/ The Neuroscience of Addiction This book addresses a growing need for accessible information on the neuroscience of addiction. In the past decade, neuroscientific research has greatly advanced our understanding of the brain mechanisms of addiction; however, this information remains largely confined to scientific outlets. As legislation continues to evolve and the stigma surrounding addiction persists, new findings on the impact of substances on the brain are an important public health issue. Francesca Mapua Filbey gives readers an overview of research on addiction including classic theories as well as current neuroscientific studies. A variety of textual supports– including a glossary, learning objectives and review questions – help students better reinforce their reading and make the text a ready-made complement to undergraduate and graduate courses on addiction. Francesca Mapua Filbey is a Professor of Cognition and Neuroscience and Bert Moore Endowed Chair of BrainHealth for the School of Behavioral and Brain Sciences at the University of Texas at Dallas. She conducts research aimed at understanding the biobehavioral mechanisms of addictive disorders for the improvement of early detection and intervention.
/ Cambridge Fundamentals of Neuroscience in Psychology Developed in response to a growing need to make neuroscience accessible to students and other non-specialist readers, theCambridge Fundamentals of Neuroscience in Psychology series provides brief introductions to key areas of neuroscience research across major domains of psychology. Written by experts in cognitive, social, affective, developmental, clinical and applied neuroscience, these books will serve as ideal primers for students and other readers seeking an entry point to the challenging world of neuroscience. Books in the Series The Neuroscience of Expertise by Merim Bilalić The Neuroscience of Intelligence by Richard J. Haier Cognitive Neuroscience of Memory by Scott D. Slotnick The Neuroscience of Adolescence by Adriana Galván The Neuroscience of Suicidal Behavior by Kees van Heeringen The Neuroscience of Creativity by Anna Abraham Cognitive and Social Neuroscience of Aging by Angela Gutchess The Neuroscience of Sleep and Dreams by Patrick McNamara The Neuroscience of Addiction by Francesca Mapua Filbey
/ The Neuroscience of Addiction Francesca Mapua Filbey University of Texas at Dallas
/ University Printing House, Cambridge CB2 8BS, United Kingdom One Liberty Plaza, 20th Floor, New York, NY 10006, USA 477 Williamstown Road, Port Melbourne, VIC 3207, Australia 314–321, 3rd Floor, Plot 3, Splendor Forum, Jasola District Centre, New Delhi – 110025, India 79 Anson Road, #06–04/06, Singapore 079906 Cambridge University Press is part of the University of Cambridge. It furthers the University’s mission by disseminating knowledge in the pursuit of education, learning and research at the highest international levels of excellence. www.cambridge.org Information on this title: www.cambridge.org/9781107127982 DOI: 10.1017/9781316412640 © Francesca Mapua Filbey 2019 This publication is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 2019 Printed in the United Kingdom by TJ International Ltd, Padstow Cornwall A catalogue record for this publication is available from the British Library. Library of Congress Cataloging-in-Publication Data Names: Filbey, Francesca M., 1972- author. Title: The neuroscience of addiction / Francesca Mapua Filbey. Other titles: Cambridge fundamentals of neuroscience in psychology. Description: Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2019. | Series: Cambridge fundamentals of neuroscience in psychology | Includes bibliographical references and index. Identifiers: LCCN 2018049853 | ISBN 9781107127982 (hardback : alk. paper) | ISBN 9781107567337 (paperback : alk. paper) Subjects: | MESH: Behavior, Addictive | Substance-Related Disorders | Brain–physiopathology | Neurosciences | Risk Factors Classification: LCC RC564 | NLM WM 176 | DDC 616.86 –dc23 LC record available at https://lccn.loc.gov/2018049853 ISBN 978-1-107-12798-2 Hardback ISBN 978-1-107-56733-7 Paperback Cambridge University Press has no responsibility for the persistence or accuracy of URLs for external or third-party internet websites referred to in this publication and does not guarantee that any content on such websites is, or will remain, accurate or appropriate.
/ To David: thank you for your love and support. To Colin: thank you for nourishing my mind. To Alastair: thank you for nourishing my spirit. To Juan and Georgina Mapua: thank you for always believing in me. To Felipe and Emerita Canlas: thank you for being my example of dedication.
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/ Table of Contents List of Plates page xi List of Figures xii List of Tables xvi Preface xvii 1 What is Addiction? 1 Learning Objectives 1 Introduction 1 The Phenomenology of Substance Use Disorders 4 The Demography of Addiction 5 The Stigma of Addiction 5 The Diagnosis of Addiction 6 A Brain Disease Model of Addiction 9 Non-Drug Addictions 12 Summary Points 14 Review Questions 14 Further Reading 14 Spotlight 15 References 16 2 Human Neuroscience Approaches Toward the Understanding of Addiction 21 Learning Objectives 21 Introduction 21 Measuring the Brain’s Electrical Activity 22 Visualizing the Brain’s Structure and Function 24 Biochemical Imaging 27 Limitations of Neuroimaging Research 28 Summary Points 29 Review Questions 29 Further Reading 29 Spotlight 1 30 Spotlight 2 32 References 32
/ 3 Brain-Behavior Theories of Addiction 34 Learning Objectives 34 Introduction 34 The Incentive-Sensitization Theory 35 The Allostatic Model: Dysregulation in Homeostasis 36 The Impaired Response Inhibition and Salience Attribution (iRISA) Syndrome Model 38 The Future of Brain-Behavior Theories of Addiction 40 Summary Points 42 Review Questions 42 Further Reading 42 Spotlight 43 References 45 4 From the Motivation to Initiate Drug Use to Recreational Drug Use: Reward and Motivational Systems 47 Learning Objectives 47 Introduction 47 Reward and Motivational Systems Guide the Direction of Behavior 48 Predicting Rewards: Evidence for the Primary Role of Dopamine 51 Final Common Pathway: All Drugs Lead to One 53 Is Addiction a Reward Deficiency Syndrome? 55 Corticostriatal Circuitry and Effort–Reward Imbalance 56 Role of Memory Systems 56 Summary Points 58 Review Questions 58 Further Reading 59 Spotlight 60 References 61 5 Intoxication 64 Learning Objectives 64 Introduction 64 Drug Pharmacodynamics 66 Actions of Addictive Drugs 66 Brain Mechanisms of Intoxication: Evidence From Neuroimaging Pharmacological Studies 68 Modulators of Intoxication: Challenges in Human Research 73 Summary Points 75 Review Questions 76 Further Reading 76 viii Table of Contents
/ Spotlight 76 References 78 6 Withdrawal 81 Learning Objectives 81 Introduction 81 What Does Withdrawal Look Like? 82 Acute Withdrawal Symptoms and Associated Neural Mechanisms 85 Protracted Withdrawal Symptoms and Associated Neural Mechanisms 87 Electrophysiological Mechanisms of Withdrawal 88 A Model of Opposing Mechanisms: Between-System Response to Drugs 90 Summary Points 91 Review Questions 92 Further Reading 92 Spotlight 1 93 Spotlight 2 94 References 94 7 Craving 98 Learning Objectives 98 Introduction 98 Cue-Elicited Craving Paradigms and Associated Neural Mechanisms 99 Neurophysiological Underpinnings of Craving 101 Contextual Cues 102 Do Drugs Hijack the Reward Circuitry of the Brain? 103 Greater Craving or Greater Attention? 105 Neuromolecular Mechanisms 106 Summary Points 107 Review Questions 107 Further Reading 108 Spotlight 108 References 110 8 Impulsivity 114 Learning Objectives 114 Introduction 114 Neuropharmacology of Impulsivity 116 Is Impulsivity Pre-existing or Drug Induced? 117 Risky Decision Making 120 Table of Contents ix
/ Inhibitory Control 121 Delay Discounting of Reward 123 Summary Points 125 Review Questions 125 Further Reading 126 Spotlight 127 References 128 9 Impacts of Brain-Based Discoveries on Prevention and Intervention Approaches 130 Learning Objectives 130 Introduction 130 Pharmacological Approaches 132 Behavioral Approaches 135 Combined Approaches 137 Treatment Outcomes 138 Summary Points 141 Review Questions 141 Further Reading 141 Spotlight 1 142 Spotlight 2 143 References 144 10 Conclusions 148 Learning Objectives 148 Introduction 148 Risk Factors Inform Better Prevention and Intervention 149 Addiction Endophenotypes 150 Sex Differences in Addiction 155 The Question of Causality 156 General Conclusions 157 Summary Points 159 Review Questions 159 Further Reading 160 Spotlight 1 161 Spotlight 2 162 References 162 Glossary 165 Index 173 Color plate section found between pages 172 and 173 x Table of Contents
/ List of Plates 1.1 A longitudinal study demonstrating neuromaturational processes from 5 to 20 years of age. 2.4 Gray matter has predominantly isotropic (soccer ball-shaped) water diffusion, while dense white matter tracks have highly anisotropic (rugby ball-shaped) diffusion of water pointing in the direction of the fiber bundle. 5.3 PET studies to determine the effects of nicotine administration. 6.3 Fast β power can be a predictor of relapse in polysubstance users during a 3-month abstinence. S7.1 Measuring ΔFosB. 8.5 Ventromedial PFC lesions lead to risky decision making. 9.3 Following methadone-assisted therapy (MAT), long-term abstinent heroin users (mean length of abstinence, 193 days) had a greater decreased response in striatal areas compared with shortterm abstinent heroin users (mean length of abstinence, 23 days) during a cue-induced craving task. 9.5 Common (a) and distinct (b) neural targets of pharmacological and cognitive-based therapeutic interventions. 10.4 Brain EEG oscillations may be useful endophenotypes for alcohol use disorders.
/ List of Figures 1.1 A longitudinal study demonstrating neuromaturational processes from 5 to 20 years of age. page 2 1.2 Animal behavioral paradigms in addiction studies. 8 1.3 Sites of action of various drugs on the mesocorticolimbic reward system. 11 S1.1 Magic mushrooms. 16 2.1 Magnetoencephalography scanner with patient. 23 2.2 Mechanisms of MRI. 24 2.3 A patient going through a magnetic resonance imaging machine. 25 2.4 Gray matter has predominantly isotropic (soccer ball-shaped) water diffusion, while dense white matter tracks have highly anisotropic (rugby ball-shaped) diffusion of water pointing in the direction of the fiber bundle. 26 2.5 MRS image of a 34-year-old man with human immunodeficiency virus (HIV) infection and alcohol dependence. 27 S2.1 What does 45 years of love look like in the brain? 31 S2.2 Associating the brain with behavior began with the field of phrenology. 32 3.1 Diagram describing the addiction cycle – preoccupation/ anticipation (“craving”), binge/intoxication and withdrawal/ negative affect– with the different criteria for substance dependence incorporated from the Diagnostic and Statistical Manual of Mental Disorders, 4th edn. 37 3.2 The iRISA model depicting the interactions between the PFC and subcortical regions in drug users and non-users. 39 3.3 Daily smoking, risky alcohol consumption and illicit drug use by people with the lowest and highest socioeconomic status (SES), in Australians aged 14 years or older, in 2013. 41 S3.1 The modern opioid epidemic. 44 4.1 Lever press (a) and intracranial self-stimulation (ICSS) (b) are two examples of experimental paradigms used to study reward and motivation in animals. 48
/ 4.2 The brain’s reward system lies in the mesocorticolimbic pathway, which is regulated by dopamine. 49 4.3 Camera lucida drawings of medium spiny neurons in the shell (top) and core (bottom) regions of the nucleus accumbens of saline- and amphetamine-pretreated rats. 50 4.4 The release of dopamine signals reward. 52 4.5 According to Kalivas and Volkow (2005), the projection from the PFC to the nucleus accumbens core to the ventral pallidum is a final common pathway for drug seeking by increases in dopamine release (via stress, a drug-associated cue or the drug itself) in the PFC. 54 4.6 Experiments on the effects of dopamine depletion on effort. 57 S4.1 (a) Sensation and novelty seeking are characteristic of adolescence. (b) Schematic of the monetary incentive delay task. 61 5.1 Alcohol intoxication may impact sensorimotor skills. 65 5.2 Mechanisms of drug action. 67 5.3 PET studies to determine the effects of nicotine administration. 70 5.4 Example of a virtual reality driving simulator device. 72 5.5 (a) Position of the amygdala (arrow). (b). Response in brain regions to emotional faces during alcohol intoxication. 73 S5.1 Law enforcement challenges during changes in cannabis legislation. 77 6.1 The severity of cannabis withdrawal symptoms across time. 84 6.2 Change in CBF in the thalamus from baseline to overnight abstinence and subjective withdrawal from nicotine as measured by the Minnesota withdrawal score from baseline to withdrawal. 87 6.3 Fast β power can be a predictor of relapse in polysubstance users during a 3-month abstinence. 89 6.4 Neuroadaptations between the reward and stress systems during withdrawal. 91 S6.1 Babies have to be weaned from opiates when born from opiate-using mothers. 93 S6.2 Can Facebook be addictive? 94 7.1 Cue-elicited craving paradigm using tactile cannabis cue paraphernalia, a neutral object (pencil) and appetitive non-drug reward cues (fruit, not shown). 101 7.2 Cue-elicited craving paradigm. 104 List of Figures xiii
/ 7.3 Representative trial from the backward-masked cue task. 105 7.4 Regulation of the dendritic structure by drugs of abuse. 106 S7.1 Measuring ΔFosB. 109 8.1 Impulsivity leads to risky behavior. 115 8.2 Corticostriatal pathways. 116 8.3 Study in stimulant-dependent individuals, their non-using siblings and non-using controls demonstrating that impulsivity traits (but not sensation seeking) may be a predisposing factor for stimulant dependence. 118 8.4 Illustration of a go/no go test. 119 8.5 Ventromedial PFC lesions lead to risky decision making. 122 8.6 Schematic of the stop circuit. 123 8.7 Illustration of a delay discounting task. 124 8.8 Schematic of the wait circuit. 124 S8.1 Adolescence is a critical neurodevelopmental period. 127 9.1 Relapse rates for drug-addicted patients compared with those suffering from diabetes, hypertension and asthma. 131 9.2 Components of comprehensive drug addiction treatment. 132 9.3 Following methadone-assisted therapy (MAT), long-term abstinent heroin users (mean length of abstinence, 193 days) had a greater decreased response in striatal areas compared with short-term abstinent heroin users (mean length of abstinence, 23 days) during a cue-induced craving task. 134 9.4 Proposed model illustrating synergistic mechanisms between behavioral and pharmacological treatment approaches for addiction. 138 9.5 Common (a) and distinct (b) neural targets of pharmacological and cognitive-based therapeutic interventions. 139 S9.1 Peer addiction recovery specialists bring different perspective to treatment. 143 10.1 Heritability (h 2 ; weighted means and ranges) of ten addictions based on a large survey of adult twins. 151 10.2 Integration of complementary technologies can be used to reveal the neurobiology of individual differences in complex behavioral traits. 152 10.3 The concept of endophenotypes is that they lie in the causal pathway between the genetic mechanisms and observable behavior. 153 10.4 Brain EEG oscillations may be useful endophenotypes for alcohol use disorders. 154 xiv List of Figures
/ 10.5 Changes in brain volume may be an endophenotype for cannabis use disorder. 155 10.6 (a) Birth cohort design. (b) The prospective study included initiation alcohol and drug use. (c) Using a prospective, longitudinal design on a birth cohort, the Dunedin Study found changes in full-scale IQ (in standard deviation units) from childhood to adulthood. 157 S10.1 Post-traumatic stress disorder (PTSD). 161 List of Figures xv
/ List of Tables 1.1 2017 Schedule of Drugs according to the US Drug Enforcement Administration (DEA). page 3 1.2 Modifications to addiction diagnosis from DSM-IV to DSM-5. 7 1.3 Outline of overlapping behavioral symptoms between SUDs and compulsive overeating (Volkow & O’Brien, 2007). 13 6.1 Drug specificity and timing of acute withdrawal symptoms. 83
/ Preface The concerted effort by the US government to determine underlying brain mechanisms for diseases during the “Decade of the Brain” in the 1990s has led to greater attention on the role of the brain in addiction. Neuroscience research has made significant progress toward our understanding of the antecedents as well as the consequences of addiction, which, in turn, has helped de-stigmatize addiction and get help to those who need it. However, to date, this information remains largely confined to scientific outlets resulting in a lag in dissemination to students and the general community. This may contribute to the lack of emphasis on addiction in most training programs, including clinical programs, despite the prevalence of addiction and its high co-morbidity with other diseases and disorders. The need for this book is further highlighted by the recent public health issues surrounding two substances: cannabis and opioids. Hence, there is a growing need for accessible information on the neuroscience of addiction that caters to both students and the general public. Approach This book has been written to fill a void in the areas of behavioral neuroscience and neuropsychopharmacology. To date, the single most relevant textbook on this topic is one focused on the use of neuroimaging tools to study addiction, rather than to explain it. It is also written for a scientific audience, not undergraduate students or lay people. As scientific inquiry and public interest in the addicted brain have grown, so too has the need for a comprehensive and accessible textbook that communicates extant neuroscience research on this topic. This book will serve as an educational tool for neuroscience and pre-med students and trainees at all levels. Undergraduate students in upper-division courses, graduate students and educated lay people are the target audience for this book. It is written at a level appropriate for individuals with minimal to no background in neuroscience so as to be accessible for scientists in other disciplines, including public policy, public health and developmental psychology, with interest in the adolescent brain. This book can serve as a supplemental textbook in upper-level college/university courses such as Brain and Behavior, Psychopharmacology, Neuropsychology, Behavioral Neuroscience and as a trade book for educated lay people
/ (as it has been written in an accessible style), and/or as a main textbook in a college/university course or seminar at the advanced undergraduate level or the graduate level (along with supplemental scientific articles). It is written in language that is accessible to students, non-specialists and educated lay people alike. This book is included in the Cambridge Fundamentals of Neuroscience in Psychology series published by Cambridge University Press. The goal of this series is to introduce readers to the use of neuroscience methods and research to inform psychological questions. Coverage and Organization This book has been written and organized to cover the neuroscientific research that supports the most widely reported stages of addiction. I wrote the first three chapters to lay the groundwork for the more indepth topics covered in the later chapters. The introductory chapter serves to provide a general foundation for the clinical and behavioral features of addiction. This is followed by a chapter that then describes the approaches used by neuroscience research, which are also consequently referred to throughout the rest of the book. This chapter, then, should provide a very basic familiarity with current scientific techniques as used to study addiction. The last of the foundational chapters describes the various theories that stimulate the investigative research described in subsequent chapters. The goal of these foundational chapters is to broadly set out the current thinking inthe field as well as provide the necessary backgroundknowledge to be able to integrate information from the subsequent chapters. The later chapters starting with Chapter 4 each focus on the important constructs related to addiction and are organized to follow a somewhat ecological order of the progression of addiction stemming from acute intoxication and rewarding effects of substance use to withdrawal symptoms and addiction interventions. These chapters cover the basic research that supports the understanding of these constructs as well as issues related to the understanding of these constructs. The concluding chapter discusses auxiliary topics relevant to these processes such as individual variability. It then provides a cohesive overview of the neuroscience of addiction zeitgeist. Features Each chapter contains comprehensive figures that best illustrate concepts or challenging topics. Each figure is referred to in the xviii Preface
/ corresponding text. Summary Points are provided at the end each chapter to help focus the reader on the most important points and to reinforce the gist of each chapter. Review Questions are also provided to challenge the reader’s understanding of each chapter. These questions are related to the important points of the chapter. The chapters also have a Further Reading section that directs readers to supplemental materials that could facilitate further learning. The Spotlight sections take current issues and integrate these timely topics with constructs from the chapter. These spotlights help put constructs into a real-world perspective that is aimed to stimulate critical thinking in readers. Preface xix
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/ CH A PTE R O N E What is Addiction? Learning Objectives • Be able to describe the clinical definition of addiction. • Be able to recognize the phenomenology of addiction. • Be able to explain how psychoactive substances are classified. • Be able to characterize the brain disease model of addiction. • Be able to understand the concept of behavioral addiction. Introduction According to the World Health Organization, there were 2 billion alcohol users, 1.3 billion smokers and 185 million drug users in the year 2000. This figure contributed to 12.4% of all deaths worldwide that year. Addiction does not discriminate. It affects both sexes, all races and all ages. However, the highest rate of addiction is in the adolescent to emerging-adult populations (ages 12–29 years) (UNODC, 2012). The high rate of substance use initiation during this period has the potential to change the tone for how the brain develops, given that this age period is when the brain undergoes critical maturation processes. Figure 1.1 illustrates brain development as a process consisting of gray matter reductions and cortical thinning that is then followed by increased white matter volume, connectivity and organization through adolescence and young adulthood (Giorgio et al., 2010; Gogtay et al., 2004; Hasanet al., 2007; Lebel et al., 2010; Shaw et al., 2008). Guided by multidisciplinary research in neuroscience, epidemiology, brain imaging and genetics, addiction is now understood to be a brain disease due to the changes it exerts on the brain. Like other brain diseases, addiction is best described using the three Ps: pervasive, persistent and pathological. Addiction is pervasive as it affects all aspects of the individual’s life. Addiction is persistent as its effects persevere despite efforts by the individual. Last, addiction is pathological because the
/ effects are uncontrollable. Thus, compulsive drug seeking and continued use despite negative consequences broadly characterize addiction. From a clinical perspective, addiction is officially diagnosed via clinical interview using guidelines such as the Diagnostic and Statistical Manual of Mental Disorders, currently in its 5th edition (DSM-5) by the American Psychiatric Association orthe International Classification of Diseases(ICD) published by the World Health Organization. According to the DSM-5, addiction is a chronic progressive disease with behavioral patterns that fall within a spectrum of severity. Thus, the DSM-5, implemented in 2014, refers to this broad spectrum as “substance use disorders” (SUDs). In the USA, the Drug Enforcement Administration (DEA) organizes drugs within a schedule of drug classes that are based on risk for abuse and harm as well as acceptable medical use (Table 1.1). Schedule I drugs have the highest risk for abuse and harm and little to no medical benefit, while schedule V drugs have low potential for abuse. Schedule I drugs include heroin, lysergic acid diethylamide (LSD), cannabis, peyote, methaqualone, and 3,4-methylenedioxymethamphetamine (ecstasy). Furthermore, drugs of abuse are classified into categories based on their mechanism of 1.0 H H A A B J K I B I J K N Q M P L D G E F C C G O 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 20 Age 5 0.1 0.0 Gray matter Figure 1.1 A longitudinal study demonstrating neuromaturational processes from 5 to 20 years of age. (From Gogtay et al., 2004. © 2004 National Academy of Sciences, USA.) (A black and white version of thisfigure will appear in some formats. For the color version, please refer to the plate section.) 2 What is Addiction?
/ Table 1.1 2017 Schedule of Drugs according to the US DEA. The DEA classifies drugs into five distinct categories or schedules depending on the drug’s acceptable medical use and the drug ’s abuse or dependency potential. Schedule I drugs have the highest potential for abuse and the potential to create severe psychological and/or physical dependence. Schedule V drugs represent the least potential for abuse. Drug schedule Classification meaning (defined by the DEA) Drugs, substances, chemicals Schedule I No currently accepted medical use High potential for abuse Heroin LSD Cannabis Ecstasy Methaqualone Peyote Schedule II High potential for abuse Severe dependence risk Vicodin Cocaine Methamphetamine Methadone Dilaudid Demerol OxyContin Fentanyl Dexedrine Adderall Ritalin Schedule III Moderate to low potential for abuse Moderate to low dependence risk Codeine Ketamine Anabolic Steroids Testosterone Schedule IV Low potential risk for abuse Low potential for dependence Xanax Darvocet Valium Ativan Ambien Tramadol Schedule V Lower potential risk for abuse Lower potential risk for dependence Robitussin Lyrica Introduction 3
/ action and behavioral effects: narcotics, cannabinoids, depressants, stimulants, hallucinogens and inhalants. For instance, some target specific receptors (e.g. cannabinoids) whereas others target multiple receptor systems (e.g. stimulants). The Phenomenology of Substance Use Disorders Addiction is often defined as compulsive drug seeking despite the negative consequences related with the substance use. Although the criteria for the clinical diagnosis of drug abuse and dependence has been and will continue to be modified based on scientific research, the behavioral sequelae associated with addiction revolve around a heightened response to rewarding stimuli and the uncontrollable behavior that individuals present in order to consume the rewarding stimuli. Various models of addictionsuggestseveral stages and processes that contribute to addiction (discussed in Chapter 3). However, they all begin with the initial hedonic or pleasurable response to substances that lends itself to increased motivation to acquire and consume substances, as well as impulsivity and loss of control over their use. Tolerance and withdrawal are also vital processes that contribute to the maintenance of substance use despite a desire to quit. What makes addiction so complex is the multidimensional processes that lead to a cascade of neural and biological events. These events increase the risk for other illnesses such as AIDS, cancer, and cardiovascular and respiratory diseases, as well as mental disorders including psychosis. Use of substances can also transmit harmful effects to unborn fetuses such as in the case of fetal alcohol syndrome, premature birth and neonatal abstinence syndrome. Individuals with addiction are also at risk for failing to meet their responsibilities. For example, substance abuse increases the risk for dropping out of school (27% of high-school dropouts smoked cannabis, 10% abused prescription drugs, 42% consumed alcohol; US Substance Abuse and Mental Health Administration, www .samhsa.gov/data), one in six unemployed individuals use substances (www.samhsa.gov/data) and ~70% of incarcerated offenders regularly used drugs prior to their incarceration (US Dept. of Justice Report, www.bjs.gov/content/dcf/duc.cfm). Most of these consequences persist despite discontinuation from drug use. Thus, prevention and treatment strategies should focus on modifying behaviors that promote protracted abstinence. Current research in SUD intervention is focusing on more targeted treatment, given that current programs have very poor success rates, with~70% relapse within the first year. 4 What is Addiction?
/ The Demography of Addiction Epidemiological studies make sense of connections between demographic factors and substance use. These studies demonstrate associations between certain demographics and prevalence of substance use. For instance, stimulant users in developed countries have been found to be typically lower-class, 20–25-year-old males (Babor, 1994). US national survey data also show that alcohol use varies by age, sex and ethnic background. For instance, young males tend to drink alcohol more than females and older individuals. Similar associations are also found in nicotine use such that higher rates of smoking are found in those of lower social class (Jarviset al., 2008). Dynamic factors, however, change the trends in substance users. For example, while opioid use was historically most prevalent in urban 18–25-year-old males in the USA, there has been a shift toward more widespread use that includes a greater number of female users in the last few years (Ciceroet al., 2014). There are also commonalities in the demographic characteristics of users across different substances. In general, substance-abusing individuals tend to be male, young and have low socioeconomic status. Notably, accessibility of substances also plays a large role in these associations, contributing to alcohol and nicotine use being the most prevalent of all substance use. However, of all of these characteristics, age appears to be the most important demographic correlate. Several factors contribute to the abuse potential within certain demographic populations. Interactions of the drug with other disorders can influence its likelihood for abuse and dependence. For instance, populations characterized as being high in risk-taking behavior are more likely to abuse substances. Psychiatric disorders that are associated with an increased risk of abuse include schizophrenia, bipolar disorder, depression and attention deficit/hyperactivity disorder (ADHD). Genetic factors also play an important role in the risk for addiction. Implicated genes are typically those that regulate dopaminergic functioning, such as the dopamine receptor D4 gene (Filbey et al., 2008). The Stigma of Addiction Historically, addiction has been and, to some extent, continues to be viewed as a “disorder of free will.” Such perception implies that addiction is a social issue that should be handled by social solutions. These putative social issues include failings in childhood upbringing including the home and school environment, aversive conditions including neglect The Stigma of Addiction 5
/ and abuse, cultural acceptance, absence of positive influences and role models, unstructured environments, and negative peer and societal influences. While some of these social factors may contribute toward the initiation of substance use, growing empirical evidence does not support social issues as the core basis of addiction. Let us take the example of alcohol. The large majority of the population consumes alcohol on a regular basis (52% of American adults are current regular drinkers); however, only 10% of the drinking population develops an addiction (Blackwell et al., 2014). This demonstrates that there is more to the equation than “free will.” Social solutions have also largely failed to remediate those who are addicted, primarily because they do not address the underlying etiology. Because of the stigma of addiction, those with addiction: 1) do not seek the necessary treatment; 2) do not receive the necessary social support; or 3) receive largely ineffective treatment that does not address the underlying mechanisms of addiction. The Diagnosis of Addiction The clinical diagnoses of mental health disorders rely on classification systems that have been developed over centuries. These classification systems differ based on their purpose for classification (clinical, research or administrative objectives), as well as emphasis on discerning features of diagnostic categories (phenomenology versus etiology). The two most prominent systems are the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD). The ICD, developed by the World Health Organization, published the first section for mental health disorders in 1949 within its 6th edition. Based on this, the American Psychiatric Association Committee on Nomenclature and Statistics developed the 1st edition of the DSM in 1952. The DSM then became thefirst official manual of mental disorders to focus on clinical use. The DSM-5, which was published in 2013 and implemented in 2014, is the most recent version. In terms of the diagnosis of addiction, the DSM-5 classifies the diagnosis of SUDs based on evidence of impaired control, social impairment, risky use and pharmacological criteria. The major modification from DSM-IV to DSM-5 is the combination of the categorical symptoms in DSM-IV into a continuum in DSM-5 (Table 1.2). Thus, rather than dimorphic diagnoses of substance abuse and dependence, a unidimensional diagnosis of SUD is evaluated on a scale from mild to severe depending on the number of symptoms presented. This decision was 6 What is Addiction?
/ based on evidence showing that symptoms of abuse and dependence were not independent of each other and formed a single dimension. As a result, two to three symptoms would classify as “mild SUD”, four to five symptoms as “moderate SUD” and six to eleven symptoms as “severe SUD.” Since the inception of this new classification system for addiction diagnosis, opponents of this system have argued that the unidimensional classification does not reflect the discrete nature of the features of addiction, namely, withdrawal, tolerance and craving. Indeed, these constructs have been viewed as conceptually and empirically distinct, and subsequent chapters will discuss the neuroscientific foundations of each of these constructs. Another modification is the overarching criteria for SUDs independent of substance, as well as the inclusion of behavioral addictions (e.g. gambling disorder). Incidentally, DSM-5 includes a section with tools to Table 1.2 Modifications to addiction diagnosis from DSM-IV to DSM-5. Criterion DSM-IV substance abuse DSM-IV substance dependence DSM-5 SUD Tolerance X X Withdrawal X X Taken more/longer than intended X X Desire/unsuccessful efforts to quit use X X Great deal of time taken by activities involved with use X X Use despite knowledge of problems associated with use X X Important activities given up because of use X X Recurrent use resulting in a failure to fulfill important role obligations X X Recurrent use resulting in physically hazardous behavior (e.g. driving) X X Continued use despite recurrent social problems associated with use X X Craving for the substance X The Diagnosis of Addiction 7
/ 1 2 111 0 9 8 7 6 5 4 3 2 1 Drug 1 2 111 0 9 8 7 6 5 4 3 2 1 Saline ? (b) Lever Electric stimulator Pump dispensing drug or saline Computer (a) Drug-tested mouse prefers chamber in which drug was given Figure 1.2 Animal behavioral paradigms in addiction studies. (a) In self-administration models, animals continuously perform an action (e.g. pressing a lever) in order to receive a 8 What is Addiction?
/ improve the diagnosis of personality disorders, and incorporates diagnoses that may be considered for future iterations of the DSM. This section (section III) includes internet gaming disorder and caffeine use disorder. A Brain Disease Model of Addiction As mentioned earlier, the view that addiction is a social issue overlooks the role of the brain in the behavioral symptoms related to addiction. By doing so, interventions attempt to modify behavior that may not be directly related to the underlying mechanisms. What are these underlying mechanisms of addiction? Much of what we know about addiction as a brain disease originates from seminal animal research that began ~30 years ago. For instance, animal experiments utilizing intracranial self-stimulation demonstrated how animals will readily self-administer drugs of abuse and how these drugs alter the animal’s reward threshold (Figure 1.2a). In a classic study of the positive reinforcing effects of morphine, Weeks and colleagues trained rats to self-deliver morphine intravenously (Weeks, 1962). They discovered that the unrestrained rats self-injected morphine and that the greater the dose, the less they selfinjected. Classical conditioning models, such as conditioned place preference, show the development of paired associations between the rewarding properties of drugs and the cue that signals exposure to the drug, suggesting adaptations in reward learning mechanisms (Figure 1.2b). Behavior sensitization models assess the result of repeated drug exposure and suggest an augmented response following continued use. These models demonstrate the progression of addiction from the initial hedonic response to the drug ( “liking” the drug) to that of yearning or craving (“wanting” the drug). For example, behavior sensitization has been described in terms of locomotor activity in rats sensitized to higher doses of amphetamine (e.g. 2.0 mg/kg intraperitoneally) where an initial slowing is later followed by an increase (Leith & Kuczenski, 1982). Another example is the reinstatement model, which also assesses how repeated drug exposure impacts behavior but is used to test reward or receive intracranial current in brain-rewarding loci (self-stimulation). (b) In placepreference models, animals spend more time in an environment where they had repeatedly received a drug, demonstrating positive reinforcing mechanisms of drugs. (From Camí & Farré, 2003. © 2003 Massachusetts Medical Society, USA.) A Brain Disease Model of Addiction 9
/ mechanisms of drug relapse. In these models, an established operant response for the drug such as lever pressing that has been extinguished re-emerges or reinstates. For example, place preference to previously drug-paired environments can be reinstated following extinction in animals. These animal models have been translated into human models (discussed in Chapter 2), and with advanced technologies (discussed in Chapter 2) and focused scientific research, there is now a growing understanding of the key role of neurobiological mechanisms underlying processes related to addiction. These processes are discussed individually in subsequent chapters. The initial effects of substances on behavior widely vary because each drug’s mechanism of action on the brain is unique. Opioids bind to μ receptors in the brain, which results in feelings of euphoria, sedation and tranquility. The importance of μ receptors is demonstrated in studies where mice lacking this receptor do not exhibit these behavioral effects, and also do not become physically addicted. Cannabis also causes relaxation but exerts its effects by binding to cannabinoid (CB1) receptors in the brain. The effects of cannabis also include a sense of well-being, as well as slowing of cognitive functions. Slowing of cognitive functions also results from alcohol, although alcohol modulates activity in several receptors including serotonin (5-hydroxytryptamine, 5- HT), nicotinic, γ-aminobutyric acid (GABA) and N-methyl-d-aspartate (NMDA) receptors. Unlike depressants, such as alcohol, psychostimulants, in general, result in opposite effects such as increased alertness, arousal, concentration and motor activity by blocking the reuptake of dopamine, norepinephrine and serotonin. This results in a rapid release and accumulation of neurotransmitters in the synaptic cleft. However, despite this wide range of mechanisms and effects, virtually all addictive substances target brain regions in the medial portion of the limbic and frontal lobes. These regions form a neural pathway that is innervated primarily by dopaminergic projections that originate from the ventral tegmental area (VTA) in the midbrain and project to the amygdala and the nucleus accumbens. Because of dopamine’s role in the hedonic response, this neural pathway is referred to as the dopaminergic reward pathway due to its role in processing rewarding drug and nondrug stimuli (illustrated in Figure 1.3). In addition to dopamine, this pathway is also modulated by opioids, GABA and endocannabinoids, and also processes emotion and motivation. This pathway is, therefore, important in the conscious experience of taking a drug, drug craving and compulsion. It is within this pathway that substances exert their effects. Thus, brain regions within this pathway are likely to endure pervasive 10 What is Addiction?
/ and potentially permanent changes. Some of the symptoms of addiction, such as tolerance and withdrawal, are examples of this adaptation. Thus, drugs of abuse alter the neural transmission and functioning of the reward pathway from its evolutionary role of sustaining the organism (i.e. via natural, non-drug rewards). The result of this dysregulated reward network is a decreased responsivity to natural rewards. This neural adaptation or “hijacking” of the brain is what classifies addiction as a brain disease. Changes in neural transmission in the mesolimbic reward pathway also lead to a cascade of events that occurs in other neurochemical systems, such as the stress system. Indeed, studies have found that chronic drug use leads to dysregulation in stress hormones such as corticotropinreleasing factor in the hypothalamic–pituitary–adrenal (HPA) axis. George Koob has described this “antireward system” as the dysregulation of the stress system that contributes to the negative emotional state occurring during abstinence from drugs (Koob, 2006). Koob has referred to this negative state as “the dark side of addiction” and it is often associated with withdrawal symptoms. Lastly, the compulsive drug seeking associated with addiction is associated with cognitive impairment such as poor decision making, inhibitory control, learning and memory, GLU 5-HT Opioid Amygdala Opioid Opioid Raphe nucleus Nucleus accumbens Ventral tegmental area GLU GABA Prefrontal cortex DA DA DA DA Locus NE ceruleus 5-HT GABA GABA GABA Amphetamines, cocaine, opioids, cannabinoids, phencyclidine Opioids, ethanol, barbiturates, benzodiazepines Figure 1.3 Sites of action of various drugs on the mesocorticolimbic reward system. Although the pathway’s primary neurotransmitter is dopamine (DA), this circuit is innervated by glutamatergic (GLU) projections,γ-aminobutyric acid (GABA) norepinephrine (NE) and serotonergic (5-HT) projections. (From Camí & Farré, 2003. © 2003 Massachusetts Medical Society, USA.) A Brain Disease Model of Addiction 11
/ which are cognitive functions within areas of the prefrontal cortex (PFC). Some of these changes in the brain are long term, which contributes to the relapsing nature of the disease despite protracted periods of abstinence. Neuroimaging studies in humans have supported the involvement of these systems in addiction. For instance, techniques such as positron emission tomography (PET) and magnetic resonance imaging (MRI) scans have shown that regions within the mesocorticolimbic pathway that include the orbitofrontal cortex, PFC, anterior cingulate gyrus, amygdala and nucleus accumbens are activated during drug intoxication. Although PET and MRI only measure neural activity indirectly, these results are likely due to increased dopamine levels in this pathway during drug consumption. Interestingly, during withdrawal, the reverse effect is observed (i.e. decreased activity). Non-Drug Addictions So far, this chapter has focused on addiction in terms of response to substances of abuse, sometimes referred to as “chemical addiction.” However, a growing area of research has found similar behavioral symptoms (tolerance, withdrawal, compulsion) that occur as a consequence of non-substance or “behavioral addictions.” These have been evidenced in compulsive activities such as eating, sex/pornography, exercising, gambling, video gaming and tanning, among others (Holden, 2010). These compulsive disorders were previously categorized as “substance-related disorders,” “impulse control disorders, not otherwise specified” or “eating disorders”. However, emergent neuroimaging studies suggest that these behavioral addictions may have overlapping mechanisms with substance addictions (Table 1.3) (Holden, 2001; Probst & van Eimeren, 2013). Non-drug addictions have also been observed in animal models. Forexample, during intravenous self-administration experiments, rats trained to press a lever for highly palatable foods such as sugar and saccharin were shown to reduce self-administration of cocaine and heroin (Lenoir & Ahmed, 2008). This unexpected finding suggests that these natural reinforcers (i.e. sweet foods) have a higher reinforcing value than cocaine, even in animals with an extensive history of drug intake. Studies by Hoebel et al. (2009) have also demonstrated behavioral plasticity following a history of intermittent sugar access, supporting the notion that sugar consumption meets the criteria for addiction. Tolerance has also been noted whereby an increase in intake is observed 12 What is Addiction?
/ during sugar self-administration (Colantuoni et al., 2001). Interestingly, withdrawal symptoms such as anxiety and depression were observed following removal of sugar or fat access (Colantuoni et al., 2002). In humans, neuroimaging studies demonstrate a neural response in the mesocorticolimbic reward system similar to drug addiction in individuals with problems with gambling (Worhunsky et al., 2014) , sex (Kuhn & Gallinat, 2014), internet/video games (Kimet al., 2014), food (Filbey et al., 2012), shopping (Dagher, 2007) and tanning (Kourosh et al., 2010). These studies suggest that the reward system is responsible for neural adaptations as a consequence of these compulsive behaviors. Pitchers et al.(2010) reported neural adaptations in the form of increased dendrites and dendritic spines within the nucleus accumbens in rats during “withdrawal” from sexual experience. Additionally, like drugs of abuse and other natural rewards, exercise in rodents has been shown to be associated with increased dopamine signaling in the nucleus accumbens and striatum (Freed & Yamamoto, 1985; Hattori et al., 1994). Notably, despite the overlap in brain regions, single-unit recordings have suggested that different cell populations are responsible for the response to self-administration of natural rewards and drugs of abuse such as cocaine or ethanol (Bowman et al., 1996; Carelli, 2002; Carelli et al., 2000; Robinson & Carelli, 2008). Importantly, emerging clinical evidence suggests that pharmacotherapies used to treat drug addiction may be a Table 1.3 Outline of overlapping behavioral symptoms between SUDs and compulsive overeating (Volkow & O ’Brien, 2007). SUDs Compulsive overeating Tolerance Increasing amounts of food to maintain satiety Withdrawal symptoms Distress and dysphoria during dieting Larger amounts used than intended Larger amounts eaten than intended Persistent desire to quit Persistent desire to curtail amount eaten Great deal of time spent using or obtaining Great deal of time spent eating Decreased social activities Activities given up from fear of rejection or due to physical limitations Continued use despite physical or psychological problems Overeating despite adverse physical and psychological consequences Non-Drug Addictions 13
/ successful approach to treating non-drug addictions. For example, naltrexone, nalmefene, N-acetylcysteine and modafinil have all been reported to reduce craving in pathological gamblers (Grant et al., 2006; Kim et al., 2001; Leung & Cottler, 2009). Summary Points • Both the animal and human literature support the notion that addiction is a brain disorder stemming from the positive reinforcing mechanisms in the mesolimbic pathway. • Chronic use leads to neuroadaptation, primarily within this pathway, that results in the behavioral symptoms of addiction. • These adaptations also lead to changes in other brain systems, including the stress system. • Through this cycle, addiction becomes a chronic, relapsing disorder. • More recently, non-drug addictions have been identified, with evidence showing parallel neural mechanisms to those of substance-based addictions. Review Questions • How are the five categories in the DEA’s classification of substances delineated? • What are the current (i.e. DSM-5) primary symptoms of SUD according to clinical guidelines? • What are the seminal animal studies that have helped shape our understanding of addiction as a brain disease? • How is dopamine critical in the processes related to addiction? Further Reading Babor, T. F. (2011). Substance, not semantics, is the issue: comments on the proposed addiction criteria for DSM-V. Addiction, 106(5), 870–872; discussion 895–877. doi:10.1111/j.1360-0443.2010.03313.x Barnett, A. I., Hall, W., Fry, C. L., Dilkes-Frayne, E. & Carter, A. (2017). Drug and alcohol treatment providers’ views about the disease model of 14 What is Addiction?
/ addiction and its impact on clinical practice: a systematic review. Drug Alcohol Rev, 37(6), 697–720. doi:10.1111/dar.12632 Burrows, T., Kay-Lambkin, F., Pursey, K., Skinner, J. & Dayas, C. (2018). Food addiction and associations with mental health symptoms: a systematic review with meta-analysis. J Hum Nutr Diet, 31(4), 544–572. doi:10.1111/ jhn.12532 Diana, M. (2011). The dopamine hypothesis of drug addiction and its potential therapeutic value. Front Psychiatry, 2, 64. doi:10.3389/ fpsyt.2011.00064 Grant, J. E. & Chamberlain, S. R. (2016). Expanding the definition of addiction: DSM-5 vs. ICD-11. CNS Spectr, 21(4), 300–303. doi:10.1017/ S1092852916000183 Hou, H., Wang, C., Jia, S., Hu, S. & Tian, M. (2014). Brain dopaminergic system changes in drug addiction: a review of positron emission tomography findings. Neurosci Bull, 30(5), 765–776. doi:10.1007/s12264-014- 1469-5 Lewis, M. D. (2011). Dopamine and the neural“now”: essay and review of addiction: a disorder of choice. Perspect Psychol Sci, 6(2), 150–155. doi:10.1177/1745691611400235 Singer, M. (2012). Anthropology and addiction: an historical review.Addiction, 107(10), 1747–1755. doi:10.1111/j.1360-0443.2012.03879.x Spotlight The magic in the mushrooms remains unknown A 2015 report published by theCanadian Medical Association Journalpointed to several small studies demonstrating that psychedelic drugs such as LSD and 3,4-methylenedioxymethamphetamine (MDMA) may be effective in reducing symptoms of post-traumatic stress disorder (PTSD) anxiety, as well as addiction (Tupperet al., 2015). A small 2014 Swiss study, for instance, found that people with terminal illness treated with a combination of LSD and psychotherapy had lower rates of anxiety (Gasseret al., 2014). A US study involving a small group of patients also found that MDMA, more commonly known as ecstasy, can greatly reduce symptoms of PTSD. However, many caution of the negative side effects of psychedelics on mood and cognition, as well as sensory processing and perception. For instance, LSD, psilocybin (obtained from magic mushrooms) and mescaline can cause psychosis and/or hallucinations (Figure S1.1). Spotlight 15
/ Since the 1950s, the therapeutic benefits of psychedelics have always been argued. However, how psychedelics affect the brain remains unknown. Furthermore, it remains to be determined for what purposes psychedelics should be used in addition to the risks and benefits associated. Stephen Kish, who studies the use of ecstasy in the treatment of PTSD, suggests that it increases a person’s sociability, which may foster patients’ interactions with their therapists (Kish et al., 2010). However, he also notes that psychedelics cause hallucinations and, in some cases, psychosis. The biggest concern in these studies is the risk that people would selfmedicate with psychedelic drugs. The fact remains that the forms available on the street are unlikely to be pure and could lead to serious health problems and even death. References Babor, T. F. (1994). Overview: demography, epidemiology and psychopharmacology–making sense of the connections. Addiction, 89(11), 1391–1396. Blackwell, D. L., Lucas, J. W. & Clarke, T. C. (2014). Summary Health Statistics for U.S. Adults: National Health Interview Survey, 2012. Vital Health Statistics, Series 10, No. 260. Hyattsville, MD: National Center For Health Statistics. Bowman, E. M., Aigner, T. G. & Richmond, B. J. (1996). Neural signals in the monkey ventral striatum related to motivation for juice and Figure S1.1 Magic mushrooms. (From https://pixabay.com/en/alone-autumn-background-britain-1239208/. Reproduced under Creative Commons CC0 license.) 16 What is Addiction?
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/ CH A PTE R TWO Human Neuroscience Approaches Toward the Understanding of Addiction Learning Objectives • Be able to identify current neuroimaging techniques used to study addiction in humans. • Be able to understand the current limitations of neuroimaging research. • Be able to describe the fundamental features of each neuroimaging technique. • Be able to understand the various brain mechanisms that neuroimaging techniques can examine. • Be able to appreciate how neuroimaging techniques can be applied in clinical and research practice. Introduction Our understanding of addiction as a brain disease can be attributed largely to the recent advancements in brain imaging techniques. While issues in methodological differences within human neuroimaging studies can add complexity to this picture, the use of multivariate approaches integrating neuroscience with other disciplines, such as behavioral studies, genetics and pharmacology, provides a deeper understanding of the mechanisms underlying addiction. In addition, translational studies that apply lessons gained from non-human studies for testing within humans have enriched our understanding of the overall mechanisms of addictive processes. How have these neuroimaging techniques advanced over the years? And what kind of information do they provide above and beyond what we can glean clinically? How can we harnessfindings from neuroimaging research in order to improve the lives of those who suffer from addiction?
/ Measuring the Brain’s Electrical Activity Introduced in the 1920s, the technique of electroencephalography (EEG) takes advantage of the electrophysiological properties of the brain. By measuring these electrophysiological signals or “brain waves,” we are able to determine patterns of electrical charges (frequency, voltage, morphology and topography) from large representative samples of cortical neurons – largely pyramidal cells. Brain function can then be inferred from these patterns that reflect neuronal factors and activities including ionic gradients from neuronal membranes, and excitatory and inhibitory post-synaptic potentials. EEG recordings are measured by electrodes placed either extracranially (on the scalp) or intracranially (via surgical placement directly on the surface of the brain) to record the electrical voltage fluctuations generated by the brain. Currently, a minimum of twenty-one electrodes is considered ideal for a clinical study, although higher density array EEG systems of up to 256 electrodes are available. Currently, while electrical signals provide high temporal resolution data regarding brain activity, the poor spatial resolution of the two-dimensional EEG representation of a threedimensional brain poses limitations in interpretation of the data. Thus, source localization is limited in extracranial EEG recordings. Furthermore, EEG recordings are the result of synchronous activity from large samples of neurons, which conceals small activity or activity from smaller samples of neurons. The net effect of electrophysiological activity in the brain also generates a magnetic field that can be detected. Magnetoencephalography (MEG) is a technique that measures the magnetic fields emitted by electrical activity in the brain (Figure 2.1). The magneticfield emitted by neurons passes through brain tissue and the skull with little distortion, thereby generating better spatial localization relative to EEG, as the scalp distorts magnetic fields less than electrical signals. Although the brain’s magnetic field is 10–15 Tesla (T; i.e. 100 million times smaller than the Earth’s magnetic field), superconducting sensors called superconducting quantum interference devices (SQUIDs) are able to detect and record this signal. More than 300 fixed SQUID sensors are embedded within the MEG helmet. SQUID sensors amplify the magneticfields generated by intracellular currents within the dendrites of pyramidal cells. These cells are perpendicular to the cortical surface. While MEG has the advantage of measuring neural activity directly, it is not sensitive beyond the first few centimeters of the cortex, as the signals from internal neurons decay quickly over distance (Cohen & Cuffin, 1991; 22 Human Neuroscience Approaches
/ Huettel et al., 2008). The MEG signal is also highly susceptible to magnetic interference such as a car driving by or other electrical sources; therefore, MEG scanners have to be in magnetically shielded rooms. Both EEG and MEG are considered direct measures of brain function to study event-related potentials/fields, or in the time-frequency domain, to study oscillatory activity. They provide very high temporal resolution in the order of milliseconds. These techniques can be conducted extracranially and are therefore non-invasive and do not require injection or exposure to X-rays. Thus, these techniques can be used in virtually all populations. Lastly, due to the passive nature of these techniques, recordings can be conducted in most settings, especially for EEG. Figure 2.1 Magnetoencephalography scanner with patient. (From https://images.nimh.nih.gov/public_il/image_details.cfm?id=80. © National Institute of Mental Health, National Institutes of Health, Department of Health and Human Services.) Measuring the Brain’s Electrical Activity 23
/ Visualizing the Brain’s Structure and Function First utilized in the 1970s, magnetic resonance imaging (MRI) is one of the most widely used neuroimaging techniques today. MRI is still considered “state of the art” given its flexibility and sensitivity as a diagnostic imaging modality that is capable of characterizing a wide range of parameters. The fundamental concept of MRI lies in the discovery of nuclear magnetic resonance of protons in water molecules and its interaction with a magnetic field. Bloch and Purcell then measured the effective precessional spin properties of protons within a given magnetic field, thereby yielding an MRI signal (Block et al., 1946; Purcell et al., 1946). During an MRI scan, a radiofrequency pulse is delivered that causes protons to spin in a different direction. When the radiofrequency pulse is turned off, the protons return back to their low-energy state and their normal alignment within the magnetic field. This return to the lowenergy state or relaxation causes release of stored energy in the form of light, which is detected by the magnetic resonance scanner and is converted to the images that we see (Figure 2.2). MRI yields high-resolution images of brain macro- and microstructure, function and neurochemical composition (Figure 2.3). Structural MRI scans provide static images of the brain’s anatomy. From these images, quantification of the structural dimensions of brain regions (e.g. volume), shape and tissue composition can be determined. On a microstructural level, diffusion tensor imaging (DTI) detects the movement of water molecules through tissue, thereby providing information on the architecture and integrity of white matterfibersin the brain. Applied magnetic field Precession Figure 2.2 Mechanisms of MRI. The MRI signal stems from the circling or precession of the spinning protons around the axis of the magneticfield (center arrow). 24 Human Neuroscience Approaches
/ DTI indexes can quantify the length of fiber bundles (e.g. tractography), as well as the directionality (e.g. fractional anisotropy) and diffusivity (e.g. trace) of water molecules through brain tissue. High fractional anisotropy and low diffusivity reflect healthy white matter (Figure 2.4). In addition to structural information, MRI also enables functional imaging that offers dynamic physiological information of the brain. Functional MRI (fMRI) paradigms provide near real-time information regarding task-induced as well as resting baseline state neural activation. The fundamental element of fMRI scans is the blood oxygenated leveldependent (BOLD) signal. Originally discovered by Seiji Ogawa in 1990, the BOLD signal refers to thein vivo change of blood oxygenation that leads to variation in the magnetic signal detectable with MRI. The BOLD signal therefore provides information on brain regions that have increased oxygenation as the result of being active and requiring more energy. It is therefore anindirect measure of neural function and relies on assumptions regarding underlying neuronal activity. fMRI also includes perfusion techniques (with or without endogenous or exogenous contrast), regional cerebral blood flow and cerebrospinal fluid pulsation measurements, as well as phase contrastflow measurements. Figure 2.3 A patient going through a magnetic resonance imaging machine. (From https://commons.wikimedia.org/wiki/File:US_Navy_030819-N-9593R-228_Civilian_technician,_ Jose_Araujo_watches_as_a_patient_goes_through_a_Magnetic_Resonance_Imaging,_(MRI)_ machine.jpg. CC-PD National Naval Medical Center, Bethesda, MD, 2003) Visualizing the Brain’s Structure and Function 25
/ Innovations in both scanner hardware and scan sequences continue to provide advancements in diagnostic MRI techniques. These improvements include higher field imaging up to 11.75 T (standard hospital MRIs are 1.5 or 3 T), multiband imaging via advanced coil technology, shorter echo time imaging and simultaneous scanning modalities including PET-MRI, SPECT-MRI and EEG-MRI, as well as the development of novel molecular MRI agents. Thus, continued advancements in our understanding of brain mechanisms via MRI techniques are still to come. Computed tomography (CT) and positron emission tomography (PET) also provide visualization of brain structure and function, respectively. However, with the advent of MRI, PET is now more widely used for detection of brain molecules and is discussed in greater detail in the following section. l1 = longitudinal (axial) diffusivity (AD) (l2+ l3 )/2 = radial diffusivity (RD) (l1+ l2+ l3 )/3 = mean diffusivity (MD) Isotropic Anisotropic l3 l3 l l2 2 l1 l1 Figure 2.4 Gray matter has predominantly isotropic (soccer ball-shaped) water diffusion, while dense white matter tracks have highly anisotropic (rugby ball-shaped) diffusion of water pointing in the direction of thefiber bundle. The measure most commonly used to characterize directional diffusion is fractional anisotropy (FA). This measure gives a value of between 0 and 1 to indicate the fraction of diffusion that is in the longitudinal direction compared with the proportion of diffusion in both transverse directions. Other measures that can be used are axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD). (From Whitfordet al., 2011.) (A black and white version of this figure will appear in some formats. For the color version, please refer to the plate section.) 26 Human Neuroscience Approaches
/ Biochemical Imaging Other imaging techniques provide quantification of brain molecules. These include magnetic resonance spectroscopy (MRS) (Figure 2.5), PET and single-photon emission computed tomography (SPECT). MRS is conducted using an MRI scanner and is based on radiofrequency signals or peaks within a spectrum that are unique to metabolites such as N-acetylaspartate (NAA), choline and creatine in brain tissue. Unlike MRS that does not use radioactive isotopes, PET and SPECT use radionucleotides that are injected into the individual. The advantages of PET and SPECT techniques include their ability to provide information 15000 Control HIV NAA Cr Cho 10000 5000 0 15000 NAA Cr Cho 10000 5000 0 HIV+Alcohol 15000 NAA Cr Cho 10000 5000 0 Alcohol 15000 NAA Cr Cho 10000 5000 0 5.0 4.0 3.0 2.0 1.0 0.0 5.0 4.0 3.0 2.0 1.0 0.0 5.0 4.0 3.0 2.0 1.0 0.0 5.0 4.0 3.0 2.0 1.0 0.0 Figure 2.5 MRS image of a 34-year-old man with human immunodeficiency virus (HIV) infection and alcohol dependence. The brain images show the parietal-occipital cortical region (in white) sampled by MRS for metabolite quanti fication. The graphs below show the MRS spectra of various brain metabolites in people with HIV infection alone, alcoholism alone, co-morbid HIV infection and alcoholism, and control subjects with neither condition. The peak representing the metaboliteN-acetylaspartate (NAA) shows a significant deficit in the HIV plus alcoholism group compared with the other groups. Cho, choline; Cr, creatine. (From Rosenbloom et al., 2010. © 2010 Alcohol Research: Current Reviews, USA.) Biochemical Imaging 27
/ on biochemistry. PET ligands can bind to molecules or neuroreceptors of interest such as glucose, dopamine, serotonin and opioid receptors. In this way, studies can quantify changes in glucose metabolism and receptors of interest. Both PET and SPECT detect γ-rays emitted from the decay of the radioactive tracer and convert these into images. However, they differ in that PET has better sensitivity for detecting γ-rays (up to 1000 times), the radiotracers have a shorter half-life and there is higher image quality relative to SPECT. In conclusion, the benefit of biochemical imaging is not only in informing mechanisms and potential biomarkers of disease states but also in establishing diagnoses and drug effects on neurotransmission and metabolism. Limitations of Neuroimaging Research Our current understanding of brain changes associated with addiction is limited by the feasibility of conducting these types of studies in humans. Specifically, while findings from association studies suggest potential mechanisms whereby addiction may relate to brain alterations, causality (i.e. that addiction led to brain changes or vice versa) can only be inferred rather than tested directly. In other words, are these brain alterations the chicken or the egg? The two possible scenarios to be considered are: 1) observed alterations are the direct result of exposure to substances; and 2) observed alterations existed prior to exposure to substances and are the risk factors that contribute to substance abuse and dependence. Without a prospective, longitudinal study that examines the brain before and after exposure to substances, the chicken or the egg debate may never be fully answered. However, there are various approachesthat attempt to provide some information that could suggest causation. Each one makes an attempt to advance our understanding; however, the vast majority of these studies contradict each other due to differences in approach. For instance, genetic, family, sibling and twin studies attempt to disentangle brain changes that may be associated with genetic factors versus exposure to substance. Our own work in cannabis users found an interaction between cannabinoid receptor genes and cannabis use on amygdala volumes, suggesting that the effect of cannabis interacts with genetic predisposition in determining the size of the amygdala (Schacht et al., 2012). However, a recent publication by Pagliaccioet al. (2015) reported no effect of cannabis use on amygdala volumes. Specifically, while the authors reported smaller amygdala volumes in cannabis users compared with non-users, there was no difference in amygdala volume between cannabis users and their siblings. These findings suggest that previously 28 Human Neuroscience Approaches
/ reported brain volume differences between users and non-users may not be due to cannabis, but rather be a genetically pre-determined brain alteration that puts one at risk for cannabis use. In short, much work remains to be done in this area, but the existing literature points to a very complicated picture likely involving a recursive function and involving several moderating and mediating variables. Summary Points • Advancements in neuroscience techniques have paved the way for the understanding that addiction is a brain disorder. • Neuroimaging techniques provide the ability to measure the electrophysiological, functional, structural and biochemical composition of the brain. • Brain imaging techniques provide evidence for associations between brain structure and function and behavioral symptoms of addiction. • Understanding neural mechanisms underlying behavioral symptoms of addiction is important in identifying potential targets for therapeutic interventions. • Future research should focus on determining the exact relationship between changes in the brain and exposure to substances. Review Questions • How have neuroimaging advancements informed our understanding of addiction? • How is EEG different from MEG? • What are the various techniques that can be used during MRI? • What can PET tell us that is different from MRI? • What chemicals can we measure using MRS? • What is the definition of “resting state” in neuroimaging terms? • What are the limitations should we keep in mind when interpreting neuroimaging findings? Further Reading Garrison, K. A. & Potenza, M. N. (2014). Neuroimaging and biomarkers in addiction treatment. Curr Psychiatry Rep, 16(12), 513. doi:10.1007/ s11920-014-0513-5 Further Reading 29