Imaging in neurodegenerative disorders [First edition] 9780191651397, 0191651397, 9780191801822, 0191801828, 9780199671618, 0199671613

Table of contents :
Cover
Imaging in Neurodegenerative Disorders
Copyright
Dedication
Contents
List of abbreviations
List of contributors
SECTION 1 Introduction
1 Epidemiology of neurodegenerative diseases
2 Metabolomics of neurodegenerative disorders
3 Transcriptome profiling in neurodegenerative disorders
4 Health economic considerations in neurodegenerative disorders
5 Symptoms of neurodegenerative diseases
SECTION 2 Imaging technique
6 Computed tomography
7 General principles of magnetic resonance imaging
8 Nuclear medicine and radiology
9 Molecular imaging and neurodegenerative disorders 10 Positron emission tomography in neurodegenerative disorders-evolving techniques and new tracers11 Radiopharmaceuticals for molecular imaging of neurodegenerative diseases
SECTION 3 Neurodegeneration: cognition
12 Neuroimaging of Alzheimer's disease
13 MRI-based imaging of Alzheimer's disease
14 Frontotemporal dementia
15 Dementia with Lewy bodies
16 Corticobasal syndrome and corticobasal degeneration
SECTION 4 Neurodegeneration: movement
17 Parkinson's disease: clinical and imaging features
18 Progressive supranuclear palsy
19 Imaging in Huntington's disease 20 Multiple system atrophy21 Clinical benefit of dopamine transporter imaging in movement disorders and dementia
SECTION 5 Neurodegeneration: strength
22 Amyotrophic lateral sclerosis
SECTION 6 Neurodegeneration: coordination
23 Spinocerebellar atrophies
24 Imaging in Friedreich's ataxia
25 Neuroimaging in human prion diseases
SECTION 7 Neurodegeneration: peripheral and autonomic nervous systems
26 Amyloidosis
27 Neurodegeneration: metabolic and toxin-related disorders
SECTION 8 Neurodegeneration: myelin
28 Demyelinating diseases
29 Charcot-Marie-Tooth disease 30 Neurodegenerative disorders of the basal gangliaSECTION 9 Neurodegeneration: trauma
31 Neurodegeneration post trauma: brain
32 Neurodegeneration post trauma: spine
33 Neurodegeneration after trauma: peripheral nerves
SECTION 10 Neuroimaging after therapy
34 Functional imaging of neurosurgery in Parkinson's disease
35 Neuroimaging after cell-based therapy
Index

Citation preview

Imaging in Neuro­degenerative Disorders

Considerate la vostra semenza: fatti non foste a viver come bruti, ma per seguir virtute e canoscenza. Consider your origin; you were not born to live like brutes, but to follow virtue and knowledge. Dante Alighieri, Italian poet (1265–1321) Divine Comedy Canto XXVI, lines 118–120. Stilicidi casus lapidem cavat, uncus aratri Continual dropping wears away a stone Lucretius, Roman poet and philosopher (c.99 BC–c.55 BC) De Rerum Natura Book I, line 313

Imaging in Neurodegenerative Disorders Edited by

Luca Saba Department of Medical Sciences, University of Cagliari, Italy

1

1 Great Clarendon Street, Oxford, OX2 6DP, United Kingdom Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries © Oxford University Press 2015 The moral rights of the authors‌have been asserted First Edition published in 2015 Impression: 1 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, by licence or under terms agreed with the appropriate reprographics rights organization. Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above You must not circulate this work in any other form and you must impose this same condition on any acquirer Published in the United States of America by Oxford University Press 198 Madison Avenue, New York, NY 10016, United States of America British Library Cataloguing in Publication Data Data available Library of Congress Control Number: 2014935547 ISBN 978–0–19–967161–8 Printed in China by Asia Pacific Offset Ltd. Oxford University Press makes no representation, express or implied, that the drug dosages in this book are correct. Readers must therefore always check the product information and clinical procedures with the most up-to-date published product information and data sheets provided by the manufacturers and the most recent codes of conduct and safety regulations. The authors and the publishers do not accept responsibility or legal liability for any errors in the text or for the misuse or misapplication of material in this work. Except where otherwise stated, drug dosages and recommendations are for the non-pregnant adult who is not breast-feeding Links to third party websites are provided by Oxford in good faith and for information only. Oxford disclaims any responsibility for the materials contained in any third party website referenced in this work.

This book is dedicated to Tiziana

Foreword

I am honoured to have been asked by Dr Saba to write one chapter for his fabulous new book in addition to writing this foreword. A foreword is generally an introduction that describes a book and specifically its subject, contents, scope, and aims. So here I go. ◆

◆

◆

Subject: As its title clearly states, this book deals with degenerative disorders of the brain and spinal cord. However, it goes much further than what one would expect from its title. Keep reading. Contents: Authors from 13 different countries have contributed 35 chapters dealing with all imaginable aspects of neurodegeneration. The contents of these chapters go beyond imaging to provide a clear, concise, and complete framework with which to understand these important and often neglected disorders. Scope: The book begins with five chapters on the clinical aspects of degenerative disorders of the central nervous system, including their genetics and economic impact. Various techniques, current and future, that are and will be used to evaluate these disorders are described in the next six chapters. The following 17 chapters are each dedicated to one disorder or a group of disorders and are complemented by another describing their differential diagnoses. The often overlooked topic of post-traumatic neurodegeneration is well explained in three chapters arranged by anatomical regions. The last four chapters

deal with a variety of topics but concentrate on different treatments and their imaging. ◆

Aims: What Dr Saba set out to accomplish is to give neuroradiologists, neurologists, and other interested specialists a book that, while concentrating on imaging, is wide enough in its approach and contents to satisfy and enlighten all of those interested in neurodegeneration. I believe that the book you hold in your hands has clearly accomplished this goal.

Yes, there are other tomes on the same topic. Some, however, cover clinical aspects, molecular genetics, and/or biomarkers while relegating neuroimaging to a lesser status (or ignoring it completely). Others deal with specific topics such as proteins, inflammation, inhibitor molecules, and protein aggregates and although they also contain wonderful information they are far away from what we clinicians do and what we require in order to take care of our patients. That is the strength of Dr Saba’s book: it contains enough basic science to make it illuminating while emphasizing the imaging and clinical aspects of these diseases. Read it, enjoy it, and learn from it. Mauricio Castillo Professor of Radiology and Chief of Neuroradiology, University of North Carolina at Chapel Hill Editor in Chief, American Journal of Neuroradiology May 2014

Preface

Neurodegenerative diseases are characterized clinically by their insidious onset and chronic progression. Neurodegenerative disease clinical syndromes are often categorized by whether they initially affect cognition, movement, strength, coordination, sensation, or autonomic control. Frequently, however, patients will present with symptoms and signs referable to more than one system. Either involvement of several systems can occur concomitantly, or else by the time the patient has functionally declined enough to seek medical attention multiple systems have become involved. While the term ‘neurodegenerative’ implies it is the loss of neurons that cause disease, it is possible that neuronal demise is merely the final stage of a preceding period of neuronal dysfunction. It is difficult to know whether clinical decline is associated with actual neuron loss, or with a period of neuronal dysfunction that precedes neuron loss. Also, particular neurodegenerative diseases are aetiologically heterogeneous. In addition to syndromically defining neurodegenerative diseases by what neuroanatomical system is involved, these disorders are broken down along other clinical lines. Early (childhood, young adulthood, or middle age) versus late (old age) onset is an important distinction. Some clinically similar neurodegenerative diseases are subcategorized by their age of onset, despite the fact at the molecular level different forms of a particular disease may have very little in common. Sporadic onset versus Mendelian inheritance constitutes another important distinction, and many named neurodegenerative diseases have both sporadic

(Mendelian inheritance is not recognizable) and Mendelian subtypes. Neurodegenerative disorders constitute a group of frequently diagnosed conditions in neurological clinics, but diagnosing these diseases can prove particularly intimidating to clinicians, because often the diagnosis cannot be critically ‘confirmed’ by a simple test. New imaging modalities have advanced to the point of high resolution, morphological, metabolic, and functional analysis. CT, MRI, nuclear medicine, and molecular imaging have recently emerged as outstanding non-invasive techniques for the study of the neurodegenerative disorders. In recent years, advances in brain imaging technology have dramatically expanded the frontiers of human investigation in the field of neurosciences. The purpose of this book is to cover all the imaging techniques and new exciting methods like new tracers, biomarkers, metabolomics, and gene-array profiling. We discuss the potential for applying such techniques clinically, and offer present and future applications as applied to the neurodegenerative disorders with the most world-renowned scientists in these fields. Given these exciting developments in imaging application for the study of neurodegenerative disease I hope that this book will be a timely and wide-ranging addition to the growing body of literature on this topic. Luca Saba June 2014, Cagliari, Italy

Acknowledgements

It is not possible to overstate my gratitude to the many individuals who helped to produce this book; their enthusiasm and dedication was unbelievable. In particular, I would like to thank Mauricio Castillo for his help and advice. I am deeply appreciative of the support offered by my colleagues at the Azienda Ospedaliero-Universitaria di Cagliari and in particular Mario Piga and Eugenio A. Genovese. I wish to acknowledge also the technologists and nurses in the Department of Radiology of the Azienda Ospedaliero-Universitaria di Cagliari, with special thanks to Angelo Porcu. I would like to express my appreciation to Oxford University Press for their professionalism in handling this project. Most

particularly I  would like to thank Peter Stevenson and Eloise Moir-Ford for their tireless dedication and advice during each stage in the production of this book. Your help was wonderful and made producing this book an enjoyable and worthwhile experience. Finally, I  would like to acknowledge Tiziana. Your patience, smile, and unfailing good cheer kept me sane! Luca Saba June 2014, Cagliari—Italy

Contents

List of abbreviations  xv List of contributors  xix

SECTION 1

Introduction 1 Epidemiology of neurodegenerative diseases  3 Giancarlo Logroscino and Rosanna Tortelli 2 Metabolomics of neurodegenerative

disorders  20

Luigi Barberini, Luca Saba, Claudia Fattuoni, Matteo Fraschini, and Francesco Marrosu 3 Transcriptome profiling in

neurodegenerative disorders  35 James D. Mills and Michael Janitz

4 Health economic considerations in

neurodegenerative disorders  42 Thomas Rapp, Pauline Chauvin, Nadège Costa, and Laurent Molinier

5 Symptoms of neurodegenerative diseases  54 Robert Laforce Jr, Manja Lehmann, Joël Macoir, Stéphane Poulin, Martin Roy, Jean-Paul Soucy, Louis Verret, Bruce L. Miller, and Rémi W. Bouchard

SECTION 2

Imaging technique 6 Computed tomography  85 Michele Anzidei, Fabrizio Boni, Giuseppe Pelle, and Carlo Catalano 7 General principles of magnetic

resonance imaging  94

Aart J. Nederveen, Matthan W. A. Caan, and Marion Smits

8 Nuclear medicine and radiology  113 Juergen Dukart and Bogdan Draganski 9 Molecular imaging and

neurodegenerative disorders  123 Vivekanandan Palaninathan, Sivakumar Balasubramanian, and D. Sakthi Kumar

10 Positron emission tomography in

neurodegenerative disorders—evolving techniques and new tracers  135 Christopher Kobylecki, Alexander Gerhard, and Karl Herholz

11 Radiopharmaceuticals for molecular imaging

of neurodegenerative diseases  151 Shankar Vallabhajosula, Brigitte Vallabhajosula, and Lilja Solnes

SEC TION 3

Neurodegeneration: cognition 12 Neuroimaging of Alzheimer’s disease  181 Charles D. Smith and Brian T. Gold 13 MRI-based imaging of Alzheimer’s disease  199 Francesco Garaci, Nicola Toschi, Tommaso Volpi, Girolamo Garreffa, Lothar Spies, Simone Lista, Roberto Floris, and Harald Hampel 14 Frontotemporal dementia  214 Jennifer L. Whitwell 15 Dementia with Lewy bodies  232 Claire Henchcliffe and Thomas F. Tropea 16 Corticobasal syndrome and

corticobasal degeneration  243 Luke A. Massey and Sean O’Sullivan

xiv

contents

SECTION 4

Neurodegeneration: movement 17 Parkinson’s disease: clinical and

imaging features  259

Ana M. Franceschi, Sofia Pina, and Mauricio Castillo 18 Progressive supranuclear palsy  292 Dominic Paviour 19 Imaging in Huntington’s disease  303 Simon J. A. van den Bogaard and Raymund A. C. Roos 20 Multiple system atrophy  316 Keita Sakurai 21 Clinical benefit of dopamine transporter imaging

in movement disorders and dementia  333 Jan Booij, Françoise J. Siepel, Tirza C. Buter, and Dag Aarsland

SECTION 5

Neurodegeneration: strength 22 Amyotrophic lateral sclerosis  345 Martin R. Turner

SECTION 6

Neurodegeneration: coordination 23 Spinocerebellar atrophies  363 Ana Solodkin, Gülin Öz, and Christopher M. Gomez 24 Imaging in Friedreich’s ataxia  385 Hamed Akhlaghi, Martin B. Delatycki, Nellie Georgiou-Karistianis, and Gary F. Egan 25 Neuroimaging in human prion diseases  394 Damien Galanaud

SECTION 7

Neurodegeneration: peripheral and autonomic nervous systems 26 Amyloidosis  401 Erez Nossek and Tali Jonas Kimchi

27 Neurodegeneration: metabolic

and toxin-related disorders  409 Amogh N. Hegde and C. C. Tchoyoson Lim

SEC TION 8

Neurodegeneration: myelin 28 Demyelinating diseases  421 Eytan Raz 29 Charcot–Marie–Tooth disease  437 José Berciano and Elena Gallardo 30 Neurodegenerative disorders

of the basal ganglia  461

Lev Bangiyev, Alexandra Roudenko, Eytan Raz, August Dietrich, and Girish M. Fatterpekar

SEC TION 9

Neurodegeneration: trauma 31 Neurodegeneration post trauma: brain  489 Erin D. Bigler 32 Neurodegeneration post trauma: spine  507 Jens A. Petersen and Spyros S. Kollias 33 Neurodegeneration after

trauma: peripheral nerves  520 Xiao-Hui Duan and Jun Shen

SEC TION 10

Neuroimaging after therapy 34 Functional imaging of neurosurgery

in Parkinson’s disease  537

Anna L. Bartels and Rudiger Hilker 35 Neuroimaging after cell-based therapy  545 Alex Tsui and Paola Piccini

Index  553

Abbreviations

AA amyloid angiopathy AAAD aromatic τ-amino acid decarboxylase ACAT acyl-CoA cholesteryl acyl transferase AChE acetylcholinesterase ACR American College of Radiology AD Alzheimer’s disease ADC apparent diffusion coefficient ADEM acute disseminated encephalomyelitis ADNI Alzheimer’s Disease Neuroimaging Initiative AI amyloidosis inflammation AIP automated image processing AL amyloidosis light chain ALS amyotrophic lateral sclerosis AOS apraxia of speech APP amyloid precursor protein ARCA autosomal recessive cerebellar ataxia ARD alcohol-related dementia ARSACS autosomal recessive spastic ataxia of Charlevoix-Saguenay AS alternative splicing ASE asymmetric spin echo ASIA American Spinal Injury Association ASL arterial spin labelling AT ataxia telangiectasia ATTR amyloidosis transthyretin AUC appropriate use criteria Aβ beta-amyloid BBB blood–brain barrier BIH benign intracranial hypertension BMI body mass index BOLD blood oxygenation level dependent BP binding potential BSE bovine spongiform encephalopathy BSI boundary shift integral bvFTD behavioural variant frontotemporal dementia CA contrast agent CAA cerebral amyloid angiopathy CADASIL cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy CBD corticobasal degeneration CBF cerebral blood flow CBS corticobasal syndrome CBV cerebral blood volume

CCA cerebellar cortical atrophy CCSVI chronic cerebrospinal venous insufficiency CDR Clinical Dementia Rating CHMP2B charged multivesicular body protein 2B Cho choline CIDP chronic inflammatory demyelinating polyneuropathy CIS clinically isolated syndrome CISS constructive interference into steady state CJD Creuzfeldt–Jakob disease CMAP compound muscle action potential CMT Charcot–Marie–Tooth disease CMTNS CMT neuropathy score CMV cytomegalovirus CNS central nervous system COI costs of illness COMT catechol-O-methyl transferase Cr creatine CSF cerebrospinal fluid CSPTC cortico-striatal-pallidal-thalamic-cortical CT computed tomography CTA CT angiography CTDI CT dose index CTE chronic traumatic encephalopathy CTS carpal tunnel syndrome CUPS clinically uncertain parkinsonian syndromes DA discriminant analysis DAT dementia of the Alzheimer’s type DAT dopamine transporter DBS deep brain stimulation DCGM diminished cerebral glucose metabolism DDS dopamine dysregulation syndrome DIR double inversion recovery DIS dissemination in space DIT dissemination in time DKI diffusion kurtosis imaging DLB dementia with Lewy bodies DLP dose length product DLPFC dorsolateral prefrontal cortex DMN default mode network DSC dynamic susceptibility contrast DSI diffusion spectrum imaging

xvi

list of abbreviations DTBZ dihydrotetrabenazine DTI diffusion tensor imaging DTT diffusion tensor tractography DWI diffusion-weighted imaging EADC European Alzheimer’s Disease Consortium ECG electrocardiogram EDV effective dopamine distribution volume EEG electroencephalography EFNS European Federation of Neurological Societies EMA European Medicines Agency EMG electromyography EOFAD early-onset familial Alzheimer’s disease EPI echo planar imaging ESR electron spin resonance ESUR European Society of Urogenital Radiology EUROSCA European Integrated Project on Spinocerebellar Ataxias FA fractional anisotropy FAP familial amyloidosis with polyneuropathy FARS Friedreich’s ataxia rating scale FCM friction Cost Method FD fractal dimension FDA Food and Drug Administration FDG 2-[18F]fluoro-2-deoxy-D -glucose FDR false discovery rate FDRI field-dependent relaxation rate increase FDS functional disability scale FID free induction decay FLAIR fluid-attenuated inversion recovery FLD frontal lobe dementia fMRI functional magnetic resonance imaging FMT fluorescence-mediated tomography FOG freezing of gait FoV field of view FRI fluorescence reflectance imaging FSE fast spin-echo FTD frontotemporal dementia FTLD frontotemporal lobar degeneration FUS fused in sarcoma (RNA-binding protein) G6PD glucose-6-phosphate dehydrogenase GABA γ-aminobutyric acid GCA global cortical atrophy GCI glial cytoplasmic inclusions GDNF glial cell line-derived neurotrophic factor GID graft-induced dyskinesias GLM general linear model Glu glutamate GluR glutamate receptor GM grey matter GRE gradient-recalled echo GRN granulin HAART highly active antiretroviral therapy HCA human capital approach HCBS hot cross bun sign HD Huntington’s disease HE hepatic encephalopathy HIE hypoxic ischaemic encephalopathy HLA human leukocyte antigen HMN hereditary motor neuropathies

HMSN hereditary motor and sensory neuropathy HPR hyperintense putaminal rim HRMAS high-resolution magic angle spinning HRSD Hamilton Rating Scale for Depression HS hippocampal sclerosis HSN hereditary sensory neuropathies HSP hereditary spastic paraparesis HSV herpes simplex virus IACRS Inherited Ataxia Clinical Rating Scale ICD impulse control disorders ICT information communication technology INAD infantile neuroaxonal dystrophy IPD idiopathic Parkinson’s disease LB Lewy body LMN lower motor neuron LN Lewy neurite LOC loss of consciousness LOR line of response lvPPA logopenic variant primary progressive aphasia MAPT microtubule associated protein tau MCI mild cognitive impairment MCS metabolic cognitive syndrome MCV motor conduction velocities MD mean diffusivity MEP motor evoked potentials MetS metabolic syndrome MFC magnetic field correlation MFF muscle fat fraction MHC major histocompatibility complex MIP maximum intensity projection MMSE mini-mental state examination MND motor neuron disease MPR multiple planar reformation MPTP 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine MPTP mitochondrial permeability transition pore MRI magnetic resonance imaging MRN magnetic resonance neurography MRPI MR parkinsonism index MRS magnetic resonance spectroscopy MRT magnetic resonance tractography MRV magnetic resonance venography MS mass spectroscopy MS multiple sclerosis MSA multiple system atrophy MT magnetization transfer MTA medial temporal atrophy MTC magnetization transfer contrast MTF modulation transfer function MTI magnetization transfer imaging MTL medial temporal lobe MTR magnetization transfer ratio MTT mean transit time NAA N-acetylaspartate naPPA non-fluent/agrammatic variant primary progressive aphasia NAT natural antisense transcripts NAWM normal-appearing white matter NBIA neurodegeneration with brain iron accumulation NCS nerve conduction studies

list of abbreviations ND neurodegenerative disorder NEI neuro-endo-immune NFT neurofibrillary tangles NGS next-generation sequencing NINDS National Institute of Neurological Disorders and Stroke NIPALS non-linear iterative partial least squares NIRS near-infrared spectroscopy NMO neuromyelitis optica NMR nuclear magnetic resonance NPS neuropsychiatric symptoms NSAID non-steroidal anti-inflammatory drug NSC neural stem cells NT neuropil threads OCT optic coherence tomography PAF pure autonomic failure PBBS peripheral benzodiazepine sites PBMC peripheral blood mononuclear cells PBR peripheral benzodiazepine receptor PCA posterior cortical atrophy PCA principal component analysis PCD programmed cell death PCR polymerase chain reaction PD Parkinson’s disease PDCP PD cognitive pattern PDD Parkinson’s disease dementia PDRP Parkinson’s disease-related pattern PDWI proton density weighted images PEP post-encephalitis parkinsonism PET positron emission tomography PG pathological gambling PHF paired helical filaments PiB Pittsburgh compound B PKAN pantothenate kinase-associated neurodegeneration PLS primary lateral sclerosis PMA progressive muscular atrophy PMC primary motor cortex PML progressive multifocal leukoencephalopathy PNFA progressive non-fluent aphasia PNI peripheral nerve injury PNS peripheral nervous system PPA primary progressive aphasia PPAOS primary progressive apraxia of speech PPMI Parkinson’s Progression Markers Initiative PPMS primary progressive multiple sclerosis PPP purchasing power parities PPV positive predictive value PRESS point resolved spectroscopy sequence PRESTO principles of echo shifting with a train of observations PrP prion protein PSP progressive supranuclear palsy PTA post-traumatic amnesia PTH parathyroid hormone RAC raclopride RBD REM sleep behaviour disorder RCFT Rey complex figure test RD radial diffusivity

REM RI RIS RNFL ROI ROS RPD RRMS SAR SARA SBM SCA SCI sCJD SCP SD SEEP

rapid eye movement reconstruction interval radiologically isolated syndrome retinal nerve fibre layer region of interest reactive oxygen species rapidly progressive dementia relapsing–remitting multiple sclerosis specific absorption rate scale for the assessment and rating of ataxia surface-based morphometry spinocerebellar ataxias spinal cord injury sporadic Creutzfeldt–Jakob disease superior cerebellar peduncle semantic dementia signal enhancement from extracellular water protons SERT serotonin transporter SMA supplementary motor area SN substantia nigra SNP single-nucleotide polymorphism SNR signal-to-noise ratio SOD superoxide dismutase SPAIR spectral adiabatic inversion recovery SPECT single photon emission computed tomography SPIR selective partial inversion recovery SPM statistical parametric mapping SPMS secondary progressive multiple sclerosis SSP stereotactic surface projection STEAM stimulated echo acquisition mode STIR short tau inversion recovery SUV standardized uptake value SUVR standardized uptake value ratios SVD singular value decomposition svPPA semantic variant primary progressive aphasia SWEDD scans without evidence of dopaminergic deficit SWI susceptibility weighted imaging T1WI T1-weighted images T2WI T2-weighted images TB tuberculosis TBI traumatic brain injury TBM tensor-based morphometry TBSS tract-based spatial statistics TDP-43 TAR-DNA binding protein 43 TMS transcranial magnetic stimulation TPN total parenteral nutrition TSE transmissible spongiform encephalopathies TSE turbo spin echo TSPO translocator protein TSS transcription start site UBO unidentified bright objects UHDRS Unified Huntington’s Disease Rating Scale UMN upper motor neuron UPDRS Unified Parkinson’s Disease Rating Scale UPHRS Unified Huntington’s Disease Rating Scale UPSIT University of Pennsylvania Smell Identification Test VaD vascular dementia VBM voxel-based morphometry

xvii

xviii

list of abbreviations vCJD VCP VD VH VMAT VOI VR

variant Creutzfeldt–Jakob disease valosin-containing protein vascular dementia visual hallucinations vesicular monoamine transporter volume of interest volume rendering

VZV WE WHO WKS WM WMH WTP

varicella-zoster virus Wernicke’s encephalopathy World Health Organization Wernicke–Korsakoff syndrome white matter white matter hyperintensities willingness to pay

Contributors

Dag Aarsland

Center for Age-Related Medicine, Department of Psychiatry, Stavanger University Hospital, Stavanger, Norway

Hamed Akhlaghi

Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia

Michele Anzidei

Department of Radiological, Oncological and Anatomopathological sciences, Sapienza—University of Rome, Rome, Italy

Sivakumar Balasubramanian

Bio-Nano Electronics Research Centre, Graduate School of Interdisciplinary New Science, Toyo University, Kawagoe, Saitama, Japan

Lev Bangiyev

Department of Radiology, NYU Langone Medical Center, New York, NY, USA

Luigi Barberini

Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, Italy

Anna L. Bartels

Department of Neurology, University Medical Centre Groningen, Groningen, The Netherlands

José Berciano

Service of Neurology, University Hospital ‘Marqués de Valdecilla’ (IFIMAV), University of Cantabria; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Santander, Spain

Erin D. Bigler

Department of Psychology, Brigham Young University, Provo, and Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA

Fabrizio Boni

Department of Radiological, Oncological and Anatomopathological sciences, Sapienza—University of Rome, Rome, Italy

Jan Booij

Department of Nuclear Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands

Rémi W. Bouchard

Clinique interdisciplinaire de mémoire, Département des sciencesneurologiques, Centre hospitalier universitaire de Québec, Québec, Canada; Faculté de médecine, Université Laval, Québec, Canada

Tirza C. Buter

Department of Nuclear Medicine, Stavanger University Hospital, Stavanger, Norway

Matthan W. A. Caan

Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands

Mauricio Castillo

Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA

Carlo Catalano

Department of Radiological, Oncological and Anatomopathological sciences, Sapienza—University of Rome, Rome, Italy

Pauline Chauvin

Université Paris Descartes—LIRAES, Paris, France

Nadège Costa

Université Toulouse Paul Sabatier, Toulouse, France

Martin B. Delatycki

Department of Clinical Genetics, Austin Health, Heidelberg, Victoria; Bruce Lefroy Centre for Genetic Health Research, Murdoch Childrens Research Institute, Parkville, Victoria, Australia

xx

list of contributors

August Dietrich

Girolamo Garreffa

Bogdan Draganski

Nellie Georgiou-Karistianis

Department of Radiology, NYU Langone Medical Center, New York, NY, USA Département des Neurosciences Cliniques, Laboratoire de Recherche en Neuroimagerie, Centre Hospitalier Universitaire Vaudois, Université de Lausanne, Lausanne, Switzerland

Xiao-Hui Duan

Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China

Juergen Dukart

Département des Neurosciences Cliniques, Laboratoire de Recherche en Neuroimagerie, Centre Hospitalier Universitaire Vaudois, Université de Lausanne, Lausanne, Switzerland

Gary F. Egan

Monash Biomedical Imaging, and School of Psychology and Psychiatry, Monash University, Clayton, Victoria, Australia

Girish M. Fatterpekar

Department of Radiology, NYU Langone Medical Center, New York, NY, USA

IRCCS Fondazione Santa Lucia and Centro Studi e Ricerche ‘Enrico Fermi’, Viminale, Rome, Italy School of Psychology and Psychiatry, Monash University, Clayton, Victoria, Australia

Alexander Gerhard

Institute of Brain, Behaviour and Mental Health, University of Manchester, Manchester; Department of Neurology, Greater Manchester Neurosciences Centre, Salford Royal Hospital, Salford, UK

Brian T. Gold

Department of Anatomy and Neurobiology, Magnetic Resonance Imaging and Spectroscopy Center, Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA

Christopher M. Gomez

Department of Neurology, University of Chicago, Chicago, IL, USA

Harald Hampel

Department of Chemical and Geological Sciences, University of Cagliari, Italy

Université Pierre et Marie Curie, Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Institut du Cerveau et de la Moelle épinière (ICM), Hôpital de la Pitié-Salpêtrière, Paris, France

Roberto Floris

Amogh N. Hegde

Claudia Fattuoni

Department of Diagnostic Imaging—Neuroradiology Section, University Hospital ‘Tor Vergata’, and Department of Biomedicine and Prevention, University of Rome ‘Tor Vergata’, Rome, Italy

Ana M. Franceschi

Department of Radiology, New York University School of Medicine, New York, NY, USA

Department of Radiology, Raffles Hospital, Northbridge Road, Singapore

Claire Henchcliffe

Weill Medical College of Cornell University, New York, NY, USA

Karl Herholz

Department of Electric and Electronic Enginering, University of Cagliari, Italy

Institute of Brain, Behaviour and Mental Health, University of Manchester, Manchester; Department of Neurology, Greater Manchester Neurosciences Centre, Salford Royal Hospital, Salford, UK

Damien Galanaud

Rudiger Hilker

Matteo Fraschini

Department of Neuroradiology, Pitié Salpêtrière Hospital, Paris, France

Elena Gallardo

Service of Neurology, University Hospital ‘Marqués de Valdecilla (IFIMAV)’, University of Cantabria and ‘Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED)’, Santander, Spain

Francesco Garaci

Department of Diagnostic Imaging—Neuroradiology Section, University Hospital ‘Tor Vergata’, and Department of Biomedicine and Prevention, University of Rome ‘Tor Vergata’, Rome, Italy

Centre for Neurology und Neurosurgery, Clinic for Neurology, Frankfurt am Main, Germany

Michael Janitz

School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia

Tali Jonas Kimchi

Department of Diagnostic Imaging, Neuroradiology Division, Tel Aviv Medical Center, Tel Aviv, Israel

list of contributors

Christopher Kobylecki

Institute of Brain, Behaviour and Mental Health, University of Manchester, Manchester; Department of Neurology, Greater Manchester Neurosciences Centre, Salford Royal Hospital, Salford, UK

Spyros S. Kollias

Department of Neuroradiology, University Hospital, Zurich, Zurich, Switzerland

D. Sakthi Kumar

Bruce L. Miller

Memory and Aging Center and Department of Neurology, University of California, San Francisco, California, CA, USA

James D. Mills

School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia

Laurent Molinier

Université Toulouse Paul Sabatier, Toulouse, France

Bio-Nano Electronics Research Centre, Graduate School of Interdisciplinary New Science, Toyo University, Kawagoe, Saitama, Japan

Aart J. Nederveen

Robert Laforce Jr

Erez Nossek

Clinique interdisciplinaire de mémoire, Département des sciences neurologiques, Centre hospitalier universitaire de Québec, Québec, Canada; Faculté de médecine, Université Laval, Québec, Canada

Manja Lehmann

Dementia Research Centre, University College London, London, UK

C. C. Tchoyoson Lim

Department of Neuroradiology, National Neuroscience Institute, Singapore; Department of Neurology, Duke-NUS Graduate Medical School, Singapore.

Simone Lista

Université Pierre et Marie Curie, Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Institut du Cerveau et de la Moelle épinière (ICM), Hôpital de la Pitié-Salpêtrière, Paris, France

Giancarlo Logroscino

Neurodegenerative Diseases Unit, Department of Basic Medical Sciences, Neurosciences and Sense Organs, Department of Clinical and Research Neurology ‘Pia Fondazione Cardinal G. Panico’ Hospital, Tricase (LE); University ‘A . Moro’, Bari, Italy

Joël Macoir

Faculté de médecine, Université Laval, Québec, Canada; Centre de recherche de l’Institut universitaire en santé mentale de Québec, Québec, Canada

Francesco Marrosu

Department of Public Health, Clinical, and Molecular Medicine, University of Cagliari, Italy

Luke A. Massey

Sara Koe PSP Research Centre, UCL Institute of Neurology, London, UK

Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands Department of Neurosurgery, Tel Aviv Medical Center, Tel Aviv, Israel

Sean O’Sullivan

Cork University Hospital, University College Cork, Ireland

Gülin Öz

Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA

Vivekanandan Palaninathan

Bio-Nano Electronics Research Centre, Graduate School of Interdisciplinary New Science, Toyo University, Kawagoe, Saitama, Japan

Dominic Paviour

Atkinson Morley Neurosciences Centre, St Georges Hospital, London, UK

Giuseppe Pelle

Department of Diagnostic Imaging and Interventional Radiology, Santa Maria Goretti Hospital, Latina, Italy

Jens A. Petersen

Department of Neurology, University Hospital Zurich, Zurich, Switzerland

Paola Piccini

Imperial College London, Division of Brain Sciences, Hammersmith Hospital, Du Cane Road, London

Sofia Pina

Neuroradiology Department, Hospital Santo António, Centro Hospitalar do Porto, Portugal

Stéphane Poulin

Clinique interdisciplinaire de mémoire, Département des sciences neurologiques, Centre hospitalier universitaire de Québec, Québec, Canada; Faculté de médecine, Université Laval, Québec, Canada

Thomas Rapp

Université Paris Descartes—LIRAES, Paris, France

xxi

xxii

list of contributors

Eytan Raz

Department of Radiology, NYU Langone Medical Center, New York, NY; Department of Neurology and Psychiatry, University La Sapienza, Rome, Italy

Raymund A. C. Roos

Department of Neurology, Leiden University Medical Centre, Leiden, The Netherlands

Alexandra Roudenko

Department of Radiology, NYU Langone Medical Center, New York, NY, USA

Martin Roy

Axe santé des populations et pratiques optimales en santé, Centre hospitalier universitaire de Québec, Québec, Canada

Luca Saba

Department of Medical Sciences, University of Cagliari, Italy

Keita Sakurai

Department of Diagnostic Radiology, Tokyo Metropolitan Medical Center of Gerontology, Tokyo, Japan

Jun Shen

Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China

Françoise J. Siepel

Center for Age-Related Medicine, Department of Psychiatry, Stavanger University Hospital, Stavanger, Norway

Charles D. Smith

Department of Anatomy and Neurobiology, Magnetic Resonance Imaging and Spectroscopy Center, Sanders-Brown Center on Aging, Department of Neurology, University of Kentucky, Lexington, KY, USA

Marion Smits

Department of Radiology, Erasmus MC—University Medical Center Rotterdam, The Netherlands

Lilja Solnes

Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Weill Cornell Medical College of Cornell University and New York Presbyterian Hospital, New York, NY, USA

Ana Solodkin

Anatomy & Neurobiology and Neurology, UC Irvine School of Medicine, Irvine, CA, USA

Jean-Paul Soucy

McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Québec, Canada; Centre hospitalier universitaire de l’université de Montréal, Montréal, Québec, Canada

Lothar Spies

Jung Diagnostics GmbH, Hamburg, Germany

Rosanna Tortelli

Neurodegenerative Diseases Unit, Department of Basic Medical Sciences, Neurosciences and Sense Organs, University ‘A . Moro’, Bari, Italy

Nicola Toschi

Medical Physics Section, Department of Biomedicine and Prevention, Faculty of Medicine University of Rome ‘Tor Vergata’, Rome, Italy and Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, USA

Thomas F. Tropea

Weill Medical College of Cornell University, New York, NY, USA

Alex Tsui

Imperial College London, Division of Brain Sciences, Hammersmith Hospital, Du Cane Road, London

Martin R. Turner

University of Oxford, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK

Brigitte Vallabhajosula

Consultant, Criminal Justice and Forensic Psychology, Larchmont, NY, USA

Shankar Vallabhajosula

Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Weill Cornell Medical College of Cornell University and New York Presbyterian Hospital, New York, NY, USA

Simon J. A. van den Bogaard

Department of Neurology, Leiden University Medical Centre, Leiden, The Netherlands

Louis Verret

Clinique interdisciplinaire de mémoire, Département des sciences neurologiques, Centre hospitalier universitaire de Québec, Québec, Canada; Faculté de médecine, Université Laval, Québec, Canada

Tommaso Volpi

Department of Diagnostic Imaging—Neuroradiology Section, University Hospital ‘Tor Vergata’, and Department of Biomedicine and Prevention, University of Rome ‘Tor Vergata’, Rome, Italy

Jennifer L. Whitwell

Department of Radiology, Mayo Clinic, Rochester, MN, USA

SECTION 1

Introduction

CHAPTER 1

Epidemiology of neurodegenerative diseases Giancarlo Logroscino and Rosanna Tortelli Introduction Neurodegenerative disorders are the main challenge for medicine and public health for healthy living in future years because of demographic changes worldwide. Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS) share two main characteristics: (1) the incidence and prevalence increase with age and (2) the majority of cases are sporadic even if some of the cases are familial, including monogenetic forms with early age of onset (under age 50). In the last two decades the epidemiology of neurodegenerative diseases has focused on descriptive studies and on a wide range of genetic and environmental risk factors. The overall goal of epidemiology is identification of cases and possibly prevention of disease. This chapter therefore focuses on the definition of disease, frequencies, and modifiable risk factors of neurodegenerative diseases (Table 1.1). Unmodifiable risk factors (mainly genetic) are not reviewed here.

Definition of disease Dementia is a loss of cognitive abilities in multiple domains that results in impairment of the normal activities of daily living and loss of independence. AD, the leading cause of dementia and the most common neurodegenerative disorder worldwide, is characterized by a progressive decline in cognitive function, which typically begins with deterioration in memory. The neuropathological hallmarks of the AD brain are diffuse and neuritic extracellular amyloid plaques, frequently surrounded by dystrophic neurites, and intracellular neurofibrillary tangles. These hallmark pathologies are often accompanied by the presence of reactive microgliosis and the loss of neurons, white matter, and synapses. Frontotemporal lobar degeneration (FTLD) is a progressive neurodegenerative disease that affects frontal and temporal regions [1]‌. It is the second most common form of early-onset dementia. FTLD is used as an umbrella term for three clinical variants that can be distinguished based on the early and predominant symptoms:  behavioural-variant frontotemporal dementia (bvFTD), semantic dementia (SD), and progressive non-fluent aphasia (PNFA). The majority of pathologies associated with FTLD clinical syndromes include either tau-positive (FTLD-TAU) or TAR DNA-binding protein 43 (TDP-43)-positive (FTLD-TDP) inclusion bodies [2,3]. Dementia with Lewy bodies (DLB) is generally considered as the second cause of degenerative dementia after AD [4,5]

in older people after age 75, and clinically combines dementia and parkinsonism in most cases [6]. Pathological hallmark of the disease are intracellular accumulation of the protein α-synuclein in form of Lewy bodies [7]. Vascular dementia (VaD) is characterized by a clinical diagnosis of dementia with impairment of executive functioning (subcortical and/or frontal type) and presence of cerebrovascular disease. Progression of the disease could be fluctuating, gradual progressive, and/or stepwise [8] Recent lines of research have shown that neurodegenerative dementia and VaD have additive effects and probably interact, and a clear distinction between AD and VaD cannot be made in most cases [9]. ALS is the most common type of adult-onset motor neuron disease. It is typically characterized by degeneration of upper (cortical) (UMN) and lower (brainstem and spinal cord) (LMN) motor neurons to various degrees, even though recent evidences have suggested that the process of degeneration extends beyond motor areas and involves other neuronal and non-neuronal cellular types. The disease is mainly sporadic (sALS); almost 10% of cases are familiar (fALS) in clinical series from referral centres. Neuropathology in ALS is heterogeneous, but typically it is characterized by the presence of ubiquitin-positive neuronal inclusions containing TDP-43 [10]. PD is an insidious and slowly progressive neurodegenerative disease, clinically characterized by bradykinesia, resting tremor, rigidity, and postural instability. Symptomatic response to levodopa therapy is used as an additional diagnostic criterion [11]. The pathological hallmark of the disease is progressive loss of dopaminergic neurons in the substantia nigra pars compacta. This is accompanied by microglial activation and intraneural accumulation of Lewy bodies (containing the protein α-synuclein) as the disease spreads from the brainstem to involve a range of other structures, including the cortex. The aetiological mechanisms underlying the neuropathological changes in these major neurodegenerative diseases remain unclear, but it can be surmised that oxidative/nitrative stress, which is cooperatively influenced by environmental factors, genetic predisposition, and senescence, may be a link between these disorders [12]. Based on advances over the past 30 years, it is now generally accepted that nearly all neurodegenerative disease are diseases of protein homeostasis, or ‘proteostasis’, caused by the misregulation of protein maintenance. Proteostasis is maintained by the proteostasis network, which comprises pathways that control protein synthesis, folding, trafficking, aggregation, disaggregation, and degradation. The decreased ability of the proteostasis

4

Section 1  introduction

Table 1.1  Frequencies of neurodegenerative diseases in three large population-based studies

Dementia AD PD ALS

Rotterdam study

Kungsholmen study

EURALS

Prevalence

6.3%

12%

–

Incidence

9.8/1000 per year

57/1000 per year

–

Prevalence

4.5%

6.4%

–

Incidence

7.2/1000 per year

44/1000 per year

–

Prevalence

1.4%

–

–

Incidence

1.7/1000 per year

–

–

Prevalence

–

–

7.9/100 000

Incidence

–

–

2.16/100 000 per year

AD, Alzheimer’s disease; ALS, amyotrophic lateral sclerosis; PD, Parkinson’s disease.

network to cope with inherited misfolding-prone proteins, ageing, and/or metabolic/environmental stress appears to trigger or exacerbate proteostasis diseases [13]. These protein misfolding disorders are characterized by pathological central nervous system protein aggregates, alterations in the solubility and metabolism of corresponding disease proteins, and mutations in genes that encode major disease proteins of familial disorders and their sporadic counterparts, including the genes encoding tau, β-amyloid (Aβ), α-synuclein, SOD1, TDP-43, and others.

Transition phenotypes The phenotypic features of these diseases are highly variable, but correlations between genetic abnormalities, clinical features and underlying neuropathology are recognized. Furthermore a large amount of overlap presentations and transition phenotypes can be often recognized. For example, it is now widely demonstrated that ALS, far from being an only motor neuron disease, is characterized by an extreme clinical heterogeneity with numerous fuzzy transition phenotypes, that spans along three principal axes (Figure 1.1): 1. UMN and LMN involvement: The archetypal phenotypic manifestation of the disease derived from the combined and contemporary presence of UMN (stiffness, hyper-reflexia, Hoffman’s and Babinski’s signs, corticobulbar reflexes) and LMN (muscular weakness and wasting, hyporeflexia, hypotonia, fasciculations, muscular cramps, dysphagia, dysarthria, dyspnea) signs in one or more body regions. Median survival time of this ‘classical ALS’ is 30 months from symptom onset. Besides this phenotypic manifestation that represents approximately 80% of cases, the different degree of involvement of UMN and LMN generates a clinical spectrum that spans from the pure involvement of the UMN (primary lateral sclerosis—PLS) to the pure involvement of LMN (progressive muscular atrophy— PMA). PLS is characterized by the exclusive presence of UMN damage without detectable LMN signs for at least 4 years after onset [14,15]. PMA is clinically characterized solely by signs of LMN dysfunction. Patients with LMN signs who at any time later in follow-up develop UMN signs are then considered to have LMN-onset ALS [16]. Also the phenotype UMN-dominant ALS (UMN-d ALS) has been clinically defined as the presence

of predominantly UMN signs with minor LMN findings at least for 4  years after onset [14]. Epidemiological and pathological studies showed that these nosological entities strictly belong to the ALS phenotype spectrum [17,18] but are distinguished from classical ALS by a more benign course and long survival [14,19–22]. 2. Cognition and behaviour: Clinical, neuroimaging, and pathological data, especially accumulated in the last decade have been suggested that ALS and frontotemporal dementia (FTD) might form part of a disease continuum, with pure ALS at one extreme and pure FTD at the other. Cognitive impairment occurs in up to 50% of cases, and one in seven patients develops frank FTD.[23]. ALS occurs in up to 15% of patients with FTD, with subtle LMN signs in a larger percentage [24]. Comorbid phenotypes (ALS/FTD and FTD/MND) are characterized by reduced survival [25–27]. Genetics and neuropathology have largely confirm the overlap between ALS and FTD. The existence of families with pure ALS, pure FTD, ALS with comorbid FTD (ALS-FTD). and FTD with comorbid motor neuron impairment (FTD/MND) has been long recognized [28]. In 2006 Manuela Neumann demonstrated that the 43-kDa TAR DNA-binding protein (TDP-43) was a common molecular signature of both the majority of ALS patients and tau protein-negative FTD [3]‌. In TDP-43-negative cases she added the fused in sarcoma (FUS) protein as a second, but quantitatively less relevant, molecular marker to her discovery [29]. These breakthroughs led to a reclassification of the diseases on the molecular neuropathology level [30]. Pathogenic mutations in the TARDBP and FUS genes were subsequently described

Continuum I

Pure UMN

II FTD III PD

Classical ALS FTD/MND

Pure LMN ALS/FTD

ALS with extrapyraminal involvement

ALS ALS

Fig. 1.1  Three axes of clinical continuum in amyotrophic lateral sclerosis.

Chapter 1 

in both ALS and FTD cases [31–33]. However, the real turning point was the recent discovery of the genetic abnormality on chromosome 9p21: a GGGGCC repeat expansion within the non-coding region of the C9ORF72 gene [34,35]. This repeat expansion accounts for 25–60% of familial ALS cases, depending on the population studied, making this the most common genetic cause of ALS and FTD, much more common than the mutations in other identified genes causing familial ALS including SOD1, TDP43, FUS, and VCP; less than 10% of sporadic ALS and FTD cases test positive for this mutation [34,35]. Neuropathology in both FTD and ALS cases with C9ORF72 expansions showed TDP-43-positive neuronal and glial inclusions and a higher proportion of nuclear RNA foci in frontal cortex and spinal cord neurons. No unique clinical phenotype was associated with this subtype of ALS or FTD, even though patients with ALS and C9ORF72 repeat expansion seem to present a recognizable phenotype (earlier disease onset, presence of cognitive and behavioural impairment, specific neuroimaging changes, family history of neurodegeneration with autosomal dominant inheritance, and reduced survival) [36,37]. 3. Extrapyramidal involvement: Extrapyramidal involvement has been described in ALS. Desai and Swash reported three patients with backward falls and retropulsion at onset [38]. In an epidemiological study in Japan incidence of extrapyramidal signs in ALS was found in 4.8%, more frequent than expected by chance, suggesting that the degeneration of basal ganglia and/ or substantia nigra may not be so rare in ALS [39]. The presence of extrapyramidal symptoms seems to be more frequent in ALS cases related to mutations of TARDBP [40,41] and if predominant UMN signs are present [42]. Post-mortem analysis of ALS cases shows the diffusion of TDP-43 pathology beyond the motor system to involve the nigrostriatal system, the neocortical and allocortical area, and the cerebellum [10]. Perhaps in the group of TDP-43 proteinopathies besides ALS and FTD an extrapyramidal syndrome may be included. AD is clinically heterogeneous in presentation and progression, demonstrating variable topographic distributions of atrophy and hypometabolism/hypoperfusion [43]. In addition, AD often keeps company with other conditions that may further nuance clinical expression, such as synucleinopathy exacerbating executive and visuospatial dysfunction and vascular pathologies (particularly small-vessel disease that is increasingly ubiquitous with human ageing) accentuating frontal-dysexecutive symptomatology. PD and dementia often overlap, and co-occur in families. A population-based case–control study reported higher frequency of first-degree relatives with PD among AD patients than among controls, corresponding to a relative risk (RR) of 2.9 [44]; the association was stronger among early-onset PD cases [45,46]. A review including 13 studies with a total of 1767 patients found a prevalence of PD with dementia (PDD) of 31.3% [47], indicating that PD patients have a 4- to 6-fold increased risk of developing dementia compared to the age-matched general population [48]. The prevalence of dementia increased as years of observation rose, up to 83% after 20 years [49]. Cognitive symptoms may be present since the earliest stage of the disease [50]. The presence of cognitive decline or dementia reduces survival in patients with PD [51]. The pathological correlate of PDD could be neocortical synucleinopathy and neocortical synucleinopathy with Aβ deposition. Accumulation

epidemiology of neurodegenerative diseases

of Aβ is associated with lower survival rates in PD patients with dementia [52]. On the other hand extrapyramidal signs (EPS) have been described in a consistent proportion of patients with AD. Portet et al., in a prospective studies on a multiethnic cohort, detected EPS in 12.3% of patients with incident AD at first evaluation and 22.6% for the last evaluation (after a median follow-up of 3.6  years) and described greater rates of cognitive decline in patients with EPS [53]. Over the last 10 years, it became evident that focal onset of AD may not be uncommon. A neuropathological study on 120 patients (100 consecutive cases with focal cortical syndromes and 20 with clinically typical AD) showed that AD is a much commoner cause of focal cortical syndromes than previously recognized, particularly in posterior cortical atrophy, PNFA and corticobasal degeneration, but rarely causes SD or FTDbv [54]. Furthermore the authors found that age at both onset and death was greater in the atypical AD cases than those with non-AD pathology, although survival was equivalent, that the focal syndrome may remain pure for many years, and that patients with atypical AD tend to be older than those with non-AD pathology [54].

Frequency (prevalence and incidence) Alzheimer’s disease The number of people affected by AD was 26.6 million worldwide in 2006 [55] and, due to dramatic increase of life expectancy over the past century across the globe, the prevalence is expected to quadruple by 2050, so that 1 in 85 persons will be living with the disease [56], and 43.0% of them are expected to need a high level of care (e.g. a nursing home). In developed countries, approximately 1 in 10 persons over 65 years of age suffers from a form of dementia compared with more than one-third of those older than 85 years [57]. The global prevalence of AD is estimated at 3.9% in people older than 60 years, with regional variations in individual continents [58]. Among regional populations of individuals aged 60 years or more, those from North America and western Europe exhibited the highest prevalence of dementia (6.4% and 5.4%, respectively), followed by those from Latin America (4.9%) and China and its western Pacific neighbours (4.0%) [58]. Almost 70% of these cases were attributed to AD. Two US studies of persons aged 65 or more reported an AD incidence of 15.0 per 1000 person-years. The incidence rates for males and females were 13.0 and 16.9 per 1000 person-years, respectively [59,60]. The prevalence and incidence rates for AD increase exponentially with age [61]. Although a consensus does not yet exist, a growing body of evidence suggests that both the prevalence and the incidence of AD may vary substantially between different ethnoracial groups. The Alzheimer’s Association estimates that the prevalence of AD and other dementias in African Americans above the age of 65 years is about twice the rate among elderly whites, while the prevalence in Hispanics is approximately one and a half times greater than in whites [62].

Amyotrophic lateral sclerosis The point prevalence in the 1990s ranged from 2.7 to 7.4 per 100 000 (average 5.2 per 100 000) in Western countries [63]. The incidence

5

6

Section 1  introduction

rate of ALS varies from approximately 0.3 to 2.5 cases per year per 100 000 persons worldwide [64]. Five per cent or more of all cases run in families (FALS) [65], with a range from 2%–15% in different populations [66], although regional and/or ethnic variations in incidence and penetrance complicate the estimation [67], as does the organization of the studies themselves, being either population- or clinic-based. Typically, in sporadic ALS (SALS) cases, but not always in FALS, males appear to predominate [68], but this may vary among ethnic backgrounds and may be trending toward equality with time [63]. Prospective population-based registries in three European countries (Italy, UK, and Ireland) show a crude annual incidence rate of ALS in the general European population of 2.16 per 100 000 person-years and an age-adjusted incidence of 2.1 per 100  000 [69]. In northern Italy during the 10-year period of observation, the Piemonte and Valle d’Aosta Register for ALS (PARALS) reported a mean annual crude incidence rate of 2.90/100 000 population, without any relevant variation during the 10-year period of the study and with a constantly higher rate among men, and a crude prevalence rate (31 December 2004) of 7.89/100 000 population [70]. A population-based study in Netherlands using capture–recapture methodology reported incidence rate of 2.77 per 100 000 person-years and a prevalence rate (2004–2009) of 10.32 per 100 000 individuals. Incidence and prevalence peaked in the 70–74 year age group followed by a rapid decline in older age. The male:female ratio in the premenopausal age group was 1.91 and in the postmenopausal age group was 1.50 [71]. Low crude and age-adjusted incidence and prevalence rates have been reported for specific ethnic groups, such as American Indians and Alaska natives [72], Africans [73], and Hispanic and ‘mixed’ ethnicity in the USA and Cuba [74,75]. In Cuba lower incidence has been reported in mixed-race populations compared to whites and blacks, suggesting variable genetic and/or environmental susceptibilities in different ethnicities and/or ancestral populations.

Parkinson’s disease PD is the second most common neurodegenerative disorder worldwide, after AD. It affects 1–2% of the population over the age of 60 years, although the disease is seen in younger individuals as well [76]. The prevalence of PD varies among different ethnic and geographic regions around the world. Approximately 1–2% of the population over 65 years suffers from PD. Overall prevalence in door-to-door studies ranged from 167 to 5 703 per 100 000, with those studying an elderly population (>60 or 65 years) reporting the highest statistics [77–82]. Early onset of sporadic PD is rare, with about 4% of patients developing clinical signs of the disease before the age of 50 years [83]. Several studies reported lower prevalence of PD in Africa [84,85], Asia [86–88], and South America [89,90] compared to Europe [91–96]. It is still questioned if this extreme variation in the reported prevalence may be due to differences in methodology, diagnostic criteria, and case-finding strategies. Some population-based studies conducted in China and South America reported prevalence rates similar to European countries [80,81,97]. Part of the variation may be explained by geographical difference in the same countries, with rural and undeveloped areas reporting lower frequencies. The age-specific prevalence of PD has been found to be 5–10-fold lower in mainland China compared with Europe in the past epidemiological

studies [88], with higher frequencies in highly developed regions in China and Singapore [87,88,98] and lower rates in rural areas [99]. The low prevalence in Africa may be due in part to population structure (shorter life expectancy compared to developed countries) [100], with rates in northern Africa similar to those in developed countries [101], and in part to ethnic differences, with lower prevalence rates reported in African Americans as compared with whites [85,102]. Overall, incidence rates for PD in studies that reported results for all age groups ranged between 1.5 and 22 per 100 000 person-years [103]. Studies restricted to older populations (>55 or 65 years) reported overall incidence rates between 410 and 529 per 100 000 person-years [104,105]. A meta-analysis of eight high-quality studies estimated the median standardized incidence rate in developed countries at 14 per 100 000 person-years [106]. There are no cases, or very few, occurring before 40 years. Also, the incidence of PD clearly increases with age steeply after age 60. However, several studies reported that incidence rates dropped in older age groups [86,107–112]. It is still a matter of debate whether this decline is real or due to underdiagnosis. In fact several studies reported increasing incidence rates up to 85 years [83,104,105,109]. Comparison among incidence studies of PD is hampered by differences in methodology and reporting. Although incidence data for PD are limited for populations other than whites, there are indications of ethnic differences. In a male North American population, incidence of PD was higher among African Americans [85]. In a multiethnic population in California, incidence of PD was highest among Hispanics, followed by non-Hispanic whites, Asians and African Americans [83]. The incidence of PD seems to be higher in men than in women. A  meta-analysis based on 17 incidence studies of PD reported a pooled age-adjusted male to female ratio of 1.46 (95% CI 1.24–1.72) with significant heterogeneity between studies [113]. Neuroprotective properties of female steroid hormones, or alternatively differences in exposure to environmental and occupational risk factors or gender-specific genetic influences, have been discussed as possible underlying causes of this gender difference [113,114]. However, gender differences in incidence of PD appear to differ by ethnicity. In Asian populations gender distribution was almost equal (M:F ratio 0.95–1.2) [113,115]. In rural areas a lower incidence of PD has been reported [116].

Modifiable risk and protective factors It is now largely accepted that most cases of neurodegenerative diseases are caused by the interaction between genetic and environmental factors that may have a beneficial or detrimental effect over the whole lifetime (Figure 1.2). The principal modifiable risk and protective factors for neurodegenerative diseases are discussed in the following sections and summarized in Table 1.2.

Cigarette-smoking Smoking is one of the most extensively studied lifestyle exposures in relation to neurodegenerative diseases. Numerous analytical studies have found a significantly increased risk of AD associated with cigarette-smoking, especially in apoE4 allele non-carriers [117,118]. Meta-analyses concluded that current smoking was

Chapter 1 

epidemiology of neurodegenerative diseases

Risk factors

Protective factors

Genetic background

Genetic backround

Environmental exposures

Psycho-social factors

Psycho-social factors

Lifestyle

Lifestyle and nutrition

Nutrition

• Toxicity • Inflammation

• Vascular mechanisms • Oxidative stress • Neurodegeneration

Burden of brain lesions Birth

Childhood–2nd decade

Adult life

Transition

Old age

Fig. 1.2  Multiple interactions in the process of neurodegeneration over life course.

associated with an increased risk of the development of AD, with RR = 1.79 [119,120]. The majority of case–control studies and all the prospective studies reported an inverse relationship of smoking with PD, without gender differences. In the large cohort studies, RRs for PD in current smokers versus never smokers ranged between 0.27 and 0.56; and in past smokers versus never smokers it was between 0.50 and 0.78 [121–123]. The inverse association persisted when possible confounders such as coffee and alcohol consumption were adjusted for. A  significant inverse dose–response relationship was detected in all prospective studies except one [124]. The largest cohort study also showed that longer duration of smoking is needed for a risk reduction [123]. In men, an inverse association was observed for cigar or pipe smoking (OR 0.46) [125]. There is some evidence that smoking may also delay onset of PD. Two retrospective case series studies reported that PD patients who smoked had later onset of disease compared to never smokers [126,127]. Despite the amount of evidences, it is still debated whether the protective effect of smoking on PD could be in part explained by various biases (information bias, selection bias, reverse causation, confounding factors). Nevertheless different forms of information and selection bias as well as reverse causation are all unlikely in a prospective design and confounding by genetic factors has been adequately addressed in family-based designs [128,129]. Furthermore, experimental studies have indicated a link between primarily nicotine and α-synuclein [130]. The relationship seems to be less strong for ALS. A  meta-analysis does not support an overall strong association of smoking with ALS risk but suggests that smoking might be associated with a higher risk of ALS in women [131]. Also in a large population-based study in the UK, smoking was associated with ALS risk and worse survival in women but not in men [132]. A pooled analysis of five prospective cohorts supports the hypothesis that cigarette-smoking increases the risk of ALS and that the risk increases as age at smoking initiation decreases [133]. Another population-based study in Netherlands found that current smoking is associated with an increased risk of ALS, as well as a worse prognosis [134].

Alcohol Middle-aged heavy drinkers, especially apoE4 allele carriers, were found to have a more than threefold higher risk of dementia and AD later in their lives [135]. On the other hand, a recent review of the literature confirmed the protective effect of light to moderate alcohol consumption on incident overall dementia and AD, in particular in the absence of the AD-associated apoE4 allele and where wine is the beverage [136]. In the Nurses Health Study [137], a cohort study of 120 000 nurses in the USA, moderate drinkers (those who consumed A c. 1145delC c. 1477C>T c. 102delC

1.6

Shown above

1.4 Brain volume, L

Intensity gain

10

15

Shown above 0

5

10

15

Disease duration, years

Disease duration, years

Fig. 14.10  Rates of brain atrophy over time in patients with MAPT or GRN mutations. The top panel shows serial MRI from two patients with progressive brain atrophy shown in red. The GRN subject shows asymmetric patterns of atrophy while the MAPT subject shows relatively symmetric patterns of atrophy. The bottom panel shows plots of brain volume over time in MAPT and GRN subjects. The different colours represent different families. Specific mutations are shown with different symbols. Mean rates of whole brain atrophy were 2.4% per year in MAPT and 3.5% per year in GRN. Bottom panel is reproduced with permission from Whitwell et al. Neurology 2011;77:393–8.

in frontal and temporal white matter tracts, including the uncinate fasciculus, superior longitudinal fasciculus, the inferior frontooccipital fasciculus that connects the parietal-occipital cortex to the dorsolateral frontal cortex, and the anterior thalamic radiation. White matter tract abnormalities also appear to exist in the absence of grey matter atrophy [130,139], suggesting that white matter measurements from DTI could be more useful biomarkers for early GRN-related disease. As with MAPT mutations, there is also some evidence for early disruptions in functional connectivity in asymptomatic patients with GRN mutations [130].

Nuclear medicine

Patients with GRN mutations have been reported to show hypoperfusion on SPECT and hypometabolism on FDG-PET in the frontal lobe, anterior cingulate, posterior temporal lobe, and inferior parietal lobe [137,140–142], with greater hypoperfusion seen across these region than patients with sporadic FTD [137]. As with structural findings, perfusion is asymmetric in the majority of patients [137]. Therefore, mutations in GRN modify patterns of both structural and functional abnormalities in patients with FTD.

223

224

SECTION 3  

neurodegeneration: cognition

C9ORF72 hexanucleotide repeat expansions

Summary

MRI

It is clear that genetic factors play an important role in determining the pattern of structural and functional abnormalities present in patients with FTD. The three main genetic abnormalities each have different imaging associations which are largely independent of clinical diagnosis. For example, patients with the same clinical diagnosis of bvFTD can show very different patterns of atrophy in the presence of these genetic abnormalities (Figure 14.9). Imaging has potential to help predict the presence of these genetic abnormalities; a feature that will be particularly important if genetic testing is unavailable. As will become clear in the following section, each genetic abnormality also has specific pathological associations which likely drive these patterns of neurodegeneration.

The C9ORF72 hexanucleotide repeat expansion was only relatively recently identified and the majority of imaging studies as this point have focused on structural imaging. Patients with the C9ORF72 hexaucleotide repeat expansion show atrophy and cortical thickness reduction predominantly targeting the frontal lobes, with involvement of orbitofrontal, medial and dorsolateral regions (Figures 14.8 and 14.9) [13,143,144]. However, atrophy is typically widespread and has also been observed in temporal and parietal lobes [13,143], and in the occipital lobes [13,145], cerebellum [13,143,145], and thalamus [143–145] in some studies. The unusual involvement of the cerebellum in C9ORF72 is consistent across studies and concurs with pathological data that demonstrate TDP-43 inclusions in the cerebellum. Atrophy is typically symmetric in the majority of patients [13,33,143]. In comparison to the other FTD genetic mutations, C9ORF72 patients tend to show greater atrophy in the frontal, parietal, and occipital lobes and cerebellum than patients with MAPT mutations and patients with sporadic bvFTD [13,143]. In contrast, patients with MAPT mutations show greater involvement of the anterior temporal lobes than C9ORF72 patients [13,143]. There is a large degree of overlap observed between C9ORF72 and GRN, although there is a suggestion that the temporal and parietal lobes may be involved to a greater degree in GRN compared to C9ORF72 [13,143], and that rates of whole brain atrophy are greater in GRN patients [143]. Atrophy in the occipital lobes and cerebellum may help differentiate subjects with the C9ORF72 hexanucleotide repeat from those with MAPT or GRN mutations, and from sporadic disease, within subjects that have the same clinical presentation of bvFTD [13]. In fact, 93% of patients with MAPT, GRN, C9ORF72, or sporadic FTD can be correctly classified using imaging features alone. There is also a suggestion that thalamic atrophy is more severe in C9ORF72 patients compared to sporadic bvFTD [144,145], although thalamic atrophy is not generally visible on MRI of individual patients [144] so may have limited clinical utility. As with MAPT and GRN mutations, patterns of atrophy in C9ORF72 can be variable at the individual level.

Nuclear medicine Hypometabolism on FDG-PET and hypoperfusion on SPECT is consistently observed in the frontal lobe, particularly involving the anterior cingulate, in patients with C9ORF72 hexanucleotide repeat expansion [146]. The temporal lobes can be involved, although posterior regions of the brain, including the posterior cingulate, are relatively spared.

Role of imaging in predicting pathology Tau pathology MRI Specific patterns of atrophy have been shown to be associated with each of the different FTLD-tau diseases that can underlie the FTD clinical syndromes. These signatures appear to be useful biomarkers of these underlying pathologies regardless of the clinical syndrome. As discussed in the previous section, mutations in the MAPT gene have been associated with striking anterior temporal atrophy. In contrast, Pick’s disease is associated with striking involvement of the prefrontal cortex, including orbitofrontal, medial, and dorsolateral aspects, and less severe involvement of the anterior temporal lobe [124,147,148]. Frontal atrophy in Pick’s disease is often described as ‘knife-edge’ in appearance due to the striking degree of tissue loss (Figure 14.11), and explains why patients with Pick’s disease usually present with a bvFTD syndrome. Patients with Pick’s disease typically show greater frontal atrophy than subjects with MAPT mutations [124]. The pathologies of PSP and CBD can also underlie the FTD clinical syndromes, although they more commonly present with the extrapyramidal syndromes of PSP syndrome and corticobasal syndrome. Both PSP and CBD target the posterior frontal cortex, with atrophy observed particularly in premotor regions. Patterns of atrophy tend to be more focal and mild in PSP, with CBD showing more widespread and asymmetric frontal involvement and often greater involvement of the striatum and parietal lobes [148– 151]. Rates of whole brain atrophy in patients with CBD are hence greater than in patients with PSP [152]. However, even though

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Fig. 14.11  Volumetric MRI demonstrating knife-edge frontal atrophy in three patients with behavioural variant frontotemporal dementia who had Pick’s disease on autopsy.

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FTLD-TDP type A

FTLD-TDP type B

FTLD-TDP type C

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R

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Fig. 14.12  Group-level maps illustrating patterns of grey matter atrophy in patients with FTLD-TDP pathology. Results are shown on 3D renderings of the brain. Data from Whitwell et al. [158].

atrophy is typically more severe in CBD than in PSP, the degree of frontal atrophy observed in patients with CBD is less severe than that observed in patients with Pick’s disease, and atrophy involves the posterior frontal lobe compared to the prefrontal cortex in Pick’s disease [148,153]. Pick’s disease also shows greater involvement of the temporal lobes than CBD [148,153]. These imaging differences have been observed within patients that have the same clinical syndrome of bvFTD, yet different pathologies, showing that structural patterns of atrophy could be useful clinical markers to help predict underlying tau pathology in these patients. While atrophy of the midbrain and superior cerebellar peduncle are typically thought of as markers of PSP, these features are not usually observed in patients with PSP pathology that present with an FTD clinical syndrome [149,153a]. White matter tract degeneration has been reported as being a feature of pathologically confirmed FTLD-tau, with predominant degeneration of the superior longitudinal fasciculus [154]. However, it is likely that this pattern of white matter tract degeneration is driven by the inclusion of patients with PSP and CBD, which are both associated with white matter volume loss in the posterior frontal lobes and body of the corpus callosum [149]. The degree and pattern of white matter tract degeneration in Pick’s disease has not been investigated.

Nuclear medicine and molecular imaging The clinical utility of FDG-PET or SPECT imaging in predicting tau pathologies in FTD is currently unknown, with little

information available on patterns of hypometabolism and hypoperfusion in autopsy-confirmed patients. However, there are PET ligands that have been proposed to bind to hyperphosphorylated tau and could therefore become valuable biomarkers for tau pathology in FTD. One such ligand, [18F]FDDNP, which appears to bind to both Aβ and hyperphosphorylated tau, has shown elevated uptake in striatum, thalamus, subthalamic nucleus, and midbrain in patients with PSP [155]. The PET ligands 18F-THK523 [156]and 18F-T807 [156a] may also show promise. More extensive studies assessing patients with different tauopathies, as well as pathological correlates, is needed before these ligands can be confirmed as useful biomarkers, and before they potentially become clinically available.

TDP pathology MRI Patterns of atrophy observed in patients with FTLD-TDP are heterogeneous, with patterns differing across the specific FTLD-TDP types (Figure 14.12). Patients with FTLD-TDP type A  pathology show widespread and asymmetric patterns of loss involving medial and lateral frontal, posterior temporal and parietal lobes, cingulate, and striatum (Figure 14.12) [151,157,158]. The widespread and asymmetric nature of these findings may explain why varied clinical diagnoses can result from this pathology. Mutations in GRN have been associated with FTLD-TDP type A, which explains why these mutations are associated with such

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widespread asymmetric patterns of atrophy. Asymmetry appears to be a feature of FTLD-TDP type A, either when a GRN mutation is present or absent, suggesting it is a useful biomarker of FTLD-TDP type A pathology [151,158]. Asymmetry may however be somewhat greater in those with a GRN mutation [151]. However, patients with GRN mutations tend to show greater atrophy in the lateral temporal lobe than patients with FTLD-TDP type A without GRN mutations [158]. Within patients with bvFTD, those with FTLD-TDP type A pathology tend to show greater frontal, temporal, and parietal atrophy than CBD, and greater parietal atrophy than Pick’s disease [153], again supporting a potential role for MRI in predicting underlying pathology in bvFTD. Patients with FTLD-TDP type B show predominant and relatively symmetric patterns of frontal and anterior temporal atrophy (Figure 14.12) [151,157,158], consistent with the fact that these patients have clinical diagnoses of bvFTD and FTD-MND [45]. The C9ORF72 hexanucleotide repeat expansion is associated with TDP type B, as well as type A [159], which likely contributes to the fact that C9ORF72 patients show widespread patterns of atrophy with a frontal predominance. Patients with FTLD-TDP type C show asymmetric anterior temporal lobe patterns of atrophy, involving both medial and lateral temporal regions (Figure 14.12), with milder involvement of the frontal lobe, likely driving the fact that these patents present with a clinical diagnosis of SD [151,157,158]. Patterns of atrophy in FTLD-TDP type C can be either left or right-sided [93]. Right-sided patterns of atrophy with additional involvement of the motor cortex have been particularly associated with the presence of corticospinal tract degeneration on pathology in patients with FTLD-TDP type C [160]. Given the relatively close clinicopathological associations in FTLD-TDP, clinical diagnosis can be helpful in predicting specific FTLD-TDP pathologies. For example, a patient with SD is highly likely to have FTLD-TDP type C pathology. However, there is still heterogeneity underlying other clinical diagnoses, such as bvFTD, and so imaging measures are still important. Imaging features that could help clinicians differentiate across these FTLD-TDP types include the degree of asymmetry, which is typically greater in types A and C than in type B [158]. Assessing patterns of lobar atrophy would also be helpful. Atrophy of the anterior temporal lobes is greater in patients with FTLD-TDP type C compared to both of the other FTLD-TDP types, while atrophy in the frontal lobes is greater in FTLD-TDP types A and B compared to type C. Patients with FTLD-TDP type A show greater parietal atrophy than the other two types.

FUS pathology MRI The pathologies characterized by the presence of FUS are much rarer than the tau or TDP pathologies. Nevertheless, imaging has been reported in these patients and a relatively consistent pattern of atrophy has been observed across patients. Patients with FUS pathology show temporoparietal patterns of atrophy, but also show a striking involvement of the caudate nucleus (Figure 14.13) [147,151,161]. The caudate nucleus is actually involved to a greater degree in FUS pathologies than in patients with FTLD-tau or FTLD-TDP [161], and therefore could be a useful biomarker of FUS pathology.

Alzheimer’s disease pathology MRI AD pathology has been observed in some patients diagnosed clinically with FTD syndromes, although it is relatively rare. There is evidence that an imaging signature of AD pathology can be detected on MRI in patients with FTD. The presence of atrophy in the temporoparietal cortices has been observed in patients with bvFTD or agPPA and AD pathology [162,163]. The temporoparietal cortex is affected to a greater degree in these patients compared to those with FTLD pathology, suggesting that it could be a useful biomarker of the presence of AD.

Nuclear medicine and molecular imaging Amyloid-binding ligands, such as Pittsburgh compound B (PiB), that can be detected using PET scanning have now been developed and provide an invaluable biomarker to infer the presence of the Aβ protein, and hence AD (Figure 14.14). Positive PiB-PET scans, demonstrating the presence of Aβ, have been observed in approximately 17% of patients with SD, bvFTD, and agPPA [108,164–167], and 8% of patients with PPAOS [6]‌. In some of the cases with positive PiB-PET scans, patterns of hypometabolism on FDG-PET were suggestive of AD, showing temporoparietal hypometabolism, and therefore there is a suggestion that FDG-PET could also be useful in predicting the presence of AD pathology in FTD [166]. It is important to point out that a positive PiB-PET scan does not rule out the possibility that patients may have mixed pathology, with AD as well as one of the FTLD pathologies.

Summary Structural and molecular imaging techniques hold a lot of promise as biomarkers to help predict underlying pathology in FTD. Specific signature patterns have been observed for each of the different FTLD-tau and FTLD-TDP pathologies, and for FTLD-FUS and AD. Variability across FTLD-tau and FTLD-TDP pathologies means there is not one signature of each of these proteins, but signatures of each specific disease characterized by these proteins. Importantly, these signature patterns of atrophy have been shown to be relatively independent of clinical syndrome, meaning that prediction of pathology should be possible within each of the FTD clinical syndromes. The different patterns of abnormality observed across the different pathologies likely explains some of the heterogeneity that can be observed across patients with the same clinical syndrome, particularly, for example, bvFTD. Similarly, heterogeneity within pathologies may explain the varying clinical diagnoses that can be associated with each pathology. Nevertheless, pathology appears to be an important determinant of neurodegeneration in FTD, and neuroimaging therefore has potential to help predict underlying pathology during life in FTD [168].

Future applications Future work on imaging in FTD will need to focus on developing specific imaging biomarkers or metrics that can be applied in a clinical setting in order to improve diagnosis and patient treatment. The ultimate goal will be to predict underlying pathology. Automated methods that integrate information from multiple modalities and assess multiple regions across the brain may prove to be the most fruitful, although visual assessment protocols or

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FTLD-FUS (2 years from onset)

FTLD-FLUS (3 years from onset)

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FTLD-TDP (6 years from onset)

Fig. 14.13  Volumetric MRI illustrating caudate atrophy in three patients with FTLD-FUS. These patients are compared to two patients with FTLD-TDP. Figure is reproduced with permission from Josephs et al. Eur J Neurol 2010;17:969–75.

simple measurements would be more universally applicable. Molecular imaging may prove to be the answer, with ligands that can bind to the major proteins underlying FTD, such as tau, becoming a reality. As treatments that target tau, and likely eventually TDP-43 and FUS, become available, imaging could be crucial in order to select appropriate patients to enter into clinical trials and as outcome measures to detect treatment effects.

Conclusions Imaging research over the past two decades has been crucial to the understanding of FTD. While the clinical syndromes of FTD have PiB positive

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PiB negative

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0 Bq/ml

Fig. 14.14  Pittsburgh compound B (PiB) PET scans from two patients with agrammatic PPA. The patient on the left shows elevated amyloid deposition in frontal and medial parietal lobes (i.e. PiB positive). The patient on the right does not show any elevated cortical amyloid deposition (i.e. PiB negative).

distinctive regional changes on imaging, it is now clear that these patterns are heterogeneous and modified, or even driven by, underlying genetic and pathological features. Understanding the complex relationships between imaging and the clinical syndrome, genetic abnormalities and pathology is necessary in order to accurately predict the underlying disease process and provide a useful clinical diagnosis for the patient. Imaging has great potential to provide such prediction and will therefore be an increasingly vital clinical tool.

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76. Jeong Y, Cho SS, Park JM, et al. 18F-FDG PET findings in frontotemporal dementia: an SPM analysis of 29 patients. J Nucl Med 2005;46:233–9. 77. Ishii K, Sakamoto S, Sasaki M, et al. Cerebral glucose metabolism in patients with frontotemporal dementia. J Nucl Med 1998;39:1875–8. 78. Raczka KA, Becker G, Seese A, et al. Executive and behavioural deficits share common neural substrates in frontotemporal lobar degeneration—a pilot FDG-PET study. Psychiatry Res 2010;182:274–80. 79. Salmon E, Kerrouche N, Herholz K, et  al. Decomposition of metabolic brain clusters in the frontal variant of frontotemporal dementia. NeuroImage 2006;30:871–8. 80. Foster NL, Heidebrink JL, Clark CM, et al. FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer’s disease. Brain 2007;130:2616–35. 81. Diehl-Schmid J, Grimmer T, Drzezga A, et  al. Decline of cerebral glucose metabolism in frontotemporal dementia:  a longitudinal 18F-FDG-PET-study. Neurobiol Aging 2007;28:42–50. 82. Renard D, Collombier L, Castelnovo G, et al. Brain FDG-PET changes in ALS and ALS-FTD. Acta Neurol Belg 2011;111:306–9. 83. Kipps CM, Hodges JR, Fryer TD, Nestor PJ. Combined magnetic resonance imaging and positron emission tomography brain imaging in behavioural variant frontotemporal degeneration: refining the clinical phenotype. Brain 2009;132:2566–78. 84. Poljansky S, Ibach B, Hirschberger B, et al. A visual [18F]FDG-PET rating scale for the differential diagnosis of frontotemporal lobar degeneration. Eur Archives Psychiatry Clin Neurosci 2011;261:433–46. 85. Womack KB, Diaz-Arrastia R, Aizenstein HJ, et al. Temporoparietal hypometabolism in frontotemporal lobar degeneration and associated imaging diagnostic errors. Arch Neurol 2011;68:329–37. 86. Dukart J, Mueller K, Horstmann A, et  al. Combined evaluation of FDG-PET and MRI improves detection and differentiation of dementia. PloS One 2011;6:e18111. 87. Chan D, Fox NC, Scahill RI, et al. Patterns of temporal lobe atrophy in semantic dementia and Alzheimer’s disease. Ann Neurol 2001;49:433–42. 88. Galton CJ, Patterson K, Graham K, et al. Differing patterns of temporal atrophy in Alzheimer’s disease and semantic dementia. Neurology 2001;57:216–25. 89. Mummery CJ, Patterson K, Price CJ, et al. A voxel-based morphometry study of semantic dementia: relationship between temporal lobe atrophy and semantic memory. Ann Neurol 2000;47:36–45. 90. Chan D, Anderson V, Pijnenburg Y, et al. The clinical profile of right temporal lobe atrophy. Brain 2009;132:1287–98. 91. Edwards-Lee T, Miller BL, Benson DF, et al. The temporal variant of frontotemporal dementia. Brain 1997;120(Pt 6):1027–40. 92. Thompson SA, Patterson K, Hodges JR. Left/right asymmetry of atrophy in semantic dementia:  behavioural-cognitive implications. Neurology 2003;61:1196–203. 93. Josephs KA, Whitwell JL, Knopman DS, et  al. Two distinct subtypes of right temporal variant frontotemporal dementia. Neurology 2009;73:1443–50. 94. Josephs KA, Whitwell JL, Vemuri P, et al. The anatomic correlate of prosopagnosia in semantic dementia. Neurology 2008;71:1628–33. 95. Omar R, Rohrer JD, Hailstone JC, Warren JD. Structural neuroanatomy of face processing in frontotemporal lobar degeneration. J Neurol Neurosurg Psychiatry 2011;82:1341–3. 96. Hsieh S, Hornberger M, Piguet O, Hodges JR. Neural basis of music knowledge: evidence from the dementias. Brain 2011;134:2523–34. 97. Johnson JK, Chang CC, Brambati SM, et al. Music recognition in frontotemporal lobar degeneration and Alzheimer disease. Cogn Behav Neurol 2011;24:74–84. 98. Brambati SM, Rankin KP, Narvid J, et  al. Atrophy progression in semantic dementia with asymmetric temporal involvement:  a tensor-based morphometry study. Neurobiol Aging 2009;30:103–11. 99. Rohrer JD, McNaught E, Foster J, et al. Tracking progression in frontotemporal lobar degeneration:  serial MRI in semantic dementia. Neurology 2008;71:1445–51.

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100. Whitwell JL, Anderson VM, Scahill RI, Rossor MN, Fox NC. Longitudinal patterns of regional change on volumetric MRI in frontotemporal lobar degeneration. Dementia Geriatric Cogn Disord 2004;17:307–10. 101. Acosta-Cabronero J, Patterson K, Fryer TD, et al. Atrophy, hypometabolism and white matter abnormalities in semantic dementia tell a coherent story. Brain 2012;134:2025–35. 102. Agosta F, Henry RG, Migliaccio R, et  al. Language networks in semantic dementia. Brain 2010;133:286–99. 103. Mahoney CJ, Malone IB, Ridgway GR, et al. White matter tract signatures of the progressive aphasias. Neurobiol Aging 2013;34:1687–99. 104. Schwindt GC, Graham NL, Rochon E, et al. Whole-brain white matter disruption in semantic and nonfluent variants of primary progressive aphasia. Hum Brain Mapp 2013;34:973–84. 104a. Guo CC, Gorno-Tempini ML, Gesierich B et al. Anterior temporal lobe degeneration produces widespread network-driven dysfunction. Brain 2013;136:2979–91. 105. Coulthard E, Firbank M, English P, et al. Proton magnetic resonance spectroscopy in frontotemporal dementia. J Neurol 2006;253:861–8. 106. Garrard P, Schott JM, MacManus DG, Hodges JR, Fox NC, Waldman AD. Posterior cingulate neurometabolite profiles and clinical phenotype in frontotemporal dementia. Cogn Behav Neurol 2006;19:185–9. 107. Desgranges B, Matuszewski V, Piolino P, et al. Anatomical and functional alterations in semantic dementia: a voxel-based MRI and PET study. Neurobiol Aging 2007;28:1904–13. 108. 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. 109. Josephs KA, Duffy JR, Fossett TR, et al. Fluorodeoxyglucose F18 positron emission tomography in progressive apraxia of speech and primary progressive aphasia variants. Arch Neurol 2010;67:596–605. 110. Talbot PR, Snowden JS, Lloyd JJ, Neary D, Testa HJ. The contribution of single photon emission tomography to the clinical differentiation of degenerative cortical brain disorders. J Neurol 1995;242:579–86. 111. Gorno-Tempini ML, Dronkers NF, Rankin KP, et al. Cognition and anatomy in three variants of primary progressive aphasia. Ann Neurol 2004;55:335–46. 112. Josephs KA, Duffy JR, Strand EA, et  al. Clinicopathological and imaging correlates of progressive aphasia and apraxia of speech. Brain 2006;129:1385–98. 113. Grossman M, McMillan C, Moore P, et  al. What’s in a name: voxel-based morphometric analyses of MRI and naming difficulty in Alzheimer’s disease, frontotemporal dementia and corticobasal degeneration. Brain 2004;127:628–49. 114. Rohrer JD, Warren JD, Modat M, et  al. Patterns of cortical thinning in the language variants of frontotemporal lobar degeneration. Neurology 2009;72:1562–9. 115. Rohrer JD, Clarkson MJ, Kittus R, et  al. Rates of hemispheric and lobar atrophy in the language variants of frontotemporal lobar degeneration. J Alzheimer’s Dis 2012;30:407–11. 116. Galantucci S, Tartaglia MC, Wilson SM, et  al. White matter damage in primary progressive aphasias: a diffusion tensor tractography study. Brain 2011;134:3011–29. 117. Grossman M, Powers J, Ash S, et al. Disruption of large-scale neural networks in non-fluent/agrammatic variant primary progressive aphasia associated with frontotemporal degeneration pathology. Brain Lang 2013;127:106–20. 117a. Josephs KA, Duffy JR, Strand EA, et  al. Syndromes dominated by apraxia of speech show distinct characteristics from agrammatic PPA. Neurology 2013;81:337–45. 118. Whitwell JL, Duffy JR, Strand EA, et al. Distinct regional anatomic and functional correlates of neurodegenerative apraxia of speech and aphasia: An MRI and FDG-PET study. Brain Lang 2013;125:245–52. 119. Whitwell JL, Duffy JR, Strand EA, et al. Neuroimaging comparison of primary progressive apraxia of speech and progressive supranuclear palsy. Eur J Neurol 2013;20:629–37.

120. Perneczky R, Diehl-Schmid J, Pohl C, Drzezga A, Kurz A. Non-fluent progressive aphasia: cerebral metabolic patterns and brain reserve. Brain Res 2007;1133:178–85. 121. Nestor PJ, Graham NL, Fryer TD, et al. Progressive non-fluent aphasia is associated with hypometabolism centred on the left anterior insula. Brain 2003;126:2406–18. 122. Abe K, Ukita H, Yanagihara T. Imaging in primary progressive aphasia. Neuroradiology 1997;39:556–9. 123. Spina S, Farlow MR, Unverzagt FW, et al. The tauopathy associated with mutation +3 in intron 10 of Tau: characterization of the MSTD family. Brain 2008;131:72–89. 124. Whitwell JL, Josephs KA, Rossor MN, et  al. Magnetic resonance imaging signatures of tissue pathology in frontotemporal dementia. Arch Neurol 2005;62:1402–8. 125. Rohrer JD, Ridgway GR, Modat M, et al. Distinct profiles of brain atrophy in frontotemporal lobar degeneration caused by progranulin and tau mutations. NeuroImage 2010;53:1070–6. 126. Spina S, Murrell JR, Huey ED, et al. Corticobasal syndrome associated with the A9D Progranulin mutation. J Neuropathol Exp Neurol 2007;66:892–900. 127. Slowinski JL, Schweitzer KJ, Imamura A, et  al. Brainstem atrophy on routine MR study in pallidopontonigral degeneration. J Neurol 2009;256:827–9. 128. van Swieten JC, Stevens M, Rosso SM, et al. Phenotypic variation in hereditary frontotemporal dementia with tau mutations. Ann Neurol 1999;46:617–26. 129. Whitwell JL, Jack CR, Jr, Boeve BF, et  al. Atrophy patterns in IVS10+16, IVS10+3, N279K, S305N, P301L, and V337M MAPT mutations. Neurology 2009;73:1058–65. 130. Dopper EG, Rombouts SA, Jiskoot LC, et  al. Structural and functional brain connectivity in presymptomatic familial frontotemporal dementia. Neurology 2013;80:814–23. 131. Miyoshi M, Shinotoh H, Wszolek ZK, et al. In vivo detection of neuropathologic changes in presymptomatic MAPT mutation carriers: a PET and MRI study. Parkinsonism Relat Disord 2010;16:404–8. 132. Kantarci K, Boeve BF, Wszolek ZK, et  al. MRS in presymptomatic MAPT mutation carriers:  a potential biomarker for tau-mediated pathology. Neurology 2010;75:771–8. 133. Arvanitakis Z, Witte RJ, Dickson DW, et al. Clinical-pathologic study of biomarkers in FTDP-17 (PPND family with N279K tau mutation). Parkinsonism Relat Disord 2007;13:230–9. 134. Pal PK, Wszolek ZK, Kishore A, et al. Positron emission tomography in pallido-ponto-nigral degeneration (PPND) family (frontotemporal dementia with parkinsonism linked to chromosome 17 and point mutation in tau gene). Parkinsonism Relat Disord 2001;7:81–8. 135. Kelley BJ, Haidar W, Boeve BF, et al. Prominent phenotypic variability associated with mutations in Progranulin. Neurobiol Aging 2009;30:739–51. 136. Whitwell JL, Jack CR, Jr, Boeve BF, et al. Voxel-based morphometry patterns of atrophy in FTLD with mutations in MAPT or PGRN. Neurology 2009;72:813. 137. Le Ber I, Camuzat A, Hannequin D, et al. Phenotype variability in progranulin mutation carriers: a clinical, neuropsychological, imaging and genetic study. Brain 2008;131:732–46. 138. Whitwell JL, Weigand SD, Gunter JL, et al. Trajectories of brain and hippocampal atrophy in FTD with mutations in MAPT or GRN. Neurology 2011;77:393–8. 139. Borroni B, Alberici A, Premi E, et al. Brain magnetic resonance imaging structural changes in a pedigree of asymptomatic progranulin mutation carriers. Rejuvenation Res 2008;11:585–95. 140. Spina S, Murrell JR, Huey ED, et  al. Clinicopathologic features of frontotemporal dementia with progranulin sequence variation. Neurology 2007;68:820–7. 141. Cruchaga C, Fernandez-Seara MA, Seijo-Martinez M, et al. Cortical atrophy and language network reorganization associated with a novel progranulin mutation. Cereb Cortex 2009;19:1751–60.

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142. Huey ED, Grafman J, Wassermann EM, et al. Characteristics of frontotemporal dementia patients with a Progranulin mutation. Ann Neurol 2006;60:374–80. 143. Mahoney CJ, Beck J, Rohrer JD, et al. Frontotemporal dementia with the C9ORF72 hexanucleotide repeat expansion: clinical, neuroanatomical and neuropathological features. Brain 2012;135:736–50. 144. Sha SJ, Takada LT, Rankin KP, et al. Frontotemporal dementia due to C9ORF72 mutations:  clinical and imaging features. Neurology 2012;79:1002–11. 145. Irwin DJ, McMillan CT, Brettschneider J, et al. Cognitive decline and reduced survival in C9orf72 expansion frontotemporal degeneration and amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry 2013;84:163–9. 146. Boeve BF, Boylan KB, Graff-Radford NR, et al. Characterization of frontotemporal dementia and/or amyotrophic lateral sclerosis associated with the GGGGCC repeat expansion in C9ORF72. Brain 2012;135:765–83. 147. Seelaar H, Klijnsma KY, de Koning I, et al. Frequency of ubiquitin and FUS-positive, TDP-43-negative frontotemporal lobar degeneration. J Neurol 2010;257:747–53. 148. Rankin KP, Mayo MC, Seeley WW, et al. Behavioural variant frontotemporal dementia with corticobasal degeneration pathology: phenotypic comparison to bvFTD with Pick’s disease. J Mol Neurosci 2011;45:594–608. 149. Josephs KA, Whitwell JL, Dickson DW, et al. Voxel-based morphometry in autopsy proven PSP and CBD. Neurobiol Aging 2008;29:280–9. 150. Whitwell JL, Jack CR, Jr, Boeve BF, et al. Imaging correlates of pathology in corticobasal syndrome. Neurology 2010;75:1879–87. 151. Rohrer JD, Lashley T, Schott JM, et al. Clinical and neuroanatomical signatures of tissue pathology in frontotemporal lobar degeneration. Brain 2011;134:2565–81. 152. Whitwell JL, Jack CR, Jr, Parisi JE, et al. Rates of cerebral atrophy differ in different degenerative pathologies. Brain 2007;130:1148–58. 153. Whitwell JL, Jack CR, Jr, Parisi JE, et al. Imaging signatures of molecular pathology in behavioural variant frontotemporal dementia. J Mol Neurosci 2011;45:372–8. 153a. Whitwell JL, Jack CR, Parisi JE et al. Midbrain atrophy is not a biomarker of progressive supranuclear palsy pathology. Eur J Neurol 2013;20:1417–22. 154. McMillan CT, Irwin DJ, Avants BB, et al. White matter imaging helps dissociate tau from TDP-43 in frontotemporal lobar degeneration. J Neurol Neurosurg Psychiatry 2013;84:949–55. 155. Kepe V, Bordelon Y, Boxer A, et  al. PET imaging of neuropathology in tauopathies: progressive supranuclear palsy. J Alzheimer’s Dis 2013;36:145–53.

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156. Fodero-Tavoletti MT, Okamura N, Furumoto S, et al. 18F-THK523: a novel in vivo tau imaging ligand for Alzheimer’s disease. Brain 2011;134:1089–100. 156a. 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;9:666–76. 157. Rohrer JD, Geser F, Zhou J, et al. TDP-43 subtypes are associated with distinct atrophy patterns in frontotemporal dementia. Neurology 2010;75:2204–11. 158. Whitwell JL, Jack CR, Jr, Parisi JE, et al. Does TDP-43 type confer a distinct pattern of atrophy in frontotemporal lobar degeneration? Neurology 2010;75:2212–20. 159. Murray ME, DeJesus-Hernandez M, Rutherford NJ, et  al. Clinical and neuropathologic heterogeneity of c9FTD/ALS associated with hexanucleotide repeat expansion in C9ORF72. Acta Neuropathol 2011;122:673–90. 160. Josephs KA, Whitwell JL, Murray ME, et  al. Corticospinal tract degeneration associated with TDP-43 type C pathology and semantic dementia. Brain 2013;136:455–70. 161. Josephs KA, Whitwell JL, Parisi JE, et  al. Caudate atrophy on MRI is a characteristic feature of FTLD-FUS. Eur J Neurol 2010;17:969–75. 162. Hu WT, McMillan C, Libon D, et  al. Multimodal predictors for Alzheimer disease in nonf luent primary progressive aphasia. Neurology 2010;75:595–602. 163. Whitwell JL, Jack CR, Jr, Przybelski SA, et al. Temporoparietal atrophy:  A  marker of AD pathology independent of clinical diagnosis. Neurobiol Aging 2011;32:1531–41. 164. Drzezga A, Grimmer T, Henriksen G, et  al. Imaging of amyloid plaques and cerebral glucose metabolism in semantic dementia and Alzheimer’s disease. NeuroImage 2008;39:619–33. 165. Engler H, Santillo AF, Wang SX, et  al. In vivo amyloid imaging with PET in frontotemporal dementia. Eur J Nucl Med Mol Imaging 2008;35:100–6. 166. Rabinovici GD, Furst AJ, O’Neil JP, et al. 11C-PIB PET imaging in Alzheimer disease and frontotemporal lobar degeneration. Neurology 2007;68:1205–12. 167. Rowe CC, Ng S, Ackermann U, et al. Imaging beta-amyloid burden in aging and dementia. Neurology 2007;68:1718–25. 168. Whitwell JL, Josephs KA. Neuroimaging in frontotemporal lobar degeneration—predicting molecular pathology. Nat Rev 2011;8:131–42.

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Dementia with Lewy bodies Claire Henchcliffe and Thomas F. Tropea Introduction Dementia with Lewy bodies (DLB) is a highly disabling progressive neurodegenerative disease that affects many ageing people throughout the world. Its core clinical features are dementia and parkinsonism, and its onset is insidious. The clinical presentation of individuals with this disorder therefore overlaps with other neurodegenerative diseases, such as Alzheimer’s disease (AD), or Parkinson’s disease (PD), and for this reason DLB is often misdiagnosed, resulting in inappropriate management and treatment. DLB remains a diagnosis based on clinical signs and symptoms. However, advances in neuroimaging have allowed researchers to define radiographically specific markers suggestive of DLB with the goal of improving diagnostic accuracy. In order to understand the importance of early and accurate diagnosis, this chapter first reviews DLB as a clinical entity focusing on epidemiology and clinical diagnosis. Thereafter the pathology of DLB is reviewed to understand how it has informed recent advances in imaging techniques. The remainder of the chapter is dedicated to reviewing the major imaging findings in the evaluation of DLB and related disorders, and ends with a consideration of the utility of imaging in the clinical diagnosis of DLB.

Epidemiology Dementia is a widespread concern and, since it is age-related, its impact will only continue to grow as the oldest members of our society continue to grow older. A meta-analysis of European studies published during the 1990s found that dementia affects up to 6.4% of all adults over 65  years of age [1]‌. Moreover, mild cognitive impairment (MCI), which may precede dementia, is also common. Incidence of MCI ranges from 1% to 6%, and prevalence from 3 to 22% [2–5; reviewed in 6]. The conversion rate from MCI to dementia has been estimated at about 6–16% per year ([7,8], reviewed in [6]). DLB is a substantial contributor to dementia as a whole. It is diagnosed in up to 5% of the general population and in up to 30.3% of adults with a diagnosis of dementia [9–14; reviewed in 15]. A new diagnosis of DLB is made in 0.1% of the general population yearly, while new diagnoses of DLB comprise about 3.2% of all new dementia cases per year [16].

Clinical features and diagnosis DLB shares many features with other types of dementia and, as such, is characterized by a progressive decline in cognition involving multiple domains that impacts upon daily function. There are some general principles that help distinguish DLB from other progressive dementias. Parkinsonism is a key feature of DLB, with bradykinesia, rigidity, gait disturbance, and sometimes tremor.

Presence of visual hallucinations and fluctuations in attention and cognitive function are also typical in DLB [17,18]. Formal neuropsychological tests may be helpful in distinguishing DLB from other common dementias such as AD, which has more prominent memory and spatiotemporal deficits, and frontotemporal dementia (FTD), which is associated with personality and behavioural abnormalities and prominent language impairment. Standard MRI may also help evaluate for evidence of vascular disease seen in multi-infarct dementia [reviewed in 19]. Nonetheless, DLB overlaps clinically with other dementing disorders including AD, FTD, and multi-infarct dementia, sometimes referred to as vascular dementia. The motor symptoms of DLB are also shared by PD, Parkinson’s disease dementia (PDD), and Parkinson’s plus syndromes such as progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and multiple system atrophy (MSA), among others. The overlap of cognitive, psychiatric, behavioural, and motor symptoms with other neurodegenerative disorders, coupled with the insidious onset of all of these, therefore renders diagnosis challenging, particularly early in the disease course. To add to diagnostic confusion, there is also debate as to whether PD and PDD versus DLB are distinct entities or parts of a spectrum. Although PD is a progressive neurological disorder characterized by motor symptoms of bradykinesia, resting tremor, and rigidity, subtle cognitive dysfunction may occur early, and PDD develops in up to 80% of PD patients if monitored for 20 years after symptom onset [20]. Criteria for differentiating PD and PDD versus DLB have been proposed [18,21,22]. According to these criteria, DLB is diagnosed when parkinsonism is seen either before, concurrent with or up to 1 year after symptoms of dementia with prominent alterations in attention, alertness, executive function, visuospatial ability, and visual hallucinations [23–25, reviewed in 26]. Throughout the past couple of decades the defining characteristics of these clinical entities have been debated, such that their exclusivity as independent entities continues to be questioned. It may be that DLB and PDD fundamentally represent the same disorder and are distinct only in site of initial onset and/or pattern of spread of pathology. However, since the vast majority of research studies have adopted the referenced criteria above, we use a similar definition in this chapter, while understanding that underlying cellular pathophysiological mechanisms may be overlapping or indeed identical.

Pathology of dementia with Lewy bodies The Lewy body (LB) and Lewy neurite (LN) are the defining pathological entity of DLB, as well as PD (Figure 15.1). LBs are

Chapter 15 

eosinophilic, filamentous inclusions composed of α-synuclein, ubiquitin, parkin, neurofilaments, lipids, and chaperone proteins that accumulate in neurons [27–29, reviewed in  30], and are accompanied by dystrophic LNs. These inclusions are found in central and peripheral autonomic neurons [28,31,32, reviewed in 30] and anatomically correlate with clinical outcome such that brainstem lesions are associated with extrapyramidal symptoms, cognitive impairment with cortical and limbic lesions, and so forth [33–35, reviewed in 30]. In DLB, cortical and midbrain LBs are necessary for a pathological diagnosis. However, DLB and AD pathology have been reported to coexist in diagnosed DLB cases (e.g. [36]), again emphasizing not only clinical overlap but also possible coexistence of the two disorders (or of an overlap disorder) in certain individuals.

Neuroimaging as a diagnostic tool and biomarker in dementia with Lewy bodies At present DLB remains a diagnosis made primarily on clinical grounds and despite the existence of approved adjunctive testing there is, as yet, no definitive diagnostic biomarker. As noted earlier, clinical diagnosis can be challenging and clinical practice is riddled with misdiagnosis. For example an autopsy analysis of 15 PD and 14 DLB cases demonstrated a sensitivity of 73.3% for clinical diagnosis with 46% positive predictive value (PPV) of PD at initial clinical visits, compared with 80% sensitivity and 64.1% PPV for PD at the final recorded visit [37]. Additionally a study comparing clinical diagnosis with post-mortem neuropathological studies [38] showed that while clinical diagnosis of DLB is highly specific (>95%) it is associated with a low sensitivity (32% for DLB and 12% for AD with DLB) when compared to neuropathological studies.

dementia with lewy bodies

As we move away from purely symptomatic therapies to those that affect the underlying pathology, it will be critical to have biomarkers that help us detect, locate, and track this pathology. Differentiation of DLB from other forms of dementia, parkinsonism, and psychosis has important therapeutic implications. Pharmacotherapy for hallucinations and behavioural agitation often includes antipsychotics, for example, but these medications can exacerbate parkinsonism further given the compromised nigrostriatal pathway. Roughly 80% of DLB patients exhibit this sensitivity to antipsychotics: the adverse reaction can be severe in 50% and is associated with increased mortality [39,40]. Moreover, individuals with DLB are often unusually sensitive to dopaminergic agents that might be used to alleviate parkinsonism, and are often very susceptible to side effects including hallucinations and confusion [41]. A valid diagnostic biomarker would improve diagnostic accuracy and facilitate earlier diagnosis, thus avoiding unnecessary tests and exposure to potential adverse effects of unwarranted medications. There is also a critical need for biomarkers to measure progression of disease, not only in the clinic but also to provide objective and accurate outcome measures in clinical trials. In this chapter we therefore focus upon current and emerging neuroimaging techniques used in DLB.

MRI The use of MRI as a diagnostic tool is primarily important in its current clinical usage to differentiate a structural or vascular aetiology versus a neurodegenerative process in patients with dementia. However, more sophisticated measures show promise for use of MRI as a potential biomarker for DLB. MRI technology has been used to describe specific patterns of grey and white matter morphological differences between DLB and other forms of

A

B

C

D

Fig. 15.1  Photomicrographs of Lewy bodies (LB). (A) Haematoxylin and eosin stain of LB located in the substantia nigra. (B) Anti-α-synuclein antibody stain of LB. (C) Anti-α-synuclein antibody stain of LB in cortex in DLB. (D) Anti-α-synuclein antibody stain of LB in cortex in AD. AD, Alzheimer’s disease; DLB, dementia with Lewy bodies. Reprinted from Clinical Neuroscience Research, 3, Popescu A. and Lippa C.F. Parkinsonian syndromes: Parkinson's disease dementia, dementia with Lewy bodies and progressive supranuclear palsy, 461–8, Copyright (2004), with permission from Elsevier.

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dementia. Voxel-based morphometry, a method used to compare local areas of grey matter concentration on MRI studies [42], has been used to show a reduction in individuals with DLB in regional grey matter in Brodman areas 6, 20–22, and 47, corresponding to areas involved in motor planning, sensory input processing, and language function, as well as in bilateral insular cortices with a relative sparing of grey matter in the hippocampus, amygdala, thalamus, and parahippocampal gyrus (see Figure 15.2) [43]. Studies using MRI to compare DLB with other forms of dementia have been informative for the clinical diagnosis of dementias. Whitwell et al. [44] describe a prominent regional grey matter loss primarily in the dorsal midbrain, substantia innominata, and hypothalamus in the DLB brain. The same study reported a trend towards a greater reduction of substantia innominata grey matter in AD than DLB, and a greater reduction of midbrain grey matter in DLB than AD. Comparison of the AD and DLB study participants showed a relative greater loss of hippocampal and temporoparietal grey matter in AD. Taken together, a pattern of focused atrophy of the midbrain and hypothalamus, with a relative sparing of the hippocampus and temporoparietal cortex is, therefore, suggestive of DLB versus AD. In a study comparing DLB and PDD utilizing voxel-based morphometric analyses, Sanchez-Castaneda et al. [45] demonstrated significant grey matter atrophy in the right superior frontal gyrus, the right premotor area, and the right inferior frontal lobe in DLB when compared to PDD. Beyer et al. [46] demonstrated a more pronounced cortical atrophy in DLB than in PDD in the temporal, parietal, and occipital lobes, while in AD there was a reduced grey matter concentration in the bilateral temporal lobes and amygdala.

Lee et al. [47] compared patients with PDD and DLB and demonstrated a decreased grey matter density in the left occipital, parietal, and striatal areas in patients with DLB. Furthermore, white matter density was decreased in bilateral occipital and left occipitoparietal areas in patients with DLB compared with those with PDD with a relatively similar effect on white matter and grey matter in the DLB group. In PDD there was markedly less atrophy in the white matter than in the grey matter. However, overall there does not seem to be a single specific pattern of grey or white matter alteration that emerges to accurately distinguish DLB from other dementias, although a diffuse pattern of white and grey matter loss of the frontal, parietal, and occipital cortices as well as hypothalamic and midbrain volume loss is suggestive of DLB. This lack of specificity may be related to poor structural resolution, poor sensitivity of MRI, or may represent a lack of structural correlates of disease between these disorders. To better evaluate white matter morphology in DLB, diffusion tensor imaging (DTI) has been employed. This is an MRI-based technique of measuring the movement of water in the extravascular, extracellular space to develop 2D or 3D models of neural tracts. The measured value is fractional anisotropy (FA) and represents the isotropy of a diffusion measure such that an FA of 0 means unrestricted diffusion, while a measure of 1 suggests restricted diffusion in a linear plane. This can be interpreted as movement in the perpendicular linear pattern to generate a series of tract images signifying neural pathways [48]. The use of DTI allowed Watson et al. [49] to study white matter tracts in AD and DLB (Figure 15.3). Areas of reduced FA in subjects with DLB were found primarily in parietooccipital white

Controls versus DLB A

B

C

Fig. 15.2  Reduced grey matter volume in dementia with Lewy bodies as compared to controls, involving bilateral frontal, temporal, and insular cortex: (A) sagittal, (B) coronal, and (C) axial planes. Right side of the head is on the right of the printed image in panel (B). Reprinted with permission from Burton E.J., Karas G., Paling S.M., et al. (2002) Patterns of cerebral atrophy in dementia with Lewy bodies using voxel-based morphometry. Neuroimage 17:618–30.

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dementia with lewy bodies

the microstructural interruption of visual association areas in patients with DLB, and showed a decreased FA in the visual association fibres of the inferior longitudinal fasciculus (ILF) compared to healthy subjects. Considering that the ILF is involved in visuospatial cognition, the authors infer a causal relationship between ILF aberration and the visual hallucinations and visuospatial distortions prominent in DLB. While MRI is useful for diagnosing vascular and structural lesions, its use has also been challenged by its lack of specificity. MRI also may mislead the interpreter to assign a vascular aetiology as a single entity when multiple pathologies are present. A large study of 1339 cases of autopsy-proven AD, PD, DLB, and 486 age-matched controls showed significantly more frequent cerebrovascular lesions (91.9% and 67.8%) in AD and in a LB variant of AD than the other groups (29.4–45.7%), with the highest frequency of old or recent infarcts and haemorrhages in AD (23.6%), but 2.0–8.3% in the other groups. Interestingly the incidence and severity of cerebrovascular lesions significantly correlated with the neuritic Braak stages of AD [51]. Another study showed that in patients with pathology-proven DLB, significantly more cortical microhaemorrhages occurred in the brains with DLB, which remained significant when compared to brains with co-occurring AD and cerebral amyloid angiopathy pathology [52]. These studies highlight the difficulty in assigning a single clinical entity to a complex medical condition such as dementia, and also serve to inform that non-specific imaging findings may be misleading. For this reason more specific imaging modalities have been developed aimed at disease-specific pathological changes.

PET and SPECT Fig. 15.3  Images represent areas of reduced fractional anisotropy (FA) (blue) and mean FA skeleton (green) in dementia with Lewy bodies versus controls, overlaid onto the Montreal Neurological Institute template image. Reductions are evident mainly in the parieto-occipital areas (precuneal and cingulate gyri), and in the temporal lobes involved in a region of the posterior thalamic radiation that included the optic radiation. Reprinted with permission from Watson R., Blamire A.M., Colloby S.J. et al. (2012) Characterizing dementia with Lewy bodies by means of diffusion tensor imaging. Neurology 79:906–14.

matter tracts, as well as the pons and thalamus, when compared to controls. In AD the changes were much more diffuse. The authors concluded that in addition to white matter pathology, there is an association between these DTI parameters and impaired episodic memory, letter fluency, and severity of motor manifestations of parkinsonism in DLB. Lee et al. [47] used DTI to compare PDD and DLB patients, and showed that when compared to controls DLB and PDD subjects had significantly lower FA in the bilateral frontal, left temporal and left parietal white matter, and in patients with DLB the pattern of FA reduction was similar to that of patients with PDD. In DLB the extent of disease burden was more severe and involved more of the bilateral insular, bilateral posterior cingular, and bilateral visual association regions. In this same study the DLB subjects performed significantly worse in visual recognition memory, semantic fluency, and ideomotor praxis than those with PDD pointing towards a structural aetiology of this discrepancy. Neurodegeneration in DLB is thought to include the visual association cortex, and thus Ota et al. [50] used DTI to evaluate

Positron emission tomography (PET) and single photon emission CT (SPECT) are both techniques of great promise in neurodegenerative disorders since they allow detection of a radiolabelled tracer injected into the patient to study a molecule of interest. Neurotransmitter system-specific modalities have been made possible by the development of radiolabelled ligands specific to dopamine- and non-dopamine-specific pathways. Here we first discuss studies that utilize non-specific PET radiotracer ligands such as 2-[18F]fluoro-2-deoxy-D -glucose (FDG), which is a non-specific marker for glucose metabolism, and SPECT measures of brain perfusion and function. In a study of 14 AD, 14 DLB participants, and 14 control subjects, Ishii et al. [53] utilized N-isopropyl-[123I]-p-iodoamphetamine (IMP) SPECT to demonstrate a relative decrease in cerebral blood flow in the occipital cortex, and higher relative perfusion in the right medial temporal lobe in the DLB group than in the AD group, suggesting that this pattern can be used to help distinguish DLB from AD. Minoshima et al. [54] utilized FDG-PET to compare AD and DLB, and showed a significant reduction in metabolism in the occipital cortex, particularly in the primary visual cortex in DLB when compared to AD. In this study, a particular strength was that diagnoses were autopsy confirmed, and primary visual cortex hypometabolism distinguished DLB versus AD with 90% sensitivity and 80% specificity, suggesting that this may be useful as a radiographic biomarker to distinguish DLB from AD. Further studies by Higuchi et al. [55] studied FDG-PET in patients diagnosed clinically with probable AD and probable DLB, and one autopsy confirmed DLB patient,

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Fig. 15.4  Sagittal SPECT image of a patient with dementia with Lewy bodies, in which the yellow arrows point to areas of occipital hypoperfusion. Reprinted with permission from Lobotesis K., Fenwick J.D., Phipps A. et al. (2001) Occipital hypoperfusion on SPECT in dementia with Lewy bodies but not AD. Neurology 56:643–9.

revealing that DLB could be distinguished from AD with a sensitivity of 86% and a specificity of 91%. In both groups there was evidence of widespread cortical hypometabolism, although this was most pronounced in the visual association cortex in the DLB group compared to that in the AD group. In a study utilizing 99mTc-hexamethylpropyleneamine oxime (HMPAO) SPECT, Lobotesis et al. [56] studied 23 individuals with clinical diagnoses of DLB, and 50 with clinically diagnosed AD, and showed that both AD and DLB showed evidence of temporoparietal hypoperfusion. However, DLB showed a more specific pattern of occipital hypoperfusion when compared to control as well as AD groups (see Figure 15.4). In this study SPECT measures (occipital and medial temporal) correctly classified 69% of all subjects with 65% sensitivity and 87% specificity for DLB versus AD and control subjects. Occipital hypoperfusion has been confirmed in numerous subsequent studies utilizing 99mTc-ethyl cysteinate dimer (ECD) SPECT showing bilateral occipital and right medial temporal lobe hypoperfusion [57], as well as in a study utilizing HMPAO or ECD-SPECT [58].

DLB

Imaging the dopamine system Imaging studies so far have targeted presynaptic dopamine transporters, dopamine biosynthesis or degradation, and postsynaptic dopamine receptors, and this has led to an interesting debate on the potential utility of this modality in clinical diagnosis and in monitoring progression. The most widely used ligands include [123I]-FP-CIT (123I-N-3-fluoropropyl-2βcarbomethoxy-3β-(4-[123I]iodophenyl)nortropane, DaTscan) (see Figure 15.6), [99 mTc]TRODAT-1 (99 mTc-[2-[[2-[[[3-(4chlorophenyl)-8-methyl-8-azabicyclo[3,2,1]oct-2-yl]methyl](2mercaptoethyl)amino]ethyl]amino]ethanethiolato(3-)-N2, N2′, S2,S2′]oxo-[1R-(exo-exo)]) and [123I]-β-CIT ([123I]β-CIT (2b-carbomethoxy-3b-(4-[123I]iodophenyl)tropane, iometopane, Dopascan), each with different kinetic profiles [59]. The most relevant studies are summarized in Table 15.1. To study the utility of dopamine transporter imaging in the diagnosis of neurodegenerative disorders, Donnemiller et al. [58] studied 6 subjects with AD and 7 with DLB at 1 and 3 hours after ligand injection, and demonstrated a decreased striatal dopamine transporter density utilizing [123I]-β-CIT SPECT at 1 and 3 hours in DLB. Considering that dopamine depletion has been demonstrated in pathology studies, this study was proof of principle that dopamine ligand binding could be studied using radiotracer studies. In a similar study utilizing [123I]FP-CIT SPECT, Ceravolo et al. [60] studied a group of 10 people with AD with parkinsonism, 15 people with DLB, and 20 people with PD. The study demonstrated

Fig. 15.5  FDG-PET images in 3D rendered brains in subjects with dementia with Lewy bodies as compared to controls. Red areas demonstrate areas of reduced FDG uptake, representing hypometabolism. Reprinted with permission from Klein J.C., Eggers C., Kalbe E. et al. (2010) Neurotransmitter changes in dementia with Lewy bodies and Parkinson disease dementia in vivo. Neurology 74:885–92.

that striatal binding of [123I]FP-CIT was similar between AD and controls, while binding was significantly lower in DLB and PD compared to controls, as well as when compared to AD with parkinsonism.

Fig. 15.6  Binding of [123I]FP-CIT, a dopamine transporter ligand, detected by SPECT and superimposed upon T1 MRI images illustrating the striatal structure in controls. Reprinted with permission from Colloby S.J., O’Brien J.T., Fenwick J.D., et al. (2004) The application of statistical parametric mapping to 123I-FP-CIT SPECT in dementia with Lewy bodies, Alzheimer’s disease and Parkinson’s disease. Neuroimage 23:956–66.

A

B

C

D

R

R

Fig. 15.7 [123I]FP-CIT SPECT images: (A) control, (B) Parkinson’s disease, (C) Alzheimer’s disease, (D) dementia with Lewy bodies. Reproduced from J Neurol Neurosurg Psychiatry, Walker Z., Costa D.C., Walker R.W. et al, 73:134–40, 2002, with permission from BMJ Publishing Group Ltd.

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Table 15.1  Summary of dopaminergic imaging studies in dementia with Lewy bodies Study

Reference

Imaging modality

Clinical diagnosis

Key findings

Donnemiller et al., 1997

[58]

[123I]β-CIT

7DLB, 6 AD

Striatal binding lower in DLB than AD

Ransmayr et al., 2001

[77]

[123I]β-CIT

20 DLB, 24 PD

Striatal binding lower in DLB and PD with a more marked asymmetry in binding in PD

Hu et al., 2000

[78]

[18F]DOPA

7DLB, 10 AD

DLB differentiated from AD with 86% sensitivity and 100% specificity

Walker et al., 2002

[61]

[123I]FP-CIT

27 DLB, 17 AD, 19 PD

DLB and PD lower binding in caudate nucleus and putamen compared to controls

Ceravolo et al., 2003

[60]

[123I]FP-CIT

20 DLB, 24 AD

Striatal binding lower in DLB compared to AD

Ceravolo et al., 2004

[79]

[123I]FP-CIT

15 DLB, 13 AD + P, 20 PD

Striatum binding lower in DLB and PD compared to AD with parkinsonism and controls

Colloby et al., 2004

[80]

[123I]FP-CIT

23 DLB, 34 AD, 38 PD

Reduced striatal uptake DLB and PD compared to AD and controls. Binding in the striatum is similar PD and DLB

Gilman et al., 2004

[63]

[11C]DTBZ

6 DLB/PD, 14 DLB/AD

Striatal binding decreased in DLB with PD greater than in DLB with AD compared to AD alone and controls

O’Brien et al., 2004

[62]

[123I]FP-CIT

23 DLB, 34 AD, 38 PD, 36 PDD

Reduced striatum binding in DLB compared to AD and controls. Similar binding in DLB and PD. DLB and AD differentiated with 78% sensitivity and 94% specificity

Walker et al., 2004

[81]

[123I]FP-CIT

21 DLB, 19 PD

Reduced striatal binding in DLB and PD compared to controls. Binding in caudate nucleus lower in DLB compared to PD

Colloby et al., 2005

[67]

[123I]FP-CIT

20 DLB, 20 PD, 15 PDD

Rates of decline over time in striatal binding similar in DLB, PDD and PD

McKeith et al., 2007

[72]

[123I]FP-CIT

94 probable DLB, 57 Diagnosed clinically probable DLB with 77.7% sensitivity and possible DLB, 147 non-DLB excluded non-DLB dementia with 90.4% specificity. Diagnostic dementia accuracy 85.7%, PPV 82.4%, negative predictive value 87.5%

Walker et al., 2007

[71]

[123I]FP-CIT

13 DLB, 6 AD, 1 CBD

88% sensitivity and 100% specificity for DLB diagnosis compared to autopsy analysis and 75% sensitivity and 42% specificity compared to initial clinical diagnosis

Colloby et al., 2008

[82]

[123I]FP-CIT

28 DLB, 33 AD

84% overlap in diagnosis of AD and DLB compared to clinical diagnosis. Sensitivity 78.6%, specificity 87.9%

Koeppe et al., 2008

[83]

[11C]DTBZ

25 DLB, 30 PD, 25 AD

Striatal binding lower in DLB and PD compared to AD and controls

Lim et al., 2009

[65]

[123I]β-CIT

14 DLB, 1 AD (autopsy confirmed)

Imaging showed 100% accuracy

Walker and Walker, 2009

[84]

[123I]FP-CIT

15 DLB, 7 AD, 1 CBD

[123I]FP-CIT- 100% sensitivity and 92% specificity for DLB

O’Brien et al., 2009

[73]

[123I]FP-CIT

44 possible DLB changed to 19 probable DLB and 7 AD

In those initially diagnosed with DLB imaging diagnosed AD with 100% specificity and probable DLB with 63% sensitivity

Rossi et al., 2009

[64]

[123I]FP-CIT

30 DLB, 30 PDD

Striatal binding lower in PDD and DLB than controls though similar between PDD and DLB

Klein et al., 2010

[65]

[18F]DOPA

8 PDD, 6 DLB, 9 PD

Lower uptake in all groups

AD, Alzheimer’s dementia; CBD, corticobasal degeneration; DLB, dementia with Lewy bodies; DTBZ, dihydrotetrabenazine; PD, Parkinson’s disease; PDD, Parkinson’s disease dementia; PPV, positive predictive value.

Walker et al. [61] studied [123I]FP-CIT SPECT in a group of 27 DLB, 17 AD, 19 drug-naive PD, and 16 control subjects and showed that in both DLB and PD, there was significantly lower uptake of tracer than in AD and controls in the caudate nucleus and the anterior and posterior putamen (Figure 15.7). O’Brien et al. [62] demonstrated a significant reduction in [123I]FP-CIT SPECT binding in the caudate and anterior and posterior putamen in subjects with DLB and PD compared with subjects with AD and controls.

Dopamine transporter loss in DLB was of similar magnitude to that seen in PD, but with a flatter rostrocaudal (caudate–putamen) gradient, while the greatest loss in all three areas was seen in those who had PD and dementia. Gilman et  al. [63] utilized (+)-[11C]dihydrotetrabenazine ([+]‌-[11C]DTBZ) and PET to study presynaptic dopamine receptor density in patients with AD, DLB, and PDD (referred to as AD/PD in the study). This study demonstrates a decrease in mean striatal binding, suggesting decreased

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monoamine density by 62–77% in the DLB group and by 45–67% in the PDD group, when compared to AD and controls. However, these findings only reached statistical significance in the caudate, not the putamen. Multimodal studies have been employed to study multiple neurotransmitter systems to better describe perturbations in signalling molecules between DLB, AD, PD, and PDD. Rossi et  al. [64] performed a dual-imaging modality study utilizing [123I]-FP-CIT-SPECT and ECD-SPECT binding in subjects with PDD and DLB, and showed that in both modalities there was reduced striatal uptake and cerebral blood flow when compared to controls. However this analysis did not demonstrate a difference between DLB and PDD, suggesting that these modalities when used concurrently are insufficiently sensitive to discriminate between these two disorders, if indeed they are distinct. Klein et al. [65] also employed multiple imaging modalities using FDG-PET (depicted in Figure 15.5), N-11C-methyl-4-piperidyl acetate (MP4A)-PET, a radiotracer for cholinergic activity, and 18F-fluorodopa (FDOPA) PET, a dopamine radiotracer, in a small study of six DLB, eight PDD, and nine non-demented PD subjects. This study showed a reduction in FDOPA-PET activity in striatum and in limbic and associative prefrontal areas in all study groups compared to controls (Figure 15.8). There was additionally a significant MP4A and FDG binding reduction in the neocortex with increasing signal diminution from frontal to occipital regions in all study groups. There was no difference between the study groups for any radioligand studied, suggesting that DLB, PDD, and PD have similar dopaminergic and cholinergic profiles. In an effort to optimize the use of FDG-PET for use in diagnosis, Lim et al. [66] utilized both FDG-PET and [123I]-β-CIT-SPECT to compare DLB and AD. In this study FDG-PET studies had a sensitivity of 83% and specificity of 93% for DLB. Hypometabolism in the lateral occipital cortex had the highest sensitivity for DLB (88%), but relative preservation of the mid or posterior cingulate gyrus, referred to as the cingulate island sign, had the highest specificity (100%). [123I]-β-CIT-SPECT achieved 100% accuracy when comparing DLB versus AD, with younger and less severely affected DLB subjects showing marked reductions in the putamen with relative sparing of the caudate nucleus, while more severely affected individuals showed widespread striatal deficits. Imaging studies are often limited in their ability to demonstrate changes in pathological processes over time. In order to study the progression of dopaminergic degeneration Colloby et al. [67] studied [123I]FP-CIT-SPECT in serial studies of controls and patients with DLB, PD, and PDD and demonstrated that rates of decline were significantly greater in the DLB, PD, and PDD patients than in controls in caudate and posterior putamen though were similar between the different patient groups. The most robust rates of change were exhibited in the caudate of PDD, with a rate of loss of −40.7% over the 1-year study time period, followed by DLB (−12.7%) and PD (−11.4%), with controls, as expected, showing the least change (−3.0%).

Non-dopaminergic pathology specific imaging modalities Although this is addressed in other chapters, a brief evaluation of non-dopaminergic pathology-based imaging is appropriate in this context, as the search for accurate diagnostics and imaging

dementia with lewy bodies

correlates of disease has led researchers to compare DLB with other disease states. The 11C-labelled Pittsburg compound B (PiB) specifically targets brain amyloid, and is visualized by PET allowing a pre-mortem evaluation of amyloid burden. Non-demented subjects with PD as well as with MCI showed no increase in amyloid deposition compared to healthy controls. PDD patients showed a non-significant increase in amyloid deposition, while DLB showed a significantly higher amyloid level then PD and healthy controls [68]. This association did not extend to motor symptoms, supporting a possible role for specifically investigating the contribution of amyloid to the underlying pathology of cognitive decline in LB disorders. An interesting adjunct to brain imaging has been the use of cardiac imaging with 123I and 99mTc-MIBI scans that have provided additional support for a diagnosis of DLB via an evaluation of sympathetic function of the heart. Inui et al. [69] showed reduced [123I]MIBG cardiac uptake in up to 91.7% of probable DLB patients compared to 33.3% of possible DLB, making combined studies of brain perfusion scans and cardiac perfusion scans highly suggestive of DLB, and raising the possibility of an additional diagnostic biomarker in the diagnosis of DLB.

Imaging as a diagnostic tool The clinical utility of neuroimaging by MRI and 99Tc-SPECT perfusion imaging has been evaluated in a retrospective case review study of patients who met clinical criteria of cognitive disorders based on a first visit with a trained physician [70]. This study showed that when a single neurodegenerative disease was suspected based on clinical criteria, coupling data from a dementia-protocolled MRI with a nuclear perfusion study confirmed the diagnosis in roughly half of the cases, contradicted the diagnosis in 30%, and was non-diagnostic in 12%. This was based on an MRI analysis focused on micro- and macrovascular disease as well as ventricular size and lobar atrophy. In patients with a more elusive clinical diagnosis, roughly half could be assigned a diagnosis based on imaging findings suggestive of specific neurodegenerative patterns of disease. This demonstrates that imaging can be helpful to confirm a clinical diagnosis or assign a diagnosis to an unclear clinical diagnosis in roughly 50% of cases. However, a multifactorial aetiology, including neurodegeneration with concurrent infarction, remains an important consideration. [123I]FP-CIT-SPECT imaging modality has been marketed under the trade name DaTSCAN, and has been employed in a number of clinical utility studies. Additionally it has been used in the USA as a valid and reliable diagnostic biomarker for parkinsonian disorders and has additionally been used in Europe for dementias. In a study by Walker et al. [71] comparing pre-mortem clinical diagnosis supplemented by [123I]FP-CIT-SPECT imaging with post-mortem neuropathology follow-up showed sensitivity of an initial clinical diagnosis of DLB was 75% and specificity was 42%. The sensitivity of the [123I]FP-CIT-SPECT imaging for the diagnosis of DLB was 88% and specificity was 100%. McKeith et al. [72] studied [123I]FP-CIT-SPECT in patients with clinically probable DLB, and demonstrated 77.7% sensitivity for diagnosing DLB versus non-DLB with 90.4% specificity. The diagnostic accuracy was 85.7% with a PPV of 82.4% and negative predictive value of 87.5% in this study. However, other studies have shown a 63% sensitivity and 100% specificity of [123I]FP-CIT-SPECT in the diagnosis of DLB [73]. Nonetheless, these data suggest that [123I]

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PD

PDD

Fig. 15.8  FDOPA-PET images demonstrate reduced FDOPA uptake (white areas) in subjects with dementia with Lewy bodies, Parkinson’s disease, and Parkinson’s disease dementia as compared to controls. Reprinted with permission from Klein J.C., Eggers C., Kalbe E. et al. (2010) Neurotransmitter changes in dementia with Lewy bodies and Parkinson disease dementia in vivo. Neurology 74:885–92.

FP-CIT-SPECT can be useful as a negative predictive biomarker as well as a positive diagnostic biomarker in the diagnosis of DLB. Kemp et al. [74] performed a retrospective case note study of 80 patients who had undergone [123I]FP-CIT-SPECT within 12–24 months for suspicion of DLB to evaluate the impact on clinical decision making of neuroimaging. They show that in patients with an abnormal [123I]FP-CIT-SPECT, 90% had a working diagnosis of DLB, while 95% with a normal [123I]FP-CIT-SPECT had an alternative clinical diagnosis than DLB, suggesting that [123I]FP-CITSPECT results therefore have a marked influence on the working clinical diagnosis in patients with suspected DLB. These results again suggest that [123I]FP-CIT-SPECT may be useful in influencing the diagnosis of DLB. Additional evidence comes from a study by Garibotto et al. [75] who studied FDG-PET and [123I]FP-CITSPECT in patients with parkinsonism and dementia. This study showed that up to 85.2% of the patients were correctly diagnosed when comparing FDG-PET alone. When [123I]FP-CIT-SPECT alone was considered, 59.3% were correctly diagnosed. However, the combination of both yielded up to 100% accuracy. The combination of imaging modalities in this study demonstrate that while a single study may not be sufficiently sensitive for the diagnosis of DLB, a combination of FDG-PET and [123I]FP-CIT-SPECT is more useful for accurate diagnosis.

Conclusions The diagnosis of DLB versus other neurodegenerative dementing disorders continues to be based on clinical indicators of disease pattern, and the clinicotemporal profile of symptom onset and symptom characteristics. Unfortunately, even in present-day practice, this still leads to misdiagnosis, particularly in early disease stages when signs and symptoms are subtle. There is a critical need for biomarkers of DLB, not only to improve diagnostic accuracy and timeliness, but also to objectively measure disease progression. The availability of such modalities cannot be more timely: as our population continues to age, the impact of these disorders will

continue to afflict our older populations and misdiagnosis will only lead to worse outcomes and inappropriate prognostication. Neuroimaging provides promise in several areas for improving DLB diagnosis and management in the future. Structural neuroimaging may point to AD rather than DLB with relatively prominent involvement of the medial temporal lobes. [123I]FP-CIT-SPECT is of use in the work-up of dementia, and reduced striatal dopamine transporter (DAT) binding suggests DLB, rather than AD. This is currently in clinical use in many countries, although not in the USA for this indication at the time of writing. Hypometabolism in the occipital and posterior temporoparietal region, demonstrated by PET or SPECT, also points towards DLB rather than AD. However, at present none of these tests stand alone as diagnostic tools in the clinic. Moreover, none are approved as screening tools. In the future, it may be that a combination of clinical findings and multiple biomarkers, incorporating molecular genetics, pathophysiology-based measures, and neuronal imaging may perform better than single markers. An example of this combined approach is the Alzheimer’s Disease Neuroimaging Initiative (ADNI), an international longitudinal study incorporating multimodal imaging data in large cohorts of control subjects and of individuals with AD (http://www.adni-info. org/) [76]. More recently, the Parkinson’s Progression Markers Initiative (PPMI) has been launched to aid in identification of PD progression markers by studying a combination of neuroimaging and clinical batteries, in combination with longitudinal cerebrospinal fluid, blood, and urine collection. This type of approach would very likely address diagnostic problems in DLB, as well as addressing markers of progression, of critical importance in new drug development.

References 1. Lobo A, Launer LJ, Fratiglioni L, et  al. Prevalence of dementia and major subtypes in Europe: A collaborative study of population-based cohorts. Neurologic Diseases in the Elderly Research Group. Neurology 2000;54(11 Suppl 5):S4–9.

Chapter 15 

2. Ganguli M, Rodriguez E, Mulsant B, et al. Detection and management of cognitive impairment in primary care:  The Steel Valley Seniors Survey. J Am Geriatr Soc 2004;52:1668–75. 3. Hanninen T, Hallikainen M, Tuomainen S, et al. Prevalence of mild cognitive impairment:  a population-based study in elderly subjects. Acta Neurol Scand 2002;106:148–54. 4. Larrieu S, Letenneur L, Orgogozo JM et al. Incidence and outcome of mild cognitive impairment in a population-based prospective cohort. Neurology 2002;59:1594–9. 5. 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;78:342–51. 6. Geda YE Mild cognitive impairment in older adults. Curr Psychiatry Rep 2012;14:320–7. 7. Daly E, Zaitchik D, Copeland M, et  al. Predicting conversion to Alzheimer disease using standardized clinical information. Arch Neurol 2000;57:675–80. 8. Petersen RC, Thomas RG, Grundman M, et al. Vitamin E and donepezil for the treatment of mild cognitive impairment. N Engl J Med 2005;352:2379–88. 9. de Silva HA, Gunatilake SB, Smith AD Prevalence of dementia in a semi-urban population in Sri Lanka: report from a regional survey. Int J Geriatr Psychiatry 2003;18:711–15. 10. Herrera E, Jr, Caramelli P, Silveira AS et al. Epidemiologic survey of dementia in a community-dwelling Brazilian population. Alzheimer Dis Assoc Disord 2002;16:103–8. 11. Rahkonen T, Eloniemi-Sulkava U, Rissanen S, et  al. Dementia with Lewy bodies according to the consensus criteria in a general population aged 755  years  years or older. J Neurol Neurosurg Psychiatry 2003;74:720–4. 12. Stevens T, Livingston G, Kitchen G, et al. Islington study of dementia subtypes in the community. Br J Psychiatry 2002;180:270–6. 13. Yamada T, Hattori H, Miura A, et al. Prevalence of Alzheimer’s disease, vascular dementia and dementia with Lewy bodies in a Japanese population. Psychiatry Clin Neurosci 2001;55:21–5. 14. Yamada T, Kadekaru H, Matsumoto S, et al. Prevalence of dementia in the older Japanese-Brazilian population. Psychiatry Clin Neurosci 2002;56:71–5. 15. Zaccai J, McCracken C. and Brayne C. A systematic review of prevalence and incidence studies of dementia with Lewy bodies. Age Ageing 2005;34:561–6. 16. Miech RA, Breitner JC, Zandi PP et al. Incidence of AD may decline in the early 90s for men, later for women: The Cache County study. Neurology 2002;58:209–18. 17. Macijauskiene J, and Lesauskaite V. Dementia with Lewy bodies: the principles of diagnostics, treatment, and management. Medicina (Kaunas) 2012;48:1–8. 18. McKeith IG, Dickson DW, Lowe J, et al. Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium. Neurology 2005;65:1863–72. 19. Karantzoulis S, and Galvin JE Distinguishing Alzheimer’s disease from other major forms of dementia. Expert Rev Neurother 2011;11:1579–91. 20. Hely MA, Reid WG, Adena MA et al. The Sydney multicenter study of Parkinson’s disease: the inevitability of dementia at 200 years. Mov Disord 2008;23:837–44. 21. 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;47:1113–24. 22. McKeith IG, Perry EK, Perry RH. Report of the second dementia with Lewy body international workshop:  diagnosis and treatment. Consortium on Dementia with Lewy Bodies. Neurology 1999;53:902–5. 23. Aarsland D, Ballard CG, Halliday G. Are Parkinson’s disease with dementia and dementia with Lewy bodies the same entity? J Geriatr Psychiatry Neurol 2004;17:137–45.

dementia with lewy bodies

24. Goldmann Gross R, Siderowf A, Hurtig HI. Cognitive impairment in Parkinson’s disease and dementia with lewy bodies: a spectrum of disease. Neurosignals 2008;16:24–34. 25. Troster AI. Neuropsychological characteristics of dementia with Lewy bodies and Parkinson’s disease with dementia:  differentiation, early detection, and implications for ‘mild cognitive impairment’ and biomarkers. Neuropsychol Rev 2008;18:103–19. 26. Johansen KK, White LR, Sando SB, Aasly JO. Biomarkers: Parkinson disease with dementia and dementia with Lewy bodies. Parkinsonism Relat Disord 2010;16:307–15. 27. Alafuzoff I, Parkkinen L, Al-Sarraj S, et  al. Assessment of alpha-synuclein pathology:  a study of the BrainNet Europe Consortium. J Neuropathol Exp Neurol 2008;67:125–43. 28. Braak H, Bohl JR, Muller CM, et al. Stanley Fahn Lecture 2005: The staging procedure for the inclusion body pathology associated with sporadic Parkinson’s disease reconsidered. Mov Disord 2006;21:2042–51. 29. Ma SY, Ciliax BJ, Stebbins G, et  al. Dopamine transporterimmunoreactive neurons decrease with age in the human substantia nigra. J Comp Neurol 1999;409:25–37. 30. Jellinger KA A critical evaluation of current staging of alpha-synuclein pathology in Lewy body disorders. Biochim Biophys Acta 2009;1792:730–40. 31. Del Tredici K, Rub U, De Vos RA, et  al. Where does parkinson disease pathology begin in the brain? J Neuropathol Exp Neurol 2002;61:413–26. 32. Den Haltog Jager WA, Bethlem J. The distribution of Lewy bodies in the central and autonomic nervous systems in idiopathic paralysis agitans. J Neurol Neurosurg Psychiatry 1960;23:283–90. 33. Braak H, Muller CM, Rub U, et  al. Pathology associated with sporadic Parkinson’s disease-where does it end? J Neural Transm Suppl 2006;70:89–97. 34. Iranzo A, Molinuevo JL, Santamaria J, et  al. Rapid-eye-movement sleep behaviour disorder as an early marker for a neurodegenerative disorder: a descriptive study. Lancet Neurol 2006;5:572–7. 35. Mosimann UP, and McKeith IG Dementia with lewy bodies-diagnosis and treatment. Swiss Med Wkly 2003;133:131–42. 36. Armstrong RA, Lantos PL, Cairns NJ. Overlap between neurodegenerative disorders. Neuropathology 2005;25:111–24. 37. Litvan I, MacIntyre A, Goetz CG et al. Accuracy of the clinical diagnoses of Lewy body disease, Parkinson disease, and dementia with Lewy bodies: a clinicopathologic study. Arch Neurol 1998;55:969–78. 38. Nelson PT, Jicha GA, Kryscio RJ et al. Low sensitivity in clinical diagnoses of dementia with Lewy bodies. J Neurol 2010;257:359–66. 39. Ballard C, Kahn Z, Corbett A. Treatment of dementia with Lewy bodies and Parkinson’s disease dementia. Drugs Aging 2011;28:769–77. 40. McKeith I, Fairbairn A, Perry R, et  al. Neuroleptic sensitivity in patients with senile dementia of Lewy body type. BMJ 1992;305:673–8. 41. Goldman JG, Goetz CG, Brandabur M, et al. Effects of dopaminergic medications on psychosis and motor function in dementia with Lewy bodies. Mov Disord 2008;23:2248–50. 42. Ashburner J, Friston KJ Voxel-based morphometry—the methods. Neuroimage 2000;11:805–21. 43. Burton EJ, Karas G, Paling SM, et  al. Patterns of cerebral atrophy in dementia with Lewy bodies using voxel-based morphometry. Neuroimage 2002;17:618–30. 4 4. 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;130:708–19. 45. 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;24:1740–6. 46. Beyer MK, Larsen JP, Aarsland D. Gray matter atrophy in Parkinson disease with dementia and dementia with Lewy bodies. Neurology 2007;69:747–54.

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47. 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;81:320–6. 48. Price SJ, Tozer DJ and Gillard JH Methodology of diffusion-weighted, diffusion tensor and magnetisation transfer imaging. Br J Radiol 2011;84 Spec No 2:S121–6. 49. Watson R, Blamire AM, Colloby SJ et  al. Characterizing dementia with Lewy bodies by means of diffusion tensor imaging. Neurology 2012;79:906–14. 50. Ota M, Sato N, Ogawa M, et  al. Degeneration of dementia with Lewy bodies measured by diffusion tensor imaging. NMR Biomed 2009;22:280–4. 51. Jellinger KA Critical evaluation of the Braak staging scheme for Parkinson’s disease. Ann Neurol 2010;67:550. 52. De Reuck J, Deramecourt V, Cordonnier C, et al. Prevalence of cerebrovascular lesions in patients with Lewy body dementia: A neuropathological study. Clin Neurol Neurosurg 2013;115:1094–7. 53. Ishii K, Yamaji S, Kitagaki H, et al. Regional cerebral blood flow difference between dementia with Lewy bodies and AD. Neurology 1999;53:413–16. 54. Minoshima S, Foster NL, Sima AA et  al. Alzheimer’s disease versus dementia with Lewy bodies: cerebral metabolic distinction with autopsy confirmation. Ann Neurol 2001;50:358–65. 55. Higuchi M, Tashiro M, Arai H, et  al. Glucose hypometabolism and neuropathological correlates in brains of dementia with Lewy bodies. Exp Neurol 2000;162:247–56. 56. Lobotesis K, Fenwick JD, Phipps A, et  al. Occipital hypoperfusion on SPECT in dementia with Lewy bodies but not AD. Neurology 2001;56:643–9. 57. Pasquier J, Michel BF, Brenot-Rossi I, et  al. Value of (99  m)Tc-ECD SPET for the diagnosis of dementia with Lewy bodies. Eur J Nucl Med Mol Imaging 2002;29:1342–8. 58. Donnemiller E, Heilmann J, Wenning GK, et  al. Brain perfusion scintigraphy with 999m  mTc-HMPAO or 999m  mTc-ECD and 123I-beta-CIT single-photon emission tomography in dementia of the Alzheimer-type and diffuse Lewy body disease. Eur J Nucl Med 1997;24:320–5. 59. Varrone A. and Halldin C. Molecular imaging of the dopamine transporter. J Nucl Med 2010;51:1331–4. 60. Ceravolo R, Volterrani D, Gambaccini G, et al. Dopaminergic degeneration and perfusional impairment in Lewy body dementia and Alzheimer’s disease. Neurol Sci 2003;24:162–3. 61. Walker Z, Costa DC, Walker RW et  al. Differentiation of dementia with Lewy bodies from Alzheimer’s disease using a dopaminergic presynaptic ligand. J Neurol Neurosurg Psychiatry 2002;73:134–40. 62. 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;61:919–25. 63. Gilman S, Koeppe RA, Little R, et  al. Striatal monoamine terminals in Lewy body dementia and Alzheimer’s disease. Ann Neurol 2004;55:774–80. 6 4. Rossi C, Volterrani D, Nicoletti V, et  al. ‘Parkinson-dementia’ diseases:  a comparison by double tracer SPECT studies. Parkinsonism Relat Disord 2009;15:762–6. 65. Klein JC, Eggers C, Kalbe E, et  al. Neurotransmitter changes in dementia with Lewy bodies and Parkinson disease dementia in vivo. Neurology 2010;74:885–92. 66. Lim SM, Katsifis A, Villemagne VL et al. The 18F-FDG PET cingulate island sign and comparison to 123I-beta-CIT SPECT for diagnosis of dementia with Lewy bodies. J Nucl Med 2009;50:1638–45.

67. Colloby SJ, Williams ED, Burn DJ, et al. Progression of dopaminergic degeneration in dementia with Lewy bodies and Parkinson’s disease with and without dementia assessed using 123I-FP-CIT SPECT. Eur J Nucl Med Mol Imaging 2005;32:1176–85. 68. Gomperts SN, Locascio JJ, Marquie M, et al. Brain amyloid and cognition in Lewy body diseases. Mov Disord 2012;27:965–73. 69. Inui Y, Toyama H, Manabe Y, et al. Evaluation of probable or possible dementia with lewy bodies using 123I-IMP brain perfusion SPECT, 123I-MIBG, and 999m  mTc-MIBI myocardial SPECT. J Nucl Med 2007;48:1641–50. 70. Borghesani PR, DeMers SM, Manchanda V, et al. Neuroimaging in the clinical diagnosis of dementia: observations from a memory disorders clinic. J Am Geriatr Soc 2010;58:1453–8. 71. Walker Z, Jaros E, Walker RW et al. Dementia with Lewy bodies: a comparison of clinical diagnosis, FP-CIT single photon emission computed tomography imaging and autopsy. J Neurol Neurosurg Psychiatry 2007;78:1176–81. 72. McKeith I, O’Brien J, Walker Z, et  al. 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;6:305–13. 73. O’Brien JT, McKeith IG, Walker Z, et  al. Diagnostic accuracy of 123I-FP-CIT SPECT in possible dementia with Lewy bodies. Br J Psychiatry 2009;194:34–9. 74. Kemp PM, Clyde K, Holmes C. Impact of 123I-FP-CIT (DaTSCAN) SPECT on the diagnosis and management of patients with dementia with Lewy bodies:  a retrospective study. Nucl Med Commun 2011;32:298–302. 75. 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:e112–7. 76. 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. 77. Ransmayr G, Seppi K, Donnemiller E, et al. Striatal dopamine transporter function in dementia with Lewy bodies and Parkinson’s disease. Eur J Nucl Med 2001;28:1523–8. 78. Hu XS, Okamura N, Arai H, et al. 18F-fluorodopa PET study of striatal dopamine uptake in the diagnosis of dementia with Lewy bodies. Neurology 2000;55:1575–7. 79. Ceravolo R, Volterrani D, Gambaccini G, et  al. Presynaptic nigro-striatal function in a group of Alzheimer’s disease patients with parkinsonism: evidence from a dopamine transporter imaging study. J Neural Transm 2004;111:1065–73. 80. Colloby SJ, O’Brien JT, Fenwick JD, et al. The application of statistical parametric mapping to 123I-FP-CIT SPECT in dementia with Lewy bodies, Alzheimer’s disease and Parkinson’s disease. Neuroimage 2004;23:956–66. 81. Walker Z, Costa DC, Walker RW et al. Striatal dopamine transporter in dementia with Lewy bodies and Parkinson disease: a comparison. Neurology 2004;62:1568–72. 82. Colloby SJ, Firbank MJ, Pakrasi S, et  al. A comparison of 99 mTc-exametazime and 123I-FP-CIT SPECT imaging in the differential diagnosis of Alzheimer’s disease and dementia with Lewy bodies. Int Psychogeriatr 2008;20:1124–40. 83. Koeppe RA, Gilman S, Junck L, et  al. Differentiating Alzheimer’s disease from dementia with Lewy bodies and Parkinson’s disease with (+)-[11C]dihydrotetrabenazine positron emission tomography. Alzheimers Dement 2008;4(1 Suppl 1):S67–76. 84. Walker RW and Walker Z. Dopamine transporter single photon emission computerized tomography in the diagnosis of dementia with Lewy bodies. Mov Disord 2009;24 Suppl 2:S754–9.

CHAPTER 16

Corticobasal syndrome and corticobasal degeneration Luke A. Massey and Sean O’Sullivan Introduction Corticobasal degeneration (CBD) was first described in 1968 by Rebeiz et al. as ‘corticodentatonigral degeneration with neuronal achromasia’ [1]‌. The authors described three patients with progressive, unilateral, slow, and awkward movements with an associated alien limb phenomenon, jerky tremor, dystonia, parkinsonism, spasticity, and abnormal gait. At post-mortem they found asymmetrical frontoparietal atrophy and basal ganglia changes with neuronal loss and gliosis and swollen neuronal bodies devoid of Nissl substance (‘achromasia’) [1]. The condition probably had been clinically described before this however, in the 1920s [2], and there is a suggestion that the French composer Maurice Ravel may have had a similar clinical syndrome [3]. CBD has gone by a variety of other names including corticonigral degeneration with neuronal achromasia [4], cortical degeneration with swollen chromatolytic neurons [5], cortical basal ganglionic degeneration [6], and corticobasal degeneration, which is the most common term now used [7]. Since the initial description by Rebeiz et  al. developments derived from clinicopathological studies and case reports and series have revealed that not only does the classic presentation of this disease—the ‘corticobasal syndrome’ (CBS)—herald diverse pathology at post-mortem, but also pathologically confirmed corticobasal degeneration can present with different clinical phenotypes during life [8]‌. For clarity in the following text, as has come to be accepted in the literature, CBS refers to the clinical presentation and ante-mortem ‘diagnosis’ and corticobasal degeneration (CBD) to the pathologically confirmed diagnosis [9].

Early clinical descriptions of corticobasal syndrome The first use of the term ‘corticobasal degeneration’ was by Gibb et al. [7]‌, who presented three patients with (1) abnormal motor function—either complex involuntary and persistent motor activity restricted to one hand or an akinetic rigid syndrome—dystonia, myoclonus, the alien hand syndrome, truncal ataxia, or chorea; (2) constructional difficulty indicative of parietal damage or dyspraxia; (3) abnormalities of eye movements—including saccadic and pursuit movements in the vertical and horizontal planes but with normal vestibuloocular reflexes; (4) pyramidal signs with hyperreflexia and extensor plantar responses. At post-mortem

there was frontoparietal cortical atrophy with neuronal loss and gliosis most severely in the parietal and frontal cortex, cortical white matter, and to a lesser extent in the corticospinal tracts, subthalamic nucleus, red nucleus, putamen, globus pallidus, substantia nigra, and locus coeruleus [7]. Similarities with progressive supranuclear palsy (PSP) clinically and Pick’s disease pathologically were already evident at this stage. In a later study of 36 patients (6 pathologically confirmed) most presented with markedly asymmetric (arm more than leg) akinesia and rigidity with apraxia and with variable addition of the other features including dystonia, myoclonus, an alien limb, an eye movement disorder primarily affecting the initiation of saccadic movements, and pyramidal features [10]. There was poor response to levodopa and no other diagnosis suggested on imaging. Examination revealed rigidity, akinesia, and apraxia with variable addition of the other features including dystonia, myoclonus, an alien limb, an eye movement disorder primarily affecting the initiation of saccadic movements, and pyramidal features. In a further series of 14 pathologically confirmed cases unilateral levodopa-unresponsive parkinsonism with limb idiomotor apraxia and gait disturbance were eventually seen in most cases but only 36% were diagnosed with CBD during life [11]. The reasons for this were felt to be lack of awareness of the variability in clinical features of CBD; that the characteristic features such as dystonia, myoclonus, and an alien limb could all be absent particularly early in the disease; and that some features such as idiomotor apraxia are not routinely assessed in the clinic. A clinical vignette study of pathologically confirmed CBD, when compared to PSP, Parkinson’s disease (PD), diffuse Lewy body disease (DLB), multiple system atrophy (MSA), post-encephalitis parkinsonism (PEP), Pick’s disease, Creutzfeld–Jakob disease (CJD), Alzheimer’s disease (AD), vascular parkinsonism, and Whipple’s disease further emphasized the low sensitivity of diagnosis, with only 35% identified early in the disease course and only up to 50% identified at the last clinic visit [12].

Defining the features of corticobasal syndrome The features found in the classic presentation of CBD are known as CBS [9]‌. This is a multisystem syndrome with extrapyramidal, cortical, oculomotor, cognitive, and neuropsychiatric features (Table 16.1).

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Table 16.1  Features of corticobasal syndrome Domain

Feature

Comments

Extrapyramidal

Rigidity

Characteristically progressive and asymmetric

Akinesia

Cortical/cognitive

Poor levodopa response

May have transient mild response

Myoclonus

Usually associated with dystonia

Dystonia

Including blepharospasm

Postural instability

Usually later

Dyspraxia

Limbs, speech, gait, eye movements, eyelid opening

Alien limb

More than limb levitation alone

Cortical sensory loss Dysphasia Visuospatial deficits Dysexecutive/frontal deficits Neuropsychiatric

Increasingly recognized as a common feature, particularly non-fluent dysphasia

Depression Apathy Sleep inversion Obsessive–compulsive behaviours Frontal release signs Bulbar symptoms

Dyspraxia and cortical sensory loss Dyspraxia commonly coexists with the parkinsonian features although it may be difficult to distinguish the subtleties of dyspraxia in the context of a severely bradykinetic, rigid, and dysonic limb with superimposed myoclonus. It is found in 80–90% of CBS [17] and in 139/193 (72%) of patients with pathologically proven CBD [15]. Idiomotor apraxia is associated with supplementary motor area disease, and the combination of idiomotor and ideational apraxia with more significant cognitive impairment [17]. Orobuccal and eyelid opening dyspraxia are also described. Cortical sensory loss is seen in less than one-quarter of cases, and visual neglect is also reported [18].

The ‘alien limb’ phenomenon

Irritability

Others

dystonia is more usually associated with PSP, and blepharospasm is less common. Myoclonus is also found in approximately 50% of corticobasal presentations of CBD, and in approximately 37% of all pathologically confirmed CBD cases included in an analyses of patients according to the presence or absence of coexisting dystonia [15]. Myoclonus is action and stimulus sensitive and occurs on a background of rigidity and dystonia [16], being present in 54/85 (64%) of CBD patients with dystonia [15]. Often there is a jerky tremor but this is not characteristic of PD and likely related to myoclonus with action and postural components.

Dyphagia and dysarthria are usually late

Chorea From Dickson et al 2002 JNEN 61(11):935, adapted from Table 3.

Patients report that the limb is acting on its own and they feel dissociated from it—the limb may rise up on its own (‘levitation’) but this is not considered on its own to be sufficient to be called an alien limb [9]‌, which may also include features such as forced grasping or reaching for objects in the immediate environment, and intermanual conflict. Although the presence of this phenomenon is often considered very suggestive of CBD, it was found in only 3/19 (16%) pathologically confirmed cases in one series [14].

Oculomotor abnormalities Extrapyramidal features The typical features include a markedly asymmetric progressive akinesia and rigidity which does not respond to levodopa therapy [7,11]. This can start in the arm or leg with associated manifestations. There may be a mild non-sustained levodopa response which is unusual to be significant enough to lead to confusion with levodopa-responsive idiopathic PD [13], although this has been described [14]. Parkinsonism was an early prominent feature seen in 9/19 (47%) pathologically confirmed CBD patients, although caution is required in generalizing these data from a predominantly movement-disorders focused Queen Square Brain Bank [14]. In a review of CBD, 153 of 169 (91%) cases described demonstrated an akinetic–rigid syndrome [15]. Dystonia with a fixed limb posture is characteristic with pain, finger and limb posturing, and impairment of gait. A recent literature review reveals that in pathologically confirmed disease 37.5% manifest dystonia mostly in the arm and in the first 2 years, but that it may appear later and affect the neck or face particularly if there was a cognitive presentation [15]. In fact, in a CBS presentation only 50% had dystonia at some stage of the illness. Axial

The presence of delayed saccadic latency supports the diagnosis of CBD [7,10] whereas abnormalities of saccadic speed and in the vertical plane are more predictive of PSP, although abnormalities in the vertical plane may be seen later in the disease but are likely to be milder than in PSP [19]. Oculomotor apraxia and a late supranuclear gaze palsy have also been described [9]‌.

Other features Axial signs including bulbar (dysarthria and dysphagia) and gait disturbance, pyramidal signs, and ataxia and chorea are also reported but are less discriminating from other diseases [9,18].

Cognitive impairment Although dementia was felt to be an exclusion criterion for the diagnosis of CBD in the early literature, cognitive deficits including aphasia and frontal lobe syndromes [11], memory disturbance and behavioural changes were recognized to often precede the more typical corticobasal features which then develop later on in the disease [11,20]. Early post-mortem studies highlighting the pathological similarities between CBD

Chapter 16 

corticobasal syndrome and corticobasal degeneration

and Pick’s disease [7,21] suggested that cognitive impairment would be important in CBD. In support of this finding, a subsequent post-mortem study showed that dementia was actually the most common presentation of CBD, with clinical diagnoses in life including AD, AD with parkinsonism, and atypical dementia of the frontotemporal type in 9/13 pathologically confirmed cases [22]. Cognitive deficits reported include apraxia, constructional, and visuospatial [23] abnormalities, acalculia, frontal dysfunction [24,25] and non-fluent aphasia [26,27]. Progressive non-fluent aphasia (PNFA) is found in over one-half of confirmed CBD during the disease [28–30]. Other presentations include apraxia of speech [18,31], and a syndrome of frontotemporal dementia behavioural variant (bvFTD) [32].

Neuropsychiatric features Depression, apathy, and irritability [11,12,24], inversion of sleep/ sleep vigilance pattern [24], emotional lability and obsessive-compulsive behaviours [10] have been reported.

Epidemiology and natural history The age of onset in CBD is the 7th decade (61–66 years) [10,11,14] with no cases identified under 45 years [11]. There is no gender predisposition [10,14] True incidence is unknown, and in a UK community-based study no cases were identified [33]. At the Queen Square Brain Bank for Neurological Disorders, in London, 19/1440 cases in the archive had a pathological diagnosis of CBD which was much less common than PD (608), PSP (179), and MSA (117) [14]. However, the incidence has been estimated to be less than 1 in 100 000 [34] and in a Russian population the age-standardized incidence rate was 0.02 per 100 000/year for CBD, compared with figures of 9.03 for PD, 0.11 for MSA, and 0.14 for PSP [35]. Data in pathologically confirmed disease indicate that virtually all patients develop asymmetric or unilateral akinetic rigid parkinsonism and a gait disorder [11]. Median survival time after onset of symptoms is 7.9 ±0.7 (range 2.5–12.5) years. The last clinical visit documented occurred a mean of 6.1± 2.0 years after symptom onset, by which time 42% exhibited cortical dementia, 43% were wheelchair bound, 14% required assistance to walk, and 93% had speech abnormalities. Clinical features associated with shortened survival included early bilateral brady­ kinesia or frontal syndrome, whereas age at onset, early onset of dysphagia, dementia, incontinence, falls, gait disturbances, aphasia, ideomotor apraxia, pyramidal signs, dystonia, or vertical supranuclear palsy had no effect on prognosis or survival. Bronchopneumonia represented the cause of death in all patients with information available [11]. Survival duration in CBD of 4–8 years [10,11,14] is less than that found in pathologically confirmed parkinsonian syndromes (8 ± 4.1 years for PSP and 7.9 ± 2.8 years for MSA) [36].

Pathology Biochemistry CBD is one of the neurodegenerative diseases associated with the deposition of insoluble fibrillary deposits containing the microtubule-associated protein tau [34].

Tau is a microtubule binding protein which stabilizes polymerization of tubulin into microtubules. Alternate splicing from the tau gene leads to six different isoforms of the protein in the brain which differ in whether they have three or four microtubule-binding domains [34,37]. In CBD tau has predominantly four microtubule binding domains [38]. CBD and PSP are associated with the common tau haplotype, H1 [39]. The microtubuleassociated protein tau (MAPT) gene is located on chromosome 17 and mutations have been associated clinically with CBS presentations (Table 16.2). Mirroring the clinical diagnostic uncertainties surrounding CBS and CBD, it was not until 2002 that standardized neuropathological criteria were agreed for the diagnosis of CBD [40]. This adds to the difficulties in studying CBD in the literature, as until this point there will be some heterogeneity in diagnostic confirmation. Although there is general consensus that CBD is a distinct entity, there is ongoing nosological debate about whether PSP and CBD form part of a spectrum of the same disease given (1) their clinical and pathological overlap, (2) that they both are associated with four-microtubule binding repeat tau deposition and a H1 haplotype, and (3) that both clinical syndromes have been reported in presentations of MAPT mutations which cannot be pathologically differentiated from CBD [41,42]. Others have suggested that CBD, PSP, and FTD should be grouped together as part of the ‘Pick complex of diseases’ [43].

Macroscopic findings Macroscopic findings are not specific (Table 16.3) but typically there is cortical atrophy in frontal lobes with lateral ventricle dilatation [44] and characteristically parasagittal cortical gyri narrowing is seen in a perirolandic distribution with the posterior superior frontal gyrus more affected than the middle or inferior frontal gyri [40]. Atrophy may be asymmetric and in cases with aphasia inferior frontal and temporal lobe involvement is seen. Loss of associated white matter is seen [44] with thinning of the corpus callosum. The thalamus may be atrophic and the caudate head flattened. The substantia nigra is pale [44] but the locus coeruleus is preserved [40] or pale [44].

Table 16.2  Tauopathies without other associated pathology Disease

3R or 4R predominant

Associated gene

PSP

4R

MAPT H1

Argyrophilic grain disease

4R

MAPT H1

CBD

4R

MAPT H1

Pick’s disease

3R

None

FTD with parkinsonism linked to chromosome 17

4R

MAPT mutations

Postencephalitic Parkinsonism

Equal

None

Parkinson’s–dementia complex of Guam

Equal

None

CBD, corticobasal degeneration; FTD, frontotemporal dementia; PSP, progressive supranuclear palsy. Adapted from Williams et al 2006 Int Med 36(10):652 Table 1

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Table 16.3  Macroscopic and microscopic findings in corticobasal degeneration Feature Macroscopic findings

Microscopic findings

(Asymmetric) posterior frontal atrophy with associated white matter atrophy and callosal thinning Atrophy of caudate and thalamus Nigral pallor Focal neuronal loss and astrogliosis in affected cortex and substantia nigra White matter myelin loss Ballooned neurons in affected cortical areas Tau-immunoreactive neuronal inclusions in affected cortex, lentiform, thalamus, subthalamus, substantia nigra and locus coeruleus Astrocytic plaques in affected cortex and basal ganglia Tau-positive threads and coiled bodies in centrum semiovale, corticospinal tracts, lentiform nucleus, pontine base, and grey matter of affected cortex, basal ganglia, thalamus, and brainstem

Adapted from Dickson et al. [40].

Microscopic findings Neuronal loss and astrogliosis are found in affected areas with spongiosis and microvacuolation in affected regions of cortex with underlying white matter myelin loss [40]. In the 3rd, 5th, and 6th cortical layers ‘swollen’, ‘achromatic’ or ‘ballooned’ neurons (BN) [1]‌are seen which are immunoractive for neurofilaments but with different immunoreactivity to Pick bodies of Pick’s disease and, if in characteristic cortical areas, are relatively specific for CBD [40]. Tau immunohistochemistry reveals pretangles, or small neurofibrillary tangles in the cortex and substantia nigra and locus coeruleus. The neuropil also contains tau-immunoreactive cell processes and the location in cell processes is characteristic of CBD. Tau-immunoreactive alphasynuclein-negative coiled bodies are found in oligodendroglia [40]. In the neocortex tau-immunoreactive astrocytic lesions are characterized by an annular cluster of processes named ‘astrocytic plaques’ and these are probably the most specific finding in CBD (Figure 16.1). There is lateral substantia nigra cell loss and astrogliosis [7].

Discerning the cause of corticobasal syndrome and the multifarious presentations of corticobasal degeneration Even in expert centres, it is acknowledged that there will probably always be patients whose clinical diagnosis in vivo will not be confirmed pathologically. In the Queen Square Brain Bank post-mortem series, the rate of correct clinical diagnoses in life for pathologically confirmed PD was only 91% [45] with correct diagnoses of PSP and MSA seen in 72% and 70% respectively [46]. Recent work has confirmed that there is even greater heterogeneity in both the presentation of cases who turn out to have corticobasal degeneration at post-mortem and in the pathological findings of those who present with a CBS during life [14,47]. Furthermore, the mix of pathologies and clinical diagnoses in case series depends on whether the patients were seen in a predominantly movement disorders or cognitive specialist clinic. In a report from a primarily movement disorders setting at the Queen Square Brain Bank for Neurological Disorders, of 19 cases with a pathological diagnosis of CBD [14], only 5 had a CBS diagnosis at time of death, i.e. approximately one-quarter of cases of CBD were accurately diagnosed during life (sensitivity 26.3%). On the other hand, of 21 CBS presentations only 5 were associated with CBD pathology, i.e. approximately one-quarter of cases of CBS turn out to have corticobasal degeneration at post-mortem and another diagnosis is more likely [14], although the figure is almost 50% if a movement disorders specialist is the attending physician. In another series from a primarily cognitive centre 18 cases with CBD pathology were associated with an executive-motor presentation (containing both features of CBS and being PSP-like but not fulfilling clinical research criteria for the diagnosis) in 7/18 [47]. At post-mortem examination of 40 patients with CBS, 14 had a pathological diagnosis of CBD (35%). This has led to the development of more complicated clinical research diagnostic criteria [18]. Given the complexity we consider those conditions that may present with CBS, and the clinical manifestations of CBD (Table 16.4), separately.

Aetiology of corticobasal syndromes Clinical features of CBS are listed in Table 16.5. In patients presenting with CBS only a minority are found to have CBD at post-mortem:

Fig 16.1  Tau-positive neuronal and glial inclusions (tangles and threads) seen with immunohistochemistry. On the right an astrocrytic plaque is demonstrated. Courtesy of Dr Janice Holton, Queen Square Brain Bank for Neurological Disorders, UCL Institute of Neurology.

Chapter 16 

corticobasal syndrome and corticobasal degeneration

Table 16.4  The Armstrong (2013) diagnostic criteria for corticobasal degeneration Probable CBS: asymmetric presentation with 2 of: ◆ limb rigidity/akinesia, ◆ limb dystonia ◆ myoclonus plus 2 of: ◆ orobuccal or limb dyspraxia cortical sensory deficit ◆ alien limb Frontal behavioural spatial syndrome: ◆ executive dysfunction ◆ behavioural or personality change ◆ visuospatial deficit Non-fluent variant of primary progressive aphasia: ◆ impaired grammar with preserved single word comprehension ◆ apraxia of speech Progressive supranuclear palsy syndrome: ◆ axial/limb rigidiy/akinesia ◆ postural instability/falls ◆ urinary incontinence ◆ behavioural changes ◆ supranuclear gaze palsy or slowing of vertical saccades Using the Armstong criteria for possible CBS only one feature from each of the extrapyramidal and cortical domains are needed; for probable CBD the onset must be insidious with gradual progression, duration greater than 1 year, age greater than 50 years, no family history in more than one relative, no MAPT mutation and the phenotype must be one of the first three with at least one CBS feature. Possible sporadic CBD criteria are less stringent [18].

◆ CBS with CBD at post-mortem—CBS-CBD: A corticobasal syndrome is found in 24–35% [14,47] but may be found in up to 47% in the literature [14]. ◆ CBS-AD: In one study this was found in 5/21 CBS cases although these had no evidence of parkinsonism at onset, nor early falls [14]; in another 23% had CBS-AD and these were more likely to fulfil clinical criteria for CBS than CBS-CBD even [47]; in a study of focal cortical presentations of AD 50% of CBS had CBS-AD [48]. ◆ CBS-PSP: PSP was the most common cause of CBS in the Queen Square Brain Bank study from a movement disorder clinic (6/21 or 29%) [14] but less common in cognitive groups where it was found in 13% [47]. The presentation with CBS is caused by increased cortical pathology either of PSP itself [49] or an associated pathology such as AD [50]. ◆ CBS-TDP: This is found in 10–13% [14,47,51]. ◆ CBS-mixed: These cases included AD pathology in addition to PSP, CBD, or FTLD-TDP [47].

Case reports of other diseases presenting with corticobasal syndrome In addition to this there have been case reports of other pathologies presenting with CBS.

Table 16.5  Aetiology of clinical corticobasal syndrome Neurodegenerative disease Corticobasal degeneration Alzheimer’s disease Progressive supranuclear palsy Frontotemporal dementia with TDP43 pathology Pick’s disease Dementia with Lewy bodies Creuzfeldt–Jakob disease Neurofilament inclusion body disease/frontotemporal dementia with FUS pathology Argyrophilic grain disease Mixed pathology Vascular disease Carotid stenosis Stroke Genetic disease MAPT mutations Progranulin mutations TDP43 mutations FUS mutations C9ORF72 mutations

Vascular disease has been implicated with rapid-onset CBS caused by carotid stenosis with multiple emboli events demonstrated on MRI [52] and a rapidly progressive CBS caused by stenosis/occlusion of the middle cerebral arteries with associated infarction on MRI [53]. Other neurodegenerative diseases reported to be associated with CBS in the literature include Pick’s disease [54], AD with diffuse dementia with Lewy bodies [55], CJD [54,56,57], and neurofilament inclusion body disease (now known as frontotemporal lobar degeneration with FUS pathology, FTLD-FUS) [58]. CBS is also reported in genetic disease including mutations of MAPT [59], TDP43 and FUS [60], PRGN [61], and C9ORF72 [62].

Clinicopathological correlations in pathologically confirmed corticobasal degeneration Conversely, patients with CBD pathology turn out to have a variety of clinical phenotypes during life (Table 16.6) [14,47]. In a cognitive setting the clinical presentations in order of frequency are CBS, executive-motor phenotype (characterized by a frontal dysexecutive sydrome similar to PSP but without falls or eye movement abnormalities and features of CBS), PNFA, FTDbv, and posterior cortical atrophy [47]; whereas in a movement disorders setting the presentations are most commonly PSP, followed by CBS, PD, FTD, Pick’s disease, spastic quadriparesis with myoclonus, and incidental pathology in a case of Tourette’s syndrome

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Table 16.6  Clinical presentation of confirmed corticobasal degeneration Presenting clinical phenotype Corticobasal syndrome Progressive non-fluent aphasia Progressive supranuclear palsy or executive-motor phenotype’ Frontotemporal dementia including the behavioural variant Posterior cortical atrophy (Alzheimer’s disease) Parkinson’s disease Pick’s disease Progressive quadriparesis with myoclonus Apraxia of speech Incidental ‘Symmetric’

[14]. The frontotemporal syndromes of PNFA and apraxia of speech are well described in the literature [31,30,63], as are frontotemporal dementia and posterior cortical atrophy [23,64], AD, and PSP. There is even a symmetric presentation without apraxia, myoclonus, dystonia, or the alien limb phenomenon and with 40% having a family history [65].

Non-imaging diagnostic tools in corticobasal syndrome/corticobasal degeneration Currently, there is little data available regarding the use of biomarkers other than brain imaging in CBS/CBD. ◆ Cerebrospinal fluid (CSF) biomarkers: The growing literature about the use of CSF analyses [66] in the related tauopathy PSP will hopefully lead to increased work and improved diagnostic accuracy in CBD [67]. In a series of 1000 patients attending a memory clinic where CSF amyloid β 42 (Aβ42), total tau (t-tau), and phosphorylated tau (p-tau) levels were measured, a CSF AD biomarker profile was seen in 38% of CBS patients, suggesting these markers are unlikely to be helpful in diagnosing CBS [68]. ◆ Neurophysiology: Abnormalities of brainstem auditory evoked potentials and somatosensory evoked potentials [69], long latency reflexes [70], and surface electromyography (EMG) [71] are described in CBS but yet to reach routine clinical practice or be confirmed in pathologically confirmed disease. ◆ Olfaction testing: Olfactory function in CBS has been shown to be more impaired than the frontal variant of FTD [72] and less impaired compared to AD patients [73] or PD, but may not be useful to differentiate between other forms of atypical parkinsonian disorders [74].

Conventional MRI As a consequence of the evolving literature concerning the pathological underpinning of CBS and the clinical phenotypes

associated with CBD, much of the earlier literature without pathological confirmation of disease needs to be read with caution. Abnormalities reported in the early literature include asymmetric cortical atrophy [75,76], T2 and proton density hyperintensities in affected cortical grey matter and underlying white matter [76], atrophy of the central corpus callosum [77], putaminal and pallidal T2 signal hyperintensity [75], signal hyperintensity, and atrophy in the primary motor and supplementary motor areas contralateral to the clinical deficit [78]. Diffuse supratentorial atrophy in the absence of midbrain atrophy, which on more detailed subjective assessment may show characteristic asymmetry affecting the side contralateral to the most affected side clinically, was felt to be characteristic [79,80]. In a series of 26 CBS patients frontal and parietal atrophy were the most common abnormalities, followed by dilatation of the lateral and third ventricles; asymmetric frontoparietal atrophy accompanied by lateral ventricular dilatation was the most reliable predictor of a clinical diagnosis of CBS when compared to PD, PSP, and MSA [81]. Occasionally focal abnormalities have been reported such as focal atrophy and signal hyperintensity on FLAIR images correlating with contralateral leg area of the homunculus [82]. A more recent study reported that all CBS have cerebral atrophy, 81% asymmetry with the side contralateral to the affected limb most affected and atrophy most severe in the anterior frontal and parietal lobes; 94% had corpus callosal atrophy [83]. Atrophy was also seen less frequently in the cerebral peduncles, pyramids, and midbrain tegmentum. 88% had signal hyperintensity on FLAIR sequences in the frontal or parietal subcortical white matter ipsilateral to the atrophic cortex with hyperintensities seen in the frontal lobes when there was motor aphasia or apraxia of speech, and parietal hyperintensities in 50% of those with parietal signs (see Figure 16.2) [83]. However, in confirmed CBD global cortical atrophy is evident with a predilection for the frontal and parietal regions which may be asymmetric [84] or symmetric [85,44]. A recent report supports a symmetric presentation of CBD presenting with frontotemporal dementia rather than a CBS (see Figure 16.3) [65]. Furthermore, abnormalities are described in the substantia nigra (smudging or thinning) and signal change in the pallidum [85], third ventricle dilatation [44,85] and midbrain atrophy [44,85] or midbrain signal abnormality [85], including periaqueductal T2 hyperintensity [44]. However, none of these features is specific enough on its own to exclude other diseases in the differential diagnosis including PD and atypical parkinsonian disorders MSA and PSP. While not typically the case, midbrain tegmental atrophy can be marked, leading to further difficulties differentiating CBD and PSP [84], although this atrophy would not be sufficient to be classified as a ‘hummingbird’ sign which is specific for PSP [44]. Other reported abnormalities in CBD include T1 signal hyperintensity in the subthalamic nucleus, signal hyperintensity on T2 weighted and FLAIR images in the subcortical white matter corresponding to axonal degeneration, and primary white matter disease in CBD [84]. A systematic blinded study of conventional MRI in patients presenting with CBS who had a subsequent pathological diagnosis revealed that posterior frontal, superior parietal, and middle corpus callosal atrophy were found both in those who had CBD at post-mortem and also those who did not (in this study this included PSP, FTD, AD and CJD) [86].

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Fig. 16.2   Asymmetric cortical atrophy and atrophy of the cerebellar peduncle in corticobasal syndrome. Reprinted from Neuroradiology, 49(11), Koyama, M., A. Yagishita, et al, Imaging of corticobasal degeneration syndrome, 905–12. Copyright (2007), with permission from Elsevier.

Fig. 16.3  Symmetrical atrophy in corticobasal degeneration. Reprinted from Parkinsonism Relat Disord, 16(3), Hassan, A., J. L. Whitwell, et al., Symmetric corticobasal degeneration (S-CBD), 208–14. Copyright (2010), with permission from Elsevier.

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Exclusion of other diseases Vascular disease Vascular disease can be detected using Diffusion-weighted imaging (DWI), as illustrated in a case presenting with a CBS and with extensive ischaemic lesions evident as diffusion-restricting lesions [53].

CJD This important mimic is most readily identified by the short disease duration and cortical diffusion restriction in addition to asymmetric functional imaging findings [57,87].

More advanced MRI techniques Volumetric MRI The first report of 3D-MR volumetry in CBS (in a proportion of whom pathological confirmation was obtained) used regiongrowing-based segmentation after manual presegmentation and found parietal and occipital lobe and white matter volumes and the corpus callosum were significantly reduced compared to controls and PSP, with PSP affecting the midbrain structures much more [88]. Similarly, using voxel-based morphometry in a clinically diagnosed cohort of CBS (called CBD syndrome in this paper) and PSP, CBS showed asymmetric atrophy of the premotor cortex, superior parietal lobules, the frontal eye fields, and striatum with PSP showing atrophy of the midbrain and pontine tegmentum, thalamus, and striatum [89]. The most detailed study in pathologically confirmed PSP and CBD shows distinct differences in the patterns of atrophy depending on the pathological diagnosis (PSP vs CBD) and the

PSP

predominance of the clinical phenotype (extrapyramidal vs cognitive): overall, in CBD a predominantly cortical pattern of atrophy was found with grey matter loss in the posterior inferior, middle, and superior frontal lobes, superior premotor cortex, and posterior temporal and parietal lobes, subcortical grey matter atrophy in the globus pallidus, putamen, and head of caudate with relative brainstem sparing; white matter volume loss was evident in posterior frontal lobes, the corpus callosum, the external capsule, and right midbrain [90]. In PSP brainstem atrophy was more pronounced (see Figure 16.4). In the extrapyramidal-predominant groups CBD had more cortical atrophy and PSP more brainstem atrophy; in the dementia-predominant groups CBD had more cortical and PSP more subcortical disease; in dementia-predominant CBD there was isolated grey matter atrophy and in extrapyramidal-predominant CBD both grey and white matter loss [91]. The authors commented on the overlap with FTD where frontal and insula atrophy are seen and that while PSP and CBD can present as similar clinical syndromes of movement disorder and cognitive disorder predominance, the pattern of disease appears to be different (see Figures 16.5 and 16.6). Differences have also been found using voxel-based morphometry (VBM) to detect differences between patients with CBS who turn out to have AD and CBD [90]. Similar to previous work both show posterior frontal and superior parietal atrophy, but in AD inferior parietal, posterior temporal and occipital cortex are affected but without differences in hippocampal volume or subcortical grey matter [90]. Greater parietal, posterior temporal, and occipital cortex atrophy in CBS-AD has been confirmed in a subsequent study which also revealed that CBS-TDP pathology was associated with

CBD

Cortical grey matter Subcortical grey matter White matter

Fig 16.4   3D renders showing the patterns of grey (shown in blue) and white (shown in yellow) matter atrophy in subjects with pathologically confirmed CBD and PSP compare to controls (p 60 ms) can be used for T2-weighted imaging to avoid this pitfall. This subtle signal change may also represent subclinical neuropathy and slight contusion trauma. In such circumstances, other findings such as nerve calibre change, fascicular abnormality,

course deviation, perineural abnormality, and regional muscle denervation should be looked for carefully to increase specificity in the diagnosis of neuropathy.

Post-trauma nerve degeneration Nerve degeneration in PNI can show changes in nerve diameter, appearance and signal intensity on MRN. The injured peripheral nerves often show focal or diffuse enlargement, abnormal hyperintensity of fascicles on T2-weighted imaging, disruption of the fascicular pattern, swelling of individual fascicles, displacement or altered nerve course, formation of a traumatic neuroma, and perineural low signal intensity indicating fibrosis [14,15]. Diffuse or focal enlargement and hyperintensity on the T2-weighted imaging are the most common features of an injured nerve(Figures 33.2–33.5). The extent of the signal and morphological abnormalities are associated with the type of injuries. In general, the morphological and signal abnormalities are restricted to the injury site and distal portion of the injured nerve in crush or transaction injuries, while diffuse abnormalities covering whole segment of nerve trunk are often found with nerve traction, or stretch injury [20]. Subtle changes such as loss or distortion of the characteristic fascicular pattern

Fig. 33.2  Magnetic resonance neurography of brachial plexus injury. Fat-suppressed T2-weighted imaging demonstrates that the upper (C5–C6), middle (C7), and lower (C8, T1) trunks of the left brachia plexus (arrows) are enlarged and have diffuse hyperintense signal. There is also oedema of the scalene muscles.

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Fig. 33.3  Magnetic resonance neurography of common peroneal nerve injury. Transverse (A) and oblique (B) fat-suppressed T2-weighted imaging demonstrate that the common peroneal nerve is enlarged and has diffuse hyperintense signal (arrows). There is also bone marrow oedema in the tibia (t) and fibula (f), indicative of a severe trauma.

Fig. 33.4  Magnetic resonance neurography of brachial plexus injury. T2-weighted imaging demonstrates that the lower (C8, T1) trunks of the right brachial plexus show fusiform enlargement and the middle (C7) trunk is mildly enlarged; both have hyperintense signal (arrows). There is a nerve root avulsion which manifests as a fluid collection around the lower trunk near the nerve root (arrowhead).

Fig. 33.5  Magnetic resonance neurography of brachial plexus injury. T2-weighted imaging demonstrates that the three trunks of the right brachial plexus are enlarged and have diffuse hyperintense signal (arrows). There is apparent soft tissue oedema in the right supraclavicular field (arrowhead).

and swelling of individual fascicles also indicate an abnormal nerve. On fat-suppressed T2-weighted imaging, due to adequate fat suppression and increased dynamic range of contrast, normal nerves are hyperintense. In such situation, asymmetric T2 hyperintensity and other signs of the abnormal nerves compared

with normal-appearing nerves on the ipsilateral or contralateral side is critical for making a correct diagnosis. The underlying aetiology of hyperintensity of injured nerve on T2-weighted imaging remains unclear. Various mechanisms have been hypothesized to explain such signal changes. In animal studies, the T2 relaxation time of normal peripheral nerves in rodents has been assigned to three components: myelinic, axonal, and extra-axonal water protons [25]. Endoneurial and/or perineurial oedema, venous congestion, obstruction of the axoplasmic flow due to entrapment or mechanical insult, altered blood–nerve barrier, and wallerian degeneration distally could each play a role in hyperintensity on T2-weighted imaging [15,26]. Pathologically, neurapraxia damages only the myelin sheath around the axon with resultant transient functional loss. In a neurapraxic injury, nerves commonly show slightly increased signal intensity on T2-weighted imaging or mild enlargement of the peripheral nerve, and these abnormal signs are often restricted to the injured site [14]. In more severe axonotmesis, the axon is usually completely ruptured, resulting in wallerian degeneration of its distal segment. However, the perineurium and epineurium remain intact. Axonotmesis exhibit increased signal intensity on T2-weighted imaging and enlargement of the nerve. The signal

Chapter 33 

neurodegeneration after trauma: peripheral nerves

abnormalities often involve the injured site and distal segments owing to wallerian degeneration. Other findings in axonotmesis include effacement, enlargement, or disruption of individual fascicles, and sometimes there may be formation of a neuroma, which has the characteristic findings of discrete nodular or fusiform nerve enlargement (Figure 33.6) [14]. Neurotmesis is the most serious injury and represents complete severance of the nerve. In the acute stage of neurotmesis, MRN can directly reveal the nerve discontinuity and the gap which is filled with fluid and granulation tissue(Figure 33.7). In the subacute and chronic stages, fibrosis at the injury site is typically seen as hypointense soft tissue within the nerve gap on T2-weighted imaging. As in axonotmesis, injury site at the level of neurotmesis often lead to formation of neuroma [14]. Traumatic nerve injury may result from traction, contusion, or penetrating trauma. In many cases, multiple types of trauma may coexist. An exact differentiation among them is not always feasible because there may be some overlap of imaging findings. Contusion trauma most often occurs in nerves that run close to bony surfaces and are therefore more easily compressed by excessive external pressure [27]. A  typical nerve contusion trauma involves the deep peroneal nerve as it runs closely opposed to the bony surfaces of the dorsal aspect of the ankle and midfoot [28]. Nerve contusion traumas are also reported to develop in the ulnar, axillary, and spinal accessory nerves in rugby players and martial artists [27,28]. Most nerve contusions are mild, usually transient and self-limiting followed by complete recovery, thus MRN is rarely used. However, repeated minor contusion trauma usually causes nerve abnormalities which can manifest as a segmental

fusiform thickening of the nerve at the site of trauma and hyperintensity of the nerve on fat-suppression T2-weighted imaging and STIR images. Traction injuries typically occur as a result of repetitive sprain or overuse [27]. A typical traction injury is the avulsion of the nerve roots encountered in brachial plexus trauma caused by road traffic accidents or during childbirth [27]. Another site of nerve traction is the popliteal fossa with the peroneal nerve involved during serious sprains, knee dislocation, or fractures [27]. In a typical traction injury, the stretched nerve appears diffusely enlarged and hyperintense due to increased water content and extends a large distance on fat-suppressed T2-weighted imaging. In mild traction traumas, the stretched nerve appears fairly normal or slightly enlarged. When the traction trauma is more significant, laceration of the peripheral nerve may occur; here MRN can show the partial or complete disruption of the fascicles. Penetrating injury, the most serious trauma, results in the nerve being partly or completely transected. After penetrating injury, regenerating axons, Schwann cells, and fibroblasts may form a non-neoplastic mass at the end of the nerve. The nerve can regrow to fill the gap between the fascicles. MRN can directly show the nerve discontinuity and the gap filled with fluid and granulation tissue. In a completely transected stump neuromas develop as small masses in continuity with the opposite edges of the severed nerve. Stump neuromas often show slight hyperintensity on T2-weighted imaging or STIR and usually have well-defined margins, or irregular or poorly defined borders when the attachment to the surrounding tissues was caused by adhesions and encasing

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Fig. 33.6  Magnetic resonance neurography of brachial plexus injury. T2-weighted imaging (A) and diffusion-weighed imaging (B) demonstrate that the middle (C7) and lower (C8, T1) trunks of the left brachial plexus (arrows) are enlarged and have hyperintense signal. Several neuromas have also formed in the lower trunk (*).

Fig. 33.7  Magnetic resonance neurography of brachial plexus injury. Contiguous T2-weighted images demonstrate that three trunks of the right brachial plexus are completely disrupted (arrows) and between the nerve gaps there is a huge fluid collection (arrowheads).

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scar tissue. Sometime, stump neuromas can be larger than the axial diameter of the native nerve. Apart from nerve changes, muscle denervation secondary to the injured nerve can indicate or further confirm the presence of PNI. In the acute stage, denervated muscles may show oedema-like signal intensity as early as 24 hours after the onset of PNI. Such muscular hyperintensity is best detected by STIR or T2-weighted fat-suppressed sequences. These denervation-related signal abnormalities do not reflect real oedema but a relative fluid shift between intra- and extravascular compartments and perfusion change. In the subacute stage (weeks to months after injury), denervated muscles commonly appear as a markedly high signal on T2-weighted imaging and a low signal on T1-weighted imaging. Besides that, minimal fatty replacement is often seen. In chronic stage (months to years after nerve injury), marked atrophy of denervated muscles occurs with variable signal intensity on T2-weighted images, and high signal on T1-weighted images as a result of fatty infiltration, suggesting irreversible damage. Once the denervated muscles show severe fatty replacement, it is unlikely that primary nerve repair will yield a satisfactory, functional recovery, outcome. MRI can therefore provide information about the site and the severity of the injury, which may help in predicting the prognosis. MRI can also reveal denervation in the discrete and restricted areas of a muscle that might been missed by needle EMG [6,29]. Denervation muscle changes should be differentiated from systemic or metabolic myopathy, myositis, disuse, muscle strains, and radiation myositis. However, the presence of muscle changes in a regional nerve distribution, diffuse rather than focal muscle involvement, and lack of fascial and subcutaneous oedema helps in the differentiation of denervation from other causes [14,17].

Post-trauma regeneration After injury, the peripheral nerves have the capability to regenerate. The regeneration process mostly depends on the length of the gap between the nerve ends after removal of irreversibly damaged tissue and terminal neuromas [22,27]. Since regeneration may fail owing to scar formation or dislocation of nerve ends, early neurosurgical intervention is still required. If the gap is short, an ‘end-to-end’ anastomosis is enough to restore continuity, whereas a nerve graft might be required when the gap is wider than 5 mm. The nerve graft guarantees a ‘proximal nerve end–graft–distal nerve end’ anastomosis. Postoperative assessment of the repaired peripheral nerves and improved nerve discontinuity at repair sites are needed. Besides the diagnosis, MRN can provide evidence of nerve regeneration and can be used to monitor the process of peripheral nerve recovery after the injury [14,15]. It can detect the nerve conduits and can demonstrate residual or recurrent pathology, complications of surgery (such as haematoma, abscess, or focal perineural encasing fibrosis), or other unexpected findings [15]. MRN is therefore a suitable and valuable tool for monitoring the process of peripheral nerve recovery and persistent nerve discontinuity repaired by surgery or nerve tube implantation [14]. In the acute stage of nerve trauma, the increased signal intensity usually peaks on days 3–14, and the hyperintensity gradually disappears and returns to normal by day 70 [30]. It is also reported that hyperintensity on T2-weighted imaging gradually decreased even after 8–10 months [6]‌. However, a T2 signal

abnormality may persist for a prolonged period of time despite clinical and functionaql improvement [14]. Current T2-based MRN still has some drawbacks in evaluating nerve regeneration. Multicomponent T1, especially T2 relaxation, may yield more important information about the microstructural changes of nerves. The multicomponent T2 relaxation time is often interpreted as evidence of tissue compartmentalization. In nerve tissues, the T2 components have been detected as associated with myelin (5–12 ms), intracellular water (15–70 ms), extracellular water (90–250  ms), and cerebrospinal fluid (2000  ms) [31]. In animal experiments, these MR properties of nerve are consistent with the microstructural changes observed in histopathology examinations [20,32,33]. In our previous experimental study [19,20], the T1 and T2 relaxation times of the normal rabbit sciatic nerve measured with a 1.5-T clinical MR system were 915 ± 41 ms (range 839–956 ms) and 40 ± 2 ms (range 36–43 ms), respectively [19]. In acute nerve traction injury, sequential MRI with T1 and T2 measurements and functional changes as well as histological assessments were studied over a 70-day follow-up period [20]. The results demonstrated that three portions of the injured nerves (distracted portion, distal portion, and proximal portion) showed varied enlargement and hyperintense signals on T2-weighted imaging and had prolonged T1 and T2 relaxation times. Obvious nerve enlargement and hyperintense signals were observed in the distracted and the distal portions, starting at 1  day after surgery and peaking at 7 days; afterwards, nerve enlargement gradually returned to normal at 10-week follow-up. Signal abnormality remained until 10-week follow-up in the distracted portions, while the distal and proximal portions gradually returned to normal at 4 weeks and 2 weeks respectively(Figure 33.8). T1 and T2 values of the distracted and distal portions showed a rapid rise to a peak at 3 days and were followed by a rapid decline until 2 weeks, after which they slowly decreased. The most pronounced and prolonged phase of T1 and T2 values increases (peak values of 1333 ± 46 ms and 79 ± 3.7 ms, respectively) observed in the distracted portions of the injured nerve at 3  days. In the distracted portions, T1 and T2 values remained elevated at 10-week follow-up. In the distal portions, T1 values returned to near-normal levels at 4-week follow-up and T2 values remained slightly higher than normal at 10-week follow-up. In the proximal portions, T1 and T2 values were essentially normalized at 2-week follow-up for T1 values and at 1-week follow-up for T2 values (Figure 33.9). Additionally, T1 and T2 values and functional changes after nerve injury showed a similar time course. These results show that signal abnormalities and the time course of T1 and T2 values correlated with nerve injury and their recovery pattern could be correlated with functional recovery. MRI may hold much promise as a means both of predicting the degree of nerve damage and of monitoring the process of nerve recovery [20]. Stanisz et al. measured the T1 and T2 relaxation times, magnetization transfer (MT), and diffusion anisotropy of rat sciatic nerve at different time point following trauma. Results showed that the MR measurements were correlated with histopathology. Observed changes in tissue microstructure, such as demyelination, inflammation, and axonal loss, can result in a significant increase in T1 and T2 relaxation times, reduction in the MT effect, and decrease in diffusion anisotropy. Thus these MR parameters are very good indicators of nerve regeneration,

Chapter 33 

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neurodegeneration after trauma: peripheral nerves

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Fig. 33.8  Magnetic resonance neurography of the stretched sciatic nerve. Sequential fat-suppressed 3D T2-weighted images demonstrate that the proximal portion (p) of the stretched sciatic nerve of a rabbit shows hyperintense signal from 1 day to 3 days after injury and thereafter returns to normal at 1 week. The distracted portion (between arrows) shows hyperintense signal during 8-week follow-up and obvious enlargement which gradually increases and then settles. The distal portion (d) manifests hyperintense signal from day 1 to 2 weeks and returns to normal at 4 weeks after injury.

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and may therefore be useful in monitoring therapies that assist nerve regeneration [32]. At present, the measurement of MR properties including proton density, T1 and T2 relaxation time, and MT ratio of nerves is achieved in humans by using clinical 3T MR scanners [34].

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Fig. 33.9  Time course of T2 values in the stretched nerve. Nerve T2 values obtained by a single-slice multi-echo spin echo (2000/160, TR/TE) in the distracted portions or in the distal portions showed a rapid rise to a peak at 3 days after injury followed by a rapid decline until 2 weeks after injury; thereafter, they slowly decreased but remained elevated at 10-week follow-up in the distracted portions. T2 values continued to decrease but remained slightly higher than normal at 10-week follow-up in the distal portions. Shen J, et al. Radiology. 2010, 254:734, RSNA.

Considering the lack of donor nerve or the secondary functional loss at the donor site, artificial nerve conduits are being increasingly used for the repair of relatively short nerve defects. The nerve tube is sutured to freshened edges of the nerve in the hope of promoting nerve regeneration. Reported rates of nerve regeneration vary from 0.5 to 1 mm per day [15]. During the immediate postoperative period, nerve tubes are seen as curvilinear hypointensities on both T1-weighted and T2-weighted imaging, and nerve tubes are often filled with fluid, accordingly showing marked hypointensity on T1-weighted imaging and hyperintensity on T2-weighted imaging. Early nerve regeneration and nerve sprout formation within nerve tubes may exhibit as tiny filling defects within the tubes on MRN, and these filling defects develop a fascicular appearance with increased T2 signal intensity over 2–4 months [15]. In our previous animal study, gadofluorine

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Fig. 33.11  MRI of transected nerve repaired with chitosan tube. Gross inspection reveals that the nerve is reconnected at 6 weeks after surgery (A). The corresponding gadofluorine-enhanced TSE T1-weighted image shows the continuity of regenerated nerve (B). Immunohistochemical staining demonstrated that there are prominent Schwann cell proliferation and axonal regeneration in the proximal portion (C, F) and distal portion (E, H) and graft inside the tube (D, G). A tiny neuroma is found at the transection site (arrows in D, G). Bars in C–H = 50 μm.

M (Gf)-enhanced MRI and nerve T2 relaxation time measurement were applied to observe the longitudinal changes of nerve repair in rat nerve transection injury with chitosan nerve tube throughout the 8-week follow-up. Results showed that the distal and proximal portions of the injured nerves and implanted chitosan tubes were clearly visualized on both T2-weighed imaging and Gf-enhanced T1-weighted images. Obvious signal abnormality in the distal portions of the nerves and mild abnormality in the proximal portions were observed 1 week after surgery on T2-weighed imaging, followed by a gradual decrease and a return to the baseline level between 5 and 8 weeks after surgery. The graft inside the tubes showed a continuous hyperintense signal on T2-weighed imaging during the entire study period (Figure 33.10), whereas the Gf-enhanced T1-weighted images revealed that the nerve continuity began to be partially restored at 4 weeks and was completely restored at 8 weeks follow-up (Figure 33.11). Furthermore, nerve T2 relaxation times and Gf enhancement, as well as functional changes, showed a similar time course. T2 relaxation times of the distal portion of transected nerves showed a rapid return to baseline level (Figure 33.12). These results indicated

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Fig. 33.12  Time course of T2 values in the nerve treated with nerve conduit. T2 values in the distal portion of transected nerves repaired with nerve conduit show a rapid increase after surgery, then slowly decrease but remain elevated until 8-week follow-up. Shen J, et al. Radiology, 2012, 262:166, RSNA.

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Fig. 33.14  In-vivo MRI monitoring of transplanted mesenchymal stem cells (MSCs). Fat-suppressed T1-weighted images (TR/TE, 650/20 ms) of the stretched nerve grafted with Gd-DTPA-labelled MSCs (A) showed a subepineurium linear high signal intensity (black arrows) in the distracted portion immediately after transplantation (0 day); thereafter, the high signal gradually diminished and diffused throughout the injured nerves by day 7 following transplantation and was no longer discernable by 14 days. No obvious hyperintense signal was found in animals treated with unlabelled cells (B). Arrowheads indicate the distracted portion of the injured nerve. Shen J et al. J Magn Reson Imaging, 2010, 32:1082.

that peripheral nerve repair with use of tissue-engineered constructs can be monitored by Gf-enhanced MRI (Figure 33.13) and T2 relaxation time measurements [35]. The regeneration and repair following nerve injury may last for many months, even up to a year. Some studies have shown that recovery of nerve function is paralleled by gradual normalization of the nerve signal on MRI, beginning at the lesion site and then further extending distally, in both experimental and human traumatic nerve injuries [16,36]. It is noted that decline of hyperintensity on MRN is closely relevant to the axonal regeneration but does not always correlate with recovery of nerve function, particular in a severe traumatic nerve injury. In the future larger. randomized controlled studies are needed to evaluate the potential of high-resolution MRN in the confident diagnosis of reinnervation [15].

Stem cell therapy Cellular therapy using Schwann cells, bone marrow mesenchymal stem cells (MSCs), neural stem cells (NSCs), neural progenitor cells, or fibroblast cells have been shown to exert a beneficial effect on peripheral nerve regeneration after PNI, and thus have been proposed as a new approach for functional reconstruction of the PNI [33,35,37]. MRI not only provides the ability to monitor the homing and engraftment of the transplanted cells but can also depict and assess the peripheral nerve regeneration non-invasively

and re-peatedly with a high-quality soft tissue contrast [33,35,38]. In animal PNI models, our previous work had demonstrated that MSCs and NSCs could be paramagnetically labelled with Gd-DTPA and a fluorescent complex and the distribution and migration of labelled cells could be followed for a short period (about 10 days) by MRI and correlated well with histology(Figures 33.14–33.15) [37,38]. MSCs and NSCs transplanted to an injured nerve could promote nerve repair. The enhanced nerve regeneration resulting from the transplantation of MSCs or NSCs could be revealed by longitudinal, non-invasive MRI by using T1 and T2 relaxation times, Gf-enhanced techniques [33,35,37]. After MSC transplantation, T1 and T2 values of the distracted and the distal portions of nerves began to slowly decrease from 1  week after injury, but remained elevated at 10-week follow-up in the cell-treated nerves, whereas T1 and T2 values of the distal portions in the non-cell-treated nerves returned to near-normal level at 6-week follow-up. Cell-treated nerves had higher T1 and T2 values in the distracted portions during the period from 8 weeks to 10 weeks after the injury, while the distal portions had higher T1 values during the period from 8 weeks to 10 weeks after the injury and higher T2 values during the period from 6 weeks to 10 weeks after the injury. Similar results were also found in our previous study by using NSC transplantation in acute traction PNI [37]. Compared with control groups, nerves grafted with NSCs had better functional recovery and showed improved nerve regeneration but exhibited a sustained increase of T1 and T2 values during

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Fig. 33.15  In-vivo MRI tracking of grafted neural stem cells (NSCs). Serial fat-suppressed T1-weighted images demonstrate that labelled cells (arrows) showed a subepineurium linear hyper intense signal immediately after injection in the distracted portions (arrowheads). Afterward, this increased signal intensity gradually diminished and diffused into the distracted and distal portions of the injured nerve (A). However, reliable detection of labelled cells at the spatial resolution of the present experiment was no longer possible by 14 days. No such high signal intensity could be found in the nerve grafted with unlabelled cells (B). Reprinted with permission from the American Journal of Roentgenology.

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Fig. 33.16  Time course of T2 values in nerves treated with neural stem cells (NSCs). For the distracted (A) and the distal portions (B) of the injured nerves treated with NSCs, T2 values reached a peak value at 1 week after injury; after that, they began to slowly decrease but remained elevated at 10-week follow-up. T2 values of the distracted and distal portions remained higher than basal levels since 4 weeks after injury. Compared with nerves treated with PBS, nerves grafted with NSCs have a sustained T2 increase in the distracted and distal portion. Reprinted with permission from the American Journal of Roentgenology.

the regeneration phase (Figure 33.16);. These results indicated that longitudinal, non-invasive MRI could be used to monitor the enhanced nerve regeneration in PNI treated with MSC or NSC transplantation, reflected by a prolonged increase in T1 and T2 values of the injured nerves [33,37]. Stem cells can also be used in combination with nerve conduit to repair nerve defects. In our previous study, tissue-engineered construct implantation was prepared by seeding MSCs into chitosan tubes to repair rat transected nerve. Nerves implanted with tissue-engineered tubes achieved slightly better functional recovery and enhanced nerve regeneration (Figure 33.17);, which is consistent with a more rapid return to baseline level of T2 relaxation times and Gf enhancement in the distal portion of transected nerves. These results indicated that peripheral nerve repair with use of tissue-engineered constructs can be monitored by Gf-enhanced MRI and T2 relaxation time measurements [35].

Advances in magnetic resonance neurography Despite the great clinical potential of MRI to investigate the human peripheral nerve system, T2-based MRI is still the basic technique utilized in peripheral nerve imaging, on which the morphological demonstration of nerves and signal alterations on T2-weighted

imaging are observed. T2-based MRN lacks specificity and quantification for diagnosis of nerve injury. Furthermore, MRN cannot image continuous nerve fibres over their entire length unless a 3D scan used, so it is not considered very useful in daily practice for examining the growth of regenerating peripheral nerves [29]. Nerve degeneration and regeneration can both affect MR properties, including T1 and T2 relaxation and MT. Apart from the previously mentioned T1 and T2 relaxation, MT measurements of peripheral nerves have also been performed to semiquantitatively or quantitatively assess injured peripheral nerves [39]. Magnetization transfer (MT) imaging is based on the principle that protons bound in macromolecules exhibit T1 relaxation coupling with protons in the aqueous phase. In general, the magnetization transfer ratio (MTR) is an index that reflects the interaction between protons that belong to the pool of relatively mobile water molecules, and protons associated with macromolecules or bound water. MT imaging allows for acquisition of high spatial resolution images and does not suffer from signal losses due to the short T2, since it can be performed with gradient-echo sequences at a short TE. In nerves it could determine the integrity of particular nerve tissues such as collagen. MTR can be used to assess the nerve injuries, and monitor nerve regeneration. With the ability for quantitative measurement, MTR allows for longitudinal studies, possibly providing additional information about the course of

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Fig. 33.17  MRI of nerve treated with mesenchymal stem cell-seeded chitosan tube. Gross inspection revealed that the nerve was already reconnected at 4 weeks after surgery and has a larger size (A). The corresponding gadofluorine-enhanced TSE T1-weighted image shows the continuity of regenerated nerve inside the tube (B). Immunohistochemical staining revealed that there is more prominent Schwann cell proliferation and axonal regeneration in the proximal portion (C, F) and distal portion (E, H) and graft inside the tube (D, G). Neuroma is present in the transection site (arrows in D, G). Bars in C–H = 50 μm.

the neuropathy over time, hence allowing treatment monitoring, or can be used for group comparison [32,39]. Diffusion-based MRN such as diffuse weighted imaging (DWI), diffuse tensor imaging (DTI), and diffusion tensor tractography (DTT) has recently been developed to increase nerve conspicuity by vascular signal suppression, and has the potential for quantification of the nerve signal intensity, apparent diffusion coefficient (ADC), fractional anisotropy (FA) values, and fibre tracking. DTI allows interrogation of the nerve architecture with a potential for both qualitative visualization and quantitative characterization. It will likely play an important role in the future in depicting functional characteristics of the peripheral nerves and increasing the specificity of the diagnosis of PNI [14,40]. DTI of peripheral nerves is based on the movements of water molecules and microstructures of the nerve tracts. Neural tissues are highly enriched in nerve fibres and tracts, and water molecules tend to move along these fibres and tracts. This motion of water molecules in a preferential orientation enables DTI clear identification of nerve tracts and their anatomy (sizes and shapes). FA of

DTI is considered a sensitive measures of nerve fibre integrity in different studies [41]. DTT refers to the analysis and reconstruction of the data obtained by DTI, by which the orientation of nerve fibres can be followed to trace specific neural pathways (Figure 33.18) [24]. In this way, nerve tractography and the calculation of quantitative parameters such as the ADC can be provided simultaneously for both qualitative visualization and quantitative characterization of the peripheral nerves. After injury, blockade of the axoplasmic flow or venous congestion as well as wallerian degeneration lead to widening of the potential space between axons and the surrounding covering, resulting in increased proton diffusion and consequently increased ADC and decreased FA values [24]. Wallerian degeneration may also reduce the anisotropy of water diffusion, which can be revealed by DTI in the changes of anisotropy [24]. Preliminary studies show that DTI can provide more physiological information and increase the specificity of MRN in detecting nerve degeneration and regeneration. Furthermore, use of 3D reconstructions, DTI tractography, will likely allow accurate delineation of the

Fig. 33.18  Diffusion tensor imaging (DTI) of the human brachial plexus. DTI was obtained by using a 3.0-T MR scanner, b value =1000. The fibres on both sides of the brachial plexus in a healthy adult are clearly demonstrated.

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whole course of peripheral nerves, even the nerve course around joints [41]. This imaging technique in particular has the potential to depict early signs of nerve regeneration and to distinguish the degeneration and regeneration of PNI [24,41]. In animal studies and preliminary human studies, FA values have been shown to decrease in parallel with axonal degeneration and demyelination, then subsequently increase with axonal regeneration and remyelination. This process was well correlated with motor and sensory recovery [23,24,40]. However, DWI and DTI are technically demanding and remain mainly in the experimental stage so far. Ghosting artefacts may frequently degrade the quality of images [41]. Spatial resolution and obtaining enough SNR is also a challenge in clinical MR scanners. For wider application of this technology, DTI is likely to continue to improve with developments in hardware such as MRI magnet size and dedicated coils to image different peripheral nerves and with development of newer acquisition and analysis software [41]. It is anticipated that increasing interest in DTI will prompt more clinical and preclinical studies and will allow us to determine the real potential of DTI to quantitatively monitor PNI and recovery. Injured nerves do not regularly show contrast enhancement after administration of a conventional MR contrast agent, i.e. Gd-DTPA, even when the blood–nerve barrier is disturbed during wallerian degeneration [16]. Recent experimental studies using novel MR contrast agents in the animal nerve injury model have shown that it is possible to increase the diagnostic yield of MRI, and even visualize cellular processes [16,22]. Gf, a novel MR contrast agent, has the ability to selectively accumulate and persist in nerve fibres undergoing wallerian degeneration and disappear with remyelination [22,42]. After injection, Gf selectively accumulates and persists in injured nerve fibres, causing an obvious contrast effect on T1-weighted MRI. An enhancement of the entire nerve segments undergoing wallerian degeneration was present within 48 h, and subsequently disappeared in parallel to the regrowth of nerve fibres. These results suggested that Gf-based MRI has the potential to assess demyelination and remyelination in peripheral nerves, to bridge the diagnostic gap between nerve injury and completed nerve regeneration, and to determine the necessity for neurolysis and engraftment if spontaneous regeneration fails [6,42]. Haematogenous macrophages play a key role in nerve injury and subsequent repair. Tracking these infiltrated haematogenous macrophages has become a new research interest in nerve injury [16]. Superparamagnetic iron oxide (SPIO) particles are superparamagnetic and can induce a significant hypointensity on T2or T2*-weighted imaging. After systematic administration, SPIO particles are rapidly phagocytosed by circulating macrophages, and their accumulation in nerve tissues after injury can result in focal decreased signal intensity on T2-weighted MRI [22]. Using SPIO-based MRN, it is known that macrophages initially migrated to the lesion site, then extended distally into the degenerating nerve stump between days 1 and 8 after injury [43]. The presence of macrophages in nerves manifested as signal loss on MRI which is caused by the iron particles being taken up by macrophages within degenerating nerve fibres [16,22,43]. Using superparamagnetic iron oxide, MR technique provides a novel in-vivo tool to assess and monitor the macrophages entering the nerve tissues.

Conclusion In traumatic PNI, with the ability to direct visualization of nerves, MRN can effectively detect the presence of the nerve injury and determine the detailed extent of the injury and demonstrate true discontinuities of the nerve. The morphological changes observed on MRN can help the treatment decision and thus are critical for management, especially of severe nerve trauma. Moreover, using quantitative measurements of nerve MR properties such as T1, T2, ADC, and MTR, MRN can be more sensitively used to detect the nerve injury. Such quantitative assessments can allow accurate longitudinal evaluation of the same injured nerves and are then very useful for dynamical monitoring of nerve regeneration after treatment with either microsurgery, nerve conduits, or stem cells. Although current knowledge of the emerging new techniques or novel tissue-specific contrast agents has been obtained from animal studies, these results indicate that MRN has the potential to accurately detect subtle nerve injury at an early stage. More importantly, it will be helpful for describing and understanding the underlying pathological process in nerve degeneration or nerve regeneration.

References 1. Stanec S, Tonkovic I, et al. Treatment of upper limb nerve war injuries associated with vascular trauma. Injury 1997;28:463–8. 2. Robinson LR. Traumatic injury to peripheral nerves. Muscle Nerve 2000;23:863–73. 3. Kouyoumdjian JA. Peripheral nerve injuries: a retrospective survey of 456 cases. Muscle Nerve 2006;34:785–8. 4. Campbell WW. Evaluation and management of peripheral nerve injury. Clin Neurophysiol 2008;119:1951–65. 5. Rotshenker S. Wallerian degeneration: the innate-immune response to traumatic nerve injury. J Neuroinflammation 2011;8:109. 6. Zhang H., B. Xiao, et al. Clinical application of magnetic resonance neurography in peripheral nerve disorders. Neurosci Bull 2006;22: 361–7. 7. Gold BG T. Storm-Dickerson, et al. The immunosuppressant FK506 increases functional recovery and nerve regeneration following peripheral nerve injury. Restor Neurol Neurosci 1994;6:287–96. 8. Zochodne DW and D. Levy. Nitric oxide in damage, disease and repair of the peripheral nervous system. Cell Mol Biol (Noisy-le-grand) 2005;51:255–67. 9. Seddon HJ, Medawar PB, et  al. Rate of regeneration of peripheral nerves in man. J Physiol 1943;102:191–215. 10. Sunderland S. A classification of peripheral nerve injuries producing loss of function. Brain 1951;74:491–516. 11. Bianchi S. Ultrasound of the peripheral nerves. Joint Bone Spine 2008;75:643–9. 12. Zhu J, Liu F, et al. Preliminary study of the types of traumatic peripheral nerve injuries by ultrasound. Eur Radiol 2011;21:1097–101. 13. Zhang Z, Meng Q, et  al. 3-T imaging of the cranial nerves using three-dimensional reversed FISP with diffusion-weighted MR sequence. J Magn Reson Imaging 2008;27:454–8. 14. Chhabra A, Andreisek G, et al. MR neurography: past, present, and future. AJR Am J Roentgenol 2011;197:583–91. 15. Thawait SK, Wang K, et  al. Peripheral nerve surgery:  the role of high-resolution MR neurography. AJNR Am J Neuroradiol 2012;33:203–10. 16. Stoll G, Bendszus M, et al. Magnetic resonance imaging of the peripheral nervous system. J Neurol 2009;256:1043–51. 17. Chhabra A. Magnetic resonance neurography-simple guide to performance and interpretation. Semin Roentgenol 2013; 48:111–25.

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18. Chhabra A, Williams EH, et al. MR neurography of neuromas related to nerve injury and entrapment with surgical correlation. AJNR Am J Neuroradiol 2010;31:1363–8. 19. Shen J, Wang HQ, et al. Magnetic resonance microneurography of rabbit sciatic nerve on a 1.5-T clinical MR system correlated with gross anatomy. Microsurgery 2008;28:32–6. 20. Shen J, Zhou CP, et  al. MR neurography:  T1 and T2 measurements in acute peripheral nerve traction injury in rabbits. Radiology 2010;254:729–38. 21. Husarik DB, Saupe N, et al. Elbow nerves: MR findings in 60 asymptomatic subjects—normal anatomy, variants, and pitfalls. Radiology 2009;252:148–56. 22. Bendszus M, Stoll G. Technology insight: visualizing peripheral nerve injury using MRI. Nat Clin Pract Neurol 2005;1:45–53. 23. Lehmann HC, Zhang J, et al. Diffusion tensor imaging to assess axonal regeneration in peripheral nerves. Exp Neurol 2010;223:238–44. 24. Takagi T, Nakamura M, et al. Visualization of peripheral nerve degeneration and regeneration: monitoring with diffusion tensor tractography. Neuroimage 2009;44:884–92. 25. Peled S, Cory DG, et al. Water diffusion T(2), and compartmentation in frog sciatic nerve. Magn Reson Med 1999;42:911–8. 26. Spratt JD, Stanley AJ, et al. The role of diagnostic radiology in compressive and entrapment neuropathies. Eur Radiol 2002;12:2352–64. 27. Tagliafico A, Altafini L, et  al. Traumatic neuropathies:  spectrum of imaging findings and postoperative assessment. Semin Musculoskelet Radiol 2010;14:512–22. 28. Toth C. Peripheral nerve injuries attributable to sport and recreation. Phys Med Rehabil Clin N Am 2009;20:77–100, viii. 29. Bendszus M, Wessig C, et al. MRI of peripheral nerve degeneration and regeneration:  correlation with electrophysiology and histology. Exp Neurol 2004;188:171–7. 30. Cudlip SA, Howe FA, et  al. Magnetic resonance neurography of peripheral nerve following experimental crush injury, and correlation with functional deficit. J Neurosurg 2002;96:755–9.

31. Does MD, Snyder RE. Multiexponential T2 relaxation in degenerating peripheral nerve. Magn Reson Med 1996;35:207–13. 32. Stanisz GJ, Midha R, et al. MR properties of rat sciatic nerve following trauma. Magn Reson Med 2001;45:415–20. 33. Duan XH, Cheng LN, et al. In vivo MRI monitoring nerve regeneration of acute peripheral nerve traction injury following mesenchymal stem cell transplantation. Eur J Radiol 2012;81:2154–60. 34. Gambarota G, Mekle R, et al. NMR properties of human median nerve at 3 T: proton density T1 T2, and magnetization transfer. J Magn Reson Imaging 2009;29:982–6. 35. Liao CD, Zhang F, et al. Peripheral nerve repair: monitoring by using gadofluorine M-enhanced MR imaging with chitosan nerve conduits with cultured mesenchymal stem cells in rat model of neurotmesis. Radiology 2012;262:161–71. 36. Bendszus M, Wessig C, et al. MR imaging in the differential diagnosis of neurogenic foot drop. AJNR Am J Neuroradiol 2003;24:1283–9. 37. Cheng LN, Duan XH, et al. Transplanted neural stem cells promote nerve regeneration in acute peripheral nerve traction injury: assessment using MRI. AJR Am J Roentgenol 2011;196:1381–7. 38. Shen J, Duan XH, et al. In vivo MR imaging tracking of transplanted mesenchymal stem cells in a rabbit model of acute peripheral nerve traction injury. J Magn Reson Imaging 2010;32:1076–85. 39. Mekle R, Mortamet B, et al. Magnetization transfer-based 3D visualization of foot peripheral nerves. J Magn Reson Imaging 2013;37:1234–7. 40. Chhabra A, Lee PP, et al. 3 Tesla MR neurography—technique, interpretation, and pitfalls. Skeletal Radiol 2011;40:1249–60. 41. Sheikh KA. Non-invasive imaging of nerve regeneration. Exp Neurol 2010;223:72–6. 42. Wessig C, Bendszus M, et al. In vivo visualization of focal demyelination in peripheral nerves by gadofluorine M-enhanced magnetic resonance imaging. 2007;Exp Neurol 204:14–9. 43. Bendszus M, Stoll G. Caught in the act: in vivo mapping of macrophage infiltration in nerve injury by magnetic resonance imaging. J Neurosci 2003;23:10892–6.

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Functional imaging of neurosurgery in Parkinson’s disease Anna L. Bartels and Rudiger Hilker Introduction: Parkinson’s disease In 1817, the neurologist James Parkinson described a disease that he called ‘shaking palsy’, or ‘paralysis agitans’: involuntary tremulous motion with lessened muscular power, in parts not in action and even when supported; with a propensity to bend the trunk forward, and to pass from walking to a running pace: the senses and intellects being uninjured [1]‌. His description led to the advent of the Parkinson’s disease (PD) syndrome, and the knowledge of the pathology and of the clinical characteristics of this disease has greatly evolved since then. In 1912, Friedrich Heinrich Lewy was the first to describe the inclusion bodies named after him that are now seen as the pathological hallmark of idiopathic PD [2]. Tretiakoff reported a loss of pigmented cells (dopaminergic neurons) in the substantia nigra pars compacta in patients with the encephalitic form of PD [3]. In the 1950s, Carlsson observed that 80% of the dopamine content in the brain is localized in the basal ganglia [4]. He established the important pathophysiological link between the neurotransmitter loss with dopamine depletion and the clinical occurrence of PD symptoms. The progressive degeneration of the nigrostriatal system causes dopaminergic denervation of the striatum. More recently, positron emission tomography (PET) studies demonstrated impaired [18F]-fluorodopa (FDOPA) uptake in the striatum of PD patients in vivo, reflecting the presynaptic dopaminergic defect in PD [5]. Parkinsonism has three cardinal motor symptoms: bradykinesia, rigidity, and tremor. Classical for PD is an asymmetrical onset of motor symptoms. The presence of two of the three cardinal signs and a consistent response to an adequate pharmacological dose of levodopa are considered essential for the clinical diagnosis of PD by most experts. If this is not the case, other atypical parkinsonian syndromes, such as progressive supanuclear palsy (PSP), multiple system atrophy (MSA), or vascular parkinsonism should be suspected. Gait problems form another spectrum of PD symptoms, and together with the loss of balance reflexes they can cause dramatic immobility and risk of falling in later disease stages. PD gait is characterized by shuffling, small steps, decreased arm swing and a forward bending posture. Furthermore, freezing of gait (FOG), a sudden disturbance where patients feel stuck with their feet being ‘glued to the floor’, occurs in about one-half of PD patients. The

influence of attention and mental loading on the occurrence of FOG [6]‌is an evident example of the important interplay between motor and cognitive basal ganglia loops, which are both involved in PD symptomatology. Although dementia develops in the majority of late-stage PD patients, cognitive dysfunction not primarily classified as dementia often accompanies earlier disease stages. The pattern of cognitive deficits in PD usually includes executive dysfunction similar to that seen in patients with frontal lesions, as well as episodic memory impairment, visuospatial dysfunction and impaired verbal fluency [7]‌. The severest deficits are usually seen with tasks that depend on context-specific planning. This deficit has been called impairment in ‘mental flexibility’. In thorough neuropsychological testing, behaviours closely associated with ‘frontal’ or executive functions are most clearly impaired. However, besides the dopaminergic deficit, PD is nowadays viewed as a multisystem neurodegenerative disease causing complex biochemical changes that explain the variable clinical picture in PD patients, including various motor and non-motor symptoms, such as cognitive deterioration, depression, vegetative dysregulation, and pharmacotoxic hallucinations. Basal ganglia (BG)-cortical networks may be differentially affected, leading to variable motor, visuomotor, and cognitive impairments. The pathophysiological basis of dysfunctional BG-cortical loops has changed the concept of the PD syndrome, primarily described as a motor disorder leaving ‘the senses and intellects. . . uninjured’, to a complex brain disease involving motor and mental dysfunction as well.

Model of basal ganglia circuitry in Parkinson’s disease Before considering changes in neuronal activity by deep brain stimulation (DBS), it is useful to give a short overview of BG organization. The BG are made up of five main subcortical nuclei: putamen, nucleus caudatus, globus pallidus, nucleus subthalamicus, and substantia nigra. The BG are thought to play a role in the initiation of voluntary movements, facilitation of some motions suppressing others, and comparison of motor commands with feedback from evolving motions. In addition to their role in motor control, they are involved in various emotional and

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cognitive functions. Brown and Marsden proposed that the BG facilitate the synchronization of cortical activity underlying the selection of appropriate movements or appropriate sequences of thoughts [8]‌. In the classical Alexander–deLong model, the BG form a complex network of parallel loops that integrate cerebral cortical regions (associative, oculomotor, limbic, and motor regions), BG nuclei, and the thalamus [9]‌. For schematic organization of BG inand outputs see Figure 34.1. The motor circuit within this complex originates in cortical motor areas which project to the striatum, especially the putamen. From the putamen, neurons in the ‘direct pathway’ project to the internal part of the globus pallidus (GPi) and the substantia nigra pars reticulata (SNr) to the output nuclei of the BG. Neurons in this pathway bear dopamine D1 receptors and coexpress the peptides substance P and dynorphin. The direct pathway provides a GABA-ergic inhibitory effect on GPi/SNr neurons, thereby reducing the inhibitory effect of the output nuclei on the thalamus and thus ‘facilitating’ the execution of movements. The ‘indirect pathway’ connects the putamen with the output nuclei via the external part of the globus pallidus (GPe) and the subthalamic nucleus (STN). Neurons contain D2 receptors and the peptide encephalin. Stimulation of striatal projection neurons in the indirect pathway leads to inhibition of the GPe, disinhibition of the STN and excitation of the GPi/SNr, enhancing the inhibitory effect on the thalamus and ‘suppressing’ the execution of movements. Thus, it was suggested that the brake–accelerator function of the BG is based on the net result of opposing effects on the BG output nuclei receiving inhibitory inputs from the direct pathway and excitatory inputs from the indirect pathway. Prefrontal Cortex

The model further proposes that dopamine exerts a dual effect on striatal neurons: exciting D1 receptors in the direct pathway and inhibiting D2 receptors in the indirect pathway, thus facilitating movement [10]. According to this ‘classical’ direct–indirect pathway model, dopamine deficiency leads to reduced inhibition of the indirect pathway and reduced excitation of the direct pathway, with the net result of an excessive activation of the BG output nuclei (GPi and SNr) and inhibition of thalamocortical and brainstem motor systems, obviously leading to parkinsonian motor features. This model serves as a good starting point, but provides no insight into the pathophysiology of specific motor abnormalities in PD. Different aspects of parkinsonian motor and non-motor symptoms cannot be explained simply as a result of augmentation in the inhibitory output from the BG. Several suggestions to refine this model have been proposed. The basal ganglia are a highly organized network formed by discrete and finely arranged cortico-BG-cortical parallel motor loops, which provide positive feedforward signalling for movement preparation and execution. Internal BG circuits would mainly serve as a feedback stabilizer. Dopamine depletion destabilizes this network and leads to increases of neuronal synchronization and oscillatory activity in several BG loops [11]. The BG are not only arranged in separate parallel circuits, but each cortical motor area (area 4, 6, supplementary motor area (SMA), dorsolateral prefrontal cortex (DLPFC)) is organized in a somatotopic manner. Functional specificity of these areas is selective: thus, normally a large proportion of pallidal neurons discharge only in relation to a specific task or a part of a motor sequence. Synchronization of circuits in GPi, as is the BASAL GANGLIA INPUTS and OUTPUTS

Premotor Cortex Primary Motor Cortex

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Caud Superior Colliculus Reticular Formation Pedunculopontine N.

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Amygdala Fig. 34.1  Schematic, semi-sagittal representation of the cerebral cortex, basal ganglia nuclei, and the thalamus, illustrating the most important afferent and efferent connections between these structures. The inputs of the basal ganglia are primarily directed to the striatum and the subthalamic nucleus (basal ganglia input structures). The outputs of the basal ganglia stem from the internal segment of the globus pallidus, the substantis nigra pars reticulata, and the ventral pallidum (basal ganglia output structures). sc, central sulcus; Acb, nucleus accumbens; Caud, caudate nucleus; Put, putamen; VA/VL/MD, ventral anterior/ventral lateral/mediodorsal thalamic nucleus; ML, midline thalamic nuclei; IL, intralaminar thalamic nuclei; SNC, substantia nigra pars compacta; SNR, substantia nigra pars reticulata; STN, subthalamic nucleus; VP, ventral pallidum; VTA, ventral tegmental area. (Courtesy by of H.J. Groenewegen, Parkinsonism and Related Disorders, Wolters E.Ch, van Laar T, Berendse H.W. Eds. VU University Press. Amsterdam. 2007.)

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case in PD, could lead to motor defects under special circumstances where critical components of the network are required [12,13]. In addition to the motor circuit, there are at least three other circuits connecting the BG to the thalamus and cortex: the oculomotor circuit, involved in control of saccadic eye movements; the dorsolateral prefrontal circuit, probably involved in aspects of memory and orientation in space; and the lateral orbitofrontal circuit, which is thought to be involved in the ability to change behavioural set [9]‌, signalling changes in reinforcement contingencies and behavioural control [14]. Those circuits project via the caudate nucleus. A distinction can be made between an ‘association neostriatum’, including anterior regions (caudate and anterior putamen) and a posterior ‘motor striatum’ [15]. Some variation in PD symptomatology could, thus, result from deficits in selective subregions of the striatum with their specific afferent and efferent connections. In PD patients, the relationship between motor and cognitive performance and striatofrontal dysfunction has been investigated using 18F-fluoro-2-deoxy-D-glucose (FDG)-PET [16]. Resting state FDG uptake can be interpreted as a marker of local synaptic activity, which is both sensitive to direct neuronal damage and secondary functional disruption distant from the primary site of pathology. Furthermore, scanning during performance of a task can assess the activation response of a brain network, which has been applied in PET [17] as well as in functional MRI (fMRI) studies. Using PET and network analysis, Lozza et al. found that bradykinesia and global motor function in PD patients was associated with increased activity in the striatum and in the mediodorsal thalamus, while decreased activity was observed in prefrontal and cingulate regions. Executive dysfunction was associated with increased metabolic activity in the left pallidum and mediodorsal thalamus as well as decreased activity in ventromedial frontal regions, striatum and left hippocampal gyrus [16]. It is still unclear how neuronal degeneration in PD impairs normal functioning of BG-cortical loops. Recent work has demonstrated abnormally synchronized oscillatory activity at low beta frequencies in BG circuits in PD [12]. Normal functioning of the BG is characterized by uncorrelated activity between their functional subcircuits. After nigrostriatal dopamine depletion, cross-connection between the ‘parallel’ BG subcircuits become more active, resulting in abnormal synchronicity within the BG. Treatment with dopaminergic drugs or high-frequency stimulation of the GPi or STN may perhaps suppress the synchronized oscillatory activity, and remove hyperexcitation of the BG output nuclei (GPi and SNr). However, DBS is not useful for cognitive and psychiatric dysfunction in patients with PD. STN more than GPi stimulation may rather exacerbate cognitive or emotional symptoms, which is a comparable effect than overstimulation due to certain dopaminergic drugs [18]. Moreover, STN stimulation may not correct dysfunctional sensory association cortices in PD [19]. Furthermore, cognitive and emotional symptoms in PD may also relate to lesions in the mesocortical dopaminergic system, projecting from the ventral tegmental area (VTA) to frontal regions [20], and to degeneration of other ascending subcortical systems, i.e., cholinergic, noradrenergic. and serotonergic projections. Furthermore, the brainstem pedunculopontine nucleus (PPN) has been shown to be involved in PD pathology in terms of gait disturbance and FOG [21]. The cortico-BG-cortical circuitry model, although simplified, may help to understand the changes of brain activation by DBS in PD

patients (see Figure 34.2) [22]. In this model, the STN plays a critical role for the modulation of cortico-striato-pallido-thalamocortical motor pathways, where motor symptoms in PD are related to overactivity of GPi afferents from the STN and to a subsequent increase in inhibitory pallidal outflow to the ventral thalamus and pons (Eidelberg et al 1994, 1997)[23,24]. Among others, the discovery of this phenomenon in animal models indeed led to the development of STN DBS in humans [25]. The activity of the STN can be modulated either by lesional subthalamotomy [26] or by functional neuromodulation (DBS).

Deep brain stimulation in Parkinson’s disease Clinical effects and structural imaging The possibility of DBS was already surmised in the 1930s, however the rationale was posited not until the 1980s [27]. Previously, neurosurgeons relied on lesioning brain structures to alleviate Cortex

1A: normal

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Fig. 34.2  Cortical-basal ganglia loops in (A) normal situation, (B) Parkinson’s disease (PD), and (C) PD with STN-DBS. Grey arrows indicate glutamatergic pathways, black arrows GABAergic pathways. In PD, dopaminergic degeneration in SNc leads to hyperactivity of the indirect pathway and reduced activity of the direct pathway. STN-DBS reverses the inhibitory output from the GPi to the thalamus and increases cortical activation. Adapted from Ballanger et al., et al. PET functional imaging of deep brain stimulation in movement disorders and psychiatry. J Cereb Blood Flow Metab 2009;29(11):1743–54.

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parkinsonian symptoms, especially tremor. In the early 1990s, DBS of the STN was shown to result in effective improvement of motor function and was thereafter an increasingly used surgical treatment in advanced PD patients [28]. Nowadays, the use of DBS is a more common treatment in up to 5% of PD patients. However, unwanted neuropsychological effects have been shown to accompany the motor benefits in some, especially advanced, PD patients with STN DBS [29]. Apart from the clinical benefit, the impact of DBS upon complex neuronal networks provides an excellent research opportunity to investigate these cerebral systems with electrophysiological and imaging tools.

Functional effects STN DBS induces a change in the pattern and periodicity of neuronal activity in the BG-thalamic network. The results of several studies are consistent with the hypothesis that STN output hyperactivity is reduced by DBS with subsequent deactivation of inhibitory BG output nuclei and a release of thalamocortical projections [30] (see Figure 34.2). The changes also include cerebellar pathways, likely via activation of adjacent cerebello-thalamic fibre bundles. Most functional imaging studies in the context of surgery have investigated modification of brain perfusion or energy metabolism by DBS. The uptake of FDG by the brain, as a measure of energy metabolism, is thought to be primarily determined by local afferent synaptic activity. Increased FDG uptake is suggested to reflect the perpetuation of neuronal membrane depolarization in response to excitation [31]. In PD patients without treatment, glucose hypermetabolism is found in the main BG output nuclei, namely the pallidum and the substantia nigra (SN), which get

strong glutamatergic projections from the excessively firing STN (see Figure 34.2). Increased striatal FDG uptake in PD patients is explained by loss of inhibitory nigrostriatal dopaminergic input, leading to functional overactivation of the putamen [32]. Metabolic studies using FDG-PET in resting PD patients have shown that STN DBS reduces this increased glucose metabolism in the putamen and GPi, thereby modulating the BG-cortical networks [33–35]; see also Figures 34.3 and 34.4 for examples of network effects as measured with FDG-PET. Recent FDG-PET studies showed increased energy metabolism in the electrode area, in the STN, and in the GP under active STN DBS, indicating that DBS exerts activation of the electrode area, of the STN target region, and of the GPi [36]. This may imply a mechanism of DBS action that is fundamentally different from lesional surgery. An FDG-PET study after unilateral subthalamotomy in PD patients showed decreased cerebral metabolic rate of glucose (CMRGl) in the STN and GPi [37]. However, in another FDG-PET study it was found that lesioning and stimulation of the STN were mechanistically similar at a system level. FDG-PET scans showed metabolic declines in the midbrain, the GPi, and the cingulated motor region (BA 24/6) after both interventions. Metabolic increases seen in the parietal cortex (BA 7/39/40) were also common in both surgical procedures, although more pronounced following STN stimulation. The metabolic reductions in the rostral pons and midbrain, in proximity to the pedunculopontine nucleus (PPN), suggest diminished outflow from the STN target nucleus to the brainstem in both interventions (see Figure 34.3) [34]. The main difference in metabolic changes between the two procedures was that metabolic declines in the pallidum were greater in the STN-lesioned patients.

Fig. 34.3  Regions of rCMRGl increases (upper row) and decreases (lower row) between the on and off conditions of bilateral STN DBS, matched to a T1-weighted MRI scan. Increases were seen in left premotor cortex, right SMA, bilaterally in the frontomesial cortex, (pre)cuneus, posterior temporal gyrus, posterior cingulated and in the right caudal cerebellum and cerebellar vermis. Decreases with DBS-on were seen in bilateral primary motor cortex, left hypothalamus and bilateral rostral cerebellum. Adapted from Hilker et al: Deep brain stimulation of the subthalamic nucleus versus Levodopa challenge in Parkinson’s disease: measuring the on- and off-conditions with FDG-PET. J Neural transmission Oct, 109(10):1257–64.

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Although the therapeutic outcome and functional mechanisms of stimulation and lesioning seem to be similar, the neuronal mechanisms underlying both interventions may be different. Evidence from experimental animal models has suggested that neurochemical and physiological changes with stimulation at the target site may not be explained simply by inhibition [38]. On the other hand, neural recordings during DBS have indicated that local inhibition is an important feature of therapeutic DBS [39]. According to the so-called dual concept of DBS action, the effect of DBS depends on the electrical properties of the stimulated target and on its position within the electrical field. In the immediate proximity of the electrode tip, depolarization blockade of cell somata inhibits neuronal firing [40]. Conversely, farther away from the epicenter of the electrical field the stimulation current activates axons passing nearby. The subthalamic-pallidal bundle connecting the STN with the main outflow pathway of the BG is thus activated in STN DBS, resulting in high-frequency glutamatergic inputs to the GPe and GPi [41]. Finally, it was hypothesized that DBS overrides synchronized bursts of the BG in the parkinsonian state with a high-frequency, more regular activity pattern in the gamma frequency range (neuronal jamming) [42]. Tonic driving of fibres crossing the subthalamic area may cause the increased subthalamic and pallidal energy metabolism under STN stimulation [36]. This likely occurs in excitatory glutamatergic efferents of STN neurons to the GPi, as well as in pallidothalamic fibres and GPe-STN outflow connections. STN DBS has also been shown to enhance glucose consumption in cortical association regions [43], which is consistent with STN inhibition and consequent reduction of inhibitory output from GPi and SNr on the thalamus according to the BG circuitry A

model. Several studies, however, show variable effects of STN DBS on thalamic activation, some reporting increased metabolic activity [43] or blood flow [44,45], whereas others found decreased thalamic blood flow [30]. Discrepant findings relating to subcortical increases and cortical decreases in activation are difficult to reconcile in the simplified BG model. The postulated overactivation of BG output in the parkinsonian condition, as represented by high firing rates in the GPi and SNr and low firing rates in the motor thalamus, may not be the pathophysiological attribute responsive to DBS treatment. The finding of reduced burst firing pattern during DBS [46] supports the notion that DBS may exert its therapeutic effects via a modulation of BG firing patterns rather than by changing firing rates. Most likely, DBS-induced metabolic activation in midbrain and thalamic areas reflects neuronal jamming of ascending and descending fibre bundles in the pallidothalamic bundle and zona incerta and in their projection sites. Furthermore, methodological differences may account for some differences in subcortical activation found in several studies. It has been suggested that increases of FDG-PET uptake values in subcortical regions are an artefact of ratio normalization to the global mean of radiotracer uptake [47]. In simulation studies comparing two groups, even small and statistically not significant differences in cortical activity resulted in subcortical increases after normalization of the uptake values to the global mean. Absolute quantification of tracer uptake, without using global normalization, would be necessary to overcome this possible bias. Due to intersubject variation, absolute measures of glucose uptake have not shown significant differences in subcortical structures between PD and control subjects [48]. On the other hand, animal studies using 2-deoxyglucose (2-DG) autoradiography in 6-OHDA-induced B

Post Parietal

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Cerebellum X=4 Fig. 34.4  (A) Parkinson’s disease-related pattern (PDRP) identified by network analysis of 18F-fluorodeoxyglucose (FDG)-PET scans from 30 PD patients and 30 age-matched normal volunteer subjects. This pattern was characterized by relative metabolic increases in the pallidum and thalamus (left) and in the pons and cerebellum (bottom). These changes covaried with metabolic decreases in the lateral premotor and parieto-occipital association areas (right). Voxels with positive region weights (metabolic increases) are colour coded from red to yellow; those with negative region weights (metabolic decreases) are colour coded from blue to purple. (B) Bar histogram of the change in PDRP expression (ΔPDRP; mean ± SE) quantified in hemispheres undergoing either STN deep brain stimulation (DBS, filled bar) or lesioning (LESION, shaded bar), and in non-operated control hemispheres (CNTL, open bar). Significant reductions in network activity were observed with both interventions (p < 0.05). However, the degree of network modulation was not different for the two treatment groups (p = 0.58). [Asterisks refer to comparisons with control hemispheres; p < 0.05.]. By courtesy of Trost et al., Network modulation by the subthalamic nucleus in the treatment of Parkinson’s disease.

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parkinsonism have demonstrated absolute metabolic increases in the GPi and GPe, STN, PPN and thalamus [49]. Furthermore, in MPTP-lesioned maquaque monkeys, chronic STN-HFS reversed abnormally decreased 2-DG uptake in the STN and reversed abnormally increased 2-DG accumulation in the GPi [50], demonstrating pathophysiological changes in metabolic activity in the BG. Finally, the spatial resolution of most PET tomographs is currently 4 mm, which can be improved with 3D volume acquisition. However, this does not allow the detailed examination of small structures like the STN or GPi. As yet, only a single study has been performed on a new-generation high-resolution PET scanner to improve the quantification of tracer metabolism in these small subcortical regions [32]. In addition to radiotracer imaging studies, few studies have investigated the pattern of fMRI activation during STN DBS (with an externalized pulse generator). Activation as measured with fMRI is based on haemodynamic responses with oximetric changes (blood oxygenation level dependent (BOLD) effect) that reflect changes in neuronal firing activity. One initial study in five PD patients showed activation in the ipsilateral GP and thalamus and in the contralateral superior cerebellum [51]. In a case report, right STN DBS with the electrode placed more laterally to the intended STN site showed less extensive changes in motor regions, but increases in superior prefrontal cortex, ACC, anterior thalamus, caudate, and brainstem, and marked decreases in medial prefrontal cortex, which was clinically associated with transient depressive mood [52]. Besides functional imaging studies in rest, activation studies with radiotracer imaging during performance of specific tasks have also been performed in DBS-treated PD patients. In these patients at rest a decrement of cerebral blood flow (rCBF) in motor and premotor regions was seen, while a unimanual motor task gave increased rCBF in SMA, anterior cingulated gyrus, and dorsolateral prefrontal cortex (DLPFC) in PD patients with STN stimulation, and a moderate but non-significant increase in SMA and anterior cingulated in patients with GPi stimulation [53].

associative territory [43], areas that are involved in the regulation of cognition, mood and behaviour. Interestingly, dorsal neocortical areas that are activated in patients with depression [56] are similar to the regions that show restored glucose metabolism after STN DBS [43]. This finding agrees with the clinical observation that PD-related depression tends to improve after STN DBS [57]. Transient neuropsychiatric side effects after device implantation have also been reported, but in the few patients studied to date they could be unrelated to specific alterations in glucose metabolism. Another FDG-PET study in nine PD patients with STN DBS demonstrated highly variable postoperative changes of both cognitive test performance and frontal glucose uptake values along with significant correlations between verbal fluency and FDG uptake in the left DLPFC (Brodmann area [BA] 9, 46), left Broca area (BA 44/45), and the right dACC (BA 32) [58]. The decrease of FDG uptake in the left OFC (BA 11/47) and dACC (BA 32) correlated with a decline of verbal learning. These data showed a significant linear relationship between frontal brain activity changes with cognitive outcome after deep STN DBS in advanced PD. Finally, a clear influence of STN DBS is seen upon the activity of cerebellar neurons. Cerebellar hypermetabolism in PD disappeared with STN stimulation [43]. Suppression of parkinsonian tremor in the DBS-on condition was found to be associated with an increase of glucose metabolism in the posterior cingulate, as well as a marked cerebellar decrease, corroborating the concept of cerebellar involvement in parkinsonian tremor. Two other PET studies showed decreased cerebellar blood flow with suppression of parkinsonian tremor by DBS of the thalamic ventral intermediate nucleus (VIM) [59,60]. Possibly, both STN and VIM DBS deactivate the cerebellum via an antidromic (from axon to soma) effect in dentate-thalamic fibres. Taken together, STN DBS seems to interact with widespread cortical and cerebellar pathways. These alterations of neuronal resting energy metabolism were reversible after turning the devices off. This indicates a temporary functional phenomenon in line with the relapse of parkinsonism in the DBS-off condition.

Relation of regional effects to parkinsonian symptoms

Pedunculopontine nucleus deep brain stimulation

Several studies investigated the relation of specific brain regions with functional changes after DBS with changes in specific symptoms. Correlations were found between STN DBS induced improvement of rigidity and decreased rCBF in the SMA, and between improvement of bradykinesia and increased rCBF in the thalamus [45]. Remarkably, bilateral STN stimulation resulted in right-sided midbrain and premotor rCBF increases [43,45]. On the other hand, unilateral STN DBS led to bilateral BG and thalamus changes [54]. The reduction of contralateral GPi activity by STN DBS induced contralateral thalamic disinhibition, causing contralateral motor cortical activation and improving ipsilateral motor symptoms. Contralateral Gpi activation by STN DBS may be explained by effects on brainstem structures, such as the pedunculopontine nucleus (PPN), which has connections to the BG in both hemispheres. Because the STN plays an important role not only in motor, but also in limbic and associative BG circuits [55], neuropsychological effects may be related to stimulation-induced alterations of neuronal activity in these non-motor BG networks. STN DBS was found to activate glucose metabolism in the frontal limbic and

It has been supposed that brainstem structures such as the PPN play an important role in postural instability and gait disturbances in PD. Recent work suggested that PPN-DBS may be beneficial in the treatment of axial symptoms particularly of FOG, in which (a combination with) lower-frequency STN stimulation was more effective in alleviating gait disturbances [61]. The mechanisms underlying these effects are currently unknown. In PD, the increased inhibitory GABAergic activity from the GPi is believed to inhibit the PPN (see Figure 34.2) [21]. One group used 15O-H2O-PET to investigate regional cerebral blood flow (rCBF) in three advanced PD patients who underwent unilateral PPN-DBS [62]. PET revealed an increase of rCBF bilaterally in the thalamus and in the cerebellum as well as ipsilateral in the ventral midbrain under active PPN stimulation. This finding is consistent with anatomical reports demonstrating descending PPN projections to the cerebellum and ascending fibres through the dorsal tegmental pathway to the thalamus [21]. At the cortical level, increases were found in the contralateral dorsolateral prefrontal cortex, anterior cingulate cortex, orbitofrontal cortex, temporal gyrus, and occipital cortex. Furthermore, PPN stimulation resulted in a lower frequency and higher amplitude of self-paced

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lower limb movements, which were associated with increased blood flow in medial sensorimotor areas [62]. These findings are in line with the hypothesis that PPN-DBS induces functional changes in neuronal networks involved in locomotion and possibly in executive function and arousal [63].

Effect of surgery on disease progression Numerous single photon emission CT (SPECT) and PET studies have investigated the nigrostriatal dopaminergic system in PD patients [5]‌. Presynaptic dopaminergic function is mostly studied with 18F-fluoro-Dopa (FDOPA), a substrate for dopa-decarboxylase in catecholaminergic neurons, and PET. More recently, also tracers for dopamine presynaptic reuptake sites (dopamine transporter or DAT sites) were developed for SPECT imaging. In PD patients, FDOPA uptake in the putamen was found to be inversely correlated to motor disability and disease duration, while caudate uptake showed a correlation with the occurrence of cognitive deficits [64]. Follow-up studies with FDOPA-PET reported an annual decline of the FDOPA uptake accounting for 9% in the putamen and for 3% in the caudate [65], while another study measured a 13% decrease per year in putaminal dopamine transporter density [66]. Furthermore, sequential FDOPA-PET scanning was performed to evaluate the efficacy of intrastriatal grafting of mesencephalic neurons before and after transplantations [67–69]. STN DBS has been proposed to potentially slow disease progression, since STN DBS is supposed to reduce glutamatergic hyperactivity from the STN towards e.g. substantia nigra pars compacta (SNc). Glutamatergic hyperactivity is under certain circumstances excitotoxic. It was postulated that reducing this hyperactivity by inhibitory STN DBS would then possibly result in less dopaminergic cell loss in the SNc. Hilker et al. [70] have investigated 30 PD patients using FDOPA-PET before and 12 to 36 months after successful STN DBS. In this study, the annual decline was in a similar range as in ordinary PD patients, namely 9.5–12.9% per year. That study, therefore, negates a neuroprotective influence by STN DBS at least on the dopaminergic system in advanced PD patients. These findings were confirmed by a later SPECT study using 123I-FP-CIT SPECT in 35 PD patients with STN DBS and 1-year follow-up [71]. The rate of striatal DAT reduction per year was 7.7% in patients with STN stimulation and 6.7% per year in medically treated patients. This study did also not support the hypothesis that STN stimulation exerts a neuroprotective effect in advanced PD.

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29. Jahanshahi M, Ardouin CM, Brown RG, et  al. The impact of deep brain stimulation on executive function in Parkinson’s disease. Brain 2000;123(Pt 6):1142–54. 30. Geday J, Ostergaard K, Johnsen E, Gjedde A. STN-stimulation in Parkinson’s disease restores striatal inhibition of thalamocortical projection. Hum Brain Mapp 2009;30:112–21. 31. Gjedde A, Marrett S, Vafaee M. Oxidative and nonoxidative metabolism of excited neurons and astrocytes. J Cereb Blood Flow Metab 2002;22:1–14. 32. Eggers C, Hilker R, Burghaus L, Schumacher B, Heiss WD. High resolution positron emission tomography demonstrates basal ganglia dysfunction in early Parkinson’s disease. J Neurol Sci 2009;276:27–30. 33. Asanuma K, Tang C, Ma Y, et al. Network modulation in the treatment of Parkinson’s disease. Brain 2006;129:2667–78. 34. Trost M, Su S, Su P, et al. Network modulation by the subthalamic nucleus in the treatment of Parkinson’s disease. Neuroimage 2006;31:301–7. 35. Wang J, Ma Y, Huang Z, Sun B, Guan Y, Zuo C. Modulation of metabolic brain function by bilateral subthalamic nucleus stimulation in the treatment of Parkinson’s disease. J Neurol 2010;257:72–8. 36. Hilker R, Voges J, Weber T, et al. STN-DBS activates the target area in Parkinson disease: an FDG-PET study. Neurology 2008;71:708–13. 37. Su PC, Ma Y, Fukuda M, et al. Metabolic changes following subthalamotomy for advanced Parkinson’s disease. Ann Neurol 2001;50:514–20. 38. Windels F, Bruet N, Poupard A, et al. Effects of high frequency stimulation of subthalamic nucleus on extracellular glutamate and GABA in substantia nigra and globus pallidus in the normal rat. Eur J Neurosci 2000;12:4141–6. 39. Dostrovsky JO, Lozano AM. Mechanisms of deep brain stimulation. Mov Disord 2002;17 Suppl 3:S63–8. 40. Filali M, Hutchison WD, Palter VN, Lozano AM, Dostrovsky JO. Stimulation-induced inhibition of neuronal firing in human subthalamic nucleus. Exp Brain Res 2004;156:274–81. 41. McIntyre CC, Savasta M, Kerkerian-Le Goff L, Vitek JL. Uncovering the mechanism(s) of action of deep brain stimulation: activation, inhibition, or both. Clin Neurophysiol 2004;115:1239–48. 42. Meissner W, Leblois A, Hansel D, et al. Subthalamic high frequency stimulation resets subthalamic firing and reduces abnormal oscillations. Brain 2005;128:2372–82. 43. Hilker R, Voges J, Weisenbach S, et al. Subthalamic nucleus stimulation restores glucose metabolism in associative and limbic cortices and in cerebellum: evidence from a FDG-PET study in advanced Parkinson’s disease. J Cereb Blood Flow Metab 2004;24:7–16. 4 4. Goerendt IK, Lawrence AD, Mehta MA, Stern JS, Odin P, Brooks DJ. Distributed neural actions of anti-parkinsonian therapies as revealed by PET. J Neural Transm 2006;113:75–86. 45. Karimi M, Golchin N, Tabbal SD, et  al. Subthalamic nucleus stimulation-induced regional blood f low responses correlate with improvement of motor signs in Parkinson disease. Brain 2008;131:2710–9. 46. Hashimoto T, Goto T, Hongo K. Neuronal responses to high-frequency stimulation in human subthalamic nucleus. Mov Disord 2009;24: 1860–2. 47. Borghammer P, Cumming P, Aanerud J, Forster S, Gjedde A. Subcortical elevation of metabolism in Parkinson’s disease—a critical reappraisal in the context of global mean normalization. Neuroimage 2009;47:1514–21. 48. Ma Y, Tang C, Moeller JR, Eidelberg D. Abnormal regional brain function in Parkinson’s disease: truth or fiction? Neuroimage 2009;45:260–6. 49. Carlson JD, Pearlstein RD, Buchholz J, Iacono RP, Maeda G. Regional metabolic changes in the pedunculopontine nucleus of unilateral 6-hydroxydopamine Parkinson’s model rats. Brain Res 1999;828:12–9. 50. Meissner W, Guigoni C, Cirilli L, et  al. Impact of chronic subthalamic high-frequency stimulation on metabolic basal ganglia activity:  a 2-deoxyglucose uptake and cytochrome oxidase mRNA study in a macaque model of Parkinson’s disease. Eur J Neurosci 2007;25:1492–500.

51. Phillips MD, Baker KB, Lowe MJ, et al. Parkinson disease: pattern of functional MR imaging activation during deep brain stimulation of subthalamic nucleus—initial experience. Radiology 2006;239:209–16. 52. Stefurak T, Mikulis D, Mayberg H, et al. Deep brain stimulation for Parkinson’s disease dissociates mood and motor circuits: a functional MRI case study. Mov Disord 2003;18:1508–16. 53. Limousin P, Greene J, Pollak P, et al. Changes in cerebral activity pattern due to subthalamic nucleus or internal pallidum stimulation in Parkinson’s disease. Ann Neurol 1997;42:283–91. 54. Arai N, Yokochi F, Ohnishi T, et  al. Mechanisms of unilateral STN-DBS in patients with Parkinson’s disease: a PET study. J Neurol 2008;255:1236–43. 55. Alexander GE, Crutcher MD, DeLong MR. Basal ganglia-thalamocortical circuits: parallel substrates for motor, oculomotor, ‘prefrontal’ and ‘limbic’ functions. Progr Brain Res 1990;85:119–46. 56. Mayberg HS. Frontal lobe dysfunction in secondary depression. J Neuropsychiatry Clin Neurosci 1994;6:428–42. 57. Ardouin C, Pillon B, Peiffer E, et  al. Bilateral subthalamic or pallidal stimulation for Parkinson’s disease affects neither memory nor executive functions: a consecutive series of 62 patients. Ann Neurol 1999;46:217–23. 58. Kalbe E, Voges J, Weber T, et  al. Frontal FDG-PET activity correlates with cognitive outcome after STN-DBS in Parkinson disease. Neurology 2009;72:42–9. 59. Deiber MP, Pollak P, Passingham R, et al. Thalamic stimulation and suppression of parkinsonian tremor. Evidence of a cerebellar deactivation using positron emission tomography. Brain 1993;116(Pt 1):267–79. 60. Parker F, Tzourio N, Blond S, Petit H, Mazoyer B. Evidence for a common network of brain structures involved in parkinsonian tremor and voluntary repetitive movement. Brain Res 1992;584:11–17. 61. Moreau C, Defebvre L, Devos D, et al. STN versus PPN-DBS for alleviating freezing of gait: toward a frequency modulation approach? Mov Disord 2009;24:2164–6. 62. Ballanger B, Lozano AM, Moro E, et al. Cerebral blood flow changes induced by pedunculopontine nucleus stimulation in patients with advanced Parkinson’s disease: a [(15)O] H2O PET study. Hum Brain Mapp 2009; 30:3901–9. 63. Alessandro S, Ceravolo R, Brusa L, et al. Non-motor functions in parkinsonian patients implanted in the pedunculopontine nucleus: focus on sleep and cognitive domains. J Neurol Sci 2010;289:44–8. 6 4. van Beilen M, Portman AT, Kiers HA, et  al. Striatal FDOPA uptake and cognition in advanced non-demented Parkinson’s disease:  a clinical and FDOPA-PET study. Parkinsonism Relat Dis 2008;14:224–8. 65. Morrish PK, Sawle GV, Brooks DJ. An [18F]dopa-PET and clinical study of the rate of progression in Parkinson’s disease. Brain 1998;119:585–91. 66. Nurmi E, Ruottinen HM, Bergman J, et  al. Rate of progression in Parkinson’s disease:  a 6-[18F]fluoro-L-dopa PET study. Mov Disord 2001;16:608–15. 67. Pogarell O, Koch W, Gildehaus FJ, et  al. Long-term assessment of striatal dopamine transporters in Parkinsonian patients with intrastriatal embryonic mesencephalic grafts. Eur J Nucl Med Mol Imaging 2006;33:407–11. 68. Remy P, Samson Y, Hantrave P, et  al. Clinical correlates of [18F] fluorodopa uptake in five grafted parkinsonian patients. Ann Neurol 1995;38:580–8. 69. Sawle GV, Bloomfield PM, Bjorklund A, et al. Transplantation of fetal dopamine neurons in Parkinson’s disease: PET [18F]6-L-fluorodopa studies in two patients with putaminal implants. Ann Neurol 1992;31:166–73. 70. Hilker R, Portman AT, Voges J, et al. Disease progression continues in patients with advanced Parkinson’s disease and effective subthalamic nucleus stimulation. J Neurol Neurosurg Psychiatry 2005;76:1217–21. 71. Lokkegaard A, Werdelin LM, Regeur L, et al. Dopamine transporter imaging and the effects of deep brain stimulation in patients with Parkinson’s disease. Eur J Nucl Med Mol Imaging 2013;34:508–16.

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Neuroimaging after cell-based therapy Alex Tsui and Paola Piccini Introduction Cell replacement therapy in Parkinson’s disease (PD) aims to physiologically correct chemical deficits and neurotransmission within degenerated nigrostriatal circuits, restoring functional and structural connectivity. Since the late 1980s, over 200 PD patients have received ventral mesencephalic (VM) fetal transplants. Although significant improvements in motor symptoms and dopaminergic medication requirements were reported in open-labelled studies, they were not reproduced in two large double-blinded randomized controlled trials. In addition, significant graft-induced dyskinesias (GIDs) have been reported in varying proportions of patients receiving transplants. In-vivo neuroimaging has been extensively used to study the properties of grafts after transplant. In particular, specific tracers with positron emission tomography (PET) have allowed researchers to non-invasively appreciate the ability of transplanted tissues to survive, grow, release dopamine and form structural and functional connections with the host brain. Tracers targeting serotonergic systems have recently provided insight into the pathogenic mechanism of GIDs [1]‌. With further advances in functional imaging, other modalities such as functional magnetic resonance imaging (fMRI) will become increasingly utilized in studying functional connectivity between graft and host and the contribution of inflammatory processes to graft complications, as well as the inclusion of non-striatal non-dopaminergic areas as targets for neurotransplantation in PD, as our understanding of non-motor PD symptoms advances.

Graft survival In order for transplanted tissue to restore dopaminergic neurotransmission and produce clinical benefits, grafts must survive within the host brain. Since the first feasibility studies in the 1980s, 18F-dopa-PET has been used to assess graft viability in vivo. By demonstrating the activity of aromatic-L-amino-acid decarboxylase (AADC) and hence the decarboxylation of L-dopa to dopamine, the rate of 18F-dopa-PET uptake provides an indirect gauge of presynaptic dopamine storage. In non-grafted PD patients, 18F-dopa uptake correlates negatively to the Unified PD Rating Scale (UPDRS) motor scores and has been shown to effectively demonstrate PD progression [2,3]. In a study involving PD, Alzheimer’s disease (AD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS) patients, there

was a significant correlation between the number of surviving dopaminergic neurons at post-mortem and the degree of 18F-dopa uptake in life [4]‌. 18F-dopa-PET can be used not just to express a percentage of uptake change from the patient’s baseline but, perhaps more importantly from a therapeutic perspective, as a percentage of uptake from age-matched healthy controls. Nigrostriatal neurons are known to form dense, overlapping projections [5]‌. As a result of redundancy caused by complex neuronal arborization, PD motor symptoms only develop years after striatal denervation has begun, with clinical signs appearing when up to 70% of nigrostriatal neurons have been lost [6]. 18F-dopa-PET can thus demonstrate reinnervation beyond a threshold in which clinical improvements can be expected, in addition to demonstrating viability. 18F-dopa-PET is therefore an ideal modality to assess the survival of transplanted tissue in cell replacement therapies. 18F-dopa-PET has been used to demonstrate graft viability in open-labelled trials and double-blinded randomized controlled trials in Europe and the United States. The team from Lund, Sweden in collaboration with colleagues from London and Marburg, Germany, reported 6-year follow-up data on six patients receiving unilateral intrastriatial fetal VM grafts [7]‌. Two of these patients also received a caudate graft. At 8–12 months follow-up, non-grafted putamen 18F-dopa uptake decreased by a mean 28%, while uptake amongst the six grafts increased by an average of 68%. At the same time, four grafted patients reported significant clinical improvements, with UPDRS ‘off’ motor scores decreasing by 18–26% at 1 year post-transplant. The amount of time spent ‘off’ was reduced by 34% in these four patients in the first postoperative year and 44% in the second year. In one patient reporting the greatest clinical and neuroimaging improvement, 18F-dopa-PET uptake increased by 22% by 6 months postoperatively and had normalized by 72 months compared to age-matched individuals, while their clinical improvement allowed L-dopa to be stopped completely. In the two patients demonstrating minimal clinical benefits from transplantation, the diagnosis of PD in one has since been in doubt and multisystem atrophy (MSA) has subsequently proposed as the more likely diagnosis. The second patient developed dementia soon after transplantation, complicating motor assessments. These two unsuccessfully graft recipients demonstrate the importance of patient selection in PD neurotransplantation. These early transplantation studies demonstrated that grafts grow, survive, and successfully restore striatal dopaminergic

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concentrations, as shown from significant increases of 18F-dopa uptake from baseline. In addition, in specific patients, restoration of extracellular dopamine also results in significant clinical improvements. After 10–56  months following the first graft, the original six patients were offered a second fetal VM graft on the contralateral striatum [8]‌. One patient declined a second graft after such significant improvements with the first graft. In the remaining five patients, the second graft demonstrated a mean 85% increase in 18F-dopa uptake from baseline at 12–18 month follow-up. In two patients, the 18F-dopa success correlated to significant clinical improvements: one was able to stop L-dopa completely while a second reduced their dose requirements by 70%. No adverse effects were observed following a second contralateral transplant. The two patients who had demonstrated poor outcomes from the first graft continued to deteriorate with minimal benefits from a second graft, despite increases in 18F-dopa uptake within the grafted striatum. The increases of 18F-dopa from baseline demonstrate that repeat transplantation surgery does not affect the viability of the first or second grafts, while continuing to afford the potential of significant clinical improvements. Although 18F-dopa-PET studies have demonstrated that grafts can survive within the host brain, only a small proportion (3–20%) of transplanted tissue actually remains viable. In order to account for this attrition, a larger amount of tissue must be transplanted from a larger number of fetal donors (at least 3–4 aborted fetuses), raising issues regarding tissue availability and ethical considerations. The Lund group has utilized neuroimaging to optimize transplantation techniques that can improve graft viability [9]‌. Five PD patients received bilateral putaminal and caudate grafts. The transplanted tissue had previously been treated with a lazaroid prior to surgery and the patients received intravenous lazaroids for 3  days postoperatively. At 10–23 months after transplantation, PET imaging demonstrated a similar degree of 18F-dopa uptake as other open-labelled trials, with a mean increase of 61% from baseline in the putamen and 24% in the caudate. Clinically, the UPDRS ‘off’ score and L-dopa dose requirement were reduced by an average of 40% and 54% respectively. Critically, the amount of tissue transplanted in the putamen and caudate was reduced by 41% and 50% respectively. These results demonstrate that with optimal tissue preparation, the same degree of presynaptic dopaminergic restoration and consequent clinical motor improvements can be achieved with significantly less donor tissue. Remy and colleagues in France reported significant correlation between 18F-dopa uptake and clinical improvement [2]‌. Following 5 patients who had received unilateral putaminal and caudate grafts with 16 serial PET scans, the 5 grafts demonstrated an average of 63% increase in uptake constant (K i) at 12 months compared to preoperative baseline. The 5 patients enjoyed on average 54.8% more ‘on’ time at 12 months. However, the degree of K i increase ranged from 5% to 185%. In addition, the authors found that when postoperative K i were compared to those of age-matched healthy individuals, patients who achieved a postoperative K i to within two standard deviations of controls were most likely to report clinically satisfying results. A greater increase in 18F-dopa uptake, suggesting significant graft survival, is more likely to restore dopaminergic circuits in the striatum to beyond a threshold and produce clinical benefits.

Investigators at the University of Tampa, Florida, demonstrated the correlation between 18F-dopa-PET and graft survival with histological evidence [10]. Four patients underwent bilateral putaminal and caudate fetal transplantation, producing on average 53% and 35% increases in 18F-dopa-PET uptake in the left and right striatum at 6-month follow-up respectively, associated with a 37% decrease in UPDRS ‘off’ motor score. However, two of the four patients died of unrelated causes 18 and 19 months after the transplantation. The significant increases in 18F-dopa-PET uptake and clinical motor improvements were strongly correlated in both patients to significant numbers of surviving grafted neurons. At post-mortem, between 28% and 78% of the host-commissural putamen was reinnervated with tyrosine hydroxylase immunoreactive neurons, suggestive of grafted nigrostriatal cells. These findings confirm that 18F-dopa uptake is a robust neuroimaging technique in demonstrating graft survival and reinnervation that consequently may lead to clinical benefits. However, despite the encouraging conclusions of earlier, open-labelled studies, two randomized double-blinded sham-surgery controlled trials failed to produce similar results. Freed and colleagues recruited 40 PD patients and randomized them to bilateral putaminal transplantation of fetal mesencephalic tissue or sham surgery [11]. No immunosuppression was used. At 1-year follow-up, the study demonstrated significant increases in 18F-dopa uptake in grafted patients, with a mean 40% increased uptake from baseline. A blinded rater was able to identify 17 of 20 grafted patients using 18F-dopa-PET images alone. Graft survival was further confirmed in two patients, who later died of causes unrelated to transplantation surgery, in which significant numbers of grafted neurons were found at post-mortem. However, no overall significant difference in UPDRS motor score was reported between transplanted and sham-surgery groups. Significant reductions in UPDRS scores were reported in younger grafted patients under 60  years old only. In addition, 15% of grafted patients reported involuntary GIDs when off medication post-surgery. The second trial by Olanow and colleagues recruited 34 PD patients, who were randomized to bilateral fetal transplantation or sham surgery [12]. At 24  month follow-up, the authors reported no significant difference in UPDRS scores despite significantly increased 18F-dopa-PET uptake in the grafted group. Post-mortem of five patients who died of unrelated causes again demonstrated surviving transplanted neurons. However, 56% of patients reported GIDs. These results initially suggest that while grafts survive, they confer clinical benefits, if any, on younger patients only. However, further follow-up of patients from the first trial contradict these conclusions. After the initial blind was lifted at 1 year, 14 of the 20 non-grafted patients underwent transplantation and added to the intervention group for follow-up. After adjusting for patients lost to follow-up, 17 patients were followed up with UPDRS assessments and neuroimaging for 2 years while 15 were followed for 4 years. Ma and colleagues subsequently reported that increases in 18F-dopa-PET uptake continued to increase from years 2 to 4 post-transplant [13]. But, more significantly, clinical improvements, as assessed by reductions in UPDRS scores, were reported across all age groups, not only younger PD patients, with mean reduction in motor ‘off’ score of 15.1% and 30.8% at year 1 and year 2 post-graft respectively. The degree of 18F-dopa-PET uptake

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increase correlated strongly to the clinical improvement. These additional findings question whether graft benefits can reliably be assessed after only 1  year, considering that the graft appears to continue to mature for at least 4 years after transplantation. In the context of likely greater plasticity potential in younger patients, the earlier appearance of clinical improvements in patients under 60, despite similar 18F-dopa-PET uptake, reinforces that even after grafts restore striatal extracellular dopamine, downstream mechanisms must also be restored and normalized before clinical benefits are produced. It is not known whether there is a limit for graft survival. However, long-term 18F-dopa-PET imaging follow-up from the first Swedish grafts reported continued normal levels of uptake compared to age-matched controls, associated with persistent motor benefits, at 16 years after transplant [14]. In the appropriate patients, the use of optimal graft tissue and transplantation techniques can produce transplanted tissues that survive, reinnervate, and produce clinical benefits for at least 16 years and counting.

Dopamine release Dopamine release from the grafted fetal tissue can be indirectly assessed via the binding of PET ligands to postsynaptic receptors. In the striatum, endogenous dopamine binds to D1 and D2 classes of postsynaptic receptors, both of which play key roles in basal ganglia motor regulation. However, benzamide PET radiotracers such as 11C-raclopride and 123I-IBZM bind to D2 receptors with low affinity. Following acute administration of amphetamine or L-dopa, the presynaptic terminal is stimulated to release dopamine, which competitively binds to postsynaptic D2 receptors and displace radioisotope tracers, leading to reduced uptake on PET imaging [15]. 11C-Raclopride has proved a stratified and quantified marker of dopamine release. In non-grafted, dopamine-naive PD patients, 11C-raclopride-PET binding is increased. Following chronic L-dopa medication, 11C-raclopride binding is normalized [16–18]. Assessing graft viability and graft dopamine release 10 years after transplantation, 18F-dopa-PET uptake and basal and post-stimulation 11C-raclopride binding were found to have normalized with age-matched controls [19]. In addition, the patient reported persistent clinical benefits and reduced L-dopa requirements. At 32 months post-graft, L-dopa was stopped completely. A low dose of L-dopa was reintroduced at 72 months post-graft, which remained sufficient to control symptoms until the 10-year follow-up, at which point the patient reported mild resting tremor only. Piccini and colleagues subsequently demonstrated a strong correlation between 18F-dopa uptake and 11C-raclopride binding, concluding that transplanted grafts survive in the host brain and continue to release dopamine, producing significant symptomatic relief despite an ongoing disease process lasting at least 10 years.

Functional and structural connectivity Although the initial chemical deficiency in PD is localized to degenerated nigrostriatial projections, subsequent downstream pathological effects later in the disease process are widespread, affecting many parallel complex circuits within the cortico-striatal-pallidal-thalamic-cortical (CSPTC) loop.

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Neuroimaging modalities such as PET and more recently fMRI have provided a window into the abnormal cortical networks and connectivity during rest and movement. PD patients demonstrate abnormal regional metabolism patterns that represent dysfunctional pathogenic networks and possible compensatory mechanisms. Neuroimaging can show how pharmacological and cell-based therapies affect or potentially normalize these downstream abnormal connectivity patterns. Early studies with transcranial magnetic stimulation (TMS) had demonstrated increased resting excitability and reduced facilitation of voluntary muscle in PD patients, suggesting abnormal control of cortical motor systems [20]. Subsequent 18FDG-PET studies demonstrated abnormal patterns of regional metabolism at rest compared to age-matched healthy controls [21]. PD patients showed reduced metabolism in the lateral premotor cortex, supplementary motor area, dorsolateral prefrontal cortex, and parieto-occipital association regions. Investigations of resting state abnormalities with fMRI regional homogeneity report reduced neural activity in putamen, thalamus, and supplementary motor area (SMA) and increased activity in the premotor areas, cerebellum, and primary motor cortices. The amplitude of neural activity change correlated to the patient’s UPDRS ‘off’ motor scores and were normalized by L-dopa medication, implicating the pathogenic nature of this abnormal cortical network activity and its underlying relation to dopaminergic deficiency [22]. The most recent fMRI studies have demonstrated pathological connectivity between cortical regions in addition to abnormal metabolism and activity (Wu et al. 2012) [23]. Using the pre-SMA and primary motor cortices as seed regions to map the connectivity of motor initiation and execution circuits respectively, the patterns of connectivity in both circuits were abnormal in PD patients compared to controls. Circuits responsible for motor initiation demonstrated greater changes in connectivity than those for motor execution, perhaps explaining the common motor features of PD such as gait and movement initiation. In the earliest functional imaging studies of PD patients during movement, H215O-PET was used to demonstrate regional cerebral blood flow, which could be used as a surrogate marker for regional neural activity. PD patients consistently demonstrated abnormal metabolism patterns during limb movements, receiving decreased regional blood flow to the SMAs and dorsolateral prefrontal cortex [24,25]. These metabolic changes were normalized with L-dopa treatment [26] and following deep brain stimulation surgery [27]. Similar metabolic changes have more recently been demonstrated with fMRI. During hand movements, PD patients demonstrated reduced activity in the SMA and medial frontal cortex compared to controls, while producing increased signal in the cerebellum ipsilateral to the side of movement [28]. However, L-dopa produced no changes to SMA activation but increased the ipsilateral cerebellar response. These changes demonstrated by H215O-PET and fMRI suggest that cortical networks in PD patients activate abnormally during movements in addition to the dysfunctional resting cortical metabolism. Normalization of some changes with L-dopa implies a relation to the underlying dopaminergic deficiency in PD while increased activity in some areas suggests compensatory mechanisms in place to offset abnormal cortical networks. H 215O-PET imaging in PD patients after fetal transplantation suggest that grafting can normalize dysfunctional cortical

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networks. H215O-PET was performed during limb movement in four patients in the 2 years after transplantation [29]. Previously reduced activity in the SMA and dorsolateral prefrontal cortex had normalized 2 years after the transplantation. Interestingly, while 18F-dopa uptake had been restored to normal levels by 6 months, H215O-PET activity was normalized by 2 years after surgery, in tandem with the greatest motor benefits. Replacement of extracellular striatal dopamine post-transplantation is only the first step towards achieving full therapeutic effects of the graft. Striatal reinnervation normalizes previously dysfunctional cortical networks, producing the most significant motor improvements as grafts mature and downstream plastic changes develop.

Graft-induced dyskinesias GIDs are involuntary movements that occur while off dopaminergic medications after fetal VM transplantation. They develop years after transplantation and progress in severity [30]. A number of trials, including the two double-blinded randomized controlled trials, reported significant prevalence of GIDs after transplants at 15% and 56.4%, reported by Freed and colleagues in 2001 and Olanow and colleagues in 2003 respectively [11,12]. However, other small open-labelled studies reported significant lower prevalence at similar follow-up points [31]. This suggests that different graft parameters and transplantation procedures can lead to GID pathogenesis. Neuroimaging has been extensively used to investigate the various proposed underlying causes of GIDs. Early 18F-dopa studies reported increased uptake in the putamen [32] and caudate [33] of patients who developed GIDs, suggesting pathogenic excess graft outgrowth or dopamine release. However, later 18F-dopa-PET studies with larger sample sizes reported no significant different in uptake between patients who developed GIDs and those who did not [11]. In addition, there was no difference between 11C-raclopride binding under basal conditions or after metamphetamine stimulation [34], concluding that there is no significant correlation between graft survival, growth, or dopamine release with GID development. It has also been proposed that localized graft inflammation could significantly contribute to GID pathogenesis. Neuroimaging has not so far supported this hypothesis. However, if inflammation were a key cause of GIDs, a number of observations remain unsatisfactorily unexplained: first, a number of patients developed GIDs even before immunosuppresion was withdrawn. Secondly, why would GIDs not be most common immediately aftersurgery, when inflammation would be at its greatest? Thirdly, GIDs have not been reported to decrease in severity, as would be expected as inflammation resolves postoperatively [35]? Lastly, Isacson and colleagues proposed that the composition of cell grafts could contribute significantly to GIDs via non-dopaminergic neurotransmission, principally via serotonin release [36]. Significantly, Politis and Piccini utilized 11C-DASB, a presynaptic terminal PET tracer, to demonstrate an excess of serotonin in grafts of PD patients who developed GIDs, with a significantly raised serotonin to dopamine ratio within these grafts [1]‌. Crucially for potential treatments, GIDs were successfully reduced with a serotonin-1A agonist that dampened serotonin release. It is suggested that consequent increased serotonin release in a denervated dopamine transporter-deficient area lacking in dopamine autoregulation

results in large swings of extracellular striatal dopamine concentrations, which produces GIDs The discovery of serotonin’s central role in GID pathogenesis has significant implications for aims in future grafts. In order to reduce the prevalence of GIDs, preoperative dissection of fetal VM tissue should aim to reduce to proportion of serotonergic neurons included. The culture and storage of transplanted tissue should be carefully monitored, as tissue storage conditions are known to affect the final tissue cell composition [37]. For example, tissue that is transplanted after a long delay is more likely to cause GIDs in the host patient compared to the use of fresh VM fetal tissue, likely secondary to increasing the serotonin to dopamine ratio in the grafted tissue [38]. In the future, as fetal tissue is hopefully replaced by pluripotent human stem cells, cell sorting should aim to reduce the proportion of serotonergic neurons included.

Non-motor symptoms of Parkinson’s disease Non-motor manifestations of PD are being increasingly recognized in addition to its motor presentations. These include equally troubling symptoms such as cognitive dysfunction, sleep disturbances, gastrointestinal dysfunction, and visual hallucinations, as well as neuropsychiatric features such as depression, anxiety, and apathy [39]. In addition to dopaminergic deficits in the basal ganglia, serotonergic, noradrenergic, and cholinergic neurotransmitter systems are also affected. Neuroimaging modalities have been increasingly utilized to appreciate the underlying mechanisms causing these symptoms and raise questions about potential future cell-based therapy targets. Cognitive dysfunction in PD is caused by a combination of abnormal dopaminergic, cholinergic, and noradrenaergic systems. Dopaminergic deficits in the dorsal striatum, but not the ventral striatum, are strongly associated with executive dysfunction; reductions in uptake of 18F-dopa [40] and 18F-FPCIT-PET [41] in the dorsal striatum positively correlate to the degree of executive dysfunction as measured by the Wisconsin card-sorting test. Similarly, as executive function deteriorates, 11C-raclopride-PET binding is increased in dorsal striatal projection targets such as the dorsolateral prefrontal cortex [42]. On a cortical network level, cognitive dysfunction is associated with hypometabolism on 18FDG-PET imaging in the occipital regions [43]. In some patients more likely to develop PD dementia, frontal and mesiofrontal regions are additionally involved. In early PD cognitive impairment, L-dopa normalizes these dysfunctional cortical metabolism patterns [44]. Dorsal striatal dopaminergic neuronal loss and reduction in dopamine release are thus thought to contribute significantly to PD executive dysfunction. Cholinergic degeneration in the basal forebrain is also correlated to PD cognitive dysfunction. Uptake of the acetylcholinesterase (ACE) ligand 11C-MPA on PET imaging, a stratified surrogate measure of ACE activity, decreases as executive functions such as working memory, episodic verbal learning, involuntary attention, response inhibition deteriorate [45]. Neuropsychiatric non-motor symptoms in PD have been linked to dysfunctional dopaminergic and serotonergic transmission. PD patients suffering from depression and anxiety demonstrate increased 11C-raclopride binding and reduced 18F-dopa uptake in the mesolimbic system [40,46]. In addition, depressed PD patients

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exhibit a globally dysfunctional serotonergic system:  striatal 11C-DASB-PET binding is increased [47], suggesting increased presynaptic serotonergic transporter activity, perhaps secondary to upregulation in a serotonin-deficient environment, while 18F-MPPF-PET uptake was decreased [48], indicating reduced postsynaptic serotonergic binding. Lastly, visual hallucinations in PD patients have been associated with reduced perfusion and blood oxygenation level-dependent (BOLD) signal intensity in occipital and temporal lobes [49] while PD patients reporting sleep disturbances demonstrate increased brainstem monoaminergic activity [50]. The effects of PD neurotransplantation on non-motor symptoms have been investigated in three patients 13–16  years after grafting [14]. Despite normalization of striatal extracellular dopaminergic concentrations and significant persistent motor improvements, the patients continued to report depression, sleep disturbances, fatigue, and visual hallucinations. 18F-dopa-PET uptake was within normal range in almost all extrastriatal areas including the locus coeruleus. However, 11C-DASB uptake was significantly reduced in the Raphe nucleus and other areas receiving serotonergic inputs from the Raphe, including the amygdala, insula, thalamus, hypothalamus, anterior cingulate cortex, posterior cingulate cortex, and prefrontal cortex. As hypothesized by Politis and colleagues, while reinnervation of striatal dopaminergic neurons produces significant motor benefits, it does not normalize non-motor symptoms. This finding highlights serotonergic reinnervation in the Raphe nucleus as a potential future target for additional cell-based therapies to treat non-motor features in PD.

Huntington’s disease The only other neurodegenerative disease in which cell-based therapy has been attempted in humans is Huntington’s disease (HD). HD is an autosomal dominant disorder characterized by expanded numbers of CAG triplet repeats in the huntingtin gene, resulting in a progressive and eventually fatal neurodegenerative disorder. Although it is not known how the mutated huntingtin gene leads to the pathology observed in HD, polyglutamine expansion at the gene product’s N-terminal is thought to interfere with proteins containing a short polyglutamine stretch, such as CREB binding protein, disrupting gene transcription. Patients typically present in midlife with a movement disorder, psychiatric symptoms, and dementia. Pharmacological options are currently available for limited symptomatic relief of psychiatric and motor presentations. Transplantation in HD has so far only taken place in a handful of feasibility studies and one small prospective pilot study. Neuroimaging was initially used to demonstrate the safety of using neurotransplantation in HD before later use for demonstration of graft viability. Kopyov and colleagues transplanted fetal striatal tissue into three patients with HD [51]. Conventional MRI demonstrated no growth of the graft into surrounding tissue, concluding that transplantation was safe and posed no threat of donor tissue extending into the host brain. PET was used only as part of the inclusion criteria. In a larger feasibility study, seven HD patients received bilateral striatal transplantation of 2–8 fetal lateral ventricular eminences [52]. The uptake of 18FDG-PET at 11  months after the second

neuroimaging after cell-based therapy

transplant demonstrated maintained striatal metabolism compared to an annual decline of between 5% and 7% in non-grafted HD patients. Although no significant decreases in UPHRS was reported, a non-significant decrease from an average of 32 to 29, in the context of maintained striatal metabolism, could nonetheless be considered encouraging in a progressive neurodegenerative disease. Bouchard-Levi and colleagues conducted the only prospective trial of HD transplantation so far. Five patients were followed up for 6  years with 18-FDG-PET imaging and clinical assessment using the unified Huntington’s disease rating scale (UHDRS). At 2 years, three patients either demonstrated significant clinical improvements or maintained their previous motor and cognitive functions, associated with increases striatal metabolism at the site of grafts as shown on 18FDG-PET [53]. Two patients progressively declined clinically while their 18FDG-PET uptake decreased, as in untreated patients. However, all but one patient reported significant increases in motor disability as assessed by the UHDRS. However, at 6 years after surgery, while 18FDG-PET demonstrated no decrease in graft metabolism, implying continuing graft viability, significant heterogeneous hypometabolism was seen elsewhere in the brain [54]. This suggests that although grafts survive and produce a transient period of clinical benefit in the selected patient cohort, HD is such a global disease that neuronal replacement therapy can only be one arm of our treatment.

Conclusions and future directions Neuroimaging has demonstrated that transplanted fetal grafts in PD patients can survive, release dopamine and remain viable for several decades. In appropriately selected patients, cell-based therapies can restore striatal dopamine to the same extracellular concentrations as age-matched healthy controls that correlate with significant motor benefits in well-selected patients. The most recent 18F-dopa-PET studies confirmed that grafts continue to mature years after transplantation, indicating that the most appropriate assessments of primary endpoints in future PD cell-based therapy studies should be designated at beyond 1–2  years after surgery, as in the original two double-blinded randomized controlled clinical trials. As functional MRI becomes increasingly more sophisticated and utilized in future transplantation trials, including the current Transeuro consortium, it is anticipated that they will confirm improvements in functional connectivity, as implied by early 15H O-PET studies. Neuroimaging has also demonstrated the 2 crucial underlying serotonergic pathophysiology and provided a curative solution to overcome GIDs, one of the biggest obstacles previously facing cell-based therapies in PD. Neuroimaging has also highlighted the most desirable characteristics in potential patients and transplanted tissue that would produce the greatest clinical benefits. PD patients with highest 18F-dopa uptake in the ventral striatum prior to surgery have reported the greatest clinical benefits. In addition, cell-based therapies have been most beneficial in patients with idiopathic Parkinson’s disease and less successful in those with atypical Parkinsonism or Parkinson’s plus syndromes. 18F-dopa-PET thus has a role in the preoperative screening of patients, selecting those with preserved ventral striatal uptake and excluding those demonstrating resting cortical metabolic patterns atypical of idiopathic PD.

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SECTION 10  

neuroimaging after therapy

The goals of cell-based therapies in PD have also been shaped by results from neuroimaging studies. Evidence of serotonergic hyperinnervation in GIDs reinforced the need for careful dissection of fetal tissues and selective cell sorting in future stem-cell-based therapies to reduce serotonergic to dopaminergic neurons transplanted. In addition, increasing recognition of serotonergic denervation in Raphe-innervated areas in depressed PD patients, as well as cholinergic, dopaminergic, and noradrenergic deficiencies in PD cognitive dysfunction, questions whether these may be potential targets for cell-based therapies for treating non-motor PD symptoms. Striatal transplantation in HD is safe and produces short-term benefits in selected patient cohorts. However, in the longer term, motor disability continues to deteriorate as a result of the global nature of HD neurodegeneration, despite proven survival of grafted tissue. Other strategies, such as neuroprotective therapies, must also be developed in parallel to cell replacement to halt or slow the underlying, progressive neurodegeneration in HD.

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disease:  PET evidence of increased dopamine turnover. Ann Neurol 2001;49:298–303. 16. Brooks DJ, Ibanez V, Sawle GV, et  al. Striatal D2 receptor status in patients with Parkinson’s disease, striatonigral degeneration and progressive supranuclear palsy, measured with 11C-raclopride and positron emission tomography. Ann Neurol 1992;31:184–92. 17. Playford ED, Jenskin IH, Passingham RE, et al. Impaired mesial frontal and putamen activation in Parkinson’s disease: a positron emission tomography study. Ann Neurol 1992;32:151–61. 18. Rinne JO, Laihinen A, Rinne UK, et al. PET study on striatal dopamine D2 receptor changes during the progression of early Parkinson’s disease. Mov Disord 1993;8:134–8. 19. Piccini P, Brooks DJ, Bjorklund A, et al. Dopamine release from nigral transplants visualized in vivo in a Parkinson’s patient. Nat Neurosci 1999;2:1137–40. 20. Vallis-Sole J, Pascual-Leone A, Brasil-Neto JP, et al. Abnormal facilitiation of the response to transcranial magnetic stimulation in patients with Parkinson’s disease. Neurology 1994;44(4): 735–41. 21. Eidelberg D, Moeller JR, Dhawan V, et al. The metabolic topography of parkinsonism. J Cerebral Blood Flow Metab 1994;14(5):783–801. 22. Wu T, Long X, Xang Y, et al. Regional homogeneity changes in patients with Parkinson’s disease. Hum Brain Mapp 2009;30:1502–10. 23. Wu et al. 2012). 24. Playford ED, Brooks DJ. In vivo and in vitro studies of the dopaminergic system in movement disorders. Cerebrovasc Brain Metab Rev 1992;4:144–71. 25. Jahanshahi M, Jenkins IH, Brown RG, et  al. Self-initiated versus externally triggered movements. An investigation using measurement of regional cerebral blood flow with PET and movement-related potentials in normal and Parkinson’s disease subjects. Brain 1995;119:913–33. 26. Rascol O, Sabatini U, Chollet F, et al. Normal activation of the supplementary motor area in patients with Parkinson’s disease undergoing long-term treatment with levodopa. J Neurol Neurosurg Psychiatry 1994;57:567–71. 27. Ceballos-Baumann AO, Boecker H, Bartenstein P, et  al. A positron emission tomographic study of subthalamic nucleus stimulation in Parkinson’s disease:  enhanced movment-related activity of motor association cortex and decrease motor cortex resting activity. Arch Neurol 1999;l56:997–1003. 28. Payoux P, Brefel-Courbon C, Ory-Magne F, et  al. Motor activity in multiple system atrophy and Parkinson’s disease. Neurology 2010;75:1174–80. 29. Piccini P, Lindvall O, Bjorklund A, et al. Delayed recovery of movement related cortical function in Parkinson’s disease after striatal dopaminergic grafts. Ann Neurol 2000;48:689–95. 30. Politis P, Lindvall O. Clinical application of stem cell therapy in Parkinson’s disease. BMC Med 2012;10:1. 31. Mendez I, Dagher A, Hong M, et al. Simultaneous intrastriatal and intranigral fetal dopaminergic grafts in patients with Parkinson’s disease: a pilot study. Report of three cases. J Neurosurg 2002;96:589–96. 32. Ma Y, Feigin A, Dhawan V, et al. Dyskinesia after fetal cell transplantation for parkinsonism: a PET study. Ann Neurol 2002;52:628–34. 33. Huang Z, de la Fuente-Fernandez R, Hauser RA. Dopaminergic alteration in Parkinson’s patients with ‘off period’ dyskinesias following striatal embryonic mesencephalic transplant. Neurology 2003;60: A126. 34. Piccini P, Pavese N, Hagell P, et al. Factors affecting the clinical outcome after neural transplantation in Parkinson’s disease. Brain 2005;128:2977–86. 35. Tsui A, Isacson O. Functions of the nigrostriatal dopaminergic synapse and the use of neurotransplantation in Parkinson’s disease. J Neurol 2011;258:1393–405. 36. Isacson O, Bjorklund LM, Schumacher JM. Toward full restoration of synaptic and terminal function of the dopaminergic system in Parkinson’s disease by stem cells. Ann Neurol 2003;53:S135–48.

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37. Fawcett JW, Barker RA, Dunnett SB. Dopaminergic neuronal survival and the effects of bFGF in explant, three dimensional and monolayer cultures of embryonic rat ventral mesencephalon. Exp Brain Res 1995;106(2):275082. 38. Hagell P, Piccini P, Bjorklund A et al. Dyskinesias following neural transplantation in Parkinson’s disease. Nat Neurosci 2002;5(7):627–8. 39. Ballanger B, Poisson A, Broussolle E, Thobois S. Functional imaging of non-motor signs in Parkinson’s disease. J Neurol Sci 2012;315:9–14. 40. Broussolle E, Dentresangle C, Landais P, et al. The relation of putamen and caudate nucleus 18F18F-dopa uptake to motor and cognitive performances in Parkinson’s disease. J Neurol Sci 1999;166:141–51. 41. Wang J, Zuo CT, Jiang YP, et al. 18F-FP-CIT PET imaging and SPM analysis of dopamine transporters in Parkinson’s disease in various Hoehr and Yahr stages. J Neurol 2007;254:185–90. 42. Kish SJ, Shannak K, Hornykiewicz O. Uneven pattern of dopamine loss in the striatum of patients with idiopathic Parkinson’s disease. Pathophysiologic and clinical implications. New Engl J Med 1988;318:876–80. 43. Jokinen P, Scheinin N, Aalto S, et al. 11C-PIB and 18F18F-FDG PET and MRI imaging in patients with Parkinson’s disease with and without dementia. Parkinsonism Relat Disord 2010;16:666–70. 4 4. Cools R, Barker RA, Sahakian BJ, Robbins TW. Enhanced or impaired cognitive function in Parkinson’s disease as a function of dopaminergic medication and task demands. Cereb Cortex 2001;11: 1136–43. 45. Bohnen NI, Kaufer DI, Hendrickson R et al. Cognitive correlates of cortical cholinergic denervation in Parkinson’s disease and parkinsonian dementia. J Neurol 2006;253:242–7.

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46. Koerts J, Leenders KL, Koning M, Portman AT, van Beilen M. Striatal dopaminergic activity (FDOPA-PET) associated with cognitive items of a depression scale (MADRS) in Parkinson’s disease. Eur J Neurosci 2007;25(10): 3132–6. 47. Boileau I, Warsh JJ, Guttman M et al. Elevated serotonin transporter binding in depressed patients with Parkinson’s disease: a preliminary PET study with 11-DASB. Mov Disord 2008;23(12):1776–80. 48. Ballanger B, Lkinger H, Eche J et al. Role of serotonergic 1A receptor dysfunction in depression associated with Parkinson’s disease. Mov Disord 2012 27 (1): 84–9. 49. Oishi N, Udaka F, Kameyama M et al. Regional cerebral blood flow in Parkinson’s disease with nonpsychotic visual hallucinations. Neurology 2005;65(11):1708–15. 50. Hilker R, Rakai N, Ghaemi M, et  al. [18F]Fluorodopa uptake in the upper brainstem measured with positron emission topography correlates with decreased REM sleep duration in early Parkinson’s disease. Clin Neurol Neurosurg 2003;105(4):262–9. 51. Kopyov OV, Jacques S, Liberman A, Duman CM, Eagle KS. Safety of intrastriatal neurotransplantation for Huntington’s disease patients. Exp Neurol 1998;149:97–108. 52. Hauser RA, Furtado S, Cimino CR, et al. Bilateral human fetal striatal transplantation in Huntington’s disease. Neurology 2002;58:687–95. 53. Bouchard-Levi AC, Remy P, Nguyen JP, et  al. Motor and cognitive improvements in patients with Huntington's disease after neural transplantation. Lancet 2000;356:1975–9. 54. Bouchard-Levi AC, Gaura V, Brugieres P, et al. Effect of fetal neural transplants in patients with Huntington’s disease 6  years after surgery: a long term follow up study. Lancet Neurol 2006;5:303–9.

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Index

Page numbers in italics refer to illustrations; those in bold refer to tables A ABP688 radioligand 172, 172 acetylcholine (ACh) 167–8 see also cholinergic imaging studies acetylcholine receptors (AChR) 168 muscarinic (mAChR) 168 nicotinic (nAChR) 168, 169 acetylcholinesterase (AChE) 130, 168–9 Alzheimer’s disease 130, 144–5 Parkinson’s disease 144 radioligands 169–70, 169 acute disseminated encephalomyelitis (ADEM) 431–2, 432, 432 adiposity 9 agrammatic aphasia and apraxia of speech (naPPA) 3, 60, 61, 214 atrophy 219, 220 metabolic changes 220, 220 MRI findings 219–20 nuclear medicine findings 220 white matter changes 219–20 AIDS 478–9, 479 akinesia 293, 463 corticobasal syndrome 244 alcohol-related disorders 7, 412–13 dementia 75 Alexander, G.E. 538 alien limb phenomenon, corticobasal ­syndrome 244 α-fetoprotein (AFP) 369 α synuclein 3, 63, 259, 284, 317 altanserin radioligand 171, 171 alternative splicing (AS) 35, 37–8 Alzheimer, Alois 58, 113 Alzheimer’s disease (AD) 3, 113, 128–30, 181 amyloid pathology 128, 138–9, 182, 182, 186–8, 186, 188 cerebral amyloid angiopathy (CAA) 403–4 predictive significance 11 atrophy patterns 99, 183–5, 186, 188–9, 189, 199–202 atypical variants 55–6, 57 biomarkers 57, 58, 118 imaging as 190–1 cholinergic changes 130, 144–5, 170, 170, 172 AChE activity 130 clinical heterogeneity 5 cognitive activation studies 190 cognitive symptoms 54–8 co-pathologies 181, 182, 185

CT findings 183 depression and 171, 172 diagnostic accuracy 113 diagnostic criteria 56–8, 58 differential diagnosis 56, 262, 263 dopaminergic imaging 144 economic burden see costs-of-illness (COI) studies enrichment trials 190–1, 191 epidemiology 5, 42 familial AD 56, 57 focal onset 5 frontotemporal dementia and 226 future decline prediction 188–90, 195 genetic risk factors 136 inflammation 141–2, 162 metabolic changes 118, 129, 136, 155, 156, 187 metabolomics approach 32 microglial activation 129, 141 modifiable risk and protective factors 6–11 MRI findings 117–18, 130, 183–6, 199–202 diffusion tensor imaging 130, 185–6, 189–90, 204 functional MRI 130, 190, 204–5 future perspectives 208–9 magnetization transfer imaging (MTI) 202–4 predictive significance 191–2 proton magnetic resonance spectroscopy 205–8 white matter hyperintensities 185, 189 neuropsychiatric symptoms 55 outcome trials 190–1 pathological cascade 58, 59 perfusion studies 154, 155 PET findings 117–18, 128–30, 138–44, 186–8, 190 amyloid tracers 138–40, 140, 140, 186–8, 190 automated analysis 136 FDG-PET 117–18, 129, 135–6, 137, 155, 156, 187, 190 predictive significance 192–4 preclinical AD 11, 57 prediction of in cognitively normal persons 191–4, 192, 193, 195 prevention 11 serotoninergic changes 145 timing of neuroimaging findings 194 transcriptome analysis 36, 37

variants 181 white matter structural changes 185–6, 189–90 Alzheimer’s Disease Neuroimaging Initiative (ADNI) 188, 190, 191, 240 American Spinal Injury Association (ASIA) impairment scale 509 amyloid 156 amyloid A protein 72 see also beta-amyloid amyloid neuropathies 404 amyloidoma, brain 404–5, 405 amyloidosis 72–3, 401 familial, with polyneuropathy (FAP) 404 leptomeningeal 406 spinal 406–7, 406 primary 406 secondary 406–7 amyloid plaques 3 detection of 130 amyloid precursor protein (APP) 156–7 amyotrophic lateral sclerosis (ALS) 3, 345 biomarker quest 346–7 future directions 354–5, 355 clinical heterogeneity 4–5, 4 cortical inhibitory influences 351 corticospinal tract involvement 347, 347, 349 diagnostic criteria 70 dopaminergic changes 352 economic burden see costs-of-illness (COI) studies epidemiology 5–6, 42 extramotor brain involvement 349–51 extrapyramidal involvement 5 familial ALS 352–3 C9ORF72-associated patients 353 homozygous ’D90A’ SOD1 patients 352–3, 353 heterogeneity 345–6 inflammation 162, 351 metabolomics approach 31–2 microglial activation 351, 352 models 353 modifiable risk and protective factors 6–11 MRI findings 347–9 diffusion tensor imaging 348, 349, 350, 350 functional MRI 350, 354 magnetic resonance spectroscopy 347–8 magnetization transfer ratio 348, 350 voxel-based morphometry 348, 348

554

index amyotrophic lateral sclerosis (ALS) (Cont.) myelin changes 350, 350 network-based approach 353–4, 354 neuroimaging role 346 PET findings 144, 349–50, 351–2 presymptomatic studies 353 Revised Functional Rating Score (ALSFRS-R) 346, 347 serotonergic dysfunction 352, 352 SPECT findings 349–50 spinal cord pathology 351 weakness 70 anoxoischaemic encephalopathy see hypoxic ischaemic encephalopathy antioxidant intake 9 aphasia 61 see also primary progressive aphasia (PPA) APOE4 allele 187, 192 apparent diffusion coefficient (ADC) 100, 101 apraxia of speech (AOS) 62 arsenic toxicity 75 artefacts, CT images 87 artefactual noise 87 arterial spin labelling (ASL) 104, 105, 106 amyotrophic lateral sclerosis 350 multiple system atrophy 326 astrocytic plaques 246, 246 astrocytoma 92 astrocytosis 143, 164 PET tracer 164 astroglia activation 161 ataxia see spinocerebellar ataxias (SCAs) ataxia telangiectasia (AT) 368, 370 ataxin1 (ATXN1) 36 athetosis 462 atypical parkinsonism 119 Australian Imaging Biomarker & Lifestyle Flagship Study of Ageing (AIBL) 190 automated methods diagnostic algorithms 119–20 FDG-PET image analysis 136 volumetry 200 autosomal dominant spinocerebellar ataxias 71 autosomal recessive ataxias 71 cerebellar type 1 (ARCA-1) 71, 72 spastic, of Charlevoix-Saguenay (ARSACS) 71 axonotmesis 520, 521, 524–5 AZ10419369 radioligand 171, 172 AZD2184 radioligand 157, 158 AZD4694 radioligand 158 B Ballint’s syndrome 181 ballismus 462 Baló’s concentric sclerosis 429–30, 430 basal ganglia 461, 537–8 circuitry 462, 462 Parkinson’s disease 537–9, 538, 539 dementia relationship 463–4 movement disorder relationships 462–3 neurodegenerative disorders 464–83 physiology 461–2 basophilic inclusion body disease (BIBD) 215 behavioural-variant frontotemporal dementia (bvFTD) 3, 59–61, 61, 214 atrophy 215–17, 216, 217, 222, 224 clinical features 60 diagnostic criteria 60 imaging studies 118, 215–18

MRI 215–17 nuclear medicine 217–18 metabolic changes 217–18 misdiagnosis 59–61 beta-amyloid (Aβ) 156–7 Alzheimer’s disease 11, 128, 135, 182, 182, 186–8, 186, 188 as biomarker 57, 58 cerebral amyloid angiopathy 103 imaging concerns 158–60, 160, 194 metabolomics approach 32 mild cognitive impairment 190 Parkinson’s disease 5 predictive significance 194 radioligands 128–9, 138–41, 157–60, 157, 158, 159 beta-site APP-cleaving enzyme (BACE1) gene 37 bilirubin encephalopathy 469–70 bioinformatics 26 bioluminescence imaging 128 biomarkers 113, 117 Alzheimer’s disease 57, 58, 118 imaging and 190–1 amyotrophic lateral sclerosis 346–7 future directions 354–5, 355 beta-amyloid (Aβ) as 57, 58 corticobasal degeneration 248 dementia with Lewy bodies 118 frontotemporal lobar degeneration 118 multiple system atrophy 317 Parkinson’s disease 284–5 progressive supranuclear palsy 297–8 bismuth toxicity 75 Bloch, Felix 94 blood 22, 23 blood oxygen-level dependence (BOLD) ­contrast 106–7 blood pressure 9 blood–brain barrier dysfunction 141 Parkinson’s disease 275 body fat 9 bovine spongiform encephalopathy (BSE) 72 brachial plexus injury 523, 524, 525 diffusion tensor imaging 531 bradykinesia 463 Parkinson’s disease 260 brain amyloidoma 404–5, 405 calcifications 468, 469, 469 iron accumulation disorders 409, 410 see also iron accumulation metabolism 104, 115, 116 see also cerebral glucose metabolism perfusion 104–5 see also cerebral blood flow (CBF) structural MRI 97–9, 98–101 transcriptome analysis 36 brain atrophy 99, 100, 101 mutation relationships 221–4, 221, 222, 223 see also specific conditions brain injury see traumatic brain injury (TBI) ‘bright claustral’ sign 465 Broca’s area 220 Bromwell, Gordon 115 C C90RF72 hexanucleotide repeat expansions 5, 70, 226 amyotrophic lateral sclerosis 5, 70, 353 frontotemporal dementia 5, 70, 214–15 imaging studies 221, 222, 224

caffeine intake 7–8 CAIDE Dementia Risk Score 11 calcifications 468, 469, 469, 476 cannabinoid receptor type 2 (CB2R) 143 carbon monoxide poisoning 396, 471–3, 473 cardiac imaging dementia with Lewy bodies 239 multiple system atrophy 328–9 Parkinson’s disease 275 carotid arteries CT angiography 89 dual-energy CT 91 plaques 89 Cartwright, M.S. 442 case management interventions 49–50 central nervous system (CNS) 22 cerebellar atrophy infantile neuroaxonal dystrophy 467 multiple system atrophy 322 see also spinocerebellar ataxias cerebellar peduncles 363, 365 cerebellum 363–8, 364, 367 afferent and efferent connectivity 363–6 cytoarchitecture 366, 367 deep nuclei 365 microcircuit 366–8, 368 multimodal analysis 380–1, 380 parcellation 363, 366 see also cerebellar atrophy cerebral amyloid angiopathy (CAA) 103, 103, 104, 401–3 clinical presentation 401–2 cognitive impairment 401–2 dementia 401–2 epidemiology 401 neuroimaging 402–3 cerebral microbleeds 402–3 convexity subarachnoid haemorrhage 403, 404 intracerebral haemorrhage 401, 402, 403, 403 white matter changes 403, 404 neuropathology 401 cerebral blood flow (CBF) 90, 92, 106, 152 mild cognitive impairment 154 mutation relationships 221, 223 radioligands 152–4, 154 see also specific conditions cerebral blood volume (CBV) 91, 92, 104 cerebral glucose metabolism 115, 116 mutation relationships 221 PET principles 115–16 predictive significance 192–4 see also FDG-PET imaging; specific conditions cerebral microbleeds 103, 203–4 cerebral amyloid angiopathy 402–3, 402 cerebrocerebellum 363 cerebrospinal fluid (CSF) 22–3 corticobasal degeneration biomarkers 248 pulsatile flow 507–8 traumatic brain injury effects 492 Charcot, Jean-Martin 66, 69, 259, 345, 421 Charcot–Marie–Tooth disease (CMT) 76 classification of subtypes 437, 438–9 clinical signs 437–40, 439, 440 CNS imaging 441 epidemiology 437 lower limb muscle imaging 443–54, 444–56 CMT type 1a 445–50 peripheral nerve imaging 441, 441–3, 442 pes cavus imaging 448, 451, 456, 457

index chemical exposures 10 chemical shift phenomenon 108 Chenomx software 28, 32 Cho-Cr ratio 208 cholinergic imaging studies 130 Alzheimer’s disease 130, 144–5 Parkinson’s disease 144, 278–9, 548 see also acetylcholine chorea 462 Huntington’s disease 68–9, 462 choreoathetoid movements 462 chronic cerebrospinal venous insufficiency (CCSVI) 422 chronic traumatic encephalopathy (CTE) 489–90 cigarette smoking 6–7 circle of Willis, CT angiography 88, 89 coat-hanger sign 69 cobalamin deficiency 74 coffee intake 8–9 cognitive symptoms 54–66 age-related changes 502 Alzheimer’s disease 54–8 cerebral amyloid angiopathy 401–2 corticobasal syndrome 64–6, 244–5 frontotemporal dementia 58–63 Lewy body dementia 63–4 multiple sclerosis 75 Parkinson’s disease 274, 278, 537, 548 Wilson’s disease 465 Compton effect 89 computed tomography (CT) 85, 127–8 axial CT 128 contrast agents 88 CT angiography (CTA) 88, 89 CT perfusion (CTp) 90–2 dementia differential diagnosis 183 dual-energy CT (DECT) 88–90, 90, 91, 92 helical CT 128 multi-energy CT 90 multi-spectral CT 90 physical principles 85–7 radiation dose considerations 87–8 spinal imaging 509, 516 concussion 489–90 contrast agents (CAs) 88 contraindications to use 88 contrast resolution, CT 86 convexity subarachnoid haemorrhage (cSAH) 403, 404 coordination disorders 70–2 ataxias 70–1 prion disorders 71–2 copper metabolism abnormalities, Wilson’s disease 410, 464–5 corpus callosum changes amyotrophic lateral sclerosis 348, 349, 354 corticobasal syndrome 248 Huntington’s disease 307 Marchiafava–Bignami disease 412–13, 413 traumatic brain injury 491, 491, 496, 500, 501, 504 cortical atrophy Alzheimer’s disease 183–5, 184, 188–9, 189, 199–202 posterior cortical atrophy (PCA) 55, 57 corticobasal degeneration 243, 245, 248, 249 global cortical atrophy (GCA) rating scale 99, 100, 100, 199–200 mild cognitive impairment 188–90, 190 see also brain atrophy

corticobasal degeneration (CBD) 64–6, 119, 215, 243 astrocytic plaques 246, 246 atrophy 224–5, 245, 248, 249, 250, 251 clinical presentation 247, 248 clinicopathological correlations 247–8 diagnostic criteria 67, 247 differential diagnosis 250, 262, 271, 271, 276 dopaminergic studies 336 epidemiology 245 microglial activation 143 non-imaging diagnostic tools 248 tau pathology 245, 246 corticobasal syndrome (CBS) 64–6, 66, 243–5, 244 aetiology 246–7, 247 alien limb phenomenon 244 cognitive symptoms 64–6, 244–5 differences between subtypes 250–2, 252 dopamine transporter (DAT) imaging 253–4 dyspraxia 244 early clinical descriptions 243 extrapyramidal features 244 metabolic changes 252 MRI findings 248–52, 268–9, 271 diffusion-weighted imaging 252 functional MRI 252 neuropsychiatric features 245 oculomotor abnormalities 244 PET findings 252–4, 253 SPECT findings 252–4 see also corticobasal degeneration (CBD) costs-of-illness (COI) studies 42–6 direct costs 43–4, 44, 45 importance of each cost component 46, 46 indirect costs 43, 45, 45 informal costs 43, 45–6, 45 total costs 43, 43, 44 Couper, John 467 Creutzfeldt, Hans-Gerhard 71, 477 Creutzfeldt-Jakob disease (CJD) 71–2, 476–8, 477 genetic 396 iatrogenic 397 MRI findings 396–7 diffusion-weighted imaging 102, 103, 395, 396 PET findings 144, 164 sporadic (sCJD) 72, 73, 395, 396, 477, 478 variant (vCJD) 72, 396–7, 477–8 Cryptococcus neoformans (cryptococcosis) 478 CT see computed tomography (CT) CT angiography (CTA) 88, 89 CT dose index (CTDI) 87 CUBE imaging technique 98 Cufflinks package 39 cyanobacteria 11 cytomegalovirus (CMV) 475–6, 476 D ‘D90A’ SOD1 mutation 352–3, 353 DAA1106 radioligand 143 DASB radioligand 145, 170, 171–2 Dawson, James Walker 426 Dawson’s fingers 426, 427, 427 DED radioligand 164, 165 deep brain stimulation (DBS), Parkinson’s ­disease 265–6, 266, 267, 282–3, 439–543 adverse effects 266 clinical effects 281, 539–40

disease progression and 343 dual concept 541 functional effects 540–2 globus pallidus interna (GPi) 265, 266 patient selection 266 pedunculopontine nucleus (PPN) 542–3 relation of regional effects to symptoms 542 subthalamic nucleus (STN) 265, 266, 540 target sites 265, 266 default mode network (DMN) 107, 108, 192, 217, 311 Dejerine, J. 440 deLong, M.R. 538 dementia 3, 232 AIDS related 478–9 alcohol-related 75 basal ganglia relationship 463–4 cerebral amyloid angiopathy 401–2 differential diagnosis 183–8 epidemiology 55 rapidly progressive (RPD) 71–2, 73 traumatic brain injury associated 502–4, 503 see also specific conditions dementia with Lewy bodies (DLB) 3, 118–19, 232, 481 amyloid pathology 139, 239 biomarkers 118 cardiac imaging 239 clinical features 232 cognitive symptoms 63–4 diagnostic criteria 66, 232 differential diagnosis 262 dopaminergic imaging 144, 236–9, 237, 238, 337 economic burden see costs-of-illness (COI) studies epidemiology 42, 232 grey matter atrophy 234, 234 imaging as a diagnostic tool 239–40 metabolic changes 119, 187, 235–6, 236 MRI findings 118, 208, 233–5 diffusion tensor imaging 234–5, 235 neurological examination 64 pathology 232–3 perfusion studies 154, 155, 236, 236 PET findings 118–19, 139, 235–9, 236, 240 SPECT findings 235–40, 236 white matter pathology 234–5 demyelinating diseases 421 acute disseminated encephalomyelitis (ADEM) 431–2, 432, 432 Charcot–Marie–Tooth disease 76 neuromyelitis optica 431, 432 osmotic demyelination syndrome 414–15, 415, 434, 434 posterior leukoencephalopathy syndrome 433–4, 433 progressive multifocal leukoencephalopathy 432–3, 433 see also multiple sclerosis (MS) dephasing 95 depression Alzheimer’s disease 171, 172 Parkinson’s disease 279, 548–9 destructive spondyloarthropathy (DSA) 406 detector collimation, CT 85 deuteriodeprenyl (DED) radioligand 143–4 Devic, Eugène 431 diabetes mellitus 9, 413 diabetic striatopathy 474–5, 475 dialysis arthropathy 406

555

556

index dietary factors 9 diffuse sclerosis 430 diffusion kurtosis imaging (DKI) 270 diffusion spectrum imaging (DSI) 379–80, 379 diffusion tensor imaging (DTI) 105–6 Alzheimer’s disease 130, 185–6, 189–90, 204 amyotrophic lateral sclerosis 350, 350 dementia with Lewy bodies 234–5, 235 Huntington’s disease 307–8 mild cognitive impairment 189–90 Parkinson’s disease 271 progressive supranuclear palsy 297 spinal imaging 510–13, 511, 513 spinocerebellar ataxias 378–9, 378 Friedreich’s ataxia 387 diffusion tensor tractography (DTT) 531 diffusion-weighted imaging (DWI) 99–102, 105, 107, 117 clinical applications 102, 102, 103 corticobasal syndrome 252 Creutzfeldt-Jakob disease 102, 103, 395 multiple system atrophy (MSA) 325 Parkinson’s disease 270 progressive supranuclear palsy (PSP) 378 spinocerebellar ataxias 378 Friedreich’s ataxia 386–8, 387, 388 dihydrotetranenazine (DTBZ) radioligand 131, 144, 166, 166 dilated perivascular spaces (DPVS) 192 direct costs of neurodegenerative diseases 43 by disease 43–4, 44 by severity stage 44, 45 discriminant analysis (DA) 27 dopamine 164–5 agonists 263 metabolism 165 presynaptic transporters 165–6 vesicular monoamine transporter 166 dopamine dysregulation syndrome (DDS) 279–80 dopamine receptors 166 activity studies 273 radioligands 166–7, 167 dopaminergic changes 144 Alzheimer’s disease 144 amyotrophic lateral sclerosis 352 corticobasal syndrome 253–4 dementia with Lewy bodies 144, 236–9 multiple system atrophy 327–8, 328 Parkinson’s disease 130, 272–3, 273, 274, 464 imaging methods 130–1, 144, 156, 167, 168, 169, 333–5 see also dopamine transporter (DAT) imaging dopamine transporter (DAT) imaging 130, 165, 328, 333 benefits of in movement disorders 333–6 corticobasal degeneration 336 multiple system atrophy 335–6 Parkinson’s disease 333–5, 334 progressive supranuclear palsy 335–6 vascular parkinsonism 336 dementia with Lewy bodies 337, 337 future developments 337–8 radioligands 166 see also dopaminergic changes; specific conditions DTBZ see dihydrotetranenazine (DTBZ) radioligand dual-energy CT (DECT) 88–90, 90, 91, 92 DWAY radioligand 171, 171

dynamic susceptibility contrast (DSC) 104 dynamin-2 (DNM2) 451 dyskinesia graft-induced 548 levodopa-induced 263–4 dyspraxia, corticobasal syndrome 244 dystonia corticobasal syndrome 244 pantothenate kinase associated ­neurodegeneration 466 spinocerebellar ataxias 371–2 E E200K mutation 396 early-onset familial Alzheimer’s disease (EOFAD) 56, 57 echo planar imaging (EPI) sequence 101 echo time (TE) 96 economic burden see costs-of-illness (COI) studies electromagnetic field exposure 10 electromyography (EMG) 521 electronic noise 86 electron spin resonance (ESR) 24, 24 essential tremor 261–2 European Integrated Project on Spinocerebellar Ataxias (EUROSCA) 375 exercise benefits 10 exploratory dataset analysis (EDA) 27 eye movement abnormalities 64 ‘eye of the tiger’ sign 409, 410, 466 F FA-85380 radioligand 169, 169 ‘face of the giant panda’ sign 410, 465 Fahr’s disease 468–9, 469 falls, recurrent 262 progressive supranuclear palsy 262, 293 fallypride radioligand 166, 167 familial amyloidosis with polyneuropathy (FAP) 404 fatal familial insomnia 397 fatigue multiple sclerosis 75 Parkinson’s disease 280 fatty acid metabolism 21 18F-AV-1 radioligand see florbetaben 18F-AV-45 radioligand see florbetapir 18F-AV-133 radioligand 167, 169 [18F]AZAN radioligand 169, 169 Fazekas rating scale 98, 100 FDDNP radioligand 128–9, 141, 160, 161 FDG-PET imaging 126, 135–8 automated analysis 136 methodological developments 135 predictive significance 192–4 see also cerebral glucose metabolism; specific conditions FDG radioligand 115, 126, 156 FDOPA radioligand 130, 144, 165, 165, 167, 168 fibre tracking 106 field of view (FoV), CT 85 ‘figure of 8’ writing task, Friedreich’s ataxia 389–91, 391 finger-tapping task study, Friedreich’s ataxia 389, 389, 390 FLB-457 radioligand 166, 167 flip angle 94 variable flip angle approach 108–9 florbetaben 128, 140, 157, 158, 159 florbetapir 128, 139–40, 140, 157, 158, 159

fluid attenuation inversion recovery (FLAIR) sequence 97, 99 Creutzfeldt-Jakob disease 395, 396 progressive supranuclear palsy (PSP) 295, 296 flumazenil binding 351, 351 fluorescence imaging 128 flutemetamol 128, 139, 157 foot muscles, Charcot–Marie–Tooth disease 446, 451 Fourier transformation 95 FP-CIT radioligand 166, 167, 237, 338 FPEB radioligand 172, 172 fractal measures 208 fractional anisotropy (FA) 106, 107, 204, 378 Alzheimer’s disease 204 risk relationship 192 corticobasal syndrome 252 dementia with Lewy bodies 234–5, 235 Friedreich’s ataxia 386–7, 387 Huntington’s disease 307, 309 multiple sclerosis 427 spinal imaging 511–12 frataxin 369 free induction decay (FID) 95 freezing of gait 293, 537 Friction Cost Method (FCM) for indirect costs 45 Friedreich, Nicholaus 71 Friedreich’s ataxia (FRDA) 71, 368–9, 385 diffusion-weighted imaging 386–8, 387, 388 functional MRI studies 389–91, 389, 390, 391 magnetic resonance spectroscopy 391 metabolic changes 388–9 optic coherence tomography 392 PET studies 388–9 transcranial sonography 391–2 volumetric studies 385–6, 386, 387 see also spinocerebellar ataxias (SCAs) frontotemporal dementia (FTD) 4, 58–9, 59, 118, 214, 215 Alzheimer’s disease pathology 226 atrophy 99, 220–4, 221–3 behavioural variant see behavioural-variant frontotemporal dementia (bvFTD) cognitive symptoms 58–63 economic burden see costs-of-illness (COI) studies epidemiology 42 future imaging applications 226–7 genetics of 214 C90RF72 hexanucleotide repeat ­expansions 221, 222, 224 GRN gene mutations 214, 221–3, 221, 222, 223 MAPT gene mutations 214, 215, 221, 221, 222 language variants 60, 61 metabolic changes 118, 121, 155 neuroimaging role 215 pathology 215 PET findings 140, 140 FDG-PET 136–7 see also frontotemporal lobar degeneration (FTLD) frontotemporal dementia-motor neuron ­disease (FTD-MND) 70, 214 frontotemporal lobar degeneration (FTLD) 3, 58, 59, 118, 215 atypical 215

index FTLD-FUS 58, 59, 59, 215, 226, 227 FTLD-tau 58, 59, 59, 215, 224–5 FTLD-TDP 58, 59, 59, 215, 225–6, 225 metabolic changes 118 neurological examination 62–3 PET findings 118 FDG-PET 118, 136–7 see also frontotemporal dementia (FTD) functional MRI (fMRI) 21, 106–7, 113, 115 Alzheimer’s disease 130, 190, 204–5 amyotrophic lateral sclerosis 350, 354 corticobasal syndrome (CBS) 252 Friedreich’s ataxia 389–91, 389, 390, 391 Huntington’s disease 309–11, 311 resting state 107 spinal imaging 513–14, 514 fused in sarcoma (FUS) protein 4–5, 215 FTLD-FUS 58, 59, 59, 215, 226, 227 G gait problems, Parkinson’s disease 537 freezing 293, 537 ganglioside-induced differentiation-associated protein 1 (GDAP1) 451 gas chromatography-mass spectrometry (GC-MS) 26, 26 gastrocnemius muscles, Charcot–Marie–Tooth disease 444, 447 GE405833 radioligand 143 general linear model (GLM) 200 Gerstmann–Straussler–Scheinker syndrome 71, 397 Gibb, W.R. 64 Glasgow Coma Scale (GCA) 490 glial cytoplasmic inclusions (GCIs), multiple system atrophy 317 gliosis see astrocytosis global cortical atrophy (GCA) rating scale 99, 100, 100 globus pallidus interna (GPi) deep brain stimulation 265, 266 Parkinson’s disease 265 glucose metabolism see cerebral glucose metabolism glutamate imaging, Huntington’s disease 309, 310 glutamate receptors 172–3, 173 GOLM Metabolome Database 26 gradient coils 95 gradient echo 96–7 gradient fields 114 granulin (GRN) gene mutations 58 frontotemporal dementia 214, 221–3, 221, 222, 223 imaging studies 221–3, 221, 222, 223 graph theory 354 grey matter volume changes see brain atrophy; specific conditions gyromagnetic ratio 94 H Hallervorden–Spatz syndrome 465 hallucinations, Parkinson’s disease 279, 549 Hazenberg, B.P. 72 head injury 10–11 heat shock proteins 451–2 heavy metal exposure 9–10 Heinemeyer, O. 442 hepatic encephalopathy 411, 471, 472 hereditary motor and sensory neuropathy (HMSN) 437 see also Charcot–Marie–Tooth disease (CMT)

hereditary spastic paraperesis 70 herpes simplex virus (HSV) 476 high-resolution magic angle spinning (HRMAS) 25 hippocampus atrophy 99, 185, 501, 501, 504 Alzheimer’s disease 183, 183, 184, 191, 199–200, 501, 501 mild cognitive impairment 191, 200 hyperintensity 98 memory function 204 sclerosis 119, 185 traumatic brain injury 500–1, 501, 504 HIV infection 478–9 Hoffman, Ed 115 homocysteine (Hcy) levels 8–9 ‘hot cross bun’ sign 321, 322, 323 Human Capital Approach (HCA) to indirect costs 45 Human Metabolome Database 26 ‘hummingbird’ sign 248, 271, 294, 294, 295 Huntington, George 68 Huntington’s disease (HD) 68–9, 119, 131–2, 462, 481–2, 482 atrophy 303, 304–5, 305, 306 cell-based therapy 549 CT findings 303 metabolic changes 304 microglial activation 143 MRI findings 119, 304–12 cortical grey matter 306, 307 diffusion tensor imaging 307–8, 309 effect size 307 extrastriatal subcortical grey matter 305–6 functional MRI 309–11, 311 iron imaging 311–12, 312 magnetic resonance spectroscopy 308–9, 310 magnetization transfer imaging 308 striatum 304–5 white matter 306–8, 307, 308 whole brain volume 306–7 perfusion study 303–4 PET findings 304 SPECT findings 303–4 hydrocephalus, cryptococcal infection 478 hyperalimentation therapy 467–8, 468 hyperammonaemic encephalopathy 411–12, 412 hyperbilirubinaemia 469–70 hyperglycaemia 474 hypoglycaemic encephalopathy 413, 414 hypoparathyroidism 468, 469 hypoxic ischaemic encephalopathy 396, 413–14, 414, 415, 482–3, 482 I IBZM see iodobenzamide (IBZM) radioligand icterus gravis 469–70 idiopathic hypoparathyroidism 468, 469 imaging 123 molecular 123–5, 124 purposes of 195 systems 123, 124 inclusion bodies 3 indirect costs of neurodegenerative diseases 43 by disease 45, 45 by severity stage 45 induction 95 infantile neuroaxonal dystrophy (INAD) 467, 467

inflammation 33, 131, 161–2 Alzheimer’s disease 141–2, 162 amyotrophic lateral sclerosis 162, 351 evaluation 131 graft inflammation 548 imaging 141–4, 162–4, 163 multiple sclerosis 33, 162 Parkinson’s disease 142–3, 162 traumatic brain injury 490–1, 498 see also microglial activation informal care provision 46–50 financial support 49 interactions among siblings 47 labour supply relationships 47–8 motives for caring 47 social assistance 49–50 sustaining informal care 48–50 informal costs of neurodegenerative diseases 43 by disease 45–6, 45 by severity stage 46 see also informal care provision information exchange 21–2 Ingenuity Pathways Analysis (IPA) 26, 27 Inherited Ataxia Clinical Rating Scale (IACRS) 374, 386–7, 389–91 insulin abnormalities 9 internuclear ophthalmoplegia (INO), multiple sclerosis 422–3, 422 intracerebral haemorrhage (ICH), cerebral amyloid angiopathy 401, 402, 403, 403 intraoperative spinal sonography (IOSS) 515 inversion recovery curve 96, 96 inversion recovery technique 108 iodobenzamide (IBZM) radioligand 166, 167 iofetamine 154, 154 ioflupane (DATSCAN) 130 iron accumulation 409, 410, 464 Huntington’s disease 311–12, 312 infantile neuroaxonal dystrophy (INAD) 467, 467 oxidative stress and 464 pantothenate kinase associated neurodegeneration (PKAN) 465–6 Parkinson’s disease 464, 479–80 superficial siderosis 403, 404 traumatic brain injury 483 iron imaging 311–12 ischaemic encephalopathy 396, 413–14, 414, 415, 482–3, 482 J Jakob, Alfons 72, 477 jaundice, neonatal 470 Josephs, K.A. 62 K Kayser–Fleischer rings, Wilson’s disease 410, 465 kernicterus 469–70, 470 k-space 115, 115 kurtosis imaging 270 L language variants frontotemporal dementia 60, 61 Larmor frequency 94, 95 Lauterbur, Paul C. 113–14 lead poisoning 75, 410–11 Leigh’s syndrome 470–1, 471 lentiform nucleus 461 leptomeningeal amyloidosis (LMA) 406 leukoencephalopathy see specific conditions; white matter degeneration

557

558

index levodopa therapy 259, 263 complications 172, 263–4, 277 imaging studies 277–8 novel approaches 264 Lewy bodies 3, 232–3, 233 see also dementia with Lewy bodies (DLB) Lewy, Friedrich Heinrich 537 Lewy neurites 232–3 Lhermitte’s phenomenon 423 liquid chromatography-mass spectrometry (LC-MS) 26 logopenic variant primary progressive aphasia (lvPPA) 55, 136, 214 long non-coding RNAs (lncRNAs) 36–7 Look-Locker approach 108 Lou Gehrig’s disease 69 lower limb muscle imaging, Charcot–Marie– Tooth disease 443–54, 444–56 M Machado–Joseph disease 371 Mad Hatter syndrome 75 magnetic resonance imaging (MRI), 3D ­imaging techniques 97–8, 98, 109 magnetic resonance imaging (MRI) 94, 127 basic principles 94–7, 113–15 diffusion tensor imaging (DTI) 105–6 diffusion-weighted imaging (DWI) 99–102, 102, 103, 105, 107, 117 echo generation 96 fluid attenuation inversion recovery (FLAIR) sequence 97, 99 functional see functional MRI morphological versus functional imaging 21 perfusion-weighted imaging 104–5, 105 protocol 109, 109 radiofrequency (RF) pulse 94, 114, 115 relaxation time 95–6, 96, 97, 114, 115 relaxometry 108–9, 109 reporting 109–10 scan acceleration 96–7 spatial encoding 95, 95 spinal imaging 509–10, 509, 510 spin precession 94–5 structural brain imaging 97–9, 98–101 susceptibility-weighted imaging (SWI) 102–4 T1-weighted imaging 97, 99, 116 T2-weighted imaging 97, 98, 98, 99, 117 see also specific conditions magnetic resonance neurography 521–3, 523 advances in 530–2 diffusion-based MRN 531–2, 531 spectral adiabatic inversion recovery (SPAIR) sequence 522 superparamagnetic iron oxide (SPIO)-based MRN 532 magnetic resonance spectroscopy (MRS) 24–5, 108, 108, 127 Alzheimer’s disease 130, 205–8, 205, 206, 207 brain amyloidoma 405, 405 Huntington’s disease 308–9 in-vivo MRS 24, 25 progressive supranuclear palsy (PSP) 297 spinal imaging 514–15, 515 spinocerebellar ataxias 375–8, 376, 377 Friedreich’s ataxia 391 transmissible spongiform encephalopathies 395 magnetization transfer imaging (MTI) 202, 271, 308, 530 Alzheimer’s disease 202–4

amyotrophic lateral sclerosis 348, 350 Huntington’s disease 308 Parkinson’s disease 271 transmissible spongiform encephalopathies 395 magnetization transfer ratio (MTR) 530–1 see also magnetization transfer imaging (MTI) manganese toxicity 411, 411 manganism 467–8, 468 Mansfield, Peter 113–14 MAPT gene mutations frontotemporal dementia 214, 215, 221, 221, 222 imaging studies 221, 221, 222 see also microtubule-associated protein tau (MAPT); tauopathies Marchiafava–Bignami disease 412–13, 413 Martinoli, C. 442 mass spectrometry (MS) 24, 25–6 matrix-assisted laser desorption ­ionization with time of flight detector (MALDI-TOF) 26 mean diffusivity (MD) 106, 107, 378 mean transit time (MTT) 91 medial temporal atrophy (MTA) Alzheimer’s disease 183 predictive value 191, 201–2 scale 99, 101, 101, 199–200 see also brain atrophy Mediterranean diet 9 memory 204–5 mercury toxicity 75 Mesulam, Marsel 61 MetaboAnalyst 28, 29 metabolic changes see cerebral glucose metabolism metabolic cognitive syndrome (MCS) 9 metabolic disorders 411, 413–15 metabolic fingerprint 23 metabolic profile 23 metabolic syndrome (MetS) 9 metabolic targeted profiling 23 metabolomic imaging 20 metabolomics 20 analytical platforms 24–6 approach to principal neurodegenerative disorders 31–3 bioinformatics 26 data analysis 26–8 network analysis 28–31, 30 systems theory definition 22 technological innovations 23–4 metaiodobenzylguanidine (MIBG) 275 methanol toxicity 473–4, 474 microbleeds 103, 203–4 cerebral amyloid angiopathy 402–3, 402 microglial activation 141, 161, 162 Alzheimer’s disease 129, 141 amyotrophic lateral sclerosis 351, 352 corticobasal degeneration 143 Huntington’s disease 143 inflammation and 143, 162 mild cognitive impairment 142 multiple system atrophy 129, 142, 142 Parkinson’s disease 142–3, 163, 275, 464 progressive supranuclear palsy 143 radioligands 162–4, 163 micro RNAs (miRNAs) 36 microtubule-associated protein tau (MAPT) 3, 37–8, 58, 245

Alzheimer’s disease 187 corticobasal degeneration 245, 245 FTLD-tau 58, 59, 59, 215, 224–5 mutation-related imaging studies 221, 221 physiological function 160 progressive supranuclear palsy 293 radioligands 160–1, 161, 187 see also MAPT gene mutations; tauopathies mild cognitive impairment (MCI) 54, 105 Alzheimer’s disease associated 57 astrocyte activation 164 cerebral blood flow 154 cognitive activation studies 190 epidemiology 232 metabolic changes 187, 187 microglial activation 142 MRI findings 188–90 diffusion tensor imaging 189–90 white matter hyperintensities 189 PET findings 141–4, 190 amyloid imaging 190 DED-PET 164, 165 FDG-PET 135–6, 190 prediction of future decline 188–90 modulation transfer function (MTF) 86 molecular imaging 123–5, 124 current trends 125 high-resolution modalities 126 history of 124–5, 125 pros and cons 128, 129 see also specific imaging modalities monoamine oxidase B (MAOB) expression 143 ‘morning glory’ sign 270, 294, 295 mossy fibre system 363–4 motor evoked potentials (MEP) 512 motor neuron disease (MND) 345 frontotemporal dementia and (FTD-MND) 70, 214 see also amyotrophic lateral sclerosis (ALS) movement disorders 66–9 basal ganglia relationships 462–3 Huntington’s disease 68–9 hyperkinetic 462–3 hypokinetic 463 multiple system atrophy 69 Parkinson’s disease 66–7 progressive supranuclear palsy 67–8 recording of abnormal movements 259 see also specific disorders MP4A radioligand 144, 169, 169 MP4P radioligand 169, 169 MPRAGE imaging sequence 97, 99 MR parkinsonism index (MRPI) 267 multi-energy CT 90 multiple myeloma 406, 407 multiple sclerosis (MS) 75, 421–31 atrophy 425 clinical presentation 422–4, 422, 423, 424 cognitive deficits 75 diagnostic criteria 76, 424–5, 425, 426 differential diagnosis 76 epidemiology 421 grey matter changes 428 inflammation 33, 162 metabolomics approach 32–3 MRI findings 426–7, 426, 427 normal-appearing white matter (NAWM) 428 pathology 425–6 radiological isolated syndrome 429, 429, 430 relapse 424

index risk factors 421–2 spinal cord lesions 427, 428, 429 variants 429–31, 430, 431 multiple system atrophy (MSA) 69, 119, 316 autonomic failure 317 cardiac sympathetic function imaging 328–9 cerebellar atrophy 322 cerebral atrophy 322–4 CFS biomarkers 317 clinical presentation 317 CT imaging 329 diagnostic criteria 69, 317–18, 319 differential diagnosis 262, 267–70, 270, 276–7 dopaminergic studies 327–8, 328, 335–6 epidemiology 316 infratentorial abnormalities 321, 322, 323 metabolic changes 155, 157, 327 microglial activation 129, 142, 142 middle cerebellar peduncular changes 321–2 MRI findings 267, 268–9, 318–26 arterial spin labelling 326 diffusion tensor imaging 325 diffusion-weighted imaging 325 magnetic resonance spectroscopy 325 quantitative volume evaluation 324–5 susceptibility-weighted imaging 326 MSA with cerebellar dysfunction (MSA-C) 69, 295, 316, 317, 372, 372, 480, 481 MSA with parkinsonism features (MSA-P) 69, 267–72, 316, 317, 480 natural history 316 neuromelanin imaging 326, 326 neuropathology 318 perfusion studies 327, 327 PET findings 142, 142, 326–9 FDG-PET 138, 138 pontine changes 322 putaminal abnormalities 137, 138, 267, 318–21 atrophy 320–1 hyperintense putaminal rim 318, 320, 322, 323 hyperintensity on T1WI 320 hypointensity 318–20 risk factors 316–17 SPECT studies 327, 327 warning signs (red flags) 317 white matter hyperintensity 322, 323, 324 multi-spectral CT 90 multivoxel or spectroscopic imaging (MRSI) 205, 206, 206 muscle denervation 526 myoclonus, corticobasal syndrome 244 N N-acetylaspartate (NAA) 108, 208 Alzheimer’s disease 130 amyotrophic lateral sclerosis 347–8 Huntington’s disease 308–9, 310 multiple system atrophy 325 progressive supranuclear palsy 297 spinocerebellar ataxias 376–8 Friedreich’s ataxia 391 natural antisense transcripts (NATs) 37 NAV4694 radioligand 157, 158 NE40 radioligand 143 near-infrared spectroscopy (NIRS) 24, 25, 25 nerve conduction studies (NCS) 521 nerve conduit repair 527–9, 528, 529 nerve regeneration 520, 526–7, 527

nervous system 22 net magnetization vector 94 network analysis 12 amyotrophic lateral sclerosis 353–4 metabolomics and 28–31, 30 Neumann, Manuela 4 neural transplants, Parkinson’s disease 281–2, 282 neurapraxia 520, 521, 524 neurodegeneration with brain iron accumulation (NBIA) 409, 410, 465–7 see also iron accumulation neurodegenerative disease 123, 151–2 aggregated proteins 152 basal ganglia associated 464–83 common features of diseases 54, 55 economic burden see costs-of-illness (COI) studies epidemiology 4, 5–6, 12 modifiable risk and protective factors 6–11, 8 prevention 11–12, 12 risk factors 152 tauopathies 37–8 time trends 12 underdiagnosis 12 neuro-endo-immune (NEI) network 22 neurofibrillary tangles (NFTs) 63 Alzheimer’s disease 3, 135–6 progressive supranuclear palsy 68, 293 neuroinflammation see inflammation neuromelanin 267, 464 multiple system atrophy 326, 326 neuromyelitis optica (NMO) 431, 432 neuronal intermediate filament inclusion disease (NIFID) 215 Neuro-protection and Natural History in Parkinson’s Plus Syndromes (NNIPPS) study 297 neuropsychiatric symptoms (NPS) Alzheimer’s disease 55 Parkinson’s disease 67, 67 neuroreceptor imaging 164 dopamine receptors 166–7, 167 neurotmesis 520, 521, 525 neurotoxic exposures 75 next-generation sequencing (NGS) 38 niacin deficiency 74 nigrostriatal degeneration see substantia nigra changes NMDA receptor 172 NNC-112 radioligand 166, 167 noise 86–7 non-coding RNAs 36–7 non-ketotic hyperglycaemic choreoathetosis 413 non-linear iterative partial least squares (NIPALS) 27 nuclear magnetic resonance (NMR) 24–5, 24, 94 nutritional factors 9 O occupational exposures 9 oldest old, epidemiology of 12 olfaction testing corticobasal syndrome 248 Parkinson’s disease 281, 284 optical imaging techniques 128 optic coherence tomography (OCT), Friedreich’s ataxia 392 optic neuritis, multiple sclerosis 422, 422

organism complexity 35 osmotic demyelination syndrome 414–15, 415, 434, 434 P pain, Parkinson’s disease 280 PANK2 gene 409, 466 pantothenate kinase associated neurodegeneration (PKAN) 465–6, 466 parathyroid disorders 468 PARK genes 259–60, 275–6 parkinsonian syndromes 63–4, 64, 480–1, 537 atypical parkinsonism 119, 267 differential diagnosis 144, 276–7 secondary parkinsonism 262 symptoms 65 vascular parkinsonism 263, 263, 336 Parkinson, James 66, 259, 479, 537 Parkinson’s disease with dementia (PDD) 5, 67, 278–9, 463–4 biomarkers 285 diagnostic criteria 68 Parkinson’s disease (PD) 3, 130–1, 259, 479–80, 537 basal ganglia circuitry 537–9, 538, 539 biomarkers 284–5 cardiac denervation 280–1 cardiac imaging 275 cholinergic changes 144, 172, 278–9, 548 clinical features 66–7, 259–60, 260, 463, 573 cognitive impairment 274, 278, 537, 548 dementia and see Parkinson’s disease with dementia (PDD) depression 279, 548–9 diagnostic criteria 260–1, 261 differential diagnosis 261–3, 267–72, 270, 276 dopamine dysregulation syndrome (DDS) 279–80 dopaminergic changes 130, 144, 272–3, 273, 274, 464 imaging methods 130–1, 144, 167, 168, 169, 333–5, 334 economic burden see costs-of-illness (COI) studies epidemiology 6, 42 hereditary 275–6 history of 259 inflammation 142–3, 162 iron accumulation 464, 479–80 levodopa complications 172 metabolic changes 119, 137–8, 155, 274–5, 541 deep brain stimulation effects 540–1, 540, 541 metabolomics approach 32 microglial activation 142–3, 163, 275 modifiable risk and protective factors 6–11 monitoring disease progression 277 MRI findings 119, 131, 266–72, 268–9 diffusion tensor imaging 271 diffusion-weighted imaging 270 magnetization transfer imaging 271 MR spectroscopy 271 non-motor symptoms 67, 67 olfactory testing 281, 284 pain 280 perfusion studies 273–4 deep brain stimulation effects 542 PET findings 119, 130–1, 272–8 activation studies 273–5 FDG-PET 119, 137–8, 138

559

560

index Parkinson’s disease (PD) (Cont.) PD-related pattern (PDRP) 137–8, 274, 541 placebo effect 281 preclinical period 11, 275–6 psychosis and visual hallucinations 279, 549 scans without evidence of dopaminergic deficit (SWEDD) 277 serotoninergic changes 145, 275, 279 sleep disturbances 280 SPECT applications 131 transcranial ultrasonography 272 transcriptome analysis 36 treatment 263–6 non-motor symptoms 264, 264 symptomatic treatment 263 see also deep brain stimulation (DBS); levodopa therapy; stem cell therapy Parkinson’s Progression Markers Initiative (PPMI) 240 PBR28 radioligand 143 pedunculopontine nucleus (PPN) deep brain stimulation 542–3 pellagra 74 perfusion-weighted imaging 104–5, 105 peripheral benzodiazepine receptor (PBR) see translocator protein (TSPO) peripheral blood mononuclear cells (PBMCs) 32 cholesterol ester metabolism 32 peripheral nerve injury (PNI) 520 classification 520, 521 contusion trauma 525 diagnosis 521 magnetic resonance neurography 521–3, 523 muscle denervation 526 nerve conduit repair 527–9, 528, 529 nerve regeneration 520, 526–7, 527 pathophysiology 520 penetrating injury 525 post-trauma nerve degeneration 523–6 stem cell therapy 529–30, 529, 530, 531 stump neuromas 525 traction injuries 525 ultrasound imaging 521 Wallerian degeneration 520, 531 peripheral nervous system (PNS) 22 peroneal muscles, Charcot–Marie–Tooth disease 444, 447 peroneal nerve injury 524 pervasive transcription 35 pes cavus, Charcot–Marie–Tooth disease 448, 451, 456, 457 pesticide exposure 10 PET see positron emission tomography (PET) P-glycoprotein 141 Phelps, Mike 115 photoelectric effect 89 physical activity benefits 10 PiB-PET imaging Alzheimer’s disease 139, 226, 227 dementia with Lewy bodies 118, 139 frontotemporal lobar degeneration 139 Parkinson’s disease 139 see also Pittsburgh compound B Pick, Arnold 58 Pick’s disease 215 atrophy 224, 224 pitch 85 Pittsburgh compound B (PiB) 118, 128, 130, 139, 157–8, 157 see also PiB-PET imaging

PK11195 radioligand 129–31, 141–3, 162–4, 163 limitations of 143, 163–4 PLA2G6 gene 467 placebo effect, Parkinson’s disease 281 plasma 22, 23 homocysteine (Hcy) levels 8–9 point resolved spectroscopy sequence (PRESS) technique 206–7 pontine nuclei 364 positron emission tomography (PET) 113, 116, 125–7, 126 basic principles 115–17 inflammation 141–4 neurochemical changes 144–5 neurodegeneration 135–8 spinal imaging 515–16 tracers see radiopharmaceuticals for imaging see also specific conditions posterior cortical atrophy (PCA) 55, 57 posterior leukoencephalopathy syndrome (PRES) 433–4, 433 post-traumatic amnesia (PTA) 490 postural instability Parkinson’s disease 260 progressive supranuclear palsy 293 presenilin-1 (PSEN1) mutation 140 primary lateral sclerosis (PLS) 4, 345, 346 primary progressive aphasia (PPA) 61, 214 classification 61, 62 diagnostic criteria 62 language characteristics 63 logopenic variant (lvPPA) 55, 136, 214 neural substrates 62 non-fluent/agrammatic (naPPA) 3, 60, 61, 214, 219–20, 220 semantic variant (svPPA) 60, 61 primary progressive apraxia of speech (PPAOS) 214 atrophy 220, 220 metabolic changes 220, 220 white matter degeneration 220 principal component analysis (PCA) 27 prion disorders 71–2 classification 72 see also transmissible spongiform encephalopathies procedural memory dysfunction 463 progranulin mutations see granulin (GRN) gene mutations progressive multifocal leukoencephalopathy (PML) 432–3, 433 progressive muscular atrophy (PMA) 4, 345 progressive non-fluent aphasia (PNFA) 3, 214 see also aphasia progressive supranuclear palsy (PSP) 67–8, 119, 215, 292, 480–1 atrophy 224–5, 293–8, 294 clinical features 292–3 CT findings 294 differential diagnosis 262, 267–71, 271, 276–7 dopaminergic studies 335–6 epidemiology 292 history of 292 metabolic changes 298, 300 microglial activation 143, 271 MRI findings 250–2, 250, 251, 252, 268–9, 293–8, 294, 295 as biomarker 297–8 diffusion tensor imaging 297 diffusion-weighted imaging 296–7

FLAIR 295, 296 magnetic resonance spectroscopy 297 quantitative measurements 295–6, 297 neurological examination 68 PET findings 298–9, 300 protective factors see risk and protective factors proteostasis 3–4 proton magnetic resonance spectroscopy (H-MRS) see magnetic resonance ­spectroscopy (MRS) psychosis, Parkinson’s disease 279 purchasing power parities (PPPs) 42 pure akinesia and gait freezing (PAGF) 293 Purkinje cells 365, 368 putaminal changes multiple system atrophy 137, 138, 267, 318–21, 320, 322, 323 Parkinson’s disease 131, 137, 138, 267 R rabies 396 Rabinovici, G.D. 72 racloplide radioligand 166, 167 radiofrequency (RF) pulse 94, 114, 115 radiopharmaceuticals for imaging 113, 115–16, 118, 126–7, 151, 153–4 amyloid pathology 128–9, 138–41, 157–60, 157, 158, 159 cerebral blood flow 152–4, 154 characteristics of 140 inflammation 141–4, 161–4 manufacture of 116 neurochemical changes 144–5, 144, 164–73 neurodegeneration 135–8 tau protein pathologies 160–1, 161 see also specific tracers Raman spectroscopy 24 rapidly progressive dementia (RPD) 71–2 differential diagnosis 73 Rebeiz, J.J. 64 reconstruction filters 85–6 reconstruction interval (RI) 85 reconstruction matrix 85 Reimers, C.D. 442 reticular formation 364 retroviruses 11 Richardson, Clifford 292 Richardson syndrome 252 rigidity corticobasal syndrome 244 Parkinson’s disease 260 risk and protective factors 6–11, 8 alcohol 7 chemical agents and pesticides 10 cigarette smoking 6–7 coffee and tea 7–8 cyanobacteria 11 diet 9 electromagnetic fields 10 head injury 10–11 heavy metals 9–10 metabolism 8–9 physical activity 10 viral infections and drugs 11 workplace exposures 9 RNA 35 micro RNAs (miRNAs) 36 non-coding RNAs 36–7 see also transcriptome RNA-binding Fox (RBFOX) family 38

index RNA editing 35, 38 RNA-Seq transcriptome profiling 38–9 single cell RNA-Seq 39 standard RNA-Seq 38–9 strand-specific RNA-Seq 39 Ro 5-4864 ligand 162 rounding errors 87 rubella 475, 476 S salience network 217 SCH-23390 radioligand 166, 167 Schilder’s disease 430 sciatic nerve magnetic resonance neurography 523, 523 regeneration 526–7, 527 semantic dementia (SD) 3, 214, 218–19 atrophy 218–19, 219 metabolic changes 219 MRI findings 218–19 nuclear medicine findings 219 senataxin 369 serial analysis 380 serine/arginine repetitive matrix 2 (SRMM2) 38 serotonin 170 graft-induced dyskinesia and 548 serotoninergic imaging studies 145 amyotrophic lateral sclerosis 352, 352 Parkinson’s disease 131, 275, 279 receptor imaging 145 transporter imaging 145 serotonin receptors 170–1 imaging studies 172 radioligands 170–1, 171 serotonin transporter (SERT) 170, 275 radioligands 170, 170 serum 22, 23 Shy–Drager syndrome 69, 480 Simon task, Friedreich’s ataxia 391, 391, 392 simultaneous analysis 380–1 single photon emission computed tomography (SPECT) 127 cerebral blood flow 154, 154 Parkinson’s disease 131 singular value decomposition (SVD) 27 sleep disturbances, Parkinson’s disease 280 slice thickness CT 85 CT angiography 88 Smith, Marion 349 smoking 6–7 somatosensory evoked potentials (SSEP) 512 Sottas, J. 440 SP203b radioligand 172, 172 SPACE imaging technique 98 spatial resolution, CT 86 spectral adiabatic inversion recovery (SPAIR) sequence 522 spectral CT 90 spinal cord amyotrophic lateral sclerosis 351 anatomy 507–8, 508 atrophy 510 injury (SCI) 508, 509 Wallerian degeneration 511 multiple sclerosis 427, 428, 429 oedema 510 spinal imaging 508–16 computed tomography 509, 516 conventional MRI 509–10, 509, 510 diffusion tensor imaging 510–13, 511, 513

functional MRI 513–14, 514 intraoperative spinal sonography (IOSS) 515 magnetic resonance spectroscopy (MRS) 514–15, 515 PET imaging 515–16 see also spinal cord spinal stroke 511 spin echo sequence 96 spinocerebellar ataxias (SCAs) 70–1, 368–72, 374 ataxia with oculomotor apraxia type 2 369 ataxia telangiectasia (AT) 368, 370 dominant 71, 369–72 magnetic resonance spectroscopy 375–8, 376, 377 microscopic analysis 379–80, 379 MRI findings diffusion tensor imaging 378–9, 378 diffusion-weighted imaging 378 morphometric MRI 373–5, 373 multimodal analysis 380–1, 380 neurochemical changes 375–8 non-hereditary/sporadic 372 recessive 71, 368–9, 369 spinocerebellar ataxia type 1 (SCA1) 370, 371 transcriptome analysis 36 spinocerebellar ataxia type 2 (SCA2) 370–1 spinocerebellar ataxia type 3 (SCA3) 371–2 spinocerebellar ataxia type 6 (SCA6) 371, 372 treatment response monitoring 378 white matter degeneration 378–9 zebrin band visualization 379, 379 see also Friedreich’s ataxia (FRDA) spinocerebellum 363 spin precession 94–5 statistical noise 86 Statistical Parametric Mapping (SPM) 298, 299 status epilepticus 396 Steele–Richardson–Olszewski syndrome 67–8, 292 stem cell therapy Huntington’s disease 549 Parkinson’s disease 264–5, 283–4, 545 dopamine release 547 functional and structural connectivity 547–8 graft-induced dyskinesias 548 graft survival 545–7 peripheral nerve injury 529–30, 529, 530, 531 stimulated echo acquisition mode (STEAM) technique 206–7 striatum changes, Huntington’s disease 304–5 stroke 296 structural noise 87 substantia nigra changes 272 amyotrophic lateral sclerosis 5 corticobasal degeneration 246, 248 Parkinson’s disease 3, 130–1, 259, 267–72, 270 as biomarker 285 iron deposition 464, 479 volume reduction 119 subthalamic nucleus (STN) 270 deep brain stimulation 265, 266, 540 clinical effects 281, 540 functional effects 540–2 Parkinson’s disease 265 superficial siderosis 403, 404

superparamagnetic iron oxide (SPIO)-based magnetic resonance neurography 532 support groups for caregivers 49 surface-based morphometry (SBM) 348–9 susceptibility-weighted imaging (SWI) 102–4 brain amyloidoma 405 clinical applications 103–4 multiple system atrophy 326 Sweet, William 115 synucleinopathies, symptoms 65 syphilis, congenital 475 syrinx 510 systems medicine 21 T T807 radioligand 161, 161 TAR DNA-binding protein 43 (TDP-43) 3, 4–5, 215 amyotrophic lateral sclerosis 345 frontotemporal lobar degeneration 58, 59, 59, 215, 225–6, 225 targeted metabolomics 23 tauopathies 37–8, 245, 293 pathology prediction 224–5 symptoms 65 see also MAPT gene mutations; microtubule-associated protein tau (MAPT); specific conditions tau protein see microtubule-associated protein tau (MAPT) thiamine deficiency 74, 412 THK253 radioligand 141 THK523 radioligand 161, 161 THK5105 radioligand 161, 161 thrombosis, deep cerebral veins 396 tics 462–3 TopHat package 39 TORCH infections 475–6, 476 toxi-metabolic syndromes 73–5 deficiency states 74–5, 74 Toxoplasma gondii (toxoplasmosis) 475, 476 tract-based spatial statistics (TBSS) 307, 309, 386 transcranial sonography (TCS) 391 Friedreich’s ataxia 391–2 transcriptional collision model 37 transcriptome 35–6 complexity 35–6 variability 35 transcriptome profiling 35–6 future directions 39 RNA-Seq 38–9 transcriptome profiling 35–6 future directions 39 RNA-Seq 38–9 transcriptome regulation 36–8 alternative splicing 37–8 non-coding RNAs 36–7 RNA editing 38 transient neurological episodes 402 translocator protein (TSPO) 131, 132, 141–3, 142, 162–3 radioligands 141–3, 163–4, 163 novel ligands 143, 164 transmissible spongiform encephalopathies advanced neuroimaging 395, 395 basic lesions 394–5 differential diagnosis 396 imaging challenges 394 MRI findings 396–7 see also prion disorders; specific encephalopathies

561

562

index transthyretin-familial amyloidosis with ­polyneuropathy (TTR-FAP) 404 traumatic brain injury (TBI) 483, 489 definitional issues 489–90 iron deposition 483 long-term degeneration 502–4, 502, 503, 504 metabolic alterations 497–9 neuroimaging findings 490–4 multiple haemorrhagic lesions 490, 490, 491 oedema 490, 491, 491, 493, 495 neuroinflammation 490–1, 498 particularly vulnerable regions for ­degeneration 500–1, 500 corpus callosum 500, 501 focal parenchymal loss 500 hippocampus 500–1, 501 primary versus chronic degeneration 494–9 shear-strain force markers 496–7 volumetric changes 491, 494 white matter damage 494–6 tremor essential 261–2 Parkinson’s disease 66–7, 260 Treponema pallidum 475 TRODAT-1 radioligand 338 tryptophan 170 tumefactive multiple sclerosis 430, 431 turbo spin echo (TSE) 96 U ultrasonography (US) 127, 272 Charcot–Marie–Tooth disease 441–2, 442 intraoperative spinal sonography (IOSS) 515

peripheral nerve injury 521 transcranial sonography (TCS) 391–2 Unified Parkinson’s Disease Rating Scale (UPDRS) 260, 545 urine 23 V variable flip angle approach 108–9 vascular dementia (VaD) 3 diagnostic criteria 98 economic burden see costs-of-illness (COI) studies epidemiology 42 vascular parkinsonism 263, 263, 336 vascular risk profile 9 verapamil 141 vesicular monoamine transporter (VMAT) 166 imaging studies 167, 272–3 radioligands 166, 166 vestibulocerebellum 33 viral infections 11 Virtual Brain 380–1, 380 VISTA imaging technique 98 visual hallucinations, Parkinson’s disease 279, 549 vitamin B1 deficiency 74 vitamin B3 deficiency 74 vitamin B12 deficiency 74 VMAT2 protein 131 voxel-based morphometry (VBM) 131, 200–1, 201, 202 Alzheimer’s disease 202, 203 predictive value 191, 203 amyotrophic lateral sclerosis 348, 348 corticobasal degeneration 250

dementia with Lewy bodies 234 multiple system atrophy 325 Parkinson’s disease 267 spinocerebellar ataxias 373, 375 Friedreich’s ataxia 385 voxel-based relaxometry 267 Parkinson’s disease 267 W Wallerian degeneration peripheral nerve injury 520, 531 spinal cord 511 WAY-100635 radioligand 171, 171 WAY radioligand 171, 171 weakness amyotrophic lateral sclerosis 70 hereditary spastic paraperesis 70 Wernicke’s encephalopathy 74, 75, 396, 412, 412 white matter degeneration 98, 99, 100, 102 microstructural changes 185–6, 189–90 mutation relationships 221, 222–3 predictive value 192, 192 see also specific conditions white matter hyperintensities (WMH), ­predictive value 192 see also specific conditions willingness to pay (WTP) approach to indirect costs 45 Wilson’s disease 409–10, 410, 464–5, 465 workplace exposures 9 Z Zaidman, C.M. 442 zebrin bands 379, 379 zinc 464