Behavioral Neuroscience of Drug Addiction (Current Topics in Behavioral Neurosciences)

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Current Topics in Behavioral Neurosciences Series Editors: Mark Geyer, La Jolla, CA, USA Bart Ellenbroek, Hamburg, Germany Charles Marsden, Nottingham, UK

About this series Current Topics in Behavioral Neurosciences provides critical and comprehensive discussions of the most significant areas of behavioral neuroscience research, written by leading international authorities. Each volume offers an informative and contemporary account of its subject, making it an unrivalled reference source. Titles in this series are available in both print and electronic formats. With the development of new methodologies for brain imaging, genetic and genomic analyses, molecular engineering of mutant animals, novel routes for drug delivery, and sophisticated cross-species behavioral assessments, it is now possible to study behavior relevant to psychiatric and neurological diseases and disorders on the physiological level. The Behavioral Neurosciences series focuses on ‘‘translational medicine’’ and cutting-edge technologies. Preclinical and clinical trials for the development of new diagnostics and therapeutics as well as prevention efforts are covered whenever possible.

David W. Self

l

Julie K. Staley

Editors

Behavioral Neuroscience of Drug Addiction

Editors Prof. Dr. David W. Self University of Texas Southwestern Medical Center Dept. Psychiatry 5323 Harry Hines Blvd. Dallas TX 75390-9070 USA [email protected]

Julie K. Staley{, Ph.D. Department of Psychiatry Yale University School of Medicine VACHS 116A2 950 Campell Ave. West Haven CT 06516 USA

ISSN 1866-3370 e-ISSN 1866-3389 ISBN 978-3-642-03000-0 e-ISBN 978-3-642-03001-7 DOI 10.1007/978-3-642-03001-7 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2009938007 # Springer-Verlag Berlin Heidelberg 2010 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microﬁlm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Product liability: The publishers cannot guarantee the accuracy of any information about dosage and application contained in this book. In every individual case the user must check such information by consulting the relevant literature. Cover illustration: Artistic representation of oscillatory synchrony and timing of neurons in networks by Gyorgy Buzsaki Cover design: WMXDesign GmbH, Heidelberg, Germany Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

Drug addiction is a chronically relapsing mental illness involving severe motivational disturbances and loss of behavioral control leading to personal devastation. The disorder afflicts millions of people, often co-occurring with other mental illnesses with enormous social and economic costs to society. Several decades of research have established that drugs of abuse hijack the brain’s natural reward substrates, and that chronic drug use causes aberrant alterations in these rewardprocessing systems. Such aberrations may be demonstrated at the cellular, neurotransmitter, and regional levels of information processing using either animal models or neuroimaging in humans following chronic drug exposure. Behaviorally, these neural aberrations manifest as exaggerated, altered or dysfunctional expression of learned behavioral responses related to the pursuit of drug rewards, or to environmental factors that precipitate craving and relapse during periods of drug withdrawal. Current research efforts are aimed at understanding the associative and causal relationships between these neurobiological and behavioral events, such that treatment options will ultimately employ therapeutic amelioration of neural deficits and restoration of normal brain processing to promote efforts to abstain from further drug use. The Behavioral Neuroscience of Drug Addiction, part of the Springer series on Current Topics in Behavioral Neurosciences, contains scholarly reviews by noted experts on multiple topics from both basic and clinical neuroscience fields. In the first two chapters, recent technological advances in the ability to monitor synaptic neuroplasticity and transient dopamine release events are discussed in relation to drug and alcohol addiction models. These studies have greatly advanced our understanding of how chronic drug exposure changes the responsiveness of primary reward substrates for drugs of abuse. Subsequent chapters illustrate how these events translate into addictive behavior and recruit additional brain regions involved in reward-related learning and behavioral disinhibition. Other chapters delve into the relationship between heightened drug responsivity and the propensity for relapse, and the neurobiology of anhedonia after chronic drug use is discontinued. Together, these chapters provide a focused and critical review of current animal models and methods along with functional relationships between v

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neurobiological and behavioral change. Many of the neurobiological and behavioral changes produced by chronic drug exposure in animals are reflected in human studies using modern neuroimaging and neurocognitive analyses, while others differ. The second part of the volume is dedicated to studies in human drug abusers, beginning with two chapters on alterations in drug and neurotransmitter receptor levels, dopamine release, and their relationship to drug taking and craving. In addition, the association of genomic markers with vulnerability to drug and alcohol addiction is reviewed in relation to genes known to be involved in transmitting drug signals and drug metabolism, and other approaches to identify novel genes associated with addicted phenotypes that could ultimately serve as targets for treatment. Three chapters discuss the various cognitive abnormalities that accompany drug addiction, including deficits in attention, memory, and executive control, susceptibility of the adolescent brain, and the impact of such changes on the inability to make appropriate and beneficial life choices in the context of a behavioral economic model of addiction. These latter chapters illustrate the global impact of druginduced alterations discussed in earlier chapters on complex neurocircuitry involved in the intricate interplay between cognitive processing and decisionmaking. Finally, while several non-pharmacological treatments for addiction have been explored, an explosion in potential pharmacological targets has lead to several novel treatments based on known and unknown mechanisms of action, and the latest findings are compared with more traditional approaches. The findings reviewed in this volume suggest that an emerging consensus exists for the underlying pathology of drug addiction. Basic neuroscience research conducted in animal models suggests that neuroadaptations in limbic brain regions promote drug-taking behavior by enhancing the neural substrates of primary drug reward, while weakening neural mechanisms of inhibitory control. The latter is paralleled in humans by deficits in neocortical function and executive/cognitive information processing. However, human neuroimaging studies also suggest that drug addiction is associated with deficits in dopaminergic neurotransmission, the major neural substrate for primary drug reward. These and other discrepancies indicate that more work is needed to reconcile the findings from animal models and human drug addiction. Furthermore, there are numerous other drug-induced changes that encompass regulation of gene expression, intracellular signaling molecules, and several other neurotransmitter, metabolic and morphological changes that have been identified, but their relevance to human behavioral change is unknown. Ultimately, it is important for animal models to better emulate human cognitive abnormalities so that critical cause-effect relationships between neurobiological and behavioral change may be determined. We hope the breadth of behavioral neuroscience endeavor contained in this volume will assist in directing future research aimed at integrating human and animal work towards a cohesive body of research with substantial implications for treatment. Dallas, TX West Haven, CT

David W. Self Julie K. Staley{

Dedication Dr. Julie Staley passed away on July 25, 2009 shortly after completing her work as co-editor for this book. Her struggle with a long-term illness never once diminished her devotion and vigor for scientific endeavor, and her profound dedication to this project. This book is dedicated to Julie, and the tireless enthusiasm she displayed in life remains an inspiration for all of us. Dr. Julie Staley

David Self

Kelly Cosgrove

Contents

Part I Preclinical Neuroscience Neuroplastic Alterations in the Limbic System Following Cocaine or Alcohol Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Garret D. Stuber, F. Woodward Hopf, Kay M. Tye, Billy T. Chen, and Antonello Bonci Dopamine Signaling in the Nucleus Accumbens of Animals Self-Administering Drugs of Abuse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Ingo Willuhn, Matthew J. Wanat, Jeremy J. Clark, and Paul E.M. Phillips Amygdala Mechanisms of Pavlovian Psychostimulant Conditioning and Relapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Deanne M. Buffalari and Ronald E. See Prefrontal Cortical Regulation of Drug Seeking in Animal Models of Drug Relapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Heather C. Lasseter, Xiaohu Xie, Donna R. Ramirez, and Rita A. Fuchs Neural Substrates of Psychostimulant Withdrawal-Induced Anhedonia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Manoranjan S. D’Souza and Athina Markou Sensitization Processes in Drug Addiction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Louk J.M.J. Vanderschuren and R. Christopher Pierce Part II Clinical Neuroscience Imaging Receptor Changes in Human Drug Abusers . . . . . . . . . . . . . . . . . . . . . . 199 Kelly P. Cosgrove

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Imaging Neurotransmitter Release by Drugs of Abuse . . . . . . . . . . . . . . . . . . . . 219 Diana Martinez and Rajesh Narendran Imaging Cognitive Deficits in Drug Abuse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Thomas Lundqvist Neural Markers of Genetic Vulnerability to Drug Addiction . . . . . . . . . . . . . 277 Daniel J. Mu¨ller, Olga Likhodi, and Andreas Heinz The Role of Executive Control in Human Drug Addiction . . . . . . . . . . . . . . . . 301 Robert Hester, Dan I. Lubman, and Murat Yu¨cel The Behavioral Economics of Drug Dependence: Towards the Consilience of Economics and Behavioral Neuroscience . . . . . . . . . . . . . . . . . . . 319 Warren K. Bickel, Richard Yi, E. Terry Mueller, Bryan A. Jones, and Darren R. Christensen Novel Pharmacological Approaches to Drug Abuse Treatment . . . . . . . . . . . 343 Ellen Edens, Alfredo Massa and Ismene Petrakis Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387

Contributors

Warren K. Bickel Departments of Psychiatry and Radiology, Center for Addiction Research, University of Arkansas for Medical Sciences, 4301 W. Markham # 554, Little Rock, AR 72205, USA, [email protected] Antonello Bonci Ernest Gallo Clinic and Research Center, University of California San Francisco, 5858 Horton Street, Emeryville, CA 94608, USA, [email protected] Deanne M. Buffalari Department of Neuroscience, Medical University of South Carolina, BSB 416 173 Ashley Avenue, Charleston, SC 29425, USA Billy T. Chen Ernest Gallo Clinic and Research Center, University of California San Francisco, 5858 Horton Street, Emeryville, CA 94608, USA Anna Rose Childress Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA, [email protected] Jeremy J. Clark Department of Psychiatry and Behavioral Science, University of Washington, Box 356560, Seattle WA 98195-6560, USA Kelly Cosgrove Departments of Psychiatry, Yale University School of Medicine & VACHS 116A6, 950 Campbell Avenue, West Haven, CT 06516, USA, [email protected]

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Manoranjan S. D’Souza Department of Psychiatry M/C-0603, University of California, 9500 Gilman Drive, La Jolla CA 92093-0603, USA Rita Fuchs Department of Psychology, University of North Carolina, CB #3270, Davie Hall, Chapel Hill, NC 27599-3270, USA, [email protected] Andreas Heinz Clinic for Psychiatry and Psychotherapy, Charite´ University Medicine, Campus Charite´–Mitte, Schumannstraße 20/21, 10117 Berlin, Germany, andreas.heinz@ charite.de F. Woodward Hopf Ernest Gallo Clinic and Research Center, University of California San Francisco, 5858 Horton Street, Emeryville, CA 94608, USA Heather C. Lasseter Department of Psychology, University of North Carolina, CB #3270, Davie Hall, Chapel Hill, NC 27599-3270, USA Daniel Lubman Department of Psychiatry, ORYGEN Research Centre, The University of Melbourne, Victoria, Australia and Department of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia, [email protected] Thomas Lundqvist Drug Addiction Treatment Centre, Lund University Hospital, 221 85 Lund, Sweden, [email protected] Athina Markou Department of Psychiatry M/C-0603, University of California, 9500 Gilman Drive, La Jolla, CA 92093-0603, USA, [email protected] Diana Martinez NYS Psychiatric Institute, 1051 Riverside Drive, New York, NY 10032, USA, [email protected] Wilbur D. Mills University of Arkansas for Medical Sciences, Departments of Psychiatry and Radiology, Center for Addiction Research, 4301 W. Markham # 554, Little Rock, AR 72205, USA

Contributors

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Ismene Petrakis Yale University School of Medicine, VA Connecticut Healthcare System, 950 Campbell Avenue #116A, West Haven, CT 06516, USA, [email protected] Paul E.M. Phillips Department of Psychiatry and Behavioral Science, University of Washington, Box 356560, Seattle WA 98195-6560, USA, [email protected] R. Christopher Pierce Department of Pharmacology and Anatomy, The Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands Donna R. Ramirez Department of Psychology, University of North Carolina, CB #3270, Davie Hall, Chapel Hill, NC 27599-3270, USA Ronald E. See Department of Neuroscience, Medical University of South Carolina, BSB 416 173 Ashley Avenue, Charleston, SC 29425, USA, [email protected] Garret D. Stuber Ernest Gallo Clinic and Research Center, University of California San Francisco, 5858 Horton Street, Emeryville, CA 94608, USA Kay M. Tye Ernest Gallo Clinic and Research Center, University of California San Francisco, 5858 Horton Street, Emeryville, CA 94608, USA Louk Vanderschuren Department of Pharmacology and Anatomy, The Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Universiteitsweg 100, 3584-CG Utrecht, The Netherlands, [email protected] Matthew J. Wanat Department of Psychiatry & Behavioral Science, University of Washington, Box 356560, Seattle WA 98195-6560, USA Ingo Willuhn Department of Psychiatry & Behavioral Science, University of Washington, Box 356560, Seattle WA 98195-6560, USA Xiaohu Xie Donna R. Ramirez, Department of Psychology, University of North Carolina, CB #3270, Davie Hall, Chapel Hill, NC 27599-3270, USA

Neuroplastic Alterations in the Limbic System Following Cocaine or Alcohol Exposure Garret D. Stuber, F. Woodward Hopf, Kay M. Tye, Billy T. Chen, and Antonello Bonci

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Ionotropic Glutamate Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3 Cocaine-Induced Synaptic Plasticity in Midbrain DA Neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 4 Cocaine-Induced Synaptic Plasticity in the NAc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 5 Amygdala Plasticity and Drugs of Abuse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 6 Alcohol and Plasticity in Glutamate Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 7 Altered Intrinsic Excitability After Alcohol or Cocaine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 8 Conclusions and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Abstract Neuroplastic changes in the CNS are thought to be a fundamental component of learning and memory. While pioneering studies in the hippocampus and cerebellum have detailed many of the basic mechanisms that can lead to alterations in synaptic transmission based on previous activity, only more recently has synaptic plasticity been monitored after behavioral manipulation or drug exposure. In this chapter, we review evidence that drugs of abuse are powerful modulators of synaptic plasticity. Both the dopaminergic neurons of the ventral tegmental area as well medium spiny neurons in nucleus accumbens show enhanced excitatory synaptic strength following passive or active exposure to drugs such as cocaine and alcohol. In the VTA, both the enhancement of excitatory synaptic strength and the acquisition of drug-related behaviors depend on signaling through the N-methyl-D-aspartate receptors (NMDARs) which are mechanistically thought to lead to increased synaptic insertion of a-amino-3-hydroxy-5-methyl-4isoxazolepropionic acid receptors (AMPARs). Synaptic insertion of AMPARs by drugs of abuse can be long lasting, depending on the route of administration, G.D. Stuber, F.W. Hopf, K.M. Tye, B.T. Chen and A. Bonci (*) Ernest Gallo Clinic and Research Center, Department of Neurology, University of California, San Francisco, CA, USA e-mail: [email protected]

D.W. Self and J.K. Staley (eds.), Behavioral Neuroscience of Drug Addiction, Current Topics in Behavioral Neurosciences 3, DOI 10.1007/7854_2009_23, # Springer‐Verlag Berlin Heidelberg 2009, published online 15 September 2009

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number of drug exposures, or whether the drugs are received passively or selfadministered. Keywords Plasticity Dopamine Glutamate Accumbens Ventral tegmental area Amygdala Drug abuse Reward

1 Introduction The mesolimbic dopamine (DA) system, formed in part by the ventral tegmental area (VTA) and nucleus accumbens (NAc), is an integral part of the brain’s natural reward circuit. The VTA is a major source of DA for brain circuits involved in encoding of reinforcement and learning, and the NAc is a critical node that integrates limbic and motivational input (including DA signals from the VTA) to influence behavioral output. Thus, these brain regions, in concert with other areas such as the prefrontal cortex, thalamus, and amygdala, are considered to play a critical role in the control of motivated and goal-directed behaviors, including the development and expression of addictive behavior (Cardinal et al. 2002; Epstein et al. 2006; Kalivas and McFarland 2003; Mogenson et al. 1980). In addition, recent studies suggest that addiction is a form of maladaptive learning, where aberrant neural links are formed between the action of taking drugs and the “reward” or alleviation of withdrawal-related negative states produced by the drugs (Hyman et al. 2006). For this reason, this chapter will briefly address the consequences of passive versus active exposure to drugs. Repeated passive exposure to a given drug can enhance or “sensitize” the locomotor-activating effects of that drug (for review, see Robinson and Berridge 1993). Since locomotor sensitization can be long-lasting and can enhance subsequent drug self-administration, sensitization has been considered a model of enhanced drug seeking during abstinence. However, although pharmacological effects through passive drug exposure can produce enduring plastic changes, human drug intake is typically active and voluntary, and associative learning between such volitional drug taking and the positive or negative reinforcing consequences may be a critical component in the development of addiction. Overall, this chapter seeks to address the hypothesis that drugs of abuse produce persistent changes in neuronal function that may drive drug seeking following periods of abstinence. The ability of drugs to alter neuronal function was originally investigated predominantly through biochemical methods such as Western Blot, which allows one to determine changes in protein levels or phosphorylation state of a given receptor or channel subunit. More recently, large-scale screening for changes in protein levels (using proteomics) or mRNA levels (using DNA microarrays) have shown great promise for indicating potential changes in receptor or channel function. Importantly, ex vivo electrophysiological techniques in brain slice have

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allowed direct examination of functional changes in excitatory synaptic strength or ion channel activity after drug exposure. Electrophysiological studies are particularly critical because they provide detailed information about the functional state of a given receptor or channel, which can occur without concurrent alterations in total protein or mRNA in brain tissue. Thus, we will examine evidence that abused substances can cause long-term changes (after one day or more of withdrawal) in glutamate receptor and ion channel function in the VTA and NAc. While many other brain regions such as the prefrontal cortex play a very important role in drugdependent behaviors (Kalivas et al. 2005), those brain regions will not be addressed here. Furthermore, we will focus here on cocaine and alcohol, although many interesting studies have also been performed in relation to other abused drugs such as morphine, nicotine, and amphetamine.

2 Ionotropic Glutamate Receptors Ionotropic glutamate receptors are generally categorized into one of two distinct classes. Activation of a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) by glutamate leads to fast onset/fast offset depolarization, which contributes to action potential induction in many cells (Bredt and Nicoll 2003). In contrast, N-methyl-D-aspartate receptors (NMDARs) have slower kinetics and are voltage-activated. That is, at resting membrane potentials they are blocked by Mg2+ and no current can pass. However, as the neuron becomes more depolarized, the Mg2+ block is alleviated, which allows for the passage of cations including Ca2+ when glutamate is bound to the receptor. Therefore, currents through the NMDAR both depolarize the neuron and elevate intracellular Ca2+, which is critical for induction of many enduring forms of synaptic plasticity. AMPARs are hetero or homomeric complexes composed of four subunit proteins (GluR1-4) (Hollmann and Heinemann 1994). In many brain regions including both the VTA and NAc, AMPARs are thought to exist as heteromeric complexes containing both GluR2/3 and GluR1 subunits in a basal state (Liu and Zukin 2007). At some synapses neuroplastic changes in AMPAR function are associated with the synaptic insertion of GluR1 subunits that form homomeric receptors lacking GluR2/3 (Bellone and Lu¨scher 2006; Plant et al. 2006). The formation and insertion of GluR2/3-lacking (GluR1 homomeric) receptors is of particular importance as these receptors pass more current while hyperpolarized at resting membrane potentials compared to when they are depolarized (rectification) as well be being permeable to Ca2+, which can facilitate further Ca2+-dependent changes in plasticity. To investigate the functional consequence of changes in GluR subunit composition of AMPARs, several compounds that are capable of selectively blocking GluR2-lacking AMPARs, such as 1-naphthylacetylsperimine (Naspm), Joro spider toxin, or philanthotoxin-7,4, have been used (Argilli et al. 2008; Conrad et al. 2008; Nilsen and England 2007).

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3 Cocaine-Induced Synaptic Plasticity in Midbrain DA Neurons There is a large body of literature demonstrating a role of DAergic transmission in learning and reinforcement (for reviews see Kelley 2004; Salamone et al. 2005; Schultz and Dickinson 2000; Wise 2004). Midbrain DA neurons exhibit bimodal firing rates (2–7 Hz tonic; 10–30 Hz Bursting) (Freeman and Bunney 1987; Schultz 1998). Tonic firing is hypothesized to lead to ambient, low levels of DA (~20 nM) capable of activating high affinity D2 receptors, while burst firing is hypothesized to cause transient surges in DA concentrations (>100 nM), which would be able to act on lower affinity D1 receptors (Missale et al. 1998). Burst firing of DA neurons in particular seems to be of distinct importance as cues that are associated with reward, which can serve to initiate goal-directed behavior, lead to phasic DA release after cue-reward learning (Phillips et al. 2003; Stuber et al. 2005). Thus, phasic activation of midbrain DA neurons is thought to promote goal-directed behavior, especially in relation to reward-predictive stimuli. Importantly, glutamatergic signaling potently modulates DAergic neuronal activity. Ionotropic glutamate receptor agonists can potently increase firing rates of DAergic neurons in vivo (Chergui et al. 1993; Grace and Bunney 1984; Johnson et al. 1992; Murase et al. 1993), while ionotropic antagonists attenuate firing (Charlety et al. 1991; Chergui et al. 1993). Furthermore, modeling studies predict that changes in AMPAR number or function should lead to profound alterations in the firing pattern of DA neurons, perhaps by indirectly inducing NMDAR activity which could lead to burst firing (Canavier and Landry 2006). Consistent with this notion, synapses onto DA neurons are highly plastic, exhibiting NMDARdependent long-term potentiation (LTP) (Liu et al. 2005; Ungless et al. 2001) and long-term depression (LTD) (Bonci and Williams 1996), as well as short-term plasticity involving changes in the amount or function of synaptic AMPA receptors (Bonci and Malenka 1999). Thus, it is hypothesized that such plasticity could modify AMPAR responses during environmental events that increase glutamate release onto DA neurons, and significantly alter the firing of DA neurons during goal-directed behavior. Indeed, nonassociative learning in relation to behavioral sensitization to cocaine, and cocaine- and morphine-induced conditioned place preference, are both blocked by VTA NMDAR antagonists (Harris and AstonJones 2003; Harris et al. 2004; Kalivas and Alesdatter 1993). Ungless et al. (2001) were the first to report that the ratio of AMPAR-mediated current to NMDAR-mediated current (termed the AMPAR/NMDAR ratio) is significantly elevated at excitatory synapses in the VTA relative to saline-injected controls 24 h after a single exposure to cocaine. The increase in the AMPAR/ NMDAR current ratio after cocaine exposure occludes further potentiation and induction of LTP, suggesting that these synapses already exist in an LTP-like state. Importantly, LTP of VTA glutamatergic synapses is observed after a single exposure to many other drugs of abuse, demonstrating a convergence of cellular responses within the VTA by all abused drugs (Saal et al. 2003). The mechanism underlying LTP of excitatory synapses onto VTA DA neurons appears to be

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mediated by the initial insertion of GluR2-lacking AMPARs (Bellone and Lu¨scher 2006; Lu¨scher and Bellone 2008; Mameli et al. 2007). This insertion of new GluR2-lacking AMPAR subunits is thought to be transient, so that the homomeric GluR1 AMPA receptors are eventually replaced by newly synthesized GluR2/3 after many hours. Furthermore, the insertion of GluR2-lacking AMPARs can be reversed by activation of the metabotropic glutamate receptor (mGluR) mGluR1, which induces replacement of GluR2-lacking receptors with GluR2-containing receptors. This is hypothesized to readjust synaptic strength back to basal levels and therefore prevent behavioral changes that could contribute to development of addiction. Thus, LTP observed in VTA DA neurons after a single cocaine exposure is transient, exhibiting potentiated AMPAR currents 5 but not 10 days later (Ungless et al. 2001). The same time course for potentiated VTA AMPAR currents has also been observed following repeated cocaine injections (Borgland et al. 2004), suggesting that increased cocaine exposure does not increase the duration of the VTA AMPAR enhancement. It also is interesting that a single cocaine injection produces changes in opiate conditioned place preference and aversion with a similar time course (altered conditioning 5 but not 10 days after cocaine exposure), and these effects of cocaine were prevented by inhibition of NMDARs in the VTA during cocaine exposure (Kim et al. 2004). These studies suggest that many forms of LTP require Ca2+ influx through NMDARs, and enhanced AMPAR number or signaling in the VTA following cocaine exposure could act to facilitate DA-mediated learning. Consistent with this hypothesis, establishment of place preference for both cocaine and morphine depends on NMDAR signaling (Harris and Aston-Jones 2003; Harris et al. 2004), as does conditioned approach behavior to cues that predict natural rewards (Stuber et al. 2008b). Reinstatement of cocaine-seeking behavior (either by electrical stimulation or by cocaine priming) following cessation of self-administration also is blocked by intra-VTA glutamate antagonism (Sun et al. 2005; Vorel et al. 2001). Pathological drug use is thought to highjack natural associative learning mechanisms in order to facilitate and exacerbate drug-seeking behavior (Hyman et al. 2006; Kelley 2004). Since the process of active drug-seeking behavior is very different from passive experimenter-delivered drug exposure, it is critical to investigate changes in synaptic plasticity in the VTA following voluntary drug-self administration. Chen and colleagues (2008) found that voluntary cocaine self-administration in rats increased excitatory synaptic strength in VTA DA neurons for up to 3 months following cessation of chronic self-administration, in stark contrast to passive involuntary cocaine administration that potentiates excitatory synaptic strength for ~1 week as described above. Interestingly, yoked rats that received the same amount and temporal pattern of involuntary cocaine did not show an increase in synaptic strength. Finally Changes in VTA NMDAR levels have also been observed during cocaine withdrawal (Lu et al. 2003; Hemby et al. 2005), although electrophysiological studies have not been performed to corroborate these results. Taken together, these data suggest that a number of behavioral conditions can produce short-term plasticity in the VTA (passive cocaine exposure, self-administration of natural reinforcers), but the combined outcome of cocaine’s pharmacological effect with

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the animal’s volition to self-administer cocaine can produce a long-lasting potentiation of glutamate transmission onto VTA DA neurons.

4 Cocaine-Induced Synaptic Plasticity in the NAc The NAc is another integral part of the brain’s reinforcement circuit, and many studies indicate that synaptic plasticity in the NAc can be altered by cocaine exposure. Neuroplasticity of glutamatergic synapses onto medium spiny NAc neurons was first observed following passive, experimenter-administered cocaine injections (Thomas et al. 2001), which found LTD at these synapses. Interestingly, in contrast to the VTA where LTP is elicited after a single cocaine exposure, NAc synaptic plasticity was observed only after five daily cocaine injections (Kourrich et al. 2007; Thomas et al. 2001). However, after extended withdrawal from repeated cocaine exposure, mice show both cocaine-induced behavioral sensitization and LTP ex vivo in NAc shell but not core. This LTP is reversed back to the basal state if animals received a cocaine injection 24 h before recording. Thus, the history of cocaine exposure and withdrawal can readily change the direction of synaptic plasticity in the NAc. The behavioral consequence of NAc LTD after repeated amphetamine exposure was demonstrated in an elegant experiment whereby a GluR-trafficking modulator prevented both NAc LTD induction ex vivo and expression of behavioral sensitization to amphetamine (Brebner et al. 2005). In addition, unlike the VTA, AMPAR potentiation in the NAc following experimenter-administered amphetamine is not attributed to changes in the composition of AMPAR subunits, since analyses of rectification index revealed no rectification before or after amphetamine exposure. These results are consistent with the finding that blocking constitutive recycling of GluR2-containing AMPARs prevented NAc LTD and reduced expression of amphetamine-induced sensitization. In parallel to studies on plasticity in the VTA, plasticity in the NAc following chronic cocaine revealed differences whether cocaine was administered passively or by voluntary self-administration (Martin et al. 2006). In cocaine self-administering rats, the ability to induce LTD in the core and shell of the NAc was occluded one day after chronic cocaine. However, after 21 days of forced abstinence, LTD induction remained occluded in the NAc core, but could be readily induced in the NAc shell, suggesting that LTD in shell synapses had reverted to normal. These results suggest that voluntary cocaine self-administration induces long-lasting glutamatergic neuroadaptations exclusively in the NAc core, a region associated with control of behavior by drug-related stimuli and relapse to drug seeking. In support of this finding, a number of studies have observed increased NAc GluR1 levels after cocaine self-administration. Importantly, a recent study found an increase in cell surface GluR1 subunits in the NAc, during abstinence from cocaine self-administration (Conrad et al. 2008). Increased GluR1 levels are associated with an increase in rectification in synaptic AMPAR currents, as would be expected from plasma

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membrane insertion of GluR1-containing AMPARs. Importantly, to demonstrate that addition of GluR1-containing AMPARs plays a significant role in cue-induced cocaine relapse this study showed that intra-NAc injection of an antagonist selective for GluR1-containing AMPARs significantly reduced the enhancement in cocaine seeking that occurs after longer withdrawal times. In another recent study (Anderson et al. 2008), viral interference of GluR1 membrane insertion in the NAc shell attenuated drug-induced reinstatement. Although there are some differences in the methods used to elicit cocaine seeking in these studies (i.e., context re-exposure vs. drug-induced reinstatement), it is clear that accumbens GluR1 is important for the expression of cocaine-seeking behavior. In addition to altered AMPAR function, recent work has shown the importance of changes in the Homer proteins, scaffolding proteins that bind directly to mGluRs and indirectly to NMDARs, as critical neuroadaptations that can drive cocaine seeking (Swanson et al. 2001). Repeated cocaine and abstinence is associated with reduced NAc protein levels of Homer1b/c and Homer2a/b isoforms, and group I mGluRs (mGluR1 and mGluR5) (see also Mitrano et al. 2008). Activation of group I mGluRs within the NAc can increase NAc glutamate levels and produce locomotor activation. However, mGluR enhancement of NAc glutamate levels and locomotor activation is blunted after 3 weeks but not 24 h of withdrawal from repeated cocaine injection, in agreement with reduced mGluR and Homer protein levels.

5 Amygdala Plasticity and Drugs of Abuse Within the amygdala, there is a great degree of subregion heterogeneity. Two amygdala subregions have been of particular interest in the context of plasticity related to drug-seeking behavior. The basolateral amygdala (BLA; composed of the lateral, basomedial, basal and accessory basal nuclei) plays a critical role in associating environmental stimuli with primary rewards (Cador et al. 1989; Cardinal et al. 2002; Davis and Whalen 2001; LeDoux 2003, 2007; Maren and Quirk 2004), which may lead to relapse of drug seeking (Everitt et al. 1999; Robbins et al. 2008). The BLA is markedly different from the central nucleus of the amygdala (CeA) in both structure and function. The CeA is thought to play a critical role in the development of ethanol dependence, purportedly by modulating anxiety or stress that may increase ethanol intake (Bajo et al. 2008; Hyytia and Koob 1995; Koob, 2004, 2009; Rassnick et al. 1993b; Roberts et al. 1996). Drug conditioning plays a tremendous role in the persistence of drug addiction, and understanding how these learned responses are encoded is critical to developing drug addiction treatments. The lateral amygdala (LA), a dorsal subnucleus of the BLA, is a site of initial convergence for afferents transmitting sensory information about conditioned and unconditioned stimuli (Azuma et al. 1984; Doron and Ledoux 1999; LeDoux 2003; Maren and Quirk 2004; McDonald 1998; Nakashima et al. 2000). This may explain why the LA typically exhibits plasticity upon the acquisition of the association between a conditioned stimulus and a primary reward

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(Cador et al. 1989; Everitt et al. 1999; Jentsch et al. 2002; Thomas and Everitt 2001; Tronel and Sara 2002; Tye et al. 2008), while regions such as the VTA or NAc show robust molecular and synaptic changes after a single exposure to cocaine (Ghasemzadeh et al. 2003; Grignaschi et al. 2004; Ungless et al. 2001). It is likely that drugs act as powerful reinforcers by hijacking the natural reward circuitry, and that the amygdala-mediated formation of conditioned stimulus (CS) drug associations endows the CS with the power to maintain (Arroyo et al. 1998; Goldberg 1975; Goldberg et al. 1975), prolong (Ciccocioppo et al. 2004; Ranaldi and Roberts 1996) and induce reinstatement of drug-seeking behaviors (Fuchs et al. 2006; Grimm and See 2000; Kantak et al. 2002; Meil and See 1996; See et al. 2001). Relapse, which is often triggered by exposure to the drug, stress, or to cues associated with the drug experience, is a major clinical problem and one of the greatest challenges of addiction. In cocaine addicts, the presentation of cocaineassociated stimuli during abstinence can elicit intense drug craving (O’Brien et al. 1998), which is accompanied by physiological arousal, including increased heart rate and skin conductance (Childress et al. 1988; Ehrman et al. 1992), which corresponds to amygdala activation (Childress et al. 1999). The amygdala activation evoked by self-reported craving in humans is also observed in animals during drugseeking behaviors. Animal models can be utilized to explore the ability of environmental cues to guide reward-seeking behavior. Additionally, LTP has been observed in the lateral amygdala during cocaine withdrawal (Goussakov et al. 2006). While rats will readily lever press to self-administer primary reinforcers, such as sucrose, cocaine, or alcohol, they will also respond for reward-paired cues in the absence of the primary reward (Davis and Smith 1976; Grimm et al. 2002; Meil and See 1997; Nie and Janak 2003). The BLA has been shown to be necessary for second-order conditioning for both natural and drug rewards (Cador et al. 1989; Cardinal et al. 2002; Davis and Whalen 2001; LeDoux 2003, 2007; Maren and Quirk 2004). Moreover, evidence suggests that BLA function is specific to reinforcing properties of the reward-associated cue and does not affect reinforcing properties of the reward itself (Balleine et al. 2003). BLA lesions attenuate responding to a cue associated with a natural reinforcer such as sexual interaction, but do not alter sexual behavior itself (Everitt 1990). Furthermore, BLA lesions do not alter cocaine self-administration but attenuate the ability of cocaine-associated cues to reinstate extinguished responses (Meil and See 1997). Acute inactivation of the BLA prevents both cue-induced and drug priming-induced reinstatement to heroin-seeking behavior (Fuchs and See 2002). Other manipulations of the BLA, including muscarinic receptor antagonism (See et al. 2003), and DA receptor antagonism (Berglind et al. 2006) also impair the ability of cocaine-associated cues to induce reinstatement of cocaine-seeking behavior. It is also noteworthy that electrical stimulation of the BLA is sufficient to reinstate cocaine-seeking behavior (Hayes et al. 2003) and that LTP has been observed in the lateral amygdala during cocaine withdrawal (Goussakov et al. 2006). Importantly, extracellular concentrations of DA in the amygdala are tightly correlated with cocaine self-administration (Hurd and Ponten 2000). Specifically, intra-amygdala administration of selective DA antagonists reduces cocaine self-administration (Caine et al. 1995), and increasing or reducing

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the concentrations of DA in the amygdala can increase or reduce cocaine selfadministration (Hurd et al. 1997; Hurd and Ponten 2000). Whether these findings pertain to the cocaine-associated cues that are presented during self-administration or to the drug itself remains to be determined, but they clearly delineate a critical role for the amygdala in cocaine self-administration. The BLA is critically involved in the formation and expression of associations between sensory cues and rewarding or aversive stimuli (Davis and Whalen 2001; Gallagher 2000; LeDoux 2007; McGaugh 2002). Specifically, when animals are trained to respond to a reward that is paired with a predictive cue and responding is subsequently extinguished by the omission of the cue and reward, presentation of the cue alone increases responding. Evidence of BLA encoding of rewardassociated cues has been observed via in vivo electrophysiological recordings. BLA neurons are phasically excited during cocaine self-administration and by cocaine-associated cues (Carelli et al. 2003) as well as in response to cues associated with natural rewards such as sucrose (Tye and Janak 2007). Dynamic changes in local levels of immediate early genes indicate the occurrence of synaptic plasticity in the amygdala following activation or manipulation of stimulus-drug associations. For example, antagonism of the NMDAR has been shown to reduce the expression of the plasticity-related immediate early gene Zif268 during classical conditioning (Mokin and Keifer 2005). Furthermore, the protein products of immediate early gene Zif268 are upregulated in the BLA, but not the CeA, following exposure to discrete cues previously associated with cocaine self-administration (Thomas et al. 2003). The infusion of Zif268 antisense oligodeoxynucleotides into the BLA abolished the previously acquired conditioned reinforcing properties of the drug-associated stimulus (Lee et al. 2005). Acute NMDAR antagonism in the BLA prior to memory reactivation also disrupted the drug-associated memory, impairing the ability of the cocaine-associated cue to exert its acquired conditioned reinforcing properties, and resulted in reduced expression of Zif268 (Milton et al. 2008). In contrast to the BLA, the CeA is widely considered to be critically involved in mediating the behavioral effects of ethanol (Gilpin et al. 2008; Koob, 1998, 2003, 2004, 2009; Koob et al. 1998; McBride et al. 2005; Richter et al. 2000; Roberto et al. 2004a, b). There is a strong connection between anxiety states and alcohol dependence (Heilig and Egli 2006; Koob 2003, Pandey et al. 2003). Acute ethanol exposure induces anxiolytic effects associated with increased brain-derived neurotrophic factor (BDNF) and tyrosine kinase B (trk B) expression, increased expression of the immediate early gene activity-regulated cytoskeleton-associated protein (Arc), and increased dendritic spine density in the CeA, but not the BLA (Pandey et al. 2008). The activation of neuropeptide Y (NPY) in the CeA can impair the motivational aspects associated with ethanol dependence (Pandey et al. 2003). Specifically, administration of NPY into the CeA attenuates the increase in drug intake associated with alcohol dependence (Gilpin et al. 2008; Thorsell et al. 2007). In vivo microdialysis and in vitro electrophysiological studies revealed that both acute and chronic ethanol alter glutamatergic transmission in the CeA (Roberto et al. 2004b). Taken together, this constellation of ethanol-related neuroadaptations

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strongly implicates the critical involvement of the CeA in alcohol abuse and dependence. The CeA is heavily involved in the anxiety related to ethanol withdrawal (Koob 2003; McBride et al. 2002), as multiple measurable changes occur in the CeA during withdrawal. Accumulating evidence shows that acute withdrawal from many drugs of abuse, including cocaine (Fu et al. 2007; Richter and Weiss 1999) and alcohol (Funk et al. 2006; Koob 2003; Merlo Pich et al. 1995; Olive et al. 2002) produce common increases in reward thresholds, anxiety-like responses, and extracellular levels of CRF in the CeA. Understanding the anxiety related to ethanol withdrawal involves a complex neurobiology involving the interaction of multiple systems. The CRF and norepinephrine systems have been shown to be closely entwined in the amygdala (Dunn et al. 2004; Emoto et al. 1993; Roozendaal et al. 2008; Smith and Aston-Jones 2008), as they reciprocally activate one another (Koob 1999a, b). Both ethanol and CRF enhance GABA release in the CeA (Nie et al. 2004) via a common pathway involving protein kinase C epsilon (Bajo et al. 2008). Another substrate for withdrawal-related anxiety is in cAMP-responsive element-binding (CREB) phosphorylation in the CeA, which is decreased during withdrawal following chronic ethanol exposure (Pandey et al. 2003). Additionally, partial deletions and deficits in amygdaloid (CREB) protein contribute to increased ethanol consumption and a predisposition to alcoholism and alcohol-related anxiety (Pandey 2004; Pandey et al. 2005).

6 Alcohol and Plasticity in Glutamate Receptors Alcohol abuse is considered the third leading preventable cause of human death (Mokdad et al. 2004). Also, because it is legal to obtain and linked to increased aggression and violence, alcoholism extracts enormous social and economic costs relative to other drugs of abuse (see Buck and Harris 1991; Harwood et al. 1998; Larimer et al. 1999; Sanchis-Segura and Spanagel 2006). Thus, changes in neuronal function after long-term alcohol intake that contribute to pathological, compulsive alcohol seeking and relapse are of great interest. In particular, alcohol seeking and intake occurs even in the face of detrimental consequences, which represents a major clinical hurdle during the process of overcoming alcohol use disorders. Interestingly, in humans, relapse to alcohol seeking is commonly associated with the appearance of negative affective states or other stressful events, as well as to alcohol-related cues (Larimer et al. 1999; Sanchis-Segura and Spanagel 2006). Relapse can thus be associated with more stressful and psychological symptoms early during withdrawal, or can be associated with increased susceptibility for relapse even after prolonged abstinence. Further, rodent studies have suggested that stress- and cue-related enhancement of drug seeking are critically regulated by the VTA and by target regions of the VTA such as the NAc (see Kalivas and McFarland 2003; Wang et al. 2007), where DA release can interact with

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glutamate-driven firing to modulate expression of behavior. Thus, we will first focus on changes in glutamatergic function in the VTA and NAc after alcohol. A number of studies using diverse alcohol exposure models have examined the effect of repeated alcohol exposure on NMDA receptor function, and several brain regions, including the NAc/striatum, show enhanced NMDAR function during early withdrawal from alcohol (Buck and Harris 1991; Dodd et al. 2000; Gulya et al. 1991; Siggins et al. 2003; Szumlinski et al. 2008a, b; Wang et al. 2007; Zhao and Constantine-Paton 2007); but see (Winkler et al. 1999). In general, increased NMDAR function during withdrawal from alcohol is thought to increase neuronal excitability and drive aversive withdrawal symptoms that increase alcohol consumption. Given the role of negative emotional and physical states in promoting alcohol seeking, NMDAR inhibitors are an attractive clinical intervention to reduce early withdrawal symptoms and decrease relapse during early withdrawal. The exact change in particular NMDAR subunit levels varies among brain regions and alcohol exposure. In the NAc, both NR1 and NR2B have been found to be increased during early withdrawal from alcohol, in addition to increased mGluR1 and Homer 2b (see below) (Szumlinski et al. 2008a, b; Zhao and Constantine-Paton 2007). Some studies have also observed alterations in NMDAR function without changes in subunit levels. For example, long-term alcohol intake can strongly upregulate the NR1-2 isoform in the NAc, which lacks an NR1 C1 cassette that is necessary for efficient trafficking or anchoring of NMDARs to the postsynaptic density, and the pattern of functional changes in NMDAR depends on whether alcohol exposure is intermittent or continuous (Zhao and Constantine-Paton 2007). In the VTA, chronic forced alcohol is associated with increased NMDAR subunit levels (Ortiz et al. 1995), although one study found reduced NMDA excitation of VTA firing in vitro during withdrawal from alcohol (Bailey et al. 1998). In addition to NMDAR changes, VTA AMPAR function is enhanced after alcohol self-administration (Stuber et al. 2008a), in agreement with an earlier study showing increased VTA GluR1 levels after long-term forced alcohol intake (Ortiz et al. 1995). Although enhanced glutamate receptor function might increase VTA excitability (Canavier and Landry 2006), early withdrawal from alcohol is associated with a decrease in activity of VTA neurons and a decrease in DA release in VTA terminal regions (Bailey et al. 1998; Diana et al. 1993; Weiss et al. 1996). This hypoactivity of the DA system is reversed by systemic inhibition of NMDARs (Rossetti et al. 1992). Thus, NMDARs in a brain region other than the VTA may control VTA firing activity during early withdrawal from alcohol. Importantly, self-administration of alcohol during withdrawal continues until NAc DA levels are normalized (Weiss et al. 1996), in agreement with observations that modulation of alcohol intake is sensitive to altering DA signaling in the NAc (Hodge et al. 1997; Rassnick et al. 1993a; Rassnick et al. 1993b; Samson and Chappell 2004) or altering VTA DA neuron activity (Hodge et al. 1993; Rodd et al. 2004). Finally, although VTA DA neurons may exhibit hypoactivity during withdrawal from alcohol, enhanced NMDAR and AMPAR function in the VTA after chronic alcohol could facilitate VTA neuron activity during consumption of alcohol, since alcohol exposure can facilitate VTA neuron firing

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(Brodie et al. 1990; Diana et al. 1993; Mereu et al. 1984) and increases DA release in the NAc (Gonzales and Weiss 1998). Of potentially great interest are possible long-term changes in VTA glutamatergic function after longer withdrawal from alcohol that may parallel alterations in glutamatergic function in both the NAc and VTA after withdrawal from cocaine self-administration (see above). In particular, enhanced glutamatergic throughput could increase firing of NAc or VTA neurons and modulate alcohol intake or facilitate relapse in the presence of alcohol-related conditioned stimuli. Altered glutamatergic function after long-term withdrawal from alcohol has been found in the NAc, where a different pattern of changes is observed relative to cocaine exposure. After alcohol, the NAc shows increases rather than decreases in NR2B, mGluR1, and Homer2b (see Szumlinski et al. 2008a, b). These altered glutamate receptor levels are observed after 2 weeks withdrawal following 3 months of alcohol drinking; after 2 months withdrawal, only an increase in Homer2b is observed. Changes in all three glutamate receptor proteins also are observed after short-term passive exposure or binge drinking (Szumlinski et al. 2008a, b; Zhao and Constantine-Paton 2007), and thus the time course of glutamate receptor changes may vary depending upon the duration and route of alcohol exposure. Interestingly, viral overexpression of Homer2b in the NAc increases alcohol preference and alcohol-related place preference, suggesting that mimicking the drinking-induced enhancement in Homer2b is sufficient to increase alcohol preference and reinforcement. Interestingly, overexpression of Homer2b in the NAc increases alcoholrelated NAc glutamate and DA release. This pattern has been associated with increased alcohol preference, and is also linked to regulation of glutamate release by mGluR1/5. Thus, increased NAc Homer2b levels, acting through trafficking of mGluRs and modulation of glutamate release, could facilitate the reinforcing effects of alcohol after chronic intake, and in this way promote alcohol seeking during withdrawal.

7 Altered Intrinsic Excitability After Alcohol or Cocaine In addition to modulation of neuronal excitability through plastic changes in glutamate receptor function, a number of studies in recent years have investigated the possibility that plastic changes in ion channel function can occur and be longlasting. Further, such intrinsic excitability changes could alter the ability of synaptically generated excitatory postsynaptic currents (EPSCs) to propagate along dendrites in order to influence action potential generation and neuronal firing. Thus, numerous ion channels in dendrites can amplify or retard passing EPSCs and greatly impact the ability of glutamate receptor activation to generate action potentials and generate LTD or LTP (Kauer and Malenka 2007). Particularly interesting is the possibility that persistent alterations in ion channel function could dramatically enhance the ability of relapse-inducing stimuli to drive firing of the VTA and NAc, and in this way promote craving and relapse.

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L-type voltage-gated calcium channels (LVGCC) are notably interesting, since altered LVGCC function seems to occur during early withdrawal from many abused drugs including alcohol, and thus could represent a common mechanism (for review, see Brooks et al. 2008; Buck and Harris 1991; Little 1999). Chronic alcohol exposure increases LVGCC levels in brain regions such as the hippocampus, with more mixed results in the few studies from the striatum (Lucchi et al. 1985; Woodward and Gonzales 1990). Also, systemic administration of LVGCC antagonists decreases alcohol withdrawal symptoms and the decreased DA levels normally evident during early withdrawal from alcohol and other drugs. Human studies are less clear with regard to changes in LVGCC in the brains of alcoholics (Kril and Harper 1989; Marks et al. 1989). However, there are human preclinical studies indicating that LVGCC antagonists may ameliorate early withdrawal symptoms, as well as reducing tolerance and cravings, with clearer evidence for drugs other than alcohol (Altamura et al. 1990; Rosse et al. 1994; Rush and Pazzaglia 1998). Interestingly, LVGCC in the striatum are required for induction of LTD (see Adermark and Lovinger 2007), and NAc LTD is necessary for the expression of behavioral sensitization to amphetamine (Brebner et al. 2005). While the mechanism underlying this requirement for LTD in behavioral sensitization is unknown, increased LVGCC levels could promote LTD induction after drug exposure and facilitate the development or expression of behavioral sensitization. Thus, like the NMDARs, LVGCC inhibitors may be a useful clinical intervention to counteract early withdrawal symptoms, even though the brain regions where LVGCC plasticity occurs and is necessary for withdrawal symptoms have not been clearly identified (see Whittington and Little 1991). Considerable work has examined changes in NAc ion channel activity after repeated cocaine injection and 3 days withdrawal. A number of functional changes that decrease intrinsic excitability are apparent in the NAc during withdrawal from cocaine, including increased potassium channel activity, decreased N- and R-type VGCC activity, and decreased sodium channel activity (Hu et al. 2004, 2005; Zhang et al. 2002). Also, calcium channels can regulate a number of physiological processes in addition to firing, including induction of glutamatergic plasticity, neurotransmitter release, and calcium-dependent activation of intracellular regulatory proteins such as kinases. These changes may reflect increased or decreased ion channel protein levels, but some cocaine-associated changes in channel function may be related to altered tonic activity of intracellular signaling molecules and ion channel phosphorylation or other modifications. Thus, as for NMDARs, investigations of plastic changes in receptor or channel function after chronic drug exposure need to carefully consider the many different regulatory steps that can impact activity of a receptor or channel, such as phosphorylation, trafficking, and membrane localization, since these might provide important information about potential therapeutic targets to reverse drug-related neuroadaptations that could facilitate relapse. Such psychostimulant-related changes in intrinsic excitability are not limited to the NAc, and have also been identified in the prefrontal cortex and subiculum (Cooper et al. 2003; Nasif et al. 2005).

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VTA DA neuron firing after alcohol exposure has also been studied. A number of groups have observed a reduction in firing during the first 1–6 days of withdrawal from alcohol (Bailey et al. 1998; Brodie 2002; Diana et al. 1993), a pattern also observed after withdrawal from exposure to other drugs of abuse (Ackerman and White 1992; Rossetti et al. 1992). However, the possible contribution of particular ion channels in this decreased VTA neuron excitability is poorly understood. Nonetheless, given the possible involvement of DA receptors in the regulation of many forms of alcohol- and drug-related behaviors (see above), changes in VTA ion channel function that persist beyond the early withdrawal period are of great interest. A recent study found that the function of the apamin-sensitive, SK-type calcium-activated potassium channel was significantly reduced in VTA DA neurons after 7 days withdrawal following repeated alcohol exposure (Hopf et al. 2007). Although alcohol exposure did not alter baseline firing rates, NMDA receptor activation increased burst firing in alcohol-treated animals, but increased the spike firing rate of VTA neurons in control animals. These findings are consistent with reductions in SK-type potassium channels in alcohol-treated animals, since inhibition of these channels facilitates glutamate-induced bursting in midbrain DA neurons (Johnson and Seutin 1997; Seutin et al. 1993; Waroux et al. 2005). Since bursting is associated with increased DA release in VTA terminal regions (see Grace 2000; Marinelli et al. 2006), reduced SK function could enhance DA release in response to alcohol-related stimuli and promote relapse to alcohol seeking. A small reduction in the Ih current was also observed after 7 days withdrawal from alcohol (Hopf et al. 2007), which was also observed after one day of withdrawal (Okamoto et al. 2006). Reduced Ih function did not change burst firing, but decreased the ability of VTA DA neurons to recover from hyperpolarization. Of particular importance are neuroadaptations that are evident after even longer periods of abstinence from alcohol. Alcoholism is considered a chronically relapsing disease (Larimer et al. 1999; Sanchis-Segura and Spanagel 2006), and neuroadaptations that persist for months to years of abstinence could play a critical role in the increased propensity for relapse in human alcoholics. In this regard, a recent study from our group observed reduced SK channel function in the NAc core after 3 weeks abstinence from alcohol self-administration, but not after abstinence from sucrose self-administration. Importantly, infusion of an SK activator into the NAc core reduced alcohol seeking but not sucrose seeking. In addition, the lateral dorsal striatum exhibited a strong basal SK regulation of firing (Pineda et al. 1992), which was not reduced during abstinence from alcohol selfadministration, and SK activators infused into the lateral dorsal striatum did not reduce alcohol seeking. Thus, SK activators were only effective in modulating alcohol seeking in regions where SK function was reduced, suggesting that SK activators might represent a novel therapeutic intervention in abstinent alcoholics. We hypothesize that decreased SK function in NAc core will enhance firing in NAc core neurons during exposure to alcohol-related stimuli, and so reduced NAc core SK function could promote behavioral activation by these alcohol-related stimuli and drive relapse. In addition, SK inhibition in the NAc is in agreement

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with an early study showing that abstinence after long-term alcohol exposure was associated with a reduced after hyperpolarization in the dentate gyrus (Durand and Carlen 1984) that is strongly although not uniquely mediated by SK potassium channels (Gu et al. 2005). Thus, results from multiple complementary techniques, including in vitro electrophysiology, biochemistry, and gene chip and proteomic studies have identified changes in SK and other ion channels and receptors after alcohol exposure. Additional behavioral pharmacology experiments have suggested that at least some of these neuroadaptations could represent critical changes in the NAc and other brain regions that might promote alcohol seeking during abstinence from alcohol.

8 Conclusions and Future Directions Although many important and tantalizing clues regarding the effect of chronic drug and alcohol exposure on plasticity in glutamate receptor and ion channel function have been demonstrated, many questions remain. Few studies have examined excitability changes beyond the period of early withdrawal, although recent studies of glutamate receptor and ion channel function have begun to characterize changes after long-term withdrawal from cocaine, alcohol, or heroin exposure. Identifying long-term changes is particularly critical since neuroadaptations persisting after weeks or months of abstinence may mediate long-term susceptibility to cravings and relapse. Importantly, these long-term neuroadaptations may also correlate with structural changes in spine density or dendritic architecture that can occur in the NAc after passive or active cocaine administration. It is also clear that selfadministration and passive administration of abused drugs can differentially alter gene expression and protein function (see Jacobs et al. 2002, 2004), perhaps due to learned cues that come to be associated with the act of voluntary self-administration and that can reinforce further alcohol or drug seeking in a conditioned, involuntary manner. Fortunately, the field possesses a battery of powerful techniques including membrane isolation and traditional Western Blot protein analyses, large scale genomic and proteomic methods, optogenetics, and ex vivo brain slice electrophysiology from animals that have learned to self-administer cocaine or alcohol. Although laborious and technically challenging in adult animals, we believe that ex vivo electrophysiology is especially critical for uncovering and defining which neuroadaptations might persist during abstinence, in part because potent functional changes can occur without altered total protein or mRNA levels. The advent of localized knockdown or cell-specific over expression of proteins of interest will aid in determining their functional role in addictive behavior. In addition, a more clear delineation of the molecular alterations apparent after abstinence could allow one to utilize specific pharmacological or molecular agents to reverse that harmful effects of molecular change (e.g., reversing the plasma membrane trafficking of particular AMPAR subunits), and perhaps provide a novel therapeutic intervention for addiction and alcoholism.

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Dopamine Signaling in the Nucleus Accumbens of Animals Self-Administering Drugs of Abuse Ingo Willuhn, Matthew J. Wanat, Jeremy J. Clark, and Paul E. M. Phillips

Contents 1

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3

4

The Dopamine System: Implication in Normal Behavior and Addiction . . . . . . . . . . . . . . . . . . 31 1.1 Drug Addiction and Dopamine Neurotransmission in Humans . . . . . . . . . . . . . . . . . . . . . . . 31 1.2 Drug Self-Administration as an Animal Model for Drug Addiction . . . . . . . . . . . . . . . . . . 31 1.3 Drug-Self-Administration and Dopamine in the Nucleus Accumbens . . . . . . . . . . . . . . . . 32 1.4 Anatomy of the Dopamine System and Dopamine Signal Transduction: Phasic and Tonic Release . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 1.5 Proposed Functions of Dopamine in the NAcc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Dopamine Detection in the Behaving Animal: In Vivo Microdialysis, Chronoamperometry, and Fast-Scan Cyclic Voltammetry (FSCV) . . . . . . . . . . . . . . . . . . . . . . . . 36 2.1 In Vivo Microdialysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2 Electrochemical Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Effects of Drugs of Abuse on Extracellular Dopamine Concentration in the NAcc . . . . . . . 38 3.1 The Dopamine Hypothesis of Addiction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2 Drug Effects on Dopamine Signaling Measured Over the Course of Minutes: Microdialysis Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.3 Drug Effects on Dopamine Signaling Measured Over the Course of Seconds to Hours: Chronoamperometry Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.4 Drug Effects on Dopamine Signaling Measured on a Subsecond Time Scale: FSCV Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Effects of Withdrawal from Drugs of Abuse on the NAcc Dopamine System . . . . . . . . . . . . 46 4.1 Tonic Dopamine During Withdrawal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.2 Phasic Dopamine During (Short-Term) Withdrawal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

I. Willuhn (*), M.J. Wanat, J.J. Clark, and P.E.M. Phillips Department of Psychiatry and Behavioral Sciences and Department of Pharmacology, University of Washington, Health Sciences Building, Box 356560, 1959 NE Pacific Street, Seattle, WA, 98195, USA e-mail: [email protected]

D.W. Self and J.K. Staley (eds.), Behavioral Neuroscience of Drug Addiction, Current Topics in Behavioral Neurosciences 3, DOI 10.1007/7854_2009_27, # Springer‐Verlag Berlin Heidelberg 2009, published online 15 September 2009

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Stimulus-Induced NAcc Dopamine Release in the Absence of Drug: Implications for Reinstatement of Drug Seeking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.1 Effects of Drug Cues on Tonic Dopamine Concentration in the NAcc . . . . . . . . . . . . . . . 49 5.2 Effects of Drug Cues on Phasic Dopamine Signaling in the NAcc . . . . . . . . . . . . . . . . . . . 50 6 The Role of NAcc Dopamine in Drug Addiction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 6.1 Motivation and Addiction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 6.2 Associative Learning and Addiction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 7 Different Functions for Phasic and Tonic Dopamine Transmission in Addiction . . . . . . . . . . 53 7.1 Dopamine Signaling in the Drug-Naı¨ve State (Fig. 2a) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 7.2 Immediate Effects of Drug Exposure (Fig. 2b) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 7.3 Long-Term Effects of Drug Exposure During Drug Withdrawal (Fig. 2c) . . . . . . . . . . . 57 8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Abstract Abuse of psychoactive substances can lead to drug addiction. In animals, addiction is best modeled by drug self-administration paradigms. It has been proposed that the crucial common denominator for the development of drug addiction is the ability of drugs of abuse to increase extracellular concentrations of dopamine in the nucleus accumbens (NAcc). Studies using in vivo microdialysis and chronoamperometry in the behaving animal have demonstrated that drugs of abuse increase tonic dopamine concentrations in the NAcc. However, it is known that dopamine neurons respond to reward-related stimuli on a subsecond timescale. Thus, it is necessary to collect neurochemical information with this level of temporal resolution, as achieved with in vivo fast-scan cyclic voltammetry (FSCV), to fully understand the role of phasic dopamine release in normal behavior and drug addiction. We review studies that investigated the effects of drugs of abuse on NAcc dopamine levels in freely moving animals using in vivo microdialysis, chronoamperometry, and FSCV. After a brief introduction of dopamine signal transduction and anatomy and a section on current theories on the role of dopamine in natural goal-directed behavior, a discussion of techniques for the in vivo assessment of extracellular dopamine in behaving animals is presented. Then, we review studies using these techniques to investigate changes in phasic and tonic dopamine signaling in the NAcc during (1) response-dependent and -independent administration of abused drugs, (2) the presentation of drug-conditioned stimuli and operant behavior in self-administration paradigms, (3) drug withdrawal, and (4) cue-induced reinstatement of drug seeking. These results are then integrated with current ideas on the role of dopamine in addiction with an emphasis on a model illustrating phasic and tonic NAcc dopamine signaling during different stages of drug addiction. This model predicts that phasic dopamine release in response to drug-related stimuli will be enhanced over stimuli associated with natural reinforcers, which may result in aberrant goal-directed behaviors contributing to drug addiction. Keywords Dopamine Drug self-administration Drugs of abuse Nucleus accumbens Drug addiction Fast-scan cyclic voltammetry Phasic and tonic dopamine signaling

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1 The Dopamine System: Implication in Normal Behavior and Addiction 1.1

Drug Addiction and Dopamine Neurotransmission in Humans

Abuse of psychoactive substances can lead to drug addiction, a maladaptive behavioral pattern of drug use that is often accompanied by drug tolerance and withdrawal symptoms and causes impairment, distress, and the habitual intake of the drug regardless of the devastating consequences (Diagnostic and Statistical Manual of Mental Disorders (DSM-IV); APA 2000). An estimated 5% of the world’s population aged 15–64 years used illicit psychoactive drugs in the past year (UN 2008). Legal psychoactive drugs, such as alcohol and tobacco, are used by at least one-quarter of the world’s adult population (UN 2008). Drug addiction is considered to be a chronic disorder because addicts are rarely able to maintain abstinence for extended periods of time despite an expressed desire to stay drugfree. This is because stress, re-exposure to the drug itself, and drug-associated cues often trigger the resumption of drug taking (Kalivas and McFarland 2003). Such cues can even promote drug-seeking behavior outside of awareness that eventually results in relapse to drug taking (Tiffany and Carter 1998). Evidence shows that drugs of abuse affect dopamine neurotransmission. Imaging studies revealed that drug-naı¨ve individuals show enhanced dopamine levels in the striatum upon exposure to psychostimulants such as cocaine and amphetamine (Volkow et al. 1997a; Drevets et al. 2001). In contrast, decreased striatal dopamine responses were reported in detoxified cocaine abusers (Volkow et al. 1997b). Furthermore, individuals with a history of abuse of alcohol (Volkow et al. 1996), cocaine (Volkow et al. 1990), heroin (Wang et al. 1997), or methamphetamine (Volkow et al. 2001) display lower levels of dopamine receptor binding compared to nonabusers (Volkow et al. 2004). Together, these imaging studies suggest an involvement of dopamine neurotransmission in the acute and long-term effects of abused drugs.

1.2

Drug Self-Administration as an Animal Model for Drug Addiction

To better understand the neurobiology of drug abuse and addiction in humans, several animal models have been developed to investigate different aspects of drug addiction (Koob and Le Moal 2008). Among these models, paradigms that incorporate self-administration of drugs are thought to best capture the human condition because animals are allowed to voluntarily seek the drug and because drugs that are self-administered by animals correspond well with those that have high abuse

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potential in humans (Koob and Le Moal 2008). However, a great number of studies have examined the effects of drugs of abuse upon passive injection by the experimenter (noncontingent or response-independent administration). Therefore, in this chapter, we will review studies that have used contingent or responsedependent (self-administration), as well as noncontingent or response-independent drug injections.

1.3

Drug-Self-Administration and Dopamine in the Nucleus Accumbens

Dopamine neurotransmission is highly implicated in the regulation of reinforcement in rodent drug self-administration paradigms. The dopamine projection from the ventral tegmental area (VTA) to the nucleus accumbens (NAcc) has been identified as a critical substrate for the expression of drug reinforcement (Ritz et al. 1987; Wise and Bozarth 1987; Koob and Bloom 1988; Kalivas and McFarland, 2003). For example, direct dopamine receptor agonists are selfadministered systemically as well as locally into the NAcc (Yokel and Wise 1978; Woolverton et al. 1984; Carlezon et al. 1995). Dopamine receptor antagonists administered systemically in low doses increase the rate of operant responding for cocaine in animals (De Wit and Wise 1977; Ettenberg et al. 1982; Roberts and Vickers 1984; Britton et al. 1991; Corrigall and Coen 1991; Caine and Koob 1994; Hemby et al. 1996), but decrease the motivation to carry out high work requirements to obtain an infusion of cocaine (Hubner and Moreton 1991; Richardson et al. 1993). Similarly, intracerebral administration of dopamine antagonists into the NAcc increases the rate of psychostimulant self-administration (Maldonado et al. 1993; Phillips et al. 1994), but decreases responding when work requirements for the drug infusion increase (McGregor and Roberts 1993). These effects have been interpreted as an attenuation of the reinforcing properties of the drug, such that more drug is taken to achieve the same level of reward, and that less effort is invested into a drug infusion with decreased rewarding properties. In contrast, lesion or inactivation of the mesolimbic dopamine system in the VTA (Roberts and Koob 1982; Shoaib et al. 1998; Xi and Stein 1999) or the NAcc (Roberts et al. 1977, 1980; Lyness et al. 1979; Pettit et al. 1984; Corrigall et al. 1992; Shoaib et al. 1998) attenuate cocaine, amphetamine, heroin, and nicotine self-administration in rats. These findings further underline the critical importance of the mesolimbic dopamine system in drug taking. However, there are some conflicting reports on whether dopamine antagonists or lesions of the mesolimbic dopamine system affect responding for all drugs of abuse (Pettit et al. 1984; Hemby et al. 1996; Di Chiara 2000; Czachowski et al. 2001). Notably, the response of the NAcc dopamine system to drug administration (discussed later) differs significantly depending on whether the animal actively self-administers a drug of abuse or whether the animal receives it independent of a response (Hemby et al. 1995, 1997; Lecca et al. 2007).

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Anatomy of the Dopamine System and Dopamine Signal Transduction: Phasic and Tonic Release

The VTA and the neighboring substantia nigra are the primary dopamine-producing nuclei in the brain (Swanson 1982). From these midbrain nuclei, relatively distinct dopamine cell groups innervate different functional domains of the striatum; i.e., sensorimotor, associative, and limbic circuits that are mainly defined by their differential cortical inputs (Carlsson et al. 1962; Dahlstrom and Fuxe 1964; Alexander and Crutcher 1990; Joel and Weiner 2000). The VTA predominantly projects to the limbic striatum including the NAcc. More than three-quarters of this mesolimbic projection stems from dopaminergic neurons (Swanson 1982). Dopamine acts on D1- or D2-type dopamine receptors upon release from dopaminergic fibers that densely innervate the striatum (Doucet et al. 1986; Ritz et al. 1987; Groves et al. 1994). Dopamine receptors are found at symmetric boutons formed by dopaminergic terminals on the neck or shaft of the spines of striatal projection neurons (Levey et al. 1993; Groves et al. 1994; Hersch et al. 1995; Caille et al. 1996). However, dopamine receptors are often found on striatal dendrites outside such synapses, in the vicinity of asymmetric boutons on the head of dendritic spines that are contacted by glutamatergic terminals (Levey et al. 1993; Caille et al. 1996). Stimulation of dopamine receptors is terminated by reuptake of extracellular dopamine into the presynaptic terminals. Most dopamine reuptake sites are located on dopaminergic fibers outside synaptic contacts of dopaminergic terminals (Nirenberg et al. 1996; Hersch et al. 1997), similar to the dopamine receptors. Together, this distribution of receptors and reuptake sites allows for “volume transmission”; i.e., the activation of extrasynaptic dopamine receptors by dopamine that diffuses a few micrometers away from its release into the synaptic cleft (Garris et al. 1994; Garris and Rebec 2002; Rice and Cragg 2008). Therefore, the measurement of extracellular dopamine concentrations is a meaningful indicator of dopamine signaling. Dopamine neurons are either hyperpolarized/quiescent or display different patterns of discharge activity: single-spike firing (2–10 Hz) or bursts of 2–6 action potentials (15–30 Hz) (Grace and Bunney 1984a, b; Freeman and Bunney 1987). Individual dopamine neurons can switch from one of these patterns to the other, as shown in freely moving rats (Freeman and Bunney 1987; Hyland et al. 2002). Salient sensory stimuli can evoke a transient increase in firing rate and burst firing (Freeman and Bunney 1987; Mirenowicz and Schultz 1996). The “tonic” extracellular concentration of dopamine, ranging from 5 to 20 nM depending on the target area, is thought to arise from basal dopamine neuron firing patterns, which predominantly consists of single-spike firing (Bunney et al. 1991; Grace 1991; Parsons and Justice 1992; Keefe et al. 1993; Floresco et al. 2003). Conversely, burst firing of dopamine neuron populations is thought to give rise to “phasic” dopamine events which can reach as high as 1 mM (Gonon 1988; Chergui et al. 1994; Garris et al. 1997). In support of this assertion, it has been shown that electrical stimulations mimicking burst firing are much more potent in triggering dopamine overflow than

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single pulse stimulations (Wightman and Zimmerman 1990; Chergui et al. 1996; Gonon and Sundstrom 1996). In summary, the signaling modes that govern communication between dopamine neurons and their target cells are rapid and transient increases in dopamine concentrations (phasic dopamine) on top of a low, slowly changing dopaminergic tone (tonic dopamine).

1.5

Proposed Functions of Dopamine in the NAcc

Since the identification of dopamine over 50 years ago (Carlsson et al. 1957), a number of ideas have been developed to explain the role of dopamine in behavior. These ideas (some of which are presented below) are not necessarily mutually exclusive, but rather focus on different aspects of dopamine function in behavior. The most uncontroversial of ideas is that dopamine is implicated in motor function (Berridge 2007; Salamone and Correa 2002) as selective degeneration of dopamine neurons in Parkinson’s disease patients or animals treated with neurotoxins causes motor deficits (Sundstrom et al. 1990; Schwarting and Huston 1996; Galvan and Wichmann 2008). However, these motor deficits are considered to be caused primarily by compromised dopamine signaling in the dorsal striatum, whereas dopamine in the ventral striatum including the NAcc is assumed to be part of a “limbic motor interface” (Mogenson et al. 1980). This idea is based on the fact that NAcc neurons receive input from limbic brain regions and send output to motor brain regions. For example, dopamine infused into the NAcc caused an increase in locomotion that can be inhibited by blocking the effect of NAcc output in one of its motor output nuclei (Jones and Mogenson 1980), supporting the proposition that NAcc dopamine serves as a modulator in the translation of motivation into action (Mogenson et al. 1980). 1.5.1

Dopamine and Motivated Behavior

Mogenson’s pioneering work inspired research that produced many lines of evidence supporting a critical role of dopamine in motivation and effort (Salamone and Correa 2002; Berridge 2007). For example, the impact of manipulations that impair dopamine signaling in the NAcc on food-seeking behavior is critically dependent upon the work requirements of the task (e.g., Salamone et al. 1991; Cousins and Salamone 1994). Thus, food-seeking that requires low effort is largely unaffected by partial NAcc dopamine depletions, whereas food-seeking that requires high effort is substantially impaired (e.g., Salamone et al. 1994; Denk et al. 2005). This suggests that dopamine may be implicated in overcoming the motivational costs required for completing tasks that involve a high level of effort (Salamone and Correa 2002; Phillips et al. 2007). Similarly, we recently suggested that NAcc dopamine, influenced by internal deprivation states (e.g., hunger and thirst), plays a key role in overcoming response costs by modulating activity originating from the frontal cortical systems that assess costs and rewards (Phillips et al. 2007). This may

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enable mesolimbic dopamine to energize goal seeking and affect cost-benefit decision making. The “incentive-salience” hypothesis of dopamine also builds upon the involvement of dopamine in motivation (Robinson and Berridge 1993; Berridge 2007). In short, incentive salience is the neural representation of motivational value in response to a reward-related stimulus that drives behavior. Dopamine in the NAcc is thought to modulate the incentive value of such reward-related stimuli (Berridge 2007). The hypothesis distinguishes between “liking” of rewards (hedonic value) and “wanting” of rewards (incentive value) (Berridge 2007). For example, enhancing dopamine levels in dopamine transporter knock-down mice increases the “wanting” of food reinforcers (Pecina et al. 2003; Cagniard et al. 2006a), whereas “liking” of food reinforcers is not affected by insults to the function of the dopamine system (Berridge et al. 1989; Pecina et al. 1997). This supports the proposition that NAcc dopamine is implicated in “wanting” or the “incentive value” of a stimulus.

1.5.2

Dopamine and Reinforcement Learning

The most compelling line of evidence for a role of dopamine in reinforcement learning comes from the firing patterns of dopamine neurons during the presentation of conditioned and unconditioned stimuli (Schultz et al. 1997). Initially, these neurons show synchronous firing patterns in response to the delivery of unpredicted rewards. After repeated pairing with a stimulus that predicts reward, dopamine neurons cease firing to reward delivery and instead respond to the cue that predicts its availability (Schultz et al. 1997; Waelti et al. 2001). This firing pattern of dopamine cells is consistent with a reward prediction signal that provides the organism with the capacity to compare the expected outcome to the actual outcome, in order to maximize reward (Montague et al. 2004). An unexpected reward following a neutral cue is a positive error and favors learning. The omission of an expected reward after a predictive cue or action is a negative error and favors extinction. In support, it has been shown that dopamine neuron firing correlates with the magnitude of the reward (Tobler et al. 2005). However, some have argued it is unlikely that dopamine serves as a teaching signal given the limited availability of afferent sensory processing and the precise timing of dopamine signals (Redgrave and Gurney 2006). Instead, it has been suggested that dopamine may contribute to a more simple, low-computation process, leading to the identification of which aspects of context and behavioral output are crucial in causing unpredicted events (Redgrave and Gurney 2006). All of the above presented evidence and theoretical framework implicate NAcc dopamine in motivation and/or reinforcement learning. What becomes evident after examining these different lines of research is that the mesolimbic dopamine system is associated with a diverse array of behaviors, which illustrate that dopamine may mediate various functions depending upon the temporal dynamics, location, and context of its release (Schultz 2007). Specifically, the functional impact of phasic and tonic dopamine release may differ, as we will explore below.

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2 Dopamine Detection in the Behaving Animal: In Vivo Microdialysis, Chronoamperometry, and Fast-Scan Cyclic Voltammetry (FSCV) A number of analytical techniques are utilized to chemically detect extracellular dopamine in vivo. These techniques differ in their time resolution ranging from milliseconds to hours. Thus, some are best suited to detect tonic changes, while others are optimized for isolating phasic dopamine release events.

2.1

In Vivo Microdialysis

Microdialysis is one of the most commonly used methods for the in vivo detection of dopamine and has excellent analyte selectivity and sensitivity. In microdialysis, extracellular dialysates of the brain are sampled through a membrane that is permeable to water and small solutes (Bito et al. 1966). The inside of the microdialysis probe inserted into the brain region of interest is continuously flushed with an isomolar solution that lacks the analyte of interest. The analyte of interest is sampled after diffusion from the extracellular space across the membrane into the microdialysis probe and thus changes in concentration can be detected. Another variant of this technique used for determining the absolute basal analyte concentrations is no-net flux microdialysis, which involves perfusion of known concentrations of the analyte of interest through the probe to establish when an equilibrium between the inside and the outside of the probe is reached (Parsons and Justice 1992). Despite the considerable size of the microdialysis probe (0.2–0.5 mm in diameter; 1–2 mm working length), several experimental studies indicate that the damage to the blood–brain barrier is minimal (e.g., Westerink and De Vries 1988; Tossman and Ungerstedt 1986). Microdialysis is a sampling technique that is not directly coupled to any particular method of chemical analysis. The vast majority of studies uses high performance liquid chromatography in conjunction with electrochemical or fluorescence detection to analyze the very small amount of chemicals in the dialysate. Due to this small amount of dialysate, the sampling time-resolution is usually between 5 and 20 min. Even though there are now technical advances that will enable sample collection in intervals significantly shorter than a minute (Bowser and Kennedy 2001), most microdialysis experiments still operate on relatively low temporal resolution and are best suited for the quantitative analysis of basal and slowly changing tonic dopamine concentrations.

2.2

Electrochemical Techniques

It is known that dopamine neuron activity responds to reward-related stimuli on a subsecond timescale (Schultz et al. 1997). To fully understand control of behavior

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by dopamine in the NAcc and its role in drug addiction, we require neurochemical information with this level of temporal resolution. Electrophysiological recordings provide excellent temporal resolution but usually do not determine the projection target of recorded neurons, and thus cannot inform us on neurotransmission in specific terminal structures. In contrast, electrochemical or voltammetric techniques combine sampling of dopamine neurotransmission in specific terminal structures with excellent temporal resolution. Although various voltammetric methods have been developed over the years, the basic principle underlying each of these variations is the application of a modest electrical potential sufficient to drive electrolysis of the analyte of interest in brain extracellular fluid (Adams 1976; Stamford 1986; Kawagoe et al. 1993). The current produced by this electrolysis can be measured at the electrode and is proportional to the number of molecules undergoing oxidation, and therefore to the concentration of analyte at the electrode surface (Adams 1976; Stamford 1986; Kawagoe et al. 1993). Adams and coworkers pioneered voltammetric recordings of dopamine in the 1970s (Adams 1976). However, the presence of high concentrations of electroactive neurotransmitter metabolites, as well as ascorbic acid and uric acid, interfered with their recordings (Marsden et al. 1988). Technical advances such as the utilization of modified electrodes and more complex input voltage command waveform to improve selectivity (e.g., Gonon et al. 1981; Gonzalez-Mora et al. 1991) have led to the development of three predominant techniques for hightemporal resolution monitoring of dopamine using carbon-fiber microelectrodes: (a) amperometry, (b) high-speed chronoamperometry, and (c) FSCV (Garris and Rebec 2002). These techniques are now often referred to as voltammetric techniques. They are very attractive tools for chemical monitoring in the brain because measurements can be made with a small probe (5–30 mm in diameter; less than 200 mm working length) that causes minimal tissue damage and allows for sampling in precise brain areas. Considering the size of the carbon fiber electrode and the size of the synaptic cleft (15–25 nm; Savtchenko and Rusakov 2007), it is evident that voltammetry monitors the dopamine overflow in the extrasynaptic extracellular space and not dopamine in the synaptic cleft. Of these techniques, (constantpotential) amperometry is the fastest and “simplest” as it applies a continuous, constant potential to the electrode. Although this variant has microsecond temporal resolution, it offers little chemical selectivity since current produced by oxidation of any compound will be detected. Thus, amperometry is of great utility in studying, for example, fast release and uptake kinetics of single cells in brain slices (e.g., Chow et al. 1992), but has found little use in behaving animals. The remaining two techniques are reviewed below.

2.2.1

In Vivo Chronoamperometry

In chronoamperometric recordings, the potential of the working electrode is stepped up, held at this higher potential, and then stepped back down, while the resulting oxidation and reduction currents from faradic processes occurring at the

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electrode are monitored. Such currents have either been analyzed on the level of seconds (for simplicity here termed: high time resolution) or averaged to achieve a better signal-to-noise ratio with a time resolution of minutes to hours (low time resolution). Because electroactive species have different chemical reversibilities, a ratio of oxidation and reduction currents (redox ratios) can assist in identifying the primary substance contributing to changes in the electrochemical signal (e.g., Gratton et al. 1989). Chemical sensitivity is improved further in this technique by using electrodes coated with Nafion, an ion-selective polymer, (Gerhardt et al. 1984) that reduces the contribution of anionic species such as ascorbic acid and the dopamine metabolite dihydroxyphenylacetic acid (DOPAC) to the signal. 2.2.2

In Vivo FSCV

Compared to chronoamperometry, FSCV is a more selective electrochemical method, because it utilizes a triangle input waveform (and not a step function) to separate electrolysis from different analytes into temporally resolved peaks in the output current. Since the voltage is swept gradually to an oxidizing potential and back, current is generated over time, during oxidation and reduction processes, whereby producing multiple electrochemical peaks for an ideal compound. This allows for the recording of a chemical signature, the voltammogram, that serves to identify the species detected, separating the signal from changes in pH and “other noise” and making the chemical resolution more robust (e.g., Baur et al. 1988, Michael et al. 1998). The voltammogram has sufficient information that it can be used with high-powered statistical analysis and provides standardized identification of the dopamine signal (Heien et al. 2004). For this chemometric approach, a socalled “training set” of phasic dopamine events of different amplitude spanning the concentration range of interest is collected (stimulated electrically with varying pulse rate and frequency). This training set is then used to perform a principal component analysis of the signal to statistically identify dopamine events.

3 Effects of Drugs of Abuse on Extracellular Dopamine Concentration in the NAcc 3.1

The Dopamine Hypothesis of Addiction

It has been proposed that the critical mechanism for the development of addiction is drug-induced activation of dopamine transmission in the NAcc, also referred to as the “dopamine hypothesis of addiction” (Fibiger et al. 1987; Wise and Bozarth 1987; Di Chiara and Imperato 1988). Electrophysiological studies have shown that acute exposure to many drugs of abuse affect the firing properties of dopamine neurons in the VTA despite their many distinct actions in the brain (for review, see Wanat et al. in press). However, to directly study dopamine signaling in the NAcc

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and to fully characterize the functions of dopamine in this target region, it is essential to study dopamine release into the extracellular space (discussed later) in addition to dopamine cell firing.

3.2

Drug Effects on Dopamine Signaling Measured Over the Course of Minutes: Microdialysis Studies

In the following sections, we will focus on studies investigating the effects of abused drugs on dopamine levels in the NAcc in freely moving animals. Microdialysis studies in behaving animals demonstrated that response-independent, systemic administration of cocaine, amphetamine, heroin, cannabinoids, nicotine, and ethanol increase dopamine levels in the NAcc (Di Chiara and Imperato 1986; Imperato and Di Chiara 1986; Imperato et al. 1986; Di Chiara and Imperato 1988; Kalivas and Duffy 1990; Kuczesnki et al. 1991; Yoshimoto et al. 1992; Pontieri et al. 1996; Tanda et al. 1997). The dose of the drug administered and the concentration of striatal dopamine following drug administration are positively correlated, as demonstrated for cocaine and ethanol (Nicolaysen et al. 1988; Bradberry 2002). Similar to response-independent drug administration, selfadministered drugs of abuse, including cocaine, amphetamine, heroin and ethanol, induce increases in concentrations of dopamine in the NAcc (Hurd et al. 1989, 1990; Pettit and Justice 1989, 1991; Weiss et al. 1992, 1993; Di Ciano et al. 1995; Wise et al. 1995a, b). Conversely, drugs with low potential for abuse do not affect dopamine overflow (Di Chiara and Imperato 1988). These findings are in agreement with the dopamine hypothesis of addiction (see Sect. 3.1), since drugs of abuse increase tonic concentrations of extracellular dopamine in the NAcc. During psychostimulant self-administration, animals learn to “load up” drug concentrations with an initial burst of operant responses before settling into a slower, more regular pattern of responding with inter-response rates varying between 2 and 20 min (Carelli and Deadwyler 1996). Response rates are inversely related to the infusion dose of cocaine or amphetamine; thus, the lower the dose the higher the number of responses (Pickens and Thompson 1968; Wilson et al. 1971; Yokel and Pickens 1973; Wise and Bozarth 1987; Di Ciano et al. 1995). However, the total intake of these drugs is elevated with higher doses. This intake pattern seems not to be due to aversive drug effects at high doses, since monkeys will choose infrequent high doses in preference to more frequent low doses (Iglauer et al. 1976) and rats show no reliable preference for one over the other (Di Ciano et al. 1995). Furthermore, animals will adjust their response rates to meet increased operant response demands (Roberts et al. 1989). Together, this suggests that animals titrate their drug intake to achieve a preferred level of intoxication. Cocaine-induced increases in extracellular NAcc dopamine concentrations are thought to be the principal neurochemical event associated with the drug’s positive reinforcing action (see Sect. 1.3; Kuhar et al. 1991; Wise and Bozarth 1987; Roberts et al. 1977; Ritz et al. 1987). For example, psychostimulants are self-administered

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directly into the NAcc (Hoebel et al. 1983; MCkinzie et al. 1999), and morphine and ethanol into the VTA (e.g., Bozarth and Wise 1981; Gatto et al. 1994). With increased drug dose the dopamine “maintenance” concentration in the NAcc is also held at an increased level (Pettit and Justice 1991). Importantly, microdialysis studies have shown that animals will maintain the increased NAcc dopamine concentration during cocaine, amphetamine, and heroin self-administration sessions at a steady level over days, just like they maintain the drug concentrations at a steady level (Pettit and Justice 1989; Pettit et al. 1990; Wise et al. 1995a, b; Ranaldi et al. 1999). Furthermore, in a microdialysis study with advanced temporal resolution that sampled dopamine every minute, Wise et al. (1995b) demonstrated that the selfadministration pattern of cocaine closely followed NAcc dopamine levels within sessions. This study demonstrated that cocaine dose-dependently increased dopamine concentrations after an infusion and that rats self-administered the next infusion as soon as the dopamine concentration diminished past a certain threshold. Consistent with this finding, it has been proposed that functional dopamine depletion in the NAcc represents a neurochemical correlate of drug craving (Dackis and Gold 1985; Koob et al. 1989). The findings presented above suggest that fluctuation in tonic dopamine concentration in the NAcc is a common denominator between different abused drugs that can regulate drug self-administration behavior.

3.3

Drug Effects on Dopamine Signaling Measured Over the Course of Seconds to Hours: Chronoamperometry Studies

In vivo chronoamperometry studies in the behaving animal have demonstrated that experimenter-administered ethanol, cocaine, and amphetamine increase extracellular concentrations of dopamine over the course of minutes to hours (Kiyatkin 1994; Di Ciano et al. 1998b; Sabeti et al. 2003). Similarly, self-administration of heroin and psychostimulants caused an increase in dopamine concentrations in the NAcc on this time scale (Gratton 1996; Di Ciano et al. 2001, 2002). Thus, chronoamperometry studies with low time resolution confirmed microdialysis findings on the basic effects of abused drugs on extracellular concentration of dopamine in the NAcc. To further investigate the temporal dynamics of dopamine signaling in animals self-administering drug, chronoamperometry with high time resolution was used to monitor dopamine on the order of seconds. NAcc dopamine concentrations were found to gradually increase preceding and to drop immediately following responsedependent (and -independent) intravenous injections of cocaine, before rising again around 4–6 min after infusion (Kiyatkin and Stein 1994, 1995). These postresponse decreases in signal were dose-dependent, absent when the infusion was withheld, and the preresponse increases became increasingly bigger when the access to the lever was blocked (Kiyatkin and Stein, 1993; Kiyatkin and Gratton 1994; Gratton 1996). Furthermore, chronoamperometry studies reported similar response patterns during operant behavior maintained by other reinforcers such as heroin

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(Kiyatkin and Stein 1993) and food (Kiyatkin and Gratton 1994), supporting the assumption that dopamine is the principal contributor to the reported biphasic signal fluctuations. In summary, the described high time resolution chronoamperometric data are in conflict with microdialysis findings which found that dopamine levels in the NAcc rise and fall in unison with oscillating blood/brain stimulant levels (Pettit and Justice 1989; Koob and Bloom 1988; Wise and Bozarth 1987; see Sect. 3.2). The validity of chronoamperometry measurements has been challenged on the basis of chemical sensitivity (Salamone 1996; Di Chiara 2002; Wightman and Robinson 2002). First, even though the electrodes are much less sensitive to DOPAC than to dopamine, the DOPAC concentration is several hundred times higher than dopamine and fluctuations in DOPAC concentration may contribute to the signal (Dayton et al. 1981; Gonon et al. 1984). Second, the voltage input step used to measure current changes that were assumed to be due to the oxidation/ reduction of dopamine also measures changes in pH that often accompany dopamine signaling (Heien et al. 2004). The argument that chemical species other than dopamine are contributing to the chronoamperometry signal is supported by the fact that the detected task-related signaling changed dramatically during the development of self-administration behavior (Gratton 1996), an observation not reported for heroin or by microdialysis studies with cocaine. Furthermore, chronoamperometry studies report drug-induced elevations in dopamine concentrations last nearly twice as long as that measured with microdialysis (Di Ciano et al. 1995). Although these results were replicated and the reported electrochemical changes were shown to be task-related, one cannot be sure about the chemical specificity of the signal (Wightman and Robinson 2002), especially in light of evidence from a microdialysis study reporting the opposite finding (Wise et al. 1995b; see Sect. 3.2). In summary, microdialysis and low time resolution chronoamperometry studies have convincingly demonstrated a link between tonic dopamine concentrations in the NAcc and the reinforcing effects of drugs of abuse. However, both techniques also raised questions. For example, what is the detailed temporal composition of dopamine signals? In contrast, chronoamperometry with high temporal resolution leaves the question of the chemical identity of observed phasic changes unanswered. These questions and some of the above described issues are not resolvable with these techniques, as the need for a combination of high temporal and chemical resolution is not satisfied by either of these techniques alone.

3.4

3.4.1

Drug Effects on Dopamine Signaling Measured on a Subsecond Time Scale: FSCV Studies Changes in Phasic Dopamine Signaling to Response-Independent Drug Administration

FSCV provides sufficient temporal and chemical resolution to study phasic dopamine signals. For example, a single phasic dopamine event in response to either

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electrical stimulation or presentation of a salient stimulus can be assessed. Furthermore, FSCV can examine the effects of drugs of abuse on phasic dopamine in both in vitro (slice) and in vivo (anesthetized, awake, and during self-administration) preparations, and can separate dopamine release and uptake. In addition to the amplitude and duration of phasic dopamine changes, the frequency of “spontaneous” dopamine transients (i.e., phasic release events found in the awake animal that are not attributed to specific events in the animal’s environment) can also be measured. Thus, findings obtained with FSCV illustrate a greater complexity of dopamine signaling than previously described by data from other techniques (see Sects. 3.2 and 3.3). The phasic NAcc dopamine response to ethanol and cannabinoids illustrates the complexity in the profile of subsecond dopamine signaling. Ethanol showed no effect on dopamine uptake in striatal brain slices collected from drug-naı¨ve rats (Samson et al. 1997; Budygin et al. 2001b; Mathews et al. 2006), but enhanced dopamine uptake in animals chronically treated with ethanol, possibly due to a compensatory mechanism resulting from elevated dopamine levels (Budygin et al. 2007). Consistent with this, both ethanol and cannabinoids attenuate electrically stimulated dopamine release in the intact animal, possibly due to increased tonic dopamine levels (see Sect. 3.2) that impair phasic dopamine release due to activation of release-regulating autoreceptors (Budygin et al. 2001a; Cheer et al. 2004). In contrast, intravenous infusions of ethanol and cannabinoids increased the amplitude and/or frequency of spontaneous phasic dopamine transients in awake, behaving animals (Cheer et al. 2004, 2007). These findings suggest that the complexity conferred by multiple mechanisms make it difficult to parsimoniously use findings from in vitro preparations and artificial electrical stimulations to make net predictions concerning the effect of drugs on phasic dopamine signaling in awake, behaving rodents. In agreement with results presented in Sect. 3.2 that indicated enhanced tonic dopamine concentrations in the NAcc, the findings presented above demonstrate increased frequency of spontaneous phasic dopamine signals by ethanol and cannabinoids. A number of FSCV studies have examined the effect of nicotine on phasic dopamine release in both in vitro and in vivo preparations. In contrast to ethanol, acute in vivo nicotine exposure enhances dopamine uptake in the striatum of the anesthetized rat (Middleton et al. 2004). Nicotine exerts frequency-dependent effects on electrically stimulated phasic dopamine release in vitro; at low firing rates dopamine release is attenuated, but at high firing rates nicotine enhances dopamine release (Zhang and Sulzer 2004). Intravenous infusions of nicotine also increase the frequency and amplitude of spontaneous phasic dopamine release events in the behaving rat (Cheer et al. 2007). Together, both in vitro and in vivo studies consistently show enhanced phasic dopamine signaling in response to nicotine. Consistent with findings from other abused substances, intravenous infusions of cocaine increase amplitude and frequency of spontaneous phasic dopamine release events in the NAcc (Heien et al. 2005; Stuber et al. 2005a, b; Cheer et al. 2007; Wightman et al. 2007; Aragona et al. 2008), as well as the amplitude of electrically

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stimulated release (Wu et al. 2001). An increase in amplitude can be explained by decreased reuptake due to the pharmacological action of cocaine. However, it is not clear why cocaine increases the frequency of dopamine release events. It seems likely that more phasic signals exceed the FSCV detection threshold due to their increased amplitude and thus become “visible” under drug exposure. Another explanation is that drug-induced behavioral hyperactivity may stimulate afferents to the VTA, and therefore may increase the firing frequency of dopamine neurons projecting to the NAcc. Notably, these findings indicate that the strong increases in tonic dopamine levels described in Sect. 3.2 do not appear to result in significant autoreceptor-mediated inhibition of dopamine neurons; thus, one could describe the phasic signals as “riding on a tonic dopamine wave” during drug exposure. Interestingly, endogenous cannabinoids modulate the cocaine-, nicotine-, and ethanol-mediated increases in phasic dopamine release, as the effects of drugs on phasic dopamine release are attenuated by systemic cannabinoid receptor antagonism (Cheer et al. 2007). While the locus of this effect is yet to be determined, it is speculated that cannabinoid receptor activation in the VTA reduces GABA release on dopamine neurons (Riegel and Lupica 2004). These findings suggest that abused drugs exert similar effects on phasic dopamine release even though their respective pharmacological and cellular effects are quite distinct.

3.4.2

Changes in Phasic Dopamine Signaling During Cocaine Self-Administration: The Role of Operant Behavior and Conditioned Stimuli

Pavlovian and operant conditioning paradigms using nondrug reinforcers have demonstrated that conditioned stimuli (CS) can elicit phasic dopamine release (Roitman et al. 2004; Day et al. 2007; Stuber et al. 2008; Owesson-White et al. 2008). In studies using drug reinforcers, it has been shown that repeated CS presentation with drug delivery subsequently can elicit an electrochemical response when the CS is presented alone (Kiyatkin and Stein, 1993; Kiyatkin and Gratton 1994; Di Ciano et al. 1998a). This response develops over time, as its development requires at least 10–50 pairings of the drug with the CS (Gratton 1996), and therefore indicates that these dopamine signals reflect a learning process. Studies that probed the contribution of CS to phasic dopamine in response to drug taking will be discussed below. Contingent and noncontingent cocaine administration produce differential longterm effects on synaptic plasticity in VTA dopamine neurons (Chen et al. 2008). The effect of cocaine infusions on phasic dopamine release also depends on whether the drug administration is contingent upon an operant response or not, as shown with FSCV (Stuber et al. 2005a). For example, in rats pressing a lever for an intravenous infusion of cocaine, rapid changes in dopamine concentrations are time-locked to specific aspects of this behavior (Phillips et al. 2003; Stuber et al. 2005a, b; Fig. 1a), whereas no changes in dopamine levels are observed within 10 s of a responseindependent cocaine administration to awake, but idle rats (Stuber et al. 2005a).

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In contrast, following these initial 10 s, the frequency of spontaneous dopamine transients increases dramatically independent of whether the administration of the drug was response-independent or -dependent (Stuber et al. 2005a). However, there is an ongoing debate regarding the exact latency of the pharmacological effects of cocaine after intravenous infusion (Espana et al. 2008; Wise et al. 2008). The considerable variance in the onset of these transients may be due to variability in the latency of drug delivery to the brain. Together, this suggests that (a) early dopamine release events (first 10 s) in animals that self-administered cocaine may be related to learned associations and (b) the increase in spontaneous dopamine transients 10 s after the beginning of the infusion may be a consequence of the pharmacological effects of cocaine that are not related to learning. Consistent with this idea, these latter spontaneous transients are correlated with cocaine levels and the animal’s locomotion (Stuber et al. 2005a). In summary, cocaine self-administration is accompanied by early phasic dopamine release that is time-locked to the onset of drug infusion but cannot be attributed to the pharmacological effects of the drug. Phillips et al. (2003) demonstrated that the largest change in dopamine concentration time-locked to cocaine self-administration behavior occurs immediately upon completion of the operant response (Fig. 1a). This effect was conditioned to an audiovisual stimulus (presented on the completion of the lever response) and was not due to the pharmacological actions of cocaine since it persisted during initial extinction trials where cocaine is replaced with saline (Stuber et al. 2005b; Fig. 1b). If the CS denoting the onset of the cocaine infusion was presented noncontingently during the period between operant responses, a similar dopamine signal could be evoked. Furthermore, this signal diminished gradually when the associative strength between the stimuli and cocaine is weakened during extinction (Stuber et al. 2005b; Fig. 1b). This phenomenon is identical to what has been observed with nondrug reinforcers, where cues that predict availability of these reinforcers are able to elicit phasic dopamine release on their own (Roitman et al. 2004, OwessonWhite et al. 2008). Therefore, the change in extracellular dopamine that occurs at completion of the operant response (Fig. 1a) may encode the association of cue and drug delivery, and thus the expectation of cocaine delivery. Phasic dopamine release has also been demonstrated just prior to the operant response for a self-administered infusion of cocaine (Phillips et al. 2003, Stuber et al. 2005a, b; Fig. 1a). These changes tended to be smaller than those following the operant response, but consistently preceded the animal’s approach to the response lever. In agreement with these antecedent neurochemical signals, dopamine neuron activation also occurs immediately before subsequent injections in rats trained to bar press for intravenous heroin (Kiyatkin and Rebec 2001). Furthermore, chronoamperometric studies detected a slow increase in dopamine-related signals (over the course of multiple seconds) in rats before they approached the lever for the next drug self-administration (Kiyatkin and Stein 1995). This temporal correlation with the drug seeking (lever approach) suggests that this component of the neurochemical signal might be causally linked to the behavior (Phillips et al. 2003, Stuber et al. 2005a, b). The temporal proximity of the signal precludes testing its role in drug seeking using conventional pharmacological approaches, since blockade of

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a

drug selfadministration infusion (maintenance) cue

1 2

3 3 3

6s 20 s

b

extinction

time cue lever press

lever press

lever press

Fig. 1 Phasic dopamine signaling in the NAcc associated with drug seeking and taking. (a) Phasic dopamine signaling consists of multiple phasic events (triangles). The first event is elicited by the animal’s approach of the operant lever (preresponse signal) (1), whereas a bigger second event is associated with the onset of the audiovisual cue (dark gray bar) that is presented in response to the lever press (postresponse signal) (2). A set of peaks (3) that is observed with an onset of approximately 10 s after the lever press and the beginning of the drug infusion (light gray bar), is thought to be a direct consequence of the pharmacological effect of the drug. The latency of this pharmacological effect relative to the operant response appears to be more variable than for the postresponse signal. (b) During extinction, the audiovisual stimulus presented after the lever press is not accompanied by a drug infusion. As a consequence, the postresponse dopamine signal becomes smaller with repeated nonreinforced responding. In contrast, the preresponse signal remains relatively stable during extinction. This suggests that the preresponse signal reflects the motivation to obtain drug, whereas the postresponse encodes the expectation of the drug infusion

this signal will perturb other phasic dopamine signals or the tonic baseline level of dopamine. However, an electrically evoked dopamine signal of similar brevity was sufficient to promote the lever approach (drug seeking), as such brief changes in dopamine, heavily influenced drug seeking by biasing the animal to initiate this behavior (Phillips et al. 2003). This may indicate a role for phasic dopamine in drug seeking or at least biasing the animal’s decision-making policies towards selecting actions leading to drug taking, rather than a direct role in drug reward. In contrast to the postresponse signal, this preresponse signal does not appear to be a learned or conditioned process because it does not disappear during extinction trials, where the CS was presented contingent to the operant response but without drug infusion (Stuber et al. 2005b; Fig. 1b). Together, these findings indicate that the preresponse dopamine signal may be involved in initiating approach behavior. In summary, these findings underline the multiple roles of phasic dopamine in operant responding for cocaine. Phasic dopamine release associated with operant behavior is comprised of signals in relation to approach, conditioned cues, and the

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pharmacological effects of the drug (Fig. 1a). Dopamine signaling is likely to play a role in motivation, but probably also encodes other information about goal-directed behavior including learned association between cues and drug reward. The extinction-resistant preresponse signal seems to be related to drug seeking, and therefore may have a motivational quality. In contrast, the postresponse signal is linked to a learned association (lever-press and cue signaling the drug infusion), and therefore may be related to the expectation of drug delivery (see Sect. 3.4.2). Compared to natural reinforcers, such behavior-related dopamine release is enhanced due to the pharmacological properties of abused drugs. Therefore, the normal function of these signals is potentially corrupted, which may explain dysregulated goaldirected behavior in addiction (see Sect. 7.2).

3.5

Summary

The findings obtained in FSCV studies examining the effects of different drugs of abuse on phasic dopamine signaling in the NAcc are in general agreement with findings made with microdialysis and low time resolution chronoamperometry (see Sects. 3.2 and 3.3), as each of these techniques have shown that response-dependent and -independent administration of abused drugs increase dopamine levels in the NAcc. Thus, both phasic and tonic concentrations of dopamine in the NAcc are enhanced by drugs, which is in agreement with the postulate of the dopamine hypothesis of addiction that drugs of abuse converge on the mesolimbic dopamine pathway. These findings also raise the question as to what extent such phasic events contribute to the observed changes in tonic dopamine levels. Some evidence indicates that spontaneous dopamine transients that presumably reflect the pharmacological effects of drugs are likely to alter overall tonic dopamine concentrations (see Sect. 3.4.2). Future studies are required to investigate a possible interaction between phasic and tonic dopamine signaling and to examine phasic dopamine release during the self-administration of a wider range of abused substances.

4 Effects of Withdrawal from Drugs of Abuse on the NAcc Dopamine System While the acute effects of drugs on the dopamine system are well cataloged, the long-term effects after cessation of drug intake are less well studied and understood. This disconnection in the research is partly because the latter effects show considerable variance that arises from the drug studied, how the drug is administered (dose, frequency, and route), and duration of abstinence after drug experience (Wanat et al. in press). It has been documented that repeated exposure to a drug of abuse causes structural changes in VTA dopamine neurons, as repeated opiate

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exposure decreases the size and caliber of dendrites and soma of VTA dopamine neurons (Sklair-Tavron et al. 1996). Along with such structural changes, many studies support the notion that multiple drug exposures lead to an altered function of the dopamine system (Koob and Le Moal 2008), though the timing of this effect can vary.

4.1

Tonic Dopamine During Withdrawal

Microdialysis studies have demonstrated that the discontinuation of chronic treatment with ethanol (Rossetti et al. 1991, 1992a, b; Diana et al. 1993; Weiss et al. 1996), morphine (Acquas et al. 1991; Pothos et al. 1991; Acquas and Di Chiara 1992; Rossetti et al. 1992a, b; Crippens and Robinson 1994), nicotine (Rahman et al. 2004), amphetamine (Rossetti et al. 1992a), or cocaine (Parsons et al. 1991; Robertson et al. 1991; Imperato et al. 1992; Rossetti et al. 1992a, b; Segal and Kuczenski 1992; Weiss et al. 1992; Diana et al. 1993; Chefer and Shippenberg 2002; Zhang et al. 2003) decreases the basal extracellular concentration of dopamine in the NAcc. However, there have been conflicting reports for withdrawal from psychostimulants. For example, several studies demonstrated a lack of change in basal concentrations of NAcc dopamine after withdrawal from amphetamine (Segal and Kuczenski 1992; Crippens et al. 1993; Crippens and Robinson 1994; Paulson and Robinson 1996). In the case of cocaine, some studies reported that withdrawal does not change tonic NAcc dopamine (Robinson et al. 1988; Kalivas and Duffy 1993; Hooks et al. 1994; Meil et al. 1995; Kuczenski et al. 1997), and yet others found an increase in basal dopamine levels (Imperato et al. 1992; Weiss et al. 1992; Johnson and Glick 1993; Heidbreder et al. 1996). While some studies reported changes in basal dopamine levels depending upon the time of cocaine withdrawal (Imperato et al. 1992; Heidbreder et al. 1996), these observed changes have not been consistent across studies. For example, some report that withdrawal after chronic cocaine exposure decreases basal dopamine levels in as early as a few hours (Zhang et al. 2003) to as long as 10 days (Parsons et al. 1991), while others found increases in basal dopamine levels during 1–4 days of withdrawal (Imperato et al. 1992; Weiss et al. 1992; Heidbreder et al. 1996), and it was reported to have no effect on basal dopamine levels after 24 h and 2 weeks withdrawal (Kalivas and Duffy 1993; Meil et al. 1995). Thus, at least for cocaine, no clear temporal effect of withdrawal on basal dopamine levels can be inferred from these studies. The studies described above utilized either conventional microdialysis or the more accurate method of determining exact basal dopamine levels, no-net flux microdialysis (Parsons and Justice 1992). The majority of studies employing no-net flux microdialysis did not observe changes in dopamine levels after withdrawal from cocaine treatment, strongly supporting no or minor effects of cocaine withdrawal on tonic dopamine concentration (Crippens et al. 1993; Kalivas and Duffy 1993; Heidbreder et al. 1996; Chefer and Shippenberg 2002).

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Together, the effects of withdrawal from chronic drug treatment on NAcc dopamine levels depend on many factors, including the drug studied and the dose of the drug administered during the chronic treatment (Kalivas and Duffy 1993). In addition, it is known that tonic dopamine levels in the NAcc are affected by drug cues (see Sect. 5.1) and by contextual cues associated with aversive stimuli (Mark et al. 1991; Saulskaya and Marsden 1995). Re-exposure to a drug-paired environment can either be rewarding or aversive depending on drug dose and time of drug-cue pairing after drug administration (Ettenberg 2004). Differences in these parameters may explain some of the discrepancies between studies presented here. In summary, according to microdialysis studies the NAcc dopamine system may undergo a depression after withdrawal from abused substances; however, this finding remains controversial regarding psychostimulants such as cocaine.

4.2

Phasic Dopamine During (Short-Term) Withdrawal

Only one study using FSCV has examined how phasic dopamine release associated with drug taking is affected by withdrawal from drug self-administration. Stuber et al. (2005b) showed that the dopamine postresponse signal associated with the completion of the operant response gradually decreases during an extinction session immediately after completion of a cocaine self-administration session, whereas the preresponse signal remained relatively stable (Fig. 1b). Thus, the overall phasic dopamine release associated with the operant response for drug delivery decreases during withdrawal. However, this study does not reveal how phasic dopamine signals are affected by long-term withdrawal. Furthermore, the self-administration behavior underwent extinction instead of abstinence, a better model for the human condition because humans do not usually undergo extinction during drug withdrawal. The term “abstinence” will be used here to describe withdrawal from drug taking without extinction of drug taking behavior. We are aware of the fact that this term is not an ideal description of animal behavior since the animal does not refrain from drug taking voluntarily. Future studies should investigate changes in phasic dopamine signaling during long-term withdrawal after extinction or abstinence from drug taking. Collectively, these studies suggest that acute exposure to drugs of abuse activates phasic and tonic dopamine signaling in the NAcc (see Sect. 3), but withdrawal from chronic drug exposure can dampen phasic and tonic dopamine levels in the absence of drug (see Sect. 4). However, withdrawal from psychostimulants does not necessarily lead to decreased tonic levels. Decreased tonic levels of dopamine in the NAcc and firing of VTA dopamine neurons during drug withdrawal have been shown to return to and above basal levels by a subsequent drug re-exposure with ethanol (Diana et al. 1993; Weiss et al. 1996), amphetamine (Robinson et al. 1988), cocaine (Pettit et al. 1990), and morphine (Sklair-Tavron et al. 1996; Diana et al. 1999). Similarly, the extinction-induced decrease of the phasic postresponse dopamine signal can be reversed to previous amplitudes during a drug-induced

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reinstatement session, as shown by FSCV (Stuber et al. 2005b). Returning dampened dopamine levels to “baseline” may be one of the driving forces in relapse to drug taking behavior, since evidence presented in Sect. 3.2 indicates that animals pursue the next drug infusion when tonic dopamine levels in the NAcc decrease past a certain threshold.

5 Stimulus-Induced NAcc Dopamine Release in the Absence of Drug: Implications for Reinstatement of Drug Seeking 5.1

Effects of Drug Cues on Tonic Dopamine Concentration in the NAcc

In the previous sections, we reviewed the powerful effects of abused drugs on dopamine signaling in the NAcc during drug taking and withdrawal. This leads to the question of whether the NAcc dopamine system is also involved in reinstatement of drug seeking behavior after abstinence or extinction of drug taking. Re-exposure to drugs of abuse on a single occasion can promote relapse to drug seeking behavior in abstinent human drug users (Jaffe et al. 1989). Similarly, exposure to a CS associated with self-administered drugs can elicit subjective states such as craving (Grant et al. 1996; Childress et al. 1999; Garavan et al. 2000), as well as drug seeking and relapse in humans (Stewart et al. 1984; Avants et al. 1995) and experimental animals (Markou et al. 1993, Robinson and Berridge 1993). Furthermore, even after extinction of self-administration behavior, drug seeking can be reinstated in animals by presentation of conditioned drug cues (e.g., de Wit and Stewart 1981; Weiss et al. 2000). It has been proposed that the ability of conditioned drug cues to increase dopamine is critical for reinstatement of drug seeking (Stewart et al. 1984), and support for this assumption is provided by findings in animals (see Sect. 3.4.2). Further evidence for the involvement of NAcc dopamine signaling in drug-induced reinstatement is provided by findings demonstrating that microinjection of amphetamine into the NAcc can reinstate heroin self-administration (Stewart and Vezina 1988) and that such drug seeking can be attenuated by drugs that decrease dopamine neuron activity (Di Ciano and Everitt 2003, 2004; Bossert et al. 2004). In contrast, a decrease in the concentration of extracellular dopamine in the NAcc may contribute to some drug withdrawal symptoms (Rossetti et al. 1992b). Decreasing withdrawal symptoms by increasing dopamine levels may therefore possibly promote the motivation to reinstate drug taking (Koob and Le Moal 2008). Therefore, we will now focus on studies examining drug cue-induced changes in dopamine release and how this relates to reinstatement of drug seeking. Several studies have observed increases in tonic dopamine concentrations in the NAcc following the noncontingent presentation of a psychostimulant-paired CS in the absence of drug. Such CS-induced effects were demonstrated after pairing of

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the CS with response-independent (Fontana et al. 1993; Di Ciano et al. 1998b) as well as with response-dependent (Gratton and Wise 1994; Kiyatkin and Stein 1996; Di Ciano et al. 1998a; Ito et al. 2000) drug administration. Similar conditioned changes in dopamine have been shown in the NAcc following the noncontingent presentation of stimuli previously paired with food (Young et al. 1998), heroin (Gratton 1996), or footshock (Wilkinson et al. 1998). In contrast to noncontingent presentation, response-contingent presentation of a CS failed to produce changes in tonic NAcc dopamine (Neisewander et al. 1996; Ito et al. 2000). In fact, one study found no difference in NAcc dopamine concentrations during cocaine self-administration without concurrent presentation of the CS as compared to self-administration of cocaine plus contingent presentation of a CS (Bradberry et al. 2000). However, it should be noted that other studies have found no change in tonic dopamine efflux in response to the noncontingent presentation of drug cues (Brown and Fibiger 1992; Bradberry et al. 2000). This discrepancy may be due to the proximity of changes in dopamine concentrations to the microdialysis detection limit, where minor differences in the study design, for example, the number of stimulus-reward pairings or CS presentations, may cause different outcomes. Overall, these findings suggest that noncontingent (but not contingent) presentation of drug-paired cues can contribute to overall changes in tonic NAcc dopamine. Few studies have examined dopamine release during actual cue-induced reinstatement of drug-seeking after abstinence or extinction of the operant behavior. One study found elevations in tonic NAcc dopamine after extinction in conjunction with robust cocaine-seeking behavior elicited by sustained (60 min) presentation of a salient discriminate stimulus that previously signaled the availability of cocaine (Weiss et al. 2000). In contrast, reinstatement elicited by brief noncontingent presentation of a visual drug cue during or after extinction did not produce significant changes in dopamine efflux, although drug-induced reinstatement showed both a behavioral and a neurochemical response (Neisewander et al. 1996; Di Ciano et al. 2001). This suggests that repeated or sustained noncontingent presentation of drug-associated cues may be necessary to elicit changes in tonic dopamine concentrations during reinstatement of drug seeking that are detectable with microdialysis.

5.2

Effects of Drug Cues on Phasic Dopamine Signaling in the NAcc

There are no studies to date that have examined cue-induced reinstatement of drug seeking with FSCV. However, Stuber et al. (2005b) demonstrated that the phasic dopamine component that occurs following a lever-press for cocaine infusion (postresponse) gradually diminishes during extinction, whereas the signal that occurs just prior to the lever-press (preresponse) seems resistant to extinction (Fig. 1b, see Sect. 4.2). During drug-induced reinstatement, postresponse but phasic dopamine release returns to the previous pre-extinction amplitude, suggesting that

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it encodes a learned association between cue and drug delivery (Stuber et al. 2005b). In addition, studies recording neuronal firing in the NAcc during withinsession extinction have shown that neurons with a postresponse discharge pattern are less active during extinction and return their activity to pre-extinction levels after reinstatement (Carelli and Ijames 2000). The absence of the postresponse signal after extinction in both striatal dopamine release and NAcc cell firing makes it an unlikely candidate as the driving force behind the initiation of the reinstatement of drug seeking. Moreover, the increase in postresponse dopamine does not cause further cocaine seeking in self-administration maintenance sessions, as it would be expected if this aspect of the dopamine signal is involved in approach behavior or drug seeking. Instead the animals typically engage in stereotypies during the immediate postresponse phase before seeking the next infusion (Stuber et al. 2005a). However, abstinent human addicts generally do not undergo extinction, and thus drug cues may retain their capacity to elicit dopamine release. Furthermore, the postresponse signal may reappear during subsequent extinction sessions, as drug seeking in response to drug cues is not permanently extinguished after a single extinction session, as shown in many studies (Shalev et al. 2002). In contrast to the postresponse dopamine signal, the preresponse dopamine signal may be an essential component of reinstatement of drug seeking since it remains relatively stable during extinction (Fig. 1b), and an electrically evoked dopamine signal of similar brevity is sufficient to induce drug seeking (Phillips et al. 2003). Additionally, animals will approach and press a lever for an infusion of dopamine into the NAcc (Dworkin et al. 1986). Such a dopamine-induced approach response may be more reliably triggered in a drug-related environment where the approach is directed towards a familiar goal. Together, the findings presented in Sects. 5.1 and 5.2 indicate that tonic and phasic dopamine concentrations in the NAcc can be increased during (noncontingent) cue-induced reinstatement of drug seeking. However, there have been few studies of this effect and their findings are somewhat inconsistent, which may reflect the highly dynamic quality of the phasic dopamine postresponse that fades quickly under extinction conditions. The lack of reinstatement-related tonic dopamine changes after brief exposure to drug cues under extinction conditions may thus be attributable to a diminished amount of phasic dopamine per lever approach (decreased postresponse dopamine), whereas higher tonic dopamine levels can be achieved with sustained cue exposure. These findings suggest that the more stable preresponse component of the dopamine signal (Fig. 1b) may be of greater significance in eliciting reinstatement of drug seeking.

6 The Role of NAcc Dopamine in Drug Addiction Altered dopamine signaling has been implicated in all stages of drug addiction, from induction to maintenance to relapse. Unlike natural reinforcers, drugs act directly on the mesolimbic dopamine system thereby bypassing sensory processing and

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adaptive mechanisms that normally control NAcc dopamine release. Most theories of drug addiction postulate that this “direct access” to dopamine signaling results in abnormal shaping of synaptic efficiency. However, addiction theories differ regarding the effect that these processes have on the organism (discussed later).

6.1

Motivation and Addiction

The “incentive-sensitization” theory of addiction proposed by Robinson and Berridge (1993) emphasizes the motivational function of NAcc dopamine. It states that hedonic processes (“liking”) associated with drug intake are not mediated by the mesolimbic dopamine projection, which instead is involved in the attribution of incentive salience to stimuli associated with rewards (“wanting”) (as described in Sect. 1.5.1). Thus, this theory postulates that NAcc dopamine mediates the motivation to pursue rewards. Addictive drugs are assumed to render these brain reward systems hypersensitive (i.e., “sensitized”) to drugs and drug-related stimuli, causing pathological wanting of drugs. According to this view, relapse to drug seeking and compulsive aspects of drug taking are mediated by the sensitized dopamine efflux in the NAcc in response to a drug-paired CS.

6.2

Associative Learning and Addiction

Dopamine receptors regulate intracellular signaling cascades that alter the expression of genes, such as immediate-early genes, which are considered to be one of the first molecular steps that ultimately result in stable neuroadaptations and behavioral changes (for review, see Davis and Squire 1984; Stork and Welzl 1999; Tischmeyer and Grimm 1999). In the striatum, such mechanisms have been shown to play a central role in learning-related changes in protein synthesis (Teather et al. 2005; Hernandez et al. 2006). Consistent with associative learning theories of addiction, psychostimulants engage a set of molecular mechanisms normally implicated in learning and memory including D1 receptors and downstream intracellular messenger cascades that may cause synaptic rearrangements (Berke and Hyman 2000; Everitt et al. 2001). Thus, psychostimulant-induced dopamine release may alter learning-related molecular changes by activating common signal transduction pathways. In fact, the effects of psychostimulants on procedural memory consolidation have been demonstrated in many studies (e.g., Puglisi-Allegra et al. 1994; Castellano et al. 1996; Cestari and Castellano 1996; Packard and White 1991). Importantly, recent studies found D1 receptor-dependent effects of cocaine on procedural learning in association with molecular changes in the striatum (Willuhn and Steiner 2006, 2008). A current influential hypothesis that incorporates these findings would suggest that addiction is due to drug-induced neuroadaptations in reward-related learning and memory processes in the NAcc (Berke and Hyman 2000; Everitt et al. 2001). Such neuroadaptations are believed to cause

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hypersensitivity to cocaine-associated cues (Di Chiara and Bassareo 2007; Everitt and Wolf 2002) and abnormal habit-like behaviors (White 1996) that become insensitive to adverse consequences with chronic drug exposure and lead to compulsive drug intake (Wolffgramm and Heyne 1995; Deroche-Gamonet et al. 2004).

7 Different Functions for Phasic and Tonic Dopamine Transmission in Addiction How can the different modes of dopamine transmission, phasic and tonic signaling, be synthesized with different theories of addiction? Most experiments do not distinguish between these two modes of neurotransmission. For example, in vivo microdialysis and low time resolution chronoamperometry measure changes in tonic levels of dopamine, but it is not clear how much phasic signaling contributes to tonic extracellular concentrations of dopamine, and, thus, these techniques potentially measure the sum of both tonic and phasic dopamine. Similarly, pharmacological manipulations of the dopamine system modify both tonic and phasic aspects of dopamine neurotransmission, as both modes presumably utilize the same mechanisms of release and reuptake. Interestingly, it is both experimentally and conceptually challenging to separate the different functions of dopamine on a behavioral level. As described in Sect. 1.5, dopamine signaling is thought to be implicated in both reinforcement learning and motivation. Dopamine is thought to facilitate reinforcement learning by “stamping in” stimulus-reward associations (Wise 2004), providing a prediction error (Montague et al. 2004), and/or by biasing action selection (Redgrave and Gurney 2006). Alternatively, dopamine is thought to facilitate motivation by enhancing the energizing effect of reward or rewardpredicting cues through assignment of incentive salience (Robinson and Berridge 1993, 2008) and/or by maintaining behavior when response costs are high (Salamone and Correa 2002). Later, we argue that the different temporal modes of dopamine signaling fulfill both learning and motivational functions. Recent research, using a genetic approach, indicates that phasic and tonic dopamine signaling may indeed subserve different functions. Specifically, reduced expression of the dopamine transporter in the striatum and thus reduced clearance of released dopamine has been shown to cause increased motivation in a previously learned task in the absence of new learning (Cagniard et al. 2006b). Importantly, mice carrying this inducible knockdown of the dopamine transporter showed increased nonbursting activity of dopamine neurons (presumably driving tonic dopamine concentration in the striatum) but no change in burst firing (presumably driving phasic dopamine release). Thus, these mice learned a behavioral task and were then rendered tonically hyperdopaminergic, which led to a better performance in this task without affecting reinforcement learning subsequently tested in another task (Cagniard et al. 2006b). This important finding suggests that tonic dopamine signaling may mediate motivational aspects of behavior. Conversely, it has been suggested that phasic signaling is particularly well suited for transmitting rapid

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time-specific information and thus provide the temporal resolution necessary to represent the contingencies in reinforcement learning (Grace 1991; Schultz 2007). FSCV recordings in the NAcc were made from rats during Pavlovian reinforcement learning support this role for phasic dopamine release (Day et al. 2007; Sunsay and Rebec 2008; Stuber et al. 2008). Early in training, phasic dopamine responses are observed primarily with reward delivery (unconditioned stimulus, US). With continued training, dopamine release is elicited by the presentation of the CS, while the response to the US is attenuated, suggesting a transfer of the phasic dopamine response from the US to the CS (Day et al. 2007; Sunsay and Rebec 2008). Such a role for dopamine in the learning of stimulus–reward associations has also been demonstrated in electrophysiological studies (Schultz et al. 1997; see Sect. 1.5.2) and cocaine self-administration studies using FSCV, as discussed in Sect. 3.4.2 (Phillips et al. 2003; Stuber et al. 2005a, b). However, phasic dopamine is also associated with initiating goal-directed behaviors, and thus may have a motivational impact as well (see Sect. 3.4.2; Phillips et al. 2003). Therefore, phasic signaling, time-locked to drug intake and drug-predicting stimuli, may contribute to both the motivational aspects of drug taking and associative learning related to drug taking. Together, these findings suggest that different time scales of dopamine transmission may have different functions, where different aspects of phasic signaling is related to both reinforcement learning and approach behavior (motivation), and tonic signaling enables motivational and motor systems but not reinforcement learning.

7.1

Dopamine Signaling in the Drug-Naı¨ve State (Fig. 2a)

In Fig. 2, we summarize data reviewed in this chapter in a simplified manner and discuss it in light of the theoretical framework of NAcc dopamine function reviewed in Sects. 1.5 and 6. In a drug-naı¨ve state (Fig. 2a), it is assumed that NAcc neurons receive physiological levels of receptor stimulation by tonic dopamine release providing normal motivational function of the organism. Phasic dopamine release and subsequent postsynaptic dopamine receptor stimulation may alter synaptic plasticity on striatal projection neurons (in concert with glutamate signals) in response to behaviorally relevant novel stimuli and natural reinforcers. Together this allows for normal goal-directed behavior and reinforcement learning. In this drug-naı¨ve state (Fig. 2a), stimuli that are specifically associated with drug intake, such as drug paraphernalia or cues predicting drug availability, will not affect dopamine neuron firing or release because the organism has not yet been exposed to the drug.

7.2

Immediate Effects of Drug Exposure (Fig. 2b)

Thus far, we have reviewed data demonstrating that acute exposure to drugs of abuse increases phasic (see Sect. 3.4) and tonic dopamine concentrations (see

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phasic baseline tonic

drug-naive

b

during drug exposure

c withdrawal (drug-free) non-drug cue

no cue

drug cue

Fig. 2 Drug-induced changes in phasic and tonic dopamine transmission in the NAcc. (a) In the drug-naı¨ve state, phasic (triangles) and tonic (solid horizontal line) dopamine signaling in the NAcc is normal. Few spontaneous phasic dopamine events (no cue) are observed. Salient stimuli (nondrug cues) can elicit phasic dopamine release and goal-directed behavior. (b) Drugs of abuse enhance tonic (dashed line) and phasic (triangles) dopamine signaling. Stimuli not associated with drug (nondrug cues) and drug-related cues both elicit phasic dopamine events, but the latter cause more robust release due to the temporal proximity to the drug administration. Furthermore, the number of spontaneous phasic dopamine events is increased. This may lead to aberrant learning of drug-cue associations and thus abnormal goal-directed behavior such as compulsive drug taking. (c) Effects of drug withdrawal on dopamine signaling are variable. For example, dampened tonic dopamine concentrations during withdrawal can be returned to and above basal concentrations by exposure to drug cues and drug context (left to right). Such drug cues may also elicit more phasic dopamine release (dashed triangle) compared to nondrug cues because (1) drugs represent a higher reward magnitude than natural reinforcers and/or (2) extended withdrawal results in incubation of drug craving. As a consequence, independent of tonic dopamine levels, seeking for drugs is more prevalent than seeking for natural reinforcers, which may promote relapse to drug taking

Sect. 3.2 and 3.3) in the NAcc. What are the potential behavioral consequences of this enhanced dopamine signaling? During drug exposure, cues predicting reinforcer availability may elicit greater amounts of dopamine due to the drug-induced increase in the amplitude of phasic dopamine signals, similar to cocaine-induced increases in electrically stimulated release (Wu et al. 2001; Figs. 2a, b). Notably, exposure to cocaine and amphetamine also affects the processing of cues that are not related to drug intake (e.g., Puglisi-Allegra et al. 1994; Castellano et al. 1996; Cestari and Castellano 1996; Packard and White 1991). Additionally, the number of

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spontaneous phasic dopamine release events is increased dramatically due to the pharmacological effect of the drug which possibly leads to an association of an increased number of environmental stimuli with the drug experience (see Sect. 3.4). Together, this may explain how contextual cues not directly predicting drug administration become powerfully associated with the drug experience. In comparison to natural reinforcers and contextual cues, drug-associated cues are even more robustly “consolidated” due to the high contiguity and contingency of drug administration and these cues (i.e., the drug-induced facilitation of dopamine signaling is strongest immediately after intake and drug cues are never experienced separately from drug administration) (Fig. 2b). Consistent with this idea, learning associations between cues and natural reinforcers transiently affects the synaptic properties of dopamine neurons, while drug experience promotes long-lasting changes to the intrinsic and synaptic properties of dopamine neurons (Chen et al. 2008; Stuber et al. 2008). Such facilitation in Pavlovian learning may then promote the development of abnormal levels of dopamine release by drug-conditioned stimuli upon re-exposure (Redish 2004; Di Chiara and Bassareo 2007). With repeated training on a Pavlovian learning task using a natural reinforcer, the phasic dopamine signal shifts from reward delivery to the cue that predicts it (Day et al. 2007; Schultz et al. 1997). In contrast, dopamine signals in response to drug delivery in a drug self-administration task may not attenuate or may attenuate slower than with natural reinforcement because drugs directly activate the dopamine system. Therefore, this teaching signal may be constantly exhibited to both drug cues and the drug delivery itself. Thus, the brain continues to perceive drug delivery as a novel reward or positive prediction error despite repeated use (Redish 2004). Additionally, drug reward may be experienced as a reward of exaggerated magnitude, and thus the positive prediction error may be extraordinarily large (Tobler et al. 2005). Together, this may lead to aberrant reinforcement learning and eventual fixation on pursuit of drugs and compulsive intake. If dopamine acts to promote the repetition of actions that immediately precede rewarding events (Redgrave and Gurney 2006), drug exposure would immensely facilitate operant behavior that leads to drug administration. Alternatively, such drug-enhanced phasic dopamine signaling could also lead to the sensitized attribution of incentive salience to drug-related cues (Robinson and Berridge 1993). Thus, enhanced phasic signaling may promote abnormal responding to drug cues, whether due to aberrant learning and memory or motivation. Enhanced tonic dopamine concentrations may produce more exploratory activity and thus greater exposure of the organism to the drug environment including drug-related cues possibly promoting continued drug intake. This role for tonic dopamine fits well with the proposition that dopamine may have a function in overcoming the motivational costs required for completing tasks (Salamone and Correa 2002; Phillips et al. 2007). Tonically elevated dopamine concentrations may therefore keep the motivational cost for pursuing drug rewards minimal. Together, we posit that the drug-induced enhancement of phasic dopamine signaling will increase phasic release in response to previously weak or neutral

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stimuli and specifically strengthen associations of drug cues and drug delivery, whereas increased tonic dopamine levels may maintain the organisms motivation to continue drug intake by promoting seeking of cues/environments associated with the drug (Fig. 2b).

7.3

Long-Term Effects of Drug Exposure During Drug Withdrawal (Fig. 2c)

Withdrawal after chronic drug exposure has variable effects on tonic NAcc dopamine levels (see Sect. 4.1). Some studies report decreased dopamine concentrations while others have found no change or even increased basal levels. Lowered tonic dopamine levels during drug withdrawal may be associated with a reduced motivational state, leading to an enhanced susceptibility to drug seeking elicited by drug cues. Drug seeking could result in further exposure to drug cues, and thus eventually lead to elevated tonic levels of dopamine, as noncontingent presentation of drug-associated cues are known to cause elevations in tonic dopamine levels (see Sect. 5.1; Fig 2c). Increases in tonic dopamine could then, in turn, facilitate seeking behavior. Although little is known about the effect of drug withdrawal on the frequency or size of phasic dopamine release, we assume based on the findings discussed above that phasic signals to drug cues will be greater compared to (nondrug) cues associated with natural reinforcers (Fig. 2c), due to the abnormally strong association between drug and drug-predicting cues that develops during drug exposure (see Sect. 7.2; Fig. 2b). One explanation for enhanced phasic dopamine release to drug cues is that drugs of abuse produce an exaggerated reward magnitude, which is known to be reflected in dopamine signaling (Tobler et al. 2005). Another possibility is that the dopamine signal to drug cues escalates over time due to incubation of drug craving during abstinence (Grimm et al. 2001). In support of this proposition, it has been demonstrated that the activation of NAcc neurons by drug-associated cues is potentiated after 1 month of abstinence from cocaine self-administration (Hollander and Carelli 2007). Because phasic dopamine release is associated with the initiation of goal-directed behaviors (Roitman et al. 2004; Phillips et al. 2003; Stuber et al. 2005a, b; see Sects. 3.4.2 and 4.2), it follows that promotion of drug seeking in response to drug-related stimuli is more likely than seeking of natural rewards in response to associated nondrug cues. This assumption is consistent with the DSM-IV criterion that human drug addiction normally constitutes a progressive “narrowing” of the behavioral repertoire to that controlled by drug reinforcement rather than that guided by natural reinforcers, such as food or sex. Taken together, we propose that independent of tonic NAcc dopamine concentrations during withdrawal exposure to drug-associated stimuli activates phasic dopamine release more than nondrug related stimuli, and thus leads to fixation on drug-related behavior and eventually relapse to drug taking.

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8 Summary In this chapter, we review the current state of knowledge on how abused drugs, drug-associated cues, drug seeking, and drug withdrawal affect phasic and tonic dopamine signaling in the NAcc in animal models of addiction. For the sake of simplicity, we have neglected to address the core and shell subdivisions of the NAcc (e.g., Zahm and Heimer 1990) and referred to the NAcc as a whole. The vast majority of the reviewed studies sampled dopamine concentrations in the NAcc core. However, greater increases in phasic and tonic dopamine concentrations in the NAcc shell compared to the core have been identified in response to psychostimulants, morphine and ethanol (e.g., Pontieri et al. 1995; Ito et al. 2000; Lecca et al. 2007; Aragona et al. 2008; Howard et al. 2008). Furthermore, it has been shown that dopamine in the shell increases following self-administration of cocaine, but not following presentation of CS associated with the drug, whereas such CS predicting cocaine caused dopamine release in the core (Ito et al. 2000). These data suggest that the NAcc shell may be mainly implicated in the primary reinforcing effects of psychostimulants, whereas the core is preferentially involved in conditioned drug responses. The reviewed results are integrated with current ideas on the role of dopamine in addiction with an emphasis on a model illustrating phasic and tonic NAcc dopamine signaling during different stages of drug addiction. Each theory of dopamine function briefly outlined in this chapter has merits and it is not our intention to verify or falsify any of them, but rather to combine their different perspectives. The purpose of this chapter is not to provide a new psychology of addiction, but rather to give an updated perspective on potential neurochemical mechanisms underlying addiction. We have only focused on dopamine signaling here, although many other neurochemical systems have been identified as important contributors to addiction. Our model predicts that phasic dopamine release in response to drug-related stimuli will be enhanced over stimuli associated with natural reinforcers, which may result in aberrant goal-directed behaviors contributing to drug addiction.

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Amygdala Mechanisms of Pavlovian Psychostimulant Conditioning and Relapse Deanne M. Buffalari and Ronald E. See

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Amygdala Anatomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 2.1 Intrinsic Circuitry of the Amygdala . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 2.2 Afferent/Efferent Amygdala Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 3 Behavioral Models of Drug Abuse and Relapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.1 Conditioned Place Preference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 3.2 Self-Administration and Reinstatement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 3.3 Summary of Behavioral Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4 Amygdala Neuronal Activity and Addiction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.1 Immediate Early Gene Expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.2 Electrophysiological Recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5 Downstream Signaling Cascades and Neuroadaptations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.1 Cellular and Molecular Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.2 Amygdala Neuroadaptations and Plasticity in Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 6.1 Summary and Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 6.2 Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 6.3 Clinical Relevance and Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Abstract Psychostimulant addiction often consists of periods of sustained drug abstinence disrupted by periods of relapse and renewed heavy drug use. Prevention of relapse remains the greatest challenge to the successful treatment of drug addiction. Drug-associated cues are a primary trigger for relapse, as they can elicit intense craving for the drug. These cues become associated with the drug reward through Pavlovian learning processes that develop over multiple drug–cue pairings. The amygdala (AMY) is critical for such drug-related learning. Intrinsic and D.M. Buffalari (*) and R.E. See Department of Neuroscience, Medical University of South Carolina, 173 Ashley Avenue, Charleston, SC 29425, USA e-mail: [email protected]

D.W. Self and J.K. Staley (eds.), Behavioral Neuroscience of Drug Addiction, Current Topics in Behavioral Neurosciences 3, DOI 10.1007/7854_2009_18, # Springer‐Verlag Berlin Heidelberg 2009, published online 15 September 2009

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extrinsic circuitry position the AMY to integrate cue and drug-related information and influence drug-seeking and drug-taking behaviors. Animal models of conditioned drug reward, drug use, and relapse have confirmed the necessary role of the AMY for drug conditioned cues to control motivated behavior. Neurons within the AMY are responsive to the primary effects of psychostimulants, and more critically, they also respond to the presentation of drug-associated cues. The mechanisms by which conditioned cues come to influence drug-seeking behavior likely involve long-term plasticity and neuroadaptations within the AMY. A greater understanding of the associative learning mechanisms that depend upon the AMY and related limbic and cortical structures, and the process by which drug cues come to gain control over behavior that maintains the addictive state, will facilitate the development of more effective addiction treatments. Keywords Amygdala Psychostimulant Pavlovian Conditioning Relapse

Abbreviations AMY CPP DA NAcc SA VTA

Amygdala Conditioned place preference Dopamine Nucleus accumbens Self administration Ventral tegmental area

1 Introduction The rewarding properties of abused drugs, especially psychostimulants, drive both the initiation and continuation of drug use during the process of addiction (Wise 1980). The reinforcing properties of abused drugs (e.g., cocaine) are readily associated with environmental stimuli, such as the environment or context in which drugs are consumed, or discrete stimuli (e.g., drug paraphernalia). Through multiple pairings, these cues acquire conditioned reinforcing properties via associative learning. This process has particular relevance to the study of addiction, as often these cues elicit intense craving for the drug (Foltin and Haney 2000), and consequently serve to trigger relapse to drug taking in abstinent users (Kirby et al. 1995). Since prevention of relapse remains the greatest challenge to the successful treatment of drug addiction, a greater understanding of the associative learning processes that maintain the addictive state would facilitate the development of more effective addiction treatments.

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Multiple brain regions have been investigated for the role that they play in associative learning. The amygdala (AMY) has been an area of intense focus, due to the extensive afferent/efferent connectivity of the AMY with several brain regions that are relevant to associative conditioning of information that regulates affective states. In particular, the AMY has been thoroughly examined for the role it plays in fear conditioning (for review, see Maren 2005; Sigurdsson et al. 2007; Sah et al. 2008). Experimental paradigms have been developed and applied to investigate the independent phases of fear conditioning (acquisition, consolidation, and reconsolidation) (Debiec and LeDoux 2004), examine different nuclei of the AMY (Wilensky et al. 2006), and investigate the role of downstream signaling and neural adaptations (Schafe et al. 2001) in order to gather a more complete picture of how the AMY mediates such behaviors. In contrast to fear conditioning, the role of the AMY in appetitive conditioning has been far less studied. This is due, in part, to the large number of appetitive learning tasks that are unaffected by AMY lesions (Baxter and Murray 2002). Despite these findings, research dating back many years (Weiskrantz 1956) has clearly demonstrated that the AMY significantly contributes to appetitive learning. Tasks such as reinforcer devaluation (Hatfield et al. 1996; Malkova et al. 1997), second-order conditioning (Hatfield et al. 1996), Pavlovian approach behaviors (Hitchcott et al. 1997), conditioned suppression (Lee et al. 2005), and conditioned orienting (Groshek et al. 2005) are all sensitive to AMY lesions or direct pharmacological manipulations within the AMY. Given this abundant evidence with appetitive reinforcers, it comes as no surprise that the role of the AMY in appetitive learning related to drugs of abuse holds considerable interest for those in the field of addiction. Formulating a comprehensive view on the role of the AMY in the Pavlovian processes involved in addictive behavior poses a difficult challenge. Variations in the behavioral procedures (e.g., different stimuli, contexts, drug doses, species) can lead to large differences in the measured behaviors. Also, experimenters use different nomenclatures and target different nuclei or subnuclei of the AMY across different species. Finally, the associative processes of interest generally develop over multiple experiences of drug-stimuli pairings. Therefore, it can be difficult to pinpoint the relevant critical period for these AMY-mediated associations. In spite of the limitations, recent studies on the role of the AMY in addiction have employed more sophisticated behavioral models and have been more specific in targeting selective components of AMY anatomy and molecular targets. More advanced techniques in areas such as immunolabeling, electrophysiological recordings, measurements of downstream signaling, behavioral tasks, and functional in vivo imaging have greatly advanced our understanding of the role of the AMY in addiction in a relatively short time frame. This review will first discuss the anatomical complexity of intra-AMY circuitry and afferent/efferent projections. We will then examine two current animal models of addiction used to evaluate AMY mediation of drug taking and drug seeking. Subsequent sections will evaluate findings on specific aspects of AMY function in neurochemistry and neuropharmacology, measurements of neuronal activity, and downstream intracellular signaling

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processes. Finally, these data will be integrated in an attempt to provide a global understanding of the role of the AMY in the mediation of the learned associations that drive addiction.

2 Amygdala Anatomy Over the last two decades, anatomical and neurophysiological studies of the role of the AMY in Pavlovian conditioning with drug reward have revealed a marked degree of anatomical complexity and specialization. The AMY is an assembly of several major nuclei, each of which can be further divided into separate subnuclei, which also have their own subdivisions. These regions are unique in terms of cell type and morphology, electrophysiological characteristics, connectivity, and neurotransmitter content (Pitkanen et al. 1997; Sah et al. 2003). While the role of individual AMY subregions in drug addiction and relapse has been delineated to some extent, the exact function of some subregions remains to be fully clarified.

2.1

Intrinsic Circuitry of the Amygdala

For the purposes of the current review, we have utilized the nomenclature summarized by Pitkanen and colleagues (1997). As studies of AMY function and addiction have focused primarily on basolateral (lateral, basolateral, and accessory basal nuclei) and central regions, the current review will discuss these AMY nuclei proper, and not address studies on the extended AMY (specifically, the bed nucleus of the stria terminalis) or other AMY regions. For a review of the role of the extended AMY and drug addiction, the reader is referred to the excellent review by Koob (2003). The four main nuclei of the AMY can be further divided, with the lateral nucleus containing dorsolateral, ventrolateral, and medial subdivisions; the central nucleus containing lateral, capsular, intermediate, and medial subdivisions; the accessory basal containing magnocellular and parvocellular subdivisions; and the basal nucleus containing magnocellular, parvicellular, and intermediate divisions. Each subdivision has varying degrees of intraconnectivity and interconnectivity with the other subdivisions. Furthermore, each nucleus has varying degrees of interconnectivity with associated regions outside of the AMY. The lateral nucleus of the AMY does not display a high degree of intradivisional connectivity, but studies have shown both feedforward and feedback inhibition (Samson and Pare 2006). The dorsolateral division sends input to both the ventrolateral and medial divisions, but receives little reciprocal input. Furthermore, little connectivity exists between the ventrolateral and medial subdivisions (Pitkanen et al. 1995). Thus, input arriving in the lateral nucleus is generally processed in a lateral–medial/ventral manner, and then proceeds to other nuclei.

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In contrast, all of the subdivisions of the basal nucleus have a high degree of intradivisional connectivity. Furthermore, the parvocellular and magnocellular divisions are interconnected, and while the intermediate subdivision receives input from the parvocellular subdivision, it does not provide projections of its own or receive many inputs from the magnocellular subdivision (Savander et al. 1995). These anatomical characteristics allow for a great degree of information processing within the basal nucleus before synaptic relays to other regions. The accessory basal nucleus subdivisions have high intraconnectivity, but few connections with one another, making it more similar to the lateral nucleus (Savander et al. 1996). The subdivisions of the central nucleus have great intradivisional and interdivisional connectivity, with the exception, to some degree, of the intermediate subdivision. The capsular and medial subdivisions are interconnected, and the lateral subdivision sends projections to both, but receives little input in return. The intermediate subdivision is somewhat isolated, with meager projections from the capsular and lateral divisions, but sending little in terms of reciprocal projections (Jolkkonen and Pitkanen 1998). Of critical importance to intra-AMY processing are the connections between nuclei, which are extensive. The lateral nucleus sends projections to all the other nuclei, although the direct efferents to the central nucleus are sparse (Pitkanen et al. 1995; Stefanacci et al. 1992). The basal nucleus projects back to the lateral nucleus and sends heavier projections onto the central nucleus (Savander et al. 1997). In fact, single neurons within the lateral and basal nuclei of the AMY innervate multiple extranuclear AMY targets (Pitkanen et al. 2003), further evidence of the highly distributed processing typical of this structure. The accessory basal nucleus has efferents to the central nucleus and back to the lateral nucleus as well (Savander et al. 1997). The central nucleus receives convergent input from lateral, basal, and accessory areas, but sends meager projections in return. Hence, it is typically designated as the output nucleus. Often overlooked when considering AMY intrinsic connectivity are the intercalated cell masses. These small groups of GABAergic neurons lie mostly between the lateral/basal/accessory basal nuclei and the more medial central nuclei (Pare and Smith 1993a) and provide an important GABAergic projection to the central AMY (Pare and Smith 1993b). The intercalated cell masses have been shown to gate information between the more lateral nuclei and the central nucleus (Royer et al. 1999), perhaps creating a scenario whereby activation of lateral regions, via activation of intercalated cells, may actually inhibit the central nucleus (Rosenkranz et al. 2006). The lack of conclusive evidence as to the influence of “input” nuclei (lateral, basal, accessory basal) over the emergent output nucleus (central) makes interpretation of the results of initiation of activity in any of these regions difficult to predict. These intra- and interdivisional connections confer upon the AMY a great capacity for the generation and processing of several representations of sensory stimuli, even within individual nuclei. While the AMY has sometimes been conceptualized as a simple lateral to medial throughput, the anatomical and physiological evidence shows otherwise. These rich interconnections subserve Pavlovian

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conditioning in drug addiction by allowing the sensory representations of drug cues to become closely associated with the rewarding consequences of the drug. Further, these representations could be updated during the process of extinction, whereby memory information about the cues is accessed (cue = drug) and compared to the current sensory input (cue = no drug) to allow for flexibility in behavioral output as contingencies are changed. Of particular interest for ongoing research is the clarification of how the representations of these sensory cues can maintain drug desire and drug seeking even after periods of abstinence and/or extinction.

2.2

Afferent/Efferent Amygdala Projections

The AMY displays a high degree of regionally specific interconnectivity with several other brain regions that have been well implicated in drug addiction (Fig. 1). The long-held view of the AMY is that lateral/basal nuclei of the AMY serve as the input target for the sensory (e.g., thalamus) and midbrain (e.g., locus coeruleus) projections, with subsequent projections modulating central nucleus activity, which ultimately regulates behavioral output via downstream brainstem projections. However, it is unlikely that information processing simply occurs in a serial input–output manner, as sensory and thalamic regions display strong direct projections to central nuclei in addition to their lateral/basal projections (LeDoux

SENSORY CORTEX THALAMUS

DA/NE CELLS

“DISCRETE CUES”

“VALENCE INFO”

LAT

CEA ICMs

INTERACTION W/NAcc & PFC tomediate drugseeking behavior

BAS

AB

DOWNSTREAM PROJECTIONS CPP/reinstatement expression Autonomic activation

Fig. 1 Neural circuitry of cue-drug associations in the amygdala. Discrete cues activate the amygdala via sensory projections and become associated with drug reward via catecholaminegic inputs. After extensive intra-amygdala processing, downstream projections mediate drug-seeking behavior and sympathetic activation. LAT lateral nucleus, BAS basal nucleus, AB accessory basal, ICMs intercalated cell masses, CEA central nucleus, DA dopamine, NE norepinephrine, NAcc nucleus accumbens, PFC prefrontal cortex

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et al. 1985; Turner and Herkenham 1991; Linke et al. 2000). In fact, thalamic inputs onto neurons of the medial subdivison of the central nucleus of the AMY display activity dependent plasticity in a manner similar to the synapses in the lateral AMY heavily implicated in fear conditioning (Samson and Pare 2006). Further, a great deal of evidence has shown a dissociation of the roles of basolateral and central nuclei across many types of appetitive conditioning (Hatfield et al. 1996; Everitt and Robbins 2000; Parkinson et al. 2000). These data support the evidence derived from AMY anatomy (see above) suggesting the likelihood that the AMY contains several representations of stimulus-value associations, and that both serial and parallel processing are involved in the learning and expression of appetitive behaviors, such as those seen in addiction. From repeated drug use emerges complex behaviors including periods of heavy use, abstinence, and relapse, which often occur in repetitive cycles. Therefore, stimulus-value representations are constantly changing and updated based on drug use and motivational state. Extensive processing likely occurs within the AMY, with an integration of afferent input from many regions necessary to produce behavior most appropriate to the current conditions via efferent projections. While the AMY receives a great deal of sensory input, little of this originates directly from primary sensory regions. Rather, sensory association cortices send heavy projections to the lateral, basal, and central nuclei (McDonald 1998). Thalamic efferents also carry sensory information to all of the AMY nuclei (van Vulpen and Verwer 1989; Doron and LeDoux 2000). Furthermore, higher association cortices project to AMY nuclei, with the projections from the prelimbic cortex targeting mainly the lateral and central nuclei, and the infralimbic cortex projecting more prominently to central and basal nuclei (McDonald et al. 1996). The hippocampus projects most strongly to the lateral and basolateral nucleus with few projections to the central nucleus (Kishi et al. 2006), and the entorhinal cortex sends diffuse efferents to all AMY nuclei (McDonald and Mascagni 1997). It is important to note the extensive overlap in these disparate projections. For example, the AMY receives prominent projections from the ventral tegmental area (VTA) (Asan 1998, Brinley-Reed and McDonald 1999), as well as the locus coeruleus (Asan 1998), and nucleus of the solitary tract (Fallon et al. 1978). The overlap of sensory and higher association cortical projections with catecholaminergic input from midbrain sources allows the AMY to integrate information involving current sensory inputs, the valence of those stimuli, and past experiences. Similar to afferent systems, efferent projections of the AMY are comparably diverse. The lateral and basolateral nuclei send outputs to prefrontal regions (McDonald 1991; Gabbott et al. 2006). The most prominent projection to the hippocampus arises from the basal nucleus (Pikkarainen et al. 1999). Other projections arise from basolateral regions to the nucleus accumbens (NAcc) (Shinonaga et al. 1994). The basolateral AMY also projects to the central AMY and beyond to the bed nucleus of the stria terminalis (Dong et al. 2001). All of these regions play prominent roles in the development and expression of addictive behaviors. The projections of the central nucleus are quite different from the lateral and basal nuclei, targeting various subcortical regions including the hypothalamus

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(Gray et al. 1989), the bed nucleus of the stria terminalis (Sun et al. 1991), and the noradrenergic neurons of the locus coeruleus (Van Bockstaele et al. 2001). These efferent connections may mediate behavioral responding to drug cues and autonomic nervous system activation to drugs of abuse. In summary, the extremely complex intrinsic and extrinsic connectivity of the AMY likely subserves several functions relevant to drug addiction. First, association of sensory input with dopaminergic (DA) and possibly noradrenergic signaling appears to contribute to the formation of associations between discrete or contextual cues with the drug-induced state. Furthermore, as it is unlikely that such memories are simply stored in the AMY, projections to the hippocampus and cortex may mediate transfer to other regions for long-term memory storage. Interconnectivity of the AMY with these regions enables integration of previously formed associations with new information (as may occur during extinction of responding for drugs of abuse). Based on the well-established role of glutamatergic projections from the prefrontal cortex to ventral and dorsal striatal regions in the reinstatement of drug seeking (McFarland and Kalivas 2001; Kalivas and McFarland 2003; McFarland et al. 2004) and subjective craving in human drug addicts (Volkow et al. 2006), the AMY probably modulates transmission in this pathway through extensive efferent projections to both regions. In fact, neurons within the NAcc receive converging input from the prefrontal cortex and AMY at the level of a single neuron (O’Donnell and Grace 1995), supporting the likelihood that these regions can modulate one another, as well as the NAcc. Further, many neurons from the AMY that project to the prefrontal cortex have collateral projections to the NAcc (Shinonaga et al. 1994). Medial prefrontal cortical neurons respond to Pavlovian conditioned stimuli, and these responses depend on AMY input (Schoenbaum et al. 2003). In fact, prefrontal neurons that receive excitatory input from the AMY are also activated by aversively conditioned cues (Laviolette et al. 2005). AMY inputs also preferentially modulate prefrontal cortical neurons that project to the NAcc (McGinty and Grace 2008). Therefore, the AMY is well positioned to modulate the output of this prefrontal–accumbens pathway that mediates drug seeking.

3 Behavioral Models of Drug Abuse and Relapse Clinical investigations, particularly when combined with modern imaging techniques, have provided unique insight into the brain structures involved in addiction. However, few of these studies are able to examine addictive behaviors at the points of initiation and progression, only their late stage resultant effects. Furthermore, these studies seldom examine patients in a drug-induced state. Increasingly sophisticated animal models of addiction have expanded our understanding of the development of motivated behaviors for drugs of abuse. The two models of greatest relevance to a discussion of the Pavlovian processes contributing to the

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Fig. 2 Phases of conditioned place preference

development of addiction are the conditioned place preference (CPP) paradigm and the drug self-administration (SA) and relapse model.

3.1

Conditioned Place Preference

The CPP model involves three phrases: habituation, conditioning, and testing (with some studies including extinction and reinstatement test phases, Fig. 2). The paradigm evaluates whether the pairing of one specific context with a drug produces a preference for that context (for review see Bardo and Bevins 2000; Tzschentke 2007). In the habituation phase, animals are acclimated to two distinct environments. Then, during training, one context is consistently paired with the subjective experience of the drug, hence the Pavlovian aspect of this task. Animals are then tested in a drug-free state for their display of a conditioned preference for one context versus the other. Preference is measured as a significant difference in the number of entries or time spent in the drug-paired context as compared to the vehicle-paired context. This behavior can then be extinguished with repeated drugfree tests or confinement of the rat to the drug-paired context without a pre-session drug injection. Subsequent testing can determine if exposure of the animal to different stimuli (e.g., drug priming injections) will reinstate the extinguished CPP. Both cocaine and amphetamine support CPP. Lesions of the lateral, central, and basolateral AMY nuclei before conditioning disrupt the acquisition of cocaine-CPP (Brown and Fibiger 1993). More specific investigations demonstrated that lesions of the lateral nuclei, but not basal or central, also attenuated both the acquisition and expression of amphetamine-CPP (Hiroi and White 1991). Recent studies confirmed

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that lesions of the basolateral AMY complex performed prior to cocaine-CPP training disrupted acquisition, with similar lesions given after conditioning disrupting cocaine-CPP extinction (Fuchs et al. 2002). Basolateral AMY inactivation with sodium-channel blockade immediately after amphetamine conditioning sessions also disrupted CPP acquisition, suggesting that the AMY may play a role in the consolidation of CPP (Hsu et al. 2002). However, although the central AMY seems to mediate expression, but not acquisition of this behavior, injections of amphetamine directly into the central AMY, but not the basolateral, supported CPP, which complicates interpretation of these results (O’Dell et al. 1999). In addition to studies that have disrupted AMY-based CPP, others have demonstrated a facilitation of CPP extinction, but not acquisition. Intrabasolateral AMY injections of the NMDA partial agonist, D-cycloserine, cause a facilitation of CPP extinction, but not acquisition (Botreau et al. 2006). Similarly, injections of glucose directly into the basolateral AMY (mainly targeting the basal nucleus) also enhanced extinction training of amphetamine-CPP (Schroeder and Packard 2003), as did the muscarinic agonist, oxotremorine (Schroeder and Packard 2004). In summary, while the role of the basolateral AMY in acquisition, expression, consolidation and extinction of this behavior has been established, the central nucleus does not seem to be required for this aspect of drug conditioned reward behavior (but see O’Dell et al. 1999).

3.2

Self-Administration and Reinstatement

While CPP has provided a useful model for studying the role of the AMY in conditioned drug reward, the procedure is inherently limited due to the lack of contingent drug administration. A more widely accepted animal model of addiction with greater face validity is the drug SA and relapse paradigm (Epstein et al. 2006). Pavlovian conditioning to drug cues and contexts plays an important role in this paradigm as well.

Fig. 3 Phases of administration and relapse paradigm

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Four main experimental phases can be assessed using the SA/relapse paradigm: acquisition, maintenance, extinction/abstinence, and reinstatement (Fig. 3). Following implantation of jugular catheters, animals learn to self-administer an intravenous (IV) drug using operant conditioning whereby a set number of operant responses (usually on a lever) results in an IV drug infusion. Each infusion may also be paired with discrete cues (e.g., lights and/or tones). Thus, throughout training, both the SA chamber and the cues presented with the infusion become associated with drug delivery and the rewarding effects of the drug. Acquisition transitions to the maintenance phase once the animal displays stable responding for the drug. Upon reaching a set SA criterion (minimum number of infusions over a set number of days), animals move from active drug SA into the extinction phase of the paradigm. During extinction, the subject is placed in the same test environment and provided access to response levers, but no drug infusions or conditioned stimulus presentations occur in response to lever responding. Therefore, animals gradually reduce responding on the previously drug-paired lever over consecutive sessions. Once an extinction criterion has been reached, subjects undergo trials of reinstatement testing. Reinstatement testing evaluates the ability of different factors to induce reinstatement of responding on the previously drug-paired lever. These tests directly model exposure of abstinent drug users to specific factors known to trigger relapse, such as drug paraphernalia, small amounts of the drug, or stress. Similar to humans, exposing animal subjects to a priming injection of the drug (drug-primed reinstatement), exposure to discrete cues (conditioned-cue reinstatement), or administration of some form of stressor (stress-induced reinstatement) will reliably reinstate drugseeking behavior (Shaham et al. 2003). Excitotoxic lesions of the basolateral AMY have no effect on cocaine SA in rats; however, these same lesions (Meil and See 1997) or reversible inactivations (Grimm and See 2000; McLaughlin and See 2003) abolish the ability of cocainepaired cues to reinstate cocaine seeking. Similar AMY lesions also attenuate the ability of a second-order cue previously associated with cocaine to support responding (Whitelaw et al. 1996; Kantak et al. 2002). Attempts to examine neurotransmitter specificity have shown that DA D1, but not D2, receptor antagonists administered directly into basolateral AMY block expression of conditioned-cue reinstatement (See et al. 2001), while muscarinic receptor antagonism blocks acquisition, but not expression of reinstatement (See et al. 2003). However, neither of these antagonists affect the re-establishment of cocaine SA. In order to more specifically examine the role that conditioned cues play in the learned associations that underlie reinstatement of cocaine seeking, our laboratory developed a Pavlovian training approach to discretely associate cocaine infusions and cues in cocaine-experienced animals (Kruzich et al. 2001). Amidst several days of active cocaine SA in the absence of discrete cues, animals experience a single learning session during which passive cocaine infusions are discretely paired with the presentation of a novel light-tone stimulus complex. Following extinction training, these cues trigger cue-induced reinstatement. This paradigm allows for the study of the acquisition and consolidation of cue–cocaine associations distinct from the expression of the learned behavior guided by conditioned cues.

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inactivations of the basolateral AMY immediately prior to the conditioning session or before reinstatement testing both disrupt conditioned-cue reinstatement, while inactivation of the central AMY disrupts expression, but not acquisition (Kruzich and See 2001). Blockade of D1 receptors in the basolateral AMY during acquisition also disrupts subsequent cue-induced reinstatement (Berglind et al. 2006). Inactivation (Fuchs et al. 2006) or NMDA receptor blockade (Feltenstein and See 2007) immediately after conditioning sessions (consolidation) disrupt cue-induced drug seeking as well. These results confirm and extend previous studies for the AMYmediated development of drug–cue associations.

3.3

Summary of Behavioral Findings

Integrating the multiple studies that have examined the role of the AMY and its neurotransmitter regulation in psychostimulant CPP and SA supports the conclusion that the AMY is indeed critical for motivated drug reward and drug seeking. Furthermore, establishment of drug–cue associations relies on a convergence of AMY inputs that include DA, glutamate, and acetylcholine. The AMY is involved at key phases in the development of these behaviors. The lateral nucleus of the AMY is essential for acquisition, expression, and extinction of cocaine-CPP. Reinstatement caused by conditioned cues is attenuated by basolateral AMY lesions, pharmacological inactivation during acquisition or consolidation, or immediately prior to reinstatement. However, it appears that the central AMY is involved in the expression of these reinstatement behaviors only. These behavioral models and resultant findings have provided the basis for the development of further studies examining measurements of neuronal activation during each phase of behavior, downstream signaling, and cellular and molecular events related to these processes, and neuroadaptations that may be critical for susceptibility to relapse.

4 Amygdala Neuronal Activity and Addiction While studies utilizing behavioral paradigms of drug reward and drug seeking have clearly implicated the AMY in addiction, other experimental approaches have been applied to directly discern neuronal activity in the AMY relevant to drugs of abuse. The two primary techniques that have been most commonly utilized are immunolabeling of immediate early genes and gene products (e.g., c-fos) and direct recordings from AMY neurons. These techniques are combined with the identification of cell type (from recordings or staining) or projection target (from tract tracing) in an attempt to examine the types of neurons activated during behavioral tasks, and where those cells may project.

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Immediate Early Gene Expression

Acute cocaine and amphetamine injections both lead to an increase in c-fos immunoreactivity in the lateral, basal, and central AMY (Brown et al. 1992; Engber et al. 1998; Mead et al. 1999). The context in which the drugs are administered also plays a critical role in neuronal activation. Amphetamine administration in the home cage has been shown to increase c-fos in the lateral, basal, and central nuclei, with a potentiated effect in the lateral and basal nuclei when administration occurs in a novel environment. Administration of amphetamine in a novel environment caused a decrease in activity of central AMY neurons (Day et al. 2001), suggesting that the activation of lateral/basal populations may be of importance for the association between drug and environment/contextual cues, which does not occur in central AMY regions. Presentation of a cocaine or amphetamine-paired environment also leads to increased c-fos expression in the lateral, basal, and central AMY (Brown et al. 1992; Mead et al. 1999). Cocaine-CPP testing caused increased c-fos immunolabeling of both projection neurons and local circuit interneurons in the AMY (Miller and Marshall 2004). These activated projection neurons send efferents to the NAcc, supporting the known interactions of AMY-PFC-NAcc in modulating these types of behaviors (Miller and Marshall 2005). Further, those neurons in the lateral and basal AMY nuclei that were activated during amphetamine-CPP testing displayed increased synaptophysin varicosities, indicative of synaptogenesis, as well as increased TrKB receptor immunoreactivity (Rademacher et al. 2006), lending support to the idea that the AMY is a critical site of drug–contextual cue association.

4.2

Electrophysiological Recordings

Neurophysiological data regarding the role of the AMY in models of addiction is fairly limited, as most studies have focused on NAcc and the VTA. However, evidence to date has demonstrated that cocaine has complex effects on AMY activity. Lateral and central AMY neurons display excitatory or inhibitory responses to IV cocaine administration (Ben-Ari and Kelly 1976; Callahan and Cunningham 1991). These studies did not differentiate between the types of neurons being recorded; however, their average firing rate indicates that the recorded population likely included many interneurons (Rosenkranz and Grace 1999). These results are not surprising, in that DA itself causes both excitatory and inhibitory responses in the AMY (Rosenkranz and Grace 1999), as do norepinephrine (Buffalari and Grace 2007) and serotonin (Rainnie 1999). The excitatory and inhibitory actions of cocaine may correlate with neuronal cell type. IV amphetamine causes mostly slow onset, long lasting inhibition in AMY neurons, with a small proportion of cells showing excitation (Bashore et al. 1978, but see Wepsic and Austin 1972).

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As IV cocaine administration causes DA efflux across many brain regions highly interconnected with the basolateral AMY, cocaine directly applied in the AMY would indicate whether these effects are due to direct actions vs. secondary effects via activity changes in afferent structures. Iontophoretic cocaine application in the AMY led to an inhibition of spontaneous activity in a large proportion of AMY neurons in the lateral, basolateral, and central nuclei (Cunningham et al. 1989), with no evidence of any neuronal excitation. For cocaine SA, IV drug delivery results in actions that are not restricted to the AMY. Therefore, the impact of cocaine is a complex mixture of excitatory and inhibitory responses in the lateral, basal, and central nuclei. Thus, a more complex relationship exists between basolateral and central AMY activity than the simple lateral to medial transmission pattern, whereby the former solely activates/inactivates the latter. Inhibition of lateral AMY neurons, which have glutamatergic projections to central AMY neurons, should cause a decrease in the firing of central AMY neurons. Alternatively, inhibition of lateral neurons could decrease excitation of intercalated cell masses neurons, and disinhibit central AMY neurons. This scenario is complicated by the actions of IV psychostimulants on AMY afferent regions. The most interesting and relevant studies of neuronal activity have involved neuronal recording in awake behaving rats actively engaged in established cocaine SA (Fig. 4) (Carelli et al. 2003). In these studies, several different types of neuronal responses have been characterized. Of interest, a subset of neurons displayed “postresponse excitations,” showing significant increases in activity just after behavioral responding. These same neurons were excited when cocaine-associated

Fig. 4 Extracellular recordings from neurons of the AMY in rats actively selfadministering cocaine (Carelli et al. 2003). Horizontal bar in top panel represents cocaine delivery (R: reinforced response), in bottom panel represents tonelight presentation (S: stimulus). Note the neuronal response to the tone-light in the absence of cocaine

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stimuli were presented in the absence of the cocaine reinforcer. Interestingly, NAcc neurons respond in a manner very similar to the AMY (Carelli 2002), perhaps partially due to basolateral AMY efferents to the NAcc. Clinical investigations of drug addiction have often combined reports of craving for the drug and general physiological indices (e.g., skin conductance responses, cortisol release, etc.) with in vivo measurements of brain activation. Craving states elicited by drug-associated cues or stressful situations in human drug addicts have been linked to activation of similar neural circuitries as reported in animal models of relapse to drug seeking. Exposure to cocaine-associated cues increases drug craving in abstinent users (Childress et al. 1993). The AMY has emerged as a structure with a prominent role in cue-induced craving in human addicts, similar to the role seen for reinstatement of drug seeking in animal models. The presentation of drug-associated cues or imagery results in activation of the AMY in abstinent cocaine users (Grant et al. 1996; Childress et al. 1999; Kilts 2001), although this AMY activation is not always apparent (Garavan et al. 2000). Studies of neuronal responses to psychostimulant cues advance our understanding of how such cues might support SA and trigger relapse. However, much work remains to be done in this area. The limited existing studies have only examined neuronal responses in naı¨ve animals or in animals reliably self-administering cocaine. Therefore, while these studies provide valuable data on the responding of AMY neurons to cocaine and cocaine-related cues, nothing is known about the development and progression of responding over the course of the addictive process. Of particular interest would be characterizing how the responses of AMY neurons, particularly those that respond to cocaine-associated cues, develop through various phases of cocaine SA and cocaine-seeking behaviors (acquisition, maintenance, and extinction). Furthermore, as the AMY plays such an important role in conditioned-cue induced reinstatement, linking the firing patterns of AMY neurons to cocaine conditioned cues during reinstatement of cocaine seeking would strengthen the argument that relapse is driven by basolateral AMY activity.

5 Downstream Signaling Cascades and Neuroadaptations The clinical profile of addiction as a chronic relapsing disorder strongly supports the role of permanent drug-induced neuroadaptations in glutamatergic and DAergic reward learning pathways. Such neuroadaptations can lead to impaired decisionmaking skills, hyperresponsivity to drug cues or contexts, and persistent habit-like behaviors that are insensitive to the negative consequences of drug abuse. The characterization of addiction as a maladaptive learning-related phenomenon has prompted examination of the effects of psychostimulants on cellular events and signaling cascades related to synaptic plasticity (Thomas et al. 2008). While the majority of studies on AMY neuroplasticity have focused on fear learning paradigms (Maren 2005; Sigurdsson et al. 2007; Sah et al. 2008), more recent

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investigations have also examined appetitive learning (Rademacher et al. 2006). Much of the work in the addiction field has centered on the NAcc and DAergic neurons of the VTA (Kauer and Malenka 2007; Thomas et al. 2008). However, based on evidence for the pivotal role of the AMY in drug–cue associative learning and cue-induced relapse, recent investigations have begun to assess the AMY as a potential site of synaptic plasticity that may account for the type of persisting changes that maintain chronic addiction (Goussakov et al. 2006; Rademacher et al. 2006). The role of DA in SA behavior has been well established, as D1 and D2 receptors play a critical role in the acquisition and reinstatement of drug seeking. DA receptors are primarily located postsynaptically on neurons of the lateral AMY (Pinto and Sesack 2008), where these associative events likely occur. DA in the basolateral AMY causes an overall decrease in the firing rate of projection neurons (Rosenkranz and Grace 1999). This reduced activity may seem counterintuitive to activation by psychostimulant SA. However, DA also preferentially facilitates input from sensory association areas (Rosenkranz and Grace 1999). This facilitation is mediated via an increase in the signal-to-noise ratio, and benefits the association of sensory information representing drug cues with reward-related information representative of the primary drug reinforcer.

5.1

Cellular and Molecular Mechanisms

D1 receptors are coupled via Gs proteins to protein kinase A activation (Missale et al. 1998). This activation has multiple potential downstream targets, including ERK and PI3-K. Acute cocaine leads to D1-dependent increases in phosphorylated ERK levels within the lateral and central AMY (Valjent et al. 2004), and inhibition of ERK prevents the acquisition of CPP (Valjent et al. 2000), a behavior dependent on AMY function. While inhibition of ERK does not affect cocaine SA, ERK plays a role in cue-induced reinstatement of psychostimulant-seeking behaviors (Lu et al. 2005). Exposure to cocaine cues after 1 day of withdrawal increased phosphorylated ERK in the basolateral but not central AMY, while the same treatment after 30 days of withdrawal caused the reverse effect. Furthermore, inhibition of ERK phosphorylation in the central AMY after 30 days decreased conditioned-cued responding, while basolateral AMY infusions had no effect. Finally, activation of ERK phosphorylation in the central AMY at 1 day of withdrawal enhanced responding for drug-associated cues (Lu et al. 2005). Akt (also known as protein kinase B) is another signaling molecule that is affected by psychostimulants. Levels of phosphorylated Akt are increased in the AMY after acute treatment with cocaine, but decreased after chronic treatment (Perrine et al. 2008). GSK-3 protein levels are also decreased after 14 days of noncontingent cocaine treatment, which is surprising given that Akt negatively

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regulates GSK-3 activity. ERK regulates further neuroadaptations that could play a significant role in addiction. For example, increased ERK proteins can cause activation of transcription factors such as CREB and Elk-1 (Valjent et al. 2001). In turn, these factors can regulate gene expression necessary for modifications at the level of the synapse. Immediate early genes such as c-fos, zif268, and JunB have also demonstrated responses to psychostimulants specific to the AMY (Radwanska et al. 2005). After extinction, re-exposure to cocaine cues upregulated zif268 in the lateral AMY regions (Thomas and Malenka 2003). Inhibition of zif268 in the basolateral AMY also blocked cue-induced reinstatement of cocaine seeking (Lee et al. 2005). These immediate early genes have also been implicated in learning and plasticity processes, further linking the convergence of the development of addiction with cellular modifications within the AMY (Nestler 2002).

5.2

Amygdala Neuroadaptations and Plasticity in Learning

While many of these effects are likely mediated via DAergic signaling at the D1 receptor, there are many other upstream regulators of ERK and other molecules. The phosphorylated ERK signal in the central AMY after 30 days of withdrawal is blocked by either NMDA receptor antagonists or mGLUR2/3 receptor agonists (Lu et al. 2005). As discussed earlier, glutamatergic afferents to the lateral AMY are likely essential to the association of cues with the rewarding properties of psychostimulants. These synapses constitute the critical site for long-term potentiation that occurs as a result of fear conditioning (Sigurdsson et al. 2007). Furthermore, BDNF can lead to activation of ERK. BDNF has been shown to have a role in various forms of neuroplasticity, including addiction (Thoenen 1995). The AMY contains BDNF-immunoreactive cells (Meredith et al. 2002), and withdrawal from cocaine SA increases BDNF protein in the AMY after 30 or 90 days (Grimm and See 2000). Several other effects of BDNF in brain regions relevant to addiction (e.g., NAcc, Graham et al. 2007 and prefrontal cortex, Hearing et al. 2008) have been investigated; however, studies have not yet been expanded to include consideration of the AMY. The relation of AMY-dependent fear learning to plasticity and AMY synapses has been well established and developed. In comparison, reward-related learning in the AMY has been far less investigated. An important first step would be to examine how acute or repeated psychostimulant administration affects plasticity at AMY synapses. This could be extended to examine plasticity (or occlusion of plasticity) in animals at various stages of CPP and SA behavior. Further investigations into the cellular and molecular mechanisms of these processes would expand our currently limited knowledge on the relation of appetitive drug-reward learning and plasticity in the AMY.

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6 Conclusions 6.1

Summary and Synthesis

The AMY clearly plays a critical role in the associative learning involved in the development of addiction. However, to understand the neurobiology of addiction, one must consider the AMY in the context of the larger limbic and cortical circuitry that underlies psychostimulant addiction. The importance of sensory cortical, thalamic, and hippocampal input that conveys information about environmental cues to the AMY has already been reviewed. DA and norepinephrine modulate information about drug reward in the AMY via projections from the VTA and LC/NTS as well. These multiple streams of information are integrated and expressed via neuronal responding at the cellular level in the AMY, likely through synaptic plasticity at inputs to lateral AMY neurons, in a manner similar to fear conditioning. The role of efferent projections from the AMY that mediate conditioned responding for psychostimulants are less clear. Prior research has implicated the prefrontal cortex and the NAcc in these processes, as well as the mesolimbic DA system originating in the VTA. The lateral/basolateral AMY projection to the NAcc is important for generating goal-directed behaviors (Setlow et al. 2002), including drug seeking (Di Ciano and Everitt 2004). Neuronal inactivation of both the basolateral AMY (Grimm and See 2000) and the NAcc core (Fuchs et al. 2004) disrupts conditioned-cue reinstatement of drug-seeking behavior. Furthermore, disruption of the interactions between these structures reduces cocaine seeking (Di Ciano and Everitt 2004). Cocaine cues also increase glutamate levels in the NAcc (Hotsenpiller and Wolf 2002), which could be due to increased AMY input. These data are congruent with human imaging studies showing that cocaineassociated cues activate both the NAcc (Kilts et al. 2001) and the AMY (Childress et al. 1999; Grant et al. 1996) in abstinent cocaine users. Increased striatal DA is associated with enhanced craving in abstinent users only when presented with cocaine-associated cues (Volkow et al. 2008), suggesting glutamatergic input from the AMY to the striatum may be critical for craving states. DA in the AMY is also important for cue-induced drug seeking (See et al. 2001). Neurons of the AMY and NAcc respond similarly to cocaine-paired cues (2002; Carelli et al. 2003), and basolateral AMY input to the accumbens is critical for reward-seeking behaviors (Ambroggi et al. 2008). Thus, projections from the AMY to the accumbens are clearly also critical for drug seeking. The prefrontal cortex is another region with a well established role in maintaining addictive behaviors. Cocaine-associated cues activate various areas of human prefrontal cortex (Childress et al. 1999; Kilts et al. 2001), and the magnitude of change in these regions correlates with self-reported craving levels (Breiter et al. 1997). Conditioned-cue induced reinstatement in rats is associated with c-fos increases in prefrontal cortex (Zavala et al. 2007) and cocaine seeking after prolonged abstinence is correlated with increased prefrontal c-fos, arc, and zif268

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gene expression (Hearing et al. 2008). Conditioned cues also increase glutamate levels in the prefrontal cortex (Hotsenpiller and Wolf 2002) and inactivation of the anterior cingulate or prelimbic cortex disrupts conditioned-cue induced cocaine seeking (McLaughlin and See 2003). The prefrontal cortex likely modulates AMY output as well as supports drug-seeking behavior via projections to NAcc.

6.2

Future Research

An area in need of further research is the cellular and molecular adaptations within the AMY that promote the transition from initiation of limited drug use to addiction. Long-term potentiation at synapses in the VTA is evident after a single cocaine (Ungless et al. 2001) or amphetamine (Faleiro et al. 2004) exposure. This plasticity is NMDA (Ungless et al. 2001) and D1 (Dong et al. 2001) receptor dependent, and is seen after repeated cocaine as well (Borgland et al. 2004). Other studies have suggested roles for ERK and PKA in the VTA in cocainedependent long-term potentiation (Thomas et al. 2008). These results are consistent with studies examining long-term potentiation and fear conditioning within the AMY. AMY plasticity is dependent on NMDA receptors, MAPK, ERK, CREB, and protein synthesis (for review see Schafe et al. 2001; Rodrigues et al. 2004). However, these two lines of study have not yet converged with an examination of whether AMY plasticity is important for drug–cue associations in addiction. Longterm plasticity and neuroadaptations could support how cues associated with psychostimulants come to exert powerful control over goal-directed behaviors, in particular drug seeking. Basolateral AMY neurons normally switch their firing during behavioral reversals to encode cue outcome rather than cue identity. After chronic cocaine administration, these neurons become inflexible, either firing in a nonspecific manner or maintaining prereversal firing patterns (Stalnaker et al. 2008). While this may partially explain decision-making deficits in addicts, it could also explain the ability of cues to precipitate drug seeking long after extinction or abstinence. Although positive reinforcement plays a major role in the development of addictive behaviors, abundant evidence suggests that tolerance to the rewarding properties of drugs causes secondary adaptations that promote increased drug use and dependence. That is, persistent drug use involves maladaptive neural activity and dysregulated homeostasis that, when the drug is withheld, results in aversive psychological and physiological effects (Kreek and Koob 1998). Such a scenario may promote drug use through negative reinforcement, whereby an individual continues use of the drug to avoid the negative consequences of drug withdrawal. Pavlovian conditioning via the AMY plays a vital role in how withdrawal symptoms precipitate drug-taking behavior. Studies have cited the involvement of medial portions of the central AMY in the negative affect associated with withdrawal stages (Koob 2008). Recent evidence also demonstrates that plasticity at

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synapses in the lateral and central AMY plays a role in the negative emotional states associated with cocaine withdrawal (Goussakov et al. 2006; Fu et al. 2007).

6.3

Clinical Relevance and Application

The role of the AMY in learning processes that contribute to psychostimulant addiction and relapse is now abundantly clear. What remains to be delineated is the role of different subnuclei, as well as how AMY neurons come to encode cocaine cues. Investigations into the neuroadaptations and synaptic plasticity that underlie these firing patterns will expand our knowledge of the precise role of the AMY in addiction. Treatment approaches must consider the powerful influence that drug cues have over abstinent drug users seeking treatment. While the strength of this conditioning can be detrimental to successful treatment, conditioning may also be used to the patients’ advantage. Exposure therapy and the formation of new associations can be applied in attempts to control relapse. While the highly context and cue-specific nature of the previously formed drug–cue associations make such treatments challenging, a better understanding of the neurophysiological and neurochemical underpinnings of AMY-based associative mechanisms contributing to addiction will facilitate the development of pharamacotherapies and behavioral treatments that will improve addiction treatment outcomes.

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Prefrontal Cortical Regulation of Drug Seeking in Animal Models of Drug Relapse Heather C. Lasseter, Xiaohu Xie, Donna R. Ramirez, and Rita A. Fuchs

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Environmental Stimulus-induced Relapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 2.1 Relapse Following Extinction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 2.2 Relapse Following Abstinence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 3 Drug-primed Relapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 3.1 Anterior Cingulate and Prelimbic Cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 3.2 Infralimbic Cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 3.3 Orbitofrontal Cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 4 Stress-induced Relapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 4.1 Anterior Cingulate and Prelimbic Cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 4.2 Infralimbic Cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 4.3 Orbitofrontal Cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

Abstract Prefrontal cortical dysfunction is thought to underlie maladaptive behaviors displayed by chronic drug users, most notably the high propensity for relapse that severely impedes successful treatment of drug addiction. In animal models of drug relapse, exposure to drug-associated stimuli, small amounts of drug, and acute stressors powerfully reinstate drug seeking by critically engaging the prefrontal cortex, with the anterior cingulate, prelimbic, infralimbic, and orbitofrontal subregions making distinct contributions to drug seeking. Hence, from an addiction treatment perspective, it is necessary to fully explicate the involvement of the prefrontal cortex in drug relapse. Keywords Anterior cingulate Drug seeking Extinction Infralimbic Orbitofrontal Prefrontal cortex Prelimbic Reinstatement H.C. Lasseter, X. Xie, D.R. Ramirez and R.A. Fuchs (*) Department of Psychology, University of North Carolina, Chapel Hill, NC 27599-3270, USA e-mail: [email protected]

D.W. Self and J.K. Staley (eds.), Behavioral Neuroscience of Drug Addiction, Current Topics in Behavioral Neurosciences 3, DOI 10.1007/7854_2009_19, # Springer‐Verlag Berlin Heidelberg 2009, published online 3 September 2009

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Abbreviations ACC AMPA BDNF BLA BM BNST CeA CS DH dlCPu GABA IL LTN NA NAcore NAshell NBm OFC p-ERK PL RGS4 TTX VH VP VTA

Anterior cingulate cortex Alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid Brain derived neurotrophic factor Basolateral amygdala Baclofen and muscimol Bed nucleus of the stria terminalis Central amygdaloid nucleus Conditioned stimulus Dorsal hippocampus Dorsolateral caudate-putamen Gamma-aminobutyric acid Infralimbic cortex Lateral tegmental nucleus Nucleus accumbens Core region of the nucleus accumbens Shell region of the nucleus accumbens Nucleus basalis of Mynert Orbitofrontal cortex Phospho-extracellular-related kinase Prelimbic cortex Regulator of G-protein signaling 4 Tetrodotoxin Ventral hippocampus Ventral pallidum Ventral tegmental area

1 Introduction Clinical studies suggest that structural, physiological, and functional abnormalities in the prefrontal cortex facilitate drug craving and drug seeking, which can be triggered by drug-associated environmental stimuli, small amounts of drug, or stress (Ehrman et al. 1992; Foltin and Haney 2000; Rohsenow et al. 2007). The transition from recreational drug use to drug addiction may be related to neural predisposition to drug addiction or neural plasticity resulting from prolonged drug exposure (Franklin et al. 2002; Volkow et al. 2002). Chronic drug users typically present with decreased gray matter density and reduced baseline blood glucose metabolism in the frontal cortex (London et al. 1999; Volkow and Fowler 2000; Franklin et al. 2002; Matochik et al. 2003). At the same time, frontal cortical

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regions of drug users exhibit heightened metabolic activity upon exposure to relapse triggers, which is positively correlated with the intensity of self-reported craving (Grant et al. 1996; Breiter et al. 1997; Childress et al. 1999; Garavan et al. 2000; Bonson et al. 2002; Sinha and Li 2007). In addition to the neural correlates of drug seeking identified in human studies, preclinical studies in rodents directly support the idea that prefrontal cortical subregions make distinct contributions to relapse behaviors (see Table 1), as will be described in this chapter. Based on its connectivity, the prefrontal cortex is ideally positioned to receive information about the salience and motivational significance of relapse triggers from limbic and sensory areas and, in turn, to exert executive control over the selection and initiation of drug-seeking behavior via its output to the motor system. The rodent prefrontal cortex is an aggregate of several cortical regions, including the anterior cingulate (ACC), prelimbic (PL), infralimbic (IL), and orbitofrontal cortices (OFC). These prefrontal cortical subregions all receive input from the mediodorsal thalamus (Uylings and van Eden 1990), but make distinct contributions to drug seeking likely based on their differential connectivity with other elements of the cue-induced, drug-induced, and stress-induced relapse circuitries summarized in Fig. 1 (for detailed reviews, see Shaham et al. 2003; Schmidt et al. 2005b; Feltenstein and See 2008). Importantly, future studies will need to verify the existence of functionally significant interconnections between the circuitry components depicted in the figure.

2 Environmental Stimulus-induced Relapse Several animal models of cue-induced drug relapse have been developed to assess the neural correlates of incentive motivation for drug elicited by drug-paired environmental stimuli. In these paradigms, rodents are trained to self-administer a drug of abuse by exhibiting an instrumental response. Over the course of selfadministration training, drug effects are paired with either the response-contingent presentation of explicit conditioned stimuli (CS) or passive contextual stimulus exposure. Through associative learning processes, these previously neutral stimuli acquire conditioned reinforcing and/or incentive motivational properties, respectively, which permit them to elicit drug seeking in the absence of drug reinforcement (Fuchs et al. 2005; Crombag et al. 2008). Similar to the results from the human neuroimaging studies discussed above, preclinical studies strongly implicate the prefrontal cortex in cue-induced drug seeking. Additionally, these studies suggest that prefrontal cortical subregions exhibit a subregion-specific involvement in mediating drug seeking, depending on the type of cues being utilized and whether drug seeking is assessed following explicit extinction training or after experimenterenforced abstinence.

TTX #

Abstinence Fos ", Arc ", RGS4 ", DAT " BM BM #, TTX #, DA antagonists # –, PL lesion # Lidocaine #, nACh agonist #

Drug priming

BM #, TTX #, D1 antagonist #, D2 antagonist

Stress

Methamphetamine Lidocaine #, nACh agonist # Heroin ania-3 ", MKP-1 ", Fos " zif268 ", c-fos ",Nr4a3 " B/M #" B/M # Ethanol Fos " IL Cocaine Fos " Fos ", pERK " TTX TTX B/M # TTX , BM TTX , BM Methamphetamine Lidocaine Lidocaine Heroin AMPA GluR2 # BM # BM #, CB1 antagonist # OFC Cocaine Arc " Fos " lesion , lOFC: BM #, lesion BM –, TTX , TTX #, lOFC: BM # "mOFC: BM – lOFC: lesion " D1 antagonist #, mOFC: BM mOFC: lesion # D2 antagonist Heroin ania-3 " " denotes an increase, # denotes a decease, and denotes no observed change, in gene or protein expression or in drug-seeking behavior. Abbreviations: ACC anterior cingulate cortex, BM baclofen plus muscimol, CB1 canabinoid 1, DAT dopamine transporter protein, D1 dopamine 1, D2 dopamine 2, IL infralimbic cortex, lOFC lateral orbitofrontal cortex, mOFC medial orbitofrontal cortex, nACh nicotinic cholinergic, p-ERK phospho-extracellular-related kinase, PL prelimbic cortex, RGS4 regulator of G-protein signaling 4, TTX tetrodotoxin

TTX #

Table 1 PFC subregional contribution to drug-seeking behavior Subregion Drug CS/extinction Context/extinction ACC/PL Cocaine Arc ", Fos "

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Relapse Following Extinction

Numerous studies suggest that drug-associated CSs or environmental contexts critically activate regions of the prefrontal cortex to reinstate extinguished drug seeking. CS-induced cocaine seeking is associated with enhanced Fos protein expression in the ACC and enhanced Arc mRNA expression in the ACC, PL, and OFC (Neisewander et al. 2000; Ciccocioppo et al. 2001; Zavala et al. 2008a). Similarly, CS-induced heroin seeking is paralleled by increased mRNA levels for several immediate-early genes, including c-fos, ania-3, MKP-1, and Nr4a3 in the ACC/PL; zif268 in the PL; and ania-3 in the OFC (Schmidt et al. 2005a, b; Koya et al. 2006). Although a cocaine-associated CS fails to alter Fos expression in the IL, cocaine-context re-exposure enhances IL Fos expression in animals that exhibit context-induced drug seeking (Hamlin et al. 2008; Zavala et al. 2008a). Furthermore, drug context-induced ethanol seeking is related to enhanced Fos protein expression in the ACC/PL (Dayas et al. 2007). While changes in immediate-early gene expression are rarely observed in saline-yoked control subjects exposed to cues or in drug-trained subjects not exposed to cues or levers (but see Hamlin et al. 2007), re-exposure to a distinct drug-paired context enhances Fos protein expression in the ACC/PL in rats with a history of passive cocaine, morphine, or nicotine treatment (Franklin and Druhan 2000; Schroeder et al. 2000, 2001; Schroeder and Kelley 2002). Thus, future studies will need to ascertain whether alterations in downstream signaling signify cue-induced incentive motivation or neuroplasticity resulting from drug exposure or behavioral experience, as each of these may influence the involvement of the prefrontal cortex in cue-induced drug seeking.

2.1.1

Anterior Cingulate and Prelimbic Cortex

Performing independent pharmacological manipulations of the ACC and PL has been technically challenging due to the location and proximity of these brain regions. Thus, despite putative differences in connectivity and function, the ACC and PL have not been consistently differentiated in experimental studies of the dorsomedial prefrontal cortex and, as a result, are discussed together here. These pharmacological studies provide direct evidence that the ACC/PL critically regulates cue-induced reinstatement of extinguished drug seeking. McLaughlin and See (2003) first demonstrated that tetrodotoxin (TTX)-induced inactivation of the ACC or PL significantly impairs CS-induced cocaine seeking. Subsequent studies employing lidocaine or baclofen plus muscimol (BM) infusions to achieve neural inactivation have further indicated that the PL mediates CS-induced motivation for methamphetamine and heroin (Hiranita et al. 2006; LaLumiere and Kalivas 2008; Rogers et al. 2008, but see Schmidt et al. 2005a, b). Furthermore, dopamine and acetylcholine neurotransmission in the PL appears to be necessary for CS-induced reinstatement of cocaine and methamphetamine seeking, respectively (Ciccocioppo et al. 2001; Hiranita et al. 2006). In turn, the PL may initiate CS-induced drug

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a

Sensory association Cortex

CS & Context

NBm

BLA

DH ACC VH PL

CeA IL

Motor cortex Spinal cord

Relapse

OFC

VTA

Thalamus dlCPu NAcore VP NAshell

b Drug ACC

VH

PL IL OFC

Motor cortex Spinal cord

Relapse

Thalamus

VTA NAcore VP NAshell

c Stress ACC CeA PL LTN

BNST

IL OFC

Motor cortex Spinal cord

Thalamus

VTA NAcore VP NAshell

Relapse

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seeking through glutamatergic innervation of the nucleus accumbens core (NAcore) given that BM-induced PL inactivation prevents heroin cue-induced drug seeking and concomitant glutamate release within the NAcore, while AMPA/kainate glutamate receptor antagonism in the NAcore abolishes CS-induced heroin seeking (LaLumiere and Kalivas 2008). Although drug-associated contexts engage a distinct neural circuitry relative to drug-paired CS, TTX-induced ACC/PL inactivation similarly impairs both context-induced and CS-induced cocaine seeking, with the former depending on functional interaction between the ACC/PL and basolateral amygdala (Fuchs et al. 2005, 2007).

2.1.2

Infralimbic Cortex

To date, studies suggest that the IL plays a differential role in CS-induced motivation for different drugs of abuse. While the ACC/PL sends projections to the NAcore, a structure that promotes CS-induced reinstatement of cocaine seeking, the IL preferentially innervates the NAshell, which is not critical for this behavior (Sesack et al. 1989; McFarland and Kalivas 2001; Fuchs et al. 2004a). Consistent with this, IL inactivation achieved through TTX or lidocaine infusions fails to alter CS-induced cocaine or methamphetamine seeking, respectively (McLaughlin and See 2003; Hiranita et al. 2006). However, the neural circuitry underlying CSinduced reinstatement of heroin seeking may engage a wider neural network than that subserving cocaine or methamphetamine seeking. In support of this, BMinduced IL inactivation prevents CS-induced heroin seeking, an effect potentially mediated by cannabinoid 1 receptor stimulation (Rogers et al. 2008; Alvarez-Jaimes et al. 2008). Furthermore, self-administered heroin enhances AMPA receptor internalization in the IL, and this neural adaptation appears to facilitate CS-induced heroin seeking (Van den Oever et al. 2008). While the NAshell is involved in context-induced cocaine seeking, the IL does not mediate this behavior given that TTX inactivation of the IL fails to alter reinstatement elicited by a previously cocaine-paired context (Fuchs et al. 2005, 2008).

< Fig. 1 Schematic illustrating the putative neural circuitries of drug-seeking behavior produced by drug-associated environmental stimuli (a), drug itself (b), and stress (c) in rodent models of drug relapse. The anterior cingulate (ACC), prelimbic (PL), infralimbic (IL), and orbitofrontal (OFC) subregions of the prefrontal cortex differentially contribute to these forms of drug seeking. Black shading denotes a lack of demonstrated contribution to cocaine seeking, whereas gray shading denotes limited contribution. Additional abbreviations: BLA basolateral amygdala, BNST bed nucleus of the stria terminalis, CeA central amygdaloid nucleus, DH dorsal hippocampus, dlCPu dorsolateral caudate-putamen, LTN lateral tegmental nucleus, NAcore nucleus accumbens core, NAshell nucleus accumbens shell, NBm nucleus basalis of Mynert, VH ventral hippocampus, VP ventral pallidum, VTA ventral tegmental area

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Orbitofrontal Cortex

The OFC may play a unique role in drug seeking given that damage to this structure produces irresponsibility, impulsivity, and perseveration in human drug users (Bechara et al. 1994). Moreover, it is a functionally heterogeneous brain region comprised of medial and lateral subregions. Fiber-sparing lesions or BM-induced inactivation of the medial OFC fail to alter CS- or context-induced cocaine seeking. In contrast, BM-induced inactivation of the lateral OFC, which is the putative functional homolog of the human medial OFC, attenuates both CS- and drug context-induced cocaine seeking (Gallagher et al. 1999; Fuchs et al. 2004b; Lasseter et al. 2008). Further, lesions of the lateral OFC made prior to selfadministration training do not alter CS-induced cocaine seeking, but potentiate cocaine-context-induced reinstatement. One possible explanation for these seemingly discrepant findings is that OFC lesion-induced neural adaptations have different effects on cocaine seeking based on cue type (Fuchs et al. 2004b; Lasseter et al. 2008).

2.2

Relapse Following Abstinence

While the previous sections discussed the role of the prefrontal cortex in cueinduced reinstatement of extinguished drug-seeking behavior, human drug users rarely undergo explicit extinction training. Because extinction training is an active learning process that induces neurobiological changes due to learning-induced neuroplasticity, different neural substrates may underlie drug seeking following extinction training versus drug-free abstinence periods (Self and Nestler 1998; Self et al. 2004). For example, while the ACC/PL critically regulates drug seeking following extinction training, its influence is diminished following abstinence without extinction training (Fuchs et al. 2006). Moreover, the duration of the abstinence period may profoundly impact relapse behaviors given that enhanced drug seeking is observed following longer drug-free periods (Tran-Nguyen et al. 1998; Grimm et al. 2001).

2.2.1

Anterior Cingulate and Prelimbic Cortex

Enduring neural adaptations are present in the ACC/PL even after extended abstinence periods, which may be sufficient to facilitate cocaine seeking. After prolonged abstinence, exposure to a cocaine-associated context, discriminative stimuli, and/or CS produces robust cocaine seeking and enhances Fos and Arc expression in the ACC as well as Fos protein expression and dopamine transporter levels in the ACC/PL (Ciccocioppo et al. 2001; Grimm et al. 2002; Schwendt et al. 2007; Hearing et al. 2008). Similar regimens also decrease levels of regulator of G-protein

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signaling 4 in the ACC/PL, which are normalized following re-exposure to a drug-associated context (Schwendt et al. 2007). Fos-mediated cue-induced neuronal activation may involve AMPA glutamate receptor signaling in a subregionspecific manner within the prefrontal cortex because cue-induced cocaine seeking is correlated with significant Fos and GluR1 subunit coexpression in the ACC and Fos and GluR4 subunit coexpression in the IL (Zavala et al. 2007). Thus, cue-induced AMPA receptor-mediated signal transduction in the ACC and IL may involve alternate signaling pathways. In particular, AMPA receptor stimulation in the ACC may facilitate cocaine seeking following abstinence given that systemic AMPA receptor antagonism attenuates this behavior and decreases Fos protein expression in the ACC (Zavala et al. 2008b). Neuroplasticity that occurs in the prefrontal cortex during early abstinence may subsequently promote drug seeking. Consistent with this, acute brain-derived neurotrophic factor (BDNF) administration into the ACC/PL after self-administration training disrupts cue-induced, as well as drug-primed, cocaine seeking following 6 days of abstinence and simultaneously enhances BDNF immunoreactivity and normalizes phospho-extracellularrelated kinase (p-ERK) levels in the NAcore (Berglind et al. 2007).

2.2.2

Infralimbic Cortex

The IL may facilitate cue-induced cocaine seeking following extended abstinence, even though it does not play a critical role in this behavior following extinction training, as discussed above. Re-exposure to cocaine-associated cues increases phosphorylated ERK levels in the IL after 30 days, but not 1 day, of abstinence (Koya et al. 2008). Furthermore, infusions of gamma-aminobutyric acid (GABA) agonists into the IL/PL transition area attenuate, while infusions of GABA antagonists enhance, this behavior following 30 days of abstinence (Koya et al. 2008). However, these effects may be partially mediated by the PL. In contrast, Peters et al. (2008a, b) has reported that GABA agonist-induced IL neural inhibition is sufficient to reinstate cocaine seeking after extended extinction training and also facilitates spontaneous recovery. Hence, while the IL may promote drug seeking following abstinence, it may also be recruited during extinction training to inhibit the same behavior.

2.2.3

Orbitofrontal Cortex

Drug-associated cues may recruit the OFC following extended drug-free periods in part via the stimulation of AMPA receptors. After abstinence, the OFC exhibits cue-induced Fos-mediated neuronal activation in conjunction with cocaine seeking, and systemic AMPA receptor antagonism attenuates cocaine seeking and decreases Fos protein expression in the OFC (Zavala et al. 2008b).

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3 Drug-primed Relapse Acute re-exposure to drugs of abuse precipitates drug craving and increases the probability of relapse in abstinent drug users (Jaffe et al. 1989). Similarly, preclinical studies verify that after self-administration and extinction training, an acute drug priming injection elicits robust reinstatement of drug seeking as well as renewal of extinguished conditioned place preference, both of which are thought to reflect motivation for drug. Numerous studies utilizing these animal models further demonstrate that drug-primed drug seeking engages the prefrontal cortex.

3.1

Anterior Cingulate and Prelimbic Cortex

The ACC/PL is critical for the ability of drug-priming injections to reinstate drug seeking for a variety of drugs of abuse, including cocaine, heroin, and methamphetamine (McFarland and Kalivas 2001; Capriles et al. 2003; Hiranita et al. 2006). For instance, ACC/PL inactivation induced by BM or TTX infusions impairs drugprimed heroin and cocaine seeking without producing nonspecific disruption in other goal-directed behaviors (McFarland and Kalivas 2001; Capriles et al. 2003; Rogers et al. 2008). Similarly, selective excitotoxic lesions of the PL attenuate cocaine-primed reinstatement of conditioned place preference (Zavala et al. 2003), while lidocaine-induced inactivation or nicotinic cholinergic receptor stimulation impairs methamphetamine-primed reinstatement (Hiranita et al. 2006). Using functional disconnection techniques, McFarland and Kalivas (2001) have demonstrated that the ventral tegmental area, ACC/PL, NAcore, and ventral pallidum form a serial circuit that mediates cocaine-primed cocaine seeking. While similar circuitry mapping studies have not been conducted for other drugs of abuse, enhanced dopamine neurotransmission in the ACC/PL appears to initiate drug seeking in rats with a history of psychomotor stimulant self-administration. Infusions of dopamine, cocaine, or amphetamine into the ACC/PL can elicit cocaine seeking, while D1 or D2 dopamine receptor antagonists disrupt both cocaine-primed reinstatement of cocaine seeking and conditioned place preference (McFarland and Kalivas 2001; Park et al. 2002; Sanchez et al. 2003; Sun and Rebec 2005; but see Capriles et al. 2003). Studies utilizing in vivo microdialysis suggest that glutamate input from the ACC/PL into the NAcore represents the critical pathway underlying drug-primed reinstatement of drug seeking. While drug priming injections substantially elevate glutamate release in the NAcore, BM-induced ACC/PL inactivation attenuates both cocaine- and heroin-primed drug seeking while simultaneously preventing the increase in glutamate release in the NAcore (McFarland et al. 2003; LaLumiere and Kalivas 2008).

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Infralimbic Cortex

The IL appears to be involved in drug-primed reinstatement in a drug-dependent fashion. It does not mediate psychomotor stimulant-primed reinstatement given that cocaine-primed drug seeking is unimpaired by either BM- or TTX-induced IL inactivation, and methamphetamine-primed reinstatement is not altered by lidocaine infusions into the IL (McFarland and Kalivas 2001; Capriles et al. 2003; Hiranita et al. 2006). Interestingly, however, BM-induced IL inactivation impairs heroin-primed reinstatement of drug seeking, suggesting that drug-primed heroin seeking engenders wider prefrontal cortical recruitment than drug-primed cocaine seeking (Rogers et al. 2008).

3.3

Orbitofrontal Cortex

The involvement of the OFC in drug-primed reinstatement of cocaine seeking is unclear. TTX- or BM-induced inactivation of the OFC fails to attenuate cocaineprimed reinstatement (Capriles et al. 2003; Fuchs et al. 2004b). In contrast, lesions of the lateral OFC, but not the medial OFC, increase cocaine-primed reinstatement in a perseverative manner (Fuchs et al. 2004b). This suggests that prolonged loss of lateral OFC output may potentiate cocaine seeking by prompting compensatory neuroadaptations in the drug-primed reinstatement circuitry (Fuchs et al. 2004b).

4 Stress-induced Relapse Psychological stress plays an important role in the initiation and maintenance of drug use. Stress can produce drug craving in current cocaine users under laboratory conditions (Sinha et al. 1999). Similarly, acute stressors, such as footshock, restraint stress, food deprivation, and pharmacological manipulations including corticotrophin-releasing factor, metyrapone, and yohimbine, reliably reinstate drug seeking in laboratory animals after prolonged drug-free periods (Shaham and Stewart 1995; Erb et al. 1996; for review, see Shaham et al. 2000). Both clinical and preclinical studies have implicated the prefrontal cortex in stress-induced relapse behaviors. However, perhaps due to fundamental differences between psychological and acute stress or methodological factors, human neuroimaging studies suggest stress-induced drug craving stems from diminished stress-induced frontal cortical activation in former drug users, while animals studies suggest stressinduced heroin seeking is correlated with enhanced PL Fos protein expression (Shalev et al. 2003; Sinha et al. 2005).

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Anterior Cingulate and Prelimbic Cortex

Dopamine neurotransmission in the ACC/PL critically mediates stress-induced reinstatement. BM- or TTX-induced inactivation of, as well as dopamine receptor antagonism within, the ACC/PL or PL alone inhibits the ability of footshock and restraint stress to reinstate cocaine seeking or cocaine-conditioned place preference (Capriles et al. 2003; McFarland et al. 2003; Sanchez et al. 2003). Furthermore, it has been theorized that within the larger stress-induced reinstatement circuitry, glutamatergic input from the ACC/PL to the NAcore may initiate stress-induced reinstatement following analysis of inputs from the extended amygdala (McFarland et al. 2003). Stressors are well-known to activate the central noradrenergic system, and this system may play a specific role in mediating drug seeking produced by acute stress. Systemic injections of alpha-2 adrenergic receptor agonists prevent footshockinduced reinstatement of cocaine seeking and inhibit stress-induced norepinephrine release within the ACC/PL (Erb et al. 2000). However, additional research is necessary to explore the direct involvement of ACC/PL norepinephrine release in this behavioral phenomenon.

4.2

Infralimbic Cortex

The IL does not appear to critically regulate stress-induced reinstatement of drug seeking. Neither BM- nor TTX-induced inactivation of the IL alters footshockinduced reinstatement of cocaine seeking (McFarland et al. 2004; Capriles et al. 2003). However, the possible involvement of the IL in the ability of stress to elicit other forms of drug-seeking behavior has yet to be explored.

4.3

Orbitofrontal Cortex

Dopamine in the OFC plays a critical role in footshock-induced drug seeking. Consistent with this, TTX-induced inactivation of the lateral OFC impairs footshock-induced reinstatement of cocaine seeking, while intra-OFC microinfusion of D1-like, but not D2-like, dopamine receptor antagonists have a similar effect on this behavior (Capriles et al. 2003).

5 Concluding Remarks Research utilizing rodent models of drug relapse has demonstrated that prefrontal cortical subregions make distinct contributions to relapse behaviors. The ACC/PL exerts critical control over drug seeking elicited by drug-paired CS, contextual

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stimuli, small amounts of drug, and stress following extinction training, but it exhibits diminished involvement in drug seeking following abstinence without extinction. Unlike the ACC/PL, the OFC does not appear to contribute critically to drug-primed reinstatement of drug seeking even though it facilitates cue-induced and stress-induced relapse behaviors. In further contrast to the ACC/PL, the IL appears to play a role in cue-induced drug seeking following abstinence whereas its involvement in cue-induced and drug-primed drug seeking following extinction training is limited to heroin-seeking behavior. Moreover, the IL does not appear to play a significant role in stress-induced relapse behaviors. In addition to the evidence indicating that the functional integrity of the prefrontal cortex is necessary for various forms of drug seeking, intriguing molecular adaptations have been identified within the ACC, PL, OFC, and IL as well as other elements of the relapse circuitry in rats following passive drug exposure, drug-self administration, abstinence or extinction training, as well as in conjunction with drug-seeking behaviors (for review, see Kalivas and O’Brien 2008; Thomas et al. 2008). Thus, from an addiction treatment perspective, it will be imperative to ascertain whether these neuroadaptations have functional significance with respect to relapse behaviors.

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Neural Substrates of Psychostimulant Withdrawal-Induced Anhedonia Manoranjan S. D’Souza and Athina Markou

Contents 1 2 3 4 5

Introduction: Anhedonia and Psychostimulants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 Psychostimulant Withdrawal in Humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 Theoretical Perspective on Psychostimulant Withdrawal-Induced Anhedonia . . . . . . . . . . . . 125 Psychostimulant Withdrawal-Induced Anhedonia in Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Assessment of Anhedonia in Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 5.1 Assessment of Brain Reward Function with the Intracranial Self-Stimulation Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 5.2 Assessing Decreases in Incentive-Motivation for Rewarding Stimuli . . . . . . . . . . . . . . 133 6 Neural Substrates and Psychostimulant Withdrawal-Induced Anhedonia . . . . . . . . . . . . . . . . 136 6.1 Neurotransmitters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 6.2 Neurohormones, Neuropeptides, and Neurotrophic Factors . . . . . . . . . . . . . . . . . . . . . . . . 149 6.3 Neurosteroids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 6.4 Endocannabinoids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 6.5 Cytokines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 7 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Abstract Psychostimulant drugs have powerful reinforcing and hedonic properties and are frequently abused. Cessation of psychostimulant administration results in a withdrawal syndrome characterized by anhedonia (i.e., an inability to experience pleasure). In humans, psychostimulant withdrawal-induced anhedonia can be debilitating and has been hypothesized to play an important role in relapse to drug use. Hence, understanding the neural substrates involved in psychostimulant withdrawal-induced anhedonia is essential. In this review, we first summarize the theoretical perspectives of psychostimulant withdrawal-induced anhedonia. Experimental procedures and measures used to assess anhedonia in experimental M.S. D’Souza and A. Markou (*) Department of Psychiatry, School of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA e-mail: [email protected]

D.W. Self and J.K. Staley (eds.), Behavioral Neuroscience of Drug Addiction, Current Topics in Behavioral Neurosciences 3, DOI 10.1007/7854_2009_20, # Springer‐Verlag Berlin Heidelberg 2009, published online 3 September 2009

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animals are also discussed. The review then focuses on neural substrates hypothesized to play an important role in anhedonia experienced after termination of psychostimulant administration, such as with cocaine, amphetamine-like drugs, and nicotine. Both neural substrates that have been extensively investigated and some that need further evaluation with respect to psychostimulant withdrawal-induced anhedonia are reviewed. In the context of reviewing the various neurosubstrates of psychostimulant withdrawal, we also discuss pharmacological medications that have been used to treat psychostimulant withdrawal in humans. This literature review indicates that great progress has been made in understanding the neural substrates of anhedonia associated with psychostimulant withdrawal. These advances in our understanding of the neurobiology of anhedonia may also shed light on the neurobiology of nondrug-induced anhedonia, such as that seen as a core symptom of depression and a negative symptom of schizophrenia. Keywords psychostimulants anhedonia dopamine glutamate intracranial self-stimulation serotonin nucleus accumbens mesolimbic pathway

Abbreviations 5-HT Ach ACTH AMPA CRF DA ICSS MDD NAc nAchR NMDA NPY VTA

5-Hydroxytryptamine (serotonin) Acetylcholine Adrenocorticotropic hormone a-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid Corticotropin-releasing factor Dopamine Intracranial self-stimulation Major depressive disorder Nucleus accumbens Nicotinic acetylcholine receptor N-methyl-D-aspartate Neuropeptide Y Ventral tegmental area

1 Introduction: Anhedonia and Psychostimulants The pleasure associated with an experience or a particular state was termed hedonia in the mid-1600s and is derived from the Greek word hedonikos (De La Garza 2005). The ability to experience pleasure is essential for learning, as well as for our

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mental well-being and social interactions. The inability to experience pleasure, or lack of interest, in rewarding activities was termed anhedonia by French psychologist T. Ribot (Auriacombe et al. 1997). People who cannot experience pleasure are socially withdrawn and lack motivation to carry out their daily activities, and their behavior is not guided by rewards, unlike healthy people. The inability to experience pleasure or lack of interest in rewarding activities can lead to psychological disturbances that characterize psychiatric disorders, such as major depression (Hasler et al. 2004) and schizophrenia (Andreasen 1982). In fact, anhedonia is a core symptom of depression and schizophrenia (American Psychiatric Association 1994). This inability to experience pleasure is hypothesized to be the result of deficits in the functioning of brain reward systems (Nestler and Carlezon 2006). A major class of drugs of abuse that initially induce pleasurable effects in humans are the psychostimulant compounds. Psychostimulants share the common property of activating motoric behavior, and this is why they are often referred to as psychomotor stimulants. Some of the most potent psychostimulants include cocaine, D-amphetamine, and methamphetamine. Nicotine, another widely abused drug in the form of tobacco smoking, is a mild psychostimulant (Grilly 2000) and will also be discussed in this review. Acute administration of these drugs induces euphoria, elation, mood elevation, alertness, focused attention, reduced fatigue, and suppressed appetite (Cami and Farre 2003; Gawin and Ellinwood 1989). The degree to which these effects are produced varies from drug to drug. Notably, however, the general motor activation does not occur in all cases. Patients with attention deficit hyperactivity disorder (ADHD) are treated with psychostimulant compounds, such as methylphenidate, to calm agitated/hyperactive behavior (Arnsten 2006). The powerful stimulation of brain reward systems after administration of psychostimulants results in activation of intrinsic “antireward” or “opponent” mechanisms in an attempt to bring back the deviated hedonic processes (Koob and Le Moal 1997; Solomon and Corbit 1974). These opposing forces are hypothesized to decay slowly, and thus persist for a while even after termination of drug use (Solomon and Corbit 1974). Withdrawal from psychostimulants results in severe affective and psychological morbidity, including anhedonia and somatic symptoms (Barr and Markou 2005; Gawin and Kleber 1986; Hughes and Hatsukami 1986; McGregor et al. 2005; Newton et al. 2004; Watson et al. 1972; Weddington et al. 1990) that are hypothesized to be the result of these opponent mechanisms (Koob and Le Moal 1997). Anhedonia resulting from psychostimulant withdrawal can be incapacitating in some subjects, and anhedonia has been suggested to increase the vulnerability to drug-use relapse in psychostimulant abusers (Leventhal et al. 2008). Hence, to reduce relapse, identifying the neural substrates that play a critical role in psychostimulant withdrawal-induced anhedonia and developing effective pharmacological medications for its treatment are essential. In this review, we will focus on the neural substrates mediating anhedonia seen after cessation of chronic administration of psychostimulant drugs. Additionally, we will discuss animal models used to assess anhedonia. The occurrence of anhedonia in psychostimulant abusers, as well as depressed patients and schizophrenia patients, may reflect

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the fact that some common abnormalities characterize these three disorders that mediate anhedonia (Markou and Kenny 2002; Markou et al. 1998; Paterson and Markou 2007). Thus, investigations of the neural substrates of depressive symptomatology may offer insights into psychostimulant withdrawal-induced anhedonia.

2 Psychostimulant Withdrawal in Humans The Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV; American Psychiatric Association 1994) defines substance withdrawal using the following three criteria: (i) the development of a substance-specific syndrome due to the cessation of, or reduction in, substance use that has been heavy and prolonged; (ii) the substance-specific syndrome causes clinically significant distress or impairment in social, occupational, or other important areas of functioning; and (iii) the symptoms are not due to a general medical condition and are not better accounted for by another mental disorder. The signs and symptoms vary depending on the drug, the amount of drug that has been used, the pattern of drug use, and the duration of drug use. Unlike other drugs of abuse, such as alcohol or opioids, in which withdrawal symptoms have a significant physical component, psychostimulant withdrawal has predominantly psychological and affective components. Withdrawal from psychostimulants, such as cocaine, amphetamine, methamphetamine, and nicotine, results in depressive symptoms, such as dysphoric mood, anhedonia, hypersomnolence, fatigue, sadness, suicidal ideation, and general malaise (Coffey et al. 2000; Gawin and Kleber 1986; Hughes 2007; Hughes and Hatsukami 1986; McGregor et al. 2005; Newton et al. 2004; Satel et al. 1991; Watson et al. 1972; Weddington et al. 1990). Subjects also experience increased appetite, anger, anxiety, irritability, agitation, poor concentration, craving, and physical discomfort. Symptoms generally peak in 2–4 days and gradually decrease over time, persisting for a few weeks. Some of the medications used to treat psychostimulant withdrawal in human subjects are listed in Table 1. These medications include dopaminergic uptake blockers (Garattini 1997; Hurt et al. 1997; Margolin et al. 1995; Srisurapanont et al. 1999), dopamine receptor agonists (Giannini et al. 1987, 1989; Gillin et al. 1994; Kampman et al. 2000; Malcolm et al. 1991; Tennant and Sagherian 1987), tricyclic antidepressants (Gawin et al. 1989; Giannini and Billett 1987; Tuma 1993), selective serotonin reuptake inhibitors (Covey et al. 2002; Killen et al. 2000; Saules et al. 2004), selective norepinephrine blockers (Cox et al. 2004), combined serotonin/norepinephrine blockers, noradrenergic and serotonergic receptor blockers (Cruickshank et al. 2008; Kongsakon et al. 2005), 5-HT1A receptor agonists (West et al. 1991), nicotinic acetylcholine receptor (nAchR) agonists (Gonzales et al. 2006; Jorenby et al. 2006; Nides et al. 2006; Shiffman 2008; Shiffman et al. 2006), and compounds that enhance glutamatergic function (Dackis et al. 2005). These studies, however, did not specifically examine whether the test

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Table 1 Clinical studies for the treatment of depressive symptoms during psychostimulant withdrawal in humans Drug of Medication Medication class and Efficacy Reference withdrawal mechanism of action (depressive symptoms) Amphetamine Amitriptyline Tricyclic Y Tuma (1993) antidepressant; norepinephrine uptake blocker Amineptine Antidepressant; Y Garattini (1997); dopaminergic Srisurapanont uptake blocker et al. (1999) Venlafaxine Combined serotonin/ Y McDowell et al. norepinephrine (2000) uptake blocker Reboxetine Antidepressant; Y Cox et al. (2004) selective norepinephrine uptake blocker N Kongsakon et al. Mirtazapine Antidepressant; (2005) noradrenergic (a2) and serotonergic (5HT2 and 5-HT3) receptor blocker Cocaine Bupropion Dopamine uptake Y Margolin et al. blocker (1995) Tennant and Bromocriptine Dopamine D2 receptor Y agonist Sagherian (1987); Giannini et al. (1987) N Gillin et al. (1994) Lisuride Ergot derivative; dopamine D2 receptor agonist N Malcolm et al. Pergolide Dopamine D1/D2 receptor agonist (1991) Hollander et al. Apomorphine Dopamine D2 receptor Y agonist (1990) Amantadine Indirect dopamine Y Tennant and agonist Sagherian (1987); Giannini et al. (1989); Kampman et al. (2000) Desipramine Tricyclic Y Giannini and Billett antidepressant; (1987); Gawin norepinephrine et al. (1989) uptake blocker Propranolol b-adrenoceptor blocker Y Kampman et al. (2001) Modafinil Glutamate-enhancer N Dackis et al. (2005) N Cruickshank et al. Methamphetamine Mirtazapine Antidepressant; (2008) noradrenergic (a2) and serotonergic (5(continued)

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Table 1 (continued) Drug of Medication withdrawal

Nicotine

Nicotine lozenges

Medication class and mechanism of action HT2 and 5-HT3) receptor blocker Nicotine receptor agonist

Efficacy Reference (depressive symptoms)

Varenicline

a4b2 nicotinic receptor partial agonist

N

Fluoxetine

Selective serotonin reuptake inhibitor Selective serotonin reuptake inhibitor Selective serotonin reuptake inhibitor

N

Shiffman et al. (2006); Shiffman (2008) Nides et al. (2006); Gonzales et al. (2006); Jorenby et al. (2006) Saules et al. (2004)

N

Covey et al. (2002)

Y

Killen et al. (2000)

Sertraline Paroxetine þ nicotine patch Buspirone

Y

N West et al. (1991) 5-HT1A receptor agonist Bupropion Dopamine uptake N Hurt et al. (1997) blocker Y effective in alleviating depressive symptoms, N not effective in alleviating depressive symptoms, mechanism of action column describes the main action of the drug, and a drug may have more actions than those reported here

compounds treated anhedonia, and most of the clinical studies were not successful in achieving their primary end point, which was to reduce relapse rates. Nevertheless, some studies did show improvement in alleviating some symptoms associated with psychostimulant withdrawal, including depression (Cox et al. 2004; Kampman et al. 2001; McDowell et al. 2000; Tuma 1993). Future clinical studies may need to be better designed in terms of patient selection (i.e., identifying and grouping patients based on symptom severity) and focusing more on specific dysfunctions, such as anhedonia (Geyer and Markou 1995; Hyman and Fenton 2003; Markou et al. 2008). Currently, sparse information is available about the severity and nature of anhedonia in psychostimulant abusers and the correlation of anhedonia with the amount of drug used. Subjective “high” from acute cocaine intoxication in abusers depends on baseline anhedonic symptoms (Newton et al. 2005; Uslaner et al. 1999). The greater the degree of anhedonia, the greater the “high” experienced after termination of cocaine administration. Based on these findings, anhedonic subjects are hypothesized to be more prone to relapse after an initial slip than people who experience less anhedonia during drug withdrawal (Leventhal et al. 2008). Treatment of protracted anhedonia seen in subjects withdrawing from psychostimulants may help in preventing relapse to drug use (Leventhal et al. 2008).

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Unclear, however, is whether the anhedonia seen during withdrawal from psychostimulant drugs is induced by abused drugs or is merely an exacerbation of preexisting anhedonia. No data exist regarding the status of the reward system at baseline or prior to drug use in individuals who become dependent on psychostimulants. Anhedonia has been proposed to be a personality “trait” rather than a “state” (Loas 1996). This hypothesis implies that anhedonia can preexist prior to drug exposure. A high degree of comorbidity exists between major depressive disorder (MDD) and substance dependence disorder (Kessler et al. 1996; Robins and Regier 1991). This high degree of comorbidity is hypothesized to be due to the use of psychostimulants and other drugs of abuse by the anhedonic subjects in attempts to alleviate the underlying brain reward deficit (i.e., self-medication hypothesis; Khantzian 1985, 1997; Markou et al. 1998). In nondrug abusers, the “high” experienced after acute amphetamine intoxication has been shown to be dependent on the presence of baseline anhedonic symptoms (Tremblay et al. 2002, 2005), similar to the “high” experienced by drug users (see above; Leventhal et al. 2008). Subjects who have baseline anhedonic symptoms experience a greater “high” compared with healthy controls. These findings suggest that anhedonic subjects may also be more vulnerable to developing drug dependence, especially psychostimulant dependence (Leventhal et al. 2008).

3 Theoretical Perspective on Psychostimulant WithdrawalInduced Anhedonia Anhedonia experienced during withdrawal from chronic psychostimulant abuse represents a reward deficit that has been theoretically attributed to a breakdown of hedonic homeostasis described by two major theories: “opponent process” theory and “hedonic set-point shift” theory (i.e., allostasis; for extensive discussions of these theories, see Koob and Le Moal 2001; Schulkin et al. 1994; Solomon 1980; Solomon and Corbit 1974). The opponent process theory posited by Solomon and Corbit (1974) postulates that whenever there is a departure from a state of homeostatic neutrality in brain reward systems, opposing processes are initiated in an attempt to bring the disturbed system back to the original state of homeostasis. According to this theory, the opposing processes are sluggish in onset, slow to build up to an asymptote, and slow to decay. Therefore, they can last longer than the effects of the original homeostasis-perturbing event and can result in depression of brain reward system functioning. Thus, excessive stimulation of brain reward systems induced by psychostimulant administration activates opposing processes to counteract this excessive stimulation. After abrupt cessation of psychostimulant exposure, specific brain circuits attempt to return to hedonic homeostasis. However, the winding up of the opposing processes initiated by the exposure to psychostimualants is a slow process, resulting in anhedonia and other psychological disturbances in the patient.

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More recently, another theory, termed hedonic set-point shift theory or allostasis, has been proposed to explain psychostimulant withdrawal-induced anhedonia (Koob and Le Moal 2001). This theory takes a more expanded view of dysregulated hedonic homeostatic balance and involves brain hormonal and stress response systems, in addition to brain reward circuitry. According to this theory, the repeated and prolonged abuse of psychostimulants results in changing of the homeostatic hedonic set point, resulting in a new elevated allostatic state. This new elevated allostatic state requires higher stimulation to lead to the same degree of hedonic pleasure. Both of these theories suggest that the brain reward system, similar to most biological systems, is homeostatically regulated by various neuronal processes. Excessive and prolonged stimulation resulting from prolonged abuse of psychostimulants can result in disturbing homeostasis, thus leading to dysregulation of brain reward circuitry and ultimately to psychopathological states such as anhedonia.

4 Psychostimulant Withdrawal-Induced Anhedonia in Animals Anhedonia-like effects have been observed in animals after administration and subsequent withdrawal from psychostimulants (Fig. 1). Some of the drug treatment regimens used for inducing psychostimulant withdrawal-induced anhedonia are described in Table 2. The first studies on psychostimulant withdrawal used a procedure in which rats received progressively increasing doses of amphetamine (Leith and Barrett 1976, 1980). Subsequent studies showed dose-dependent effects of cocaine (Kokkinidis and McCarter 1990), amphetamine (Kokkinidis et al. 1980), and nicotine (Bozarth et al. 1998) on brain reward function. Similar effects were observed when cocaine was self-administered by the subjects for prolonged periods of time (Kenny et al. 2003b; Markou and Koob 1991, 1992a, b). Psychostimulant withdrawal effects on brain reward function have also been successfully demonstrated with nicotine and amphetamine delivered subcutaneously via osmotic minipumps (Epping-Jordan et al. 1998; Paterson et al. 2000) that release fixed amounts of drug per unit time, thus maintaining constant drug blood levels. With nicotine, withdrawal effects can be induced either by discontinuing the administration of nicotine (i.e., spontaneous withdrawal) or by administering a nAchR antagonist (i.e., precipitated withdrawal; Epping-Jordan et al. 1998). In addition to anhedonia, animals also exhibit other depression-like symptoms during psychostimulant withdrawal, such as decreased locomotor activity, decreased appetite, increased anxiety, decreased grooming behavior, and mild somatic symptoms (Barr and Markou 2005). These anhedonia-like effects seen following psychostimulant withdrawal in animals are reversed by pharmacological agents currently available for depression/anhedonia and/or drug abuse treatment. These pharmacological treatments include the tricyclic antidepressant desipramine (Markou et al. 1992; Paterson et al. 2008b), serotonergic treatments involving the administration of a selective serotonin reuptake inhibitor with or without a

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Fig. 1 Withdrawal from psychostimulants, such as cocaine (a), amphetamine (b), and nicotine (c), induces transient deficits in brain reward function. These reward deficits, reflecting a state of anhedonia, can be quantified through the use of the intracranial selfstimulation (ICSS) procedure. Psychostimulant withdrawalinduced anhedonia is reflected in elevated current intensity reward thresholds for self-stimulation of the posterior lateral hypothalamus. Cocaine, amphetamine, and nicotine figures taken with permission from Markou and Koob (1991), Paterson et al. (2000), and Harrison et al. (2001), respectively. Please note that direct comparisons of the magnitude of the effects cannot be made among all figures. Different stimulation parameters and stimulation hardware systems were used to conduct the studies with cocaine, amphetamine, and nicotine

5-HT1A receptor antagonist (Harrison et al. 2001; Markou et al. 2005; Muscat et al. 1992b), the atypical antidepressant bupropion (Cryan et al. 2003; Paterson et al. 2007), and the dopamine receptor agonist bromocriptine (Markou and Koob 1992a; Figs. 2 and 3).

5 Assessment of Anhedonia in Animals Several paradigms have been used to assess anhedonia in animals that can be broadly divided into those that (i) directly measure the status of the brain reward system or (ii) assess the incentive-motivation for rewarding stimuli.

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Table 2 Treatment regimens used to induce psychostimulant withdrawal-induced anhedonia in experimental animals Location of ICSS Treatment regimen Reference electrode Cryan et al. (2003); Paterson et al. Posterior D-amphetamine (continuous infusion (2000) lateral using subcutaneous osmotic hypothalamus minipump), 5 or 10 mg kg1 day1 for 6 days D-amphetamine administered Markou et al. (2005); Harrison i.p. three times per day (6 a.m., et al. (2001) 12 a.m., 6 p.m.) for 4 days in a rising-dose regimen starting at 1 mg kg1 and stabilizing at 5 mg kg1 (i.e., 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5 mg kg1; total dose, 50 mg kg1) Leith and Barrett (1980) D-amphetamine administered i.p. 5 mg kg1 for 7 days followed by 10 mg kg1 for 7 days Leith and Barrett (1976) D-amphetamine administered i.p. three times per day (8 a.m., 2 p.m., 8 p.m.) beginning with a dose of 1 mg kg1 which was increased in 1 mg kg1 steps at each injection so that for the last injection on the fourth day the animal received 12 mg kg1 (i.e., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 mg kg1; total dose, 78 mg kg1) D-amphetamine administered i.p. Barr et al. (2002) three times per day (9 a.m., 5 p.m., 12 p.m.), starting with a dose of 1 mg kg1 and escalating by 1 mg kg1 for each subsequent dose (doses expressed as salt) for the first 3 days for nine doses. On day 4, subjects received three doses of 10 mg kg1. Animals therefore received a total of 12 injections over the 4 day period (i.e., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10 mg kg1; total dose, 75 mg kg1) Kokkinidis et al. (1980); Predy and Substantia nigra D-amphetamine administered Kokkinidis (1981) 7.5 mg kg1 i.p. with two injections per day separated by 6 h for 10 consecutive days Ventral D-amphetamine administered Borowski and Kokkinidis (1992) tegmental 10 mg kg1 i.p. for 12 consecutive area days (continued)

Neural Substrates of Psychostimulant Withdrawal-Induced Anhedonia Table 2 (continued) Location of ICSS Treatment regimen electrode Cocaine administered 25 mg kg1 i.p. for 18 days or 30 mg kg1 i.p. for 3 days Posterior lateral Cocaine administered as eight hypothalamus injections of 15 mg kg1 i.p. over 9 h Cocaine administered 0.5 mg kg1 as “binge” intravenous selfadministration (3, 6, 12, 24, or 48 h of cocaine self-administration after stabilization of intake at 3 h day1) Cocaine self-administered for a prespecified number of injections (10, 20, 40, or 80 injections; equivalent to 4.94 0.23, 9.88 0.46, 19.64 0.94, or 39.53 1.84 mg kg1). Each injection was 0.25 mg kg1. The time necessary to self-administer the specific number of injections varied between subjects (10 injections, 40 6.9 min; 20 injections, 99 11.9 min; 40 injections, 185 10.9 min; 80 injections, 376 19.9 min) Nicotine administered via osmotic pump with continuous infusion of 3.16 mg kg1 day1 base or 6.32 mg kg1 day1 base for 7, 21, or 28 days

5.1

129

Reference Frank et al. (1992)

Baldo et al. (1999)

Markou and Koob (1991, 1992a, b), Markou et al. (1992)

Kenny et al. (2003a, b)

Harrison et al. (2001), Epping-Jordan et al. (1998), Cryan et al. (2003), Skjei and Markou (2003), Paterson et al. (2007)

Assessment of Brain Reward Function with the Intracranial Self-Stimulation Procedure

Intracranial self-stimulation (ICSS) is a procedure that provides an operational measure of brain reward system function, reflected in reward thresholds. Effects of manipulations, such as stress, social defeat, or psychostimulant withdrawal, on brain reward function can be reliably assessed using the ICSS procedure. ICSS involves the implantation of microelectrodes into discrete brain sites hypothesized to be part of the brain reward circuits, such as the medial forebrain bundle including the posterior lateral hypothalamus (Baldo et al. 1999), ventral tegmental area (VTA; Borowski and Kokkinidis 1992; Frank et al. 1992), and substantia nigra (Predy and Kokkinidis 1981). Among other actions, stimulation of the medial forebrain bundle may activate excitatory inputs to the dopaminergic system and thereby transynaptically excite the brain reward system (Bielajew and Shizgal

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Fig. 2 The atypical antidepressant bupropion, which is approved for smoking cessation by the Food and Drug Administration (FDA), attenuated elevated reward thresholds seen during withdrawal from nicotine. This figure shows intracranial self-stimulation (ICSS) data (mean SEM) expressed as percentage of pretreatment (drug-naive) baseline. Chronic administration of bupropion/saline and nicotine/saline was carried out using subcutaneous osmotic minipumps that delivered drug chronically. Chronic bupropion prevented the elevation in reward thresholds seen during spontaneous nicotine withdrawal after termination of nicotine administration induced by the removal of the minipump that contained nicotine (indicated by the arrowhead). The bupropion/ saline pump was left in place. *p < 0.05, significant difference between the nicotine/saline-treated group and the saline/saline-treated control group. #p < 0.05, significant difference between the nicotine/saline-treated group and the nicotine/bupropion-treated group. Bupropion acts by blocking the uptake of monoamines, such as dopamine and norepinephrine (Figure taken with permission from Paterson et al. 2007)

1986; Yeomans et al. 2000). Although several methods exist to determine brain reward thresholds, the field has settled on the use of primarily two procedures: the rate-frequency curve-shift procedure (Campbell et al. 1985) and the discrete-trial current-intensity procedure (Kornetsky and Esposito 1979; Markou and Koob 1992b). We will describe these two procedures briefly because they are the ones used most extensively in the studies discussed in this review. In the discrete-trial current-intensity procedure, the subject receives a rewarding noncontingent electrical stimulus and has a time-window (i.e., limited hold period) during which the subject can perform an operant response to receive a contingent electrical stimulus identical in all parameters to the noncontingent stimulus. Failure to respond within the limited hold period results in termination of the trial and initiation of an intertrial interval. The current intensity of the noncontingent and contingent stimuli is varied according to the psychophysical method of limits to allow the determination of a current-intensity threshold. The threshold value is defined as the midpoint in microamps between the current-intensity level at which the animal made two or more positive responses out of the three stimulus presentations and the level at which the animal made less than two positive responses at two

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Fig. 3 Serotonergic (fluoxetine + 5-HT1A receptor antagonist) treatment reversed the elevations in brain reward thresholds observed during amphetamine and nicotine withdrawal. (a) Amphetamine withdrawal resulted in elevated thresholds (mean SEM). p-MPPI (3 mg kg1) + fluoxetine (2.5 or 5 mg kg1) dose-dependently lowered the threshold elevations of amphetamine-withdrawing rats. Arrow indicates the time-point at which one of the various treatments was administered. *p < 0.05, significant differences from the corresponding control group. (b) Nicotine withdrawal resulted in elevated reward thresholds (mean SEM). p-MPPI + fluoxetine lowered the threshold elevations of nicotine-withdrawing rats. Arrow indicates the time-point at which one of the various treatments was administered. *p < 0.05, significant differences from the corresponding control group (Figure taken with permission from Harrison et al. 2001)

consecutive intensities. This reward threshold is the current intensity of the stimulation that is perceived as rewarding, and changes in this threshold indicate changes in reward sensitivity and brain reward function. Lowering of thresholds indicates reward enhancement or facilitation, while elevation of thresholds indicates a decrease in brain reward function and may be considered homologous, or at least analogous, to anhedonia-like symptoms seen in humans.

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The rate-frequency procedure involves the systematic manipulation of the frequency of the stimulation and assessment of the rate of responding at each frequency (Campbell et al. 1985; Gallistel and Freyd 1987). This procedure leads to a sigmoidal function, similar to dose–response relationships with pharmacological stimulation. The reward threshold is usually defined as the frequency that supports an arbitrary percentage of the asymptotic level of responding, such as 50 or 75%. Other methods of threshold estimation involve the extrapolation of where a linear extension of the linear part of the rate-frequency curve meets the x-axis (i.e., frequencies are depicted on this axis; Miliaressis et al. 1986). In rats, independent of whether the rate-frequency function or the discrete-trial current-intensity procedure was used, withdrawal from all drugs of abuse, including cocaine (Kokkinidis and McCarter 1990; Markou and Koob 1991), amphetamine (Barr et al. 2002; Leith and Barrett 1980; Paterson et al. 2000), methamphetamine (Hoefer et al. 2006), and nicotine (Epping-Jordan et al. 1998), is associated with impaired brain reward function and elevated brain reward thresholds. The ICSS procedures also provide measures of motor performance by measuring either the latency to respond after the presentation of the noncontingent electrical stimulus in the discrete-trial procedure or the asymptotic rate of responding in the ratefrequency procedure. In the discrete-trial procedure, the noncontingent electrical stimulus signifies the initiation of a trial during which a reward is available to the subject upon performance of the required operant response. Markou and Koob (1992b) showed that the response latency measure, but not the threshold measure, was sensitive to nonspecific motor impairments. Response latency was unaffected during withdrawal from cocaine (Markou and Koob 1991), amphetamine (Paterson et al. 2000; Wise and Munn 1995), or nicotine (Kenny et al. 2003a), indicating that psychostimulant withdrawal is associated with specific impairment of rewardrelated processes. Such specificity is particularly important for nicotine withdrawal in which the presence of somatic signs of withdrawal could affect motor performance, unlike with amphetamine and cocaine that are not characterized by somatic signs. Similarly, in the rate-frequency procedure, changes in reward efficacy result in leftward (reward facilitation) or rightward (reward attenuation) shifts in the rate-frequency function and have been demonstrated to reflect changes in reward sensitivity and not nonspecific motoric effects (Edmonds and Gallistel 1974). In summary, the ICSS paradigm offers one of the best objective and quantitative measures of brain reward system function (Carlezon and Chartoff 2007; Markou and Koob 1993; Nestler et al. 2002). Additionally, ICSS studies in mice (Elmer et al. 2005; Gill et al. 2004; Stoker et al. 2008; Zacharko et al. 1990) can be very useful because genetically modified mice can be used to specifically identify the neurobiological substrates underlying drug withdrawal-induced anhedonia. Importantly, in terms of pharmacological isomorphism (i.e., whether a pharmacological treatment effective in treating the human condition shows a response in the animal model; Geyer and Markou 1995), the predictive validity of this procedure has been established by showing that medications with efficacy in the treatment of depression/anhedonia and/or drug abuse reversed brain reward deficits in rats undergoing withdrawal from cocaine, amphetamine, or nicotine

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(Harrison et al. 2001; Markou et al. 1992; Paterson et al. 2008a). However, the ICSS paradigm is not without its disadvantages. Some of the major disadvantages of this procedure include the need for stereotaxic surgery and the extensive training required before the implementation of experimental manipulations.

5.2

5.2.1

Assessing Decreases in Incentive-Motivation for Rewarding Stimuli Progressive-Ratio Responding for a Natural Reinforcer

The progressive-ratio schedule of reinforcement provides a measure of the motivation to consume a drug (Hodos 1961; Markou et al. 1993; Richardson and Roberts 1996). This procedure assesses the amount of effort that an animal is willing to emit to obtain a reward (e.g., sucrose). Decreases in the amount of effort that the animal emits in a progressive-ratio schedule provides a measure of avolition, that is, lack of motivation, in addition to reinforcement strength (i.e., incentive). A typical training schedule is the following (Hoefer et al. 2006). Animals are initially trained on a fixed-ratio schedule of reinforcement for 1 h daily for 10 days. The requirement for obtaining reinforcement is gradually increased from a fixed-ratio 1 to fixed-ratio 10 over these 10 days. After this fixed-ratio training is completed, animals are placed on a progressive-ratio schedule to obtain the reinforcement, in which the response requirements necessary to receive a single reinforcement increase according to the following progression: [5e0.2 (total rewards +1) 5], resulting in progressive response requirements of 1, 2, 4, 6, 9, 12, 15, 20, 25, 32, 40, 50, 62, 77, 95, 118, 145, 178, 219, 268, etc., to receive a reward. The number of lever presses for each reward continues to increase until the animal fails to obtain the next reward within the allowed time, commonly 60 min. The final completed ratio is defined as the breakpoint. For psychostimulant compounds, such a response progression results in rats reaching a breakpoint within 4–6 h. Rats undergoing withdrawal from amphetamine after a 4-day escalating dose regimen of D-amphetamine exhibited lower breakpoints for sucrose reward compared with saline-treated controls (Barr and Phillips 1999; Orsini et al. 2001). Similar decreases in breakpoints for sucrose were observed after the termination of an escalating dose regimen of methamphetamine (Hoefer et al. 2006). Such decreases in breakpoints likely reflect decreased motivation to obtain reward during psychostimulant withdrawal.

5.2.2

Sucrose Preference/Consumption

Sucrose consumption/preference is another proposed measure of anhedonia (Casarotto and Andreatini 2007; Papp et al. 1991). Sucrose is a natural reinforcer, and reduced preference for a sucrose solution in rats has been hypothesized to reflect

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decreased sensitivity to reward and has been argued to be homologous with anhedonia (Muscat and Willner 1992; Papp et al. 1991). Rats show inverted U-shaped concentration-dependent consumption and preference for sucrose (Muscat et al. 1991). The intake increases as the concentration of the sucrose solution increases, but for very high concentrations the intake is lower. Operant responding for sucrose shows a similar inverted U-shaped function (Guttman 1953). Sucrose consumption/preference typically is monitored by tracking a decrease in consumption of and/or preference for palatable, low-concentration (1%) sucrose solution over repeated tests. Prior to the first sucrose preference test, animals are subjected to 48 h of forced exposure to 1% sucrose solution to habituate them to the sucrose solution and eliminate avoidance of a novel taste due to neophobia. During this initial exposure, sucrose is the only fluid available for consumption. The following 2 days, the rats have free access to food and water. The animals then are submitted to water deprivation for 16 h prior to performing the sucrose preference test. Animals are single-housed, and testing often occurs in the home cage. During the test, two preweighed bottles, one containing tap water and one containing 1% sucrose solution, are presented to the animal for 1 h. The bottles are weighed again after 1 h, and the weight difference is considered to be the rat’s intake from each bottle. The sum of the water and sucrose intake defines total intake, and sucrose preference is expressed as the proportion of sucrose intake to total intake using the following formula: % Preference ¼ (Flavor intake/total intake) 100. After the sucrose preference test, the animals have free access to food and water. After the baseline sucrose preference test, the animals are subjected to an experimental manipulation either in the form of chronic mild stress or psychostimulant treatment. Care must be taken to ensure that the total fluid intake is not significantly different between the experimental groups. The validity of sucrose drinking as a measure of reward sensitivity has been established by several studies (Muscat et al. 1992a, b; Muscat and Willner 1992). The test has been successfully used by some groups to demonstrate anhedonia-like behavior in animals subjected to chronic mild stress (Casarotto and Andreatini 2007; Papp et al. 1991; Sampson et al. 1992; Willner et al. 1992), but not in animals withdrawing from psychostimulants. This test, however, is well known to produce unreliable results – not all studies have found a decrease in sucrose intake in animals subjected to chronic mild stress (Matthews et al. 1995).

5.2.3

Positive and Negative Contrast Procedures

The contrast paradigm is used to study anhedonia by assessing the effect of devaluation or enhancement of incentive value of a sweet solution reinforcer after preexposure to a different concentration of the sweet solution (Barr and Phillips 2002). Two types of contrast procedures include successive negative contrast and successive positive contrast. In the successive negative contrast procedure, animals are initially trained to obtain a reinforcer of a consistent value (e.g., 32% sucrose solution). If this reinforcer is replaced by a reinforcer of a lesser value (e.g., 4% sucrose solution), then animals consume significantly lower levels of the sucrose solution compared

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with animals that only had continual access to the 4% solution. For example, animals are divided into two groups. One group is exposed to and trained on a 32% sucrose solution, and the other group is exposed to and trained on a 4% sucrose solution. Initially, animals are given two 1-h habituation sessions to their respective sucrose solutions in their home cages on alternate days. Animals are then given access to their respective solutions for a 5-min period once per day in the testing apparatus for 10 days or until most rats reach an asymptotic level of consumption of their respective sucrose solutions. Once stable intake is reached, all animals are given access to a 4% sucrose solution for 5 min for the next 8 days. Animals trained on the 32% sucrose solution show decreased intake of the 4% sucrose solution initially compared with animals trained on the 4% sucrose solution for all 10 days. Intake levels return to baseline levels over time. This phenomenon has been widely demonstrated across species, including rodents, primates, and humans (Grigson et al. 1994; Schnorr 1967; Specht and Twining 1999). However, to assess the effect of psychostimulant withdrawal-induced anhedonia, drug treatment is carried out after animals have reached asymptotic levels of intake. Animals withdrawing from an escalating dose regimen of amphetamine showed an exaggerated decrease in intake of the lower-concentration reinforcer (Barr and Phillips 2002). Furthermore, while control animals showed a rapid increase in consumption of sucrose solution, animals undergoing amphetamine withdrawal required a longer time to return to prewithdrawal levels of consumption. Such an effect is interpreted as a decrease in the perception of reward during amphetamine withdrawal. Similarly, amphetamine withdrawal is also associated with a failure to display successive positive contrast (Vacca and Phillips 2005). The procedure for successive positive contrast is similar to that described above, with the exception that animals are initially exposed to and trained on a 4% sucrose solution. Once animals reach an asymptotic level of intake (e.g., after 10 sessions), one of the groups is exposed to a 32% solution (i.e., a shift in incentive value from 4 to 32%). Normal animals show an enhanced intake of the higher-concentration reinforcer. Animals withdrawn from an escalating dose of amphetamine do not show this enhanced intake of the higher-concentration reinforcer (Vacca and Phillips 2005).

5.2.4

Incentive-Motivation for Sexual Reward

Another procedure used to study anhedonia is decreased motivation to approach a sexually receptive conspecific (Barr et al. 1999; Yirmiya 1996). Anhedonic human subjects show significant reductions from previous levels of sexual interest. Further difficulties in sexual functioning are seen in humans suffering from depression (American Psychiatric Association 1994). In this test, male rats are introduced to a cage and allowed to habituate for 5 min. Subsequently, a receptive female rat is introduced, and copulatory behavior is videotaped for 25 min. The following indices are measured: anticipatory precopulatory activity, intromission latency (time to first intromission), ejaculation latency (time from first mount to ejaculation), and number of ejaculations. Rats undergoing amphetamine withdrawal

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exhibited decreased motivation to obtain access (i.e., decreased anticipatory precopulatory activity) to sexually receptive conspecifics, while measures of copulatory activity were not affected (Barr et al. 1999).

5.2.5

Novelty-Induced Conditioned Place Preference

This procedure is based on the principle that rats spend more time in novel environments (Hughes 1965) and interact more with novel objects (Bevins et al. 2002) than familiar environments and objects, presumably because novelty has reinforcing value. This procedure is similar to the conventional place conditioning procedure, with the exception that a novel object is used as a reward, and one side of the two-chamber box is consistently associated with this novel object (Bevins and Bardo 1999). Specifically, the animals are initially placed at the center of a chamber having two distinct end compartments, and the initial compartment preference of the rat is noted. The animal is then restricted to the nonpreferred compartment with a novel object (i.e., conditioning against a preference is carried out). The animals are similarly restricted to the preferred chamber but without an object. During the postconditioning test in the absence of an object, animals prefer the side associated with a novel object. Withdrawal from nicotine blocked the acquisition of conditioned place preference for novelty reward (Besheer and Bevins 2003). This blockade occurred in the absence of underlying deficits in information processing, such as interactions with an object, novelty detection, environmental familiarization, and expression of learning. This procedure has not been used with other psychostimulant drugs, such as cocaine or amphetamine. The predictive validity of this procedure, in terms of pharmacological isomorphism, has not yet been assessed.

6 Neural Substrates and Psychostimulant Withdrawal-Induced Anhedonia The mesocorticolimbic dopaminergic pathway is one of the critical reward pathways in the brain mediating the reinforcing effects of both natural and unnatural rewards (Koob 1992; Wise 1978). All psychostimulant drugs exert their reinforcing actions partly by activating this reward pathway. More recently, studies have also shown that this pathway plays an important role in reward anticipation and seeking (Nicola et al. 2005; Pessiglione et al. 2006; Schultz 1998). One characteristic of anhedonia is the lack of reward anticipation and seeking. Evidence suggests that dysregulation of the mesocorticolimbic brain reward pathway plays an important role in depression and its constituent symptoms, such as anhedonia (Bressan and Crippa 2005; Di Chiara et al. 1999; Nestler and Carlezon 2006; Shirayama and Chaki 2006). Thus, dysregulation of the mesocorticolimbic reward pathway may

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play an important role in the anhedonia seen during withdrawal from psychostimulant drug exposure. The mesocorticolimbic dopaminergic system consists of dopaminergic neurons originating in the VTA that project to limbic and cortical areas, such as the nucleus accumbens (NAc), prefrontal cortex, amygdala, hippocampus, lateral hypothalamus, entorhinal cortex, and lateral septal area (Beckstead et al. 1979; Fallon and Moore 1978; Lindvall and Stenevi 1978). The NAc is composed of small populations of g-aminobutyric acid (GABA) and cholinergic interneurons, in addition to a large number (about 90%) of efferent GABAergic medium spiny projecting neurons (Chang et al. 1982; Meredith et al. 1993). The mesolimbic dopaminergic neurons are influenced either at the level of the cell body in the VTA or at their terminals in the NAc, or both, by projections from several brain areas. For example, NAc neurons receive excitatory glutamatergic projections from the lateral hypothalamus, prefrontal cortex, amygdala, and hippocampus (Brog et al. 1993; Geisler and Zahm 2005; Kelley and Domesick 1982; Kelley et al. 1982; McDonald 1991; Rosin et al. 2003; Sesack and Pickel 1992). They also receive serotonergic projections from the raphe nucleus, noradrenergic projections from the locus coeruleus (LC), and cholinergic inputs from the pedunculopontine (PPTg) and laterodorsal tegmental nucleus (LDT; Forster and Blaha 2000; Oakman et al. 1995; Omelchenko and Sesack 2006; Overton and Clark 1997; Phillipson 1979; Semba and Fibiger 1992). Furthermore, several peptidergic nuclei arising in the hypothalamus also influence the mesocorticolimbic dopaminergic system (Hsu et al. 2005; Mignot 2004; Saito et al. 1999). The output of the NAc is in the form of GABAergic projections to the VTA/substantia nigra, ventral pallidum, hypothalamus, amygdala, and interpenduncular nucleus (Groenewegen and Russchen 1984; Heimer et al. 1991; Nauta et al. 1978). The various neurotransmitter systems, such as dopaminergic, glutamatergic, GABAergic, adrenergic, cholinergic, and serotonergic, regulate the mesolimbic system through an array of presynaptic and postsynaptic receptors. These projections from the hypothalamus, prefrontal cortex, amygdala, and hippocampus to the mesolimbic reward pathways contain emotional, contextual, and motivational information and thus modulate the response of the mesolimbic system to appetitive and aversive stimuli (Faure et al. 2008; Hyman and Malenka 2001; Hyman et al. 2006). In short, the mesocorticolimbic system is affected by a complex interplay of many neurotransmitters, neurohormones, and other neuromediators. These influences play an important role in regulating this pathway, and disturbances in these neurotransmitter systems and/or receptors or downstream mediators have a possible role in the anhedonia seen during withdrawal from psychostimulants after prolonged use. In the sections below, the roles of each neurotransmitter system that are likely to be involved in psychostimulant-induced anhedonia are discussed (Table 3). In many cases, the potential role of altered neurotransmission in mediating anhedonia is not known, with the exception of inferences made from the temporal correlation of the neurochemical and behavioral events associated with drug withdrawal. Some studies, however, have investigated the direct contribution of specific neurotransmitter changes in anhedonia.

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Table 3 Changes in neural substrates associated with psychostimulant withdrawal Neurotransmitter Change Psychostimulant Reference Dopamine Decreased basal Cocaine, Broom and Yamamoto extracellular dopamine amphetamine, (2005), Imperato et al. levels in the NAc nicotine (1992), Hildebrand et al. (1998), Rahman et al. (2004), Rada et al. (2001), Rossetti et al. (1992), Weiss et al. (1997), Parsons et al. (1991) Decreased firing of VTA Cocaine, Ackerman and White dopaminergic neurons amphetamine, (1992), Henry et al. nicotine (1989), Liu and Jin (2004) Decreased density of Cocaine Pilotte et al. (1994), dopamine transporter Sharpe et al. (1991) Cocaine Macedo et al. (2004), Decreased dopamine D1 receptor binding in the Stefanski et al. (1999) NAc and dorsal striatum Increased dopamine D2 Cocaine Macedo et al. (2001), receptor binding in the Macedo et al. (2004) striatum and premotor cortex Cocaine Neisewander et al. (2004) Increased dopamine D3 receptor binding in the NAc Serotonin Decreased serotonin levels Cocaine, Parsons et al. (1995, in the NAc, striatum, amphetamine, 1996), Persico et al. hippocampus, and methamphetamine (1995), Schmidt et al. prefrontal cortex (1985), Tonge (1974) Cocaine, nicotine Baumann and Rothman Increased sensitivity of (1998), Suemaru et al. 5-HT2A/2C (2001), Yasuda et al. (2002) Cocaine Parsons et al. (1995), Increased sensitivity of Yan et al. (2000) presynaptic 5-HT2A receptors Baumann and Rothman Decreased sensitivity of 5- Cocaine (1998) HT1A receptors Norepinephrine Decreased norepinephrine Amphetamine Vogel et al. (1985) levels in the frontal cortex, hypothalamus, and caudate Decreased a2 receptor Cocaine Giralt and Garcia-Sevilla binding (1989) Upregulation of Cocaine Beveridge et al. (2005), norepinephrine Macey et al. (2003) transporter levels in the bed nucleus of the stria terminalis, basolateral amygdala, cortex, hypothalamus, and hippocampus of primates (continued)

Neural Substrates of Psychostimulant Withdrawal-Induced Anhedonia Table 3 (continued) Neurotransmitter Change Glutamate Decreased extracellular glutamate Increased AMPA receptor subunits GluR1 and GluR2 in the NAc Increased NMDA receptor subunits NR1, NR2A, and NR2B in the NAc, VTA, central nucleus of the amygdala, basolateral amygdala, and frontal cortex Reduced phosphorylation of NR1 in the frontal cortex Decreased mGluR5 in the NAc Increased mGluR5 mRNA in the NAc shell and dorsal striatum Increased mGluR8 mRNA in the NAc and dorsal striatum GABA Increased GABA levels and turnover rate

Decreased mRNA of the b3 subunit of the GABAA receptor in the cortex and caudate putamen Increased parvalbuminpositive neurons Neurohormones, Increased CRF levels in neurothe hypothalamus peptides, and amygdala neurotrophic Increased levels of factors prodynorphin mRNA in the striatum

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Psychostimulant Cocaine, amphetamine Cocaine

Reference Baker et al. (2003), Giorgetti et al. (2002) Lu et al. (2003)

Cocaine

Crespo et al. (2002), Lu et al. (2003, 2005), Loftis and Janowsky (2000)

Cocaine

Loftis and Janowsky (2002)

Cocaine, amphetamine Cocaine

Mao and Wang (2001), Swanson et al. (2001) Ghasemzadeh et al. (1999)

Amphetamine

Parelkar and Wang (2008)

Cocaine, amphetamine

Cocaine

Dworkin et al. (1995), Lynch and Leonard (1978), Xi et al. (2003) Suzuki et al. (2000)

Amphetamine

Mohila and Onn (2005)

Cocaine

Erb et al. (2004), Gardi et al. (1997), Richter and Weiss (1999) Andersson et al. (2003), Isola et al. (2008), Spangler et al. (1993), Turchan et al. (1998) Wahlestedt et al. (1991)

Cocaine, amphetamine, nicotine

Decreased NPY levels in Cocaine the NAc and prefrontal cortex Increased BDNF in the Cocaine NAc

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Neurotransmitters Dopamine

Dopamine plays a critical role in mediating the rewarding effects of all drugs of abuse, including psychostimulants. Withdrawal from psychostimulants results in a decrease in dopaminergic tone. Specifically, microdialysis studies show decreased basal levels of dopamine in the NAc during withdrawal from psychostimulants, such as cocaine (Imperato et al. 1992; Parsons et al. 1991; Robertson et al. 1991; Rossetti et al. 1992), amphetamine (Broom and Yamamoto 2005; Rossetti et al. 1992; Weiss et al. 1997), and nicotine (Hildebrand et al. 1998; Rada et al. 2001; Rahman et al. 2004). Consistent with decreased dopamine levels in the NAc, decreased activity of mesolimbic dopaminergic neurons also occurs during withdrawal from psychostimulants (Ackerman and White 1992; Henry et al. 1989; Liu and Jin 2004). Withdrawal from cocaine results in a decrease in the density of dopaminergic transporter levels (Pilotte et al. 1994; Sharpe et al. 1991), and withdrawal from cocaine and amphetamine-like drugs results in decreased density and sensitivity of dopamine D1 receptors (Macedo et al. 2004; Stefanski et al. 1999, 2002). Rats exposed to chronic nicotine treatment exhibit decreased sensitivity to the ICSS threshold-elevating effects of the dopamine D1 receptor antagonist SCH 23390 administered into the anterior VTA compared with saline-treated control rats, while the response to the D2-like receptor antagonist eticlopride was unaltered (Bruijnzeel and Markou 2005). These findings indicate a downregulation in the number or function of D1 receptors after chronic nicotine administration. Additionally, upregulation of dopamine D2 receptors in the striatum have been reported during withdrawal from cocaine (Macedo et al. 2004). Finally, increases in dopamine D3 receptor binding in the NAc core have been reported during withdrawal from cocaine self-administration (Neisewander et al. 2004). Several pharmacological manipulations aimed at correcting the deficits in dopaminergic neurotransmission seen during psychostimulant withdrawal have been effective in reversing psychostimulant withdrawal-induced anhedonia in animal models, thus implicating these deficits in the mediation of anhedonia. Increasing dopaminergic neurotransmission by using a dopamine reuptake blocker, such as bupropion, alleviates the elevation of ICSS thresholds seen during withdrawal from nicotine (Cryan et al. 2003; Paterson et al. 2007). However, the lowering of ICSS thresholds was also seen in saline controls, suggesting that the effect of bupropion is nonspecific (i.e., statistically additive) and related to the reward-facilitating effects of bupropion independent of the “allostatic baseline” state of the animals. Importantly, bupropion has other mechanisms of action, such as inhibition of norepinephrine uptake, which may also play a role in the “therapeutic” properties of bupropion. Bromocriptine, a dopamine D2 receptor agonist, also reversed ICSS threshold elevations observed in rats undergoing cocaine withdrawal (Markou and Koob 1992a). Clinically, bupropion alleviated cocaine withdrawal symptoms only in a subset of patients who reported depressive symptomatology at the beginning of the

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study (Margolin et al. 1995). Bromocriptine alleviated dysphoria associated with cocaine withdrawal in human subjects (Giannini et al. 1987; Tennant and Sagherian 1987). Nevertheless, more recent studies generally did not demonstrate efficacy of bromocriptine in alleviating the affective aspects of cocaine withdrawal (Eiler et al. 1995; Handelsman et al. 1997). Bromocriptine, in combination with the tricyclic antidepressant desipramine, was successful in attenuating symptoms of cocaine withdrawal, indicating that bromocriptine may be most effective as an adjunct therapy. The dopamine D2 receptor agonist apomorphine rapidly reversed depressive symptomatology observed during cocaine withdrawal in humans (Hollander et al. 1990). The indirect dopamine agonist amantadine has also proven to be more effective than direct agonists at treating symptoms of cocaine withdrawal (Giannini et al. 1989; Tennant and Sagherian 1987), particularly when used in patients with severe withdrawal symptoms (Kampman et al. 2000). Altogether, these results suggest that the dopaminergic system is critically involved in anhedonia seen during psychostimulant withdrawal.

6.1.2

Serotonin

Both acute and chronic administration of psychostimulants leads to adaptive changes in the brain serotonergic system (Carrasco and Battaglia 2007; Cunningham et al. 1992a, b; Hoplight et al. 2007). Importantly, the serotonergic system is a critical neurotransmitter system for the action of currently used antidepressant medications, such as selective serotonin reuptake inhibitors (Ban 2001). Thus, serotonin is likely to play an important role in psychostimulant withdrawal-induced anhedonia. Deficits in serotonergic transmission have been observed during psychostimulant withdrawal. Withdrawal from psychostimulants results in decreased serotonin levels in the NAc (Parsons et al. 1995, 1996) and other brain regions, such as the hippocampus, striatum, and prefrontal cortex (Persico et al. 1995; Schmidt et al. 1985; Tonge 1974). Impaired serotonin release in response to fenfluramine has been reported during cocaine withdrawal (Baumann et al. 1995; Benwell and Balfour 1979; Darmani 1997). Withdrawal from psychostimulants also results in changes in serotonergic receptor sensitivity. Specifically, withdrawal from cocaine is associated with an increased prolactin response to systemic administration of 5HT2A/2C receptor agonists and a decreased response to a 5HT1A receptor agonist, suggesting an increased sensitivity of postsynaptic 5HT2A/2C receptors and decreased sensitivity of 5HT1A receptors (Baumann and Rothman 1998). A similar increase in sensitivity of 5HT2A/2C receptors has also been observed during withdrawal from nicotine (Suemaru et al. 2001; Yasuda et al. 2002). Withdrawal from chronic cocaine is also associated with an increase in sensitivity of presynaptic 5-HT2A receptors, and intra-NAc application of serotonin or a 5HT2A receptor agonist increased dopamine overflow in withdrawing rats, presumably attributable to an increased sensitivity of these 5HT2A receptors (Parsons et al.

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1995; Yan et al. 2000). Finally, withdrawal from nicotine resulted in increased sensitivity of 5-HT1A receptors located in the dorsal raphe (Rasmussen and Czachura 1997). Reversal of serotonergic deficits by coadministration of the selective serotonin reuptake inhibitors fluoxetine or paroxetine and the 5HT1A receptor antagonist p-MPPI (4-[20 -methoxy-phenyl]-piperazine) reversed ICSS threshold elevations seen in animals withdrawing from amphetamine or nicotine (Harrison et al. 2001; Markou et al. 2005). However, administration of either compound alone had no effect on threshold elevations. Surprisingly, coadministration of fluoxetine and p-MPPI elevated ICSS thresholds in saline-exposed control rats, an effect opposite to that seen in psychostimulant-withdrawing animals (Harrison et al. 2001; Harrison and Markou 2001). Furthermore, although fluoxetine and p-MPPI attenuated reward deficits associated with nicotine withdrawal, the combined effect had no effect on the somatic signs of nicotine withdrawal, providing evidence for different mechanisms underlying these two components of nicotine withdrawal. Reversal of threshold elevations during withdrawal from nicotine or amphetamine was also achieved by chronic administration of the atypical antipsychotic clozapine (Semenova and Markou 2003; Fig. 4). Clozapine has a mixed pharmacological profile and exhibits high affinity for several subtypes of serotonin receptors (5-HT2A, 5-HT2C, 5-HT6, 5-HT7), dopamine receptors (D2, D4), muscarinic receptors (M1), and adrenergic receptors (a1; Goudie et al. 1998; Meltzer 1994; Zhang and Bymaster 1999). Evidence suggests that the antagonistic properties of clozapine at 5-HT2A and D4 receptors are critical for the clinical actions of clozapine (Meltzer 1994).

6.1.3

Norepinephrine

Another neurotransmitter that may play an important role in mediating anhedonia during psychostimulant withdrawal is the norepinephrine system. The brain noradrenergic system comprises two main ascending projections: the dorsal noradrenergic bundle (DNB) and the ventral noradrenergic bundle (VNB). The DNB originates in the A6 area of the LC and projects to the hippocampus, cerebellum, VTA, and forebrain. The VNB arises in a number of pons and medulla nuclei and innervates the hypothalamus, midbrain, and extended amygdala (Moore and Bloom 1979). The firing of midbrain dopaminergic neurons, which form the mesolimbic dopaminergic system, is influenced by noradrenergic projections from the LC (Grenhoff et al. 1993; Liprando et al. 2004). The noradrenergic pathways support ICSS (Ritter and Stein 1974), and drugs acting on the noradrenergic system can modulate ICSS thresholds (Wise 1978). Additionally, the biochemical effects of psychostimulants include blockade of norepinephrine reuptake and enhancement of norepinephrine release, or both (Holman 1994; Howell and Kimmel 2008; Weinshenker and Schroeder 2007). The actions of several clinically effective antidepressant medications, such as imipramine and desipramine, are mediated via inhibition of norepinephrine reuptake (Richelson and Pfenning 1984). This evidence

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Fig. 4 Clozapine, an atypical antipsychotic with activity predominantly involving serotonergic and dopaminergic systems, was administered to rats prior to inducing spontaneous nicotine withdrawal. The figure shows that clozapine administration blocked threshold elevations (defined as 10% or more above baseline) in 50% of rats 6 h following termination of nicotine administration. Clozapine pretreatment (before any exposure to nicotine) by itself resulted in elevations of reward thresholds in some animals, and these animals were termed clozapine-sensitive rats. These clozapine-sensitive rats showed greatest threshold elevations during nicotine withdrawal. Hence, the right panel of graphs shows data in which rats sensitive to the initial mildly aversive effects of clozapine pretreatment were excluded. Fisher’s exact test revealed significantly fewer rats showing withdrawal after clozapine pretreatment compared with vehicle-pretreated rats when clozapine- or vehicle-sensitive rats were excluded (*p < 0.05). Numbers in parentheses are the total number of rats per group. clz clozapine, n nicotine, s saline, v vehicle (Figure taken with permission from Semenova and Markou (2003))

strongly suggests an involvement of the norepinephrine system in psychostimulant withdrawal-induced anhedonia. Norepinephrine levels are decreased in several brain regions, such as the frontal cortex, hypothalamus, caudate, and pons-medulla, during withdrawal from chronic amphetamine administration (Vogel et al. 1985). Changes in norepinephrine function have also been observed following chronic psychostimulant administration. After chronic cocaine exposure, decreased binding of the a2-adrenoceptor agonist clonidine occurs in the rat cortex and hypothalamus (Giralt and Garcia-Sevilla 1989). Rats chronically treated with cocaine showed a decreased mydriatic response to clonidine (Pitts and Marwah 1989). Chronic cocaine administration was also associated with desensitization of postsynaptic a2 adrenoceptors coupled with growth hormone secretion (Baumann et al. 2004). Chronic cocaine also resulted in upregulation of norepinephrine transporter levels in the bed nucleus of the stria terminalis (Macey et al. 2003), basolateral amygdala, cortex, hypothalamus, and hippocampus of primates (Beveridge et al. 2005). The tricyclic antidepressants desipramine and imipramine, which act by blocking norepinephrine reuptake (Richelson and Pfenning 1984), attenuated

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elevations in ICSS thresholds associated with cocaine and amphetamine withdrawal, respectively (Kokkinidis et al. 1980; Markou et al. 1992). Specifically, repeated daily administration of desipramine shortened the duration of ICSS threshold elevations associated with cocaine withdrawal in rats. Such an effect was correlated with b-adrenoceptor downregulation (Markou et al. 1992), suggesting that altered norepinephrine signaling mediated anhedonia associated with cocaine withdrawal. Consistent with this finding, the b-adrenoceptor blocker propranolol decreased cocaine withdrawal severity in humans (Kampman et al. 2001). Kokkinidis et al. (1980) showed that treatment with either amitriptyline or imipramine counteracted depressed responding for self-stimulation of the substantia nigra seen during amphetamine withdrawal. Chronic, but not acute, administration of desipramine prevented ICSS threshold elevations and prevented increases in somatic signs observed during nicotine withdrawal (Paterson et al. 2008a; Fig. 5). This pattern of results suggests that b-adrenoceptor downregulation (Markou et al. 1992) or some other neuroadaptations induced by chronic desipramine administration is critical for the antianhedonic effect of desipramine. Additional actions of desipramine that may contribute to its antianhedonic properties include noncompetitive blockade of N-methyl-D-aspartate (NMDA) receptors (Sernagor et al. 1989; Watanabe et al. 1993) and nAchRs (Izaguirre et al. 1997; Rana et al. 1993). Further work is necessary to clarify which actions of desipramine mediate the reversal of anhedonia. As mentioned above, bupropion also attenuated elevations in ICSS thresholds seen during nicotine withdrawal (Cryan et al. 2003; Malin et al. 2006; Paterson et al. 2007). In addition to its action on the dopamine system, bupropion also acts as a norepinephrine transporter inhibitor (Damaj et al. 2004; Ferris et al. 1983) and exhibits low levels of dopamine transporter occupancy at therapeutic levels (Learned-Coughlin et al. 2003; Meyer et al. 2002) while enhancing norepinephrine turnover (Golden et al. 1988a, b). These observations raise the possibility that the effects of bupropion on the noradrenergic system could be more important than its effects on the dopamine transporter in its antismoking properties.

6.1.4

Glutamate

Glutamate is the major excitatory neurotransmitter, and acute administration of psychostimulants, such as cocaine, amphetamine, and nicotine, increases glutamate release in both the NAc and VTA (Reid et al. 2000, 1997; Smith et al. 1995; Xue et al. 1996). Chronic administration of psychostimulants leads to adaptations in the glutamatergic system (Lu et al. 2005, 2003; Swanson et al. 2001), and these adaptations may play a critical role in psychostimulant withdrawal-induced anhedonia. Increasing evidence suggests that glutamatergic substrates play a role in the pathophysiology of depression. NMDA receptor antagonists have antidepressantlike effects in animal models of depression (Paul and Skolnick 2003). Ketamine, an NMDA receptor antagonist, produces rapid, but transient, antidepressant effects in human subjects suffering from depression (Berman et al. 2000; Krystal 2007). This rapid action of ketamine may be useful in reversing anhedonia in subjects

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Fig. 5 Chronic desipramine (DMI) prevented brain reward threshold elevations during nicotine withdrawal. The figure shows the effects of chronic DMI (15 mg kg1 day1, salt, administered via osmotic minipumps) treatment on brain reward thresholds measured using the intracranial selfstimulation (ICSS) procedure. Brain reward thresholds (mean SEM) during nicotine/saline withdrawal expressed as percent of prenicotine/saline baseline thresholds. *p < 0.05, **p < 0.01, significant differences from the saline/vehicle group. #p < 0.01, significant difference from the nicotine/DMI group (p < 0.05)

withdrawing from psychostimulants and may provide a major advantage over conventional antidepressants that take approximately 3–6 weeks to be effective (Frazer and Morilak 2005). The above evidence makes a strong case for the evaluation of glutamate in psychostimulant withdrawal-induced anhedonia. Withdrawal from chronic cocaine and amphetamine resulted in a decrease in extracellular mesolimbic glutamate levels (Baker et al. 2003; Giorgetti et al. 2002). Together with the decrease in mesolimbic extracellular glutamate levels, several studies have also reported changes in AMPA and NMDA receptors. An increase in the AMPA glutamate receptor subunits GluR1 and GluR2 and the NMDA receptor subunit NR1 in the NAc have been observed during cocaine withdrawal (Crespo et al. 2002; Loftis and Janowsky 2000; Lu et al. 2003). Increases in NMDA and AMPA subunits have also been seen during cocaine withdrawal in the VTA (NR1 and GluR2), central nucleus of the amygdala (NR1 and GluR2), and basolateral amygdala (GluR1, NR2A, and NR2B; Lu et al. 2003, 2005). Cocaine withdrawal is also associated with reduced phosphorylation of serine residues 896 and 897 of the NMDA NR1 subunit in the frontal cortex (Loftis and Janowsky 2002). Furthermore, microinjection of AMPA into the VTA during amphetamine withdrawal enhanced the release of glutamate and dopamine in the VTA, suggesting an enhancement in AMPA receptor expression in the VTA (Giorgetti et al. 2001). However, not all studies have reported an increase in NMDA and AMPA receptors during cocaine withdrawal. Some studies report decreases in the levels of

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NMDA and AMPA receptor subunits during amphetamine and cocaine withdrawal (Lu et al. 1999; Lu and Wolf 1999; Yamaguchi et al. 2002). These discrepancies appear to be mainly attributable to the time-point at which brain tissue was collected following cessation of cocaine or amphetamine administration (early [24 h] vs. late [14, 30, 90 days]) and the experience of the animals following withdrawal (extinction vs. nonextinction training). In addition to changes in ionotropic receptors, changes in metabotropic receptors also occur after termination of chronic psychostimulant administration. Specifically, group I metabotropic glutamate receptors are downregulated (Swanson et al. 2001), including mGluR1 and mGluR5, and mGluR5 mRNA is upregulated (Ghasemzadeh et al. 1999) during cocaine withdrawal. A recent study showed that cocaine withdrawal resulted in a decrease in membrane-bound mGluR1, especially in large dendrites of the NAc (Mitrano et al. 2008). Repeated cocaine also prolonged the decrease in mGluR2/3 function in the NAc (Xi et al. 2002). A transient increase in mGluR1 levels and a prolonged decrease in mGluR5 levels in the NAc have also been reported during amphetamine withdrawal (Mao and Wang 2001). A more recent study suggested that mGluR8 mRNA levels in the NAc and dorsal striatum are elevated during amphetamine withdrawal (Parelkar and Wang 2008). Withdrawal from nicotine is also associated with impaired glutamatergic neurotransmission (for review, see Kenny and Markou 2001; Markou 2007). Systemic administration or microinjection of the mGluR2/3 receptor agonist LY354740 into the VTA precipitated ICSS threshold elevations in chronically nicotine but not saline-treated rats, suggesting that mGlu2/3 receptors mediate the anhedonic aspects of nicotine withdrawal (Kenny et al. 2003a). Administration of the mGluR2/3 receptor antagonist LY341495 attenuated ICSS threshold elevations associated with nicotine withdrawal (Kenny et al. 2003a). Reward deficits seen during nicotine withdrawal were also attenuated by coadministration of the mGluR2/3 antagonists LY341495 and MPEP (Liechti and Markou 2007). With regard to changes in ionotropic and glutamatergic receptors during withdrawal from psychostimulants, compounds opposing these changes may be effective in reversing psychostimulant withdrawalinduced anhedonia.

6.1.5

GABA

GABA is the major inhibitory neurotransmitter in the mammalian brain and plays an important role in the negative regulation of brain reward function. The GABA transaminase inhibitor g-vinyl GABA, which increases GABA levels, dose-dependently elevated brain reward thresholds (Kushner et al. 1997). This effect is hypothesized to be mediated by the GABAB receptor. Systemic administration of the GABAB receptor agonist CGP 44532 elevated ICSS thresholds, presumably by enhancing GABAergic neurotransmission via postsynaptic GABAB receptors (Macey et al. 2001). However, systemic administration the GABAB receptor antagonists CGP 56433A or CGP 51176 also elevated brain

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reward thresholds (Macey et al. 2001). GABAB receptor antagonists are suggested to elevate reward thresholds by enhancing GABA release by acting on presynaptic GABAB receptors (Macey et al. 2001). Thus, negative modulation of brain reward function via the GABAB receptor is complex. Both agonists and antagonists at this receptor show negative modulation of reward, presumably depending on activation of postsynaptic or presynaptic GABAB receptors, respectively. Evidence also suggests that MDD is associated with dysfunction of GABAergic transmission (Brambilla et al. 2003; Krystal et al. 2002; Lloyd et al. 1989), and pharmacological agents modulating GABAergic transmission show antidepressant-like activity in animal models of depression. For example, the GABAB receptor positive allosteric modulator CGP 7930, the GABAB agonists baclofen and SKF 97541, and GABAB antagonists SCH 50911 and CGP 56433A all showed antidepressant-like activity in preclinical animal models (Frankowska et al. 2007; Mombereau et al. 2004). Moreover, evidence suggests that the GABAergic system negatively regulates the rewarding effects of psychostimulants. The GABAB receptor positive modulator GS39783 attenuated the reward-facilitating effects of cocaine (Slattery et al. 2005), and the GABAB receptor positive allosteric modulator BHF177 decreased the reward-facilitating effects of nicotine (Paterson et al. 2008b) measured by ICSS. Withdrawal from cocaine resulted in increased extracellular GABAergic content in the NAc core (Xi et al. 2003). These increased GABA levels were attributable to desensitization of presynaptic GABAB autoreceptors that play an important role in regulating synaptic GABA levels. Another study also reported an increase in GABA turnover rate upon withdrawal from cocaine (Dworkin et al. 1995). Cocaine withdrawal resulted in decreased mRNA of the GABAA receptor b3 subunit in the cortex and caudate putamen (Suzuki et al. 2000). In the same study, 1 week of withdrawal from cocaine increased mRNA expression of the a1 and b3 subunits of the GABAA receptor in the frontal cortex and hippocampus. In a more recent study, rats undergoing withdrawal from cocaine showed increased pentobarbital-induced sleep time (Ma et al. 2008). This study also found increased GABAA receptor a subunit levels and increased glutamic acid decarboxylase levels in the hypothalamus during cocaine withdrawal. Similarly, GABA concentrations were increased in the amygdala during withdrawal from amphetamine (Lynch and Leonard 1978). Finally, increased GABAergic interneurons immunoreactive for the calcium binding protein parvalbumin were found in the anterior cingulate cortex during amphetamine withdrawal (Mohila and Onn 2005). This latter finding is interesting because a recent postmortem study of patients with a history of depression found a decrease in parvalbumin-positive neurons in the dorsolateral prefrontal cortex (Rajkowska et al. 2007). The GABAergic system is also involved in mediating anxiety (Pilc and Nowak 2005). Thus, changes in the GABAergic system following chronic administration of psychostimulants and subsequent withdrawal may mediate anxiety symptoms, rather than depression/anhedonia, seen during psychostimulant withdrawal (Murphy et al. 2001). However, the negative regulation of reward function by GABAergic compounds and the antidepressant effect of GABAergic agents suggest that

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further exploration of the role of the GABAergic system in psychostimulant withdrawal-induced anhedonia is needed.

6.1.6

Acetylcholine

Acetylcholine is another neurotransmitter that plays an important role in regulating the mesocorticolimbic reward circuit (Calabresi et al. 2000; Clarke and Pert 1985; Zhou et al. 2003a). Evidence suggests that the cholinergic system contributes to the reinforcing effects of psychostimulants. For example, the acetycholinesterase inhibitor physostigmine decreased cocaine self-administration (de la Garza and Johanson 1982). The nAchR antagonist mecamylamine blocked the heightened dopaminergic response and psychomotor effects of cocaine-induced sensitization (Schoffelmeer et al. 2002). In humans, stimulation of the central cholinergic system with cholinomimetics or cholinesterase inhibitors induced depressive phenotypes, including depressed mood, dysphoria, and anhedonia (Dilsaver 1986). Depressed human subjects are hypersensitive to a cholinergic challenge compared with healthy control subjects (Dilsaver 1986). Anticholinergic agents, such as antimuscarinic (Chau et al. 2001) and antinicotinic compounds (Shytle et al. 2002; Slemmer et al. 2000), have antidepressant activity. Many conventional antidepressants have nAChR antagonist properties (Izaguirre et al. 1997; Rana et al. 1993; Shytle et al. 2002). nAChR blockade is suggested to be crucial to the antidepressant effects of the tricyclic antidepressant amitriptyline in mice (Caldarone et al. 2004). Altogether, these observations suggest that acetylcholine may play a role in psychostimulant withdrawal-induced anhedonia. However, nAChR antagonists precipitate the affective, somatic, and neurochemical components of nicotine withdrawal in rodents chronically treated with nicotine (Epping-Jordan et al. 1998; Hildebrand et al. 1998, 1999). Thus, nAChR blockade is highly unlikely to attenuate ICSS threshold elevations associated with nicotine withdrawal. However, nicotine withdrawal may be mediated by hyperfunctioning of nAchR receptors. The nAChR antagonist mecamylamine did not improve nicotine withdrawal in human smokers (Rose et al. 2001). Nicotine and nicotine receptor agonists (a4b2) also showed antidepressant activity (Ferguson et al. 2000; Semba et al. 1998) similar to nAchR antagonists (see above). To our knowledge, no studies have evaluated the effects of anticholinergic agents on anhedonia during psychostimulant withdrawal. Interestingly, however, high rates of tobacco smoking are observed in depressed patients compared with the general population (Aubin et al. 1996), supporting the hypothesis that nAchR activation with nicotine contained in tobacco smoke may be useful as self-medication of depressive symptoms (Markou et al. 1998). Altogether, the anticholinergic effect of currently available antidepressants and the regulation of the mesolimbic reward pathway by cholinergic neurons suggest that acetylcholine may be an important substrate that needs further evaluation with respect to psychostimulant withdrawalinduced anhedonia.

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149

Neurohormones, Neuropeptides, and Neurotrophic Factors

Several neuropeptides originating in the hypothalamus play a critical role in mediating mood, stress, and hedonia. These include corticotropin-releasing factor (CRF), vasopressin, melanin concentrating hormone (MCH), melanin stimulating hormone (MSH), and orexin. Other neuropeptides, such as opioids, neuropeptide Y (NPY), and brain-derived neurotrophic factor (BDNF), have also been found to play a critical role in the pathophysiology of depression. Changes in these neuropeptides have been reported following psychostimulant administration. Although much work is still required to assess the role of these mediators in psychostimulant withdrawal-induced anhedonia, we will discuss them here as possible mediators needing further evaluation. CRF is a 41-amino acid peptide that controls hormonal and behavioral responses to stressors (Turnbull and Rivier 1997). It is synthesized in the paraventricular nucleus of the hypothalamus and induces the release of adrenocorticotropic hormone (ACTH) from the anterior lobe of the pituitary. ACTH in turn stimulates glucocorticoid synthesis and secretion from the adrenal cortex. The hypothalamus, anterior lobe of the pituitary gland, and adrenal gland together form the hypothalamic-pituitary-adrenal (HPA) axis. In addition to the hypothalamic CRF system, CRF immunoreactivity has also been found in the neocortex, extended amygdala, medial septum, thalamus, cerebellum, and autonomic midbrain and hindbrain nuclei (Swanson et al. 1983). Evidence also suggests that extrahypothalamic CRF systems play an important role in drug dependence and withdrawal (for review, see Koob 2008). The HPA axis plays an important role in mediating stress (Koob 2008), and thus CRF levels can be expected to rise during stressful events, such as psychostimulant withdrawal. Increased levels of CRF have been reported in the hypothalamus and amygdala during cocaine withdrawal (Erb et al. 2004; Gardi et al. 1997; Richter and Weiss 1999; Zhou et al. 2003b). Although increased CRF levels may contribute significantly to anxiety associated with cocaine withdrawal (Basso et al. 1999), intracerebral infusion of only relatively large CRF doses have resulted in increased ICSS thresholds (Macey et al. 2000), indicating a possible role for elevated CRF levels in anhedonia. A recent study by Bruijnzeel et al. (2007) showed that a systemic CRF1 receptor antagonist blocked elevations of ICSS thresholds in animals undergoing nicotine withdrawal precipitated by injection of the nAchR antagonist mecamylamine (Bruijnzeel et al. 2007). However, the CRF1 antagonist did not block elevations of brain reward thresholds seen during spontaneous nicotine withdrawal. This discrepancy may be due to the time at which the CRF1 antagonist was administered. During precipitated withdrawal, the CRF1 antagonist was administered 30 min before administration of the nAchR antagonist. During spontaneous withdrawal, the CRF1 antagonist was administered 6 h after removal of nicotinereleasing osmotic pumps. Thus, the CRF1 antagonist is hypothesized to block the initiation of brain reward deficits, but not to reverse the deficit once the process is initiated (Bruijnzeel et al. 2007).

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The nonpeptide vasopressin is another neuromediator mainly known for its role in fluid metabolism. It also plays a role in regulating the HPA axis and stress adaptation (Aguilera and Rabadan-Diehl 2000). Vasopressin is released from the posterior lobe of the pituitary, and extrahypothalamic vasopressin-containing neurons have been identified in the rat medial amygdala and bed nucleus of the stria terminalis (Caffe et al. 1987). Vasopressin acts on V1a and V1b vasopressin receptors expressed mainly in limbic areas and the hypothalamus (Lolait et al. 1995). Vasopressin is hypothesized to play an important role in the pathophysiology of depression. Abnormalities in vasopressin expression and receptor function have been found in both clinical depression and genetic animal models of depression (Keck et al. 2003; Zhou et al. 2001). Furthermore, the nonpeptide V1b receptor antagonist SSR149415 exerted antidepressant-like effects in rodents (Griebel et al. 2002). Blockade of V1b receptors in the lateral septum and central nucleus of the amygdala have been shown to play an important role in mediating the antidepressant effects of this compound (Salome et al. 2006; Stemmelin et al. 2005). Recent evidence suggests that vasopressin may also be involved in mediating the negative affect associated with drug withdrawal. Specifically, the V1b antagonist blocked the aversive effects of heroin withdrawal (Zhou et al. 2008), and increased vasopressin mRNA levels have been observed during cocaine withdrawal (Zhou et al. 2005). The role of vasopressin in regulating stress and the HPA axis, and the antidepressant effects of a V1b antagonist, suggest that vasopressin may be an important substrate of psychostimulant withdrawal-induced anhedonia. MCH is another neuropeptide that has received much attention in depression and hedonia associated with food reward (Saper et al. 2002). Increasing evidence suggests that melanin may play a role in the regulation of mood and stress (Nestler and Carlezon 2006; Shirayama and Chaki 2006; Smith et al., 2009). It is synthesized by neurons in the lateral hypothalamus that have extensive projections throughout the brain and play a role in regulating the mesolimbic reward pathway (Bittencourt et al. 1992; Smith et al. 2005). Melanin acts through MCH1 and MCH2 G-protein-coupled receptors. The MCH1 receptor shows dramatic enrichment in the NAc (Saito et al. 1999), and several MCH1 receptor antagonists administered systemically or directly into the NAc have exhibited antidepressant-like effects (Borowsky et al. 2002; Georgescu et al. 2005). Although studies evaluating the role of MCH in drug reward are generally lacking, one study has shown that MCH may play a role in mediating the effects of cocaine. Mice lacking MCH1 did not show locomotor sensitization or a conditioned increase in locomotor activity after cocaine treatment (Tyhon et al. 2006). Melanocortins are another group of neuromediators mainly involved in regulating appetite (Fan et al. 1997). Melanocortins are synthesized by proopiomelanocortin (POMC)-expressing neurons in the arcuate nucleus in the hypothalamus (Adan et al. 2006). The melanocortins consist of a, b, and g MSHs. Increasing evidence suggests an interaction between melanocortins and the mesolimbic pathway. For example, microinjection of a melanocortin agonist into the VTA increased NAc dopamine levels (Lindblom et al. 2001). The melanocortin receptor agonist MTII enhanced the effects of amphetamine on ICSS thresholds (Cabeza de Vaca et al. 2002). The key

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receptor through which melanocortins act is the MC4 receptor, which is widely distributed in the NAc (Alvaro et al. 1996; Hsu et al. 2005). Nonpeptide MC4 receptor antagonists showed some antidepressant-like effects in animal models (Chaki et al. 2003). Evidence suggests that melanocortins and the MC4 receptor system play a role in mediating psychostimulant reward, especially cocaine reward. For example, repeated cocaine administration enhanced melanocortin levels in the brain (Sarnyai et al. 1992; Tong and Pelletier 1992) and upregulated MC4R mRNA in the NAc (Alvaro et al. 2003). MC4 knockout mice do not show locomotor sensitization upon repeated cocaine administration (Hsu et al. 2005). Presently, however, no evidence has supported the role of melanocortin neuropeptides during psychostimulant withdrawal. Orexin (hypocretin) is another peptide produced by neurons located in the perifornical area of the lateral hypothalamus that appears to play a critical role in appetite regulation, arousal, motivation, and stress (Boutrel et al. 2005; Koob 2008; Mignot 2004; Sakurai et al. 1998). Orexin neurons project extensively throughout the brain (Peyron et al. 1998), and orexin A and orexin B are the two currently known orexin peptides. These peptides act via orexin-1 (OX1) and orexin-2 (OX2) receptors (Sakurai et al. 1998). Orexin neurons regulate the mesolimbic system by providing strong innervation to the dopaminergic neurons in the VTA via the OX1 receptor, and thus regulate reward, including drug reward (Korotkova et al. 2003; Nakamura et al. 2000). Orexin peptides have been implicated in the modulation of noradrenergic (Horvath et al. 1999), cholinergic (Burlet et al. 2002), and serotonergic (Brown et al. 2001, 2002) systems and the HPA axis (Jaszberenyi et al. 2000; Kuru et al. 2000). Orexin plays a critical role in regulating sleep–wake cycles, and orexin deficiency causes narcolepsy. Patients with narcolepsy have a high incidence of depression (Daniels et al. 2001). Abnormalities in orexin signaling are hypothesized to be related to the sleep abnormalities reported in depression (Allard et al. 2004). However, infusion of orexin A in the lateral ventricle or VTA elevated ICSS thresholds, indicating a decrease in excitability of brain reward systems (Boutrel et al. 2005). This effect is similar to that seen after intracerebroventricular infusion of CRF (Macey et al. 2000) or during drug withdrawal (Epping-Jordan et al. 1998; Markou and Koob 1991). Chronic cocaine administration upregulated OX2 receptor protein levels in the NAc (Zhang et al. 2007). The effect of orexin receptor agonists and antagonists on psychostimulant withdrawal-induced anhedonia should be evaluated. Endogenous opioids are another important group of neuromediators. Three distinct families of endogenous opioid peptides have been identified, including endorphin, dynorphin, and enkephalin. These endogenous peptides act on m, k, and d receptors, respectively, which are widely distributed throughout the brain (Bodnar 2007). Dynorphin has the greatest affinity for k receptors. Endorphins have equal affinity for m and d receptors. Enkephalins have higher affinity for d receptors. Opioid receptors distributed along the mesolimbic system play a critical role in modulating the reinforcing properties of psychostimulant drugs (Corrigall et al. 1999; Kuzmin et al. 1998; Ward and Roberts 2007). Much focus has been on k opioid receptors in mediating dysphoria and the negative affective states associated

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with psychostimulant withdrawal. Upregulation of the dynorphin system is hypothesized to mediate the dysphoria and negative emotional symptoms experienced by humans during cocaine withdrawal (Shippenberg et al. 2007). In rats, withdrawal from cocaine (Andersson et al. 2003; Spangler et al. 1993), amphetamine (Turchan et al. 1998), and nicotine (Isola et al. 2008) is associated with increased levels of prodynorphin mRNA (the precursor for dynorphin) in the striatum. k receptor agonists induced a conditioned place aversion in mice (McLaughlin et al. 2006) and dysphoria and depression in humans (Pfeiffer et al. 1986). The k receptor agonist U69593 dose-dependently elevated ICSS thresholds, reflecting a decrease in brain reward function (Todtenkopf et al. 2004). More recently, U69593 antagonized the lowering of ICSS thresholds induced by acute cocaine administration (Tomasiewicz et al. 2008). k opioid receptor antagonists on their own did not influence brain reward function assessed in the ICSS procedure (Todtenkopf et al. 2004). However, k receptor antagonists have antidepressant-like effects in the forced swim test (Mague et al. 2003; McLaughlin et al. 2003). To date, however, no study has evaluated the effects of k receptor antagonists during psychostimulant withdrawal. Enkephalin, another opioid peptide that acts mainly on the d opioid receptor, is reported to play a role in reward processes in proenkephalin knockout mice (Skoubis et al. 2005). The d receptor agonist SNC80 has demonstrated antidepressant activity (Broom et al. 2002; Saitoh et al. 2004). However, the effects of these compounds on psychostimulant withdrawal remain to be evaluated. NPY is a 36-amino acid peptide that is expressed extensively in the brain, including the LC, hypothalamus, amygdala, hippocampus, NAc, and neocortex (Adrian et al. 1983; Larhammar et al. 1998). Central NPY colocalizes with norepinephrine, GABA, somatostatin, and agouti-related protein (Kask et al. 2002). The CNS effects of NPY are mediated through Y1, Y2, Y4, Y5 heterogeneous G-proteincoupled receptors (Kask et al. 2002). Extensive evidence suggests that NPY plays a critical role in depression and mood disorders. In humans, NPY levels in cerebrospinal fluid are lower in depressed patients compared with healthy controls (Heilig et al. 2004; Widerlov et al. 1988). Clinically effective antidepressant treatments (e.g., repeated electroconvulsive therapy and the selective serotonin reuptake inhibitor fluoxetine) elevated brain NPY levels in rats (Baker et al. 1996; Heilig et al. 1988; Stenfors et al. 1989). In animals, centrally administered Y1 receptor agonists have antidepressant-like effects (Redrobe et al. 2002; Stogner and Holmes 2000). Cocaine withdrawal resulted in significant reductions in NPY levels in the NAc and cortex (Wahlestedt et al. 1991). A study investigating the role of NPY in brain reward function, however, showed that both NPY and a Y1 agonist failed to attenuate the elevations in brain reward threshold associated with precipitated nicotine withdrawal (Rylkova et al. 2008). However, NPY and the Y1 receptor agonist [D-His26]-NPY reduced the somatic signs of nicotine withdrawal. The same study also showed that NPY decreased the rewarding effects of ICSS, and this effect was mediated via Y1 receptor-dependent mechanisms. BDNF is a member of the nerve growth factor family (Barde et al. 1982). In addition to neuronal development and survival, BDNF is hypothesized to play an important role in learning, motivation, and mood regulation (Lipsky and Marini

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2007). BDNF signal transduction is mediated by binding to two different transmembrane receptors, the high-affinity tyrosine kinase receptor B (TrKB), which specifically recognizes BDNF, and the low-affinity p75 neurotrophin receptor (Klein et al. 1991). BDNF is expressed in many brain sites, including the amygdala (Rattiner et al. 2004), striatum (Yurek et al. 1996), and prefrontal cortex (Bland et al. 2005). The TrKB receptor is expressed in all mesencephalic dopaminergic neurons (Numan and Seroogy 1999). Evidence suggests that BDNF plays an important role in depression and the mode of action of antidepressants. Specifically, serum BDNF levels are low in patients suffering from depression (Karege et al. 2002) and return to normal levels upon treatment with antidepressants (Shimizu et al. 2003). However, the role of BDNF in depression and antidepressant action in animal models depends on the brain region involved. For example, repeated stress, which can lead to depression-like effects in animals, decreased BDNF levels in the hippocampus (Duman and Monteggia 2006). Injection of BDNF in the hippocampus led to antidepressant-like effects (Shirayama et al. 2002). Intra-VTA BDNF exerted a depression-like effect in the forced swim test, and blockade of BDNF action in the NAc using viral-mediated overexpression of dominant negative mutant TrkB induced an antidepressant-like effect in the NAc in the same test (Eisch et al. 2003). BDNF also plays an important role in psychostimulant reinforcement and withdrawal. BDNF administered directly into the VTA or NAc led to a profound increase in cocaine reward in several behavioral paradigms (Filip et al. 2006; Horger et al. 1999; Lu et al. 2004). Increases in BDNF have been reported in the NAc during cocaine withdrawal (Grimm et al. 2003). However, significant increases were also seen at 30 and 90 days of cocaine withdrawal, and these increases are unlikely to be associated with anhedonia that is most commonly observed during early cocaine withdrawal. Further work is necessary to understand the role of BDNF in psychostimulant withdrawal-induced anhedonia and other depressive symptoms.

6.3

Neurosteroids

Neurosteroids are neuromediators that may play a role in mediating psychostimulant withdrawal-induced anhedonia. Neurosteroids are synthesized from cholesterol and include 3a-hydroxy compounds (e.g., pregnenolone, dihydroepiandrosterone [DHEA], and their sulfate derivatives [DHEA-S]) and metabolites of progesterone (e.g., 3a-hydroxy-5a-pregnan-20-one, also known as allopregnanolone; (Corpechot et al. 1993). In the brain, in addition to their traditional action via nuclear receptors, both gonadal steroids and neurosteroids show rapid, nongenomic, and stereospecific steroid actions transmitted via specific membrane receptors, such as GABAA, ionotropic glutamate, and sigma receptors (Falkenstein et al. 2000; Jung-Testas et al. 1989). DHEA-S has shown antidepressant effects in both humans and animals (Maayan et al. 2005; Wolkowitz et al. 1997). Levels of DHEA and DHEA-S are altered following cocaine abuse. In hospitalized chronic cocaine users, discontinuation (1–3 weeks) of cocaine resulted in increased plasma cortisol levels that were highest at the beginning of abstinence and then subsequently decreased, whereas

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levels of DHEA-S were low at the beginning of abstinence and increased later (Buydens-Branchey et al. 2002). In another study of cocaine abusers (3 weeks of inpatient therapy for cocaine dependence and 10-week follow-up), significantly lower levels of DHEA-S were observed in abusers who relapsed (Shoptaw et al. 2004; Wilkins et al. 1997). Another study also showed that only patients in whom spontaneous increases in DHEA-S levels were observed were successful at abstaining from cocaine use over time (Wilkins et al. 2005). Wilkins et al. (2005) suggested that increased circulating DHEA-S levels result in improved mood, and this effect may enhance resiliency during withdrawal. A recent study in smokers, however, did not find differences in brain DHEA-S between those who relapsed within 4 weeks of their quit date and those who remained abstinent (Ceballos and al’Absi 2006).

6.4

Endocannabinoids

The endocannabinoid system consists of endogenous ligands, cannabinoid receptors, and several proteins/enzymes responsible for their synthesis and degradation. The role of the endocannabinoid system on hedonic activity has been evaluated using the ICSS procedure. The CB1 receptor agonists WIN 55,212, HU210, and CP55940 dose-dependently elevated brain reward thresholds, indicating an inhibitory influence of these drugs on brain reward mechanisms (Vlachou et al. 2005). A fatty acid amide hydrolase inhibitor and a selective anandamide uptake inhibitor, both of which increase endocannabinoid levels, also inhibited the rewarding effects of electrical brain stimulation (Vlachou et al. 2006). The CB1 receptor agonist WIN 55,212-2 reversed the threshold-lowering effects of cocaine, whereas pretreatment with the CB1 antagonist SR141716A reversed the inhibitory effects of WIN 55,212 on cocaine-induced lowering of brain reward thresholds (Vlachou et al. 2003). Thus, the CB1 receptor agonist blocked the reward-facilitating effects of cocaine. Endocannabinoids appear to negatively regulate brain reward, and endocannabinoid receptor antagonists may facilitate reward and alleviate psychostimulant withdrawal-induced anhedonia. However, one study showed that the CB1 antagonist SR141716A (1–10 mg kg1, i.p.) decreased the reinforcing value of electrical medial forebrain bundle stimulation, supporting a facilitatory role for endogenous cannabinoids in brain reward function (Deroche-Gamonet et al. 2001). The reason for this discrepancy is not clear, and further work is necessary to evaluate the role of the cannabinoid system on hedonic activity, and especially its effect during psychostimulant withdrawal-induced anhedonia.

6.5

Cytokines

Although not traditionally associated with psychostimulants or psychostimulant withdrawal, increasing evidence suggests interactions between immune signaling molecules, such as cytokines, and the brain (Connor et al. 1998; Sugama et al. 2004).

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Cytokines are also hypothesized to play a critical role in major depression and the mode of action of currently available antidepressants (Dunn et al. 2005; Kenis and Maes 2002; Leonard 2000; Maes 2008; Raison et al. 2009). A recent study showed that monocytes in cocaine-dependent subjects have a decreased capacity to express tumor necrosis factor-a (TNF-a) and interleukin-6 (IL-6), which are important for activating the immune system in the fight against pathogens (Irwin et al. 2007). During cocaine withdrawal, proliferative responses of peripheral blood lymphocytes in response to stimulation by mitogen concanavalin A were decreased, indicating a suppression of the immune system in response to stress resulting from the cessation of cocaine administration (Avila et al. 2003, 2004). Furthermore, lipopolysaccharide (LPS)-induced secretion of proinflammatory cytokines was suppressed following treatment with the amphetamine-derivative 3,4-methylenedioxymethamphetamine (MDMA, which is not actually classified as a psychostimulant but rather as an entactogen; Nichols 1986), shifting T-cell cytokine production away from a T-helper 1 (Th1) direction and toward Th2 (Connor 2004; Connor et al. 2005), similar to results found with cocaine (Stanulis et al. 1997). To our knowledge, no studies have yet determined the effects of psychostimulant withdrawal on cytokine levels in the brain. Because stress is associated with increased levels of cytokines in the brain (Nguyen et al. 2000), and peripheral administration of cytokines can induce affective states such as anhedonia (Anisman and Merali 1999; Asnis and De La Garza 2005; De La Garza 2005; but see Dunn et al. 2005), further examination of psychostimulant withdrawal-induced anhedonia and the immune system is required.

7 Summary and Conclusions Anhedonia, the inability to experience pleasure, is a core symptom of psychostimulant withdrawal and a core symptom of psychiatric disorders, such as depression and schizophrenia (American Psychiatric Association 1994). The existence of a high degree of comorbidity between psychopathological conditions, such as depression and schizophrenia, with drug dependence suggests that some drug abusers may have some form of subclinical anhedonia that they attempt to ameliorate by using drugs of abuse, such as psychostimulants (Khantzian 1985; Markou et al. 1998). Thus, abuse of psychostimulants and their subsequent withdrawal may result in anhedonia not only from a de novo perturbation of the normal hedonic state but also from a worsening of preexisting subclinical anhedonia. Anhedonia during psychostimulant withdrawal is hypothesized to increase vulnerability of the individual to drug use relapse (Leventhal et al. 2008). Therefore, understanding the neurobiological substrates that mediate this state is essential. In this review, we described several methods used to assess anhedonia and incentive-motivation in animals, such as the ICSS procedure, the progressiveratio schedule for sucrose reward, sucrose preference tests, positive and negative

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contrast tests, incentive-motivation for sexual reward, and novelty-induced conditioned place preference. Psychostimulant withdrawal-induced anhedonia models described in this review have high etiological validity in which the etiology of the condition is identical in humans and experimental animals, good construct validity in cases in which a dependent measure is used that accurately and reliably assesses the construct of anhedonia, and predictive validity in which treatments effective in treating anhedonia in humans have been shown to be effective in the models. Several classes of antidepressant medications, such as monoamine oxidase inhibitors, tricyclic antidepressants, and selective serotonin reuptake inhibitors, are all effective in reversing brain reward deficits seen in animals during psychostimulant withdrawal (Harrison et al. 2001; Paterson et al. 2008a). Thus, considering all of the aforementioned features, psychostimulant withdrawal-induced anhedonia in animals is a well-validated animal model that can be used to understand the neurobiological substrates of reward and motivational deficits in humans. Although animal procedures can be used to quantify anhedonia in experimental animals, this is not the case for most assessments of anhedonia in humans. Most frequently, with few exceptions (e.g., Pizzagalli et al. 2005, 2008), anhedonia is measured in humans using subjective tests (Leventhal et al. 2006). Furthermore, none of the established methods of assessing anhedonia in animals or humans have an exact parallel that can be used for measuring anhedonia in the other species. To improve the success of compounds that preclinical studies suggest may have efficacy in treating anhedonia in patients (i.e., translational predictability), a desperate need exists to develop methods or tests for anhedonia that can be used both in humans and animals (Markou et al. 2008). Additionally, potential clinical treatments have been used as magic bullets, with the expectation that they will prevent relapse while simultaneously alleviating all symptoms associated with psycho-stimulant withdrawal. This magic bullet approach may need to be reevaluated, and future studies may need to adopt multiple strategies to prevent relapse, including focusing on specific symptoms, such as psychostimulant withdrawalinduced anhedonia (Hyman and Fenton 2003; Markou et al. 2008). In this chapter, we reviewed the current knowledge on the neural substrates of anhedonia. Some of these substrates, such as monoamine neurotransmitters (dopamine, serotonin, and norepinephrine) have been extensively studied. A great deal of evidence suggests that a decrease in extracellular levels of monoamine neurotransmitters during psychostimulant withdrawal directly modulate anhedonia (Parsons et al. 1991, 1996; Rada et al. 2001; Rahman et al. 2004; Rossetti et al. 1992). In addition to a decrease in monoamine levels, dysregulation also occurs in the functioning of receptors that mediate monoaminergic neurotransmission (Baumann et al. 1995; Bruijnzeel and Markou 2005; Stefanski et al. 2002; Suemaru et al. 2001). Several typical and atypical antidepressants and other compounds that act on the monoaminergic system reverse the anhedonia-like effects of psychostimulant withdrawal in animals (Harrison and Markou 2001; Paterson et al. 2007). In addition to monoamines, disturbances in neuronal signaling mediated via amino acid neurotransmitters, such as glutamate and GABA, have also been reported during psychostimulant withdrawal (Baker et al.

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2003; Xi et al. 2003). Several metabotropic glutamatergic receptor compounds have been shown to positively or negatively regulate anhedonia-like effects in animals (for review, see Markou 2007). GABAergic compounds have not yet been directly evaluated in psychostimulant withdrawal-induced anhedonia. In addition to these classical neurotransmitters, several neuromediators, such as neurohormones and neuropeptides, are receiving greater attention primarily because of their role in stress, depression, and psychostimulant-induced or food reward. These include CRF, orexin, melanocortins, MCH, vasopressin, opioids, BDNF, and NPY (Boutrel 2008; Koob 2008; Nestler and Carlezon 2006; Shirayama and Chaki 2006). Among these, CRF1 receptor antagonists have been shown to reverse anhedonia-like effects of nAchR antagonist-precipitated nicotine withdrawal (Bruijnzeel et al. 2007). Similarly, neurosteroids, cannabinoids, and cytokines are potential substrates of anhedonia, based primarily on studies that have evaluated their role in depression and psychostimulant reward (Boutrel 2008; Raison et al. 2008; Wolf and Kirschbaum 1999). However, the functional significance of these neural substrates in mediating psychostimulant withdrawal-induced anhedonia remains to be determined. In conclusion, withdrawal from psychostimulants results in an anhedonic state that can increase the vulnerability of an individual to drug use relapse. Identifying the neural substrates and molecules whose abnormal functioning may be responsible for the induction and maintenance of this state is imperative, and attempting to alleviate this state by developing pharmacological medications aimed at normalizing the functioning of these substrates will be important. Furthermore, some of the substrates involved in psychostimulant withdrawal-induced anhedonia may be common to non-drug-induced anhedonia seen in MDD and schizophrenia. Thus, understanding the neural mechanisms of psychostimulant withdrawal-induced anhedonia will not only help to decrease the burden of psychostimulant dependence, but may also provide insights into other psychopathological states, such as depression and schizophrenia. Acknowledgments This work was supported by NIH research grants U01 MH69062, R01 DA11946 and R01 DA232090, and research grant 15RT-0022 from the Tobacco-Related Disease Research Program of the State of California. The authors wish to thank Mr. Michael Arends for outstanding editorial assistance and Ms. Janet Hightower for graphics.

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Sensitization Processes in Drug Addiction Louk J.M.J. Vanderschuren and R. Christopher Pierce

Contents 1 Incentive Sensitization: Basic Tenets and Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 2 Sensitization of the Incentive Value of Drugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 3 Sensitization After Extended Drug Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 4 Sensitization and Relapse to Drug Seeking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 5 Limitations of the Incentive-Sensitization View of Addiction . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 6 Sensitization in Humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

Abstract In 1993, Robinson and Berridge published their first review that laid out the incentive sensitization theory of addiction (Robinson and Berridge 1993 Brain Res Rev 18:247). Its basic point is that repeated exposure to drugs of abuse causes hypersensitivity to drugs and drug-associated stimuli of the neural circuits mediating incentive salience, an important way in which motivational stimuli influence behavior. In laymen’s terms, it states that this drug-induced hypersensitivity of motivational circuitry would mediate an increase in drug “wanting,” thus being responsible for the dramatically exaggerated motivation for drugs displayed by addicts. This theory has been exceptionally influential, as evidenced by the fact that the original review paper about this theory (Robinson and Berridge 1993 Brain Res Rev 18:247) has been cited 2,277 times so far, and subsequent updates of this view

L.J.M.J. Vanderschuren (*) Rudolf Magnus Institute of Neuroscience, Department of Neuroscience and Pharmacology, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands e-mail: [email protected] R.C. Pierce Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA

D.W. Self and J.K. Staley (eds.), Behavioral Neuroscience of Drug Addiction, Current Topics in Behavioral Neurosciences 3, DOI 10.1007/7854_2009_21, # Springer‐Verlag Berlin Heidelberg 2009, published online 3 September 2009

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(Robinson and Berridge 2000 Addiction 95(Suppl 2):S91; Robinson and Berridge 2001 Addiction 96:103; Robinson and Berridge 2003 Ann Rev Psychol 54:25) have been cited 274, 297, and 365 times, respectively, adding up to more than 3,200 citations within 15 years. The present chapter aims to delineate the merits and limitations of the incentive sensitization view of addiction, and whether incentive sensitization occurs in humans. We conclude that since incentive sensitization most prominently occurs after the first few drug exposures, it may represent an important initial step in the addiction process. During the expression of full-blown addiction, characterized by loss of control over drug intake and use of large quantities of drugs, the expression of incentive sensitization may be transiently suppressed. However, detoxification and the gradual disappearance of tolerance and withdrawal symptoms may unmask sensitization, which could then play an important role in the high risk of relapse. Keywords Incentive sensitization Reinforcement Motivation Self-administration Psychomotor activity

1 Incentive Sensitization: Basic Tenets and Observations One behavioral effect that many drugs of abuse have in common is that they evoke characteristic patterns of hyperactivity in laboratory animals (Wise and Bozarth 1987). Upon repeated exposure to drugs of abuse, particularly psychostimulants and opiates, there is a progressive and persistent increase in this drug-induced hyperactivity (Segal and Mandell 1974; Post and Rose 1976; Babbini and Davis 1972; Clarke and Kumar 1983). Interestingly, drug-induced enhancements in dopamine overflow in the nucleus accumbens (Di Chiara and Imperato 1988) have been intimately implicated in drug-induced hyperactivity, and sensitization of hyperactivity has been found to be associated with an increased capacity of drugs to enhance mesoaccumbens dopaminergic activity (Kalivas and Stewart 1991; Pierce and Kalivas 1997; Vanderschuren and Kalivas 2000). In addition, the mesoaccumbens dopamine projection is known to mediate motivational influences on behavior (not to be mistaken with subjective feelings of pleasure) (Berridge 2007; Salamone et al. 2005; Cardinal et al. 2002). Together, this fuelled the notion that repeated drug exposure causes brain motivational circuitry to become persistently hyperresponsive, or sensitized, to drugs and drug-associated stimuli. Sensitization of the mesoaccumbens dopamine system, which plays a prominent role in determining the manner that stimuli are perceived as desirable (Berridge and Robinson 1998; Berridge 2007), causes drugs and drug-associated stimuli to become excessively “wanted.” Importantly, according to the incentive-sensitization hypothesis, this exaggerated drug wanting can lead to the compulsive pursuit and consumption of the drug without sensitization of the drug’s subjective pleasurable effects (Robinson and Berridge 1993, 2008).

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2 Sensitization of the Incentive Value of Drugs An augmented psychomotor response to a drug challenge has long been considered a standard method to determine whether behavioral or incentive sensitization has occurred. However, at present, there seems to be a shift in the way of thinking about the standard readout of sensitization, as the increased motivation to self-administer drugs is clearly a more relevant way to address addiction-like behavior in animals. Moreover, although the neural substrates of drug-induced hyperactivity and its positive subjective properties overlap to a large extent (Wise and Bozarth 1987; Koob 1992; Pierce and Kumaresan 2006), measurement of locomotion is, at best, an indirect measure of a drug’s impact on reward and incentive motivational processes. Nonetheless, several important findings support the incentive sensitization view of addiction. For example, it has been repeatedly shown in two different but widely used models that nonvolitional drug treatment enhances the positive subjective and/ or reinforcing properties of drugs. Thus, repeated drug exposure enhances their capacity to evoke subsequent conditioned place preference, apparent as either lower drug doses or fewer conditioning trials being effective, or an increase in place preference magnitude (Lett 1989; Gaiardi et al. 1991; Shippenberg and Heidbreder 1995; Shippenberg et al. 1996; Meririnne et al. 2001; Manzanedo et al. 2005; Narita et al. 2004; Harris and Aston-Jones 2003a, b; Simpson and Riley 2005). Repeated drug pretreatment was also shown to enhance drug self-administration, apparent as either faster acquisition, acquisition at lower doses, or increased responding during early maintenance (Piazza et al. 1989, 1990; Horger et al. 1990, 1992; Valadez and Schenk 1994; Pierre and Vezina 1997, 1998; Covington and Miczek 2001). In addition, pretreatment with amphetamine also accelerated the escalation of cocaine self-administration, when animals were permitted long daily access to the drug (Ferrario and Robinson 2007). Even stronger evidence that preexposure to drugs enhances their motivational properties came initially from Philips and colleagues (Mendrek et al. 1998), findings that were subsequently replicated and extended by Vezina and colleagues and others (Lorrain et al. 2000; Covington and Miczek 2001; Suto et al. 2002, 2003; Vezina et al. 2002). These researchers showed that treating rats with amphetamine or cocaine according to a schedule that evokes sensitization to its psychomotor stimulant properties also caused these animals to work harder to receive amphetamine and cocaine under a progressive-ratio schedule that measures effort required for self-administration. This indicates that sensitized animals are more motivated to work for the drug. Importantly, not only experimenter-delivered drug pretreatment, but also a period of drug self-administration (as would happen in the real world, where drug exposure rarely occurs as passive treatment) has been shown to subsequently enhance its motivational properties. Thus, Roberts and colleagues have shown that a period of drug (cocaine or heroin) self-administration leads to increased break-points under a progressive ratio schedule of reinforcement (Liu et al. 2005b, 2007; Morgan et al. 2005, 2006; Ward et al. 2006). Remarkably, in these studies, sensitization of the incentive value of cocaine appeared to be most

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pronounced in animals with limited drug self-administration experience (Morgan et al. 2006). This is consistent with findings from psychomotor sensitization experiments, where sensitization can be induced by a single, or only a few drug exposures (Magos 1969; Robinson 1984; Robinson et al. 1982; Peris and Zahniser 1987; Kalivas and Alesdatter 1993; Vanderschuren et al. 1999a, 2001). Somewhat contrasting the findings from Roberts and colleagues, but consistent with findings from studies using extended access models of self-administration (discussed later), prolonged drug self-administration has been shown to increase the incentive value of the drug. Deroche et al. (1999) compared groups of rats with short (6 daily sessions) and long (29 daily sessions) cocaine self-administration experience, and found that certain motivational properties of the drug were augmented in the animals with the long cocaine taking history. The long-experience group showed a leftward shift in the dose–response curve for cocaine in an extinction–reinstatement paradigm, which assesses relapse to drug seeking after detoxification. In addition, the animals with long cocaine experience took less time to traverse a runway for cocaine reinforcement, suggesting that these animals were more motivated for the drug. Interestingly, cocaine-induced conditioned place preference did not differ between these groups, suggesting that not all positive motivational properties of the drug were enhanced (Deroche et al. 1999). Unfortunately, self-administration under a progressive ratio schedule was not tested in this study, precluding a direct comparison with the work of Roberts and colleagues. However, in a subsequent study from this group, break-points under a progressive ratio schedule for cocaine increased with prolonged cocaine self-administration experience, but this was only found in a subgroup of animals that also showed other characteristics of loss of control over drug use (Deroche-Gamonet et al. 2004).

3 Sensitization After Extended Drug Exposure Since drug addiction is usually the consequence of prolonged use of large quantities of drugs, there has been an increasing interest in recent years in animal models that emulate excessive drug intake. The most well-known of these is the so-called escalation model (Ahmed and Koob 1998). In this paradigm, animals are allowed to self-administer drugs for either brief or prolonged daily sessions. In the case of cocaine, this usually means 1 h vs. 6 h of access to cocaine. Animals that selfadminister cocaine for 6 h/day then display a gradual increase, or escalation, of drug intake, whereas cocaine intake in the animals that self-administer the drug for 1 h/day remains stable over time (Ahmed and Koob 1998). Clearly, if incentive sensitization underlies drug addiction, then animals with escalated cocaine intake would show behavioral changes associated with sensitization. When motivation to self-administer cocaine was assessed using a progressiveratio schedule of reinforcement, animals with a history of longer daily access to cocaine or heroin self-administration displayed increased breakpoints (Paterson

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and Markou 2003; Wee et al. 2008; Lenoir and Ahmed 2008; Orio et al. 2009). However, Roberts and colleagues did not find that escalation of cocaine intake is associated with increased motivation for the drug (Liu et al. 2005a), and one study actually showed that access to large quantities of the drug decreased the subsequent motivation to take cocaine (Morgan et al. 2006). A recent analysis from this same laboratory showed that escalation of cocaine intake increased the motivation for cocaine at high unit doses, but reduced the rate of responding at a threshold dose of cocaine (i.e., the lowest dose that reliably supports self-administration) (Oleson and Roberts 2009). These data suggest that animals with a history of escalated cocaine intake consume more of the drug if large amounts are available, but are not willing to pay a high price for a low amount of the drug (Oleson and Roberts 2009). In contrast, escalation of heroin self-administration was shown to increase the value of the drug, as the maximum price animals were willing to pay for heroin (number of responses/reinforcer, as well as response rate) was increased (Lenoir and Ahmed 2008). Together, these data show that a history of escalated drug intake may indeed increase the incentive motivational value of the drug, possibly depending on a number of experimental variables. However, animals with escalated drug intake fail to show two other indices of behavioral sensitization, including increased behavioral activation and nucleus accumbens dopamine in response to a challenge injection of the drug. Thus, after escalated cocaine or heroin self-administration, no psychomotor sensitization was found (Ben-Shahar et al. 2004, 2005; Ahmed and Cador 2006; Lenoir and Ahmed 2007; Ferrario et al. 2005; Knackstedt and Kalivas 2007). These data suggest that increased responsiveness to the psychomotor effects and increased motivation for the drug are not necessarily a manifestation of the same neurobehavioral process (see also Lack et al. 2008). One study found that stereotyped head movements rather than psychomotor activity were sensitized after cocaine treatment in animals that had self-administered cocaine in long-access sessions (Ferrario et al. 2005; but see Knackstedt and Kalivas 2007). Since the effect of psychomotor stimulant drugs on stereotyped behavior depends on dorsal rather than ventral striatal dopamine (Joyce and Iversen 1984; Kelly et al. 1975), this suggests that escalated animals would show increased dopaminergic responsiveness in dorsal, but not ventral striatum. Indeed, animals with a history of escalated cocaine self-administration do not show hyperdopaminergic responses to the drug in the nucleus accumbens, in terms of increased dopamine concentrations after cocaine (Ahmed et al. 2003). In fact, dopamine reactivity in the nucleus accumbens to cocaine has even been shown to be dramatically decreased after excessive cocaine self-administration (Mateo et al. 2005). In view of the sensitized stereotypy observed after escalated cocaine self-administration, it would therefore be of interest to investigate dopaminergic reactivity to the drug in dorsal striatum after escalated cocaine self-administration. Taken together, prolonged or excessive drug self-administration can result in exaggerated motivation for drugs, but whether this is the result of a neural and behavioral sensitization process is unclear, because two hallmark features of behavioral sensitization are absent following excessive drug self-administration.

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Thus, augmented psychomotor stimulant effects of drugs and an increased dopamine overflow in nucleus accumbens are absent, the latter of which should mediate the excessive salience of drugs and drug-associated cues. While cocaine selfadministration under progressive ratio schedules may reflect motivation for the drug, it has been suggested that the upward shift in the dose–response curve for cocaine under this schedule in escalated animals (Paterson and Markou 2003; Wee et al. 2008; Lenoir and Ahmed 2008) signifies a downregulation of brain reward processes. Thus, if animals are more motivated to work for any unit dose of cocaine, this suggests that they need more drug to achieve the desired positive effect (Ahmed 2005; Lenoir and Ahmed 2008, but see Oleson and Roberts 2009). On the other hand, the leftward shift in the dose–response curve for amphetamine seen in sensitized animals (Vezina et al. 2002) may rather indicate enhanced motivation for the drug, since animals only work harder for low unit doses. This issue clearly warrants further investigation.

4 Sensitization and Relapse to Drug Seeking The long-lasting character of sensitization suggests that sensitization plays a role in persistent aspects of addiction, besides a long-lasting increase in the motivation for drugs. Perhaps the most important persistent aspect of addiction is the risk of relapse to drug abuse, which can remain present for years or even decades after detoxification. Relapse to drug seeking after detoxification is widely investigated using the extinction–reinstatement model (Shaham et al. 2003). In this paradigm, drug self-administration is acquired by animals and subsequently extinguished by no longer reinforcing operant behavior with drug delivery. The critical test is the reinstatement of operant behavior, which can be achieved by drug priming (usually the same drug that was initially self-administered), response-contingent presentation of drug-associated cues, or stress. Since operant behavior is not reinforced during this phase of the experiment, it is interpreted as reflecting drug seeking rather than drug taking. Using a pharmacological approach, it was shown that dopaminergic and opioid drugs that evoked sensitized psychomotor responses also reinstated drug seeking after extinction (De Vries et al. 1998, 1999, 2002; Vanderschuren et al. 1997; 1999b, c; Dias et al. 2004). Interestingly, there was considerable overlap between the drugs that reinstated cocaine seeking and heroin seeking, but the pharmacological profile of relapse to psychostimulant and opiate seeking was not identical, suggesting that the neural substrates of relapse to drug seeking are to a certain extent drug-specific. Additional evidence for a positive relation between psychomotor sensitization and reinstatement of drug seeking comes from the observation that AMPA receptor stimulation in the nucleus accumbens is important for both processes (Bell and Kalivas 1996; Cornish et al. 1999; Pierce et al. 1996; Park et al. 2002; Famous et al. 2008; Ping et al. 2008; Conrad et al. 2008). Furthermore, rats pretreated with amphetamine and subsequently trained to selfadminister cocaine were more motivated to work for the drug under a progressive

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ratio schedule of self-administration (Suto et al. 2003), and were also more sensitive to the reinstating effect of intra-accumbens treatment with AMPA (Suto et al. 2004). Further evidence for the involvement of nucleus accumbens AMPA receptor stimulation in both reinstatement of cocaine seeking and expression of sensitization to the psychomotor stimulant effect of cocaine was shown in a recent study that manipulated expression of the AMPA receptor subunit GluR1 in the accumbens. This study found that overexpression of GluR1 reduced sensitization and reinstatement, whereas downregulation of GluR1 enhanced both processes (Bachtell et al. 2008). However, as mentioned above, animals showing escalated cocaine or heroin intake show no psychomotor sensitization, but they do display an increased sensitivity to the ability of the drug to reinstate extinguished drug seeking (Ben-Shahar et al. 2004, 2005; Ahmed and Cador 2006; Lenoir and Ahmed 2007; Ferrario et al. 2005; Mantsch et al. 2004; Knackstedt and Kalivas 2007). Furthermore, some studies have not found a relationship between the ability of drugs to evoke a sensitized psychomotor response and to reinstate drug seeking (Sutton et al. 2000). Together, these data suggest that part of the neural changes underlying psychomotor sensitization also play a role in relapse to drug seeking even if these are two different manifestations of behavior, but also that the mechanisms involved are not identical and may even differ between animals with different drug taking histories.

5 Limitations of the Incentive-Sensitization View of Addiction Although repeated drug exposure can enhance the incentive value of drugs, there are two lines of evidence that argue against the notion that incentive sensitization can fully explain drug addiction. First, by and large, a few drug exposures usually suffice to induce psychomotor sensitization. In fact, even a single drug exposure can induce long-lasting behavioral sensitization and associated neural changes (Magos 1969; Robinson 1984; Robinson et al. 1982; Peris and Zahniser 1987; Kalivas and Alesdatter 1993; Vanderschuren et al. 1999a, 2001). However, before full-blown drug addiction develops, people have usually taken large quantities of drugs over prolonged periods of time. Thus, casual drug use may not be without lasting consequences, and the incentive sensitization evoked by limited drug exposure may be an important first step in the addiction process, but sensitized individuals are not necessarily addicted. Second, repeated pretreatment with drugs increases the incentive value of drugs, but also of nondrug reinforcers. Thus, drug pretreatment facilitates sexual behavior (Fiorino and Phillips 1999a, 1999b), approach behavior to sexual stimuli and palatable food (Nocjar and Panksepp 2002, 2007), as well as lever pressing for food (Nordquist et al. 2007; Olausson et al. 2006). Interestingly, amphetamine-pretreated rats do not consume more sucrose when it is freely available (Nordquist et al. 2007), suggesting that it is the motivation for food (“wanting”) rather than its hedonic value (“liking”) that is increased in amphetaminesensitized rats (Robinson and Berridge 1993). In addition, drug pretreatment has

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been shown to enhance the impact of food-associated stimuli on behavior in paradigms measuring conditioned approach, responding for conditioned reinforcement, and Pavlovian-to-instrumental transfer (Harmer and Phillips 1998; Taylor and Jentsch 2001; Taylor and Horger 1999; Mead et al. 2004; Olausson et al. 2004; Wyvell and Berridge 2001). Importantly, animals pretreated with amphetamine displayed not only enhanced acquisition of conditioned approach, but also accelerated acquisition of conditioned inhibition (Harmer and Phillips 1999). This excludes the possibility that the increases in conditioned responding for food are solely the result of increased locomotor activity in sensitized animals. Together, these data show that in animals showing psychomotor sensitization, the motivation for nondrug reinforcers, as well as the ability of stimuli associated with them to gain control over behavior, is augmented. Thus, repeated drug exposure appears to evoke a generalized enhancement in the responsiveness of the neural systems involved in motivation, rather than specific increases in the motivation for artificial (i.e., drug) reinforcers. Indeed, sensitization of the psychomotor stimulant properties of cocaine does not appear to alter the relative preference of animals for highly palatable sweet solutions over the drug (Lenoir et al. 2007). Together, these observations in sensitized animals are inconsistent with one of the core symptoms of addiction as formulated in DSM IV (American Psychiatric Association 2000); i.e., the sacrifice or disinterest in natural reinforcement such as social and professional activities in favor of drug-related activities.

6 Sensitization in Humans An important question regarding incentive sensitization that has eluded researchers is whether it occurs among human drug addicts. One practical explanation for the lack of evidence is that ethical reasons prohibit sensitization experiments in humans in the same way as they are usually performed in animals. Thus, experiments that compare the responsivity to drugs in drug-experienced vs. drug-naı¨ve individuals have not been widely performed, nor have many studies assessed the gradual change in drug effects during the first few drug exposures. Of the relatively few studies that have examined whether sensitization of behavioral or neurochemical effects of drugs occurs in humans, the most clear-cut data come from studies in drug-naı¨ve individuals. Thus, studies from Strakowski and colleagues (Strakowski et al. 1996, 2001; Strakowski and Sax 1998) have shown that effects of amphetamine on parameters such as activity, energy, elevated mood, euphoria, speech, and eye-blink rate are increased in subjects that receive amphetamine for the second or third time, as compared to the first amphetamine exposure. Interestingly, effects of amphetamine on “drug liking” were actually highest after the first (compared to third) amphetamine treatment (Strakowski et al. 2001). A recent comprehensive study on the behavioral and neurochemical effects of amphetamine for up to 1 year after the first amphetamine treatment also demonstrated the occurrence of sensitization (Boileau et al. 2006). In this study,

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drug-naı¨ve volunteers were treated with amphetamine three times over 5 days, and then again 14 days and 1 year later. As compared to the first amphetamine dose, the effects on agreeableness and clearheadedness were largest at the 14-day follow-up, and the effects on energy were elevated at the 14-day, as well as the one-year follow-up. In addition, effects on dopamine release were increased in both dorsal and ventral striatum at both follow-up times as measured by positron emission tomography (PET) scans of dopamine D2 receptor displacement (Boileau et al. 2006). Thus, as found in rodents, sensitization in humans can be observed in drug-naı¨ve individuals after repeated amphetamine treatment. However, these studies contrast with negative findings on the sensitization of behavior and striatal dopamine release effects of amphetamine when comparing the second to first amphetamine exposure in drug-naı¨ve individuals (Wachtel and De Wit 1999; Kegeles et al. 1999). As would be expected from animal studies, sensitizing behavioral effects of amphetamine and cocaine have not been observed in humans with a history of psychostimulant abuse, even if they had never developed addiction and were drug-free at the time of the experiment (Kelly et al. 1991; Johanson and Uhlenhuth 1981; Rothman et al. 1994), although one study found sensitization of cardiovascular (but not subjective) effects of cocaine in former addicts (Kollins and Rush 2002). These findings are consistent with the notion that sensitization occurs during initial drug exposures, and once sensitization has occurred, it is long-lasting but no further sensitization can be induced due to a ceiling effect. One form of psychostimulant sensitization that is often observed in human addicts is the occurrence of paranoid psychosis. Interestingly, psychosis can be re-evoked or exacerbated in abstinent individuals with doses of drugs that are lower than the ones that were initially used when psychosis developed (Sato et al. 1983), comparable with the expression of sensitized psychomotor activity in rodents. The relationship between psychosis and psychostimulant addiction is not clear, as both positive and negative correlations between psychosis and cocaine craving have been reported (Reid et al. 2004; Bartlett et al. 1997). The question remains whether behavioral and neurochemical sensitization can be observed in human addicts. As far as we know, no pertinent study has as yet addressed the issue of whether the behavioral effects of drugs are sensitized in addicts, although the high motivation to obtain drugs that characterizes addiction can in itself be taken as evidence for incentive sensitization. However, in contrast to drug-naı¨ve individuals, studies on dopaminergic sensitization in human addicts have yielded negative evidence. In humans addicted to alcohol or cocaine, challenges with amphetamine or methylphenidate resulted in reduced dopamine release in the striatum compared to controls as assessed using PET scan of dopamine D2 receptor binding (Martinez et al. 2005, 2007; Volkow et al. 1997), opposite to increases that would be expected from a sensitization hypothesis. Interestingly, cocaine-dependent subjects also reported a reduced “high” after methylphenidate, suggesting reduced drug “liking” (Volkow et al. 1997).

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7 Conclusions Sensitization to the psychomotor activating effects of drugs of abuse can be readily observed after repeated drug treatment, and psychomotor sensitization is an effect that drugs of abuse probably have in common. This behavioral change is usually associated with hyperresponsiveness of the mesoaccumbens dopamine projection (Robinson and Berridge 1993; Pierce and Kalivas 1997; Vanderschuren and Kalivas 2000). Since this pathway mediates motivational influences on behavior (Berridge 2007; Salamone et al. 2005; Cardinal et al. 2002), its heightened sensitivity has been hypothesized to underlie the excessive motivation for drugs in addicts (Robinson and Berridge 1993). The available data suggest an involvement of sensitization in certain, but not all, phases and aspects of drug addiction. In rodents, primates (Bradberry 2007) and humans (discussed earlier), psychomotor and dopaminergic sensitization is quite consistently found after limited drug experience; it develops quickly after the first few drug exposures in otherwise drugnaive individuals. This sensitization is associated with enhanced rewarding and reinforcing effects of drugs. However, after excessive drug intake, there is neither sensitization of the psychomotor effects of drugs nor sensitization of the nucleus accumbens dopamine response, even if enhanced motivation for the drug persists. This suggests that psychomotor sensitization does not play a major role in the genuine expression of addictive behavior. Indeed, animal studies aimed at emulating addiction-like behavior in rats have found that enhanced motivation for cocaine either is not apparent during loss of control over drug intake (Vanderschuren and Everitt 2004), or precedes the development of this effect (Deroche-Gamonet et al. 2004). Together with findings that sensitized animals show increased motivation for natural reinforcers, this suggests that incentive sensitization represents a generalized increase in the sensitivity of neural systems that regulate motivated behavior. We, therefore, think that incentive sensitization is an important initial step in the addiction process, whereby the first few occasions of drug use enhance the attractiveness of drugs and promote further use. During these drug use episodes, the association of environmental stimuli with drug effects will be readily made, and these drug-associated cues would quickly gain control over behavior (Everitt and Robbins 2005). The combination of these phenomena would greatly enhance the likelihood of further drug use, which may then escalate into large quantities of drug intake with prolonged use, culminating in the loss of control over drug intake that characterizes addiction. During this latter phase of the addiction process, sensitization is masked by other behavioral and neural changes such as a reduced sensitivity of brain reward pathways (Koob et al. 2004). However, this does not mean that the neural changes underlying sensitization have been reversed. In fact, animal studies have shown that sensitization can be extremely long-lasting (e.g., Paulson et al. 1991), and is most prominently observed after a drug-free period (Pierce and Kalivas 1997; Vanderschuren and Kalivas 2000). Thus, after detoxification, sensitization could re-emerge to play a role in some of the more persistent aspects of addiction, such as the high risk for relapse that can remain for a lifetime.

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Imaging Receptor Changes in Human Drug Abusers Kelly P. Cosgrove

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cocaine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Cocaine and the Dopamine Transporter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Cocaine and the D2/3 Receptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Cocaine and the Serotonin Transporter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Cocaine and the mu-Opioid Receptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Alcohol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Alcohol and the D2/3 Receptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Alcohol and the Dopamine Transporter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Alcohol and the Serotonin Transporter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Alcohol and the mu-Opioid Receptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Alcohol and the GABAA-BZ Receptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Nicotine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Nicotine and the b2-Nicotinic Acetylcholine Receptor (b2*-nAChR) . . . . . . . . . . . . . . 4.2 Nicotine and the D1 Receptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Nicotine and the D2/3 Receptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Nicotine and the Dopamine Transporter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Nicotine and the Serotonin Transporter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Opiates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Opiate Dependence and the mu-Opioid Receptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Opiate Dependence and the D2/3 Receptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract This chapter will review the literature on differences in the brain chemistry of alcohol- and drug-dependent individuals compared to healthy controls as measured with positron emission tomography and single photon emission computed tomography. Specifically, alterations in dopamine, serotonin, opioid, and K.P. Cosgrove Department of Psychiatry 116A6, Yale University School of Medicine and the VACHS, 950 Campbell Avenue, West Haven, CT 06516, USA e-mail: [email protected]

D.W. Self and J.K. Staley (eds.), Behavioral Neuroscience of Drug Addiction, Current Topics in Behavioral Neurosciences 3, DOI 10.1007/7854_2009_24, # Springer‐Verlag Berlin Heidelberg 2009, published online 15 September 2009

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GABA systems in cocaine, alcohol, nicotine, and heroin dependence have been examined. These neurochemical systems are integrated and play significant roles in a final common pathway mediating addiction in the brain. One recurrent finding is that dopaminergic dysfunction is prevalent in both alcohol and drug dependent populations, and specifically there is a lower availability of dopamine type 2/3 receptors in cocaine-, alcohol-, nicotine-, and heroin-dependent individuals compared to healthy controls. The development of novel radiotracers that target additional receptor systems will further our understanding of the neurochemical basis of addiction. Keywords Imaging PET SPECT Drug abuse Alcohol Cocaine Nicotine

Abbreviations PET SPECT DA 5-HT GABAA-BZR b2*-nAChR D1 D2/3

Positron emission tomography Single Photon emission computed tomography Dopamine Serotonin GABAA-benzodiazepine receptor b2*-subtype of the nicotinic acetylcholine receptor Dopamine type 1 receptor Dopamine type 2/3 receptor

1 Introduction This chapter will briefly review the literature on differences in the brain chemistry of individuals with alcohol and drug dependence versus healthy, nondependent individuals in vivo as measured with positron emission tomography (PET) and single photon emission computed tomography (SPECT) imaging. Importantly, in humans we are not able to obtain “baseline” scans on drug dependent individuals from when they were drug-naı¨ve; thus, data collected in drug abusers are typically compared to age-matched healthy controls. This means that we do not know whether the brain changes are pre-existing and represent a vulnerability to addiction, or whether they are a consequence to drug use. PET and SPECT receptor imaging is typically used to examine the neurochemistry of addiction during early detoxification and also during longer periods of withdrawal. The length of abstinence is an important variable to take into account in imaging studies, because different receptor systems have been shown to change during the recovery from drug and alcohol dependence, some in as little as several weeks; thus, length of

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abstinence will be noted when it is available. We cannot currently determine in humans whether differences in receptor or transporter availability are pre-existing or are a consequence; thus, this alternative conclusion is a caveat in all sections of this chapter. It is also important to note that many individuals with drug addiction also drink alcohol and/or smoke cigarettes, making it a complex process to determine which variable or combination of variables are contributing to the receptor changes. In the majority of studies, to date, smoking is not systematically controlled; thus, additional research will be required to untangle these relationships. PET and SPECT may also be used to measure striatal dopamine (DA) transmission, e.g., drug-induced changes in DA release, and this is covered in another chapter by Dr. Martinez. We refer to receptor “availability” as opposed to receptor “levels” or “density” throughout the chapter to note that these studies are measuring receptors that are available to be bound by the radiotracer. This is because the radiotracer cannot bind to receptors that are already occupied, perhaps by the drug; e.g., cocaine, or an endogenous neurotransmitter such as dopamine. We are currently able to image a limited number of receptor systems in the brain; thus, the scope of this review is limited to existing radiotracers that are currently approved for use in humans.

2 Cocaine 2.1

Cocaine and the Dopamine Transporter

Cocaine acts directly at the DA transporter by blocking the reuptake of dopamine. Several studies report higher striatal DA transporter availability in acutely abstinent cocaine-dependent subjects compared to healthy controls using [123I]beta-CIT SPECT (Jacobsen et al. 2000; Malison et al. 1998). Additionally, higher DA transporter availability was associated with worse scores on the Hamilton depression inventory (Malison et al. 1998). However, no difference in striatal DA transporter availability between chronic cocaine abusers (last use of cocaine was 5 8 days) versus controls, but lower striatal DA transporter availability in detoxified cocaine abusers (last use 42 7 days) was reported using [11C]cocaine PET (Wang et al. 1997a). Differences in these studies are likely due to differences between the radiotracers [123I]beta-CIT and [11C]cocaine. Specifically, [11C]cocaine is limited due to its low specific to nonspecific binding ratio and rapid clearance, and thus is more of a blood flow agent. It is also important to note that we do not know the halflife of cocaine in the brain; thus, while it rapidly clears in the blood it may remain in the brain for a longer period of time and interfere with radiotracer binding. A recent study using [99mTc]TRODAT, which labels striatal DA transporters, and SPECT also reported higher striatal DA transporter availability in recently abstinent (7.5 9.7 days) cocaine dependent subjects vs. controls (Crits-Christoph et al. 2008). And, DA transporter levels tended to decrease with days since last use, suggesting that cocaine-induced increases in DA transporter availability may

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decrease during more prolonged abstinence. Because cocaine acts by blocking the reuptake of dopamine at the DA transporter, an increase in DA transporter availability suggests that chronic cocaine use induces a compensatory upregulation of the DA transporter. This is further supported by the data suggesting that when cocaine is removed, e.g., during prolonged abstinence, DA transporter availability decreases.

2.2

Cocaine and the D2/3 Receptor

There is a transient increase in DA levels in brain when cocaine blocks the DA transporter. The increase in DA levels is critical to cocaine’s reinforcing effects, and thus cocaine-induced changes in DA receptor levels have been the subject of many studies (Table 1). For example, recently detoxified (within 1 week) cocaine abusers had lower striatal dopamine type 2/3 (D2/3) receptor availability measured with [18F]N-methylspiroperidol PET (Volkow et al. 1990) compared to healthy controls. In the same study, cocaine abusers (n ¼ 3) with 1 month of abstinence had similar striatal D2/3 receptor availability compared to controls (Volkow et al. 1990). A subsequent study, also using [18F]N-methylspiroperidol PET, in a larger sample of cocaine abusers (n ¼ 20) demonstrated lower striatal D2/3 receptor availability at 1 and 4 months of abstinence compared to healthy controls (Volkow et al. 1993), suggesting that the initial decrease in D2/3 receptor availability persists. These findings were further replicated using [11C]raclopride PET. Specifically, chronic cocaine abusers had lower striatal D2/3 receptor availability during withdrawal, at approximately 2 weeks of abstinence (Martinez et al. 2004) and at 3–6 weeks of abstinence compared to healthy controls (Volkow et al. 1997). Taken together, these studies suggest that the striatal dopamine system compensates for chronic cocaine use by decreasing the number of postsynaptic D2/3 receptors, and this decreased availability persists with prolonged abstinence. The ultimate effect in a chronic cocaine abuser is a dysregulated DA system which has important treatment implications; e.g., these individuals may be more resistant to treatment drugs that target DA neurotransmission. There does not appear to be a correlation between availability of D2/3 receptors and the reinforcing effects of cocaine in cocaine addiction. While high versus low striatal D2/3 receptor availability predicts Table 1 Studies examining D2/3 receptor availability in cocaine dependence compared to controls Radiotracer Time abstinent Main finding Reference 1 week Lower availability Volkow et al. (1990) [18F]N-methylspiroperidol 1 month Similar availability Volkow et al. (1990) [18F]N-methylspiroperidol 1 and 4 months Lower availability Volkow et al. (1993) [18F]N-methylspiroperidol 2 weeks Lower availability Martinez et al. (2004) [11C]raclopride 3–6 weeks Lower availability Volkow et al. (1997) [11C]raclopride 11 C and 18F radioisotopes are used with PET

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unpleasant versus pleasurable experiences, respectively, to stimulants in healthy controls (Volkow et al. 1999, 2002a); this correlation was not replicated in cocainedependent individuals (Martinez et al. 2004). Lower D2/3 receptor availability in dependent individuals compared to healthy controls has emerged as a consistent marker across all addictions. An alternative conclusion may be that addicted individuals have decreased D2/3 receptor availability prior to any drug use and that this represents vulnerability rather than consequence.

2.3

Cocaine and the Serotonin Transporter

While much of the work on the reinforcing and withdrawal effects of cocaine has focused on DA neurotransmission, serotonin (5-HT) dysfunction may also be involved since cocaine exhibits high affinity for the 5-HT transporter and increases 5-HT reuptake, which likely contributes to changes in mood associated with cocaine. Specifically, dopamine may be more involved in the locomotor activating effects and euphoric effects of cocaine, while serotonin may be important in the worsening mood that is reported during cocaine withdrawal. We can measure the 5-HT transporter in the diencephalon and brainstem with [123I]beta-CIT and SPECT. Using this paradigm, Jacobsen et al. (2000) reported higher diencephalon and brainstem 5-HT transporter availability in smoked cocaine-dependent subjects during acute abstinence (e.g., 3.7 3.8 days) compared to healthy controls. Higher 5-HT transporter availability is likely a compensatory upregulation to chronic cocaine use. Because the transporters remove extracellular serotonin, increased transporter availability may result in a decrease in synaptic serotonin levels, which may influence mood during acute withdrawal from cocaine. However, no correlations between transporter availability and measures of mood, impulsivity, or aggression were found (Jacobsen et al. 2000). Additional studies are needed to determine whether chronic cocaine use leads to changes in 5-HT transporters over more prolonged abstinence.

2.4

Cocaine and the mu-Opioid Receptor

The opioid system is widely implicated in the reinforcing effects of most drugs of abuse including cocaine. There are three opioid receptors (mu, delta, kappa) that are typically studied, and the mu-opioid receptor is known for modulating the positive reinforcing effects of drugs of abuse. [11C]carfentanil PET has been used to measure mu-opioid receptor availability. In cocaine addicts during acute withdrawal (1–4 days), mu-opioid receptor availability was higher in the frontal and temporal cortices and anterior cingulate compared to healthy controls, and was positively correlated with the severity of craving on the day of the scan (Gorelick

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et al. 2005; Zubieta et al. 1996). Additionally, mu-opioid receptor availability increases over the first week of abstinence, remains elevated up to 4 weeks of abstinence, and begins to decrease to control levels by 12 weeks of abstinence (Gorelick et al. 2005; Zubieta et al. 1996). A recent study examined the relationship between mu-opioid receptor availability and time to relapse in cocaine users (Gorelick et al. 2008). Specifically, higher mu-opioid receptor availability at 1 and 12 weeks of abstinence in the frontal and temporal cortices was significantly associated with a shorter time to relapse. Put another way, the greater the decline in mu-opioid receptor availability during a 12-week abstinence period, the longer the time to relapse after discharge. These studies suggest that the mu-opioid receptor plays a critical role in craving and relapse in cocaine users and may direct pharmacotherapies during abstinence and/or may provide a biochemical marker that helps identify individuals at high-risk of early relapse.

3 Alcohol 3.1

Alcohol and the D2/3 Receptor

Alcohol’s rewarding effects are largely linked to the mesolimbic dopamine system (Vengeliene et al. 2008), and specifically, the D2/3 receptor plays a role in mediating the reinforcing effects of alcohol. As mentioned previously, [11C]raclopride is widely used to examine the availability of D2/3 receptors. Here, the studies examining D2/3 receptor availability used [11C]raclopride unless otherwise noted. Initial studies in this area indicated lower striatal D2/3 receptor availability in alcohol dependent subjects compared to controls (Hietala et al. 1994; Volkow et al. 1996a). Two additional studies by Dr. Heinz and colleagues found lower D2/3 receptor availability with [18F]desmethoxyfallypride PET in alcoholdependent subjects compared to controls (Heinz et al. 2004b, 2005b). And, two SPECT studies reported nonsignificantly lower D2/3 receptor availability in alcohol-dependent subjects compared to controls (Guardia et al. 2000; Repo et al. 1999). While many studies examine alcohol-dependence as a heterogeneous population, there is some evidence to subgroup alcohol dependence, specifically into Cloninger’s Type I and II subtypes (Cloninger et al. 1988). Type I alcoholics are described as displaying a late-onset of alcoholism, (>25 years old), and they are initially more affected by the antianxiety effects of alcohol. Type II alcoholics typically have an early-onset of alcoholism (<25 years old), are more affected initially by the rewarding effects of alcohol, e.g., thrill seeking, and display antisocial and impulsive personality traits and also are characterized as having higher rates of criminal behavior. A preliminary study in Type II alcoholics found lower D2/3 receptor availability during early withdrawal, e.g., within 1 month, in the caudate and putamen, and lower availability in the caudate during late withdrawal, 1–4 months later (Volkow et al. 2002b) compared to controls, suggesting that changes in

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D2/3 receptor availability are initially lower and remain lower during prolonged withdrawal. An [123I]epidepride SPECT study in Type I alcoholics with at least 1 week of abstinence (range 1 week to 4 years) found lower D2/3 receptor availability compared to controls in the left temporal pole (Kuikka et al. 2000). Thus, even when examining alcohol dependence by distinct subgroups, lower D2/3 receptor availability appears to be a consistent marker of alcohol dependence. Several studies have examined the relationship between D2/3 availability and alcohol behaviors. Specifically, low availability of D2/3 receptors in ventral striatum was associated with a high level of craving and higher cue-induced (alcohol associated vs. control cue) brain activation in alcohol dependent subjects who had been abstinent on an average of 36 days (Heinz et al. 2004b). Additionally, low availability of D2/3 receptors was associated with a greater average daily amount of alcohol consumed, suggesting that low availability may be an indicator of greater severity of alcohol dependence (Martinez et al. 2005). These two studies suggest that low availability is associated with more marked severity of disease, e.g., increased consumption and increased craving. However, an interesting study used [123I]iodobenzamide (IBZM) SPECT to correlate striatal D2/3 receptor availability during detoxification with treatment outcome at 3 months (Guardia et al. 2000). Alcohol dependent subjects who relapsed within 3 months had higher D2/3 receptor availability during detoxification compared to subjects who remained abstinent. This suggests that low DA levels or higher D2/3 receptor availability may indicate a vulnerability to early relapse. These studies generally support the hypothesis that alcohol dependence is linked to a dysregulation in dopamine neurotransmission.

3.2

Alcohol and the Dopamine Transporter

While alcohol does not act directly at the DA transporter, a dysfunction in the dopaminergic pathway as evidenced by reduced D2/3 receptor availability appears to extend to the DA transporter. Alcohol-dependent individuals have been reported to have lower (Laine et al. 1999; Repo et al. 1999) (Tiihonen et al. 1995), higher (Tiihonen et al. 1995) or similar (Volkow et al. 1996b) DA transporter availability compared to healthy controls (See Table 2). Volkow et al. (1996b) reported no difference in a small study of five alcohol-dependent individuals compared to controls using [11C]d-threo methylphenidate PET. However, in a larger study of 27 alcohol-dependent individuals, there was lower DA transporter availability in human alcohol drinkers who were imaged with [123I]beta-CIT and SPECT on admission for detoxification compared to healthy controls (Laine et al. 1999). Interestingly, these subjects were imaged again after 4 weeks of abstinence and there was an increase in DA transporter availability, which in some subjects approached or surpassed the level of DA transporter availability in control subjects (Laine et al. 1999). In a similar study, higher availability of DA transporters in alcoholics was associated with increased novelty seeking (Laine et al. 2001). Repo et al. (1999) found lower DA transporter availability in Type I alcoholics compared

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Table 2 Studies examining DA transporter availability in alcohol dependence compared to controls Radiotracer Time Main finding Reference abstinent 4–8 weeks Lower availability/ Tiihonen et al. (1995) [123I]beta-CIT nonviolent 4–8 weeks Higher availability/violent Tiihonen et al. (1995) [123I]beta-CIT 52 48 Similar availability Volkow et al. (1996b) [11C]-dthreomethylphenidate days 1–4 days Lower availability Laine et al. (1999) [123I]beta-CIT 4 weeks Similar availability Laine et al. (1999) [123I]beta-CIT 7–165 days Lower availability/type I Repo et al. (1999) [123I]PE2I 3–5 weeks Similar availability Heinz et al. (1998) [123I]beta-CIT 3–5 weeks Similar availability Heinz et al. (2000) [123I]beta-CIT 1–5 days Higher availability/ Cosgrove et al. [123I]beta-CIT nonsmokers (2009a, b) 1–2 days Similar availability/smokers Cosgrove et al. [123I]beta-CIT (2009a, b) 11 C radioisotopes are used with PET, 123I radioisotopes are used for SPECT

to controls, and Tiihonen et al. (1995) found lower versus higher DA transporter availability in nonviolent versus violent alcohol-dependent individuals, respectively, compared to controls. Thus, Type I alcoholics and nonviolent alcoholics tend to have lower DA transporter availability than controls. Conversely, two studies by Heinz and colleagues found no difference in DA transporter availability between alcohol-dependent individuals at 3–5 weeks of abstinence and healthy controls (Heinz et al. 1998, 2000). Many alcohol-dependent individuals also smoke cigarettes, although the majority of these studies did not control for tobacco smoking. Recently, it has been suggested that tobacco smoking may mediate the effects of alcohol at the DA transporter. Specifically, during acute abstinence, heavy drinkers had higher levels of DA transporters compared to nondrinkers and this effect was restricted to nonsmokers (Cosgrove et al. 2009b). This suggests that tobacco smoking suppresses alcohol-induced increases in DA transporters during early withdrawal. Additional studies specifically designed to assess the effects of tobacco smoking in more severe alcohol dependence need to be performed.

3.3

Alcohol and the Serotonin Transporter

A large preclinical literature links serotonergic dysfunction along with specific genetic and social/environmental interactions to alcohol dependence (Wrase et al. 2006). Because serotonin is critically tied to mood regulation, it is thought that dysregulated serotonin in alcohol dependence may be tied to negative mood states including depression and anxiety which are highly comorbid with alcohol dependence. 5-HT transporter availability has been reported to be lower (Heinz et al. 1998; Szabo et al. 2004) and similar (Brown et al. 2007) in recent and long-term

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abstinent alcoholics compared to controls. Specifically, lower midbrain and brainstem 5-HT transporter availability was found in 22 alcohol-dependent men after 3–5 weeks with [123I]beta-CIT SPECT (Heinz et al. 1998) and at least 2 years* of abstinence* (n ¼ 15) with [11C]McN5652 and PET (Szabo et al. 2004) compared to healthy controls, which was correlated with depression and anxiety during withdrawal (Heinz et al. 1998). A more recent study reported no difference in 5HT transporter availability between 30 alcoholics who were abstinent on an average of 14 days, and 18 healthy controls using [11C]DASB PET (Brown et al. 2007), which is a more selective ligand for 5-HT transporters than [123I]b-CIT. There are some limitations to [123]b-CIT SPECT measurement of 5-HT transporter availability. For example, the radiotracer may be influenced by endogenous 5-HT (Heinz et al. 2004a) and/or there may be difficulties in quantification due to the low signal in the neocortex (Kuikka et al. 1995; vanDyck et al. 2000). However, the majority of these studies did not control for tobacco smoking. A recent study suggests that heavy drinkers have higher 5-HT transporters than nondrinkers, and this difference is mediated by tobacco smoking. Specifically, heavy drinker nonsmokers had significantly higher 5-HT transporters compared to nondrinker nonsmokers, but heavy drinker smokers had similar 5-HT transporter levels compared to nondrinker smokers (Cosgrove et al. 2009b). Additionally, there was a significant positive correlation between diencephalon and brainstem 5-HT transporter availability and days since last drink, suggesting that during acute withdrawal, there is a compensatory increase in 5-HT transporters in nonsmokers but not smokers. This suggests that smoking suppresses the alcohol-induced increase in 5-HT transporter availability over the first week of abstinence. Thus, the serotonin transporter may be involved in mediating the effects of drinking and smoking on serotonergic tone during acute alcohol withdrawal.

3.4

Alcohol and the mu-Opioid Receptor

The opioid system is responsible for modulating reward to natural reinforcers such as food and also to drugs of abuse including alcohol. The endogenous opioid system is made up of mu-, delta-, and kappa-opioid receptors, which are modulated by the endogenous opioid peptides enkephalin, beta-endorphin, and dynorphin. Agonists at the mu-opioid receptor stimulate the mesolimbic dopamine system and are rewarding. Alcohol indirectly interacts with the opioid receptor by increasing endogenous levels of beta-endorphin and enkephalin, which bind the mu-opioid receptor. Much of the research has focused on the mu-opioid receptor, which can be measured in vivo with [11C]carfentanil PET. Two studies to date have examined mu-opioid receptor availability in alcohol dependent subjects. In the first study, 25 alcohol dependent subjects at 1–3 weeks of abstinence had higher mu-opioid receptor availability in the ventral striatum, including the nucleus accumbens, compared to healthy controls which was positively correlated with alcohol craving (Heinz et al. 2005a). Interestingly, a subset of these alcohol dependent subjects

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remained abstinent and was scanned again 5 weeks later, and their mu-opioid receptor availability remained elevated, suggesting this elevation is long lasting. In the second study, eight alcohol dependent subjects scanned at 4 days of abstinence exhibited lower mu-opioid receptor availability in the right dorsal lateral prefrontal cortex, the right anterior frontal cortex and the right parietal cortex compared to healthy controls (Bencherif et al. 2004). They did not examine the nucleus accumbens. Additionally, they report that lower mu-opioid receptor availability was correlated with higher craving in these areas. While more research needs to be done in this area to determine changes in the availability of mu-opioid receptors in the recovery from alcohol dependence, these studies highlight the role of the mu-opioid receptor in alcohol craving during acute abstinence. This is consistent with the use of naltrexone, a mu-opioid antagonist, as an anticraving drug in the treatment of alcoholism.

3.5

Alcohol and the GABAA-BZ Receptor

GABA receptors are ligand gated ion channels that are the primary mechanism for modulating inhibitory synaptic transmission in the brain, and it is well established that GABAA receptors have a central role in modulating the effects of ethanol in the central nervous system (Davies 2003; Kumar et al. 2004). Alterations in GABAergic neurotransmission are strongly associated with symptoms of alcohol tolerance, dependence, and withdrawal, and thus the GABAA receptor has been a target of neuroimaging research. The GABAA-benzodiazepine receptor (GABAA-BZR) can be measured with [123I]iomazenil SPECT or [11C]flumazenil PET. These two radiotracers have comparable quantitation in vivo (Bremner et al. 1999). SPECT studies have demonstrated lower GABAA-BZR availability in alcohol dependent subjects at approximately 1 month (Abi-Dargham et al. 1994), 3 months (LingfordHughes et al. 1998, 2000), and 7 months (range of 2–18 months) (Lingford-Hughes et al. 2005) of alcohol withdrawal, and similar GABAA-BZR availability in alcohol-dependent subjects at approximately 3 weeks of withdrawal vs. controls (Litton et al. 1993). However, the most recent study, which was the first to evaluate GABAA-BZRs while controlling for tobacco smoking status, demonstrated higher availability of GABAA-BZRs at about 1 week withdrawal in alcohol-dependent nonsmokers that was suppressed in alcohol-dependent smokers, suggesting that tobacco smoke suppressed the alcohol-induced increase in GABAA-BZR availability (Staley et al. 2005). Additionally, this elevation was correlated with more severe alcohol withdrawal symptoms in alcohol-dependent nonsmokers, which were not apparent in alcohol-dependent smokers, suggesting that tobacco smoking may block some symptoms of alcohol withdrawal by suppressing the increased availability of GABAA-BZR during acute withdrawal from alcohol. At 4 weeks of abstinence the higher GABAA-BZR availability in alcohol-dependent nonsmokers had decreased and was similar to the alcohol-dependent smokers and controls suggesting a normalization of the receptor over time (Staley et al. 2005). These findings have several

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implications. One, they suggest that GABAA-BZR availability undergoes a compensatory increase during early recovery from alcohol dependence and decreases during prolonged withdrawal. Two, the changes over time are mediated by tobacco smoking, which has important clinical implications. If the nicotine in tobacco smoke is responsible for suppressing the alcohol-induced increase in GABAA-BZRs and suppressing some of the associated alcohol withdrawal symptoms, it suggests that smokers who desire to quit smoking and drinking when they enter treatment may benefit from nicotine replacement therapy to help cope with both nicotine and alcohol withdrawal symptoms.

4 Nicotine 4.1

Nicotine and the b2-Nicotinic Acetylcholine Receptor (b2*-nAChR)

Nicotine, the primary addictive ingredient in tobacco smoke, acts at the nicotinic acetylcholine receptor (nAChR) and exerts its reinforcing effects at nAChRs containing the b2*-*subunit* (b2*-nAChR). It is well known from preclinical and postmortem studies that chronic nicotine and tobacco smoking upregulate b2*nAChRs and this has recently been confirmed in vivo. Specifically, there is a higher availability of b2*-nAChRs in tobacco smokers during acute abstinence compared to never smokers in the cerebellum, striatum, and throughout the cortex (Mukhin et al. 2008; Staley et al. 2006). Additionally, this upregulation, or higher availability, is temporary (Mamede et al. 2007) and in most people decreases to nonsmoker levels between 6 and 12 weeks of abstinence from tobacco smoking (Cosgrove et al. 2009a) (See Fig. 1). Studies are ongoing to determine variables that may affect individual differences in the regulation of the b2*-nAChR during prolonged

Fig. 1 Reprinted with permission from Archives of General Psychiatry (2009), 66, 672. These are mean parametric images in transaxial view illustrating b2*-nAChR availability in nonsmokers and smokers during acute and prolonged abstinence. Measurements for the smokers were obtained at 1 day and 1, 2, 4, and 6–12 weeks of abstinence. The color scale is shown with red, yellow, green, and blue corresponding to values of b2*-nAChR availability (VT/fP). Note the higher b2*-nAChR availability in the striatum, cerebellum and cortex in the smokers at 1 week of abstinence compared to the nonsmokers which decreases over time

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abstinence. Individual differences in the rate of normalization during prolonged abstinence may have individual-specific treatment indications. For example, if a faster rate of normalization is associated with increased craving, those individuals may benefit from nicotine replacement therapy, which may slow the normalization of the receptor and decrease craving.

4.2

Nicotine and the D1 Receptor

The dopamine type 1 (D1) receptor is important in tobacco smoking due to its involvement in the mesolimbic reward pathway and because it has been implicated in cognition. The cognitive impairments and deficits that some smokers report during withdrawal make this an important area of research. However, to date, only one study has evaluated differences in D1 receptor availability between smokers and nonsmokers. Tobacco smokers had significantly lower D1 receptor availability compared to nonsmokers measured with [11C]SCH 23390 and PET (Dagher et al. 2001). This difference was most profound in the ventral striatum. This suggests that the dopaminergic system may be underactive in smokers, and thus may promote continued tobacco smoking. It is also possible that because chronic tobacco smoking results in sustained dopamine and other neurotransmitter release by both nicotine and monoamine oxidase inhibitors, which are in tobacco smoke, there is a resulting downregulation of D1 receptor. Future studies should address whether D1 receptor availability changes over time during withdrawal from tobacco smoking and whether it is correlated with changes in cognition.

4.3

Nicotine and the D2/3 Receptor

The D2/3 receptor is critical to the rewarding effects of all drugs of abuse. Several studies have examined differences in D2/3 availability between tobacco smokers and nonsmokers. Two studies have reported no difference in striatal D2/3 receptor availability between actively smoking tobacco smokers and nonsmokers using [123I]IBZM and SPECT (Yang et al. 2006, 2008). In a subsequent larger study, tobacco smokers had significantly lower D2/3 receptor availability in the bilateral putamen while actively smoking and 24 h after smoking cessation compared to never smokers using [11F]fallypride PET (Fehr et al. 2008). Additionally, D2/3 receptor availability correlated positively with craving in the ventral striatum, and negatively in the anterior cingulate and inferior temporal cortex. Differences between the studies may be due to the higher resolution of PET, differences between radiotracers, and/or because the tobacco smokers in Fehr et al. (2008) smoked for more years than in Yang et al. (2006). This finding of decreased D2/3 receptor availability in tobacco smokers compared to controls is consistent with findings of lower D2/3 receptor availability in individuals dependent on cocaine, alcohol and heroin as discussed earlier in this chapter. This suggests that dopamine

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D2/3 dysfunction is pervasive in addiction in general, and not specific to a particular class of drugs.

4.4

Nicotine and the Dopamine Transporter

The dopamine transporter is responsible for regulating dopamine levels in the synapse. Since tobacco smoking increases dopamine release, we might expect that chronic tobacco smoking would lead to changes in dopamine transporter numbers. One initial study showed no difference between smokers and nonsmokers in striatal DA transporter availability with [123I]beta-CIT and SPECT (Staley et al. 2001). However, two recent studies using [99mTc]TRODAT and SPECT, report significantly lower striatal DA transporter availability in smokers vs. nonsmokers (Newberg et al. 2007; Yang et al. 2008). The lower DA transporter availability in smokers is likely a downregulation in response to tobacco smoking-induced increases in synaptic DA. Lower DA transporter availability was associated with increased severity of nicotine dependence as measured by the Fagerstro¨m Test for Nicotine Dependence (Yang et al. 2008), further suggesting that heavier smoking, which may lead to increased dopamine release results in greater downregulation of the DA transporter.

4.5

Nicotine and the Serotonin Transporter

Tobacco smoking results in the release of a cascade of neurotransmitters including serotonin. This may lead to adaptations in the serotonin transporter, which is responsible for removal of serotonin from the synapse. Only one study using [123I]beta-CIT and SPECT has examined potential differences in 5-HT transporter availability between smokers and nonsmokers (Staley et al. 2001). The study found modestly higher 5-HT transporter availability in men smokers compared to nonsmokers in the brainstem (10%), but not in the diencephalon. This provides some evidence that smoking may regulate the 5-HT transporter in men, but additional studies with more selective radiotracers are needed to confirm this finding. This finding is important because the serotonin system is responsible for mood regulation and a poor mood during tobacco smoking withdrawal is a primary reason tobacco smokers are unable to successfully quit smoking.

5 Opiates 5.1

Opiate Dependence and the mu-Opioid Receptor

The mu-opioid receptor has been widely studied because it is critical for the rewarding properties of many drugs of abuse, especially opiate addiction. In a

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preliminary study, men who were dependent on heroin, but recently detoxified (n ¼ 3), e.g., 1 week, had higher mu-opioid receptor availability measured with [11C]carfentanil PET in the inferofrontal cortex and anterior cingulate compared to healthy controls (n ¼ 3) (Zubieta et al. 2000). This is likely due to an upregulation of the mu-opioid receptors in response to chronic heroin use, which may be implicated in the tolerance to the rewarding effects that is reported by heroin users. Additional studies are needed to determine receptor changes during prolonged withdrawal.

5.2

Opiate Dependence and the D2/3 Receptor

Very few studies have been conducted on receptor availability in heroin and/or opiate-dependent subjects in large part because of the physical withdrawal symptoms associated with imaging them acutely after their last drug administration. As previously discussed, the D2/3 receptor is a critical reward substrate and low D2/3 receptor availability may represent a vulnerability to addiction or a consequence of chronic use. Two studies found that opiate-dependent men had significantly lower striatal D2/3 receptor availability compared to controls using [123I]IBZM SPECT (Zijlstra et al. 2008) and [11C]raclopride (Wang et al. 1997b). Additionally, Zijlstra et al. (2008) reported that lower striatal D2/3 availability was associated with more years of opiate and polydrug use, suggesting that the lower D2/3 availability is tied to the chronicity of drug use in general. This suggests that low D2/3 receptor availability and dopaminergic dysfunction is more likely a consequence of chronic drug use.

6 Conclusions In this chapter, we have reviewed differences in the brain chemistry of drug- and alcohol-dependent individuals compared to healthy controls. Cocaine dependence is associated with a higher availability of DA and 5-HT transporters and mu-opioid receptors during acute abstinence compared to healthy controls. The higher DA transporter and mu-opioid receptor availability tends to decrease over prolonged abstinence from cocaine. Alcohol dependence is associated with higher GABAABZR availability compared to controls during acute abstinence that decreases with prolonged abstinence. Additionally, the alcohol-induced changes in GABAA-BZR and DA and 5-HT transporter availability appear to be modulated by comorbid tobacco smoking. Importantly, the rates of comorbidity of substance dependence with tobacco smoking and other drugs of abuse are high and additional studies that systematically examine these variables are needed. Less research has been conducted on tobacco smoking and heroin dependence. One consistent finding is that tobacco smokers have higher beta2*-nAChR availability than nonsmokers which

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decreases with prolonged abstinence. A main theme throughout this chapter has been a lower availability of D2/3 receptors in drug-dependent individuals compared to healthy controls. This is true for cocaine, alcohol, nicotine, and heroin and thus suggests that low D2/3 availability is a hallmark feature of drug dependence. It is still not clear, however, whether this predates the drug use, and thus confers a vulnerability, or whether it is a consequence of chronic drug consumption. Additionally, the polydrug issue should not be overlooked. While it is still highly likely that low D2/3 is a marker for vulnerability to become dependent on each of these drugs, it has not been ruled out that this effect is primarily due to comorbid tobacco smoking or alcohol consumption. The neurochemical systems outlined in this review that are involved in addiction are likely integrated and play significant roles in a final common pathway mediating addiction in the brain. While we can currently image only a handful of chemical sites in the brain, the recent explosion in radiotracer development will ultimately increase our understanding of addiction in the living human brain. Acknowledgment This work was supported by National Institute of Health grants RO1 DA015577, KO2 DA21863, KO1 DA20651, P50 DA13334, and P50 AA15632.

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Zijlstra F, Booij J, van den Brink W, Franken IH (2008) Striatal dopamine D2 receptor binding and dopamine release during cue-elicited craving in recently abstinent opiate-dependent males. Eur Neuropsychopharmacol 18:262–270 Zubieta JK, Gorelick DA, Stauffer R, Ravert HT, Dannals RF, Frost JJ (1996) Increased mu opioid receptor binding detected by PET in cocaine-dependent men is associated with cocaine craving. Nat Med 2:1225–1229 Zubieta J, Greenwald MK, Lombardi U, Woods JH, Kilbourn MR, Jewett DM, Koeppe RA, Schuster CR, Johanson CE (2000) Buprenorphine-induced changes in mu-opioid receptor availability in male heroin-dependent volunteers: a preliminary study. Neuropsychopharmacology 23:326–334

Imaging Neurotransmitter Release by Drugs of Abuse Diana Martinez and Rajesh Narendran

Contents 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Positron Emission Tomography Radioligand Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Using PET to Image Neurotransmitter Release . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 Loss of Sensitivity in Measurement of Dopamine Transmission . . . . . . . . . . . . . . . . . . . . . . . . 223 Modulation of Imaging of Dopamine Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224 Imaging Changes in Endogenous Dopamine with Other Substances of Abuse . . . . . . . . . . 225 Imaging Dopamine Transmission in the Extrastriatal Regions . . . . . . . . . . . . . . . . . . . . . . . . . 227 Imaging Other Neurotransmitter Release Using PET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 PET Radioligand Imaging in Cocaine Dependence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Imaging Dopamine Release in Cocaine Dependence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Functional Significance of Low D2 Receptor in Cocaine Dependence . . . . . . . . . . . . . . . . . . 233 Dopamine Transmission and Cocaine-Seeking Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 Sensitization and Chronic Cocaine Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Imaging Cue-Induced Craving in Cocaine Dependence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 Imaging Dopamine Transmission in Other Addictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 14.1 Behavioral Correlates of Low D2 Receptor BP in Alcohol Dependence . . . . . . . . . . 237 14.2 Alcohol Dependence and Presynaptic Dopamine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

Abstract Previous studies have shown that imaging with positron emission tomography (PET) and single photon emission computed tomography (SPECT) radiotracers that are specific for brain dopamine receptors can be used to indirectly image the change in the levels of neurotransmitters within the brain. Most of the studies in addiction have focused on dopamine, since the dopamine neurons that project to the striatum have been shown to play a critical role in mediating addictive behavior. These imaging studies have shown that increased extracellular dopamine produced by psychostimulants can be measured with PET and SPECT. However, there are some technical issues associated with imaging changes in dopamine, and these are D. Martinez (*) and R. Narendran NYS Psychiatric Institute, 1051 Riverside Drive, New York, NY 10032, USA e-mail: [email protected]

D.W. Self and J.K. Staley (eds.), Behavioral Neuroscience of Drug Addiction, Current Topics in Behavioral Neurosciences 3, DOI 10.1007/7854_2009_34, # Springer‐Verlag Berlin Heidelberg 2009, published online 15 September 2009

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reviewed in this chapter. Among these are the loss of sensitivity, the time course of dopamine pulse relative to PET and SPECT imaging, and the question of affinity state of the receptor. In addition, animal studies have shown that most drugs of abuse increase extracellular dopamine in the striatum, yet not all produce a change in neurotransmitter that can be measured. As a result, imaging with a psychostimulant has become the preferred method for imaging presynaptic dopamine transmission, and this method has been used in studies of addiction. The results of these studies suggest that cocaine and alcohol addiction are associated with a loss of dopamine transmission, and a number of studies show that this loss correlates with severity of disease. Keywords PET SPECT Neuroimaging Addiction Alcohol dependence Dopamine Neurotransmission

Abbreviations BP D2 GABA IV PET PO SPECT

Binding potential Dopamine type 2 receptor Gamma-aminobutyric acid Intravenous Positron emission tomography Per os Single photon emission computed tomography

1 Positron Emission Tomography Radioligand Imaging Positron emission tomography (PET) uses receptor specific agonists and antagonists that are labeled with a positron-emitting radionuclide, usually carbon-11 (11C) or fluorine-18 (18F) to image these receptors in human brain imaging studies. The specific techniques involved in PET radioligand imaging have been reviewed previously (Carson 1986; Slifstein and Laruelle 2001). Briefly, the radionuclide is incorporated into the receptor-specific molecule, so that as the ligand binds to the receptor in the brain, it can be visualized with imaging. As the positron emitted by the radionuclide encounters an electron, an annihilation event occurs which produces two gamma rays about 180 apart. These gamma rays are detected by scintillators of the PET scanner. Using coincidence detection, an image of the receptor-bound radioligand can be obtained which results in the ability to quantify neuroreceptors in vivo in the human brain. To date, a number of radiotracers are available to image neurochemistry, including the dopamine receptors and transporters, serotonin receptors/transporters, GABA and glutamate receptors, opioid receptors,

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and others. In addition to PET, single photon emission computed tomography (SPECT), which uses radionuclides that emit photons, can also be used to image brain receptors and transporters. While SPECT has a lower resolution than PET, it has the advantage of using radionuclides with a longer half-life (usually iodine-123 (123I) for brain receptor imaging) which reduces the need to generate the radiopharmaceutical onsite and allows greater flexibility. The main outcome measure used in PET and SPECT imaging studies of clinical populations is called “binding potential” (BP), which is the product of receptor density and affinity of the radiotracer for the receptor. BP is usually measured as either BPP (BP relative to the free fraction of radiotracer in the arterial plasma) or BPND (BP relative to the free fraction of radiotracer in the brain tissue) (Innis et al. 2007). Since BP is a composite of both receptor density and affinity, most human imaging studies cannot differentiate these two parameters. Thus, a difference in BP seen between two groups could result from either a difference in receptor density or affinity. However, PET studies performed with receptor antagonists, are expected to be less affected by the affinity state of the receptor, whereas PET studies performed with an agonist may provide information regarding receptor affinity state.

2 Using PET to Image Neurotransmitter Release In addition to the imaging receptors, PET and some radiotracers can be used to indirectly image the change in the levels of neurotransmitters within the brain. The most frequently used radiotracer for this purpose is the radiotracer [11C]raclopride for PET and [123I]iodobenzamide (IBZM) for SPECT, which bind to the D2 family of receptors (referred to as D2 for simplicity) and can be used to measure changes in extracellular dopamine in the striatum. Previous imaging studies have shown that radioligand binding to the D2 receptor is sensitive to changes in the level of endogenous dopamine in the brain and that increases in extraneuronal dopamine decrease [11C]raclopride or [123I]IBZM binding (since fewer D2 receptors are available to bind to the radioligand). In these studies, dopamine levels are increased by the administration of a psychostimulant (such as methylphenidate or amphetamine), which results in a large increase in extracellular dopamine. Therefore, in the same individual, a comparison of BP prior to and following stimulant administration provides an indirect measure of dopamine transmission. This is depicted in Fig. 1, where an individual subject’s scan is shown at baseline (left panel) and following the administration of methylphenidate (right panel). As shown in Fig. 1, [11C]raclopride binding is reduced following methylphenidate due to the reduction in the D2 receptors available to bind to the radiotracer. Alternatively, decreases in dopamine levels in the striatum result in increased [11C]raclopride binding, given that more D2 receptors are available to the radiotracer. This is shown in Fig. 2, where the depletion of endogenous dopamine increases the percentage of receptors available to bind to the radiotracer, by reducing the pool of receptors occupied by dopamine. A paradigm has been

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Fig. 1 Using PET and [11C]raclopride to measure changes in endogenous dopamine in the striatum. The left panel shows baseline D2 binding to the radiotracer in a healthy control and the right panel shows D2 binding following the administration of methylphenidate (60 mg PO). Methylphenidate blocks the dopamine transporter on the dopamine nerve terminals in the striatum, resulting in a large increase in extracellular dopamine levels. As a result, fewer D2 receptors are available to bind to [11C]raclopride. Thus, the decrease in [11C]raclopride binding provides an indirect measure in stimulant-induced increases in endogenous dopamine

Fig. 2 PET and [11C]raclopride can also be used to measure a reduction in endogenous dopamine, using alpha-methylparatyrosine (AMPT), which inhibits tyrosine hydroxylase and reduces dopamine production. The left panel shows baseline D2 binding in a healthy control and the right panel shows D2 binding following the administration of AMPT (120.7 9.2 mg kg 1). Following 48 h of treatment, endogenous dopamine levels are significantly reduced, resulting in an increase in D2 receptor availability for the radiotracer

developed for use in human volunteers to acutely deplete dopamine using the drug alpha-methylparatyrosine (AMPT), which inhibits tyrosine hydroxylase and reduces endogenous levels of dopamine in the brain (Laruelle et al. 1997a, b). Using this paradigm, AMPT has been used in both PET and SPECT studies to image the percent of D2 receptors occupied by endogenous dopamine, and occupancies ranging from 9 to 28% have been reported in control subjects (Laruelle et al. 1997a, b; Abi-Dargham et al. 2000; Verhoeff et al. 2001, 2002). Taken

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together, these studies in human volunteers show that pharmacologic manipulations that either increase or decrease endogenous levels of dopamine provide reliable changes in radioligand binding ([11C]raclopride for PET and [123I]IBZM for SPECT) that mirror the change in endogenous dopamine; i.e., radioligand binding decreases in the setting of higher levels of endogenous dopamine and is increased when dopamine levels are reduced.

3 Loss of Sensitivity in Measurement of Dopamine Transmission Studies in nonhuman primates using this PET technique and microdialysis have shown that there is a linear correlation between the stimulant-induced change in BP and extracellular dopamine (Breier et al. 1997; Laruelle et al. 1997a, b). In other words, the magnitude of the increase in endogenous dopamine is faithfully mirrored by the decrease in radiotracer binding. However, there is a significant loss of sensitivity: each percent decrease in [11C]raclopride BP corresponds to a 54% increase in extracellular dopamine measured with microdialysis (Breier et al. 1997). Thus, in healthy controls the administration of amphetamine (0.3 mg kg 1 iv), in general, produces an average decrease in striatal raclopride binding on the order of 10–25% (Drevets et al. 2001; Martinez et al. 2003; Munro et al. 2006) despite the fact that animal studies using microdialysis have shown that amphetamine produced a several 100-fold increase in extrasynaptic dopamine (Breier et al. 1997; Laruelle et al. 1997a, b). Thus, a major limitation of this technique is its comparatively low sensitivity; i.e., relatively large increases in extracellular dopamine are associated with relatively modest effects on D2 antagonist radiotracer binding. In addition to this low sensitivity, previous studies have shown a ceiling effect of about 40% (Laruelle et al. 1997a, b; Price et al. 1997). In other words, even large doses of intravenous (IV) amphetamine do not result in more than approximately 40% reduction in radiotracer binding, despite the enormous increase in synaptic dopamine. Decreases in [11C]raclopride and [123I]IBZM BP have been measured following a number of challenges in anesthetized animals, and the literature is quite consistent in the range of radiotracer displacement, which is 10 to 48% (Schlaepfer et al. 1997; Dewey et al. 1993; Laruelle et al. 1997a, b; Price et al. 1997; Volkow et al. 1999a, b, c). Thus, less than half of the radiotracer-specific binding is vulnerable to changes in synaptic dopamine. This low sensitivity and ceiling effect may be related to the fact that D2 receptors are configured in interconvertible states of high or low affinity for agonists. The high affinity sites (D2high) are G-protein-coupled D2 receptors, whereas the low affinity sites (D2low) are those uncoupled with G-proteins. In vitro, approximately 50% of D2 receptors are configured in the high affinity state (Zahniser and Molinoff 1978; Sibley et al. 1982; George et al. 1985; Seeman and Grigoriadis 1987; Richfield et al. 1989). Antagonists, such as [11C]raclopride, bind with equal affinity to both states. The agonist dopamine is not expected to compete efficiently with

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[123I]IBZM or [11C]raclopride binding to D2low. This factor would leave less than 50% of the antagonist binding susceptible to endogenous competition by dopamine. These observations suggest that the ideal radiotracer for endogenous competition studies would be a D2 receptor agonist. Consistent with such a hypothesis are recent imaging studies in anesthetized nonhuman primates and cats that suggest dopamine D2 agonist radiotracers such as [11C]-N-propyl-nor-apomorphine (NPA), [11C]-methoxy-NPA, and [11C]-(+)-propyl-hydroxy-naphthoxazine (PHNO) are more vulnerable to endogenous competition by dopamine relative to the reference D2 antagonist radiotracers (Narendran et al. 2004; Ginovart et al. 2006; Seneca et al. 2006). A recent study with [11C]PHNO and amphetamine in humans demonstrated that D2 agonist are vulnerable to endogenous competition by dopamine following an acute amphetamine (0.38–0.45 mg kg 1 PO) challenge (Willeit et al. 2008). Unfortunately, this study failed to contrast the vulnerability of the antagonist [11C] raclopride with that of the agonist [11C]PHNO in the same subjects and did not allow for definitive conclusions to be drawn with respect to superiority of D2 agonists over antagonists in measuring dopamine transmission. The replication of the results previously observed in anesthetized animal studies and in humans will not only allow for the use of more sensitive probes to image dopamine transmission but also allow for the measurement of dopamine D2 receptors configured in a state of high affinity for the agonists in health and disease. Another puzzling observation besides the low sensitivity and ceiling effect is related to the significant temporal discrepancy between the microdialysis measures (peak extracellular DA surge between 10 and 20 min, followed by rapid decrease over 100–120 min) and radiotracer displacement (sustained change in BP for 4–5 h) following amphetamine (Laruelle et al. 1997a, b; Carson et al. 2001). This long lasting decrease in D2 receptor BP that has been observed for both agonist and antagonist radiotracers has been reported to subside, with BP returning to preamphetamine values in approximately 24–48 h (Cardenas et al. 2004; Houston et al. 2004; Narendran et al. 2007). Thus, the exact mechanism behind the decrease in D2 radiotracer binding is not known. While competition between extracellular dopamine and the radiotracer for the receptor is often used as the model to explain the decrease in radiotracer binding, other phenomena, such as receptor affinity state, internalization or polymerization may also be involved (Laruelle 2000; Logan et al. 2001).

4 Modulation of Imaging of Dopamine Transmission Notably, PET imaging studies have also shown that stimulant-induced increases in endogenous dopamine can be modulated. Based on microdialysis studies in animals, these studies show that the administration of medications that are known to modulate presynaptic dopamine release also affect changes in [11C]raclopride binding. Microdialysis studies have shown that the pretreatment of N-methyl-Daspartate (NMDA) receptor antagonists increase stimulant-induced presynaptic

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dopamine release, and this same phenomenon has been shown with imaging. An imaging study by Kegeles et al. (2000) used SPECT and the radiotracer [123I] IBZM, which is very similar to [11C]raclopride and also images the D2 receptor family, to investigate the effect of NMDA antagonism on stimulant-induced dopamine release in the striatum. [123I]IBZM BP was obtained before and after the administration of amphetamine (0.25 mg kg 1 iv) in healthy subjects under the control condition and during the infusion of the NMDA antagonist ketamine. The results of this study showed that, in the control condition, amphetamine produced an average 5.5 3.5% change in [123I]IBZM binding in the striatum compared to 12.8 8.8% in the same subjects who had been pretreated with ketamine (Kegeles et al. 2000). This type of design has also been used in imaging studies of nonhuman primates. Imaging with [11C]raclopride and an amphetamine challenge, one study showed that pretreatment with a metabotropic glutamate receptor group II agonist (which inhibits glutamate transmission) also increased dopamine release, similar to the effect seen with ketamine in human subjects (van Berckel et al. 2006). Alternatively, another PET study in baboons showed that pretreatment with gamma vinylGABA, a irreversible inhibitor of GABA-transaminase which potentiates GABA transmission in the brain, significantly attenuated the ability of cocaine to displace [11C]raclopride, presumably due to GABA-induced increased inhibition of the dopamine neurons (Dewey et al. 1998). Thus, these studies show that mechanisms known to affect dopamine transmission in the striatum modulate radiotracer displacement in the direction predicted by the microdialysis studies, which add support to the theory that radiotracer imaging can be used to measure changes in endogenous dopamine in the human brain.

5 Imaging Changes in Endogenous Dopamine with Other Substances of Abuse The majority of PET imaging studies investigating changes in endogenous dopamine have been performed using a psychostimulant challenge. Drugs such as cocaine or methylphenidate block the dopamine transporter, which regulates synaptic dopamine by the reuptake of dopamine back into the dopamine neuron. Other stimulants, such as amphetamine, release dopamine by forcing the dopamine transporter (and the vesicular monoamine transporter) to work in reverse, causing cytosolic dopamine to be released into the synapse. Thus, a number of studies in healthy human subjects have shown that the administration of a psychostimulant, including amphetamine, cocaine, methylphenidate result in a reproducible decrease in [11C]raclopride binding. In human studies, the challenges used have included methylphenidate (IV and PO) (Volkow et al. 1994, 2001a, b), amphetamine (Drevets et al. 1999; Martinez et al. 2003), and cocaine (IV and intranasal) (Schlaepfer et al. 1997; Cox et al. 2009). In each of these studies, the decrease in [11C]raclopride following psychostimulant administration was approximately 10% at the level of the whole striatum with some studies showing a preferential effect

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(i.e., greater radiotracer displacement) in the ventral striatum (VST) (15–20%), which in humans includes the nucleus accumbens. This selectivity is important, given that dopamine transmission in the nucleus accumbens is closely associated with the reinforcing and rewarding effects of drugs of abuse (Di Chiara and Imperato 1988; Wise and Rompre` 1989). However, microdialysis studies in animals show that most drugs of abuse, not just psychostimulants, increase dopamine levels in the striatum, often with a preferential effect (greater dopamine release) on the VST (Di Chiara and Imperato 1988; Wise and Rompre` 1989). For example, ethanol has been shown to increase striatal dopamine in microdialysis studies, although not through a direct effect on the dopamine nerve terminal in the striatum. Ethanol acts to release striatal dopamine by activation of the mu opioid receptors in the ventral tegmental area (VTA) of the midbrain, where the dopamine cell bodies are located (Herz 1997). The mu receptors are located on the GABA interneurons in the VTA, which inhibit the dopamine neurons. Activation of these receptors by alcohol administration results in a decrease in the inhibitory activity of these interneurons, so that the dopamine neurons increase their firing, producing increased levels of dopamine in the striatum (Herz 1997). Thus, based on these microdialysis experiments, it would be expected that the increases in dopamine levels induced by drugs of abuse other than stimulants could be measured with PET. However, previous studies examining the effect of an alcohol challenge on [11C]raclopride binding in healthy control subjects do not provide consistent results. The first study was performed by Salonen et al. (1997) and it showed no effect of alcohol on [11C]raclopride binding, despite the fact that a high dose of ethanol was administered (1 g kg 1 ethanol; 40 vol%). A second study reported that oral alcohol (1 ml kg 1 of 95% USP alcohol) produced a 14–15% decrease in [11C]raclopride binding in the VST (Boileau et al. 2003). No displacement was seen in the caudate and putamen outside of the VST. Thus, since the earlier study of Salonen et al. measured dopamine release in the striatum as a whole, it is possible that this study did not see an effect that was limited to the VST (which makes up a small percentage of the whole striatum) (Salonen et al. 1997). However, Yoder et al have performed two studies showing that alcohol does not produce a measurable displacement of [11C]raclopride in the VST (Yoder et al. 2005, 2007). In these studies, alcohol was administered intravenously as a “clamp,” which produces a stable breath concentration over the time course of the scan at two different doses (60 and 80 mg%). The results were surprising, given that a high range dose of ethanol was administered to produce a steady state throughout the scan, which produced significant subjective effects. In a more recent study by this group, subjects were presented alcohol-associated cues that were dissociated from the actual administration of alcohol, and showed that the cues for alcohol resulted in a decrease in [11C]raclopride binding, whereas the administration of alcohol in the absence of a cue increased radiotracer binding (Yoder et al. 2009). Similar findings have been reported with studies investigating the effects of marijuana and tobacco on [11C]raclopride binding. Animal studies have shown that tetrahydrocannabinol (THC) increases the firing rate of dopamine neurons and striatal dopamine release via indirect excitatory action on the dopaminergic cell

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bodies in the VTA (Tanda et al. 1997; Cheer et al. 2004). However, a recent study by Stokes et al. (2009) showed that, in control subjects, THC administration (10 mg PO) did not affect [11C]raclopride binding. A number of studies have investigated the effect of tobacco smoking on extracellular dopamine measured with PET. Using PET and [11C]raclopride, Brody et al. showed that smoking a regular cigarette, compared to baseline or smoking a denicotinized cigarette, results in a decrease in radiotracer binding in the VST in dependent tobacco smokers (Brody et al. 2004, 2009). Similar results have been reported by another group who also compared the effects of nicotine-containing and denicotinized cigarettes on [11C]raclopride binding in smokers (Scott et al. 2007). Another PET study showed that nicotine gum resulted in a decrease in striatal [11C]raclopride binding in smokers, whereas no effect was seen in nonsmokers (Takahashi et al. 2008). However, other studies using similar methods have not shown a significant decrease in [11C]raclopride BP following nicotine administration. These studies include a [11C]raclopride scan following cigarette smoking (in smokers) (Barrett et al. 2004), nicotine administered as a nasal spray in smokers (Montgomery et al. 2007), and a study in monkeys administering high dose IV nicotine (Tsukada et al. 2002). Taken together, these imaging studies investigating the effects of ethanol, THC, and nicotine suggest that dopamine release by ethanol and THC may be less robust than that seen with tobacco smoking. However, even within the studies examining the effect of nicotine on [11C]raclopride BP, the results show some discrepancies. In contrast, to date, no study has been published using a psychostimulant challenge showing no effect on [11C]raclopride binding. One reason for this may be the magnitude of dopamine release. As described above, psychostimulant administration results in a several 100-fold increase in extracellular dopamine, whereas other drugs of abuse, which indirectly affect the dopamine nerve terminals, generally result in a 100–200% increase in dopamine levels (Di Chiara and Imperato 1988; Wise and Rompre` 1989). As described above, there is a loss of sensitivity when measuring dopamine release with PET. Thus, while dopamine transmission may still be altered by drugs of abuse in the human brain, these alterations may not be measured as robustly with PET. In addition, it is interesting to note that the one study imaging both tobacco smokers and nonsmokers showed [11C]raclopride displacement only in the smokers, no change was seen in the nonsmokers, suggesting that dopamine transmission may be altered in addicted subjects compared to controls when administered their drug of abuse (Takahashi et al. 2008).

6 Imaging Dopamine Transmission in the Extrastriatal Regions Since the introduction of the high affinity D2 PET radioligands [11C]FLB 457 (Halldin et al. 1995) and [18F]fallypride (Mukherjee et al. 1995), several groups have confirmed their increased signal-to-noise ratio relative to [11C]raclopride (Suhara et al. 1999; Olsson et al. 2004; Slifstein et al. 2004) and reported on their

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ability to reliably measure D2 receptor availability (or binding potential, BPND) in the human cortex (Vilkman et al. 2000; Sudo et al. 2001; Mukherjee et al. 2002; Cropley et al. 2008). Despite numerous investigations, the question of whether these high affinity D2 PET ligands can be used to measure dopamine transmission in regions with relatively low D2 receptor densities such as the dorsolateral prefrontal cortex (3–5% D2 receptor density than the striatum) is still unresolved (Aalto et al. 2005; Riccardi et al. 2005; Montgomery et al. 2006; Cropley et al. 2008). Human data with [18F]fallypride, from three different groups, evaluating amphetamine-induced DA transmission concluded that [18F]fallypride can be used to measure DA release not only in the striatum but also in a limited number of extrastriatal regions such as the medial temporal lobe (amygdala and hippocampus) and midbrain (Riccardi et al. 2005; Slifstein et al. 2007; Cropley et al. 2008). However, two of the three investigations reported that [18F]fallypride cannot be used to measure amphetamine-induced DA release (greater than 5% decrease in radioligand binding that is statistically significant) in the cortical regions of interest due to its relatively low signal-to-noise ratio in these regions (Riccardi et al. 2005; Slifstein et al. 2007). The third study by Cropley et al. reported a statistically significant decrease of [18F] fallypride binding ( 13 4%) in the medial OFC, but not in the temporal cortex. Other cortical regions such as the dorsolateral prefrontal cortex, the medial prefrontal cortex, and anterior cingulate were not evaluated in this study either due to relatively low binding potential (BPND < 0.5) or poor reproducibility for BPND. A more recent human study contrasted the in vivo binding of [11C]FLB 457 and 11 [ C]fallypride in the cortex with respect to their signal-to-noise ratio and vulnerability to endogenous competition by DA (Narendran et al. 2009). The results of this study demonstrated that the signal-to-noise ratio of [11C]FLB 457 is on average 60% higher than that of [11C]fallypride in the cortical regions of interest (for example, DLPFC [11C]FLB 457 BPND is 0.6 0.3, [11C]fallypride 0.4 0.2). The results of this study also demonstrated for the first time that this higher signal-to-noise ratio of [11C]FLB 457 allows for the successful imaging of amphetamine-induced DA release in the cortical regions of interest. The mean displacement in the cortical regions of interest ranged from 5 to 13%. More exciting was the fact that the amphetamine-induced displacement of [11C]FLB 457 was detected in the prefrontal cortical regions of interest such as the dorsolateral prefrontal cortex ( 13%), medial prefrontal cortex ( 7%), and anterior cingulate cortex ( 9%). If further validation of this technique is successful, this technique would potentially allow for the imaging of prefrontal cortical dopamine transmission in several neuropsychiatric disorders such as addiction, schizophrenia, and ADHD.

7 Imaging Other Neurotransmitter Release Using PET While the dopamine system has been shown to allow measurement of dopamine release measured with PET radioligand imaging, the same is not true for other transmitter systems. A number of attempts have been made to use PET imaging of

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the serotonin system to measure changes in endogenous levels of this neurotransmitter. A previous study in healthy humans using the PET radiotracer [11C] WAY100635, which labels the pre and postsynaptic 5-HT1A receptors, used rapid tryptophan depletion to acutely reduce brain serotonin levels but showed no significant effect (Rabiner et al. 2002). Similar results were seen for the other 5HT1A receptor radiotracer, [18F]MPPF, using methods to alter brain serotonin levels with tryptophan infusion and depletion in addition to fenfluramine administration to induce serotonin release (Udo de Haes et al. 2002). PET imaging studies that label the postsynaptic 5-HT2A receptor, instead of the 5-HT1A receptor, have showed similar results with no change in radiotracer binding following fenfluraminestimulated serotonin release (Staley et al. 2001; Hirani et al. 2003). Lastly, a PET imaging study in healthy human volunteers using the radiotracer [11C]DASB, which labels the serotonin transporter, showed that using rapid tryptophan depletion to acutely reduce brain serotonin levels, had no significant effect on BP (Talbot et al. 2005). With respect to the GABA system, a recent study has shown that an increase in endogenous levels of GABA stimulated by a GABA transporter blocker drug tiagabine can be detected as an increase in [11C]flumazenil binding (Frankle et al. 2009). The principle underlying this hypothesis is a “GABA-shift” – the enhancement in receptor affinity for benzodiazepine – site substrates resulting from increased GABA transmission in the brain (Tallman et al. 1978; Braestrup et al. 1982). The replication and further validation of this method is likely to allow for the characterization of GABA-ergic abnormalities in addictive disorders.

8 PET Radioligand Imaging in Cocaine Dependence The most studied addiction using PET radioligand imaging is cocaine dependence, and most of these studies have focused on imaging the D2 receptor and dopamine release. Studies measuring D2 receptor binding have been performed using both [18F] N-methylspiroperidol and [11C]raclopride, and show that cocaine dependence is associated with a decrease in D2 receptor binding. The first of these, published in 1990, showed that cocaine dependence was associated with a 35% decrease in D2 receptor BP in the striatum compared to healthy control subjects (Volkow et al. 1990). Subsequent studies, performed with [11C]raclopride, have shown decreases in D2 receptor binding of 11–15% in cocaine-dependent individuals compared to control subjects (Volkow et al. 1993, 1997; Martinez et al. 2004). These results have led to the investigation of whether this decrease is reversible. Only one study has been done to address this question in human subjects and showed that the decrease in D2 receptors persisted in a group of cocaine-dependent subjects who were rescanned after 3 months of inpatient treatment (Volkow et al. 1993). This finding is in agreement with a study in rhesus monkeys, which showed that D2 receptor availability was decreased by 15–20% within 1 week of cocaine selfadministration, and that in some monkeys these decreases persisted for up to 1 year of abstinence (Nader et al. 2006).

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The decrease in D2 receptors was first described in cocaine abusers and was initially thought to result from sustained exposure to cocaine. However, subsequent studies showed a decrease in D2 receptor binding in a number of other addictive behaviors, such as heroin addiction (Wang et al. 1997), alcohol dependence (Hietala et al. 1994; Volkow et al. 1996), methamphetamine abuse (Volkow et al. 2001a, b), and obesity (Wang et al. 2001). As a result, it has been suggested that low D2 receptor availability might serve as a biomarker for addiction in general, and may reflect a reduced sensitivity to naturally occurring reinforcers or a propensity to depend on pharmacological stimulation to experience reward (Volkow et al. 2002a, b, c; Melis et al. 2005). Thus, the question that arises is whether low D2 receptor BP is a risk factor for cocaine dependence, which may be present prior to the onset of dependence. Studies in both rhesus monkeys and human subjects have sought to address this question. In rhesus monkeys exposed to a social hierarchy, social dominance is associated with a higher striatal D2 receptor binding compared to subordinate animals (Grant et al. 1998; Morgan et al. 2002). In addition, in the rhesus monkeys low D2 receptor BP was predictive of greater cocaine self-administration (Morgan et al. 2002). A subsequent study in rhesus monkeys also showed that low D2 receptor binding predicted the choice to selfadminister cocaine, but in this case the differences in binding were independent of social stress (Nader et al. 2006). In human volunteers, imaging studies in nonaddicted participants have investigated the behavioral significance on D2 receptor binding in the context of addiction. One of these reported that the nonaddicted siblings of cocaine abusers had higher D2 receptor binding compared to controls (Volkow et al. 2006a, b). Similar results have been reported in a study of social drinkers, where subjects with a strong family history of alcohol dependence had higher D2 receptor BP in the striatum compared to social drinkers with no family history of alcoholism (Volkow et al. 2006a, b). Since the family history positive subjects would be expected to have a high risk for alcohol dependence, but are not dependent themselves, these findings suggest that increased D2 receptor BP may be protective (Volkow et al. 2006a, b). In another study in human volunteers, high striatal D2 receptor BP in healthy controls was predictive of an unpleasant reaction to the psychostimulant methylphenidate, whereas low D2 binding was associated with a pleasurable experience, suggesting that high D2 receptor binding may confer a resilience to the development of addictive behaviors, whereas low D2 BP may reflect a vulnerability (Volkow et al. 1999a, b, c, 2002a, b, c). However, not all human PET studies show results that are in agreement with this theory, and some have shown no difference in D2 receptor BP in family history positive and negative social drinkers or in the reaction to psychostimulant administration (Martinez et al. 2003; Munro et al. 2006). In addition, while some studies have shown that low D2 receptor binding is associated with a risk for addiction and suggest that this neurobiologic marker might occur prior to the onset of addiction, other studies in nonhuman primates have also shown that chronic exposure to cocaine itself also reduce D2 receptor binding (Farfel et al. 1992; Moore et al. 1998; Nader et al. 2002, 2006).

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9 Imaging Dopamine Release in Cocaine Dependence As described above, PET imaging with [11C]raclopride and a pharmacologic challenge that releases presynaptic dopamine can be used to image changes in the level of endogenous dopamine. Using these methods, Volkow et al. (1997) showed that cocaine dependence is associated with a decrease in [11C]raclopride displacement in the striatum following methylphenidate (0.5 mg kg 1 iv) (Volkow et al. 1997). The results of this study showed that the cocaine-dependent subjects had an average of 9% decrease in [11C]raclopride binding compared to a 21% decrease in healthy controls, suggesting that this disorder is associated with a loss of dopamine transmission. The cocaine-dependent subjects also reported a decrease in the positive effects of the stimulant compared to the controls. Using SPECT and an amphetamine challenge (0.3 mg kg 1 IV), Malison et al. (1999) performed a similar study in cocaine abusers and controls and reported a 1% change in binding in the cocaine abusers compared to a 10% decrease in controls. These studies suggest that cocaine dependence is associated with a decrease in presynaptic dopamine release, and this hypothesis is supported by a PET study that imaged presynaptic dopamine stores in the striatum. Using the levodopa analog 6-[18F]-fluoro-L-DOPA (FDOPA), which provides a measure of presynaptic dopamine activity, Wu et al. (1997) showed that cocaine-dependent subjects who had been abstinent for 11–30 days had lower uptake compared to controls, although this was not seen in subjects who had been abstinent for only 1–10 days. As noted by Wu et al. (1997), the time frame of the decrease in presynaptic dopamine corresponds with the reported peak time of cocaine craving and dysphoria during abstinence, and a higher risk of relapse (Gawin and Kleber 1986; Satel et al. 1991a, b). At the time these imaging studies were performed, the resolution of the PET (and SPECT) scanners that were available only allowed measurement of the striatum as a whole, and the signal from the caudate, putamen, and VST could not be differentiated. However, with higher resolution scanners, the substructures of the striatum may now be measured separately (Drevets et al. 2001; Mawlawi et al. 2001). Using a higher resolution camera, we previously published studies in cocaine-dependent subjects and matched healthy controls investigating both baseline D2 receptor binding and the dopamine transmission using [11C]raclopride and a psychostimulant challenge (amphetamine 0.3 mg kg 1 iv). In these studies, the striatum was subdivided into subregions based on its anatomy, function, and connections to other brain regions, as shown in Fig. 3. Animal studies have shown that dopamine transmission in the nucleus accumbens is most closely correlated with the addictive properties of drugs (Di Chiara and Imperato 1988; Wise 1996), and in higher primates, the nucleus accumbens is part of the VST, which includes the nucleus accumbens, in addition to the ventral caudate and ventral putamen (Lynd-Balta and Haber 1994a, b). The VST (also called the limbic striatum) receives most of its glutamatergic input from the amygdala, hippocampus, orbitofrontal and anterior cingulate cortex (Kunishio and Haber 1994; Lynd-Balta and Haber 1994a, b; Haber et al. 2000). The associative striatum includes the caudate and anterior putamen

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Associative: pre-AC putamen

Associative: pre-AC caudate Limbic: Ventral Striatum

Rostral to Anterior Commissure (AC) - 6mm

Associative: post-AC caudate Sensorimotor: post-AC putamen Caudal to Anterior Commissure (AC) - 3mm

Fig. 3 Subdivisions of the striatum. With greater resolution of PET scanners, it has been possible to measure the signal emitted from the substructures of the striatum. As a result, anatomic markers, including the anterior commissure (AC), are used to divide the striatum into subregions. These include the caudate and putamen rostral to the AC (pre-AC caudate and pre-AC putamen), the caudate and putamen caudal to the AC (post-AC caudate and post-AC putamen), and the ventral striatum which includes the nucleus accumbens. Based on the connectivity of these regions, they have been grouped into the following functional subdivisions: limbic (ventral striatum), associative (pre-AC caudate, post-AC caudate, and pre-AC putamen), and sensorimotor (postAC putamen)

(rostral to the anterior commissure), is largely involved in cognition, and receives excitatory input from the dorsolateral prefrontal cortex and other associative cortices (Haber et al. 2000; Joel and Weiner 2000). The sensorimotor striatum consists of the posterior putamen (caudal to the anterior commissure), which mostly receives input from motor and premotor areas (Haber et al. 2000; Joel and Weiner 2000). More recent studies have imaged baseline D2 receptor binding and stimulantinduced dopamine release separately in the limbic, associative, and sensorimotor striatum. The investigation of baseline D2 receptor binding showed that cocaine dependence was associated with a decrease in all three striatal subdivisions (15% in the limbic and associative striatum and 17% in the sensorimotor striatum) compared to the healthy controls (Martinez et al. 2004). The study that imaged dopamine transmission, using amphetamine as the challenge, showed that cocaine dependence was associated with a marked reduction in [11C]raclopride displacement in each of the functional subregions ( 12% in HC vs 1% in CD for the limbic striatum, 7% for HC and 3% for CD in the associative striatum, and 14% for the HC and 4% for the CD in the sensorimotor striatum) (Martinez et al. 2007a, b). Thus, the results of these studies confirm the findings of the previous studies showing that cocaine dependence is associated with both a decrease in D2 receptor BP and a blunted dopamine response to a psychostimulant

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challenge. In addition, these alterations in D2 binding and dopamine release were similar across the subdivisions of the striatum in cocaine dependence. However, as discussed below, while there were no differences in the neurochemistry across these subregions, there were regional differences in the behavioral correlates of dopamine transmission.

10

Functional Significance of Low D2 Receptor in Cocaine Dependence

Overall, these imaging studies have consistently shown that cocaine dependence is associated with both a reduction in D2 receptor BP and a decrease in dopamine transmission. The next question is what behavioral significance do these findings have? As described above, studies in nonaddicted human subjects have suggested that high levels of D2 receptor binding may be protective against the development of addiction and, in animals, low D2 BP was shown to be associated with a propensity to self-administer cocaine. Our group recently performed a study investigating the correlation between D2 receptor binding and the choice to self-administer cocaine in human cocaine-dependent subjects (Martinez et al. 2004). In this study, cocainedependent subjects were scanned with [11C]raclopride and were shown to have a decrease in D2 receptor availability compared to a group of matched healthy controls. In addition to the PET scans, the cocaine-dependent volunteers underwent cocaine self-administration sessions. Two types of sessions were performed, sample sessions and choice sessions, and each type was performed three times (total of six sessions) with doses of 0, 6, and 12 mg of smoked cocaine. In the sample sessions, the participants self-administered a single dose of smoked cocaine and were asked to rate the subjective effects of cocaine as described previously (Foltin et al. 2003). The three choice sessions began with a response-independent or “priming” dose of cocaine followed by five opportunities to choose between the same dose of cocaine and a $5.00 voucher. Notably, in the sample sessions, the positive effects of the 6-mg dose did not differ from that of the 0-mg dose, whereas the 12-mg dose was rated as having higher positive subjective effects than either 0 or 6 mg. In other words, the 6-mg dose, which is a low dose, was perceived as not differing from placebo. Despite this, in the choice sessions, the 6-mg dose was selfadministered more frequently than the placebo (0 mg). These findings show that the reinforcing effects of drugs of abuse are more complex than simply the pleasurable or euphoric effects they produce, and previous studies have shown similar results. Fischman (1989) studied a group of chronic cocaine abusers presented with a dose of cocaine that was too low to produce subjective effects, yet still chose cocaine over placebo (Fischman 1989) and Lamb et al. (1991) showed that opiate-dependent subjects would work to self-administer a dose of morphine that was indistinguishable from placebo.

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Thus, we investigated the correlation between D2 receptor BP and the results from the self-administration sessions (Martinez et al. 2004). No correlation was seen with either the positive effects of any dose of cocaine nor was there a correlation with the choice to self-administer cocaine. These results show that while low D2 receptor availability is associated with cocaine dependence, this parameter does not predict the choice to self-administer cocaine in human cocaine-dependent subjects. Thus, while studies in monkeys show that low D2 receptor binding is predictive of the choice to self-administer cocaine prior to cocaine exposure, our results show that once addiction is established, D2 receptor binding does not correlate with selfadministration. In addition, while low D2 receptor availability has been shown to correlate with the pleasurable response to psychostimulants in control subjects, this phenomenon does not seem to be occurring in addicted subjects. Taken together, the results of these imaging studies show that low D2 receptor BP may correlate with a positive response to a psychostimulant and serves as a risk factor for cocaine dependence. Of the individuals who become addicted, most would be expected to have lower than average D2 receptor binding. However, within the population of cocaine abusers, BP does not predict drug-seeking behavior.

11

Dopamine Transmission and Cocaine-Seeking Behavior

As described above, PET imaging can be used to measure dopamine transmission in addition to D2 receptor binding. Thus, our group has also investigated the correlation between dopamine release and the choice to self-administer cocaine, using the sessions described in the previous section. Twenty four cocaine-dependent participants and matched healthy controls underwent two scans with [11C]raclopride, under a baseline condition and following 0.3 mg kg 1 iv amphetamine administration. As described, cocaine dependence was associated with a blunted dopamine response compared to the controls. However, this study also showed that, within the cocaine-dependent subjects, blunted dopamine transmission in the VST was predictive of the choice for cocaine over money (Martinez et al. 2007a, b). In other words, within the cocaine-dependent subjects there was a range of dopamine release, and those with the greatest blunting of [11C]raclopride displacement were more likely to choose cocaine over the monetary reinforcer. The self-administration sessions were developed as a laboratory model of relapse and are based on animal studies showing that a priming dose of cocaine reinstates cocaine self-administration (Self et al. 1996; Khroyan et al. 2000; Shaham et al. 2003). Thus, the results of this study suggests that the cocaine-dependent subjects who are the most vulnerable to relapse are those with the lowest presynaptic dopamine function. Thus, two studies have been performed showing that cocaine dependence is associated with a decrease in dopamine release, this one (Martinez et al. 2007a, b) and that of Volkow et al. (1997). Both of these found no correlation between the subjective effects of the psychostimulant administered (methylphenidate in the study of Volkow et al. and amphetamine in our study), cocaine and dopamine release

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measured with PET. In contrast, studies in healthy controls have shown a significant association between psychostimulant-induced euphoria and psychostimulant-induced radiotracer displacement in the striatum (Volkow et al. 1999a, b, c; Drevets et al. 2001; Abi-Dargham et al. 2003; Martinez et al. 2003). In addition, the studies in cocaine dependence reported that these participants experienced less of a positive effect in response to the psychostimulant compared to controls (Volkow et al. 1997; Martinez et al. 2007a, b), again suggesting that there is a significant separation between the positive euphoric effects of drugs of abuse and its reinforcing effects.

12

Sensitization and Chronic Cocaine Exposure

It is striking that three independent studies in human cocaine-dependent subjects have demonstrated a blunted dopamine response to a psychostimulant when much of the preclinical animal literature suggests the opposite: that chronic cocaine exposure should produce an exaggerated dopaminergic response to a stimulant. In the preclinical studies, long-term exposure of an animal to cocaine results in sensitization, which is an enhanced or exaggerated dopamine response to a psychostimulant (Pettit et al. 1990; Kalivas and Duffy 1993; Bradberry 2000; Vezina 2004). Sensitization occurs when an animal chronically administered cocaine (which acutely increases extraneuronal dopamine) undergoes a period of abstinence. Following this period of abstinence, a subsequent dose of a psychostimulant (such as cocaine or amphetamine) results in an exaggerated release of dopamine. In these studies, sensitization has been shown to be long lasting and animals exposed to cocaine have been shown to cross sensitize to amphetamine (i.e., a dose of amphetamine following cocaine exposure also elicits an exaggerated dopamine response) (Pierce and Kalivas 1995). In light of this research, it would be expected that cocaine abusers administered a psychostimulant would show an excess of DA release rather than a blunted effect. The study of Volkow et al. (1997) and our study were performed on participants who had been abusing cocaine for prolonged periods of time and the scans were performed following a period of abstinence (3–6 weeks in the study of Volkow et al. and 14 days in our study), such that sensitization should have been elicited. Therefore, these studies show that chronic cocaine exposure in humans is associated with a decrease rather than an increase in striatal DA transmission, and suggest that sensitization may not be present in humans who have been exposed to cocaine for several years. The reason for this discrepancy between the human and animal studies is not known. In a recent review, Bradberry et al. addressed this issue and concluded that sensitization can be elicited when animals are exposed to a recreational schedule of cocaine, but that a chronic regimen of cocaine self-administration at a higher dose did not produce sensitization (Bradberry 2006). In humans, sensitization has been difficult to demonstrate and has largely been investigated behaviorally. Early studies in chronic stimulant abusers reported that these subjects developed psychosis more readily when reexposed to drug (Sato et al.

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1983; Satel et al. 1991a, b) but more recent studies that measure sensitization (by measuring motor behaviors, vital signs, or subjective effects) have produced mixed results (Rothman et al. 1994; Strakowski et al. 1996; Gorelick and Rothman 1997). However, a recent PET study using the radiotracer [11C]raclopride demonstrated sensitization to amphetamine in nondependent human subjects. In this study, 10 healthy men with limited past exposure to stimulants were administered oral amphetamine (0.3 mg kg 1) on five occasions followed by a period of 14 days of abstinence. In the presensitization condition, amphetamine produced an 18% decrease in raclopride BP, whereas the sensitization dose of amphetamine produced a 28% decrease in the VST. Seven of the subjects returned for PET scans at 1 year, and sensitization was still detected (24% [11C]raclopride displacement). Together, the preclinical and human studies suggest that sensitization can be elicited with limited lifetime exposure. The studies in human cocaine abusers were performed in subjects who had chronic exposure to cocaine. Thus, it can be hypothesized that early on in cocaine use there is a sensitized dopamine response. In the early stages of drug use, the degree of dopamine release correlates with the euphorigenic effects of the drug, as shown in the studies of the control subjects. However, once addiction is established, one of the most significant changes in neurochemistry appears to be a blunted dopaminergic response. A more in-depth review of this topic has been previously published by Narendran and Martinez (2008).

13

Imaging Cue-Induced Craving in Cocaine Dependence

Instead of a pharmacologic challenge to release dopamine, some recent studies have used a sensory stimulus to release dopamine. Two studies have investigated the effect of drug-related cues on [11C]raclopride binding in cocaine dependence (Volkow et al. 2006a, b; Wong et al. 2006), using a video of persons engaged in cocaine use compared to a neutral video (nature scenes). The study of Volkow et al. (2006a, b) showed a decrease in [11C]raclopride binding in the dorsal caudate and putamen following the cocaine video compared to the neutral video, with no effect in the VST (Volkow et al. 2006a, b). Wong et al. showed a decrease in BP in the left anterior putamen in the cocaine subjects who craved cocaine, while no significant change was seen in cocaine abusers who did not crave cocaine (Wong et al. 2006). In both studies, the magnitude of [11C]raclopride displacement correlated with increased craving for cocaine. In these studies, the magnitude of cue-induced [11C]raclopride displacement was low and similar to that seen in our study using IV amphetamine (5% for the video vs 3% with amphetamine in the caudate and 6% for the video vs 1% for amphetamine in the putamen). In addition, Volkow et al. showed that cue-induced changes in dopamine correlated with severity of addiction, such that greater dopamine release in the dorsal striatum correlated with higher scores of severity (Volkow et al. 2006a, b). This finding suggests that dopamine release in response to a cue correlates with craving for drug and might thus correlate with a greater risk of relapse. Our

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data demonstrates that cocaine-dependent subjects with the lowest amphetamineinduced dopamine release are more likely to self-administer cocaine, and thus greater deficits in dopamine release may be indicative of risk for relapse. The reason for this difference is not clear, although it has been suggested that setshifting depends on dopamine transmission in the dorsal striatum and reversal learning is mediated by dopamine in the VST (Voorn et al. 2004). Thus, dopamine transmission in the ventral vs dorsal striatum may play a critical role in relapse.

14

Imaging Dopamine Transmission in Other Addictions

As mentioned above, cocaine dependence is the most studied addiction and fewer studies imaging presynaptic dopamine release have been published in other addictions. For example, methamphetamine abuse has been shown to be associated with dopamine neuronal injury, such that blunted dopamine release would be expected in this setting. Previous studies have shown that this disorder is associated with reduced levels of the dopamine transporter (which serves as a marker for dopamine neuronal integrity), reduced D2 receptors, and a reduction in the Vesicular Monoamine Transporter 2 (a marker for presynaptic stores of dopamine), which provide strong evidence that this addiction is also associated with reduced dopamine transmission (Martinez et al. 2007a, b). Alcohol dependence has also been studied using PET to investigate both baseline D2 receptor binding and presynaptic dopamine release. A number of studies have been performed investigating baseline D2 receptor binding: six of these showed a decrease in D2 receptor BP while two showed no significant difference between alcohol-dependent subjects and healthy controls (Martinez et al. 2007a, b). The studies showing a decrease in D2 receptor BP have shown that this decrease occurs in the striatum measured as a whole in addition to each of the subdivisions of the striatum. The two studies showing no difference between the alcohol-dependent subjects and healthy controls were performed measuring the striatum as a whole only and were performed with SPECT rather than PET, although these differences in imaging methodology are unlikely to explain the differences in the results (Repo et al. 1999; Guardia et al. 2000; Kuikka et al. 2000). Notably, one study has been performed imaging baseline D2 receptors following 1–4 months of abstinence and showed that no recovery of D2 receptors occurred within this time frame (Volkow et al. 2002a, b, c).

14.1

Behavioral Correlates of Low D2 Receptor BP in Alcohol Dependence

Previous studies have investigated the behavioral significance of reduced D2 receptor BP in alcohol dependence. In a seminal study, Heinz et al. showed that low

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D2 receptor BP in the VST is associated with greater alcohol craving and greater cue-induced activation of the medial prefrontal cortex and anterior cingulate using functional magnetic resonance imaging (fMRI) (Heinz et al. 2004). These findings led the authors to hypothesize that dopaminergic dysfunction in the VST may attribute incentive salience to alcohol-associated stimuli, such that alcohol cues elicit craving and excessive activation of the networks associated with attention and behavior control (Heinz et al. 2004).

14.2

Alcohol Dependence and Presynaptic Dopamine

Presynaptic dopamine function in the striatum has been investigated in alcohol dependence using a number of PET imaging methods, including scanning with the radiotracers [18F]DOPA (neuronal uptake of this tracer provides a measure of presynaptic dopamine stores), (+)[18F]dihydrotetrabenazine (labels the type 2 vesicular monoamine transporters of the dopamine vesicles), and [11C]raclopride with an amphetamine challenge. Two studies have been performed using [18F]DOPA; one reported an increase in uptake and the other reported no difference between alcohol-dependent subjects and healthy controls (Tiihonen et al. 1998; Heinz et al. 2005). Tiihonen et al. reported an increase in [18F]DOPA uptake in the putamen and caudate in alcohol-dependent subjects compared to healthy controls, a finding that suggests that alcoholics have increased presynaptic dopamine function (Tiihonen et al. 1998). Alternatively, Heinz et al. showed no difference in [18F] DOPA uptake in alcohol-dependent subjects, although uptake in the putamen negatively correlated with craving for alcohol, suggesting that alcohol-dependent subjects with reduced dopamine stores may be more susceptible to the reinforcing effects of alcohol (Heinz et al. 2005). One study has been performed with the PET radioligand (+)[18F]dihydrotetrabenazine, which provides a measure of presynaptic dopamine vesicles in the striatum, and reported a decrease in the caudate and putamen of alcohol-dependent subjects compared to controls (Gilman et al. 1998). However, levels of VMAT2 were not specifically measured in the VST. Two studies have been performed using [11C]raclopride and a stimulant challenge to investigate dopamine transmission in alcohol dependence. In a study of recently detoxified alcohol-dependent volunteers, our group showed that dopamine transmission was reduced in the VST only in the alcohol-dependent subjects compared to healthy controls: no differences were seen in the associative and sensorimotor striatum between the two groups (Martinez et al. 2005). In a subsequent study, Volkow et al. used [11C]raclopride and methylphenidate to increase dopamine levels and showed that alcohol dependence was associated with a decrease in presynaptic dopamine release in the VST and putamen (Volkow et al. 2007). This study investigated the brain glucose metabolism of the prefrontal with [18F]fluorodeoxyglucose in addition to dopamine transmission, and showed that in controls, but not in alcoholics, metabolism in orbitofrontal cortex was negatively associated with methylphenidate-induced dopamine increases in VST.

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This finding supports the hypothesis that the orbitofrontal cortex modulates the value of a reward by regulating the magnitude of dopamine release in the VST, and that this regulation is disrupted in alcohol dependence (Volkow et al. 2007).

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Imaging Cognitive Deficits in Drug Abuse Thomas Lundqvist

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imaging Cognitive Deficits in Cannabis Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Imaging Cognitive Deficits in Cannabis Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Imaging Cognitive Deficits in Amphetamine, Methamphetamine, MDMA, and Cocaine Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Imaging Cognitive Deficits in Amphetamine and Methamphetamine Users . . . . . . . . 3.3 Imaging Cognitive Deficits in MDMA (3,4-Methylenedioxymethamphetamine, Ecstasy) Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Imaging Cognitive Deficits in Cocaine Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Imaging Cognitive Deficits in Heroin and Methadone use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Imaging Cognitive Deficits in Heroin and Methadone Users . . . . . . . . . . . . . . . . . . . . . . . 5 Imaging Prenatal Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Summary on Prenatal Children Exposed to Drugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Imaging Cognitive Deficits in Children Exposed to Illicit Drugs . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract The neuropsychological network is a complex structure. To identify processes location and network capacity the brain imaging techniques together and in combination with other neuropsychological techniques and the expanding of well elaborated designs provide us with a multidimensional understanding, and contributes to the understanding of each illicit drug’s character, which is of importance in designing of new treatment programs and clinical practice. Cannabis, MDMA, amphetamine, cocaine, and heroin abusers display both acute effects and chronic effects, deficits in attention, memory, and executive functioning. These deficits may last beyond the period of intoxication and cumulate with years of use. T. Lundqvist Drug Addiction Treatment Centre, Lund University hospital, Lund, SE-22185, Sweden e-mail: [email protected]

D.W. Self and J.K. Staley (eds.), Behavioral Neuroscience of Drug Addiction, Current Topics in Behavioral Neurosciences 3, DOI 10.1007/7854_2009_26, # Springer‐Verlag Berlin Heidelberg 2009, published online 18 September 2009

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Cannabis users may recruit an alternative neural network as a compensatory mechanism during performance of tasks of attention. There is some evidence indicating the detrimental effects of cannabis on the maturing adolescent brain. Stimulant dependence is characterized by a distributed alteration of functional activation. Attenuated anterior and posterior cingulate activation, reduced inferior frontal and dorsolateral prefrontal cortex activation, and altered posterior parietal activation point towards an inadequate demand-specific processing of information. On an individual level they exhibit process-related brain activation differences that are consistent with a shift from context-specific, effortful processing to more stereotyped, habitual response generation. Finally, opiate use appears to decrease the ability to shift cognitive set and inhibit inappropriate response tendencies. Keywords Cannabis Marijuana Heroin Amphetamine Methamphetamine MDMA Cocaine Methadone Cognitive deficits Chronic drugabuse Prefrontal cortex Residual effects Brain imaging Prenatal exposure Hippocampus Amygdale

Abbreviations 5-HT ACC ADHD BOLD BP CB1 CBF CBV DA DAT DLPFC ECF ERP fMRI MDMA METH MRI MRS OFC PET PFC PVC

Serotonin Anterior cingulate cortex Attention-Deficit/Hyperactivity Disorder Blood-oxygen-level-dependent Binding potential Cannabinoid receptor Cerebral blood flow Cerebral blood volume Dopamine DA transporter Dorsolateral prefrontal cortex Executive cognitive functioning Event-related potential Functional MRI 3,4-Methylenedioxymethamphetamine, ecstasy Methamphetamine Structural magnetic resonance imaging Magnetic resonance spectroscopy Orbitofrontal cortex Positron emission tomography Prefrontal cortex Primary visual cortex

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rCBF SPET SPECT SUR THC VBM WCST

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Regional cerebral blood flow Single photon emission tomography Single photon emission computed tomography Specific uptake ratio Delta-9-tetrahydroxycannabinol Voxel-based morphometry Wisconsin Card Sorting Test

1 Introduction Scientific advances, clinical observations, conventional wisdom, and well-reasoned theoretical mechanisms have provided information on drugs’ neurobiological effects, helped to explain the causes and mechanisms of vulnerability to drug abuse, and yielded important insights into abusers’ subjective experiences and behaviors, including their struggles in recovery. The application of advanced neuroimaging techniques has allowed important advances to be made in research being conducted on why stable disorders are produced in the brain mechanisms responsible for the cognitive processes and on determining exactly what mechanisms drugs of abuse are involved in. There are five primary brain imaging techniques which reveal different aspects of brain structure or function (Fowler et al. 2007): 1. Structural magnetic resonance imaging (MRI) provides information on the location, shapes, and sizes of the brain’s various regions and subregions. 2. Functional MRI (fMRI) detects changes in the local magnetic field that occur as a result of changes in the ratio of oxygenated to deoxygenated hemoglobin in arterial blood vessels in specific brain regions during a cognitive task. Researchers read images as maps of cellular activity levels in cross section or area of the brain. 3. Magnetic resonance spectroscopy (MRS) can be used to detect and measure important chemical contents within the brain. To be visible in an MRS image, a chemical must respond in a unique way to magnetization and energy stimulation, and it must be present in relatively high concentrations (in millimolar range). It reveals the location and concentrations of target chemicals in the brain tissue. 4. Positron emission tomography (PET). 5. Single photon emission computed tomography (SPECT). Both PET and SPECT are called “nuclear medicine techniques” because they require injecting molecules labeled with radioactive isotopes into the bloodstream of the person being studied. They make it possible to study the drug’s effects on key components of cell to cell communication, including cell receptors, transporters, and enzymes involved in the synthesis or metabolism of neurotransmitters. For further information about the different techniques the interested reader is referred to Fowler et al. (2007). To reduce the scope of this review the focus will be on the abuse of cannabis, amphetamine, and methamphetamine (METH), 3,4-methylenedioxymethamphetamine or ecstasy (MDMA), cocaine, and heroin.

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Further, the review focuses on studies providing a picture of the state of the art with regard to cognitive deficits of attention, memory, and executive functions. Finally, it will also have the focus on data that have likely clinical implications.

2 Imaging Cognitive Deficits in Cannabis Users 2.1

Summary

Both neuropsychological assessment studies and studies based on brain imaging techniques indicate deficits in attention, memory, and executive functioning induced by cannabis. Acute neuropsychological effects (within 12–24 h) include deficits in attention, executive functioning, and short-term memory (Pope et al. 1995; O’Leary et al. 2002). Some studies indicate long-term effects (after 24 h to 28 days) on short-memory and attention (Schwartz et al. 1989; Pope et al. 2001; Bolla et al. 2002; Eldreth et al. 2004) and motor function (Pillay et al. 2008). Solowij et al. (2002) and Yu¨cel et al. (2008) found that these deficits may last beyond the period of intoxication and cumulate with years of use. This is a recently developed field of investigation. Thus, future research may reveal more subtle deficits in neurocognitive functioning as the assessments process improves. Other interesting areas for future research are reported by Eldreth et al. (2004), Kanayama et al. (2004), and Yurgelun-Todd et al. (1999), hypothesizing that marijuana user may recruit an alternative neural network as a compensatory mechanism during performance of tasks of attention, except for the visual–spatial working memory, where it is necessary to work harder. Exogenous cannabinoids alter the normative functioning of the endogenous cannabinoid system. There is a trend of a cumulative effect of marijuana over time and that a younger age at onset of use may predispose individuals to structural white matter damage (Arnone et al. 2008). The adolescent brain is susceptible to cannabinoids. The studies by Padula et al. (2007), Tapert et al. (2007), and Jacobsen et al. (2007) indicate that cannabis use during the teenage years will have consequences in the adulthood. The neuropsychological network is a complex structure and to identify process in this network includes mapping the areas where the process takes place. Jager et al. (2007) conclude that lower brain activation may not signify neurocognitive impairment, but could be the expression of a non-cognitive variable related to frequent cannabis use; for example, changes in cerebral perfusion or differences in vigilance.

2.2

Imaging Cognitive Deficits in Cannabis Users

Cognitive deficits associated with the acute and chronic use of cannabis have important theoretical and clinical significance and using brain imaging techniques

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may reveal neurotoxic effects of cannabis. Thus, the deficits reflect changes to the underlying cortical, subcortical, and neuromodulatory mechanisms that underpin cognition. Many studies have reported behavioral alterations and cellular effects in connection with cannabis (see reviews in Martin and Cone 1999; Solowij 1999). Neuroimaging data have been derived from studies focusing both on acute marijuana exposure and on chronic abusers, in resting conditions, where the subject is instructed to lie down, relax, and not to think, and in activated conditions with a cognitive challenge paradigm. 2.2.1

Resting Paradigm

Several studies, with different techniques (CBF, PET SPECT, fMRI), have shown subnormal cerebral blood flow (CBF) (Tunving et al. 1985; Mathew et al. 1986; Mathew et al. 1989) or lower cerebellar metabolism (Volkow et al. 1996; Amen and Waugh 1998) in long-term cannabis users who were assessed within one week of cessation of use. Lundqvist et al. (2001) measured brain blood flow levels after cessation of cannabis use (mean 1.6 days). The findings showed significantly lower mean hemispheric blood flow values and significantly lower frontal values in the cannabis subjects, compared to normal controls. Block et al. (2000a) found that after 26 h of controlled abstinence, young frequent marijuana users showed hypoactivity relative to controls in a large region of bilateral posterior cerebellar hemispheres, vermis, and in left and right ventral prefrontal cortex (Brodmann’s area 11). Compared with average whole brain activity in controls, marijuana users showed 9% lower values. Acute exposure to marijuana has resulted in dose related increases in CBF measures among experienced users (Mathew and Wilson 1991; Mathew et al. 1993). In a PET study, Volkow et al. (1991) showed that effects can be individually related. In a subsequent study, Volkow et al. (1996) found, similar to Mathew and Wilson (1991), that besides an increase in the global metabolism, the users also showed regional metabolic increases in orbitofrontal cortex (OFC), prefrontal cortex (PFC), and basal ganglia compared to the normal group. Mathew et al. (1997, 1999) also reported regional flow increases that reached statistical significance in frontal regions, insula, cingulate gyrus, and subcortical regions. Block et al. (1999) found that chronic marijuana use was related to a decrease in memoryrelated activation in users relative to controls. Block et al. (2000b) also used MRI to investigate brain structure in young currently frequent marijuana users. The users showed no evidence of cerebral atrophy or global or regional changes in tissue volumes compared to controls. 2.2.2

Cognitive Challenge Paradigm

Cognition in an everyday situation demands cognitive effort. It is, therefore, necessary to involve studies which have a challenge within their paradigm.

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Yurgelun-Todd et al. (1999) assessed chronic marijuana smokers twice with fMRI, after 24 h and 28 days of abstinence. A visual working memory task with known sensitivity was used as a cognitive challenge paradigm. Cannabis users who completed 24 h of washout showed diminished activation in the dorsolateral prefrontal cortex (DLPFC) during the challenge paradigm, compared to the control subjects. This effect remained diminished after 28 days of washout, although some increase in the DLPFC activation was noted relative to the 24-h time point. In contrast, the cannabis users produced increased activation in the cingulate during both washout conditions, whereas controls did not. These results indicate that even after an extended washout period, specific deviating patterns of cortical activation exist in subjects with a history of heavy marijuana use. Ilan et al. (2004) studied the effects of marijuana on neurophysiological signals of working and episodic memory. The results suggested that marijuana disrupted both sustained and transient attention processes resulting in impaired memory task performance. In subjects most affected by marijuana, a pronounced event-related potential (ERP) difference between previously studied words and new distracter words was also reduced, suggesting disruption of neural mechanisms underlying memory for recent study episodes. Block et al. (2002) measured CBF during the performance of verbal memory recall tasks and during a selective attention task. Memory-related blood flow in frequent marijuana users showed decreases relative to controls in PFC, increases in memory-relevant regions of cerebellum, and altered lateralization in hippocampus. The greatest differences between users and controls occurred in brain activity related to episodic memory encoding, which may infer with the individual subjective history. You do not recall because the event was not coded. O’Leary et al. (2000, 2002) observed increased rCBF after inhalation of cannabis in orbital and mesial frontal lobes, insula, temporal poles, and anterior cingulate cortex (ACC), as well as in the cerebellum. The increases in rCBF in anterior brain regions were predominantly in “paralimbic” regions that may be related to marijuana’s moodrelated effects. Reduced rCBF was observed both during resting as in activated conditions, acutely intoxicated, in brain regions that may be a part of an attentional network (parietal lobe, frontal lobe and thalamus). Reduced rCBF was also observed in temporal lobe auditory regions, and in visual cortex. However, auditory activation paradigm did not show rCBF increases in temporal lobe auditory regions that were significantly different from baseline condition. Additionally, marijuana decreased rCBF in comparison to baseline in brain regions known to be involved in attentional modulation of sensory processing. These findings suggest that it may be possible to isolate the mood-enhancing effects of marijuana rCBF increases in ventral forebrain) from cannabis’ effect on perception, attention, and behavior (decreased rCBF in sensory regions and attention-related brain systems).

2.2.3

Extended Washout Period

Brain imaging studies also discuss how long the washout period is. Loeber and Yurgelun-Todd (1999) postulate that a washout period of 3 days is the minimum

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required in order to show negligible levels of metabolites. In another study, using a challenge paradigm, Yurgelun-Todd et al. (1999) found that even after an extended washout period (28 days), specific differential patterns of cortical activation exist in subjects with a history of heavy marijuana use. In a study on cannabis and motor function using finger-tapping tasks, Pillay et al. (2008) present results suggesting that residual (after 28 days) diminished brain activation is still observed in motor cortical circuits after discontinuing cannabis use. Eldreth et al. (2004) used PET and a modified version of the Stroop task to determine if 25-day abstinent heavy marijuana users have persistent deficits in executive cognitive functioning (ECF) and brain activity. The 25-day abstinent marijuana users showed no deficits in performance compared to controls. Despite the lack of performance differences, the marijuana users showed hypoactivity in the left perigenual ACC and the left lateral prefrontal cortex and hyperactivity in the hippocampus bilaterally. These results suggest that marijuana users display persistent metabolic alterations in brain regions responsible for ECF. It may be that marijuana users recruit an alternative neural network as a compensatory mechanism during performance on a modified version of the Stroop task. Kanayama et al. (2004) found similar result in an fMRI study that heavy long-term cannabis abusers displayed greater and more widespread brain activation than normal subjects attempting to perform a spatial working memory task. This observation suggests that heavy long-term cannabis abusers may experience subtle neurophysiological deficits, and that they compensate for these deficits by “working harder” – calling upon additional brain regions to meet the demands of the task. Sneider et al. (2008) examined changes in regional blood volume in the frontal and temporal lobe, and the cerebellum during 28 days of supervised abstinence from cannabis. Dynamic susceptibility contrast MRI data were collected on longterm cannabis users between 6 and 36 h after the subjects’ last reported cannabis use (Day 0), and again after 7 and 28 days of abstinence. The findings demonstrate that at Day 7, cannabis users continued to display increased blood volumes in the right frontal region, the left and right temporal regions, and the cerebellum. However, after 28 days of abstinence, only the left temporal area and cerebellum showed significantly increased cerebral blood volume (CBV) values in cannabis users. These findings suggest that while CBV levels begin to normalize with continued abstinence from cannabis, specifically in frontal areas, other temporal and cerebellar brain regions show slower CBV decreases. The first brain imaging study focusing on long-term cannabis use and harmful effects on brain tissue were performed by Yu¨cel et al. (2008). The group studied the association of heavy cannabis use with gross anatomical abnormalities in two cannabinoid receptorrich regions of the brain, the hippocampus and the amygdala, using high-resolution (3-T) MRI. The results show that cannabis users had bilaterally reduced hippocampal and amygdala volumes, with a relatively greater magnitude of reduction in the former. Left hemisphere hippocampal volume was inversely associated with cumulative exposure to cannabis during the previous 10 years and subthreshold positive psychotic symptoms. Positive symptom scores were also associated with cumulative exposure to cannabis. Although cannabis users performed significantly worse

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than controls on verbal learning, this did not correlate with regional brain volumes in either group. These results provide new evidence of exposure-related structural abnormalities in the hippocampus and amygdala in long-term heavy cannabis users and corroborate similar findings in the animal literature. The authors conclude that heavy daily cannabis use across protracted periods exerts harmful effects on brain tissue and mental health.

2.2.4

Focus on the Growing Adolescent Brain, Neuroadaptation, Stress, and Dysfunction of Hippocampus and Amygdala

Li et al. (2005) used fMRI to examine whether recent cannabis abuse contributed to stress-induced blood-oxygen-level-dependent (BOLD) contrast in a group of cocaine-dependent individuals. All subjects were abstinent for at least 15 days and drug free as confirmed by urine drug screening before the imaging session. These results provide evidence that recent cannabis abuse is associated with decreased activation in the frontal cortex during an emotional stress task. The results also suggest an abnormal cognitive control mechanism during affective processing in association with heavy cannabis use. Phan et al. (2008) used fMRI and a well validated task to probe amygdala responses to threat signals in recreational cannabis users after a double-blind crossover administration of delta-9tetrahydroxycannabinol (THC) or placebo. They found that THC significantly reduced amygdala reactivity to social signals of threat, but did not affect activity in primary visual and motor cortex. Nestor et al. (2008) designed two experiments. Experiment 1 involved a face-name task, previously shown to activate the hippocampal region. Based on the results of experiment 1, experiment 2 used fMRI and a modified version of the face-name task to examine cortical and (para)hippocampal activity during learning and recall. Results showed that cannabis users had significantly higher BOLD activity in the right parahippocampal gyrus during learning. Hypoactivity in frontal and temporal cortices and relative hyperactivity in the parahippocampus identify functional deficits and compensatory processes in cannabis users. Padula et al. (2007) collected fMRI data from adolescent marijuana users aged 16–18 years after 28 days’ monitored abstinence as participants performed a spatial working memory task. Marijuana users showed differences in brain response to a spatial working memory task despite adequate performance, suggesting a different approach to the task via altered neural pathways. Tapert et al. (2007) used fMRI to study inhibitory processing in abstinent adolescent marijuana users. Histories of marijuana use were compared with BOLD response to a go/no-go task during fMRI after 28 days of monitored abstinence. They concluded that adolescent marijuana users relative to non-users showed increased brain processing effort during an inhibition task in the presence of similar task performance, even after 28 days of abstinence. Thus, increased brain processing effort to achieve inhibition may predate the onset of regular use or result from it. Chang et al. (2006) studied the reversibility of cognitive deficits (attention and memory) to assess if chronic

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marijuana use does alter or does not alter cortical networks, or that although such changes occur the brain adapts to the drug-induced changes. BOLD fMRI was performed in chronic marijuana users (abstinent user and active user) during a set of visual-attention tasks with graded levels of difficulty. The BOLD signals in the right frontal and medial cerebellar regions normalized with duration of abstinence in the abstinent users. Active marijuana users, with positive urine tests for THC, showed greater activation in the frontal and medial cerebellar regions than abstinent marijuana users and greater usage of the reserve network (regions with load effect), suggesting a neuroadaptive state. Both earlier age of first use and greater estimated cumulative dose of THC exposure were related to lower BOLD signals in the right prefrontal region and medial cerebellum. The altered BOLD activation pattern in the attention network and hypoactivation of the cerebellum suggest neuroadaptive processes or alteration of brain development in chronic marijuana users. These changes also may be related to marijuana-induced alteration in resting CBV/flow or downregulation of cannabinoid (CB1) receptors. The greater activation in the active compared with abstinent marijuana users demonstrates a neuroadaptive state in the setting of active marijuana use, while the long-term chronic effect of marijuana on the altered brain network may be reversible with prolonged abstinence. Cannabis is typically consumed in the context of ongoing tobacco use. Jacobsen et al. (2007) focused on the possible interacting effects of these drugs on brain function and cognition during adolescent development. Verbal learning and memory were assessed in adolescent users of tobacco and cannabis. They used fMRI to examine brain function and functional connectivity while a subset of these subjects performed a verbal working memory task. They found that delayed recall of verbal stimuli deteriorated during nicotine withdrawal among cannabis users. During high verbal working memory load, nicotine withdrawal selectively increased taskrelated activation of posterior cortical regions and was associated with disruption of frontoparietal connectivity in adolescent cannabis users relative to comparison subjects. These observations suggest that cannabis use during adolescent development may disrupt neurocircuitry supporting verbal memory formation and that deficits associated with disruption of these neurocircuits are unmasked during nicotine withdrawal.

2.2.5

Associative Memory

The neuropsychological network is a complex structure and to identify the processes in this network includes mapping the areas where the process takes place. Jager et al. (2007) studied non-acute effects of frequent cannabis use on hippocampus-dependent associative memory, investigated with fMRI in frequent cannabis users. Structural changes in the (para)hippocampal region were measured using voxel-based morphometry (VBM). Cannabis users displayed lower activation than non-users in brain regions involved in associative learning, particularly in the (para)hippocampal regions and the right DLPFC, despite normal performance. VBM-analysis of the (para)hippocampal regions revealed no differences in brain

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tissue composition between cannabis users and non-users. No relation was found between (para)hippocampal tissue composition and the magnitude of brain activity in the (para)hippocampal area. Therefore, lower brain activation may not signify neurocognitive impairment, but could be the expression of a non-cognitive variable related to frequent cannabis use; for example, changes in cerebral perfusion or differences in vigilance. Sneider et al. (2006) examined differences in relative CBV in focal regions of interest, including the frontal lobe, the temporal lobe, and the cerebellum, during a period of supervised abstinence from cannabis. Cannabis users demonstrated significantly increased CBV in the right frontal area, in the left temporal area, and in the cerebellum relative to comparison subjects. Among the cannabis users, there were no significant correlations between regional CBV and either total lifetime episodes of smoking or urinary THC concentrations. Further studies beyond 6–36 h of abstinence from cannabis are necessary to determine whether increased CBV values persist for several weeks or eventually normalize. Jager et al. (2006) assessed brain function in frequent but relatively moderate cannabis users in the domains of working memory and selective attention. They used fMRI to examine verbal working memory and visuo-auditory selective attention in frequent cannabis users (after 1 week of abstinence). Cannabis users and controls performed equally well during the working memory task and the selective attention task. Furthermore, cannabis users did not differ from controls in terms of overall patterns of brain activity in the regions involved in these cognitive functions. However, for working memory, a more specific region-of-interest analysis showed that, in comparison to the controls, cannabis users displayed a significant alteration in brain activity in the left superior parietal cortex. They concluded that no evidence was found for long-term deficits in working memory and selective attention in frequent cannabis users after 1 week of abstinence. Nonetheless, frequent cannabis use may affect brain function, as indicated by altered neurophysiological dynamics in the left superior parietal cortex during working memory processing.

3 Imaging Cognitive Deficits in Amphetamine, Methamphetamine, MDMA, and Cocaine Users 3.1

Summary

Aron and Paulus (2007) reviewed all publications in PubMed that conducted comparison studies between healthy volunteers and cocaine-, amphetamine-, or METH-dependent individuals using fMRI. The purpose of that review was to summarize the neural substrate dysfunctions and disrupted cognitive, affective, and experiential processes observed in METH and cocaine-dependent individuals. They found that stimulant dependence is characterized by a distributed alteration of functional activation to a number of experimental paradigms. Attenuated anterior

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and posterior cingulate activation, reduced inferior frontal and DLPFC activation, and altered posterior parietal activation point towards an inadequate demandspecific processing of information. Processes reported most consistently to be deficient in these functional neuroimaging studies include inhibitory control, executive functioning, and decision making. One emerging theme is that stimulant-dependent individuals show specific, rather than generic, brain activation differences; i.e., instead of showing more or less brain activation regardless of task, they exhibit process-related brain activation differences that are consistent with a shift from context-specific, effortful processing to more stereotyped, habitual response generation.

3.1.1

Amphetamine and Methamphetamine

Neuroimaging studies on stimulant users have demonstrated alterations in frontal, temporal, and subcortical brain metabolism (Gouzoulis-Mayfrank et al. 1999; Iyo et al. 1997; Volkow et al. 2001a), changes in brain metabolites suggestive of neuronal injury in the basal ganglia and frontal cortex (Ernst et al. 2000), and decreased density of dopaminergic neurons in the caudate and putamen (McCann et al. 1998; Sekine et al. 2001; Volkow et al. 2001b). The dopamine transporter (DAT) increases with abstinence could indicate that METH-induced DAT loss reflects temporary adaptive changes (i.e., downregulation). During the first month of abstinence this downregulation could be a mask of the residual drug, that is blocking the measurement of the transporter in brain and not necessarily a true change in the DAT. Further, that the loss reflects DA terminal damage but that terminals can recover, or that remaining viable terminals increase synaptic arborization (Volkow et al. 2001b). Because neuropsychological tests did not improve to the same extent, this suggests that the increase of the DATs was not sufficient for complete function recovery. Recent investigations have documented deficits in learning, delayed recall, processing speed, and working memory (Rippeth et al. 2004; Simon et al. 2000) and the psychiatric symptoms in METH users may be attributable to the decrease in DAT density in the OFC and the DLPFC (Sekine et al. 2003). Neuroimaging studies demonstrate abnormalities in brain structure and chemistry convincingly in individuals who used METH and in children with prenatal METH exposure, especially in the striatum (Chang et al. 2007).

3.1.2

MDMA

MDMA users are suggested to exhibit difficulties in coding information into longterm memory to display impaired verbal learning, and to be more easily distracted, and less efficient at focusing attention on complex tasks (McCardle et al. 2004). A converging result in the MDMA studies is that the degree of executive impairment increases with the severity of use, and that the impairments are relatively lasting over time. Some studies indicate sex differences (Reneman et al. 2001; Von Geusau

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et al. 2004), in that women might be more susceptible than men, and that men performed significantly worse on the tasks that tap on cognitive flexibility and on the combined executive function tasks. In male users, cognitive flexibility was impaired and increased perseverative behavior was observed. The inability to adjust behavior rapidly and flexibly may have repercussions for daily life activities. 3.1.3

Cocaine

Cocaine induces constriction of coronary and cerebral vessels in both humans and in animal models (Vitullo et al. 1989; Flores et al. 1990), reflecting the ability to use cognitive capacity. Several studies demonstrate both deficits in neuropsychological performance and abnormalities in brain perfusion or metabolism in chronic cocaine abusers and that both of these flow deficits can improve during abstinence (Holman et al. 1993; O’Malley et al. 1992). Several studies have reported impaired cognitive function in stimulant (cocaine) abusers (Washton and Tatarsky 1984; Ardila et al. 1991; Mittenberg and Motta 1993). Mittenberg and Motta (1993) found significant residual impairment in verbal learning efficiency subsequent to chronic cocaine use that result from memory storage difficulties rather than attentional impairment or general intellectual reduction. Chronic cocaine users display impaired attention, learning, memory, reaction time, and cognitive flexibility. Shortly after cessation, patients (Hoff et al. 1996) display impairment on measures of spatial but not verbal memory and cognitive flexibility. Roselli and Ardila (1996) reported significant correlations between the chronicity of use of cocaine and other drugs and moderate executive performance deteriorations, evaluated by the Wisconsin Card Sorting Test (WCST). Murphy et al. (2006) found that drug groups may be compared with control subjects using event-related fMRI without the need for any post hoc procedures to correct for possible drug-induced cardiovascular alterations. Thus, fMRI activation differences reported between these drug groups can be more confidently interpreted as reflecting neuronal differences. In a review, Verdejo-Garcı´a et al. (2007) conclude that drug abusers present significant alterations in extensive areas of the cortex (especially in the frontal and temporal cortex), subcortex (amygdala, hippocampus and insular cortex), and basal regions (striatum). These alterations are associated with abnormal patterns of activation during cognitive memory tasks, inhibition and decision-making. Cocaine abusers present more pronounced and generalized alterations in the brain. By combining neuropsychological and neuroimaging findings, it is has been shown that the motivational, memory, and executive control processes can play a key role in rehabilitating drug addicts.

3.2

Imaging Cognitive Deficits in Amphetamine and Methamphetamine Users

Volkow et al. (2001b) assessed the effects of protracted abstinence compared to the loss of DATs in striatum, in METH abusers using PET. Brain DATs in five METH

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abusers evaluated during short abstinence (<6 months) and then retested during protracted abstinence (12–17 months) showed significant increases with protracted abstinence. The DAT increase with abstinence could indicate that METH-induced DAT loss reflects temporary adaptive changes (i.e., downregulation), that the loss reflects DA terminal damage but that terminals can recover, or that remaining viable terminals increase synaptic arborization. Because neuropsychological tests did not improve to the same extent, this suggests that the increase of the DATs was not sufficient for complete functional recovery. Few studies have explicitly attempted to examine the cognitive functioning of METH users, but recent investigations have documented deficits in learning, delayed recall, processing speed, and working memory (Rippeth et al. 2004; Simon et al. 2000). Studies in abstinent METH users have demonstrated reductions in DAT binding potential (BP), as well as cognitive and motor deficit. However, it is not yet clear whether cognitive deficits and brain DAT reductions fully reverse with sustained abstinence, or whether behavioral deficits in METH users are related to DA deficits. McCann et al. (2008) conducted a study to further investigate potential persistent psychomotor deficits secondary to METH abuse, and their relationship to brain DAT availability, as measured using quantitative PET methods with [(11)C]WIN 35428. The subjects underwent psychometric testing to test the hypothesis that METH users would demonstrate selective deficits in neuropsychiatric domains known to involve DA neurons (e.g., working memory, executive function, motor function). A subset of subjects also underwent PET scanning with [(11)C]WIN 35428. METH users were found to have modest deficits in short-term memory, executive function, and manual dexterity. Exploratory correlational analyses revealed that deficits in memory, but not those in executive or motor function, were associated with decreases in striatal DAT BP. These results suggest a possible relationship between DAT BP and memory deficits in abstinent METH users, and lend support to the notion that METH produces lasting effects on central DA mechanisms in human. Marijuana is the most common secondary drug of abuse among METH users (Simon et al. 2000). Gonzales et al. (2004) measured neurocognitive performance of METH users discordant for history of marijuana exposure. A comprehensive neuropsychological battery was administered and performance was quantified for five cognitive ability areas. A group using both METH and marijuana demonstrated the greatest neuropsychological impairment, with statistically significant differences observed between the METH users only and control group in learning, retention/retrieval, and a summary score of global neuropsychological performance. The association between level of DA D2 receptors and metabolism in the OFC in METH abusers suggests that D2 receptor-mediated dysregulation of the OFC could underlie a common mechanism for loss of control and compulsive drug intake in drug-addicted subjects (Volkow et al. 2001a). Further, chronic METH use may cause DAT reduction in the OFC, DLPFC, and amygdala in the brain. Thus, psychiatric symptoms in METH users may be attributable to the decrease in DAT density in the OFC and the DLPFC (Sekine et al. 2003).

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Salo et al. (2007) used a computerized measure of selective attention and singlevoxel proton MRS. They examined the relationship between attentional control and brain metabolite levels in the ACC and primary visual cortex in currently abstinent METH abusers. The METH abusers exhibited reduced attentional control (i.e., increased Stroop interference) compared to the controls. Changes in neurochemicals within frontostriatal brain regions including ACC may contribute to deficits in attentional control among chronic METH abusers. Chou et al. (2007) examined the change of DAT binding in METH abusers over a 2-week period of abstinence and its association with cognitive function. At baseline conditions, the values of specific uptake ratio (SUR) of DAT binding measured by SPECT were lower in METH abusers than in controls. After a 2-week period of abstinence, DAT binding was partially recovered and there were no statistic differences in SUR between METH abusers and controls. There was a borderline correlation between the changes of DAT binding in the right, but not the left, striatum, and the %Error of WCST. These findings indicate that DAT binding in METH abusers can be reversed in a short period of abstinence. The recovery of DAT binding was asymmetric and possibly parallel with the improvement of cognitive function. Tipper et al. (2005) looked at working memory performance by measuring the efficiency of cortical processing in brain regions associated with working memory using BOLD fMRI with healthy participants. An inverted U-shaped relationship was observed between the amount of D-amph administered and working memory processing efficiency. This relationship was specific to brain areas functionally defined as working memory regions and to the encoding/maintenance phase (as opposed to the response phase) of the task. The results are consistent with the hypothesis that the neurochemical effects of amphetamine modulate the efficiency of a verbal working memory system. Since language dominance is related to motor dominance, amphetamine might also affect cerebral dominance for language. Sommer et al. (2006) tested this hypothesis, in study where language activation was measured twice with fMRI in a double-blind crossover design 2 h after amphetamine or placebo administration. Language-related activation increased significantly in task-related areas, but the individual lateralization index was not affected in the amphetamine condition as compared to placebo. This finding suggests that short-termed alterations in the dopaminergic neurotransmission do not affect language dominance. In a review, Baicy and London (2007) found that brain imaging studies have revealed differences in the brains of research participants who have used METH chronically and then abstained from taking the drug. These abnormalities are prominent in cortical and limbic systems, and include deficits in markers of dopaminergic and serotonergic neurotransmitter systems, differences in glucose metabolism and deficits in gray matter. These abnormalities accompany cognitive deficits, including evidence of impaired inhibitory control. Cortical deficits in abstinent METH abusers can affect a wide range of functions that can be important for success in maintaining drug abstinence. These include but are not limited to modulation of responses to environmental stimuli as well as internal triggers

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that can lead to craving and relapse. Potential therapies may combine behavioral approaches with medications that can improve cognitive control.

3.3

Imaging Cognitive Deficits in MDMA (3,4Methylenedioxymethamphetamine, Ecstasy) Users

Among the designer drugs, the amphetamine analogues are the most popular and extensively studied, ecstasy in particular. They are used recreationally with increasing popularity despite animal studies showing neurotoxic effects to serotonin (5-HT) and/or DA neurons. Most of these studies provide suggestive evidence that MDMA is neurotoxic to 5-HT neurones, and (meth)amphetamine to DA neurones in humans. These effects seem to be dose-related, leading to functional impairments such as memory loss, and are reversible in several brain regions. Reneman et al. (2001) found in a PET-study an indication that heavy use of MDMA is associated with neurotoxic effects on 5-HT neurons that women might be more susceptible than men, and that MDMA-induced neurotoxic changes in several brain regions of female ex-MDMA users are reversible. McCardle et al. (2004) suggests that MDMA users exhibit difficulties in coding information into long-term memory, display impaired verbal learning, are more easily distracted, and are less efficient at focusing attention on complex tasks. Parrott and Lasky (1998) assessed the effects upon mood and cognition, before, during, and after a Saturday night dance. Three groups of young people were compared. Each subject completed a cognitive test and mood scale battery four times: an initial drug-free baseline, at a Saturday night dance/club (on-drug), then 2 days later, and 7 days later. The consumption of cannabis and cocaine at the club was similar across groups. However, 2 days afterwards, cognitive performance on both tasks (verbal recall, visual scanning) was significantly reduced on MDMA. Memory recall was also significantly impaired in drug-free MDMA users, with regular ecstasy users displaying the worst memory scores at every test session. This agrees with previous findings of memory impairments in drug-free ecstasy users. Von Geusau et al. (2004) found that male MDMA users performed significantly worse on the tasks that tap on cognitive flexibility and on the combined executive function tasks. Female users showed no impairments on any of the tasks. The authors conclude that the data suggest that a history of MDMA use selectively impairs ECF. In male users, cognitive flexibility was impaired and increased perseverative behavior was observed. The inability to adjust behavior rapidly and flexibly may have repercussions for daily life activities over an extended period. Jager et al. (2008) investigated specific effects of ecstasy on working memory, attention, and associative memory, using fMRI. Neurocognitive brain function in three domains – working memory, attention, and associative memory – was assessed with performance measures and fMRI. Use of ecstasy had no effect on working memory and attention, but drug use was associated with reduced associative memory performance. Multiple regression analysis showed that associative memory

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performance was affected by amphetamine much more than by ecstasy. Both drugs affected associative memory-related brain activity, but the effects were consistently in opposite directions, suggesting that different mechanisms are at play. The authors proposed that this could be related to the different neurotransmitter systems these drugs predominantly act upon, that is, 5-HT (ecstasy) vs. DA (amphetamine) systems.

3.4 3.4.1

Imaging Cognitive Deficits in Cocaine Users Neuropsychological Findings

Several studies have reported impaired cognitive function in stimulant (cocaine) abusers. Ardila et al. (1991) gave a basic neuropsychological assessment battery to chronic freebase cocaine (“crack”) abusers. In general, performance was lower than expected according to their age and educational level. Subjects showed significant impairment in short-term verbal memory and attention subtests. Neuropsychological test scores were correlated with lifetime amount of cocaine used, suggesting a direct relationship between cocaine abuse and cognitive impairment. A pattern of cognitive decline is proposed. O’Malley et al. (1992) found mild but significant impairments in tests of attention and memory in heavy cocaine abusers. These subjects also performed poorly on the Category test, but surprisingly were superior in verbal fluency tests. Shortly after cessation, patients (Hoff et al. 1996) display impairment on measures of spatial but not verbal memory, and cognitive flexibility. 3.4.2

Imaging Findings

In two studies, Tomasi et al. (2007a, b) report evidence of abnormalities in thalamocortical responses in cocaine abusers that are likely to contribute to the impairments in sensory processing and in attention, and of impaired function of regions involved with executive control, attention, and vigilance in cocaine abusers. Hurt et al. (2008) assessed the effect of prenatal cocaine exposure on the PFC with fMRI. In this sample of adolescents, participants who were exposed to cocaine and participants not exposed were similar in performance on an ECF task and in fMRI activation patterns during task performance. 3.4.3

Therapy Issues

Several studies have focused on therapy issues. Cognitive behavioral and related therapies for cocaine dependence may exert their effects, in part, by enhancing cognitive control over drug use behavior. Brewer et al. (2008) examined the neural correlates of cognitive control as related to treatment outcomes for cocaine dependence. Treatment-seeking cocaine-dependent individuals performed a Stroop color-word interference task while undergoing fMRI prior to initiating treatment.

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During Stroop performance, individuals activated brain regions similar to those reported in nonaddicted individuals, including the ACC, DLPFC, parietal lobule, insula, and striatum. Activations at treatment onset correlated differentially with specific outcomes: longer duration of self-reported abstinence correlated with activation of ventromedial PFC, left posterior cingulate cortex, and right striatum; percent drug-free urine screens correlated with striatal activation; and treatment retention correlated with diminished activation of DLPFC. A modest correlation between Stroop effect and treatment retention was found. The functions of specific brain regions underlying cognitive control relate differentially to discrete outcomes for the treatment of cocaine dependence. These findings implicate neurocircuitry underlying cognitive control in behavioral treatment outcome and provide insight into the mechanisms of behavioral therapies for cocaine dependence. They also suggest that neural activation patterns during cognitive control tasks are more sensitive predictors of treatment response than behavioral measures.

3.4.4

Craving Issues

Imaging studies have shown an association between DA increases in striatum and cue induced craving in cocaine abusers. However, the extent to which DA increases reflect a primary rather than a secondary response to the cues remains unclear. Sinha et al. (2005) tried to identify neural activity associated with stress and stress-induced cocaine craving, to understand the neurobiology of cocaine craving and relapse. BOLD signal changes were assessed in an fMRI session with healthy controls and treatment-engaged, abstinent, cocaine-dependent individuals (patients). They were assessed recalling three personal stress and three personal neutral situations. Patients failed to activate the ACC region, and related circuits during stress, regions associated with control, and regulation of emotion and distress states. Instead, they exhibited greater craving-related activation in the dorsal striatum, a region related to reward pathways and part of the obsessive-compulsive circuitry. Such functional alterations in stress processing may underlie the stress-related vulnerability to cocaine relapse often observed in cocaine-dependent individuals in early recovery. In two studies, Volkow et al. (2006, 2008) looked at craving as the key contributor to relapse. In 2006, they tested whether DA increases follow conditioned stimuli in human subjects addicted to cocaine and whether this is associated with drug craving. They tested cocaine-addicted subjects using PET and [11C]raclopride (DA D2 receptor radioligand sensitive to competition with endogenous DA). The result provided evidence that DA in the dorsal striatum (region implicated in habit learning and in action initiation) is involved in craving and a fundamental component of addiction. Because craving is a key contributor to relapse, strategies aimed at inhibiting DA increases from conditioned responses are likely to be therapeutically beneficial in cocaine addiction. In 2008, the same group (Volkow et al. 2008) evaluated the extent to which DA increases can induce craving in cocaine abusers. Using PET they show that in cocaine abusers oral methylphenidate (20 mg), which significantly increased DA in striatum, did not induce craving unless subjects were

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concomitantly exposed to cocaine cues (video scenes of subjects self-administering cocaine). This suggests that DA increases associated with conditioned cues are not primary responses but rather reflect downstream stimulation of DA cells (presumably glutamatergic afferents from PFC and/or amygdala). Inasmuch as afferent stimulation of DA neurons results in phasic cell firing, these findings suggest that “fast” (burst) DA increases (firing), in contrast to the “slow” DA increases as achieved when using oral methylphenidate (mimicking tonic DA cell firing), are required for cues to trigger craving. The fact that methylphenidate induced craving only when given with the cocaine cues highlights the context dependency of methylphenidate’s effects and suggests that its use for the treatment of ADHD subjects with co-morbid drug abuse should not increase craving. Kufahl et al. (2008) investigated how cocaine expectation modulates human brain responses to acute cocaine administration. Changes in BOLD signals were measured, and online behavioral ratings during cocaine expectation and acute cocaine administration were recorded. The results suggest that cocaine expectation modulates neuralsensitivity adaptation between the expected events and the actual outcome, but did not modulate the pharmacological characteristics of cocaine. In addition, the amygdala-lateral OFC circuitry plays an important role in mediating stimulus– outcome relations and contextual factors of drug abuse.

3.4.5

Stress Issues

Acute stress is probably associated with relapse in cocaine addiction, possibly through the activation of craving-related neural circuitry. Duncan et al. (2007) investigated neural responses to cocaine cues and acute stress in an fMRI study. Their findings suggest that stress may precipitate relapse in cocaine addiction by activating brain areas that mediate reward processing and the attentional and mnemonic bias for drug use reminders. Goldstein et al. (2007a) attempted to examine the brain’s sensitivity to monetary rewards of different magnitude in cocaine abusers and to study its association with motivation and self-control. Subjects performed a forced-choice task under three monetary value conditions while brain activation was measured with fMRI. The findings suggest that in cocaine addiction (1) activation of the corticolimbic reward circuit to gradations of money is altered; (2) the lack of a correlation between objective and subjective measures of state motivation may be indicative of disrupted perception of motivational drive, which could contribute to impairments in self-control; and (3) the lateral PFC modulates trait motivation and deficits in selfcontrol, and a possible underlying mechanism may encompass a breakdown in PFC-OFC communication. In a second study, Goldstein et al. (2007b) studied the PFC core role in monitoring attention and behavioral control especially under novelty, and that neural habituation responses may be modified in drug addiction. Subjects performed an incentive sustained attention task twice, under novelty and after practice, during fMRI. The authors report a differential pattern of neural responses to repeated presentation of an incentive sustained attention task in

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cocaine addiction. The results suggest a disruption in drug addiction of neural habituation to practice that possibly encompasses opponent anterior vs. posterior brain adaptation to the novelty of the experience: overly expeditious for the former but overly protracted for the latter. In a third study, Goldstein et al. (2007c) evaluated the subjective sensitivity to reward gradients and its association with neural responses to sustained reward in cocaine addicted participants. An fMRI task that utilized monetary reward was used as feedback in a blocked design. Results revealed that whereas control subjects valued high money more than low money, over half of the cocaine addicted subjects valued all monetary amounts equally. The results provide evidence of restricted subjective sensitivity to gradients of reward in cocaine addiction and of the involvement of frontolimbic brain regions (including the OFC) in this deficit. 3.4.6

Sex Differences

Different clinical trajectories of cocaine-dependent men and women may be a consequence of distinct neurobiological substrates and that hypoperfusion of the OFC probably is a biological mediator of relapse due to impulsivity or impaired decision-making. Adinoff et al. (2006) assessed rCBF between abstinent cocainedependent men and women and sex-matched healthy controls. Regional CBF appears to be reduced in the bilateral OFC in cocaine-dependent men and in the medial OFC in cocaine-dependent women. Sex differences in the medial and lateral OFC rCBF may be relevant to understanding relapse characteristics differentiating men and women addicted to cocaine. Li et al. (2006) explored the neural correlates underlying distress processing in antisocial personality in cocaine-dependent individuals. Abstinent cocaine-dependent individuals took part in script-guided stress imagery in an fMRI study. They found that the effect size of activity change in the medial PFC is associated with lower socialization score (i.e., greater sociopathy) and with the change in heart rate, but only among female participants. These results highlight important sex differences in the association between antisocial personality and distress processing in cocaine-dependent individuals.

4 Imaging Cognitive Deficits in Heroin and Methadone use 4.1

Summary

In a review, Gruber et al. (2007) proposed that findings to date suggest that the use of opiates has both acute and long-term effects on cognitive performance. Neuropsychological data indicate deficits in attention, concentration, recall, visuospatial skills, and psychomotor speed with both acute and chronic opioid use. The longterm effects of opiate use appear to have the greatest impact on ECF, including the ability to shift cognitive set and inhibit inappropriate response tendencies.

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Imaging Cognitive Deficits in Heroin and Methadone Users Heroin

Neuropsychological assessment in heroin use indicates that heroin addiction has a negative effect on impulse control, while attention and mental flexibility/abstract reasoning ability were not affected (Pau et al. 2002). Subtle impulse control difficulty may appear as a result of 5 years of heroin use. Other cognitive skills studied, including attention and mental flexibility/abstract reasoning, appeared to be unaffected (Davis et al. 2002). Franken et al. (2003) investigated heroin-related visual information processing by employing ERPs. The authors conclude that processing of drug-related information is abnormal in heroin dependent patients. Heavy long-term user has been shown to be associated with greater likelihood of neuropsychological impairment as assessed by at battery including WAIS, aphasia tests, and the Halstead battery (Grant et al. 1977). Hill et al. (1979), who studied opiate abusers with an almost exclusive drug preference for heroin, found impairment of Tactual Performance for memory and location and Tapping Tests, but not on the Category Test, a measure of abstract reasoning ability. The authors concluded that, since performance in the Category Test is thought to be related to damage to the frontal lobes, this brain region may be less affected by opiate abuse. This conclusion is supported to some extent by results from studies that have failed to detect a difference between opiate users and controls on other measures of neuropsychological functioning believed to correlate with frontal lobe damage; for example, abstract thinking (Bruhn and Maage 1975) or verbal fluency (Rounsaville et al. 1982). An interesting study was performed by Ornstein et al. (2000) comparing groups of subjects whose primary drug of abuse was amphetamine or heroin, together with age- and IQ-matched control subjects. The study consisted of a neuropsychological test battery which included both conventional tests and also computerized tests of recognition memory, spatial working memory, planning, sequence generation, visual discrimination learning, and attentional set-shifting. Many of these tests have previously been shown to be sensitive to cortical damage (including selective lesions of the temporal or frontal lobes) and to cognitive deficits in dementia, basal ganglia disease, and neuropsychiatric disorder. Qualitative differences, as well as some commonalities, were found in the profile of cognitive impairment between the two groups. The chronic amphetamine abusers were significantly impaired in performance on the extra-dimensional shift task (a core component of the WCST) whereas in contrast, the heroin abusers were impaired in learning the normally easier intradimensional shift component. Both groups were impaired in some of tests of spatial working memory. However, the amphetamine group, unlike the heroin group, was not deficient in an index of strategic performance on this test. The heroin group failed to show significant improvement between two blocks of a sequence generation task after training and additionally exhibited more perseverative behavior on

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this task. The two groups were profoundly, but equivalently impaired on a test of pattern recognition memory sensitive to temporal lobe dysfunction. These results indicate that chronic drug use may lead to distinct patterns of substance-specific cognitive impairment that may be associated with dysfunction of different components of cortico-striatal circuitry. Although opioid receptor function in humans is clearly downregulated during opioid dependence, the receptor situation in early abstinence is not understood. Williams et al. (2007) sought to examine changes in opioid receptor availability in early abstinence using [11C]-diprenorphine PET. Compared to controls, participants with opioid dependence had increased [11C]-diprenorphine binding in the whole brain and in 15 of the 21 a priori regions studied. The result suggests that opioid receptor binding is increased throughout the brain in early abstinence from dependent opioid use. These data are in line with findings in cocaine and alcohol dependence. Kosel et al. (2008) examined acute effects of intravenous diacetylmorphine (heroin) administration. The results indicate that the cerebellum is an important component in functional brain systems subserving sensory and motor integration, learning, modulation of affect, motivation, and social behavior, which all play important roles in reinforcing properties of opioids. Botelho et al. (2006) studied the profile of vascular alterations in heroin addicts before and, in one of them, 10 weeks after an ultra-rapid heroin detoxification, using the functional technique of SPET. The result revealed decreased perfusion in heroin addicts in regions involved in the control of attention, motor speed, memory, and visual– spatial processing. A reduction in regional perfusion may reflect ongoing subtle neurocognitive deficits, which are consistent with the maintenance of asymmetry of the basal nuclei.

4.2.2

Methadone

Environmental drug-related cues have been implicated as a cause of illicit heroin use during methadone maintenance treatment of heroin dependence. Langleben et al. (2008) tried to identify the functional neuroanatomy of the brain response to visual heroin-related stimuli in methadone maintenance patients, using eventrelated fMRI. Brain responses to heroin-related stimuli and matched neutral stimuli in patients were compared. Patients were studied both before and after administration of their regular daily methadone dose. The findings suggest that the medial PFC and the extended limbic system in methadone maintenance patients with a history of heroin dependence remains responsive to salient drug cues, which suggests a continued vulnerability to relapse. Vulnerability may be highest at the end of the 24-hour interdose interval. Chronic opiate craving and anhedonia have been associated with low availability of DA D2 receptors (D2Rs) and cue-elicited craving has been linked with endogenous DA release. Zijlstra et al. (2008) studied D2R availability and cue-elicited endogenous DA release in abstinent opiate-dependent males with [123I]IBZM SPECT. Craving was manipulated with a video containing heroin-related stimuli. Opiate-dependent subjects demonstrated higher DA release

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after cue-exposure in the right putamen than controls. Chronic craving and anhedonia were positively correlated with DA release. The researchers concluded that treatment strategies that increase D2Rs may, therefore, be an interesting approach to prevent relapse in opiate addiction. To establish whether chronic opiate use might impair brain DA mechanisms in humans, Shi et al. (2008) assessed DAT uptake function in the striatum (caudate and putamen). They analyzed the correlation between DAT in the striatum and heroin craving and subjective anxiety in former heroin users with prolonged abstinence and in patients receiving methadone maintenance treatment, using PET. Methadone maintenance treatment subjects had reduced DAT uptake function in the bilateral caudate and putamen. Prolonged abstinence subjects showed significantly lower DAT uptake function in the bilateral caudate. Moreover, in comparison to the prolonged abstinence subjects, the methadone maintenance treatment subjects showed significant decrease of DAT uptake in the bilateral putamen, but not in bilateral striatum. However, DAT uptake in the bilateral caudate was significantly correlated with subjective anxiety in methadone maintenance treatment subjects. In conclusion, chronic opioid use induces long-lasting striatum DA neuron impairment, and prolonged withdrawal from opioids can benefit the recovery of impaired DA neurons in the brain. Ersche et al. (2006) assumed that methadone users were less sensitive to punishment on immediately preceding unsuccessful trials. The authors sought to explore this from a neural perspective by performing a post hoc analysis of data from a previous PET study. They found significant over-activation in the lateral OFC in methadone users compared with both heroin users and controls concomitant with the greatest overall tendency to “play risky.” Heroin users showed significant under-activation in this area compared with the other two groups whilst exhibiting the greatest overall tendency to “play safe.”. Correlational analysis revealed that abnormal task-related activation of the left OFC was associated with dose of methadone in methadone users and with duration of intravenous heroin use in heroin users. The findings suggest that the interplay between processes involved in integrating penalty information for the purpose of response selection may be altered in opiate users. This change was reflected differentially in task-related pattern of OFC activation depending on the opiate used.

5 Imaging Prenatal Conditions 5.1

Summary on Prenatal Children Exposed to Drugs

Prenatal marijuana exposure alters neural functioning on visuospatial working memory. Cocaine in utero results in behavioral and neurodevelopmental abnormalities that persist into adulthood.

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269

Imaging Cognitive Deficits in Children Exposed to Illicit Drugs

Smith and co-workers (2006) investigated the long lasting neurophysiological effects of prenatal marijuana exposure on visuospatial working memory in 18– 22-year-olds using fMRI. Multiple regression analyses (including controls with no exposure) revealed that as the amount of prenatal marijuana exposure increased, there was significantly more neural activity in the left inferior and middle frontal gyri, left parahippocampal gyrus, left middle occipital gyrus, and left cerebellum. There was also significantly less activity in right inferior and middle frontal gyri. These results suggest that prenatal marijuana exposure alters neural functioning during visuospatial working memory processing in young adulthood. Walhovd et al. (2007) studied morphometric cerebral characteristics in children with prenatal poly-substance exposure compared to controls. Compared to controls, the substance-exposed children had smaller intracranial and brain volumes, including smaller cerebral cortex, amygdala, accumbens area, putamen, pallidum, brainstem, cerebellar cortex, cerebellar white matter, and inferior lateral ventriclesand thinner cortex of the right AC and lateral OFC. Pallidum and putamen appeared especially reduced in a subgroup exposed to opiates. Only volumes of the right AC, the right lateral OFC, and the accumbens area, showed some association with ability and questionnaire measures. The sample studied is rare and hence small, so conclusions cannot be drawn with certainty. Morphometric group differences were observed, but associations with previous behavioral assessment were generally weak. Some of the volumetric differences, particularly thinner cortex in part of the right lateral OFC, may be moderately involved in cognitive and behavioral difficulties more frequently experienced by opiate and poly-substance-exposed children. Arnold et al. (2008) found that exposure to cocaine in utero results in behavioral and neurodevelopmental abnormalities that persist into adulthood. The authors report a case of focal MR imaging signal-intensity changes in the substantia nigra, locus ceruleus, and other selected nerve tracts and nuclei in a child exposed prenatally to cocaine and other drugs. Rivkin et al. (2008) used volumetric MRI to study brain volumes in 10–14-year-old children with and without intrauterine exposure to cocaine, alcohol, nicotine, or marijuana. The findings suggest that intrauterine exposures to cocaine, alcohol, and nicotine are individually related to reduced head circumference, cortical gray matter, and total parenchymal volumes as measured by MRI at school age. ¨ hman, MD and Professor Emeritus, Lund Acknowledgment I would like to acknowledge Rolf O University, Lund, Sweden for his thorough remarks on the manuscript.

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Neural Markers of Genetic Vulnerability to Drug Addiction Daniel J. Mu¨ller, Olga Likhodi, and Andreas Heinz

Contents 1 2 3 4

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 Heritability and Epidemiology of Nicotine and Alcohol Dependence . . . . . . . . . . . . . . . . . . . 280 Defining Dependence: Clinical Definitions and Complexities in Addiction Studies . . . . . 280 Dysfunction of the Brain Reward System in Alcohol and Nicotine Dependence – Rationales for Potential Genetic Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 5 Strategies of Genetic Analyses in Alcohol and Nicotine Dependence: Principles of Genetic Linkage, Candidate Gene/Whole Genome Association Studies, and Neuroimaging Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 6 Linkage Studies in Alcohol and Nicotine Dependence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 7 Association Studies in Alcohol and Nicotine Dependence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286 8 Genome-Wide Association Studies in Alcohol and Nicotine Dependence . . . . . . . . . . . . . . 289 9 Combined Neuroimaging and Genetics Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 10 Summary and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294

Abstract This chapter will summarize genetics findings derived from various strategies and highlight important neural markers (or correlates) in some specific and extensively studied genes. Most studies highlighted here focus on alcohol and nicotine dependence (AD and ND, respectively). AD and ND are among the most prevalent addictive disorders worldwide, are among the best studied, and are also associated globally with the largest socioeconomic impact. D.J. Mu¨ller and A. Heinz (*) Department of Psychiatry, Clinic for Psychiatry and Psychotherapy, Charite´ University Medicine, Campus Charite´ Mitte, Schumannstrasse 20/21, 10117, Berlin, Germany Neurogenetics Section, Centre for Addiction and Mental Health, Department of Psychiatry University of Toronto, Toronto, ON, Canada e-mail: [email protected] O. Likhodi Neurogenetics Section, Centre for Addiction and Mental Health, Department of Psychiatry, University of Toronto, Toronto, ON, Canada

D.W. Self and J.K. Staley (eds.), Behavioral Neuroscience of Drug Addiction, Current Topics in Behavioral Neurosciences 3, DOI 10.1007/7854_2009_25, # Springer‐Verlag Berlin Heidelberg 2009, published online 18 September 2009

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We describe different mechanisms through which genes can have an impact on the addictive behaviors, distinguishing between the genes that inscribe the proteins affecting the metabolism of the addictive substance (e.g., ADH/ALDH for alcohol or CYP2A6 for nicotine) and genes that code for the brain transmitter systems, such as genes involved in cerebral neurotransmission thought to be involved in addiction (e.g., brain reward system, mood regulation, opioid system). Strategies include linkage analyses, association studies, whole genome association studies as well as intermediate/endophenotype studies. Moreover, some important findings derived from animal studies and from neuroimaging studies are highlighted. In conclusion, we provide the reader with an overview of most important studies related to AD and ND and give an outlook how these findings may become useful and beneficial in the future. Keywords Alcohol dependence Nicotine dependence Genetics Association studies Genome-wide studies Brain reward system

Abbreviations AD ND WHO NIAAA DSM-IV ICD-10 GABA NMDA OPRM1 DNA rSA COGA ADH ALDH DRD2 DRD4 DAT DbH COMT CYP2A6 eCB 5-HTT ADHD MAO-A

Alcohol dependence Nicotine dependence World Health Organization National Institute on Alcohol Abuse and Alcoholism Diagnostic and Statistical Manual of Mental Disorders edition IV International Statistical Classification of Diseases and Related Health Problems Gamma-aminobutyric acid N-methyl-D-aspartic acid Opioid receptor, mu 1 Deoxyribonucleic acid Regions for substance abuse Collaborative Study on the Genetics of Alcoholism Alcohol dehydrogenase genes Aldehyde dehydrogenase genes Dopamine receptor D2 variants Dopamine receptor D4 variants Dopamine transporter Dopamine beta-hydroxylase Catechol-O-methyltransferase Cytochrome P450 2A6 enzyme Endocannabinoid The serotonin transport protein or 5-hydroxy-tryptamine Attention-deficit/hyperactivity disorder Monoamine-oxidase gene A

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Whole genome association study Single nucleotide polymorphism Blood oxygen level-dependent Serotonin transporter polymorphism in the promoter region Magnetic resonance imaging Functional MRI Positron emission tomography Single photon emission computed tomography Diffusion tension imaging Copy number variations

1 Introduction It has long been recognized that addictive behaviors run in families, and therefore a familial and/or genetic component in the etiology of addiction has been postulated. In fact, it is now commonly accepted that genetic factors do confer susceptibility to all major addictions (Kendler et al. 2000; Schumann 2007; Tyndale 2003). Adoption studies that controlled for shared environment among close relatives demonstrated for example that adopted offspring have the same risk of alcohol dependence as their nonadopted siblings, thus confirming a genetic predisposition in alcohol dependence (Ball 2008). Twin studies were able to quantify the genetic contribution (heritability) by comparing rates of alcohol/ substance dependencies among monozygotic and dizygotic twins. At present, estimates of heritability for all major addictive disorders range between 40% and 80% (Goldman et al. 2005). This chapter will summarize genetics findings derived from various strategies and highlight important neural markers (or correlates) in some specific and extensively studied genes. Most studies highlighted here focus on alcohol and nicotine dependence (AD and ND, respectively). AD and ND are among the most prevalent addictive disorders worldwide, are among the best studied, and are also associated globally with the largest socioeconomic impact. Moreover, there is a high comorbidity between AD and ND suggesting a common underlying vulnerability (Dawson et al. 2008; Madden and Heath 2002). It has been hypothesized that alcohol abuse may enhance the reinforcing effects of nicotine and that each drug becomes a pharmacological cue for the expectation of the other (Littleton et al. 2007; Meyerhoff et al. 2006). Furthermore, nicotine may improve cognitive performance compromised by alcohol in chronic alcohol misusers (Ceballos 2006). In summary, both alcohol and nicotine dependence are prominent addictive disorders that frequently co-occur and that may share some overlap in the predisposing neurobiological pathways. Nonetheless, some studies that are presented here refer to other addictions (e.g., illicit drugs) as some studies included various subgroups of addictions in their research.

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2 Heritability and Epidemiology of Nicotine and Alcohol Dependence Intake and abuse of any given addictive substance varies according to geographical, cultural, religious, and social/legal factors. Consequently, the prevalence of addictive substances varies considerably among different regions in the world. Nonaddictive consumption of alcohol has been reported to depend in an estimated 70% on other than genetic factors; however, alcohol dependence depends at least 50% on genetic factors. In contrast, consumption and dependence on nicotine are both estimated to be determined by genetic factors by about 50% (Maes et al. 1999; True et al. 1999). The nonaddictive consumption of alcohol and nicotine is probably more genetically determined in males than in females; in contrast, alcohol and nicotine dependence has been reported to be equally determined by genetic factors in males and females (Han et al. 1999; Kendler et al. 1994). Worldwide, average alcohol consumption has been estimated to be the highest in the former Soviet Union countries and South America, with North America in the middle, and the lowest in the Middle East (WHO 2004). The prevalence of alcohol dependence in North America appears to be stable affecting 5–8% of men and 2–3% of women. In terms of ethnicities, the highest rates are shared between Native Americans and white men (7.47%) and the lowest in Asian women (NIAAA 2001–2002). Alcohol dependence is typically more prevalent in males but associated with similar rates of relapses (<50%) in both genders, although risk factors for relapses have been reported to vary among both genders (Walitzer and Dearing 2006). While subtle but significant structural brain deficits and impaired cognitive performances have been reported in smokers (Gallinat et al. 2006; Neuhaus et al. 2006), toxicity-induced brain damages or cognitive deficits are substantially more pronounced in alcohol dependent patients (Oscar-Berman and Marinkovic 2003). According to the WHO, smoking is more prevalent in males worldwide except in a few countries such as Norway, Sweden, and New Zealand. It has been estimated that one-third of the male adult global population smokes. Despite a decline of smoking prevalence in the industrially developed countries, smoking rates increase in developing countries. Thus, smoking related diseases are expected to increase worldwide and are held responsible, on average, for 15 years of premature death (WHO 2008).

3 Defining Dependence: Clinical Definitions and Complexities in Addiction Studies Alcohol and drugs (e.g., cocaine, opiates, nicotine, etc.) act on the brain in different ways and exert different effects as well as side-effects. Nonetheless, the current definitions of dependence in commonly used classification systems (DSM-IV or ICD-10) share common items for dependence as all forms of dependence lead

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ultimately to similar pathological features. The latter are mainly characterized by tolerance, withdrawal symptoms, and a person’s marked inability or unsuccessful efforts to reduce or to stop the substance intake linked with a persistent desire of consumption. Tolerance can be interpreted as the brain’s auto-regulatory attempt to restore a perturbed homeostasis. For example, chronic alcohol consumption stimulates inhibitory (sedating) GABA-A receptors which may subsequently lead to or exacerbate a downregulation of GABA-A receptors. Furthermore, alcohol impairs normal functioning of glutamatergic NMDA receptors, causing an upregulation of NMDA receptors. Any longer interruption of alcohol intake may cause a sudden peak of disinhibited and enhanced excitatory glutamatergic neurotransmission along with simultaneously reduced inhibitory input in downregulated GABA-A receptors (Heinz et al. 2003b). These excitatory effects can ultimately trigger other excitatory transmitter systems such as the noradrenergic center of the locus coeruleus. Altogether, these mechanisms precipitate to the observed withdrawal symptoms such as tachycardia, increased sweating, irritability, potential epileptic seizures, and so on. A clear sign of alcohol dependence is given by emerging withdrawal symptoms in the morning after interruption of alcohol intake during sleep time. Likewise, in the commonly applied Fagerstro¨m Test for Nicotine Dependence, one of the key questions to assess severity of nicotine dependence is to ask subjects how much time elapses before they light the first cigarette after waking-up (Heatherton et al. 1991). The persistent desire or unsuccessful efforts to cut down or control substance use is another hallmark of dependence that is commonly observed in addictions. Other symptoms listed in DSM or ICD (such as, e.g., continuation of substance use despite knowledge of having a persistent or recurrent physical or psychological problem) further include presence of negative consequences and reduced behavior control typically emerging in severe and chronic addiction. However, even though the recognition and distinction of dependence (according to DSM or ICD-10) can usually be achieved with high reliability among clinicians, the clinical course and symptoms of substance dependence itself are highly heterogeneous. Genetic factors have been shown to differ according to an individual’s age at initiation (Rose 1998) and may also differ in other important phenotypes such as the amount of substance intake as well as with the individual success rates in cessation. Thus, studies that include subjects primarily based on a lifetime diagnosis of dependence are likely to increase their signal-to-noise ratio considerably, if they do not control for heterogeneous covariables. On the other hand, the distinction between abuse and dependence is also not unambiguous. From a clinical view, it makes sense to distinguish between abuse and dependence. However, there is little knowledge thus far that helps to define a “threshold” between abuse and dependence in biological terms. The common notion that use, abuse, and dependence are categorical entities will most probably not be transferable when dealing with and expecting findings from research in addiction neurobiology. In other words, for addictions, it is unlikely that gene

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variants differ categorically between alcohol users, abusers, and dependents. This dimensional spectrum in addiction needs to be taken into account very cautiously when selecting the controls for subsequent case–control studies. The studies presented and discussed in this chapter, in general, mostly refer to individuals being diagnosed with a clinical definition of dependence, unless otherwise specified.

4 Dysfunction of the Brain Reward System in Alcohol and Nicotine Dependence – Rationales for Potential Genetic Mechanisms Repeated alcohol and nicotine/drug intake trigger pleasurable feelings in most individuals. It has long been observed that both substances increase dopaminergic neurotransmission in different areas of the brain including the ventral striatum (nucleus accumbens), also known as the reward center (Mereu et al. 1984, 1987). It was earlier hypothesized that an increased dopaminergic activity in the ventral striatum mediates pleasurable feelings (e.g., Gunne et al. 1972). More recent studies, however, suggested that dopaminergic transmission acts more in reinforcing behavioral patterns (“wanting”), without necessarily creating pleasurable effects (Berridge and Robinson 1998). Reinforcement may nonetheless represent a powerful factor in developing dependence or obsessions towards a substance. In Alcoholism, the dopaminergic system may play a prominent role in stimulusinduced craving and the motivation for alcohol intake (Heinz et al. 2003b). Chronic alcohol intake may lead to a lifelong sensitization of the dopaminergic system with altered dopamine release triggered by alcohol-related stimuli and reduced responses to conventional reinforcers such as monetary reward (Berridge and Robinson 1998; Heinz et al. 2004; Wrase et al. 2007). This could explain the high rates of relapses even after long periods of abstinence. Likewise, nicotine has been shown to activate the ventral striatum through an increased dopaminergic transmission (Berridge and Robinson 1998). Consistent to this hypothesis, clinical trials have shown bupropion to be an effective drug in individuals who decided to quit smoking (Wilkes 2008): bupropion exerts its effects by blocking the dopamine transporter, which activity is also known to be modulated by gene variants (Heinz et al. 2000). Based on the role of dopamine in the manifestation of addictions, genetic variants in the dopaminergic system have become prime targets for genetic addiction studies. If dopamine explains the “wanting” of a substance, which brain systems may then be responsible for pleasurable effects; the “liking” of a substance? There is evidence from numerous animal studies and from clinical trials that several additional transmitter systems are involved in generating pleasurable effects, e.g., the opioid system (Ait-Daoud et al. 2001; Heinz et al. 2005b; Volpicelli et al. 1995). A genetically determined increased stimulation of the opioidergic system appears

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indeed to be associated with alcohol dependence (Froehlich et al. 2000). The opioidergic receptor antagonist naltrexone reduces alcohol-induced pleasurable feelings and has been shown to reduce the relapse rate compared to placebo from about 80% to 60% (O’Malley et al. 1992). This variability in response may be explained by genetic factors: Oslin et al. (2003) reported the Asp40 allele of the mu-opioid receptor (OPRM1) gene to be associated with better response (lower relapse rates) to naltrexone (Oslin et al. 2003) – a finding that has recently been replicated (Anton et al. 2008; Ray and Hutchison 2007). Nicotine has been suggested to create pleasurable effects through brain nicotinic cholinergic receptors that facilitates neurotransmitter release (e.g., dopamine), producing stimulation and mood modulation (Benowitz 2008). Another important genetic predisposition towards substance addictions may be determined by a dysfunction of the serotonergic system (Heinz et al. 2003a; Wrase et al. 2006). This is evidenced by studies in adult nonhuman primates where early separation from their mothers has been reported to correlate with long-term dysfunction of the serotonergic system evidenced by variables indicating a low serotonin turnover. The primates were shown to display depressed mood states, anxiety, greater aggressiveness, and excessive alcohol intake (Heinz et al. 1998a; Higley et al. 1991; Knutson et al. 1998). Chronic alcohol intake, possibly mediated through neurotoxic effects, may further contribute to a dysregulation of the serotonergic system associated with negative mood states (Heinz et al. 1998b). This would be consistent with the hypothesis of dysregulation in the serotonin system hypothesized in depressive disorders (Malison et al. 1998). A dysfunction of the serotonergic system may also interact with a second alcohol-associated phenotype: it impairs alcohol-induced GABAergic sedation, thus increasing the tolerance for alcohol intake (Doudet et al. 1995; Hinckers et al. 2006; Schuckit et al. 1999). Higher tolerance for negative drug effects is likely to predispose to the development of drug dependence: it has been reported that individuals with higher tolerance (also referred to as low response) are more likely to develop alcohol dependence and to be at higher risk for relapses (Schuckit and Smith 1996). As will be described later on, gene variants that reduce biologic tolerance to alcohol or nicotine have in fact been found to be associated with AD and ND. Stress and toxic effects could be the causes for dysregulated serotonergic neurotransmission (Heinz et al. 2001); this has recently been confirmed in studies with rodents, where early parental separation has been found to influence the developing serotonergic system (Jezierski et al. 2006). There is evidence that not all individuals are equally vulnerable to stress effects, and that their impact is modulated by the genetic constitution, e.g., of the serotonin transporter, thus promoting depression, anxiety, and alcohol abuse in the presence of different levels of negative life events (Caspi et al. 2003). In summary, dysfunctions of several neurotransmitter systems (glutamatergic, dopaminergic, opioidergic, serotonergic, and cholinergic) are hypothesized to predispose to alcoholism and the dysfunctions may, most likely to a significant extent, be genetically determined.

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5 Strategies of Genetic Analyses in Alcohol and Nicotine Dependence: Principles of Genetic Linkage, Candidate Gene/Whole Genome Association Studies, and Neuroimaging Studies The exact genetic mechanisms in the etiology of AD and ND are not yet sufficiently understood. Although environmental factors, perhaps by modulating epigenetic mechanisms (i.e., gene expression controlled by potentially reversible changes in DNA methylation and/or chromatin structure) are thought to contribute to substance dependence (Tsankova et al. 2007), the first step in molecular genetic studies involves analyzing genomic DNA sequence variation patterns in subjects affected with an addictive behavior compared to nonaffected individuals. Despite the rapidly increasing knowledge about molecular mechanisms involved in addictions, genetic analyses remain complicated since risk conferring sequence variation may potentially occur anywhere in the genomic DNA (e.g., in promoter regions, in intronic and/or exonic regions, or in intragenic regions). Little is known about the functional relevance of gene sequence variations in most cases; i.e., will any variation be harmless (and represent a false positive finding) or will it affect gene expression, protein structure, or mRNA stability? Over the past 15 years, several complementary approaches have widely been applied in medical genetics to identify genes involved in the etiology of heritable disorders: Linkage analyses are based on the principle that two loci that are close to each other are linked and coinherited, meaning that they are not transmitted independently. Thus, in linkage analyses two chromosomal loci are being considered: one that may harbor the putative genetic risk variant and one that is the marker with known chromosomal locus. Large family pedigrees are typically chosen for linkage studies using trait markers spanning the whole genome. Complex statistical analyses seek to determine those markers that are typically inherited (and are “linked”) to the disorder. By this, conclusions may be drawn that indicate the location of susceptibility genes. Genetic association studies compare DNA sequence variations between unrelated cases and controls. Typically, controls are being recruited from a nonaffected population (called case–control design) or from healthy family members (called family-based design). Association studies can either be performed with candidate genes that have been hypothesized to play an etiological role in a given disorder (called functional candidate genes) or may be chosen based on their specific location that has emerged from linkage studies (called positional candidate genes or “hot spots”). More recently, driven by the rapid advances in biotechnology, a “combination” of both approaches has been developed, the whole genome association studies (WGAS). Similar to linkage analyses, WGAS try to identify loci (susceptibility regions) in a hypothesis-free based model and do not need to include families or large pedigrees, similar as in candidate gene case–control studies.

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However, they use allelic variance as in association studies instead of the trait markers used in linkage analyses. Another important approach is the combination of genetics with (neuro)imaging studies. Imaging studies elicit quantifiable measures that allow assessing traits involved in addiction susceptibility, which are tested for association with candidate genes. Such kind of studies are also referred to as endophenotype (or intermediate phenotype) studies and are mainly indexed with morphometric, metabolic, or functional characteristics present in the genes-to-behaviors pathways (Gallinat et al. 2008). The concept of endophenotypes tries to overcome the hurdles of complexity in psychiatric disorders expected to be associated with many genes of subtle effects by refining a phenotype with quantitative measures that may involve fewer genes with larger effects. A classic endophenotype must meet several criteria: (1) it must be associated with the disorder, (2) it must be heritable, (3) it must be state independent, (4) it must cosegregate in families, and (5) it must be found in some (currently) unaffected relatives (Gottesman and Gould 2003). Prominent examples in psychiatry are genotype effects on monoamine transporter expression or brain activation associated with working memory function (Egan et al. 2001; Heinz and Smolka 2006). The difficulties arise from the fact that some of the required criteria are not easy to validate.

6 Linkage Studies in Alcohol and Nicotine Dependence Uhl (2004) reviewed earlier set of genome scan findings and reported about a remarkable convergence for several classes of substances. Sixteen regions for Substance Abuse (rSA) were identified on chromosome 2, 3, 4, 9, 10, 11, 12, 13, and X with two “hot spots” on chromosomes 3, 11, and 13 as well as 5 rSA on chromosome 4 (Uhl 2004). These data further suggest multiple gene effects each with modest or little size-effect consistent with a complex inherited disorder. The author also points out that due to the small effect sizes there is also a likelihood of false negative findings, so that more chromosomal regions could be involved in harboring susceptibility genes/gene variants. On the other hand, it cannot be ruled out that that these findings contain some positive chromosomal regions. Nonetheless, the convergence of different studies is still interesting as it was achieved in different clinical samples, different addictions, and different ethnicities suggesting that common allelic variants confer susceptibility for addiction across these heterogeneous studies. Earlier results from linkage studies in the large “Collaborative Study on the Genetics of Alcoholism” (COGA), were reported on chromosomes 1 and 7, with some evidence for chromosome 4 (Reich et al. 1998). A review on linkage findings on smoking related behaviors highlighted chromosomes 1, 2, 4, 5, 6, 9, 10, 11, 14, 17, 18, and 21 emphasizing, however, the nonreplications across studies (Li et al. 2004). More studies have been performed than presented here and are summarized in a recent review (Ball 2008). However, a large number of ambiguous results remain present across studies and across phenotypes, even

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though newer studies suggest at least some substantial chromosomal overlap, or interesting findings in regions with plausible genes involved in addiction. Inconclusive findings may be due to methodological factors that varied considerably in terms of data set selection, disease modeling, statistical analyses, etc. Furthermore, negative findings do not disprove linkage, and therefore further investigations are warranted to identify susceptibility loci for addiction in the future. In a recent paper, it has been concluded that gene variants with small effect sizes, such as in any complex disorder, are nonetheless more likely to be detected with association studies, in particular with genome-wide association studies (Uhl et al. 2008).

7 Association Studies in Alcohol and Nicotine Dependence Numerous association studies on addiction disorders have been published with candidate genes using case–control designs. An exhaustive and comprehensive summary of these numerous studies in both, AD and ND, would go beyond the scope of this paragraph and the reader is referred to recent reviews (Ball 2008; Ho and Tyndale 2007; Ko¨hnke 2008). Nonetheless, we will highlight some examples and discuss strength and limitations. Major findings, in candidate genes have recently been reviewed by Ko¨hnke (2008). Positive findings were reported for the alcohol dehydrogenase genes (ADH), the aldehyde dehydrogenase genes (ALDH), for genes involved in the dopaminergic transmitter pathways (DRD2, DRD4, DAT, DbH, COMT, MAOA, and COMT), (you need to put these in the abbreviations) the gamma-aminobutyric acid (GABA) receptor genes, genes coding for glutamatergic (NMDA) receptors, as well as genes involved in the opioid, the cholinergic, and the serotonin system and the gene coding for NP-Y. Negative studies have also been reported rendering an inconclusive picture. For smoking behaviors, candidate gene studies focused on variants involved in nicotine metabolism (CYP2A6), genes of the dopamine, the serotonin, the cholinergic system, the gamma-aminobutyric acid (GABA) gene, the opiate and endocannabinoid (eCB) systems genes as well as the brain-derived neurotrophic factor (BDNF) gene (Ho and Tyndale 2007). Dopamine receptor D2 variants, in particular the TaqIA polymorphism, were among the first studied and the TaqIA polymorphism was (albeit inconsistently) associated with alcoholism since the early 1990s (Noble 2000). In nicotine dependence, studies on the DRD2 gene have also yielded positive results. However, one meta-analysis differentiating smoking-related phenotypes such as smoking initiation, smoking persistence, cigarette consumption, or cessation did not detect meaningful significant associations (Munafo et al. 2004). Taken together, the role for DRD2 in AD and ND awaits further elucidation, since some studies suggest that DRD2 polymorphisms may be related to an even broader phenotype in the processing of reward-related stimuli rather than being associated with any specific addictive disorder per se (Kienast and Heinz 2006). More

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recently, Laucht et al. (2008) dissected different stages in smoking progression and reported that smoking initiation was associated with allelic variations of the DRD4 gene, whereas smoking continuation and dependence were associated with the DRD2 gene (Laucht et al. 2008). The importance of such findings is given by the fact that the progression from use/abuse to dependence may be modulated by different genes at different stages. Refining the addiction phenotype in various “progression phenotypes” may help to elucidate the role of dopamine genes in addictive behaviors. Studies in nonhuman primates mentioned above (Heinz et al. 1998b, 2003a) reported a lower serotonin turnover rate after exposure to early separation stress, which was associated with increased alcohol consumption in a free choice paradigm. Further studies in nonhuman primates indicated that serotonin turnover rates are reduced if they were exposed to social isolation stress but only in carriers of a copy of the short (s) allele (with lower transcriptional activity) of the serotonin transporter (5-HTT) gene (Bennett et al. 2002). Lower extracellular serotonin concentrations were in turn associated with an elevation of serotonin transporters in the raphe (brainstem) area and lower response to alcohol conferring higher risk for alcoholism (Heinz et al. 1998b; Schuckit et al. 1999). Based on these findings one would speculate that carriers of the s-allele could be at greater risk for alcoholism and/or other addictions. Although some studies confirmed an association between alcoholism related phenotypes and the s-allele of the 5-HTT gene, findings were controversial since significant findings were also obtained in carriers of the long (l) allele (Dick and Foroud 2003). Interestingly, elevated serotonin transporters and a low response to alcohol were associated with the l/l genotype, which increases the expression and function of serotonin transporters in healthy controls without excessive stress exposure (Lesch et al. 1996; Reimold et al. 2007). These findings indicate that the same phenotype – elevated serotonin transporters associated with a low response to acute alcohol effects – can either result directly from genetic effects (e.g., in carriers of two long alleles of the serotonin transporter gene) or from the interaction between stress exposure and genetic vulnerability, e.g., in carriers of serotonin transporter loss-of-function(s) alleles, thus rendering simple genetic association studies inconsistent. Taken together, it appears crucial to take gene–environment effects into account to unravel the impact of the 5-HTT gene in addictions, which is to identify stressful life events and protective factors and include those in gene–environment analyses (Gorwood et al. 2007; Lesch 2005). Over the past few years, a growing number of gene–environment interactions between the 5-HTT gene and adverse life events have been reported in various psychiatric syndromes such as depression (Caspi et al. 2003) and adult ADHD (Mu¨ller et al. 2008; Retz et al. 2008). Of interest, a similar interaction was reported for a functional polymorphism in the promoter region of the monoamine-oxidase gene A (MAO-A) with childhood sexual abuse in women and alcoholism: following trauma, the allele conferring lower activity of the MAO-A enzyme confers increased risk for alcoholism and antisocial alcoholism in adult women (Ducci et al. 2008).

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The most consistent findings as to how genetic variant affect risks to develop AD and/or ND are perhaps currently been given by genes involved in the metabolism of these substances. Alcohol is metabolized by ADH into acetalehyde which is subsequently converted to acetate by ALDH. A large group of people of Asian origin possess gene variants in the ALDH2 gene that impairs the enzyme function partially leading to an acetaldehyde syndrome (Ball 2008). Accumulation of acetaldehyde is responsible for unpleasant effects such as palpitations, skin flushing and palpitations. The ALDH*2 allele leads to an impaired enzyme activity and numerous studies demonstrated a protective effect in carriers of the ALDH*2 variant. It has been shown that individuals with the impaired ALDH2 enzyme function have a reduced likelihood by the factor 10 to become alcohol dependent (Ko¨hnke 2008). Similar to ALDH, there are several ADH isoenzymes where only a few appear to be primarily involved in ethanol metabolism. The most prominent and most well studied is the ADH1B isoenzyme. Allele*2 is associated with a reduced functioning leading to an accumulation of acetaldehyde, leading to severe and unpleasant hangover symptoms. Although the protective effects on alcoholism have been suggested to be stronger in the ALDH gene (Dick and Foroud 2003), association studies with the ADH1B*2 polymorphism have been consistently reported to reduce significantly the risk for alcoholism. The ADH1B*2 polymorphism is almost exclusively found in individuals of Asian ethnicity, but other ADH gene variants are also found in other ethnicities. It goes almost without saying, that the effects of having both impaired ADH and ALDH enzymes are additive, with an even higher protection against alcoholism. Interestingly, the region containing the ADH enzymes (chromosome 4) has been implicated in linkage analyses on alcohol dependence (Reich et al. 1998), providing some evidence that linkage studies are able to detect genes with significant effects in alcohol addiction, provided that the effect is not too subtle. As for nicotine use/abuse, a comparable effect to ADH/ALDH in alcohol is given by the nicotine metabolizing enzyme CYP2A6, which accounts predominantly for the conversion of nicotine into cotinine (Yamazaki et al. 1999). Several polymorphism-affecting enzyme activities have been detected and the investigation are still ongoing. Nonetheless, individuals with lower or absent enzyme activity have lower risk of smoking, decreased cigarette consumption, shorter smoking durations, decreased withdrawal symptoms during abstinence, and increased cessation (Ho and Tyndale 2007). In summary, despite numerous candidate gene association studies being performed over almost two decades, the most convincing findings were obtained in metabolizing enzymes for alcohol and nicotine with only limited evidence for initially suggested “key players” in the central nervous system such as dopamine system genes, etc. Questions were raised on how these inconsistencies can be explained. There are several factors that have to be kept in mind in association studies that are often hampered by false positive findings. The sample size (of cases and controls) were often relatively small and did not have sufficient statistical power. The ascertainment and definition of dependence has been very heterogeneous among studies and

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many studies did not differentiate sufficiently between use, abuse, dependence or did not consider the impact of gene in different stages of progression in addiction. As depicted above, important environmental factors such as adverse life events, etc., have yet virtually not been taken into account, even though it is well known that stressful experiences or events trigger addictive behaviors. The genetic analyses were often not covering the whole gene, and different gene variants were seldom considered, although for metabolizing enzymes, other gene variants may further enhance or counterbalance the effects on enzyme activity. Many genes and functional relevance of many gene variants are not yet known. There is increasing evidence that variants formerly described as “silent” may however impact on gene expression by affecting mRNA stability or ribosomal binding, etc. New hope has arisen with the development of genome-wide association studies which is described in more detail in the following section.

8 Genome-Wide Association Studies in Alcohol and Nicotine Dependence With the rapid advances in molecular genetics over the past years, it has become feasible in high-throughput facilities to perform WGAS in affected individuals compared to controls. Compared to classical candidate gene association studies, this approach offers the advantage of being independent of a priori hypotheses to define a plausible candidate gene. Compared to linkage analyses, WGAS are more likely to detect genes with even subtle effects. More importantly, an unprecedented number (>500,000 and more) of gene polymorphisms (SNPs) can be performed in each single individual in one experiment. Drawbacks are given by the lower robustness to allelic heterogeneity compared to linkage analyses, the higher costs and by the fact that this methodology is restricted to SNPs and excludes other variants (e.g., repeat-polymorphisms). Furthermore, the hurdles of multiple testings and data handling has yet not been resolved sufficiently. Nonetheless, WGAS offer a powerful opportunity to identify chromosomal regions that allow to “pin down” regions with novel candidate genes that confer susceptibility to a given phenotype. Several WGAS have now been performed in the field of alcohol, nicotine, or drug addictions. Uhl et al. (2008) have been attempting to unravel convergent findings arising out of WGAS in alcohol dependence, methamphetamine dependence, and nicotine dependence. They highlighted about 100 genes involved in cell adhesion, enzymatic activity, protein translation, trafficking and degradation, transcriptional regulation, receptor, ion channel and transport processes, disease processes, cell structures, and other functions. Gene names (abbreviated) and their chromosomal location are listed in Figure 1. It is interesting to note that virtually none of the genes implicated in candidate gene association studies were detected by those WGAS. The authors emphasize the importance of cell adhesion involved in neuronal connection processes, which may be critical in developing susceptibility

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Fig. 1 Results from whole genome association studies in addictions (Uhl et al. 2008)

to addictions. Limitations are given by the fact that the analyses did not differentiate among the various substances but has focused on variants that enhance vulnerability to many addictions. Chances of either false positive or false negative findings cannot be ruled out, and other gene variants are likely to exist that are more specifically associated for any given addictive substance (Uhl et al. 2008). In summary, WGAS may be powerful techniques to identify genes involved in addiction, and as illustrated in this example, new candidate genes have been suggested that need subsequent replication and further detailed investigation. The main conclusion at this time appears to be that no genes with major effect sizes have been detected with either one of the illustrated strategies (Fig. 1).

9 Combined Neuroimaging and Genetics Studies Anxiety and depression are negative mood states and often associated with alcoholism (Enoch et al. 2008), most likely in an attempt of “self medicating” unpleasant feelings. Genetic factors contributing to anxiety or lower emotional resilience against negative aversive stimuli may thus predispose to initiation, continuation, and relapse of alcohol intake. Catechol-O-methyltransferase (COMT) degrades the catecholamine neurotransmitters dopamine, epinephrine, and norepinephrine. A functional polymorphism in the COMT gene (val158met) accounts for a 4-fold variation in enzyme activity with a lower activity attributed to the met158 allele (Lotta et al. 1995). Homozygous met allele carriers were associated with higher levels of dimensionally measured anxiety among women in two populations (Enoch et al. 2003). Consistent with findings of COMT gene modulating anxiety, the

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met allele has been associated with increased social drinking (Enoch 2006; Kauhanen et al. 2000) and alcoholism (Tiihonen et al. 1999; Wang et al. 2001). However, negative findings were reported as well (Ohara et al. 1998) which would be more consistent with a small effect of the size of COMT in anxiety. The observed association between the COMT (on a gene level) and alcoholism (on a behavioral level), however, raises the question if the effect of the COMT gene may be associated with an intermediate or endophenotype (“upstream” from the gene, but “downstream” from the “behavioral” level) modulating anxiety with higher effect sizes. Based on the endophenotype concept depicted above, the use of functional imaging has been proven to represent an excellent tool to assess genotype effects on central processing of emotional stimuli (Hariri and Weinberger 2003). Smolka et al. (2005) assessed the effects of COMT genotype on brain activation by standardized affective visual stimuli (unpleasant, pleasant, and neutral). The analysis of genotype effects was restricted to brain regions with robust activation by the task. Numbers of met158 alleles were correlated with the blood oxygen leveldependent (BOLD) response elicited by pleasant or unpleasant stimuli compared with neutral stimuli. COMT genotype showed a significant impact on brain activation to unpleasant stimuli. The reactivity to unpleasant stimuli was significantly positively correlated with the number of met158 alleles in the limbic system (left hippocampus, right amygdala, right thalamus), connected prefrontal areas (bilateral ventrolateral prefrontal cortex, right dorsolateral prefrontal cortex), and the visualspatial attention system (bilateral fusiform gyrus, left inferior parietal lobule). Consistent with the initial hypothesis of higher effect sizes, the COMT genotype explained about 25% of the interindividual variance in BOLD response elicited by unpleasant stimuli (Smolka et al. 2005). This finding was replicated and indicated that increased responses to unpleasant stimuli may be regarded as one factor in developing anxiety, which may explain why subjects with this COMT genotype may suffer from an increased risk to develop anxiety and alcohol-related problems (Drabant et al. 2006). The serotonin transporter polymorphism in the promoter region (5-HTTLPR) has been shown to account for substantial portions of interindividual variance in amygdala response during the presentation of aversive stimuli (Hariri et al. 2002; Heinz et al. 2005a). During the activation of aversive, but not pleasant pictures, healthy carriers of the s-allele showed stronger activation of the amygdala. This is of interest, since the s-allele has been shown to predispose to anxiety/depression related traits (Levinson 2006) explaining, however, only up to 4% of the variance in the trait anxiety (Lesch et al. 1996). The fMRI study by Heinz et al. (2005a) noted that carriers of the s-allele also displayed greater coupling between the amygdala and the ventromedial prefrontal cortex. In other words, during stressful situations, a dysfunctional coupling between both areas may be associated (in carriers of the s-allele) with an impaired capacity to regulate emotional states – a suggested risk factor for drug and alcohol abuse. In another study by (Canli et al. 2006) the phasic activation model of the amygdala was challenged by using a multimodal magnetic resonance imaging approach (functional, perfusion and structural). Further data analysis and

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comparison of methodological approaches between different imaging groups suggested that s-carriers display increased amygdala activation not only in response to aversive stimuli but also when confronted with undefined and potentially aversive situations (e.g., lying in a noisy scanner and watching a fixation cross), conforming the importance of baseline selection in imaging studies (Canli and Lesch 2007; Heinz et al. 2007). Findings in COMT and 5-HTT genes prompted Smolka et al. (2007) to study both genes and investigate their combined effects in central processing of aversive stimuli. In addition to the previously investigated 5-HTTLPR polymorphism, the recently detected A–G substitutions in the 5-HTTLPR has been included in order to provide a more precise functional characterization of the 5-HTT gene. The authors found that processing of aversive stimuli in the amygdala, hippocampus, and limbic cortex is additively affected by the COMT and 5-HTT polymorphisms and explained about 40% of the interindividual variance in the fMRI BOLD response (Smolka et al. 2007). Altogether, these imaging-genetics studies highlighted here may explain the neural correlates of increased vulnerability for addictions and how this may be modulated by allelic variance in, e.g., COMT and 5-HTT genotypes. Moreover, these studies lend further support to the use of functional imaging for the assessment of gene (or gene–gene effects) on intermediate phenotypes in humans. Nonetheless, when interpreting these results further, one should be aware of the pleiotropic effects COMT and 5-HTT gene variants exert. For example, Schmack et al. (2008), by measuring brain activations by fMRI elicited by the anticipation of monetary gains and losses, observed a linear relationship between the number of met(158) alleles of the COMT gene and ventral striatal activity to loss incentives (Schmack et al. 2008). This higher reactivity in carriers of the Met alleles further strengthens the role of this particular COMT polymorphism in the processing of aversive stimuli, which may contribute to the development of addictive behaviors. However, the many reported associations between the 5-HTTLPR and different phenotypes (e.g., psychosomatic disorders, suicide, eating disorders, ADHD, and others) lend further support to the notion of many – not yet fully resolved – complexities inherent to this polymorphism (Serretti et al. 2006).

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Summary and Outlook

In this chapter, we have summarized strategies applied in the search of genes involved in addictive behaviors with emphasis on studies of alcohol and nicotine addiction. Strategies include linkage analyses, association studies, WGAS, as well as intermediate/endophenotype studies. Despite considerable efforts over the past two decades, the results are not conclusive thus far with little roles in clinical practice. Nonetheless, the genetic studies in addiction have taught scientists important lessons. First, there is little evidence to date for genes with major effects, thus the “common (gene) variants in common (addiction) disorders” hypothesis appears

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to be confirmed. More importantly, the role of classical “suspects” such as the DRD2 gene or the 5-HTT gene are now far better understood in that these genes appear to be associated with a much broader phenotype than originally speculated. Therefore, variants of these genes have little predictive power (or low effect sizes) explaining the inconsistent findings in often underpowered studies. More recent association studies have included larger sample sizes and aimed to cover whole gene systems with dense SNP coverage of genes (Schumann et al. 2008). Thus far, gene variants that have most consistently been proven to confer risk (or protection) are those genes coding for metabolizing proteins (e.g., ALDH, CYP2A6). Second, environmental factors such as early isolation stress has demonstrated to be of crucial importance at least for two major candidates such as the 5-HTT and MAOA genes in modulating predisposition of addictive behaviors. Such gene environment studies will perhaps pose the key to unravel the mechanisms involved in addiction (Agrawal and Lynskey 2008). The environmental effects probably include potentially reversible epigenetic factors and such studies are now evolving more rapidly (Tsankova et al. 2007). Third, all strategies applied thus far have proven some specific pitfalls adherent to them, but have also proven their validity. The strategies cannot be interpreted as being exclusive, but being complementary approaches. For example, WGAS have generated and will continue to generate novel and most interesting candidate genes useful for subsequent association studies and endophenotype studies. Fourth, some findings have been proven to be valuable for pharmacogenetics, that is to allow for an individualized drug treatment based on the genetic make up (e.g., naltrexone response dose based on OPRM1 genotypes, nicotine patch dose based on the CYP2A6 genotypes). This can be explained by the fact that pharmacogenetic phenotypes may be “simpler complex phenotypes”, because knowledge of pharmacological mechanisms are better understood than mechanisms in complex disorders, such as addictions (Goldstein 2005). Fifth, the phenotype definition has to be re-evaluated: a clinical phenotype may not in all cases be the best choice, sometimes referred to with the notion that “genes do no read DSM (or ICD).” Furthermore, it appears that comorbidities, although frequently present in addictions, and the progression of addictions from use, abuse, etc., have not been considered adequately. The endophenotype approach has taught important lessons and functional imaging studies have been proven to be invaluable and powerful tools. Imaging studies allow to detect neural correlates in brain areas that would otherwise not be detected. The genotype effects (e.g., 5-HTT or COMT) modulating aversive effects are prominent examples in that matter and may help to understand and to explain the pleiotropic effects of many genes. Sixth, novel tools have become available that are most likely to foster the advances in research considerably. Novel laboratory techniques emerged that allow to test for an unprecedented amount of genetic polymorphisms. Novel strategies in bioinformatics have been developed that will help, for example, to investigate gene–gene interactions, e.g., between COMT and metabotropic glutamate receptor 3 (mGluR3) genes and hippocampal volume in alcoholics (Puls et al. 2008). Multimodal (including PET, SPECT, MRI, and fMRI) and novel imaging techniques (e.g., diffusion tension imaging, DTI) are likely to represent other promising in vivo

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assessment techniques (Harris et al. 2008; Kalus et al. 2005; Wrase et al. 2008). Final, novel achievements and discoveries in the area of human genetics (e.g., completion of the HapMap project, discovery of Copy Number Variations, CNV) have attracted considerable attention and has been revolutionizing the pace of molecular genetic findings (Manolio et al. 2008; Sebat 2007). Given the findings to date, the insights being generated over the past years and the novel developments generated regarding strategies and techniques, one may wish to say that we are living a very exciting era in addiction research. Not with irony, one could conclude at this point that the “glass is half full” meaning that optimism is more than justified. It will probably soon become realistic to delineate algorithms that include variables (gene variants, life events, functional imaging assessments, type of substance, etc.) that will allow to estimate an individual’s vulnerability profile for the initiation and progression to any given addiction as well as to predict treatment outcome, at least partially, based on genotype information. The ethical challenges related to addiction genetics are an imminent task that needs to be addressed, to avoid stigmatization or discrimination based on an individual’s genetic data (Caron et al. 2005). It will be a matter of debate on who will have access to an individual’s genetic data. The pleiotropic effects of many gene variants will create particular challenges. Education of physicians is another important task in order to provide guidelines on how to communicate genetic findings. Thus, despite the potentials of developing preventive strategies and novel forms of treatment based on the rapidly evolving knowledge in commonly devastating addictive disorders, ethical issues will have to be carefully weighed against.

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The Role of Executive Control in Human Drug Addiction Robert Hester, Dan I. Lubman, and Murat Yu¨cel

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302 2 Executive Control Processes and Their Constituent Neural Network . . . . . . . . . . . . . . . . . . . . . 303 3 Neuroanatomy of Executive Control Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 4 Executive Control Dysfunction in Addicted Drug Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 5 Attentional Bias for Drug-Related Stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 6 Executive Control Dysfunction in “At-Risk” Individuals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308 7 Future Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311

Abstract Recent neurobiological models propose that executive control deficits play a critical role in the development and maintenance of drug addiction. In this review, we discuss recent advances in our understanding of executive control processes and their constituent neural network, and examine neuropsychological and neuroimaging evidence of executive control dysfunction in addicted drug users. We explore the link between attentional biases to drug-related stimuli and treatment outcome, and discuss recent work demonstrating that the hedonic balance between drug cues and natural reinforcers is abnormal in addiction. Finally, we consider the potential impact of early drug use on the developing adolescent brain, and discuss research examining premorbid executive control impairments in drug-naı¨ve “at-risk” populations. R. Hester (*) Department of Psychology, University of Melbourne, Melbourne, VIC, 3010 Australia e-mail: [email protected] D.I. Lubman Orygen Youth Health Research Centre, Faculty of Youth Mental Health, University of Melbourne, Melbourne, VIC, 3010 Australia M. Yu¨cel Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, VIC, 3010 Australia

D.W. Self and J.K. Staley (eds.), Behavioral Neuroscience of Drug Addiction, Current Topics in Behavioral Neurosciences 3, DOI 10.1007/7854_2009_28, # Springer‐Verlag Berlin Heidelberg 2009, published online 15 September 2009

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Keywords Addiction Drug dependence Cognition Executive Cognitive control Cognitive neuroscience

function

Abbreviations WM WCST PET EEG fMRI ERP ACC PTSD rACC SUD

Working memory Wisconsin card sorting test Positron emission tomography Electroencephalography Functional Magnetic Resonance Imaging Event related potential Anterior cingualte cortex Post-traumatic stress disorder Rostral anterior cingulate cortex Substance use disorder

1 Introduction Contemporary models of human drug addiction emphasize neuropsychological and neurobiological dysfunction of complex processes within the brain (Everitt and Robbins 2005; Koob 2006; Robinson and Berridge 2008). In these models, cognitive factors, such as a diminished capacity to control one’s own behavior, in conjunction with a strong motivation to consume a drug, is considered critical. Decades of research has demonstrated the powerful reinforcing properties of addictive drugs via their influence on the neurotransmitter dopamine within the mesocorticolimbic system of the brain (Volkow et al. 1999). However, this attribute alone does not explain the maintenance of drug taking behavior, particularly if it is likely to result in serious adverse consequences. Recent work has argued that executive control deficits also play a critical role in the development and maintenance of drug addiction (Jentsch and Taylor 1999; Goldstein and Volkow 2002; Lubman et al. 2004; Garavan and Stout 2005; Yucel et al. 2007a). Current research indicates that executive control processes are fundamental for successfully inhibiting the immediate pursuit of pleasurable stimuli, and for the development of adaptive patterns of behavior – both key factors in drug addiction (Kalivas and Volkow 2005). The aim of this review is to outline the evidence for compromised executive control processes, and the neural mechanisms that underlie them, thereby contributing to prolonged drug consumption. This review will examine evidence of executive control dysfunction in dependent drug users, drug-naı¨ve “at-risk” populations and its predictive value for identifying those individuals who transition from use to dependence. In this review, the term “drug” will be used throughout to encompass all psychoactive substances (including alcohol) that are abused.

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2 Executive Control Processes and Their Constituent Neural Network All goal-directed behavior, however trivial, might be said to require executive involvement: Cowan’s operational definition of executive functions includes all processes that can be influenced by instructions or incentives (Cowan 2001). In general, executive functions serve the same explanatory roles as control processes (Atkinson and Shiffrin 1968; Schneider and Shiffrin 1977; Shiffrin and Schneider 1977); that is, those nonroutinized, attentionally-demanding, consciously-available, volitional processes that initiate a certain action or interrupt and adjust ongoing actions. Measuring individual differences in these processes has typically involved cognitive tasks that increase demands for specific aspects of control, such as inhibition, selective attention, or task switching. Examples of tests used to examine executive processes include dual-task performance, Stroop, Wisconsin Card Sorting, Tower of London, delayed alternation, and assorted working memory (WM) tasks. Such paradigms have proved a reliable method for demonstrating executive control deficits across a range of clinical conditions (Dalrymple-Alford et al. 1994; Diamond 1996; Baddeley et al. 1997; Barkley 1997; Diamond et al. 1997; Konrad et al. 2000; Baddeley et al. 2001; Bennetto et al. 2001; Gilotty et al. 2002; Sharma and Antonova 2003; Simon et al. 2003), with recent work also indicating a strong relationship between executive control deficits on laboratory tasks and real-world behavioral problems (Burgess et al. 1998; Kibby et al. 1998; Moriyama et al. 2002; Kalechstein et al. 2003a; Odhuba et al. 2005; Chaytor et al. 2006).

3 Neuroanatomy of Executive Control Processes In attempting to identify their anatomical loci, cognitive neuroimaging experiments have operationalized executive functions in various ways, including dual-task coordination (D’Esposito et al. 1995), task switching (Dove et al. 2000; Sohn et al. 2000), memory updating (Salmon et al. 1996), and response sequencing, monitoring and manipulation (Owen et al. 1996). A consensus implicating the dorsolateral prefrontal cortex as critical for executive functioning has emerged, as this region has been observed in a number of studies using a range of different tasks (Owen et al. 1996; Smith and Jonides 1999; Owen et al. 2000; Petrides 2000; Postle et al. 2000; Bor et al. 2001; Szameitat et al. 2002; Sylvester et al. 2003). This consensus is also consistent with the human lesion literature, which implicates the frontal lobes in organizing, regulating, and producing coherent behavior (Luria 1973; Stuss and Benson 1987). Patients with frontal lobe lesions appear to lose important aspects of autonomous executive control, as evidenced by the loss of behavioral control to environmental contingencies. Classic examples of such behavior include capture errors (automatically following cues with prepotent responses) and utilization behaviors (reaching out and using objects in the environment in an automatic manner) (Lhermitte 1986).

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However, it is clear that executive functions are not located solely in prefrontal regions (Andres 2003). Those neuroimaging studies that have localized executive functions to the dorsolateral prefrontal cortex have also observed extensive parietal, premotor, cingulate, occipital, and cerebellar activation. Consistent with these findings, functional imaging studies of “classic” executive tasks such as the Tower of London, the Wisconsin Card Sorting Task, and Stroop Test reveal extensive activation in the frontal lobes, as well as in temporal, parietal, occipital, and cerebellar regions (Berman et al. 1995; Prabhakaran et al. 1997; Monchi et al. 2001; Newman et al. 2003). Other investigators have argued for distinct or interacting prefrontal and anterior cingulate contributions to executive processes (Gehring and Knight 2000; MacDonald et al. 2000), or have suggested that regions underlying executive functions may contribute to many other cognitive processes, such that executive functions are accomplished by distributed networks of activated areas (Carpenter et al. 2000; Miller and Cohen 2001). Despite the challenge that this may present for localizing executive functions, one should still be able to identify the underlying neuroanatomical circuitry, although a more sophisticated level of description may be required; the hallmark of executive functions may not be a particular gyrus or gyri, but may be reflected in dynamic patterns of activation within an entire task-related circuit.

4 Executive Control Dysfunction in Addicted Drug Users Significant impairments on clinical neuropsychological (e.g., Stroop test, WCST) and experimental measures of executive control (e.g., Go/No-go task, Eriksen Flanker task, Simon Task) have been identified in a range of dependent drugusing groups (Hoff et al. 1996; Bolla et al. 1999, 2000; Simon et al. 2000; Rosselli et al. 2001; Fillmore and Rush 2002; Salo et al. 2002; Simon et al. 2002; Solowij et al. 2002; Kalechstein et al. 2003b; Goldstein et al. 2004; Lundqvist 2005; Li et al. 2006; Verdejo-Garcia et al. 2006; McHale and Hunt 2008). Neuroimaging studies have identified an association between these executive control deficits and dysfunction in prefrontal (particularly dorsolateral and inferior frontal), anterior cingulate, and orbitofrontal regions (Bolla et al. 2001, 2003, 2004; Goldstein et al. 2001; Franklin et al. 2002; Paulus et al. 2002; Kaufman et al. 2003; Hester and Garavan 2004; Gruber and Yurgelun-Todd 2005; London et al. 2005; Tapert et al. 2007; Paulus et al. 2008). Individual studies have also identified changes within subcortical (thalamus and basal ganglia), parietal, temporal, and cerebellar regions, although these findings are less consistent. The variability in brain regions implicated across studies is partly related to differences in the task demands of the cognitive paradigms administered. Similarly, the characteristics of the sample (e.g., demography, education, premorbid intelligence, comorbid psychiatric history), the duration and frequency of drug use, neuroimaging technique [e.g., positron emission tomography (PET), electroencephologram (EEG), functional magnetic resonance imaging (fMRI)], and type of drug used all appear to subtly

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influence the pattern of behavioral and neural executive control deficits observed (see also Yucel et al. 2007a). It is important to point out that many studies that observe differences in functional brain activity between drug-using groups and matched control participants do not observe a significant difference in executive control performance (Goldstein et al. 2001; Tapert et al. 2007). Indeed, some studies manipulate task difficulty to ensure equivalent performance between drug users and controls so as to ensure that the differences in brain function observed are not overly influenced by performance differences, or by factors that potentially contribute to performance differences (e.g., fatigue, frustration, effort)(Kaufman et al. 2003; Yucel et al. 2007b). The interpretation of findings from such studies has generally focused on identifying brain regions or brain networks that behave differently in drug users. For example, studies have found that equivalent executive control performance in drug users is typically associated with higher levels of activity within brain regions related to the task (e.g., prefrontal regions; Gruber and Yurgelun-Todd 2005; Tapert et al. 2007), or the recruitment of additional analogous brain regions (Desmond et al. 2003; Yucel et al. 2007b) that suggest compensatory patterns of activity. The identification of executive control deficits in addicted drug users has typically involved a comparison between an actively using addicted group, or those who have recently become abstinent, and a nondrug-using control group. Little research has examined the trajectory of executive control deficits during sustained abstinence, or longitudinally examined the impact of relapse or continued use on performance. Simon et al. (2004) found that methamphetamine users who had relapsed by 3-month follow-up had significantly poorer executive control performance than demographically comparable participants who had remained abstinent. However, the “relapse” group (and for some tests the abstinent group) also had significantly poorer executive control performance than a group of comparable users who had not attempted abstinence and continued to use methamphetamine. The results of this study highlight the critical need for longitudinal research examining how cognitive performance, and in particular executive control, is influenced by continued drug use, abstinence, and relapse.

5 Attentional Bias for Drug-Related Stimuli One suggested mechanism by which executive control dysfunction influences further drug consumption is via specific attentional biases to drug-related stimuli (e.g., drug paraphernalia). Human drug addiction is a complex multifactorial phenomenon that features, with remarkable consistency, a difficulty in directing attention away from salient drug-related stimuli. Behavioral studies have shown that processing a nonsalient stimulus in the presence of a salient drug-related stimulus presents a significant difficulty for those dependent on cocaine (Copersino et al. 2004; Hester et al. 2006), alcohol (Sharma et al. 2001; Ryan 2002; Cox et al. 2003; Duka and Townshend 2004a, b), cannabis (Field et al. 2004a), nicotine

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(Wertz and Sayette 2001; Powell et al. 2002; Waters et al. 2003; Bradley et al. 2004; Field et al. 2004b), or heroin (Lubman et al. 2000; Franken et al. 2003). Similarly, electrophysiological studies, which are able to directly quantify the allocation of processing resources to specific stimuli independently of conscious awareness, demonstrate enhanced event-related potential (ERP) responses to drugrelated stimuli compared to nonsalient stimuli across a range of addicted populations (Warren and McDonough 1999; Herrmann et al. 2000, 2001; Franken et al. 2003; van de Laar et al. 2004; Lubman et al. 2007b, 2008. Together, these studies provide evidence that drug-related stimuli capture processing resources and influence behavior. The basis of this attentional bias in addicted users may relate to the reinforcing properties of drugs and their influence on the mesocorticolimbic “reward” network, and consequently, the influence of the limbic system on attention and executive control. The mesocorticolimbic neural circuit, which includes the nucleus accumbens, amygdala and hippocampus, has been associated with the acute reinforcing properties of addictive drugs (Everitt et al. 1999). Repeated administration of a drug alters the responsiveness of these brain regions, insofar as they become sensitized to the association between the drug, its many related stimuli (e.g., context and surroundings in which it is taken), and the euphoria that accompanies intoxication. Indeed, studies of drug craving where drug-related stimuli are presented to either active or abstinent users have demonstrated significant activation in regions such as the amygdala, nucleus accumbens, and hippocampus (Grant et al. 1996; Maas et al. 1998; Childress et al. 1999; Garavan et al. 2000; Ciccocioppo et al. 2001; Kilts et al. 2001; Bonson et al. 2002; Brody et al. 2002; Tapert et al. 2003; Franken et al. 2004a). This type of conditioned associative learning is typically found with other reinforcing stimuli (e.g., food, pain), and items conditioned in this way are reinforced as salient to the individual (Berridge and Robinson 1998). The salience of a stimulus determines its capacity to hold attention, and to an extent, to direct attention. Learning the salience of stimuli and, in turn, allowing salience to reflexively direct our attention (particularly visual attention) appears to have a logical and evolutionary advantage. Thus, when navigating a complex multistimulus environment, our attention is captured by those items which we find rewarding (e.g., food) or that could harm us (e.g., predators). As salience directs attention relatively automatically (Pessoa and Ungerleider 2004), a greater level of executive control must be imposed to ignore a salient cue in order to focus on a less salient stimulus. Exerting cognitive control is associated with activation in the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex, and inferior parietal regions, during selective attention paradigms such as the Stroop Test (Kerns 2006). The strong attentional bias that chronic users typically demonstrate for drugrelated stimuli highlights its potential role in maintaining addictive behavior. If a user’s attentional system is sensitive to directing attention toward drug-related stimuli in their environment, re-encountering these stimuli will cue attention, and consequently craving. Indeed, several studies have reported a correlation between craving and drug cue-elicited ERP responses (Franken et al. 2003, 2004b; Namkoong

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et al. 2004; Lubman et al. 2008). While the relationship between craving and relapse during abstinence is complex, users typically report that cravings occur prior to and during the period of highly ritualized and automatic drug-taking behavior that follows an impulsive urge to use (Miller and Gold 1994). Recent studies have demonstrated that the extent of an individual users’ attentional bias for drug-related stimuli can robustly predict the likelihood of successfully ceasing cigarette smoking (Waters et al. 2003), or remaining abstinent during treatment for alcohol (Cox et al. 2002), cocaine (Carpenter et al. 2005), and heroin (Marissen et al. 2006) dependence. The study by Cox et al. (2002) measured attentional bias for alcohol-related stimuli over two time-points and demonstrated that levels of bias increased prior to relapse. Recently, Lubman et al. (2009) utilized a multimethod approach to examine hedonic responses to natural reinforcers and drug cues among heroin users on opiate substitution treatment. Across a range of response measures (i.e., self report, expressive, reflex modulation, and cortical/attentional), they consistently found altered processing of drug and pleasant pictures in opiate-dependent individuals relative to controls. The opiate-dependent group demonstrated enhanced attentional processing of drug-related stimuli as well as reduced responsiveness to natural reinforcers, and subjective valence ratings of pleasant pictures consistently predicted regular (at least weekly) heroin use at 6-month follow-up, even after controlling for baseline craving scores and heroin use. While few other addiction studies have included a nondrug-related emotionally salient class of stimuli (e.g., sexual imagery, highly aversive images) in their study design, these results support the notion that the hedonic balance between drug cues and natural reinforcers is abnormal in heroin users, with drug-related stimuli capturing relatively more attentional and hedonic resources than natural rewards. Research with other clinical (e.g., Major Depression, PTSD) populations suggest that the neural mechanisms underlying attentional biases for emotionally-salient information may be related to a reciprocal suppression effect (Bush et al. 2000). In these studies, the processing of nonsalient incongruent Stroop stimuli resulted in a pattern of increased dorsal ACC and dorsolateral prefrontal cortex activity, while the processing of evocative or emotionally salient words activated limbic areas such as the rostral anterior cingulate cortex (rACC), insula, and amygdala (Mayberg et al. 1999). Interestingly, during the latter condition, dorsal ACC and dorsolateral prefrontal cortex regions demonstrated decreased activation (when compared to the incongruent Stroop condition), further supporting the notion of a reciprocal suppression effect, whereby emotional words appear emotionally salient and are associated with decreased activity of executive control regions. In general, research examining the neural bases of attentional biases in drug users has been limited. Goldstein et al. (2007) administered an emotional stroop task that presented drug-related words to participants and required them to ignore the evocative content of the stimuli while performing a cognitive operation (responding to the word’s ink color). The design of their task prevented the detection of an attentional bias; however, brain activation in response to drug-related words (relative to neutral words) by their sample of dependent cocaine users indicated significant activity in

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ACC and mesial orbitofrontal regions, with individual differences in performance correlating with activity in these regions. Recent work has also demonstrated that cognitive measures of executive control dysfunction, in the absence of evocative drug-related stimuli, are also capable of predicting treatment outcomes. Bowden-Jones et al. (2005) demonstrated that decision-making deficits (e.g., the inability to inhibit the selection of immediately rewarding stimuli associated with poorer outcomes over the longer term) predicted those alcohol-dependent patients would relapse, after completing a 21-day inpatient program, in the following 3 months. Passetti et al. (2008) have recently demonstrated a similar relationship using performance on the Cambridge Gamble Task and the Iowa Gambling Task to predict relapse rates in dependent opiate users. This study highlighted that impulsiveness for reward, rather than impulsiveness per se (measured by tasks such as the Go/No-go), predicted relapse rates. Streeter et al. (2008) also found that baseline Stroop task performance predicted those patients who failed to complete an outpatient treatment trial for cocaine dependence. These results, while consistent with previous studies demonstrating a relationship between cognitive task performance and treatment completion (Aharonovich et al. 2003, 2006), utilized measures of treatment compliance rather than relapse to drug taking. However, the relationship between treatment completion and cognitive function may be mediated by the treatment approach. For example, compliance rates for cognitively demanding treatments (e.g., cognitive behavior therapy) are more influenced by individual differences in cognitive ability than nondemanding forms of treatment (e.g., medication trials). To date, Paulus et al. (2005) have conducted the only study to demonstrate that neural activation patterns (measured by fMRI) during a decision-making task can also be used to predict relapse risk. Treatment-seeking methamphetamine dependent patients were administered a two-choice prediction task 3–4 weeks after starting an abstinence-based treatment program. The patients were followed up at 12 months post-discharge and assessed for drug-taking behavior in the intervening period. After categorizing participants as “relapsers” or “nonrelapsers,”, the fMRI data analysis indicated a network of regions that differentiated the two groups, including significantly lower levels of activity in dorsolateral prefrontal, insula, parietal, and temporal cortex regions. Activity in the right insula, right posterior cingulate, and right middle temporal cortex best differentiated relapsers from nonrelapsers, correctly predicting 17 of 18 relapsers and 19 of 22 nonrelapsers (94% sensitivity, 86% specificity). The data highlight the potential for neuroimaging studies of executive control to play a role in predicting those patients at risk of relapsing during the early stages of treatment.

6 Executive Control Dysfunction in “At-Risk” Individuals The consistent finding of executive control deficits across cross-sectional studies of drug addicted populations raises questions of whether such deficits directly relate to the addictive process, or to some extent, represent premorbid vulnerabilities.

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While animal research has robustly demonstrated the neurotoxic effects of chronic drug abuse on cortical regions critical to executive control, few human studies have explored whether such neural and behavioral deficits relate to pre-existing vulnerabilities that may be further exacerbated by chronic drug consumption (Lubman et al. 2007a). Using a longitudinal approach, Tarter and colleagues (Tarter et al. 2003, 2004; Habeych et al. 2005; McNamee et al. 2008) have examined which cognitive factors predict the later development of drug dependence in children (from the age of 10) who have a parent with a diagnosed substance use disorder (SUD). Their data has demonstrated a strong predictive relationship between “neurobehavioral disinhibition,”, a composite index of personality and neuropsychological tests that measure executive control, and the development of SUD by the age of 19. Individual differences in neurobehavioral disinhibition have also been associated with fMRI activity in prefrontal regions of adolescents at risk of SUD (McNamee et al. 2008). Studies of high-risk populations (e.g., a family history of alcoholism) suggest impairments in frontal functioning are apparent prior to drug use exposure (Monti et al. 2005; Schweinsburg et al. 2005) and can predict later substance use (Deckel and Hesselbrock 1996; see also Ivanov et al. 2008 for a review). Schweinsburg et al. (2005) demonstrated that on a Go/No-go fMRI paradigm, adolescents with a positive family history of alcoholism demonstrated less inhibitory frontal response than those with no family history, despite similar task performance between groups. Deckel and Hesselbrock (1996) examined the ability of neuropsychological and behavioral tests of anterior brain functioning to predict changes in adolescent alcohol-related behaviors 3 years after the initial assessment. Tests of executive functioning, in subjects with a positive family history of alcoholism, were the only measures to predict later alcohol consumption. Other populations that are at-risk of developing drug dependence are those children with diagnoses of oppositional defiant disorder, conduct disorder, and attention deficit hyperactivity disorder (Myers et al. 1995; Zoccolillo et al. 1997; Riggs 1998; Whitmore et al. 2000; Finn et al. 2005), as well as young people with psychiatric disorders such as schizophrenia and bipolar disorder (Dixon 1999; Batel 2000; Crome 2000; Soyka 2000; Chambers et al. 2001; Altamura 2007; Thoma et al. 2007). These disorders are consistently associated with impairments in executive control as well as disruptions to frontal brain circuitry, highlighting the role that executive deficits play in increasing risk for drug addiction. Recent advances in developmental neuroscience have highlighted that frontal brain regions do not fully mature until midway through the third decade of life (Paus 2005), and appear to be affected by episodes of developmental trauma as well as exposure to psychoactive drugs (Lubman and Yucel 2008). Indeed, there is growing evidence that psychoactive substances impact differentially on both behavior and brain function during adolescence, with the adolescent brain appearing to be more sensitive to the neurotoxic effects of a broad range of psychoactive substances (Lubman and Yucel 2008). In addition, childhood mistreatment, which is common among drug users, has also been shown to affect brain development (Teicher et al. 2003). Thus, vulnerable adolescents (early trauma and/or family

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history of addiction) may develop further deficits in frontal functioning following early sustained drug exposure, thereby substantially increasing their risk of transitioning from drug use to drug dependence. However, longitudinal studies that examine the impact of psychoactive drugs on the developing human brain have yet to be conducted.

7 Future Studies Together, these findings highlight the need for prospective studies across addicted populations that systematically examine structural, functional, and cognitive changes within frontal brain networks, both pre- and post-treatment. Longitudinal research documenting the development of executive and hedonic functioning during adolescence (prior to the onset of drug use) in high-risk populations is also required, so as to determine how early drug use impacts upon developmental trajectories. Such studies would improve our understanding of neurobiological risk for addictive disorders, development of related neuropsychological and neurobiological impairments, as well as potential prognostic markers for treatment and recovery. To date, cognitive and neurobiological research in the addiction field has tended to focus on identifying factors that increase risk for later drug dependence. More recently, however, there has been growing interest in the role of protective factors (such as executive skills), which improve resilience. Studies that examine both risk and protective factors, as well as environmental variables that promote them, have the potential to foster a greater understanding of brain–behavior relationships as well as pathways into (or away from) addictive disorders. There is little evidence regarding how, and to what extent, the brain recovers following detoxification and protracted abstinence, or the specific role of treatment in the recovery of affected neurobiological systems. Such data would be particularly salient for rehabilitation settings, and would also provide critical information regarding prognostic outcomes. For instance, the degree to which identified executive control impairments recover with abstinence remains unclear. There is some evidence to suggest that the functional impairments observed in the executive control network are exacerbated during the early stages of withdrawal. Thus, at the time when inhibitory control and decision-making abilities are most needed, it appears that the neural systems underlying them are most impaired (Copersino et al. 2004; Jacobsen et al. 2007). This has clear treatment implications, including the need for interventions that bolster executive control during periods of increased risk, as well as the potential for utilizing neuropsychological paradigms to predict early relapse. Clearly, more research is required in this domain. Finally, while current diagnostic criteria promote the physiological features of drug dependence (i.e., tolerance and withdrawal), it is arguably the neuropsychological component (i.e., impaired control over one’s behavior) that has the most significant impact on affected individuals and the wider community. Although there is growing recognition that deficits in executive control are a key feature of

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addictive disorders, we hope that recent advances and future studies in the neuropsychological and neuroimaging fields will further inform diagnostic conceptualizations. This would facilitate recognition of the clinical need to incorporate the management of such deficits within standard care, as well as promote the development of interventions (both pharmacological and psychological) that reduce the impact of attentional biases, enhance executive skills (i.e., improved decisionmaking and inhibitory control), and improve hedonic responding to prosocial relationships and activities. Acknowledgment Drs Hester and Yu¨cel are supported by Australian National Health and Medical Research Council Career Development Grants (519730 (RH) and 509345 (MY)).

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The Behavioral Economics of Drug Dependence: Towards the Consilience of Economics and Behavioral Neuroscience Warren K. Bickel, Richard Yi, E. Terry Mueller, Bryan A. Jones, and Darren R. Christensen

Contents 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction to Behavioral Economics of Drug Dependence . . . . . . . . . . . . . . . . . . . . . . . 2 Demand and Substance Abuse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Delay Discounting and Drug Dependence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Delay Discounting as a Measure of Time Perception and Temporal Horizon . . . . . . . . . . . . 5 Neural Correlates of Delay Discounting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Competing Neurobehavioral Systems and Addiction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract In this chapter, we review the research in this growing field by first discussing the concepts related to price and consumption (demand), its applications to the study of drug consumption and drug seeking, and the impact of other commodities on drug consumption. We then review the discounting of future commodities and events among the addicted, review the most recent research examining the neural correlates of discounting, and describe and review the new theory of addiction that results from that research. We conclude by addressing the next research steps that these advances engender. Keywords Behavioral economics Demand curve Demand intensity Price elasticity Commodity interactions Cross-price elasticity Substitute-complement continuum Delay discounting Hyperbolic delay function Preference reversals Impulsivity Temporal horizon Past discounting Neural correlates Brain imaging Competing neurobehavioral systems Impulsive system Executive system W.K. Bickel (*), R. Yi, E.T. Mueller, B.A. Jones, and D.R. Christensen Center for Addiction Research, University of Arkansas for Medical Sciences, 4301, W. Markham Street, # 843, Little Rock, AR 72205, USA e-mail: [email protected]

D.W. Self and J.K. Staley (eds.), Behavioral Neuroscience of Drug Addiction, Current Topics in Behavioral Neurosciences 3, DOI 10.1007/7854_2009_22, # Springer‐Verlag Berlin Heidelberg 2009, published online 15 September 2009

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Abbreviations fMRI

Functional MRI

1 Introduction In his book, Consilience: The Unity of Knowledge, Wilson (1998), the famed naturalist, anticipated much of the contemporary concern of scientists and science organizations. These concerns have to do with formulating and expanding translational science. One area of investigation that predated these concerns has been the application of economic ideas and concepts to the problem of addiction. The first substantive application of this approach was made by Becker and Murphy (1988) in their classic paper, “A Rational Theory of Addiction”. They proposed that addicts make choices between the value of continued drug use and, for example, the value of future health; they discount the value of future health and decide in favor of continued consumption of their addictive substance. This theory suggests that past consumption engenders future consumption, that consumption enhances demand, and that there is an important role in decision-making for the discounting of future events. Shortly after that seminal work, addiction researchers began to empirically explore the themes proposed by Becker and Murphy. First, using the laboratory procedures developed in operant psychology, they examined the effects of price, including what constitutes the costs of the consumption of drugs. Second, and later, they considered the impact that discounting the value of future consumption may have on choices between immediate and future opportunities (intertemporal choice), and they began to analyze discounting as a behavioral phenomenon. Currently, this field is influenced by technological advances in neuroimaging, which have lead to a new translational research field referred to as neuroeconomics. Importantly, this more recent work in neuroeconomics has contributed to a new conceptual model of addiction.

1.1

Introduction to Behavioral Economics of Drug Dependence

Behavioral economics evolved from the synthesis of behavior analysis (formerly known as operant psychology) and economics. Behavior analysts had long endeavored to account for what an organism does by studying operant responses and reinforcers (Skinner 1938, 1953, 1974; Baum 2005). Recognizing that responses and reinforcers are analogous to the price and commodity concepts of economics, behavioral economics applied concepts from economic consumer demand theory to the study of reinforcement.

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Given the reinforcing nature of abused drugs, behavioral economics is particularly relevant to the study of drug dependence. Behavioral economics characterizes drug dependence as a pattern of choosing drug-related behavior rather than behavior that leads to economic, social, and biological well-being (Bickel and DeGrandpre 1995; Bickel et al. 1993). Behavioral economics research has contributed to the development of two principles for understanding why such choices are made and how such behavior patterns can be altered: (a) the law of demand and (b) availability of alternative reinforcers.

2 Demand and Substance Abuse The law of demand states that consumption of a commodity decreases as constraints are placed upon that consumption (Bickel and DeGrandpre 1996a, Vuchinich and Tucker 1988). For behavioral economists, constraints are costs measured as money or as energy expended by an individual to obtain a reinforcer. For example, price could be measured in one behavioral economics drug-research laboratory as the price a participant is hypothetically willing to pay for a unit of a drug, and in another laboratory price could be the number of moderately effortful plunger-pulls that are required from a subject before a drug dosage is delivered. A behavioral-economic analysis-of-demand study implements a range of prices for a commodity such as a drug, and determines amount of consumption for the individual participant at each price. The data may be collected by directly measuring operant responses like plunger-pulls (Bickel et al. 1991) or by querying the participant about hypothetical purchases of the commodity (Jacobs and Bickel 1999). When consumption is plotted as a function of price, the participant’s demand curve for that commodity is portrayed. Fig. 1, taken from Johnson and Bickel (2006), exhibits hypothetical 100 Reinforcer Administrations

Fig. 1 Displays demand curves for two reinforcing commodities. Here price is operationalized as FR requirement and consumption is operationalized as reinforcer administrations. Double logarithmic axes are used to facilitate observation of proportional change in consumption in relation to changes in price. The two curves are similar in general form but differ in specific respects. The similarities and differences are describable in terms of the concepts of demand intensity and demand elasticity, described in the text

Reinforcer A Reinforcer B

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demand curves for two reinforcing commodities, A and B, with price plotted on the X-axis and consumption plotted on the Y-axis. As “price” in this experiment was the number of required plunger pulls, the X-axis is labeled as “FR Requirement,” which is the fixed ratio (FR) of plunger pulls to reinforcer deliveries. Reinforcer A and Reinforcer B might be a subject’s consumption of Drug A and Drug B if Fig. 1 were from a behavioral-economic drug-use study. Demand intensity is the term used to refer to consumption at a specific price on a subject’s demand curve. It can be observed in Fig. 1 that there is greater demand intensity for Reinforcer A than for Reinforcer B at the lowest price. This might suggest that if A and B were drugs, A has a greater abuse liability (Griffiths et al. 1979) than B. However, this relationship between the demand intensities of commodities A and B is reversed as price increases. This undermines our simple description of the abuse liability of drugs A and B. Such dynamic relationships between features of commodities are commonly observed in behavioral-economic studies – including studies of drugs as commodities. The ability to reveal and simply portray some complex relationships regarding commodities is one of the great virtues of demand-curve analysis. The concept of demand elasticity is important in describing many demand-curve phenomena. In Fig. 1, for example, Reinforcer A is described as having greater mean elasticity than Reinforcer B because the reduction in consumption of A as a function of increases in price occurs at a more rapid rate (the curve for Reinforcer A falls more precipitously than the curve for Reinforcer B). The elasticity concept can also describe regions or points on the single demand curve for a given commodity. In Fig. 1 (note the logarithmic axes), the 10-fold increase in the price of Reinforcer B from FR 1 to FR 10 does not result in a decrease in the consumption of Reinforcer B by a factor of 10. This change in consumption that is less-than-proportional to the change in price defines commodity B as price inelastic across this range of prices. By contrast in Fig. 1, a 10-fold price increase for Reinforcer A from FR 10 to FR 100 reduces its consumption by a factor greater than 10. This change in consumption that is greater-than-proportional to the change in price defines commodity A as price elastic across that price range. The behavioreconomic use of the term “elastic” is based upon much quantitative rigor and mathematical theorizing about the form of demand curves. However, the fundamental generality from demand-curve analysis is simple: for most commodities and consumers of them, the relationship between consumption and the constraint on consumption (i.e., price) consists of an inelastic price range and an elastic price range. This relationship holds for the commodities known as drugs of dependence and those who consume them; and it has important implications for combating drug dependency. A goal of drug-dependence therapy should be to change the cost of drug consumption to a price on the drug-consumer’s demand curve for that drug to a price at which demand is highly elastic. Part of this therapy may necessarily be the changing of the effective “price of drug consumption” to include behaviors that include acquiring the monetary means to obtain the drug (Bickel et al. 1998). The second guiding principle for a behavioral-economic understanding of drug dependence is that consumption of reinforcing commodities such as a drug of abuse may be affected by the concurrent availability of other reinforcers

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(Vuchinich and Tucker 1988). That is, there may be interactions between abused drugs and other reinforcing commodities. Behavioral economics interprets the notion of interactions between commodities by quantitatively analyzing demand, and, again, the concept of elasticity is important. The analyses determine crossprice elasticity – so named because it describes the relation between the price of one commodity and the consumption of another commodity. The quantitative analyses determine the appropriateness of describing an interaction between commodities with one of three terms: substitute, complement, or independent. The three panels of Fig. 2 (from Johnson and Bickel 2003) illustrate these three categories of the demand curve interrelations for two different commodities. We know, for example, that increasing the price of Coca Cola will cause an increase in the consumption of Pepsi even though the price of Pepsi remains unchanged. This defines Pepsi as a behavioral-economic substitute for Coca Cola. The demand curves for this relationship are portrayed in the top panel of Fig. 2. We also know that the consumption of hot-dog buns will go down if the price of hot-dog franks goes up, even if the price of hot-dog buns does not change. Thus hotdog buns conform to the behavioral-economic definition of a complement to hotdog franks (Fig. 2, bottom panel). Substitutes and complements may be construed as occupying the opposite ends of a continuum of relationships between two commodities. Between the two extremes of the continuum lie independents. Ballpoint pens constitute a commodity independent of bread, for example, because while an increase in the price of bread causes consumption of bread to decrease (consistent with the law of demand), it would have no effect on the rate of consumption of ballpoint pens (Fig. 2, middle panel). The unqualified behavioraleconomic terms “substitute,” “independent,” and “complement” imply asymmetrical – that is, unidirectional – relationships. It is also true, for example, that increasing the price of Pepsi, but not Coca Cola, will cause an increase in the consumption of Coca Cola. This additional information about the demand for Coca Cola and Pepsi justifies calling them symmetrical substitutes. Behavioral-economic research has demonstrated how analysis of cross-price elasticities and the terms of the substitute–complement continuum may be used to describe the interaction between a reinforcing drug and another commodity. Bickel et al. (1995) reanalyzed data and determined cross-price elasticities from 16 drug self-administration studies that had arranged concurrently available reinforcers, at least one of which was a drug. The reanalysis showed that (a) each type of interaction was observed across different types of experimental arrangements; (b) in the studies reporting individual-subject data, the types of interactions between commodities generally were consistent across subjects, thus supporting the general appropriateness of the classification category; and (c) the interactive relations between the commodities were not always symmetrical. The fact that the studies used a variety of drug reinforcers (methadone, cocaine, PCP, pentobarbital, etonitazene, ethanol, coffee, cigarettes, morphine) and several species (rats, humans, baboons, and rhesus monkeys) lends support to the general descriptive utility of the concepts in the substitute–complement continuum as a framework for describing interactions with substances of abuse.

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Recent studies have used the concepts of the substitute–complement continuum to explicate issues of drug abuse. For example, cigarette smokers’ cross-price elasticities for the nicotine gum, denicotinized cigarette, and money commodities have been determined in operant laboratories so as to explore the extent to which nicotine gum and denicotinized cigarettes are economic substitutes for cigarettes (Johnson et al. 2004; Shahan et al. 2000). Another experiment explored the interaction effects of nicotine gum (a pharmacological substitute for cigarettes) and money (an economic independent) by making each of them available concurrently

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with cigarettes in one condition and both available with cigarettes in another condition (Johnson and Bickel 2003). These studies have revealed the weakness of alternative commodities containing nicotine (i.e., pharmacological substitutes) as economic substitutes for cigarettes, with the implication that nonpharmacological variables such as the sensations experienced in the act of inhaling may be important reinforcers that contribute to the maintenance of smoking (Shahan et al. 1999, 2001). In sum, behavioral-economic analyses have revealed that there are distinctions between economic substitute and pharmacological substitute concepts that should not be ignored by those in the field of drug addiction (Carroll et al. 1989; Hursh and Bauman 1987; Johnson and Bickel 2003; Lea and Roper 1977). Behavioral-economic studies of commodity interactions have led to a better understanding of polydrug misuse (Sumnall et al. 2004) or abuse (Petry 2001; Petry and Bickel 1998; Spiga et al. 2005) in natural situations. They have established classes of drugs that are economic substitutes (Petry and Bickel 1998) or complements (Petry 2001; Spiga et al. 2005), and determined the directionality of these relations – that is, whether they are symmetrical or asymmetrical (Petry and Bickel 1998), and if asymmetrical, which drug is the substitute or complement (Petry 2001; Spiga et al. 2005). Behavioral-economic study of interactions with drugs of abuse can determine the populations of users susceptible to undesirable outcomes in natural environments with numerous drugs available. Such studies can generally be the basis for developing an understanding of the mechanisms of economic substitution or complementarity (Petry and Bickel 1998; Spiga et al. 2005). This will enable drug-abuse practitioners to devise therapies that promote healthy behaviors that function as substitutes for addictive drugs and complements to healthy living.

3 Delay Discounting and Drug Dependence Another behavioral-economic concept that has recently garnered much interest is delay discounting, wherein a delay to the receipt of a commodity reduces the subjective value or utility of that commodity. Drug-dependent individuals are often faced with choosing between a small and immediate reinforcer (e.g., drug high or intoxication) and larger, but delayed reinforcement (e.g., better health, improved social standing, and improved interpersonal relationships), and they are often mired in a pattern of preference for the former choice. This pattern of substance abusers discounting delayed commodities differently from nonabusers suggests that a better understanding of delay discounting processes may advance the understanding of drug dependence; the extent to which someone discounts delayed reinforcement may be an important individual trait among those susceptible to drug dependence, or a driving state in the lives of drug-dependent individuals. Behavioral-economic procedures for measuring the extent of an individual’s delay discounting have been devised. In a typical human-subject assessment of discounting, a participant is given a series of binary choices (trials) between receiving a smaller, immediate reward and a larger, delayed reward (e.g., between

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receiving $10 now or $100 in 6 months). A given series of trials will assess the participant’s present subjective value for a particular large-magnitude reward to be received at a specified distance in the future; it is as if one series of trials resulted in a statement of this nature by the participant: “receiving $37 now has the same value to me as waiting 6 months to receive $100.” The magnitude of the smaller immediate reward ($37 in this example) is called the indifference point for the large amount ($100 in this example) to be received at the particular time in the future (6 months in this example). Several series of trials are administered to the participant in a delay discounting assessment procedure. Each is for the same large amount of reward, but each is for that reward amount to be received at a different distance in the future. Thus, several indifference points are determined for the same large amount, but each represents the receipt of that reward at a different time in the future. When a number of indifference points have been determined for the participant by using an assessment procedure, a function can be plotted showing the participant’s subjective value for the large reward as a function of its receipt at future times – her discount function. Such a function will decrease as a function of time till future receipt – a pictorial illustration of the discounting of delayed rewards. The equations that best describe discount functions – also called utility functions – have been the subject of much scientific exploration. Traditional economic and financial models of discounted utility estimate that discounting occurs as an exponential function of delay (Becker and Murphy 1988; Kirby 1997). Thus, (1) shows that as delay d increases, the subjective value of the delayed reward v decreases at an exponential rate, k. Since the discount rate is used to describe the rate of devaluation of the commodity with passage of time to its receipt, discount functions with high k values are portrayed as steeply falling discounting curves, and low values of k indicate shallow discounting curves. v ¼ ekd

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An extensive body of literature has compared exponential and hyperbolic models of discounting (see review in Green and Myerson 2004), and the hyperbolic model has been shown to generally describe human choice behavior regarding delayed rewards better than the exponential model. In addition to the superior fit of Mazur’s equation to observed data, the hyperbolic model theoretically accounts for the ubiquitous phenomenon of preference reversals, whereas the exponential model does not. Preference reversals refer to the

The Behavioral Economics of Drug Dependence 1.0 0.8

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common experience of planning to engage in a beneficial activity when the activity is some temporal distance away, only to fail to follow through when time for the activity arrives. Common are experiences like planning to get up early to exercise only to hit the snooze button the next morning and return to sleep, or departing for a restaurant with the intention of skipping desert only to have a breakdown of willpower when the desert cart happens to be rolled by one’s table. Changes in preference are more than an anecdotal phenomenon, however, as they have been observed in controlled laboratory conditions with either human or nonhuman animal subjects (e.g., Green and Estle 2003). Some of the essential problems in drug dependence (planning to quit but not following through; quitting and relapsing) conceptually map onto the phenomena of preference reversals and delay discounting, so a scientific understanding of these phenomena may be critical to understanding drug dependence. To be sure, numerous studies have empirically shown that drug-dependent individuals differ from nondependent individuals on measures of delay discounting (reviews in Bickel and Marsch 2001; Reynolds 2006). Findings indicate that steeper discount rates are found among alcoholics (Vuchinich and Simpson 1998), opioid users (Madden et al. 1999; Madden et al. 1997), cocaine users (Heil et al. 2006), cigarette smokers (Mitchell 1999; Bickel et al. 1999), and pathological gamblers (Petry and Casarella 1999) compared to controls. In addition to the theoretical understanding gained by differentially associating high discount rates with substance-abusing populations, the discount rate measure may be useful as a diagnostic tool of treatment success. Some recent research indicates that a person’s rate of delay discounting may predict the ability to abstain from cigarette smoking. Yoon et al. (2007) measured rates of discounting among female smokers who became pregnant and spontaneously quit smoking. Numerous prenatal and postnatal delay discounting assessments were collected along with indicators of smoking status (maintained abstinence or relapse to smoking). These revealed that participants with high prenatal discount rates were more likely to return to smoking following childbirth, while those with low discount rates were more likely to remain abstinent. Dallery and Raiff (2007) used a laboratory model of

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relapse and showed that cigarette smokers with high delay discounting rates were more likely to relapse in their experiment than smokers with low discount rates. Finally, Krishnan-Sarin et al. (2007) found that adolescent smokers who highly discounted rewards in an experiential discounting task were less likely to maintain abstinence during a cessation program than adolescents who discounted less. Though the results to date are limited to nicotine addiction, further work may show that delay-discounting rates can predict abstinence from other substances of abuse.

4 Delay Discounting as a Measure of Time Perception and Temporal Horizon Delay discounting measures have often been referred to as indexes of impulsivity (Monterosso and Ainslie 1999; Bickel and Marsch 2001). However, impulsivity is a complex construct (Evenden 1999; Eysenck 1993; Parker et al. 1993) that is difficult to define and is operationalized in numerous ways. Thus, efforts have been made to clarify the relationship between impulsivity and delay discounting (Bickel et al. 2008; Reynolds et al. 2008). The ability to inhibit a prepotent response has been considered a behavioral indicator of impulsivity as assessed via self-report measures. Thus, some researchers have made comparisons among self-report measures of impulsivity, measures of behavioral inhibition, and measures of delay discounting. These studies have produced mixed results. While several studies have found correlations between delay-discounting rates and self-report measures of impulsivity (Kirby et al. 1999; Mobini et al. 2007), others have found no such relationship (Mitchell 1999; Dom et al. 2006; de Wit et al. 2007; Reynolds et al. 2006). In this absence of consensus about what delay discounting rates may be indexing, two alternate hypotheses have been proposed. One hypothesis suggests that measures of delay discounting reflect individual differences in time perception. This approach is based on evidence that delaydiscounting tasks engage brain structures related to the perception of time (Wittmann et al. 2007). The hypothesis proposes that the internal clock of a steep discounter operates at a faster speed than normal, so that a few seconds of inaccuracy each minute turn into hours or days of inaccurate time perception over longer periods of time (Wittmann and Paulus 2008). According to this hypothesis, a drugaddicted individual is a person who discounts future reinforcers excessively, and they do this because they overestimate the temporal distance to the delayed reinforcer, which subjectively reduces its value. The hypothesis thus suggests that it is the reduced value of distant rewards due to a malfunctioning internal timing mechanism in the brain that causes drug addicted individuals to prefer the immediate rewards of drug effects over the distant rewards associated with nondrugrelated behavior. This proposed relationship between delay discounting and time perception has to date been the subject of little empirical investigation.

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A second hypothesis suggests that (delay) discounting is considered a measure of temporal horizon, or the windows of time individuals use to view or make decisions. The temporal horizon concept has previously been operationalized in assessments that asked participants to name/describe events that may occur in the future (Future Time Perspective; Wallace 1956). According to the present hypothesis involving temporal horizon, events in an individual’s temporal window, either in the past or the future, have the capacity to affect current behavior while events outside of the window cannot. The notion that either future or past events may affect current behavior has recently appeared in a generalization of the delay discounting construct to the notion of temporal discounting – the discounting of events in both the future and past. Past discounting refers to a reduction in the subjective value of previous outcomes. The discounting of past events has been studied, and the discounting of future and past events appear to share qualitative features (e.g., the hyperbolic function) and are highly correlated (Yi et al. 2006; Bickel et al. 2008). The symmetry of future and past discounting in the more general measure of temporal discounting makes temporal discounting comparable to the measure of an individual’s temporal horizon, likewise a symmetrical concept. Also, because the notion of temporal discounting is inclusive of both past and future discounting it has a feature that conceptually differentiates it from the concept of impulsivity. The notion of past discounting refers to an individual’s preference for having received a reward at a point in the more distant past compared to having received it just prior to the present. As neither of these events involves possible acquisition in the future, they also do not involve a prepotent future-oriented response that is subject to possibly being inhibited. This absence of possible inhibition differentiates past discounting – and by generalization, temporal discounting – from the concept of impulsivity. Rather than discounting being an index of impulsivity, the present hypothesis implies that the degree that an individual discounts is an index of the size of their temporal window, and that size determines the distance, in both the future and past, of events that can influence present behavior. Thus, this hypothesis states that a steep discounter has a limited view (horizon) of the future and has difficulty learning from experiences too far in the past, or even remembering what has occurred beyond their past temporal horizon. Though research to date bearing upon the temporal horizon hypothesis of discounting is suggestive, future work will undoubtedly put it to the test.

5 Neural Correlates of Delay Discounting Recently, neurobiological research has incorporated brain-imaging technologies to study the areas of brain activation during the deliberation about intertemporal events that occurs in discounting assessments (McClure et al. 2004; McClure et al. 2007). The application of brain imaging technologies to identify the neural correlates of delay discounting and other behavior falls within the domain of a nascent discipline called neuroeconomics. By combining neuroscience, psychology, and economics,

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neuroeconomics provides a neurobiological framework for understanding behavior. Recently, neuroeconomic approaches, and functional magnetic resonance imaging (fMRI) in particular, have been applied toward the understanding of decisionmaking processes. The fMRI brain-scanning paradigm identifies differences in the magnetic properties of nonoxygenated and oxygenated blood levels in brain regions. The use of fMRI is based on the assumption that increases in brain activation produce an increase in oxygen metabolism, which in turn increases blood flow to the activated regions. Since active regions do not consume all the concentration of oxygen in the blood, the local concentration of oxygenated blood in those regions increases. An increase in oxygenated blood compared to baseline is measured as a difference in magnetic resonance and is assumed to represent the increase in neural activation in the associated brain region. The fMRI procedure has been used to examine the effects of decision-making tasks on neural activation and has found differential brain region activation, which depends upon the decision alternatives made available to the participant being scanned. Evidence suggests that specific brain regions are involved in different tasks (Jentsch and Taylor 1999). Namely, the prefrontal cortex is involved during cognitive processes such as considering the future, while the amygdala, an area associated with more emotional responses, is activated when the participant makes a response associated with an immediate outcome. For example, McClure et al. (2004) examined brain activation using an intertemporal decision-making procedure, where participants chose between alternatives that had short or long delays to the receipt of different monetary values; the more immediate alternative had a lower monetary value than the later alternative, and receipt of the two alternatives were separated by a minimum delay of 2 weeks. Using fMRI, McClure et al. found differential activation in limbic areas such as the ventral striatum, medial orbitofrontal cortex, and medial cortex when the binary choice pair included an alternative with an immediate outcome. In contrast, when all decision epochs were analyzed, including those involving both immediate and delayed alternatives, the lateral and prefrontal cortices were activated uniformly. Additionally, McClure et al. determined which areas of the brain were activated under different amounts of decision difficulty. They proposed that areas involved in decision-making would be differentially activated depending on the similarity or difference between the participant’s available choice alternatives. They construed decision difficulty as the percentage difference between the smaller and larger alternative; large differences were classified as easier decisions, while small differences were considered to constitute more difficult tasks. As they expected, activation was greater in areas associated with cognition, the dorsolateral prefrontal and parietal cortices, when the subjects were given difficult rather than easy decisions (see also Kable and Glimcher 2007). McClure et al. (2007) later found similar results in an experiment in which primary reinforcers, small volumes of juice squirts, were used as outcomes of the participants’ choices. Some fMRI results suggest that specific brain regions appear to be activated by positive and negative feedback. Hariri et al. (2006) examined individual differences

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in a delay discounting procedure where participants were given positive and negative feedback about their responses that were ultimately to be rewarded by the receipt (or not) of money. Hariri et al. found that individuals with the strongest preference for immediate rewards exhibited the greatest activation in the ventral striatum for both positive and negative reward, while differential activation of the ventral striatum in response to negative versus positive feedback also covaried with interindividual variability in delay discounting. Consistent with the results of McClure et al. (2004), Hariri et al. also found that activity in the dorsolateral prefrontal cortex was significantly negatively correlated with individual preference for immediate rewards. The neurobiological consensus from these neuroimaging experiments is that separate brain regions are differentially activated by delayed and immediate outcomes. Specifically, difficult decisions about delayed rewards increase neural activation in the dorsolateral prefrontal cortices and parietal regions, while easier decisions about immediate rewards increase activation in limbic regions. This phenomenon has stimulated the development of models conceptualizing addiction as the result of a competition between two separate and interdependent brain regions.

6 Competing Neurobehavioral Systems and Addiction Addiction models hypothesizing the competition between neural systems or brain regions can be traced to the “somatic marker” framework (Damasio 1994). This theory proposes that decision-making is based on the collection of bodily sensations and brain signals that mediate affective and emotional responses. These somatic markers are associated with particular situations; negative somatic markers warn the organism of future negative events, while positive somatic markers signal future reinforcement. Moreover, this hypothesis assumes behavior is the result of activation from two separate regions: the limbic region, associated with processing emotion (centered around the amygdala), and the cortical region, associated with cognitive tasks (centered around the prefrontal cortex). The competing neurobehavioral systems hypothesis further develops that model by proposing that the impulsive (limbic) and executive (cortical) regions compete for overall dominance (McClure et al. 2004; Bickel et al. 2007) and that addiction, or behavior oriented to immediacy, is the result of signals from cortical areas being overwhelmed by signals from limbic areas. Implicit in the competing neurobehavioral systems hypothesis is the assumption that affective signals are valued on a common neural “metric” allowing a composite signal to emerge from the activation of different brain regions. This hypothesis could explain why some people become addicted while others do not. The argument suggests that brain differences affect individuals’ decisions between using and not using drugs. In some individuals, a relatively strong impulsive system or relatively weak executive system makes them vulnerable to the immediately reinforcing effects of an addictive substance.

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Thus, these individuals’ executive systems are quickly overwhelmed by signals from the impulsive systems associated with drug use. Research across a wide group of studies suggests that brain regions associated with the impulsive system are critical to drug sensitization and responsiveness (Jentsch and Taylor 1999). For example, Harmer and Phillips (1998) found that repeated exposure to amphetamine can produce dopaminergic sensitization within the amygdala and can facilitate appetitive learning. Similarly, Harmer et al. (1997) found that prior treatment with D-amphetamine enhanced dopamine release in the amygdala of nonhumans. Furthermore, Lee and Ma (1995) found that injections of amphetamine and cocaine increased norepinephrine release in the hippocampus and reduced avoidance latencies to foot shock, while another study showed that lesions to the amygdala had an opposite effect, impairing foot shock avoidance in a similar procedure (Cestari et al. 1996). Some of the research has emphasized the amygdala as the locus of sensitization to drug-related cues. Using imaging technology on drug-dependent humans, Grant et al. (1996) found that drug-associated stimuli (syringes, pictures of white powder, etc.) produced significant increases in metabolic activity within the amygdala. Moreover, nonhuman studies have found the amygdala to be associated with stimulus–response contingencies related to relapse (Meil and See 1995). These results suggest that drug-related stimuli impact the amygdala; they acquire functions in conditioned autonomic and instrumental responding, and they lead to drugseeking behavior. There is also evidence of changes to cortical regions as a function of drug use. These regions are assumed to control an array of cognitive skills such as planning, inhibition, decision-making, memory, etc. (Collette et al. 1999; Fey 1951; Robbins 1990). Damage to these regions increases preference for immediate rewards (Damasio 1996) while lesions have been shown to result in behavioral perseveration and inhibitory deficits (Dias et al. 1996a). Drug consumption has been associated with similar cognitive deficits. For example, long-term exposure to drugs of abuse has been found to have adverse affects on the various executive functions of the frontal cortex. Such effects include decreased activation during cognitively intensive tasks and potentiation of autonomic responses (Jentsch and Taylor 1999), as well as deficits in attention, verbal memory and delayed recall, and behavioral disinhibition (Castner and Goldman-Rakic 1997; McKetin and Mattick 1998; Rogers et al. 1999; Olausson et al. 1998). Together, the effects of substances of abuse on amygdala and cortical regions support the competing neurobehavioral systems hypothesis: drug consumption is associated with deficits of the executive system and exaggerated responding of the impulsive system. Related research has suggested that there exist “hot” and “cold” decision-making processes, where the former is associated with emotional and autonomic responses, and the latter is associated with more cognitive and rational decisions (Glimcher and Rustichini 2004). Theorists have suggested that these “hot” and “cold” processes combine during decision-making, where each system offsets the other (Seguin and Zelazo 2005). In general, these hypotheses are

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consistent with other neuroeconomic theories that implicate separate neural regions for impulsive and self-controlled choice (Boettiger et al. 2007). Some of the research suggesting differences in brain region activation of nonaddicted and drug-dependent individuals during their discounting of delayed rewards has been in the neuroimaging area. For example, Paulus et al. (2002) compared 10 methamphetamine-dependent subjects with 10 age- and educationmatched controls and measured performance on 2 decision-making tasks during an fMRI session. Compared to the control subjects, the methamphetamine-dependent subjects had greater preference for immediate outcomes and had less activation in the dorsolateral prefrontal cortex. To summarize, separate brain regions appear to be involved in the decision-making process and an individual’s choice of immediate over long-term alternatives is associated with a dominance of limbic system responding over responding originating in cortical areas, and this predominance of the impulsive system may be an important contributor to addiction.

7 Conclusion In this chapter, we have briefly reviewed the behavioral economics and neuroeconomics of addiction. We described how economic factors such as demand intensity and elasticity can provide detailed understanding of the effects of price on drug consumption, and how alternatively available commodities can impact drug taking. We also have reviewed the research on intertemporal choice and data that addicts discount the future substantially more than nonaddicts. Importantly, as this research began to use imaging approaches, findings were obtained that helped develop and support the competing neurobehavioral decision system’s hypothesis. As a result, this has furthered the view that addiction is an excess of control over an individual’s behavior by the impulsive system relative to the executive system. This hypothesis about addiction is important because it suggests that interventions in addiction should have two targets – the impulsive system and the executive system. Specifically, it suggests the impulsive system needs to be constrained and the executive system should be strengthened. Although the exact means of accomplishing those results remain to be concretely determined and tested, this view provides excitingly new hypotheses to examine. Another area that remains to be explored is the neural correlates of demand and price. Surprisingly, this important aspect of the behavioral economics of addiction has yet to be developed. Perhaps advances in this area will indicate that addicts’ decisions relating to demand and price are predominately influenced by the impulsive system, so that immediate reinforcement from addictive drugs dominates the individual’s personal economy. It would be important to examine the effects of therapeutic strengthening of the executive system: would this restore the balance between the two systems, producing an individual with greater demand for the commodities involved in better health rather than those involved in drug consumption?

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The various concepts and procedures that together define this translational research domain provide the real opportunity to shape our understanding of addiction and to impact our approach to interventions. The successful exploration of this potential will require that more addiction researchers employ the methods and analytical approaches of behavioral economics and neuroeconomics. We hope that this chapter furthers that development, and that it results in a consilience of scientific approaches to addiction. Acknowledgments The writing of this chapter was supported by National Institute on Drug Abuse Grants R37 DA 006526-18, R01 DA 11692-10, R01 DA022386-02, and R01 DA02408001A1, Wilbur Mills Chair Endowment, and in part by the Arkansas Biosciences Institute, a partnership of scientists from Arkansas Children’s Hospital, Arkansas State University, the University of Arkansas-Division of Agriculture, the University of Arkansas, Fayetteville, and the University of Arkansas for Medical Sciences. The Arkansas Biosciences Institute is the major research component of the Tobacco Settlement Proceeds Act of 2000.

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Novel Pharmacological Approaches to Drug Abuse Treatment Ellen Edens, Alfredo Massa, and Ismene Petrakis

Contents 1 2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Nicotine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 2.1 Neurobiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 2.2 Treatment Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346 3 Alcohol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 3.1 Neurobiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 3.2 Treatment Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 4 Cannabis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356 4.1 Neurobiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356 4.2 Treatment Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 5 Stimulants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360 5.1 Neurobiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360 5.2 Cocaine Treatment Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 5.3 Methamphetamine Treatment Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 6 Opioids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 6.1 Neurobiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 6.2 Treatment Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371

Abstract The field of pharmacologic addiction treatment is expanding rapidly. While there are currently several FDA-approved medications for nicotine, alcohol, and opiate dependence, research into novel pharmacological approaches for these and additional substances is legion. Each drug of abuse, while sharing a common

E. Edens, A. Massa and I. Petrakis (*) West Haven Veterans Administration Medical Center, #116-A 950 Campbell Avenue, West Haven, CT 06516, USA e-mail: [email protected]

D.W. Self and J.K. Staley (eds.), Behavioral Neuroscience of Drug Addiction, Current Topics in Behavioral Neurosciences 3, DOI 10.1007/7854_2009_29, # Springer‐Verlag Berlin Heidelberg 2009, published online 15 September 2009

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final neural pathway of increasing dopaminergic tone, has unique and individual characteristics that are important in developing improved and varied treatments. In this chapter, we discuss such research and present the neurobiological underpinnings of these explorations. In general, addiction treatment is focused on four areas: (1) reducing withdrawal discomfort, (2) diminishing cravings, (3) blocking rewarding effects of the drug, and (4) treating comorbidities, such as depression or ADHD. We present current ideas in pharmacologic research for nicotine, alcohol, cannabis, stimulants, and opiates. Keywords Pharmacotherapy Alcohol Stimulants Nicotine Marijuana Opioids Treatment

Abbreviations ADHD AG BID CB CM CNS CSAT CYP DAT DATA FAAH FDA GABA GVG LAAM MAO MAOI NAc NAChR NMDA NRT OPRM RCT SSRI THC VTA 5-HT

Attention deficit hyperactivity disorder Arachidonoylglycerol Twice daily Cannabinoid Contingency management Central nervous system Center for Substance Abuse Treatment Cytochrome P450 Dopamine transporters Drug Addiction Treatment Act Fatty-acid amide hydrolase Food and Drug Administration g-Aminobutyric acid Gamma-vynil GABA Levo-alpha acetyl methadol Monoamine oxidase Monoamine oxidase inhibitor Nucleus accumbens Nicotine acetylcholine receptors N-methyl-D-aspartate Nicotine replacement therapy m-Opioid receptor Randomized controlled trial Selective serotonin reuptake inhibitor Tetrahydrocannabinol Ventral tegmental Area Serotonin

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1 Introduction An explosion in neuroscience research has led to improvements in our understanding of the neurobiology of addictive disorders and in their potential treatments. These disorders share a common neurobiology of reward, but also differ with regards to specific effects on neurotransmitters. Treatments for these disorders have targeted both common and specific sites of action. In general, there is considerable evidence that all drugs of abuse converge on a common circuitry in the brain’s limbic system (Nestler 2005). Despite disparate mechanisms of action involving neurotransmitters such as glutamate, g-aminobutyric acid (GABA), serotonin, endogenous opioids, and endocannabinoids, considerable evidence has emerged showing that each drug of abuse ultimately acts via the dopaminergic projections of the ventral tegmental area (VTA) of the midbrain to the nucleus accumbens (NAc ) pathway to increase dopaminergic transmission, thus mediating the acute rewarding effects of all drugs of abuse as well as the chronic changes in reward associated with addiction. Other brain systems that play a vital role in the addictive process and interact with the VTA-NAc pathway include the amygdala, hippocampus, hypothalamus, and several regions of the frontal cortex. Some of these brain areas are central to the brain’s memory system and play a key role in producing powerful emotional memories important in the path to addiction (Nestler 2005). In this chapter, the five major classes of drugs of abuse will be reviewed for their basic neurobiology, treatments targeting, withdrawal and abstinence initiation, and potential new pharmacologic treatment for relapse prevention. These include nicotine, alcohol, cannabis, stimulants (cocaine and methamphetamine), and opiates. Finally, some general principles about treatment of addictive disorders will be addressed.

2 Nicotine 2.1

Neurobiology

When inhaled, nicotine rapidly crosses into the brain where it binds to nicotine acetylcholine receptors (nAChR), ligand-gated ion channels that open upon nicotine binding. The a4b2* nAChR is the most abundant nAChR type found in mammalian brains and is thought to be the primary mediator of nicotine dependence (Maskos et al. 2005; Picciotto et al. 1998). Stimulation of nAChRs results in the release of multiple neurotransmitters, dopamine being the most important in mediating the addictive properties of nicotine (Benowitz 2008b). Additionally, nicotine directly augments the release of glutamate and, with chronic administration, inhibits the release of GABA – both synergistically enhancing dopamine release. With chronic smoking, the activity of brain monoamine oxidase (MAO,

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an enzyme responsible for metabolizing monoamines such as dopamine, norepinephrine, and serotonin) is reduced, thus even further increasing these neurotransmitters in the brain.

2.2 2.2.1

Treatment Approaches Withdrawal/Abstinence/Initiation

Nicotine withdrawal is characterized by increased appetite, poor concentration, insomnia, irritability, reduced heart rate, anxiety, and low mood (Benowitz 2008a). With repeated nicotine exposure, nAChRs become upregulated in the brain in response to nicotine-induced receptor desensitization (Benowitz 2008b). Current thinking suggests that craving and withdrawal symptoms occur when previously desensitized receptors regain their functionality in the absence of smoking, such as after a night of sleep. This is supported by research showing that typical daily smokers maintain levels of nicotine enough for near saturation of the brain receptors (Benowitz 2008b; Brody et al. 2006). Also contributing to low rates of success at quitting, a relative dopamine depletion, and subsequent feeling of general anhedonia, occurs upon nicotine cessation. Conditioned behaviors (pairing of the pharmacologic actions of a drug with specific behaviors) also promote continued smoking even when the majority of nAChRs are saturated and desensitized (Balfour 2004; Benowitz 2008b).

Nicotine Replacement Therapies Nicotine replacement therapies (NRT) were the first proven effective medications for the treatment of nicotine dependence and remain a mainstay and first-line pharmacotherapy in the treatment of nicotine withdrawal symptoms. NRTs come in the form of gum, patch, lozenge, sublingual tablet (not available in the United States), nasal spray, and inhaler. Analyses comparing the effectiveness of different preparations do not demonstrably support one form over another (Buchhalter et al. 2008). Novel approaches using NRT utilize unique delivery systems. Currently, three new formulations are being researched: the straw, nicotine drops, and a pulmonary inhaler (delivery mechanism different than currently available inhalers). To conceptualize the nicotine straw, tiny nicotine beads are attached inside and swallowed while consuming a beverage. With the straw and drops, nicotine is absorbed via the intestine rather than bucally, as with the gum or lozenge. Safety and pharmacokinetic studies are limited by potential drug–beverage interactions (Buchhalter et al. 2008; D’Orlando and Fox 2004; Westman et al. 2001). Research on pulmonary inhalers is spurred by the promise of delivering nicotine to the lungs in a manner similar to inhaled cigarettes, thus allowing for rapid reduction in withdrawal symptoms as well as mimicking more closely the behavioral and

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sensory aspects of smoking. Current inhalers deliver nicotine bucally only to the mouth and throat. Drawbacks include technical challenges to engineering such a device as well as potential abuse liability. While NRTs have aided many people worldwide to quit smoking, they do not meet the needs of all smokers and have limited utility in long-term relapse prevention.

Nicotinic Receptor Antagonists/Partial Agonists Mecamylamine is a nicotine antagonist with antihypertensive effects. It does not appear to trigger withdrawal (Eissenberg et al. 1996) and may help with smoking cessation by reducing craving, negative affect, and appetite (Buchhalter et al. 2008; Rose et al. 1998). Randomized controlled trials (RCT) evaluating mecamylamine and the nicotine patch together have yielded conflicting results on its utility in enhancing cessation rates (Glover et al. 2007; Rose et al. 1994, 1998). Varenecline, a partial agonist of the a4b2 and full agonist of the a7 nACh receptors, has become an additional first-line option for smoking cessation pharmacotherapy (Fiore et al. 2008; Hays and Ebbert 2008). Varenecline (marketed as Chantix) gained Food and Drug Administration (FDA) approval in 2006 after several RCTs demonstrated its efficacy and safety tolerability (Gonzales et al. 2006; Jorenby et al. 2006; Nides et al. 2006; Oncken et al. 2006). Varenecline binds to the a4b2 nACh receptor triggering partial stimulation – and subsequent release of dopamine in the brain reward center – while simultaneously and competitively inhibiting nicotine delivered by cigarettes (Hays and Ebbert 2008). Varenecline is typically dosed 1 mg twice daily. This dosing is achieved after an upward taper over a week’s period prior to an individual’s quit smoking date. Six months of therapy has been shown superior to 12 weeks in achieving abstinence (Tonstad et al. 2006).

Antidepressants It has long been recognized that individuals with depression suffer disproportionately from nicotine dependence, that cessation may precipitate symptoms of depression or a major depressive episode, and that nicotine may have antidepressant properties (Hughes et al. 2007). These observations provide rationale for research using antidepressants in the treatment of nicotine dependence. Studied antidepressants include: bupropion, nortriptyline, reboxetine, venlafaxine, fluoxetine, paroxetine, meclobromide, and selegiline. Currently, only bupropion and nortriptyline show clear efficacy in smoking cessation (approximately doubling the quit rate) and only bupropion is FDA approved for use in both nicotine dependence and depression. Bupropion has both adrenergic and dopaminergic actions and appears to antagonize the nicotinic acetylcholinergic receptor (Hughes et al. 2007). Smokers interested in quitting are encouraged to begin bupropion 1 week prior to a set quit date,

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similar to varenecline. The dose is typically tapered from 150 mg daily to 150 mg twice daily over 3 days and maintained for at least 7 weeks. Nortriptyline is considered second-line treatment in smoking cessation. According to the U.S. Surgeon General’s report, it is generally started 10–28 days before a quit attempt at a dose of 25 mg daily, increasing gradually to a target dose of 75– 100 mg per day and continuing for 12 weeks or up to 6 months (Fiore et al. 2008). Despite the high comorbidity with major depression, selective serotonin reuptake inhibitors (SSRIs), including fluoxetine, paroxetine, and sertraline, have generally provided disappointing results for smoking cessation. One large multicenter, placebo-controlled trial showed improved short-term abstinence rates in individuals taking fluoxetine compared to placebo; however, this effect was lost at 6-month follow-up (Hughes et al. 2007; Niaura et al. 2002). Venlafaxine, an inhibitor of both serotonin and norepinephrine, was studied in combination with NRT and showed no main effect of treatment on abstinence. Post hoc analysis, however, demonstrated some limited efficacy among light smokers both at the end of treatment and at 1-year follow up (Cinciripini et al. 2005). Reboxetine, currently available only in Europe, is a selective norepinephrine uptake inhibitor that also inhibits nAChR activity. While no human trials have been published for smoking cessation, reboxetine is known to decrease nicotine self-administration in animals, and thus warrants continued investigation into its utility (Foulds et al. 2006; Rauhut et al. 2002). Meclobomide and selegiline, two monoamine oxidase inhibitors (MAOI), have also been evaluated as potential treatments (George and Weinberger 2008). Meclobomide is a reversible MAO-A inhibitor not currently available for use in the United States. The sole study evaluating efficacy in smokers showed a statistically significant improvement in self-reported abstinence rates; however, only trend significance was found in verified cotinine plasma levels (Berlin et al. 1995). Selegiline, an irreversible inhibitor of MAO-B available for use as an antidepressant and treatment for Parkinson’s disease in the U.S., has shown more promise for smoking cessation. In one trial, smokers treated with selegiline combined with NRT showed double the abstinence rates at 1-year follow-up (25 vs. 11%) compared with smokers given placebo with NRT. This effect, however, was not powered to show statistical significance (Biberman et al. 2003). Additionally, besides inhibiting MAO-B, a recent study demonstrated selegiline as also an inhibitor of CYP2A6, the enzyme primarily responsible for nicotine metabolism. Thus, there are two potential mechanisms by which selegiline may aid in smoking cessation (Siu and Tyndale 2008). In summary, some, but not all, antidepressants have demonstrated efficacy in the treatment of nicotine dependence.

Central Adrenergic Agonists Central a2-adrenergic agonists, such as clonidine and lofexidine, decrease noradrenergic cell firing and subsequent neurotransmitter release, thus reducing overall noradrenergic activity (Carter 1997). It is hypothesized that these agents prevent the

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sympathetic overload accompanying various drug withdrawal states and have been found to reduce various symptoms of opiate, alcohol, and cannabis withdrawal (Baumgartner and Rowen 1987; Gossop 1988; Hart et al. 2008). A multitrial metaanalysis of clonidine’s effectiveness upon smoking cessation at 12-week follow-up found small but significant benefit of use. The authors noted potential study biases, however, and warned that side effects may limit clonidine’s effectiveness (Gourlay et al. 2004). Clonidine, though not FDA approved, is considered a second-line therapy and is typically dosed 0.10 mg twice daily oral or 0.10 mg day1 transdermal, increasing by 0.10 mg day1 per week if needed (Fiore et al. 2008).

Immunotherapy Interest in immunotherapy for the treatment of substance use disorders is emerging. The majority of currently available pharmacotherapies act centrally in the brain to block the rewarding properties of or reduce withdrawal symptoms from drugs of abuse. Yet, what if the drug could be neutralized peripherally before ever reaching the brain? This is the basic premise upon which the development of nicotine vaccines lies. Currently, all vaccinations for nicotine in the pipeline are conjugates, meaning nicotine is linked to a carrier protein capable of mounting an immune response. To date, three companies have begun clinical trials of their antinicotine vaccines (Cerny and Cerny 2008). Two studies have demonstrated efficacy as long as a sufficient antibody level is achieved (Cornuz et al. 2008; Hatsukami et al. 2005; Maurer and Bachmann 2007).

Other Medications Naltrexone and nalmefene, two opioid antagonists, have been evaluated as potential treatments for nicotine dependence. While naltrexone is currently FDA approved for treatment of alcohol dependence, two studies failed to demonstrate its efficacy in treating nicotine withdrawal symptoms or promoting smoking cessation (Buchhalter et al. 2008). More recently, however, pretreatment with naltrexone prior to cessation was found to be more helpful among individuals with higher levels of depressive symptomatology (Walsh et al. 2008). This point highlights an emerging understanding among researchers and clinicians that no single treatment will be a panacea for addiction. Rather, treatments will need to be tailored to the specific characteristics of individuals in order to maximize effectiveness. Rimonabant is a cannabinoid-1 (CB1) antagonist temporarily approved (and most recently suspended due to psychiatric side effects) for use in Europe in the treatment of obesity (Buchhalter et al. 2008). Rimonabant may aid smoking cessation by activating and restoring balance to the endocannabinoid system and, in particular, addressing concerns regarding postcessation weight gain. A meta-analysis of three trials (n ¼ 1567) indicate dosing of 20 mg daily conferred a 1½-fold increase in

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odds of quitting but its effect on maintaining abstinence remains inconclusive (Cahill and Ussher 2007). GABAergic neurons provide inhibitory input to the dopaminergic neurons projecting from the VTA to the NAc. Thus, medications affecting GABA neurotransmission may confer benefits for addiction treatment by modulating the dopaminergic tone in these brain regions integral to the reward process. To date, research on vigabatrin, baclofen, gabapentin, and tiagabine (all GABA-related medications) has been minimal in smoking cessation and the few trials available have not shown demonstrable efficacy (Buchhalter et al. 2008; Cousins et al. 2001; Sofuoglu et al. 2005; White et al. 2005). 2.2.2

Relapse Prevention

Relapse to smoking following a period of cessation appears to be the rule, rather than the exception. At this time, no pharmacotherapy is indicated for long-term use. However, a 2009 meta-analysis of behavioral and pharmacologic relapse prevention interventions concluded that extended treatment with varenecline – but not bupropion – may aid in relapse prevention (Hajek et al. 2009; Tonstad et al. 2006). Additionally, the authors noted that additional studies with extended use of NRT are warranted. While complete abstinence from drugs of abuse, particularly illicit ones, remains the preferred goal of treatment, there nonetheless remains interest in helping individuals reduce consumption of licit substances either as an intermediate goal to abstinence or in lieu of abstinence altogether. This approach undergirds interest in pharmacotherapies targeting the cytochrome P450 (CYP) system. Nicotine is primarily metabolized into cotinine via this system and genetic variations in the allele coding for the CYP2A6 enzyme have been linked to differences in nicotine metabolism and smoking behaviors (Schoedel et al. 2004; Tyndale and Sellers 2002). Certain ethnic groups with higher rates of slow nicotine metabolism have been shown to take in less nicotine per cigarette with reduced risk of lung cancer (Benowitz et al. 1999, 2002). Thus, inhibiting CYP2A6 may reduce nicotine intake among smokers, and thereby reduce adverse consequences. At this time, given the current socio-political climate and known serious health consequences to even lowrate smoking, harm reduction approaches tend to be less well supported than abstinence approaches to treating nicotine dependence.

3 Alcohol 3.1

Neurobiology

Alcohol, like all drugs of abuse, increases the release of dopamine in the brain’s mesocorticolimbic system (Weiss et al. 1993). However, unlike stimulants, which directly increase dopamine, alcohol’s mechanism of action is believed to work

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through indirect effects on modulating neurotransmitters. For example, acute alcohol administration leads to inhibition of GABA in the VTA and NAc that, in turn, disinhibits the dopaminergic neurons found there. Chronic alcohol use sensitizes the system and leads to relative dopamine deficiency (Koob and Le Moal 2001; Petrakis 2006). Alcohol directly influences the GABAA receptor complex, but it is plausible that its effects on the GABA receptors are modulated via other GABA mechanisms as well (Krystal et al. 2006; Petrakis et al. 2001a). The glutamate receptors include the N-methyl-D-aspartate (NMDA) receptors, the a-amino-3-hydroxy-5-methyl-4-isoxazoleproprionate (AMPA) receptors, and kainate receptors (Dingledine et al. 1999). The NMDA glutamate receptor plays a central role in the neuropharmacological effects of alcohol (Tsai et al. 1995). Alcohol is an NMDA receptor antagonist (Hoffman et al. 1989) and it has been hypothesized that the changes in the NMDA receptor or its function may underlie the neurobiological changes associated with alcohol dependence, withdrawal, and related behavioral phenomena such as “craving” (Davis and Wu 2001). The endogenous opioid system has also been implicated in the rewarding effects of alcohol. Alcohol modulates release of opioid neuropeptides, and this effect is more striking in animals bred for alcohol preference (Froehlich 1997). Moreover, human studies have shown central deficiencies of endorphins in alcohol-dependent individuals (Genazzani et al. 1982). In animal studies, serotonergic function has consistently been associated with the regulation of alcohol intake. Specifically, a central serotonergic deficiency correlates with high alcohol intake. In contrast, human studies have not reliably detected abnormal serotonergic function in alcohol-dependent individuals (Petrakis et al. 1999, 2001b; Roy and Linnoila 1989). Nonetheless, recent evidence is emerging implicating serotonin dysregulation in a subset of individuals with alcohol dependence, highlighting the notion that alcohol dependence is likely a heterogeneous disorder (Johnson et al. 2000a; Johnson 2000).

3.2

Treatment Approaches

3.2.1

Withdrawal/Abstinence

Alcohol withdrawal is characterized by a wide range of symptoms from mild autonomic instability, tremor, nausea, and psychiatric distress (including anxiety, insomnia, psychomotor agitation, and dysphoria) to more severe cases with accompanying hallucinations, delirium tremens, seizures, and even death. The presence of withdrawal symptoms is influenced by both the frequency and quantity of drinking (Petrakis 2006). The goals of an optimal pharmacologic treatment for alcohol withdrawal include: (1) treating immediate symptoms, (2) preventing complications, and (3) initiating long-term preventive therapy (Ait-Daoud et al. 2006).

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Benzodiazepines and Barbiturates Benzodiazepines and barbiturates are cross-tolerant at the GABAA receptor and benzodiazepines, in particular, have been the mainstay of pharmacologic treatment of alcohol withdrawal up to the present (Petrakis 2006). These medications, however, have significant limitations including their abuse potential, pharmacologic interaction with alcohol, and their cognitive and psychomotor side effects (AitDaoud et al. 2006). Additionally, there is some evidence that their use may increase the risk of relapse, particularly in individuals with genetic predisposition to alcohol and comorbid anxiety and personality disorders (Longo et al. 2002; Malcolm et al. 2002). Given these concerns, interest remains in developing additional therapies for alcohol withdrawal in particular, genetically driven treatments that may provide more specificity and compliance.

Nonbenzodiazepine GABA Modulators Carbamazepine and valproate, both commonly used anticonvulsants, have been evaluated in the treatment of alcohol withdrawal and found to be safe alternatives to benzodiazepines with several significant advantages including reduced adverse side effects, no demonstrable abuse potential, and no potentiation of alcohol’s cognitive and psychomotor effects (Ait-Daoud et al. 2006; Longo et al. 2002; Malcolm et al. 2001, 2002). Although not completely understood mechanistically, these medications may suppress “kindling” via facilitation of GABA neurotransmission and subsequent suppression of excitatory glutamatergic transmission (Ait-Daoud et al. 2006; Hillemacher et al. 2006; Petrakis 2006). The “kindling hypothesis” proposes that heavy alcohol use with repeated withdrawal episodes is associated with neuronal changes and increasingly severe alcohol withdrawal symptoms (Brown et al. 1988). Gabapentin, an anticonvulsant with structural similarity to GABA, garnered initial interest when clinical cases and open-label trials indicated it might have particular efficacy in reducing generalized tonic-clonic seizures associated with alcohol withdrawal (Bonnet et al. 2003; Myrick and Anton 1998; Rustembegovic et al. 2004). However, a 2003 controlled study of gabapentin 400 mg qid compared to placebo found no differences in the frequency or severity of withdrawal symptoms (Ait-Daoud et al. 2006; Bonnet et al. 2003). Additionally, a recent doubleblind RCT found the anticonvulsants gabapentin and valproic acid to be no better than placebo for mild to moderate withdrawal symptoms, preventing relapse, or reducing depressive symptoms (Trevisan et al. 2008). Baclofen is a GABAB receptor agonist approved for the treatment of spasticity. Consistent with preclinical, case reports, and open-label studies (Addolorato et al. 2002b, 2003; Colombo et al. 2000), a 2006 randomized single-blind study compared baclofen to diazepam in patients needing alcohol detoxification. Both medications significantly decreased alcohol withdrawal symptoms with no significant between-medication differences (Addolorato et al. 2006; Leggio et al. 2008).

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Other Medications Though most detoxification strategies in current clinical use exert effects via the GABAergic system, there is theoretical evidence to support the use of glutamatergic agents, such as topiramate, in treating alcohol withdrawal. A 2005 in-patient study demonstrated that topiramate 50 mg daily (N ¼ 25) was as efficacious as lorazepam up to 4 mg daily (N ¼ 27) in treating alcohol withdrawal, and allowed for simple transition to out-patient care on the same regimen without the potential for abuse or the increased risk of relapse commonly seen in alcoholics treated with benzodiazepines (Choi et al. 2005).

3.2.2

Relapse Prevention

As with smoking, relapse is common among alcohol-dependent individuals with up to 25–50% of treated individuals returning to alcohol use in the 2 years post treatment and vulnerability to relapse persisting for years (Kalivas and Volkow 2005). Research suggests that drug relapse is a complex phenomenon moderated by both biological and environmental factors (Koob and Le Moal 2001; Sinha et al. 2003). Historically, research into the pharmacotherapy of drug addiction has focused on modulating dopamine function. Yet, direct dopaminergic agents have yielded disappointing results in preventing relapse despite the plausible hypothesis that agonists would decrease drug intake/drinking by mimicking alcohol properties and antagonists could interfere with alcohol’s pleasurable effects (Petrakis 2006). Instead, more recent evidence has emerged that once addicted, the final common pathway for drug-seeking behavior is the glutamatergic projection from the prefrontal cortex to the accumbens (Gass and Olive 2008; Kalivas and Volkow 2005). Thus, pharmacotherapies targeting this system may provide novel approaches for relapse prevention.

Glutamate Modulators Acamprosate, FDA approved in 2004 for use in alcohol-dependent individuals, is believed to exert its effect through antagonism of NMDA glutamate receptors. Clinically, it may help patients achieve abstinence by reducing distress and interfering with the processes of reward and conditioning (Mason 2001; Petrakis 2006; Weinstein et al. 2003). Acamprosate obtained FDA approval largely on the basis of European studies, as the multisite US trial did not find an overall effect. Post-hoc analysis of this US trial, however, suggested therapeutic benefit in a subgroup of individuals with a goal of abstinence (Mason et al. 2006) and a large meta-analysis of 20 studies confirmed this finding (Mann et al. 2004). Still, a recent large, multisite, clinical trial conducted in the US did not demonstrate acamprosate’s superiority to placebo on percent days abstinent or return to heavy drinking

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(Anton et al. 2006), though complete abstinence was not measured. The typical acamprosate dosing is two 333 mg tablets three times daily. Other glutamatergic medications under investigation include memantine and topiramate. Clinical trials with memantine, an NMDA antagonist currently approved for use in Alzheimer’s disease, are ongoing, though a preliminary double-blind pilot study yielded negative results compared to placebo (Evans et al. 2007). Topiramate has demonstrated more positive results in clinical trials and is one of the most promising new medications for alcohol dependence. A double-blind RCT of 150 alcohol-dependent individuals treated with either 300 mg daily of topiramate or placebo found improvement in multiple clinical outcome domains, including measures of drinking and overall quality of life and well-being ratings (Johnson et al. 2003, 2004; Ma et al. 2006). A subsequent 14-week multisite trial of 371 men and women replicated the findings of improved drinking outcomes.(Johnson et al. 2007) In fact, the effect size for topiramate is larger than for any other medication to treat alcohol dependence, including that of naltrexone. However, topiramate’s utility is somewhat compromised by the side effect burden. This same trial reported retention rates among those randomized were 61.2% for the topiramate group and 76.6% for the placebo group (p < 0.001). m-Opioid Antagonists In 1994, oral naltrexone, an antagonist at the m-opioid receptor, was FDA approved for use in the treatment of alcohol dependence based on two relatively small studies (total n ¼ 167) demonstrating a modest but significant effect on maintaining abstinence rates and reducing relapse for 12 weeks in recently abstinent alcoholics (Johnson 2008; O’Malley et al. 1992; Volpicelli et al. 1992). Two subsequent large meta-analyses upheld the findings of oral naltrexone’s efficacy in reducing relapse (Bouza et al. 2004; Srisurapanont and Jarusuraisin 2005). The effect size from these study reviews, however, appeared small, with a number needed to treat of 7 (i.e., seven individuals requiring treatment with naltrexone in order to prevent one individual’s relapse) (Johnson 2008). Certain clinical characteristics have been associated with greater effectiveness of oral naltrexone in preventing relapse, including strong cravings for alcohol, and having a high familial loading for alcohol dependence (Monterosso et al. 2001). Naltrexone is typically dosed at 50–100 mg daily. Because of relatively good tolerability and ease of dosing, naltrexone may be particularly useful in primary care settings where alcohol dependence is commonly encountered. A major threat to the efficacy of oral naltrexone is the issue of compliance (Volpicelli et al. 1997). Consequently, three extended-release formulations of naltrexone for deep intramuscular injection have been developed. One of these, Vivitrol (Alkermes, Inc., Cambridge, MA, USA), gained FDA approval after a large, double blind, RCT demonstrated reduced heavy drinking days in men – but not women – with alcohol dependence receiving a high-dose (380 mg) intramuscularly (Garbutt et al. 2005). The lack of efficacy in women has been ascribed to less familial loading for alcohol dependence, higher placebo response, greater affective

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symptoms, more clinical heterogeneity or, perhaps, naltrexone’s effects on hormones mediating the menstrual cycle (Johnson 2008; Rossmanith et al. 1989). In addition to oral and depot naltrexone, there is some evidence that another m-opioid antagonist, nalmafene, might be efficacious in treating alcohol dependence (Mason et al. 1999) though initial findings failed replication in a relatively small multisite study (Anton et al. 2004). Nalmafene has a relative lack of doserelated hepatotoxicity, a decided advantage over naltrexone in this population at risk for hepatic injury; however, the evidence is too scarce to promote its clinical utility (Petrakis 2006). There has been considerable recent interest in the moderating effects of genetic variation in the m-opioid receptor (OPRM) upon response to opioid antagonist pharmacotherapy. It has been proposed that individuals with the Asp variant of a m-opioid receptor (OPRM1) gene exhibit a greater response to opioid antagonist therapy (Oslin et al. 2003). Replication studies, however, have yielded mixed results (Anton et al. 2008; Arias et al. 2008; Gelernter et al. 2007).

Serotonergic Modulators Preclinical studies in animals support the notion that selective serotonin reuptake inhibitors (SSRIs) suppress ethanol consumption (Boyce-Rustay et al. 2006; Gill et al. 1988) and that medication effects diminish when they are discontinued (Johnson 2008; Murphy et al. 1988). Despite this, clinical trials using SSRIs have generally yielded disappointing results (Gorelick and Paredes 1992; Kabel and Petty 1996; Kranzler et al. 1995). More recently, there has been renewed understanding in how differing serotonergic agents might produce different results depending on the particular subtype of alcoholism an individual has (Johnson et al. 2000a; Johnson 2000). Using Cloninger’s classification scheme,(Cloninger 1987) Type I alcoholics are characterized by later age of onset, greater anxiety and guilt, and lower familial loading for alcohol dependence. In contrast, Cloninger Type-2 alcoholics demonstrate an early age of onset (before 25), greater antisocial and impulsive traits, and high familial loading. Solid evidence exists supporting the notion of serotonergic dysfunction in Type-2 compared with Type-1 alcoholism (Ballenger et al. 1979; Fils-Aime et al. 1996; Johnson et al. 2000a; Virkkunen and Linnoila 1990). Despite this, research has not shown benefit of SSRIs in this earlyonset typology. Instead, surprisingly two studies have demonstrated improved drinking outcomes among Type-1 late-onset individuals when treated with SSRIs (Dundon et al. 2004; Pettinati et al. 2000). Recent decades have generated an explosion of knowledge in serotonin receptor subtypes and functions and this knowledge is being applied to the addiction research field. The serotonin-1 (5-HT1) partial receptor agonist, buspirone, for example, has been examined for its efficacy in reducing ethanol consumption. Buspirone is FDA approved for the treatment of generalized anxiety disorder. While buspirone has not shown efficacy among alcohol-dependent individuals without comorbidity, there might be benefit among alcohol-dependent individuals with a comorbid anxiety

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disorder (Malec et al. 1996). Ondansetron is a serotonin-3 (5-HT3) antagonist that has shown more promise, particularly for individuals with Type-2, early-onset, alcoholism. Two studies (combined n = 361) demonstrated ondansetron was superior to placebo in drinking outcomes, including fewer drinks per day and drinks per drinking day, and found these benefits augmented in early- versus late-onset alcoholism (Johnson et al. 2000a; Kranzler et al. 2003). This line of research reiterates an emerging theme in addiction research: dependence syndromes are likely heterogeneous, thus requiring individualized therapies to optimize outcomes. Other Medications Disulfiram exerts its effect through inhibition of alcohol dehydrogenase, an enzyme responsible for metabolizing alcohol’s primary metabolite, acetaldehyde. In large quantities, acetaldehyde is toxic to humans. Disulfiram has been used in the treatment of alcohol dependence since the 1940s, is FDA approved, and works as an aversive therapy. Once taken, it will prevent further metabolism of acetaldehyde and the individual will experience flushing, sweating, headache, nausea, and vomiting. Association of these symptoms with drinking discourages alcohol intake. Because of the risk of the disulfiram–alcohol interaction, individuals using disulfiram should be highly motivated to quit drinking (Johnson 2008). However, disulfiram is limited in patient acceptability. There are also some reports of disulfiraminduced psychosis. Although such reports were generally found in early literature where higher doses of disulfiram were used, nevertheless, disulfiram should be used with caution in patients who are vulnerable to develop psychosis. It has since been recognized that disulfiram affects dopamine beta-hydroxylase centrally and may be the mechanism by which this side effect is produced. Additionally, this mechanism has been hypothesized to contribute to its potential to treat cocaine dependence (discussed later). A typical daily dose of disulfiram is 250 mg daily. Care should be taken to ensure an individual has not consumed alcohol within 12 h of dosing (Antabuse, 2001) Finally, there is some preliminary evidence that baclofen, a GABAB receptor agonist, reduces cravings and may have a place in relapse prevention (Addolorato et al. 2002a, 2006; Leggio et al. 2008).

4 Cannabis 4.1

Neurobiology

For years, conventional wisdom held that marijuana (Cannabis sativa) was not truly addictive; however, several recent discoveries have disproved this assumption. First, it is now known that D9-tetrahydrocannabinol (D9-THC), the main psychoactive ingredient of marijuana, activates the mesocorticolimbic system, the

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same system responsible for the reinforcing properties of all drugs of abuse (Nordstrom and Levin 2007; Tanda and Goldberg 2003; Tanda et al. 1997). Second, a withdrawal syndrome consisting of irritability, anxiety, depressed mood, decreased appetite, sleep difficulty, and physical discomfort has now been validated and well characterized (Budney et al. 2007; Nordstrom and Levin 2007). D9-THC acts primarily through the endocannabinoid system in the brain. This system modulates diverse physiologic functions including motor function, memory, motivation and drive, pain, and emotion (Clapper et al. 2008; Piomelli 2003; Xie et al. 2007) and is widely distributed throughout the brain. While understanding of the neurobiology has lagged behind other prototypical drugs of abuse, the field has accelerated substantially since the identification and cloning of the first cannabinoid receptor subtype, CB1, in 1990 (Matsuda et al. 1990). CB1 is one of the most abundant neuromodulatory receptors in the brain and is expressed at high levels in the hippocampus, cortex, cerebellum, and basal ganglia. It is believed that D9-THC acts in humans primarily via presynaptic CB1 receptor activation (Huestis et al. 2001; Maldonado et al. 2006; Tanda and Goldberg 2003; Van Sickle et al. 2005; Wilson and Nicoll 2002). Two endocannabinoids, anandamide and 2-arachidonoylglycerol (2-AG), have been identified as important neurotransmitters for the endocannabinoid system. These neurotransmitters are thought to be deactivated via a two-step process. First, the endocannabinoids are taken up into neurons via as-yet-unidentified protein transporters and second, are hydrolyzed intracellularly by specific hydrolases. Current research into novel pharmacotherapies for cannabis dependence includes CB1 receptor agonist/antagonists and inhibitors of endocannabinoid degradation. Additional therapeutic options will depend upon further characterization and understanding of the cannabinoid system.

4.2 4.2.1

Treatment Approaches Withdrawal/Abstinence Initiation

Cannabis withdrawal is now a well-recognized clinical entity with symptoms including insomnia, irritability, appetite suppression, anxiety, restlessness, and low mood (Budney and Hughes 2006; Budney et al. 2004). Unlike nicotine and alcohol, there are currently no FDA-approved medications for the treatment of any aspect of cannabis dependence including withdrawal, relapse prevention, or harm reduction. Research in this area, however, is burgeoning (Nordstrom and Levin 2007).

Cannabinoid Receptor Agonists Oral D9-THC, also known as dronabinol, has shown great promise in treating withdrawal symptoms. In a human laboratory study, dronabinol, compared with

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placebo, was shown to improve sleep, maintain appetite, and decrease anxiety and craving; it had no effect on irritability or social withdrawal (Haney et al. 2004). Several later out-patient studies – performed among nontreatment-seeking individuals – again demonstrated dronabinol’s efficacy in reducing withdrawal symptoms, with higher dosages producing greater suppression of symptoms (Budney et al. 2007; Haney et al. 2008). Significantly, intoxicating effects were not observed (Elkashef et al. 2008a).

Central Adrenergic Agonists As with nicotine and alcohol withdrawal, preclinical data demonstrates cannabis withdrawal is associated with noradrenergic hyperactivity (Lichtman et al. 2001) and blocking this overactivity may help alleviate various symptoms of cannabis withdrawal (Hart 2005). A recent small (n ¼ 8) in-patient study among nontreatment-seeking male regular marijuana users compared the efficacy of four different dosing regimens upon withdrawal symptoms and relapse prevention: placebo, oral D9-THC only, oral lofexidine (central a2-receptor agonist), and finally, a combination of both oral D9-THS and lofexidine. Oral D9-THC reversed some withdrawal symptoms but did not decrease marijuana relapse, while lofexidine did not robustly attenuate withdrawal but did decrease marijuana relapse. The combination treatment produced the most robust improvement in withdrawal symptomatology and relapse prevention (Haney et al. 2008).

Mood Stabilizers and Antidepressants There is some preclinical data suggesting a role for lithium in attenuating cannabis withdrawal. Lithium was found to dose-dependently prevent symptoms of cannabinoid withdrawal in rats. The investigators speculated that this action was not due to lithium’s mood stabilizing effects – as valproic acid, another mood stabilizer, did not attenuate symptoms – but rather a result of lithium’s enhancement of oxytocin activity in the central nervous system (CNS). This hypothesis was based on the fact that oxytocin administration mimicked lithium’s effect upon withdrawal, and pretreatment with an oxytocin antagonist blocked lithium’s action (Cui et al. 2001). A recent small open-label in-patient study among treatment-seeking individuals found some benefit of lithium in alleviating withdrawal symptoms (Winstock et al. 2009). Consistent with the preclinical finding in rats that valproic acid did not reduce withdrawal symptoms (Cui et al. 2001), a study in humans evaluating divalproex effects on marijuana withdrawal found it actually worsened mood and cognitive performance (Haney et al. 2004). Published trials on bupropion, like divalproex, found it worsened symptoms during withdrawal as well (Haney et al. 2001). It may be that a medication with stimulant side effects, such as bupropion, is ill-suited for cannabis withdrawal symptoms. As such,

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nefazodone, an antidepressant thought to exert its action by antagonizing the serotonin 5-HT2a receptor with sedative and anxiolytic effects, has been studied. During withdrawal, nefazodone reduced anxiety and muscle pain, but had limited effect on other withdrawal symptoms (Elkashef et al. 2008a; Haney et al. 2003b).

4.2.2

Relapse Prevention

CB1 Antagonists Rimonabant, the first specific CB1 receptor antagonist discovered, has been shown to block dysfunctional craving of food and drugs and is currently undergoing study in the treatment of obesity, smoking cessation and alcohol abuse (Howlett et al. 2004; Xie et al. 2007). As might be expected given its mechanism of action, pretreatment with rimonabant has been shown to decrease marijuana drug effect in a dose-dependent fashion (Huestis et al. 2001). At this time, however, and despite its promise, there is a paucity of clinical evidence to promote use in relapse prevention (Elkashef et al. 2008a). Additionally, there is concern for psychiatric side effects which prompted Europe to suspend its approval of rimonabant for obesity treatment.

Opioid Antagonists Early preclinical work in animals suggested opioid antagonists block the reinforcing effects of cannabinoids (Kaymakcalan et al. 1977; Navarro et al. 1998). This discovery laid the groundwork for trials in humans. Disappointingly, however, such trials have not demonstrated effectiveness in blocking the subjective effects of cannabis (Wachtel and de Wit 2000). Rather, a small study (n ¼ 9) among regular marijuana smokers actually found naltrexone pretreatment increased the subjective effects of oral D9-THC (Haney et al. 2003a).

Anandamide Deactivation Inhibitors After reuptake into neurons and glia, anandamide, one of the two known endogenous cannabinoids, is hydrolyzed by the activity of fatty-acid amide hydrolase (FAAH) (Desarnaud et al. 1995). Some current preclinical research utilizing FAAH inhibitors to enhance anandamide signaling in the brain has shown the ability to potentiate stress-coping behaviors, oppose the anhedonic effects of stress, and promote normal positive responses to pleasurable stimuli in rodents (Clapper et al. 2008). No studies have been conducted in humans.

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Other Medications Buspirone, an anxiolytic with serotonergic actions, has shown preliminary promise for use in marijuana-dependent individuals. A 12-week open-label trial used flexible dosing of buspirone up to a maximum of 60 mg day1. The participants had used marijuana for an average of 13 years. The median follow-up time was 23 days. Statistically significant reductions in frequency and duration of craving and degree of irritability were found. Additionally, there was a nearly 80% reduction in use observed during the study. The authors conclude a placebo-controlled trial is warranted (McRae et al. 2006). As with its use in treating withdrawal symptoms, a 14-week placebocontrolled trial of divalproex observed no group difference on any outcome measure – retention, psychological measures of outcomes, or urine analyses (Levin et al. 2004). Other studies, ongoing or recently completed, are evaluating efficacy of the antiparkinson drug, selegiline, antidepressants (venlafaxine, fluoxetine, atomoxetine), GABAergic medications (gabapentin and baclofen), and the cannabinoid agonist, dronabinol, in treating marijuana dependence (Health 2008).

5 Stimulants 5.1

Neurobiology

While all drugs of abuse ultimately exert their actions on the VTA-NAc reward pathway, stimulants such as cocaine and methamphetamine do so directly. By inhibiting dopamine reuptake from the synaptic cleft via binding to dopamine transporters (DAT), cocaine increases synaptic dopamine availability. Like cocaine, methamphetamine elevates dopamine concentrations at the extracellular level by inhibiting dopamine reuptake at the DAT and actually promotes dopamine release from the presynaptic neuron. Methamphetamine is taken up into the intracellular space and may produce toxicity as dopamine accumulates and undergoes oxidation (Carboni et al. 2001; Davidson et al. 2001). Additionally, stimulants block norepinephrine and serotonin transporters. Glutamate and GABA neurons have also shown importance in the neurobiology of stimulant addiction (Kalivas 2007a, b). While there are no current FDA-approved pharmacotherapies for treating stimulant dependence, hundreds of published trials exist evaluating medications for potential to aid in the initiation of abstinence and/or prevent relapse. Medications targeting various transmitter systems implicated in addiction generally, and stimulants specifically, including dopamine, glutamate, and GABA, have been investigated.

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Cocaine Treatment Approaches

Cocaine withdrawal syndrome, while not life threatening – as is alcohol and sedative/hypnotic withdrawal – nor as medically uncomfortable as opiate withdrawal, nonetheless, describes a cluster of symptoms including intense cocaine craving, sleep disturbances, fatigue, anhedonia, apathy, dysphoria, carbohydrate craving, anxiety, and decreased activity level (Brower et al. 1988; Cottler et al. 1993; Kampman et al. 1998). Moreover, the presence of severe withdrawal symptoms has predictive validity for poorer outcomes, highlighting the clinical utility of ameliorating cocaine withdrawal symptoms (Kampman et al. 2001a; Sofuoglu et al. 2006). While many agents investigated for use in cocaine dependence have targeted these symptoms, the majority of trials have focused on relapse prevention. Many medications have been identified because of their mechanism of action and so, for purposes of this review, they will be grouped this way. These include medications primarily modulating dopamine, adrenergic blockers, and medications affecting GABA and glutamate. Other medications, including acetylcholine receptor agents and immunotherapies, have also been evaluated. 5.2.1

Dopaminergic Agents

Bupropion represents a novel class of antidepressant with dopaminergic and adrenergic – but little serotonergic – effects. As such, its side effect profile differs from other antidepressants with diminished weight gain and sexual dysfunction (Stahl et al. 2004). Given bupropion’s actions upon dopaminergic and noradrenergic systems, bupropion was hypothesized to have efficacy for treating cocaine withdrawal. However, while a small (n ¼ 10) laboratory study found benefit in selfreported effects (Oliveto et al. 2001), randomized controlled trials (RCT) have not consistently supported these findings. Three double blind, placebo-controlled trials have been published using bupropion for cocaine dependence, two of which were conducted in methadone-maintained cocaine-dependent individuals. The first found no group differences between bupropion and placebo on primary outcome measures. A post-hoc analysis of individuals with high depression ratings, however, revealed a significant reduction in cocaine-positive urines among bupropion compared to placebo-treated individuals, suggesting bupropion may be more effective in individuals with comorbid depression (Margolin et al. 1995). The second study combined bupropion with contingency management (CM) (n ¼ 106) and reported more positive results in support of bupropion treatment (Poling et al. 2006). Adding to the inconsistencies, however, the third RCT found no better outcomes on any primary measure when comparing bupropion with placebo (both combined with cognitive behavioral therapy) (Shoptaw et al. 2008a). Thus, these results indicate that bupropion may be effective particularly in conjunction with certain psychosocial treatments, or for a subgroup of patients with comorbid psychiatric disorders (Levin et al. 2002; Margolin et al. 1995).

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Amantadine is an antiviral agent with low abuse potential used primarily for the treatment of Parkinson’s disease and extra pyramidal symptoms (Burgyone et al. 2004; Uitti et al. 1996). Its actions promote the release and inhibit the reuptake of dopamine. Additionally, amantadine antagonizes the glutamatergic NMDA receptor as well as cholinergic receptors (Vamvakides 1991; Wenk et al. 1995). Preclinical studies have suggested amantadine may decrease cocaine withdrawal symptoms (King et al. 1994). RCTs have been inconsistent. Some studies have found no effect of amantadine (Alterman et al. 1992; Kampman et al. 1996; Weddington et al. 1989). In contrast, two double-blind RCT found amantadine was associated with decreased cocaine cravings, greater study retention among participants, better global ratings of outcomes, and superior outcomes on urine screens compared with placebo (Kampman et al. 2000; Shoptaw et al. 2002). The latter included individuals with severe cocaine withdrawal symptoms. Disappointingly, however, a replication study was conducted that did not confirm these findings (Kampman et al. 2006). Bromocriptine is a dopamine agonist with high affinity for the D2 dopaminergic receptor and partial agonism at the D1 receptor. It is used for treatment of hyperprolactinemic disorders, Parkinson’s disease, and acromegaly (Gorska 2000; Jackson et al. 1988). Laboratory studies have found no effect of bromocriptine in the subjective response to cocaine (Kumor et al. 1989; Preston et al. 1990b). Two double-blind, placebo-controlled RCTs did not find bromocriptine better than placebo in treatment retention, cocaine positive urine samples, or craving (Gorelick and Wilkins 2006; Handelsman et al. 1997). Disulfiram is used in the treatment of alcoholism because it inhibits the enzyme aldehyde dehydrogenase. Disulfiram also inhibits the enzyme dopamine beta hydroxylase, which converts dopamine to noradrenaline, leading to an increase in dopamine concentration (Suh et al. 2006). Although disulfiram was first tested for cocaine dependence because of the high comorbidity between cocaine dependence and alcohol dependence (Carroll et al. 1998), its efficacy for cocaine dependence has been confirmed in nonalcohol-dependent individuals. Disulfiram has been found to promote cocaine abstinence within buprenorphine (George et al. 2000) and methadone-maintained opioid individuals (Petrakis et al. 2000). In the largest clinical trial to date (n ¼ 121), disulfiram in combination with two types of psychotherapy, was found more effective than placebo in reducing cocaine use even in the nonalcoholic patients (Carroll et al. 2004). Its use, however, has been limited by concerns about safety. Laboratory studies performed with controlled conditions have shown disulfiram increases plasma cocaine levels and decreases clearance. While no significant cardiovascular effects were found in two trials of disulfiram combined with intravenous or smoked cocaine, a study evaluating the cardiovascular response to intranasal cocaine combined with disulfiram demonstrated a significant increase in systolic and diastolic blood pressure and heart rate (Baker et al. 2007; Hameedi et al. 1995; McCance-Katz et al. 1998). As such, safety concerns due to the possible interaction effects with cocaine and with alcohol in clinical populations may limit its utility in clinical practice.

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Other investigators have tested the efficacy of agonist replacement medications such as methylphenidate, dextroamphetamine, and oral cocaine in the treatment of cocaine addiction and found them to be generally effective. While sustainedrelease methylphenidate was found to decrease cocaine use among cocainedependent patients with comorbid attention deficit hyperactivity disorder (ADHD) symptoms (Levin et al. 2007), the immediate-release form was not found to be effective in a similar sample (Schubiner et al. 2002). Additionally, sustained-release dextroamphetamine has been demonstrated to decrease cocaine use when compared with placebo (Grabowski et al. 2004; Shearer et al. 2003). Oral cocaine has been shown to decrease cocaine cravings and relapse in a caseseries of coca-paste smokers (Llosa 1994) as well as decrease the subjective and physiological responses to intravenous cocaine administration (Walsh et al. 2000). Although it is plausible that medications with slow release formulations have diminished abuse potential compared to immediate release forms, abuse liability, nonetheless, is of great concern when proposing agonist pharmacotherapies for the treatment of cocaine addiction.

Adrenergic Antagonists/Central Agonists Adrenergic blockers are widely used in the treatment of hypertension and other cardiovascular disorders (Lindholm et al. 2005). They have also been found helpful in performance anxiety (Hartley et al. 1983). Given the numerous physiological effects of cocaine mediated by the adrenergic system, research on adrenergic antagonists’ utility in cocaine addiction has been conducted. Disappointingly, laboratory studies have suggested that both peripherally (Sofuoglu et al. 2000b) and centrally acting adrenergic agents (Sofuoglu et al., 2000a) affect the cardiovascular system (heart rate and blood pressure) but not the subjective effects of cocaine ingestion. Clinical trials have found similar results (Kampman et al. 2006; Kampman et al. 2001b).

5.2.2

GABAergic Modulators

As previously mentioned, GABA systems interact with dopaminergic neurons to modulate dopaminergic tone in the reward centers of the brain. Preclinical models of cocaine relapse reveal that decreased GABA release in the ventral pallidum is associated with cocaine-seeking behavior and that GABA agonist injection in the CNS of primates and rats decreases dopamine extracellular levels (Brebner et al. 2000; Kalivas and Duffy 1995; Kita and Kitai 1988; Morgan and Dewey 1998). Gabapentin, tiagabine, and gamma-vynil GABA (GVG) are each GABA-related anticonvulsants studied for the treatment of cocaine addiction. Gabapentin is an agonist at the GABA receptor site. Tiagabine binds presynaptically to GABA transporter type-1, thereby blocking GABA reuptake. GVG is an irreversible

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inhibitor of GABA-transaminase and is not currently approved for use due to serious side effects, including visual field deficits (Adkins and Noble 1998; Angehagen et al. 2003; Letterman and Markowitz 1999). Gabapentin failed to show a decrease in the subjective effects of cocaine or the choice of self-administration in cross-over studies and was also found to be no better than placebo in decreasing cravings or increasing abstinence from cocaine in two double-blind, placebo-controlled trials (Bisaga et al. 2006; Gonzalez et al. 2007; Hart et al. 2007; Hart et al. 2004). Tiagabine was shown more effective than placebo in two studies (Gonzalez et al. 2003, 2007). However, concerns over the possibility of inducing seizures has limited its utility. There have been only two clinical studies about the safety and effectiveness of GVG in the treatment of cocaine addiction. These two open label studies conducted in a Mexican government designated addiction treatment showed GVG may prolong abstinence and have an impact upon cocaine cravings. Importantly, there was no evidence of visual field deficits in either of these studies (Brodie et al. 2003, 2005). Baclofen, a GABAB agonist, is FDA approved for the treatment of spasticity (Flannery et al. 2004). Preliminary studies have shown that baclofen may decrease cocaine self-administration and attenuate the subjective effects of cocaine (Brebner et al. 2000; Roberts and Andrews 1997). Two experimental in-patient studies testing baclofen for the treatment of cocaine addiction have been published with limited results. While one study found baclofen outperformed placebo in decreasing cocaine self-administration (Haney et al. 2006), no differences were found in either study on the subjective response to cocaine use (Rotheram-Fuller et al. 2007). Furthermore, a double-blind, placebo-controlled RCT tested the efficacy of baclofen on cocaine-dependent patients (n ¼ 70) and found significant reductions in positive urine drug screens in the baclofen-treated group but no between-group differences in cravings or treatment retention (Shoptaw et al. 2003). Valproic acid, a simple, branched-chain carboxylic acid, is available in four oral preparations: divalproex sodium, sodium valproate, divalproex sodium sprinkle capsules, and valproic acid, and is FDA approved for the treatment of seizure disorders and Bipolar I disorder (Davis et al. 2000). While three open label studies found valproic acid decreased cocaine use and cravings for cocaine (Halikas et al. 2001; Myrick et al. 2001; Salloum et al. 2007), one placebo-controlled trial found no differences between valproate and placebo in urine screens or self-report drug use (Reid et al. 2005b). As such, valproic acid has little current evidence to support its utility in the treatment of cocaine dependence.

5.2.3

Glutamatergic Modulators

Modafinil, an FDA-approved therapy for narcolepsy, is a promising investigational medication to treat cocaine addiction. Its hypothesized mechanism of action lies in its ability to increase metabolic pools of glutamate which, in turn, activate metabotropic glutamate presynaptic receptors and, ultimately lead to elevated extracellular

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glutamate levels (Ferraro et al. 1998). Additionally, modafinil binds dopamine and norepinephrine transporters (Madras et al. 2006) and decreases GABA activity (Ballon and Feifel 2006). Modafinil is a mild stimulant and may decrease symptoms of cocaine withdrawal (Dackis et al. 2003; Rush et al. 2002). Laboratory studies have shown modafinil attenuates cocaine self-administration, subjective effects of cocaine, cravings for cocaine, and cocaine-induced euphoria after intravenous and smoked cocaine administration in cocaine-dependent subjects (Ballon and Feifel 2006; Hart et al. 2008; Malcolm et al. 2006). The first double-blind, placebocontrolled, out-patient RCT showed the modafanil-treated group had significantly higher rates of negative urine screens compared to placebo; however, no differences were found in treatment retention, cocaine craving, and withdrawal measures (Dackis et al. 2005). A multisite, RCT tested modafinil’s efficacy in abstinence facilitation but found no statistically significant difference in cocaine nonuse days. A post-hoc analysis showed a significant effect of modafinil but only in the subgroup of cocaine-dependent patients without alcohol dependence (Elkashef et al. 2008b). Despite this trial’s negative findings, overall research findings suggest modafinil may still have a place in cocaine dependence treatment. Further studies are warranted. Topiramate is an anticonvulsive with complex mechanisms of action, including modulating voltage-activated sodium channel and cation influx (Stefan and Feuerstein 2007). One double-blind, placebo-controlled pilot study has been published examining topiramate’s role in the treatment of cocaine addiction. The study demonstrated improved abstinence rates during the last 5 weeks of the trial in the topiramate group compared to placebo (Kampman et al. 2004). This single positive result warrants replication in order to justify further conclusions or treatment recommendations. N-acetylcysteine, a prodrug that increases glutamate levels (Baker et al. 2003), has been shown safe and tolerable in healthy cocaine-dependent individuals by a double-blind, placebo-controlled cross-over study (LaRowe et al. 2006). A follow up study using similar methodology found attenuated cue reactivity with N-acetylcysteine (LaRowe et al. 2007), and a recent open label study demonstrated most participants taking N-acetylcysteine were either completely abstinent or had significantly decreased their cocaine use (Mardikian et al. 2007). To date, no RCTs have been conducted.

5.2.4

Immunotherapies

Like vaccines being developed for nicotine addiction, cocaine vaccines link a component of the cocaine molecule to a carrier protein known to reliably elicit an immune response from the vaccine recipient. The TA-CD vaccine, which links succinylnorcocaine to cholera toxin B, has been shown to produce dose-dependent specific antibodies against cocaine and is, generally, well tolerated (Kosten et al. 2002). This vaccine prevents cocaine from crossing the blood–brain barrier and

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blocks the euphoric effects of cocaine. A Phase IIA trial tested the immunogenicity, safety, and clinical efficacy of the TA-CD vaccine and found that subjects receiving the higher doses (2000 mg) produced higher mean antibody titers, and were significantly more likely to provide cocaine-free urines (Martell et al. 2005). These promising results merit further investigation.

5.2.5

Other Medications

Mecamylamine is an antihypertensive and nicotine receptor antagonist (Rose et al. 1989). Studies on cocaine craving discovered nicotine is able to accentuate cueinduced cocaine craving as well as rates of cocaine self-administration (Reid et al. 1998). Therefore, a nicotine antagonist, such as mecamylamine, has hypothetical treatment potential for cocaine addiction. One double-blind, placebo-controlled, cross-over study found cue-induced cocaine craving was reduced by mecamylamine (Reid et al. 1999). However, another RCT performed in opioid-maintained cocaine-dependent patients found no significant between-group differences in positive urine screens or reported abstinence rates (Reid et al. 2005a).

5.3

Methamphetamine Treatment Approaches

Compared with cocaine, many fewer RCTs have been conducted on potential medications for the treatment of methamphetamine addiction. Moreover, results have often been disappointing. For example, neither baclofen, gabapentin, ondansetron, nor mirtazapine have been found superior to placebo in decreasing cocaine use or cocaine cravings (Cruickshank et al. 2008; Heinzerling et al. 2006; Johnson et al. 2008). The antidepressant sertraline was actually associated with worse outcomes compared to placebo, including a higher number of positive urine screens and lower retention rates (Shoptaw et al. 2006). Similarly, a RCT using aripiprazole found it worsened drug use outcomes compared to placebo (Tiihonen et al. 2007). Nonetheless, some studies have shown potential promise. For example, one placebo-controlled RCT comparing the stimulant, methylphenidate, to placebo reported significantly fewer positive urines in the treated group (Tiihonen et al. 2007). Additionally, the antidepressant, bupropion, demonstrated some efficacy in increasing the number of weeks abstinent, though only in the subgroup of low to moderate methamphetamine users (Elkashef et al. 2008b; Shoptaw et al. 2008b). In a laboratory study using naltrexone, diminished subjective effects of methamphetamine were found, though no objective behavioral or physiological effects were observed (Jayaram-Lindstrom et al. 2004). Finally, a 9-week open label trial of the anticonvulsant vigabatrin was promising (Brodie et al. 2005).

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6 Opioids 6.1

Neurobiology

There are three main opioid receptors (m, g, k) as well as three main types of endogenous opioids (B-endorphins, enkephalins, and dynorphins). The term opioid is used to refer to the class of compounds and other synthetic chemicals that bind to one or more types of opioid receptors. The term opiate is correctly used only to refer to compounds directly synthesized from poppy plant products (Brownstein 1993). Like all drugs of abuse, opioids increase dopaminergic transmission. Opioids achieve this by inhibiting GABAergic interneurons in the VTA, which in turn disinhibits VTA dopamine neurons. They also act directly on the NAc by increasing dopamine through opioid receptors (Nestler 2005).

6.2 6.2.1

Treatment Approaches Withdrawal/Abstinence Initiation

Opioids have a clearly defined withdrawal syndrome characterized by restlessness, irritability, mydriasis, nausea, vomiting, abdominal cramping, tachycardia, hypertension, perspiration, rhinorrhea, and lacrimation. The onset and duration of withdrawal depends on the half-life of the opioid used. For example, heroin withdrawal symptoms typically start between 4 and 6 h and peak in 2–3 days, whereas methadone withdrawal typically begins 36–72 h after last use, peaking in 4–7 days (O’Connor and Fiellin, 2000). Because of the subjective distress associated with opioid withdrawal, withdrawal completion rates tend to be low and relapse following detoxification is common (O’Brien 2008; Stimmel et al. 1977). Different options for opioid detoxification are available and include: (1) substitution with another, typically longer-acting, opioid; (2) treatment of the underlying neurobiology using central a-adrenergic agents; and (3) symptomatic treatment of the withdrawal syndrome. m-Opioid Agonists Using one opiate to replace another in order to ease withdrawal symptoms has long been a tool employed by the addiction community. Methadone, one of the longer acting agonists, has historically been the preferred opioid used as substitution therapy. However, its use is restricted to in-patient settings or out-patient licensed programs. Thus, the 2002 FDA approval of buprenorphine, a partial m-opioid receptor agonist available for use in general out-patient settings, has helped to revolutionize opioid-substitution therapy and widened public access to such

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treatments (Fiellin et al. 2004). Methadone and buprenorphine should be started at low, but adequate, doses to ease withdrawal symptom onset (20–35 mg and 4–16 mg daily, respectively) and tapered slowly over several days or weeks to minimize withdrawal symptoms. As might be expected, discontinuation of these medications may also lead to withdrawal. Because methadone is a full agonist – as opposed to buprenorphine’s partial agonist activity – methadone is associated with a severer and longer-lasting withdrawal syndrome than buprenorphine at equivalent doses (Kosten and O’Connor 2003b).

Central Adrenergic Agonists Clonidine and lofexidene, both central a2-adrenergic agonists, are frequently used to treat symptoms associated with opioid withdrawal including tachycardia, tremors, sweating, and increased anxiety (Kosten and O’Connor 2003a). Lofexidene is a clonidine analogue and may be as effective as clonidine for opioid withdrawal while producing fewer symptoms of hypotension and sedation, a common side effect of clonidine (Lin et al. 1997). Rapid and ultra-rapid detoxification may use an opioid antagonist to precipitate withdrawal and then treat the symptoms with a2-adrenergic agonists, augmenting with sedatives, antiemetic agents, analgesics, and anesthetics. Studies, however, do not support the long term effectiveness of such rapid detoxification measures (Collins et al. 2005). A recent meta-analysis on the management of opioid withdrawal found that buprenorphine was more effective than clonidine, but found no significant differences when comparing completion rates between methadone and buprenorphine (Gowing et al. 2006).

6.2.2

Relapse Prevention

m-Opioid Agonists Methadone was synthesized for analgesia prior to World War II in Germany and is now used for the treatment of opioid dependence (Joseph et al. 2000; Strain et al. 1999). The successful work of Dole, Nyswander, and Kreek (Dole and Nyswander 1966; Dole et al. 1966) using methadone for opioid dependence set the stage for the extensive system of opioid maintenance treatment programs in existence today. Federal regulations and legislation such as the Narcotic Addict Treatment Act created a joint responsibility for methadone between the FDA and the Drug Enforcement administration (DEA) in order to ensure quality treatment while preventing diversion for illicit use. Methadone maintenance is now available through special licensed programs with oversight by the Center for Substance Abuse Treatment (CSAT) of SAMHSA (Fiellin and O’Connor 2002; Joseph et al. 2000). Methadone prevents withdrawal symptoms, cravings for opioids, and, at higher doses, may block the effects of other opioid agonists. Additionally, it aids in

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balancing the hormonal disruptions found in addicted individuals, such as down regulation of the hypothalamic-pituitary-adrenal axis and abnormal functioning of the hypothalamic-pituitary-gonadal axis (Kosten and George 2002). Methadone maintenance is associated with a significant decrease in morbidity and mortality; it reduces illicit drug use, crime, risk for human immunodeficiency virus infection, and death. It also improves employment and social adjustment (Ball et al. 1998; Barthwell et al. 1989; Bell et al. 1997; Caplehorn et al. 1994; Goldstein and Herrera 1995; Hser et al. 2001; Klee 1993; Rounsaville and Kleber 1985). Many studies have shown that higher doses of methadone are associated with lower rates of opioid use and improved retention in treatment (Amato et al. 2005). Recently, methadone has been associated with potentially life threatening cardiac outcomes including ECG changes and arrhythmias (Krantz et al. 2003). In 1993, the FDA-approved levo-a-acetylmethadol (LAAM), a methadone derivative for the treatment of opioid addiction. Because of its extremely long half-life, LAAM may be dosed every other day or three times a week (Finn and Wilcock 1997). Despite this advantage, reports of serious cardiac arrhythmias, both in the United States and in Europe, have limited its use (FDA 2001). Indeed, the product has been withdrawn from the United States market by its manufacturer. m-Opioid Partial Agonist/Antagonist Buprenorphine, marketed as Suboxone, is a combination tablet of both buprenorphine and naloxone. Naloxone, a m-opioid antagonist not absorbed orally or sublingually, will, nonetheless, precipitate acute withdrawal if taken intravenously by an opioid-dependent individual (Strain et al. 1997). This formulation was specifically developed to decrease the risk of diversion and overdose (Chiang and Hawks 2003; Preston et al. 1990a). Buprenorphine treatment has been shown to increase treatment retention and is as effective as methadone in the detoxification of opioid addicts (Bickel et al. 1988). Additionally, buprenorphine was found to significantly reduce opioid use when compared to low dose methadone and to be as effective as high dose methadone and LAAM in some (Johnson et al. 1992, 2000b; Strain et al. 1994), but not all (Connock et al. 2007), trials. Buprenorphine maintenance was effective in a primary care clinic and in opioid-dependent adolescents (Marsch et al. 2005; Woody et al. 2008). A study evaluating a long-acting buprenorphine formulation has been completed but is not yet published (Montoya and Vocci 2008). m-Opioid Antagonists Naltrexone, an oral competitive antagonist at the m-opioid receptor, prevents and reverses the effects of opioid agonists such as heroin. Naltrexone is currently approved by the FDA for the treatment of opioid and alcohol dependence. Four major reviews have been published in the last 6 years regarding the effectiveness of oral naltrexone. All of them conclude that there is limited evidence supporting the

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efficacy of naltrexone in the treatment of opioid dependence, although the quality of the evidence is heterogeneous and relatively poor (Adi et al. 2007; Kirchmayer et al. 2002; Minozzi et al. 2006). Naltrexone has shown to be effective in opioiddependent health care professionals (Ling and Wesson 1984; Roth et al. 1997). Because of issues of compliance with oral naltrexone, there has been an interest in a sustained-release formulation of this medication. The only RCT testing the efficacy of sustained-release intramuscular naltrexone concluded that high-dose naltrexone (384 mg) had the lowest dropout rate (48 days compared to 27 days for placebo), but there was no between-group difference in the proportion of opioid negative urine samples.(Lobmaier et al. 2008) Some countries have been using naltrexone implants as well as the sustainedrelease formulation to treat opioid addiction; however, little data exists with regards to its effectiveness (Lobmaier et al. 2008). Augmentation strategies may also improve naltrexone’s effectiveness. For example, lofexidine was found to increase opioid abstinence and reduce cue-induced opioid craving in naltrexone treated opioid-dependent patients compared to placebo-naltrexone treated patients (Sinha et al. 2007).

Other Medications Studies evaluating novel approaches are underway. Aprepitant, a substance P antagonist, is an FDA-approved medication for the prevention of acute and delayed chemotherapy related nausea and vomiting. Substance P has been associated with an increase in dopamine in the shell of the nucleus accumbens and is being tested to determine its effect in opioid-dependent patients (Le Foll et al. 2000; Reddy et al. 2006). Other studies have found a role for the cannabinoid system in opioid dependence (Manzanedo et al. 2004; Trang et al. 2007). There have been several trials testing NMDA receptor agonists and antagonists as well. Memantine, an NMDA receptor antagonist, was recently tested in a 8-week in-patient study. Memantine produced modest reductions in subjective ratings of drug quality, liking, willingness to pay for the drug, and craving for heroin; however, few changes were found in the reinforcing effects of heroin (Comer and Sullivan 2007). Other preclinical studies have shown the group II metabotropic glutamate receptor agonist, LY379268, which decreases glutamate release, attenuates cueinduced reinstatement of heroin seeking behaviors in rats.(Bossert et al. 2006a, b).

7 Conclusion Currently, addiction treatment is generally focused on four areas: (1) reducing withdrawal discomfort, (2) diminishing cravings, (3) blocking rewarding effects of the drug, and (4) treating comorbidities, such as depression or ADHD. This last area of intervention raises an important point emerging in both addiction treatment

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and in health care as a whole: individual characteristics, including comorbidities and genomics, are likely to play a substantial role in future pharmacotherapies. As illustrated by the differential effects of antidepressants among depressed samples with addictive disorders, serotonergic agents among Type I or II alcohol-dependent individuals, and naltrexone among individuals with allelic variations of the m-opioid receptor, individually-tailored approaches to treatment are likely to be the norm rather than the exception. The world of addiction research is expanding at a rapid and exciting pace and reason for hope among treatment providers and individuals struggling with addictions is justified. Nonetheless, numerous questions continue to beg exploration and, given the enormous toll addictive disorders exact from individuals and society at large, a need for a wider range of effective pharmacologic treatment remains. Acknowledgments Support was provided by the he VISN I Mental Illness Research Education and Clinical Center (MIRECC) (PI ¼ Rounsaville) and the VA Alcohol Center (PI ¼ Krystal). We wish to thank Dr. Erin O’Brien for her assistance in preparing this manuscript.

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Index

A Abused drugs, 31, 39, 40, 46, 49 Acamprosate, 353, 354 Acetaldehyde, 356 Acetylcholine, 105 Addiction, 229–230 ADH1B isoenzyme, 288 Adoption studies, 279 Afferent/efferent AMY projections, 78–80 Akt. See Protein kinase B Alcohol, 187 Alcohol dehydrogenase gene (ADH), 286, 288 Alcohol dependence, 200, 204–206, 208, 209, 238–239 Aldehyde dehydrogenase gene (ALDH), 286, 288 Allele, 283, 287, 288, 290–292 Alpha-methylparatyrosine (AMPT), 222 Amantadine, 362 a-Amino-3-hydroxy-5-methyl-4isoxazolepropionic acid (AMPA), 6, 7 AMPA receptor, 184, 185 Amphetamine, 181, 184–187 methamphetamine (METH), 249, 256–265 3,4-methylenedioxymethamphetamine, ecstasy (MDMA), 249, 256–265 Amphetamine administration, 85 Amygdala, 253–255, 259, 264, 269, 291, 292 Amygdala (AMY) mechanisms anatomy, 76

cue-drug association, 78 neuronal activity, 84–87 AMY neuronal activity, 84–87 Anhedonia, psychostimulants acetylcholine, 148 in animals, 126–127 BDNF, 152 contrast paradigm, 134–135 CRF, 149 cytokines, 154–155 depressive symptoms, 123–124 Diagnostic and Statistical Manual of Mental Disorders, 4th edition, 122 dopamine, 140–141 endocannabinoid system, 154 GABA, 146–148 glutamate, 144–146 hedonic set-point shift theory, 125 hypothalamic-pituitary-adrenal (HPA) axis, 149 intracranial self-stimulation (ICSS), 129–132 melanin concentrating hormone (MCH), 150 melanocortins, 150 mesocorticolimbic dopaminergic pathway, 136 neural substrates, 138–139 neurosteroids, 153–154 neurotransmitter systems, 137 nonpeptide vasopressin, 150 norepinephrine, 142–144 NPY, 152

387

388

NAcc, 137 opponent process theory, 125 orexin, 151 progressive-ratio responding, 133 serotonin, 141–142 sucrose consumption/preference, 133–134 treatment regimens, 128–129 Animal models, 31–32 Anterior cingulate cortex (ACC), 103, 105, 107–113 Aprepitant, 370 Association studies, 284–290, 292, 293 Associative learning, 52–54 Atomoxetine, 360 Atypical antidepressant bupropion, 130

B Baclofen, 350, 353, 356, 364, 366 Basolateral amygdala, 9 Behavioral economics, drug dependence cross-price elasticity, 323, 324 demand curves and intensity, 321–322 Mazur’s equation, 326–327 price and commodity concepts, 320 Binding potential (BP), 221 Blood oxygen level dependent (BOLD) response, 291, 292 Brain, 200–203, 205, 207, 208, 212 Brain-derived neurotrophic factor (BDNF), 152 Brain imaging, 329 cerebral blood flow (CBF), 251, 252, 265 event-related potential (ERP), 252, 266 functional MRI (fMRI), 249, 251–256, 258, 260–265, 267, 269 magnetic resonance spectroscopy (MRS), 249, 260 positron emission tomography (PET), 249, 251, 253, 258, 259, 261, 263, 267, 268 single photon emission computed tomography (SPECT), 249, 251, 260, 267 structural magnetic resonance imaging (MRI), 249, 251, 253, 269 Brain reward function assessment, 129–133

Index

Bromocriptine, 362 Buprenorphine, 362, 367–369 Bupropion, 347, 350, 358, 361, 366 Buspirone, 355, 360

C Candidate genes, 284, 286, 289, 290, 293 Cannabis, marijuana, 250–255, 259, 268, 269 Carbamazepine, 352 Catechol-O-methyltransferase (COMT), 286, 290–293 Chromosomes, 285 Chronic cocaine exposure, 235–236 Chronoamperometry, 36–38, 40–41, 46, 53 Clonidine, 348, 349, 368 Clozapine, 143 Cocaine, 3–17, 181–188, 249, 254, 256–265, 267–269 Cocaine administration, 85, 86 Cocaine dependence, 229–233 Cognitive control, 306 Cognitive deficits, 247–269 Commodity interactions, 324, 325 Competing neurobehavioral systems, 331–33 COMT val158 met polymorphism, 290 Conditioned drug cues, 49 Conditioned place preference, 181 Conditioned place preference (CPP) mdoel, 81–82 Conditioned stimuli (CS), 43–46, 49, 50, 52, 54 Contingency management (CM), 361 Copy number variations (CNV), 294 Corticotropin-releasing factor (CRF), 149 Cross-price elasticity, 323, 324 Cytochrome P450 2A6 enzyme (CYP2A6), 286, 288, 293 Cytochrome P450, CYP, 350 Cytokines, 154–155

D Delay discounting, drug dependence, 325–329 Demand curve, 321–322 Demand intensity, 322

Index

Dextroamphetamine, 363 Disulfiram, 356, 362 Dopamine (DA), 4, 105, 108, 110, 112, 140–141, 180, 183, 184, 187, 201–207, 210–212, 282, 283, 286–288, 290 Dopamine D2 agonist radiotracers, 224 Dopamine D2 receptor, 187 Dopamine hypothesis of addiction, 38–39 Dopamine metabolite dihydroxyphenylacetic acid (DOPAC), 41 Dopamine receptor, 202–205, 210–212, 286 Dopamine receptor antagonists, 32 Dopamine receptor D2 (DRD2), 286, 287, 293 Dopamine receptors, 33, 52 Dopamine reuptake sites, 33 Dopaminergic uptake blockers, 122 D2 PET radioligands, 227 Dronabinol, 357, 358, 360 Drug abuse, 247–269 Drug abuse and relapse, 80–84 Drug addiction, 31, 37, 51–53, 57, 201 neural network, 303 neuroanatomy, 303–304 Drug cues, 48–51, 55–57 Drug dependence, 309, 310 Drug-naı¨ve state, 54 Drug seeking, 31, 44–46, 49–52, 57, 58 Drug-self-administration, 31, 32, 39, 40, 48, 56 Drugs of abuse cocaine, 105, 107–112 ethanol, 105 gamma-aminobutyric acid (GABA), 109 glutamate, 107, 109, 110, 112 heroin, 105, 107, 110, 111, 113 methamphetamine, 105, 107, 110, 111 morphine, 105 nicotine, 105

389

Ethical issues, 294 Event-related potential (ERP) responses, 306 Executive control processes, 303–304 Executive function, 250, 258, 259, 303, 304 Executive system, 331, 332

F FAAH inhibitors, 359 Fast-scan cyclic voltammetry (FSCV), 36–38, 41–46, 48–50, 54 Fluoxetine, 347, 348, 360 Functional MRI (fMRI), 291–293

G GABA-A, 281 Gabaa-benzodiazepine receptor (GABAA-BZR), 208, 209 Gabapentin, 350, 352, 360, 363, 364, 366 GABA shift, 229 g-Aminobutyric acid (GABA), 146–148 Gamma-vynil GABA (GVG), 363, 364 Gene expression, 284 Genome wide association studies (GWAS), 286, 289–290 Glutamate, 4–9, 11–17, 144–146

H Heritability, 279, 280 Heroin, 181–185, 249, 265–268 Hippocampus, 252–255 5-HTT promoter polymorphism (5-HTTLPR), 291, 292 5-Hydroxy-tryptamine (5-HTT), 287, 291–293 Hyperactivity, 180, 181 Hyperbolic delay function, 326–327

I E Early gene expression, 85 Early separation, 287 Endogenous dopamine level, 222 Endophenotypes, 285 Ethanol, 9, 11, 12

Imaging neurotransmitter release alpha-methylparatyrosine (AMPT), 222 binding potential (BP), 222 cocaine dependence, 230–233 [11C]raclopride, 221, 226 D2 PET radioligands, 227

390

endogenous dopamine level, 222 GABA transmission, 225 loss of sensitivity, 223–224 microdialysis studies, 224 mu opioid receptors, 226 PET, 220–221 scintillators, 221 serotonin transporter, 229 tetrahydrocannabinol (THC), 226 Immunotherapy, 349 Impulsive system, 331, 332 Impulsivity, 328 Incentive salience, 179 Incentive-salience hypothesis, 35, 53 Incentive sensitization, 180, 181, 185–186, 187 Infralimbic cortex (IL), 103, 105, 107, 109, 111–113 Intracranial self-stimulation (ICSS) atypical antidepressant bupropion, 130 response latency, 132 serotonergic treatment, 131

Index

conditioned place preference, 110, 112 extinction training, 103, 108–110, 113 self-administration, 109, 110 Molecular markers ania-3, 105 Arc, 105, 108 brain-derived neurotrophic factor (BDNF), 109 Fos, 105, 108, 109, 111 MKP-1, 105 Nr4a3, 105 zif 268, 105 Monoamine oxidase (MAO), 345 Monoamine-oxidase gene (MAO-A), 287 Motivated behavior, 34–35 Motivation, 32, 34, 35, 45, 46, 49, 52–54, 56, 57, 180–187 Motivational costs, 34, 56 mRNA, 284, 289 mu-opioid receptor, 283

N L Levo-a-acetylmethadol (LAAM), 369 Linkage analyses, 284, 285, 288, 289, 292 Linkage studies, 284–286, 288 Lithium, 358 Locomotion, 181 Lofexidine, 348, 358, 370 Long-term potentiation (LTP), 6–8, 10, 14 Low D2 receptor, 233–234, 237 LTP. See Long-term potentiation

M Magnetic resonance imaging (MRI), 293 Mecamylamine, 347, 366 Meclobromide, 347 Memantine, 354, 370 Mesolimbic pathway Methadone, 265–268, 361, 362, 367–369 Methylphenidate, 187, 363, 366 Microdialysis, 36, 39–41, 46–48, 50, 53 Mirtazapine, 366 Modafinil, 364, 365 Models of cue-induced drug relapse abstinence, 103, 108–109, 113

N-acetylcysteine, 364 Nalmafene, 355 Naloxone, 369 Naltrexone, 283, 293, 349, 354, 355, 359, 366, 369–371 Nefazodone, 359 Neural correlates, 329–331 Neural network, 303 Neuroadaptations, 87–89 Neuroanatomy, 303–304 Neuroimaging studies, 208, 284–285 Neuropeptide Y (NPY), 152 Neuroplasticity, 87, 89 drug users, 103, 108, 110, 111 rat/rodents, 103, 105, 110, 113 Neurosteroids, 153–154 Nicotine replacement therapies (NRT), 346 Nicotinic acetylcholine receptor (nAChR), 209 N-methyl-D-aspartic acid (NMDA), 13, 16, 281, 286, 351, 353, 354, 362, 370 Norepinephrine, 112, 142–144 Nortriptyline, 347, 348 Nucleus accumbens

Index

Nucleus accumbens (NAcc), 4, 5, 8–10, 12–16, 32–35, 37–52, 54, 55, 57, 137, 180, 183–185

O Ondansetron, 356 Opioid, 184 Orbitofrontal cortex (OFC), 103, 105, 108, 109, 111–113 Oxytocin, 358

P Paroxetine, 347, 348 Past discounting, 329 Pavlovian psychostimulant conditioning and relapse afferent/efferent AMY projections, 78–80 amphetamine administration, 85 AMY anatomy, 76 amygdala (AMY) mechanisms, 76–78 AMY neuronal activity, 84–87 cocaine administration, 85, 86 conditioned place preference (CPP) mdoel, 81–82 drug abuse and relapse, 80–84 early gene expression, 85 neuroadaptations, 87–89 protein kinase B, 88 Phasic dopamine, 34, 36, 38, 41–46, 48–57 Phasic dopamine events, 33, 34, 36, 38, 41–46, 48–57 Phasic signals, 43, 53, 54, 56, 57 Plasticity, 4–9, 11–14, 15, 17 Pleiotropic effects, 292, 293 Positron emission tomography (PET), 187, 200–208, 210, 212, 293 alpha-methylparatyrosine (AMPT), 222 binding potential (BP), 222 cocaine dependence, 230–231 [11C]raclopride, 221 endogenous dopamine level, 222 neurotransmitter release, 222 scintillators, 221 Preference reversals, 326, 327 Prefrontal cortex, 251, 253 Prelimbic cortex (PL), 103, 105–110, 112

391

Prenatal, 257, 262, 268–269 Presynaptic dopamine function, 238–239 Price elasticity, 323 Progressive-ratio schedule, 181 Protein kinase B, 88 Psychomotor activity, 183, 187 Psychostimulant, 180, 184, 187 Psychostimulants, anhedonia acetylcholine, 148 in animals, 126–127 BDNF, 152 contrast paradigm, 134–135 CRF, 149 cytokines, 154–155 depressive symptoms, 123–124 Diagnostic and Statistical Manual of Mental Disorders, 4th edition, 122 dopamine, 140–141 dopaminergic uptake blockers, 122 endocannabinoid system, 154 GABA, 146–148 glutamate, 144–146 hedonic set-point shirft theory, 125 hypothalamic-pituitary-adrenal (HPA) axis, 149 intracranial self-stimulation (ICSS), 129–132 melanin concentrating hormone (MCH), 150 melanocortins, 150 mesocorticolimbic dopaminergic pathway, 136 neural substrates, 138–139 neurosteroids, 153–154 neurotransmitter systems, 137 nonpeptide vasopressin, 150 norepinephrine, 142–144 NPY, 152 NAcc, 137 opponent process theory, 125 orexin, 151 progressive-ratio responding, 133 serotonin, 141–142 sucrose consumption/preference, 133–134 treatment regimens, 128–129

392

Psychostimulant self-administration, 32, 39

R Rate-frequency procedure, 132 Reboxetine, 347, 348 Receptors alpha-2 adrenergic, 112 alpha-amino-3-hydroxy-5-methyl-4isoxazolepropionic acid (AMPA), 107, 109 AMPA, 351 Cannabinoid (CB1), 349, 357, 359 D1, 110, 112 D2, 110, 112 Dopamine1 (D1), 362 Dopamine2 (D2), 362 GABAA, 351, 352 GABAB, 352, 356, 364 kainate, 107 nicotine acetylcholine (nAChR), 345, 346, 348 m-opioid receptor (OPRM), 355 Reinforcement, 181, 182, 186 Reinforcement learning, 35, 53, 54, 56 Reinforcer, 183, 185, 186 Reinstatement, 182, 184, 185 Reinstatement of drug seeking, 49–51 Relapse, 182, 184–185 drug-primed, 109–111, 113 environmental stimulus-induced, 103–109 stress-induced, 103, 111–113 Relapse circuitry amygdala, 107, 112 nucleus accumbens core (NAcore), 107, 109, 110, 112 nucleus accumbens shell (NAshell), 107 prefrontal cortex, 102, 103, 105, 107–109, 111 thalamus, 103 ventral pallidum, 107 ventral tegmental area, 107, 110 Residual, 253, 257, 258 Reward, 4, 6, 7, 9–12 Reward prediction signal, 35 Reward system, 282–283 Rimonabant, 349, 359

Index

S Selegiline, 347, 348, 360 Self-administration, 181–185 Sensitization, 179–188, 235–236 Serotonergic system, 283 Serotonin, 141–142 Serotonin (5-HT), 203, 206–207, 211 Serotonin transporter (5-HTT), 229, 287, 292, 293 Sertraline, 348, 366 Single nucleotide polymorphism (SNP), 289, 293 Single photon emission computed tomography (SPECT), 200, 201, 203–208, 210–212, 221–222, 293 Spontaneous dopamine transients, 42, 44, 46 Substance abuse, 285 Substance dependence, 281 Substance use disorder (SUD), 309 Substitute-complement continuum, 323

T Temporal horizon, 329 Tiagabine, 350, 363, 364 Tobacco smoking, 206–212 Tonic dopamine, 34–36, 40–43, 46–51, 53–57 Tonic extracellular concentration of dopamine, 53 Topiramate, 353, 354, 365 Twin studies, 279

V Vaccines, 349, 365 Valproic acid divalproex, 358, 360, 364 valproate, 352, 364 Varenecline, 347, 348, 350 Venlafaxine, 347, 348, 360 Ventral tegmentral area (VTA), 4–8, 10, 12–14, 16, 32, 33, 38, 40, 43, 46–48 Vigabatrin, 350, 366

W Withdrawal, 31, 46–49, 55, 57

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