding in the diagnosis of X-linked adrenoleukodystrophy. AJNR Am J Neuroradiol 1997; 18: 95–100 [37] Berry-Kravis E, Abrams L, Coffey SM et al. Fragile X-associated tremor/ataxia syndrome: clinical features, genetics, and testing guidelines. Mov Disord 2007; 22: 2018–2030, quiz 2140 [38] Weiler IJ, Spangler CC, Klintsova AY et al. Fragile X mental retardation protein is necessary for neurotransmitter-activated protein translation at synapses. Proc Natl Acad Sci U S A 2004; 101: 17504–17509 [39] Hagerman PJ, Hagerman RJ. Fragile X-associated tremor/ataxia syndrome (FXTAS). Ment Retard Dev Disabil Res Rev 2004; 10: 25–30 [40] Kanekar S, Verbrugge J. Metabolic disorders of the brain: part II. Semin Ultrasound CT MR 2011; 32: 615–636 [41] van der Knaap MS, Barth PG, Gabreëls FJ et al. A new leukoencephalopathy with vanishing white matter. Neurology 1997; 48: 845–855 [42] Sijens PE, Boon M, Meiners LC, Brouwer OF, Oudkerk M. 1 H chemical shift imaging, MRI, and diffusion-weighted imaging in vanishing white matter disease. Eur Radiol 2005; 15: 2377–2379

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Part XIV Cerebellar Degeneration and Dysfunction

35 Normal Anatomy and Pathways of Cerebellum

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36 Imaging of Cerebellar Degeneration and Cerebellar Ataxia

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35 Normal Anatomy and Pathways of Cerebellum Sangam G. Kanekar and Jeffrey D. Poot For centuries, the cerebellum was thought to be purely a motor control device, with its major role in motor behavior and coordination. Over the last two decades, however, it has been increasingly recognized that the cerebellum contributes to cognitive processing and emotional control. Although this role has been pointed out by various anatomists and physiologists in the past, it was further solidified with advancement of molecular and functional imaging, which includes positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). Functional neuroimaging showed that the various anatomical regions of the cerebellum were activated during higher-level tasks. The posterior lobe and lobule VI of the cerebellum seem to be involved in higher-level tasks, such as verbal and working memory as well as executive functions. Left frontal and parietotemporal cortex hypoperfusion are interpreted to be associated with disruption of cerebellocortical connections, which can lead to cognitive problems. Embryology and anatomy of the cerebellum remain challenging because of its various nuclei and connections; however, understanding the anatomy and various tract connections has become of vital importance in the era of molecular and cellular imaging. This chapter discusses the embryology, gross anatomy with vascularity, histopathology, and nuclear and tract anatomy, followed by state-of-the-art cross-sectional imaging.

35.1 Embryology The cerebellum is first seen at 5 to 6 weeks as rhombic lips of the thinned roof of the fourth ventricle of the hindbrain.1,2 Rhombic lips are formed when the dorsolateral aspects of the alar plates bend medially. They compress craniocaudally to form the cerebellar plate. The cerebellar plate in a 12-week embryo demonstrates the midline vermis and the two lateral cerebellar hemispheres. The transverse fissure separates the nodule from the vermis and the lateral flocculus from the hemispheres. By the second month, this cerebellar plate consists of neuroepithelial (inner germinal) layer, mantle and marginal layers. By 19 weeks, neuroblasts dividing in the inner germinal layer migrate to the surface, proliferate, and form the external granular (germinal) layer.1,3,4 Ultimately, these cells form a proliferative zone and continue to divide. The outer germinal layer produces basket, granule, and stellate cells. The inner germinal layer produces the Purkinje and Golgi cells and the cells of the deep cerebellar nuclei (dentate, globose, emboliform, and fastigii). Radial glial cells extend from the ventricular layer to the surface of the marginal layer and guide the migration of the developing neurons. Neuroblasts of both dividing cell layers produce glia.

35.2 Gross Anatomy The anatomy of the cerebellum can be defined by both its structure and function. The largest structure in the posterior fossa is the cerebellum. The cerebellum has a midline vermis with two lateral cerebellar hemispheres. Multiple fissures are found

within the cerebellum, with the primary fissure separating the cerebellum into anterior and posterior lobes.5 Additionally, a posterolateral fissure along the ventral inferior surface separates the posterior lobe from the flocculonodular lobe. See ▶ Table 35.1 for further details of the names of the lobules, divisions, and subdivisions (▶ Fig. 35.1).5,6 Along the inferior surface are cerebellar tonsils. Along the surface of the cerebellum, the small ridges that course medial to lateral are called folia. Three white matter peduncles attach to the dorsal aspect of the pons and medulla (▶ Table 35.1). Regions of the cerebellum can be organized according to their function. Cerebellar pathways are involved with the articulation of speech, respiratory movements, and motor learning. The vermis is involved with medial motor systems of the trunk.5,7,8 The flocculonodular lobes are involved with balance and eye movements. The lateral cerebellar hemispheres involve the motor systems of the distal appendicular muscles and are also involved with motor planning. The three cerebellar peduncles are the superior cerebellar peduncle, also known as the brachium conjunctivum, which has mainly cerebellar outputs; the middle cerebellar peduncle, also known as brachium pontis, has mainly cerebellar inputs; and the inferior cerebellar peduncle, also known as the restiform body, has mainly cerebellar inputs. The lateral hemispheres of the cerebellum involve motor planning for the extremities and influence the lateral corticospinal tract.7,8,9 The intermediate hemispheres involve distal limb coordination and influence the lateral corticospinal and rubrospinal tracts. The vermis involves proximal limb and trunk coordination and influences the anterior corticospinal, reticulospinal, vestibulospinal, and tectospinal tracts. The flocculonodular lobe involves balance and vestibulo-ocular reflexes and influences the medial longitudinal fasciculus. The four deep cerebellar nuclei, lateral to medial, are the dentate, emboliform, globose, and fastigial nuclei. These nuclei handle all output tracts. The dentate nuclei receive input from the lateral hemispheres and are active just before voluntary movement.7,8,9 The interposed nuclei, which are made up of the emboliform and globose nuclei, receive input from the intermediate hemispheres and are active during movements. The fastigial nuclei have input mainly from the vermis, with a small input from the flocculonodular lobe. Projections from the inferior vermis and flocculi extend to the vestibular nuclei.

35.2.1 Vascular Supply to the Cerebellum Three main pairs of arteries supply the cerebellum and are branches of the vertebral and basilar arteries (▶ Fig. 35.2).10 First, the posterior inferior cerebellar artery (PICA) usually arises from the vertebral artery to supply the most inferior half of the cerebellum and inferior vermis as well as the lateral medulla. The anterior inferior cerebellar artery (AICA) arises from the lower basilar artery and supplies the inferior lateral pons, middle cerebellar peduncles and ventral aspect of the cerebellum, including the flocculus. The superior cerebellar artery (SCA)

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Normal Anatomy and Pathways of Cerebellum Table 35.1 Anatomical nomenclature of the cerebellum according to Bolk with lobule numerals according to Larsell and classic nomenclature of the vermis and hemispheres Hemisphere Divisions (Bolk)

Fissures (Bolk)

Vermis

Hemispheres (Classic)

Lobule # of Larsell

Lobule name (classic)

I

Lingula

Vincingulum lingulae

II III

Central lobulus

Ala lobulus centralis

IV V

Culmen

Anterior quadrangular lobule

Lobulus simplex

VI

Declive

Posterior quadrangular lobule

Ansiform lobule Crus I

VIIA

Folium

Superior semilunar lobule

VIIB

Tuber

VIII

Pyramis

Biventral lobule (1)

IX

Uvula

Biventral lobule (2) Tonsil

Anterior lobe

Primary fissure



Intercrural fissure ●

Crus II Ansoparamedial fissure

Paramedian lobule

Horizontal fissure

Prepyramidal fissure

Inferior semilunar Lobule Gracile lobule

Secondary fissure Dorsal paraflocculus Ventral paraflocculus

Accessory paraflocculus Posterolateral fissure

Flocculus

X

Nodulus

Flocculus

arises toward the top of the basilar artery and supplies the upper lateral pons, superior cerebellar peduncles, superior half of the cerebellar hemispheres, including the deep cerebellar nuclei, and superior vermis. The veins are located on the surface of the cerebellar hemispheres and generally drain into the adjacent sigmoid and transverse sinuses, with the exception of the vermian veins.10 Anatomically, they are disposed in two sets: superior and inferior (▶ Fig. 35.3). The superior cerebellar veins pass partly forward and medialward, across the superior vermis, to end in the straight sinus and the internal cerebral veins, and partly lateralward to the transverse and superior petrosal sinuses. The inferior cerebellar veins of large size end in the transverse, superior petrosal, and occipital sinuses.

35.3 Histopathology

Fig. 35.1 Sagittal T1-weighted image of midline cerebellum shows various lobes of the cerebellum. Ce, central; Cu, culmen; D, declive; F, folium; L, lingual; N, nodule; P, pyramid; T, tuber; To, tonsil; U, uvula.

The three layers that make up the cerebellar cortex4,11,12 are the granule cell, Purkinje cell, and molecular layers. Within the cerebellum are two types of synaptic inputs. Mossy fibers extend through the cerebellar white matter to form excitatory synapses onto granule cells. These granule cells send projections into the molecular layer, which form parallel fibers, which run perpendicular to dendritic trees of the Purkinje cells. Each parallel fiber forms excitatory synapses with multiple Purkinje cells. The axons of the Purkinje cells direct all output. These form inhibitory synapses on the deep cerebellar and vestibular nuclei. The deep cerebellar nuclei send output through the

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Fig. 35.3 Sagittal maximum intensity projection from time-of-flight magnetic resonance venogram shows internal cerebral veins (curved arrow), vein of Galen (arrowhead), straight sinus (fat arrow), precentral cerebellar vein (single zigzag arrow), and inferior vermian vein (double zigzag arrow).

35.3.1 Nuclear and Tract Anatomy Fig. 35.2 Maximum-intensity projection from time-of-flight magnetic resonance angiography shows normal vessels of the posterior fossa: vertebral arteries (arrow), basilar artery (fat arrow), posterior cerebral arteries (thin arrows), superior cerebellar arteries (zigzag arrows), anterior inferior cerebellar arteries (arrowheads), posterior cerebellar arteries (curved arrow).

excitatory synapses. In addition to mossy fibers, there are also climbing fibers, which arise from the neurons of the contralateral inferior olivary nucleus. Climbing fibers cause a modulatory effect on the response of Purkinje cells. There are several inhibitory interneurons of the cerebellum. Basket and stellate cells are in the molecular layer and receive excitatory synaptic inputs from the granule cell parallel fibers. Stellate cells terminate on Purkinje cell dendrites; basket cells terminate on Purkinje cell bodies, both having a strongly inhibitory effect. Golgi cells are found in the granule cell layer and receive excitatory inputs in the molecular layer from granule cell parallel fibers. These provide feedback inhibition on the granule cell dendrites. In summary, mossy fibers, climbing fibers, granule cell parallel fibers, and deep cerebellar nuclei have excitatory synapses. Purkinje cells, stellate cells, basket cells, and Golgi cells contain inhibitory synapses.

Cerebellar Output Pathways Coordination deficits usually occur ipsilateral to a lesion because pathways involving the cerebellum and the lateral motor systems have crossed sides twice. The first crossing occurs at the superior cerebellar peduncle, and the other occurs with the decussation of the corticospinal and rubrospinal tracts. The lateral cerebellar hemisphere projects to the dentate nucleus. In turn, the dentate nucleus projects through the superior cerebellar peduncle to the contralateral ventral lateral nucleus of the thalamus.13,14,15 Additional cerebellar outputs project to the thalamic ventral anterior and intralaminar nuclei. Additional dentate nucleus output fibers project to the rostral parvocellular division of the red nucleus, which in turn project to the inferior olive. From the ventral lateral nucleus, fibers project to motor cortex, premotor cortex, supplementary motor area, and the parietal lobe to assist in motor planning of the corticospinal systems. There are additional projections from the thalamus to the prefrontal association cortex, thought to be involved with cognitive function. The intermediate hemisphere projects to the interposed nuclei. The interposed nuclei then project through the superior cerebellar peduncle to the contralateral ventral lateral thalamic nucleus.15,16 This projects to the motor, supplementary motor, and premotor cortex. Additional fibers project to the contralateral magnocellular division of the red nucleus to influence the rubrospinal tract.

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Fig. 35.4 Axial T2 image through the cerebellum at the level of pons (p) shows middle cerebellar peduncle (mc), vermis (v), and nodulus (arrow).

Fig. 35.5 Axial T2 image through the cerebellum shows inferior cerebellar peduncle (white dots), nodulus (star), uvula (u), pyramid (p), and tonsils (arrows).

The cerebellar vermis has connections from the anterior corticospinal, reticulospinal, vestibulospinal, and tectospinal tracts (▶ Fig. 35.4, ▶ Fig. 35.5, ▶ Fig. 35.6).15,16 The vermis has projections to the fastigial nuclei. The flocculonodular lobes and inferior vermis project mainly to the vestibular nuclei with few projections to the fastigial nuclei. Outputs from the fastigial nuclei travel along the uncinate fasciculus (fibers within the superior cerebellar peduncle), and juxtarestiform body (fibers within the inferior cerebellar peduncle). The fibers from the uncinate fasciculus ultimately influence the anterior corticospinal tract. The fibers from the juxtarestiform body influence the ipsilateral reticular formation, which influences the reticulospinal tracts and the vestibular nuclei, which in turn influences the vestibulospinal tracts. There are few direct connections to lower motor neurons via the fastigial neurons projecting to the upper cervical spinal cord.

Cerebellar Input Pathways Inputs to the cerebellum involve multiple areas of the central nervous system. One main source of input is from the corticopontine fibers from the cerebral cortex that travel in the internal capsule and cerebral peduncles. Primary sensory cortex, primary motor cortex, and part of the visual cortex comprise most of the corticopontine fibers, which travel to the ipsilateral pons, synapsing in the pontine nuclei. Pontocerebellar fibers cross the midline to enter the contralateral middle cerebellar peduncle. There are four spinocerebellar tracts14,15,16: the dorsal and ventral spinocerebellar tracts, which involve the lower extremities; and the cuneocerebellar and rostral spinocerebellar tracts,

Fig. 35.6 Coronal T2 image shows horizontal fissure (fat arrows), superior semilunar (single arrow), inferior semilunar (double arrow), biventer (zigzag arrow), and dentate nucleus (arrowhead).

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Cerebellar Degeneration and Dysfunction which involve the upper extremities and neck. Proprioception about limb movements from the lower extremities projects via the dorsal spinocerebellar tract and from the upper extremities via the cuneocerebellar tract. The activity of spinal cord interneurons, which reflects the magnitude of activity in descending pathways in the lower extremities, projects via the ventral spinocerebellar tract and in the upper extremity via the rostral spinocerebellar tract. The dorsal spinocerebellar tract ascends in the dorsolateral funiculus, entering via the dorsal roots and ascending in the gracile fasciculus. Some fibers synapse in the nucleus dorsalis of Clark, which is a long column of cells in the dorsomedial spinal cord gray matter intermediate zone from C8 to L2/L3. Fibers from the nucleus dorsalis of Clark ascend ipsilaterally and travel to the ipsilateral cerebellar cortex via the inferior cerebellar peduncle. In the cuneocerebellar tract, fibers from the upper extremities enter the cuneate fasciculus and ascend ipsilaterally to synapse in the external cuneate nucleus, which then ascends in the inferior cerebellar peduncle to the ipsilateral cerebellum. These pathways provide rapid feedback regarding ongoing movements, allowing for fine adjustments. The ventral spinocerebellar tract arises from neurons along the outer edge of the central gray matter, then crosses the ventral commissure of the spinal cord, ascending just ventral to the dorsal spinocerebellar tract. These fibers join the superior cerebellar peduncle and cross a second time, reaching the cerebellum ipsilateral to the beginning of the pathway. The rostral cerebellar tract is similar to the ventral spinocerebellar tract, but it involves the upper extremity and enters the cerebellum through the inferior and superior cerebellar peduncles. The inferior olivary nuclear complex has olivocerebellar fibers, which cross the medulla to enter the contralateral cerebellum. These form the bulk of the inferior cerebellar peduncle and terminate as climbing fibers. The parvocellular red nucleus has inputs from the contralateral dentate nucleus. A circuit is formed from the lateral cerebellar hemisphere to the dentate nucleus, to the contralateral parvocellular red nucleus, to the inferior olive via the central tegmental tract, and then back to the original cerebellar hemisphere via the inferior cerebellar peduncle. The cerebral cortex, other brainstem nuclei, and the spinal cord also send fibers to the inferior olivary nuclear complex. The lateral reticular nucleus receives similar inputs and projects to the cerebellum via the inferior cerebellar peduncle but gives rise to mossy fibers. Primary vestibular nuclei project to the ipsilateral inferior cerebellar vermis and flocculonodular lobe via the juxtarestiform body. The flocculus receives visual inputs that are important for the control of smooth pursuit eye movements. Noradrenergic inputs from the locus ceruleus and serotonergic inputs from the raphe nuclei project diffusely throughout the cerebellar cortex.

35.4 Diffusion Tensor Imaging of the Cerebellum Technical details and physics of diffusion tensor imaging (DTI) are beyond the scope of this chapter and are discussed in detail in Chapter 5. The degree of water mobility in a given brain tissue can be studied by adding diffusion gradients to standard magnetic resonance image (MRI) sequences.17,18 Depending on

the local microstructure, diffusion can be isotropic, which means that the magnitude of diffusion is equal in all directions, or anisotropic, which means that the magnitude of diffusion differs along the various directions in space. Diffusion is typically isotropic in CSF and anisotropic along the white matter tracts. DTI allows study of the three-dimensional shape and direction of diffusion by adding diffusion gradients along multiple orthogonal directions in space. When the complete tensor of the diffusion is measured, the degree of anisotropic diffusion can be calculated (by fractional anisotropy [FA]).17,18 The degree of anisotropic diffusion can be represented as a two-dimensional FA map. FA values vary between 0 (maximal isotropic diffusion) and 1 (maximal anisotropic diffusion). High FA values are typically found along fiber tracts (e.g., corticospinal tracts, corpus callosum). Measurement of the entire diffusion tensor also provides information about the principal direction of diffusion within the brain. Consequently, the FA maps can be colorcoded. By convention, regions with a predominant left-to-right diffusion are color-coded in red; regions with a superiorinferior diffusion direction are coded in blue; and anterior–posterior diffusion is color coded in green. The intensity of the color-coding is related to the magnitude of the FA value.

35.4.1 Diffusion Tensor Imaging of the Anatomy of the Brainstem and Cerebellum Brainstem and cerebellum represent a crossroad between the fiber bundles of the spinal cord, midbrain, and cerebral hemispheres and vice versa.19,20 The five major white matter tract connections include the superior (SCP), middle (MCP), and inferior cerebellar peduncles (ICP), the corticospinal tract, and the medial lemniscus (ML).

The Superior Cerebellar Peduncle The SCP is the main pathway that connects the cerebellum with the thalamus. The dentatofugal course originates from the hilus of the dentate nucleus, runs within the superior cerebellar peduncle, goes through the mesencephalic tegmentum and the red nucleus, and reaches the ventrolateral thalamus. On axial images, the SCP is identified at the dentate nucleus level, with a linear green- or blue-colored course (▶ Fig. 35.7).

Transverse Fibers and the Middle Cerebellar Peduncle The MCP is part of the pontocerebellar tracts seen wrapping around the pons with dorsal anterior–posterior (green) and ventral left–right (red) directionality (▶ Fig. 35.8).20,21,22 The transverse pontine fibers (TPFs, red) are also part of the MCP and appear on structural anisotropic axial images as large Hshaped red areas of the pons, circumscribing the two corticospinal tracts (▶ Fig. 35.9). Morphologically, TPFs can be dissected into ventral transverse fibers, medial fibers, and dorsal fibers. Ventral fibers (▶ Fig. 35.9) are connected to the lateral hemispheric part of lobules VIIIB, VIIIA, VII, and VI of the cerebellum. All these fibers converge caudally and constitute part of the MCP. Their course remains ventrolateral to the ipsilateral

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Fig. 35.7 Axial diffusion tensor imaging colored maps at the level of pons shows superior cerebellar peduncle (yellow arrows), fourth ventricle (red dot), cerebellar vermis (red arrow), cerebellar hemisphere (C).

dentate nucleus. The dorsal fibers (▶ Fig. 35.9) are more medial within the MCP and travel along the lateral border of the ipsilateral dentate nucleus. Most end within the anterior lobe, including its vermian, paravermian, and hemispherical parts. The medial transverse fibers between the two corticospinal tracts (▶ Fig. 35.9) are connected to the dorsolateral part of the prefrontal cortex through the rostral posterior limb of the internal capsule and through the ventromedial, ipsilateral crus cerebri. Besides the transverse pontine fibers, various other tracts connecting the cerebellum with parietal, occipital, and orbital cortex may be seen. The most complete corticopontocerebellar pathway corresponds to projections from the pericentral cortex to the hemispherical parts of the cerebellar anterior lobe (HVVIIA) through the ipsilateral, ventromedial crus cerebri and through the rostroventral transverse fibers.

Fig. 35.8 Axial diffusion tensor imaging–colored maps at the level of pons shows middle cerebellar peduncle (yellow arrows), dentate nucleus (blue arrows), fourth ventricle (red dot), cerebellar vermis (red arrow), and cerebellar hemisphere (C).

of the medulla and pons, represented by the color blue (inferior-superior direction) in its inferior half and green in its superior half.

Corticospinal (and Corticonuclear) Tract The corticospinal and corticonuclear tracts are major descending pathways connecting the motor cerebral cortex with the spinal cord (▶ Fig. 35.10).22 The course of these tracts can be followed from the sensorimotor cortex into the caudal part of the posterior limb of the internal capsule, the ventromedial part of the crus cerebri, in the pons between the ventral and dorsal segments of the transverse pontine fibers, and then into the most ventral part of the medulla oblongata in front of the inferior olivary nucleus.

Inferior Cerebellar Peduncle

Medial Lemniscus

The ICP originates in the caudal medulla oblongata, traverses the pons, and sends branches into the cerebellar cortex. The tract enters the cerebellar white matter dorsal to the central tegmental tract, ventral to the superior cerebellar tract, and between the lateral wall of the fourth ventricle and the MCP. Tracts then run within the cerebellar white matter above the dentate nucleus and reach the vermis and paravermis of the cerebellar anterior lobe, especially in lobules IV-VI and IX. On axial DTI maps, the ICP is identified along the dorsal aspect

The ML is an important pathway for ascending sensory fibers that is best seen dorsal to the dorsal transverse pontine fibers at the level of the MCP on DTI axial images (▶ Fig. 35.10).20,22 The tracts ascend within the rostromedial part of the medulla oblongata, dorsal and medial to the corticospinal tract, and spread upward and dorsal to the inferior olivary nucleus, and ventral to the central tegmental tract. At the mesencephalic level, the course is located in a ventrolateral position, dorsal to the substantia nigra and lateral to the red nucleus, and at the

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Fig. 35.9 Axial diffusion tensor imaging–colored maps at the level of midpons show the main tracts running to and fro from the cerebellum and brainstem. CST, corticospinal tract; CTT, central tegmental tract; dTF, dorsal transverse fibers (mainly connected to prefrontal fibers); LMT, longitudinal medial tract; ML, medial lemniscus; mTF medial transverse fibers; SCP, superior cerebellar peduncle rvTF, rostroventral transverse fibers.

telencephalic level, it terminates within the ventroposterior nucleus of the thalamus.

35.5 Magnetic Resonance Spectroscopy Proton magnetic resonance spectroscopy (MRS) is a noninvasive technique used for biochemical analysis of small volumes of interest (voxel) within the brain parenchyma. Spectroscopy measures the concentration of the particular metabolite, thus assessing the biochemical constituents of the brain tissue. Physics and the relevant technical details are discussed in detail in Chapter 3. MRS of posterior fossa is more challenging than MRS of the supratentorial area because of potential technical difficulties (e.g., field inhomogeneity) and surrounding bone; however, continuous improvement in MRS techniques has overcome these difficulties. Calculated metabolites and their ratios depend on the metabolite concentrations and the relaxation properties of the tissue characterized by the relaxation times T1 and T2. The cerebellar hemispheres are different from the supratentorial white and gray matter embryologically, in cytoarchitecture, and in metabolic activity.23,24 Embryologically, the cerebellum, which is derived from the metencephalon, originates from the rhomb-

Fig. 35.10 Axial color-coded FA maps at the level of pontomesencephalic junction. The anatomical landmarks are labeled as follows: CST corticospinal tract, CTT central tegmental tract, ML medial lemniscus, SCP superior cerebellar peduncle, SN substantia nigra, rvTF rostroventral transverse fibers (mainly connected to the sensorimotor cortical fibers), vTD ventral tegmental decussation beneath the overlying red nucleus.

encephalon (hindbrain), and is different from the cerebral hemispheres, which derive from the telencephalon that comes from the prosencephalon (forebrain). Most of the cerebellum is myelinated from deep to superficial white matter by 1 to 3 months after birth on T1-weighted images, whereas the cerebral parenchyma takes almost 18 months to complete its myelination. This is important to understand when reading the MRS of the cerebellum in pediatric patients. Cerebellar MRS may be performed using either a single or multi voxel, depending on the size and the area of interest to be analyzed. Depending on the echo time, MRS can be performed using either short (< 30 milliseconds [ms]), intermediate (144 ms), or long (270 ms) echo time (TE). MRS with short TE (30 ms) shows a greater number of metabolites compared with long (270 ms) or intermediate (144 ms) TE. Most prominent peaks seen on short TE are lipid (resonates at 0.9 to 1.4 parts per million [ppm]), lactate (1.3 ppm), N-acetyl aspartate (NAA) (2 ppm), glutamine/GABA (2.2 to 2.4 ppm), creatine (Cr, 3 ppm), choline (Cho, 3.2 ppm), and myoinositol (mI, 3.5 ppm). Each metabolite resonates at a specific frequency, expressed as ppm, and each one reflects specific cellular and biochemical processes. In short, NAA is a neuronal marker, Cr provides a measure of energy stores, Cho is a measure of increased cellular turnover, mI is a measure of cellular signal transduction and osmoregulation, and lactate reflects anaerobic metabolism.

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35.5.1 Spectrum Analysis Because of its different anatomical and cellular composition, the spectroscopic peaks show mild variations with respect to the cerebral parenchyma. The largest metabolic difference between the cerebellum and other brain regions is the high levels of Cr both in the vermis and in the cerebellar hemispheres.24,25 The exact reason for this difference is not clear but is thought to be due to the different cellular composition of cerebellar cortex compared with that of the neocortex. Compared with the six-layered neocortex, the cerebellar cortex has a uniform three-layer structure, consisting of a superficial “molecular” layer containing mainly axons and dendrites of the cerebellar neurons, a Purkinje cell layer, and a “granular” layer consisting of a multitude of densely packed small granule cells. Anatomically, the pons is characterized by a high density of fiber bundles (i.e., white matter) and thus shows a similar spectroscopic pattern as observed in supratentorial brain white matter. It shows high levels of NAA and Cho and low levels of Cr with respect to the cerebellum. The high NAA signal presumably reflects the high axonal/neuronal density of the pons. The cerebellum shows a smaller NAA:Cr ratio compared with the pons and the thalamus (▶ Fig. 35.11). Quantitative MRS shows cerebellar levels of Cho, Cr, and NAA to be 2.5, 9.1, and 9.6 mM, and for the pons, concentrations of 2.9, 6.0, and 12.1 mM, respectively.25,26 Cho:Cr is significantly greater in the vermis (0.83 ± 0.10) than in the cerebellar hemisphere (0.76 ± 0.11), and NAA:Cho was significantly lower in the vermis (1.19 ± 0.12) than in the cerebellar hemisphere (1.35 ± 0.16).

35.6 Role of Cerebellum in Cognition and Neuropsychiatric Disorders Traditionally, the role of the cerebellum was thought to be limited to the coordination of voluntary movement, gait, posture, speech, and motor functions. However, during the past three decades, neuroanatomical, neuroimaging, and clinical studies have provided evidence of cerebellar involvement in cognitive and linguistic processing. Evidence that supports the idea of the cerebellum’s role in cognitive functions are neuropsychological deficits in patients with cerebellar lesions, activation of the cerebellum in normal subjects as they perform a cognitive task on functional MRI, and anatomical connections showing links between the cerebellum and cerebral cortex that are known or thought to be involved in cognition.27 Although the cerebellum constitutes only 10% of the total brain weight, it contains more than half of all the neurons in the brain.28 The cerebellum has 40 million nerve fibers for connections to neocortical areas, compared with the one million nerve fibers of the visual system. Neuroanatomical studies have shown bidirectional pathways connecting the cerebellum to important parts of the cerebral cortex involved in cognitive regulation. The frontocerebellar connections consist of closed corticocerebellar loops in which the (dorso)lateral part of the prefrontal cortex connects to the cerebellum via pontine nuclei, and the cerebellum sends projections back to the prefrontal cortex via the dentate nucleus and thalamus. The cerebellum is connected to the cerebrum via three cerebellar peduncles. These are connections to many brain areas relevant to cognition

Fig. 35.11 Short echo time (TE) (30) magnetic resonance spectroscopy through the cerebellar vermis shows normal spectrum. N-acetyl aspartate (NAA) at two parts per million (ppm), creatine (Cr) at 3 ppm, choline (Cho) at 3.2 ppm, and myoinositol (ins) at 3.5 ppm.

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Cerebellar Degeneration and Dysfunction and behavior, including the dorsolateral prefrontal cortex, the medial frontal cortex, the parietal and superior temporal areas, the anterior cingulate, and the posterior hypothalamus. There are also noradrenergic, serotonergic, and dopaminergic inputs to the cerebellum from the brainstem nuclei. Recent functional neuroimaging demonstrated that there seems to be a functional organization of the cerebellum: motor and sensorimotor tasks activate the anterior lobe, adjacent parts of lobule VI, and lobule VIII. In contrast, the posterior lobe (lobules VI and VII, in particular) is found to be involved in higher-level tasks for language, verbal working memory, and executive tasks.29 It was further shown that there is lateralization of the function within the cerebellar hemispheres; logical reasoning and language processing were predominately on the right side and visuospatial and attentional skills on the left side of the cerebellum. Cellular as well as functional reduction is seen with the aging process. A 10 to 40% decrease in the Purkinje cell layer and a reduction in the area of the dorsal vermis have been reported with aging. Congenital and acquired lesions of the cerebellum and posterior fossa have been seen in association with various cognitive dysfunctions. Various congenital malformations may manifest with selective neuropsychological deficits involving mainly executive functions and visuospatial and linguistic abilities. The extent of these symptoms largely depends on the area involved and the extent of the pathology. Children with cerebellar hypoplasia have significant problems in attention, processing speed, and visuospatial functions, whereas patients with olivopontocerebellar atrophy demonstrate multiple deficits in intellect, memory, attention, language, and visuospatial and executive functions compared with a control group.30 The congenital and early acquired cerebellar problems have a more pronounced effect on cognitive functioning than lesions acquired later in life. This observation supports the idea that the cerebellum has an important influence on the developing structures and function of the supratentorial brain, enabling the perfection of its higher-level functions. Various acquired lesions affecting the cerebellar parenchyma, especially during childhood, also have a significant impact on cognition.31 Two major types of clinical scenarios seen with acquired lesions are the cerebellar cognitive affective syndrome (CCAS) and the posterior fossa syndrome (PFS).27 CCAS is characterized by executive dysfunctions, such as disturbances in planning, set-shifting, abstract reasoning, and working memory; visuospatial deficits, such as impaired visuospatial organization and memory; mild language symptoms, including agrammatism and anomia; and, finally, behavioral–affective disturbances, consisting of blunting of affect or disinhibited and inappropriate behavior.27,32,33 Clinical analysis revealed that lesions of the posterior lobe of the cerebellum (PICA territory) result in cognitive symptoms, whereas the vermis is mostly involved in patients with behavioral–affective disturbances. Patients with superior cerebellar artery territory lesions have been reported to have clinically significant cognitive or linguistic disturbances. Posterior fossa syndrome is a well-recognized clinical entity seen most commonly following posterior fossa tumor resection, but it may also be seen with trauma, vascular insults, and infectious causes.34 PFS is characterized by a broad spectrum of linguistic, cognitive, and behavioral–affective disturbances. Signs

and symptoms of this syndrome depend largely on the hemisphere involved. Tumors infiltrating the right cerebellar hemisphere are associated with difficulties in verbal processing and complex language tasks, whereas tumors of the left cerebellar hemisphere correlate with deficits in nonverbal and spatial processing. The exact pathogenesis of PFS is elusive. The hypothetical explanation given is that it is due to the phenomenon of cerebellocerebral diaschisis, which represents the metabolic impact of a cerebellar lesion in a distant but anatomically and functionally connected supratentorial region.35,36,37 Left cerebellar damage was related to typical right hemispheric dysfunctions, such as attention deficits and visuospatial disturbances, whereas right cerebellar damage was associated with typical left-hemispheric deficits, such as disrupted language skills. PFS is also thought to be due to multiple bilateral injuries to the proximal dentate-thalamocortical pathways and/or functional disruption of the white matter bundles containing efferent axons within the superior cerebellar peduncles.

References [1] Ten Donkelaar HJ, Lammens M. Development of the human cerebellum and its disorders. Clin Perinatol 2009; 36: 513–530 [2] Millen KJ, Gleeson JG. Cerebellar development and disease. Curr Opin Neurobiol 2008; 18: 12–19 [3] Zervas M, Blaess S, Joyner AL. Classical embryological studies and modern genetic analysis of midbrain and cerebellum development. Curr Top Dev Biol 2005; 69: 101–138 [4] Palay SL, Chan-Palay V. Cerebellar Cortex: Cytology and Organization. New York: Springer-Verlag; 1974:1–348 [5] Schmahmann JD, Doyon J, Toga AW, Petrides M, Evans A. MRI Atlas of the Human Cerebellum. London: Academic Press; 2000 [6] Triulzi F, Parazzini C, Righini A. MRI of fetal and neonatal cerebellar development. Semin Fetal Neonatal Med 2005; 10: 411–420 [7] Ito M. Cerebellar circuitry as a neuronal machine. Prog Neurobiol 2006; 78: 272–303 [8] Ito M. Control of mental activities by internal models in the cerebellum. Nat Rev Neurosci 2008; 9: 304–313 [9] Katz DB, Steinmetz JE. Psychological functions of the cerebellum. Behav Cogn Neurosci Rev 2002; 1: 229–241 [10] Tatu L, Moulin T, Bogousslavsky J, Duvernoy H. Arterial territories of human brain: brainstem and cerebellum. Neurology 1996; 47: 1125–1135 [11] Crépel F, Mariani J, Delhaye-Bouchaud N. Evidence for a multiple innervation of Purkinje cells by climbing fibers in the immature rat cerebellum. J Neurobiol 1976; 7: 567–578 [12] Voogd J, et al. The cerebellum, chemoarchitecture and anatomy. In: Swanson LW, ed. Handbook of Chemical Neuroanatomy, Vol. 12. New York: Elsevier; 1996:1–369 [13] Altman J, Bayer SA. Development of the Cerebellar System in Relation to its Evolution, Structure, and Functions. Boca Raton: CRC Press; 1997 [14] Middleton FA, Strick PL. Anatomical evidence for cerebellar and basal ganglia involvement in higher cognitive function. Science 1994; 266: 458–461 [15] Schmahmann JD. An emerging concept: the cerebellar contribution to higher function. Arch Neurol 1991; 48: 1178–1187 [16] Schmahmann JD, Sherman JC. Cerebellum cognitive affective syndrome. Int Rev Neurobiol 1997; 41: 433–400 [17] Wakana S, Jiang H, Nagae-Poetscher LM, van Zijl PCM, Mori S. Fiber tractbased atlas of human white matter anatomy. Radiology 2004; 230: 77–87 [18] Golay X, Jiang H, van Zijl PC, Mori S. High-resolution isotropic 3D diffusion tensor imaging of the human brain. Magn Reson Med 2002; 47: 837–843 [19] Salamon N, Sicotte N, Alger J et al. Analysis of the brain-stem white-matter tracts with diffusion tensor imaging. Neuroradiology 2005; 47: 895–902 [20] Nagae-Poetscher LM, Jiang H, Wakana S, Golay X, van Zijl PC, Mori S. Highresolution diffusion tensor imaging of the brain stem at 3 T. AJNR Am J Neuroradiol 2004; 25: 1325–1330 [21] Habas C, Cabanis EA. Anatomical parcellation of the brainstem and cerebellar white matter: a preliminary probabilistic tractography study at 3 T. Neuroradiology 2007; 49: 849–863

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Normal Anatomy and Pathways of Cerebellum [22] Chokshi FH, Poretti A, Meoded A, Huisman TA. Normal and abnormal development of the cerebellum and brainstem as depicted by diffusion tensor imaging. Semin Ultrasound CT MR 2011; 32: 539–554 [23] Costa MO, Lacerda MT, Garcia Otaduy MC, Cerri GG, Da Costa Leite C. Proton magnetic resonance spectroscopy: normal findings in the cerebellar hemisphere in childhood. Pediatr Radiol 2002; 32: 787–792 [24] Jacobs MA, Horská A, van Zijl PC, Barker PB. Quantitative proton MR spectroscopic imaging of normal human cerebellum and brain stem. Magn Reson Med 2001; 46: 699–705 [25] Michaelis T, Merboldt KD, Bruhn H, Hänicke W, Frahm J. Absolute concentrations of metabolites in the adult human brain in vivo: quantification of localized proton MR spectra. Radiology 1993; 187: 219–227 [26] Pouwels PJW, Frahm J. Regional metabolite concentrations in human brain as determined by quantitative localized proton MRS. Magn Reson Med 1998; 39: 53–60 [27] Schmahmann JD, Sherman JC. The cerebellar cognitive affective syndrome. Brain 1998; 121: 561–579 [28] Andersen BB, Korbo L, Pakkenberg B. A quantitative study of the human cerebellum with unbiased stereological techniques. J Comp Neurol 1992; 326: 549–560

[29] Stoodley CJ, Schmahmann JD. Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. Neuroimage 2009; 44: 489–501 [30] Kish SJ, el-Awar M, Schut L, Leach L, Oscar-Berman M, Freedman M. Cognitive deficits in olivopontocerebellar atrophy: implications for the cholinergic hypothesis of Alzheimer’s dementia. Ann Neurol 1988; 24: 200–206 [31] Tavano A, Borgatti R. Evidence for a link among cognition, language and emotion in cerebellar malformations. Cortex 2010; 46: 907–918 [32] Schmahmann JD, Caplan D. Cognition, emotion and the cerebellum. Brain 2006; 129: 290–292 [33] Schmahmann JD, Weilburg JB, Sherman JC. The neuropsychiatry of the cerebellum: insights from the clinic. Cerebellum 2007; 6: 254–267 [34] Pollack IF. Posterior fossa syndrome. Int Rev Neurobiol 1997; 41: 411–432 [35] Murdoch BE. The cerebellum and language: historical perspective and review. Cortex 2010; 46: 858–868 [36] Catsman-Berrevoets CE, Aarsen FK. The spectrum of neurobehavioural deficits in the Posterior Fossa Syndrome in children after cerebellar tumour surgery. Cortex 2010; 46: 933–946 [37] De Smet HJ, Baillieux H, Wackenier P et al. Long-term cognitive deficits following posterior fossa tumor resection: a neuropsychological and functional neuroimaging follow-up study. Neuropsychology 2009; 23: 694–704

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36 Imaging of Cerebellar Degeneration and Cerebellar Ataxia Sangam G. Kanekar and Kyaw Tun Ataxia in Greek means “absence of order.” It is a movement disorder resulting from the incoordination of movements and inadequate postural control and manifests as balance and walking disturbances. Normal motor control is the outcome of normal postural tonus, muscle coordination, and balance working together in unison. Various classifications of ataxia can be found in the literature. On the basis of their clinical signs and symptoms, they can be classified into acute or chronic onset. Chronic ataxias are further divided into (1) acquired ataxias, which are due to exogenous or endogenous nongenetic causes; (2) hereditary ataxias; and (3) nonhereditary degenerative ataxias. ▶ Fig. 36.1 enumerates the common causes of cerebellar ataxia.

a history of antecedent illness. The neurologic symptoms depend largely on localization of a lesion. Hemispheric lesions result in ipsilateral limb hypotonia, dysmetria, and tremor; vermian lesions manifest with dysarthria, truncal titubation, and gait abnormalities. Neuroimaging plays an important role in identifying the pathology and associated or impending complications, such as upward or downward cerebellar herniation or acute hydrocephalus.

36.1.1 Cerebellar Stroke Bland and hemorrhagic strokes cause disruption in the cerebellar connections and cellular dysfunction and can lead to acuteonset ataxia.

36.1 Acute-Onset Ataxia

Infarct

Acute cerebellar ataxia is most commonly seen in younger children, between 2 and 4 years of age. In 70% of cases, there is

On magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI) can detect the acute infarct within the first

Fig. 36.1 Causes of chronic ataxia. CNS, central nervous system.

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Fig. 36.3 Acute cerebellitis. Sagittal T2-weighted imaging shows diffuse hyperintensity in the cerebellar cortex in a child with acuteonset ataxia after viral (arrows) infection.

Fig. 36.2 Acute right cerebellar stroke. Diffusion-weighted magnetic resonance image shows area of increased signal intensity (arrow), suggestive of cytotoxic edema from infarction.

6 hours after onset of symptoms. As the vasogenic edema develops, an area of T2 hyperintensity is seen in this region, with or without mass effect, in the fourth ventricle, depending on the size (▶ Fig. 36.2). Magnetic resonance angiography of the head and neck can identify the associated abnormality, such as atherosclerosis, aneurysm, dissection, or vasculitis.

Hemorrhage Computed tomography (CT) head examination is still the first line of imaging choices in evaluating cerebellar hemorrhages. CT shows hyperdensity with a surrounding area of edema in the region of the hemorrhage. On MRI, gradient-echo or susceptibility-weighted imaging is more sensitive in detecting small hemorrhages resulting from the presence of blooming effect from the hemorrhage.

36.1.2 Cerebellitis Cerebellitis is an important cause of acute ataxia, particularly in children and young adults, after an infectious illness, especially a viral illness. Varicella infections are the most common causes of acute cerebellar ataxia in children, sometimes referred to as post-chickenpox cerebellitis.1 Brainstem encephalitis may also involve the cerebellar tracts, resulting in ataxia. Ataxic symptoms are thought to be due to either direct viral invasion in the cerebellum or autoimmune reaction to infectious agents. On pathology there is extensive infiltration of the leptomeninges

and molecular layers of the cerebellar cortex by mature Tlymphocytes, monocytes or macrophages, and eosinophils, associated with a loss of Purkinje cells. CT is insensitive in diagnosis of cerebellitis. MRI shows asymmetric or symmetric, bilateral, and diffuse T2 hyperintense signal in the cerebellar gray matter (▶ Fig. 36.3). Leptomeningeal enhancement may be seen on postcontrast scans. Rarely, subcortical areas and the deep white matter of the cerebellar hemisphere are involved.

36.1.3 Toxic Cerebellitis Cerebellar cells, particularly Purkinje neurons and cerebellar circuits, are susceptible to toxins and poisons, such as drugs, anticonvulsant overdoses, alcohol ingestion, organic chemicals, heavy metals, and various chemotherapeutic agents. These toxins are thought to cause hypoxia or to interfere with oxidative metabolism, leading to a decrease in the blood oxygen level to Purkinje cells, which in turn leads to cerebellar degeneration and atrophy.2 Imaging findings are nonspecific. Brain MRI is normal in most of the cases. In a few cases, mild cortical and white matter hyperintensity is seen on T2-weighted images. In cases of chronic exposure (e.g., antiepileptic drugs), atrophy may be present (▶ Fig. 36.4).

36.2 Chronic-Onset Ataxia Chronic-onset ataxias can be generally classified into (1) inherited and (2) sporadic causes. Inherited causes can be further subdivided into (a) autosomal dominant, (b) autosomal recessive, and (3) X-linked.

36.3 Autosomal Dominant Inherited Type Of the several different types of autosomal dominant central nervous system (CNS) ataxia, the two most commonly encountered in clinical practice are spinocerebellar ataxia (SCA) and

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Fig. 36.4 Cerebellar atrophy from chronic antiepileptic therapy. Axial computed tomographic scan of the brain shows generalized prominence of cerebellar folia and diffuse thickening of the calvarium.

episodic ataxia type 2. The overall prevalence of SCA is estimated to be about 3 per 100,000.

36.3.1 Spinocerebellar Ataxia The SCAs are caused by expansion of CAG triplet repeats in the coding region of the disease gene, resulting in production of a mutant protein with an abnormally long polyglutamine

stretch.3 To date, 27 loci for SCAs and the gene for dentatorubral and pallidoluysian atrophy (DRPLA) have been discovered. This numbering is based on the order in which the causative genetic mutation was identified. Detail clinical, genetic, and pathogenetic features of all the 27 SCAs are beyond the scope of this chapter. SCA shows wide ethnic and geographic variations; SCA2 is most common in Cuba, and DRPLA is most common in Japan. Worldwide, SCA3 is the most common genotype (21%), whereas SCA1 and SCA2 account for about 6% and 15% of cases, respectively.4 Clinical manifestation of SCA includes ataxia of gait, ataxia of stance, dysmetria, kinetic tremors, and nystagmus. Signs and symptoms, however, largely depend on the genetic defect and the predominate anatomical degeneration. On histopathology, besides the cerebellum, there is degeneration of the extracerebellar structures like the cerebral cortex, basal ganglia, brainstem, cranial nerves, and spinal cord. This degeneration shows individual variations, depending on the type of SCA; for SCA2, degeneration of the cerebellar cortex and of the pontocerebellar petal system is marked, and the dentate nucleus is spared, whereas in SCA1, the degenerative process affects mostly the spinocerebellar system, and the dentate nucleus, the Purkinje cells, and the pontocerebellar petal system are relatively spared.5,6 Differentiating various SCAs by their structural properties or even newer molecular or cellular imaging techniques (diffusion tensor imaging [DTI], magnetic resonance spectroscopy [MRS]) is not possible. On MRI, appearances are broadly divided into pure cerebellar atrophy (SCA4, SCA5, SCA8, SCA9, SCA10, SCA11, SCA14, SCA15, SCA16, SCA18, SCA21, and SCA22) (▶ Fig. 36.5), olivopontocerebellar atrophy (SCA1, SCA2, SCA3, SCA6, SCA7, and SCA13) (▶ Fig. 36.6), and global cerebral and cerebellar atrophy (DRPLA, SCA12, SCA17, and SCA19).7 Cellular imaging techniques have been used to differentiate the various types of SCAs with limited success. Proton MRS shows decreased NAA:creatine (Cr) ratio, N-acetyl aspartate (NAA) concentration, and choline (Cho):Cr ratio in the pons and deep cerebellum. There is a direct correlation of the severity of clinical deficit with atrophy of the brainstem, decreased NAA:Cr ratio in the pons, and increased apparent diffusion coefficient (ADC) value in the brainstem and cerebellum in SCA1.8

Fig. 36.5 Spinocerebellar ataxia, pure cerebellar atrophy type (SCA8). (a) Sagittal T1- and (b) axial T2-weighted imaging shows diffuse prominence of the cerebellar folia. Pons is normal in size and morphology.

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Fig. 36.6 Spinocerebellar ataxia, olivopontocerebellar atrophy type (SCA3). (a) Sagittal T1- and (b) axial T2-weighted images show prominence of the cerebellar folia (cerebellar atrophy) (arrow) with atrophy of lower pons (arrowhead in a and arrow in b).

36.3.2 Episodic Ataxia Type 2 Episodic ataxia type 2 (EA2) is secondary to the mutation in the CACNA1A gene in Purkinje cells that encodes α-1 subunits in neuronal calcium channels.9 This mutation is also associated with familial hemiplegic migraine and SCA6. Onset of EA2 is usually in childhood, with recurrent ataxia that may last hours to days. In a minority of patients, MRI of EA2 shows a corticocerebellar atrophy (CCA) pattern; that is, atrophy of cerebellar folia without any signal changes and with normal morphology of cervical spinal cord and brainstem, although most patients have no abnormalities in the cerebellum or brainstem.10 Proton MRS shows decreased Cr concentration with normal NAA and Cho concentration in the cerebellar vermis and deep cerebellar hemispheres without cerebellar atrophy in MRI. High lactate peaks may also be observed in cerebellum and cerebrum. These findings are assumed to be attributable to early calcium channel dysfunction.10

36.4 Autosomal Recessive Ataxia 36.4.1 Friedreich’s Ataxia Friedreich’s ataxia is the most common recessively inherited ataxia, with mean age of onset around 15 years. Clinical manifestation includes gait ataxia, limb ataxia, dysarthria, tendon areflexia, proprioceptive loss, and Babinski sign. Most patients have an unstable expansion of a repeated trinucleotide (GAA) sequence within the first intron of the FRDA gene on chromosome 9q13–21.1.11 Expanded GAA sequence reduces the transcriptional and translational efficiency, which leads to a partial deficiency of the protein frataxin. It is believed that lack of this protein can result in accumulation of iron in mitochondria, especially in the cerebellum and cortical spinal tract, which leads to atrophy and degenerative changes. As a result, the size of dentate nuclei and superior and inferior cerebellar peduncles is reduced. The corticospinal tracts are severely degenerated beyond the cervicomedullary junction, which becomes progressively more severe moving down the spinal cord. Loss of cells is also involved in cranial nerves VIII, X, and XII, which in turn results in facial weakness and difficulties with speech and swallowing.

On MRI, the cervical spinal cord or, rarely, the length of the entire cord shows atrophy from degeneration, with myelin loss and gliosis of the posterior columns and roots and the spinocerebellar and corticospinal tracts (▶ Fig. 36.7). Atrophy in the cerebellum is more pronounced in the superior vermian and paravermian area.12 Atrophy in the cerebellum and medulla has direct correlation with the severity of ataxia, as mentioned by França et al.13 DWI and DTI show white matter signal changes or damages in inferior and superior cerebellar peduncles, corticospinal tracts along internal capsule, pyramids and optic radiations.14 On MRS imaging, a decreased NAA:Cr ratio with a normal Cho:Cr ratio is observed in the pons, deep cerebellar hemisphere, and cerebral white matter.13

36.4.2 Ataxia Telangiectasia Ataxia telangiectasia (AT) usually manifests with progressive cerebellar ataxia between the ages of 1 and 3 years. Initial signs of truncal ataxia may be seen between 6 and 12 months, and usually patients with AT become unable to walk by the age of 8 to 12 years. Telangiectasia component has later onset, usually around 3 to 6 years of age, and sometimes as late as adolescence. Common clinical features include choreoathetotic involuntary movement, mental retardation, endocrine abnormalities abnormalities, such as diabetes mellitus, and recurrent respiratory tract infections that lead to bronchiectasis and chronic bronchitis. Recurrent infections are due to lymphocytopenia and a decrease or absence of immunoglobulins A and E (IgA and IgE). The lymphocytes are also very sensitive to ionizing radiation; thus, the AT patient has a high risk of lymphoma and leukemia.15 Because of the high risk of lymphoproliferative neoplasm, examination of AT patients using CT or nuclear medicine studies should be quite restrictive, and MRI should be the mainstay of imaging evaluation. On MRI, AT usually shows a pattern of CCA without signal changes in the brainstem and cerebellum. Atrophy of the cerebellar vermis and cerebellar hemispheres is most pronounced because of the loss of Purkinje and granule cells from the cerebellar cortex. Cerebellar atrophy starts from the lateral portion of the hemispheres and progresses to the inferior and superior parts and eventually to diffuse involvement of the cerebellum.15 In DWI, the ADC values are increased in the cerebellar white

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Fig. 36.7 Friedreich’s ataxia. (a) Sagittal and axial T2-weighted images show mild atrophy of the cervical cord and cerebellum. (b) Axial image also shows mild increased signal intensity in the posterior columns (arrow) from myelin loss and gliosis.

Fig. 36.8 Ataxia telangiectasia. (a) Sagittal T1-weighted imaging shows moderate cerebellar atrophy. (b) Axial gradient-echo image shows multiple hypointense foci in the white matter bilaterally, probably resulting from multiple cerebral capillary telangiectasia (arrows).

matter and cortex, with normal values in the cerebral hemispheres.16 On susceptibility-weighted or gradient echoweighted imaging, foci of hypointensity can be observed in the cerebral parenchyma, which corresponds to capillary telangiectasia, especially in 3 T MRI (▶ Fig. 36.8). Proton MRS studies show a significant decrease in NAA and Cr in the cerebellar vermis.17 On perfusion single-photon emission computed tomography, decreased cerebellar blood flow is also documented in AT patients with cerebellar atrophy.18

36.5 Metabolic Causes Various metabolic conditions involve the posterior fossa structures and may lead to morphologic or myelination abnormalities, or disturbances at the neurotransmitter or disruption

pathways. Clinical symptoms and signs of metabolic diseases are nonspecific and are often challenging for the clinician. Ataxia may be an initial or an associated symptom in the various organelle-based (lysosomal, peroxisomal, mitochondrial disorders) or nonorganelle-based metabolic disorders (aminoaciduria, organic acidemia, nuclear DNA repair defects, a defect in the gene encoding myelin proteins, and miscellaneous, including primary myelin disorders [Alexander’s disease], vacuolating leukoencephalopathy [Canavan’s disease, megalencephalic leukoencephalopathy with subcortical cysts, vanishing white matter disease], hypomyelination with atrophy of the basal ganglia and cerebellum, and merosin-deficient congenital muscular dystrophy).19,20 Metabolic causes leading to neurodegenerative disorders are discussed in detail in Chapter 34, Inborn Errors of Metabolism.

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Imaging of Cerebellar Degeneration and Cerebellar Ataxia

36.5.1 X-Linked Inherited Ataxia Fragile X Tremor Ataxia Syndrome Fragile X tremor ataxia syndrome (FXTAS) is an X-linked inherited disease with the mutation in fragile X mental retardation 1 gene (FMR1). The full mutation is an expansion of a CGG repeat in the gene to greater than 200. Usually, the normal range of CGG repeats in the FMR1 gene is less than 54. An adult man who carries a permutation in the 55 to 200 range may have cerebellar ataxia, tremor, parkinsonian features, and dementia.21 FXTAS is a late-onset neurodegenerative disorder that predominately affects men. Clinical signs and symptoms include ataxia, intention tremor, rigidity, and cognitive decline. On MRI, the main characteristic features include disproportionate atrophy of the cerebral and cerebellar hemispheres, middle cerebellar peduncles, pons, medulla, and corpus callosum for the age. Symmetric T1 hypointense and T2 hyperintense signals are seen in the peridentate white matter and middle cerebellar peduncles.22 Similar signal intensity changes are also seen in the cerebrum and cerebellum white matter, often bilateral, symmetric, patchy, or confluent, with involvement of the deep and periventricular white matter and with sparing of the subcortical U-fibers and cortical and deep gray matter. Diffuse atrophy of the cerebrum, particularly in the frontal and parietal lobes, with dilatation of the lateral ventricle and cerebellar atrophy, is also seen with normal pons morphology.23 A definite diagnosis is made on the basis of detection of the FMR1 permutation.

36.6 Sporadic Causes of Ataxia 36.6.1 Alcoholic Cerebellar Degeneration Although population-based epidemiologic studies are not available, alcoholic cerebellar degeneration (ACD) is probably the most common form of chronic cerebellar ataxia. ACD is likely due to the combination of nutritional deficiency of vitamin B1, as in Wernicke’s encephalopathy, and the toxic actions of alcohol and its derivative acetaldehyde on the neurons. Degeneration predominately affects the cerebellar cortex of the anterior superior vermis and adjacent hemispheres, parts of the cerebellum that mainly receive spinal afferents. Alcohol and acetaldehyde, its highly toxic derivative, have various deleterious actions on central neurons, including depression of neuronal firing by interaction with γ-aminobutyric acid (GABA)-ergic inhibitory mechanisms, increased lipid peroxidation, and reduction of antioxidant concentrations.24 Wernicke’s encephalopathy (WE) is the clinical syndrome, with ataxia, peripheral neuropathy, seizures, and mental confusion resulting from thiamine deficiency. Besides alcoholism, thiamine deficiency can be caused by malabsorption secondary to gastrointestinal neoplasm, bowel surgery, hyperemesis, severe malnutrition, and prolonged hyperalimentation. The neuropathological hallmarks of WE are hemorrhagic lesions that are centered on the third ventricle and affect the mammillary bodies and thalamic nuclei. If ACD and WE are on the same spectrum, WE is the acute phase that affects the cerebellum as well as other parts of

Fig. 36.9 Wernicke’s encephalopathy. (a, b) Axial fluid-attenuated inversion recovery (FLAIR) images show bilateral symmetric hyperintensity in the medial aspect of the thalami (arrows, a). Edema and hyperintensity are also seen in the mamillary bodies (fat arrow, b).

CNS, such as the mammillary bodies and thalami. ACD is the chronic variant, with cerebellar atrophy. Ataxia usually occurs subacutely in chronic alcohol users, and symptoms may stabilize for years. Strict abstinence improves ataxia; however, ataxia progresses in patients who continue to consume alcohol. On MRI, the main feature of ACD is vermal cerebellar atrophy. However, many chronic alcohol users have cerebellar atrophy without ataxia.25 WE may show symmetric areas of increased T2-weighted signal intensity in mesial thalami, mammillary bodies, and periaqueductal white matter (▶ Fig. 36.9). Contrast enhancement of the thalamus and mammilary bodies is strongly associated with alcoholism. Nonalcoholic causes may have atypical findings, such as T2 hyperintensity in the cerebellum, cranial nerve nuclei, red nuclei, dentate nuclei, splenium, and cerebral cortex. One should remember, however, that lack of imaging abnormalities does not exclude WE.

36.6.2 Paraneoplastic Cerebellar Degeneration Paraneoplastic cerebellar degeneration (PCD) is an immunemediated degenerative disorder of the cerebellar cortex that can occur in almost every neoplastic process. It is most commonly seen with small-cell lung cancer, cancer of the breast and ovary, and Hodgkin’s lymphoma. Clinically, it may manifest as pure ataxia, or it may be associated with other syndromes, like paraneoplastic encephalomyelitis. Approximately 50% of patients with PCD will have various antibodies in serum or cerebrospinal fluid (CSF).26 For example, anti-Yo antibody is associated with ovarian and breast cancer, anti-Tr antibody is associated with Hodgkin’s lymphoma, and anti-CV 2 is associated with small-cell lung cancer and malignant thymoma. The hallmark of PCD is a diffuse loss of Purkinje cells associated with inflammatory infiltrates in the cerebellar cortex, deep cerebellar nuclei, and inferior olivary nuclei. In PCD, initial MRI is often unremarkable; however, the presence of any antineuronal antibodies will confirm the diagnosis of PCD. Rarely, some cases show a transient diffuse

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Cerebellar Degeneration and Dysfunction

Fig. 36.10 Paraneoplastic syndrome. (a) Coronal T1-weighted imaging shows mild cerebellar atrophy (arrows) in a lung cancer patient with acute-onset ataxia. (b) Axial fluorodeoxyglucose (FDG) image shows intense uptake in the primary tumor (arrow) with mediastinal lymph node metastasis.

cerebellar hemispheric enlargement or cortical-meningeal enhancement. During this phase, fluorodeoxyglucose (FDG)positron emission tomography (PET) can show cerebellar hypermetabolism. In the later stages, cerebellum shows atrophy on MRI (▶ Fig. 36.10) and global decrease in FDG uptake in the cerebrum and cerebellum, with sparing of brainstem, indicating damage of cerebellar efferents to the thalamus and forearm (reverse cerebellar diaschisis).27 Generally, PCD does not respond to treatment of primary malignancy or immunosuppressive therapy. However, in some rare cases, plasma exchange, intravenous immunoglobulins, and steroids may be helpful, especially if treatment is within the first month after manifestation of ataxia.28

36.6.3 Gluten Ataxia Gluten ataxia is the most common clinical manifestation in patient with gluten-sensitive small bowel disorder. Diagnosis of gluten ataxia relies mainly on increased serum antigliadin antibodies and typical histologic findings from duodenal biopsy. Anti-gliadin antibodies target Purkinje cells and eventually lead to cerebellar cortical atrophy. However, anti-gliadin antibodies can also be found in patients with Friedreich’s ataxia and multisystem atrophy.29 The imaging pattern of gluten ataxia is nonspecific. On MRI, diffuse cortical cerebellar atrophy can be seen.

Fig. 36.11 Superficial siderosis. Axial T2-weighted imaging shows hypointense blooming strips over the cerebellar surface from hemosiderin deposition (arrows).

On proton MRS, there is decreased NAA, NAA:Cho, and NAA:Cr ratios, with an increased Cho:Cr ratio in the deep cerebellum.

36.6.4 Siderosis of the Central Nervous System Superficial siderosis is characterized by deposition of free iron and hemosiderin along the pial and subpial structures in the CNS, spinal cord, and cranial nerves, especially in the second and eighth cranial nerves.30 Causes of these subarachnoid hemorrhages may be secondary to vascular malformation, such as arteriovenous malformation and aneurysm, hemorrhagic tumor, posttraumatic or postsurgical causes. Siderosis is commonly seen in the cerebellopontine cistern and over the cerebellum, dependent structures in the cranial cavity. Clinical symptoms usually include progressive cerebellar ataxia, sensorineural hearing loss, and pyramidal signs.30 In diagnosing superficial siderosis, MRI is the mainstay. T2weighted imaging shows linear hypointensity along the surface of the brainstem, cerebellum, cranial nerves, and spinal cord in subarachnoid space (▶ Fig. 36.11). Similar changes may be also seen over the cerebral cortex. Gradient echo (T2*) and susceptibility-weighted imaging will show blooming artifact in the hemosiderin deposits in the subarachnoid space and further increase the sensitivity of detection.

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36.6.5 Multiple-System Atrophy Multiple-system atrophy (MSA) is a neurodegenerative disorder with clinical symptoms of cerebellar ataxia, parkinsonism, and autonomic nervous dysfunction. MSA can be further subdivided into MSA-C, cerebellar-predominant type (formerly known as olivopontocerebellar degeneration), and MSA-P, parkinsonism type (formerly known as striatonigral degeneration). The characteristic pathological feature of MSA is the presence of glial cytoplasmic inclusions in oligodendroglial cells at autopsy. The MSA-P type is characterized clinically by parkinsonian symptoms with prominence of rigidity. Unlike Parkinson’s disease, however, less than 15% of MSA-P patients show response to levodopa. In MSA-P, the nigrostriatal system is the main site of disease, but less severe neurodegeneration can be widespread and normally includes the olivopontocerebellar system. MRI shows atrophy of the putamen, with T2 hypointense signal along posterolateral margins of putamen secondary to iron deposition. On fluid-attenuated inversion recovery (FLAIR) imaging, a hyperintense rim around the above-mentioned T2 hypointensity may be seen as a result of the accumulation of water associated with cell loss and gliosis. In addition, on DWI, abnormal increased diffusion is often observed in the putamen and middle cerebellar peduncle as a result of neuronal loss and loss of fiber tracts in this region. For similar reasons, MRS also shows a reduction in the NAA:Cr ratio within the putamen and base of the pons. The MSA-C type usually manifests with ataxia in the lower extremities, which progresses to the upper extremities and eventually ends up with bulbar manifestation. In the MSA-C type, the olivopontocerebellar system is mainly involved, along with loss of pontine neurons and transverse pontocerebellar fibers and atrophy of middle cerebellar peduncles. The main characteristic feature in MRI is atrophy of the pons, with flattening of the inferior portion, which resembles “the loss of normal pregnant belly of pons.” In T2-weighted imaging, atrophy of brainstem, middle cerebellar peduncles (▶ Fig. 36.12), and cerebellum with characteristic hyperintensity in these same structures may show classic “hot cross bun” sign. In DWI, there is increased diffusion in the putamen, cerebellum, and middle

Fig. 36.12 Multiple-system atrophy. Axial T2-weighted imaging shows mild atrophy and patchy hyperintensity in the middle cerebellar peduncles bilaterally (black arrows).

cerebellar peduncles. Taoka et al report that in DTI, microstructural change appears selectively in the middle cerebellar peduncles, with sparing of both superior and inferior cerebellar peduncles.31 Brenneis et al found that there is direct correlation between the amount of loss of the volume of the brainstem and cerebellar hemispheres and the severity of cerebellar ataxia.32 In MRS, a decrease in the NAA:Cr ratio in the lentiform nucleus was more pronounced in the MSA-P type than in the MSA-C type.33

Fig. 36.13 Joubert malformation. (a) Sagittal T1-weighted imaging shows dysplastic superior vermis (arrow), enlargement of the fourth ventricle, and elongation of the interpeduncular cistern. (b) Axial T1-weighted imaging shows elongated superior cerebellar peduncles giving appearance of “molar tooth” (arrows).

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Cerebellar Degeneration and Dysfunction

36.6.6 Congenital Structural Malformation Cerebellar malformation can be grossly divided into hypoplasia (a small cerebellum that has fissures of normal size compared with the folia) or dysplasia (an abnormal folial pattern or the presence of heterotopic nodules of gray matter).34 These malformations may be either focal (localized to either a single hemisphere or the vermis) or generalized (involving both the cerebellar hemispheres and the vermis). Irrespective of the type, any type of malformation may manifest with ataxia. Associated brainstem anomalies may be seen either in association with, or isolated from, similar symptoms. Localized cerebellar hypoplasia is mostly due to prenatal disruption of the cerebellar development, usually by infarct or hemorrhage; rarely, it may be genetic. Severe generalized cerebellar hypoplasia is usually a sign of a wider disorder, including hindbrain malformations, malformations of cortical development, chromosomal abnormalities, or a metabolic disorder of glycosylation, particularly type 1a. Vermian dysplasias such as molar tooth–type malformation caused by mutations of primary ciliary protein genes (JSRD) and rhomboencephalosynapsis, are commonly associated with ataxia. MRI is diagnostic in such cases and shows vermis hypoplasia with the distinctive “molar tooth” sign (▶ Fig. 36.13).35 Ataxia results from abnormal function of the primary cilia, specialized membrane-bound structures that project from the neuron and ependyma surface.35 Other imaging findings include dysmorphic midbrain, thin midbrain-pons junction, enlarged horizontal nondecussating superior cerebellar peduncles (molar tooth on axial images), and a triangular-shaped fourth ventricle (“bat wing” on axial images). Other rare malformations that may be seen with inherited cerebellar ataxia include rhomboencephalosynapsis (characterized by a midline continuity of the infratentorial structures, such as the deep cerebellar nuclei, superior cerebellar peduncles, and cerebellar hemispheres) and cerebellar cortical dysgenesis. The Dandy-Walker malformation is not a common cause of ataxia in children. Arnold-Chiari malformation, a congenital malformation, may manifest with ataxia at a later age. Chiari type I is secondary to mismatch between the (small) posterior fossa size and normal cerebellum size.36 Symptoms are due mainly to brainstem compression and include hypersomnolence, central apnea, torticollis, ataxia, neck or back pain, or bulbar signs. On CT or MRI, cerebellar tonsils will be equal or more than 5 mm below the foramen magnum or opisthion-basion line (▶ Fig. 36.14). The morphology of the tonsils is a more important factor than the extent of descent, such as pointed, triangular, or peglike rather than round. The foramen magnum will be crowded, with effacement or cisterns. On T2-weighted MRI, the tonsillar folia will be oriented in the vertical or oblique plane with or without syringohydromyelia in the cervical cord. The most important MRI sequence is phase-contrast cine MRI showing pulsatile systolic tonsillar descent with obstruction of CSF flow through the foramen magnum.37

Fig. 36.14 Chiari I malformation. Cerebellar tonsils are elongated and extend below the foramen magnum (arrow). Cervical cord shows syringohydromyelia (fat arrow).

References [1] Mascalchi M, Vella A. Magnetic resonance and nuclear medicine imaging in ataxias. In: Subramony SH, Dürr A, eds. Handbook of Clinical Neurology. Vol. 103, 3rd series, Ataxic Disorders. Amsterdam: Elsevier B.V.; 2012;85–110 [2] Luef G, Burtscher J, Kremser C et al. Magnetic resonance volumetry of the cerebellum in epileptic patients after phenytoin overdosages. Eur Neurol 1996; 36: 273–277 [3] Storey E. Dominantly inherited ataxias. Part II. J Clin Neurosci 1998; 5: 369– 377

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Imaging of Cerebellar Degeneration and Cerebellar Ataxia [4] Bird TD. Hereditary ataxia overview online. Available in: GeneReviews. 2008. Available at: http://www.ncbi.nlm.nih.gov/bookshelf [5] Iwabuchi K, Tsuchiya K, Uchihara T, Yagishita S. Autosomal dominant spinocerebellar degenerations: clinical, pathological, and genetic correlations. Rev Neurol (Paris) 1999; 155: 255–270 [6] Gilman S, Sima AA, Junck L et al. Spinocerebellar ataxia type 1 with multiple system degeneration and glial cytoplasmic inclusions. Ann Neurol 1996; 39: 241–255 [7] Manto MU. The wide spectrum of spinocerebellar ataxias (SCAs). Cerebellum 2005; 4: 2–6 [8] Guerrini L, Lolli F, Ginestroni A et al. Brainstem neurodegeneration correlates with clinical dysfunction in SCA1 but not in SCA2: a volumetric, diffusion and proton spectroscopy MR study. Brain 2004; 127: 1785–1795 [9] Jen JC, Graves TD, Hess EJ, Hanna MG, Griggs RC, Baloh RW, CINCH investigators. Primary episodic ataxias: diagnosis, pathogenesis and treatment. Brain 2007; 130: 2484–2493 [10] Harno H, Heikkinen S, Kaunisto MA et al. Decreased cerebellar total creatine in episodic ataxia type 2: a 1H MRS study. Neurology 2005; 64: 542–544 [11] Campuzano V, Montermini L, Moltò MD et al. Friedreich’s ataxia: autosomal recessive disease caused by an intronic GAA triplet repeat expansion. Science 1996; 271: 1423–1427 [12] Della Nave R, Foresti S, Tessa C et al. ADC mapping of neurodegeneration in the brainstem and cerebellum of patients with progressive ataxias. Neuroimage 2004; 22: 698–705 [13] França MC, Jr, D’Abreu A, Yasuda CL et al. A combined voxel-based morphometry and 1H-MRS study in patients with Friedreich’s ataxia. J Neurol 2009; 256: 1114–1120 [14] Mantovan MC, Martinuzzi A, Squarzanti F et al. Exploring mental status in Friedreich’s ataxia: a combined neuropsychological, behavioral and neuroimaging study. Eur J Neurol 2006; 13: 827–835 [15] Tavani F, Zimmerman RA, Berry GT, Sullivan K, Gatti R, Bingham P. Ataxiatelangiectasia: the pattern of cerebellar atrophy on MRI. Neuroradiology 2003; 45: 315–319 [16] Firat AK, Karakaş HM, Firat Y, Yakinci C. Quantitative evaluation of brain involvement in ataxia telangiectasia by diffusion weighted MR imaging. Eur J Radiol 2005; 56: 192–196 [17] Wallis LI, Griffiths PD, Ritchie SJ, Romanowski CA, Darwent G, Wilkinson ID. Proton spectroscopy and imaging at 3 T in ataxia-telangiectasia. AJNR Am J Neuroradiol 2007; 28: 79–83 [18] Jiang H, Tang B, Xia K et al. Mutation analysis of the ATM gene in two Chinese patients with ataxia telangiectasia. J Neurol Sci 2006; 241: 1–6 [19] Kanekar S, Gustas C. Metabolic disorders of the brain: part I. Semin Ultrasound CT MR 2011; 32: 590–614 [20] Kanekar S, Verbrugge J. Metabolic disorders of the brain: part II. Semin Ultrasound CT MR 2011; 32: 615–636

[21] Hagerman PJ, Hagerman RJ. Fragile X-associated tremor/ataxia syndrome (FXTAS). Ment Retard Dev Disabil Res Rev 2004; 10: 25–30 [22] Ginestroni A, Guerrini L, Della Nave R et al. Morphometry and 1H-MR spectroscopy of the brain stem and cerebellum in three patients with fragileX-associated tremor/ataxia syndrome. AJNR Am J Neuroradiol 2007; 28: 486– 488 [23] Jacquemont S, Hagerman RJ, Leehey M et al. Fragile X premutation tremor/ ataxia syndrome: molecular, clinical, and neuroimaging correlates. Am J Hum Genet 2003; 72: 869–878 [24] Mameli M, Botta P, Zamudio PA, Zucca S, Valenzuela CF. Ethanol decreases Purkinje neuron excitability by increasing GABA release in rat cerebellar slices. J Pharmacol Exp Ther 2008; 327: 910–917 [25] Hillbom M, Muuronen A, Holm L, Hindmarsh T. The clinical versus radiological diagnosis of alcoholic cerebellar degeneration. J Neurol Sci 1986; 73: 45– 53 [26] Perlman SL. Ataxias. Clin Geriatr Med 2006; 22: 859–877 [27] Anderson NE, Posner JB, Sidtis JJ et al. The metabolic anatomy of paraneoplastic cerebellar degeneration. Ann Neurol 1988; 23: 533–540 [28] David YB, Warner E, Levitan M, Sutton DM, Malkin MG, Dalmau JO. Autoimmune paraneoplastic cerebellar degeneration in ovarian carcinoma patients treated with plasmapheresis and immunoglobulin: a case report. Cancer 1996; 78: 2153–2156 [29] Abele M, Bürk K, Schöls L et al. The aetiology of sporadic adult-onset ataxia. Brain 2002; 125: 961–968 [30] Fearnley JM, Stevens JM, Rudge P. Superficial siderosis of the central nervous system. Brain 1995; 118: 1051–1066 [31] Taoka T, Kin T, Nakagawa H et al. Diffusivity and diffusion anisotropy of cerebellar peduncles in cases of spinocerebellar degenerative disease. Neuroimage 2007; 37: 387–393 [32] Brenneis C, Boesch SM, Egger KE et al. Cortical atrophy in the cerebellar variant of multiple system atrophy: a voxel-based morphometry study. Mov Disord 2006; 21: 159–165 [33] Watanabe H, Fukatsu H, Katsuno M et al. Multiple regional 1H-MR spectroscopy in multiple system atrophy: NAA/Cr reduction in pontine base as a valuable diagnostic marker. J Neurol Neurosurg Psychiatry 2004; 75: 103–109 [34] Patel S, Barkovich AJ. Analysis and classification of cerebellar malformations. AJNR Am J Neuroradiol 2002; 23: 1074–1087 [35] Brancati F, Dallapiccola B, Valente EM. Joubert syndrome and related disorders. Orphanet J Rare Dis 2010; 5: 20 [36] Poretti A, Huisman TA, Scheer I, Boltshauser E. Joubert syndrome and related disorders: spectrum of neuroimaging findings in 75 patients. AJNR Am J Neuroradiol 2011; 32: 1459–1463 [37] Ventureyra EC, Aziz HA, Vassilyadi M. The role of cine flow MRI in children with Chiari I malformation. Childs Nerv Syst 2003; 19: 109–113

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Part XV Motor Neuron Disorders

37 Overview of Motor Neuron Disorders

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38 Neuroimaging of Motor Neuron Disorders

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Motor Neuron Disorders

37 Overview of Motor Neuron Disorders Divisha Raheja and Zachary Simmons The terms motor neuron disease (MND) and amyotrophic lateral sclerosis (ALS) are often used synonymously, particularly in the United Kingdom. However, an understanding of some basic neuroanatomy will help clarify the terms upper motor neuron (UNM) and lower motor neuron (LMN) and so will facilitate a more sophisticated understanding of MNDs as a family of related disorders, ranging from pure UMN to pure LMN to mixed UMN-LMN syndromes. The role of imaging in the diagnosis of MNDs has traditionally been limited to excluding alternative diagnoses. However, significant advances have been made in the last few years in the development of techniques to detect early signs of UMN involvement. This chapter focuses primarily on the clinical features of MND. The role of imaging in the diagnosis and understanding of MND is discussed in detail in Chapter 38.

37.1 Upper Motor Neuron Disorders Upper motor neurons (▶ Fig. 37.1) are neurons that have cell bodies in the primary motor cortex (Brodmann area 4) and the premotor area (Brodmann area 6) of the brain; they give rise to descending corticobulbar and corticospinal tracts that terminate on interneurons or motor neurons in cranial nerve motor nuclei or in spinal cord gray matter. UMN disorders are characterized by poor motor control, with loss of dexterity, spasticity, and pseudobulbar (spastic bulbar) palsy. Loss of muscle strength is generally mild until the disease is advanced because the LMNs are spared.

37.1.1 Primary Lateral Sclerosis Primary lateral sclerosis (PLS) is a progressive disorder primarily affecting the UMNs. Originally described by Charcot in 1865 and Erb in 1875,1,2 it accounts for 3 to 5% of cases of MND.3 Symptoms in PLS usually begin in the fifth to sixth decade as a slowly evolving spastic paraparesis that spreads to the upper extremities and the bulbar muscles. Rarely, onset can be in the bulbar region, or it can ascend or descend in a hemiplegic fashion before progression to the other side (Mills syndrome).4,5 Progression is usually slow, over many years. The weakness is generally mild, and patients report clumsiness, a stiff and awkward gait, poor coordination, and loss of dexterity. Muscle atrophy is mild, if present, generally a result of disuse, and there are no sensory symptoms. Bulbar symptoms usually begin as a mild spastic dysarthria and can progress to severe dysarthria and dysphagia associated with sialorrhea. Patients not uncommonly develop emotional lability, characterized by inappropriate laughter or crying, also known as pseudobulbar affect. Muscle cramps and spasms are common symptoms as well. Bladder function is rarely affected and is usually a late occurrence. Dementia is not a common feature, although subtle cognitive difficulties may be seen on neuropsychological testing in more than 30% of PLS patients, most commonly involving executive function.6 Patients usually do not have visual symptoms, but

eye movement abnormalities can be seen. Prognosis is significantly better than that for ALS,7 with patients demonstrating slow progression over many years. Diagnostic criteria were proposed by Pringle et al in 1992, but specificity is low, and these criteria are no longer in wide use. The Pringle criteria required the disease to be limited to UMN findings for at least 3 years to exclude ALS.4 More recently, it has been proposed that the diagnosis of PLS should be considered if the patients do not exhibit any LMN findings clinically or electrophysiologically 4 years after the onset of symptoms.3,5 Updated diagnostic criteria have been published (▶ Table 37.1). The diagnosis is mainly clinical, with ancillary tests used to exclude other disorders. Recommended laboratory studies for assessment of UMN dysfunction include vitamin B12 levels, copper levels, human T-cell lymphotrophic virus (HTLV) titers, hexosaminidase A levels (for adult-onset Tay-Sachs disease), and very-long-chain fatty acids to assess for adrenomyeloneuropathy. Serum creatine kinase (CK) levels usually are normal and so are electrodiagnostic studies (nerve-conduction studies and needle electromyography). Historically, imaging studies have been performed to exclude mass lesions of the brain or spinal cord or to assess for other central nervous system diseases, such as multiple sclerosis, but modern imaging techniques may play a much more meaningful role. Neuroimaging techniques, including magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET) scans, and diffusion tensor imaging show changes along the corticospinal tracts, discussed in detail in Chapter 38. Treatment is mainly supportive. Most recommendations for symptomatic treatment have been directed toward ALS patients, but a similar approach can be used for those with PLS. Baclofen, benzodiazepines, and tizanidine are usually the firstline drugs for treatment of spasticity. Dantrolene also is used, although less frequently, and some patients may benefit from the insertion of an intrathecal baclofen pump for spasticity that is refractory to oral agents.8,9 Botulinum toxin intramuscular injections may improve function in selected muscles. Pseudobulbar affect can be controlled with tricyclic antidepressants, selective serotonin reuptake inhibitors, or a combination of dextromethorphan and quinidine.10,11,12 Sialorrhea generally responds to anticholinergic agents, such as glycopyrrolate, amitriptyline, benztropine, hyoscyamine, and transdermal scopolamine.13,14

37.1.2 Hereditary Spastic Paraplegia Hereditary spastic paraplegia (HSP) is a rare, genetically heterogeneous group of disorders characterized by progressive lower extremity spasticity. The incidence has been reported to range from 3 to 10 per 100,000 population.15 Patients commonly show symptoms in the second to fourth decade of life, although juvenile onset has been reported. Retrograde “dying back” degeneration of axons of the corticospinal tracts and posterior columns is the common pathological feature.15,16 The genetics of HSP are extraordinarily heterogeneous. Although the most common mode of inheritance is autosomal dominant, it may be

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Overview of Motor Neuron Disorders

Fig. 37.1 Anatomy of upper and lower motor neurons: Upper motor neurons arise from the cerebral cortex, giving rise to the corticospinal and corticobulbar tracts that terminate on cranial nerve motor nuclei in the brainstem and in the anterior horns of the spinal cord. Lower motor neurons arise from the cranial nerve nuclei in the brainstem and from the anterior horns of the spinal cord, terminating on the muscle.

Table 37.1 Diagnostic categories of primary lateral sclerosis (PLS)3 Autopsy-proven PLS

Clinically diagnosed PLS with degeneration in the motor cortex and corticospinal tracts; no loss of motor neurons in the spinal cord or brainstem; no gliosis in the anterior horn cells, and no Bunina or ubiquitinated inclusions

Clinically pure PLS

Evident UMN signs; no focal muscle atrophy or visible fasciculation; no denervation in EMG 4 yr from symptom onset; age at onset after 40 yr; secondary and mimicking conditions excluded by laboratory and neuroimaging

UMN-dominant ALS

Symptoms less than 4 yr or disability from predominantly UMN signs but with minor EMG denervation or LMN signs on examination; not sufficient to meet diagnostic criteria for ALS

PLS plus

Predominant UMN signs also with clinical, laboratory, or pathologic evidence of dementia, parkinsonism, or sensory tract abnormalities. Note: If cerebellar signs, urinary incontinence, or orthostatic hypotension are evident, multiple-system atrophy could be considered

Symptomatic lateral sclerosis

Clinically diagnosed PLS with evident possible cause (human immunodeficiency virus, paraneoplastic syndrome)

Abbreviations: EMG, electromyography; LMN, lower motor neuron; UMN, upper motor neuron. Source: Modified and reprinted with permission. Copyright © 2006 by Wolters Kluwer Health

inherited in an autosomal recessive or X-linked fashion. A total of 31 genes and 20 loci are known to be causative. Nearly 40% of the autosomal dominant families and 10% of the sporadic cases have been linked to the SPAST gene, located on chromosome 2.17,18 Hereditary spastic paraplegia is classified as pure, or uncomplicated, when the symptoms are primarily those of lower extremity spasticity. Other features also include mild sensory loss in the distal lower extremities and urinary urgency

or frequency.19 Complex or complicated HSP is phenotypically diverse and manifests as spastic paraplegia in association with other neurologic abnormalities, which may include a combination of ataxia, amyotrophy, pigmentary retinopathy, mental retardation, epilepsy, dementia, peripheral neuropathy, deafness, and ichthyosis.15 Diagnosis is mainly clinical and is based on the individual and family history. In the absence of family history, diagnostic evaluation should be directed toward other conditions that may mimic HSP: structural causes, such as

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Motor Neuron Disorders hydrocephalus and myelopathy; degenerative or inflammatory processes, such as multiple sclerosis and leukodystrophies; infections, such as syphilis, HTLV, and human immunodeficiency virus (HIV); metabolic disorders, including deficiencies of vitamin B12, copper, and vitamin E; and paraneoplastic disorders. Electrodiagnostic studies are normal in uncomplicated cases but may demonstrate peripheral neuropathy in some complicated cases. The primary role of imaging is to rule out other pathologies, such as multiple sclerosis or myelopathy from compressive, inflammatory, or ischemic etiologies. Treatment is mainly supportive. Medications to reduce spasticity include baclofen, tizanidine, benzodiazepines, and dantrolene. Intrathecal baclofen and botulinum toxin intramuscular injections are alternative options if the desired effect cannot be achieved with the oral medications.

37.2 Lower Motor Neuron Disorders Lower motor neurons (▶ Fig. 37.1) are somatic efferent neurons whose cell bodies are located either in cranial nerve motor nuclei or in the ventral spinal cord. These are the final pathways between the central nervous system and the skeletal muscles. Muscle weakness, atrophy, fasciculations, and cramps are the primary clinical symptoms in LMN diseases and are the result of muscle denervation. Electrodiagnostic studies are key tests in the diagnosis of these disorders. Lowamplitude motor nerve-conduction studies with relatively preserved latencies and conduction velocities result from axonal loss. Sensory nerve-conduction studies are normal in pure LMN disorders. On needle electromyography, fibrillation potentials and positive sharp waves with or without fasciculation potentials can be seen, suggesting an active denervating process; motor unit action potentials of large amplitude, increased duration, and increased polyphasia reflect chronic denervation and reinnervation.

Muscle biopsy may be useful when clinical and electrodiagnostic findings are mild and nonspecific, particularly in early stages of the disease. Biopsies serve to rule out other causes of muscle weakness, such as a primary muscle disease, or an inflammatory process, such as vasculitis. When viewed under the microscope, denervated muscle fibers appear small and angulated and demonstrate dark staining on oxidative enzyme and nonspecific esterase stains. Progressive chronic denervation and reinnervation usually lead to loss of randomness of the normal “checkerboard” pattern of muscle fibers, eventually resulting in groups of only one muscle fiber type, known as fiber type grouping (▶ Fig. 37.2).

37.2.1 Spinal Muscular Atrophy Spinal muscular atrophy (SMA) comprises a group of hereditary disorders characterized by progressive degeneration of anterior horn cells and selected brainstem motor nuclei, resulting in muscle atrophy and symmetric, predominantly proximal muscle weakness, in association with tongue fasciculations and markedly reduced to absent deep tendon reflexes.20 The first few cases of infantile, progressive weakness were described by Werdnig in 1891 and Hoffman in 1893.21,22,23 The International Consortium classified SMAs on the basis of age of onset and highest level of function (▶ Table 37.2).20 SMA I is characterized by severe, generalized muscle weakness and hypotonia at birth with rapidly progressive respiratory failure. Death usually occurs by 2 years of age.21,22 SMA II usually manifests in early childhood. These children are able to sit without assistance, but they never walk or stand unassisted. In SMA III, patients have proximal muscle weakness after the age of 18 months, are able to walk unassisted, and usually survive to adulthood.24 SMA IV has onset in adulthood and is characterized by proximal muscle weakness in a limb girdle pattern, leading to progressive difficulty in walking, climbing stairs, and getting up from a seated position. Life expectancy is normal.25 Fasciculations are commonly seen in SMA types III and IV. Bulbar muscle weakness is

Fig. 37.2 Muscle biopsy from an amyotrophic lateral sclerosis (ALS) patient showing darkly staining angulated denervated muscle fibers (arrow) on nonspecific esterase staining (a). Adenosine triphosphatase stain showing fiber-type grouping and loss of randomness (arrow) of type 1 (lightly stained) and type 2 (darkly stained) fibers (b).

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Overview of Motor Neuron Disorders Table 37.2 Classification of spinal muscular atrophy (SMA)20,21,22,24,25 SMA type

Age of onset

Inheritance

SMA I (Werdnig-Hoffman disease)

Infancy

Autosomal recessive

Death by 2 yr of age

SMA II

6–18 mo

Autosomal recessive

10–40 yr; may be able to stand unassisted, but never walk

SMA III (Kugelberg-Welander disease)

After 18 mo

Autosomal recessive

Able to walk; survive till adulthood

SMA IV (adult onset)

After 20 yr

Autosomal recessive

Normal life span

a common feature in SMA type I but usually is not seen in the others. Almost all cases are inherited in an autosomal recessive fashion. The gene responsible for SMA was identified in 1995 as the survival motor neuron gene (SMN), located on the long arm of chromosome 5.26 In humans, two forms of the SMN gene exist: SMN1 encodes for the full-length messenger RNA and hence is responsible for coding the SMN protein; SMN2 is identical to SMN1 except for a transition of cytosine to thymine at position 840 in exon 7, which results in a truncated protein that is not functional and is easily degraded. About 90% of the mRNA transcripts from SMN2 lack exon 7, but a small fraction can be translated into normal SMN protein. In the absence of SMN1, patients are dependent for survival on SMN2 to make the SMN protein. Thus, the number of SMN2 copies has major implications for the phenotype. The larger the number of SMN2 copies, the better the prognosis.27,28 When SMA is suspected clinically, the diagnosis is confirmed by molecular genetic testing to identify homozygous deletions of the SMN gene on chromosome 5q. CK levels are usually normal in SMA I and II, although they can be elevated up to 10 times the upper limit of normal in types III and IV. Electrodiagnostic studies and muscle biopsies demonstrate chronic denervation as described above. The mainstay of treatment is supportive care, including therapy, nutritional support, respiratory care, orthotics, and orthopedic interventions. Gene therapy and stem cell therapy are under investigation.

37.2.2 Kennedy’s Disease Kennedy’s disease, also known as bulbospinal muscular atrophy, is an X-linked recessive, adult-onset LMN disorder, described in 1968.29 Patients commonly manifest symptoms in their third or fourth decades of life with bulbar dysfunction and proximal muscle weakness. Gynecomastia is common. Although sensory symptoms are not common, sensory abnormalities have been noted on electrodiagnostic studies. Consistent with this, nerve biopsies and autopsies have revealed that there is degeneration not only of the anterior horn cells, but also of the dorsal root ganglia, resulting in sensory nerve fiber loss. This broader understanding resulted in renaming the disorder in 1982 as X-linked recessive bulbospinal neuronopathy.30 The genetic defect was recognized by La Spada et al in 1991 to be a cytosine-adenine-guanine (CAG) trinucleotide repeat expansion within exon 1 of the androgen receptor gene located on the X chromosome.31 The number of repeats in normal individuals varies between 11 and 30; the range is from 40 to 65 in symptomatic individuals. Very low repeat numbers (i.e., less than 11) are associated with mental retardation, and repeats between 30 and 40 are associated with reduced cognitive func-

Survival/prognosis

tion.32,33 The number of repeats has an inverse correlation with the age of onset but is unrelated to the rate of progression.33,34 Clinically, patients have progressive, painless, asymmetric, proximal muscle weakness; wasting of the facial, bulbar, and limb muscles; and endocrinologic abnormalities, such as progressive testicular atrophy, azoospermia, infertility, and gynecomastia. Degeneration of the dorsal root ganglia results in sensory loss in the distal extremities. Muscle fasciculations are common, including facial and perioral fasciculations in more than 90% of patients.33,35 Muscle cramps are common as well. The disease follows a slowly progressive course, with a median survival of more than 20 years from the onset of symptoms. The average life span of patients is only minimally reduced, with a reported 10-year survival rate of 82%, compared with 95% among age-matched controls.34,36 Thus, distinguishing between Kennedy’s disease and the much more rapidly progressive ALS is critically important for patient care purposes. The diagnostic evaluation includes molecular genetic testing for the expansion of CAG repeats on exon 1 of the androgen receptor gene. CK levels are almost always high and may be up to 10 times normal. Electrodiagnostic studies usually reveal decreased sensory and motor nerve amplitude, indicative of axonal degeneration of both sensory and motor axons.37 These findings are more severe in the upper than in the lower extremities, unlike the length-dependent process in peripheral neuropathies. Both active and chronic denervation are seen on needle electromyography examination, with chronic changes predominating. MRI may reveal decreased diameter of the cervical spinal cord.38 Treatment is supportive. Symptomatic therapy for muscle cramps and physical therapy are the mainstays. Experimental trials in animals suggested a role for androgen reduction therapy in slowing progression of the disease.39 However, randomized, placebo-controlled trials testing the androgenreducing agents leuprorelin and dutasteride did not reveal any significant effects on swallowing and muscle strength.40,41

37.2.3 Hirayama’s Disease Hirayama’s disease, also known as juvenile spinal muscular atrophy of the distal upper extremity or monomelic amyotrophy, was originally described in Japan by Hirayama and colleagues in 1959.33 Most reported cases have been from Asia, particularly Japan, but additional cases have since been recognized in the western hemisphere. It is a rare disease, affecting primarily young men, most commonly between the ages of 15 and 25 years, showing insidious, asymmetric atrophy of the hand and forearm. The C7-T1 myotomes are most commonly affected, while sparing the brachioradialis muscle, but proximal weakness has also been noted. The weakness is predominantly unilateral in most affected individuals, although asymmetric and

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Motor Neuron Disorders

Fig. 37.3 Hand atrophy in an amyotrophic lateral sclerosis (ALS) patient. (a) Palmar aspect and (b) dorsal aspect representing the classic claw hand due to intrinsic hand muscle atrophy.

rarely symmetric bilateral upper extremity weaknesses have been reported. It is a benign disease, with an initial progressive course followed by stabilization after 2 to 3 years.42,43,44 The cause of the disease is unproven. It is speculated that repeated flexion and extension of the neck lead to flattening of the spinal cord secondary to microvascular ischemia. The first autopsy case was described in 1982 as demonstrating atrophic changes and gliosis in the anterior horn cells, with anteroposterior flattening of the cervical cord.45 MRIs have been extremely valuable in the diagnosis and are discussed in detail in Chapter 38. Early diagnosis is important because intervention with a cervical collar at early stages can prevent recurrent flexion changes and prevent further progression.46,47

37.3 Disorders of Both Upper and Lower Motor Neurons 37.3.1 Amyotrophic Lateral Sclerosis Jean-Marie Charcot first described ALS in 1869.48,49 ALS is a fatal neurodegenerative disorder affecting both the upper and lower motor neurons in the cerebral cortex, brainstem, and spinal cord. The course of the disease is inexorably progressive, resulting in death from respiratory failure. More than 90% of the cases are sporadic (SALS). Only about 5% are familial (FALS),50 most commonly autosomal dominant. The lifetime risk of acquiring ALS by age 70 is between 1 in 400 and 1 in 1,000.51 The incidence of ALS worldwide is between 0.3 and 2.5 cases per 100,000 population per year.52,53 In Europe and the United States, the incidence is estimated to be 2 per 100,000 per year, with a prevalence of about 5.4 per 100,000.51,54 ALS is known by several others names, including Charcot disease (primarily in France), motor neuron disease, and (mostly in the United States) as Lou Gehrig’s disease after the famous baseball player who developed the disease in the 1930s.

Fig. 37.4 Tongue atrophy in an amyotrophic lateral sclerosis (ALS) patient.

Clinical Presentation and Epidemiology Patients most commonly have a combination of UMN and LMN findings. LMN symptoms and signs include asymmetric, painless weakness associated with muscle atrophy ( ▶ Fig. 37.3, ▶ Fig. 37.4), fasciculations, and muscle cramps, whereas UMN findings usually are characterized by spasticity and brisk reflexes. Bulbar involvement is common, resulting in dysarthria and dysphagia. Bulbar onset is seen in about 25% of patients; 70% initially develop symptoms in the extremities.55 Less than 5% of the time, trunk involvement with progressive respiratory failure may be seen at onset. Patients with ALS may initially have predominantly or exclusively UMN or LMN symptoms and signs, although most eventually develop both. The overall incidence of SALS is higher in men than in women, although there is a mild female predominance among bulbar-onset patients. FALS is equally common in men and women. The peak incidence is in the sixth to seventh decades and is about 10 years older for sporadic than familial patients. Mean survival from symptom onset to death is approximately

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Overview of Motor Neuron Disorders 3 years, but the survival curve has a relatively long tail, with one in five patients living 5 years and one in 10 patients surviving 10 years or longer.53,55 Older age at onset, early respiratory muscle dysfunction, and bulbar onset are associated with shorter survival. Fatigue and decreased exercise capacity are common symptoms in ALS, and ultimately most patients require assistance with activities of daily living. Dysphagia eventually occurs in most patients, placing them at risk for weight loss and malnutrition, further compromising the prognosis. Respiratory dysfunction usually presents with exertional dyspnea and orthopnea. Eventually, progressive weakening of the respiratory muscles leads to respiratory failure and death. Cognitive impairment, usually in the form of frontotemporal dysfunction, occurs in 30 to 50% of patients, with about 15% developing a full-blown frontotemporal dementia (FTD).56,57,58

Cause, Pathogenesis, and Genetics The cause of ALS remains unknown. The pathophysiological mechanisms underlying the disease process appear to involve a complex interaction of genetic and molecular pathways. Various mechanisms, including viral infections, activation of the immune system, exogenous toxins, mitochondrial dysfunction, neuroinflammation, oxidative stress, axonal transport, and protein misfolding and degradation, have been considered and investigated over the years.59,60,61 The incidence of ALS has been noted to be higher in football players, smokers, and personnel who served in armed forces.62,63,64 The role of androgen toxicity and protective effect of estrogens have been speculated as contributing to higher incidence of the disease in men.65,66 Glutamate-induced neurotoxicity has also been implicated in the pathogenesis. Neurotoxins like β-methyl-amino-L-alanine have been associated with the epidemic of ALS and parkinsonism on the island of Guam, although there is not universal agreement about this.67,68,69 The discovery of mutations in the superoxide dismutase 1 (SOD1) gene in patients with FALS by Siddique and colleagues was a landmark in the history of ALS research.70 This gene, located on chromosome 21, encodes for the enzyme Cu-Zn superoxide dismutase, and variants of it are responsible for up to 20% of FALS cases. SOD1 mutations have also been identified in about 1 to 4% of sporadic cases.71 Cu-Zn superoxide dismutase catalyzes the dismutation of superoxide (O2-), resulting in oxygen and hydrogen peroxide. Mutations in SOD1 result in a toxic gain of function of the enzyme, leading to the generation of free radicals and causing progressive neuronal death. SOD1associated FALS is usually inherited in an autosomal dominant fashion, although recessive transmission has been described in patients from Sweden and Finland.61,72 The transactive response TAR-DNA binding protein (TARDP) gene codes for the TDP-43 protein, which normally is localized in the nucleus; however, the cleaved form can be seen in the cytoplasm in pathological conditions. This TDP-43 protein is the major disease protein in ubiquitin-positive, tau-negative, and αsynuclein negative frontotemporal dementia (FTD) and is also present as cytoplasmic inclusions in almost all patients with ALS, strongly suggesting an overlap between FTD and ALS.73 Mutations in the TARDP gene account for 5 to 10% of FALS cases and are found in up to 2% of patients with SALS.53,55,71 Various other genes, including OPTN, FUS, and ANG (involved in RNA

metabolism) have been implicated in both FALS and SALS. The recent connection between the open reading frame 72 gene (C9ORF72), located at 9p21, and ALS has raised new hopes for a better understanding of ALS pathophysiology because of its unique features and apparently widespread occurrence. A hexanucleotide (GGGGCC) repeat expansion in the noncoding promoter region of C9ORF72 has been identified in 24 to 46% of FALS patients and in 4 to 21% of SALS patients, making it the most commonly mutated ALS gene.74,75,76

Diagnosis The clinical hallmark of ALS is the presence of both UMN and LMN signs at the bulbar, cervical, thoracic, and lumbar levels. Often, diagnosis is delayed because of the insidious onset of symptoms, with a median time to diagnosis from onset of symptoms of about 14 months.77 There is no single blood test, imaging study, or other biomarker that is specific for ALS. The diagnosis is based on an appropriate history and neurologic examination, supported by electrodiagnostic studies. A variety of blood tests are done to rule out ALS mimics. Examination of cerebrospinal fluid (CSF) should be performed if an infectious or infiltrative process is suspected or if an acquired demyelinating polyneuropathy, such as chronic inflammatory demyelinating polyneuropathy, is being considered. Other than mildly to moderately elevated serum CK levels and mildly elevated CSF protein levels, blood and CSF tests are expected to be normal in ALS. Electrodiagnostic studies are an invaluable tool in the diagnosis of ALS. They assist the physician in assessing the extent of LMN involvement, and can be abnormal early in the course of the disease. Sensory nerve–conduction studies are invariably normal. Motor nerve–conduction studies may be normal or may reveal reduced amplitudes suggesting axonal loss. Needle examination characteristically reveals widespread active denervation (fibrillation potentials, positive sharp waves, and fasciculation potentials) and often shows chronic neurogenic changes as well, without a specific nerve root or peripheral nerve distribution.78 Imaging is performed primarily to exclude other structural, inflammatory, or infiltrative processes. For example, cervical stenosis with multilevel neural foraminal stenosis can result in upper motor neuron signs resulting from myelopathy and lower motor neuron findings from superimposed polyradiculopathy. Proton density–weighted MRI may reveal hyperintensity within the motor tracts (specifically the internal capsule) in ALS patients. Newer imaging techniques, including MRS, functional MRI, PET, and diffusion tensor imaging may show early changes suggestive of UMN involvement.79 These topics are discussed in detail in Chapter 38. The El Escorial diagnostic criteria were established by the World Federation of Neurology in 1991 and were subsequently modified to increase the sensitivity.80 Clinical, electrodiagnostic, and (optionally) neuropathological findings are used to arrive at a diagnosis of clinically definite, probable, laboratorysupported probable, or possible ALS based on a thorough evaluation at the bulbar, cervical, thoracic, and lumbar levels (▶ Table 37.3). The Awaji criteria were introduced in 2008 to increase the diagnostic sensitivity. These criteria emphasized the use of electrodiagnostic findings in the clinical context and not as separate stand-alone data and thus recommended eliminating the category of probable laboratory-supported ALS.

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Motor Neuron Disorders Table 37.3 Revised El Escorial criteria81 The diagnosis of ALS requires:

(A) The presence of: ● (A:1) Evidence of LMN degeneration by clinical, electrophysiological or neuropathologic examination ● (A:2) Evidence of UMN degeneration by clinical examination, and ● (A:3) Progressive spread of symptoms or signs within a region or to other regions, as determined by history or examination, together with (B).

The clinical diagnosis of ALS, without pathological confirmation, may be categorized into various levels of certainty:









(B) The absence of: ● (B:1) Electrophysiologic or pathological evidence of other disease processes that might explain the signs of LMN and/or UMN degeneration, and ● (B:2) Neuroimaging evidence of other disease processes that might explain the observed clinical and electrophysiological signs

Clinically definite ALS: UMN and LMN signs in three regions Clinically probable ALS: UMN and LMN signs in at least two regions with UMN signs rostral to LMN signs Clinically probable ALS: Laboratorysupported: clinical signs of UMN and LMN dysfunction in only one region or UMN signs alone in one region, and LMN signs defined by EMG criteria in at least two limbs, with proper application of neuroimaging and clinical laboratory protocols to exclude other causes Possible ALS: UMN and LMN signs in one region, UMN signs alone in two or more regions, or LMN signs above UMN signs

Abbreviations: EMG, electromyography; LMN, lower motor neuron; UMN, upper motor neuron. Source: Modified and reprinted with permission. Copyright © 2000 by Informa Healthcare. Source: Brooks BR, Miller RG, Swash M, Munsat TL; World Federation of Neurology Research Group on Motor Neuron Diseases. El Escorial revisited: revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Other Motor Neuron Disord 2000;1(5):293–299

The Awaji criteria also recommended that fasciculation potentials be taken as evidence of lower motor neuron dysfunction, thus eliminating the often-challenging need to find fibrillation potentials and positive sharp waves, particularly in cranial nerve innervated muscles or clinically unaffected limb muscles.81,82

Treatment The mainstay of treatment for ALS patients is supportive, and thus palliative care and maximizing quality of life are the focus of management.83,84 Riluzole, an inhibitor of glutamate release, is the only approved drug for ALS. It is a disease-modifying drug that has been shown to prolong survival, on average, by 2 to 3 months.85 Symptomatic treatment includes management of muscle cramps, spasticity, sialorrhea, constipation, depression, anxiety, and pseudobulbar affect. Assistive devices and durable medical equipment eventually fill the homes of most patients with ALS, including orthotics, pivot discs, transfer boards, walkers, wheelchairs, and smaller devices for assistance with activities of daily living.86 Respiratory support in the form of noninvasive ventilation is recommended when patients develop orthopnea, dyspnea on exertion, or morning headaches or when the forced vital capacity is less than 50% of the predicted value.87 Randomized controlled trials have shown that noninvasive ventilation prolongs survival and improves quality of life in patients without severe bulbar dysfunction and also improves some quality-of-life indices, such as sleep.13,88 Most

patients eventually require tracheostomy and mechanical ventilation to sustain life, but less than 10% choose this option in most Western countries.89 Malnutrition negatively affects prognosis and quality of life. A gastrostomy tube is recommended when weight loss exceeds 10% of body weight or the body mass index is less than 20 kg/m2 and is best done when the forced vital capacity is greater than 50% of predicted.13,90 The care of ALS patients is highly complex and best served by a multidisciplinary approach in specialized ALS centers with neurologists, nurse coordinators, respiratory therapists, nutritionists, physical and occupational therapists, and social workers. Such care can improve quality of life and prolong survival.91,92

References [1] Charcot JM. Sclérose des cordons latéraux de la moelle épiniére chez une femme hystérique atteinte de contracture permanente des quatre membres. Bull Soc Med Hop Paris. 1865; 2 suppl 2: 24–42 [2] Erb W. Ueber einen wenig bekannten spinalen Symptomencomplex. Berl Klin Wchnschr. 1875; 12: 357–359 [3] Gordon PH, Cheng B, Katz IB et al. The natural history of primary lateral sclerosis. Neurology 2006; 66: 647–653 [4] Pringle CE, Hudson AJ, Munoz DG, Kiernan JA, Brown WF, Ebers GC. . Primary lateral sclerosis: clinical features, neuropathy, and diagnosis criteria. Brain 1992: 495–520 [5] Singer MA, Statland JM, Wolfe GI, Barohn RJ. Primary lateral sclerosis. Muscle Nerve 2007; 35: 291–302 [6] Grace GM, Orange JB, Rowe A, Findlater K, Freedman M, Strong MJ. Neuropsychological functioning in PLS: a comparison with ALS. Can J Neurol Sci 2011; 38: 88–97

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Overview of Motor Neuron Disorders [7] Strong MJ, Gordon PH. Primary lateral sclerosis, hereditary spastic paraplegia and amyotrophic lateral sclerosis: discrete entities or spectrum? Amyotroph Lateral Scler Other Motor Neuron Disord 2005; 6: 8–16 [8] Ashworth N, Satkunam L, Deforge D. Treatment for spasticity in amyotrophic lateral sclerosis/motor neuron disease. Cochrane Database Syst. 2012 Feb 15;2:CD004156 [9] Marquardt G, Lorenz R. Intrathecal baclofen for intractable spasticity in amyotrophic lateral sclerosis. J Neurol 1999; 246: 619–620 [10] Schiffer RB, Herndon RM, Rudick RA. Treatment of pathologic laughing and weeping with amitriptyline. N Engl J Med 1985; 312: 1480–1482 [11] Andersen G, Vestergaard K, Riis JO. Citalopram for post-stroke pathological crying. Lancet 1993; 342: 837–839 [12] Brooks BR, Thisted RA, Appel SH et al. AVP-923 ALS Study Group. Treatment of pseudobulbar affect in ALS with dextromethorphan/quinidine: a randomized trial. Neurology 2004; 63: 1364–1370 [13] Miller RG, Jackson CE, Kasarskis EJ et al. Quality Standards Subcommittee of the American Academy of Neurology. Practice parameter update: the care of the patient with amyotrophic lateral sclerosis: drug, nutritional, and respiratory therapies (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 2009; 73: 1218–1226 [14] Andersen PM, Borasio GD, Dengler R et al. EFNS Task Force on Diagnosis and Management of Amyotrophic Lateral Sclerosis. EFNS Task Force on Management of Amyotrophic Lateral Sclerosis: guidelines for diagnosing and clinical care of patients and relatives. Eur J Neurol 2005; 12: 921–938 [15] Salinas S, Proukakis C, Crosby A, Warner TT. Hereditary spastic paraplegia: clinical features and pathogenetic mechanisms. Lancet Neurol 2008; 7: 1127–1138 [16] Behan WMH, Maia M. Strümpell’s familial spastic paraplegia: genetics and neuropathology. J Neurol Neurosurg Psychiatry 1974; 37: 8–20 [17] Depienne C, Stevanin G, Brice A, Durr A. Hereditary spastic paraplegias: an update. Curr Opin Neurol 2007; 20: 674–680 [18] Finsterer J, Löscher W, Quasthoff S, Wanschitz J, Auer-Grumbach M, Stevanin G. Hereditary spastic paraplegias with autosomal dominant, recessive, X-linked, or maternal trait of inheritance. J Neurol Sci 2012; 318: 1–18 [19] Harding AE. Hereditary “pure” spastic paraplegia: a clinical and genetic study of 22 families. J Neurol Neurosurg Psychiatry 1981; 44: 871–883 [20] Munsat TL. Workshop report: International SMA collaboration. Neuromuscul Disord 1991; I: 81 [21] Werdnig G. Die frühinfantile progressive spinale Amyotrophie. Arch Psychiatr Nervenkr. 1894; 26: 706–744 [22] Hoffmann J. Uber die hereditare progressive spinale Muskelatrophie im Kindesalter. Munch Med Wochenschr 1900; 47: 1649–1651 [23] Baioni MTC, Ambiel CR. Spinal muscular atrophy: diagnosis, treatment and future prospects. J Pediatr (Rio J) 2010; 86: 261–270 [24] Kugelberg E, Welander L. Heredofamilial juvenile muscular atrophy simulating muscular dystrophy. AMA Arch Neurol Psychiatry 1956; 75: 500–509 [25] Moulard B, Salachas F, Chassande B et al. Association between centromeric deletions of the SMN gene and sporadic adult-onset lower motor neuron disease. Ann Neurol 1998; 43: 640–644 [26] Lefebvre S, Bürglen L, Reboullet S et al. Identification and characterization of a spinal muscular atrophy-determining gene. Cell 1995; 80: 155–165 [27] Taylor JE, Thomas NH, Lewis CM et al. Correlation of SMNt and SMNc gene copy number with age of onset and survival in spinal muscular atrophy. Eur J Hum Genet 1998; 6: 467–474 [28] Kolb SJ, Kissel JT. Spinal muscular atrophy: a timely review. Arch Neurol 2011; 68: 979–984 [29] Kennedy WR, Alter M, Sung JH. Progressive proximal spinal and bulbar muscular atrophy of late onset. A sex-linked recessive trait. Neurology 1968; 18: 671–680 [30] Harding AE, Thomas PK, Baraitser M, Bradbury PG, Morgan-Hughes JA, Ponsford JR. X-linked recessive bulbospinal neuronopathy: a report of ten cases. J Neurol Neurosurg Psychiatry 1982; 45: 1012–1019 [31] La Spada AR, Wilson EM, Lubahn DB, Harding AE, Fischbeck KH. Androgen receptor gene mutations in X-linked spinal and bulbar muscular atrophy. Nature 1991; 352: 77–79 [32] Manning JT. The androgen receptor gene: a major modifier of speed of neuronal transmission and intelligence? Med Hypotheses 2007; 68: 802–804 [33] Finsterer J. Perspectives of Kennedy’s disease. J Neurol Sci 2010; 298: 1–10 [34] Atsuta N, Watanabe H, Ito M et al. Natural history of spinal and bulbar muscular atrophy (SBMA): a study of 223 Japanese patients. Brain 2006; 129: 1446–1455

[35] Finsterer J. Bulbar and spinal muscular atrophy (Kennedy’s disease): a review. Eur J Neurol 2009; 16: 556–561 [36] Chahin N, Klein C, Mandrekar J, Sorenson E. Natural history of spinal-bulbar muscular atrophy. Neurology 2008; 70: 1967–1971 [37] Ferrante MA, Wilbourn AJ. The characteristic electrodiagnostic features of Kennedy’s disease. Muscle Nerve 1997; 20: 323–329 [38] Sperfeld A-D, Bretschneider V, Flaith L et al. MR-pathologic comparison of the upper spinal cord in different motor neuron diseases. Eur Neurol 2005; 53: 74–77 [39] Katsuno M, Adachi H, Doyu M et al. Leuprorelin rescues polyglutaminedependent phenotypes in a transgenic mouse model of spinal and bulbar muscular atrophy. Nat Med 2003; 9: 768–773 [40] Katsuno M, Banno H, Suzuki K et al. Japan SBMA Interventional Trial for TAP144-SR (JASMITT) study group. Efficacy and safety of leuprorelin in patients with spinal and bulbar muscular atrophy (JASMITT study): a multicentre, randomised, double-blind, placebo-controlled trial. Lancet Neurol 2010; 9: 875–884 [41] Fernández-Rhodes LE, Kokkinis AD, White MJ et al. Efficacy and safety of dutasteride in patients with spinal and bulbar muscular atrophy: a randomized placebo-controlled trial. Lancet 2011; 10: 140–147 [42] Hirayama K, Toyokura Y, Tsubaki T. Juvenile muscular atrophy of unilateral upper extremity: a new clinical entity. Psychiatr Neurol Jpn. 1959; 61: 2190– 2197 [43] Hirayama K, Tsubaki T, Toyokura Y, Okinaka S. Juvenile muscular atrophy of unilateral upper extremity. Neurology 1963; 13: 373–380 [44] Hirayama K. Juvenile muscular atrophy of distal upper extremity (Hirayama disease): focal cervical ischemic poliomyelopathy. Neuropathology 2000; 20 Suppl: S91–S94 [45] Hirayama K, Tomonaga M, Kitano K, Yamada T, Kojima S, Arai K. Focal cervical poliopathy causing juvenile muscular atrophy of distal upper extremity: a pathological study. J Neurol Neurosurg Psychiatry 1987; 50: 285–290 [46] Hassan KM, Sahni H, Jha A. Clinical and radiological profile of Hirayama disease: A flexion myelopathy due to tight cervical dural canal amenable to collar therapy. Ann Indian Acad Neurol 2012; 15: 106–112 [47] Tokumaru Y, Hirayama K. [Cervical collar therapy for juvenile muscular atrophy of distal upper extremity (Hirayama disease): results from 38 cases] [in Japanese] Rinsho Shinkeigaku 2001; 41: 173–178 [48] Charcot JM, Joffroy A. Deuxcas d’atrophie musculaire progressive avec lesions de la substance grise et de faisceaux anterolateraux de la moelle epiniere. Arch Physiol Norm Pathol. 1869; 1: 354–357 [49] Charcot JM. De la sclerose laterale amyotrophique. Prog Med. 1874; 2: 325– 327, 341–342, 453–455 [50] Byrne S, Walsh C, Lynch C et al. Rate of familial amyotrophic lateral sclerosis: a systematic review and meta-analysis. J Neurol Neurosurg Psychiatry 2011; 82: 623–627 [51] Wijesekera LC, Leigh PN. Amyotrophic lateral sclerosis. Orphanet J Rare Dis 2009; 4: 3 [52] Sathasivam S. Motor neurone disease: clinical features, diagnosis, diagnostic pitfalls and prognostic markers. Singapore Med J 2010; 51: 367–372, quiz 373 [53] Pratt AJ, Getzoff ED, Perry JJ. Amyotrophic lateral sclerosis: update and new developments. Degener Neurol Neuromuscul Dis 2012; 2012: 1–14 [54] Chiò A, Logroscino G, Traynor BJ et al. Global epidemiology of amyotrophic lateral sclerosis: a systematic review of the published literature. Neuroepidemiology 2013; 41: 118–130 [55] Kiernan MC, Vucic S, Cheah BC et al. Amyotrophic lateral sclerosis. Lancet 2011; 377: 942–955 [56] Phukan J, Elamin M, Bede P et al. The syndrome of cognitive impairment in amyotrophic lateral sclerosis: a population-based study. J Neurol Neurosurg Psychiatry 2012; 83: 102–108 [57] Ringholz GM, Appel SH, Bradshaw M, Cooke NA, Mosnik DM, Schulz PE. Prevalence and patterns of cognitive impairment in sporadic ALS. Neurology 2005; 65: 586–590 [58] Lomen-Hoerth C, Murphy J, Langmore S, Kramer JH, Olney RK, Miller B. Are amyotrophic lateral sclerosis patients cognitively normal? Neurology 2003; 60: 1094–1097 [59] Cleveland DW, Rothstein JD. From Charcot to Lou Gehrig: deciphering selective motor neuron death in ALS. Nat Rev Neurosci 2001; 2: 806–819 [60] Vucic S, Kiernan MC. Pathophysiology of neurodegeneration in familial amyotrophic lateral sclerosis. Curr Mol Med 2009; 9: 255–272 [61] Pasinelli P, Brown RH. Molecular biology of amyotrophic lateral sclerosis: insights from genetics. Nat Rev Neurosci 2006; 7: 710–723

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Motor Neuron Disorders [62] Chiò A, Benzi G, Dossena M, Mutani R, Mora G. Severely increased risk of amyotrophic lateral sclerosis among Italian professional football players. Brain 2005; 128: 472–476 [63] Horner RD, Grambow SC, Coffman CJ et al. Amyotrophic lateral sclerosis among 1991 Gulf War veterans: evidence for a time-limited outbreak. Neuroepidemiology 2008; 31: 28–32 [64] Wang H, O’Reilly ÉJ, Weisskopf MG et al. Smoking and risk of amyotrophic lateral sclerosis: a pooled analysis of 5 prospective cohorts. Arch Neurol 2011; 68: 207–213 [65] Blasco H, Guennoc A-M, Veyrat-Durebex C et al. Amyotrophic lateral sclerosis: a hormonal condition? Amyotroph Lateral Scler 2012; 13: 585–588 [66] McCombe PA, Henderson RD. Effects of gender in amyotrophic lateral sclerosis. Gend Med 2010; 7: 557–570 [67] Cox PA, Sacks OW. Cycad neurotoxins, consumption of flying foxes, and ALSPDC disease in Guam. Neurology 2002; 58: 956–959 [68] Chiu AS, Gehringer MM, Braidy N, Guillemin GJ, Welch JH, Neilan BA. Gliotoxicity of the cyanotoxin, β-methyl-amino-L-alanine (BMAA). Sci Rep 2013; 3: 1482 [69] Karamyan VT, Speth RC. Animal models of BMAA neurotoxicity: a critical review. Life Sci 2008; 82: 233–246 [70] Siddique T, Figlewicz DA, Pericak-Vance MA et al. Linkage of a gene causing familial amyotrophic lateral sclerosis to chromosome 21 and evidence of genetic-locus heterogeneity. N Engl J Med 1991; 324: 1381–1384 [71] Chen S, Sayana P, Zhang X, Le W. Genetics of amyotrophic lateral sclerosis: an update. Mol Neurodegener 2013; 8: 28 [72] Orrell RW. Amyotrophic lateral sclerosis: copper/zinc superoxide dismutase (SOD1) gene mutations. Neuromuscul Disord 2000; 10: 63–68 [73] Neumann M, Sampathu DM, Kwong LK et al. Ubiquitinated TDP-43 in frontotemporal lobar degeneration and amyotrophic lateral sclerosis. Science 2006; 314: 130–133 [74] DeJesus-Hernandez M, Mackenzie IR, Boeve BF et al. Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron 2011; 72: 245–256 [75] Renton AE, Majounie E, Waite A et al. ITALSGEN Consortium. A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD. Neuron 2011; 72: 257–268 [76] Rademakers R, van Blitterswijk M. Motor neuron disease in 2012: Novel causal genes and disease modifiers. Nat Rev Neurol 2013; 9: 63–64 [77] Chiò A. ISIS Survey: an international study on the diagnostic process and its implications in amyotrophic lateral sclerosis. J Neurol 1999; 246 Suppl 3: III1–III5

[78] Daube JR. Electrodiagnostic studies in amyotrophic lateral sclerosis and other motor neuron disorders. Muscle Nerve 2000; 23: 1488–1502 [79] Wang S, Melhem ER, Poptani H, Woo JH. Neuroimaging in amyotrophic lateral sclerosis. Neurotherapeutics 2011; 8: 63–71 [80] Brooks BR, Miller RG, Swash M, Munsat TL World Federation of Neurology Research Group on Motor Neuron Diseases. El Escorial revisited: revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Other Motor Neuron Disord 2000; 1: 293–299 [81] de Carvalho M, Dengler R, Eisen A et al. Electrodiagnostic criteria for diagnosis of ALS. Clin Neurophysiol 2008; 119: 497–503 [82] Costa J, Swash M, de Carvalho M. Awaji criteria for the diagnosis of amyotrophic lateral sclerosis:a systematic review. Arch Neurol 2012; 69: 1410– 1416 [83] Simmons Z, Bremer BA, Robbins RA, Walsh SM, Fischer S. Quality of life in ALS depends on factors other than strength and physical function. Neurology 2000; 55: 388–392 [84] Simmons Z, Felgoise SH, Bremer BA et al. The ALSSQOL: balancing physical and nonphysical factors in assessing quality of life in ALS. Neurology 2006; 67: 1659–1664 [85] Miller RG, Mitchell JD, Moore DH. Riluzole for amyotrophic lateral sclerosis (ALS)/motor neuron disease (MND). Cochrane Data System Rev. 2012;(3 [86] Simmons Z. Management strategies for patients with amyotrophic lateral sclerosis from diagnosis through death. Neurologist 2005; 11: 257–270 [87] Hardiman O. Management of respiratory symptoms in ALS. J Neurol 2011; 258: 359–365 [88] Bourke SC, Tomlinson M, Williams TL, Bullock RE, Shaw PJ, Gibson GJ. Effects of non-invasive ventilation on survival and quality of life in patients with amyotrophic lateral sclerosis: a randomised controlled trial. Lancet Neurol 2006; 5: 140–147 [89] Rabkin J, Ogino M, Goetz R et al. Tracheostomy with invasive ventilation for ALS patients: neurologists’ roles in the US and Japan. Amyotroph Lateral Scler Frontotemporal Degener 2013; 14: 116–123 [90] Greenwood DI. Nutrition management of amyotrophic lateral sclerosis. Nutr Clin Pract 2013; 28: 392–399 [91] Aridegbe T, Kandler R, Walters SJ, Walsh T, Shaw PJMC, McDermott CJ. The natural history of motor neuron disease: assessing the impact of specialist care. Amyotroph Lateral Scler Frontotemporal Degener 2013; 14: 13–19 [92] Van den Berg JP, Kalmijn S, Lindeman E et al. Multidisciplinary ALS care improves quality of life in patients with ALS. Neurology 2005; 65: 1264– 1267

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Neuroimaging of Motor Neuron Disorders

38 Neuroimaging of Motor Neuron Disorders Divisha Raheja and Zachary Simmons The diagnosis of motor neuron disorders (MNDs) is based on evidence of upper motor neuron (UMN) or lower motor neuron (LMN) dysfunction or both. LMN signs, such as muscle atrophy and fasciculations, are relatively easy to recognize clinically and are further aided by electrodiagnostic studies, which can often identify denervation and reinnervation of muscle before the development of symptoms or of abnormalities on neurologic examination. Thus, these studies have become standard, clinically useful instruments in the neurologist’s armamentarium. In contrast, UMN dysfunction is diagnosed by clinical examination alone. The lack of objective markers for UMN involvement, particularly before such findings are clinically apparent, delays the diagnosis in many patients and precludes early initiation of neuroprotective treatment and the inclusion of these patients in clinical treatment trials. Electrodiagnosis has been used in an attempt to address this gap. However, transcranial magnetic stimulation (TMS) lacks sensitivity for subclinical UMN deficits. The triple stimulation technique (TST) combines collision studies with TMS and has shown some promise, but the sensitivity is also low. For example, in patients who went on to develop amyotrophic lateral sclerosis (ALS), but who did not meet the criteria for definite or probable ALS at testing, the TST was abnormal in only 4 of 18 patients.1 Traditionally, the role of neuroimaging has been to exclude “ALS mimic” syndromes; from the clinician’s perspective, this means that MNDs are diagnosed using clinical and electrodiagnostic means and that imaging findings in these patients are expected to be normal or nonspecifically abnormal. However, several advanced MR-based and functional neuroimaging techniques have substantially increased our knowledge about the pathophysiology of, and the dynamic changes that occur in, the human brain in MNDs. These techniques show promise for aiding clinical diagnosis and monitoring clinical progression in MNDs. This chapter focuses on these newer imaging techniques in ALS and other MNDs.

38.1 Conventional Magnetic Resonance Imaging Conventional magnetic resonance imaging (MRI) is routinely used to look for other pathologies, such as cerebral mass lesions, multiple sclerosis, cervical spondylotic myelopathy, conus lesions, or lumbosacral radiculopathy. In patients with ALS, a few subtle changes have been reported on T1-weighted, T2-weighted, proton density, and fluid-attenuated inversion recovery images (FLAIR), which are not diagnostic but are supportive of the diagnosis of a MND in patients with high clinical suspicion. Hyperintensities of the corticospinal tract (CST) in ALS have been reported by some as best detected on T2-weighted images and by others as best seen on proton density or FLAIR sequences.2 They are most readily identified in the posterior limb of internal capsule and are best monitored on the coronal images from centrum semiovale to the brainstem (▶ Fig. 38.1a, b). Increased T2 signal is also seen in the extramotor frontotem-

poral regions (▶ Fig. 38.1 c). The sensitivity of these changes in patients with ALS and primary lateral sclerosis (PLS) ranges from 15 to 76% in various studies, with a reported sensitivity close to 62% with combined application of all three sequences.3, 4 Specificity for ALS is not high, however, as these changes can be seen in normal healthy individuals, in other diseases, such as leukodystrophies, or after liver transplantation. There is no relationship between the degree of CST hyperintensity and the severity of clinical UMN involvement.5 The precentral cortex can appear as a hypointense rim on T2weighted and FLAIR images in patients with ALS (▶ Fig. 38.1 d). The mechanism has been thought to be a T2 shortening effect that results from excessive iron accumulation, fibrillary gliosis, or macrophage infiltration,6,7 but this is neither specific nor sensitive for ALS pathology. Hyperintensity of the anterolateral columns of the cervical cord has been observed on T2-weighted images in patients with ALS, consistent with the degeneration of the CST at autopsy, and is more specific than signal changes in the brain.2,8,9,10 A recent study with 7 T MRI revealed similar T2 hyperintensities in bilateral lateral segments of the spinal cord.11 Spinal cord imaging may be abnormal in some non-ALS MNDs. Cervical cord imaging findings have been described in detail in patients with Hirayama’s disease. These include loss of attachment of the dura to the lamina, asymmetric lower spinal cord atrophy, spinal cord T2 hyperintensity, loss of cervical lordosis in the neutral position, and forward displacement of the dura with flexion MRI (▶ Fig. 38.2).12,13,14 Flexion MRI should be considered in patients with a high clinical suspicion of Hirayama’s disease to increase the sensitivity. Loss of attachment of the dura is described as the most specific finding, with a sensitivity of 70 to 90%13,15 Reduced diameters of the cervical and thoracic spinal cords can be seen in patients with Kennedy’s disease,16 and spinal cord atrophy is a common feature in patients with pure or complicated hereditary spastic paraplegia (HSP).17,18

38.2 Voxel-Based Morphometry Voxel-based morphometry (VBM) is an automated statistical approach that is used to detect the regional differences in brain tissue density and tissue amount. The technique typically uses T1-weighted volumetric MRI scans and performs statistical tests across all voxels in the image to identify volume differences between groups.19 Global atrophy, as noted by reduced brain parenchymal fraction (BPF) in comparison to healthy control subjects, has been reported in some studies of patients with ALS.20 Regional gray matter (GM) atrophy of not only the motor cortex, but also the frontotemporal and parietal regions, has been noted in patients with ALS without cognitive impairment (▶ Fig. 38.3).20,21 Atrophy of the frontal regions has been noted to be severe in patients with ALS and frontotemporal dementia (FTD). ALS patients with mild cognitive impairment without evidence of frank FTD have also demonstrated gray matter loss in the frontal, parietal, and limbic regions compared with patients with no cognitive loss.3,22

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Fig. 38.1 Brain magnetic resonance imaging findings in amyotrophic lateral sclerosis (ALS). (a,b) Hyperintensity in the subcortical white matter on axial and sagittal fluid-attenuated inversion recovery images (arrows) in a 43-yearold woman with ALS. (c,d) T2-weighted images obtained from a 58-year-old patient with ALS with dementia show symmetric hyperintensity in the anterior temporal subcortical white matter (arrows) (c) and hypointensity along the precentral cortices (arrowheads, d). (Reprinted with permission from the American Society of Neuroradiology and Agosta F, Chiò a, Cosottini M, et al. The present and the future of neuroimaging in amyotrophic lateral sclerosis. AJNR. Am J Neuroradiol 2010;31(10):1769– 1777.)

Fig. 38.2 Magnetic resonance imaging of the cervical spine in an 18-year-old man with Hirayama’s disease. (a) Axial T2-weighted image demonstrates loss of attachment at the C5 level (arrow). (b) Neutral-position T2-weighted image demonstrates subtle atrophy at C5-C6 (arrow). (c) Flexion T2weighted image demonstrates 6 mm of anterior dural shift with near-complete obliteration of the subarachnoid space at C5–C6 (arrow). (Reprinted with permission from the American Society of Neuroradiology, Lehman VT, Luetmer PH, Sorenson EJ, et al. Cervical spine MR imaging findings of patients with Hirayama disease in North America: a multisite study. AJNR Am J Neuroradiol 2013;34(2):451–456.)

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Fig. 38.3 Regional gray matter atrophy in the brains of amyotrophic lateral sclerosis (ALS) patients compared with controls. Atrophy of the gray matter is present in the precentral and postcentral gyri, extending from the primary motor cortex to premotor, parietal, and frontal regions in a group of 17 ALS patients compared with controls. The differences between groups are superimposed on a standard normalized T1-weighted image. (Image reprinted with permission from the American Society of Neuroradiology, Agosta F, Chiò A, Cosottini M, et al. The present and the future of neuroimaging in amyotrophic lateral sclerosis. AJNR Am J Neuroradiol 2010;31(10):1769–1771.)

VBM studies have also provided evidence of white matter (WM) atrophy in extramotor areas, such as the corpus callosum, cerebellum, frontotemporal and occipital regions, supporting the theory that ALS is a multisystem disease and suggesting that extramotor involvement may be seen early in the disease.20,23,24 A few longitudinal studies have looked at the progression of GM loss and have demonstrated greater GM atrophy in the motor and extramotor frontal regions with disease progression, more pronounced in rapidly progressing cases.25,26 Global and regional atrophy in the precentral cortex and the corpus callosum have been reported in patients with PLS compared with controls.27 Regional GM and WM atrophy, mainly in the pericentral regions and specifically the precentral gyrus have been reported in patients with HSP, correlating with the most affected cortical region (i.e., the motor cortex). These changes are more prominent in patients with complicated than pure HSP. Corpus callosum thinning has been noted to be a common feature in patients with complicated HSP.28

38.3 Magnetic Resonance Spectroscopy Magnetic resonance spectroscopy (MRS) is a noninvasive technique to evaluate the chemical environment of the brain.

Because N-acetyl aspartate (NAA) is primarily found in neurons, whereas creatine (Cr) and choline (Cho) can be derived from all brain cells, the absolute concentration of NAA and the NAA/Cr and NAA/Cho ratios are considered markers of neuronal structural integrity. MRS studies can be performed from a single voxel using a single-voxel spectroscopy technique or using a chemical-shift imaging technique in which multiple voxels can be studied simultaneously. Proton MRS studies reveal reduced concentrations of NAA or reduced NAA:Cr, NAA:Cho, and NAA:Cr+Cho ratios in the motor cortex in ALS and PLS patients (▶ Fig. 38.4).29,30,31,32,33 These changes are most prominent in the precentral gyrus and the corona radiata, but they can also be seen in the premotor regions, primary sensory cortex, and extramotor frontal regions, with relative sparing of the parietal lobes. Similar changes are also seen in the brainstem, primarily in the pons and upper medulla of patients with prominent UMN or bulbar signs.34 Reduced concentrations of NAA correlate with disease severity in patients with ALS as measured with the ALS Functional Rating Scale-Revised and with UMN signs.29,35,36 Bulbar onset patients tend to have a lower NAA:Cr + Cho ratio than limbonset patients, and the frontal NAA:Cr ratio correlates well with cognitive dysfunction.34,37,38 Myo-inositol, another spectroscopic biomarker for glial activity, is also noted to be increased in the motor cortices of patients with ALS.

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Fig. 38.4 Two-dimensional multivoxel spectroscopy of the motor cortex in a control subject (a–c) and a patient with amyotrophic later sclerosis (ALS) patient (d–f). Axial T2-weighted image shows the grid and the volume of interest (solid white rectangle, a and d). Reduced NAA/Cr and NAA/Cho ratios are demonstrated in the ALS patient (e, f) compared with the control subject (b,c). (Image reprinted with permission from John Wiley & Sons, Wang S, Melhem ER. Amyotrophic lateral sclerosis and primary lateral sclerosis: The role of diffusion tensor imaging and other advanced MR-based techniques as objective upper motor neuron markers. Ann NY Acad Sci 2005;1064:61–77.)

38.4 Diffusion Tensor Imaging Diffusion tensor imaging (DTI) is a relatively new MR-based technique that allows estimation of the orientation of white matter fiber bundles based on the diffusion characteristics of water. This technique permits the detection of brain injuries earlier than they can be detected by conventional imaging techniques. Diffusivity of water is generally higher along the direction of the fiber tracts than perpendicular to them. A quantitative measure of the overall presence of obstacles to diffusion is called mean diffusivity (MD). The MD is a measure of diffusivity of water molecules irrespective of the direction and hence is higher in less restricted environments, such as cerebrospinal fluid. The directionality of diffusion can be quantified by fractional anisotropy (FA), which ranges from zero (no directional dependence of diffusion) to one (diffusion along a single direction). Thus, any structural change in the white matter or axonal loss would affect the diffusion characteristics and lead to an increase in the MD and a decrease in FA.39,40,41 Increased MD and decreased FA along the CST have been reported in multiple studies as a measure of UMN dysfunction in patients with ALS and PLS (▶ Fig. 38.5).41,42,43,44 These

changes are thought to be secondary to loss of pyramidal motor neurons in the primary motor cortex and axonal degeneration of the CST, together with the proliferation of glial cells, extracellular matrix expansion, and intraneuronal abnormalities.7,41 The posterior limb of the internal capsule (PLIC) is predominantly involved, although changes have been reported in the corpus callosum and in subcortical regions beneath the motor and premotor cortex. Patients with bulbar-onset ALS have the most significant decrease in FA (▶ Fig. 38.6).41 Decreased FA has been correlated with severity and disease progression in ALS patients in some studies, but other studies have failed to confirm this finding.2,45 A few studies have focused on differences in white matter involvement in patients with ALS and PLS, suggesting different pathology of the two diseases. Patients with PLS are noted to have decreased FA along the whole length of CST from the primary motor cortex to the medullary pyramids. The rostral part of the CST is significantly involved in patients with PLS, specifically, the corpus callosum and the subcortical regions underlying the primary motor cortices. In contrast, although ALS patients also are noted to have decreased FA along the CST, the rostral portions of the CST and the callosal fibers are not as involved as in patients with PLS. The extent of these changes does not correlate with the duration of the disease in

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Fig. 38.5 Graphs of mean fractional anisotropy (FA) in the right (Mean_R) and left (Mean_L) corticospinal tracts and in the left and right precentral (Pre_L and Pre_R) and postcentral (Post_L and Post_R) regions of 28 ALS patients (green) and 26 healthy controls (blue). Mean values shown with SDs (black lines). Significant parameters marked by a red asterisk (p < 0.0041 on left and p < 0.001 on right); ns indicates not significant. CT = controls; PA = ALS patients. (Image reprinted with permission from Sage CA, Peeters RR, Görner A, Robberecht W, Sunaert S. Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis. Neuroimage 2007;34 (2):486–99).

Fig. 38.6 Diffusion tensor imaging in 15 ALS patients compared with controls demonstrating reduced fractional anisotropy in the pyramidal tract, corpus callosum, and thalamus (a,b), and under the motor and premotor cortex (c,d). (Images reprinted with permission from Oxford University Press, Sach M, Winkler G, Glauche V, et al. Diffusion tensor MRI of early upper motor neuron involvement in amyotrophic lateral sclerosis. Brain 2004;127(Pt 2):340–350.)

PLS patients, but it tends to worsen with the disease duration and severity in ALS patients.44,46 Cervical cord FA has been reported to be decreased in patients with ALS compared with controls and to correlate with disease severity.47 Longitudinal follow-up of these patients demonstrates a significant decrease in the cord FA and an increase in the MD over time, despite the stability of the brain CST FA and MD.45 DTI studies recorded with a voxel-based

approach also have shown extramotor involvement in the corpus callosum, premotor white matter, prefrontal white matter, and temporal regions.48,49 Patients with progressive muscular atrophy (PMA) usually do not demonstrate these changes, as would be expected from lack of UMN involvement in these patients.50 However, a few studies have revealed that patients with PMA who demonstrated decreased FA values in PLIC similar to those seen in patients

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Motor Neuron Disorders

Fig. 38.7 Diffusion tensor tractography of a control subject (a) compared with patient with amyotrophic lateral sclerosis (ALS). (b). Reduced fiber density of the corticospinal tract (green) is demonstrated in the ALS patient. (Images reprinted with permission from John Wiley & Sons, Wang S, Melhem ER. Amyotrophic lateral sclerosis and primary lateral sclerosis: the role of diffusion tensor imaging and other advanced MRbased techniques as objective upper motor neuron markers. Ann NY Acad Sci 2005;1064:61– 77.)

with UMN signs eventually developed ALS, suggesting a role for DTI as an early marker of UMN involvement.42,43 Diffusion tensor imaging also allows interregional fiber tracking. Known as diffusion tensor tractography, this technique allows identification of major white matter tracts as they course through the brain. Further quantification of the white matter tracts can be performed using a region-based approach. A decreased number of CST fibers is seen in patients with ALS and PLS with severe clinical deficits compared with normal subjects (▶ Fig. 38.7).7,48

38.5 Magnetization Transfer Imaging Magnetization transfer ratio (MTR) is an MR-based parameter that measures the exchange of magnetization between free protons (water molecules) and those bound to macromolecules and is thus thought to reflect alterations in macromolecular structures. Reduced MTR values are indicative of inability of neuronal macromolecules to exchange magnetization with the surrounding free water molecules, which correlates with axonal degeneration and demyelination.51 MT imaging also improves the visibility of gadolinium-enhancing lesions by suppressing the surrounding normal brain parenchyma and leading to contrast augmentation. Reduced MTR values are seen along the CST and in the precentral gyrus in patients with ALS, consistent with the pathology of the disease. This reduction has been reported in two different studies as ranging from 2.6 to 20% compared with controls.52,53 Hyperintensity along the CST has been reported in a single study using T1-weighted MT contrast images in 80% of ALS patients compared with controls.54 Reduced MTR values also have been reported in the nonprimary motor cortices, including the premotor cortex (superior and middle frontal gyri) and the motor-related parietal cortices. Prefrontal and temporal lobes demonstrate MTR reductions in patients with or without frontotemporal dementia, suggesting extramotor involvement in ALS patients, in line with neuropathological findings and with other nuclear imaging studies, such as functional MRIs and PET scans.55 It is not clear whether these changes correlate with the severity of the disease. Longitudinal studies are needed to establish the utility of the MT imaging as a surrogate marker for disease severity and evolution.

38.6 Functional Imaging 38.6.1 Positron Emission Tomography Positron emission tomography allows noninvasive quantification of cerebral blood flow, metabolism, and receptor binding. It uses positron-emitting radioisotopes as molecular probes to assess biochemical processes in vivo. The typical agents used for PET studies are fluorodeoxyglucose (FDG), carbon 11labeled deoxyglucose, or methionine. Different analogs, such as dopamine, amyloid, and benzodiazepine receptor ligands, have been used with radioactive fluorine, oxygen, or carbon to analyze activity in neurologic patients. Positron emission tomography studies have been described at rest and during motor activation tasks in patients with ALS, PMA, and controls. Reduced global cerebral blood flow suggesting hypometabolism has been reported in some studies of patients with ALS56,57 but not in others.58 In contrast, regional cerebral blood flow (rCBF) has been consistently noted to be reduced in those with ALS. In the resting state, the reduction is mainly in the primary sensorimotor cortex and the adjacent premotor, parietal, and insular cortices.58,59 PET studies of patients with ALS during a motor activation task reveal reduced rCBF in the medial prefrontal cortex, anterior cingulate gyrus, and parahippocampal gyrus. These changes beyond the primary sensorimotor cortices during performance of a simple motor task likely demonstrate the neural plasticity and the development of new synapses and pathways to compensate for the loss of pyramidal neurons. These rCBF changes were not demonstrated in patients with primary LMN disorders, such as PMA.56,60 The presence or absence of cognitive deficits in patients with ALS leads to different findings on PET. Studies of ALS patients with cognitive deficits or of nondemented patients with impaired verbal fluency revealed impaired activation in the cortical and subcortical regions, including the dorsolateral prefrontal cortex, premotor cortex, insular cortex, and anterior thalamic nuclear groups compared with cognitively intact patients.59,61 Ligand-based PET studies have been performed using 11C-flumazenil, which binds to the gamma-aminobutyric acid A (GABA A) receptor, thought to serve as a marker for neuronal loss. These studies show reduced binding of 11C-flumazenil in not only the motor/premotor regions, but also the association cortices, specifically the prefrontal cortex (▶ Fig. 38.8).62 ALS

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Fig. 38.8 Relative decreases in 11C-flumazenil binding in ALS patients (a) compared to controls (b). Regions of decreased binding are superimposed on an average MRI constructed from spatially normalized MRI data of normal control subjects. (Image reprinted with permission from Lloyd CM, Richardson MP, Brooks DJ, Al-Chalabi A, Leigh PN. Extramotor involvement in ALS: PET studies with the GABA(A) ligand 11Cflumazenil. Brain. 2000;123: 2289–96)

patients with poor verbal fluency demonstrate decreased 11Cflumazenil binding in the right inferior frontal gyrus, superior temporal gyrus and the anterior insula. The left inferior and middle frontal gyrus and the cuneus were noted to be involved in patients with poor performance on confrontation naming tests.63 A recent study using [18F]FDG PET revealed large hypometabolic areas in the frontal and parietal regions bilaterally in bulbar-onset patients compared to control subjects and to spinal-onset onset patients, associated with lower neuropsychological testing scores in bulbar patients (▶ Fig. 38.9).64

38.6.2 Functional Magnetic Resonance Imaging Functional MRI (fMRI) is a noninvasive tool based on the bloodoxygen–dependent contrast method, relying on the T2 effect of deoxyhemoglobin in the tissues. Patients with ALS have been noted to demonstrate increased activation of the premotor cortex, supplementary motor area, basal ganglia, and cerebellum during simple motor tasks, such as finger tapping.65,66,67,68 This shift of activity with upper limb movement and the increased

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Fig. 38.9 Positron emission tomography studies showing (in red) areas in which there is increased [18F]fluorodeoxyglucose (FDG) uptake in control subjects as compared to patients with bulbar amyotrophic lateral sclerosis (ALS). These areas include bilateral prefrontal cortex, premotor cortex, right insula, anterior cingulate gyrus and inferior parietal lobes patients. (Image reprinted with permission from Cistaro A, Valentini MC, Chiò A, et al. Brain hypermetabolism in amyotrophic lateral sclerosis: a FDG PET study in ALS of spinal and bulbar onset. Eur J Nucl Med Mol Imaging 2012;39(2):251–259.)

ipsilateral involvement of the sensorimotor cortex support the concept of functional reorganization to compensate for the loss of pyramidal neurons.65,66,67,69 fMRI studies during a motor imagery task also revealed increased activation of the premotor areas, a finding that became more prominent with longer disease duration.70 Another fMRI study of ALS patients during motor imagery tasks revealed reduced activation of the parietal and medial frontal regions, areas that are usually involved in motor imagery tasks. This finding suggests reduced activation of the usual networks, possibly related to involvement of the prefrontal cortex by the underlying disease (▶ Fig. 38.10).71 Impaired activation of the middle and inferior frontal gyri,

anterior cingulate gyrus, and the parietal and temporal lobes has been demonstrated in ALS patients during letter fluency and confrontation naming tasks, corresponding to clinical deficits in these spheres by the patients.72

38.7 Conclusion Although imaging studies have traditionally served a negative (“rule-out”) rather than a positive (“rule in”) role in MNDs, this is changing. Voxel-based morphometry, magnetic resonance spectroscopy, diffusion tensor imaging, magnetization transfer imaging, and functional studies all show promise in

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Fig. 38.10 Functional magnetic resonance imaging demonstrating areas of activation during motor imagery in healthy controls (a) and in amyotrophic lateral sclerosis (ALS) patients (b). Areas of significantly reduced activation during motor imagery in ALS patients compared with healthy controls are demonstrated in (c). (Image reprinted with permission from Elsevier, Stanton BR, Williams VC, Leigh PN, et al. Cortical activation during motor imagery is reduced in amyotrophic lateral sclerosis. Brain Res 2007;1172:145–151.)

identifying, localizing, and quantitating abnormalities associated with UMN dysfunction; in demonstrating involvement of brain regions outside of the primary motor cortex, including nonmotor areas; and in advancing our understanding of brain plasticity. Given the lack of biological markers for ALS and other MNDs, imaging studies have the potential to play important roles, not only for clinical diagnosis and follow-up, but as research tools for a better understanding of the widespread areas of the brain that are affected by this heterogeneous group of disorders.

References [1] Kleine BU, Schelhaas HJ, van Elswijk G, de Rijk MC, Stegeman DF, Zwarts MJ. Prospective, blind study of the triple stimulation technique in the diagnosis of ALS. Amyotroph Lateral Scler 2010; 11: 67–75 [2] Filippi M, Agosta F, Abrahams S et al. European Federation of Neurological Societies. EFNS guidelines on the use of neuroimaging in the management of motor neuron diseases. Eur J Neurol 2010; 17: 526–e20 [3] Agosta F, Chiò A, Cosottini M et al. The present and the future of neuroimaging in amyotrophic lateral sclerosis. AJNR Am J Neuroradiol 2010; 31: 1769–1777

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Motor Neuron Disorders [4] Charil A, Corbo M, Filippi M et al. Structural and metabolic changes in the brain of patients with upper motor neuron disorders: a multiparametric MRI study. Amyotroph Lateral Scler 2009; 10: 269–279 [5] Hecht MJ, Fellner F, Fellner C, Hilz MJ, Heuss D, Neundörfer B. MRI-FLAIR images of the head show corticospinal tract alterations in ALS patients more frequently than T2-, T1- and proton-density-weighted images. J Neurol Sci 2001; 186: 37–44 [6] Wang S, Melhem ER, Poptani H, Woo JH. Neuroimaging in amyotrophic lateral sclerosis. Neurotherapeutics 2011; 8: 63–71 [7] Wang S, Melhem ER. Amyotrophic lateral sclerosis and primary lateral sclerosis: The role of diffusion tensor imaging and other advanced MRbased techniques as objective upper motor neuron markers. Ann N Y Acad Sci 2005; 1064: 61–77 [8] Thorpe JW, Moseley IF, Hawkes CH, MacManus DG, McDonald WI, Miller DH. Brain and spinal cord MRI in motor neuron disease. J Neurol Neurosurg Psychiatry 1996; 61: 314–317 [9] Terao S, Sobue G, Yasuda T, Kachi T, Takahashi M, Mitsuma T. Magnetic resonance imaging of the corticospinal tracts in amyotrophic lateral sclerosis. J Neurol Sci 1995; 133: 66–72 [10] Mascalchi M, Salvi F, Valzania F, Marcacci G, Bartolozzi C, Tassinari CA. Corticospinal tract degeneration in motor neuron disease. AJNR Am J Neuroradiol 1995; 16 Suppl: 878–880 [11] Cohen-Adad J, Zhao W, Keil B et al. 7-T MRI of the spinal cord can detect lateral corticospinal tract abnormality in amyotrophic lateral sclerosis. Muscle Nerve 2013; 47: 760–762 [12] Huang Y-L, Chen C-J. Hirayama disease. Neuroimaging Clin N Am 2011; 21: 939–950, ix–x [13] Lehman VT, Luetmer PH, Sorenson EJ et al. Cervical spine MR imaging findings of patients with Hirayama disease in North America: a multisite study. AJNR Am J Neuroradiol 2013; 34: 451–456 [14] Hassan KM, Sahni H, Jha A. Clinical and radiological profile of Hirayama disease: A flexion myelopathy due to tight cervical dural canal amenable to collar therapy. Ann Indian Acad Neurol 2012; 15: 106–112 [15] Chen C-J, Hsu H-L, Tseng Y-C et al. Hirayama flexion myelopathy: neutralposition MR imaging findings—importance of loss of attachment. Radiology 2004; 231: 39–44 [16] Sperfeld A-D, Bretschneider V, Flaith L et al. MR-pathologic comparison of the upper spinal cord in different motor neuron diseases. Eur Neurol 2005; 53: 74–77 [17] Hedera P, Eldevik OP, Maly P, Rainier S, Fink JK. Spinal cord magnetic resonance imaging in autosomal dominant hereditary spastic paraplegia. Neuroradiology 2005; 47: 730–734 [18] Sperfeld A-D, Baumgartner A, Kassubek J. MRI of the upper spinal cord in pure and complicated hereditary spastic paraparesis. Eur Neurol 2005; 54: 181–185 [19] Whitwell JL. Voxel-based morphometry: an automated technique for assessing structural changes in the brain. J Neurosci 2009; 29: 9661–9664 [20] Mezzapesa DM, Ceccarelli A, Dicuonzo F et al. Whole-brain and regional brain atrophy in amyotrophic lateral sclerosis. AJNR Am J Neuroradiol 2007; 28: 255–259 [21] Grosskreutz J, Kaufmann J, Frädrich J, Dengler R, Heinze HJ, Peschel T. Widespread sensorimotor and frontal cortical atrophy in amyotrophic lateral sclerosis. BMC Neurol 2006; 6: 17 [22] Chang JL, Lomen-Hoerth C, Murphy J et al. A voxel-based morphometry study of patterns of brain atrophy in ALS and ALS/FTLD. Neurology 2005; 65: 75–80 [23] Agosta F, Pagani E, Rocca MA et al. Voxel-based morphometry study of brain volumetry and diffusivity in amyotrophic lateral sclerosis patients with mild disability. Hum Brain Mapp 2007; 28: 1430–1438 [24] Grosskreutz J, Peschel T, Unrath A, Dengler R, Ludolph AC, Kassubek J. Whole brain-based computerized neuroimaging in ALS and other motor neuron disorders. Amyotroph Lateral Scler 2008; 9: 238–248 [25] Avants B, Khan A, McCluskey L, Elman L, Grossman M. Longitudinal cortical atrophy in amyotrophic lateral sclerosis with frontotemporal dementia. Arch Neurol 2009; 66: 138–139 [26] Agosta F, Gorno-Tempini ML, Pagani E et al. Longitudinal assessment of grey matter contraction in amyotrophic lateral sclerosis: a tensor based morphometry study. Amyotroph Lateral Scler 2009; 10: 168–174 [27] Tartaglia MC, Laluz V, Rowe A et al. Brain atrophy in primary lateral sclerosis. Neurology 2009; 72: 1236–1241 [28] Kassubek J, Juengling FD, Baumgartner A, Unrath A, Ludolph AC, Sperfeld AD. Different regional brain volume loss in pure and complicated hereditary spastic paraparesis: a voxel-based morphometric study. Amyotroph Lateral Scler 2007; 8: 328–336

[29] Mitsumoto H, Ulug AM, Pullman SL et al. Quantitative objective markers for upper and lower motor neuron dysfunction in ALS. Neurology 2007; 68: 1402–1410 [30] Pohl C, Block W, Karitzky J et al. Proton magnetic resonance spectroscopy of the motor cortex in 70 patients with amyotrophic lateral sclerosis. Arch Neurol 2001; 58: 729–735 [31] Block W, Karitzky J, Träber F et al. Proton magnetic resonance spectroscopy of the primary motor cortex in patients with motor neuron disease: subgroup analysis and follow-up measurements. Arch Neurol 1998; 55: 931–936 [32] Pioro EP, Antel JP, Cashman NR, Arnold DL. Detection of cortical neuron loss in motor neuron disease by proton magnetic resonance spectroscopic imaging in vivo. Neurology 1994; 44: 1933–1938 [33] Rooney WD, Miller RG, Gelinas D, Schuff N, Maudsley AA, Weiner MW. Decreased N-acetylaspartate in motor cortex and corticospinal tract in ALS. Neurology 1998; 50: 1800–1805 [34] Cwik VA, Hanstock CC, Allen PS, Martin WR. Estimation of brainstem neuronal loss in amyotrophic lateral sclerosis with in vivo proton magnetic resonance spectroscopy. Neurology 1998; 50: 72–77 [35] Ellis CM, Simmons A, Andrews C, Dawson JM, Williams SC, Leigh PN. A proton magnetic resonance spectroscopic study in ALS: correlation with clinical findings. Neurology 1998; 51: 1104–1109 [36] Kaufmann P, Pullman SL, Shungu DC et al. Objective tests for upper motor neuron involvement in amyotrophic lateral sclerosis (ALS). Neurology 2004; 62: 1753–1757 [37] Suhy J, Miller RG, Rule R et al. Early detection and longitudinal changes in amyotrophic lateral sclerosis by (1)H MRSI. Neurology 2002; 58: 773–779 [38] Strong MJ, Grace GM, Orange JB, Leeper HA, Menon RS, Aere C. A prospective study of cognitive impairment in ALS. Neurology 1999; 53: 1665–1670 [39] Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 1994; 103: 247–254 [40] Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B 1996; 111: 209–219 [41] Ellis CM, Simmons A, Jones DK et al. Diffusion tensor MRI assesses corticospinal tract damage in ALS. Neurology 1999; 53: 1051–1058 [42] Sach M, Winkler G, Glauche V et al. Diffusion tensor MRI of early upper motor neuron involvement in amyotrophic lateral sclerosis. Brain 2004; 127: 340– 350 [43] Graham JM, Papadakis N, Evans J et al. Diffusion tensor imaging for the assessment of upper motor neuron integrity in ALS. Neurology 2004; 63: 2111–2119 [44] Agosta F, Galantucci S, Riva N et al. Intrahemispheric and interhemispheric structural network abnormalities in PLS and ALS. Hum Brain Mapp 2014; 35: 1710–1722 [45] Agosta F, Rocca MA, Valsasina P et al. A longitudinal diffusion tensor MRI study of the cervical cord and brain in amyotrophic lateral sclerosis patients. J Neurol Neurosurg Psychiatry 2009; 80: 53–55 [46] Ciccarelli O, Behrens TE, Johansen-Berg H et al. Investigation of white matter pathology in ALS and PLS using tract-based spatial statistics. Hum Brain Mapp 2009; 30: 615–624 [47] Valsasina P, Agosta F, Benedetti B et al. Diffusion anisotropy of the cervical cord is strictly associated with disability in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry 2007; 78: 480–484 [48] Sage CA, Peeters RR, Görner A, Robberecht W, Sunaert S. Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis. Neuroimage 2007; 34: 486–499 [49] Sage CA, Van Hecke W, Peeters R et al. Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis: revisited. Hum Brain Mapp 2009; 30: 3657– 3675 [50] Cosottini M, Giannelli M, Siciliano G et al. Diffusion-tensor MR imaging of corticospinal tract in amyotrophic lateral sclerosis and progressive muscular atrophy. Radiology 2005; 237: 258–264 [51] Wolff SD, Balaban RS. Magnetization transfer imaging: practical aspects and clinical applications. Radiology 1994; 192: 593–599 [52] Tanabe JL, Vermathen M, Miller R, Gelinas D, Weiner MW, Rooney WD. Reduced MTR in the corticospinal tract and normal T2 in amyotrophic lateral sclerosis. Magn Reson Imaging 1998; 16: 1163–1169 [53] Kato Y, Matsumura K, Kinosada Y, Narita Y, Kuzuhara S, Nakagawa T. Detection of pyramidal tract lesions in amyotrophic lateral sclerosis with magnetization-transfer measurements. AJNR Am J Neuroradiol 1997; 18: 1541–1547 [54] da Rocha AJ, Oliveira ASB, Fonseca RB, Maia AC, Jr, Buainain RP, Lederman HM. Detection of corticospinal tract compromise in amyotrophic lateral sclerosis with brain MR imaging: relevance of the T1-weighted spin-echo

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[55]

[56] [57]

[58] [59]

[60]

[61] [62]

magnetization transfer contrast sequence. AJNR Am J Neuroradiol 2004; 25: 1509–1515 Cosottini M, Pesaresi I, Piazza S et al. Magnetization transfer imaging demonstrates a distributed pattern of microstructural changes of the cerebral cortex in amyotrophic lateral sclerosis. AJNR Am J Neuroradiol 2011; 32: 704–708 Dalakas MC, Hatazawa J, Brooks RA, Di Chiro G. Lowered cerebral glucose utilization in amyotrophic lateral sclerosis. Ann Neurol 1987; 22: 580–586 Hatazawa J, Brooks RA, Dalakas MC, Mansi L, Di Chiro G. Cortical motor-sensory hypometabolism in amyotrophic lateral sclerosis: a PET study. J Comput Assist Tomogr 1988; 12: 630–636 Kew JJ, Leigh PN, Playford ED et al. Cortical function in amyotrophic lateral sclerosis. A positron emission tomography study. Brain 1993; 116: 655–680 Kew JJM, Goldstein LH, Leigh PN et al. The relationship between abnormalities of cognitive function and cerebral activation in amyotrophic lateral sclerosis: a neuropsychological and positron emission tomography study. Brain 1993; 116: 1399–1423 Kew JJM, Brooks DJ, Passingham RE, Rothwell JC, Frackowiak RSJ, Leigh PN. Cortical function in progressive lower motor neuron disorders and amyotrophic lateral sclerosis: a comparative PET study. Neurology 1994; 44: 1101– 1110 Abrahams S, Goldstein LH, Kew JJ et al. Frontal lobe dysfunction in amyotrophic lateral sclerosis. A PET study. Brain 1996; 119: 2105–2120 Lloyd CM, Richardson MP, Brooks DJ, Al-Chalabi A, Leigh PN. Extramotor involvement in ALS: PET studies with the GABA(A) ligand 11Cflumazenil. Brain 2000; 123: 2289–2296

[63] Wicks P, Turner MR, Abrahams S et al. Neuronal loss associated with cognitive performance in amyotrophic lateral sclerosis: an 11-Cflumazenil PET study. Amyotroph Lateral Scler 2008; 9: 43–49 [64] Cistaro A, Valentini MC, Chiò A et al. Brain hypermetabolism in amyotrophic lateral sclerosis: a FDG PET study in ALS of spinal and bulbar onset. Eur J Nucl Med Mol Imaging 2012; 39: 251–259 [65] Konrad C, Henningsen H, Bremer J et al. Pattern of cortical reorganization in amyotrophic lateral sclerosis: a functional magnetic resonance imaging study. Exp Brain Res 2002; 143: 51–56 [66] Stanton BR, Williams VC, Leigh PN et al. Altered cortical activation during a motor task in ALS. Evidence for involvement of central pathways. J Neurol 2007; 254: 1260–1267 [67] Konrad C, Jansen A, Henningsen H et al. Subcortical reorganization in amyotrophic lateral sclerosis. Exp Brain Res 2006; 172: 361–369 [68] Lulé D, Ludolph AC, Kassubek J. MRI-based functional neuroimaging in ALS: an update. Amyotroph Lateral Scler 2009; 10: 258–268 [69] Schoenfeld MA, Tempelmann C, Gaul C et al. Functional motor compensation in amyotrophic lateral sclerosis. J Neurol 2005; 252: 944–952 [70] Lulé D, Diekmann V, Kassubek J et al. Cortical plasticity in amyotrophic lateral sclerosis: motor imagery and function. Neurorehabil Neural Repair 2007; 21: 518–526 [71] Stanton BR, Williams VC, Leigh PN et al. Cortical activation during motor imagery is reduced in mayotrophic lateral sclerosis. Brain Res 2007; 1172: 145–151 [72] Abrahams S, Goldstein LH, Simmons A et al. Word retrieval in amyotrophic lateral sclerosis: a functional magnetic resonance imaging study. Brain 2004; 127: 1507–1517

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Part XVI Clinical Approach and Treatment

39 Reversible versus Nonreversible Dementia: Practical Approach

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40 Advances in the Treatment of Dementia

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41 Imaging of Deep Brain Stimulation

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Clinical Approach and Treatment

39 Reversible versus Nonreversible Dementia: Practical Approach Sol De Jesus and Sangram Kanekar The advancement of neuroimaging has afforded significant insight into progressive neurodegenerative disorders and their mimics. Many primary and secondary disease entities can manifest with memory dysfunction. Although Alzheimer’s disease (AD) remains the most common primary type of dementia, the clinician must consider many preventable disorders before making a final diagnosis of AD. Distinguishing between a preventable, potentially reversible cause and an irreversible (progressive) cause has serious implications for future planning in regard to the patient’s medical, social, and economic spheres. The diagnosis of dementia has historically involved clinical suspicion alone and, when available, confirmation via postmortem neuropathological analysis. Although the diagnosis of dementia remains largely clinical, neuroimaging can help direct the diagnostic workup. This chapter concentrates on summarizing preventable and potentially reversible presentations of memory dysfunction. The reader is referred to previous chapters in this text for discussion of primary progressive and more common reversible types.

39.1 Prevalence Aging is the most significant risk factor for dementia. Most individuals will age successfully. As the population ages, however, the risk for dementia also inevitably increases. The Federal Interagency Forum on Aging-Related Statistics reports that the older population considered age 65 and older, is expected to double by 2030.1 The World Health Organization (WHO)2,3 estimates that 35.6 million individuals worldwide suffer from dementia, with 7.7 million new cases per year. Dementia prevalence may be as high as 40% in people age 90 and older, whereas it is only 1 to 2% at age 65.4 The Aging, Demographic and Memory Study (ADAMS), published in 2007, attempted to estimate the prevalence in the United States of AD, vascular dementia, and other dementias, including the reversible dementia types. In a sample of more than 800 patients, ADAMS estimated prevalence at 12.7% for the reversible dementia syndromes.5 This number will vary from 1 to 30%, depending on the population studied.6,7,8,9 Most available studies are retrospective, and prevalence will vary depending on the specialties involved, the difference in classification of reversible disease entities between studies, and the study setting. The statistics presented do not take into account the percentage of dementia patients who may have concomitant reversible causes or the duration of the disease process and its association with reversibility.

39.2 Diagnostic Evaluation The initial clinical symptoms of dementia are variable and may include the presence of impaired memory with an associated decline in one or more cognitive memory, (language, executive function, visual spatial, attention, and praxis), or behavior and

personality changes.10 These deficits must impair the individual’s overall functional state in the context of daily living. The recommended diagnostic evaluation for suspected dementia includes complete history, physical, and neurologic examination, bedside cognitive screen (Mini-Mental State Examination, Montreal Cognitive Assessment), serum studies (complete blood cell count, basic metabolic panel, vitamin B12, thyroid-stimulating hormone, liver function tests), and imaging (computed tomography [CT] and/or magnetic resonance imaging [MRI]).11,12,13 The American Academy of Neurology (AAN) latest practice parameters now recommend neuroimaging as an important diagnostic ancillary tool.13 This initial screening is helpful in capturing possible dementia mimics. For atypical and unclear presentations, such as early onset (< 65 years of age) or rapidly progressive deterioration, it is necessary to expand the basic screening. Neuroimaging modalities used in dementia include CT, MRI, positron emission tomography (PET), singlephoton emission computed tomography (SPECT), and functional MRI (fMRI). The role of each imaging modality, whether it is clinical or research based, and its use in dementia and dementia mimics are summarized in (▶ Table 39.1). In clinical

Table 39.1 Utility of different imaging modalities in the evaluation of cognitive dysfunction26,58 Imaging modality

Type of imaging

Utility Study in Dementia Evaluation

CT

X-ray: structural

Rule out space-occupying lesions (hemorrhage, tumors, ventricular dilation) and assess generalized atrophy

MRI

Electromagnetic: structural

Sequence dependent: ● T1: anatomy and atrophy assessment ● T2-FLAIR: atrophy and small vessel cerebrovascular disease burden ● Gradient echo: visualization of microhemorrhages, which may be associated with amyloid angiopathy

PET

Gamma rays: functional

Provides supporting information regarding regional cerebral metabolism changes

SPECT

Gamma rays: functional

Provides supporting information regarding cerebral function via blood flow measurement

fMRI

Electromagnetic: functional

Mostly research based, providing information regarding cortical connectivity and synaptic dysfunction

Abbreviations: CT, computed tomography; fMRI, functional magnetic resonance imaging; MRI, magnetic resonance imaging; PET, positron emission tomography; SPECT, single-photon emission computed tomography.

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Reversible versus Nonreversible Dementia: Practical Approach

Fig. 39.1 Algorithm for reversible and preventable dementia. HIV, human immunodeficiency virus; MS, multiple sclerosis; NPH, normal pressure hydrocephalus; SDH, subdural hematoma. SLE, systemic lupus erythematosus; vit, vitamin. This simple guide does not include all the possible reversible entities.

practice, CT and MRI are sufficient for the initial investigations; however, the increase in the availability of PET and SPECT has also made these imaging modalities an option in clinical practice to support a suspected diagnosis. Close attention to the temporal course of the memory dysfunction, focal neurologic examination findings, fluctuation of symptoms, and comorbid conditions can direct the clinician to the appropriate investigation and help delineate a precise cause between reversible and irreversible. Dementia and the dementia mimics can manifest as acute, subacute, or chronic. Memory dysfunction may be the main initial symptom, or it may be an accompanying symptom with a medical illness or other primary cause. Medical illnesses may exacerbate baseline neurologic deficits that will then uncover a primary progressive neurodegenerative disease state, as seen in metabolic encephalopathy. Although the underlying disorder may not be reversible, all attempts should be made to address the concomitant disease exacerbating the baseline neurologic deficits. The differential diagnosis for reversible dementia can be daunting and can be subdivided into the categories of vascular, infectious, traumatic, autoimmune, metabolic, toxic, idiopathic, neoplasms, and others (▶ Fig. 39.1).

39.3 Reversible versus Irreversible Causes of Dementia Primary neurodegenerative diseases involving memory dysfunctions are progressive in nature. There is continued degeneration of cellular networks and brain substance either by misfolded protein accumulation, poor synaptic transmission, or abnormal metabolism/neurotransmitter levels. The course of these illnesses is complicated by concomitant medical disease, which may delay or cloud the final diagnosis. As there are no known disease-modifying therapies, efforts are directed at

treating symptoms. In 2002, Hejl et al14 defined potentially reversible types of dementia as arrestable spontaneously or via treatment or a disease entity that may be contributing to the memory complaints or dementia. True reversibility of many of these disease types remains unclear.15,16,17,18

39.3.1 Preventable Entities Obstructive sleep apnea has been linked to memory dysfunction. If screened appropriately, it may be readily identified in individuals who snore and complain of excessive daytime sleepiness, mood changes, and difficulty maintaining attention. These individuals tend to be younger than the typical dementia patient, being diagnosed during middle age. Diagnosis is confirmed via polysomnography when breathing is interrupted for longer than 10 seconds, 5 or more times per hour. Studies have shown anatomical brain changes in patients with chronic untreated sleep apnea.19 Brain structural changes affecting white matter were further delineated by Macey et al20 in a 2008 report using diffusion tensor imaging modalities. The question remains whether true reversibility is obtainable if the destruction seen in imaging occurs from intermittent periods of hypoxia.21,22 Pseudodementia was introduced in 1961 by Leslie Kiloh. The term has been reserved for the manifestation of memory dysfunction as consequence of psychiatric disorder, traditionally depression.23 The incidence of depression is high in the aging population, and depression often manifests with cognitive symptoms.24 The Geriatric Depression Scale and formal neuropsychiatric testing, along with basic dementia screening, have been useful in identifying this cohort of patients.25 Neuroimaging is an ancillary tool to exclude underlying structural changes that explain the neuropsychiatric changes. Most imaging cases will be unremarkable. However, nonspecific changes have been cited in the literature to include white matter

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Clinical Approach and Treatment

Fig. 39.2 Vascular dementia. (a) Axial diffusionweighted imaging (November 2004) shows a large area of restricted diffusion in the right parietal lobe (arrow), suggestive of hyperacute stroke. Within 4 months, this patient had signs and symptoms of dementia. (b) Axial computed tomography scan image (February 2005) shows a large area of hypodensity in the right frontoparietal lobe (arrowheads) with ex vacuo dilatation of the lateral ventricle suggestive of encephalomalacia and loss of parenchyma.

lesions.26 Once the psychiatric disorder is treated, memory complaints are expected to reverse, although they may do so in a delayed fashion. Dementia may also be accompanied by depression, and treating the depression may restore some function but will not reverse underlying pathology.17 Small vessel, lacunar disease, and vascular dementia represent the second most common type of dementia (▶ Fig. 39.2). Vascular dementia is irreversible and progressive; however, there is a theoretical stage of preventability if risk factors are addressed before evidence of irreversible ischemic change is seen. The National Institute of Neurological and Communicative Disorders and Stroke (NINDS)/Association Internationale pour la Recherche et l’Enseignement en Neurosciences (AIREN) criteria27 for vascular dementia require evidence of cerebrovascular disease by CT or MRI. These changes include diffuse white matter small-vessel disease or focal large-vessel ischemic changes involving strategic memory pathways or brain structures. The risk factors for vascular dementia are similar to those for vascular and cardiac disease and include hypertension, hyperlipidemia, diabetes mellitus, obesity, smoking, and homocysteinuria.28 Multiple scales have attempted to correlate the degree of white matter changes to the degree of small-vessel disease and cognitive impairment. Whereas significant territorial infarcts have been identified, such as thalamic lacunar infarct and associated higher degree of cognitive impairment, overall there is no single scale that is used uniformly to quantify the degree of tissue involvement.29,30,31 Imaging correlates for vascular changes are summarized in ▶ Table 39.226,32; refer to Chapter 21 for more extensive discussion.

39.4 Potentially Reversible Entities Many different approaches have been designed to further classify the potentially reversible disease types, including association with other medical diseases or other neurologic signs. The following sections comprise a straightforward collection of the different possible potential reversible entities and the most common neuroimaging correlates available.

Table 39.2 Imaging correlates for vascular changes in the different imaging modalities26,32 Computed tomography

Magnetic resonance imaging

SPECT

Hypoattenuation of subcortical regions

T1: hypointensities and enlarged perivascular spaces

Used as an ancillary tool when diagnosis is unclear; seen as a patchy reduction in blood flow dependent on the site of the lesion

Enlarged perivascular spaces (lacunae)

T2/FLAIR: hyperintensities involving periventricular white matter, subcortical deep gray nuclei

Abbreviations: FLAIR, fluid-attenuated inversion recovery; SPECT, single-photon emission computed tomography.

39.4.1 Structural Because the brain is encased within the skull, pressure changes via intracranial hemorrhage or structural lesions will cause destruction to local cell bodies and their connections. Clinical changes secondary to a bleed or structural lesion (neoplasm) can manifest with memory dysfunction and/or a state of confusion. Additional neurologic symptoms, history of trauma, and rate of progression may provide further clues as to location and suspected injury.

Intracranial Hemorrhage There are different types of intracranial hemorrhages (subarachnoid, subdural, epidural, lobar), and manifestation varies based on the type of bleed. The subdural hematoma has been considered one of the most likely to mimic dementia. At-risk populations include elderly adults, alcoholics, those with generalized atrophy, and those with iatrogenic (postprocedural) causes. Generalized atrophy results in stretching of bridging veins, which may cause spontaneous or posttraumatic bleeding

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Reversible versus Nonreversible Dementia: Practical Approach

Fig. 39.3 Subdural hematoma in 61-year-old man with dementia. (a) Axial computed tomography and (b) axial T2-weighted imaging show left holohemispheric subdural hematoma (arrowheads) causing effacement and compression of the cortical sulci and left cerebral parenchyma.

within the subdural space. The cognitive and neurologic changes from the compression unilaterally or bilaterally, depending on the degree of cerebral atrophy, may vary in clinical signs and symptoms. CT scan is the imaging modality of choice for identification of acute ICH, revealing a crescentshaped collection with a subdural hematoma (▶ Fig. 39.3). MRI can provide further information about the age of bleed. It is expected that, with evacuation of blood and relief of pressure on cortical structures, cognitive symptoms improve. Brand et al33 described cognitive impairment as an expected sequel within their cohort of patients who had subarachnoid, subdural, and intracerebral hemorrhage at 6 months’ follow-up. Improvement was variable, and full reversibility is not mentioned.

Normal Pressure Hydrocephalus Normal pressure hydrocephalus (NPH) is identified by the clinical triad of cognitive dysfunction, urinary incontinence, and gait abnormalities (described as a “magnetic gait”). Memory complaints tend to be seen later in the disease course. Neuroimaging reveals enlarged ventricles out of proportion to the degree of atrophy (▶ Fig. 39.4). NPH is described as a communicating hydrocephalus, and presumed pathophysiology is impaired cerebrospinal fluid (CSF) absorption, as evident in radionuclide cisternography and MRI CSF flow studies. Clinical response to shunting procedures and serial lumbar punctures is variable in patients with idiopathic NPH.6 Cognitive deficits are the least likely to show any major improvement, although overall long-term improvement was seen in as many as 75% of patients who did have the shunting procedure.34 Duration of symptoms before intervention may correlate with the degree of response. NPH must be distinguished from hydrocephalus ex vacuo.

Meningioma Meningiomas are dural-based, nonglial tumors that arise from arachnoid cap cells. They exhibit indolent growth and cause neurologic symptoms by compression of localized tissue and

Fig. 39.4 Axial computed tomography scan of the head shows moderate dilatation of the lateral ventricles out of proportion to the convexity sulci, suggestive of normal pressure hydrocephalus.

increased intracranial pressure. Peak incidence occurs in the sixth and seventh decade of life; however, they may be seen at any age. The tumor is a homogeneous mass that is easily identified by the dural tail on enhanced imaging (▶ Fig. 39.5). Total resection of tumor in elderly patients is associated with increased morbidity and mortality, but significant cognitive improvement has been seen.35,36

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Fig. 39.5 Sagittal T1-weighted imaging shows an extraxial mass (star) centered over the planum sphenoidale causing severe mass effect on the frontal lobes (arrow).

39.4.2 Systemic Metabolic and Toxic Disorders Systemic manifestations of metabolic and toxic disorders may include alteration of consciousness, encephalopathy, decreased arousal, and deficits involving the different cognitive domains. Electrolyte abnormalities and organ failure will lead to abnormalities in brain metabolism and injury to cognitive pathways in a localized or diffuse fashion. Hepatic disease (chronic liver disease, Wilson’s disease) on MRI reveals T1/T2 hyperintensities along the deep gray matter structures (▶ Fig. 39.6), which have been shown to regress in follow-up imaging after liver transplantation.37,38 Uremic patients can also present with impairment of higher cortical

Fig. 39.7 A 63-year-old uremic man with impairment of higher cortical functions. Axial fluid-attenuated inversion recovery (FLAIR) image shows mild expansion of the cortical gyri with bright signal intensity suggestive of cortical edema (white arrows). Focal hyperintensity is seen in the occipital cortex (black arrows) and subjacent white matter bilaterally resulting from associated posterior reversible encephalopathy syndrome.

functions. When uremia is severe, a posterior reversible leukoencephalopathy or cerebral cytotoxic and vasogenic edema on MRI can be seen (▶ Fig. 39.7).39 Toxic effects of alcohol or inhalant abuse, heavy metal exposure, and malnutrition involve all the major organs, including

Fig. 39.6 Chronic liver failure with cognitive decline. (a) Axial T1 image shows symmetric hyperintensity in the globus pallidus bilaterally. This region shows normal signal intensity on (b) fluid-attenuated inversion recovery images.

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Reversible versus Nonreversible Dementia: Practical Approach Table 39.3 Systemic (metabolic/toxic) disease entities and their imaging correlates17,40,41,42,43,44,59,60 Alcohol/vitamin deficiency

Heavy metal Other substances

Disease entity

Imaging correlates

Marchiafava-Bignami disease

Generalized atrophy, T2 hyperintensities involving corpus callosum or T1 hypointensities along same region suggestive of cavitation

Acquired hepatocellular degeneration

Diffuse microcavitation at gray/white matter junction

Wernicke-Korsakoff (thiamine deficiency)

T2/FLAIR and diffusion hyperintensities involving mammillary bodies, thalamus, cerebral aqueduct/third ventricle, fornix Atrophy of mammillary bodies

Methanol poisoning

Bilateral putaminal hemorrhagic necrosis

Cerebellar degeneration

Cerebellar cortical atrophy

Iron/manganese

T1 hyperintensities within basal ganglia

Lead

T2 hyperintensities within basal ganglia, hypothalamus, and pons

Inhaled toluene

Diffuse white matter changes: toxic leukoencephalopathy

Abbreviation: FLAIR, fluid-attenuated inversion recovery.

the central nervous system (CNS). Acutely, exposure to these toxins may cause cognitive changes that are potentially reversible; however, correlation between the exposure period or the pattern of ingestion and the likelihood of reversibility remains unclear. Long-term alcohol use leads to direct end-organ alterations (Marchiafava-Bignami disease, acquired hepatocerebral degeneration, Korsakoff ’s syndrome, cerebellar degeneration) and changes to physiologic homeostasis by malnutrition and vitamin deficiency (Marchiafava-Bignami disease, pellagra, Wernike’s encephalopathy).40 The same disease entities may have direct influence on, or lead to, nutritional deficiencies, which ultimately have an end-organ effect.40 As summarized by Bjork and Gilman41, functional neuroimaging modalities such as fMRI, diffusion tensor imaging, magnetic resonance spectroscopy, and PET are aiding in our understanding of the effects of acute alcohol exposure in resting-state connections, alcohol’s influence on the composition of brain tissue, regional changes, and dopamine involvement.41 Brain lesions are varied to include decreased brain volume, which is now another area of interest that uses voxel-based morphometry.42,43,44 Imaging correlates for systemic disease entities are summarized in (▶ Table 39.3). To complete the discussion of toxic exposure manifesting as cognitive deficits, it is important to consider medication effects and polypharmacy. Medications like benzodiazepines and the combination of medications, especially in elderly patients, may be a simple reversible inciting factor for the signs of memory dysfunction. Other medications to consider as possible offending agents include antibiotics, chemotherapeutic agents (especially those introduced intrathecally), anticonvulsants, and psychiatric agents.

39.4.3 Infectious Causes HIV–Associated Neurocognitive Disorder As highlighted in the 2013 Conference on Retroviruses and Opportunistic Infections human immunodeficiency virus (HIV) CNS-associated changes remain poorly understood.45 HIVassociated neurocognitive disorder (HAND) is considered a milder form of cognitive dysfunction seen in early infection and in the age of antiretroviral therapy (ART) has been a topic of great interest. It has been difficult to ascertain whether

cognitive dysfunction is from direct neuroinvasion, neurotoxicity from ART therapy, or an immune response to retroviral treatment, aging of the HIV population, comorbid factors, or (a more likely explanation) a combination thereof. Although highly active antiretroviral therapy has been noted to decrease the progression to AIDS dementia, it does not appear to reverse injury present before initiation of retroviral therapies or to halt fully any further neurocognitive changes.46 Imaging studies reveal global brain atrophy, white matter changes, and basal ganglia signal changes in patients with HAND.47

Whipple’s Disease, Chronic Meningitis, and Central Nervous System Lyme Disease Whipple’s disease, a systemic disorder, can lead to cognitive dysfunction. Associated CNS involvement varies from 6 to 63%, depending on the study, although it is rare to have isolated CNS Whipple’s disease.48,49 Whipple’s disease should be considered in the setting of progressive cognitive dysfunction with associated gastrointestinal disturbances. Imaging may be nonspecific and can show focal or diffuse gadolinium-enhancing lesions, mostly within white matter, but meningeal involvement is also seen.50 Follow-up imaging may be useful in assessing for the resolution of lesions once treatment has been started or in cases where recurrence is a possibility. Chronic meningitis is defined as meningeal inflammation with abnormal CSF findings lasting more than 4 weeks.51,52,53 Numerous infectious and noninfectious causes for chronic meningitis are possible; however, in up to one-third of patients, the cause remains unclear.53 Imaging findings reveal pachymeningeal enhancement, and hydrocephalus as a complication of the inflammatory response. Lyme disease involves the central nervous system in 10 to 20% of patients during the acute disseminated phase of the infection.54 Cognitive changes seen in Lyme encephalopathy are suspected to occur in response to the inflammatory changes and not irreversible cellular injury. Lyme remains a clinical diagnosis with supporting serologic studies and or spinal fluid testing when CNS is involved. Imaging changes are nonspecific and include T2/fluorescent-attenuated inversion recovery (FLAIR) hyperintensities. Clinical response to antibiotic therapy continues to be excellent.54,55

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39.4.4 Inflammatory Limbic encephalitis clinically manifests as a subacute dementia with associated psychiatric symptoms. It is a paraneoplastic or autoimmune-associated disorder that requires investigation for possible underlying neoplasm. MRI findings include T2/FLAIR hyperintensities with predilection for the temporal lobe; however, the lentiform nucleus can also be involved (▶ Fig. 39.8).17,56 Reversibility of symptoms with removal of neoplasm, if present, or immunosuppression has been documented.57

39.4.5 Neurodegenerative Dementias Neurodegenerative dementias are covered extensively in previous chapters. ▶ Fig. 39.9 provides the most common primary progressive neurodegenerative disorders divided by cortical, subcortical, and mixed dementia types. Brain CT, MRI, and functional imaging will allow for further supportive evidence via classic imaging correlates as described in ▶ Table 39.4 for some of the primary neurodegenerative dementias.

39.5 Conclusion This chapter briefly summarizes reversible and irreversible dementias and neuroimaging. The differential diagnosis for memory dysfunction is vast and complex because they exist as primary conditions or as manifestations of underlying medical illness. Obtaining comprehensive history, complete neurologic examination, and baseline medical screen in accordance with neuroimaging may guide diagnosis and treatment. True reversibility of dementia mimics remains unclear and will require

Fig. 39.8 8 Limbic encephalitis from ovarian cancer in 59-year-old woman. Axial fluid-attenuated inversion recovery image shows symmetrical hyperintensity in the hippocacampi bilaterally.

Fig. 39.9 Algorithm for irreversible dementia. AD, Alzheimer’s disease; ALS, amyotrophic lateral sclerosis; CADASIL, cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy; CBDG, corticobasal degeneration; FTLD, frontotemporal dementia; MCI, mild cognitive impairment; MSA, multiple system atrophy; PSP, progressive supranuclear palsy. This simple guide does not include all possible irreversible entities.

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Reversible versus Nonreversible Dementia: Practical Approach Table 39.4 Progressive neurodegenerative disorders and most commonly associated imaging correlates12,26,58,61–66 Disease entity

Imaging modality

Imaging correlates

Alzheimer’s disease

CT/MRI

Medial temporal lobe, hippocampal atrophy

SPECT/ FDG-PET

Hypoperfusion/hypometabolism of temporal and parietal lobes, posterior cingulate and inferior frontal regions (Murray)

CT/MRI

Similar atrophic changes to AD except higher likelihood of preserving medial temporal lobe structures (O’Brien)

SPECT

Hypoperfusion of temporoparietal lobes and occipital lobes, changes in dopamine transporter

Frontotemporal dementia

CT/ MRI

Frontal and/or frontal temporal lobe “knife-edge” atrophy

Multiple system atrophy

MRI

Cerebral atrophy particularly of brainstem structures (pons, middle cerebellar peduncles) with presence of “hot cross bun” sign

Progressive supranuclear palsy

MRI

Midbrain atrophy “humming bird/penguin” sign

Corticobasal degeneration

MRI

Frontal and/or parietal degeneration (asymmetric)

Creutzfeldt-Jakob disease (prion)

MRI

DWI hyperintensity along thalamus, striatum and cortex (cortical ribboning)

Huntington’s disease

MRI

Caudate atrophy, T2, FLAIR hypointensity along basal ganglia within areas of iron deposition (Degnan)

Dementia with Lewy bodies

Abbreviations: CT, computed tomography; FDG, fluorodeoxyglucose; FLAIR, fluid-attenuated inversion recovery; MRI, magnetic resonance imaging; PET, positron emission tomography; SPECT, single-photon emission computed tomography

further evaluation in clinical studies. However, any potentially reversible disease should be considered and addressed in patients with memory dysfunction. As for primary neurodegenerative dementias, current investigational efforts are using structural and functional imaging modalities for earlier identification of anatomical changes and biomarkers in the pursuit of neuroprotective therapies that eventually might lead to potential cures.

References [1] Federal Interagency Forum on Aging-Related Statistics. Older Americans 2012: Key Indicators of Well-Being. Federal Interagency Forum on AgingRelated Statistics. Washington, DC: U.S. Government Printing Office; 2012 [2] World Health Organization. Dementia fact sheet. 2012. (http://www.who.int/ mediacentre/factsheets/fs362/en/index.html [3] World Health Organization (WHO). Dementia: a public health priority. Geneva: WHO; 2012 [4] Galasko D. The diagnostic evaluation of a patient with dementia. Continuum (Minneap Minn) 2013; 19 2 Dementia: 397–410 [5] Plassman BL, Langa KM, Fisher GG et al. Prevalence of dementia in the United States: the aging, demographics, and memory study. Neuroepidemiology 2007; 29: 125–132 [6] Piccini C, Bracco L, Amaducci L. Treatable and reversible dementias: an update. J Neurol Sci 1998; 153: 172–181 [7] Arnold SE, Kumar A. Reversible dementias. Med Clin North Am 1993; 77: 215–230 [8] Weytingh MD, Bossuyt PM, van Crevel H. Reversible dementia: more than 10% or less than 1%? A quantitative review. J Neurol 1995; 242: 466–471 [9] Freter S, Bergman H, Gold S, Chertkow H, Clarfield AM. Prevalence of potentially reversible dementias and actual reversibility in a memory clinic cohort. CMAJ 1998; 159: 657–662 [10] Ropper AH, Samuels MA. Adams and Victor’s Principles of Neurology. 9th ed. New York: McGraw Hill; 2009;410–429 [11] Knopman DS, DeKosky ST, Cummings JL et al. Report of the Quality Standards Subcommittee of the American Academy of Neurology. Practice parameter: diagnosis of dementia (an evidence-based review). Neurology 2001; 56: 1143–1153 [12] Sorbi S, Hort J, Erkinjuntti T et al. EFNS Scientist Panel on Dementia and Cognitive Neurology. EFNS-ENS Guidelines on the diagnosis and management of disorders associated with dementia. Eur J Neurol 2012; 19: 1159–1179

[13] Lee L, Weston WW, Heckman G, Gagnon M, Lee FJ, Sloka S. Structured approach to patients with memory difficulties in family practice. Can Fam Physician 2013; 59: 249–254 [14] Hejl A, Høgh P, Waldemar G. Potentially reversible conditions in 1000 consecutive memory clinic patients. J Neurol Neurosurg Psychiatry 2002; 73: 390– 394 [15] Waldemar G. Reversible dementia’s do they exist? Pract Neurol 2002; 2: 138– 143 [16] Tripathi M, Vibha D. Reversible dementias. Indian J Psychiatry 2009; 51 Suppl 1: S52–S55 [17] Kabasakalian A, Finney GR. Reversible dementias. Int Rev Neurobiol 2009; 84: 283–302 [18] Loannidis P, Karacostas D. How reversible are reversible dementias? Euro Neurolog Rev. 2011; 6: 230–233 [19] O’Donoghue FJ, Wellard RM, Rochford PD et al. Magnetic resonance spectroscopy and neurocognitive dysfunction in obstructive sleep apnea before and after CPAP treatment. Sleep 2012; 35: 41–48 [20] Macey PM, Kumar R, Woo MA, Valladares EM, Yan-Go FL, Harper RM. Brain structural changes in obstructive sleep apnea. Sleep 2008; 31: 967– 977 [21] Macey PM. Is brain injury in obstructive sleep apnea reversible? Sleep 2012; 35: 9–10 [22] Muñoz A, Mayoralas LR, Barbé F, Pericás J, Agusti AG. Long-term effects of CPAP on daytime functioning in patients with sleep apnoea syndrome. Eur Respir J 2000; 15: 676–681 [23] Caine ED. Pseudodementia: current concepts and future directions. Arch Gen Psychiatry 1981; 38: 1359–1364 [24] Lima-Silval B, Yassuda MS. The relationship between memory complaints and age in normal aging. Dementia & Neuropsychologia 2009; 3: 94–100 [25] Welsh-Bohmer KA, Morgenlander JC. Determining the cause of memory loss in the elderly. From in-office screening to neuropsychological referral. Postgrad Med 1999; 106: 99–100, 103–104, 106 passim [26] O’Brien J, Barber B. Neuroimaging in dementia and depression. Adv Psychiatr Treat 2000; 6: 109–119 [27] Román GC, Tatemichi TK, Erkinjuntti T et al. Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology 1993; 43: 250–260 [28] Purandare N. Preventing dementia: role of vascular risk factors and cerebral emboli. Br Med Bull 2009; 91: 49–59 [29] Román G, Pascual B. Contribution of neuroimaging to the diagnosis of Alzheimer’s disease and vascular dementia. Arch Med Res 2012; 43: 671–676 [epub] [30] Black S, Gao F, Bilbao J. Understanding white matter disease: imaging-pathological correlations in vascular cognitive impairment. Stroke 2009; 40 Suppl: S48–S52

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Clinical Approach and Treatment [31] Murray ME, Knopman DS, Dickson DW. Vascular dementia: clinical, neuroradiologic and neuropathologic aspects. Panminerva Med 2007; 49: 197–207 [32] van Straaten EC, Scheltens P, Barkhof F. MRI and CT in the diagnosis of vascular dementia. J Neurol Sci 2004; 226: 9–12 [33] Brand C, Alber B, Fladung AK et al. Cognitive performance following spontaneous subarachnoid haemorrhage versus other forms of intracranial haemorrhage. Br J Neurosurg 2014–80 [34] McGirt MJ, Woodworth G, Coon AL, Thomas G, Williams MA, Rigamonti D. Diagnosis, treatment, and analysis of long-term outcomes in idiopathic normal-pressure hydrocephalus. Neurosurgery 2005; 57: 699–705 [35] Konglund A, Rogne SG, Lund-Johansen M et al. Outcome following surgery for intracranial meningiomas in the outcome following surgery for intracranial meningiomas in the elderly. Acta Neurol Scand 2013; 127: 161–169 [36] Tucha O, Smely C, Lange KW. Effects of surgery on cognitive functioning of elderly patients with intracranial meningioma. Br J Neurosurg 2001; 15: 184–188 [37] Pujol A, Pujol J, Graus F et al. Hyperintense globus pallidus on T1-weighted MRI in cirrhotic patients is associated with severity of liver failure. Neurology 1993; 43: 65–69 [38] Litwin T, Dzieżyc K, Poniatowska R, Członkowska A. Effect of liver transplantation on brain magnetic resonance imaging pathology in Wilson disease: a case report. Neurol Neurochir Pol 2013; 47: 393–397 [39] Kang E, Jeon SJ, Choi SS. Uremic encephalopathy with atypical magnetic resonance features on diffusion-weighted images. Korean J Radiol 2012; 13: 808–811 [40] Mancall EL. Nutritional disorders of the nervous system. In: Neurology and General Medicine. New York: Churchill Livingstone; 1995:285–301 [41] Bjork JM, Gilman JM. The effects of acute alcohol administration on the human brain: Insights from neuroimaging. Neuropharmacology 2014 [42] Hillbom M, Saloheimo P, Fujioka S, Wszolek ZK, Juvela S, Leone MA. Diagnosis and management of Marchiafava-Bignami disease: a review of CT/MRI confirmed cases. J Neurol Neurosurg Psychiatry 2014; 85: 168–173 [43] Charness ME. Brain lesions in alcoholics. Alcohol Clin Exp Res 1993; 17: 2–11 [44] Sullivan EV, Pfefferbaum A. Neuroimaging of the Wernicke-Korsakoff syndrome. Alcohol Alcohol 2009; 44: 155–165 [45] Spudich SS, Ances BM. Neurologic complications of HIV infection: highlights from the 2013 Conference on Retroviruses and Opportunistic Infections. Top Antivir Med 2013; 21: 100–108 [46] Spudich S. HIV and neurocognitive dysfunction. Curr HIV/AIDS Rep 2013; 10: 235–243 [47] Steinbrink F, Evers S, Buerke B et al. German Competence Network HIV/AIDS. Cognitive impairment in HIV infection is associated with MRI and CSF pattern of neurodegeneration. Eur J Neurol 2013; 20: 420–428

[48] Panegyres PKE, Edis R, Beaman M, Fallon M. Primary Whipple’s disease of the brain: characterization of the clinical syndrome and molecular diagnosis. QJM 2006; 99: 609–623 [49] Louis ED, Lynch T, Kaufmann P, Fahn S, Odel J. Diagnostic guidelines in central nervous system Whipple’s disease. Ann Neurol 1996; 40: 561–568 [50] Dönmez FY, Ulu E, Başaran C et al. MRI of recurrent isolated cerebral Whipple’s disease. Diagn Interv Radiol 2010; 16: 112–115 [51] Helbok R, Broessner G, Pfausler B, Schmutzhard E. Chronic meningitis. J Neurol 2009; 256: 168–175 [52] Zunt JR, Baldwin KJ. Chronic and subacute meningitis. Continuum (Minneap Minn) 2012; 18 6 Infectious Disease: 1290–1318 [53] Syed N, Saxena A, Hartley L. Investigating chronic meningitis. Arch Dis Child Educ Pract Ed 2009; 94: 138–143 [54] Halperin JJ. Lyme disease: a multisystem infection that affects the nervous system. Continuum (Minneap Minn) 2012; 18 6 Infectious Disease: 1338– 1350 [55] Halperin JJ. Nervous system lyme disease: diagnosis and treatment. Curr Treat Options Neurol 2013; 15: 454–464 [56] Sureka J, Jakkani RK. Clinico-radiological spectrum of bilateral temporal lobe hyperintensity: a retrospective review. Br J Radiol 2012; 85: e782–e792 [57] Asztely F, Kumlien E. The diagnosis and treatment of limbic encephalitis. Acta Neurol Scand 2012; 126: 365–375 [58] Masdeu JC. Neuroimaging of Dementia. New York: Elsevier; 707–771 [59] Jain N, Himanshu D, Verma SP, Parihar A. Methanol poisoning: characteristic MRI findings. Ann Saudi Med 2013; 33: 68–69 [60] Filley CM. Toluene abuse and white matter: a model of toxic leukoencephalopathy. Psychiatr Clin North Am 2013; 36: 293–302 [61] Agosta F, Caso F, Filippi M. Dementia and neuroimaging. J Neurol 2013; 260: 685–691 [62] Haines A, Katona C. Dementia in old age. Occas Pap R Coll Gen Pract 1992: 62–66 [63] Petersen RC, Stevens JC, Ganguli M, Tangalos EG, Cummings JL, DeKosky ST Report of the Quality Standards Subcommittee of the American Academy of Neurology. Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review). Neurology 2001; 56: 1133– 1142 [64] Murray AD. Imaging approaches for dementia. AJNR Am J Neuroradiol 2012; 33: 1836–1844 [65] Degnan AJ, Levy LM. Neuroimaging of rapidly progressive dementias, part 1: neurodegenerative etiologies. AJNR Am J Neuroradiol 2014; 35: 418–423 [66] Degnan AJ, Levy LM. Neuroimaging of rapidly progressive dementias, part 2: prion, inflammatory, neoplastic, and other etiologies. AJNR Am J Neuroradiol 2014; 35: 424–431

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Advances in the Treatment of Dementia

40 Advances in the Treatment of Dementia Madhav Thambisetty, Néstor Gálvez-Jiménez, and Thyagarajan Subramanian Dementias can be broadly classified as reversible and nonreversible. This classification is bound to change over time as disease pathology becomes more clear-cut, and newer modalities of treatment that target the pathology are discovered, and diseases that were considered nonreversible are likely to become reversible. For now, however, this classification structure is useful, to enhance our understanding and explore imaging consequences as well as opportunities.

40.1 Treatment of Reversible Dementias

triamterene alone. The use of these compounds by themselves has produced numerous complications, including potentially lethal status dystonicus, which is associated with acute-onset thalamic, tegmentum, and brainstem MRI findings (▶ Fig. 40.1) within 2 weeks of starting therapy in some patients.6 Although this change in MRI can be reversed in some patients while treatment is continued further on D-penicillamine, these reports of severe complications have led to the development of safer new regimens. The new regimen that has become standardized uses zinc and triamterene in combination initially for 3 months, and then if there is satisfactory reduction in copper excretion in the urine and sufficient zinc in the measured urine samples, patients may continue on zinc alone. From an imaging standpoint, patients can be followed up by using periodic MRI to determine whether the copper deposits in the basal ganglia are clearing. However, the radiologic outcomes suggest better chelation with D-penicillamine as opposed to treatment with zinc.7 To date, zinc does not appear in imaging studies as abnormal signals. Nevertheless, most treatment protocols in contemporary medicine tend to initiate treatment with zinc and triamterene. In addition to chelation, many Wilson’s disease patients require anti-parkinsonian medications, botulinum toxin therapy for focal dystonia, and medications for psychiatric symptom management.

Conventional treatment strategies continue to be the mainstays in the treatment of reversible dementias. These include identification of reversible causes and prompt initiation of treatment. Examples include vitamin B12 replenishment in the case of B12 deficiency via parenteral administration initially and then further correction using appropriate investigations. The imaging finding in patients with dementia in the case of B12 deficiency is that of subacute combined degeneration, classically seen predominantly in the spinal cord. These changes completely reverse in 3 to 4 months if treatment is initiated promptly.1,2,3 Nitrous oxide use in anesthesia can potentially exacerbate previous borderline cases of B12 deficiency. Therefore, imaging hallmarks of the B12 deficiency characterized in cross-section images by bilateral paired areas of T2 hyperintensity, seen as an “inverted V” or “inverted rabbit ears” in the expected anatomical location of the dorsal columns, is often considered pathognomonic for B12 deficiency, even in the absence of classic neurologic deficits on clinical testing. These changes are reversible with proper initiation of treatment. Recent work also suggests that diffusion tensor imaging (DTI) may be useful as a technique to detect changes resulting from B12 deficiency in the brain.4 However, the use of DTI in monitoring treatment efficacy with B12 repletion is unknown. Correcting hypothyroidism and treating infectious causes of dementia (human immunodeficiency virus and syphilis) also alter imaging pathology, although neurosyphilis-induced imaging changes are only partially altered by treatment. Normal pressure hydrocephalus (NPH) is treated with surgical placement of a ventriculoperitoneal or lumboperitoneal shunts. Better shunt designs have allowed minimization of the complications caused by excess removal of cerebrospinal fluid (CSF) after the placement of shunts. Understanding the fluid mechanics of CSF drainage in NPH and the prudent use of improved technology have allowed this complication to be minimized.5

A number of antioxidants and free radical scavengers have been investigated as either common preventative or ameliorative therapies for all neurodegenerative disorders. This sweeping approach, although it has a strong scientific basis, has not had positive results in clinical research testing. The number of agents in this category that have undergone testing is beyond the scope of this chapter. Some examples include coenzyme Q10, several vitamins, and several natural products. To date, none of these agents has shown proven benefits and none is recommended as standard treatment. Health care providers must be aware, however, that some patients take these medications as nutritional aids and nutraceuticals. Ongoing research efforts continue to explore this overarching approach to all neurodegenerative disorders. An example of recent success is the use of vitamins B6, B12, and folic acid in combination in patients with elevated homocysteine to prevent brain cortical atrophy as evidenced by serial MRI studies.8 Whereas the vast majority of these vitamin and herbal agents, antioxidants, and free radical scavengers have no deleterious consequences, some do and can actually cause rare imaging changes.

40.1.1 Wilson’s Disease

40.2.1 Alzheimer’s Disease

New treatment regimens are in place for Wilson’s disease, which is reversible if recognized and treated early. The magnetic resonance imaging (MRI) findings in Wilson’s disease are discussed elsewhere in this book (Chapter 20). Here we discuss the new treatment protocols, their rationale, and imaging consequences. The new regimen avoids D-penicillamine or

Two types of medications are used to treat the symptoms of Alzheimer’s disease (AD),9 and a useful treatment schematic drawing that summarizes current strategies and their basis is shown in ▶ Fig. 40.2. First, to enhance cerebral cholinergic activity in AD, anticholinesterases have been used. There is consistent evidence from numerous trials that, on average, subjects

40.2 Nonreversible Dementias

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Fig. 40.1 Axial fluid attenuated inversion recovery (FLAIR) images (b), in Wilson disease patient with chelation-induced dystonia, show increased areas of high signal intensity in the bilateral thalami, tegmentum, pons, upper medulla, and adjacent cerebellum after treatment with trientine, compared with (a) MRI performed 4 months earlier. (Reprinted with permission from Kim B, Chung SJ, Shin H-W. J Clin Neurosci 2013;20:606–608.)

treated with these medications show statistically significant improvements (versus placebo) on measures of cognition (the cognitive subscale of the Alzheimer’s Disease Assessment Scale [ADAS] and the Mini-Mental State Examination), as well as on measures of overall improvement from the clinician and caregiver in all stages of AD.10 However, the magnitude of benefit on the cognitive scales is minimal. Many patients have claimed significant subjective improvement. The ADAS-cog is an approximately 70-point scale, however, and the 2- to 4-point improvement from treatment results in modest “real-life” change. From a neuroimaging standpoint, measuring the effects of treatment using structural imaging remains a work in

progress. Several methods, including manual or automated measurement of the hippocampus or the ventricles, are being tested and with refinement of techniques will become available in the near future. Needless to say, these methods will prove of great value in treating AD patients and monitoring AD clinical symptoms.11 Another important issue in imaging is a safety issue related to the transdermal formulation of rivastigmine. Local burns are possible because of the small metal content in the patches. Because patients with dementia might not remember that they have a patch on or that a patch has not been removed, it is important to instruct caregivers and to also check patients’ skin for any transdermal patches of rivastigmine.

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Fig. 40.2 Mechanism of action of cholinesterase inhibitors. (Modified with permission from Hanson MR, Galvez-Jimenez N. Cleve Clin J Med 2000;67 (6):441–448.)

Second, modulation of the N-methyl-D-aspartate (NMDA) receptor with the use of memantine, a noncompetitive NMDA receptor antagonist, is standard therapy for most AD patients. It is not clear how this medication causes an improvement in cognition. Like the cholinesterase inhibitors, memantine has shown a statistically significant benefit on measures of cognition and global impression in moderate to severe AD. There is evidence that the combination of both medications may be better than either alone. Again, the effect is modest at best. Because of the modest effects and relatively high cost of the medications, resource utilization will likely become an even greater issue in treatment considerations than it is at present.

In addition to cognitively directed medical treatment, the occurrence of emotional changes and depression in AD is high. These result from progressive deterioration of affective systems and can be intermixed with behavioral changes (e.g., aggressiveness, psychosis) as the disease progresses. Selective serotonin reuptake inhibitors (SSRIs) are a viable initial treatment for emotional changes and depressive symptoms. Novel approaches to the treatment of AD include the development of an AD vaccine by immunizing against the β-amyloid (Aβ) protein.12 Although initial trials have not been successful, the concept of developing a vaccine to retard AD pathology is intriguing, and several lines of research are pursuing this idea.

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Clinical Approach and Treatment Another notion is to modulate the neuroimmune system to mitigate and perhaps retard neurodegeneration. Strategies include development of a specific set of antibodies and novel gene therapy that targets the immune system to clear pathological proteins from the brain.13 Finally, the gene therapy approach using growth factor injection into the brain has been attempted with limited success but has not met the standards for widespread application. These experimental strategies remain of interest as future AD therapies. The availability of good imaging surrogates for AD has been a major advance in facilitating research into AD experimental therapeutics. The recent incorporation of neuroimaging as well as CSF biomarkers in the revised diagnostic criteria for AD14 reflects the considerable advances in our knowledge about their association with disease onset, severity, and progression. Moreover, their usefulness in defining a “preclinical” stage of AD15 is likely to accelerate the development of disease-modifying treatments in at-risk older individuals before the onset of clinical symptoms. The neuroimaging methods specified by the revised criteria to define the presence of an “AD pathophysiological process” include positive positron emission tomography (PET) amyloid imaging, decreased16 fluorodeoxyglucose uptake on PET in the temporoparietal cortex, and disproportionate atrophy on structural MRI in the medial, basal, and lateral temporal lobes and medial parietal cortex.17 It is worth noting in this context that the Food and Drug Administration recently approved florbetapir (Amyvid; Eli Lilly, Indianapolis, IN), the first radioligand for in vivo imaging of brain amyloid burden in humans by PET18 and other similar applications.16

40.2.2 Parkinson’s Disease Advances in the treatment of Parkinson’s disease (PD), PD-plus syndromes, secondary parkinsonisms, and complications of these disorders have been major accomplishments in the past decade.19 Besides the use of levodopa in combination with carbidopa or benseraside (dopa decarboxylase inhibitors), several additional pharmacologic agents have been approved as medications for parkinsonism. One class of agents is the dopamine agonists. Although this class has been used for treating PD for many years, several new developments have occurred in this field over the past several years. Two previously extensively used dopamine agonists, bromocriptine and pergolide, have been abandoned as treatments for parkinsonism because their use has been associated with high risk for cardiac valvular disease.20 This risk is attributed to the ergot-like properties of these agents. Lisuride, another ergot-like dopamine agonist, is not available in the United States, but it is widely used in Asia and in the European Union. Two well-established nonergot dopamine agonists, ropinirole and pramipexole, are mainstays in the treatment of PD. They are of much less use in other forms of parkinsonism and need to be avoided in patients with Lewy body dementia because they cause a significant increase in the risk for hallucinations. These long-acting agents are also to be avoided in PD patients with dementia. Two nonenteral dopamine agonists are also available. Rotigotine is available as a once a day transdermal patch, and apomorphine is available as a subcutaneous injection. These agents are of putative benefit in patients who may have significant trouble with oral drugs or as adjuncts to other ongoing PD therapies. Another class of agents

routinely used are the monoamine-oxidase B inhibitors. Selegiline, a drug introduced in the early 1990s, and rasagiline, a more recent introduction, are both useful in the treatment of PD. These medications are both used in early disease for mild symptoms and in more advanced disease as adjunct therapy to other anti-parkinsonian medications to minimize complications. Examples of such complications include drug-induced dyskinesias and end-of-dose wearing off. Finally, carboxy-Omethyltransferase inhibitors remain available as adjunct agents to enhance the action of levodopa in patients with more advanced disease. Two agents, entacapone and talcapone, are available, although talcapone requires careful hepatic monitoring and hence is not often used. There has been a major reduction in the use of anticholinergic agents in the treatment of PD. Previous mainstays, benztropine and trihexyphenidyl (Artane) are no longer considered effective choices in most PD patients because their risks outweigh their benefits, as it is being increasingly recognized that there are long-term complications of chronic use of anticholinergic agents, especially in terms of causing cognitive side effects. In general, the modern approach to PD therapy is via rational polypharmacy in which neurologists use a prudent combination of two or more medications to manage the symptoms optimally while minimizing side effects.

40.2.3 Parkinson-Plus and Secondary Parkinsonisms Parkinson-plus and secondary parkinsonisms require quite different therapeutic approaches, often comprising much larger doses of levodopa. For example, multiple-system atrophy (MSA) patients frequently need 3 to 4 times the mean daily dose of levodopa compared with a PD patient with a similar degree of parkinsonism. The rationale for this increased need is based in the pathology of secondary parkinsonism, where there is damage to the dopaminergic targets and hence a higher dose is needed. It is also important to note that most dopamine agonists have a minimal role in the treatment of secondary parkinsonisms. A rare complication of the use of large doses of levodopa is caused by its abrupt withdrawal. The syndrome of acute levodopa withdrawal resembles neuroleptic malignant syndrome, which clinically manifests with hyperthermia, tachycardia, generalized stiffness, and occasionally dystonia. Imaging shows cerebellar white matter signal abnormalities that reverse when the patient is successfully treated by restoring dopamine and shares some features of posterior reversible encephalopathy syndrome (PRES) (▶ Fig. 40.3). From an imaging perspective, this condition must be kept in mind when a parkinsonian patient presents with findings that resemble PRES. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) or the globus pallidus internal segment (GPi) and lesioning of the STN or GPi have been shown to be effective in ameliorating PD symptoms in patients who are unable to get satisfactory relief with pharmacotherapy or who experience unacceptable side effects from pharmacotherapy. DBS has not been helpful in most patients with secondary parkinsonism. DBS is being investigated in other neurodegenerative disorders and is a major advancement in the field. The imaging aspects of DBS are discussed in Chapter 41.

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Fig. 40.3 Imaging changes in acute L-dopa withdrawal-induced neuroleptic malignant syndrome (NMS).

The imaging implications of the therapies discussed so far for PD and related disorders are the following: in PD patients with drug-induced dyskinesias, the choreiform movements cause major motion artifacts during imaging. Because drug-induced dykinesias are uniquely temporally related to the short-acting levodopa formulation, a short-term withholding of levodopa overnight is generally a safe practice and will abolish the druginduced dyskinesias to permit imaging. This short-term medication withdrawal should not be prolonged beyond what is necessary to obtain a good imaging study to avoid the risk of neuroleptic malignant syndrome (discussed earlier). Amantadine, a drug with multiple sites of action, can be effective to mitigate dyskinesias. This could be a strategy to suppress drug-induced dyskinesias to facilitate imaging. Another imaging implication applies to patients who are NPO (i.e., not taking any food or liquids orally) in preparation for surgery or anesthesia. In such patients, the lack of dopaminergic medications will cause their symptoms to be become more manifest. Specifically, resting tremor, significant bradykinesia, and dystonia could be apparent in PD patients who are NPO before surgery. These symptoms could compromise quality of brain imaging. Although general anesthesia may solve the positioning of a parkinsonian patient with significant resting tremor this option might not be available and is associated with considerable risk. In this scenario, the use of injectable apomorphine along with an antinausea agent may provide excellent short-term relief of symptoms for approximately 1 to 2 hours, enabling a high-quality imaging study to be accomplished. Another, less-effective option is the use of the rotigotine patch, although the patch will need to be removed immediately before the beginning of the imaging study. These options may be of particular benefit in patients who are undergoing DBS surgery and are NPO in preparation for such surgery. Sedation in such patients interferes with the ability of the neurologist to perform intraoperative neurophysiologic monitoring until the sedation wears off. This delay could potentially be avoided with the use of parenteral dopaminergic agents. Many new approaches to pharmacotherapy in PD are in development and have been recently reviewed.21 These experimental approaches include new medications to mitigate druginduced dyskinesias, methods to prolong the effective duration

of action of L-dopa formulations, and novel methods to deliver L-dopa. One novel method that is in advanced stages of testing is a levodopa formulation that is administered into the duodenum via a subcutaneous pump.22 This technology is in advanced clinical trials and is expected to reach patients in the next few years. Levodopa formulations that have longer half-lives and novel routes of delivery (intranasal) are also under investigation. Two multicenter research studies to investigate gene therapy using recombinant adenoassociated virus to modulate either the subthalamic nucleus or the striatal expression of aromatic amino acid decarboxylase are in advanced stages of testing. A European effort to evaluate the use of fetal tissue transplants in PD patients is also under way. Many novel small molecules and natural products for PD are also in advanced stages of testing. Targeted therapeutic agents that attempt to reverse the primary pathology in PD are also under investigation. There is now compelling evidence that protein repair in degenerating dopaminergic neurons is faulty and that pathologically misfolded proteins can potentially spread disease in a prion-like fashion within the brain. So efforts are being made to intervene therapeutically at multiple molecular targets and at secondary gial response to the primary pathology. Each of these studies will benefit from a reliable imaging biomarker; hence, there is a worldwide effort to discover reliable biomarkers for disease identification and disease progression in PD. Huntington’s disease (HD) therapy has made some incremental gains in the past few decades with the acceptance of tetrabenazine as a suitable choice of treatment for HD chorea. Tetrabenazine is extremely short acting and has little risk of causing drug-induced parkinsonism in HD patients, unlike the older medications used in the treatment of chorea, like haloperidol and risperidone (Resperidal). These antidopaminergic medications frequently caused secondary drug-induced parkinsonism that worsened disability in HD patients and accelerated mortality. Therefore, traditional typical antipsychotic agents are avoided; tetrabenazine or, if needed, atypical antipsychotic agents are used to control chorea in HD. Neuropsychiatric complications of HD are also treated with alternative medications like propranolol for anxiety and impulse control or prudent use of selective serotonin reuptake inhibitors that have adjunct antianxiety benefits. Rarely, HD patients may be on a

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Clinical Approach and Treatment combination therapy of tetrabenazine and an antiparkinsonian medication. In such patients, if motion artifacts from chorea are of concern, brief discontinuation of the antiparkinsonian medication while continuing the tetrabenazine may provide sufficient suppression of movement without interfering with cognitive function, as is associated with other methods of pharmacologic immobilization of patients, like using conscious sedation or deeper forms of anesthesia. Chorea in HD may also benefit from amantadine treatment or, in selected patients, riluzole. Less commonly, nabilone, a synthetic cannabinoid, is prescribed for chorea.23 The juvenile form of HD (Westphal variant) manifests with akinesia and rigidity but not chorea. These patients are treated with high doses of levodopa, similarly to how other secondary forms of parkinsonism are treated. Patients with both the adult form and the juvenile form of HD benefit from cognitive therapies. So choline esterase inhibitors and memantine appear to benefit symptomatic cognitive decline in HD patients. New experimental therapeutic approaches in development for HD include gene therapy using anti-sense RNA technology to silence the trinucleotide repeats or other approaches to negate the deleterious effects of the huntingtin protein. Although such exciting new possibilities are in development, thhe current approach is to use evidence-based pragmatic treatments that provide comfort and symptom management in HD.24 Amyotrophic lateral sclerosis (ALS) is extensively covered elsewhere in this book. However, its treatment remains a challenge.25 Riluzole has become standard treatment for ALS, but its effects are minimal in mitigating the symptoms. ALS management has remained largely symptomatic and to provide relief from pain and suffering. A promising ALS stem cell trial that attempts to diminish the inflammation in the spinal cord using stem cell transplants directly placed surgically is ongoing. This experimental approach has strong preclinical and clinical safety data and holds much promise.26 Degenerative disorders of the cerebellum remain an area of active preclinical research but without much clinical relief to patients. Advances in understanding the genetics of these diseases are likely to inspire the discovery of new treatments in the near future. Other advances in pharmacologic approaches are in the symptomatic treatment of comorbidities in degenerative disorders. Sialorrhea, a major feature of many neurodegenerative disorders like PD, AD, MSA, HD, and ALS, is effectively treated with the injection of salivary glands with botulinum toxins. Periodic botulinum toxin injection is also effective in treating focal cervical dystonia, which accompanies many degenerative disorders (e.g., retrocollis in progressive supranuclear palsy, anterocollis in MSA and foot dystonia in PD). Botulinum toxin injections also provide relief for focal dystonia in pyknodysostosis (PKND). The feature of pseudobulbar affect seen in many neurodegenerative disorders like vascular dementias, parkinsonism, AD, and rarely ALS now has a newly approved treatment using dextromethorphan hydrobromide and quinidine sulfate as a combined formulation. Although the effects from this treatment have been modest, a new option to treat this symptom that can be quite disabling and socially embarrassing is now available. Depression, hallucinations, and sleep disturbances are now more frequently recognized as comorbid

illnesses in many neurodegenerative disorders. The approval and availability of a wide array of atypical antipsychotic agents (e.g., Quetiapine and clozapine) that do not cause extrapyramidal side effects has been a major advancement in treating such symptoms.

40.3 Summary In this chapter, we review a broad range of treatments for dementias that may have relevance to the imaging sciences. The key issue is whether treatments alter imaging or pose unique opportunities and challenges to neuroimaging. Some well-known examples of imaging findings after treatment are considered, and rarer examples are provided as a window to the future. Disease entities and treatment entities we consider here are the broad categories of progressive dementias, reversible dementias, and symptomatic treatments.

References [1] Gürsoy AE, Kolukısa M, Babacan-Yıldız G, Celebi A. Subacute combined degeneration of the spinal cord due to different etiologies and improvement of MRI findings. Case Rep Neurol Med 2013; 2013: 159649 [2] Naidich MJ, Ho SU. Case 87: Subacute combined degeneration. Radiology 2005; 237: 101–105 [3] Pittock SJ, Payne TA, Harper CM. Reversible myelopathy in a 34-year-old man with vitamin B12 deficiency. Mayo Clin Proc 2002; 77: 291–294 [4] Gupta PK, Gupta RK, Garg RK et al. DTI correlates of cognition in conventional MRI of normal-appearing brain in patients with clinical features of subacute combined degeneration and biochemically proven vitamin B12 deficiency. AJNR Am J Neuroradiol 2014; 35: 872–877 [5] Mpakopoulou M, Brotis AG, Gatos H, Paterakis K, Fountas KN. Ten years of clinical experience in the use of fixed-pressure versus programmable valves: a retrospective study of 159 patients. Acta Neurochir Suppl (Wien) 2012; 113: 25–28 [6] Huang CC, Chu NS. Acute dystonia with thalamic and brainstem lesions after initial penicillamine treatment in Wilson’s disease. Eur Neurol 1998; 39: 32–37 [7] da Costa MdoD, Spitz M, Bacheschi LA, Leite CC, Lucato LT, Barbosa ER. Wilson’s disease: two treatment modalities. Correlations to pretreatment and posttreatment brain MRI. Neuroradiology 2009; 51: 627–633 [8] Douaud G, Refsum H, de Jager CA et al. Preventing Alzheimer’s diseaserelated gray matter atrophy by B-vitamin treatment. Proc Natl Acad Sci U S A 2013; 110: 9523–9528 [9] Farrimond LE, Roberts E, McShane R. Memantine and cholinesterase inhibitor combination therapy for Alzheimer’s disease: a systematic review. BMJ Open 2012; 2: 8 [10] Birks J. Cholinesterase inhibitors for Alzheimer’s disease. Cochrane Database Syst Rev 2006: CD005593 [11] Filippi M, Agosta F, Frisoni GB et al. Magnetic resonance imaging in Alzheimer’s disease: from diagnosis to monitoring treatment effect. Curr Alzheimer Res 2012; 9: 1198–1209 [12] Morgan D, Diamond DM, Gottschall PE et al. A beta peptide vaccination prevents memory loss in an animal model of Alzheimer’s disease. Nature 2000; 408: 982–985 [13] Sabbagh JJ, Kinney JW, Cummings JL. Animal systems in the development of treatments for Alzheimer’s disease: challenges, methods, and implications. Neurobiol Aging 2013; 34: 169–183 [14] Jack CR, Jr, Albert MS, Knopman DS et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 257–262 [15] Sperling RA, Aisen PS, Beckett LA et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 280–292

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Advances in the Treatment of Dementia [16] Bertelson JA, Ajtai B. Neuroimaging of dementia. Neurol Clin 2014; 32: 59–93 [17] McKhann GM, Knopman DS, Chertkow H et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 263–269 [18] Yang L, Rieves D, Ganley C. Brain amyloid imaging—FDA approval of florbetapir F18 injection. N Engl J Med 2012; 367: 885–887 [19] Devos D, Moreau C, Dujardin K, Cabantchik I, Defebvre L, Bordet R. New pharmacological options for treating advanced Parkinson’s disease. Clin Ther 2013; 35: 1640–1652 [20] Cosyns B, Droogmans S, Rosenhek R, Lancellotti P. Drug-induced valvular heart disease. Heart 2013; 99: 7–12 [21] Olanow CW, Schapira AH. Therapeutic prospects for Parkinson’s disease. Ann Neurol 2013; 74: 337–347

[22] Nyholm D, Klangemo K, Johansson A. Levodopa/carbidopa intestinal gel infusion long-term therapy in advanced Parkinson’s disease. Eur J Neurol 2012; 19: 1079–1085 [23] Armstrong MJ, Miyasaki JM American Academy of Neurology. Evidence-based guideline: pharmacologic treatment of chorea in Huntington disease: report of the guideline development subcommittee of the American Academy of Neurology. Neurology 2012; 79: 597–603 [24] Mestre TA, Ferreira JJ. An evidence-based approach in the treatment of Huntington’s disease. Parkinsonism Relat Disord 2012; 18: 316–320 [25] Gibson SB, Bromberg MB. Amyotrophic lateral sclerosis: drug therapy from the bench to the bedside. Semin Neurol 2012; 32: 173–178 [26] Riley J, Federici T, Polak M, et al. Intraspinal stem cell transplantation in amyotrophic lateral sclerosis: a phase I safety trial, technical note, and lumbar safety outcomes. Neurosurgery 2012; 71: 405–416

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Clinical Approach and Treatment

41 Imaging of Deep Brain Stimulation Falgun H. Chokshi Deep brain stimulation (DBS) has revolutionized the treatment of treatment-refractory movement disorders. It has also rejuvenated the field of functional neurosurgery by allowing precise anatomical neuromodulation of select intracranial nuclei.1 Historically, DBS probes were placed via burr holes and local anesthesia, using only surgical anatomical landmarks and radiography for guidance.2,3 Subsequent work by Horsley established the foundations of the stereotactic technique,4 which led to development of several stereotactic atlases.1 Next came the era of stereotactic technique combined with ventriculography, which was brought to surgical practice in 1947.5 It was not until the advent of magnetic resonance imaging (MRI) that DBS, and functional neurosurgery at large, matured significantly further, with better localization of target nuclei. The role of the neuroradiologist is crucial in the screening, presurgical imaging, and postsurgical evaluation of patients receiving DBS. As an important member of the multidisciplinary team, a neuroradiologist often works closely with the neurologist and neurosurgeon to treat the patient. The purpose of this chapter is to discuss the underlying imaging and management principles of DBS, including basal ganglia anatomy, indications for DBS, techniques for target visualization, and postoperative evaluation. Furthermore, we briefly discuss the role of MRI risk and safety as they relate to DBS.

41.1 Anatomy of Target Nuclei Three nuclei have historically been targeted for DBS in movement disorders: (1) the ventral intermediate nucleus (VIM) of the thalamus, (2) globus pallidus interna (GPi), and (3) subtha-

lamic nucleus (STN).6 Methods for targeting these nuclei are discussed later in the section on techniques. The VIM is anatomically located in the cephalad-lateral portion of the thalamus. Adjacent structures include the ventral lateral nucleus anteriorly, the ventral posteromedial and posterolateral nuclei posteriorly, the posterior limb of the internal capsule laterally, and the medial thalamic nuclei medially. The basal ganglia are paired structures and comprise the caudate nuclei (CN), putamena (PN), and globus pallidi (GP) (▶ Fig. 41.1). They play a fundamental role in integrating complex movement, having connections with almost all parts of the brain.7,8 The STN and substantia nigra (SN) are considered by some, including our group, to be part of the basal ganglia.7,9,10 These five deep gray nuclei are metabolically quite active, have high energy demands, and are susceptible to both systemic diseases and those altering cerebral perfusion and/or oxygenation.7,8,9,11 The CN, PN, and GP are grouped into the corpus striatum; and the CN, PN, and nucleus accumbens (NA) are called the striatum (or neostriatum). These classifications are due to neurochemical, histologic, and connectional similarities among the nuclei.7,10 Five nuclei constitute the basal ganglia, but only two are routinely targeted for DBS: (1) the GP (GPi) and (2) the STN (▶ Fig. 41.2). The GPi is in the medial portion of the GP (laterally is the GP externa). It lies medial to the lamina interna and lateral to the internal capsule.12 The STN is located in the rostral midbrain and lies posterior and medial to the cerebral peduncles (crus cerebri). Each STN is a small biconvex structure whose orientation is oblique, with the superior pole located posterior and lateral to the inferior pole.12,13

Fig. 41.1 Cross-sectional anatomy of the basal ganglia and their associated structures. VA/VL, ventral anterior/ventral lateral thalamic nucleus. (Illustration by Eric Jablonowski.)

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Fig. 41.2 Deep brain stimulation (DBS) device and targets. (a) Diagrammatic representation of the DBS device showing both intracranial and extracranial components. (b) Coronal depiction of globus pallidus (GP) targeting by DBS lead. (c) Coronal depiction of subthalamic nucleus targeting by DBS lead. (Illustration by Eric Jablonowski.)

41.2 Indications for Deep Brain Stimulation Common indications for DBS include Parkinson’s disease (PD),14 essential tremor (ET),15 chronic pain,16 and dystonia.17,18 Initial work on the effects of DBS focused on chronic stimulation of the VIM of the thalamus in patients with PD or ET, with some subjects showing complete relief from tremor.19 Subsequently, DBS was used in patients with severe akinetic-rigid PD and motor fluctuations, where the STN was stimulated.19 Patients showed a reproducible diminishment of their symptoms.14 Eventually, the Food and Drug Administration approved DBS of the bilateral STN in patients with advanced PD in 2002 and GPi stimulation in 2003.6

41.3 Techniques Imaging techniques for DBS fall into one of three main categories: (1) screening for potential DBS patients; (2) targeting of nuclei, namely, the GPi and STN; and (3) postoperative lead placement confirmation and complication assessment.

41.3.1 Screening of Patients Patients who are clinically deemed candidates for DBS (usually for advanced PD) typically undergo a screening brain MRI to exclude other causes for their symptoms (e.g., thalamic tumor causing PD-like symptoms). The presence of leukoencephalop-

athy, severe brain atrophy, multiple lacunar infarcts, severe ventriculomegaly, and mass, such as atrioventricular malformation or tumor, along the expected path of the lead, are all contraindications to DBS (▶ Fig. 41.3).20,21,22 At our institution, all patients considered for DBS are screened using a standard noncontrast protocol comprising five sequences (▶ Table 41.1). A detailed report is transcribed, which helps the neurosurgical team in treatment planning.

41.3.2 Nuclei Targeting Currently, the vast majority of nuclei targeting focuses on the GPi and the STN,6 which is the focus of this section. This is a critical part of the patient’s treatment plan, with the outcome of the DBS placement hinging on the proper presurgical planning of the trajectory.20 Targeting of nuclei can be indirect (statistically determined coordinates relative to the anterior-posterior commissure line and related to known anatomical atlases) (▶ Fig. 41.4) or direct (direct visualization via MRI) and using an MRI-computed tomography (CT) fusion technique incorporating a stereotactic atlas.6 At our institution, a “homegrown” hybrid targeting system has been developed (unpublished data), encompassing the information provided by a Talaraich atlas and via threedimensional (3D) T1-weighted images of the brain during preoperative evaluation. Additionally, an abbreviated preoperative MRI of the brain is performed on the day of surgery, as described in ▶ Table 41.1.

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Fig. 41.3 Screening magnetic resonance imaging (MRI) showing thalamic tumor causing parkinsonian symptoms. (a) Axial T1 magnetizationprepared rapid acquisition gradient echo (MP RAGE) and (b) axial fluid-attenuated inversion recovery (FLAIR) images show an expansile lesion (white arrows) in the left thalamus, which was thought to cause the patient’s parkinsonian symptoms. DBS surgery was not scheduled.

Table 41.1 Deep brain stimulation magnetic resonance imaging (MRI) protocols at Emory Neuroradiology Screening MRI

1.5 T MRI with transmit-receive head coil only protocol Volumetric axial T1-weighted Reformed to coronal and sagittal planes Volumetric axial T1-weighted inversion recovery Axial DWI and ADC maps Axial T2-weighted GRE Axial FLAIR Axial T2-weighted FSE

Preoperative MRI

Post-contrast volumetric axial T1-weighted Post-contrast volumetric axial T1-weighted inversion recovery

Postoperative MRI

Volumetric axial T1-weighted Volumetric axial T1-weighted inversion recovery Axial T2-weighted GRE Axial FLAIR Axial T2-weighted FSE

Abbreviations: ADC, apparent diffusion coefficient; DWI, diffusionweighted imaging; FLAIR, fluid-attenuated inversion recovery; FSE, fast spin echo; GRE, gradient echo.

Historically, many centers have used microelectrode recording (MER) of neurons along the path of the implant lead to identify the signature of groups of neurons, thereby using this method to localize target nuclei. A detailed discussion of this technique is beyond the scope of this chapter, and the reader is referred to select publications.23,24,25

Subthalamic Nucleus Using the anterior-posterior commissure (AC-PC) line, some authorities have reported the coordinates of the STN as 12 mm

lateral, 3 mm posterior, and 3 mm inferior to the mid-commissural point26; 9 to 12 mm lateral, 1 to 2 mm posterior, and 5 mm inferior to the mid AC-PC line27; 12.12 mm lateral, 2.41 mm posterior, and 2.39 mm relative to the mid-commissural point (▶ Fig. 41.5).28 Direct targeting of the STN is also used, first described using coronal T2-weighted images.23 The STNs are visible as hypointense structures having biconvex shapes, placed in the upper midbrain.29 Additionally, these authorities used the red nucleus as an internal reference point to determine the anteroposterior orientation and location of the STN. Additionally, Dormont and colleagues30 showed that the anatomical location of the STN had corresponding iron deposition, explaining the T2-hypointense signal of this nucleus. Various studies have examined STN visualization at 3 T magnetic field strength (▶ Fig. 41.6). Slavin and colleagues found that high-resolution contiguous T2-weighted fast spin echo images allowed direct visualization of the STN.27 A multigradient-echo fast low-angle shot technique also allowed simultaneous acquisition of 3D T1-weighted images for stereotactic use and T2* contrast to detect the STN.31 Some authorities have relied on the red nucleus as an internal landmark, also showing that 3D reconstruction of images aids targeting better than 2D images.32 Liu and colleagues have found that quantitative susceptibility weighted imaging (SWI) at 3 T field strength is significantly better at visualizing the STN compared with conventional T2-weighted fast spin-echo imaging.33

Globus Pallidus Interna Initial work on direct targeting of the GPi focused on using axial turbo-spin echo proton attenuation-weighted images, described by Hirabayashi and colleagues as accurate in 71% of the 48 patients they imaged.34 Subsequent studies corroborated the value of this sequence in targeting the GPi in children with dystonia, showing clear visualization of the internal and external pallidum, the putamen, and the pallidocapsular

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Fig. 41.4 Anterior-posterior commissure (AC-PC) line. Sagittal postcontrast magnetizationprepared rapid acquisition gradient echo (MPRAGE) image showing the anatomical relationships between the AC and PC, with delineation of the AC-PC line (white solid arrows). The midpoint of the AC-PC line is conventionally the coordinate (0, 0, 0).

Fig. 41.5 Hybrid targeting of the subthalamic nucleus (STN). Hybrid targeting system at Emory Neurosurgery showing atlas-based regions superimposed on three-dimensional magnetic resonance images. Sagittal (a), coronal (b) and axial (c) images show that the STN (red), thalami (green), caudate (blue), and zona incerta (yellow) are highlighted.

border.35 In this study, targeting was typically aimed for the posteroventral pallidum. Additionally, in children, direct MR targeting of the GPi was shown to be superior to atlas-based targeting.36 At our institution, as discussed, we use a hybrid system to localize the GPi (▶ Fig. 41.7). Coordinates that we use for the GPi are 21 mm lateral to the AC-PC line, 1 mm posterior to

the AC-PC line midpoint, and 4 mm inferior to the AC-PC line midpoint.

41.4 Postoperative Imaging Postoperative imaging of DBS evaluates (1) early and late operative complications and (2) implantable lead positioning relative

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Clinical Approach and Treatment to the intended target. Both CT and MRI are useful. ▶ Table 41.1 lists the MRI protocol at our institution.

41.4.1 Complications Complications can be divided into early and late. Early complications mainly involve ischemia and intracranial hemorrhage (▶ Fig. 41.8), namely, intraparenchymal hematoma, subdural hematoma, and epidural hematoma.37,38 Some studies suggest age, bleeding disorders, gender, and hypertension as predisposing factors.39,40 The risk of bleeding using single-electrode versus multielectrode recording (MER) is controversial.40,41 Of note, seizures following DBS are rare,42 prompting a noncontrast head CT evaluation to assess for hemorrhage.42,43 Late complications predominantly include infection, which ranges from 1 to 22.2% in incidence.44,45 Infection in a DBS patient can result in prolonged hospitalization, long-term antibiotic therapy, or even removal of the leads.23 Antibioticcontaining cement has shown some success in blocking microbacterial proliferation.45 Lead migration is another complication, for which the published incidence to date is 4 to 5%.42 Frank electrode breakage is rare (▶ Fig. 41.9), usually associated with connecting the lead and extension behind the ear instead of the calvarium.42,46,47

41.4.2 Lead Placement Confirmation Fig. 41.6 Subthalamic nucleus (STN) on susceptibility-weighted imaging (SWI). Axial SWI image showing that the STNs (black arrows) are lateral and posterior to the substantia nigra (white arrows), lateral to the symmetric red nuclei (dashed arrows).

Confirmation of DBS lead positioning is a key step in identifying the relationship between the electrode and target and to validate the precision of targeting. Various approaches to this assessment have been published, but no single approach is

Fig. 41.7 Hybrid targeting of the globus pallidus interna (GPi). Hybrid targeting system at Emory Neurosurgery showing atlas-based regions superimposed on three-dimensional magnetic resonance images. Sagittal (a), coronal (b) and axial (c) images show that the GPi (red), GP externa (GPe, green), putamen/caudate (blue), and anterior commissure (yellow) are highlighted.

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Fig. 41.8 Early complication, brainstem hemorrhage. Axial noncontrast computed tomography (CT) image of the head immediately following bilateral deep brain stimulation (DBS) lead placement (black arrows on scout image (a) and axial image (b). Acute intraparenchymal hematoma (white arrows) surrounding the right lead tip, extending into the right side of the brainstem (c,d).

Fig. 41.10 Confirmation of deep brain stimulation (DBS) leads in subthalamic nucleus (STN), magnetic resonance imaging (MRI). (a) coronal and (b) parasagittal T1 inversion recovery (IR) threedimensional images of the brain show proper placement of DBS leads in the subtemporal nucleus bilaterally (white arrows).

Fig. 41.9 Deep brain stimulation lead fracture evaluation by computed tomography (CT). Oblique coronal maximum intensity projection (MIP) image of patient suspected of lead fracture. The image supports that the leads are intact and tips are in the region of the subthalamic nucleus bilaterally.

universally accepted (▶ Fig. 41.10). For example, Yelnik and colleagues reported using the Schaltenbrand and Wharen atlas fused onto anatomical MRI to show differences in the effect of DBS in patients with akinesia and rigidity of stimulation based on lead placement in the internal versus external GP.48 Using a 3D atlas and MR fusion method in a group of DBS patients with STN targeting, Yelnik and colleagues were also able to show that stimulation of the zona incerta and the lenticular fasciculus around the STN allowed amelioration of PD

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Clinical Approach and Treatment symptoms.49 Many other atlas and MR fusion-based evaluation methods have also been studied.13,50 Postoperative MRI is quite helpful when DBS failure occurs. In a study of 41 patients with suboptimal DBS results, Okun and colleagues51 found the main causes for failures to be suboptimal medical treatment (73%), suboptimal pacemaker programming (54%), and suboptimal lead placement (46%). In another study, by Anheim and colleagues,52 the precise localization of electrode placement in the STN is necessary for proper response to DBS.

41.5 Risk and Safety of Magnetic Resonance Imaging Whereas CT can detect most DBS-related complications, such as acute intracranial hemorrhage and lead fracture, we believe that MRI is the modality of choice to assess for both complications and lead placement confirmation.6 However, there is a risk of electrode element heating due to energy deposition during radiofrequency electromagnetic pulse generation.53,54,55,56 After such risks were reported, the manufacturer of the DBS system updated the safety guidelines in November 2005. The major parts of this update include (1) the pacemaker device must be “off,” 2) use a 1.5-T MR system, 3) use of a transmitreceive-type radiofrequency head coil that does not cover the chest area, and (4) optimization of MR parameters to keep the specific absorption rate (SAR) below 0.1 W/kg in the head.6 These guidelines pose a set of dilemmas for both the neuroradiologist and the referring physician. For example, without the availability of a transmit-only head coil, DBS patients cannot undergo MRI using these guidelines. In fact, they cannot have an MRI safely on any other part of the body once the leads are in place, according to the manufacturer’s guideline.6 Additionally, variability in SAR has been shown, to the point where some authorities question the need for such a strict cutoff for the SAR.57,58,59

41.6 Summary Deep brain stimulation continues to evolve as a novel means of restoring functionality and decreasing morbidity in patients with movement disorders resistant to medical therapy. Rapid development of DBS targeting and postoperative evaluation techniques is under way, with ever-emerging indications for DBS coming to light, including for major depression, obsessivecompulsive disorder, and anorexia nervosa. The integral roles of neuroimaging and the neuroradiologist are paramount in the pre- and post-DBS periods and will continue to grow.

41.7 Acknowledgments Thanks to Mark E. Mullins, MD, PhD, for editorial assistance and guidance and Eric Jablonowski for medical illustrative services.

References [1] Zrinzo L. The role of imaging in the surgical treatment of movement disorders. Neuroimaging Clin N Am 2010; 20: 125–140

[2] Cooper IS. The Vital Probe: My Life as an Experimental Brain Surgeon. 1st ed. New York: Norton; 1981 [3] Cooper IS. Involuntary Movement Disorders. New York: Hoeber Medical Division; 1969 [4] Horsley V. The structure and functions of the cerebellum examined by a new method. Brain 1908; 31: 45–124 [5] Spiegel EA, Wycis HT, Marks M, Lee AJ. Stereotaxic apparatus for operations on the human brain. Science 1947; 106: 349–350 [6] Dormont D, Seidenwurm D, Galanaud D, Cornu P, Yelnik J, Bardinet E. Neuroimaging and deep brain stimulation. AJNR Am J Neuroradiol 2010; 31: 15–23 [7] Groenewegen HJ. The basal ganglia and motor control. Neural Plast 2003; 10: 107–120 [8] Herrero MT, Barcia C, Navarro JM. Functional anatomy of thalamus and basal ganglia. Childs Nerv Syst 2002; 18: 386–404 [9] Lim CC. Magnetic resonance imaging findings in bilateral basal ganglia lesions. Ann Acad Med Singapore 2009; 38: 795–798 [10] Lincoln CM, Bello JA, Lui YW. Decoding the deep gray: a review of the anatomy, function, and imaging patterns affecting the basal ganglia. Neurographics. 2012; 2: 92–102 [11] Finsterer J. Central nervous system imaging in mitochondrial disorders. Can J Neurol Sci 2009; 36: 143–153 [12] Yelnik J. Functional anatomy of the basal ganglia. Mov Disord 2002; 17 Suppl 3: S15–S21 [13] Yelnik J, Bardinet E, Dormont D et al. A three-dimensional, histological and deformable atlas of the human basal ganglia. I. Atlas construction based on immunohistochemical and MRI data. Neuroimage 2007; 34: 618–638 [14] Limousin P, Pollak P, Benazzouz A et al. Effect of parkinsonian signs and symptoms of bilateral subthalamic nucleus stimulation. Lancet 1995; 345: 91–95 [15] Hariz GM, Lindberg M, Bergenheim AT. Impact of thalamic deep brain stimulation on disability and health-related quality of life in patients with essential tremor. J Neurol Neurosurg Psychiatry 2002; 72: 47–52 [16] Cruccu G, Aziz TZ, Garcia-Larrea L et al. EFNS guidelines on neurostimulation therapy for neuropathic pain. Eur J Neurol 2007; 14: 952–970 [17] Hung SW, Hamani C, Lozano AM et al. Long-term outcome of bilateral pallidal deep brain stimulation for primary cervical dystonia. Neurology 2007; 68: 457–459 [18] Coubes P, Roubertie A, Vayssiere N, Hemm S, Echenne B. Treatment of DYT1generalised dystonia by stimulation of the internal globus pallidus. Lancet 2000; 355: 2220–2221 [19] Benabid AL, Pollak P, Gervason C et al. Long-term suppression of tremor by chronic stimulation of the ventral intermediate thalamic nucleus. Lancet 1991; 337: 403–406 [20] Landi A, Parolin M, Piolti R et al. Deep brain stimulation for the treatment of Parkinson’s disease: the experience of the neurosurgical department in Monza. Neurol Sci 2003; 24 Suppl 1: S43–S44 [21] Loher TJ, Burgunder JM, Pohle T, Weber S, Sommerhalder R, Krauss JK. Longterm pallidal deep brain stimulation in patients with advanced Parkinson disease: 1-year follow-up study. J Neurosurg 2002; 96: 844–853 [22] Welter ML, Houeto JL, Tezenas du Montcel S et al. Clinical predictive factors of subthalamic stimulation in Parkinson’s disease. Brain 2002; 125: 575–583 [23] Kocabicak E, Temel Y. Deep brain stimulation of the subthalamic nucleus in Parkinson’s disease: surgical technique, tips, tricks and complications. Clin Neurol Neurosurg 2013; 115: 2318–2323 [24] Shamir RR, Zaidel A, Joskowicz L, Bergman H, Israel Z. Microelectrode recording duration and spatial density constraints for automatic targeting of the subthalamic nucleus. Stereotact Funct Neurosurg 2012; 90: 325–334 [25] Krack P, Batir A, Van Blercom N et al. Five-year follow-up of bilateral stimulation of the subthalamic nucleus in advanced Parkinson’s disease. N Engl J Med 2003; 349: 1925–1934 [26] Schlaier J, Schoedel P, Lange M et al. Reliability of atlas-derived coordinates in deep brain stimulation. Acta Neurochir (Wien) 2005; 147: 1175–1180, discussion 1180 [27] Slavin KV, Thulborn KR, Wess C, Nersesyan H. Direct visualization of the human subthalamic nucleus with 3 T MR imaging. AJNR Am J Neuroradiol 2006; 27: 80–84 [28] Andrade-Souza YM, Schwalb JM, Hamani C et al. Comparison of three methods of targeting the subthalamic nucleus for chronic stimulation in Parkinson’s disease. Neurosurgery 2005; 56 Suppl: 360–368, discussion 360–368 [29] Bejjani BP, Dormont D, Pidoux B et al. Bilateral subthalamic stimulation for Parkinson’s disease by using three-dimensional stereotactic magnetic resonance imaging and electrophysiological guidance. J Neurosurg 2000; 92: 615–625

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| 12.09.15 - 10:56

Imaging of Deep Brain Stimulation [30] Dormont D, Ricciardi KG, Tandé D et al. Is the subthalamic nucleus hypointense on T2-weighted images? A correlation study using MR imaging and stereotactic atlas data. AJNR Am J Neuroradiol 2004; 25: 1516–1523 [31] Elolf E, Bockermann V, Gringel T, Knauth M, Dechent P, Helms G. Improved visibility of the subthalamic nucleus on high-resolution stereotactic MR imaging by added susceptibility (T2*) contrast using multiple gradient echoes. AJNR Am J Neuroradiol 2007; 28: 1093–1094 [32] Andrade-Souza YM, Schwalb JM, Hamani C, Hoque T, Saint-Cyr J, Lozano AM. Comparison of 2-dimensional magnetic resonance imaging and 3-planar reconstruction methods for targeting the subthalamic nucleus in Parkinson’s disease. Surg Neurol 2005; 63: 357–363 [33] Liu T, Eskreis-Winkler S, Schweitzer AD et al. Improved subthalamic nucleus depiction with quantitative susceptibility mapping. Radiology 2013; 269: 216–223 [34] Hirabayashi H, Tengvar M, Hariz MI. Stereotactic imaging of the pallidal target. Mov Disord 2002; 17 Suppl 3: S130–S134 [35] Vayssiere N, Hemm S, Zanca M et al. Magnetic resonance imaging stereotactic target localization for deep brain stimulation in dystonic children. J Neurosurg 2000; 93: 784–790 [36] Vayssiere N, Hemm S, Cif L et al. Comparison of atlas- and magnetic resonance imaging-based stereotactic targeting of the globus pallidus internus in the performance of deep brain stimulation for treatment of dystonia. J Neurosurg 2002; 96: 673–679 [37] Novak KE, Nenonene EK, Bernstein LP et al. Two cases of ischemia associated with subthalamic nucleus stimulator implantation for advanced Parkinson’s disease. Mov Disord 2006; 21: 1477–1483 [38] Binder DK, Rau GM, Starr PA. Risk factors for hemorrhage during microelectrode-guided deep brain stimulator implantation for movement disorders. Neurosurgery 2005; 56: 722–732 [39] Sansur CA, Frysinger RC, Pouratian N et al. Incidence of symptomatic hemorrhage after stereotactic electrode placement. J Neurosurg 2007; 107: 998– 1003 [40] Xiaowu H, Xiufeng J, Xiaoping Z et al. Risks of intracranial hemorrhage in patients with Parkinson’s disease receiving deep brain stimulation and ablation. Parkinsonism Relat Disord 2010; 16: 96–100 [41] Temel Y, Wilbrink P, Duits A et al. Single electrode and multiple electrode guided electrical stimulation of the subthalamic nucleus in advanced Parkinson’s disease. Neurosurgery 2007; 61 Suppl 2: 346–355, discussion 355–357 [42] Boviatsis EJ, Stavrinou LC, Themistocleous M, Kouyialis AT, Sakas DE. Surgical and hardware complications of deep brain stimulation: a seven-year experience and review of the literature. Acta Neurochir (Wien) 2010; 152: 2053–2062 [43] Chou YC, Lin SZ, Hsieh WA et al. Surgical and hardware complications in subthalamic nucleus deep brain stimulation. J Clin Neurosci 2007; 14: 643–649

[44] Fenoy AJ, Simpson RK, Jr. Management of device-related wound complications in deep brain stimulation surgery. J Neurosurg 2012; 116: 1324–1332 [45] Temel Y, Ackermans L, Celik H et al. Management of hardware infections following deep brain stimulation. Acta Neurochir (Wien) 2004; 146: 355– 361, discussion 361 [46] Schwalb JM, Riina HA, Skolnick B, Jaggi JL, Simuni T, Baltuch GH. Revision of deep brain stimulator for tremor: technical note. J Neurosurg 2001; 94: 1010–1012 [47] Blomstedt P, Hariz MI. Hardware-related complications of deep brain stimulation: a ten year experience. Acta Neurochir (Wien) 2005; 147: 1061–1064 [48] Yelnik J, Damier P, Bejjani BP et al. Functional mapping of the human globus pallidus: contrasting effect of stimulation in the internal and external pallidum in Parkinson’s disease. Neuroscience 2000; 101: 77–87 [49] Yelnik J, Damier P, Demeret S et al. Localization of stimulating electrodes in patients with Parkinson’s disease by using a three-dimensional atlas-magnetic resonance imaging coregistration method. J Neurosurg 2003; 99: 89–99 [50] Nowinski WL, Belov D, Pollak P, Benabid AL. Statistical analysis of 168 bilateral subthalamic nucleus implantations by means of the probabilistic functional atlas. Neurosurgery 2005; 57 Suppl: 319–330 [51] Okun MS, Tagliati M, Pourfar M et al. Management of referred deep brain stimulation failures: a retrospective analysis from 2 movement disorders centers. Arch Neurol 2005; 62: 1250–1255 [52] Anheim M, Batir A, Fraix V et al. Improvement in Parkinson’s disease by subthalamic nucleus stimulation based on electrode placement: effects of reimplantation. Arch Neurol 2008; 65: 612–616 [53] Oluigbo CO, Rezai AR. Magnetic resonance imaging safety of deep brain stimulator devices. Handb Clin Neurol 2013; 116: 73–76 [54] Baker KB, Tkach JA, Phillips MD, Rezai AR. Variability in RF-induced heating of a deep brain stimulation implant across MR systems. J Magn Reson Imaging 2006; 24: 1236–1242 [55] Rezai AR, Baker KB, Tkach JA et al. Is magnetic resonance imaging safe for patients with neurostimulation systems used for deep brain stimulation? Neurosurgery 2005; 57: 1056–1062, discussion 1056–1062 [56] Rezai AR, Phillips M, Baker KB et al. Neurostimulation system used for deep brain stimulation (DBS): MR safety issues and implications of failing to follow safety recommendations. Invest Radiol 2004; 39: 300–303 [57] Larson PS, Richardson RM, Starr PA, Martin AJ. Magnetic resonance imaging of implanted deep brain stimulators: experience in a large series. Stereotact Funct Neurosurg 2008; 86: 92–100 [58] Rezai AR, Finelli D, Nyenhuis JA et al. Neurostimulation systems for deep brain stimulation: in vitro evaluation of magnetic resonance imaging-related heating at 1.5 tesla. J Magn Reson Imaging 2002; 15: 241–250 [59] Finelli DA, Rezai AR, Ruggieri PM et al. MR imaging-related heating of deep brain stimulation electrodes: in vitro study. AJNR Am J Neuroradiol 2002; 23: 1795–1802

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Index Note: Page numbers set bold or italic indicate headings or figures, respectively.

3 3-Hydroxy-3-methylglutaryl-CoA lyase deficiency 307

5 5-fluorouracil 272

1 18-q syndrome 307

A ABCA7 gene 120 Abnormal movement complaints 6 ACD, see Alcoholic cerebellar degeneration (ACD) Acetylcholinesterase (AChE) activity – in dementia with Lewy bodies 39 – in multiple-system atrophy 182 – in Parkinson’s disease 171 – in progressive supranuclear palsy 183 Acquired hepatocellular degeneration 367 Acyl-CoA oxidase deficiency 307 Addison syndrome 296, 297 Adenosing triphosphate-binding cassette 120 Adrenomyeloneuropathy (AMN) 307, 313, 313, 340 Adult autosomal dominant leukoencephalopathies 307 Aging brain – as primary dementia risk factor 362 – brain function in 77, 77 – cerebrospinal fluid in 26, 70, 71 – cerebrovascular changes in 74, 75 – cerebrovascular reactivity in 75 – cortical thickness in 71, 72 – creatine levels in 26 – diffusion tensor imaging in 42, 168 – fractional anisotropy 168 – functional magnetic resonance imaging in 77, 77 – gray matter in 70, 71–72 – iron deposition in 73, 73, 80, 80, 81 – magnetic resonance spectroscopy in 26 – metabolism in 75, 76 – microbleeds in 74, 75 – perfusion changes in 65, 66, 75 – positron emission tomography in 75, 76 – signs and symptoms associated with 78 – structural changes in 70, 71–75 – structural imaging in 16 – volume changes in 70, 71–72 – white matter in 42, 71, 73, 73, 74 AICA, see Anterior inferior cerebellar artery (AICA) Aicardi Goutiers syndrome (AGS) 215 Alcoholic cerebellar degeneration (ACD) 328, 333, 333 Alcoholic encephalopathy 301 Alcoholic-related dementia (ARD) 301, 303

Alexander disease 307, 332 Alkylating agents 272 ALS, see Amyotrophic lateral sclerosis (ALS) ALS2 gene 11 Alsin 11 Alzheimer, Alois 3, 113 Alzheimer’s disease (AD) – amyloid deposition in 36, 83, 98, 113, 121, 121, 128, 130, 139, 139, 140, 141, 145–148 – animal model of, amyloid plaques in 139, 142–144 – antidepressants in 373 – apathy in 115 – aphasia in 114–115 – atypical presentation of 115 – biomarkers in 93, 99, 116, 122 – cerebrovascular disease and 60 – cholesterolemia and 93 – cholinesterase inhibitors in 373 – clinical features of 114 – clinical overlaps with 10 – cognitive deficits in 114 – cortical signature of 124–125, 125 – delusions in 115 – dementia with Lewy bodies vs. 44, 151–153, 155 – diagnosis of 115, 130 – diffusion tensor imaging in 42, 43 – dopamine system in 35 – dopaminergic system in 36 – early diagnosis of 130 – early-onset 119 – familial autosomal dominant 113 – frontal variant 115 – functional magnetic resonance imaging in 54, 134, 135 – GABA system in 36–37 – genetics in 9, 11, 113, 119, 120 – gray matter in 100, 139, 140 – hallucinations in 115 – in epidemiology of neurodegenerative diseases 5 – in genome-wide association studies 119 – in history of neurodegenerative disease 3 – increase in 113 – iron accumulation in 80, 139 –– animal models of 83 – iron mapping in 83, 84 – late-onset 119 – Lewy bodies in 151 – logopenic-variant primary progressive aphasia variant of 115 – magnetic resonance imaging in 124, 124, 126, 133 – magnetic resonance spectroscopy in 26, 137 – magnetization transfer ratio in 144, 148 – memory deficits in 114 – metabolism in 128, 129, 134 – microscopic magnetic resonance imaging in 139, 139, 140–148 – mild cognitive impairment and 93, 93, 93, 115–116

mild cognitive impairment vs. 100 neurofibrillary tangles in 113, 121 neuropathology of 113, 121, 121 neuropsychiatric features of 115 paranoia in 115 patient complaints in 6 perfusion in 65, 66 perfusion-weighted imaging in 133, 134 – plasma biomarkers of 122 – positron emission tomography in 35, 37, 116, 127–128, 129–130 – posterior cortical atrophy variant 115 – preclinical 124 – prodromal 115–116 – serotonin system in 35–36 – severity of 115 – single photon emission computed tomography in 35, 116, 127 – structural imaging in 17, 17, 18, 116 – tacrine in 37 – tau protein in 121, 128, 130 – therapeutic evaluation in 37 – treatments for 371 – vascular dementia vs. 66 – white matter in 125, 126, 153, 155 Amantadine 375–376 American Society of Neuroradiology 2 Amino acid (AA) disorders – as inborn errors of metabolism 307, 314, 314 – clinical overlaps with 10 Amino acid degrader 272 Aminoaciduria 314, 314 AMN, see Adrenomyeloneuropathy (AMN) Amnesia – in Alzheimer’s disease 114 – in mild cognitive impairment 100, 101 – in normal pressure hydrocephalus 256 – in prion disease 239 – in strategic single-infarct dementia 204 – in voltage-gated potassium channel encephalopathy 250 – in Wernicke-Korsakoff syndrome 300 Amyloid angiopathy 307 Amyloid deposition – in Alzheimer’s disease 36, 83, 98, 113, 121, 121, 128, 130, 139, 139, 140, 141, 145–148 – in animal model of Alzheimer’s disease 139, 142–144 – in Creutzfeldt-Jakob disease 28, 240 – in frontotemporal lobar degeneration 98 – in mild cognitive impairment 95, 98–99 – in prion disease 240 – iron and 83, 143 – magnetization transfer ratio and 144, 148 – morphology of 147 – on MRI 143 Amyloid precursor protein (APP) 9, 11, 119, 120, 121, 140, 142–145 – – – – – – – –

Amyloid-related imaging abnormalities (ARIAs), in Alzheimer’s disease 127 Amyotrophic lateral sclerosis (ALS) – cause of 345 – clinical presentation of 344, 344 – corticospinal tract in 45–46, 46, 349 – diagnosis of 345, 346 – diffusion tensor imaging in 45, 46, 352, 353–354 – epidemiology of 344 – familial 344 – frontotemporal dementia vs. 7 – frontotemporal lobar degeneration and 157 – functional magnetic resonance imaging in 355, 357 – genetics in 11, 345 – iron mapping in 85, 85 – magnetic resonance imaging in 349, 350 – magnetic resonance spectroscopy in 29, 30, 351, 352 – magnetization transfer ratio in 354 – patient complaints in 6 – positron emission tomography in 354, 355–356 – sporadic 344 – treatment of 346, 376 – voxel-based morphometry in 349, 351 Amyvid tracer 128 Angiography, see Computed tomography angiography (CTA), Digital subtraction angiography (DSA), Magnetic resonance angiography (MRA) Animal models, of Alzheimer’s disease – amyloid plaques in 139, 142–144 – iron mapping in 83 Anosmia – in herpes simplex encephalitis 232 – in Parkinson’s disease 172 Anterior inferior cerebellar artery (AICA) 318, 320 Anterior-posterior commissure (AC-PC) line 381 Anti-AMPAR encephalitis 279 Anti-CV2/CRMP-5 encephalitis 281 Anti-GABABR encephalitis 279 Anti-GAD encephalitis 279 Anti-glutamic acid decarboxylase syndrome 245 Anti-Hu encephalitis 280 Anti-Ma2 encephalitis 281 Anti-NMDAR encephalitis 279 Anti-Ri encephalitis 281 Anti-Tr encephalitis 281 Anti-VGKC encephalitis 279 Anti-voltage-gated potassium channel encephalopathy 250, 250 Anti-Yo encephalitis 281 Antibodies – in celiac disease 247 – in gluten sensitivity dementia 247 – in immune-mediated dementias 245, 245 – in limbic encephalitis 277 – in paraneoplastic syndrome 20, 276–277, 279

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Index – in systemic lupus erythematosus 248 Antidepressants – in Alzheimer’s disease 373 – in motor neuron disease 340 Antiphospholipid syndrome (APS) 214 Apathy, in Alzheimer’s disease 115 Aphasia, see Logopenic aphasia (LPA), Primary progressive aphasia (PPA), Progressive nonfluent aphasia (PNFA) – in Alzheimer’s disease 114–115 – in frontotemporal dementia 37, 44 – in vascular dementia 195 Apo E4 – in Alzheimer’s disease 93, 133 – in mild cognitive impairment 93 ApoE gene 10, 11, 113, 119, 120 Apolipoprotein E 11, 196 Apomorphine 374–375 APP gene 9, 11, 119, 120, 121–122, 196 – See also Amyloid precursor protein (APP) APS, see Antiphospholipid syndrome (APS) ARD, see Alcoholic-related dementia (ARD) Argyrophilic fibrillary inclusions – in frontotemporal degeneration 17 – in frontotemporal lobar degeneration 158, 159 – in multiple-system atrophy 180 – in neurodegenerative disease 3 Arnold-Chiari malformation 336 Artane 374 Arterial spin labeling (ASL) 64, 65, 102, 103 Arylsulfatase A deficiency 306, 307– 308 ASL, see Arterial spin labeling (ASL) Ataxia – cerebellar –– acute-onset 328, 329–330 –– autosomal dominant inherited 329, 330–331 –– autosomal recessive 331, 332 –– causes of 328 –– chronic 328, 329 –– defined 328 –– in gluten sensitivity 247 –– in prion disease 239 –– inherited 328, 330–331 –– metabolic 332 –– sporadic 328 – drug-induced 328 – Friedreich 328, 331, 332 – gluten 328, 334 – in fragile X-associated tremor/ataxia syndrome 307, 313, 328, 333 – spinocerebellar 330 –– genetic testing with 4 –– genetics in 11, 330 –– in classification of cerebellar ataxia 328 –– magnetic resonance spectroscopy in 330 –– patient complaints in 6 Ataxia telangiectasia (AT) 328, 331, 332 Ataxins 11 ATP7B gene 11 Attention complaints 6 ATXN genes 11 AV-45 tracer 128

B Bacterial infections 233, 234–235 Bacterial meningitis 233 Bálint syndrome 115 Bartonella henselae 235 Basal ganglia – in aging brain 78, 80 – in Alzheimer’s disease 83, 100, 128 – in Behçet disease 251 – in CADASIL 210, 221 – in CARASIL 211 – in cerebral amyloid angiopathy 211, 211 – in children 73 – in corticobasal degeneration 183 – in Creutzfeldt-Jakob disease 28 – in deep brain stimulation 378 – in dementia with Lewy bodies 151, 154 – in frontotemporal lobar degeneration 158 – in functional magnetic resonance imaging 57 – in HIV infection 227 – in Huntington disease 19, 187–188 – in hyperparathyroidism 298 – in Leigh disease 311 – in MELAS 215, 215, 312 – in mixed dementia 208 – in multiple-system atrophy 180 – in myoclonic epilepsy with ragged red fibers 313 – in parathyroidism 298, 298 – in Parkinson’s disease 38, 84, 169 – in Parkinson’s diseases 38 – in polyarteritis nodosa 221 – in primary angiitis of central nervous system 217 – in prion disease 240, 241 – in progressive supranuclear palsy 21, 182 – in secondary parkinsonism 186–187 – in systemic lupus erythematosus 217 – in vascular parkinsonism 191, 191, 194, 200, 206, 207–208 Basilar artery 320 Behavior complaints 6 Behavioral variant frontotemporal dementia (bvFTD) 157–159 Behçet disease 217, 220, 250 Benseraside 374 Benzodiazepines 367 Benztropine 374 Bifunctional protein deficiency 307 BIN1 gene 120 Binswanger’s disease 3, 3 Biomarkers – in Alzheimer’s disease 93, 99, 116 – in mild cognitive impairment 93 BK virus 233 Blood-brain barrier (BBB) – in combined radiotherapy and computed tomography dementia 273 – in lead toxicity 302 – in manganese toxicity 303 – in mercury toxicity 303 – in parathyroidism 298 – in radiotherapy 269, 269 – in thiamine deficiency 300 Blood-oxygen-level-dependent (BOLD) contrast 51, 51, 171–172

– See also Functional magnetic resonance imaging (fMRI) BOLD, see Blood-oxygen-leveldependent (BOLD) contrast Borrelia burgdorferi 233 Botulinum toxin 376 Bradykinesia, in Parkinson’s disease 38, 171 Brain sagging 159, 162 Brain tumors – chemotherapy for, dementia and 271, 273 – cognitive effects of 266 – metastatic 267 – radiotherapy for –– blood-brain barrier in 269, 269 –– computed tomography and, dementia in combined 272, 274 –– dementia and, for brain tumors 268, 270, 270, 271–272 –– diffuse late atrophy in 270 –– imaging of effects of 269 –– in pediatric patients, brain effects of 273 –– mechanism of brain effects in 268, 268, 269 –– photon-cell interaction in 268, 268, 269 – treatment of 266 – whole-brain radiation for 267 Brainstem – anatomy, diffusion tensor imaging of 322, 323–324 – in Alzheimer’s disease 121 – in amyotrophic lateral sclerosis 45 – in Arnold-Chiari malformation 336 – in ataxia telangiectasia 331 – in Behçet disease 220, 251, 251 – in carbon monoxide exposure 303 – in dementia with Lewy bodies 150– 151 – in dentatorubral-pallidoluysian atrophy 189, 189 – in HIV infection 227 – in Leigh disease 311 – in Lyme disease 233 – in multiple-system atrophy 335 – in normal pressure hydrocephalus 258, 259 – in paraneoplastic cerebellar degeneration 281 – in Parkinson’s disease 173 – in progressive supranuclear palsy 21, 183 – in spinal muscular atrophy 342 – in spinocerebellar ataxia 330 Brainstem encephalitis, in paraneoplastic syndrome 279, 280 Breast cancer 276, 277 Bridging integrator 1 120 Bromocriptine 374 Brownian motion 42 Bulbar problems 6 Bulbospinal muscular atrophy 343 bvFTD, see Behavioral variant frontotemporal dementia (bvFTD)

C C9ORF72 gene 11, 158 C11-PIB tracer 128, 130 CADASIL, see Cerebral autosomal dominant arteriopathy (CADASIL)

Canavan disease 25, 307, 332 CARASIL, see Cerebral autosomal recessive arteriopathy (CARASIL) Carbidopa 374 Carbon monoxide exposure 303, 304 Carboplatin 272 Carboxy-O-methyltransferase inhibitors 374 Carboxylase deficiency 307 Carmustine 272 Cat scratch disease 235 Caudate nuclei (CN) 378, 378 CBD, see Corticobasal degeneration (CBD) CCA, see Corticocerebellar atrophy (CCA) CD, see Celiac disease (CD) CD-2 associated protein 120 CD2AP gene 120 CD33 antigen 120 CD33 gene 120 Celiac disease (CD) 247, 248, 334 Central nervous system tumors, see Brain tumors Cerebellar ataxia – acute-onset 328, 329–330 – autosomal dominant inherited 329, 330–331 – autosomal recessive 331, 332 – causes of 328 – chronic 328, 329 – defined 328 – in gluten sensitivity 247 – in prion disease 239 – inherited 328, 330–331 – metabolic 332 – sporadic 328 Cerebellar degeneration – alcoholic 328, 333, 333 – in toxic cerebellitis 329 – paraneoplastic 277, 278, 281, 328, 333, 334 Cerebellar embryology 318 Cerebellar hemorrhage 329 Cerebellar infarct 328, 329 – See also Infarct(s) Cerebellar input pathways 321 Cerebellar output pathways 320, 321 Cerebellar stroke 328, 329 Cerebellitis – acute 329, 329 – toxic 329, 330 Cerebellum – congenital malformations of 335– 336, 336 – diffusion tensor imaging of 322, 323–324 – gross anatomy of 318, 319 – histopathology of 319, 321 – magnetic resonance spectroscopy of 324, 325 – vascular supply of 318, 320 Cerebral amyloid angiopathy (CAA) 211, 211, 212 Cerebral autosomal dominant arteriopathy (CADASIL) – as inborn error of metabolism 307 – genetics in 11, 196, 196 – imaging of 210, 210 – in classification of vascular dementia 201 – vasculitis in 221, 222 – white matter in 210, 210

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Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Index Cerebral autosomal recessive arteriopathy (CARASIL) 201 – as inborn error of metabolism 307 – genetics in 11, 196, 196 – imaging of 211 Cerebral blood flow, see Perfusion Cerebral microbleeds (CMBs), in aging brain 74, 75 Cerebral peduncles 378 – in Behçet disease 220 – in corticobasal degeneration 183 – in multiple-system atrophy 180, 183 Cerebroretinal vasculopathy (CRV) 196, 196 Cerebrospinal fluid (CSF) – in aging 26, 70, 71 – in Alzheimer’s disease 93, 114, 116, 122, 124 – in amyotrophic lateral sclerosis 29, 345 – in Behçet disease 218 – in Creutzfeldt-Jakob disease 28, 239, 240 – in HIV infection 226 – in mucopolysaccharidoses 309, 309 – in normal pressure hydrocephalus 23, 257, 257 –– cine phase-contrast magnetic resonance quantification of flow in 259, 260 –– flow void sign in 258, 258 –– shunting 262 – in paraneoplastic cerebellar degeneration 333 – in paraneoplastic syndrome 276, 279 – in primary angiitis of central nervous system 216 – in progressive multifocal leukoencephalopathy 23 – in sarcoidosis 252 – in Sjögren syndrome 219, 249 – in voltage-gated potassium channel encephalopathy 250 – in voxel-based morphometry 15, 170 Cerebrotendinousxanthomatosis 307 Cerebrovascular disease (CVD) 60, 206, 209 – See also Vascular dementia (VaD) Cerebrovascular reactivity (CVR), in aging brain 75 Charcot, Jean-Martin 3 Charged multivesicular body protein 2B 157 Chemical-shift imaging 25 Chemotherapy, dementia and 271, 273 – See also specific agents Childhood ataxia with central nervous system hypomyelination 314, 315 Cholesterolemia – Alzheimer’s disease and 93 – mild cognitive impairment and 93 Choline (Cho) – in Alzheimer’s disease 26 – in dementia with Lewy bodies 27 – in frontotemporal dementia 27, 27– 28 – in gluten sensitivity 247 – in HIV infection 31 – in multiple sclerosis 30–31 – in traumatic brain injury 287, 289

– peak, in magnetic resonance spectroscopy 25, 25 Cholinesterase inhibitors – in Alzheimer’s disease 373 – in dementia with Lewy bodies 156 – in mild cognitive impairment 108 Chronic lymphocyte inflammation with pontine perivascular enhancement response to steroids (CLIPPERS) 279 Chronic traumatic encephalopathy (CTE) 284 Cisplatin 272 Cisternography, in normal pressure hydrocephalus 260, 261 CJD, see Creutzfeldt-Jakob disease (CJD) Climbing fibers 320 Clinical approach 6, 6, 7–8 CLIPPERS, see Chronic lymphocyte inflammation with pontine perivascular enhancement response to steroids (CLIPPERS) CLU gene 120 Clusterin 120 CMB, see Cerebral microbleeds (CMBs) CN, see Caudate nuclei (CN) Cobalamin deficiency 301, 302 Cockayne syndrome 307 Cognitive complaints 6 Cognitive impairment, see Mild cognitive impairment (MCI) – in Alzheimer’s disease 114 – in amyotrophic lateral sclerosis 345 – in Behçet disease 251 – in brain tumor therapy 268 – in brain tumors 266 – in chemotherapy 272 – in meningioma 266 – in multiple sclerosis 245, 246–247 – in normal pressure hydrocephalus 365 – in Parkinson’s disease 38 – in sarcoidosis 252 – in Sjögren syndrome 249 – in systemic lupus erythematosus 248 – in vascular dementia 364 – radiation-induced 270, 270 – with chemotherapy 272 COL4A1-related brain small vessel disease 215 Complaints, patient, in presentation of neurodegenerative disease 6 Complement component receptor 1 120 Computed tomography (CT) – hemorrhage on 329 – in dementia evaluation 362 – in dementia imaging 14 – in frontotemporal lobar degeneration 161 – in history of neuroimaging 2, 2 – in hyperparathyroidism 298 – in Kearns-Sayre disease 312 – in Krabbe disease 307 – in normal pressure hydrocephalus 257 – in Sjögren syndrome 250 – in traumatic brain injury 284 – radiotherapy and, dementia in combined 272, 274 Computed tomography angiography (CTA) 60, 60, 61–62 – multidetector row 61

– postprocessing in 61, 61 Computed tomography cisternography, in normal pressure hydrocephalus 260, 261 Computed tomography perfusion (CTP) 63, 64 Concussion 284 – See also Traumatic brain injury (TBI) Congenital cerebellar malformations 335–336, 336 Copper accumulation, in Wilson disease 187 Corpus callosum, thinning, in normal pressure hydrocephalus 258, 258 Cortical basal syndrome (CBS), patient complaints in 6 Cortical thickness – in aging brain 71, 72 – in Alzheimer’s disease 125 – in Parkinson’s disease 171 – in Parkinson’s diseases 169 – in traumatic brain injury 285 Corticobasal degeneration (CBD) – clinical overlaps with 10 – functional imaging in 184 – magnetic resonance imaging in 183 – Parkinson’s disease vs. 29 – patient complaints in 6 – single photon emission computed tomography in 184 – structural imaging in 18, 20, 183 – voxel-based morphometry in 184 Corticocerebellar atrophy (CCA) 331 Corticonuclear tract 323, 324 Corticospinal tract (CST) – in adrenoleukodystrophy 313 – in amyotrophic lateral sclerosis 45– 46, 46, 349, 353–354 – in cerebellar anatomy 323, 324 – in Friedreich ataxia 331, 332 – in hepatic encephalopathy 300 – in hereditary spastic paraplegia 340 – in Krabbe disease 307 – in motor neuron anatomy 341 – in primary lateral sclerosis 341 – in vascular dementia 201, 202 CR1 gene 120 Creatine (Cr) – in Alzheimer’s disease 26, 104 – in Creutzfeldt-Jakob disease 28 – in dementia with Lewy bodies 27 – in HIV infection 31 – in Huntington disease 29 – in Krabbe disease 308 – in Marchiafava-Bignami disease 302 – in mild cognitive impairment 104 – in multiple sclerosis 31 – in normal aging 26 – in traumatic brain injury 287, 289 – in Wernicke-Korsakoff syndrome 301 – peak, in magnetic resonance spectroscopy 25, 25 Creutzfeldt-Jakob disease (CJD), see Prion diseases – diagnosis of 239, 239, 240 – diffusion tensor imaging in 44, 45 – diffusion weighted imaging in 28, 44, 45 – genetic 239 – iatrogenic 239 – magnetic resonance imaging in 44, 45, 239, 239, 240, 240, 241–242

– magnetic resonance spectroscopy in 28 – patient complaints in 6 – positron emission tomography in 243 – single photon emission computed tomography in 242 – sporadic 239 – structural imaging in 23 – thalamic involvement in 242, 242 – variant 239 Cryptococcosis 236 CSF, see Cerebrospinal fluid (CSF) CST, see Corticospinal tract (CST) CT, see Computed tomography (CT) CTA, see Computed tomography angiography (CTA) CTE, see Chronic traumatic encephalopathy (CTE) CTP, see Computed tomography perfusion (CTP) Cushing syndrome 297, 297 CVD, see Cerebrovascular disease (CVD) CVR, see Cerebrovascular reactivity (CVR) Cyclophosphamide, in primary angiitis of the central nervous system 216, 218 Cysticercosis 235, 236 Cytosine arabinoside 272

D D-Penicillamine, in Wilson disease treatment 371 Dandy, Walter 2 Dandy-Walker malformation 336 Dardarin 11 DATs, see Dopamine transporters (DATs) DaTSCAN 166 DDS, see Dialysis disequilibrium syndrome (DDS) Deep brain stimulation (DBS) – anatomy in 378 – complications in 382, 383 – electrode breakage in 382, 383 – history of 378 – in secondary parkinsonism 374 – indications for 379 – infection in 382 – iron mapping and 84 – lead placement confirmation in 382, 383 – magnetic resonance imaging in 380, 383, 384 – nuclei targeting in 379, 381–382 – patient screening for 379, 380 – techniques in 379, 381–382 Deep cerebellar nuclei 318 Default mode network (DMN) 54, 55, 135 Delusions, in Alzheimer’s disease (AD) 115 Dementia, see specific dementias – chemotherapy and 271, 273 – cost of, global 5 – defined 2 – diagnostic evaluation in 362, 362 – flowchart for assessment and investigation of 8 – in history of neurodegenerative disease 2–3

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Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Index – in normal pressure hydrocephalus 256 – potentially reversible 364 – prevalence of 362 – preventable 363 – radiotherapy and 268, 270, 270, 271–272 – reversible vs. irreversible 363 – senility vs. 2 – structural imaging of –– arguments against 14 –– computed tomography in 14 –– in aging 16 –– modalities in 14 –– MRI in 14, 14, 15 –– voxel-based methods in 15, 16 – treatment of –– nonreversible 45 –– reversible 371 – vasculitis and 217–220, 222 Dementia lacking distinctive histology (DLDH) 159 Dementia with Lewy bodies (DLB) – acetylcholinesterase activity in 39 – Alzheimer’s disease vs. 44, 151–153, 155 – clinical features of 151 – clinical overlaps with 10 – cortical atrophy in 151 – diagnosis of 151 – differential diagnosis of 151 – diffusion tensor imaging in 44, 153 – dopamine system in 38 – dopaminergic imaging in 153 – frontotemporal dementia vs. 154 – genetics of 151 – hippocampus in 152, 155 – history of 150 – Lewy bodies in 151 – magnetic resonance imaging in 152, 152, 153–155 – magnetic resonance spectroscopy in 26 – management of 154 – metabolism in 154 – neuropathology of 151 – nicotinic acetylcholine receptors in 39 – Parkinson’s disease and 39, 150, 153 – patient complaints in 6 – perfusion in 154 – positron emission tomography in 38, 154 – possible 151 – probable 151 – single photon emission computed tomography in 38, 154 – structural imaging in 18, 152, 152, 153–155 – volume loss in 152, 153–154 – white matter in 153, 155 Dengue fever 188 Dentate nucleus 318, 320, 321, 322, 323 – in Alzheimer’s disease 83 – in Friedreich ataxia 331 – iron deposition in 73, 80, 82, 83 Dentatorubral-pallidoluysian atrophy (DRPLA) 186–187, 188, 189, 307, 328 Depression – in aging 364 – in Alzheimer’s disease 373 – in CADASIL 221

– in corticobasal degeneration 18 – in Cushing syndrome 297 – in dementia with Lewy bodies 18, 151 – in Huntington disease 188 – in Lyme disease 233 – in mild cognitive impairment 92 – in paraneoplastic syndrome 276 – in Parkinson’s disease 172–173 – in Sjögren syndrome 219 – in traumatic brain injury 284, 287 – in voltage-gated potassium channel encephalopathy 250 – in Wilson disease 190 – pseudodementia in 14, 363 Diabetes mellitus 298, 298 – Alzheimer’s disease and 119 – ataxia telangiectasia and 331 – cerebral microbleeds in 75 – MELAS and 215 – mild cognitive impairment and 91 – paraneoplastic syndrome and 279 – secondary parkinsonism and 187 – small-vessel disease and 206, 364 – vascular dementia and 194 Dialysis dementia 299, 300 Dialysis disequilibrium syndrome (DDS) 299 Diffuse late atrophy, radiationinduced 270 Diffusion tensor imaging (DTI), see Fractional anisotropy (FA), Mean diffusivity (MD) – basic concepts in 42, 96–97 – diffusion weighted imaging vs. 42 – in aging brain 42, 168 – in Alzheimer’s disease 42, 43 – in amyotrophic lateral sclerosis 45, 46, 353–354 – in Creutzfeldt-Jakob disease 44, 45 – in dementia with Lewy bodies 44, 153 – in frontotemporal dementia 44 – in frontotemporal lobar degeneration 160 – in HIV infection 228 – in Huntington disease 45 – in mild cognitive impairment 104, 105–106 – in motor neuron disease 45, 46–47, 352, 353–354 – in multiple sclerosis 46, 47–48, 246 – in multiple-system atrophy 180 – in Parkinson’s disease 44, 167 – in Parkinson’s diseases 168 – in progressive supranuclear palsy 44 – in traumatic brain injury 285, 286– 287 – of brainstem 322, 323 – of cerebellum 322, 323–324 – white matter in 42 Diffusion weighted imaging (DWI) – diffusion tensor imaging vs. 42 – in cerebellar infarct 328, 329 – in Creutzfeldt-Jakob disease 28, 44, 45 – in HIV infection 227, 227 – in hyperglycemia 298, 299 – in limbic encephalitis 277 – in multiple-system atrophy 180 – in normal pressure hydrocephalus 260 – in progressive supranuclear palsy 183

Digital subtraction angiography (DSA), in primary angiitis of central nervous system 216, 217–219 Disseminated necrotizing leukoencephalopathy 273 DJ1 gene 11 DLB, see Dementia with Lewy bodies (DLB) DMN, see Default mode network (DMN) Donepezil hydrochloride, for mild cognitive impairment 108 Dopamine agonists 374 Dopamine system – in Alzheimer’s disease 35 – in dementia with Lewy bodies 38, 151 – in Parkinson’s disease 38 Dopamine transporters (DATs), in Parkinson’s disease 166 Dopaminergic system – in Alzheimer’s disease 36 – in dementia with Lewy bodies 153 – in Parkinson’s disease 38, 169 – in secondary parkinsonism 186 Dorsal fibers 322 Dorsal spinocerebellar tracts 321 DRPLA, see Dentatorubralpallidoluysian atrophy (DRPLA) Drug-induced ataxia 328 Drug-induced cerebellitis 329, 330 Drug-induced dementia 304 Drug-induced parkinsonism 169 – See also Parkinsonism, Secondary parkinsonism DSA, see Digital subtraction angiography (DSA) DSC, see Dynamic susceptibility contrast (DSC) DWI, see Diffusion weighted imaging (DWI) Dynamic susceptibility contrast (DSC) – in Alzheimer’s disease 133 – in normal cerebral blood flow 65 – in normal pressure hydrocephalus 260 – mechanism of 63

E Echo planar imaging (EPI), in fMRI 52, 52 Electroencephalography (EEG) – in Creutzfeldt-Jakob disease 44 – in paraneoplastic syndrome 276 – in prion disease 239 – in prion disease transmission 239 Emboliform nucleus 318 Embryology, of cerebellum 318 Encephalitis lethargica 188 Endocrine-related dementia – Cushing syndrome in 297, 297 – Hashimoto encephalopathy in 296 – hyperglycemia in 298, 298 – parathyroidism in 298, 298 – thyroid hormones in 296, 297 – type 2 diabetes in 298, 298 Entacapone 374 Ephrin receptor EphA1 120 EPI, see Echo planar imaging (EPI) Epidemiology, of neurodegenerative diseases 5, 5 Episodic ataxia type 2 (EA2) 328, 331

Essential tremor (ET) – deep brain stimulation for 379 – Parkinson’s disease vs. 39, 166 ET, see Essential tremor (ET) Executive skills complaints 6 External lumbar drainage (ELD), in normal pressure hydrocephalus 262

F FA, see Fractional anisotropy (FA) Fabry disease 210, 307, 308, 309 Familial British dementia (FBD) 196, 196 Fastigial nucleus 318 Fat suppression 24 Fatal familial insomnia (FFI) 239 FBD, see Familial British dementia (FBD) FCSRT, see Free and Cued Selective Reminding Test (FCSRT) Fenton reaction 80 FFI, see Fatal familial insomnia (FFI) Fiber type grouping 342, 342 Fluorine 18 (18F)-labeled glucose 34 Fluoroethyl methyl amino-2 naphthyl ethylidene malononitrile (18FDDNP) 129 fMRI, see Magnetic resonance imaging (MRI) FOG, see Freezing of gait (FOG) Folia 7, 318 Fractional anisotropy (FA) 42 – See also Diffusion tensor imaging (DTI) – defined 42 – equation for 42 – in aging 168 – in Alzheimer’s disease 43 – in amyotrophic lateral sclerosis 352, 353 – in cerebellum 322 – in dementia with Lewy bodies 153 – in glioblastoma multiforme 267 – in motor neuron disease 352 – in multiple sclerosis 246 – in Parkinson’s disease 167 – in Parkinson’s diseases 168–169 – in traumatic brain injury 287 Fragile X-associated tremor/ataxia syndrome (FXTAS) 307, 313, 328, 333 Free and Cued Selective Reminding Test (FCSRT) 114 Freezing of gait (FOG), in Parkinson’s disease 171 Friedreich’s ataxia 328, 331, 332 Frontal variant Alzheimer’s disease 115 Frontotemporal brain sagging syndrome 159, 162 Frontotemporal dementia (FTD) – amyotrophic lateral sclerosis vs. 7 – behavioral variant 157–159 – dementia with Lewy bodies vs. 154 – diffusion tensor imaging in 44 – familial 158 – genetics in 11 – magnetic resonance spectroscopy in 27, 27, 28 – motor neuron disease and 27 – patient complaints in 6 – positron emission tomography in 37, 37 – serotonin system in 37

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Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Index – structural imaging in 17, 19 – syndromes 157, 158 – variants of 27 Frontotemporal lobar degeneration (FTLD) – age at onset 157 – amygdala in 159 – amyloid imaging in 98 – amyotrophic lateral sclerosis and 157 – clinical features of 157, 158 – clinical overlaps with 10 – computed tomography in 161 – dementia lacking distinctive histology in 159 – diagnosis of 157 – diffusion tensor imaging in 160 – exclusion criteria for 157 – functional magnetic resonance imaging in 160 – genetics in 157 – hippocampus in 159 – inclusion criteria for 157 – magnetic resonance imaging in 160, 162 – neuropathology of 158 – Pick disease in 158, 159 – positron emission tomography in 37, 161, 163 – prevalence of 157 – single photon emission computed tomography in 161 – structural imaging of 159, 160–162 – subtypes of 158 – tau 158, 159 – volume loss in 159, 160 – with ubiquitin and transactiveresponse-43 positive inclusions 159 FTD, see Frontotemporal dementia (FTD) FTLD, see Frontotemporal lobar degeneration (FTLD) Fucosidosis 307 Functional magnetic resonance imaging (fMRI) – activation in 52, 53 – advantages of 56 – blocked paradigm designs in 52, 53 – blood-oxygen-level-dependent contrast in 51, 51 – default mode network in 54, 55, 135 – disadvantages of 56 – echo planar imaging in 52, 52 – future of 56 – in aging brain 77, 77 – in Alzheimer’s disease 54, 134, 135 – in amyotrophic lateral sclerosis 355, 357 – in frontotemporal lobar degeneration 160 – in mild cognitive impairment 54, 105, 106–107 – in motor neuron disease 355, 357 – in multiple sclerosis 54, 56 – in neurodegenerative disorders 54, 56, 56 – in parathyroidism 298 – in Parkinson’s disease 171, 174 – in Parkinson’s diseases 175–176 – in traumatic brain injury 288, 289 – networks in 53, 55 – pharmacological 55, 57 – physics of 51

– physiology of 51 – positron emission tomography in 56 – postprocessing in 52 – resting-state 51, 53, 55 Fungal infections 236 FUS gene 157

G GABA, see Gamma-aminobutyric (GABA) system Gait impairment – in normal pressure hydrocephalus 256, 365 – in Parkinson’s disease 171 Galactosemia 307 Galanthamine, for mild cognitive impairment 108 Gamma-aminobutyric (GABA) system – in alcoholic cerebellar degeneration 333 – in Alzheimer’s disease 36–37 Gangliosidosis 307 GB3, see Globotriaosylceramide (GB3) GBM, see Glioblastoma multiforme (GBM) Gene therapy – for Huntington disease 376 – in Alzheimer’s disease 374 – in Parkinson’s disease 375 Genetic testing, in history of neurodegenerative disease 4 Genetics – in adrenomyeloneuropathy 313 – in Alzheimer’s disease 9, 11, 113, 119, 120 – in amyotrophic lateral sclerosis 345 – in dementia with Lewy bodies 151 – in dentatorubral-pallidoluysian atrophy 189 – in fragile X-associated tremor/ataxia syndrome 313, 333 – in Friedreich ataxia 331 – in frontotemporal lobar degeneration 157 – in hereditary spastic paraplegia 341 – in Huntington disease 9, 11, 187 – in Kennedy disease 343 – in myoclonic epilepsy with ragged red fibers 312 – in prion diseases 11, 240 – in spinal muscular atrophy 343 – in vanishing white matter disease 314 – in vascular dementia 196, 196 – of neurodegenerative disease 9 – of spinocerebellar ataxia 330 Genome-wide association studies, of Alzheimer’s disease 119 Gerstmann-Straussler-Scheinker disease (GSS) 239, 242 Giant cell arteritis 221 Glioblastoma multiforme (GBM) 266, 267 Glioma, low-grade 266, 267 Global cerebral and cerebellar atrophy 330 Globoid cell leukodystrophy 306, 308 Globose nucleus 318 Globotriaosylceramide (GB3) 210 Globus pallidi (GP) 378, 378 Globus pallidus interna (GPi) 378–379, 380, 382

Glucose metabolism, see Metabolism Glutamate excitotoxicity, with alcohol 301 Glutamine and glutamate (Glx) – in amyotrophic lateral sclerosis 29 – in dementia with Lewy bodies 27 – in Huntington disease 29 – in traumatic brain injury 287, 289 – peak, in magnetic resonance spectroscopy 25, 26 Glutaricaciduria type 1 307 Gluten sensitivity 245, 247, 248, 328, 334 Glx, see Glutamine and glutamate (Glx) Golgi cells 320 GP, see Globus pallidi (GP) Granule cell – chemotherapy and 272 – in ataxia telangiectasia 331 – in cerebellar histology 319 – in hypoglycemia 298 Gray matter – in aging brain 70, 71–72 – in Alzheimer’s disease 100, 139, 140 – in mild cognitive impairment 100 – normal-appearing, in multiple sclerosis 30 GRN gene 157 GSS, see Gerstmann-StrausslerScheinker disease (GSS)

H HA-HP, see Hemiatrophyhemiparkinsonism (HA-HP) syndrome Haber-Weiss reaction 80 Hallervorden-Spatz syndrome 186 Hallucinations – in Alzheimer’s disease (AD) 115 – in dementia with Lewy bodies 151 – in Parkinson’s disease 174 – in Parkinson’s diseases 175 Haloperidol 55, 376 HAND, see HIV-associated neurocognitive disorders (HAND) Hashimoto encephalopathy (HE) 245, 296 HD, see Huntington disease (HD) HDLS, see Hereditary diffuse leukoencephalopathy with neuroaxonal spheroids (HDLS) HE, see Hashimoto encephalopathy (HE) Heavy metal poisoning 302, 367 Hemiatrophy-hemiparkinsonism (HAHP) syndrome 186 Hemiparkinsonism with hemiparesis 186 Hemorrhage – cerebellar 329 – intracranial 364, 365, 382, 383 Hemosiderin 15, 74, 127, 139, 201, 202, 334, 334 Hepatic disease 366 Hepatic encephalopathy (HepE) 300, 300 Hereditary cerebral amyloid angiopathy 196, 196 Hereditary diffuse leukoencephalopathy with neuroaxonal spheroids (HDLS) 196, 197, 197

Hereditary endotheliopathy with retinopathy, nephropathy and stroke (HERNS) 196, 196, 201 Hereditary spastic paraplegia (HSP) 340, 349 Hereditary vascular retinopathy (HVR) 196, 196 HERNS, see Hereditary endotheliopathy with retinopathy, nephropathy and stroke (HERNS) Herpes simplex encephalitis (HSE) 232, 232 Hexamethylpropylene amine oxime (HMPAO) 34 HIF1α, see Hypoxia-inducible factor 1α (HIF1α) Hirayama disease 343, 349, 350 History, of neurodegenerative diseases 2, 2, 3–4 HIV, see Human immunodeficiency virus (HIV) HIV encephalitis (HIVE) 226–227 HIV-associated neurocognitive disorders (HAND) 367 – as term 226 – incidence of 226 – symptoms of 226 – treatment of 228 HIV-associated neurocognitive impairments (HNCIs) 31 HMPAO, see Hexamethylpropylene amine oxime (HMPAO) HNCIs, see HIV-associated neurocognitive impairments (HNCIs) Hockey-stick sign 242, 242 Hodgkin lymphoma 276, 277, 279, 280, 281, 333 Homocystinuria 213, 214, 364 Hounsfield, Godfrey 2 HSE, see Herpes simplex encephalitis (HSE) HSP, see Hereditary spastic paraplegia (HSP) HTRA serine protease 11 HTRA1 gene 11 HTT gene 11 Human immunodeficiency virus (HIV), see entries at HIV – history of 226 – infection –– cerebrospinal fluid in 226 –– congenital, CNS infection in 228, 229 –– in brain 227 –– magnetic resonance spectroscopy in 31, 31, 228, 228 –– opportunistic CNS infections in 228, 228–229 – overview of 226 – structural imaging in 22 Huntington disease (HD) 4 – cerebral cortex in 187 – course of 188 – diagnosis of 188 – gene therapy for 376 – genetics in 9, 11, 187 – in diffusion tensor imaging 45 – in history of neurodegenerative disease 4 – in magnetic resonance spectroscopy 28 – in secondary parkinsonism 187, 188 – magnetic resonance imaging in 188

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Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Index – patient complaints in 6 – structural imaging in 19, 20 – treatment of 375 – white matter in 45 Huntington protein 11 Hydroxyglutaricaciduria 307 Hyperhomocysteinemias 301, 307, 314 Hyperparathyroidism 298, 298 Hypoglycemia 298, 298 – See also Diabetes mellitus Hypokinesis complaints 6 Hypomelanosis of Ito 307 Hypomyelination with atrophy basal ganglia/cerebellum 307 Hypoperfusion encephalopathy 205, 205 Hyposmia, in Parkinson’s disease 172 Hypothyroidism 296, 296–297, 371 Hypoxia-inducible factor 1α (HIF1α) 269, 269

I Iatrogenic Creutzfeldt-Jakob disease (iCJD) 239 – See also Creutzfeldt-Jakob disease (CJD) iCJD, see Iatrogenic Creutzfeldt-Jakob disease (iCJD) ICP, see Inferior cerebellar peduncle (ICP) Idiopathic Parkinson’s disease (IPD) 20 – See also Parkinson’s disease (PD) Ifosfamide 272 Illicit drugs 304 Imaging, see specific modalities – in clinical approach 6–7, 8 – structural –– in Alzheimer’s disease 17, 17, 18, 116 –– in corticobasal degeneration 18, 20 –– in Creutzfeldt-Jakob disease 23 –– in dementia with Lewy bodies 18 –– in frontotemporal dementia 17, 19 –– in history of neurodegenerative disease 2, 2 –– in HIV dementia 22 –– in Huntington disease 19, 20 –– in idiopathic Parkinson’s disease 20 –– in mild cognitive impairment 16, 21 –– in multiple-system atrophy 21 –– in normal pressure hydrocephalus 23 –– in Parkinsonian disorders 20, 21–22 –– in progressive multifocal leukoencephalopathy 22 –– in progressive supranuclear palsy 21 –– in reversible dementia 22 –– of dementia ––– arguments against 14 ––– computed tomography in 14 ––– modalities in 14 ––– MRI in 14, 14, 15 Immune reconstitution inflammatory syndrome (IRIS) 228 Immune-mediated dementia – anti-voltage-gated potassium channel encephalopathy in 250 – antibodies in 245, 245 – Behçet disease in 250 – categories of 245, 245

celiac disease in 247 multiple sclerosis in 245 sarcoidosis in 251 Sjögren encephalopathy in 249 systemic lupus erythematosus in 248 Inborn errors of metabolism – amino acid disorders as 314, 314 – classification of 307 – Fabry disease as 308, 309 – globoid cell leukodystrophy as 306, 308 – Kearns-Sayre syndrome as 311 – Leigh disease as 311, 311 – lysosomal storage disease as 306, 307–311 – MELAS as 312, 312 – metachromatic leukodystrophy as 306, 307–308 – mitochondrial dysfunction as 310, 311–312 – mucopolysaccharidoses as 309, 309, 310 – myoclonic epilepsy with ragged red fibers as 312 – neuronal ceroid-lipofuscinosis as 310, 311 – peroxisomal disorders as 313, 313, 314–315 – vanishing white matter disease as 314, 315 Incontinence, urinary – in adrenomyeloneuropathy 313 – in normal pressure hydrocephalus 256 Incontinentiapigmenti 307 Infarct(s), see Vascular dementia (VaD) – cerebellar 328, 329 – in antiphospholipid syndrome 214 – in Behçet disease 251 – in CADASIL 210, 210 – in CARASIL 211 – in COL4A1 small-vessel disease 215 – in dementia with Lewy bodies 93 – in Fabry disease 309 – in HIV vasculopathy 228 – in MELAS 214, 312 – in multi-infarct dementia 194, 201, 201, 202 – in sickle cell disease 213, 213 – in Sjögren syndrome 249 – in strategic-infarct dementia 194, 204, 204, 205 – in systemic lupus erythematosus 249 – in tuberculosis 234 – lacunar 217, 364, 379 – watershed 202, 203–204 Infectious dementia, see HIV-associated neurocognitive disorders (HAND) – bacterial infections in 233, 234–235 – bacterial meningitis in 233 – cryptococcosis in 236 – cysticercosis in 235, 236 – fungal 236 – herpes simplex encephalitis in 232, 232 – herpes virus in 232, 232 – Lyme disease in 233 – parasitic 235, 236 – syphilis in 233, 234 – tuberculosis in 234, 235 – viral 232, 232, 233 – – – – –

Inferior cerebellar peduncle (ICP) 323 Inferior cerebellar veins 319 Inferior vermian vein 320 Intercellular adhesion molecule-1 (ICAM-1) 269, 269 Internal capsule 378 Intracranial hemorrhage 364, 365, 382, 383 Intracranial hypotension 159 IRIS, see Immune reconstitution inflammatory syndrome (IRIS) Iron accumulation – abnormal 80 – amyloid plaques and 143 – assessment of, with quantitative magnetic resonance imaging 80, 81– 82 – in aging brain 73, 73, 80, 80, 81 – in Alzheimer’s disease 80, 83, 84, 139 –– animal models of 83 – in motor neuron diseases 85, 85 – in multiple-system atrophy 84 – in Parkinson’s disease 84 – in Parkinson’s diseases 85 – in progressive supranuclear palsy 84 – in secondary parkinsonism 186 – in substantia negra 85, 85 Ischemic encephalopathy 205, 205

J Japanese B encephalitis 188, 233 JC virus 233 Joubert malformation 335 Juvenile spinal muscular atrophy of distal upper extremity 343

K KD, see Krabbe disease (KD) Kearns-Sayre syndrome 307, 311 Kennedy disease 343 Korsakoff psychosis (KP) 300 Korsakoff syndrome (KS) 300 – See also Wernicke-Korsakoff syndrome KP, see Korsakoff psychosis (KP) Krabbe disease (KD) 306, 307–308 KS, see Korsakoff syndrome (KS) Kugelberg Welander disease 343 Kuru 239 – See also Creutzfeldt-Jakob disease (CJD), Prion diseases

L L-asparaginase 272 Lactate peak – in alcoholic-related dementia 301 – in episodic ataxia type 2 331 – in HIV infection 31 – in Leigh disease 311 – in magnetic resonance spectroscopy 25, 25, 26 – in mitochondrial encephalomyopathy 215 – in multiple sclerosis 30 Lacunar infarct 189, 217, 364, 379 – See also Infarct(s) Language complaints 6 – See also Aphasia Larmor frequency 24 LE, see Limbic encephalitis (LE)

Lead poisoning 302, 367 Leigh disease 307, 311, 311 Levodopa – in dementia with Lewy bodies 151, 154 – in history of neurodegenerative disease 4 – in Huntington disease 376 – in multiple-system atrophy 374 – in Parkinson’s disease 374 – in vascular parkinsonism 191 – subcutaneous pump 375 – withdrawal 374, 375 Lewy bodies (LBs) – classical brainstem 150 – cortical 150, 150 – defined 150 – in Alzheimer’s disease 121, 122, 151 – in dementia with Lewy bodies 27, 151 – in diagnosis of dementia with Lewy bodies 151 – in mild cognitive impairment 93 – in Parkinson’s disease 20, 166 – in Parkinson’s diseases 7 – in secondary parkinsonism 186 – types of 150, 150 Lewy body dementia, see Dementia with Lewy bodies (DLB) Limbic encephalitis (LE) 250, 277, 278, 368, 368 Lipid peak, see Magnetic resonance spectroscopy (MRS) – in HIV infection 31 – in magnetic resonance spectroscopy 25 – in Marchiafava-Bignami disease 302 – in multiple sclerosis 30 Lisuride 374 Lithium toxicity 328 Liver failure 366 Logopenic aphasia (LPA) – as Alzheimer’s disease variant 115 – patient complaints in 6 Lomustine 272 Low-grade glioma 266, 267 LPA, see Logopenic aphasia (LPA) LRRK2 gene 11 Lung cancer 277, 279–280 Lyme disease 233, 367 Lymphoma 268, 276, 277, 279, 280, 333 Lysosomal storage disorders 306, 307– 311 – See also Inborn errors of metabolism

M Maeda syndrome, see Cerebral autosomal recessive arteriopathy (CARASIL) Magnetic resonance angiography (MRA) – contrast-enhanced 61, 63 – phase contrast 62 – time of flight 61, 62 Magnetic resonance imaging (MRI), see Imaging – amyloid plaques on 143 – fragile X-associated tremor/ataxia syndrome in 314 – functional –– activation in 52, 53 –– advantages of 56

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Index –– blocked paradigm designs in 52, 53 –– blood-oxygen-level-dependent contrast in 51, 51 –– default mode network in 54, 55, 135 –– disadvantages of 56 –– echo planar imaging in 52, 52 –– future of 56 –– in aging brain 77, 77 –– in Alzheimer’s disease 54, 134, 135 –– in amyotrophic lateral sclerosis 355, 357 –– in frontotemporal lobar degeneration 160 –– in mild cognitive impairment 54, 105, 106–107 –– in motor neuron disease 355, 357 –– in multiple sclerosis 54, 56 –– in neurodegenerative disorders 54, 56, 56 –– in parathyroidism 298 –– in Parkinson’s disease 171, 174 –– in Parkinson’s diseases 175–176 –– in traumatic brain injury 288, 289 –– networks in 53, 55 –– pharmacological 55, 57 –– physics of 51 –– physiology of 51 –– positron emission tomography in 56 –– postprocessing in 52 –– resting-state 51, 53, 55 – high-resolution, in traumatic brain injury 284 – in adrenomyeloneuropathy 313, 313 – in alcoholic cerebellar degeneration 333 – in Alzheimer’s disease 124, 124, 126, 133 – in aminoaciduria 314, 314 – in amyotrophic lateral sclerosis 349, 350 – in anti-NMDAR encephalitis 279 – in Arnold-Chiari malformation 336, 336 – in ataxia telangiectasia 331 – in Behçet disease 251, 251 – in CADASIL 210 – in cerebellitis 329, 329 –– toxic 329, 330 – in cerebral amyloid angiopathy 211, 211–212 – in corticobasal degeneration 183 – in Creutzfeldt-Jakob disease 44, 45, 239, 239, 240, 240, 241–242 – in deep brain stimulation 380, 383, 384 – in dementia evaluation 362 – in dementia structural imaging 14, 14, 15 – in dementia with Lewy bodies 152, 152, 153–155 – in dentatorubral-pallidoluysian atrophy 189 – in Fabry disease 309, 309 – in fragile X tremor ataxia syndrome 333 – in Friedreich ataxia 331, 332 – in frontotemporal lobar degeneration 160, 162 – in glioblastoma multiforme 267 – in gluten sensitivity 247 – in Hashimoto encephalopathy 297

in hepatic encephalopathy 300, 300 in herpes simplex encephalitis 232 in history of neuroimaging 2 in Huntington disease 188 in Kearns-Sayre syndrome 312 in Krabbe disease 308, 308 in Leigh disease 311, 311 in limbic encephalitis 277, 278 in maple syrup urine disease 314 in Marchiafava-Bignami disease 302 in MELAS 312, 312 in meningioma 267 in metachromatic leukodystrophy 306, 308 – in mild cognitive impairment 99 –– volumetry 99, 99, 100–102 – in motor neuron disease 349, 350 – in mucopolysaccharidoses 309, 309 – in multiple sclerosis 246, 246–247 – in multiple-system atrophy 21, 168, 180, 180, 335, 335 – in neuronal ceroidlipofuscinosis 310, 311 – in neurosyphilis 234 – in normal pressure hydrocephalus 257–258 – in paraneoplastic cerebellar degeneration 333, 334 – in paraneoplastic syndrome 277, 278, 279 – in Parkinson’s disease 167 – in progressive supranuclear palsy 22, 22, 182, 183 – in sarcoidosis 252, 252–253 – in siderosis of central nervous system 334, 334 – in striatal encephalitis 279 – in subacute sclerosing panencephalitis 233 – in systemic lupus erythematosus 248–249 – in traumatic brain injury 284 – in uremic encephalopathy 299, 299 – in vascular dementia 195–196, 201– 202 – in vascular parkinsonism 169, 191 – in voltage-gated potassium channel encephalopathy in 250, 250 – in Wilson disease 190 – iron imaging with 80, 81–82 – microscopic, in Alzheimer’s disease 139, 139, 140–148 – perfusion-weighted 63, 65 –– in Alzheimer’s disease 133, 134 Magnetic resonance spectroscopy (MRS) – chemical shift in 24 – data acquisition in 24 – fat suppression in 24 – future perspectives in 32 – in aging 26 – in Alzheimer’s disease 26, 137 – in amyotrophic lateral sclerosis 29, 30, 351, 352 – in ataxia telangiectasia 332 – in Creutzfeldt-Jakob disease 28 – in Cushing syndrome 298 – in dementia with Lewy bodies 26 – in dialysis dementia 299 – in episodic ataxia type 2 331 – in Friedreich ataxia 331 – in frontotemporal dementia 27, 27, 28 – – – – – – – – – – – – –

in gluten ataxia 334 in gluten sensitivity 247 in Hashimoto encephalopathy 297 in hepatic encephalopathy 300 in HIV infection 31, 31, 228, 228 in Huntington disease 28 in Krabbe disease 308 in Leigh disease 311 in Marchiafava-Bignami disease 302 in MELAS 215 in metachromatic leukodystrophy 306 – in mild cognitive impairment 104, 104 – in motor neuron disease 351, 352 – in multiple sclerosis 30, 30, 246 – in multiple-system atrophy 335 – in Parkinson’s disease 29 – in primary lateral sclerosis 351 – in radiation-induced dementia 271 – in spinocerebellar ataxia 330 – in thiamine deficiency 301 – in traumatic brain injury 287, 289 – in vanishing white matter disease 315 – metabolite peaks in 25, 25 – nuclear magnetism in 24 – of cerebellum 324, 325 – shimming in 24 – short versus long echo time in 25 – single-voxel spectroscopy versus chemical-shift imaging in 25 – techniques 25 – water suppression in 24 Magnetization transfer ratio (MTR) – amyloid plaques and 144, 148 – in Alzheimer’s disease 144, 148 – in amyotrophic lateral sclerosis 354 – in hepatic encephalopathy 300 – in motor neuron disease 354 Malaria 235 Manganese toxicity 187, 303, 367 Maple syrup urine disease 307, 314, 314 MAPT gene 11 Marchiafava-Bignami disease (MBD) 302, 367 MBD, see Marchiafava-Bignami disease (MBD) MCI, see Mild cognitive impairment (MCI) MCP sign 314 MD, see Mean diffusivity (MD), Mixed dementia (MD) Mean diffusivity (MD), see Diffusion tensor imaging (DTI) – defined 42 – equation for 42 – in Alzheimer’s disease 43 – in amyotrophic lateral sclerosis 353 – in dementia with Lewy bodies 153 – in motor neuron disease 352 – in multiple sclerosis 246 Medial fibers 322 Medial lemniscus (ML) 323, 324 Medial transverse fibers 322 Medication effects 367 Medication toxicity 304 Megalencephalic leukoencephalopathy with calcifications and cysts 307 MELAS, see Mitochondrial myopathy, encephalopathy, lactic acidosis, and strokelike episodes (MELAS) – – – – – – – – – – –

Memantine 156 Membrane-spanning 4-domains subfamily A 120 Memory, see Amnesia – in Alzheimer’s disease 114 – in mild cognitive impairment 100, 101 – in normal pressure hydrocephalus 256 Meningioma 266, 267, 365, 366 Meningitis – bacterial 233 – chronic 367 – tubercular 234, 235 Menkes disease 307 Mercury poisoning 303 MERRF, see Myoclonic epilepsy with ragged red fibers (MERRF) Metabolic-related dementia – as potentially-reversible 366 – dialysis dementia in 299, 300 – dialysis disequilibrium syndrome in 299 – hepatic encephalopathy in 300, 300 – uremic encephalopathy in 299, 299 Metabolism, see Inborn errors of metabolism, Positron emission tomography (PET) – in aging brain 75, 76 – in Alzheimer’s disease 128, 129, 134 – in dementia with Lewy bodies 154 – in mild cognitive impairment 95, 98 – in multiple-system atrophy 181 – in traumatic brain injury 290 Metachromatic leukodystrophy (MLD) 306, 307–308, 328 Metastatic disease 267 Methanol poisoning 367 Methotrexate (MTX) 268, 270, 272, 273 Microscopic magnetic resonance imaging (μMRI), see Magnetic resonance imaging (MRI), microscopic Microtubule inhibitors 272 Microtubule-associated protein tau 11 MID, see Multi-infarct dementia (MID) Middle cerebellar peduncles (MCP) 314, 322, 324 Mild cognitive impairment (MCI) – Alzheimer’s disease and 93, 93, 93, 115–116 – Alzheimer’s disease vs. 100 – amnesic 100, 101 – amyloid imaging in 95, 98–99 – analytical epidemiology of 91, 91 – Apo E4 and 93 – arterial spin labeling in 102, 103 – as intermediate state between normal cognition and dementia 90 – biomarkers in 93 – cholesterolemia and 93 – clinical features of 92, 92 – clinical trials for 108 – descriptive epidemiology of 91 – diagnostic algorithm for 94, 94 – diagnostic concept and evolution 90, 91 – diagnostic guidelines for 93 – differential diagnosis of 92 – diffusion tensor imaging in 104, 105–106 – epidemiology of 90, 91

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Index – follow-up for 108 – functional magnetic resonance imaging in 54, 105, 106–107 – gray matter in 100 – image analysis in 107 – in Parkinson’s disease 173 – in single photon emission computed tomography 95, 95, 96–97 – interventional epidemiology of 92 – Lewy bodies in 93 – magnetic resonance imaging in 99 –– volumetry 99, 99, 100–102 – magnetic resonance spectroscopy in 104, 104 – medication for 108 – metabolism in 95, 98 – neuropathology of 92–93, 93, 94 – outline of 90 – perfusion in 95, 95, 96–97, 102, 103 – positron emission tomography in 95, 98 – prevention of 108 – radiation-induced 270, 270 – risk factors for 93 – structural imaging in 16 – subtypes of 92 – symptoms of 92 – underlying diseases with 92, 92 Mild traumatic brain injury (mTBI) 284 – See also Traumatic brain injury (TBI) Mini-Mental State Examination (MMSE) 115, 266 Mitochondrial dysfunction 310, 311– 312 Mitochondrial myopathy, encephalopathy, lactic acidosis, and strokelike episodes (MELAS) 214, 215, 307, 312, 312 Mixed dementia (MD) 208, 208 ML, see Medial lemniscus (ML) MLD, see Metachromatic leukodystrophy (MLD) MND, see Motor neuron disease (MND) Molar tooth-type malformation 335, 336 Molecular layers, in cerebellar histology 319 Monomelic amyotrophy 343 Mood disorders, in Parkinson’s disease 173 – See also Depression Motor complaints 6 Motor neuron anatomy 341 Motor neuron disease (MND), see Amyotrophic lateral sclerosis (ALS), Multiple sclerosis (MS) – clinical overlaps with 10 – diffusion tensor imaging in 45, 46– 47, 352, 353–354 – frontotemporal dementia and 27 – functional magnetic resonance imaging in 355, 357 – genetics in 11 – iron mapping in 85, 85 – lower –– Hirayama disease in 343 –– Kennedy disease in 343 –– spinal muscular atrophy in 342, 343 –– upper vs. 340 – magnetic resonance imaging in 349, 350

– magnetic resonance spectroscopy in 351, 352 – magnetization transfer ratio in 354 – positron emission tomography in 354, 355–356 – upper –– hereditary spastic paraplegia in 340 –– lower vs. 340 –– primary lateral sclerosis in 340, 341 – voxel-based morphometry in 349, 351 Moyamoya, in sickle cell disease 213, 213–214 MPS, see Mucopolysaccharidoses (MPS) MRA, see Magnetic resonance angiography (MRA) MRI, see Magnetic resonance imaging (MRI) MRI parkinsonism index (MRPI) 22 MRS, see Magnetic resonance spectroscopy (MRS) MS4A4E gene 120 MS4A6A gene 120 MSA, see Multiple-system atrophy (MSA) mTBI, see Mild traumatic brain injury (mTBI) MTR, see Magnetization transfer ratio (MTR) MTX, see Methotrexate (MTX) Mucopolysaccharidoses (MPS) 307, 309, 309, 310 Multi-infarct dementia (MID) 194, 201, 201, 202 Multiple sclerosis (MS) – as immune-mediated 245 – cognitive dysfunction in 245, 246– 247 – diffusion tensor imaging in 46, 47– 48, 246 – functional magnetic resonance imaging in 54, 56 – magnetic resonance imaging in 246, 246–247 – magnetic resonance spectroscopy in 30, 30, 246 – normal-appearing gray matter in 30 – normal-appearing white matter in 30 – positron emission tomography in 246 – single photon emission computed tomography in 246 – Sjögren syndrome vs. 249 – spinal cord involvement in 47 – subtypes of 245 – white matter in 47, 47–48 – whole-brain atrophy in 246, 247 – “black holes” in 30 Multiple sulfatase deficiency 307 Multiple-system atrophy (MSA) 335 – acetylcholinesterase activity in 182 – argyrophilic fibrillary inclusions in 180 – C-type 21, 335 – clinical overlaps with 10 – dementia with Lewy bodies and 150 – diffusion tensor imaging in 180 – diffusion weighted imaging in 180 – functional imaging in 181, 181 – iron accumulation in 84

– magnetic resonance imaging in 21, 168, 180, 180 – metabolism in 181 – microglia in 182 – olivo-ponto-cerebellar network in 180 – P-type 335 – Parkinson’s disease vs. 29, 168, 180, 182 – patient complaints in 6 – positron emission tomography in 181, 181 – progressive supranuclear palsy vs. 183 – structural imaging in 21, 21, 180, 180 – transcranial ultrasound in 181 – voxel-based morphometry in 180 Multitensor tractography, in traumatic brain injury 286, 286 Myo-inositol (mI) – in Alzheimer’s disease 26, 104 – in dialysis dementia 299 – in frontotemporal dementia 27, 27– 28 – in magnetic resonance spectroscopy 25, 26 – in mild cognitive impairment 104, 104 Myoclonic epilepsy with ragged red fibers (MERRF) 307, 312

N N-acetyl aspartate (NAA) – in Alzheimer’s disease 26, 137 – in amyotrophic lateral sclerosis 29, 351 – in Creutzfeldt-Jakob disease 28 – in dementia with Lewy bodies 27 – in frontotemporal dementia 27, 27– 28 – in gluten sensitivity 247 – in HIV infection 31, 31 – in Krabbe disease 308 – in magnetic resonance spectroscopy 25, 25 – in Marchiafava-Bignami disease 302 – in metachromatic leukodystrophy 306 – in mild cognitive impairment 104, 104 – in motor neuron disease 351 – in multiple sclerosis 30–31 – in normal aging 26 – in Parkinson’s disease 29 – in traumatic brain injury 287, 289 – in Wernicke-Korsakoff syndrome 301 NAA, see N-acetyl aspartate (NAA) NAGM, see Normal-appearing gray matter (NAGM) National Institute of Neurological Disorders and Stroke-Association Internationale and the Association Internationale pour la Recherche et l’Enseignement en Neurosciences (NINDS– AIREN) 199–200 NAWM, see Normal-appearing white matter (NAWM) NBIA, see Neurodegeneration with brain iron accumulation (NBIA) NCC, see Neurocysticercosis (NCC)

NCL, see Neuronal ceroid-lipofuscinosis (NCL) Neuroblastoma 276, 280 Neuroborreliosis 233 Neurocysticercosis (NCC) 235, 236 Neurodegeneration with brain iron accumulation (NBIA) 186–187 – See also Iron accumulation Neurodegenerative diseases – clinical approach in 6, 6, 7–8 – defined 42 – epidemiology of 5, 5 – genetics of 9 – history of 2, 2, 3–4 – pathology in 7, 9–10 Neurofibrillary tangles (NFTs), see Tau protein – in Alzheimer’s disease 80, 95, 113, 116, 121, 121, 129, 137 – in dementia with Lewy bodies 150 – in progressive supranuclear palsy 182 – in repetitive brain trauma 290 Neurogenic locus notch homolog protein 3 11 Neuroimaging, see Imaging Neuronal ceroid-lipofuscinosis (NCL) 307, 310, 311 Neurosarcoidosis 245, 251, 252–253 Neurosyphilis 3, 233, 234, 342, 371 Nicotinic acetylcholine receptors, in dementia with Lewy bodies 39 NINDS-AIREN, see National Institute of Neurological Disorders and StrokeAssociation Internationale and the Association Internationale pour la Recherche et l’Enseignement en Neurosciences (NINDS– AIREN) Nitrous oxide 371 Noguchi, Hideyo 3 Nonketotichyperglycinemia 307 Normal pressure hydrocephalus (NPH) – as potentially-reversible dementia 365 – brainstem changes in 258, 259 – cerebrospinal fluid in 23, 257, 257 –– cine phase-contrast magnetic resonance quantification of flow in 259, 260 –– flow void sign in 258, 258 –– in normal pressure hydrocephalus ––– cine phase-contrast magnetic resonance quantification of flow in 259, 260 ––– flow void sign in 258, 258 –– shunting 262 – cingulate nucleus sign in 258, 259 – clinical features of 256 – corpus callosal thickening in 258, 258 – dementia in 256 – diagnosis of 256, 262 – diffusion weighted imaging in 260 – dynamic susceptibility contrast MRI 260 – epidemiology of 256 – external lumbar drainage in 262 – gait disturbance in 256 – imaging in 256, 257–261 – management of 262 – nuclear imaging in 260, 261 – patient complaints in 6 – perfusion in 67

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Index – periventricular white matter changes in 257, 257, 258–259 – positron emission tomography in 261 – shunting in 262 – single photon emission computed tomography in 260 – spinal tap in 262 – structural imaging in 23 – urinary incontinence in 256 – vascular parkinsonism vs. 191 – ventriculomegaly in 256, 257 Normal-appearing gray matter (NAGM), in multiple sclerosis 30 Normal-appearing white matter (NAWM), in multiple sclerosis 30 NOTCH3 gene 11 NPH, see Normal pressure hydrocephalus (NPH) Nuclear magnetism 24 Nutritional-deficiency related dementia – vitamin B12 deficiency in 301, 302 – Wernicke-Korsakoff syndrome in 300, 301

O Obstructive sleep apnea 363 Olfaction, see Anosmia, Hyposmia Olivocerebellar fibers 322 Olivopontocerebellar atrophy 21, 326, 330, 331, 335 Ovarian teratoma 276, 277, 279 Overlap, clinical 7, 10

P PACNS, see Primary angiitis of the central nervous system (PACNS) Pancreatic carcinoma 276 Pantothenate kinase-associated neurodegeneration (PKAN) 186–187 Parallel fibers 319 Paraneoplastic cerebellar degeneration (PCD) 281, 328, 333, 334 Paraneoplastic encephalitis 279 Paraneoplastic syndrome (PNS) – antibodies in 20, 276, 279 – brainstem encephalitis in 279, 280 – cerebellar degeneration in 277, 278 – clinical features of 276 – electroencephalography in 276 – imaging in 277 – limbic encephalitis in 277, 278 – pathology of 277 – pathophysiology of 277 – screening for 277 – striatal encephalitis in 279 – treatment of 277 Parasitic infections 235, 236 Parathyroidism 298, 298 Parkin 11 Parkinson, James 2 Parkinsonism, see Secondary parkinsonism – drug-induced 169 – postinfectious 187 – vascular –– in magnetic resonance imaging 169, 191 –– in secondary parkinsonism 187, 190

–– lacunar infarcts in 187 –– normal pressure hydrocephalus vs. 191 –– perfusion in 66 –– treatment of 191 Parkinson’s disease (PD), see Vascular parkinsonism (VP) – akinetic-rigid 171 – anosmia in 172 – behavioral symptoms in 173 – bradykinesia in 171 – cognitive symptoms in 173 – dementia with Lewy bodies and 39, 150 – depression in 173 – diagnosis of –– differential 167 –– early 166 –– imaging in 166 – differential diagnosis in 29 – diffusion tensor imaging in 44, 167 – dopamine system in 38 – dopaminergic system in 38, 169 – essential tremor vs. 39, 166 – fractional anisotropy in 167 – freezing of gait in 171 – functional magnetic resonance imaging in 171, 174 – gait impairment in 171 – hallucinations in 174 – hyposmia in 172 – imaging –– in diagnosis 166 –– of motor hallmarks 169 –– of nonmotor features 172 – iron mapping in 84 – Lewy bodies in 20, 166 – magnetic resonance imaging in 167 – magnetic resonance spectroscopy in 29 – mild cognitive impairment in 173 – mood disorders in 173 – multiple-system atrophy vs. 29, 168, 180, 182 – positron emission tomography in 37, 166 – premotor symptoms in 172 – progressive supranuclear palsy vs. 44, 183 – psychosis in 174 – rapid eye movement behavior disorder in 172 – rest tremor in 169 – rigidity in 171 – secondary parkinsonism vs. 20, 66 – serotonin system in 38–39, 170 – single photon emission computed tomography in 166, 169, 171, 174 – transcranial ultrasound in 167 – treatments for 374 – tremor-dominant 171 Parkinson’s disease dementia (PDD) – atrophies in 173 – dementia with Lewy bodies vs. 151, 153 – white matter hyperintensities in 173 Parkinson’s diseases (PD) – diagnosis of –– early 167–168 –– imaging in 167–168 – diffusion tensor imaging in 168 – fractional anisotropy in 168–169

– FreeSurfer software in 169 – functional magnetic resonance imaging in 176 – genetics in 11 – hallucinations in 175 – imaging –– in diagnosis 167–168 –– of motor hallmarks 169–170 –– of nonmotor features 175–176 – in history of neurodegenerative disease 3 – iron mapping in 85 – Lewy bodies in 7 – positron emission tomography in 38, 169 – progressive supranuclear palsy vs. 7 – single photon emission computed tomography in 167, 169 – substantia negra in 169 – supranuclear palsy vs. 7 – voxel-based morphometry in 169– 170 Parkinson’s diseases dementia (PDD) – clinical overlaps with 10 – patient complaints in 6 PARKN gene 11 Pathology, in neurodegenerative disease 7, 9–10 Patient complaints 6 PCA, see Posterior cortical atrophy (PCA) PCD, see Paraneoplastic cerebellar degeneration (PCD) Pediatric patients – congenital HIV in 228 – magnetic resonance spectroscopy in 324 – radiotherapy brain effects in 271, 273 Pelizaeus-Merzbacher disease 307 Perfusion – arterial spin labeling for 64, 65 – computed tomography 63, 64 – dynamic susceptibility contrast for 63, 65 – in aging 65, 66, 75 – in Alzheimer’s disease 65, 66 – in dementia with Lewy bodies 154 – in mild cognitive impairment 95, 95, 96–97, 102, 103 – in normal pressure hydrocephalus 67 – in sickle cell disease 67, 67 – in vascular dementia 66, 66 – in vascular parkinsonism 66 – magnetic resonance 63, 65 Perfusion-weighted imaging (PWI), in Alzheimer’s disease 133, 134 Pergolide 374 Perivascular spaces (PVSs) 207, 207 Peroxisomal disorders 307, 313, 313, 314–315 Peroxisome biogenesis defects 307 PET, see Positron emission tomography (PET) Phenylketonuria 307, 314 Phenytoin toxicity 328 Phosphatidylinositol-binding clathrin assembly protein 120 Photon-cell interaction, in radiotherapy 268, 268, 269 PICA, see Posterior inferior cerebellar artery (PICA)

PICALM gene 120 Pick bodies 159 – See also Argyrophilic fibrillary inclusions Pick disease 158, 159 – in frontotemporal dementia 37 – in history of neurodegenerative disease 3 PKAN, see Pantothenate kinaseassociated neurodegeneration (PKAN) Plasma biomarkers, in Alzheimer’s disease 122 PLS, see Primary lateral sclerosis (PLS) PML, see Progressive multifocal leukoencephalopathy (PML) Pneumoencephalogram 2 PNFA, see Progressive nonfluent aphasia (PNFA) PNS, see Paraneoplastic syndrome (PNS) Polyarteritis nodosa 221 Polyomavirus 233 Polypharmacy 367 Positron emission tomography (PET), see Metabolism – fluorine 18 (18F)-labeled glucose in 34 – functional magnetic resonance imaging vs. 56 – importance of 34 – in aging brain 75, 76 – in Alzheimer’s disease 35, 35, 116, 127–128, 129–130 – in amyotrophic lateral sclerosis 354, 355–356 – in dementia evaluation 362 – in dementia with Lewy bodies 38, 154 – in frontotemporal dementia 37, 37 – in frontotemporal lobar degeneration 37, 161, 163 – in hypothyroidism 296, 297 – in mild cognitive impairment 95, 98 – in motor neuron disease 354, 355– 356 – in multiple sclerosis 246 – in multiple-system atrophy in 181, 181 – in normal pressure hydrocephalus 261 – in paraneoplastic cerebellar degeneration 334, 334 – in Parkinson’s disease 37, 166 – in Parkinson’s diseases 38, 169 – in prion disease 243 – in progressive supranuclear palsy 183 – in traumatic brain injury 290 – mechanism of 34 Posterior cortical atrophy (PCA) – as Alzheimer’s disease variant 115 – patient complaints in 6 Posterior inferior cerebellar artery (PICA) 318, 320 Posterior reversible encephalopathy syndrome (PRES) 299, 366, 374 Postinfectious parkinsonism 187 PPA, see Primary progressive aphasia (PPA) Pramipexole 374 Praxis complaints 6 Precentral cerebellar vein 320

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Index PRES, see Posterior reversible encephalopathy syndrome (PRES) Presenelin 1 11, 113, 120, 120 Presenelin 2 11, 113, 120, 120 PRG gene 11 Primary angiitis of the central nervous system (PACNS) 216, 217–219, 245 Primary lateral sclerosis (PLS) 340, 341, 349, 351, 352–353, 354 Primary progressive aphasia (PPA) – in frontotemporal dementia 37 – language deficit 115 – patient complaints in 6 – semantic variant 115, 157 Prion diseases, see Creutzfeldt-Jakob disease (CJD) – acquired 239 – clinical features of 239 – genetic 239 – genetics in 11, 240 – imaging in 240, 241–243 – incidence of 239 – neuropathology of 240 – sporadic 239 Prion protein 11 PRNP gene 11 Progranulin 11, 157 Progressive multifocal leukoencephalopathy (PML) – in HIV infection 228 – structural imaging in 22 Progressive muscular atrophy (PMA) 353 Progressive nonfluent aphasia (PNFA) – frontal lobe in 7 – in frontotemporal degeneration 18, 27, 157 – in magnetic resonance imaging 159, 162 – neuropathology of 158 – patient complaints in 6 Progressive supranuclear palsy (PSP) – cholinergic function in 183 – clinical overlaps with 10 – diffusion weighted imaging in 183 – functional imaging in 183 – iron accumulation in 84 – magnetic resonance imaging in 182, 183 – multiple-system atrophy vs. 183 – neurofibrillary tangles in 182 – Parkinson’s disease vs. 44, 183 – Parkinson’s diseases vs. 7 – patient complaints in 6 – positron emission tomography in 183 – serotonin system in 39 – structural imaging in 21, 182, 183 – voxel-based morphometry in 183 Propionic acidemia 307 Propranolol 376 Prostate cancer 276 PSEN1 gene 11, 113, 120, 120 PSEN2 gene 11, 113, 120, 120 Pseudodementia 14, 92, 256, 363 PSP, see Progressive supranuclear palsy (PSP) Pulvinar sign 242, 242 Pure cerebellar atrophy 330, 330 Purkinje cell – 5-Fluorouracil and 272 – in ataxia telangiectasia 331 – in celiac disease 247, 334

– in cerebellar histology 319 – in cerebellitis 329 – in paraneoplastic cerebellar degeneration 281, 333 – in toxic cerebellitis 329 Putamena 378, 378 PVSs, see Perivascular spaces (PVSs) PWI, see Perfusion-weighted imaging (PWI)

Q QSM, see Quantitative susceptibility mapping (QSM) Quantitative susceptibility mapping (QSM) 83

R Radiotherapy – blood-brain barrier in 269, 269 – computed tomography and, dementia in combined 272, 274 – dementia and, for brain tumors 268, 270, 270, 271–272 – diffuse late atrophy in 270 – imaging of effects of 269 – in pediatric patients, brain effects of 273 – mechanism of brain effects in 268, 268, 269 – photon-cell interaction in 268, 268, 269 Rapid eye movement behavior disorder (RBD), in Parkinson’s disease 172 Rasagiline 374 RBD, see Rapid eye movement behavior disorder (RBD) RBT, see Repetitive brain trauma (RBT) Refsum disease 307, 328 Renal cell carcinoma 276, 277 Repetitive brain trauma (RBT) 284, 288, 290 – See also Traumatic brain injury (TBI) Rest tremor, in Parkinson’s disease 169 Retinal vasculopathy + leukoencephalopathy 215 Reversible dementia, structural imaging in 22 Rhombic lips 318 Rhomboencephalosynapsis 336 Riluzole 346, 376 Risperidone 376 Rituximab 277 Rivastigmine 56, 108, 229, 372 Ropinirole 374 Rotigotine 374–375

S Sacks, Oliver 4 Sarcoidosis 245, 251, 252–253 SCA, see Superior cerebellar artery (SCA) Scans without evidence of dopaminergic deficiency (SWEDDs), in Parkinson’s disease 169 SCD, see Sickle cell disease (SCD), Subacute combined degeneration (SCD) sCJD, see Sporadic Creutzfeldt-Jakob disease (sCJD)

SD, see Semantic dementia (SD) Secondary parkinsonism, see Parkinsonism, Vascular parkinsonism (VP) – dentatorubral-pallidoluysian atrophy as 186–187, 188 – Huntington disease as 188 – Parkinson’s disease vs. 20, 66 – pathology of 186 – substantia nigra pars compacta in 186 – treatments for 374 – Wilson disease as 190 Selective serotonin reuptake inhibitors (SSRIs), in Alzheimer’s disease 373 Selegiline 374 Semantic dementia (SD) – anterior temporal atrophy in 158 – as frontotemporal syndrome 157 – magnetic resonance imaging in 159 – patient complaints in 6 Semantic variant of primary progressive aphasia (svPPA) 115, 157 Senility 2 Serotonin system – in Alzheimer’s disease 35–36 – in frontotemporal dementia 37 – in Parkinson’s disease 38–39, 170, 174 – in progressive supranuclear palsy 39 Shimming 24 Shunting, in normal pressure hydrocephalus 67, 258–260, 262, 371 Sialic-acid binding immunoglobulinlike lectin 6 120 Sialorrhea 340, 376 Sickle cell disease (SCD) – imaging in 212, 213–214 – perfusion in 67, 67 Siderosis of central nervous system 127, 211–212, 218, 328, 334, 334 SIGLEC6 gene 120 Single photon emission computed tomography (SPECT) – Alzheimer’s disease in 35, 116, 127 – corticobasal degeneration in 184 – dementia with Lewy bodies in 38, 154 – frontotemporal lobar degeneration in 161 – importance of 34 – mechanism of 34 – mild cognitive impairment in 95, 95, 96–97 – multiple sclerosis in 246 – normal pressure hydrocephalus in 260 – Parkinson’s disease in 166, 169, 171, 174 – Parkinson’s diseases in 167, 169 – prion disease in 242 – Sjögren syndrome in 249 – traumatic brain injury in 290 – vascular dementia in 205 Single-voxel spectroscopy 25 Sjögren encephalopathy dementias 245, 249 Sjögren syndrome (SS) 218, 249, 250 SLE, see Systemic lupus erythematosus (SLE)

Sleep apnea 363 SMA, see Spinal muscular atrophy (SMA) Smell, see Anosmia, Hyposmia SN, see Substantia nigra (SN) SNCA gene 11 SNpc, see Substantia nigra pars compacta (SNpc) Solvent toxicity 328 SORL1 gene 113, 119, 120 Sortilin-related receptor 113, 119, 120 SPECT, see Single photon emission computed tomography (SPECT) Spectroscopy, see Magnetic resonance spectroscopy (MRS), Twodimensional correlated spectroscopy (2D-COSY) Speech complaints 6 – See also Aphasia Spinal cord – in adrenomyeloneuropathy 313 – in amyotrophic lateral sclerosis 45, 349 – in Friedreich ataxia 331 – in hereditary spastic paraplegia 349 – in Hirayama disease 344 – in Kennedy disease 343 – in Lyme disease 234 – in motor neuron disease 349 – in multiple sclerosis 47 – in multiple-system atrophy 180 – in neurosyphilis 233 – in primary angiitis of central nervous system 216 – in superficial siderosis 334 – in vitamin B12 deficiency 301 Spinal muscular atrophy (SMA) 342, 343 Spinocerebellar ataxias 330 – genetic testing with 4 – genetics in 11, 330 – in classification of cerebellar ataxia 328 – magnetic resonance spectroscopy in 330 – patient complaints in 6 Spinocerebellar atrophy 7 Spinocerebellar tracts 321 Spirochetes 233, 234 Sporadic Creutzfeldt-Jakob disease (sCJD) 239 – See also Creutzfeldt-Jakob disease (CJD) SS, see Sjögren syndrome (SS) Stellate cells 320 Stiff-person syndrome 276, 276, 279 STN, see Subthalamic nucleus (STN) Strategic-infarct dementia 194, 204, 204, 205 Striatal encephalitis, in paraneoplastic syndrome 279 Stroke, cerebellar 328, 329 – See also Infarct(s) Structural imaging, see Imaging Subacute combined degeneration (SCD), in vitamin B12 deficiency 301 Subacute sclerosing panencephalitis (SSPE) 233, 233 Subcortical vascular encephalopathy 194 Substantia nigra (SN) – in deep brain stimulation 378

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Index – in Parkinson’s disease early diagnosis 166 – in Parkinson’s diseases 169 – in secondary parkinsonism 186 – iron accumulation in 85, 85 Substantia nigra pars compacta (SNpc) 186–187, 378 Substantia nigra pars reticulata 378 Subthalamic nucleus (STN) 378, 378, 379, 380, 381–382 Sulfite oxidase deficiency 307 Superficial siderosis 127, 211–212, 218, 328, 334, 334 Superior cerebellar artery (SCA) 318, 320 Superior cerebellar peduncle 322, 323 Superior cerebellar veins 319 Superoxide dismutase 1 11 Susac syndrome 219 Susceptibility-weighted imaging (SWI) 83 – in Alzheimer’s disease 127 – in Fabry disease 309 – in multi-infarct dementia 202 – in traumatic brain injury 285, 286 – subthalamic nucleus in 382 SWEDDs, see Scans without evidence of dopaminergic deficiency (SWEDDs) SWI, see Susceptibility-weighted imaging (SWI) Syphilis 3, 233, 234, 342, 371 Systemic lupus erythematosus (SLE) 216, 245, 248, 248, 249, 363

T Tacrine, in Alzheimer’s disease 37 Taenia solium 235, 236 Talcapone 374 Tamoxifen 272 Tau bodies 158, 159 Tau imaging 128, 129 Tau protein, see Neurofibrillary tangles (NFTs) – in Alzheimer’s disease 114, 116, 121, 121, 122, 124, 128, 129, 130 – in corticobasal degeneration 183 – in frontotemporal dementia 27 – in frontotemporal lobar degeneration 157–158 – in mild cognitive impairment 93 – in Pick disease 159 – in progressive supranuclear palsy 182 – in repetitive brain trauma 290 – in traumatic brain injury 290 Tay-Sachs disease 328, 340 TBI, see Traumatic brain injury (TBI) TDP-43 protein 345 Testicular cancer 276, 277, 279 Tetrabenazine 375–376 TG, see Transglutaminase (TG) Thalamic tumor 380 Thalidomide 272 Thiamine deficiency 300, 301, 367 Thymoma 276, 280 Thyroid hormones 296, 297 TNF-α, see Tumor necrosis factor α (TNF-α) Toluene inhalation 302, 367 Toxic cerebellitis 329, 330 Toxin-related dementias – alcoholic-related dementia in 301

– carbon monoxide exposure in 303, 304 – heavy metal poisoning in 302, 367 – medications in 304 Toxoplasmosis 236 Transactive response-DNA binding protein (TARDP) 157, 159 Transcranial ultrasound – in multiple-system atrophy 181 – in Parkinson’s disease 166–167 Transglutaminase (TG) antibodies 247 Transverse pontine fibers (TPFs) 322– 323 Traumatic brain injury (TBI) – chronic traumatic encephalopathy in 284 – computed tomography in 284 – diffusion tensor imaging in 285, 286–287 – functional magnetic resonance imaging in 289 – magnetic resonance imaging in 284 –– high-resolution 284 – magnetic resonance spectroscopy in 287, 289 – metabolism in 290 – mild 284 – pathophysiology of 284 – patient complaints in 6 – positron emission tomography in 290 – repetitive brain injury in 284 – single-photon emission computed tomography in 290 – susceptibility-weighted imaging in 285, 286 Tremor, see Essential tremor (ET), Rest tremor Triamterene 371 Trichothiodystrophy with photosensitivity 307 Trihexyphenidyl 374 Tropheryma whippelii 235 Trypanosomiasis 235 Tuberculosis 234, 235 Tumor necrosis factor α (TNF-α) 252, 269, 269, 272 Tumors, see Brain tumors, Paraneoplastic syndrome (PNS), specific cancers Two-dimensional correlated spectroscopy (2D-COSY) 288 Type 2 diabetes mellitus 298, 298 – See also Diabetes mellitus

U Ubiquitin inclusions – in frontotemporal dementia 27 – in frontotemporal lobar degeneration 159 – in Lewy bodies 151 Ultrasound, transcranial – in multiple-system atrophy 181 – in Parkinson’s disease 166–167 Urea cycle defects 307 Uremic encephalopathy 299, 299 Urinary incontinence – in adrenomyeloneuropathy 313 – in normal pressure hydrocephalus 256

V VaD, see Vascular dementia (VaD) Valosin-containing protein 11, 157 Vanishing white matter disease (VWMD) 314, 315 Variant Creutzfeldt-Jakob disease (vCJD) 239 – See also Creutzfeldt-Jakob disease (CJD) Varicella zoster meningoencephalitis 229 Vascular dementia (VaD) – Alzheimer’s disease vs. 66 – as preventable 364 – classification of 197, 201 – clinical criteria for 199, 199 – cortical 199 – defined 23 – diagnostic criteria for 194 – genetics in 196, 196 – hypoperfusion encephalopathy in 205, 205 – imaging in 200, 200, 201 – in Diagnostic and Statistical Manual 194 – ischemic encephalopathy in 205, 205 – large-vessel 200, 201–202 – magnetic resonance imaging in 195–196, 201–202 – mixed dementia in 208, 208 – neuroanatomic-behavior considerations in 197 – pathologic syndromes in 194 – pathophysiology of 195, 195, 196 – patient complaints in 6 – perfusion in 66, 66 – perivascular spaces in 207, 207 – single photon emission computed tomography in 205 – small-vessel 201, 206, 206, 207 – subcortical 199 – watershed infarcts in 202, 203–204 Vascular endothelial growth factor (VEGF) 269, 269 Vascular parkinsonism (VP), see Secondary parkinsonism – in secondary parkinsonism 187, 190 – lacunar infarcts in 187 – magnetic resonance imaging in 169, 191 – normal pressure hydrocephalus vs. 191 – perfusion in 66 – treatment of 191 Vasculitis – in Behçet disease 217, 220 – in CADASIL 221, 222 – in giant cell arteritis 221 – in polyarteritis nodosa 221 – in Sjögren syndrome 218 – in Susac syndrome 219 – in systemic lupus erythematosus 216 – in Wegener granulomatosis 219 – primary central nervous system 216, 217–219 – secondary 216, 220, 222 vCJD, see Variant Creutzfeldt-Jakob disease (vCJD) VCP gene 11

VEGF, see Vascular endothelial growth factor (VEGF) Vein of Galen 320 Ventral intermediate nucleus (VIM) 378 Ventral spinocerebellar tracts 321 Ventral transverse fibers 322 Ventriculomegaly, in normal pressure hydrocephalus 256, 257 Vermian dysplasia 336 Vertebral arteries 320 VGKC-E, see Voltage-gated potassium channel encephalopathy (VGKC-E) VIM, see Ventral intermediate nucleus (VIM) Vinblastine 272 Vincristine 272 Vindesine 272 Vinorelbine 272 Viral encephalitides 232, 232, 233 Virchow-Robin spaces (VRSs) 207, 207 Visual complaints 6 Vitamin B1 deficiency 300, 301 Vitamin B12 deficiency 301, 302, 371 Vitamin deficiencies, patient complaints in 6 Vitamin E deficiency 328 Voltage-gated potassium channel encephalopathy (VGKC-E) 250, 250, 279 Voxel-based morphometry (VBM) 15 – in Alzheimer’s disease 125, 125 – in amyotrophic lateral sclerosis 349, 351 – in corticobasal degeneration 184 – in dementia imaging 15, 16 – in dementia with Lewy bodies 152, 153–154 – in mild cognitive impairment 16, 99–100, 100, 102 – in motor neuron disease 349, 351 – in multiple-system atrophy 180 – in Parkinson’s diseases 169–170 – in primary lateral sclerosis 351 – in progressive supranuclear palsy 183 Voxel-based relaxometry (VBR), in dementia imaging 15 VP, see Vascular parkinsonism (VP) VRSs, see Virchow-Robin spaces (VRSs) VWMD, see Vanishing white matter disease (VWMD)

W Watershed infarcts 202, 203–204 WBRT, see Whole-brain radiation (WBRT) Weakness, as complaint 6 Wegener granulomatosis 219 Werdnig-Hoffman disease 343 Wernicke encephalopathy (WE) 300, 333 Wernicke-Korsakoff syndrome (WKS) 300, 301, 367 West Nile encephalitis 187, 233 Whipple disease 235, 367 White matter (WM) – in aging brain 42, 71, 73, 73, 74 – in CADASIL 210, 210 – in Huntington disease 45 – in mucopolysaccharidoses 309 – in multiple sclerosis 47, 47–48

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Index – in normal pressure hydrocephalus 257, 257, 258–259 – macrostructural lesions in, in aging brain 73, 73 – microstructural changes in, in aging brain 74 – normal-appearing, in multiple sclerosis 30 – water diffusion in 42 White matter hyperintensities (WMHs) – in Alzheimer’s disease 125, 126, 153, 155 – in dementia with Lewy bodies 152, 155 – in Parkinson’s disease dementia 173 Whole-brain radiation (WBRT) 267 Wilson disease 187, 190, 190, 307, 366

– genetics in 11 – in secondary parkinsonism 186 – treatment of 371 WKS, see Wernicke-Korsakoff syndrome WM, see White matter (WM) WMHs, see White matter hyperintensities (WMHs)

X X-linked adrenoleukodystrophy 307

Z Zinc, in Wilson disease treatment 371

α α-galactosidase A (α-Gal A) 210, 306, 308 α-synuclein, see Lewy bodies (LBs) – in amyotrophic lateral sclerosis 345 – in dementia with Lewy bodies 18, 150, 150, 151, 154 – in multiple-system atrophy 180 – in neurodegenerative disease 2 – in Parkinson’s disease 11, 186 – in Parkinson’s diseases 7

“ “Black holes”, in multiple sclerosis 30

“Double panda” sign 190 “Face of the giant panda” sign 190 “Face of the miniature panda” sign 190 “Hot cross bun” sign 168, 180 “Hummingbird” sign 21, 22, 259 “Mickey mouse” sign 183 “Morning glory” sign 183 “Penguin silhouette” 21, 22, 183 “Proteinopathies” 2 “Slit” sign 180 “Upper midbrain profile sign” 258, 259

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Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.