Neuroimaging in Addiction [1. ed.] 0470660147, 9780470660140

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Neuroimaging in Addiction

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Neuroimaging in Addiction Bryon Adinoff Department of Psychiatry, UT Southwestern Medical Center and VA North Texas Health Care System, Dallas, TX, USA

Elliot A. Stein National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA

A John Wiley & Sons, Ltd., Publication

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c 2011 by John Wiley & Sons, Ltd This edition first published 2011  Wiley-Blackwell is an imprint of John Wiley & Sons, formed by the merger of Wiley’s global Scientific, Technical and Medical business with Blackwell Publishing. Registered office: John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK Editorial offices: 9600 Garsington Road, Oxford, OX4 2DQ, UK 350 Main Street, Malden, MA 02148-5020, USA 2121 State Avenue, Ames, Iowa 50014-8300, USA 111 River Street, Hoboken, NJ 07030-5774, USA For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com/wiley-blackwell. The right of the author to be identified as the author of this work has been asserted in accordance with the UK Copyright, Designs and Patents Act 1988. 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, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought. Library of Congress Cataloging-in-Publication Data Adinoff, Bryon. Neuroimaging in addiction / Bryon Adinoff and Elliot Stein. p. ; cm. Includes bibliographical references and index. ISBN 978-0-470-66014-0 (cloth) 1. Compulsive behavior–Magnetic Resonance Imaging. 2. Brain–Effect of drugs on. 3. Brain–Radiography. I. Stein, Elliot. II. Title. [DNLM: 1. Behavior, Addictive–radiography. 2. Brain–drug effects. 3. Brain– radiography. 4. Magnetic Resonance Imaging–methods. 5. Substance-Related Disorders–radiography. 6. Tomography, X-Ray Computed–methods. WM 176] RC533.A35 2011 616.8’047572–dc23 2011021437 A catalogue record for this book is available from the British Library. This book is published in the following electronic formats: ePDF 9781119998464; Wiley Online Library 9781119998938; ePUB 9781119972709; Mobi Pocket 9781119972716 Typeset in 9/11pt Times by Aptara Inc., New Delhi, India 1 2011 Cover figure courtesy of Dr. Xujuan Geng of the NIDA-IRP, where this work was done. Dr. Geng is now Assistant Professor, Department of Psychiatry, University of North Carolina, USA.

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To my lovely wife, Trish, and my wonderful children, Zack and Holly. Their love and support through the years have calmed my limbic hot spots. Bryon Adinoff

To Marsha, Lindsay and Matthew: All that I am, all that I do, is better because of you. Elliot Stein

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Contents Foreword

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Edythe D. London

List of Contributors 1 Introduction

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Bryon Adinoff and Elliot A. Stein References

2 An Integrated Framework for Human Neuroimaging Studies of Addiction from a Preclinical Perspective

5

9

Karen D. Ersche and Trevor W. Robbins 2.1 2.2 2.3 2.4 2.5 2.6 2.7

Introduction A Conceptual Framework for Understanding Drug Addiction Based on Preclinical Observations Neuropharmacological Considerations Neuropathology of Chronic Drug Abuse Impulsivity: An Endophenotype for Drug Addiction Compulsivity: Craving versus Drug-Seeking Summary References

3 Structural and Functional Neuroimaging Methods: Applications to Substance Abuse and Addiction

9 9 15 15 17 20 25 26

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Yihong Yang, Svetlana Chefer, Xiujuan Geng, Hong Gu, Xi Chen, and Elliot A. Stein 3.1 3.2 3.3 3.4

Introduction MRI Based Imaging Tools and their Application to Drug Abuse Research Molecular Imaging with PET and SPECT Summary and Peek into the Future References

4 Functional Neuroimaging of the Acute Effects of Drugs of Abuse

39 40 59 69 69

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Laurence John Reed and David J. Nutt 4.1 4.2 4.3 4.4 4.5

Introduction Fundamental Neuronal Systems Related to Abuse Liability in Humans Psychostimulants Alcohol Cannabis and the Cannabinoids

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4.6 4.7

Opioids Conclusions and Future Directions References

5 Reward Processing

96 98 99

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Anne Beck, Anthony A. Grace, and Andreas Heinz 5.1 5.2 5.3 5.4 5.5 5.6

Introduction Neurotransmitter Systems Implicated in Reward Processing Neurotransmitter Systems Involved in Drug-Related Reward Processing Alterations in the Mesostriatal System in Addiction Summary and Outlook Acknowledgments References

6 A Neuroimaging Approach to the Study of Craving

107 107 110 116 122 123 123

133

Francesca M. Filbey, Eric D. Claus, and Kent. E. Hutchison 6.1 6.2 6.3 6.4 6.5 6.6 6.7

A Neuroimaging Approach to the Study of Craving Neural Response During Cue-Elicited Craving Associations between Neural and Subjective Response During Cue-Elicited Craving Modulators of Neural Response During Cue-Elicited Craving Effects of Intervention on the Neural Response During Cue-Elicited Craving Summary and Integration of Findings Conclusions References

7 Impulsivity and Addiction

133 134 141 142 147 149 151 151

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Hugh Garavan 7.1 7.2 7.3 7.4 7.5 7.6 7.7

Introduction Impulsivity as Reward versus Control The Neurobiology of Impulsivity Impulsivity and Risk for Developing a Drug Use Disorder Impulsivity in Current Users Impulsivity, Abstinence, and Relapse Conclusion References

159 159 161 163 165 168 170 171

8 Cognitive Disruptions in Drug Addiction: a Focus on the Prefrontal Cortex

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Rita Z. Goldstein, Scott J. Moeller, and Nora D. Volkow 8.1 8.2 8.3 8.4 8.5

Introduction Attention Working Memory Decision-Making Pre-Morbid Vulnerabilities

179 181 190 193 198

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8.6 8.7 8.8 8.9 8.10

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Other Brain Regions Limitations Across All Studies Treatment Implications General Summary and Conclusions Acknowledgments References

Neural Mechanisms of Stress and Addiction

198 199 200 200 201 201

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Dongju Seo and Rajita Sinha 9.1 9.2 9.3 9.4 9.5

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Stress and Addiction Neural Circuits of Stress Regulation Dysfunction in the Neural Circuits Underlying Stress and Addiction Interplay of Gene, Stress, and Drug Intake Acknowledgments References

Anatomical and Neurochemical Evidence of Neurotoxic Changes in Psychostimulant Abuse and Dependence

211 212 218 222 224 224

237

Young Hoon Sung and Perry F. Renshaw 10.1 Introduction 10.2 Characteristics of Psychostimulants 10.3 Quantitative MR Morphology Changes Associated with Psychostimulant Dependence 10.4 Gross Anatomic Changes in Brain Structures and Subtle Neurotoxicity 10.5 Relationship between Errant Neuromodulation by Drug Abuse and Cognitive Abnormalities 10.6 Neurochemical Alterations and Psychostimulant Dependence 10.7 Abnormal White Matter Integrity in Psychostimulant Dependence 10.8 Alcohol and Opiate Addiction 10.9 Conclusion References

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Neuroimaging in Behavioral Addictions

237 238 239 240 241 244 249 251 252 253

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Bryon Adinoff and Cythnia R. Harrington 11.1 11.2 11.3 11.4 11.5 11.6

12

Introduction Diagnostic Considerations Mesostriatal Dopamine Pathway Reward Craving Future Directions References

Imaging Genetics and Addiction

263 264 265 269 273 277 279

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Vibhuti Srivastava and David Goldman 12.1 Introduction 12.2 Domains of Vulnerability 12.3 Cognitive Function

287 289 300

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12.4 12.5 12.6 12.7

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Brain Morphometric Changes Bridging Gaps Imaging Pharmacogenetics Conclusion Glossary References

The Diagnostic and Therapeutic Potential of Neuroimaging in Addiction Medicine

301 302 304 306 308 309

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Martina Reske and Martin P. Paulus 13.1 Can fMRI Become the ECG in Addiction Medicine, or What Are the Treatment Implications of Neuroimaging Research in Drug Addiction? 13.2 Functional Neuroimaging in Addiction: Relevant Cognitive Constructs to Address during Treatment 13.3 Drug Challenge Studies Enhance Knowledge on Pharmacokinetics and Drug-Experience-Relationships 13.4 Imaging Symptom Severity 13.5 Neuroimaging-Based Monitoring of Treatment Regimes and the Prediction of Treatment Outcomes 13.6 Assessing the Relapse Potential Using fMRI 13.7 Neurofeedback as a Therapeutic Approach? 13.8 Methodological Challenges to Utilize Functional Neuroimaging as a Clinical Test 13.9 The Near Future of Brain Imaging in Addiction Medicine References

Index

321 322 325 325 326 329 334 335 336 338

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Foreword Edythe D. London, PhD Semel Institute of Neuroscience and Biobehavioral Sciences, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, USA

Addictive disorders are among the primary preventable causes of major health problems. They also present therapeutic challenges, and often are treatment-resistant and characterized by relapse. The quest for effective addiction treatments has evolved in parallel with major technical advances in the field of brain imaging, which have yielded convincing illustrations that addictions are “brain diseases.” With this in mind, it seems appropriate that a thorough understanding of how disturbances in brain circuitry promote and maintain addiction can help advance the development of effective addiction therapies. Publication of Neuroimaging in Addiction is timely in view of substantial changes in technology and approaches since the appearance of a previous volume on the same subject, almost a decade ago [1]. Relevant advances include the development of new imaging techniques and their application to clinical problems. For example, although a patent was issued for the use of diffusion tensor imaging (DTI) in 1996 [2], it was years later, after the technique was deemed feasible for studies of the brain, that there was a proliferation of studies using DTI for assessment of white matter. Notably, most articles using DTI in studies of substance abuse have appeared in the literature only within the past three years. Similarly, while the technique of determining functional connectivity, using functional MRI in the resting state, was described in the late 1990s [3, 4], this approach has only been applied in addiction research in recent years [5]. Not only has the last decade seen the application of new imaging techniques, but there have also been substantial advancements in functional and structural image analysis procedures, which have greatly influenced the flexibility, scope, and sensitivity of neuroimaging studies [6]. With that in mind, the editors of this book, Bryon Adinoff and Elliot Stein, have assembled an outstanding group of international scholars who contributed to the present volume. The book provides a logical sequence of chapters, beginning with a presentation of current knowledge regarding the neural circuits and neurotransmitters affected by the acute and chronic administration of drugs of abuse, with a focus on findings gleaned primarily from animal studies. After a description of various imaging modalities and how they are used in studies of addiction, the next chapters deal with the acute effects of drugs of abuse, reward processing and craving, and the progression of changes that occur as addiction develops. The subsequent chapters discuss impulsive behavior and neuroimaging studies of disruptions in cognitive function, such as changes in decisionmaking, that contribute to the maintenance of addictions and that can interfere with behavioral treatments. Next, there is a chapter exploring the role of stress in the development of addiction and in relapse to substance abuse followed by a chapter that presents anatomical evidence for structural changes associated with addictive disorders.

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Foreword

In view of research developments over the past decade, including evidence that various addictions (alcohol, drugs, sugar, etc.) involve the same neurotransmitters and circuits, as well as commonalities in genetic markers of addiction vulnerability, the book generally considers addictions as a group of disorders that share neural substrates, without a primary focus on any one substance of abuse. This is exemplified by a chapter which has been devoted to neuroimaging studies of non-chemical addictions. Given the enormous contribution of uncontrolled eating to obesity, diabetes, and other highly prevalent and debilitating diseases, such as cardiovascular disease and stroke, major attention to non-substance addictions is warranted. Brain imaging studies point to commonalities in the neural correlates of these disorders, suggesting that approaches aimed at correcting neural function in common circuitry may be useful in treating the array of addictive disorders. Such approaches have the potential to reduce the burden of disease across a variety of syndromes that feature loss of self-control as a symptom. With respect to addiction vulnerability, linkage analyses, candidate-gene analyses and genome-wide association studies have yielded findings that have implicated specific genes. Nonetheless, because of the profound influences of epigenetic and environmental factors, intermediate phenotypes at the level of neural systems can provide valuable correlates of behavioral measures. Furthermore, assessments of neural markers and responses can be used in studies of the mechanisms by which genotype can influence behavior. Considering these issues, a chapter in this volume focuses on the use of brain imaging studies to describe relevant intermediate phenotypes that are linked to addiction. The volume closes with a chapter that integrates the previous chapters and provides examples and considerations of how brain imaging can be used to predict risk for addiction, diagnosis of addictive disorders, and personalization of treatment. Identification of individuals with neural phenotypes that confer risk for addiction can help target those who might maximally benefit from targeted preventive interventions. Such prophylaxes include educational programs, behavioral approaches, and even vaccines against drug addictions, which are currently under investigation. Although success in clinical trials can be predicted from self-reports of drug use and urine screening [7], which are less costly than neuroimaging, it is possible that identification of dysfunction at the circuit level may be useful in selecting an appropriate targeted treatment. The birth of the field of brain imaging brought with it the hopes of diagnosing neuropsychiatric diseases that are difficult to discern from one another, and identifying the most relevant therapeutic targets. Although the use of brain imaging for diagnostic purposes has not been as successful as predicted 30 years ago, the increasingly progressive development of brain imaging technologies has provided us with the means to clarify the links between neural circuits and behavioral states that lead to and result from addictive disorders. This volume brings us up to date on how imaging technologies are applied in understanding addiction and the therapeutic targets that it presents. Research in the next decade promises equally exciting advances in molecular brain imaging techniques and their application in drug abuse research. At the very least, positron emission tomography research is at the brink of providing new radiotracers that extend our ability to study the brain of drug-abusing individuals and to evaluate effects of treatments. For example, while currently available radiotracers can be used to assess striatal and extrastriatal D2-like dopamine receptor availability, ongoing development focuses on tracers for quantitative assay of dopamine dynamics in low-receptor areas of brain, such as the cerebral cortex. Furthermore, ongoing research is directed at overcoming the radiation dosimetry limitations of nuclear medicine approaches (PET and SPECT scanning), which restrict their

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Foreword

use in children and in multiple assessments of human subjects of any age. A promising area of technological development is the use of nonradioactive magnetonanoparticles, which are detectable by external imaging [8]. Another area of potential advancement in drug abuse research involves the use of realtime functional MRI feedback in facilitating behavioral change. In this regard, real-time functional MRI has been used to show that individuals can voluntarily control activation in a particular brain region, influencing the perception of pain [9]. It is conceivable that addiction-relevant behavioral states, such as craving, could be influenced as well. Whereas these anticipated advances are the subject of future reviews, this highly informative volume describes the brain circuits and neurochemical pathways that contribute to addictive disorders with various technical approaches and how they have been used to elucidate the neural correlates of addictive behaviors and their links to genetics. It serves as an excellent reference volume to both researchers and students interested in the translational neurobiology of addictive disorders. Edythe D. London, PhD

References 1. Kaufman, M. (2001) Brain Imaging in Substance Abuse: Research, Clinical and Forensic Applications, Humana Press, Totowa, NJ. 2. Basser, P.J., Mattiello, J., and LeBihan, D. (1996) Method and System for measuring the diffusion tensor and for diffusion tensor imaging. U. S. Pat. No. 5539310, issued Jul 23. 3. Biswal, B., Yetkin, Z.F., Haughton, V.M., and Hyde, J.S. (1995) Functional connectivity in the motor cortex of resting human brain using echoplanar MRI. Magn Reson Med, 34, 537–541. 4. Lowe, M.J., Mock, B.J., and Sorenson, J.A. (1998) Functional connectivity in single and multislice echoplanar imaging using resting state fluctuations. Neuroimage, 7, 119–132. 5. Hong, L.E., Gu, H., Yang, Y., Ross, T.J. et al. (2009) Association of nicotine addiction and nicotine’s actions with separate cingulate cortex functional circuits. Arch Gen Psychiatry, 66(4), 431–41. 6. Smith, S.M., Jenkinson, M., Woolrich, M.W. et al. (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage, 23(Suppl. 1), S208–S219. 7. Dean, A.C., London, E.D., Sugar, C.A. et al. (2009) Predicting adherence to treatment for methamphetamine dependence from neuropsychological and drug use variables. Drug Alcohol Depend., 105(1–2), 48–55. 8. Akhtari, M., Bragin, A., Cohen, M. et al. (2008) Functionalized magnetoparticles for MRI diagnosis and localization in epilepsy. Epilepsia, 49(8), 1419–1430. 9. deCharms, R.C., Maeda, F., Glover, G.H. et al. (2005) Control over brain activation and pain learned by using real-time functional MRI. Proc. Natl. Acad. Sci., USA, 102(51), 18626–18631.

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List of Contributors Bryon Adinoff, M.D. Professor and Distinguished Professor in Drug and Alcohol Abuse Research, Chief, Division on Addictions, Department of Psychiatry, UT Southwestern Medical Center, VA North Texas Health Care System, Dallas, TX, USA Anne Beck, Ph.D. Postdoctoral Fellow, Department of Psychiatry and Psychotherapy Charit´e, Universit¨atsmedizin Berlin Charit´e Campus Mitte Charit´eplatz 1, Berlin, Germany Svetlana Chefer, Ph.D. Senior Research Scientist, Neuroimaging Research Branch, National Institute of Drug Abuse, National Institutes of Health, Baltimore, MD, USA Xi Chen, Ph.D. Postdoctoral Fellow, Neuroimaging Research Branch, National Institute of Drug Abuse, National Institutes of Health, Baltimore, MD, USA Eric D. Claus, Ph.D. Research Scientist, University Mind Research Network, Albuquerque, NM, USA Karen D. Ersche, Dipl-Psych, M.Sc., Ph.D., CPsychol Senior Research Associate, Department of Psychiatry and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK Francesca M. Filbey, Ph.D. Assistant Professor, School of Behavioral and Brain Sciences University, University of Texas at Dallas, Dallas, TX, USA Hugh Garavan, Ph.D. Associate Professor, Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA Xiujuan Geng, Ph.D. Assistant Professor, Department of Psychiatry, University of North Carolina School of Medicine, Chapel Hill, NC, USA David Goldman, M.D. Chief, Laboratory of Neurogenetics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA Rita Z. Goldstein, Ph.D. Scientist, Medical Research, Brookhaven National Laboratory, Upton, NY, USA Anthony A. Grace, Ph.D. Distinguished Professor of Neuroscience, Professor of Psychiatry and Psychology, Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA Hong Gu, Ph.D. Research Associate, Neuroimaging Research Branch, National Institute of Drug Abuse, National Institutes of Health, Baltimore, MD, USA Cynthia R. Harrington, M.D., Ph.D. Clinical Instructor, Department of Dermatology, UT Southwestern Medical Center, Dallas, TX, USA Andreas Heinz, M.D. Director and Chair, Department of Psychiatry and Psychotherapy Charit´e, Universit¨atsmedizin Berlin Charit´e Campus Mitte Charit´eplatz 1, Berlin, Germany Kent E. Hutchison, Ph.D. Professor, Department of Psychology, University of Colorado at Boulder, Boulder, CO, USA

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List of Contributors

Edythe D. London, Ph.D. Thomas P. and Katherine K. Pike Professor of Addiction Studies, Professor of Psychiatry and Biobehavioral Sciences, Professor of Molecular and Medical Pharmacology, Department of Psychiatry and Biobehavioral Sciences, Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, USA Scott J. Moeller, Ph.D. Postdoctoral Research Associate, Medical Research, Brookhaven National Laboratory, Upton, NY, USA David J. Nutt, DM FRCP FRCPsych FMedSci The Edmond J Safra Chair in Neuropsychopharmacology, Director, Neuropsychopharmacology Unit, Imperial College London, London, UK Martin P. Paulus, M.D. Professor of Psychiatry, Department of Psychiatry, Laboratory of Biological Dynamics and Theoretical Medicine, University of California San Diego, La Jolla, CA, USA Laurence John Reed, Ph.D., MRCPsych Clinical Senior Lecturer in Addiction Neurobiology, Neuropsychopharmacology Unit, Centre for Pharmacology and Therapeutics, Division of Experimental Medicine, Department of Medicine, Imperial College London, London, UK Perry F. Renshaw, M.D., Ph.D., M.B.A. Professor, Department of Psychiatry, University of Utah, Salt Lake City, UT, USA Martina Reske, Ph.D. Postdoctoral Researcher, Institute of Neuroscience and Medicine 4, Forschungszentrum J¨ulich, J¨ulich, Germany Trevor W. Robbins, Ph.D., F.R.S., FMedSci Professor of Cognitive Neuroscience and Experimental Psychology, Director of the Behavioural and Clinical Neuroscience Institute, Head of Department of Experimental Psychology, University of Cambridge, Cambridge, UK Dongju Seo, Ph.D. Associate Research Scientist, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA Rajita Sinha, Ph.D. Professor, Department of Psychiatry and Child Study, Yale University School of Medicine, New Haven, CT, USA Vibhuti Srivastava, Ph.D. Post Doctoral Fellow, Department of Molecular Pathology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA Elliot A. Stein, Ph.D. Chief of Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA Young-Hoon Sung, M.D., M.S. Assistant Professor, Department of Psychiatry, University of Utah, Salt Lake City, UT, USA Nora D. Volkow, M.D. Director, National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD, USA Yihong Yang, Ph.D. Chief of MR Imaging and Spectroscopy Section, Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA

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Chapter 1

Introduction

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Chapter 1 Introduction Bryon Adinoff1,2 and Elliot Stein3 1

Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX 2 VA North Texas Health Care System, Dallas, TX 3 National Institutes on Drug Abuse-Intramural Research Program, Baltimore, MD

Derived from addictionem, meaning “an awarding, a devoting,” the term addiction evolved in the 1600s to suggest a tendency of habits and pursuits. Used in the modern sense since the 1800s with reference to tobacco, opium, and spirits, addiction now describes a symptom complex of loss of control, compulsive use, and continued use despite adverse consequence. Although “dependence” was used by DSM III to describe the physical dependence upon drugs and alcohol (as evidenced by tolerance and withdrawal) and subsequently by DSM III-R and IV to include the three Cs (Control, Compulsive use, and Consequences), there is now relatively widespread agreement that “addiction” best denotes the symptom cluster that is the focus of this volume: Neuroimaging in Addiction [1]. As this book goes to press, the DSM-V work group on substance-related disorders has recommended that “addiction” replace “dependence” as the diagnostic label that defines these behaviors, concerns regarding its vagueness, associated stigma, overuse, and non-scientific formulation non-withstanding [2]. “Addiction,” however, has also been usurped in the public domain to describe any behavior that is performed in excess, including Internet use, sex, chocolate, shopping, pornography, gambling, tanning, or eating. Whether or not these behaviors are truly “addictive,” and whether these behaviors are consistent with a disease process, begs the question of how to definitively identify this disorder. The diagnosis of substance use disorders (in addition to other so-called “process” or “behavioral” addictions), unfortunately, shares a dilemma encountered throughout psychiatry – the diagnosis is based solely on descriptive, symptomatic checklist criteria. The use of biological measures, such as blood tests, physiological measures (e.g., blood pressure), electrocardiograms, or x-rays, to diagnosis disease states, which are standard protocol throughout the rest of medicine, continues to elude our field. The absence of accurate (or even partially accurate) biological markers to guide the diagnosis of neuropsychiatric disorders remains a critical limiting factor in discerning a neurobiologically-based disease from a non-pathological behavioral state and may, in part, be responsible for the poor outcome prognoses for many of our patients suffering from addiction. We believe that neuroimaging techniques offer the best hope to realize this Holy Grail of psychiatry.

Neuroimaging in Addiction, First Edition. Bryon Adinoff and Elliot Stein.  C 2011 John Wiley & Sons, Ltd. Published 2011 by John Wiley & Sons, Ltd.

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Introduction

When the editors began their training, brain imaging was in its early stages of development and implementation as a diagnostic tool. Researchers and clinicians were suddenly provided the opportunity to safely, and with relatively minimal patient discomfort, investigate the human brain in situ. The promises inspired by structural and functional brain imaging were profound. The 1990s were pronounced “The Decade of the Brain” and it was assumed that these tools would herald the neurobiologically based diagnosis and targeted treatment of psychiatric disorders by the turn of the twenty-first century. This, of course, did not happen. What did happen, however, were stunning technical advancements in assessing brain activity that allowed an unparalleled investigation of neural processes, exponentially increasing our understanding of how the brain perceives, integrates, and responds to sensory and affective stimuli. Steady progress has also been evident in unveiling the neurobiological differences in individuals with psychiatric disorders, albeit not (as of yet) with the diagnostic sensitivity and specificity required for clinical use. These advances, perhaps most impressive in the addictive disorders, has motivated the publication of Neuroimaging in Addiction. The accomplishments in understanding the neural processes involved in addiction are due, at least in part, to superb animal models that closely mimic the repetitive and compulsive drug-taking behaviors observed in addicted humans. Neuroimaging techniques have provided the interface necessary to translate these anatomical, cellular and circuitry models into the human addicted brain. A major accomplishment of these closely aligned approaches is the elucidation of biologic processes that are shared across several substances of abuse. The growing confluence of these two approaches signaled to the editors that the timing was propitious to summarize the neuroimaging findings to-date and has guided two key concepts encapsulated in Neuroimaging in Addiction. First, the chapters have been organized by key constructs shared across the various substances of abuse, starting with a description of shared disruptions in neurocircuitry and extending to experiential, cognitive and behavioral processes such as reward salience, craving, stress, and impulsivity. This approach, rather than a categorical approach based upon a specific drug of abuse, supports the common DSM-IV behavioral criteria used to describe all additive disorders. Second, the title of the book refers to Addiction in the singular, denoting a common disease process that is differentially manifested (i.e., a shared etiology and neurocircuitry that is variably expressed with different drug choices) rather than a spectrum disorder (i.e., each substance addiction encapsulates its own etiologic and biologic profile with shared symptoms across each substance). This distinction has critical implications for our understanding, as well as treatment, of addiction. Guided by this framework, the contributors to Neuroimaging in Addiction detail the state-of-the-art in their respective fields. Although the original intent of the editors was to specifically highlight the advances of neuroimaging in addiction, each chapter has also evolved into a superb overview of the construct or topic approached and thus simultaneously provides the reader with an excellent textbook on addiction neurobiology. This extensive overview emphasizes the remarkable progress that has occurred in our field over the past ten years. Yet, as noted earlier, these great leaps forward have not been paralleled with similar progress in the diagnosis or treatment of addiction. Making accurate diagnoses on an individual subject/patient basis remains elusive, as does our ability to assess treatment efficacy. Nevertheless, dramatic advances in imaging technology, coupled with those in other fields (e.g., genomics, drug discovery), promise such breakthroughs in the nottoo-distant future. New technologies have and will continue to offer new insights in

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References

the structure and function of both the healthy brain and its pathophysiology. Justified excitement in the neuroimaging field can be seen in the recent advances in the ability to perform white matter tract tracing in situ, combine the excellent temporal resolution of EEG with the superb spatial resolution of fMRI in combined recording studies, and measure the important neurotransmitters glutamate and GABA via MR spectroscopy. New PET ligands are starting to emerge from the lab, promising the ability to make molecular measurements of compounds based on scientific hypotheses, not simply because a ligand was available. And new hardware continues to be developed, whether it be ever higher field MRI scanners (a human 11.7 T scanner is currently in development) or the exciting recent PET camera insert into a standard 3T MRI, allowing for the first time simultaneous measurements. Finally, especially in the field of MRI, new analysis methods are continually being developed to better extract information from the rich MRI signal. These developments include the rapidly evolving field of resting state functional connectivity, and its analysis using network and multivariate analyzes, although only the former has yet to be applied to the addiction field. Elucidating subject-specific differences in brain functioning will enable the identification of neural correlates of behavioral complexes, unique intermediate phenotypes, and/or substance-specific disruptions as well as targeted treatment approaches and objective assessments of treatment efficacy. Clarification of the distinct and overlapping neural networks defining addictive and other psychiatric disorders, including schizophrenia, bipolar, post-traumatic stress, and antisocial social personality disorders, will allow increasingly focused treatment approaches. Finally, it is likely that identifying neural signatures of addiction will markedly diminish the stigma associated with addictive disorders. Such biological markers should lessen the fear and shame that accompanies this disease, and in turn, remove self-imposed, social, and medical obstacles in seeking and obtaining treatment. It is our hope that scientists, clinicians, and students will find the material in this volume useful as we continue our journey to understand the addicted brain with the goal of improved prevention and treatment outcomes for our patients.

References 1. O’Brien, C.P., Volkow, N., and Li, T.K. (2006) What’s in a word? Addiction versus dependence in DSM-V. American Journal of Psychiatry, 163, 764–765. 2. Erickson, C.K. (2007) Terminology and characterization of “Addiction”, in The Science of Addiction: From Neurobiology to Treatment, W. W. Norton & Company, New York, pp. 1–31.

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An Integrated Framework for Human Neuroimaging Studies of Addiction from a Preclinical Perspective

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Chapter 2 An Integrated Framework for Human Neuroimaging Studies of Addiction from a Preclinical Perspective Karen D. Ersche1 and Trevor W. Robbins1,2 1

University of Cambridge, Behavioural & Clinical Neuroscience Institute, Cambridge, UK 2 University of Cambridge, Department of Experimental Psychology, Cambridge, UK

2.1 Introduction Preclinical research into the neural substrates of drug dependence focused attention onto the dopamine-dependent functions of the nucleus accumbens of the ventral striatum in rewarded behavior (see recent review [1]. More recent analyzes have shown the importance of considering the neural context of the ventral striatum in subserving such behavior [2], including limbic-cortical and prefrontal interactions with the striatum. It is this framework of preclinical research that has guided the yet more complex issues of the neural substrates of addiction, particularly in humans, to a variety of drugs of abuse, including stimulants and opiates.

2.2 A Conceptual Framework for Understanding Drug Addiction Based on Preclinical Observations Understanding the neural basis of drug addiction has required an integrated approach from both studies in cognitive and affective neuroscience on human volunteers and clinical patients, and also from behavioral neuroscientists and psychopharmacologists conducting well-controlled animal experiments. However, it was discoveries derived from experiments with animals that provided the first clues about how the brain might mediate reinforcement processes relevant to addiction, and it is this literature that underpins many of today’s sophisticated investigations of the neural substrates of human addiction. Perhaps the seminal discovery was that by Roberts et al. [3], who showed that depleting dopamine from the mesolimbic dopamine system appeared to block the selfadministration of intravenous cocaine in rats in a way that could not easily be accounted for as a motor deficit (given the implication of dopamine in Parkinson’s disease). Previous work by several groups beginning with Crow [4] had implicated mesolimbic dopamine Neuroimaging in Addiction, First Edition. Bryon Adinoff and Elliot Stein.  C 2011 John Wiley & Sons, Ltd. Published 2011 by John Wiley & Sons, Ltd.

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in a “brain reward system” from studies on intracranial self-stimulation via implanted electrodes in the medial forebrain bundle.

2.2.1 The Pivotal Role of the Nucleus Accumbens One of the terminal regions of the mesolimbic dopamine system is a structure in the basal forebrain, associated with both the basal ganglia and the limbic system, the nucleus accumbens. Much interest was already focused on the role of the nucleus accumbens in reward processes when Hoebel et al. [5] showed that rats would self-administer damphetamine directly into this region unilaterally in very small volumes – with little evidence of other “hot-spots.” Phillips et al. [6] confirmed this finding with evidence from bilateral self-administered infusions that were up-regulated by simultaneously adding dopamine D1 or D2 receptor antagonists to the infusate – suggesting that the rats were “regulating” their preferred level of dopamine receptor stimulation, as rates of selfadministration increased, again contrary to what would be expected of a purely motor function for these neurons. Two other classic studies have confirmed an important focus on dopamine-dependent functions of the nucleus accumbens, while broadening its involvement to include nonstimulant drugs such as heroin and alcohol. DiChiara and Imperato [7], using in vivo microdialysis, have shown that many drug withdrawal states, whether from stimulants such as cocaine, nicotine, alcohol or heroin, all increase levels of dopamine sampled in the nucleus accumbens. This does not, of course, suggest that such an effect is sufficient or even necessary for drug reinforcement, as many other receptor-types and brain regions may be implicated for example in alcohol reinforcement, but the commonality is significant. However, Koob and LeMoal [8] have also highlighted many other neurochemical and neuroendocrine changes occurring in drug withdrawal. A second landmark study was that of Bozarth and Wise [9], which appeared to dissociate the positive reinforcing effects of opiates from their physical withdrawal signs. The latter were attributed to brain-stem systems, but rats would self-administer morphine directly into the vicinity of the dopamine cell bodies in the ventral tegmental area (VTA) in the absence of any obvious precipitated signs of withdrawal – implicating a dopamine system in the positively reinforcing actions of opiates. However, it was shown subsequently that not only did morphine selfadministration occur in the nucleus accumbens but also that it was, perhaps surprisingly, not blocked by dopamine depletion from that structure (see [8] for a review). Thus, the nucleus accumbens clearly had an important role in opioid reinforcement, but its contribution to opioid self-administration was independent of its dopamine input.

2.2.2 The Nucleus Accumbens as a Limbic-Motor Interface (see Figure 2.1) The nucleus accumbens, as mentioned above, is a potential “interface,” as described by Mogenson et al. [10], between the limbic system and the striatum (or between “motivation and action” as some have also suggested). Major inputs to the nucleus accumbens include from the prefrontal cortex, hippocampus and amygdala (Figure 2.1). The role of amygdala afferents to the nucleus accumbens in aspects of addiction was first suggested by parallel studies in rats and human drug abusers. It had already been shown that some

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Figure 2.1 Neural circuitry associated with the neuopathology of drug addiction, involving brain systems such as the nucleus accumbens, of which both the shell and the core are implicated in producing the powerful reinforcing effects of addictive drugs such as cocaine. Interactions between the nucleus accumbens, the basolateral amygdala and the hippocampus are important for conditioned reinforcement and the processing of contextual information, which underlie the feelings of drug cravings in the face of drug-related stimuli. Executive control from the prefrontal cortex over the nucleus accumbens and the dorsomedial striatum are needed to guide behavior according to the individual’s expectations, values and goals. In the case of habitual behaviors, however, which occur independently from a goal, control from the prefrontal cortex gradually shifts towards the dorsolateral striatum. It has been hypothesized that stimulus-response habit learning plays an important role in development of drug addiction, as it may underlie the transition from the hedonically driven recreational drug use to more habitual, and eventually compulsive patterns of drug-taking, as seen in drug-addicted individuals. Green/blue arrows indicate glutamatergic projections; orange arrows indicate dopaminergic projections; pink arrows indicate GABAergic projections; Acb, nucleus accumbens, BLA, basolateral amygdala; CeN, central nucleus of the amygdala; VTA, ventral tegmental area; SNc, substantia nigra pars compacta. GP, globus pallidus (D, dorsal; V, ventral). Reproduced with permission from Figure 2.1b of Everitt and Robbins [25].

of the propensity for stimulant drugs to potentiate effects of appetitive conditioned reinforcers was dependent upon an input to the nucleus accumbens from the basolateral amygdala (BLA) [11]. This result suggested that stimulus-reward associations could be mediated in part by the amygdala and that this information was conveyed to the nucleus accumbens where it could be “gain-amplified” by its dopamine input. In human imaging studies, it was later shown that the amygdala was one of several brain regions

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in the temporal lobe activated in stimulant-dependent individuals by cues associated with the abused drug [12, 13]. Correspondingly, in studies of “drug-seeking” behavior in which rats worked under a so-called “second order” schedule to obtain intra-venous (i.v.) cocaine, performance is maintained at least partly by the cues associated with the drug, which are presented contingently as conditioned reinforcers during instrumental performance [14]. However, excitotoxic damage to the amygdala [15] and also to the core region of the nucleus accumbens to which it projects [16] blocked the acquisition of this drug-seeking behavior. There is also neurochemical specificity in this interaction: dopamine receptor blockade, but not AMPA receptor blockade, in the BLA reduced established cue-controlled cocaine-seeking. However, the reverse was true in the nucleus accumbens core sub-region [17]. Moreover, a disconnection experiment of the BLA and accumbens core by blocking dopamine receptors in the BLA on one side and AMPA receptors in the accumbens core on the other, dramatically reduces cocaine-seeking, indicating that these two regions are probably serially connected in functional terms [18], that is they are part of a common amygdala-ventral striatal system (see Figure 2.1). Other studies have revealed the importance of this amygdalo-striatal system in relapse, as measured in the reinstatement-extinction paradigm [19]. It is, however, of note that the predictive validity of the reinstatement paradigm and its functional equivalence to humans have been called into question [20, 21]. Specifically, it has been criticized that the reinstatement model depends on extinction, which does not mimic most situations in humans that lead to drug abstinence, and therefore, may not be suitable to model relapse. The role of the hippocampus has been less clear. Fundamental studies have of course accorded this structure a role in memory and learning, but perhaps the most plausible contribution to addiction is modulation of the shell sub-region of the nucleus accumbens via its projections there, and its possible mediation of motivational aspects of context (as distinct from discrete cue) conditioning. Theta-bust stimulation of the hippocampus (a form of experimental deep brain stimulation) reinstates extinguished cocaine-seeking in a way that indicated a dependence on glutamate transmission in the VTA [22] – and a possible mechanism for the effects of context re-exposure on relapse. In fact, inactivation of the dorsal hippocampus does attenuate context-induced reinstatement of drug-seeking in rats [23]. Many electrophysiological studies indicate that amygdala, hippocampal and prefrontal cortical inputs may influence drug-seeking behavior via their convergence on the nucleus accumbens, possibly competing for access to different response selection mechanisms gated by the ascending dopamine system and the cortico-striatal-pallidothalamic circuitry (Figure 2.1: [24]).

2.2.3 The Dorsal Striatum and Habits Burgeoning evidence supports the notion that as drug-seeking becomes compulsive, there is a shift in the control of behavior from the prefrontal cortex to the striatum, and from the ventral striatum (i.e., nucleus accumbens) to the dorsal striatum (i.e., caudate-putamen in the rat) (see Figure 2.1: [25]. Similar views have been expressed by other authors [8, 26]. Some of the early evidence depended on observations that chronic i.v. self-administration of cocaine in rhesus monkeys initially produced changes in the expression of D1 receptors that were initially limited to the ventral striatum but then spread throughout the caudate nucleus and putamen [27]. Additionally, Ito et al. [28, 29] found that the conditioned reinforcer in a second order schedule evoked

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release of dopamine in the dorsal striatum in rats that had been well-trained, but not the nucleus accumbens shell or core, which were only sensitive to cocaine itself or to changes in the presentation of the CS, respectively. Vanderschuren et al. [30] have shown that dopamine receptor blockade in the dorsal striatum was effective in blocking drug-seeking, but similar infusions in the nucleus accumbens core were without effect, even though this structure is implicated in initial learning of the second order schedule of cocaineseeking. Everitt et al. [31] has summarized the most recent evidence, which includes the observations that the presentation of drug cues to cocaine-dependent individuals not only induces drug craving correlated with activation of the amygdala and other limbic regions, but also the dorsal striatum [32]. These observations have been interpreted as supporting the hypothesis that stimulusresponse habit learning plays an important role in the transition from abuse to the compulsive drug-seeking behavior of addiction [25]. This hypothesis is mostly supported by evidence that the dorsolateral striatum in rats [33], and more recently in humans [34], is implicated in habit learning for natural reinforcers such as food. The neuroanatomical substrate for this transition is not yet clear, but may depend on the serial, cascading nature of feedback circuitry unidirectionally from successive components of the ventral to the dorsal striatum [35]. This hypothesis remains to be tested in detail, especially for non-stimulant drugs of abuse. An especially crucial task is to elucidate the mechanisms responsible for the transition from habit learning to the compulsive behavior, characteristic of drug-seeking, defined as repeated behavior that has dysfunctional consequences (see Figure 2.2).

Action-to-Habit Theory

Incentive-Sensitization Theory

(Robbins & Everitt 1992, Everitt & Robbins, 2005)

(Robinson & Berridge, 1993, 2000)

euphoria drug liking

Pavlovian conditioning

initial drug use

pleasure, euphoria

Pavlovianinstrumental transfer

stimulus – outcome learning

repeated drug use

sensitization of the pleasurable effects of the drug (drug wanting)

Instrumental conditioning

continued drug use

action – outcome learning

regular drug use

attempts to abstain from drugs

compulsive desire for the drug due to incentive salience

repeated drug use

habit learning

habitual drug use leading to compulsive drug-taking

Failure of inhibitory control

MESOLIMBIC → NIGROSTRIATAL DOPAMINE SYSTEMS

initial drug use

MESOLIMBIC DOPAMINE SYSTEM

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Figure 2.2 Stages in the process of addiction explained by the Incentive-Sensitization Theory and the Action-to-Habit Theory. The steps related to Pavlovian conditioning are indicated by light blue arrows, whereas the steps related to instrumental conditioning are indicated by dark blue arrows.

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Everitt and Robbins [25] propose several factors affecting this transition, but one especially relevant to the neurocircuitry of drug abuse is the prefrontal cortex, given that damage to this region commonly results in perseverative behavior, reminiscent of compulsivity.

2.2.4 Prefrontal Cortex and Top-Down Control The prefrontal cortex (PFC) has major roles in the control of many aspects of behavior, often via its influence on parallel cortico-striatal “loops,” that are implicated in different aspects of motor and cognitive output. For example, frontal dysfunction, as a consequence of drug-taking, may lead to impairments in volitional control over drug-seeking and taking behavior. Metabolic under-activity and structural changes within the orbitofrontal cortex have especially been related to stimulant dependence [36, 37], but it is likely that many sectors of the PFC and the neocortex in general are impacted in addiction to several other drugs of abuse. A key question, however, is to what extent neurobiological abnormalities in drugaddicted individuals are caused by the toxic effects of drug exposure, and may be reversible, versus the possibility that they are not necessarily caused by drug exposure and may thus exist premorbidly. Animal research is being increasingly directed to this question. For example, Porrino et al. [38] using the 2-[14 C] deoxyglucose method to map changes after increasing durations of self-administered cocaine exposure in rhesus monkeys have shown initially significant decreases in PFC metabolism in the caudal sectors of the medial wall of the gyrus rectus (area 14), cingulate areas 24 and 25, and caudal portions of area 32 – all regions implicated in the control of autonomic and visceral function. Decreases were further found in such regions as the insula, with increases in the dorsolateral and dorsomedial PFC. Following chronic self-administration, metabolic decreases extended into the anterior cingulate cortex and the orbitofrontal cortex (areas 12 and 13), and now also began to appear in the dorsolateral prefrontal cortex. These changes have been paralleled by detailed neuroanatomical and electrophysiological studies of the sequelae of chronic drug exposure in rodents (e.g., [39, 40]. The precise impact of these various changes will depend on a sophisticated understanding of the behavioral functions of the various regions of the PFC, including the orbitofrontal cortex. Excitotoxic lesions of this area certainly impair performance on second-order schedules of cocaine seeking, seemingly increasing response output without respect to the presence or absence of drug-paired conditioned reinforcers [41, 42]. Regions of the medial (prelimbic) PFC are believed to have important roles in instrumental (i.e., action-outcome) learning in rats [34], which presumably competes with S-R habit learning, and thus damage to this area will presumably lead to habit-dominated behavior. Inactivation of the rodent medial PFC also leads to enhanced signs of relapse to cocaine-taking on the extinction-reinstatement paradigm [19]. The detrimental influence of cortical damage on cognitive function, and in particular the effects of orbitofrontal cortex damage on decision-making [43, 44] will clearly have deleterious effects on chronic drug users, leading to cognitive impairments for example in working memory, but also to maladaptive behavioral choices that further enhance the drive to addiction. The PFC also probably mediates many subjective aspects of cognition, including attributional processes relevant to drug addiction, but these more represent a challenge for imaging investigations in humans rather than allowing an experimental approach with animal studies.

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2.4

Neuropathology of Chronic Drug Abuse

2.3 Neuropharmacological Considerations While all drugs of abuse increase extracellular levels of dopamine in the nucleus accumbens in rodents, stimulant drugs are distinct in this regard because they exert a direct influence on the levels of mesolimbic dopamine, stimulant drugs such as amphetamine and cocaine acutely enhance dopamine neurotransmission either by blockade of its reuptake or by directly releasing it from pre-synaptic sites [45, 46], for review). Opiates, by contrast, exert their action mainly through μ-opioid receptors, indirectly increasing dopamine but decreasing norepinephrine levels. Several lines of investigation have indicated an effect of 5-HT in mediating opiate reinforcement, but at present, the mechanism is still unclear [47,48]. Ethanol acts on multiple neurotransmitter systems, and increases extracellular dopamine concentrations in the ventral striatum by activating GABAA receptors or by inhibiting NMDA receptors in the ventral tegmental area. Chronic drug-induced dopamine release has been associated with significant reduction of dopamine receptors in the striatum [36, 38, 49], which seems to be linked with reduced metabolism in the prefrontal cortex [36, 37, 50]. Whether or not the effects on the dopamine system are permanent is still a matter of debate, but for amphetamines, it seems that drug-induced changes in the dopamine system are dose-dependent [51] and long-lasting [52–55]. Yet, there is converging evidence from studies investigating brain glucose metabolism, brain metabolites and dopamine transporter density which suggests recovery from some of the drug-induced dopaminergic alterations following protracted abstinence in amphetamine users (proton MRS: [56]; PET dopamine-ligand: [55]; FDG-PET: [57]. For opiates, there is evidence for marked changes in the density of μ-opioid receptors throughout the brain [58, 59]. Although methadone is used as a substitute for heroin for the treatment of opiate-dependent individuals, the long-lasting effects of these two opiates differ. Thus, methadone administered in a maintenance regimen results in an up-regulation of μ-opioid receptors, which persists even after detoxification from opiates [60]. Conversely, post-mortem analyzes of chronic heroin users have shown a down-regulation of μ-opioid receptors [61]. Regarding monoaminergic neurotransmission, chronic opiate use has been associated with reduced densities of norepinephrine (α2) and dopamine (D2) receptors [61, 62], but no evidence for neurotoxic effects on dopamine neurons has been identified [63]. In particular, the effects on dopaminergic function/activity overall in opiate users are less pronounced than in stimulant users [63].

2.4 Neuropathology of Chronic Drug Abuse Neuroimaging studies using structural magnetic resonance imaging (MRI) have provided further evidence that abnormalities associated with chronic drug abuse are not only of a functional, but also of a morphological nature. For stimulant drugs, it seems that the structural abnormalities are relatively specific to the stimulant of abuse. For example, chronic amphetamine abuse has been associated with profound reductions in gray matter in the cingulate, limbic, and paralimbic and prefrontal cortex [64–66] and enlarged striatal volume [67–69]. Neuropathology in chronic cocaine users, in contrast, appears to be associated with gray matter reductions predominantly in ventromedial prefrontal, orbitofrontal and temporal cortices [70–72]. However, as in the case of amphetamine abusers, enlargements of the striatum have also been reported in cocaine-dependent

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individuals [73]. Reduced prefrontal gray matter has also been documented in opiatedependent individuals [74–78], and subcortically, opiate users seem to have less gray matter in the thalamus [79].

2.4.1 Neurocognitive Impairments Associated with Chronic Drug Abuse Behavior of chronic drug users often seems ill-judged as exemplified by the sharing of needles [80], the increased frequency of accidents [81, 82] or tendencies to indulge in other risky behaviors such as driving under the influence of drugs [83–85]. Neuropsychological studies investigating decision-making abilities in chronic drug users with different experimental paradigms have provided ample evidence for impaired decisionmaking in this population. However, decision-making performance differs between the types of substance used. For example, on the Iowa Gambling Task (IGT) [43], decisionmaking is addressed through the selection of cards by the participant, on the basis of expected reward contingencies. Alcohol, stimulant users and polydrug users all make risky choices by preferentially selecting cards from decks that involve large rewards but also large losses [86–88]. However, the IGT is a complex task that involves stimulusreinforcement and reversal learning, and working-memory, as well as decision-making cognition [89, 90]. The Cambridge Gamble Task was developed in order to isolate the assessment of risk from the learning component that is central to the IGT, by requiring participants to make a choice between two mutually exclusive and explicit options and to place bets on the expected outcome. Each trial is independent from its predecessor and learning effects are obviated [91]. Chronic amphetamine users overall chose the favorable option significantly less frequently than controls and opiate users [92]. Yet, although amphetamine users chose disadvantageously, they neither increased their gambles on the less favorable options nor did they significantly choose against the odds on the risky conditions. The use of disadvantageous decision-making strategies in stimulant users have been shown on various experimental paradigms, including the Cambridge Risk Task [93]. It appears that the poor decision-making performance in amphetamine users is due to an impairment in correctly estimating outcome probabilities and may not reflect a rewardseeking strategy per se. This proposal finds support from neuroimaging research, showing that methamphetamine users were not impaired regarding the sensitivity to positive or negative feedback, but showed disruptions in the neural network implicated in processing of feedback information, on the basis of which outcome probabilities were estimated [94, 95]. Interestingly, on self-report measures such as the Melbourne Decision-Making Questionnaire [96], which assesses competent and maladaptive styles of decision-making, stimulant-dependent individuals also report making less rational and more maladaptive strategies of decision-making than non-drug using controls [97]. Amphetamine users’ reports of reduced competent decision-making were significantly associated with an increased tendency to postpone decisions, suggesting that amphetamine users delay decisions more frequently because they lack competent strategies in dealing with decisional conflicts. Chronic use of psychoactive substances is associated with widespread deficits in neuropsychological function [98–100]. Deficits are pronounced not simply in decision-making [86–88,101] but also in other executive functions such as response inhibition [102–104], planning [105, 106], working memory [107–110], attention [111–114], and associative learning [105].

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The diverse impairments in executive function seen in individuals with histories of substance dependence are in keeping with the knowledge base generated by pre-clinical studies in animal models. However, a key question is whether the neurobiological abnormalities seen in drug-dependent individuals are a predisposing cause of their addiction or an effect o their long-term exposure to potentially neurotoxic drugs of abuse.

2.4.2 Neuropathology Associated with the Clinical Phenotype Drug addiction, or drug dependence, as outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV TR, [115], is characterized by impulsive and maladaptive behavior to obtain and consume an increasing amount of drugs at the expense of the individual’s social and personal life. However, not everybody who takes drugs develops addictive behavior. The transition from recreational drug use to addiction is often described as a process in which natural rewards are gradually replaced by drug rewards; an initially hedonic motivation to consume drugs is replaced over time by a less pleasurable, more habitual pattern of drug consumption [25]. As described above, it is widely agreed from preclinical studies the pathophysiology of drug addiction involves neuroadaptive changes within large-scale cortico-striato-thalamic networks implicated in the processing of natural rewards and the regulation of behavior [8,25,116]. The two psychological dimensions of impulsivity and compulsivity have been associated with dopaminergic mechanisms [25, 117–119], and growing evidence suggests that both constructs play an important role in the development of substance dependence.

2.5 Impulsivity: An Endophenotype for Drug Addiction Impulsivity is a heterogeneous construct but generally the term impulsivity is used to describe “a predisposition towards rapid, unplanned reactions to internal or external stimuli without regard to the negative consequences of these reactions to themselves or others” [120]. The loss of control over substance intake or the distraction resulting from drug-related stimuli is often referred to as an example of impulsive behavior in substance-dependent individuals [120]. Impulsive behavior may arise from a breakdown of inhibitory control mechanisms that are necessary to adjust ongoing behavior appropriately to situational demands [26,121]. Experimentally, motor impulsivity can be assessed using response inhibition paradigms with tasks such as the Go/No-Go or the Stop Signal test [122–124], which can be implemented in experimental animals as well as humans. Indeed, there is evidence that chronic drug users are impaired in inhibiting a previously rewarded response [103,104,125,126] and disruptions in inhibitory control mechanisms, regulated by the prefrontal cortex, have been considered as a key component in theoretical models of addictive drug-taking [26, 127–130]. However, it has proven difficult to address the issue of causality, whether impulsivity leads to drug abuse and dependence or whether it is a consequence of it, through disruptions of top-down control, in human studies alone. The difficulty is that it is practically impossible to monitor drug exposure in humans (which can of course, be prenatal). This is obviously easier to do in animal studies and recent evidence has provided support for the former view. For example, following the screening of a normal population of Lister hooded rats for high and low levels of impulsivity in an attentional task, akin to a continuous performance test, and monitoring the incidence of premature (“false alarm”)

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responses, which can be excessive in a small proportion of animals, rats then underwent a positron emission tomography (PET) scan to determine dopamine D2/3 receptor density in the dorsal and ventral striatum before being given free access to cocaine. Consistent with later work in humans [131], low D2/D3 receptor levels in the ventral striatum were associated with increased trait impulsivity [118]. Highly impulsive rats were not only more likely to escalate their cocaine intake compared with their less impulsive counterparts, they also had a greater propensity to show compulsive drug-seeking behaviors following prolonged exposure to cocaine. Compulsivity was defined on the basis of operational measures matched to the criteria listed in DSM-IV. Thus, high impulsive rats were more likely to elect to receive electric foot-shock when placed on a cocaine-seeking/taking schedule that had a punishment contingency – thus, they persisted in responding for cocaine even though they received shock for doing so. Additionally, matching another criterion, these rats would work more persistently on a progressive ratio schedule. Only a relatively small subset (0.01

unrelated drug users’ current unrelated former drug users controls siblings drug users

Duration of drug abstinence (years)

Figure 2.3 Comparisons between stimulant-dependent individuals (N = 29), their full biological bothers/sisters (N = 30), unrelated healthy control volunteers (N = 30) and unrelated former drug-dependent individuals (N = 25) with regard to impulsive personality traits, as reflected by the BIS-11 total score. (a) BIS-11 scores were not only significantly increased in the stimulantdependent individuals but also in their non-drug-taking biological brothers/sisters and in former drug users. (b) The levels of impulsivity reported by former drug users were significantly associated with the period of time they had been abstinent from all drugs of abuse (except nicotine).

2.5.1 Impulsivity Associated with Attention Hyperactivity Disorder Impulsivity is also a cardinal symptom of ADHD, and adolescents with ADHD have increased levels of drug and alcohol use than their peers without ADHD [147–149]. The risk for developing substance abuse disorders in ADHD patients seems to be mediated

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by co-morbid conduct or defiant disorder but not by ADHD alone [150–152]. Although substance abuse is common in ADHD, differential diagnosis of ADHD in substance abusing populations is a challenge for clinicians [153, 154], as both disorders have overlapping phenotypes. Methylphenidate, the pharmacological treatment of choice for ADHD, has similar pharmacological properties to cocaine [155], and cocaine is (after marijuana) the second most frequently abused drug in patients with ADHD [156–159]. It has been estimated that approximately 30% of individuals seeking treatment for stimulant dependence have co-morbid ADHD symptoms [160,161]. Both stimulant dependence and ADHD have been associated with abnormalities in dopaminergic neurotransmission, as reflected by reduced dopamine release [162, 163] and a decreased dopamine receptor availability [164, 165]. Perhaps surprisingly, however, these disorders respond differentially to treatment with the indirect dopamine agonist methylphenidate. While methylphenidate is effective in reducing illicit stimulant abuse in adults with ADHD [166], it is only of limited use for improving ADHD symptoms in patients with co-morbid substance use disorder [167], and is not appropriate for the treatment of stimulant-dependent individuals who do not have ADHD. The differential response to methylphenidate may indicate that, despite phenotypic similarities, the chemical neuropathology of both disorders is distinct.

2.6 Compulsivity: Craving versus Drug-Seeking Psychologically, the term “compulsion” refers to an inappropriate repetition or perseveration of responding. In the context of drug addiction, compulsivity has been defined as persistence or perseverance of behavior in the absence of reward or despite punishment [168]. Thus, compulsivity reflects the persistence with which drug-dependent individuals act to obtain and consume drugs, despite the risk of job loss, family break-up or imprisonment precipitated by further drug use. In contrast to impulsivity, which may also involve actions in the face of negative consequences, compulsive behavior is not spontaneous or premature but involves well-established, habitual behavior patterns that become out of control. Importantly, while impulsivity may represent a vulnerability marker for substance dependence, it is also observed in recreational as well as in drugaddicted individuals. Compulsive drug-taking, by contrast, is not seen in recreational drug users. It is thought to develop in a transitional process from voluntary, hedonicallymotivated drug use to patterns of drug-taking in which the control over drug use is progressively compromised such that drug-taking becomes increasingly habitual and eventually compulsive. It should be acknowledged that compulsive drug-seeking has also been explained in terms of a motivational system that has gone awry, producing extreme urges to consume drugs, which drug-dependent individuals find difficult to resist [169]. These urges are usually referred to as cravings and will be discussed separately from compulsive-drug seeking.

2.6.1 Craving Craving can be described as a strong desire to replace a negative feeling (e.g., an urge to use) with a positive feeling (e.g. pleasure) [170]. At a conceptual level, craving refers to learned responses to stimuli, which involve highly pleasurable outcomes, and in the context of drug addiction, craving is thought to play an important role in relapse following drug abstinence [171,172]. The concept of craving has often been explained in

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terms of the Sensitization Hypothesis. Robinson and Berridge [171, 172] developed this hypothesis from studies on rats given amphetamine according to a repeated treatment regimen, which produces a gradually augmented locomotor and stereotyped response to the drug, associated with neuroadaptations in the ascending dopamine systems. Based on rather different, complementary studies on ingestive behavior in rats, these authors hypothesized that the mesolimbic dopamine system plays a role in a motivational process defined as “wanting” rather than “liking” (as measured by reflexive appetitive responses to gustatory stimuli that are not affected by manipulations of mesolimbic dopamine). They thus hypothesized that drugs such as cocaine produce a sensitized “wanting” response, presumably analogous to “craving,” independent of subjective “liking” of the drug effect, consistent with phenomena of tolerance, and mediated by enhanced striatal dopamine activity interacting with Pavlovian to instrumental processes (see Figure 2.2). This theory has some analogies with that of Everitt and Robbins [25] who propose instead that the transition to compulsive behavior occurs as the control of instrumental drug seeking switches to a habit mode, controlled by the dorsal striatum. Although conscious experiences of cravings for the drug are common in addiction and have been widely studied, there are drug-dependent individuals who do not report feelings of cravings. Neuroimaging research suggests that inter-individual differences in drug cravings are associated with inter-individual variability in cue-induced dopamine release in the midbrain. For example, Wong et al. [173] measured occupancy of dopamine receptors in the striatum in cocaine-dependent individuals who were watching a drugrelated video. They found that reports of craving were associated with a significant release of dopamine in the striatum (as measured by decreased [11 C]raclopride binding). However, not all stimulant-dependent individuals experienced such cue-induced cravings, and most importantly, those individuals who did not crave, also did not show changes in dopamine release while watching the cocaine-video. In other words, there seem to be inter-individual differences in the responsiveness of the dopamine system to drug-related cues among stimulant-dependent people who are otherwise indistinguishable in terms of their drug-taking histories, clinical symptoms or demographic variables. These findings are consistent with the notion that disruptions in the dopamine system in stimulant dependence are heterogeneous, that is, approximately two thirds of stimulant-dependent individuals are thought to have reduced dopamine function, whereas one third is believed to have normal or increased dopamine neurotransmission [174]. However, the clinical implications of these inter-individual differences in stimulant-dependent individuals are still not fully understood. Functional neuroimaging studies have investigated the brain responses to the drug itself (“drug response studies”) or to drug related stimuli (“cue-response” or “cue-exposure” studies) and related them to drug users’ subjective reports of craving during scanning. Regardless of the neuroimaging technique used (i.e., single photon emission computed tomography, positron emission tomography or functional magnetic resonance imaging), the neural network underpinning craving is largely consistent across studies, involving brain regions such as the anterior cingulate cortex (ACC), OFC, DLPFC and amygdala [175–177]. The fact that these brain regions not only activated during feelings of craving but are also implicated in working memory, emotional processing, allocation of attention, reward anticipation, and motivational drive may reflect the complexity of the craving phenomenon. The majority of studies reviewed by Dom et al. [177] reported OFC activation during cue exposure paradigms across different substance user groups. Orbitofrontal activation was most consistently related to drug administration and the subjective experience of

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drug cravings [177]. Orbitofrontal involvement in craving seems to be associated with the duration of drug abstinence. For example, cue-induced OFC activation has been reported more frequently in studies during early abstinence or withdrawal, than in studies involving patients who had been drug abstinent for a longer period of time. Most cue-exposure paradigms that activate the OFC also engage the DLPFC [177, 178]. In contrast to OFC activation, involvement of the DLPFC has shown to be associated with engagement of working memory rather than emotional feelings of craving [175]. In view of the close relationship between subjective feelings of craving and relapse, it has been suggested that a willingness to give in to cravings and drug-taking might be reflected in prefrontal (OFC and DLPFC) activation in response to drug exposure. Wilson et al. [178] found that only drug users not enrolled in treatment showed activation in the prefrontal cortex when exposed to their drug of choice. In light of a possible role for OFC function in encoding motivation value with respect to the selection of appropriate behavioral responses [179], the absence of activation in drug users in treatment is striking. Thus Wilson et al. [178] have suggested that lack of prefrontal activation in response to drug-related cues may reflect the users’ intention to control their drug use, namely not to give in to the urge to seek drugs after the experiment. This view is consistent with drug users’ self-reports, stating that the urge to use is subjectively greater when individuals are prepared to give in to their feelings, compared to attempts in exerting control over their drug use [180].

2.6.2 Compulsive Drug-Taking The persistence with which individuals act to obtain and consume drugs despite the aversive consequences precipitated by further drug use, is a hallmark of addiction. It reflects a shift in the control over behavior from the prefrontal to nigrostriatal brain systems. Dopamine neurotransmission is believed to play an important part in this process as it drives conditioned learning and facilitates the formation of habits, as described by the Action-to-Habit Theory shown in Figure 2.2. Thus, Pavlovian conditioning plays an important role in forming associations between neutral stimuli such as music, certain people, or internal states such as feeling low and the pleasant aspects of drug-taking. Regular drug use involves repetitive, profound stimulation of the mesolimbic dopamine system, which affects learning in three different ways: firstly, it strengthens these stimulus – outcome associations, such that for example a certain song evokes memories of cocaine; secondly, it also strengthens associations between actions that lead to drug use and the effects of the drug. In other words, the pleasurable effects of drugs facilitate the learning of where to get the good quality drugs or how to best prepare or mix them to obtain the desired effects; and thirdly, the drug-induced stimulations of the dopamine system also facilitate habit-learning. This means that actions leading to drug use are directly reinforced as well, creating powerful stimulus-response habits (see [25], and above). For example, hearing a certain piece of music directly activates the habit of ringing the dealer’s number to score some drugs, including the subsequent habitual patterns of preparing and using the drugs. In drug-dependent individuals, in whom executive prefrontal control is compromised, these habitual responses can get out of control because they occur automatically and are no longer motivated by a goal. Growing evidence has shown that biased attention towards drug-related cues plays a key role in drug addiction and contributes to the maintenance of drug-taking behavior [181, 182]. Experimentally, attentional bias can be measured by the modified Stroop paradigm. The classical Stroop test requires attentional control to over-ride an automatic

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but incorrect response tendency, that is, it measures how well a person exerts control over an automatic behavior (word reading) in favor of a more unusual behavior (color naming) [183, 184]. In the modified Stroop test, color words are replaced with drug-related words to measure the degree of involuntary attention, or attentional bias, towards drug-related words compared with neutral words [185–187]. Several studies have shown that in drug users, the increased salience of drug-related cues (as measured by attentional bias in drug-word Stroop paradigms) has predictive value for the risk of relapse [188–191]. Although the exact neurochemical mechanisms underpinning attentional bias to drugrelated stimuli are still elusive, attentional bias seems to be amenable to dopaminergic modulation [192–194]. We have recently shown that dopaminergic modulation of attentional bias towards drug-related words is influenced by compulsivity in stimulant dependence [195]. Thus we found that greater attentional bias for stimulant-related words was associated with increased prefrontal activation in stimulant-dependent individuals. Importantly, this impairment of attentional control in people with stimulant dependence was specific to drug-related cues and did not generalize in their performance on the color-word Stroop. We also found that the selective dopamine D2/D3 receptor agonist pramipexole abolished attentional bias and normalized associated fronto-cerebellar brain responses in stimulant users who reported relatively low levels of compulsive stimulant use, as shown in Figure 2.4. In stimulant users with highly compulsive patterns of drug (a)

Behavioral performance during drug-word Stroop

low compulsive stimulant users

highly compulsive stimulant users

Amisulpride

(b)

Task-related brain activation during drug-word Stroop

low compulsive stimulant users

Placebo

highly compulsive stimulant users

Pramipexole

Figure 2.4 Behavioral performance and task-related brain activation during the drug-word Stroop (drug words versus neutral words) in high and low compulsivity stimulant-dependent individuals (a) High compulsivity drug users showed a different profile in response to acute dopaminergic treatment compared with low compulsivity drug users, as reflected in greater attentional bias for drug-related words relative to neutral words. Attentional bias was measured by each volunteer’s median response latency of correctly identified colors of drug-related words minus the median response latency of correctly identified colors of matched neutral words. (b) Dopaminergic drug effects on left prefrontal cortex during the drug-word Stroop are modulated by compulsivity of stimulant dependence. Box plots show functional activation associated with attentional bias for drug-related words in the left prefrontal cortex and right cerebellum in high and low compulsivity sub-groups indicating differential effects of pramipexole. Reproduced with permission from Figure 2.3 Ersche et al. [195].

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use, pramipexole had exactly the opposite effect. In contrast to drug-related compulsivity, levels of impulsivity in chronic drug users were not significantly related to the variability in attentional bias towards drug-related words and the associated fronto-attentional networks. These findings are important in two regards: they provide evidence that drugrelated compulsivity in humans is modulated by dopaminergic neurotransmission, and they show, once again, that there is a heterogeneity in response to dopaminergic agents in stimulant-dependent individuals, which should be taken into account when interpreting the studies of dopamine neurotransmission in stimulant abusers.

2.6.3 Assessment of Compulsivity in Drug Addiction In current clinical practice, there are no objective instruments available for the assessment of drug-related compulsivity. Self-report measures of compulsivity in substance dependence are available, for example the Obsessive-Compulsive Drug Use Scale (OCDUS, [196], which is equivalent to the Yale-Brown Obsessive Compulsive Scale (YBOCS, [197] that is widely used to assess symptomatology in patients with Obsessive-Compulsive Disorder (OCD). Correspondingly, the OCDUS measures the time, interference and distress caused by drug-related thoughts or urges and the drug users’ efforts to resist them. Similar to patients with OCD, drug-dependent individuals experience extreme difficulties in shifting their thoughts and behaviors away from drugs and drug-related activities, and these difficulties are captured by the OCDUS. The comparison bears the question as to whether compulsivity in drug-dependent individuals and in patients with OCD exhibit similar neuropathology. Meunier et al. (in press) recently showed that resting state connectivity of the OFC was associated with OCDUS and YBOCS scores in stimulant-dependent and OCD patients, respectively. Animal models of addiction suggest that perseverative responding, as measured by reversal learning paradigms, might serve as a good example for these rigid maladaptive behavior patterns seen in drug-addicted individuals [40]. Indeed, both animals experimentally treated with stimulants and humans chronically using cocaine, have demonstrated severe difficulties in adapting their behavior to changes in stimulus-reward contingencies. In particular, they continue choosing the previously rewarded stimulus, despite receiving negative feedback [198–200], a response pattern that has been termed response perseveration. Figure 2.5 shows drug users’ performance on a probabilistic reversal learning task, which has two stages: (i) to learn a stimulus – reward association, and (ii) to adjust behavioral responding when the reinforcement contingencies reverse [198]. As shown in Figure 2.5a drug users had no problems learning the stimulus – reward association, but when the rule changed and the stimulus – reward contingencies reversed, the cocaine users had enormous problems adjusting to responding to the new rule, as they showed a “sticky response” to the previously rewarded stimulus, as shown by a high number of perseverative errors, shown in Figure 2.5b. No response perseveration was observed in the other drug user groups; a finding that does not concur with the notion that perseverative responding reflects the rigid and maladaptive behavior seen in drug-dependent individuals. At least two mechanisms might explain this maladaptive behavior. On the one hand, response perseveration may reflect impairment in the detection of alterations in reinforcement contingencies, suggesting abnormal striatal function. Dopaminergic neurons projecting to the striatum seem to signal reward prediction errors, for both positive and negative outcomes [201, 202]. Alternatively, response perseveration might reflect a failure in using information about changes in reinforcement contingencies to adjust

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(a)

Overall errors during probabilistic reversal learning

Controls

Amph.

Cocaine

Opiates

(b)

Ex-drug

Summary

Perseverative errors immediately after the rule change

Controls

Amph.

Cocaine Opiates Ex-drug

Figure 2.5 Performance during probabilistic reversal learning in different groups of chronic drug users and non-drug-taking control volunteers. (a) All participants learned the stimulus-reward associated during the first stage of the task, but when the rule changed and the reinforcement contingencies reversed, cocaine-dependent individuals had enormous problems in adjusting their behavior according to the new rule. (b) Responses recorded immediately after the rule change showed that cocaine-dependent individuals perseverated on the previously rewarded response. Reproduced with permission from Figure 1 Ersche et al (198)

behavior, linked to intact function of the OFC [203]. Functional MRI studies have suggested that probabilistic reversal learning is associated with activity in both OFC and striatum [204–206]. Both these regions are implicated in the transition from recreational to compulsive drug use, as shown in Figure 2.1 [25]. We recently conducted a pharmacological fMRI study to understand the modulatory role of dopamine on brain function during perseverative responding in stimulant dependence. Since we wished to understand how reversal learning performance is related to compulsivity in psychiatric patients more generally, we included a second control group of patients with obsessive-compulsive disorder (OCD). We observed response perseveration in stimulant-dependent individuals but not in OCD patients. Furthermore, the objective indices of compulsivity (i.e., stimulus perseveration) and subjective measures of compulsivity (i.e., the OCDUS and YBOCS scores) were unrelated in both the drug users and the patients with OCD. Our findings suggest that there are different aspects of compulsivity that may have different neuropathological underpinnings. For example, in stimulant users, perseverative responses on placebo were associated with altered task-related patterns of brain activation. We further found that a single dose of the dopamine D2/D3 agonist pramipexole abolished perseverative responding and normalized the associated brain responses (Ersche et al. in press). The observation that a dopamine agonist improves behavioral performance and activation in related brain systems, supports the hypothesis of significant dopaminergic dysfunction in stimulant dependence [162, 207].

2.7 Summary We have reviewed the contemporary literature on the neurobiological underpinning of stimulant dependence from its beginning with preclinical studies on experimental animals in the 1970s. This has led to a focus on dopamine-dependent functions of the striatum,

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an emphasis that has been reinforced by the effective use of PET for studying this neurotransmitter system in human drug abusers. Stimulant drugs such as amphetamines and cocaine have been of particular interest because they directly enhance dopamine neurotransmission and chronic abuse has been associated with long-lasting changes in dopaminergic brain systems. Nevertheless, animal studies have also indicated the importance of other brain regions in aberrant conditioning and executive control processes that modulate striatal function. In addition, preclinical work has begun to address a central issue in the field, that of vulnerability factors and individual differences that distinguish drug-addicted individuals, as well as the cognitive sequelae of stimulant addiction – which has also inspired much functional brain imaging research in human drug abusers. Hopefully, this paradigm can now be extended to include other drugs of abuse, although it is anticipated that different neurobehavioral principles may well apply in these cases.

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189. Cox, W.M., Hogan, L.M., Kristian, M.R., and Race, J.H. (2002) Alcohol attentional bias as a predictor of alcohol abusers’ treatment outcome. Drug Alcohol Depend, 68, 237–243. 190. Marissen, M.A.E., Franken, I.H.A., Waters, A.J. et al. (2006) Attentional bias predicts heroin relapse following treatment. Addiction, 101, 1306–1312. 191. Waters, A.J., Shiffman, S., Sayette, M.A., et al. (2003) Attentional bias predicts outcome in smoking cessation. Health Psychology, 22, 378–387. 192. Franken, I.H.A., Hendriks, V.M., Stam, C.J., and Van Den Brink, W. (2004) A role for dopamine in the processing of drug cues in heroin dependent patients. European Neuropsychopharmacology, 14, 503–508. 193. Hitsman, B., MacKillop, J., Lingford-Hughes, A. et al. (2008) Effects of acute tyrosine/phenylalanine depletion on the selective processing of smoking-related cues and the relative value of cigarettes in smokers. Psychopharmacology, 196, 611–621. 194. Munafo, M.R., Mannie, Z.N., Cowen, P.J. et al. (2007) Effects of acute tyrosine depletion on subjective craving and selective processing of smoking-related cues in abstinent cigarette smokers. J Psychopharmacol, 21, 805–814. 195. Ersche, K.D., Bullmore, E.T., Craig, K.J. et al. (2010a). Influence of compulsivity of drug abuse on dopaminergic modulation of attentional bias in stimulant dependence. Arch Gen Psychiatry, 67, 632–644. 196. Franken, I.H.A., Hendriks, V.M., and Van Den Brink, W. (2002) Initial validation of two opiate craving questionnaires: The Obsessive Compulsive Drug Use Scale and the Desires for Drug Questionnaire. Addict Behav, 27, 675–685. 197. Goodman, W.K., Price, L.H., Rasmussen, S.A. et al. (1989) The Yale-Brown Obsessive Compulsive Scale. 1. Development, use, and reliability. Arch Gen Psychiatry, 46, 1006–1011. 198. Ersche, K.D., Roiser, J.P., Robbins, T.W., and Sahakian, B.J. (2008) Chronic cocaine but not chronic amphetamine use is associated with perseverative responding in humans. Psychopharmacology, 197, 421–431. 199. Jentsch, J.D., Olausson, P., De La Garza, I.R., and Taylor, J.R. (2002) Impairments of reversal learning and response perseveration after repeated, intermittent cocaine administrations to monkeys. Neuropsychopharmacology, 26, 183–190. 200. Schoenbaum, G., Saddoris, M.P., Ramus, S.J., et al. (2004) Cocaine-experienced rats exhibit learning deficits in a task sensitive to orbitofrontal cortex lesions. Eur J Neurosci, 19, 1997–2002. 201. Kawagoe, R., Takikawa, Y., and Hikosaka, O. (1998) Expectation of reward modulates cognitive signals in the basal ganglia. Nat Neurosci, 1, 411–416. 202. Schultz, W., Dayan, P., and Montague, P.R. (1997) A neural substrate of prediction and reward. Science, 275, 1593–1599. 203. Kringelbach, M.L. and Rolls, E.T. (2004) The functional neuroanatomy of the human orbitofrontal cortex: Evidence from neuroimaging and neuropsychology. Progress in Neurobiology, 72, 341–372. 204. Cools, R., Clark, L., Owen, A.M., and Robbins, T.W. (2002) Defining the neural mechanisms of probabilistic reversal learning using event-related functional magnetic resonance imaging. J Neurosci, 22, 4563–4567. 205. Cools, R., Lewis, S.J.G., Clark, L. et al. (2007a) L-DOPA disrupts activity in the nucleus accumbens during reversal learning in Parkinson’s disease. Neuropsychopharmacology, 32, 180–189. 206. Dodds, C.M., Muller, U., Clark, L. et al. (2008) Methylphenidate has differential effects on blood oxygenation level-dependent signal related to cognitive subprocesses of reversal learning. J Neurosci, 28, 5976–5982. 207. Volkow, N.D., Wang, G.J., Fowler, J.S., and Thanos P (2001c) Role of dopamine in drug abuse and addiction in human subjects: Results from imaging studies. Molecular Psychiatry, 6, S3–S4.

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Chapter 3 Structural and Functional Neuroimaging Methods: Applications to Substance Abuse and Addiction Yihong Yang, Svetlana Chefer, Xiujuan Geng, Hong Gu, Xi Chen, and Elliot A. Stein Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, Baltimore, MD, USA

3.1 Introduction Although stretching back to the mid 1950s for Positron Emission Tomography (PET) scanning, and the early 1970s for magnetic resonance imaging (MRI), both modalities quickly became commercialized and entered the research and clinical arenas in the mid 1980s. Among the precipitating factors, it was the advent of flurodeoxyglucose (18 F) to measure brain metabolism and [O15 ]H2 0 to measure cerebral blood flow, followed by the development of specific radioligands, which propelled the nascent field of functional imaging. The development of functional MRI in the early 1990s rapidly followed. In the relatively short period thereafter, non-invasive imaging technology has exploded onto the human neuroscience research arena. Initial efforts in the field were mostly limited to understanding the tool and the signal they provided. Probing the healthy brain quickly emerged and rapidly helped create and then dominated new research fields like cognitive neuroscience. While research pioneers began applying these tools to pathological populations quite early, it has only been little more than a decade where imaging has been almost routinely used to understand the underlying pathophysiology of neuropsychiatric disorders. What has greatly aided this nascent inquiry is the impressive and continued development of novel radiotracers, in the case of PET imaging, and exciting new pulse sequences and hardware to acquire higher quality and diverse signals from MRI. Notably are the continued developments in signal processing and statistics that have tremendously aided the field in how to extract, analyze, and interpret the MRI signal(s). Most of what we know about the neurobiology of drug abuse was derived from the many excellent preclinical models and the mechanistic, yet invasive, studies possible in these models. Prior to neuroimaging, most human drug abuse research was dominated by careful behavioral pharmacological studies. However, soon after fMRI was developed, studies

Neuroimaging in Addiction, First Edition. Bryon Adinoff and Elliot Stein.  C 2011 John Wiley & Sons, Ltd. Published 2011 by John Wiley & Sons, Ltd.

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in the late 1990s began to appear addressing many of the pharmacological questions posed in preclinical studies. However, for the most part it was not until the turn of the century that the full benefits of neuroimaging in humans became apparent as the tools of cognitive neuroscience merged with neuroimaging and were applied to various disease models. Then and still today, these studies have been dominated by functional imaging modalities. More recently, newer modalities like diffusion tensor imaging (DTI) including tractography, quantitative morphometric measures such as cortical thickness, voxel-based and deformation based morphometry, resting state connectivity and magnetic resonance spectroscopy (MRS) have been added to the research armamentarium. This chapter aims to briefly discuss the basic principals of emission and magnetic resonance imaging and illustrate some of the cutting edge techniques employed by each. Many of the techniques described herein are employed and discussed in subsequent chapters of this volume. Additional technical details and a deeper explanation of the physics and mathematics behind each of the imaging methods described herein can be found in several excellent texts on MR [1–3] and molecular based imaging methods [4,5]. We have attempted to offer the reader an overview of modern non-invasive imaging as it has been successfully applied to better understand the neurobiology of drug abuse. We offer brief examples of each tool’s use in the addiction field. All of the chapters in this volume that follow build on these exemplars to provide a contemporary foundation of drug addiction neurobiology.

3.2 MRI Based Imaging Tools and their Application to Drug Abuse Research Magnetic resonance imaging (MRI) uses a powerful magnetic field and radio frequency waves to make images of the body. Briefly, an individual is first placed in a large static magnetic field (Bo), which aligns the magnetization of some nuclei (predominantly hydrogen nuclei or protons in water) in the body. Radio frequency fields are briefly applied to produce an electromagnetic field and systematically flip the spins of the aligned protons. When the RF is turned off, the protons return to their pre-excited state due to the strong static field. This causes the nuclei to produce a rotating magnetic field detectable by the scanner – and this information is recorded to construct an image of the scanned area. Additional magnetic fields are applied during the scan to make the magnetic field strength depend on the position within the patient, allowing for precise position localization. Image contrasts can be produced because the protons in different types of tissue (e.g., gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF)) return to their equilibrium state at different rates. By changing pulse sequence parameters of scanner acquisition, contrast between different types of tissue can be enhanced. Two parameters are discussed in this chapter. The spin-lattice relaxation time, known as T 1 , is the time it takes for the signal to recover approximately 63% [1-(1/e)] of its initial value after being flipped into the magnetic transverse plane. In contrast, the spin-spin relaxation time, known as T 2 , is the time it takes for the transverse signal to reach 37% (1/e) of its initial value after flipping into the magnetic transverse plane.

3.2.1 Structural MRI Structural MRI is a widely used imaging technique in basic research and clinical practice. With advances in image acquisition and analytical techniques, structural MRI enables the

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Figure 3.1 Illustration of anatomical brain images and cortical thickness maps derived from a threedimensional (3-D) brain volume. (a) A T1 -weighted image of an axial brain section showing higher signal intensity in white matter than gray matter and (b) a T2 -weighted MRI image of the same brain section showing reversed image contrast, (c) a cortical thickness map obtained from a 3-D T1-weighted brain volume image. T1 images are used in research studies to localize brain activations, while T2 images are traditionally used in clinical evaluations.

investigation of global and local morphological changes of brain tissue. Image contrast between GM, WM and CSF can be optimized by appropriate weighting of the longitudinal (T1 ) and transverse (T2 ) relaxation times. T1 -weighted structural images (Figure 3.1a) provide clear contrast between GM and WM [268] and most structural MRI analyses are thus performed on this type of image. T2 -weighted (Figure 3.1b) and proton density images are generally employed to detect brain abnormalities in patient populations, including chronic substance abuse [222] and drug dependence [269].

3.2.1.1 Region-of-Interest (ROI) Based Analysis Numerous structural MRI methods have been developed to investigate volumetric structural changes in longitudinal and cross-sectional studies. A conventional approach is based on a priori definition of hypothesized brain regions of interest (ROIs) followed by measurements of tissue volume of each ROI. The inter-subject variability in head size can be corrected by normalizing the ROI volume by the total intracranial volume. The procedures for this method are relatively straightforward, and the results are less sensitive to signal-to-noise ratio (SNR) and image inhomogeneity. However, this approach is extremely laborious since it requires the manual tracing of ROIs on a large number of subjects, and the operators need to be well-trained to produce consistent results and inter- and intra-rater reliability is an important factor that should always be considered. These limitations might be overcome by using semi-automated or fully automated methods for ROI generation [256]. Another major limitation of ROI-based analyses is the requirement of a priori hypotheses about brain structures involved in certain brain pathology, which may not always be known. Further, the affected region might be only part of a pre-defined anatomical region and thus any differences might be obscured by partial volume measures. To overcome these limitations, alternative approaches based on voxel-wise analysis have been developed.

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3.2.1.2 Voxel-Based Morphometry (VBM) and Deformation-Based Morphometry (DBM) Computational neuro-morphometry is a methodology to characterize local differences in brain anatomy. In contrast to conventional ROI-based analysis, computational morphological techniques offer automatic, voxel-wise analyses across the whole brain, which not only significantly improve the efficiency of the study, but also allow for the development of novel hypotheses from whole brain examination. These techniques require images from different subjects to be spatially normalized to a standard stereotaxic template such as the Talaraich or Montreal Neurological Institute (MNI) space, with further analyses performed on the registered images or the deformation fields that register the images to the template. Voxel-based morphometry (VBM) [6–9] employs statistical analysis to investigate local volume/density of brain tissue. In general, VBM methods first segment images into GM, WM and CSF [10], register the images onto a standard brain template or a study-specific template, correct for the volume changes caused by image warping, and smooth the registered images to reduce errors caused by individual mismatches. Analysis of statistical parameter mapping (SPM) is then applied to provide inferences about differences in the local volume/density of various brain tissue compartments. SPM refers to the assessment of spatially extended statistical processes to test neuroimaging hypotheses [11] and has been implemented in the SPM software package (http://www.fil.ion.ucl.ac.uk/spm). Deformation-based morphometry (DBM) [6–8,12] analyzes deformation fields that register the images to a template. Transformations that align each image to the template are estimated by image registration and are defined by displacement vectors in each voxel. The shape information can be characterized by computing the local Jacobian, a matrix of first-order derivatives, of the deformation field. The determinant of the Jacobian matrix provides information on local volume effects. The Jacobian determinant in each voxel specifies whether the change in each voxel is due to volume reduction or enlargement. A value greater than 1 indicates volume expansion, whereas a value less than 1 means volume contraction. Statistical analyses performed on the displacement vectors or the Jacobian determinant maps are able to characterize alterations in local tissue shape and volume between different individuals. The performance of VBM and DBM is highly dependent on the accuracy of the underlying transformations calculated from image registration, which is an estimate of the spatial correspondence between individual images [13, 14]. Due to the complexity of brain structure and high dimensional degrees of freedom of the transformation, it is very difficult to obtain perfect transformation. Various studies have attempted to improve registration accuracy by better modeling the deformation fields [15–17], constraining transformations to be consistent [18–20], and incorporating specific anatomical features other than image intensity [21]. An unbiased template is critical for generating reliable group analysis results. Recently reported group-wise image registration techniques enable joint estimation of the transformations and generation of an unbiased template [22, 23].

3.2.1.3 Cortical Surface-Based Analyses Brain morphological information includes tissue density and volume, as well as cortical folding, thickness and area. These topographical properties are of great interest for the investigation of neural development, aging, and pathology [24–26]. High resolution

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structural MRI can be used to generate cerebral cortical surface models, from which surface indices can be computed. Over the last two decades, there have been numerous efforts to automate and improve cortical surface-based analyses. The human cerebral cortex is highly folded with variable patterns across individuals [26]. Although the underlying neurobiology involved in gyrification is still largely unknown [27], it has been shown that the human cortex folding pattern is closely related with structural and functional specializations [28, 29]. It has also been reported that the folding pattern of human cerebral cortex can predict its cytoarchitecture [30]. After constructing cortical surfaces from MRI data [31], quantitative descriptions of folding patterns can be computed based on multiple scales, such as global scale with gyrification index [32], sulcus scale [33], gyrus scale [34], and local scale using curvature and surface ratio [35]. Statistical analyses of these indices can then be applied to assess folding pattern abnormalities. A cross-sectional and longitudinal study of sulcal changes demonstrated decreases with aging in surface area, mean thickness, GM volume, sulcal depth measures, and sulcal curvature [36]. Human cortical thickness (Figure 3.1c) varies between 1 and 5 mm, with an average of approximately 2.5 mm, and changes in thickness is taken to imply changes in neuron density [37]. Cortical thickness is a relatively invariant brain parameter during mammalian evolution [38], whereas cortical surface is almost linearly correlated with brain volume. Changes in cortical thickness have been found during normal aging [39] and in patient populations such as autism [40] and schizophrenia [41]. There has been growing interest in the measurements of cortical thickness to investigate neuropsychiatric disorders. The computational and statistical analysis pipeline for cortical thickness studies has been developed in a few public software packages such as FreeSurfer (http://surfer.nmr.mgh.harvard.edu/).

3.2.1.4 Application to Addiction Structural MRI techniques have demonstrated altered brain morphology in chronic drug users, particularly in the frontal lobes. An early study measured the volume of prefrontal and temporal cortices in polysubstance abusers with a semi-automated ROI segmentation procedure and found smaller prefrontal lobes in drug abusers [42]. Using manual segmentation techniques, decreased volume of the amygdala but not hippocampus was seen in cocaine dependent individuals [43]. Voxel-wise structural analyses of GM and WM densities have been performed on chronic cocaine users, and decreased GM density in orbitofrontal, anterior cingulate, insular and superior temporal cortices have been reported [44, 45]. Similarly, decreased GM density has been seen in prefrontal and temporal regions in opiate-dependent subjects [46]. A recent study in abstinent methamphetamine-dependent subjects revealed that GM density was reduced in the bilateral insula and left middle frontal gyrus compared to controls, and that impulsivity was positively correlated with GM density in the posterior cingulate cortex and ventral striatum and negatively correlated with that in the left superior frontal gyrus [47]. In addition, amygdalar density was associated with the length of abstinence. Our group has recently shown that GM density is lower in left prefrontal cortex in heavy smokers and was inversely related to lifetime cigarette usage [48]. In contrast, left insular cortex GM density was higher in smokers and associated with the Toronto Alexithymia Scale (TAS-20) and with difficulty-identifying-feelings factor (Figure 3.2). Using DBM, alcohol dependence was associated with significant atrophy in the frontal and temporal

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Figure 3.2 Voxel-based morphometry (VBM) analysis on smokers. Gray matter density was lower in the left prefrontal cortex (PFC) (blue, a), but higher in the left insular cortex (red, b) in high pack-year smokers (from [48]).

lobes, and abstinence-associated tissue volume recovery was significant in the focal parts of the fronto-ponto-cerebellar circuit that was adversely affected by heavy drinking [49]. Cortical regions involved in executive regulation of reward and attention were found to be significantly thinner in cocaine addicts, with thickness correlated with reduced key-presses during judgment and decision making of relative preference [50]. Recently, reduced thickness of the left medial orbitofrontal cortex (mOFC) has been reported in smokers [51], with mOFC thickness correlating negatively with the amount of cigarettes consumed/day and the magnitude of lifetime exposure to tobacco smoke.

3.2.2 Diffusion Imaging Diffusion refers to the random thermal motion of small particles (e.g., water molecules) in a given medium (e.g., biological tissue). This phenomenon can be illustrated by placing a drop of dye into a tank of water. The dye will spread in a spherically symmetric pattern over time before reaching the boundary of the tank. The effects of molecular diffusion on MR signals have been studied since the 1950s [52, 53], and a critical improvement of

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diffusion measurement using MR spectroscopy was achieved in the 1960s [54]. Diffusionweighted imaging (DWI) was developed in the 1980s [55] to produce MR images of biological tissues weighted by the local characteristics of water diffusion. It was found that the diffusion process in brain WM was dependent on tissue orientation, or anisotropy, probably due to specific micro- and/or macro-structure of the tissue, leading to directiondependent restriction to water molecule diffusion [56]. Some ten years later, diffusion tensor imaging (DTI) was developed [57,58] that utilizes a mathematical model, a tensor of 3×3 elements, to quantify the anisotropic diffusion in biological tissues. Recently, “high-order” diffusion imaging methods [73] have been proposed to overcome certain limitations of DTI (a 2nd-order mathematical model) in handling complex WM structures (e.g., fiber crossings). While there is considerable technical development still needed, tractography (or fiber tracking) techniques based on diffusion anisotropy are able to delineate neuronal pathways [60–63] and provide information on structural connectivity between brain regions [64]. Diffusion imaging has been widely used to assess WM integrity in neurological and psychiatric disorders [65, 66].

3.2.2.1 Diffusion Weighted Imaging DWI is based on a classic experiment [54] designed to measure spin-echo signal attenuation as a result of phase dispersion of diffusive nuclear spins in the presence of diffusion-sensitive magnetic gradients. DWI is a combination of an imaging sequence with diffusion-sensitive gradients to map diffusion coefficients of diffusive particles in a medium. Diffusion coefficients measured in biological samples are often influenced by restricted diffusion due to complex microscopic structures and macroscopic motion such as blood perfusion, and therefore is usually termed “apparent diffusion coefficient” or ADC. One of the most successful clinical applications of DWI is the assessment of early ischemic stroke, since DWI is highly sensitive to changes occurring in the lesion [56].

3.2.2.2 Diffusion Tensor Imaging (DTI) DTI is a diffusion imaging technique to characterize diffusion anisotropy [57]. Molecular diffusion in biological tissues is often anisotropic due to distinct restrictions along different geometric directions. Water molecules in a fiber fascicle, for example, typically diffuse faster along the fibers compared to that across the fibers. Diffusion in such an anisotropic medium is characterized by multiple diffusion coefficients, accounting for diffusion variations along different directions. In the formulation of DTI [57, 58], the diffusion coefficient is described by a 3 × 3 tensor. To determine the six independent elements of the tensor, at least six diffusion-weighted measurements with independent gradient directions are needed, as well as a reference image without diffusion weighting. Measurements along more independent directions generally improve the accuracy of the tensor calculation [69]. Diffusion tensors can be analyzed based on the eigen-analysis theorem [70], and several indices derived from the tensor have been widely used to characterize biological tissues. Mean diffusivity (MD) is the average diffusion strength over all directions, while fractional anisotropy (FA) describes the degree of the diffusion anisotropy. FA has been used to assess white matter integrity, although the underlying structural and functional biological significance of the measure and specificity of FA alterations are still under investigation.

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Figure 3.3 A T1-weighted image (a) and corresponding maps from diffusion weighted images at identical location demonstrating mean diffusivity (b) and fractional anisotropy (FA) (c). (d) Voxelwise analysis of FA in smokers (transverse, sagittal and coronal views through the prefrontal cortex). Lower white matter integrity (i.e., FA) is seen in the left prefrontal area (red) in a highly dependent group of smokers (Fagerstr¨om Test of Nicotine Dependence, FTND), compared to a matched control group. The FA analysis is projected onto a white matter skeleton (green) of the right hemisphere MNI brain (from [48]).

Nevertheless, axonal membranes have been demonstrated to be the primary determinant of water diffusion anisotropy in neural tissue, while myelin can modulate the degree of the anisotropy [71]. An example of MD and FA maps are shown in Figure 3.3b and 3.3c. The MD map shows high intensities in ventricles and gray matter where diffusion is usually high and isotropic, whereas the FA map highlights the WM tracts where the diffusion is highly anisotropic.

3.2.2.3 “High-Order” Diffusion Imaging The diffusion tensor model has limitations in handling complex brain structures in which the patterns of diffusion are far more complex than a single ellipsoid as modeled in DTI. For example, in brain areas with two or more neural tracts traveling in different directions, the information of the fiber crossings would not be correctly revealed from

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conventional DTI. This is due to the fundamental limitations of the tensor model, in which a second-order approximation (in terms of mean square fitting) is used to describe the real three-dimensional diffusion process [62]. “High-order” diffusion techniques based on higher-order mathematical models have been proposed to tackle this problem. These methods include high angular resolution diffusion imaging (HARDI), diffusion spectrum imaging (DSI), “Q-ball” imaging, and high-order tensor imaging [72–77]. It should be noted that longer image acquisition time and more sophisticated analysis approaches are usually needed for such high-order diffusion imaging.

3.2.2.4 Tractography Based on Diffusion Imaging Diffusion anisotropy can be used to perform tractography, a procedure used to demonstrate neural tracts. In DTI, for example, the primary eigenvector (predominant axis) of the diffusion tensor indicates the orientation of fiber boundaries, and thus can be used to track neural pathways [60, 61]. The fundamental assumption of diffusion tractography is that water diffusion is least hindered along the neural tracts. In practice, diffusion along many orientations is measured in each image voxel and pathway, expected to correspond to axonal fibers, is reconstructed based on the criterion to integrate them. Two algorithms are commonly used for diffusion tractography: deterministic and probabilistic. In deterministic tractography, a pathway can be reconstructed by starting from a seed point and following the local diffusion orientation in a step-by-step fashion, until reaching an end point [60–62]. However, while gaining much attention of late, this algorithm is vulnerable to errors caused by imaging noise and inappropriate diffusion modeling, with any error potentially accumulating along the tracking steps. In contrast, probabilistic tractography aims to develop a full representation of the uncertainty associated with any possible pathways, and therefore is able to track through regions of high uncertainty and to quantify the confidence of the tracked pathways [63, 78]. Diffusion based tractography is currently the only noninvasive tool for identifying neural pathways in vivo. Compared to invasive approaches that make use of active axonal transport, it is an indirect marker of fiber connections and is quantitatively difficult to interpret. Nevertheless, diffusion tractography has shown great potential to address scientific and clinical questions that cannot be answered by traditional tractography techniques.

3.2.2.5 Application to Addiction Diffusion-based imaging, particularly DTI, has been used to investigate the effect of drug dependence on WM integrity. In studies using ROI analyses, lower FA and/or higher MD has been shown in the genu of the corpus callosum, in heroin [79], cocaine [80] and chronic alcohol [81] abusers, and studies using whole brain voxel-wise analyses demonstrated significant prefrontal WM alteration in chronic marijuana and cigarette smokers, respectively [82, 48]. As shown in Figure 3.3d, WM integrity (i.e., FA) was lower in the left prefrontal area in highly nicotine-dependent subjects (Fagerstr¨om Test of Nicotine Dependence; FTND), compared to a matched control group. Tractography studies also revealed abnormalities in alcohol dependent relative to control subjects in frontal lobes [83, 84]. White matter alterations have been correlated to various cognitive dysfunctions, including increased impulsivity in cocaine dependence [85], impaired performance

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on the Iowa Gambling Task in MDMA users [86] and impaired executive functions in cocaine impaired children [87].

3.2.3 Functional MRI Functional MRI (fMRI), in a broad sense, refers to a set of MRI techniques that are able to measure neuronal activity, as most commonly employed, using changes in brain hemodynamic features as indirect markers of brain activity. To distinguish from the resting-state fMRI described in the next section, this section focuses on task-driven fMRI, in which neuronal activation is induced by external stimulation or task performance of the subject. In contrast to indirect, hemodynamic measures, several emerging methods have been proposed to directly measure changes in neural firing using a) MRI signal changes caused by electrical current [88] or b) using diffusion-based fMRI, in which changes in water diffusion is thought to reflect cellular swelling accompanying with ion movements during electrophysiological activity [89]. However, the vast majority of fMRI experiments utilize hemodynamic parameters as an indirect measure of neuronal activity, primarily due to the high sensitivity of the measurement. This section therefore focuses on the discussion of these hemodynamic based fMRI techniques.

3.2.3.1 BOLD Imaging Task induced neuronal activity is accompanied by a local increase in glucose and oxygen consumption. As a response of such elevated local metabolism, blood supply to the activated regions increases, characterized by elevated cerebral blood flow (CBF) and blood volume (CBV) [90]. But this increase in blood supply overcompensates for the increase in oxygen metabolism [91]. As a result, the blood oxygenation in the veins and capillaries of the activated region becomes more oxygenated in the stimulated state compared to the control state. Hemoglobin in erythrocytes takes on different MR properties during the oxygenated and deoxygenated states. Deoxygenated blood is paramagnetic and results in more attenuation of the local MR signal, whereas oxygenated blood is diamagnetic and causes less MR signal attenuation. As such, MR signal intensity is modulated by the amount of deoxyhemoglobin in the voxel [92]. This forms the basis of blood oxygenation level dependent (BOLD) fMRI [93] and is schematically represented in Figure 3.4. Echo planar imaging (EPI) provides optimal BOLD contrast and rapid EPI scanning and is the most widely used pulse sequence for fMRI.

3.2.3.2 ASL Perfusion Imaging Besides BOLD, CBF can also be used as an index reflecting brain activation. Arterial spin labeling (ASL) perfusion imaging uses magnetically labeled water in arteries as an endogenous tracer to quantify brain tissue CBF [94]. ASL perfusion imaging has the advantage of providing quantitative measures of CBF in both stimulation and control states, whereas BOLD can only measure relative signal changes between the two states. Therefore, ASL perfusion imaging is particularly useful for studies in which signals in both baseline and stimulation-induced state are important or when events occur less frequently and on a longer time scale, such as the measurement of chronic and acute effects of a drug or cue induced craving or other emotional states. On the other hand,

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Figure 3.4 Schematic illustration of BOLD signal biophysics. (a) Stimulus-induced neuronal activity is accompanied by a local increase of cerebral blood flow, which overcompensates for the local increase in oxygen metabolism, resulting in more oxygenated hemoglobin in the veins and capillaries. Hemoglobin in erythrocytes has different magnetic properties in the oxygenated and deoxygenated states. (b) Oxygenated blood is diamagnetic (similar to brain tissue) and results in less variations in the magnetic field surrounding the vessels than that of deoxygenated blood. (c) As a result, the local MR signal attenuates less during activation, compared to rest, due to the relatively more homogeneous magnetic filed.

ASL techniques typically have lower sensitivity compared to BOLD and the quantification of CBF is not straightforward [95, 96].

3.2.3.3 VASO Imaging Vascular space occupancy (VASO) imaging has been proposed to monitor CBV changes associated with stimulation-driven brain activation [97]. In this technique, the signal from blood is selectively suppressed by manipulating its magnetization. Since local CBV increases with elevated neuronal activity, more blood signal is suppressed in activated brain regions. As a result, the VASO signal decreases following stimulation compared to baseline. VASO imaging may have improved spatial specificity over BOLD, as the signal is thought to originate primarily from small veins [97]. An fMRI technique that measures BOLD, CBF and CBV simultaneously in a single scan has been developed to assess different aspects of hemodynamic changes accompanying brain activation [98]. This technique can also be used to measure cerebral metabolic rate of oxygen (CMRO2 ) in a single scan, although a number of biophysical assumptions still need to be fully validated [99].

3.2.3.4 Hemodynamic Response Function The temporal dynamics of BOLD, ASL and VASO imaging include several phases. Following a brief stimulus, a decrease of the hemodynamic signal, called initial dip, is often

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observed [100–103], and is attributed to the rapid rise in oxygen consumption before CBF can increase. The hemodynamic signal then increases robustly following the initial dip, peaking at about 5 seconds after stimulus onset. This positive BOLD signal is caused by a mismatch between a mild increase of oxygen consumption and an overcompensating CBF [91, 104]. The vast majority of fMRI experiments are based upon this positive hemodynamic response. Finally, an undershoot following the positive signal can be seen, particularly in BOLD, which is usually larger in amplitude and longer in duration than the initial dip. The undershoot in BOLD is thought to be caused by a delayed return of venous blood volume [105, 106] or a transient decrease in venous oxygenation [107–109].

3.2.3.5 fMRI Experimental Design Based on the characteristics of the hemodynamic response function, fMRI experiments are usually carried out as “block-designs” or “event-related designs”. A block-design experiment consists of epochs when the subject is continuously performing a task interspersed with blocks when the subject is resting or performing a control task; block lengths typically vary between 10-30 seconds. In general, the sensitivity of block-design studies is typically quite high, because the fMRI signal generated per unit time is high. In contrast, an event-related experiment consists of a set of stimuli with duration shorter than the hemodynamic response, such that each stimulus is expected to generate a single hemodynamic response. Event-related designs have the advantage that the responses to individual events can be distinguished, so that individual responses can be analyzed separately with, for example, incorrect responses analyzed separately from correct responses or discarded.

3.2.3.6 Mechanisms of fMRI Although the precise neurovascular coupling mechanisms remain unknown, one or more diffusible substances are thought to be released from active nerve fibers to mediate metabolism and blood flow [110]. Candidate vasoactive mediators include nitric oxide, adenosine and changes in K+ or hydrogen ions (i.e., pH); alternative or complementary neurogenic mechanisms have also been hypothesized [111, 112]. Using simultaneous BOLD and electrophysiological recordings in non-human primates, the BOLD signal was best predicted by changes in local field potentials, suggesting that BOLD most strongly reflects the inputs to and intracortical processing of the local brain area [113]. It is important to note that the hemodynamic fMRI signal is an indirect assessment of underlying neuronal activity that is generated by as yet incompletely understood transduction mechanisms, and therefore its spatial and temporal characteristics may not match those of the underlying neuronal activity [113, 114]. In particular, one should be cautious when interpreting any pharmacological challenge study, and indeed any fMRI studies employing a chronic neuropsychiatric patient population. Careful positive and negative control procedures that are able to disambiguate true neuronal activity changes from signal changes that are influenced by potential changes in hemodynamics via direct influence on the vasculature or indirect modulations of the neurovascular coupling agents must always be considered.

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3.2.3.7 Application to Addiction A combination of fMRI with pharmacological challenge (called pharmacological MRI or phMRI) has been successfully used to investigate the acute effects of drugs of abuse in the brain. Although it lacks the ability to examine specific transmitter/receptor systems compared with PET (see below), phMRI offers the advantages of high temporal and spatial resolution. The high temporal resolution allows characterization of the response based upon known drug pharmacokinetics. Activation sites in the human brain following the administration of cocaine or nicotine have been reported [115, 116]. Functional MRI has also been prominently used to assess cognitive and affective alterations as a consequence of chronic drug abuse with or without an acute drug manipulation (i.e., state vs. trait analysis) [117,118]. The design of most task-based activation studies employ either a between group or a within group, pre-post manipulation, to examine alterations in cognitive and affective constructs that have been implicated in this phenotype. For example, the neurobiological bases of cue-induced reactivity have been studied, mostly to produce conscious craving [119], although more recently, Childress et al. [254] demonstrated limbic activation following brief stimuli that were presented outside of conscious awareness. Multimodal imaging (cue activation, DTI, VBM and functional connectivity) has very recently been applied to understand the relationship between and within cue responsive regions [120]. Craving provocation can also be induced following acute drug administration [115, 121]. Others have examined alterations in response inhibition in cocaine [68] and marijuana users [122], and working memory in smokers [123,124]. In a series of studies we [125, 126] identified attention related regions altered in smokers in the presence and absence of acute nicotine patch administration (Figure 3.5).

3.2.4 Resting-State Functional Connectivity In contrast to task-driven functional MRI, resting-state functional connectivity (rsFC) measures spontaneous brain activity in the absence of specific extrinsic stimulation or task performance [127]. In rsFC studies, participants are usually instructed to lie still with their eyes closed or looking at a fixation crosshair. By inspecting the lowfrequency spontaneous fluctuations of the fMRI signals acquired during this “resting state”, it has been consistently observed that brain regions within specific networks, for example, sensorimotor [127], visual [128], and auditory [129], exhibit coherent signal fluctuations. As illustrated in Figure 3.6, fMRI signals from the left and right primary sensorimotor cortices are highly synchronized. In addition to these primary sensory and motor networks, similar observations have been reported for brain networks reflecting higher-level cognitive processes, such as language [130], attention [131], and frontal opercular networks [132]. Among these observations, one of the most extensively investigated resting-state networks (RSNs) is the so called default-mode network (DMN), which includes medial prefrontal cortex, posterior cingulate cortex, bilateral parietal cortices, and the medial temporal lobes [133, 134]. The DMN regions usually show higher perfusion and oxygen consumption rates than the whole brain average at rest, but decreased activity during attention-demanding tasks [135, 136].

3.2.4.1 Mechanisms of Functional Connectivity The spontaneous fluctuations of resting-state fMRI signal are thought to reflect interregional coherence of neuronal activity. However, the underlying mechanism and genesis

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Figure 3.5 Main effects of nicotine in cue-only trials. Group activation maps are overlaid onto a rendered anatomical scan in Talairach space. In left angular gyrus (ang G), posterior cingulate cortex (PCC), subparietal sulcus (SPS), left middle frontal gyrus (MFG), anterior cingulate cortex (ACC) and cuneus, nicotine decreased the BOLD signal related to cue-only trials, causing significant deactivations. Data in the inset graph are presented as averages ± SEM (n = 17). Significant differences from zero in one-sample t-tests (∗∗ P < 0.01, ∗∗∗ P < 0.001) and significant differences between the nicotine and placebo session in paired t-tests (### P < 0.001) are indicated (from [125]).

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of the synchronized intrinsic fluctuations remain to be fully elucidated. Mapping of RSN has been demonstrated across various species, including humans [127], monkeys [137], and rodents [138], and under different levels of consciousness [139–142]. Simultaneous recording of fMRI and intracortical neurophysiological signals in nonhuman primates reveal correlations between low-frequency BOLD signal and local neuronal activity [143]. Loss of interhemispheric rsFC caused by section of the corpus callosum suggests that anatomical connections are necessary for functional connectivity [144]. Studies inspecting the correspondence of rsFC and anatomical connectivity via diffusion tensor imaging further demonstrate that regions directly linked by WM fiber tracts show high rsFC, but high rsFC strength is not necessarily indicative of high anatomical connectivity strength [145, 146].

3.2.4.2 Analyses of Resting-State fMRI Several approaches have been developed to analyze resting-state fMRI data. Seed-based correlation analysis (SCA) is a hypothesis-driven method, which involves the pre-selection of one or more regions of interest (ROIs) as “seeds” and the computation of crosscorrelations between the fMRI signals of a seed and all other brain voxels. A typical sensorimotor “functional connectivity” map can be robustly obtained using SCA by placing the seed ROI within one side of the primary motor cortex, as shown in Figure 3.6. Unlike SCA, independent component analysis (ICA) is a data-driven analysis method, which separates the brain into distinct networks by decomposing the resting-state fMRI data into component maps with maximal spatial independence from each other along with their associated time courses. Using ICA, RSNs representing known sensory and cognitive systems have been identified, similar to those revealed by SCA [67]. The advantage of ICA over SCA is that multiple RSN can be generated simultaneously without prerequisite hypotheses for determining seed ROIs. In addition, RSNs identified by ICA are potentially less affected by artifacts caused by the cardiac and respiratory cycles, since the effects of such physiological noise may be accounted for and eliminated by additional ICA components. A relatively new technique, amplitude of low-frequency fluctuations (ALFF), can be utilized to quantify the local strength of the fMRI signal fluctuations in each voxel or predefined ROI [147]. An improved version, fractional ALFF (fALFF), has been more recently proposed to reduce contributions from non-neuronal factors, such as those from vasculature and CSF [148]. Based upon measures of structural and functional connectivity, graph-theory based network analysis has been introduced to quantify brain network topology. For example, by parcellating the human brain into 90 cortical and subcortical regions and calculating the partial correlation of resting-state time series among these regions, it has been shown that functional brain networks demonstrate small-world properties with high global and local efficiency of parallel information processing and low wiring costs [149]. Graphbased network analyses on the brain’s structural connectivity also revealed its small-world characteristics, which implies high resilience to localized damage [150, 151].

3.2.4.3 Application to Addiction A number of drug addiction related rsFC studies have recently been reported from our lab and others that investigated nicotine [152, 153], cocaine [154], and heroin

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addiction [155, 156], along with prescription opioid dependence [157]. We [152] identified a brain circuit from the dorsal anterior cingulate (ACC) to the ventral striatum (VS) whose connectivity strength was inversely correlated with nicotine addiction severity as measured by the FTND score, but was not altered by acute nicotine patch administration. Compared with a placebo patch, acute nicotine patch increased seven specific cingulate to cortical circuits that are consistent with the behavioral characteristics of smoking. A subsequent gene x circuit study found that the nAChR α5 subunit gene functional variant rs16969968 was associated with a virtually identical dorsal ACC-VS/extended amygdala rsFC circuit strength, such that (1) the risk allele leads to reduced rsFC in the circuit; (2) this gene-derived rsFC strength was reduced in smokers compared to nonsmokers; and (3) reduction of rsFC in the circuit predicts more severe nicotine addiction in smokers [153]. In a recent study to assess chronic cocaine effects on brain reward networks, Gu et al. [154] placed multiple seed ROIs along the mesocorticolimbic (MCL) system, and observed rsFC decreases within MCL components, and between MCL nodes and frontal cortical regions, insula, striatum, and thalamus, as summarized by the schematic diagram in Figure 3.7. A secondary regression analysis on these impaired circuits revealed that the rsFC strength between ventral tegmental area and bilateral thalamus was negatively correlated with years of cocaine use.

Figure 3.7 Schematic representation of regions showing decreased functional connectivity (indicated by the colored lines) in cocaine users compared with matched healthy controls. The lines match the color of the seed ROIs where the decreased connectivity was associated. Amygdala and rACC, as well as the VTA and MD thalamus, each showed a reduction when the other was the seed region. VTA showed reduced connectivity to much of the thalamus, including the mediodorsal thalamus seed region. VTA: ventral tegmental area; NAcc: nucleus accumbens; Amy: amygdala; Hip: hippocampus; Thal: thalamus; rACC: rostral anterior cingulate cortex; mPFC: medial prefrontal cortex; Ins: insula; LN: lentiform nucleus (from [154]).

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3.2.5 Magnetic Resonance Spectroscopy (MRS) In vivo MRS is a noninvasive technique to quantify regional concentration of metabolites and neurotransmitters. The MRS measurement is based on the dispersion of magnetic resonance frequency of the measured nuclei. Magnetic resonance frequency depends not only on the types of nuclei (e.g., 1 H vs. 31 P), but also on the electronic environment of the nucleus (e.g., protons in –CH3 vs. –CH2 –). The dependence of magnetic resonance frequency on the electronic environment in a molecule is called chemical shift, which is the fundamental basis of MRS. The amount of chemical shift is usually very small, and is often expressed as parts per million (ppm) of the operating frequency of the magnet. The first study of in vivo 31 P MRS was conducted on a mouse brain using a conventional spectrometer with an RF coil surrounding the entire head [158]. MRS has been utilized to measure signals from 1 H, 31 P, 13 C, 19 F, 15 N, 23 Na and 7 Li in vivo, among which 1 H MRS has the highest sensitivity and is most widely used in biological studies.

3.2.5.1 Proton MRS In 1 H MRS, the signal from water is usually more than 104 times stronger than those from metabolites. Therefore, water suppression techniques such as VAPOR (VAriable Pulse power and Optimized Relaxation delays) [159] are necessary to isolate the metabolite signals from the strong water signal. As many as 19 metabolites can be detected via 1 H MRS at ultra-high magnetic fields [160]. In 1 H MRS, N-Acetyl Aspartate (NAA), creatine (Cr) and choline (Cho) usually have high concentrations in the brain. These compounds also contain a group of protons that provide a singlet resonance, and therefore are the most reliable metabolites to measure in 1 H MRS. NAA, which provides the most prominent singlet resonance at 2.02 ppm, is believed to be a marker of mature neurons, and a reduction of NAA level in the brain is indicative of neuronal loss and/or dysfunction. Creatine (Cr) and phosphocreatine (PCr), usually referred to as ‘total creatine (tCr)’, give their singlet resonances at almost the same frequencies at 3.03 and 3.93 ppm. tCr plays a crucial role in tissue energy metabolism. The total choline (tCho) resonance at 3.2 ppm contains contributions from free Cho, glycerophosphorylcholine (GPC) and phosphorylcholine (PC). tCho are involved in phospholipid synthesis and degradation. Despite their lower concentrations and complex spectral patterns, the neurotransmitters glutamate (Glu), glutamine (Gln) and γ -aminobutyric acid (GABA) are also detectable in 1 H MRS. Glu is the major excitatory neurotransmitter in the mammalian brain, while Gln is synthesized from Glu by Gln synthetase in the astroglia and is broken down to Glu by phosphate-activated glutaminase in neurons. Glu and Gln have similar chemical structures and thus very similar frequencies in 1 H spectroscopy. Their resonances at 2.35 ppm (Glu) and 2.46 (Gln) can be separated at high magnetic fields > 7 T, although some techniques have been developed to resolve them at lower fields [161, 162]. GABA is an inhibitory neurotransmitter in the mammalian brain. With a low concentration of around 1 mM and overlapping with other metabolites, GABA detection is usually achieved by spectral editing methods.

3.2.5.2 Spatial Localization Localized MRS techniques can obtain metabolite signals from specific brain ROIs. Spatial localization is crucial for MRS measurements because it not only removes the unwanted

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signal from outside the ROI, but also improves the spectral resolution by reducing the spatial variations in the B0 magnetic field over the small, localized volume. The first localized MRS was obtained using a surface coil, which achieves a small volume excitation [165]. Similar to MRI, phase encoding can be used to obtain spatial distributions of metabolites. Magnetic resonance spectroscopic imaging (MRSI), or chemical shift imaging (CSI), can achieve multi-voxel localization, while single volume spectroscopy (SVS) measures metabolite information from a single voxel or ROI. The advantages of SVS include optimized shimming of the magnetic field in the voxel of interest, and minimized contamination from the outer volume. Single volume localization is usually achieved by the combination of spatial encoding B0 gradients and slice selective RF pulses in three dimensions. The most commonly used localization sequences for proton SVS are point resolved spectroscopy (PRESS) [166] and stimulated echo acquisition mode (STEAM) [167]. In the PRESS sequence, three selective RF pulses, one 90◦ and two 180◦ , are used to achieve slice selections in three dimensions, and the magnetization is fully refocused. In contrast, three selective 90◦ pulses are used for localization in the STEAM sequence, and only half of the magnetization is refocused. As a result, only about half of the signal intensity can be obtained using STEAM compared to PRESS using the same echo time (TE). On the other hand, very short TE (∼ 1 ms) can be achieved with the STEAM sequence [159] to minimize signal attenuation due to transverse relaxation and J-coupling (see Spectral Editing section). Therefore, the STEAM method with short TE is suitable for the detection of metabolites with short transverse relaxation time (T2 ) or J-coupling, while PRESS with a medium or long TE is commonly applied to measure metabolites uncontaminated by macromolecular signals, which have much shorter T2 than the metabolites.

3.2.5.3 Spectral Quantification After data acquisition, spectral quantification is a crucial step to estimate metabolite levels. Due to severe overlap of spectral peaks and a complex baseline, it is usually very difficult to quantify the in vivo proton spectrum by simply calculating the relative resonance areas, which is a routine way to obtain concentration ratio between different compounds in chemical NMR. Therefore several algorithms, such as LCModel [168] and jMRUI [169], have been developed to obtain metabolite concentrations in vivo. LCModel is a widely used program incorporating prior knowledge of metabolites for spectral fitting. After phase, frequency and line shape corrections using an unsuppressed water spectrum as reference [170], quantification is achieved via a linear combination of high-resolution metabolite MR spectra, which are referred to as a “basis set.” The Cramer-Rao lower bound (CRLB), which presents the lowest possible standard deviation of all unbiased model parameters, is commonly used for estimating errors in the fitting [171]. An example of LCModel fitting of a proton spectrum from a rat brain is illustrated in Figure 3.8.

3.2.5.4 Spectral Editing and 2-D MRS In vivo proton spectroscopy is subject to severe spectral overlapping due to a large number of resonances in a small range of chemical shift frequencies. For example, the three resonances of GABA at 1.89, 2.28 and 3.01 ppm are concealed under the much stronger signals of NAA, Glu and tCr, respectively. Several editing techniques such as MEGA-PRESS [163, 164] have been proposed to separate the desired spectral peak using the different

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properties of these protons. The magnetic resonance frequencies of nuclear spins are not only subject to the influence of electron distributions (chemical shift), but also affected by coupling to other spins through bonding electrons, which is called J-coupling. In a MEGA-PRESS sequence, two selective 180◦ pulses are added to a conventional PRESS sequence for spectral editing, which refocuses the magnetization evolution due to J coupling of the GABA-H4 spin (3.01 ppm). When editing pulses are turned ON/OFF in a two-step acquisition, the J evolution of GABA-H4 is refocused/intact respectively, while the strong singlet resonance tCr at 3.03 ppm remains unchanged between the two scans. Subtraction of one scan from another will eliminate the tCr resonance while preserving the GABA-H4 at almost the same frequency, as shown in Figure 3.9a. MEGA-PRESS can also be used to separate J-coupled metabolites such as lactate (Lac) [172] and N-Acetyl Aspartyl Glutamate (NAAG) [173]. By acquiring a series of spectra with varying durations of certain evolution periods and Fourier transforming into frequency domain, one can expand the resonances into another spectral dimension. Two-dimensional (2D) MRS has been successfully used in chemistry and molecular biology to separate crowded resonances with similar frequencies and to determine their coupling relations. However, 2D spectroscopy has not been applied widely to in vivo studies because of its long scan time and unavoidable head motion, which will lead to artifacts in the 2D spectrum. J-PRESS [174] is probably the mostly used 2D spectroscopy in vivo, as shown in Figure 3.8b. One form of J-PRESS, TE-averaged PRESS [161], is proposed to separate Glu from Gln. It is also used to resolve tCho from lipid

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sidebands [175] and to suppress myo-inositol (mI) for glycine detection [59]. Techniques using 2D spectroscopy have shown great potential to improve spectral quantification in vivo and is under active development [176].

3.2.5.5 Applications to Addiction There is growing interest in using non-invasive MRS to study neurochemical alterations in active or abstinent drug dependent individuals [177]. Psychostimulants such as methamphetamine (METH) and cocaine have been shown to induce long-term neurotoxicity, which can lead to cognitive, neurological, and psychiatric consequences (see also Chapter 2 in this volume). MRS is a powerful tool to study metabolite alterations caused by addiction related neuroadaptations, which is invaluable for understanding the etiology of drug abuse. For example, NAA is reduced in the basal ganglia and frontal GM in METH users [178–181] and in the thalamus and frontal GM in cocaine users [182,183], implying neuronal injury and reduction of tissue density in these regions. The level of tCho was decreased in the frontal GM and basal ganglia in METH [178, 179, 181] and cocaine [184] users, suggesting reduced membrane turnover rate in this etiology. Alterations of tCr and mI levels have been shown in drug users [185, 186]. Drug-induced neurotransmitter

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adaptations have also been observed. Reduction of Glu and/or Gln level in the frontal GM has been reported in METH users [187]. Recently, a study using TE-averaged PRESS to selectively detect Glu observed lower levels in the anterior cingulate cortex of chronic cocaine users [188]. Studies using 2D spectroscopy and difference spectroscopic techniques have shown lower GABA levels in the prefrontal cortex [189] and occipital cortex [190] in cocaine users. These findings suggest a metabolic/neurotransmitter dysregulation associated with drug addiction. There are also a number of MRS studies examining alterations in alcohol [191–196] and nicotine dependence [197–199].

3.3 Molecular Imaging with PET and SPECT 3.3.1 Principles of Emission Computed Tomography Positron emission tomography (PET) and single photon emission tomography (SPECT) are radionuclide imaging methods providing relatively noninvasive measurements of the three-dimensional distribution of radiolabeled compounds in living organisms. Radionuclide imaging is an in vivo analog to tissue dissection, autoradiography, and other techniques that involve both sectioning and imaging or counting excised tissue samples into which a compound, labeled with a radioisotope probe, has been introduced. The ability of PET and SPECT to image specific biomolecules allowing both the temporal and the spatial biodistribution of a radiotracer to be determined quantitatively in vivo is unmatched by any other method currently available. Based on the radiotracer principle developed by George DeHevesy at the beginning of the twentieth century [200], these methods conceptually consist of three components: (i) scanners that provide 3 dimensional, tomographic images of the concentration of radioactivity in the body; (ii) ligands labeled with radioactive atoms; and (iii) mathematical models that describe the in vivo behavior of radioligands and allow the physiological process under study to be quantified from the image. To accurately derive quantitative information from PET or SPECT scans, a variety of factors must be taken into account. These include that the total activity observed by the scanner represents a combination of the activity coming from the radiotracer specifically bound to receptor targets, nonspecifically bound radiotracer, and free radiotracer. The relative contribution of these activity sources to the total activity fluctuates over the course of a scan session in an interdependent manner. Additional factors that play a role in the interpretation of data from PET/SPECT studies are the distribution of the receptors in the brain, peripheral clearance of the radiotracer, regional cerebral blood flow, and transport of the radiotracer across the blood–brain barrier. The potential for these factors to vary from subject to subject necessitates that they be accounted for to provide interpretable results. The principles for detecting the emerging radiation and forming images are similar for single photon and positron emitters, but the underlying physics and the instruments employed are different [4, 201]. Although both SPECT and PET detect radiotracer distribution, it is the chemical versatility of the positron emitters, the ability to measure their concentration quantitatively with relatively little attenuation by tissue, the greater sensitivity and the superior resolution of PET, which differentiates these two methods. Many positron-labeled compounds have been synthesized, enabling a wide range of biological processes to be measured quantitatively, non-invasively, and repeatedly. These include cerebral metabolism [202], blood flow [203]), neurotransmitter release [204],

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and enzyme activity [205–208]. Examples of those processes studied as they have been applied to drug abuse are given below.

3.3.2 Brain Activation PET and SPECT allow the mapping of neuronal activity using an index of changes in energy demand in specific brain areas as a surrogate of neuronal activity. Most often, the measure has been the regional cerebral metabolic rate for glucose (rCMRglc) using the PET tracer [18 F]fluorodeoxyglucose (FDG). Assays of regional cerebral blood flow (rCBF) have also been conducted with PET using [15 O]H2 0 and with SPECT using [99m Tc]d,lhexamethylpropyleneamine oxime (HMPAO). Measurements of CBF and metabolism are intimately related to local neuronal activity [90] and use the same principle discussed earlier for BOLD fMRI imaging. The measurement of rCMRglc utilizes FDG and is based on the technique previously developed to measure rCMRglc in laboratory animals with [14 C]-deoxyglucose and tissue autoradiography [209] and adapted for PET by using 18 F as the label [210, 211]. Briefly, after intravenous injection, FDG is transported across the blood-brain barrier and is phosphorylated in tissue to FDG-6-P, which is metabolically trapped in the same manner as DG-6-P. PET images are obtained up to 45 min after the injection. Blood is sampled to measure the concentration of glucose and FDG in plasma over time. An operational equation is used to convert images of FDG concentration to quantitative images of rCMRglc. Typical normal values of rCMRglc are 6-7 for GM and 2.5-3 mg/min per g tissue for WM [212, 213]. Methods to measure rCBF with PET and SPECT are based on a model developed by Kety to measure rCBF in laboratory animals with diffusible radiotracers and tissue autoradiography [214]. In the conventional concept, fluid flow is the volume of fluid that passes a particular point per unit time. A more meaningful physiological concept, however, is that of tissue perfusion, the volume of blood flowing through a particular volume or weight of tissue per unit time. Although there are different approaches, they all involve administering a diffusible radiotracer, blood sampling to determine the time-activity curve in arterial blood and the application of a model to generate images of rCBF from PET images of radioactivity. Average values for rCBF in normal subjects are 40-60 ml/min per 100 g in GM and 20-30 ml/per 100 g in WM [215, 216]. rCBF assays with SPECT and PET have a faster time resolution than that of rCMRglc, which has a single uptake period > 30 min; HMPAO is distributed within approximately 90 sec post-injection, while [15 O]H2 0 usually takes 1–1.5 min. Since these [15 O]H2 0 injections can be repeated about every 8 minutes, they are better suited to capture changes in neural activity that reflect transient behavioral states, such as for example “rush” in drug abuse. However, recent MRI developments offer, in principle, better temporal and spatial resolution and do not involve ionizing radiation and its attendant perception of risk, which presents many advantages, based on the needs of the paradigm. The spatial resolution of fMRI, nominally between 1-4 mm is, depending on scanner quality and imaging parameters, generally better than that of an emission imaging method such as PET (∼2–8 mm), or SPECT (6 mm), which is advantageous since the regional representation in cortex and some subcortical nuclei involves areas that are smaller than the current resolution of most PET or SPECT scanners. Additionally, multiple studies can be done in the same subject without the limitation of radiation exposure. Its temporal resolution (generally 2–5 seconds) is also superior to the best achieved with PET technology

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(45 seconds for “activation” studies using [15 O]H2 0), although still not ideal since functional processes in brain occur in the millisecond range.

3.3.2.1 Application to Addiction Clinical studies that have used PET and SPECT imaging to characterize the effects of stimulants on background activity have focused primarily on long-term changes in individuals with complex histories of multidrug use. Early PET imaging documented decreased CBF in the prefrontal cortices of chronic cocaine users [217]. Additional studies with PET and SPECT (Figure 3.10) have confirmed those results, demonstrating that brain perfusion deficits occur with high frequency [218–220,231]. Local perfusion deficits have been linked closely to changes in cerebral metabolism. Measures of brain glucose metabolism in chronic users document transient increases in metabolic activity in dopamine-associated brain regions during cocaine withdrawal [221] with glucose metabolism higher in the orbitofrontal cortex and the striatum than in healthy comparison subjects. During more protracted withdrawal (1–6 weeks since last use), brain metabolism was lower in cocaine abusers than in comparison subjects, an effect that was most accentuated in the frontal cortex (Figure 3.11), and persisted for months of detoxification. The same pattern of decreased glucose metabolism [221] and perfusion deficits [217] was observed in the prefrontal cortices of a subset of cocaine users who were imaged on multiple occasions. More recently, mood disturbances have been linked to regional cerebral metabolic abnormalities in methamphetamine abusers [224].

3.3.3 Neuroreceptors and Other Molecular Targets for PET and SPECT Radiotracers The uniqueness of PET and SPECT vs fMRI and MRS lies in their sensitivity, which is in the nanomolar-to-picomolar range (whereas that for MRI is in the millimolar range), and the specificity of radiotracers available for a variety of different cellular and molecular

B

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B

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Figure 3.10 Transaxial SPECT images obtained with HMPAO in a subject reporting recent use of cocaine. The left figure uses standard a hot-body (yellow-red) color scale (left), while the isocount map (linear color table referenced to the maximum activity in the cerebellum) was used to identify perfusion defects as those cortical regions with less than 60% of the maximum cerebellar activity (right). A, B, C – 2 cm, 4 cm and 6 cm above the orbitomeatal line. Arrows on the right panel show multiple small focal perfusion defects involving the right inferior parietal, left temporal and left frontal cortex (from [218]).

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Comparison Subject

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Figure 3.11 PET images obtained with FDG showing the difference in glucose metabolism level between cocaine abuser and normal comparison subject (from [283]). Copyright © 2002, reprinted with permission from American Psychiatric Association.

binding sites. In spite of the short half-lives of medical positron emitters, many labeled drugs and radiotracers for specific enzymes, transporters and receptors are available and employed in a variety of research fields (Table 3.1). Organic radiotracers most often incorporate C-11 (half-life = 20 minutes) or F-18 (halflife = 110 minutes). The radiation doses associated with these short-lived nuclides is often low enough to allow repeated studies on the same subject. Thus, longitudinal studies as well as “test-retest” studies involving administration of pharmacologically active drugs between two imaging studies are possible. SPECT cameras have been optimized for clinical radiopharmaceuticals. Among the available radionuclides, I-123 (half-life = 13 h) has the best properties for labeling low molecular weight organic compounds while retaining biological activity, although recent advances in the radiopharmaceutical chemistry of Tc-99m have been impressive [225]. If a chemical structure of a drug allows a label with a positron emitter, radiolabeling the drug itself allows study of its brain distribution and assessment of regional drug binding site concentration. For example, cocaine, methamphetamine and methylphenidate radiolabeled with [C-11] were successfully used with PET [226–229, 244]. Visualization of enzymatic activity in vivo and enzyme pharmacodynamics was conducted using [11 C]clorgyline and [11 C]L-deprenyl, radioligands for monoamine oxidase (MAO) A and B, respectively [205, 206, 230]. Since these enzymes metabolize monoamines, their inhibition is associated with increases in synaptic dopamine. Examples of images obtained with different radioligands are shown in Figure 3.12. Receptor occupancy by the drug and indirect measurement of endogenous neurotransmitters released by the drug or during cognitive task performance can be measured in

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Table 3.1 A selection of effective radiotracers for studying human brain receptors and other protein targets with PET Radioligand

Molecular target

Reference

2[18 F]F-A-85380 5[123 I]I-A-85380 [11 C]SCH 23390 [11 C]NNC 112 [11 C]Raclopride [18 F]Fallypride [123 I]IBZM [11 C]CFT [11 C]Cocaine [11 C]methylphenidate [18 F]MPPF [11 C]DWAY [11 C]WAY-100635 [11C]MDL 100907 [11 C]DASB [18 F]SP203 [11 C]MePPEP [11 C]Flumazenil [18 F]SPA-RQ [11 C]Diprenorphine [11 C]Carfentanil [11 C](R)-Rolipram [11 C]PBR28 [18 F]PBR06 [11 C](R)-PK 11195 [11 C]clorgyline [11 C]deprenyl [11 C]PIB

α4β2 nAChR α4β2 nAChR D1 D1 D2 /D3 D2 /D3 D2 /D3 DAT DAT DAT 5-HT1A 5-HT1A 5-HT1A 5-HT2A SERT mGluR5 CB1 Bz NK1 OR OR PDE4 TSPO TSPO TSPO MAO-A MAO-B Ab-plaque

[261] [262] [260] [263] [259] [264] [265] [266] [281] [225] [268] [282] [282] [270] [271] [272] [273] [274] [275] [276] [277] [257] [285] [278] [279] [206] [205] [280]

Target abbreviations: α4β2 nAChR, α4β2 sub-type of nicotinic acetylcholine receptor; D1 dopamine sub-type 1 receptor; D2 /D3 , dopamine sub-type 2 and 3 receptors; DAT, dopamine transporter; 5-HT1A , serotonin receptor sub-type-1; 5-HT2A , serotonin receptor sub-type-2; SERT, serotonin transporter; mGluR5 , metabotropic glutamate sub-type 5 receptor; CB1 , cannabinoid sub-type 1 receptor; Bz, benzodiazepine binding site of GABAA receptors; NK1 , neurokinin-1 receptor; OR, opiate receptor; PDE4, phosphodiesterase-4; TSPO, translocator protein (18 kDa; formerly known as the ‘peripheral benzodiazepine receptor’ or PBR); MAO-A, monoamine oxidase sub-type A; MAO-B, monoamine oxidase sub-type B; Ab-plaque, brain amyloid plaque.

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N

O

11CH 3

C111 CH3

N CO2CH3

MAO A

OCOC6H5 C1

DA DA transporters

MAO

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DA

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DA

DA

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N H3 11C

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Nicotinic receptors

D

signal

H CONHCH2 N CH HO O11CH3 2 6 Cl

Cl

OH HO HC

O 18F

OH

DA Figure 3.12 PET images obtained with different radioligands (from [284]).

pharmacological displacement studies [232–238]. In these studies, experiments are usually performed using two consecutive PET scans in a paired-bolus paradigm. In general, the first scan occurs prior to any challenge condition to assess radiotracer binding at baseline while the second scan is initiated before or during the challenge. In this instance, the stimulus can be a drug that will either compete directly with the radiotracer for binding sites or will produce a change in neurotransmitter that will compete with the radiotracer. This approach was used to measure nAChRs occupancy after cigarette smoking using a recently developed PET and SPECT radioligands 2-[18 F]fluoro-3-(2(S)azetidinylmethoxy)pyridine (2FA) and 5-[123 I]iodo-3-(2(S)-azetidinylmethoxy]pyridine (5IA) that binds to the α4β2∗ nAChRs [235, 258] (Figure 3.13). Because these radioligands compete with nicotine for nAChRs binding sites, the difference between radioligand binding in the first and second scans was used to calculate receptor occupancy by nicotine.

3.3.4 Output Measures of PET and SPECT Studies The conceptual basis for neuroreceptor imaging quantification is based directly on in vitro work with membrane preparations. In vitro, it is possible to derive the affinity (1/KD ) and number (Bmax ) of binding sites using a radioactive tracer by manipulating the concentration of the unlabeled ligand; however in vivo studies of this type can be performed only with a limited number of radiotracers given the high concentration of unlabeled drug required, which can easily exceed the safety threshold of the ligand.

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MRI

kBq/mL 9

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100 80 60 Baseline Baseline

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Figure 3.13 nAChRs availability after cigarette smoking measured with (top panels) the PET radioligand 2-[18 F]fluoro-3-(2(S)-azetidinylmethoxy) pyridine) (2FA) (from [235]) and (bottom panel) the SPECT radioligand 5-[123 I] iodo-3-(2(S)-azetidinylmethoxy) pyridine) (5IA) (from [258]). PET images obtained before (top row) and 3.1 hours after (bottom row) variable amounts of cigarette smoking. Figure demonstrates decreased radioligand binding, and thus nAChRs occupancy, as a function of nicotine dose. Smoking to satiety (3 cigarettes) led to the greatest decrease of radioactivity throughout the entire brain, equal to the level of non-displaceable radioligand accumulation, indicating that most brain nAChRs are occupied by nicotine 3 h after cigarette smoking. Parametric images of nAChR availability obtained with SPECT before and after smoking to satiety (2.4 cigarettes). Decreased radioligand binding as a result of nAChR occupancy can be observed up to 6 h after smoking.

Therefore, in most cases it is not possible to independently measure KD or Bmax in humans using PET or SPECT. Instead, the outcome measure derived in neuroreceptor imaging studies is called the binding potential (BP), which is proportional to Bmax /KD [239]. A variety of model-based methods have emerged to allow for the determination of BP by relating the activity observed in a ROI to the activity in the arterial plasma over the time course of the scan. The derivation of the outcome measures can be divided into equilibrium, kinetic, and graphical methods. Equilibrium methods derive information about the

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receptors by analyzing the activity distribution at equilibrium [240,241]. Kinetic methods determine quantitative information regarding the receptors by estimating the rate constants that govern the transfer between the arterial, brain, and receptor compartments [239]. Graphical analysis transforms the data into variables that are linearly related, with the parameters then determined by means of linear regression [242, 243].

3.3.4.1 Application to Addiction PET and SPECT have contributed greatly to our understanding of the pharmacokinetics and pharmacodynamics of abused drugs, and have provided valuable information in terms of drug toxicity, drug interactions and drug mechanisms, and changes in brain chemistry that may account for the addictive actions of these drugs. The dopaminergic system has been the most investigated neurochemical system. Effects of cocaine on D2 receptor binding was evaluated using [11 C]raclopride [244]. All subjects reported subjective stimulation and euphoria in response to cocaine administration, with corresponding decreases in D2 receptor binding that likely reflected cocaine-induced elevations in dopamine. Another study demonstrated reduced dopamine release in the striatum and reduced self-reports of “high” in response to methylphenidate in cocaine addicts vs. healthy control [227]. A relationship between dopamine receptor densities and behavioral effects of stimulants also has been documented. D2 receptor levels were determined in healthy men who had no history of drug abuse [246]. Subjects who reported liking the effects of methylphenidate had significantly lower D2 receptor levels in the striatum when compared to subjects who disliked the drug. In addition, there was a direct relationship between the intensity of unpleasant effects and D2 receptor levels (Figure 3.14). These results indicate that

Publisher's Note: Image not available in the electronic edition

Figure 3.14 D2 receptor levels (Bmax /KD ) in healthy subjects who reported the effects of methylphenidate as pleasant or unpleasant (from [246]).

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subjective responses to stimulants in humans may be correlated with D2 receptor levels, and that intrinsic, inherited low levels of D2 receptors may contribute to stimulant abuse. Studies of D2 receptor availability using [11 C]raclopride have also shown a reduction in D2 receptors in heroin abuse [247] and alcoholism [248, 249]. A study [257] reported a reduction in D1 receptor availability in the striatum using [11 C]SCH23390, with a greater decrease in the ventral striatum compared with the caudate and putamen. In contrast to a hypo functional dopaminergic system in cocaine dependence, an upregulation of α4β2 nAChRs measured with PET and SPECT radioligands 2FA and 5IA, respectively, has been observed in smokers vs. nonsmokers [250, 251] (Figure 3.15). Studies that assess specific neurotransmitter systems in activated, as well as resting states, offer promise in improving the understanding of behavioral correlates of drug abuse. A particularly productive strategy has been the use of PET in conjunction with neuropsychological testing to allow correlation of imaging data with uniquely human aspects of the effects of drugs, such as euphoria and craving. In an interesting series of studies, tobacco-induced increase in dopamine level measured by a decrease in [11 C]raclopride binding was observed in smokers who were allowed to smoke during the scan and who had abstained for up to one day prior to the experiment [252, 253] with binding reductions correlated with reported euphoric effects from smoking. Finally PET studies have also been employed to evaluate the neurochemical consequences of candidate therapies for drug abuse [254]. Using [11 C] raclopride, the authors demonstrated that a reduction in D2 receptor availability from pre- to post-treatment was correlated with the total puff volume of a cigarette smoked during scanning. Because percent change in receptor availability is an indirect measure of change in synaptic DA concentration (and therefore DA release), the association between this measure and total puff volume indicates that smoking-induced DA release is dose-dependent, regardless of treatment effects or smoking status, at least early in abstinence as was studied here.

3.3.5 Limitations There are some prominent limitations narrowing the employment of PET and SPECT in human research. One of the primary factors constraining both the scope and advancement of their applications is the shortage of radiotracers with sufficient specificity and appropriate kinetics to allow the study of specific molecular targets. As a result, in many cases, research questions are framed around the availability of those radiotracers rather than around a hypothesis based on current and emerging scientific knowledge. This might also be one of the factors contributing to the small number of brain targets implicated in human studies of a wide variety of abused drugs. A second major limitation is the inability to administer radiotracers to minors in a research setting, thus precluding a better understanding of the state of brain chemistry either prior to drug use onset or at very early stages of use, prior to full dependence, information desperately needed to better understand those brain changes already seen in chronic abusers and determine which may be pre-existing and which is a consequence of dependence. Nevertheless, as much as contributing to basic knowledge of the effects of abused drugs on the brain, molecular imaging has the potential to contribute to development of medications for treatment of drug abuse, and even to tailor therapies to individual patients’ needs.

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VT/fP 15 Control

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Figure 3.15 nAChRs availability measured by the PET radioligand 2FA in smokers and non-smokers. Top panel – voxel-based average parametric maps (using SPM) of nAChRs availability (VT /fP ) in (top) nonsmoking controls, (middle) smokers and (bottom) difference between smokers and nonsmokers. The difference maps demonstrate the widespread clusters in which VT /fP in smokers was greater than in non-smokers (P = 0.01, corrected). Bottom panel – differences between smokers and nonsmokers (VT /fP) in seven brain ROIs using three modeling approaches: Logan graphical analysis, 1- and 2-tissue compartment models (1TCM and 2TCM, respectively). Values are given as mean ± SD. CC-corpus callosum; FrCx-frontal cortex; Cb-cerebellum, Put – putamen; Midbr – midbrain; Th – thalamus. (from [250]).

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References

3.4 Summary and Peek into the Future While somewhat beyond the scope of both this chapter and volume, it is worth mentioning that neuroimaging in general, and MRI based methods in particular, nicely lend themselves to translational research. The field of addiction neurobiology has traditionally been dominated by extensive preclinical animal models that have yielded a rich understanding of the acute and chronic consequences of drug administration. More recently, such neuroadaptations have been extended to novel models of drug cue responding, and drug seeking reinstatement and subsequent relapse models along with the interactions of drug-induced plasticity and cognitive dysregulation. Further, with the advent of dedicated, small bore, high field animal scanners and microPET cameras, and the application of phMRI to animal imaging, much of what has been learned from these more invasive, mechanistic studies can be profitably and more directly applied to human based clinical and treatment research with the hope of better treatment outcome for our patients. We have reviewed the major structural and functional methods available in contemporary MRI, PET, and SPECT research and illustrated briefly how they may be usefully applied to better understand the causations and consequences of human drug dependence. While we predominantly emphasized acquisition methods, tools and radiotracer ligands, novel analyses continue to emerge in this still young field that extend signal detection, extraction and analysis leading to novel addiction applications. The rapidly emerging field of imaging genetics (discussed later in this volume), coupled with careful behavioral, cognitive and demographic history promises to open an important new window into individual differences, better differential diagnosis, treatment planning, and outcome predictions. This will allow the field to move beyond group based analyses and into the important clinical realm of individual differences, with the prospect of developing the imaging equivalent of what a stethoscope and cardiac stress test have brought to the field of cardiology. An important outstanding application for neuroimaging is the ability to identify those most vulnerable to developing dependence after casual use, potentially leading to interventions early in the disease trajectory where it is likely to be most efficacious. Conversely, it is important to identify those who seem to enjoy relative protection from such development, despite early use and adverse environmental and perhaps genetic traits. It is hoped that imaging based biomarkers might serve in this important role.

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196. Miese, F., Kircheis, G., Wittsack, H.J. et al. (2006) H-1-MR spectroscopy, magnetization transfer, and diffusion-weighted imaging in alcoholic and nonalcoholic patients with cirrhosis with hepatic encephalopathy. American Journal of Neuroradiology, 27, 1019–1026. 197. Durazzo, T.C., Gazdzinski, S., Banys, P., and Meyerhoff, D.J. (2004) Cigarette smoking exacerbates chronic alcohol-induced brain damage: a preliminary metabolite imaging study Alcoholism. Clinical and Experimental Research, 28, 1849–1860. 198. Epperson, C.N., O’Malley, S., Czarkowski, K.A. et al. (2005) Sex, GABA, and nicotine: The impact of smoking on cortical GABA levels across the menstrual cycle as measured with proton. magnetic resonance spectroscopy Biological Psychiatry, 57, 44–48. 199. Gallinat, J. and Schubert, F. (2007) Regional cerebral glutamate concentrations and chronic tobacco consumption. Pharmacopsychiatry, 40, 64–67. 200. de Hevesy, G. and Chiewitz, O. (1935) Radioactive indicators in the study of phosphorus metabolism in rats. Nature, 136, 754–755. 201. Daube-Witherspoon, M.E. and Herscovitch, P. (1996) Positron emission tomography, in Nuclear Medicine, (eds J.S. Harbert, W.E. Eckelman, and R.D. Neumann), Thieme, New-York, 121–43. 202. Huang, S.C., Phelps, M.E., Hoffman, E.J. et al. (1980) Noninvasive determination of local cerebral metabolic rate of glucose in man. Am J Physiol, 238, E69–E82. 203. Frackowiak, R.S. and Friston, K.J. (1994) Functional neuroanatomy of the human brain: positron emission tomography – a new neuroanatomical technique. JAnat, 184, 211–225. 204. Laruelle, M. and Huang, Y. (2001) Vulnerability of positron emission tomography radiotracers to endogenous competition: New insights. J Nucl Med, 45, 124–138. 205. Fowler, J.S., Volkow, N.D., Wang, G.J. et al. (1996a) Inhibition of monoamine oxidase B in the brains of smokers. Nature, 379, 733–736. 206. Fowler, J.S., Volkow, N.D., Wang, G.J. et al. (1996b) Brain monoamine oxidase A inhibition in cigarette smokers. Proc Natl Acad Sci USA, 93, 14065–14069. 207. Fowler, J.S., Volkow, N.D., Wang, G.J. et al. (1998) Neuropharmacological actions of cigarette smoke: Brain monoamine oxidase B (MAO B) inhibition. J Addict Dis, 17, 23–34. 208. Fowler, J.S., Wang, G.J., Volkow, N.D. et al. (2000) Maintenance of brain monoamine oxidase B inhibition in smokers after overnight cigarette abstinence. Am J Psychiatry, 157, 1864–1866. 209. Sokoloff, L., Reivich, M., Kennedy, C. et al. (1977) The [14C]deoxyglucose method for the measurement of local cerebral glucose utilization; theory, procedure, and normal values in the conscious and anesthetized albino rat. J Neurochem, 28, 897–916. 210. Phelps, M.E., Huang, S.C., Hoffman, E.J. et al. (1979) Tomographic measurement of local cerebral glucose metabolic rate in humans with (F-18)2-fluoro-2-deoxy-D-glucose: validation of method. Ann Neurol, 6, 371–388. 211. Reivich, M., Kuhl, D., Wolf, A. et al. (1979) The [18 F]fluorodeoxyglucose method for the measurement of local cerebral glucose utilization in man. Circ Res, 44, 127–1237. 212. Sasaki, H., Kanno, I., Murakami, M. et al. (1986) Tomographic mapping of kinetic rate constants in the fluorodeoxyglucose model using dynamic positron emission tomography. J Cereb Blood Flow Metab, 6, 447–454. 213. Hatazawa, J., Ito, M., Matsuzawa, T. et al. (1988) Measurement of the ratio of cerebral oxygen consumption to glucose utilization by positron emission tomography: its consistency with the values determined by the Kety-Schmidt method in normal volunteers. J Cereb Blood Flow Metab, 8, 426–432. 214. Kety, S.S. (1951) The theory and applications of the exchange of inert gas at the lungs and tissues. Pharmacol Rev, 3, 1–41. 215. Leenders, K.L., Perani, D., Lammertsma, A.A. et al. (1990) Cerebral blood flow, blood volume and oxygen utilization: Normal values and effect of age. Brain, 113, 27–47. 216. Herscovitch, P., Raichle, M.E., Kilbourn, M.R., and Welch, M.J. (1987) Positron emission tomographic measurement of cerebral blood flow and permeability-surface area product of water using [15 O]water and [11 C]butanol. J Cereb Blood Flow Metab, 7, 527–542. 217. Volkow, N.D., Mullani, N., Goul, K.L. et al. (1988) Cerebral blood flow in chronic cocaine users: A study with positron emission tomography. Br J Psychiatry, 152, 641–648.

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References

258. Esterlis, I., Cosgrove, K.P., Batis, J.C. et al. (2010) Quantification of smoking-induced occupancy of b2-nicotinic acetylcholine receptors: Estimation of nondisplaceable binding. J Nucl Med, 51, 1226–1233. 259. Farde, L., Ehrin, E., Eriksson, L. et al. (1985) Subsituted benzamides as ligands for visualization of dopamine receptor binding in the human brain by positron emission tomography. Proc Natl Acad Sci USA, 82, 3863–3867. 260. Farde, L., Halldin, C., Stone-Elander, S., and Sedvall, G. (1987) PET analysis of human dopamine receptor subtypes using 11 C-SCH 23390 and 11 C-raclopride. Psychopharmacology (Berl), 92, 278–284. 261. Kimes, A.S., Horti, A.G., London, E.D. et al. (2003) 2-[18 F]F-A-85380: PET imaging of brain nicotinic acetylcholine receptors and whole body distribution in humans. FASEB J, 17, 1331– 1333. 262. Fujita, M., Ichise, M., van Dyck, C.H. et al. (2003) Quantification of nicotinic acetylcholine receptors in human brain using [123 I]5-I-A-85380 SPET. Eur J Nucl Med Mol Imaging, 30, 1620–1629. 263. Halldin, C., Foged, C., Chou, Y.H. et al. (1998) Carbon-11-NNC 112: a radioligand for PET examination of striatal and neocortical D1-dopamine receptors. J Nucl Med, 39, 2061–2068. 264. Mukherjee, J., Christian, B.T., Dunigan, K.A. et al. (2002) Brain imaging of 18 F-fallypride in normal volunteers: blood analysis, distribution, test-retest studies, and preliminary assessment of sensitivity to aging effects on dopamine D-2/D-3 receptors. Synapse, 46, 170–188. 265. Kung, H.F., Alavi, A., Chang, W. et al. (1990) In vivo SPECT imaging of CNS D-2 dopamine receptors: initial studies with iodine-123-IBZM in humans. J Nucl Med, 31, 573–579. 266. Rinne, J.O., Laihinen, A., N˚agren, K. et al. (1995) PET examination of the monoamine transporter with [11 C]b-CIT and [11 C]b-CFT in early Parkinson’s disease. Synapse, 21, 97–103. 267. Mugler, J.P. 3rd and Brookeman, J.R. (1991) Rapid three-dimensional T1-weighted MR imaging with the MP-RAGE sequence. J Magn Reson Imaging, 1, 561–567. 268. Passchier, J., van Waarde, A., Pieterman, R.M. et al. (2000) In vivo delineation of 5-HT1A receptors in human brain with [18 F]MPPF. J. Nucl Med, 41, 1830–1835. 269. Lyoo, I., Streeter, C., Ahn, K. et al. (2004) White matter hyperintensities in subjects with cocaine and opiate dependence and healthy comparison subjects. Psychiatry Res, 131, 135–145. 270. Hinz, R., Bhagwagar, Z., Cowen, P.J. et al. (2007) Validation of a tracer kinetic model for the quantification of 5-HT(2A) receptors in human brain with [(11)C]MDL 100,907. J Cereb Blood Flow Metab, 27, 161–172. 271. Ginovart, N., Wilson, A.A., Meyer, J.H. et al. (2001) Positron emission tomography quantification of [(11)C]-DASB binding to the human serotonin transporter: modeling strategies. J Cereb Blood Flow Metab, 21, 1342–1353. 272. Brown, A.K., Kimura, Y., Zoghbi, S.S. et al. (2008) Metabotropic glutamate subtype-5 (mGluR5) receptors quantified in human brain with a novel radioligand for positron emission tomography. J Nucl Med, 49, 2042–2048. 273. Terry, G.E., Hirvonen, J., Liow, J.S. et al. (2010) Imaging and quantitation of cannabinoid CB1 receptors in human and monkey brains using (18 )F-labeled inverse agonist radioligands. J Nucl Med, 51, 112–120. 274. Odano, I., Halldin, C., Karlsson, P. et al. (2009) [18 F]flumazenil binding to central benzodiazepine receptor studies by PET–quantitative analysis and comparisons with [11C]flumazenil. Neuroimage, 45, 891–902. 275. Yasuno, F., Sanabria, S.M., Burns, D. et al. (2007) PET imaging of neurokinin-1 receptors with [(18 )F]SPA-RQ in human subjects: assessment of reference tissue models and their test-retest reproducibility. Synapse, 61, 242–251. 276. Jones, A.K., Cunningham, V.J., Ha-Kawa, S.K. et al. (1994) Quantitation of [11C]diprenorphine cerebral kinetics in man acquired by PET using presaturation, pulse-chase and tracer-only protocols. J Neurosci Methods, 51, 123–134.

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277. Frost, J.J., Douglass, K.H., Mayberg, H.S. et al. (1989) Multicompartmental analysis of [11C]carfentanil binding to opiate receptors in humans measured by positron emission tomography. J Cereb Blood Flow Metab, 9, 398–409. 278. Fujimura, Y., Zoghbi, S.S., Sim`eon, F.G. et al. (2010) Quantification of translocator protein (18 kDa) in the human brain with PET and a novel radioligand, 18 F-PBR06. J Nucl Med, 50, 1047–1053. 279. Kropholler, M.A., Boellaard, R., Schuitemaker, A. et al. (2005) Development of a tracer kinetic plasma input model for (R)-[11 C]PK11195 brain studies. J Cereb Blood Flow Metab, 25, 842–851. 280. Klunk, W.E., Engler, H., Nordberg, A. et al. (2004) Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol, 55, 306–319. 281. Fowler, J.S., Volkow, N.D., Wolf, A.P. et al. (1989) Mapping cocaine binding sites in human and baboon brain in vivo. Synapse, 4, 371–377. 282. Andr´ee, B., Halldin, C., Pike, V.W. et al. (2002) The PET radioligand [carbonyl-(11)C]desmethylWAY-100635 binds to 5-HT(1A) receptors and provides a higher radioactive signal than [carbonyl-(11)C]WAY-100635 in the human brain. Nucl Med, 43, 292–303. 283. Goldstein, R.Z. and Volkow, N.D. (2002) Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am J Psychiatry, 159, 1642–1652. 284. Volkow, N.D., Fowler, J.S., and Wang, G.J. (2003) Positron emission tomography and singlephoton emission computed tomography in substance abuse research. Semin Nucl Med, 33, 114–28. 285. Fujita, M., Imaizumi, M., Zoghbi, S.S. et al. (2008) Kinetic analysis in healthy humans of a novel positron emission tomography radioligand to image the peripheral benzodiazepine receptor, a potential biomarker for inflammation. NeuroImage, 40, 43–52.

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Functional Neuroimaging of the Acute Effects of Drugs of Abuse

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Chapter 4 Functional Neuroimaging of the Acute Effects of Drugs of Abuse Laurence John Reed and David J. Nutt Neuropsychopharmacology Unit, Centre for Pharmacology and Therapeutics, Division of Experimental Medicine, Department of Medicine, Imperial College London, UK

4.1 Introduction Drug abuse, addiction and dependence to compounds with abuse potential such as stimulants, alcohol, and opioids provide an enormous and growing public health problem with multiple medical, psychiatric and psychosocial sequelae. However, present methods for assessment of the abuse potential of existing substances of abuse as well as novel pharmaceuticals are complex and largely framed at a behavioral level – a methodology that may be criticized for lacking neurobiological specificity. The ‘abuse liability’ of any particular pharmacological compound or substance is a sophisticated neuroethological construct. Understanding how substances with abuse liability affect central nervous system function, and how this in turn translates into natural behavior is in no sense trivial. In particular, the process is sensitive to interactions between the ‘abuse liability’ of a particular substance (based upon its pharmacodynamics, pharmacokinetics and exposure history) and the ‘abuse liability’ of a person (based upon genetic, developmental, educational and social predispositions) and indeed context (based upon stressors, drug cues in the immediate environment). However, this construct is extremely important in addiction neurobiology, particularly in the area of therapeutics, given the well known abuse potential of a wide variety of stimulants, sedatives and analgesic agents – providing a pressing need to understand this process more fully. This chapter critically reviews studies examining the acute effects of the range of substances with abuse liability and their impact on neurotransmitter and neuronal network function in human and nonhuman primates, exploiting a now substantial body of evidence from modern neuroimaging techniques. [1–4] A standardized definition of abuse liability or potential is provided by Schoedel and Sellers, [5] as ‘Abuse potential has been defined as the ability of a drug to produce positive subjective or reinforcing effects, which is thought to be predictive of risk for “addiction.” Abuse liability from a regulatory and public health perspective refers not only to abuse potential, but also to all factors impacting the risk of misuse, abuse, or diversion. Abuse liability also includes the potential for negative outcomes resulting Neuroimaging in Addiction, First Edition. Bryon Adinoff and Elliot Stein.  C 2011 John Wiley & Sons, Ltd. Published 2011 by John Wiley & Sons, Ltd.

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from abuse (e.g., addiction, overdose, or toxicity). It should be stressed that current abuse liability assessment relies almost entirely upon behavioral surrogate markers – such as subjective liking, or repeat self administration in an acute laboratory setting – without an understanding of the underlying neuronal basis for enhanced reinforcement responses. This deficiency can be addressed by the use of multimodal neuroimaging strategies focusing on processes of interest. The study of the acute brain effects of drugs with abuse liability has, however, been relatively little studied. While a substantial body of work now exists concerning this fundamental feature of the effects of abuse liable drugs, there are significant omissions.

4.2 Fundamental Neuronal Systems Related to Abuse Liability in Humans The fundamental neuronal systems relevant to abuse liability have for some time focused upon appetitive motivational networks [6], shown in Figure 4.1. These systems have Executive function impulse control Decision-making CORTEX

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VTA/SN DMH VMH

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Gustatory visceral input

Figure 4.1 Major subcortical and cortical nuclei of mammalian appetitive motivational networks. The neuroanatomy of motivated behavior has been elucidated in preclinical models by examining feeding behavior [6], with the assumption that these form the substrates upon which drugs with abuse liability can act. Key regions identified are the ventral striatum (nucleus accumbens), ventral tegmental area, amygdala and hypothalamus, the latter not currently being considered to play a major role in substance abuse. Of note are the relatively minor roles of cortical regions in modulating the behavior of these networks, at least in the rodent. Copyright © 2004, reprinted with permission from Elsevier.

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4.3

Psychostimulants

emphasized the overlaps between drug seeking and food seeking behavior and the contribution of opioid and dopaminergic neurotransmission pathways. For example, Baldo and Kelley [7] distinguished motivational processes and consummatory processes with distinct neuronal network and neurotransmitter contributions. In particular, opioids have been related to consummatory processes, with Berridge [8] identifying particular “hedonic hotspots” relating opioid effects to consummatory processes. Barbano and Cador [9] synthesized the two component processes as “Opioids for hedonic experience and dopamine to get ready for it,” suggesting that at least two systems are relevant to considering the impact of substances with abuse liability. For example, the intravenous administration of glucose has been shown, albeit with some difficulty and sex differences, to displace striatal [11 C]raclopride in man [10]. However, more recently an emerging perspective suggests the even more fundamental system of movement control is involved. This system addresses movement per se as a reinforced behavior [11, 12] and emphasizes links with motor learning [13]. To quote Shadmehr [11], “Let us assume that the purpose of any movement is to position our body in a more rewarding state,” nicely makes the point that continuous online “reward related” processes are determining action. Of particular note are the studies of Kurniawan et al. [14] who were able in a human fMRI study to dissociate effort related responses in the ventral pallidum from reward related responses in ventral striatum. While at present this research has focussed on the contributions of dopaminergic neurotransmitter systems to movement control and learning, the role of opioids in control of effort and motivation to motor control is at an early stage. It is anticipated that this will be a key area of development.

4.3 Psychostimulants 4.3.1 Neuropharmacology of Acute Psychostimulant Administration The psychostimulants amphetamine, cocaine, and methamphetamine comprise a group of drugs with analogous effects to epinephrine on the body, stimulating both the central nervous system and the sympathetic nervous system, producing increased heart rate, locomotor activity, excitement and euphoria. At high doses, amphetamine increases the concentration of dopamine in the synaptic cleft by inducing the release of dopamine from the nerve terminal and dopamine containing synaptic vesicles. Furthermore, amphetamine can bind to monoamine oxidase and the dopamine re-uptake transporter in dopaminergic neurones, reducing the clearance of free dopamine in the nerve terminal. It is important to note that amphetamine has a similar effect on noradrenergic neurones, similarly inducing the release of norepinephrine into the synaptic cleft and inhibiting re-uptake. Cocaine primarily binds to dopamine re-uptake transporters on the pre-synaptic membranes of dopaminergic neurones, inhibiting dopamine removal from the synaptic cleft and hence degradation by monoamine oxidase within the nerve terminal.

4.3.2 Receptor Positron Emission Tomography and Acute Psychostimulant Administration The potentiation of cerebral dopamine in response to pharmacological challenges can be noninvasively measured by functional neuroimaging assessments of the competition between dopamine and radiolabelled PET ligands that bind to dopamine receptors. The

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most sensitive of these techniques uses PET to measure changes in the binding potential of the dopamine D2/D3-receptor antagonist [11 C]raclopride, by virtue of the relatively low affinity of raclopride for the dopamine D2/3 receptor – but also rendering the tracer only suitable for measuring striatal, but not extrastriatal displacement. Each of the psychostimulants has been shown to increase synaptic dopamine, resulting in displacement of [11 C]raclopride in both human and nonhuman primates [15–18]. These studies progressively optimized the methodology necessary for detection of this signal and the demonstration of preferential ventral versus dorsal striatal dopamine increases. Utilizing evidence from preclinical studies of the functional-anatomical organization of subdivisions of the striatum, Martinez et al. [19] demonstrated preferential amphetamineinduced dopamine release within limbic and associative striatal subregions, which correlated with subjective experience of euphoria, in comparison with associative striatum, which demonstrated lesser amphetamine induced displacement and lesser correlation with subjective responses (Figure 4.2).

Figure 4.2 Pattern of psychostimulant-induced dopamine release in the striatum. A key informative experimental strategy for examining the function of the striatal dopamine system in healthy and clinical human populations is exemplified by this figure from Martinez, et al. [19]. Using [11 C] raclopride PET to measure dopamine D2 receptor availability in the presence of placebo or following amphetamine challenge, to induce endogenous dopamine release and displacement of the PET tracer, the regional extent and magnitude of dopamine release can be estimated and compared between clinical groups. Copyright © 2003, reprinted with permission from Nature Publishing Group.

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The combination of amphetamine-induced dopamine release measured by displacement of raclopride has provided a powerful model system to investigate alterations in dopaminergic neurotransmission in a variety of conditions. In a landmark example, Volkow et al. [20] demonstrated a linear relationship between methylphenidate-induced “high” and dopamine receptor occupancy as indicated by [11 C]raclopride displacement (Figure 4.3). A more recent study by Munro et al. [21] demonstrated increased amphetamine-induced dopamine release in men compared with women, despite no differences in baseline receptor density. Given that there is a known increased risk of substance misuse in men, this may be etiologically relevant, and it is certainly relevant to assessing the sex make up of studies conducted using this technique. Of particular relevance to substance misuse and dependence are a series of studies that examined the impact of these conditions and relevant risk factors on this model system to infer alterations in dopaminergic neurotransmission relevant to the etiology of that condition. Martinez et al. [22, 23] demonstrated blunting of amphetamine-induced [11 C]raclopride displacement in subjects with cocaine dependence, and the degree of blunting predicted self-administration of this drug. A subsequent study further demonstrated lower endogenous dopamine in cocaine dependent individuals following depletion of dopamine using alpha-methyl-p-tyrosine (AMPT) [24], supporting either down-regulation of dopaminergic neurotransmission as a consequence or a predisposition to cocaine dependence and self-administration. In an analogous study, Martinez et al. [25] demonstrated that alcohol dependence was associated with a decrease in dopamine D2/3 receptors in each striatal subdivision, with amphetamine-induced dopamine release being reduced in the limbic striatum only. Some evidence supporting the etiological disambiguation of these alterations in dopaminergic neurotransmission has been provided by Munro et al. [26] who, in an adequately powered study of 41 individuals with and without a family history of alcoholism – a strong predisposing factor to later alcohol dependence – failed to demonstrate any difference in amphetamine induced displacement of raclopride. A potential model to understand the impact of repeated administration of psychostimulants on dopaminergic function is that of sensitization, where repeated administration gives rise to increased psychomotor response and dopamine release potentially reinforcing the behavior. Boileau et al. [27] administered amphetamine on three occasions within 14 days to healthy volunteers and demonstrated, consistent with sensitization model, increased psychomotor response and amphetamine-induced raclopride displacement following the third dose – an effect which was sustained for at least one year. It is intriguing to consider that the sensitization model – which this study supports – is not readily reconcilable with the observations made in stimulant or alcohol dependent subjects, implying additional mechanisms may come into play. Alternative dopamine receptor tracers have also been deployed to examine amphetamine-induced dopamine release in areas of the brain other than the striatum. In particular, the PET tracer [18 F]-fallypride has much higher affinity for the dopamine D2/3 receptor and is thus able to demonstrate a signal in thalamic, temporal and frontal cortical regions in primates [28]. A tracer selective for the D3 receptor [11 C]-PHNO [29] has been shown to be more sensitive than [11 C]raclopride to endogenous dopamine increases as assessed by examining the dose-response of amphetamine (0.1, 0.5 and 2 mg/kg; i.v.) in the cat [30]. A series of studies examining the relative sensitivity of various tracers [11 C]FLB 457, [11 C]NPA, [11 C]fallypride and [11 C]raclopride [31–33] to amphetamine-induced displacement in human has demonstrated potential advantages of the novel tracers in either region or extent of displacement but also methodological constraints. This will be an area of ongoing development, particularly given the utility of the amphetamine-induced displacement model for investigation of dopaminergic

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Figure 4.3 Psychostimulant-induced dopamine release and subjective responses. Correlation of subjective responses and dose of the psychostimulant methyphenidate was achieved by Volkow et al. [20] using [11 C] raclopride PET to measure dopamine D2 receptor availability and magnitude of displacement. Of note, while displacement was observed in all subjects, not all found this to be subjectively enjoyable with those with higher initial dopamine D2 availability more likely to find the drug experience aversive. Copyright © 1999, reprinted with permission from American Society for Pharmacology and Experimental Therapeutics.

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function in man. An example of how this field of research may develop is the demonstration that amphetamine-induced dopamine increase can be modulated by the metabotropic glutamate receptor agonist LY354740 [34], a candidate treatment not only for treatment of psychosis, but also relevant to the pharmacotherapy of stimulant misuse.

4.3.3 Resting Blood Flow or Metabolism and Acute Psychostimulant Administration The impact of acute psychostimulant administration on cerebral perfusion, metabolism or proxy measures such as Blood Oxygen Level Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) has been employed to identify particular neural networks relevant to the subjective and physiological responses. In the earliest study using fMRI, Breiter and colleagues employed intravenous cocaine to investigate the relationship between subjective effects and BOLD signal to identify what they considered to be the “reward circuitry” of the brain [35–37]. Those regions correlating with measures of “rush” comprised a very distributed network comprising ventral tegmental area, pons, basal forebrain, caudate, cingulate, and most regions of lateral prefrontal cortex, see Figure 4.4. However, regions including the ventral striatum were more correlated with craving than with rush ratings.

Figure 4.4 Demonstration of fMRI BOLD measures and subjective response to psychostimulant exposure. In one of the first demonstrations of pharmacological fMRI, or phMRI, Brieter et al. [16] evaluated the brain regional response to cocaine, and were able to correlate brain regional increase in BOLD (blood oxygen level dependent) signal time-series with subjective responses. Of note, a multiplicity of other cortical and subcortical areas is also identified, indicating the complex nature of the response. Copyright © 1997, reprinted with permission from Elsevier.

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In a more naturalistic replication of this study employing self-administration of cocaine in non-treatment seeking subjects, Risinger et al. [38] found negative correlations between limbic, paralimbic, and mesocortical regions including the nucleus accumbens (NAc), inferior frontal/orbitofrontal gyrus (OFC), and anterior cingulate (AC) signals to measures of high. In contrast, craving correlated positively with activity in these regions.

4.3.4 Neural Network Function and Acute Psychostimulant Administration Another approach to examining the acute effects of psychostimulants is to examine pharmacological modulation of particular fMRI tasks. One particularly influential task was the Monetary Incentive Delay Task employed by Knutson et al. [39]. This task demonstrated selective engagement of the ventral striatum in response to cues of increasing monetary gain, together with engagement of anterior insular cortex [involved in interoception or awareness of body state [40]] and anterior cingulate cortex [involved in control of autonomic arousal [41]]. Acute, double-blind administration of amphetamine showed attenuation of responses to the MID task [42], Figure 4.5. The authors suggested that these findings may relate to an “equalization” of anticipation of gains and losses, a process which could perhaps explain the diminution of the incentive value of “natural” rewards seen in drug addictions. Alternatively, given that responding on the task was little affected, this may simply represent a “ceiling” of the evoked BOLD response in striatal regions given that both drug and task would be expected to elevate BOLD response independently.

4.4 Alcohol Acute alcohol effects are particularly sensitive to dose, rapidity of consumption, timing (rising versus declining phase) and other diverse factors, including social context and inter-individual differences.

4.4.1 Neuropharmacology of Acute Alcohol Administration Although alcohol is usually classified as a central nervous system depressant, a specific molecular target has recently been identified on the GABA-A receptor [43], although some controversy remains [44], as well as “between system” effects on both opioid and dopaminergic systems [45, 46].

4.4.2 Receptor Positron Emission Tomography and Acute Alcohol Administration With respect to alcohol challenge and measurement of dopamine release using neuroimaging, Boileau et al. [47] demonstrated displacement of [11 C]raclopride, although very high doses were required. A subsequent replication by Urban et al. [48] in healthy young adults detected raclopride displacement in all striatal subregions with the largest effect observed in the ventral striatum. Interestingly, there was a relationship between ventral striatal [11 C]raclopride displacement and measures of subjective ‘activation’ in men but not in women.

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Alcohol

Figure 4.5 Interaction of incentive delay task responses and psychostimulant effects measured using fMRI. The monetary incentive delay task [39] has been widely adopted as a naturalistic task to engage and evaluate networks engaged in motivated behavior, contrasting incentivised and non-incentivised trials and demonstrating clear responses within the ventral striatum. Contrasting the effect of amphetamine versus placebo on such incentivised responses and demonstrating a psychopharmacological interaction, Knutson et al. [42] (© 2004, with Permission from Elsevier), showed attenuation of incentivised responses in the ventral striatum, perhaps reflecting that the system has achieved a “ceiling” response.

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Investigation of opioid receptor involvement in acute alcohol effects has not, at present, been conducted. This is a significant omission as studies in abstinent alcohol dependent individuals have demonstrated elevation of striatal μ-opioid receptor availability using [11 C]carfentanil [45]. Moreover, this and an earlier study by Bencheriff et al. [50] were both able to relate opioid receptor availability to alcohol craving measures, which strongly suggests an involvement of endogenous opioids in acute alcohol effects.

4.4.3 Resting Blood Flow or Metabolism and Acute Alcohol Administration Neuroimaging of brain regional effects of acute alcohol ingestion initially employed [18 F]fluorodeoxyglucose (FDG) measures of brain metabolism, demonstrating reduced cerebellar and cerebral metabolism with “sparing” of limbic and particularly striatal regions [51–53]. However, a later dose-ranging study from the same group [54], while demonstrating this pattern of response at low dose, showed even more profound decrements at high dose and no relationship to cognitive measures. These results suggested that alcohol was serving as an alternative brain substrate for metabolism rather than identifying networks involved per se. Similarly, the vasodilatatory effects of alcohol may render measures of cerebral blood flow unreliable in the identification of particular networks in acute alcohol responses [55], given that prefrontal regional cerebral blood flow increases whereas metabolism decreases. Using more controlled intravenous alcohol clamp conditions, Schreckenberger et al. [56] demonstrated activations in the bilateral striatum and frontal cortex on the influx phase of alcohol administration using [18 F]FDG PET, but were unable to demonstrate any particular subjective correlate of this response. Although inverse correlations with attentional measures were demonstrated in cortical regions, this could arise simply from non-specific dose/metabolism effects. In an intriguing study investigating the influence of the A1 allele of the ankyrin repeat and protein kinase domain-containing protein (ANKK1) TaqIa polymorphism, associated with lower dopaminergic tone and greater risk for alcoholism, the acute effect of alcohol challenge on [18 F]FDG PET demonstrated divergent responses between A1+ and A1- men to alcohol [57, 58]. Furthermore, the investigators were able to demonstrate a relationship between metabolism in orbitofrontal and striatal regions and reduced anxiety and fatigue in A1+ men, suggestive of a causal association.

4.4.4 Neural Network Function and Acute Alcohol Administration Pharmacological fMRI approaches to examine the acute effects of alcohol have met with some success, although the potential of confounding vasodilatory effects with similar dose-response relationships should give grounds for caution. For example, Tims et al. [59] examined high and low responders to alcohol following an alcohol challenge on fMRI activation in a verbal working memory task and were able to demonstrate a significant group by condition interaction in inferior frontal and cingulate regions, such that alcohol attenuated the group differences found under placebo. While the authors claim that the “group by condition effect remained even after controlling for cerebral blood flow, age, and typical drinking quantity,” the fact that no differences in response were evident under alcohol challenge is somewhat counterintuitive, given that this is exactly what differentiates the groups. More recently, the growing interest in the existence of resting state networks, identified by spontaneous correlations of fMRI signal fluctuations under non-task conditions and their pharmacological modulation, led Esposito et al. [60] to investigate alcohol effects on such networks. They were able to demonstrate increases

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Cannabis and the Cannabinoids

in spontaneous BOLD signal fluctuations, albeit in the visual networks, which are not usually thought of as relevant to alcohol effects.

4.5 Cannabis and the Cannabinoids Aside from the fact that cannabis is one of the most widely abused illicit drugs, it is its relationship to psychiatric disorder, in particular psychosis and schizophrenia that have provided a spur to understanding their acute action. The cannabinoids refer to the 60+ psychoactive compounds derived from Cannabis Sativa, most importantly delta-9-tetrahydrocannabinol (9-THC) and cannabidiol (CBD). Multiple studies have been conducted under both naturalistic and controlled conditions examining acute and non-acute (residual) effects on subjective experience and neuropsychological function (reviewed [61, 62]).

4.5.1 Neuropharmacology of Acute Cannabinoid Administration Two cannabinoid receptors, CB1 (central) and CB2 (peripheral), have been identified, with the CB1 receptor clearly implicated in the behavioral effects of cannabinoids. In studies of CB1 receptor “knockouts,” attenuation of opioid effects was also demonstrated [63], implying that the effects of opioids are contingent upon intact cannabinoid signaling. Conversely, in one laboratory-based controlled study, haloperidol treatment did little to affect the subjective experience of 9-THC and worsened neuropsychological performance, indicating that the effects of 9-THC are not substantially mediated via dopaminergic neurotransmission [64].

4.5.2 Receptor Positron Emission Tomography and Acute Cannabinoid Administration The acute administration of cannabis/cannabinoids and measurement of dopamine release using [11 C]raclopride PET have recently been studied with somewhat equivocal results. While Bossong et al. [65] detected a small degree of [11 C]raclopride displacement to inhaled cannabis in a small group of healthy volunteers, Stokes et al. [66] were not able to demonstrate displacement of [11 C]raclopride in striatal regions with oral THC challenge. A later reanalysis of this study [67] was able to detect displacement in extrastriatal prefrontal cortical regions – but not striatal regions – to an extent exceeding test-retest variability.

4.5.3 Resting Blood Flow or Metabolism and Acute Cannabinoid Administration Initial studies of the acute effects of the cannabinoid THC employed [18 F]FDG and intravenous administration in healthy volunteers demonstrated variable cerebral effects, but with consistent increases in cerebellar metabolism [68]. A later study employing similar methodology included chronic marijuana users. In this latter group, increases in prefrontal and striatal regions suggested reinforcing properties of THC in the chronic user group, whether innate or acquired [69]. A series of studies by Mathew et al. [70–73] employed measures of cerebral perfusion using [15 O]H2 O in a progressively larger group

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of chronic cannabis users following intravenous placebo-controlled THC infusion. These studies found progressive increases in prefrontal, insula and anterior cingulate perfusion relating to measures of intoxication and depersonalization. Interestingly, a distorted sense of time was related to increases in cerebellar perfusion [70].

4.5.4 Neural Network Function and Acute Cannabinoid Administration Studies of the interaction of acute cannabis/cannabinoids with functional neuroimaging measures of network recruitment and task performance have focussed on timing, motor coordination and emotional responsivity. O’Leary et al. [74] used [15 O]H2 O PET measures of rCBF during self pacing and demonstrated negative correlations between cerebellar perfusion and rate of self pacing, which they designated the “cerebellar clock.” Similarly, Weinstein et al. [75] showed alterations in brain regions associated with coordination and visual integration with THC, albeit in a complex pattern. An interesting series of studies comparing and contrasting CBD and THC effects on emotional processing [76, 77] showed attenuation of amygdala activation and connectivity to face stimuli with CBD and, conversely, enhanced responses to THC, putatively related to development of paranoia. These studies are reviewed in Mart´ın-Santos et al. [78] and give further context to these findings in a review of functional imaging studies in chronic versus light cannabis use.

4.6 Opioids 4.6.1 Neuropharmacology of Acute Opioid Administration The central actions of opioid drugs have been clarified by the identification of opiate receptors in the central and peripheral nervous systems, in particular the μ-receptor which is most relevant to analgesic and euphoriant effects, is found in, among other regions, the dorsal root ganglia of the spinal cord, the ventral tegmental area and the ventral striatum. Opiates are administered by a variety of routes, including orally and transdermally, however for maximal euphoriant effects rapid intravenous administration or smoking are preferred. For example heroin is administered via intravenous injection “shooting up” or by inhalation “chasing the dragon.” Administration is followed by a euphoriant ”rush,” a subsequent “high” comprising a state of mental detachment and feelings of extreme well-being. It is notable that the opioids do not possess classical psychostimulant activity on acute administration with only minor changes to heart rate or locomotor activity, and in contrast have sedative effects “nodding” or “gouching” leading to difficulty with concentration, drowsiness and sleep. While, the link between μ opioids and the limbic dopaminergic system is well established in rodents [79], the extent of dopamine release is lower than that seen with psychostimulants.

4.6.2 Receptor Positron Emission Tomography and Acute Opioid Administration Studies of the acute effects of μ opioids on [11 C]raclopride binding in humans have been surprisingly rarely studied, in comparison with the number of studies on psychostimulants, given that the abuse liability of the two drug classes are comparable.

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Hagelberg et al. [80, 81] studied the effects of alfentanil, a selective μ opioid agonist, on [11 C]raclopride binding in healthy volunteers and found a significant increase in binding potential compared with placebo, indicating a reduction in endogenous dopamine release. This observation was made despite the fact that this dose produced definite positive subjective and analgesic effects. In opioid dependent volunteers, Daglish et al. [82] studied the response to high doses of diacetylmorphine (heroin) or hydromorphone (another μ opioid agonist) in two separate groups of volunteers, totaling 14 participants. Again, despite profound subjective and objective opioid effects, this group was unable to demonstrate any significant reduction in raclopride binding potential. This latter observation has been replicated in a further group of opioid dependent individuals (Nutt & Lingford-Hughes, personal communication). These results suggest that either the magnitude of μ opioid-stimulated dopamine release is not readily detectable using this methodology, or that the interaction of opioid and dopamine is more complex, perhaps relating to anticipation or withdrawal phenomena, or to negative reinforcement processes. These latter possibilities remain to be explored.

4.6.3 Resting Blood Flow or Metabolism and Acute Opioid Administration Studies of opioid administration on functional neuroimaging of neural network function have been extensive, given the importance of μ opioids in anesthesia and analgesia. In particular, studies have explored the interaction of μ opioids and pain processing. A particular problem, which becomes evident on examining μ opioid effects, are that μ opioids decrease respiration, leading to carbon dioxide retention and vasodilatation. The extent to which investigators have addressed this confound is often difficult to judge. In one of the earlier series of studies, Firestone et al. [83] studied the perfusion response to fentanyl using [15 O]H2 O PET and showed regional brain activity increases in thalamus, anterior cingulate and caudate nuclei. In a subsequent study examining the interaction of fentanyl and pain stimulation, Adler et al. [84] found a slightly different pattern of changes in rCBF and fentanyl with thalamus and anterior cingulate changes, together with a paradoxical increase in pain processing networks with fentanyl. In contrast, using [18 F]FDG PET, a tracer insensitive to changes in perfusion, London et al. [85] showed, in a placebo-controlled study of intramuscularly administered morphine, widespread reductions in cortical and subcortical metabolism which did not correlate with measures of euphoria. In a more recent study of rCBF changes with pulsed arterial spin labeling (pASL) using fentanyl in a randomized placebo-controlled trial in 24 individuals, prominent bilateral increases in thalamic pulvinar and parahippocampal gyri were observed (Reed, Mehta and Zelaya, personal communication). Similarly, Schlaepfer et al. [86] were able to distinguish the acute effects of the μ opioid agonist hydromorphone, which significantly increased regional CBF bilaterally in the anterior cingulate cortex, amygdalae, and thalamus, from those of the κ agonist butorphanol. A low and high dose remifentanyl challenge study in healthy human volunteers was conducted by Wagner et al. [87] using [15 O]H2 O and demonstrated dose-dependent increases in rCBF in bilateral frontal and association cortices and decreases in midbrain gray matter and cerebellum – ostensibly without change in cardiorespiratory parameters. With respect to acute opioid effects on pain processing, Casey et al. [88] and Petrovic et al. [89] used [15 O]H2 O PET to demonstrate that both opioid (fentanyl) and placebo induced analgesia produced increases in anterior cingulate cortex. It is interesting to note that none of the above studies demonstrated consistent changes in the high dopaminergic

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areas classically associated with psychostimulant action, such as the ventral striatum, while a consistent observation of thalamic and anterior cingulate cortex involvement is evident.

4.6.4 Neural Network Function and Acute Opioid Administration Becerra et al. [90] examined the effects of morphine on central nervous system circuitry using fMRI in a pilot study in opioid-naive healthy volunteers. Positive signal changes in “reward” structures, including the nucleus accumbens, sublenticular extended amygdala, orbitofrontal cortex, and hippocampus, were demonstrated. This was only partially replicated by Lepp¨a et al. [91], who exploited the very rapid half-life of remifentanyl to give repeated pulses, demonstrating activation of cingulate, orbitofrontal, posterior parietal and insular cortices, and amygdala. Pharmacological fMRI approaches to acute opioid action have similarly focussed on the interaction with pain processing, combining acute opioid administration with painful stimulation and examining the impact of acute opioids on the pain “matrix.” For example, Wise et al. [92,93] demonstrated reduction in the amplitude of the pain-related BOLD response in bilateral insular cortex and anterior cingulate with remifentanil. This same group took particular care to address the potential confounding effects of respiratory depression and vasodilatation, using hypercapnic challenge and control tasks under both remifentanyl and placebo conditions [94]. This showed a complex picture, with remifentanyl having little effect on hypercapnic challenge responses but diminution of control task responses. While the conclusion of this study was that μ opioids did not have generalized neurovascular effects, it clearly indicates the need for caution. A later study from this group demonstrated that remifentanyl led to depression of regions associated with the volitional control of respiration [95], which raises the question of potential dose related effects on hypercapnia. The effects of μ opioids on pain elicited brain responses were further explored by Oertel et al. [96] using a series of doses of alfentanil. Responses in brain regions associated with the affective dimensions of pain (parahippocampal regions) and those in somatosensory regions diminished at progressively higher doses. The face-validity of these investigations of the interaction of acute μ opioids and pain stimuli have led to a model system being exploited in analgesic development and for investigating inter-individual differences in pain responsivity. Intriguingly, there are few studies that have examined acute μ opioid agonist action on processing of other stimuli relating to, for example, reward processing which are conceptually relevant to the abuse liability of opioids. However, studies such as that of Petrovic et al. [97], who employed the nonselective opioid antagonist naloxone to modulate reward processing using fMRI to demonstrate an attenuation of anterior cingulate responses to reward receipt, together with enhancement of aversive processing in bilateral insula, suggests that this may be a valuable future development.

4.7 Conclusions and Future Directions This chapter has reviewed the current state of the literature with respect to the major categories of substances with abuse liability from the perspectives of their pharmacology, their impact on neurotransmitter release, measured using receptor PET, and engagement of brain networks, using PET measures of perfusion or metabolism and more recently fMRI measures. While a large number of studies have been reviewed, the majority of studies

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References

using the full range of techniques have tended to focus upon the psychostimulants, with alcohol and other compounds receiving relatively lesser interest, so that a less consistent picture emerges. For the psychostimulants, there is abundant evidence for dopamine release within ventral striatal networks, and increased activity of ventral striatal and other networks in their acute effects. However, the evidence for such dopamine release becomes progressively more inconsistent for those drugs with lesser psychostimulant action, such as alcohol, the cannabinoids and opioids, providing something of a puzzle. One suggestion is that the overwhelming dopamine release seen with psychostimulants has effects on other neurotransmitter systems, which then also contribute to the reinforcement process. In view of the increasing evidence of alterations in μ opioid receptor distribution in addictive disorders [98], the use of [11 C]carfentanil PET to examine the range of acute drug effects on the opioid system – in particular the psychostimulants – will be illuminating. With respect to pharmacological fMRI approaches to acute drug effects, an increased use of combinatorial designs where acute drug administration is presented against a background of known pharmacological blockade will be very useful in the dissection of the distinct pharmacological actions of a drug. This will prove invaluable in the study of what will probably prove to be multiple “within-system” and “between-system” effects, not only of acute drug effects in themselves but, over time and with repeated use, the development of addiction itself.

References 1. Laruelle, M. and Huang, Y. (2001) Vulnerability of positron emission tomography radiotracers to endogenous competition. New insights. Q J Nucl Med., 45, 124–38. Review. 2. Frost, J.J. and Wagner, H.N. (1985) Imaging opiate receptors in the human brain by positron tomography. J. Comput. Assist. Tomogr., 9, 231–6. 3. Matthews, P.M. and Jezzard, P. (2004) Functional magnetic resonance imaging. J. Neurol. Neurosurg. Psychiatry., 75, 6–12. 4. Honey, G. and Bullmore, E. (2004) Human pharmacological MRI. Trends Pharmacol. Sci., 25, 366–74. 5. Schoedel, K.A. and Sellers, E.M. (2008) Assessing abuse liability during drug development: changing standards and expectations. Clin Pharmacol Ther, 83(4), 622–6. 6. Kelley, A.E. (2004) Ventral striatal control of appetitive motivation: role in ingestive behavior and reward-related learning. Neurosci Biobehav Rev., 27(8), 765–76. 7. Baldo, B.A. and Kelley, A.E. (2007) Discrete neurochemical coding of distinguishable motivational processes: insights from nucleus accumbens control of feeding. Psychopharmacology (Berl), 191(3), 439–59. 8. Berridge, K.C. (2009) ‘Liking’ and ‘wanting’ food rewards: brain substrates and roles in eating disorders. Physiol Behav., 97, 537–50. 9. Barbano, M.F. and Cador, M. (2007) Opioids for hedonic experience and dopamine to get ready for it. Psychopharmacology (Berl), 191(3), 497–506. 10. Haltia, L.T., Rinne, J.O., Merisaari, H., et al. (2007) Effects of intravenous glucose on dopaminergic function in the human brain in vivo. Synapse, 61(9), 748–56. 11. Shadmehr, R. (2010) Control of movements and temporal discounting of reward. Curr Opin Neurobiol. 12. Shadmehr, R., Orban de Xivry, J.J., Xu-Wilson, M. and Shih, T.Y. (2010) Temporal discounting of reward and the cost of time in motor control. J Neurosci., 30(31), 10507–16. 13. Wickens, J.R., Reynolds, J.N. and Hyland, B.I. (2003) Neural mechanisms of reward-related motor learning. Curr Opin Neurobiol., 13(6), 685–90.

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14. Kurniawan, I.T., Seymour, B., Talmi, D., et al. (2010) Choosing to make an effort: the role of striatum in signaling physical effort of a chosen action. J Neurophysiol., 104(1), 313–21. 15. Dewey, S.L. (1993) Striatal binding of the PET ligand 11C-raclopride is altered by drugs that modify synaptic dopamine levels. Synapse, 13, 350–56. 16. Breier, A., Saunders, R., Carson, R.E., et al. (1997) Schizophrenia is associated with elevated amphetamine-induced synaptic dopamine concentrations: Evidence from a novel positron emission tomography method. Proc Natl Acad Sci USA, 94, 2569–74. 17. Carson, R.E., Breier, A., de Bartolomeis, A., et al. (1997) Quantification of amphetamineinduced changes in [11C]raclopride binding with continuous infusion. J Cereb Blood Flow Metab, 17, 437–47. 18. Drevets, W.C., Price, J.C., Kupfer, D.J., et al. (1999) PET measures of amphetamineinduced dopamine release in ventral versus dorsal striatum. Neuropsychopharmacology., 21(6), 694–709. 19. Martinez, D., Slifstein, M., Broft, A., et al. (2003) Imaging human mesolimbic dopamine transmission with positron emission tomography. Part II: amphetamine-induced dopamine release in the functional subdivisions of the striatum. J Cereb Blood Flow Metab., 23(3), 285–300. 20. Volkow, N.D., Gillespie, H., Mullani, N., et al. (1996) Brain glucose metabolism in chronic marijuana users at baseline and during marijuana intoxication. Psychiatry Res., 67(1), 29–38. 21. Munro, C.A., McCaul, M.E., Oswald, L.M., et al. (2006) Striatal dopamine release and family history of alcoholism. Alcohol Clin Exp Res., 30(7), 1143–51. 22. Martinez, D., Broft, A., Foltin, R.W., et al. (2004) Cocaine dependence and d2 receptor availability in the functional subdivisions of the striatum: relationship with cocaine-seeking behavior. Neuropsychopharmacology, 29(6), 1190–202. 23. Martinez, D., Narendran, R., Foltin, R.W., et al. (2007) Amphetamine-induced dopamine release: markedly blunted in cocaine dependence and predictive of the choice to self-administer cocaine. Am J Psychiatry., 164(4), 622–9. 24. Martinez, D., Greene, K., Broft, A., et al. (2009) Lower level of endogenous dopamine in patients with cocaine dependence: findings from PET imaging of D(2)/D(3) receptors following acute dopamine depletion. Am J Psychiatry., 166(10), 1170–7. 25. Martinez, D., Gil, R., Slifstein, M., et al. (2005) Alcohol dependence is associated with blunted dopamine transmission in the ventral striatum. Biol Psychiatry., 58(10), 779–86. 26. Munro, C.A., McCaul, M.E., Wong, D.F., et al. (2006) Sex differences in striatal dopamine release in healthy adults. Biol Psychiatry., 59(10), 966–74. 27. Boileau, I., Dagher, A., Leyton, M., et al. (2006) Modeling sensitization to stimulants in humans: an [11C]raclopride/positron emission tomography study in healthy men. Arch Gen Psychiatry., 63(12), 1386–95. 28. Slifstein, M., Narendran, R., Hwang, D.R., et al. (2004) Effect of amphetamine on [(18)F]fallypride in vivo binding to D(2) receptors in striatal and extrastriatal regions of the primate brain: Single bolus and bolus plus constant infusion studies. Synapse., 54(1), 46–63. 29. Narendran, R., Slifstein, M., Guillin, O., et al. (2006) Dopamine (D2/3) receptor agonist positron emission tomography radiotracer [11C]-(+)-PHNO is a D3 receptor preferring agonist in vivo. Synapse., 60(7), 485–95. 30. Ginovart, N., Galineau, L., Willeit, M., et al. (2006) Binding characteristics and sensitivity to endogenous dopamine of [11C]-(+)-PHNO, a new agonist radiotracer for imaging the highaffinity state of D2 receptors in vivo using positron emission tomography. J Neurochem., 97(4), 1089–103. Epub 2006 Apr 5. 31. Narendran, R., Hwang, D.R., Slifstein, M., et al. (2004) In vivo vulnerability to competition by endogenous dopamine: comparison of the D2 receptor agonist radiotracer (-)-N-[11C]propylnorapomorphine ([11C]NPA) with the D2 receptor antagonist radiotracer [11C]-raclopride. Synapse., 52(3), 188–208.

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32. Narendran, R., Frankle, W.G., Mason, N.S., et al. (2009) Positron emission tomography imaging of amphetamine-induced dopamine release in the human cortex: a comparative evaluation of the high affinity dopamine D2/3 radiotracers [11C]FLB 457 and [11C]fallypride. Synapse., 63(6): 447–61. 33. Narendran, R., Mason, N.S., Laymon, C.M., et al. (2010) A comparative evaluation of the dopamine D(2/3) agonist radiotracer [11C](-)-N-propyl-norapomorphine and antagonist [11C]raclopride to measure amphetamine-induced dopamine release in the human striatum. J Pharmacol Exp Ther., 333(2), 533–9. 34. Berckel, B.N., Kegeles, L.S., Waterhouse, R., et al. (2006) Modulation of amphetamineinduced dopamine release by group II metabotropic glutamate receptor agonist LY354740 in non-human primates studied with positron emission tomography. Neuropsychopharmacology., 31(5), 967–77. 35. Breiter, H.C., Gollub, R.L., Weisskoff, R.M., et al. (1997) Acute effects of cocaine on human brain activity and emotion. Neuron., 19(3), 591–611. 36. Gollub, R.L., Breiter, H.C., Kantor, H., et al. (1998) Cocaine decreases cortical cerebral blood flow but does not obscure regional activation in functional magnetic resonance imaging in human subjects. J Cereb Blood Flow Metab., 18(7), 724–34. 37. Breiter, H.C. and Rosen, B.R. (1999) Functional magnetic resonance imaging of brain reward circuitry in the human. Ann N Y Acad Sci., 877, 523–47. Review. 38. Risinger, R.C., Salmeron, B.J., Ross, T.J., et al. (2005) Neural correlates of high and craving during cocaine self-administration using BOLD fMRI. Neuroimage., 26(4), 1097–108. 39. Knutson, B., Adams, C.A., Fong, G.W. and Hommer, D. (2001) Anticipation of Increasing Monetary Reward Selectively Recruits Nucleus Accumbens. The Journal of Neuroscience, 21, RC159(1-5). 40. Craig, A.D. (2009) How do you feel–now? The anterior insula and human awareness. Nat Rev Neurosci., 10(1), 59–70. 41. Critchley, H.D., Melmed, R.N., Featherstone, E., et al. (2002) Volitional control of autonomic arousal: a functional magnetic resonance study. Neuroimage., 16(4), 909–19. 42. Knutson, B., Bjork, J.M., Fong, G.W., et al. (2004) Amphetamine modulates human incentive processing. Neuron, 43, 261–9. 43. Santhakumar, V., Wallner, M. and Otis, T.S. (2007) Ethanol acts directly on extrasynaptic subtypes of GABAA receptors to increase tonic inhibition. Alcohol., 41(3), 211–21. 44. Lovinger, D.M. and Homanics, G.E. (2007) Tonic for what ails us? high-affinity GABAA receptors and alcohol. Alcohol, 41(3), 139–43. 45. Gianoulakis, C. (2001) Influence of the endogenous opioid system on high alcohol consumption and genetic predisposition to alcoholism. J Psychiatry Neurosci., 26(4), 304–18. Review. 46. Barrett, S.P., Pihl, R.O., Benkelfat, C., et al. (2008) The role of dopamine in alcohol self-administration in humans: Individual differences. Eur Neuropsychopharmacol., 18(6), 439–47. 47. Boileau, I., Assaad, J.M., Pihl, R.O., et al. (2003) Alcohol promotes dopamine release in the human nucleus accumbens. Synapse., 49(4), 226–31. 48. Urban, N.B., Kegeles, L.S., Slifstein, M., et al. (2010) Sex Differences in Striatal Dopamine Release in Young Adults After Oral Alcohol Challenge: A Positron Emission Tomography Imaging Study With [(11)C]Raclopride. Biol Psychiatry. 49. Heinz, A., Reimold, M., Wrase, J., et al. (2005) Correlation of stable elevations in striatal muopioid receptor availability in detoxified alcoholic patients with alcohol craving: a positron emission tomography study using carbon 11-labeled carfentanil. Arch Gen Psychiatry., 62(1), 57–64. 50. Bencherif, B., Wand, G.S., McCaul, M.E., et al. (2004) Mu-opioid receptor binding measured by [11 C]carfentanil positron emission tomography is related to craving and mood in alcohol dependence. Biol Psychiatry., 55(3), 255–62. 51. Volkow, N.D., Hitzemann, R., Wolf, A.P., et al. (1990) Acute effects of ethanol on regional brain glucose metabolism and transport. Psychiatry Res., 35(1), 39–48.

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52. Volkow, N.D., Hitzemann, R., Wang, G.J., et al. (1992) Decreased brain metabolism in neurologically intact healthy alcoholics. Am J Psychiatry., 149(8), 1016–22. 53. Wang, G.J., Volkow, N.D., Franceschi, D., et al. (2000) Regional brain metabolism during alcohol intoxication. Alcohol Clin Exp Res., 24(6), 822-9. 54. Volkow, N.D., Wang, G.J., Franceschi, D., et al. (2006) Low doses of alcohol substantially decrease glucose metabolism in the human brain. Neuroimage., 29(1), 295–301. 55. Volkow, N.D., Mullani, N., Gould, L., et al. (1988) Effects of acute alcohol intoxication on cerebral blood flow measured with PET. Psychiatry Res., 24(2), 201–9. 56. Schreckenberger, M., Amberg, R., Scheurich, A., et al. (2004) Acute alcohol effects on neuronal and attentional processing: striatal reward system and inhibitory sensory interactions under acute ethanol challenge. Neuropsychopharmacology., 29(8), 1527–37. 57. London, E.D., Berman, S.M., Mohammadian, P., et al. (2009) Effect of the TaqIA polymorphism on ethanol response in the brain. Psychiatry Res., 174(3), 163–70. 58. Berman, S.M., Noble, E.P., Mohammadian, P., et al. (2009) Laterality of cortical response to ethanol is moderated by TaqIA A1 allele. Synapse., 63(9), 817–21. 59. Trim, R.S., Simmons, A.N., Tolentino, N.J., et al. (2010) Acute ethanol effects on brain activation in low- and high-level responders to alcohol. Alcohol Clin Exp Res., 34(7), 1162–70. 60. Esposito, F., Pignataro, G., Di Renzo, G., et al. (2010) Alcohol increases spontaneous BOLD signal fluctuations in the visual network. Neuroimage., 53(2), 534–43. 61. Gonzalez, R., Carey, C. and Grant, I. (2002) Nonacute (Residual) neuropsychological effects of cannabis use: A qualitative analysis and systematic review. Journal of Clinical Pharmacology, 42, 48S–57S. 62. Gonzalez, R. (2007) Acute and non-acute effects of cannabis on brain functioning and neuropsychological performance. Neuropsychol Rev., 17(3), 347–61. 63. Martin, M., Ledent, C., Parmentier, M., et al. (2000) Cocaine, but not morphine, induces conditioned place preference and sensitization to locomotor responses in CB1 knockout mice. Eur J Neurosci., 12(11), 4038–46. 64. D’Souza, D.C., Braley, G., Blaise, R., et al. (2008) Effects of haloperidol on the behavioral, subjective, cognitive, motor, and neuroendocrine effects of Delta-9-tetrahydrocannabinol in humans. Psychopharmacology (Berl), 198(4), 587–603. 65. Bossong, M.G., van Berckel, B.N., Boellaard, R., et al. (2009) Delta 9-tetrahydrocannabinol induces dopamine release in the human striatum. Neuropsychopharmacology., 34, 759–66. 66. Stokes, P.R., Mehta, M.A., Curran, H.V., et al. (2009) Can recreational doses of THC produce significant dopamine release in the human striatum? Neuroimage., 48(1), 186–90. 67. Stokes, P.R., Egerton, A., Watson, B., et al. (2010) Significant decreases in frontal and temporal [11C]-raclopride binding after THC challenge. Neuroimage., 52(4), 1521–7. 68. Volkow, N.D., Gillespie, H., Mullani, N., et al. (1991) Cerebellar metabolic activation by delta9-tetrahydro-cannabinol in human brain: a study with positron emission tomography and 18F-2-fluoro-2-deoxyglucose. Psychiatry Res., 40(1), 69–78. 69. Volkow, N.D., Wang, G.J., Fowler, J.S., et al. (1996) Relationship between psychostimulantinduced “high” and dopamine transporter occupancy. Proc Natl Acad Sci U S A., 93(19), 10388–92. 70. Mathew, R.J., Wilson, W.H., Coleman, R.E., et al. (1997) Marijuana intoxication and brain activation in marijuana smokers. Life Sci., 60(23), 2075–89. 71. Mathew, R.J., Wilson, W.H., Turkington, T.G. and Coleman, R.E. (1998) Cerebellar activity and disturbed time sense after THC. Brain Res., 797(2), 183–9. 72. Mathew, R.J., Wilson, W.H., Chiu, N.Y., et al. (1999) Regional cerebral blood flow and depersonalization after tetrahydrocannabinol administration. Acta Psychiatr Scand., 100(1), 67–75. 73. Mathew, R.J., Wilson, W.H., Turkington, T.G., et al. (2002) Time course of tetrahydrocannabinol-induced changes in regional cerebral blood flow measured with positron emission tomography. Psychiatry Res., 116(3), 173–85.

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74. O’Leary, D.S., Block, R.I., Turner, B.M., et al. (2003) Marijuana alters the human cerebellar clock. Neuroreport., 14(8), 1145–5. 75. Weinstein, A., Brickner, O., Lerman, H., et al. (2008) Brain imaging study of the acute effects of Delta9-tetrahydrocannabinol (THC) on attention and motor coordination in regular users of marijuana. Psychopharmacology (Berl), 196(1), 119-31. 76. Bhattacharyya, S., Morrison, P.D., Fusar-Poli, P., et al. (2010) Opposite effects of delta-9tetrahydrocannabinol and cannabidiol on human brain function and psychopathology. Neuropsychopharmacology, 35(3), 764–74. 77. Fusar-Poli, P., Allen, P., Bhattacharyya, S., et al. (2010) Modulation of effective connectivity during emotional processing by Delta 9-tetrahydrocannabinol and cannabidiol. Int J Neuropsychopharmacol., 13(4), 421–32. 78. Mart´ın-Santos, R., Fagundo, A.B., Crippa, J.A., et al. (2010) Neuroimaging in cannabis use: a systematic review of the literature. Psychol Med., 40(3), 383–98. 79. Di Chiara, G. and Imperato, A. (1988) Opposite effects of mu and kappa opiate agonists on dopamine release in the nucleus accumbens and in the dorsal caudate of freely moving rats. J Pharmacol Exp Ther, 244, 1067–80. 80. Hagelberg, N., Kajander, J., Nagren, K., et al. (2002) Mureceptor agonism with alfentanil increases striatal dopamine D2 receptor binding in man. Synapse, 45, 25–30. 81. Hagelberg, N., Aalto, S., Kajander, J., et al. (2004) Alfentanil increases cortical dopamine D2/D3 receptor binding in healthy subjects. Pain, 109, 86–93. 82. Daglish, M.R., Williams, T.M., Wilson, S.J., et al. (2008) Brain dopamine response in human opioid addiction. Br J Psychiatry., 193(1), 65–72. 83. Firestone, L.L., Gyulai, F., Mintun, M., et al. (1996) Human brain activity response to fentanyl imaged by positron emission tomography. Anesth. Analg., 82, 1247–51. 84. Adler, L.J., Gyulai, F.E., Diehl, D.J., et al. (1997) Regional brain activity changes associated with fentanyl analgesia elucidated by positron emission tomography. Anesth. Analg., 84, 120–6. 85. London, E.D., Broussolle, E.P. and Links, J.M. (1990) Morphine-induced metabolic changes in human brain. Studies with positron emission tomography and [fluorine 18]fluorodeoxyglucose. Arch Gen Psychiatry., 47(1), 73–81. 86. Schlaepfer, T.E. (1998) Site of opioid action in the human brain: mu and kappa agonists’ subjective and cerebral blood flow effects. Am-J-Psychiatry, 155(4), 470–3. 87. Wagner, K., Willoch, F. and Kochs, E.F., et al. (2001) Dose-dependent regional cerebral blood flow changes during remifentanil infusion in humans: a positron emission tomography study. Anesthesiology, 94, 732–9. 88. Casey, K.L., Svensson, P. and Morrow, T.J. (2000) Selective opiate modulation of nociceptive processing in the human brain. J Neurophysiol., 84(1), 525–33. 89. Petrovic, P., Kalso, E., Petersson, K.M. and Ingvar, M. (2002) Placebo and opioid analgesia—Imaging a shared neuronal network. Science, 295, 1737–40. 90. Becerra, L., Harter, K., Gonzalez, R.G. and Borsook, D. (2006) Functional magnetic resonance imaging measures of the effects of morphine on central nervous system circuitry in opioidnaive healthy volunteers. Anesth Analg., 103(1), 208–16. 91. Lepp¨a, M., Korvenoja, A., Carlson, S., et al. (2006) Acute opioid effects on human brain as revealed by functional magnetic resonance imaging. Neuroimage., 31(2), 661–9. 92. Wise, R.G., Rogers, R., Painter, D., et al. (2002) Combining fMRI with a pharmacokinetic model to determine which brain areas activated by painful stimulation are specifically modulated by remifentanil. Neuroimage., 16(4), 999–1014. 93. Wise, R.G., Williams, P., and Tracey, I. (2004) Using fMRI to quantify the time dependence of remifentanil analgesia in the human brain. Neuropsychopharmacology., 29(3), 626–35. 94. Pattinson, K.T., Rogers, R., Mayhew, S.D., et al. (2007) Pharmacological FMRI: measuring opioid effects on the BOLD response to hypercapnia. J Cereb Blood Flow Metab., 27(2), 414–23. 95. Pattinson, K.T., Governo, R.J., MacIntosh, B.J., et al. (2009) Opioids depress cortical centers responsible for the volitional control of respiration. J Neurosci., 29(25), 8177–86.

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96. Oertel, B.G., Preibisch, C., Wallenhorst, T., et al. (2008) Differential opioid action on sensory and affective cerebral pain processing. Clin Pharmacol Ther., 83(4), 577–88. 97. Petrovic, P., Pleger, B. and Seymour, B. (2008) Blocking central opiate function modulates hedonic impact and anterior cingulate response to rewards and losses. J Neurosci., 28(42), 10509–16. 98. Volkow, N.D. (2010) Opioid-dopamine interactions: implications for substance use disorders and their treatment. Biol Psychiatry., 68(8), 685–6.

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Reward Processing

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Chapter 5 Reward Processing Anne Beck1 , Anthony A. Grace2 , and Andreas Heinz1 1

Department of Psychiatry and Psychotherapy, Charit´e Universit¨atsmedizin Berlin, Campus Charit´e Mitte 2 Departments of Neuroscience, Psychiatry and Psychology, University of Pittsburgh, USA

5.1 Introduction Dependence on alcohol and other drugs of abuse is one of the most significant mental disorders in males in industrialized Western nations and is the number one risk factor for more than 60 chronic diseases [1]. In Europe, the percentage in the general population drinking alcohol varies from 62% in Romania up to 95% in Denmark. On average, 5% are addicted [2]. In the United States, approximately 84% of the adult population drinks alcohol, with an incidence of 7.4% dependence [3]. Based on epidemiological addiction surveys, about 2.4 million people consume cannabis and about 645 000 use other illegal drugs in Germany; the US has the highest level of illicit drug use (e.g., cocaine) in the world [4]. Alcohol is the most commonly abused drug in the world. Studies show that relapse rates in alcohol-dependent patients are rather high – without further therapeutic intervention, up to 85% of all detoxified patients resume alcohol intake, even in the absence of withdrawal symptoms [5]. However, even with a qualified therapy, the risk of relapse is generally above 50% within the first two years of abstinence [6]. For decades, addiction research has tried to identify and investigate the main factors contributing to the development and maintenance of addictive disorders.

5.2 Neurotransmitter Systems Implicated in Reward Processing 5.2.1 Basic Neuroscience of Reward Systems: The Mesostriatal Dopaminergic System One key factor involved in the addictions is the processing of reward, particularly the processing of drug-related rewards. Animal studies show that the anticipation, as well as the actual receipt of rewards elicit increased firing of dopaminergic neurons projecting to the ventral striatum and other parts of the mesolimbic/mesostriatal system [7, 8]. Reward processing in humans can be studied using a broad variety of stimuli, including primary reinforcers (e.g., food [9–15] and sex [16]), sensory stimuli serving as conditional reinforcers (e.g., olfactory, auditory or tactile pleasant or aversive stimuli [17]) and Neuroimaging in Addiction, First Edition. Bryon Adinoff and Elliot Stein.  C 2011 John Wiley & Sons, Ltd. Published 2011 by John Wiley & Sons, Ltd.

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Figure 5.1 Schematic illustration of the core regions of the brain reward system.

abstract, secondary reinforcers (e.g., monetary rewards [18,19]). Multiple imaging studies in humans revealed that primary as well as secondary reinforcers stimulate the same reward system, which includes this mesolimbic or mesostriatal dopaminergic pathway. The mesostriatal system originates in the ventral tegmental area (VTA) of the midbrain and connects to the limbic system via the nucleus accumbens, the amygdala, and the hippocampus as well as to the medial prefrontal cortex (MPFC) [20, 21] (Figure 5.1). The latter is known to be involved in modulating behavioral responses to stimuli that activate anticipation of reward (motivation) and reinforcement [22]. Moreover, in imaging studies with healthy controls, the MPFC (including Brodman areas 10, 12, 32) is involved in the processing of the actually received reward, while the ventral striatum is implicated in the anticipation of these rewarding stimuli. Knutson and colleagues [18] observed activation of the ventral striatum during the processing of reward-indicating cues in a monetary incentive delay paradigm (Figure 5.2). In this paradigm, different shapes indicated the possibility to win or lose money depending on the reaction time of a button press during the short presentation of a white target square [18]. During the outcome period, results indicated that when volunteers received $5.00 after anticipating a $5.00 win, MPFC activity increased. In contrast, when volunteers did not receive $5.00 after anticipating a $5.00 win, MPFC activity decreased relative to outcomes with no incentive value [19]. Studies by Breiter et al. [23] and

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Figure 5.2 Brain response measured with functional magnetic resonance imaging during the processing of reward-indicating cues in healthy volunteers. During the processing of stimuli indicating monetary reward in a monetary incentive delay paradigm (cf. [18]; see B) healthy controls showed significant activation of bilateral ventral striatum, the core region of the so called reward system (cf. [16]; see A). Referring to the work by Schultz and colleagues [7] the reward expectation phase is related to an increased firing rate of dopaminergic midbrain neurons (see C). (Reprinted with permission from AAAS.) Section D depicts cues used in the different trails, indicating whether different amounts of money (number of horizontal lines) could be won, lost or whether there will be no consequences depending on reaction time (circle, square, triangle).

Hommer et al. [24] revealed similar activity patterns [23, 24]. Taken together, these results indicate that different regions of the reward system are mediating different aspects of the reward process [18, 19, 23, 24]. Besides studies on the basis of reward processing in healthy individuals, studies have examined predisposing factors as well as neurobiological changes in subjects with a high risk for developing substance abuse and addiction. Drugs of abuse acquire different degrees of control over thoughts and actions based not only on the effects of drugs

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themselves, but also on predispositions of the individual. It has been postulated that drug-associated cues acquire the ability to develop and maintain drug-taking behavior, in part because they acquire incentive motivational properties through Pavlovian learning processes, which will be discussed in detail later in this chapter. A review of the preclinical literature by Flagel et al. [25] explored individual differences in the tendency to attribute incentive salience to cues that predict rewards. They describe that some rodents learn to quickly approach a reward-indicating cue even if it is located distant from where the reward will be delivered, probably by attributing incentive salience to the cue. These so-called “sign-trackers” showed a greater propensity for psychomotor sensitization upon repeated cocaine application [26]. Flagel et al. [25] suggested that these individual differences in the tendency to attribute incentive salience to cues predictive of reward might confer vulnerability to compulsive behavioral disorders, including addiction. Serotonin also appears to play a role in reward processing. Although low serotonin turnover rates (as measured with the serotonin metabolite 5-hydroxyindoleacetic acid (5-HIAA) in the cerebrospinal fluid) and reduced serotonin transporter availability appear to increase negative mood rather than reduce reward-related positive mood states [27, 28], serotonin release in the ventral striatum appears to be directly rewarding [29]. Furthermore, hedonic pleasure associated with the consumption, but not the expectation of reward, was associated with opioidergic neurotransmission via mu-opioidreceptors [30, 31].

5.3 Neurotransmitter Systems Involved in Drug-Related Reward Processing 5.3.1 Methodical Approaches for Studying Neuronal Systems Relevant for Drug Reward Response Blood oxygen level dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) studies allow the assessment of brain activity during the processing of (drug-related) rewards. FMRI measures the hemodynamic response related to neuronal activity in the brain and is one of the most prominent neuroimaging techniques because of its noninvasiveness, sensitivity, lack of radiation exposure, and relatively wide availability. Changes in blood flow and blood oxygenation in the brain are closely linked to neuronal activity [32–34]. In the neurosciences, subjects are commonly exposed to alternating presentations of, for example, rewarding stimuli (win, loss etc.) and a fixation (baseline) condition (crosshair). This design enables investigators to examine the main effects of brain activity related to different stimuli relative to a neutral condition (e.g., rewarding stimulus vs. fixation). In this context, the nature of the stimuli used and their explicit versus implicit emotional valence is a critical issue. Referring to drug reward research, some studies have used alcohol-related words [35] and others alcohol-related pictures, either with [36] or without [37] a sip of alcohol (“priming dose”). Moreover, in some studies patients were not detoxified and thus able to consume larger amounts of alcohol, at least to a later time point [36], while in other studies the patients were detoxified and participated in an inpatient treatment program, where relapse would cause termination of treatment [37–41]. In contrast to the measurement of cerebral blood flow with fMRI, in vivo neurotransmitter release and neuroreceptor and transporter availability can be measured directly with

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Figure 5.3 Competition between radioligand binding to dopamine D2 receptors and endogenous dopamine can be used to measure dopamine concentrations in vivo: Upper part: Presynaptic dopamine release is depicted on the left side (with dopamine transporters for reuptake). Postsynaptic dopamine D2 receptors are indicated by indented boxes on the right side. High endogenous dopamine release blocks a considerable amount of dopamine D2 receptors, which then cannot bind a radioligand tracer, resulting in low radioligand binding. Lower part: Reduced dopamine release (e.g., due to blockade of dopamine synthesis) decreases dopamine concentration and results in less binding of endogenous dopamine to D2 receptors, which can now be marked by radioligand tracers. Alterations in radioligand binding can be quantified and reflect changes in endogenous dopamine concentrations.

Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT). In this context, several radioligands compete with endogenous neurotransmitters for binding at receptors or transporters, for exmaple, binding of the radioligand raclopride to dopamine D2/D3 receptors before and after experimental stimulation of dopamine release can be used to quantify extracellular dopamine concentrations [42–44] (see Figure 5.3).

5.3.2 The Role of the Mesostriatal Dopaminergic System in Drug Reward Dysfunctions in dopaminergic neurotransmission during reward processing have been suggested to play a major role in addictive disorders. All drugs of abuse induce dopamine release in the ventral striatum, one of the core regions of the reward circuitry. For example, Doyon et al. [45] designed a rodent study to clarify the role of dopamine in the nucleus accumbens during operant ethanol self-administration (ethanol seeking and ethanol consumption) to define the relationship between ethanol in the brain and the accumbal dopamine response after oral self-administration of ethanol. Using microdialysis, these investigators showed a significant increase in dopamine during placement of the trained alcohol-seeking rats into the operant chamber. These findings indicated that the dopamine response is associated not only with ethanol itself, but also with

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the stimulus properties of ethanol presentation. Doyon [46] also showed that the smell or taste of alcohol transiently elevates dopamine levels in the nucleus accumbens. In this context, it was postulated that the probability of displaying a certain behavior was increased if dopamine was released (e.g., during drug consumption) [47, 48]. In human studies, the displacement of radioligand binding to dopamine D2 receptors indicated alcohol-associated dopamine release [49, 50]. In humans, there is indirect evidence from pharmacological [51,52] and clinical [53,54] studies that also suggest a fundamental role of dopamine in (drug) reward processing. Moreover, inspired by animal experiments, a study by Knutson et al. [18] showed that, in healthy volunteers, presentation of a salient stimulus that had reliably predicted reward evoked a phasic functional activation of the ventral striatum [18]. Of course, a phasic activation of the ventral striatum measured with fMRI appears on a much longer time scale (around 10s) compared with the phasic increase in dopamine firing observed by Schultz et al. (see below), which ranges in the tenths of seconds scale (see Figure 5.2). Therefore, fMRI cannot directly assess alterations in phasic dopamine firing, but rather assesses changes in neuronal functioning that can nevertheless act as a surrogate marker for a briefer dopaminergic input. Indeed, the application of dopamine D1/D2 receptor agonists has been shown to predictably affect the BOLD response in fMRI [55, 56]. Wise [57] suggested that dopamine release itself is rewarding and associated with hedonic feelings of pleasure [57]. However, observing that a drug reinforces behavior does not necessarily imply that the drug effect is subjectively pleasant. Robinson and Berridge distinguished between hedonic or pleasant drug effects (“liking”) and the craving for such a positive effect (“wanting”) [58]. They suggested that drug effects associated with drug liking are mediated by opioidergic neurotransmission in the ventral striatum, including the nucleus accumbens, similar to the endorphin release following the pleasurable effects perceived during consumption of primary reinforcers such as food [59]. Indeed, animal research suggests that dopamine release is not directly rewarding but instead reflects an error of reward prediction. According to this hypothesis, dopamine is released whenever an incoming reward exceeds the predicted reward and the positive difference between received and predicted reward is reflected in dopamine firing [7]. Likewise, dopamine firing is reduced whenever the outcome is worse than expected since the incoming reward is smaller than the expected positive reinforcement [7]. Conditioned cues that have been regularly paired with the reward, indicating an upcoming reward, appear to acquire the same capacity to elicit a short, phasic increase in dopaminergic firing. The rewardindicating cue itself appears unpredicted and hence exceeds the individual’s expectation. In contrast, a reward that arrives exactly as predicted by the previous conditioned cue will no longer elicit dopamine release, because the difference between the incoming and the expected reward is zero, meaning that the reward arrives exactly as predicted by the cue (Figure 5.4) [7]. Robinson and Berridge [59] suggested that a phasic increase in dopamine would facilitate the allocation of attention (attribution of “incentive salience”) towards salient, conditioned reward-indicating stimuli [58]. This process can thus motivate an individual to display a particular behavior in order to receive the reward. Hence, dopamine contributes to the control of goal-directed behavior because it encodes the expected magnitude of a potential reinforcer and attributes incentive salience to reward-indicating stimuli. As a consequence, the nucleus accumbens acts as a “sensory-motor gateway,” which controls the effects of salient environmental stimuli on prefrontal and limbic brain areas that regulate attention and motor output [60]. This attribution of incentive salience to cues also plays a major role in learning theories regarding addictive behaviors, which suggest that originally neutral stimuli (NS) can be

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No prediction Reward occurs

R received = 1, R expected = 0: 1 – 0 = +1

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R

Reward predicted Reward occurs

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CS

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0 CS

1

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2s

Figure 5.4 Alterations in phasic dopamine release reflect an error of reward prediction (modified from Schultz et al., 1997 [7]. Reprinted with permission from AAAS.): Upper part: When no reward (R) is expected but surprisingly occurs, the difference between the received and the expected reward (arbitrarily set at 1) is positive, which is reflected by an increase in dopamine firing. Middle part: A conditioned stimulus (CS) that reliably predicts reward is attributed with incentive salience and carries the same positive value as the reward that it predicts. Whenever the CS appears unexpectedly, it elicits a phasic dopamine response due to a positive difference between the received and the expected value of the cue. Arrival of the reward itself, on the other hand, no longer elicits dopamine firing as long as this reward is fully predicted by the preceding salient stimulus, because the reward received is exactly the same as the expected reward. Lower part: When the expected reward does not appear, the difference between the received and the expected reward is negative, reflected in a phasic decrease of dopaminergic firing.

associated with (1) affectively positive effects of the drug of abuse (the unconditioned response [UCR] to the drug of abuse) or with (2) a compensatory, homeostatic process that counteracts the expected drug effect [61, 62]. Such cues thus become conditioned stimuli (CS), which can elicit an urge or “craving” as a conditioned response (CR) for the affectively positive effects of alcohol or for the alleviation of the homeostatic (aversive) counter-adaptive process [63–65] (Figure 5.5). It is important to note that such formerly neutral stimuli, which become associated with the effects of the drug, can be both internal or external cues, for example, the context or environment that characterized former drug consumption or internal stimuli, such as feelings of loneliness or memories of conflict situations, which had previously been associated with drug intake [64–66]. Indeed, neurophysiological studies in animals have shown that repeated administration of psychostimulants activates the ventral subiculum, a region of the hippocampus that has been associated with contextual processing [67]. One method to investigate alterations in incentive salience attribution to drug-related stimuli is by use of the so-called cue-reactivity paradigms [64]. In the context of these psychophysiological paradigms, it is feasible to examine conditioned reaction on conceptually different levels [68]: (1) craving,

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Figure 5.5 Model of conditioned drug and alcohol craving: A previously neutral stimulus (e.g., a beer glass) can become regularly associated with (a) an increase in blood alcohol concentration, which elicits an affectively positive response (UCR). The beer glass thus turns into a conditioned stimulus (CS), which can elicit an attenuated positive effect and craving for the “full” affectively positive effect of alcohol. Furthermore, the positive effect of alcohol itself (b) can act as an unconditioned stimulus (UCS) that elicits a homeostatic counter-adaptive process as an unconditioned response (UCR). If the CS is presented without subsequent alcohol intake, the counter-adaptive process is experienced as aversive and craving for alcohol emerges to reduce this aversive state. The balance between these two processes appears to be influenced by context: it has been suggested that a drug is more rewarding when given in drug-related context, thereby overcoming any negative associations by increasing the positive response without decreasing the negative one (cf. [64]).

anxiety or joy can be elicited on a subjective level, (2) an increased heart rate, brain activity or skin conductance reflects alterations on a physiological level, and (3) on a behavioral level, the amount of drug consumption or the latency until relapse can be observed [69]. In clinical studies, the startle response and associated event-related potentials (ERPs) measured with electroencephalograms (EEGs) can be used as neurophysiological

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Figure 5.6 Brain response during the processing of alcohol-related pictures in alcohol-dependent patients. During the presentation of alcohol-related stimuli (C), detoxified alcohol-dependent patients significantly activated the putamen (A) and the anterior cingulate (B), brain regions associated with attentional processes and habit formation (cf. [37]).

indicators of appetitive or aversive reactions towards visual stimuli. For example, in alcohol-dependent patients, it has been observed that alcohol cues often elicit a physiological response similar to appetitive cues, which is not necessarily reflected in a conscious feeling of attraction or pleasure [66]. In addition, Lubman and colleagues observed that heroin users demonstrated reduced responsiveness to natural reinforcers across a broad range of psychophysiological measures [70]. Cue-induced functional brain activation can be indirectly assessed by measuring changes in cerebral blood flow with positron emission tomography (PET) or single photon emission computed tomography (SPECT) or by measuring the blood oxygen level dependent (BOLD) response with functional magnetic resonance tomography (fMRI) as described above. Although these studies revealed considerable inter-individual variance in response to the presentation of drugassociated stimuli, there are some core regions that were activated in most studies [71, 72] (Figure 5.6). These core regions are presented in Table 5.1.

5.3.3 Other Neurotransmitters Involved in Drug Reward Besides dopamine, other neurotransmitters have also been implicated in the development and maintenance of drug-dependence. A long-term sensitization towards the effects of

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Table 5.1 Brain regions commonly activated in “cue-reactivity” paradigms Anterior cingulate (ACC) and adjacent medial prefrontal cortex Orbitofrontal cortex (OFC) Basolateral amygdala

Ventral striatum (including the nucleus accumbens) Dorsal striatum

Attention and memory processes; encodes the motivational value of stimuli Evaluation of reward of stimuli Specifies the emotional salience of stimuli; initiates conditioned and unconditioned approach and avoidance behavior Connects motivational aspects of salient stimuli with motor reactions Consolidates stimulus-reactionpatterns; habit formation

[35–37, 39, 107, 111]

[36, 41] [111, 143]

[38, 40, 41]

[37, 107, 110]

drugs and drug-associated cues can be caused by structural changes in striatal GABAergic neurons, which are innervated by dopaminergic neurons and play a major role in the signal transfer towards the thalamus and the cortex [73]. For example, alcohol stimulates GABAA receptors and inhibits the function of glutamatergic NMDA-receptors [74,75]. The alcohol-induced inhibition of glutamatergic signal transduction results in up-regulation of NMDA receptors [76,77]; during withdrawal, loss of alcohol-associated inhibition of NMDA receptor function may result in hyperexcitation and clinically manifest as withdrawal symptoms [78]. Repeated withdrawals, in turn, elicit enhanced glutamate release [79] and increased glutamatergic neurotransmission in pathways from the prefrontal cortex (PFC), amygdala and hippocampus to the nucleus accumbens and ventral tegmental area (VTA), which may play a major role in triggering relapse via glutamatergic stimulation of dopamine release in the ventral striatum [79–81]. A PET imaging study suggested that detoxified alcohol-dependent individuals display increased μ-opiate receptors in the ventral striatum, which appear to increase the pleasurable effects of alcohol intake [82]. This increase was correlated with the severity of alcohol craving [83]. In accordance with these observations, it is well documented that naltrexone (a μ-opiate receptor antagonist) can suppress alcohol craving and the subjective “high,” that is, drug “liking,” associated with alcohol intake [84]. In animal experiments, naltrexone reduced both dopamine release in the ventral striatum and alcohol consumption [85]. In humans, several clinical studies showed that naltrexone treatment can reduce the relapse risk of alcoholics and lower the amount of consumed alcohol [86, 87], particularly if administered to patients with a high affinity, gain-of-function μopiate receptor genotype [88,89]. Other transmitter systems regulating striatal dopamine release are the cannabinoid and cholinergic systems [90–92].

5.4 Alterations in the Mesostriatal System in Addiction Brain imaging studies with positron emission tomography (PET) have revealed a reduction of availability and sensitivity of central dopamine D2 receptors in detoxified alcohol-dependent patients. This reduction may reflect a compensatory down-regulation

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in central D2 receptors and is correlated with lifetime alcohol intake and subsequent relapse risk [93, 94]. Similar results were observed in cocaine abusers [95]. Further, PET studies showed that alcohol craving was specifically correlated with a low dopamine synthesis capacity measured with F-DOPA PET and with reduced dopamine D2 receptor availability in the ventral striatum including the nucleus accumbens [39, 96]. During detoxification and early abstinence, dopamine dysfunction may be further augmented by reduced intra-synaptic dopamine release: rodent experiments showed that extracellular dopamine concentrations decreased rapidly during alcohol detoxification [97] and a PET study showed that dopamine release following amphetamine administration was significantly reduced in detoxified alcoholics, indicating that presynaptic dopamine storage capacity is reduced during early abstinence [98]. Together, these studies indicate that after detoxification, overall dopaminergic neurotransmission in the ventral striatum of alcohol-dependent patients is reduced rather than increased or sensitized. Nevertheless, this dopamine dysfunction appears to be associated with an increased neuronal response to drug-associated stimuli. In a multimodal imaging study combining PET and functional magnetic resonance imaging (fMRI), ventral striatal dopamine D2 receptor down-regulation was not only correlated with the severity of alcohol craving but also with increased processing of alcohol-associated cues in the anterior cingulate and medial prefrontal cortex [39]. These brain areas are associated with motivated attention [99], error monitoring [100] and risk evaluation [101] and an increased activation in these regions during the processing of alcohol cues has been associated with an increased relapse risk [37]. Moreover, a study by Volkow and colleagues [102] proposed that genetic factors might also contribute to vulnerability towards addictive behaviors. This study, examining first-degree relatives of alcohol-dependent patients, suggested that low dopamine D2 receptor availability was also found among first-degree relatives of alcohol-dependent patients, while high availability of dopamine D2 receptors among relatives appears to be protective [102]. In contrast, dopaminergic mechanisms have also been implicated in a phenomenon called sensitization: when a rat is given a stimulant drug within a particular context (e.g., their home cage), the rat will show an increased psychomotor response to the same dose of the drug when applied again in the same context in which it had been previously administered [103–105]. Sensitization may thus reflect the increasing motivational, positively reinforcing effects of commonly abused drugs, which occur with repeated drug consumption [59]. However, to date, clinical studies have not found clear evidence that a sensitized dopamine system underlies the observed dysfunctions in the processing of, for example, alcohol-related cues and the associated relapse risk. Rather, dysfunction of reward-associated learning may explain why detoxified alcohol-dependent patients display an increased preference for alcohol and alcohol-related cues, even in the face of a reduced, rather than sensitized, dopaminergic neurotransmission in the brain reward system [106]. On the other hand, sensitization may play a role in the context-dependent relapse that is experienced with drug abuse. Thus, in animal models of sensitization, a major role for the hippocampus ventral subiculum has been noted [67]. In particular, this region, known for its involvement in context-dependent processing, underlies the increased dopamine system responsivity that occurs with amphetamine administration in the drug-sensitized animal. Furthermore, the sensitized response to psychostimulants is reversed whenever the ventral subiculum is inactivated. This suggests that, in the sensitized state, contextual cues acting via the ventral subiculum may set the dopamine system to be hyper-responsive to drug administration, thereby making the drug “more rewarding” than it would be in unrelated contexts.

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However, if dopamine dysfunction in detoxified alcohol-dependent patients, for example, interferes with phasic changes in dopaminergic neurotransmission that reflect an error of reward prediction, alcohol-dependent patients should have problems in attributing salience to newly learned stimuli which are presented unexpectedly and indicate the availability of (non-alcohol) reward. Indeed, while the above mentioned brain areas revealed increased neural activity during the presentation of drug-related stimuli [36–38, 40, 107–111], a reduced functional activation of the ventral striatum was found in alcohol-dependent individuals who were confronted with newly-learned cues indicating the availability of monetary reward [40]. This reduced activation of the ventral striatum correlated with the severity of alcohol craving and was not explained by differences in performance or mood between alcohol-dependent and control subjects. Reduced brain activation to newly learned, reward-indicating stimuli may thus interfere with the patients’ motivation to experience new and potentially rewarding situations. Is this true for all addictions? A study by Garavan et al. [107] showed that cocaine users displayed a reduced brain response towards sexual stimuli compared to cocaine-associated cues in regions such as the ACC and caudate nucleus. This finding is in accordance with the hypothesis that cocaine, alcohol and other drugs of abuse “hijack” a dysfunctional reward system, which tends to respond too strongly to drug-associated cues while failing to adequately process conventional reinforcers such as food, sex or money [40, 112]. This hypothesis is consistent with the propensity of drug abuse to increase contextual focus on drug seeking behavior while attenuating motivational drive toward non-drug goal-directed behavior [113]. Studies in behavioral addictions, like pathological gambling, speculated (by analogy to drug dependence) that there also is a reduction in the sensitivity of the reward system. Reuter and colleagues [54] investigated pathological gamblers and controls during a guessing game using fMRI. They observed a reduction of ventral striatal and ventromedial prefrontal activation in the pathological gamblers, which was moreover negatively correlated with gambling severity, linking hypoactivation of these areas to disease severity [54]. In another study among pathological gamblers, de Ruiter et al. (2009) showed a diminished reward and punishment sensitivity, as indicated by hypoactivation of the ventrolateral prefrontal cortex when money is gained and lost [114]. The work of Schultz et al. [7] suggests that phasic alterations in dopamine release are not only required to learn new stimulus-reward associations but are also necessary to “unlearn” (extinguish) established associations [7]. According to Schultz, a phasic dip of dopamine release occurs whenever a conditioned stimulus is not followed by the anticipated reward (Figure 5.4). Under this condition, a phasic decrease in dopamine input to the ventral striatum would break the positive feedback loop with the hippocampus while removing inhibition from the prefrontal cortex, enabling the subject to switch to other goal-directed behaviors. On the other hand, the above described tonic down-regulation of dopamine synthesis and storage and dopamine D2 receptor availability in the ventral striatum of detoxified alcohol-dependent patients and cocaine abusers can interfere with this dopamine-dependent signaling of an error in reward expectation [39,115]. Therefore, it may be difficult for alcohol- and other drug-addicted individuals to divert attention away from conditioned cues, which no longer deliver drug-related reward but which have previously regularly been well-learned to signal the availability of drug (possibly via glutamate-dependent long-term potentiation within the ventral hippocampus-ventral striatal pathway, which has been associated with perseverative behavior) [115]. Thus dopamine dysfunction may interfere with the phasic dopamine-dependent error signal indicating that drug-associated cues are no longer followed by reward – this may help to

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explain why patients continue to consume drugs even though they often feel that this is no longer rewarding and results in manifold negative consequences. Striatal dopamine release is regulated, at least in part, via glutamatergic neurotransmission from, among other regions, the hippocampus, which plays a major role in memory processes [116]. In rats that had formerly consumed cocaine, the stimulation of glutamatergic neurons in the hippocampus results in dopamine release in the ventral striatum and leads to renewed drug consumption [117]. Hippocampal stimulation thus may reflect real-life situations in which contextual, drug-associated cues activate the hippocampus and trigger memories associated with previous drug use (Figure 5.7). In this context, hippocampal activation appears to indirectly stimulate dopaminergic neuronal activity in the ventral tegmentum, which projects to the ventral striatum, where dopamine release facilitates drug intake [118]). Indeed, such an augmented phasic event may actually cause the system to become “stuck” in a drug-seeking state. Thus, phasic dopamine events are known to specifically potentiate hippocampal-accumbens pathways [115], which in turn drive an increased phasic response of the dopamine system [119]. Furthermore, this

Figure 5.7 Model of a neuronal network that includes dopamine-related prediction of unexpected or novel reward and reward-associated stimuli. The discrepancy between expected and actual sensory information is calculated in the hippocampus (CA1) and activates dopaminergic neurons in the brainstem (ventral tegmental area; VTA) via glutamatergic projections to the nucleus accumbens (ventral striatum), which indirectly stimulates dopamine neuronal activity via descending GABAergic projections to the ventral pallidum and to the VTA. The VTA in turn interacts with neuronal transmission in CA1 via increased dopamine release in the hippocampus and thus modulates memory performance. The prefrontal cortex contributes to executive control functions and modulates - just as the limbic system - the firing rate of dopaminergic neurons that project from the brain stem (VTA) to the nucleus accumbens (modified from [116] and see [144]).

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increase in dopamine release would also attenuate prefrontal input, thereby decreasing behavioral flexibility mediated by this structure [115]. Given that the hippocampalaccumbens pathway is proposed to mediate contextual events, this would cause the system to remain in a positive feedback loop, whereby reinforcement-driven dopamine release would potentiate contextual focus on drug seeking while attenuating the propensity of the prefrontal cortex to shift to other goal-directed behaviors [113]. Therefore, the normal balance between focusing on reward-generating tasks (hippocampus) and shifting attention to other goal-directed behaviors (prefrontal cortex) is hijacked, causing an exclusive focus on drug-seeking, reward-related behaviors. On the other hand, Kalivas and coworkers [79] suggest that descending glutamatergic pathways from the prefrontal cortex regulate striatal and prefrontal dopamine release [80]; according to animal experiments, such glutamatergic projections play a central role when drug-associated cues trigger relapse in rodents during drug withdrawal [79] (cf. Figure 5.8). However, whether

Figure 5.8 Dopaminergic and other monoaminergic modulation of GABAergic and glutamatergic neurotransmission during alcohol consumption and withdrawal. The sedative effects of alcohol and other drugs of abuse are mediated by stimulation of GABAergic neurotransmission (increased neuronal inhibition), resulting in a down-regulation of GABAA receptors. Furthermore, alcohol inhibits glutamatergic neurotransmission (decreased excitatory neuronal activation) and thus leads to upregulation of glutamate NMDA receptors. During withdrawal, GABAergic receptors are no longer activated by alcohol (missing inhibition), and up-regulated glutamate receptors are no longer functionally inhibited by direct ethanol effects (increased excitation), which can result in severe withdrawal symptoms [74, 76]. Alterations in the activation of glutamatergic neurons projecting from the prefrontal cortex to midbrain dopamine neurons can directly affect dopaminergic projections to the prefrontal cortex and indirectly (via GABAergic interneurons) to the striatum [80]. Glutamatergic neurotransmission is shown in green, GABAerg´ıc in red and dopaminergic in blue.

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these glutamatergic effects are mediated via prefrontal cortical afferents or instead originate in the ventral hippocampus, as suggested by Goto and Grace [115] is unclear. Such a dissociation between drug reward and drug intake may be a manifestation of the transition from a ventral striatal reward-driven behavior (“liking”) to a dorsal striatal habit-driven behavior (“wanting”), which has been observed in primates during long-term drug self-administration [120] and in drug-dependent humans exposed to stimuli which induce craving [121, 122]. Increased reactivity to alcohol-associated cues, for example, may also underlie the clinical observation that many detoxified alcoholdependent patients report difficulty remaining abstinent when confronted with alcohol advertisements in typical drinking situations (e.g., when sitting alone at home and watching a football game). Moreover, it can be very difficult to motivate detoxified drug addicts to replace the drug by other reinforcers such as social interactions or new hobbies – this problem can be explained when it is taken into account that drugdependent patients display reduced neuronal responses to new, reward-indicating stimuli. Instead, drug-dependent patients often show increased brain activation in association with craving when drug-specific cues are presented, which may make it very difficult for them to divert attention from drug-associated cues and to focus on non-drug related, potentially rewarding situations [40, 81, 123]. Taken together, these findings indicate reduced neural activity in reward circuitry in response to non-drug related, reward-indicating cues and increased activity during the presentation of drug-associated stimuli in addiction. As described above, similar findings of reduced neural activity in reward circuitry have been reported for non-substancerelated addictive behaviors such as pathological gambling [54], as discussed further in Chapter 11 on Neuroimaging in Behavioral Addictions.

5.4.1 Other Possible Mediators of Reward Dysfunction in Addiction A recent study by de Greck et al. [124] investigated why abstinent alcohol-dependent patients show a proceeding neglect of formerly important, self-relevant interests, and the authors examined whether there is an association between altered self-referential processing and reward system dysfunction. In this fMRI study, alcohol-dependent patients displayed reduced signal changes in reward circuitry during personal reference with no differentiation between high and low self-referential stimuli. This finding is in line with other studies in healthy subjects, which demonstrated involvement of the ventral striatum and the ventromedial prefrontal cortex (VMPFC) in personal reference, thus demonstrating that the reward circuitry is recruited during the evaluation of high versus low selfreferential stimuli [125–128]. Notably, VMPFC activation during personal relevance tasks cannot be attributed to episodic memory retrieval but seems to be specifically related to self-referential processing [129]. In summary, these results may point to a role of the reward circuitry during personal reference, which may be disturbed in patients suffering from alcohol and drug dependence. Another factor that may indicate dysfunction of reward processing in drug and alcoholdependence is the inability to delay reward or choose a later and larger reward instead of an immediate but smaller positive reinforcement, which is an aspect of impulsivity (other aspects include reduced inhibitory control). Indeed, it has been suggested that patients suffering from alcohol dependence or other forms of drug abuse display dysfunctional reward expectation with an overemphasis on immediate rewards and a neglect for delayed outcomes. Regarding the correlational relationship between the described delay

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discounting and impulsivity, it is worth mentioning that a series of studies suggests that alcohol- and drug dependent patients are more impulsive than controls [130–133], and that impulse control disorders (i.e., impulsive-violent behavior) are more common among alcohol- and drug dependent patients than in healthy volunteers [134–136]. Impaired reward expectation, putatively associated with dysfunction of the ventral striatum and other areas associated with the brain reward system, may therefore contribute to impulsivity and impulsive aggression. Indeed, Beck et al. [137] observed that in alcohol-dependent patients, a reduced activation of the ventral striatum during reward anticipation (i.e., the processing of reward-indicating cues) was associated with increased impulsivity [137]. This finding suggests that subjects who have difficulties maintaining reward expectation, while they appear to be hyper-responsive to immediate rewards, might also display high impulsivity [138]. Increased neuronal responses to immediate reward and decreased brain activation elicited by cues that indicate upcoming reward may thus lead to increased delay discounting, that is, an inability to wait for a larger but delayed reward. Moreover, reduced neuronal responsiveness to delayed reward may provoke increased reward-seeking behavior as a means of compensation [139]. In addition, acute drug and alcohol administration seems to impair conditioning of reward-seeking responses and inhibitory control processes. A recent study by Loeber and Duka [140] investigated healthy social drinkers during an instrumental reward-seeking procedure, with abstract stimuli serving as positive (S+, always predicting a win) or negative reinforcers (S-, always predicting a loss) and a standard “stop signal” task [140]. After the administration of alcohol, participants more often chose the stimulus associated with a negative outcome (i.e., loss of money). This finding was observed although alcohol was not affecting explicit knowledge of stimulus–response outcome contingencies. In addition, alcohol increased stop signal reaction times, indicating a disinhibiting effect of alcohol, which was in turn associated with an unfavorable response probability towards the negative stimuli. These results demonstrate that alcohol interferes with inhibitory control of behavioral responses to external signals and impairs association of cues with negative outcomes, which may help to explain why alcohol-dependent patients have difficulties learning from the aversive consequences of excessive alcohol intake. Moreover, Park et al. [141] observed that learning speed and performance in a probabilistic reversal learning task in alcohol-dependent patients was reduced compared to healthy controls, and that this reduction was associated with dysfunctional connectivity between the ventral striatum (VS) and dorsolateral prefrontal cortex (dlPFC) during prediction error processing. Interestingly, severity of craving for the drug of abuse was also correlated with the dysfunctional VS-dlPFC connectivity, suggesting that a reduced ability to learn new reward contingencies may dispose to crave an all too well-known reinforcer: the drug of abuse.

5.5 Summary and Outlook Altogether, a series of studies demonstrate a dysfunction of dopaminergic neurotransmission in the reward circuitry of drug-dependent individuals. These studies also suggest that functional activation is potentially associated with striatal dopaminergic signaling, such as the attribution of salience to reward predicting stimuli and the computation of prediction errors [7, 58, 142]. In this context, drug-dependent patients display an increased processing of drug-related cues and a dysfunctionally decreased attention attribution towards other nondrug-related reinforcers.

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References

The neurobiological mechanisms underlying addictive behaviors and the associated relapse risk are promising tools for identifying patients with high cue-induced reactivity who may also be particularly vulnerable to relapse. These patients could benefit from cognitive-behavioral therapies, including exposition therapy, to extinguish conditioned reaction towards addiction-related cues and to encourage self-efficacy. Another therapeutic approach could be to train the individual to experience non-drug-related rewards to consolidate the processing of non drug-related stimuli. Moreover, neurobiological research may help to reduce the stigma of addiction. Even though the diagnosis of drug dependence is not bound to a detection of brain-related changes, these changes might account for why addicted individuals often relapse against their declared intention as a result of complex biological mechanisms that are not readily subject to intentional control. This implies that drug-dependent patients do not suffer from “weak willpower” or “bad intentions”, as suggested early during the twentieth century. Instead, brain imaging studies suggest that drug-associated cues activate limbic brain areas, which appear to be in part genetically influenced and to respond in an automated, habit-like manner [66, 96]. Therefore, it seems plausible that patients experience drug craving and drug seeking behavior “against their own conscious will”. These individuals should not be blamed for their behavior, but instead be treated with the same respect as other patients in the health care system.

5.6 Acknowledgments This work was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft; HE 2597/4-3; 7-3; Exc 257), by the Bernstein Center for Computational Neuroscience Berlin (BMBF grant 01GQ0411) and NGFN (BMBF grant 01 GS 08 159).

References 1. World Health Organization (2008) World Health Statistics, World Health Organization, Geneva. 2. World Health Organization (2001) Alcohol in the European Region – consumption, harm and policies, World Health Organization, Geneva. 3. World Health Organization (2004) WHO Global Status Report on Alcohol 2004, World Health Organization. 4. Degenhardt, L., Chiu, W.T., Sampson N. et al. (2008) Toward a global view of alcohol, tobacco, cannabis, and cocaine use: findings from the WHO World Mental Health Surveys. PLoS Med., 5(7), e141. 5. Boothby, L.A. and Doering, P.L. (2005) Acamprosate for the treatment of alcohol dependence. Clin Ther, 27, 695–714. 6. Mann, K. (2002) Neue Therapieans¨atze bei Alkoholproblemen. Pabst Science Publishers, Lengerich. 7. Schultz, W., Dayan, P., and Montague, P.R. (1997) A neural substrate of prediction and reward. Science, 275(5306), 1593–1599. 8. Wightman, R.M. and Robinson, D.L. (2002) Transient changes in mesolimbic dopamine and their association with ‘reward’. Journal of Neurochemistry, 82(4), 721–735. 9. Berns, G.S., McClure, S.M. and Pagnoni, G. (2001) Predictability modulates human brain response to reward. Journal of Neuroscience, 21(8), 2793–2798. 10. Del Parigi, A., Chen, K.W., Gautier, J.F. et al. (2002) Sex differences in the human brain’s response to hunger and satiation. American Journal of Clinical Nutrition, 75(6), 1017–1022.

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Chapter 6

A Neuroimaging Approach to the Study of Craving

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Chapter 6 A Neuroimaging Approach to the Study of Craving Francesca M. Filbey1 , Eric D. Claus2 , and Kent. E. Hutchison2,3 1

Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas 2 The Mind Research Network, Albuquerque, NM 3 Department of Psychology, University of New Mexico, University of Colorado at Boulder

6.1 A Neuroimaging Approach to the Study of Craving Over the years, there has been considerable debate about how to best conceptualize and measure craving, much of which is beyond the scope of this volume (for more information see reviews by [1, 2]. Craving has often been defined as a strong subjective desire to use alcohol or drugs. Despite the debate over the years, craving has been and will likely continue to be one of the primary targets of medication development for alcohol and drug use disorders. For example, one of the putative mechanisms of action for naltrexone in the treatment of alcohol dependence is its action on craving (Anton et al., 2006), and studies that have tested other medications often target craving [3, 4]. The definition and measurement of craving has been contentious. Research that evolved in the 1980s and 1990s advanced a cue reactivity approach to the study of craving. In this approach, individuals were exposed to drug cues (e.g., sight of drug paraphernalia, smell of alcohol) and self-report measures of craving collected. The measurement of craving in the context of cue reactivity research emphasized a strong theoretical framework grounded in learning theory such as Pavlovian conditioning. Cue reactivity research also emphasized experimental control in an attempt to improve reliability and validity in the measurement of craving (see [5, 6]. Nonetheless, the subjective nature of this research has been viewed as imperfect and has been criticized on the basis that it does not predict drug use behavior prospectively (see [1]. For example, methodological issues such as limited scales and investigations that are not ecologically valid are some of the caveats that diminish the accuracy, reliability, and validity of subjective craving measures in laboratory settings. In addition, it has been difficult to integrate this approach with numerous animal studies that have produced valuable information on the neurobiological mechanisms that underlie “craving” and the motivation to use alcohol and drugs. In fact, luminaries in the field have made public pleas for research reconciling human and animal approaches to the study of craving (Li, 2002).

Neuroimaging in Addiction, First Edition. Bryon Adinoff and Elliot Stein.  C 2011 John Wiley & Sons, Ltd. Published 2011 by John Wiley & Sons, Ltd.

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In the animal literature, the actions of drugs have long been linked to the mesolimbic and mesocortical pathways in the brain, which are the neural substrates that putatively underlie the attribution of incentive salience to alcohol and other drugs of abuse [7–9]. Clearly, the ability to focus on the neurobiology of craving in humans is critical for developing a more detailed understanding of the pathophysiological mechanisms that underlie craving, critical in terms of providing a target and venue to test new treatment approaches, and critical in terms of connecting this research to animal models. Recently, scientists have begun to use neuroimaging approaches to focus on the neurobiology of craving in humans. The primary advantage of this approach is that it does not rely on subjective responses, thus, addressing some of the limitations of accuracy and validity in behavioral investigations. Specifically, neuroimaging provides a more direct, biological measure of the brain networks that underlie craving and therefore is less likely to be biased than subjective reports. A neuroimaging approach also allows for greater consistency between animal and human models because of the focus on neurobiology. In this chapter, we provide a summary of the regions that have been reported in the neuroimaging literature of alcohol and drug (nicotine, cocaine, opioids, cannabis) cueelicited craving (Section 6.1), the reported associations between the neural response and subjective report of craving (Section 6.2), some of the variables that have been found to modulate the neural response to cue-elicited craving (Section 6.3), and the effects of intervention on this neural response (Section 6.4).

6.2 Neural Response During Cue-Elicited Craving 6.2.1 Alcohol One of the early studies that examined the neural response to alcohol cues used ethanol odor to elicit fMRI blood oxygenated level dependent (BOLD) response among alcohol dependent individuals and controls [10]. The findings from this study revealed significant increases in subjective craving and significant increases in activation of the cerebellum and amygdala in alcohol dependent subjects but not in controls. In a different study, visual alcohol stimuli induced a significant activation of brain areas such as the fusiform gyrus, basal ganglia, and orbitofrontal gyrus, as compared to abstract control pictures [11]. Two other studies used a combination of taste stimulation (sip of alcohol) with visual stimulation (picture of alcohol stimuli) and found that alcohol stimuli increase activation in the prefrontal cortex (PFC) and anterior thalamus [12], whereas the second study noted activation in the prefrontal cortex and anterior limbic areas (Myrick et al., 2002). The separate contribution of visual stimulation versus gustatory stimulation was not examined. In a follow-up study, alcohol dependent individuals were compared to social drinkers. Consistent with their previous study, the alcohol dependent individuals demonstrated greater BOLD response in the PFC and anterior limbic areas after a sip of alcohol and exposure to visual alcohol cues in the scanner [13]. More recently, investigators utilized a block design alternating between the taste of alcohol and an appetitive control taste (i.e., juice, see [14, 15] in heavy drinking adults. The results of this study suggested that the taste of an alcohol beverage is a very powerful cue, producing significant BOLD response in the striatum, VTA, and PFC, as compared to a control taste (see Figure 6.1).

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Figure 6.1 Greater BOLD response to alcohol cues in reward network [14, 15]; VS/NAc = ventral striatum/nucleus accumbens; ACG/MPFC = anterior cingulate gyrus/medial prefrontal cortex; VMPFC = ventromedial prefrontal cortex; OFC = orbitofrontal cortex; R = right. Reprinted with permission from Neuropsychopharmacology (14).

6.2.2 Nicotine Similar to alcohol, the study of cue reactivity in individuals who smoke cigarettes has received a fair amount of attention in the past decade. Many of the earlier studies focused on determining those brain regions that were more active during cigarette-cue presentation when compared to neutral cues. Studies of craving in smokers have employed mostly visual cues in the form of pictures or videos, although some studies have used the manipulation of an actual cigarette (tactile cue). While both visual cues and tactile cues are expected to be powerful at eliciting craving responses, it could be argued that holding a cigarette would engage a slightly different set of brain regions or could elicit greater craving responses, given that holding a cigarette is more highly predictive of subsequent smoking of the cigarette than seeing a cigarette. Comparing BOLD activation in smokers when holding a cigarette versus control cues (i.e., golf ball, roll of tape), significant differences were found in the anterior cingulate cortex (ACC), posterior cingulate gyrus, middle temporal gyrus, as well as other areas in parietal and occipital cortex [16]. In a second study by Franklin et al. [17] using arterial spin labeling (ASL) along with tactile and video cues (see Figure 6.2), smoking cues elicited greater activation compared to neutral cues in a priori regions of interest such as the amygdala, ventral striatum, hippocampus, insula, OFC, and thalamus [17]. Finally, Brody et al. [19,20] also used video presentation while participants held either a cigarette or a pen in a PET study. When comparing the response to cigarette cues to neutral cues, smokers showed significantly more activity in the ACC and anterior temporal lobe compared to non-smoking participants. Fewer studies have examined how smokers and non-smoking control groups differ on neural measures of cue reactivity. The advantage of this between group design is that

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Figure 6.2 Perfusion imaging paradigm utilizing tactile cigarette cues (taken from [17, 18]). Reprinted with permission from Neuropsychopharmacology (Franklin TR, Wang Z, Wang J, Sciortino N, Harper D, Li Y, et al. Limbic Activation to Cigarette Smoking Cues Independent of Nicotine Withdrawal: A Perfusion fMRI Study. Neuropsychopharmacology. 2007 Nov;32(11):2301–9).

it becomes possible to determine whether the relative observed increases in activity are the result of increased activation to a smoking cue or decreased activity to non-smoking cues in smokers. For example, it may be the case that smokers lose incentive motivation for all other stimuli while motivation increases for smoking related cues, or that smoking motivation remains constant, but motivation for other stimuli actually drops over time. For example, Due and colleagues [21] showed that smokers activated regions within inferior

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and middle frontal gyrus, VTA, thalamus, amygdala, hippocampus, and intraparietal sulcus to a greater degree for smoking cues compared to neutral cues, and these differences were greater than the differences observed for non-smoking control subjects. In all cases, smokers showed roughly equivalent levels of activation as control to the neutral cue, but showed elevated levels of activation during the smoking cues. A second study also examined between group differences in a task designed to measure automatic motor representations in response to smoking related cues [22]. In this study, smokers showed greater activation to smoking cues compared to controls in a motivational network consisting of DLPFC, dorsal and ventral striatum, insula, parahippocampal gyrus, and an action knowledge/tool use network that included superior parietal cortex, premotor cortex, and cerebellum. These results suggest that group differences emerge as a result of increased activation to smoking cues rather than decreases in activation to nonsmoking cues. A more recent study by Zhang et al. [23] reported that viewing pictures elicited greater BOLD response in the DLPFC, dorsal medial prefrontal cortex (DMPFC), ACC, occipital cortex, and insula/operculum in cigarette smokers vs. controls. The authors further performed functionally connectivity analyzes during resting fMRI and found that several correlations between connectivity strength and cue-elicited activity between these regions (e.g., connectivity strength between DLPFC and DMPFC was positively correlated with the cue-elicited activity in DMPFC) suggesting that network connectivity within this network may facilitate the response to smoking cues [23].

6.2.3 Cocaine Neuroimaging studies of cocaine craving have also demonstrated activation in the ACC, posterior cingulate, nucleus accumbens, dorsal striatum, amygdala, OFC, and insula [24, 25], 2007; [26]; Kosten et al., 2001; [27–29]. Because dopamine receptors are ubiquitous in these areas, studies have also directly examined how dopamine receptors react following cocaine cues. For example, using PET, current cocaine users were exposed to a 10-min videotape of cocaine use and a 45-min audiotape of a variety of pleasurable experiences from cocaine use as described in interviews with cocaine abusers [30]. The change in occupancy at D2-like receptors was measured as well as the change of intrasynaptic dopamine (endogenous dopamine release), which was inferred based on the displacement of radiotracer [(11)C]raclopride. The results showed that receptor occupancy by dopamine increased significantly in the putamen of participants who reported cueelicited craving compared to those who did not. Further, the intensity of the self-reported craving was positively correlated with the increase in dopamine receptor occupancy in the putamen. These results provide direct evidence that occupancy of dopamine receptors in human dorsal striatum increased in proportion to subjective craving, presumably because of increased release of intrasynaptic dopamine. Consistent with this study, using fMRI and a modified Stroop task, Goldstein et al. [24] examined neural response to cocaine cues in the mesencephalon, an area dense with dopamine receptors that respond to motivationally salient or conditioned stimuli. During the task, current cocaine users were asked to respond to the color of drug-related versus neutral words. The imaging results showed that the drug words, but not neutral words, activated the mesencephalon in the cocaine users, but not the controls. These studies demonstrate the role of dopamine in this pathway during cue-elicited craving. Several studies have also suggested the involvement of a distributed neural network that integrates emotion with memory in the link between environmental cues and cocaine craving [26, 28, 31–34]. For example, Grant and colleagues [35] conducted a PET study

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that exposed cocaine abusers to a videotape showing cocaine use and cocaine-related paraphernalia, and found that exposure to cocaine-related stimuli induced increases in regional cerebral metabolic rates for glucose in memory-related areas, including the DLPFC, the medial OFC, and the temporal lobe. Further, subjective craving was positively correlated with increases in glucose metabolism in the DLPFC and cerebellum. With regards to areas that regulate emotion, the involvement of the limbic system, including the amygdala, nucleus accumbens, and insula [28], in response to cocaine cues is similar to what has been noted in the animal literature. For example, Bonson et al. [36] reported that the combination of “evocative scripts,” videos and paraphernalia is related to activation of lateral amygdala, among others, and that this activation is positively correlated with self-reported craving. The scripts used in this study described stimuli that were later presented to the participants. During the script, the participants were encouraged to imagine themselves in a setting where they would have been making art (neutral cue) or would have been using cocaine. The script was constructed to vividly describe the emotions and sensations associated with either making art or using cocaine, and it was read in an effusive manner. The script-reading was then followed by simultaneous presentations of videos (e.g., handling of art supplies, handling of cocaine-related objects) and objects placed in front of the video monitor (e.g., paint brush, paper, art pencils, glass crack pipe, mirror, razor blade, straw, etc.). These findings replicated earlier reports of involvement of the limbic system in response to cocaine cues [31]. Taken together, cue-elicited studies of cocaine show that limbic activation is a component of cue-induced cocaine craving. Specificity of these regions to craving for cocaine cues was investigated in a study by Garavan et al. [37]. In this study, cocaine-users and controls underwent an fMRI scan while viewing three separate videos containing either cocaine use (population specificity), outdoor scenes (content specificity) or explicit sexual content (natural cue). The results from this study identified “craving sites” that were not only activated in cocaine users during the cocaine videos, but that also had greater activation compared to the controls, and compared to the nature scenes. The analyzes revealed 13 sites that were largely left-lateralized and included wide areas in the frontal, parietal, and limbic lobes, as well as the insula. Of these putative “craving sites”, only the ACC, inferior parietal lobe, and the caudate showed significantly greater activation during the cocaine videos compared to the sex videos in the cocaine users. This latter finding was interpreted by the authors as the cocaine cues activating mostly similar substrates as naturally evocative stimuli in the cocaine users. These data suggest that cocaine craving is not associated with a dedicated and unique neuroanatomical circuitry; instead, unique to the cocaine user is the ability of learned, drug-related cues to produce brain activation comparable to that seen with nondrug evocative stimuli in healthy comparison subjects. A later study from the same group also attempted to disentangle the processes that underlie attention to cocaine cues [38]. In this study, active cocaine users were administered a working memory task during which participants were required to remember a sequence of five random (high working memory load) or sequential (low working memory load) digits and to recall the list while presented on a randomized background with either neutral pictures from the International Affective Picture System (IAPS) or cocaine-related pictures (e.g., home-made crack cocaine smoking device). The behavioral results showed that cocaine users had significantly poorer attentional control under high working memory loads (i.e., increased response times and reduced recall accuracy) and that this effect was greater when the recall list had the cocaine background compared to the neutral background, suggesting greater interference of drug cues. Further, the presentation of

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the cocaine background was associated with greater activation in occipital cortex, suggesting greater visual processing of the irrelevant stimuli. These results evidence deficits in cognitive control in cocaine users especially during high cognitive demands (i.e., high working memory load), which may be related to relapse.

6.2.4 Opioids Similar alterations in brain activation have also been reported in heroin dependent individuals while performing cue-elicited craving tasks. Different stimuli such as auditory autobiographical scripts [39], videos [40], and pictures [41] have been utilized to investigate cue-elicited craving for heroin. These studies have identified the OFC, inferior frontal, and anterior cingulate gyrus as key sites involved in opiate craving [39, 40]. According to Daglish et al. [42], the brain networks involved in drug craving are “normal” networks found universally, but are hypersensitive to drug related cues. For example, in a randomized single-blind PET study by Sell and colleagues [40], opiate addicts who were current heroin injectors were presented with alternating drug related and neutral video cues followed by either heroin or placebo in order to examine patterns of brain activity in the presence and absence of heroin. The results showed involvement of networks that are involved in different cognitive processes such as reward-motivation, memory and emotion. Specifically, the authors reported that subjective craving correlated strongly with increases in regional blood flow in areas of the reward pathway, such as the inferior frontal gyrus and OFC, as well as areas related to memory, such as the precuneus, and areas sub-serving emotion, such as the insula. Studies of connectivity between these regions have shown two separate connectivity patterns associated with the OFC and anterior cingulate regions [42], such that the ACC region was associated with activity in the left temporal region and the left OFC region activity correlated with activity in the right OFC, left parietal, and posterior insular regions. The patterns of functional connectivity reflect the ability of drug-related stimuli to activate attentional and memory circuits to a greater degree than non-drug-related stimuli. The authors argue that neural circuits of dependence and craving are not specific “craving” or “addiction” brain regions but are “normal” circuits activated to a greater degree. To further investigate the relationship between these areas of activation, a recent study examined factorial interactions between the cues and the subjects in heroin users [43]. Using a repeated two-way ANOVA for factorial analyzes, the authors showed interactions between video (neutral, heroin-related) cues and subjects (heroin-dependent, controls) in the VTA, amygdala, fusiform cortex, precuneus, superior frontal, dorsal lateral prefrontal, and orbitofrontal cortices. They reported that the neural response patterns in the prefrontal systems were dynamic, with decreased response to neutral-cues and increased response to heroin cues. Moreover, the areas within the prefrontal cortex were significantly inter-correlated during response to heroin cues. The authors posit that the dynamic response patterns in the prefrontal cortex system may underlie the reported deficits in executive control in heroin-dependent subjects.

6.2.5 Cannabis As described above, there are a growing number of imaging studies examining cue effects in individuals who abuse alcohol, nicotine, cocaine, and heroin. In spite of this, functional

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Figure 6.3 Tactile presentations of a used marijuana pipe elicited greater BOLD response to marijuana cues compared to a neutral object in the orbitofrontal cortex that are associated with marijuana-related problems (taken from [45]). Taken from Proceedings of the National Academy of Sciences (45).

imaging studies on the effects of marijuana cues on brain function have been relatively sparse. Nonetheless, amid inconsistencies in imaging findings of altered brain structure as a result of marijuana use, existing functional imaging studies suggest that marijuana use is associated with functional changes in the brain (for review see [44]). In the only neuroimaging study of cue-elicited craving in marijuana, Filbey and colleagues [45] used fMRI to examine the neural response to marijuana tactile cues in regular marijuana users. In this study, exposure to a used marijuana pipe elicited greater neural response in the OFC, anterior cingulate, amygdala and insula [45]. This pattern of activation was also found to be significantly positively correlated with drug-related problems as measured by the Marijuana Problem Scale (MPS) [45] (see Figure 6.3).

6.2.6 Summary Any lack of consistency between imaging studies of cue-elicited craving may be the result of the cue modality used to elicit a craving response. For instance, it would be expected that tactile cues may engage sensorimotor regions to a greater degree than visual cues, and thus pose as a stronger elicitor of craving responses. In addition to cue modality, it is possible that within the visual cue domain the stimuli used can have substantial effects on observed activation patterns. For example, [46] investigated the different neural responses to pictures that represented different time points within a cigarette smoking sequence. Based on previous evidence that images representative of initiation of smoking elicit higher levels of craving than images representative of end stages of smoking, they examined the neural responses to these different image types in nonsmokers, nondeprived smokers, and deprived smokers. For pictures representing the beginning of the smoking sequence, they found increased activation in regions such as the ACC, ventral striatum, OFC, DLPFC, insula, and VTA. At the end of the sequence they found activations in OFC, DLPFC, and left insula and deactivations in ventral striatum, ACC, right DLPFC, and right insula. This pattern of activations was similar across deprived

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and nondeprived smokers, although deprived smokers did show greater activation in DLPFC during stimuli that represented the beginning of the smoking initiation sequence, suggesting a role of DLPFC in drug seeking (i.e., mobilizing resources to obtain a drug goal). Thus, some of the inconsistencies found in the visual cue presentation studies may have resulted from the use of stimuli that were biased towards scenes depicting initiation or termination of smoking. Future studies should address potential experimental design confounds, and this may increase the replicability of findings. To summarize, neuroimaging investigations of craving in the addictions have utilized a variety of methods for inducing craving (e.g., picture stimuli, videos, odors, taste, tactile) and underscored the importance of mesolimbic and prefrontal structures with respect to cue-elicited craving. For example, dorsal and lateral portions of the frontal cortex (i.e., DLPFC), including middle and superior frontal gyri have been reported across several studies [21,47–50]. The DLPFC has been implicated in studies of cognitive control and goal directed behavior [51, 52], and its presence in cue reactivity studies may be the result of seeking out drug rewards or planning future use. The anterior cingulate cortex has also consistently appeared across studies [47–50]; the anterior cingulate has been implicated in reward and motivational processes predominantly as it relates to conflict monitoring and resolution and integrates cognitive and emotional information in making decisions [53–57]. Regions within the temporal cortex, such as the parahippocampal gyrus and medial and superior temporal gyrus, also commonly show greater activation during drug cues compared to control cues [21, 47–49, 58]. Although these regions are not usually implicated in reward processing, they play a significant role in object recognition and memory processes [59], suggesting that drug cues have gained special status through habitual use, and the processing of these stimuli lead to increased spreading of activation to drug-related concepts. Finally, several studies have found evidence of increased activation in the dorsal visual processing stream [60], a network that is involved in spatial attention that connects to regions involved in sensorimotor integration [61]. These areas include the precuneus, inferior parietal lobe, as well as the superior temporal gyrus. Other regions that emerge less consistently include the insula [50], posterior cingulate cortex/retrospenial area [47, 49, 58], and thalamus [21, 49, 58], all of which are embedded in larger networks of reward processing, incentive motivation, and goal directed behavior (see Figure 6.8).

6.3 Associations between Neural and Subjective Response During Cue-Elicited Craving While each of the above studies investigated general appetitive responses to cues, a different analysis strategy has focused on the relationship between cue reactivity related activation and measures of subjective craving. By examining the relationship between subjective measures of craving and brain activity, it may be possible to delineate those areas that are involved in the conscious experience of craving. Further, these brain regions may ultimately lead to better predictions of treatment outcomes if they contain less measurement error than traditional questionnaire based methods. Using variants of the tasks described above, studies have begun to present correlations between activity and subjective craving. One of the most frequently reported correlations between brain activation and subjective craving is found in the DLPFC [17, 34, 36, 49, 62]. This finding is intriguing as DLPFC has been implicated in many studies of executive control, working

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memory and goal directed behavior [35, 64], which suggests that subjective craving may result from the motivation system usurping brain regions (including the ACC) involved in control to obtain a drug. In addition to DLPFC, some evidence of positive correlations between activity and subjective craving has suggested a role for OFC and insula in craving [36]; Daglish, 2001; [13, 19, 65, 66] as well as brainstem, amygdala, and hippocampus [40, 63]. However, McClernon et al. [50] found negative relationships with craving and activation in ventral striatum, occipital lobe, and globus pallidus in the context of a multiple regression controlling for dependence severity, negative affect, and sex (see Figure 6.4). The lack of consistency may be an artifact of experimental design. The exact method for evaluating the relationship between subjective craving measures has varied across studies in terms of the time at which craving was assessed (in-scanner ratings, pre-scan craving, post-scan, or post-scan–pre-scan), as well as the measure that was used to assess subjective craving (e.g., Shifmann-Jarvik, QSU, single item). However, there is still promise that with larger samples and specific tests of task differences, reliable correlates of craving will be confirmed. In addition, it is important to note that the subjective experience of craving would not be expected to covary with alternating stimuli in the scanner because craving is more persistent and long-lasting (i.e., continues despite the removal of the cue).

6.4 Modulators of Neural Response During Cue-Elicited Craving Emerging studies are extending the craving literature by examining factors (e.g., acute intoxication, family history, genotype) that may moderate neuronal responses to cues. For example, in a study by Bragulat et al. [68] it was found that in a group of 10 hazardous drinkers, a moderate dose of alcohol enhanced the effect of olfactory cues in the ventral striatum, medial frontal gyrus, OFC and posterior cingulate, suggesting that a moderate dose of alcohol administration may exaggerate the effect of alcohol cues [68]. In another report, heavy drinkers with and without a family history were compared after exposure to olfactory cues and after exposure to intravenous dose of alcohol. The results suggested that family history influence how the brain responds to alcohol cues (Karaken et al., 2010). Gender has also been reported to modulate response to cues. For example, in a PET study by Kilts et al. [29], cocaine and neutral script-guided imagery were presented to a group of matched cocaine-dependent males and females. The results showed that exposure to cocaine cues in women was associated with less activation of the amygdala, insula, OFC, and ventral cingulate cortex. Additionally, there was greater activation of the central sulcus and widely distributed frontal cortical areas in women compared to men. These findings suggest the presence of gender differences in the neural response to cocaine cues. The authors suggest that this difference may reflect behavioral differences between genders in conditioned associations to cocaine use or in volitional regulation. Similarly, withdrawal is assumed to influence craving and thus may be useful in better delineating those brain regions that are involved in motivational processes underlying drug seeking than correlational studies. For example, McClernon et al. [69] examined the effects of withdrawal on cue reactivity by comparing individuals after smoking ad lib until the scan session or after abstaining for 24 hours prior to the scanning session. As expected, withdrawal resulted in significantly higher self-reported withdrawal symptoms as well as craving compared to the ad lib session. Comparison of the withdrawal and ad lib session data for the smoking picture > control picture contrast revealed significant differences in DLPFC, posterior cingulate cortex, occipital cortex, postcentral gyrus, and

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caudate, such that greater response to the smoking cues compared to the control cues was observed only during the 24-hour withdrawal session and not the ad lib session. McBride et al. [49] found that a 12-hour abstinence manipulation showed differences in ACC, PCC, precuneus, middle temporal gyrus (MTG), ventral pallidum, with the abstinent smokers showing greater brain response than non-abstinent smokers, again suggesting the potentiating effects of withdrawal on cue reactivity. Finally, David et al. [70] investigated the effects of abstinence on activation to infrequently presented smoking cues in eight female smokers and found between session differences in ventral striatum (VS), although the direction of the effect was such that abstinence showed less VS activity overall, potentially the result of increased dopaminergic activity caused by the relative increase in nicotine levels in the ad lib sessions. Overall, the evidence for the effects of withdrawal on neural response are equivocal, although the evidence leans towards enhanced neural response to drug related cues when undergoing withdrawal compared to ad lib drug consumption. Expectancy or drug availability within the context of the study is another significant modulator of craving. Many craving studies do not make substances available to subjects during or immediately after study participation, potentially limiting the effects of craving [70, 71]. Further, it may be the case that the relative inability of craving measures to predict future relapse behavior may be the result of the forced disconnect between cue presentation and subsequent use in the context of a laboratory setting; outside the laboratory, when cues are presented to participants, the potential to consume may be much higher. For instance, Yoon et al. [73] reported in a group of outpatient alcohol dependent patients that self-reported craving was directly associated with indices of morbidity to alcohol dependence [73]. To better understand how such availability effects may influence brain activation patterns during the presentation of cues, two smoking studies have explicitly manipulated whether participants would be able to smoke immediately after the fMRI session. Both studies used between-subjects designs in which half of the participants were instructed that they would be allowed to smoke during a break immediately after the cue-reactivity portion of the scan, and the other half were told that they would have to wait several hours to smoke after the scan [16, 49]. When comparing the two groups on a smoking vs. neutral cue contrast, both studies found greater activation in DLPFC, ventromedial PFC (VMPFC), and cuneus. McBride et al. [49] also found that the group expecting to be able to smoke immediately after the scan activated ACC (Figure 6.5). Interestingly, in the Wilson et al. study, the results largely showed that responses to the neutral cue were greater than for the smoking cue for the group that had the cigarette available, and no difference in the group that did not have the ability to smoke. The only regions where signal increased in the availability group were VMPFC and precentral gyrus. In contrast to Wilson and colleagues, McBride found increases in the anterior cingulate gyrus in the smoking > control cue contrast for the group that expected to be able to smoke immediately after the scanning session, whereas the group who did not expect to smoke after the scan showed no such effect. The conflicting results between the two in terms of the nature of the effects observed likely resulted from design differences. The design used in McBride consisted of a longer cue exposure period (three 2-minute long presentations of cigarette cues in McBride vs. one 74-second long cue presentation in Wilson), potentially leading to increased power to detect differences in BOLD response. From both available studies investigating availability effects, DLPFC emerged as an important region that may underlie craving responses. Specifically, these studies suggest that DLPFC plays an important role in potentially planning future use in

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that it integrates motivational states and drug availability, facilitating plans for future drug consumption. Emotional regulation also appears to play a role in the BOLD response to cues. In a recent study by Zijlstra et al. [41], the relationship between the neural response to heroin cues and anhedonia (hyposensitivity to pleasant stimuli) was examined [41]. During fMRI, the authors presented detoxified opioid-dependent participants with pictures of neutral and pleasant images from the IAPS and heroin-related pictures showing heroin preparatory objects or the actual act of heroin preparation. The Snaith-Hamilton Pleasure Scale (SHAPS) was used to measure anhedonia. With regards to anhedonia, there was no observable difference between the opioid-dependent participants and controls (i.e., both groups showed a negative correlation between scores on SHAPS and activation in medial prefrontal gyrus). These findings suggest that the response to heroin cues in opioid dependent subjects is specific to the drug and is not due to abnormalities related to emotion. More recently, investigators have begun to emphasize the integration of neuroimaging and genetic approaches to the study of craving (e.g., [28, 30, 44]). As described above, Filbey and colleagues utilized an approach that involves exposing individuals to the taste of alcohol versus a novel control taste (litchi juice) in the scanner. In a follow up study, Filbey et al. [14, 15] examined the effects of genetic variations in the mu-opioid receptor-1 (OPRM1) and dopamine D4 receptor (DRD4) genes on alcohol cue-elicited BOLD response. The results indicated that the “G” allele of the OPRM1 a118g polymorphism and the 7 repeat allele of the DRD4 VNTR polymorphism were associated with greater BOLD response to alcohol cues [14, 15]. In a related study, BOLD response to alcohol cues, as well as acute behavioral responses to alcohol and treatment outcomes, were modulated by a polymorphism in the cannabis receptor gene (CNR1), such that carriers of the C allele had greater subjective craving for alcohol and had more positive outcomes after pharmacotherapy [74]. Similarly, the CNR1, in addition to the fatty acid amide hydrolase (FAAH) gene, also appear to influence the neural mechanisms that underlie cue-elicited craving for marijuana [75]. In this study, between-group comparisons were carried out between CNR1 A/G and CNRI1 A/A genotypes and between FAAH C/C and FAAH A/C individuals. CNR1 comparisons showed that the CNR1 A/G carriers had significantly greater activity in the expected reward areas of the brain such as the OFC, inferior frontal gyrus (IFG), insula, and caudate during the marijuana cue compared to the A/A carriers. Additionally, carriers of the FAAH C/C genotype had greater activation in the OFC, IFG, insula, nucleus accumbens, compared to the FAAH A/A and A/C genotype group. These findings provide evidence of aberrant patterns of neural response in marijuana users with CNR1 A/G or FAAH C/C genotypes in response to marijuana cues. This study also showed an additive effect of these alleles, such that the greater number of risk alleles (i.e., CNR1 G, FAAH C) the greater the neural response to cues in reward areas such as the striatum. In summary, there are several variables that influence the BOLD response to craving, including acute intoxication, withdrawal, expectancy and genetic factors. These variables contribute to the inconsistencies in the craving literature. One potential concern, especially relevant for earlier studies, is the use of underpowered samples. For example, some of the samples in the investigations noted above consisted of 10 or fewer subjects per group (e.g., six abstinent alcohol-dependent individuals comprised the sample in Wrase et al., [11]. Small sample sizes mean that subject characteristics may vary considerably across studies, leading to different findings. Clearly, there are individual differences that

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moderate these neurobiological mechanisms that cannot be examined or controlled in studies with small sample sizes. Studies with larger sample sizes are necessary to account for individual differences that may influence the BOLD response after exposure to drugs. Greater sample sizes are critical for producing more consistent effects and enabling one to make more generalized inferences.

6.5 Effects of Intervention on the Neural Response During Cue-Elicited Craving As an illustration of the utility of neuroimaging of cue-elicited craving in terms of providing a neurobiological target for screening medications, a recent study suggested that naltrexone and ondansetron reduced cue-elicited activation of ventral striatum, consistent with their putative mechanism of action [76] (Figure 6.6). In this relatively large study, 90 heavy drinkers were scanned 7 days after being randomized to naltrexone, ondansetron, or placebo. Naltrexone treatment, with or without ondansetron, was associated with a decrease in BOLD response in the ventral striatum during exposure to alcohol cues. In addition, the combination of naltrexone and ondansetron decreased craving for alcohol. Other studies have used a neuroimaging approach to examine the effect of medications on the mechanisms that underlie craving. For example, visual alcohol cues were compared with neutral cues in 10 abstinent alcohol-dependent individuals and controls before and after amisulpride treatment. Amisulpride treatment reduces cue elicited BOLD response [77]. Using a different approach, a PET study was used to test the theory that dopamine receptor changes in the ventral striatum may alter error detection and enhance reward sensitivity. In this study, lower D2 receptor availability during baseline in the ventral striatum measured by PET was associated with greater craving during visual stimuli and BOLD response in medial prefrontal cortex and anterior cingulate in detoxified alcohol-dependent males [78]. All of these studies are consistent with the notion that changes in dopamine receptor function in striatum and PFC play an important role in the mechanisms that underlie craving and represent an important target for medication development.

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Figure 6.6 Effects of Naltrexone on cue-elicited craving for alcohol vs. control in social drinkers and alcohol-dependent patients (Taken from [76]). Reprinted with permission from Archives of General Psychiatry (75).

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Similar findings have also been reported in tobacco dependence. For example, in a study of the effects of nicotine replacement therapy (NRT), Janes et al. [47] found that smokers showed activation in the typical regions associated with craving (i.e., ACC, posterior cingulate, insula, DLPFC, dorsomedial PFC, inferior parietal lobe, and middle temporal gyrus) both before and after 2 months of NRT. Interestingly, activation increased in the smoking > neutral contrast post-treatment compared to pre-treatment in DLPFC, ACC, posterior cingulate, superior temporal gyrus, inferior parietal lobe (IPL) and caudate. The authors suggest that these mechanisms may contribute to the maintenance and relapse vulnerability in smokers. On the other hand, in a separate study, McClernon et al. [79] only found changes in amygdala activation over the course of three scanning sessions (baseline, 2–4 weeks post-treatment, 2–4 weeks post-abstinence), such that activation to control cues increased while activation to smoking cues showed a general decrease in activation. One significant difference between the two studies is that McClernon also had participants smoke reduced nicotine content cigarettes, which likely led to extinction of the association between nicotine induced reward and smoking related cues and thus decreased the learned reward value of smoking cues. The lack of consistency between the two NRT studies is surprising given the similarity in samples (all female participants in the Janes et al. study versus 14/16 females in the McClernon et al. study) and cue task design (both used photographs of smoking-related stimuli). However, it may be possible that similar widespread effects observed in Janes et al. would also appear in the McClernon study if pairwise comparisons were presented between sessions 1 and 2 or 2 and 3. Similar unexpected findings have also been reported in methadone maintenance patients. Specifically, Langleben et al. [81] reported that the administration of a daily methadone dose acutely reduced the neural response to cues in the insula, amygdala, and hippocampal complex, but not the orbitofrontal and ventral anterior cingulate cortices. The authors suggest that certain areas within the medial prefrontal cortex and the extended limbic system in methadone maintenance patients do not respond to treatment and continue to be sensitive to the effects of heroin cues. Thus, regions not usually implicated in habit formation are evidenced to play a significant role in treatment. Similar BOLD changes as a result of treatment in these cognitive areas have also been reported in tobacco and cocaine dependence via cue-elicited craving paradigms. For instance, Brody et al. [58] scanned smokers while resisting craving urges during the viewing of smoking related videos. They found that resisting craving engaged areas such as dorsal ACC, dorsomedial PFC, precuneus, and posterior cingulate, when compared to an active craving condition. These regions are typically involved in conflict detection and cognitive control of attentional resources, suggesting that the resist condition enhanced conflict that potentially led to an increased need to implement control. A similar relationship between brain activity evoked by drug cues and inhibitory control (as indicated by relapse) has been shown by Kosten et al. [82]. They studied 17 recovering cocaine-dependent patients while viewing images of cocaine-related cues. Patients then entered a 10-week outpatient clinical trial where urine was assessed three times weekly. Worse treatment response correlated with greater BOLD activation in posterior cingulate cortex, as well as left precentral and superior temporal cortices, and right middle temporal and lingual cortices. The left posterior cingulate cortex, which was also reported in the earlier mentioned smoking studies (i.e., NRT and craving resistance), was the most sensitive to relapse. PCC activation distinguished eight non-relapsers from nine relapsers. Importantly, selfreports of craving during fMRI did not differ between non-relapsers and relapsers and did not correlate with treatment effectiveness scores. These results suggest that alteration of function in the posterior cingulate cortex and other brain regions is associated with an

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increased potential for relapse, which is not dependent on the conscious perception of “craving.” Thus, cue-elicited craving tasks combined with imaging may provide important and useful information for the study of incentive salience as it relates to understanding treatment response, which cannot be obtained using introspective or other behavioral measures alone. The use of neuroimaging in treatment studies provide not only clues as to the mechanisms that underlie therapeutic effects, but also may be able to predict those individuals who are likely to relapse after completing a treatment program. While the initial studies investigating the effects of treatment on brain activation patterns have shown little replicability, larger sample sizes that include a non-treatment control group will likely provide a more comprehensive understanding of the mechanisms underlying change during treatment.

6.6 Summary and Integration of Findings Many of the reports cited above have been integrated in previous reviews (e.g., [81, 82]; Koob and Volkow, 2010) (see Figure 6.7 for Koob and Volkow’s model). The model presented in Figure 6.7 depicts the structures that play a significant role in cue elicited craving responses (ventral tegmental area, dorsal striatum, anterior cingulate cortex,

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Figure 6.7 Illustration of brain regions involved in addiction from Koob and Volkow (Neuropsychopharmacology Reviews, 2010). In this model, drug cues act on both dorsal (DS) and ventral (VS) striatum and amygdala (AMG) via the hippocampus (HIPPO) and basolateral amygdala (BNST). Reprinted with permission from Neuropsychopharmacology (96).

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OFC, and insula). While the findings discussed in the current review largely agree with this model, some inconsistencies have emerged such as an inability to observe all regions within the network in a given study. However, the lack of consistency may be the result of individual differences or contextual factors such as withdrawal and their impacts on cue-elicited responses, factors which the current review has addressed. For example, as reviewed above, relative periods of abstinence appear to influence neural responsivity throughout these motivational networks. In addition, polymorphisms in genes related to reward processing influence neural responses to drug related cues, even in the absence of between genotype group differences in drug use severity. These results suggest that a variety of potentially confounding factors contribute to relative levels of response in studies of craving, suggesting that future studies will require rigorous control over these factors in order to confirm animal models of cue reactivity and development of new theoretical positions regarding the mechanisms of cue elicited craving. Other reviews have focused on neuroimaging approaches related to cue-elicited craving in the context of translational models that attempt to link genetic variation with variation in neuroimaging measures and variation in clinical outcomes (e.g., [83,84]. The model presented in Figure 6.8 depicts the structures that play a significant role in the incentive motivation network (ventral tegmental area, caudate, putamen, anterior cingulate cortex, and insula), which in turn is thought to impact the experience of craving and drug use behavior. As noted above, previous studies have repeatedly implicated specific structures (e.g., ventral tegmental area, ventral striatum/nucleus accumbens, and perigenual anterior cingulate cortex) in the motivation to use drugs and the attribution of

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Figure 6.8 Disequilibrium model of addiction. SNP = single nucleotide polymorphism; G = gene; OFC = orbitofrontal cortex; IFG = inferior frontal gyrus; dlPFC = dorsolateral prefrontal cortex; mPFC = medial prefrontal cortex; Ins = insula; VTA = ventral tegmental area; Put = putamen; Caud = caudate; ACC = anterior cingulate cortex.

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References

incentive salience to drug cues, in animals and humans (see London, 2000; [30, 81]. The model also includes structures that may play an important role in the reflective control network. The control network (top of Figure 6.8) consists of regions in the PFC, including the orbitofrontal cortex (OFC), dorsolateral prefrontal cortex (DLPFC), medial prefrontal cortex (mPFC), and inferior frontal gyrus (IFG). The cognitive neuroscience literature has implicated these areas in reflection and control, as well as the evaluation of the magnitude of reward and craving for alcohol and drugs (e.g., [30, 85–87]. Despite the limitations of recent neuroimaging approaches to the study of craving and the models that are derived from those approaches, the study of craving has clearly advanced as a result of these efforts. In fact, these approaches are now well positioned to lead to new insights into the treatment of addiction. Increasingly, these mechanisms are the target of treatment development efforts. For example, medications like naltrexone and topiramate clearly target the incentive and reward value of alcohol and craving [88,89]. Medications, such as ondansetron [92], olanzapine [3], and aripiprazole [93], that are in development or have been previously tested also target the incentive network.

6.7 Conclusions In conclusion, one of the major advantages of using a neuroimaging approach to the study of craving in humans is the ability to assess some of the same mechanisms that have emerged in the basic neuroscience literature over the last two decades. Another advantage is that neuroimaging measures are not subjective and therefore not prone to demand bias and other nuisance factors. These aspects resolve two of the main concerns voiced in previous reviews of the craving literature (see [95]). Despite differences in the type of cue, the type of drug, or other design details, there is now a reasonable convergence and overlap in the findings as they pertain to the biological mechanisms that underlie craving (also see [83]). To date, however, meta-analyzes have focused on single regions of interest [16], which is reflective of the complicated and highly variable methodologies utilized thus far in this topic. Thus, important questions such as whether these networks are consistent across drugs and what kind of paradigm is best to assess craving remain unanswered. Future studies should consider utilizing quantitative metaanalytic techniques such as activation likelihood estimation (ALE) to better understand these issues. As the neuroimaging approach to cue-elicited craving matures, it will increasingly be used to examine medications or psychosocial treatments that target the neurobiology that underlies craving. In so doing, this approach may lead to new and more effective treatments for addiction, as well as identifying genetic variations that may predict the effect of these treatments.

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3. Hutchison, K.E., Ray, L., Sandman, E., Rutter, M.C., Peters, A., Davidson D. et al. (2006) The effect of olanzapine on craving and alcohol consumption. Neuropsychopharmacology., 31(6), 1310–7. 4. Johnson, B.A., Ait-Daoud, N., Bowden, C.L. et al. (2003) Oral topiramate for treatment of alcohol dependence: a randomised controlled trial. Lancet, 361(9370), 1677–85. 5. Niaura, R.S., Rohsenow, D.J., Binkoff, J.A. et al. (1988) Relevance of cue reactivity to understanding alcohol and smoking relapse. J Abnorm Psychol., 97(2), 133–52. 6. Drummond, D.C. (2000) What does cue-reactivity have to offer clinical research? Addiction, 95(Suppl 2), S129–S144. 7. Berridge, K.C. and Robinson, T.E. (1998) What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? Brain Res Brain Res Rev., 28(3), 309–69. 8. Robinson, T.E. and Berridge, K.C. (1993) The neural basis of drug craving: an incentivesensitization theory of addiction. Brain Res Brain Res Rev., 18(3), 247–91. 9. Wise, R.A. (1988) The neurobiology of craving: implications for the understanding and treatment of addiction. J Abnorm Psychol., 97(2), 118–32. 10. Schneider, F., Habel, U., Wagner, M. et al. (2001) Subcortical correlates of craving in recently abstinent alcoholic patients. American Journal of Psychiatry, 158(7), 1075–83. 11. Wrase, J., Grusser, S.M., Klein, S. et al. (2002) Development of alcohol-associated cues and cue-induced brain activation in alcoholics. Eur Psychiatry, 17(5), 287–91. 12. George, M.S., Anton, R.F., Bloomer, C. et al. (2001) Activation of prefrontal cortex and anterior thalamus in alcoholic subjects on exposure to alcohol-specific cues. Arch Gen Psychiatry, 58(4), 345–52. 13. Myrick, H., Anton, R.F., Li, X. et al. (2004) Differential brain activity in alcoholics and social drinkers to alcohol cues: relationship to craving. Neuropsychopharmacology, 29(2), 393– 402. 14. Filbey, F.M., Claus, E., Audette, A.R. et al. (2008) Exposure to the taste of alcohol elicits activation of the mesocorticolimbic neurocircuitry. Neuropsychopharmacology, 33(6), 1391– 401. 15. Filbey, F.M., Ray, L., Smolen, A. et al. (2008) Differential neural response to alcohol priming and alcohol taste cues is associated with DRD4 VNTR and OPRM1 genotypes. Alcohol Clin Exp Res., 32(7), 1113–23. 16. Wilson, S.J., Sayette, M.A., Delgado, M.R., and Fiez, J.A. (2005) Instructed smoking expectancy modulates cue-elicited neural activity: a preliminary study. Nicotine Tob Res., 7(4), 637–45. 17. Franklin, T.R., Wang, Z., Wang, J. et al. (2007b) Limbic activation to cigarette smoking cues independent of nicotine withdrawal: a perfusion fMRI study. Neuropsychopharmacology, 32(11), 2301–9. 18. Brody, A.L., Mandelkern, M.A., London, E.D. et al. (2002b) Brain metabolic changes during cigarette craving. Archives of General Psychiatry, 59(12), 1162–72. 19. Brody, A.L., Mandelkern, M.A., London, E.D. et al. (2002b) Brain metabolic changes during cigarette craving. Archives of General Psychiatry, 59(12), 1162–72. 20. Brody, A.L., Mandelkern, M.A., Olmstead, R.E. et al. (2007) Neural substrates of resisting craving during cigarette cue exposure. Biol Psychiatry, 62(6), 642–51. 21. Due, D.L., Huettel, S.A., Hall, W.G., and Rubin, D.C. (2002) Activation in mesolimbic and visuospatial neural circuits elicited by smoking cues: evidence from functional magnetic resonance imaging. Am J Psychiatry, 159(6), 954–60. 22. Yalachkov, Y., Kaiser, J., and Naumer, M.J. (2009) Brain regions related to tool use and action knowledge reflect nicotine dependence. J Neurosci., 29(15), 4922–9. 23. Zhang, X., Salmeron, B.J., Ross, T.J. et al. (2011) Factors underlying prefrontal and insula structural alterations in smokers. Neuroimage, 54(1), 42–48. 24. Goldstein, R.Z., Alia-Klein, N., Tomasi, D. et al. (2009) Anterior cingulate cortex hypoactivations to an emotionally salient task in cocaine addiction. Proc Natl Acad Sci USA, 106(23), 9453–8.

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25. Volkow, N.D., Wang, G.J., Telang, F. et al. (2008) Dopamine increases in striatum do not elicit craving in cocaine abusers unless they are coupled with cocaine cues. Neuroimage, 39(3), 1266–73. 26. Wexler, B.E., Gottschalk, C.H., Fulbright, R.K. et al. (2001) Functional magnetic resonance imaging of cocaine craving. Am J Psychiatry, 158(1), 86–95. 27. Childress, A.R., Franklin, T., Listerud, J. et al. (2002) Neuroimaging of cocaine craving states: cessation, stimulant administration and drug cue paradigms. In Neuropsychopharmacology: a fifth generation of progress (eds K.L. Davis, D. Charney, J.T. Coyle, & C. Nemeroft), Lippincott Williams & Wilkins, Philadelphia. 28. Kilts, C.D., Schweitzer, J.B., Quinn, C.K. et al. (2001) Neural activity related to drug craving in cocaine addiction. Arch Gen Psychiatry, 58(4), 334–41. 29. Kilts, C.D., Gross, R.E., Ely, T.D., and Drexler, K.P. (2004) The neural correlates of cue-induced craving in cocaine-dependent women. Am J Psychiatry, 161(2), 233–41. 30. Wong, D.F., Kuwabara, H., Schretlen, D.J. et al. (2006) Increased occupancy of dopamine receptors in human striatum during cue-elicited cocaine craving. Neuropsychopharmacology, 31(12), 2716–27. 31. Childress, A.R., Mozley, P.D., McElgin, W. et al. (1999) Limbic activation during cue-induced cocaine craving. Am J Psychiatry, 156(1), 11–8. 32. Volkow, N.D., Wang, G.J., Fowler, J.S. et al. (1999) Association of methylphenidate-induced craving with changes in right striato-orbitofrontal metabolism in cocaine abusers: implications in addiction. Am J Psychiatry, 156(1), 19–26. 33. Wang, G.J., Volkow, N.D., Fowler, J.S. et al. (1999) Regional brain metabolic activation during craving elicited by recall of previous drug experiences. Life Sci., 64(9), 775–84. 34. Maas, L.C., Lukas, S.E., Kaufman, M.J. et al. (1998) Functional magnetic resonance imaging of human brain activation during cue-induced cocaine craving. Am J Psychiatry, 155(1), 124–6. 35. Grant, S., London, E.D., Newlin, D.B. et al. (1996) Activation of memory circuits during cueelicited cocaine craving. Proc Natl Acad Sci USA, 93(21), 12040–5. 36. Bonson, K.R., Grant, S.J., Contoreggi, C.S. et al. (2002) Neural systems and cue-induced cocaine craving. Neuropsychopharmacology., 26(3), 376–86. 37. Garavan, H., Pankiewicz, J., Bloom, A. et al. (2000) Cue-induced cocaine craving: neuroanatomical specificity for drug users and drug stimuli. Am J Psychiatry, 157(11), 1789–98. 38. Hester, R. and Garavan, H. (2009) Neural mechanisms underlying drug-related cue distraction in active cocaine users. Pharmacol Biochem Behav., 93(3), 270–7. 39. Daglish, M.R., Weinstein, A., Malizia, A.L. et al. (2001) Changes in regional cerebral blood flow elicited by craving memories in abstinent opiate-dependent subjects. Am J Psychiatry., 158(10), 1680–6. 40. Sell, L.A., Morris, J.S., Bearn, J. et al. (2000) Neural responses associated with cue evoked emotional states and heroin in opiate addicts. Drug Alcohol Depend., 60(2), 207–16. 41. Zijlstra, F., Veltman, D.J., Booij, J. et al. (2009) Neurobiological substrates of cue-elicited craving and anhedonia in recently abstinent opioid-dependent males. Drug Alcohol Depend., 99(1–3), 183–92. 42. Daglish, M.R., Weinstein, A., Malizia, A.L. et al. (2003) Functional connectivity analysis of the neural circuits of opiate craving: “more” rather than “different”? Neuroimage, 20(4), 1964–70. 43. Yang, Z., Xie, J., Shao, Y.C. et al. (2009) Dynamic neural responses to cue-reactivity paradigms in heroin-dependent users: an fMRI study. Hum Brain Mapp., 30(3), 766–75. 44. Quickfall, J. and Crockford, D. (2006) Brain neuroimaging in cannabis use: a review. J Neuropsychiatry Clin Neurosci., 18(3), 318–32. 45. Filbey, F.M., Schacht, J.P., Myers, U.S. et al. (2009b) Marijuana craving in the brain. Proc Natl Acad Sci USA, 106(31), 13016–21. 46. Stippekohl, B., Winkler, M., Mucha, R.F. et al. (2010) Neural responses to BEGIN- and ENDstimuli of the smoking ritual in nonsmokers, nondeprived smokers, and deprived smokers. Neuropsychopharmacology. 35(5), 1209–25.

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47. Janes, A.C., Frederick, B., Richardt, S. et al. (2009) Brain fMRI reactivity to smoking-related images before and during extended smoking abstinence. Exp Clin Psychopharmacol., 17(6), 365–73. 48. Lee, J.-H., Lim, Y., Wiederhold, B.K., and Graham, S.J. (2005) A functional magnetic resonance imaging (fMRI) study of cue-induced smoking craving in virtual environments. Applied Psychophysiology and Biofeedback, 30(3), 195–204. 49. McBride, D., Barrett, S.P., Kelly, J.T. et al. (2006) Effects of expectancy and abstinence on the neural response to smoking cues in cigarette smokers: an fMRI study. Neuropsychopharmacology, 31(12), 2728–38. 50. McClernon, F.J., Kozink, R.V., and Rose, J.E. (2008) Individual differences in nicotine dependence, withdrawal symptoms, and sex predict transient fMRI-BOLD responses to smoking cues. Neuropsychopharmacology, 33(9), 2148–57. 51. Miller, E.K. and Cohen, J.D. (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202. 52. Wallis, J.D. and Miller, E.K. (2003) Neuronal activity in primate dorsolateral and orbital prefrontal cortex during performance of a reward preference task. The European Journal of Neuroscience, 18(7), 2069–81. 53. Allman, J.M., Hakeem, A., Erwin, J.M. et al. (2001) The anterior cingulate cortex. The evolution of an interface between emotion and cognition. Annals of the New York Academy of Sciences, 935, 107–17. 54. Bush, G., Luu, P., and Posner, M.I. (2000) Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences|Trends in Cognitive Science, 4(6), 215–22. 55. Bush, G., Vogt, B.A., Holmes, J. et al. (2002) Dorsal anterior cingulate cortex: A role in rewardbased decision making. Proceedings of the National Academy of Sciences of the United States of America, 99(1), 523–8. 56. Peoples, L.L. (2002) Will, anterior cingulate cortex, and addiction. Science, 296(5573), 1623–4. 57. Vogt, B.A., Finch, D.M., and Olson, C.R. (1992) Functional heterogeneity in cingulate cortex: the anterior executive and posterior evaluative regions. Cerebral Cortex|Cerebral Cortex (Cary), 2(6), 435–43. 58. Brody, A.L., Mandelkern, M.A., Olmstead, R.E. et al. (2007) Neural substrates of resisting craving during cigarette cue exposure. Biol Psychiatry, 62(6), 642–51. 59. Souza, M.J., Donohue, S.E., and Bunge, S.A. (2009) Controlled retrieval and selection of actionrelevant knowledge mediated by partially overlapping regions in left ventrolateral prefrontal cortex. Neuroimage, 46(1), 299–307. 60. Ungerleider, L.G. and Haxby, J.V. (1994) ‘What’ and ‘where’ in the human brain. Curr Opin Neurobiol., 4(2), 157–65. 61. Margulies, D.S., Vincent, J.L., Kelly, C. et al. (2009) Precuneus shares intrinsic functional architecture in humans and monkeys. Proc Natl Acad Sci USA, 106(47), 20069–74. 62. McClernon, F.J., Hiott, F.B., Huettel, S.A., and Rose, J.E. (2005) Abstinence-induced changes in self-report craving correlate with event-related FMRI responses to smoking cues. Neuropsychopharmacology, 30(10), 1940–7. 63. Smolka, M.N., Buhler, M., Klein, S. et al. (2006a) Severity of nicotine dependence modulates cue-induced brain activity in regions involved in motor preparation and imagery. Psychopharmacology, 184(3–4), 577–88. 64. Fassbender, C., Murphy, K., Foxe, J.J. et al. (2004) A topography of executive functions and their interactions revealed by functional magnetic resonance imaging. Brain Res Cogn Brain Res., 20(2), 132–43. 65. Naqvi, N.H. and Bechara, A. (2009) The hidden island of addiction: the insula. Trends Neurosci., 32(1), 56–67. 66. Naqvi, N.H., Rudrauf, D., Damasio, H., and Bechara, A. (2007) Damage to the insula disrupts addiction to cigarette smoking. Science, 315(5811), 531–4.

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References

67. Bragulat, V., Dzemidzic, M., Talavage, T. et al. (2008) Alcohol sensitizes cerebral responses to the odors of alcoholic drinks: an fMRI study. Alcohol Clin Exp Res., 32(7), 1124–34. 68. McClernon, F.J., Kozink, R.V., Lutz, A.M., and Rose, J.E. (2009) 24-h smoking abstinence potentiates fMRI-BOLD activation to smoking cues in cerebral cortex and dorsal striatum. Psychopharmacology (Berl), 204(1), 25–35. 69. David, S.P., Munafo, M.R., Johansen-Berg, H. et al. (2007) Effects of Acute Nicotine Abstinence on Cue-elicited Ventral Striatum/Nucleus Accumbens Activation in Female Cigarette Smokers: A Functional Magnetic Resonance Imaging Study. Brain Imaging Behav., 1(3–4), 43–57. 70. Carter, B.L. and Tiffany, S.T. (2001) The cue-availability paradigm: the effects of cigarette availability on cue reactivity in smokers. Exp Clin Psychopharmacol., 9(2), 183–90. 71. Droungas, A., Ehrman, R.N., Childress, A.R., and O’Brien, C.P. (1995) Effect of smoking cues and cigarette availability on craving and smoking behavior. Addict Behav., 20(5), 657–73. 72. Yoon, G., Kim, S.W., Thuras, P. et al. (2006). Alcohol craving in outpatients with alcohol dependence: rate and clinical correlates. J Stud Alcohol, 67(5), 770–777. 73. Hutchison, K.E., Haughey, H., Niculescu, M. et al. (2008) The incentive salience of alcohol: translating the effects of genetic variant in CNR1. Arch Gen Psychiatry., 65(7), 841–50. 74. Filbey, F.M., Schacht, J.P., Myers, U.S. et al. (2009a) Individual and additive effects of the CNR1 and FAAH genes on brain response to marijuana cues. Neuropsychopharmacology, 35(4), 967–75. 75. Myrick, H., Anton, R.F., Li, X. et al. (2008) Effect of naltrexone and ondansetron on alcohol cueinduced activation of the ventral striatum in alcohol-dependent people. Arch Gen Psychiatry, 65(4), 466–75. 76. Hermann, D., Smolka, M.N., Wrase, J. et al. (2006) Blockade of cue-induced brain activation of abstinent alcoholics by a single administration of amisulpride as measured with fMRI. Alcohol Clin Exp Res., 30(8), 1349–54. 77. Heinz, A., Siessmeier, T., Wrase, J. et al. (2004) Correlation between dopamine D(2) receptors in the ventral striatum and central processing of alcohol cues and craving. Am J Psychiatry, 161(10), 1783–9. 78. McClernon, F.J., Hiott, F.B., Liu, J. et al. (2007a) Selectively reduced responses to smoking cues in amygdala following extinction-based smoking cessation: results of a preliminary functional magnetic resonance imaging study. Addict Biol., 12(3–4), 503–12. 79. Langleben, D.D., Ruparel, K., Elman, I. et al. (2008) Acute effect of methadone maintenance dose on brain FMRI response to heroin-related cues. Am J Psychiatry, 165(3), 390–4. 80. Kosten, T.R., Scanley, B.E., Tucker, K.A. et al. (2006) Cue-induced brain activity changes and relapse in cocaine-dependent patients. Neuropsychopharmacology, 31(3), 644–50. 81. Kalivas, P.W. and Volkow, N.D. (2005) The neural basis of addiction: a pathology of motivation and choice. Am J Psychiatry, 162(8), 1403–13. 82. Wiers, R.W., Bartholow, B.D., Van Den Wildenberg, E. et al. (2007) Automatic and controlled processes and the development of addictive behaviors in adolescents: a review and a model. Pharmacol Biochem Behav., 86(2), 263–83. 83. Hutchison, K.E. (2010) Substance Use Disorders: Realizing the Promise of Pharmacogenomics and Personalized Medicine. Annual Review of Clinical Psychology 6, 577–89 84. Hutchison, K.E. (2008) Alcohol dependence: neuroimaging and the development of translational phenotypes. Alcohol Clin Exp Res., 32(7), 1111–2. 85. Bechara A. (2005) Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nat Neurosci., 8(11), 1458–63. 86. Goldstein, R.Z. and Volkow, N.D. (2002) Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am J Psychiatry, 159(10), 1642–52. 87. Volkow, N.D. and Fowler, J.S. (2000) Addiction, a disease of compulsion and drive: involvement of the orbitofrontal cortex. Cereb Cortex., 10(3), 318–25. 88. Anton, R.F. (2008) Naltrexone for the management of alcohol dependence. N Engl J Med., 359(7), 715–21.

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89. Komanduri R. (2003) Two cases of alcohol craving curbed by topiramate. J Clin Psychiatry, 64(5), 612. 90. Johnson, B.A., Roache, J.D., Ait-Daoud, N. et al. (2002) Ondansetron reduces the craving of biologically predisposed alcoholics. Psychopharmacology (Berl), 160(4), 408–13. 91. Kenna, G.A., McGeary, J.E., and Swift, R.M. (2004a) Pharmacotherapy, pharmacogenomics, and the future of alcohol dependence treatment, part 1. Am J Health Syst Pharm., 61(21), 2272–9. 92. Li, T.K. (2000) Clinical perspectives for the study of craving and relapse in animal models. Addiction, 95(Suppl 2), S55–S60. 93. Anton, R.F., O’Malley, S.S., Ciraulo, D.A., Couper, D., Gastfriend, D.R., Johnson, B.A., et al. (2006) Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence: The COMBINE Study: A Randomized Controlled Trial. JAMA: Journal of the American Medical Association. 94. Daglish, M.R., Weinstein, A., Malizia, A.L., Wilson, S., Melichar, J.K., Britten, S., et al. Changes in regional cerebral blood flow elicited by craving memories in abstinent opiate-dependent subjects. Am J Psychiatry. 2001 Oct;158(10): 1680–6. 95. Kareken, D.A., Bragulat, V., Dzemidzic, M., Cox, C., Talavage, T., Davidson, D., O’Connor, S.J. NeuroImage (2010). Family history of alcoholism mediates the frontal response to alcoholic drink odors and alcohol in at-risk drinkers. 50(1): 267–76. 96. Koob, G.F. and Volkow, N.D. (2010) Neurocircuitry of addiction. Neuropsychopharmacology 35, 217–38. 97. London, E.D., Ernst, M., Grant, S., Bonson, K., Weinstein, A. (2000) Orbitofrontal cortex and human drug abuse: functional imaging. Cereb Cortex. 2000 Mar;10(3): 334–42.

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Chapter 7

Impulsivity and Addiction

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Chapter 7 Impulsivity and Addiction Hugh Garavan Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA

7.1 Introduction The role of personality or cognitive factors in drug abuse has been a particular topic of interest. One such factor, impulsivity, has received considerable attention as it may be relevant to drug risk (e.g., by contributing to initial use or to the transition from recreational use to abuse), to the continuation of use (e.g., drugs may detrimentally affect the neurocognitive systems that control impulses) or to relapse (e.g., suppressing powerful drug urges may be essential for abstinence). This chapter will address impulsivity and how it might contribute to each of these periods in the addiction lifecycle. The focus will be on drug addictions but it is worth noting that evidence is mounting that proposed behavioral addictions, such as gambling, may share impairments in impulsivity and associated neurobiological differences [1, 2]. A quick review of the relevant research reveals that impulsivity is a multi-faceted concept that is measured in many different ways. While impulsivity, in general, typically refers to actions made without sufficient forethought or consideration of their consequences, the study of impulsivity ranges from self-report measures of personality dimensions (e.g., Barratt Impulsiveness Scale, [3]; Eysenck Impulsiveness Questionnaire, [4]; Temperament and Character Inventory- Revised, [5] to laboratory measures of impulsive choices (e.g., selecting immediate over delayed rewards), impulsive responses (e.g., failure to countermand a motor response) or impulsive information selection (e.g., insufficient sampling of available information prior to making a decision). A summary of these measures and their application to various drug addicted groups is provided in a recent review by Verdajo-Garc´ıa and colleagues [2]. Neuroimaging studies, for obvious reasons, tend to favor laboratory-based tasks of impulsivity, although personality measures are often incorporated by assessing their relationship to observed activation patterns or brain structure [6–8].

7.2 Impulsivity as Reward versus Control Although correlations between cognitive and personality measures of impulsivity tend to be weak [9, 10], it is a curious phenomenon that drug addicted individuals tend to show heightened impulsivity on both [2]. An important distinction that might help integrate the varied dimensions of impulsivity is that between the reward or reinforcement Neuroimaging in Addiction, First Edition. Bryon Adinoff and Elliot Stein.  C 2011 John Wiley & Sons, Ltd. Published 2011 by John Wiley & Sons, Ltd.

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Behavioural Impulsivity

“Impulse Control” Goal-driven Regulation

“Impulse Drive” Motivation for Immediate Reward

Figure 7.1 A schematic of behavioural impulsivity. Regions highlighted include the ventral striatum as one source of the reinforcement-related, motivational system that creates an impulse drive for immediate gratification and the right inferior prefrontal cortex as one source of top-down, goaldriven regulation that creates impulse control. These two regions are chosen as exemplars for these two components of impulsivity but, as described in the main text, other limbic and prefrontal systems are also involved.

seeking drives and the cognitive control system that attempts to exert control over these drives. We might refer to these, respectively, as impulse drive and impulse control (see Figure 7.1). Impulse drive could, for instance, result from a ventral striatum system that attributes excessive incentive salience to drugs and drug-related stimuli and that biases the organism towards immediate rewards. In this regard, impulse drive is not being proposed as a psychological process distinct from the reward-seeking processes that are already known but, to the contrary, the intention is to make the point that understanding “impulsivity” requires us to incorporate the reward and reinforcement processes that provide the motivation to seek gratification. Impulse control, on the other hand, would be the domain of a largely prefrontal system responsible for suppressing these drives and drug-seeking behaviors in favor of longer-term goals consistent with the prefrontal cortex’s role in regulating behavior and maintaining goals, including abstract long-term goals. Within this framework, impulsive behavior can thus be seen to result from either an excessive drive for reinforcement and/or by compromised top-down, prefrontally-mediated control over these drives. Consequently, the observable impulsivity of a drug-addicted individual could result from excessive drive for reward or compromised control over those drives, with each driven by altered functioning in neuroanatomically distinct regions. Consistent with this framework, a recent study [6] has functionally linked the prefrontal and ventral striatal systems by showing an antagonism between the two. In this experiment, choices were made for either immediate reward or in the service of long-term goals, with the latter yielding a delayed, larger reward. Selections in favor of immediate

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7.3

The Neurobiology of Impulsivity

rewards were associated with increased activity in the ventral tegmental area and the ventral striatum. Activity levels in these regions were reduced when subjects selected in favor of the long-term goal. Significantly, when selecting in favor of the long-term goal, activity levels in the ventral striatum were negatively correlated with activity in the left inferior prefrontal cortex and lateral orbitofrontal cortex. Moreover, the strength of the negative correlation between the prefrontal and striatal regions was correlated, across individuals, to self-report personality measures of impulsivity. Consequently, the coupling between prefrontal cortex and the striatum suggests that compromise to the “impulse drive” (i.e., reward seeking) and “impulse control” systems could underlie the pathological drug urges or impaired control over those urges observed in drug users. Thinking of impulsivity in terms of a trait that emerges from the functioning of either the impulse drive or impulse control systems thus provides a rational basis for assaying impulsivity with such seemingly distinct processes as an inability to delay gratification or withhold a prepotent motor response. One further speculation that emerges from distinguishing between impulse drive and impulse control is that the components of the impulse drive and impulse control systems might show independent, inter-individual variation. Thus, the integrity of the impulse drive system may be independent of the integrity of the impulse control system and the strength of relationship between psychometric and cognitive measures may reflect the extent to which they assess these different components.

7.3 The Neurobiology of Impulsivity Considerable progress has been made in understanding the components of the impulse drive and impulse control systems. What is referred to here as the impulse drive system arises from the brain’s reward seeking, appetitive motivation or salience attribution systems; it is proposed here that it is the aberrant functioning of these systems that produces the motivation for reward, most apparent as the motivation for immediate gratification, and which then presents as impulsive behavior. These systems include the ventral striatum, insula, orbitofrontal cortex, amygdala, hypothalamus, periaqueductal gray and other brainstem nuclei ([11–13]). These brain regions appear to be central to maintaining representations of reward states and incentivized, reward-related stimuli [12, 14], in representing interoceptive, subjective feelings such as cravings [15], and in motivating consummatory behavior. In contrast, the impulse control system is typically associated with the frontal lobes. Right inferior frontal cortex has been linked through lesion, Transcranial Magnetic Stimulation (TMS) and functional Magnetic Resonance Imaging (fMRI) studies to motor response inhibition [16–19] (see Figure 7.2). Recently, repetitive TMS (rTMS) has identified the left prefrontal cortex with controlling the desire for immediate gratification [20]. Figner and colleagues showed that choices for smaller, more immediate rewards over delayed rewards increased following disruptive rTMS over left but not right lateral PFC. This result is consistent with earlier studies linking both gray matter volume and functional activation of lateral PFC to smaller discounting of delayed rewards [21, 22]. That is, those with greater volumes and greater activation did not de-value delayed rewards as much as those with smaller gray matter volumes or smaller levels of activation. Motor response inhibition has been a particular favorite of neuroimaging studies. Although this may appear to be an overly simplistic operationalization of impulse control when one considers the impulse control required of a drug addicted individual attempting

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Figure 7.2 Areas activated during motor response inhibition (top row; n = 71) showing a largely right hemisphere network of dorsal and inferior prefrontal, parietal and midline regions [18]. The bottom left corner shows the right frontal operculum identified as critical for response inhibition in a lesion study [15] while the bottom right areas show the overlap between this region and the activations above. Combined, the fMRI and lesion data converge on an important role for the right inferior frontal gyrus is motor inhibition.

to maintain abstinence, there is converging evidence that the right prefrontal cortex may be involved in many aspects of impulse control extending beyond motor inhibition. For example, right prefrontal cortex has been implicated in the suppression of drug cravings in cocaine users [23]. In this study, the magnitude of fMRI activation change in the right inferior frontal gyrus when inhibiting craving responses to a cocaine video, relative to when not inhibiting, was negatively correlated with a similar difference score in the right nucleus accumbens. Depue and colleagues showed that the active suppression of the sensory components of memories in a Think/No Think paradigm was associated with activation in right inferior frontal cortex [24]. A similar relationship between prefrontal regions and sensory processing was shown by de Fockert and colleagues who demonstrated that increased working memory load increased activity levels in prefrontal cortex (including bilateral inferior and middle frontal gyri) while simultaneously increasing the distraction caused by (and sensory processing of) irrelevant faces [25]. Adopting this paradigm to drug abuse, Hester and colleagues showed that irrelevant drug stimuli produced heightened activity in visual cortex in cocaine users relative to drug-na¨ıve controls [26]. Moreover, among users, those who showed the greatest activity in right prefrontal cortex showed the smallest behavioral interference caused by the distracting drug stimuli. One challenge associated with identifying core loci for impulse control is the flexibility of human cognition paralleled in the functional plasticity and adaptability of the PFC [27, 28]. Even with regard to the very specific operationalization of motor response impulsivity addressed above, inter- and intra-individual variation can be observed in the specific cognitive operations being engaged and there is still controversy regarding the

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Impulsivity and Risk for Developing a Drug Use Disorder

precise cognitive functions being subserved by regions such as the right inferior frontal cortex [29,30]. Braver and colleagues make a useful distinction between proactive and reactive control [31]. With regard to impulse control, one might be successful on a response inhibition task by maintaining high levels of proactive control (e.g., sustained attention or concentration; maintaining task goal representations in a highly active/accessible state) or by having good reactive control (e.g., being especially adept on countermanding responses or “slamming on the brakes”). Consequently, poor performance (e.g., high numbers of commission errors or lengthy Stop-Signal Reaction Times [SSRT, a calculation of the time required to inhibit motor responses, [32]]) may reflect either poor proactive or reactive control being applied to a task and the relative contribution of both will likely dictate the functional activation patterns observed in individuals. In this vein, the impulse control deficits in drug-addicted individuals may derive from poor proactive, rather than reactive, control. For example, Li and colleagues showed that the increased SSRTs of cocaine-addicted subjects were accompanied by smaller response slowing on trials that followed both errors and successful inhibitions [33]. Post-trial slowing of this kind might reflect performance monitoring abilities, which others have suggested are compromised in addiction, including nicotine dependence [34, 35]. Of note, the group difference in SSRT between cocaine-addicted and control participants [33] was eliminated if one factored out the post-trial slowing effect, suggesting that the apparent impulse control deficit of users may be driven by deficits in a proactive rather than reactive control system. Conversely, a further implication of the cognitive flexibility with which people can perform laboratory tasks is that cortical areas that are not specifically involved in reactive, response inhibition may nonetheless make important contributions to successful performance. Although typically not activated in imaging studies of motor response inhibition, there is considerable evidence of a role for the orbitofrontal cortex (OFC) in impulse control. For example, OFC damage in a rodent model increases SSRT [36] while patients with lesion damage to the OFC show increased self-report and cognitive measures of impulsivity and altered time perception relative to healthy controls and non-OFC lesioned patients (Berlin et al., 2004). That said, many behaviors that appear impulsive might not be driven by a deficit in impulsivity (i.e., either impulse drive or impulse control) per se. For example, Torregrossa and colleagues argue that the most robust deficit in OFC-damaged animals is in reversal learning. Hence, seemingly impulsive behaviors, such as perseverative responding and a failure to alter responding when the rewards for a learned behavior are devalued, may, in fact, reflect impairment in the ability to update the value of an outcome, especially under changing circumstances [37].

7.4 Impulsivity and Risk for Developing a Drug Use Disorder The common observation of heightened impulsivity in users of different drugs has led to the hypothesis that impulsivity may precede drug use and may, in fact, increase an individual’s risk of use and abuse [2]. Drug use is typically initiated during adolescence, a period of multiple social, personality, cognitive and emotional changes and one that is also characterized by immature frontal lobe development; myelination, synaptic pruning and reorganization, dendritic and axonal arborization are not completed until early adulthood [38, 39]. These developmental processes likely impact on the cognitive abilities of adolescents. For example, poorer performance on a response inhibition task in 12 year

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olds relative to 27 year olds has been associated with elevated levels of prefrontal, parietal and medial activation, which was interpreted to reflect the greater demands placed on the maturing brain [40]. Longitudinal studies of behavioral disinhibition also support the link between early disinhibition and later drug use. For example, behavioral and cognitive measures of impulsivity in 10–12 year olds predict drug use at age 19 [41]. Similarly, disinhibition measured at age 11 predicted the age at which adolescents subsequently had their first drink [42]. Wong and colleagues obtained measures of behavioral control in children aged 3 to 5 and every three years thereafter until ages 15–17 and showed that both the earliest measures of behavioural control and the rate of development of control throughout childhood predicted onset of drinking by age 14 and onset of drunkenness by age 17 [43]. Cognitive measures assessed in the laboratory at age 14, such as the SSRT, can predict conduct disorder, including substance use, at age 16 [44]). Beyond laboratory tasks, specific personality risk factors correlate with specific patterns of drug use and psychiatric co-morbidities, with cocaine use, for example, being associated with an impulsive personality and antisocial personality disorder [45]. In keeping with these personality factors conferring drug use risk, personality-specific, early-adolescent interventions have proven effective in reducing the initiation of cannabis, cocaine and other drug use [46]. A recent, cross-sectional, study of alcohol use in a non-clinical sample of college students showed that 50% of the variance on the Alcohol Use Disorders Identification Test (AUDIT, a measure of alcohol consumption, drinking behavior, and alcohol-related problems) could be explained by measures of alcohol-attentional bias (an alcohol Stroop task), impulsivity (a delayed discounting task), and impulse control (a Go/NoGo task) [111]. Consistent with impulse drive and impulse control being alcohol risk factors, among those who were below the AUDIT threshold for problematic drinking, inhibitory control was the only significant predictor of AUDIT scores while the delayed discounting task best discriminated problem from non-problem drinkers. (In contrast, attentional bias significantly predicted AUDIT scores among those above the threshold, consistent with attentional bias arising from repeated exposure to alcohol cues associated with consumption.) Verdejo-Garc´ıa et al. [2], in an extensive review of this literature, present three lines of evidence supportive of heightened impulsivity preceding use. First, heightened impulsivity is observed in those at high risk of developing substance dependence such as adolescents, those with ADHD, or those with drug-dependent parents. Second, pathological gamblers who show dependence-like symptoms also show heightened impulsivity; although their gambling may have exposed them to the endogenous neurochemistry associated with gambling-related rewards, they have not been exposed to the potent pharmacological elements that might be considered to cause impulsivity in drug users. Finally, there is considerable evidence of a genetic contribution to dependence and genetic linkage studies are identifying genes related to impulsivity and drug use [47,48]. As noted above, what is being referred to here as impulse drive has been associated with ventral striatal functioning. Considerable evidence links the ventral striatum to the anticipation of reward [49] and to aberrant functioning within it in current alcohol, cannabis and nicotine users [50–53]. The typical observation is for drug users to show relative hypoactivity in the ventral striatum when anticipating monetary reward, which is thought to reflect reward deficiency [54]. This anhedonia may explain the heightened reinforcement value of drugs for these individuals and may, therefore, provide the motivation to consume. A rodent model of impulsivity links the inability to withhold premature

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7.5

Impulsivity in Current Users

responding for reward that is observed in a subset of wildtype rats to reductions in D2 receptor levels in the ventral striatum [55]. Moreover, these animals have increased cocaine self-administration relative to non-impulsive animals. This study is notable insofar as it links a heritable, trait-like variable, which under this chapter’s framework would be considered increased impulse drive, to a neurochemical alteration that precedes use and that is related to increased subsequent use of cocaine. Subsequent testing shows that these animals progressed to persistent drug taking even in the presence of negative consequences (electric shocks) indicative of compulsive drug taking [56]. Similar effects have been observed in humans. For example, Volkow and colleagues showed that the subjective pleasure reported by drug-na¨ıve individuals following an IV administration of the stimulant methylphenidate was related to their D2 receptor levels in the striatum [57]. An important paper that addresses resistance to, rather than risk of, drug use, shows an opposite effect, with increased D2 receptor levels in the caudate and ventral striatum of those who have a family history of alcoholism but who themselves are not alcohol dependent [7]. In addition, activity levels in the striatum were correlated with prefrontal activity levels in those with a family history of alcoholism, suggesting an important role for a close functional coupling between the prefrontal and striatal systems. Finally, a non-human primate model, valuable for being able to disentangle the temporal order of brain changes and drug exposure, shows that the same dopamine system can be affected by environmental influences [58]. Primates who ascend to a position of social dominance show increases in D2 receptor levels relative to their own baseline, and, critically, subsequently show reduced levels of cocaine self-administration. Although the focus of these latter studies on the ventral striatum D2 system is largely reward and reinforcement processes, it is proposed, as outlined above, that the functioning of this system is what generates the reward-seeking impulse drive. Thus, the “resistance” shown by the non-alcohol dependent individuals and the dominant primates may be attributed to them not having a reward deficiency and not therefore having an excessive impulse drive. Indeed, D2 receptor systems have been linked to both impulsivity and substance use. In a genetic study, Esposito-Smythers and colleagues showed an association between the DRD2 TaqIA polymorphism, conduct disorder and substance misuse: a hierarchical regression analysis revealed an interaction linking those with this polymorphism and selfreported impulsivity (an impulsivity rating from the Schedule for Affective Disorders and Schizophrenia for School-Age Children - Present and Lifetime Version; [59] to problematic alcohol and other drug use [47]. Similarly, combined D2/D3 binding in the striatum as measured by Positron Emission Tomography is related to self-report impulsivity (Barratt Impulsiveness Scale, [3] and methamphetamine use [60].

7.5 Impulsivity in Current Users It should be noted that longitudinal investigations are not the norm in addiction studies due, presumably, to their prohibitive costs and high attrition rates. As a result, it can be difficult to determine from studies on current or former users if impairments in impulsivity reflect pre-existing traits or cumulative drug exposure effects. Animal models, consequently, are valuable in this regard. A number of such studies suggest that drug exposure might lead to increases in impulsive behavior [61–63]. For example, the review by Robinson and Kolb shows that drugs such as nicotine, morphine, amphetamine and cocaine administered in animal models produce long-lasting changes in dendritic length,

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Poorer inhibitory control of cocaine users was associated with reduced activation in the anterior cingulate and the right insula/IFG. 60

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Users

Figure 7.3 The poorer inhibitory control of 13 current cocaine users relative to 14 drug-na¨ıve controls was associated with reduced activation in the anterior cingulate and the right insula/right IFG [69].

branching and/or density in multiple brain regions including those in prefrontal cortex and the ventral striatum. Jentsch and colleagues showed that even a single dose of cocaine could impair reversal learning in monkeys and that repeated exposure produced reversal impairments and perseverative responding for up to 30 days [61]. As noted above, studies on human drug users provide considerable evidence of heightened impulsivity and diminished inhibitory control based on both self-report and laboratory measures (reviewed in [2]). For example, greater discounting of delayed hypothetical rewards, a quintessential measure of impulsive choice (i.e., the inability to delay gratification), has been observed in opiate-dependent patients compared with controls [64, 65], in opiate-dependent individuals who report sharing needles compared with those who do not [66], in current smokers compared with both never-smokers and ex-smokers of cigarettes [67], and in individuals with early-onset alcoholism compared to those with late-onset alcoholism [68]. With regard to impulse control, it has been shown that, compared to drug-na¨ıve controls, abusers of opiates [69], cocaine [70, 73]), nicotine [71], and alcohol [72] are poorer at inhibiting their motor responses (see Figure 7.3). Neuroimaging studies have linked these impairments to activation differences in medial and lateral PFC for impulse control. What might be described as a systems-level difference between cocaine users and controls in response to increased inhibitory demands was reported by [73]. In this study, impulse control was paired with a working memory load. Participants had to store a variable number of letters in working memory while making button press responses to a serial stream of letters. They were instructed to respond to all letters except those in the memory set which enabled the researchers to investigate the effects of the memory set size on impulse control. These investigators observed that fMRI BOLD activation associated with motor inhibition increased in control subjects in the prefrontal cortex and reduced in the cerebellum as the working memory load increased. In cocaine users, prefrontal increases were not observed; instead, cerebellar activity increases, the very opposite to what was observed in controls, was seen, indicating a

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reliance on what might be considered a more rudimentary and less flexible cognitive system in the users. To our knowledge, there are no neuroimaging studies of drug users performing a delayed discounting task. However, research into individual differences on this measure [74–76] suggest that functional differences in the ventral striatum of drug users are probable, consistent with the studies on reward anticipation noted above. The nature of the neuroanatomical deficits associated with impulse control in drug users can vary between drug-using groups, which may reflect the specific pharmacological actions of the drugs and the extent of brain alteration arising from the specific drug of abuse. Of course, many other factors affecting neurocognitive function may be confounded with the drug under investigation. These might include pre-existing differences between users of different drugs (which may have guided which drug an individual abuses), the co-morbidities associated with different drugs, lifestyle differences and miscellaneous drug-use characteristics such as age of onset (e.g., typically earlier for alcohol and cannabis than for heroin), route of administration, frequency of use, and extent of polydrug use [77]. These endemic uncertainties notwithstanding, a series of studies employing the same Go/NoGo task tested in different drug using groups yields some insight into the similarities and differences in neurocognitive function in users of different drugs (see Figure 7.4). Kaufman et al. [70] observed poorer performance relative to drug-na¨ıve

Figure 7.4 The Go/NoGo task schematic shows a 1 Hz serial stream of letters X and Y. Subjects are instructed to make a button-press response when the letters alternate, indicated by green arrows, and to withhold responding when a letter repeats, indicated by the red arrow. (The study with cannabis users involved a more complicated Go/NoGo task using Stroop-like stimuli that required subjects to inhibit either when a stimulus repeated or when the word and its font colour were incongruent). The brain images show 8mm radius spheres centred on the centres-of-mass of between-group differences in event-related activation for successful inhibitions. The areas shown for the cannabis and ecstasy results had greater activation in the drug users relative to their control groups (both groups performed comparably to their control groups) with the opposite pattern observed for the nicotine and cocaine studies (in which both drug-using groups performed worse than their control groups). Overlapping effects are observed in the anterior cingulate (relative hypoactivity for both cocaine and nicotine users) and in the right inferior parietal lobule (relative hypoactivity for the nicotine group and hyperactivity for the cannabis and ecstasy groups).

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controls and reduced activation in the anterior cingulate and anterior insula in current cocaine users. That cocaine might have a direct effect on impulse control was suggested by an IV administration of cocaine to cocaine users which improved performance and increased activation in right dorsolateral and inferior frontal cortex [78]. In contrast, Roberts et al. [79] tested ecstasy users on an almost identical task. Motor performance was similar to controls but increased activity in the users was observed in the right middle and inferior frontal gyri and the right inferior parietal lobule. This was interpreted as indicating that inhibiting required greater “effort” (i.e., cortical resources) in the users. However, it should be noted that the evidence for heightened impulsivity in ecstasy users is mixed. For example, ecstasy users revealed elevated impulsivity on the Matching Familiar Figures Test [80–83] and on trait impulsivity [80, 84, 85] in comparison to drug-na¨ıve controls. However, there are reports of failures to replicate these deficits on a response inhibition Go/NoGo task [86, 87], a Stop Signal Test [88], Stroop test [89], and on trait impulsivity [90]. The neuroimaging data might help resolve these inconsistencies by showing that comparable performance can be attained by the users but doing so requires greater activation on their part. Thus, the increased demand placed on the impulse control system of the users indicates a subtle impairment in this cognitive function. Cannabis users, performing a more complicated Go/NoGo task (primarily designed to assess error awareness but which also assesses motor impulse control) showed comparable performance to controls and increased activation in the right inferior parietal lobule, right putamen, and right middle cingulate gyrus, a pattern of results that is broadly similar to that reported above for recreational ecstasy users [91]. Nicotine users, on the other hand, demonstrated significantly poorer motor response inhibition compared to exsmokers and controls [92]. Compared to controls, smokers demonstrated reduced activity in the right dorsal and ventrolateral PFC, anterior cingulate cortex and in parietal and temporal cortex. Interestingly, long-term nicotine use has been associated with reduced gray matter volume in right PFC [93–95], potentially implicating changes in gray matter prefrontal volume in impulse control deficits. These studies indicate that drug-related differences in inhibitory control can be expressed in performance and/or brain activity levels. They underscore the importance of considering performance differences when interpreting brain activation. What are presumably subtle impairments in prefrontal function may not present as a performance deficit (at least not until one “stresses” the system with a particularly demanding task) if there is sufficient reserve capacity to increase activation levels. More pronounced deficits may be revealed in both impaired performance and relative prefrontal hypoactivity. This series of studies, using a relatively straightforward response inhibition task, complement the extant literature by demonstrating that impaired inhibitory control, presenting as either impaired performance and/or altered brain activation patterns, is a general characteristic of current drug users.

7.6 Impulsivity, Abstinence, and Relapse Relapse is, in many regards, a defining characteristic of drug dependence. There is, however, relatively little empirical data on the neurobiology of successful abstinence despite its potential value for informing therapeutic interventions. The extant literature has typically investigated short-term abstinence and has revealed many persistent deficits, which,

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for example for cocaine users, are more pronounced in heavy users in lateral and medial prefrontal regions associated with cognitive control [96, 97]. Abstinent marijuana users show a similar pattern of lateral and medial hypoactivity but have also been reported to show bilateral hippocampal hyperactivity [98]. There is, however, evidence to suggest that prolonged abstinence will correct the general pattern of prefrontal hypoactivity in users (see below) with, for example, cocaine abstinence reducing high-risk responses on a gambling task [99]. Longitudinal studies have identified attentional biases to drugs and drug-related cues as particularly effective in predicting treatment outcome. Stroop-like tasks, in which drugstimuli are irrelevant but distracting (e.g., subjects are asked to identify the font color of drug-related words), provide a measure of attentional bias for drug-related stimuli. These attentional bias measures have been shown to predict relapse better than other standard dependence measures such as self-reports of dependence or drug use histories [100, 101]. The ability to direct attention away from drug cues might be considered another important dimension of inhibitory control (note the papers cited earlier that implicate similar right prefrontal circuitry in this and motor response suppression). There is, however, more direct evidence linking impulsivity and abstinence: Higher scores on a self-report measure of impulsivity (the Barratt Impulsiveness Scale) has been shown to predict poorer treatment outcome [102, 103]. During abstinence, impulse control might be important for suppressing drug-seeking behaviors and drug cravings. Although subjective reports of craving often prove to be poor predictors of subsequent abstinence, cognitive and neuroimaging measures appear to do better [104, 105]. However, the neuroimaging literature on predicting relapse is small and has employed a variety of tasks that were not necessarily designed to induce a craving response or to assess the user’s ability to suppress that response. Nonetheless, the existing results do identify prefrontal systems, among other regions, as effective predictors of treatment outcome. For example, using a two-button prediction task, Paulus and colleagues showed activation levels in prefrontal, temporal, and posterior cingulate regions early in abstinence to predict subsequent relapse for methamphetamine users [106]. Gr¨usser et al. [104] found that activity in response to alcohol-related stimuli in the putamen, anterior cingulate and medial prefrontal cortex predicted relapse. Although it does not follow from these findings that impulse control should also predict abstinence, the predictive value of prefrontal cortex suggests that regulatory processes may be involved. A recent study in cigarette smokers shows that practicing self-control (i.e., small acts of impulse control such as avoiding sweets, which were practiced over two weeks before quitting) significantly improved abstinence rates; 27% in the self-control group relative to 12% in a control condition were still abstinent one month after quitting [107]. Given the important role that cognitive processes may play in avoiding relapse in drug users and gamblers ([1, 100, 101, 108]), it may be the case that the best predictors of treatment outcome are those that reflect cognitive control over drug urges rather than the drug urges themselves. This is supported by a study by Brewer et al. [109] who identified cognitive control prefrontal regions in addition to other subcortical and posterior cingulate regions as being the best predictors of treatment outcome in a treatment-receiving sample of cocaine users. Further evidence for the assertion that impulse control might contribute to successful abstinence arises from cross-sectional research of abstinent former-users using the same Go/NoGo task described above. These studies are showing an apparent reversal in activation patterns in that the prefrontal hypoactivity of current users is mirrored in prefrontal hyperactivity in abstinent users. For

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Figure 7.5 Error-related activation for non-smokers, current smokers and former smokers showing reduced activation levels in the current smokers and elevated activation in the former smokers.

example, Connolly et al. (Under Review) shows that both short-term abstinent cocaine users (1–5 weeks) and long-term abstinent users (40–102 weeks) present with fMRI hyperactivity in cognitive control regions relative to drug-na¨ıve controls. That is, the brain regions involved in impulse control (e.g., right middle and inferior frontal gyri), which are consistently shown to be hypoactive in current users, show elevated activity in former users compared to drug-na¨ıve controls. A similar effect was observed for former cigarette smokers (abstinent for two years). Using the same Go/NoGo task, current smokers showed reduced activity relative to controls in dorsolateral PFC and the anterior cingulate (see Figure 7.4) while the former smokers revealed greater inhibition- and error-related activation in the anterior cingulate relative to the current smokers (Figure 7.5; [92]). As both the nicotine and cocaine abstinence studies were cross-sectional in design, it is not possible to determine if the heightened activity of the former users reflected a pre-existing trait that might have facilitated abstinence or if it arose during the abstinence period in response, perhaps, to the frequent needs to inhibit. A longitudinal study would resolve this but, for now, either possibility suggests that heightened impulse control facilitates abstinence.

7.7 Conclusion Preclinical studies suggest that ventral striatal functioning, which is associated with the motivation for drugs (i.e., the impulse to use), can confer risk or protection against drug self-administration. The human studies show that the integrity of both the ventral striatal system and the prefrontal impulse control system are also related to drug use and relapse risk. The body of evidence suggests, therefore, that individual differences in impulsivity, manifest perhaps in the integrity of these systems and their interaction, may contribute significantly to the abuse of drugs. One challenge that emerges from this proposal is that interventions that reduce impulsivity, through pharmacological or cognitive training methods, should reduce drug use and might be a useful complement to existing drug treatment programs [107]. By providing a window into the functioning of the reinforcement seeking and impulse control neurobiological systems, neuroimaging can identify which processes best predict treatment outcome and/or are most amenable to therapeutic intervention. The preceding review, despite emphasizing the role of right PFC, shows that a number of prefrontal regions have been implicated in impulse control.

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References

Future neuroimaging studies can help pinpoint the exact mechanisms involved and the dynamics by which control over impulses can be exerted.

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64. Madden, G.J., Bickel, W.K., and Jacobs, E.A. (1999) Discounting of delayed rewards in opioiddependent outpatients: exponential or hyperbolic discounting functions? Exp. Clin. Psychopharmacol., 7, 284–293. 65. Madden, G.J., Petry, N.M., Badger, G.J., and Bickel, W.K. (1997) Impulsive and self-control choices in opioid-dependent patients and non-drug using control participants: drug and monetary rewards. Exp. Clin. Psychopharmacol., 5, 256–262. 66. Odum, A.L., Madden, G.J., Badger, G.J., and Bickel, W.K. (2000) Needle sharing in opioiddependent outpatients: psychological processes underlying risk. Drug Alcohol Depend., 60, 259–266. 67. Bickel, W.K., Odum, A.L., and Madden, G.J. (1999) Impulsivity and cigarette smoking: delay discounting in current, never, and ex-smokers. Psychopharmacology, 146, 447–454. 68. Dom, G., D’haene, P., Hulsyijn, W., and Sabbe, B. (2006) Impulsivity in abstinent early- and late-onset alcoholics: differences in self-report measures and a discounting task. Addiction, 101, 50–59. 69. Forman, S.D., Dougherty, G.G., Casey, B.J. et al. (2004) Opiate addicts lack error-dependent activation of rostral anterior cingulate. Biological Psychiatry, 55, 531–537. 70. Kaufman, J., Ross, T.J., Stein, E.A., and Garavan, H. (2003) Cingulate hypoactivity in cocaine users during a GO/NOGO task as revealed by event-related fMRI. The Journal of Neuroscience, 23(21), 7839–7843. 71. Mitchell, S.H. (2004) Measuring impulsivity and modeling its association with cigarette smoking. Behav Cogn Neurosci Rev, 3(4), 261–275. 72. Kamarajan, C., Porjesz, B., Jones, K.A. et al. (2005) Alcoholism is a disinhibitory disorder: neurophysiological evidence from a Go/No-Go task. Biol. Psychol., 69, 353–373. 73. Hester, R. and Garavan, H. (2004) Executive dysfunction in cocaine addiction: evidence for discordant frontal, cingulate, and cerebellar activity. J Neurosci., 24(49), 11017– 11022. 74. Hariri, A.R., Brown, S.M., Williamson, D.E. et al. (2006) Preference for immediate over delayed rewards is associated with magnitude of ventral striatal activity. J. Neurosci., 26, 13213– 13217. 75. Kable, J.W. and Glimcher, P.W. (2007) The neural correlates of subjective value during intertemporal choice. Nat. Neurosci., 10, 1625–1633. 76. Marco-Pallar´es, J., Mohammadi, B, Samii, A., and Munte, T.F (2010) Brain activations reflect individual discount rates in intertemporal choice. Brain Research, 1320, 123–129. 77. Fern´andez-Serrano, M.J., P´erez-Garc´ıa, M., and Verdejo-Garc´ıa, A. (2010) What are the specific vs. generalized effects of drugs of abuse on neuropsychological performance? Neurosci Biobehav Rev. [Epub ahead of print]. 78. Garavan, H., Kaufman, J.N., and Hester, R. (2008) Acute effects of cocaine on the neurobiology of cognitive control. Philos Trans R Soc Lond, B, Biol Sci., 363(1507), 3267–3276. 79. Roberts, G.M.P. and Garavan, H. (2010) Evidence of increased activation underlying cognitive control in ecstasy and cannabis users. NeuroImage, 52, 429–435. 80. Morgan, M.J. (1998) Recreational use of “ecstasy” (MDMA) is associated with elevated impulsivity. Neuropsychopharmacology, 19, 252–264. 81. Morgan, M.J., McFie, L., Fleetwood, H., and Robinson, J.A. (2002) Ecstasy (MDMA): are the psychological problems associated with its use reversed by prolonged abstinence? Psychopharmacology (Berl), 159, 294–303. 82. Morgan, M.J., Impallomeni, L.C., Pirona, A., and Rogers, R.D. (2006) Elevated impulsivity and impaired decision-making in abstinent Ecstasy (MDMA) users compared to polydrug and drug-naive controls. Neuropsychopharmacology, 31, 1562–1573. 83. Quednow, B.B., Kuhn, K.U., Hoppe, C. et al. (2007) Elevated impulsivity and impaired decision-making cognition in heavy users of MDMA (“Ecstasy”) Psychopharmacology (Berl), 189, 517–530. 84. Parrott, A.C., Sisk, E., and Turner, J.J. (2000) Psychobiological problems in heavy ‘ecstasy’ (MDMA) polydrug users. Drug Alcohol Depend., 60, 105–110.

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85. Butler, G.K. and Montgomery, A.M., 2004. Impulsivity, risk taking and recreational ‘ecstasy’ (MDMA) use. Drug Alcohol Depend 76, 55–62. 86. Fox, H.C., McLean, A., Turner, J.J. et al. (2002) Neuropsychological evidence of a relatively selective profile of temporal dysfunction in drug-free MDMA (“ecstasy”) polydrug users. Psychopharmacology (Berl), 162, 203–214. 87. Gouzoulis-Mayfrank, E., Thimm, B., Rezk, M. et al. (2003) Memory impairment suggests hippocampal dysfunction in abstinent ecstasy users. Prog Neuropsychopharmacol Biol Psychiatry, 27, 819–827. 88. von Geusau, N.A., Stalenhoef, P., Huizinga, M. et al. (2004) Impaired executive function in male MDMA (“ecstasy”) users. Psychopharmacology (Berl), 175, 331–341. 89. Dafters, R.I. (2006) Impulsivity, inhibition and negative priming in ecstasy users. Addict Behav, 31, 1436–1441. 90. Clark, L., Roiser, J.P., Robbins, T.W., and Sahakian, B.J. (2009) Disrupted ‘reflection’ impulsivity in cannabis users but not current or former ecstasy users. J Psychopharmacol., 23, 14–22. 91. Hester, R., Nestor, L., and Garavan, H. (2009) Impaired error awareness and anterior cingulate cortex hypoactivity in chronic cannabis users. Neuropsychopharmacology, 34(11), 2450–2458. 92. Nestor, L., McCabe, E., Jones, J., Clancy, L., Garavan, H., (2011) Differences in “bottomup” and “top-down” neural activity in current and former cigarette smokers: Evidence for neural substrates which may promote nicotine abstinence through increased cognitive control. Neuroimage. [Epub ahead of print] PMID: 21440645. 93. Brody, A.L., Mandelkern, M.A., Jarvik, M.E. et al. (2004) Differences between smokers and nonsmokers in regional gray matter volumes and densities. Biol Psychiatry, 55(1), 77–84. 94. Gazdzinski, S., Durazzo, T.C., Studholme, C. et al. (2005) Quantitative brain MRI in alcohol dependence: preliminary evidence for effects of concurrent chronic cigarette smoking on regional brain volumes. Alcohol Clin Exp Res, 29(8), 1484–1495. 95. Gallinat, J., Meisenzahl, E., Jacobsen, L. K. et al. (2006) Smoking and structural brain deficits: a volumetric MR investigation. Eur J Neurosci, 24(6), 1744–1750. 96. Bolla, K.I., Eldreth, D.A., London, E.D. et al. (2003) Orbitofrontal cortex dysfunction in abstinent cocaine abusers performing a decision-making task. NeuroImage, 19(3), 1085–1094. 97. Bolla, K., Ernst, M., Kiehl, K. et al. (2004) Prefrontal cortical dysfunction in abstinent cocaine abusers. J Neuropsychiatry Clin Neurosci, 16(4), 456–464. 98. Eldreth, D.A., Matochik, J.A., Cadet, J.L., and Bolla, K.I. (2004) Abnormal brain activity in prefrontal brain regions in abstinent marijuana users. NeuroImage 23, 914–920. 99. Bartzokis, G., Lu, P.H., Beckson, M. et al. (2000) Abstinence from cocaine reduces high-risk responses on a gambling task. Neuropsychopharmacology, 22(1), 102–103. 100. Cox, W.M., Hogan, L.M., Kristian, M.R., and Race, J.H. (2002) Alcohol attentional bias as a predictor of alcohol abusers’ treatment outcome. Drug Alcohol Depend, 68, 237–243. 101. Waters, A.J., Shiffman, S., Sayette, M.A. et al. (2003) Attentional bias predicts outcome in smoking cessation. Health Psychol, 22, 378–387. 102. Moeller, F.G., Dougherty, D.M., Barratt, E.S. et al. (2001) The impact of impulsivity on cocaine use and retention in treatment. J. Subst. Abuse Treat, 21, 193–198. 103. Patkar, A.A., Murray, H.W., Mannelli, P. et al. (2004) Pre-treatment measures of impulsivity, aggression and sensation seeking are associated with treatment outcome for African–American cocaine-dependent patients. J. Addict. Disord., 23, 109–122. 104. Gr¨usser, S.M., Wrase, J., Klein, S. et al. (2004) Cue-induced activation of the striatum and medial prefrontal cortex is associated with subsequent relapse in abstinent alcoholics. Psychopharmacology (Berl), 175, 296–30. 105. Kosten, T.R., Scanley, B.E., Tucker, K.A. et al. (2006) Cue-induced brain activity changes and relapse in cocaine dependent patients. Neuropsychopharmacology, 31, 644–650. 106. Paulus, M.P., Tapert, S.F., and Schuckit, M.A. (2005) Neural activation patterns of methamphetamine-dependent subjects during decision making predict relapse. Archives of General Psychiatry, 62(7), 761–768.

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107. Muraven, M. (2010) Practicing Self-Control Lowers the Risk of Smoking Lapse. Psychology of Addictive Behaviors, 24, 446–452. 108. Passetti, F., Clark, L., Mehta, M.A. et al. (2008) Neuropsychological predictors of clinical outcome in opiate addiction. Drug Alcohol Depend., 94, 82–91. 109. Brewer, J.A., Worhunsky, P.D., Carroll, K.M. et al. (2008) Pretreatment brain activation during stroop task is associated with outcomes in cocaine-dependent patients. Biol Psychiatry, 64, 998–1004. 110. Connolly, C.G., Foxe, J.J., Nierenberg, J. et al. (Under Review) Neurocognitive markers associated with cocaine abstinence. 111. Murphy, P. and Garavan, H. (2011) Cognitive predictors of problem drinking and AUDIT scores among college students. Drug Alcohol Depend, 115(1–2), 94–100. PMID: 21145183.

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Chapter 8

Cognitive Disruptions in Drug Addiction: a Focus on the Prefrontal Cortex

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Chapter 8 Cognitive Disruptions in Drug Addiction: a Focus on the Prefrontal Cortex Rita Z. Goldstein1 , Scott J. Moeller1 , and Nora D. Volkow2,3 1

Medical Department, Brookhaven National Laboratory, Upton, NY 2 National Institute on Alcohol & Alcoholism, Bethesda, MD 3 National Institute on Drug Abuse, Bethesda, MD

8.1 Introduction Drug addiction is marked by mild, yet pervasive, neurocognitive disruptions [1, 2] that may accelerate its course and threaten sustained abstinence [3]. For example, behavioral and cognitive disinhibition at 10–12 years of age predicts substance use disorders in 19 year olds [4] and addicted individuals with better cognitive functioning have lower rates of attrition from treatment [5,6]. In particular, attention that is biased towards the drug and drug-related cues and away from other stimuli and reinforcers predicts relapse in abstaining individuals [7], while attenuating such attentional bias improves clinical prognosis [8,9]. Disruptions to memory and higher-order executive functioning, including decision-making (DM), are also consistently reported in addiction [10]. Yet despite the growth of the research on the cognitive deficits in drug addiction, the nature of these deficits remains uncertain and the study of their putative neuropathological mechanisms is yet to mature. Therefore, the goal of the current chapter is to comprehensively review functional magnetic resonance (fMRI) and positron emission tomography (PET) studies that compare drug-addicted individuals to healthy controls performing tasks of attention, memory, and DM.1 Given its critical role in all these cognitive functions [11], and in the 1 Excluded from this review are studies that did not include a control group (unless when not necessary or not feasible, such as when studying direct drug administration, abstinence, treatment, or relapse). Also excluded are studies in which methodological concerns precluded deriving definitive conclusions: those without a control task or baseline condition, those that were underpowered [N < 10 in each study group; also, within the included studies, analyses pertaining to such underpowered subgroups are not reviewed. Note that a more stringent cut-off, such as N < 15, would have excluded the majority of the studies reviewed (See Table 8.1)], and those that lacked explicit group comparisons or reported technical difficulties. Finally, excluded are related topics that appear in other chapters in this book (e.g., reward processing, inhibitory control, stress and cue reactivity).

Neuroimaging in Addiction, First Edition. Bryon Adinoff and Elliot Stein.  C 2011 John Wiley & Sons, Ltd. Published 2011 by John Wiley & Sons, Ltd.

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Figure 8.1 The prefrontal cortex, displaying regions of interest that underlie the cognitive disruptions in drug addiction. Specific regions of interest include the lateral and medial OFC [Brodmann area (BA) 47/12, 10, 11, clearly visible in (a) and 25 visible in (b)], the ACC [(BA 24, 32; clearly visible in (b)], the DLPFC (BA 9, 46) and inferior frontal gyrus/ventrolateral PFC [(BA 8, 44-46); clearly visible in (c)]. Modified and adapted with permission from Nature Publishing Group [12].

core neuropsychological symptoms of addiction as exemplified by the Impaired Response Inhibition and Salience Attribution (I-RISA) model (where excessive salience is attributed to drug-related cues at the expense of decreased sensitivity to other reinforcers and with a concomitant decrease in the ability to inhibit maladaptive or disadvantageous behaviors) [12], our focus is on the prefrontal cortex (PFC) (see Figure 8.1 for brain areas of interest). Our review does not focus on any specific drug of abuse; instead, it is guided by the role of the PFC – and its major subdivisions including the orbitofrontal cortex (OFC), dorsolateral prefrontal cortex (DLPFC), and anterior cingulate cortex (ACC) – in the selected cognitive functions in drug addiction. These regions were selected because of their identified roles in attention [14] [perhaps especially selective attention [15]], memory [16, 17], and DM [18]. Activation of the DLPFC appears to be common to all these functions [19]. For the OFC, Brodmann Areas (BA) are 10–14 and 47 (or 47/12) [20] but also the inferior and subgenual regions of the ACC BA 24, 25 and 32 in the ventromedial PFC [21] [see also OFC inclusion as part of orbital and medial PFC networks) [22]], for the

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8.2

Attention

DLPFC we reviewed BAs 6, 8, 9, 10 and 46 [23], the ACC encompasses the rostral and caudal-dorsal BAs 24 and 32 which are included within the medial PFC [that also includes BA 6, 8, 9, 10 [24]]. We also reviewed the inferior frontal gyrus/ventrolateral PFC, encompassing inferior portions of BA 8 and 44–46 [25] – with BA 44 increasingly recognized to play a key role in inhibitory control of cognitive, motor, and emotional functions [26] including control of craving [27] – as likely contributing to impulsivity, risk taking and disadvantageous DM in addiction. We focus on cross sectional studies (Table 8.1; in the text below, BA is only mentioned for studies not included in the table) although for each cognitive domain we also review the impact of direct drug administration (not included in Table 8.1) bookended by a brief definitional introduction and concluding summary.

8.2 Attention Attention refers to the action of selectively processing a particular stimulus while ignoring irrelevant others [56]. Posner and Petersen [57] proposed that the sources of attention form a specific system of interconnected anatomical areas (encompassing the PFC, parietal cortex, and thalamus), which can be further subdivided into three networks that carry out the functions of alerting, orienting, and executive control [57, 58]. Tasks that measure attention necessarily vary by the sensory modality involved. Early work on attention often explored auditory attention, relying especially on dichotic listening tasks. In these tasks, auditory information is presented in both ears, and the subject needs to repeat one of the two message streams verbatim. Usually, subjects can only attend to the message they are repeating [56]. Other tasks measure visual attention, often asking subjects to visually track a certain stimulus. In particular, the popular continuous performance/oddball tasks index sustained attention and vigilance by asking subjects to respond to an infrequently occurring stimulus while ignoring other continuously changing stimuli. These tasks have been employed for over 50 years with numerous alterations of task components (e.g., sensory modality, target type, frequency of target presentation, and stimulus duration) [59].

8.2.1 Attention without Drug Administration Compared to matched healthy control subjects, cocaine addicted individuals showed higher activations in the DLPFC as modulated by attention load, and more deactivations in the ACC, while visually tracking two to four of 10 moving target balls [34]. Similarly, while completing an oddball task that required pressing for infrequent targets with the right hand and more frequent non-targets with the left hand, nicotine addicted individuals showed less activation in the medial PFC as correlated with longer smoking duration [30]. In contrast, although methamphetamine-dependent individuals committed more errors and were less able to discriminate between targets and nontargets on an auditory continuous performance task, they showed relative regional metabolism that was higher in the middle ACC and right lateral OFC, but lower in the bilateral infragenual ACC, than matched controls [32]. In this study, direction of correlations with task-related errors was opposite for the two study groups (positive for controls, negative for addicted subjects). These results corroborate an earlier study by the same research group in which negative correlations were further observed between the left infragenual ACC and drug use [31].

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Only cross-sectional (between-group) results are included in this table. C controls, S subjects, Sg gamblers, Samp amphetamine users, Sopi opiate users, ex past users, Sat satiety, Abs abstinence. fMRI functional magnetic resonance imaging, FDG positron emission tomography (PET) with [18 F]fluorodeoxyglucose for glucose metabolism, H2 15 O PET for blood flow. CPT continuous performance test, WM working memory, DD delay discounting, IGT Iowa gambling task, RT reaction time. NR not reported, NA not applicable, NS not significant. T Task, N neutral, BL baseline. ACC anterior cingulate cortex, dACC dorsal ACC, MCC middle cingulate cortex. pgACC perigenual ACC, ifgACC infragenual ACC, rACC rostral ACC, scACC subcallosal ACC, vACC ventral ACC, FC frontal cortex, aFC anterior FC, mFC middle FC, IFC inferior FC, PFC prefrontal cortex, mPFC medial PFC, dmPFC dorsomedial PFC, vmPFC ventromedial PFC, DLPFC dorsolateral PFC, vlPFC ventrolateral PFC, IFG inferior frontal gyrus, OFC orbitofrontal cortex, mOFC medial OFC, MedFG medial frontal gyrus, MidFG middle frontal gyrus, SFG superior frontal gyrus, INS insula, SMA supplementary motor area. (+) positive correlation, (–) negative correlation. R right, L left, B bilateral, CL central. If available: ⇑ increase/activation/hyperactivation, ⇓ decrease/deactivation/hypoactivation, and Brodmann Areas. Subject column is in italics if groups are matched on 2 of the following: age, sex, race, education.

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8.2.2 Attention During Direct Drug Administration Allen et al. investigated how alcohol affects attention during a virtual driving task with oddball stimuli [60]. Young healthy subjects completed this task while sober, or with moderate (0.04% g/ml) or high (0.09% g/ml) levels of blood alcohol concentration. Results showed dose-dependent linear increases in reaction time and line crossings during (or after) oddball stimuli. Other behavioral effects were non-linear (e.g., the moderate alcohol dose was associated with the slowest speed and least amount of errors). Numerous PFC regions showed incremental fMRI blood oxygen level dependent (BOLD) signal decreases with increasing alcohol doses (BOLD signal was higher in sober > moderate > high alcohol levels), including the ACC (BA 24), inferior frontal gyrus (BA 45, 46) and DLPFC (BA 9, 10, 46); the ACC further correlated with performance hits on the oddball task [60]. Pending replication, this study is applicable to understanding (and possibly preventing) alcohol-driven impairments during real-life tasks, with perhaps the greatest relevance to individuals with alcohol use disorders. Visual spatial attention was also disrupted with acute marijuana administration. Current marijuana abusers (≥1 joint/day for ≥5 years) performed a 3-dimensional maze task after smoking a cigarette with or without tetrahydrocannabinol (THC) (17 mg). Compared to the placebo cigarette, smoking the THC cigarette resulted in more collisions on the maze task [and more errors on higher-order executive control tasks, including the Wisconsin Card Sorting Task and the Cambridge Risk task] and increased relative glucose metabolism in the ACC (BA 24) and right DLPFC (BA 6, 8) [61]. In contrast to alcohol or marijuana, visual attention may be enhanced by nicotine. For example, in minimally deprived smokers, a nicotine (vs. placebo) patch improved attentional focus (as measured with reaction time and omission error reductions) on a spatial attention resource allocation task, with associated enhanced deactivations in the default mode network (including BA 24, 32) [29]. Consistent with prior studies by this research group [28], in this study nicotine also enhanced DLPFC activations (BA 8, 9), especially during low intensity cue trials (with an opposite pattern for high intensity cues) and as associated with smaller reaction time benefits, therefore implicating this region in compensatory responses during performance of this task [29]. Nicotine’s impact on the PFC may be modulated by individual differences in abstinence and gender. For example, after two weeks of abstinence (smoking denicotinized cigarettes in addition to using a nicotine patch), regular smokers evidenced lower cigarette smoking and craving as associated with relative metabolic decreases in the right ACC during performance of an auditory 1-back continuous performance task [62]. A nicotine patch also decreased the gender differences (females > males) in PFC metabolism when performing a similar task [63].

8.2.3 Interim Summary: Attention In cross-sectional studies of attention, select PFC subregions including the ACC, DLPFC, and OFC have shown significant activation differences between drug-addicted individuals and healthy controls even when the groups did not differ on task performance (which was the rule rather than the exception). Involvement of the ACC supports the idea that executive control is an integral part of attention [57, 58]; its compromise in addicted individuals during attention possibly underlies the drug-related attention bias [64–67] and decreased performance monitoring in addiction. Involvement of the DLPFC suggests

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disruptions in drug addiction of vigilance, sustained attention or suppression of inappropriate responses [68] including those necessary to control attention [69]; note prior research in our laboratory pointing to an association between the DLPFC with inhibitory control in cocaine addiction [70]. Involvement of OFC and rostroventral ACC subregions suggests disruptions in drug addiction of goal driven behaviors and emotion regulation including hedonic experiences [71]. Together, these studies indicate abnormalities in drug addiction of PFC regions mediating alerting, orienting, and executive control as directly correlated with worse task performance and more drug use. Despite these recurring effects, inconsistencies have been frequently encountered. For example, in some PFC regions addicted individuals showed heightened responses compared with controls, while other regions/studies showed the opposite pattern (see Table 8.1). Further, although higher activation was typically associated with better function (shorter drug use duration, fewer errors), the direction of these brain-behavior correlations also varied (across studies and groups). Some of the conflicting direction of results may be driven by the use of different imaging modalities (PET vs. fMRI). In particular, because glucose metabolic measures reflect brain responses over a 30–45 minute period, PET results may reflect a longer-lasting response that is opposite in direction to an initial or more short-lasting response; the much higher temporal resolution of fMRI (seconds), on the other hand, enables the detection of areas with more dynamic patterns of activation/deactivation. Discrepant results could also be due to opposite effects of the different drugs of abuse on glucose metabolism (but also blood vessels and flow). For example, while marijuana intoxication is associated with higher levels of glucose metabolism in PFC (particularly in OFC) in marijuana abusers [72], alcohol administration, although increasing cerebral blood flow (CBF) in PFC [73], reduces whole brain glucose metabolism (also reducing normalized metabolism in PFC) in alcohol-dependent [74] and healthy control subjects [75]. Note that the discrepancy between CBF and glucose metabolism with alcohol administration is likely to reflect confounds in the glucose metabolic measure from the use of alternative brain energy substrates (i.e., acetate) during intoxication [75], and the fact that CBF measures reveal activity over 60 seconds. Route of drug administration (injection vs. smoking) may have also contributed to discrepancies in results. Overall, additional studies are clearly needed to probe for the neural correlates of attentional disruptions in drug addiction. It is especially important to employ active control tasks and randomized designs over separate days (for testing the effects of different drugs), and to specify direction of effects (activations vs. deactivations). Given the robust group differences in PFC activation during the performance of these attention tasks, the precise mechanism for achieving “normal” (i.e., similar to a healthy control group) task performance levels in the addicted individuals remains to be better understood. Although it is likely that compensatory mechanisms are involved (e.g., DLPFC hyperactivations, recruitment of subcortical regions), the dynamics and limits of their involvement (e.g., potential premature breakpoints when more effort is needed) remain to be determined. It is also important to conduct longitudinal within-study designs to address impact on results of length of abstinence, treatment, and course of illness. Given the marked inconsistencies when studying acute drug effects on attention, the direct between-drug comparisons in the same subject samples would be revealing. Such comparisons may highlight differential long-term effects of the substances under study. Comparison between different subject groups, and further pursuing different patterns in brain-behavior correlations, will help elucidate possible variability in regional function.

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Figure 8.2 Schematic of an N-back task. Subjects indicate whether the current stimulus was presented one trial before (1-back; left column), two trials before (2-back; middle column), or three trials before (3-back; right column). The higher the N, the higher is the working memory load and the more difficult the task.

8.3 Working Memory Working memory (WM) refers to the capacity to temporarily store and manipulate information required to complete complex tasks [76]. During WM tasks, participants typically attend to presented information (e.g., words or number strings) and then repeat the information. In one classical neuropsychological task – digit span – an examiner reads a list of numbers to the research participant, and the participant needs to repeat the string of numbers in the correct order. Another popular WM task is the N-back task. In this task, subjects are presented with a sequence of stimuli and then indicate when the current stimulus matches the one that was presented N steps earlier in the sequence. Thus, participants determine whether the currently presented stimulus is the same as the one that immediately preceded it (1-back), the same as two items before (2-back), the same as three items before (3-back), and so on. Each step reflects increasing task difficulty (Figure 8.2).

8.3.1 Working Memory without Drug Administration Hyperactivations in the ACC were reported in alcohol-dependent subjects performing WM tasks [39, 40]. Heightened bilateral response of the ACC (but also the right inferior frontal gyrus and bilateral DLPFC, BA 10) was also observed in heavy marijuana users (daily smoking, 5100–54 000 total smoking occasions) performing a short-delay WM task (similar to a 0- and 1-back task) compared with a visual perception (control) task [35]. A study of nicotine dependence corroborated these findings, showing more activation in smokers than healthy controls in the right medial superior frontal cortex and bilateral anterior PFC, the latter negatively correlated with lifetime cigarette smoking; administration of nicotine to smokers did not alter these results [42]. Addicted individuals had worse behavioral WM performance in two studies only. In a study of 3,4-methylenedioxymethamphetamine (MDMA; “ecstasy”), overall recall for a face-number matching task was worse in young (mean age 22 years) current users than in

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controls as associated with higher responses in the left DLPFC (superior frontal gyrus) and lower responses in the ACC during encoding [41]. Similarly, cocaine addicted individuals were slower than controls across all conditions of a verbal 2-back WM task, displaying lower accuracy in the most difficult conditions, as associated with higher activations in the DLPFC and more deactivations in the ACC (controls showed higher activations in the left inferior frontal gyrus instead) [33]. DLPFC hypoactivations to WM tasks were also reported, all in marijuana users: (1) in a study mentioned above but in other sub regions of the bilateral DLPFC (BA 6, 9) [35]; and (2) in studies of users of lower severity and duration of use. For example, such hypoactivations were noted in (A) adolescent marijuana users (14 uses/month for the last 3 months) performing a visuospatial 2-back (vs. 0 back or fixation) task [37]; and in (B) young (mean age 24 years) but heavier marijuana users (7 years of use, 19 days and 83 joints/past month) learning to match numbers to faces [36]. Such hypoactivations may be driven by a dampened response to practice as possibly indicative of premature habituation while response to novelty may elicit the opposite pattern as indicative of compensatory hyperactivation and inefficiency when processing information requiring frequent updating [38].

8.3.2 Working Memory During Direct Drug Administration Despite lack of a behavioral effect and compared to placebo, a moderate dose of oral caffeine (100 mg) enhanced task-related responses (2-back > 0-back) in the bilateral medial frontopolar cortex (BA 10), extending to the right ACC (BA 32), in moderate coffee drinkers after overnight abstinence [77]. Although caffeine is known to alter the BOLD signal independent of task-related activation [and has been used as a means to increase signal gain [78, 79]], and while appropriate controls remain to be included (e.g., noncoffee drinkers, different caffeine doses), these results suggest that caffeine enhances activity in PFC regions that underlie WM. Alcohol [consumed over 8 min by young (mean age 23 years) healthy subjects (0.75 mL/kg for males, .68 mL/kg for females)] also did not impact behavior on an event-related visual WM load task (low, medium or high load: tracking color of 2, 4, or 6 dots) that was performed 65 min after consumption [80]. Nevertheless, there was a significant alcohol effect in the bilateral inferior frontal gyrus (BA 9) (where alcohol increased activation) and right middle and superior frontal gyri (BA 6, 11) (where alcohol attenuated signal reduction), and a memory load × alcohol interaction in the bilateral DLPFC (middle frontal gyrus BA 9, 6) (with alcohol, activation was no longer modulated by WM load) [80]. In a more recent study, alcohol intoxication and expectancy had opposite effects on neuronal activation in healthy non-dependent social drinkers (but not alcohol na¨ıve such that alcohol expectation could be created) such that intoxication (.08% blood alcohol level) decreased neuronal activation to a WM 3-back task especially in the dorsal ACC (BA 24, 32) and other PFC regions, while expectancy (being correctly or incorrectly informed about drink identity) increased neuronal activation in the same areas [81]. Similar studies need to be performed with alcohol-dependent individuals for ascertaining relevance to addiction.

8.3.3 Interim Summary: Working Memory Similarly to attention, most imaging studies suggest no differences in WM performance between addicted individuals and healthy controls. Nevertheless, although the number

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of studies is still low, results suggest DLPFC hyperactivations (except in young users where DLPFC is hypoactive), and ACC hypoactivations, in addicted as compared to control subjects when task performance is matched. In contrast, when the expected behavioral compromises are reported, the DLPFC is hypoactive. Further, alcohol may decrease DLPFC support of WM functions while caffeine has the opposite effect as possibly generalizable to other depressants and stimulants, respectively. Variability in the studies underscores the importance of choosing the appropriate control task. For example, the discrepancy in results between marijuana and alcohol users on the one hand, and MDMA and cocaine users on the other hand, may be attributed to the comparison condition used (e.g., comparison to encoding vs. recall or degrees of memory load). Indeed, directly contrasting the 1- to the 2-back condition (i.e., focusing on the most challenging task condition while holding constant all other factors) revealed the expected hypoactivation in the DLPFC (controls > addicted), as associated with worse performance [33] (Figure 8.3). It is interesting to note that correlations between WM regional activations with task performance or drug use were largely not significant. We interpret this to reflect the possibility that drug abusers are able to maintain adequate levels of cognitive performance in WM tasks by recruiting alternative neuronal networks to those used by controls, which could explain the DLPFC hyperactivation and the ACC hypoactivation. However, the functional significance of PFC hypoactivation seen in young drug users despite lack of behavioral compromises or with alcohol is unclear (it may be related to premorbid vulnerabilities and other comorbidities as remains to be explored, see below). Therefore, impact on PFC results of task-related parameters and individual differences (e.g., abstinence, severity of addiction) remain to be studied, preferably in larger sample sizes. Indeed, the expected negative correlation

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between the right anterior PFC with use duration was significant in the largest sample size study (of adult nicotine users) reported above [42].

8.4 Decision-Making Effective DM consists of selecting an advantageous response when multiple options are available [82]. Drug-addicted individuals are broadly thought to make disadvantageous decisions, choosing to pursue drugs over other competing reinforcers. A common task probing advantageous versus disadvantageous DM is the Iowa Gambling Task (IGT) [83] (Figure 8.4). In this task, subjects choose from any of four flipped-over decks of cards; unbeknownst to the participants, two decks are considered high-risk (choosing these decks results in high gains but even higher losses), while the other two decks are low-risk (choosing these decks results in small gains but smaller longer-term losses). Accordingly, maximizing profit on this gambling task requires that participants consistently select from the two low-risk decks, a strategy that healthy control subjects learn promptly. In contrast, patients with damage to the ventromedial PFC (which includes parts of the medial OFC), but not DLPFC, consistently select from the high-risk decks [82]. One drawback of the IGT is that volunteers need to learn reward-punishment associations over the course of the task, which means that poor learners may develop a less successful

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Figure 8.4 Schematic of the Iowa Gambling Task, showing a representative trial. On each trial, subjects choose one of four decks of cards, two being disadvantageous (high gains but higher losses) and two being advantageous (low gains but lower losses). After indicating their choice, subjects see the amount of money gained or lost on that trial. The total money won is also typically displayed (here it is displayed in green in the cash pile top row). Image from the Wikimedia Commons, a freely licensed media file repository.

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Figure 8.5 Schematic of the Cambridge Risk Task, showing a representative trial. Subjects are told that a golden token is located either in a blue box or in a red box; their task is to guess which color box contains the token. Subjects can either opt for a high probability of guessing correctly, associated with lower reward if correct, or they can opt for low probability of guessing correctly, associated with higher reward if correct. The higher probability choices are considered advantageous. Subjects select the bet amount (square on right). The total amount of points gained is displayed (on left). From CANTAB demonstration: http://www.camcog.com/science/decision-making.asp.

DM strategy than good learners [84]. Consequently, the Cambridge Risk Task [85] was developed to investigate DM independently from learning. In this task, participants make bets when outcome probabilities and reward values are explicit, a context that circumvents the need for learning (Figure 8.5). Despite the explicit contingencies, chronic drug users are impaired on the Cambridge Risk Task, attributed to impairments in correctly estimating outcome probabilities [85]. Similarly to the IGT, the Cambridge Risk Task recruits the OFC/medial PFC (BA 10, 11, 47) [85]. However, because it presumably depends on fewer cognitive processes than the IGT [86], the Cambridge Risk Task may be more selective for interrogating the OFC. Another common method for assessing disadvantageous DM is with tasks of delay discounting that measure whether subjects are willing to forgo an immediate but lesser reward for a more distant but higher reward as associated with the ACC and inferior frontal gyrus [87]. Drug-addicted individuals discount delayed reward more steeply than controls, interpreted to reflect failure in considering future outcomes [88]. Such deficits could also result from a decreased threshold for distress at not being able to achieve immediate gratification. Dysfunction of the ACC that modulates activity of the amygdala (responsible for negative emotion) could contribute to this deficit.

8.4.1 Decision-Making without Drug Administration 8.4.1.1 Gambling Tasks The IGT or Cambridge Risk Task were compared to active control tasks in five imaging studies (4 PET for normalized/regional CBF, 1 fMRI) (Table 8.1). In two of these studies, the drug-addicted individuals made more disadvantageous [51] or risky choices [54] relative

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to controls. In most of these studies, drug-addicted individuals showed hypoactivations in the ACC (BA 24, 32) and DLPFC (BA 9, 10, 46). The OFC showed mixed results such that the lateral OFC was hypoactive [in marijuana users [51]] while the medial OFC was mostly hyperactive [in cocaine addicted individuals [50]; in current users of amphetamine or opiates and previously drug-dependent subjects [53]; and in gamblers [52], although in this study this same region (BA 25/11) was hypoactive in substance-dependent individuals]. Associations of the OFC with better task performance (lower risk taking) or lower drug use [50, 51, 54] speak to this region’s role in the effort that is needed to make advantageous decisions as negatively impacted by severity of use [note, however, opposite correlations with drug use for the right medial OFC [54]]. Similarity of results across current and past users [53] bolsters the idea that such OFC dysfunction during DM endures well into abstinence (Figure 8.6). The differential lateral vs. medial OFC results deserve a particular discussion given an influential meta-analysis of the OFC, which suggested that reward tends to activate medial OFC regions, whereas punishment tends instead to activate more lateral OFC regions [89]. In addiction, the medial OFC is uniquely implicated in drug expectation [90] and craving suppression in cocaine addicted individuals [27, 91] as associated with midbrain cue reactivity [92] and dopamine receptor availability [93]. Structurally, the medial OFC shows gray matter reductions in addicted individuals as correlated with risk-taking on a gambling task [94] and driven by low function of the monoamine oxidase A genotype, which regulates metabolism of monoamine neurotransmitters including dopamine [95]. Taken together, medial OFC hyperactivations across the gambling tasks reviewed here suggest dopaminergically driven disruptions in reward processing, possibly associated with enhanced salience of the risky conditions and reduced recruitment of emotional and behavioral resources that could tag as motivationally salient the alternative, less risky yet delayed, choices. The medial OFC also receives significant serotonergic innervation, which is believed to regulate behavioral flexibility, and thus serotonergic disruption in addiction could also contribute to this deficit [96]. An explicit comparison between gambling and drug cue-reactivity tasks could help demarcate the role of the OFC in compulsivity in addiction.

8.4.1.2 Delay Discounting Methamphetamine-dependent subjects were compared to matched healthy controls on delay discounting choices classified as hard versus easy in two studies; despite variability in group performance differences between these studies, task-related PFC hypoactivations were noted in the addicted individuals, an effect that was driven by the easy choices (where responses were increased in the addicted subjects only) [46, 47]. The ventrolateral PFC correlated with lower delay discounting/better DM [46] whereas the superior frontal gyrus correlated with steeper discounting/poorer DM [47], correlations that remain to be replicated in larger sample sizes. Despite marked behavioral differences between smokers and non-smokers during performance of a related task (a sequential gambling task), where information on ‘what might have been’ guided future decisions in the latter but not former group, PFC group differences were not significant [48]. Nevertheless, subjective craving correlated with rostral ACC ‘fictive error’ response but only for unsated smokers. A similar dissociation between behavioral and neural group differences were reported in abstinent alcohol-dependent men during a similar reward-guided DM task with dynamically changing response-outcome contingencies [49]. In this latter study, there were group differences

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Figure 8.6 Task-related activation in the left medial OFC (BA 11) in drug users (i.e., current amphetamine and opiate users and former amphetamine- and opiate-users) compared with controls, shown in (a) sagittal, (b) coronal, and (c) axial planes. (d) Mean size and variability of the effect for each group (x = −32, y = 32, z = −20). Modified and adapted with permission from Springer [52].

in functional connectivity between the right DLPFC and ventral striatum to win more than loss trials and the stronger this frontostriatal connectivity, the faster the learning and the lower the craving. These results suggest that functional connectivity analyses may be more sensitive to performance variables than the traditional regional analyses.

8.4.1.3 Other Choice Tasks When performing a two-choice prediction task, methamphetamine-dependent abstinent inpatients used a lose-shift strategy more than controls as negatively correlated with sobriety duration and associated with hypoactivations within regions of the task main

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effects including the right inferior frontal gyrus, OFC, and bilateral DLPFC [43, 44]. While the inferior frontal gyrus and middle frontal gyrus were more active when “successfully” predicting the outcome of the stimulus (low error rate) in the healthy control subjects, these regions were most active when the outcome of the stimulus was most unpredictable (50% error rate) in the drug-addicted individuals [44]. Lower adaptability to error on this same task and DLPFC group differences even for non-addicted young stimulant users suggest these findings could be markers of a predisposition to addiction [45]. Importantly, higher baseline task-related activations in the right DLPFC (BA 6, 8/9), right inferior frontal gyrus (BA 45), and left ACC (BA 32) predicted 1-year abstinence among 40 methamphetamine-dependent treatment-seeking individuals [97]. This study directly speaks to how cognitive disruptions in drug addiction may contribute to detrimental clinical outcome: hypoactivations in these areas may be associated with worse DM (and other related processes such as executive control), enhancing the possibility of relapse.

8.4.2 Decision-Making During Direct Drug Administration Healthy control subjects were administered the benzodiazepine lorazepam (at 0, .25 and 1 mg) during a risk-taking task; the numerous neural changes with lorazepam [e.g., inverted u-shaped dose effect in the right inferior frontal gyrus (BA 47), right ACC (BA 24), and left middle frontal gyrus (BA 6); linear dose effect increases in a more ventral region of the right ACC (BA 24), left superior frontal gyrus (BA 8), and right middle frontal gyrus (BA 10); attenuation of win activations in the medial PFC] remain to be interpreted vis-`a-vis a design that causes behavioral differences [98]. Importantly, DM during acute intoxication remains to be studied in drug-addicted individuals. As a gross approximation, one could explore the impact of emotion on DM and risk taking in healthy individuals or in related psychopathology.

8.4.3 Interim Summary: Decision-Making Of the 10 neuroimaging studies that compared drug-addicted individuals to healthy control subjects during the performance of DM tasks, six studies reported group differences in task performance. Nine of these studies reported task-related hypoactivations in the addicted individuals relative to controls in PFC regions encompassing the DLPFC, lateral OFC and ACC. Given the conflicting direction of correlations with task performance and drug use, the specific contribution of these regions to disadvantageous DM in addicted individuals remains to be explored, preferentially in direct drug administration designs. Nevertheless, the role of these regions in DM tasks has been widely explored in healthy individuals, emphasizing their contributions to sensory integration, motor planning [99] and control implementation [100] (DLPFC), performance monitoring [100] (ACC) and disambiguating the relationship between multiple choices and their (expected) outcomes (OFC) [101, 102]. The medial OFC, in contrast, was mostly hyperactive in addicted individuals during performance of gambling tasks (IGT and Cambridge Risk Task), possibly reflecting the DM effort when comparing the different choice options (e.g., delayed vs. immediate reward) as associated with lower drug use severity. Direct comparisons between positive and negative reinforcers and between reward and punishment in addiction will help delineate the contribution of the lateral vs. medial OFC to disadvantageous DM including risk taking and impulsivity in addiction.

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8.5 Pre-Morbid Vulnerabilities Pre-morbid vulnerabilities such as prenatal alcohol exposure or family history of alcoholism have been examined in several recent studies in adolescents (mean ages 13–15 years). Heavy prenatal alcohol exposure was associated with greater fMRI response across widespread cortical regions including bilateral middle, superior and inferior frontal gyri (BA 6, 8, 9, 10, 13, 47) to the demands of a 2-back spatial WM task [103]. Prenatal exposure to cocaine showed a similar pattern: the left DLPFC (BA 9, 46) showed a greater WM response in the exposed group than in controls, primarily during negative emotional arousal (i.e., memory load prefrontal activation decreased in the presence of emotional distraction in the controls, but it increased in the exposed group) [104]. In contrast, subjects with denser family histories of alcohol use disorders (i.e., more parents and/or grandparents with the disorder) showed less cingulate and medial frontal gyri activation during a simple vigilance condition relative to a spatial WM task, although results may have been driven by the more posterior cingulate regions [105] and null results were also reported [106]. Clearly, the impact of these vulnerability factors on brain responses to a cognitive challenge remains to be clarified with further research. A second type of vulnerability involves carrying risk alleles for certain genes. One such gene is the catechol-O-methyltransferase (COMT) enzyme that helps regulate dopamine levels in PFC. Specifically, the COMT gene has G–A transition in exon 3 that results in a substitution of methionine for valine at codon 158 (val158 met). The val allele is associated with a 3- to fourfold increase in enzyme activity and decreased PFC dopamine levels. In healthy cigarette smokers, only val/val subjects (but not met/met or val/met subjects) showed an impact of smoking (as usual) vs. abstinence (≥14 hours) in both task performance (faster reaction time) and fMRI increased activations in PFC [dorsal ACC/medial PFC (BA 32) and bilateral DLPFC (BA 46)] during the hardest 3-back condition on a visual n-back task [107]. These findings suggest that the COMT val allele may confer increased susceptibility to nicotine dependence and smoking relapse especially during a challenging context (where vulnerable individuals may use drugs for self-medication/cognitive enhancement purposes). Genes may also impact the propensity for disadvantageous DM. For example, among 200 healthy male Caucasian young participants, subjects who carried at least one copy of the 7-repeat allele of the DRD4 polymorphism showed an increased gambling propensity 60 min after dopaminergic stimulation (with 300 mg L-DOPA vs. placebo) [108]. A third type of vulnerability involves having a comorbid psychopathology. Compared to community controls, adolescents with substance dependence and a comorbid conduct disorder showed lower activation in frontal regions encompassing the medial and middle frontal gyri, and the ACC, during a gambling task [55]. Relevance of results to addiction in adults, and generalization to cognitive functions other than WM load and gambling, remain to be explored.

8.6 Other Brain Regions Given its role as the hub of the brain’s executive control network, we focused this chapter on the anterior PFC [11]. However, the “default mode network,” encompassing the posterior cingulate cortex, precuneus, lateral parietal and medial temporal cortices (as well as the medial PFC), has also been implicated in executive control processes such as disinhibition and attention shifting (e.g., internal to external reference) [109–111].

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Limitations Across All Studies

We have recently suggested that methylphenidate, a dopamine agonist, attenuates the cocaine cue-induced engagement of this network in cocaine-addicted individuals if administered orally, potentially due to reduced engagement of the individual’s attention by a cocaine-related context [112]2 . Indeed, preliminary fMRI results suggest that oral methylphenidate improves executive function (by decreasing impulsivity) and normalizes underlying ACC responses in cocaine-addicted individuals [113]. The in-depth exploration of how this brain network [as well as other networks and interconnected brain regions such as the striatum [114], insula [115, 116], and cerebellum [117]] impacts cognitive function in drug addiction is beyond the scope of the current chapter and remains to be undertaken separately.

8.7 Limitations Across All Studies Further studies are needed to address methodological issues, variability in study design, and factors affecting the interpretability of findings (e.g., inclusion/exclusion of active control conditions, group differences in behavioral performance, and type of addictive substance). A notable difficulty in many of the reviewed studies pertains to using a region of interest analysis that is not necessarily confirmed with the more stringent statistical corrections of whole brain analyses. For example, to overcome issues of low power, data is collapsed across all study subjects and task conditions, and reported results pertain to post-hoc analyses in these main regions (that showed activations in all subjects to all task conditions vs. a baseline). Whole brain comparisons between groups and/or within task conditions (to explore potential main and interaction effects) are often lacking. Further, anatomical definitions are not always clear, and actual data (mean and variance, scatterplots for correlations) are not consistently provided. This field would clearly benefit from more studies of adequate power, more stringent statistical thresholds and standardization for reporting location and magnitude of regional effects. Further studies are also needed to explore the potentially modulating effects of individual characteristics. For example, the impact on cognitive function of recency of drug comorbidities (i.e., effects of nicotine histories in BOLD activation responses in alcoholics) and drug use/length of abstinence (e.g., brief: less than one week vs. longer periods of abstinence: more than two weeks, a month, a year, a decade or more) remain key unclear issues. In a neuropsychological study, we have previously reported that recent use of stimulants (cocaine) may reduce or mask underlying cognitive impairments in cocaine addicted individuals [2], possibly by enhancing function of the dopaminergicallyinnervated compromised PFC. Recent studies in addition in our group [113] similarly suggest that stimulants such as methylphenidate (or other dopamine agonists) improve brain-behavior responses to executive function PFC-mediated tasks in stimulant addicted individuals [118, 119]. Similar patterns were observed in the reviewed studies. For example, when cocaine addicted individuals tested positive for cocaine in urine (indicating use within 72 hours), they responded faster than those who tested negative (indicating more protracted last use) in an intermediately difficult sustained attention task condition (note, however, that the omnibus interaction between task condition and urine status did not reach significance, precluding definitive conclusions) [34]. In tasks of DM,

2 Hahn

et al. also demonstrated a nicotine-induced decrease in default mode regions correlating with better attention.

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group differences were observed for most studies of methamphetamine users, except in the study in which subjects had the shortest abstinence (5–7 days versus an average of several weeks; Table 8.1). Together, these results support the self-medication hypothesis where drug self-administration is posited to ameliorate underlying emotional and cognitive deficits [120, 121], although longitudinal within-subject designs would allow the empirical examination of this important issue.

8.8 Treatment Implications Despite these difficulties and remaining questions, the studies reviewed here are essential to our understanding of drug addiction and for prevention and intervention purposes: (1) brain imaging studies and/or neuropsychological tests that could serve as biomarkers of PFC function could be used to identify children/adolescents who would benefit from more intensive drug abuse prevention efforts; and (2) cognitive dysfunction has direct relevance to the treatment course of those suffering from a substance use disorder. First, cognitive dysfunction is associated with higher attrition from treatment [5, 6]. Second, understanding key PFC circuits implicated in cognitive dysfunction in drug addiction could facilitate efforts to formulate targeted therapeutic remedies. For example, one could use or develop a pharmacological agent that improves neuropsychological and associated PFC functioning without a detrimental impact on mood or drug craving. For example, as mentioned above, we recently showed that an oral dose of methylphenidate (which similarly to cocaine blocks the dopamine transporter, thus increasing dopamine extracellular availability) improved cognitive performance (decreasing errors of commission, a measure of impulsivity) both in cocaine-addicted individuals and healthy controls during performance of a salient task (pressing for color of drug vs. neutral words while receiving monetary remuneration for accurate performance) as associated with normalized activation of both major ACC subdivisions (dorsal BA 24, 32 and rostroventromedial BA 10, 32) in the addicted subjects [113]. Another example is the use of varenicline (which, like nicotine, binds to nicotinic acetylcholine receptors stimulating moderate levels of dopamine release) that has been suggested to be more effective than nicotine replacement therapies or bupropion in the treatment of nicotine addiction as potentially attributed to its impact on strengthening DLPFC and ACC responses to a cognitive task (a visual WM load) [122]. Other approaches for normalizing the PFC in addicted individuals remain to be explored. For example, low-frequency repetitive transcranial magnetic stimulation that transiently disrupted the right (but not left) DLPFC in addicted individuals transiently reduced craving for cocaine [123] and alcohol [124]. Whether such interventions designed to enhance PFC functioning (thereby enhancing attention and shifting it away from drug-related cues, strengthening WM and attenuating risk-taking) could reduce drug-seeking in addicted individuals remains a pertinent question for future research.

8.9 General Summary and Conclusions Our chapter summarized neuroimaging studies in drug-addicted individuals vs. healthy control subjects performing attention, WM, and DM tasks. Although this field is gaining strides, much work remains. Nevertheless, an emerging pattern across all cognitive domains examined points to task-related hypoactivity of the PFC, and especially the DLPFC and ACC, in the drug-addicted individuals. Despite this overwhelming pattern,

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task-related hyperactivations were also observed mostly in the context of equal task performance pointing to compensatory responses to achieve adequate levels of cognitive function. Hyperactivations were also observed for the medial OFC, and in at-risk adolescents (those with family history of or prenatally predisposed to drugs including alcohol), again supporting compensatory processes (e.g., DM effort) or inefficient processing when task demands increase (i.e., during the most challenging task condition). Given that associations with behavior were mostly in the expected direction (decreased regional responses correlated with more drug use and worse task performance), such a combined pattern of hypoactivations and the associated compensatory hyperactivations may be a good target for intervention efforts, especially in cognitive rehabilitation longitudinal designs potentially enhancing clinical outcome. Initial targets could be improving ACC-driven performance monitoring and DLPFC-driven error resolution for enhancing OFC-driven DM. Together, such PFC normalization could culminate in reduced automatic/habitual drug-seeking behaviors as associated with enhanced avoidance of its adverse consequences and approach of alternative non-drug reinforcement.

8.10 Acknowledgments This chapter was supported by grants from the National Institute on Drug Abuse (to RZG: 1R01DA023579; to SJM: 1F32DA030017-01). Notice: This manuscript has been authored by Brookhaven Science Associates, LLC under Contract No. DE-AC02-98CHI-886 with the US Department of Energy. The United States Government retains, and the publisher, by accepting the article for publication, acknowledges, a world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the United States Government purposes.

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97. Paulus, M.P., Tapert, S.F., and Schuckit, M.A. (2005) Neural activation patterns of methamphetamine-dependent subjects during decision making predict relapse. Arch Gen Psychiatry, 62, 761–768. 98. Arce, E., Miller, D.A., Feinstein, J.S. et al. (2006) Lorazepam dose-dependently decreases risk-taking related activation in limbic areas. Psychopharmacology (Berl), 189, 105–116. 99. Kim, J.N. and Shadlen, M.N. (1999) Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nat Neurosci, 2, 176–185. 100. MacDonald, A.W. 3rd, Cohen, J.D., Stenger, V.A., and Carter, C.S. (2000) Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science, 288, 1835–1838. 101. Seo, H. and Lee, D. (2010) Orbitofrontal cortex assigns credit wisely. Neuron, 65, 736–738. 102. Schoenbaum, G., Roesch, M.R., Stalnaker, T.A., and Takahashi, Y.K. (2009) A new perspective on the role of the orbitofrontal cortex in adaptive behaviour. Nat Rev Neurosci, 10, 885–892. 103. Spadoni, A.D., Bazinet, A.D., Fryer, S.L. et al. (2009) BOLD response during spatial working memory in youth with heavy prenatal alcohol exposure. Alcohol Clin Exp Res, 33, 2067–2076. 104. Li, Z., Coles, C.D., Lynch, M.E. et al. (2009) Prenatal cocaine exposure alters emotional arousal regulation and its effects on working memory. Neurotoxicol Teratol, 31, 342–348. 105. Spadoni, A.D., Norman, A.L., Schweinsburg, A.D., and Tapert, S.F. (2008) Effects of family history of alcohol use disorders on spatial working memory BOLD response in adolescents. Alcohol Clin Exp Res, 32, 1135–1145. 106. Pulido, C., Anderson, K.G., Armstead, A.G. et al. (2009) Family history of alcohol-use disorders and spatial working memory: effects on adolescent alcohol expectancies. J Stud Alcohol Drugs, 70, 87–91. 107. Loughead, J., Wileyto, E.P., Valdez, J.N. et al. (2009) Effect of abstinence challenge on brain function and cognition in smokers differs by COMT genotype. Mol Psychiatry, 14, 820–826. 108. Eisenegger, C., Knoch, D., Ebstein, R.P. et al. (2010) Dopamine receptor D4 polymorphism predicts the effect of L-DOPA on gambling behavior. Biol Psychiatry, 67, 702–706. 109. Raichle, M.E., MacLeod, A.M., Snyder, A.Z. et al. (2001) A default mode of brain function. Proc Natl Acad Sci U S A, 98, 676–682. 110. Greicius, M.D., Krasnow, B., Reiss, A.L., and Menon, V. (2003) Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A, 100, 253–258. 111. Raichle, M.E. and Snyder, A.Z. (2007) A default mode of brain function: a brief history of an evolving idea. Neuroimage, 37, 1083–1090; discussion 1097–1089. 112. Volkow, N.D., Wang, G.J., Tomasi, D. et al. (2010b) Methylphenidate attenuates limbic brain inhibition after cocaine-cues exposure in cocaine abusers. PLoS ONE, 5, e11509. 113. Goldstein, R.Z., Woicik, P.A., Maloney, T. et al. (2010) Oral methylphenidate normalizes cingulate activity in cocaine addiction during a salient cognitive task. Proc Natl Acad Sci U S A, 107(38), 16667–16672. 114. Aarts, E., Roelofs, A., Franke, B. et al. (2010) Striatal dopamine mediates the interface between motivational and cognitive control in humans: evidence from genetic imaging. Neuropsychopharmacology, 35, 1943–1951. 115. Naqvi, N.H., Rudrauf, D., Damasio, H., and Bechara, A. (2007) Damage to the insula disrupts addiction to cigarette smoking. Science, 315, 531–534. 116. Garavan, H. (2010) Insula and drug cravings. Brain Struct Funct, 214, 593–601. 117. Miquel, M., Toledo, R., Garcia, L.I. et al. (2009) Why should we keep the cerebellum in mind when thinking about addiction? Curr Drug Abuse Rev, 2, 26–40. 118. Ersche, K.D., Bullmore, E.T., Craig, K.J. et al. (2010) Influence of compulsivity of drug abuse on dopaminergic modulation of attentional bias in stimulant dependence. Arch Gen Psychiatry, 67, 632–644. 119. Li, C.S., Morgan, P.T., Matuskey, D. et al. (2010) Biological markers of the effects of intravenous methylphenidate on improving inhibitory control in cocaine-dependent patients. Proc Natl Acad Sci U S A, 107(32), 14455–14459.

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120. Khantzian, E.J. (1985) The self-medication hypothesis of addictive disorders: focus on heroin and cocaine dependence. Am J Psychiatry, 142, 1259–1264. 121. Khantzian, E.J. (1997) The self-medication hypothesis of substance use disorders: a reconsideration and recent applications. Harv Rev Psychiatry, 4, 231–244. 122. Loughead, J., Ray, R., Wileyto, E.P. et al. (2010) Effects of the alpha4beta2 partial agonist varenicline on brain activity and working memory in abstinent smokers. Biol Psychiatry, 67, 715–721. 123. Camprodon, J.A., Martinez-Raga, J., Alonso-Alonso, M. et al. (2007) One session of high frequency repetitive transcranial magnetic stimulation (rTMS) to the right prefrontal cortex transiently reduces cocaine craving. Drug Alcohol Depend, 86, 91–94. 124. Mishra, B.R., Nizamie, S.H., Das, B., and Praharaj, S.K. (2010) Efficacy of repetitive transcranial magnetic stimulation in alcohol dependence: a sham-controlled study. Addiction, 105, 49–55.

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Neural Mechanisms of Stress and Addiction

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Chapter 9 Neural Mechanisms of Stress and Addiction Dongju Seo and Rajita Sinha Department of Psychiatry, Yale University School of Medicine, New Haven, CT

9.1 Stress and Addiction Stress refers to a state of disrupted homeostasis in response to threatening or challenging stimuli [1–3]. Stressors can be social-emotional, physiological and pharmacological components in nature [3]. For example, emotional stressors can be marital discord, family problems, job loss, interpersonal problems, and loss of loved ones. Physiological stressors include hunger, sleep deprivation, physical pain and, in the case of addiction, drug withdrawal. An example of pharmacological stressors is heavy use of alcohol and psychoactive drugs [3]. Stress activates a cascade of emotional and physiological responses (e.g., glucocorticoid release) and requires adaptive processes to reinstate homeostasis [3–5]. Stress can increase anxiety [6, 7], but is differentiated from anxiety disorders. Anxiety disorders encompass chronic states of worry, tension, and nervousness [8] and are typically accompanied by negative mood, arousal, and increased reactivity to stress [9, 10]. Stress often generates negative emotions such as sadness, fear and anger, especially with increasing levels of challenge, and greater unpredictability and uncontrollability [3]. Cognitive, emotional, and behavioral regulation are key factors in adapting and coping with stressful situations. Sudden, extremely stressful events such as traumas can significantly impair one’s adaptability [2, 3, 11]. Such extreme, life-threatening stressors could induce a cluster of clinical symptoms, such as posttraumatic stress disorder (PTSD), marked by excessive anxiety, hyper-vigilance, emotional numbness, and intrusive recall of traumatic events [8]. Chronic, persistent stress also leads to prolonged homeostatic dysregulation and increases the risk for maladaptive behaviors, including addiction [3, 5, 12]. For example, chronic pain, a persistent physiological stressor, often generates negative emotional reactions and many patients with chronic pain suffer from emotional distress and addictive disorders [13–15], supporting a link between stress-related illness and addiction. It is well established that stress significantly increases susceptibility to addiction and addiction relapse [3, 16, 17]. Persistent drug craving, an important risk factor for relapse, is frequently induced by stress exposure [3]. Patients with addictive disorders also frequently suffer from stress-related emotional disorders such as depression and

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anxiety [18], which often complicates the assessment and treatment of addictive disorders. These findings suggest that there is a presence of shared brain circuits associated with experience and regulation of stress and addiction. Research also indicates that an overlap between stress and reward circuitry may underlie the pathophysiology of addiction and stress-related illnesses [3, 19, 20]. Although these studies have provided some insights into the neurobiology of stress and addiction, specific neurobiological interactions between them remain unclear, especially in humans. One of the difficulties is the identification of common biological substrates for stress and addiction as well as neural patterns of dysfunctional interactions between these two factors. It is also important to consider premorbid stress vulnerabilities and continuous exposure to life stressors that may alter brain systems of stress and reward regulation, which could increase vulnerability to the development of addiction and addiction relapse. In order to elucidate the neural mechanisms underlying stress and addiction, this chapter reviews relevant neuroimaging studies and discusses the functional aspects of brain systems underlying stress regulation as well as patterns of regulatory deficits associated with stress and addiction pathology. The chapter is broadly divided into three main sections. First, we review the neural circuitry of stress regulation, with an emphasis on functional neural mechanisms of emotion regulation associated with stress. Second, the dysfunctional neural circuits underlying stress and addiction is examined, including the neurobiology of relevant psychiatric disorders. Finally, interactions among biological predisposing factors, stress, and drug intake are covered in an effort to understand the neurobiological dysfunction that underlies perpetuating vicious cycles of stress, craving and relapse, aggravating the severity of illnesses. Understanding the neurobiological mechanisms of addictive disorders in the context of chronic stress will provide insights into the etiology of the complex psychopathological phenotype and into the development of effective treatments for psychiatric disorders associated with both stress and addiction.

9.2 Neural Circuits of Stress Regulation 9.2.1 The Cortico-Striatal-Limbic Circuit Neurobiological evidence suggests that there is a significant overlap between stress and reward modulation systems in the cortico-striatal-limbic circuit [21]. Chronic stress states and substance abuse each result in neuroadaptations in this circuit [22], and dysfunction in this circuit is known to increase drug craving and vulnerability to relapse [19, 23, 24]. The cortico-striatal-limbic circuit has been identified as a key system for stress and reward regulation [22]. It consists of regulatory brain regions such as the prefrontal cortex (PFC; dorsolateral, ventromedial, orbitofrontal), anterior cingulate cortex (ACC) as well as limbic-striatal regions involved in emotional reactivity and arousal, including the amygdala, striatum and hippocampus [25–27]. This section reviews the functional role of each brain region in the cortico-striatal-limbic circuit in order to understand mechanisms underlying emotion regulation associated with stress and addiction.

9.2.1.1 Dorsolateral and Lateral PFC The dorsolateral prefrontal cortex (DLPFC) is a major regulatory region of cognition and emotion, and the activity of DLPFC has been consistently found in studies on cognitive

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control of emotion. For example, neuroimaging studies have found that DLPFC activity increases during conscious, voluntary control of emotions such as negative affect [28,29], erotic arousal [30] and sad feelings [31]. The DLPFC integrates and evaluates information received from other brain regions and mediates cognitive processes for the execution of planned behaviors [27, 32]. The DLPFC is also involved in reward-related decision making based on information encoded by the orbitofrontal cortex (OFC) [33]. The lateral PFC interacts with the OFC [34] and integrates cognitive and emotional information [35] for further implementation of intended goals [36].

9.2.1.2 Medial PFC and Orbitofrontal Cortex The medial PFC and OFC play a crucial role in emotional control. Anatomically, the medial PFC is connected with the medial part of OFC, which has dense projections to the amygdala and hippocampus [37]. The orbitomedial cortex also sends cortical outputs to the hypothalamus and brain stem, and these networks have connections with ventral striatum, ventromedial caudate and putamen, allowing the modulation of reward related behaviors [38, 39]. The OFC has been associated with reversal learning involving positive or negative emotional feedback with the medial division, which is more associated with reward sensitivity and with the lateral division, which is more sensitive to punishment reactivity [40]. It is also known to promote flexible encoding of emotional response in the amygdala [40–42]. Further, the ventromedial PFC has been shown to modulate amygdalar activity during the regulation of negative emotion [43], suggesting the role of this region in emotional control.

9.2.1.3 Anterior Cingulate Cortex (ACC) The ACC plays a crucial role in cognitive control by monitoring cognitive [44, 45] and emotional [46] conflicts. It is thought to guide human behavior by generating postconflict behavioral adjustment [47] and avoidant learning [48]. Specifically, the rostral ACC (rACC) is anatomically and functionally connected to the medial OFC [37] and the basolateral amygdala, and modulates amygdala-dependent fear conditioning [49]. The rACC has been associated with affective aspects of conflict monitoring and control. For example, in a study using an emotional Stroop test, the rACC was activated when taskirrelevant information was distracting due to emotional content [50]. The dorsal ACC (dACC) is anatomically connected to the motor system and lateral PFC [51] and has been associated with cognitive processing, motor control [50], and reward processing [52]. It has been suggested that the dACC and dorsolateral PFC closely interact in monitoring and in evaluating reward values [52]. Monkey single-cell recording studies indicate that the dACC is associated with behaviors related to anticipation, reward value, and error cues [53]. In humans, the dACC has also been shown to be involved in rewardrelated decision-making and promoting behavioral responding [54, 55]. The dACC is also thought to integrate reward prediction error signals received from the mesencephalic dopamine (DA) system to facilitate decision making and motor control [56].

9.2.1.4 Amygdala and Nucleus Accumbens (NAcc) The amygdala and nucleus accumbens are core regions in limbic-striatal regions involved in emotional response and arousal. In particular, the amygdala responds to both positive

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and negative emotions [57], such that amygdalar activity increases in the presence of negative stimuli [58] as well as positive stimuli [59]. However, when fear stimuli were directly compared with positive stimuli, the amygdala was more responsive to fear stimuli [60]. A meta-analysis examining fifty-five positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) studies on emotion demonstrated that fear stimuli reliably activated greater amygdala responses [61]. The amygdala is also found to be activated by stressful stimuli [62], indicating a specific role of the amygdala in emotional stress and aversive processing. In contrast to the amygdala, the NAcc has been associated with pleasant emotion, reward [63, 64], and reward prediction [65]. Increased NAcc activity is observed in response to positive emotion [66], and DA levels in the NAcc are elevated in response to food [67], sexual arousal, [68] and drugs [69]. Several researchers propose that DA transmission is involved in reinforcement learning by processing the temporal difference error (TDE) signal, with positive TDEs for a better outcome of expected reward and with negative TDEs for a worse outcome of expected reward [65, 70, 71]. Thus, the neurotransmitter dopamine responds to conditioned, reward predicting stimuli [70] and has been associated with reward-seeking behavior [63, 72, 73]. To summarize, brain regions in the cortico-striatal-limbic circuit functionally interact with each other to modulate emotion associated with stress and reward. Specifically the PFC (dorsolateral, ventromedial, orbitofrontal) exerts regulatory control to modulate emotional responses in limbic-striatal regions, and the limbic-striatal system closely communicates with prefrontal regions via feedback circuits. Therefore, the interaction between prefrontal regulation and the limbic-striatal system is crucial in understanding neural processes underlying stress and addiction. In the next section, we review the role of the cortico-striatal-limbic circuit in stress regulation in more detail.

9.2.2 Stress and Limbic-Striatal Circuit Acute stress triggers the release of neurochemicals including norepinephrine from the locus coeruleus and corticotrophin-releasing hormone (CRH) from the hypothalamus. These neurochemicals activate the autonomic nervous system and the HPA axis [74, 75]. The amygdala is a major brain region where stress stimulates the release of CRH and norepinephrine [76, 77]. In response to stress-evoking stimuli or under threatening situations, the amygdala modulates vigilance by lowering sensory threshold to facilitate information processing [78, 79]. Research also suggests that acute stress induction in healthy individuals often elicits heightened amygdala responses to both threat-related and positive stimuli, resulting in failure to detect real threats due to indiscriminate hyper-vigilance [62]. Acute stress also up-regulates serine protease tissue-plasminogen activator in the amygdala, resulting in stress-related anxious behavior in mice [80]. Neuroimaging studies showed that subjective experience of emotional distress is associated with heightened limbic activity during social stress conditions [81] and with hyperactivity in the amygdala during the experience of negative emotion [61]. Increased amygdala activity has also been reported during the stimulation of pain [82], due to an overlap between emotion and pain modulatory circuits [13]. The amygdala contributes to enhanced pain processing and chronic pain [83], suggesting an important role of amygdala in experience of both emotional and physiological distress. The amygdala is anatomically and functionally connected with the NAcc via dense sensory projections [84, 85], and the mesolimbic DA system has been associated with

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Stress > Neutral (Limbic-striatal regions) (A)

(B)

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Figure 9.1 Brain activity in limbic-striatal regions during stress exposure compared to the neutral condition (p < 0.05, whole-brain FWE corrected). While experiencing brief imagery of personalized stressful scenarios, (A) increased amygdalar activity was evident in twenty healthy men and (B) elevated striatal activity was shown in forty three healthy individuals (23 women). Coordinates are given in MNI space. This material is reproduced with permission of John Wiley & Sons, Inc. [91].

emotional and physiological distress in both animal and human studies. In preclinical studies, striatal DA release was found in rats following tail pinch-induced stress [86]. This is consistent with stress-induced DA release reported in other laboratory studies [87, 88]. In human studies, emotional distress has also been associated with mesolimbic DA release. During a psychosocial stress task, elevated levels of DA release in the ventral striatum was found in healthy individuals with low parental care in early life [89]. This is also consistent with a human neuroimaging study demonstrating increased ventral striatal activity in anticipation of aversive stimuli during aversive conditioning [90]. In addition, during stress imagery, brain activity in the amygdala and striatum was increased in healthy individuals [91] (see Figure 9.1). The NAcc is also known to be involved in pain and analgesia [92]. Activation of the NAcc has been demonstrated during the experience of physiological stress such as the presentation of a noxious pain stimulus [93]. The availability of D2 dopamine receptors in the striatum is also associated with tolerance to cold pain [94, 95]. Among fibromyalgia patients, a disturbance of DA transmission was shown with lowered tolerance to a saline injection [96]. These findings suggest that the mesolimbic DA system could be sensitized by physiological distress. It has been suggested that mesolimbic DA release in response to stress is modulated by glucocorticoid hormones [97]. Amphetamine-induced cortisol responses were associated with amphetamine-induced DA release in the ventral striatum [98]. Consistent with this, during a psychological stress task, DA release in the ventral striatum was associated with stress-induced cortisol levels [99]. Taken together, these results suggest that the limbic-striatal regions play a crucial role in processing of emotional as well as physiological stressors.

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VmPFC Lateral PFC

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Figure 9.2 Brain activity in prefrontal regions during stress exposure relative to the neutral condition (p < 0.01, whole-brain FWE corrected). During stress exposure, activations in selected areas of prefrontal cortex including the ventromedial PFC, lateral PFC, orbitofrontal cortex, and anterior cingulate cortex increased in forty three healthy individuals (23 women). VmPFC = Ventromedial Prefrontal cortex; OFC = Orbitofrontal cortex; ACC = Anterior cingulate cortex; Coordinates are given in MNI space. This material is reproduced with permission of John Wiley & Sons, Inc. [91].

9.2.3 Stress and Prefrontal Regulation The PFC plays a crucial role in stress processing and regulation [22]. For example, a fMRI study demonstrated that several regions of the PFC including the ventromedial PFC, medial OFC, and lateral PFC were strongly activated during stress exposure in healthy individuals [91] (see Figure 9.2). An amygdala response to aversive stress or dangerous objects is a natural adaptive mechanism to protect the organism. However, prolonged stimulation of the amygdala due to continuous negative emotional reactions or stressors can lead to dysfunction of this region and its interconnected brain structures, including the PFC [100]. Research shows that the amygdala has an inverse relationship with the PFC, such that increased medial PFC/DLPFC activity reduces the activity of amygdala and levels of negative emotion [28, 101]. Further the ventromedial PFC is known to modulate the hypothalamic–pituitary–adrenal (HPA) axis during a stressful experience [102, 103]. Increased activity in the ventromedial PFC is associated with decreased amygdalar activity and cortisol response while regulating negative emotion [43]. Research also suggests that the integrity of connectivity between the ventromedial PFC and the amygdala is crucial in appropriate fear extinction following stress experiences [104]. These findings demonstrate a close association between the PFC and amygdala and suggest that sustained amygdala over-activity could adversely influence prefrontal regulatory function. In support of this, hypersensitive amygdala activity blocks DLPFC inhibition in order to maximize external sensory input and respond to potential danger in the environment [100], suggesting the counteracting role of the amygdala in PFC regulation especially under stress. Consistent with this research, impairment in prefrontal functions during stress has been reported. For example, peer-reared Rhesus monkeys exposed to early life stress displayed

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increased volume in the dorsomedial PFC and dorsal ACC [105] and decreased overall serotonin1A receptor (5-HT1A R) density, compared to mother-reared monkeys [106], suggesting that traumatic early life stress could result in brain abnormalities and increase risk for stress-related disorders. In addition, noise-induced stress exposure impaired prefrontal dysfunction during a spatial working memory task in monkeys. In this study, stress-induced deficits in PFC cognitive function were recovered by the pretreatment of DA antagonist, haloperidol, indicating the mediation of hyperdopaminergic mechanisms in stress-induced PFC dysfunction [107]. In humans, a recent fMRI study indicated that chronic psychosocial stress impaired prefrontal regulatory control during an attentionshifting task [108], further confirming the adverse impact of stress on prefrontal regulatory function. It has been suggested that under stress, maladaptive and habitual patterns governed by the amygdala and striatum could dominate behavior, while weakening prefrontal regulatory function [23,109]. These results suggest that continuous stimulation of the limbic-striatal circuit under chronic stress could lead to prefrontal regulatory impairment, which may further disinhibit the activity of amygdala and striatum and aggravate the severity of emotional distress.

9.2.4 Stress-Related Illnesses and Dysfunction in the Stress Circuit Although stress reactivity is an adaptive response, chronic stress could adversely impact the brain and lead to maladaptive behaviors. Specifically, dysfunctional interactions between the limbic-striatal circuit and prefrontal regulation could underlie the pathophysiology of stress-related illnesses such as PTSD and chronic pain syndrome. In neuroimaging studies of veterans with PTSD compared to combat-exposed veterans without PTSD, heightened amygdala responses were consistently observed [110] including hyperactive amygdala response to negative facial stimuli [111]. Decreased prefrontal regulatory function was also found in PTSD patients, especially in the ventromedial PFC and ACC. For example, hypoactivity in the ventromedial PFC (BA10) was observed in PTSD patients [110, 112] during traumatic imagery and recall. Hypofunction in the rostral ACC was also found during recall of traumatic events in PTSD patients [112–116]. Consistent with this, decreased activation in the ACC (BA 32) was elicited during the retrieval of emotional words associated with childhood trauma [117]. In PTSD patients, treatment responders displayed larger rACC volume and better regulatory control over fear relative to non-treatment responders [118]. Dysfunction in the limbic-striatal circuit and prefrontal regulation is also found in patients suffering from chronic pain. A recent literature review concluded that patients with chronic pain syndrome have significant impairments in subcortical emotional structures such as the amygdala and the NAcc resulting from persistent stimulation of pain [13]. Considering the functional relationship between the amygdala and the PFC [28], deficits in the limbic-striatal circuit could lead to prefrontal dysfunction in patients with chronic pain. Consistent with this, several neuroimaging studies have reported that patients with chronic pain have abnormal prefrontal regulatory systems, including the OFC and DLPFC [119, 120]. Reduced blood flow in the OFC and DLPFC was also found in groups of patients with chronic pain [121], suggesting potential regulatory difficulties in this clinical population. To summarize, persistent stimulation of the limbic-striatal systems caused by emotional and physiological stressors could impair the prefrontal regulatory system, further increasing the severity of emotional distress and vulnerability to other clinical disorders (see Figure 9.3).

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Dorsolateral PFC Ventromedial PFC Orbitofrontal Cortex Regulatory Control

ACC Conflict monitoring

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Weakened Control - Stress-related emotional disorders - Addictive behaviors

Disinhibition Amygdala Modulation: CRH, norepinephrine Hypervigilance

Ventral Striatum Dopamine release Vunerability to addiction

Chronic stress

Figure 9.3 Schematic drawing of the neural circuitry for chronic stress and the development of addiction. Persistent stimulation of the limbic-striatal circuit debilitates the prefrontal regulatory system in individuals under chronic emotional and physiological stress. Deficient prefrontal regulation in turn leads to disinhibition in the amygdala and ventral striatum, which could further increase vulnerability to stress-related emotional disorders and to addiction.

9.3 Dysfunction in the Neural Circuits Underlying Stress and Addiction 9.3.1 Stress and Addiction Stress is an important risk factor in the development of addictive disorders. Stress, aversive emotion, and withdrawal-related distress increase vulnerabilities to drug craving [122, 123]. In a preclinical study, peer-reared monkeys in early life tend to show significantly greater amounts of alcohol consumption in adults compared to motherreared monkeys [124]. Stress associated with neonatal isolation was found to increase cocaine self-administration in rats [125]. Strong associations between stress and alcohol consumption were also reported in humans [23]. In a study examining behavioral patterns of New York City residents after the September 11 terrorist attacks, increases in trauma-related substance use (cigarettes, alcohol, and marijuana use) persisted, although symptoms of PTSD and depression were reduced within 6 months [126]. Stress is a multifaceted process involving concomitant changes in multiple domains such as behavioral, emotional, and physiological systems in central and peripheral

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physiology. Although some studies using only self-reported measures reported conflicting findings [127–129], a growing number of studies with more sophisticated and comprehensive stress measures including HPA activity [130], ecological momentary assessment [131], cumulative lifetime adversities [132], cardiovascular measures [133], fMRI [134], and single photon emission computed tomography [135], confirmed a significant relationship between stress and addiction, supporting the need to further understand neural mechanisms underlying this association. A review based on preclinical studies provides a neurobiological model for the association between stress and addiction, such that striatal DA release in response to stress is modulated by glucocorticoid hormones, which could increase the sensitization of the reward system and vulnerability to drug intake under chronic stress conditions [97]. This is consistent with a priming model suggested by Kalivas and McFarland that connectivity among the amygdala, ventral striatum, and ACC could be a core neural circuit of drug cue and stress related drug reinstatement [136], highlighting the role of the cortico-striatal-limbic circuit in the relationship between stress and addiction. In a series of neuroimaging studies, dysfunction in the cortico-striatal-limbic regions was also associated with elevated levels of emotional distress and drug craving. For example, in a study using a stress imagery paradigm, patients with cocaine dependence showed decreased activity in the ACC compared to healthy controls. High levels of stressinduced cocaine craving were associated with increased activity in the striatal region in these patients [137, 138]. In another study, reduced activity in left ACC during stress exposure was associated with increased cocaine craving in patients with cocaine dependence [139]. In women with cocaine dependence, decreased activity in the medial PFC during stress exposure was associated with socially maladaptive behaviors [140]. These results suggest that hypofunction in regulatory regions, including the medial PFC and ACC, as well as increased activity in the striatum during stress exposure, contributes to drug craving and dysfunctional behaviors in individuals with addictive disorders.

9.3.2 Stress-Related Illnesses and Addiction Despite the impact of co-occurring stress-related illnesses (e.g., PTSD or chronic pain) and addictive behaviors, pathogenic processes involved in these comorbid conditions remain unclear. For the neural mechanisms underlying this co-morbidity, prefrontal hypofunction and disrupted limbic-striatal function may provide a viable model for the link between stress-related disorders and addiction. High rates of co-morbidity with substance use disorders (SUD) are found in PTSD patients [141–144]. PTSD patients with SUD tend to display higher severity of addiction symptoms and poorer substance use treatment outcomes compared with SUD patients or SUD patients with other co-morbid psychiatric disorders [145–147]. As an explanation for the neurobiology underlying the comorbidity between PTSD symptoms and addictive behaviors, several studies suggest dysfunction in the limbic-striatal system. In a preclinical study, male mice with prenatal stress show excessive amounts of alcohol consumption as well as altered DA and glutamate transmission in regions of the limbic forebrain during adulthood [148]. It is also suggested that drug addiction in PTSD patients is mediated by interactions between CRH and noradrenergic systems, especially in the hypothalamus and amygdala [144]. PTSD patients also showed reduced activity in the bilateral striatum during reward conditions (monetary gain vs. loss) compared to healthy controls [149], suggesting altered function in reward circuits in these patients. These findings suggest

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that repeated stress exposure could overly sensitize the cortico-striatal-limbic circuit, increasing vulnerability to substance abuse in PTSD patients. In patients with chronic pain, a significant comorbidity with substance abuse has been reported [150]. Individuals treated for chronic pain have a high prevalence of substance abuse for multiple agents, including opiates and alcohol [151, 152]. As a neurobiological explanation, a recent review proposed that comorbid substance abuse in patients with chronic pain syndrome might result from a deficit in DA transmission in the NAcc, a component of the reward system, and in the amygdala, an aversion system component [13]. Consistent with this, a preclinical review suggests that dysfunction in limbic-forebrain regions is an important factor in explaining the link between pain and drug self-administration in rodents [153]. Along with dysfunction in the limbic-striatal circuit, the OFC, a major brain region for the regulation of addiction urge [154, 155], was found to be impaired in patients with chronic pain [121,156,157]. It has also been shown that patients with chronic pain have impaired emotional decision making, in which the OFC plays a critical role [158]. In a study examining the neural substrates of addiction in chronic pain, OFC hypofunction was specifically associated with analgesic overuse in patients with chronic headache [159], suggesting an important role of decreased OFC regulatory function in the development of addiction in patients with chronic pain. Taken together, repeated stress exposure over-stimulates the limbic-striatal system, which could weaken PFC regulatory function and increase vulnerability to substance abuse in PTSD [144] and chronic pain [13] patients. While these neuroimaging studies provide initial evidence to support the above hypothesis, there remains a dearth of studies that have concurrently investigated chronic stress and addictive processes. In future studies, the functional interactions between prefrontal regulation and activation of the limbic-striatal circuit should be further clarified in tasks that concurrently examine stress and addictive processes in order to better understand the effects of stress and addictive disorders, especially in patients with stress-related illnesses.

9.3.3 Addiction and Heightened Sensitivity to Stress 9.3.3.1 Stress Sensitivity in Addiction Patients Individuals with addictive disorders often suffer from significant difficulty with life stressors [23], and stress contributes to repetitive substance abuse and increases susceptibility to relapse [23, 138]. Addiction patients also display an increased sensitivity to stressinduced craving. For instance, stress has been associated with increased drug craving in patients with cocaine dependence [160]. In cocaine-dependent patients, both stress and drug cue exposures facilitate craving and drug seeking behaviors, whereas healthy social drinkers show no increased craving during stress exposures [161]. Cocaine relapse outcomes were also strongly predicted by stress-induced craving and arousal [162, 163], highlighting the critical role of stress in drug craving and relapse. It has been suggested that continued substance use leads to neuroadaptive brain changes that are tied to increased craving and susceptibility to addiction relapse [19, 23, 164]. Chronic substance abuse also increases increases negative emotionality accompanied with compulsive craving [133]. Research suggests that anxiety and negative emotionality tied to compulsive craving indicates a shift in hedonic tone in substance dependent individuals [165, 166], which could further increase stress sensitivity. Consistent with this, alcohol-dependent patients are shown to experience negative affect, anxiety, and dysphoria during withdrawal states [165] and these negative emotional

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states are often accompanied by abnormal physiological reactions during stress and cue exposures [133, 161]. Research has shown that negative emotion associated with alcohol consumption also reinforces compulsive alcohol intake in alcohol-dependent patients [167]. These results suggest a presence of altered reactivity in stress and reward circuits in individuals with addiction disorders.

9.3.3.2 Neural Dysfunction Underlying Negative Reinforcement, Stress, and Relapse Negative emotional arousal has been associated with hyperactivity in the amygdala [168]. The amygdala is also a major region for modulating stress response due to abundant populations of corticotrophin-releasing factor (CRF) receptors, cell bodies, and terminals [169, 170]. It has been proposed that sensitized CRF and norepinephrine systems, especially in the amygdala, play a central role in negative reinforcement associated with drug abuse [74, 169]. Negative reinforcement is characterized by compulsive drug intake in spite of a decreased reward state and increased negative emotional arousal [169]. In the initial stage, drugs of abuse tend to facilitate DA transmission in the striatum and elicit elevated mood (e.g., [171]). However, chronic and repeated drug intake decreases the reward state and stimulates the stress circuit, where the role of the extended amygdala is highlighted. This triggers compulsive drug seeking, negative mood, heightened stress sensitivity and irritability [169, 172]. In support of this, hyperactivity in both the amygdala and striatum were reported during cue-induced cocaine craving states [173, 174]. Given close functional and anatomical connections between the amygdala and the PFC, repeated stimulation of the limbic-striatal circuit could adversely impact prefrontal regulatory function. Consistent with this, dysfunction of the cortico-striatal-limbic circuit has been consistently found in substance abuse studies (e.g., [175]). More specifically, prefrontal dysfunction in individuals with substance abuse was dominantly found in areas of OFC, ACC, and ventromedial PFC [176]. For example, reduced striatal D2 receptor availability was highly correlated with reduced activity in the OFC and ACC in detoxified alcohol-dependent patients [155, 176]. It is notable that the OFC plays a crucial role in the regulation of anxiety and has been found to be impaired in individuals with anxietyrelated disorders [177, 178]. Negative emotion is a dominant characteristic of anxiety disorders [179,180], suggesting the potential link between OFC dysfunction and negative affect displayed in patients with addictive disorders. In addition, alteration in the neural circuit involved in the medial PFC and ACC has been associated with drug abuse [181–184] including hypoactive PFC and ACC shown in cocaine dependent patients [185]. In a study using script-guided imagery paradigm, abstinent cocaine-dependent patients displayed hypoactivity in the ACC and medial PFC, but hyperactivity in the dorsal striatum during stress exposure compared to healthy controls [138] (see Figure 9.4). In addition, stress-induced activity in the ventromedial PFC was associated with a time to relapse in individuals with cocaine dependence [21]. Abstinent alcohol-dependent patients displayed increased levels of anxiety and craving during stress exposure. Additionally, hypoactivity in the ventromedial PFC and rostral ACC during stress exposures was significantly associated with increased alcohol craving and subsequent relapse after treatment in these patients [186]. Preclinical studies also showed that the dorsomedial PFC, including the ACC, played a preventive role in drug reinstatement elicited by drug cues, drug, and stress [136,187]. These results further confirm the importance of integrity in the PFC regulatory control system in stress-induced drug/alcohol craving and relapse.

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led to the identification of broad areas involving dopaminergic neurons in midbrain, striatum, and forebrain and the administration of dopamine antagonists disrupted selfstimulation leading to devaluation of reward (review by [29]). Drugs of abuse lead to an enhancement in mesocorticolimbic dopamine activity and this circuit is extensively involved in reward to natural reinforcers such as food, drink, and sex, as well as addictive agents. Ventral tegmental area (VTA) and the basal forebrain, composed of the nucleus accumbens (NAc), amygdala, olfactory tubercle, and frontal and limbic cortices are the main components of this system. Mesocorticolimbic dopaminergic system is central to maintaining a healthy response to several stimuli. Dopamine release is most salient in the anticipatory phase of reward (review by [30]). Addictive agents modify the reward circuit and associated neurotransmitter functions and motivation to maintain drug use can be reinforced either by administration of high dosages of drugs [31] or smaller doses over a prolonged period [32]. Such reinforcement is a result of the modulation of anticipatory neural activity by the predictive stimuli/drug. Alcohol and other substances of abuse affect natural learning systems. These systems have been modeled through temporal-difference reinforcement learning (TDRL), based on a reward-error signal (review by [33]). There is phasic activation of dopamine neurons during reward predictive stimuli and associated anticipatory phase and this dopamine neuron activity resembles the prediction error signal of temporal difference learning. Suri and Schultz [34] demonstrated that the tonic dopaminergic activity during the anticipatory phase reflected the prediction signals involved in the processing of neuronal activity. Significantly elevated levels of extracellular dopamine in human striatum were observed even at a moderately low oral dose of d-amphetamine [35]. In the striatum of both addicted patients [36] and rats [37] that are more impulsive and predisposed to consume large amounts of cocaine, there are differences in dopamine receptor D2 (DRD2) availability that appear to reflect both pre-existing and drug-induced effects on dopamine function. This evidence suggests that the dopamine pathway holds some of the key genes for imaging genetics of addiction. A general view on the involvement of dopamine systems in addiction involves development of neurons and circuits, neurotransmitter synthesis and trafficking, receptor expression and signal transduction. In addition to being relevant to reward and thereby addiction, dopaminergic genes have been investigated in a wide range of neuropsychiatric disorders and behavioral phenotypes. The sequences and sequence variations of numerous dopamine-relevant genes are available in public databases. As will be illustrated by specific examples, variant discovery has been prolific and includes variations of different types – single nucleotide polymorphisms (SNPs), short tandem repeats (STRs) and Insertion-deletion (indel). In addition, extensive information is now available on population variations and linkage disequilibrium relationships between loci, with frequencies of specific loci ranging from very rare (single) to highly abundant. In several instances, functional polymorphisms, or multiple functional polymorphisms, are known, including both coding sequence and regulatory variants of different types. For example, a series of human variants in dopamine and serotonin G-protein coupled receptors (GPCR) were placed on the canonical GPCR structure [38]. This was done to correlate the frequency and evolutionary conservation of genetic variants with their position in the receptor proteins. From this analysis it was demonstrated that, on a genetic basis, humans are neurochemically individual, even if only their monoamine neurotransmitter receptors are considered. Biochemical and physiological effects of several of these genetic variants have been detected for a majority of receptors and enzymes in dopaminergic pathways

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(review by [39]). In short, the mesostriatal pathway is perhaps better understood from the standpoint of genetic expression-function than practically any other domain affecting brain functioning. A reasonable expectation is that imaging genetic studies that exploit this information will be informative for linking inter-individual variation in functional reward circuits to various addictions. It has been postulated that lower concentration/expression of DRD2 receptors alters sensitivity to rewarding stimuli and increased vulnerability to addiction. Several studies have detected lower availability of the DRD2 receptor in cocaine, alcohol, heroin, methamphetamine, MDMA and ketamine abuse and dependence using various imaging techniques (review by [40]). For example, over expression of DRD2 in nucleus accumbens of rats trained to self-administer alcohol reduces alcohol intake [41]. Several studies have associated the DRD2 gene to addiction [42]; reviewed in Le Foll et al. [43] and to the so-called “reward deficiency syndrome” [44]. DRD2 SNPs have also have been investigated in pathological gambling, another reward related phenotype [45–47]. However, DRD2 genetic studies are complex and single marker studies with nonfunctional markers and without control for ancestry are recipes for false positive findings. For example, a frequently studied locus Taq1A [rs1800497 T/C located in ANKK1 gene, NCBI genome build 37.1 (DRD2 locus)] has been associated with DRD2 receptor binding [48, 49] and availability [50, 51]. However, there are negative studies on the involvement of Taq1A polymorphism and in some of the studies the effects of prior drug exposure may not have been parsed out [52, 53]. Drug exposure leading to dopamine release can down regulate DRD2 receptors. The Taq1A locus itself shows substantial inter-population variation in allele frequencies and several carefully done association studies on severely addicted individuals have yielded negative findings. There is some evidence for false-positive associations to addiction and other diseases because of anomalously low allele frequencies. Thus, the positive associations of the Taq1A allele with addictive disorders previously reported may have been a result of anomalously low allele frequencies observed in the specific control populations recruited for these studies rather than higher allele frequencies in the addicted populations. As later studies included more appropriately matched control groups, the association between the Taq1 locus and addictive disorders was no longer apparent [52]. On the other hand, the DRD2 gene harbors several functional variations that can be characterized as single loci, and the effects of these loci can also be captured by haplotype-based analysis of the gene. With regard to individual loci, the 311Cys allele (of Ser311Cys; rs1801028 C/G SNP) accommodates DRD2 receptor ligands but inadequately transduces signal [54]. Whereas this hypo-functional allele is common in American Indians, it was not associated with addictive disorders [55]. For DRD2, haplotype-based studies capturing effects of various functional loci that have been identified at the gene have yielded apparently valid associations to addictive behaviors, and these associations ultimately reflect the actions of functional loci imbedded in the haplotypes. For example, two studies involving three datasets have implicated DRD2 haplotypes in opioid addiction [56, 57]. However, DRD2 haplotype-based studies have not been performed with neuroimaging phenotypes. Several other genes, modulating dopamine function and reinforcement, are also important candidates for imaging genetic studies of addiction. In addition to DRD2, as summarized in Table 12.1, catechol-o-methyltransferase (COMT) and DAT (SLC6A3 gene – popularly known as DAT) gene polymorphisms have been investigated in imaging genetics of addiction. COMT is an enzyme involved in the breakdown of DA while, as mentioned earlier, DAT is responsible for the reuptake of synaptic DA. Both of these proteins are

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widespread in the brain and are key molecules for maintaining optimum DA levels. Cocaine and amphetamine block the dopamine transporter (reviewed in [58]) and this can lead to a massive decrease in dopamine reuptake and increase in synaptic dopamine. Modulation of neuronal activity in the prefrontal cortex and ventral striatum during reward anticipation by COMT genotypes was seen along with a nonlinear multiplicative effect of COMT and DAT genotypes on ventral striatal responses [59]. Similar findings were reported for these two genes in reward related behavior [60]. The endogenous opioid system is also involved in addiction and reward (reviewed in [61–63]. Pre-clinical studies show that mu-opioid receptors (MOPR) mediate alcohol consumption wherein MOPR knockout mice demonstrate decreased ethanol intake [64,65]. Anticipating discussion of the pharmacogenetics of addiction treatment later in this chapter, clinical studies have established the utility of the MOPR antagonist, naltrexone, in reducing alcohol intake [66]. Opioid receptor-mu 1 (OPRM1) deserves special mention, as a functional genetic variation- rs1799971 (A118G/Asn40Asp) in this gene [67] has been variably associated with different addictions and more consistently associated with treatment response to naltrexone in alcoholism [68, 69]. Recently, Asp40 was shown to modulate dopaminergic activity in striatum, as detected by displacement of C11 raclopride [24], illustrating the ability of a functional polymorphism to predict a profound difference in drug-mediated dopamine release.

12.2.2 Stress Resiliency Stress appears to be an important contributor to addictive behaviors and may moderate or mediate pre-existing vulnerability, the process of addiction and relapse. Several stressors such as foot shock, food deprivation and chemical stressors reinstate drug-seeking behavior in rats previously trained to self-administer drugs (reviewed in [70]). In humans, childhood trauma, neglect, sexual abuse, and stress later in life appear to predispose to addictive disorders (reviewed in [71]). Due to pre-existing genetic factors, environmental interactions and the allostatic effects of addictive drugs (to be discussed below), alcoholdependent patients across diverse populations, are more anxious [72]. Concurrent stress increases negative mood states in cocaine-dependent individuals leading to increased rates of cocaine use and relapse (reviewed in Erb [73]). A consequence of chronic rewarding drug intake is neuroadaptations, which are proposed to induce a new allostatic mood set point. According to this model, as a cause and consequence of the addiction, an individual becomes chronically more anxious and dysphoric, leading to vulnerability to stress-, as well as cue-, induced reinstatement of drug use [74–76]. The two stress-related systems that are profoundly modulated in this way are the hypothalamic-pituitary-adrenal axis and the amygdala-hippocampus circuit, the latter normally involved in the assignment of emotional valence, amplitude and context of cues. Genes that modulate pre-existing and drug-induced variations in the function of the HPA axis and the brain amygdala/hypothalamic axis are likely to play important roles in vulnerability, relapse, and potentially recovery [77]. The neuroanatomical localization of these networks points to targets for imaging genetic studies.

12.2.2.1 Hypothalamic-Pituitary-Adrenal Axis The HPA axis is central to environmental stressor responses. A twin study showed that after repeated exposure to the same psychosocial stressor, inter individual response

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variation is heritable (h2 = 0.33–0.97; [78]). The HPA axis can also modulate reward and motivation. Decreased resiliency in terms of an inability to remain in substance abuse treatment is associated with higher salivary cortisol level in response to a computerized stress task [79]. Stimulatory and inhibitory neurotransmitter circuits regulate the functioning of the HPA axis. CRH and NPY are both stress-related neuropeptides. Preclinical data suggests that CRH mediates the anxiogenic and aversive effects of withdrawal from many addictive substances [80–82]. Barr et al. have shown that CRH variants predict stress-induced alcohol consumption as well as cerebrospinal fluid levels of corticotropin-releasing hormone, hypothalamic-pituitary-adrenal axis activity, and temperament in rhesus macaques [83,84]. Corticotropin releasing hormone-binding protein (CRH-BP) is a modulator of CRH that directly inhibits its actions by binding, and inactivating, the neuropeptide released into the synapse (reviewed in [85]). It is a potent neuroendocrine modulator of CRH function. Genetic variations in CRH-BP are linked to anxiety disorders in Native Americans and alcohol use disorders in Caucasians [86, 87]). In an intermediate phenotype-based linkage study, it was found that CRH-BP influences alcoholism-associated variation in EEG activity [86]. The cellular endpoint of cortisol response involves activation of the cortisol receptor, translocation of the receptor to the nucleus and binding to gene regulatory elements. Variation at several of the transducing genes, or the genes that are transcriptionally modulated, could alter cortisol response. Binder et al. [88] described a functional haplotype of FKBP5, an accessory protein that regulates the translocation of the activated cortisol receptor to the nucleus. This polymorphism modulates responses to stress, for example exerting a powerful effect on vulnerability to PTSD [89]. However, imaging genetic studies have yet to be reported for FKBP5 or the other two genes just mentioned, CRH and CRH-BP, as integral to HPA function and already associated with addictions and stress-modulated behavior.

12.2.2.2 Emotional Processing In addition to the HPA, circuits within and between amygdala, hippocampus, and certain regions of prefrontal cortex play a crucial role in stress-related responses to addiction and related etiology [77]. Impaired processing of emotional faces has been reported in alcohol-dependent subjects reflecting deficient activation of amygdala and hippocampus [90]. Three genes, NPY, 5-HTT (SLC6A4 solute carrier family 6 (neurotransmitter transporter, serotonin), member 4), and COMT, are expressed in and modulate the amygdalahippocampus circuit and have been studied with imaging genetic paradigms. Neuropeptide Y (NPY) is one of the most abundantly expressed peptides in the CNS and NPY receptors are highly expressed in human amygdala, cerebral cortex, and striatum [91]. NPY acts as an endogenous anti-stress and anti-anxiety mediator during prolonged and repeated exposure to stress (reviewed in [92]). It is involved in neuroadaptations following alcohol intake and moderates the intake of alcohol in rodents. Genetic variation in the NPY gene has recently been shown to influence stress resiliency and alcohol consumption in non-human primates [93]. In humans, this peptide is a crucial contributor to stress modulated addictive behaviors. A profound effect of NPY genetic variations on the levels of neuropeptide and its messenger RNA was detected in plasma, post-mortem brain and lymphoblastoid cell lines. Lower diplotype-driven NPY expression predicted higher emotion-induced activation of the amygdala and was associated with stronger

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Figure 12.1 NPY diplotype driven mRNA expression affects amygdala and hippocampal activation in response to threat-related facial expressions. Top: Statistical parametric maps showing NPY diplotype-biased (LL = lowest; LH = intermediate; HH = highest NPY mRNA expression associated diplotypes) mean right dorsal amygdala and right hippocampal activation overlaid on an average sagittal and coronal MRI [8; reproduced with permission from Nature Publishing Group, License Date: Dec 13, 2010; License Number: 2567251455821]. Bottom: Right amygdala (black diamonds) and hippocampal (gray squares) activities (means and s.e.m.) grouped by NPY diplotypes – higher amygdala and hippocampal activation in individuals with the low-NPY-expression (LL) than in high-expression (HH) diplotype group.

brain responses to emotional and stress challenges, and higher levels of trait anxiety ([8]; Figure 12.1). The 5-HTT gene is a part of the serotonin (5-HT) neurotransmitter system. Serotonergic neurons from the dorsal raphe nucleus project widely to diverse brain regions but, relevant to emotion and stress response, these regions include the hippocampus, frontal cortex, and amygdala [94]. Low levels of the 5-HT metabolite, 5-hydroxyindolacetic acid (5-HIAA) have been reported in the cerebrospinal fluid of early onset alcohol-dependent subjects [95] and the administration of serotonin 3A receptor (HTR3A) antagonists to the central nucleus of amygdala blocks oral ethanol consumption in rats [96]. A common tandem repeat polymorphism in the HTT promoter regulates transcription in an allele-dependent

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manner [97]. In humans, this locus has three relatively common functional alleles [98] and there is an orthologous polymorphism in the promoter region of the rhesus macaque serotonin transporter gene [99]. The human polymorphism (HTTLPR) alters serotonin transporter availability in vivo [100]. Lower expression alleles have been associated with increased amygdala activity in response to emotional probes [5, 101–104]. More recently, HTTLPR was found to predict connectivity of the amygdala with brain regions that modulate emotional responses [105, 106]. HTTLPR modulates stress and emotion responses in the human [107] and rh-HTTLPR modulates such responses in the macaque [108, 109]. COMT gene (discussed above in the reward domain, and below in the cognition domain) has a common functional missense variant Val158Met in which the Met allele leads to a reduction in enzyme activity [110]. Additionally, the higher activity Val158 allele resides on different haplotype backgrounds, one of which appears to lead to a less translatable mRNA [111]. In one of the first imaging genetic studies, Val158Met was shown to predict frontal metabolic activity during cognitive and executive functioning [112], with the lower activity Met158 allele, which leads to higher dopamine levels in PFC, predicting better executive cognitive performance and cortical efficiency. Met158 is associated with better executive cognitive performance in populations ranging from normal controls to schizophrenic patients [112, 113] to patients who had suffered head injury [114]. Under more stressful conditions, the performance of Met158 carriers deteriorates [115], potentially reflecting the inverted U relationship of frontal dopamine concentrations to executive cognitive performance [16]. This gene has been named the Warrior/Worrier gene [1,116] because the Val158 allele reciprocally predicts better resiliency and reduced levels of trait anxiety [87], especially under conditions of moderate to very high stress exposure [117]. The effects of COMT on emotional processing have been observed in several imaging genetic studies. The lower activity Met158 allele predicted stronger metabolic response to emotional stimuli in amygdala and PFC [118, 119], including an additive effect with HTTLPR [120]; (Figure 12.2). In this study, the additive genotype effect of COMT and HTTLPR was associated with stimulus-induced activation and accounted for 40% of interindividual variance in averaged fMRI BOLD response of amygdala, hippocampal, and limbic cortical regions elicited by unpleasant stimuli. COMT low activity allele also predicted impaired regional mu-opioid system responses to a pain stressor, with stronger affective response to pain and lower pain threshold [7]; also [121, 122]. As listed in Table 12.1, and in line with the underexploited status of candidate genes in addiction, only the COMT Val158Met SNP has been analyzed using imaging genetics paradigms in the direct context of addiction, and those studies were conducted only in individuals with nicotine addiction and alcoholism. In nicotine addicted subjects, Val158 was associated with an increased BOLD signal in right and left dorsolateral prefrontal cortex and dorsal cingulate/medial prefrontal cortex [14]; (Figure 12.3). Smokers with homozygous Val/Val genotypes were more sensitive to the effects of an abstinence challenge on brain function and working memory than carriers of the Met allele. In other studies on COMT genetic variation, the Met allele was associated with decreased immediate reward bias and differences in decision-making in alcohol-dependent subjects [15], while cigarette smokers carrying Val158 showed evidence of increased blood flow in regions previously linked to nicotine craving [12]. These studies appear to identify novel brain–behavior mechanism through which genetic variations in COMT alter vulnerability to addiction.

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Figure 12.2 Additive effect of COMT and 5-HTT genotypes on limbic reactivity to aversive stimuli. Combined effect of COMT and 5-HTT (5-HTTLPR and rs25531) genotypes on limbic activation elicited by unpleasant stimuli in the amygdala, hippocampal formation and cingulate cortex; reproduced from [120] with permission from Nature Publishing Group, license no. 2577800422000, license content date Dec 28, 2010.

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Visual N-back working memory task activation. (a) Mean activation for abstinent and smoking sessions identified by a parametric model of increased working memory load (p < 0.05, corrected). Brain rendering performed with CARET. (b) Mean percent signal change (N-back minus 0-back) for the 1-back, 2-back and 3-back contrasts calculated from a priori regions of interest. Abstinence effects differed among the three genotype groups (Wald χ 2(2) = 15.06, P = 0.0005).

Figure 12.3 Association of COMT Val158 allele with an increased BOLD signal in right and left dorsolateral prefrontal cortex and dorsal cingulate/medial prefrontal cortex in nicotine addicts. Reproduced from [167] with permission from Nature Publishing Group, license no. 2657260311319, license content date Dec 9, 2008.

12.3 Cognitive Function Dependence on addictive agents leads to various degrees of cognitive impairment and impulse control, working memory and decision-making abilities. Many neuropsychological tasks are used to evaluate cognitive functioning. Executive cognitive functioning can be assessed by simulating real-life decisions (for example, gambling tasks) and by cognitive challenges. Some of the commonly used tasks are the Iowa Gambling Task (IGT), Wisconsin Card Sorting Test (WCST), N-back test and Delay Discounting. Alcohol- and other substance-dependent individuals perform poorly on these tasks (IGT: [123]; WCST: [124]; DD: [125]; N-Back Test: [126]). The decisions of addicted patients tend to reflect a bias towards short-term gains over longer-term losses – a bias that is frequently unfavorable in real life situations (reviewed in [127]). One study examining poly-substance abusers concluded that they performed more poorly than controls even when matched on impulsivity scales [128]. Substance use was associated with lower bilateral dorsolateral frontal lobe cerebral oxygenation during the IGT [128] and frontal lobe hemodynamic differences were reported in substance abusers compared to controls [129]. Several genes have been linked to cognitive dysfunction accessed via the IGT, WCST and N-back tasks. Common polymorphisms in these genes associate with cognitive alterations

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and also link with vulnerability to addiction (reviewed in [130]). As discussed in the previous section, the COMT Val158Met polymorphism has been extensively associated with executive cognition in studies measuring performance and associated brain imaging responses. Again, studies conducted in addicted patients are sparse, but tend to bear out findings made in healthy controls studied for the addiction-relevant intermediate phenotype. As would have been predicted from studies in healthy controls and clinical populations, subjects performing the Delay Discounting task that carry the COMT Met 158 (low activity) allele showed decreased immediate reward bias and less fronto-parietal activity during decision making, indicative of greater cortical efficiency. However there was no interaction between genotype and drug abuse history. Nonetheless, a genetic effect was apparent, since this study compared alcohol abstinent people with healthy control subjects (Table 12.1; [15]).

12.4 Brain Morphometric Changes Structural brain changes are well documented in alcohol-dependent subjects (reviewed in [131,132]). Age associated brain volume reductions are significantly greater in people with alcoholism and other drug dependencies. In alcoholism, both white matter and gray matter are reduced [133–137], with the reduction partially reversible with abstinence ([138–140]). Reductions in regional brain volume are also observed in addiction to other drugs including nicotine [141, 142], methamphetamine [143], heroin [144], cocaine and amphetamine dependence [145]. The mechanisms of these structural alterations are not well understood and not all addicted patients exhibit the same volume losses. Environmental, physiological, exposure pattern, or genetic factors (or an interaction among these) likely influence the course and severity of these volumetric alterations. Various interdependent pathways beginning from drug metabolism, neuronal protection, oxidative stress and apoptosis (or necrosis) may be involved. In a recent study, we hypothesized that the excessive environmental oxidative challenge posed by alcoholism could lead to insufficient clearance of damaging free radicals that in turn can cause cell death. Alcohol causes an imbalance in anti- and pro-oxidant levels both through its metabolism and by release of excitotoxic neurotransmitters during alcohol withdrawal. Superoxide dismutase 2 (SOD2) is a critical free radical scavenger and genetic variations impairing its activity could be pivotal in oxidative stress-related structural brain alterations. In alcohol-dependent subjects who had not consumed alcohol at the highest levels, an SOD2 diplotype that includes a common missense allele Ala16 and that impairs SOD2 activity predicted greater gray matter shrinkage (Table 12.1, Figure 12.4; [22]). This diplotype carries two copies of the lower activity haplotype. Genetic effects of 5HTT and BDNF genotypes were observed in a study that evaluated high-risk offspring from a longitudinal cohort of multiplex alcohol dependent pedigrees. There was a significant reduction in the volume of orbitofrontal cortex (OFC) in high-risk individuals. Though no main genotypic effects were observed, the combination of the 5-HTT S-allele and the BDNF Met66 allele predicted reduced OFC volume in the right hemisphere, total brain volume, white matter and gray matter volume [23]. A gene x gene interaction involving two different genes predicted hippocampal volumes in another study of alcohol dependent patients. There was a synergistic effect of mGluR3 (metabotropic glutamate receptor 3) and COMT on hippocampal volume in alcohol-dependent individuals, but not controls [21].

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Diplotype rs10370TT-rs4880GG (Ala16Ala)

Diplotype rs10370TT-rs4880AA (Val16Val)

Figure 12.4 Coronal brain sections representing segmented gray-matter in representative chronic alcohol-dependent individuals containing SOD2 vulnerability (left panel) and protective (right panel) diplotypes. Segmented gray matter from an alcoholic with a functional SOD2 diplotype significantly associated with gray matter shrinkage (green) and compared to an alcoholic with the protective diplotype (red) – The left panel with Ala16Ala diplotype seems to show greater gray matter shrinkage compared to the right panel, suggesting that Ala16Ala is the risk diplotype associated with higher gray matter shrinkage in alcohol dependent people. Figure reproduced from [22]; Copyright © 2009, Nature Publishing Group.

12.5 Bridging Gaps There is a gap between intermediate phenotype and disease. All the imaging genetic studies mentioned above exemplify the utility of joining neuroimaging with genetics in addictions research; still this approach has so far not led to any change in clinical practice. A key problem is the disease definition itself, which mismatches to the intermediate phenotypes that can be measured and that reflects the response of the brain to the addictive agent and processes involved in vulnerability to addiction. Resolving this gap requires a long-term effort to measure and refine these phenotypes in more patients and, eventually, the incorporation of such measures into diagnostic schemes. There is a big gap between gene and disease. The genes that are involved in addiction vulnerability are not “genes for addiction.” These genes are usually pleiotropic in action, not just having isolated effects on addiction vulnerability, and their common functional variants were not selected because they had an ability to predispose to addiction, but for other “purposes” such as set points in emotion, resilience, reward, and cognition. There is also a gap in the diversity and comparability of what is measured across different studies and populations. Functional alleles have indeed been related to intermediate phenotypes relevant to addiction. Sometimes, as with the relationship between COMT and executive cognitive function, the same gene/phenotype correlations have been observed across diverse clinical populations. However, the genotype effect may only be observable in people who have been previously exposed to the drug. For example, the relationship of a functional variant of SOD2 to brain volume [22] would probably not have been observed in healthy controls that had not experienced the oxidative stress to which the alcoholic brain is subjected. On the other hand, genotype effects on intermediate phenotypes can be reduced in addicted patients. A reduction-of-function NPY genotype was associated with lower NPY neuropeptide levels both in alcoholics as well as controls, but the effect appears to be blunted in alcohol-dependent subjects [8]. Also, HTTLPR predicted serotonin transporter density in controls, but not in alcohol-dependent individuals [4].

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Clinicians do not have the luxury of using genetic and neuroimaging predictors in pristine controls. Therefore, it is important to contrast findings in populations with different exposures and in both addicted individuals and persons at risk. Table 12.1 summarizes reports in which genes predicting brain anatomical and functional indices have been evaluated and Table 12.2 lists the studies that assess the role of genetic variations on regional receptor binding and ligand displacement within populations of addicted individuals. The studies in these tables indicate the importance and power of functional imaging correlates in the genetics of addiction, and contrast the effect of genetic variants on brain volume or function in alcohol/substance dependent and non-dependent individuals. In the following section we discuss the possibility of tying neuroimaging-genetics studies with other relevant phenotypes. We mainly concentrate on two areas: alcohol flushing response (inter-individual variation in metabolism) and imaging pharmacogenetics (inter-individual variation in treatment response in imaging context).

12.5.1 Imaging Genetics in the Context of Other Intermediate Phenotypes Brain imaging represents a powerful means of accessing variation in the structure and function of the brain. Imaging genetic studies, however, are likely to be far more powerful when other molecular and physiological intermediate phenotypes are measured that access important elements of risk. Indeed, this is an inherent feature of fMRI, PET, and SPECT studies in which brain metabolism or molecules are measured in the context of specific emotional or cognitive probes that elicit measurable responses. Thus, brain metabolic activity can be measured under conditions of equivalent cognitive performance, emotional activation or pain. Each of those measures represents an intermediate phenotype whose importance is more than ancillary. In addition, by measuring drug and drug metabolite levels, the effects of pharmacokinetic genetic differences can be better controlled, enhancing the ability to detect effects of genes on pharmacodynamic variation. The list of ancillary intermediate phenotypes that might usefully be combined with brain imaging is long, and at present these largely represent gaps in knowledge. For example, a list of intermediate phenotypes in alcoholism that might be coupled with imaging genetics would include cue, stress and drug-induced craving, alcohol response including sedation and activation, alcohol withdrawal, alcohol metabolism (as will be briefly discussed), a variety of cellular changes including differences in function of immune cells, and a host of molecular changes including neurotransmitter and endocrine hormone levels, serum proteins, levels of RNA, protein and lipid oxidant damage, acetaldehyde adducted molecules, and (as will be briefly discussed) epigenetic genomic change. In several instances, these ancillary intermediate phenotypes point directly to genes and molecular networks that ultimately mediate the gene effects on the brain imaging response. This has been illustrated for several functional loci including NPY, BDNF and HTTLPR where gene effects are manifest at the level of RNA (or even transcription factor binding to promoter), protein (neuropeptide, enzyme, receptor), brain imaging response and complex phenotype. In each of these cases a dilution of effect is seen as one moves from gene to molecule to behavior. Thus, the ancillary intermediate phenotypes are invaluable both to enable the effects of genes on imaging phenotypes to be detected, and to retrace the pathway by which the genetic variant alters the imaging phenotype and behavior. Some specific examples of using intermediate phenotypes in addiction related genetic imaging studies are provided.

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Ethanol

ADH1B

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acetate

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Figure 12.5 Alcohol metabolism pathway.

12.5.1.1 Alcohol Flushing Response: Opportunities in Biochemical Genetics Addictions and related phenotypes are heritable and, similar to any other complex disorder, addictions are also influenced by an interaction between physiological, genetic, environmental, and socio-economic-demographic factors. This gap can be bridged by proxy phenotypes that more directly access such associated domains, and that can be quantified. A case in point in which an intermediate phenotype better accesses a genetic effect on vulnerability is the flushing response associated with alcohol intake in East Asian populations. Approximately half the population in Japan flushes after consuming relatively small quantities of alcohol (i.e., the equivalent of two cans of beer). The genetic variants responsible for this phenotype, which is abundant throughout East Asia, occur in two enzymes: Alcohol dehydrogenase 2 (ALDH2 Glu487Lys) and aldehyde dehydrogenase 1B (ADH1B His47Arg). ALDH2 and ADH1B are key enzymes that catalyze successive steps in the metabolism of ethanol (Figure 12.5). Blockade of ALDH2 by disulfiram, antiprotozoal drugs such as metronidazole, or the naturally occurring Lys487 causes acetaldehyde to accumulate, stimulates histamine release and thereby stimulates an aversive flushing reaction. Acetaldehyde is also a mutagen and carcinogen causing DNA damage leading to an increased predisposition to upper GI and breast cancer (reviewed in [146]). Although carriers of these alleles have a biological alarm system discouraging alcohol consumption and substantially reducing the risk of alcoholism, many Lys487 carriers nevertheless drink or even become alcohol dependent patients, and thus are at enhanced risk for cancer, as well as other consequences of alcoholism. Alcohol flushing response is not an example of “imaging genetics,” but it is included in this section because there is an obvious gap in knowledge to define the neural responses to alcohol for the 500 million humans who carry the ALDH2 Lys487 variant. Is the only signal one corresponding to aversive flushing – a peripheral adverse effect, or is there perhaps a counteracting reinforcing effect of acetaldehyde in the brain itself, a proposal for which there is some evidence from pre-clinical data [147].

12.6 Imaging Pharmacogenetics The preceding discussion on the effects of a naturally occurring genetic variant on response to a drug or drug-associated cues also directly leads to the question of how the effect of drugs used to treat drug addiction are modulated by genotype. If imaging genetics can be used for pharmacogenetics, it may be possible to detect genotype effects via more tightly focused, inexpensive and rapid studies that also have the salient advantage

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of minimizing risk and inconvenience to patients enrolled in human studies. The effect of medication/genotype combinations to modify brain responses might not be detectable in those not addicted. For example, drugs that modify craving in response to drug and stress cues are unlikely to elicit similar responses in addicted patients and controls. One would also not expect gene-modified responses to be equal across populations. In response to an image of an uncapped syringe, an IV heroin addicted subject, but not a non-addicted control subject, is likely to release striatal dopamine and experience subjective craving. Medications that modulate emotion (for example, CRF antagonists and selective serotonin reuptake inhibitors) also need to be pharmacogenetically studied in relevant clinical contexts, as well as the several genes mentioned earlier as modifying stress, pain and emotion. Here we discuss two examples of addiction imaging pharmacogenetics. Both involve genetic variation in a gatekeeper molecule with which the drug is potentially interacting, although in both instances the basis of the pharmacogenetic effect may lie at the circuit level.

12.6.1 Naltrexone, Alcoholism, Striatal Dopamine Release, and OPRM1 Naltrexone is a potent opioid antagonist mainly targeting mu-opioid receptors [148] at the dosages clinically used. Naltrexone has proven to be helpful in medical management of alcohol addiction leading to a reduction in alcohol induced craving and relapse [66, 69, 149, 150]. Mu-opioid receptors naturally bind beta-endorphin, and a missense variant Asn40Asp in the OPRM1 gene [67] alters the binding pattern and the extracellular domain of this receptor [151, 152]. Asp40 allele carriers appear to have higher craving in response to alcohol cues [153, 154]. Alcohol-dependent patients either heterozygous or homozygous for Asp40 allele were better responders to naltrexone [68]. This interaction of OPRM1 haplotype with naltrexone treatment outcome was recently replicated in the multicenter COMBINE study. The Asp40 allele ([69, 155]- Figure 12.6) and an Asp40containing haplotype [156] predicted a high likelihood of good treatment outcome. Via neuroimaging, naltrexone has been shown to decrease alcohol cue-induced activation of ventral striatum [157], and OPRM1 Asn40Asp was recently shown to modulate striatal dopamine response following alcohol administration [24]. Filbey et al. [19] also observed a high correlation of striatal response to measures of alcohol use in Asp40 carrying alcohol dependent people (Table 12.1). Therefore, it would be interesting to see how naltrexone modulates dopamine responses in OPRM1 Asp40 carriers, potentially providing a functional explanation for the greater efficacy of naltrexone in Asp40 carriers.

12.6.1.1 Smoking, Connectivity and CHRNA5 The nicotine-acetylcholine receptor gene cluster CHRNA5-CHRNA3-CHRNB4 was identified and securely replicated in nicotine dependence (and lung cancer) by large genomewide association studies [158–161]. The CHRNA5 gene has a functional missense variant (Asp398Asn) and there also appears to be a locus in the region modulating level of expression of the gene [159, 162, 163]. Many factors influence predisposition to nicotine addiction and it is therefore not surprising that CHRNA5, on its own, contributes less than 5% of the variance in vulnerability. Identifying the brain circuits that link the risk alleles to smoking would be a next logical step. Adopting that approach via a resting state functional connectivity imaging study of smokers and nonsmokers, we found that

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Figure 12.6 *Asp40 allele of a functional OPRM1 polymorphism (Asn40Asp) predicts better clinical outcome in alcoholics treated with naltrexone compared to placebo. Figure reproduced from [155; Copyright © (2008) American Medical Association. All rights reserved].

the Asn398 allele predicts connectivity strength between the dACC and ventral striatum/ extended amygdala ([164]; Figure 12.7), a circuit previously identified by a smoking severity phenotype [165]. The Asn398 allele appears to reduce connectivity strength in both smokers and non-smokers, and distinguishes smokers from non-smokers. This is the first example where a specific brain circuit function affected by a genetic variant explains approximately 10-12% of the variance in predisposition to nicotine addiction.

12.7 Conclusion Advances in human genetics have raced ahead of their effective application. The canonical human genome sequence and a list of some of the 22 million sequence variants that are rapidly expanding due to multinational efforts such as the 1000 genomes project have created a knowledge resource. The capability to routinely genotype panels of a million genetic markers and the not-yet routine ability to sequence genomes or subgenomes have enabled genome-wide surveys for genetic association and genetic variation. Another field that is quickly catching up is epigenetics, the secondary alterations of DNA and chromatin that enable differentiation and development and which, at the molecular level, mediate gene x environment interactions. The first applications of genomics to addiction phenotypes have already yielded one major discovery: a functional locus at CHRNA5 that accounts for a small, but important, part of the overall variance in vulnerability. However, GWAS studies of complex phenotypes have several specific limitations that lead to two

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Figure 12.7 *Asn398 (risk allele) associated with reduced functional connectivity between dACC and eight brain circuits. (A) More risk alleles are associated with lower connectivity strength across all studied brain regions (regions 1 to 8; Pcorrected < 0.05); (B) Effect of genotypes on eight brain circuits investigated in this study- Asn398 associated with reduced connectivity across all eight regions studied. C and D. Comparison of smokers (SK) and nonsmokers (NS) - genotype driven reduction in functional connectivity in brain regions 1 and 2 (dACC with right and left ventral striatum respectively) distinguishes SK from NSK. *figure reproduced from [164].

general limitations: power and explanatory capability. Power of a genetic study estimates the likelihood of detecting a significant association, given certain parameters, while the explanatory capability is limited by the variance of the phenotype in question. Neuroimaging can help address both of these problems. The “early returns” on the effect sizes of genes on intermediate phenotypes is that they are larger than the “geneonly” studies, as illustrated in this chapter by imaging genetic studies involving CHRNA5, NPY and other genes, and also by a GWAS study recently performed on EEG. The functional validation provided by these sorts of studies will frequently be more practically achievable than will replication of very small effect sizes in populations that differ in crucial respects, that is, drug exposure. The ability of imaging genetics to explain the pathway by which a gene influences a complex trait such as addiction is perhaps more important, as brain circuits will ultimately serve as targets in treatment and prevention. At the frontier of pharmacogenetics, the study of combined effects of genes and treatments in addicted patients will enable the more rapid evaluation of efficacy of those treatments, and of the effects of functional polymorphisms that modify efficacy. The obstacles to addiction imaging genetics are many, including the expense, the relatively small sample sizes of imaging studies, and difficulties inherent in studying addiction including collecting careful exposure histories and matching controls with

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patient populations. However, the effects of functional loci on particular circuits can be large – similar to the effects of the drugs themselves. In imaging studies, genetic analyzes will come to be viewed as both an essential tool to rule out confounds such as differences in ethnic background or frequencies of functional alleles between cases and controls. Increasingly, it will also be used as a tool for discovery, and to explicate the mechanisms of loci discovered through GWAS, deep sequencing, or other future technical advancements.

Glossary Gene: Stretch of DNA which mostly codes for proteins, but sometimes also codes for small non-coding RNA molecules. Promoter: Specific DNA sequence in the gene where transcription (RNA synthesis) is initiated through the binding of RNA polymerase enzyme. Candidate gene: A gene which is thought to be involved in disease etiology based on the evidence from biochemical and/or genetic studies. Functional locus: The physical site or location of a specific gene on a chromosome which is involved in the trait or disease and also affects protein expression. Allele: One of the two (or more) forms of DNA sequence. Polymorphism: Variation in DNA sequence among individuals, groups, or populations. Functional polymorphism: A variation in DNA sequence which alters the expression and/or function of protein. Orthologous polymorphism: A polymorphism which is present in different species, often closely related (such as chimpanzee and humans). Single nucleotide polymorphism (SNP): A type of polymorphism involving a single DNA base change. Common SNPs usually occur at a frequency of 1% or more in the population. Short tandem repeats (STR): A type of polymorphism consisting of short sequences of DNA (2-5base pairs) repeated several times in a head-tail manner. Indel: Insertion-deletion polymorphism where one or more than one DNA bases are inserted in, or deleted from the genome. Variant (genetic): Altered form of gene and/or protein arising due to the existence of polymorphism(s) in DNA sequence. Missense polymorphism: A polymorphism in the DNA sequence which leads to the substitution of one amino acid for another. Haplotype: A combination of alleles at different loci on the same chromosome (maternal or paternal). Diplotype: A combination of alleles on both maternal and paternal chromosomes. Linkage disequilibrium: The excess and complementary deficit of combinations of alleles at two different loci, which is based on rarity of meiotic recombination between loci on the same chromosome. Effect size: A concept from descriptive statistics routinely applied to population and family based association studies including GWAs. Effect size calculations estimate the magnitude and direction of the difference between groups without considering the level or state of actual statistical significance (usually represented by the p-value). Linkage study: A study involving a large affected or high risk family or several smaller families which aims to identify the disease causing gene locus. This analysis makes use of the traits co-inherited with the disease which are stably transmitted in the families.

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High throughput genotyping: Refers to a number of techniques which are used to simultaneously assess large number of polymorphisms in the genome. Genome wide association study (GWA): Refers to one of the high throughput genotyping methods where a large number of SNPs across the genome are genotyped in multiple individuals and association analyses are performed with specific traits/diseases. Genomics: Study of the genome of an organism; includes the base-by-base sequencing of the entire DNA, gene mapping and annotation. Pharmacogenetics/Pharmacogenomics: A branch of genetics which studies the effect of individual genetic variation on drug response and metabolism. Transcription: The process of RNA synthesis where DNA codes for RNA. Translation: The process of biosynthesis of proteins via RNA. Knockout mice: Genetically engineered mice where the expression of one or more genes is shut off. Iowa gambling task: A psychological task where participants are asked to choose a card and each choice may lead to a monetary gain. Delay discounting task: A task used to measure impulsivity where participants are asked to choose between smaller but more immediate rewards versus larger but delayed. Wisconsin card sorting test: A neurocognitive test of frontal lobe function that requires the subject to switch strategies that are needed to match cards to a target.

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Chapter 13

The Diagnostic and Therapeutic Potential of Neuroimaging in Addiction Medicine

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The Diagnostic and Therapeutic Potential of Neuroimaging in Addiction Medicine Martina Reske1,2 and Martin P. Paulus1,3 1

Department of Psychiatry, University of California San Diego, CA, USA 2 Institute of Neuroscience and Medicine, J¨ulich, Germany 3 Psychiatry Service, Veterans Affairs San Diego Health Care System, San Diego, CA, USA

13.1 Can fMRI Become the ECG in Addiction Medicine, or What Are the Treatment Implications of Neuroimaging Research in Drug Addiction? Traditionally, medicine has sought to use new technologies to refine diagnosis or treatment of medical conditions. For example, the availability of imaging tools such as echocardiography in cardiology has helped to differentiate pre-load versus after-load dysfunctions, which has direct implications for treatment. However, psychiatry in general and addiction medicine in particular appears to have a long way to go to link recently developed technologies and already characterized brain processing differences in substance users to specific treatments. Different implementations of functional brain imaging into the drug treatment process can be pictured. They have, at least partially, already been implemented and validated successfully: Nowadays, brain imaging is not only being used to assess the physiological basis of psychopathological symptoms and thus to assess symptom severity throughout the therapeutic process. Neuroimaging has also been considered for identifying the best suited therapy and to predict treatment outcomes. Finally, neuroimaging itself can be utilized as a therapeutic tool. For instance, recent developments in the acquisition and analysis of brain activation data allow for a direct modulation of brain activation to alter neural circuits therapeutically (i.e., the use of real-time fMRI as “neurofeedback”). There are numerous reasons why these innovative opportunities have not been appreciated and realized appropriately. Previously, addiction treatment had focused on reducing the most obvious target behavior, that is, drug use, as the main variable of interest. Moreover, no comprehensive approaches to utilize an individual’s behavior, rather than

Neuroimaging in Addiction, First Edition. Bryon Adinoff and Elliot Stein.  C 2011 John Wiley & Sons, Ltd. Published 2011 by John Wiley & Sons, Ltd.

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verbal report, about the intent to use have been developed. Finally, most therapies are aimed at changing behavior to reduce drug taking. On the other hand, brain imaging and systems neuroscience tools are poised to fundamentally change the way we treat drug addiction. Specifically, the main target may no longer be whether an individual is using or not using a drug but may focus on specific brain processing alterations. Candidate brain processes are those associated with an increased risk for becoming drug addicted, for continuing to use the drug despite adverse consequences or for heightened risks to relapse after abstinence. These critical brain processes that are either pre-existent in individuals prone to develop addiction or are altered by the disease of addiction have already been characterized in detail. Addiction medicine now has to move from a symptomatic view of addiction to a brain processing view of this disease to use the newly acquired knowledge for the diagnostic and therapeutic process. Here, we review some potential candidate neuropsychological processes and speculate as to how one may begin to monitor and modulate these processes in light of treatment progression. We point out how neuroimaging can contribute to this development and propose that neuroimaging is useful in linking psychologically defined processes to implementation in specific neural substrates. This is of particular importance if similarly defined processes (e.g., inhibition) are actually implemented in different brain systems for different functions. Moreover, we propose that the degree of dysfunction can be more quantitatively and comprehensively assessed using neuroimaging. This recent approach may impact the diagnostic process and may lead to the development of a more predictive severity measure of the disease. We will describe how neuroimaging can be used to predict relapse which, reversely, should impact treatment planning. We illustrate how continued monitoring of brain processes can provide an ongoing assessment of the efficacy of certain treatments and which steps need to be taken prior to clinicians actually using neuroimaging results. For example, can we combine fMRI and incentive-based interventions to modify reward-related processing in the striatum to such an extent that cognitive control processes can come online to reduce the propensity of drug-taking behavior? Finally, we suggest that neuroimaging itself can be included into the therapeutic process by summarizing real-time and neurofeedback imaging studies aimed at the voluntary modulation of one’s own (deficient) brain capacities. Ultimately, brain imaging may become the ECG of the addiction medicine specialist because it can provide information about the brain processes that underlie a subject’s probability to use or crave for drugs. In addition, neuroimaging may be used to differentiate different forms of addiction that require a specific treatment planning. However, to accomplish this goal, large scale clinical trials will be required that can begin to truly move neuroimaging from an experimental technique to a clinically useful tool. This chapter will summarize the current state of research on neuroimaging in addiction medicine and will outline potential future clinical applications.

13.2 Functional Neuroimaging in Addiction: Relevant Cognitive Constructs to Address during Treatment Numerous cognitive domains have been shown to be impaired in substance users (see Chapter 8 for details). The nature of these neuropsychological deficits mirrors disruption in frontal, temporal, parietal, and basal ganglia systems. Variations within the literature are likely to reflect the marked heterogeneity within dependent and/or abusing individuals. Besides differences in drug use patterns (drug type, duration, dose), non-drug

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related factors such as demography, education, psychiatric or neurological comorbidities are known to affect cerebral processes. The presumably most relevant and promising neuropsychological constructs in light of significance for drug use and treatment shall be highlighted here.

13.2.1 Executive Functioning Executive functions subsume a group of abilities involving organization and integration of information and include the development and implementation of action schemes to perform tasks in a non-habitual manner. Substance users show deficits in three related components of executive functioning: the expectation component based on reward predictions, the compulsive “drive” component which is a motivational state and lastly the decision-making component. The latter is based on the motivational properties of the stimulus and the relative importance given to the expectation of immediate reward over possible long-term losses. Findings on substance users suggest that executive dysfunctions, particularly related to computation of motivational valences and decision-making guidance, are an important factor contributing to the risk of substance use [1]. Dependent populations have been studied the most. For example, methamphetamine dependent subjects exhibit significant impairments on a number of neuropsychological tests, including verbal fluency, tests on attention and psychomotor speed as well as on learning and memory tests [2]. In studies examining cocaine-dependent individuals, some investigators have found severe executive functioning deficits [3] whereas others have found mild [4,5] or a lack of executive impairment [5]. Nevertheless, some investigators have argued that cocaine users show particular impairments on tasks involving executive control. Interestingly, it appears that the use of cocaine in these individuals is associated with mild cognitive improvement [6, 7]. Up to now, it remains unclear whether executive dysfunction is a general characteristic of stimulant users or whether it characterizes a particular subgroup of individuals. Moreover, it is not clear whether executive dysfunctions emerge as a consequence of use or whether they are part of a pre-existing condition. Our own results on occasional, non-dependent users elaborate a differentiated view: while verbal fluency [8], verbal learning and verbal memory [9] capacities were shown to be impaired in very early users of psychostimulants, suggesting a pre-existing cognitive condition, we further revealed that visual attention, task switching, problem solving, and inhibitory processing were intact in early stages of stimulant uses (unpublished observations). However, the latter were negatively affected by cumulative stimulant use, leading to severe impairments in later stages of stimulant dependence.

13.2.2 Decision-Making Decision-making refers to the process of transforming options into actions according to the individual’s preference. Associated is the experiencing of outcomes which then leads to a different psychological and physiological state of the decision-maker. Operationally, one can divide decision-making into three stages that occur over time [10]: the assessment and formation of preferences among possible options, the selection and execution of an action (and inhibition of inappropriate actions), and the experience or evaluation of an outcome. An important aspect during the first stage – assessment and formation of preferences among possible options – is to assign value or utility to each of its available

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options [11]. This value determines the preference structure within the decision-making situation. In particular, the brain must not only evaluate what is occurring now, but also what may or may not occur in the future [12]. We have clear evidence from a series of studies that substance using individuals show various decision-making dysfunctions. Such alterations of decision-making may even be present before the individual develops substance dependence [13–15] and thus could serve as a predictor. In particular, decisions by methamphetamine dependent individuals are more influenced by the immediately preceding choice [16] and show a more rigid stimulus-response relationship [17]. Moreover, methamphetamine dependent subjects were shown to be less well able to adjust their decision-making to short-term versus long-term gains [18]. Others have found that methamphetamine dependent subjects exhibit an inefficiency of cortical processing which is related to enhanced delay discounting [19]. Finally, these individuals have greater difficulty inhibiting inappropriately selected actions [20]. In comparison, amphetamine dependent individuals showed greater orbitofrontal cortex activation during decision-making [21], which may point toward altered decision-strategies.

13.2.3 Interoceptive Processing How we “feel” and how we “interact with others” are important determinants of decision-making and drug-taking behavior, and likely contribute to relapse in substance using individuals. Nevertheless, little is known about the interaction between the feeling state and the process of choosing an action. Traditional decision-making psychology has sought to establish rules and quantitative theories about how individuals establish preferences among uncertain options. These theories have been based on the concept of utility [22, 23], a measure of human value, which is related to the degree of preference for an option in a decision-making situation. More recently, several investigators have augmented this approach to include affective or visceral factors [24–26], which profoundly change the preference structure of available options. In particular, individuals often under-appreciate, hardly remember, and have difficulty explaining the influence of these factors on their decision-making. It is critical to evaluate decision-making in the context of different feeling states to understand the behavioral and neural processes that drive individuals to take drugs repeatedly, to again consume drugs during or after treatment. Interoception is sensing the physiological condition of the body [27], representing the internal state [28] within the context of ongoing activities, and initiating motivated action to homeostatically regulate the internal state [29]. It includes a range of sensations such as pain, temperature, itch, tickle, sensual touch, muscle tension, air hunger, stomach pH, and intestinal tension, which together provide an integrated sense of the body’s physiological condition. These sensations travel via small-diameter primary afferent fibers, which eventually reach the anterior insular cortex for integration [30]. Two aspects of interoception are important for addiction: First, the evaluation of the signal is highly dependent on the homeostatic state of the individual. For example, the same degree of heat (or cold) can be motivationally rewarding or punishing depending on the individual’s core body temperature. Second, interoceptive sensations are often associated with intense affective and motivational components. Future research will focus on the relevance of interoceptive feeling states for both, executive functioning and decision making, to further understand drug-taking behavior and relapse. Neuroimaging tools together with self-reports and clinical assessments will allow for a comprehensive assessment of drug-seeking behavior. Using this approach a therapist may be able to

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develop a therapeutic approach that is tailored to the individual’s needs by taking into account the interoceptive sensitivity to drug craving stimuli.

13.3 Drug Challenge Studies Enhance Knowledge on Pharmacokinetics and Drug-Experience-Relationships Animal studies and human behavioral studies clearly show that drugs of abuse powerfully affect reward learning, motivation and memory. However, it remains unclear why not all subjects consistently select them over other types of reward. Neuroimaging studies of acute drug effects (see Chapter 4 for details) combined with subjective ratings of drug effects (e.g., craving, “rush”, “high”) can enhance knowledge on temporal properties and their interplay with subjective aspects of drug use. Ultimately this will help identify the underlying basis of compulsive drug-intake. Breiter and colleagues [31] were among the first to administer cocaine during functional brain imaging. The group reported focal increases in BOLD (blood oxygenation level dependent) signal at the time of onset of subjective measures of euphoria in the brain reward circuitry, basal ganglia as well as paralimbic regions. Brain activation correlated with rush ratings was noted in a subset of regions in animal experiments associated with brain reward. Others replicated and extended these results. For instance, cocaine induced activations within orbitofrontal and anterior prefrontal areas [32] or differential time courses of activation within the Nc. Accumbens and the ventral tegmentum [33] were described with the latter indicating a Nc. Accumens-to-tegmentum feedback relationship. Among others, Garavan and colleagues [33] investigated the direct influence of drugs of abuse on cognitive processes known to facilitate drug taking behavior. They reported that acute cocaine administration improves inhibitory performance in cocaine users. This improvement was accompanied by activation increases in brain regions particularly relevant to inhibition, such as dorsolateral and inferior frontal areas. Furthermore, cocaine normalized brain activation in regions previously shown to be hypoactive in cocaine users. Understanding acute drug effects may suggest candidates for long-term impairments elicited by drugs of abuse. Frequent drug-induced activations in circumscribed brain regions may lead to them subsequently being functionally downregulated. This can then negatively impact those cognitive functions these regions are normally associated with [34]. The link, for instance, between mesolimbic and prefrontal circuits offers a startingpoint for researchers and clinicians to study the interaction between rewarding and motivational processes. The better understanding of physiological properties of drugs of abuse and associated subjective experiences will soon lead to more efficacious pharmacological and clinical interventions.

13.4 Imaging Symptom Severity One aspect in considering the therapeutic applications of neuroimaging is the possibility of using these technologies to assess illness severity throughout the therapeutic process. Rather than characterizing symptoms of addiction on a dimensional scale, addiction medicine only recently began to refrain from following an entirely categorical approach. Typically, substance abuse and dependence had been considered an all-or-none condition, namely that dependence or abuse were either present or absent. Both conditions were assumed to arise once an individual reaches a defined threshold. This categorical model

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is implemented in current classification systems but is clearly at odds with the clinical reality: Substance use disorders are not sufficiently quantified by presence or absence of abuse or dependence [35] and a dimensional description of symptoms in different stages of addiction disorders is being developed. For instance, some investigators have argued that the expression of substance use disorders should be based on a general liability model across drug classes, which results in a quantitative rather than discrete expression of a disorder phenotype [36]. Others proposed a severity index of drug use, which takes into account the number of uses and duration of use [37]. The severity index was shown to be related to executive functioning measures. For example, the severity of cocaine use was inversely related to performance on the Stroop-interference test [38] and predicted greater disinhibition behavior [39]. A series of neuropsychological studies already acknowledged this matter of debate and has for instance shown that cognitive performance of cocaine users is related to amount of recent use [40], the duration of use [41, 42] as well as to the recency of use [43, 44]. Some authors [45] have reported a dose-related association between increasing number of joints per week and greater neurocognitive impairments. However, others found no relationship between amount of stimulant use and cognitive functioning [46] or even reported “improved function” with use.

13.4.1 Brain Activation as a Matter of Symptom Severity Recently, brain imaging studies specifically aimed at the neurofunctional characterization of symptom severity. Modeling nicotine dependence parametrically, Smolka and colleagues [47], for instance, tested the hypothesis whether brain activation elicited by smoking cues increases with severity of nicotine dependence. The authors report significant associations of dependence measures and anterior cingulate cortex (ACC), parahippocampal and parietal activation (see Figure 13.1). The potentially central role of the ACC in this regard is underlined by a replication study [48]. In particular, Hong and colleagues [49] provide further evidence that ACC activation is correlated with symptom severity. However, these investigators point out that the ACC has to be considered within a cerebral network and showed that the severity of nicotine dependence was inversely correlated with connectivity between dorsal ACC and striatum, that is, greater addiction severity was associated with weaker functional connectivity between the dorsal ACC and the striatum. The striatum is particularly known for its relevance for decision making and reward processing. Even short-term nicotine challenge did not abolish this relation. The knowledge about such underlying physiological characteristics may help researchers to identify new, optimally targeted therapies, both pharmacologically and neurofunctionally. For example, knowing which neural substrates exhibit altered levels of functioning can spur the development of medications, cognitive behavioral or neuroimaging based trainings (see below) that specifically target optimal activation in these brain areas. As a consequence, proper engagement of these brain systems may lower susceptibility to relapse and thereby improve treatment outcomes.

13.5 Neuroimaging-Based Monitoring of Treatment Regimes and the Prediction of Treatment Outcomes Clinicians recognize that psychological interventions can profoundly alter patients’ sets of belief, ways of thinking or behavior, but the putative mechanisms and underlying

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changes in the brain have only recently attracted the attention they deserve. Indirectly or unintentionally, most substance treatments already employ paradigms that target brain areas crucial for addictive behavior. For example, prefrontal cortex functioning will be addressed by improvement of response selection by exploring advantages and disadvantages of continued drug use. On the other hand, the development of drug-refusal skills and coping mechanisms to overcome craving will improve dorsal ACC functioning. Cognitive and affective neuroscience could help elucidate psychological phenomena characterizing successful psychotherapy such as emotion regulation or increased inhibitory control. So far, addiction medicine, however, has only rarely integrated brain imaging in such ways but researchers recognize this necessity and usefulness [50–53].

13.5.1 Methodological Considerations Extending the initial use of fMRI to characterize the neural correlates of major psychiatric symptoms and syndromes, researchers have begun to develop study designs that incorporate fMRI into the treatment process. While the majority of presently published papers retrospectively related treatment outcomes to brain function, researchers now aim at prospective predictions which in the end will allow for an individual treatment planning. It has been reliably shown that training- and learning-related changes in the brain can be characterized applying brain imaging techniques like fMRI, making it likely that therapy-associated effects can be monitored in patients with substance use disorders. Understanding the biological differences between patients who do and who don’t respond to available treatments will extend our knowledge about fundamental differences between certain psycho- and pharmacotherapies and might ultimately help clinicians to

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identify the most suited therapy. The knowledge gained from those studies is a first step in identifying brain regions and processes involved in successful or unsuccessful treatment attendances. These could then be used in future prospective studies to examine the predictive utility of neuroimaging. Moreover, clinicians may be enabled to identify subjects at high risk to re-engage in drug use behavior after treatment, for instance due to pronounced prefrontal dysfunctions, so that a continued emphasis and extension of treatment could be indicated in these individuals.

13.5.2 Prediction of Outcomes A wealth of studies reported learning- and training-related changes in brain functions, both, on the structural and functional level. Only recently, researchers tried to predict training outcomes from brain activation, which holds a vast potential for psychotherapy. Visual and auditory learning success could be predicted by fusiform and fronto-temporal activation [54, 55]. In contrast to psychiatry, neurology adopted neuroscience methods early on and clearly identified five potential roles for neuroimaging, for instance with respect to dementia: (1) as a cognitive neuroscience research tool, (2) for prediction of which normal or slightly impaired individuals will develop dementia and over what time frame, (3) for early diagnosis of Alzheimer’s disease (AD) in demented individuals, (sensitivity) and separation of AD from other forms of dementia (specificity), (4) for monitoring of disease progression, and (5) for monitoring response to therapies [56]. Neurologists have shown that lower motor cortex activation using fMRI was the best predictor of improvement and increases in motor cortex activation after treatment for stroke. It has been hypothesized that predictions of therapy-related behavioral gains after stroke may most accurately be achieved when acquiring baseline measures of brain function along with clinical variables [57]. Such roles are not unlike those that we would like to see implemented in psychiatry and particularly within the field of addiction. While fMRI-based treatment predictions in addiction are rare, first attempts were taken to predict cognitive behavioral therapy outcomes in post-traumatic stress disorder and depression. Activation in brain regions involved in fear processing was either shown to be overactive [58, see Fig. 13.2] or diminished [59] in subsequent non-responders, suggesting a complex mechanism affecting success of cognitive behavioral therapy.

13.5.3 Outcome Predictions in Addiction Medicine Drug researchers have only started to monitor the therapeutic process and clear predictions cannot be drawn at this stage. Applying a Stroop test during fMRI, Brewer et al. [60, see Fig. 13.3] found self-reported longest durations of cocaine abstinence following treatment correlated with activation of the right putamen, left VMPFC extending into the OFC and ventral ACC, as well as the posterior cingulate cortex. Inverse correlations between abstinence and activation emerged for the left DLPFC. Although approaches to use neuroimaging in a clinical setting have been described for neurological and some psychiatric disorders, there have been very few attempts to integrate neuroimaging technology into addiction medicine. Large-scale studies will be needed to describe the potential of neuroimaging information to predict different types of treatment of substance use disorders. For instance, neuroscientists may want to outline

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and compare the potential of pre-treatment fMRI results assessing core symptoms of substance use (e.g., executive control, decision making, cue-reactivity). On the other hand, the relevance of cerebral activation patterns for different treatment regimes and therapies such as cognitive behavioral therapy, currently the most effective treatment for substance dependence, has to be identified.

13.6 Assessing the Relapse Potential Using fMRI 13.6.1 Terminology, Clinical Reality and Models on Relapse A central characteristic of addictive behaviors is their chronically relapsing nature [61]. However, “relapse” represents a somewhat arbitrary binary judgment imposed on a complex clinical condition. Moreover, the use of the term “relapse” has been criticized because it connotes an unrealistic and inaccurate conception of how successful change can occur over time [62]. Relapse is a complex process and includes multiple dimensions such as the process prior to re-use of the drug, the event of using the drug, the level to which the use returns, and the consequences associated with use [63]. Several models that stress cognitive behavioral [64], person-situation interactional [65], cognitive appraisal [66], and outcome expectation factors [67,68] have been put forth to explain the process of relapse. These models differ in the extent to which the focus is on the person (decreased self-efficacy), the situation (exposure to high-risk environments), and/or their interaction (insufficient mobilization of coping skills) [69]. On the other hand, psychobiological models of relapse have been based on opponent process and acquired motivation theories [70], craving or loss of control [71], urges or craving [72], withdrawal [73, 74], and kindling processes [74]. These models focus on the fact that brain reward systems become sensitized to drugs and drug-associated stimuli [75] which results in increased “drug-wanting” and increased susceptibility to relapse. Some investigators have suggested that relapse is best understood as having multiple and interactive determinants that vary in their temporal proximity from and their relative influence on relapse. Therefore, an adequate assessment and prediction model must be

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sufficiently comprehensive to include theoretically relevant variables from each of the multiple domains and different levels of potential predictors [63]. Some have pointed out that relapse is not a binary but a continuous outcome across multiple dimensions [62].These include the threshold, duration, prior sobriety level, number of substances involved, as well as consequences. Relapse further encompasses a multidimensional response pattern with the occurrence of negative life events, cognitive appraisal variables including self-efficacy, expectancies, and motivation for change. Also, coping resources, craving experiences [76,77] and relevant affective characteristics [61] need to be considered. Thus, relapse is a complex and multi-dimensional process and it is not surprising that multiple assessment domains can reliably characterize and predict relapse susceptibility.

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13.6.2 Neurobiology of Relapse Surprisingly little research has been conducted to relate neurobiological variables to relapse susceptibility. Some of the cognitive variables that are known to correlate with increased risks for relapse, for example, executive functioning, can be readily assessed using functional brain imaging. The value of functional neuroimaging on its own compared to clinical, socio-demographical and neuropsychological variables for the prediction of relapse in substance abuse has not been clearly delineated yet. A combination of data from all those modalities, however, may soon enable the clinician to deduce individual predictions, even though current imaging studies rely on group analyzes. Further, other functional brain imaging techniques such as PET, SPECT and MEG have already proven their relevance in predicting mild cognitive impairment [78] and depression [79] and may also become useful for the prediction of relapse to stimulant use. In addition to cognitive, psychophysiological, socio-demographic, social and psychological tests, measuring brain functioning during cognitively or emotionally challenging tasks provide insights into the nature of associated cerebral dysfunctions in substance dependent subjects. Results from these initial studies offer evidence of the potential to predict treatment outcomes from brain activation. These studies, which are outlined in the following section, typically targeted cognitive domains known to show deficiencies in substance users. Decision-making, for example, has been proposed to represent an essential ingredient for understanding relapse [80]. It has been found that longer durations of the disorder as well as decision-making and neurocognitive indicators of disinhibition were significant predictors of subsequent relapse [81]. Self-reported impulsivity or reward sensitivity, however, were not as predictive. Performance on two tests of decision-making, but not on tests of planning, motor inhibition, reflection impulsivity or delay discounting, was found to predict abstinence from illicit drugs at 3 months with high specificity and moderate sensitivity [82]. Thus, decision-making paradigms are good behavioral candidates to predict clinical outcomes. Refining these paradigms to better probe specific aspects of this complex process particularly during fMRI may enhance their prediction accuracy in the future. In a longitudinal study of methamphetamine dependent individuals we used a two-choice prediction task to probe simple decision-related processing. By comparing brain activation during a two-choice response task relative to a two-choice prediction condition, we were able to separate sensorimotor processing from prediction and decision-making. Individuals who engage in this task show bilateral activation of prefrontal cortex, striatum, posterior parietal cortex, and anterior insula during decision-making [83]. Individuals who relapsed later on, but not those who did not, showed attenuated or reversed activation patterns in prefrontal, parietal, and insular cortical regions. Optimized prediction calculations based on step-wise discriminant function analyzes revealed that right insula, right posterior cingulate and right middle temporal gyrus response best differentiated between relapsing and non-relapsing methamphetamine dependent subjects [84, see Fig. 13.4]. In combination, we obtained 94% sensitivity, with 86% specificity using this approach. Using Cox Regression analyzes, we were able to predict time to relapse [84]. This study demonstrated that the attenuated activation patterns during decision-making may play a critical role in processes that “set the stage” for relapse [63]. Others focused on the relevance of drug cue induced brain activation for the prediction of relapse and showed that it can distinguish between subjects subsequently abstinent or relapsing [85]. Kosten and colleagues [86], for example, presented videotapes showing cocaine smoking to recently abstinent cocaine dependent subjects while acquiring BOLD

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Figure 13.5 Cocaine cue-induced activation of the left precentral cortex, left posterior cingulate and right superior temporal, right lingual and inferior occipital correlate with Treatment Effectiveness Score [86]. This figure summarizes the use of neuroimaging in three domains: (1) identifying individuals at risk for drug addiction or those who are resilient to transitioning into dependence; (2) use of real-time fMRI as a therapeutic tool; and (3) using fMRI to predict long-term outcomes. Source: Neuropsychopharmacology [86], which is published by Nature Publishing Group.

fMRI along with subjective drug craving. Activation in sensory, posterior cingulate and superior temporal cortical as well as lingual and occipital gyri showed significant correlations with treatment effectiveness. Subsequent relapsers and abstinent patients differed in their activation in the right temporal and precentral, left occipital and posterior cingulate cortical regions (see Figure 13.5) and this physiological activation was a better predictor of relapse than subjective reports of craving. Focusing on striatum, ACC and medial prefrontal cortex, Gr¨usser et al. [87] showed that cue-induced brain activation was a better predictor of subsequent relapse in abstinent alcoholics than overt craving. Preliminary results specifically highlight roles for the medial prefrontal cortex, the sensory cortex, the anterior and posterior cingulate gyri, the insula and the right middle temporal gyrus in relapse processes. Such recent developments in fMRI study designs and analyzes show that fMRI may become a useful tool to identify people at high risk for relapse to substance abuse. This may hold true at stages where both, patients and therapists may have few other means for identifying such increased susceptibility to relapse. This innovative field of addiction research may have far-reaching effects involving neuroscientists, clinicians, statisticians and last but not least ethics specialists. As results from functional imaging gain more relevance for predictions or responses in individual patients, researchers will need to better anticipate and evaluate implied ethical concerns. The medical and social consequences of predicting drug dependency or relapse, based on functional brain images, will have to be acknowledged. How reliable are predictions based on groups of patients for an individual in treatment? How will this knowledge of increased relapse potentials be used? Can intensified treatment programs be proposed to patients with increased relapse risks only? Should low dose pharmaco- or psychotherapies be carried out in those patients only? Effects on health insurance and stigmatization are obvious issues. Measures

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to guarantee confidentiality will have to be identified. Individuals at high risk for problem use, relapse or poor treatment outcomes may be liable to increased stress of such testing and predictions. These themselves may increase relapse risks. Researchers, clinicians and ethicians better address these challenges in a timely manner.

13.7 Neurofeedback as a Therapeutic Approach? Neurofeedback, that is, the combination of introspective examination or modulation of ongoing psychological processes and neuroimaging, aims to address one of the most interesting questions in cognitive and clinical neuroscience: can subjects gain control over their brain processes and therewith mediate mental and cognitive experiences? In general, neurofeedback brings brain activation into awareness by applying operant conditioning methodology. Activation of a specific brain area is targeted through immediate feedback and positive reinforcement. Rather than modulating brain activity through the engagement in a cognitive task, subjects are supposed to learn to reliably evoke a certain mental state corresponding to a certain activity. Biofeedback based on neuroimaging techniques was originally developed with EEG slow cortical potentials [88, 89]. Several clinical applications have been reported in neurology and psychiatry, for instance training completely paralyzed subjects to spell [90], suppression of epileptic seizures [91] or enhancement of ADHD symptoms [92, 93]. Selfregulation of electric brain activity is facilitated by minimum delays between physiologic activity and feedback and thus requires an ultra-rapid processing of brain activation. Recent statistical and methodological achievements processing and presenting fMRI signal to subjects within 1.3 seconds [94] introduced neurofeedback into the fMRI environment (for review see [95,96]). Several studies with healthy subjects investigated the feasibility to use fMRI BOLD response with feedback of the BOLD signal as reward and reported successful regulation of brain activity in targeted regions of interest. First studies on pain reduction through ACC activity modulation are promising [97]. Brain regions with particular relevance for addiction such as ACC [97], insular cortex [98], frontal cortex [99] and amygdala have also successfully been targeted. Importantly, researchers report accompanying behavioral modifications. The question whether changes in brain activity derive from or elicit changes in cognitive processes has not been answered yet, but behavioral changes and learning, however, may be based on both intercorrelated mechanisms. Taken together, there is growing evidence that neurofeedback using EEG or fMRI could become a valuable treatment module in neuropsychiatric disorders including substancerelated disorders. Detailed neurofunctional models are still needed to identify best target brain areas. Compared to neurological disorders where for example, lesions can be clearly characterized, psychiatric syndromes or cognitive constructs may pose too wide-spread networks of cerebral activation to be targeted by fMRI-based neurofeedback. The clinical efficacy, specificity and reliability of neurofeedback have to be established in further studies. Further, studies will be needed to show whether manipulations established during brain imaging can be transferred into everyday life. First results already describe at least short term effects of neurofeedback on linguistic task performance [99] and a persistence of volitionally induction of brain activation beyond feedback presentation [100]. The potential of this approach for long-term treatment effects is still being investigated. Given economic reasons, it will also be important to characterize conditions and patients who could profit the most from this method.

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Methodological Challenges to Utilize Functional Neuroimaging as a Clinical Test

13.8 Methodological Challenges to Utilize Functional Neuroimaging as a Clinical Test Neuroimaging studies typically report differences in brain activation from a group perspective rather than individual subjects. In comparison, a good clinical test is used in an individual subject to predict the presence or absence of a condition, for example, the risk to relapse. For addiction medicine and neuroscientists, it will thus be important to determine whether fMRI can be used as such a test to become clinically useful. Main target areas will be to, for instance, identify subjects at risk for substance dependence, for substance related problems or for relapse. The sensitivity of a clinical test is defined as the proportion of people with the given disease who will have a positive result and is calculated from those individuals only who have the disease. A test with a high sensitivity is useful for “ruling out” a disease if a person tests negative. A test’s specificity on the other hand describes the proportion of people without the disease who will have a negative result. A test with a high specificity is useful for “ruling in” a disease if a person tests positive. However, the major limitation of sensitivity and specificity is that they are of no practical use when it comes to helping the clinician estimate the probability of disease in individual patients. Likelihood ratios (e.g., see [101]) are alternative statistic for summarizing diagnostic accuracy. They have several particularly powerful properties that make them more useful clinically than other statistics [102]. Likelihood ratios summarize how many times more (or less) likely subjects with the condition are to have that particular test result compared to subjects without the condition. In clinical practice it is essential to know how a particular test result predicts the risk of a pathological, which is not true for measures such as sensitivity or specificity, which quantify how a pathological condition predicts a particular test result. These statistics have recently been applied in psychiatry in general [103] and to functional neuroimaging in particular [104]. Several advantages of the likelihood ratio approach can be pointed out: First, one can obtain statistical estimates of the confidence intervals of the likelihood ratio, which enables one to determine how good / bad a particular fMRI paradigm is as a clinical test. Second, the resulting numbers can be readily and practically applied to other populations and future measurements given the same fMRI paradigms and analysis pathway. Third, statistics are comparable across different experimental approaches, which enables one to directly compare the test characteristics of fMRI with that of other clinical tests, for example, self- or clinician rating scales. Consequently, addiction medicine and neuroscientists should focus on clearly outlining the relevance and usefulness of fMRI experiments, paradigms, designs, and results. This will help when identifying the best strategies to incorporate the advantages of neuroimaging tools into diagnosis and treatment of substance use disorders. Timeline Follow-Back Latent trajectory class analyzes pose a useful method for aggregating individuals who have similar symptom patterns over time into course of illness groups [105]. This statistical approach may have a quantitative advantage over rulesbased assignment based on a priori criteria because it attempts to maximize the extraction of information from empirical data rather than following a specific operational convention of rules. Recently, latent trajectory class approaches have been utilized to examine substance use trajectories [106]. These approaches reveal substantial commonality for disorders associated with a variety of substance types [107]. Others have argued that a number of factors such as age, type of substance, problem severity, and chronicity,

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influence the longitudinal expression of substance use disorders [108]. Specifically, six trajectory classes have been identified in men with substance use disorders [109]. Among these classes were those with early-onset and severe substance use disorder symptoms persisting into adulthood, individuals with early-onset who improve over time, and other groups with symptoms that emerge later and with varying degrees of severity and persistence [109]. Others have found associations between the longitudinal courses of substance use disorders and psychiatric comorbidity, marital and employment status [110]. Using similar approaches in individuals with alcohol or tobacco use disorders, five different trajectory types were found, which were associated with densities of family history or substance abuse, gender, alcohol expectancy, behavioral undercontrol, and childhood stressors [111]. In summary, the longitudinal expression of substance use disorder is complex, nonuniform, and may not be adequately quantified by a binary state of relapse. Therefore, predicting outcomes using functional neuroimaging or other measures may need to focus on drug use trajectories rather than relapse versus non-relapse only.

13.9 The Near Future of Brain Imaging in Addiction Medicine Functional brain imaging has become a powerful tool to investigate the physiological basis of psychiatric or neurological disorders, to monitor treatment efficacy, and recently to predict clinical outcome measures. Moreover, new approaches to use the methodology itself to modify aberrant brain activation have been presented. However, brain imaging has not yet fully been integrated into the diagnostic and therapeutic process. Since MRI scanners are widely available, the use of this technology for identifying subjects at high risk and for predicting individual differences in treatment responses and relapses is particularly appealing. Such studies will have concrete implications for the understanding, diagnosis and treatment of substance use disorders. Neuroscientists and clinicians are beginning to identify concrete approaches on how to best incorporate the gained knowledge on dysfunctional cognitive and cerebral capacities into the planning and monitoring of drug treatment programs. Nevertheless, there is still strong skepticism among practitioners and neuroscientists to the idea of applying neuroscience into the treatment process. Efficacy of treatment, however, is more likely to be based on brain functional differences than on how a patient is diagnosed. Visionaries already picture cognitive emotional stress tests to predict treatment responsiveness [53]. Neuroimaging has provided a revolutionary means of mapping addiction to physiological dysfunctions in the brain. Moreover, different imaging techniques can augment our knowledge about addiction by yielding valuable information on a molecular, structural, and functional level. These results vastly exceed the acquisition of one behavioral or experience-based measure like reaction time or craving. The most important conceptual challenge for both clinicians and neuroscientists, will now be to more precisely define the problems to be treated in substance use disorders. Experimental designs need to be adopted to monitor cerebral activation and resources throughout the treatment process. However, more work is needed to better delineate how distinct psychological concepts can be related to activation patterns in circumscribed brain regions. This way, these brain areas can be used for targeted specific interventions. In addition, it is still unclear which neuropsychological concepts are most relevant for drug seeking, drug taking, and drug craving. These questions can be addressed only if neuroimagers work in close collaboration

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with addiction medicine specialists such that neuroimaging and drug treatment approaches can merge into one promising field to enhance addiction medicine. Two developments over the past 10 years make it possible to speculate how modern neuroscience can radically alter the practical treatment of drug addiction. First, MRI scanners are now widely available and are capable of providing high quality functional neuroimaging read outs. Second, the knowledge base about the functional state of different brain systems has exploded over the last decade, which provides a sound and scientific basis for understanding the role of different brain structures in addiction. Taken together, these developments can be used to assess the role that specific brain processes play in the current state of the individual. Moreover, using other biological markers, such as specific genotypes, can further differentiate the relative contribution of specific behavioral processes (e.g., approach versus avoidance type behaviors) to the disease of addiction. Some neuroscientists, for instance, have begun investigating the relevance of different genotypes for substance users’ brain functioning. Weinberger’s group was among the first to relate the known diverse behavioral responses to amphetamine to different dopaminergic genotypes [112]. Specifically, this group revealed that individual variations in catechol O-methyltransferase (COMT) val158 met genotypes were associated with differential effects of amphetamine to prefrontal functioning during a working memory task [112]. Also, McClernon and colleagues [113] provided some evidence that a dopamine receptor 4 variable number tandem repeat (DRD4 VNTR) polymorphism is related to specific brain responses to smoking cues in brain areas associated with executive and somatosensory processing. These or other genetic variations may explain differential individual working profiles of psychoactive substances and may have promoted substance use in the first place. It could, for example, be hypothesized that a given polymorphism leads to deficient prefrontal processing strategies, resulting in insufficient inhibitory processing and subsequently increasing the chances for substance use. Besides a potential causal relationship, certain genotypes may need to be considered prior to instituting pharmacological or psychological treatment regimes. Drawing on the same example of prefrontal functioning in diverse genotypes, clinicians and therapists could benefit from a neurofunctional and genetic characterization of a particular patient to identify the best suited treatment targets. For example, a given genotype may be a relevant factor to consider when implementing therapy with dopaminergic agents or treatments to substance use disorders. Such approaches illustrate one application of functional neuroimaging in pharmacogenomics. In the near future, such findings from interdisciplinary experiments need to be integrated into both the diagnostic and especially therapeutic processes. Ideally, individual patients with substance related problems will soon be characterized comprehensively by psychiatrists, therapists, neuroscientists and geneticist to individually plan the treatment process. Along with adopting functional imaging results into the diagnostic process, researchers will have to demonstrate its use to select potential subsequent treatments and to reliably predict treatment outcomes. For instance, brain imaging can already group usually heterogeneous substance users according to distinct etiologies and brain function data could thus help to trigger special clinical interventions. Moreover, brain imaging information has already been shown to precisely reflect severity of substance related problems. In order to help clinicians to assess illness severity marker or serve as a reliable relapse predictor, neuroimaging measures need to closely track disease state both when it is symptomatic as well as when the disorder is asymptomatic. Thus, it is not sufficient to show that ill individuals differ from healthy subjects but also that recovered or asymptomatic individuals with substance use disorders have altered

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processing in specific brain structures when compared to those individuals without substance use disorders. Such consistent distinctions would thus enable us to make clinical predictions about individuals who are at high risk for experiencing exacerbation of substance use problems. As others have pointed out, combining neuroimaging approaches within medications studies (e.g. of cocaine abusers) could prove useful for targeting specific pharmacological agents to subgroups of patients, prediction of response to medication and relapse to use [114]. Clearly, functional neuroimaging is playing an increasingly important role in addiction medicine. In order for this modality to be useful for defining diagnostic categories or monitoring treatment success, neuroscientists now need to push the limits of this technology to clearly show its ability to define clinically-relevant information on a singlesubject basis.

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84. Paulus, M.P., Tapert, S.F., and Schuckit, M.A. (2005) Neural activation patterns of methamphetamine-dependent subjects during decision making predict relapse. Arch Gen Psychiatry, 62, 761–8. 85. McClernon, F.J., Hiott, F.B., Liu, J. et al. (2007) Selectively reduced responses to smoking cues in amygdala following extinction-based smoking cessation: results of a preliminary functional magnetic resonance imaging study. Addict Biol, 12, 503–12. 86. Kosten, T.R., Scanley, B.E., Tucker, K.A. et al. (2006) Cue-induced brain activity changes and relapse in cocaine-dependent patients. Neuropsychopharmacology, 31, 644–50. 87. Grusser, S.M., Wrase, J., Klein, S. et al. (2004) Cue-induced activation of the striatum and medial prefrontal cortex is associated with subsequent relapse in abstinent alcoholics. Psychopharmacology (Berl), 175, 296–302. 88. Birbaumer, N., Ghanayim, N., Hinterberger, T. et al. (1999) A spelling device for the paralysed. Nature, 398, 297–8. 89. Rockstroh, B., Elbert, T., Birbaumer, N., and Lutzenberger, W. (1990) Biofeedback-produced hemispheric asymmetry of slow cortical potentials and its behavioural effects. Int J Psychophysiol, 9, 151–65. 90. Birbaumer, N., Hinterberger, T., Kubler, A., and Neumann, N. (2003) The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome. IEEE Trans Neural Syst Rehabil Eng, 11, 120–3. 91. Kotchoubey, B., Strehl, U., Uhlmann, C. et al. (2001) Modification of slow cortical potentials in patients with refractory epilepsy: a controlled outcome study. Epilepsia, 42, 406–16. 92. Strehl, U., Leins, U., Goth, G. et al. (2006) Self-regulation of slow cortical potentials: a new treatment for children with attention-deficit/hyperactivity disorder. Pediatrics 118: e1530e1540. 93. Beauregard, M. and Levesque, J. (2006) Functional magnetic resonance imaging investigation of the effects of neurofeedback training on the neural bases of selective attention and response inhibition in children with attention-deficit/hyperactivity disorder. Appl Psychophysiol Biofeedback, 31, 3–20. 94. Weiskopf, N., Mathiak, K., Bock, S.W. et al. (2004) Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Trans Biomed Eng, 51, 966–70. 95. deCharms, R.C. (2007) Reading and controlling human brain activation using real-time functional magnetic resonance imaging. Trends Cogn Sci, 11, 473–81. 96. Sitaram, R., Caria, A., Veit, R. et al. (2007) FMRI brain-computer interface: a tool for neuroscientific research and treatment. Comput Intell Neurosci, 254–87. 97. deCharms, R.C., Maeda, F., Glover, G.H. et al. (2005) Control over brain activation and pain learned by using real-time functional MRI. Proc Natl Acad Sci U S A, 102, 18626–31. 98. Caria, A., Veit, R., Sitaram, R. et al. (2007) Regulation of anterior insular cortex activity using real-time fMRI. Neuroimage, 35, 1238–46. 99. Rota, G., Sitaram, R., Veit, R. et al. (2009) Self-regulation of regional cortical activity using real-time fMRI: the right inferior frontal gyrus and linguistic processing. Hum Brain Mapp, 30, 1605–14. 100. deCharms, R.C., Christoff, K., Glover, G.H. et al. (2004) Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage, 21, 436–43. 101. Florkowski, C.M. (2008) Sensitivity, Specificity, Receiver-Operating Characteristic (ROC) Curves and Likelihood Ratios: Communicating the Performance of Diagnostic Tests. Clin Biochem Rev, 29(Suppl. 1), S83–S87. 102. Deeks, J.J. and Altman, D.G. (2004) Diagnostic tests 4: likelihood ratios. BMJ, 329, 168–9. 103. Crawford, J.R., Garthwaite, P.H., and Betkowska, K. (2009) Bayes’ theorem and diagnostic tests in neuropsychology: interval estimates for post-test probabilities. Clin Neuropsychol, 23, 624–44. 104. Medina, L.S., Bernal, B., and Ruiz, J. (2007) Role of functional MR in determining language dominance in epilepsy and nonepilepsy populations: a Bayesian analysis. Radiology, 242, 94–100.

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References

105. Sobell, L.C., Agrawal, S., Annis, H. et al. (2001) Cross-cultural evaluation of two drinking assessment instruments: alcohol timeline followback and inventory of drinking situations. Subst Use Misuse, 36, 313–31. 106. Kaloyanides, K.B., McCabe, S.E., Cranford, J.A., and Teter, C.J. (2007) Prevalence of illicit use and abuse of prescription stimulants, alcohol, and other drugs among college students: relationship with age at initiation of prescription stimulants. Pharmacotherapy, 27, 666–74. 107. Kane, J.M., Safferman, A.Z., Pollack, S. et al. (1994) Clozapine, negative symptoms, and extrapyramidal side effects. J Clin Psychiatry, 55(Suppl B), 74–7. 108. Kaufman, J.N., Ross, T.J., Stein, E.A., and Garavan, H. (2003) Cingulate hypoactivity in cocaine users during a GO-NOGO task as revealed by event-related functional magnetic resonance imaging. J Neurosci, 23, 7839–43. 109. Clark, D.B., Jones, B.L., Wood, D.S., and Cornelius, J.R. (2006) Substance use disorder trajectory classes: diachronic integration of onset age, severity, and course. Addict Behav, 31, 995–1009. 110. Chi, F.W. and Weisner, C.M. (2008) Nine-year psychiatric trajectories and substance use outcomes: an application of the group-based modeling approach. Eval Rev, 32, 39–58. 111. Jackson, K.M., Sher, K.J., and Wood, P.K. (2000) Trajectories of concurrent substance use disorders: a developmental, typological approach to comorbidity. Alcohol Clin Exp Res, 24, 902–13. 112. Mattay, V.S., Goldberg, T.E., Fera, F. et al. (2003) Catechol O-methyltransferase val158-met genotype and individual variation in the brain response to amphetamine. Proc Natl Acad Sci USA 100: 6186-91. 113. McClernon, F.J., Hutchison, K.E., Rose, J.E., and Kozink, R.V. (2007) DRD4 VNTR polymorphism is associated with transient fMRI-BOLD responses to smoking cues. Psychopharmacology (Berl), 194(4), 433–41. 114. Elkashef, A. and Vocci, F. (2003) Biological markers of cocaine addiction: implications for medications development. Addict Biol, 8, 123–39.

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Index Page numbers in italics denote figures, those in bold denote tables. abstinence 12, 15, 19, 162, 168–70, 170, 238, 275, 298 alcohol 191, 251 central effects amygdalar density 43 dopaminergic system 117 cocaine 247, 328, 330 cognitive performance effects 182–7 and craving 20, 22, 145 methamphetamine 43, 242, 248, 250 nicotine 143–4, 145, 188, 300 abuse see drug abuse abuse potential 85–6 MDMA 239 neuronal systems related to 86–7, 86 acetaldehyde 303, 304 action-to-habit theory 13, 22 activation likelihood estimation 151 addiction 3, 211–12, 289 behavioral see behavioral addictions brain regions involved in 149 see also individual regions cognitive disruption 186–7 disequilibrium model 150 mesostriatal system in 116–21 neural dysfunction in 218–24 neural mechanisms of 211–33 neuropharmacology 15–17 preclinical studies 9–14 and stress 218–21 see also craving; and individual drugs ADHD 19–20, 164, 334 alcohol abstinence 191, 251 abuse 15, 92–5, 122, 251–2, 305 cognitive disruption 183, 185 DAT protein 288, 294–5

neuropharmacology 92 resting blood flow 94 central effects 301, 302 amygdala 134 corpus callosum 251 glutamatergic system 116, 301 hypothalamus 251 neural network function 94–5 nucleus accumbens 135 orbitofrontal cortex 301 prefrontal cortex 134, 135, 169 ventral striatum 67, 118, 135, 266, 305 ventral tegmental area 116 craving 147 flushing response 304 metabolism 304 neuroimaging studies BOLD 134, 135, 146 PET 92–4, 93 alcohol dehydrogenase 304, 304 Alcohol Use Disorders Identification Test 164 alleles 308 Asn398 307 asp40 305, 306 amphetamine 10, 15–16, 21, 117, 238–9 addiction 290–1 central effects 240, 241 blood flow 241 cortisol response 215 dopaminergic system 26, 265, 295 orbitofrontal cortex 195, 196, 324 and impulsivity 165 neuropathology 15 neuropharmacology 87 see also psychostimulants

Neuroimaging in Addiction, First Edition. Bryon Adinoff and Elliot Stein.  C 2011 John Wiley & Sons, Ltd. Published 2011 by John Wiley & Sons, Ltd.

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amygdala 10, 11, 21, 108, 212, 213–14, 293 in addiction 11–12, 13, 54, 149, 219, 222 alcohol 134 nicotine 135 basolateral 11 in craving 137, 138, 142, 273 in cue-reactivity 116, 142 density 43 in emotional reactivity 295–8 in impulsive behavior 161 in motivation 86 in neurofeedback 334 neuropeptide Y affecting 296–7, 297 in reward processing 98 in stress 214–15, 216–17, 218, 221, 295, 296 amygdalo-striatal system 12 anhedonia 146, 164 animal models 4, 24, 69, 117, 134, 150, 165, 238–9, 265, 269, 288 anisotropy 45–7, 46, 249, 249 anterior cingulate cortex 14, 21, 52, 54, 92, 97, 180, 212, 213, 326, 327 in addiction cannabis 168 cocaine 222 disequilibrium model 150 nicotine 135, 327 in craving 273 in cue-reactivity 116, 141 in stress 216 anxiety disorders 211, 221, 296 apparent diffusion coefficient 45, 250 appetitive motivational networks 86–7, 86 arterial spin labeling perfusion imaging see ASL perfusion imaging ASL perfusion imaging 48–9 Asn398 allele 307 Asp40 allele 305, 306 associative learning deficit 16 attention 181, 182, 188–90 deficit see cognitive deficit during drug administration 188–90, 190 without drug administration 181 attention deficit hyperactivity disorder see ADHD autism 43

Barratt Impulsiveness Scale 18–19, 19, 159 behavioral addictions 3, 263–83 craving in 273–7, 274 diagnosis 264–5 mesostriatal dopaminergic system in 265–9, 266, 267 reward processing in 118, 269–72 see also individual addictions behavioral impulsivity see impulsivity benzoylmethylecgonine see cocaine biofeedback see neurofeedback blood oxygenation dependent imaging see BOLD studies body mass index 264, 275 BOLD studies 48, 49, 115, 188, 290, 330 alcohol craving 134, 135, 146 cannabis addiction 140 in craving 275, 276 in cue-reactivity 147, 148 and emotional regulation 144–5 in neurofeedback 334 neurotransmitter studies 110 nicotine addiction 52, 135, 137 psychostimulant addiction 91, 91, 92 in reward processing 110–11, 115, 271, 274 working memory task 192, 300 brain drug effects see individual drugs imaging see neuroimaging morphometric changes 301–2, 302 symptom severity 325–6, 327 see also individual regions Brodmann areas 180–1, 180, 187 Cambridge Gamble Task see Cambridge Risk Task Cambridge Risk Task 16, 194–5, 194 cannabis abuse 95–6 cognitive disruption 183, 186 neural network function 96 neuropharmacology 95 resting blood flow 95–6 anterior cingulate cortex effects 168 craving 139–40, 140, 274 neuroimaging studies BOLD 140 PET 95

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catechol-O-methyltransferase see COMT gene caudate-putamen 12 cerebellar clock 96 chemical shift imaging 55, 56, 246 CHRNA5 gene 305–6 chronic pain syndrome 217, 220 cocaine 239 abstinence 247, 328, 330 abuse 15, 20, 58 anatomic changes 240–1 cognitive deficit 182, 183, 186 executive dysfunction 323 inhibitory control in 166 neurochemical alterations 247–8 neuropathology 15–16 voxel-based morphometry 244 central effects 241–2, 333 anterior cingulate cortex 222 cerebellar neurotoxicity 244 corpus callosum 250 dorsal striatum 137 gray matter density/volume 242–4, 243 nucleus accumbens 137, 138 ventral tegmental area 54, 54 craving 137–9 cues 138 and impulsivity 165 neuroimaging studies BOLD 91 MRI 239–40, 240 neuropharmacology 87 neurotransmitter effects dopaminergic system 26 GABAergic system 248 glutamatergic system 248 see also psychostimulants cognitive deficit 16, 179–207, 182–7, 241–4 abstinence effects 182–7 attention 181, 182, 188–90 decision-making 184–7, 193–7 evaluation of 300–1 frontolimbic and temporal abnormalities 241–2 pre-morbid vulnerabilities 198 study limitations 199–200

treatment implications 200 working memory 183–4, 190–3, 192 cognitive restraint 276, 277 compulsivity 13, 18, 20–5 assessment of 24–5 craving 20–2 drug-seeking behavior 12, 22–4, 23 see also craving; impulsivity COMT gene 198, 294, 298–300, 299, 300, 337 conditioned stimulus 113, 113, 114, 118 corpus callosum 68 in addiction 47 alcohol 251 cocaine 250 methamphetamine 242, 244 section of 53 cortical surface-based analyses 42–3 cortical thickness 43 cortico-striatal-limbic circuit 212–14 in stress/addiction 223–4 see also amygdala; anterior cingulate cortex; nucleus accumbens; orbitofrontal cortex; prefrontal cortex corticotropin-releasing hormone 214, 221 corticotropin-releasing hormone binding protein 296 Cramer-Rao lower bound 56 craving 20–2, 113, 114, 116, 133–56 abstinence effects 20, 22, 145 alcohol 134–5, 135 behavioral addictions 273–7, 274 BOLD studies 275, 276 brain regions involved in amygdala 137, 138, 142, 273 dorsal striatum 276 hippocampus 275 orbitofrontal cortex 140 ventral striatum 142, 144, 147, 147 cannabis 139–40, 140 cocaine 137–9 and drug availability 144, 145 drug-seeking behavior 12, 22–4, 23 emotional regulation 144–5 genetic aspects 146 neural response 141–2 intervention effects 147–9, 149 modulators of 142–7

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craving (Continued ) nicotine 135–7, 136 opioids 139 subjective response 141–2 see also addiction cue-reactivity 113, 144, 195, 329 BOLD studies 147, 148 brain regions involved in 116, 135, 141, 142, 150 see also craving cuneus 52, 139, 144, 327 DAT protein 288, 292, 294–5 decision-making 323–4 cognitive disruption 184–7, 193–7 during drug administration 197 without drug administration 194–7 see also cognitive deficit impairment of 16 default-mode network 51, 270 deformation-based morphometry 42 delay discounting task 195–6, 196, 300, 309 depression 212, 328, 331 diffusion imaging 44–8 in addiction 46, 47–8 higher-order 46–7 tractography based on 47 diffusion tensor imaging 40, 45–6, 46 psychostimulant abuse 249–50, 249 diffusion-weighted imaging 45 diplotype 291, 296, 297, 301, 302, 308 disequilibrium model 150 disinhibition see impulsivity dopamine dysregulation syndrome 268 dopaminergic system 15 in addiction 293 amphetamine 26, 265, 295 cocaine 26 gambling 268 psychostimulants 90 in craving 137 fMRI 25 G-protein coupled receptors 293 gene modulation 294–5 mesostriatal see mesostriatal dopamine system

in reward processing 111–15, 113–15, 294 striatal 305 dorsal striatum 266 in cocaine addiction 137 in craving 276 in cue-reactivity 116 in habit learning 12–14, 13, 272 in stress 221 dorsolateral prefrontal cortex 14, 122, 150, 180, 212–13 cortical thickness 243 in disequilibrium model 150 in nicotine addiction 298, 300 in stress 212–13 dorsomedial prefrontal cortex 137 drive see impulse drive drug abuse 39–82, 85–104 impulsivity as risk factor 163–5 potential for 85–7, 86 psychostimulants see psychostimulant abuse see also addiction; craving; and individual drugs drug addiction see addiction drug availability, and craving 144, 145 drug challenge studies 325 drug-seeking behavior 12, 22–4, 23 DSM-III-R 3 DSM-IV 4, 17 DSM-V 3 ecstasy see MDMA EEG 5, 114, 296, 307, 334 effect size 288, 307, 308 electroencephalography see EEG emission computed tomography see PET; SPECT emotional processing 296–300, 297, 299, 300 BOLD studies 144–5 brain regions involved in amygdala 295–8 hippocampus 296, 297, 299 emotional regulation 144–5 endophenotypes 223, 287 see also impulsivity endorphins 112, 272, 305 ethanol see alcohol

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event-related designs 50 event-related potentials 114–15 executive functioning 298, 323 expectancy 144, 336 and craving 145 Eysenck Impulsiveness Questionnaire 159 Fagerstr¨om Test of Nicotine Dependence 46, 47 fMRI 5, 39, 48–51 addiction studies 51, 52, 117 psychostimulant abuse 91 ASL perfusion imaging 48–9 BOLD studies see BOLD studies as diagnostic tool 321–2 methodological challenges 335–6 dopamine and brain function 25 experimental design 50 hemodynamic response function 49–50 mechanisms 50 motor response inhibition 162 phenotype studies 290–1 relapse potential 329–34 resting-state functional connectivity 51–4, 52 VASO imaging 49 food craving 273–5, 274 frontolimbic abnormalities 241–2 functional brain imaging see neuroimaging; and individual techniques functional locus 288, 306, 308 functional magnetic resonance imaging see fMRI

genetic studies 287–317 addiction phenotypes 290–1 alcohol flushing response 304, 304 brain morphometric changes 301–2, 302 cognitive function 300–1 craving 146 domains of vulnerability 289, 293–300 reward sensitivity 289, 293–5 stress resiliency 295–300 gene-environmental interaction 222–3 molecular-functional locus 288 pharmacogenetics/pharmacogenomics 304–6, 306, 307, 309 pre-morbid vulnerabilities 198 protein expression 292 genetic variants 308 genome wide association studies 288, 309 genomics 288, 309 genotyping, high throughput 288, 309 glucocorticoids 215 glucocorticoid gene 223 glutamatergic system 5, 11, 12, 55, 245 in addiction 119–20, 120 alcohol 116, 301 cocaine 248 in reward processing 116 in stress 219 Go/No-Go task 17, 164, 167 gray matter density/volume changes 242–4, 243 prefrontal cortex 15–16 MRI 41 gyrification index 43 gyrus scale 43

G-protein coupled receptors 293 GABAergic system 5, 11 in addiction 120 cocaine 248 in reward processing 116, 119, 120 gambling, pathological 264, 271–2 craving in 276–7 dopaminergic system in 268 gamma-aminobutyric acid see GABA genes 308 candidate 308 and disease 302 modulation of dopamine function 294–5 see also individual genes

habit learning 12–14, 13 haplotype 292, 294, 296, 298, 301, 305, 308 Heath, Robert 271 hedonic motivation 11, 17, 20, 87, 110, 112, 189, 220, 274 hemodynamic response function 49–50 heroin 10, 15, 18, 47, 53, 67, 96, 97, 115, 139, 167, 247, 294 central effects 301 craving 144, 146 dopamine receptor downregulation 265 high angular resolution diffusion imaging 47

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hippocampus 10, 11, 12, 43, 108, 118 in addiction 54, 119, 149 nicotine 136 overeating 270 in craving 142, 273, 275 in emotional processing 296, 297, 299 glutamatergic neurons in 119 memory role 119, 119 neuropeptide Y affecting 296–7, 297 in reward processing 98, 120 in stress 212 5-HTT gene 297–8 hydromorphine 97 hyperthermia 239 hypothalamic-pituitary-adrenal axis 242, 295–6 hypothalamus 213 in alcohol addiction 251 in impulsivity 161 in motivation 86 sexual cue reward 277 in stress 214, 219 imaging genetics see genetic studies Impaired Response Inhibition and Salience Attribution 180 impulse control 160, 160 disorders of 122, 264 impulse drive 160, 160, 164 impulsivity 17–20, 159–76 abstinence and relapse 168–70, 170 and ADHD 19–20 brain regions involved in hypothalamus 161 orbitofrontal cortex 161, 163 prefrontal cortex 17, 160, 161, 162 ventral tegmental area 161 in current drug users 165–8, 166, 167 drugs affecting 165 neurobiology 161–3, 162 and reward processing 121–2 as reward vs control 159–61, 160 and risk of drug abuse 163–5 see also compulsivity incentive salience 13, 110, 112–13, 113, 134, 148, 150, 160 incentive-sensitization theory 13 independent component analysis 53 inferior parietal lobe 138, 141, 147, 327

insertion-deletion (indel) 293, 308 intermediate phenotypes 287, 303 interoceptive processing 324–5 Iowa Gambling Task 16, 48, 193, 193, 300, 309 J-coupling 57, 58 lateral prefrontal cortex 91, 212–13 learning systems 293 liking 121 limbic-striatal circuit 212, 214–15, 215 dysfunction 217, 219–23 stimulation of 217, 218 linkage disequilibrium 308 linkage studies 308 locus coeruleus 214 magnetic resonance imaging see MRI magnetic resonance spectroscopy see MRS marijuana see cannabis Matching Familiar Figures task 168 MDMA 48, 190, 192, 240 abuse potential 239 cognitive disruption 184 neurotoxicity 239, 240, 244, 248, 294 medial prefrontal cortex 51, 54, 108, 117, 213, 298, 300, 333 in cue-reactivity 116, 117, 147 MEGA-PRESS 56–7 Melbourne Decision-Making Questionnaire 16 memory 137 deficit 16, 239, 323 drugs affecting 179 opioids 139 episodic 121, 270 hippocampus in 119, 119 working see working memory mesostriatal dopamine system 107–10, 108, 109 in addiction 116–21 behavioral addictions 265–9, 266, 267 in reward processing 111–15, 113–15 methamphetamine 16, 58, 239 abstinence 242, 248, 250 abuse cognitive disruption 182, 184–5

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dopaminergic system in 267 executive dysfunction 323 neurochemical alterations 248–9 central effects 242 corpus callosum 242, 244 nucleus accumbens 242 white matter changes 250 neuropharmacology 87 see also psychostimulants 3,4-methylenedioxymethamphetamine 239 methylphenidate 20, 66, 89, 165, 199 molecular-functional locus 288 monetary incentive delay task 92, 93 monoamine oxidase A gene 223 Montreal Neurological Institute (MNI) space 42 morphine 10, 97, 98 and impulsivity 165 motivation neuroanatomical pathways 86 see also craving; reward processing motor response inhibition 162 MRI 15, 39, 40–54 diffusion imaging 44–8 functional see fMRI pharmacological 51 phenotype studies 290–1 principles of 40 psychostimulant abuse 239–40, 240, 244–7, 245, 246 structural see structural MRI MRS 40, 55–9 in addiction 58–9 proton 55 spatial localization 55–6 spectral editing and 2-D imaging 56–8, 58 spectral quantification 56, 57 N-back task 300 naltrexone 147, 147, 305–6, 306 negative reinforcement 97, 221–2, 222 neural networks alcohol abuse 94–5 cannabis/cannabinoid abuse 96 opioid abuse 98 psychostimulant abuse 92 neuroadaptation 295

neurochemical changes cocaine 247–8 methamphetamine 248–9 neurocognitive impairment 16, 167, 179, 326, 331 see also cognitive deficit neurofeedback 321, 334 neuroimaging acute drug effects 85–104 craving 133–56 diagnostic/therapeutic potential 321–43 future of 336–8 genetic studies 287–317 methods 39–82 preclinical studies 9–35 reward processing 107–29 see also individual drugs and methods neuropathology 10, 11, 15–17 amphetamine abuse 15 and clinical phenotype 17 cocaine abuse 15–16 see also cognitive deficit; neurocognitive impairment neuropeptide Y 223, 296–7, 297 neuropharmacology 15–20 neuroreceptors 61–4, 62, 63, 64, 65 binding potential 65 G-protein coupled 293 neurotoxic changes 237–59 neurotransmitter systems dopaminergic system see dopaminergic system GABAergic system see GABAergic system glutamatergic system see glutamatergic system in reward processing 107–16, 108, 109 serotoninergic system 110, 239, 293, 297–8 see also individual neurotransmitters nicotine abstinence 143–4, 145, 170, 170, 188, 300 addiction 305–6 cognitive disruption 182, 184, 185 functional connectivity 54, 54 central effects amygdala 135 anterior cingulate cortex 135, 327

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nicotine (Continued ) dorsolateral prefrontal cortex 298, 300 hippocampus 136 prefrontal cortex 44, 46 ventral striatum 135, 137, 307 craving 135–7, 136, 143–4 abstinence/expectancy effects 145 gender-related 143 and intensity of use 143, 326 subjective 143 tactile cues 135, 136 and impulsivity 165 neuroimaging studies 44, 46 BOLD 52, 135, 137 replacement therapy 147–8 novelty 18, 86, 191 nucleus accumbens 10, 54, 86, 117, 162, 213–14, 265, 293 in addiction alcohol 135 cocaine 137, 138 methamphetamine 242 opioids 98 in craving 92, 146 in cue-reactivity 116 dopamine in 15, 111–12 as limbic-motor interface 10–12, 11 in reward processing 108, 119 as sensory-motor gateway 112 in stress 215 obesity see overeating obsessive-compulsive disorder 24, 25 Obsessive-Compulsive Drug Use Scale 24 opioids abuse 15, 96–8, 251–2 neuropharmacology 96 PET imaging 96–7 resting blood flow 97–8 central effects 98 endogenous opioid system 295 nucleus accumbens 98 ventral striatum 96 ventral tegmental area 96 craving 139 orbitofrontal cortex 14, 98, 180, 241, 270 in addiction 61 alcohol 301

amphetamine 195, 196, 324 disequilibrium model 150 in craving 140 in cue-reactivity 116, 135 in impulsivity 161, 163 in stress 213, 216, 218 thickness 44 overeating 264, 269–71 dopaminergic system in 266–7, 267 food craving 273–5, 274 parahippocampus 273, 327 Parkinson’s disease 268 PET 5, 18, 39, 59–68 addiction studies 61, 61, 66–7, 66, 68, 116–17 alcohol 92–4, 93 cannabis 95 opioids 96–7 psychostimulants 87–91, 88, 90 brain activation 60–1 limitations 67, 68 neuroreceptors 61–4, 62, 63, 64, 65 output measures 64–6 principles of 59–60 reward processing 115 pharmacogenetics/pharmacogenomics 304–6, 306, 307, 309 phenotype clinical 17 and disease 302 imaging studies 290–1 intermediate 287, 303 planning, deficit in 16 point resolved spectroscopy 56 polymorphisms 308 functional 308 insertion-deletion 293, 308 missense 308 orthologous 308 serotonin transporter gene 223 single nucleotide (SNP) 288, 293, 308 positron emission tomography see PET posttraumatic stress disorder 211, 217, 328 addiction in 219 pramipexole 25 pre-morbid vulnerabilities 198 preclinical studies 9–35

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prefrontal cortex 12, 15, 22, 23, 57, 58, 119, 120, 120, 180 in addiction 10, 11, 43 alcohol 134, 135, 169 cognitive disruption 179–207 disequilibrium model 150 nicotine 44, 46 dorsolateral see dorsolateral prefrontal cortex dorsomedial 137 GABA levels 59 in impulsivity 17, 160, 161, 162 lateral 91, 212–13 medial see medial prefrontal cortex in stress 216–17, 216 and top-down control 14 ventrolateral 118 ventromedial 121, 144 premotor cortex 137, 327 priming 269 process addictions see behavioral addictions promoters 308 proton MRS 55 psychostimulants 87–92, 238–9 abuse cognitive deficit 241–4, 243 dopamine release 90 neural network function 92 neurochemical alterations 244–9 treatment 252–3 central effects frontolimbic/temporal region 241–2 gray matter changes 242–4, 243 resting blood flow 91–2, 91 ventral striatum 91, 92, 93 white matter changes 249–50, 249 neuroimaging studies BOLD 91, 91, 92 MRI 239–40, 240, 244–7, 245, 246 PET 87–91, 88, 90 neuropharmacology 87 neurotoxicity 237–59 anatomic changes 237–8, 240–1 cerebellar 244 frontotemporal 237–8 see also individual drugs PTSD see posttraumatic stress disorder

Q-ball imaging 47 radiotracers 63 regions of interest 41, 53 relapse 168–70, 170, 221–2, 222, 329–30 neurobiology 331–4, 332, 333 potential for 329–34 repetitive behavior disorders 268 response inhibition 16 response perseveration 24, 25 resting blood flow alcohol abuse 94 cannabis/cannabinoid abuse 95–6 opioid abuse 97–8 psychostimulant abuse 91–2, 91 resting-state functional connectivity 51–4, 52 in addiction 53–4, 54 analysis 53 mechanism 51, 53 reward deficiency syndrome 294 reward processing 10, 107–29 behavioral addictions 118, 269–72 brain regions involved in amygdala 98 hippocampus 98, 120 nucleus accumbens 108, 119 ventral striatum 108, 109, 111–12, 121, 271 ventral tegmental area 108, 119 neuroimaging studies 107–29 BOLD 110–11, 115, 271, 274 PET 115 SPECT 115 neurotransmitter systems in 107–10, 108, 109 dopaminergic system 111–15, 113–15, 294 GABAergic system 116 glutamatergic system 116 mesostriatal dopamine system 111–15, 113–15 serotoninergic system 110 sensitivity 289, 293–5 schizophrenia 43 seed-based correlation analysis 53 selective serotonin reuptake inhibitors 305

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sensation-seeking 19 see also impulsivity sensitization hypothesis 21 serine protease tissue-plasminogen activator 214 serotonin transporter gene 223, 298 polymorphism 223 serotoninergic system 239 G-protein coupled receptors 293 5-HTT gene 297–8 in reward processing 110 severity of use 325–6 Sexual Addiction Screening Test-Revised 265 sexual behaviors, compulsive 264–5 craving in 277 sexual gratification, neural response to 271 shape analysis 244 short tandem repeats 293, 308 sign-trackers 110 signal-to-noise ratio 41 single nucleotide polymorphisms (SNPs) 288, 293 single photon emission tomography see SPECT single volume spectroscopy 56 Snaith-Hamilton Pleasure Scale 146 South Oaks Gambling Screen 264 SPECT 59–68 addiction studies 61, 61, 66–7, 66, 68 psychostimulant abuse 241 brain activation 60–1 drug-related reward processing 115 limitations 67 neuroreceptors 61–4, 62, 63, 64, 65 output measures 64–6 principles of 59–60 startle response 114–15 statistical parameter mapping 42 stimulated echo acquisition mode 56 stimulus-outcome association 22 Stop Signal task 17, 122, 163, 168 stress 211–12 and addiction 218–20 brain regions involved in amygdala 214–15, 216–17, 218, 221, 295, 296 cortico-striatal-limbic circuit 212–14

dorsal striatum 221 dorsolateral prefrontal cortex 212–13 hippocampus 212 hypothalamus 214, 219 limbic-striatal circuit 214–15, 215 orbitofrontal cortex 213, 216, 218 prefrontal cortex 216–17, 216 ventral striatum 218, 219, 222 early life 223 genetic aspects 222–3 neural dysfunction 217–22, 218, 221–2, 222 neural mechanisms 212–18, 218 dysfunction of 217–22, 218, 221–2, 222 prefrontal regulation 216–17, 216 resiliency 295–300 emotional processing 296–300, 297, 299, 300 hypothalamic-pituitary-adrenal axis 295–6 sensitivity to 220–1 stress-related illness 217–18, 218 and addiction 219–20 PTSD 211, 217, 218 stressors 211 striatum see individual areas Stroop test 22–3, 23, 168, 250, 328 brain activation in 330 structural MRI 40–4, 41 in addiction 43–4, 44 cortical surface-based analyses 42–3 deformation-based morphometry 42 region-of-interest based analysis 41 voxel-based morphometry 42, 44 substance abuse see drug abuse substance dependence see addiction sulcus scale 43 supplementary motor area 184–7, 327 symptom severity 325–6 brain activation 326, 327 Talairach space 42 tanning, excessive 264, 272 Temperament and Character Inventory-Revised 159 temporal region abnormalities 241–2 timeline follow-back latent trajectory class analysis 335–6

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Index

top-down control 14 Toronto Alexithymia Scale 43 tractography 40, 45, 47 trait-impulsivity 19 transcription 309 translation 309 treatment cognitive deficit 200 monitoring of 326–9 neurofeedback 321, 334 outcome prediction 328–9, 329 psychostimulant abuse 252–3 unconditioned response 113 utility 324 VAPOR (VAriable Pulse power and Optimized Relaxation Delays) 55 vascular space occupancy imaging see VASO imaging VASO imaging 49 ventral striatum 9, 12, 15, 18, 54, 54, 107, 196, 213, 265, 270 in addiction 43 alcohol 67, 118, 135, 266, 305 nicotine 135, 137, 307 opioids 96 psychostimulants 91, 92, 93 in craving 142, 144, 147, 147 in cue-reactivity 116, 150 in impulsivity 160, 161, 164–6, 167 in motivation 86 in reward processing 108, 109, 111–12, 121, 271

in stress 218, 219, 222 see also nucleus accumbens ventral subiculum 113, 117 ventral tegmental area 15, 91, 108, 224, 265, 293, 293831 in addiction 10, 11, 119 alcohol 116 cocaine 54, 54 disequilibrium model 150 opioids 96 in impulsivity 161 in motivation 86 in reward processing 108, 119 ventrolateral prefrontal cortex 118 ventromedial prefrontal cortex 121, 144 voxel-based morphometry 42, 44, 243 cocaine abuse 244 wanting 121 Warrior/Worrier gene 298 Wernicke’s encephalopathy 251 white matter changes in 249–50, 249 MRI 41 neuropsychological abnormalities 250 tract tracing 5 Wisconsin Card Sort task 250, 309 working memory 14, 16, 138, 141, 162, 183–4, 190–3, 192, 300 brain regions involved in 21 drugs affecting 191 nicotine 298 Yale Food Addiction Scale 264 Yale-Brown Obsessive Compulsive Scale 24

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