Neuropsychological Aspects of Substance Use Disorders : Evidence-Based Perspectives 9780199930951, 9780199930838

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Neuropsychological Aspects of Substance Use Disorders

National Academy of Neuropsychology Series on Evidence-Based Practices SERIES EDITOR

L. Stephen Miller SERIES CONSULTING EDITORS

Glenn J. Larrabee Martin L. Rohling Civil Capacities in Clinical Neuropsychology Edited by George J. Demakis Secondary Influences on Neuropsychological Test Performance Edited by Peter A. Arnett Neuropsychological Aspects of Substance Use Disorders Edited by Daniel N. Allen and Steven Paul Woods

Neuropsychological Aspects of Substance Use Disorders Evidence-Based Perspectives EDITED BY DANIEL N . ALLEN AND STEVEN PAUL WOODS

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1 Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam Oxford is a registered trademark of Oxford University Press in the UK and certain other countries. Published in the United States of America by Oxford University Press 198 Madison Avenue, New York, NY 10016

© Oxford University Press 2014 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, by license, or under terms agreed with the appropriate reproduction rights organization. Inquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above. You must not circulate this work in any other form and you must impose this same condition on any acquirer. Library of Congress Cataloging-in-Publication Data Neuropsychological aspects of substance use disorders : evidence-based perspectives / edited by Daniel Allen and Steven Paul Woods. pages cm—(National Academy of Neuropsychology series on evidence-based practices) Includes bibliographical references and index. ISBN 978–0–19–993083–8 1. Substance abuse—Treatment—Case studies. 2. Clinical neuropsychology. 3. Evidence-based psychiatry. I. Allen, Daniel N. II. Woods, Steven Paul, 1972– RC564.N476 2014 616.8—dc23 2013026889

9 8 7 6 5 4 3 2 1 Printed in the United States of America on acid-free paper

To my wife Ann, for her steadfast support and friendship over these many years. D.N.A. To Kimberly, who for the past 20 years has kept me laughing and grounded, all the while being extraordinarilypatient and unwavering in her support (and tolerance) of my obsession with clinical neuropsychology. Also to my parents, who have been steadfast in their encouragement.And finally, to my mentors and students, who have inspired, challenged, and impressed me so very much along the way. S.P.W.

Contents

Preface to the National Academy of Neuropsychology Series on Evidence-Based Practices ix Preface to the Third Volume in the National Academy of Neuropsychology Series on Evidence-Based Practices xi Acknowledgments xiii Contributors xv 1. Introduction 1 Daniel N. Allen and Steven Paul Woods 2. Neural Substrates of Substance Use Disorders 19 Veronica Bisagno and Jean Lud Cadet 3. Behavioral and Neuro-economic Approaches to Substance Use Disorders 35 David P. Jarmolowicz, Derek D. Reed, and Warren K. Bickel 4. Genetic Influences on Addiction 58 Amanda M. Barkley-Levenson and John C. Crabbe 5. Treatment of Addictions and Effects of Neuropsychological Impairment on Mechanisms of Behavior Change 82 Marsha E. Bates, Jennifer F. Buckman, and Justine C. Bates-Krakoff 6. Alcohol 103 Rosemary Fama and Edith V. Sullivan 7. Cannabis 134 Jordan E. Cattie and Igor Grant 8. Cocaine 157 Antonio Verdejo-García 9. Methamphetamine 183 Jennifer E. Iudicello, Khalima Bolden, Stefanie R. Griglak, and Steven Paul Woods

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Contents

10. Club Drugs 204 John E. Fisk and Catharine Montgomery 11. Opioids 231 Olga Rass, Rebecca L. Schacht, Cherie L. Marvel, and Miriam Z. Mintzer 12. Inhalants 254 Michael John Takagi, Daniel I. Lubman, Susan M. Cotton, and Murat Yücel 13. Serious Mental Illness and Substance Use Disorder Comorbidity 280 Daniel N. Allen, Bern G. Lee, and Nicholas S. Thaler 14. Infectious Disease and Substance Use Disorder Comorbidity 310 Jennifer M. Loftis, Marilyn Huckans, Erica Weber, and Steven Paul Woods 15. Traumatic Brain Injury and Substance Use Disorder Comorbidity 341 Tresa Roebuck Spencer, Elisabeth A. Wilde, and Angelle Sander 16. Everyday Functioning in Substance Use Disorders 364 J. Cobb Scott, Kaitlin Blackstone, and Thomas D. Marcotte 17. Prescription Drug Abuse 390 Kai-Hong Jeremy Mao, Lauren N. Buchheim, and Jason P. Caplan 18. Central Nervous System Risk Factors for and Consequences of Adolescent Substance Use on Brain Structure and Function 409 Susan F. Tapert, Lotte Berke, Norma Castro, and Sunita Bava Index 435

Preface to the National Academy of Neuropsychology Book Series on Evidence-Based Practices

The field of clinical neuropsychology has advanced extensively and successfully in the worlds of psychology and neurology by following two major tenets. The first has been the constant focus on exploring and understanding the complex and intricate relationship between observed behavioral function and brain structure (and, of course, changes to that structure). From early observations of the relationship between brain injury and behavior to today’s combination of psychometric testing, cognitive neuroscience, and functional neuroimaging techniques, this focus has served the field extremely well. The second has been the rigorous adherence to careful, replicable scientific principles of questioning and theorizing, data collection, and use of sophisticated statistical analysis in testing, evaluating, and interpreting information about brain/behavior relationships. It is in the spirit of this strong foundation of empirical evidence aimed at improving the quality of informed clinical decision-making that the National Academy of Neuropsychology (NAN) Series on Evidenced-Based Practices was developed and came to fruition. For a significant amount of time, members of the neuropsychology community, and in particular the membership of the NAN, have voiced a desire for the development and availability of thorough and accurate resources that are directly applicable to the everyday needs and demands of clinical neuropsychology in a meaningful and accessible way, but provide the latest knowledge based on the most recent and rigorous scientific evidence within the field. The NAN Book Series on Evidence-Based Practices is meant to provide just such a series of resources. At its inception, it was important to first identify an excellent publisher with a history of publishing significant psychological and scientific volumes who would share this vision and provide significant support for a quality product. After lengthy research and discussions with multiple publishers, the venerable Oxford University Press (OUP), one of the most renowned and respected publishing companies in existence, was selected by the NAN Board of Directors. For

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their part, OUP have committed to the long-term development and support of the NAN Book Series and, as can be seen in the pages herein, they have spared no effort or expense to provide the finest-quality venue for the success of the Series. The Book Series is designed to be a dynamic and ever-growing set of resources for the science-based clinical neuropsychologist. As such, the volumes are intended to individually focus on specific significant areas of neuropsychological inquiry in depth, and together cover the majority of the broad clinical area of neuropsychology. This is a challenging endeavor, and one that relies on the foremost experts in the neuropsychological field to provide their insight, knowledge, and interpretation of the empirically supported evidence within each focused topic. It is our hope that the reader recognizes the many established scholars from our field who have taken on the task of volume editor and chapter author. While each volume is intended to provide an exhaustive review of its particular topic, there are several constants across the volumes. Importantly, each volume editor and respective chapter authors have committed to constraining themselves to providing only evidence-based information that meets that definition. Second, each volume maintains a broad consistency in format, including an introductory chapter outlining the volume, and often a final discussion chapter summarizing the state of the art within its topic area. Each volume provides a comprehensive index, and each chapter provides relevant references for the reader. Third, each volume is designed to provide information that is directly and readily usable, in both content and format, to the clinical neuropsychologist in everyday practice. As such, each volume and chapter within the volume is obliged to provide information in such a way as to make it accessible as a “pull off the shelf ” resource. Finally, each volume is designed to work within a pedagogical strategy so that it educates and informs the knowledgeable neuropsychologist, giving a greater understanding of each particular volume focus, and provides meaningful (read “useful”) information geared towards enhancing her/his practice of neuropsychology. In keeping with the educational focus of the Series, an additional aspect is a collaboration of the Series contributors and the NAN Continuing Education Committee such that each Series volume is available to be used as a formal continuing education text via the CEU system of NAN. It is my hope, and the hope of the consulting editors who provide their time, expertise, and guidance in the development of the NAN Series, that this will become an oft-used and ever expanding set of efficient and efficacious resources for the clinical neuropsychologist and others working with the plethora of persons with brain disorders and dysfunction. L. Stephen Miller Editor-in-Chief National Academy of Neuropsychology Series on Evidence-Based Practices July, 2012

Preface to the Third Volume in the National Academy of Neuropsychology Series on Evidence-Based Practices

Substance use disorders continue to be a major health concern in the United States and worldwide. Clinical neuropsychologists are often asked to differentiate between the neuropsychological effects of a significant drug history versus the effects of current use, make judgments on whether a history of substance abuse is causally related to the onset of neuropsychological problems or potentially exacerbating comorbid neurological conditions, and comment on the risks of substance-related neuropsychological deficits for everyday functioning and outcomes (e.g., treatment adherence). Providing the latest empirical evidence regarding the specific sequelae and neuropsychological deficits associated with substance abuse disorders will assist clinicians in answering these questions, and that is the main objective of the current volume. It is well known that substance use impacts brain structure and function, and may produce both acute and chronic neuropsychological abnormalities. While there is a large body of neuropsychological literature regarding some substances (such as alcohol) that dates back to the 1960s, the neuropsychological impact of other substances (e.g., methamphetamine) has developed much more recently. In concert with these recent developments in neuropsychology, a substantial body of knowledge regarding psychobiological, behavioral, and genetic factors that contribute to the onset and maintenance of substance use disorders has emerged. Similarly, translational research has attempted to move from these basic scientific findings to the development of evidence-based interventions for treatment of substance use disorders. Substance use disorders make up one of the most prevalent major health challenges that we face today. They are pervasive, often chronic, afflict millions of persons worldwide, and have a major negative impact on society. The role of legal and illegal substances on neurocognition is complex and varied, and the subject of great controversy. Opinions about their relative influence on cognition differ across many dimensions, including acute or chronic impact, legal or illegal

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substances, direct or indirect effects, or behavioral or genetic etiology, to name just a few issues. Here, in this third volume of the National Academy of Neuropsychology’s Series on Evidence-Based Practices—Neuropsychological Aspects of Substance Use Disorders:  Evidence-Based Perspectives—Drs. Daniel N.  Allen and Steven Paul Woods have brought together a remarkable group of international experts in substance use disorders and in neuropsychological measurement to provide a straightforward but detailed set of windows into these complex relationships. This important volume provides an empirically derived set of descriptions of the relationships between differing substances of abuse and neurocognition to inform researchers and practitioners alike, and make available the latest science examining these relationships. Dr. Allen is the Lincy Professor of Psychology at the University of Nevada, Las Vegas. He is a Fellow of NAN and the American Psychological Association, and the current president-elect of NAN. He has received numerous awards for his scholarly work including the Nelson Butters Award and Early Career Award from NAN. He has had a longstanding interest in and a significant research history studying the comorbidity of substance use with other mental health disorders and their combined impact on neuropsychological performance. Dr.  Woods is a Professor in the Department of Psychiatry at the University of California, San Diego, and received the 2007 Early Career Award for Contributions to Clinical Neuropsychology from NAN. His research interests focus on applying cognitive models of memory to examine central nervous system (CNS) effects of substance abuse and HIV infection. His work has been funded by the National Institutes of Health (NIH) and he has an extensive publishing record in some of our best neuropsychology journals. We are extremely fortunate to have two such productive, thoughtful, and accomplished volume editors. Importantly, both are also clinical neuropsychologists and have developed this volume to provide information of relevance to the practicing clinician. This volume covers a wide variety of the most notable substances of abuse, from alcohol, cannabis, and cocaine, through “exotic” designer or club drugs, to prescription drug abuse. Each chapter goes into detail regarding the current empirical knowledge base of the role of each on neuropsychological performance, and, when appropriate, the impact of neurocognition on substance use. Additionally, there are substantive chapters on the neural substrates of addictions, the neuro-economic approaches to understanding the addiction process, the influence of genetics on addiction, and the multidirectional relationship of addictions treatment and neuropsychological impairment. As are previous volumes in the Series, this volume is aimed primarily at neuropsychologists, but it should also be of use to a multitude of professionals who deal with the complexity of disentangling the interactions between cognitive processes, neuropsychological performance, and the many influences of substances of use and abuse. L. Stephen Miller

Acknowledgments

First and foremost, we are indebted to the National Academy of Neuropsychology, without whom this volume in the Book Series on Evidenced-Based Practice would not have been possible. We are especially grateful for the guidance and wisdom of Series Editor, L. Stephen Miller, who has been a tremendous resource to us throughout this process. The initial review and comments of Consulting Editors, Glenn Larrabee and Martin Rohling were also instrumental in shaping the tone and content of this book. We also extend our gratitude to the authors of the various chapters in this edited volume, who took valuable time out of their very busy professional schedules to contribute to this effort. We are also appreciative of the invaluable assistance and support of Oxford University Press, with  specialthanks to Joan Bossert, Vice President/Editorial Director in the Medical Division; Miles Osgood, Assistant Editor in the Academic Division; and our Project Manager, Joseph Lurdu Antoine, A. Finally, we also express our gratitude to the VA Healthcare System and National Institutes of Health (NIH), particularly the National Institute on Drug Abuse (NIDA), National Institute on Alcohol Abuse and Alcoholism (NIAAA), and National Institute of Mental Health (NIMH), who sponsored much of the science reviewed in this volume, and to the Lincy Foundation, for their support of education, medical, social service scientific research.

Contributors

Daniel N. Allen, Ph.D. Lincy Professor of Psychology Department of Psychology University of Nevada Las Vegas Las Vegas, Nevada Marsha E. Bates, Ph.D. Research Professor of Psychology Director, Cognitive Neuroscience Laboratory Center of Alcohol Studies Rutgers, The State University of New Jersey Piscataway, New Jersey Sunita Bava, Ph.D. Department of Psychiatry University of California, San Diego San Diego, CA Lotte Berke, B.S. Department of Psychiatry University of California, San Diego San Diego, CA Veronica Bisagno Instituto de Investigaciones Farmacológicas (ININFA-UBA-CONICET) Junín 956, piso 5, C1113-Buenos Aires, Argentina

Warren K. Bickel, Ph.D. Director Addiction Recovery Res earch Center Virginia Tech Carilion Research Institute Professor of Psychology Virginia Tech Virginia Tech Carilion Research Institute Roanoke, Virginia Kaitlin Blackstone Graduate Student Researcher SDSU/UCSD Joint Doctoral Program in Clinical Psychology HIV Neurobehavioral Research Program San Diego, California Khalima Bolden, M.S. Graduate Student Researcher SDSU/UCSD Joint Doctoral Program in Clinical Psychology Translational Methamphetamine AIDS Research Center VA San Diego Healthcare System San Diego, California Lauren N. Buchheim, B.S. Creighton University School of Medicine Omaha, Nebraska

Contributors

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Jennifer F. Buckman, Ph.D. Assistant Professor Center of Alcohol Studies Rutgers,The State University of New Jersey 607 Allison Road Piscataway, New Jersey Jean Lud Cadet, M.D. Associate Director for Diversity and Outreach Chief, Molecular Neuropsychiatry Research Branch Co-Director, Fellowship in Addiction and Aging National Institute on Drug Abuse (NIDA) Intramural Research Program Baltimore, Maryland Jason P. Caplan, M.D. Associate Professor of Psychiatry Chair of Psychiatry Creighton University School of Medicine St. Joseph’s Hospital and Medical Center Phoenix, Arizona Norma Castro, M.A. Department of Psychiatry University of California, San Diego San Diego, CA Susan Cotton Orygen Youth Health Research Centre Centre for Youth Mental Health University of Melbourne Melbourne Australia Jordan E. Cattie, M.S. Graduate Student Researcher San Diego State University/University of Calfornia, San Diego (SDSU/ UCSD) Joint Doctoral Program in Clinical Psychology HIV Neurobehavioral Research Program

San Diego, California John C. Crabbe, Ph.D. Professor, Behavioral Neuroscience Senior Research Career Scientist, Research Service Department of Veterans Affairs Medical Center Director, Portland Alcohol Research Center Rosemary Fama, Ph.D. Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford, California John E. Fisk, PhD Professor of Psychology School of Psychology University of Central Lancashire Preston, United Kingdom Antonio Verdejo-García PhD Departamento de Personalidad, Evaluación y Tratamiento Psicológico Universidad de Granada Granada, Spain Igor Grant, M.D. Distinguished Professor of Psychiatry Director, HIV Neurobehavioral Research Program University of California, San Diego San Diego, California Stefanie R. Griglak Department of Psychiatry University of California, San Diego (UCSD) San Diego, California Department of Psychology Trinity College Hartford, Connecticut

Contributors

Marilyn Huckans, Ph.D. Staff Psychologist and Neuropsychologist Portland VA Medical Center Associate Professor, Department of Psychiatry Oregon Health and Science University Portland VA Medical Center Portland, Oregon Jennifer E. Iudicello, Ph.D. Postdoctoral Research Fellow University of California, San Diego HIV Neurobehavioral Research Program (HNRP) Training in Research on Addictions in Interdisciplinary NeuroAIDS (TRAIN) San Diego, California David P. Jarmolowicz Assistant Professor Department of Applied Behavioral Science University of Kansas Lawrence, Kansas Justine C. Bates-Krakoff Department of Psychology Pace University 861 Bedford Road Pleasantville, New York Bern G. Lee, B.A. Ph.D. Candidate Department of Psychology University of Nevada, Las Vegas Las Vegas, Nevada Amanda M. Barkley-Levenson, B.S. Ph.D. Candidate Portland Alcohol Research Center Department of Behavioral Neuroscience Oregon Health & Science University and VA Medical Center Portland, Oregon

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Jennifer M. Loftis, Ph.D. Associate Professor, Department of Psychiatry Oregon Health and Science University Research Scientist Portland VA Medical Center Portland, Oregon Daniel I. Lubman Turning Point Alcohol and Drug Centre Eastern Health and Monash University Melbourne, Australia Kai-Hong Jeremy Mao, B.S. Creighton University School of Medicine Omaha, Nebraska Thomas D. Marcotte, Ph.D. Associate Professor of Psychiatry University of California, San Diego HIV Neurobehavioral Research Program San Diego, California Cherie L. Marvel Dept. of Neurology, Division of Cognitive Neuroscience Johns Hopkins University School of Medicine Baltimore, Maryland Miriam Z. Mintzer, Ph.D. Associate Professor Johns Hopkins University School of Medicine Behavioral Pharmacology Research Unit Behavioral Biology Research Center Baltimore, Maryland

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Catharine Montgomery, Ph.D. Reader in Psychopharmacology School of Natural Sciences and Psychology Liverpool John Moores University Tom Reilly Building, Byrom St, Liverpool L3 3AF United Kingdom Olga Rass Johns Hopkins University School of Medicine Behavioral Pharmacology Research Unit Behavioral Biology Research Center Baltimore, Maryland Derek D. Reed Assistant Professor Department of Applied Behavioral Science University of Kansas Lawrence, Kansas Angelle Sander Associate Professor Department of Physical Medicine and Rehabilitation Baylor College of Medicine Houston, Texas Rebecca L. Schacht Johns Hopkins University School of Medicine Behavioral Pharmacology Research Unit Behavioral Biology Research Center Baltimore, Maryland J. Cobb Scott, Ph.D. Assistant Professor Yale University Department of Psychiatry National Center for Post-Traumatic Stress Disorder Clinical Neurosciences Division VA Connecticut Healthcare System New Haven, Connecticut

Contributors

Tresa Roebuck Spencer, Ph.D. University of Oklahoma Cognitive Science Research Center Norman, Oklahoma Edith V. Sullivan, Ph.D. Professor Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford, California Michael John Takagi, Ph.D. Orygen Youth Health Research  Centre Centre for Youth Mental Health University of Melbourne Parkville, Australia Susan F. Tapert, Ph.D. Acting Chief, Psychology Service VA San Diego Healthcare System Professor of Psychiatry University of California, San Diego San Diego, California Nicholar S. Thaler, Ph.D. Post doctoral Fellow Semel Institute University of California, Los Angeles Los Angeles, California Erica Weber, M.S. Graduate Student Researcher SDSU/UCSD Joint Doctoral Program in Clinical Psychology HIV Neurobehavioral Research Program San Diego, California

Contributors

Elisabeth A. Wilde, Ph.D. Baylor College of Medicine, Departments of Physical Medicine and Rehabilitation, Neurology, and Radiology and Michael E. DeBakey Veterans Affairs Medical Center, Traumatic Brain Injury Center of Excellence Houston, Texas

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Steven Paul Woods, Psy.D. Professor of Psychiatry University of California, San Diego HIV Neurobehavioral Research Program Translational Methamphetamine AIDS Research Center (TMARC) Training in Research on Addictions in Interdisciplinary NeuroAIDS (TRAIN) San Diego, California

Posterior

Anterior

PFC

ACC

Thal

DS

OFC

Hipp

NAc Amygdala

V T A

Figure 2.1 Mesocorticolimbic reward circuit. Regions of the brain that are involved in reward and addiction: the ventral tegmental area (VTA) ventral striatum (nucleus accumbens, NAc) involved in responding to rewarding stimulus; the amygdala and hippocampus (Hipp), which participate in memory functions; the mediodorsal thalamus, key component of thalamo-cortico-basal ganglia circuits implicated in aberrant habit-learning disorders; and the prefrontal cortex/orbitofrontal cortex (PFC/OFC) and anterior cingulate cortex (ACC), which participate in executive control and emotion regulation.

Holistic Strategy

Piecemeal Strategy order of color pens

Control: Accuracy = 32, Strategy = 5 Time = 75 sec

Control: Accuracy = 28, Strategy = 2 Time = 128 sec

Alcoholic: Accuracy = 30, Strategy = 5 Time = 141 sec

Alcoholic: Accuracy = 27, Strategy = 1 Time = 156 sec

Figure 6.1 Examples of drawings copied by controls (above) and alcoholics (below) illustrating holistic (left) and piecemeal (right) strategies. Subjects drew first with a red pen, then with a blue pen, a pink pen, and a green pen, using each for 30 seconds. Modified from Rosenbloom et al., Brain Imaging and Behavior (2009).

Inhalant users vs healthy controls > 0.05 0.04 0.03 0.02 0.01 0

Figure 12.3 Regional callosal-width alterations for inhalant versus community controls, with significant and trend-level expansions denoted by color-coding according to significance.* *Reproduced from Takagi et al. (2011).

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Introduction DA N I E L N. A L L E N A N D ST E V E N PAU L   WO O D S

I have absolutely no pleasure in the stimulants in which I sometimes so madly indulge. It has not been in the pursuit of pleasure that I have periled life and reputation and reason. It has been the desperate attempt to escape from torturing memories, from a sense of insupportable loneliness and a dread of some strange impending doom. — Edgar Allan Poe1 Substance use disorders continue to be a major health concern in the United States and worldwide, although their causes and effective treatments remain elusive. What is clear is that they are responsible for a great deal of public health burden, bringing great distress and harm to those who have the disorders, as well as their family members, friends, and the healthcare system. This burden is compounded the by presence of comorbid psychological, emotional, social, and medical dysfunctions, which for some drive the development of substance abuse and for others are a consequence of it. There is now a substantial body of evidence that substance use directly impacts brain structure and function, providing insights into a number of mechanisms that put some individuals at increased risk to transition from recreational use to addiction. It is also well documented that some substances have higher addictive potential and produce greater negative physical, psychological, and neurological effects compared to others, with evidence from postmortem and antemortem human investigations, animal studies, and in

1. From S. H. Whitman (1860). Edgar Poe and His Critics, pp 74–75. New York: Rudd & Carleton.

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vitro modeling demonstrating greater neurotoxic effects of some substances over others. Persisting neural injury and associated neuropsychological abnormalities associated with prolonged and heavy use are oftentimes the motivation for referral to clinical neuropsychologists for evaluation. In this context, clinical neuropsychologists are often asked to differentiate between the neuropsychological effects of a significant drug history versus the effects of current use, make judgments about whether a history of substance abuse is causally related to the onset of neuropsychological problems or potentially exacerbates neuropsychological deficits resulting from preexisting and commonly comorbid neurological or psychiatric disorders, and comment on the adverse impact of substance-related neuropsychological deficits on everyday functioning; for example, treatment adherence, ability to manage finances, and job fitness. In forensic settings, neuropsychological evaluation may be requested to provide insight into substance-induced neuropsychological deficits as a mitigating factor in sentencing. While neuropsychological studies of substances such as alcohol began to appear in the neuropsychological literature as early as the 1960s and 1970s (Goldstein & Chotlos, 1965, 1966; Jones & Parsons, 1971; Tarter & Parsons, 1971), it is only more recently that scientists have begun to intensely investigate the consequences of other commonly abused drugs on neurological integrity and associated neuropsychological functioning. In fact, over the past two decades, the literature documenting the neurophysiological effects of various substances has virtually exploded. This literature has documented neuropsychological abnormalities and provided insights into psychobiological, behavioral, and genetic factors that contribute to the onset and maintenance of substance use disorders and associated neuropsychological abnormalities. Translational research has attempted to move these basic scientific findings from the bench to the bedside, supporting development of evidence-based prevention and intervention programs that target substance use and abuse, considering, among other things, the manner in which neuropsychological deficits interact with treatment effectiveness and outcome. This research has provided a strong empirical foundation that has direct implications for clinical neuropsychological practice. However, given the diverse nature and sheer volume of this work, there is an evident need to provide the practitioner with a cogent and up-to-date summary of current developments, which is the goal of this volume. Chapters in this volume provide the latest empirical evidence regarding epidemiological, genetic, and psychobiological factors that contribute to the development and maintenance of substance use disorders, as well as their associated social, behavioral, psychiatric, and neuropsychological sequelae, in order to assist clinicians in answering relevant referral questions. In the following introductory sections, we provide a brief history of substance use and abuse, including consideration of legislative efforts to control access, distribution, and use; incidence and prevalence of substance use disorders in the United States; a review of current diagnostic practices with regard to substance-related disorders, including general issues regarding assessment of substance use disorders in the clinic; tailoring assessment batteries to increased sensitivity to the effects of such substances; as well as an overview of the organization of the book and contents of its chapters.

Introduction

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A BRIEF HISTORY OF SUBSTANCE USE AND ABUSE

Ancient History Archeologists have provided abundant evidence for the use of psychoactive substances dating back to prehistoric times. Production and use of alcohol may date as far back as 9000 years in China’s Henan province, with archeological evidence documenting wine making and viniculture to the Neolithic period, as early as 5400–5000 B.C. (McGovern et al., 2004). Infrared and chemical analyses of ancient clay jars dating back to 5400–5000 B.C. found in a mud-brick building in the Hajji Firuz Tepe in the Zagros mountain of present-day Iran (see Figure 1.1) clearly indicated the presence of chemicals that were common to wine production (McGovern et al., 1997). These included high amounts of tartaric acid, which only naturally occurs in large amounts in grapes, as well as natural preservatives derived from tree resins, which are later well documented in wine production in ancient Egypt and the Near East. Stoppers were also found at the Hajji Firuz Tepe site, which would have allowed sealing of the jars to prevent the grape product from turning from wine to vinegar. At the beginning of the Old Kingdom Period of Egypt

Figure 1.1 Neolithic jar used to store wine, found at Hajji Firuz Tepe (Iran).∗ From: McGovern, P. E., Hartung, H., Badler, V. R., Glusker, D. L., & Exner, L. J. (1997). The beginnings of winemaking and viniculture in the ancient Near East and Egypt. Expedition, 39(1), 3–21. Used with permission. All rights reserved. ∗ One of six jars once filled with resinated wine from the “kitchen” of a Neolithic residence at Hajji Firuz Tepe (Iran), dating back to 5400–5000 BC. UPM no. 69012015. H. 23.5 cm. Photograph courtesy of Hasanlu Project, University of Pennsylvania Museum.

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N E U R O P SYC HO L O G IC A L A SP E C T S O F SU B STA N C E U SE D I S O R D E R S

(ca. 2700 B.C.), there was also clear evidence for large-scale wine production. Scenes depicting wine-making were illustrated on tomb walls, with wine included as a common provision for the afterlife, sealed in wine jars (see Figure 1.2). Archeological evidence also supports the early use of other substances, such as opium and cannabis. Earliest documentation of opium use dates back to the third millennium B.C. Clay tablets found in the ruins of the city of Nippur just south of Baghdad dating back to 3400 B.C. indicate that Sumerians in Mesopotamia cultivated opium poppies in order to isolate opium (Brownstein, 1993; Schiff, 2002). Opium was also used in ancient Egypt, although restricted to occasions such as religious ceremonies and rituals. There is also evidence for opium use from Greek and Roman literature, in Arabia, and later its introduction into China between the eleventh and thirteenth centuries A.D. by Arabian traders (Schiff, 2002). The use of cannabis for medicinal purposes may date back to circa 2700 B.C., where Chinese

Figure 1.2 Early Dynastic wine jar from royal tomb in Egypt.∗ From: McGovern, P. E., Hartung, H., Badler, V. R., Glusker, D. L., & Exner, L. J. (1997). The beginnings of winemaking and viniculture in the ancient Near East and Egypt. Expedition, 39(1), 3–21. Used with permission. All rights reserved. ∗ (a) Early Dynastic “wine jar” from a royal tomb at Abydos, Egypt, with stopper showing serekh (the early hieroglyphic form of the cartouche) of Den, a Dynasty 1 pharaoh (see insert). (b) The early hieroglyphic sign for “grapevine/vineyard” (arrowed) occurs on a more elaborate cylinder seal impression on a jar stopper with the serekh of Khasekhemwy, a Dynasty 2 pharaoh. (a) UPM no. E6943. H. 66.5 cm (Petrie 1901:I:29, pls. 40.26 and 52.743); unprovenanced stopper UPM no. 60-15-23. Photograph courtesy of the Egyptian Section, UPM, modified by P. Zimmerman, MASCA. (b) Drawing after Kaplony 1963-64; Fig. 310.

Introduction

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Figure 1.3 Chinese ideogram for marijuana. The Chinese ideogram for marijuana (“ma”) shows two plants, male and female, under a drying shed. From Aldrich, M. (1997). History of Therapeutic Cannabis. In M. L. Mathre (Ed.), Cannabis in Medical Practice, p. 36. Jefferson, NC: McFarland and Company Inc.

legend attributes to Emperor Chen Nung the discovery of its healing properties (Mack & Joy, 2000). Early Chinese medical writings include a pictogram for marijuana (see Figure 1.3) depicting two plants drying under the roof of a shed (Marthe, 1997). There is also evidence for the use of cannabis in religious rituals in India as early as 2000 B.C., with medicinal use described in Greek and Roman writing as early as A.D. 1–25 (Mack & Joy, 2000).

Modern History Following the Middle Ages, substance use patterns in Europe were largely influenced by expanding exploration of the New World. Brecher (1972) provides a detailed history of substance use and abuse, and notes that in a relatively short period of time, substance use patterns in Western Europe shifted from a primarily alcohol-based culture to become a multi-drug culture. Tobacco was introduced to Europe by sailors who had explored the New World and learned to use tobacco (both smoking and chewing tobacco leaves) from American Indians. Caffeine was introduced to Europe by explorers and traders who discovered coffee in Turkey and Arabia, the kola nut in Western Africa, and tea produced largely in China. Peyote had been used by the Aztecs as part of religious rituals dating back to pre-Columbian times, and Incan rituals involving peyote and the chewing of leaves from the coca plant were encountered by the Spanish conquerors of Mexico. While cocaine was not isolated from coca leaves until 1844, the use of coca leaves occurred long before then. Although cannabis use dates back to 2000 B.C. in Chinese culture, the plant was not indigenous in the New World. It was introduced to Chile by the Spaniards in 1545, and was later cultivated by Jamestown settlers and elsewhere as early as 1611, where it was grown for hemp

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fibers (Brecher, 1972). While isolation of opium from poppies dates back to as early as 3400 B.C. (Brownstein, 1993), it was not until the early nineteenth century that opium became a part of European and American cultures. In the United States, opium was used for medicinal purposes from the early to mid 1800s, primarily as an analgesic, but also for treatment of a variety of “nervous” conditions. Schiff (2002) indicates that, during the Civil War, the “Union Army used 2.8 million ounces of opium tincture and powder and about 500,000 opium pills” (p. 189). Recreational use of opium was fueled by a number of influences, including its spread across the country by Chinese workers who were building the transcontinental railroad in the 1860s (Brecher, 1972). Although its use was primarily medicinal at that time, recreational use of opium (typically smoking) did occur, and its potential for addiction and associated negative consequences were also clearly documented (Levine, 1974).

Reasons for Substance Use The reasons for substance use in ancient times closely mirror those of today. For alcohol, not only was it used for medicinal purposes over the millennia, but it had a number of other beneficial aspects that increased life expectancy and reproductive success, such as counteracting potentially harmful microorganisms in water supplies, increasing the nutritive value of the natural product, and decreasing the risk of developing a number of medical conditions (McGovern, 2009). Use of naturally occurring substances for medicinal purposes is also well documented. For example, early Hindu texts describe some of the benefits of alcohol use, concluding that alcohol has medicinal purposes if used in moderation (Dasgupta, 2011). Use of substances was also an integral part of religious ceremonies and rituals through the ages, with some of the more common examples including the use of peyote in the Native American Church and the service of wine during Communion and the Eucharist in the Christian religion. Finally, it is clear that alcohol and other drugs have been used for their hedonic effects since ancient times. Thus, while many of these substances were initially used for medicinal or religious purposes, recreational use was also common, prompting legislative efforts and cultural and religious prohibitions to control the misuse of substances since our earliest recorded history. Writings from religious and other sources highlight the potential negative consequences of substance misuse, acknowledging the negative effects that substance intoxication has on human reasoning processes as well as the potential of repeated intoxication for development of what are now referred to under current diagnostic practice as “substance abuse” and “substance dependence.” The Panchsheel in Buddhism, dating back to 5000 B.C., warns against intoxication out of respect for a clear mind (Dasgupta, 2011). Sumerian wisdom literature from the Shuruppak texts dating back to three millennia B.C. suggest, “A drunkard will drown the harvest” (Lambert, 1996). Similarly, there are Jewish and Christian prohibitions against alcohol intoxication. In the Old Testament, Proverbs 23:29–30 says, “Who has woe? Who has sorrow? Who has strife? Who

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has complaints? Who has needless bruises? Who has bloodshot eyes? Those who linger over wine. . . .” And in the New Testament:  “Do not get drunk on wine, which leads to debauchery. . . .” (Ephesians 5:6). Consumption of alcohol and other intoxicants is prohibited in Muslim cultures because of their adverse effects on the body and mind. While many of these ancient writings also acknowledge the potentially beneficial aspects of some substances, their prohibitions against substance intoxication resonate with modern understandings of the deleterious effects that substances may have on our physical health, cognition, and psychosocial functioning. With regard to formal legislation, in the United States, the first law regulating opium was passed in California in 1872, with the Territory of Oregon passing the first comprehensive substance abuse law in 1877, making it illegal for anyone to possess opium, cocaine, and a number of other substances, without a prescription (Levine, 1974). Some years later, similar legislation was introduced in various states to prohibit peyote use. Prohibitions against tobacco use began in the early part of the twentieth century, with laws prohibiting cigarette smoking in fourteen states by 1921 (Brecher, 1972). During the same period, alcohol prohibition movements were common and eventually resulted in the Eighteenth Amendment to the United States Constitution, making it illegal to produce, transport, or sell alcohol (Brecker, 1972). This led to an era commonly referred to as “Prohibition” in the United States, which began in 1920. The Eighteenth Amendment was eventually repealed in 1933 due to public opposition, among other considerations, as were many anti-tobacco laws, highlighting the very real tensions that continue to exist between drug policy, public opinion, and drug use. Legislation prohibiting the use of marijuana also began as early as 1927, when Louisiana passed legislation including fines and imprisonment for the possession or sale of marijuana (Brecker, 1972). Efforts to control substance use at the state levels were characterized by a lack of uniformity and were challenged to keep pace with the emergence of new substances, such as barbiturates, amphetamines, and LSD (lysergic acid diethylamide). These considerations prompted federal legislation, beginning in 1906 with the passage of the Pure Food and Drug Act, which, among other things, required that medicines containing opiates be labeled as such, with a clear indication of the amount of opiates contained (Brecker, 1972). Formation of the Federal Bureau of Narcotics (later renamed the “Drug Enforcement Administration”) occurred in 1932, followed shortly thereafter by the proposal of the “Uniform Anti-Narcotics Act” and passage of the “Marihuana Tax Act” in 1937 (Brecker, 1972). Efforts at the federal level culminated in the passage of the “Controlled Substances Act” in 1970, which was the first comprehensive legislation providing integrated regulations to implement legal control over the burgeoning number of natural, semi-synthetic, and synthetic compounds that had become substances of abuse at that time. Included in this legislation were five separate “schedules” that attempted to classify substances based on their accepted medical uses and their potential for abuse, and stipulated penalties for violations in each schedule based on whether the individual was considered a “user,” an “addict,” or a “trafficker” of the substance. The most severe penalties were specified for drugs classified in

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Schedule I, which includes marijuana, heroin, and LSD, among others, while the least severe penalties were specified for Schedule V drugs, which included substances that could be sold over the counter without a prescription, such as cough syrups. The legislation also allowed for the classification of new drugs under each of the five schedules, and was notable in its call for increased research in the areas of prevention and treatment. Thus, history provides a clear picture of the tensions that exist today between the use of substances for recreational, religious, and medicinal purposes, with the ever present potential for addiction and the negative consequences that result therefrom. EPIDEMIOLOGY Each of the chapters in Section II of this volume provides relevant epidemiological information for specific substances, so here we will discuss more general current trends in substance use disorders. Estimates indicate that 18.9 million adults in the United States were diagnosed with substance abuse or dependence in 2011, or approximately 8% of the adult population (Substance Abuse and Mental Health Services Administration [SAMHSA], 2012). Approximately 23.5  million Americans age twelve and older required intervention for substance use (SAMHSA, 2010). Treatment and other related costs in the United States resulting from substance-related disorders has been estimated to be $510.8 billion (Miller & Hendrie, 2009). It is projected that disability caused by substance use disorders will surpass that caused by any other physical disease worldwide by 2020 (World Health Organization, 2004). It is apparent from these and other statistics that substance use disorders are a major public health concern in the United States and worldwide. There are numerous socioeconomic and sociopolitical factors that assist in understanding changes in drug use over time. One is the relative accessibility of illicit substances within the general population. Increased access is associated with increased use, so much effort has been directed toward limiting the availability and access to both licit and illicit substances, through drug enforcement policy targeting the import and distribution of illicit drugs, age restrictions, increased taxes, and restriction of use in public places for licit substances like tobacco and alcohol. These efforts have met with mixed success, as evidenced by epidemiological studies indicating that substance use in the general population for specific substances exhibits divergent trends, with the use of some substances increasing and the use of others decreasing over the same time periods. Public policy, sociopolitical influences, and psychological factors such as the perceived risk of negative consequences associated with use of a particular substance play an important role in incidence of use, so efforts to prevent or decrease substance use have often focused on educating the public regarding the risks of use. These efforts have often focused on adolescents, since risk-perception is an important factor influencing decisions to use substances, such that adolescents who perceive a high risk of harm from substance use are less likely to use than those who perceive a low risk of harm (Johnston, O’Malley, Bachman, & Schulenberg, 2012).

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Some cases in point include recent trends regarding adolescent and young adult use of alcohol, tobacco, and marijuana (SAMHSA, 2010, 2012, 2013). Changes in alcohol use and risk perception from 2002 to 2011 among adolescents ages twelve to seventeen indicate that past-month binge alcohol use decreased to an all-time low of 7.4% for past-month episodes in 2011, from 10.7% in 2002. This decrease in use was accompanied by a commensurate increase in the perception of great risk from binging of 38.2% in 2002 to 40.7% in 2011. Incidence of drinking by underage individuals twelve to twenty years of age also declined, from 28.8% in 2002 to 25.1% in 2011, as did the number of individuals twelve and older who had driven under the influence of alcohol during the past year, from 14.2% in 2002 to 11.1% in 2011. The dissociation between public policy, sociopolitical influence, incidence of drug use, and perceived risk is also apparent when comparing the data for alcohol and tobacco use with changes in marijuana use rates over the past twenty years. As mentioned before, cannabis is classified as a Schedule I substance according to the 1970 Controlled Substance Act, making it comparable to drugs like heroin with regard to legal penalties for use, possession, and distribution. However, there has been a move toward legalizing marijuana in a number of states, accompanied by an increased public perception of the acceptability of its use. Polling data in 1969 indicated that 84% of Americans were against legalizing marijuana, while 12% were in favor of it, compared with 2011 data indicating 46% of American are against legalization and 50% are in favor of it (Gallup Politics, 2013). At the same time, the perception of negative consequences associated with cannabis use have decreased among adolescent users, from their peak in 2005, when 55.0% of adolescents perceived risk of great harm from smoking marijuana once a month, to 44.8% in 2011. Over this same period, the percentage of adolescents reporting marijuana use over the past month has increased from 6.8% in 2005 to 7.9% in 2011 (SAMSHA, 2013). Whether efforts designed to increase awareness of risks associated with tobacco and alcohol use through media campaigns, educational programming, and other efforts are causally related to decreases in alcohol use and smoking among adolescents, and increases in marijuana use, cannot be determined. However, the findings regarding alcohol and tobacco use do support an apparent relationship between increased perceived risk and decreased reporting of substance use, and are also consistent with opposite trends noted for drugs such as marijuana. Thus, while the causal relationships between changes in public policy, social acceptability, perceived risk, and incidence of use cannot be directly determined, the interrelationship between them nevertheless does highlight the fact that the incidence of use of any substance is most likely determined by factors other than, or in addition to, the psychoactive effects and additive properties of a particular substance. CURRENT DIAGNOSTIC PRACTICES The two most common diagnostic systems for substance related disorders include the International Classification of Diseases–10 (ICD-10) and the Diagnostic and

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Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSMIV-TR). There are differences between these two classification systems: for example, the ICD-10 includes diagnostic categories for Harmful Use and Dependence Syndrome, while the DSM-IV-TR labels generally comparable disorders as Substance Abuse and Substance Dependence. However, they also have much in common, and efforts have been made to develop comparable diagnostic criteria across both systems, so we will focus our discussion on the DSM-IV-TR diagnostic criteria, which are in common use in the United States. The DSMIV-TR classifies disorders arising from the use of substances under the general category of Substance-Related Disorders. Within this general category, there are two main subcategories that include Substance Use disorders and SubstanceInduced disorders. Substance use disorders include the diagnoses of Substance Abuse and Substance Dependence. These two diagnoses constitute the most common Substance-Related disorder diagnoses. Substance-Induced disorders include conditions whose causes are thought to be etiologically related to the use of substances, such as withdrawal effects from cessation of cocaine use, psychotic symptoms caused by the use of amphetamines, or dementia caused by the neurotoxic effects of alcohol.

Substance Use Disorders (Abuse and Dependence) Substance Abuse is diagnosed when it is determined that substance use has led to significant recurrent negative consequences in one or more of four domains over the same 12-month period. These domains include legal, interpersonal, work, or school, or hazardous behaviors. Examples of behaviors that would meet these criteria include a failure to fulfill major role obligations at work, at school, or in the family; use of substances in situations where their intoxicating effects increase the risk of physical harm (such as driving a motor vehicle); or repeated interpersonal problems that are associated with substance use. Some have suggested that individuals falling in this diagnostic category may be more accurately characterized by the term “substance misuse,” since physiological dependence and a pattern of compulsive use are not required to make this diagnosis. A Substance Dependence diagnosis is made when substance use persists despite leading to three or more recurrent negative cognitive, behavioral, or physiological consequences over a 12-month period. Symptoms meeting criteria may include physiological dependence as indicated by tolerance or withdrawal, unsuccessful attempts to cut down use even though desiring to do so, using substances in larger doses or for longer periods of time than originally intended, spending substantial time in the procurement or use of substances or recovering from their effects, failure to fill major role obligations as a result of substance use, or continuing to use substances despite the knowledge that they worsen current medical or psychiatric conditions. Thus, a major distinction between substance dependence and substance abuse is the compulsive use of the substances with an inability to control their use, despite the realization that use causes negative consequences.

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The diagnostic criteria for substance dependence also include an indicator for the presence of physiological dependence, since physiological dependence is not required to make the diagnosis, but it is an important treatment consideration when withdrawal effects are expected as a result of substance use cessation. Finally, the DSM IV-TR provides six course specifiers for a diagnosis of substance dependence, four that allow the clinician to indicate different states of remission (partial early or partial sustained remission, and full early or full sustained remission), as well as two indicators to identify whether the individual is on an agonist therapy or in a controlled environment. Substance Abuse and Dependence diagnostic criteria are applied to 13 categories of substances in the DSM: Alcohol, Amphetamines, Caffeine, Cannabis, Cocaine, Hallucinogens, Inhalants, Nicotine, Opioids, Phencyclidine; and Sedatives, Hypnotics, and Anxiolytics, as well as two other categories, Polysubstance and Other. The Other category is used for substances that are not included in the 13 specific drug categories (e.g., anabolic steroids, some over-the-counter and prescription medications). In addition, if a diagnosis of substance dependence is present for a specific substance, a Substance Abuse diagnosis cannot also be given for that same substance. However, in the case of polysubstance use, which is quite common, multiple diagnoses can be made. For example, an individual could be diagnosed with alcohol dependence, cocaine abuse, and cannabis abuse. There is a special diagnostic category of Polysubstance Dependence, which is only diagnosed when an individual who uses multiple substances does not meet criteria for dependence on any of those substances when considered individually, but when the combined effects of the individual substances are considered together, the individual would meet diagnostic criteria for substance dependence.

Substance-Induced Disorders While substance-induced disorders are less common than substance use disorders in most outpatient settings, they are important to consider when evaluating individuals who use substances. These disorders include substance intoxication, substance withdrawal, as well as eight other substance-induced psychiatric disorders (substance-induced delirium, persisting dementia, persisting amnestic disorder, psychotic disorder, mood disorder, anxiety disorder, sexual dysfunction, and sleep disorders). The diagnoses of substance intoxication and substance withdrawal are relatively straightforward when the substance responsible for these conditions has been identified. Sometimes, in cases where multiple substances are being used at the same time, associations between withdrawal or intoxication effects with a particular substance can be more difficult to establish. For the other substance-induced disorders, it can be quite challenging to make the diagnosis, because it is often unclear whether the psychiatric symptoms are better characterized as stemming from a primarily psychiatric or primarily neurological disorder, or rather result from the use of a particular substance. The criteria itself require that there be evidence from physical examination, laboratory studies, or subject history that establishes an etiological link from the substance

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use to the substance-induced disorder. Even when such information is available, the temporal associations between the substance-induced diagnosis under consideration and patterns of substance use can be difficult to establish. For example, in the case of an individual with heavy alcohol use and periodic depressive episodes, it is not always clear whether the alcohol use preceded the onset of depressive symptoms or, alternatively, whether alcohol was used in attempt to cope with preexisting symptoms of depression. Collateral sources of information such as family members or other informants and available medical records can be helpful in establishing the temporal associations between onset of substance use and the emergence of psychiatric symptomatology. However, even in cases where an adequate patient history is available, there is often a good deal of clinical judgment used in establishing these diagnoses. Clinical decision-making may be further informed by considering a number of factors such as family history of mental disorder, prior episodes of the disorder under consideration, and whether or not psychiatric symptoms are consistent with typical intoxication or withdrawal effects for various substances. For example, onset of major depressive symptoms during alcohol intoxication is consistent with the physiological effects of the substance; however, similar symptoms during methamphetamine intoxication are not consistent with the physiological effects of the drug. In the former case, a diagnosis of alcohol-induced mood disorder could be considered, but in the latter case it could not. When the disorder does not occur during intoxication or withdrawal, it must begin within one month of cessation of drug use. Psychiatric symptoms that persist for longer than a month may be better classified as a primary psychiatric disorder. For example, in the case of an individual who begins experiencing psychotic symptoms during methamphetamine intoxication or withdrawal, if the psychotic symptoms continue to persist over months, the diagnosis might be changed to a primary psychotic disorder, with substance use considered an environmental risk factor that contributed to the onset of a psychotic disorder in an individual who was already predisposed to develop one. It is also noteworthy that neuropsychologists are uniquely trained to evaluate at least two of these disorders:  Substance-Induced Persisting Dementia, and Substance-Induced Persisting Amnestic Syndrome. As with the other disorders, the link between substance use and the onset of these neurological conditions requires extensive history-taking, and it can be difficult to establish the etiological link from the substance to the disorder itself. However, neuropsychological evaluation may provide very useful information that helps establish differential diagnoses between dementia and amnestic disorders, as well as provide evidence that will assist in differentiating substance-induced persisting dementia from other forms of dementia. Specifically, since well-established profiles of neuropsychological functioning are available for many degenerative dementias, comparisons between the cognitive profiles of individuals suspected to have substance-induced persisting dementia with those having other established conditions may provide invaluable information in arriving at the correct diagnosis. A final point of clarification is that, like the substance use disorders, substance-induced disorders are diagnosed according to the 13 drug categories previously described. However, not all substance-induced disorders can be caused by all 13 drug categories. For example, while there are diagnoses

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for cocaine-induced psychotic, anxiety, and sleep disorders, there are no diagnoses for Cocaine-Induced Persisting Dementia or Persisting Amnestic Disorder.

DSM-V The DSM-V is slated for publication in the summer of 2013 and is purported to incorporate a number of significant changes to the Substance-Related Disorders diagnostic criteria. At the time of this publication, the general title of the category is slated to be changed from Substance-Related Disorders to Substance-Related and Addictive Disorders. This change was incorporated in order to allow other non-substance-related addictive disorders to be included as the subcategory NonSubstance-Related Disorders, which includes the DSM-IV diagnosis of Pathological Gambling that was formerly classified as an Impulse Control Disorder and is now referred to as Gambling Disorder. Another major change was the combining of the DSM-IV Substance Abuse and Dependence diagnoses into the single diagnostic category of Substance Use Disorders (SUD). Rather than having two distinct disorders, the DSM-V will categorize the more general SUD according to level of severity, based on the number of diagnostic criteria that are met. Meeting 0 or 1 of the diagnostic criteria would result in no diagnosis, while meeting 2 to 3 would be categorized as a mild SUD, 4 to 5 as a moderate SUD, and 6 or more as a severe SUD. This classification scheme incorporating levels of severity within a single SUD diagnosis was accomplished by collapsing the DSM-IV Substance Abuse and Substance Dependence criteria. The DSM-IV criteria regarding recurrent legal problems was deleted from the SUD diagnosis, given concerns that this criterion had a lower prevalence rate than the other criteria, perhaps unfairly biased diagnoses of substance abuse in populations who have more involvement with the legal system, and would be difficult to consistently diagnose internationally, given differences in laws across international jurisdictions. An additional criterion of Substance Craving was added, given that research supports this as a key symptom of addictive disorders. The reasons for these changes are multiple, but combining the DSM-IV abuse and dependence criteria and grading according to the level of severity is thought to more accurately reflect the symptoms of individuals with substance use disorders, thereby having the potential to improve patient care and create greater precision in research. Also, there was a concern that the term “dependence” implied physiological dependence, which was not a criterion necessary to meet the DSM-IV diagnosis of Substance Dependence. Physiological dependence on some substances occurs as a normal response of neural systems that adapt to the exposure. Addiction, on the other hand, involves other adaptations that are distinct from those resulting in physiological dependence, wherein individuals experience loss of control and compulsively use substances despite their expectation of adverse consequences. Finally, the DSM-V has moved away from the classification of neurocognitive disorders based on classic distinctions between dementia and amnestic syndromes, preferring a classification system that distinguishes between major and mild neurocognitive disorders. Thus, the DSM-IV disorders of Substance-Induced Persisting Dementia and Substance-Induced Persisting Amnestic Disorder will now fall under the classification of Substance-Induced Major Neurocognitive Disorder, with an

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additional classification for Substance-Induced Mild Neurocognitive Disorder. There are other changes to the current Substance-Related Disorders section of the DSM-IV, and considered together, these changes will significantly alter the way that substance related disorders are characterized and diagnosed in the DSM-V. CLINICAL CONSIDERATIONS In order to accurately diagnose substance use disorders, a biopsychosocial history is required. Collateral information obtained from family members, case managers, social workers, physicians, and other professionals is helpful in this regard, since individuals who use substances often minimize the frequency, amount, and psychosocial impact of their use. This is particularly true when negative legal or employment consequences could result from a substance use diagnosis. Additionally, since there are acute and long-term effects of substance use on cognitive abilities, including learning and memory, clients may not be able to accurately report their history of use. In cases where the clinician suspects a substance-related disorder but also feels clients are minimizing their use, informant reports are particularly helpful. However, there are also several structured methods that might assist the clinician in obtaining a more accurate substance use and psychosocial history. When available, a systematic review of medical records can provide valuable information, as they may provide insights into medical conditions that might be caused by and/or commonly co-occurring with substance use (e.g., liver disease, traumatic brain injury, and infectious diseases, such as hepatitis C), as well as provide information about prescription drugs that have a high potential for abuse, such as narcotics and benzodiazepines. Useful findings from laboratory tests may also be found in medical records, including results from tests that are specifically designed to detect the presence of substances in the system (e.g., urine toxicology), as well as test results that are consistent with the use of substances, such as elevated liver-function tests, which are associated with heavy alcohol use. There are also a number of psychometrically validated evaluation procedures that can assist in arriving at a diagnosis. For example, the Substance Abuse Subtle Screening Inventory-3 (SASSI-3; Miller & Lazowski, 1999) and the Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993) may help identify individuals who are at increased risk for substance abuse. Similarly, structured interviewing procedures like the Timeline Follow Back assessment procedure, can be used to assist the client in recalling instances of substance use. In this procedure, calendars are presented to the client covering the prior four months, and the client is asked to recall significant events that occurred over that time period; for example, birthdays, anniversaries, sporting events, etc. Clients are then instructed to recall instances of substance use that occurred proximal to these significant events, which provide memory anchors in a visually structured format to facilitate accurate recall of use. Detailed information on prior episodes of inpatient and outpatient treatment for substance abuse is also recommended and may facilitate dispositional planning.

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While the diagnosis of substance-induced disorders relies heavily on many of the common elements previously discussed, in some cases more specialized procedures are also required. For example, in cases where the diagnosis of substanceinduced persisting dementia is being considered, specialized medical procedures are commonly used in arriving at a diagnosis, including neurological evaluations, neuroimaging procedures, and neuropsychological evaluation, to document and profile cognitive strengths and weaknesses. In cases of mood, anxiety, developmental (e.g., attention-deficit/hyperactivity [ADHD]), and personality (e.g., antisocial) disorders that commonly occur in substance abuse and dependence, questionnaires (e.g., the Minnesota Multiphasic Personality Inventory-2 [MMPI2], Wender-Utah Rating Scale) and semi-structured interviews (e.g., Structured Clinical Interview for DSM-IV SCID) designed to evaluate the presence and severity of these symptoms may also assist in arriving at a diagnosis. Specialized procedures may also be required for diagnosis of other disorders such as substance-induced sleep disorders (e.g., sleep studies) and sexual dysfunction (e.g., nocturnal penile tumescence-monitoring for male erectile dysfunction). Thus, for many substance-induced disorders, diagnosis is reached after extensive evaluation by a number of professionals, each with specialized expertise and functioning within the context of an interdisciplinary team. Selection of an appropriate neuropsychological battery for individuals with substance-related disorders will depend on commonly referenced factors, such as time, resources, referral questions, setting (e.g., inpatient versus outpatient) and the specific characteristics of the individual client (e.g., physical and sensory limitations). In general, a test battery that emphasizes the domains that are known to be the most strongly affected in persons with substance-related disorders is recommended to enhance sensitivity, including episodic learning and memory, executive functions, information processing speed, motor coordination, and visuoperceptual skills. Among the various executive functions, particular emphasis might be placed on cognitive flexibility, impulsivity and disinhibition, novel problem-solving, and decision-making. Symptom validity tests and embedded measures of test-taking effort are also particularly valuable in the clinical evaluation of persons with substance-related disorders. Inclusion of performance-based and self- and other-report measures of neurobehavioral (e.g., Frontal Systems Behavior Scale [FrSBe]) and real-world (e.g., instrumental activities of daily living [ADLs]) may also facilitate diagnoses by documenting the real-world impact of observed neurocognitive problems (see Scott et al., this volume). ORGANIZATION OF THE BOOK The 18 chapters in this volume are organized into three sections that are designed to provide the reader with a translational overview of basic research and treatment findings regarding addictions, neuropsychological and neurological sequelae of the most common substances of abuse, and consideration of special issues that might confound the interpretation of neuropsychological test results.

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Section I (Chapters 1–5) provides an overview of addictions, including diagnoses based on the DSM-IV, as well as the most current conceptualizations of addiction from the perspectives of psychobiology, genetics, behavioral and neuro-economics, and treatment. This section provides an understanding of translational research and uses findings from basic science to develop state-of-the-art evidence-based interventions and illustrates how these interventions might be modified given presenting neuropsychological deficits, which should assist clinicians with treatment planning. Additionally, this section is designed to provide the reader with a broad evidence-based conceptual framework for the chapters of the following sections II and III. Section II (Chapters 6–12) reviews the most common substances of abuse, including coverage of structural and functional neuroimaging findings, epidemiological evidence, and neuropsychological sequelae. Substances included in this section represent the most commonly encountered drugs of abuse, including alcohol, cannabis, cocaine, methamphetamine, club drugs, opioids, and inhalants, with chapters written by some of the world’s leading experts in these complex areas of clinical research. Some substances were not included in this section, such as nicotine, hallucinogens, and polysubstance use, because the literature either does not support long-term effects on cognition, or there is simply not enough evidence to allow for meaningful conclusions to be drawn about persisting neuropsychological effects as a consequence of their use. Section III (Chapters 13–18) includes coverage of several special topics, including specific issues related to psychiatric (i.e., severe mental illness), medical (i.e., infectious disease), and neurological (i.e., traumatic brain injury) comorbidities, as well as special populations (i.e., adolescents) and the real-world impact of neuropsychological deficits in persons with substance-related disorders. Topics were selected for inclusion in this section if 1) they represented areas of common concern faced by clinical neuropsychologists, and 2) the potential neuropsychological effects of substance use and test results were confounded by the presence of these factors. This overview of the history and diagnosis of substance-related disorders is intended to provide a brief background to the area of substance use and abuse and to illustrate the breadth of information that has come to bear on our understanding of the interplay between substance use and the brain over the past 30 years. This book endeavors to provide the most up-to-date information regarding substance use disorders, and assist clinicians in answering questions regarding impact of substance use on neuropsychological functioning. References Brecher, Edward M. and the editors of Consumer Reports (1972). Licit and Micit Drugs. The Consumers Union Report on Narcotics, Stimulants, Depressants, Inhalants, Halluncinogens, and Marijuana—including Caffeine, Nicotine, and Alcohol. Boston: Little, Brown and Company. Brownstein, M. J. (1993). A brief history of opiates, opioid peptides, and opioid receptors. Proceedings from the National Academy of Sciences, 90(12), 5391–5393. Dasgupta, A. (2011). The Science of Drinking: How Alcohol Affects Your Body and Mind. Lanham, Maryland: Rowman & Littlefield.

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Gallup Politics (2013). Record-High 50% of Americans Favor Legalizing Marijuana Use. Available at http://www.gallup.com/poll/150149/record-high-americans-favorlegalizing-marijuana.aspx. Goldstein, G., & Chotlos, J. W. (1965). Dependency and brain damage in alcoholics. Perceptual and Motor Skills, 21(1), 135–150. Goldstein, G., & Chotlos, J. W. (1966). Stability of field dependence in chronic alcoholic patients. Journal of Abnormal Psychology, 71(6), 420. Johnston, L. D., O’Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2012). Monitoring the Future: National Survey Results on Drug Use,1975–2011: Volume I, Secondary School Students. Ann Arbor, MI:  Institute for Social Research, the University of Michigan. Retrieved from http://monitoringthefuture.org/pubs/monographs/mtf-vol1_2011. pdf. Jones, B., & Parsons, O. A. (1971). Impaired abstracting ability in chronic alcoholics. Archives of General Psychiatry, 24(1), 71–75. Levine D. G., Preston P. A., Lipscomb S. G. Am J Psychiatry. 1974 Sep;131(9):1036–7. A historical approach to understanding drug abuse among nurses. Lambert, W. G. (1996). Babylonian Wisdom Literature. Winona Lake, IN: Eisenbrauns. Mack, A., & Joy, J. (2000). Marijuana as Medicine? The Science Beyond the Controversy. The National Academy Press. Washington, DC. Marthe, M. L. (Ed.) (1997). Cannabis in Medical Practice:  A  Legal, Historical and Pharmacological Overview of the Therapeutic Use of Marijuana. Jefferson, NC: McFarland and Company. McGovern P. E., Zhang J., Tang J., Zhang Z., Hall G. R., Moreau R. A., Nuñez A, Butrym E. D., Richards M. P., Wang C. S., Cheng G., Zhao Z., Wang C. Proc Natl Acad Sci U S A. 2004 Dec 21;101(51):17593–8. Fermented beverages of pre- and proto-historic China. McGovern, P. E. (2009). Uncorking the Past: The Quest for Wine, Beer, and Other Alcoholic Beverages. Berkeley, CA: University of California Press. McGovern, P. E., Hartung, H., Badler, V. R., Glusker, D. L., & Exner, L. J. (1997). The beginnings of winemaking and viniculture in the ancient Near East and Egypt. Expedition, 39(1), 3–21. Miller, T., & Hendrie, D. (2009). Substance Abuse Prevention Dollars and Cents: A Cost-Benefit Analysis (DHHS Pub. No. SMA 07-4298). Rockville, MD: Substance Abuse and Mental Health Services Administration, Center for Substance Abuse Prevention. Miller, F. G., & Lazowski, L. E. (1999). The Adult SASSI-3 Manual. Springville, IN:  The SASSI Institute. Saunders, J. B., Aasland, O. G., Babor, T. F., de la Fuente, J. R., & Grant, M. (1993). Development of the Alcohol Use Disorders Identification Test (AUDIT):  WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption—II. Addiction, 88(6), 791–804. Schiff, P. L. (2002). Opium and its alkaloids. American Journal of Pharmaceutical Education, 66, 186–194. Substance Abuse and Mental Health Services Administration (SAMHSA). (2010). Results from the 2009 National Survey on Drug Use and Health: Vol. I. Summary of National Findings. (Office of Applied Studies, NSDUH Series H-38A, DHHS Publication No. SMA 10-4856 Findings). Rockville, MD: SAMHSA. Substance Abuse and Mental Health Services Administration. (2012). Results from the 2011 National Survey on Drug Use and Health:  Mental Health Findings (No. HHS

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Publication No. SMA 12-4725). Rockville, MD: Substance Abuse and Mental Health Services Administration. Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality. (Jan. 3, 2013). The NSDUH Report: Trends in Adolescent Substance Use and Perception of Risk from Substance Use. Rockville, MD: SAMHSA. Tarter, R. E., & Parsons, O. A. (1971). Conceptual shifting in chronic alcoholics. Journal of Abnormal Psychology, 77(1), 71–75. World Health Organization (WHO). (2004). Promoting Mental Health:  Concepts, Emerging Evidence, Practice. Summary Report. Geneva, Switzerland: WHO. Available at http://www.who.int/mental_health/evidence/en/promoting_mhh.pdf.

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Neural Substrates of Substance Use Disorders V E R O N I CA B I S AG N O A N D J EA N L U D   CA D E T

Drug addiction is a serious public health problem that manifests as a compulsive drive to take the drug without regard to severe adverse consequences (Volkow & Li, 2005). Drugs of abuse have well-known pharmacological consequences in the brain. Although these are necessary, they are not sufficient for the development and the maintenance of the addicted state. Other influencing factors include access to drugs, social environment, genetic predisposition, as well as other psychiatric comorbidities (Volkow, 2004). Addiction is a primary, chronic disease of brain reward, motivation, memory, and related circuitry, as defined by the American Society of Addiction Medicine (2013). Figure 2.1 provide a depiction of key brain regions and circuitry implicated in addiction. Dysfunctions within these brain circuits (Figure 2.1) are associated with characteristic biopsychosocial manifestations of addiction. Those include the inability to abstain from drug seeking and taking, unbearable craving, and behavioral impairments of various kinds, such as the inability to recognize the deterioration of interpersonal relationships (Volkow, Wang, Fowler, & Tomasi, 2012). The addicted state is also accompanied by repeated cycles of remission and relapses that are associated with adverse neuropsychiatric consequences, including depression and psychotic episodes, depending on the primary drug of choice (Wilson & Cadet, 2009). Without treatment or engagement in recovery activities, addiction is progressive and results in medical complications, risks of incarceration, social isolation, and/or premature death (Volkow et al., 2012). Preclinical and clinical studies suggest that addiction is secondary to regional neuroadaptations in the brain. Preclinical models have consistently demonstrated the importance of the mesocorticolimbic brain reward system in drug dependence (Everitt & Robbins, 2006), while neuroimaging studies in drug-dependent

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Anterior

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Figure 2.1 Mesocorticolimbic reward circuit. Regions of the brain that are involved in reward and addiction: the ventral tegmental area (VTA), ventral striatum (Nucleus Accumbens, Nac) involved in responding to rewarding stimulus; the amygdala (Amyg) and hippocampus (Hipp) that participate in memory functions; the mediodorsal thalamus (Thal), key component of thalamo-cortico-basal ganglia circuits working in tandem with the dorsal striatum (DS), implicated in aberrant habit learning disorders and the prefrontal cortex/orbitofrontal cortex (PFC/OFC) and anterior cingulate cortex (ACC) that participate in executive control and emotion regulation. (See color insert).

individuals have documented significant alterations in these same brain regions (Volkow & Li, 2005). The mesocorticolimbic components include: 1. the ventral tegmental area (VTA) and ventral striatum, which are involved in responding to rewarding stimuli (Wise, 2009); 2. the amygdala and hippocampus, which participate in memory functions, especially those related to learning cue and context associations (Malenka, 2003); 3. the mediodorsal thalamus, an intermediary node linking the midbrain and prefrontal cortex, and a key component of thalamo-cortico-basal ganglia circuits implicated in aberrant habit-learning disorders (Hyman, 2005); and

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4. the prefrontal/orbitofrontal cortex (PFC/OFC) and anterior cingulate cortex, which regulate certain aspects of diverse emotions, cognition, and executive function, while exerting inhibitory control on various behavioral processes (Everitt & Robbins, 2006).

DOPAMINERGIC PROJECTIONS FROM THE VENTRAL TEGMENTAL AREA AND SUBSTANTIA NIGRA PARS COMPACTA Drugs are taken because of their hedonic properties, and these rewarding effects are linked to their ability to increase dopamine (DA) in the dorsal striatum and nucleus accumbens (NAc) (Wise, 2009). DA neurons of the ventral midbrain can be divided into two main subpopulations: 1) the nigrostriatal projection, which arises from the substantia nigra pars compacta (SNpc) and projects to dorsal aspects of the striatum; and 2) the mesolimbic projection from the ventral tegmental area (VTA) to the NAc and other limbic regions (Figures  2.1 and 2.2). The DA neurons from the SNpc are involved mostly in motor functions, although

rg mine D o p a minal ter D2

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Figure 2.2 Cortical inputs reach GABA-releasing striatal output neurons where they exert glutamate-mediated excitation. Medium-size GABA-containing spiny neurons represent the main (95%) striatal neuronal population. They participate in the modulation of output signals from the basal ganglia via interaction with parvalbumin-containing GABA-releasing interneurons, NADPH diaphorase-, and somatostatin-positive interneurons. They also interact with large cholinergic aspiny interneurons. D1 receptors are found predominantly in striatonigral neurons of the “direct pathway,” whereas D2 receptors are mainly expressed by the striatopallidal neurons of the “indirect pathway.” Abbreviations: AC, adenylyl cyclase; Ach, acetylcholine; GP, globus pallidus; NO, nitric oxide, PKG, protein kinase G; sGC, soluble guanylyl cyclase.

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recent evidence has accumulated to support an important role for these neurons in the rewarding process (Wise, 2009). Located medially to the SNpc, VTA DA neurons are known to play an important role in motivation and reinforcement. In contrast to SNpc DA neurons, they project to the ventral striatum, including the NAc core, shell, and olfactory tubercle, as well as to the amygdala, septum, hippocampus, and the PFC (Fields, Hjelmstad, Margolis, & Nicola, 2007; Ikemoto, 2007). In addition to DA neurons, the VTA contains a significant proportion of gamma-aminobutyric acid (GABA) producing neurons, which project to the PFC, NAc, and other brain regions (Fields et al., 2007). GABAergic VTA neurons also form local contacts onto both dopaminergic and nondopaminergic VTA neurons. Interestingly, a subset of VTA DA neurons expresses the vesicular glutamate transporter VGLUT2, indicating the potential to co-release of DA and glutamate (Hnasko, Hjelmstad, Fields, & Edwards, 2012). DA neurons transmit DA signals via tonic and phasic modes (Grace, Floresco, Goto, & Lodge, 2007). In their tonic mode, DA neurons maintain steady, baseline levels of DA within downstream structures that are vital to their normal functions (Schultz, 2007). In their phasic mode, DA neurons increase or decrease their firing rates sharply for 100–500 milliseconds, causing large changes in DA concentrations in downstream structures, changes that can last for several seconds (Schultz, 2007).

INFORMATION ENCODED BY DA RELEASE DA release is used to signal novel and motivationally relevant environmental events (Bromberg-Martin, Matsumoto, & Hikosaka, 2010). For example, when an organism encounters a novel stimulus, whether it be a positive stimulus such as a food reward or a negative stimulus such as psychological stress, there are alterations in the activity of DA cells in the VTA and in DA release in axon terminal fields in the PFC, NAc, and/or amygdala (Hyman, 2005; Abercrombie, Keefe, DiFrischia, & Zigmond,1989; Lataster et al., 2011). DA is also important for the motivation and reinforcement of actions. Drugs that interfere with DA transmission interfere with reinforcement learning, while manipulations that enhance DA transmission, such as brain stimulation and addictive drugs, often act as reinforcers (Wise, 2004; Wise, 2012). In addition, DA transmission is crucial for creating a state of motivation to seek rewards (Berridge & Robinson, 1998). DA release is not necessary for all forms of reward learning and may not always be “liked” in the sense of causing pleasure, but it is critical for causing goals to become “wanted” in the sense of motivating actions to achieve the desired goals (Berridge & Robinson, 1998; Palmiter, 2008). DA release might indeed be a sine qua non for the generation of motivated behaviors during diverse rewarding experiences. This idea is supported by observations that most DA neurons are strongly activated by unexpected primary rewards such as food and water, often producing phasic “bursts” of activity (Schultz, 2007) and phasic excitations including multiple spikes (Grace et  al., 2007). Dopamine responses

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might thus encompass a “reward prediction error” phenomenon that reports differences between the reward that is received and the predicted reward (Schultz et al., 2007). Thus, a larger than predicted reward might cause greater firing of DA neurons (positive prediction error), whereas a lesser reward might inhibit phasic firing (negative prediction error). A reward that is predictable has no influence on the firing of DA neurons (zero prediction error). These diverse set-ups or responses might influence the physiological responses to drugs of abuse that exert varied responses in animals, depending on environmental cues. This is consistent with the fact that DA reward responses are accompanied by synchronous phasic bursts, a response pattern that shapes DA release in target structures (Grace et al., 2007). These phasic bursts, in turn, influence learning and motivation in manner distinct from tonic DA activity (Grace et  al., 2007; Schultz, 2007). Thus, it is not farfetched to suggest that humans treat rewarding and aversive events in similar physiological ways that reflect the predictability of motivational salience, whether positive or negative. Indeed, both rewarding and aversive events trigger attentional orienting, changes in cognitive processing, as well as increases in general motivation, because these are necessary to engage working memory to hold information in mind, to resolve conflict during decision-making, and to store the resulting behaviors in long-term memory (see Hyman, 2005; Bromberg-Martin et al., 2010). DA neurons modulate signals related to processes such as goal seeking, engage motivationally salient situations, or react to alerting changes in the environment (Bromberg-Martin et al., 2010). DA neurons are proposed to transmit their signals to distinct brain structures in order to support distinct neural systems for motivated cognition and behaviors. Some DA neurons support brain systems that assign motivational value, promoting actions to seek rewarding events, avoid aversive events, and ensure that alerting events can be predicted and/or prepared for. Other DA neurons support brain systems that are engaged by motivational salience, including orienting to detect potentially important events, cognitive processing to choose a response and to remember its consequences, and motivation to persist in the pursuit of an optimal outcome. Ultimately, DA neurons tailor their signals to support multiple neural networks with distinct roles in motivational control. This discussion thus emphasizes the important role that DA systems might play in some of the common manifestations of various substances whose initial biochemical effects might be on presumably disparate neurotransmitter systems.

COMMON FEATURES OF DRUGS OF ABUSE ASSOCIATED WITH NA C DYSFUNCTIONS The discovery that electrical stimulation of specific brain areas can induce reward (Olds, & Milner, 1954) has led to the theory that the mesotelencephalic DA system is the neurobiological substrate for the rewarding effects of both opiates and psychostimulants (Wise, 1978). Moreover, Di Chiara and Imperato (1988)

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provided substantial evidence that addictive drugs consumed by humans increase DA concentrations in the rat mesolimbic DA system. These biochemical events might indeed be responsible for some of the similar clinical observations in humans who are addicted to drugs. Specifically, drugs of abuse can cause very similar biochemical, physiological, and molecular effects in various brain regions, including in the VTA and NAc (Nestler, 2005; Cadet, Jayanthi, McCoy, Beauvais, & Cai, 2010). Stimulants such as cocaine and methamphetamine directly increase dopaminergic transmission in the NAc by blocking the DA transporter or causing DA release through the vesicular monoamine transporter (Pontieri, Tanda, & Di Chiara, 1995; Xi et al., 2009). Opiates cause increased synaptic DA levels by inhibiting GABAergic VTA interneurons, a process that produces disinhibition of dopaminergic VTA neurons (Shabat-Simon, Levy, Amir, Rehavi, & Zangen, 2008). Other substances such as nicotine also appear to activate VTA DA neurons directly via stimulation of nicotinic cholinergic receptors on those neurons and indirectly via stimulation of its receptors on glutamatergic nerve terminals that innervate the DA cells (Dani & Zhou, 2001). Alcohol, by promoting GABAA receptor function, may inhibit GABAergic terminals in VTA and disinhibit VTA DA neurons (Boehm et al., 2004). Cannabinoid mechanisms appear to be more complex and involve activation of cannabanoid type 1 receptors on glutamatergic and GABAergic nerve terminals in the NAc and on NAc neurons themselves (Howlett et al., 2004). These biochemical differences notwithstanding, there are many similarities in the brain regions that are influenced by these psychoactive drugs. There are, however, substantial differences in the neurobiological mechanisms and chronic neuroadaptations that are consequences of the self-administration of opiates and psychostimulants (Badiani, Belin, Epstein, Calu, & Shaham, 2011). In addition, different clinical patterns of drug abuse are also observed for cocaine and methamphetamine addiction (Simon et al., 2002), cannabis abuse (Hall & Degenhardt, 2009), and opiate addiction (Darke, 2012). These clinical, biochemical, and molecular differences need to be taken into consideration when approaching the discussion of the neural substrates of drug addiction and while planning therapeutic approaches to individuals addicted to these various substances. These differences might also impact on the neuropsychological sequelae of these licit and illicit drugs. The NAc is part of the ventral striatal complex and serves as a critical region where motivations derived from limbic regions interface with motor control circuitry to regulate appropriate goal-directed behavior (Wise, 2004; Hyman, 2005). Like other parts of the ventral striatal complex, the NAc receives extensive excitatory afferents from the cerebral cortex and thalamus. It projects to the ventral pallidum, which innervates the mediodorsal and other thalamic divisions, thus completing cortico–striato–pallidal–thalamocortical loops (Zahm & Brog, 1992; O’Donnell, Lavín, Enquist, Grace, & Card, 1997). Excitatory cortical afferents to the NAc typically synapse onto the spines of medium spiny neurons. The so-called triad of elements—spine, glutamate synapse, and DA synapse—creates the potential for DA to modulate discretely specific sources of glutamate transmission onto distal dendritic compartments as opposed to a

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more generalized effect on overall cell excitability (Sesack, Carr, Omelchenko, & Pinto, 2003). It is also noteworthy that there is indication that these dopaminergic and/or glutamatergic pathways that interact at the levels of the VTA, NAc, and other limbic regions might also be the generators of the positive emotional effects of natural rewards, such as food, sex, and social interactions (Volkow et al., 2012; Wise, 2012). These same regions appear to be the culprits that foster compulsive food consumption (Wang, Volkow, Thanos, & Fowler, 2009), pathological gambling (van den Brink, 2012), and sexual addictions (Blum et  al., 2012), among other compulsive behaviors such as Facebook addiction (Andreassen, Torsheim, Brunborg, & Pallesen, 2012).

ROLE OF THE NIGROSTRIATAL AND CORTICOSTRIATAL PATHWAYS IN DRUG ADDICTION In addition to the mesoaccumbens systems, the nigrostriatal dopaminergic pathway appears to be involved in addictive processes. The dorsal striatum represents the main input into the basal ganglia (Graybiel, Aosaki, Flaherty, & Kimura, 1994; Figure 2.2). In addition to inputs from the SNpc, striatal projection neurons receive a large convergence of afferents from all areas of the cortex, which has a crucial integrative and computational role that mediates, in part, sequences that direct acquisition of motor skills (Calabresi, Picconi, Tozzi,, & Di Filippo 2007), the selection and initiation of actions (Graybiel et al., 1994), and stimulus– response (habit) learning, including drug-taking behaviors (White & McDonald, 2002; Everitt & Robbins, 2006). Dopaminergic afferents from SNpc also converge on these cells, and there is evidence that glutamate and DA receptors can form heterodimers that are highly organized molecular complexes where different classes of receptors are clustered (Fuxe et al., 2008). These observations support the idea that the dorsal striatum might participate in important integrative steps in the development and maintenance of drug addiction. These integrative processes probably occur at the level of medium-size GABA-containing spiny neurons that represent the main (95%) neuronal population of the striatum (Figure 2.2), where they modulate the output signals of the basal ganglia through interaction with three major subclasses of interneurons:  fast-spiking, parvalbumin-containing, GABA-releasing interneurons; low-threshold spike, nicotinamide adenine dinucleotide phosphate-oxidase (NADPH) diaphorase- and somatostatin-positive interneurons; and large cholinergic aspiny interneurons (Kawaguchi, Wilson, Augood, & Emson, 1995; Tepper & Bolam, 2004). Cortical inputs reach GABA-releasing neurons that output from the striatum, on which they exert a powerful glutamate-mediated excitatory influence (Calabresi et  al., 2007). Long-lasting, activity-dependent synaptic changes are thought to underlie the ability of the brain to translate experiences into memories and seem to represent the cellular model underlying learning and memory processes.

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ROLE OF PRE-FRONTAL CORTICAL REGIONS IN DRUG ADDICTION Pre-frontal cortical areas work in tandem with striatal regions via corticostriatal networks that are modulated by DA (Figure 2.2). These include the dorsolateral PFC, which is involved in higher cognitive operations and decision-making (Brass, Ullsperger, Knoesche, von Cramon, & Phillips, 2005); the OFC, which is involved in salience attribution and goal-directed behaviors (Cardinal et  al., 2002); and the anterior cingulate cortex (ACC), involved in inhibitory control and awareness (Shimamura, 2000). Thus, improper DA modulation of these prefrontal regions in addicted subjects could underlie the enhanced incentive motivational value of drugs and the user’s loss of control over drug intake (Volkow et al., 2012). Under normal circumstances, organisms value many goals, making it necessary to select among them. A significant aspect of addiction is the pathological narrowing of goal selection to those that are drug-related (Volkow et al., 2012). The representation of goals, assignment of value to them, and selection of actions based on the resulting valuation depend on the PFC (Matsumoto, Suzuki, & Tanaka, 2003). Successful completion of goal-directed behavior, whether searching for food or drugs, requires a complex and extended sequence of actions that must be maintained despite obstacles and distractions. The cognitive control that permits goal-directed behaviors to proceed to a successful conclusion is thought to depend on the active maintenance of goal representations within the PFC (Matsumoto et al., 2003). Furthermore, phasic DA release influences the ability to update information within the PFC, including the selection of goal-directed behaviors (D’Ardenne et al., 2012). If phasic DA release provides a gating signal in the PFC, addictive drugs might produce a potent but highly distorted signal that disrupts normal DA-related learning in the PFC, as well as in the nucleus accumbens and dorsal striatum. In the addicted individual, this disruption might be reflected by neuronal adaptations to repetitive and excessive bombardment by high levels of synaptic DA that generate progressive decreases in the salience of natural rewards. This would then be followed by weaker DA release to subsequent reward-related cues in comparison to release caused by progressively increasing the amount of illicit drugs that are self-administered in settings that help provoke environment–drug interaction-dependent potentiated DA release in both the NAc and the dorsal striatum. This discussion might explain, in part, the repeated cycles of relapse and remission, with the remission period serving to recalibrate systems that become even more sensitive to the physiological and behavioral effects of renewed drug use. Although more neuroimaging studies are necessary to test the ideas of potentiated interactions between these various brain regions during resumption of drug use, these types of studies have provided consistent evidence for the involvement of the PFC, the NAc, and dorsal striatum in the addiction process (see below). In parallel, preclinical research has expanded our understanding of the complex role that the various regions in the PFC play in cognitive processes, including executive functions such as inhibitory control, decision-making, emotional regulation,

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purposefulness, motivation, and salience attribution, among others (Chudasama & Robbins 2006). The OFC has been shown to participate in outcomes related to primary reinforcers in both nonhuman and human studies (Lucantonio et  al., 2012). These neurons encode details concerning the sensory properties of rewards, such as visual, olfactory, and gustatory aspects, and the size or timing of past or future rewards, as well as the magnitude of more abstract rewards and penalties (Lucantonio et al., 2012). Impairments in the OFC and ACC are associated with compulsive behaviors and impulsivity, and it has also been postulated that impaired modulation of these regions by DA might underlie the compulsive and impulsive aspects of drug-taking and abuse (Volkow et al., 2012). For example, preclinical and clinical studies have revealed that low striatal DA receptor 2 (D2R) levels are associated with impulsivity, and in rodents, impulsivity predicts compulsive cocaine administration. In turn, overexpression of D2R in the striatum interferes with compulsive alcohol and cocaine intake (Volkow et al., 2011). Moreover, addicted subjects experience decreased DA release in striatal regions; this decreased release might serve to exacerbate the low signaling that already exists in brains with reduced striatal D2R levels (Volkow et al., 2011). In any case, the low levels of D2 receptors that project via the indirect striatal pathway might leave unopposed the actions of D1-like DA receptors through the direct basal ganglia pathway. This unopposed circuit might serve to promote hyperconnections between basal ganglionic and cortical pathways, with addictions being disorders of “hyperconnected brain circuits.” Impaired self-control plays a fundamental role in drug-taking behaviors in addiction. Successful self-regulation functions require top-down control from the PFC to the striatal and limbic regions involved with rewards and emotions (Volkow et  al., 2012). Impaired self-control in addicted subjects is believed to reflect disrupted prefrontal regulation of striatal regions. The level of impairment is influenced by the emotional state (negative mood increases impairment) and the context (exposure to unexpected cues can also impair it). Damage to the OFC also interferes with the inhibition of responding to formerly rewarding cues that are no longer reinforcing, thus favoring the emergence of perseverative behaviors even when these are no longer reinforcing (Lucantonio et al., 2012). Thus, dysregulated activity of the OFC could underlie both the impulsive choices for immediate rewards and compulsive drug intake even when the drug-induced DA increases may be profoundly attenuated in addicted subjects (as described above). This loss of control might continue even when drug-taking has become less rewarding or when adverse consequences far outweigh the psychological or physiological benefits of drug-taking. To summarize this section on the PFC, it can be concluded that structural and/ or functional abnormalities in these sub-regions may lead to cycles of impulsivity and compulsivity that contribute to the clinical observations of binge/intoxication, withdrawal, preoccupation/anticipation/craving (Koob & Volkow, 2010). The following sections describe these stages and some adaptations that have been described on neuronal circuits that underlie them.

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NEURAL SUBSTRATES OF DRUG WITHDRAWAL All drugs of abuse are associated with a motivational withdrawal syndrome characterized by dysphoria, irritability, emotional distress, and sleep disturbances that persist even after protracted withdrawal (Koob & Volkow, 2010). Brain imaging studies have also revealed decreases in endogenous opioids during cocaine withdrawal, which may contribute to the irritability, malaise, and dysphoria that occur during this phase of motivational withdrawal (Zubieta et  al., 1996). The mechanisms underlying acute withdrawal are likely to be drug-specific and reflect adaptations in the molecular targets of these drugs, including changes in transcription, post-translational protein modifications, as well as long-lasting epigenetic changes (Robison & Nestler, 2011; Cadet & Jayanthi, 2013). For example, during the first few days of cocaine withdrawal, enhanced sensitivity of the brain to the effects of GABA-enhancing drugs occurs (Volkow et al., 1998). Interestingly, preclinical studies have also found increased GABA transmission in thalamic nuclei after a short cocaine “binge” protocol (Bisagno et al., 2010), and following a short methylphenidate “binge” administration (Goitia et al., 2012). During protracted withdrawal (once the signs and symptoms of acute withdrawal have subsided), the OFC is hypoactive in cocaine and methamphetamine users (Volkow et al., 2012). Moreover, in cocaine and methamphetamine users, the degree of this hypometabolism seems to correlate with decreased DA D2 receptors in striatum. After detoxification, addicted subjects (to various drug classes including cocaine, heroin, alcohol, methamphetamine, nicotine, cannabinoids), consistently showed significant reductions in D2R availability in the striatum, which persists for months (Volkow et al., 2011). In the case of marijuana abusers, on a visual attention paradigm, decreased activation in the right prefrontal, medial, dorsal parietal cortices and medial vermis of the cerebellum were detected in both abstaining and active marijuana users when compared to controls. Additional data show that marijuana use is associated with subtle cognitive impairments and the activation of the frontal lobe, dorsolateral PFC, and the hippocampus (see Wilson & Cadet, 2009, for a review).

NEURAL SUBSTRATES OF CRAVING AND RELAPSE Subjective drug craving is the conscious representation of drug wanting; subjective urges may only be attended to or strongly experienced if drugs are not readily available or if the addicted person is making efforts to limit use (Hyman, 2005). It is well documented that environmental variables such as cocaine-associated cues can effectively elicit physiological responses and self-reports of cocaine craving and withdrawal (Ehrman et al., 1992). One potential mechanism for this finding is cue-induced DA release in the dorsal striatum (Volkow et al., 2006). Interestingly, oral methylphenidate administration in cocaine abusers significantly increased DA in the striatum as measured by displacement of C11 raclopride, but failed to induce craving unless subjects were concomitantly exposed to cocaine cues (Volkow et al., 2008). Similarly, drug-associated cues have been shown to modulate

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the brain metabolic effects of stimulants in cocaine abusers. Also, the brain metabolic effects of methylphenidate were enhanced in cocaine abusers when methylphenidate was administered in the presence of methylphenidate-associated cues (Volkow et al., 2003). Drug-induced increases in self-reports of drug “highs” were also greater when subjects received methylphenidate in the presence of methylphenidate-associated cues, and self-report measures were significantly correlated with brain metabolic effects. Similar results have been reported for subjects who had minimal experience with stimulant drugs (Volkow et al., 2006). The accumulating evidence supports a role for limbic neuronal hyperexcitability in cocaine craving. For example, re-exposure to drug cues resulted in an increased activation in the amygdala among cocaine-using individuals as well as decrements in activation in the PFC. Cocaine use appears to induce regional brain dysfunction in the PFC (and inhibitory control of the amygdala by the PFC), the anterior cingulate cortex, and the basal ganglia (reviewed in Nnadi, Mimiko, McCurtis, & Cadet, 2005). Relapse after detoxification is often precipitated by cues, such as people, places, paraphernalia, or bodily feelings associated with prior drug use, and also by stress (Koob & Volkow, 2010). Stress and stress hormones such as cortisol have physiological effects on reward pathways, but it is interesting to note that stress shares with addictive drugs the ability to trigger the release of DA (Marinellii & Piazza, 2002). Addicted subjects are liable to return to compulsive drug-taking long after experiencing acute withdrawal symptoms (Langleben et al., 2008). The gradual reorganization of reward and memory circuits, brought about by chronic drug abuse, is hypothesized to be crucial to the mounting of these responses. Both DA and glutamate have been identified in preclinical studies as contributing to the neuroplastic adaptations that occur during and after the process of drug self-administration. Dysfunctions in these systems working in tandem might play a substantial role in the varied manifestations of drug craving and seeking, drug taking and abusing, as well as associated neuropsychological sequelae that are often reported in certain addicted individuals. CONCLUDING REMARKS To conclude, mesocorticolimbic DA circuits and their interactions with cortical glutamatergic and striatal GABAergic projections provide the structural and functional neural substrates for the psychological awarding effects of drugs such as cocaine, methamphetamine, and opioids, among other licit and illicit drugs. These connections are also important for the rewarding effects of natural awards such as food and sexual activities. However, after repeated exposure to these drugs, these connections become dysfunctional and lead to the development of addiction that is manifested by repeated cycles of craving, drug abuse, abstinence, and relapse. These behavioral abnormalities are consequent to differential transcriptional and epigenetic alterations within these circuits. We posit that these dysfunctions secondary to repeated drug exposure create a state of “regional hyperconnectedness” that causes drug-related stimuli to be overvalued.

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The molecular and cellular bases for this structural or functional hyperconnectedness depend on the specific classes of drugs of abuse. Dissection of the neural substrates of these dysfunctional circuits should provide for a better understanding of the neuropsychological manifestations of licit and illicit drug abuse. Such elucidation should then provide better rationales for therapeutic interventions in cases of drug addiction that negatively impact the lives of so many of our patients and their family members. References Abercrombie, E. D., Keefe, K. A., DiFrischia, D. S., & Zigmond, M. J. (1989). Differential effect of stress on in vivo dopamine release in striatum, nucleus accumbens, and medial frontal cortex. Journal of Neurochemistry, 52(5), 1655–1658. American Society of Addiction Medicine. (2013). Available at http://www.asam.org/ research-treatment/definition-of-addiction. Andreassen, C. S., Torsheim T., Brunborg, G. S., & Pallesen, S. (2012). Development of a Facebook Addiction Scale. Psychological Reports, 110(2), 501–517. Badiani, A., Belin D., Epstein D., Calu D., & Shaham, Y. (2011). Opiate versus psychostimulant addiction: the differences do matter. Nature Reviews. Neuroscience, 12(11), 685–700. doi:10.1038/nrn3104. Berridge, K. C., & Robinson, T. E. (1998). What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? Brain Research. Brain Research Reviews, 28(3), 309–369. Bisagno, V., Raineri, M., Peskin, V., Wikinski, S. I., Uchitel, O. D., Llinás, R. R., & Urbano, F. J. (2010). Effects of T-type calcium channel blockers on cocaine-induced hyperlocomotion and thalamocortical GABAergic abnormalities in mice. Psychopharmacology (Berl), 212(2), 205–214. doi:10.1007/s00213-010-1947-z. Boehm, S. L. 2nd, Ponomarev, I., Jennings, A. W., Whiting, P. J., Rosahl, T. W., Garrett, E. M., . . . & Harris, R. A. (2004). Gamma-aminobutyric acid A  receptor subunit mutant mice: new perspectives on alcohol actions. Biochemical Pharmacology, 68(8), 1581–1602. Brass, M., Ullsperger, M., Knoesche, T. R., von Cramon, D. Y., & Phillips, N. A. (2005). Who comes first? The role of the prefrontal and parietal cortex in cognitive control. Journal of Cognitive Neuroscience, 17(9), 1367–1375. Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010). Dopamine in motivational control:  rewarding, aversive, and alerting. Neuron, 68(5), 815–834. doi:10.1016/j.neuron.2010.11.022. Blum, K., Werner, T., Carnes, S., Carnes, P., Bowirrat, A., Giordano, J., Oscar-Berman M., & Gold, M. (2012). Sex, drugs, and rock ‘n’ roll: hypothesizing common mesolimbic activation as a function of reward gene polymorphisms. Journal of Psychoactive Drugs, 44(1), 38–55. Cadet, J. L., Jayanthi, S., McCoy, M. T., Beauvais, G., & Cai, N. S. (2010). Dopamine D1 receptors, regulation of gene expression in the brain, and neurodegeneration. CNS and Neurological Disorders Drug Targets, Nov;9(5), 526–538. Cadet, J. L., & Jayanthi, S. (2013). Epigenetics of methamphetamine-induced changes in glutamate function. Neuropsychopharmacology, 38(1), 248–249. Calabresi, P., Picconi, B., Tozzi, A., & Di Filippo, M. (2007). Dopamine-mediated regulation of corticostriatal synaptic plasticity. Trends in Neurosciences, 30(5), 211–219. doi. org/10.1016/j.tins.2007.03.001.

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Behavioral and Neuro-economic Approaches to Substance Use Disorders DAV I D P.   JA R M O L OW I C Z , D E R E K D.   R E E D , A N D WA R R E N K .   B I C K E L

People do not always respond rationally. Many eat too much and exercise too little, despite knowing the long-term consequences of these decisions. Others spend too much on hobbies or habits, hurting their long term-fitness, or take risks with low chances of success. Despite their irrationality (i.e., foregoing future or certain benefits to enjoy immediate or risky outcomes), these behavioral patterns are widespread and predictable (Madden & Bickel, 2009). Over the past 40  years, these patterns have been studied both in the laboratory and the field. Regularities have been observed, and a robust field, broadly called behavioral economics, has emerged. This field has had a pronounced impact on our understanding of behavioral disorders such as addiction (Bickel, Jarmolowicz, Mueller, & Gatchalian, 2011), obesity (Carr, Daniel, & Epstein, 2011), mental illness (MacKillop & Tidey, 2011), gambling (Miedl, Peters, & Buchel, 2012), and poor health behavior (Melanko & Larkin, 2013). Despite the broad impact of behavioral economics, no area has benefitted more from this approach than addiction research. Delay-discounting studies have highlighted the myopic choices of addicted individuals (Bickel, Jarmolowicz, Mueller, Koffarnus, & Gatchalian, 2012). Research translating the microeconomic principles of demand has illuminated many important aspects of drug reward (Bickel, Marsch, & Carroll, 2000). And, explorations of decisions under uncertain conditions have helped us understand the addicted individuals’ risky decisions (Yi, Chase, & Bickel, 2007). Clearly, behavioral economic research has enhanced our ability to predict the seemingly irrational behavior of individuals suffering from addition. More recently, researchers in the emerging area of neuroeconomics (Glimcher, Camerer, Poldrack, & Fehr, 2008)—an interdisciplinary field that merges the insights from behavioral economics with those from economics, cognitive

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psychology, and neuroscience—have begun mapping the neural substrates of behavioral economic performance, and enhancing our fundamental understanding of the neurobiology of these seemingly irrational response patterns. The current chapter reviews the behavioral and neuroeconomics of addiction. As such, the principles, procedures, addiction-related insights, neurobiological underpinnings, and treatments inspired by key areas of behavioral and neuroeconomic addiction research are described. DELAY DISCOUNTING All things being equal, we typically choose more immediate rewards (e.g., $1000 now) over more delayed rewards (e.g., $1000 a week from now). Similarly, larger rewards are typically preferred to smaller rewards (e.g., $1000 versus $900). Choices wherein both the immediacy and magnitude of the rewards vary, however, entail trade-offs between the magnitude and delay of each option. For example, although a participant may prefer $1000 today over $900 today, the same participant may prefer $900 today over $1000 to be delivered next month. This suggests that waiting a month devalued the $1000 reward. This devaluation process, typically called delay discounting, is consistent in form (i.e., qualitatively similar) but not degree (i.e., quantitatively different) across participants. This may help explain why addicted individuals may do many of the same things as non-addicted individuals (e.g., drink alcoholic beverages), yet to a greater (i.e., problematic) degree.

Measuring Delay Discounting Many approaches have been used to measure delay discounting. These vary from ready-made questionnaires (Kirby, Petry, & Bickel, 1999) to dynamic computerized assessments, sometimes including real monetary (Reynolds & Schiffbauer, 2004) or consumable (Jimura, Myerson, Hilgard, Braver, & Green, 2009) rewards. Encouragingly, these paradigms generally yield comparable results, suggesting that delay discounting is a robust phenomenon. Two common tests are the adjusting amount procedure (Du, Green, & Myerson, 2002) and the monetary choice questionnaire (Kirby et al., 1999). Common variants of the adjusting amount procedure present a delayed reward that is twice as large as the immediate reward (e.g., $500 now vs. $1000 a month from now), and subsequently “titrate” the size of the immediate reward based on the participant's choices. These choices are repeated until the participant is indifferent between the two alternatives (called the indifference point). This is repeated across delays (e.g., 1 day, 1 week, 1 month, 6 months, 1 year, 5 years, and 25 years), and nonlinear regression is used to quantify the rate at which rewards are devalued (see “Quantifying Delay Discounting,” below). A second common approach to measuring delay discounting is the monetary choice questionnaire (MCQ; Kirby et  al., 1999). The MCQ is a validated questionnaire (Kirby, 2009)  with adequate test-retest reliability (Kirby, 2009) used to rapidly collect delay-discounting data in settings wherein the adjusting delay

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procedure may not be feasible (e.g., clinics, classrooms). The MCQ consists of a series of 27 (Kirby et al., 1999) choices between rewards that differ in both their magnitude and immediacy. For example, participants may be asked to choose between “$31 today” and “$85 in seven days.” Answers are then used to quantify individuals’ discounting rates, based on an algorithm (see Kirby et al., 1999, for details). One remarkable feature of delay-discounting assessments is their flexibility. For example, modified delay-discounting tasks have been used to demonstrate that individuals discount consumable commodities such as sex (Jarmolowicz, Bickel, & Gatchalian, 2013), drugs (Bickel, Landes, et al., 2011), or food (Jimura et al., 2009) more rapidly than money. Similarly, modified delay-discounting tasks have demonstrated that individuals discount past rewards like future rewards (Yi, Gatchalian, & Bickel, 2006); discounting for abstract rewards such as freedom or health (Petry, 2003) is qualitatively similar to discounting of money; and that losses are discounted more rapidly than gains (Estle, Green, Myerson, & Holt, 2006).

Quantifying Delay Discounting Discounting data can be analyzed many ways. We direct readers interested in a comprehensive descriptions of these quantitative approaches elsewhere (Green & Myerson, 2004). The two most common approaches to analyzing discounting data are described below. Hyperbolic functions fitted to the data using nonlinear regression adequately describe delay-discounting data (Green & Myerson, 2004; Mazur, 1987). Specifically, delay-discounting data are typically well described by Mazur's (1987) hyperbolic discounting formula [V = A/(1+kD)], which describes how the value (V) of some amount (A) of a reward relates to the delay to that reward (D) and the rate by which the organism discounts delayed rewards (k). The one free parameter (i.e., discounting rate [k]) provides a descriptor of the entire curve, and can be used in additional statistical analyses. Area under the curve (AUC) is another common approach to quantifying delay-discounting data. Touted as a theory-free approach to quantifying discounting data (Myerson, Green, & Warusawitharana, 2001), AUC makes fewer assumptions about the expected form of the data. Simply put, AUC is calculated by connecting the indifference points and calculating the area of the resulting figure below that curve. Higher AUC is roughly equivalent to lower k values (i.e., higher AUC, more self-control). Like k, AUC is a single value that can be used to describe the entire parametric function and can be used in subsequent statistical analyses.

Delay Discounting and Addiction-Related Phenomena Regardless of how delay discounting is quantified, it consistently relates to addiction-related phenomena. For example, many studies have compared

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discounting rates in individuals with substance use disorders to those of non-users (see Bickel et al., 2012, for a review). These studies have demonstrated that individuals who are users or are dependent on nicotine (Bickel, Odum, & Madden, 1999), alcohol (Vuchinich & Simpson, 1998), cocaine (Bickel, Landes, et  al., 2011), methamphetamine (Hoffman et  al., 2006), and/or heroin (Kirby et  al., 1999)  discount delayed rewards more rapidly than non-users. The consistency of this finding across most drugs of abuse, along with the elevated rates of discounting seen with behavioral addictions such as gambling (Petry, 2001) and obesity (Weller, Cook III, Avsar, & Cox, 2008), spurred the conclusion that elevated delay-discounting rates are a trans-disease process that undergirds many maladaptive behaviors (Bickel et al., 2012). The literature on discounting across commodities suggests that individuals discount consumable goods such as food (Odum, Baumann, & Rimington, 2006), sex (Jarmolowicz et al., 2013), and drugs (Bickel, Landes, et al., 2011) more rapidly than money. This may help explain some of the particularly impulsive choices that addicted individuals make regarding pleasurable activities such as sexual encounters and drug use. Interestingly, these elevated discounting rates appear to exist because consumable commodities (e.g., drugs) do not retain their value, rather than because their immediate delivery is particularly appealing (Bickel, Landes, et al., 2011). Delay discounting also relates to the “loss of control” that is a hallmark of addiction (Monterosso & Ainslie, 2007). Notably, addicted individuals may express a desire to engage in drug-free activities (e.g., exercise), yet choose drug use when the two alternatives are later presented. For example, when drinking his morning coffee, an alcoholic may say that he is going to go to the gym after work, yet change his mind in favor of happy hour in the bar as the workday winds down. These preference reversals (Thaler, 1981) are predicted by the hyperbolic discounting models used in behavioral economics, but not by the exponential discounting functions favored by traditional economic approaches (see Figure 3.1). Specifically, when having his morning coffee (t1), gym attendance's delayed reward of increased fitness is more desirable, yet as time passes, and the workday is almost finished (t2), the smaller yet more immediate rewards associated with alcohol use become more desirable. Hence, a preference reversal in favor of alcohol use occurred as the opportunity to drink became more proximal.

The Neuroeconomics of Delay Discounting Given the relatively recent emergence of neuroeconomics, the neural substrates of delay discounting have already received considerable attention. For example, McClure, Laibson, Loewenstein, and Cohen (2004) used fMRI to measure brain activity as participants made choices between smaller yet more immediate, and larger yet more delayed rewards. McClure et al. found relatively higher levels of activation in reward-oriented prelimbic areas when participants chose immediate rewards, and relatively higher levels of activation in prefrontal areas when participants chose the delayed rewards.

Desirability →

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Preference reversal

The workday

t1

t2

Time →

Figure 3.1 Diagram of how one's preference for a healthy activity (i.e., exercise) versus an unhealthy activity (i.e., drinking) may change from one timepoint (t1) to another timepoint (t2). These preference reversals are predicted by hyperbolic discounting of delayed rewards. See the text for further details.

McClure et al.'s (2004) finding fueled the development of the competing neurobehavioral decision systems (CNDS) model (Bickel et al., 2007). The CNDS, a two-system model broadly consistent with models independently developed by a diverse set of researchers (Bechara, 2005; Jentsch & Taylor, 1999; Kahneman, 2011), posits that delay discounting reflects the relative balance of activation in two interacting brain systems (Bickel et al., 2007). The evolutionarily older impulsive system (limbic and prelimbic areas) is reward-driven and favors immediate rewards. By contrast, the evolutionarily younger executive system (prefrontal areas; e.g., dorsolateral prefrontal cortex [DLPFC]), which is involved in higher-order cognitive functions such as memory, planning, and inhibition, favors larger yet delayed rewards. Consistent with findings by McClure et al. (2004), data from a preponderance of imaging studies demonstrate relatively higher levels of impulsive system activation when individuals choose immediate rewards, and relatively higher levels of activation in the executive system when individuals choose larger yet delayed rewards (see Bickel, Jarmolowicz, et al., 2011, for a discussion). Stronger support for the CNDS can be found in studies using transcranial magnetic stimulation (TMS), an emerging technology that uses magnetic fields to temporarily increase or decrease activation in targeted brain regions. TMS, by directly manipulating brain activation, moves beyond the neural correlates obtained from neuroimaging and permits causal inferences about the neural underpinning of behavior. For example, Figner et  al. (2010) had three groups of participants complete a delay-discounting assessment after receiving TMS (targeting the left DLPFC, right DLPFC, or a control condition). Subjects receiving TMS to the left DLPFC discounted delayed rewards more rapidly (i.e., chose the smaller yet sooner reward more often) than subjects in the other two groups, suggesting that the left DLPFC subserves delay discounting (also see Cho et al., 2010).

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Studies examining brain differences in substance-using versus control participants during intertemporal choice (i.e., delay discounting) tasks are consistent with the CNDS model and have expanded our understanding of addiction. For example, Hoffman et  al. (2006) compared patterns of neural activation during delay-discounting tasks in currently abstinent methamphetamine-dependent individuals with those of non-users. Hoffman et  al. (2006) found that the non-users had higher levels of executive-system activation during choices for delayed rewards than did the methamphetamine-dependent individuals. Similarly, when comparing the neural correlates of delay discounting in methamphetamine abusers to non-users, Monterosso et al. (2007) found higher levels of executive system activation during difficult choices in non-using, relative to the methamphetamine-abusing, subjects. Consistent with Hoffman et al. (2006) and Monterosso et al. (2007), Meade, Lowen, Maclean, Key, and Lukas (2011) found lower levels of executive system activation during difficult delay-discounting choices in cocaine-dependent persons. This is consistent with the CNDS model, which posits that delay discounting reflects the relative strength of the impulsive and executive systems. These studies suggest that the higher discounting rates seen in drug users are a result of hypoactivation of the executive system during intertemporal choice. DEMAND AND REWARD VALUATION Consumer behavior is impacted by the relative cost of commodities in multiple, but orderly ways. For example, if the price of coffee increases, you may buy less coffee, or you may switch to tea. But if you really need that morning lift, yet dislike tea, you may simply pay more. Similarly, a sale on hotdog buns may spur the purchase of additional hot dogs. These tendencies, long studied by economists as “consumer demand,” have been successfully applied to understanding a wide range of behaviors, including substance use disorders (Bickel et al., 2000). The principles presented below spring from a long-standing tradition of translating economic principles to understand the behavior of non-human animals (Hursh, 1980). Further translation has brought these insights to bear on aberrant patterns of human behavior such as drug use (Bickel & Madden, 1999) and overeating (Carr et al., 2011). For example, the tendency to work for a reward is stronger when the reward can only be obtained in the experiment (called a closed economy) than when the reward is available elsewhere (called an open economy; Hursh, 1980). The amount of work that an individual will do for a reward is consistently related to the amount of that reward received for that work. This ratio of work performed (i.e., cost) to reward (i.e., benefit) is called the unit price (Hursh, 1980). Unit price draws a parallel between working (cost) for a given reward (benefit) and paying (cost) for a given commodity (benefit), facilitating the translation of economic principles for the study of behavior (e.g., substance abuse). Unit price can be manipulated in at least two ways: 1) you can require the same amount of work while increasing or decreasing the amount of reward (Bentzley, Fender, & Aston-Jones, 2013), or 2)  you can increase or decrease the amount of

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work required per unit of reward (Johnson & Bickel, 2006). Both manipulations yield the same result; little change in the number of rewards earned (consumption) over the lower range of unit prices (i.e., demand is inelastic), followed by a precipitous drop in consumption as the price is further increased (i.e., demand becomes elastic). This pattern of change in demand from inelastic to elastic, summarized as elasticity of demand, differs between both rewards (e.g., cigarettes and money; Johnson & Bickel, 2006)  and individuals (e.g., problem drinkers versus casual drinkers; MacKillop & Murphy, 2006)  in ways which may be relevant to understanding addiction. Although unit price and elasticity of demand provide insight into individuals’ responding for, and consumption of, a reward (e.g., drugs), non-laboratory environments entail opportunities to concurrently respond for various rewards. The principles of demand provide unique insights into the interactions between various commodities. Commodities can be substitutes, complements, or independents. If the price of coffee increased, causing a decrease in coffee consumption and an increased consumption of tea, tea and coffee would be seen as substitutes. By contrast, if increased coffee prices lead one to buy less coffee and less creamer, the two commodities are complements. The unit price of one commodity, however, often has little if any impact on the consumption of other commodities. For example, an increase in the price of coffee does not generally impact the number of hotdogs purchased. Because the price of coffee did not impact hot dog purchases, the two commodities are called independents.

Measuring Demand As noted above, the principles that have been helpful in understanding addiction were originally investigated in non-human animals (Hursh, 1980). As a result, the early studies examining these principles in humans used preparations modified from non-human animal studies. These approaches are typically referred to as demand curve analyses (Bickel et al., 2000). For example, Bickel and Madden (1999) compared demand for cigarettes and money in four subjects. Each day the participants completed one three-hour session wherein they earned either cigarette puffs (3 puffs) or money (20 cents) by pulling a standard response plunger. Participants could only earn one reward type (i.e., cigarettes or money) in a given session, and the number of plunger pulls for that reward increased each day (from 3 to 10,000 over 7 days) until a session occurred wherein the participant did not earn any rewards (at which point the process was repeated for the other reward type). Participants consistently completed higher response requirements for the cigarette puffs than for the money, indicating less elastic demand for cigarettes than for money. Although the experimental control afforded by the laboratory techniques described above has robustly demonstrated these behavioral economic principles in human subjects, the techniques are labor-intensive and require specialized equipment (e.g., a very well-ventilated room). These obstacles have limited their

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widespread use. Jacobs and Bickel (1999), however, developed a quicker approach to assessing demand for drugs in clinical settings. This general approach, which will hereafter be referred to as the hypothetical purchase task (HPT), asks subjects how many units of a given commodity that the participant would purchase across a wide range of prices. The Jacobs and Bickel (1999) study administered an HPT for cigarettes and an HPT for heroin (14 questions, prices ranging from $0.05– $1,120.00 during each assessment) to heroin-dependent individuals. Demand for heroin was notably higher than demand for cigarettes (i.e., consumption changed from inelastic to elastic at higher prices) in all participants. Like the procedures for assessing delay discounting (described above), this general approach is extraordinarily flexible, and it has been adapted to examine demand for other commodities such as alcohol (MacKillop & Murphy, 2006), and even for use in virtual-reality paradigms (Acker & MacKillop, in press). Moreover, recent studies have validated the HPT (MacKillop et al., 2008); have found that the HPT yields findings similar to those from tasks that use real rewards (Amlung, Acker, Stojek, Murphy, & MacKillop, 2012); and have found that the increasing sequence of prices used in the HPT yield similar findings to those from a randomized series of prices (Amlung & MacKillop, 2012). Moreover, and importantly, responses on the HPT predict treatment success in alcohol-dependent individuals (MacKillop & Murphy, 2007).

Quantifying Demand Demand-curve analyses provide parametric data on both consumption (rewards earned or purchased) and expenditure (number of responses emitted or money spent) across a wide range of choices. Much like the quantification of delay-discounting rates (described above), these data are typically analyzed using nonlinear regression. The analyses of these data are somewhat more involved; thus we direct interested readers elsewhere for the details of the analysis (see Hursh, 1980; Hursh & Silberberg, 2008). Analyzed, these demand and output curves provide five measures of responding. Most of these measures are illustrated in Figure 3.2. The first two measures are the simplest, conceptually. Breakpoint refers to the lowest cost at which no rewards are earned/consumed, and intensity of demand refers to the number of rewards that are earned/consumed when the cost per reward is 1. The final three measures relate to the translation in consumption/ output as consumption moves from inelastic (increases in price have little effect on consumption) to elastic (increases in price have marked effects on consumption). The first of these measures, Pmax, refers to the price of the reward (i.e., number of responses or monetary expenditure per reward) when consumption moves from inelastic to elastic. Similarly, Omax refers to the maximum response output (i.e., expenditure) for the reward at any price. Omax is typically, but not necessarily, the expenditure at Pmax (Hursh, 1980). Lastly, elasticity of demand is an aggregate measure (derived from the newer exponential demand model; Hursh & Silberberg, 2008) of elasticity within a given curve. Combined, these five metrics provide a very sensitive measure of effects of price on consumption.

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Omax Inelastic

Self-Administrations

100

Elastic

100000

10000 10 1000

Responses Per Session

Intensity of Demand when Unit Price = 1

Pmax 1 10000

1000

100

10

1

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Fixed Ratio

Figure 3.2 Diagram depicting several of the behavioral economic measures of demand described in the text. At lower prices (i.e., 1–1000), demand is inelastic, but at 1000, demand becomes elastic. That point coincides with Omax (maximum response output) and Pmax (the price where demand becomes elastic).

Demand and Addiction-Related Phenomena Much like delay discounting, demand and response output functions have contributed to our understanding of addiction-related phenomena. In fact, these two principles (i.e., delay discounting and demand) are the core of the reinforcer pathologies approach to addition (Bickel, Jarmolowicz, et al., 2011), which posits that two fundamental aspects of addiction are an inability to delay gratification (delay discounting) and excessive demand for the reinforcer to which the individual is addicted (demand). This excessive demand for addictive reinforcers is evident in studies comparing demand for cigarettes versus money in smokers (Johnson & Bickel, 2006), heroin versus cigarettes in heroin addicts (Jacobs & Bickel, 1999), alcohol in problem versus casual drinkers (MacKillop & Murphy, 2006), and food in obese relative to healthy-weight individuals (Epstein et  al., 2004). Moreover, demand for heroin (Petry & Bickel, 1998) and opioid maintenance drugs such as buprenorphine (Petry & Bickel, 1999) is inelastic, even when other rewards such as other drugs or money are available, supporting the need for large rewards in programs that incentivize abstinence (e.g., Higgins et al., 1991).

Neurobehavioral Underpinning of Demand/Reinforcer Valuation Whereas the neuroeconomic paradigms used to study delay discounting have been well developed, few studies have examined demand in ways that are comparable to demand-curve analyses. Instead, understanding of the neural correlates

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of demand is derived from other, related, areas of inquiry. The current discussion will focus on the understanding of demand that can be gleaned from neuroimaging studies of cost/benefit valuation and reward processing. Unit price can be seen as a simple calculation of the cost (i.e., responses or monetary investment) to benefit (i.e., quantity of the reinforcer received) ratio associated with responding in a particular context. As such, the literature on neural correlates of cost–benefit valuation may be particularly relevant to our understanding of demand. Building on their considerable research on cost–benefit valuation in non-humans, Croxson, Walton, O'Reilly, Behrens, and Rushworth (2009) conducted an informative imaging study on cost–benefit valuation in a sample of 19 human subjects. Specifically, subjects were provided with a cue that indicated 1) the number of responses required during the effort phase of the trial (i.e., cost), and 2) the amount of money they would receive for doing so. This cue remained in place for 2.5 to 4 seconds before the participants moved into the effort phase of the trial. During the effort phase, participants responded by clicking on white boxes that appeared in random locations on the screen, and subsequently received the indicated reward once the response requirement was met. Impulsive system activation (i.e., anterior cinglate cortex and striatum) during the cue phase decreased as the unit price (i.e., cost–benefit ratio) increased. Supplemental analyses indicated that both the relative effort and relative reward on a given trial contributed to this pattern. Similar patterns of neural activation were observed during the effort phase of the trial. Thus, the neurobehavioral correlates of cost–benefit valuation appear to be cue-elicited and a product of both reward and effort anticipation. Although the connection is less direct, research using the monetary-incentive delay task (MID; Knutson, Adams, Fong, & Hommer, 2001) may provide insight into the high levels of demand often seen in addicted individuals. The MID task 1) briefly presents a cue indicating how much money they would be working to earn or avoid losing, 2) presents a brief delay (e.g., 2–3 seconds; often referred to as the “anticipatory phase”), then 3) presents a target which the participant must click within a specified timeframe to 4) receive the signaled incentive (often referred to as the “reward receipt phase”). Research using this paradigm has uncovered lower than control subjects’ levels of impulsive system (i.e., ventrial striatum) activation in detoxified alcoholics (Wrase et al., 2007), cigarette smokers (Rose et al., 2012), methamphetamine users (Schouw et al., 2013), and polysubstance-abusing adolescents (Schneider et al., 2012), but not in cocaine-dependent individuals (Jia et al., 2011). These decreased levels of anticipatory neural activation may drive demand by requiring higher levels of consumption to achieve the same levels of rewarding brain activation. Moreover, this decreased impulsive system activation was correlated with higher levels of cue-elicited craving in detoxified alcoholics (Wrase et  al., 2007), correlated with impulsivity as measured by the Barrett Impulsivity Scale in alcoholics (Beck et  al., 2009), and risk taking as measured by the Cambridge Gambling Task (Schneider et al., 2012). Less clear, however, is whether these decreased levels of impulsive-system activity reflect a predisposition towards, or an effect of, use. Data showing decreased reward anticipatory impulsive system activity in children of alcoholics (Yau et al., 2012) suggest that

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these patterns may be part of a genetic predisposition towards use. Alternatively, data showing that administration of oral methylphenidate prior to the MID task decreased reward-anticipatory responding in non-drug users (Schouw et  al., 2013) suggest that these patterns of neural activation may result from use. Thus, these patterns may both predate and be worsened by use.

DECISION MAKING UNDER RISK AND UNCERTAINTY In addition to excessively devaluing future events and overvaluing rewards, individuals often make risky decisions. Maurizo Tosi (1969) unearthed dice during his 1968 archaeological expedition into Shahr-e Sūkhté, the “Burnt City” of Iran, suggesting that games of chance were present nearly 1,000 years before the onset of the Iron Age (i.e., 5,000 years ago). Today, such games are ever-present, with over $40 billion dollars lost in legal gambling per year (Ghezzi, Lyons, & Dixon, 2000) in the United States alone. Like delay discounting, this risk-taking appears irrational, and thus more amenable to behavioral than standard economic analysis. Risk implies that an outcome is uncertain. Risky decisions feature a probability of return that is less than 1.0. Risk also implies that the outcome is potentially harmful to the decision-maker, as is the case with substance abusers, compulsive gamblers, and hedonists (Marsch, Bickel, Badger, & Quesnel, 2007). Neoclassical economic approaches to risk posit that the expected utility of an outcome is a product of the value and probability of that outcome. For example, a 50% gamble of $100 has an expected utility of $50 (0.50 x $100). This rational-choice approach, however, makes unrealistic assumptions (Herrnstein, 1990; Thaler, 1980), and thus fails to account for real-world phenomena such as individuals’ continuing to use drugs despite the known risk of addiction. Clearly, risk valuation entails more than simple expected utility. In addition to outlining cognitive biases that drive irrational choice, Kahneman and Tversky's prospect theory (Kahneman & Tversky, 1979)  provides a cogent summary of the behavioral economic approach to risk. Prospect theory posits that decisions made under risk and uncertainty are influenced by the individual's subjective valuation of gains and losses. For example, individuals exhibit loss aversion (i.e., losses are valued more than gains). Specifically, losing $100 is more aversive than gaining $100 is appealing. For example, when asked to choose between a 50% chance of losing $100 and a guarantee of losing $45 (note, the expected utility of the risky choice is greater than the guarantee), most individuals choose the guaranteed smaller amount (i.e., risk aversion). However, when the scenario is presented as a 50% chance of winning $100 and a guarantee of winning $55 (in this case, the guarantee is greater than the expected utility of the risky choice), most individuals will prefer to take the risk (i.e., risk seeking). This valuation process is formalized as two distinct gain-loss functions in Figure 3.3. Note, however, that prospect theory assumes individual differences in risk seeking/aversion. Thus, not all decision-makers follow the exact value function indicated in Figure  3.3. These differences may be important for understanding behavioral disorders (e.g., addiction, gambling).

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Value

Loss

Gain

Figure 3.3 Depiction of prospect theory value function, indicating risk-seeking valuation for gains in the upper-right quadrant and risk-aversion valuation in the lower-left quadrant.

Understanding one's propensity to value risky rewards over certain ones is a core tenet of the behavioral economics of risk. Interestingly, although the devaluation of probabilistic rewards follows a similar discounting function as delay discounting (Rachlin, Raineri, & Cross, 1991), these decision-making patterns do not seem to be manifestations of a single construct (Jarmolowicz, Bickel, Carter, Franck, & Mueller, 2012). For example, Myerson, Green, Hanson, Holt, and Estle (2003) showed that, despite similar discounting functions, participants exhibited differential magnitude effects on the two tasks. Specifically, larger rewards resulted in lower delay-discounting rates (i.e., more self-control) than smaller rewards, whereas larger probabilistic rewards resulted in higher probability discounting rates (i.e., more rapid devaluation) than smaller rewards. The differential discounting of delayed and probabilistic rewards suggests that decision-making is a multifaceted construct.

Measuring the Economics of Risk Like delay discounting and demand analysis, individuals’ responses to risk have been measured in many ways. Approaches to assessing risk, however, are less standardized than delay discounting and demand assessment. Therefore, we will provide a brief description of one prominent approach and direct readers elsewhere for an exhaustive discussion of these methods (Green & Myerson, 2004). Probability discounting tasks are one approach to assessing risky decisions. Probability discounting is typically measured using procedures similar to those for delay discounting. Participants make a series of hypothetical choices regarding monetary rewards. Response patterns across these trials result in indifference points that are used in quantitative analyses. Unlike delay discounting—which pits smaller, immediate rewards against larger, delayed ones—probability discounting involves the choice between smaller, certain rewards and larger, uncertain ones (e.g., “Would you rather have $50 for sure, or a 25% chance of receiving $100?”). The probability of receiving the larger reward (typically from 0.01 to 0.95) is then manipulated across a series of trials until the indifference point is determined. Like delay-discounting assessment, probability-discounting procedures are remarkably flexible. For example, the assessment of probability discounting of

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losses only involves a simple rewording of the choice scenario. Rather than choosing between a small but certain reward and a larger amount with less than certain odds, the loss assessment involves a chance to lose the larger amount, rather to receive it. Such analyses are often conducted within individual subjects to evaluate differences between probabilistic discounting of gains and losses. For probability discounting of gains, responding subjective valuation above expected values would indicated risk-seeking (subjective values less than expected values indicate risk aversion). The reverse conclusions are made when considering probability discounting of losses.

Quantifying Risky Decision-Making Using Probability Discounting Given the similarities between delay and probability discounting, nearly identical quantitative models sufficiently describe the two functions. Probability discounting, like delay discounting, is best described using a hyperbolic function: [V = A/(1 + kΘ)] where the parameters V, A, and k are identical to those used in delay discounting. Unlike delay discounting, however, the hyperbolic function for probability discounting examines how the value (V) of a reward decreases as the odds against (Θ; calculated using the formula Θ = 1–ρ/ρ, where ρ is the probability of receiving the larger uncertain reward.

Risk Valuation and Addiction-Oriented Phenomena Behavioral economic approaches have yielded important and interesting conclusions about risk and reward-valuation in addicts. At a basic level, organisms exhibit evidence of risk-valuation in their foraging patterns (Caraco, Martindale, & Whittam, 1980). Specifically, risk-sensitive foraging (RSF) suggests that survival prospects modulate risk seeking and risk aversion. For example, when sated, organisms are risk averse. Risk seeking occurs when organisms are deprived, as this increases their chances of survival. From an evolutionary perspective, risk sensitivity may be explained by relative access to rewards. In addiction, Bickel, Giordano, and Badger (2004) evaluated whether opioid-dependent-individuals’ risk sensitivity is modulated by relative amounts of reward access. Specifically, participants made hypothetical choices between risky and non-risky sources of heroin while imagining feeling either high (sated) or sick from withdrawal symptoms (deprived). As predicted by RSF theory, addicts made riskier choices during the deprivation scenario, suggesting that risky choices for drugs may be a product of evolutionary selection. The above findings complement self-report scales of addicts’ valuation of and opinions on risk. In a study of 50 injection-drug users and 50 matched control participants, Marsch et al. (2007) asked a series of questions aimed at assessing risk perception. The drug users perceived risk in more activities, commodities, and outcomes than did controls. The drug users were also more likely to perceive risk for items most personally relevant to them. Thus, the drug users

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clearly acknowledge the risks associated with their behavior. These data provide some descriptive evidence that drug users’ valuation of risk is greater than non-users’. Given that addiction has been conceptualized as a pattern of risk-seeking behaviors, probability-discounting data have generated new questions about decision-making processes underlying addiction. As a measure of risky decision-making, probability discounting integrates the procedures used in delay discounting and risk valuation. Researchers’ success in differentiating addicts from non-addicts using probability-discounting measures has, however, been mixed. For example, Mitchell (1999) found that smokers’ delay-discounting rates and personality/impulsivity scale scores were higher than non-smokers’, yet the two groups did not differ on probability discounting. Yi et al. (2007) replicated these findings but found that smokers and non-smokers differed within the task when higher probabilities were presented. Thus, differences in delay and probability discounting may be due to scaling factors in the tasks. Yi and Landes (2012) investigated whether delay and probability discounting would differ in cigarette smokers following 24 hours of abstinence. Following abstinence, delay discounting of gains and losses of money was steeper, but delay discounting of gains and losses of cigarettes and probability discounting of gains and losses of cigarettes and money were unaffected. Note that probability discounting of both gains and losses has also failed to predict alcohol use, despite delay discounting serving as a significant predictor (Takahashi, Ohmura, Oono, & Radford, 2009). Interestingly, the only addiction-oriented phenomenon reliably related to probability discounting is pathological gambling (Petry, 2001). Petry (2012) showed that gamblers’ delay-discounting rates did not improve following treatment for pathological gambling, but probability-discounting rates decreased, and improvements predicted treatment success.

Neural Substrates of Risk and Uncertainty Using fMRI technologies, neuroeconomic researchers are able to examine neuronal differences in human participants as risky choices are presented. For example, Volz, Schubotz, and von Cramen (2004) examined decision-making on probabilistic reward schedules. Interestingly, risky decision-making was associated with the posterior frontomedial cortex substrate regardless of whether the probability of the risk was explicitly stated or learned through trial and error. Although these data aid in identifying biological correlates of risky decision-making, they do not provide an account of whether these mechanisms differ with regards to gains and losses. Toward that end, Montague and Berns (2002) reviewed neuroeconomic findings and found that valuation of risky decisions and losses may be computationally predicted within orbitofrontal and striatal neurons. Risky decisions have typically been regarded as gambles, given the less than certain outcome and concomitant chance of potential loss. This is congruent with conceptualizations of risky health decisions, such as needle sharing, binge drinking, or smoking (Inukai & Takahashi, 2006); in essence, one gambles one's health

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for immediate pleasure despite odds that long-term gains will be compromised. Not surprisingly, gambling games are often used to model the neural processes associated with risky decision-making. Findings from such studies may therefore further the understanding of both the functional and biological foundations of risky decisions. Habib and Dixon (2010) examined neurological indicators of gains and losses in pathological and non-pathological gamblers during simulated slot machine spins. While no overt behavioral differences were identified, significant neurological differences were observed between the groups with respect to responses to “near-misses” on the slot machine (i.e., two of three symbols appearing when three symbols in a row were considered “wins”). Non-pathological gamblers demonstrated activation in the brain indicating a loss-function. That is, for non-pathological gamblers, near-misses featured neural substrates similar to those experienced during losses. On the contrary, pathological gamblers activated areas of the brain indicating that near-misses were valued similarly to reward or gain processes. These data suggest that neurological indicators may help explain functional reasons for gambling addiction; overvaluation of gambles that almost pay off might actually serve a reinforcement function for risk-taking. In an application of a sequential investment game, Chiu, Lohrenz, and Montague (2008) measured brain activity of non-smokers and smokers (both unsated and sated) as they made investments in a hypothetical stock market. Participants were able to monitor fictive markets that described the money they would have made had they made different choices. This procedure enabled the researchers to monitor effects of fictive (information from historical data on what would have happened if they had made alternative choices) and experiential error signals. While all participants exhibited distinct neural correlates to the error signals, the fictive error signals were only predictive of subsequent choice for non-smokers. That is, smokers did not appear to use the “what if ” information when making subsequent investments. When unsated smokers were provided the opportunity to smoke throughout the day of the research session, their responses trended toward those of non-smokers, but were still not significantly predictive. Little neural research has been published on probability discounting, perhaps due to the relatively scant experimental evidence of its clinical utility. Nevertheless, two recent studies warrant discussion. Peters and Buchel (2009) examined within subjects the neural substrates of delay and probability discounting using an fMRI scanner. Delay and probability discounting exhibited considerable overlap in both the ventral striatum and orbitfrontal cortex. Domain-specific valuation was also observed. Delay discounting was evident in the fronto-polar and lateral parietal cortex, and posterior cingulate cortex, but not during probability discounting. However, probability discounting featured domain-specific activation in the superior parietal cortex and middle occipital regions. Thus, delay and probability discounting share some reward-valuation mechanisms, but also feature domain-specific activation, indicative of differing decision-making pathways. In a follow-up study (Miedl et al., 2012), they found that gamblers exhibited weak associations between reward valuation and activation in the ventral striatum and orbitfrontal cortex areas—areas previously associated with overlap between delay

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and probability discounting. Thus, additional research is necessary in order to understand the neural substrates of probability discounting and their relationship to risky decision-making and addiction. IMPLICATIONS FOR TREATMENT The value of behavioral economics, in addition to providing scientific insights into addictive behavior, is providing methods to change that behavior. Although many behavior-change methods are related to behavioral economics, space limitations prevent a comprehensive description here (see Bickel, Jarmolowicz, et al., 2011, for a more detailed review). Instead, we present two specific areas more closely aligned with the goals of this volume: 1) manipulating the price of drug use, and 2) providing training that allows for greater control by future events.

Increasing the Price of Drug Use Behavioral economics identifies a very reliable phenomenon; namely, that consumption decreases as the price of a commodity increases (Johnson & Bickel, 2006). “Price” is broadly defined and includes anything that functionally increases the effort or time necessary to obtain a commodity. For example, restrictions against smoking in public places increases the cost of smoking and often increases the wait for a smoking break. These restrictions have reduced the rates of smoking (Brigham, Gross, Stitzer, & Felch, 1994) and increased quitting rates for smokers (Farrelly, Evans, & Sfekas, 1999). Taxes also provide increased price and, as taxes have increased in the United States, smoking has decreased, with the greatest reduction in the states that have high cigarette prices (Al-Delaimy et al., 2007). These straightforward approaches, however, require modification when considering illicit drug use. Currently, the basis for most U.S. drug policy is increasing the price of illicit drugs by punishing users (i.e., criminal sanctions for the use and possession of drugs). However, applying these methods via the criminal justice system entails consequences that are both probabilistic and unfold over long arcs of time (Kleiman, 2010). Probabilistic and delayed consequences are discounted relative to more certain and more immediate events. An alternative means to increase the price of drug use without the use of punishing events is reinforcing abstinence with procedures referred to as contingency management. For example, Higgins et  al. (1991) rewarded cocaine-dependent individuals providing drug-free urine samples with vouchers redeemable for merchandise or services. More specifically, urine samples were provided three times a week across a multi-week trial. The samples were tested immediately, followed by the provision of vouchers that increased in value for continued abstinence. Drug-positive urine (indicating recent use) caused the current reward to be omitted and the value of future rewards to be reset to a lower value. To date, this approach is one of the most efficacious treatments for substance dependence, and it uses positive consequences that are administered with certainty

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over short time frames. Meta-analyses have found that the delay between receipt of the urine sample and the administration of the reward modulated the effect; that is, the greater the delay, the less of the effect (Griffith, Rowan-Szal, Roark, & Simpson, 2000). This technique has been applied to cigarette smoking (Dallery & Glenn, 2005), alcoholism (McDonell et  al., 2012), heroin use (Bickel, Amass, Higgins, Badger, & Esch, 1997), and methamphetamine use (Roll et al., 2006).

Training for Greater Control by Future Events As noted earlier, neuroeconomics (i.e., the competing neural systems hypothesis; Bickel et al., 2007; Koffarnus, Jarmolowicz, Mueller, & Bickel, 2013) has provided unique insights into addiction. From this perspective, the excessive discounting evident among addicted individuals often reflects the relative balance between the impulsive and executive systems such that the executive system is hypoactive. This suggests an approach to the treatment of addiction; namely, decreasing discounting rates by restoring the balance to these systems by strengthening the influence of the executive system. Bickel and colleagues (Bickel, Yi, Landes, Hill, & Baxter, 2010) demonstrated this approach by using working-memory training to decrease discounting rates in individuals addicted to stimulants. In this study, participants’ discounting rates were first assessed prior to their being randomized to two groups. One group underwent 4–15 sessions of active working-memory training. This training had them practice memory tasks involving the recall and categorization of numbers and words. The other group received sham working-memory training; that is, they were provided the same tasks, but these were modified so that cues indicated the correct responses (i.e., they did not have to work to get the answer). Participants’ rates of discounting were then redetermined. Discounting rates decreased for 9 of the 14 participants who underwent the training; whereas only 2 of the 13 who received the sham training had decreased discounting rates. Moreover, another study has shown that working-memory training in and of itself can lead to reduction in alcohol consumption in problem drinkers (Houben, Wiers, & Jansen, 2011). These and ongoing studies demonstrate the utility of neuroeconomic approaches to treatment. CONCLUSION In this chapter, we briefly reviewed the behavioral economics and neuroeconomics of addiction, including the independent variables, methods for analysis, and dependent measures derived from behavioral economic and neuroeconomic measures. Based on these approaches, we have discussed a new conceptual model of addictive behavior referred to as reinforcement pathologies. This new model emphasizes important processes that are relevant to addiction; namely, 1) the persistently high valuation of a commodity or substance, and 2) the preference for the

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immediate acquisition and/or consumption of that commodity, despite long-term negative outcomes. This model permits a novel way to organize and illustrate the many expressions of addiction that share trans-disease processes (Bickel et  al., 2012). The value of trans-disease processes is that an advance made in understanding one disorder can be applied to every disorder that shares that process. This chapter illustrates some of the ways behavioral economics and neuroeconomics have contributed to novel understanding of disorders. However, the ultimate test of this approach is whether it can induce long-lasting behavior change that improves individuals’ lives. Clearly, the use of incentives to alter the “price structure” of those with addiction is having considerable impact. What remains to be done is to ascertain whether decreasing discounting rates can contribute over and above the use of incentives, and whether these and other behavioral economic and neuroeconomic approaches can be integrated into a whole system of care that proves to be efficacious. We will not know whether this can be achieved for some time, but given the current increase in research in this area, the future looks promising. References Acker, J., & MacKillop, J. (2013). Behavioral economic analysis of cue-elicited craving for tobacco: A virtual reality study. Nicotine & Tobacco Research, 15(8), 1409–1416. Al-Delaimy, W. K., Pierce, J. P., Messer, K., White, M. M., Trinidad, D. R., & Gilpin, E. A. (2007). The California Tobacco Control Program's effect on adult smokers: (2) Daily cigarette consumption levels. Tobacco Control, 16(2), 91–95. Amlung, M. T., Acker, J., Stojek, M. K., Murphy, J. G., & MacKillop, J. (2012). Is talk “cheap”? An initial investigation of the equivalence of alcohol purchase task performance for hypothetical and actual rewards. Alcoholism, Clinical & Experimental Research, 36(4), 716–724. Amlung, M. T., & MacKillop, J. (2012). Consistency of self-reported alcohol consumption on randomized and sequential alcohol purchase tasks. Frontiers in Psychiatry, 65(3), 1–6. Bechara, A. (2005). Decision making, impulse control and loss of willpower to resist drugs: A neurocognitive perspective. Nature Neuroscience, 8(11), 1458–1463. Beck, A., Schlagenhauf, F., Wustenberg, T., Hein, J., Kienast, T., Kahnt, T., . . . & Wrase, J. (2009). Ventral striatal activation during anticipation correlated with impulsivity in alcoholics. Biological Psychiatry, 66, 734–742. Bentzley, B. S., Fender, K. M., & Aston-Jones, G. (2013). The behavioral economics of drug self-administration: A review and new analytical approach for within-session procedures. Psychopharmacology, 226(1), 113–125. Bickel, W. K., Amass, L., Higgins, S. T., Badger, G. J., & Esch, R. A. (1997). Effects of adding behavioral treatment to opioid detoxification with buprenorphine. Journal of Consulting & Clinical Psychology, 65(5), 803–810. Bickel, W. K., Giordano, L. A., & Badger, G. J. (2004). Risk sensitive foraging theory elucidates risky choices made by heroin addicts. Addiction, 99(7), 855–861. Bickel, W. K., Jarmolowicz, D. P., MacKillop, J., Epstein, L. H., Carr, K., Mueller, E. T., & Waltz, T. (in press). The behavioral economics of reinforcement pathologies. In H. J.  Shaffer (Ed.), Addiction Syndrome Handbook. Washington, DC:  American Psychological Association.

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Bickel, W.  K., Jarmolowicz, D.  P., Mueller, E.  T., & Gatchalian, K.  M. (2011). The behavioral economics and neuroeconomics of reinforcer pathologies:  Implications for etiology and treatment of addiction. Current Psychiatry Reports, 13(5), 406–415. Bickel W. K., Jarmolowicz D. P., Mueller E. T., Koffarnus M. N., Gatchalian K. M. (2012). Excessive discounting of delayed reinforcers as a trans-disease process contributing to addiction and other disease-related vulnerabilities: emerging evidence. Pharmacol Ther. Jun;134(3):287–97. Bickel, W. K., Landes, R. D., Christensen, D. R., Jackson, L., Jones, B. A., Kurth-Nelson, Z., & Redish, A. D. (2011). Single- and cross-commodity discounting among cocaine addicts:  The commodity and its temporal location determine discounting rate. Psychopharmacology, 217(2), 177–187. Bickel, W. K. & Madden, G. J. (1999). A comparison of measures of relative reinforcing efficacy and behavioral economics: Cigarettes and money in smokers. Behavioural Pharmacology, 10, 627–637. Bickel, W. K., Marsch, L. A., & Carroll, M. E. (2000). Deconstructing relative reinforcing efficacy and situating the measures of pharmacological reinforcement with behavioral economics: A theoretical proposal. Psychopharmacology, 153(1), 44–56. Bickel, W. K., Miller, M. L., Yi, R., Kowal, B. P., Lindquist, D. M., & Pitcock, J. A. (2007). Behavioral and neuroeconomics of drug addiction: Competing neural systems and temporal discounting processes. Drug & Alcohol Dependence, 90S, S85–S91. Bickel, W.  K., Odum, A.  L., & Madden, G.  J. (1999). Impulsivity and cigarette smoking:  Delay discounting in current, never, and ex-smokers. Psychopharmacology, 146(4), 447–454. Bickel, W.  K., Yi, R., Landes, R.  D., Hill, P.  F., & Baxter, C. (2010). Remember the future:  Working memory training decreases delay discounting among stimulant addicts. Biological Psychiatry, 69(3), 260–265. Brigham, J., Gross, J., Stitzer, M. L., & Felch, L. J. (1994). Effects of a restricted work-site smoking policy on employees who smoke. American Journal of Public Health, 84(5), 773–778. Caraco, T., Martindale, S., & Whittam, T.  S. (1980). An empirical demonstration of risk-sensitive foraging preferences. Animal Behaviour, 28(3), 820–830. Carr, K. A., Daniel, T. O., & Epstein, L. H. (2011). Reinforcement pathology and obesity. Current Drug Abuse Reviews, 4(3), 190–196. Chiu, P. H., Lohrenz, T. M., & Montague, P. R. (2008). Smokers’ brains compute, but ignore, a fictive error signal in a sequential investment task. Nature Neuroscience, 11(4), 514–520. Cho, S. S., Ko, J. H., Pellecchia, G., Van Eimeren, T., Cilia, R., & Strafella, A. P. (2010). Continuous theta burst stimulation of right dorsolateral prefrontal cortex induces changes in impulsivity level. Brain Stimulation, 3(3), 170–176. Croxson, P. L., Walton, M. E., O’Reilly, J. X., Behrens, T. E., & Rushworth, M. F. (2009). Effort-based cost–benefit valuation and the human brain. Journal of Neuroscience, 29(14), 4531–4541. Dallery, J., & Glenn, I. M. (2005). Effects of an internet-based voucher reinforcement program for smoking abstinence:  A  feasibility study. Journal of Applied Behavior Analysis, 38(3), 349–357. Du, W., Green, L., & Myerson, J. (2002). Cross-cultural comparisons of discounting delayed and probabilistic rewards. Psychological Record, 52, 479–492. Epstein, L. H., Wright, S. M., Paluch, R. A., Leddy, J., Hawk, L. W., Jr., Jaroni, J. L., . . . & Lerman, C. (2004). Food hedonics and reinforcement as determinants of laboratory food intake in smokers. Physiology & Behavior, 81(3), 511–517.

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4

Genetic Influences on Addiction A M A N DA M .   BA R K L EY- L E V E N S ON A N D J O H N C .   C RA B B E

It is now accepted that addiction represents a disease state that arises from a complex combination of genetic and environmental contributions. Heritability estimates (i.e., the proportion of trait variability that can be attributed to genetic variation) for risk for abuse of different substances, based on twin studies, range from 0.39 for hallucinogens to 0.72 for cocaine (Goldman et al., 2005). Thus, genetic factors play a clear role in addiction. Research to date has made use of both human subjects and animal models of substance abuse in order to better understand which genes and genetically influenced traits might predispose an individual towards substance abuse and addiction. However, addiction and related behaviors are complex traits, and probably arise from a similarly complex interplay of numerous genes and environmental contributions. Although this chapter cannot address all the possible genetic influences on addiction, we will attempt to provide a broad overview of the field with a special focus on genes and traits that have shown strong concordance for their involvement in both the animal and human literatures. We also emphasize genetic factors that appear to predispose towards multiple addictions or polydrug abuse. Throughout, we direct the reader to relevant reviews of the extensive research literature.

METHODS FOR BEHAVIORAL GENETICS RESEARCH In order to examine genetic contributions to complex disorders such as addiction most effectively, a combination of human and animal research is useful. Each approach has its strengths and weaknesses, and it is the work of both of these fields in conjunction that provides strong evidence for genes that are involved and possible therapeutic strategies in the future.

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Research Strategies for Human Subjects In human research, studies are usually designed to assess correlation between a certain genotype and the trait of interest: often the trait is a categorical diagnosis of dependence. This is done using related individuals, as in a family study, or across a subset of the population, as with genome-wide association studies. The classic approach is the twin study (Boomsma et al., 2002). Twin studies assess trait concordance between pairs of monozygotic and dizygotic twins. All same-sex monozygotic twins are essentially genetically identical, while dizygotic twins share 50% of their genetic inheritance (as do siblings, and parent-offspring pairs). If one member of each twin pair in a sample has a certain trait (e.g., drug dependence), finding that monozygotic co-twin concordance is greater than dizygotic co-twin concordance provides evidence of genetic influences on the trait, as well as heritability estimates. Studies with related individuals can therefore allow us to differentiate genetic from environmental contributions to the trait, as well as evaluate the important role of gene–environment interactions. Cases where a risk-promoting gene exerts its deleterious effect only in the presence of specific environmental risk factors are probably the norm (for discussion, see Young-Wolff et al., 2011). Similar strategies can be applied with adoption studies, wherein trait concordance is compared between adopted children and their biological and adoptive parents, as well as between biological and adoptive siblings. Greater concordance between biological than adoptive relatives suggests genetic influences on the trait, whereas greater concordance among adoptive relatives would indicate a significant role for environmental factors. Adoption and twin study approaches can also be combined by studying twins raised apart (e.g., Kaplan, 2012; Kendler 2000). In the case of monozygotic twins raised apart, it is possible to measure trait concordance in genetically identical individuals raised in different environments. In principle, this experimental design combines the advantages of twin and adoption studies and allows for easier determination of genetic and environmental contributions to the trait of interest. A second approach often used in human studies is the candidate gene approach. With this experiment design, individuals with a given trait or diagnosis and healthy controls are tested for the presence or absence of a particular genetic variant of interest (i.e., the candidate gene). If a particular genetic variant is found more frequently in cases than controls, this suggests that this variant may be involved in the development of that trait or disorder. The candidate genes studied in addiction are often chosen because of their relationship to the pharmacological actions of the drug of interest. For example, because of cocaine’s actions on the dopamine system, many candidate gene studies for cocaine addiction have investigated the possible role of the genes encoding the dopamine receptors or dopamine transporter (e.g., Lohoff et al., 2010). Animal studies can also help inform the choice of candidate genes (see below) and often suggest novel genes and pathways to pursue in human research (Ermann & Glimcher, 2012). Population genetic variability can also be correlated with diagnosis (or any other addiction-relevant trait) without specifying any particular genes of interest. In such a genome-wide association study (GWAS), very large numbers of unrelated individuals are studied in a hypothesis-free approach that allows for identification of

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novel candidate genes associated with a given disease or trait (Cichon et al., 2009). GWAS are generally aimed at detecting simple, relatively common genetic variants known as single nucleotide polymorphisms (SNPs). SNPs consist of a change to the genetic code of only one nucleotide and can occur in coding or non-coding regions of genes, or in intergenic regions of DNA. While nonsynonymous SNPs (those that result in a change to the amino acid sequence of the gene product) are very likely to have functional significance, synonymous SNPs can also have effects via changes to mitochondrial RNA (mRNA) and protein folding (Hunt et al., 2009). The goal of a GWAS is to find SNPs that increase the risk of a particular disease, which are identified in the population by a statistical association between that SNP and the disorder. GWAS are best at detecting relatively common SNPs that presumably represent only a small part of the genetic risk for the disease of study. Rare genetic variant contributions are less likely to be identified by GWAS because there is little chance that they will be present with sufficient frequency in the sample population to show a statistically significant association with the trait. Despite this limitation, GWAS can be a useful tool to determine new candidate genes for further study. For all human-subjects methods discussed above, a symptom or trait-based approach may yield more informative results than a simple case-control design based solely on diagnosis. There can often be a great deal of phenotypic heterogeneity within a sample of individuals with a given disorder, particularly for psychiatric disorders where multiple permutations of symptoms can lead to the same DSM diagnosis. One approach to overcome this issue is to assess more specific aspects of the disorder, sometimes known as endophenotypes. First proposed by Gottesman and Shields in 1973, endophenotypes (also called intermediate phenotypes) are objective trait markers that are related to a disorder, but are less complex, both genetically and phenotypically. Useful endophenotypes for study should be heritable, should segregate with a disease and occur more frequently in non-affected family members than in the general population, and should be part of the causal mechanism of the disease (Gottesman & Gould 2003; Singh & Basu, 2009). Endophenotypes are presumably more proximal to the relevant genes and have fewer genetic determinants than the disorders they are related to, and the effect sizes of the genes involved are therefore likely to be larger (but see Flint & Munafò, 2007). Consequently, identifying SNPs and other genetic variants underlying these traits may prove easier than finding genes related to the overlying complex diagnoses. One other issue regarding human subjects is that most studies deal with individuals already experiencing addictive drug use. For such individuals, it is difficult to determine whether the neurobiological end points under investigation were antecedents of the addiction (risk factors), or whether they represent consequences of excessive drug use. This is an area where animal subjects are of great use, as described in the next section.

Research Strategies for Animal Subjects Animal research provides behavioral geneticists with invaluable techniques for studying genetic contributions to complex traits. These techniques are most

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widely used with rodents, but research is also done in species ranging from invertebrates to non-human primates. One common tool is inbred mouse and rat strains. An inbred strain is generated from repeated sibling matings, and because genetic variability decreases with each generation of brother–sister matings, all members of an inbred strain are presumed to be genetically identical. There are more than a hundred common inbred mouse strains, and many inbred rats as well. Using these strains, the effects of specific treatments or experimental manipulations can be assessed while controlling for the influence of genetic variation. Inbred strains also provide a way to assess environmental contributions and gene by environment interactions, since all the animals presumably share the same genetic makeup. Testing a panel of different inbred strains can be a useful means by which to estimate the heritability of a trait, as we would expect greater variation between strains than within them if genetics contribute to the trait. However, the testing environment can significantly affect behavior (Crabbe et al., 1999), and failure to maintain a consistent environment across animals will complicate the interpretation of inbred strain data, as strains can differ in their responses to specific environmental effects (Wahlsten et al., 2006). Inbred strains may also show innate behavioral differences that are relevant to a disease of interest. For example, the C57BL/6J (B6) and DBA/2J (D2) inbred mouse strains differ drastically for alcohol drinking, and have been subsequently widely studied as a way to examine genes affecting alcohol intake and other traits related to the rewarding effects of drugs (e.g., Fish et al., 2010; Fidler et al., 2006). Populations derived from inbred strains can be used for gene mapping studies to try to determine the location of chromosomal regions and specific genes that are involved. Selective breeding is another approach that allows the creation of novel genetic models of complex traits (Crabbe, 1989). The trait of interest (such as preference for alcohol) is measured in each generation, and high performers are bred with high performers and low performers with low. Over successive generations of this breeding scheme, if the trait is genetically influenced, the lines will diverge as mice within each line become more genetically similar for the trait-relevant genes. The phenotype of each line should strengthen over time, with the result (in this example) being a “low preference” line and a “high preference” line. These lines can then be used to assess other traits that may be under similar genetic control, as well as specific biological differences between the lines (Crabbe et al., 1990). Selected lines can also be used for gene-mapping studies of the trait (Grisel, 2000; Wehner et al., 2001). Other, more targeted genetic approaches can also be used (Fowler & Kenny, 2012). This includes the generation of transgenic animals that over-express a certain gene or express a particular mutation or version of the gene. Knockout animals can also be produced, which completely lack a gene. These animals, however, may have developmental compensations for the loss of this gene, which is completely absent from all tissues throughout development; consequently, there may be interpretational complications when using these animals. Newer methods allow for tissue-specific and temporally specific knockout or knockdown of gene expression, and these methods ameliorate the risk of developmental

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compensation. Though beyond the scope of this chapter, advances in the study of mechanisms affecting gene expression (e.g., epigenetic modifications, micro RNA) are likely to also prove important for determining the genetic basis of addictive disorders.

GENETIC RISK FACTORS FOR ADDICTION

Alcohol Alcohol has a complex pharmacology with widespread effects in the brain, and a similarly diverse number of genes have been implicated as possible contributors to the risk for alcoholism and alcohol use disorders (AUDs). Some of the earliest-examined genes are those involved in the metabolism of alcohol, particularly those that encode the enzymes that convert alcohol to acetylaldehyde (alcohol dehydrogenase, ADH) and acetylaldehyde to acetic acid (aldehyde dehydrogenase, ALDH). Certain polymorphisms reduce the efficiency of ALDH, which results in a buildup of acetylaldehyde and produces aversive effects such as flushing and nausea. Studies have shown that individuals heterozygous and homozygous for one of these polymorphisms in the ALDH genes have a significantly reduced risk of developing an AUD, most likely because of the aversive effects of even moderate drinking (see Agrawal and Bierut, 2012, for review). Implicated variants of the ADH genes also lead to an ultimate buildup of acetylaldehyde and produce effects similar to the ALDH polymorphisms. This line of research has contributed to the development of an effective pharmaceutical treatment for alcohol abuse, disulfiram (Antabuse®). Disulfiram is an ALDH–inhibitor, and alcohol intake during disulfiram treatment leads to the buildup of acetylaldehyde and subsequent negative physical effects. A recent meta-analysis showed that disulfiram treatment in a majority of the studies surveyed had a significantly better effect on abstinence outcomes than placebo or alternative treatments (Jørgensen et al., 2011). However, disulfiram’s actions are largely peripheral and probably do little to reduce alcohol cravings, which may contribute to issues related to poor compliance (e.g., Fuller et al., 1986). Some of the best-replicated single-gene influences on AUDs involve polymorphisms in the gene encoding the alpha-2 subunit of the gamma-aminobutyric acid (GABA) receptor A subtype, GABRA2 (for review, see Borghese & Harris, 2012). Alcohol is known to act at the GABA-A receptor, and multiple GWAS and candidate gene studies have indicated a relationship between AUDs and SNPs in GABRA2 in various populations. These SNPs are synonymous, but it is possible that they may alter the amount of protein that is produced. In animal studies, knock-in mice with an alcohol-insensitive version of the alpha-2 subunit show a reduction in sensitivity to alcohol’s aversive effects, as well as a total loss of alcohol-induced motor stimulation (Blednov et al., 2011). This provides further evidence for the potential role of variation in the alpha-2 subunit in AUDs. Genetic research into AUDs also provides one of the better success stories of translation from animal to human studies. Quantitative trait loci gene-mapping

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studies in inbred strains and selectively bred mice yielded a potential gene associated with alcohol-withdrawal severity, Mpdz (the human gene is written as MPDZ) (Shirley et al., 2004; Fehr et al., 2002). A good methodological review of such studies has been published, detailing the Mpdz story (Milner & Buck, 2010). This gene represented a novel potential target for study in relation to alcoholism, and consequently has been investigated in human studies. However, although genetic variation in MPDZ in humans is related to alcohol intake, it has not yet been implicated in human alcohol-withdrawal severity (Karpyak et  al., 2009). This highlights a challenge: animal studies may provide new targets for research, but the relationship between genes and behavior may not be one-to-one when translating across species.

Nicotine The most widely studied genes in relation to nicotine dependence are those that encode the nicotinic acetylcholinergic receptors, or CHRNs. Nicotinic acetylcholinergic receptors are made up of five subunits, and a gene cluster encoding the alpha-5, alpha-3, and beta-4 subunits (referred to as CHRNA5-A3-B4) has been particularly strongly implicated in nicotine dependence and related behaviors (Maes et  al., 2011; Berrettini & Doyle, 2012; Ware et  al., 2012). Both twin studies and GWAS have found a link to one or more of the genes in this cluster, particularly in relation to cigarettes smoked per day (see Berrettini & Doyle, 2012, for review). Variants of these genes do seem to have functional significance, with polymorphisms probably affecting receptor modification and sensitization/ desensitization. Though not as well replicated, on a different chromosome, variants in CHRNA4 (encoding the alpha-4 subunit) may also be related to nicotine dependence (Breitling et  al., 2009). A  meta-analysis of linkage studies found a relationship between SNPs in the region encoding this gene and maximum number of cigarettes smoked per day (Han et  al., 2011). Varenicline, an approved smoking-cessation drug, is a partial agonist for alpha-4-beta-2 receptors, and various CHRNA4 SNPs have been associated with its efficacy (King et al., 2012). It should be noted that the most robust human genetics findings for the CHRN genes come from Caucasian study populations, and the risk-conveying allele for the CHRNA5 SNP is relatively rare in Asian and African populations (Berrettini & Doyle, 2012). This highlights the need to study a wide variety of populations in order to best understand the genetic risk for addiction. Additionally, some of the risk alleles in the CHRNA5-A3-B4 cluster have also been implicated in alcohol abuse, indicating a possible common pathway underlying multiple addictions (Ware et al., 2012). Studies in animal models have also suggested a role for the CHRN genes in nicotine-related behaviors. Over-expression of the CHRNA5-A3-B4 gene cluster in mice leads to increased nicotine sensitivity and enhanced acquisition of nicotine self-administration, as well as reduced dopaminergic neuron activation in the ventral tegmental area after acute nicotine administration (Gallego et al., 2012). Interestingly, mice lacking CHRNA5 also will self-administer greater quantities

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of nicotine than wild-type mice (Fowler et  al., 2011). A  study with engineered mice provided evidence that alpha-4 subunit-containing receptors are involved in nicotine reward, as well as tolerance and sensitization following chronic administration (Tapper et  al., 2004). It has been speculated that the alpha-5 subunit, and in particular its expression relative to the beta-4 subunit, may be important in mediating sensitivity to the aversive effects of nicotine (Frahm et  al., 2011). Consequently, changes in the balance between these subunits through either over-expression or knockout might lead to enhanced nicotine self-administration due to decreased drug aversion. The risk allele for human CHRNA5 is partially deficient in signaling and also appears to attenuate nicotine aversion in people with this genotype. Thus, convergent data from the human and animal literatures implicate variation in the CHRN receptor subunit genes as a potential risk for nicotine dependence and related behaviors.

Psychostimulants Abused psychostimulants include cocaine, amphetamines, methamphetamine, and methamphetamine-like compounds such as MDMA (Ecstasy). Of these substances, cocaine shows the greatest genetic risk for addiction. The most widely studied genes for stimulant abuse are those in the dopaminergic system, particularly genes encoding dopamine receptors and the dopamine transporter. Given the likely involvement of mesolimbic dopamine transmission in a variety of addictions, these genes have also been studied in relation to many abused substances. Behavior genetic animal studies of stimulant abuse initially focused heavily on dopaminergic genes and the genes coding for the monoamine transporters (Sora et al., 2010). The rewarding effects of cocaine were believed to be due to its blockade of the dopamine transporter, which prolongs dopamine action at the synapse. However, studies of dopamine transporter knockout mice demonstrated that these animals still found cocaine rewarding. This led to an increased interest in the role of other transporters such as the serotonin and norepinephrine transporters. Experiments with serotonin and norepinephrine transporter knockout mice, as well as combinations of multiple knockouts of the monoamine transporters, suggested that multiple transporters are normally involved in cocaine reward and that significant developmental compensations occur in knockout animals missing one or more transporters. Knocking out various dopamine and serotonin receptors has produced mixed effects on cocaine reward and self-administration. Dopamine D2 receptors, however, have shown evidence of a relationship to psychostimulant reward. D2-like agonists are self-administered by rats, and antagonists thought to be selective for these receptors can attenuate cocaine reward (Caine et  al., 1999, 2002). D2 knockout mice, however, show only limited reduction in cocaine self-administration. This suggests that, similar to the monoamine transporter knockout studies, there may be developmental compensations in the knockout mice and the involvement of multiple receptors in the wild-type animals. In humans, a SNP causing a nonsynonymous coding change in the gene encoding the D2 receptor (DRD2) has been widely studied in relation to drug abuse.

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This polymorphism, known as Taq1A, may result in changes in substrate binding of the resulting receptor (Thompson et al., 1997). Association studies of this SNP with psychostimulant abuse have yielded mixed results. Initial reports showed an association of a specific allele in cocaine-dependent subjects, but additional studies of different populations and of methamphetamine abusers have not shown a significant relationship (for review, see Le Foll et  al., 2009). A  meta-analysis of association studies for this polymorphism and stimulant abuse showed no overall association and a high degree of heterogeneity across studies (Gorwood et  al., 2012). However, the Taq1A polymorphism has also occasionally been associated with alcohol, nicotine, and opiate abuse, and it has been suggested that it may predispose towards substance abuse in general. Nonetheless, the association with alcohol dependence also has not fared well in meta-analyses (Munafò et al., 2007). Polymorphisms in the genes coding for the dopamine D1, D3, and D4 receptors have all been studied as well for an association with stimulant abuse. Results again have been mixed, depending on the experiment (Le Foll et al., 2009). A 2008 meta-analysis of linkage and association studies of methamphetamine dependence and related traits showed a significant relationship between polymorphisms in the D4 receptor gene and methamphetamine abuse (Bousman et al., 2009). A polymorphism in the catechol-O-methyltransferase (COMT) gene, which codes for an enzyme involved in dopamine metabolism, was also shown to be associated with methamphetamine abuse. COMT has been implicated in cocaine-induced paranoia as well (Ittiwut et al., 2011). COMT variants have also been shown to influence susceptibility to MDMA-induced hyponatremia, and to interact with heavy MDMA use to produce cognitive deficits. Thus, there appears to be some overlap for the genes conferring risk for psychostimulant abuse. However, many human genetic associations have been difficult to replicate, and no single allele seems to have a consistent significant effect on stimulant use and abuse.

Opioids The best-studied genetic contributions to opioid addiction are SNPs in the μ-opioid receptor gene (OPRM1). The μ-opioid receptor is known to mediate in part the rewarding effects of multiple drugs of abuse as well as natural reinforcers (Le Merrer et al., 2009). In humans, there is a common missense variant that causes an A-G substitution and is thought to have potential functional consequences, such as lower mRNA and protein expression, and reduced receptor-binding potential (Beyer et al., 2004; Zhang et al., 2005; Ray et al., 2011). An equivalent mutation in the mouse gene results in loss of function, as seen with lower mRNA and protein levels (Mague et al., 2009). These mice also have reduced locomotor activation and anti-nociception in response to morphine treatment. In human studies, diverse populations have shown an association between OPRM1 SNPs and opioid dependence. A  significant association was found in both Swedish (Bart et al., 2004) and Hong Kong Chinese heroin addicts (Szeto et al., 2001), as well as in opioid-dependent individuals in a continental Indian population (Kapur et  al., 2007). However, as with many potential single-gene

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effects, results have been mixed, and negative results have been obtained as well (e.g., Franke et al., 2001; Glatt et al., 2007; Levran et al., 2008). The μ-opioid receptor also represents a target for pharmacological treatments of opioid and other substance abuse. Naltrexone and buprenorphine are two current drugs for opioid dependence that target this receptor. Naltrexone is a non-selective opioid receptor antagonist, and it is used in the treatment of opioid abuse as well as in AUDs and nicotine dependence (Ross & Peselow, 2009). Buprenorphine is a partial agonist for the μ-opioid receptor and is used as a maintenance treatment for opioid dependence (Bart, 2012). In addition to OPRM1, a possible role for prodynorphin has also been investigated in opioid abuse. Prodynorphin is the precursor for dynorphins, endogenous ligands of the κ-opioid receptor. These peptides can decrease both basal and drug-induced dopamine levels in addiction-related brain regions (Bruijnzeel, 2009). Association studies of SNPs in the prodynorphin gene (PDYN) have been carried out for various substance abuse disorders, including opiate addiction. A  SNP in the PDYN promoter was found to be weakly associated with opioid dependence in African Americans, but not European Americans (Ray et al., 2005). A study of a SNP in the PDYN gene itself, however, found an association with opioid addiction in European Americans, but not African Americans (Clarke et al., 2012). A study in a Han Chinese population found an association between SNPs in both PDYN and its promoter and heroin addiction (Wei et al., 2011). The μ-opioid receptor also helps mediate the response to multiple other drugs of abuse (e.g., alcohol, nicotine). Consequently, SNPs in the OPRM1 represent another potential convergent genetic pathway for substance abuse. Similarly, PDYN SNPs have shown some association with alcohol and cocaine dependence. As with DRD2, the opioid system is heavily involved in mediating rewarding and other effects of multiple abused drugs, and genetic variation in this system is likely to be involved with addiction liability for a variety of drugs and behavioral addictions.

Cannabis The genetics underlying cannabis use have not yet been thoroughly explored. Twin studies suggest that 40%–48% of variance in cannabis use initiation is accounted for by shared genes, while 51%–59% of the variance in problematic use is genetic (Verweij et  al., 2010). There has been some investigation of the variants in the cannabinoid receptor 1 gene and the fatty acid amide hydrolase gene in relation to cannabis dependence symptoms (see Agrawal & Lynskey, 2009, for review). Some evidence suggests cannabinoid receptor 1 gene SNPs are associated with cannabis-related problems and heavier use. However, results are mixed, and most of the limited number of existing association studies used an outcome measure of general substance abuse rather than cannabis-specific symptoms. Fatty acid amine hydrolase is involved in the metabolism of endogenous cannabinoids, and has consequently been a target of study. A missense mutation in the promoter region of fatty acid amide hydrolase gene has repeatedly shown

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association with drug and alcohol abuse, but individuals homozygous for the risk allele actually show decreased likelihood of cannabis dependence (Tyndale et al., 2007). Attempts have also been made to find an association with GABRA2, with varying levels of success (Lind et  al., 2008; Agrawal et  al., 2006; Corley et al., 2008). However, this does suggest a potential genetic overlap with risk for alcohol abuse. Overall, many more studies are needed, and translation from the growing animal literature will probably help inform us of other potential genes to examine in human populations.

Behavioral Addictions There has been increasing interest in studying non-drug addictions, sometimes referred to as “behavioral addictions.” Potential behavioral addictions include a range of behaviors such as sex, eating, shopping, gambling, and computer/ Internet use. The most well-studied of these by far is disordered gambling. The heritability estimate for gambling addiction is approximately 0.5 (Goldman et al., 2005). Candidate gene studies have suggested a role for variants in the dopamine D1 receptor gene. The Taq1A polymorphism of the DRD2 gene has also been associated with problem gambling (Comings et al., 1996), though this association is not found in all studies (da Silva Lobo et al., 2007). Pathological gambling may share many features with risky and impulsive decision making (see below), and genetic research into these latter behaviors could shed further light on candidate genes for gambling disorders. In general, much more exhaustive study will be needed to determine specific and replicable genetic contributions to gambling addiction. Compulsive eating is another area where there is growing interest in research. It is unknown to what extent compulsive eating and overeating are heritable. However, studies of compulsive eating suggest that some of the same brain systems may underlie both this behavior and drug addiction. In particular, the reward system circuitry appears to be relevant to both substance abuse and compulsive eating. Dopamine release from the nucleus accumbens and ventral tegmental area, like that seen with drug use, occurs with ingestion of palatable foods (see Frascella et al., 2010, for review). Consequently, reward-related genes such as DRD2 and OPRM1 are likely to be associated with compulsive eating and food addiction. However, there may also be genes more specific to ingestive behaviors and energy balance that prove crucial for promoting disordered food consumption. GENETICS OF ENDOPHENOTYPES AND PREDISPOSING TRAITS As mentioned earlier, the phenotypic and genetic complexity of substance abuse disorders can make research into their genetic origins highly challenging. Consequently, one popular avenue of research is to focus on endophenotypes for substance abuse, as well as behavioral and personality traits that may be predisposing towards addiction in general. Here we describe some of the research into the genetics underlying these intermediate phenotypes.

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Impulsivity and Conduct Disorder Trait impulsivity and conduct disorder have been frequently associated with a variety of substance abuse disorders. In the case of impulsivity, it is unclear whether impulsive behavior increases the likelihood of developing substance abuse, whether substance abuse enhances impulsive choices, or both. Behaviorally, impulsivity has been demonstrated repeatedly to co-occur with substance abuse. In a delay-discounting task, cocaine and methamphetamine users show steeper discounting than controls, indicating a preference for smaller immediate rewards over larger delayed rewards. Similar deficits are also seen with alcohol abusers, MDMA users, and current smokers (see Verdejo-Garcia et al., 2008, for review). This suggests that trait impulsivity is highly connected to substance abuse and addiction. A  limitation of these studies, however, is that the individuals tested are already substance users. This makes it difficult to parse the directional relationship between substance abuse and impulsive decision making. Animal studies allow the use of naïve subjects, and these experiments further support a connection between impulsivity and substance use. For example, naïve animals from mouse and rat lines selected for high alcohol drinking show greater impulsive behavior on delay discounting and behavioral inhibition tasks than animals selected for low drinking (e.g., Steinmetz et al., 2000; Wilhelm & Mitchell, 2008; Oberlin & Grahame, 2009). The dopaminergic and serotonergic systems are thought to be involved in impulsivity (Pattij & Vanderschuren, 2008), and genes from these systems are frequent candidates for study in the genetics of impulsive choice. A polymorphism in the dopamine transporter gene that is thought to decrease dopamine availability in the striatum has been shown to be associated with increased risky decision making on a measure of impulsivity (Mata et al., 2012). A SNP in the serotonin 5-HT2A receptor gene has also been associated with poorer performance on a behavioral inhibition task (Nomura et al., 2006). In general, more replication is needed in genetic studies of impulsivity, as well as systematic meta-analysis of the existing literature. Also, impulsivity is itself a complex trait, and can be broadly differentiated into multiple types. While some aspects of impulsivity as assessed in humans can be translated fairly directly into rodent behaviors (e.g., delay discounting, motor response inhibition), others are more difficult to model (e.g., lack of planning; for review, see Dick et al., 2010). Like impulsivity, conduct disorder is frequently associated with substance abuse, and genes associated with conduct disorder might also be related to substance abuse. This evidence of shared underlying genetics reinforces the idea that there might be shared risk for these behaviors and addiction. It is also possible that substance abuse and conduct disorder may serve to reinforce one another reciprocally. As with impulsivity, dopamine-related genes have been frequently studied in relation to conduct disorder. Particular attention has been paid to the Taq1A SNP of DRD2. One association study in adolescents examined the Taq1A SNP and conduct disorder, impulsivity, and substance abuse. The “risk” allele of Taq1A was found to have a mediating relationship with conduct disorder/impulsivity and substance abuse, such that carriers of

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this allele who also had conduct disorder or impulsive behavior were also more likely to have problematic alcohol or drug use (Esposito-Smythers et al., 2009). Unlike for impulsive behavior, there are no obvious rodent animal models for conduct disorder.

Novelty- and Sensation-Seeking Novelty- and sensation-seeking have also been shown to be related to substance use disorders (Roberti, 2004; Milivojevic et al., 2012). Novelty/sensation-seeking is related to impulsivity, though these behaviors do not necessarily require impulsive choices. Individuals who are high novelty/sensation-seekers tend to look for new and stimulating experiences and are prone to boredom when not in these types of situations. There has been some speculation that these individuals are therefore more likely to experiment with drugs, and thus are more likely to develop dependencies (Zuckerman, 1991). It is also possible, as with impulsivity, that similar genetics underlie novelty/sensation-seeking and susceptibility to drug abuse. Multiple studies have found some evidence supporting an association between polymorphisms in the dopamine D4 receptor gene and novelty-seeking in populations without psychiatric diagnoses (e.g., Munafò et al., 2008; Schinka et al., 2002; Okuyama et al., 2000). A study of substance abusers found an association between substance use and novelty-seeking, but was unable to demonstrate an association with a particular dopamine D4 receptor gene allele (Gelernter et al., 1997). Animal studies are able to provide further evidence for the involvement of the dopamine system in novelty-seeking. Rat lines selected for high and low avoidance behavior show differences in measures of novelty-seeking and also differ in dopamine release from the nucleus accumbens after acute treatment with morphine, cocaine, or amphetamine (Giorgi et al., 2007). This enhanced mesolimbic dopaminergic activity is also seen in a different rat model of sensation-seeking, as well as in sensation-seeking humans (see Blanchard et al., 2009, for review). This underlying difference in dopamine activity might help explain the relationship between novelty-seeking and substance abuse.

Electrophysiological and Brain Imaging Markers Certain patterns of electrophysiological brain activity show a significant degree of heritability and have also been found to be reliable endophenotypes for substance use disorders (Ceballos et al., 2009). In normal brain functioning, different frequencies of oscillations in brain signals have been associated with different sensory and cognitive processes. During a resting state, for example, oscillations in electroencephalogram (EEG) patterns probably reflect intrinsic brain activity and maintenance of brain networks. Features of these oscillations have been repeatedly associated with substance abuse, particularly AUDs (Porjesz & Begleiter, 2003). For example, there are several EEG patterns that are seen frequently with both alcoholics and at-risk individuals with a family history of alcoholism (see

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Rangaswamy & Porjesz, 2008, for review). Experiments in animal models of alcohol consumption have shown similar electrophysiological differences between naïve mice from high- and low-drinking genetic backgrounds as those seen in humans with high and low risk for developing alcoholism (Criado & Ehlers, 2009). These oscillations, therefore, appear to be state-independent (i.e., they are present even when an AUD is not) and related to risk for alcohol abuse. In cocaine abusers, several EEG features have been found to predict treatment outcomes and particularly time spent in treatment (Prichep et al., 1999, 2002). Rigorous genetic analyses of electrophysiological markers are still in their early stages. However, there is evidence to suggest the involvement of several genes, including the alcoholism-implicated gene GABRA2 (Rangaswamy & Porjesz, 2008). SNPs in glutamate, serotonin, and corticotropin-releasing hormone receptor genes have also been associated with various EEG features and alcohol dependence (Chen et al., 2009, 2010; Zlojutro et al., 2011). These abnormal EEG patterns presumably reflect underlying differences in brain functioning, and electrophysiological endophenotypes are therefore likely to have functional significance that may contribute to the risk of developing an AUD. More recently, brain imaging techniques have been increasingly common, and functional MRI (fMRI) studies have offered some ability to connect behavioral endophenotypes and genetic risk for substance abuse. Activity in the frontal lobes during a verbal working memory task was found to differ in adolescents who were positive or negative for a family history of alcohol (Cservenka et al., 2012). Using a combination of frontal connectivity MRI to assess brain activity and diffusion tensor imaging to reveal brain structure, Herting and colleagues showed unusual functional and structural connectivity in drug-naïve adolescents with a positive family history compared to those with a negative family history (Herting et al., 2011). Other imaging techniques such as magnetic resonance spectroscopy have also found metabolic abnormalities in the brains of abstinent alcoholics and polysubstance abusers (Abe et al., 2012). In the case of nicotine dependence, a variant of the CHRNA5 gene has been associated with decreased resting functional connectivity in the dorsal anterior cingulate-ventral striatum/extended amygdala circuit. This relationship is observed in both smokers and non-smokers, though circuit strength is able to distinguish these groups and predict addiction severity in smokers (Hong et al., 2010).

Drug Cue Reactivity and Attentional Bias Previous research has shown that drug-dependent individuals demonstrate greater reactivity to drug-related cues than to neutral stimuli, and also show greater cue reactivity than non-dependent control subjects (Yalachkov et  al., 2012). Cue reactivity can be determined through measures of physiological arousal and self-reported craving, or it can be assessed using brain-imaging techniques to identify region-specific activation in response to cue presentation. Activation-based measures of cue reactivity are associated with increased self-reported craving (e.g., Goudriaan et al., 2010), and both have been shown

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to predict future relapse (e.g., Fatseas et  al., 2011; Janes et  al., 2010). There is some evidence for genetic contributions to individual differences in cue reactivity in various substance abuse disorders. For example, non-dependent drinkers who are family history–positive for an AUD have been shown to have greater brain activation in response to visual and olfactory alcohol cues than individuals who are family history–negative (Kareken et al., 2010; Dager et al., 2013). Studies of smoking cue reactivity have indicated possible roles for SNPs in the CHRNA5, dopamine transporter, and dopamine D4 receptor genes (Janes et al., 2012; Franklin et al., 2009; McClernon et al., 2007). In a study of cocaine-dependent individuals, cue reactivity was associated with a GABRA2 SNP, whereas a SNP in OPRM1 was found to be protective against cue-elicited craving (Smelson et al., 2012). Enhanced attentional bias to drug cues is a specific form of cue reactivity wherein attention is preferentially paid to drug-related stimuli over neutral stimuli. This shift in focus can be demonstrated by the ability of drug cues to interfere with performance on attention tests such as the visual dot-probe and Stroop tasks. Attentional bias for drug cues is frequently seen in substance use disorders and may represent differences in the cognitive processing of drug-related stimuli in addicted versus non-addicted individuals (Field & Cox, 2008). There is a limited number of studies exploring the genetics of attentional bias in addiction, but existing studies do suggest some genetic influence. Nicotine-naïve children of parents who smoke, for example, show greater attentional bias for smoking stimuli than children of non-smokers (Lochbuehler et al., 2012). As with smoking cue reactivity, attentional bias to smoking cues has been associated with variation in the dopamine transporter gene (Wetherill et al., 2012). The relationship between alcohol intake and attentional bias for alcohol cues has been shown to be mediated by variants in the OPRM1 and dopamine D4 receptor genes in adolescent and young adult drinkers, respectively (Pieters et al., 2011).

Cognition and Executive Functions Deficits in cognitive abilities, and particularly executive functions, have been identified in many substance abuse disorders (e.g., van der Plas et  al., 2009). It has been postulated that these deficits arise from the drug use itself and are due to neurobiological changes caused by repeated exposure as well as cycles of dependence and withdrawal (Vik et al., 2004). However, there is some evidence that genetics also contribute to these deficits, though research in this area is limited. Most of the evidence for a genetic link to cognitive impairment in substance abuse comes from studies of alcoholics and their offspring. Children of alcoholics have been shown to perform worse on tasks of cognitive and executive functions than children with no family history of AUDs (Díaz et al., 2008; Nigg et al., 2004; Gierski et al., 2013), though this group difference is not always observed (Ozkaragoz et al., 1997). The relationship between familial alcoholism and cognitive function appears to be mediated in part by factors such as familial

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density of AUDs (Corral et al., 1999) and presence of antisocial personality disorder (Poon et al., 2000). These studies suggest that cognitive impairment does not stem entirely from alcohol use and might in fact precede AUDs as a contributor to abuse liability. The genetics of amphetamine-induced changes in cognitive functions have also been examined. There is some evidence that COMT genotype can affect performance on measures of executive function after treatment with amphetamine (Mattay et al., 2003), but other studies have failed to replicate this effect (Wardle et al., 2013). Some candidate genes have been studied in relation to impairment in cognitive and executive functions in methamphetamine dependence as well, though these studies have largely been restricted to HIV-infected methamphetamine users. In this population, however, there is some evidence that polymorphisms in the dopamine D3 receptor and COMT genes may affect the relationship between methamphetamine use and deficits in cognitive and executive function (Gupta et al., 2011; Bousman et al., 2010). Genetic variance in metabolic clearance of methamphetamine might also help explain individual differences in use-associated cognitive deficits. Specifically, methamphetamine users with higher cytochrome P450-D6 activity (and therefore faster metabolic clearance of methamphetamine) have been shown to have worse performance on cognitive tasks than slower metabolizers (Cherner et al., 2010). GENETICS OF TREATMENT RESPONSE One avenue of genetic research that may prove especially valuable for translation to the clinic is determining subpopulations of addicted individuals for whom particular pharmacotherapies are especially effective. The genetics of differential response to existing treatments for addiction are not yet well understood, but there do appear to be genetic factors that increase or decrease the efficacy of some addiction treatments. For example, certain OPRM1 SNPs increase the likelihood of abstinence with naltrexone treatment in alcoholics (Sturgess et al., 2011). Similarly, some variants of CHRN genes have been reported to influence the success of smoking cessation after various treatments (see Gold & Lerman, 2012, for review). Variants of cytochrome P450 enzymes have been seen to have different effects on the dose needed in methadone-maintenance treatment of heroin addiction. Some variants are associated with need for an increased maintenance dose, while others are associated with the need for lower doses (Kreek et al., 2012). Ultimately, a better understanding of genetic variants that underlie treatment response will enable the classification of subtypes of addicted individuals and the ability to customize the most efficacious treatment strategy possible based on genotype. CONCLUSION Addiction is a complex disorder with a similarly complex etiology. Both genetic and environmental factors, as well as their interactions, are crucial for determining risk of developing a substance use disorder. Though there are probably some drug-specific genes that are related only to certain substance abuse disorders,

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many genes have been found to be associated with risk of addiction in general. This suggests that there are likely to be some converging genetic risk factors for addiction across a variety of substances and behavioral addictions. Although numerous genes have been investigated for their association with substance abuse, there remains much that is not known about the genetics underlying these disorders. A  continued effort to translate findings between human and animal studies may help pave the way for greater discoveries in the future. ACKNOWLEDGMENT Preparation of this chapter was supported by NIH-NIAAA grants AA13519, AA10760, AA20245, a grant from the US Department of Veterans Affairs, and USAMRMC Grant 10234005.05. AMB-L was supported by an Oregon Health & Science University Graduate Research Scholar award. References Abe, C., Mon, A., Durazzo, T. C., Pennington, D. L., Schmidt, T. P., & Meyerhoff, D. J. (2012). Polysubstance and alcohol dependence:  Unique abnormalities of magnetic resonance-derived brain metabolite levels. Drug & Alcohol Dependence [Epub ahead of print]. doi:10.1016/j.drugalcdep.2012.10.004. Agrawal, A., & Bierut, L. J. (2012). Identifying genetic variation for alcohol dependence. Alcohol Research, 34, 274–281. Agrawal, A., Edenberg, H.J., Foroud, T., Bierut, L.J., Dunne, G., Hinrichs, A.L., Nurnberger, J.I., Crowe, R., Kuperman, S., Schuckit, M.A., Begleiter, H., Porjesz, B., & Dick, D.M. (2006). Association of GABRA2 with drug dependence in the collaborative study of the genetics of alcoholism sample. Behavior Genetics, 36 (5):640–650. Agrawal, A., & Lynskey, M.  T. (2009). Candidate genes for cannabis use disorders: Findings, challenges and directions. Addiction, 104, 518–532. Bart, G. (2012). Maintenance medication for opiate addiction: The foundation of recovery. Journal of Addictive Diseases, 31, 207–225. Bart, G., Heilig, M., LaForge, K.  S., Pollak, L., Leal, S.  M., Ott, J., & Kreek, M.  J. (2004). Substantial attributable risk related to a functional mu-opioid receptor gene polymorphism in association with heroin addiction in central Sweden. Molecular Psychiatry, 9, 547–549. Berrettini, W. H., & Doyle, G. A. (2012). The CHRNA5-A3-B4 gene cluster in nicotine addiction. Molecular Psychiatry, 17, 856–866. Beyer, A., Koch, T., Schröder, H., Schulz, S., & Höllt, V. (2004). Effect of the A118G polymorphism on binding affinity, potency and agonist-mediated endocytosis, desensitization, and resensitization of the human mu-opioid receptor. Journal of Neurochemistry, 89, 553–560. Blanchard, M. M., Mendelsohn, D., & Stamp, J. A. (2009). The HR/LR model: Further evidence as an animal model of sensation seeking. Neuroscience Biobehavioral Review, 33, 1145–1154. Blednov, Y.  A., Borghese, C.  M., McCracken, M.  L., Benavidez, J.  M., Geil, C.  R., Osterndorff-Kahanek, E., & Harris, R.A. (2011). Loss of ethanol conditioned taste aversion and motor stimulation in knockin mice with ethanol-insensitive α2-containing GABA(A) receptors. Journal of Pharmacology & Experimental Therapeutics, 336, 145–154.

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Corley, R.P., Zeiger, J.S., Crowley, T., Ehringer, M.A., Hewitt, J.K., Hopfer, C.J., Lessem, J., McQueen, M.B., Rhee, S.H., Smolen, A., Stallings, M.C., Young, S.E., & Krauter, K. (2008). Association of candidate genes with antisocial drug dependence in adolescents. Drug and Alcohol Dependence, 96 (1–2):90–98. Corral, M. M., Holguín, S. R., & Cadaveira, F. (1999). Neuropsychological characteristics in children of alcoholics:  Familial density. Journal of Studies on Alcohol, 60, 509–513. Crabbe, J.  C. (1989). Genetic animal models in the study of alcoholism. Alcoholism, Clinical & Experimental Research, 13, 120–127. Crabbe, J. C., Phillips, T. J., Kosobud, A., & Belknap, J. K. (1990). Estimation of genetic correlation: Interpretation of experiments using selectively bred and inbred animals. Alcoholism, Clinical & Experimental Research, 14, 141–151. Crabbe, J.  C., Wahlsten, D., & Dudek, B.  C. (1999). Genetics of mouse behavior: Interactions with laboratory environment. Science, 284, 1670–1672. Criado, J. R., & Ehlers, C. L. (2009). Event-related oscillations as risk markers in genetic mouse models of high alcohol preference. Neuroscience, 163, 506–523. Cservenka, A., Herting, M. M., & Nagel, B. J. (2012). Atypical frontal lobe activity during verbal working memory in youth with a family history of alcoholism. Drug & Alcohol Dependency, 123, 98–104. Dager, A.  D., Anderson, B.  M., Stevens, M.  C., Pulido, C., Rosen, R., Jiantonio-Kelly, R. E., & Pearlson, G.D. (2013). Influence of alcohol use and family history of alcoholism on neural response to alcohol cues in college drinkers. Alcoholism, Clinical & Experimental Research, 37 Suppl 1, E161–E171. Díaz, R., Gual, A., García, M., Arnau, J., Pascual, F., Cañuelo, B., . . . & Garbayo, I. (2008). Children of alcoholics in Spain: From risk to pathology. Results from the ALFIL program. Society of Psychiatry & Psychiatric Epidemiology, 43, 1–10. Dick, D. M., Smith, G., Olausson, P., Mitchell, S. H., Leeman, R. F., O’Malley, S. S., & Sher, K. (2010). Understanding the construct of impulsivity and its relationship to alcohol use disorders. Addiction Biology, 15, 217–226. Ermann, J., & Glimcher, L. H. (2012). After GWAS: Mice to the rescue? Current Opinion in Immunology, 24, 564–570. Esposito-Smythers, C., Spirito, A., Rizzo, C., McGeary, J.  E., & Knopik, V.  S. (2009). Associations of the DRD2 TaqIA polymorphism with impulsivity and substance use:  Preliminary results from a clinical sample of adolescents. Pharmacology, Biochemistry, & Behavior, 93, 306–312. Fatseas, M., Denis, C., Massida, Z., Verger, M., Franques-Rénéric, P., & Auriacombe, M. (2011). Cue-induced reactivity, cortisol response and substance use outcome in treated heroin dependent individuals. Biological Psychiatry, 70, 720–727. Fehr, C., Shirley, R. L., Belknap, J. K., Crabbe, J. C., & Buck, K. J. (2002). Congenic mapping of alcohol and pentobarbital withdrawal liability loci to a 11 C]raclopride positron emission tomography study. Biological Psychiatry, 71(8), 677–683. Vadhan, N. P., Hart, C. L., van Gorp, W. G., Gunderson, E. W., Haney, M., & Foltin, R. W. (2007). Acute effects of smoked marijuana on decision making, as assessed by a modified gambling task, in experienced marijuana users. Journal of Clinical & Experimental Neuropsychology, 29(4), 357–364. Van Sickle, M. D., Duncan, M., Kingsley, P. J., Mouihate, A., Urbani, P., Mackie, K., . . . & Sharkey, K. A. (2005). Identification and functional characterization of brainstem cannabinoid CB2 receptors. Science Signaling, 310, 329. Van Ours, J. C. (2006). Cannabis, cocaine and jobs. Journal of Applied Econometrics, 21, 897–917. Vanyukov, M. M., & Tarter, R. E. (2000). Genetic studies of substance abuse. Drug & Alcohol Dependence, 59(2), 101–123. Varma, V. K., Malhotra, A. K., Dang, R., Das, K., & Nehra, R. (1988). Cannabis and cognitive functions: a prospective study. Drug & Alcohol Dependence, 21(2), 147–152. Verdoux, H., Gindre, C., Sorbara, F., Tournier, M., & Swendsen, J. D. (2003). Effects of cannabis and psychosis vulnerability in daily life: an experience sampling test study. Psychological Medicine, 33(01), 23–32. Wade, D. T., Makela, P., Robson, P., House, H., & Bateman, C. (2004). Do cannabis-based medicinal extracts have general or specific effects on symptoms in multiple sclerosis? A double-blind, randomized, placebo-controlled study on 160 patients. Multiple Sclerosis, 10(4), 434–441.

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Cocaine A N TO N I O V E R D E J O - G A R C Í A

EPIDEMIOLOGY AND PSYCHOSOCIAL CORRELATES After cannabis, cocaine is the most frequently used illicit drug in Western societies (European Monitoring Centre for Drugs and Drug Abuse [EMCDDA] Annual Report, 2012; United States National Survey on Drug Use and Health [NSDUH] Annual Report 2011). In the European Union, 6% of the total population between 18 and 35  years of age (8  million youths) have used cocaine at least once in their life, and these figures are higher in Canada (9%) and the United States (16%) (EMCDDA Annual Report, 2012). Moreover, cocaine is frequently used in combination with other drugs; mostly alcohol, but also cannabis, other stimulants, and heroin (EMCDDA, 2012). Notwithstanding its prevalent use, cocaine is a powerfully addictive drug associated with substance dependence and a number of physical, neurological, mental health, and psychosocial harms (Chen et al., 2011; Cunha et al., 2011). Not surprisingly, it is also one of the main drugs motivating treatment requests in Europe (70,000 new requests, 15% of all treatment demands; EMCDDA, 2012) and the United States (0.5 million new demands in the last year; NSDUH, 2011). During the past decade, mounting neuroscientific evidence has revealed that cocaine use is associated with significant alterations in different brain areas, including prefrontal, temporal, limbic, striatal, and cerebellar regions (Goldstein & Volkow, 2011; Hester et al., 2006). On a neuropsychological level, these brain alterations are accompanied by protracted deficiencies in domains of attention, memory, and executive functions (Fernandez-Serrano, Perez-Garcia, & Verdejo-Garcia, 2011). Notably, these impairments have been found to predict poor treatment engagement and prognosis (Severtson et al., 2010; Turner et al., 2009) and a

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greater propensity to drug relapse (Streeter et al., 2008). Whereas some of these impairments may have been present before any exposure to cocaine, and as such may act as a vulnerability marker for cocaine use and dependence, animal research also suggests that chronic cocaine use causes harmful effects to the brain, resulting in substantial neurocognitive deficits. In this chapter, we will 1) review the neuropharmacological and neurotoxic mechanisms by which cocaine exerts its effects on the brain, based on acute administration and animal studies, respectively; 2)  describe the most consistent brain dysfunctions and neuropsychological deficits associated with cocaine use, based on neuroimaging and cognitive studies conducted in cocaine-dependent abstinent users; and 3) analyze the contribution of relevant comorbidities to the clinical presentation of these deficits. NEUROPSYCHOPHARMACOLOGY AND ACUTE EFFECTS Cocaine increases excitatory transmission in catecholamines (especially dopamine) and glutamate prefrontal–limbic–striatal synapses (Kalivas & Volkow, 2005). These excitatory effects are, under certain doses, associated with increases in prefrontal functioning and cognitive performance (Garavan et  al., 2008; Kufahl et al., 2005). The acute administration of cocaine in human controlled trials has been associated with increased accuracy in motor performance, attention, and stimulus discrimination–response control tests (Fillmore et al., 2005; Higgins et al., 1990, 1993). In response control tests, doses in the range of 100 to 300 mg produce dose-related performance improvements (Fillmore et  al., 2005, 2006). These improvements have been associated with cocaine-induced activations in dorsolateral and medial prefrontal regions (Garavan et al., 2008). Conversely, increasing cocaine doses to circa 300 mg in humans, or 3 mg/ kg in monkeys, has shown to impair different aspects of executive functions, including working memory (Gould et  al., 2012)  and motor response inhibition (Fillmore et al., 2006). In animal models, acute cocaine administration has also shown to produce dose-related deficits on stimulus–shift reversal learning (Jentsch et al., 2002) and suppression of punishment-induced cognitive shifting (Simon et al., 2009). Functional neuroimaging studies measuring responses to acute cocaine administration have shown brain activation patterns comparable to neuropsychological performance results. Cocaine produces positive blood-oxygen-level-dependent (BOLD) signals in different sections of the Prefrontal cortex (PFC) (frontopolar, anterior orbital, and dorsolateral), which may support increased attentional vigilance, but cocaine simultaneously reduces BOLD signals in temporal/limbic and striatal regions relevant to information integration and executive control (Kufahl et al., 2005). Evidence regarding the acute effects of cocaine on brain functioning are also in agreement with the neuropsychological profile revealed during cocaine withdrawal, characterized by deficits in verbal memory and shifting with relative sparing of attentional functions (Beatty et al., 1995; Kelley et al., 2005).

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NEUROTOXICITY There are several mechanisms by which cocaine produces prolonged neuroadaptive effects in the brain systems acutely impacted by its administration. These neuroadaptive mechanisms include:  1)  depletion of monoamine and glutamate receptors in prefrontal–limbic–striatal systems (Kalivas & Volkow, 2005; Wayment et al., 2001); 2) overexpression of cocaine-mobilized transcription factors (Robison & Nestler, 2011); 3)  epigenetic changes (Wong et  al., 2011); 4)  suppression of neurogenesis (Sudai et  al., 2011); and 5)  persistent up-regulation of the stress neuroendocrine systems (Koob & Kreek, 2007). Most of these mechanisms have been revealed through animal models designed to mimic the long-term sequelae of cocaine dependence in humans (e.g., extended cocaine self-administration). These models have demonstrated that cocaine self-administration produces prolonged decreases in metabotropic glutamate receptors and dopamine D2 receptors in the medial prefrontal and orbitofrontal cortex (Ben-Shahar et  al., 2012, 2013; Briand et  al., 2008; Kasanetz et  al., 2012). Further neuroadaptations in glutamate and dopamine systems encompass up-regulation of AMPA-type glutamate receptors, changes in intrinsic membrane excitability, and decreased extra-cellular neurotransmitter levels, all having a profound impact on synaptic connections between the striatum, the limbic system, and the PFC (Bonci et al., 2003; Wolf, 2010). Another well-established neuroadaptive mechanism occurs by cocaine-induced mobilization of gene transcription factors; for example, it has been shown that cocaine-induced over-expression of the deltaFOS B is associated with orbitofrontal dysfunction and deficient impulse-control in cocaine-treated rats (Winstanley et al., 2009). There is also evidence for more stable cocaine-induced epigenetic changes in DNA methylation and histone modification causing global brain and hippocampal attrition, and deficits in probes of attention and working memory (He et  al., 2006; Novikova et  al., 2008). Cocaine can also block cell proliferation and neurogenesis in the dentate gyrus of the hippocampus, negatively impacting working memory (Sudai et  al., 2011), and can persistently elevate cortisol and corticotropin-releasing hormone levels (Smith et al., 2004), contributing to memory impairment and executive dysfunction (Gold et al., 2005).

STRUCTURAL AND FUNCTIONAL NEUROIMAGING FINDINGS

Volumetric Abnormalities Studies investigating brain volumetric differences between abstinent cocaine users and non–drug using controls are particularly useful for identifying long-term structural brain sequelae of cocaine use. Here we specifically review studies that selected participants with more than two weeks of abstinence, a time window relevant to capturing representative effects of long-term use, rather than of acute, withdrawal, or residual effects (MacKey & Paulus, 2013). The findings from these

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studies reveal that cocaine dependent individuals exhibit significant gray matter reductions in different sections of the PFC, the temporal cortex and adjacent limbic regions, the caudate nucleus, and the cerebellum (McKey & Paulus, 2013). PFC gray matter reductions are present within the PFC encompass the ventral medial prefrontal (including orbitofrontal) cortex, the inferior frontal gyrus, and the right anterior cingulate cortex (Matochik et  al., 2003; Moreno-Lopez et  al., 2012; Tanabe et  al., 2009), which are primarily involved in executive functions (Glascher et al., 2012). Medial orbitofrontal deficits may be particularly enduring, since they have been detected in cocaine-dependent users with reported abstinence durations over four years (Tanabe et  al., 2009). Cocaine users also show white matter reductions in regions adjacent to the inferior and medial frontal gyri and to the right anterior cingulate cortex (Moreno-Lopez et al., 2012). Gray matter reductions within the temporal cortex include the middle temporal gyrus, the parahippocampal gyrus, and the temporal pole (Albein-Urios et al., 2013b; Hanlon et al., 2011; Moreno-Lopez et al., 2012), which have been associated with deficits in verbal declarative memory and social cognition (Olson et al., 2007). In Brain white matter abnormalities encompass the right superior and transverse temporal gyrus (Moreno-Lopez et  al., 2012). In addition, cocaine-dependent users show gray matter volume reductions in the caudate nucleus, including the adjacent lenticular nucleus white matter (Hanlon et al., 2011; Moreno-Lopez et al., 2012) and the cerebellum (Hanlon et al., 2011; O’Neill et al., 2001), which have been associated with reversal learning deficits (Ersche et al., 2011). In Cerebellum notably, some of the volumetric studies have correlated the brain structural measures with the neuropsychological performance of abstinent cocaine users. These subsidiary analyses have shown that PFC volumes are negatively associated with general executive control performance (Fein et al., 2001), that caudate volumes are associated with perseveration errors during set-shifting performance (Hanlon et al., 2011), and that medial orbitofrontal gray matter is associated with preference for risky rewarding choices during decision-making (Tanabe et al., 2009). It should be noted that these structural neuroimaging findings have been obtained in individuals for which cocaine was the primary drug of choice, but who concurrently used other drugs, including alcohol, cannabis, or other stimulants. Whereas there is currently no conclusive evidence concerning the interactive effect of these drugs on brain structure, preliminary evidence suggests that the combined use of cocaine and alcohol may have synergistic detrimental effects on anterior cingulate cortex volumes (O’Neill et al., 2001).

Functional Neuroimaging—Cognitive Functions Findings Functional neuroimaging studies in cocaine-dependent individuals have focused on the neuropsychological domains of cue-reactivity, attention, executive functions, and decision-making (Crunelle et al., 2012), constructs that are meaningfully associated with the clinical concepts of craving, self-control, or relapse. The most consistent finding stemming from cue-reactivity studies in abstinent users

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is the significantly increased activation of the anterior cingulate cortex (Childress et  al., 1999; Wexler et  al., 2001), a region predominantly involved in cognitive control and arousal regulation (Paus, 2001). Interestingly, studies that have utilized cognitive control tasks requiring working memory and response-inhibition skills consistently report that cocaine-dependent individuals exhibit decreased anterior cingulate activation, frequently in combination with decreased dorsolateral PFC activation (Bolla et al., 2004; Goldstein et al., 2007; Kluber et al., 2005; Li et  al., 2008). During working memory and sustained attention tasks, cocaine-dependent users additionally demonstrate hypoactivity in the precuneus and the thalamus (Goldstein et  al., 2007; Kluber et  al., 2005). During attempts to exert cognitive control over negative emotions, cocaine-dependent users primarily show reduced activation of the right inferior frontal gyrus (Albein-Urios, Verdejo-Roman et al., 2012). With regard to decision-making tasks, it has been demonstrated that during Iowa Gambling Task decisions involving reward and risk of punishment, cocaine users exhibit increased activation of the orbitofrontal cortex and putamen, combined with decreased activation of the dorsolateral PFC, parietal cortex, and cerebellum (Bolla et  al., 2003). When performing a social decision-making task based on moral dilemmas, cocaine-dependent users moreover exhibit decreased activation of the medial PFC (including the anterior cingulate cortex), the insula, and the periaqueductal gray (Verdejo-Garcia et  al., 2012). Recently, functional imaging studies have started to apply functional connectivity measures to reveal cocaine-related deficits in large-scale functional brain networks. The findings obtained with this approach illustrate that cocaine-dependent users exhibit diminished resting-state and working-memory task–related connectivity within lateral prefrontal-parietal networks (Kelly et al., 2011), and diminished resting-state and emotion regulation task–related connectivity within fronto-limbic circuits connecting the orbitofrontal cortex with the anterior insula and the amygdala (Albein-Urios, Verdejo-Roman et al., 2012; Gu et al., 2010; Verdejo-Garcia et al., 2012). Conversely, it has been shown that cocaine-dependent users may present abnormally increased connectivity within the default mode network (Camchong et al., 2011), which may negatively impact executive functions, particularly on tasks that normally require default network deactivations. NEUROPSYCHOLOGICAL SEQUELAE OF COCAINE USE In this section, we focus on neuropsychological studies including abstinent cocaine-dependent users and matched control groups of non–drug using participants. We also include findings from recreational (non-dependent) stimulant users, especially when these findings enlighten the understanding of cocaine-related effects on specific neuropsychological domains. When available, we specifically raise evidence from twin studies and meta-analytic data, including the effect sizes of group differences for each domain. The section is organized by neuropsychological domain, following a basic-to-more complex functions gradient. Neuropsychological findings are summarized in Table 8.1.

Table 8.1. Summary of Findings Attention Boosting

Learning/Memory Non-significant

Executive Functions Detrimental

Decision-Making Unknown

Emotion Processing Unknown

Monoamines

Stress systems Epigenetic changes

Chatecholamines

Chatecholamines

Volumetric deficits

Anterior cingulate

Temporal cortex Parahippocampus

Medial orbitofrontal cortex

Amygdala

Functional brain correlates

↓ Anterior cingulate ↓ Dorsolateral PFC ↓ Precuneus ↓ Thalamus

Catecholamines Overexpression of transcription factors Dorsolateral PFC Orbitofrontal cortex Anterior cingulate Caudate ↓ Dorsolateral PFC ↓ Inferior frontal gyrus ↓ Anterior cingulate ↓ Cerebellum

↑ Dorsolateral PFC ↑ Inferior frontal gyrus ↑ Amygdala

Impacted by comorbidities

Cluster B Personality disorders

↑ Orbitofrontal ↑ Caudate ↓ Dorsolateral PFC ↓ Ventral anterior cingulate ↓ Insula ADHD

Impact on outcomes

Higher risk of relapse

Higher risk of relapse

Unknown

Acute Effects Neurotoxic mechanisms

Mood disorders Anxiety disorders ADHD Cluster B and C personality disorders Lower treatment retention Lower treatment engagement Higher risk of relapse

Unknown

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Perceptual and Motor Skills Empirical and meta-analytic evidence do not support a significant impact of cocaine use on basic perceptual or motor skills (Jovanovski et al., 2005).

Language Empirical and meta-analytic evidence do not support a significant impact of cocaine use on language comprehension or basic production (Jovanovski et al., 2005).

Attention Cocaine users exhibit a strong attentional bias towards cocaine-related words and pictorial stimuli (Hester et  al., 2006), and the neural networks engaged during attentional bias and selective attention are predictive of cocaine relapse (Clark et al., 2012; Marhe et al., 2013). These findings illustrate the notion that attention is prominently involved in the neural-functional pathology associated with cocaine addiction. Moreover, a twin study, case-control studies, and meta-analytical data indicate that cocaine use is associated with medium to large effect-size deficits in attentional components, including measures of attentional span (digits, Paced Auditory Serial Addition Task), attentional response speed (symbol search or cancellation tests), and vigilance or sustained attention (continuous performance tests) (Gooding et al., 2008; Jovanovski et al., 2005; Toomey et al., 2003). There is also evidence that the intensity of the cocaine-related attentional bias correlates with greater error commission rates during sustained attention (Liu et al., 2011), suggesting that there is a trade-off between the cocaine-induced sensitization of the attentional salience network and the poorer functioning of broader attentional networks. This is probably due to exhaustion of the catecholamine prefrontal-thalamic pathways (Tomasi et  al., 2007). Multiple studies have also demonstrated cocaine-related deficits on the Stroop and other attentional interference tests, in which higher amounts of cocaine use and peak cocaine use are associated with greater interference times (Albein-Urios et al., 2012; Bolla et al., 1999, 2004). However, it is not clear to what extent these findings primarily reflect attentional or executive functioning deficits.

Memory Memory deficits are relevant to particular aspects of cocaine addiction treatment, including remembering appointments, learning counseling contents and guidelines, or transferring knowledge into everyday life. Several well-controlled case-control studies have demonstrated that cocaine-dependent users have medium effect-size deficits in verbal learning and memory, including shallower learning slopes, poorer immediate and delayed recall, and less efficient

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recognition (Fox et al., 2009; Hulka et al., 2013). Recreational (non-dependent) cocaine users exhibit a more specific profile of deficits, which primarily encompasses poorer learning and recognition accuracy (Reske et  al., 2010). Reports of cocaine lifetime “amount of use” and “years of use” are negatively correlated with verbal learning and immediate recall scores (Hulka et al., 2012), fitting with neuroimaging evidence showing reduced gray matter in prefrontal and temporal regions relevant to information encoding and retrieval (Moreno-Lopez et al., 2012). We have a less precise knowledge of the cognitive profile associated with visual memory, since most studies have employed the Rey Complex Figure Test, in which patterns of performance can be impacted by planning deficits (Shin et al., 2006) Clinical and empirical applications of the Rey–Osterrieth Complex Figure Test Nature Protocols 1, –892–899 (2006). However, factor-analysis evidence indicates that verbal and visual learning/memory indices load on a single cognitive domain (episodic memory), which is negatively associated with severity of cocaine use and with reduced metabolism of the dorsolateral PFC (Goldstein et al., 2004). The molecular mechanisms supporting cocaine-related episodic memory deficits may include drug-induced protracted elevations in cortisol levels and long-term depletion of dopamine signaling pathways in the dorsolateral prefrontal and temporal regions (Beveridge et al., 2006; Goldstein et al., 2004). As for procedural memory, one case-control study found no detrimental impact of cocaine on this domain; in fact, cocaine users showed superior learning and performance (van Gorp et al., 1999), which was in agreement with findings of enlarged dorsal striatal volumes in cocaine-dependent samples (MacKey & Paulus, 2013).

Executive Functions Defective functioning of the executive components of working memory, response inhibition, and set shifting have been shown to significantly predict poor cocaine treatment prognosis, including lower engagement with treatment, shorter retention times, and higher rates of drug use during and following treatment (Severtson et al., 2010; Streeter et al., 2008; Turner et al., 2009; Verdejo-Garcia et al., 2012). Case-control studies and quantitative review evidence reliably demonstrate that cocaine users have substantial deficits in working-memory probes, including tests with higher organizational (e.g., letter and number sequencing) and updating (e.g., N-back) demands (Fernandez-Serrano et al., 2011). Because cocaine users have shown significantly poorer working-memory performance than pathological gamblers (who share addiction vulnerabilities and learning mechanisms but lack drug-induced neurotoxicity), it has been posited that these deficits are specifically attributable to cocaine exposure, and not to overall “addiction” neuroadaptations (Albein-Urios et  al., 2012). As for response inhibition, multiple studies have revealed significant deficits in cocaine users performing both attentional (e.g., Stroop) and motor response control (e.g., Stop-Signal, Go/ No-Go) tasks (Fillmore & Rush, 2002; Verdejo-Garcia et al., 2007; Verdejo-Garcia

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& Perez-Garcia, 2007). However, there is still debate on whether these deficits are sequelae of cocaine exposure (Bolla et al., 1999, 2004; Verdejo-Garcia et al., 2005) or predate cocaine-use onset (Ersche et al., 2012). A more inclusive notion is that response-inhibition dysfunction somehow predisposes to cocaine-use initiation, but then is further deteriorated by cocaine exposure (Albein-Urios et  al., 2012; Belin et  al., 2008). With regard to set-shifting skills, there is considerable case-control evidence showing that cocaine-dependent individuals have specific deficits in switching between cognitive or affective sets that have previously been reinforced, as shown by comparisons with non–drug using controls (Fernandez-Serrano et  al., 2012)  and other groups of substance users, such as amphetamine or opiate users (Ersche et al., 2008). This deficit is clearly illustrated by cocaine-dependent users’ greater perseveration patterns on reversal learning tasks (Ersche et al., 2008; Fernandez-Serrano et al., 2012) and also by disproportionally higher perseveration error rates during the third-criterion switch of the Wisconsin Card Sorting Test, when participants are first required to sort by a previously reinforced rule (color) (Woicik et  al., 2011). Compared to cocaine-dependent users, recreational cocaine users are mainly characterized by subtler response-inhibition deficits and set shifting—but not obvious perseveration—errors (Colzato et al., 2007, 2009; Reske et al., 2011), with no indication of working-memory deficits (Colzato et al., 2009). Because executive functions overly rely on monoamine neural pathways, this is likely to be the main mechanism by which cocaine exerts its detrimental effects on working memory, inhibition, and switching (Filip et  al., 2010; Wayment et  al., 2001). However, whereas there is evidence of primarily dopamine D1 receptor and noradrenaline involvement in working memory, inhibition deficits have been associated with alterations in dopamine D2 receptors and noradrenaline transmission, and shifting deficits with alterations in serotonin transmission (Clarke et al., 2007; Robbins & Arnsten, 2009; Robbins & Roberts, 2007).

Decision-Making The poor ability to make decisions based on long-term (rather than immediate) outcomes has been associated with drug relapse in alcohol- and opiate-dependent users (Passetti et al., 2008). Although there is yet no equivalent evidence in cocaine users, poor decision-making is likely to affect the prognosis of cocaine dependence, due to the significance and durability of this type of deficit in cocaine users (Verdejo-Garcia, Rivas-Pérez et al., 2007). Most studies focusing on this domain have utilized the Iowa Gambling Task, a computer test that measures decision-making under conditions of uncertain reward/punishment outcomes (Bechara et al., 2002). In the task, participants make a series of 100 card choices from four decks: Risky decks (Decks A  and B) offer high rewards ($100 per choice) but higher losses, and Safe decks (Decks C and D) offer only $50 per choice, but small losses, resulting in profit over time. Quantitative review evidence suggests that cocaine users have

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medium-size deficits in this task, exemplified by overall preference for risky vs. safe decks (Fernandez-Serrano et al., 2011). The task can also be analyzed in a progressive learning-slope fashion, by computing the net preference for safe vs. risky decks every 20 trials. This strategy results in five blocks, of which it is thought that blocks 1 to 3 reflect learning of task contingencies, whereas blocks 4 and 5 reflect overt risk-based decision-making (Bechara et al., 2005). Most studies in cocaine dependence have revealed that cocaine users demonstrate both a slower learning slope between blocks 1 and 3, and a preference for risky choices during blocks 4 and 5 (Barry & Petry, 2008; Cunha et  al., 2011; Verdejo-Garcia, Perales & Perez-Garcia, 2007). Cocaine users are also less able to benefit from previous exposure to the task, since they maintain a risky decision-making pattern during an immediately repeated second administration of the task (Verdejo-Garcia, Benbrook et al., 2007). The application of the cognitive “expectancy-valence” model to deconstruct the factors impairing Iowa Gambling Task performance in cocaine users have further revealed that both the slower learning and the risk preference can be explained by a disproportionately higher influence of rewards vs. punishments on choices (Stout et al., 2004, 2005; Yechiam et al., 2005). A similar interpretation stems from the analysis of the skin conductance reactivity to rewards vs. punishment outcomes: stimulant users exhibit elevated sensitivity to reward (Bechara et al., 2002). Comparatively less is known about the potential contribution of hyposensitivity to punishment in the context of risky decision-making in cocaine users. Although cocaine dependence is associated with diminished error-detection (Franken et al., 2007), decision-making studies can only conclude that cocaine users compute rewards over punishments, but there is no significant indication that punishments are neglected (Leland & Paulus, 2005). Due to the difficulties in interpreting Iowa Gambling Task results (e.g., poor performance has been associated with other cognitive skills such as working memory or reversal learning), more specific decision-making tools have been proposed (Brand et al., 2006; Clark & Robbins, 2002), but they have not been applied in the context of cocaine dependence. A decreased engagement of the neural network supporting affective evaluative processes (ventral medial prefrontal cortex-insula-striatum-periaqueductal gray) has shown to underlie decision-making deficits in cocaine users (Verdejo-Garcia et al., 2012), which may result from premorbid or acquired alterations in dopamine, serotonin, and orexin transmission (Borgland et al., 2009; Homberg, 2012).

Emotion Processing Emotion recognition and experience deficits are particularly relevant to social behavior and social networking, which are robust clinical predictors of addiction recovery (Mutschler et  al., 2013). Quantitative review evidence indicates that cocaine dependence is associated with medium effect-size deficits in emotion recognition, as measured by facial emotional expression-decoding tests (Fernandez-Serrano et  al., 2011). Specifically, cocaine users have poorer

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recognition of expressions of fear and anger (Fernandez-Serrano et  al., 2010; Kemmis et al., 2007), and these deficits are associated with cumulative levels of cocaine exposure (Fernandez-Serrano et al., 2010; Morgan & Marshall, 2013). The design and utilization of neuropsychological tests to measure emotional experience is more challenging, and electrophysiological measures have been integrated in cognitive studies to address this aspect. Combined results from event-related brain potentials and subjective reports have demonstrated that cocaine users show a reduced sensitivity to monetary gradients (e.g., they are less able to distinctively value smaller vs. larger monetary rewards) especially during protracted abstinence (Goldstein et al., 2007; Parvaz et al., 2012). By measuring the subjective reports to sets of emotionally laden pictorial stimuli, it has also been shown that cocaine users experience less arousal in response to a range of non–drug positive emotional stimuli (Aguilar de Arcos et al., 2005). These studies suggest that cocaine users may have a reduced or narrower ability to experience the emotions normally attached to positive reinforcers, but more research is warranted to develop and test proper neuropsychological measures of emotional experience. Overall, evidence indicates that cocaine users have significant deficits in both emotion recognition and experience. Several molecular mechanisms, including cocaine-induced up-regulation and subsequent exhaustion of monoamine systems relevant to affective processing (Scheggi et al., 2011), changes in gene expression impacting on amygdala or striatum functions (Sillivan et  al., 2011), and neuroendocrine changes in the oxytocin system (McGregor & Bowen, 2012) have been proposed to underlie these deficits. COMMON COCAINE COMORBIDITIES AND IMPLICATIONS FOR NEUROPSYCHOLOGICAL FUNCTIONING The most common comorbidities among cocaine-dependent individuals include alcohol dependence; mood and anxiety disorders (depression, bipolar disorder, obsessive compulsive disorder, or post-traumatic stress disorder); attention-deficit/hyperactivity disorder (ADHD); and personality disorders from Clusters B (antisocial personality disorder and borderline personality disorder) and C (obsessive-compulsive personality disorder and avoidant personality disorder) (Vergara-Moragues et al., 2012).

Cocaine Dependence and Alcohol Dependence The simultaneous use of both substances leads to the formation of “cocaethylene” (Jatlow et al.,), an active metabolite associated with higher cocaine and cortisol plasma concentrations and increased susceptibility to cardiovascular toxicity (Farre et al., 1997). Therefore, it is reasonable to assume that the combined use of both drugs is associated with cumulative or synergistic detrimental effects on cognition. However, neuropsychological studies do not support the notion that the combined use of both substances produces greater cognitive deficits (Pennings et al., 2002), and some studies have even suggested that the use of alcohol may

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partially buffer cocaine-related deficits due to its vasodilatation effects (Abi-Saab et al., 2005; Robinson et al., 1999). Hence, there is currently no solid evidence of synergistic detrimental effects of cocaine and alcohol on the neuropsychological performance in cocaine users. However, this issue warrants further exploration, since relevant confounders such as subtle differences in drug behavior patterns (e.g., concurrent users’ taking less cocaine than non-alcohol users) or lack of sensitivity of the tests employed (mostly psychomotor, attention, and memory tasks) may have contributed to these negative findings. For example, the combined effects of both drugs have never been examined using tests of reversal learning, decision-making, or emotion recognition, which rely on neural networks that are negatively associated with cardiovascular activity (Kimmerly et al., 2005).

Mood and Anxiety Disorders Despite being the most prevalent psychiatric comorbidities associated with cocaine addiction (Vergara-Moragues et  al., 2012), there is remarkably little research on the impact of mood and anxiety disorders on neuropsychological performance in cocaine-dependent individuals. In agreement with the broader neuropsychological literature, most available studies have estimated the impact of general depression or anxiety symptoms to be mild and non-significant for cocaine users’ performance in tests of attention, memory, and set shifting (Horton & Roberts, 2003; Roberts & Horton, 2003; Woicik et  al., 2009). However, the comorbidity with specific diagnoses of the anxiety spectrum has a verified impact on attentional and response-inhibition functions. In particular, it has been shown that cocaine-dependent individuals with post-traumatic stress disorder demonstrate an increased attentional bias towards cocaine-related material (Tull et al., 2012), which has been linked to poor selective attention in independent studies (Liu et al., 2011). Furthermore, there is evidence indicating that the presence of obsessive-compulsive symptoms maximizes cocaine-dependent users’ deficits in an anti-saccadic inhibition test (Rosse et al., 1994), which is in agreement with recent findings showing that obsessive-compulsive disorders are primarily characterized by response-inhibition deficits (Robbins et al., 2012). Therefore, some specific anxiety disorders have a substantial detrimental impact on attention and response inhibition in cocaine-dependent users. More research is needed in relation to mood disorder comorbidities, which share with cocaine addiction neuroadaptive changes in prefrontal, limbic, and hypothalamic-pituitary-adrenal (HPA) systems, and selective profiles of executive dysfunction and decision-making deficits (Lim et al., 2013).

Attention-Deficit/Hyperactivity Disorder (ADHD) ADHD is a developmental disorder that is associated with deficits in attention, working memory, response inhibition, and impulsive decision-making, which often continues during adulthood. It is also considerably prevalent among

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cocaine-dependent users (between 10% and 14%, according to recent studies) (van Emmerik-van Oortmerssen et  al., 2012). However, again surprisingly, few studies have directly addressed its impact on cocaine-induced cognitive deficits. Recent evidence has shown that adults with cocaine dependence and ADHD perform significantly more poorly than ADHD non-substance users on measures of response inhibition and impulsive decision-making (Crunelle et  al., 2012). Furthermore, there is questionnaire-derived evidence showing that cocaine-dependent users with ADHD self-report higher disinhibition and behavioral dysregulation symptoms compared to cocaine-dependent users without ADHD (Vergara-Moragues et  al., 2011). Therefore, the available evidence suggests that the comorbid presence of ADHD may have a mild impact on cocaine-dependent users’ response inhibition, self-regulation, and impulsive decision-making. However, well-controlled studies on neuropsychological performance differences between cocaine-dependent users with vs. without ADHD are still lacking.

Personality Disorders Cocaine dependence is often associated with concurrent personality disorders, particularly from Clusters B and C (Chen et al., 2011), and this comorbidity is associated with significantly worse treatment prognosis and psychosocial outcomes (Lopez-Quintero et  al., 2011; McMahon & Enders, 2009). It has been demonstrated that the comorbidity between cocaine dependence and Cluster B disorders is specifically associated with large effect-size deficits in sustained attention and medium effect-size deficits in response inhibition compared to the performance of cocaine-dependent users without personality disorders (Albein-Urios et al., 2013). Specifically, sustained attention deficits are observed in the fluctuation index of a response-speed cancellation task (Brickenkamp, 2002), suggesting poor strategic allocation of attentional resources, and response-inhibition deficits are revealed in the inhibition condition of the Delis-Kaplan Executive Function System (D-KEFS) Stroop test (Delis, Kaplan, & Kramer, 2001). When the comorbid group’s performance is contrasted with that of a non-drug control group, both deficits achieve large effect sizes. Comparatively less is known about the neuropsychology of the comorbidity with Cluster C disorders, although recent evidence points to a differential impact of these disorders on working-memory functioning (Albein-Urios, et al., 2013), presumably through overstimulation of the updating system’s resources (Spinhoven et al., 2009). CONCLUSIONS In summary (see also Table  8.1), cocaine dependence is associated with neuropsychological deficits (of medium to large effect sizes) in the domains of attention (selective and sustained), episodic memory, executive functions (working memory, inhibition, and shifting), decision-making, and emotion

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processing. It has been demonstrated that comorbidity with anxiety-spectrum obsessive-compulsive or post-traumatic stress disorders further impacts inhibition deficits, whereas the comorbidity with Axis II personality disorders further deteriorates sustained attention and response inhibition. Attention, executive functions, and decision-making have been robustly associated with cocaine treatment and cocaine cessation outcomes, and it is reasonable to argue that these deficits may negatively affect other relevant outcomes, including productivity, social networking, and quality of life. Therefore, we may conclude that the neuropsychological sequelae of cocaine dependence deserve further investigation and require specific clinical attention.

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Opioid-related cognitive deficits show some improvement with long-term maintenance treatment. Early withdrawal from opioids is associated with cognitive deficits, which decrease with time. However, neurocognitive changes associated with substance dependence, such as impaired executive functioning, may be evident even after prolonged abstinence.

Conclusions Results of studies on the cognitive effects of opioids are mixed. Most consistently, opioids have a detrimental effect on perception, information processing, and psychomotor performance following acute administration in healthy, non– drug-abusing volunteers. These effects tend to subside with the development of tolerance after a short time of stable dosing (Zacny, 1995). In individuals with opioid dependence, some impairment has been found, relative to non–drug users, across a number of cognitive domains. However, these impairments may not be of clinical or functional relevance (e.g., may not noticeably affect daily tasks such as driving a car) and may reflect premorbid cognitive differences (Mintzer & Johnson, 2007). The progression to dependence may introduce additional cognitive impairment due to changes in executive functions and self-regulation (Kalivas, 2009; Kalivas & O’Brien, 2008; Volkow et al., 2003a; Volkow et al., 2011). Cognitive deficits show some improvement over time in opioid maintenance treatment Withdrawal and short-term abstinence are associated with impaired executive functions, which may be a significant cognitive contributor to relapse (Ersche & Sahakian, 2007; Rapeli et al., 2006). Some recovery of functioning is seen in former opioid abusers; however, the impairments can remain after several years of abstinence (Ersche et al., 2006). Experimental, observational, and longitudinal studies provide initial insight into the neuropsychological sequelae of opioid use, although additional studies are needed for a comprehensive understanding that accounts for potential confounds inherent in the current literature, including small sample size, differences in cognitive task batteries, and individual differences.

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SUBSTANCE COMORBIDITIES AND IMPLICATIONS FOR NEUROPSYCHOLOGICAL FUNCTIONING Opioid-dependent individuals, particularly heroin users, are typically polydrug users. Polydrug use and larger numbers of substance-dependence diagnoses are associated with decreased psychosocial function, as evidenced by larger numbers of mental health diagnoses, suicide attempts, and heroin overdoses (Shand et al., 2011). Alcohol and opioid dependence are highly comorbid, with 65%–89% of individuals meeting criteria for both (Darke & Ross, 1997; Wu et al., 2011). Heavy alcohol consumption is strongly associated with subsequent neurocognitive deficits (Meyerhoff et  al., 2005). Lifetime rates of comorbid dependence for methamphetamine or cannabis in heroin-dependent individuals are also high (39% and 36%, respectively; Darke and Ross, 1997). Rates of cocaine use in heroin users, including those in methadone maintenance treatment, range from 30%–80% (Leri et  al., 2003). Benzodiazepines, whose deleterious effects on cognitive functioning are well documented (Hindmarch, 2009), are also widely used by opioid abusers, such that between 51% and 70% of methadone- and buprenorphine-maintained patients test positive for benzodiazepines (Jones et al., 2012). Nicotine dependence is also highly comorbid with opioid dependence, with over one-half of opioid-dependent individuals also meeting criteria for nicotine dependence (Wu et al., 2011). In addition to polydrug use, violence exposure is nearly universal in heroin users. As a result, up to 50% of opioid-dependent individuals meet criteria for post-traumatic stress disorder (PTSD), which can also compromise neuropsychological functioning (Darke et al., 2010; Horner & Hamner, 2002; Mills et al., 2005). Violent traumatic events may result in traumatic brain injury, which is also associated with long-term cognitive impairment (Khan et al., 2003). Serious health problems that affect neurocognitive functioning, such as HIV infection, are also very common in opioid-dependent individuals (De Cock et al., 2012; Gannon et al., 2011). Opioid-dependent individuals often present as clinically complex, with multiple health, psychosocial, and addiction issues, many of which are likely to contribute to neurocognitive deficits. As a result, multiple levels of care are often required to improve patients’ functioning.

CONCLUSION Acute and chronic opioid use affects multiple levels of functioning by changing neurophysiological structure and function, modulating perception and cognition, and compromising physical and mental health. Comorbid psychiatric disorders and health conditions, predating or resulting from opioid use and dependence, complicate the success of current treatment interventions. Comprehensive research that considers these complexities is needed to optimize opioid dependence interventions and develop effective public health policies.

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Inhalants M I C H A E L J O H N TA K AG I , DA N I E L I .   L U B M A N , S U S A N M .   C OT TON , A N D M U R AT   Y ÜC E L

“Inhalant abuse” refers to the deliberate inhalation of chemical vapors in order to achieve intoxication or altered mental states (Lubman, Hides, & Yücel, 2006). Internationally, inhalants are frequently one of the first drugs young people use, and there is a wide range of commercially available products that emit psychoactive vapors (e.g., shoe polish, lighter fluid, petrol [gasoline], spray paints, glues, and adhesives) (Cámara-Lemarroy, Gónzalez-Gónzalez, Rodriguez-Gutierrez, & Gónzalez-Gónzalez, 2012; Howard, Bowen, Garland, Perron, & Vaughn, 2011; Yücel, Takagi, Walterfang, & Lubman, 2008). In the United States, those who reported having ever used inhalants is second only to cannabis in lifetime prevalence, with 13% of eighth-graders and 10% of tenth-graders reporting lifetime inhalant use (Johnston, O'Malley, Bachman, & Schulenberg, 2012). Within Australia, 22% of 12-year-olds, 19% of 15-year-olds, and 11% of 17-year-olds report lifetime inhalant use (White & Hayman, 2006b). In contrast, rates of regular inhalant abuse among adolescents appears to be significantly less. Estimates from school-based surveys indicate that 2%–3% of Australian adolescents aged 12–17 years have used inhalants on more than ten occasions (White & Hayman, 2006b). Similarly, in the United States, 3.2% of eighth-graders and 1.0% of twelft h-graders report inhalant use in the past 30 days (Johnston et al., 2012). While rates of regular use among adolescents appear low, the potential consequences of chronic use during this developmental period are concerning. The most frequently abused inhalants contain a range of chemicals that are neurotoxic (e.g., toluene) and are readily absorbed into lipid-rich tissues, such as the central nervous system (Chadwick, Anderson, Bland, & Ramsey, 1989). Considering the significant cognitive and neurobiological changes occurring during adolescence, the potential harms from chronic inhalant abuse are significant.

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In this chapter, we will review the neuropsychological and neurobiological effects of inhalant abuse during adolescence. Specifically, we will focus on toluene and toluene-containing products because they are frequently abused and are more widely studied than other inhalant chemicals, and toluene is hypothesized to be the most toxic chemical contained within frequently abused inhalants (Yücel et al., 2008). We will highlight: 1) definitional issues and the characterization of inhalants; 2) the epidemiology and psychosocial correlates of adolescent inhalant misuse; 3) the neuropsychopharmacology of inhalants and their toxic effects; 4) neuroimaging studies investigating adolescent inhalant misuse; 5)  neuropsychological sequelae of adolescent inhalant misuse; and 6)  clinical implications of inhalant use in young people. INHALANT MISUSE: WHAT IS IT? Adolescent glue-sniffing was first noted in the United States during the 1940s, and reports of petrol- or gasoline-sniffing first appeared during the following decade. In the 1960s and 1970s, inhalant use became popular among children in the United States, with a significant number of published articles and case studies detailing their effects (Barman, Sigel, Beedle, & Larson, 1964; Brozovsky & Winkler, 1965; Dodds & Santostefano, 1964; Easson, 1962; Edwards, 1960; Glaser & Massengale, 1962; Grabski, 1961; Knox & Nelson, 1966; Massengale, Glaser, Lelievre, Dodds, & Klock, 1963; Merry & Zachariadis, 1962). It is only more recently that group studies investigating inhalant abuse amongst children and adolescents have emerged (Allison & Jerrom, 1984; Rosenberg, Grigsby, Dreisbach, Busenbark, & Grigsby, 2002; Takagi, Lubman, Cotton, et  al., 2011; Takagi, Lubman, Walterfang, et  al., 2011; Takagi, Yücel, et  al., 2011; Unger, Alexander, Fritz, Rosenberg, & Dreisbach, 1994; Yücel et al., 2010). Inhalants can be classified in a number of ways and represent a wide range of products. An example of a clarification scheme for these products can be seen Table  12.1. It is important to note that, although these products are classified under the blanket term “inhalants,” there are many different chemicals in these products, each with diverse chemical profiles and levels of toxicity. For example, acetone is generally considered one of the least toxic solvents, while toluene (often found in paint) is thought to be extremely toxic and may cause more severe medical, neurological, and neuropsychological consequences than any other substance (Hartman, 1995; Yücel et al., 2008). The heterogeneity in chemical composition of commonly used inhalants and the lack of knowledge surrounding their pharmacological effects pose problems for developing more useful classification systems, such as classification based on pharmacological effects, patterns of use, and phenomenology (Howard et al., 2011; Takagi, Yucel, & Lubman, 2010). In order to inhale the vapors, the user commonly places volatile compounds into a plastic bag from which the concentrated vapors are inhaled, referred to as “bagging.” This method is used for paint or some other form of dissolved solid (e.g., glue) (Sharp, 1992). Another method is to soak a rag in the solvent and hold the soaked cloth over the nose and mouth, or insert the rag directly into

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Table 12.1. Inhalants and Their Common Chemical Constituents* Volatile Solvents

• • • • • •

Correction fluids (1,1,1-trichloroethane) Dry-cleaning fluids (trichloroethylene, 1,1,1-trichloroethane) Glues (n-hexane, toluene, xylene) Nail polish remover (acetone, esters) Paint thinners and removers (dichloromethane, toluene, xylene) Petrol/gasoline (benzene, n-hexane, toluene, xylene

Aerosols • Deodorants and hairsprays • Fabric protector sprays • Spray paints (toluene, methyl isobutyl ketone) • Vegetable oil sprays Gases

• • • •

Bottled gas (propane) Cigarette lighter fluid (butane) Medical anesthetics (ether, chloroform, nitrous oxide) Whipped cream chargers (nitrous oxide)

* Reproduced from Lubman, Hides, & Yücel (2006).

the mouth, a method referred to as “huffing.” Other methods include “sniffing,” which involves sniffing the substance directly from the container (i.e., whipped cream), while some abusers take the more simple but dangerous approach of placing the solvent directly into their mouths. PREVALENCE OF INHALANT USE With respect to availability, inhalants are easily accessible because they are relatively inexpensive, easy to steal and conceal, are legal to purchase, and are widely available in everyday household products. According to the 2012 Youth Risk Behavior Survey (CDC, 2012), a nationwide survey that monitors health-risk behaviors among youth and adults in the United States, 11.4% of students had sniffed glue, breathed the contents of aerosol spray cans, or inhaled any paints or sprays to get high one or more times during their life. Furthermore, the prevalence of lifetime inhalant use was higher among ninth-grade (12.7%), tenth-grade (11.8%), and eleventh-grade (11.1%) students than those in the twelfth grade (9.3%). This is similar to the results of the 2012 Monitoring the Future survey, an ongoing nationwide study of the behaviors, attitudes, and values of American secondary students and young adults (see Table 12.2). Similarly, within Australia, 17% of students aged 12–17 report deliberately sniffing inhalants at least once in their lifetimes (White & Hayman, 2006b). The highest rates of inhalant use in Australia (those who reported having ever used inhalants) have been recorded for 12-year-olds (21%) compared to 16.5% for 15-year-olds and 10% for 17-year-olds.

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Table 12.2. Proportion of American Students Who Have Used Inhalants in 2011* Grade Eighth

Lifetime Use % 13.1

Use in the Past Year % 7.0

Use in the Past 30 Days 3.2

Tenth

10.1

4.5

1.7

8.1

3.2

1.0

Twelfth

* Adapted from Johnston et al. (2012).

It is important to note that population or school-based surveys often exclude young people who habitually abuse inhalants or are at high risk of becoming inhalant users. These at-risk adolescents demonstrate high rates of absenteeism, are more likely to be suspended and/or expelled from school, and may be homeless (Mosher, Rotolo, Phillips, Krupski, & Stark, 2004). In 1998, the National Alternative High School Youth Risk Behavior Survey in the United States, which included expelled students and those at a high risk of school dropout, reported a lifetime inhalant-use prevalence of 25% throughout secondary school (Grunbaum et al., 2000). This is probably a more accurate description of youth inhalant use and supports the notion that traditional school-based surveys underestimate rates of actual inhalant use. In contrast to the lifetime rates of inhalant abuse, rates of chronic inhalant misuse among adolescents appear to be significantly less (see Table 12.3). An analysis of the 2000 and 2001 National Household Survey on Drug Abuse showed 0.4% of American adolescents aged 12–17 met the DSM-IV criteria for inhalant abuse or dependence (Wu, Pilowsky, & Schlenger, 2004). Overall, the true extent of chronic inhalant use is difficult to determine, as the current data are based on population and school-based surveys, which do not include adolescents who are homeless, truant, incarcerated, or have dropped out of school. Thus, conclusions cannot be definitively drawn, and further epidemiological research is needed to definitively map chronic inhalant use internationally. It is also important to note definitional issues in identifying “chronic” inhalant use. There is a lack of consensus in what defines “chronic” use, with some studies identifying use on 20 or more occasions as “chronic” (Johnston, O'Malley, Table 12.3. Frequency of Lifetime Inhalant Use in the United States in 2011* Number of Occasions Grade Eighth Tenth Twelfth

1–2 7.5% 5.9% 4.3%

3–5 2.5% 1.7% 1.5%

6–9 1.1% 0.9% 0.9%

* Adapted from Johnston et al. (2012).

10–19 0.8% 0.6% 0.7%

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& Bachman, 2003), while others define chronic as using on 10 or more occasions (White & Hayman, 2006a). Inconsistencies in defining the parameters of inhalant use are among the methodological issues frequently criticized in inhalant research conducted to date. PROFILE OF INHALANT USERS Inhalant use occurs more frequently among young people from lower socioeconomic backgrounds (Dinwiddie, 1994). Chronic use is typically more common among males than females (Johnston, O'Malley, Bachman, & Schulenberg, 2004; White, 2001), although recent epidemiological data in the United States suggest slightly more females use than males in the younger age groups (e.g., eighth grade) (Johnston et al., 2012). There is little research investigating the social correlates of inhalant use. However, a number of criminal, educational, family, and interpersonal difficulties have been associated with inhalant use. McGarvey, Cantebury, and Waite (1996) compared adolescents who had abused or not abused inhalants in a juvenile correctional facility in the United States in terms of two major areas; family relations and delinquency. Family problems and delinquent behaviors were more prevalent in inhalant users than in non-users. In a similar study, Howard and Jenson (1999) examined inhalant abuse among adolescent delinquents on probation in the United States. Inhalant users reported significantly more gang activity, intentions to engage in illegal activity, and substance-related criminality compared to non-inhalant delinquents. Additionally, age of initiation of inhalant use, gang membership, truancy, and substance-related criminality significantly predicted lifetime inhalant use (Howard & Jenson, 1999). These findings were supported by Perron and Howard (2009), who reported that, among lifetime adolescent inhalant users, 18.6% met DSM-IV criteria for inhalant abuse, and 28.3% met criteria for inhalant dependence. Furthermore, these inhalant users (regardless of DSM-IV diagnosis) were more likely than non-users to have psychiatric symptomatology (e.g., depression and anxiety), report more antisocial behavior and suicidality, experience more traumatic events, and have higher comorbid substance-use problems (Perron & Howard, 2009). Peer groups also play an important role in the initiation of inhalant use, with most adolescents using inhalants in small groups (Carroll, Houghton, & Odgers, 1998). These groups appear to glorify chronic inhalant abuse, using it as a part of their identity, despite the poor reputation of inhalant abuse among non-using peers (Carroll et al., 1998). Many users report inhaling paint as a means of embracing their outsider status, and reported using inhalants for shock value (MacLean, 2005). A desire to fit in with a group of peers is also a significant factor for an individual's desire to abuse inhalants (MacLean, 2005). However, involvement with a non-inhalant-using peer group with negative opinions regarding inhalant abuse can lead to a significant reduction in inhalant use (Beauvais, Wayman, Jumper-Thurman, Plested, & Helm, 2002).

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Inhalant use has also been frequently associated with family dysfunction and abuse. Young inhalant-abusers typically come from single-parent homes with high levels of conflict, substance use, and a lack of family support (Dinwiddie, 1994; Massengale et  al., 1963; McGarvey et  al., 1996; Oetting, Edwards, & Beauvais, 1988). Carroll, Houghton, and Odgers (1998) examined volatile substance use among Western Australian adolescents and found that adolescents who abused inhalants were characterized by delinquency, trouble with the law (theft and prostitution), and a family history of drug and alcohol problems. In addition to the psychosocial difficulties associated with dysfunctional and abusive family environments (Mitchell et al., 2001), childhood maltreatment can also affect a range of neurodevelopmental processes (i.e., cortical pruning abnormalities, delays in myelination), which can in turn affect normal cognitive development (Lubman & Yücel, 2008). Comorbid drug use is also common among inhalant users. Dinwiddie, Reich, and Cloninger (1991) found that over 90% of inhalant users reported lifetime experience with three or more other classes of illicit drugs, and over two-thirds had also used substances from every class of drug recorded in the study (cannabinoids, opioids, stimulants, depressants, and hallucinogens). Inhalant abuse is significantly associated with later substance use, including cocaine, amphetamines, and narcotics, among juvenile offenders (Young, Longstaffe, & Tenenbein, 1999). Similarly, Dinwiddie and colleagues (1990) found that individuals with a lifetime history of inhalant use were 5–10 times more likely to report opioid, stimulant, depressant, and hallucinogen use compared to non-inhalant users. Inhalant abusers also show high levels of psychopathology, including both Axis I and Axis II psychiatric diagnoses. Dinwiddie, Reich, and Cloninger (1990) found inhalant abusers had a markedly increased likelihood of receiving a diagnosis of antisocial personality disorder and becoming addicted to alcohol and other drugs, and they appeared more vulnerable to depression and anxiety. Participants reporting any solvent use also had a significantly increased risk of suicidal ideation and suicide attempts compared to non-users, with half of the solvent users reporting suicidal ideation and 30% reporting a history of a suicide attempt. Similarly, Sakai, Hall, Mikulich-Gilbertson, and Crowley (2004) examined 847 adolescents being treated for substance dependence. They found that inhalant users were significantly more likely to have major depression, have attempted suicide, and received a diagnosis of dependence on alcohol, amphetamines, nicotine, cocaine, or hallucinogens (Sakai et al., 2004). An important but often overlooked aspect of inhalant use is the phenomenology of inhalant intoxication. As discussed earlier, it is often assumed that adolescents misuse inhalants because they are cheap, readily available, and legal, and once other drugs become available, inhalant use decreases significantly. While this is certainly true, it does not completely explain why some users continue to use inhalants concurrently with other drugs (e.g., alcohol and cannabis) and, in some cases, identify inhalants as their drug of choice (see Takagi et  al., 2010). Within the inhalant literature, the intoxication experience of inhalants varies significantly, with some reports of an alcohol-like intoxication and light-headedness,

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and others reporting significant hallucinations and delusions (MacLean, 2005, 2008; Takagi et al., 2010). The heterogeneity of the chemicals contained in frequently used inhalants probably explains the variability in subjective intoxication experience, and recent research shows this experience can play a significant role in a young person's decision to continue use. Takagi, Yücel, and Lubman (2010) examined subjective intoxication experiences in a sample of adolescent inhalant users who chronically sniffed spray paint. The young people were divided into groups based on their paint-color preferences to examine how different types of paint (chrome vs. non-chrome) were experienced by adolescent users. Both chrome and non-chrome users reported high levels of pleasure from sniffing paint, and the chrome-using group were more likely to report deliberately inhaling to experience altered perceptions (such as visual and auditory hallucinations) relative to non-chrome users. In addition, a significantly greater proportion of chrome users reported that the perceptual alterations they experienced after sniffing paint differed between paint colors, with chrome colors being associated with more vivid hallucinations. Chrome paint users were also more likely to be motivated by the potential to hallucinate. The reason for these subjective differences is unclear, because the chemical makeup of paints is not readily available. The notion that inhalant users select products to experience specific pleasurable effects is rarely considered, but it has significant implications for understanding inhalant abuse and developing effective treatment strategies. In summary, the term “inhalants” represents a wide range of readily available, legal, and toxic chemicals. Inhalants are the fourth-most-abused substance by young adolescents (Johnston et al., 2012); however, they are frequently the first drugs tried (Young et  al., 1999). Determining the number of chronic inhalant users is difficult, as school-based surveys frequently do not include adolescents at risk for inhalant abuse. Among the chronic inhalant-using group, comorbid substance use, psychopathology, family dysfunction, and delinquency are common. However, the causal nature of associations between these variables has yet to be delineated. Longitudinal, prospective studies are necessary in order to more definitively infer causality. However, the complexity of comorbid problems associated with chronic inhalant users makes them an extremely difficult population to engage and retain in research studies and in treatment. This is an important consideration when reviewing the few studies examining inhalant abuse that have been conducted, as well as the methodological concerns identified. Methodological issues such as small sample size, non-comprehensive assessments, unmatched control groups, and high participant attrition rates are common to these studies and are difficult to address, given the complexity of this population. TOXIC EFFECTS OF CHRONIC INHALANT ABUSE Broadly speaking, the acute physical effects of inhalant intoxication are euphoria and an initial rapid light-headed “high” sometimes associated with an anesthetic-type feeling (Brouette & Anton, 2001). At higher doses, disorientation, slurred speech, hallucinations, and ataxia may occur, with seizures, coma,

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and cardiopulmonary arrest possible at extreme doses (Kurtzman, Otsuka, & Wahl, 2001). There are no safe levels of use, as evidenced by “sudden sniffing death” (SSD), which can occur as a direct toxic effect of the substances inhaled (Bass, 1970). It is thought that certain substances sensitize the heart to adrenaline, and that this sensitivity, in conjunction with stress, physical exertion, or anxiety, may lead to cardiac arrhythmia (DCPC, 2002). Due to the rapid nature of SSD, the precise type of arrhythmia is rarely recorded, but in those few cases, the substances of abuse are typically toluene, chlorofluorocarbons, or butane (DCPC, 2002). The neuropsychopharmacology of inhalants is not well understood, although there is growing evidence from the animal literature that commonly used inhalants, including toluene, share common cellular mechanisms, and appear to have actions similar to those of CNS depressants (e.g., alcohol) (Lubman, Yücel, & Lawrence, 2008). The mechanism of action for toluene involves multiple neurotransmitter systems; it acts as a NMDA receptor antagonist decreasing the ability of cells to respond to glutamate (an action similar to alcohol's), increasing dopamine release in several brain areas (e.g., the ventral tegmental area and nucleus accumbens), and modifing serotonin transmission (Bowen, Batis, Paez-Martinez, & Cruz, 2006; Cruz, 2011; Cruz & Dominguez, 2011; Lubman et  al., 2008). However, there is evidence to suggest that, at sufficient doses, toluene has hallucinatory effects and cannot be necessarily considered a CNS depressant. A review by Cruz and Dominguez (2011) discusses the pharmacology of inhalant hallucinatory intoxication and identifies the etiology of other hallucinatory drugs as an interaction between mainly dopaminergic, glutamatergic, and serotonergic systems. The authors conclude all of these actions are likely to play a role in toluene-induced hallucinations, but the specific mechanisms remain unclear. While the specific neuropsychopharmacological mechanisms are not completely understood, chronic inhalant exposure is associated with significant toxic effects (Dinwiddie, 1994). However, the vast majority of research has been conducted on small and selective samples of adult inhalant users (Filley, Heaton, & Rosenberg, 1990; Hormes, Filley, & Rosenberg, 1986b; Yamanouchi et al., 1995), or occupationally exposed adults (Spurgeon, 2002). Inhalants are known to cause permanent damage to the cardiac, renal, pulmonary, hepatic, and hematological systems. For example, in chronic abusers, toluene causes thinning and ruptures of the alveolar walls, resulting in emphysema (Brouette & Anton, 2001). Additionally, there is also significant risk of asphyxiation while “bagging” if an individual loses consciousness. Chronic abuse of benzene (also found in paint) has been implicated in bone marrow disorders that lead to diseases such as leukemia, lymphoma, and multiple myeloma (Kurtzman et  al., 2001; Rosner & Grunwald, 1980; Vigliani & Saita, 1964). A number of renal disorders are associated with chronic inhalant abuse (especially toluene-containing compounds), including interstitial nephritis, renal tubular acidosis, Goodpasture's syndrome, glomerulopathy, tubulopathy, and renal failure (Brouette & Anton, 2001; Iqbal, 2001; Kurtzman et al., 2001; Olgar et al., 2008).

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Considering that inhalants are typically abused during early adolescence, the introduction of toluene may interfere with normal development, leading to cognitive impairment and neurobiological abnormalities, particularly in the prefrontal cortex. Animal studies support the notion that inhalants are damaging to the developing brain, with research showing a permanent reduction in forebrain myelination in rats after prenatal exposure to toluene (Gospe & Zhou, 1998) and behavioral abnormalities evident in rats exposed to toluene post-natally during periods of high synaptogenesis (Bowen et al., 2006; Chien, Chan, Tang, & Chen, 2005). Significant brain remodelling occurs throughout the adolescent period, characterized by increased development of white matter tracts (i.e., myelination) and decreases in cortical gray matter through synaptic pruning (Paus, 2005). The increased myelination and elimination of excess synaptic connections through apoptotic processes results in refinement of the neural circuitry. This, in turn, strengthens the remaining functional connections and reduces competition from suboptimal associations. As a result, communication across distributed systems and associated cognitive, emotional, and social processing are vastly improved. Considering the significant structural and functional changes occurring in the brain during adolescence (see Chapter  18 of this volume), inhalant abuse may have marked and long-term consequences due to the toxic and lipophilic nature of inhalants. As a class, inhalants have been reported to have a more formidable toxic profile than any other abused substance (Ramsey, Anderson, Bloor, & Flanagan, 1989), and considering the young age at which abuse typically occurs, the risk of neurobiological and neuropsychological disturbances in adolescent inhalant users appears to be significant (Lubman & Yücel, 2008). NEUROBIOLOGICAL CONSEQUENCES OF INHALANT ABUSE As with the neuropsychological literature, relatively few studies have investigated the neurobiological consequences of inhalant misuse in humans. A handful of autopsy studies have been performed and documented significant white matter abnormalities (e.g., demyelination, colossal thinning and diffuse, deep white matter hyperintensities) among chronic inhalant users (Filley, Halliday, & Kleinschmidt-DeMasters, 2004; Fornazzari, Pollanen, Myers, & Wolf, 2003; Rosenberg, Kleinschmidt-DeMasters, et al., 1988). However, the majority of studies have used neuroimaging techniques to investigate the neurobiological consequences of inhalant misuse. Such research includes computed tomography (CT) to investigate structural abnormalities (Ashikaga, Araki, Muria, & Ishida, 1995; Ehyai & Freemon, 1983; Escobar & Aruffo, 1980; Fornazzari, Wilkinson, Kapur, & Carlen, 1983; Kamran & Bakshi, 1998; Lazar, Ho, Melen, & Daghestani, 1983); magnetic resonance imaging (MRI), both T1- and T2-weighted, to examine abnormalities in both white and grey matter (Ashikaga et  al., 1995; Aydin et  al., 2002; Aydin et  al., 2003; Caldemeyer, Pascuzzi, Moran, & Smith, 1993; Deleu & Hanssens, 2000a; Filley et al., 1990; Hsu et al., 2012; Ikeda & Tsukagoshi, 1990; Kamran & Bakshi, 1998; Ohnuma, Kimura, & Saso, 1995; Okada et  al., 1999; Rosenberg et  al., 2002; Rosenberg, Kleinschmidt-DeMasters, et  al., 1988;

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Rosenberg, Spitz, Filley, Davis, & Schaumburg, 1988; Ryu et al., 1998; Sotirchos, Saidha, & Becker, 2012; Takagi, Lubman, Walterfang, et  al., 2011; Unger et  al., 1994; Yamanouchi et al., 1995; Yamanouchi et al., 1997); single-photon emission computed tomography (SPECT) to measure cerebral blood flow (Kucuk et  al., 2000; Okada et  al., 1999; Ryu et  al., 1998); magnetic resonance spectroscopy (MRS) to measure biochemical changes in specific brain regions (Aydin et  al., 2003); and diffusion tensor imaging (DTI) to examine the integrity of white matter tracts (Yücel et al., 2010). Evidence from the neuroimaging literature consistently supports the notion of long-term harms from chronic inhalant abuse, with evidence of diffuse atrophy of the cerebrum, cerebellum, and brainstem, sulcal widening, and ventricular dilation (Aydin et  al., 2002; Filley et  al., 1990; Fornazzari et  al., 1983; Hormes, Filley, & Rosenberg, 1986a; Lazar et al., 1983; Rosenberg et al., 2002; Rosenberg, Kleinschmidt-DeMasters, et  al., 1988; Rosenberg, Spitz, et  al., 1988). Broadly speaking, the abnormalities are greater in periventricular, subcortical (e.g., basal ganglia), and white matter regions, relative to cortical and gray matter regions, and are characterized by demyelination, hyperintensities, callosal thinning and loss of gray matter–white matter boundaries (Ashikaga et al., 1995; Aydin et al., 2002; Caldemeyer et  al., 1993; Deleu & Hanssens, 2000b; Ikeda & Tsukagoshi, 1990; Ohnuma et al., 1995; Rosenberg et al., 2002; Takagi, Lubman, Walterfang, et al., 2011; Yamanouchi et al., 1995). Several studies (Aydin et al., 2002; Aydin et  al., 2003; Filley et  al., 1990; Rosenberg et  al., 2002; Unger et  al., 1994)  have identified associations between neurobiological abnormalities and parameters of inhalant abuse (i.e., duration and frequency of use). Furthermore, two studies noted an association between neuropsychological impairment and white matter pathology (Filley et al., 1990; Yamanouchi et al., 1997). However, as highlighted by Rosenberg et al. (2002), comorbid issues that are frequently associated with inhalant use can significantly influence neurobiological findings, meaning that it is difficult to clearly delineate inhalant-specific effects. Indeed, only a handful of studies have attempted to control for such comorbid effects. Rosenberg et  al. (2002) compared 55 volatile-substance users (mean age = 30.1 years) and 61 cocaine users (mean age = 29.4 years) using structural MRI (T1- and T2-weighted images were obtained). While the inhalant and drug-using control groups were not statistically matched on substance use, polysubstance use (alcohol, cannabis, and cocaine) was frequent in both groups. Overall, the volatile substance group had significantly more brain abnormalities (44%) relative to the cocaine group (25%) and performed worse on tests of executive functions (Rosenberg et al., 2002). In all, 22% of the volatile substance users had decreased signal intensity in the basal ganglia, 40% demonstrated pontine abnormalities, and 42% had cerebellar abnormalities (see Figure 12.1 for an example). Solvent users also demonstrated significantly more diffuse abnormalities of the cerebral white matter, which was associated with greater cognitive impairment. Given the cross-sectional nature of the study, it was not possible to determine the permanency of the identified abnormalities. Nevertheless, although the two groups were not statistically matched on substance use, it is possible that the

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Figure 12.1 MRI scan of a 25-year-old chronic inhalant user. T2-weighted images reveal cerebral atrophy, enlargement of the lateral ventricles, and severe, diffuse, increased signal in cerebral white matter.* *Reproduced from Rosenberg et al. (2002).

cumulative effect of polysubstance abuse (including inhalants) is significantly more detrimental to neuropsychological performance (i.e., executive functioning) and neurobiological health relative to polysubstance abuse excluding inhalants. Yücel et al. (2010) investigated the neurobiological effects of adolescent inhalant misuse and found significant white matter abnormalities among a small sample of young inhalant users. Using diffusion tensor imaging (DTI), the research team examined white matter integrity in 11 adolescent inhalant users (mean age = 18.2 years, range = 14–22), 11 drug-using controls (mean age = 19.4 years), and eight community controls (mean age = 19.7 years). White matter integrity was measured by fractional anisotropy (FA), a broad measure reflecting the myelination of axons, axonal density, and axonal diameter. An abnormally low FA is suggestive of reduced white matter integrity. All participants were statistically equivalent on age and gender, and the drug-using groups were statistically equivalent on cannabis and alcohol consumption. Inhalant users demonstrated abnormally low FA relative to controls in the white matter fibers adjacent to the hippocampus and the splenium of the corpus callosum (see Figure 12.2). These findings are consistent with white matter– related cognitive deficits (e.g., slowed processing speed and memory retrieval) previously reported among volatile substance users (see Yücel et  al., 2008).

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Drug-using controls also showed abnormalities relative to controls, with lower FA in the same white matter fibers adjacent to the hippocampus. Interestingly, when the drug-using groups were compared, volatile substance users revealed lower FA in the corpus callosum compared with cannabis users (Figure 12.2). Furthermore, early initiation of volatile substance misuse was positively associated with lower frontal white matter integrity, suggesting that earlier use of volatile substances may result in greater harm (see Figure 12.2) (Yücel et al., 2010). Building on the findings of Yücel et al. (2010), Takagi et al. (2013) examined size and shape alterations in the corpus callosum (CC) of a sample of adolescent inhalant-users using structural T1-weighted MRI. Participants included 14 inhalant users (mean age 17.3 years), 11 drug-using controls (mean age 19.7), and 9 community controls (mean age = 19.5). Unitary CC measures such as total CC area, length, mean thickness, and bending angle were compared between groups using analysis of covariance. The inhalant group was significantly younger than the other two groups, and age was included as a covariate for all analyses. All three groups, however, were matched on gender, and the drug-using groups were statistically equivalent on cannabis and alcohol consumption. The CC was globally thinner in the inhalant group compared with controls, with these changes being disproportionately located towards the genu (see Figure 12.3). This region has interhemispheric connections with prefrontal brain regions associated with impulse control (e.g., orbitofrontal cortex) (Pandya &

Figure 12.2 Differences in fractional anisotropy between inhalant users, drug-using controls, and community controls.* *Reproduced from Yücel et al. (2010).

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Inhalant users vs healthy controls > 0.05 0.04 0.03 0.02 0.01 0

Figure 12.3 Regional callosal-width alterations for inhalant versus community controls, with significant and trend-level expansions denoted by color-coding according to significance.* (See color insert). *Reproduced from Takagi et al. (2013).

Seltzer, 1986), with abnormalities associated with increased levels of impulsivity (Matsuo et al., 2010; Moeller et al., 2005). This finding is consistent with the findings of earlier neuropsychological studies that identified executive deficits among young inhalant users (e.g., Rosenberg et al (2002)). Surprisingly, there were no significant differences between the drug-using controls and inhalant groups on any CC measure. Yücel et al. (2010) previously identified significant reductions in FA in the right and left CC among adolescent inhalant users compared with drugusing controls, suggesting reduced white matter integrity. However, the authors highlight that, while volume as measured by shape change and microstructure as measured by FA are related, they do not necessarily co-vary, so it is possible that changes in FA precede volumetric change. NEUROPSYCHOLOGICAL CONSEQUENCES OF INHALANT MISUSE Relatively little research has been conducted in human adolescent inhalant-misusing subjects. The lack of human research is, in part, due to the serious and complex issues that characterize inhalant-abusing adolescents (i.e., delinquency, family dysfunction, mental health, comorbid drug use, and medical/ neurological comorbidities), making them difficult to access, engage, and maintain in research. As a result, inhalant research is frequently criticized on methodological grounds (Lubman et al., 2006; Takagi, Lubman, & Yücel, 2008), such as small sample sizes, non-comprehensive assessments, and unmatched control groups. As a result, it is difficult to identify any inhalant-specific deficits among adolescent users. The vast majority of adolescent inhalant studies have identified a broad spectrum of neuropsychological deficits, including poorer general intellectual functioning (Allison & Jerrom, 1984; Berry, Heaton, & Kirby, 1977; Chadwick et  al., 1989; Ehyai & Freemon, 1983; Filley et  al., 1990; Fornazzari et  al., 1983;

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Ikeda & Tsukagoshi, 1990; Kamran & Bakshi, 1998; Korman, Matthews, & Lovitt, 1981; Kucuk et al., 2000; Lazar et al., 1983; Massengale et al., 1963; Ryu et  al., 1998; Scott & Scott, 2011; Takagi, Lubman, Cotton, et  al., 2011; Takagi et al., 2008; Takagi, Yücel, et al., 2011; Tushima & Towne, 1977; Yamanouchi et al., 1997); attention (Allison & Jerrom, 1984; Deleu & Hanssens, 2000b; Dodds & Santostefano, 1964; Filley et al., 1990; Kamran & Bakshi, 1998; Massengale et al., 1963; Ryu et al., 1998; Vilar-Lopez et al., 2013); memory (Allison & Jerrom, 1984; Berry et  al., 1977; Chadwick et  al., 1989; Deleu & Hanssens, 2000b; Dodds & Santostefano, 1964; Ehyai & Freemon, 1983; Filley et al., 1990; Fornazzari et al., 1983; Hormes et al., 1986a; Kamran & Bakshi, 1998; Lazar et al., 1983; Massengale et al., 1963; Rosenberg, Kleinschmidt-DeMasters, et al., 1988; Rosenberg, Spitz, et al., 1988; Ryu et al., 1998; Tushima & Towne, 1977); speed of information processing (Chadwick et al., 1989; Rosenberg et al., 2002; Tushima & Towne, 1977); language, including verbal fluency and language comprehension (Berry et  al., 1977; Filley et al., 1990; Hormes et al., 1986a; Rosenberg et al., 2002; Rosenberg, Kleinschmidt-DeMasters, et  al., 1988; Rosenberg, Spitz, et  al., 1988); visuospatial functioning (Chadwick & Anderson, 1989; Hormes et al., 1986a; Massengale et al., 1963; Rosenberg, Kleinschmidt-DeMasters, et al., 1988; Rosenberg, Spitz, et  al., 1988); motor dexterity (Berry et  al., 1977; Chadwick et  al., 1989; Dodds & Santostefano, 1964; Fornazzari et  al., 1983; Massengale et  al., 1963; Tushima & Towne, 1977); verbal and visuospatial learning (Deleu & Hanssens, 2000b; Ryu et  al., 1998); and executive functions (e.g., insight, planning, working memory, inhibition) (Filley et al., 1990; Hormes et al., 1986a; Kamran & Bakshi, 1998; Rosenberg et  al., 2002; Rosenberg, Kleinschmidt-DeMasters, et  al., 1988; Rosenberg, Spitz, et al., 1988; Takagi, Lubman, Cotton, et al., 2011; Takagi, Yücel, et al., 2011). However, only a handful of these studies attempted to address the numerous comorbidities associated with inhalant misuse (e.g., polysubstance use, psychopathology, abuse), making it difficult to tease apart the cognitive effects of inhalant misuse and other commonly comorbid issues. The first inhalant studies to examine adolescent inhalant users and address commonly comorbid issues were conducted by Rosenberg et al. (2002) and Kucuk et al. (2000). Relative to other inhalant studies, the participants were matched (or statistically controlled for in the case of Rosenberg et al., 2002) on a wider range of demographic and psychosocial variables. With respect to neuropsychological performance, both studies reported that controls and inhalant users performed worse than published normative standards, although the two groups did not significantly differ on measures of general intellectual ability. However, Rosenberg et al. (2002) reported significant differences between the groups on measures of executive abilities and working memory after controlling for demographic and psychosocial variables. Further, Rosenberg et  al. (2002) identified several neuropsychological abnormalities in the control group (alcohol-, cannabis-, and cocaine-using controls). Previous research has identified significant neuropsychological abnormalities associated with alcohol and cannabis abuse (see chapters 6 and 7 of this volume). Rosenberg and colleagues’ study (2002) supports these results, reporting poor

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neuropsychological performance among substance abusing controls. While the inhalant and drug-using control groups were not statistically matched on substance use, polysubstance abuse was frequent in both groups (alcohol, cannabis, and cocaine). Nevertheless, inhalant users (mean age = 30.1 years, SD = 8) were found to have significantly greater deficits on measures of executive functioning (set shifting and working memory). It is possible that the cumulative effect of polysubstance abuse plus inhalant abuse is significantly more detrimental to neuropsychological performance (i.e., executive functions) relative to polysubstance abuse excluding inhalants. In contrast, the results of Kucuk and colleagues’ study (2000) do not fully support this notion, as there were no significant differences between inhalant users and controls on measures of intellectual ability. It is important to note the participants in the Kucuk et al. (2000) study were significantly younger (mean age = 17.3, range 16–18) and thus had less exposure to inhalant use. Furthermore, in the Kucuk et al. study (2000), participants had abstained from inhalant use for at least one month (range = 1–11 months) prior to assessment, which may have contributed to the lack of significant differences. Finally, the measures used by the investigators were global measures of cognitive functioning (IQ) that did not probe specific cognitive domains. More recently, Takagi and colleagues (2011) examined verbal memory, learning, and executive functions in a sample of adolescent inhalant (n = 21, age range 14–21) and cannabis (n = 21, age range 15–21) users. In an attempt to address many of the common comorbidities often associated with inhalant users, the authors recruited a control group from the general population (n = 21, age range 13–24) and a drug-using control group (primarily cannabis users) from the same geographical area as the inhalant sample. All three groups were statistically equivalent at the group level on age, sex, and education, and the inhalant- and drug-using control group were statistically equivalent on measures of psychopathology, general intellectual abilities, family dynamics, socioeconomic status, and substance use measures (e.g., alcohol, cannabis, and tobacco). All participants completed the Rey Auditory Verbal Learning Test (RAVLT) (Rey, 1941)  which is used as a measure of immediate verbal memory, learning performance, and executive aspects of learning and memory (i.e., interference susceptibility). Both drug-using groups performed significantly worse on several trials of the RAVLT compared to controls, demonstrating deficits in immediate verbal memory, learning performance, and memory retrieval. This is not surprising, however, considering the significant differences between the drug-using groups and controls on demographic measures, psychopathology, and drug use. Interestingly, the inhalant- and drug-using controls did not significantly differ on any measure, with the exception of proactive interference (old learning interfering with new learning, RAVLT Trial 1 minus List B), with the inhalant group demonstrating significantly more susceptibility relative to drug-using controls. The authors hypothesized that the difficulty in successful proactive interference resolution demonstrated by the inhalant group relates to inhalant-specific deficits in executive functioning. This is consistent with the findings of Rosenberg et al. (2002),

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who also identified executive deficits among inhalant users relative to drug-using controls after statistically controlling for several variables. Further examining executive functions in inhalant users, Takagi et al. (2011) used computerized versions of the Stroop Color and Word Test and Go/No-Go tasks to probe executive functioning in a subset of the previously examined inhalant cohort (n = 19 in each group, age range 13–24). The authors examined reaction times and errors for both tasks (omission and commission), as well as the Stroop effect. As in the previous study, all groups were statistically equivalent at the group level on age, sex, and education, and the inhalant and drug-using control group were statistically equivalent on measures of psychopathology, general intellectual abilities, family dynamics, socioeconomic status, and substance use measures. Surprisingly, there were no significant differences between any of the groups on any measure, suggesting intact response inhibition (Go/No-Go) and interference control (Stroop). The authors discussed several possibilities to explain the surprising lack of significant differences between all three groups. This included being underpowered; many comparisons had medium to large effect sizes (e.g., Cohen's d > 0.5) and did not survive post-hoc analysis due to corrections for multiple comparisons. The authors also discussed the possibility that the clinical groups had not used inhalants long enough to see the broad range of neuropsychological deficits identified in the adult population, and it would be worth utilizing more sensitive, experimental cognitive tasks to explore subtle deficits. Vilar-Lopez and colleagues (2013) utilized the Attention Network Task (ANT) (Fan, McCandliss, Sommer, Raz, & Posner, 2002)  to examine attention networks in a subset of the inhalant cohort described in Takagi et  al. (2011) (n = 19 for inhalant and drug-using controls, n = 18 for community controls, age range 12–25). The ANT was chosen because it is a specific yet comprehensive and well-validated task that measures three aspects of attention:  alerting (achieving and maintaining an alert state); orienting (selection of information from sensory input); and executive control (resolving conflict among responses). Inhalants have been hypothesized to be toxic to white matter (Rosenberg et al., 2002; Takagi, Lubman, Walterfang, et al., 2011; Yücel et al., 2010), and white matter abnormalities are frequently associated with deficits in attention and linked to the efficiency of attentional networks (Niogi, Mukherjee, Ghajar, & McCandliss, 2010; Silk, Vance, Rinehart, Bradshaw, & Cunnington, 2009; Vilar-Lopez et al., 2013). There were no significant differences between the groups on the alerting, orienting, or executive control measures. With respect to errors, the inhalant group committed significantly more errors across all conditions of the ANT relative to both community controls and drug-using controls. Furthermore, the authors controlled for speed and accuracy trade-off (i.e., sacrificing accuracy for increased response time) by calculating inverse efficiency scores, which showed a similar pattern of performance across groups; that is, all three groups adopted a similar strategy, and the pattern of results was not due to impulsive responding or a specific strategy to respond quickly. The authors concluded that the patterns of behavioral results for the inhalant group are consistent with a deficit in sustained

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attention or the ability to maintain an alert state for a sustained period of time while performing an activity. The bulk of research conducted to date has found that chronic adolescent inhalant misuse is associated with significant neuropsychological impairment. Evidence from the animal literature also supports the notion of developmental harms resulting from adolescent inhalant abuse; however, the methodological difficulties associated with the human inhalant literature limit any conclusions that can be drawn. The psychosocial difficulties frequently associated with the inhalant population create significant challenges for recruitment and retention into studies, as inhalant users are often deviant, move frequently, and typically live on the fringes of society (Oetting & Webb, 1992). This presents methodological difficulties that limit the generalizability of neuropsychological studies examining inhalant abuse, including lack of well-matched control groups and difficulties controlling for the numerous comorbid issues associated with inhalant abuse (e.g., polysubstance use, psychopathology), which can significantly influence neuropsychological performance. The handful of studies that have attempted to address the comorbid issues have identified inhalant-specific deficits in executive functions, working memory, sustained attention, and interference susceptibility relative to drug-using controls. This is somewhat surprising, considering the widespread neuropsychological and neurobiological impairments associated with adult inhalant users; however, there are several factors that should be considered. It is possible that the well-matched adolescent samples had not used inhalants for a sufficient amount of time to manifest the gross neuropsychological and neurobiological abnormalities seen in adult users. For example, the sample recruited by Takagi et al. (2011) had only used regularly (daily or almost daily) for approximately two years. It is therefore possible that any potential cognitive impairment has not yet fully developed because of the relatively short duration of regular use. Given that most of the existing research is cross-sectional in nature, it is not possible to identify causal mechanisms (i.e., does inhalant use lead to poor cognitive functioning, or vice versa?). Alternatively, the plasticity of the adolescent brain (Barnea-Goraly et al., 2005; Paus, 2005)  may minimize any potential inhalant-related brain insult, thereby temporarily masking any underlying deficits. In this case, the cognitive and neurobiological deficits frequently identified in adult inhalant users may only be evident later, in the context of continued use. Finally, it may be that, relative to other substances, inhalants are not as severely toxic to the adolescent human brain and cognition as previously hypothesized. For example, in the studies conducted by Takagi et al. (2011; 2011) and Vilar-Lopez et al. (2013), the performance of the inhalant group and the drug-using controls raises several important questions regarding the hypothesized toxicity of inhalants and substance-specific cognitive deficits among regular adolescent substance users. Cognitive deficits identified in previous adult and adolescent studies may reflect adolescent substance use and social and environmental circumstances rather than the specific effects of inhalant use. Indeed, regular inhalant users are characterized by poor family environments, comorbid substance use, poor academic performance, and

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psychopathology (Howard et al., 2011; Oetting & Webb, 1992; Sakai et al., 2004; Takagi et al., 2008), all of which could negatively influence cognition. Future studies will need to carefully consider these broader variables when examining the specific impairments associated with inhalant exposure. CLINICAL IMPLICATIONS Inhalant misuse is disproportionately associated with young, marginalized individuals who are characterized my numerous medical, socioeconomic, psychosocial, mental health, and substance abuse comorbidities. It is therefore not surprising that inhalant users represent a diagnostic and treatment dilemma for a broad spectrum of clinicians. Clinical treatment is further complicated by the heterogeneity of chemicals contained within commonly abused inhalants, of which there is limited neuropsychopharmacological knowledge. The literature examining the clinical treatment of inhalant users does not support any clear direction. Konghom et al. (2010) reviewed the inhalant treatment literature and found no conclusions can be drawn regarding evidence-based treatments for inhalant users. Similarly, a recent systematic review by MacLean and colleagues (2012) examined psychosocial therapeutic interventions for volatile substance use. They concluded that there were no clear evidence-based treatments for inhalant users. However, family therapy, activity-based programs, and indigenous-led residential approaches do merit further investigation. There are also reports based on individual cases where therapeutic interventions such as cognitive behavioral therapy–based brief interventions have had some success (Ogel & Coskun, 2011). It is clear that further research is required to establish the effectiveness of any specific treatment for inhalant users. While a clear treatment model for inhalant users does not currently exist, there is a clear role for clinical neuropsychology in helping to determine appropriate treatment approaches, given that cognitive dysfunction is a hallmark of inhalant misuse. Although there is some evidence that inhalant-specific cognitive dysfunction may be more executive in nature (e.g., sustained attention), inhalant users most frequently present with a broad range of cognitive dysfunction across multiple domains. A case study published by Takagi et al. (2008) illustrates the heterogeneity of cognitive deficits among inhalant users, as well as the important role that neuropsychology can play in treatment planning. Two young, female inhalant users (JP and HB) participated in a neuropsychological assessment at the request of the Department of Human Services (DHS) in Melbourne, Australia, which provides state-based care for young people in the event their primary caregivers are unable to care for them. HB was 15  years old, and she was no longer attending school, was unemployed, was not socializing well (frequent fights, cruelty to others and animals, and few friends), exhibited signs of depression (low mood, poor sleep/concentration, self-harm, suicidal ideation, lethargy and tiredness), and routinely abused a number of substances (cannabis, alcohol, inhalants, and tobacco). At the time of the assessment, she was a daily tobacco user, and a weekly cannabis

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and alcohol user. She reported that she began sniffing paint on a daily basis at the age of 14 years and had continued at that level until three months prior to assessment (daily use for approximately 12  months), at which point she reduced her use to less than once per month. HB received a Global Assessment of Functioning (GAF) score of 50 during the assessment. JP was 16 years old, and although she was living with her mother, she had been under the supervision of DHS for many years; her mother had a severe drug problem and was not able to provide her with adequate supervision or support. She frequently absconded from home, had few friends, had attempted suicide twice in the past nine months, routinely engaged in deliberate self-harm, and was often involved in physical altercations with other adolescents. Additionally, she exhibited signs of depression (low mood, poor sleep and concentration, self-harm, suicidal ideation, lethargy and tiredness) and received a GAF score of 50. At the time of assessment, she was no longer in school and was unemployed. She reported sniffing paint daily beginning at the age of 15 years, but she had only used inhalants three times in the past three months and had been abstinent for the past month. The case managers for both girls suspected they were intellectually disabled, and DHS was interested in obtaining additional funding to support JP and HB. Part of the assessment included the Wechsler Intelligence Scale for Children-IV (WISC-IV) (Wechsler, 2003). The results revealed that JP was functioning well below the expected age range for her group (Full Scale IQ = 53); however HB was functioning within the average range for her age group (Full Scale IQ = 97). HB's case management team were surprised at the results of her assessment, as they expected her to perform significantly worse than she had. As a result, the focus of her treatment shifted to her mental health and behavioral issues instead of a presumed cognitive deficit. In contrast, JP's case management team were not surprised and utilized the recommendations from the neuropsychological report to implement strategies for effective behavioral management of her cognitive deficits. The cases presented by Takagi et al. (2008) provide a brief illustration of the heterogeneity of cognitive functioning among adolescent inhalant users and highlight the dramatic inter-individual variability that can exist across individuals with very similar psychosocial backgrounds and patterns of substance use. Furthermore, these cases highlight the utility of neuropsychology in the clinical management of inhalant users. HB's case managers focused their attention on her psychosocial issues rather than on her presumed intellectual disability. Her treatment included attending to her mental health and behavioral issues to prepare her for reentering the school environment. In the case of JP, her scores confirmed her clinical team's hypothesis and allowed them to focus on managing her intellectual disability. This included teaching her practical behavioral techniques to assist her in everyday living (e.g., encouraging her to think on paper, allowing extra time to absorb information, using repetition, etc.). These cases highlight the complex interaction between inhalant use and cognition and the important influence psychosocial and motivational factors have on neuropsychological outcomes.

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These cases also illustrate that behavioral problems, poor engagement, and lack of motivation are important issues to consider when conducting a neuropsychological assessment with adolescent inhalant users. Indeed, individuals chronically exposed to inhalants typically find it difficult to engage in such tasks presenting a difficult challenge to clinicians seeking to obtain reliable measures of intellectual and cognitive functioning. These two cases illustrate the typically complex presentation (i.e., disadvantaged psychosocial backgrounds, comorbid psychopathology, and poor motivation/engagement) associated with adolescent inhalant abuse. Future studies must consider such factors carefully when attempting to examine the neuropsychological sequelae of inhalant abuse among adolescents, and it remains a critical issue for clinicians to consider when attempting to engage an adolescent inhalant-user in treatment. CONCLUSIONS The bulk of research conducted to date has found that chronic inhalant abuse is frequently associated with neuropsychological impairment and neurobiological abnormalities; however, it is difficult to separate inhalant-specific effects from the effects of polysubstance use, psychopathology, trauma, and numerous other psychosocial factors that characterize inhalant users. In order to identify causal mechanisms, prospective studies are required to map the impact of inhalant misuse on behavior, psychopathology, social interaction, cognition, and neurobiology. With respect to clinical treatment, future research should examine clinical trials of focused interventions to improve social circumstances and prevent poor functioning in young inhalant users. This may include clinical management through pharmacological or psychological interventions. Maclean et  al. (2012) highlight the possible utility of family therapy, activity-based programs, and culturally specific residential approaches. Ascertaining clinical best practice is an essential step to improve the quality of life of these young people. References Allison, W., & Jerrom, D. (1984). Glue sniffing: A pilot study of the cognitive effects of long-term use. International Journal of Addiction, 19(4), 453–458. Ashikaga, R., Araki, Y., Muria, K., & Ishida, O. (1995). Cranial MRI in chronic thinner intoxication. Neuroradiology, 37(6), 443–444. Aydin, K., Sencer, S., Demir, T., Ogel, K., Tunaci, A., & Minareci, A. (2002). Cranial MR findings in chronic toluene abuse by inhalation. American Journal of Neuroradiology, 23, 1173–1179. Aydin, K., Sencer, S., Ogel, K., Genchellac, H., Demir, T., & Minareci, O. (2003). Single-voxel proton MR spectroscopy in toluene abuse. Magnetic Resonance Imaging, 21(7), 777–785. Barman, M., Sigel, N., Beedle, D., & Larson, R. (1964). Acute and chronic effects of glue sniffing. California Medicine, 100(1), 19–22. Barnea-Goraly, N., Menon, V., Eckert, M., Tamm, L., Bammer, R., Karchemisky, A., . . . & Reiss, A. (2005). White matter development during childhood and

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13

Serious Mental Illness and Substance Use Disorder Comorbidity DA N I E L N.   A L L E N , B E R N G .   L E E , A N D N I C H O L AS S .   T H A L E R

Substance use disorders (SUDs) occur at a high rate in serious mental illnesses (SMIs). Despite extensive literature describing brain abnormalities in SMIs and SUDs, brain dysfunction for individuals who have SMI with comorbid SUDs (SMI+SUD) remains poorly understood. The current chapter summarizes relevant literature regarding neuropsychological functioning when substance use is present in individuals who are diagnosed with SMI, focusing primarily on schizophrenia (SZ). We focus on SZ because it is one SMI with disproportionate rates of comorbid SUDs, and strong evidence supporting structural and functional brain abnormalities. We also focus our discussion on the neuropsychological effects of alcohol, since alcohol use disorders (AUDs) are the most common comorbid SUD for individuals with SMI, and are associated with significant cognitive impairment and functional and structural brain abnormalities. Neuropsychological deficits associated with AUD comorbidity in SZ are discussed, and a model is proposed to explain neuropsychological functioning in SMI + SUD based on existing research, taking into account the multiple sources that contribute to neuropsychological deficits that fluctuate in relation to disorder-specific factors (e.g., substance intoxication, symptom exacerbation, psychotropic medication compliance) as well as deficits that are stable and suggest lasting or permanent impairment. Finally, literature relevant to schizophrenia and alcoholism is reviewed to support the basic components of the model, with the understanding that, while the model has more general application to various psychiatric-substance use comorbidities, the neuropsychological literatures regarding schizophrenia and alcoholism are the most extensive, and so are able to provide strong empirical support for the factors included in the model.

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EPIDEMIOLOGY AND RELATED CHARACTERISTICS A recent report from the Substance Abuse and Mental Health Services Administration (SAMHSA, 2012) indicates that in 2011, 19.6% of the adult population in the United States (45.6 million people) were diagnosed with a DSM-IV mental illness, an estimate that has remained stable since 2008. During the same year, 18.9 million adults were diagnosed with a SUD, of whom 8.0 million (42.3%) also had a DSM-IV mental illness. Figure 13.1 presents a breakdown of the percentage of individuals diagnosed with a DSM-IV mental illness who also had an alcohol, illicit drug, or SUD (alcohol and illicit drug combined) based on severity of mental illness (adapted from SAMHSA, 2012, Figures  4.4, 4.5, and 4.6). Of interest, the table indicates that SUDs occur with increased frequency as the severity of mental illness increases, from an overall percentage of 5.8 when no mental illness is present, to 22.6% when mental illness is serious/severe. It is also noteworthy that at all levels of mental illness severity, AUDs occur significantly more often than illicit drug use disorders. Of the illicit drugs, marijuana was used the most often in the past year, with 25.2% of individuals with a mental illness indicating use, compared to 11.8% of those without a mental illness, followed by use of prescription drugs for non-medical reasons (11.9% vs. 4.0%, respectively), and then cocaine use (3.2% vs. 1.2%, respectively). Estimates of SUDs also vary 25 Substance use disorder

% Substance dependence or abuse (DSM-IV)

Alcohol use disorder Illicit drug use disorder

20

15

10

5

0 Serious mental illness

Moderate mental Mild mental illness illness Mental illness severity

No mental illness

Figure 13.1 Substance, alcohol and illicit drug use disorders among individuals with mental illnesses in 2011.

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by mental illness diagnosis. Up to 65% of individuals with SZ have lifetime diagnoses of SUD (Cuffel, 1992; Mueser et al., 1990; Regier et al., 1990). It has been consistently documented that SUD comorbidity is associated with a wide variety of negative outcomes in SMI, including treatment noncompliance, increased suicidality, increased psychotic symptoms, poorer community adjustment, more frequent hospitalizations, and increased violence (Drake et  al., 1991; Drake & Wallach, 1989; Havassy & Arns, 1998; Mazza et al., 2009; Steadman, Mulvey & Monahan, 1998; Westermeyer, 2006), among others. Neuropsychological deficits are a common feature of SMI and SUD, and are significant predictors of treatment and functional outcomes. Alcoholism researchers have actively investigated relationships among neuropsychological abilities and treatment outcomes since the 1970s (Berglund et al., 1977; Chotlos et al., 1970; Clark & Haughton, 1975; O'Leary et al., 1979), noting that cognitive impairment will not only interfere with treatments requiring attention, memory, abstraction, and other complex cognitive abilities, but if left unaddressed will also interfere with adjustment in the community (see Allen, Goldstein, & Seaton, 1997; Goldman, 1990). Chapter 5 in this volume provides more detail regarding these matters, and makes clear the significance of neuropsychological function in predicting a variety of treatment outcomes following cessation of substance use, while Chapter 16 reviews the impact that neuropsychological deficits have on functional outcomes. There have been numerous studies reporting similar associations among neuropsychological abilities and both short- and long-term outcomes in SZ (Bell & Bryson, 2001; Evans et al., 2003; Green, 1996; Green et al., 2000; Green et  al., 2004; Robinson et  al., 2004; Schretlen et  al., 2000; Twamley et al., 2002; Velligan et al., 2000). In fact, recognition that neuropsychological dysfunctions are among the most debilitating features of SZ has prompted their identification as targets for new treatments (e.g., Gold, 2004; Green & Neuchterlein, 1999; Harvey et  al., 2004; Hyman & Fenton, 2003). Despite the significance of neuropsychological deficits in both SMI and SUD with regard to treatment, these deficits in SMI+SUD have received relatively little attention. BRAIN DYSFUNCTION IN SMI AND SUD Neuropsychological evaluation has provided many insights into the cognitive deficits arising from the neuropathology of SMI and SUDs. Chapter 6 of this volume provides an in-depth review of the neuropsychological consequences of AUDs, so we will focus our review on neuropsychological and neuroimaging studies concerning SZ and SMI+SUD comorbidity.

Schizophrenia NEUROPSYCHOLOGY

Schizophrenia (SZ) is classified as a psychotic disorder in the DSM-IV, for which neuropsychological deficits have been clearly documented (for reviews, see Goldberg & Gold, 1995; Heinrichs & Zakzanis, 1998). Two issues that have

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emerged from neuropsychological investigations concern whether SZ is best characterized by a number of discrete cognitive deficits or by a more generalized deficit, as well as the matter of neuropsychological heterogeneity. Consistent with the suggestion of generalized deficit, many studies report that individuals with SZ perform worse than healthy control groups on most if not all neuropsychological tests (Schaefer, Giangrande, Weinberger, & Dickinson, in press). Because of this, a challenge for neuropsychological studies has been to identify differential deficits; that is, deficits that are more severe than expected given the presence of generalized cognitive impairment (Chapman & Chapman, 1973). These differential deficits are of primary interest because they may reflect dysfunction of specific brain regions or neural circuits central to SZ pathophysiology. Based on meta-analysis, Heinrichs and Zakzanis (1998) provided evidence for variability in the severity of impairment across neuropsychological domains with a possible selective impairment of verbal memory, and decreased processing speed may also be a central feature of cognitive dysfunction in SZ (Dickinson et al., 2007; Schaefer et al., in press). However, neuropsychological studies also indicate individuals with SZ have diminished simple apprehension and sustained attention (Asarnow et  al., 1991; Gold et al., 1994; Nuechterlein, 1991), language comprehension (Condray et al., 1992; Purisch et al., 1978), and executive abilities, such as concept formation, problem solving, sequencing, cognitive flexibility, and working memory (Braff et al., 1991; Fleming et al., 1995; Fleming et al., 1997; Goldstein et al., 1996; Levin et al., 1989; Park & Holtzman, 1992; Schwartz et al., 1991). One issue that complicates straightforward interpretation of these group findings is the presence of marked heterogeneity in neuropsychological performance among individuals with SZ (Seaton et al., 2001). Gerald Goldstein was perhaps the first to address this matter using statistical approaches (e.g., cluster analysis) designed to determine whether there were homogeneous subgroups within the more general SZ population based on neuropsychological test performance. Through a series of investigations using a variety of neuropsychological evaluation procedures and conducted by different researchers at different sites (e.g., Goldstein, 1990; Heinrichs & Awad, 1993; Hill et al., 2002; Palmer et al., 1997), four or five subgroups were identified, including a group that demonstrated severe impairment and another that appeared to perform at normal levels on most neuropsychological tests. Two or three intermediate clusters also emerged. One demonstrated relatively severe motor impairment with relative sparing of other abilities, and another demonstrated selective impairment on the Wisconsin Card Sorting Test. The cluster with minimal cognitive impairment was initially referred to as “neuropsychologically” normal but is probably better characterized as “high cognitive functioning” (Allen et  al., 2003), similar to usage of the term in the autism literature, where affected individuals may demonstrate average to above average performance on most tests despite the presence of disordered thinking. The results of these investigations suggest that SZ is not like other disorders that are adequately characterized by a single neuropsychological profile, but rather may be better understood as a heterogeneous disorder characterized by four or five unique neuropsychological profiles that differ from each other in both

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level and pattern of neuropsychological test performance. In this sense, examination of mean differences across all individuals with SZ may obscure meaningful differential deficits that are observed when clusters are examined. The clusters may be clinically useful, as they provide an indication of expected patterns of neuropsychological test performance in schizophrenia, but further work examining clinical course, outcome, and pathophysiology is needed to determine whether these clusters represent actual subtypes of SZ. NEUROIMAGING

Functional and structural neuroimaging studies of SZ have identified several general findings, including increased ventricular–brain ratio suggesting a general loss of neural tissue, as well as decreased gray matter volume and reduction of the normal asymmetry between left and right hemispheres. Cortical gray matter volume reductions have also been identified in fronto-striatal and temporo-limbic regions (Ananth et al., 2002; Gur et al., 2000a; Gur et al., 2000b; Harvey et al., 1993; Lim et al., 1996; Sullivan et al., 1998). For the frontal lobes, functional and structural abnormalities have been identified in dorsolateral prefrontal cortex (Gur et  al., 2000a; Ragland et  al., 1998; Weinberger et  al., 1992), medial prefrontal cortex and anterior cingulate (Ananth et  al., 2002; Haznedar et  al., 1997), frontal eye fields (Sweeney et  al., 1998), and orbital frontal cortex (Gur et al., 2000a; Malaspina et al., 1998). Temporal lobe abnormalities involving the superior temporal gyrus, including primary and secondary auditory cortex (Menon et al., 1995; McCarley et al., 1999; Shenton, Dickey, Frumin, & McCarley, 2001; Sullivan et al., 1998) and medial temporal lobe structures (Arnold et al., 1995; Bogerts et al., 1990; Suddath et al., 1989; Suddath et al., 1990) have also been reported. With regard to neuropsychological heterogeneity, some neuroimaging differences have been reported among the neuropsychological clusters described earlier. Allen, Seaton, et al. (2000) found significant differences on global sulcal widening as identified by CT scan between a neuropsychologically impaired cluster and the high-functioning cluster. MRI-based analyses further indicated presence of substantial generalized cortical thinning in the neuropsychologically impaired group with very little thinning observed in the high-functioning group (Cobia, Csernansky, & Wang, 2011). Similarly, MRS spectroscopy identified reduction in left prefrontal cortex, temporal lobe, and basal ganglia neuronal cell bodies for cognitively impaired patients, compared with those who were cognitively intact, as indicated by a number of biochemical differences including decreased levels of sphosphomonoester and elevated phosphocreatine and adenosine triphosphate in the cognitively impaired group (Pettegrew et al., 1991, 2003, 2008).

SMI SUD Comorbidity NEUROPSYCHOLOGY

Investigation of substance use comorbidity on neuropsychological functioning in individuals with SMI has increased significantly over the past 10 years, driven by

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the expectation that neuropsychological and brain abnormalities associated with substance use would have compounding effects on those abnormalities already present in SMI. Most studies involve individuals diagnosed with SZ or experiencing their first psychotic episode. Methodologies of these studies vary dramatically, such that some have included individuals experiencing their first psychotic episode, with others including those with a confirmed SZ diagnosis who were recently diagnosed or diagnosed for many years. For substance use, some studies examine those with a lifetime history of substance use, while others include only those with DSM-IV substance use disorders, sometimes current and sometimes lifetime diagnoses. Investigators have directed various levels of attention toward ensuring substance use specificity in the SUD groups, with most including individuals with all types of substance use in the SMI+SUD groups, and others including only individuals with predominant use of a particular substance (e.g., alcohol, cocaine, marijuana). Some studies employ comprehensive neuropsychological testing, while others employ simple screening procedures. Others rely on retrospective chart reviews to establish substance use history or psychiatric diagnosis. Sample size also varies substantially, and groups are sometimes poorly matched on important demographic variables (e.g., age, years of education). Not surprisingly, these and other methodological differences have contributed to a mixed set of findings. Some studies find no differences between SZ+SUD compared to SZ alone (Addington & Addington, 1997; Cleghorn et al., 1991; Cooper et al, 1999; Nixon, Hallford & Tivis, 1996; Pencer & Addington, 2003; Scott et al., 1982), suggesting an absence of compounding effects of SUDS on neuropsychological deficits of SMI. Even more surprising, others find that SZ+SUD patients have less severe neuropsychological impairment than those with SZ (Carey et al., 2003; Herman 2004; Joyal et al., 2003; Rabin et al., 2012; Smelson et al., 2002), or perform better on some tests but worse on others (Sevy et al., 1990). There are numerous possible explanations for the latter findings, including higher levels of premorbid functioning in the substance-using groups, but methodological limitations preclude a definitive explanation. However, from the studies that selectively include individuals with SMI and predominant alcohol use, there are several findings that appear consistent and would be expected based on the SZ and AUD literatures. Increased cerebellar signs, such as balance and gait disturbances, are a prominent feature of AUD and have been identified in SZ+AUD patients as would be expected based on the additive effects of AUD on SZ (Deshmukh et  al., 2002; Sullivan et  al., 2004). Allen et al. (1999) also reported three main findings for a large sample of male veterans evaluated with the Halstead-Reitan Neuropsychological Test Battery (Reitan & Wolfson, 1993). First, on most of the neuropsychological tests, the AUD group performed better than the SZ group, who performed better than the SZ+AUD group, although many of the differences between the SZ and SZ+AUD group were not statistically significant. Second, compared to the SZ+AUD group, the SZ group performed significantly better on tasks assessing visuoconstructional and visuoperceptual abilities, and also exhibited less overall cognitive impairment. Third, the SZ+AUD group exhibited an acceleration in cognitive decline after the

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age of 40 when compared to the SZ group, a finding subsequently confirmed by Mohamed et al. (2006). Further evidence of age-related effects was provided by Goldstein et al. (2002), who found a powerful age effect on sensory-perceptual functions for both SZ+AUD and SZ groups, as well as greater impairment of higher integrative perceptual functions in the SZ+AUD group. Others have also reported significantly greater impairment of attention, working memory, executive functions, and verbal memory in SZ+AUD patients compared to those with SZ alone (Manning et al., 2009; Thoma et al., 2006). Lastly, a review of findings from 10 studies indicated that SZ+AUD comorbidity was associated with increased cognitive impairment in 8 of the 10 studies, including more severe impairment in attention, intelligence, working memory, verbal learning and memory, visual learning and memory, reasoning, and problem solving (Ralevski et  al., 2012). Thus, with careful selection of study participants for the substance use groups in these studies, findings suggest a compounding effect of AUD on neuropsychological deficits of SZ in a manner that is consistent with predictions from studies of alcoholism that have found pronounced visuoconstructional, executive function, and age-associated acceleration in cognitive decline, which typically begins after the age of 40. The finding of increased memory deficits in SZ+AUD is also consistent with the mild to moderate memory disturbances identified in AUD (see Chapter 6 of this volume) worsening already deficient memory abilities in SZ. NEUROLOGICAL AND NEUROIMAGING FINDINGS

While these studies suggest a unique profile of neurocognitive dysfunction associated with SZ+AUD comorbidity, the neuropathology underlying these deficits remains unclear. A number of recent neurological and neuroimaging studies have provided some insight into this issue. An initial examination was accomplished by Allen, Goldstein, et  al. (2000) using a modified version of the Neurological Evaluation Scale (NES; Buchanan & Heinrichs, 1989; Sanders et al., 1998). When compared to SZ, greater impairment was present in SZ+AUD on an NES factor assessing sensory perceptual functions that is correlated with heteromodal cortex volumes quantified by MRI in the left hemisphere in first-episode psychoses (Keshavan et  al., 2003). Group differences were attributed to the combined effects of prefrontal and temporoparietal white matter volume reductions, which are common in AUD, to the heteromodal cortex pathology which occurs in SZ (Schlaepfer et al., 1994; Sullivan et al., 1998). Neuroimaging studies have also provided evidence for structural differences between SZ and SZ+AUD. A  series of volumetric MRI studies by Sullivan, Mathalon, and coworkers (Mathalon et al., 2003; Sullivan et al., 2000a; Sullivan et  al., 2003)  suggest a number of abnormalities reflecting the compounding effects of alcoholism on the pathophysiology of schizophrenia. For example, in a study of the cerebellum, fourth-ventricle enlargement was present in SZ, decreased cerebellar volume was present in AUD, while fourth-ventricle enlargement as well as decreased vermian gray matter and cerebellar volume were present in SZ+AUD (Sullivan et al., 2000a). Similarly, in an investigation of the pons and thalamus, AUD was associated with significant reductions in

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pons and thalamus volumes, while patients with SZ who were taking atypical antipsychotic medications exhibited reductions in the thalamus but not the pons (Sullivan et al., 2003). Both the alcoholism and the SZ+AUD groups exhibited reductions in the pons when compared to the SZ group, again suggesting a compounding effect of alcoholism. Finally, in a study of gray and white matter, Mathalon et  al. (2003) reported that in comparison to normal controls, SZ, and AUD groups, individuals with SZ+AUD exhibited the greatest overall volume deficits, with the most marked deficits present in superior anterior temporal and prefrontal regions. Thus, presence of cerebellar, temporal, and frontal abnormalities in SZ+AUD identified using MRI are consistent with behavioral observations of balance and gait disturbances, as well as deficits involving executive functions, working memory and learning and memory, although additional research is necessary to establish clear links between disrupted cognition with specific neural circuitry and brain regions.

Summary Based on these neuropsychological, neurological, and neuroimaging studies, patients with SZ+AUD exhibit greater neurocognitive impairment on tasks typically associated with prefrontal, parietal, and temporal lobe structures. Evidence for decreased white and gray matter pathology in heteromodal, prefrontal, and superior anterior temporal regions has been reported, as well as volume reductions in pontine structures and the cerebellum. These functional and neuroanatomical abnormalities are specific to SZ+AUD and are largely consistent with predictions based on studies of either SZ or AUD in regard to the compounding effects of AUD on SZ pathophysiology. While these findings suggest a compounding effect of AUD on the pathophysiology of SZ, they require additional investigation. For example, the finding by Allen et al. (1999) of an age-associated acceleration of brain deterioration in SZ+AUD is significantly limited by the cross-sectional nature of the research design. Also, there has not been a systematic investigation of the temporal course of functional or structural brain recovery in SZ+AUD following cessation of drinking. Thus, it is not clear whether SZ complicates the course of recovery that typically follows the cessation of drinking in alcoholism. Similarly, it is not clear whether the persisting dysfunction observed in SZ+AUD following extended sobriety simply represents compounding effects of alcoholism on the preexisting deficits of SZ, or if there is an interaction between SZ and AUD, causing some brain structures affected in SZ to be more susceptible to the damaging effects of excessive alcohol use. Finally, while it is clear that substance use disorders are associated with a variety of negative outcomes in SZ and SMI, whether these negative outcomes are associated with increased cognitive deficits awaits further investigation. A MODEL OF NEUROPSYCHOLOGICAL FUNCTIONING SMI+SUD A model is presented in Figure  13.2 to organize and explain the relationships between the multiple factors that contribute to neuropsychological deficits in

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SMI+SUD. It is organized according to three general factors that interact to effect cognitive function in SMI+SUD. These factors are 1) a Predispositional Factor, 2) an Illness Expression Factor, and 3) an Illness Modifying Factor. Each of these three general factors encompasses disorder-specific risk factors identified in the literature as leading to cognitive deficits in SMI+SUD. The predispositional factor accounts for the cognitive deficits ostensibly resulting from genetic or early environmental influences (e.g., perinatal brain insult in SZ, antisocial personality, learning disabilities, ADHD). Cognitive deficits arising from predispositional factors are present prior to onset of the disorders and reflect either prodromal signs or (putative) genetically determined characteristics of these disorders. In the model, these predispositional risk factors are indicated as “serious mental illness” or “substance use disorder.” The illness expression factor includes risk factors that influence cognitive function and emerge with the onset of illness, either as a direct expression of the illness itself or as a result of treatment, such as illness severity, psychotropic medication effects, neuromedical risk factors, and neurotoxic effects of substances. The illness modifying factor encompasses the risk factors that are responsible for episodic, temporally unstable cognitive deficits (e.g., medication noncompliance, symptom exacerbation, substance intoxication and withdrawal), and thus have mediating or moderating roles on the neuropsychological deficits associated with the predispositional and illness expression factors. The ellipse representing the illness modifying factor is formed by a dashed line, which connotes the episodic nature of the deficits. The “final neuropsychological profile” represents the combined effects of the predispositional, illness expression, and illness modifying factors on cognition and underlying brain function. At the bottom of Figure 13.2, the brackets provide a further important distinction in the model; predispositional and illness expression factors are responsible for enduring neuropsychological deficits that persist over time, while the illness modifying factor causes transient deficits representing state-like cognitive abnormalities. Arrows in Figure 13.2 reflect the compounding effects of one factor on another, and in this sense suggest that brain dysfunction in SMI+SUD accrues over time, from early development until onset of the disorders in late adolescence or early adulthood, to changes occurring later in life that result from disorder chronicity. Arrow 1 specifies that the illness modifying factor contributes directly to neuropsychological functioning (final neuropsychological profile) by producing transient abnormalities that are unique, and are not simply the result of predispositional or illness expression factors. An example would be changes in attention associated with substance intoxication or withdrawal effects. The illness modifying factor also influences expression of neuropsychological deficits arising from the predispositional and illness expression factors (arrows 2 and 3) by causing either selective improvement or deterioration of, for example, extant attention abnormalities. Arrows 4 and 5 indicate that the predispositional and illness expression factors have direct influences on neuropsychological functioning that persist even in the absence of influence from the illness modifying factor. These neuropsychological deficits represent enduring features of the disorders, some of which may be trait markers and potential endophenotypes for the specific disorders.

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4

2

Illness modifying factor

6

1

Final neuropsycho-logical profile

3 Illness expression factor

Enduring deficits

5

Transient deficits

Figure 13.2 A model of neuropsycho-logical dysfunction in serious mental illness with comorbid substance use disorder. Note: See text for explanation of the numbered arrows in the model.

Arrow 6 indicates that the predispositional factor has a direct influence on the illness expression factor, a relationship intended to indicate the core influence of neuropathophysiology arising from the predispositional factor in the model. The complexity of the current model provides some idea of the multiple factors requiring consideration in order to understand neuropsychological dysfunction in dual-diagnosis patients. EMPIRICAL SUPPORT FOR MODEL FACTORS AND VARIABLES Table 13.1 contains specific variables that are categorized according to the three general factors identified in Figure 13.2. These variables are clearly identified in the literature as contributing to brain dysfunction and associated neuropsychological abnormalities observed in SZ and AUD, so we will focus the remainder of the discussion on these disorders. Also, the model's reference to the “final neuropsychological profile” is intended to encompass the cognitive abilities that are uniquely impacted by SMI+SUD comorbidity, depending on, for example, the psychiatric diagnosis or type of substance use disorder under consideration. The model could just as well include specific domains such as memory or attention, rather

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Table 13.1. Predispositional, Illness Expression and Illness Modifying Variables That Contribute to Neuropsychological Deficits in Serious Mental Illness with Comorbid Substance Use Disorders Predispositional (Genes/ Environment) Factor

Illness Expression Factor

Illness Modifying Factor

Serious Mental Illness

SZ Illness Severity

Psychosis Severity

Psychotropic Medications

Medication Compliance

Neuromedical Risk

Long-Term Cessation of Use

Neurotoxic Effects

Medical Improvement

SUD Illness Severity

Acute Intoxication Effects Withdrawal Effects

Substance Use Disorder

than the more general indication “Final Neuropsychological Profile.” With those points acknowledged, the model has broad applicability to psychiatric-substance use comorbidity involving various substances and psychiatric conditions, as they differentially impact discrete cognitive domains.

Predispositional Factor Variables In the model, predispositional factor variables are specified as SMI (for SZ) or SUD (for AUD) to indicate that some cognitive deficits are present prior to the onset of the disorder. Neurodevelopmental theories have been proposed to explain SZ that posit either excessive or inadequate synaptic pruning during critical developmental periods driven by aberrant genetic processes (Feinberg, 1997; Murray & Lewis, 1987; Weinberger, 1987). Neurocognitive deficits that are present prior to onset may reflect this abnormal development, and as such some neuropsychological deficits are suggested to be endophenotypes for SZ because they are more closely related to the SZ genotype than is the phenotypical expression of the disorder (Cannon et al., 1994). Neurocognitive endophenotypes are found in high-risk individuals prior to the onset of illness (Cornblatt & Erlenmeyer-Kimling, 1985; Cornblatt et al., 1999), in biological relatives of affected individuals (Cannon et al., 1994; Faraone et al., 1995; Franke et al., 1993; Keefe et al., 1994; Mirsky et al., 1992), as well as in apparently healthy adolescents who later go on to develop SZ (David et al., 1998; Davidson et al., 1999), all suggestive of abnormal brain functioning prior to the onset of the disorder. Neuroimaging studies parallel these neurocognitive findings, reporting abnormalities in first-episode never-medicated patients (Bagary et al., 2003; Gilbert et al., 2001; Kasai et al., 2003a; Keshavan et al., 2002a) as well as individuals at high genetic risk for SZ (Keshavan et al., 2002b; Keshavan et  al., 2002c; Seidman et  al., 2002). Based on these studies, in individuals who develop SZ, it appears that premorbid brain dysfunction persists as a stable component of the disorder following onset.

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As in SZ, AUD has a substantial genetic component as demonstrated by twin, adoption, and high-risk studies (Cloninger & Begleiter, 1990; Murray & Stabenau, 1982), and a number functional brain abnormalities have been investigated as potential endophenotypes, using neuropsychological tests. Initial work found that adults with AUD had increased incidence of severe “minimal brain dysfunction” (MBD), hyperactivity, and attention deficits during childhood, and that adults who reported increased MBD in childhood also experienced more severe adverse effects stemming from AUD as adults (Alterman et  al., 1982; Alterman et  al., 1986; Blouin et  al., 1978; Frances et  al., 1980; Goodwin et al., 1975; Wender et al., 1981). Neuropsychological evaluations of children at high risk for AUD indicate they exhibit attention, information processing, visuospatial, executive, and motor deficits compared to children at low risk for AUD (Corral et al., 1999; Hill et al., 1999; Hill et al., 2000; Schandler et al., 1988; Sher et al., 1991). These findings have not always been replicated (Alterman et al., 1986), and family density of AUD may partially explain differences between studies, in that selective cognitive deficits may be present only when there is a high density of AUD in the family history (Corral et al., 1999). Also, some deficits may represent developmental delays that become less apparent with age, including motor deficits and information-processing deficits indicated by P300 electroencephalographic abnormalities (Bauer & Hesselbrock, 2003; Hill et al., 1999; Hill et al., 2000; Ledin & Odkvist, 1991). Decreased right amygdala volume has also been reported for high-risk offspring who do not have an AUD (Hill et  al., 2001). It appears then, that some premorbid functional brain abnormalities represent maturational delays, and are more pronounced in children at high genetic risk for AUD than they are in adolescents or adults who are at risk.

Illness Expression Factor Variables The onset of SZ or AUD is associated with the manifestation of the illness expression factor variables, which include SZ illness severity, AUD illness severity, psychotropic medications, neuromedical risk, and neurotoxic effects of alcohol (see Table 13.1). SZ illness severity reflects brain abnormalities associated with baseline levels of illness severity, apart from the exacerbations and remissions in symptoms that occur as part of the natural course of SZ. Studies examining illness course and severity demonstrated that some patients exhibit good outcomes with few psychotic episodes and minimal lasting impairment, while others have a severe form of the illness and exhibit poor outcomes with a deteriorating course, severe residual symptoms, and marked brain abnormalities (Bleuler, 1968; Carpenter & Strauss, 1991; Ciompi, 1980; Tsuang et al., 1979). Diagnostic systems attempt to capture this heterogeneity by proposing various subgroups of patients with SZ, and most include a group characterized by poor outcomes, increased negative symptoms, and neuropsychological deficits (American Psychiatric Association, 1994; Andreasen & Olson, 1982; Carpenter et al., 1988; Crow, 1980; Keefe et al., 1987). As previously reviewed, neuropsychological studies have also demonstrated this

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heterogeneity, leading to attempts to identify subgroups of patients with schizophrenia based on neuropsychological profiles. These studies have consistently identified a subgroup characterized by near-normal test performance as well as one with severe cognitive impairment, with initial evidence supporting neuropathological differences among these subgroups. In addition, these findings are consistent with research indicating that patients who have poorer outcomes have more severe neurocognitive deficits (Buchanan et al., 1994; Heaton et al., 1994; Keefe et al., 1987; Murray, 1994), as do those who are treatment-resistant (Bartko et al., 1989; Kolakowska et al., 1985; Schulz et al., 1989; Smith et al., 1999). For AUD, numerous studies have also indicated that some individuals exhibit a severe course with poor outcome, while others have a more benign course; and numerous typologies have been proposed (Cloninger et al., 1987; Hauser & Rybakowski., 1997; Hesselbrock et al., 1985a, 1985b; Morey et al., 1984; Winokur et al., 1971). For example, Cloninger and coworkers (1987) distinguish between Type I  and Type II alcoholics, the latter exhibiting stronger genetic heritability, greater psychiatric comorbidity, and poorer outcome. A  number of studies indicate that those with a more severe course also exhibit more severe cognitive deficits and corresponding brain dysfunction (Hesselbrock et al., 1985a, 1985b). Consequently, disorder severity is included as an illness expression variable in the model, because it is expected that for both SZ and AUD, illness severity is associated with neuropsychological deficit following onset of the disorders. “Psychotropic medication” is included as an illness expression factor variable primarily for SZ, because in most cases long-term treatment with antipsychotic and other psychotropic medications is required after onset of the disorder. In this sense, psychotropic medication is a disorder-specific and stable variable that influences brain function and structure. While antipsychotic medications have direct effects on brain function, some investigators have found beneficial effects of typical antipsychotics on some attentional measures (Cassens et al., 1990; Cleghorn et  al., 1990; Goldberg & Weinberger, 1996; Serper et  al., 1990; Verdoux et  al., 1995), while others have reported adverse effects (Magliozzi et al., 1989: Spohn et al., 1985) or no effects (Seidman et al., 1993; Strauss et al., 1985). Typical antipsychotics may improve secondary memory, with more beneficial effects on verbal compared to visual memory (Gilbertson & van Kammen, 1997; Nigal et al., 1991). Atypical antipsychotic medications, or serotonin-dopamine antagonists, may improve deficits in executive abilities, verbal fluency, and fine motor skills (for review, see Keefe et al., 1999). However, results from the Clinical Antipsychotic Trials in Intervention Effectiveness CATIE trial on 817 patients indicated that, after two months of treatment with either olanzapine, perphenazine, quetiapine fumarate, or risperidone, all groups evidenced small but significant improvements in neuropsychological functioning, and there were no differences based on medication type (Keefe et al., 2007). For SUDs where medication is a component of treatment, such as methodone or buprenorphine treatment for heroin addiction, medications may also be considered part of the model for those disorders. Another illness expression factor variable is neuromedical risk, which represents neuropsychological deficits associated with medical risk factors, such as poor

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nutritional status, cirrhosis of the liver, or head injury (Adams & Grant, 1986; Arria et al., 1991; Grant et al., 1984; Moss et al., 1992). Poor nutrition (thiamin deficiency) rather than alcohol neurotoxicity causes Wernicke-Korsakoff 's disorder, which is characterized by severe and persistent anterograde amnesia (Butters & Cermak, 1980; Victor et  al., 1989). Cirrhosis of the liver occurs in about 30% of chronic alcoholics, and in its severe form it causes increased nitrogenous compounds in the blood, decreased protein synthesis, and decreased hepatic blood flow, all of which can produce functional brain abnormalities evidenced by neurocognitive deficits (Arria et al., 1991; Edwin et al., 1999; Moss et al., 1992; Tarter et al., 1990). Head injury also occurs with increased frequency in AUD because alcohol produces ataxia and incoordination that increase risk of accidents (see Chapter 15 of this volume). Neuromedical risk factors such as HIV/HCV infection have increased prevalence in many SUDs (see Chapter 14) as well as in SMIs, and poor nutrition is also a consideration in SMI, although cognitive effects of such factors are not clearly documented in SMI. Neuropsychological deficits associated with some of these medical conditions do not fully improve after psychiatric stabilization or cessation of substance use, and so may continue to produce cognitive deficits. In the model, a distinction is made between “neuromedical risk” and the “neurotoxic effects of alcohol,” because while it is presumed that neuropsychological deficits in AUD are caused by the neurotoxic effects of alcohol, it is also clear that much of the brain dyfunction is caused by the aforementioned medical conditions.

Illness Modifying Factor Variables Predispositional and illness expression factor variables contribute directly to neuropsychological function, but their influences are also influenced by the illness modifying factor, whose variables are primarily responsible for transient neuropsychological abnormalities in SZ and AUD. As can be seen from Table 13.1, illness modifying variables for SZ include psychosis severity and medication compliance, while medical improvement, withdrawal and intoxication effects, and long-term cessation of substance use are specific to AUD. Two types of abnormalities are included here. First, some aspects of brain dysfunction and associated neurocognitive deficits have been viewed as mediating vulnerability markers for schizophrenia; i.e., while they are persistent and enduring features of the disorder, they fluctuate in relation to symptom severity and medication status (Nuechterlein & Dawson, 1984; Condray et al., 1992). This is in contrast to the second type of dysfunction arising from illness modifying variables, which totally resolves with clinical stabilization. Both types of abnormalities produce temporal fluctuations in brain structure and function, and so are included within the Illness Modifying factor. ILLNESS MODIFYING VARIABLES IN SCHIZOPHRENIA

Psychosis severity is included as an illness modifying variable primarily for SZ because of the primacy of these symptoms to the disorder. In the model, psychosis severity reflects that, regardless of SZ illness severity, most individuals exhibit a variable course characterized by periods of high levels of psychosis

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as well as periods of relative quiescence, and that neuropsychological function fluctuates in relation to this variable course. Symptom syndrome severity (disorganization, reality distortion, psychomotor poverty) is associated with activation of specific cortical regions (Ebmeier et al., 1993; Liddle, 1996; Liddle et al., 1992; Shenton et al., 1992), and specific symptoms have been associated with structural and functional abnormalities in corresponding brain regions. For example, relationships are present between abnormalities in auditory association cortices and the experience of auditory visual hallucinations (Gaser et al., 2004; Lennox et  al., 2000; Silbersweig et  al., 1995). Similar relationships are apparent between symptom syndromes and neurocognitive test performance, so that patients with more severe symptoms exhibit more severe deficits on selective neurocognitive tests (Allen, Anastasiou, et al., 2000; Himelhoch et al., 1996; Liddle, 1996). These relationships appear quite robust in patients treated with antipsychotics, but not in antipsychotic-free patients (Allen, Anastasiou, et al., 2000; Himmelhoch et al., 1996) suggesting that antipsychotic drugs may modify relationships between symptoms, cognition, and associated brain activation (Silbersweig et al., 1995). Medication compliance is included within the illness modifying factor, because compliance is notoriously poor in SZ, and it moderates the pharmacological effects of antipsychotic medications on brain function. Poor compliance is associated with poorly controlled symptoms and increased psychotic relapse, with associated disturbances in thinking and cognition. Medication compliance is considered primary for SZ because, even though medications are sometimes used in the treatment of AUD, their effects (if any) on neuropsychologcial function have not been clearly demonstrated. ILLNESS MODIFYING VARIABLES IN ALCOHOLISM

Cessation of alcohol use results in substantial improvement in cognitive function with marked spontaneous improvement in neurocognitive function during the first few weeks of sobriety. For younger individuals with AUD (age < 35), cognitive functioning improves rapidly and can return to normal levels following prolonged cessation of drinking (Eckardt et al., 1995). Older individuals exhibit more severe cognitive deficits, slower recovery of cognitive function following cessation of drinking, and a possible age-associated acceleration in cognitive decline (Ellenberg et al., 1980; Forsberg & Goldman, 1985; Horner et al., 1999; Munro et  al., 2000; Rourke & Grant, 1999), although this age-related acceleration has not been found consistently (Ellis & Oscar-Berman, 1989; Grant et al., 1984; Sullivan et al., 2000b). Studies examining individuals abstinent for longer periods of time (three years or more) are less conclusive but suggest that deficits persist in short-term memory, attention, and visuospatial abilities (Brandt et al., 1983; Yohman et al., 1985). Neuroimaging studies have also demonstrated that prolonged abstinence is associated white matter volume increases (Mann et al., 1995; Pfefferbaum et al., 1998; Shear et al., 1994). With cessation of drinking, improvement in medical status also occurs, which is indicated in Table 13.1 by the medical improvement variable. Poor nutritional

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status, impaired hepatic function, and disrupted neuronal function are but three conditions that may substantially improve following cessation of alcohol use and contribute to improved brain function (Adams & Grant, 1986; Arria et al., 1991; Goldstein, 1987; Tarter et al., 1990; Victor et al., 1989). The final illness modifying variables are acute intoxication effects and withdrawal effects, because brain function is altered during intoxication and withdrawal. Changes in brain functioning during alcohol intoxication is evidenced by a number of motor and cognitive abnormalities, including ataxia and impairment of memory, attention, and judgment, while withdrawal symptoms can include grand mal seizures, delirium, and hallucinations. These symptoms typically subside and brain function normalizes with the resolution of acute intoxication and withdrawal. CONCLUSION Unique structural and functional brain abnormalities characterize SZ+AUD, and differentiate it from SZ or AUD alone. These abnormalities represent the compounding effects of excessive alcohol use on neuropathology already present in SZ. So, while some functional similarities are noted across SZ and AUD, including impairment of attention, learning and memory, abstraction abilities, and executive functions, differences are also present. For example, functional impairment demonstrated through neuropsychological evaluations suggests that deficits in adults with SZ are more severe than in AUD. Language abilities that are impaired in SZ are not typically impaired in AUD, while a characteristic feature of AUD is impaired visuospatial abilities, which are not typically differentially impaired in SZ. Also, persisting neurocognitive dysfunction in alcoholism occurs primarily in older alcoholics with chronic and severe drinking histories, while cognitive deficits are typically present throughout the course of SZ in the large majority of cases. It is suggested that the unique pathophysiology of SZ+AUD results from a combination of premorbid and disorder-related factors that are unique to each disorder. Neuromedical risk factors contribute unique variance primarily to brain function in AUD, while antipsychotic medication effects are unique to SZ. Additionally, in both SZ and AUD, some neuropsychological deficits are stable trait-like features and have been identified as potential endophenotypes, while others fluctuate over time as a result of alcohol use/non-use (AUD), medical status (AUD), changes in psychosis severity (SZ), and antipsychotic medication regimens and compliance (SZ). Volumetric MRI investigations have provided evidence suggesting structural differences between individuals with SZ and SZ+AUD, although the cognitive deficits present in SZ+AUD are likely to result from dysfunction of interrelated brain regions and neural circuits, rather than dissociated lesions of discrete brain structures. Therefore, functional neuroimaging studies may provide further clarification of these matters in SZ+AUD, and in other SMI and SUD comorbidities. The model proposed to account for the unique pattern of structural and function brain abnormalities in SZ+AUD suggests that brain dysfunction arises from three general factors, each containing disorder-specific variables that are unique to either SZ or AUD. While other models have been proposed to explain the high

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rate of substance abuse in SZ (Mueser et al., 1998) or neurobiological mechanisms responsible for comorbidity (Chambers et  al., 2001), the current model focuses on explaining neuropsychological dysfunction in SZ+AUD. The model provides some direction for research by posing a number of interesting theoretical questions, including the extent to which interaction among the variables within and between the various factors impact brain function. For example, at the predispositional factor level, does the expression of SZ and AUD in the same individual represent a genetic liability to both disorders, or a common disordered neurobiological pathway? Is substance abuse in schizophrenia an attempt to overcome symptoms or negative side effects associated with antipsychotic treatment? At the illness expression level, are SZ and AUD illness-severity effects additive or synergistic? Do AUD and SZ illness severity co-vary, or do some patients exhibit severe SZ and mild AUD, or mild SZ and severe AUD, and how does this co-variation contribute to brain function? Because many of the relationships between variables in the model have not been directly investigated, the model suggests numerous questions that can be addressed in future studies. However, the model can also provide direction for clinical evaluation. Since variables in the model contribute to neuropsychological functioning, clinical assessment should consider these variables. Given genetic and early environmental influences on cognition, historical information is critical regarding family history of SMI and SUD, birth complications, educational difficulties such as learning disability and ADHD, behavioral disorders such as conduct disorders, as well as other premorbid indicators of later SMI or SUD expression. Illness course and severity, psychotropic medication effects, and common neuromedical risk factors should be reviewed as contributing factors to current neuropsychological functioning. The expected profile of neuropsychological deficits based on the primary substance of abuse should be considered, along with the frequency and duration of substance use. A final consideration is that recent changes in neuropsychological status may be accounted for by any of the illness modifying factor variables, including length of sobriety, compliance with medications, and improvement in medical conditions known to disrupt cognitive functioning. While acknowledging that the literature regarding neuropsychological effects of SUD+SMI comorbidity is still quite limited, we hope that consideration of these various factors will assist in guiding the evaluation of these complex patients, identifying key variables that contribute to current neuropsychological profiles, and assisting in treatment recommendations and planning.

References Adams, K. M., & Grant, I. (1986). Influence of premorbid risk factors on neuropsychological performance in alcoholics. Journal of Clinical & Experimental Neuropsychology, 8, 362–370. Addington, J., & Addington, D. (1997). Substance abuse and cognitive functioning in schizophrenia. Journal of Psychiatry & Neuroscience, 22, 99–104.

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Infectious Disease and Substance Use Disorder Comorbidity J E N N I F E R M .   L O F T I S , M A R I LY N H UC K A N S , E R I CA W E B E R , A N D ST E V E N PAU L   WO O D S

Certain patterns of substance use (e.g., injection) and accompanying high-risk sexual behaviors can greatly increase the likelihood of transmitting and acquiring blood-borne infections, such as human immunodeficiency virus (HIV) and hepatitis C virus (HCV). Infectious diseases are thus highly prevalent among persons with substance use disorders (SUD) and can both directly (e.g., virally mediated neural injury) and indirectly (e.g., via comorbidities, such as depression) contribute to neuropsychological impairment, declines in everyday functioning, suboptimal disease management, and poor health outcomes (see Figure 14.1). While there has been a large number of reports documenting the neurocognitive deficits associated with viral infections and SUD separately, relatively fewer studies have examined the additive effects of their comorbidity. In this chapter, we review the literature on the individual and combined effects of HIV, HCV, and SUD on brain structure and function, including their possible neurobiological mechanisms (e.g., oxidative stress), neurocognitive profiles (e.g., executive dysfunction), and impact on real-world behaviors, such as treatment adherence. OVERALL EPIDEMIOLOGY OF THE SUD AND INFECTIOUS DISEASE COMORBIDITY Relative to the general population, the incidence and prevalence of HIV and HCV are elevated among both injection and non-injection drug users. Injection drug use (IDU) remains a primary mode of HCV transmission risk, occurring in up to 90% of injection drug users (IDUs) (Patrick et al., 2000). In contrast, HIV occurs in approximately 10%–15% of IDUs (CDC, 2012), who comprise 10%– 15% of the HIV epidemic in the United States (CDC, 2012). Non-IDUs are also at risk for infectious disease, due at least in part to high transmission-risk sexual

Substance use • • • •

Type Recency Onset Severity CNS injury Infectious disease

• Type • Immunovirologic burden • Treatment

• Direct • Virologic • Indirect • Oxidative stress • Vasculopathy • Inflammation

Sociodemographics

Neurocognitive impairment

Neural systems • Frontostriatal • Temporolimbic

Neuropsychiatric factors

• • • • •

Attention Working memory Processing speed Memory Executive functions

Functional outcomes • ADL disability • Medication nonadherence • Unemployment • Poorer health & wellbeing

Medical comorbidities

Secondary factors

Figure 14.1 Factors affecting neuropsychological function in patients with HIV, HCV, and comorbid substance use disorders.

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behaviors (e.g., unprotected intercourse while intoxicated). Perhaps because HCV viral counts are typically low or undetectable in genital secretions, sexual activity is much less efficient for transmitting HCV than for HIV and other sexually transmitted diseases (Terrault et al., 2013). However, sexual transmission of HCV is theoretically more likely when serum-derived body fluids cross mucosal surfaces, such as when the mucosal integrity of the genital tract is compromised or other bacterial or viral infections are present; these factors may in part contribute to a recent increase in the incidence of sexually transmitted HCV among predominantly HIV-seropositive men who have sex with men (Brejt, Gilleece, & Fisher, 2007). Drug use may further increase the risk of HIV and HCV transmission in part through its association with high-risk sexual behaviors as well as through its impact on cellular responses and immunity; for example, methamphetamine exposure hampers the antiviral responses of macrophages and hepatocytes to HIV and HCV, respectively, thus increasing the likelihood of infection following exposure (Liang et al., 2008; Ye et al., 2008). Regardless of the mode of transmission, individuals with SUD are at increased risk for HCV and HIV infection. For example, using a comprehensive database of all 293,445 veterans who had received healthcare in any Veterans Administration facility in the Northwest across a six-year period, one study found that veterans with SUD were more likely to test positive for HCV and to be co-infected with HIV than those without SUD (Huckans et al., 2005). Of those with SUD who had been tested, 28.9% were HCV infected, 3.8% were HIV infected, and 1.8% were co-infected. Despite high infection rates, only 59.6% of veterans with SUD had been tested for HCV, only 19.2% had been tested for HIV, and only 16.3% had been co-tested, indicating that more intensive testing and co-testing efforts are needed within addiction populations. Viewed from a different angle, numerous studies have examined the prevalence of comorbid SUD in persons living with infectious disease (Schulden et al., 2012; Loftis & Hauser, 2008). Overall, these studies indicate that the frequency of SUD (and other psychiatric disorders) is notably higher within HIV and HCV infected samples than within the general U.S. population, where fewer than 10% of persons > 12 years old met criteria for an SUD in the past year (SAMHSA, 2010). In HIV, for example, the prevalence of current comorbid SUD ranges from 10% (Merlin et al., 2012) to 56% (Mirza et al., 2012), with lifetime SUD diagnoses ranging from approximately 70% to 80% (e.g., Heaton et al., 2010). In HCV, the prevalence of current SUD is similar and broadly ranges from 26% (Zickmund et al., 2004) to 49% (Lehman & Cheung, 2002).

HEPATITIS C VIRUS (HCV)

Independent Effects on the Central Nervous System NEURAL MECHANISMS

HCV is a single-stranded RNA flavivirus with six different primary genotypes that preferentially affects hepatic cells. HCV replicates in monocytes/macrophages and T and B lymphocytes, with the former thought to be a mechanism whereby

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HCV crosses the blood–brain barrier (BBB). Indeed, HCV is neurotropic, such that its RNA is detectable in cerebrospinal fluid and brain parenchyma (e.g., Letendre et  al., 2007). For example, HCV sequences and proteins have been found in brain macrophage/microglia cells, and activation of these brain cells in patients with HCV is associated with higher expression of messenger ribonucleic acid (mRNA) transcripts for key immune activation cytokines (e.g., interleukin [IL]-1 and tumor necrosis factor [TNF]-alpha) than in seronegatives (Wilkinson et al., 2010). HCV is also associated with positive neuroimaging findings at the group level (e.g., Bokemeyer et al., 2011; Grover et al., 2012; Weissenborn et al., 2009). Several studies have used magnetic resonance spectroscopy (MRS) and positron emission tomography (PET) to demonstrate white matter damage, as well as microglial activation and altered brain metabolism in HCV (Forton et al., 2005; Forton et al., 2008; Taylor et al., 2004; Weissenborn et al., 2004; Grover et al., 2012). Virally mediated neurotoxic effects of HCV appear to be most prominent in brain regions rich with dopaminergic transporters, such as the basal ganglia— a brain area that also shows damage following chronic psychostimulant abuse (Nath et al., 2001). While the mechanisms of such injury are currently unknown, inflammation, oxidative damage, and glutamate imbalance contribute to neural injury in SUD and HCV (Martin-Thormeyer & Paul, 2009). These neuroimaging markers of central nervous system (CNS) immune activation and inflammation are consistent with reports of alterations in the peripheral cytokine milieu. When analyzing a small panel of one or several blood immune factors, previous studies have revealed significantly increased levels of specific blood immune factor levels, including IL-6, IL-18, IL-10, IL-4, TNF-alpha, and RANTES (Regulated upon Activation, Normal T-cell Expressed, and Secreted), in untreated HCV+ adults compared with seronegatives (e.g., Loftis et al., 2008) . Moreover, alterations in these peripheral immune factors may be associated with neuropsychiatric impairments in adults with HCV. Hilsabeck and colleagues (2010) examined serum levels of interferon (IFN)-alpha, IL-6, and TNF-alpha in relation to cognition; in HCV+ adults with detectable IFN-alpha levels (n = 17), higher IL-6 levels correlated with worse visual memory and sustained visual attention, and higher TNF-alpha levels correlated with worse visual memory and visual perception. Byrnes et  al. (2012) found that HCV eradication had a beneficial effect on cerebral metabolism with significant improvements in total verbal learning recall, verbal memory recognition, and visuospatial memory. These findings raise the possibility that HCV infection could be directly related to neuropsychological dysfunction. NEUROCOGNITIVE IMPAIRMENT

About one-third of persons with HCV demonstrate mild cognitive impairment on neuropsychological tests, most consistently in the domains of verbal learning, reasoning and mental flexibility, working memory, attention and concentration, and speeded information processing (e.g., Cherner et  al., 2005; Forton et  al., 2002; Karaivazoglou et  al., 2007; Letendre et  al., 2005; Martin & Novak et al., 2004; McAndrews et al., 2005; von Giesen et al., 2004; Weissenborn et al., 2004; Hilsabeck, Hassanein, Carlson, Zeigler, & Perry, 2003; Hilsabeck, Perry, &

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Hassanein, 2002). Although patterns of cognitive impairment can vary, executive dysfunction in particular has been repeatedly demonstrated in individuals with HCV (e.g., Weissenborn et al., 2004; Cherner et al., 2005). For example, one study found that 22.2% of HCV+ veterans evidenced impairments in an executive domain consisting of reasoning, abstraction, and mental flexibility compared with only 5.4% of HCV– veterans (Huckans et al., 2009). Compared with HCV– adults, HCV+ adults have also been shown to exhibit an increased tendency to choose smaller immediate rewards over larger delayed rewards on a delay discounting task, a common behavioral measure of impulsivity; this increased impulsivity was associated with worse performance on a range of executive function tasks, suggesting that executive dysfunction may contribute to altered decision-making styles in HCV-infected adults (Huckans et al., 2011). It remains to be determined whether this altered executive function and decision-making style contribute to increased risk of HCV infection as well as increased rates of SUD among this population. Deficits in memory, attention and concentration, speeded information processing, and psychomotor speed are also commonly observed (Perry, Hilsabeck, & Hassanein, 2008). Studies show that attention/concentration difficulties tend to appear earlier during the course of HCV infection, while other neuropsychological effects such as executive dysfunction are more likely to develop later (e.g., Hilsabeck et al., 2003; Weissenborn et al., 2004). For example, consistent with previous studies using more comprehensive neuropsychological batteries, Umaki and Denney (2013) recently reported that, compared with HCV– men who were incarcerated, HCV+ inmates performed significantly worse on two indices (Attention Index and Total Scale Score) and four subtests (Coding, Digit Span, Story Memory, and Story Recall) of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS; Randolph, 1998). These authors found that the Coding subtest (i.e., a multifactorial measure of attention/ concentration, speeded information processing, and psychomotor speed) showed the largest effect size in this HCV+ group with relatively well-managed disease. However, many studies that evaluate cognitive function in adults with HCV include participants with more severe degrees of hepatic involvement, such as cirrhosis and/or minimal hepatic encephalopathy (reviewed in Senzolo et al., 2011)— clinical conditions that can contribute to systemic inflammation and may also alter cognitive abilities. For example, Hilsabeck and colleagues (2003) reported that the degree of fibrosis was positively related to cognitive impairment, and suggested that the latter might parallel progressive liver damage in persons living with advanced HCV. Furthermore, Sun and colleagues (2013) found that HCV viral load positively correlated with global cognitive deficit scores, with particularly strong associations on measures of executive functions, attention, and information processing speed. Thus, while HCV-associated neurocognitive impairment may be observed in the absence of severe liver disease, the risk and severity of such deficits appears to increase somewhat with advancing HCV disease severity. EVERYDAY FUNCTIONING

HCV-associated cognitive and neuropsychiatric impairment have also been studied as potential risk factors contributing to problems in everyday function. For

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example, the cognitive performance of individuals with HCV shows significant intra-individual variability across domains (i.e., neurocognitive dispersion), and this higher dispersion is associated with an increased risk of unemployment among individuals with higher overall mean neurocognitive ability (Morgan, Woods, Rooney, et al., 2012). Moreover, although the cognitive deficits observed in HCV cannot be accounted for fully by comorbid depression, fatigue, or substance use (e.g., Perry et al., 2008), depression rating scale scores (but not quality of life scores) have been found to correlate with cognitive impairments (Bieliauskas et al., 2006) as well as daily functioning (Vigil et al., 2008) in individuals with HCV. In one study, difficulties in processing speed and depressive symptoms were associated with declines in instrumental activities of daily living (IADLs), while motor impairment and general emotional distress were associated with declines in physical activities of daily living (Vigil et al., 2008). Thus, the pattern and progression of cognitive and neuropsychiatric impairment associated with HCV appear to be important clinical considerations that interfere with an individual's daily life. As discussed previously, observed deficits in attention and executive function additionally contribute to reduced impulse control and altered decision-making style in adults with HCV (Huckans et al., 2011), which could further impact HCV disease course and treatment outcomes, especially in patients with comorbid SUD.

Combined Effects of SUD and HCV Considering the known, independent effects of SUD and HCV on brain structure and function, one might surmise that their comorbidity could amplify the magnitude of neurocognitive impairment. In one of the few studies to prospectively evaluate this important question, Huckans et  al. (2009) investigated cognitive differences in the following three groups of veterans:  HCV+/ SUD–, HCV+/SUD+, and those who had neither HCV nor SUD. The groups differed on domains of verbal memory, auditory attention, speeded visual information processing, and reasoning or mental flexibility. Follow-up comparisons indicated that, among participants without a SUD, HCV was a factor in impairments in verbal learning, auditory attention, and reasoning and mental flexibility. However, HCV+ patients with a SUD history were more impaired than HCV–/SUD– controls on tests of speeded visual information processing, suggesting that any additive or synergistic effects of SUD on HCV-induced cognitive impairments may be domain-specific. Taken together, these results show that cognitive impairment can be associated with HCV even in the absence of substance abuse. More recently, Devlin and colleagues (2012) examined the effects of HCV and substance use history on cognitive performance in patients with and without HIV. The authors reported that HCV infection was consistently associated with poorer performance in psychomotor speed and learning, whereas substance use was less strongly associated with cognitive performance. However, as in the Huckans and colleagues (2009) study, the synergistic effects of substance abuse may have been masked in the Devlin et  al. (2012) study

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because research participants had mixed (and sometimes remote) substances use disorders. A few research groups are investigating CNS mechanisms associated with the neurocognitive effects of HCV and SUD. For example, using neuroimaging techniques, Taylor and colleagues (2004) found that N-acetylaspartate (NAA), a marker of neuronal integrity, was lower in the white matter of patients with HCV and a history of methamphetamine (MA) abuse, compared to that of participants with HCV and no history of MA abuse. Importantly, this reduction in NAA was correlated with worse global neuropsychological deficit scores in the individuals with a history of MA addiction. However, at least one study of MA users (without HCV or HIV) found that impairments in cognitive function were unrelated to prior drug use histories, including self-reported age at first use, total years of use, route of consumption, or length of abstinence (Cherner et al., 2010). Thus, more research is needed, as the additive effects of SUD and viral infection on specific cognitive domains and CNS neuropathologies are yet to be defined.

HUMAN IMMUNODEFICIENCY VIRUS (HIV)

Independent Effects on the Central Nervous System NEURAL MECHANISMS

HIV is a lentivirus, which belongs to the class of enveloped viruses known as Retroviridae that replicate by integrating themselves into the DNA of the host cell. HIV preferentially infects T-helper cells along with monocytes and macrophages. Although its primary adverse effects are immunological, HIV is also highly neurotropic, meaning that it is able to infiltrate the CNS. In fact, the brain is the second most common organ (to the lung) infected by HIV (Masliah et al., 2000), causing widespread neurological damage (Gonzalez-Scarano & Martin-Garcia, 2005). Since it is blocked from crossing the BBB on its own, HIV enters the CNS through a “Trojan horse” mechanism via infected monocytes and cluster of differentiation 4 (CD4+) lymphocytes (Hult et al., 2008). Although the virus does not directly infect neurons, it often causes damage to brain parenchyma through both direct (e.g., viral proteins) and indirect (e.g., inflammatory; Genis et  al., 1992) processes. After crossing the BBB, HIV-infected monocytes may differentiate into perivascular macrophages or infect microglia, which causes brain injury through the release of neurotoxic substances, such as chemokines and cytokines (Kaul, Garden, & Lipton, 2001). Overall, brain pathology has been observed in over 50% of HIV-infected adults (Ellis et al., 2007), in addition to non-infectious findings and minimal non-diagnostic abnormalities such as white matter hyperintensities (e.g., Filippi et al., 2001). HIV preferentially affects fronto-striato-thalamo-cortical (FSTC) circuitry, resulting in both structural (e.g., white matter hyperintensities) and functional (e.g., abnormal brain perfusion) damage evident throughout the frontal cortex, cerebral white matter, and striatum. For example, the frontal cortex and striatum of HIV-infected individuals appear to be especially susceptible to structural

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abnormalities (e.g., Castelo et al., 2007), neuroinflammation (e.g., elevated myoinositol and choline; Chang et al., 2005), neuronal injury (i.e., decreased N-acetyl aspartate; Chang et al., 2005), and altered blood-oxygen-level dependent (BOLD) response during the performance of cognitive tasks (e.g., Melrose et  al., 2008). Beyond these frontostriatal pathways, imaging evidence also implicates abnormalities in medial temporal structures (e.g., hippocampus), which may, in combination with FSTC injury, increase the risk of episodic memory impairment (e.g., Maki et al., 2009). Such neural abnormalities are typically most prevalent in individuals with more severe disease (e.g., reduced brain volumes; Stout et al., 1998). For instance, Jernigan and colleagues (2011) reported that lower nadir CD4 was a significant factor in most measures of structural damage (e.g., less cerebral white matter) as determined via morphometric analyses. However, neuropathology may still be evident in medically asymptomatic individuals with optimal viral control (e.g., Wilkinson et al., 1997), suggesting that neural abnormalities are prevalent beyond the immuno-compromised. NEUROCOGNITIVE IMPAIRMENT

HIV-associated neuropathologies can produce mild to moderate impairment in numerous lower- (e.g., motor skills) and higher-order (e.g., executive functions and episodic memory) cognitive abilities. Depending on the stage of HIV disease, HIV-associated neurocognitive disorders (HAND) are evident in an estimated 30%–50% of individuals with HIV (e.g., Heaton et al., 2010), with yearly incidence rates of approximately 10%–25% (e.g., Robertson et al., 2007). Most recently, an NIH working group (Antinori et al., 2007) updated the research diagnostic criteria for HAND, producing diagnoses that account for level of cognitive impairment, everyday functioning declines, and potential confounds (e.g., depression). Although the neurocognitive profile of HIV infection has been historically described as “spotty” (e.g., Butters et  al., 1990), impairment is most commonly observed in the areas of executive functions, working memory, information-processing speed, episodic memory, and motor skills, with relative sparing of simple attentional, language, visuoperceptual, and somatosensory functions (e.g., Heaton et al., 1995; Heaton et al., 2011). Since the advent of combined antiretroviral therapy (cART), data suggest that the overall profile of impairment has evolved. For example, Heaton and colleagues (2011) compared impairment profiles from the pre-cART era to the current treatment guidelines and observed a shift from primary deficits in the domains of information processing and motor speed to greater impairments in episodic memory and executive functions. More specifically, the pattern of episodic memory impairment in HAND is most consistent with the prototypical mixed encoding and retrieval profile that is often observed in populations with compromised frontostriatal systems (e.g. Parkinson's disease), with impairment most evident on more executively demanding free-recall tasks, but normalized performance on more structured recognition trials (e.g., Cattie et al., 2012). HAND is also characterized by decreased use of higher-order organizational encoding strategies (e.g., Delis et al., 1995), including semantic clustering during list learning (e.g., Gongvatana et al.,

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2007). Regarding executive functions, many individuals with HIV evidence deficits on a wide variety of higher-order processes, the most studied of which are abstraction and novel-problem solving (e.g., Heaton et al., 1995), cognitive flexibility (e.g., Reger et al., 2002), pre-potent response inhibition (e.g., Martin, Pitrak et al., 2004), and planning (e.g., Bartok et al., 1997). Emergent data also indicate that individuals infected with HIV may be prone to risky decision-making (Hardy et al., 2006), perhaps as a function of cognitive impulsivity. Overall, etiological commonalities throughout neurocognitive findings in HIV seem to point to a primarily dysexecutive syndrome, which may impact various domains of functioning through deficient higher-order strategic abilities (e.g., semantic clustering in verbal learning; Gongvatana et al., 2007) as well as weakened mechanisms for cognitive control (e.g., intraindividual variability; Morgan et al., 2011). EVERYDAY FUNCTIONING

The myriad medical, psychiatric, and neurocognitive complications that may accompany HIV infection can decrease one's efficiency in completing instrumental and even basic ADLs. Neurocognitive impairment, in particular executive dysfunction and deficits in episodic memory, is an important and independent contributor to ADL declines. However, even subtle HIV-associated neurocognitive deficits can contribute to problems in numerous aspects of everyday functioning (Morgan, Woods, Grant, et  al., 2012), including dependence in IADLs (Heaton et al., 2004), poorer health-related quality of life (e.g., Trepanier et al., 2005), increased engagement in risk behaviors (e.g., Gonzalez et al., 2005), and even higher mortality rates (e.g., Sevigny et al., 2007). Subsequent research demonstrated that HIV-associated neuropsychological impairment is associated with poorer performance on laboratory medication-management tasks (e.g., Patton et al., 2012), higher rates of self-reported problems with medication management (e.g., Woods et al., 2008), and non-adherence as measured by electronic medication monitors (e.g., Hinkin et al., 2004). The complex relationship between neurocognition and adherence is also cyclical, in that significant evidence demonstrates that poor adherence worsens disease outcomes, which in turn increases risk for cognitive decline (Ettenhofer et al., 2010).

Combined Effects of SUD and HIV Given their high rates of comorbidity, the combined effects of SUD and HIV on brain structure and function have garnered considerable scientific interest (e.g., Meyer et al., 2013). Below we review the literature on the CNS effects of some of the most common drugs of abuse, including methamphetamine, cocaine, alcohol, cannabis, and opioids. METHAMPHETAMINE AND HIV

Among substances of abuse, MA dependence is a major risk factor for becoming infected with HIV (e.g., risky sexual behaviors during periods of MA intoxication), and as such, these two conditions commonly co-occur (Buchacz et al., 2005).

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Within such high-risk situations, HIV-infected MA users are more likely to transmit the virus to non-infected sexual or needle-sharing partners, due to increased viral load as a result of the medication non-adherence frequently observed in MA users (e.g., Moore et al., 2012). The combination of these conditions appears to be more neurotoxic than either condition by itself. First, MA increases the expression of HIV cofactors (e.g., CXCR-2) and can increase replication of the virus in astrocytes (e.g., Gavrilin, Mathes, & Podell, 2002). At the neuronal level, MA decreases the function of the BBB, allowing greater numbers of HIV-infected leukocytes to be transported into the brain (e.g., Liang et al., 2008). Additionally, HIV-induced damage of the BBB may also allow for increased concentration of MA within the neural pathways (Kousik, Napier, & Carvey, 2012). These neurobiological changes result in significant ill effects for neuronal integrity; for example, magnetic resonance spectroscopy has shown relationships between increased cerebral metabolites (e.g., myo-inositol) and HIV plasma viral load in this population, suggesting that MA use may moderate CNS effects of HIV (Chang et al., 2005). However, the neuroimaging literature on the comorbid presentation of HIV and MA is not entirely straightforward. Although associated with similar neural pathway dysfunction, the resulting independent neuropathology of MA use on frontostriatal systems (e.g., increases in cortical and basal ganglia volume; Jernigan et al., 2005) appears to be distinct from that of HIV infection (e.g. frontal cortex and caudate atrophy), which may be a result of differential mechanisms of injury (e.g., inflammation, neuronal loss). Indeed, MA use in the presence of HIV may produce an additive neuronal injury (e.g., interneuron loss; Chana et al., 2006) and abnormal brain metabolism in frontostriatal pathways (Chang et al., 2005). Given the opposing neurotoxic effects of these conditions on brain structure, it is not surprising that interaction effects are not seen when examining overall impact on volume (e.g., Jernigan et al., 2005). However, the expected additive deficit on brain function from these two neurotoxic conditions has been notably absent in a number of studies. For instance, Ances and colleagues (2011) observed independent trend-level and significant effects of HIV and MA, respectively, on cerebral blood flow surrounding the lenticular nuclei, but no interaction of the two conditions on this outcome on a functional task. Similar findings in magnetic resonance spectroscopy failed to find an interaction of these conditions on cerebral metabolites, but some evidence suggests that MA decreases neuronal integrity in the context of poorly controlled HIV disease (Taylor et al., 2007). Even more unexpectedly, Archibald and colleagues (2012) found that the combination of HIV and MA appeared to ameliorate the negative effect of either condition alone on decreased blood flow during a complex motor task. In sum, these mixed findings suggest that further research, perhaps using different technology (e.g., diffusion tensor imaging) or methodology (e.g., fMRI with executive tasks) is necessary to elucidate the interaction of HIV and MA on macro neural systems. Despite the complexity of the imaging literature, the combination of HIV and MA has been reliably associated with higher rates of neuropsychological impairment than either of the independent conditions, with specific deficits in learning, memory, motor skills, and attention/working memory (Rippeth et al., 2004).

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HIV+ MA users with dopamine-specific genetic variant positivity (i.e., rs6280TC) may demonstrate particularly higher rates of impairment (Gupta et  al., 2011). Carey and colleagues (2006) observed additive deleterious effects of HIV and MA, such that individuals in the immunosuppressed comorbid sample were impaired on more neurocognitive domains than all other MA/HIV comparison groups. In fact, MA use has been linked to worse neurocognitive functioning in acute and early HIV infection, a critical period of immune activation and alterations of brain metabolism that is hypothesized to set the stage for future neurocognitive outcomes (Weber, Morgan, et al., 2013). Speaking to the pattern of neural injury observed in this group, Gonzalez, Bechara, and Martin (2007) found increased working memory deficits and risky decision-making in a sample of HIV-infected MA users relative to substance-free individuals with HIV. Extending the impact of HIV and MA to the ecologically relevant domain of social cognition, Homer and colleagues (2013) observed additive effects on emotional theory of mind, as well as an independent effect of MA on cognitive theory of mind. Importantly, these neurocognitive deficits may amplify the risk of poorer functional outcomes (e.g., Sadek et al., 2007). In fact, neurocognitive deficits in this population may greatly increase the likelihood of poorer disease outcomes (Ellis et al., 2003), in part due to this population's high rate of cART non-adherence (Moore et al., 2012). ALCOHOL AND HIV

Alcohol-use disorders are highly prevalent within HIV-infected adults (see Conigliaro et al., 2006), as alcohol use is frequently associated with risky behaviors (e.g., disinhibition leading to risky sex) that increase the likelihood of HIV transmission (Stein et al., 2005). Once an individual contracts HIV, heavy alcohol use is associated with immune suppression (e.g., Wang et al., 2002), potentially complicated by poor cART adherence (e.g., Arnsten et al., 2001), and contributes to more rapid disease progression (Samet et al., 2003). The combined effects of alcohol and HIV infection on neural systems suggest an overall picture of additive neurotoxicity, possibly through glutamate imbalance and oxidative damage that are present in both conditions (Zhao et al., 2004; Melendez et al., 2005) and alcohol-induced damage to the BBB (e.g., Singh et al., 2007), although interactive mechanisms also exist (e.g., alcohol's potentiation of HIV-specific protein-induced gp120 apoptosis; Chen et al., 2005) (see Persidsky et al., 2011). These effects are seen at the neural level, in which imaging studies have revealed the exacerbation of HIV-associated white matter damage (e.g., fractional anisotropy in the corpus callosum; Pfefferbaum et al., 2007) and increased metabolic abnormalities (i.e., reduced N-acetylaspartate and creatine; Pfefferbaum et al., 2005). While early neurocognitive studies on HIV/alcohol comorbidity were heavily confounded by polysubstance use (Durvasula, 2007), more recent studies have been able to better isolate the impact of this combined condition on cognitive impairment. Additive effects on cognition have been seen in HIV-infected alcohol users on measures of selective attention (Schulte et al., 2005), psychomotor speed (Durvasula et al., 2006), and verbal reasoning and auditory processing (Green et al., 2004). A recent study examining the impact of HIV and alcohol-use disorders found

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significant impairment of the sequencing and recall of remote semantic knowledge in the comorbid (HIV+ALC) group, but with intact recognition abilities, as consistent with disrupted retrieval processes and frontostriatal dysfunction (Fama et al., 2011). Beyond these demonstrations of additivity, it has also been theorized that alcohol use may synergistically act with the immune system to produce additional damage to neurocognitive status (e.g., thrombocytopenia and small thymus volume, associated with poor cognitive performance; Miguez-Burbano et al., 2009). Furthermore, Fama and colleagues (2009) found a synergistic pattern in which the comorbid HIV-plus-alcohol group was impaired on immediate episodic memory relative to healthy adults, but the deficit was also amplified as compared to either single risk-factor group. Importantly, these augmented neurocognitive deficits appear to contribute independently to poorer health-related quality of life, which is greater in this comorbid group than either risk group alone (e.g., Rosenbloom et al., 2007; Sassoon et al., 2012). Indeed, alcohol use is associated with increased neuropsychiatric symptoms during acute and early HIV infection (Weber et al., 2013) and with worse everyday functioning outcomes among HIV-infected persons without histories of immune suppression (Blackstone et al., 2013).

CANNABIS AND HIV

Marijuana is commonly used among HIV-infected adults, as both a recreational drug as well as an adjunct medicinal therapy (Furler et al., 2004). Medical marijuana is often prescribed to this population to treat symptoms of pain, nausea, and appetite loss (Ellis et al., 2009). Although efficacious in reducing targeted symptoms (e.g., Ellis et al., 2009), marijuana use often results in the suppression of the immune system, which may be of concern in an immunocompromised condition such as HIV (e.g., Ongradi et al., 1998). In general, cannabis is largely not associated with frank neurocognitive impairment; a meta-analytic study revealed only a small effect of long-term cannabis use on the ability to learn and remember new information (Grant et al., 2003; see Chapter 7, this volume). Despite its common co-occurrence, few studies have directly examined the impact of cannabis use in HIV-infected populations. In a four-group design, Cristiani and colleagues (2004) found that frequent marijuana users with symptomatic HIV disease experienced greater memory impairment relative to non- or minimal-users and those with less severe disease status. More recently, Gonzalez and colleagues (2011) revealed an additive effect between HIV and history of marijuana dependence on a series of complex motor tasks requiring procedural learning (e.g., Star Mirror Tracing Task) in a cohort of abstinent polysubstance users. However, given marijuana's bestowed nickname of “the gateway drug” (DuPont, 1984) and the likelihood of other comorbid past and current substance use (Hall & Lynskey, 2005), it may be difficult for retrospective research studies to isolate the neurocognitive impact of cannabis use in HIV infection that is not confounded by related variables (e.g., overall severity of substance use). Continued efforts are needed to determine the impact of cannabis on neural systems in HIV infection, as medicinal cannabis is utilized more broadly for treating neuropathic pain in this population.

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COCAINE AND HIV

Although cocaine use is highly prevalent among HIV-infected adults (e.g., approximately 40%; Heaton et al., 2010), evidence for their comorbid neurotoxicity has been mixed across a number of research modalities. For instance, the administration of cocaine to a rodent model of HIV has shown evidence of neural injury (e.g., astrogliosis) as well as neurobehavioral deficits (e.g., impairment on a spatial learning task; Griffin et al., 2007), whereas few immune, CNS inflammatory, or neurobehavioral changes were observed in a SIV-macaque model (Weed et  al., 2012). However, some studies suggest that these effects appear to be minimal, but evident, at the neural level relative to the greater HIV-associated impact. For example, in light of a significant reduction of dopamine transporters (DATs) in the basal ganglia in HIV-infected non-substance users, Chang and colleagues (2008) found trend-level evidence for further DAT reduction in comorbid HIV/cocaine users, with low DAT associated with greater neurocognitive impairment. Beyond dopaminergic systems, other candidate hypotheses for comorbid neurotoxicity include cocaine's deleterious effects on the BBB (e.g., increased HIV neuroinvasion; Zhang et al., 1998), amplification of HIV replication (e.g., Peterson et  al., 1991), and increased expression of inflammatory processes (e.g., Gan et al., 1998). Although previous studies often grouped cocaine users with general samples of “stimulant” or “substance” users due to high rates of comorbidity with other substances such as methamphetamine (e.g., Levine et al., 2006), several more-recent studies have sought to clarify the cocaine-specific impact on neurocognition in the context of HIV infection. For example, Meade, Lowen, and colleagues (2011) studied delay discounting, a measure of cognitive impulsivity that focuses on the relationship between the delay of a reward and its perceived value and often plays a role in risky decision-making (Ainslie, 1975), in HIV-infected samples with varying degrees of cocaine use (i.e., naïve, recovered users, and active users). With baseline deficits in decision-making among HIV-infected adults (e.g., Hardy et al., 2006) in mind, participants with any cocaine history performed worse than naïve HIV-infected adults. Although not statistically significant, analyses revealed a medium-to-large effect of cocaine use on delay discounting performance, which was supplemented by additional analyses revealing relatively decreased activation of the active users’ executive centers (e.g., frontal poles) when making decisions on a separate, fMRI-suited delayed discounting task. These neurocognitive deficits translate to ecologically relevant impairments as well. A study by this same group demonstrated that neurocognitive impairment partially mediated the relationship between cocaine dependence and non-adherence in the context of HIV infection, highlighting the role of cocaine on cognition in important health outcomes (Meade, Conn, et al., 2011).

OPIOIDS AND HIV

With a significant proportion of opioid users utilizing injection as a primary route of drug delivery (e.g., SAMHSA, 2010), HIV features prominently among

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comorbid conditions in this opioid use. Opioids are easily able to cross the BBB and result in feelings of euphoria, which is probably related to the drug's mechanism of action in dopaminergic pleasure centers of the brain. Despite this obvious neural influence, the independent effect of opioids on neurocognition remains unclear (e.g., Pau et  al., 2002). As such, the literature is similarly mixed when considering HIV comorbidity. At the neuronal level, opioids appear to exacerbate synaptodentritic injury through a combination of excitotoxic and inflammatory events (see Hauser et al., 2012). Concordantly, several studies have demonstrated that opiate (e.g., heroin) use may exacerbate rates of HIV-associated neurocognitive impairment (e.g., Margolin et al., 2002), and conversely, that HIV infection independently affects neurocognition, particularly in the areas of verbal memory, attention, and motor speed, above and beyond relevant cofactors (e.g., severity of substance use, depression; Applebaum et  al., 2010). Despite these findings, multiple studies have been unable to conclude that there is such an independent effect on neurocognition (e.g., Selnes et al., 1997). Regardless, the health impact of neurocognitive deficits, particularly in the areas of problem-solving and cognitive flexibility, appears to play a role in adherence to cART (Avants et al., 2001) in opiate users, suggesting that comprehensive evaluation of this population is necessary.

TRIMORBIDITY: SUD, HIV, AND HCV Individuals who are infected with one virus have an increased risk of co-infection with the other due to overlapping risk factors (e.g., IDU, high-risk sexual behaviors). Over 30% of individuals infected with HIV are also seropositive for HCV (Ryan et  al., 2004; Backus et  al., 2009), and approximately 5%–10% of those with HCV are co-infected with HIV (Bini et al., 2006). Independently of SUD, co-infection increases the risk of infectious disease–related morbidity and mortality (e.g., Lacombe & Rockstroh, 2012). Although several studies have found evidence for an additive negative effect of co-infection versus mono-infection on cognition (Hilsabeck, Castellon, & Hinkin, 2005; Martin, Novak et al., 2004), particularly in the area of psychomotor speed (Martin-Thormeyer & Paul, 2009), not all reports have been consistent (Simioni et al., 2010). The apparent additive effects of HIV-HCV co-infection appear to be independent of SUD (Martin-Thormeyer & Paul, 2009) and other psychiatric co-morbidity (Sun et al., 2013). Several other studies have evaluated the cognitive effects of HCV, HIV, and SUD in concert. One recent study evaluated neuropsychological functioning among 115 HIV+ and 72 HIV– adults, many of whom had SUD and/or HCV (Devlin et  al., 2012). Findings showed that detectable HIV RNA and HCV serostatus was significantly associated with poorer cognitive function across multiple cognitive domains, whereas there were much weaker effects for SUD (e.g., stimulants). In another study, of over 400 participants, which included healthy comparison subjects alongside persons with and without HCV, HIV, and MA dependence, Cherner and colleagues (2005) showed that each of the

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clinical risk factors was independently associated with worse neurocognitive performance across multiple domains, even after adjusting for potentially confounding factors. These data suggest possible additive effects of HIV, HCV, and SUD, as rates of global and domain-specific cognitive impairments significantly increased in step with the number of risk factors present. In a follow-up study, Letendre et al. (2005) reported that the independent HCV effect among co-infected SUDs may be a function of viral or immune-mediated factors (e.g., TNF-alpha). Although additional studies are needed across other SUDs (e.g., cocaine, alcohol) and indicators of brain structure and function (e.g., neuroimaging), this early research suggests that individuals with comorbid HIV, HCV, and SUD are at proportionally increased risk for neurocognitive impairment. SUD'S AND INFECTIOUS DISEASE'S COMORBID IMPACT ON OTHER NEUROPSYCHIATRIC OUTCOMES Beyond the combined impact of SUD and infectious disease on neurocognitive outcomes, emerging research has begun to explore the effect of this comorbidity on broader neuropsychiatric outcomes (e.g., depression, apathy). One mechanism for this phenomenon is that neuropathological changes that occur in the frontal lobes may extend beyond the dorsolateral prefrontal cortex, which is primarily associated with neurocognitive deficits in executive functions (e.g., inhibition), to the ventromedial prefrontal cortex and therefore other neuropsychiatric outcomes. Specifically, increasing attention has been focused on apathy, or the decrease in self-initiated cognitive, emotional, and behavioral activity (Marin, 1990), as an early behavioral indicator of neurocognitive disorders in infectious disease (e.g., Paul et al., 2005; Posada et al., 2010) and a significant predictor of declines in everyday functioning (e.g., Kamat et al., 2012). Similar patterns have been observed in the context of substance use (e.g., MA; Cattie, Woods, Iudicello, et al., 2012), so it may be likely that the combination of conditions would increase the risk for clinically significant apathy. However, the relationship between these conditions might be more dynamic rather than strictly based on their neuroanatomical substrates. For example, depression may be elevated in both infectious disease and SUD (e.g., Oser et al., in press; Weber, Morgan, et al., 2013; cf. Vigil et al., 2007), but some evidence suggests that substance use may be utilized as a coping mechanism for neuropsychatric distress, rather than an outcome of the inverse (e.g., Oser et al., in press). Indeed, individuals with acute and early HIV infection have a fivefold greater risk of experiencing clinically significant neuropsychiatric distress than their seronegative counterparts, which was significantly related to high-risk alcohol use (Weber, Morgan, et al., 2013). While the order of condition onset may be unclear, it may be hypothesized that a proportion of individuals with newly diagnosed infectious disease may use substances in order to decrease their symptoms of distress, given the effectiveness of certain substances (e.g., alcohol) as mood-altering agents (e.g., anxiolytics; Steele et al., 1986). Therefore, clinicians may seek to explore high-risk substance use in their

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patients with infectious disease, not only as it may relate to immediate health risk, but additionally as a potential indicator of underlying mood disorders. CLINICAL RECOMMENDATIONS Considering the significant CNS complications introduced by HIV and HCV infection, substance users with (or clearly at risk for) these conditions may be targeted for neuropsychological screening. Screening is important because research has shown that even mild levels of neuropsychological impairment can have significant effects on real-world functions, such as employment (van Gorp et  al., 1999), medication non-adherence, (Hinkin et al., 2002), and automobile-driving safety (Marcotte et al., 1999). Since self-report of cognitive symptoms does not correspond well to actual neurocognitive functioning, and brief mental status testing is generally inaccurate in HIV (e.g., Carey et al., 2004), a brief battery of standard clinical tasks may be preferred for screening purposes. For example, several studies now suggest that a brief measure of verbal learning (e.g., Hopkins Verbal Learning Test) and psychomotor speed (e.g., Grooved Pegboard, Digit Symbol) are reasonable screeners for neurocognitive impairment in HIV (Carey et  al., 2004). Brief assessment of daily functioning (ideally by informants) may also be worth the added effort with, for example, the Lawton and Brody IADL questionnaire (Lawton & Brody, 1969), which has been used extensively in HIV (e.g., Heaton et al., 2004), as well as in HCV (Vigil et al., 2007) and addictions (e.g., Sadek et al., 2007). Quick and effective methods for assessing substance use and psychiatric disorders are also available for health care practitioners. A 2011 study found that the Center for Epidemiological Studies Depression scale (CES-D; Radloff, 1977) and Alcohol Use Disorders Identification Test (AUDIT; Bush et  al., 1998)  are good options for screening patients with HIV, as both scales show high discrimination and sensitivity (Chishinga et  al., 2011). Similarly, preliminary testing of a brief screening tool, the Substance Abuse and Mental Illness Symptoms Screener (SAMISS; Whetten et al., 2005), found that this screener was highly predictive of having a general mental disorder and substance use disorder among patients with HIV. The Kreek-McHugh-Schluger-Kellogg scale (KMSK; Kellogg et al., 2003) is a validated instrument that quantifies lifetime use of alcohol, cocaine, heroin, and tobacco and has shown utility among patients with viral hepatitis (Jackson et al., 2010). As with the management of most diseases, a comprehensive, multidisciplinary approach is ideal for the treatment of SUD in patients with comorbid chronic viral infections (i.e., HIV, HCV). Targeted education efforts about HCV and HIV risks among persons with SUD are also clearly warranted (Gonzales et al., 2006). Ideally, educational interventions to prevent infectious diseases should be utilized in populations at risk for transmission and acquisition of HIV (e.g., IDUs). Continued efforts toward integration of HCV and HIV screening and testing into mental health and addiction programs are also clearly indicated (Huckans et  al., 2005). Co-testing, particularly in HCV-infected individuals, is not done

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routinely despite shared risk factors for HIV and HCV. Many programs are nevertheless beginning to successfully integrate HCV treatment for individuals with SUD into healthcare settings, including primary care, methadone treatment and other substance-abuse treatment programs, infectious disease clinics, and clinics in correctional facilities (Sylvestre et al., 2005; Sylvestre et al., 2004; Cournot et  al., 2004; Backmund et  al., 2001; Boutwell et  al., 2005). This is important because the multimorbidity of HIV and HCV with SUD (Fig. 14.1), including neurocognitive impairment, psychiatric distress, and medical complications, may increase the already substantial challenges of SUD treatment. For instance, persons with comorbid HCV and HIV undergoing antiviral therapy may experience increased drug cravings (Marks & Milby, 2009). Antiviral therapies can produce significant side effects such as fatigue, nausea, and depression that can affect cognition and motivation, which in turn may interfere with ongoing SUD interventions. By the same token, clients’ engagement in infectious-disease clinic services provides healthcare providers an opportunity to more regularly monitor issues related to the management of SUD. In turn, SUD can also influence the availability and effectiveness of antiviral treatment. Infectious-disease physicians may be reticent about prescribing antiviral therapy to persons with active SUD. For example, several studies report that alcohol use is the most common reason for excluding patients from antiviral therapy for HCV and that abstinence may increase clinicians’ willingness to administer treatment (Cawthorne et al., 2002; Morrill et al., 2005). SUD might also adversely impact the effectiveness of antiviral therapy; for example, by enhancing HCV (e.g., Pessione et al., 1998; Cromie et al., 1996) and HIV (Ellis et  al., 2003)  viral replication; indeed, abstinence from SUD has been associated with reductions in viral load (Cromie et al., 1996). A third mechanism by which SUD might adversely affect antiviral therapy is by interfering with medication adherence. In HIV, for example, current use of methamphetamine is strongly associated with a notably increased risk of non-adherence to cART (e.g., Moore et al., 2012). Thus, effective screening and management of SUD may positively impact health outcomes in persons with HIV and HCV by enhancing the availability and effectiveness of antiviral therapies by identifying potential clients at high risk of treatment failure. A treatment model is advocated that uses a multidisciplinary team (including mental health providers, addictions and infectious disease specialists, and hepatologists) to provide frequent neuropsychological, substance use, and medical monitoring (Hauser et al., 2004). Given the higher rates of HIV and HCV in patients with SUD, especially IDU, new harm-reduction approaches are needed for these populations that focus beyond prevention to the functioning and well-being of those already infected. For example, reducing heavy alcohol use in addition to slowing HCV progression shows promise for improving quality of life (Costenbader et  al., 2007) and depressive symptoms (Sullivan et al., 2011). In persons with comorbid HCV, HIV, or co-infection, medication-assisted therapies can reduce the negative consequences of drug use. These treatments alone, or when combined

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with brief counseling or education, can lead to impressive outcomes (Bruce et al., 2010). In a comprehensive review, Altice et al. (2010) provided a list of evidence-based medication-assisted therapies that have been evaluated for opioid dependence and alcohol-use disorders in patients with HIV or HCV. Medications that showed some efficacy included methadone, buprenorphine, buprenorphine-naloxone, and naltrexone (for opioid dependence and for alcohol dependence). Given that neuroinflammation and neurodegeneration of the CNS may be responsible for neuropsychological dysfunction observed in patients with comorbid HIV and/or HCV and SUD (Fig. 14.1), the use of anti-inflammatory approaches for the treatment of substance use, mood, and cognitive disorders is being examined at both pre-clinical and clinical levels (Loftis et  al., 2010; Altice et al., 2010; Abbasi et al., 2012). Indeed, many antidepressant medications have specific anti-inflammatory effects (Lim et al., 2009; Carvalho & Pariante, 2008) and significant immunoregulatory activities. Therefore, therapeutic development aimed at reducing neuroinflammation may be a logical approach to ameliorating, or reversing, CNS injury in the setting of SUD and comorbid viral infection. We are unaware of any empirically supported cognitive neurorehabilitation approaches for persons with SUD and infectious disease. In fact, there is a paucity of such approaches in any of these conditions considered by themselves (see Weber, Blackstone, et  al., 2013). Therefore, the development and validation of effective, multimodal therapies for neurocognitive disorders in persons with SUD and infectious disease is of paramount importance. Innovative pharmacotherapy approaches may be nicely complemented by cognitive and behavioral strategies, especially regarding the enhancement of medication adherence, which is a major concern in this complicated clinical group. Given the paucity of effective interventions to maintain antiviral adherence in infectious disease, using theory-guided studies to validate neurocognitive targets represents a potentially significant initial step toward effective, low-cost interventions to improve health outcomes. If effective, these types of mechanistic manipulations may become the basis for larger-scale, low-cost cognitive interventions with significant everyday functioning and health outcome implications for persons living with infectious disease and SUD. ACKNOWLEDGMENTS The preparation of this chapter was supported by F31DA034510, P30MH062512, P50DA026306, P50DA018165 and T32DA031098. This material is the result of work supported with resources and the use of facilities at the Portland Veterans Affairs Medical Center, Portland, Oregon; Oregon Health & Science University, Portland Oregon; and the Methamphetamine Abuse Research Center (MARC), Portland, Oregon. The views expressed in this article are those of the authors and do not reflect the official policy or position of the Department of the Navy, Department of Defense, nor the United States Government.

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Stout, J. C., Ellis, R. J., Jernigan, T. L., Archibald, S. L., Abramson, I., Wolfson, T., . . . & The HIV Neurobehavioral Research Center (HNRC) Group. (1998). Progressive cerebral volume loss in human immunodeficiency virus infection: A longitudinal volumetric magnetic resonance imaging study. Archives of Neurology, 55, 161–168. Substance Abuse and Mental Health Services Administration. (2010). Results from the 2009 National Survey on Drug Use and Health: Volume I. Summary of National Findings. Rockville, MD: SAMHSA. Sullivan, L. E., Goulet, J. L., Justice, A. C., & Fiellin, D. A. (2011). Alcohol consumption and depressive symptoms over time: A longitudinal study of patients with and without HIV infection. Drug & Alcohol Dependence, 117, 158–163. Sun, B., Abadjian, L., Rempel, H., Monto, A., & Pulliam, L. (2013). Differential cognitive impairment in HCV coinfected men with controlled HIV compared to HCV monoinfection. Journal of Acquired Immune Deficiency Syndromes, 62, 190–196. Sylvestre, D. L., Loftis, J. M., Hauser, P., Genser, S., Cesari, H., Borek, N., . . . & Francis, H. (2004). Co-occurring Hepatitis C, substance use, and psychiatric illness: Treatment issues and developing integrated models of care. Journal of Urban Health, 81, 719–734. Sylvestre, D.  L. (2005). Treating hepatitis C virus infection in active substance users. Clinical Infectious Diseases, 40(Suppl 5), S321–S324. Taylor, M.  J., Letendre, S.  L., Schweinsburg, B.  C., Alhassoon, O.  M., Brown, G.  G., Gongvatana, A., . . . & The HNRC Group. (2004). Hepatitis C virus infection is associated with reduced white matter N-acetylaspartate in abstinent methamphetamine users. Journal of the International Neuropsychological Society, 10, 110–113. Taylor, M.  J., Schweinsburg, B.  C., Alhassoon, O.  M., Gongvatana, A., Brown, G.  G., Young-Casey, C., . . . & The HIV Neurobehavioral Research Center (HNRC) Group. (2007). Effects of human immunodeficiency virus and methamphetamine on cerebral metabolites measured with magnetic resonance spectroscopy. Journal of Neurovirology, 13, 150–159. Terrault, N. A., Dodge, J. L., Murphy, E. L., Tavis, J. E., Kiss, A., Levin, T. R., . . . & Alter, M.  J. (2013). Sexual transmission of hepatitis C virus among monogamous couples: The HCV partners study. Hepatology, 57, 881–889. Trepanier, L.  L., Rourke, S.  B., Bayoumi, A.  M., Halman, M.  H., Krzyzanowski, S., & Power, C. (2005). The impact of neuropsychological impairment and depression on health-related quality of life in HIV-infection. Journal of Clinical & Experimental Neuropsychology, 27, 1–15. Umaki, T. M., & Denney, R. L. (2013). Neurocognitive deficits associated with the hepatitis C virus among incarcerated men. Clinical Neuropsychologist, 27, 426–436. van Gorp, W., Baerwald, J. P., Ferrando, S. J,, McElhiney, M. C., & Rabkin, J. G. (1999). The relationship between employment and neuropsychological impairment in HIV infection. Journal of the International Neuropsychological Society, 5, 534–539. Vigil, O., Posada, C., Woods, S. P., Atkinson, J. H., Heaton, R. K., Perry, W., . . . & The HNRC Group. (2008). Impairments in fine-motor coordination and speed of information processing predict declines in everyday functioning in hepatitis C infection. Journal of Clinical & Experimental Neuropsychology, 30, 805–815. Vigil, O., Woods, S.  P., Moran, L.  M., Letendre, S.  L., Young-Casey, C., Grant, I., & Atkinson, J.  H. (2007). Is hepatitis C infection associated with increased risk of depression in persons with methamphetamine dependence? American Journal of Addictions, 16, 418–423. von Giesen, H.  J., Heintges, T., Abbasi-Boroudjeni, N., Kucukkoylu, S., Koller, H., Haslinger, B. A., . . . & Arendt, G. (2004). Psychomotor slowing in hepatitis C and HIV infection. Journal of Acquired Immune Deficiency Syndromes, 35, 131–137.

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Wang, X., Douglas, S. D., Metzger, D. S., Guo, C. J., Li, Y., O'Brien, C. P., . . . & Ho, W. Z. (2002). Alcohol potentiates HIV-1 infection of human blood mononuclear phagocytes. Alcoholism, Clinical & Experimental Research, 26, 1880–1886. Weber, E., Blackstone, K., & Woods, S.  P. (2013). Cognitive neurorehabilitation of HIV-associated neurocognitive disorders:  A  qualitative review and call to action. Neuropsychology Review, 23, 81–98. Weber, E., Morgan, E. E., Iudicello, J. E., Blackstone, K., Grant, I., Ellis, R. J., . . . & The TMARC Group. (2013). Substance use is a risk factor for neurocognitive deficits and neuropsychiatric distress in acute and early HIV infection. Journal of NeuroVirology, 19, 65–74. Weed, M., Adams, R.  J., Hienz, R.  D., Meulendyke, K.  A., Linde, M.  E., Clements, J.  E., . . . & Zink, M.  C. (2012). SIV/macaque model of HIV infection in cocaine users: Minimal effects of cocaine on behavior, virus replication, and CNS inflammation. Journal of Neuroimmune Pharmacology, 7, 401–411. Weissenborn, K., Krause, J., Bokemeyer, M., Hecker, H., Schuler, A., Ennen, J. C., . . . & Boker, K. W. (2004). Hepatitis C virus infection affects the brain-evidence from psychometric studies and magnetic resonance spectroscopy. Journal of Hepatology, 41, 845–851. Weissenborn, K., Tryc, A.  B., Heeren, M., Worthmann, H., Pflugrad, H., Berding, G., . . . & Goldbecker, A. (2009). Hepatitis C virus infection and the brain. Metabolic Brain Disease, 24, 197–210. Whetten, K., Reif, S., Swartz, M., Stevens, R., Ostermann, J., Hanisch, L., & Eron, J. J. Jr. (2005). A brief mental health and substance abuse screener for persons with HIV. AIDS Patient Care & STDs, 19, 89–99. Wilkinson, I.  D., Miller, R.  F., Miszkiel, K.  A., Paley, M.  N. J., Hall-Craggs, M.  A., Baldeweg, T., . . . & Harrison, M.  J. G. (1997). Cerebral proton magnetic resonance spectroscopy in asymptomatic HIV infection. AIDS, 11, 289–295. Wilkinson, J., Radkowski, M., Eschbacher, J. M., & Laskus, T. (2010). Activation of brain macrophages/microglia cells in hepatitis C infection. Gut, 59, 1394–1400. Woods, S. P., Moran, L. M., Carey, C. L., Dawson, M. S., Iudicello, J. E., Gibson, S., . . . & The HIV Neurobehavioral Research Center (HNRC) Group. (2008). Prospective memory in HIV infection:  Is “remembering to remember” a unique predictor of self-reported medication adherence? Archives of Clinical Neuropsychology, 23, 257–270. Ye, L., Peng, J. S., Wang, X., Wang, Y. J., Luo, G. X., & Ho, W. Z. (2008). Methamphetamine enhances hepatitis C virus replication in human hepatocytes. Journal of Viral Hepatitis, 15, 261–270. Zhang, L., Looney, D., Taub, D., Chang, S. L., Way, D., Witte, M. H., . . . & Fiala, M. (1998). Cocaine opens the blood-brain barrier to HIV-1 invasion. Journal of NeuroVirology, 4, 619–626. Zhao, J., Lopez, A. L., Erichsen, D., Herek, S., Cotter, R. L., Curthoys, N. P., & Zheng, J. (2004). Mitochondrial glutaminase enhances extracellular glutamate production in HIV-1-infected macrophages: Linkage of HIV-1 associated dementia. Journal of Neurochemistry, 88, 169–180. Zickmund, S., Hillis, S.  L., Barnett, M.  J., Ippolito, L., & LaBrecque, D.  R. (2004). Hepatitis C virus-infected patients report communication problems with physicians. Hepatology, 39, 999–1007.

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Traumatic Brain Injury and Substance Use Disorder Comorbidity TRESA ROEBUCK SPENCER, ELISABETH A. WI LDE, AND ANGELLE SANDER

The Centers for Disease Control and Prevention (CDC) define traumatic brain injury (TBI) as an injury to the head that involves at least one of the following: 1) decreased level of consciousness, 2)  amnesia, 3)  skull fracture, or 4)  objective neurological or neuropsychological abnormality or diagnosed intracranial lesion (Marr & Coronado, 2004). TBI is a leading cause of death and disability in the United States affecting persons of all ages, sexes, races/ethnicities, and incomes (Coronado et al., 2011). Approximately 1.7 million people sustain a TBI annually. Estimates indicate that 80,000 to 90,000 persons have new onset of disability each year due to TBI (Thurman, Alverson, Dunn, Guerrero, & Sniezek, 1999), and at least 5.3  million Americans are living with disability due to TBI (Langlois, Rutland-Brown, & Wald, 2006). Falls and motor vehicle accidents are the two most common causes of injury, with males overall showing higher rates of TBI than females. TBI ranges in severity from mild to severe and results in some disturbance in cognitive, behavioral, emotional, and/or physical functioning. These effects may be transient, long-lasting, or permanent, depending on injury specifics and severity. Moderate to severe TBI typically results in a prolonged recovery course, the need for acute medical care and rehabilitation, and the potential for persisting disabilities (Roebuck-Spencer & Sherer, 2008). In contrast, prospective studies and meta-analyses have generally concluded that symptoms and cognitive impairments following uncomplicated (i.e., absence of findings on imaging) mild TBI (mTBI) usually improve over a period of days to weeks in the majority of patients, with no indication of permanent impairment by three months post-injury (Belanger & Vanderploeg, 2005; Levin et  al., 1987; Schretlen & Shapiro, 2003). Although persisting symptoms may remain in a minority of individuals outside of this window (Lange, Brickell, Ivins, Vanderploeg, & French, 2012), evidence indicates that incomplete recovery from mTBI may be associated with or complicated

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by preexisting or comorbid psychiatric, medical, psychosocial, or litigation factors in some cases (Iverson, 2005). Additional evidence indicates that repeated mTBI and complicated mTBI (i.e., mTBI with associated neuroimaging findings) may also place individuals at risk for a prolonged or atypical recovery course (Dagher, Richard-Denis, Lamoureux, de Guise, & Feyz, 2013; Guskiewicz et al., 2003; Ravdin, Barr, Jordan, Lathan, & Relkin, 2003). The relationship between substance abuse and traumatic brain injury (TBI) has been well documented, with substantial evidence that substance abuse leads to both increased risk for TBI and negative outcomes across a variety of domains (Corrigan, 1995; Corrigan, Bogner, & Holloman, 2012; Graham & Cardon, 2008; Parry-Jones, Vaughan, & Miles Cox, 2006; Taylor, Kreutzer, Demm, & Meade, 2003). Alcohol has been the most widely studied substance of abuse in TBI, with much of the literature on substance abuse and TBI focusing primarily on alcohol. Thus, this chapter reviews the relationship between TBI and alcohol abuse with specific focus on the prevalence of alcohol abuse prior to, at the time of, and following TBI; the effects of alcohol use on recovery and outcome following TBI; the neural underpinnings and neuroimaging findings of combined TBI and alcohol abuse; and the effects of alcohol/substance abuse treatment following TBI. When relevant literature is available, the relationship between TBI and more general substance abuse will also be reviewed.

HISTORY OF TBI IN ALCOHOL AND SUBSTANCE USE DISORDERS History of TBI is frequent among individuals receiving treatment for alcohol and substance use disorders (Corrigan & Deutschle, 2008)  and, when present, is associated with worse and earlier onset of substance abuse, greater treatment needs, more psychiatric comorbidities, and overall poorer outcomes (Corrigan & Deutschle, 2008; Felde, Westermeyer, & Thuras, 2006; Walker, Cole, Logan, & Corrigan, 2007). The relationship between alcohol abuse and TBI is complex and probably circular in nature. Evidence from birth-cohort studies shows that adolescents who drank regularly were twice as likely to sustain a TBI compared with adolescents who had never used alcohol (Winqvist, Jokelainen, Luukinen, & Hillbom, 2006). Additionally, initial alcohol-related TBI injuries sustained after age 12 were associated with a four-fold increased risk of repeat TBI by age 34 (Winqvist et al., 2008). Consequently, the risk for conduct disorder, mood disorder, and substance abuse is increased in adolescents who suffered a significant TBI during early childhood (McKinlay, Grace, Horwood, Fergusson, & MacFarlane, 2009; McKinlay, Grace, Horwood, Fergusson, & MacFarlane, 2010). These relationships are further complicated by family and social factors, with evidence that childhood TBI is more likely for individuals with parents who drank alcohol heavily and that repeat injuries are more common for those living in urban areas (Winqvist, Jokelainen, Luukinen, & Hillbom, 2007; Winqvist et al., 2008).

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ALCOHOL AS RISK FACTOR FOR TBI Alcohol use and intoxication have long been documented as risk factors for TBI. Across studies, 23% to 56% of individuals sustaining TBIs have been documented as being intoxicated at the time of injury (Cherner, Temkin, Machamer, & Dikmen, 2001; Dikmen, Machamer, Donovan, Winn, & Temkin, 1995; Parry-Jones et  al., 2006; Rimel, Giordani, Barth, & Jane, 1982). Rates of other substance intoxication at time of injury are less well known due to lack of regular screening, but positive toxicology reports at the time of injury have been reported as being as high as 37% (Bombardier, Rimmele, & Zintel, 2002). A disproportionate number of those intoxicated at time of injury are young, male, injured in car accidents or assaults, and have a pre-injury history of substance use disorders. Documented alcohol use and intoxication were 21% and 12% respectively following TBI due to motor vehicle incidents and 41% and 23% following assault-related injuries (Langlois et al., 2003). Likewise, violence-related TBI injuries are also frequent in patients with a history of drug or combined drug and alcohol abuse (Drubach, Kelly, Winslow, & Flynn, 1993). Intoxication at the time of injury is also a good indicator of premorbid problematic drinking with the likelihood of pre-morbid problematic alcohol use increasing as blood-alcohol level (BAL) increases (Dikmen et al., 1995; Rivara et al., 1993). Pre-injury alcohol and substance abuse has been documented in one-third to slightly over one-half of persons hospitalized for TBI and in two-thirds of rehabilitation samples (Corrigan, 1995). Rates vary widely across studies, ranging from 15%–66%, with this variability probably associated with sampling and measurement artifacts (Bombardier et  al., 2002; Bombardier, Temkin, Machamer, & Dikmen, 2003; Cherner et  al., 2001; Corrigan, 1995; Dikmen et al., 1995; Kreutzer, Wehman, Harris, Burns, & Young, 1991; Kreutzer, Witol, & Marwitz, 1996; Parry-Jones et  al., 2006; Ponsford, Whelan-Goodinson, & Bahar-Fuchs, 2007; Tate, Freed, Bombardier, Harter, & Brinkman, 1999). Lower rates are typically found in studies using retrospective reporting methods or medical record review, and higher rates are typically found in hospitalized samples or from family report. Individuals with a history of TBI are generally found to be heavier drinkers prior to their injury than their same-age peers (Kolakowsky-Hayner et  al., 1999; Kreutzer, Doherty, Harris, & Zasler, 1990; Taylor et al., 2003). Those with a history of heavy alcohol abuse are more likely to be male, over 30 years of age, unmarried, to have lower education and socioeconomic status, to have been intoxicated at the time of their injury, and to have violence-related injuries (Taylor et al., 2003). Pre-injury substance abuse rates are also quite high, ranging from 21%–37% (Bombardier et al., 2002; Burnett, Silver, Kolakowsky-Hayner, & Cifu, 2000; Drubach et al., 1993; Kreutzer et al., 1991; Taylor et al., 2003). Premorbid alcohol and substance abuse is also high in ethnic minority groups who sustain TBI, with 50.5% of minority samples meeting criteria for heavy/moderate alcohol use and 34.2% reporting pre-morbid illicit drug use in those who sustain TBI (Burnett et al., 2000).

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NATURAL HISTORY OF POST-INJURY ALCOHOL AND SUBSTANCE USE Multiple studies show that alcohol use initially following injury declines substantially, particularly in the first month to year (Bombardier et  al., 2003; Dikmen et al., 1995; Hibbard, Uysal, Kepler, Bogdany, & Silver, 1998; Kreutzer et al., 1990; Ponsford et al., 2007). However, alcohol abuse remains a significant problem for many people following TBI, with estimates ranging from 7% to 48% (Bombardier et  al., 2003; Dikmen et  al., 1995; Horner et  al., 2005; Kreutzer, Witol, Sander, et  al., 1996; Ponsford et  al., 2007; Simpson & Tate, 2002). Longitudinal analysis of alcohol use patterns indicate that persons who are abstinent from alcohol at early post-injury periods may resume use at later periods, although generally still lower than pre-injury levels (Corrigan, Smith-Knapp, & Granger, 1998; Dikmen et al., 1995; Kelly, 1995; Kreutzer, Witol, Sander, et al., 1996; Ponsford et al., 2007; Soderstrom, 1989). In an evaluation of psychiatric disorders several years post-TBI, Hibbard and colleagues (1998) found that 28% of the sample met criteria for substance abuse disorder after injury, compared with 40% prior to injury. Of those with a pre-injury substance abuse problem, 80% had no problems post-injury, indicating recovery after injury. In contrast, of those with post-injury substance abuse problems, 71% had no such problem prior to injury, indicating that TBI may trigger substance abuse in some subgroups. Nonetheless, it is still relatively infrequent for individuals with TBI to develop new-onset substance abuse problems, with only 7% of those reporting abstinence or normal drinking prior to injury falling in this category (Bombardier et al., 2003). RISK FOR FUTURE SUBSTANCE USE PROBLEMS Studies exploring potential risk factors for post-injury alcohol abuse document that pre-injury problematic alcohol use is highly predictive of post-injury heavy alcohol use and related problems (Bombardier et al., 2003; Kreutzer, Witol, Sander, et al., 1996; Ponsford et al., 2007). Those most at risk to return to heavy drinking are male and younger in age, have a history of substance abuse prior to TBI, have had a diagnosis of depression since TBI, are in fair to moderate mental health, and have better physical functioning (Horner et al., 2005). Patients with fewer impairments (and less injury severity) are also more likely to drink or resume drinking post-injury than are more impaired patients (Taylor et al., 2003). Similarly, greater depth of coma (lower Glasgow Coma Scale) is predictive of decreased drinking at one year post-injury (Dikmen et al., 1995), presumably due to factors such as greater disability, reduced independence, and greater need for supervision, which all lead to decreased access to alcohol. Higher BAL at the time of injury also predicted greater decreases in alcohol use at one year post-injury (Dikmen et al., 1995). These findings may be related to the fact that BAL is related to higher levels of drinking pre-injury, allowing for a greater range of change. Conversely, decreased alcohol use may reflect actual positive and proactive changes in drinking behaviors due to increased awareness of the negative consequences of excessive alcohol use, as indicated by positive

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correlations between higher BAL and self-reported readiness to change alcohol use (Bombardier, Ehde, & Kilmer, 1997). Understanding these patterns of change and risk factors for later problematic alcohol use is critical for treatment planning and may help us understand patterns of cognitive impairment and mitigate poor outcome. OUTCOMES FOLLOWING TBI AND ALCOHOL AND SUBSTANCE USE DISORDERS There is strong evidence that intoxication at the time of injury is related to acute complications, longer hospital stays, and poorer discharge status (Corrigan, 1995). However, intoxication at the time of injury is highly correlated with problematic pre-injury alcohol use, so it is possible that these negative outcomes are actually related to the latter. The effect of alcohol intoxication at time of injury on the severity of injury and subsequent early recovery process is still not well understood, with some evidence suggesting deleterious effects of alcohol at the time of injury, and other evidence suggesting a neuroprotective effect. Positive blood-alcohol levels at the time of injury are associated with improved survival rates (Berry et al., 2011; Berry et al., 2010; Ward, Flynn, Miller, & Blaisdell, 1982) and decreased length of acute hospital stay (Fuller, 1995). The exact explanation for the survival benefit at higher alcohol levels is unclear, but there is speculation that these findings may be attributable to alcohol blunting the catecholamine response after severe TBI (Berry et al., 2010). Additionally, alcohol intoxication at the time of injury was related to better outcome at six months post-injury in a study of hospitalized patients with mTBI, although the initial sedating effects of alcohol may have led to overestimation of the injury's severity (Jacobs et al., 2010). Many more studies have documented negative impact of alcohol and substance use on outcomes following TBI, and these will be reviewed in more detail below. Alcohol abuse prior to TBI has consistently been found to mediate outcome from TBI. In a review of the effects of general substance abuse on outcome following TBI, Corrigan (1995) documented that a history of substance abuse is related to a wide range of outcomes, including higher mortality rates, poorer neuropsychological functioning, increased chance of repeated injury, late deterioration, and worse functional outcome. These findings, however, should be interpreted cautiously, given that problematic pre-injury alcohol and substance use is confounded by many factors that may predispose individuals to poor outcome, including poorer premorbid psychosocial functioning, lower educational attainment, greater likelihood of problems with the law, lower perceived social support, and greater prevalence of other substance abuse (Cherner et al., 2001).

Medical and Neuroimaging Outcomes Intoxication and a history of premorbid alcohol use are related to worsening injury severity indicators and early medical outcomes. Patients with positive BALs on hospital admission following trauma have lower levels of consciousness when

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admitted, longer duration of coma, and longer lengths of hospitalization (Solomon & Malloy, 1992; Sparadeo & Gill, 1989). Post-traumatic amnesia and loss of consciousness were significantly longer in groups of patients with pre-injury alcohol abuse, regardless of whether they were intoxicated or not at the time of injury (De Guise et al., 2009). Pre-injury history of alcohol abuse also appears to exacerbate the effects of TBI on brain structure and function. In a series of 56 consecutive TBI patients, those with a history of alcohol abuse demonstrated greater volumes of intracranial hemorrhage on neuroimaging, even when controlling for injury severity (Ronty, Ahonen, Tolonen, Heikkila, & Niemela, 1993). These authors also demonstrated more pronounced local brain atrophy over time compared to non-drinking controls, as well as a weaker response to quantitative electroencephalography (QEEG). While both TBI and drug and alcohol abuse independently result in significant brain structural changes, there is evidence of even greater neuropathological change to the brain when they co-occur. In a quantitative MRI (QMRI) study, Barker and colleagues (1999) found that the effects of substance abuse in combination with TBI resulted in greater atrophic changes in the brain on QMRI than seen for either group alone. A positive BAL at the time of injury was also associated with later increased atrophy on QMRI (Wilde et al., 2004). Additionally, other authors have reported similar findings with evoked response potentials (ERPs), such that the combination of a pre-injury history of heavy alcohol use and TBI results in an impaired response of the brain to cognitive stimulation greater than that seen for either condition alone (Baguley et al., 1997).

Neurocognitive and Neurobehavioral Outcomes Alcohol intoxication at the time of injury and alcohol abuse prior to injury are each associated with increasing risk for poor neuropsychological outcomes (Kelly, Johnson, Knoller, Drubach, & Winslow, 1997). However, the literature in this area is relatively small, and studies are equivocal as to whether there is an interactive effect of alcohol and TBI on cognition above and beyond the effects of alcohol or TBI alone. Further, confounds with pre-injury demographic variables and methodological differences between studies (e.g., differing sample characteristics, exclusion criteria, time-post-injury assessment points, and covariates, etc.) cloud the ability to draw strong conclusions from this literature. Several studies show that alcohol intoxication at the time of injury can have significant effects on cognition. For instance, Bombardier and colleagues (1998) found that higher BAL at the time of injury was related to lower performance on a brief battery of neuropsychological tests administered during inpatient rehabilitation within 60 days of injury. Relationships were strongest when the BAL-positive group was examined in isolation, and were seen across tests of attention, orientation, naming, memory, conceptual reasoning, judgment, verbal learning, and processing speed. Relationships between cognition and BAL decreased from 30 to 60 days post-injury, suggesting that the effect of BAL on cognition is strong at

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early time points in the recovery course but diminishes over time. Brooks and colleagues (1989) found that drinking at the time of injury was related to neuropsychological functioning up to seven years post-injury, and that these relationships were stronger than reports of pre-injury habitual drinking. Verbal learning and memory were found to be the most vulnerable to combined alcohol use at injury and TBI. Notably, this study relied on retrospective family and patient reports of drinking patterns rather than objective BAL levels and thus may be subject to bias. Kelly and colleagues (1997) examined the effects of BAL at the time of injury on neuropsychological status within two months of injury in patients with TBI grouped by normal, alcohol-positive, or drug-positive toxicology screens. TBI sustained in individuals with history of alcohol intoxication at time of injury showed worse cognitive outcomes than those with negative toxicology screens, with particular difficulty on tests of verbal intelligence, verbal memory, and attention and concentration. There were no significant differences between the groups with positive alcohol and positive drug toxicology screens. Greater impairments in verbal intelligence over performance intelligence is unusual, given that long-standing alcohol abuse has frequently been related to greater effects on visual-spatial functioning (Ridley, Draper, & Withall, 2013). Although the authors excluded individuals with history of learning disability and other premorbid neurological problems, it is possible these findings reflect confounds of premorbid functioning rather than the combined effects of alcohol intoxication and TBI. In a separate study controlling for levels of pre-injury alcohol abuse, hospital admission BAL was predictive of poorer delayed verbal memory, greater decrement in verbal memory over time, and poorer visuospatial functioning (Tate et al., 1999). Interestingly, a history of alcohol abuse prior to injury was not predictive of neuropsychological functioning in this study, but the potential strength of this relationship may have been mitigated by this variable's being dichotomized. Other studies demonstrate no effect of BAL on cognitive functioning following TBI. Kaplan and Corrigan (1992b) found that, although BAL was related to length of time to admission to rehabilitation, it was not related to neuropsychological functioning assessed during inpatient rehabilitation. Likewise, in a large study, Schutte and Hanks (2010) found that BAL had a statistically significant effect on injury severity and functional measures at early time points but was not related to functional or cognitive measures at one year post-injury. Other studies have examined the effect of pre-injury alcohol abuse on cognitive functioning following TBI, with some studies failing to find a relationship (Barker et al., 1999). Wilde and colleagues (2004) found consistently worse performance on neuropsychological variables in patients with a history of TBI and pre-injury alcohol use compared to TBI only, although these differences did not reach statistical significance. In one of the largest and best controlled studies to date, Dikmen and colleagues (1993) examined the relationship between pre-injury alcohol and TBI. Groups were divided based on levels of pre-injury drinking and injury severity and examined at one month and one year post-injury. TBI severity was strongly and consistently related to neuropsychological functioning at both time points, with

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greater injury severity corresponding to lower cognitive performance. Degree of pre-injury alcohol abuse was also related to performance on neuropsychological measures, but less so than injury severity. Deficits observed were consistent with those typically associated with alcohol abuse (Ridley et al., 2013), and were found in the areas of performance intelligence, psychomotor speed and coordination, and problem solving/abstraction. However, contrary to expectations, verbal intelligence was also associated with alcohol abuse severity, similar to that seen in the study by Kelly and colleagues (1997). Relationships between alcohol abuse and neuropsychological functioning may be related to premorbid demographic factors, given that higher levels of pre-injury drinking were confounded by lower levels of education, lower income, and greater likelihood of premorbid psychiatric disorders. Thus, lower cognitive skills, especially verbal intellectual skills, may have predated alcohol use and subsequent TBI. Finally, there was no interaction between injury severity and degree of pre-injury alcohol abuse on any neuropsychological measure, indicating a lack of evidence that alcohol abuse exacerbates neuropsychological deficits following with TBI. More recently, Ponsford and colleagues (2013) examined the effect of pre-injury alcohol use on neuropsychological functioning, and found that 27.8% of the sample drank at harmful or hazardous levels prior to injury, with 16.7% drinking at this level at 6–9 months post injury and 20% at 12–15 months. This study found that harmful or hazardous alcohol use in the 12  months prior to injury was associated with poorer verbal learning and memory and slowed processing speed. Similar to previous studies, this study was not able to rule out the presence of premorbid alcohol-related cognitive impairments, which if present would have negatively influenced post-injury assessments. A significant strength of this study is that it examined the effects of continued post-injury drinking on neuropsychological functioning and found that post-injury drinking affected measures of executive functions. Similarly, a separate study also found that alcohol use following TBI was related to impairments on tests of processing speed and executive functions (Jorge et al., 2005). More research is clearly needed to determine the relationships between post-injury alcohol use and increasing cognitive impairments, risk for future TBI, and poor functional outcome. Mood disorders: Finally, previous alcohol abuse increases the risk of developing mood disorders after TBI, which in turn, increases the risk of alcohol abuse relapse and results in difficulties returning to productive activities (Jorge et al., 2005). History of alcohol abuse was also found to be related to emotional deterioration six months or later during recovery from TBI (Dunlop et al., 1991). Functional Status: Although some studies have shown no differences between TBI patients with and without a pre-injury history of alcohol abuse with regard to global and neurobehavioural outcomes at discharge from acute care (De Guise et  al., 2009), others have shown poorer functional status at admission to rehabilitation for individuals with a pre-injury history of alcohol abuse (Vickery, 2008). With regard to longer term functional status, there is significant evidence that pre-injury alcohol abuse leads to lower rates of employment and productivity (Jorge et al., 2005; MacMillan, Hart, Martelli, & Zasler,

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2002; Ronty et  al., 1993; Sherer, Bergloff, High, & Nick, 1999; Wagner, 2002) and less likelihood of living independently (MacMillan et  al., 2002). In contrast, Sander and colleagues (1997) found greater levels of drinking in productive/employed patients, however, this pattern of drinking is likely due to these individuals being less impaired and having greater financial and transportation resources. Additionally, being employed at the time of follow-up and not having a pre-injury history of substance abuse were associated with higher life satisfaction at both 1 and 2 years after injury (Corrigan, Bogner, Mysiw, Clinchot, & Fugate, 2001). Risk for Subsequent TBI: Finally, multiple studies demonstrate an increased incidence of re-injury or subsequent TBI for individuals with a pre-injury historyww of alcohol and substance abuse (Drubach et al., 1993; Kaplan & Corrigan, 1992a; Kreutzer, Witol, & Marwitz, 1996; Solomon & Malloy, 1992; Winqvist et al., 2008), which only further exacerbates poor outcome, particularly with respect to neuropsychological, neurobehavioral, and functional outcomes.

MILD TBI AND ALCOHOL ABUSE Most of the above cited studies include samples drawn from trauma and rehabilitation settings. Because the vast majority of patients with mTBI are released from emergency departments, treated in outpatient settings, or never seek treatment, they are not represented well by the above studies. Lange and colleagues (2007) specifically examined the effects of day of injury intoxication and pre-injury alcohol abuse on short-term cognitive outcome following uncomplicated mTBI. Pre-injury alcohol abuse had the most influence on cognitive performance, with effects being the greatest for individuals that were also intoxicated at the time of injury. However, these findings occurred only on a limited number of variables (i.e., Trailmaking Test, Part B and variables from the Hopkins Verbal Learning Test) and were small in magnitude accounting for less than 5% of the variance in outcomes. Despite this, the role of alcohol abuse remains important in mTBI. Although most individuals with mTBI recover well with resolution of reported symptoms and cognitive dysfunction within three months, a minority continue with persisting symptoms. Among other psychological and psychosocial factors, a history of premorbid alcohol/substance abuse is one of the factors that place an individual at risk for an incomplete or complicated recovery following mild TBI (Iverson, 2005). Recent reviews regarding substance abuse in TBI patients concur that substance abuse rates decline even after mTBI, however, an emerging literature suggests mTBI may cause subtle impairments in cognitive, executive, and decision-making functions that are often poorly recognized in early diagnosis and treatment. When combined with difficulties in psychosocial adjustment and coping skills, these impairments may increase the risk for chronic substance abuse in a subset of mTBI patients. Thus, mTBI and substance abuse are bidirectionally related with regard to risks and treatment (Graham & Cardon, 2008).

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ALCOHOL MISUSE AND TBI WITHIN THE MILITARY Much attention has been given to the co-morbidities of mTBI and psychiatric disorders amongst military personnel having served in the current Iraq (Operation Iraqi Freedom; OIF) and Afghanistan (Operation Enduring Freedom; OEF) conflicts. Current estimates of TBI in deployed Service Members range from 7.6% to 22.8% (Hoge et al., 2008; Schwab et al., 2007; Tanielian & Jaycox, 2008; Terrio et al., 2009; Vasterling et al., 2006), with the vast majority of these injuries classified as mTBI (Hoge et  al., 2008; Tanielian & Jaycox, 2008). Co-morbidities between mTBI and psychiatric disorders in military Service Members are high (Hoge et al., 2008; Lew et al., 2008; Tanielian & Jaycox, 2008) with reports that psychiatric comorbidities in Service Members with mTBI lead to worse outcomes (Hoge et al., 2008) and more complex treatment regimens (French & Parkinson, 2008). TBI may also increase the risk for developing psychiatric disorders including posttraumatic stress disorder (PTSD) with prevalence rates for OEF/OIF veterans as high as 30-39% for those with a history of mTBI (Carlson et al., 2011; Cohen et al., 2010; Sundin, Fear, Iversen, Rona, & Wessely, 2010). The relationship between substance abuse and traumatic brain injury (TBI) has been documented in veterans of the Afghanistan and Iraq conflicts (Carlson et al., 2010; Heltemes, Dougherty, MacGregor, & Galarneau, 2011; Olson-Madden et al., 2010). Of the approximately two million service personnel discharged from the military since 1992, those with mTBI were 2.6 to 5.4 times more likely to be discharged with alcohol or substance use difficulties than those without mTBI (Dedert et al., 2009). Evidence from a sample of OEF and OIF veterans reflects current alcohol abuse or dependence in 52% to 54% of veterans with TBI (Graham & Cardon, 2008). In a large study of military veterans, 85% of those with a confirmed diagnosis of TBI had at least one co-morbid psychiatric disorder and 64% had two or more psychiatric diagnoses. Compared to veterans with negative TBI screens, those with positive screens were two times more likely to have depression and substance-related diagnoses (Carlson et al., 2010). Heltemes and colleagues (2011) found that alcohol abuse disorders were higher among service members with a history of mTBI compared with other injuries (6.1% vs. 4.9%). Although this difference did not reach statistical significance, it was suggested that respondents may have underreported problematic alcohol abuse and that rates may actually increase over time.

NEURAL MECHANISMS AND NEUROIMAGING FINDINGS IN TBI AND ALCOHOL/SUBSTANCE USE DISORDERS As detailed in other chapters, alcohol and other substance use disorders are associated with structural and functional alterations in several brain regions, with consequences to both white and gray matter, and with significant cognitive sequelae. Although perhaps most evident using advanced imaging techniques and in cases of severe and prolonged alcohol abuse, structural change can be evident on conventional imaging, even in younger individuals, as depicted in Figure 15.1, which

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B

Figure 15.1 Sagittal T1-weighted MRI image of a 26 year old OIF/OEF veteran with a history of an alcohol use disorder in the absence of a history of TBI (B) in relation to a healthy male veteran of similar age. Despite his age, significant global white matter volume loss is apparent, particularly in the corpus callosum and cerebellum, findings which may also be expected with a history of TBI.

demonstrates global white matter loss (most prominent in the corpus callosum and cerebellum in the sagittal image) in a young veteran with a significant alcohol use disorder in the absence of a history of TBI. Albeit through entirely different mechanisms of injury, TBI may also compromise similar brain structures and circuitry, enhancing vulnerability to development of co-morbidities, exacerbating or complicating symptom presentation, and complicating recovery. There is a striking overlap in several key brain structures implicated in TBI, alcohol abuse, and PTSD (relevant to military populations). As previously indicated, mTBI may increase alcohol or illicit substance use in persons with no history of significant substance use prior to injury in civilian populations (Hibbard et al., 1998). Previous reports have suggested that TBI may increase the risk for development of substance use disorders via structural brain changes, such as disruption of incentive-motivation circuitry via mesolimbic dopaminergic pathways including the striatum, ventral tegmental area, nucleus accumbens, and orbitofrontal cortex (Fann et al., 2004; Hibbard et al., 1998). Alternatively, TBI may cause persistent executive dysfunction (e.g., impulsivity, risky decision-making), thereby creating or exacerbating vulnerability to substance use as well as posing a risk factor for decreased compliance with mental health treatment programs. Figure 15.2 illustrates the substantial overlap between these disorders in terms of the affected brain regions, with alcohol use disorders and TBI both often associated with ventromedial frontal, ventral striatum and corpus callosum compromise, areas shown to mediate reward, decision-making, and processing speed. TBI and PTSD have both been associated with injury to the hippocampus, a structure implicated in memory, though alcohol abuse has also been implicated in other components of memory circuits. Concomitant mTBI may exacerbate PTSD due to damaged pathways regulating fear responses (Bryant, 2011), and neural changes that may alter the initial encoding of trauma and the processing of trauma-related emotions. Added stress related to TBI-induced physical symptoms or either TBI- or

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TBI

corpus callosum ventral striatum orbitofrontal

hippocampus amygdala frontal lobes cingulate

PTSD

ETOH insula

Structures involved in multiple brain networks mediating functions such as emotion, reward, memory, impulsivity/disinhibition, executive functioning, and processing speed which may be compromised in mTBI, PTSD, and/or ETOH, and exacerbate symptoms when >1 is present.

Figure 15.2 Overlapping brain structures involved mild TBI, PTSD, and/or alcohol use disorders.

PTSD-related functional impairment can further exacerbate symptoms and create a vulnerability to alcohol misuse. The insula, an emotional processing and reward-circuitry structure, has been implicated in both alcohol use and PTSD. Finally, all three disorders have been associated with compromise to the frontal lobes and cingulate regions, and amygdala, areas considered critical to executive functions, impulse control, emotional processing, and inhibition. Given that both TBI and substance abuse independently result in central nervous system compromise, one thought has been that the effects of each are simply additive and result in poorer outcome with regard to cognitive deficits and greater abnormalities on imaging (Ruff et  al., 1990; Solomon & Malloy, 1992; Wilde et al., 2004) in patients with both a history of substance abuse and TBI. However, evidence also suggests that certain substances of abuse present at the time of injury may interact pathophysiologically with mechanisms of head injury to potentiate TBI-induced damage, over and above the additive effect of the two factors. The presence of alcohol may specifically exacerbate head injury through vascular effects, altered platelet aggregation, and the induction of lipid peroxidation and other free radical reactions (Albin & Bunegin, 1986; Charness, 1993; Flamm et al., 1977; Franco, Spillert, Spillert, & Lazaro, 1988; Luna, Maier, Sowder, Copass, & Oreskovich, 1984; Solomon & Malloy, 1992; Sparadeo & Gill, 1989). Platelet response in patients with severe TBI is significantly reduced

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(Nekludov, Bellander, Blomback, & Wallen, 2007) and may be further exacerbated by alcohol, potentially leading to greater risk of mass lesions following TBI (Ruff et  al., 1990). Human studies have also suggested that alcohol may enhance common mechanisms associated with TBI-induced secondary injury such as hypoxia, edema and altered/reduced cerebral blood flow (Alexander, Kerr, Yonas, & Marion, 2004; DeCrescito, Demopoulos, Flamm, & Ransohoff, 1974; Sparadeo, Barth, & Stout, 1992). Further, animal studies have also confirmed that alcohol led to increased hemorrhage volume, worsened hemodynamic responses, increased resuscitation requirements, decreased survival and impaired respiratory control (Zink & Feustel, 1995; Zink, Stern, McBeth, Wang, & Mertz, 2006). TREATMENT OF SUBSTANCE USE DISORDERS FOLLOWING TRAUMATIC BRAIN INJURY Despite the high incidence of substance misuse in persons with TBI, there has been minimal research on effective intervention. Many of the traditional treatments for substance abuse in the general population, such as 12-step programs, may be difficult for persons with TBI to benefit from. These programs often rely on recall of past events, abstract thinking, self-reflection, and goal directedness, which are often impaired in persons with TBI. While treating substance misuse for persons with TBI can be challenging, it is possible. Early models of treating substance abuse in persons with TBI focused on adapting traditional substance abuse treatment components for use with persons who have cognitive impairment (Blackerby & Baumgarten, 1990; Langley & Kiley, 1992; Sparadeo, Strauus, & Kapsalis, 1992). Typical adaptations include use of repetition, concrete examples, use of visual aids, peer-modeling, and slower pace of instruction. Unfortunately, methodological limitations preclude firm conclusions regarding the effectiveness of these adapted interventions. Many rehabilitation experts have advocated for a community-based case management approach to preventing substance misuse after TBI. Corrigan and colleagues (1995) were instrumental in starting the TBI Network, which is a comprehensive case management approach to helping persons at risk for substance misuse after TBI. The basic tenet of this approach is that clients with TBI require assistance with all aspects of their lives in order to maintain abstinence from substance use. These programs take a “whatever it takes” approach to helping clients be successful, including job training and placement, neuropsychological assessment, ongoing client and family education, training in advocacy and accessing resources, and peer support. The majority of clients who completed this and similar programs showed improved abstinence from alcohol and an increase in productive activity at 6 months (Corrigan et al., 1995) and 1 year (Bogner, Corrigan, Spafford, & Lamb-Hart, 1997) and increased life and family satisfaction at nine months (Heinemann, Corrigan, & Moore, 2004). Those referred earlier following injury showed greater gains in physical well-being, employment, and community integration.

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The TBI Network, as well as other similar programs, is based on the concept that persons who misuse alcohol or other drugs are more likely to change their behavior if they are at a certain stage of readiness, and there is substantial evidence that intervention works best when matched with readiness to change. The occurrence of trauma has been shown to result in increased readiness to change alcohol and drug use (Bombardier & Rimmele, 1998; Gentilello et al., 1988; Gentilello et al., 1999), potentially because of the life-threatening nature of trauma. This readiness may increase when alcohol or drugs were directly involved in the trauma (Bombardier et al., 1997). Motivational Interviewing (MI) is a therapy technique that may help increase readiness to change alcohol and/or drug use. The goal of MI is to guide clients to awareness of a discrepancy between their life goals and a particular problem behavior (Miller & Rollnick, 2002). Cox and colleagues (2003) demonstrated the effectiveness of a 12-session MI-based therapy program for improving motivation, reducing negative affect, and reducing substance use in persons with TBI. These gains, noted immediately following treatment, were maintained at follow-up (an average of 9  months). Corrigan and colleagues (2005) found no difference between a group receiving a brief MI, conducted by telephone, and an attention control group; however, this may have been due to the very brief treatment and the fact that it was conducted by phone. Brief intervention is a form of therapy that combines motivational interviewing and education. It has been shown to be effective in reducing substance use based on a randomized clinical trial in general trauma patients (Gentilello et al., 1999; Schermer, Moyers, Miller, & Bloomfield, 2006; Soderstrom et al., 2007; Sommers et al., 2006). These studies have led to inclusion of screening and brief intervention as a mandated standard of care for trauma patients by the American College of Surgeons for Level I  trauma centers (American College of Surgeons, 2006). Unfortunately, most of the studies conducted with general trauma patients have systematically excluded persons with TBI, based on inclusion criteria requiring cognitive ability to participate in intervention and assessment prior to trauma discharge (Corrigan, Bogner, Hungerford, & Schomer, 2010). In a recent study, Sander and colleagues (2012) conducted a randomized controlled trial of brief intervention versus standard of care in a sample of persons with TBI who screened as having an at-risk history for substance abuse. The intervention consisted of presentation of a 10-minute educational DVD that contained information on the negative impact of drug and alcohol use on recovery from TBI. This presentation was followed by a brief (15 to 20 minute) motivational interview, conducted to be compatible with participants’ readiness to change. Results were somewhat disappointing in that, out of 4 outcome variables assessed at 3 months post-intervention, the only treatment effect found was for expectancies that alcohol would result in physical and cognitive impairment. This effect was moderated by injury severity, and only seen for those with severe TBI. Attribution of injury to alcohol use was associated with the expectation that alcohol use would result in cognitive and physical impairment and in greater readiness to change. About one third of persons in both groups continued to show problem alcohol use

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at follow-up. On the positive side, even though the intervention did not lead to reduced alcohol use, results indicate that persons with severe TBI are able to learn information about the impact of alcohol use on recovery. The results raise questions about the potential effectiveness of brief interventions for persons with TBI and indicate that adaptations for cognitive deficits may need to be made, including media education, booster sessions, written goals and reminders. The results also indicate that attribution of the injury to alcohol use could potentially increase readiness to change in some settings, and might be used to generate discussion about the negative impact of alcohol use. In a recent study, Tweedly and colleagues (2012) compared an education only, education plus MI, and an attention control group at 12 months post-injury. The interventions had been delivered at 6 months post-injury. There was no significant effect due to the small sample size, but there was trend for the treatment groups to show a decrease in frequency and quantity of alcohol consumption relative to the control group. While inconclusive, the research to date has several implications for clinicians treating persons with TBI who have a dual diagnosis of substance abuse or who have histories that put them at risk for substance abuse. At the very least, brief screening for an at-risk history should be conducted with all persons who sustain TBI. This screen can be incorporated into the traditional neuropsychological assessment. Persons who are identified as being at risk based on this screen should be provided with education on the potential negative impact of alcohol and drug use on recovery from TBI. Use of multimedia, such as video, is more likely to accommodate the cognitive deficits often associated with TBI. Provision of written information is also important to compensate for memory deficits. The Ohio Valley Center for Brain Injury Prevention and Rehabilitation has developed such materials, which can be downloaded or purchased (http://ohiovalley.org/ informationeducation). While providing education is important, it is unlikely to be sufficient to prevent substance abuse, especially in those with a more severe at-risk history. Referrals for additional treatment will be required. Unfortunately, most states do not have the integrated network needed to ensure successful treatment of persons with TBI who have substance abuse issues. In the absence of such a network, partnerships between neuropsychology and community providers, such as substance abuse counselors, could be forged. The neuropsychologist may provide inservices to these professionals and serve as a consultant on how to adapt treatment to compensate for cognitive deficits. The Rehabilitation Research and Training Center on Developing Strategies to Foster Participation for Individuals with Traumatic Brain Injury is developing educational materials for substance abuse counselors on how to work with persons with TBI. These materials will soon be available at www.tbicommunity.org. Finally, abstinence from drug and alcohol use is more likely to be achieved if the person with TBI is successful and happy in other areas of their lives. A referral for case management services, where available, can help them to manage their lives in a way that is incompatible with substance abuse. Paradoxically, persons with TBI who are more active and participate more in their

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communities are more likely to be exposed to situations involving alcohol and drugs. Helping them to anticipate this and to develop strategies to combat use can be beneficial. In conclusion, the relationship between TBI and alcohol abuse is very complex and likely circular in nature such that the presence of either may increase risk for the other. There is clear evidence of worsening outcomes when TBI and alcohol abuse co-occur. Intoxication at the time of injury and pre-injury alcohol abuse increase risk for greater initial injury severity and poor cognitive outcome. Evidence presented within this chapter highlights the complex interactions between TBI and alcohol abuse and discusses considerations when planning treatment for patients with these co-morbid diagnoses.

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Everyday Functioning in Substance Use Disorders J. C O B B S COT T, K A I T L I N B L AC K STON E , A N D T H O M A S D.   M A R COT T E

Illicit substance use is highly prevalent in the United States. In 2011, 8.7% (22.5 million) of individuals over 12 years of age reported using illicit drugs in the past month, and 47% (121.1 million) reported illicit drug use over their lifetime (Substance Abuse and Mental Health Services Administration, 2012). Although most individuals who drink alcohol or experiment with illicit substances do not experience substance-related problems, a significant minority will experience some problems as a result of substance use. For example, 8% of the U.S. population met criteria for a substance-use disorder in 2011 (Substance Abuse and Mental Health Services Administration, 2012). Such recurring substance use can lead to alterations in neurotransmission and neurophysiology, adversely affect cognitive functioning, and result in poor downstream psychosocial outcomes (e.g., co-occurring psychiatric problems, legal problems). Substance-use disorders (SUD) result in the use of the drug at the expense of other important daily activities, resulting in detrimental effects on social and occupational functioning. To this end, it is estimated that abuse of illicit drugs and alcohol costs the United States $428 billion annually in costs related to lost work productivity, accidents, crime, and healthcare (National Drug Intelligence Center, 2011; Rehm et  al., 2009). Understanding the factors that contribute to poor functional outcomes in SUD is therefore critical to improving treatment efforts and lessening the economic and social impact of substance use. Given this need, it is somewhat surprising that (with the exception of treatment outcomes) the literature investigating factors associated with poor functional outcomes in SUD is fairly sparse. Substance addiction is increasingly conceptualized as a chronic, relapsing illness (e.g., Le Moal & Koob, 2007; McLellan, Lewis, O'Brien, & Kleber, 2000). Given the chronicity of the disorder for many and the fact that important social, occupational, and recreational activities are

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often given up to use the substance, it is clear that SUD may have negative consequences for a number of life domains. Furthermore, chronic substance use is often accompanied by additional risk factors for poor functional outcomes (e.g., unstable housing, impulsive personality traits). Moreover, even mild neuropsychological deficits, such as those often observed in SUD, can significantly increase the risk for declines in daily functioning and exert adverse effects on instrumental activities of daily living (e.g., financial management), treatment adherence, driving, and employment (e.g., Green, 1996; Heaton et al., 2004; Marcotte et al., 2008). Accordingly, there may be two parallel processes by which SUD exert an increased risk for declines in everyday functioning; both directly, simply via drug-use behaviors as a replacement for normal daily activities; as well as indirectly, for example, via the downstream effects of substance-induced neurotoxicity and neurocognitive impairment, which may limit one's capacity to successfully complete daily activities. The aim of this chapter is to provide an overview and critical review of the literature examining the contribution of neuropsychological deficits to everyday functioning outcomes of SUD in adults. In the first section, we will briefly review the mechanisms underlying addiction, the pathophysiological effects of SUD, and neurocognitive functioning in substance-use disorders. Next, we discuss methods for assessing everyday functioning and the overall limitations of this literature. We devote the majority of the chapter to studies examining the functional impact of SUD and will highlight research examining overall everyday functioning, occupational outcomes, driving, impulsivity and violence, and treatment outcomes. Finally, gaps in the literature and future research directions are considered. NEUROPATHOPHYSIOLOGY AND NEUROPSYCHOLOGY OF SUBSTANCE USE DISORDERS Addiction is a complex brain-behavior process that is moderated by a number of genetic, environmental, and developmental factors (e.g., Sloboda, Glantz, & Tarter, 2012). Many models have been proposed to explain the neurobiological and neurobehavioral mechanisms underlying substance addiction (e.g., Bechara, 2005; Goldstein & Volkow, 2002; Le Moal & Koob, 2007). Although they differ somewhat, most models of addiction implicate alterations in communication among brain networks that regulate risk and reward (e.g., orbitofrontal cortex, striatum), emotion (e.g., anterior cingulate, limbic structures), and certain neurocognitive processes, such as response inhibition (e.g., inferior frontal cortex) and working memory (e.g., dorsolateral prefrontal cortex). (See Chapter 2 of this volume for more detail regarding the neural substrates of addiction.) Chronic substance use also increases risk of injury to brain structure and functions in regions overlapping with those implicated in addictive processes. For example, studies of chronic cocaine users have documented brain volume loss in prefrontal cortex and temporoparietal and insular cortices (e.g., Ersche et al., 2011; Franklin et al., 2002). In chronic methamphetamine use, volume changes

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have most often been observed in the basal ganglia, nucleus accumbens, cingulate cortex, and parietal lobes (Chang, Alicata, Ernst, & Volkow, 2007; Jernigan et al., 2005; Thompson et al., 2004). Such changes to the structural integrity of the brain could contribute to further enhancement of the mechanisms underlying substance addiction and create a more severe clinical profile, especially if prefrontal networks involved in monitoring and inhibiting responses are affected. There is a vast number of substances with neurobehavioral effects and the potential for abuse. Here, neurocognitive findings regarding drugs of abuse are only minimally reviewed, as the studies examining the cognitive deficits associated with SUD are covered extensively in other chapters in this volume. Since a majority of drugs of abuse act on the mesocorticolimbic dopaminergic system, common cognitive deficits across various classes of drugs are to be expected (Fals-Stewart & Bates, 2003; Rogers & Robbins, 2001). Across most substance of abuse, deficits in attention and working memory, episodic learning and memory, and executive functions (e.g., novel problem-solving, mental flexibility, response inhibition, decision making) are commonly reported with chronic users. However, because specific drugs of abuse also differentially affect other neurotransmitter systems (e.g., GABA) in the brain, the pattern of cognitive impairments may differ, depending on the specific neuropharmacological action of the drug (Barker, Greenwood, Jackson, & Crowe, 2004; Gonzalez, 2007; Grant & Rourke, 2009; Scott et al., 2007). To this end, an extensive literature has documented both common and specific effects of chronic substance abuse on neuropsychological functioning. Moreover, it is clear that the nature and magnitude of the cognitive deficits observed in SUD are likely to have an impact on everyday functioning abilities in this population.

SUBSTANCE USE AND EVERYDAY FUNCTIONING OUTCOMES

Measuring Everyday Functioning Recognition of the independent effects of cognitive deficits on everyday, “real world” functioning was a significant development in clinical neuropsychological research. However, everyday functioning is an expansive concept that involves a broad range of behaviors. A major challenge in trying to predict real-world functioning is the lack of a gold standard for determining impairments in everyday abilities. Given that the criterion is behavior in the natural environment, numerous measurement challenges are introduced in determining outcome measures (Morgan & Heaton, 2009). An individual's natural environment is variable across patients, and the factors that determine functional success or impairment are multifaceted. Moreover, these factors vary with the demands of an individual's environment. Given these challenges, many different approaches have been developed to examine real-world performance, each with its own strengths and weaknesses (for a thorough review, see Marcotte, Scott, Kamat, & Heaton, 2009). Self-report is perhaps the most common method of assessing daily functioning, as it is easy

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to obtain, resource-efficient, and offers the unique ability to identify declines specific to the demands of an individual's life. However, self-report is susceptible to biases, including desire for secondary gain (e.g., litigation or disability status), poor insight (e.g., Cahn-Weiner, Ready, & Malloy, 2003), or depressed or anxious mood (e.g., Errico, Nixon, Parsons, & Tassey, 1990; Horner, Harvey, & Denier, 1999). For example, in a sample of individuals in substance abuse treatment, Richardson-Vejlgaard and colleagues (2009) found that complaints of everyday cognitive problems were related to depression but not to neuropsychological performance, highlighting the limitations of self-report (cf. Weinborn, Woods, O'Toole, Kellogg, & Moyle, 2011). “Other” or “informant” (e.g., spouse) reports offer an additional perspective on how a patient/participant handles everyday activities, although informants may overestimate a patient's ability (e.g., Loewenstein et al., 2001) or may not observe the patient in functionally challenging situations. Clinician ratings, including the Global Assessment of Functioning (GAF), reduce patient bias but predominantly rely on a brief impression from the patient's behavior and report in the clinic, which may not reflect behavior in the natural environment. Some studies have examined “manifest functioning,” or external documentation of how the individual performs in the real world (e.g., employment status, medication records). This approach provides a more “objective” estimate of a patient's functioning in daily life and may provide insight into compensatory strategies, although it is limited by the availability of data. Investigators have also recently developed more direct (i.e., “performance-based”), objective methods of examining the impact of cognitive deficits on real-world functioning (e.g., Giovannetti et  al., 2008; Patterson, Goldman, McKibbin, Hughs, & Jeste, 2001; Scott et al., 2011), which can provide insight into functional capacity for everyday tasks. However, these performance-based measures are often resource- and time-intensive and sometimes have limited empirical evidence of their reliability and validity. Importantly, such performance-based assessments measure an individual's capacity to perform a task in the laboratory but do not directly assess what an individual may actually do in real life (e.g., cooking performed in the laboratory versus a home, where there may be many distractions and competing priorities). Direct observation in a patient's natural environment is perhaps the most valid method for determining daily-functioning outcomes, although the resources necessary to accomplish this approach typically limit its use. Using these methods in combination may improve the accuracy in determining the severity of functional impairment in individuals with SUD.

Methodological Limitations Before discussing studies examining everyday functioning in SUD, it is useful to discuss the methodological limitations that specifically affect this literature. First, substance-use characteristics (e.g., amount, frequency, duration of abstinence) are often heterogeneous both between and within samples, which may affect the internal validity of studies and increase contradictory findings across

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the literature. Second, poly-substance use is quite common among individuals with substance-use disorders. If investigators only include individuals who have primarily used one substance, the results may not be generalizable to other substance users (impacting external validity); on the other hand, including a majority of subjects who are poly-substance users increases generalizability but limits the specific conclusions that can be drawn about a particular substance (impacting internal validity). Third, some studies require that participants meet criteria for a substance-use disorder, whereas others may focus on substance users who have consumed a specific substance a certain number of times, or use cut-scores from screening instruments to suggest high-risk substance-use behaviors (e.g., Alcohol Use Disorders Identification Test; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). These limitations complicate efforts to draw conclusions across the research literature addressing functional outcomes in SUD and can reduce the reliability of clinical decisions regarding functional outcomes. Given the established genetic and neurobehavioral risk factors for substance dependence (e.g., Rhee et  al., 2003), another methodological challenge that is sometimes discussed in the neuropsychological literature is whether cognitive deficits found in substance users might predate the onset of substance abuse as opposed to being a consequence of substance use (Block, Bates, & Hall, 2003; Vanyukov et  al., 2003). Given the moderate association between cognition and functional outcomes across a variety of neurological and neuropsychiatric disorders (e.g., Cahn-Weiner et al., 2003; Evans et al., 2003; Heaton et al., 2004), it appears reasonable to extend this limitation to everyday-functioning outcomes as well, such that individuals with lower functioning prior to the initiation of substance misuse might be more prone to chronically use substances than individuals with higher functioning. However, in the absence of longitudinal research that follows participants through various stages of drug use (e.g., pre-initiation, establishment of dependence, abstinence), such questions will remain unanswered. Nonetheless, it is likely that these neuropsychological deficits and functional outcomes, even if they predate substance-use problems, are exacerbated by the initiation of substance use or by chronic substance abuse and may be at least partially ameliorated with extended abstinence (e.g., Iudicello et al., 2010) or with interventions designed to increase functional capacity (e.g., Mueser, Campbell, & Drake, 2011).

Impact of Substance Use Disorders on Instrumental Activities of Daily Living As mentioned above, research supports the utility of neuropsychological deficits in predicting functional outcomes in neurological and neuropsychiatric disorders (e.g., Cahn-Weiner et al., 2003; Evans et al., 2003; Green, 1996; Heaton et al., 2004; Marcotte et  al., 2008), although the literature investigating the contribution of neuropsychological performance to functional outcomes in SUD is relatively limited. In alcohol-use disorders, a small body of research has shown that cognitive functioning is a significant predictor of a variety of daily functioning outcomes.

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Specifically, alcohol-associated deficits in novel problem-solving, cognitive flexibility, and response inhibition have emerged as the strongest independent neuropsychological predictors of poor functional outcomes, such as educational status, occupational outcomes, and poorer quality of life following treatment (Ihara, Berrios, & London, 2000; Miller, 1991; Moriyama et al., 2002; Tuck & Jackson, 1991; Wicks, Hammar, Heilig, & Wisén, 2001; Zinn, Stein, & Swartzwelder, 2004). Although the proportion of variance accounted for by neurocognition in these studies has been relatively small (e.g., 26% in Zinn et  al., 2004), initial studies indicate that targeted assessment of the dysexecutive aspects of the neurobehavioral profile of chronic alcohol use, including the propensity for habitual behavior and difficulties with impulse control, may provide more accurate predictions of functional outcomes (Ihara et al., 2000; Moriyama et al., 2002). Further research is clearly warranted in order to further delineate the specific aspects of neurocognitive functioning that may be driving functional declines in alcohol-use disorders. A few studies have examined the contribution of cognitive deficits to reports of general psychosocial functioning in SUD. For example, among chronic MDMA (“Ecstasy”) users, neuropsychological dysfunction has been linked to irritability, depression, and poorer physical health (Fisk, Montgomery, & Murphy, 2009). In methamphetamine dependence, Sadek and colleagues (2007) reported that individuals with a history of chronic methamphetamine use endorsed higher rates of cognitive complaints and greater declines in daily functioning, including difficulties with managing money, preparing meals, and working, than healthy comparison subjects. Similarly, Blackstone et  al. (2013) showed that neurocognitive impairment in HIV-infected individuals with methamphetamine dependence was associated with dependence in instrumental, but not basic, activities of daily living. Although the use of performance-based tests to predict functional outcomes in SUD is still in its infancy, a few studies have provided initial evidence that such measures may prove useful in understanding functional outcomes in this population. Laloyaux and colleagues (2012) reported that individuals with alcohol dependence evidenced significantly reduced performance on a performance-based computerized shopping task, which was associated with both duration of illness and self-ratings of daily functioning in a variety of domains. Performance on this measure was also associated with cognitive deficits in processing speed, verbal memory, and executive functions. In a cohort of methamphetamine-dependent individuals, Henry and colleagues (2010) showed that chronic methamphetamine users performed more poorly on a performance-based measure assessing a range of daily activities, including medication and financial management, comprehension and planning, and setting up travel arrangements (UCSD Performance Based Skills Assessment [UPSA]; Patterson et  al., 2001)  compared to non–substance using individuals. In the methamphetamine-dependent group, these deficits were associated with both executive dysfunction and greater frequency of methamphetamine use. Together, these studies begin to provide construct validity for direct, performance-based tasks to assess daily functioning in SUD and identify several important areas of everyday functioning that may be particularly susceptible to SUD-associated neurocognitive dysfunction.

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Prospective memory, or “remembering to remember,” has emerged as a particularly relevant construct for everyday-functioning outcomes in SUD. Individuals with SUD, including cannabis (Bartholomew, Holroyd, & Heffernan, 2010; Montgomery & Fisk, 2007), methamphetamine (Iudicello, Weber, Grant, Weinborn, & Woods, 2011; Rendell, Mazur, & Henry, 2009), benzodiazepines (Rich, Svoboda, & Brown, 2006), alcohol use (Griffiths et al., 2012; Heffernan & O'Neill, 2012), and MDMA (Rendell, Gray, Henry, & Tolan, 2007), have been shown to evidence deficits in prospective memory abilities. Moreover, poorer prospective memory performance has been associated with increased memory complaints in daily life in both clinical (Weinborn, Woods, O'Toole, et al., 2011) and non-clinical (Hadjiefthyvoulou, Fisk, Montgomery, & Bridges, 2011) SUD samples, even when controlling for other cognitive abilities and affective distress. Prospective memory is also associated with both laboratory measures of risky decision-making in MDMA users (Weinborn, Woods, Nulsen, & Park, 2011)  and reports of risky sexual and drug-use behaviors in individuals with SUD (Martin et al., 2007; Weinborn et al., 2013). Taken together, these studies suggest that prospective memory may be particularly ecologically relevant for daily-functioning outcomes in SUD. Neurobehavioral symptoms, including disinhibition and executive dysfunction, have been noted clinically in certain groups of SUD patients (e.g., stimulant abusers), but only recently has empirical evidence been collected to examine their nature and magnitude (e.g., Pluck et  al., 2012; Semple, Zians, Grant, & Patterson, 2005). An emergent literature has also demonstrated their importance in predicting declines in everyday functioning in SUD even after accounting for other established predictors of functioning, including overall level of cognitive functioning (Cattie, Woods, Iudicello, Posada, & Grant, 2012; Verdejo-García, Bechara, Recknor, & Pérez-García, 2006). Thus, assessing neurobehavioral symptoms in addition to psychiatric and neuropsychological assessments might provide incremental validity to predictions of everyday-functioning outcomes in certain SUD populations. It deserves mention that SUD are often accompanied by comorbid psychiatric disorders, which should be taken into account when examining everyday functioning (see Chapter 13 of this volume for more detail). For example, numerous studies investigating functional outcomes in SUD have shown that the affective distress that frequently accompanies SUD has a significant role both in predicting self-reported daily functioning (Bedi & Redman, 2008; Weinborn, Woods, O'Toole, et  al., 2011)  and in affecting individual outcomes, such as treatment success (e.g., Glenn & Parsons, 1991). Moreover, as described above, psychiatric comorbidity (e.g., depression) can frequently interfere with subjectively and objectively measured daily outcomes. For example, Obermeit and colleagues (in press) recently showed that history of attention-deficit/hyperactivity disorder (ADHD) was uniquely associated with an increased risk of declines in IADLs, increased cognitive complaints, and unemployment among chronic methamphetamine users. Therefore, clinicians and researchers should consider level of affective distress and neuropsychiatric comorbidity when evaluating daily-functioning problems.

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Employment Although most illicit substance users are employed (between 65% and 85% are employed full or part-time; French, Roebuck, & Alexandre, 2001; Substance Abuse and Mental Health Services Administration, 2012), employment difficulties are common in SUD, especially those with psychiatric comorbidities (Hser, Huang, Chou, & Anglin, 2007; Johansson, Alho, Kiiskinen, & Poikolainen, 2007; Laudet, 2012). Determining the relationships between substance use, cognitive functioning, and employment is a complex undertaking. While substance use is associated with incident unemployment, the reverse is also true—unemployment itself may lead to increased substance use (Paul & Moser, 2009). In addition, comparisons across studies are complicated by differing definitions of “employment” (e.g., paid vs. not paid, full-time/part-time/temporary) and the criteria used for classification of substance-use disorders. Other factors complicate interpretation of the relationships between SUD and employment. Employment may be influenced by factors such as educational status, level of occupational attainment, availability of other sources of financial support, and access to jobs, and incident unemployment may be a consequence of drug-related health issues, increased absenteeism, violation of workplace policies, loss of driving privileges, or stigma (Baldwin, Marcus, & De Simone, 2010). In addition, many studies have found sex differences in the relationship between substance use and employment, as well as different patterns in adolescents/young adults vs. older adults (Henkel, 2011). For example, substance use in adolescence is frequently associated with reduced educational attainment and a lesser likelihood of employment in adulthood (Ringel, Ellickson, & Collins, 2007); substance use in college attendees has also been associated with reduced future employment (Arria et  al., 2013). Many studies do not incorporate these myriad factors into their statistical models. The literature linking the cognitive effects of substance use and vocational functioning are surprisingly sparse. Executive functioning performance in chronic alcoholics has been related to post-treatment occupational status (Moriyama et al., 2002), and decision-making deficits, assessed via the Iowa Gambling Task, have been shown to be predictive of reduced levels of employment in alcohol and stimulant abusers (Bechara et al., 2001). Methamphetamine abusers evidence problems with unemployment (Webster, Staton-Tindall, Duvall, Garrity, & Leukefeld, 2007), particularly among those with comorbid medical conditions, such as HIV (Blackstone et  al., 2013)  and, in an Australian cohort, were particularly likely to go to work when under the influence and miss work due to their drug use, compared to users of other substances (Roche, Pidd, Bywood, & Freeman, 2008). Neurocognitive impairment has also been shown to be an independent predictor of unemployment in methamphetamine-dependent participants, with executive functions, verbal fluency, working memory, and learning associated with employment status (Weber et al., 2012). Increased impulsivity has also been associated with lower income and greater rates of unemployment (Semple, Zians, Grant, & Patterson, 2006).

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Automobile Driving Safely driving an automobile can be negatively affected by inattention, risky decisions and behaviors, slowed reaction times, and poor hazard perception, which are all common sequelae of substance use. The relationship between driving performance and substance use has been examined via epidemiological records, emergency department records, roadside assessments, on-road evaluations (closed and open course) and laboratory-based simulators. While there is a large body of literature examining alcohol and cannabis, the extant literature on other substances (e.g., heroin) is limited. In addition, although numerous studies examine the effects of substance use on cognition and, to a lesser degree, driving behavior, there is a surprising dearth of studies directly linking substance-related neuropsychological changes and their impact on driving abilities. Perhaps the clearest is the relationship between driving performance and alcohol, the most commonly detected drug among drivers (Williams, 2006). Alcohol can affect steering control, lane maintenance, divided attention, vigilance, and judgment, as well as other abilities. Impaired driving, or “driving under the influence,” is frequently determined based upon blood alcohol levels (typically a cut-point of .02% to .08%). Performance can be reduced at even low blood-alcohol content (BAC) levels, although there is actually no clear threshold where driving becomes “impaired” (Ogden & Moskowitz, 2004). Alcohol-related crash rates are higher in regions where the legally accepted BAC levels are higher (Fell, Fisher, Voas, Blackman, & Tippetts, 2009). Because much is known about the effects of BAC levels on driving, these have served as a benchmark for comparing the effect of other substances on driving (Ramaekers, 2003). Many states prohibit “driving under the influence” of “illegal drugs,” although the laws are not uniform and often difficult to enforce (Walsh, 2009). Marijuana (cannabis sativa) acutely results in reduced learning, attention, processing speed, and psychomotor abilities, depending upon the dose, but the long-term effects of chronic use on cognition remains controversial (see Chapter 7 of this volume). In a variety of driving studies, cannabis intoxication has resulted in delayed reaction times and poor lane tracking (Ramaekers, Berghaus, van Laar, & Drummer, 2004). While low doses of cannabis may result in moderate driving impairments, the effects are dramatically increased with concurrent alcohol consumption (Ramaekers et  al., 2004; Ramaekers, Robbe, & O'Hanlon, 2000). Unlike with alcohol, there are no clear per se limits for the concentration of delta-9-tetrahydrocannabinol (THC) in blood or urine, since THC, the primary psychoactive component of cannabis, can be detected for days or weeks after intake. Whereas drivers who ingest alcohol often have a false sense of confidence, most studies find that after smoking cannabis, participants drive more slowly than when not under the influence, perhaps due to their awareness of impairment and increased cautiousness (Grotenhermen et al., 2007). However, this behavior may not adequately compensate for unanticipated hazards and the need to react under time pressure. Most studies have been carried out under controlled experimental

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conditions, and the real-world impact of low-dose cannabis use on driving safety remains uncertain (Sewell, Poling, & Sofuoglu, 2009). While stimulants in small doses may improve performance on some tasks, including driving, long-term use of drugs such as cocaine and methamphetamine can negatively impact cognitive functioning (Jovanovski, Erb, & Zakzanis, 2005; Scott et al., 2007). There is limited research on driving in stimulant users. Field reports of individuals under the influence of stimulants note speeding, poor lane maintenance, agitation, and risk-taking (Logan, 1996). Most laboratory studies have only used low doses of such substances (e.g., cocaine), and it is anticipated that higher doses would result in impaired performance on driving tasks, including impaired decision-making and increased risk-taking. Simulator studies have shown increased speeding, weaving, and risky driving in methamphetamine-dependent individuals, some of whom have used methamphetamine within hours of testing (Bosanquet et  al., 2013); similar driving behaviors are associated with laboratory measures of risk-taking and executive dysfunction in currently abstinent methamphetamine users (Marcotte et  al., 2013). Methamphetamine may also be associated with elevated risk-taking and impulsivity (Gonzalez, Bechara, & Martin, 2007; Semple et  al., 2005), and one study found a 2.5-fold increase in the number of fatal crashes involving methamphetamine (Schwilke, Sampaio dos Santos, & Logan, 2006). Studies have found modest effects of MDMA on vehicle control and greater risk-taking on a driving simulator (Brookhuis, de Waard, & Samyn, 2004), although an on-road study found that certain aspects of driving were improved after MDMA (e.g., maintenance of lane position), whereas others were negatively impacted (e.g., overshooting a lead car's speed decelerations) (Ramaekers, Kuypers, & Samyn, 2006).

Impulsivity, Violence, and Legal Outcomes Acute and chronic substance use is consistently associated with patterns of impulsive behaviors (e.g., de Wit, 2009) and increased risk of violence (e.g., Fishbein, 2000; Hoaken & Stewart, 2003), which can have important adverse legal repercussions. Although illicit substance-use behavior is inherently tied to increased exposure of criminal activity (e.g., via drug procurement) and environments in which violent behavior is more prevalent, there is evidence that both acute and chronic substance use independently increase such behaviors in users, regardless of environmental stimuli or context (e.g., in a laboratory setting; Boles & Miotto, 2003). Clinically, individuals displaying more frequent violent behavior show earlier onset and greater severity of substance abuse and are less responsive to treatment than nonviolent individuals (Hubbard, Rachel, Ginzburg, & Marsden, 1989). Of note, similar neurocognitive impairments have been implicated in both the development of substance abuse (e.g., Schafer & Fals-Stewart, 1997)  and aggressive behaviors (e.g., Barratt, Stanford, Kent, & Felthous, 1997; Paschall & Fishbein, 2002). Specifically, substance abuse and violent behaviors are characterized by deficits in executive functions, such as the ability to assess and predict consequences of one's behaviors (Fishbein, 2000). Although research has

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established some of the potential neural (e.g., prefrontal lobe dysfunction; Liu, Matochik, Cadet, & London, 1998) and cognitive (e.g., poor inhibitory control, decision-making; Bolla et al., 2004; Gonzalez et al., 2007) mechanisms by which impulsivity and aggression may function in substance users inside the laboratory, a direct causal link between neurocognitive performance and “real world” violence and legal outcomes has not been well sestablished in the SUD literature. Given the parallel neurocognitive relationships observed between substance abuse and aggression, these might be particularly salient behaviors to examine in this population. To date, two studies have directly examined the association between cognition and aggressive or violent behaviors. Fishbein (2000) demonstrated that, within a cohort of mixed-substance users, violent offenders exhibited greater executive dysfunction than both nonviolent offenders and normal substance-abstinent comparison participants. Weinborn and colleagues (2013) demonstrated the unique role of time-based prospective memory deficits in predicting real-world impulsive behaviors, such as significant criminal history status, within two cohorts of mixed (e.g., alcohol, heroin) substance abusers and high-risk users. One neurocognitive construct that may be of particular relevance in aggression associated with SUD is social cognition (i.e., how individuals store, process, and use information about other people). For example, using animal models, methamphetamine use has been linked to deficits in social behavior (e.g., social withdrawal; Clemens et al., 2004; Syme & Syme, 1974), and within the laboratory, methamphetamine-dependent individuals show impaired performance on social cognition tasks compared to non-users (Homer, Halkitis, Moeller, & Solomon, 2012; Kim, Kwon, & Chang, 2011). Importantly, social cognitive deficits in the general population are associated with self-reported aggressive behaviors outside of the laboratory (e.g., Weimer & Guajardo, 2005). These findings suggest that social cognition may be an important neurocognitive mechanism by which aggressive or violent behaviors are moderated among methamphetamine users. Interventions aimed at improving such risky behaviors may benefit from specifically targeting social-cognitive skills in this population. In contrast to the literature on alcohol and stimulants, the evidence linking cannabis and opioid use to violent behaviors is mixed. For instance, laboratory-based studies examining aggression following THC administration in humans have shown contradictory findings. Some research has demonstrated null or even negative effects of marijuana on aggression, regardless of dose or aggression provocation level (Cherek & Dougherty, 1995; Taylor et al., 1976), whereas others showed variable, dose-dependent increased aggressive responding to THC administration (Cherek et al., 1993; Myerscough & Taylor, 1985). Of note, cross-sectional and correlational data examining real-world outcomes of marijuana users generally reveal positive associations between marijuana use and violence (e.g., homicide, partner violence, likelihood of future violence), though other studies found that this relationship was dependent on age, ethnicity, and/or use of other substances. Similarly, several studies have suggested that the violence observed in the context of opioid use in the laboratory may be largely explained by participants’

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preexisting personality traits rather than the effect of the drug (Gerra et  al., 2001). Importantly, the largely transient neurocognitive impairments in attention and learning associated with marijuana or opioid intoxication (Ersek, Cherrier, Overman, & Irving, 2004; Hart, van Gorp, Haney, Foltin, & Fischman, 2001) are not domains commonly associated with aggression and impulsive behaviors. As a result, violent behaviors may not be an ecologically important functional outcome in the context of marijuana or opioid use.

Treatment Compliance and Outcomes The recalcitrant and fluctuating nature of substance-use disorders make treatment inherently complex (i.e., up to 40%–60% relapse rate; McLellan et al., 2000), which highlights the importance of identifying potentially salient and modifiable risk factors impacting such treatment failures. Most consistently, ineffective coping strategies (Gossop, Stewart, Browne, & Marsden, 2002; Tapert, Ozyurt, Myers, & Brown, 2004), younger age of SUD onset, substance abuse frequency (e.g., Brecht, Greenwell, & Anglin, 2005; Dean et al., 2009), psychiatric co-morbidities (McLellan, Luborsky, Woody, O'Brien, & Druley, 1983; Xie, McHugo, Fox, & Drake, 2005), shorter treatment length (e.g., treatment drop-out; Gossop et al., 2002), and, of particular relevance to this readership, neurocognitive impairments (e.g., Aharonovich, Nunes, & Hasin, 2003) have all been associated with poorer SUD treatment outcomes, such as increased risk of positive urine toxicologies or number of substance-use days. Regarding neurocognitive functioning, it may be face valid that deficits in learning, memory, executive functions, and verbal communication, commonly observed impairments in SUD, may directly and adversely affect treatment efficacy, yet these relationships are multifaceted, and research is only beginning to illuminate the pathways by which neurocognition affects substance-use treatment. One of the most consistent findings in the literature is that greater cognitive impairment is associated with worse treatment retention with marijuana use (e.g., Aharonovich, Brooks, Nunes, & Hasin, 2008), cocaine use (Aharonovich et al., 2006; Turner, LaRowe, Horner, Herron, & Malcolm, 2009), and mixed SUD (Fals-Stewart & Lucente, 1994; Fals-Stewart, 1993; Teichner, Horner, Roitzsch, Herron, & Thevos, 2002). The mechanism by which cognitive impairment may lead to treatment dropout has not been fully elucidated, though studies suggest these findings are independent of demographics, depression, or drug-use severity (Aharonovich et al., 2006). Of note, Patkar and colleagues (2004) found that greater self-reported sensation seeking, impulsivity, and aggression were associated with fewer days in treatment and greater dropout rates in individuals in treatment for cocaine dependence. One indirect process by which neurocognitive impairment may impact treatment retentionis is via executive dysfunctionrelated impulsivity, which may itself lead to increased drop-out rates. Another hypothesized mechanism by which neurocognitive impairments may affect substance abuse treatment is via direct interference with treatment comprehension and implementation. For instance, worse attention and

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global neurocognitive abilities have been associated with poorer recall of treatment-relevant information (e.g., Sanchez-Craig & Walker, 1982; Teichner et  al., 2002), reduced quality of coping skills acquired (Kiluk, Nich, & Carroll, 2011), fewer treatment objectives achieved (Teichner, Horner, & Harvey, 2001), and, among alcohol-use disorders, fewer drink-refusal skills learned (Smith & McCrady, 1991). These studies suggest that neurocognitive functioning plays a direct role in the ability to benefit from the primary “active ingredient” of substance-use treatment (i.e., the content of the treatment program). Therefore, ensuring that substance-use treatments are presented at the appropriate neurocognitive level and format (e.g., repetition, reduced speed of information presentation) for patients is critical for treatment comprehension and effectiveness. Substance use–associated neurocognitive deficits may also play an additional indirect role in treatment outcomes by impacting treatment motivation and participation. For instance, among alcohol-use disorders, impairment in executive functions, processing speed, and memory have been associated with reduced readiness to change drinking behaviors (Le Berre et al., 2012), greater “denial-related” treatment goals (Rinn, Desai, Rosenblatt, & Gastfriend, 2002), and an increased number of post-treatment drinking days (Morrison, 2011). Relatedly, although increased self-efficacy has been associated with greater ability to resist drinking behaviors following alcohol-use treatment, this relationship does not appear to hold among individuals with neurocognitive impairment, suggesting a potentially differential pattern of predictors for treatment outcomes among individuals with alcohol-use disorders and cognitive deficits (Bates, Pawlak, Tonigan, & Buckman, 2006; Morgenstern & Bates, 1999). Additionally, in a sample of mixed-substance abusers, neurocognitive impairment was associated with less positive participation in treatment as assessed by the clinical staff, and inability to follow treatment program rules (Fals-Stewart & Lucente, 1994; Fals-Stewart, 1993). In this manner, cognitive impairment may be indirectly affecting one's ability to successfully participate in treatment programs. In addition to program compliance and outcomes, medication adherence may be an important treatment construct in individuals with SUDs. Given their high comorbidity, the majority of research informing medication adherence outcomes in substance-use disorders is within the context of HIV infection. These studies indicate that current drug use, but not alcohol use, is associated with up to a four-fold increased risk of antiretroviral treatment non-adherence among HIV-infected individuals, with stimulant users at the greatest risk for suboptimal adherence (Hinkin et  al., 2007; Moore et  al., 2012). Importantly, the strongest effects on non-adherence are observed among current, but not remote, users, potentially indicating that the acute effects of substances (i.e., state effects) interfere most with medication adherence (Hinkin et  al., 2007; Moore et  al., 2012). Among these individuals, global neurocognitive impairment conferred a 2.5-fold increase for poorer medication adherence, independent of age, with executive dysfunction, poorer verbal memory, and psychomotor slowing predictive of non-adherence (Hinkin et al., 2004). Given their impairment among individuals with SUD and importance for adherence in HIV, executive functions, memory,

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and psychomotor abilities may be critical targets for remediation. Additionally, the use of external reminders (e.g., alarms to target memory and executive dysfunction) and prepackaged medications (e.g., blister packs to target psychomotor difficulties) may be particularly indicated in this population in order to overcome such impairments. In sum, treatment outcomes represent a critical area of everyday functioning among individuals with SUD, particularly given that they may directly impact a variety of other functional abilities in this population. Thus, “fine-tuning” substance abuse treatments to match the neurocognitive functioning of a specific patient population may be critical in order to increase the efficacy of treatment. Weinstein and Shaffer (1993) propose a detailed guide delineating how neurocognitive impairment may be incorporated and addressed in substance-use treatment. For example, the authors recommend an initial evaluation of neurocognitive abilities at the beginning of treatment, and they suggest several compensatory strategies and program adaptations that may improve treatment comprehension and reduce patient frustration (e.g., for difficulties in sustained attention, teach verbal mediation in order to keep the patient focused on the task at hand; see Weinstein & Shaffer, 1993, for a more detailed review). Taken together, SUD treatment programs may benefit from the integration of neurocognitive remediation techniques (e.g., compensatory strategies) in order to reach optimal treatment outcomes. Clinically, it is critical to understand how neurocognitive impairments may impact SUD treatment in order to anticipate and remediate both the direct (e.g., poor learning comprehension may be addressed via having the patient repeat instructions out loud) and indirect (e.g., poor motivation may be addressed by creating explicit, individualized treatment goals) outcomes before they occur. (See Bates et al., Chapter 5 of this volume for more information on treatment.)

Summary and Future Directions Substance-use disorders are chronic mental health conditions with a relapsing course and concomitant negative effects on functioning. Cognitive deficits appear to both be involved in neurobiological vulnerabilities for establishing addictive behaviors and result from the chronic use of some substances. The nature and magnitude of cognitive deficits associated with SUD increase the risk of poorer health outcomes, including problems in daily functioning (e.g., unemployment), high-risk behaviors, and treatment non-adherence and relapse. Accordingly, consideration of neuropsychological functioning has clear implications for the clinical management of persons with SUD, although research focusing on the relationship between cognitive functioning and functional outcomes in SUD is still limited. Studies that have examined the relationship between cognitive deficits in substance-dependent individuals and a variety of outcomes, including employment, driving, and overall problems with instrumental activities of daily living, have generally found a moderate relationship between these variables. The strongest relationships have generally been observed in individuals with alcohol and stimulant-use disorders, as these appear to involve the greatest risk

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of neurocognitive deficits combined with poor functional outcomes. However, research on these relationships in certain drugs of abuse such as opiates and benzodiazepines is clearly lacking. Overall, executive dysfunction appears to be especially detrimental for functional outcomes in SUD, although deficits in other neurocognitive domains are clearly associated with functional outcomes, depending on the substance. Neurocognitive deficits (e.g., impulse control) may also contribute to increased aggression or violence with chronic stimulant use, although the relationship of such cognitive predictors to aggression in the context of marijuana and opiates is less clear. It is clear, however, that treatment outcomes represent a critical area of research and clinical intervention in SUD, as neurocognitive functioning may mediate and/or moderate treatment capacity and compliance in substance-misusing individuals. Overall, though, it is clear that many research questions remain to be explored in this burgeoning field. A majority of studies to date that have examined relationships between substance-use problems and everyday outcomes have utilized self-report measures of functioning, despite the limitations outlined above. Future studies would benefit from taking advantage of additional methods of assessing functioning that have shown utility in predicting everyday-functioning outcomes, including combinations of methods such as self-report and performance-based measures, which may enhance prediction models. Additional research that attempts to delineate key neurocognitive contributors to functional impairment (e.g., response inhibition) and their interaction with SUD-related factors (e.g., treatment motivation) would be of tremendous benefit in designing interventions to counteract the effects of such deficits. Relatedly, examination of ecologically relevant, translational areas of cognitive functioning may be particularly important in order to delineate the multifaceted mechanisms underlying functional outcomes among substance users. For example, in schizophrenia, social cognition, which has demonstrated SUD-related declines (e.g., methamphetamine; Homer et al., 2012), is strongly associated with functional outcomes, including interpersonal skills, independent of traditional neurocognitive tests (Couture, Penn, & Roberts, 2006; Pinkham & Penn, 2006). Consideration of social cognition among SUD populations may therefore provide a novel, integrative approach to predicting real-life social skills and outcomes, and thus may help identify a specific target for remediation that may have a significant impact in the daily lives of individuals with SUD. In addition, given the significant comorbidity of SUD with psychiatric disorders, the examination of symptom profiles that are more likely to result in neurocognitive impairments and associated functional deficits would make valuable contributions to the literature. Due to neuropsychology's increased focus on predicting functional outcomes (e.g., Marcotte et al., 2009), many studies in recent years have examined the utility of cognitive measures with evidence of ecological validity in predicting such “real life” abilities, and these advances may be beneficial for research in SUD populations. For example, studies that have examined executive impairments in SUD with tests that focus on ecological validity have shown promising results in associations with daily-functioning outcomes (Ihara et al., 2000; Moriyama et al., 2002; Verdejo-García et al., 2012). For example, Verdejo-Garcia and Perez-Garcia

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(2007) showed that deficits exhibited in individuals with SUD on the Behavioral Assessment of the Dysexecutive Syndrome (BADS; Wilson, Alderman, Burgess, Ernslie, & Evans, 1996) were associated with greater reports of everyday executive problems, including disinhibition and disorganized behavior. Moreover, as described above, studies examining prospective memory, which has putative relevance for medication management and adherence, treatment outcomes, and vocational skills, have shown it to be a good indicator of functioning in SUD and other disorders with significant deficits related to prefronto-striatal system involvement (e.g., Pirogovsky, Woods, Filoteo, & Gilbert, 2012; Weinborn et al., 2013; Woods et al., 2009). Future research aimed at developing tailored treatments for SUD individuals with cognitive impairment could also provide valuable contributions to the literature and enhance treatment effectiveness. For example, given the evidence thus far, SUD treatment of individuals with neurocognitive impairment would likely be more effective by not only directly targeting the observed deficit, but also by anticipating and adjusting (e.g., with intensive motivational interviewing) for the concomitant treatment-interfering effects that frequently co-occur early in the course of treatment. References Aharonovich, E., Brooks, A.  C., Nunes, E.  V., & Hasin, D.  S. (2008). Cognitive deficits in marijuana users: Effects on motivational enhancement therapy plus cognitive behavioral therapy treatment outcome. Drug & Alcohol Dependence, 95(3), 279–283. doi:10.1016/j.drugalcdep.2008.01.009 Aharonovich, E., Hasin, D. S., Brooks, A. C., Liu, X., Bisaga, A., & Nunes, E. V. (2006). Cognitive deficits predict low treatment retention in cocaine dependent patients. Drug & Alcohol Dependence, 81(3), 313–322. doi:10.1016/j.drugalcdep.2005.08.003 Aharonovich, E., Nunes, E., & Hasin, D. (2003). Cognitive impairment, retention and abstinence among cocaine abusers in cognitive-behavioral treatment. Drug & Alcohol Dependence, 71(2), 207–211. Arria, A.  M., Garnier-Dykstra, L.  M., Cook, E.  T., Caldeira, K.  M., Vincent, K.  B., Baron, R.  A., & O’Grady, K.  E. (2013). Drug use patterns in young adulthood and post-college employment. Drug & Alcohol Dependence, 127(1–3), 23–30. doi:10.1016/j.drugalcdep.2012.06.001 Baldwin, M. L., Marcus, S. C., & De Simone, J. (2010). Job loss discrimination and former substance use disorders. Drug & Alcohol Dependence, 110(1–2), 1–7. doi:10.1016/j. drugalcdep.2010.01.018 Barker, M. J., Greenwood, K. M., Jackson, M., & Crowe, S. F. (2004). Persistence of cognitive effects after withdrawal from long-term benzodiazepine use: A meta-analysis. Archives of Clinical Neuropsychology, 19(3), 437–454. doi:10.1016/S0887-6177(03)00096-9 Barratt, E. S., Stanford, M. S., Kent, T. A., & Felthous, A. (1997). Neuropsychological and cognitive psychophysiological substrates of impulsive aggression. Biological Psychiatry, 41(10), 1045–1061. Bartholomew, J., Holroyd, S., & Heffernan, T. M. (2010). Does cannabis use affect prospective memory in young adults? Journal of Psychopharmacology, 24(2), 241–246. doi:10.1177/0269881109106909

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Prescription Drug Abuse K A I - H O N G J E R E M Y M AO , L AU R E N N.   B UC H H E I M , A N D JA S ON P.   CA P L A N

The potential ill effects engendered by misuse of therapeutic agents have long been recognized. Indeed, the Italian adventurer and amateur physician Giovanni Casanova was credited with saying, “In the hands of the wise, poison is medicine. In the hands of a fool, medicine is poison,” in the mid-1700s. With the massive increase in variety of prescribed psychotropic drugs over the past few decades, however, prescription drug abuse has rapidly become a significant public health issue. Tracking the phenomenon of prescription drug abuse and suggesting approaches to its treatment is more difficult than doing so for other categories of drugs of abuse since, while other clusters of drugs are epidemiologically clustered by drug structure or chemistry, prescription drugs are simply grouped together by virtue of having a federally approved medicinal indication. This designation is a purely societal construct, since authorities may disagree as to whether a drug has therapeutic use or not. Examples of such controversy include marijuana, which is broadly advocated for the treatment of a number of ills, including glaucoma and chemotherapy-induced nausea, and 3,4-methylenedioxy-N-methylamphetamine (MDMA or Ecstasy), which is now being studied for the treatment of post-traumatic stress disorder. Both are currently listed as Schedule I controlled substances in the United States, as having no accepted medicinal use (Table 17.1). Population-based surveys of misuse of prescribed substances are likely to produce less reliable answers than those addressing the use of drugs without legally validated medical indications. Answering in the affirmative to a question of recent heroin use is probably indicative of an addiction-spectrum problem, whereas doing the same to a query regarding misuse of prescribed medication might just mean the respondent did not entirely follow the prescriber's directions for a substance without abuse potential.

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Table 17.1. Drug Enforcement Agency Definitions of Scheduled Substances Substance Category Schedule I

Definition

Example Drugs

• Includes drugs or other substances with a high potential for abuse • No currently accepted medical use in the United States • Low level of safety

Heroin, Marijuana, Phencyclidine (PCP), Lysergic Acid Diethylamide (LSD), Peyote

Schedule II

• Drugs with high abuse potential • Have currently accepted medical use

Cocaine, Methamphetamine, Amphetamines, Dextroamphetamine, Methylphenidate, Morphine, Oxycodone, Fentanyl, Pentobarbital

Schedule III

• Drugs have abuse potential less than that of Schedule II drugs • Have currently accepted medical use

Hydrocodone, Vicodin, Butalbital, Fiorinal, Buprenorphine, Ketamine, Anabolic Steroids

Schedule IV

• Drugs have lower abuse potential than those of Schedule III drugs • Have currently accepted medical use

Alprazolam, Diazepam, Propoxyphene, Carisoprodol

Schedule V

• Drugs have low abuse potential • Have recognized medical uses • Some pharmaceuticals contain drugs with higher abuse potential but in much lower concentrations relative to other ingredients

Cough medicines with codeine containing less than 200 mg of codeine per 100 ml

Nonetheless, in the context of this broad and somewhat nebulous classification, data released by the Substance Abuse and Mental Health Services Administration in 2011 revealed that prescription drugs had become the second most abused class of illicit substances in the United States, with only marijuana ranking ahead of them. In that same year, the United States Department of Justice indicated that the number of deaths resulting from prescription medication overdose surpassed those caused by heroin, methamphetamine, and cocaine combined (2011). This troubling trend was underscored by a number of untimely and prominent celebrity deaths (including those of Whitney Houston, Heath Ledger, Brittany Murphy, and Anna Nicole Smith) attributed to prescription medication toxicity. Older adults seem particularly vulnerable to misuse and abuse of prescription medications (SAMHSA, 2006), possibly because they are prescribed more prescription medications than younger adults (e.g., benzodiazapines for sleep), with this

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increased exposure elevating the risk for abuse. Data from the National Survey on Drug Use and Health indicates that non-medical use of prescription medications was the second most common form of substance abuse among adults older than fifty-five (SAMHSA, 2006). To properly assess, treat, and prevent pathological prescription drug use, clinicians must familiarize themselves with the terminology addressing consumption of these substances. Terms used in this context include:  nonmedical use of substances, substance misuse, physiological dependence, psychological dependence, and pseudoaddiction (Table 17.2). Since a review of all potentially abusable prescription drugs would far exceed the pages of this text, this chapter will focus on issues surrounding the abuse of two chief classes of prescription medication—stimulants and sedative-hypnotics. While opioids are the most commonly abused class of prescription medications, they are addressed in Chapter 11 of this volume. We will, however, briefly address issues specific to the abuse of the prescription formulations of opioids. PRESCRIPTION STIMULANT ABUSE Parallel with improvements in diagnostic practices and public awareness of attention-deficit/hyperactivity disorder (ADHD), prescription stimulant use has become more commonplace throughout the United States (Goldman, Genel, Bezman, & Slanetz, 1998). This class of drugs remains the most effective method of treating ADHD and has proven safe if properly utilized. Stimulants have also demonstrated efficacy in the treatment of narcolepsy, obesity, and treatment-refractory depression. The increased availability of prescription stimulants has resulted in rising concerns about their abuse potential. Over the past decade, several studies Table 17.2. Terminology Defining Inappropriate Use of Prescription Drugs Term Nonmedical use

Definition Use of drugs not prescribed for the user, specifically for the experience caused by the drug

Misuse

While drug is prescribed for the user, it is used in a manner or for a purpose other than how it is prescribed

Abuse

Maladaptive pattern of substance use resulting in clinically significant impairment or distress

Physiological Dependence

Increasing tolerance for a drug and/or physical withdrawal syndrome when the drug is discontinued

Psychological Dependence (Addiction)

Psychological symptoms featuring loss of control, drug-seeking behaviors, and continued use in face of adverse consequences

Pseudoaddiction

Incomplete treatment of pain resulting in drug-seeking and other behaviors mimicking addiction—behaviors resolve once pain is adequately managed

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have explored the abuse potential of commonly prescribed stimulants, which include dextroamphetamine (Dexedrine), mixed amphetamine salts (Adderall), and methylphenidate (Ritalin). Here, we will attempt to clarify the abuse profiles of prescription stimulants and those who misuse and abuse them. STIMULANT USE/EPIDEMIOLOGY The use of prescription stimulants has increased significantly over the past two decades. In fact, adults presenting for treatment with stimulants increased five-fold from 1992 to 2002 (U.S. Department of Health and Human Services, 2004). McCabe and colleagues (2007) reported a 4.7% lifetime prevalence of illicit prescription stimulant use amongst U.S. adults, with progression of 31% of those to stimulant abuse and 13% to stimulant dependence. Several hypotheses have been proposed as to why there has been such a dramatic elevation in stimulant use, with the most prominent being the increase in ADHD diagnoses. The percentage of children diagnosed with ADHD increased from 7.8% in 2003 to 9.5% in 2007 (U.S. Department of Health and Human Services, 2007). It is estimated that approximately 60% of ADHD patients are treated with prescription stimulants (Zuvekas & Vitiello, 2012). The National Institute on Drug Abuse (2011) reported that between 1991 and 2010, prescriptions for stimulants increased from 4 million to 45 million. In 2010, the National Survey on Drug Use and Health (NSDUH) estimated that 1.1  million Americans aged 12 and older were actively misusing prescription stimulants, a phenomenon especially prominent amongst U.S.  secondary and college students. According to a 2010 survey, prescription medications and over-the-counter drugs were the most commonly abused drugs after alcohol, marijuana, and tobacco amongst twelfth-grade students (Johnston, O'Malley, Bachman, & Schulenberg, 2011). An earlier survey reported 14.1% lifetime use of amphetamines and 9.9% past-year use within the twelfth-grade demographic (Johnston, O'Malley, Bachman, & Schelenberg, 2005). College students had a reported 5.7% past-year use, which was more than double that of their same-age peers not attending college (Johnston, O'Malley, & Bachman, 2003). In a single college survey done by McCabe and colleagues in 2006, 3% of students described medically appropriate lifetime stimulant use, with 2% reporting past-year use specifically for ADHD treatment. However, the same survey showed 8.1% of the undergraduate population endorsing illicit prescription stimulant use and 5.4% reporting illicit use in the past year (McCabe, Teter, & Boyd, 2006). With nearly three times as many students abusing stimulants as those receiving them for therapeutic indications, it is clear that there is a growing trend towards using stimulants for either performance enhancement or recreational use rather than for prescribed purposes. Several studies have described specific characteristics of the cohort most likely to be prescribed stimulants and to engage in abuse of these drugs. This demographic appears to comprise mostly students of either secondary school or college. McCabe and colleagues (2006) reported that students who are in a fraternity

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or sorority, have lower grade-point averages, come from higher-income families, are of the Jewish faith or without a religious affiliation, were more likely to receive a prescription for stimulants. Perhaps unsurprisingly, the study also noted that these same demographic descriptors applied to the population most likely to abuse stimulant medications (McCabe et al., 2006). Multiple studies have further shown that males are more likely to use stimulants than are their female counterparts. Moreover, it would seem that illicit stimulant use is fairly limited to traditional-aged students (18–24), with virtually no students older than 25 years reporting unlawful use in a survey by Babcock and Byrne (2000). Prescription stimulant use has been shown to vary considerably between academic institutions, with reports ranging from 3% to 36% of students (McCabe et al., 2006). Diversion of prescriptions is quite common within school systems, with 23% of secondary school students who were receiving prescription stimulants reporting that they had been approached to sell, trade, or give away their medication (McCabe et  al., 2004). An earlier study by Musser and colleagues (1998) indicated that 16% of secondary school students receiving prescribed stimulants were prompted to give away their medications to peers. Another study of Canadian students who were prescribed stimulants showed that 15% gave their medications away, while 7% had sold them (Poulin, 2001). Diversion within the collegiate population seems to be even more common, with 54% of prescription stimulant–using students being approached by peers to divert their medication (McCabe et al., 2006). This seems to be the primary mechanism of obtaining stimulants, as the overwhelming majority of illicitly obtained stimulants (92%) were reportedly received from friends or peers. Very few users receive their prescription via the Internet (McCabe et al., 2006). The ready availability of these drugs in academic settings probably plays a significant role in the prevalence of their abuse, as does the false perception that the designation of “prescription drugs” makes them relatively “safe” to use (Friedman, 2006). Some treaters have voiced concerns regarding how commonly stimulants are being prescribed and their appropriateness for ADHD treatment. In fact, bupropion (a dopaminergic agent primarily used as an antidepressant that carries little risk of abuse) has been proposed as a suitable first-line therapy for young adults attending college who are diagnosed with ADHD, especially if their diagnosis is complicated with a substance use disorder (Wilens et al., 2001); however, stimulants have been proven to be the most effective treatment for ADHD when used as prescribed (Pliszka, 2007). While there is clear therapeutic indication for the use of medications, questions persist as to whether prescription of stimulants places the patient at higher risk for developing an abuse problem. McCabe and colleagues (2006) found that students who began use of stimulants during secondary school or college were three to seven times more likely to abuse prescription drugs than their peers not receiving a prescription for these substances. This did not prove true for students that began prescription stimulant use during elementary school, whose abuse rate was comparable to that of peers who had never received a prescription (McCabe et al., 2006). Faraone and Wilens (2003) found that patients who used prescription stimulants for ADHD were at no increased risk of using

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stimulants illicitly, and stimulant treatment of ADHD actually demonstrated a protective effect against abuse. Abusers of prescription stimulants have been shown to be much more likely to engage in abuse of other substances (McCabe et  al., 2004). Non-prescribed-stimulant users reported significantly more alcohol binge drinking, marijuana, cocaine, Ecstasy, and hallucinogen use compared to nonusers (McCabe et al., 2006). Marijuana has shown to have a particularly strong association with stimulant abuse. Females who used marijuana had an 11.4-fold increased likelihood of abusing stimulants, while males were 12.1 times more likely to do so (Lynskey et al., 2007). DETECTION OF PRESCRIPTION STIMULANTS Much effort has been put into finding ways to quantify stimulant use, especially methylphenidate (Ritalin). Established ways of detecting methylphenidate in serum, plasma, and urine include capillary electrophoresis-mass spectrometry, gas chromatographic-mass-spectrometry (GC-MS), liquid-chromatographic-m ass-spectrometry (LC-MS), and ultraviolet analysis. These methods are designed to detect both the drug and its metabolites. For methylphenidate, the use of ritalinic acid levels for detection is able to provide additional evidence of use since methylphenidate is quickly hydrolyzed once ingested, with approximately 75% of the original dose excreted as ritalinic acid in the urine (Soldin, Chan, Hill, & Swanson, 1979; Marchei et al., 2010). Though the medication can be detected through these means, blood levels have not shown to be useful in determining therapeutic levels and proper management in patients (Marchei et al., 2008). The half-life of methylphenidate is fairly short at two to three hours (Kimko, Cross, & Abernethy, 1999), which makes detection in cases of prescription-stimulant abuse fairly difficult. There has recently been a shift in creating methods to detect methylphenidate levels by less invasive or noninvasive means to allow for a more accessible way to determine recent use or misuse. Marchei and colleagues have demonstrated the utility of hair, oral fluids, and sweat as biological matrices for testing purposes (Marchei et al., 2007). Another study, with Sticht and colleagues in 2007, showed similar results in regard to using hair samples for methylphenidate detection using GC-MS (Sticht, Sevecke, Käferstein, Döpfner, & Rothschild, 2007). Both successfully studies demonstrated that hair could be used to detect recent or chronic misuse of methylphenidate. In a 2010 case report, Marchei and colleagues observed that the use of oral fluids was comparable to using plasma for detection of methylphenidate through liquid-chromatographic analysis. Similar results were produced by a study by Joseffson and Rydberg in 2011. Sweat patches allow for the detection of methylphenidate five hours after administration of the medication (Marchei et al., 2010). As previously mentioned, those who abuse prescription stimulants often abuse other substances. Further study has been dedicated to using established methods, which include LC-MS and GC-MS, to simultaneously detect several

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prescription and illicit drugs with one test. Fernandez and colleagues (2007) examined liquid-chromatography-tandem mass spectrometry as a potential method to detect ritalinic acid along with hallucinogens, chlorpheniramine, and ketamine in urine. They were able to effectively detect these substances in an artificially prepared sample with over 87% recovery of their designated analytes. This method was subsequently successfully applied to three separate cases using authentic urine samples (Fernandez et al., 2007). Similar studies have been done to test the abilities of the various methods of testing for simultaneous drugs in urine, especially with GC-MS and enzyme-immunoassay testing. Urine toxicology is useful in the detection of dextroamphetamine (Dexedrine) and mixed amphetamine salts (including Adderall and Adderall XR). Dextroamphetamine is the d-isomer of amphetamine (George & Braithwaite, 2000)  and will trigger a positive finding on urine toxicology screening for amphetamine. Since one of the components of mixed amphetamine salts is also amphetamine, they are easily detected through urine toxicology as well. Clinically, however, the question often arises of how to differentiate whether detected amphetamine is due to a medicinal source or from an illicit form. Significant investigation has been devoted to answering this question, especially in the context of Adderall use. Resulting data have shown that this can be achieved via the analysis of the enantiomer products of Adderall, which include d- and l-amphetamine in a 3:1 ratio, respectively (Cody, Valtier, & Nelson, 2003). When urine is tested, it should be expected to retain this enantiomer ratio immediately after administration if the substance used is indeed Adderall. Differing ratios would be expected in illicit forms of amphetamines and thus would translate accordingly when analyzed. In fact, one would expect either the absence of l-amphetamine or a 1:1 ratio of both enantiomers if illicit amphetamines were used, as these products consist of d-amphetamine–only formulations and racemic mixtures, respectively (Cody, Valtier, & Nelson, 2003). Since these ratios are expected to change according to the rate of metabolism, not the rate of excretion, the timing of the sample collection after initial drug ingestion can influence the results. For Adderall, the d-enantiomer is metabolized faster than the l-enantiomer and there is a shift towards a 1:1 d-/l-amphetamine ratio, thus resembling results collected from someone who has used a racemic mixture. However, a 1:1 ratio after ingestion of Adderall may be obtained no earlier than 72 to 132 hours after ingestion (Cody, Valtier, & Nelson, 2003). Thus, it is very important to obtain a thorough history defining the timing of the last dose of drug taken. DRUG EFFECTS Stimulants increase CNS dopamine (DA) by affecting the transport mechanism of the neurotransmitter. The increase in DA levels results in an activation of the motor inhibitory system in the orbital-frontal-limbic axis, resulting in the inhibition of impulsiveness allowing for increased focus of attention (Berman, Kuczenski, McCracken, & London, 2009). Though methylphenidate and amphetamines have different mechanisms of action, they both result

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in DA increase (Morton & Stockton, 2000). Methylphenidate directly blocks dopamine transporters (DAT), resulting in the increase in postsynaptic DA, with 50% of the transporters in the brain blocked at therapeutic doses (Volkow, Fowler, Wang, Ding, & Gatley, 2002). Amphetamines also block the ability of DAT to clear DA from the synapse but simultaneously bind to monoamine transporters, resulting in increased extracellular DA, norepinephrine, and serotonin (Berman et al., 2009). While the most common adverse effects of prescription stimulants include anxiety and insomnia, perhaps their most publicized and frequently discussed effect is anorexia. In 2006, McGough and colleagues found the most frequently reported side effects of methylphenidate were headache, insomnia, and anorexia. These effects are a result of the shift in neurotransmitter concentration in the CNS after administration of the medications. Other behavioral changes include emotional lability, perseverative movements, and states of euphoria (Berman et  al., 2009). Stimulation of DA D1 receptors in the nucleus accumbens and striato-orbitofrontal cortex has been implicated as the responsible pathway of these euphoric states (Morton & Stockton, 2000). In settings of abuse, methylphenidate and amphetamine have similar toxicity profiles, which include euphoria, delirium, and confusion. Psychosis can also occur, typically in cases of chronic abuse, with prominent paranoia and both auditory and visual hallucinations (Morton & Stockton, 2000; Spensley & Rockwell, 1972). Moreover, aggressive behavior, extreme anger, and panic states have been seen in cases of chronic abuse and in the context of large doses used over a short period (Morton & Stockton, 2000). Side effects typically resolve over a matter of hours from the cessation of use, but they have been reported to last several weeks (Schuckit, 1995). Amphetamines have been associated with a greater intensity of adverse effects than methylphenidate (Efron, Jarman, & Barker, 1997), particularly in terms of insomnia, negative affect, irritability, and nightmares. Withdrawal symptoms include depression, dysphoria, psychomotor retardation, increased appetite, and fatigue (Caplan, Epstein, Quinn, Stevens, & Stern, 2007). One of the more controversial negative consequences of chronic stimulant use in children has been reduced growth in both height and weight. Poulton (2005) reviewed 29 reports concerning the growth effects of prescription stimulants, and 11 described a reduction in height. Other studies have found no such effect (Pliszka, Matthews, Braslow, & Watson, 2006; Spencer et  al., 2006). In 2007, a three-year prospective study done by Swanson and colleagues reported that there was both a height and weight growth deceleration, with an average of 2.0  cm and 2.7 kg lower values than those predicted. It remains unclear whether these changes in growth are a result of the medication's effect on appetite or if this is a direct sequela of chronic use. While therapeutic use of stimulants is typically via the oral route, abuse may occur via intranasal administration, smoking, or injecting of stimulants. Though little is known about additional adverse effect of alternative-route-administered stimulants, it is clear that these methods have been utilized to attempt to

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expedite delivery of the drug to the brain. Intravenous delivery of methylphenidate allows for uptake in the brain within four to eight minutes and has been associated with psychosis in multiple reports dating back to 1963 (Volkow et al., 1999, McCormick & McNeel, 1963). Intranasal use has become the second most popular route of illicit use (Teter, McCabe, LaGrange, Cranford, & Boyd, 2006). Reported symptoms of intranasal methylphenidate use have included anxiety, paranoia, depression, suicidal ideation, loss of consciousness, and death (Morton & Stockton, 2000). Interest in the neurophysiological effects of prescription stimulants has been the topic of much recent research. Ricaurte and colleagues found that chronic Adderall treatment in both primates and humans resulted in a 30% to 50% reduction in striatal DA. This was also the case for DA's major metabolites, rate-limiting enzyme, and membrane transporter (Ricaurte et  al., 2005). In subjects experiencing decline in dopaminergic functioning for reasons other than stimulant use, these changes have been associated with both cognitive and motor deficits, including abstraction, mental flexibility, attention, and response inhibition (Volkow et  al., 1998). Furthermore, it was found that DA levels in the caudate were much lower than in the putamen, which is the opposite of the changes seen in dopaminergic loss due to Parkinson's disease. This suggests that chronic stimulant use is more likely to result in cognitive rather than motor deficits, while the primary presentation of Parkinson's disease features motoric rather than cognitive problems (Moszczynska et al., 2004). White matter is also affected by chronic stimulant use. Magnetic resonance imaging (MRI) studies by Castellanos and Tannock (2002) found that white matter volume in medicated children with ADHD is greater than in those who had not received prescription stimulants. It was initially thought that early stimulant treatment normalized white matter volume, but recent evidence indicates that the increase in white matter is actually a result of hypertrophy (Thompson et al., 2004), and could, in fact, be due to gliosis as a result of neuronal damage (Yang, Wang, Cheng, & Xu, 2011). Studies examining the cognitive effects of stimulants in adults without an ADHD diagnosis have not produced consistent results. Smith and Farah (2011) undertook a review of the available literature, classifying effects on three types of cognition: learning, working memory, and cognitive control. In terms of learning, they surmised that all but one available study showed no significant improvement in memory after short delay, but retention was consistently enhanced by stimulants after longer delays (one hour to one week). This improvement was only seen for tasks of declarative learning. Tests of probabilistic and procedural learning revealed no consistent improvement with stimulants. When examining studies of working memory and stimulants, they noted that results from the available studies were mixed, with some showing enhancement and others with null results. No study demonstrated any impairment of working memory attributable to stimulants. Of note, the greatest improvements in working memory were seen in subjects who had the lowest baseline performance (including those from studies with overall null results), thus suggesting that stimulants are better able to correct deficits of working memory rather than enhance the working memory of

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normal or above-normal subjects. Finally, when examining the effects of stimulants on cognitive control (defined as the “guidance of cognitive processes in situations where the most natural, automatic, or available action is not necessarily the correct one”) Smith and Farah again note inconsistent results. Improvement in performance is again most clearly demonstrated in subjects with the least-optimal baseline performance, again underscoring the ability of these medications to correct rather than enhance. There is a paucity of data regarding the cognitive effects of long-term use of stimulants in ADHD (see Iudicello et al., Chapter 9, this volume, for a review of chronic methamphetamine use). SEDATIVE-HYPNOTIC ABUSE Assignment of a drug to the sedative-hypnotic class indicates that it is able to cause sedation (with concomitant relief of anxiety) or to encourage sleep. Since there is considerable chemical variation within the group, this drug classification is based on clinical use rather than similarity in chemical structure. Under this very broad definition, the term sedative-hypnotics has historically described a number of different chemical classes, though when used in the context of prescription drug abuse, it primarily refers to the benzodiazepines, barbiturates, and the newer generation of hypnotics popularly referred to as the “z-drugs” (i.e., zaleplon [Sonata], zolpidem [Ambien], and eszopiclone[Lunesta]). For the purposes of this chapter, we will chiefly focus on the benzodiazepines (BZDs) and “z-drugs,” since these agents are currently prescribed (and abused) in numbers far greater than are the barbiturates, whose heyday as drugs of abuse occurred in the 1960s and 1970s when they were prescribed far more frequently. All BZDs in clinical use can promote the binding of the major inhibitory neurotransmitter gamma-aminobutyric acid (GABA) to the GABAA subtype of GABA receptors, which exist as multi-subunit, ligand-gated chloride channels. BZDs allosterically increase the affinity of GABA for the GABAA receptor, resulting in the opening of chloride channels, influx of chloride ions into the neuron, and subsequent neuronal hyperpolarization. BZDs are widely used in the treatment of anxiety and insomnia, and are employed in the treatment of several other conditions, including psychosis, depression, social phobia (social anxiety disorder), obsessive-compulsive disorder, drug withdrawal, and to combat the adverse effects induced by various other psychotropics (Pollack et al., 1993). Although structurally different, the newer non-benzodiazepine hypnotic agents or “z-drugs” do resemble the benzodiazepines in their function, side-effect profiles, and patient counseling information. As a class, they are typically referred to as “benzodiazepine receptor agonists” (BzRAs). These agents potentiate the effects of GABA by binding to the benzodiazepine site on the GABAA receptor, with high affinity only for receptors containing the alpha1 subunit (formerly called “omega-1 receptors”). This selective binding profile of the BzRAs may explain the relative absence of anticonvulsant and myorelaxant effects, as well as the preservation of stages 3 and 5 sleep (deep, slow wave sleep) (Sanger, 2004).

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The BzRAs were approved in the United States for the treatment of insomnia in the early 1990s and quickly ranked among the most widely prescribed medications. The BzRAs are potent drugs intended for short-term use (2–3 weeks), with eszopiclone providing an exception to the rule of short-term use, since patients with chronic insomnia appear to benefit for up to 12 months of use without developing tolerance or dependence (Scharf, 2006). EPIDEMIOLOGY OF SEDATIVE HYPNOTIC MISUSE BZD misuse and abuse are challenging to define. Criteria used to define BZD dependence according to Chen and colleagues (2011) have included: unsuccessful attempts to cut back or terminate use, feeling uncomfortable when not taking BZDs (Khong et  al., 2004), having a history of long-term use, and dosage escalation and high anxiety levels despite taking BZDs. There are three chief subpopulations who misuse BZDs (Ashton, 2005): patients who are prescribed BZDs therapeutically for the short term and take them for the long term (estimated at 4  million people in the United States, with half of them probably dependent); patients who are prescribed BZDs therapeutically but then increase the dose on their own by presenting to multiple prescribers or acquiring them illicitly (prevalence unknown); and patients who seek BZDs for recreational use without any initial therapeutic indication or prescription (probably a small proportion of BZD abusers, though at present there is no estimate on the actual prevalence) (Chen et al., 2011). This third group tends to consist of polysubstance abusers who seek BZDs to enhance the effects of other drugs, alleviate withdrawal effects of other drugs, or simply to produce their own psychotropic effects (Chen et al., 2011). Data from the Drug Abuse Warning Network (DAWN) Report (SAMHSA, 2010) indicate that in 2010, of the 4 million drug-related emergency department visits made by patients age 21 or older, 1.9  million (47.2% or 849.4 visits per 100,000 population) involved drug misuse or abuse. BZDs accounted for 168.8 visits per 100,000 population, an increase of 139%, with 237,550 more visits in 2010 than in 2004. Although current abuse of barbiturates is low compared with that of other classes of abused drugs, their narrow margin of safety, risk of dependence, and abuse liability remain a health concern. According to the DAWN 2007 report, there were 9,877 emergency department visits in the United States involving nonmedical use of barbiturates, representing approximately 1.2% of total visits for nonmedical use of pharmaceuticals (Fritch et al., 2011; SAMHSA 2010). Notably, 1,663 of the total visits involving barbiturates were categorized as suicide attempts (Fritch et al., 2011). DETECTION OF SEDATIVE HYPNOTICS Several commercial screening tests are now available to provide an initial test for the presence of BZDs. Their applicability will depend on the tissue/specimen being examined (i.e., blood/plasma/serum or urine) and the type of BZD in the specimen (Drummer, 1998). The widespread use of BZDs and qualitative nature

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of these tests renders them unable to differentiate between therapeutic use and abuse, or one-time versus chronic dosing (Moeller et al., 2008). Immunoassays are the most common method for screening biological fluids for BZDs (Shaw, 2001). Manufacturers of diagnostic kits for screening of benzodiazepines currently use a cut-off concentration of either 200 or 300 ng/mL; unfortunately, there is no pharmacokinetic basis for the selection and application of these cut-off values (Shaw, 2001). The cut-off values used today are intended to detect medium to short half-life benzodiazepines, with or without any active metabolites (Shaw, 2001). Many sedative-hypnotics are not detected on standard screening tests for drugs of abuse due to differences in parent drug structure, metabolite structure, and the extent to which the parent drug is metabolized (Barrett et al., 1999). Recent technological advances in the development of ultrasensitive quantitative methods for drugs and metabolites in oral fluid have contributed to the growing interest in the use of oral fluid in drug-testing programs (Fritch et al., 2011). Without observed collections, drug abusers are frequently motivated to “tamper” with their urine specimens by various means, such as substitution with clean specimens or fluids resembling urine and by the addition of various chemicals that are designed to either destroy drugs present or interfere with their measurement. Saliva, on the other hand, can be collected by the examiner without allowing the subject the opportunity to adulterate the sample. DRUG EFFECTS According to Lader (2011), sedation is the most common subjective effect of the BZDs, and in healthy volunteers, increased sedation can be seen after each dose even after a week of treatment (Bond et al., 1983). Immediate effects of BZDs also include anterograde amnesia with disruption of both short- and long-term memory function. The extent of amnesia is systematically related to the dose effects and half-life differences of the different BZDs (Roth, Roehrs, Wittig, & Zorick, 1984). A large, prospective, population-based study done by Billioti de Gage and colleagues (2012) of elderly people who were free of dementia and did not use BZDs until at least the third year of follow-up, indicated that new use of BZDs was associated with an approximately 50% increase in the risk of dementia. Studies of cognitive effects of long-term BZD use have produced conflicting results (Stewart, 2005). Barker and colleagues (2004a) undertook a meta-analysis of studies published between 1980 and 2000 examining cognitive function in patients with chronic BZD use. The authors note several limitations of this meta-analysis, including broad heterogeneity of definitions of long-term use, type and dose of BZD used, co-occurring medical and psychiatric diagnoses, and time-span allowed between the last dose of BZD and cognitive testing. This final potential confounder is of particular importance since it may limit the ability to differentiate between the effects of acute and chronic use. Cognitive functions were divided into 12 categories:  sensory processing, nonverbal memory, speed of processing, attention/concentration, general intelligence, working memory, psychomotor speed, visuospatial, problem solving, verbal memory, motor control/performance, and verbal reasoning. The meta-analysis revealed significant

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decline across each of these domains. Studies that included samples comprised of at least 40% male subjects were noted to have greater effect sizes, leading the authors to theorize that men may be more vulnerable to the long-term cognitive effects of BZD use. A higher effect size was also seen for decline in verbal memory in subjects who had used BZDs for at least eight years, potentially indicating that cognitive effects increase with the duration of use. The results of this meta-analysis by Barker and colleagues raised a question that the same group attempted to answer with a second meta-analysis (2004b): Are the cognitive effects of long-term BZD use reversible with cessation? The authors again noted limitations due to heterogeneity of definitions and measures across multiple studies, but were again able to divide assessment of cognitive function into the same 12 domains. Here, the meta-analysis revealed that all 12 areas of cognitive function improve with the cessation of BZDs, but even after this improvement, measures of cognition do not return to the same levels as seen in non-BZD-using control subjects in every domain, except for sensory processing. The authors note that these results can be applied to the first six months after cessation (median time from cessation to testing across all studies was three months), and that further improvement in function may occur after six months of abstinence. Use of prescription medications in ways other than prescribed can have a variety of adverse health consequences, including overdose, toxic reactions, and serious drug interactions leading to life-threatening conditions, such as respiratory depression, hypertension or hypotension, seizures, cardiovascular collapse, and death (SAMSHA, 2006). Although all BZDs can be abused, agents that have the shortest half-life with the highest potency (i.e., alprazolam [Xanax], triazolam [Halcion]) and greatest lipophilia (i.e., diazepam [Valium]) tend to have the most abuse potential (Moeller et al., 2008). The BZD withdrawal syndrome is a cluster of somatic, psychological, and behavioral symptoms which arise upon abrupt discontinuation or dose reduction of BZD in a dependent individual (Latt et al, 2009). BZD dependence may occur after regular, daily use for four to six weeks, even at therapeutic doses (Marshall et  al., 2009). Pertusson (1994) describes symptomatic patterns resulting from withdrawal from normal-dosage benzodiazepine treatment, with the most common being a short-lived “rebound” anxiety and insomnia, beginning within one to four days of discontinuation and depending on the half-life of the particular drug. The second pattern is the full-blown withdrawal syndrome, usually lasting for 10–14 days; finally, a third pattern may represent the return of anxiety symptoms, which then persist until some form of treatment is instituted (Pertusson et al., 1994). About a third of long-term (beyond six months) users of benzodiazepines experience withdrawal after stopping the drug, including anxiety, insomnia, muscle spasms and tension, and perceptual hypersensitivity, with the potential for seizures and psychosis (Lader et  al., 2011). Safe cessation of the drug requires gradual tapering. Pregabalin has shown recent promise as a possible adjunctive agent to ameliorate withdrawal (Hadley et  al., 2012), as has prolonged-release melatonin (Kunz et al., 2012).

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PRESCRIPTION OPIOID ABUSE As mentioned previously, opioids are the most commonly abused class of prescription drugs. While the details of opioid abuse are discussed elsewhere in this volume (see Chapter 11), the abuse of prescription opioids carries some risks not found with abuse of illicit formulations. Reviews of studies examining the neurocognitive effects of prescribed opioids themselves consistently remark on the significant heterogeneity of studies published, noting broad variations in agents studied, doses used, and (perhaps most significantly) patient population (Mailis-Gagnon et al., 2012; Tannenbaum et al., 2012). Since chronic opioid users are typically prescribed these drugs for chronic pain, the nature and intensity of the pain being experienced can be a significant confounder of any test of cognitive performance. Published studies include patients with both cancer and non-cancer etiologies of pain, inconsistent monitoring and correlation of pain levels with performance, and inconsistent monitoring and reporting of dose amount and timing, rendering it impossible to draw any consistent conclusion about the neurocognitive effects of the long-term use of these agents. Besides the effects of the opioid itself, the primary additional concern with prescription opioid abuse is toxicity from any additional agent included in the formulation. The drugs most commonly included with tablets of prescription opioids are acetaminophen (found in Vicodin, Percocet, Panlor, Trezix, Zerlor, Fioricet with codeine, and Tylenol #3) and aspirin (found in Fiorinal with codeine, Synalgos-DC, and Percodan). Acetominophen is highly toxic in overdose, with significant potential to cause fulminant hepatic failure that is likely to result in need for transplantation, or death if not adequately treated. Effects of aspirin toxicity include nausea, vomiting, tinnitus, and dizziness. Aspirin toxicity may also result in gross metabolic disturbances that can result in death. Various non-traditional preparations of prescription opioids are available that have resulted in ingenious methods of abuse. Fentanyl, for instance, is available as a transdermal patch. Reports of abuse have included injection of liquid extracted from the patch (Reeves & Ginifer, 2002), patches chewed like gum (Carson, Knight, Dudley, & Garg, 2010), patches steeped in hot water with the resulting solution ingested like tea (Barrueto, Howland, Hoffman, & Nelson, 2004), and patches swallowed whole (Thomas, Winecker, & Pestaner, 2008). Each of these routes of administration carries significant risk of morbidity and mortality. RESPONSE TO THE PROBLEM The growing significance of prescription-drug abuse has led to a number of efforts to limit the ease with which these substances can be obtained and abused. The pharmaceutical industry (perhaps sensing that prescriber's concerns over abusability might result in a market niche for drugs that can be marketed as “non-abusable”) have developed a number of drug formulations and dispensing mechanisms that limit abuse potential. This approach is typified by lisdexamfetamine (marketed as Vyvanse) for the treatment of ADHD. Lisdexamfetamine consists of dextroamphetamine linked to the essential amino acid L-lysine. Lisdexamfetamine is a prodrug in that it has no psychopharmacological activity until it is subjected to hydrolysis by red

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blood cells, which cleaves the lysine from the compound, producing the psychoactive dextroamphetamine. Administration by parenteral routes does not increase the rate by which the prodrug is metabolized to the active agent, thus limiting the reward from either intravenous injection or increased dosage (Blick & Keating, 2007). From a public health perspective, governmental agencies have also taken steps to address prescription drug abuse. Most states now have some form of prescription drug monitoring program via which prescribers and pharmacists can electronically access records of prescription drugs obtained by patients. The extent to which these drugs are recorded (e.g., Schedule II only, Schedule II and III, or all schedules) vary from state to state (Gugelmann & Perrone, 2011). Limitations of these programs include the lack of a national database (i.e., a prescriber in one state cannot access the prescription records from a state in which they are not licensed) and the ability of determined abusers to defeat the system by using pseudonyms or other means of disguising their identity. Publicity both from public health agencies and the lay press has also brought attention to this issue, and organized education programs have been initiated to address this burgeoning epidemic (Yu, 2012). References Ashton, H. (2005). The diagnosis and management of benzodiazepine dependence. Current Opinion in Psychiatry, 18, 249–255. Babcock, Q., & Byrne, T. (2000). Student perceptions of methylphenidate abuse at a public liberal arts college. Journal of American College Health, 49, 143–145. Barker, M. J., Greenwood, K. M., Jackson, M., & Crowe, S. F. (2004a). Cognitive effects of long-term benzodiazepine use:  A  meta-analysis. Central Nervous System Drugs, 18, 37–48. Barker, M. J., Greenwood, K. M., Jackson, M., & Crowe, S. F. (2004b). Persistence of cognitive effects after withdrawal from long-term benzodiazepine use: A meta-analysis. Archives of Clinical Neuropsychology, 19, 437–454. Barrett, A. M., Walshe, K., Kavanagh, P. V., McNamara, S. M., Moran, C., Burdett, J., & Shattock, A. G. (1999). A comparison of five commercial immunoassays for the detection of flunitrazepam and other benzodiazepines in urine. Addiction Biology, 4, 81–87. Barrueto, F. Jr., Howland, M. A., Hoffman, R. S., & Nelson, L. S. (2004). The fentanyl teabag. Veterinary & Human Toxicology, 46, 30–31. Berman, S. M., Kuczenski, R., McCracken, J. T., & London, E. D. (2009). Potential adverse effects of amphetamine on brain and behavior: A review. Molecular Psychiatry, 14, 123–142. Billioti de Gage, S., Bégaud, B., Bazin, F., Verdoux, H., Dartigues, J. F., Pérès, K., . . . & Pariente, A. (2012). Benzodiazepine use and risk of dementia: Prospective population based study. British Medical Journal, 345, e6231. Blick, S. K., & Keating, G. M. (2007). Lisdexamfetamine. Paediatric Drugs, 9,129–135. Bond, A. J., Lader, M. H., & Shotriya, R. (1983). Comparative effects of a repeated dose regime of diazepam and buspirone on subjective ratings, psychological tests and the EEG. European Journal of Clinical Pharmacology, 24, 463–467. Caplan, J. P., Epstein, L. A., Quinn, D. K., Stevens, J. R., & Stern, T. A. (2007). Neuropsychiatric effects of prescription drug abuse. Neuropsychology Review, 17, 363–380. Carson, H. J., Knight, L. D., Dudley, M. H., & Garg, U. (2010). A fatality involving an unusual route of fentanyl delivery: Chewing and aspirating the transdermal patch. Legal Medicine (Tokyo, Japan), 12, 157–159.

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Central Nervous System Risk Factors for and Consequences of Adolescent Substance Use on Brain Structure and Function S U S A N F. TA P E RT, L OT T E B E R K , N OR M A CAST RO , A N D S U N I TA   BAVA

Adolescent substance use is a widespread problem, highlighted by recent data from Monitoring the Future survey (Johnston et al., 2012) showing increases in use of some substances (e.g., marijuana), yet a decline in perceived risk of use by youth. Substance use in adolescence is of particular concern because, during this time of brain development, emerging sophisticated cognitive functions influence risk-taking and vulnerability to psychopathology, and substance use may present neurotoxic effects on neuromaturation. Those who have their first alcoholic beverage prior to age 14 are four times more likely to develop alcohol dependence in their lifetime, compared with those who have their first drink after age 20 (Grant & Dawson, 1997). Similarly, marijuana use before age 15 is linked to a seven-fold increased likelihood of a future substance use disorder (SAMSHA, 2009). Early initiation of substance use has also been associated with negative events such as motor vehicle accidents, unintentional injuries, and physical fights (Hingson, Edwards, Heeren, & Rosenbloom, 2009). In this chapter, we will review neuroimaging studies on adolescent brain development. Next, we will discuss how these changes relate to the increased risk of alcohol and other drug use. In addition, the neural consequences of adolescent alcohol and substance use will be discussed. Last, we discuss the potential reversibility of the negative consequences of alcohol and substance use. BRAIN DEVELOPMENT IN ADOLESCENCE

Gray and White Matter: Brain Structure and Functional Changes Adolescence is a critical neurodevelopmental period when the brain undergoes considerable maturation, including changes in cortical volume and refinement

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in cortical connections (Huttenlocher & Dabholkar, 1997; Yakovlev & Lecours, 1967). Maturing neural circuitry in areas that include the prefrontal cortex (PFC) and the limbic system along with white matter and fiber projections have been associated with advancements in cognition and behavior, but also make the adolescent brain more vulnerable to potential neurotoxic effects from substance use (Casey & Jones, 2010; Giedd, 2004; Sowell, Trauner, Gamst, & Jernigan, 2002). Although overall brain volume remains largely unchanged after puberty, ongoing synaptic refinement and myelination results in reduced gray matter and increased white matter volume by late adolescence (Casey, Jones, & Hare, 2008; Giedd, 2004; Sowell et al., 2003; Yakovlev & Lecours, 1967). Cortical gray matter follows an inverted U-shaped developmental course, with cortical volume peaking around ages 12–14 (Giedd, 1999; Gogtay et  al., 2004; Sowell et al., 2003). Cortical gray matter loss during late childhood and adolescence is thought to be related to the pruning of synapses, and begins primarily in dorsal parietal cortices, then proceeds to sensorimotor cortices, progresses anteriorly to the frontal cortex, then posteriorly to parietal, occipital, and finally temporal cortices (Gogtay et al., 2004). Longitudinal studies of gray matter reduction during adolescence reveal declines in the medial parietal cortex, posterior temporal and middle frontal gyri, and the cerebellum (Giorgio et al., 2010). The mechanisms underlying the decline in cortical volume and thickness are suggested to involve pruning of the superfluous synaptic connections, reduction in the glial cells, and decrease in neuropil and intra-cortical myelination (Huttenlocher & Dabholkar, 1997; Paus, Keshavan, & Giedd, 2008; Tamnes et al., 2009). Variation in cortical development may coincide with difference in gray matter maturation in various brain regions. Cubic growth trajectories (i.e., gray matter cortical thickness increased during childhood, decreased during adolescence, and became stable during adulthood) are present in the medial and lateral PFC, precentral motor, somatosensory, lateral temporal, and lateral occipital regions. Quadratic growth trajectories, characterized by an unstable increases and decreases in cortical thickness, are present in the insula and anterior cingulate areas, whereas the posterior orbitofrontal and frontal operculum, piriform cortex, medial temporal cortex, subgenual cingulate areas, and medial occipitotemporal cortex displays linear growth trajectories (Shaw et al., 2008). These cortical growth curves exemplify that adolescence is a critical neurodevelopmental period and can be vulnerable to neurotoxins that could potentially affect higher-order cognitive functions, such as executive functions, visuospatial performance, speeded processing, and declarative memory (Bava, Jacobus, Mahmood, Yang, & Tapert, 2010; Casey & Jones, 2010). In contrast, white matter development is generally characterized by progressive linear volume increases in fronto-parietal, corticospinal, and other fiber pathways supporting refined motor functioning, higher-order cognition, and cognitive control (Bava et  al., 2010). Moreover, increased white matter volume of the corpus callosum (i.e., the white matter bundle that connects the left and right hemispheres of the brain) suggests greater interhemispheric communication (Barnea-Goraly et al., 2005; Giedd, 1999). Greater white matter integrity has been

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associated with more efficient connectivity between brain regions and is related to better behavioral performance (Tuch et  al., 2005). Diffusion tensor imaging (DTI), a magnetic resonance imaging (MRI) technique, measures the diffusion of water molecules to assess white matter quality (Basser, 1995). Two common parameters used in DTI research are fractional anisotropy (FA), a measure of the directionality of water movement within axons, and mean diffusivity (MD), an index of the overall displacement of water molecules. In general, higher FA and lower MD signify a more organized, healthy, and developed white matter, which in turn implies quicker neural processing (Roberts & Schwartz, 2007). Increases in FA and decreases in MD are typically evident in white matter during adolescence (Schmithorst, Wilke, Dardzinski, & Holland, 2002). Brain areas with the most prominent FA changes during adolescence are the superior longitudinal fasciculus, superior corona radiata, thalamic radiations, and the posterior limb of the internal capsule (Bava et al., 2010).

Neurotransmitters During adolescence, neurochemistry influences brain development. Specific neurotransmitters, including dopamine and gamma-aminobutyric acid (GABA), undergo profound changes during this critical time. Dopamine (DA) receptors have been found to change considerably in brain regions associated with reward circuitries (i.e., PFC, striatum, and nucleus accumbens; Spear, 2009). In particular, the density of dopaminergic connections in the PFC increases through development (Tunbridge et al., 2007). Dopaminergic neurons here have been found to project to limbic regions that correspond to cognitive, emotional, and behavioral changes (Casey et al., 2008). Dopaminergic activity has been found to decrease substantially in the nucleus accumbens during adolescence, potentially increasing adolescents’ propensity to engage in risky and novel behaviors to compensate for this DA deficit (Casey & Jones, 2010; Spear, 2002). Two DA receptor subtypes (D1 and D2) have notable connectivity expression during adolescence. The D1 receptor binding potential decreases non-linearly in cortical areas including the dorsolateral PFC, frontal anterior cingulate, and occipital cortex from adolescence well into young adulthood (Jucaite, Forssberg, Karlsson, Halldin, & Farde, 2010). The D2 receptor, specifically the TH proteins and mRNA levels, peak in the PFC during infancy but reach complete maturation by adolescence (Weickert et al., 2007). Overall, these dopaminergic neuronal alterations during adolescence are crucial during brain maturation. The inhibitory GABAergic system also shows development during adolescence. Animal models illustrate that fibers from the basolateral amygdala form connections with GABAergic receptors in the PFC throughout periadolescence (Cunningham, Bhattacharyya, & Benes, 2002, 2008). GABAergic inputs have also been found in the pyramidal cells, which undergo considerable maturation during the perinatal and adolescent period (Akil & Lewis, 1992; Cruz, Eggan, & Lewis, 2003). Other brain regions (i.e., PFC) that correspond with executive functions and working memory also undergo maturation (Rao, Williams,

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& Goldman-Rakic, 2000; Uhlhaas et  al., 2009). Human studies illustrate that GABAergic interneuron input in the PFC decreases from adolescence to adulthood (Lewis, 1997; Spear, 2000). Aberrations in these neuronal changes during adolescence have been associated with impairments in maturation and cognitive abilities (Paus et al., 2008) in conjunction with social interactions and impulsive and risky behaviors like substance use (Chambers & Potenza, 2003).

Sexual Dimorphism in Brain Structures Structure and functional sex-based differences are present during brain development (Caviness, Kennedy, Richelme, Rademacher, & Filipek, 1996; De Bellis, Keshavan, Beers, Hall, Frustaci, Masalehdan, 2001; Giedd, Castellanos, Rajapakse, Vaituzis, & Rapoport, 1997; Lenroot et al., 2007). Overall, adolescent males have larger total brain volume than females, specifically in the amygdala and globus pallidus (Caviness et  al., 1996; Reiss, Abrams, Singer, Ross, & Denckla, 1996). Conversely, adolescent females have notably larger caudate nuclei, hippocampal, and cingulate gyrus volumes (Caviness et al., 1996; Giedd et al., 1997; Wilke, Krageloh-Mann, & Holland, 2007). Age-related dimorphic differences have also been found in cortical and subcortical gray and white matter volumes. Adolescent males show more prominent gray matter reduction and white matter volume increase than females (Blanton et al., 2004; De Bellis et al., 2001). However, female cortical and subcortical gray matter volumes typically mature one to two years earlier than males (Caviness et al., 1996; Lenroot & Giedd, 2006). HOW DO THESE CHANGES RELATE TO INCREASED RISK OF ALCOHOL AND DRUG USE?

Cognitive and Social Development Neuromaturation plays an important role in cognitive and behavioral changes. In particular, the PFC is crucial in the development of executive functions; i.e., working memory, planning, problem solving, and inhibitory control (Anderson, Anderson, Northam, Jacobs, & Catroppa, 2001). The relation between adolescent brain development and higher order functioning has been demonstrated in studies examining white matter integrity using FA. IQ was found to be positively correlated with FA, indicating greater fiber organization in white matter integrity in frontal and occipito-parietal areas (Schmithorst, Wilke, Dardzinski, & Holland, 2005). Adolescents’ reading skill development is associated with maturation of white matter in the left temporal and parietal regions, the left internal capsule, and corona radiata (Beaulieu et al., 2005; Nagy, Westerberg, & Klingberg, 2004; Qiu, Tan, Zhou, & Khong, 2008). Improved phonological processing and receptive vocabulary is associated with increased lateralization of the arcuate fasciculus in the language-dominant hemisphere (Lebel & Beaulieu, 2009). Better visuospatial construction and psychomotor performance are related to greater white matter integrity in the corpus callosum (Fryer et al., 2008). Auditory-verbal memory

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and visual-perceptual memory are associated with white matter integrity in the left uncinate fasciculus and the temporo-parietal regions, respectively (Mabbott, Rovet, Noseworthy, Smith, & Rockel, 2009). Although these findings indicate that neuropsychological functioning is related to the development of white matter circuitry, cross-sectional approaches offer limited conclusions. One longitudinal study investigated whether change in white matter integrity after a 16-month follow-up was associated with cognitive functioning in adolescents. This study showed that better complex attention, working memory, and verbal fluency were related to an increase in white matter integrity (Bava et  al., 2010). Therefore, the results suggest that changes in white matter integrity during adolescence are associated with a change in neuropsychological functioning.

Socio-Emotional Processing During adolescence, social relations become particularly important. Adolescents become increasingly self-aware and more conscious of the perspectives of peers and societal pressures that in turn influence their social behaviors (Berzonsky & Adams, 2003; Choudhury, Blakemore, & Charman, 2006). Brain regions associated with socio-emotional processing continue to develop throughout adolescence. Functional MRI (fMRI) studies demonstrate that blood oxygen level–dependent (BOLD) response contrast to facial affect processing was more prominent during adolescence than adulthood in the amygdala, orbitofrontal cortex, and anterior cingulate cortex (Monk et al., 2003; Yang, Menon, Reid, Gotlib, & Reiss, 2003). Adolescents also show elevated activity in bottom-up emotion processing (Hare et al., 2008), suggesting they are more susceptible to emotional influences than adults. Overall, these data suggest that poor decisions by adolescents are often made in states of emotional reactivity. However, research has shown that adolescents may perform better than adults (for example, when promised a financial reward), and some adolescents are more likely to engage in risky behaviors (Casey & Jones, 2010).

Reward Sensitivity As the brain develops, there are neural underpinnings that enhance reward-related circuitry that play a role in risk-taking behaviors and poor decision-making during adolescence. Two central theories of reward processing have been established. The first central theory proposes that hypoactivation of the ventral striatum is associated with reward-seeking behaviors during adolescence. Conversely, the second proposes that hyperactivation in the ventral striatum enhances reward-seeking behaviors. Recent fMRI data support the second theory, showing an overactive reward system during adolescence (Galvan, 2010). Studies also illustrate increased activation in the ventral striatum in adolescence compared to that in children and adults during reward anticipation and reward receipt (Geier, Terwilliger,

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Teslovich, Velanova, & Luna, 2010; Galvan et  al., 2006; Van Leijenhorst et  al., 2010). Furthermore, when compared to adults, adolescents demonstrate attenuated responses in the ventral striatum during incentive cues, followed by a heightened response in the ventral striatum during response preparation (reward anticipation) on reward trials (Geier et al., 2010). Also, studies have demonstrated an increase of dopamine release in the ventral striatum during reward-related tasks (Aarts et al., 2010; Koepp et al., 1998; Urban et al., 2012). This increase of dopamine may be a key reinforcer of reward-seeking behaviors during adolescence. Pubertal maturation must also be taken into consideration when addressing reward-seeking behaviors, as sensation seeking increases during this time (Galvan, Hare, Voss, Glover, & Casey, 2007). Adolescents with more advanced pubertal maturation exhibited less striatal and more medial PFC reactivity during reward outcome than similar-aged adolescents with less advanced maturation (Forbes et al., 2010). Testosterone was also positively correlated with striatal reactivity in boys during reward anticipation and negatively correlated with striatal reactivity in girls and boys during reward outcome (Forbes et al., 2010). These data suggest that reward-related brain functioning changes with puberty and is associated with adolescents’ responding to reward-seeking behaviors. Areas such as the medial PFC have been implicated as playing an important role in self-processing and social cognition and may be a contributing factor during adolescents’ responses to rewards in social context.

Risk-Taking Behaviors Adolescents have a propensity for engaging in risk-taking behaviors, which are thought to be a function of immature development in limbic areas. Behavioral and fMRI studies illustrate that poorer fronto-limbic white matter integrity has been linked to increased risk-taking behaviors (Jacobus et al., 2012). Furthermore, increased activation in subcortical structures has been found during risky behaviors, along with decreased activation in the PFC (Hare et  al., 2008). More specifically, studies have shown, for example, activation in the ventral striatum and medial PFC in high-risk gamblers and activation in the dorsolateral PFC in low-risk gamblers. Interestingly, the activation in the medial PFC was positively correlated with risk-taking (Van Leijenhorst et al., 2010). Similarly, adolescents with family history of alcohol or substance use disorders were found to exhibit less brain response in the right dorsolateral PFC and right cerebellar regions during risk-taking tasks compared to that of controls (Cservenka & Nagel, 2012). Related to risk-taking behavior is the ability to inhibit inappropriate responses. Response inhibition is associated with substance and alcohol dependence, particularly in adolescents, as this executive function is not yet fully developed (Casey & Jones, 2010). Specifically, poor response inhibition has been linked to an increased risk of problem drinking and substance use in adolescents (Nigg et  al., 2006). Other studies with adolescents have supported these findings by investigating the

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predictive power of neural activation during a response inhibition task. Norman and colleagues (2011) found that less brain activation in the frontal, temporal, parietal, and basal ganglia areas during a response inhibition task predicted future alcohol and other substance use. Another study found that abnormal activation patterns during a response inhibition task predicted substance use 18  months later (Mahmood, Goldenberg, Thayer, Migliorini, Simmons, & Tapert, 2013). Overall, adolescents show a less mature connection between emotion processing and overall control (Hare et al., 2008), putting them at increased risk for engaging in potentially dangerous behaviors.

Family History of Alcoholism Adolescents with a family history of alcoholism (family history positive, FHP) are at risk for developing an alcohol use disorder (AUD), being four times more likely to exhibit alcohol use than their counterparts (family history negative, FHN; Lieb et al., 2002) and illustrating neural deficits prior to the onset of personal drinking. Wetherill and colleagues (2012) found that FHP substance-naïve youth demonstrated reduced functional connectivity in the posterior parietal cortex and the dorsolateral PFC during a visual working memory task, compared to FHN substance-naïve youth. FHP nondrinkers show spatial deficits and memory problems (Ozkaragoz & Noble, 1995), increased BOLD activation in frontal and subcortical regions (Spadoni, Norman, Schweinsburg, & Tapert, 2008), and attenuated frontal response during inhibitory processing than FHN youths (Schweinsburg et  al., 2004). These studies demonstrate that preexisting differences in neural functioning may be at least in part rendered by a genetic component (Grant, 1998; Warner & White, 2003).

Externalizing Factors “Externalizing traits” such as conduct disorder, oppositional defiant disorder, and attention-deficit/hyperactivity disorder (ADHD), along with juvenile delinquency, aggression, impulsivity, high novelty-seeking (Sher, Bartholow, & Wood, 2000; Zuckerman & Kuhlman, 2000), and sensation-seeking (Martin et  al., 2004), are associated with increased risk for SUD (Bukstein, Brent, & Kaminer, 1989). Among these factors, those associated with the highest rates of substance use disorder are ADHD and conduct disorder (Wilens, Faraone, Biederman, & Gunawardene, 2003). For example, teens with ADHD are six times more likely to develop an AUD (Kollins, 2008), and they report significantly more alcohol-related problems and greater frequency of drinking (Molina & Pelham, 2003). Conduct disorder is the most common psychiatric diagnosis associated with SUD in adolescents (Couwenbergh et al., 2006), as 50% of teens with an SUD also meet criteria for conduct disorder. Those with conduct disorder are more likely to initiate use of substances and develop substance-related problems at a younger age than those without conduct disorder. This early onset of substance

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use (i.e., at or before age 14) is associated with an increased likelihood of developing dependence (Grant, 1998; Grant, Stinson, & Harford, 2001). NEURAL CONSEQUENCES OF ADOLESCENT ALCOHOL AND SUBSTANCE USE

As adolescence is a time of brain development and maturation, alcohol and drug use during this period could interrupt typical brain development, leading to cognitive impairment and increased risk of further alcohol-use and dependence (Brown, Tapert, Granholm, & Delis, 2000).

Alcohol Less is known about the effects of alcohol use on the adolescent brain than on the adult brain (Monti et al., 2005), for which a large body of research shows adverse effects of alcohol on both brain structure and function (see Fama & Sullivan, Chapter 6, this volume). However, because neuromaturation is an ongoing process during adolescence, these results may not generalize to adolescent users. Studies examining neuropsychological functions in heavy-drinking adolescents indicate that alcohol may influence the developing brain. Early research illustrated that adolescent heavy drinkers showed impaired neurocognitive functioning in language skills (Moss, Kirisci, Gordon, & Tarter, 1994), attention and information processing (Tarter, Mezzich, Hsieh, & Parks, 1995; Tapert, Granholm, Leedy, & Brown, 2002), future planning and abstract reasoning (Giancola, Mezzich, & Tarter, 1998), verbal and nonverbal information retrieval, and visuospatial functioning (Brown et  al., 2000). Recent development in neuroimaging techniques has allowed the investigation of the detrimental impact of substance use on brain development by examining white matter changes, cortical volumes, and BOLD response. Studies including adolescents with AUD (i.e., maladaptive patterns of use, leading to clinically significant impairments), or binge drinkers (4+ drinks in one occasion for females; 5+ drinks in one occasion for males) have consistently reported alterations in white matter integrity. DTI studies illustrate differences in the corpus callosum in adolescents with AUD and those without AUD (De Bellis et al., 2008). Similar findings were reported for binge drinking adolescents with higher FA (indicating better white matter integrity) in superior corona radiata, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, superior longitudinal fasciculus (Jacobus et al., 2009), corpus callosum, superior longitudinal fasciculus, and corona radiata (McQueeny et  al., 2009). Longitudinal studies have illustrated changes in white matter anisotropy and diffusivity among adolescents with heavy alcohol use patterns. Bava and colleagues (2013) found that heavy drinking adolescents had declines in white matter integrity in the right and left superior longitudinal fasciculus and posterior corona radiata, compared to their baseline values 18 months earlier. These findings suggest that heavy drinking during adolescence may have harmful effects on white matter development. Binge drinking may affect the cerebellum, as a higher peak number of drinks during a binge was linked to smaller bilateral cerebellar gray

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matter and smaller left-hemisphere cerebellar white matter. These smaller cerebellar volumes were seen after controlling for potentially confounding variables such as recent tobacco, marijuana, and other drug use; gender; family history of substance use disorder; and intracranial volume (Lisdahl, Thayer, Squeglia, McQueeny, & Tapert, 2013). Hippocampal volume is smaller in adolescents with AUD (De Bellis et al., 2000; Nagel, Schweinsburg, Phan, & Tapert, 2005). De Bellis and colleagues (2000) found that both left and right reduced hippocampal volume correlated positively with age of onset, and negatively with the duration of AUD. However, Nagel and colleagues (2005) did not find that hippocampal volume was related to alcohol-use characteristics. Therefore, it is possible that the adolescents with AUD have a preexisting smaller hippocampus. Furthermore, smaller PFC volumes were found to be associated with adolescent-onset AUD (De Bellis et al., 2005), even in adolescents without comorbid attention and mood disorders (Medina et al., 2008). FMRI studies of heavy-drinking adolescents have shed light both on deficits in neuropsychological functioning and brain activity. Altered brain response was found in adolescents with AUD on a spatial working memory task (Tapert et al., 2004), and adolescents binge drinkers showed altered brain responses during a verbal encoding task (Schweinsburg, McQueeny, Nagel, Eyler, & Tapert, 2010), spatial working memory task (Squeglia, Schweinsburg, Pulido, & Tapert, 2011), and decision-making task (Xiao et  al., 2012). Attenuated frontal and parietal response was found among AUD adolescent females during a spatial working memory task (Tapert et al., 2001). Although longitudinal research is necessary, alcohol-related prefrontal and hippocampal alterations might explain the cognitive and memory disadvantages, especially since studies showed that cognitive disadvantages might persist into adulthood (Brown et al., 2008; Hanson, Medina, Padula, Tapert, & Brown, 2011). Longitudinal studies with baseline measurements before the onset of drinking are necessary to determine whether these differences were preexisting. Recently, an fMRI study compared the brain response to working memory in adolescents before the onset of drinking and at a three-year follow-up (Squeglia et al., 2012a). Adolescents who had transitioned into heavy drinkers were compared with those who continued to be nondrinkers. This study showed that adolescents who transitioned into heavy drinkers showed different brain activation before the onset of drinking. That is, different neural response patterns might be risk factors for future alcohol use. Squeglia and colleagues (2009) examined longitudinal data to compare neuropsychological functioning in adolescents binge drinkers without AUD. Moderateand heavy-drinking adolescents showed overall reduction in task performance. Gender specific patterns where observed, with females displaying greater reduction in visuospatial task performance and males in poorer sustained attention (Squeglia et  al., 2009). Structural gender differences have also been noted in adolescents with AUD, with females and males having smaller and larger PFCs, respectively, compared to controls (Medina et al., 2008). Recently, Squeglia and colleagues (2012b) investigated structural differences and their relationship

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to neuropsychological performance. Gender differences in the gray matter of binge-drinking adolescents were found. Namely, females showed thicker cortices in left frontal regions, which was associated with poorer performance on visuospatial, inhibition, and attention tasks, whereas males showed thinner cortices in these areas, which was associated with worse attention. Young female binge drinkers may be more vulnerable to the impairing effects of alcohol, as is evidenced by limited frontal response to a spatial working memory task and reduced gray matter volume in AUD females compared to males (Caldwell et  al., 2005; Schweinsburg et al., 2003; Squeglia et al., 2012c).

Marijuana Marijuana use in adolescence is associated with altered brain structure, function, and neuropsychological performance. Marijuana-using adolescents perform worse on neuropsychological tests of problem solving than non-using adolescents (Lane, Cherek, Tcheremissine, Steinberg, & Sharon, 2007), with deficits also observed on measures of attention, nonverbal memory, and learning problems (Harvey, Sellman, Porter, & Frampton, 2007). Importantly, after 28 days of abstinence, diminished performances were still shown for measures of cognitive functions reflecting psychomotor speed, complex attention, verbal memory, planning, and sequencing ability (Medina et al., 2007). This may point to a greater sensitivity of the adolescent brain to repeated marijuana exposure, as studies in adults have generally found few persisting marijuana use-related deficits. Studies examining white matter show alterations in white matter integrity in adolescent marijuana users compared to non-users, particularly in fronto-parietal circuitry and pathways connecting the frontal and temporal lobes (Bava et  al., 2009). Moreover, age of onset was related to poorer frontal white matter integrity in chronic adult marijuana users, which was associated with increased impulsivity (Gruber, Silveri, Dahlgren, & Yurgelun-Todd, 2011). Adolescent marijuana use has also been associated with altered cortical volumes. Marijuana-using adolescents had larger cerebellar volumes than non-users (Medina, Nagel, & Tapert, 2010), and female marijuana users have larger PFC volumes than same-gender non-users (Medina et al., 2009), suggesting the possibility of attenuated synaptic pruning. However, another study showed decreased right medial orbital PFC in marijuana-abusing adolescents (Churchwell, Lopez-Larson, & Yurgelun-Todd, 2010). Moreover, the medial and orbital PFC (moPFC) volume was positively correlated with age of first use. That is, early initiation may lead to reduced moPFC volume, or alterations in this region may be related to the initiation of marijuana use. One study examined whether structural brain alterations were present before onset of marijuana use (Cheetham et al., 2012). Right orbitofrontal cortex volumes at age 12 predicted initiation of marijuana use at age 16 when controlling for other substance use. Volumes of other brain regions (e.g., amygdala, hippocampus, and anterior cingulate cortex) did not predict marijuana use at the four-year follow-up time point (Cheetham et al., 2012). These results

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suggest that there may be regional volume vulnerabilities that increase the risk of marijuana use. Besides alterations in white matter integrity and volume, research has also showed differences in brain response patterns in adolescent marijuana users. Namely, heavy marijuana-using adolescents show alteration in brain activity compared to non-users during tasks of spatial working memory (Schweinsburg et al., 2008; Schweinsburg et al., 2010), inhibitory processing (Tapert et al., 2007), and verbal memory (Jacobsen, Pugh, Constable, Westerveld, & Mencl, 2007). Most studies suggest that adolescent marijuana users show a brain activation pattern that is less efficient than non-users’ during tasks of verbal learning and cognitive control (Schweinsburg et al., 2008; Schweinsburg et al., 2010; Tapert et al., 2007). Moreover, adolescent marijuana users show an altered cerebral blood flow compared to controls’ (Jacobus et al., 2012), but this difference disappeared after four weeks of monitored abstinence.

Interaction Between Alcohol and Marijuana To examine the neuropsychological effects of alcohol and marijuana use among adolescents, most studies describe the influence of alcohol or marijuana separately. However, alcohol and marijuana are often used together: 58% of adolescents who drink also use marijuana (Medina et al., 2007). Similar to the results from studies focused on alcohol and marijuana use separately, concomitant users also show changes in brain structure and function, as well as a diminished performance in neuropsychological functioning. Research on concurrent alcohol and marijuana use in adolescents is important not only because of the high rates of comorbidity, but also because of the pharmacological interplay between alcohol and marijuana on neurotoxicity. Animal studies have showed THC administration alone did not result in neurodegeneration; however, the same amount of THC combined with a small dose of ethanol induced neuronal cell death in the developing brain (Hansen et al., 2008). This effect is similar to that observed at high doses of ethanol alone. In contrast, research with human adolescents has suggested neuroprotective properties of marijuana. For example, a comparison of hippocampal volumes between adolescent drinkers, drinkers who also use marijuana, and controls (Medina et al., 2007) showed that drinkers had smaller hippocampal volumes and abnormal asymmetry compared to controls, but this was not the case for those reporting both alcohol and marijuana use. Another study suggesting neuroprotective properties of marijuana showed more coherent white matter tracts in binge-drinking adolescents who also use marijuana compared to those who engaged in binge drinking alone (Jacobus et al., 2009). The protective effect of marijuana was also shown in a functional study where alcohol hangover symptoms in non-marijuana users predicted worse verbal learning and memory scores, whereas this was not the case for adolescents who used both alcohol and marijuana (Mahmood, Jacobus, Bava, Scarlett, & Tapert, 2010).

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Conversely, other studies showed a diminished cognitive performance in adolescents using both marijuana and alcohol. That is, alcohol and marijuana use are associated with alterations in prefrontal, cerebellar, and hippocampal volumes; reduced white matter microstructural integrity; and atypical brain-activation patterns (Bava et al., 2009; Medina et al., 2010). Adolescents who use both alcohol and marijuana show a diminished performance on tasks of attention, information processing, spatial skills, learning and memory, planning, and problem solving, even after 28 days of sustained abstinence (Medina et al., 2007; Tapert & Brown, 1999; Tapert et al., 2002).

Stimulants Limited studies have been conducted on the effects of adolescent stimulant use (e.g., illicit amphetamine, methamphetamine, cocaine, and misused prescription stimulants) on brain functioning. The adult literature has shown that cocaine (Jacobsen, Giedd, Gottschalk, Kosten, & Krystal, 2001) and methamphetamine (Jernigan et al., 2005) use are linked to anatomical and neurochemical changes, along with dopaminergic and serotonergic dysfunction (Sekine et  al., 2001; Volkow et  al., 2001). Overall, illicit stimulant use has been associated with deficits on working memory, inhibition, learning, and set-shifting (Goldstein, Volkow, Wang, Fowler, & Rajaram, 2001; McKetin & Mattick, 1998; Nordahl, Salo, & Leamon, 2003). Similarly, during adolescence, an eight-year follow-up longitudinal study illustrated that more frequent stimulant use (primarily methamphetamine) was associated with deficits in attention, psychomotor processing, and working memory in substance-dependent adolescents, compared to their counterparts (Tapert et al., 2002). Less is known regarding misuse of prescription stimulants, but little evidence suggests persistent adverse sequelae.

Methylenedioxymethamphetamine (MDMA) Methylenedioxymethamphetamine (MDMA), with its stimulating, hallucinogenic, and socioempathic acute effects, has been documented to cause long-lasting serotonergic neurotoxicity in both human and animal studies (Parrott et  al., 2002; Taffe et al., 2002). MDMA use has been associated with deficits in spatial learning (Skelton et al., 2009), verbal learning, memory, and sustained attention (McCardle, Luebbers, Carter, Croft, & Stough, 2004); spatial working memory (Fox, Parrott, & Turner, 2001); and verbal fluency, impulsivity, and mental processing speed (Halpern et al., 2004). In young adults, greater MDMA use has been linked to certain changes in brain structures and neurochemical changes; i.e., neuronal abnormalities in the frontal cortex (Reneman, Majoie, Flick, & den Heeten, 2002). Animal studies have demonstrated that greater MDMA use is associated with neurodegenerative reactions in multiple brain structures during adolescent brain development, including the cortex, thalamus, striatum, hypothalamus, and

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hippocampus (Dzietko et  al., 2010). Collectively, these data suggest deficits in neuromaturation and cognitive functioning. Since prevalence rates of MDMA use are much lower in adolescent populations, few studies have investigated the influence of MDMA on adolescent brain development. Jacobsen and colleagues (2004) have found that MDMA use during adolescence has been associated with prolonged reaction time in selective, simple, and divided attention tasks; along with cognitive impairments and dysfunction of inhibitory circuits in the left hippocampus (Jacobsen, Mencl, Pugh, Skudlarski, & Krystal, 2004). Animal studies have also found alterations in hippocampal neurogenesis associated with MDMA use in adolescent rats, suggesting that hippocampal damage is related to the observed cognitive deficits in human adolescents (Catlow et al., 2010).

Opioids In the past decade, opioids prescribed for pain relief have been increasingly misused (i.e., used more than the prescribed amount, or used without a healthcare provider's prescription), including among youth. The use of heroin is quite rare, with an annual prevalence of 0.6% in twelfth-graders nationwide (Johnston et al., 2012). However, misuse of opium-derived and synthetic analgesics, typically prescribed for pain management, has increased in recent years. OxyContin and Vicodin are the most widely misused, with 4.3% and 7.5% of twelfth-graders reporting past-year use, respectively (Johnston et  al., 2012). The potential neurobiological implications of opioid abuse have not been studied in teenagers. Animal models have suggested that adolescents may be more susceptible to the dopamine-altering effects of opioid drugs, but studies in humans are needed. Neurocognitive Recovery As discussed, alcohol consumption and substance use among adolescents is associated with neural consequences. Research indicates that they are associated with deficits in memory, attention, and executive functions. These deficits are associated with alterations in prefrontal, hippocampal, and cerebellar structure and function, as well as poor white matter integrity (Bava et al., 2010). These findings support the negative influence of alcohol and substance use on the healthy development of the adolescent brain. However, an important question remains:  Are these negative effects reversible? Studies measuring cognitive functioning after a short period of abstinence have shown deleterious neurocognitive effects of adolescent heavy marijuana use (Brown et  al., 2000; Medina et  al., 2007; Tapert & Brown, 1999, Tapert et al., 2002). Research examining neurocognition in adolescent marijuana users after one month of abstinence indicates that impairments persist beyond the effects of recent use (Lane et al., 2007; Medina et al., 2007; Hanson et al., 2010; Schweinsburg et  al., 2010). Although some studies control for functioning at baseline, one test point after a period of abstinence does not inform us whether

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the marijuana users might improve after a longer time of abstinence. One study (Schweinsburg et al., 2010) has addressed whether the deficits seen in abstinent users represent persistent changes. In this cross-sectional investigation, the fMRI response during a spatial working memory task was compared between three groups of adolescents (ages 15–17): a) recent marijuana users, b) abstinent users, and c) non-users. The recent users (two to seven days’ abstinent) differed from the abstinent users (27–60 days’ abstinent) by showing more fMRI response in areas related to working memory updating, spatial rehearsal strategies, and inhibitory control. These results suggest that there might be improvements of neurocognition after a longer period of abstinence. Another study (Hanson et  al., 2010)  characterized neurocognitive changes among adolescent marijuana users (aged 15–19) across the first weeks of abstinence and recovery for word list learning (after two weeks) and verbal working memory (after three weeks). This study showed that attention accuracy deficiency persisted in users throughout the three-week abstinence period. Although this study did not include brain imaging techniques, these results implicate possible hippocampal, subcortical, and PFC abnormalities. Both of these studies suggest partial recovery; however, longer periods of abstinence would give more insight into the potential reversibility of the deficits that occur in functioning. A  longitudinal study compared different kinds of adolescent marijuana users (light, heavy, current, and former users; Fried, Watkinson, & Gray, 2005)  after controlling for baseline performance scores, matched substance use, and psychiatric disorders. Similar to results from previous studies, current heavy marijuana users showed cognitive impairments compared to non-users, showing lower scores on overall IQ, processing speed, and memory (Fried et al., 2005). Interestingly, cognitive impairments were not found in former marijuana smokers, suggesting a potential recovery after three months of abstinence. One of the strengths of the Fried study was that cognitive functioning was measured before onset of use. Even though this design controls for premorbid confounds, it is important to note that the subjects were part of a study on prenatal exposure to alcohol, nicotine, or marijuana. Although this was controlled for in the analyses, there might be preexisting neurological abnormalities. Research suggests that diminished neurocognitive functioning in heavy marijuana using adolescents is reversible. However, this might be different for concurrent alcohol and marijuana use. Recently, a study followed adolescents for ten years and compared neuropsychological performance between three groups: adolescents with AUD/SUD, adolescents in remission, and adolescents with no AUD/ SUD history (Hanson, Cummins, Tapert, & Brown, 2011). Controlling for age and education, adolescents with a history of AUD/SUD (either current or remitted) showed a decline in performance on tasks measuring visuospatial construction and language (Hanson et al., 2011). These results suggest that AUD/SUD during adolescence affects neurocognitive functioning into young adulthood, even for those who had had no alcohol or drug dependence symptoms or problems in the past two years.

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Recently, a large-scale study showed that persistent cannabis use was associated with decreased neuropsychological functioning (Meier et al., 2012). The subjects were tested before the onset of cannabis use and again 25 years later, at age 38. Even after controlling for education, persistent cannabis use was associated with cognitive problems, in particular among those who started using cannabis during adolescence. Importantly, among adolescent-onset users, neuropsychological functioning was not fully restored after cessation of cannabis use (Meier et  al., 2012). Thus, alcohol and substance use during adolescence appear to be partially, but not completely, irreversible. Further research is necessary to examine neurocognitive functioning separately for alcohol and substance use disorders.

CONCLUSION Adolescence is a unique life period for neurodevelopment. While the brain undergoes maturation, adolescents engage in risky behaviors that may interrupt normal brain development and potentially lead to later substance use disorders. Preexisting (i.e., familial alcoholism, ADHD) features appear to contribute to neural abnormalities during development. Brain systems associated with impulse control, risky behaviors, and reward sensitivity are typically not fully developed in adolescence, but appear less developed in youths who later go on to use substances. These less mature frontal inhibitory control networks may help facilitate the enactment of risky behavioral tendencies, which may include harmful levels of substance use. Cross-sectional studies have reported differences in brain functioning between adolescent substance users and non-users on a range of tasks. Adolescent binge drinking and alcohol use disorders have been associated with poorer performances on tests of memory, visuospatial functioning, executive functioning, attention, and language. MRI studies illustrate structural and functional deficits in the PFC and hippocampus, along with poorer white matter integrity. Gender differences have also been noted, with female users generally showing more abnormalities than male users. Marijuana-using adolescents have also shown poorer performance than non-users on tests of learning, working memory, attention, executive functioning, and inhibition. Recent longitudinal neuroimaging studies largely support these cross-sectional findings. The degree to which affected brain systems and functions recover is still not clear, and longitudinal studies are needed to determine if the adverse neurocognitive effects of adolescent alcohol and substance use are reversible, and how these might be linked to real-world outcomes such as academic performance, occupational attainment, social functioning, and health and well-being. ACKNOWLEDGEMENTS This research was supported by the National Institutes of Health R01 DA021182, R01 AA013419, and U01 AA021692 to S. F. Tapert. We extend our appreciation to participants and their families, and the Adolescent Brain Imaging Project, whose support was vital to the completion of this research.

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Index

abstinence from alcohol, 117–120 former opioid abusers compared with non-drug-abusing control participants, 242 former opioid abusers compared with opioid maintenance patients, 241–242 acute effects of alcohol, 105–106 of cannabis, 139, 141–143 of club drugs, 205–207 of cocaine, 158 of inhalants, 259–261 of methamphetamine, 184 in non-substance-abusing populations, 143 of opioids, 234–235, 239–240 addiction-oriented phenomena, risk valuation and, 47–48 addiction-related phenomena delay discounting and, 37–38 demand and, 43 addictions behavioral, 67 drug, 20–21, 25, 26–27 See also genetic influences on addiction ADHD (attention-deficit/hyperactivity disorder) about, 146–147 cocaine use, 167–168 methamphetamine use, 194 prescription stimulant use, 392–395 adolescents about, 423 brain structure and function, 409–412 cognitive development, 412–413 effect of alcohol on, 416–418 effect of cannabis on, 418–419

effect of MDMA (Ecstasy, XTC), 420–421 effect of opioids on, 421 effect of stimulants on, 420 externalizing factors, 415–416 functional brain changes, 409–411 neural consequences, 416 neurocognitive recovery in, 421–423 risk-taking behaviors in, 414–415 social development, 412–413 socio-emotional processing, 413 aerosols, 256t affective comprehension, 111–112 alcohol about, 103, 121 abstinence from, 117–120 acute and chronic physiological effects of, 105–106 alcohol abuse, 103–104 alcohol misuse and TBI within military, 350 alcohol-related brain damage, 112 alcohol use disorders (AUDs), 103–105 cognitive effects of alcoholism, 106–112 comorbidities, 121 dependence on, 104, 166–167 effect on adolescents, 416–418 factors influencing dependence on and recovery from alcoholism, 120–121 functional connectivity, 114–115 genetic risk factors for addiction, 62–63 HIV and, 320–321 interaction with marijuana, 419–420 magnetic resonance imaging (MRI), 113–114 mild TBI (mTBI) and alcohol abuse, 349 motor effects of alcoholism, 106–112 recovery, 117–120

436

alcohol (cont.) as risk factor for TBI, 343 structure-function relations in alcoholism, 116–117 alcoholism cognitive effects of, 106–112 family history of, 415 functional brain changes associated with, 112 illness modifying variables in, 294–295 motor effects of, 106–112 recovery from, 120–121 structural brain changes associated with, 112 structure-function relations in, 116–117 alcohol-related brain damage, 112 alcohol use disorders (AUDs), 103–105 Alcohol Use Disorders Identification Test (AUDIT), 85, 325 amygdala, 20–22 animal subjects, research strategies for, 60–62 ANT (Attention Network Task), 269 anterior cingulate cortex, 20–22, 26 anxiety disorders, cocaine use and, 167 area under the curve (AUC), 37 attention alcohol, 106 cannabis use, 141–142 club drugs, 220–221 cocaine use, 163 genetic influences and, 70–71 inhalants, 266–267, 269–270 methamphetamine (MA) use, 191–192 opioids, 236, 240–242 attentional bias, 70–71 attention-deficit/hyperactivity disorder (ADHD). See ADHD (attentiondeficit/hyperactivity disorder) Attention Network Task (ANT), 269 AUC (area under the curve), 37 AUDIT (Alcohol Use Disorders Identification Test), 85, 325 AUDs (alcohol use disorders), 103–105 automobile driving, 372–373 backward digit and Corsi span, 214, 217 BADS (Behavioral Assessment of Dysexecutive Syndrome), 214, 215, 378–379 “bagging,” 255, 261

INDEX

balance, affected by alcohol, 110 behavioral addictions, 67 behavioral and neuro-economic approaches about, 35–36, 51–51 decision making under risk and uncertainty, 45–50 delay discounting, 36–40 demand, 40–45 implications for treatment, 50–51 reward validation, 40–45 Behavioral Assessment of Dysexecutive Syndrome (BADS), 214, 215, 378–379 behavioral genetics research, methods for, 58–62 Behavior Rating Inventory of Executive Function (BRIEF), 214 benzene, chronic abuse of, 261 benzodiazepines, 245, 399–403 “Big Five” personality traits, 205 binge-like use, 184 biological influences, associated with alcohol use disorders, 104–105 BOLD (blood-oxygen-level-dependent) signal, 158 brain damage, alcohol-related, 112 brain imaging markers, 69–70 brain metabolism, on opioids, 237–238 brain structure and function in adolescence, 409–411 changes associated with alcoholism, 112 consequences on, 409–423 development in adolescence, 409–412 dysfunction in serious mental illnesses (SMIs), 282–287 dysfunction in substance use disorders (SUDs), 282–287 effects of alcohol, 112–117 effects of club drugs, 210–213 effects of cocaine, 159–161 effects of inhalants, 262–266 effects of methamphetamine, 186–188 effects of opioids, 235–238 sexual dimorphism in, 412 breakpoint, 42 BRIEF (Behavior Rating Inventory of Executive Function), 214 brief intervention, 354 Brief Visual Memory Test, 190 Brief Visuospatial Memory Test-Revised, 190

Index

buprenorphone maintenance patients, compared with methadone maintenance patients, 242 California Computerized Assessment Package, 191 California Verbal Learning Test, 190 Cambridge Neuropsychological Test Automated Battery (CANTAB), 214, 217 cannabis acute effects of, 139, 141–143 attention, 141–142 Chinese ideogram for, 5f effect on adolescents, 418–419 epidemiology and psychosocial correlates, 134–136 evidence-based practice recommendations, 148–149 genetic risk factors for addiction, 66–67 HIV and, 321 interaction with alcohol, 419–420 neuropsychological sequelae, 141–146 neuropsychopharmacology, 136–137 neurotoxicity, 138 structural and functional neuroimaging findings, 138–141 substance comorbidities and implications for cognitive function, 146–148 cannabis use disorders (CUDs), 135–136 CANTAB (Cambridge Neuropsychological Test Automated Battery), 214, 217 cART (combined antiretroviral therapy), 317 CBT (Cognitive Behavioral Therapy), 88, 93–94 Center for Epidemiological Studies Depression scale, 325 central nervous system independent effects of hepatitis C virus on, 312–315 risk factors for, 409–423 CFQ (Cognitive Failures Questionnaire), 217–218 “change talk,” 86 CHRNA5-A3-A4 gene, 63–64, 70, 71, 72 chronic physiological effects, of alcohol, 105–106 classical lesion model, 116

437

Clinical Antipsychotic Trials in Intervention Effectiveness CATIE trial, 292 clinical considerations, 14–15 clinical implications, of inhalant abuse, 271–273 clinical recommendations HCV (hepatitis C virus), 325–327 HIV (human immunodeficiency virus), 325–327 Clinical Trials Network (CTN), 85–86 closed economy, 40 club drugs about, 222–223 attention, 220–221 common morbidities of, 221–222 epidemiology and psychosocial correlates, 204–205 executive functions, 214–217 functional neuroimaging findings, 210–213 learning, 218–220 memory, 218–220 neuropsychological sequelae, 214–221 neuropsychopharmacology, 205–207 neurotoxicity, 207–210 processing speed, 220–221 self-report executive functions, 217–218 structural neuroimaging findings, 210–213 CNDS (neurobehavioral decision systems) model, 39–40 cocaine about, 168–169 acute effects of, 158 ADHD (attention-deficit/hyperactivity disorder), 167–168 attention, 163 common comorbidities, 166–168 dependence on, 166–167 epidemiology and psychosocial correlates, 157–158 functional neuroimaging findings, 159–161, 161t HIV (human immunodeficiency virus) and, 322 neuropsychological sequelae, 161–166 neuropsychopharmacology, 158 neurotoxicity, 158–159 structural neuroimaging findings, 159–161, 161t codeine, 231, 233, 403 See also opioids; prescription drug abuse

438

cognition changes in over time, 117–121, 243–244 effects of alcoholism on, 106–112 effects of cannabis on, 146–148 effects of cocaine on, 160–161 effects of methamphetamine on, 193 effects of opioids on, 243–244 functions of, 71–72 See also neuropsychological sequelae Cognitive Behavioral Therapy (CBT), 88, 93–94 cognitive development, risk factors in adolescence, 412–413 Cognitive Failures Questionnaire (CFQ), 217–218 combined antiretroviral therapy (cART), 317 comorbid drug use, 259 comorbidities alcohol use, 121 cannabis use, 146–148 club drug use, 221–222 cocaine use, 166–168 inhalant use, 258–259 methamphetamine, 194 opioid use, 245 of serious mental illnesses, 280–296 of substance use disorders, 280–296 complements, 41 complicated mTBI, 342 compulsive eating, 67 COMT gene, 65, 72 conduct disorder, 68–69, 415–416 consumption, 41 contingency management, 50, 87 cortical inputs, 20f corticostriatal pathway, role of in drug addiction, 25 craving, neural substrates of, 28–29 CTN (Clinical Trials Network), 85–86 CUDs (cannabis use disorders), 135–136 DALY (disability-adjusted life year), 104 DA release, information encoded by, 22–23 DAWN (Drug Abuse Warning Network) Report, 400 decision making cocaine use, 165–165 under risk and uncertainty, 45–50

INDEX

delay discounting about, 36 addiction-related phenomena and, 37–38 measuring, 36–37 in methamphetamine users, 189 neuroeconomics of, 38–40 quantifying, 37 demand about, 40–41 addiction-related phenomena and, 43 elasticity of, 41, 42 measuring, 41–42 neurobehavioral underpinning of, 43–45 quantifying, 42–43, 42f demand curve analyses, 41 dependence on alcohol, 104, 166–167 on alcoholism, 120–121 on cocaine, 166–167 dependent opioid abusers, 240–241 designer drugs. See club drugs dextroamphetamine (Dexedrine), 396 Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V), 13–14 Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR), 9–10 diagnostic practices, 9–14 diffusion tensor imaging (DTI) about, 115 for club drug use, 212 as a tool for studying methamphetamine use, 186–187 disability-adjusted life year (DALY), 104 dopaminergic projections, from ventral tegmental area and substantia nigra pars compacta, 21–22 double dissociation model, 116 DRD2 gene, 64–65, 67 driving, 372–373 Drug Abuse Warning Network (DAWN) Report, 400 drug addiction brain regions for reward and addiction, 20–21 role of nigrostriatal and corticostriatal pathways in, 25

Index

role of pre-frontal corticol regions in, 26–27 drug cue reactivity, 70–71 drug enforcement agency definitions, 391t drugs associated with NAc dysfunctions, 23–25 effects of, 396–399, 401–403 increasing price of, 50–51 d statistics, 188 DTI. See diffusion tensor imaging (DTI) EBPs. See evidence-based practices (EBPs) economics of risk, measuring, 46–47 Ecstasy. See MDMA (Ecstasy, XTC) EEG (electroencephalogram) pattern, 69 effects of drugs, 396–399, 401–403 intoxication, 293, 295 effects, acute of alcohol, 105–106 of cannabis, 139, 141–143 of club drugs, 205–207 of cocaine, 158 of inhalants, 259–261 of methamphetamine, 184 in non-substance-abusing populations, 143 of opioids, 234–235, 239–240 effects, intermediate of cannabis, 139–140 effects, lasting of cannabis use, 140–141, 144–145 effects, physiological alcohol, 105–106 opioids, 234–235 effects, residual, with cannabis use, 143–144 effects, subacute, with cannabis use, 143–144 effects, subjective, with opioids, 234–235 effects, withdrawal, 28, 243–244, 293, 295 elasticity of demand, 41, 42 electroencephalogram (EEG) pattern, 69 electrophysiological brain activity, 69 emotional processing affected by alcohol, 110–112 affected by cocaine use, 165–166 employment, 371 endophenotypes, genetics of, 67–72

439

environmental influences, associated with alcohol use disorders, 104–105 epidemiology about, 8–9 alcohol, 103–104 cannabis, 134–136 club drugs, 204–205 cocaine, 157–158 infectious disease, substance use disorder comorbidity and, 310, 312 inhalants, 256–258 methamphetamine, 183 opioids, 231–232 sedative hypnotic misuse, 400 serious mental illness and substance use comorbidity, 281–282 stimulant use, prescription, 393–395 substance use disorders (SUDs), 281–282 everyday functioning about, 364–365 automobile driving, 372–373 with cannabis use, 145–146 employment, 371 future directions, 377–379 hepatitis C virus (HCV), 314–315 human immunodeficiency virus (HIV), 318 impact on daily living activities, 368–370 impulsivity, 373–375 legal outcomes, 373–375 measuring, 366–367 methodological limitations, 367–368 neuropathophysiology of substance use disorders, 365–366 neuropsychology of substance use disorders, 365–366 outcomes, 366–379 treatment compliance and outcomes, 375–377 violence, 373–375 evidence-based practices (EBPs) about, 83–86, 83t examples of, 86–89 mechanisms of behavior change in, 90–93 neuropsychological considerations in, 89–90 recommendations, 148–149 web resources, 84

440

executive functions about, 71–72 affected by alcohol, 107 affected by cannabis, 142–143 affected by club drugs, 214–217 affected by cocaine, 164–165 affected by inhalants, 267–270 affected by methamphetamine (MA), 188–189 affected by opioids, 241–244 genetic influences and, 71–72 executive system, 39 “expectancy-valence” model, 165 experimental studies, of acute drug effects (opioids), 239–240 explicit memory, 109–110 exponential demand model, 42 externalizing factors, as risk factor in adolescence, 415–416 Family Behavior Therapy (FBT), 87–88 family history, of alcoholism, 415 FBT (Family Behavior Therapy), 87–88 Fentanyl, 403 “final neuropsychological profile,” 289 Finger Tapping, 191, 239 fMRI (functional magnetic resonance imaging), 113–114, 187 Frontal Systems Behavioral Scale (FSBS), 214 FSBS (Frontal Systems Behavioral Scale), 214 functional brain changes in adolescence, 409–411 associated with alcoholism, 112 functional connectivity, 114–115 functional magnetic resonance imaging (fMRI), 113–114, 187 functional neuroimaging findings alcohol, 113–115 cannabis, 138–141 club drugs, 210–213 cocaine, 159–161, 161t methamphetamine, 187–188 opioids, 236–238 functional outcomes and status, 348–349 See also everyday functioning future directions, 377–379 GABA-releasing striatal output neurons, 20f GABRA2 gene, 62, 70, 71

INDEX

gait, affected by alcohol, 110 gamma-hydroxy butyric acid (GHB, G, Liquid Ecstasy, Liquid X), 204–205, 210 gases, 256t gasoline-sniffing. See inhalants genetic influences on addiction about, 58, 72–72 endophenotypes and predisposing traits, 67–72 genetic risk factors for addiction, 62–67 methods for behavioral genetics research, 58–62 genome-wide association study (GWAS), 59–60 GHB, 205, 207, 210 glue-sniffing. See inhalants Go/No-Go test, 214, 215, 237, 269 gray matter, 409–411 Grooved Pegboard, 191 GWAS (genome-wide association study), 59–60 HAND (HIV-associated neurocognitive disorders), 317 hand-eye coordination, impairment in, 239 HCV. See hepatitis C virus (HCV) hepatitis C virus (HCV) clinical recommendations, 325–327 combined effects of SUD and, 315–316 everyday functioning, 314–315 independent effects on central nervous system of, 312–315 neural mechanisms, 312–313 neurocognitive impairment, 313–314 prevalence of, 310–311 heroin, 231–232 See also opioids; prescription drug abuse hippocampal volume deficits, 113, 417 hippocampus, 20–22 history of drug use and abuse, 3–6 of TBI in alcohol and substance use disorders, 342, 344 HIV. See human immunodeficiency virus (HIV) HIV-associated neurocognitive disorders (HAND), 317 Hopkins Verbal Learnings Test-Revised, 190

Index

HPT (hypothetical purchase task), 42 “huffing,” 255–256 human immunodeficiency virus (HIV) about, 376–377 alcohol and, 320–321 cannabis and, 321 clinical recommendations, 325–327 cocaine and, 322 combined effects of SUD and, 318–323 everyday functioning, 318 independent effects on central nervous system, 316–318 methamphetamines and, 318–320 neural mechanisms, 316–317 neurocognitive impairment, 317–318 opioids and, 322–323 prevalence of, 310–311 human subjects, research strategies for, 59–60 hydrocodone, 231, 240, 391 See also opioids; prescription drug abuse hypothetical purchase task (HPT), 42 illness expression factor variables, 290t, 291–293 illness modifying factor variables, 288, 289f, 290t, 293–295 impairment, neurocognitive associated with methamphetamine use, 195 hepatitis C virus (HCV), 313–314 human immunodeficiency virus (HIV), 317–318 implications for treatment, 50–51 implicit memory, 110 impulsive system, 39 impulsivity, 373–375 independents, 41 indifference point, 36 infectious diseases about, 310, 311f clinical recommendations, 325–327 comorbid impact on neuropsychiatric outcomes, 324–325 epidemiology of, 310, 312 hepatitis C virus (HCV), 312–316 human immunodeficiency virus (HIV), 316–323 trimorbidity, 323–324 information processing, speed of with methamphetamine use, 191

441

inhalants about, 254–255, 273 clinical implications, 271–273 misuse of, 255–256 neurobiological consequences of abuse of, 262–266 neuropsychological consequences of misuse of, 266–271 prevalence of, 256–258, 257t profile of users, 258–260 toxic effects of chronic abuse of, 260–262 inhibitory GABAergic system, 411–412 intensity of demand, 42 intermediate effects, of cannabis, 139–140 International Classification of Diseases-10 (ICD-10), 9–10 intervention, brief, 354 intoxication effects, 293, 295 Iowa Gambling Task, 165–165, 371 ketamine (Ket, K, Special K), 204–205, 210 Korsakoff ’s syndrome (KS), 121 language abilities cannabis, 143–144, 146 club drugs, 215 cocaine use, 163 inhalants, 267 methamphetamine (MA) use, 192 lasting effects, of cannabis, 140–141, 144–145 laws, 7–8 See also schedules of controlled substances Lawton and Brody IADL questionnaire, 325 learning. See memory legal outcomes, 373–375 Liquid Ecstasy. See gamma-hydroxy butyric acid (GHB, G, Liquid Ecstasy, Liquid X) Liquid X. See gamma-hydroxy butyric acid (GHB, G, Liquid Ecstasy, Liquid X) long-term cessation, 293, 295 “loss of life years,” 104 magnetic resonance imaging (MRI) for alcohol users, 113–114 for opioid users, 235–236

442

magnetic resonance imaging (MRI) (cont.) as a tool for studying methamphetamine use, 186 magnetic resonance spectroscopy (MRS), 187 “manifest functioning,” 367 marijuana. See cannabis MDMA (Ecstasy, XTC) about, 204–205, 369 effect on adolescents, 420–421 neuropsychopharmacology, 205–207 neurotoxicity, 207–210 structural and functional neuroimaging findings, 210–213 measuring delay discounting, 36–37 demand, 41–42 economics of risk, 46–47 everyday functioning, 366–367 medical improvement, 293–294 medical outcomes, following TBI and alcohol and substance use disorders, 345–346 medication compliance, 293–294 memory alcohol, 109–110 cannabis use, 142 club drugs, 218–220 cocaine use, 163–164 inhalants, 267–268 methamphetamine (MA), 190–191 opioids, 240–243 processes affected by alcohol, 109–110 mental illnesses. See serious mental illnesses (SMIs) mesocorticolimbic reward circuit, 19f MET (Motivational Enhancement Therapy), 86–87 methadone, 232 See also opioids methadone maintenance patients, compared with buprenorphone maintenance patients, 242 methamphetamine (MA) about, 183–184 ADHD (attention-deficit/hyperactivity disorder), 194 attention, 191–192 comorbid factors, 194

INDEX

delay discounting, 189 executive functions, 188–189 HIV (human immunodeficiency virus) and, 318–320 language abilities, 192 learning, 190–191 memory, 190–191 motor skills, 191 neurocognitive impairment associated with use of, 195 neuroimaging in, 186–188 neuropsychological profile of, 188 neuropsychopharmacology of use, 184–185 neurotoxicity of, 185–186 psychomotor skills, 191 relationship with neurocognitive functioning and use of, 193–194 social cognition, 193 speed of information processing, 191 treatment of neurocognitive impairment, 195 visuospatial abilities, 192 working memory, 191–192 methodological limitations, of everyday functioning, 367–368 methylphenidate (Ritalin), 395, 396–399 MID (monetary-incentive delay task), 44 mild TBI (mTBI), 341, 349 military, alcohol misuse and TBI within, 350 mixed amphetamine salts (Adderall/ Adderall XR), 396 moderate to severe TBI, 341 monetary-incentive delay task (MID), 44 mood, affected by cocaine use, 167 mood disorders, 348 morbidities, of club drugs, 221–222 morphine, 233–234 See also opioids; prescription drug abuse Motivational Enhancement Therapy (MET), 86–87 Motivational Interviewing (MI), 354 motor effects, of alcoholism, 106–112 motor skills cocaine use, 161 methamphetamine use, 191 MPDZ gene, 63 MRI. See MRI (magnetic resonance imaging)

Index

MRS (magnetic resonance spectroscopy), 187 mTBI (mild TBI), 341, 349 NAc dysfunctions, drugs of abuse associated with, 23–25 National Institute of Drug Abuse (NIDA), 83t, 85 National Institute on Alcohol Abuse and Alcoholism (NIAAA), 83t, 85, 103 National Survey on Drug Use and Health (NSDUH), 231 N-Back Task, 192, 214 neural consequences, of adolescent alcohol and substance abuse, 416 neural mechanisms, in TBI and alcohol/ substance use disorders, 350–353, 351f neural substrates about, 19–21, 19f, 29–29 of craving, 28–29 dopaminergic projections from ventral tegmental area and substantia nigra pars compacta, 21–22 drugs of abuse associated with NAc dysfunctions, 23–25 of drug withdrawal, 28 information encoded by DA release, 22–23 of relapse, 28–29 of risk, 48–50 role of nigrostriatal and corticostriatal pathways in drug addiction, 25 role of pre-frontal cortical regions in drug addiction, 26–27 of uncertainty, 48–50 neurobehavioral decision systems (CNDS) model, 39–40 neurobehavioral outcomes, following TBI and alcohol and substance use disorders, 346–349 neurobiological consequences, of inhalant abuse, 262–266 neurocognitive functioning, relationship with methamphetamine use, 193–194 neurocognitive impairment associated with methamphetamine use, 195 hepatitis C virus (HCV), 313–314 human immunodeficiency virus (HIV), 317–318

443

neurocognitive outcomes, following TBI and alcohol and substance use disorders, 346–349 neurocognitive recovery, in adolescents, 421–423 neuro-economic approaches. See behavioral and neuro-economic approaches neuroeconomics defined, 35 of delay discounting, 38–40 neuroimaging alcohol, 112–117 cannabis use, 138–141 club drugs, 210–213 cocaine use, 159–161, 161t inhalants, 262–266 MDMA (Ecstasy, XTC) use, 210–213 methamphetamine use, 186–188 opioid use, 235–238 outcomes following TBI and alcohol and substance use disorders, 345–346 for schizophrenia, 284 serious mental illnesses (SMIs), 286–287 substance use disorders (SUDs), 286–287 in TBI and alcohol/substance use disorders, 350–353, 351f neurological findings, for SMIs and SUDs, 286–287 neuromedical risk, 292–293 neuropathophysiology, of substance use disorders, 365–366 neuropsychiatric outcomes, SUDs and infectious diseases comorbid impact on, 324–325 neuropsychological consequences, of inhalant abuse, 266–271 neuropsychological functioning implications from cocaine use on, 166–168 implications from opioid use, 245 model of, SMI + SUD, 287–289, 289f neuropsychological impairment, effects of on mechanisms of behavior change about, 82–83, 94–94 evidence-based practice (EBP), 83–86, 83t examples of evidence-based practices, 86–89

444

neuropsychological impairment, effects of on mechanisms of behavior change (cont.) mechanisms of behavior change in evidence-based practice, 90–93 neuropsychological considerations in evidence-based practices, 89–90 patient-treatment matching, 93–94 neuropsychological interventions, development of, 93–94 neuropsychological profile, of methamphetamine use, 188 neuropsychological sequelae alcohol, 106–112 cannabis, 141–146 club drugs, 214–221 cocaine, 161–166 inhalants, 266–271 methamphetamine, 188–193 opioids, 239–244 serious mental illness and substance use comorbidity, 284–286 neuropsychology serious mental illnesses (SMIs), 284–286 substance use disorders (SUDs), 284–286 of substance use disorders, 365–366 neuropsychopharmacology alcohol, 105–106 cannabis, 136–137 club drugs, 205–207 cocaine, 158 inhalants, 261 methamphetamine, 184–185 opioids, 233–235 neurotoxicity alcohol, 112 cannabis, 138 club drugs, 207–210 cocaine, 158–159 inhalants, 262 methamphetamines, 185–186 opioids, 235 neurotransmitters, 411–412 NIAAA (National Institute on Alcohol Abuse and Alcoholism), 83t, 85, 103 nicotine, 24, 63–64 NIDA (National Institute of Drug Abuse), 83t, 85 nigrostriatal pathway, role of in drug addiction, 25

INDEX

“nodding,” 234 non-dependent opioid abusers, 240 non-drug abusing control participants compared with abstinent former opioid abusers, 242 compared with opioid maintenance patients, 241 in experimental studies of acute drug effects, 239–240 nonmedical use of substances, 392, 392t Northwest Frontier Addiction Technology Transfer Center, 83t novelty-seeking, 69 NSDUH (National Survey on Drug Use and Health), 231 nucleus accumbens, 20–22 observational studies, of opioid use, 241–243 Omax, 42 open economy, 40 opiates, 24 See also opioids opioid maintenance patients changes in cognition over time, 243 compared with abstinent former opioid abusers, 241–242 compared with non-drug-abusing control participants, 241 opioid receptors, 234 opioids about, 65–66, 245 brain metabolism, 237–238 effect on adolescents, 421 epidemiology, 231–232 HIV (human immunodeficiency virus) and, 322–323 implications for neuropsychological functioning, 245 neuroimaging, 235–238 neuropsychological sequelae, 239–244 neuropsychopharmacology, 233–235 neurotoxicity, 235 observational studies, 241–243 physiological effects, 234–235 prescription abuse, 403 psychosocial correlates, 232–233 resting state neuroimaging studies, 237–238 structural imaging studies, 235–236

Index

subjective effects, 234–235 substance comorbidities, 245 task-related neuroimaging studies, 236–237 OPRM1 gene, 65–66, 67, 71, 72 outcomes everyday functioning, 366–379 following TBI and alcohol and substance use disorders, 345–349 Paced Auditory Serial Addition Task, 192 patient-treatment matching, 93–94 patterns of use, club drugs, 204–205 PDYN gene, 66 perceptual motor functioning, with cannabis use, 142 perceptual skills, with cocaine use, 161 perfusion weighted imaging (PWI), 212 personality disorders, with cocaine use, 168 PET. See positron emission tomography (PET) petrol-sniffing. See inhalants pharmacokinetics, club drugs, 205–207 physiological dependence, 392, 392t physiological effects alcohol, 105–106 opioids, 234–235 PM (prospective memory), 109, 190–191, 220, 370 Pmax, 42 positron emission tomography (PET) for club drug use, 211 as a tool for studying methamphetamine use, 187–188 posttraumatic stress disorder (PTSD), 350, 351–352 potential years of life lost due to premature death (PYLL), 104 predisposing traits, genetics of, 67–72 predispositional factor variables, 288, 289f, 290–291, 290t pre-frontal cortical regions, role of in drug addiction, 26–27 prescription drug abuse about, 390–392 detection of prescription stimulants, 395–396 detection of sedative hypnotics, 400–401 drug effects, 396–399, 401–403

445

epidemiology of sedative hypnotic misuse, 400 prescription opioid abuse, 403 prescription stimulant abuse, 392–393 response to the problem, 403–404 sedative-hypnotic abuse, 399–400 stimulant use/epidemiology, 393–395 prescription opioid abuse, 403 prescription stimulants, 392–393, 395–396 prevalence, of inhalant use, 256–258, 257t Prize-Based Contingency Management, 87 Prize Incentives Contingency Management, 87 probability discounting, quantifying risky decision making using, 47 processing speed, club drugs, 220–221 profiles, of inhalant users, 258–260 prospective memory (PM), 109, 190–191, 220, 370 prospective memory impairment, 91 prospect theory, 45–46, 45f proton magnetic resonance spectroscopy, for club drug use, 212 pseudoaddiction, 392, 392t psychological dependence, 392, 392t psychomotor skills, with methamphetamine (MA) use, 191 psychosis severity, 293–294 psychosocial correlates cannabis, 134–136 club drugs, 204–205 cocaine, 157–158 opioids, 232–233 psychostimulants, 64–65 “psychotropic medication,” 292 PTSD (posttraumatic stress disorder), 350, 351–352 pubertal maturation, 414 PWI (perfusion weighted imaging), 212 PYLL (potential years of life lost due to premature death), 104 quantifying delay discounting, 37 demand, 42–43, 42f risky decision making using probability discounting, 47 quantitative electroencephalography (QEEG), 346 quantitative MRI (QMRI), 346

446

RAVLT (Rey Auditory Verbal Learning Task), 190, 218–219, 268 rCBF (regional cerebral blood flow), 139 recovery from alcohol abuse, 117–120 from alcoholism, 120–121 neuropsychological considerations and, 89–90 recreational drugs. See club drugs regional cerebral blood flow (rCBF), 139 reinforcement pathologies, 51–51 reinforcer pathologies, 43 reinforcer valuation, neurobehavioral underpinning of, 43–45 relapse, neural substrates of, 28–29 remote memory, 110 research strategies for animal subjects, 60–62 for human subjects, 59–60 residual effects, with cannabis use, 143–144 “resting-state connectivity,” 238 resting state neuroimaging studies, 237–238 reward sensitivity, 413–414 reward system, 234 reward validation, 40–41 Rey Auditory Verbal Learning Task (RAVLT), 190, 218–219, 268 Rey Complex Figure Test, 164, 190, 192, 219 risk decision making under, 45–50 factors for addiction, genetic, 62–67 factors for central nervous system, 409–423 for future substance use problems after TBI, 344–345 measuring economics of, 46–47 neural substrates of, 48–50 neuromedical, 292–293 quantifying risky decision making using probability discounting, 47 for subsequent TBI, 349 risk-sensitive foraging (RSF), 47 risk-taking behaviors, in adolescence, 414–415 risk valuation, addiction-oriented phenomena and, 47–48 Ritalin (methylphenidate), 395, 396–399

INDEX

Rivermead Behavioural Memory Test (RBMT), 218 RSF (risk-sensitive foraging), 47 SAMISS (Substance Abuse and Mental Illness Symptoms Screener), 325 SAMSHA (Substance Abuse and Mental Health Services Administration), 83t, 85, 104 schedules of controlled substances, 7–8, 391 schizophrenia (SZ), 282–284, 293–296 sedative-hypnotics, 399–403 abuse, 395–400 detection of, 400–401 drug effects, 401–403 epidemiology, 400 self-report executive functions, 217–218 sensation-seeking, 69 serious mental illnesses (SMIs) about, 295–296 brain dysfunction in, 282–287 comorbidity of, 280–296 empirical support for factors and variables of model, 289–295 epidemiology and related characteristics, 281–282 model of neuropsychological functioning SMI + SUD, 287–289, 289f neuroimaging findings, 286–287 neurological findings, 286–287 neuropsychology, 284–286 sexual dimorphism, 412 “sniffing,” 255–256 social cognition alcohol and emotion processing, 110–112 cocaine and emotion processing, 160, 165–166 everyday functioning, 375, 378 HIV and methamphetamine, 320 with methamphetamine, 193 social development, risk factors in adolescence, 412–413 Society of Clinical Psychology, 83t, 85 socio-emotional processing, risk factors in adolescence, 413 solvents, volatile, 256t Special K. See ketamine (Ket, K, Special K)

Index

speeded processing, with cannabis use, 141–142 stimulants about, 24 detection of, 395–396 drug effects, 396–399 effect on adolescents, 420 epidemiology, 393–395 prescription, 395–399 Stockings of Cambridge task, 223 Stop Signal Task, 189 Stroop Color Word Test, 189, 191, 269 Stroop Test, 214, 216 structural brain changes, associated with alcoholism, 112 structural imaging studies, 235–236 structural magnetic resonance imaging, 113 structural neuroimaging findings alcohol, 112–113, 115–117, 118–119 cannabis, 138–141 club drugs, 210–213 cocaine, 159–161, 161t inhalants, 262–266 methamphetamine, 186–187 opioids, 235–236 structure-function relations, in alcoholism, 116–117 subacute effects, with cannabis use, 143–144 subjective effects, with opioids, 234–235 substance abuse, 10–11 See also substance use disorders (SUDs) Substance Abuse and Mental Health Services Administration (SAMSHA), 83t, 85, 104, 281 Substance Abuse and Mental Illness Symptoms Screener (SAMISS), 325 substance comorbidities cannabis, 146–148 opioids, 245 substance dependence See also substance use disorders (SUDs) substance-induced disorders, 11–13 substance misuse, 392, 392t substance use diagnostic practices, 9–14 epidemiology, 8–9 history of, 3–6 reasons for, 6–8 substance use disorders (SUDs)

447

about, 1–3, 295–296 brain dysfunction in, 282–287 comorbidity of, 280–296 diagnostic criteria for, 10–11 empirical support for factors and variables of model, 289–295 epidemiology and related characteristics, 281–282 model of neuropsychological functioning SMI + SUD, 287–289, 289f neuroimaging findings, 286–287 neurological findings, 286–287 neuropsychology, 284–286 See also Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR); diagnostic practices substantia nigra pars compacta, dopaminergic projections from, 21–22 substitutes, 41 “sudden sniffing death,” 260–261 SZ. See schizophrenia (SZ) task-related neuroimaging studies, opioids, 236–237 TBI. See traumatic brain injury (TBI) The TBI Network, 354 Test for Attentional Performance (TAP), 221 Timed Gait tasks, 191 TMS (transcranial magnetic stimulation), 39 TMT-B, 214, 215 Token Economy, 87 toxicity, of chronic inhalant abuse, 260–262 Trail Making Test, 189, 191 training, for greater control, 51 trait impulsivity, 68–69 transcranial magnetic stimulation (TMS), 39 traumatic brain injury (TBI) about, 341–342 alcohol risk factors for, 343 history of, 342, 344 mild TBI and alcohol abuse, 349 within military, 350 neural mechanisms, 350–353, 351f neuroimaging findings, 350–353, 351f outcomes, 345–349 risk of future substance abuse problems, 344–345 treatment, 353–356

448

treatment compliance and outcomes, 375–377 implications for, 50–51 of substance use disorders following TBI, 353–356 See also treatment of addiction treatment of addictions about, 82–83, 94–94 behavioral and neural economics, 50–51 evidence-based practice (EBP), 83–86, 83t examples of evidence-based practices, 86–89 mechanisms of behavior change in evidence-based practice, 90–93 neuropsychological considerations in evidence-based practices, 89–90 patient-treatment matching, 93–94 treatment response, genetics of, 72 trimorbidity, 323–324 2012 Vital and Health Statistics, 103 2012 Youth Risk Behavior Survey, 256 uncertainty decision making under, 45–50 neural substrates of, 48–50 unit price, 40 urine toxicology, 396 ventral striatum, 20–22, 49, 70, 351–352, 413–414 See also nucleus accumbens ventral tegmental area, dopaminergic projections from, 20–22

INDEX

verbal abilities. See language abilities violence, 373–375 visuospatial abilities alcohol, 108–109 cannabis, 142 methamphetamine, 192 volumetric abnormalities, 159–160 WAISprocessing speed index, 191 WE (Wernicke’s encephalopathy), 121 Wernicke-Korsakoff ’s disorder, 293 Weschler Intelligence Scale for ChildrenIV (WISC-IV), 272 white matter, 409–411 WHO (World Health Organization), 104 Wisconsin Card Sorting Test (WCST), 165, 188–189, 214, 215 withdrawal effects, 28, 243–244, 293, 295 word-recall tests, 190 working memory alcohol, 107, 116–117 club drugs, 214–215, 217 cocaine, 158–159, 160–161, 164–165 inhalants, 267–268, 270 with methamphetamine use, 191–192 opioids, 236–237, 242–243 training, 51, 94 See also executive functions XTC. See MDMA (Ecstasy, XTC) “z-drugs,” 399–400