Cognition and Addiction: A Researcher’s Guide from Mechanisms Towards Interventions 9780128152997, 9780128152980

Table of contents :
Cognition and Addiction
Copyright
Dedication
Contributors
Biographies
Foreword
Acknowledgments
Introduction
References
1 -
Cognition: the interface between nature and nurture in addiction
Introduction
Cognition to bridge the gap between neurobiological models and social accounts of addiction
Evidence for the double role of cognition in addiction vulnerability and consequences
Longitudinal studies
Endophenotype studies
Neurotoxicity-controlled studies
Dependent versus recreational users
Stimulant users versus gamblers
Cognition at the interface between nature and nurture
References
2 -
From impulses to compulsions
Introduction
Animal models of drug-seeking habits and compulsions
Neural circuits: transitioning from the ventral to dorsal striatum
Devolving from prefrontal to striatal control
Translating animal models to understand compulsivity in people with substance use disorders
Recommendations for future research
References
3 -
Dual models of drug addiction: the impaired response inhibition and salience attribution model
Dual models of addiction
Neuroimaging evidence for dual models
Conclusions
References
4 -
Decision-making deficits in substance use disorders: cognitive functions, assessment paradigms, and levels of evidence
Introduction
First dimension: cognitive functions of decision-making
Second dimension: assessment paradigms for decision-making
Self-reports
Barratt Impulsivity Scale
Monetary choice questionnaire
UPPS impulsive behavior scale
Eysenck impulsiveness scale (I7)
Sensation seeking scales
Temporal experience of pleasure scale
Effect expectancy questionnaire
Rewarding events inventory
Reinforcement survey schedule
Consideration of future consequences scale
Sensitivity to reinforcement of addictive and other primary rewards
Substance use risk profile scale
Concluding remarks for self-reports
Behavioral task
Delay discounting task
Balloon analogue risk task
Iowa gambling task
Cambridge gambling task/risk task
Game of dice task
Effort expenditure to reward task
Beads task, box task
Risk gains task
Concluding remarks on behavioral tasks
Computational modeling
Computational models of behavioral tasks
Concluding remarks on computational models
Neuroimaging
Task-based fMRI evidence in SUD
fMRI and delay discounting tasks
fMRI and balloon analogue risk task
fMRI and Iowa gambling task
fMRI and cambridge gambling task
Model-based fMRI approaches
Concluding remarks on the task-based fMRI
Third dimension: levels of evidence in decision-making studies
Three-dimensional matrix of evidence: cognitive functions, assessment paradigm, and levels of evidence
Summary and concluding remarks
References
5 -
Social cognition in addiction
Introduction
Definitions of socio-cognitive functions and their measurement
Studies on social cognition and interaction in substance use disorders
Alcohol
Emotion recognition and cognitive empathy
Emotional empathy
Perspective-taking and ToM
Social decision-making
Moral decision-making
Cannabis
Emotion recognition and cognitive empathy
Emotional empathy
Perspective-taking and ToM
Social decision-making
Social reward
Stimulants
Emotion recognition and cognitive empathy
Emotional empathy
Perspective-taking and ToM
Social decision-making
Moral decision-making
Social reward
Entactogenes
Emotion recognition and cognitive empathy
Emotional empathy
Perspective-taking and ToM
Social decision-making
Opioids
Emotion recognition and cognitive empathy
Emotional empathy
Perspective-taking and ToM
Social decision-making
Polysubstance use
Emotion recognition and cognitive empathy
Emotional empathy
Perspective-taking and ToM
Moral decision-making
Discussion
Open questions
Relevance for treatment
Conclusion
Acknowledgments
References
6 -
A neurocognitive model of the comorbidity of substance use and personality disorders
Cross-sectional and longitudinal evidence
Broad symptoms dimensions and impulsive personality traits
Neurocognitive functioning
Personality disorder and executive functioning
Substance use disorder and executive functioning
Comorbidity and executive functioning
A preliminary neurocognitive model
Future directions
References
7 -
Cognitive risk factors for alcohol and substance addictions
Structure of cognitive function
Selective attention, working memory, and general executive function
Response inhibition
Delay discounting
Reward-based decision-making
Intelligence quotient
Discussion
References
8 -
Neuropsychological deficits in alcohol use disorder: impact on treatment
Introduction
Altered brain structure and function in alcohol use disorder
Attention, working memory, and executive functions
Episodic memory
Semantic memory
Procedural memory
Perceptive memory and visuospatial abilities
Emotional processes and theory of mind
Emotions
Social cognition
Reversibility of cognitive deficits and cerebral damage with abstinence
Brain recovery
Neuropsychological recovery
Apparent discrepancies
Episodic memory
Executive functions
Other functions
Factors influencing the recovery
Clinical implication and relapse factors
Motivation
Decision-making
New complex learning
Interpersonal relationships
Alcohol-related neurocognitive complications
Wernicke's encephalopathy
Korsakoff's syndrome
Marchiafava-Bignami disease
Hepatic encephalopathy
Central pontine myelinolysis
Recommendations for researchers and clinicians
Modalities of screening and assessment
Heterogeneity of the neuropsychological profile
Differential diagnosis
Age-alcohol use disorder interaction
Alzheimer's disease
Frontotemporal lobar degeneration
Treatment modifications
Neuropsychological rehabilitation
Conclusion
References
9 -
Tobacco addiction: cognition, reinforcement, and mood
Introduction
Scope of the problem
Smoking prevalence
Smoking-related morbidity and mortality
Smoking cessation
Electronic cigarettes
Tobacco policy in the United States and the world
Pharmacology
Chemicals in tobacco smoke
Acetylcholine system
Neural effects of nicotine
Addiction liability
Cognitive effects of nicotine and tobacco
Short-term effects
Long-term effects
Withdrawal effects
Nicotine reinforcement
Reinforcement enhancement
Neural mechanisms
The emotion-smoking relationship
Smoking as a maladaptive response to negative mood
Neural mechanisms
The role of the insular cortex
Cause, consequence, or shared underlying mechanism
Smoking cessation and mood
Recommendations for clinicians and researchers
Summary and conclusions
References
10 -
Cognitive sequelae of cannabis use
Introduction
Neuropharmacology of cannabis
Cognitive deficits associated with cannabis
Acute effects of cannabis intoxication on cognition
Reviews
Notable cross-sectional studies
Nonacute or residual/long-term effects of cannabis use on cognition
Reviews and meta-analyses
Longitudinal studies
Notable cross-sectional studies
Clinical significance of cognitive deficits associated with cannabis
Recommendations for researchers/clinicians interested in cognitive profiling in the context of cannabis
Conclusion
References
11 -
Cognitive deficits in people with stimulant use disorders
State of the problem
Neuroadaptive effects of stimulants
Cognitive profiles
Acute effects
Long-term effects
Recovery
Moderators
Age of onset
Cumulative exposure
Route of administration
Clinical significance of cognitive deficits associated with stimulants use
Memory
Attention
Working memory and executive functions
Impulsivity and decision-making
Summary
Recommendations for researchers and clinicians interested in cognitive assessment in the context of stimulants use
References
12 -
Cognitive consequences of 3,4-methylenedioxymethamphetamine use
Introduction-epidemiology of 3,4-methylenedioxymethamphetamine use
Neuropharmacological/neuroadaptive effects of 3,4-methylenedioxymethamphetamine
Pharmacokinetics and pharmacodynamics
Animal research
Human imaging
Potential adverse effects and pharmacologically confounding factors
Cognitive deficits associated with 3,4-methylenedioxymethamphetamine
Functional imaging
Clinical significance of cognitive deficits associated with 3,4-methylenedioxymethamphetamine
Recommendations for researchers/clinicians interested in cognitive profiling in the context of 3,4-methylenedioxymethamphet ...
Key points and conclusion
References
Further reading
13 -
Cognitive consequences of opioid use
Introduction
Long-term cognitive deficits associated with opioids
Neuropsychological functioning in mixed opioid using and dependent populations
Neuropsychological functioning in illicit heroin using and dependent populations
Neuropsychological functioning in abstinent former heroin-dependent populations
Neuropsychological functioning in methadone users
Neuropsychological functioning and use of buprenorphine
Combinations of opioids (morphine, tramadol, fentanyl, oxycodone buprenorphine, and/or methadone)
Methodological issues related with the study of the neuropsychological correlates of chronic opioid use, abuse, and depende ...
Context
The population studied
Recruitment
Sample size
Substance misuse and dependence
Effects of polysubstance use
Chronicity and severity of use
Time window (moment of evaluation)
Other relevant factors
Data gathering (diagnostic and screening instruments)
Type of neuropsychological tests
Defining the population
Data analysis
Conclusion
References
14 -
Predictors of problem gambling and other addictive behaviors: from context to genes
Introduction
Individual risk factors for problem gambling
Personality
(Neuro)cognitive factors
Genetic risk
Social and individual predictors of problem gambling: from family to friends and from alcohol to academic achievement
Summary and discussion
Conclusion
References
15 -
Cognitive factors in gambling disorder, a behavioral addiction
Introduction
The cognitive model of gambling
Neurocognitive correlates of gambling disorder
Specific cognitive distortions in gambling
Illusion of control
Anthropomorphism of gambling games
Immersion in the game
Treatment and intervention
Conclusion
Funding
Conflict of Interest statement
References
16 -
Cognitive factors associated with gaming disorder
From internet addiction to gaming disorder
Cognitive factors associated with gaming disorder
Cognitive deficits
Inhibitory control and other executive functions
Decision-making and related processes
Cognitive biases
Attentional biases
Dysfunctional cognitions about gaming
Key points and conclusion
Conflict of interest
References
17 -
Cognitive bias modification in the treatment of addiction
Introduction
Attentional bias modification
Approach bias modification
Memory bias modification-evaluative conditioning
Neurocognitive effects of cognitive bias modification
Toward optimized clinical applications of cognitive bias modification in addiction
References
18 -
Peer-reviewed working memory training: is it an effective intervention for addiction?
Introduction
Methods
Results
N-back (n=32); Kirchner (1958)
Jungle memory (n=2); Alloway (2009)
PSSCogRehab (n=1) Bracy (1994)
CogMed (n=31) Klingberg et al., (2002)
Lumosity (n=4) Lumos Labs (2005)
Neuroracer (n=1); Project:EVO (n=2); both versions of the same product founded by Gazzaley and Akili Interactive
NeuroNation (n=1); Ahmadi and Futorjanski (2011)
Curb Your Addiction (n=2); Brooks (2016).
Discussion
Peer-reviewed working memory training paradigms
Near and far transfer effects of peer-reviewed working memory training paradigms
Previous research into working memory training and implications for addiction
Limitations
Conclusions
References
19 -
Inhibitory control training
Introduction: alignment between the training and cognitive changes that characterize addiction
Description of the training and proposed mechanisms
Evidence for the efficacy of inhibitory control training
Efficacy in people with substance use disorder
Mechanisms of action of inhibitory control training
Conclusions and recommendations
References
20 -
Goal-based interventions for executive dysfunction in addiction treatment
Goal-based interventions for cognitive deficits associated with addiction
Intervention approaches and mechanisms
Evidence of the efficacy of the training
Discussion of the neurocognitive mechanisms in light of evidence
Recommendations for researchers and clinicians interested in using goal-based interventions
References
21 -
Neurocognitive mechanisms of mindfulness-based interventions for addiction
Introduction
Mindfulness as a means of targeting mechanisms of addiction
Clinical format and efficacy of mindfulness-based interventions for addiction
Neurocognitive mechanisms of mindfulness as a treatment for addiction
Effects of mindfulness on ``top-down'' mechanisms of cognitive control
Attentional control
Regulation of automaticity
Inhibitory control
Effects of mindfulness on enhancing cognitive regulation of reward, negative emotion, and cue reactivity
Amplifying reward and positive affect
Dampening negative affect and stress
Regulating craving and cue reactivity
Hypothesized roles of core mindfulness elements in addiction treatment
Future directions for mindfulness-based interventions and addiction
Funding
References
22 -
Brain stimulation as an emerging treatment for addiction
Noninvasive modulation of neural circuitry in humans
Preclinical foundation
Moving to the clinic
What is transcranial magnetic stimulation?
Using repetitive transcranial magnetic stimulation to modulate cortical-striatal connectivity
Applications to substance use disorders
Applications to smoking
Application to alcohol
Application to cocaine
Application to other substance using populations
Application to compulsive eating and gambling
Integration of neuromodulation with cognitive and pharmacotherapies
Repetitive transcranial magnetic stimulation with cognitive therapy
Repetitive transcranial magnetic stimulation and pharmacotherapy
Summary
References
23 -
Pharmacological cognitive enhancers
Introduction
Cognitive function within the context of substance use disorder
Executive functioning
Automatic cognitive processes
Cognitive deficits in substance use disorders
Target mechanisms
Cholinergic medications
Galantamine
Rivastigmine
Donepezil
Varenicline
Monoamine transporter inhibitors
Modafinil
Methylphenidate
Oral methamphetamine/d-amphetamine
Atomoxetine
Antipsychotic
Haloperidol
Alpha2-adrenergic agonist
Guanfacine
Glutamatergic medications
Memantine
d-Cycloserine
Minocycline
N-Acetylcysteine
GABAergic medications
Tiagabine
Exogenous sex steroids
Estradiol
Progesterone
Conclusions
Acknowledgments
References
24 -
Cognitive research on addiction in a changing policy landscape
Introduction
Cognitive research on addiction
Aberrant learning
Impulsivity to compulsivity
Impaired impulse inhibition
Cognitive research on addiction and its (so far) limited impact on policy
Potential policy impacts of cognitive accounts of addiction
Drug policy
Addiction treatment policy
Criminal justice policy
An avenue for a greater impact on mental health and criminal justice policy
Public policy can powerfully affect cognitive research
Loosening of restrictions on use of psychedelics in clinical research
Legalization of recreational cannabis
Conclusion
References
25 -
Population neuroscience in addiction research
Population neuroscience: an overview
Genes and gene regulation
Built and social environment
Brain structure and function
Population neuroscience: addiction research
The Saguenay Youth Study
IMAGEN study
Findings
Challenges and outlook
Where do we go next?
Acknowledgments
References
26 -
Drug use and self-awareness of treatment need: an exemplar of how population-based survey studies can address questions rel ...
Introduction
Methods
Sample
Outcome variable
Drug use variables
Sociodemographic and general health covariates
Statistical analyses
Results
Drug use predictors of TxUnaware status
Drug use predictors of TxAware status
Graded effects on treatment need awareness
Discussion
Acknowledgments
References
Appendix
27 -
Genetics, imaging, and cognition: big data approaches to addiction research
Cognition: online-based research
Big data and neuroimaging
Genetics and addiction: meta- and megaanalyses
General discussion
References
28 -
Modeling neurocognitive and neurobiological recovery in addiction
Modeling neurocognitive and neurobiological recovery in addiction
Neurocognitive deficits in addiction
Neurocognitive changes during abstinence
Neurobiological abnormalities in addiction
Neurobiological changes during abstinence
Conclusions and outlook
References
29 -
Clinical translation and implementation neuroscience for novel cognitive interventions in addiction medicine
Introduction
Neuroscience-based cognitive interventions
Neuroscience-informed psychoeducation and metacognitive training
Neuroscience-informed cognitive modifications
Attention bias interventions
Saliency-based interventions
Memory-based interventions
Interoceptive-based interventions
Inhibitory control interventions
Neurocognitive rehabilitation
Integrative cognitive interventions: introducing NEAT program
Other neuroscience-informed interventions
Future directions
Acknowledgments
References
30 -
Synergistic opportunities in combined interventions for addiction treatment
Introduction
Combining top-down and bottom-up approaches
Interventions tapping into decision-making
Conclusion
References
Index
A
B
C
D
E
F
G
H
I
K
L
M
N
O
P
R
S
T
U
V
W

Citation preview

Cognition and Addiction A Researcher’s Guide from Mechanisms Towards Interventions

Edited by

Antonio Verdejo-Garcia

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2020 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www. elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-815298-0 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Nikki Levy Acquisition Editor: Joslyn Chaiprasert-Paguio Editorial Project Manager: Tracy Tufaga Production Project Manager: Mohana Natarajan Cover Designer: Greg Harris Typeset by TNQ Technologies

I have been dedicated to edit this book during the last couple of years, but the knowledge and collaborations that have made it possible span over 15 years. Thus, I would like to dedicate this book to my mentors: Professors Miguel Perez-Garcı´a, Antoine Bechara, and Karen Bolla and to the many colleagues and friends (and colleagues who became friends) with whom I have shared amazing research and life adventures in Granada, Barcelona, Iowa, Baltimore, Cambridge, and Melbourne. To my parents, for fighting to give me a university education and teaching me to always decide by myself. Huge thanks to my wife, Natalia, for being an inspiration and spring of energy for so many personal and career choicesdI look up to you every single day. Antonio Verdejo-Garcı´a, Melbourne, May 2019

Contributors Merideth A. Addicott, Department of Psychiatry, University of Arkansas for Medical Science, Little Rock, AR, United States Robin L. Aupperle, Laureate Institute for Brain Research, Tulsa, OK, United States Alex Baldacchino, Division of Populations and Health Science, St Andrews Medical School, University of St Andrews, St Andrews, Fife, United Kingdom Joël Billieux, Addictive and Compulsive Behaviours Laboratory, Institute for Health and Behaviours, University of Luxembourg, Esch-sur-Alzette, Luxembourg Marilisa Boffo, Addiction Development and Psychopathology (ADAPT) Laboratory, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands; Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, The Netherlands

Nicolas Cabé, Normandie Univ, UNICAEN, PSL Université de Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, Caen, France Zhipeng Cao, School of Psychology, University College Dublin, Dublin, Ireland Adrian Carter, Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia; UQ Centre for Clinical Research, University of Queensland, Brisbane, QLD, Australia Natalie Castellanos-Ryan, Universite de Montreal, CHU Ste Justine, Montreal, QC, Canada Luke Clark, Centre for Gambling Research at UBC, Department of Psychology, University of British Columbia, Vancouver, BC, Canada Patricia Conrod, Universite de Montreal, CHU Ste Justine, Montreal, QC, Canada

Matthias Brand, General Psychology: Cognition and Center for Behavioral Addiction Research (CeBAR), University of Duisburg-Essen, Duisburg, Germany; Erwin L. Hahn Institute for Magnetic Resonance Imaging, Essen, Germany

Fleur Davey, Research and Development Department, NHS Fife, Dunfermline, Fife, United Kingdom

Damien Brevers, Laboratory of Psychological Medicine and Addictology, Faculty of Medicine, BrugmannCampus, Université Libre de Bruxelles, Brussels, Belgium; Research in Psychology Applied to Motor Learning, Faculty of Motor Sciences, Erasme Campus, Université Libre de Bruxelles, Brussels, Belgium

Logan T. Dowdle, Department of Psychiatry and Behavioral Sciences, College of Medicine, Medical University of South Carolina. Charleston, SC, United States

Gabriel Brooks, Centre for Gambling Research at UBC, Department of Psychology, University of British Columbia, Vancouver, BC, Canada S.J. Brooks, School of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool, United Kingdom; Department of Neuroscience, Uppsala University, Uppsala, Sweden M. Aryana Bryan, Center on Mindfulness and Integrative Health Intervention Development, University of Utah, Salt Lake City, UT, United States

Andrew Dawson, Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia

Timothy C. Durazzo, Stanford University and Palo Alto VA Medical Center, Stanford, CA, United States Hamed Ekhtiari, Laureate Institute for Brain Research, Tulsa, OK, United States Mario Ferrari, Centre for Gambling Research at UBC, Department of Psychology, University of British Columbia, Vancouver, BC, Canada Matt Field, Department of Psychology, University of Sheffield, Sheffield, South Yorkshire, United Kingdom S. Funk, Department of Psychology, University of Cape Town, Cape Town, South Africa

xv

xvi

Contributors

Gloria Garcia-Fernandez, School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia; Faculty of Psychology, University of Oviedo, Oviedo, Spain Eric L. Garland, Center on Mindfulness and Integrative Health Intervention Development, University of Utah, Salt Lake City, UT, United States

Daniel H. Lench, Department of Psychiatry and Behavioral Sciences, College of Medicine, Medical University of South Carolina. Charleston, SC, United States Angéline Maillard, Normandie Univ, UNICAEN, PSL Université de Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, Caen, France

Rita Z. Goldstein, Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States

Pierre Maurage, Laboratory for Experimental Psychopathology, Psychological Science Research Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium

Raul Gonzalez, Center for Children and Families, Department of Psychology, Florida International University, Miami, FL, United States

Dieter J. Meyerhoff, University of California San Francisco and San Francisco VA Medical Center, San Francisco, CA, United States

Renee D. Goodwin, Department of Epidemiology and Biostatistics, School of Public Health, The City University of New York, New York, NY, United States; Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States

Scott J. Moeller, Department of Psychiatry, Stony Brook University School of Medicine, Stony Brook, NY, United States

Anna E. Goudriaan, Amsterdam UMC, Department of Psychiatry, University of Amsterdam, the Netherlands; Amsterdam Institute for Addiction Research; Arkin Mental Health; Department of Quality of Care and Research and Jellinek, Amsterdam, The Netherlands Wayne Hall, UQ Centre for Clinical Research, University of Queensland, Brisbane, QLD, Australia; Centre for Youth Substance Abuse Research, University of Queensland, Brisbane, QLD, Australia; National Addiction Centre, Kings College London, London, WC2R 2LS, United Kingdom Adam W. Hanley, Center on Mindfulness and Integrative Health Intervention Development, University of Utah, Salt Lake City, UT, United States Colleen A. Hanlon, Department of Psychiatry and Behavioral Sciences, College of Medicine, Medical University of South Carolina, Charleston, SC, United States Matthew O. Howard, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States Andrew Jones, Department of Psychological Sciences, University of Liverpool, Liverpool, Merseyside, United Kingdom Daniel L. King, School of Psychology, The University of Adelaide, Adelaide, SA, Australia; College of Education, Psychology and Social Work, Flinders University, Adelaide, SA, Australia Jacob W. Koudys, University of Toronto, Toronto, ON, Canada

Catharine Montgomery, School of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool, United Kingdom Laura O’Halloran, School of Psychology, Trinity College Dublin, Dublin, Ireland Ileana Pacheco-Colón, Center for Children and Families, Department of Psychology, Florida International University, Miami, FL, United States Martin P. Paulus, Laureate Institute for Brain Research, Tulsa, OK, United States Tomás Paus, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada; Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada MacKenzie R. Peltier, Yale School of Medicine, Department of Psychiatry, New Haven, CT, United States; VA Connecticut Healthcare System, West Haven, CT, United States Brian Pennie, School of Psychology, Trinity College Dublin, Dublin, Ireland Anne Lise Pitel, Normandie Univ, UNICAEN, PSL Université de Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, Caen, France Marc N. Potenza, Departments of Psychiatry and Neuroscience and Child Study Center, Yale School of Medicine, New Haven, CT, United States; The Connecticut Council on Problem Gambling, Wethersfield, CT, United States; The Connecticut Mental Health Center, New Haven, CT, United States

Contributors

Boris B. Quednow, Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland; Neuroscience Centre Zurich, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland C. Rabier, Department of Psychology, University of Cape Town, Cape Town, South Africa Kavya Raj, Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia Tonisha Kearney Ramos, Department of Psychiatry and Behavioral Sciences, College of Medicine, Medical University of South Carolina, Charleston, SC, United States Tara Rezapour, Institute for Cognitive Science Studies, Tehran, Iran; Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran Carl A. Roberts, Institute of Psychology, Health and Society, University of Liverpool, Liverpool, United Kingdom Adam J. Rubenis, Turning Point Alcohol and Drug Centre, Melbourne, VIC, Australia Anthony C. Ruocco, University of Toronto, Toronto, ON, Canada H.B. Schiöth, Department of Neuroscience, Uppsala University, Uppsala, Sweden Ryan Smith, Laureate Institute for Brain Research, Tulsa, OK, United States Mehmet Sofuoglu, Yale School of Medicine, Department of Psychiatry, New Haven, CT, United States; VA Connecticut Healthcare System, West Haven, CT, United States

xvii

Douglas Steele, Institute of Neuroscience, Ninewells Hospital Medical School, University of Dundee, Dundee, Tayside, United Kingdom Ryan M. Sullivan, Department of Psychiatry, Stony Brook University School of Medicine, Stony Brook, NY, United States; Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, United States Serenella Tolomeo, Division of Populations and Health Science, St Andrews Medical School, University of St Andrews, St Andrews, Fife, United Kingdom Tess den Uyl, Addiction Development and Psychopathology (ADAPT) Laboratory, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands Alireza Valyan, Allameh Tabataba’i University, Tehran, Iran Antonio Verdejo-Garcia, School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia Fausto Viader, Normandie Univ, UNICAEN, PSL Université de Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, Caen, France Robert Whelan, School of Psychology, Trinity College Dublin, Dublin, Ireland Reinout W. Wiers, Addiction Development and Psychopathology (ADAPT) Laboratory, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands Oulmann Zerhouni, UFR Sciences Psychologiques et Sciences de l’Éducation (SPSE), Université Paris Nanterre, Nanterre, France Anna Zilverstand, Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States

Biographies Antonio Verdejo-García has a PhD in Psychology (Addiction Neuropsychology, University of Granada, 2006) and a Masters in Psychological and Biomedical Aspects of Health and Illness (University of Granada, 2002). After his PhD, he continued specialized training in addiction neuroscience in highly prestigious research centers: Johns Hopkins Medical Institute (Neurology), IMIM-Hospital del Mar (Pharmacology) and the University of Cambridge (Behavioural and Clinical Neuroscience Institute). Currently, Antonio Verdejo-García is an Australian Medical Research Future Fund Fellow and holds a Full ProfessoreResearch appointment at the Turner Institute for Brain and Mental Health (Monash University), where he is the Deputy Lead of the Addiction and Mental Health Program. He also holds honorary appointments at Turning Point, Australia’s leading national addiction treatment and

research center, and the University of Granada, and he is the Chair of the Neuroscience Interest Group of the International Society of Addiction Medicine. Professor Verdejo-García has led numerous studies on the cognitive and neural substrates of substance and behavioral addictions, and new cognitive training and remediation interventions for treating substance use disorders. He is internationally recognized as an expert in this field, as evinced by several international Editorial Board positions including top-ranked addiction journals. He has published more than 200 peer-reviewed articles, and his work has attracted over 10,000 citations and has been translated into clinical trials of neurocognitive interventions and policy recommendations regarding application of neuroscience principles for the prevention and treatment of addictions.

xix

Foreword Since the seminal paper of Dr. Leshnerdthen the director of the National Institute on Drug Abusedin Science in 1997, addiction is generally regarded to be a biopsychosocial disorder with strong genetic and neurobiological underpinnings and a chronic-intermittent course with periods of recovery followed by relapse and often with serious psychosocial deterioration. Based on animal studies and human genetic and neuroimaging studies, Dr. Leshner concluded that addiction is a brain disease and that addicted people are patients who deserve (reimbursed) treatment. According to the underlying biopsychosocial model, addiction is the outcome of a preexisting hyperactive brain reward system in combination with a deficient cognitive control system in combination with neuroplastic changes caused by continued drug use. Originally, the model was predominantly presented in terms of dysfunctional brain structures and neurotransmitter abnormalities with relatively little attention to the cognitive representations of these abnormalities. As a consequence, the link between the neurobiological abnormalities and the behavioral manifestations of addictions was incomplete, and the search for new treatments was mainly directed to the discovery of new medications against relapse. Recent developments in our knowledge about the neurocognitive aspects of the development of addictive behaviors, the neurocognitive consequences of chronic drug use, and the potential treatments directed at improvement of preexisting and drug-induced neurocognitive deficits have created new hope for patients with an addiction. This is the first book that provides a comprehensive review of what we now know about cognition and addiction. An impressive lineup of experts presents a broad and in-depth overview of what is known about the cognitive underpinnings of addiction, neurocognitive approaches to treatment and future research perspectives. The book starts with a well-balanced presentation of what we know about preexisting (genetic and learned) cognitive processes that are responsible for the change from recreational drug use to goal-driven, drug seeking and finally ending in chronic compulsive and habitual addictive behaviors leading to physical and/or psychosocial decline (Chapters 1e7). In addition to the well-known dual-process model, the authors present extensions and/or integrated

variations of this model with new perspectives for treatment. In the Chapters 14e16, similar reviews are presented for gambling and gaming disorders. In addition to their role as risk factors in the development of addiction, cognitive impairments are often a consequence of chronic, excessive drug use with negative effects on the course of the disorder and on treatment effectiveness. In the Chapters 8e13, reviews on the consequences of drug use are presented for the different substances of abuse, including alcohol, tobacco, cannabis, cocaine, MDMA, and opioids. These chapters invariably show not only that drug use may lead to cognitive impairments but also that (sustained) abstinence will lead to partial or complete recovery of cognitive functions and probably to better long-term outcomes of treatment. Treatment is the focus of the next part of the book (Chapters 17e23). In a series of highly informative reviews, the authors show that there are currently many more treatment options than motivational interviewing and cognitive behavioral treatment, including cognitive bias modification, working memory training, inhibition control training, goal management training, mindfulness-based relapse prevention (MBRP), transcranial magnetic stimulation, and the use of cognition-enhancing medications. With the exception of approach bias modification and MBRP, these new interventions need to be tested in large-phase III trials, but most of them show great promise and will redirect treatment from talking to training and from face-to-face interventions to online treatments. In the last two chapters of the book (Chapters 29 and 30), the authors provide an integrated review of both existing and new treatment options and a theory-based proposal for optimal combinations of cognitive interventions based on a thorough understanding of the underlying cognitive models. Chapters 24e28 are more contemplative in nature and make the reader think about population neuroscience, the use of cognitive information in combination with genetics, the cognitive effects of reduced consumption versus complete abstinence, and finally the (limited) impact of scientific knowledge about cognition and addiction on policy development. This book is a remarkable set of reviews and position papers presented as chapters edited by one of the best

xxi

xxii Foreword

scientists in the field of cognition and addiction. Thanks to his broad and in-depth knowledge of the field and his ability to recruit the best experts on such diverse topics; this book is currently by far the best introduction to cognition and addiction for neuroscience and psychology students and researchers and for clinical psychologists, psychiatrists, neurologists, and for all other people

interested in the topic. I therefore highly recommend this book to everybody in the field. Wim van den Brink, MD PhD Em. Professor of Psychiatry and Addiction, Amsterdam University Medical Centers, Amsterdam, The Netherlands

Acknowledgments The Editor (Antonio Verdejo-García) is funded by an Australian Medical Research Future Fund, Next Generation Clinical Researchers CDF2 Fellowship (MRFF 1141214) and wish to acknowledge this support, trusting that the book will contribute to the fellowship legacy by training a new generation of addiction researchers. The Editor would also like to acknowledge all the contributors for generously sharing their unique knowledge and limited timedthey make up an outstanding cast and are the ones who give true value to this book. The Editor would also like to specially

acknowledge Professor Wim van den Brink, a trailblazer addiction researcher and inspirational figure for many (back then) young researchers in cognition and addiction and a tireless supporter of early career scientists, for writing the foreword of the book. Last not least, sincere acknowledgments to the Elsevier editorial team including Joslyn Chaiprasert-Paguio, who planted the first seed of this book, and Tracy Tufaga, who has invaluably helped to make it grow.

xxiii

Introduction Cognition refers to mental processes and encompasses “all forms of knowing and awareness, such as conceiving, remembering, judging, imagining, and problem solving” (APA Dictionary of Psychology). Addiction is a mental health condition and, not surprisingly, is associated with cognitive biases and deficits. The last 20 years have witnessed an unprecedented expansion in the understanding of the cognitive underpinnings of addiction vulnerability and chronicity. This growth has been fueled by theoretical and technological advancement. Neurobehavioral models of addiction have shed light on the cognitive mechanisms of aberrant reward valuation, cognitive control, and decision-making (Bickel et al., 2018; Everitt and Robbins, 2016; Goldstein and Volkow, 2002; Verdejo-Garcia and Bechara, 2009), overcoming old views about addictive behavior been “self-destructive” or “morally weak” (discussed in Hyman, 2007, Am J Bioeth). The advent of automated computerized testing, neuroimaging, and other biomedical techniques such as gene sequencing and manipulation, and novel intervention approaches such as cognitive training and remediation, neuroscience-informed psychotherapies and neuromodulation have fueled the knowledge gain and revamped the landscape of cognition and addiction research (Ekhtiari et al., 2019; Kwako et al., 2016; Mackey et al., 2016). This book attempts to provide a comprehensive view of this renewed landscape. The book is structured in four main sections: (1) Cognitive Principles, (2) Cognitive Risk Factors and Consequences, (3) Cognitive Interventions, and (4) New Vistas. Section (1) covers updated neurocognitive theories of addiction and its evidence base. These include views on addiction as a disorder that involves an aberrant transition from impulsivity to compulsivity, impaired response inhibition and salience attribution, decision-making dysfunctions, social cognition and interaction deficits, and personality comorbidities underpinned by common alterations in executive functions. Section (2) reviews the cognitive alterations that underlie vulnerability to substance use and

gambling addictions, and the cognitive sequela associated with the chronic use of different substances, such as alcohol, cannabis, stimulants and opioids, and gambling modalities. The cognitive profiles associated with different drugs and addictive behaviors are discussed in the context of current topics and controversies, such as the therapeutic effects of cannabinoids, the aftermath of the synthetic opioid crisis, or the advent of online gambling and gaming activities (Curran et al., 2016; Karilla et al., 2018; KardefeltWinther et al., 2017). Section (3) introduces novel neuroscience-informed treatment approaches to rescue cognitive deficits and improve clinical outcomes for people with addictions. These approaches include computerized cognitive training programs to retrain salience-related biases and build response inhibition and working memory capacity, cognitive remediation therapies focused on executive functions, mindfulness interventions targeting cognitive control and monitoring, and pharmacological enhancement and brain-stimulation techniques. Finally, Section (4) provides unique new vistas on research approaches that are pushing the boundaries of the cognition and addiction field. These exciting new avenues include population neuroscience, namely, the application of cutting-edge neuroscience tools to population-based cohorts, neuroepidemiology, i.e., the leverage of epidemiology data to address neurocognitive questions, neuroethics, and longitudinal brain mapping. In addition, it provides a pedagogic approach to the use of new techniques for “big data” collection and analysis, and the application of genetic and neuroimaging techniques to understand the lifespan of addiction pathophysiology, from preterm vulnerability to adult abstinence-based neuroplasticity and recovery. The book has been conceived with an inclusive and international perspective and includes contributors from Europe, Africa, America, Australia, and the Middle East, which provide a truly global viewpoint. The contributors are outstanding researchers and

xxv

xxvi

Introduction

clinicians, and the contents are geared towards a broad audience including research students, researchers and academics, and frontline clinicians interested in learning and applying cognitive principles and techniques in addiction research, prevention, assessment, treatment, and recovery. We hope to succeed in our main goal, that readers share our enthusiasm about this fascinating field.

References Bickel, W.K., Mellis, A.M., Snider, S.E., Athamneh, L.N., Stein, J.S., Pope, D.A., 2018. 21st century neurobehavioral theories of decision making in addiction: Review and evaluation. Pharmacol. Biochem. Behav. 164, 4e21. https://doi.org/10.1016/j.pbb.2017.09.009. Curran, H.V., Freeman, T.P., Mokrysz, C., Lewis, D.A., Morgan, C.J., Parsons, L.H., 2016. Keep off the grass? Cannabis, cognition and addiction. Nat. Rev. Neurosci. 17 (5), 293e306. https://doi.org/10.1038/nrn.2016.28. Ekhtiari, H., Tavakoli, H., Addolorato, G., Baeken, C., Bonci, A., Campanella, S., Castelo-Branco, L., Challet-Bouju, G., Clark, V.P., Claus, E., Dannon, P.N., Del Felice, A., den Uyl, T., Diana, M., di Giannantonio, M., Fedota, J.R., Fitzgerald, P., Gallimberti, L., GrallBronnec, M., Herremans, S.C., Herrmann, M.J., Jamil, A, Khedr, E., Kouimtsidis, C., Kozak, K., Krupitsky, E., Lamm, C., Lechner, W.V., Madeo, G., Malmir, N., Martinotti, G., McDonald, W.M., Montemitro, C., Nakamura-Palacios, E.M., Nasehi, M., Noël, X., Nosratabadi, M., Paulus, M., Pettorruso, M., Pradhan, B., Praharaj, S.K., Rafferty, H., Sahlem, G., Salmeron, B.J., Sauvaget, A., Schluter, R.S., Sergiou, C., Shahbabaie, A., Sheffer, C., Spagnolo, P.A., Steele, V.R., Yuan, T.F., van Dongen, J.D.M., Van Waes, V., Venkatasubramanian, G., Verdejo-García, A., Verveer, I., Welsh, J.W., Wesley, M.J., Witkiewitz, K., Yavari, F., Zarrindast, M.R., Zawertailo, L., Zhang, X., Cha, Y.H., George, T.P., Frohlich, F., Goudriaan, A.E., Fecteau, S., Daughters, S.B., Stein, E.A., Fregni, F., Nitsche, M.A., Zangen, A., Bikson, M., Hanlon, C.A., 2019 Sep. Transcranial electrical and magnetic stimulation (tES and TMS) for addiction medicine: A consensus paper on the present state of the science and the road ahead. Neurosci.

Biobehav. Rev. 104, 118e140. https://doi.org/10.1016/j.neubiorev. 2019.06.007. Epub 2019 Jul 2. Review. PubMed PMID: 31271802. Everitt, B.J., Robbins, T.W., 2016. Drug addiction: updating actions to habits to compulsions ten years on. Annu Rev Psychol 67, 23e50. https://doi.org/10.1146/annurev-psych-122414-033457. Goldstein, R.Z., Volkow, N.D., 2002. Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am. J. Psychiatry 159 (10), 1642e1652. Hyman, S.E., 2007. The neurobiology of addiction: implications for voluntary control of behavior. Am. J. Bioeth. 7 (1), 8e11. Kardefelt-Winther, D., Heeren, A., Schimmenti, A., van Rooij, A., Maurage, P., Carras, M., Edman, J., Blaszczynski, A., Khazaal, Y., Billieux, J., 2017. How can we conceptualize behavioural addiction without pathologizing common behaviours? Addiction 112 (10), 1709e1715. https://doi.org/10.1111/add.13763. Karila, L., Marillier, M., Chaumette, B., Billieux, J., Franchitto, N., Benyamina, A., 2018. New synthetic opioids: Part of a new addiction landscape. Neurosci. Biobehav. Rev. https://doi.org/10.1016/j.neubiorev.2018.06.010 pii: S0149-7634(18)30114-3. Kwako, L.E., Momenan, R., Litten, R.Z., Koob, G.F., Goldman, D., 2016. Addictions neuroclinical assessment: a neuroscience-based framework for addictive disorders. Biol. Psychiatry 80 (3), 179e189. https:// doi.org/10.1016/j.biopsych.2015.10.024. Mackey, S., Kan, K.J., Chaarani, B., Alia-Klein, N., Batalla, A., Brooks, S., Cousijn, J., Dagher, A., de Ruiter, M., Desrivieres, S., Feldstein Ewing, S.W., Goldstein, R.Z., Goudriaan, A.E., Heitzeg, M.M., Hutchison, K., Li, C.S., London, E.D., Lorenzetti, V., Luijten, M., Martin-Santos, R., Morales, A.M., Paulus, M.P., Paus, T., Pearlson, G., Schluter, R., Momenan, R., Schmaal, L., Schumann, G., Sinha, R., Sjoerds, Z., Stein, D.J., Stein, E.A., Solowij, N., Tapert, S., Uhlmann, A., Veltman, D., van Holst, R., Walter, H., Wright, M.J., Yucel, M., Yurgelun-Todd, D., Hibar, D.P., Jahanshad, N., Thompson, P.M., Glahn, D.C., Garavan, H., Conrod, P., 2016. Genetic imaging consortium for addiction medicine: from neuroimaging to genes. Prog. Brain Res. 224, 203e223. https://doi.org/10.1016/ bs.pbr.2015.07.026. Verdejo-García, A., Bechara, A., 2009. A somatic marker theory of addiction. Neuropharmacology 56 (Suppl. 1), 48e62. https://doi.org/ 10.1016/j.neuropharm.2008.07.035.

Chapter 1

Cognition: the interface between nature and nurture in addiction Antonio Verdejo-Garcia School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia

Introduction A key question in the field of addiction is whether some people are hardwired to develop addictive disorders or if, conversely, drugs and “addictive products” or related contexts generate addictions. Historically, the answers to this question have fluctuated as a function of prevailing theories. Old moral views and personality models saw inherent weaknesses in the individual (Eysenck, 1997; Peele, 1987). Learning theories have focused on the ability of drugs and “addictive products” (e.g., electronic gaming machines) to generate aberrant learning, which is then resistant to extinction relatively uniformly across individuals (Robinson and Berridge, 2003, 2008). Social theories have treated addiction as a manifestation of a certain context and environment (e.g., the classic studies on Vietnam War veterans) (Moore, 1993; Zinberg, 1984). Nowadays, there is agreement that none of these models, in isolation, can satisfactorily explain the nature and the course of addiction, but at the same time, there is a lack of comprehensive frameworks. Contemporary models have embraced a biopsychosocial approach, but there is a bias toward the bio-, at least in discovery science and therapeutic development, especially after the advent of neuroimaging tools and genetic manipulation techniques within animal studies (Hall et al., 2015a). The current prevailing view is that addiction is a “brain disorder,” characterized by drug- or gambling-related neuroadaptations that ultimately have an impact on psychological functioning (changes in thinking, emotions, and behaviors) and social interaction (Volkow et al., 2011; Volkow et al., 2016). Critics of this view argue that it mistakenly reproduces a physical disease model (inadequate for mental disorders or social constructs), lacks integration of social and environmental drivers, and fosters feelings of hopelessness and

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00001-0 Copyright © 2020 Elsevier Inc. All rights reserved.

externalization among people with addiction (e.g., “I have a disease, I can’t do anything about it”) (Hall et al., 2015b). In this chapter, I will argue that a focus on cognitiond encompassing thinking, emotion, and related behaviors, as well as their neural underpinningsdcan provide a more comprehensive and integrative understanding of the nature and the course of addiction. Cognition sits at the interface of biological, psychological, and social drivers of addictive disorders, hinging on the interplay between nature and nurture. Genetic and early environmental influences shape the cognitive traits that make us vulnerable or resilient to drug use/gambling and related social contexts (e.g., product availability, peer pressure) (Belcher et al., 2014). At the same time, drugs and gambling modify learning and cognitive control processes and change the way we interact with others and the environment (Everitt and Robbins, 2016; Goldstein and Volkow, 2011; Moeller and Goldstein, 2014). By focusing on cognition, we can overcome the reductionism of the “disease model,” i.e., it’s not in the brain, it’s at the interface between the brain and the environment, and foster self-agency about recovery, i.e., it’s not indelible, the same influences that originally shaped cognition in a certain way can help restore or compensate cognitive mechanisms to facilitate recovery (Garavan and Weierstall, 2012). To articulate this vision, I will first discuss the role of cognition in contemporary addiction theories and outline a cognition-centered integrative approach. Next, I will summarize cognitive neuroscience evidence showing that individual variations in core cognitive processes, particularly reward-related processes and higher-order cognitive skills, plus disorder-specific impacts on such processes, can explain both addiction vulnerability and disordered states and chronicity.

1

2 Cognition and Addiction

Cognition to bridge the gap between neurobiological models and social accounts of addiction Contemporary theories of addiction generally posit neurobiological alterations in three systems: the incentive salience (or reward) system, the stress system, and the executive control system, which map into the striatum, extended amygdala, and prefrontal cortex circuits, respectively (Koob and Volkow, 2010). Incentive salience alterations are responsible for reward sensitization (increased motivation toward drugs/gambling resulting from repeated administrationdi.e., instead of the expected habituation) and reward prediction errors (i.e., expecting more reward than what is actually received). Heightened motivation toward drugs/gambling also occurs at the cost of reduced motivation toward natural reinforcers (Goldstein and Volkow, 2002, 2011). Alterations in the stress system account for persistently elevated negative affect, which can manifest as chronic stress and depression, as well as predominance of negative reinforcement mechanisms in the control of behavior. That is, negative affective states become the norm, behaviors are mostly energized to try to get rid of unpleasant feelings, and this behavior results in short-term relief but long-term augmentation of the stress response. Finally, executive alterations are responsible for the tendency to focus on immediate responses and shortterm outcomes, neglecting goals and long-term consequences. Different theories emphasize two or more of these alterations and related brain systems. For example, “dual models” focus on the imbalance between incentive salience (ventral striatum) and executive control (dorsolateral prefrontal cortex [DLPFC] and anterior cingulate cortex) (McClure and Bickel, 2014). Stress models emphasize the link between negative affect (hypothalamicepituitarye adrenal axis, amygdala, hippocampus) and poor control over stress-related responses (dorsal striatum, DLPFC), which has been ascribed to impulsivity (e.g., negative urgencydthe tendency to act impulsively under negative affect) or compulsivity (e.g., repetitive behaviors that “fly under the radar” of top-down executive supervision) (Figee et al., 2016; Verdejo-García et al., 2007). Executive control and decision-making models highlight the misalignment between goal-related systems (ventromedial prefrontal cortex) and motivational, emotional, and contextual drivers (striatum, insula, amygdala/hippocampus) (Bechara, 2005; Redish et al., 2008; Verdejo-García and Bechara, 2009). Although the significance of these models dwells in the way they characterize the drivers of addictive behaviors, while acknowledging that these drivers are shaped by and operate in social contexts, they often come across as reductionist biological accounts of behavior.

Translating the key notions of these models into a cognitive framework can help to broaden their scope and impact. Essentially, contemporary neurobiological theories characterize the “backend” of addiction-related alterations. The “frontline” is the complex harmful behavior and the negative social consequences that policy makers and preventionists try to counteract and clinicians have to face (i.e., uncontrolled drug/gambling use, distress, deterioration of health and quality of life, lack of social support, personal and social burden). In between these two, there is a range of cognitive alterations involving reward valuation and learning, emotion processing and affect regulation, and executive control and decision-making affecting personal and social domains (e.g., valuation of individual rewards such as salary and career and social rewards such as friendships and relationships). And these alterations trace back to specific traits that interact with environmental and social factors before and during the emergence of addiction and can potentially cooperate with contextual factors in the path to addiction recovery (Celma-Merola et al., 2018). As addictions take time to develop, we can chronologically map the unfolding of cognitive drivers and their interaction with environmental and social factors. Cognitive-affective traits, such as reward sensitivity, negative affectivity, and impulsivity/self-control, influence child and adolescent learning and academic and social development. The interaction between these traits and environmental/social influences (socioeconomic disadvantage, trauma, poor parenting, academic failure, peer pressure or social isolation) predicts the onset of addictive behaviors. Once initiated, drug use and gambling contribute to exacerbate preexisting traits, for example, they sensitize reward learning and stress responses and deteriorate cognitive control, fostering impulsive decisions and compulsive behaviors. These changes contribute to worsen the contextual milieu (i.e., loss of productivity, income, social capital, and support) and foster a spiral of distress and impoverishment of quality of life. Although this scenario describes the “typical” developmental course of addictive behaviors across adolescence and young adulthood, it can also apply to late-onset addiction, in which interpersonal and social factors (e.g., trauma, relationship problems, unemployment) interact with cognitive characteristics (e.g., emotion regulation and resilience against negative affect, self-control, cognitive flexibility) as well as drugand gambling-related effects to generate protection against, or escalation toward, addiction problems. Therefore, cognitive traits predict the onset of addictive behaviors via direct and indirect pathways (e.g., interaction with academic and social factors and stressful life events). At the same time, drug use and gambling deteriorate or exacerbate cognitive traits and their underlying neural processes, giving rise to abnormal cognitive states or cognitive

Cognition: the interface between nature and nurture in addiction Chapter | 1

deficits, as well as contextual and social factors (i.e., disordered states), leading to the vicious cycle of addiction.

Evidence for the double role of cognition in addiction vulnerability and consequences The view that cognition may be the key to unlock the nature and the course of addiction stands on evidence from longitudinal studies, endophenotype-based approaches, and “neurotoxicity-controlled designs” comparing people with addiction versus nonaddicted recreational users and people with substance versus behavioral addictions. Longitudinal designs enable researchers to identify cognitive traits as well as other factors that predate the onset of addictive behaviors and to track the changes that result from drug/ gambling use once initiated. It is worth noting that animal studies can also successfully address this transition (from trait characteristics to disorder-related states) but this approach will be covered in Chapter 2, and thus here I will only focus on human research. Endophenotype studies rely on the assumption that cognition is an intermediate feature between the biological drivers and the complex behavioral manifestations of addictive disorders (Verdejo-García et al., 2008). As such, the cognitive traits that predispose certain people to addiction can be identified in their first-degree relatives, and the cognitive differences between people with addiction and their unaffected relatives reflect drug use/gambling-related changes. Similarly, studies comparing people with addiction and recreational users, or people with substance versus behavioral addictions, can contribute to disentangling addiction-related traits and addictionresulting deficits. In the following sections, I summarize relevant evidence from each of these approaches.

Longitudinal studies A key assumption of the cognitive framework articulated in this chapter is that certain cognitive traits and skills predate and influence onset of addictive behaviors. Before discussing the evidence, it is important to note that although “traits” and “skills” have been traditionally approached from different disciplines, i.e., personality versus cognitive sciences, respectively, now we know that they are meaningfully intertwined and underpinned by common neural circuits and processes. As an example, the construct of “conscientiousness,” which has a long tradition in personality science, is very similar to the concept of “cognitive control” or “disinhibition” within modern cognitive neuroscience (Nigg, 2017). In support of this view, these two constructs have similar developmental trajectories (peaking between 12 and 16 years during childhood and

3

adolescence) and overlapping brain underpinnings involving lateral prefrontal cortex and anterior cingulate regions (Vijayakumar et al., 2014a,b). Moreover, conscientiousness and cognitive control are strongly correlated with intelligence quotient (IQ) and particularly fluid intelligence, probably reflecting meaningful interactions between trait characteristics and cognitive skills, as well as between these two and some of the social-contextual determinants of IQ (e.g., social disadvantage correlates with low conscientiousness, poorer cognitive control and low IQ) (Yücel et al., 2012). It could be argued that more “affective” traits such as reward sensitivity or negative emotionality do not fit in this pattern, but there are good reasons to think they do. For example, the trait of reward sensitivity overlaps with the ability to learn from reward feedback, which is critical for cognitive and social development as well as academic achievement (Telzer, 2016). In this context, longitudinal evidence has established that lower general cognitive skills, lower conscientiousness (or higher disinhibition/impulsivity), and higher negative affectivity predate and predict the onset of substance use/ gambling and the development of addictive disorders. In population-based cohorts within the general population, lower cognitive ability (IQ) measured in late adolescence predicts the risk of subsequent substance addiction during adult life (Latvala et al., 2016). Although the association seems primarily due to genetic influences, these influences are indirectly inferred from behavioral genetic analyses, which can slightly overestimate genetic versus environmental effects (Joseph, 2002; Kendler et al., 2016). Interestingly, the most predictive aspects of IQ in relation to addiction are the inductive and verbal domains (strongly associated with cognitive control) versus the visuospatial and technical domains. Within high-risk cohorts, which target individuals at greater risk of developing addiction problems by virtue of family history or personality, studies have consistently found that high levels of impulsivity/ disinhibition (or low levels of conscientiousness and cognitive control) measured in childhood predict the onset of addictive behaviors in adolescence and the development of addictive disorders (substance use disorders and gambling disorder) during young adulthood (Acheson et al., 2011; Lovallo et al., 2013; Slutske et al., 2012; Tarter et al., 2004; Tarter et al., 2003). This association is direct and significant after controlling for other environmental and social factors (e.g., background, socioeconomic status, parenting), although it is possible that some of these factors conflate into impulsivity measures within the high-risk samples. Recently, longitudinal studies have focused on adolescence as a natural model of risk within the general population and have incorporated multisite designs and imaging measures as part of multimodal assessment protocols including history (i.e., background characteristics), personality, and cognitive/brain measures. One of

4 Cognition and Addiction

these studies, the IMAGEN consortium, found that a combination of background characteristics, the personality facets of conscientiousness and anxiety sensitivity, and structural (GM:WM ratio; parenchymal volume) and cognitive-evoked functional brain features (right precentral gyrus activation during reward outcome and inhibition failure and bilateral superior frontal gyrus during reward outcome) predict future binge drinking (a proxy of problematic alcohol use and addictive behaviors) (Whelan et al., 2014). Conversely, ventromedial prefrontal cortex activation during emotional reactivity and face recognition was a better predictor of current binge drinking, probably reflecting alcohol-related neuroadaptations. Altogether, longitudinal results support the dynamic model sketched in the previous sections, whereby cognitive traits and difficulties associated with cognitive control/ disinhibition and negative affectivity contribute to addiction vulnerability and are subsequently exacerbated by addictive behaviors.

Endophenotype studies In a series of studies including sibling pairs with the same biological parents, one affected and one unaffected with stimulant use disorders, Ersche and colleagues identified cognitive and brain features associated with addiction vulnerability (i.e., shared by affected and unaffected siblings) and changes associated with stimulant use. With regard to trait impulsivity, indicated by self-reports reflecting different aspects of impulsiveness and novelty seeking, stimulant users showed greater impulsivity than both sibling and controls, but there were also sizable differences between siblings and controls (Ersche et al., 2010). Therefore, this approach shows that impulsivity is a vulnerability factor for addiction, but crucially it is also exacerbated by substance use. Interestingly, the main differences between siblings and controls were in the nonplanning aspect of impulsivity (theoretically associated with the construct of conscientiousness) (Whiteside and Lynam, 2001), whereas the main difference between stimulant users and siblings was in the disinhibition aspect of novelty seeking, which reflects behavioral dysregulation, as manifested, for example, in uncontrolled drug use and/or gambling. In a subsequent study that included trait measures of impulsivity and compulsivity, emotional sensitivity (negative affectivity and stress) and selfevaluation (self-efficacy, locus of control, and social comparison), and cognitive measures of executive control and disinhibition, results showed that stimulant users, siblings, and controls differed in most of these measures (Ersche et al., 2012b). Both sibling pairs differed from controls on impulsivityecompulsivity, negative affectivity, and self-evaluation, suggesting that stimulant use exacerbates the traits that are already elevated in nonaffected

siblings. In the executive tests of working memory and planning, stimulant users performed poorer than siblings and controls, and siblings poorer than controls. Conversely, inhibitory control measured with the Stop-Signal Task (a motor impulsivity task) differed between the sibling pairs and controls but not between stimulant users and their siblings, suggesting that inhibitory control deficits are more of a vulnerability for and less of a consequence of addiction. Interestingly as well, there were no significant differences between the groups on tests of memory or attention, suggesting that only certain cognitive functions, i.e., those related to cognitive control/executive functions, play a crucial role in the nature and course of addiction. Finally, structural neuroimaging measures revealed a set of regions displaying overlapping abnormalities in both stimulant users and their siblings, compared with healthy controls: the putamen and the amygdala had both significantly larger volumes, while the posterior insula, the left postcentral gyrus, and the superior temporal gyrus had lower volumes (Ersche et al., 2012a). Altogether, endophenotype studies support the notion that specific trait characteristics, including heightened impulsivity and negative affectivity and related neural systems, confer vulnerability to addiction and are subsequently exacerbated by drug use, leading to greater deficits in executive functions and further behavioral dysregulation.

Neurotoxicity-controlled studies This section summarizes the findings from two approaches: (i) comparing dependent versus recreational cocaine users and (ii) comparing substance users and gamblers. The first approach offers insights into which aspects of cognition deteriorate as a function of addiction progression (i.e., those observable in dependent vs. recreational users and controls). The second approach offers insights into which aspects of cognition are more sensitive to substanceinduced neuroadaptations (i.e., those observable in substance users vs. gamblers) and which aspects are related to common vulnerability factors (overlapping between substance users and gamblers and different from healthy controls).

Dependent versus recreational users In a series of studies by Quednow and colleagues, people with cocaine addiction (meeting Diagnostic and Statistical Manual of Mental Disorders [DSM] criteria for dependence) and those with recreational cocaine use (but not dependence) underwent trait and neuropsychological assessments. Both groups used cocaine as their primary drug (>40 g per month) and were currently abstinent, but critically cocaine exposure was eight times higher in the dependent versus the recreational group, as indicated by

Cognition: the interface between nature and nurture in addiction Chapter | 1

hair toxicology analyses. With regard to trait and cognitive aspects of impulsivity, findings showed significant differences between the two cocaine groups and controls in general impulsiveness and novelty seeking. However, only attentional-impulsive traits differed between dependent and recreational usersdthey were higher in the dependent group. In addition, there were no differences between the groups on cognitive measures of disinhibition, including attentional and motor impulsivity tasks (Rapid Visual Processing or Stop-Signal Task) (Vonmoos et al., 2013b). When examining executive functions, findings showed significant differences between dependent users, recreational users, and controls in tests of working memory and recall consistency (an index of strategic retrieval). In addition, sustained attention was affected in recreational users and correlated with cumulative cocaine exposure (Vonmoos et al., 2013a). A subsequent study in this cohort incorporated measures of decision-making, involving both individual rewards and social rewards. Both dependent and recreational cocaine users exhibited social decision-making deficits (more self-serving behavior in social economic exchange games). However, only dependent users showed deficits in tasks involving individual-based rewards, including the Iowa Gambling Task and Delay Discounting, which also correlated with cocaine dose and duration (Hulka et al., 2014). Altogether, these studies suggest that impulsivity and related cognitive constructs (sustained attention) represent vulnerability factors or early signs of exposure to drug use, whereas working memory and executive-memory retrieval deficits are specific consequences of substance use.

Stimulant users versus gamblers These studies compared the trait characteristics and the cognitive performance of people with cocaine addiction versus those with gambling disorder and minimal exposure to substance use, which was restricted to alcohol abuse (as per DSM-IV-TR) and smoking. In an initial study examining trait impulsivity and executive functions, findings showed that the negative urgency trait (acting impulsively under negative affect) was higher in cocaine users versus gamblers and controls and higher in gamblers than controls. Conversely, positive urgency (acting impulsively under negative affect) was elevated in cocaine users and gamblers versus controls (showing no differences between each other) (Albein-Urios et al., 2012). The analysis of executive function tests showed that cocaine users had poorer working memory than both gamblers and controls, whereas response inhibition performance (measured with an attentional inhibition task, the Stroop Test) was similar among cocaine users and gamblers and in both cases poorer than in controls. A subsequent study examined the same cohort using the brain and behavioral

5

measures of cognitive flexibility (reversal learning performance and related brain activation) (Verdejo-Garcia et al., 2015). The perseveration error rate (an index of cognitive inflexibility) was higher in cocaine users versus gamblers and controls. In addition, cocaine users showed less DLPFC activation during reversal shifting (i.e., the “flexibility” trials) versus gamblers and controls, whereas both cocaine users and gamblers showed reduced ventrolateral prefrontal cortex (VLPFC) activation compared with controls and no differences between each other. The DLPFC is strongly and generally implicated in executive control and seems to be specifically affected by cocaine use, whereas the VLPFC is more specifically implicated in goal-related cognitive control and seems to be similarly associated with cocaine and gambling use. Altogether, these studies suggest that positive urgency and reduced response inhibition represent vulnerability factors for substance use and behavioral addictive disorders, whereas working memory and cognitive flexibility deficits are specific consequences of substance use.

Cognition at the interface between nature and nurture Here, I started by proposing that cognition sits at the interface between the vulnerability for and the consequences of addiction and that it is pivotal to understand the interplay between individual-based factors (genetics, neuroadaptations) and environmental and social drivers. The evidence reviewed suggests that an array of cognitive traits and skills, which encompasses “personality” concepts of conscientiousness and negative affectivity (as manifested in emotional sensitivity or negative urgency) and “cognitive” constructs associated with fluid intelligence, attention, (dis)inhibition, and social decisionmaking, are part of the vulnerability to substance use and behavioral addictive disorders, whereas traits associated with behavioral dysregulation and cognitive deficits in working memory, cognitive flexibility, and reward-based decision-making are associated with the consequences of substance and behavioral addictions. Of note, there is an important overlap and continuity between premorbid traits and “disordered states,” given conceptual similarities and shared variance between, e.g., fluid intelligence, conscientiousness, and executive functions. The relevance of social cognition processes in the vulnerability to addiction (revealed by endophenotype and neurotoxicity-controlled studies) also illustrates the interactive nature of cognitionedrugs/addictive productse environment relationships, as faulty social decision-making processes can facilitate deviant social behavior (reduced social integration or heightened sensitivity to peer pressure) and drug/gambling use, which can ultimately exacerbate

6 Cognition and Addiction

and broaden social-cognitive deficits, fostering a vicious cycle of social impoverishment. Cognition is central to addiction development and maintenance. Higher-order cognitive traits and processes such as conscientiousness and cognitive control over salient stimuli (attention) and emotions (negative affectivity) as well as social-cognitive characteristics determine vulnerability for addictive disorders. At the same time, substance use and behavioral addictions are associated with cognitive deficits in working memory, mental flexibility, and rewardbased decision-making, which are essential for goal achievement and successful social interactions. These cognitive alterations can then contribute to explaining the core manifestations of addiction (e.g., the persistence of behavior despite negative consequences, recurrent cravings, relapses) and compromise treatment and recovery goals.

References Acheson, A., Vincent, A.S., Sorocco, K.H., Lovallo, W.R., 2011. Greater discounting of delayed rewards in young adults with family histories of alcohol and drug use disorders: studies from the Oklahoma family health patterns project. Alcohol Clin. Exp. Res. 35 (9), 1607e1613. Albein-Urios, N., Martinez-González, J.M., Lozano, Ó., Clark, L., VerdejoGarcía, A., 2012. Comparison of impulsivity and working memory in cocaine addiction and pathological gambling: implications for cocaine-induced neurotoxicity. Drug Alcohol Depend. 126 (1), 1e6. Bechara, A., 2005. Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nat. Neurosci. 8 (11), 1458e1463. Belcher, A.M., Volkow, N.D., Moeller, F.G., Ferré, S., 2014. Personality traits and vulnerability or resilience to substance use disorders. Trends Cognit. Sci. 18 (4), 211e217. Celma-Merola, J., Abella-Pons, F., Mata, F., Pedra-Pagés, G., VerdejoGarcia, A., 2018. Self-changing behaviour in smoking cessation linked to trait and cognitive impulsivity. Addiction 113 (1), 107e112. https://doi.org/10.1111/add.13942. Ersche, K.D., Jones, P.S., Williams, G.B., Turton, A.J., Robbins, T.W., Bullmore, E.T., 2012a. Abnormal brain structure implicated in stimulant drug addiction. Science 335 (6068), 601e604. Ersche, K.D., Turton, A.J., Chamberlain, S.R., Müller, U., Bullmore, E.T., Robbins, T.W., 2012b. Cognitive dysfunction and anxious-impulsive personality traits are endophenotypes for drug dependence. Am. J. Psychiatry 169 (9), 926e936. Ersche, K.D., Turton, A.J., Pradhan, S., Bullmore, E.T., Robbins, T.W., 2010. Drug addiction endophenotypes: impulsive versus sensationseeking personality traits. Biol. Psychiatry 68 (8), 770e773. Everitt, B.J., Robbins, T.W., 2016. Drug addiction: updating actions to habits to compulsions ten years on. Annu. Rev. Psychol. 67, 23e50. Eysenck, H., 1997. Addiction, personality and motivation. Hum. Psychopharmacol. Clin. Exp. 12 (2), S79. Figee, M., Pattij, T., Willuhn, I., Luigjes, J., van den Brink, W., Goudriaan, A., et al., 2016. Compulsivity in obsessiveecompulsive disorder and addictions. Eur. Neuropsychopharmacol. 26 (5), 856e868.

Garavan, H., Weierstall, K., 2012. The neurobiology of reward and cognitive control systems and their role in incentivizing health behavior. Prev. Med. 55, S17eS23. Goldstein, R.Z., Volkow, N.D., 2002. Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am. J. Psychiatry 159 (10), 1642e1652. Goldstein, R.Z., Volkow, N.D., 2011. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 12 (11), 652. Hall, W., Carter, A., Forlini, C., 2015a. The brain disease model of addiction: is it supported by the evidence and has it delivered on its promises? Lancet Psychiatry 2 (1), 105e110. Hall, W., Carter, A., Forlini, C., 2015b. Brain disease model of addiction: misplaced priorities? Lancet Psychiatry 2 (10), 867. Hulka, L., Eisenegger, C., Preller, K., Vonmoos, M., Jenni, D., Bendrick, K., et al., 2014. Altered social and non-social decisionmaking in recreational and dependent cocaine users. Psychol. Med. 44 (5), 1015e1028. Joseph, J., 2002. Twin studies in psychiatry and psychology: science or pseudoscience? Psychiatr. Q. 73 (1), 71e82. Kendler, K., Ohlsson, H., Edwards, A., Lichtenstein, P., Sundquist, K., Sundquist, J., 2016. A novel sibling-based design to quantify genetic and shared environmental effects: application to drug abuse, alcohol use disorder and criminal behavior. Psychol. Med. 46 (8), 1639e1650. Koob, G.F., Volkow, N.D., 2010. Neurocircuitry of addiction. Neuropsychopharmacology 35 (1), 217. Latvala, A., Kuja-Halkola, R., D’onofrio, B.M., Larsson, H., Lichtenstein, P., 2016. Cognitive ability and risk for substance misuse in men: genetic and environmental correlations in a longitudinal nation-wide family study. Addiction 111 (10), 1814e1822. Lovallo, W.R., Farag, N.H., Sorocco, K.H., Acheson, A., Cohoon, A.J., Vincent, A.S., 2013. Early life adversity contributes to impaired cognition and impulsive behavior: studies from the Oklahoma Family Health Patterns Project. Alcohol Clin. Exp. Res. 37 (4), 616e623. McClure, S.M., Bickel, W.K., 2014. A dual-systems perspective on addiction: contributions from neuroimaging and cognitive training. Ann. N.Y. Acad. Sci. 1327 (1), 62e78. Moeller, S.J., Goldstein, R.Z., 2014. Impaired self-awareness in human addiction: deficient attribution of personal relevance. Trends Cognit. Sci. 18 (12), 635e641. Moore, D., 1993. Beyond Zinberg’s “social setting”: a processural view of illicit drug use. Drug Alcohol Rev. 12 (4), 413e421. Nigg, J.T., 2017. Annual research review: on the relations among selfregulation, self-control, executive functioning, effortful control, cognitive control, impulsivity, risk-taking, and inhibition for developmental psychopathology. J. Child Psychol. Psychiatry 58 (4), 361e383. Peele, S., 1987. A moral vision of addiction: how people’s values determine whether they become and remain addicts. J. Drug Issues 17 (2), 187e215. Redish, A.D., Jensen, S., Johnson, A., 2008. Addiction as vulnerabilities in the decision process. Behav. Brain Sci. 31 (4), 461e487. Robinson, T.E., Berridge, K.C., 2003. Addiction. Annu. Rev. Psychol. 54, 25e53. https://doi.org/10.1146/annurev.psych.54.101601.145237. Robinson, T.E., Berridge, K.C., 2008. The incentive sensitization theory of addiction: some current issues. Phil. Trans. Biol. Sci. 363 (1507), 3137e3146.

Cognition: the interface between nature and nurture in addiction Chapter | 1

Slutske, W.S., Moffitt, T.E., Poulton, R., Caspi, A., 2012. Undercontrolled temperament at age 3 predicts disordered gambling at age 32: a longitudinal study of a complete birth cohort. Psychol. Sci. 23 (5), 510e516. Tarter, R.E., Kirisci, L., Habeych, M., Reynolds, M., Vanyukov, M., 2004. Neurobehavior disinhibition in childhood predisposes boys to substance use disorder by young adulthood: direct and mediated etiologic pathways. Drug Alcohol Depend. 73 (2), 121e132. Tarter, R.E., Kirisci, L., Mezzich, A., Cornelius, J.R., Pajer, K., Vanyukov, M., et al., 2003. Neurobehavioral disinhibition in childhood predicts early age at onset of substance use disorder. Am. J. Psychiatry 160 (6), 1078e1085. Telzer, E.H., 2016. Dopaminergic reward sensitivity can promote adolescent health: a new perspective on the mechanism of ventral striatum activation. Dev. Cogn. Neurosci. 17, 57e67. Verdejo-García, A., Bechara, A., 2009. A somatic marker theory of addiction. Neuropharmacology 56, 48e62. Verdejo-García, A., Bechara, A., Recknor, E.C., Pérez-García, M., 2007. Negative emotion-driven impulsivity predicts substance dependence problems. Drug Alcohol Depend. 91 (2), 213e219. Verdejo-Garcia, A., Clark, L., Verdejo-Román, J., Albein-Urios, N., Martinez-Gonzalez, J.M., Gutierrez, B., Soriano-Mas, C., 2015. Neural substrates of cognitive flexibility in cocaine and gambling addictions. Br. J. Psychiatry 207 (2), 158e164. Verdejo-García, A., Lawrence, A.J., Clark, L., 2008. Impulsivity as a vulnerability marker for substance-use disorders: review of findings from high-risk research, problem gamblers and genetic association studies. Neurosci. Biobehav. Rev. 32 (4), 777e810. Vijayakumar, N., Whittle, S., Dennison, M., Yücel, M., Simmons, J., Allen, N.B., 2014a. Development of temperamental effortful control mediates the relationship between maturation of the prefrontal cortex and psychopathology during adolescence: a 4-year longitudinal study. Dev. Cogn. Neurosci. 9, 30e43.

7

Vijayakumar, N., Whittle, S., Yücel, M., Dennison, M., Simmons, J., Allen, N.B., 2014b. Prefrontal structural correlates of cognitive control during adolescent development: a 4-year longitudinal study. J. Cogn. Neurosci. 26 (5), 1118e1130. Volkow, N.D., Baler, R.D., Goldstein, R.Z., 2011. Addiction: pulling at the neural threads of social behaviors. Neuron 69 (4), 599e602. Volkow, N.D., Koob, G.F., McLellan, A.T., 2016. Neurobiologic advances from the brain disease model of addiction. N. Engl. J. Med. 374 (4), 363e371. Vonmoos, M., Hulka, L.M., Preller, K.H., Jenni, D., Baumgartner, M.R., Stohler, R., et al., 2013a. Cognitive dysfunctions in recreational and dependent cocaine users: role of attention-deficit hyperactivity disorder, craving and early age at onset. Br. J. Psychiatry bjp. bp. 112.118091. Vonmoos, M., Hulka, L.M., Preller, K.H., Jenni, D., Schulz, C., Baumgartner, M.R., Quednow, B.B., 2013b. Differences in selfreported and behavioral measures of impulsivity in recreational and dependent cocaine users. Drug Alcohol Depend. 133 (1), 61e70. https://doi.org/10.1016/j.drugalcdep.2013.05.032. Whelan, R., Watts, R., Orr, C.A., Althoff, R.R., Artiges, E., Banaschewski, T., et al., 2014. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature 512 (7513), 185. Whiteside, S.P., Lynam, D.R., 2001. The five factor model and impulsivity: using a structural model of personality to understand impulsivity. Pers. Indiv. Differ. 30 (4), 669e689. Yücel, M., Fornito, A., Youssef, G., Dwyer, D., Whittle, S., Wood, S.J., et al., 2012. Inhibitory control in young adolescents: the role of sex, intelligence, and temperament. Neuropsychology 26 (3), 347. Zinberg, N.E., 1984. Drug, Set, and Setting: The Basis for Controlled Intoxicant Use. Yale University Press, New Haven, CT.

Chapter 2

From impulses to compulsions Kavya Raj1 and Antonio Verdejo-Garcia2 1

Brain, Mind and Society Research Hub, Monash University, Melbourne, VIC, Australia; 2School of Psychological Sciences and Turner Institute for

Brain and Mental Health, Monash University, Melbourne, VIC, Australia

Introduction Addiction can be conceptualized as a loss of control over drug-seeking and -taking behavior, whereby drug use that was initially voluntary and recreational progresses to a habit, and ultimately a compulsion, continuing to persist despite harmful consequences (Belin et al., 2013; Everitt and Robbins, 2005, 2013, 2016). It is hypothesized that this behavioral transition from impulsive choices to compulsive drug seeking and taking is underpinned by a shift in the neural loci of control from the ventral to the dorsal striatum (Belin and Everitt, 2008; Everitt and Robbins, 2013, 2016), as well as a progression from prefrontal cortical to subcortical striatal control (Chen et al., 2013; Everitt and Robbins, 2016; Murray et al., 2012; Renteria et al., 2018). This chapter summarizes the animal and human evidence that sustains this notion, starting with preclinical studies, continuing with human neuroimaging and cognitive studies, and concluding with ideas for future directions.

Animal models of drug-seeking habits and compulsions A key approach to understanding the transition from voluntary to compulsive drug use is through the lens of Pavlovian and instrumental learning (Robbins and Everitt; Everitt and Robbins, 2013). According to this perspective, initial drug use is goal-directed and controlled by actionoutcome (A-O) mechanisms. In this context, drug-related actions are sensitive to changes in outcome value, and thus drug seeking can stop if drug value decreases or is outweighed by alternative reinforcers (Everitt and Robbins, 2005). However, as drug use escalates, a parallel instrumental learning mechanism comes to dominate responding, and behavior shifts to a stimuluseresponse (S-R) habit process that is insensitive to outcome devaluation, i.e., precludes stopping or changing behavior, even when the

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00002-2 Copyright © 2020 Elsevier Inc. All rights reserved.

outcomes of such behaviors are negative (Zapata et al., 2010). Environmental stimuli associated with the effects of drug self-administration gain incentive salience via Pavlovian conditioning, and drug-seeking responses are automatically elicited by conditioned S-R loops (Everitt and Robbins, 2005). Drug-seeking and -taking behavior controlled by this S-R process is severed from the value of the drug and can operate without full engagement of cognitive control processes (Belin et al., 2013). Whether drug use is controlled by A-O or S-R mechanisms, at a behavioral level, can be tested through outcome devaluation procedures to observe whether instrumental responding for drug rewards changes as the outcome value changes (Dickinson et al., 2002). The shift from A-O to S-R mechanisms mediating drug use is documented by a consistent body of animal research. In a sophisticated series of experiments, Olmstead et al. (2001) utilized a heterogenous chained schedule of intravenous cocaine self-administration in which rats performed an initial drug-seeking response, which in turn provided access to a secondary drug-taking response that resulted in delivery of cocaine. After briefly establishing cocaine selfadministration, drug-seeking responses were shown to be sensitive to devaluation when the taking link of the chained schedule was extinguished, indicating that at an initial stage of cocaine seeking, behavior is goal-directed and influenced by the value of the response outcome. To replicate and extend this finding, Zapata et al. (2001) utilized a modified chained schedule of intravenous cocaine self-administration and demonstrated that, following a moderate number of training sessions, initial cocaine seeking is goal directed and sensitive to devaluation. However, as sessions increased and the cocaine-seeking experience was extended, 43% of the sample continued to respond after the outcome had been devalued, indicating that drug-seeking behavior had become habitual for this subgroup and was operating through an S-R mechanism. Similarly, while

9

10

Cognition and Addiction

instrumental responding for alcohol at early stages of training is goal-directed, after 8 week of training, behavioral control over alcohol seeking shifts to S-R habit mechanisms and is no longer sensitive to devaluation (Corbit et al., 2012). These findings illustrate how escalation of drug use results in resistance of drug seeking to outcome devaluation, and thus habitual S-R behaviors. While habitual drug-seeking and -taking behavior alone cannot provide a comprehensive explanation for addiction, it provides the foundation for compulsive drug use (Everitt and Robbins, 2016). Perseveration of habitual drug-seeking behavior despite drastically negative outcomes, such as deterioration of health and family bonds or loss of social support and employment, is a core aspect of addiction (Ersche et al., 2011). Aversive conditioning studies in rats show that, after sufficient training and exposure, responding for oral cocaine (Miles et al., 2003) and alcohol (Dickinson et al., 2002) perseveres even after devaluation through nausea-inducing lithium chloride injections (Nachman, 1963). Recent evidence suggests that as few as 16 training sessions are sufficient to render alcohol-seeking behavior unresponsive to devaluation through aversive conditioning (López et al., 2016). Drug reinforcers are significantly more resistant to aversive conditioning than natural foodebased reinforcers, for which responding is more readily reverted to an A-O process, perhaps due to evolutionary reasons (Dickinson et al., 2002; Miles et al., 2003). This indicates that drug reinforcers induce an S-R habit process far more rapidly than natural rewards. Moreover, drug-induced difficulty to shift between S-R and A-O processes suggests that a dominant habit system may become “compulsive” and trigger automatic drug-seeking behaviors under aversive conditions.

Neural circuits: transitioning from the ventral to dorsal striatum Converging evidence from animal research shows that the transition from voluntary to habitual and eventually compulsive use is neurally underpinned by a progression from the ventral to the dorsal striatum, via dopaminergic circuits (Belin and Everitt, 2008; Everitt and Robbins, 2005; Vanderschuren et al., 2005; Vanderschuren and Everitt, 2005). This transition has been neatly revealed in animal models of stimulant addiction. Early acquisition of cocaine seeking depends on the nucleus accumbens core, a region of the ventral striatum; lesions to this area disrupt establishment of drug-seeking behavior (Ito et al., 2004). Yet, as cocaine exposure escalates and becomes chronic, drug-related neuroadaptationsdsuch as changes in metabolic activity and density of dopamine transporter binding sitesdincreasingly extend from the ventral striatum to encompass dorsal striatal regions, the caudate and putamen

(Letchworth et al., 2001; Porrino et al., 2004). In rats with well-established habitual cocaine-seeking behavior, presentation of drug-related cues during cocaine seeking results in marked increases of extracellular dopamine in the dorsal striatum but not in the nucleus accumbens core or shell (Ito et al., 2002). Moreover, dopamine receptor antagonist infusion into this region of the dorsal striatum in which elevated extracellular dopamine is detected significantly reduces S-R driven, cocaine-seeking behavior (Belin and Everitt, 2008; Vanderschuren et al., 2005). The same infusion in the nucleus accumbens, however, produces no effect on habitual cocaine seeking (Vanderschuren et al., 2005), indicating that once behavior has become S-R dominant, ventral striatal regions that are entwined with A-O mechanisms lose influence over actions that have become compulsive. In addition to the ventral striatum progressively losing control over drug-seeking behavior, the transition from voluntary to compulsive drug use is further underpinned by a transition within competing regions of the dorsal striatum: from the posterior dorsomedial striatum (pDMS, early acquisition) to the anterior dorsolateral striatum (aDLS, habitual drug seeking) (Balleine et al., 2009; Murray et al., 2012; Yin et al., 2004). Electrophysiological evidence from rodents performing motivational tasks shows that early acquisition correlates with DMS activity, but as training extends and behavior becomes S-R dominant, DLS activity increases (Thorn et al., 2010). At early stages of cocaine self-administration, pharmacologically disabling the pDMS by infusing that region with a D2 agonist infusion reduces initial cocaine seeking (Murray et al., 2012). At the same time point, deactivating the aDLS with the same D2 agonist infusion has no effect on cocaine seeking, indicating that early A-Oedriven, drug seeking is dependent on the DMS and not the DLS. However, once self-administration is well-established and S-R driven, cocaine-seeking behavior can only be reduced by disabling the aDLS, whereas disabling the pDMS had no effect on behavior (Murray et al., 2012). A similar study utilizing microinjections of lidocaine to deactivate striatal regions has shown that disabling the DLS in cocaine-seeking rats can successfully renew A-O control over compulsive cocaine-seeking behavior (Zapata et al., 2010), signifying that, within the dorsal striatum, the DMS relinquishes control to the DLS as drug use becomes S-R driven. Similar findings are reported for alcohol seeking (Corbit et al., 2012). Deactivating the DMS at early stages of alcohol exposure shifts control to the habit system, preventing behavior from being sensitive to devaluation. However, after prolonged alcohol exposure, deactivation of the DLS (yet not DMS) is needed to reengage the goal-directed system. Moreover, this rich body of research suggests that control over actions occurs through competing goal-directed (DMS) and habit (DLS) systems,

From impulses to compulsions Chapter | 2

and that while typically the DMS and DLS are simultaneously able to control the same behavior (Renteria et al., 2018; Yin et al., 2006), this competition may be repeatedly dominated by the DLS in substance use disorders, ultimately resulting in compulsive drug use. It is important to note that while control of behavior may devolve to the DLS, ventral regions of the striatum may continue to influence and interact with the dorsal striatum. Belin and Everitt (2008) showed that combining a unilateral lesion of the nucleus accumbens core with contralateral dorsolateral striatum dopamine receptor blockade reduces habitual cocaine intake, yet leaving newly learned instrumental-seeking responses unaffected (Belin and Everitt, 2008; Everitt and Robbins, 2016). Therefore, notwithstanding the progressive transition from ventral to dorsal striatal regions, ongoing interactions between the nucleus accumbens core and DLS will have a critical role in the formation and persistence of compulsive drug-seeking behavior.

Devolving from prefrontal to striatal control Along with the progressive strengthening of DLS-driven S-R habit processes, compulsive drug use is concurrently mediated by weakened prefrontal control over striatal regions (Koob and Volkow, 2010; Renteria et al., 2018). Habitual behavior that occurs in the face of aversive conditions would typically promote the transition back to A-O processes and encourage ventral striatal regions to regain control of behavior (Everitt and Robbins, 2016; Murray et al., 2012); however, in substance use disorders, drugseeking behavior perseveres despite harmful outcomes (Ersche et al., 2011). Dorsomedial and ventral striatal regions that are integral to the functioning of the goal-directed system receive prefrontal cortical projections, and prefrontal cortex (PFC) hypofunction has been reported across substance use disorders (Goldstein and Volkow, 2011). Thus, a contributing factor to the development and maintenance of compulsive drug seeking may be hypofunction of prefrontal regions and weakened cortical projections to the striatum, which enables the DLS habit system to remain dominant in controlling behavior (Limpens et al., 2014). Animal studies indicate that hypofunction of the prelimbic cortexdwhich is homologous to the dorsolateral prefrontal cortex (DLPFC) in humans (Bizon et al., 2012; Koob and Volkow, 2016)dmay be tightly linked to compulsive drug seeking (Chen et al., 2013; Bishop et al., 2010; Limpens et al., 2014). In fact, lesions to the prelimbic area enable the formation of inflexible S-R habits (Killcross and Coutureau, 2003). A seminal study by Chen et al. (2013) demonstrated that, in rats that compulsively seek

11

cocaine despite electric foot shocks, extended cocaine self-administration induced substantial hypoactivity in the prelimbic cortex. Furthermore, in vivo optogenetic stimulation of the prelimbic cortex significantly diminished cocaine-seeking responses. In contrast, in vivo optogenetic inhibition of the prelimbic cortex during cocaine self-administration significantly increased cocaine-seeking responses. More recent work by Limpens et al. (2014) provides comparable findings and directly implicates the prelimbic cortex in loss of executive control. Inactivation of the prelimbic cortex significantly reduces a previously conditioned suppression of cocaine seeking in rats, thereby enabling the expression of compulsive drug-seeking behavior. Hypofunction of the prelimbic cortex has also been reported in opiate reward learning, wherein downregulation of prefrontal-excitatory N-methyl D-aspartate receptors substantially increases sensitivity to the rewarding effects of opiate administration (Bishop et al., 2010). Together, these results not only indicate that extended drug use induces marked neuroplastic changes in prefrontal excitability but also that these changes are critically involved in the expression of compulsive drugseeking behavior. Moreover, when considered alongside previous animal research (Belin and Everitt, 2008; Zapata et al., 2010), these findings support the hypothesis that compulsive drug-seeking behavior may be driven by a combination of DLS hyperactivation and prelimbic/ frontal hypoactivation, whereby weakened prefrontal activity enables the striatum and habit system to maintain control. In addition to the prelimbic cortex, hypoactivation of the orbitofrontal cortex (OFC) (key region for decisionmaking in humans) has also been implicated in compulsive drug-seeking behavior (Renteria et al., 2018). Habitual ethanol use in mice induces marked reductions in OFC excitability (Renteria et al., 2018). The OFC directly projects onto the DMS and is critically involved in excitatory circuits of the goal-directed system (Renteria et al., 2018). Ethanol-induced suppression of OFC excitability reduces glutamatergic transmission from the OFC to the DMS via the direct output pathway and thus disrupts goal-directed control over ethanol-seeking behavior (Renteria et al., 2018). Importantly, stimulating the OFC to increase excitatory activity in the OFC-striatal circuit notably reinstates goal-directed control over previous S-R ethanol-seeking behavior (Renteria et al., 2018). Yet again, these findings indicate that habit-driven drug seeking may stem from drug-induced OFC hypoactivation and subsequent disruption of communication to striatal regions critical for goal-directed control. Collectively, animal research across models of various substance use disorders indicates that compulsive drug-seeking behavior may arise from a complex interaction between an intrastriatal shift

12

Cognition and Addiction

toward dorsolateral control and PFC hypoactivation, which together enable rigid S-R habit processes to continue controlling and perpetuating drug-seeking behavior under harmful or aversive conditions.

Translating animal models to understand compulsivity in people with substance use disorders Advancing from animal research, human studies also indicate that compulsive drug use may be underpinned by an over reliance on S-R mechanisms as well as prefrontalstriatal adaptations (Ersche et al., 2011; Sjoerds et al., 2013; Vollstädt-Klein et al., 2010). In this section, we focus on cue-reactivity studies, in which drug-related cues are used to provoke a conditioned craving response analogous to habitual S-R behaviors (Koob and Volkow, 2010; Pickens et al., 2011; Sinha and Li, 2007; Tricomi et al., 2009; Yoder et al., 2009). Craving (a compelling desire to use the drug) is a paramount clinical phenomenon and a proxy of compulsive drug use in clinical studies (Skinner and Aubin, 2010; Tiffany et al., 2012). The cue-reactivity procedure enables researchers to observe behavioral and neural responses to drug-related cues, providing an indirect measure of the severity of compulsive and automatic responding in drug users (Carter and Tiffany, 1999; Tricomi et al., 2009). A consistent finding that emerges in cue-reactivity studies across various substance use populations is the importance of the dorsal striatum (caudate and putamen in humans) in the transition from recreational to compulsive use (Ersche et al., 2011, 2012a,b; Sjoerds et al., 2013; Volkow et al., 2006; Wong et al., 2006; Zhou et al., 2018). Early functional magnetic resonance imaging (MRI) and positron emission tomography studies in cocaine users established that cue-induced craving is associated with increased metabolic activity, dopamine release, and dopamine receptor occupancy in the dorsal striatum (Garavan et al., 2000; Volkow et al., 2006; Wong et al., 2006). Interestingly, the caudate (which is the human homologue of the DMS in rodents; Balleine and O’Doherty, 2009) response to sexually explicit stimuli is blunted in cocaine users, suggesting that S-R tendencies are sensitized to drug cues and relatively insensitive to natural reinforcers (Garavan et al., 2000). Furthermore, increases in dopamine receptor occupancy (Wong et al., 2006) and dopamine release (Volkow et al., 2006) in the dorsal striatum are proportionate to increases in cue-induced cocaine cravings and also corresponded with addiction severity (Volkow et al., 2006). In addition to functional differences, structural MRI studies indicate that, when compared with their noncocaine using siblings, cocaine-dependent individuals have enlarged putamen volumes (Ersche et al., 2011, 2013),

which may suggest that greater dorsal striatal volume increases risk for drug-related S-R conditioning and substance use disorders (Everitt and Robbins, 2016). Together, these findings suggest that dorsal striatal regions (the putamen and caudate) are crucial to S-R driven habitual craving responses in cocaine users, and that dopamine dysfunction within these regions may play a fundamental role in the severity of drug use. Similar findings have been reported in alcoholdependent individuals (Sjoerds et al., 2013, 2014; Vollstädt-Klein et al., 2010). Alcohol-related cues provoke greater cue-induced activation in the left dorsal striatum in heavy drinkers (Schulte et al., 2012; Vollstädt-Klein et al., 2010). In addition, Vollstädt-Klein et al. (2010) found that while heavy drinkers show more cue-evoked activation in the dorsal striatum, light drinkers show a stronger response in the ventral striatum. Moreover, they showed a significant positive relationship between activation of the dorsal striatum and scores on an obsessive-compulsive scale (a proxy of compulsivity), whereas ventral striatal activation had a negative relationship with the same scale. This study provides persuasive support for the notion of a ventral to dorsal striatal control shift as alcohol use becomes more severe and compulsive (Vollstädt-Klein et al., 2010). More recent work by Sjoerds et al. (2013, 2014) has shown that, during an instrumental learning task designed to explore potential imbalances between goaldirected and habit learning processes, alcohol-dependent individuals demonstrate a significant overreliance on S-R habit learning, resulting in poor task performance outcomes. Additionally, poor task performance coincides with decreased activation of brain regions implicated in goal-directed processes and increased activation in habit learning brain regions, namely the posterior putamen (analogous to the DLS in rodents). These results provide considerable evidence to indicate an overactive and inflexible habit system in alcohol-dependent individuals, which appears to heavily depend on dorsal striatal regions (Sjoerds et al., 2013). Furthermore, duration of alcohol dependence was strongly associated with greater cue-induced activation of the posterior putamen (Sjoerds et al., 2014), further supporting the hypothesized ventral to dorsal striatal shift as behavior advances toward addiction. As the putamen is analogous to the DLS in rodents and the caudate analogous to the DMS, this is a crucial detail for the S-R habit hypothesis first proposed by Everitt and Robbins (2005), which would predict greater putamen adaptations as seen in rodent studies (Everitt and Robbins, 2016). Changes in neural activation and adaptations in the dorsal striatum are also observed in nicotine- (McClernon et al., 2009) and cannabis-use disorder groups (Zhou et al., 2018, 2019). Following a 24-h period of abstinence in adult tobacco smokers, smoking cues elicited greater activation

From impulses to compulsions Chapter | 2

in the dorsal striatum, specifically the putamen, relative to neutral cues (McClernon et al., 2009). Participants with cannabis dependence, compared with drug-naïve controls, show an aberrant pattern of functional connectivity involving less efficient communication between both ventral and dorsal striatum and the PFC (Zhou et al., 2018). More specifically, when comparing dependent versus nondependent cannabis users exposed to a cue-reactivity paradigm, dependent users alone demonstrated heightened dorsal striatal activation during cue exposure (Zhou et al., 2019). Cumulatively, the results of human imaging cuereactivity studies support the consensus within animal literature and suggest that dorsostriatal adaptations are involved in compulsive drug use. In addition to evidence of dorsostriatal changes, human studies also implicate hypoactivation in multiple prefrontal regions across substance use disorders (Goldstein and Volkow, 2011; Zilverstand et al., 2018; see Chapter 3 in this book), much the same as animal research. Individuals with alcohol use disorder exhibit decreased engagement of the ventromedial PFC (encompassing the OFC), which directly projects to the ventral striatum, indicating OFC-striatal dysfunction (Sjoerds et al., 2013). Similarly, individuals with alcohol use disorder who relapse into compulsive use show hypoactivation of the medial PFC during a goal-directed decision-making task (Sebold et al., 2017), again suggesting disrupted prefrontal control of behavior. Structural imaging research by Ersche et al. (2013) demonstrates significant reductions in OFC gray matter volume in compulsive cocaine users, while recreational cocaine users exhibit increased OFC volume. The increase in gray matter volume for recreational users may reflect a protective factor or potential resilience against progressing to compulsive drug use (Ersche et al., 2013). Lastly, more recent preliminary research aiming to directly translate animal studies on hypoactivation of the prelimbic cortex (which is the homolog of the human DLPFC) and optogenetic stimulation has utilized repetitive transcranial magnetic stimulant (rTMS) to electrically stimulate the DLPFC in human cocaine users (Terraneo et al., 2016). rTMS of the DLPFC significantly improved symptomology in individuals with cocaine use disorder, as well as substantially reducing craving for cocaine, signifying that as with in vivo optogenetic stimulation of the prelimbic cortex in animals reducing cocaine-seeking responses, electrical stimulation of the human homolog has similar potential to reduce drug-seeking behaviors (Terraneo et al., 2016). For a more detailed discussion on brain stimulation findings, see Chapter 22 of this book. Altogether, findings from structural MRI, functional imaging using tasks of cue-reactivity (dorsal striatal hyperactivity) and decision-making (PFC hyporeactivity), and emerging brain stimulation studies indicate that PFC

13

hypoactivation may strongly contribute to the perpetuation of compulsive drug-seeking behavior, wherein multiple prefrontal regions are critically involved in enabling dorsostriatal S-R control of behavior.

Recommendations for future research Animal experiments and cross-sectional human neuroimaging studies strongly support the role of dorsal striatal control and weakened prefrontal regulation in compulsive drug use. However, human studies have not yet fully demonstrated the proposed dynamic shift between impulses and compulsions or ventral to dorsal striatal control that putatively occurs in each individual over time. We propose two ways to trace this dynamic shift in the future. One is through longitudinal studies. Assessing PFCstriatal function and related phenotypes (cue-conditioned craving, impulsivity, and compulsivity) among children and adolescents before exposure to drugs and then throughout adolescence and young/mid adulthood would compellingly tell us if neural and cognitive transitions similar to those observed in animals occur over the course of human addiction. The advent of new ambitious and exciting longitudinal research initiatives, such as the Adolescent Brain Cognitive Development (ABCD) study (https://abcdstudy.org/), has created a unique opportunity to pursue this otherwise incredibly complex challenge. The ABCD study will track the neurobiological and behavioral development of >11,000 children and includes detailed measures of cognition, brain function, and drug use, providing a unique opportunity to address longitudinal research questions. The study also has a remarkable open data sharing policy, opening endless possibilities for early career researchers. Another potential approach is using rapidly evolving epigenetic tools. Discovering epigenetic markers of ventral and dorsal striatal nuclei gene expression and conducting intraindividual analyses of changes in these markers over the course of substance use and addiction, paralleled by detailed phenotyping, would be another persuasive (although currently futuristic) approach.

References Balleine, B.W., O’Doherty, J.P., 2009. Human and rodent homologies in action control: corticostriatal determinants of goal-directed and habitual action. Neuropsychopharmacology 35 (1), 48e69. Balleine, B.W., Liljeholm, M., Ostlund, S.B., 2009. The integrative function of the basal ganglia in instrumental conditioning. Behav. Brain Res. 199 (1), 43e52. https://doi.org/10.1016/j.bbr.2008.10.034. Belin, D., Everitt, B.J., 2008. Cocaine seeking habits depend upon dopamine-dependent serial connectivity linking the ventral with the dorsal striatum. Neuron 57 (3), 432e441. https://doi.org/10.1016/j. neuron.2007.12.019.

14

Cognition and Addiction

Belin, D., Belin-Rauscent, A., Murray, J.E., Everitt, B.J., 2013. Addiction: failure of control over maladaptive incentive habits. Curr. Opin. Neurobiol. 23 (4), 564e574. https://doi.org/10.1016/j.conb.2013.01.025. Bishop, S.F., Lauzon, N.M., Bechard, M., Gholizadeh, S., Laviolette, S.R., 2010. NMDA receptor hypofunction in the prelimbic cortex increases sensitivity to the rewarding properties of opiates via dopaminergic and amygdalar substrates. Cerebr. Cortex 21 (1), 68e80. https://doi.org/ 10.1093/cercor/bhq060. Bizon, J., C Foster, T., E Alexander, G., Glisky, E., 2012. Characterizing cognitive aging of working memory and executive function in animal models. Front. Aging Neurosci. 4, 19. https://doi.org/10.3389/fnagi. 2012.00019. Carter, B.L., Tiffany, S.T., 1999. Meta-analysis of cue-reactivity in addiction research. Addiction 94 (3), 327e340. https://doi.org/10. 1046/j.1360-0443.1999.9433273.x. Chen, B.T., Yau, H.-J., Hatch, C., Kusumoto-Yoshida, I., Cho, S.L., Hopf, F.W., Bonci, A., 2013. Rescuing cocaine-induced prefrontal cortex hypoactivity prevents compulsive cocaine seeking. Nature 496, 359. https://doi.org/10.1038/nature12024. Corbit, L.H., Nie, H., Janak, P.H., 2012. Habitual alcohol seeking: time course and the contribution of subregions of the dorsal striatum. Biol. Psychiatry 72 (5), 389e395. https://doi.org/10.1016/j.biopsych.2012. 02.024. Dickinson, A., Wood, N., Smith, J.W., 2002. Alcohol seeking by rats: action or habit? Q. J. Exp. Psychol. (3), 965e985. https://doi.org/10. 1080/027249902440001. Ersche, K.D., Roiser, J.P., Abbott, S., Craig, K.J., Mller, U., Suckling, J., et al., 2011. Response perseveration in stimulant dependence is associated with striatal dysfunction and can be ameliorated by a D2/ 3receptor agonist. Biol. Psychiatry 70 (8), 754e762. https://doi.org/ 10.1016/j.biopsych.2011.06.033. Ersche, K.D., Jones, P.S., Williams, G.B., Turton, A.J., Robbins, T.W., Bullmore, E.T., 2012a. Abnormal brain structure implicated in stimulant drug addiction. Science 335 (6068), 601e604. https://doi.org/10. 1126/science.1229223. Ersche, K.D., Turton, A.J., Chamberlain, S.R., Müller, U., Bullmore, E.T., Robbins, T.W., 2012b. Cognitive dysfunction and anxious-impulsive personality traits are endophenotypes for drug dependence. Am. j. psychiatry 169 (9), 926e936. https://doi.org/10.1176/ appi.ajp.2012.11091421. Ersche, K.D., Jones, P.S., Williams, G.B., Smith, D.G., Bullmore, E.T., Robbins, T.W., 2013. Distinctive personality traits and neural correlates associated with stimulant drug use versus familial risk of stimulant dependence. Biol. Psychiatry 74 (2), 137e144. https://doi.org/ 10.1016/j.biopsych.2012.11.016. Everitt, B.J., Robbins, T.W., 2005. Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nat. Neurosci. 8 (11), 1481e1489. https://doi.org/10.1038/nn1579. Everitt, B.J., Robbins, T.W., 2013. From the ventral to the dorsal striatum: devolving views of their roles in drug addiction. Neurosci. Biobehav. Rev. 37 (9), 1946e1954. https://doi.org/10.1016/j.neubiorev.2013.02. 010. Everitt, B.J., Robbins, T.W., 2016. Drug addiction: updating actions to habits to compulsions ten years on, vol. 67. Social Science Research Network, pp. 23e50. https://doi.org/10.1146/annurev-psych-122414033457. Garavan, H., Pankiewicz, J., Bloom, A., Cho, J.K., Sperry, L., Ross, T.J., et al., 2000. Cue-induced cocaine craving: neuroanatomical specificity

for drug users and drug stimuli. Am. J. Psychiatry 157 (11), 1789e1798. https://doi.org/10.1176/appi.ajp.157.11.1789. Goldstein, R.Z., Volkow, N.D., 2011. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 12, 652. https://doi.org/10.1038/nrn3119. Ito, R., Dalley, J.W., Robbins, T.W., Everitt, B.J., 2002. Dopamine release in the dorsal striatum during cocaine-seeking behavior under the control of a drug-associated cue. J. Neurosci. 22 (80), 1e7. https://doi. org/10.1523/JNEUROSCI.22-14-06247.2002. Ito, R., Robbins, T.W., Everitt, B.J., 2004. Differential control over cocaine-seeking behavior by nucleus accumbens core and shell. Nat. Neurosci. 7, 389e397. https://doi.org/10.1038/nn1217. Killcross, S., Coutureau, E., 2003. Coordination of actions and habits in the medial prefrontal cortex of rats. Cerebr. Cortex 13 (4), 400e408. https://doi.org/10.1093/cercor/13.4.400. Koob, G.F., Volkow, N.D., 2010. Neurocircuitry of addiction. Neuropsychopharmacology 35, 217. https://doi.org/10.1038/npp.2009. 110. Koob, G.F., Volkow, N.D., 2016. Neurobiology of addiction: A neurocircuitry analysis. The Lancet Psychiatry 3 (8), 760e773. https://doi. org/10.1016/S2215-0366(16)00104-8. Letchworth, S.R., Porrino, L.J., Smith, H.R., Friedman, D.P., Nader, M.A., 2001. Progression of changes in dopamine transporter binding site density as a result of cocaine self-administration in Rhesus monkeys. J. Neurosci. 21 (8), 2799e2807. https://doi.org/10.1523/ JNEUROSCI.21-08-02799.2001. Limpens, H.W., Damsteegt, J., Broekhoven, R.H., Voorn, M., Vanderschuren, P.L., 2014. Pharmacological inactivation of the prelimbic cortex emulates compulsive reward seeking in rats. Brain Research 1628. https://doi.org/10.1016/j.brainres.2014.10.045. López, M., Soto, A., Bura, S., 2016. Alcohol seeking by rats becomes habitual after prolonged training. Psicothema 28 (4), 421e427. https:// doi.org/10.7334/psicothema2016.114. McClernon, F.J., Kozink, R.V., Lutz, A.M., Rose, J.E., 2009. 24-h smoking abstinence potentiates fMRI-BOLD activation to smoking cues in cerebral cortex and dorsal striatum. Psychopharmacology 204 (1), 25e35. https://doi.org/10.1007/s00213-008-1436-9. Miles, F.J., Everitt, B.J., Dickinson, A., 2003. Oral cocaine seeking by rats: action or habit? Behav. Neurosci. 117 (5), 927e938. https://doi. org/10.1037/0735-7044.117.5.927. Murray, J.E., Belin, D., Everitt, B.J., 2012. Double dissociation of the dorsomedial and dorsolateral striatal control over the acquisition and performance of cocaine seeking. Neuropsychopharmacology 37 (11), 2456e2466. https://doi.org/10.1038/npp.2012.104. Nachman, M., 1963. Learned aversion to the taste of lithium chloride and generalization to other salts. J. Comp. Physiol. Psychol. 56 (2), 343e349. Olmstead, M.C., Lafond, M.V., Everitt, B.J., Dickinson, A., 2001. Cocaine seeking by rats is a goal-directed action. Behav. Neurosci. 115 (2), 394e402. Pickens, C.L., Airavaara, M., Theberge, F., Fanous, S., Hope, B.T., Shaham, Y., 2011. Neurobiology of the incubation of drug craving. Trends Neurosci. 34 (8), 411e420. https://doi.org/https://doi.org/10. 1016/j.tins.2011.06.001. Porrino, L.J., Daunais, J.B., Smith, H.R., Nader, M.A., 2004. The expanding effects of cocaine: studies in a nonhuman primate model of cocaine self-administration. Neurosci. Biobehav. Rev. 27 (8), 813e820. https://doi.org/10.1016/j.neubiorev.2003.11.013.

From impulses to compulsions Chapter | 2

Renteria, R., Baltz, E.T., Gremel, C.M., 2018. Chronic alcohol exposure disrupts top-down control over basal ganglia action selection to produce habits. Nat. Commun. 9 (1), 1e11. https://doi.org/10.1038/ s41467-017-02615-9. Robbins, T.W., Everitt, B.J., 1999. Drug addiction: bad habits add up. Nature 398 (6728), 567e570. https://doi.org/10.1038/19208. Schulte, T., Oberlin, B.G., Kareken, D.A., Marinkovic, K., MüllerOehring, E.M., Meyerhoff, D.J., Tapert, S., 2012. How acute and chronic alcohol consumption affects brain networks: insights from multimodal neuroimaging. Alcohol Clin. Exp. Res. 36 (12), 2017e2027. Sebold, M., Nebe, S., Garbusow, M., Guggenmos, M., Schad, D.J., Beck, A., et al., 2017. When habits are dangerous: alcohol expectancies and habitual decision making predict relapse in alcohol dependence. Biol. Psychiatry 82 (11), 847e856. https://doi.org/https:// doi.org/10.1016/j.biopsych.2017.04.019. Sinha, R., Li, C.S., 2007. Imaging stress- and cue-induced drug and alcohol craving: association with relapse and clinical implications. Drug Alcohol Rev. 26 (1), 25e31. https://doi.org/10.1080/ 09595230601036960. Sjoerds, Z., de Wit, S., van den Brink, W., Robbins, T.W., Beekman, A.T.F., Penninx, B.W.J.H., Veltman, D.J., 2013. Behavioral and neuroimaging evidence for overreliance on habit learning in alcohol-dependent patients. Transl. Psychiatry 3, e337. https://doi.org/ 10.1038/tp.2013.107. Sjoerds, Z., van den Brink, W., Beekman, A.T.F., Penninx, B.W.J.H., Veltman, D.J., 2014. Cue reactivity is associated with duration and severity of alcohol dependence: an fMRI study. PLoS One 9 (1), 1e9. https://doi.org/10.1371/journal.pone.0084560. Skinner, M.D., Aubin, H.J., 2010. Craving’s place in addiction theory: contributions of the major models. Neurosci. Biobehav. Rev. 34 (4), 606e623. https://doi.org/10.1016/j.neubiorev.2009.11.024. Terraneo, A., Leggio, L., Saladini, M., Ermani, M., Bonci, A., Gallimberti, L., 2016. Transcranial magnetic stimulation of dorsolateral prefrontal cortex reduces cocaine use: a pilot study. Eur. Neuropsychopharmacol. 26 (1), 37e44. https://doi.org/https://doi.org/10. 1016/j.euroneuro.2015.11.011. Thorn, C.A., Atallah, H., Howe, M., Graybiel, A.M., 2010. Differential dynamics of activity changes in dorsolateral and dorsomedial striatal loops during learning. Neuron 66, 781e795. https://doi.org/10.1016/j. neuron.2010.04.036. Tiffany, S.T., Friedman, L., Greenfield, S.F., Hasin, D.S., Jackson, R., 2012. Beyond drug use: a systematic consideration of other outcomes in evaluations of treatments for substance use disorders. Addiction 107 (4), 709e718. https://doi.org/10.1111/j.1360-0443.2011.03581.x. Tricomi, E., Balleine, B.W., O’Doherty, J.P., 2009. A specific role for posterior dorsolateral striatum in human habit learning. Eur. J. Neurosci. 29 (11), 2225e2232. https://doi.org/10.1111/j.1460-9568.2009.06796.x. Vanderschuren, L.J.M.J., Everitt, B.J., 2005. Behavioral and neural mechanisms of compulsive drug seeking. Eur. J. Pharmacol. 526 (1e3), 77e88. https://doi.org/10.1016/j.ejphar.2005.09.037. Vanderschuren, L.J.M.J., Di Ciano, P., Everitt, B.J., 2005. Involvement of the dorsal striatum in cue-controlled cocaine seeking. J. Neurosci. 25 (38), 8665e8670. https://doi.org/10.1523/jneurosci.0925-05.2005.

15

Volkow, N.D., Wang, G.-J., Telang, F., Fowler, J.S., Logan, J., Childress, A.-R., et al., 2006. Cocaine cues and dopamine in dorsal striatum: mechanism of craving in cocaine addiction. J. Neurosci. 26 (24), 6583e6588. https://doi.org/10.1523/JNEUROSCI.1544-06. 2006. Vollstädt-Klein, S., Wichert, S., Rabinstein, J., Bühler, M., Klein, O., Ende, G., et al., 2010. Initial, habitual and compulsive alcohol use is characterized by a shift of cue processing from ventral to dorsal striatum. Addiction 105 (10), 1741e1749. https://doi.org/10.1111/j. 1360-0443.2010.03022.x. Wong, D.F., Kuwabara, H., Schretlen, D.J., Bonson, K.R., Zhou, Y., Nandi, A., et al., 2006. Increased occupancy of dopamine receptors in human striatum during cue-elicited cocaine craving. Neuropsychopharmacology 31 (12), 2716e2727. https://doi.org/10.1038/sj. npp.1301194. Yin, H.H., Knowlton, B.J., Balleine, B.W., 2004. Lesions of dorsolateral striatum preserve outcome expectancy but disrupt habit formation in instrumental learning. Eur. J. Neurosci. 19, 181e189. https://doi.org/ 10.1111/j.1460-9568.2004.03095.x. Yin, H.H., Knowlton, B.J., Balleine, B.W., 2006. Inactivation of dorsolateral striatum enhances sensitivity to changes in the actioneoutcome contingency in instrumental conditioning. Behav. Brain Res. 166 (2), 189e196. https://doi.org/https://doi.org/10.1016/j.bbr.2005.07.012. Yoder, K.K., Morris, E.D., Constantinescu, C.C., Cheng, T.-E., Normandin, M.D., O’Connor, S.J., Kareken, D.A., 2009. When what you see isn’t what you get: alcohol cues, alcohol administration, prediction error, and human striatal dopamine. Alcohol Clin. Exp. Res. 33 (1), 139e149. https://doi.org/10.1111/j.1530-0277.2008. 00821.x. Zapata, A., Minney, V.L., Shippenberg, T.S., 2001. Shift from goaldirected to habitual cocaine seeking after prolonged experience in rats. J. Neurosci. 30 (46), 15457e15463. https://doi.org/10.1523/ JNEUROSCI.4072-10.2010. Zapata, A., Minney, V.L., Shippenberg, T.S., 2010. Shift from goaldirected to habitual cocaine seeking after prolonged experience in rats. J. Neurosci. 30 (46), 15457e15463. https://doi.org/10.1523/ JNEUROSCI.4072-10.2010.Shift. Zhou, F., Zimmermann, K., Xin, F., Scheele, D., Dau, W., Banger, M., et al., 2018. Shifted balance of dorsal versus ventral striatal communication with frontal reward and regulatory regions in cannabisdependent males. Hum. Brain Mapp. 39 (12), 5062e5073. https:// doi.org/10.1002/hbm.24345. Zhou, X., Zimmermann, K., Xin, F., Derck, R., Sassmannshausen, A., Scheele, D., et al., 2019. Cue-reactivity in the ventral striatum characterizes heavy cannabis use, whereas reactivity in the dorsal striatum mediates dependent use. BioRxiv (Preprint). https://doi.org/10.1101/ 516385. Zilverstand, A., Huang, A.S., Alia-Klein, N., Goldstein, R.Z., 2018. Neuroimaging impaired response inhibition and salience attribution in human drug addiction: a systematic review. Neuron 98 (5), 886e903. https://doi.org/10.1016/j.neuron.2018.03.048.

Chapter 3

Dual models of drug addiction: the impaired response inhibition and salience attribution model Anna Zilverstand1 and Rita Z. Goldstein2 1

Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States; 2Departments of Psychiatry and Neuroscience, Icahn School of

Medicine at Mount Sinai, New York, NY, United States

Dual models of addiction Addiction is a chronically relapsing disorder, characterized by continued drug seeking despite reduced pleasure from drug-taking and substantial negative consequences to the individual and their kin. Neurobiological models of drug addiction seek to explain this perplexing phenomenon by understanding the changes in the brain driving this behavior. Early neurobiological models focused on the role of the reward system (also named “reinforcement,” “approach,” “drive,” “motivational,” or “dopamine” system), proposing that it was the repeated activation of the positive reinforcement system that fueled repeated drug taking (Wise and Bozarth, 1987). This theory was later refined as the “incentive-sensitization theory,” which proposed that the attribution of incentive salience is the underlying function of the dopaminergic system, and that the increased attribution of salience to drugs or drug cues is associated with a “sensitization” or upregulation of this system in drug addiction (Robinson and Berridge, 1993). However, while these early models explained how drug taking would propel the urge to seek drugs, they could not explain the inability of addicted individuals to inhibit this urge (Jentsch and Taylor, 1999). Therefore, later dual models of addiction proposed an interaction between a “drive” system (e.g., the reward system) and a “control” system located in the prefrontal cortex, which would need to be deployed to inhibit a sensitized reward system, but shows impairments with chronic drug use (Jentsch and Taylor, 1999). A contemporary updated dual model, primarily based on evidence from human neuroimaging studies, is the impaired Response Inhibition and Salience Attribution (iRISA) model (Goldstein and Volkow, 2002, 2011; Zilverstand et al., 2018). The iRISA model proposes

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00003-4 Copyright © 2020 Elsevier Inc. All rights reserved.

that the impairments of two neuropsychological functionsdimpaired response inhibition and salience attributiondand their underlying neural substrates contribute to the clinical symptomatology of addiction encompassing craving, intoxication, bingeing, and withdrawal across a broad range of substance addictions, including nicotine, alcohol, and illicit drug addictions (Goldstein and Volkow, 2002, 2011; Zilverstand et al., 2018). This model proposed for the first time that broad higher-order cognitive functions involved in the “ability to track, update, and modulate the salience of a reinforcer as a function of context and expectation” and the “ability to control and inhibit prepotent responses,” and their underlying neural networks, were impaired in human drug addiction (Goldstein and Volkow, 2002). Indeed, recent evidence suggests that impairments in iRISA in human drug addiction are linked to the altered function of six large-scale brain networks: the limbic-orbitofrontal reward network, the fronto-insular-parietal salience network, the prefrontal executive network, the fronto-parietal selfdirected network, the subcortical habit, and memory networks (Fig. 3.1). The conclusions made and the evidence reviewed in this chapter are, unless stated otherwise, a summary of the evidence discussed in a systematic review of 105 task-related neuroimaging studies published from 2010 until 2018, which compared individuals with alcohol, cannabis, heroin, stimulant, and other addictions to healthy controls (Zilverstand et al., 2018). With very rare exceptions, findings were consistent across different drugaddicted populations and are hence discussed under the assumption that the iRISA model provides a common model of addiction, independent of the primary drug of choice.

17

18

Cognition and Addiction

FIGURE 3.1 Evidence from more than 100 neuroimaging studies in drug-addicted individuals supports dual models of addiction, such as the impaired response inhibition and salience attribution (iRISA) model. Findings demonstrate that abnormal levels in the reward and salience networks can be linked to a shift in incentive salience, with decreased incentive salience of nondrug-related stimuli and increased salience of drug-related cues, whereas changed function of the salience and executive networks underlies impaired response inhibition. Additionally, neuroimaging data suggest that altered function of the habit, memory, and self-directed networks underlie altered learning processes linked to both impaired response inhibition and salience attribution. Adapted from Zilverstand, A., Huang, A.S., Alia-Klein, N., Goldstein, R.Z., 2018. Neuroimaging impaired response inhibition and salience attribution in human drug addiction. A systematic review. Neuron 98, 886e903.

Neuroimaging evidence for dual models The reward network consists of the ventral striatum, subgenual/rostral cingulate, and orbitofrontal and anterior prefrontal cortex, which together support the appraisal of incentive value by estimating subjective value based on expected reward outcomes. This network has been termed the “reward network,” as these brain regions show a consistently strong response during rewarding events (in contrast to the “salience network,” which reacts robustly to both pleasant and unpleasant events). In task neuroimaging studies, the reward network showed enhanced activation levels when, compared to controls, addicted individuals were exposed to drug cues (Filbey et al., 2016; Hong et al., 2017; Kim et al., 2014; Kober et al., 2016; Li et al., 2012, 2015; Ray et al., 2015; Tabatabaei-Jafari et al., 2014), providing evidence for an increased incentive value of drug cues to drug users. In contrast, studies that used gambling paradigms in drug users as compared with controls found reduced activation levels of the reward network during gain anticipation (Luijten et al., 2017), as well as during loss anticipation and realization of monetary loss (Gowin et al., 2017; Gradin et al., 2014; Worhunsky et al., 2017), suggesting reduced incentive value of monetary rewards and losses in drug addiction. Studies employing socialemotional stimuli to provoke an affective reaction similarly demonstrated a blunted reward network response in addicted individuals (Asensio et al., 2010; Caldwell et al., 2015; Canterberry et al., 2016; Costumero et al., 2017; Hong et al., 2017; Seo et al., 2013; Wesley et al., 2016), further indicating that the incentive value of nondrugrelated stimuli may be decreased in drug addiction. Taken together, these findings suggest a shift of incentive value away from nondrug rewards and toward drug-related cues across addicted populations, as proposed by the iRISA

model. Importantly, this shift in incentive value was stronger in drug-addicted individuals who had used drugs more frequently and for longer durations, in general manifesting more severe addiction, suggesting that these changes may be a direct consequence of drug use (Zilverstand et al., 2018). Stronger abnormalities in the activation levels of the reward network were also linked to a greater likelihood of relapse in drug users seeking to remain abstinent (Li et al., 2015; Seo et al., 2013), indicating that the incentive value shift toward drug-related stimuli contributes to the maintenance of drug seeking and taking in human drug addiction. Finally, treatment of individuals with drug addiction led to a reduction of abnormalities in the reward network (Zilverstand et al., 2016), suggesting such behavioral and neural normalizations may be a treatment target, potentially reducing drug use and enhancing abstinence. A second network shown to have abnormal brain function in drug addiction is the salience network, which encompasses the insula, dorsal anterior cingulate cortex, and inferior parietal cortex. This network integrates interoceptive information with external inputs to detect salient events, controlling the allocation of attentional control toward them. Similar to the reward network, the salience network was found to be hyperengaged when drug users were confronted with drug-related stimuli (Kühn and Gallinat, 2011; Zilverstand et al., 2018) and hypoengaged when drug users were anticipating monetary gains (Luijten et al., 2017) or anticipating or realizing monetary loss in gambling tasks (Gowin et al., 2017; Gradin et al., 2014; Stewart et al., 2014; Wesley et al., 2011) or when they were confronted with social-emotional stimuli in emotion provocation task designs (Asensio et al., 2010; Costumero et al., 2017; Gilman et al., 2010; Hong et al., 2017; Landi et al., 2011; Maurage et al., 2012; Seo et al., 2013). Beyond the

Dual models of drug addiction: the impaired response inhibition and salience attribution model Chapter | 3

evidence discussed above for the altered processing of incentive value (as indexed by the reward network), the observed changes in the salience network provide evidence for altered salience processing, with a shift in incentive salience away from nondrug-related stimuli and toward drug-related cues, in concordance with the impaired salience attribution hypothesis of the iRISA model. However, in contrast to the reward network, the salience network has also been implicated in impaired response inhibition (Goldstein and Volkow, 2002, 2011; Zilverstand et al., 2018), the second aspect of the iRISA model. In individuals with drug addiction, this network showed reduced activation levels during tasks that require either the inhibition of a motor response or cognitive self-regulation (Albein-Urios et al., 2012; Czapla et al., 2017; Fryer et al., 2013; Harle et al., 2014; Hu et al., 2015; Jan et al., 2014; Kober et al., 2014; Luijten et al., 2013; Moeller et al., 2014a,b). Importantly, both the hyperengagement of the salience network when confronted with drug-related stimuli and the hypoactivation of this network during inhibitory control tasks have been linked to clinical outcomes, such as the likelihood of relapse in individuals seeking abstinence (Kober et al., 2014; Luo et al., 2013; Moeller et al., 2016; Prisciandaro et al., 2013; Worhunsky et al., 2013). Again, therapeutic interventions could (partially) normalize engagement of the salience network (Zilverstand et al., 2016). However, in contrast to the observed effects in the reward network, alterations in salience network function did not show a linear relationship with lifetime duration of drug use or addiction severity (Zilverstand et al., 2018), which may be explained by different factors. First, the relationship between drug use and engagement of the salience network may be nonlinear over time, as has been suggested by a reversal in the incentive salience shift at 1month abstinence in cocaine-addicted individuals (Parvaz et al., 2016). Second, altered processing in the salience network may be linked to other factors, such as comorbid pathologies. A previous systematic review across different clinical populations (including addicted individuals) found a strong relationship between abnormal function of the salience network and increased anxiety levels (Zilverstand et al., 2017). Third, altered salience processing may be a precursor to drug use rather than a consequence. Indeed, abnormal activation levels in the salience network during inhibitory control tasks have been shown in children (7e12 years old) with a family history of addiction (Hardee et al., 2014). Taken together, evidence from human neuroimaging studies suggests that abnormal function of the salience network is linked to alterations both in salience attribution and response inhibition, while the factors contributing to these alterations remain to be further studied. A third network of interest is the executive network, encompassing the ventral and dorsal prefrontal cortex in its core and extending into the premotor cortex, supplementary

19

motor area, and the superior parietal lobe. This network plays a primary role in goal representation and response selection during motivated behavior, and hence has a crucial function in inhibitory control and self-regulation. However, similarly to the reward and salience networks, the executive network also shows increased engagement during exposure to drug-related cues (Albein-Urios et al., 2012; Harle et al., 2014; Hu et al., 2015; Kober et al., 2014; Moeller et al., 2014a), suggesting recruitment of cognitive resources when cues with high incentive salience/valence are encountered by drug-addicted individuals. In contrast, when individuals with addiction anticipated monetary gains (Luijten et al., 2017), or experienced or anticipated monetary losses during gambling tasks (Gowin et al., 2017; Gradin et al., 2014), or when chronic drug users were confronted with potentially emotion provoking socialemotional stimuli (Caldwell et al., 2015; Costumero et al., 2017; Hong et al., 2017; Kim et al., 2014; Landi et al., 2011; Maurage et al., 2012; Payer et al., 2012; Roberts and Garavan, 2013), the executive network showed reduced activation levels (as compared with control participants). These hypoactivations are in line with reduced deployment of executive functions during the processing of nondrug stimuli, which have lowered valence and salience in addicted populations. Beyond this shared role in altered salience attribution, reduced activation levels in the executive network have been observed during tasks requiring the inhibition of a behavioral response or cognitive selfregulation in drug-addicted individuals as compared with healthy controls (Albein-Urios et al., 2012; Harle et al., 2014; Hu et al., 2015; Kober et al., 2014; Moeller et al., 2014a), supporting a functional role of this network in response inhibition. A stronger disengagement of the executive network has further been linked to higher addiction severity (Cousijn et al., 2012; Harle et al., 2014; VollstädtKlein et al., 2011) and increased risk for relapse (Costumero et al., 2017; Kober et al., 2014; Moeller et al., 2016; Seo et al., 2013; Worhunsky et al., 2013), while abstinence and treatment have been shown to increase activation levels in this network (Gradin et al., 2014; Kober et al., 2014; Tabatabaei-Jafari et al., 2014; Worhunsky et al., 2013; Zilverstand et al., 2016, 2018). Finally, disengagement of the executive network during inhibitory control tasks has also been associated with increased risk for addiction based on family history (Hardee et al., 2014) and increased escalation of drug use in adolescence (Norman et al., 2011; Wetherill et al., 2013), suggesting that impairments in response inhibition may (in part) be a precursor of addiction. Of note is that, in contrast to the strong association between a hypoengagement of the executive network and negative clinical outcomes, there is little evidence for a link between the hyperengagement of the executive network during drug-cue exposure and clinical outcomes. Even in studies that did not use an explicit inhibitory control task

20

Cognition and Addiction

design (e.g., exposure to negative stimuli (Seo et al., 2013); exposure to drug stimuli (Cousijn et al., 2012; TabatabaeiJafari et al., 2014; Vollstädt-Klein et al., 2011); exposure to erotic stimuli (Costumero et al., 2017); monetary gambling task (Gradin et al., 2014)), negative clinical outcomes were linked to lower and not higher executive network engagement. In summary, the reduced ability to recruit the executive network for response inhibition seems to be predictive of an individual’s trajectory in addiction, while its involvement in approach behavior (when encountering drug cues) has not been linked to clinical outcomes. Finally, beyond these discussed fundamental changes in the reward, salience, and executive networks, neuroimaging data also point to crucial changes in networks underlying habitual and flexible learning, which have been extensively studied using preclinical models (Everitt and Robbins, 2005, 2016), but have been less often a focus of investigation in human neuroimaging studies (for a discussion, see Zilverstand et al., 2018). The habit network, which in its core encompasses the dorsal caudate/putamen, supports simple stimulus-response learning during automatization of behavior or habit learning. In contrast, the memory network, consisting of the hippocampus, parahippocampus, rhinal, and retrosplenial cortex, is a flexible, relational memory system, which supports adaptive, voluntary, and flexible learning processes based on associative learning between multiple cues (as compared with the simple associations formed during habitual learning). Third, the self-directed network, in its core comprised of the dorsomedial prefrontal cortex, the posterior cingulate, and the precuneus, is involved in self-focused learning processes, such as selfawareness and self-reflection. As shown for the executive network, all three of these learning networks have been implicated in impaired salience processing, as they are hyperengaged by drug-related cues with high salience in drug users (Arcurio et al., 2015; Filbey et al., 2016; Kober et al., 2016; Li et al., 2012, 2015; Ray et al., 2015; Wang et al., 2014), but hypoengaged when drug-addicted individuals anticipate monetary gains (Luijten et al., 2017) or anticipate or experience monetary loss during gambling tasks (Gowin et al., 2017; Gradin et al., 2014; Stewart et al., 2014; Wesley et al., 2011) or when they are being confronted with social-emotional stimuli (Caldwell et al., 2015; Canterberry et al., 2016; Gilman et al., 2010; Hong et al., 2017; Kim et al., 2014; Landi et al., 2011; Maurage et al., 2012; Seo et al., 2013; Wesley et al., 2016). More specifically, hyperengagement during drug-cue exposure generally involved all three learning networks, while disengagement during monetary tasks was primarily observed in the habit learning and self-directed networks, and disengagement when confronted with social-emotional stimuli was mainly observed in the self-directed and memory networks (Zilverstand et al., 2018). This consistent pattern of altered engagement of these learning networks as

modulated by the cues’ incentive salience suggests that learning is an important aspect of impaired salience attribution in human drug addiction. And while the clinical relevance of these alterations remains to be fully explored, partly because this question was rarely the main focus of the reviewed neuroimaging studies, at least one study reported that increased engagement of the habit network during drug-cue exposure was linked to increased likelihood to relapse in initially abstinent-addicted individuals (Prisciandaro et al., 2013). Beyond a potential role for altered learning processes in impaired salience attribution, neuroimaging studies have also reported altered learning processes during tasks taxing the capacity for response inhibition in human drug addiction. In adults, the memory networkdwhich supports more flexible learningdwas hypoengaged during inhibitory control tasks (Harle et al., 2014; Hu et al., 2015; Kober et al., 2014; Luijten et al., 2013; Schulte et al., 2012), with therapeutic interventions generally increasing engagement of this network in addicted individuals (Zilverstand et al., 2016). In contrast, youth at risk for addiction specifically demonstrated decreased involvement of the habit network during inhibitory control tasks (Hardee et al., 2014), and hypoengagement of this network was also linked to escalation of drug use in adolescence (Norman et al., 2011; Wetherill et al., 2013). Taken together, these findings suggest a role for altered learning processes in impaired salience attribution and response inhibition in human drug addiction, with alterations in habit, flexible, and self-directed learning depending on the task context and age of the individual.

Conclusions In summary, a large body of evidence from more than a 100 task-based neuroimaging studies comparing drug-addicted individuals with healthy controls and tracking their clinical outcomes supports the validity of dual addiction models, such as the iRISA model. Taken together, the discussed evidence supports that alterations in the brain networks underlying salience processing (reward and salience networks) and response inhibition (salience and executive networks) not only reliably differentiate drug users from healthy control participants but also provide important biomarkers for tracking and predicting clinical outcomes in addiction. Additionally, the reviewed evidence revealed alterations in learning processes, which may be important subprocesses in changed salience processing and response inhibition in drug addiction. Other subprocesses invoke stress reactivity, for example, due to its function in detecting not only external but also internal salient events and its role in the allocation of attentional control, the salience network could be conceptualized as a stress reactive system, as has been previously suggested in the somatic-marker theory of addiction (Verdejo-García and

Dual models of drug addiction: the impaired response inhibition and salience attribution model Chapter | 3

Bechara, 2009). This would be in line with other theories of addiction such as the “three functional domain model” on incentive salience, executive function, and negativeemotional states (Koob and Volkow, 2016), which proposed that a third brain system that is reactive to stress interacts with appetitive (e.g., the reward network) and inhibitory processes (e.g., the executive network). This perspective would also converge with the “Competing Neurobehavioral Decision Systems (CNDS)” theory (Bickel et al., 2018), which proposes that parts of the salience network act as a moderating system between appetitive and inhibitory processes (Bickel et al., 2018). The difference between the iRISA model and such triadic theories would then be in the assignment of the core mechanisms of addiction to a dual (vs. a tripartite) process of imbalance between appetitive and inhibitory systems, with impairments in learning, stress reactivity, and attention allocation attributed to secondary processes modulating/ moderating the core two processes. As such, the iRISA model provides a more parsimonious description of the reviewed evidence as remains to be empirically tested using computational/modeling approaches. Such approaches can, for example, parse apart different processes underlying inhibitory control (e.g., anticipation of conflict, learning rate, response inhibition), hence allowing to separate learning deficits from the execution of response inhibition itself. Initial studies using this approach indeed provide evidence for the involvement of learning deficits in impaired inhibitory control in alcohol addiction (Hu et al., 2015) and those at risk for stimulant dependence (Harle et al., 2014). A further, systematic investigation of how both impaired iRISA could be modulated by learning processes, or other factors such as stress reactivity, remains an exciting future research endeavor in the addiction field.

References Albein-Urios, N., Verdejo-Román, J., Asensio, S., Soriano-Mas, C., Martínez-González, J.M., Verdejo-García, A., 2012. Re-appraisal of negative emotions in cocaine dependence: dysfunctional corticolimbic activation and connectivity. Addict. Biol. 19, 415e426. Arcurio, L.R., Finn, P.R., James, T.W., 2015. Neural mechanisms of highrisk decisions-to-drink in alcohol-dependent women. Addict. Biol. 20, 390e406. Asensio, S., Romero, M.J., Palau, C., Sanchez, A., Senabre, I., Morales, J.L., Carcelen, R., Romero, F.J., 2010. Altered neural response of the appetitive emotional system in cocaine addiction: an fMRI study. Addict. Biol. 15, 504e516. Bickel, W.K., Mellis, A.M., Snider, S.E., Athamneh, L.N., Stein, S., Pope, D.A., 2018. 21st century neurobehavioral theories of decision making in addiction: review and evaluation. Pharmacol. Biochem. Behav. 164, 4e21. Caldwell, B.M., Harenski, C.L., Harenski, K.A., Fede, S.J., Steele, V.R., Koenigs, M.R., Kiehl, K.A., 2015. Abnormal frontostriatal activity in recently abstinent cocaine users during implicit moral processing. Front. Hum. Neurosci. 9, 565.

21

Canterberry, M., Peltier, M.R., Brady, K.T., Hanlon, C.A., 2016. Attenuated neural response to emotional cues in cocaine-dependence: a preliminary analysis of gender differences. Am. J. Drug Alcohol Abuse 42, 577e586. Costumero, V., Bustamante, J.C., Rosell-Negre, P., Fuentes, P., Llopis, J.J., Ávila, C., Barrós-Loscertales, A., 2017. Reduced activity in functional networks during reward processing is modulated by abstinence in cocaine addicts. Addict. Biol. 22, 479e489. Cousijn, J., Goudriaan, A.E., Ridderinkhof, K.R., van den Brink, W., Veltman, D.J., Wiers, R.W., 2012. Approach-bias predicts development of cannabis problem severity in heavy cannabis users: results from a prospective FMRI study. PLoS One 7. Czapla, M., Baeuchl, C., Simon, J.J., Richter, B., Kluge, M., Friederich, H.C., Mann, K., Herpertz, S.C., Loeber, S., 2017. Do alcohol-dependent patients show different neural activation during response inhibition than healthy controls in an alcohol-related fMRI go/no-go-task? Psychopharmacology (Berl.) 234, 1001e1015. Everitt, B.J., Robbins, T.W., 2005. Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nat. Neurosci. 8, 1481e1489. Everitt, B.J., Robbins, T.W., 2016. Drug addiction: updating actions to habits to compulsions ten years on. Annu. Rev. Psychol. 67, 23e50. Filbey, F.M., Dunlop, J., Ketcherside, A., Baine, J., Rhinehardt, T., Kuhn, B., DeWitt, S., Alvi, T., 2016. fMRI study of neural sensitization to hedonic stimuli in long-term, daily cannabis users. Hum. Brain Mapp. 37, 3431e3443. Fryer, S.L., Jorgensen, K.W., Yetter, E.J., Daurignac, E.C., Watson, T.D., Shanbhag, H., Krystale, J.H., Mathalon, D.H., 2013. Differential brain response to alcohol cue distractors across stages of alcohol dependence. Biol. Psychol. 92, 282e291. Gilman, J.M., Davis, M.B., Hommer, D.W., 2010. Greater activation in left hemisphere language-related regions during simple judgment tasks among substance-dependent patients in treatment for alcoholism. Alcohol. Clin. Exp. Res. 34, 331e341. Goldstein, R.Z., Volkow, N.D., 2002. Drug addiction and its underlying neurobiological Basis : neuroimaging evidence for the involvement of the frontal cortex. Am. J. Psychiatry 159, 1642e1652. Goldstein, R.Z., Volkow, N.D., 2011. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 12, 652e669. Gowin, J.L., May, A.C., Wittmann, M., Tapert, S.F., Paulus, M.P., 2017. Doubling down: increased risk-taking behavior following a loss by individuals with cocaine use disorder is associated with striatal and anterior cingulate dysfunction. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2, 94e103. Gradin, V.B., Baldacchino, A., Balfour, D., Matthews, K., Steele, J.D., 2014. Abnormal brain activity during a reward and loss task in opiate dependent patients receiving methadone maintenance therapy. Neuropsychopharmacology 39, 1e35. Hardee, J.E., Weiland, B.J., Nichols, T.E., Welsh, R.C., Soules, M.E., Steinberg, D.B., Zubieta, J.K., Zucker, R.A., Heitzeg, M.M., 2014. Development of impulse control circuitry in children of alcoholics. Biol. Psychiatry 76, 708e716. Harle, K.M., Shenoy, P., Stewart, J.L., Tapert, S.F., Yu, A.J., Paulus, M.P., 2014. Altered neural processing of the need to stop in young adults at risk for stimulant dependence. J. Neurosci. 34, 4567e4580.

22

Cognition and Addiction

Hong, J.S., Kim, S.M., Jung, H.Y., Kang, K.D., Min, K.J., Han, D.H., 2017. Cognitive avoidance and aversive cues related to tobacco in male smokers. Addict. Behav. 73, 158e164. Hu, S., Ide, J.S., Zhang, S., Sinha, R., Li, C.S.R., 2015. Conflict anticipation in alcohol dependence - a model-based fMRI study of stop signal task. NeuroImage Clin. 8, 39e50. Jan, R.K., Lin, J.C., McLaren, D.G., Kirk, I.J., Kydd, R.R., Russell, B.R., 2014. The effects of methylphenidate on cognitive control in active methamphetamine dependence using functional magnetic resonance imaging. Front. Psychiatry 5, 1e18. Jentsch, J.D., Taylor, J.R., 1999. Impulsivity resulting from frontostriatal dysfunction in drug abuse: implications for the control of behavior by reward-related stimuli. Psychopharmacology (Berl.) 146, 373e390. Kim, S.M., Han, D.H., Min, K.J., Kim, B.N., Cheong, J.H., 2014. Brain activation in response to craving- and aversion-inducing cues related to alcohol in patients with alcohol dependence. Drug Alcohol Depend. 141, 124e131. Kober, H., DeVito, E.E., Deleone, C.M., Carroll, K.M., Potenza, M.N., 2014. Cannabis abstinence during treatment and one-year follow-up: relationship to neural activity in men. Neuropsychopharmacology 39, 1e11. Kober, H., Lacadie, C.M., Wexler, B.E., Malison, R.T., Sinha, R., Potenza, M.N., 2016. Brain activity during cocaine craving and gambling urges: an fMRI study. Neuropsychopharmacology 41, 628e637. Koob, G.F., Volkow, N.D., 2016. Neurobiology of addiction : a neurocircuitry analysis. Lancet Psychiatry 3, 760e773. Kühn, S., Gallinat, J., 2011. Common biology of craving across legal and illegal drugs - a quantitative meta-analysis of cue-reactivity brain response. Eur. J. Neurosci. 33, 1318e1326. Landi, N., Montoya, J., Kober, H., Rutherford, H.J.V., Mencl, W.E., Worhunsky, P.D., Potenza, M.N., Mayes, L.C., 2011. Maternal neural responses to infant cries and faces: relationships with substance use. Front. Psychiatry 2, 1e13. Li, Q., Wang, Y., Zhang, Y., Li, W., Yang, W., Zhu, J., Wu, N., Chang, H., Zheng, Y., Qin, W., et al., 2012. Craving correlates with mesolimbic responses to heroin-related cues in short-term abstinence from heroin: an event-related fMRI study. Brain Res. 1469, 63e72. Li, Q., Li, W., Wang, H., Wang, Y., Zhang, Y., Zhu, J., Zheng, Y., Zhang, D., Wang, L., Li, Y., et al., 2015. Predicting subsequent relapse by drug-related cue-induced brain activation in heroin addiction: an event-related functional magnetic resonance imaging study. Addict. Biol. 20, 968e978. Luijten, M., Veltman, D.J., Hester, R., Smits, M., Nijs, I.M.T., Pepplinkhuizen, L., Franken, I.H.A., 2013. The role of dopamine in inhibitory control in smokers and non-smokers: a pharmacological fMRI study. Eur. Neuropsychopharmacol. 23, 1247e1256. Luijten, M., Schellekens, A.F., Kühn, S., Machielse, M.W.J., Sescousse, G., 2017. Disruption of reward processing in addiction. JAMA Psychiatry 74, 387. Luo, X., Zhang, S., Hu, S., Bednarski, S.R., Erdman, E., Farr, O.M., Hong, K., Sinha, R., Mazure, C.M., Li, C.R., 2013. Neural Predictors of Relapse in Cocaine Dependence. Maurage, P., Joassin, F., Philippot, P., Heeren, A., Vermeulen, N., Mahau, P., Delperdange, C., Corneille, O., Luminet, O., de Timary, P., 2012. Disrupted regulation of social exclusion in alcoholdependence: an fMRI study. Neuropsychopharmacology 37, 2067e2075.

Moeller, S.J., Konova, A.B., Parvaz, M.A., Tomasi, D., Lane, R.D., Fort, C., Goldstein, R.Z., 2014a. Functional, structural, and emotional correlates of impaired insight in cocaine addiction. JAMA Psychiatry 71, 61. Moeller, S.J., Froboese, M.I., Konova, A.B., Misyrlis, M., Parvaz, M.A., Goldstein, R.Z., Alia-Klein, N., 2014b. Common and distinct neural correlates of inhibitory dysregulation: stroop fMRI study of cocaine addiction and intermittent explosive disorder. J. Psychiatr. Res. 58, 55e62. Moeller, S.J., Bederson, L., Alia-Klein, N., Goldstein, R.Z., 2016. Neuroscience of Inhibition for Addiction Medicine: From Prediction of Initiation to Prediction of Relapse. Elsevier B.V. Norman, A.L., Pulido, C., Squeglia, L.M., Spadoni, A.D., Paulus, M.P., Tapert, S.F., 2011. Neural activation during inhibition predicts initiation of substance use in adolescence. Drug Alcohol Depend. 119, 216e223. Parvaz, M.A., Moeller, S.J., Goldstein, R.Z., 2016. Incubation of cueinduced craving in adults addicted to cocaine measured by electroencephalography. JAMA Psychiatry 73, 1127. Payer, D.E., Nurmi, E.L., Wilson, S.A., McCracken, J.T., London, E.D., 2012. Effects of methamphetamine abuse and serotonin transporter gene variants on aggression and emotion-processing neurocircuitry. Transl. Psychiatry 2, e80. Prisciandaro, J.J., Myrick, H., Henderson, S., Mcrae-clark, A.L., Brady, K.T., 2013. Prospective associations between brain activation to cocaine and no-go cues and cocaine relapse. Drug Alcohol Depend. 131, 44e49. Ray, S., Haney, M., Hanson, C., Biswal, B., Hanson, S.J., 2015. Modeling causal relationship between brain regions within the drug-cue processing network in chronic cocaine smokers. Neuropsychopharmacology 40, 2960e2968. Roberts, G.M.P., Garavan, H., 2013. Neural mechanisms underlying ecstasy-related attentional bias. Psychiatry Res. Neuroimaging 213, 122e132. Robinson, T.E., Berridge, K.C., 1993. The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res. Rev. 18, 247e291. Schulte, T., Mueller-Oehring, E.M., Sullivan, E.V., Pfefferbaum, A., 2012. Synchrony of corticostriatal-midbrain activation enables normal inhibitory control and conflict processing in recovering alcoholic men. Biol. Psychiatry 71, 269e278. Seo, D., Lacadie, C.M., Tuit, K., Hong, K.-I., Constable, R.T., Sinha, R., 2013. Disrupted ventromedial prefrontal function, alcohol craving, and subsequent relapse risk. JAMA Psychiatry 70, 727e739. Stewart, J.L., May, A.C., Poppa, T., Davenport, P.W., Tapert, S.F., Paulus, M.P., 2014. You are the danger: attenuated insula response in methamphetamine users during aversive interoceptive decisionmaking. Drug Alcohol Depend. 142, 110e119. Tabatabaei-Jafari, H., Ekhtiari, H., Ganjgahi, H., Hassani-Abharian, P., Oghabian, M.-A., Moradi, A., Sadighi, N., Zarei, M., 2014. Patterns of brain activation during craving in heroin dependents successfully treated by methadone maintenance and abstinence-based treatments. J. Addict. Med. 8, 123e129. Verdejo-García, A., Bechara, A., 2009. A somatic marker theory of addiction. Neuropharmacology 56, 48e62. Vollstädt-Klein, S., Kobiella, A., Bühler, M., Graf, C., Fehr, C., Mann, K., Smolka, M.N., 2011. Severity of dependence modulates smokers’ neuronal cue reactivity and cigarette craving elicited by tobacco advertisement. Addict. Biol. 16, 166e175.

Dual models of drug addiction: the impaired response inhibition and salience attribution model Chapter | 3

Wang, Y., Wang, H., Li, W., Zhu, J., Gold, M.S., Zhang, D., Wang, L., Li, Y., Yan, X., Cheng, J., et al., 2014. Reduced responses to heroincue-induced craving in the dorsal striatum: effects of long-term methadone maintenance treatment. Neurosci. Lett. 581, 120e124. Wesley, M.J., Hanlon, C.A., Porrino, L.J., 2011. Poor decision-making by chronic marijuana users is associated with decreased functional responsiveness to negative consequences. Psychiatry Res. Neuroimaging 191, 51e59. Wesley, M.J., Lile, J.A., Hanlon, C.A., Porrino, L.J., 2016. Abnormal medial prefrontal cortex activity in heavy cannabis users during conscious emotional evaluation. Psychopharmacology (Berl.) 233, 1035e1044. Wetherill, R.R., Castro, N., Squeglia, L.M., Tapert, S.F., 2013. Atypical neural activity during inhibitory processing in substance-naïve youth who later experience alcohol-induced blackouts. Drug Alcohol Depend. 128, 243e249. Wise, R.A., Bozarth, M.A., 1987. A psychomotor stimulant theory of addiction. Psychol. Rev. 94, 469e492.

23

Worhunsky, P.D., Stevens, M.C., Carroll, K.M., Rounsaville, B.J., Calhoun, V.D., Pearlson, G.D., Potenza, M.N., 2013. Functional brain networks associated with cognitive control, cocaine dependence and treatment outcome. Psychol. Addict. Behav. 27, 477e488. Worhunsky, P.D., Potenza, M.N., Rogers, R.D., 2017. Alterations in functional brain networks associated with loss-chasing in gambling disorder and cocaine-use disorder. Drug Alcohol Depend. 178, 363e371. Zilverstand, A., Parvaz, M.A.M.A., Moeller, S.J.S.J., Goldstein, R.Z.R.Z., 2016. Cognitive interventions for addiction medicine: understanding the underlying neurobiological mechanisms. Prog. Brain Res. 224, 285e304. Zilverstand, A., Parvaz, M.A., Goldstein, R.Z., 2017. Neuroimaging cognitive reappraisal in clinical populations to define neural targets for enhancing emotion regulation. A systematic review. Neuroimage 151, 105e116. Zilverstand, A., Huang, A.S., Alia-Klein, N., Goldstein, R.Z., 2018. Neuroimaging impaired response inhibition and salience attribution in human drug addiction. A systematic review. Neuron 98, 886e903.

Chapter 4

Decision-making deficits in substance use disorders: cognitive functions, assessment paradigms, and levels of evidence Alireza Valyan1, Hamed Ekhtiari2, Ryan Smith2 and Martin P. Paulus2 1

Allameh Tabataba’i University, Tehran, Iran; 2Laureate Institute for Brain Research, Tulsa, United States

Introduction As human beings, we make decisions frequently and in different situations that cover many aspects of our daily personal and professional lives. Thus, it is not surprising that a deficit in this highly important cognitive process affects our well-being and causes different forms of physical and mental health disorders (Cáceda et al., 2014). Among various mental health disorders, substance use disorder (SUD) has a strong conceptual relationship with decision-making dysfunctions (DMDs) (Stoops and Kearns, 2018). There is some empirical support that DMDs contribute to several stages of SUD from initiation to maintenance (Koffarnus and Kaplan, 2018). Furthermore, DMDs could be targets for the design and implementation of intervention strategies and treatment protocols in SUD (Verdejo-García et al., 2018). However, as shown previously (Ekhtiari et al., 2017), empirical evidence in this field is limited and inconsistent. Despite the previous attempts to develop a framework that can address various aspects of DMD in SUDs together (Kwako et al., 2017), methodological deficits in assessment paradigms and lack of empirical evidence make any rigorous scientific conclusion very hard and sometimes impossible. In this chapter, based on the most recent findings on DMD in SUD, we will try to organize and review the available evidence in a matrix that has three dimensions: (1) cognitive functions, (2) assessment paradigms, and (3) levels of evidence. In the first dimension, we will address four major cognitive functions of decision-making: (1) temporal function, (2) value/reward function, (3) risk/ probability function, and (4) learning function. In the

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00004-6 Copyright © 2020 Elsevier Inc. All rights reserved.

second dimension of the matrix of evidence, the main assessment paradigms adopted in published studies are organized in four categories: (1) self-assessment, (2) behavioral tasks, (3) computational models, and (4) neuroimaging with a focus on functional magnetic resonance imaging (fMRI). In the third dimension, available evidence will be organized in five levels of quality: (1) systematic reviews and metaanalyses, (2) randomized controlled trials (RCTs), (3) cohort studies, (4) case-control studies, and (5) cross-sectional studies. We hope this three-dimensional matrix will provide a structured framework allowing newcomers to the field to have an organized overview on DMD in SUD. We also hope this three-dimensional matrix will show the current gaps in available evidence, critical needs, and the emerging potential for production of new evidence. We will conclude this chapter with a short discussion of the challenges and hopes in studying DMD in SUD and provide some suggestions for future studies.

First dimension: cognitive functions of decision-making Decision-making is a complex cognitive process and can be decomposed into four major functions as follows. 1. Temporality: This function reflects the role of temporal perspective and delay and their related processes in decision-making. Deficits in temporal aspects of decision-making may play a significant role in SUD (Ekhtiari et al., 2017). Among different temporal aspects of decision-making, impulsivity and higher delay discounting (i.e., preferring sooner smaller rewards over the larger but delayed ones) are more frequently reported to be related to 25

26

Cognition and Addiction

SUD (Bickel et al., 2017b). The relationship between temporal functions and SUD has been investigated widely in the SUD literature for alcohol (Bernhardt et al., 2017), tobacco (Farris et al., 2017), marijuana (Gunn et al., 2017), nicotine (Kobiella et al., 2014), methamphetamine (Kogachi et al., 2017), cannabis (Lee et al., 2015), and cocaine (Ross et al., 2013). In addition to discriminating people with SUDs from healthy controls, delay discounting is reported to be associated with the severity of SUDs (e.g., Garrison et al., 2017). Several studies suggested that intervention strategies can be designed with the aim of improving temporal aspects of DMD, which ultimately result in lower substance demand (Bulley and Gullo, 2017) and consumption level (Chiou and Wu, 2017; Stein et al., 2016). 2. Risk/Probability: One of the other cognitive components of DMD in SUD is related to the extent to which SUD patients get involved in risky behavior. In other words, an individual’s risk perception, risk evaluation, and risk-taking or risk-averse mindset plays a pivotal role in DMDs (Orsini et al., 2015). The relationship between risk and SUD has been widely addressed in the SUD literature. It is believed that, in general, SUD might play a role in engaging in riskier behaviors, which itself results in more substance consumption (Brand et al., 2006; Bechara, 2003; Balogh et al., 2013; Thylstrup and Hesse, 2018). People with SUD engage more in other risky behaviors as well, such as risky sexual behaviors (Sanudo et al., 2018; Saw et al., 2018). Thus, in addition to the fact that poor risk perception and high-risk behavior can characterize patients with SUD, it is also argued that the severity of SUD is associated with severity of risk-taking in decision-making (Spear, 2018). Furthermore, recent findings showed that impaired activation in brain areas involved in decisionmaking and risk estimation may predict future alcohol consumption in adolescents (Morales et al., 2018). Thus, altering the mechanisms involved in risky decision-making may be an important target for treatment (Claus et al., 2018). A recent study showed that transcranial direct current stimulation (tDCS) over the dorsolateral prefrontal cortex (dlPFC) can reduce risk-taking behavior in one- and 2-month follow-up visits after the intervention in a sample of veterans (Gilmore et al., 2018). 3. Reward/Value: There is empirical evidence for reward and/or value aspects of decision-making deficits in SUD. For example, either overestimating the positive consequences or underestimating the negative effects can be important factors associated with various SUDs (Volkow et al., 2010; Bruijnzeel, 2017; Zimmermann et al., 2018). For example, it has been shown that abstinence from tobacco can result in decreased reward sensitivity (Hughes et al., 2017a). Several recent findings have shown that this decrease is related to lower activation in different areas of the brain (e.g., frontoparietal and striatal), during the

reward anticipation phase and higher activation during the outcome delivery phase, in patients with SUD compared with healthy controls (Costumero et al., 2017; Luijten et al., 2017; Ostlund and Cui, 2018; Yanes et al., 2018). Different labs have investigated the reward sensitivity aspect of DMD as a potential treatment target in SUD. For example, it has been shown that a 25e30 min brief motivational intervention followed by a 25e30 min substancefree activity session and a 25e30 min educative session reduces the value of the substance in a sample of heavy drinking college students (Dennhardt et al., 2015a). In addition, modulating the reward-related pathway in the brain (e.g., frontal-cingulate) appears to change druge nondrug reward processing in SUD patients (Baker et al., 2017). Finally, reward sensitivity has been shown to be a neurobiological predictor of successful intervention therapies in alcohol-dependent patients (Becker et al., 2018). 4. Learning: Finally, the fourth dimension of decisionmaking deficits in SUD pertains to learning abilities. Here, we narrow our focus to deficits in learning in response to gains or losses, which can be viewed as forms of reinforcement learning (RL) based on positive reinforcement (Wise and Koob, 2014) or negative punishment (Thompson et al., 2012). For example, it has been shown that poor performance in learning can distinguish cocaine-dependent patients from healthy controls (Patzelt et al., 2014). Other findings suggest that acute and chronic use of cannabis is positively associated with impaired reward learning (Lawn et al., 2016). More recently, it was found that chronic nicotine exposure can result in altered anterior cingulate cortex (ACC) function during RL (Wei et al., 2018).

Second dimension: assessment paradigms for decision-making The accurate measurement of key constructs and processes often represents a significant challenge when exploring any complex cognitive function like decision-making. In the following section, we focus on four major assessment paradigms for DMDs: self-reports, behavioral tasks, computational modeling, and fMRI (Ekhtiari et al., 2017). This will include a review of the most important tools in each paradigm, a brief background for each tool and its structure, and some recent evidence using such tools in the field of SUD.

Self-reports As assessment tools, self-reports in the form of questionnaires have a long history in the scientific world and numerous studies have been designed and interpreted based on self-reports. Self-reports are rapid, inexpensive, and especially efficient when the objectives of a study require large samples to be briefly screened or assessed. That makes

Decision-making deficits in substance use disorders Chapter | 4

self-reports the first choice when an individual’s reportable traits and tendencies are of interest. Self-reports have face validity and the meaning of the items and the idea behind them are to a large extent clear for both the researcher and the respondent. In the field of decision-making in SUD, self-reports have been used for a variety of constructs. Several aspects of decision-making deficits (i.e., temporal, probability, value, and learning functions) have been assessed via self-reports in substance-related disorder studies. This includes people with alcohol (Foster et al., 2015), nicotine (Stein et al., 2016; Muench and Juliano, 2017), methamphetamine (Voon et al., 2015), and heroin (Walter et al., 2015) use disorders. In this regard, the temporal dimension of decision-making, either measured with delay discounting (Kirby, 2009) or impulsivity (e.g., Stevens et al., 2017) and value (reward) dimensions (Foster et al., 2015; Walter et al., 2015; Kulis et al., 2017), has been addressed more directly. However, assessing the other two aspects of decision-making (i.e., probability and learning) with selfreports needs more scrutiny. In the following section, we describe some of the most utilized self-report measures to evaluate different aspects of decision-making (ordered by their number of citations in the literature) with an emphasis on their history and theoretical background.

Barratt Impulsivity Scale Background: Among the various self-reports that target any of the four dimensions of decision-making deficits, the Barratt Impulsivity Scale (BIS) is one of the most frequently used instruments (Patton et al., 1995). In its first version, BIS-10 consisted of 25 impulsiveness and 35 filter items (to determine the eligibility of the respondents) (Barratt, 1959, 1965). Here, impulsiveness is defined as the tendency to take chances or to seek adventure without consideration of future consequences. It is composed of four factors: (1) cognitive response speed, (2) lack of impulsive control, (3) adventure seeking, and (4) extraversion and risk-taking. Structure: The most used version of the BIS consists of 30 items, and participants are asked to rate their endorsement on a 4-point Likert scale from rarely/never, occasionally, often, to almost always/always. According to Patton et al. (1995), three second-order constructs cover the main components of impulsiveness. The first construct is attentional impulsiveness or making quick decisions and includes attention (five items) and cognitive instability (three items). A sample statement in this subscale is “I am restless at the theater or lectures.” Statements such as “I act on the spur of the moment” or “I act on impulse” are addressing the motor impulsiveness or acting without thinking as the second construct in BIS-11. The third

27

construct addresses the nonplanning impulsiveness or lack of forethought. Participants are asked to rate statements like “I get easily bored when solving thought problems” (Stanford et al., 2009; Patton and Stanford, 2011). A brief version of the scale has also been developed (Steinberg et al., 2013), which includes eight items from the original version. This version has better psychometric features compared with the original BIS-11 (Mathias et al., 2018). Evidence in SUD: While mostly known for addressing impulsiveness, BIS-11 also has some items that target risktaking behavior; thus, one can assume that this scale is related to two dimensions of decision-making deficits: temporal (impulsivity) and risk functions. Some casecontrol studies have utilized BIS-11 in conjunction with a delay discounting task (DDT) to highlight that heavier drinkers with lower scores in BIS-11 discount the value of delay reward more steeply (Adams et al., 2017). Others have studied the relationship between SUD and impulsivity in a cross-sectional setting and showed that personality traits related to impulsivity (along with sensation seeking) can predict substance use in a sample of university students (Hamdan-Mansour et al., 2018). Furthermore, BIS-11 has been used along with neuroimaging techniques to study structural deficits in alcohol dependents (Grodin et al., 2017) and brain morphometry in methamphetamine users (Kogachi et al., 2017), showing that greater impulsivity has a positive relationship with structural abnormalities in different regions of the brain. However, although showing sound internal consistency and adequate power to differentiate SUD patients from healthy controls, additional psychometric evaluations revealed that in comparison with other scales (e.g., Eysenck I7 and Multidimensional Personality Questionnaire), BIS11 did not show the adequate power in presenting the subdomains of impulsivity as proposed by Barratt (Luengo et al., 1991). Thus, some scholars have recommended switching to other impulsiveness scales to study a conceptually broad construct of impulsivity or alternatively adopting the BIS-Brief version (Reise et al., 2013; Steinberg et al., 2013).

Monetary choice questionnaire History and Background: Delay discounting, defined as preferring a sooner smaller reward to a larger but delayed one, has been assumed to be an important personality characteristic influencing decision-making patterns (Rachlin, 1974; Ainslie, 1975; Mazur, 1987; Rachlin et al., 1991). The first version of a delay discounting self-report scale, the Monetary Choice Questionnaire (MCQ), was developed to assess the relationship between discounting rate and reward magnitude (Kirby and Marakovic, 1996). In 1999, MCQ was used for the first time in the field of

28

Cognition and Addiction

SUD, with results suggesting that delay discounting can quantify the DMDs in SUDs (Kirby et al., 1999). Structure: In the first version of MCQ (Kirby and Marakovic, 1996), rewards were divided into three classes: small (30$ to 35$), medium (55$ to 65$), and large rewards (70$ to 80$). The questionnaire consisted of 21 instructions and choice trials and 5 additional questions about demographic characteristics. In each choice trial, participants were asked to choose between immediate smaller monetary rewards and larger delayed ones (e.g., “Would you prefer $54 today or $55 in 117 days?”). For each indifference point (i.e., the point in which immediate and delayed offers are of the same value for the participant), a discounting rate (k) is calculated either based on a hyperbolic or exponential discounting function (Green and Myerson, 1996; Madden et al., 1999). The first appearance of the scale in the field of SUD was in a case-control study showing that heroin addicts discount the delayed rewards more steeply than the controls (Kirby et al., 1999). Later, Kirby (2009) suggested that so long as other test conditions are held constant, delay discounting is stable and can be treated as a personal trait. Evidence in SUD: Addressing the temporal aspect of decision-making deficits, MCQ has been used in a variety of settings, from case-control and cross-sectional to monitoring longitudinal studies. For example, using MCQ, some studies have shown that there is a positive relationship between substance demand intensity and delayed reward discounting (Amlung et al., 2017; Farris et al., 2017; Hofmeyr et al., 2017). Along these lines, Schuster et al. (2019) have recently used MCQ to investigate the multivariable correlates of cannabis use disorder, and Hobkirk et al. (2019) have used the scale to study reward circuitry in cocaine users. Thus, the MCQ might be used along with other scales to assess the efficacy of intervention strategies in SUD patients, as more recently has been done using a working memory training intervention in a sample of alcohol dependents (Khemiri et al., 2019; Hendershot et al., 2018).

UPPS impulsive behavior scale History and Background: Among the different scales measuring impulsivity, UPPS is the only one that emphasizes the multifaceted and multidimensional nature of this construct. Based on the five-factor model of personality (FFM) (Costa and McCrae, 1990), it is argued that lower levels of self-control are related to both impulsiveness (in the neuroticism domain) and self-discipline (in the conscientiousness domain). Thus, Whiteside and Lynam (2001) tried to assess the relationship between common scales of impulsiveness and FFM. This resulted in the integrated UPPS scale reflecting the four main attributes of impulsivity. Later, it was argued that positive emotions

might also have an influence on impulsive actions and, as a result, the fifth dimension of personality has been added to the scale forming the UPPS-P (Lynam et al., 2006; Cyders et al., 2007). Structure: In its original version, the UPPS addressed four aspects of personality. The first dimension was Urgency, reflecting the tendency to act rashly under extreme negative emotions (e.g., “I always keep my feelings under control.”). The second dimension was lack of Premeditation, which means the tendency to act without thinking (e.g., “I am not one of those people who blurt out things without thinking.”). Lack of Perseverance was the third dimension and addressed the inability to remain focused on a task (e.g., “Unfinished tasks really bother me.”). The fourth dimension targeted Sensation Seeking, the tendency to seek out novel and thrilling experiences (e.g., “I would enjoy parachute jumping.”). It consisted of 45 items and the respondent was asked to rate the statements in a 4-point Likert scale (1 ¼ strongly agree to 4 ¼ strongly disagree) (Whiteside and Lynam, 2001). In the UPPS-P version, it was assumed that there is room for impulsive behavior under extremely positive emotions (Positive Urgency) adding an additional 14 items (e.g., “Others would say I make bad choices when I am extremely happy about something.”) to address this dimension (Cyders et al., 2007; Lynam et al. 2006, 2007). During the past decade, several versions of this scale have been developed targeting different groups and adapted for different national contexts. Cyders et al. (2014) have developed a short version (S-UPPS-P) that has only four items in each dimension. There is also a child version developed by Zapolski et al. (2010) that used one or two syllable words targeting children at the age of 7e13 years. Evidence in SUD: Designed to measure impulsivity, UPPS-P addresses the temporal aspect of DMD. In the field of SUD, several studies have utilized this scale. Rømer Thomsen et al., 2018, used the scale in conjunction with standard questionnaires of problematic substance use to show that different aspects of impulsivity have different relationships with addictive behaviors. According to their findings, relationships were found between lack of perseverance and sensation seeking, alcohol, and urgency with cannabis use. Furthermore, Tran et al., 2018, have used UPPS-P in a sample of young participants (aged 18e30) to highlight the pivotal role of both positive and negative urgency in early problematic alcohol use in adults. Luba et al. (2018) have used UPPS’s positive urgency to show that stimulant expectancies might increase marijuana use behavior. Furthermore, with the idea that different substances will cause different clinical characteristics, GarcíaMarchena et al. (2018) have used UPPS to show that severe cocaine use, compared with alcohol abuse, can cause increased impulsivity.

Decision-making deficits in substance use disorders Chapter | 4

Eysenck impulsiveness scale (I7) Background: The project of developing a unified scale of personality began more than 70 years ago (Eysenck, 1947). Then, with the idea of presenting such a scale in a hierarchical format (i.e., having first-order and second-order constructs), three aspects of Psychoticism, Extraversion, and Neuroticism were introduced in the Eysenck Personality Questionnaire (EPQ) (Eysenck and Eysenck 1969, 1975, 1976). Impulsivity and sensation seeking/venturesomeness were considered as the “primaries” for the socalled higher-order constructs. However, in nascence, impulsivity was further divided into risk-taking, nonplanning, liveliness, and (narrow) impulsivity (Eysenck and Eysenck, 1978). As a result, a 63-item self-report scale was created with an additional 21 items to address empathy. Structure: The first version of this scale (I5) consisted of 63 items in accordance with the EPQ. This scale differentiates impulsiveness (doing things without thinking) and venturesomeness (sensation seeking and risk-taking). The I6 version of the scale aimed at children and had 23 Yes/No questions which measured impulsiveness (Eysenck et al., 1984). The final version of the Eysenck impulsiveness scale (I7) is composed of 54 items and measures three subscales: impulsiveness (19 items), venturesomeness (16 items), and empathy (19 items) (Eysenck et al., 1985b). Participants are asked to answer either YES or NO to a number of questions. In the impulsivity subscale, some sample questions include “Do you often buy things on impulse?“, “Do you mostly speak before thinking things out?”, and “Do you prefer to ‘sleep on it’ before making decisions?” There are several adaptations of the scale tested in several countries (Amini et al., 2016; Francis et al., 2006; Heaven, 1991; Eysenck et al., 1985a; Tiwari et al., 2009). Evidence in SUD: While some addictive behaviors, e.g., pathologic gambling (Harries et al., 2018), have received empirical diagnostic and predictive supports from the I7 impulsivity sale, there is a limited amount of published evidence in the field of SUDs. Ekhtiari et al. (2008), for example, have used the Eysenck I7 questionnaire along with other self-report measures of impulsivity in a group of patients with opioid use disorder and found that, compared with other scales, I7 had better distinguished between the patients and healthy controls.

Sensation seeking scales Background: Sensation seeking, as a trait, is defined as the tendency toward new and intense experiences. Zuckerman et al. (1964) developed the sensation seeking scale (SSS) with the aim of assessing various aspects of sensation seeking in different psychopathologies like neuroticism, antisocial behavior, and psychopathy.

29

Structure: The original scale in its final format, SSS-V, consists of 40 items. In each item, participants are asked to consider two different situations or activities and select the one that they prefer. Later on, Arnett (1994) revised the original version and developed a 20-item questionnaire, the Arnett Inventory of Sensation Seeking Scale (AISSS), which addressed two areas of sensation seeking: novelty (odd items) and intensity (even items). In this regard, the novelty subscale of sensation seeking reflects the extent participants welcome new experiences (e.g., “I would like to travel to places that are strange and far away”). The intensity subscale, on the other hand, targets how intense the new experiences are for the participant (e.g., “In general I work better when I’m under pressure”). Arnett also changed the responding pattern to a 4-point Likert scale (A ¼ describes me very well to D ¼ does not describe me at all). Furthermore, in search for a brief version of the SSS, Hoyle et al. (2002) developed an eight-item version of the scale (Brief Sensation Seeking Scale) and further studies have revealed that this version has the same validity and reliability scores as the original one (Sousa et al., 2018; Stephenson et al., 2007). Evidence in SUD: SSSs address the risk functions in decision-making deficits in SUD. For example, HamdanMansour et al. (2018) have utilized the AISSS in conjunction with the BIS-11 to reveal that there is a positive relationship between sensation seeking and SUD among a group of university students. In a case-control setting, Mahoney et al. (2015) used the impulsive sensation seeking scale, which is a 19-item questionnaire based on the work of Zuckerman and Kuhlman (2000), to differentiate among healthy controls and participants with either cocaine or methamphetamine use disorders. Furthermore, in a cross-sectional setting, Hefner and Gordijn (2018) investigated whether severity of marijuana use disorder was positively correlated with the disinhibition subscale of the SSS.

Temporal experience of pleasure scale Background: The theoretical foundations of the temporal experience of pleasure scale (TEPS) are rooted in the concepts of anticipatory and consummatory joy in the anhedonia literature (Depue and Collins, 1999; Berridge and Robinson, 1998). Here, anticipatory joy reflects the fact that expected pleasure acts as a positive trigger for the decision, while consummatory joy reflects the online pleasure in response to real-time stimuli. In an attempt to measure people’s dispositions in these two dimensions, Gard et al. (2006) developed and tested the TEPS and found that these two dimensions measure different constructs. Anticipatory pleasure was positively associated with reward responsiveness and imagery, while consummatory pleasure reflected openness to different experiences

30

Cognition and Addiction

and appreciation of positive stimuli. In this regard, one can consider the TEPS as one of the scales targeting brain reward processing systems. Structure: In its original form, the TEPS consisted of 95 items. Participants were asked to rate each statement (e.g., “I enjoy taking a deep breath of fresh air when I walk outside”) in a 6-point Likert scale (one ¼ very true for me to six ¼ very false for me). His score is then included within the anticipatory subscale (e.g., “When something exciting is coming up in my life, I really look forward to it”), consummatory subscale (“The sound of crackling wood in the fireplace is very relaxing”), and the overall score (TEPS-Total). In its final format, the scale has 18 statements (10 items reflecting anticipatory and 9 items reflecting consummatory pleasure) (Gard et al., 2006). The psychometric characteristics of both trait version and a state version of the TEPS have been evaluated in different disorders from SUD (Garfield et al., 2016) to schizophrenia (Horan et al., 2005) and Parkinson’s disease (Leentjens et al., 2008). Evidence in SUD: In the field of SUD, TEPS is assumed to address the reward aspect of decision-making. Cassidy et al. (2012) found that lower hedonic responses might have a positive relationship with heavy cannabis use in patients with psychotic disorders. In a similar attempt within a population of cigarette smokers, Leventhal et al. (2014) have shown that smokers have an imbalance in valuating substance-related reinforcers over substance-free ones. Although the distinguishing power of the scale (i.e., between anticipatory and consummatory pleasure) was low (Garfield et al., 2017), the relationship between reward sensitivity and tobacco abstinence (Hughes et al., 2017a) as well as opioid use (Lubman et al., 2018) is generally empirically supported.

Effect expectancy questionnaire Background: Expectancies from substance use, as a part of social learning theory, is assumed to influence the pattern of substance consumption in individuals. First, Marlatt et al. (1973) attempted to show, using a taste-rating task, that the alcohol expectancy can determine drinking patterns in nonalcoholics. As some behavioral effects of alcohol drinking have been linked to alcohol expectancy, Brown et al. (1980) developed a self-report scale based on 125 interviews and found six different groups of alcohol expectancies. This includes alcohol as (1) a positive transforming agent, (2) an enhancer of social and physical pleasure, (3) a sexual experience facilitator, (4) a promoter for power and aggression, (5) an enhancer of social assertiveness, and (6) a mediator for relaxation and tension reduction. In another study, they extended the utility of the scale to adult alcoholics and showed that these expectancies affect different patterns of alcohol consumption (Brown

et al., 1985). In addition to alcohol, Schafer and Brown (1991) have analyzed the content of 794 self-reports and identified six major expectancies for marijuana use and five for cocaine. Their attempt resulted in two questionnaires for marijuana and cocaine use effect expectancies. Afterward, and based on the cognitive mediators of behavior approach, Leigh and Stacy (1993) have developed a similar scale to assess alcohol outcome expectancies. They have argued that other scales of expectancy assessment are focused only on the positive consequences, while from a learning theory point of view both positive and negative reinforcements can have an influence on the behavior. Structure: The primitive version of the scale (Alcohol Expectancy Questionnaire) consisted of 30 statements in five groups of expectancies and participants were asked to express their opinion in an agree/disagree format. The first subscale was an enhancement in social and physical pleasure (e.g., “Having a few drinks is a nice way to celebrate special occasions.”). Enhancing sexual performance and experience was the second subscale (e.g., “After a few drinks, I am more sexually responsive.”) and increasing power and aggression was the third one (e.g., “If I’m feeling restricted in any way, a few drinks make me feel better.”). Respectively, the fourth subscale was increasing social assertiveness (“If I have a couple of drinks it is easier to express my feelings.”), and reducing tension was the last one (e.g.,“ Alcohol enables me to fall asleep more easily.”) (Brown et al., 1980). In the marijuana and cocaine version of the effect expectancies measures (i.e., MEEQ and CEEQ), one example question is “what effects do you expect from a moderate and normal use of marijuana/cocaine?” This resulted in different expectancies for marijuana and cocaine use disorders (Schafer and Brown, 1991). In a similar questionnaire, the alcohol outcome expectancies questionnaire, participants are asked to say how likely the items happen to them when they drink alcohol. In this self-report, expectancies are assessed in six subscales as social facilitation, sexual enhancement, tension reduction, emotional, physical, and social effects (Leigh and Stacy, 1993). Evidence in SUD: Effect expectancies questionnaires have the potentials to deepen our understanding of the learning aspects of decision-making deficits in SUD. However, one can assume the reward/value aspects of DMD have also some traces in these questionnaires. In the field of SUD with the use of the cognitive impairment subscale of the Marijuana Effect Expectancy Questionnaire, Gunn et al. (2017) have shown that the expectancy of behavioral impairment due to use of marijuana were negatively correlated with the behavioral measures of impulsivity and risk-taking. Furthermore, Patton et al. (2018) have divided alcohol-related expectancies into positive (reflecting the alcohol consumption as a rewarding

Decision-making deficits in substance use disorders Chapter | 4

outcome) and negative (acting as relieving the undesirable feelings). However, some studies argued that negative and positive expectancies do not have the same prediction validity, as negative expectancies were not found related to alcohol consumption (Mezquita et al., 2018). On the other side, in a randomized trial with a cognitive behavioral therapy, Coates et al. (2018) argued that little improvements have been made in positive expectancies of alcohol consumption without significant change in craving and impulsivity. Nevertheless, one key point that should not be overlooked in the inconsistencies in results from the effect expectancies questionnaires is the significant effect of age and sex in how people expect to be affected by drugs.

Rewarding events inventory Background: Initially, Baron et al. (1981) extracted 16 items to assess reinforcement preference as a function of substance use and sexual behavior. In a similar study, MacPhillamy and Lewinsohn (1982) have introduced the Pleasure Events Schedule (PES) as a behavioral self-report inventory to find the potentially reinforcing events in people’s life. Other examples include the Leisure Interest Checklist (LIC) that was developed to find the activities with subjective positive value in coping with stress in a population of college students (Rosenthal et al., 1989), and the Pleasant Activities List (PAL) that measures how much people are engaged with joyful activities (Roozen et al., 2008). The last member of this family, Rewarding Events Inventory (REI), is a newly developed scale to assess the frequency and joyfulness of the activities based on assessing 21 former reward inventories, anhedonia scales, and apathy scales (Hughes et al., 2017b). Structure: The main structure of all different versions of reward inventories is the same. A list of activities is presented to the participants and they are asked to rate each activity in two dimensions: frequency of engagement and level of enjoyment. However, primary scales (e.g., PES, PAL, and LIC) had a long list (about 150 items) and included some outdated activities and events. Furthermore, most of these scales are future-oriented and address hypothetical enjoyment from rewards and have low quality in psychometric measures. REI was developed to fill these gaps. REI consisted of 58 up-to-date items and participants are asked to express their level of wanting (e.g., how much would they be willing to spend time, money, or effort to be able to experience it?) and frequency (e.g., how often the event has occurred to them in the last week). There are four subscales addressed in this scale as socializing (e.g., “Meet someone new”); active hobbies (e.g., “Do gardening or yard work”); passive hobbies (e.g., “Surf the Internet”); and sex/ drug use (e.g., “Drink alcohol”). Hughes et al. (2017b) showed that REI has an acceptable level of validity and reliability.

31

Evidence in SUD: Reward inventories have been mainly used in case-control and cross-sectional studies in the field of SUD, focusing on the reward aspect of decisionmaking deficits. Roozen et al. (2008), for example, found that SUD is positively associated with less engagement in pleasant activities, both in frequency and level of joyfulness dimensions. Being less sensitive to rewards (anhedonia) is also seen as an important construct in SUD studies. In a systematic review, Hughes et al. (2018) found that despite all differences in the sample size and targeting groups, one can argue that early abstinence from nicotine can lead to less sensitivity to nondrug-related rewards (e.g., music, money, etc.). This reward sensitivity phenomenon should be explored in more details among other groups of SUDs with different duration of abstinence.

Reinforcement survey schedule Background: The estimated value of reward/punishment (positive/negative reinforcers) is a significant part of the decision-making process. In this line, Cautela and Kastenbaum (1967) developed a Reinforcement Survey Schedule (RSS) to assess how participants value different activities. They had asked participant how joyful or pleasurable an activity or a specific event is for them. As the original version was limited to adult participants, Cautela (1977) developed an adolescent reinforcement survey schedule (ARSS). 16 years later, on 1983, Cautela and Lynch with a thorough review have identified 14 versions of the instrument with different target groups including aged persons, children, juvenile offenders, and males versus females (Cautela and Lynch, 1983b). ARSS has further been analyzed by Holmes and colleagues (Holmes et al. 1987, 1991) and later on Murphy et al. (2005) used this modified version to develop a substance use version of the measure (ARSS-SUV). Structure: In its original form, RSS consisted of 139 reinforcers in four categories. The first category comprises the reinforcers that are palpable like eating or drinking. The second category reflects reinforcers that are presented just in facsimile form like watching sports or reading a book. The third category is about reinforcers that are presented just in an imaginary scenario such as a job situation or being in a party. The fourth category consists of contextual situations reflecting daily-life reinforcing activities like the things that participant thinks about or do frequently (Cautela and Kastenbaum, 1967; Cautela and Lynch, 1983a, 1983b). The substance use version of the instrument (ARSS-SUV) assesses the frequency of engagement and the joyfulness of 45 activities, both with and without drug use. Participants are asked for the frequency and pleasure of each activity twice and on a 5-point Likert scale. The total reinforcement ratio is calculated for each participant as a value between zero and one while the higher values reflect

32

Cognition and Addiction

related joyfulness of the activities with drug use (Murphy et al., 2005). Evidence in SUD: Targeting the reward dimension of the decision-making deficits, ARSS-SUV is used to identify the individualized variations in patients with SUDs and thus provides valuable clinically relevant information for treatment strategies. Dennhardt et al. (2015b) showed that a brief intervention could reduce the reward value of the drug-related reinforcers and subsequently reduce drug consumption. Hallgren et al. (2016) analyzed the psychometric properties of ARSS-SUV-alcohol use among college student drinkers and showed that “with whom?” is more important than the reinforcer itself. They have identified activities grouped by peer interaction, sexual activity, school, and family interaction. Here, identifying the drugfree activities and contextual situations in which the relative value of drug-free activates increases may inform interventional strategies in a range from motivational interventions to contingency management.

Consideration of future consequences scale Background: Considering future events or the so-called future time perspective (FTP) has long been assumed to play an important role in decision-making (Wallace, 1956; Kastenbaum, 1961). In a systematic review and metaanalysis, Kooij et al. (2018) have identified 212 studies addressing different aspects of FTP, of which consideration of future consequences scale (CFCS) is one of the most used ones. Reviewing the most common scales to date, Strathman et al. (1994) introduced a scale to measure to what extent individuals consider immediate or delayed consequences of their decisions. They prepared a primary 24-item self-report questionnaire and after reliability assessment and factor analysis, they reached a 12-item version of the instrument. The scale was further validated and a short version of CFCS consisting of eight items is also developed (Petrocelli, 2003). Structure: The original version of CFCS consisted of 12 items, in which participants were asked to indicate whether or not the statement is among their characteristics in a 5-point Likert scale from 1 (¼extremely uncharacteristic) to 5 (¼extremely characteristic). Sample statements are like “Often I engage in a particular behavior in order to achieve outcomes that may not result for many years” and “I think it is more important to perform a behavior with important distant consequences than a behavior with lessimportant immediate consequences.” The higher score participants gain, the more it is likely that they consider the potential consequences of their decisions (Strathman et al., 1994). Petrocelli (2003) proposed omitting four items from the original work to make the scale more stable. He also identified two underlying factors acting as subscales to

predict different aspects of temporality in decision-making: CFC-Immediate and CFC-Future. Here, items in CFCImmediate subscale (e.g., “My behavior is only influenced by the immediate outcomes of my actions.”) reflect how much the participant is concerned with the immediate consequences of her decisions. On the other side, items in the CFC-Future subscale (e.g., “I am willing to sacrifice my immediate happiness or well-being in order to achieve future outcomes.”) reflect the consideration of later future consequences. Evidence in SUD: While both temporality and reward aspects of decision-making deficits might be addressed by CFCS, the evidence in the field of SUD is inconsistent. For example, while some scholars found that interventions targeting improvement in FTP has a positive relation with the decrease in alcohol consumption in students (Beenstock et al., 2010), others showed that although having some relations with the alcohol-related problems, there is no significant relationship between consideration of future consequences and alcohol consumption (Acuff et al., 2017). With regard to CFCS subscales, Adams (2012) showed that CFC-Immediate has a positive relationship with health-related behaviors (smoking status and Body Mass Index) while she could not find such a relation with CFCS-Future and these variables. This puts forward the idea that the time interval between baseline measurement and the follow-up seems to play a significant role in the interpretation of the results.

Sensitivity to reinforcement of addictive and other primary rewards Background: The subjective value of substance-related rewards versus primary substance-free rewards is an influencing factor in SUDs. In other words, the key question here is how the valuation of these two types of rewards shifts in individuals due to substance use. Based on some experiences with animals, Goldstein et al. (2010) developed the Sensitivity to Reinforcement of Addictive and other Primary Rewards (STRAP-R) to distinguish between liking and wanting of expected substance-related rewards versus food and sex. The idea of liking versus wanting is the core concepts in the inventive sensitization theory of SUD which assumes SUD as a disorder caused primarily by a shift in the saliency of the substance-related stimuli (Berridge and Robinson, 2016; Robinson and Berridge, 2008). Structure: In the original form, participants were asked to think about their most favorite food, sexual activity, and drug or alcohol. Then, for each of these categories, they respond the extent they like (i.e., how pleasant it is) and they want (i.e., if they want that) in three different hypothetical situations: currently, in general, and under drug influence (Goldstein et al., 2010). In another attempt and based on the original scale, Arulkadacham et al. (2017)

Decision-making deficits in substance use disorders Chapter | 4

have used items related to the alcohol and coffee to find if they can distinguish between liking and wanting in different groups of patients. To assess subjective wanting, participants were asked “How much do you want to drink it?” and in the linking part “How pleasant would it be to drink it?” For each reward, a Likert scale from one (¼somewhat) to five (extremely) shows the participants’ opinion. Evidence in SUD: As a recently developed instrument, there are not many pieces of evidence with SUDs using STRAP-R, and thus, it is unlikely that one can form a sound judgment about the psychometric and clinical quality of the scale. Goldstein et al. (2010) have found that the relative value of the rewards changed in the patients with SUD and this change was greater in lower ages. They have concluded that these patients attribute salience to the rewards related to the substance of their choice as well as a reduction in the nonsubstance-related rewards. This has also been extended to alcohol and caffeine, and Arulkadacham et al. (2017) showed that in patients with high risk, compared to the liking, wanting has the key role leading to more alcohol consumption and there is a significant difference between caffeine and alcohol at the neural level. However, the difference between liking and wanting effect vanishes in higher levels of consumption.

Substance use risk profile scale Background: Based on the personality risk model, the Substance Use Risk Profile Scale (SURPS) is developed to link different patterns of substance use to four dimensions of personality as happiness, anxiety sensitivity, impulsivity, and sensation seeking (Woicik et al., 2009). Based on this, SURPS assesses personality traits and positive and negative reinforcement processing associated with SUD. Structure: In the original form, the scale consists of 28 items and participants were asked to rate the statements in a Likert scale from one (¼strongly disagree) to four (¼strongly agree). In this version, seven items (e.g., “I feel that I’m a failure.”) measure happiness. Anxiety sensitivity is addressed with five items (e.g., “It frightens me when I feel my heart beat change”). In addition to that, impulsivity is the target of five items (e.g., “I usually act without stopping to think.”) and six items are assigned to the sensation seeking aspect of substance use risk (e.g., “I am interested in experience for its own sake even if it is illegal.”). Besides, a shorter, 23-item version of the scale has been developed and showed acceptable reliability and validity (Woicik et al., 2009). Evidence in SUD: In the field of SUD, Bernhardt et al. (2017) have used SURPS to assess the relation between impulsivity and the personality traits related to SUD in a group of young patients. They have shown that compared

33

with the control group, patients with alcohol use disorder have lower risk-seeking behavior in losses and higher risk aversion in gains. In another study, Pearson et al. (2018) have used SURP scale and found that marijuana-related perceptions (i.e., descriptive norms, injunctive norms, and internalization of marijuana use culture) play a mediating role in the relationship between personality traits and marijuana consumption.

Concluding remarks for self-reports A number of different self-report measures have been developed to measure various cognitive components of the DMDs, as shown in Table 4.1. The subscales addressed by each self-report and a sample question for each subscale are provided. Reviewing the body of evidence yielded from decision-making self-reports in the field of SUD reveals that different dimensions of impulsivity with focus to temporal function of decision-making are covered more extensively. The reward/value function of decision-making is standing in the second rank in the literature. Here, variables like pleasure, liking, and wanting are addressed with the self-report measures. The risk/probability dimension of the decision-making deficits in SUD is generally not addressed independently within self-report measures. However, real-life risk-taking and experience seeking is well covered with self-reports mainly along with impulsivity constructs. There are dimensions in risk preferences that could be addressed better with behavioral tasks. A similar situation exists for the learning function of decisionmaking. Learning function can be addressed indirectly in self-reports but behavioral tasks especially with computational modeling can provide a more detailed picture. Despite the effectiveness and ease of use, there are some weaknesses that should be considered when using selfreports. First, due to the face validity feature, there is always the risk that respondents do not show their real opinions. Second, many self-reports measure general behaviors and although it is shown that self-reports have better internal validity, it is argued that their predictive power is low especially when used as the only assessment paradigm (Cyders and Coskunpinar, 2011), although some of the most recent findings show that they can predict realworld outcomes moderately well, especially when these real-world outcomes are self-reported as well (Eisenberg et al., 2018). In addition to that, as mentioned in our previous work, as an assessment tool, questionnaires alone might not have a sufficient necessary power to “tease apart” different neural and cognitive aspects of DMDs in SUD (Ekhtiari et al., 2017). This can be a major deficit when we want to target these neural and cognitive aspects in certain interventions.

34

Cognition and Addiction

TABLE 4.1 Summary of self-reports for assessing decision-making deficits (DMD) in SUD. Selfreport

Original work

BIS

Barratt (1985)

MCQ

UPPS-P

I7

Format of answers

Aspects of DMD

Subscales

Sample item/Statement

4-point likert scale

Attentional impulsiveness Motor impulsiveness Nonplanning impulsiveness

I am restless at the theater or lectures. I act on the spur of the moment. I get easily bored when solving thought problems.

Temporal

Kirby and Marakovic (1996)

Option selection

-

Would you prefer $54 today or $55 in 117 days?

Temporal

Whiteside and Lynam (2001) Lynam et al. (2006)

4-point likert scale

Negative urgency Lack of ppremeditation Lack of perseverance Sensation seeking Positive urgency

I always keep my feelings under control. I am not one of those people who blurt out things without thinking. Unfinished tasks really bother me. I would enjoy parachute jumping. Others would say I make bad choices when I am extremely happy about something.

Temporal Temporal Temporal

Impulsiveness Venturesomeness Empathy

Do you often buy things on impulse? Do you think hitchhiking is too dangerous a way to travel? Do you find it silly for people to cry out of happiness?

Temporal

Risk Temporal

Eysenck et al. (1985b)

Yes/No

AISSS

(Zuckerman, 1984) (Arnett, 1994)

Item selection

Novelty Intensity

I would like to travel to places that are strange and far away. In general, I work better when I’m under pressure.

Temporal

TEPS

Gard et al. (2006)

6-pointlikert scale

Anticipatory pleasure Consummatory pleasure

When something exciting is coming up in my life, I really look forward to it. The sound of crackling wood in the fireplace is very relaxing.

Reward

EEQ

(Brown et al., 1980) (Leigh and Stacy, 1993)

Agree/ disagree

Enhances in social and physical pleasure Enhancing sexual performance and experience Increasing power and aggression Increasing social assertiveness Reducing tension

Having a few drinks is a nice way to celebrate special occasions. After a few drinks, I am more sexually responsive. If I am feeling restricted in any way, a few drinks make me feel better. If I have a couple of drinks, it is easier to express my feelings. Alcohol enables me to fall asleep more easily.

Reward

REI

Hughes et al. (2017b)

4-pointlikert scale

Wanting Frequency

How much would you be willing to spend time, money, or effort to be able to experience (the event)? How often (the event) has occurred to you in the last week?

Reward

RSS-SUV

Cautela (1977) Murphy et al. (2005)

5-likert scale

Frequency (past month) Joyfulness

How often (the activity) has occurred to you in the past month (with and without drug)? How much would you enjoy doing (the activity) (with and without drug)?

Reward

CFCS

(Strathman et al., 1994) (Petrocelli, 2003)

5-likert scale

CFCF-immediate CFCS-future

My behavior is only influenced by the immediate (i.e., a matter of days or weeks) outcomes of my actions. I am willing to sacrifice my immediate happiness or well-being to achieve future outcomes.

Temporal

Risk -

Continued

Decision-making deficits in substance use disorders Chapter | 4

35

TABLE 4.1 Summary of self-reports for assessing decision-making deficits (DMD) in SUD.dcont’d Selfreport

Original work

Format of answers

Subscales

Sample item/Statement

Aspects of DMD

STRAP-R

Goldstein et al. (2010)

5-likert scale

Wanting Liking

How much do you want to drink it? (Now, in general, under drug influence.) How pleasant would it be to drink it? (Now, in general, under drug influence.)

Reward

SURPS

Woicik et al. (2009)

4-likert scale

(Lack of) happiness Anxiety sensitivity Temporal Impulsivity Sensation seeking

I feel that I am a failure. It frightens me when I feel my heart beat change. I usually act without stopping to think. I am interested in experience for its own sake even if it is illegal

Temporal Risk

I7, Eysenck Impulsiveness Scale; - AISSS, Arnett Inventory of Sensation Seeking Scale; BIS, Barrat Impulsiveness Scale; - CFCS, Consideration of Future Consequences Scale; - EEQ, Effect Expectancy Questionnaire; - MCQ, Monetary Choice Questionnaire; - REI, Rewarding Events Inventory; - RSS-SUV, Reinforcement Survey Schedule-Substance Use Version; - STRAP-R, Sensitivity to Reinforcement of Addictive and other Primary Rewards; - SURPS, Substance Use Risk Profile Scale; - TEPS, Temporal Experience of Pleasure; UPPS-P, Negative Urgency, Lack of Lack of Premeditation, Lack of Perseverance, Sensation Seeking and Positive Urgency.

Behavioral task Despite the advantages of self-reports in studying DMDs, not all aspects of the construct under investigation can easily be addressed by self-reports. Behavioral tasks, on the other hand, are translatable (i.e., applicable for animal and human), enable process models, stimulate “real-life” decision-making situations, and go beyond verbal behavior of the participants. Therefore, behavioral tasks are utilized to quantify objective decision-making tendencies, which may not be experimentally accessible via self-report (Cyders and Coskunpinar, 2011). In this section, we review the most commonly used behavioral tasks addressing the four dimensions of DMDs in SUD. For each task, we supply a short history and background to clarify the aims and directions of the task. Then, a short description of the task structure and its variations and some of the recent evidence in the field of SUD are provided.

Delay discounting task Background: Delay discounting is defined as “the phenomenon in which the value of a reward decreases as the time delays until its receipt increases” (Goldstein and Naglieri, 2011). During the past two decades, delay discounting and its synonyms (delayed gratification, time preference, time delay discounting, intertemporal choice, discounting of delayed rewards, etc.) have been increasingly investigated in a number of behavioral disorders form SUD to obesity, gambling, and sexual behaviors (Bickel et al. 2012, 2014). As a personal state or trait (Odum, 2011), some scholars consider delay discounting as a dimension of impulsivity (Barkley et al., 2001; Green et al., 1994). However, recent findings show that at least not all

aspects of impulsivity have a positive correlation with delay discounting (Fecteau et al., 2014). Structure: It is possible to use self-reports, behavioral tasks, or both to find out participants’ delay discounting rate (van Gelder et al., 2013). In different forms of DDTs, and despite all detailed considerations, the idea behind these tasks is almost the same. Two options are presented to the participants: a smaller but immediate reward and a larger but delayed one. After choosing either of the immediate/delayed rewards, the amount of the reward (in the adjusting amount approach) or the delay intervals (in the adjusting delays approach) will be adjusted. Thus, there would be an indifference point for each participant, at which, the direction of the decision changes. Based on either a hyperbolic or exponential model fitness, a discounting rate is assigned to each participant, showing the extent to which s/he discounts the delayed rewards and prefers the immediate ones (Matta et al., 2012). The original format was with two decks of actual cards (Green et al. 1996, 1999); while nowadays, the computerized versions are mostly used (Myerson et al., 2003) in different versions, e.g., random adjustment version (Richards et al., 1999), short version (Cherek et al., 1997) and 5-trial adjusting DDT (Koffarnus and Bickel, 2014). However, despite having a similar conceptual foundation, there are some methodological considerations in measuring delay discounting with behavioral tasks and several studies have shown that these parameters can significantly influence the findings and their interpretation (Matta et al., 2012; Robles and Vargas, 2008). These parameters include but are not limited to adjusting approach (time adjusting vs. reward adjusting), type of reward (monetary or nonmonetary) (Estle et al., 2007), magnitude of the reward (Green et al., 1997), framing effect (Radu

36

Cognition and Addiction

et al., 2011), delayed gain or loss (Weatherly et al., 2010), and the intertrial interval (Smethells and Reilly, 2015). In addition to these items, the discounting calculation method (hyperbolic, exponential and the area under the curve) is another methodological parameter to be considered (Green and Myerson, 1996; Madden et al., 1999). Evidence in SUD: In the field of SUD, DDTs has been used extensively in recent years in several settings from case-control to cross-sectional studies (Amlung et al., 2017; Owens et al., 2017; Gowin et al., 2018; Koffarnus and Kaplan, 2018). In addition, several studies attempted to modulate delay discounting with interventions (Bickel et al., 2015; Gray and MacKillop, 2015; Koffarnus et al., 2013; Ryan, 2013). Among interventions designed to modulate delay discounting rate, episodic future thinking (EFT) seems more promising in the field of SUD (Daniel et al., 2013; Kaplan et al., 2016). Chiou and Wu (2017) have shown that EFT not only modulates discounting rate but also decreases cigarette consumption. This evidence is replicated in e-cigarette (Stein et al., 2018) and alcohol (Bulley and Gullo, 2017).

BART. In many of them, BART demonstrated acceptable psychometric characteristics and reasonable shared variance with other measures of risk-taking behavior (Aklin et al., 2005; Crowley et al., 2006; Fernie et al., 2010; Lejuez et al., 2002; MacPherson et al. 2010a, 2010b; White et al., 2008). However, there are some methodological parameters to decide among in using this task, i.e., the optimum number and type of balloons and reward magnitude/type (Wallsten et al., 2005a; Dahne et al., 2013). Evidence in SUD: In a case-control study, Claus et al. (2018) have used BART with fMRI to show that neural mechanisms of risky decision-making are different among adolescents reporting alcohol and/or marihuana use. In a cross-sectional setting, Gunn et al. (2017) have used BART to show how the expectancy impairments reduce marijuana-induced risk-taking. BART has also been used to test the effectiveness of different intervention strategies for SUD treatment such as community-based services (Forster et al., 2017), contingency-based management (Beckham et al., 2018), tDCS (Guo et al., 2018; Kaplan et al., 2016), and computerized therapy strategies (Zhu et al., 2018).

Balloon analogue risk task

Iowa gambling task

Background: Lejuez et al. (2002) developed the Balloon Analogue Risk Task (BART), inspired by Slovic’s devil task (Slovic, 1966), as a laboratory measure to assess realworld risk-taking behavior. In the BART, participants are asked to pump a balloon to gain as much reward as possible with the caution that the balloon may explode in any time; and the more they pump, the more will be the risk that balloon bursts. Here, like the real-world situations, being riskier will bring more reward to a turning point after which increasing in risk-taking might result in a loss. Later, Pleskac et al. (2008) developed another version of the task in which participants are asked about the number of pumps they desire and the balloon would be pumped automatically up to that number with the possibility of a burst in every pump. A youth version of the task, in which monetary balance is replaced with a point meter has also been developed. Here, participants are told that their final points will determine the final reward (Lejuez et al., 2007). Structure: In its original form, pictures of balloons are presented to the participants asking which balloon they want to pump. Each pump brings a reward (a 5-cent gain per pump) and the participant may press a “stop” button to send the gained money to the depository. However, the cash balance is not shown to the participant. Each trial consists of three different colors of balloons, 30 balloons each, with different probabilities of burst and the task is done for 90 trials. The dependent variable here is the number of pumps, which represents the amount of risk that each participant takes (Lejuez et al., 2002). Numerous studies have investigated the reliability and validity of

Background: In general, in gambling tasks, participants should bet on two different options: one with greater gains (and losses) but less success likelihood and the other with a higher probability of success but fewer gains (and losses). Among different gambling tasks, Iowa gambling task (IGT) is perhaps the most commonly used. With the aim of assessing the role of the ventromedial prefrontal cortex (vmPFC), Bechara et al. (1994) developed a task to simulate the real-world decision-making under ambiguity in the laboratory. The idea here was to let the participants select one card from four decks of cards with different probabilities of gain and loss, with the aim of maximizing their monetary gain. The most important point here is that the number of trials and the reward/punishment rules is hidden from the participants. In this regard, the IGT is addressing the nonconscious biasing step in decision-making (Bechara et al., 1997). Structure: Originally, the IGT was comprised four decks of cards with different monetary gains and losses (A and B with $100 and C and D with $50 gain/loss). Due to the gambling nature of the task, cards with more rewards would have higher punishment probability. Participants are told that they can select one card in each trial to maximize their initial balance of $2000 (Bechara et al., 1994). It has been shown that the task has three stages: (1) prehunch stage (with an anticipatory skin conductance response), (2) hunch stage (that participants recognize the advantageous cards while being unable to explain the logic), and (3) conceptual stage (in which 70% of the participants find the

Decision-making deficits in substance use disorders Chapter | 4

logic behind the task and it happens around the card number 80 on average) (Garon et al., 2006). Evidence in SUD: IGT has been widely used in a variety of SUDs to depict DMDs (Biernacki et al., 2016; Brevers et al., 2013; Bickel et al., 2017a; Kovács et al., 2017). However, not everyone supports the IGT as an optimum behavioral task to differentiate SUDs from healthy controls on DMDs or as a reasonable tool to predict or monitor treatment outcome (Buelow and Suhr, 2009; Gansler et al., 2011; Lin et al., 2013).

Cambridge gambling task/risk task Background: The first version of Cambridge gambling task (CGT) (or risk task) was developed to assess orbitofrontal cortex activity while choosing between different options associated with rewards (Rogers et al., 1999b). The idea was to show an array of boxes (in two colors) to the participants and ask them to guess the box with a hidden yellow coin (token). But before showing the result, they are requested to bet on their choice. The probability of success will be reflected in the ratio of the colored boxes (Rogers et al., 1999a). Structure: The main outcomes of the CGT are the decision-making time (i.e., how much it takes for the participant to find the targeting box), risk aversion/risktaking (i.e., the number of alternatives chosen with less/ more probability), and risk adjustment (i.e., the rate by which individuals change their choices after receiving rewards). In the CGT, the gambling amount could be either ascending or descending and the participants can stop the increase/decrease process by pressing a button and fixing the desired amount to bet. The ratio of the colored boxes varies in each trial and could be 5:1, 4:2, and 3:3 while reward to loss balance changes from 10:90 to 50:50 (Rogers et al., 1999b). Evidence in SUD: Initially, Rogers et al. (1999a) have shown that chronic amphetamine abusers have more suboptimal decisions in CGT and this was positively associated with the years of use. This finding is being replicated in recent years among different SUDs (Aharonovich et al., 2018; Grant and Chamberlain, 2018; Rochat et al., 2018). tDCS over DLPFC is shown to modulate a risk-taking measure in a version of CGT among cigarette smokers in association to their nicotine-taking behavior (Fecteau et al. 2007, 2014).

Game of dice task Background: In most of gambling tasks, the rules of reward and punishment and their probabilities are not clear to the participants. These tasks, including IGT, are basically addressing the decision-making under ambiguity and not under risk. With these assumptions, Brand et al. (2005)

37

developed the game of dice task (GDT) to investigate decision-making under risk. In the GDT, participants are asked to guess a dice and receive gains/losses relative to the success probability of their guess (larger rewards/losses are assigned to the less probable options). Structure: In its original format, each participant receives an initial balance of 1000 V with the aim of maximizing this initial balance. In each trial, a dice is thrown and participants must guess the number. The answer could be a single number (1e6) or any combination of two, three, or four numbers. The amount of gain/loss to bet would also be decreased as the likelihood of the correct answer increases. Thus, the wining chance would be 16% for the first set (single number guess) and 33%, 50%, and 67% for the combination sets, respectively. In this regard, one- and twonumber guesses are the risky and disadvantageous option, the three-number sets are neutral, and the four-number guesses are advantageous options (Brand et al., 2005). Before the task starts, the rules of rewards and punishments are presented explicitly to the participants. An interesting study has shown that these changes, compared to the conventional gambling tasks (e.g., IGT), would lead to different results as only the final trial of IGT is correlated with GDT performance (Brand et al., 2007). This might confirm that decision-making under risk and decisionmaking under ambiguity should be distinguished in studying DMDs. Evidence in SUD: Brand et al. (2008) have shown that patients with opioid use disorder have poor performance in the GDT, and this is positively correlated with their executive functioning. In a cross-sectional study to find the effect of family history on the risk for alcoholism, Kumar et al. (2018) used both IGT and GDT. Although authors have not found any correlation between measures of these two tasks, they found that alcohol naive offspring at high risk for alcoholism show significantly lower performance in both tasks.

Effort expenditure to reward task Background: Effort expenditure for reward task (EEfRT) was introduced as a measure of motivation, effort-based decision-making and reward learning under the coste benefit decision-making paradigm (Floresco and Whelan, 2009; Treadway et al., 2009). In this multitrial task, participants have the chance to choose between two alternatives in each trial to gain the reward (Treadway et al., 2012). Structure: During the EEfRT, participants are asked to choose between two options. One with low effort and less gain, and the other which is more effortful but brings more gains. Doing the task, a virtual bar shows the points or monetary reward that has been gained. The easy task option is to press a button with the pointer finger of the dominant

38

Cognition and Addiction

hand and the effortful task is doing the same but with the lower finger of the nondominant hand. The easy task must be done 30 times in 7 seconds and the hard one must be done 100 times in 21 s (Treadway et al. 2009, 2012). In another version, Lawn et al. (2016) have implemented EEfRT with a probabilistic reward task. In this version, the rewards of the task are probabilistic in a way that one-third of the trials have 12% (low), one-third have 50% (medium), and the remaining trials have 88% (high) probability of success. Thus, even completion of the task (either the easy or the hard one) does not necessarily lead to the reward. If a participant fails to complete the task, her record would be excluded from the analysis. Here, the outcome variables are the winning chance in each trial (in case of completion), the amount of reward (money) gained through effortful option, and the expected value (i.e., adjusting the probability and magnitude of the reward) (Lawn et al., 2016). Evidence in SUD: To show DMDs in SUD, EEfRT has limited and inconsistent evidence. Wardle et al. (2012) have shown that caffeine can increase probability of taking more effortful options in EEfRT. Lawn et al. (2016) have used EEfRT to demonstrate how chronic effects of cannabis use can impair effort-related decision-making and reward learning. Furthermore, Hughes et al. (2017a) have reported that while abstinence from tobacco increased reward sensitivity in self-reports, EEfRT performance did not show any change. Despite conflicting evidence, EEfRT still provides important potentials to investigate the reward and learning aspects of DMDs in close relationship with motivation in SUDs in future (Pacheco-Colon et al., 2018).

Beads task, box task Background: As a probabilistic inference task, beads/box task (BT) assesses the level of effort to gather information before making a probabilistic decision (Phillips and Edwards, 1966). BT measures tendency to gather less information to make decisions (Jumping to Conclusion (JTC) bias) based on the optimal stopping paradigm (Moutoussis et al., 2011). Structure: In its original form, two bags (jars) with equal number of beads are presented to the participants. Beads are in two colors (blue and red) and each bag has a dominant color with the predetermined ratio (80:20). A single bead from an array of (predetermined) beads is shown to the participants and s/he is asked to guess the bag from which it has been taken. Participants can guess immediately or ask for another bead to be shown. The more draws they make, the more likely they guess correctly but with smaller gains (Phillips and Edwards, 1966; Moutoussis et al., 2011). In another version, four jars are shown in two pairs of colors (i.e., yellow and black/pink and green) and participants are first asked to express whether

they need more information or not and then they guess the jar (Garety et al., 1991). Dudley et al. (1997) have replaced the beads with students (boys and girls) or words (positive and negative) and schools instead of jars. In the box version of the task, a series of gray boxes are shown to the participants with two different underlying patterns. Participants can uncover the gray boxes to see the pattern by clicking on them. They should guess which pattern is the dominant one. Again, the more boxes they uncover, the more likely they will find the answer but it will reduce their gain (Balzan et al., 2017). Despite the conceptual similarity between BTs, there are some parameters to select among such as the number of trials, the number of beads, and the form of the feedback. Furthermore, it must be noted that participants have asked less information in the beads task compared with the box task. Decision-making error (whether the participant guessed correctly) and time to the decision (how much it takes to find the correct answer) are of interest. Evidence in SUD: Although BTs are originally developed in the context of paranoid and delusional patients’ population, it could be used to address the temporal aspect of DMDs in SUD. It has been shown that the construct of impulsivity in decision-making can be further divided into two subscales: waiting impulsivity and reflective impulsivity (Voon, 2014). Waiting impulsivity shows premature decision-making before receiving the cues to reward (Voon et al., 2016). Reflective impulsivity, on the other hand, shows to what extent the individual gathers information before making a decision (Kagan, 1966) and is related to the JTC bias. Beads Task has been utilized to measure reflective impulsivity in pathologic gambling (Djamshidian et al., 2012). In the field of SUDs, Clark et al. (2006) have shown that smoking has a negative effect on the individual’s performance in Beads Task. Banca et al. (2016) have also used Beads Task in conjunction with several other behavioral tasks and with the help of computational modeling showed that binge drinkers accumulate less evidence before making decisions. Similar results have been replicated after acute administration of Methylphenidate (Voon et al., 2016). However, the field of SUD has few pieces of evidence addressing reflection and waiting impulsivity and future studies with the help of different variations of Beads Task may deepen our understanding in this area.

Risk gains task Background: Although originally designed to address the risk and reward learning aspects of DMDs, risk gains task (RGT) has the potential to be utilized the temporal aspects of DMD. Although originally designed to address the risk and reward learning aspects of DMDs, RGT has the potential to be utilized the temporal aspects of DMD. The task

Decision-making deficits in substance use disorders Chapter | 4

has been used in patients with HIV (Connolly et al. 2014), and depression (Engelmann et al. 2013) apart from SUD. Structure: In the RGT, three numbers are presented to participants in an ascending order (e.g., 20, 40, and 80) for the duration of one second each, and they can earn the value of each number by selecting one of them. Thus, participants are requested to either select a number right after seeing it on the screen or wait for the next larger value. However, they are informed that in waiting for the two larger values (i.e., 40 and 80), there is a risk of having the numbers in red which means they have lost 40 or 80 points, respectively (Paulus et al., 2003). The RGT consist of three trial types in a randomized manner: 40 nonpunished trials and 90 punished trials. Here, the outcome measures are the degree of risk-taking (i.e., the relative frequency of nonrisk option vs. risky options or the magnitude of outcomes) and the response to punishment as a function of previous trial outcome (punished vs. nonpunished or the direction of the outcome) that shows sensitivity to punishment (Kruschwitz et al., 2012). Evidence in SUD: The RGT has been used in several studies in the field of SUD. First, and in a case-control study, Leland and Paulus (2005) used the RGT in a group of undergraduate students and showed that in comparison with never users, stimulant users made more risky decisions. This evidence was further clarified with the help of neuroimaging techniques (fMRI) in a sample of

39

occasional stimulant users and it was reported that while having no behavioral difference with controls, stimulant users presented less differentiated neural processing of risky and safe options (Reske et al., 2015). Another study replicated this evidence in a group of problem stimulant users compared with those who desisted from stimulant use (Blair et al., 2018). Same results are also reported by Gowin et al. (2017) in a group of participants with cocaine use disorder who preferred risky options more often following a loss. Furthermore, Gowin et al. (2014) used RGT and showed that methamphetamine dependence can result in different behavioral neural processing patterns in making risky decision. This result has also been strengthened when Bischoff-Grethe et al. (2017) have shown in response to anticipated gain or loss, there is an attenuated neural response in chronic methamphetamine users.

Concluding remarks on behavioral tasks The area of behavioral tasks in studying decision-making is not limited to what we have mentioned here, and there are several other tasks developed in this field that are not addressed above. However, to make this chapter as relevant and concise as possible, we have focused on the behavioral tasks that are used more frequently in the field of SUD and are assumed to be involved in our framework. Table 4.2 presents a list of the most commonly used behavioral tasks.

TABLE 4.2 Summary of behavioral tasks for assessing decision-making deficits(DMD) in SUD. Selfreport

Original work

DDT

Green et al. (1996)

BART

Lejuez et al. (2002)

Aspects of DM deficits

Outcome variables

Temporal

Discounting rate

Risk

The number of pumps

Snapshot

Which do you prefer to receive? $19 in 10 days

$17 in 2 days

Choose

Choose

A

$0.25

pump

B

$0.50

pump

C

$0.75

pump

D

$1.00

E

Cash-out

F pump

Continued

TABLE 4.2 Summary of behavioral tasks for assessing decision-making deficits(DMD) in SUD.dcont’d Selfreport

Original work

IGT

Bechara et al. (1994)

Aspects of DM deficits Risk

Outcome variables The final balance, the number of advantageous and disadvantageous cards

Snapshot Winnings: 0

1000

2000

3000

4000

5000

6000

7000

8000

Cash Borrowed

You have won $100! A

CGT/ RT

Rogers et al. (1999a)

Risk

B

C

The final balance, time to find the hidden coin

POINTS: 100

75

RED

GDT

Brand et al. (2005)

D

Risk

The final balance, number of correct and incorrect guesses

BLUE

Dice

Money Balance Gain/Loss

Parcipant: # 1 Sex: Female Age: 35 Years of Educaon: 12

+ 1000 €

Round: 1 of 18

Possible Combinaons

Gains/Losses 1000€

8

500€ 200€ 100€

EEfRT

Treadway et al. (2009)

Reward

The final balance, the number of easy tasks chosen, the number of effortful tasks chosen

Choose Your Task Probability of wins: 50% Easy Task $ 1.00

Easy Task $ 1.00

Press E

Press I

to Complete

BT

Phillips and Edwards (1966)

Temporal

The number of draws before guessing, the final balance

Failure to complete You have completed the task

85% green beads 15% yellow beads

15% green beads 85% yellow beads

Jar A

Jar B

Part 1 20 beads

Part 2 38 beads

RGT

Paulus et al. (2003)

Risk/ learning

The relative frequency of safe versus risky options overall and as a function of previous trial outcome

A

1 seconds 2 seconds 3 seconds

20

C

40

40

1 seconds You Win

-80

20

1 seconds 2 seconds 3 seconds

20

B

You Lose

D

2 seconds 3 seconds

1 seconds

2 seconds 3 seconds

You Win

You Lose

20

-40

BART, Balloon Analogue Risk Task; BT, Beads Task; CGT, Cambridge Gambling Task; - DDT, Delay Discounting Task; EEfRT, Effort Expenditure to Reward Task; IGT, Iowa Gambling Task; RGT, Risk Gains Task; RT, Risk Task.

Decision-making deficits in substance use disorders Chapter | 4

The table shows the targeted aspects of decision-making in addition to the variable(s) being assessed and a snapshot of how the task looks like. As in the case of self-report measures, behavioral tasks have also been utilized to address different functions of decision-making. However, contrary to self-reports, here the risk/probability function is the most prominently featured. In this regard, parameters targeting the risk seeking or risk aversion behavior in combination with reward function are of interest in the gambling/bandit tasks. Besides, there are tasks that assess the temporal function, either in the form of delay discounting rate or impulsivity. On the other hand, although there are some tasks that address the learning functions in SUD, similar to self-reports, the learning from reward and punishment is not adequately investigated through the behavioral tasks. We will discuss in the next section how computational models can compensate this gap. Furthermore, behavioral tasks are useful when there is some doubt about having a common insight about a phenomenon between the researcher and the participants. Here, a behavioral task can be designed to measure a specific cognitive process of the phenomenon while controlling for other processes in a simulation of the real-world situations in the laboratory. This places behavioral tasks in a position that can help us reach a better understanding of the cognitive processes contributing to the different aspects of decision-making deficits in SUDs (Ekhtiari et al., 2017). However, behavioral tasks have their own weaknesses. Apart from the low construct validity and reliability, it should be noted that behavioral tasks take just a snapshot of the behavior under investigation. Furthermore, they have a low nomothetic span regarding the integrity with other measures, e.g., self-reports (Coskunpinar et al., 2012) and recent findings show that in comparison to self-reports, they have less power in reflecting real-world outcomes (Eisenberg et al., 2018).

Computational modeling Different behavioral assessment paradigms have been used in the field of SUD, and these behavioral tasks are designed to assess the underlying cognitive processes engaged during decision-making (e.g., learning from experiences or sensitivity to punishment and reward). Although behavioral paradigms have helped to improve our understanding of the cognitive aspects of decision-making deficits, the heuristically proposed dysfunctions in SUD may not exactly match what is being observed on these tasks (Busemeyer and Stout, 2002). Computational models can help with the idea of decomposing overall performance of the participants on behavioral tasks into component processes that are quantified by computational parameters. Thus, computational models can provide a more profound understanding of the cognitive substrates of decision-making deficits based on

41

individual differences. Moreover, having more personally characterized information about the different aspects of decision-making deficits, there may be a greater chance to design and implement customized and mechanistically targeted intervention strategies (Ahn et al., 2016). The goals for computational models are to optimally fit the behavioral data and to determine how these models differ for individuals with SUD. Subsequently, latent variables can be obtained to address the targeting underlying cognitive processes involved in decision-making and its deficits (Ahn and Busemeyer, 2016). In addition to teasing apart the fundamental cognitive processes contributing to an individual’s behavior, hidden variables (i.e., parameters) estimated by these computational models can also highlight individual characteristics and differentiate participants where conventional behavioral analyses paradigms might fail to do so or do so with less quantitative precision (Ekhtiari et al., 2017). Computational models can be categorized into two approaches: data-driven and theory-driven (Huys et al., 2016). In data-driven approaches, machine-learning techniques are utilized in a number of different settings in SUD. This includes case-control studies to differentiate substance users from healthy controls (e.g., Sun, 2017; Mumtaz et al., 2018) and cross-sectional studies to determine the correlation between different aspects of substance use severity and decision-making deficits (e.g., Squeglia et al., (2016); Bae et al., (2017); Alghamdi et al., 2018). Furthermore, data-driven computational models are also used to design (Kahler et al., 2017) and predict the effectiveness of treatment strategies (e.g., Acion et al., (2017); Larney et al., 2018). In comparison, theory-driven or model-based approaches to computational modeling provide a more mechanistic view of the phenomenon under investigation. Here, the idea is to differentiate the underlying processes that shape decision-making and behavior. The theorydriven approaches can be further divided into three broad levels of description: synthetic or neural process models (e.g., biophysically detailed models), algorithmic models (i.e., models that describe tractable, step-by-step computational processes to implement a particular function), and optimal (i.e., Bayesian) computation-level models that describe the mathematical problem that the brain is required to solve (Huys et al., 2016). There are also different types of models that span these levels of description. For example, one recently proposed class of models are called active inference models. According to these models, agents simultaneously seek to minimize uncertainty about their environment and choose actions expected to produce the observations they prefer under their model (Schwartenbeck et al., 2015). Over time, agents also acquire expectations about their own patterns of

42

Cognition and Addiction

actions, and decision-making can be influenced by these expectations. Aside from providing an optimal computational and algorithmic level of description, active inference also includes a neural process theory that associates particular neural populations with (for example) representing beliefs about the causes of observations, particular patterns of synaptic connections with encoding relationships between causes and observations, and the role of various neuromodulators (e.g., the role of dopamine in encoding confidence in action selection). Theoretically, in these models, SUD could involve underconfidence in the successful implementation of long-term action plans, strong expectations to engage in certain actions, and/or very strong and precise preferences for drug-related observations, among other possible mechanisms. However, limited work to date has investigated these possible mechanisms empirically (Friston et al., 2017). A more commonly used class of models with a history of use in empirical studies are based on reinforcement learning (RL). This is to some extent because they have been around much longer and they represent the affective (i.e., rewarding and punishing outcomes) and motivational (i.e., reward-seeking) aspects of decision-making in a simple and straightforward manner. Specifically, RL models describe trial and error value learning processes during decision-making tasks in which the values of different possible actions in different possible states are learned through repeated observations of the outcomes produced by those actions. Quantifying these values in the way they are learned may provide a better understanding of the cognitive basis of choice processes in the brain. This is done by comparing the model’s behavior under different parameter values to the trial-by-trial behavior observed in the experiment to find a parameter values that best reproduce of participants behavior, typically using statistical techniques like maximum likelihood estimation and hierarchical Bayesian modeling (Daw, 2011; Lee, 2011). RL has three main components: states, actions, and outcomes. States are the current situations that the agent experiences, such as the present stimuli and the agent’s location. In each state, the agent selects an action which will bring her to a new state and also lead to receiving an outcome that can be positive or negative to different degrees (positive or negative reward values). In this regard, reinforcement is the process by which, given a certain state, outcomes influence the probability of future actions. The goal of the agent is to choose the sequence of actions (policies) that leads to the highest amount of reward in the long run (Maia, 2009). Furthermore, this learning to maximize approach can be classified into two general classes of algorithms: goaldirected or habitual. In the first approach, decisions are

made prospectively, based on an internal learned model of the environment. Here, an expected future is simulated using the so-called model-based (MB) learning system and evaluating potential outcomes fuels the decision-making process. Habit-based algorithms depend on the history of past experienced rewards, regardless of the changes in the immediate model of the environment. Here, the so-called model-free (MF) learning system calculates and uses a reward prediction error signal (i.e., the difference between what was expected and what was received) in the decisionmaking process (Daw et al., 2005; Huys et al., 2016; Keramati et al., 2011; Maia, 2009; Voon et al., 2017). Recent findings have suggested interactions between reward prediction error, model-based, and model-free learning processes in the brain (Sambrook et al., 2018). However, it is argued that the amount of resources needed for the model-based/model-free (MB/MF) learning systems, and estimates of their relative reliability in a given context, shapes their role in the decision-making process. That is while the MB system seems to be more flexible and convenient in new situations; due to evaluating all possible outcomes, it needs more resources and is thus more expensive. MF systems, in contrast, need fewer resources and are more efficient in situations with resource scarcity (Huys et al., 2016). Based on this classification, it is thought that SUDs can be understood as a result of shifting from MB- to more MF-guided behavior (Lucantonio et al., 2014; Sebold et al., 2014; Huys et al., 2014). There is also evidence showing that not only can this transition initiate SUDs (Galandra et al., 2017), but it also may lead to more relapses (Sebold et al., 2017).

Computational models of behavioral tasks Several computational models have been developed to explain SUD-related dysfunctions in MB learning systems. Here, we review the two main behavioral paradigms in assessing decision-making deficits: probabilistic risk-taking tasks (exemplified by the IGT) and sequential risk-taking tasks (exemplified by the BART). For the IGT, three initial computational models were introduced by Busemeyer and Stout 2002. The first is the strategy-switching heuristic choice model in which an initial tendency leads the participant to the “disadvantageous” decks. Nevertheless, after receiving several losses, the participant “switches” choice toward the “advantageous” decks. In this model, three parameters are of interest: the initial tendency to choose the disadvantageous decks and two other parameters that determine the shape of a logistic function, representing the switching tendency based on the losses received from the disadvantageous decks.

Decision-making deficits in substance use disorders Chapter | 4

The second approach is the Bayesian-Expected Utility Model in which it is assumed that choices are made based on the premises of bounded rationality. In this model, participants use Bayesian inference to estimate the probability of future gains or losses based on the experienced outcomes in each trial. The Bayesian-expected utility model has three major elements: (1) probability of receiving a loss from a specific deck in each trial; (2) the utility of a possible gain/loss in each trial; and (3) the choice rule, which is based on a comparison of the expected utility for each deck and whether that deck is really chosen or not. Here, the parameters of interest are the probability of guessing the maximum utility option and two other parameters that shape the utility function for gains and losses (Busemeyer and Stout, 2002). The third approach to explain how participants make decisions in the IGT is the Expectancy-Valence Learning (EVL) model and its variations. The idea here is that after receiving gains/losses in each trial, participants would experience a positive/negative emotional reaction (valence). The valence experienced in each trial shapes the participants’ choice through the process of expectancy learning. Then, the probabilistic choice is made as a function of expectancies associated with each deck. Here, the parameters of interest are the weight of attention to losses, the learning rate, and the sensitivity switching. In a newer version of the EVL model, Ahn et al., 2008, have introduced Prospective Valence Learning (PVL) with some modifications in the utility learning and the choice probability functions. In this version, there are three parameters of interest. This includes (1) a loss parameter that means being insensitive to losses produces a poor IGT performance; (2) a recency parameter that shows how a former experiences decay rate may result in poor performance on the IGT; and (3) a sensitivity parameter, showing to what extent the participant is focused on the optimization of her former choices instead of choosing randomly. A twoparameter version of the expectancy-valence model also exists in which the consistency parameter is assumed constant. It is shown that this modified model has clearer results (Humphries et al., 2015). It is also shown that PVL can better explain IGT performance in SUD patients compared with healthy controls (Ahn et al., 2016; Baitz, 2016; Dai et al., 2015). In a conceptually similar attempt, Wallsten et al., 2005b, have introduced three models to explain overall performance in the BART. They have assumed a baseline model in which no learning or sequential decision-making occurs. In addition to that, there is a target model in which no further evaluation is made by participants, although there is still a trial-by-trial learning function. Finally, the learning and evaluation model assumes that in each trial

43

there is an option evaluation in addition to trial-by-trial learning. They have shown that in the BART, the best explanation for the performance of the participants is that they neglected the probabilistic nature of the trial outcome and used their judgment to guess the next trial’s probability in a Bayesian updating process. Examining the correlation between the parameters of the computational model and substance use risk, it was also shown that the reinforcement specificity (monetary vs. sexual reinforcements) has no significant influence on predicting BART performance (Prause and Lawyer, 2014).

Concluding remarks on computational models The role of computational models as an assessment paradigm for studying DMDs in SUD is largely related to the behavioral paradigm utilized. Thus, as most of the computational models are developed for the behavioral risk-taking assessment tasks (e.g., IGT and BART), computational models are mostly targeting the temporal, risk, and reward aspects of decision-making deficits. For example, Gullo and Stieger (2011) have shown that during an IGT task, anticipatory stress in heavy drinkers increased attention to losses and thus resulted in better task performance. Lane et al., 2006, have also used the EVL model to show that different substances have distinct effects on several aspects of reward/punishment sensitivity. They have shown that alcohol increases attention to risky rewards while decreasing responding to risky losses. Marijuana, on the other hand, increased the tendency to take risky decisions due to influences on learning/memory and not on the motivational aspects. This finding has been confirmed by studies in which, even after punishments, people with alcohol use disorder did not change their choices (Reiter et al., 2016). Furthermore, it is argued that computational models can help us quantify and trace the changes in neurobiological and neurophysiological mechanisms (e.g., Pavlovian and Instrumental learning) involved in relapse due to alcohol-related contextual parameters (Heinz et al., 2017). In summary, computational models can help us model some of the fundamental computations in the human brain, especially in reward learning processes (Hauser et al., 2019). However, determining the exact cognitive processes under investigation, designing new and to-the-point tasks suitable for modeling, and implementing test-re-test longitudinal studies to show the reliability and validity of the tasks and model parameters are among the main challenges in utilizing computational models (Ahn and Busemeyer, 2016). Fig. 4.1 presents a classification of the computational models used in the assessment of decision-making deficits in the field of SUD.

44

Cognition and Addiction

Prospect Valence Learnign Models

Predicon Error Signal Processing

Model-free Learning System

Synthec Models

Biophysical Models

Bayesian Models

Algorithmic Models

Learnign and Evaluaon Model

Expectancy Valance Learnign Models

Model-Based Learning System

Machine Learning Approches

Reinforcement Learning

Theory-Driven Models

Computaional fMRI Approaches

Data-Driven Models

Computaonal Models for Decision-Making Deficits FIGURE 4.1 Classification of computation models for studying decision-making deficits in substance use disorder.

Neuroimaging Decision-making, as a multifaceted phenomenon, has several representations of different levels, from attitudes and behaviors to physiological processes and neural circuitry. Respectively, DMDs in SUD can be studied at different levels to characterize behavioral, cognitive, and neural substrates of this phenomenon. Among these aspects, conscious attitudes and trait aspects of decisionmaking (measured by self-report), nonconscious dimensions and behaviors (targeted by behavioral assessment paradigms), and hidden cognitive processes (addressed by computational models) have been reviewed in the preceding sections. In this section, we move to another brainbased level of description in SUD, which reflect neural circuitry activations, and which is mainly addressed by neuroimaging methods. However, although different neuroimaging techniques have been utilized, according to the aims of this chapter, we would limit this section to the fMRI studies and the role that task-based fMRI techniques plays in studying DMDs in SUD. Simply put, the ultimate goal of fMRI studies is to examine the functional activity/connectivity of different brain regions/networks, to find the relationship between behaviors, cognition, and their neural substrates. This is attempted in fMRI mainly using the blood-oxygen-leveldependent (BOLD) signal. The idea here is that activity in different brain areas modulates the level of blood

oxygenation/deoxygenation in that specific area (or possibly in other parts). Thus, monitoring BOLD signal might represent different levels of activities in the brain. Different imaging protocols could be implemented according to the aims of the investigations. Task-based fMRI is used in the studies in which the participant is asked to perform a behavioral task while lying in the scanner to find the correlations between behavioral tasks performance and the underlying brain circuitry. In the area of SUD, fMRI is used to provide a better understanding of the biological underpinnings of substance use, e.g., initiation, maintenance, consequences, and recovery (Tarokh, 2012). In the field of DMDs, the neurocognitive components of decision-making can also be studied using functional neuroimaging. It is argued that there are four main systems involved in DMDs in SUD including the midbrain striatum system, amygdalahippocampal system, insula ACC system, and PFC system (Ekhtiari et al., 2017; Zilverstand et al., 2018). To illustrate how task-based fMRI protocols can help us going through neural/physiological levels of decisionmaking deficits, in this section, we review some of the latest evidence in addiction medicine. To keep the section adequately concise, we limit our focus to the four mostly utilized behavioral tasks in DMD studies: DDT; IGT; BART; and CGT.

Decision-making deficits in substance use disorders Chapter | 4

Task-based fMRI evidence in SUD fMRI and delay discounting tasks SUD is proven to have a negative effect on delay discounting rates, that is, substance users discount delayed rewards more significantly, compared with healthy control groups. As a behavioral trait addressing the temporal aspect of the decision-making deficits, delay discounting has neural representations in the brain circuitry that can be used to distinguish between different groups of patients and healthy individuals (Claus et al., 2011; Peters and Büchel, 2011). In this regard, Elton et al., 2017, have shown that two large-scale brain networks are involved in DDTs: (1) a temporal lobe network that has a positive relationship with DDT related impulsivity and (2) a frontoparietalestriatal network that has a negative relation with DDT related impulsivity. Furthermore, in a recent study, Nestor et al., 2018, have investigated the neural correlates of current smokers, exsmokers, and healthy control group and showed that both current smokers and ex-smokers have less activity in left amygdala during positive response outcomes and ACC during the positive and negative outcomes. They have also shown that activity in ACC and middle frontal gyrus was negatively associated with nicotine dependency and consumption level. It has also been shown that alcohol consumption level was positively correlated with attenuated frontal and parietal activity in a DDT (Herman et al., 2018). This is consistent with former findings that alcohol consumption severity has a positive correlation with activity in paracingulate gyrus and frontal lobe in delayed decisions compared to impulsive decisions (Lim et al., 2017). Task-based fMRI could also be used to study the interventional strategies designed to improve delay discounting performance. Schmaal et al., 2014, have shown that receiving a 200 mg modafinil can improve delay discounting performance in alcohol-dependent patients. fMRI data showed that this improvement was due to increased activation in frontoparietal regions while reducing ventromedial Prefrontal Cortex (vmPFC) activation. Lempert et al., 2017, have also shown that recalling positive memories can improve delay discounting performance. This effect was also reported by other scholars (Peters and Büchel, 2010; Daniel et al., 2013). However, positive memory recalling might be considered as a reward as fMRI showed higher activity in the striatum and temporoparietal junction (which is related area in reward processing), while recalling positive memories.

fMRI and balloon analogue risk task BART mainly addresses the risk aspect of the decisionmaking deficits. However, as a sequential risk-taking

45

paradigm, it is assumed that one can assess the reward dimension of decision-making with this behavioral task as well. In this regard, an exploratory study showed greater activity in the ACC and inferior frontal gyrus/anterior insula in loss aversion status (i.e., deciding not to inflate more), as well as more activity in vmPFC when participants choose the more risky options (i.e., continuing to inflate) (Fukunaga et al., 2012). Similar results are reported for college-aged cannabis users, as cannabis users showed more activity in left inferior frontal gyrus, as well as the finding that cannabis uses severity can influence the activity in the precuneus (Paneto, 2017). However, recent findings with a different gambling task showed the influence of the expected value on the role of ACC and inferior frontal gyrus during risky choices (Fukunaga et al., 2018). Kohno et al., 2014, have shown that neural correlates of risky decision-making during BART are different in methamphetamine-dependent patients and a healthy control group. Based on this study, the activation in the ventral striatum (more in SUD) and right dlPFC (less in SUD) was positively associated with the number of pumps in the BART task. The role of the reward-seeking medial frontal cortex (mPFC) in the excessive substance use was formerly shown, as activation in mPFC during BART, could predict alcohol consumption in a 1 week follow-up (Bogg et al., 2012). Claus et al. (2018) used BART with fMRI in the adolescents, who were using alcohol, marijuana, or both and found that all subjects had greater activation in the dorsal ACC (dACC), anterior insula, ventral striatum, and lateral PFC. Patients who used both substances showed decreased responses in dACC, insula, striatum, and superior parietal lobe during risky decision-making. In addition to finding the neural substrates of risky decision-making, task-based fMRI is used to investigate interventional strategies. In a community-based substance use treatment program, Forster et al. (2016) used fMRI during BART and showed that an improvement in riskinformed outcome expectations was positively associated with more activation in the caudal ACC in high-risk responses and less activation in the caudal ACC and inferior frontal gyrus during negative responses. Besides, less activation in the vmPFC (responding to higher rewards) could predict lower consumption levels in the follow-up (Forster et al., 2017).

fMRI and Iowa gambling task Among the gambling tasks that address the risk-taking and reward learning aspects of the decision-making deficits, the IGT is the one that relies on decision-making under ambiguity. It is argued that IGT performance can distinguish between healthy participants and patients with medial frontal damages, including substance dependents (Tanabe

46

Cognition and Addiction

et al., 2007). Using IGT and fMRI, Lin et al. (2012) reported that heroin dependents had greater activity in right orbitofrontal cortex and medial PFC compared with the control group. Similar results are shown in a study to find the neural correlates of affective decision-making, where binge drinkers showed worse performance in IGT. Furthermore, greater activation in insula compared with the orbitofrontal cortex was positively related to drinking severity (Carbia et al., 2017; Xiao et al., 2013). Another study, using a modified version of IGT (Thompson et al., 2012), showed more activation in frontostriatal and limbic regions during IGT in the substance-dependent participants (Yamamoto et al., 2014). In prospective settings, IGT-based fMRI was used to show that heavy cannabis users had more activity while winning, in the regions involved in decision-making (i.e., right orbitofrontal cortex, right insula, and left superior temporal gyrus). Furthermore, win-related activity and activity related to the anticipating loss outcomes in the areas related to executive functions (i.e., right insula, right caudate, and right ventrolateral PFC) could predict cannabis use in a 6-month follow-up (Cousijn et al., 2013). In addition to that, Fukunaga et al. (2013) modified the IGT and added positively framed, negatively framed, and control messages about the long-term deck payoffs and showed that substance-dependent participants have had lower neural sensitivity in the ACC and anterior insula, showing worse performance after receiving negatively framed messages that reflects lower levels of risk aversion. However, recent findings reveal that because neural substrates of realworld risk-taking behavior are not localized in a single region, studies with an overall data driven from the whole brain activity might inform interventional strategies more substantially (Sherman et al., 2018).

fMRI and cambridge gambling task CGT and risk task address the risk-taking and reward processing aspects of the decision-making deficits. Using fMRI with the CGT, it has been shown that compared with controls, patients with SUD had higher levels of risk-taking that was correlated with lower activity in ventral striatum especially during the reward anticipation phase (Schneider et al., 2012). Later on, a longitudinal study of adolescents showed that lower activities in the ventral striatal and midbrain and dlPFC during the reward anticipation phase in the CGT can predict SUD in a 2-year follow-up (Büchel et al., 2017). In a most recent attempt to find the neurobiological processes during the CGT, Yazdi et al. (2019) found different neural circuits activation during different phases of the task. Specifically, they have shown that during the

betting stage, frontal lobe and putamen, and in the outcome (i.e., reward anticipation) stage, caudate nucleus and ventral and dACC have shown greater activity. However, as there is not much evidence in the SUD with the CGT, future studies might show if CGT/risk task could be combined with fMRI protocols to provide a better understanding of the neural basis of real-world risk-taking behaviors.

Model-based fMRI approaches With the power of computational models in recognizing the underlying processes of cognition and behaviors, there are some hopes that model-based fMRI might also fill the gap between physiological and behavioral levels of cognitive phenomena. Similar to the computational models for behavioral tasks, computational neuroimaging approaches are used to provide individual-specific analysis, which might lead to single-subject predictions and more personalized treatment strategies (Stephan et al., 2017). For example, in an attempt to find if a failure in neural mechanisms of prediction error (a physiological signal that leads the feedback-based decision-making) could play a role in substance-dependent patients’ performance in IGT. Tanabe et al. (2013) used the computational neuroimaging approach. They found that compared with the healthy controls, in substance dependents, ventral striatum and medial orbitofrontal cortex did not track the prediction error during the IGT. It is also argued that substance use might attenuate prediction error signal due to higher levels of activity in mesolimbic areas (García-García et al., 2017). Beylergil et al. (2017) have also used parametric modele based fMRI analysis to examine the neural mechanisms of prediction errors during a reward-guided task (Deserno et al., 2015). They revealed that alcohol dependency and the level of consumption made the left dlPFC less engaged in the processing of the negative prediction error signals. In this regard, Cservenka et al. (2017) have developed and tested an alcohol-specific prediction error task using fMRI and found that compared to social drinkers, alcohol dependents showed greater prediction error-related activity in the left superior parietal lobule, lateral occipital cortex, and postcentral gyrus, which were not normally addressed in prior studies. In another recent study, cocaine users completed a loss-learning task while being scanned and the results showed that cocaine deprivation increased positive learning and positive prediction error responses (Wang et al., 2019).

Concluding remarks on the task-based fMRI As shown in the preceding sections, task-based fMRI might be used in different settings and with different protocols to

Decision-making deficits in substance use disorders Chapter | 4

make a link between decision-making deficits at the behavioral level and their neurobiological representations. Oldham et al. (2018) investigated several fMRI studies using the Monetary Incentive Delay Task and found that during the anticipation phase several areas including the striatum, amygdala, and thalamus are involved. Another systematic review showed that impairment in six largescale brain networks is involved in different substancerelated behaviors apart from the substance type (reward, habit, salience, executive, memory, and self-directed). Based on this, while the executive and salience networks are most involved in the initiation phase of substance use, reward network deficiencies might play a more prominent role in the next phases (Zilverstand et al., 2018). It is, however, argued that there are some inconsistent findings with regard to the role of specific areas in decisionmaking deficits, for example, the role of the striatum in reward processing (hyperactive or hypoactive) in substance dependents is inconsistent between studies. Luijten et al. (2017) suggested that different levels of activities in striatal regions during different stages of reward-based decisionmaking tasks might be interpreted with different theories (i.e., reward deficiency for the striatal hyperactivation during the anticipatory phase and the learning deficit theory for the striatal hyperactivation during the outcome phase). This could highlight the multidimensional aspect of decision-making deficits and the need to consider several theories to get the most out of experimental findings. Furthermore, one possible suggestion is to have a global view of the brain communications, based on the functional connectivity of different brain networks and using other brain mapping techniques (e.g., Positron Emission Tomography) to tease apart the substance-related neural activities (Sharma, 2017; Suckling and Nestor, 2017). In this regard, in a multimodal imaging study, Vuletic et al. (2018) have shown that frontal, striatal, and limbic regions are involved in methamphetamine use disorder. However, decision-making deficits studies can more benefit from these methodological innovations.

Third dimension: levels of evidence in decision-making studies The body of evidence in decision-making studies in SUD can be understood based on the well-known quality of evidence pyramid under the evidence-based practice paradigm (McGovern and Carroll, 2003). As a guiding tool to understand the different levels of evidence and despite different versions, the general rule is that as one goes down the pyramid, the number of publications in the field increases while the quality of evidence decreases. In this

47

regard, as we have narrowed our review to human studies, we start this section with cross-sectional studies from the bottom of the pyramid. Then, going upward, we have casecontrol studies, cohort studies, randomized controlled studies, and finally metaanalyses and systematic reviews. Now we review each level briefly to form the third dimension of our framework. Cross-sectional studies: These studies are mostly designed to investigate a correlation between a targeting variable (i.e., outcome) and the variables having the potential effect (i.e., independent variables). In these studies, the outcome behavior can be explained based on the parameters involved (unlike case-control studies). In the decision-making deficits studies in SUD, the crosssectional setting is used to investigate if there is a correlation between a variable of interest in decision-making and outcomes related to SUDs, e.g., substance consumption severity, demand, time to replace, risky behaviors, and etc. For instance, Adams et al. (2017) showed that acute alcohol consumption does not have a meaningful relationship with delay discounting, although it is argued that delay discounting has a positive relationship with the intention to quit smoking (Athamneh et al., 2017). In another crosssectional setting, Courtney et al. (2018) showed that reward system activation has a positive relationship with drinking. As an advantage, by utilizing data collection and analysis tools that are easy to implement, cross-sectional studies seem to be able to recruit larger sample sizes. However, these studies cannot be considered as causee effect models. In other words, cross-sectional studies only show that there is a correlation between the target and independent variable(s) but this does not mean that there is a causal relationship between the two. There are potentials toward more causal interpretation from cross-sectional data with quasiexperimental data analysis methods, but, at the end of the day, even these advanced statistical methods cannot claim causality with cross-sectional data (Marinescu et al., 2018; Pearce and Lawlor, 2016). Case-control studies: The main objective of a casecontrol study is to investigate a variable in different groups of participants. Thus, case-control studies are mostly limited to the group of interest and are not going to cover the whole population. In the field of decision-making deficits in SUD, case-control designs are used to show how a variable can characterize a specific decision-making deficit among patients. In addition to that, in case-control studies for DMDs in SUDs, we are looking for differences in DM between healthy controls and people with SUD or within the groups with SUD matched or controlled for other variables such as age and education. However, there is a difference between case-control studies and

48

Cognition and Addiction

historical cohort studies in which we consider DMD as a risk for SUD. Among the recent evidence in case-control studies, Costumero et al. (2017) have shown that reduced activity in functional networks during the processing of nondrug-related rewards can characterize the cocainedependent patients from healthy controls as they have showed diminished modulation in the left frontoparietal network in response to unpredicted erotic pictures. In another case-control study, delay discounting was shown to have the power to distinguish between different groups of smokers (Hofmeyr et al., 2017). Case-control studies (compared to prospective ones) are faster to complete, do not face major dropout, and as a result, will be accomplished with a lower cost. However, there is always the risk of having bias both in sample selection and in interpreting the results. For anyone in the SUD research business, it is clear that finding a control group matched even for demographics is not easy. We know that even controlling for confounding variables with statistical methods in casecontrol studies does not completely solve the problem. Considering these limitations, running case-control studies for DMDs in SUDs can be an important initial step to show if our variable of interest is basically able to differentiate between groups. However, finding relationship between variables of interests and measures of disease severity will increase the quality of evidence. But, eventually, prospective studies will provide a more causal insight to the importance of DMDs in the pathogenesis of SUDs. The aim of prospective studies, which are usually implemented in a longitudinal setting, is either to predict a future variable based on the current state or to monitor a specific decision-making variable in a sample. Here, maintaining a high retention rate in different follow-up phases is a big challenge. Cohort studies: These studies are designed in a naturalistic longitudinal settings and aimed to find the status of the desired outcomes in a period of time. In these studies, participants that already have the attributes under investigation are included. Due to the time-consuming nature of these studies, the cost would be high and there is always the risk of participant withdrawal or change in the research methodology due to the unpredicted conditions. In the field of decision-making deficits in SUD, in a longitudinal observational cohort study, it is shown that lower impulsivity can predict the poorer quality of life in the period of 6e9 weeks after treatment in a population of methamphetamine-dependent individuals (Rubenis et al., 2018). Furthermore, sometimes, the objective is to find if any variable in a sample population can predict a specific behavioral index in the future. In the field of decisionmaking deficits in SUD, for example, it is shown that neural responses to negative outcomes, from a reward

processing view, can predict the effectiveness of a community-based treatment in a sample of individuals with SUD (Forster et al., 2017). In another study, Sebold et al. (2017) showed that alcohol expectancies can predict future alcohol dependence in 48 weeks follow-up. In another setting in this level, the status of a specific variable is monitored during a period of time and in a longitudinal setting. Stewart et al. (2017) showed that transition from stimulant use to stimulant use disorder within a 3-year period in a group of occasionally stimulant users was related to the gradual alteration in the neural substrates of reward processing (i.e., anterior cingulate and insula cortices). Randomized controlled studies: In these studies, normally there are two groups of participants: control group and intervention/treatment group. The main idea here is that participants are divided in these two groups randomly (unlike cohort studies). This will decrease different biases in the study and may lead to discover the effects of intervention/treatment based on the comparison of the results of the two groups. In the field of decision-making deficits in SUD, a number of RCTs have been implemented especially with the aim of evaluation of treatment strategies. For example, in a double blind placebo-controlled study, within a 2 years period, it is shown that chronic nicotine dependence has some effects on the behavioral and neural aspects of cognitive flexibility in smokers with regard to reward sensitivity (Lesage et al., 2017). In another attempt, Kulis et al. (2017) utilized a random controlled setting to test and compare the effectiveness of two substance use prevention programs that were designed and implemented to improve decision-making and drug resistance skills in urban American Indian youth community. Systematic Review and Metaanalyses: Finally, in the peak of the pyramid, we have the systematic reviews and metaanalyses. These settings are used when a significant body of evidence in the lower levels is available. Thus, systematic reviews and metaanalyses are designed to find an answer to the key questions and debating topics in the field. In the field of decision-making deficits in SUD, for example, Barlow et al. (2017) have reviewed the relationship between time-discounting and tobacco smoking. They have examined 69 studies and found that time-discounting is considered to play as a risk in tobacco smoking. In another attempt, Hughes et al. (2018) tried to find if nicotine deprivation can result in less sensitivity to rewards in abstinent smokers. They have found that despite different findings in the field, one cannot assert that abstinence increases consummatory anhedonia (i.e., learning from the rewards). However, abstinence is considered to play a role in anticipatory anhedonia.

Levels of evidence

49

Neuroimaging

Computaonal Models

Self-reports

Decision-making deficits in substance use disorders Chapter | 4

FIGURE 4.2 A three-dimension framework to organize available literature from decision-making deficits studies in substance use disorder.

Lack of replicated longitudinal and interventional evidence for DMDs in SUDs is the one of the main reasons for the current large gap between DM studies and daily clinical practice in addiction medicine.

Three-dimensional matrix of evidence: cognitive functions, assessment paradigm, and levels of evidence As we discussed through this chapter and summarized in Fig. 4.2, each piece of evidence provided through publications addressing DMDs in SUD can be positioned in a three-dimensional matrix. This heuristic framework is helpful to conceptually organize aberrant evidence in the field for newcomers and also provide a map that shows serious gaps in the field. In a summary, the first dimension consists of the cognitive functions of decision-making deficits. Here, we have the value, probability, temporal, and learning functions as described in the initial section. In the next dimension, the assessment paradigms that could be

utilized in investigating the DMDs in SUD. Here, we have reviewed self-reports, behavioral tasks, computational models, and fMRI techniques. The last dimension of the space is the quality of the evidence which includes crosssectional, case-control, cohort, randomized controlled, metaanalyses, and systematic reviews. In this dimension, we also have narrowed our work to the experimental human studies and thus two bottom levels of the pyramid (i.e., reports and opinions and animal trials) are not included in Fig. 4.2. We hope having a research matrix based on the available evidence in the proposed three-dimensional space will help researchers to fill the gaps with quality evidence in the future.

Summary and concluding remarks DMDs are assumed to have a significant role in the initiation, maintenance, and recovery of SUDs. That is why many scholars have attempted to investigate theses deficits from different perspectives and with several research settings. This has resulted in a large body evidence for DMDs

50

Cognition and Addiction

in the field of SUD addiction medicine with many contradictions and gaps. However, it seems that having a simple heuristic framework may help researchers to better position their research projects to fill the gaps in studies of DMDs in SUDs. With this objective, in this chapter, we have introduced a three-dimensional matrix in which recent findings in the field can be positioned and interpreted. As the first dimension of the framework, we have investigated the cognitive functions of the decision-making deficits as temporal functions, value/reward functions, risk/ probability functions, and learning functions. Here, reviewing the recent findings in the field reveals that not all aspects of the decision-making deficits are investigated equally and adequately. The second dimension is addressing the assessment paradigms being used in the studies of the decision-making deficits in SUD. Here, we have reviewed self-reports, behavioral tasks, computational modeling, and fMRI paradigms. Fig. 4.2 shows a unified form of the assessment paradigms with regard to units of analysis, levels of assessment, and levels of targeting constructs. As shown in this figure, self-reports are most used for the higher-level constructs (i.e., personal states, attitudes, and traits) and moving to the right end of the continuum, studying the lower level units of analysis (i.e., behaviors, cognitive processes, and neural circuits) will bring the need for other assessment paradigms such as behavioral tasks, computational models, and neuroimaging methods. Furthermore, a combination of different paradigms also can be utilized to perform second-level assessments where task-based fMRI, model-based fMRI, more theory-driven computational models, and multimodal neuroimaging methodologies are relevant. Currently, finding links between different levels of assessment is a big challenge in the field. Recent findings show that there is not a significant overlap between variances in each two pairs from self-reports, behavioral tasks, neuroimaging data, and actual behavior in daily life (Eisenberg et al., 2018; Thompson et al., 2017). The third dimension of the framework is addressing the levels of evidence in studying DMDs in the field of SUD. This includes a range of studies from cross-sectional settings and case-control studies to cohort longitudinal studies, RCTs and systematic reviews, and metaanalyses. Here, we argue that to achieve a better understandings of the neurocognitive substrates of the DMDs in SUD, there is a need for more prospective studies especially in the form of prediction, monitoring, and interventional settings. This is because we need to monitor the status of the targeting variables and design and implement more effective interventional strategies, in particular for treatment and rehabilitation purposes. As such, while more case-control and cross-sectional studies are needed to find new potential biomarkers of decision-making deficits, causal studies are

needed to validate these potential biomarkers toward their clinical application. Finally, we assert that for the variables related to DMD to play a biomarker role, a number of criteria must be met. First, they must be quantifiable as researchers can use quantitative measures to assess those variables. Second, they have to be replicable. Unless several studies can simulate the same assessment conditions and replicate the experiments, we cannot name our findings as a biomarker. Reliability is another major challenge in all assessment paradigms. Unless a marker could have a reasonable level of reliability in multiple assessments, it is hard to be able to use it as a biomarker to inform clinical decisions. Not only reliability is necessary for the interpretation of the results in longitudinal studies but it is also a must-have feature if we are looking forward to having effective interventions targets. Fourth, we like to have a clear mechanistic relationship between the targeting decision-making variable and the pathological characteristics of SUDs. Finally, decisionmaking variables must be clinically meaningful to be labeled as a biomarker. This means that they must have the power to (1) distinguish between different groups of patients and healthy control groups, (2) show a relationship with SUD severity and drug consumption, (3) have the power to predict future states within a causal relationship, and (4) could be utilized in the assessment of the effectiveness of interventional strategies. Unfortunately, there is no single biomarker available yet not only in DMDs in SUD but also for all other neural and cognitive functions in the entire field of psychiatry. However, there is a significant hope for future breakthroughs, and we hope the heuristic framework introduced in this chapter could contribute a small role to harmonize research in the field to move forward. We conclude this chapter pointing out that there is still no consensus among scholars about the most promising DMDs and their assessment tools to serve as biomarkers in addiction medicine. In this regard, there are two main areas in which further investigations are needed for filling the gaps. First, we need more quality evidence for the current DMD measures. Design and implementation of more prospective studies are necessary to find the longer-term behavior of the variables related to DMDs. Furthermore, using more combined methodologies (e.g., multimodal neuroimaging and computational modeling) could also be used for this objective. Second, we need to design and develop new assessment tools to cover all areas of decisionmaking deficits to shape a better and holistic more comprehensive view of the phenomenon. Here, again multidimensional studies (i.e., addressing more than one aspect of decision-making deficits) might help scholars finding the internal mechanistic interactions of the lower level variables (Fig. 4.3).

Decision-making deficits in substance use disorders Chapter | 4

FIGURE 4.3 Units of analysis and levels of assessment paradigms in decision-making deficits studies in substance use disorder.

51

52

Cognition and Addiction

References Acion, L., Kelmansky, D., van der Laan, M., Sahker, E., Jones, D.S., Arndt, S., 2017. Use of a machine learning framework to predict substance use disorder treatment success. PLoS One 12 (4), e0175383. https://doi.org/10.1371/journal.pone.0175383. Acuff, S.F., Soltis, K.E., Dennhardt, A.A., Borsari, B., Martens, M.P., Murphy, J.G., 2017. Future so bright? Delay discounting and consideration of future consequences predict academic performance among college drinkers. Exp. Clin. Psychopharmacol. vol. 25 (5), 412e421. https://doi.org/10.1037/pha0000143. Adams, J., 2012. Consideration of immediate and future consequences, smoking status, and body mass index. Health Psychol. 31 (2), 260. Adams, S., Attwood, A.S., Munafo, M.R., 2017. Drinking status but not acute alcohol consumption influences delay discounting. Hum. Psychopharmacol. 32 (5) https://doi.org/10.1002/hup.2617. Aharonovich, E., Campbell, A.N.C., Shulman, M., Hu, M.-C., Kyle, T., Winhusen, T., Nunes, E.V., 2018. Neurocognitive profiling of adult treatment seekers enrolled in a clinical trial of a web-delivered intervention for substance use disorders. J. Addict. Med. 12 (2), 99e106. Ahn, W.-Y., Busemeyer, J.R., 2016. Challenges and promises for translating computational tools into clinical practice. Curr. Opin. Behav. Sci. 11, 1e7. Ahn, W.-Y., Busemeyer, J.R., Wagenmakers, E.-J., Stout, J.C., 2008. Comparison of decision learning models using the generalization criterion method. Cogn. Sci. 32 (8), 1376e1402. Ahn, W.Y., Dai, J., Vassileva, J., Busemeyer, J.R., Stout, J.C., 2016. Computational modeling for addiction medicine: from cognitive models to clinical applications. Prog Brain Res. 224, 53e65. Elsevier. Ainslie, G., 1975. Specious reward: a behavioral theory of impulsiveness and impulse control. Psychol. Bull. 82 (4), 463. Aklin, W.M., Lejuez, C.W., Zvolensky, M.J., Kahler, C.W., Gwadz, M., 2005. Evaluation of behavioral measures of risk taking propensity with inner city adolescents. Behav. Res. Therapy 43 (2), 215e228. Alghamdi, W., Stamate, D., Stahl, D., Zamyatin, A., Murray, R., Di Forti, Marta, 2018. A new machine learning framework for understanding the link between cannabis use and first-episode psychosis. Stud. Health Technol. Inform. 248, 9e16. Amini, M., Namdari, K., Shirani, M., 2016. Validity and reliability of eysenck and murray impulsivity scale and divided attention questionnaire (DAQ) in students of university of Isfahan, Iran. J. Fundamentals .Mental Health 18 (3), 125e129. Amlung, M., Vedelago, L., Acker, J., Balodis, I., MacKillop, J., 2017. Steep delay discounting and addictive behavior: a meta-analysis of continuous associations. Addiction 112 (1), 51e62. https://doi.org/ 10.1111/add.13535. Arnett, J., 1994. Sensation seeking: a new conceptualization and a new scale. Personal. Individ. Differ. 16, 289. Arulkadacham, L.J., Richardson, B., Staiger, P.K., Kambouropoulos, N., O’Donnell, Renee, L., Ling, M., 2017. Dissociation between wanting and liking for alcohol and caffeine: a test of the Incentive Sensitisation Theory. J. psychopharmacol. 31 (7), 927e933. https://doi.org/ 10.1177/0269881117711711. Athamneh, L.N., Stein, J.S., Bickel, W.K., 2017. Will delay discounting predict intention to quit smoking? Exp. Clin. Psychopharmacol 25 (4), 273e280. https://doi.org/10.1037/pha0000129. Bae, Y., Yoo, B.W., Lee, J.C., Kim, H.C., 2017. Automated network analysis to measure brain effective connectivity estimated from EEG data of patients with alcoholism. Physiol. Meas. 38 (5), 759.

Baitz, H.A., 2016. Component processes of decision making in persons with substance use disorders. Doctoral dissertation, Arts & Soc. Sci.: Dep. Psychol. Baker, T.E., Lesperance, P., Tucholka, A., Potvin, S., Larcher, K., Zhang, Y., et al., 2017. Reversing the atypical valuation of drug and nondrug rewards in smokers using multimodal neuroimaging. Biol. Psychiatry 82 (11), 819e827. https://doi.org/10.1016/j.biopsych. 2017.01.015. Balogh, K.N., Mayes, L.C., Potenza, M.N., 2013. Risk-taking and decision-making in youth. Relationships to addiction vulnerability. J. Behav. Addict. 2 (1), 1e9. Balzan, R.P., Ephraums, R., Delfabbro, P., Andreou, C., 2017. Beads task vs. box task: the specificity of the jumping to conclusions bias. J. Behav. Ther. Exp. Psychiatry 56, 42e50. Banca, P., Lange, I., Worbe, Y., Howell, N.A., Irvine, M., Harrison, N.A., et al., 2016. Reflection impulsivity in binge drinking. Behavioural and volumetric correlates. Addict. Biol. 21 (2), 504e515. Barkley, R.A., Edwards, G., Laneri, M., Fletcher, K., Metevia, L., 2001. Executive functioning, temporal discounting, and sense of time in adolescents with attention deficit hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD). J. Abnorm. Child Psychol. 29 (6), 541e556. Barlow, P., McKee, M., Reeves, A., Galea, G., Stuckler, D., 2017. Timediscounting and tobacco smoking: a systematic review and network analysis. Int. J. Epidemiol. 46 (3), 860e869. https://doi.org/10.1093/ ije/dyw233. Baron, A., DeWaard, R.J., Galizio, M., 1981. Factor-analytically derived subscales for the Reinforcement Survey Schedule: reinforcer preferences as a function of drug use and sex. Behav. Modif. 5 (2), 203e220. Barratt, E.S., 1959. Anxiety and impulsiveness related to psychomotor efficiency. Percept. Mot. Skills 9 (3), 191e198. Barratt, E.S., 1965. Factor analysis of some psychometric measures of impulsiveness and anxiety. Psychol. Rep. 16 (2), 547e554. Barratt, E.S., 1985. Impulsiveness subtraits: arousal and information processing. Motivation, Emotion, and Personality 5, 137e146. Bechara, A., 2003. Risky business. Emotion, decision-making, and addiction. J. Gambl. Stud. 19 (1), 23e51. Bechara, A., Damasio, A.R., Damasio, H., Anderson, S.W., 1994. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition 50 (1e3), 7e15. Bechara, A., Damasio, H., Tranel, D., Damasio, A.R., 1997. Deciding advantageously before knowing the advantageous strategy. Science 275 (5304), 1293e1295. Becker, A., Gerchen, M.F., Kirsch, M., Hoffmann, S., Kiefer, F., Kirsch, P., 2018. Striatal reward sensitivity predicts therapy-related neural changes in alcohol addiction. Eur. Arch. Psychiatry Clin. Neurosci. 268 (3), 231e242. Beckham, J.C., Adkisson, K.A., Hertzberg, J., Kimbrel, N.A., Budney, A.J., Stephens, R.S., et al., 2018. Mobile contingency management as an adjunctive treatment for co-morbid cannabis use disorder and cigarette smoking. Addict. Behav. 79, 86e92. Beenstock, J., Adams, J., White, M., 2010. The association between time perspective and alcohol consumption in university students: crosssectional study. Eur. J. Public Health 21 (4), 438e443. Bernhardt, N., Nebe, S., Pooseh, S., Sebold, M., Sommer, C., Birkenstock, J., et al., 2017. Impulsive decision making in young adult social drinkers and detoxified alcohol-dependent patients: a crosssectional and longitudinal study. Alcohol Clin. Exp. Res. 41 (10), 1794e1807. https://doi.org/10.1111/acer.13481.

Decision-making deficits in substance use disorders Chapter | 4

Berridge, K.C., Robinson, T.E., 1998. What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? Brain Res. Rev. 28 (3), 309e369. Berridge, K.C., Robinson, T.E., 2016. Liking, wanting, and the incentivesensitization theory of addiction. Am. Psychol. 71 (8), 670. Beylergil, S.B., Beck, A., Deserno, L., Lorenz, R.C., Rapp, M.A., Schlagenhauf, F., et al., 2017. Dorsolateral prefrontal cortex contributes to the impaired behavioral adaptation in alcohol dependence. NeuroImage. Clin. 15, 80e94. Bickel, W.K., Jarmolowicz, D.P., Mueller, E.T., Koffarnus, M.N., Gatchalian, K.M., 2012. Excessive discounting of delayed reinforcers as a trans-disease process contributing to addiction and other diseaserelated vulnerabilities: emerging evidence. Pharmacol. Therapeut. 134 (3), 287e297. Bickel, W.K., Koffarnus, M.N., Moody, L., Wilson, A.G., 2014. The behavioral-and neuro-economic process of temporal discounting: a candidate behavioral marker of addiction. Neuropharmacology 76, 518e527. Bickel, W.K., MacKillop, J., Madden, G.J., Odum, A.L., Yi, R., 2015. Experimental manipulations of delay discounting & related processes: an introduction to the special issue. J. Exp. Anal. Behav. 103 (1), 1e9. https://doi.org/10.1002/jeab.133. Bickel, W.K., Moody, L.N., Eddy, C.R., Franck, C.T., 2017a. Neurocognitive dysfunction in addiction: testing hypotheses of diffuse versus selective phenotypic dysfunction with a classification-based approach. Exp. Clin. Psychopharmacol 25 (4), 322. Bickel, W.K., Moody, L.N., Eddy, C.R., Franck, C.T., 2017b. Neurocognitive dysfunction in addiction: testing hypotheses of diffuse versus selective phenotypic dysfunction with a classification-based approach. Exp. Clin. Psychopharmacol 25 (4), 322e332. https:// doi.org/10.1037/pha0000115. Biernacki, K., McLennan, S.N., Terrett, G., Labuschagne, I., Rendell, P.G., 2016. Decision-making ability in current and past users of opiates: a meta-analysis. Neurosci. Biobehav. Rev. 71, 342e351. Bischoff-Grethe, A., Connolly, C.G., Jordan, S.J., Brown, G.G., Paulus, M.P., Tapert, S.F., et al., 2017. Altered reward expectancy in individuals with recent methamphetamine dependence. J. Psychopharmacol. 31 (1), 17e30. Blair, M.A., Stewart, J.L., May, A.C., Reske, M., Tapert, S.F., Paulus, M.P., 2018. Blunted frontostriatal blood oxygen levele dependent signals predict stimulant and marijuana use. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3 (11), 947e958. Bogg, T., Fukunaga, R., Finn, P.R., Brown, J.W., 2012. Cognitive control links alcohol use, trait disinhibition, and reduced cognitive capacity. Evidence for medial prefrontal cortex dysregulation during rewardseeking behavior. Drug Alcohol Depend. 122 (1e2), 112e118. Brand, M., Fujiwara, E., Borsutzky, S., Kalbe, E., Kessler, J., Markowitsch, H.J., 2005. Decision-making deficits of korsakoff patients in a new gambling task with explicit rules: associations with executive functions. Neuropsychology 19 (3), 267. Brand, M., Labudda, K., Markowitsch, H.J., 2006. Neuropsychological correlates of decision-making in ambiguous and risky situations. Neural Netw. 19 (8), 1266e1276. Brand, M., Recknor, E.C., Grabenhorst, F., Bechara, A., 2007. Decisions under ambiguity and decisions under risk: correlations with executive functions and comparisons of two different gambling tasks with implicit and explicit rules. J. Clin. Exp. Neuropsychol. 29 (1), 86e99.

53

Brand, M., Roth-Bauer, M., Driessen, M., Markowitsch, H.J., 2008. Executive functions and risky decision-making in patients with opiate dependence. Drug Alcohol Depend. 97 (1e2), 64e72. Brevers, D., Bechara, A., Cleeremans, A., Noël, X., 2013. Iowa Gambling Task (IGT): twenty years afteregambling disorder and IGT. Front. Psychol. 4, 665. Brown, S.A., Goldman, M.S., Inn, A., Anderson, L.R., 1980. Expectations of reinforcement from alcohol: their domain and relation to drinking patterns. J. Consult. Clin. Psychol. 48 (4), 419. Brown, S.A., Goldman, M.S., Christiansen, B.A., 1985. Do alcohol expectancies mediate drinking patterns of adults? J. Consult. Clin. Psychol. 53 (4), 512. Bruijnzeel, A.W., 2017. Reward processing and smoking. Nicotine Tob. Res. 19 (6), 661e662. Büchel, C., Peters, J., Banaschewski, T., Bokde, A.L.W., Bromberg, U., Conrod, P.J., et al., 2017. Blunted ventral striatal responses to anticipated rewards foreshadow problematic drug use in novelty-seeking adolescents. Nat. Commun. 8, 14140. Buelow, M.T., Suhr, J.A., 2009. Construct validity of the Iowa gambling task. Neuropsychol. Rev. 19 (1), 102e114. Bulley, A., Gullo, M.J., 2017. The influence of episodic foresight on delay discounting and demand for alcohol. Addict. Behav. 66, 1e6. https:// doi.org/10.1016/j.addbeh.2016.11.003. Busemeyer, J.R., Stout, J.C., 2002. A contribution of cognitive decision models to clinical assessment: decomposing performance on the Bechara gambling task. Psychol. Assess. 14 (3), 253. Cáceda, R., Nemeroff, C.B., Harvey, P.D., 2014. Toward an understanding of decision making in severe mental illness. J. Neuropsychiat. Clin. Neurosc. 26 (3), 196e213. Carbia, C., Cadaveira, F., Caamaño-Isorna, F., Holguín, R., Socorro, Corral, M., 2017. Binge drinking trajectory and decision-making during late adolescence. Gender and developmental differences. Front. Psychol. 8, 783. Cassidy, C.M., Lepage, M., Harvey, P.-O., Malla, A., 2012. Cannabis use and anticipatory pleasure as reported by subjects with early psychosis and community controls. Schizophr. Res. 137 (1e3), 39e44. Cautela, J.R., 1977. Behavior Analysis Forms for Clinical Intervention. Research Press. [1](1983). Cautela, J.R., Kastenbaum, R., 1967. A reinforcement survey schedule for use in therapy, training, and research. Psychol. Rep. 20, 1115e1130 (3_suppl). Cautela, J., Lynch, E., 1983. Reinforcement survey schedules: scoring, administration, and completed research. Psychol. Rep. 53 (2), 447e465. Cherek, D.R., Moeller, F.G., Dougherty, D.M., Rhoades, H., 1997. Studies of violent and nonviolent male parolees: II. Laboratory and psychometric measurements of impulsivity. Biol. Psychiat. 41 (5), 523e529. Chiou, W.-B., Wu, W.-H., 2017. Episodic future thinking involving the nonsmoking self can induce lower discounting and cigarette consumption. J. Stud. Alcohol Drugs 78 (1), 106e112. Clark, L., Robbins, T.W., Ersche, K.D., Sahakian, B.J., 2006. Reflection impulsivity in current and former substance users. Biol. Psychiat. 60 (5), 515e522. Claus, E.D., Kiehl, K.A., Hutchison, K.E., 2011. Neural and behavioral mechanisms of impulsive choice in alcohol use disorder. Alcohol Clin. Exp. Res. 35 (7), 1209e1219.

54

Cognition and Addiction

Claus, E.D., Ewing, S.W.F., Magnan, R.E., Montanaro, E., Hutchison, K.E., Bryan, A.D., 2018. Neural mechanisms of risky decision making in adolescents reporting frequent alcohol and/or marijuana use. Brain Imaging Behav. 12 (2), 564e576. Coates, J.M., Gullo, M.J., Feeney, G.F.X., Young, R.M., Connor, J.P., 2018. A randomized trial of personalized cognitive-behavior therapy for alcohol use disorder in a public health clinic. Front. Psychiatry 9. Connolly, C.G., Bischoff-Grethe, A., Jordan, S.J., Woods, S.P., Ellis, R.J., Paulus, M.P., et al., 2014. Altered functional response to risky choice in HIV infection. PloS One 9 (10), e111583. Coskunpinar, A., Dir, A.L., Cyders, M.A., 2012. Measurement of constructs using self-report and behavioral lab tasks. is there overlap in nomothetic span and construct representation for impulsivity? Clin Psychol Rev. Costa, P.T., McCrae, R.R., 1990. Personality in Adulthood. Guilford Press, New York. Costumero, V., Bustamante, J.C., Rosell-Negre, P., Fuentes, P., Llopis, J.J., Ávila, C., Barrós-Loscertales, A., 2017. Reduced activity in functional networks during reward processing is modulated by abstinence in cocaine addicts. Addict. Biol. 22 (2), 479e489. https:// doi.org/10.1111/adb.12329. Courtney, A.L., Rapuano, K.M., Sargent, J.D., Heatherton, T.F., Kelley, W.M., 2018. Reward system Activation in response to alcohol advertisements predicts college drinking. J. Stud. Alcohol Drugs 79 (1), 29e38. Cousijn, J., Wiers, R.W., Ridderinkhof, K.R., van den Brink, W., Veltman, D.J., Porrino, L.J., Goudriaan, A.E., 2013. Individual differences in decision making and reward processing predict changes in cannabis use. A prospective functional magnetic resonance imaging study. Addict. Biol. 18 (6), 1013e1023. Crowley, T.J., Raymond, K.M., Mikulich-Gilbertson, S.K., Thompson, L.L., Lejuez, C.W., 2006. A risk-taking “set” in a novel task among adolescents with serious conduct and substance problems. J. Am. Acad. Child Adolesc. Psychiatry 45 (2), 175e183. Cservenka, A., Courtney, K.E., Ghahremani, D.G., Hutchison, K.E., Ray, L.A., 2017. Development, initial testing and challenges of an ecologically valid reward prediction error fMRI task for alcoholism. Alcohol Alcohol 52 (5), 617e624. Cyders, M.A., Coskunpinar, A., 2011. Measurement of constructs using self-report and behavioral lab tasks: is there overlap in nomothetic span and construct representation for impulsivity? Clin. Psychol. Rev. 31 (6), 965e982. Cyders, M.A., Smith, G.T., Spillane, N.S., Fischer, S., Annus, A.M., Peterson, C., 2007. Integration of impulsivity and positive mood to predict risky behavior: development and validation of a measure of positive urgency. Psychol. Assess. 19 (1), 107. Cyders, M.A., Littlefield, A.K., Coffey, S., Karyadi, K.A., 2014. Examination of a short English version of the UPPS-P impulsive behavior scale. Addict. Behav. 39 (9), 1372e1376. Dahne, J., Richards, J.M., Ernst, M., MacPherson, L., Lejuez, C.W., 2013. Assessment of risk taking in addiction research. In: The WileyBlackwell Handbook of Addiction Psychopharmacology, pp. 209e231. Dai, J., Kerestes, R., Upton, D.J., Busemeyer, J.R., Stout, J.C., 2015. An improved cognitive model of the Iowa and Soochow Gambling Tasks with regard to model fitting performance and tests of parameter consistency. Front. Psychol. 6, 229.

Daniel, T.O., Stanton, C.M., Epstein, L.H., 2013. The future is now. Reducing impulsivity and energy intake using episodic future thinking. Psychological science 24 (11), 2339e2342. Daw, N.D., 2011. Trial-by-trial data analysis using computational models. In: Decision making, affect, and learning: Attention and performance XXIII, 23, pp. 3e38. Daw, N.D., Niv, Y., Dayan, P., 2005. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nat. Neurosci. 8 (12), 1704. Dennhardt, A.A., Yurasek, A.M., Murphy, J.G., 2015a. Change in delay discounting and substance reward value following a brief alcohol and drug use intervention. J. Exp. Anal. Behav. 103 (1), 125e140. https:// doi.org/10.1002/jeab.121. Dennhardt, A.A., Yurasek, A.M., Murphy, J.G., 2015b. Change in delay discounting and substance reward value following a brief alcohol and drug use intervention. J. Exp. Anal. Behav. 103 (1), 125e140. Depue, R.A., Collins, P.F., 1999. Neurobiology of the structure of personality: dopamine, facilitation of incentive motivation, and extraversion. Behav. Brain Sci. 22 (3), 491e517. Deserno, L., Beck, A., Huys, Q.J.M., Lorenz, R.C., Buchert, R., Buchholz, H.-G., et al., 2015. Chronic alcohol intake abolishes the relationship between dopamine synthesis capacity and learning signals in the ventral striatum. Eur. J. Neurosci. 41 (4), 477e486. Djamshidian, A., O’Sullivan, Sean, S., Sanotsky, Y., Sharman, S., Matviyenko, Y., Foltynie, T., et al., 2012. Decision making, impulsivity, and addictions. Do Parkinson’s disease patients jump to conclusions? Mov. Disord. 27 (9), 1137e1145. Dudley, R.E.J., John, C.H., Young, A.W., Over, D.E., 1997. The effect of self-referent material on the reasoning of people with delusions. Br. J. Clin. Psychol. 36 (4), 575e584. Eisenberg, I.W., Bissett, P., Enkavi, A.Z., Li, J., MacKinnon, D., Marsch, L., Poldrack, R., 2018. Uncovering Mental Structure through Data-Driven Ontology Discovery. Ekhtiari, H., Safaei, H., Djavid, E., Gholamreza, Atefvahid, M.K., Edalati, H., Mokri, A., 2008. Reliability and validity of Persian versions of eysenck, barratt, dickman and zuckerman questionnaires in assessing risky and impulsive behaviors. Iran. J. Psychiatry Clin. Psychol. 14 (3), 326e336. Ekhtiari, H., Victor, T.A., Paulus, M.P., 2017. Aberrant decision-making and drug addictiondhow strong is the evidence? Curr. Opin. Behav. Sci. 13, 25e33. Elton, A., Smith, C.T., Parrish, M.H., Boettiger, C.A., 2017. Neural systems underlying individual differences in intertemporal decisionmaking. J. Cogn. Neurosci. 29 (3), 467e479. https://doi.org/ 10.1162/jocn_a_01069. Engelmann, J.B., Maciuba, B., Vaughan, C., Paulus, M.P., Dunlop, B.W., 2013. Posttraumatic stress disorder increases sensitivity to long term losses among patients with major depressive disorder. PloS One 8 (10), e78292. Estle, S.J., Green, L., Myerson, J., Holt, D.D., 2007. Discounting of monetary and directly consumable rewards. Psychol. Sci. 18 (1), 58e63. https://doi.org/10.1111/j.1467-9280.2007.01849.x. Eysenck, H.J., 1947. Student selection by means of psychological testsda critical survey. Br. J. Educ. Psychol. 17 (1), 20e39. Eysenck, S.B.G., Eysenck, H.J., 1969. Scores on three personality variables as a function of age, sex and social class. Br. J. Soc. Clin. Psychol. 8 (1), 69e76.

Decision-making deficits in substance use disorders Chapter | 4

Eysenck, H.J., Eysenck, S.B., Giuletta, 1975. Manual of the Eysenck Personality Questionnaire (Junior and Adult): Hodder and Stoughton. Eysenck, H.J., Eysenck, S.B.G., 1976. Eysenck Personality Questionnaire: Educational and Industrial Testing Service. Eysenck, S.B.G., Eysenck, H.J., 1978. Impulsiveness and venturesomeness. Their position in a dimensional system of personality description. Psychol. Rep. 43 (3_Suppl. l), 1247e1255. Eysenck, S.B.G., Easting, G., Pearson, P.R., 1984. Age norms for impulsiveness, venturesomeness and empathy in children. Personal. Individ. Differ. 5 (3), 315e321. Eysenck, H.J., Barrett, P., Eysenck, S.B.G., 1985a. Indices of factor comparison for homologous and non-homologous personality scales in 24 different countries. Personal. Individ. Differ. 6 (4), 503e504. Eysenck, S.B.G., Pearson, P.R., Easting, G., Allsopp, J.F., 1985b. Age norms for impulsiveness, venturesomeness and empathy in adults. Personal. Individ. Differ. 6 (5), 613e619. Farris, S.G., Aston, E.R., Abrantes, A.M., Zvolensky, M.J., 2017. Tobacco demand, delay discounting, and smoking topography among smokers with and without psychopathology. Drug Alcohol Depend. 179, 247e253. https://doi.org/10.1016/j.drugalcdep.2017.06.042. Fecteau, S., Knoch, D., Fregni, F., Sultani, N., Boggio, P., PascualLeone, A., 2007. Diminishing risk-taking behavior by modulating activity in the prefrontal cortex: a direct current stimulation study. J. Neurosci. 27 (46), 12500e12505. Fecteau, S., Agosta, S., Hone-Blanchet, A., Fregni, F., Boggio, P., Ciraulo, D., Pascual-Leone, A., 2014. Modulation of smoking and decision-making behaviors with transcranial direct current stimulation in tobacco smokers: a preliminary study. Drug Alcohol Depend. 140, 78e84. https://doi.org/10.1016/j.drugalcdep.2014.03.036. Fernie, G., Cole, J.C., Goudie, A.J., Field, M., 2010. Risk-taking but not response inhibition or delay discounting predict alcohol consumption in social drinkers. Drug Alcohol Depend. 112 (1e2), 54e61. Floresco, S.B., Whelan, J.M., 2009. Perturbations in different forms of cost/benefit decision making induced by repeated amphetamine exposure. Psychopharmacology 205 (2), 189e201. Forster, S.E., Finn, P.R., Brown, J.W., 2016. A preliminary study of longitudinal neuroadaptation associated with recovery from addiction. Drug Alcohol Depend. 168, 52e60. Forster, S.E., Finn, P.R., Brown, J.W., 2017. Neural responses to negative outcomes predict success in community-based substance use treatment. Addiction 112 (5), 884e896. https://doi.org/10.1111/add. 13734. Foster, D.W., Neighbors, C., Pai, A., 2015. Decisional balance: alcohol decisional balance intervention for heavy drinking undergraduates. Subst. Use Misuse 50 (13), 1717e1727. https://doi.org/10.3109/ 10826084.2015.1036883. Francis, L.J., Lewis, C.A., Ziebertz, H., 2006. The Short-form Revised Eysenck Personality Questionnaire (EPQ-S). A German edition. Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., Pezzulo, G., 2017. Active inference: a process theory. Neural Computation 29 (1), 1e49. Fukunaga, R., Brown, J.W., Bogg, T., 2012. Decision making in the Balloon Analogue Risk Task (BART). Anterior cingulate cortex signals loss aversion but not the infrequency of risky choices. Cognit. Affect Behav. Neurosci. 12 (3), 479e490. Fukunaga, R., Bogg, T., Finn, P.R., Brown, J.W., 2013. Decisions during negatively-framed messages yield smaller risk-aversion-related brain activation in substance-dependent individuals. Psychol. Addict. Behav. 27 (4), 1141.

55

Fukunaga, R., Purcell, J.R., Brown, J.W., 2018. Discriminating formal representations of risk in anterior cingulate cortex and inferior frontal gyrus. Front. Neurosci. 12. Galandra, C., Basso, G., Cappa, S., Canessa, N., 2017. The alcoholic brain: neural bases of impaired reward-based decision-making in alcohol use disorders. Neurol. Sci. 1e13. Gansler, D.A., Jerram, M.W., Vannorsdall, T.D., Schretlen, D.J., 2011. Does the Iowa Gambling Task measure executive function? Arch. Clin. Neuropsychol. 26 (8), 706e717. García-García, I., Zeighami, Y., Dagher, A., 2017. Reward prediction errors in drug addiction and Parkinson’s disease. From neurophysiology to neuroimaging. Curr. Neurol. Neurosci. Rep. 17 (6), 46. García-Marchena, N., Ladrón de Guevara-Miranda, D., Pedraz, M., Araos, P.F., Rubio, G., Ruiz, J.J., et al., 2018. Higher impulsivity as a Distinctive Trait of severe cocaine addiction among individuals Treated for cocaine or alcohol Use Disorders. Front. Psychiatry 9, 26. Gard, D.E., Gard, M.G., Kring, A.M., John, O.P., 2006. Anticipatory and consummatory components of the experience of pleasure: a scale development study. J. Res. Personal. 40 (6), 1086e1102. Garety, P.A., Hemsley, Wessely, S.M.R.C., 1991. Reasoning in deluded schizophrenic and paranoid patients: biases in performance on a probabilistic inference task. J. Nerv. Ment. Dis. 179 (4), 194e201. Garfield, J.B.B., Cotton, S.M., Lubman, D.I., 2016. Psychometric properties, validity, and reliability of the Temporal Experience of Pleasure Scale state version in an opioid-dependent sample. Drug Alcohol Depend. 161, 238e246. Garfield, J.B.B., Cotton, S.M., Allen, N.B., Cheetham, A., Kras, M., Yücel, M., Lubman, D.I., 2017. Evidence that anhedonia is a symptom of opioid dependence associated with recent use. Drug Alcohol Depend. 177, 29e38. Garon, N., Moore, C., Waschbusch, D.A., 2006. Decision making in children with ADHD only, ADHD-anxious/depressed, and control children using a child version of the Iowa Gambling Task. J. Atten. Disord. 9 (4), 607e619. Garrison, K.A., Yip, S.W., Balodis, I.M., Carroll, K.M., Potenza, M.N., Krishnan-Sarin, Suchitra, 2017. Reward-related frontostriatal activity and smoking behavior among adolescents in treatment for smoking cessation. Drug Alcohol Depend. 177, 268e276. https://doi.org/ 10.1016/j.drugalcdep.2017.03.035. Gilmore, C.S., Dickmann, P.J., Nelson, B.G., Lamberty, G.J., Lim, K.O., 2018. Transcranial Direct Current Stimulation (tDCS) paired with a decision-making task reduces risk-taking in a clinically impulsive sample. Brain stimul. 11 (2), 302e309. Goldstein, S., Naglieri, J.A., 2011. Encyclopedia of Child Behavior and Development. Springer ScienceþBusiness Media (Springer reference, New York. Goldstein, R.Z., Woicik, P.A., Moeller, S.J., Telang, F., Jayne, M., Wong, C., et al., 2010. Liking and wanting of drug and non-drug rewards in active cocaine users: the STRAP-R questionnaire. J. Psychopharmacol. 24 (2), 257e266. Gowin, J.L., Stewart, J.L., May, A.C., Ball, T.M., Wittmann, M., Tapert, S.F., Paulus, M.P., 2014. Altered cingulate and insular cortex activation during risk-taking in methamphetamine dependence: losses lose impact. Addiction 109 (2), 237e247. Gowin, J.L., May, A.C., Wittmann, M., Tapert, S.F., Paulus, M.P., 2017. Doubling down: increased risk-taking behavior following a loss by individuals with cocaine use disorder is associated with striatal and anterior cingulate dysfunction. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2 (1), 94e103.

56

Cognition and Addiction

Gowin, J.L., Sloan, M.E., Ramchandani, V.A., Paulus, M.P., Lane, S.D., 2018. Differences in decision-making as a function of drug of choice. Pharmacol., Biochem., Behav. 164, 118e124. https://doi.org/10.1016/ j.pbb.2017.09.007. Grant, J.E., Chamberlain, S.R., 2018. Caffeine’s influence on gambling behavior and other types of impulsivity. Addict. Behav. 76, 156e160. https://doi.org/10.1016/j.addbeh.2017.08.007. Gray, J.C., MacKillop, J., 2015. Impulsive delayed reward discounting as a genetically-influenced target for drug abuse prevention: a critical evaluation. Front. Psychol. 6, 1104. https://doi.org/10.3389/fpsyg.2015. 01104. Green, L., Myerson, J., 1996. Exponential versus hyperbolic discounting of delayed outcomes: risk and waiting time. Am. Zool. 36 (4), 496e505. Green, L., Fry, A.F., Myerson, J., 1994. Discounting of delayed rewards: a life-span comparison. Psychol. Sci. 5 (1), 33e36. Green, L., Myerson, J., Lichtman, D., Rosen, S., Fry, A., 1996. Temporal discounting in choice between delayed rewards: the role of age and income. Psychol. Aging 11 (1), 79. Green, L., Myerson, J., McFadden, E., 1997. Rate of temporal discounting decreases with amount of reward. Mem. Cogn. 25 (5), 715e723. Green, L., Myerson, J., Ostaszewski, P., 1999. Amount of reward has opposite effects on the discounting of delayed and probabilistic outcomes. J. Exp. Psychol. Learn. Mem. Cogn. 25 (2), 418. Grodin, E.N., Cortes, C.R., Spagnolo, P.A., Momenan, R., 2017. Structural deficits in salience network regions are associated with increased impulsivity and compulsivity in alcohol dependence. Drug Alcohol Depend. 179, 100e108. https://doi.org/10.1016/j.drugalcdep. 2017.06.014. Gullo, M.J., Stieger, A.A., 2011. Anticipatory stress restores decisionmaking deficits in heavy drinkers by increasing sensitivity to losses. Drug Alcohol Depend. 117 (2e3), 204e210. Gunn, R.L., Skalski, L., Metrik, J., 2017. Expectancy of impairment attenuates marijuana-induced risk taking. Drug Alcohol Depend. 178, 39e42. https://doi.org/10.1016/j.drugalcdep.2017.04.027. Guo, H., Zhang, Z., Da, S., Sheng, X., Zhang, X., 2018. High-definition transcranial direct current stimulation (HD-tDCS) of left dorsolateral prefrontal cortex affects performance in Balloon Analogue Risk Task (BART). Brain Behav. 8 (2), e00884. Hallgren, K.A., Greenfield, B.L., Ladd, B.O., 2016. Psychometric properties of the adolescent reinforcement survey schedule-alcohol use version with college student drinkers. Subst. Use Misuse 51 (7), 812e822. Hamdan-Mansour, Ayman, M., Mahmoud, K.F., Al, S., Ahmad, N., Arabiat, D.H., 2018. Impulsivity and sensation-seeking personality traits as predictors of substance use among university students. J. Psychosoc. Nurs. Ment. Health Serv. 56 (1), 57e63. https://doi.org/ 10.3928/02793695-20170905-04. Harries, M.D., Redden, S.A., Grant, J.E., 2018. An analysis of treatmentseeking behavior in individuals with gambling disorder. J. Gambl. Stud. 34 (3), 999e1012. Hauser, T.U., Will, G.-J., Dubois, M., Dolan, R.J., 2019. Annual research review: developmental computational psychiatry. J. Child Psychol. Psychiatry 60 (4), 412e426. Heaven, P.C.L., 1991. Venturesomeness, impulsiveness, and Eysenck’s personality dimensions. A study among Australian adolescents. J. Genet. Psychol. 152 (1), 91e99. Hefner, A.D., Gordijn, C.M., 2018. Personality Traits Differences Among Marijuana, Alcohol, and Other Substance Users. Brenau University.

Heinz, A., Deserno, L., Zimmermann, U.S., Smolka, M.N., Beck, A., Schlagenhauf, F., 2017. Targeted intervention: computational approaches to elucidate and predict relapse in alcoholism. Neuroimage 151, 33e44. https://doi.org/10.1016/j.neuroimage.2016.07.055. Hendershot, C.S., Wardell, J.D., Vandervoort, J., McPhee, M.D., Keough, M.T., Quilty, L.C., 2018. Randomized trial of working memory training as an adjunct to inpatient substance use disorder treatment. Psychol. Addict. Behav. 32 (8), 861. Herman, A.M., Critchley, H.D., Duka, T., 2018. Binge drinking is associated with attenuated frontal and parietal activation during successful response inhibition in fearful context. Eur. J. Neurosci. 1e14. Hobkirk, A.L., Bell, R.P., Utevsky, A.V., Huettel, S., Meade, C.S., 2019. Reward and executive control network resting-state functional connectivity is associated with impulsivity during reward-based decision making for cocaine users. Drug Alcohol Depend. 194, 32e39. Hofmeyr, A., Monterosso, J., Dean, A.C., Morales, A.M., Bilder, R.M., Sabb, F.W., London, E.D., 2017. Mixture models of delay discounting and smoking behavior. Am. J. Drug Alcohol Abuse 43 (3), 271e280. https://doi.org/10.1080/00952990.2016.1198797. Holmes, G.R., Heckel, R.V., Chestnut, E., Harris, N., Cautela, J., 1987. Factor analysis of the adolescent reinforcement survey schedule (ARSS) with college freshmen. J. Clin. Psychol. 43 (4), 386e390. Holmes, G.R., Sakano, Y., Cautela, J., Holmes, G.L., 1991. Comparison of factor-analyzed adolescent reinforcement survey schedule (ARSS) responses from Japanese and American adolescents. J. Clin. Psychol. 47 (6), 749e755. Horan, W.P., Kring, A.M., Blanchard, J.J., 2005. Anhedonia in schizophrenia: a review of assessment strategies. Schizophr. Bull. 32 (2), 259e273. Hoyle, R.H., Stephenson, M.T., Palmgreen, P., Lorch, E.P., Donohew, R.L., 2002. Reliability and validity of a brief measure of sensation seeking. Personal. Individ. Differ. 32 (3), 401e414. Hughes, J.R., Budney, A.J., Muellers, S.R., Lee, D.C., Callas, P.W., Sigmon, S.C., et al., 2017a. Does tobacco abstinence decrease reward sensitivity? A human laboratory test. Nicotine Tob. Res. 19 (6), 677e685. https://doi.org/10.1093/ntr/ntw204. Hughes, J.R., Callas, P.W., Priest, J.S., Etter, J.-F., Budney, A.J., Sigmon, S.C., 2017b. Development of a self-report measure of reward sensitivity: a test in current and former smokers. Nicotine Tob. Res. 19 (6), 723e728. Hughes, J.R., Klemperer, E.M., Peasley-Miklus, Catherine, 2018. Possible new symptoms of tobacco withdrawal II. Anhedoniada systematic review. Nicotine Tob. Res. nty171. Humphries, M.A., Bruno, R., Karpievitch, Y., Wotherspoon, S., 2015. The expectancy valence model of the Iowa Gambling Task: can it produce reliable estimates for individuals? J. Math. Psychol. 64, 17e34. Huys, Q.J.M., Tobler, P.N., Hasler, G., Flagel, S.B., 2014. The role of learning-related dopamine signals in addiction vulnerability. Prog. Brain Res. 211, 31e77. Elsevier. Huys, Q.J.M., Maia, T.V., Frank, M.J., 2016. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat. Neurosci. 19 (3), 404. Kagan, J., 1966. Reflection-impulsivity. The generality and dynamics of conceptual tempo. J. Abnorm. Psychol. 71 (1), 17. Kahler, C.W., Lechner, W.J., MacGlashan, J., Wray, T.B., Littman, M.L., 2017. Initial progress toward development of a voice-based computerdelivered motivational intervention for heavy drinking college students: an experimental study. JMIR Mental Health 4 (2).

Decision-making deficits in substance use disorders Chapter | 4

Kaplan, B.A., Amlung, M., Reed, D.D., Jarmolowicz, D.P., McKerchar, T.L., Lemley, S.M., 2016. Automating scoring of delay discounting for the 21-and 27-item monetary choice questionnaires. Behav. Analyst 39 (2), 293e304. Kastenbaum, R., 1961. The dimensions of future time perspective, an experimental analysis. J. Gen. Psychol. 65 (2), 203e218. Keramati, M., Dezfouli, A., Piray, P., 2011. Speed/accuracy trade-off between the habitual and the goal-directed processes. PLoS Comput. Biol. 7 (5), e1002055. Khemiri, L., Brynte, C., Stunkel, A., Klingberg, T., JayaramLindström, N., 2019. Working memory training in alcohol use disorder. A randomized controlled trial. Alcohol Clin. Exp. Res. 43 (1), 135e146. Kirby, K.N., 2009. One-year temporal stability of delay-discount rates. Psychon. Bull Rev. 16 (3), 457e462. Kirby, K.N., Marakovic, N.N., 1996. Delay-discounting probabilistic rewards: rates decrease as amounts increase. Psychon. Bull Rev. 3 (1), 100e104. Kirby, K.N., Petry, N.M., Bickel, W.K., 1999. Heroin addicts have higher discount rates for delayed rewards than non-drug-using controls. J. Exp. Psychol. Gen. 128 (1), 78. Kobiella, A., Ripke, S., Kroemer, N.B., Vollmert, C., Vollstadt-Klein, S., Ulshofer, D.E., Smolka, M.N., 2014. Acute and chronic nicotine effects on behaviour and brain activation during intertemporal decision making. Addict. Biol. 19 (5), 918e930. https://doi.org/10.1111/ adb.12057. Koffarnus, M.N., Bickel, W.K., 2014. A 5-trial adjusting delay discounting task: accurate discount rates in less than one minute. Exp. Clin. Psychopharmacol. 22 (3), 222. Koffarnus, M.N., Kaplan, B.A., 2018. Clinical models of decision making in addiction. Pharmacol., Biochem., Behav. 164, 71e83. https:// doi.org/10.1016/j.pbb.2017.08.010. Koffarnus, M.N., Jarmolowicz, D.P., Mueller, E.T., Bickel, W.K., 2013. Changing delay discounting in the light of the competing neurobehavioral decision systems theory: a review. J. Exp. Anal Behav. 99 (1), 32e57. https://doi.org/10.1002/jeab.2. Kogachi, S., Chang, L., Alicata, D., Cunningham, E., Ernst, T., 2017. Sex differences in impulsivity and brain morphometry in methamphetamine users. Brain Struct. Funct. 222 (1), 215e227. https://doi.org/ 10.1007/s00429-016-1212-2. Kohno, M., Morales, A.M., Ghahremani, D.G., Hellemann, G., London, E.D., 2014. Risky decision making, prefrontal cortex, and mesocorticolimbic functional connectivity in methamphetamine dependence. JAMA psychiatry 71 (7), 812e820. Kooij, Dorien, T.A.M., Kanfer, R., Betts, M., Rudolph, C.W., 2018. Future time perspective: a systematic review and meta-analysis. J. Appl. Psychol. 103 (8), 867. Kovács, I., Richman, M., Janka, Z., Maraz, A., Andó, B., 2017. Decision making measured by the Iowa Gambling Task in alcohol use disorder and gambling disorder: a systematic review and meta-analysis. Drug Alcohol Depend. 181, 152e161. Kruschwitz, J.D., Simmons, A.N., Flagan, T., Paulus, M.P., 2012. Nothing to lose: processing blindness to potential losses drives thrill and adventure seekers. Neuroimage 59 (3), 2850e2859. Kulis, S.S., Ayers, S.L., Harthun, M.L., 2017. Substance use prevention for urban American Indian youth: a efficacy trial of the culturally adapted living in 2 worlds program. J. Prim. Prevent. 38 (1e2), 137e158. https://doi.org/10.1007/s10935-016-0461-4.

57

Kumar, R., Kumar, K.J., Benegal, V., 2018. Cognitive and behavioural dispositions in offspring at high risk for alcoholism. Asian J. Psychiat. 35, 38e44. Kwako, L.E., Momenan, R., Grodin, E.N., Litten, R.Z., Koob, G.F., Goldman, D., 2017. Addictions Neuroclinical Assessment: a reverse translational approach. Neuropharmacology 122, 254e264. https:// doi.org/10.1016/j.neuropharm.2017.03.006. Lane, S.D., Yechiam, E., Busemeyer, J.R., 2006. Application of a computational decision model to examine acute drug effects on human risk taking. Exp. Clin. Psychopharmacol 14 (2), 254. Larney, S., Hickman, M., Fiellin, D.A., Dobbins, T., Nielsen, S., Jones, N.R., et al., 2018. Using routinely collected data to understand and predict adverse outcomes in opioid agonist treatment: protocol for the Opioid Agonist Treatment Safety (OATS) Study. BMJ open 8 (8), e025204. Lawn, W., Freeman, T.P., Pope, R.A., Joye, A., Harvey, L., Hindocha, C., et al., 2016. Acute and chronic effects of cannabinoids on effortrelated decision-making and reward learning: an evaluation of the cannabis ’amotivational’ hypotheses. Psychopharmacology 233 (19e20), 3537e3552. https://doi.org/10.1007/s00213-016-4383-x. Lee, M.D., 2011. How cognitive modeling can benefit from hierarchical Bayesian models. J. Math. Psychol. 55 (1), 1e7. Lee, D.C., Stanger, C., Budney, A.J., 2015. A comparison of delay discounting in adolescents and adults in treatment for cannabis use disorders. Exp. Clin. Psychopharmacol 23 (2), 130e137. https://doi.org/ 10.1037/a0038792. Leentjens, A.F.G., Dujardin, K., Marsh, L., Martinez-Martin, P., Richard, I.H., Starkstein, S.E., et al., 2008. Apathy and anhedonia rating scales in Parkinson’s disease: critique and recommendations. Mov. Disord. 23 (14), 2004e2014. Leigh, B.C., Stacy, A.W., 1993. Alcohol outcome expectancies: scale construction and predictive utility in higher order confirmatory models. Psychol. Assess. 5 (2), 216. Lejuez, C.W., Read, J.P., Kahler, C.W., Richards, J.B., Ramsey, S.E., Stuart, G.L., et al., 2002. Evaluation of a behavioral measure of risk taking: the balloon analogue risk task (BART). J. Exp. Psychol. Appl. 8 (2), 75. Lejuez, C.W., Aklin, W., Daughters, S., Zvolensky, M., Kahler, C., Gwadz, M., 2007. Reliability and validity of the youth version of the balloon analogue risk task (BARTeY) in the assessment of risktaking behavior among inner-city adolescents. J. Clin. Child Adolesc. Psychol. 36 (1), 106e111. Leland, D.S., Paulus, M.P., 2005. Increased risk-taking decision-making but not altered response to punishment in stimulant-using young adults. Drug Alcohol Depend. 78 (1), 83e90. Lempert, K.M., Speer, M.E., Delgado, M.R., Phelps, E.A., 2017. Positive autobiographical memory retrieval reduces temporal discounting. Soc. Cogn. Affect. Neurosci. 12 (10), 1584e1593. https://doi.org/10.1093/ scan/nsx086. Lesage, E., Aronson, S.E., Sutherland, M.T., Ross, T.J., Salmeron, B.J., Stein, E.A., 2017. Neural Signatures of cognitive flexibility and reward sensitivity following nicotinic Receptor stimulation in dependent smokers: a randomized trial. JAMA psychiatry 74 (6), 632e640. https://doi.org/10.1001/jamapsychiatry.2017.0400. Leventhal, A.M., Trujillo, M., Ameringer, K.J., Tidey, J.W., Sussman, S., Kahler, C.W., 2014. Anhedonia and the relative reward value of drug and nondrug reinforcers in cigarette smokers. J. Abnorm. Psychol. 123 (2), 375.

58

Cognition and Addiction

Lim, A.C., Cservenka, A., Ray, L.A., 2017. Effects of alcohol dependence severity on neural correlates of delay discounting. Alcohol Alcohol 52 (4), 506e515. https://doi.org/10.1093/alcalc/agx015. Lin, B., Qian, R.B., Fu, X.M., Ji, X.B., Wei, X.P., Niu, C.S., Wang, Y.H., 2012. Impulsive decision-making behaviors in heroin addicts. A study of functional magnetic resonance imaging. Zhonghua Yi Xue Za Zhi 92 (15), 1033e1036. Lin, C.-H., Song, T.-J., Chen, Y.-Y., Lee, W.-K., Chiu, Y., 2013. Reexamining the validity and reliability of the clinical version of the Iowa gambling task: evidence from a normal subject group. Front. Psychol. 4, 220. Luba, R., Earleywine, M., Farmer, S., Slavin, M., Mian, M., Altman, B., 2018. The role of impulsivity and expectancies in predicting marijuana Use: an application of the acquired Preparedness model. J. Psychoact. Drugs 1e9. Lubman, D.I., Garfield, J.B.B., Gwini, S.M., Cheetham, A., Cotton, S.M., Yücel, M., Allen, N.B., 2018. Dynamic associations between opioid use and anhedonia: a longitudinal study in opioid dependence. J. Psychopharmacol. 32 (9), 957e964. Lucantonio, F., Caprioli, D., Schoenbaum, G., 2014. Transition from ‘model-based’to ‘model-free’behavioral control in addiction: involvement of the orbitofrontal cortex and dorsolateral striatum. Neuropharmacology 76, 407e415. Luengo, M.A., Carrillo-De-La-Pena, M.T., Otero, J.M., 1991. The components of impulsiveness. A comparison of the I. 7 impulsiveness questionnaire and the barratt impulsiveness scale. Personal. Individ. Differ. 12 (7), 657e667. Luijten, M., Schellekens, A.F., Kühn, S., Machielse, M.W.J., Sescousse, G., 2017. Disruption of reward processing in addiction. An image-based meta-analysis of functional magnetic resonance imaging studies. JAMA Psychiatry 74 (4), 387e398. Lynam, D.R., Smith, G.T., Whiteside, S.P., Cyders, M.A., 2006. The UPPS-P: assessing five personality pathways to impulsive behavior. In: West Lafayette. Purdue University. Lynam, D., Smith, G.T., Cyders, M.A., Fischer, S., Whiteside, S.A., 2007. The UPPS-P: A Multidimensional Measure of Risk for Impulsive Behavior (Unpublished technical report). MacPherson, L., Magidson, J.F., Reynolds, E.K., Kahler, C.W., Lejuez, C.W., 2010a. Changes in sensation seeking and risk-taking propensity predict increases in alcohol use among early adolescents. Alcohol Clin. Exp. Res. 34 (8), 1400e1408. MacPherson, L., Reynolds, E.K., Daughters, S.B., Wang, F., Cassidy, J., Mayes, L.C., Lejuez, C.W., 2010b. Positive and negative reinforcement underlying risk behavior in early adolescents. Prev. Sci. 11 (3), 331e342. MacPhillamy, D.J., Lewinsohn, P.M., 1982. The pleasant events schedule: studies on reliability, validity, and scale intercorrelation. J. Consult. Clin. Psychol. 50 (3), 363. Madden, G.J., Bickel, W.K., Jacobs, E.A., 1999. Discounting of delayed rewards in opioid-dependent outpatients: exponential or hyperbolic discounting functions? Exp. Clin. Psychopharmacol 7 (3), 284. Mahoney 3rd, J.J., Thompson-Lake, Daisy, G.Y., Cooper, K., Verrico, C.D., Newton, T.F., La Garza, R.2nd de, 2015. A comparison of impulsivity, depressive symptoms, lifetime stress and sensation seeking in healthy controls versus participants with cocaine or methamphetamine use disorders. J. Psychopharmacol. 29 (1), 50e56. https://doi.org/10.1177/0269881114560182.

Maia, T.V., 2009. Reinforcement learning, conditioning, and the brain: successes and challenges. Cognit. Affect Behav. Neurosci. 9 (4), 343e364. Marinescu, I.E., Lawlor, P.N., Kording, K.P., 2018. Quasi-experimental causality in neuroscience and behavioural research. Nat. Human Behav. 1. Marlatt, G.A., Demming, B., Reid, J.B., 1973. Loss of control drinking in alcoholics: an experimental analogue. J. Abnorm. Psychol. 81 (3), 233. Mathias, C.W., Stanford, M.S., Liang, Y., Goros, M., Charles, N.E., Sheftall, A.H., et al., 2018. A test of the psychometric characteristics of the BIS-Brief among three groups of youth. Psychol. Assess. 30 (7), 847. Matta, A.da, Gonçalves, F.L., Bizarro, L., 2012. Delay discounting: concepts and measures. Psychol. Neurosci. 5 (2), 135e146. Mazur, J.E., 1987. An adjusting procedure for studying delayed reinforcement. In: Commons, M.L., Mazur, J.E. (Eds.). Nevin, JA, pp. 55e73. McGovern, M.P., Carroll, K.M., 2003. Evidence-based practices for substance use disorders. Psychiatr. Clin. N. Am. 26 (4), 991. Mezquita, L., Camacho, L., Suso-Ribera, C., Ortet, G., Ibáñez, M.I., 2018. Development and validation of the alcohol expectancy questionnaire short form (EQ-SF). Adicciones 920. Morales, A.M., Jones, S.A., Ehlers, A., Lavine, J.B., Nagel, B.J., 2018. Ventral striatal response during decision making involving risk and reward is associated with future binge drinking in adolescents. Neuropsychopharmacology 1. Moutoussis, M., Bentall, R.P., El-Deredy, W., Dayan, P., 2011. Bayesian modelling of Jumping-to-Conclusions bias in delusional patients. Cogn. Neuropsychiatry 16 (5), 422e447. Muench, C., Juliano, L.M., 2017. Predictors of smoking lapse during a 48hour laboratory analogue smoking cessation attempt. Psychol. Addict. Behav. 31 (4), 415e422. https://doi.org/10.1037/adb0000246. Mumtaz, W., Kamel, N., Ali, S.S.A., Malik, A.S., 2018. An EEG-based functional connectivity measure for automatic detection of alcohol use disorder. Artif. Intell. Med. 84, 79e89. Murphy, J.G., Correia, C.J., Colby, S.M., Vuchinich, R.E., 2005. Using behavioral theories of choice to predict drinking outcomes following a brief intervention. Exp. Clin. Psychopharmacol 13 (2), 93. Myerson, J., Green, L., Hanson, J.S., Holt, D.D., Estle, S.J., 2003. Discounting delayed and probabilistic rewards: processes and traits. J. Econ. Psychol. 24 (5), 619e635. Nestor, L.J., McCabe, E., Jones, J., Clancy, L., Garavan, H., 2018. Shared and divergent neural reactivity to non-drug operant response outcomes in current smokers and ex-smokers. Brain Res. 1680, 54e61. Odum, A.L., 2011. Delay discounting: I’m ak, you’re ak. J. Exp. Anal. Behav. 96 (3), 427e439. Oldham, S., Murawski, C., Fornito, A., Youssef, G., Yücel, M., Lorenzetti, V., 2018. The anticipation and outcome phases of reward and loss processing. A neuroimaging meta-analysis of the monetary incentive delay task. Hum. Brain Mapp. 39 (8), 3398e3418. Orsini, C.A., Moorman, D.E., Young, J.W., Setlow, B., Floresco, S.B., 2015. Neural mechanisms regulating different forms of risk-related decision-making. Insights from animal models. Neurosci. Biobehav. Rev. 58, 147e167. Ostlund, S.B., Cui, Y., 2018. Not worth the wait. Cocaine alters reward processing in the nucleus accumbens. Neuropsychopharmacology 1.

Decision-making deficits in substance use disorders Chapter | 4

Owens, M.M., Amlung, M.T., Beach, S.R.H., Sweet, L.H., MacKillop, J., 2017. Delay discounting differences in brain activation, connectivity, and structure in individuals with addiction: a systematic review protocol. Syst. Rev. 6 (1), 138. https://doi.org/10.1186/s13643-0170537-0. Pacheco-Colon, I., Limia, J.M., Gonzalez, R., 2018. Nonacute effects of cannabis use on motivation and reward sensitivity in humans: a systematic review. Psychol. Addict. Behav. 32 (5), 497e507. https:// doi.org/10.1037/adb0000380. Paneto, A., 2017. Risky Decision-Making in College-Aged Cannabis Users. A Preliminary fMRI Analysis. Indiana University. Patton, J.H., Stanford, M.S., 2011. Psychology of impulsivity. In: The Oxford Handbook of Impulse Control Disorders, pp. 262e275. Patton, J.H., Stanford, M.S., Barratt, E.S., 1995. Factor structure of the Barratt impulsiveness scale. J. Clin. Psychol. 51 (6), 768e774. Patton, K.A., Connor, J.P., Rundle-Thiele, S., Dietrich, T., Young, R.M.D., Gullo, M.J., 2018. Validation of the adolescent drinking expectancy questionnaire and development of a short form. Drug Alcohol Rev. 37 (3), 396e405. Patzelt, E.H., Kurth-Nelson, Z., Lim, K.O., MacDonald 3rd, A.W., 2014. Excessive state switching underlies reversal learning deficits in cocaine users. Drug Alcohol Depend. 134, 211e217. https://doi.org/ 10.1016/j.drugalcdep.2013.09.029. Paulus, M.P., Rogalsky, C., Simmons, A., Feinstein, J.S., Stein, M.B., 2003. Increased activation in the right insula during risk-taking decision making is related to harm avoidance and neuroticism. Neuroimage 19 (4), 1439e1448. Pearce, N., Lawlor, D.A., 2016. Causal inference-so much more than statistics. Int. J. Epidemiol. 45 (6), 1895e1903. https://doi.org/ 10.1093/ije/dyw328. Pearson, M.R., Hustad, J.T.P., Neighbors, C., Conner, B.T., Bravo, A.J., 2018. Personality, marijuana norms, and marijuana outcomes among college students. Addict. Behav. 76, 291e297. https://doi.org/ 10.1016/j.addbeh.2017.08.012. Peters, J., Büchel, C., 2010. Episodic future thinking reduces reward delay discounting through an enhancement of prefrontal-mediotemporal interactions. Neuron 66 (1), 138e148. Peters, J., Büchel, C., 2011. The neural mechanisms of inter-temporal decision-making. Understanding variability. Trends Cogn. Sci. 15 (5), 227e239. Petrocelli, J.V., 2003. Factor validation of the consideration of future consequences scale: evidence for a short version. J. Soc. Psychol. 143 (4), 405e413. Phillips, L.D., Edwards, W., 1966. Conservatism in a simple probability inference task. J. Exp. Psychol. 72 (3), 346. Pleskac, T.J., Wallsten, T.S., Wang, P., Lejuez, C.W., 2008. Development of an automatic response mode to improve the clinical utility of sequential risk-taking tasks. Exp. Clin. Psychopharmacol 16 (6), 555. Prause, N., Lawyer, S., 2014. Specificity of reinforcement for risk behaviors of the Balloon Analog Risk Task using math models of performance. J. Risk Res. 17 (3), 317e335. Rachlin, H., 1974. Self-control. Behaviorism 2 (1), 94e107. Rachlin, H., Raineri, A., Cross, D., 1991. Subjective probability and delay. J. Exp. Anal. Behav. 55 (2), 233e244. Radu, P.T., Yi, R., Bickel, W.K., Gross, J.J., McClure, S.M., 2011. A mechanism for reducing delay discounting by altering temporal attention. J. Exp. Anal. Behav. 96 (3), 363e385. https://doi.org/ 10.1901/jeab.2011.96-363.

59

Reise, S.P., Moore, T.M., Sabb, F.W., Brown, A.K., London, E.D., 2013. The barratt impulsiveness scalee11: reassessment of its structure in a community sample. Psychol. Assess. 25 (2), 631. Reiter, A.M.F., Deserno, L., Kallert, T., Heinze, H.-J., Heinz, A., Schlagenhauf, F., 2016. Behavioral and neural signatures of reduced updating of alternative options in alcohol-dependent patients during flexible decision-making. J. Neurosci. 36 (43), 10935e10948. https:// doi.org/10.1523/JNEUROSCI.4322-15.2016. Reske, M., Stewart, J.L., Flagan, T.M., Paulus, M.P., 2015. Attenuated neural processing of risk in young adults at risk for stimulant dependence. PLoS One 10 (6), e0127010. Richards, J.B., Zhang, L., Mitchell, S.H., Wit, H.de, 1999. Delay or probability discounting in a model of impulsive behavior: effect of alcohol. J. Exp. Anal. Behav. 71 (2), 121e143. Robinson, T.E., Berridge, K.C., 2008. The incentive sensitization theory of addiction. Some current issues. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363 (1507), 3137e3146. Robles, E., Vargas, P.A., 2008. Parameters of delay discounting assessment: number of trials, effort, and sequential effects. Behav. Process. 78 (2), 285e290. Rochat, L., Maurage, P., Heeren, A., Billieux, J., 2018. Let’s open the decision-making umbrella: a framework for conceptualizing and assessing features of impaired decision making in addiction. Neuropsychol. Rev. 1e25. Rogers, R.D., Everitt, B.J., Baldacchino, A., Blackshaw, A.J., Swainson, R., Wynne, K., et al., 1999a. Dissociable deficits in the decision-making cognition of chronic amphetamine abusers, opiate abusers, patients with focal damage to prefrontal cortex, and tryptophan-depleted normal volunteers: evidence for monoaminergic mechanisms. Neuropsychopharmacology 20 (4), 322e339. Rogers, R.D., Owen, A.M., Middleton, H.C., Williams, E.J., Pickard, J.D., Sahakian, B.J., Robbins, T.W., 1999b. Choosing between small, likely rewards and large, unlikely rewards activates inferior and orbital prefrontal cortex. J. Neurosci. 19 (20), 9029e9038. Roozen, H.G., Wiersema, H., Strietman, M., Feij, J.A., Lewinsohn, P.M., Meyers, R.J., et al., 2008. Development and psychometric evaluation of the pleasant activities list. Am. J. Addict. 17 (5), 422e435. Rosenthal, T.L., Montgomery, L.M., Shadish Jr., W.R., Lichstein, K.L., 1989. Leisure interest patterns and subjective stress in college students. Behav. Res. Ther. 27 (1), 59e64. Ross, E.L., Yoon, J.H., Mahoney 3rd, J.J., Omar, Y., Newton, T.F., Garza, La, Richard, 2nd de, 2013. The impact of self-reported life stress on current impulsivity in cocaine dependent adults. Prog. Neuropsychopharmacol. Biol. Psychiatry. 46, 113e119. https:// doi.org/10.1016/j.pnpbp.2013.06.002. Rubenis, A.J., Fitzpatrick, R.E., Lubman, D.I., Verdejo-Garcia, A., 2018. Impulsivity predicts poorer improvement in quality of life during early treatment for people with methamphetamine dependence. Addiction 113 (4), 668e676. https://doi.org/10.1111/add.14058. Ryan, F., 2013. Cognitive Therapy for Addiction. Motivation and Change. John Wiley & Sons, Chichester, West Sussex, Malden, MA. Sambrook, T.D., Hardwick, B., Wills, A.J., Goslin, J., 2018. Model-free and model-based reward prediction errors in EEG. Neuroimage 178, 162e171. Sanudo, A., Andreoni, S., Sanchez, Z.M., 2018. Alcohol-induced risk behaviors among Brazilian nightclub patrons: a latent class analysis. Public health 155, 99e106. https://doi.org/10.1016/j.puhe.2017. 11.019.

60

Cognition and Addiction

Saw, Y.M., Saw, T.N., Chan, N., Cho, S.M., Jimba, M., 2018. Genderspecific differences in high-risk sexual behaviors among methamphetamine users in Myanmar-China border city, Muse, Myanmar: who is at risk? BMC Public Health 18 (1), 209. https://doi.org/10.1186/ s12889-018-5113-6. Schafer, J., Brown, S.A., 1991. Marijuana and cocaine effect expectancies and drug use patterns. J. Consult. Clin. Psychol. 59 (4), 558. Schmaal, L., Goudriaan, A.E., Joos, L., Dom, G., Pattij, T., van den Brink, W., Veltman, D.J., 2014. Neural substrates of impulsive decision making modulated by modafinil in alcohol-dependent patients. Psychol. Med. 44 (13), 2787e2798. https://doi.org/10.1017/S00332 91714000312. Schneider, S., Peters, J., Bromberg, U., Brassen, S., Miedl, S.F., Banaschewski, T., et al., 2012. Risk taking and the adolescent reward system. A potential common link to substance abuse. Am. J. Psychiatry 169 (1), 39e46. Schuster, R.M., Hareli, M., Moser, A.D., Lowman, K., Gilman, J., Ulysse, C., et al., 2019. Cross-domain correlates of cannabis use disorder severity among young adults. Addict. Behav. 93, 212e218. Schwartenbeck, P., FitzGerald, T.H.B., Mathys, C., Dolan, R., Wurst, F., Kronbichler, M., Friston, K., 2015. Optimal inference with suboptimal models: addiction and active Bayesian inference. Med. Hypotheses 84 (2), 109e117. Sebold, M., Deserno, L., Nebe, S., Schad, D.J., Garbusow, M., Hägele, C., et al., 2014. Model-based and model-free decisions in alcohol dependence. Neuropsychobiology 70 (2), 122e131. Sebold, M., Nebe, S., Garbusow, M., Guggenmos, M., Schad, D.J., Beck, A., et al., 2017. When habits are dangerous: alcohol expectancies and habitual decision making predict relapse in alcohol dependence. Biol. Psychiatry 82 (11), 847e856. https://doi.org/ 10.1016/j.biopsych.2017.04.019. Sharma, S., 2017. Translational multimodality neuroimaging. Curr. Drug Targets 18 (9), 1039e1050. Sherman, L., Steinberg, L., Chein, J., 2018. Connecting brain responsivity and real-world risk taking. Strengths and limitations of current methodological approaches. Dev. Cogn. Neuros-Neth. 33, 27e41. Slovic, P., 1966. Risk-taking in children: age and sex differences. Child Dev. 169e176. Smethells, J.R., Reilly, M.P., 2015. Intertrial interval duration and impulsive choice. J. Exp. Anal. Behav. 103 (1), 153e165. https:// doi.org/10.1002/jeab.131. Sousa, C., Gonçalves, G., Sousa, A., Pinto, E., 2018. An assessment of the psychometric properties of the Brief Sensation Seeking Scale and its prediction in safety performance in a Portuguese adult sample. Curr. Psychol. 1e13. Spear, L.P., 2018. Effects of adolescent alcohol consumption on the brain and behaviour. Nat. Rev. Neurosci. 19 (4), 197. Squeglia, L.M., Ball, T.M., Jacobus, J., Brumback, T., McKenna, B.S., Nguyen-Louie, Tam, T., et al., 2016. Neural predictors of initiating alcohol use during adolescence. Am. J. Psychiatry 174 (2), 172e185. Stanford, M.S., Mathias, C.W., Dougherty, D.M., Lake, S.L., Anderson, N.E., Patton, J.H., 2009. Fifty years of the barratt impulsiveness scale. An update and review. Personal. Individ. Differ. 47 (5), 385e395. Stein, J.S., Wilson, A.G., Koffarnus, M.N., Daniel, T.O., Epstein, L.H., Bickel, W.K., 2016. Unstuck in time: episodic future thinking reduces delay discounting and cigarette smoking. Psychopharmacology 233 (21e22), 3771e3778. https://doi.org/10.1007/s00213-016-4410-y.

Stein, J.S., Tegge, A.N., Turner, J.K., Bickel, W.K., 2018. Episodic future thinking reduces delay discounting and cigarette demand: an investigation of the good-subject effect. J. Behav. Med. 41 (2), 269e276. https://doi.org/10.1007/s10865-017-9908-1. Steinberg, L., Sharp, C., Stanford, M.S., Tharp, A.T., 2013. New tricks for an old measure: the development of the barratt impulsiveness scaleebrief (BIS-brief). Psychol. Assess. 25 (1), 216. Stephan, K.E., Schlagenhauf, F., Huys, Q.J.M., Raman, S., Aponte, E.A., Brodersen, K.H., et al., 2017. Computational neuroimaging strategies for single patient predictions. Neuroimage 145, 180e199. Stephenson, M.T., Velez, L.F., Chalela, P., Ramirez, A., Hoyle, R.H., 2007. The reliability and validity of the Brief Sensation Seeking Scale (BSSS-8) with young adult Latino workers: implications for tobacco and alcohol disparity research. Addiction 102, 79e91. Stevens, A.K., Littlefield, A.K., Talley, A.E., Brown, J.L., 2017. Do individuals higher in impulsivity drink more impulsively? A pilot study within a high risk sample of young adults. Addict. Behav. 65, 147e153. https://doi.org/10.1016/j.addbeh.2016.10.026. Stewart, J.L., Butt, M., May, A.C., Tapert, S.F., Paulus, M.P., 2017. Insular and cingulate attenuation during decision making is associated with future transition to stimulant use disorder. Addiction 112 (9), 1567e1577. https://doi.org/10.1111/add.13839. Stoops, W.W., Kearns, D.N., 2018. Decision-making in Addiction. Current Knowledge, Clinical Implications and Future Directions. Elsevier. Strathman, A., Gleicher, F., Boninger, D.S., Edwards, C.S., 1994. The consideration of future consequences: weighing immediate and distant outcomes of behavior. J. Personal. Soc. Psychol. 66 (4), 742. Suckling, J., Nestor, L.J., 2017. The neurobiology of addiction: the perspective from magnetic resonance imaging present and future. Addiction 112 (2), 360e369. https://doi.org/10.1111/add.13474. Sun, D., 2017. Development of new diagnostic techniquesemachine learning. In: Substance and Non-substance Addiction. Springer, pp. 203e215. Tanabe, J., Thompson, L., Claus, E., Dalwani, M., Hutchison, K., Banich, M.T., 2007. Prefrontal cortex activity is reduced in gambling and nongambling substance users during decision-making. Hum. Brain Mapp. 28 (12), 1276e1286. Tanabe, J., Reynolds, J., Krmpotich, T., Claus, E., Thompson, L.L., Du, Y.P., Banich, M.T., 2013. Reduced neural tracking of prediction error in substance-dependent individuals. Am. J. Psychiatry 170 (11), 1356e1363. Tarokh, L., 2012. Neuroimaging in addiction. Acta Psychiatrica Scandinavica 126 (3), 230. Thompson, L.L., Claus, E.D., Mikulich-Gilbertson, S.K., Banich, M.T., Crowley, T., Krmpotich, T., et al., 2012. Negative reinforcement learning is affected in substance dependence. Drug Alcohol Depend. 123 (1e3), 84e90. Thompson, P.M., Andreassen, O.A., Arias-Vasquez, A., Bearden, C.E., Boedhoe, P.S., Brouwer, R.M., et al., 2017. ENIGMA and the individual. Predicting factors that affect the brain in 35 countries worldwide. Neuroimage 145, 389e408. Thomsen, R., Kristine, Callesen, M.B., Hesse, M., Kvamme, T.L., Pedersen, M.M., Pedersen, M.U., Voon, V., 2018. Impulsivity traits and addiction-related behaviors in youth. J. Behav. Addict. 1e14. Thylstrup, B., Hesse, M., 2018. Why run the risk? Motivation for offences by patients with substance use and antisocial personality disorders which they rated as most risky to their own well-being. Crim. Behav. Ment. Health 28 (2), 187e201. https://doi.org/10.1002/cbm.2059.

Decision-making deficits in substance use disorders Chapter | 4

Tiwari, T., Singh, A.L., Singh, I.L., 2009. The short-form revised Eysenck personality questionnaire. A Hindi edition (EPQRS-H). Ind. Psychiatry J. 18 (1), 27. Tran, J., Teese, R., Gill, P.R., 2018. UPPS-P facets of impulsivity and alcohol use patterns in college and noncollege emerging adults. Am. J. Drug Alcohol Abuse 44 (6), 695e704. Treadway, M.T., Buckholtz, J.W., Schwartzman, A.N., Lambert, W.E., Zald, D.H., 2009. Worth the ‘EEfRT’? The effort expenditure for rewards task as an objective measure of motivation and anhedonia. PLoS One 4 (8), e6598. Treadway, M.T., Bossaller, N.A., Shelton, R.C., Zald, D.H., 2012. Effortbased decision-making in major depressive disorder: a translational model of motivational anhedonia. J. Abnorm. Psychol. 121 (3), 553. van Gelder, Jean-Louis, Hershfield, H.E., Nordgren, L.F., 2013. Vividness of the future self predicts delinquency. Psychol. Sci. 24 (6), 974e980. Verdejo-García, A., Alcázar-Córcoles, Miguel, A., Albein-Urios, Natalia, 2018. Neuropsychological interventions for decision-making in addiction. A systematic review. Neuropsychol. Rev. 1e14. Volkow, N.D., Wang, G.-J., Fowler, J.S., Tomasi, D., Telang, F., Baler, R., 2010. Addiction. Decreased reward sensitivity and increased expectation sensitivity conspire to overwhelm the brain’s control circuit. Bioessays 32 (9), 748e755. Voon, V., 2014. Models of impulsivity with a focus on waiting impulsivity. Translational potential for neuropsychiatric disorders. Curr. Addict. Rep. 1 (4), 281e288. Voon, V., Chang-Webb, Y.C., Morris, L.S., Cooper, E., Sethi, A., Baek, K., et al., 2015. Waiting impulsivity: the influence of acute methylphenidate and feedback. Intl. J. Neuropsychopharmacol. 19 (1) https://doi.org/10.1093/ijnp/pyv074. Voon, V., Chang-Webb, Y.C., Morris, L.S., Cooper, E., Sethi, A., Baek, K., et al., 2016. Waiting impulsivity: the influence of acute methylphenidate and feedback. Int. J. Neuropsychopharmacol. 19 (1). Voon, V., Reiter, A., Sebold, M., Groman, S., 2017. Model-based control in dimensional psychiatry. Biol. Psychiatry 82 (6), 391e400. Vuletic, D., Dupont, P., Robertson, F., Warwick, J., Zeevaart, J.R., Stein, D.J., 2018. Methamphetamine dependence with and without psychotic symptoms. A multi-modal brain imaging study. NeuroImage. Clin. 20, 1157e1162. Wallace, M., 1956. Future time perspective in schizophrenia. J. Abnorm. Soc. Psychol. 52 (2), 240. Wallsten, T.S., Pleskac, T.J., Lejuez, C.W., 2005a. Modeling behavior in a clinically diagnostic sequential risk-taking task. Psychol. Rev. 112 (4), 862. Wallsten, T.S., Pleskac, T.J., Lejuez, C.W., 2005b. Modeling behavior in a clinically diagnostic sequential risk-taking task. Psychol. Rev. 112 (4), 862. Walter, M., Denier, N., Gerber, H., Schmid, O., Lanz, C., Brenneisen, R., et al., 2015. Orbitofrontal response to drug-related stimuli after heroin administration. Addict. Biol. 20 (3), 570e579. https://doi.org/ 10.1111/adb.12145. Wang, J.M., Zhu, L., Brown, V.M., La Garza II, Richard de, Newton, T., King-Casas, B., Chiu, P.H., 2019. In cocaine dependence, neural prediction errors during loss avoidance are increased with cocaine deprivation and predict drug use. Biol. Psychiatry Cogn. Neurosci. Neuroimagin 4 (3), 291e299. Wardle, M.C., Treadway, M.T., Wit, H.de, 2012. Caffeine increases psychomotor performance on the effort expenditure for rewards task.

61

Pharmacol. Biochem. Behag. 102 (4), 526e531. https://doi.org/ 10.1016/j.pbb.2012.06.016. Weatherly, J.N., Derenne, A., Terrell, H.K., 2010. College students discount money “won” more than money “owed”. Psychol. Rec. 60 (3), 463e471. https://doi.org/10.1007/BF03395721. Wei, Z., Han, L., Zhong, X., Liu, Y., Zha, R., Wang, Y., et al., 2018. Chronic nicotine exposure impairs uncertainty modulation on reinforcement learning in anterior cingulate cortex and serotonin system. Neuroimage 169, 323e333. White, T.L., Lejuez, C.W., Wit, H.de, 2008. Test-retest characteristics of the balloon analogue risk task (BART). Exp. Clin. Psychopharmacol 16 (6), 565. Whiteside, S.P., Lynam, D.R., 2001. The five factor model and impulsivity: using a structural model of personality to understand impulsivity. Personal. Individ. Differ. 30 (4), 669e689. Wise, R.A., Koob, G.F., 2014. The development and maintenance of drug addiction. Neuropsychopharmacology 39 (2), 254. Woicik, P.A., Stewart, S.H., Pihl, R.O., Conrod, P.J., 2009. The substance use risk profile scale: a scale measuring traits linked to reinforcementspecific substance use profiles. Addict. Behav. 34 (12), 1042e1055. Xiao, L., Bechara, A., Gong, Q., Huang, X., Li, X., Xue, G., et al., 2013. Abnormal affective decision making revealed in adolescent binge drinkers using a functional magnetic resonance imaging study. Psychol. Addict. Behav. 27 (2), 443. Yamamoto, D.J., Reynolds, J., Krmpotich, T., Banich, M.T., Thompson, L., Tanabe, J., 2014. Temporal profile of fronto-striatallimbic activity during implicit decisions in drug dependence. Drug Alcohol Depend. 136, 108e114. Yanes, J.A., Riedel, M.C., Ray, K.L., Kirkland, A.E., Bird, R.T., Boeving, E.R., et al., 2018. Neuroimaging meta-analysis of cannabis use studies reveals convergent functional alterations in brain regions supporting cognitive control and reward processing. J. Psychopharmacol. 32 (3), 283e295. Yazdi, K., Rumetshofer, T., Gnauer, M., Csillag, D., Rosenleitner, J., Kleiser, R., 2019. Neurobiological processes during the Cambridge gambling task. Behav. Brain Res. 356, 295e304. Zapolski, T.C.B., Stairs, A.M., Settles, R.F., Combs, J.L., Smith, G.T., 2010. The measurement of dispositions to rash action in children. Assessment 17 (1), 116e125. Zhu, Y., Jiang, H., Su, H., Zhong, N., Li, R., Li, X., et al., 2018. A newly designed mobile-based computerized cognitive addiction therapy app for the improvement of cognition impairments and risk decision making in methamphetamine use disorder. Randomized controlled trial. JMIR mHealth and uHealth 6 (6), e10292. Zilverstand, A., Huang, A.S., Alia-Klein, N., Goldstein, R.Z., 2018. Neuroimaging impaired response inhibition and salience attribution in human drug addiction. A systematic review. Neuron 98 (5), 886e903. Zimmermann, K., Kendrick, K., Scheele, D., Dau, W., Banger, M., Maier, W., et al., 2018. Altered reward processing in abstinent dependent cannabis users. Social context matters. In bioRxiv 278044. Zuckerman, M., 1984. Experience and desire: a new format for sensation seeking scales. J. Behav. Assess. 6 (2), 101e114. Zuckerman, M., Kuhlman, D.M., 2000. Personality and risk-taking. Common bisocial factors. J. Personal. 68 (6), 999e1029. Zuckerman, M., Kolin, E.A., Price, L., Zoob, I., 1964. Development of a sensation-seeking scale. J. Consult. Psychol. 28 (6), 477.

Chapter 5

Social cognition in addiction Boris B. Quednow1, 2 1

Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of

Zurich, Zurich, Switzerland; 2Neuroscience Centre Zurich, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland

Introduction Humans have developed specific cognitive functions to understand themselves and others, to predict and affect the behavior of others, and to dynamically interact with their social environment (Amodio and Frith, 2006; Fiske and Taylor, 2013; Lieberman, 2007). These higher cognitive functions have been congregated under the umbrella term social cognition. This broad array of functions includes more perceptive abilities, such as emotion perception and recognition, self-awareness and self-perception, emotional empathy, and mental and emotional perspective-taking also called Theory of Mind (ToM), as well as interactive social functions, such as social gaze contact, social decisionmaking, and the ability to perceive reward from social contacts, and, finally, social attitudes and values, such as altruism, fairness, trust, morale, stereotypes, and prejudices (Amodio and Frith, 2006; Fiske and Taylor, 2013; Lieberman, 2007; Rilling and Sanfey, 2011). Unsurprisingly, given that social functioning in daily life depends on intact social cognition, disturbances in these functions have been shown to be crucial factors in the development, progress, and prognosis of psychiatric conditions such as schizophrenia (Couture et al., 2006; Green, 2016) and depression (Weightman et al., 2019). Analogously, it has been proposed that dysfunctional social cognition and interaction likewise play a key role in the origin and course of substance use disorders (SUDs) (Homer et al., 2008; Volkow et al., 2011; Quednow, 2017). It has also been suggested that chronic drug use, e.g., of stimulants, impacts the frontal-striatal reward system by enhancing the value of the substance, while simultaneously reducing sensitivity to the rewards obtained via social activities (Tobler et al., 2016; Preller et al., 2014a). Accordingly, drug-induced changes in brain regions and neurotransmitter systems involved in social cognition, social interaction, and social reward processing are assumed to contribute to a further

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00005-8 Copyright © 2020 Elsevier Inc. All rights reserved.

decrease in social contacts and social support, leading to an increase in social isolation, aggression, and depressive symptoms (Quednow, 2017; Homer et al., 2013). This coincides with a further reduction in social reward resources, ongoing social withdrawal, and the establishment of the drug as the main source of reward, resulting in maintained substance use and recurrent relapses (Quednow, 2017) (see Fig. 5.1). Taken together, drug-related changes in social reward and social cognition likely contribute to the social problems and the decay of social relationships in individuals with SUD. Therefore, it is also likely that disturbances in social perception and behavior strongly compromise therapeutic relationships and, thus, hamper the success of any addiction treatment. Consequently, interpersonal problems related to social cognition deficits likely contribute to high relapse rates found across a range of SUD. Importantly, social cognitive deficits can recover partially simply through drug abstinence, e.g., as shown for cocaine users (Vonmoos et al., 2019) and alcohol dependence (AD) (Erol et al., 2017), and several treatment approaches have been demonstrated to have a normalizing effect on social cognitive abilities, e.g., in major depression (Weightman et al., 2019), which encourages the inclusion of available social training techniques and development of social competences and social reward in psychotherapy of SUD. However, specific treatment modules focusing on the rehabilitation of social cognition and reward are so far lacking for SUD, even though they might have a positive impact on the overall treatment success. At the beginning of this chapter, brief definitions of the most important socio-cognitive functions will be given. Thereafter, investigations characterizing, quantifying, and explaining disturbances of different socially related mental functions will be reviewed with respect to specific SUD. Overall, only performance measures and measures of behavior will be discussed in this chapter, while

63

64

Cognition and Addiction

FIGURE 5.1 The proposed role of social cognition and interaction in maintenance and relapse of substance use disorders. Quednow, B.B., 2017. Social cognition and interaction in stimulant use disorders. Curr. Opin. Behav. Sci. 13, 55e62.

questionnaire-based research will be omitted to keep the focus on cognitive processes. Moreover, although empathogens such as 3,4-methylenedioxymethamphetamine (MDMA) have a very low addictive potential, they (i) play a role in addiction medicine primarily as a frequently co-used substance class and (ii) havedper definitionda strong effect on social cognition and thus they will be discussed below, alongside addiction-related substances.

Definitions of socio-cognitive functions and their measurement Emotion recognition, also called cognitive empathy, affect recognition, or emotion perception, is the capability to recognize and understand the emotions of others from faces, voices, gestures, and situational contexts (Banziger et al., 2009). Numerous studies with drug users have employed emotional facial expression tasks, primarily based on the famous picture set of Ekman and Friesen (1976). Beyond simple static face stimuli with different intensities of emotion expression, presentations of dynamic emotion expressions using short movies or morphed Ekman faces have also been developed (e.g., Harris et al., 2014; Holland et al., 2018). The Comprehensive Affect Test System (CATS-A) includes emotion recognition from Ekman faces and from voices, as well as the ability to align the

emotional load of faces and voices (Schaffer et al., 2009). The Multifaceted Empathy Task (MET) also deserves mention as an example of the assessment of cognitive empathy using complex, emotionally laden scenes (Dziobek et al., 2008). Emotional empathy, also called affective empathy, is defined as a person’s emotional response to another person’s emotional state, i.e., the ability to feel what another person feels (Mehrabian and Epstein, 1972). Beyond basic cognitive empathy, emotional empathy can also be measured, e.g., with the MET (Dziobek et al., 2008) or Empathy-for-Pain tasks (e.g., Lamm et al., 2011). Mental and emotional perspective-taking, also called mentalizing or ToM, reflects the ability “to propositionally reason from one’s theory of how minds operate and how social situations affect mental states in general, in order to represent the mental state of a particular individual given a particular situation” (3, p. 263). A famous emotion recognition taskdalthough perhaps more commonly interpreted as a ToM-taskdthe Reading the Mind in the Eyes Task (RMET), in which emotional states have to be inferred only from eye pairs, has been applied frequently in drug-using populations (Baron-Cohen et al., 2001). In addition, tests measuring the ability to detect social faux pas’ (Baron-Cohen et al., 1999) and to understand humor (Uekermann et al., 2006, 2007) have been used to measure

Social cognition in addiction Chapter | 5

aspects of perspective-taking and ToM. A final exampledand one with high ecological validitydis the video-based Movie for the Assessment of Social Cognition (MASC), which assesses the understanding of emotions, thoughts, and intentions and concepts such as false belief, faux pas, metaphor, and sarcasm in everyday-life situations (Dziobek et al., 2006). Social decision-making describes the ability to process multiple alternatives and to choose an optimal course of action in a social environment, which is usually operationalized using socially interactive tasks derived from game theory (Sanfey, 2007). A variety of such social decision-making games is discussed elsewhere (Houser and McCabe, 2009; Sanfey and Dorris, 2009). Moral decision-making or moral judgment is a complex cognitive process enabling individuals to judge actions of other individuals on the basis of habits, values, and norms orienting the conduct in a certain social group (Moll et al., 2005). This kind of behavior is usually operationalized by presenting hypothetical moral dilemmasdoften with varying levels of personal involvementdand asking for preferred choices (Christensen and Gomila, 2012). A great variety of text- and picture-based vignettes with hypothetical dilemmas have been developed, such as the well-known examples of Greene et al. (2001) and Koenigs et al. (2007), but also newer stimulus sets (Clifford et al., 2015). Moreover, vignettes specifically related to addiction have also been used (Fisher, 2011). Recently, a normative moral video databasedthe Moral and Affective Film Setdhas also been proposed (McCurrie et al., 2018). Social reward can be defined as perceiving pleasure during social interactions or social commitment. Of note, reward by non-social objects, such as money, gifts, and drugs of abuse, as well as social reward, all activate the same reward-related networks in the brain (Lin et al., 2012; Izuma et al., 2008). So far, experimental tasks usually applied in social neuroscience contexts have been developed to measure facets of social reward such as positive social feedback (Campbell-Meiklejohn et al., 2010), initiated joint attention (Schilbach et al., 2010), or charitable decision-making (Hare et al., 2010).

Studies on social cognition and interaction in substance use disorders Alcohol Emotion recognition and cognitive empathy Since the end of the 1980s, emotion recognition from faces has been investigated in individuals with AD across dozens of studies. Most of these identified either emotion-specific or global impairments, or both, in the decoding of emotions from faces (Erol et al., 2017; Valmas et al., 2014; Townshend and Duka, 2003; Quaglino et al., 2015;

65

D’Hondt et al., 2015; Maurage et al., 2008; Philippot et al., 1999; Foisy et al., 2005, 2007; Kornreich et al., 2001, 2002, 2013a, 2016). However, there are also several studies that were not able to identify impaired performance in facial emotion recognition tasks in similar patient groups (Kornreich et al., 2016; Sprah and Novak, 2008; Oscar-Berman et al., 1990; Uekermann et al., 2005; Cermak et al., 1989). Moreover, there are contradicting results regarding the impact of abstinence: while the first longitudinal study showed no improvement of impaired emotional face recognition after 3 months of abstinence (Foisy et al., 2007), a recent study showed almost complete recovery of such deficits in the same time period (Erol et al., 2017). Two cross-sectional studies reported sustained emotion recognition impairments in midterm abstinent (>2 months) AD individuals (Foisy et al., 2005; Kornreich et al., 2001), whereas another cross-sectional study did not detect accuracy changes in a face recognition task in patients with very long abstinence periods (>12 months, mean 75 months) (Fein et al., 2010). However, the latter study showed slower early processing of emotional facial stimuli in long-term abstinent AD patients using an electrophysiological eventrelated potentials paradigm (Fein et al., 2010). Finally, a recent meta-analysis including only 12 of the previous studies confirmed global face recognition deficits of moderate effect size (total score Cohen’s d ¼ 0.65), while also demonstrating that the strongest deficits are for the recognition of disgust (d ¼ 0.62) and anger (d ¼ 0.47) and the weakest for happiness (d ¼ 0.19) (Bora and Zorlu, 2017). In addition to face recognition deficits, worse emotion recognition from voices (prosody) has been shown repeatedly in AD patients (Uekermann et al., 2005; Monnot et al., 2001; Maurage et al., 2009). Given that deficits in emotion recognition from body postures and music have also been reported, it has therefore been suggested that AD individuals suffer from a generalized emotional decoding impairment (Kornreich et al., 2013a; Maurage et al., 2009). In line with findings showing face and prosody recognition deficits in AD patients, emotion recognition (cognitive empathy) from complex emotionally laden scenes, as presented in the MET, was also recently demonstrated to be diminished (Grynberg et al., 2017). Furthermore, a number of studies have indicated that the integrated processing of face and voice recognition in cross-modal conditions is specifically affected in AD patients (Valmas et al., 2014; Kornreich et al., 2016; Maurage et al., 2007). Patients with AD not only fail in the categorization of emotions from faces or voices but also generally overestimate the intensity of emotional expressions (Philippot et al., 1999), have a globally increased identification threshold for emotions (D’Hondt et al., 2015), or misidentify emotions (Freeman et al., 2018). It has been shown that episodic memory and cognitive flexibility are strongly associated with emotional face recognition, suggesting that socio-cognitive deficits

66

Cognition and Addiction

might be partially explained by more basic cognitive impairments (Quaglino et al., 2015). However, another study demonstrated that visuomotor impairment cannot completely explain face recognition deficits in AD (Maurage et al., 2008). In addition, face recognition abilities in AD patients have been shown to be modulated by gender (Valmas et al., 2014) and are related to self-reported interpersonal problems (Kornreich et al., 2002). As several studies have found that emotion recognition performance is correlated with severity markers of chronic alcohol use, such as binge drinking, early onset of use, drinks consumed in the last 3 months, and number of detoxifications, it has been suggested that such deficits may be acquired more than they are signs of a predisposition (Valmas et al., 2014; Townshend and Duka, 2003; Monnot et al., 2001; Freeman et al., 2018; Lannoy et al., 2018). A structural imaging study found that AD individuals displayed reduced gray matter volume in the inferior frontal cortex (IFC) and insula. IFC gray matter volume was correlated with number of detoxifications and with the recognition of fearful faces (Trick et al., 2014). A combined structural and functional imaging (fMRI) study revealed that patients with AD displayed decreased activation in response to aversive faces in bilateral fusiform gyrus, right middle frontal gyrus, right inferior parietal lobule, and left cerebellum, which were largely explained by gray matter differences. Moreover, because an increased activation of the anterior cingulate cortex (ACC) was correlated with less previous lifetime alcohol intake, longer abstinence periods, and less subsequent binge drinking, the authors concluded that chronic alcohol use appears to impair treatment outcome via exerting neurotoxic effects on the ACC (Charlet et al., 2014). Schuckit et al. (2016) showed that baseline fMRI activation patterns, specifically in insular and frontal regions, predicted heavy drinking and alcohol problems in AD 5 years later. Importantly, only poorer emotion recognition performance (but not emotional perspective-taking and affective responsiveness) at baseline was able to predict treatment outcomes of AD, such as relapse or dropout (Rupp et al., 2017). Taken together, these findings suggest that patients with AD display difficulties in the recognition and integration of emotions from faces, voices, and other sources. These deficits might be partially induced by neurotoxic effects of chronic alcohol intake, but they may also be useful for the prediction of treatment success.

Emotional empathy In an emotional contagion task using emotional facial expressions, AD patients reported fewer positive and more negative emotions when confronted with, respectively, valent face stimuli than controls, indicating emotional empathy to be lower for positive emotions, but increased

for negative. In addition, the mimicry of angry faces, as assessed by filming the participants while they were watching the stimuli (Dethier and Blairy, 2012), was more pronounced in Type-II AD patients (according to Cloninger (1987) subtypes of alcoholism) than Type-I patients and controls (Dethier and Blairy, 2012). In contrast, emotional empathy was not altered in AD individuals in a study applying the MET, although their cognitive empathy was impaired (Grynberg et al., 2017).

Perspective-taking and ToM The most commonly used ToM taskdthe RMETdhas been applied in seven published studies, of which four found a significant deficiency in “mind reading” in individuals with AD (Gizewski et al., 2013; Maurage et al., 2011; Thoma et al., 2013; Nandrino et al., 2014), while three did not report any group differences (Kornreich et al., 2011; Matyassy et al., 2006; Kopera et al., 2018). A recent meta-analysis that included all of these studiesdwith the exception of the newest one (Kopera et al., 2018)dplus an unpublished doctoral thesis came to the conclusion of a significant deficit in RMET performance with a moderate effect size (Cohen’s d ¼ 0.46) existing in patients with AD (Bora and Zorlu, 2017). An fMRI study additionally reported that a diagnosis of AD was associated with decreased activity of the right anterior insular cortex while performing the RMET (Gizewski et al., 2013). The second most commonly used ToM task is the Faux pas test: two studies have found impaired performance on this task in AD (Thoma et al., 2013; Cox et al., 2018), while one has not (Amenta et al., 2013). Deficits in the understanding of irony and humor have also been reported for AD individuals in three independent studies (Uekermann et al., 2007; Cermak et al., 1989; Amenta et al., 2013), although one further study did not detect changes in the Strange Stories test assessing the comprehension of metaphors and irony (Bosco et al., 2014). Furthermore, two video-based paradigms for the assessment of ToM and mental perspective-taking have revealed a deficit in the affective component of ToM, while its cognitive component was preserved (Nandrino et al., 2014; Maurage et al., 2016). AD individuals were also shown to display deficits in False Belief tasks (specifically in the tracking of others beliefs) (Maurage et al., 2015) and in social problem solving, both of which depend on the ability to infer mental states of others (Schmidt et al., 2016). Two meta-analyses so far have assessed the effect sizes across several ToM paradigms and both concluded that ToM abilities are impaired in AD individuals: the first of these included 8 studies with 187 patients and 187 controls and found a very strong effect size (Hedges’ g ¼ 1.62) (Onuoha et al., 2016). The second one included

Social cognition in addiction Chapter | 5

12 studies with 317 patients and 298 controls and reported a moderate effect size (d ¼ 0.58) (Bora and Zorlu, 2017). Importantly, ToM deficits in AD have been shown to be correlated with executive and memory functions as well as with depressive symptoms (Uekermann et al., 2007; Thoma et al., 2013; Nandrino et al., 2014), and to increase with the duration of AD (Gizewski et al., 2013; Cox et al., 2018). Taken together, a number of studies have shown impairments in ToM and perspective-taking abilities that might be induced or at least partially caused by chronic alcohol intake. Moreover, these disturbed abilities might be linked to other cognitive or emotional impairments and may not be changes specific to mentalizing.

Social decision-making AD individuals have been consistently reported to reject unfair offers in the ultimatum game more often than healthy controls (Tsukue et al., 2015; Brevers et al., 2013, 2015). The proportion of rejected unfair offers has been shown to be correlated with elevated physiological arousal as assessed by the skin conductance response (Brevers et al., 2015) as well as with reward impulsivity measured using a delay discounting task (Tsukue et al., 2015). These findings indicate that AD individuals have a higher sensitivity to unfairness, or that they have more problems with controlling their emotions in unfair situations, resulting in more aggressive or retributive responses (Tsukue et al., 2015; Brevers et al., 2013, 2015).

Moral decision-making Two studies have demonstrated that patients with AD endorse utilitarian choices significantly more than controls (Khemiri et al., 2012; Carmona-Perera et al., 2014). Another study also observeddalthough below the margin to be reported as statistically significantdelevated utilitarian choices in individuals with AD (Kornreich et al., 2013b). Moreover, in a group of polysubstance users, severity of alcohol use specifically predicted the proportion of utilitarian judgments (Carmona-Perera et al., 2012). Notably, moral judgment was changed in spite of individuals having sufficient knowledge of explicit social and moral norms and normal responses to non-moral or impersonal moral dilemmas (Khemiri et al., 2012). Moreover, neither non-social decision-making, measured with the Iowa Gambling Task, nor trait impulsivity and mood predicted moral judgments (Carmona-Perera et al., 2014; Kornreich et al., 2013b). However, poorer decoding of fear and disgust from faces was correlated with more utilitarian choices (Carmona-Perera et al., 2014) and AD individuals did not show aversive psychophysiological responses (heart rate) to personal moral violations (Carmona-Perera et al., 2013). To summarize, AD seems to be associated with

67

changes in moral judgment and, specifically, it may be that alcohol-induced damage to the ventromedial prefrontal cortex (VMPFC) causes emotional dysfunction, leading to a more utilitarian approach to moral judgment (Khemiri et al., 2012).

Cannabis Emotion recognition and cognitive empathy Several studies have investigated emotion recognition from faces in moderate, heavy, and dependent cannabis users and all reported a generalized deficit in this kind of emotion processing (Platt et al., 2010; Hindocha et al., 2014; Bayrakci et al., 2015; Huijbregts et al., 2014). This effect was found in current (Platt et al., 2010; Hindocha et al., 2014; Huijbregts et al., 2014) and medium-term abstinent users (mean 3.2 months) (Bayrakci et al., 2015), thus the deficits are likely not fully explained by acute and postacute detrimental effects of D-9-tetrahydrocannabinol (THC) on emotion recognition (Hindocha et al., 2015). Importantly, higher levels of schizotypy in cannabis users also failed to explain the results in one of the studies (Hindocha et al., 2014) and deficits might be more pronounced in the recognition of negative emotions (Bayrakci et al., 2015). Interestingly, in a well-powered fMRI investigation, it was demonstrated that adolescent cannabis users showed a stronger activation of the bilateral amygdala in response to angry faces, while their cortical areas did not discriminate angry versus neutral faces, unlike in controls (Spechler et al., 2015). The authors concluded that early cannabis use might be associated with hypersensitivity to signals of threat, perhaps placing users at risk for mood disorders in adulthood. Moreover, enhanced recognition of angry faces (Ernst et al., 2010), as well as misattribution of sad faces (Fishbein et al., 2016), has been shown to predict later initiation of cannabis use in adolescents. In sum, these findings all point to emotion recognition problems in cannabis users that might be partially present before onset of use and worsened further by acute, postacute, and chronic cannabis effects.

Emotional empathy Unpublished pilot data from our lab suggest that at least moderate use of cannabis (mean 2.8 g/week) was not associated with changes in cognitive and emotional empathy in the MET in a low-powered sample of 21 users versus 21 healthy controls (Diener, 2014). Published data on emotional empathy measured with a behavioral task in cannabis users are not available yet. However, an early interview-based study showed that smoking cannabis acutely decreased affective resonance between the cannabis-intoxicated individuals and their non-intoxicated interaction partners (Janowsky et al., 1979).

68

Cognition and Addiction

Perspective-taking and ToM Perspective-taking abilities seem to be largely intact in chronic cannabis users when measured with the RMET (Platt et al., 2010; Diener, 2014), a cartoon-based fMRI task (Roser et al., 2012), and the MASC (Diener, 2014). Moreover, when assessed using the Eyes and Hinting Test, cannabis use was not related to ToM performance in schizophrenia patients (Helle et al., 2017). However, a small fMRI study found some differences in brain activations (but no performance differences) between chronic cannabis users (n ¼ 15) and controls (n ¼ 14) while watching cartoon stories in which the characters show various facets of cooperative behavior (Roser et al., 2012).

Social decision-making Studies investigating social behavior with game-theoretical approaches in cannabis users have not yet been published. In our small pilot study in the context of a master thesis, we found no significant differences in prosocial behavior between modest chronic cannabis users and matched controls in three neuroeconomic gamesdthe Promise Task, the Distribution Game, and the Dictator Game. Nevertheless, there was a statistical trend (P ¼ 0.052) for cannabis users to share more money than controls did with the opposite player in the Dictator Game, indicating rather prosocial fairness preferences (Diener, 2014).

Social reward Employing a novel interpersonal pleasant touch fMRI paradigm to dependent, but recently abstinent, male cannabis users and healthy controls, Zimmermann et al. (2019) found that, relative to the controls, cannabis users reported lower reward responsiveness to female touch, as well as decoupling of striatal activations and subjectively reported reward experiences. However, neural processing of pleasant touch in general was seemingly unchanged in dependent cannabis users (Zimmermann et al., 2019).

Stimulants Emotion recognition and cognitive empathy Most studies with cocaine and methamphetamine users have revealed that their ability to identify basic facial affect expressions is largely unimpaired (Hulka et al., 2013; Woicik et al., 2009; Verdejo-Garcia et al., 2010, 2017; Fox et al., 2011; Romero-Ayuso et al., 2016; Payer et al., 2008). However, a few studies have found specific alterations in fear (Ersche et al., 2015; Kemmis et al., 2007; Morgan and Marshall, 2013; Kim et al., 2011) and anger processing (Ersche et al., 2015) from faces in regular users of cocaine or methamphetamine. Moreover, in polysubstance users,

fear and anger recognition performance was negatively correlated with cocaine use intensity (Fernandez-Serrano et al., 2010), while another study did not show an effect of stimulant polysubstance use on facets of cognitive empathy when assessed with complex stimuli (Kroll et al., 2018a). Using a facial affect matching task, an fMRI study in methamphetamine users also did not detect task-related changes, but rather different cortical activation patterns in regions relevant for social cognition (Payer et al., 2008). Non-medical users of methylphenidate without attentiondeficit/hyperactivity disorder (ADHD), who have taken the drug for neuroenhancement purposes, exhibited problems with cognitive empathy for complex emotional scenes measured with the MET (Maier et al., 2015). Interestingly, cocaine users with a comorbid ADHD diagnosis also showed impaired cognitive empathy in the MET (Wunderli et al., 2016). One investigation has shown that chronic cocaine users displayed problems in emotion recognition from voices (prosody) as well as in the detection of matches and mismatches between emotional faces and voices when both were presented together (Hulka et al., 2013). Differences in study sample characteristics may account for some of the discrepant results discussed above, given that most of the studies had relatively small sample sizes and often included stimulant-preferring polysubstance users with further psychiatric comorbidities. Accordingly, Ersche et al. (2015) demonstrated that fear and anger recognition deficits in cocaine users were mainly explained by lower IQ and concurrent opioid dependence, respectively, while an additional impact of ADHD on cognitive empathy has recently been shown (Wunderli et al., 2016). Of note, wellpowered studies did not find alterations in visual emotional processing, but rather deficient prosodic emotion recognition, in relatively pure recreational and dependent cocaine users with a low burden of psychiatric comorbidities (Hulka et al., 2013; Preller et al., 2014b).

Emotional empathy In the Zurich Cocaine Cognition Study (ZuCo2St), recreational and dependent cocaine users (Preller et al., 2014b) as well as stimulant polysubstance users (Kroll et al., 2018a) reported lower emotional empathy ratings to the photorealistic affective stimuli in the MET. In cocaine users, implicit emotional empathy was correlated specifically with weekly and lifetime cocaine dose, and emotional empathy deficits were generally most pronounced in early age-ofonset users (Preller et al., 2014b). Interestingly, comorbid ADHD had an additional impact on emotional empathy but did not explain the empathy impairment in general (Preller et al., 2014b). In a longitudinal analysis of the ZuCo2St sample, it was shown that emotional empathy can recover when cocaine use is reduced or ceased (Vonmoos et al., 2019).

Social cognition in addiction Chapter | 5

An fMRI study has shown that methamphetamine users showed reduced emotional empathy in a cartoon-based task, which was accompanied by lower activation of the orbitofrontal cortex (OFC), both temporal poles, and the right hippocampus, relative to healthy controls (Kim et al., 2010). Finally, neuroenhancement methylphenidate users taking relatively low doses did not exhibit abnormalities in emotional empathy (Maier et al., 2015).

Perspective-taking and ToM Dependent, but not recreational, cocaine users committed more errors in the video-based MASC, suggesting that worse mental perspective-taking is associated with cocaine addiction, yet not recreational use, or related premorbid characteristics (Preller et al., 2014b). Additionally, moderate correlations between task performance and several subjective and objective cocaine intake indices have been found (Preller et al., 2014b). Importantly, a concurrent ADHD diagnosis had a modulating impact on perspectivetaking, i.e., only severe users with a comorbid ADHD symptomatology showed significant impairments (Wunderli et al., 2016; Preller et al., 2014b). Studies using the RMET demonstrated that methamphetamine (Kim et al., 2011; Henry et al., 2009) but not cocaine users (Kemmis et al., 2007; Preller et al., 2014b) displayed alterations of “mind reading” from eye pairs. A single study in methamphetamine users, investigating perspective-taking with a story-based task, found only a trend for impaired ToM abilities in the drug users (Kim et al., 2011). A trend for weaker perspective-taking abilities has also been reported for non-medical methylphenidate users (Maier et al., 2015).

Social decision-making In the ZuCo2St, recreational and dependent cocaine users showed reduced prosocial decisions in comparison with a control group in two social interaction tasks, given that cocaine users preferred higher monetary payoffs for themselves and cared primarily about efficiency and less about fairness (Hulka et al., 2014). As no correlation between fairness preferences and cocaine use intensity was found, the authors proposed that self-serving behavior might represent a predisposition for stimulant use (Hulka et al., 2014); however, in a longitudinal analysis of these data, the reduction in cocaine use was weakly associated with improved social decision-making, indicating that these deficits might be at least partially drug-induced (Vonmoos et al., 2019). Interestingly, it was also reported that methylphenidate neuroenhancement users display altered social decision-making (Maier et al., 2015). Verdejo-Garcia et al. (2017) investigated social decision-making during fMRI in cocaine-dependent individuals with and without a comorbid personality disorder.

69

The acceptance rate for fair and unfair offers was not affected in cocaine users. However, compared with controls, cocaine users displayed reduced activation in the dorsolateral prefrontal cortex during evaluation of unfair offers and reduced activation in the subgenual ACC and the midbrain during rejection of these offers. Additionally, cocaine users showed increased activation in superior frontal and lateral OFC regions during the evaluation of unfair offers, which was correlated with deficient facial affect recognition (Verdejo-Garcia et al., 2017).

Moral decision-making An fMRI study did not find differences in the behavioral responses to moral dilemmas between cocaine-dependent patients and healthy controls, although the patients displayed decreased activation of the ACC, left insula, and brain stem as well as reduced functional connectivity between ACC, thalamus, insula, and brain stem (VerdejoGarcia et al., 2014). Recently, it was additionally shown that cocaine-using incarcerated individuals displayed impaired picture discrimination in the ventral ACC, VMPFC, lateral OFC, and left ventral striatum compared with non-cocaine-using incarcerated individuals when identifying pictures that did or did not depict immoral actions (Caldwell et al., 2015).

Social reward In an interactive social gaze paradigm, cocaine users showed blunted emotional responses and less activation of the VMPFC during social gaze interaction, supporting the assumption that social eye-contact might be less rewarding for them (Preller et al., 2014a). Importantly, the activation of the VMPFC was correlated with the size of the social network of the cocaine users, indicating that a blunted ability to perceive this implicit form of social reward is reflected in diminished real-life social functioning (Preller et al., 2014a). In another complex fMRI experiment, cocaine users also displayed a reduced reward signal in the VMPFC in the context of positive social feedback. The social rewarderelated activation in the VMPFC overlapped with a reduced response to object reward, which was additionally correlated with years of cocaine use (Tobler et al., 2016). As the VMPFC has been proposed to be critically involved in the encoding and maintenance of reward value (Peters and Buchel, 2010), it was proposed that chronic cocaine users suffer from a generalized impairment in value processing, likely generalizing to their social lives (Tobler et al., 2016). However, a recent study investigating aging cocaine users with a Social Incentive Delay Task, in which the positive feedback is simply given by happy faces and short positive statements, did not find an effect of chronic cocaine exposure on this facet of social

70

Cognition and Addiction

reward processing (Bedi et al., 2018). Finally, Hyatt et al. (2012) applied an interactive competitive domino game during fMRI, to investigate social reward in current and former cocaine users. Remarkably, only former but not current cocaine users showed altered activation of the dorsal caudate nucleus compared with controls, indicating changes in the reward processing related to social competition. Notably, the VMPFC was included in this region-ofinterest-based analysis, thus, these results are difficult to compare with subsequent studies on social reward discussed above.

Entactogenes Emotion recognition and cognitive empathy Acute MDMA intake has repeatedly been shown to reduce the identification of negative emotions (Bedi et al., 2010; Hysek et al., 2012, 2014a,b; Kirkpatrick et al., 2014), while one study additionally found increased recognition of positive emotions (Hysek et al., 2012). The valencedependent acute effects of MDMA were found in an emotional face recognition task (Bedi et al., 2010; Hysek et al., 2014a) and the RMET (Hysek et al., 2012), whereas cognitive empathy performance measured with the MET was not affected by acute MDMA intake (Hysek et al., 2014a; Kuypers et al., 2017; Schmid et al., 2014). In contrast to the acute effects, two studies have recently shown that chronic users of MDMA exhibit superior cognitive empathy compared with controls when assessed with complex emotionally laden scenes from the MET (Carlyle et al., 2019; Wunderli et al., 2018). However, as lower cognitive empathy was clearly correlated with higher MDMA concentrations in hair; it was concluded that the differences at the group level were likely explained by higher social affiliation motivations of the users, while at higher chronic doses MDMA might nevertheless impair cognitive empathy (Wunderli et al., 2018). Moreover, the subjective response to a social exclusion paradigm (cyberball game) was not altered in MDMA users (Carlyle et al., 2019; Batschelet et al., 2015), although unpublished MRI data from our group have suggested increased activation of pain-related circuits such as the ACC during exclusion in MDMA users (Batschelet et al., 2015).

Emotional empathy Wunderli et al. (2018) and Carlyle et al. (2019) both showed that chronic MDMA use was not associated with changes of the emotional empathy domain of the MET.

Perspective-taking and ToM In the MASC, chronic MDMA users displayed better performance than well-matched control individuals, indicating

superior mental and emotional perspective-taking in the MDMA users (Wunderli et al., 2018).

Social decision-making Stewart et al. (2014) investigated the acute effects of MDMA on the Ultimatum Game in chronic MDMA users, compared with non-intoxicated controls, and found increased cooperative behavior on the dictator and ultimatum games under the influence of the drug. However, on the second measurement, 3 days after the MDMA intake, the groups did not differ in any of the social decisionmaking parameters. In contrast, in the study of Wunderli et al. (2018), chronic MDMA users (off drug) displayed more prosocial decisions in Distribution and Dictator Games compared with controls.

Opioids Emotion recognition and cognitive empathy Deficits in emotion perception were initially found in detoxified heroin users as well as methadone-maintained heroin users using the picture-based Emotional Facial Expression Decoding Test (Kornreich et al., 2003). In contrast, in a well-powered study, McDonald et al. (2013) reported that only methadone- and buprenorphinemaintained heroin-dependent individuals, but not abstinent heroin users without opioid-maintenance therapy, showed generally impaired emotion recognition in the video-based Awareness of Social Inference Test (TASIT). Importantly, these group differences disappeared if a general cognitive performance measure was introduced as a covariate in the model, suggesting that deficits in emotion recognition are part of a more general cognitive impairment (McDonald et al., 2013). Using the CATS-A and the MET, Kroll et al. (2018b) recently demonstrated emotion recognition/cognitive empathy deficits for faces, voices, and complex affective scenes in a group of nonemedical prescription opioid users (NMPOU) without a history of heroin use, in which recreational or addicted users were included. Interestingly, a global cognitive empathy score was correlated with morphine equivalent opioid concentrations in hair, indicating a dose-response relationship regarding these deficits. Moreover, in this study, performance in executive function tasks was also correlated with cognitive empathy measures in NMPOU; however, accounting for executive function in the statistical model did not change the group differences in cognitive empathy (Kroll et al., 2018b). The same study population of NMPOU were also tested for their physiological stress response to social exclusion, with an interesting finding: on the one hand, NMPOU showed hyperreactivity of the endocrinological stress axis and poorer regulation of the parasympathetic nervous system in response to social

Social cognition in addiction Chapter | 5

exclusion, while on the other hand their self-ratings suggested that these users were aware but less emotionally affected by the rejection (Kroll et al., 2019). Taken together, these results suggest thatdat least recent or currentdopioid use is associated with emotion recognition and cognitive empathy impairments, which cooccurs withdbut may not be completely explained bydbroader cognitive deficits.

Emotional empathy Studies in heroin users are thus far lacking, but NMPOU were not impaired in emotional empathy measured with the MET (Kroll et al., 2018b). Nonetheless, preliminary results suggest that heroin users showed lower emotional empathy in an Empathy-for-Pain task (Sara L. Kroll, Linköping University, Sweden, personal communication).

Perspective-taking and ToM Again, methadone- and buprenorphine-maintained heroindependent individuals, but not abstinent heroin users without opioid-maintenance therapy, showed worse social inference performance in the TASIT, specifically regarding their ability to detect sarcasm. Social inference problems in these patients were also largely explained by their global cognitive impairments (McDonald et al., 2013).

Social decision-making Hou et al. (2016) investigated a small sample of heroindependent individuals with regard to their decisions in the Ultimatum Game and found that, in contrast to healthy controls, heroin users displayed higher rejection rates of most unfair offers under low-offer conditions, while most unfair offers were more likely to be accepted in high-offer conditions. Furthermore, rejection rates of most unfair offers under low-offer conditions were correlated with trait impulsivity measured using the Barratt impulsiveness scale. The authors concluded that heroin users acted more impulsively under low-offer conditions but became more tolerant of inequity specifically in the high-offer condition (Hou et al., 2016).

Polysubstance use Emotion recognition and cognitive empathy Using a computer-based Ekman Faces Test, deficits in recognition of facial emotion expressions have been reported for substance-dependent individuals with polysubstance use (Fernandez-Serrano et al., 2010; Verdejo-Garcia et al., 2007).

71

Across both of these studies, the effects on fear recognition were largest compared with other emotions. In contrast, a group of mainly recreational polysubstance users did not show any deficits in cognitive empathy (MET) and emotion recognition measures (CATS-A) (Kroll et al., 2018a).

Emotional empathy Recreational polysubstance users showed lower emotional empathy when assessed with the MET, and these problems clearly increased with the number of substances used (Kroll et al., 2018a). In contrast, only the number of substances, but not single substance classes, such as stimulants, predicted this deficit when introduced in multiple regression models.

Perspective-taking and ToM In a single study, primarily recreational polysubstance users did not show any abnormalities in the MASC, which measures mental and emotional perspective-taking (Kroll et al., 2018a).

Moral decision-making Two studies have shown that polysubstance-dependent individuals displayed more utilitarian choices when responding to moral dilemmas (Kornreich et al., 2013b; Carmona-Perera et al., 2012).

Discussion Although the importance of impairments in social cognition and interaction for the development, maintenance, and treatment of SUD is self-evident (Yacubian and Buchel, 2009), only few studies have yet objectified socio-cognitive dysfunctions in substance users by means of psychological test paradigms. The only exception to this is emotion recognition in chronic alcohol use, as it has been researched for almost 30 years in a large number of studies, which have provided comprehensive evidence for a generalized emotion recognition deficit in AD that seem to bedat least in partdinduced by chronic alcohol exposure. While discussing most of the yet available studies above, it was demonstrated that each substance class is associated with a range of specific impairments in social cognitive functions (see Table 5.1 for an overview). However, it was also shown above that there are a number of blank spots on the social cognition and interaction map of substance use that have to be filled in by future studies.

72

Cognition and Addiction

TABLE 5.1 Changes in performance and behavioral measures of social cognition and interaction in addiction and chronic drug use.

Substance

Emotion recognition and cognitive empathy

Emotional empathy

Perspective-taking and Theory of Mind

Social decisionmaking

Moral decisionmaking

Social reward

Alcohol

Y

[, Y, /

Y

Y

Y

?

Cannabis

Y

?(/)

/

?(/)

?

Y

Stimulants

Y, /

Y

Y, /

Y

Y

Y, /

Opioids

Y, /

/

Y, /

Y

?

?

Entactogens

[

/

/

[, /

?

?

Polysubstance use

Y, /

Y

/

?

Y

?

Y, finding(s) suggest(s) decrease or impairment; /, finding(s) suggest(s) no change; [, finding(s) suggest(s) increase or superiority; ?, not published yet; brackets represent data from so far unpublished pilot studies.

Open questions It must be noted that, for most of the substances and functions, the number of available studies is low and, furthermore, most of the studies that have been reported have had rather small sample sizes. Therefore, more studies with larger samples are needed to better characterize the true, specific, socio-cognitive profile of each substance. Although most substance users consume more than only one psychoactive compound, we also currently have only little information about how different substances might interact with each other regarding social cognition. As an example, we have shown detrimental effects of increasing polydrug use on emotional empathy (Kroll et al., 2018a), although this study was too small to directly assess specific drug combinations. It must also be noted that protective drug effects are possible, e.g., when considering that MDMA use has been associated with superior sociocognitive abilities (Wunderli et al., 2016). Thus, in samples with mixed stimulant and MDMA use, potential negative effects of stimulants might be compensated by higher socio-cognitive competences of people using MDMA. Moreover, most of the studies discussed have used relatively passive “first-person” paradigms, such as emotion recognition or ToM tasks, which probably provide only insufficient information about the real daily-life social problems users experience in their interactions with other people. Thus, more “second-person” approaches assessing behavioral and neuronal changes in real-life social interactions should be employed, as they are likely more ecologically valid (Schilbach, 2016). In addition, the relationship between several facets of social cognition (e.g., between emotional empathy and perspective-taking abilities), as well as between social cognition and non-social cognition, needs to be addressed in further studies, as several studies have indicated that

socio-cognitive impairments coincide with changes, specifically reductions, in other cognitive functions (Uekermann et al., 2007; Quaglino et al., 2015; Thoma et al., 2013; Nandrino et al., 2014).1 Nevertheless, it is important to better understand the origin and specificity of socio-cognitive disturbances, especially if targeted socio-cognitive training schemes are to be developed for the improvement of treatment outcomes (see below). Remarkably, it is also not yet fully clear how lifestyle differences, intellectual abilities, and psychiatric comorbidities might influence socio-cognitive deficits in substance users: for example, thus far it has been shown that facial affect recognition deficits of cocaine users might be explained by their opioid co-consumption and lower IQ (Ersche et al., 2015), while perspective-taking and cognitive empathy deficits may only appear if a comorbid ADHD diagnosis is present (Wunderli et al., 2016; Preller et al., 2014b). Finally, the question also of if (and which) sociocognitive dysfunctions are predisposed or drug-induced is important for the implementation of new treatments, as acquired impairments are likely to be easier to rehabilitate than predisposed impairments and perhaps “hardwired” dysfunctions. To date, some studies have shown that socio-cognitive problems predict initiation and onset of drug use (Ernst et al., 2010; Fishbein et al., 2016), while others clearly showed recovery of such functions with prolonged abstinence (Vonmoos et al., 2019; Erol et al., 2017). It has also been proposed that mentalizing deficits in SUD share similarities with such impairments in developmental disorders, such as autism and borderline personality disorder, which argues that such deficits may be rather predisposed (Savov and Atanassov, 2013). 1. Of note, no coherent theory of social cognition or its assumed subcomponents, such as “cognitive empathy” and ToM, has been developed so far, such that different definitions exist and their concepts sometimes overlap (e.g., emotion perception, cognitive empathy, and ToM).

Social cognition in addiction Chapter | 5

The notion that drugs might be instrumentalized by some users to self-medicate social cognitive deficits has also been suggested (Fein, 2015). However, predispositions to, as well as chronic drug effects on, social cognition deficits might be substance- as well as function-specific and future longitudinal studies are needed to delineate and characterize these different factors for each substance-using population.

Relevance for treatment It has been shown that specific impairments in social cognitive functions of substance users are related to reallife social functioning (Preller et al., 2014a,b; Kornreich et al., 2002; Kopera et al., 2018; Janowsky et al., 1979; Bedi et al., 2018) and that they can be used to predict treatment outcomes (Charlet et al., 2014; Schuckit et al., 2016; Rupp et al., 2017). Although not yet investigated, it is also conceivable that these disturbances of social perception, valuation, and behavior may directly affect the therapeutic relationship between substance users and their psychiatrist or psychologist and, thus, hamper the success of their addiction treatment. Thus, interpersonal problems affecting all social relationships, including relationships with therapists, might partially explain the high relapse rates after treatment in most SUD (Milivojevic and Sinha, 2018). Moreover, as social cognition and prosocial behavior can covary with alterations in substance use across time, indicating their potential for plasticity (Vonmoos et al., 2019; Erol et al., 2017), new treatments of SUD might address these specific social problems more distinctively to improve the therapeutic relationship and their overall social functional level and, consequently, the treatment success. Promising results in this regard are emerging from other neuropsychiatric disorders, such as traumatic brain injury (Milders, 2018; Vallat-Azouvi et al., 2018), schizophrenia (Kurtz et al., 2016; Fiszdon and Reddy, 2012; Wolwer and Frommann, 2011; Vaskinn et al., 2019), autism (Berggren et al., 2018; Happe and Conway, 2016; Bishop-Fitzpatrick et al., 2013), and depression (Weightman et al., 2019) suggesting that sociocognitive abilities are trainable per se and that this can also have a positive impact on treatment outcome. However, social cognitive training schemes specialized for SUD are not available thus far, although recently some encouraging results emerged from very small studies with SUD patients applying mentalization-based interventionsda psychodynamic approach shown to be effective in borderline personality disorder that targets reflective functions such as perspective-taking (Philips et al., 2018; Suchman et al., 2017). Nevertheless, as is visible in the summary provided in Table 5.1, a single treatment or training approach for all kinds of SUD will not be feasible, as the substance user populations differ in their deficits and, therefore, substance-

73

specific (if not patient-specific) therapy modules have to be developed (Rolland et al., 2019).

Conclusion When taken together, the research into SUD shows substance-specific profiles of impairments in a variety of socio-cognitive functions and indicates that predisposed or drug-related changes in social reward and social cognition may contribute to the social problems and the decay of social relationships in people with SUD. Beyond that, although not investigated yet, it is likely that disturbances in social perception and behavior compromise any social interactions, including therapeutic relationships, thus hindering the success of each addiction treatment approach. Accordingly, interpersonal problems related to social cognition deficits might partially account for high relapse rates for those enrolled in any kind of psychological or psychopharmacological treatment developed so far. Additionally, specific social reward deficits might also explain why the social consequences of drug use (e.g., imprisonment or familial problems) do not discourage substance-dependent individuals enough to cease using the drug (Preller et al., 2014a). Therefore, a new focus on psychosocial treatments of stimulant addiction might be needed to address these social dysfunctions to improve the therapeutic relationship and treatment success (Quednow, 2016). Specifically, the rehabilitation of social reward might be a promising avenue for providing an alternative to bypass the accrual of drug-related reward system maladaptations in SUD (Quednow, 2017; Preller et al., 2014a; Verdejo-Garcia, 2014).

Acknowledgments The author is grateful to Dr David Cole for critical comments and suggestions regarding the first draft of this chapter.

References Amenta, S., Noel, X., Verbanck, P., Campanella, S., 2013. Decoding of emotional components in complex communicative situations (irony) and its relation to empathic abilities in male chronic alcoholics: an issue for treatment. Alcohol Clin. Exp. Res. 37 (2), 339e347. Amodio, D.M., Frith, C.D., 2006. Meeting of minds: the medial frontal cortex and social cognition. Nat. Rev. Neurosci. 7 (4), 268e277. Banziger, T., Grandjean, D., Scherer, K.R., 2009. Emotion recognition from expressions in face, voice, and body: the Multimodal Emotion Recognition Test (MERT). Emotion 9 (5), 691e704. Baron-Cohen, S., O’Riordan, M., Stone, V., Jones, R., Plaisted, K., 1999. Recognition of faux pas by normally developing children and children with Asperger syndrome or high-functioning autism. J. Autism Dev. Disord. 29 (5), 407e418.

74

Cognition and Addiction

Baron-Cohen, S., Wheelwright, S., Hill, J., Raste, Y., Plumb, I., 2001. The “Reading the Mind in the Eyes” Test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism. J. Child Psychol. Psychiatry 42 (2), 241e251. Batschelet, H., Siegle, C., Quednow, B.B., Preller, K.H., 2015. Increased Neural Response to Social Exclusion in Chronic MDMA (“Ecstasy”) Users. DGPPN, Berlin. Bayrakci, A., Sert, E., Zorlu, N., Erol, A., Saricicek, A., Mete, L., 2015. Facial emotion recognition deficits in abstinent cannabis dependent patients. Compr. Psychiatry 58, 160e164. Bedi, G., Hyman, D., de Wit, H., 2010. Is ecstasy an “empathogen”? Effects of þ/3,4-methylenedioxymethamphetamine on prosocial feelings and identification of emotional states in others. Biol. Psychiatry 68 (12), 1134e1140. Bedi, G., Hao, X., Van Dam, N.T., Cooper, Z.D., Rubin, E., Vadhan, N.P., et al., 2018. Social motivational processing and interpersonal function in aging cocaine smokers. Addict. Biol. Available online: http://doi. org/10.1111/adb.12669. Berggren, S., Fletcher-Watson, S., Milenkovic, N., Marschik, P.B., Bolte, S., Jonsson, U., 2018. Emotion recognition training in autism spectrum disorder: a systematic review of challenges related to generalizability. Dev. Neurorehabil. 21 (3), 141e154. Bishop-Fitzpatrick, L., Minshew, N.J., Eack, S.M., 2013. A systematic review of psychosocial interventions for adults with autism spectrum disorders. J. Autism Dev. Disord. 43 (3), 687e694. Bora, E., Zorlu, N., 2017. Social cognition in alcohol use disorder: a metaanalysis. Addiction 112 (1), 40e48. Bosco, F.M., Capozzi, F., Colle, L., Marostica, P., Tirassa, M., 2014. Theory of mind deficit in subjects with alcohol use disorder: an analysis of mindreading processes. Alcohol Alcohol 49 (3), 299e307. Brevers, D., Noel, X., Ermer, E., Dabiri, D., Verbanck, P., Kornreich, C., 2013. Unfairness sensitivity and social decision-making in individuals with alcohol dependence: a preliminary study. Drug Alcohol Depend. 133 (2), 772e775. Brevers, D., Noel, X., Hanak, C., Verbanck, P., Kornreich, C., 2015. On the relationship between emotional state and abnormal unfairness sensitivity in alcohol dependence. Front. Psychol. 6, 983. Caldwell, B.M., Harenski, C.L., Harenski, K.A., Fede, S.J., Steele, V.R., Koenigs, M.R., et al., 2015. Abnormal frontostriatal activity in recently abstinent cocaine users during implicit moral processing. Front. Hum. Neurosci. 9, 565. Campbell-Meiklejohn, D.K., Bach, D.R., Roepstorff, A., Dolan, R.J., Frith, C.D., 2010. How the opinion of others affects our valuation of objects. Curr. Biol. 20 (13), 1165e1170. Carlyle, M., Stevens, T., Fawaz, L., Marsh, B., Kosmider, S., Morgan, C.J., 2019. Greater empathy in MDMA users. J. Psychopharmacol. 33 (3), 295e304. Carmona-Perera, M., Verdejo-Garcia, A., Young, L., MolinaFernandez, A., Perez-Garcia, M., 2012. Moral decision-making in polysubstance dependent individuals. Drug Alcohol Depend. 126 (3), 389e392. Carmona-Perera, M., Reyes Del Paso, G.A., Perez-Garcia, M., VerdejoGarcia, A., 2013. Heart rate correlates of utilitarian moral decisionmaking in alcoholism. Drug Alcohol Depend. 133 (2), 413e419. Carmona-Perera, M., Clark, L., Young, L., Perez-Garcia, M., VerdejoGarcia, A., 2014. Impaired decoding of fear and disgust predicts utilitarian moral judgment in alcohol-dependent individuals. Alcohol Clin. Exp. Res. 38 (1), 179e185.

Cermak, L.S., Verfaellie, M., Letourneau, L., Blackford, S., Weiss, S., Numan, B., 1989. Verbal and nonverbal right hemisphere processing by chronic alcoholics. Alcohol Clin. Exp. Res. 13 (5), 611e616. Charlet, K., Schlagenhauf, F., Richter, A., Naundorf, K., Dornhof, L., Weinfurtner, C.E., et al., 2014. Neural activation during processing of aversive faces predicts treatment outcome in alcoholism. Addict. Biol. 19 (3), 439e451. Christensen, J.F., Gomila, A., 2012. Moral dilemmas in cognitive neuroscience of moral decision-making: a principled review. Neurosci. Biobehav. Rev. 36 (4), 1249e1264. Clifford, S., Iyengar, V., Cabeza, R., Sinnott-Armstrong, W., 2015. Moral foundations vignettes: a standardized stimulus database of scenarios based on moral foundations theory. Behav. Res. Methods 47 (4), 1178e1198. Cloninger, C.R., 1987. Neurogenetic adaptive mechanisms in alcoholism. Science 236 (4800), 410e416. Couture, S.M., Penn, D.L., Roberts, D.L., 2006. The functional significance of social cognition in schizophrenia: a review. Schizophr. Bull. 32 (Suppl. 1), S44eS63. Cox, S., Bertoux, M., Turner, J.J.D., Moss, A., Locker, K., Riggs, K., 2018. Aspects of alcohol use disorder affecting social cognition as assessed using the Mini Social and Emotional Assessment (miniSEA). Drug Alcohol Depend. 187, 165e170. Dethier, M., Blairy, S., 2012. Capacity for cognitive and emotional empathy in alcohol-dependent patients. Psychol. Addict. Behav. 26 (3), 371e383. D’Hondt, F., de Timary, P., Bruneau, Y., Maurage, P., 2015. Categorical perception of emotional facial expressions in alcohol-dependence. Drug Alcohol Depend. 156, 267e274. Diener, P., 2014. Soziale Kognition bei Cannabiskonsumenten (Master thesis). University of Zurich, Zurich. Dziobek, I., Fleck, S., Kalbe, E., Rogers, K., Hassenstab, J., Brand, M., et al., 2006. Introducing MASC: a movie for the assessment of social cognition. J. Autism Dev. Disord. 36 (5), 623e636. Dziobek, I., Rogers, K., Fleck, S., Bahnemann, M., Heekeren, H.R., Wolf, O.T., et al., 2008. Dissociation of cognitive and emotional empathy in adults with Asperger syndrome using the Multifaceted Empathy Test (MET). J. Autism Dev. Disord. 38 (3), 464e473. Ekman, P., Friesen, W.V., 1976. Pictures of Facial Affect. Consulting Psychologists Press, Palo Alto. Ernst, M., Luckenbaugh, D.A., Moolchan, E.T., Temple, V.A., Jenness, J., Korelitz, K.E., et al., 2010. Decision-making and facial emotion recognition as predictors of substance-use initiation among adolescents. Addict. Behav. 35 (3), 286e289. Erol, A., Akyalcin Kirdok, A., Zorlu, N., Polat, S., Mete, L., 2017. Empathy, and its relationship with cognitive and emotional functions in alcohol dependency. Nord. J. Psychiatry 71 (3), 205e209. Ersche, K.D., Hagan, C.C., Smith, D.G., Jones, P.S., Calder, A.J., Williams, G.B., 2015. In the face of threat: neural and endocrine correlates of impaired facial emotion recognition in cocaine dependence. Transl. Psychiatry 5, e570. Fein, G., Key, K., Szymanski, M.D., 2010. ERP and RT delays in longterm abstinent alcoholics in processing of emotional facial expressions during gender and emotion categorization tasks. Alcohol Clin. Exp. Res. 34 (7), 1127e1139. Fein, G., 2015. Commentary on Maurage et al.: theory of Mind Difficulties in Patients with Alcohol Dependence. Alcohol Clin. Exp. Res. 39 (7), 1118e1119.

Social cognition in addiction Chapter | 5

Fernandez-Serrano, M.J., Lozano, O., Perez-Garcia, M., VerdejoGarcia, A., 2010. Impact of severity of drug use on discrete emotions recognition in polysubstance abusers. Drug Alcohol Depend. 109 (1e3), 57e64. Fishbein, D.H., Novak, S.P., Ridenour, T.A., Thornburg, V., Hammond, J., Brown, J., 2016. Neurocognitive characteristics of early marijuana use initiation in adolescents: a signature mapping analysis. J. Stud. Alcohol Drugs 77 (3), 431e440. Fisher, C.B., 2011. Addiction research ethics and the Belmont principles: do drug users have a different moral voice? Subst. Use Misuse 46 (6), 728e741. Fiske, S.T., Taylor, S.E., 2013. In: Carmichael, M. (Ed.), Social Cognition: From Brains to Culture. SAGE, London. Fiszdon, J.M., Reddy, L.F., 2012. Review of social cognitive treatments for psychosis. Clin. Psychol. Rev. 32 (8), 724e740. Foisy, M.L., Philippot, P., Verbanck, P., Pelc, I., van der Straten, G., Kornreich, C., 2005. Emotional facial expression decoding impairment in persons dependent on multiple substances: impact of a history of alcohol dependence. J. Stud. Alcohol 66 (5), 673e681. Foisy, M.L., Kornreich, C., Fobe, A., D’Hondt, L., Pelc, I., Hanak, C., et al., 2007. Impaired emotional facial expression recognition in alcohol dependence: do these deficits persist with midterm abstinence? Alcohol Clin. Exp. Res. 31 (3), 404e410. Fox, H.C., Bergquist, K.L., Casey, J., Hong, K.A., Sinha, R., 2011. Selective cocaine-related difficulties in emotional intelligence: relationship to stress and impulse control. Am. J. Addict. 20 (2), 151e160. Freeman, C.R., Wiers, C.E., Sloan, M.E., Zehra, A., Ramirez, V., Wang, G.J., et al., 2018. Emotion recognition biases in alcohol use disorder. Alcohol Clin. Exp. Res. Available online: http://doi.org/10. 1111/acer.13802. Gizewski, E.R., Muller, B.W., Scherbaum, N., Lieb, B., Forsting, M., Wiltfang, J., et al., 2013. The impact of alcohol dependence on social brain function. Addict. Biol. 18 (1), 109e120. Green, M.F., 2016. Impact of cognitive and social cognitive impairment on functional outcomes in patients with schizophrenia. J. Clin. Psychiatry 77 (Suppl. 2), 8e11. Greene, J.D., Sommerville, R.B., Nystrom, L.E., Darley, J.M., Cohen, J.D., 2001. An fMRI investigation of emotional engagement in moral judgment. Science 293 (5537), 2105e2108. Grynberg, D., Maurage, P., Nandrino, J.L., 2017. Preserved affective sharing but impaired decoding of contextual complex emotions in alcohol dependence. Alcohol Clin. Exp. Res. 41 (4), 779e785. Happe, F., Conway, J.R., 2016. Recent progress in understanding skills and impairments in social cognition. Curr. Opin. Pediatr. 28 (6), 736e742. Hare, T.A., Camerer, C.F., Knoepfle, D.T., Rangel, A., 2010. Value computations in ventral medial prefrontal cortex during charitable decision making incorporate input from regions involved in social cognition. J. Neurosci. 30 (2), 583e590. Harris, R.J., Young, A.W., Andrews, T.J., 2014. Dynamic stimuli demonstrate a categorical representation of facial expression in the amygdala. Neuropsychologia 56, 47e52. Helle, S., Loberg, E.M., Gjestad, R., Schnakenberg Martin, A.M., Lysaker, P.H., 2017. The positive link between executive function and lifetime cannabis use in schizophrenia is not explained by current levels of superior social cognition. Psychiatry Res. 250, 92e98.

75

Henry, J.D., Mazur, M., Rendell, P.G., 2009. Social-cognitive difficulties in former users of methamphetamine. Br. J. Clin. Psychol. 48 (Pt 3), 323e327. Hindocha, C., Wollenberg, O., Carter Leno, V., Alvarez, B.O., Curran, H.V., Freeman, T.P., 2014. Emotional processing deficits in chronic cannabis use: a replication and extension. J. Psychopharmacol. 28 (5), 466e471. Hindocha, C., Freeman, T.P., Schafer, G., Gardener, C., Das, R.K., Morgan, C.J., et al., 2015. Acute effects of delta-9tetrahydrocannabinol, cannabidiol and their combination on facial emotion recognition: a randomised, double-blind, placebo-controlled study in cannabis users. Eur. Neuropsychopharmacol. 25 (3), 325e334. Holland, C.A.C., Ebner, N.C., Lin, T., Samanez-Larkin, G.R., 2018. Emotion identification across adulthood using the Dynamic FACES database of emotional expressions in younger, middle aged, and older adults. Cognit. Emot. 1e13. Homer, B.D., Solomon, T.M., Moeller, R.W., Mascia, A., DeRaleau, L., Halkitis, P.N., 2008. Methamphetamine abuse and impairment of social functioning: a review of the underlying neurophysiological causes and behavioral implications. Psychol. Bull. 134 (2), 301e310. Homer, B.D., Halkitis, P.N., Moeller, R.W., Solomon, T.M., 2013. Methamphetamine use and HIV in relation to social cognition. J. Health Psychol. 18 (7), 900e910. Hou, Y., Zhao, L., Yao, Q., Ding, L., 2016. Altered economic decisionmaking in abstinent heroin addicts: evidence from the ultimatum game. Neurosci. Lett. 627, 148e154. Houser, D., McCabe, K., 2009. Chapter 5 - Experimental neuroeconomics and non-cooperative games. In: Glimcher, P.W., Camerer, C.F., Fehr, E., Poldrack, R.A. (Eds.), Neuroeconomics. Academic Press, London, pp. 47e62. Huijbregts, S.C., Griffith-Lendering, M.F., Vollebergh, W.A., Swaab, H., 2014. Neurocognitive moderation of associations between cannabis use and psychoneuroticism. J. Clin. Exp. Neuropsychol. 36 (8), 794e805. Hulka, L.M., Preller, K.H., Vonmoos, M., Broicher, S.D., Quednow, B.B., 2013. Cocaine users manifest impaired prosodic and cross-modal emotion processing. Front. Psychiatry 4, 98. Hulka, L.M., Eisenegger, C., Preller, K.H., Vonmoos, M., Jenni, D., Bendrick, K., et al., 2014. Altered social and non-social decisionmaking in recreational and dependent cocaine users. Psychol. Med. 44 (5), 1015e1028. Hyatt, C.J., Assaf, M., Muska, C.E., Rosen, R.I., Thomas, A.D., Johnson, M.R., et al., 2012. Reward-related dorsal striatal activity differences between former and current cocaine dependent individuals during an interactive competitive game. PLoS One 7 (5), e34917. Hysek, C.M., Domes, G., Liechti, M.E., 2012. MDMA enhances “mind reading” of positive emotions and impairs “mind reading” of negative emotions. Psychopharmacology (Berl.) 222 (2), 293e302. Hysek, C.M., Schmid, Y., Simmler, L.D., Domes, G., Heinrichs, M., Eisenegger, C., et al., 2014a. MDMA enhances emotional empathy and prosocial behavior. Soc. Cognit. Affect Neurosci. 9 (11), 1645e1652. Hysek, C.M., Simmler, L.D., Schillinger, N., Meyer, N., Schmid, Y., Donzelli, M., et al., 2014b. Pharmacokinetic and pharmacodynamic effects of methylphenidate and MDMA administered alone or in combination. Int. J. Neuropsychopharmacol. 17 (3), 371e381.

76

Cognition and Addiction

Izuma, K., Saito, D.N., Sadato, N., 2008. Processing of social and monetary rewards in the human striatum. Neuron 58 (2), 284e294. Janowsky, D.S., Clopton, P.L., Leichner, P.P., Abrams, A.A., Judd, L.L., Pechnick, R., 1979. Interpersonal effects of marijuana. A model for the study of interpersonal psychopharmacology. Arch. Gen. Psychiatry 36 (7), 781e785. Kemmis, L., Hall, J.K., Kingston, R., Morgan, M.J., 2007. Impaired fear recognition in regular recreational cocaine users. Psychopharmacology (Berl.) 194 (2), 151e159. Khemiri, L., Guterstam, J., Franck, J., Jayaram-Lindstrom, N., 2012. Alcohol dependence associated with increased utilitarian moral judgment: a case control study. PLoS One 7 (6), e39882. Kim, Y.T., Lee, J.J., Song, H.J., Kim, J.H., Kwon, D.H., Kim, M.N., et al., 2010. Alterations in cortical activity of male methamphetamine abusers performing an empathy task: fMRI study. Hum. Psychopharmacol. 25 (1), 63e70. Kim, Y.T., Kwon, D.H., Chang, Y., 2011. Impairments of facial emotion recognition and theory of mind in methamphetamine abusers. Psychiatry Res. 186 (1), 80e84. Kirkpatrick, M.G., Lee, R., Wardle, M.C., Jacob, S., de Wit, H., 2014. Effects of MDMA and Intranasal oxytocin on social and emotional processing. Neuropsychopharmacology 39 (7), 1654e1663. Koenigs, M., Young, L., Adolphs, R., Tranel, D., Cushman, F., Hauser, M., et al., 2007. Damage to the prefrontal cortex increases utilitarian moral judgements. Nature 446 (7138), 908e911. Kopera, M., Trucco, E.M., Jakubczyk, A., Suszek, H., Michalska, A., Majewska, A., et al., 2018. Interpersonal and intrapersonal emotional processes in individuals treated for alcohol use disorder and nonaddicted healthy individuals. Addict. Behav. 79, 8e13. Kornreich, C., Blairy, S., Philippot, P., Hess, U., Noel, X., Streel, E., et al., 2001. Deficits in recognition of emotional facial expression are still present in alcoholics after mid- to long-term abstinence. J. Stud. Alcohol 62 (4), 533e542. Kornreich, C., Philippot, P., Foisy, M.L., Blairy, S., Raynaud, E., Dan, B., et al., 2002. Impaired emotional facial expression recognition is associated with interpersonal problems in alcoholism. Alcohol Alcohol 37 (4), 394e400. Kornreich, C., Foisy, M.L., Philippot, P., Dan, B., Tecco, J., Noel, X., et al., 2003. Impaired emotional facial expression recognition in alcoholics, opiate dependence subjects, methadone maintained subjects and mixed alcohol-opiate antecedents subjects compared with normal controls. Psychiatry Res. 119 (3), 251e260. Kornreich, C., Delle-Vigne, D., Knittel, J., Nerincx, A., Campanella, S., Noel, X., et al., 2011. Impaired conditional reasoning in alcoholics: a negative impact on social interactions and risky behaviors? Addiction 106 (5), 951e959. Kornreich, C., Brevers, D., Canivet, D., Ermer, E., Naranjo, C., Constant, E., et al., 2013a. Impaired processing of emotion in music, faces and voices supports a generalized emotional decoding deficit in alcoholism. Addiction 108 (1), 80e88. Kornreich, C., Brevers, D., Ermer, E., Hanak, C., Verbanck, P., Campanella, S., et al., 2013b. Polysubstance dependent patients display a more utilitarian profile in moral decision-making than alcohol-dependent patients, depressive patients and controls. Drug Alcohol Depend. 132 (3), 434e440. Kornreich, C., Petit, G., Rolin, H., Ermer, E., Campanella, S., Verbanck, P., et al., 2016. Decoding of nonverbal language in alcoholism: a perception or a labeling problem? Psychol. Addict. Behav. 30 (2), 175e183.

Kroll, S.L., Wunderli, M.D., Vonmoos, M., Hulka, L.M., Preller, K.H., Bosch, O.G., et al., 2018a. Socio-cognitive functioning in stimulant polysubstance users. Drug Alcohol Depend. 190, 94e103. Kroll, S.L., Nikolic, E., Bieri, F., Soyka, M., Baumgartner, M.R., Quednow, B.B., 2018b. Cognitive and socio-cognitive functioning of chronic non-medical prescription opioid users. Psychopharmacology (Berl.) 235 (12), 3451e3464. Kroll, S.L., Williams, D.P., Thoma, M., Staib, M., Binz, T.M., Baumgartner, M.R., et al., 2019. Non-medical prescription opioid users exhibit dysfunctional physiological stress responses to social rejection. Psychoneuroendocrinology 100, 264e275. Kurtz, M.M., Gagen, E., Rocha, N.B., Machado, S., Penn, D.L., 2016. Comprehensive treatments for social cognitive deficits in schizophrenia: a critical review and effect-size analysis of controlled studies. Clin. Psychol. Rev. 43, 80e89. Kuypers, K.P., Dolder, P.C., Ramaekers, J.G., Liechti, M.E., 2017. Multifaceted empathy of healthy volunteers after single doses of MDMA: a pooled sample of placebo-controlled studies. J. Psychopharmacol. https://doi.org/10.1177/0269881117699617. Lamm, C., Decety, J., Singer, T., 2011. Meta-analytic evidence for common and distinct neural networks associated with directly experienced pain and empathy for pain. Neuroimage 54 (3), 2492e2502. https:// doi.org/10.1016/j.neuroimage.2010.10.014. Lannoy, S., Dormal, V., Brion, M., Gaudelus, B., Billieux, J., Maurage, P., 2018. Affective impairments in binge drinking: investigation through emotional facial expression decoding. Compr. Psychiatry 83, 59e63. Lieberman, M.D., 2007. Social cognitive neuroscience: a review of core processes. Annu. Rev. Psychol. 58, 259e289. Lin, A., Adolphs, R., Rangel, A., 2012. Social and monetary reward learning engage overlapping neural substrates. Soc. Cognit. Affect Neurosci. 7 (3), 274e281. Maier, L.J., Wunderli, M.D., Vonmoos, M., Rommelt, A.T., Baumgartner, M.R., Seifritz, E., et al., 2015. Pharmacological cognitive enhancement in healthy individuals: a compensation for cognitive deficits or a question of personality? PLoS One 10 (6), e0129805. Matyassy, A., Kelemen, O., Sarkozi, Z., Janka, Z., Keri, S., 2006. Recognition of complex mental states in patients with alcoholism after long-term abstinence. Alcohol Alcohol 41 (5), 512e514. Maurage, P., Campanella, S., Philippot, P., Pham, T.H., Joassin, F., 2007. The crossmodal facilitation effect is disrupted in alcoholism: a study with emotional stimuli. Alcohol Alcohol 42 (6), 552e559. Maurage, P., Campanella, S., Philippot, P., Martin, S., de Timary, P., 2008. Face processing in chronic alcoholism: a specific deficit for emotional features. Alcohol Clin. Exp. Res. 32 (4), 600e606. Maurage, P., Campanella, S., Philippot, P., Charest, I., Martin, S., de Timary, P., 2009. Impaired emotional facial expression decoding in alcoholism is also present for emotional prosody and body postures. Alcohol Alcohol 44 (5), 476e485. Maurage, P., Grynberg, D., Noel, X., Joassin, F., Hanak, C., Verbanck, P., et al., 2011. The “Reading the Mind in the Eyes” test as a new way to explore complex emotions decoding in alcohol dependence. Psychiatry Res. 190 (2e3), 375e378. Maurage, F., de Timary, P., Tecco, J.M., Lechantre, S., Samson, D., 2015. Theory of mind difficulties in patients with alcohol dependence: beyond the prefrontal cortex dysfunction hypothesis. Alcohol Clin. Exp. Res. 39 (6), 980e988.

Social cognition in addiction Chapter | 5

Maurage, P., D’Hondt, F., de Timary, P., Mary, C., Franck, N., Peyroux, E., 2016. Dissociating affective and cognitive theory of mind in recently detoxified alcohol-dependent individuals. Alcohol Clin. Exp. Res. 40 (9), 1926e1934. McCurrie, C.H., Crone, D.L., Bigelow, F., Laham, S.M., 2018. Moral and Affective Film Set (MAAFS): a normed moral video database. PLoS One 13 (11), e0206604. McDonald, S., Darke, S., Kaye, S., Torok, M., 2013. Deficits in social perception in opioid maintenance patients, abstinent opioid users and non-opioid users. Addiction 108 (3), 566e574. Mehrabian, A., Epstein, N., 1972. A measure of emotional empathy. J. Pers. 40 (4), 525e543. Milders, M., 2018. Relationship between social cognition and social behaviour following traumatic brain injury. Brain Inj. 1e7. Milivojevic, V., Sinha, R., 2018. Central and peripheral biomarkers of stress response for addiction risk and relapse vulnerability. Trends Mol. Med. 24 (2), 173e186. Moll, J., Zahn, R., de Oliveira-Souza, R., Krueger, F., Grafman, J., 2005. Opinion: the neural basis of human moral cognition. Nat. Rev. Neurosci. 6 (10), 799e809. Monnot, M., Nixon, S., Lovallo, W., Ross, E., 2001. Altered emotional perception in alcoholics: deficits in affective prosody comprehension. Alcohol Clin. Exp. Res. 25 (3), 362e369. Morgan, M.J., Marshall, J.P., 2013. Deficient fear recognition in regular cocaine users is not attributable to elevated impulsivity or conduct disorder prior to cocaine use. J. Psychopharmacol. 27 (6), 526e532. Nandrino, J.L., Gandolphe, M.C., Alexandre, C., Kmiecik, E., Yguel, J., Urso, L., 2014. Cognitive and affective theory of mind abilities in alcohol-dependent patients: the role of autobiographical memory. Drug Alcohol Depend. 143, 65e73. Onuoha, R.C., Quintana, D.S., Lyvers, M., Guastella, A.J., 2016. A metaanalysis of theory of mind in alcohol use disorders. Alcohol Alcohol 51 (4), 410e415. Oscar-Berman, M., Hancock, M., Mildworf, B., Hutner, N., Weber, D.A., 1990. Emotional perception and memory in alcoholism and aging. Alcohol Clin. Exp. Res. 14 (3), 383e393. Payer, D.E., Lieberman, M.D., Monterosso, J.R., Xu, J., Fong, T.W., London, E.D., 2008. Differences in cortical activity between methamphetamine-dependent and healthy individuals performing a facial affect matching task. Drug Alcohol Depend. 93 (1e2), 93e102. Peters, J., Buchel, C., 2010. Neural representations of subjective reward value. Behav. Brain Res. 213 (2), 135e141. Philippot, P., Kornreich, C., Blairy, S., Baert, I., Den Dulk, A., Le Bon, O., et al., 1999. Alcoholics’ deficits in the decoding of emotional facial expression. Alcohol Clin. Exp. Res. 23 (6), 1031e1038. Philips, B., Wennberg, P., Konradsson, P., Franck, J., 2018. Mentalizationbased treatment for concurrent borderline personality disorder and substance use disorder: a randomized controlled feasibility study. Eur. Addict. Res. 24 (1), 1e8. Platt, B., Kamboj, S., Morgan, C.J., Curran, H.V., 2010. Processing dynamic facial affect in frequent cannabis-users: evidence of deficits in the speed of identifying emotional expressions. Drug Alcohol Depend. 112 (1e2), 27e32. Preller, K.H., Herdener, M., Schilbach, L., Stampfli, P., Hulka, L.M., Vonmoos, M., et al., 2014a. Functional changes of the reward system underlie blunted response to social gaze in cocaine users. Proc. Natl. Acad. Sci. U.S.A. 111 (7), 2842e2847.

77

Preller, K.H., Hulka, L.M., Vonmoos, M., Jenni, D., Baumgartner, M.R., Seifritz, E., et al., 2014b. Impaired emotional empathy and related social network deficits in cocaine users. Addict. Biol. 19 (3), 452e466. Quaglino, V., De Wever, E., Maurage, P., 2015. Relations between cognitive abilities, drinking characteristics, and emotional recognition in alcohol dependence: a preliminary exploration. Alcohol Clin. Exp. Res. 39 (10), 2032e2038. Quednow, B.B., 2016. The rise of the ego: social cognition and interaction in cocaine users. In: Preedy, V.R. (Ed.), Neuropathology of Drug Addictions and Substance Misuse, vol. 2. Academic Press, London. Quednow, B.B., 2017. Social cognition and interaction in stimulant use disorders. Curr. Opin. Behav. Sci. 13, 55e62. Rilling, J.K., Sanfey, A.G., 2011. The neuroscience of social decisionmaking. Annu. Rev. Psychol. 62, 23e48. Rolland, B., D’Hondt, F., Montegue, S., Brion, M., Peyron, E., D’Aviau de Ternay, J., et al., 2019. A patient-tailored evidence-based approach for developing early neuropsychological training programs in addiction settings. Neuropsychol. Rev. 29 (1), 103e115. https://doi.org/ 10.1007/s11065-018-9395-3. Romero-Ayuso, D., Mayoral-Gontan, Y., Trivino-Juarez, J.M., 2016. Emotional intelligence, risk perception in abstinent cocaine dependent individuals. Actas Esp. Psiquiatr. 44 (2), 72e78. Roser, P., Lissek, S., Tegenthoff, M., Nicolas, V., Juckel, G., Brune, M., 2012. Alterations of theory of mind network activation in chronic cannabis users. Schizophr. Res. 139 (1e3), 19e26. Rupp, C.I., Derntl, B., Osthaus, F., Kemmler, G., Fleischhacker, W.W., 2017. Impact of social cognition on alcohol dependence treatment outcome: poorer facial emotion recognition predicts relapse/dropout. Alcohol Clin. Exp. Res. 41 (12), 2197e2206. Sanfey, A., Dorris, M., 2009. Chapter 6 - Games in humans and nonhuman primates: scanners to single units. In: Glimcher, P.W., Camerer, C.F., Fehr, E., Poldrack, R.A. (Eds.), Neuroeconomics. Academic Press, London, pp. 63e80. Sanfey, A.G., 2007. Social decision-making: insights from game theory and neuroscience. Science 318 (5850), 598e602. Savov, S., Atanassov, N., 2013. Deficits of affect mentalization in patients with drug addiction: theoretical and clinical aspects. ISRN Addict. 2013, 250751. Schaffer, S.G., Wisniewski, A., Dahdah, M., Froming, K.B., 2009. The comprehensive affect testing system-abbreviated: effects of age on performance. Arch. Clin. Neuropsychol. 24 (1), 89e104. Schilbach, L., Wilms, M., Eickhoff, S.B., Romanzetti, S., Tepest, R., Bente, G., et al., 2010. Minds made for sharing: initiating joint attention recruits reward-related neurocircuitry. J. Cogn. Neurosci. 22 (12), 2702e2715. Schilbach, L., 2016. Towards a second-person neuropsychiatry. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371 (1686), 20150081. Schmid, Y., Hysek, C.M., Simmler, L.D., Crockett, M.J., Quednow, B.B., Liechti, M.E., 2014. Differential effects of MDMA and methylphenidate on social cognition. J. Psychopharmacol. 28 (9), 847e856. Schmidt, T., Roser, P., Juckel, G., Brune, M., Suchan, B., Thoma, P., 2016. Social cognition and social problem solving abilities in individuals with alcohol use disorder. J. Clin. Exp. Neuropsychol. 38 (9), 974e990. Schuckit, M.A., Smith, T.L., Paulus, M.P., Tapert, S.F., Simmons, A.N., Tolentino, N.J., et al., 2016. The ability of functional magnetic resonance imaging to predict heavy drinking and alcohol problems 5 Years later. Alcohol Clin. Exp. Res. 40 (1), 206e213.

78

Cognition and Addiction

Spechler, P.A., Orr, C.A., Chaarani, B., Kan, K.J., Mackey, S., Morton, A., et al., 2015. Cannabis use in early adolescence: evidence of amygdala hypersensitivity to signals of threat. Dev. Cogn. Neurosci. 16, 63e70. Sprah, L., Novak, T., 2008. Behavioural inhibition system (BIS) and behavioural activation system (BAS) as predictors of emotional and cognitive deficits observed in alcohol abstainers. Psychiatr. Danub. 20 (2), 184e193. Stewart, L.H., Ferguson, B., Morgan, C.J., Swaboda, N., Jones, L., Fenton, R., et al., 2014. Effects of ecstasy on cooperative behaviour and perception of trustworthiness: a naturalistic study. J. Psychopharmacol. 28 (11), 1001e1008. Suchman, N.E., DeCoste, C.L., McMahon, T.J., Dalton, R., Mayes, L.C., Borelli, J., 2017. Mothering from the inside out: results of a second randomized clinical trial testing a mentalization-based intervention for mothers in addiction treatment. Dev. Psychopathol. 29 (2), 617e636. Thoma, P., Winter, N., Juckel, G., Roser, P., 2013. Mental state decoding and mental state reasoning in recently detoxified alcohol-dependent individuals. Psychiatry Res. 205 (3), 232e240. Tobler, P.N., Preller, K.H., Campbell-Meiklejohn, D.K., Kirschner, M., Kraehenmann, R., Stampfli, P., et al., 2016. Shared neural basis of social and non-social reward deficits in chronic cocaine users. Soc. Cognit. Affect Neurosci. 11 (6), 1017e1025. Townshend, J.M., Duka, T., 2003. Mixed emotions: alcoholics’ impairments in the recognition of specific emotional facial expressions. Neuropsychologia 41 (7), 773e782. Trick, L., Kempton, M.J., Williams, S.C., Duka, T., 2014. Impaired fear recognition and attentional set-shifting is associated with brain structural changes in alcoholic patients. Addict. Biol. 19 (6), 1041e1054. Tsukue, R., Okamoto, Y., Yoshino, A., Kunisato, Y., Takagaki, K., Takebayashi, Y., et al., 2015. Do individuals with alcohol dependence show higher unfairness sensitivity? The relationship between impulsivity and unfairness sensitivity in alcohol-dependent adults. Alcohol Clin. Exp. Res. 39 (10), 2016e2021. Uekermann, J., Daum, I., Schlebusch, P., Trenckmann, U., 2005. Processing of affective stimuli in alcoholism. Cortex 41 (2), 189e194. Uekermann, J., Channon, S., Daum, I., 2006. Humor processing, mentalizing, and executive function in normal aging. J. Int. Neuropsychol. Soc. 12 (2), 184e191. Uekermann, J., Channon, S., Winkel, K., Schlebusch, P., Daum, I., 2007. Theory of mind, humour processing and executive functioning in alcoholism. Addiction 102 (2), 232e240. Vallat-Azouvi, C., Azouvi, P., Le-Bornec, G., Brunet-Gouet, E., 2018. Treatment of social cognition impairments in patients with traumatic brain injury : a critical review. Brain Inj. 1e7. Valmas, M.M., Mosher Ruiz, S., Gansler, D.A., Sawyer, K.S., OscarBerman, M., 2014. Social cognition deficits and associations with drinking history in alcoholic men and women. Alcohol Clin. Exp. Res. 38 (12), 2998e3007. Vaskinn, A., Lovgren, A., Egeland, M.K., Feyer, F.K., Ostefjells, T., Andreassen, O.A., et al., 2019. A randomized controlled trial of training of affect recognition (TAR) in schizophrenia shows lasting

effects for theory of mind. Eur. Arch. Psychiatry Clin. Neurosci. 269 (5), 611e620. https://doi.org/10.1007/s00406-019-00997-z. Verdejo-Garcia, A., Rivas-Perez, C., Vilar-Lopez, R., Perez-Garcia, M., 2007. Strategic self-regulation, decision-making and emotion processing in poly-substance abusers in their first year of abstinence. Drug Alcohol Depend. 86 (2e3), 139e146. Verdejo-Garcia, A., Del Mar Sanchez-Fernandez, M., AlonsoMaroto, L.M., Fernandez-Calderon, F., Perales, J.C., Lozano, O., et al., 2010. Impulsivity and executive functions in polysubstanceusing rave attenders. Psychopharmacology (Berl.) 210 (3), 377e392. Verdejo-Garcia, A., Contreras-Rodriguez, O., Fonseca, F., Cuenca, A., Soriano-Mas, C., Rodriguez, J., et al., 2014. Functional alteration in frontolimbic systems relevant to moral judgment in cocaine-dependent subjects. Addict. Biol. 19 (2), 272e281. Verdejo-Garcia, A., Verdejo-Roman, J., Albein-Urios, N., MartinezGonzalez, J.M., Soriano-Mas, C., 2017. Brain substrates of social decision-making in dual diagnosis: cocaine dependence and personality disorders. Addict. Biol. 22 (2), 457e467. Verdejo-Garcia, A., 2014. Social cognition in cocaine addiction. Proc. Natl. Acad. Sci. U.S.A. 111 (7), 2406e2407. Volkow, N.D., Baler, R.D., Goldstein, R.Z., 2011. Addiction: pulling at the neural threads of social behaviors. Neuron 69 (4), 599e602. Vonmoos, M., Eisenegger, C., Bosch, O.G., Preller, K.H., Hulka, L.M., Baumgartner, M., et al., 2019. Improvement of emotional empathy and cluster B personality disorder symptoms associated with decreased cocaine use severity. Front. Psychiatry 10, 213. https:// doi.org/10.3389/fpsyt.2019.00213. Weightman, M.J., Knight, M.J., Baune, B.T., 2019. A systematic review of the impact of social cognitive deficits on psychosocial functioning in major depressive disorder and opportunities for therapeutic intervention. Psychiatry Res. 274, 195e212. Woicik, P.A., Moeller, S.J., Alia-Klein, N., Maloney, T., Lukasik, T.M., Yeliosof, O., et al., 2009. The neuropsychology of cocaine addiction: recent cocaine use masks impairment. Neuropsychopharmacology 34 (5), 1112e1122. Wolwer, W., Frommann, N., 2011. Social-cognitive remediation in schizophrenia: generalization of effects of the training of affect recognition (TAR). Schizophr. Bull. 37 (Suppl. 2), S63eS70. Wunderli, M.D., Vonmoos, M., Niedecker, S.M., Hulka, L.M., Preller, K.H., Baumgartner, M.R., et al., 2016. Cognitive and emotional impairments in adults with attention-deficit/hyperactivity disorder and cocaine use. Drug Alcohol Depend. 163, 92e99. Wunderli, M.D., Vonmoos, M., Treichler, L., Zeller, C., Dziobek, I., Kraemer, T., et al., 2018. Social cognition and interaction in chronic users of 3,4-methylenedioxymethamphetamine (MDMA, “ecstasy”). Int. J. Neuropsychopharmacol. 21 (4), 333e344. Yacubian, J., Buchel, C., 2009. The genetic basis of individual differences in reward processing and the link to addictive behavior and social cognition. Neuroscience 164 (1), 55e71. Zimmermann, K., Kendrick, K.M., Scheele, D., Dau, W., Banger, M., Maier, W., et al., 2019. Altered striatal reward processing in abstinent dependent cannabis users: social context matters. Eur. Neuropsychopharmacol. 29 (3), 356e364.

Chapter 6

A neurocognitive model of the comorbidity of substance use and personality disorders Jacob W. Koudys and Anthony C. Ruocco University of Toronto, Toronto ON, Canada

The question of why addiction is so highly comorbid with other psychiatric disorders has been the topic of scientific research for decades. The clinical significance of this comorbidity is reflected in the terms “dual diagnosis” and “concurrent disorders,” which have variously been applied to individuals who have both an addictive disorder and another psychiatric disorder, such as a depressive, anxiety, or psychotic disorder (Kessler, 2004; Khan, 2017). Comorbidity research in addiction has ranged from studies focused on treatment-related outcomes to those investigating the etiology of these disorders (Kendler et al., 2003; Krueger et al., 2002; Newton-Howes et al., 2017; van den Bosch and Verheul, 2007). Although this research has often centered on depressive and anxiety disorders (e.g., Lai et al., 2015), a growing body of research has also examined personality disorders (PDs). As defined in the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013), PDs represent a pervasive and inflexible pattern of inner experience and behavior as represented by a disturbance in at least two of four symptom areas: affect regulation, impulse control, identity, and interpersonal functioning. PDs are separated into three “clusters” denoted by the predominant symptoms that characterize the individual diagnoses contained within them: Cluster A is the odd or eccentric cluster (paranoid, schizoid, and schizotypal PDs); Cluster B is the dramatic, emotional, or erratic cluster (antisocial [ASPD], borderline [BPD], histrionic, and narcissistic PDs); and Cluster C is the anxious or fearful cluster (avoidant, dependent, and obsessive-compulsive PDs). Much of the research on the comorbidity of addiction and PDs has focused on substance use disorders (SUDs), revealing a high comorbidity with

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00006-X Copyright © 2020 Elsevier Inc. All rights reserved.

Cluster B PDs, especially ASPD and BPD (Dolan-Sewell et al., 2001; Fenton et al., 2012; McGlashan et al., 2000; Trull et al., 2010). Why addictiondand SUDs in particulardis so markedly comorbid with PDs has been investigated from many different perspectives, including their shared symptom dimensions, personality traits, and genetics. Surprisingly, little attention has been paid to neurocognitive functioning despite the preponderance of cognitive deficits in both classes of disorders. In this chapter, we draw together theory and empirical findings to populate the gaps linking SUDs and PDs according to a neurocognitive framework. Neurocognitive functions refer to a set of cognitive abilities, such as attention, memory, and “executive functions” (EFs; e.g., working memory, cognitive flexibility, and response inhibition), which have their basis in brain function. SUDs and PDs share disturbances in neurocognitive functioning that may help to explain their high comorbidity. Before reviewing the neurocognitive evidence, we begin by summarizing research that supports both cross-sectional and longitudinal associations between SUDs and PDs. Next, we describe broad symptom dimensions and impulsive personality traits that cut across SUDs and PDs, which we suggest are conceptually relevant to understanding the neurocognitive deficits shared across the disorders. We subsequently review empirical research on neurocognitive functioning in Cluster B PDs and SUDs, focusing mainly on EFs because they represent key cognitive abilities that facilitate effective self-regulation and could be impacted by problematic substance use. Finally, we present a preliminary heuristic model that describes the neurocognitive dysfunctions that potentially underlie the comorbidity of SUDs and Cluster B PDs.

79

80

Cognition and Addiction

Cross-sectional and longitudinal evidence Cluster B PDs have been the main topic of study for most cross-sectional studies investigating the relationship between SUDs and PDs. In the National Epidemiologic Survey on Alcohol and Related Conditions, ASPD and BPD were found to be comorbid with SUDs at an odds ratio higher than all other psychiatric disorders investigated (Grant et al., 2016). Compared to schizotypal, avoidant, and obsessive-compulsive PDs, BPD was more frequently comorbid with SUDs in the Collaborative Longitudinal Personality Disorders Study (McGlashan et al., 2000). In partial explanation of these differences in the comorbidity of PDs with SUDs, a twin study on cannabis use disorder found that the genetic risk associated with ASPD and BPD traits explained 32%e60% of the variance, while avoidant and dependent PD traits explained 16% and 11%, respectively (Gillespie et al., 2018). From a symptom perspective, Cluster B PD symptoms are uniquely associated with alcohol use disorder (AUD) beyond what is accounted for by major personality traits (Trull et al., 2004). SUDs also appear to run in families affected with BPD (Ruocco et al., 2018). Importantly, the high level of comorbidity between SUDs and BPD does not appear to be due to overlapping symptoms (i.e., problematic substance use as an indicator of impulsivity in BPD; Trull et al., 2018). Cross-sectional studies, however, are limited in the inferences that can be drawn about the causal link between SUDs and PDs. There is good reason to suspect that PDs could precede the development of SUDs: PDs have an early onset (i.e., at least by early adulthood) and are pervasive, inflexible, enduring, and distressing and/or impairing (American Psychiatric Association, 2013). Given its intransigent quality, it is reasonable to surmise that the pathological personality traits underlying PDs may predispose people for addiction. Only a small number of studies have investigated longitudinal relationships between the onset of SUDs and PDs, but in those that have, a nuanced relationship between SUDs and PDs is evident. Most research findings suggest that the presence of PDs increases the likelihood of SUD onset and contributes to its maintenance. In a multiyear longitudinal study, specific PDs (i.e., BPD, ASPD, and schizotypal PD) were positively related to SUD persistence, while mood and anxiety disorders were not (Fenton et al., 2012). Another study found that PDs are the comorbid disorders most related to the transition from substance use initiation to dependence, although the confidence intervals overlapped with that of mood disorders (Lopez-Quintero et al., 2011). Compared to obsessivecompulsive PD, BPD has been shown to be more related to increased vulnerability for the onset of substance dependence, a finding that is particularly interesting given that this was true irrespective of its remission status

(Walter et al., 2009). A longitudinal study based on data contained in the Taiwan National Health Insurance Research Database found that a PD diagnosis conferred the highest risk for a subsequent diagnosis of a SUD, above that associated with affective psychoses, neurotic disorders, schizophrenia, and adjustment reaction (Chiu et al., 2018). In summary, these findings underscore the high diagnostic comorbidity between SUDs and PDs. Not only are Cluster B PDs especially comorbid with SUDs but also the presence of these diagnoses appears to connote a vulnerability to later developing a SUD. Both the cross-sectional and longitudinal associations between Cluster B PDs and SUDs suggest that common individual difference factors (e.g., symptom dimensions, personality traits, and neurocognitive variables) cutting across the diagnoses could account for their strong associations.

Broad symptoms dimensions and impulsive personality traits The DSM-5 classification system employs a categorical approach that conceptualizes psychiatric disorders as discrete diagnostic entities. In contrast, other frameworks of psychopathology, such as the Hierarchical Taxonomy of Psychopathology (HiTOP; Kotov et al., 2017), adopt a dimensional conceptualization of psychiatric illness. Theory drives the construction of the HiTOP model through factor analyses of dimensions, defined as “psychopathologic continua that reflect individual differences in maladaptive characteristics across the entire population” (p. 456). This definition emphasizes the universality and richness of information provided by a dimensional framework for psychopathology. In the HiTOP model, overarching super spectra sit atop the hierarchy and govern the spectra (internalizing, thought disorder, disinhibited externalizing, antagonistic externalizing, and detachment) below them. From here, these spectra break into subfactors (e.g., internalizing splits into sexual problems, eating pathology, fear, and distress). Finally, within these subfactors, there are homogeneous componentsd“groups of related symptoms” (p. 456)dand maladaptive traitsd“specific pathology personality characteristics” (p. 456). Factors at every level represent constellations of lower-level factors, but there are some interstitial relationships among the immediately adjacent factor levels. Although HiTOP is at the early stages of providing a framework of the broader structure of psychopathology, it presents a conceptual basis for understanding why certain diagnoses are more likely to be comorbid. An explicit aim of the HiTOP initiative is to incorporate comorbidities by assigning syndromes to spectra. Metaanalysis of quantitative models fitted to comorbidity data corroborates the existence of latent liability factors that affect the manifestation of frequently comorbid diagnoses (Krueger and Markon, 2006).

Neurocognitive model of addiction comorbidity Chapter | 6

Traditionally, SUDs and PDs have often been conceptualized as distinct diagnostic entities. While they may be classified as separate diagnoses in conventional nosologies like the DSM-5, their frequent comorbidity and shared “impulsigenic” phenotypic identity (Lacey and Evans, 1986) suggest that higher-order factors (and presumably associated neurocognitive functions, as we will review below) influence their joint manifestation. In application to the specific comorbidity of Cluster B PDs and SUDs, the spectra of greatest relevance are disinhibited externalizing and antagonistic externalizing. The former breaks into substance abuse and antisocial subfactors, with the latter shared interstitially with antagonistic externalizing (Kotov et al., 2017). Based on the emerging HiTOP model, SUDs, ASPD, and BPD are speculated to share a broader externalizing symptom dimension, which helps to explain their substantial comorbidity. This implies that impulsive personality traits may also be important to consider as cross-cutting individual difference variables that might partially explain the comorbidity of these diagnoses. Impulsivity has long been recognized as a trait linking the comorbidity of SUDs and PDs (Moeller et al., 2001). For decades, researchers have sought to elucidate the different components of impulsivity (e.g., Twain, 1957), which is now recognized as a multidimensional construct. Of the most prominent developments to emerge from research on the multidimensional nature of impulsivity, a personality-based structural model of impulsivity was produced based on factor analysis of self-report measures (Whiteside and Lynam, 2001). Operationalized in the UPPS-P Impulsive Behavior Scale (Lynam et al., 2006), the model comprises five impulsive personality traits: negative urgency, the tendency toward rash actions during negative affect; (lack of) premeditation, the tendency toward delaying action in favor of planning ahead and careful consideration; (lack of) perseverance, the tendency to persist in completing a task and avoiding boredom; sensation seeking, the tendency to pursue adventure or excitement; and most recently, positive urgency, the tendency toward rash actions during positive affect (Cyders et al., 2007). A lack of premeditation is most congruent with traditional understandings of impulsivity (Whiteside and Lynam, 2001), but the other facets capture additional elements purportedly related to impulsivity. Indeed, it has been suggested that general impulsivity, specifically excluding sensation seeking, may be an endophenotype for substance dependence (Ersche et al., 2010). The UPPS-P model of impulsive personality traits helps to clarify which specific traits are unique to PDs versus SUDs and it advances the body of knowledge that explains the overlap between them. In general, metaanalytic findings on the relationship between UPPS-P traits and psychopathology reveal that negative urgency is the trait most strongly correlated with all forms of psychopathology

81

(Berg et al., 2015), within which BPD holds the strongest relationship. Indeed, negative affectivity operates indirectly through negative urgency to affect alcohol-related problems, and negative urgency is directly related to risk behavior (Wray et al., 2012). Wray et al. further note that these findings suggest that control deficits may contribute to the mechanism of addiction rather than attempts to selfmedicate to alleviate negative affect. In a similar vein, mediation models demonstrate that the relationship between negative urgency and problematic drinking operates through distinct pathways (i.e., is uniquely mediated by drinking motives and affective instability) compared with sensation seeking and lack of premeditation (Adams et al., 2012; Chugani et al., 2018; Coskunpinar and Cyders, 2012). With these mediation models in mind, attention to the interaction between affect and impulsivity in the study of SUD has been gaining traction (Smith and Cyders, 2016; Verdejo-García et al., 2007). Sensation seeking appears to share the weakest relationship with general psychopathology, but lack of perseverance and lack of premeditation are especially related to BPD and SUDs (Berg et al., 2015). Given this evidence, SUDs and PDs may be linked through externalizing symptoms and impulsive personality traits, especially negative urgency. Negative urgency is highly related to externalizing behavior (Settles et al., 2012) and increases risk for SUD onset, further highlighting the importance of considering these findings within broader symptom and personality domains related to disinhibition and negative affectivity. These symptoms and traits could partially explain the strong cross-sectional and longitudinal associations between SUDs and PDs and are critical for implicating the role of negative affect in their comorbidity.

Neurocognitive functioning Until this point, we have discussed major conceptual links between SUDs and PDs based on broad symptom dimensions and specific personality traits that cut across traditional diagnostic categories. These findings converge on externalizing symptoms and impulsive personality traits as central to the comorbidity of SUDs and PDs, and more specifically, Cluster B PDs. The comorbidity of these disorders can also be scrutinized at a neurocognitive level, where deficits in certain cognitive abilities may underlie some forms of psychopathology and personality traits. While the cognitive underpinnings of externalizing symptoms and impulsive personality traits are yet to be fully elucidated, it is reasonable to speculate that EFs are especially relevant to these dimensions because the cognitive functions comprising EFs are crucial for effective selfregulation (Hofmann et al., 2012). Indeed, EF deficits are commonly found in association with externalizing symptoms and disorders and impulsive personality traits (e.g., Cyders and Coskunpinar, 2012; Young et al., 2009).

82

Cognition and Addiction

Accordingly, we begin the next section by summarizing research on EFs in Cluster B PDs because neurocognitive research on PDs has concentrated mainly on ASPD and BPD. Next, we discuss research on EFs in SUDs, including studies investigating their longitudinal associations and the interaction of EFs with emotion and reward. We end this section with a brief summary of theoretical models that postulate a role for EFs and other factors in the comorbidity of SUDs and PDs.

Personality disorder and executive functioning A range of neurocognitive functions have been investigated in Cluster B PDs. EFs are of particular relevance to the comorbidity of SUDs and Cluster B PDs, defined as “a set of cognitive control processes.which regulate lower level processes (e.g., perception, motor responses) and thereby enable self-regulation and self-directed behavior toward a goal, allowing us to break out of habits, make decisions and evaluate risks, plan for the future, prioritize and sequence our actions, and cope with novel situations” (Snyder et al., 2015, “Introduction,” para. 1). Models conceptualizing various components of EFs have been put forward (e.g., Hasher et al., 2007; Miyake and Friedman, 2012), delineating specific cognitive functions, such as working memory (including updating, or accessing and deleting, its contents), shifting (or flexibility moving between different tasks or set ways of thinking or responding), and restraining or inhibiting a dominant or prepotent response. Other EFs, such as decision-making and planning, are likely to involve several of these cognitive functions (Miyake and Friedman, 2012). Decision-making is typically studied in PDs and SUDs using tests of delay discounting and gambling tasks (Bickel et al., 2014; de Wit, 2009; Dom et al., 2018; Paret et al., 2017), that require participants to choose among stimuli of varying quality, assessing individual differences in aspects of reward processing. In delay discounting paradigms, impulsive choice is often exhibited as a disproportionate preference for immediate rewards of lower value over later rewards of higher value (Hamilton et al., 2015). In gambling tasks, such as the Iowa Gambling Task (IGT; Bechara et al., 1997), impulsive choice is often exhibited as a greater preference for higher rewards despite unfavorable odds or more severe punishers. As mentioned, most neurocognitive investigations of PDs have focused on ASPD and BPD. In studies related to ASPD, psychopathy and antisocial behaviors have been the primary focuses. For example, early metaanalyses reveal an overall deficit in EFs ranging in magnitude from medium (Ogilvie et al., 2011) to medium-to-large (Morgan and Lilienfeld, 2000) in various antisocial behavior groups, with small but statistically significant decrements for ASPD

more specifically. Working memory and attentional aspects of EFs are the cognitive abilities most strongly associated with antisocial behavior groups, although the precise subcomponents of these cognitive functions as they relate to EFs (e.g., updating, shifting, and inhibition) are not consistently delineated. A metaanalysis of individuals with ASPD and a history of violent, aggressive, or criminal behavior revealed a small-to-medium effect size difference in EF performance compared with healthy, nonviolent controls (Sedgwick et al., 2017). Additionally, a more nuanced relationship has been uncovered between EFs and specific facets of psychopathy: impulsive and antisocial behaviors appear to be more consistently related to EF deficits (Baskin-Sommers et al., 2015), converging with metaanalytic findings (Morgan and Lilienfeld, 2000; Ogilvie et al., 2011). While it is not yet clear whether decisionmaking is disrupted in ASPD, it is suggested that comorbid addiction may increase the likelihood of greater delay discounting in people with ASPD (Turner et al., 2017). BPD is associated with a range of neurocognitive deficits, and EFs are among the most prominent (Koudys et al., 2018). Metaanalyses show large decrements in EFs (Ruocco, 2005; Unoka and Richman, 2016), more specifically on tests of attention, cognitive flexibility, and planning. There is preliminary evidence that a deficit in planning (i.e., lower deliberation time before solving a problem) aggregates in the relatives of individuals with BPD (Gvirts et al., 2012). Interestingly, the comorbidity of BPD with another Cluster B PD is associated with greater EF deficits (Unoka and Richman, 2016). A recent study also found deficits in sustained visual attention and verbal and visuospatial working memory in BPD compared to healthy controls (Thomsen et al., 2017), further supporting a prominent disturbance of cognitive abilities that rely on EFs in BPD. The disorder is also characterized by higher delay discounting and more disadvantageous decisions on a gambling task (Paret et al., 2017). Only a small number of studies have examined interactions between EFs and emotions in BPD, yielding some evidence for a unique impact of negative emotional stimuli on the performance of tests of working memory and inhibitory control (Winter, 2016).

Substance use disorder and executive functioning A large body of research has investigated the neurocognitive features of SUDs, revealing different patterns of cognitive deficits depending on the substance in question, the number of different substances used, acute versus longterm toxicological effects of the substance, and the timeframe within which the cognitive functions are assessed. It is beyond the scope of this chapter to discuss the findings

Neurocognitive model of addiction comorbidity Chapter | 6

for each substance in detail, as they are presented in Chapters 7 through 13. To contextualize the findings that follow, an overview of decision-making models in addiction is warranted. One model outlines three imbalanced systems in addiction: stimulus appraisal related to motivation, motivational state triggering, and motivational state execution (Noël et al., 2013). This model implies an element of control (i.e., execution) over motivation influenced by the environment while allowing for the separate influence of dysfunctional cognitive bias or physiological triggering. Akin to this model, but further refined to focus on decision-making and cognitive tasks pertinent to assessment in addiction, decision-making can be conceptualized in three stages: preference formation, choice implementation, and feedback processing (Verdejo-García et al., 2018). Within each of these stages, there are distinct constituent components that sharply clarify the relevance of various performance-based decision-making tests. For instance, the IGT maps onto uncertainty valuation within preference formation and to reward/punishment learning, memory, and consistency within feedback processing. Together, these models highlight the importance of considering how decision-making concords with traditional EFs in research on SUDs. In a comprehensive review, Fernández-Serrano et al. (2011) concluded that EF deficits were present across studies of users of alcohol, cannabis, methamphetamines, cocaine, heroin, and 3,4-methylenedioxymethamphetamine. Some EF deficits appear to persist, albeit at an attenuated level of severity, in users who achieve long-term abstinence. These deficits were most severe in polysubstance usedespecially when involving methamphetamine or cocainedand the effects were often less severe when considering alcohol in isolation. Metaanalyses focusing on specific substances indicate somewhat differing neurocognitive features: chronic heroin use is associated with impulsivity and cognitive flexibility deficits (Baldacchino et al., 2012); inhibition is the most impaired cognitive function in individuals remitted from AUD for at least 12 weeks (Stavro et al., 2013); polydrug use that includes ecstasy is associated with updating and switching deficits, but not inhibition deficits (Roberts et al., 2016); nonacute effects of cannabis do not include cognitive deficits related to EF (Grant et al., 2003; Schreiner and Dunn, 2012); and a comprehensive measure of EFs is the cognitive index most associated with chronic methamphetamine use, next to indices of memory and learning (Scott et al., 2007). SUDs have also been studied from the perspective of decision-making, an aspect of neurocognitive functioning that partially overlaps with EFs (Del Missier et al., 2012). The IGT evaluates one’s ability to make advantageous choices among decks that offer variable levels of reward and punishment (Bechara et al., 1997). Although the IGT is

83

frequently studied in isolation in SUDs (Dom et al., 2018), accompanying test batteries with decision-making tasks like the IGT permits a more comprehensive assessment of EF. For instance, a machine-learning approach revealed that cocaine-dependent individuals can be discriminated from healthy controls based on their profiles of selfreported impulsivity and performance on the Immediate Memory Task (IMT), Stop Signal Task, Delay Discounting Task, Delay Discounting questionnaire, IGT, and Probabilistic Reversal Learning task (Ahn et al., 2016). As part of this classification, the Stop Signal Task and Probabilistic Reversal Learning task did not meaningfully contribute to the prediction of cocaine dependence, but lower IGT scores, higher commission errors on the IMT, lower IMT discriminability (i.e., lower ability to discriminate targets from catch stimuli), and steeper delay discounting were significant predictors. The relationship of emotion and affective states with EF has also gained increased traction in research on SUDs. There is growing recognition that emotion is particularly important for understanding the etiologic role of impulsivity in SUDs and PDs (Trull et al., 2018). Although neurocognitive deficits generally predict SUD onset and differentiate patterns of use (Martinez-Loredo et al., 2018), prospective research of SUD onset from a family study suggests potential for an increased level of specificity (Groenman et al., 2015). The researchers suggested that so-called “cold” EFs (i.e., those that do not explicitly manipulate emotion or reward) do not predict SUDs and nicotine dependence, but the investigation of “hot” EFs (i.e., in this case, EFs that minimally involves reward-related processes) could yield precise predictive information. Although there are numerous ways that emotional stimuli or contexts could be introduced to cognitive testing, relatively little research has been conducted on the interaction between emotion and EFs in SUDs. This is important given evidence that the performance of healthy individuals on a response inhibition task may be affected by emotion, especially when highly arousing images are presented (Verbruggen and De Houwer, 2007). Beyond the various aspects of EFs and potential interactions with contexts involving emotion or reward, it is important to consider the potential reciprocal relationships between neurocognitive deficits, substance use, and SUDs. EF deficits could place individuals at risk for later developing SUDs, and problematic substance use could exacerbate existing neurocognitive weaknesses (Moeller et al., 2016). This is essentially a question of whether neurocognitive deficits are the cause of SUDs, a consequence of SUDs, or both. Verdejo-García et al. (2008) identified impulsivity as a vulnerability marker for SUD onsetd through multiple channelsdbut the authors assert that the extant evidence supporting neurocognitive functions underlying impulsivity as both a cause and consequence of

84

Cognition and Addiction

SUDs requires alternate models of explanation. A recent review dissected impulsivity into impulsive action (i.e., deficient response inhibition) and impulsive choice (i.e., deficient ability to defer gratification), indicating that both may be vulnerability markers for SUD onset (Grant and Chamberlain, 2014). Indeed, metaanalytic results reveal that substance dependence is related to impaired response inhibition after controlling for other psychopathology (Lipszyc and Schachar, 2010), and addictive behaviors are related to impulsive choice (MacKillop et al., 2011). While multiple discrete EFs appear to be affected in SUDs, some research favors the importance of considering impulsive choice in SUD onset. Impulsive choice on the IGT, but not impulsive action on the Stop Signal Inhibition task, was prospectively related to heavy, maladaptive alcohol use (Goudriaan et al., 2011). Similarly, a comprehensive metaanalysis of neurocognitive functioning in BPD and associations with comorbid psychiatric disorders revealed that the comorbidity of BPD with a history of substance use is associated with greater decision-making dysfunction and gross EF deficits (Unoka and Richman, 2016). This should be considered in light of moderate correlations among decision-making tasks in addiction (Monterosso et al., 2001), and that decision-making deficits do not greatly differ as a function of substance type (Gowin et al., 2018). The specific models of addiction described at the beginning of this section elaborate on how decisionmaking processes may go awry, although the stages of these models require more extensive empirical validation. In any case, these models deconstruct decision-making processes in a way that promotes recognition of how impulsivity may influence various stages of decisionmaking, whether in relation to emotion or otherwise.

Comorbidity and executive functioning Linking models of addiction to personality psychopathology, addiction researchers have specifically proffered explanations for the frequent comorbidity of SUDs and PDs centered around EF deficits and emotional impulsivity. The necessity of such proposals is apparent in the face of attempts to parse the attribution of cognitive deficits in comorbid PDs and SUDs (Berenson et al., 2016; Coffey et al., 2011). One model describes a genetically evidenced dual system of personality with a competing bottom-up emotion-based drive and a top-down control system (Ellingson et al., 2013). This model is built around the interaction between these two systems and allows for a broad conceptualization of what bottom-up drive might entail. In similar recognition of the need to integrate disparate constructs into addiction research, another framework outlines the importance of concurrently investigating incentive salience, EFs, and negative emotionality (Kwako et al., 2016). This model nods to the importance of

dimensional psychopathology constructs, such as those conceptualized in HiTOP; moreover, it proposes cognitive tasks to more comprehensively investigate addiction and its links well to potential neurobiological underpinnings related to dopaminergic function and reward learning.

A preliminary neurocognitive model Several heuristic models have been proposed to describe the comorbidity between PDs and other psychiatric disorders (for a detailed review, see Lyons et al., 1997). The models are based on etiology (genetic, environmental), pathophysiology (biological, psychological), and symptoms (or phenotypes). Based on the evidence that we have reviewed on the longitudinal association between PDs and SUDs, we propose that the substantial comorbidity of Cluster B PDs and SUD can be partially explained by a “risk factor” model (Lyons et al., 1997), wherein the presence of a PD increases the likelihood of substance use and the later development of SUD. However, as presented in Lyons et al. (1997), the model presumes that the two syndromes have distinct etiologies. We suggest that the comorbidity of SUD and Cluster B PDs reflects both shared and distinct etiologies and pathophysiologies. Therefore, the base of the model is built on an undifferentiated pool of etiology that proceeds through pathophysiology and emerges as PD symptoms, SUD symptoms, and externalizing symptoms/impulsive personality traits. This illustrates the concept of pleiotropy, wherein comorbidity of the two syndromes results from a shared etiology and pathophysiology (Lyons et al., 1997), while allowing for future research to parse out potentially heterogeneous or unique etiological elements with distinct pathways linked to pathophysiology and symptoms. We theorize that a set of shared etiology factors (whether genetic, environmental, and/or an interaction between the two) predisposes individuals to EF disruptions, including in working memory, cognitive flexibility, and inhibitory control, consequently impacting decision-making and the ability to control one’s behavior in the face of negative emotions. In the model depicted in Fig. 6.1, we highlight EF deficits because they are the focus of this chapter and they likely reflect interactions between biological and psychological systems involved in behavioral and emotional control. Disruptions of these systems are theorized to underlie the externalizing symptoms and impulsive personality traits that are shared between PDs and SUDs, with the former more likely to emerge as a syndrome earlier than the latter. A final level of complexity is added to the model for substances that are likely to directly influence the pathophysiology of SUD by way of their toxicological influences on biological (and corresponding psychological) systems that subserve behavioral and emotional control. In these situations, problematic substance use can exacerbate existing EF

Neurocognitive model of addiction comorbidity Chapter | 6

85

by the current diagnostic nosology. As the nosology of personality psychopathology is clarified, personality phenotypes related to EF deficits may exist independently of polythetic DSM-5 diagnostic criteria because bounded symptom constellations may not communicate valuable information in and of themselves. Given that PD diagnoses require pervasive and persistent symptoms, the sequencing of PDs to SUDs may be a diagnostic artifact by which addiction nearly always follows PDs. However, there is a current dearth of evidence for PD symptoms (vs. diagnoses) relating to later SUD, aside from impulsive personality traits and externalizing symptoms that are relevant to Cluster B PDs. This warrants caution in relying solely on evidence from categorical PD diagnoses and their longitudinal associations with SUD initiation. Therefore, the model should be considered preliminary given the limits of the research studies to date and should be updated, as research adopting a dimensional conceptualization of personality psychopathology is generated.

Future directions

FIGURE 6.1 Heuristic model illustrating the role of executive functions in the comorbidity of personality disorders and substance use disorders. Syndromes are represented in rectangles. The coloring of the arrow connecting substance use disorder and executive function deficits communicates the uncertainty in the extent to which toxicology effects are cyclical. PD, personality disorder; SUD, substance use disorder.

weaknesses and lead to greater problems in behavioral control that become manifest at the level of symptoms and phenotypes (e.g., externalizing symptoms, impulsive personality traits). Whether some individuals are more vulnerable to these effects (i.e., people with greater impulsive personality traits, more severe EF deficits, and/or specific genetic predispositions) requires further investigation. It also remains a point of future inquiry whether these toxicological effects could consequently increase the likelihood of PD onset (or worsen the severity of PD symptoms) in individuals with SUD alone. Our neurocognitive model overlaps in many respects with the previously described models (e.g., Ellingson et al., 2013; Kwako et al., 2016; Trull et al., 2018), although we focus more specifically on EFs as a central manifestation of the pathophysiology that arises from the genetic and/or environmental etiologies of SUDs and PDs. Our model also applies more directly to PDs characterized by externalizing symptoms and impulsive personality traits (i.e., Cluster B PDs) and could help to explain why less impulsive PDs are not as likely to be comorbid with SUDs. It should also be noted that the risk factor component of this model is limited

In summary, there is consistent evidence from crosssectional and longitudinal studies that links PDs and SUDs. The bridge between the diagnoses is likely to be formed by externalizing psychopathology and impulsive personality traits, especially when considering the comorbidity of SUDs with the dramatic, emotional, or erratic Cluster B PDs. At a neurocognitive level, these symptoms and traits are undergirded in part by EFs, which facilitate adaptive goal-directed behaviors and contribute to effective decision-making. When these cognitive functions are disrupted, as is often the case in ASPD and BPD, the likelihood of substance use initiation may increase and consequent substance use could in turn have deleterious effects on cognition. Our model attempts to synthesize these findings using a neurocognitive frame of reference, although there are many places where the model can be clarified and updated based on future research advances. First, more longitudinal research is needed to understand how EFs contribute to substance use initiation and subsequent problematic use in the context of dimensional personality psychopathology constructs, such as those described in HiTOP (e.g., disinhibited externalizing) and relevant personality trait domain qualifiers in the 11th revision of the International Classification of Diseases (e.g., disinhibition) (Tyrer et al., 2019). Relatedly, it will be important to clarify whether substance use problems emerge concurrently with personality psychopathology or whether the former occurs (or perhaps worsens) after the latter has developed. Second, research in this area would benefit from a more consistent and comprehensive operationalization of EFs and other cognitive constructs relevant to PDs and SUDs. Such efforts have already begun,

86

Cognition and Addiction

but it is apparent that congruously mapping factor analytic results and novel dimensional conceptualizations of cognition to previous research has been challenging (MacKillop et al., 2016; Morris and Voon, 2016; Stahl et al., 2014). Third, there is little research on SUDs that links emotion and reward to cognition, let alone the comorbidity of SUDs and PDs. This is surprising given the relevance of emotion- and reward-cognition interactions to both forms of psychopathology and the potential to illuminate how emotion could impact EFs to produce PDs and SUDs. Fourth, we focused our discussion primarily on ASPD and BPD because these diagnoses are the most frequently studied PDs in neurocognitive research. Accordingly, our model is most relevant to these PD diagnoses, and more research on other forms of personality psychopathology is needed to clarify the broader applicability of the model. Similarly, we based our neurocognitive model on SUDs without considering other addictive disorders (e.g., gambling disorder), which could broaden understanding of the role of neurocognitive function in the comorbidity of PDs and addiction. Taken together, significant advancements in these areas will elucidate key transdiagnostic factors that account for the comorbidity of PDs and SUDs, which could accelerate treatment research for individuals with these concurrent disorders.

References Adams, Z.W., Kaiser, A.J., Lynam, D.R., Charnigo, R.J., Milich, R., 2012. Drinking motives as mediators of the impulsivity-substance use relation: pathways for negative urgency, lack of premeditation, and sensation seeking. Addict. Behav. 37 (7), 848e855. https://doi.org/10. 1016/j.addbeh.2012.03.016. Ahn, W.Y., Ramesh, D., Moeller, F.G., Vassileva, J., 2016. Utility of machine-learning approaches to identify behavioral markers for substance use disorders: impulsivity dimensions as predictors of current cocaine dependence. Front. Psychiatry 7. https://doi.org/10.3389/ fpsyt.2016.00034. American Psychiatric Association, 2013. Diagnostic and Statistical Manual of Mental Disorders (DSM-5Ò). American Psychiatric Association Publishing. Baldacchino, A., Balfour, D.J.K., Passetti, F., Humphris, G., Matthews, K., 2012. Neuropsychological consequences of chronic opioid use: a quantitative review and meta-analysis. Neurosci. Biobehav. Rev. 36 (9), 2056e2068. https://doi.org/10.1016/j.neubiorev. 2012.06.006. Baskin-Sommers, A.R., Brazil, I.A., Ryan, J., Kohlenberg, N.J., Neumann, C.S., Newman, J.P., 2015. Mapping the association of global executive functioning onto diverse measures of psychopathic traits. Personal. Disord. 6 (4), 336e346. https://doi.org/10.1037/ per0000125. Bechara, A., Damasio, H., Tranel, D., Damasio, A.R., 1997. Deciding advantageously before knowing the advantageous strategy. Science 275 (5304), 1293e1295. https://doi.org/10.1126/science.275.5304.1293.

Berenson, K.R., Gregory, W.E., Glaser, E., Romirowsky, A., Rafaeli, E., Yang, X., Downey, G., 2016. Impulsivity, rejection sensitivity, and reactions to stressors in borderline personality disorder. Cogn. Ther. Res. 40 (4), 510e521. https://doi.org/10.1007/s10608-015-9752-y. Berg, J.M., Latzman, R.D., Bliwise, N.G., Lilienfeld, S.O., 2015. Parsing the heterogeneity of impulsivity: a meta-analytic review of the behavioral implications of the UPPS for psychopathology. Psychol. Assess. 27 (4), 1129e1146. https://doi.org/10.1037/pas0000111. Bickel, W.K., Koffarnus, M.N., Moody, L., Wilson, A.G., 2014. The behavioral- and neuro-economic process of temporal discounting: a candidate behavioral marker of addiction. Neuropharmacology 76, 518e527. https://doi.org/10.1016/j.neuropharm.2013.06.013. Chiu, M.L., Cheng, C.F., Liang, W.M., Lin, P.T., Wu, T.N., Chen, C.Y., 2018. The temporal relationship between selected mental disorders and substance-related disorders: a nationwide population-based cohort study. Psychiatry J. 12. https://doi.org/10.1155/2018/5697103. Chugani, C.D., Byrd, A.L., Pedersen, S.L., Chung, T., Hipwell, A.E., Stepp, S.D., 2018. Affective and sensation-seeking pathways linking borderline personality disorder symptoms and alcohol-related problems in young women. J. Personal. Disord. Advance online publication https://doi.org/10.1521/pedi_2018_32_389. Coffey, S.F., Schumacher, J.A., Baschnagel, J.S., Hawk, L.W., Holloman, G., 2011. Impulsivity and risk-taking in borderline personality disorder with and without substance use disorders. Personal. Disord. 2 (2), 128e141. https://doi.org/10.1037/a0020574. Coskunpinar, A., Cyders, M.A., 2012. Mediationemoderation analysis of problematic alcohol use: the roles of urgency, drinking motives, and risk/benefit perception. Addict. Behav. 37 (7), 880e883. https://doi. org/10.1016/j.addbeh.2012.03.014. Cyders, M.A., Coskunpinar, A., 2012. The relationship between self-report and lab task conceptualizations of impulsivity. J. Res. Personal. 46 (1), 121e124. https://doi.org/10.1016/j.jrp.2011.11.005. Cyders, M.A., Smith, G.T., Spillane, N.S., Fischer, S., Annus, A.M., Peterson, C., 2007. Integration of impulsivity and positive mood to predict risky behavior: development and validation of a measure of positive urgency. Psychol. Assess. 19 (1), 107e118. https://doi.org/ 10.1037/1040-3590.19.1.107. de Wit, H., 2009. Impulsivity as a determinant and consequence of drug use: a review of underlying processes. Addict. Biol. 14 (1), 22e31. https://doi.org/10.1111/j.1369-1600.2008.00129.x. Del Missier, F., Mäntylä, T., de Bruin, W.B., 2012. Decision-making competence, executive functioning, and general cognitive abilities. J. Behav. Decis. Mak. 25 (4), 331e351. https://doi.org/doi:10.1002/ bdm.731. Dolan-Sewell, R.T., Krueger, R.F., Shea, M.T., 2001. Co-occurrence with syndrome disorders. In: Livesley, J.W. (Ed.), Handbook of Personality Disorders: Theory, Research, and Treatment. Guilford, New York, NY, pp. 84e104. Dom, G., Sabbe, B., Hulstijn, W., van Den Brink, W., 2018. Substance use disorders and the orbitofrontal cortex: systematic review of behavioural decision-making and neuroimaging studies. Br. J. Psychiatry 187 (3), 209e220. https://doi.org/10.1192/bjp.187.3.209. Ellingson, J.M., Verges, A., Littlefield, A.K., Martin, N.G., Slutske, W.S., 2013. Are bottom-up and top-down traits in dual-systems models of risky behavior genetically distinct? Behav. Genet. 43 (6), 480e490. https://doi.org/10.1007/s10519-013-9615-9.

Neurocognitive model of addiction comorbidity Chapter | 6

Ersche, K.D., Turton, A.J., Pradhan, S., Bullmore, E.T., Robbins, T.W., 2010. Drug addiction endophenotypes: impulsive versus sensationseeking personality traits. Biol. Psychiatry 68 (8), 770e773. https:// doi.org/10.1016/j.biopsych.2010.06.015. Fenton, M.C., Keyes, K., Geier, T., Greenstein, E., Skodol, A., Krueger, B., et al., 2012. Psychiatric comorbidity and the persistence of drug use disorders in the United States. Addiction 107 (3), 599e609. https://doi.org/10.1111/j.1360-0443.2011.03638.x. Fernández-Serrano, M.J., Pérez-García, M., Verdejo-García, A., 2011. What are the specific vs. generalized effects of drugs of abuse on neuropsychological performance? Neurosci. Biobehav. Rev. 35 (3), 377e406. https://doi.org/10.1016/j.neubiorev.2010.04.008. Gillespie, N.A., Aggen, S.H., Neale, M.C., Knudsen, G.P., Krueger, R.F., South, S.C., et al., 2018. Associations between personality disorders and cannabis use and cannabis use disorder: a population-based twin study. Addiction 113 (8), 1488e1498. https://doi.org/10.1111/add. 14209. Goudriaan, A.E., Grekin, E.R., Sher, K.J., 2011. Decision making and response inhibition as predictors of heavy alcohol use: a prospective study. Alcohol Clin. Exp. Res. 35 (6), 1050e1057. https://doi.org/10. 1111/j.1530-0277.2011.01437.x. Gowin, J.L., Sloan, M.E., Ramchandani, V.A., Paulus, M.P., Lane, S.D., 2018. Differences in decision-making as a function of drug of choice. Pharmacol. Biochem. Behav. 164, 118e124. https://doi.org/10.1016/j. pbb.2017.09.007. Grant, B.F., Saha, T.D., Ruan, W.J., Goldstein, R.B., Chou, S.P., Jung, J., et al., 2016. Epidemiology of DSM-5 drug use disorder: results from the National Epidemiologic Survey on Alcohol and Related Conditions-III. JAMA Psychiatry 73 (1), 39e47. https://doi.org/10. 1001/jamapsychiatry.2015.2132. Grant, I., Gonzalez, R., Carey, C.L., Natarajan, L., Wolfson, T., 2003. Non-acute (residual) neurocognitive effects of cannabis use: a metaanalytic study. J. Int. Neuropsychol. Soc. 9 (5), 679e689. https:// doi.org/10.1017/S1355617703950016. Grant, J.E., Chamberlain, S.R., 2014. Impulsive action and impulsive choice across substance and behavioral addictions: cause or consequence? Addict. Behav. 39 (11), 1632e1639. https://doi.org/10.1016/ j.addbeh.2014.04.022. Groenman, A.P., Oosterlaan, J.G., Corina, U., Vuijk, P.J., Rommelse, N., Franke, B., et al., 2015. Neurocognitive predictors of substance use disorders and nicotine dependence in ADHD probands, their unaffected siblings, and controls: a 4-year prospective follow-up. J. Child Psychol. Psychiatry 56 (5), 521e529. https://doi.org/10.1111/jcpp. 12315. Gvirts, H.Z., Harari, H., Braw, Y., Shefet, D., Shamay-Tsoory, S.G., Levkovitz, Y., 2012. Executive functioning among patients with borderline personality disorder (BPD) and their relatives. J. Affect. Disord. 143 (1), 261e264. https://doi.org/10.1016/j.jad.2012.05.007. Hamilton, K.R., Mitchell, M.R., Wing, V.C., Balodis, I.M., Bickel, W.K., Fillmore, M., et al., 2015. Choice impulsivity: definitions, measurement issues, and clinical implications. Personal. Disord. 6 (2), 182e198. https://doi.org/10.1037/per0000099. Hasher, L., Lustig, C., Zacks, R.T., 2007. Inhibitory mechanisms and the control of attention. In: Conway, A., Jarrold, C., Kane, M., Miyake, A., Towse, J. (Eds.), Variation in Working Memory. Oxford University Press, New York, NY, pp. 227e249.

87

Hofmann, W., Schmeichel, B.J., Baddeley, A.D., 2012. Executive functions and self-regulation. Trends Cognit. Sci. 16 (3), 174e180. https:// doi.org/10.1016/j.tics.2012.01.006. Kendler, K.S., Prescott, C.A., Myers, J., Neale, M.C., 2003. The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Arch. Gen. Psychiatry 60 (9), 929e937. https://doi.org/10.1001/archpsyc.60.9.929. Kessler, R.C., 2004. The epidemiology of dual diagnosis. Biol. Psychiatry 56 (10), 730e737. https://doi.org/10.1016/j.biopsych.2004.06.034. Khan, S., 2017. Concurrent mental and substance use disorders in Canada. Health Rep. 28 (8), 3e8. Retrieved from: https://www150.statcan.gc. ca/n1/pub/82-003-x/2017008/article/54853-eng.htm. Kotov, R., Krueger, R.F., Watson, D., Achenbach, T.M., Althoff, R.R., Bagby, R.M., et al., 2017. The hierarchical taxonomy of psychopathology (HiTOP): a dimensional alternative to traditional nosologies. J. Abnorm. Psychol. 126 (4), 454e477. https://doi.org/10.1037/ abn0000258. Koudys, J.W., Gulamani, T., Ruocco, A.C., 2018. Borderline personality disorder: refinements in phenotypic and cognitive profiling. Curr. Behav. Neurosci. Rep. 5 (1), 102e112. https://doi.org/10.1007/ s40473-018-0145-x. Krueger, R.F., Hicks, B.M., Patrick, C.J., Carlson, S.R., Iacono, W.G., McGue, M., 2002. Etiologic connections among substance dependence, antisocial behavior and personality: modeling the externalizing spectrum. J. Abnorm. Psychol. 111 (3), 411e424. https://doi.org/10. 1037/0021-843X.111.3.411. Krueger, R.F., Markon, K.E., 2006. Reinterpreting comorbidity: a modelbased approach to understanding and classifying psychopathology. Annu. Rev. Clin. Psychol. 2, 111e133. https://doi.org/10.1146/ annurev.clinpsy.2.022305.095213. Kwako, L.E., Momenan, R., Litten, R.Z., Koob, G.F., Goldman, D., 2016. Addictions neuroclinical assessment: a neuroscience-based framework for addictive disorders. Biol. Psychiatry 80 (3), 179e189. https://doi. org/10.1016/j.biopsych.2015.10.024. Lacey, J.H., Evans, C.D.H., 1986. The impulsivist: a multi-impulsive personality disorder. Br. J. Addict. 81 (5), 641e649. https://doi.org/ 10.1111/j.1360-0443.1986.tb00382.x. Lai, H.M., Cleary, M., Sitharthan, T., Hunt, G.E., 2015. Prevalence of comorbid substance use, anxiety and mood disorders in epidemiological surveys, 1990e2014: a systematic review and meta-analysis. Drug Alcohol Depend. 154, 1e13. https://doi.org/10.1016/j. drugalcdep.2015.05.031. Lipszyc, J., Schachar, R., 2010. Inhibitory control and psychopathology: a meta-analysis of studies using the stop signal task. J. Int. Neuropsychol. Soc. 16 (6), 1064e1076. https://doi.org/10.1017/ S1355617710000895. Lopez-Quintero, C., Pérez de los Cobos, J., Hasin, D.S., Okuda, M., Wang, S., Grant, B.F., Blanco, C., 2011. Probability and predictors of transition from first use to dependence on nicotine, alcohol, cannabis, and cocaine: results of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Drug Alcohol Depend. 115 (1), 120e130. https://doi.org/10.1016/j.drugalcdep.2010.11.004. Lynam, D.R., Smith, G.T., Whiteside, S.P., Cyders, M.A., 2006. The UPPS-P: Assessing Five Personality Pathways to Impulsive Behavior. Technical Report. Purdue University, West Lafayette, IN.

88

Cognition and Addiction

Lyons, M.J., Tyrer, P., Gunderson, J., Tohen, M., 1997. Special feature: heuristic models of comorbidity of Axis I and Axis II disorders. J. Personal. Disord. 11 (3), 260e269. https://doi.org/10.1521/pedi. 1997.11.3.260. MacKillop, J., Amlung, M.T., Few, L.R., Ray, L.A., Sweet, L.H., Munafò, M.R., 2011. Delayed reward discounting and addictive behavior: a meta-analysis. Psychopharmacology 216 (3), 305e321. https://doi.org/10.1007/s00213-011-2229-0. MacKillop, J., Weafer, J., Gray, J.C., Oshri, A., Palmer, A., de Wit, H., 2016. The latent structure of impulsivity: impulsive choice, impulsive action, and impulsive personality traits. Psychopharmacology 233 (18), 3361e3370. https://doi.org/10.1007/s00213-016-4372-0. Martinez-Loredo, V., Fernandez-Hermida, J.R., La Torre-Luque, A., Fernandez-Artamendi, S., 2018. Polydrug use trajectories and differences in impulsivity among adolescents. Int. J. Clin. Health Psychol. 18 (3), 235e244. https://doi.org/10.1016/j.ijchp.2018.07.003. McGlashan, T.H., Grilo, C.M., Skodol, A.E., Gunderson, J.G., Shea, M.T., Morey, L.C., et al., 2000. The collaborative longitudinal personality disorders study: baseline Axis I/II and II/II diagnostic co-occurrence. Acta Psychiatr. Scand. 102 (4), 256e264. https://doi.org/10.1034/j. 1600-0447.2000.102004256.x. Miyake, A., Friedman, N.P., 2012. The nature and organization of individual differences in executive functions: four general conclusions. Curr. Dir. Psychol. Sci. 21 (1), 8e14. https://doi.org/10.1177/ 0963721411429458. Moeller, F.G., Barratt, E.S., Dougherty, D.M., Schmitz, J.M., Swann, A.C., 2001. Psychiatric aspects of impulsivity. Am. J. Psychiatry 158 (11), 1783e1793. https://doi.org/10.1176/appi.ajp.158.11.1783. Moeller, S.J., Bederson, L., Alia-Klein, N., Goldstein, R.Z., 2016. Neuroscience of inhibition for addiction medicine: from prediction of initiation to prediction of relapse. Prog. Brain Res. 223, 165e188. https://doi.org/10.1016/bs.pbr.2015.07.007. Monterosso, J., Ehrman, R., Napier, K.L., O’Brien, C.P., Childress, A.R., 2001. Three decision-making tasks in cocaine-dependent patients: do they measure the same construct? Addiction 96 (12), 1825e1837. https://doi.org/10.1046/j.1360-0443.2001.9612182512.x. Morgan, A.B., Lilienfeld, S.O., 2000. A meta-analytic review of the relation between antisocial behavior and neuropsychological measures of executive function. Clin. Psychol. Rev. 20 (1), 113e136. https:// doi.org/10.1016/S0272-7358(98)00096-8. Morris, L.S., Voon, V., 2016. Dimensionality of cognitions in behavioral addiction. Curr. Behav. Neurosci. Rep. 3 (1), 49e57. https://doi.org/ 10.1007/s40473-016-0068-3. Newton-Howes, G.M., Foulds, J.A., Guy, N.H., Boden, J.M., Mulder, R.T., 2017. Personality disorder and alcohol treatment outcome: systematic review and meta-analysis. Br. J. Psychiatry 211 (1), 22e30. https://doi.org/10.1192/bjp.bp.116.194720. Noël, X., Brevers, D., Bechara, A., 2013. A neurocognitive approach to understanding the neurobiology of addiction. Curr. Opin. Neurobiol. 23 (4), 632e638. https://doi.org/10.1016/j.conb.2013.01.018. Ogilvie, J.M., Stewart, A.L., Chan, R.C.K., Shum, D.H.K., 2011. Neuropsychological measures of executive function and antisocial behavior: a meta-analysis. Criminology 49 (4), 1063e1107. https:// doi.org/10.1111/j.1745-9125.2011.00252.x. Paret, C., Jennen-Steinmetz, C., Schmahl, C., 2017. Disadvantageous decision-making in borderline personality disorder: partial support from a meta-analytic review. Neurosci. Biobehav. Rev. 72, 301e309. https://doi.org/10.1016/j.neubiorev.2016.11.019.

Roberts, C.A., Jones, A., Montgomery, C., 2016. Meta-analysis of executive functioning in ecstasy/polydrug users. Psychol. Med. 46 (8), 1581e1596. https://doi.org/10.1017/S0033291716000258. Ruocco, A.C., 2005. The neuropsychology of borderline personality disorder: a meta-analysis and review. Psychiatry Res. 137 (3), 191e202. https://doi.org/10.1016/j.psychres.2005.07.004. Ruocco, A.C., Daros, A.R., Chang, J., Rodrigo, A.H., Lam, J., Ledochowski, J., McMain, S.F., 2018. Clinical, personality, and neurodevelopmental phenotypes in borderline personality disorder: a family study. Psychol. Med. 1e12. https://doi.org/10.1017/ S0033291718002908. Schreiner, A.M., Dunn, M.E., 2012. Residual effects of cannabis use on neurocognitive performance after prolonged abstinence: a metaanalysis. Exp. Clin. Psychopharmacol. 20 (5), 420e429. https://doi. org/10.1037/a0029117. Scott, J.C., Woods, S.P., Matt, G.E., Meyer, R.A., Heaton, R.K., Atkinson, J.H., Grant, I., 2007. Neurocognitive effects of methamphetamine: a critical review and meta-analysis. Neuropsychol. Rev. 17 (3), 275e297. https://doi.org/10.1007/s11065-007-9031-0. Sedgwick, O., Young, S., Baumeister, D., Greer, B., Das, M., Kumari, V., 2017. Neuropsychology and emotion processing in violent individuals with antisocial personality disorder or schizophrenia: the same or different? A systematic review and meta-analysis. Aust. N.Z. J. Psychiatry 51 (12), 1178e1197. https://doi.org/10.1177/000486741 7731525. Settles, R.E., Fischer, S., Cyders, M.A., Combs, J.L., Gunn, R.L., Smith, G.T., 2012. Negative urgency: a personality predictor of externalizing behavior characterized by neuroticism, low conscientiousness, and disagreeableness. J. Abnorm. Psychol. 121 (1), 160e172. https://doi.org/10.1037/a0024948. Smith, G.T., Cyders, M.A., 2016. Integrating affect and impulsivity: the role of positive and negative urgency in substance use risk. Drug Alcohol Depend. 163, S3eS12. https://doi.org/10.1016/j.drugalcdep. 2015.08.038. Snyder, H.R., Miyake, A., Hankin, B.L., 2015. Advancing understanding of executive function impairments and psychopathology: bridging the gap between clinical and cognitive approaches. Front. Psychol. 6. https://doi.org/10.3389/fpsyg.2015.00328. Stahl, C., Voss, A., Schmitz, F., Nuszbaum, M., Tüscher, O., Lieb, K., Klauer, K.C., 2014. Behavioral components of impulsivity. J. Exp. Psychol. Gen. 143 (2), 850e886. https://doi.org/10.1037/a0033981. Stavro, K., Pelletier, J., Potvin, S., 2013. Widespread and sustained cognitive deficits in alcoholism: a meta-analysis. Addict. Biol. 18 (2), 203e213. https://doi.org/10.1111/j.1369-1600.2011.00418.x. Thomsen, M.S., Ruocco, A.C., Carcone, D., Mathiesen, B.B., Simonsen, E., 2017. Neurocognitive deficits in borderline personality disorder: associations with childhood trauma and dimensions of personality psychopathology. J. Personal. Disord. 31 (4), 503e521. https://doi.org/10.1521/pedi_2016_30_265. Trull, T.J., Freeman, L.K., Vebares, T.J., Choate, A.M., Helle, A.C., Wycoff, A.M., 2018. Borderline personality disorder and substance use disorders: an updated review. Borderline Personal. Disord. Emot. Dysregul. 5 (1), 15. https://doi.org/10.1186/s40479-018-0093-9. Trull, T.J., Jahng, S., Tomko, R.L., Wood, P.K., Sher, K.J., 2010. Revised NESARC personality disorder diagnoses: gender, prevalence, and comorbidity with substance dependence disorders. J. Personal. Disord. 24 (4), 412e426. https://doi.org/10.1521/pedi.2010.24.4.412.

Neurocognitive model of addiction comorbidity Chapter | 6

Trull, T.J., Waudby, C.J., Sher, K.J., 2004. Alcohol, tobacco, and drug use disorders and personality disorder symptoms. Exp. Clin. Psychopharmacol 12 (1), 65e75. https://doi.org/10.1037/1064-1297.12.1.65. Turner, D., Sebastian, A., Tüscher, O., 2017. Impulsivity and Cluster B personality disorders. Curr. Psychiatry Rep. 19 (3), 15. https://doi.org/ 10.1007/s11920-017-0768-8. Twain, D.C., 1957. Factor analysis of a particular aspect of behavioral control: impulsivity. J. Clin. Psychol. 13 (2), 133e136. https://doi.org/ 10.1002/1097-4679(195704)13:23.0.CO;2-X. Tyrer, P., Mulder, R., Kim, Y.R., Crawford, M.J., 2019. The Development of the ICD-11 Classification of Personality Disorders: an Amalgam of Science, Pragmatism, and Politics. Annual Review of Clinical Psychology. https://doi.org/10.1146/annurev-clinpsy-050718-095736. Unoka, Z., Richman, M.J., 2016. Neuropsychological deficits in BPD patients and the moderator effects of co-occurring mental disorders: a meta-analysis. Clin. Psychol. Rev. 44, 1e12. https://doi.org/10.1016/j. cpr.2015.11.009. van den Bosch, L.M., Verheul, R., 2007. Patients with addiction and personality disorder: treatment outcomes and clinical implications. Curr. Opin. Psychiatry 20 (1), 67e71. https://doi.org/10.1097/YCO. 0b013e328011740c. Verbruggen, F., De Houwer, J., 2007. Do emotional stimuli interfere with response inhibition? Evidence from the stop signal paradigm. Cognit. Emot. 21 (2), 391e403. https://doi.org/10.1080/02699930600625081. Verdejo-García, A., Bechara, A., Recknor, E.C., Pérez-García, M., 2007. Negative emotion-driven impulsivity predicts substance dependence problems. Drug Alcohol Depend. 91 (2e3), 213e219. https://doi.org/ 10.1016/j.drugalcdep.2007.05.025. Verdejo-García, A., Chong, T.T.J., Stout, J.C., Yücel, M., London, E.D., 2018. Stages of dysfunctional decision-making in addiction.

89

Pharmacol. Biochem. Behav. 164, 99e105. https://doi.org/10.1016/j. pbb.2017.02.003. Verdejo-García, A., Lawrence, A.J., Clark, L., 2008. Impulsivity as a vulnerability marker for substance-use disorders: review of findings from high-risk research, problem gamblers and genetic association studies. Neurosci. Biobehav. Rev. 32 (4), 777e810. https://doi.org/10. 1016/j.neubiorev.2007.11.003. Walter, M., Gunderson, J.G., Zanarini, M.C., Sanislow, C.A., Grilo, C.M., McGlashan, T.H., et al., 2009. New onsets of substance use disorders in borderline personality disorder over 7 years of follow-ups: findings from the Collaborative Longitudinal Personality Disorders Study. Addiction 104 (1), 97e103. https://doi.org/10.1111/j.1360-0443. 2008.02413.x. Whiteside, S.P., Lynam, D.R., 2001. The Five Factor Model and impulsivity: using a structural model of personality to understand impulsivity. Pers. Indiv. Differ. 30 (4), 669e689. https://doi.org/10.1016/ S0191-8869(00)00064-7. Winter, D., 2016. Attention to emotional stimuli in borderline personality disorder e a review of the influence of dissociation, self-reference, and psychotherapeutic interventions. Borderline Personal. Disord. Emot. Dysregul. 3 (1), 11. https://doi.org/10.1186/s40479-016-0047-z. Wray, T.B., Simons, J.S., Dvorak, R.D., Gaher, R.M., 2012. Trait-based affective processes in alcohol-involved “risk behaviors”. Addict. Behav. 37 (11), 1230e1239. https://doi.org/10.1016/j.addbeh.2012. 06.004. Young, S.E., Friedman, N.P., Miyake, A., Willcutt, E.G., Corley, R.P., Haberstick, B.C., Hewitt, J.K., 2009. Behavioral disinhibition: liability for externalizing spectrum disorders and its genetic and environmental relation to response inhibition across adolescence. J. Abnorm. Psychol. 118 (1), 117e130. https://doi.org/10.1037/a0014657.

Chapter 7

Cognitive risk factors for alcohol and substance addictions Natalie Castellanos-Ryan and Patricia Conrod Universite de Montreal, CHU Ste Justine, Montreal, QC, Canada

Substance misuse and addictions are highly prevalent worldwide and represent a major health, social, and economic burden to societies (United Nations, 2018; Degenhardt and Hall, 2012). Adolescence is an important developmental period for the onset of substance use and misuse (see Conrod and Nikolaou, 2016), with some epidemiologic studies in the United States showing that up to 36% of high school students attending grade 12 report being drunk over the last 12 months and around 50% report having ever tried any illicit substance (Johnston et al., 2016). Similarly, Canadian statistics show that 42% of adolescents attending grades 9 through 11 (or 3e5 of high school) have used cannabis in the last year, and by grade 11%, 86% of them were drinking alcohol (Dubé et al., 2009). Youth substance use is also prevalent in Europe, with 37% of youth in the United Kingdom trying an illegal drug by the age of 15 years, and around 23% of 15 year olds reporting having been drunk in the last 4 weeks (Statistics Team NHS Digital, 2017). Short-term correlates of early onset drinking and drug use include psychosocial immaturity, poor grades, school dropout, higher risk for assault, teenage pregnancy and sexually transmitted diseases, suicide, homicide, and accidental injuries, including death from alcohol poisoning, with long-term consequences including higher risk of developing substance use disorders (SUDs) and a range of behavioral and mental health problems (Chassin et al., 2010; Grant and Dawson, 1998; King et al., 2006; Odgers et al., 2008; Single et al., 2000; Rioux et al., 2017; Scholes-Balog et al., 2016). Given these serious co-occurring difficulties and the increased risk for adult SUDs observed as the age of substance use onset decreases (Grant and Dawson, 1998; Rioux et al., 2017), identifying risk and protective factors in childhood and adolescence related to substance use initiation and frequency is essential for developing evidence-based targeted prevention and clinical practice guidelines.

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00007-1 Copyright © 2020 Elsevier Inc. All rights reserved.

Several studies show that heavy, long-term cannabis use is associated concurrently with impaired neurocognitive function in animals and human adults (e.g, Fried et al., 2005; Lubman et al., 2015; Rubino and Parolaro, 2016; Verrico et al., 2014). Similarly, studies comparing adolescents using cannabis on a regular basis (i.e., weekly) with controls reported that they perform worse on tasks assessing attention (Hanson et al., 2010; Mathias et al., 2011; Medina et al., 2007), verbal memory (Hanson et al., 2010; Medina et al., 2007), intelligence (Harvey et al., 2007), and executive function (EF) (Mathias et al., 2011; Medina et al., 2007; Harvey et al., 2007; Grant et al., 2012; Lisdahl and Price, 2012) and have reduced processing speed on different tasks (Medina et al., 2007; Lisdahl and Price, 2012; Gruber et al., 2012). Some of the same cognitive factors have been identified as important correlates of alcohol use and other drug misuse (Jacobus and Tapert, 2013, 2014; Squeglia et al., 2009), with alcohol use disorder being particularly related to wide-ranging deficits in response inhibition, cognitive flexibility, and working memory (WM) (Giancola and Mezzich, 2000; Giancola and Moss, 1998; Moss et al., 1994). However, most studies examining the association between cognition and substance use are cross-sectional caseecontrol or retrospective studies. While such designs are able to identify concurrent cognitive correlates of substance use or investigate the differences between clinical samples of substance users versus nonusers or normative samples, they cannot inform on the directionality of effects, i.e., whether strengths or deficits in cognitive function predate and confer risk for or protect against later substance use problems or whether they are consequences of substance use. That is, most human studies do not include a proper assessment of cognitive function before substance use onset.

91

92

Cognition and Addiction

This is an important limitation, as presubstance use performance on certain cognitive tasks has been shown to be associated with later substance use onset and increased alcohol and cannabis use frequency (Castellanos-Ryan et al., 2013a; Squeglia et al., 2014; White and Batty, 2012; Meier et al., 2018) or be associated with change in cannabis use frequency and severity (Cousijn et al., 2014). Thus, to be able to identify and differentiate between cognitive risk and protective factors from the cognitive consequences of substance use, it is important to give weight to the studies that allow us to make this distinction, e.g., those with prospective longitudinal designs.

Structure of cognitive function Substance misuse is often viewed as the result of faulty decision-making or problem-solving. Optimal decisionmaking is defined as the process of choosing a particular action among a number of alternative options, which is expected to result in the most beneficial outcome. Based on Luria’s problem-solving model (Luria, 1966), decisionmaking is thought to involve four different phases: (1) an input or problem representation phase in which a problem is perceived and an attempt is made to understand it; (2) a planning or a processing phase in which alternative options are evaluated; (3) an output or execution of the plan phase, during which the solution is executed; and (4) a monitoring or a review phase during which the solution is evaluated and errors detected and corrected. Faulty problem-solving or decision-making can result from deficits at different points or phases of the decision-making process. For example, substance misusing individuals could make poor choices because (a) they value immediate outcomes or rewards and discount the value of delayed rewards (often referred to as temporal/delay discountingda deficit associated with phase two of Luria’s problem-solving model) (Green and Myerson, 2004; Kirby et al., 1999); (b) they have a strong tendency to produce habitual/default actions prematurely or are incapable to override or stop habitual/default actions (i.e., deficits in response/motor inhibition; related to the third phase of the problem-solving model) (Aron et al., 2014; Chambers et al., 2009; Verbruggen and Logan, 2009); (c) failure to reflect on the consequences of their choices (i.e., broadly defined as a deficit in affective “decision-making” (Bechara, 2005); related to phase four of the problem-solving model); and/or (d) either incapacity to perceive or attend to important information in their environment that may help them make better decisions, that is, deficits in selective attention or interference control (Friedman and Miyake, 2004) or deficits in WM (Hofmann et al., 2012), both considered core processes necessary for EF (see Miyake et al., 2000; Stuss, 2011; Zelazo et al., 2004), which are involved in all phases of the problemsolving model.

Finally, a more general or global cognitive factor, namely intelligence, has also been implicated in faulty decision-making and substance misuse (Moss et al., 1994). Intelligence is considered a more global measure of cognitive function, as it is believed to be a combination of cognitive or intellectual abilities required to obtain knowledge and to use that knowledge to solve problems (Resing and Drenth, 2007). Thus, in theory, faulty problemsolving could result from low intelligence, which could be involved in all phases of the problem-solving model. In this chapter, we attempt to integrate several models of EF and decision-making and highlight the dissociable deficits and mechanisms that have been implicated in substance use initiation and substance use problems by reviewing the evidence focusing on the following cognitive function domains: (1) basic EF processes implicated at all stages of problem-solving, such as poor selective attention or WM; (2) response inhibition; (3) delay discounting; (4) reward-based decision-making; and (5) intelligence.

Selective attention, working memory, and general executive function EFs normally refer to the ability to monitor, direct, and regulate cognition and behavior in relation to goals rather than immediate stimuli (Nigg et al., 2004; Finn et al., 1999) or more generally to the cognitive processes that allow one to behave in a contextually appropriate manner (Spreen and Strauss, 1998). Although commonly equated to functions pertaining to the prefrontal cortex, EFs comprise many component processes, which are supported differentially by a series of parallel neural loops that connect regions of the prefrontal cortex, basal ganglia, and thalamus (Middleton and Strick, 2001). Selective attention, WM, set shifting, interference control, inhibition, decision-making, and planning are commonly studied as component processes of executive functioning (Nigg et al., 2004; Pennington and Ozonoff, 1996). Recognizing the heritable nature of problematic alcohol and drug use, many studies have investigated differences in executive functioning between drug-naïve children with low and high genetic risk for alcoholism and have found mixed results with respect to performance on tests of planning, conceptual shifting, and psychomotor functioning (Nigg et al., 2004; Bauer and Hesselbrock, 1999; Corral et al., 2003; Leonard and Das Eiden, 2002; Poon et al., 2000). The P300 amplitudes, which are an event-related potential that can be recorded by electroencephalography and are often used as a measure of cognitive function in decision-making processes, have also been shown to differentiate children with a family history of alcoholism from controls (Carlson et al., 2007; Hill et al., 1999). Deckel et al. (1995), who assessed different neurocognitive

Cognitive risk factors for alcohol and substance addictions Chapter | 7

measures in relation to alcohol use in sons of fathers with alcohol use disorder, found that performance in neurocognitive tests measuring frontal and/temporal neocortical functioning was predictive of age of first drink and frequency of heavy drinking (drinking to get intoxicated). These findings suggest that disturbances in the prefrontal or anterior neocortex, potentially implicated in EFs, may be a risk factor in the development of substance misuse. Indeed, a number of longitudinal studies suggest that executive functioning in adolescents may be predictive of future alcohol and drug use, above and beyond basic attentional deficits (Aytaclar et al., 1999; Tapert et al., 2002a,b). Although more research is needed to establish the mechanisms that underlie the link between EF and substance misuse, some researchers suggest that deficits in EF lead to alcohol or substance misuse because they result in poor self-regulation (Giancola and Mezzich, 2000; Tarter et al., 1989). In terms of specific core components of EF, WM is thought to play a central role in self-regulation (Barkley, 1997; Finn, 2002) and is considered to be a limited capacity process that keeps stimuli activated in mind so that they may be retained and effectively used to guide behavior (Finn, 2002). WM allows to hold and manipulate information temporarily (Aronen ET Vuontela et al., 2005; Rypma et al., 2002) and to integrate it into long-term memory, which is fundamental for processes such as learning, reading, and reasoning (Aronen et al., 2005). Components of the WM system include attention capacity and selective attention, also referred to as attentional control and shifting (Finn, 2002), and thus, findings related to selective attention will also be reviewed here. Several studies show that deficits in WM capacity and selective attention have been associated with substance misuse (Finn and Hall, 2004), with, for example, longitudinal studies showing that poor WM capacity in early adolescent nondrinkers was associated with increased alcohol use by mid adolescence (Khurana et al., 2013), and poor performance on tests of attention in mid adolescence (14e16 years) predicted more frequent substance use in early adulthood (22e24 years) (Tapert et al., 2002b). Similarly, a recent study showed that poor short-term memory and WM capacity, as assessed by the paired associative learning and self-ordered pointing tasks, respectively, in noncannabis user at 13e14 years was associated with earlier onset of cannabis use (Castellanos-Ryan et al., 2017). Poor WM has also been associated with other externalizing problems, including aggression and attentional deficit and hyperactivity disorder (ADHD) (Barkley, 1997; Peeters et al., 2014; Séguin et al., 1995, 2004; Young et al., 2009), and thus could also be considered an important liability factor in an externalizing pathway to substance use. That said the association between WM and substance use may be complex and nuanced, with developmental and other cognitive factors potentially moderating the association. For

93

example, differences in WM have been found in several studies comparing children of parents with alcohol use disorder versus those whose parents had no alcohol use disorder (Ozkaragoz et al., 1997; Whipple et al., 1988) but, interestingly, not when comparing adults with and without family history of alcohol use disorder (Gillen and Hesselbrock, 1992; Sher et al., 1991). Gillen and Hesselbrock (Gillen and Hesselbrock, 1992) suggest that the deficits in WM found in childhood may reflect a lag in development, and that with age, these become less evident or even disappear. This theory is supported in a more recent study comparing children from families with different densities of alcoholism, i.e., no family history of alcoholism, low density (children of father, but no other relatives, with alcohol use disorder), and high density (children of father and at least two other relatives with alcohol use disorder), in which scores on tasks of WM and intelligence quotient (IQ), but not scores on more complex EF tasks (Mazes and Wisconsin card sorting task), differentiated the children of father and at least two other relatives with alcohol use disorder from those with no family history of alcohol use disorder (Corral et al., 1999). However, a follow-up study carried out on some of this sample three and half years later (when participants were aged 11e17 years) showed that the differences on WM and IQ scores previously found were no longer detectable (Corral et al., 2003). Other researchers have proposed that WM is associated with other risk factors for substance use, such as delay discounting, response inhibition, and impulsivity (Bobova et al., 2009; Ellingson et al., 2014). For example, it is posited that WM may influence other cognitive processes related to response inhibition and addictive behavior, such as delay discounting (Bobova et al., 2009), and place an individual at increased risk for later substance use problems because the inability to recall past events and/or future consequences (e.g., previous or potential instances of problematic substance use) reduces the value placed on future events. Others propose that WM interacts with other cognitive processes involved in drug and alcohol attitudes and norms to confer risk for substance misuse (e.g, Finn and Hall, 2004). For example, proponents of the dual-process model of substance use posit that substance use and problems are influenced by the interaction between two types of cognitive processes: (1) a set of processes that is reflective, controlled, deliberate, and executive in nature, in which WM figures highly, and (2) cognitive processes that are more automatic, implicit, or associative in nature (Wiers et al., 2007; Barrett et al., 2004; Kane and Engle, 2002). In this model, it is suggested that the ability to control attentional resources (by way of WM capacity) can moderate the effects that automatic cognitive processes (drug-related associations in memory, which can be activated by environmental or internal stimuli without the need for deliberate recollection) have on substance use behavior. It is posited that activated

94

Cognition and Addiction

mental representations about substances of abuse are more likely to influence behavior among individuals with lower WM capacity because they are less likely to engage higher cognitive processes to produce logical counterarguments to drug cues/norms that are often implicitly presented to youth through traditional and social media and peer influences. At least one study has supported the role of WM capacity in this dual-process model by showing that drug-related associations in memory predicted drug use more strongly in adolescents with lower levels of WM capacity (Grenard et al., 2008). In summary, there is substantial evidence showing that poor WM capacity plays an important role in the development of substance use, but further longitudinal studies are needed to clarify its potential indirect influence through other cognitive processes, or its additive or interactive effects with independent processes, in the prediction of substance use and misuse.

Response inhibition Response inhibition is another key component of executive functioning and is the capacity to override or stop habitual, default, or learned responses or actions. In the last 20 years, there has been much interest in studying the role of response inhibition in the development of different psychopathologies in youth, with a number of studies showing that response inhibition is an important correlate or contributing factor for substance use, and impulsive behavior, more generally (Castellanos-Ryan et al., 2011, 2014; Uekermann et al., 2003; Courtney et al., 2012; Kamarajan et al., 2005; Lawrence et al., 2009). Indeed, substance use, including both alcohol and cannabis use, has often been associated with poor response or behavioral inhibition (Hester and Garavan, 2004; Kaufman et al., 2003; Nigg et al., 2006; Wiers et al., 1998; Li et al., 2006; Smith et al., 2014), with a growing body of research suggesting that deficits in response inhibition predate substance use initiation (Castellanos-Ryan et al., 2014; Norman et al., 2011; Morin et al., 2019; Wetherill et al., 2013). For example, boys with positive family history of alcohol use disorder, who are considered at risk for future substance use and addiction, perform worse on response inhibition tasks than controls who do not have a family history (Nigg et al., 2004). Moreover, a number of longitudinal studies show that poor response inhibition during early adolescence is associated with greater substance use and related problems later in adolescence (Nigg et al., 2006; Tarter et al., 2004). A recent study by Squeglia et al. (2014) showed that poorer baseline (at 12e14 years) performance on tests of cognitive inhibitioninterference, as assessed by a stroop inhibition task, predicted higher drinking quantity and frequency, as well as cannabis use frequency by ages 17e18, above and beyond important covariates, including family history of substance use, pubertal development, and psychopathology symptoms. All these studies suggest that problems in self-regulation,

which is a characteristic in many substance misusers, can result from a general inability to regulate or inhibit impulses (or “prepotent” responses). However, two recent machine learning studies investigating multivariate predictors of early onset binge drinking and cannabis use failed to identify response inhibition on a go/no-go task as a unique classifying feature when other self-report measures of impulsivity were also included in the model (Whelan et al., 2014; Afzali et al., 2019). Therefore, while poor response inhibition might be related to future substance use and misuse, it appears to be implicated in a broader personality or temperament that overlaps with impulsivity and, therefore, might not be a specific risk factor for SUDs. Problems in self-regulation and deficits in response inhibition appear more generally implicated in externalizing problems, with studies showing that deficits in response inhibition are associated with ADHD (Lijffijt et al., 2005) and conduct disorder (CD) (Hobson et al., 2011). Go/no-go and stop signal reaction studies show that aggressive adolescent males and children with ADHD and CD have reduced response inhibition (more commission errors) (Lijffijt et al., 2005; Oosterlaan and Sergeant, 1998). Children with ADHD are also slow to inhibit their responses on an stop signal reaction time (SSRT) (Castellanos et al., 2006). Furthermore, other studies have shown that response inhibition predicts common variance shared across externalizing symptoms, rather than substance use behaviors specifically (Castellanos-Ryan et al., 2014, 2016), and that the association between deficits in response inhibition and substance use does not survive control for other externalizing problems (Castellanos-Ryan et al., 2011, 2014). Taken together, these findings suggest that although deficits in response inhibition are clearly associated with later substance use initiation and misuse, they represent a more general liability to externalizing problems and may represent an important determining factor in what is often referred to as the externalizing pathway to substance use. These findings suggest that substance use is only indirectly related to response inhibition through conduct problems.

Delay discounting Optimal decision-making is defined as the process of choosing a particular action, among a number of alternative options, which is expected to result in the most beneficial outcome. Most agree that basic/core EF processes such as selective attention, response inhibition, and WM, reviewed above, are key in optimal decision-making and problemsolving (Carlson et al., 2013), regardless of context. These EF processes can affect decision-making and problem-solving in emotionally neutral context or conditions and thus are often referred to as “cool” EF processes. However, decision-making often occurs in emotional or rewarding contexts and is sensitive to motivational cues.

Cognitive risk factors for alcohol and substance addictions Chapter | 7

Consequently, faulty decision-making observed among substance-using individuals could make poor choices because they overvalue immediate outcomes or rewards and discount the value of delayed rewards (often referred to as temporal or delay discounting) (Green and Myerson, 2004; Kirby et al., 1999). While EF deficits have been associated with externalizing behaviors, including substance misuse, many posit that substance use problems result more specifically from failures in optimally incorporating temporal factors in decision-making (Bickel et al., 2014; Rachlin, 1997). This conclusion is partly supported by a literature reporting that while response inhibition and WM are associated with externalizing problems in general, executive and other cognitive functions under rewarding or affective contexts and cognitive measures that assess reward and temporal processing such as delay discounting seem to be uniquely associated with substance use (Castellanos-Ryan et al., 2014, 2016; Bickel et al., 2012). High delay discounting has been consistently associated with cigarette smoking (Baker et al., 2003), SUDs (Bickel et al., 2012, 2014; Bickel and Marsch, 2001), and early onset alcoholism (Dom et al., 2005). It has also been shown to predict the increases in number of drugs used and drug use quantity (Johnson et al., 2007; Vuchinich and Simpson, 1998; Ohmura et al., 2005; Takahashi et al., 2009). Longitudinal and prospective studies have also shown that steeper delay discounting in adolescence predicts the onset of smoking (Audrain-McGovern et al., 2009) and later alcohol and drug use (Castellanos-Ryan et al., 2014, 2016), even when controlling for important covariates, such as other externalizing psychopathology and other cognitive factors. Nevertheless, the literature is also mixed, with some studies showing that conduct problems, antisocial personality disorder (ASPD), and ADHD are associated with steep delay discounting (e.g., Bobova et al., 2009; Acheson et al., 2011), while other studies suggest that individuals with ADHD, and potentially other non-substance-related externalizing problems, may be more sensitive to delay aversion than to discounting of rewards per se (Sonuga-Barke et al., 2003).

Reward-based decision-making As complex cognitive processes involve the recruitment of multiple cognitive functions, advantageous long-term decision-making around delayed rewards also requires an intact reward valuation system (Bechara, 2005; Vassileva and Conrod, 2018) and can be impacted by heightened reward sensitivity (Castellanos-Ryan et al., 2011). Studies show that adults with SUDs (Bechara, 2005) perform poorly on reward-based or affective decision-making tasks, such as the Iowa Gambling Task or Cambridge Gambling Task, which involve making decisions about potential rewards. Impairments have been reported in alcohol-dependent adults (Rogers et al., 1999; Petry, 2001; Bechara et al., 2002),

95

abstinent marijuana and cocaine users (Bolla et al., 2003, 2005), MDMA (methylenedioxymethamphetamine or ecstasy) users (Morgan et al., 2006), and heavy binge drinking college students (Goudriaan et al., 2007). Longitudinal and prospective studies provide some evidence that affective decision-making may also be associated with future substance use. Using a rewarded go/no-go task, the present authors showed that reward sensitivity specifically predicted higher binge drinking in adolescents and did not predict risk for other externalizing problems (Castellanos-Ryan et al., 2011). Another longitudinal study showed that male college-aged drinkers who displayed a disadvantageous pattern of affective decision-making, as assessed by the Iowa Gambling Task, exhibited significantly increased heavy drinking episodes and frequency and quantity of alcohol consumption over a 2 year follow-up (Goudriaan et al., 2007). Similarly, poor decisionmaking has been shown to predict ecstacy use 18 months later in adult females (Schilt et al., 2009). As suggested by these behavioral results, there is emerging evidence from neuroimaging studies of performance tasks that individuals who are prone to substance use problems can be distinguished from other clinically disinhibited/impulsive groups based on motivational or reward sensitivity, rather than on general deficits in response inhibition or other EF processes. For example, high-functioning drug users (Yechiam et al., 2005) and adolescents with pure substance-using profiles (Castellanos-Ryan et al., 2011) show impulsivity specifically in reward conditions as opposed to a general tendency toward errors in response inhibition. These findings were replicated with additional functional magnetic resonance imaging measures, showing that adolescents at risk for early onset substance misuse that did not cooccur with other forms of externalizing problems were specifically predicted by a unique brain activity pattern during reward anticipation, while externalizing symptoms were predicted by prefrontal cortical activity on a go/no-go task (Castellanos-Ryan et al., 2014). According to this large neuroimaging study of 2200 adolescents, early onset substance use that is uncomplicated by ADHD or CD is associated with greater left medial and lateral orbital frontal cortex responding and reduced inferior frontal gyrus responding when anticipating reward (Castellanos-Ryan et al., 2014). As these two structures are, respectively, implicated in reward valuation and stopping behavior, these findings potentially indicated a critical role of overvaluation of rewards in adolescent substance misuse and decision-making.

Intelligence quotient Results from studies examining the association between intelligence (IQ) and substance use and misuse have been mixed. While some studies have shown null findings for the association between IQ and later substance use (Ensminger et al., 2002; Fergusson et al., 2005), others show that low

96

Cognition and Addiction

IQ is associated with SUDs among adolescents (Moss et al., 1994) and adults (Osler et al., 2006; Mortensen et al., 2005), and other studies indicate that substance use may be associated with higher IQ in adolescence and adulthood (White and Batty, 2012; Castellanos-Ryan et al., 2013b, 2017; Hanson et al., 2011; Johnson et al., 2009). Discrepancies in results may result from the confounding effects that comorbid psychopathology may have on these associations, as some studies have shown that once psychopathology symptoms, such as antisocial and anxiety symptoms, are taken into account, the positive associations between IQ and later substance use become clearer or stronger. For example, a study by White et al. (2012) found that global IQ (including both verbal and spatial IQ) assessed at 11 years in a very large population British cohort was associated with increased risk of illegal drug use at 42 years of age, associations that became stronger when controlling for antisocial behavior and anxiety in the model. This was the case for all substances, except for ecstasy and anxiolytic drug use, with all associations being stronger for women than men (White et al., 2012). Similarly, a prospective study of European adolescents where a bifactor structure of psychopathology was modeled, separating variance that is common or shared across psychopathology symptoms (often referred to as the P factor) from variances unique to substance use and other psychopathologies, showed a complex pattern of relationships between IQ and substance use (Castellanos-Ryan et al., 2016). Lower verbal and performance IQ, assessed at 14 years, were associated with the common variance across psychopathology (P factor) at 16 years, but higher verbal IQ, but not performance IQ, was specifically associated with substance use 2 years later. Thus, once other psychopathology symptoms are accounted for, it seems that it is higher verbal IQ that is associated with adolescent substance misuse. There are several hypotheses about why this is the case, with some suggesting that good cognitive abilities are necessary for early access to substances, which can be seen as a valuable resource (Hyman et al., 2006). Another hypothesis is that verbal IQ is also related to parent education and socioeconomic status, which has a positive relationship with alcohol and cannabis use (Patrick et al., 2012), despite having a negative relationship with smoking (Patrick et al., 2012) and substance-related harms (Grant and Dawson, 1998). Although the mechanisms behind these links are still unclear, there is some evidence suggesting that the positive relationship between social economic status (SES) and higher alcohol and cannabis use is due to the fact that high SES youth have access to certain contexts that are especially supportive of excessive alcohol and marijuana use, e.g., colleges and universities (Schulenberg and Maggs, 2002), or that these youth, especially those living in high SES neighborhoods, are less supervised by parents and are exposed to more substanceusing peers (Trim and Chassin, 2008).

Discussion The literature reviewed suggests that neurocognitive deficits, particularly related to delay discounting and rewardbased decision-making, may directly predispose to adolescent cannabis and alcohol use and later substance use problems. Findings also suggest deficits in WM and response inhibition confer risk for substance use initiation and problems, but more indirectly, through a predisposition to other externalizing psychopathology. Furthermore, emerging evidence suggests that these cognitive risk factors confer risk by impacting on how young people evaluate drug-related messages and cues (WM), how youth form decisions around rules and conformity generally (EFs), how well they are able to tolerate delayed reward and feedback (temporal discounting), how well they are able to inhibit an urge or reinforced behavior/habit (response inhibition), and how susceptible they are to reward cues (reward sensitivity). Fig. 7.1 summarizes how these cognitive functions might be related to dimensions of psychopathology related to SUDs. This review also suggests that each of these domains can explain some of the mechanisms through which cognitive variables confer risk, thus providing insights into how drug prevention interventions targeting neurocognitive risk factors might be developed. WM training interventions are not necessarily proving to be effective treatments for SUD (see Chapter 18), but if used in combination with drug cue reactivity or implicit cognition paradigms, it is conceivable that more specific effects will occur on drugrelated outcomes. Similarly, brief cognitive behavioral interventions designed to help young people to better manage their reward sensitivity or poor response inhibition have been shown to be helpful in reducing risk for early onset substance misuse (Conrod et al., 2010, 2013). An important test of the causal effect of these cognitive variables on substance use behaviors would necessarily involve intervening on these risk factors and demonstrating that their modification leads to reductions in risk. Studies investigating such treatment mechanisms are very rare in this field and definitely warrant further attention. Other important methodological considerations further limit the interpretation of the reviewed findings, such as an overreliance on cross-sectional/concurrent associations, small samples, and not considering important covariates and correlates in the models, particularly other psychopathology. While high risk and longitudinal studies are providing more clarity on the nature of the relationship between cognitive risk factors and substance use outcomes, they have also illuminated some limitations in being able to capture the evolving nature of the relationship between cognitive functions and substance use outcomes. One possible explanation for variable findings on the relationship between cognitive functioning and substance use

Cognitive risk factors for alcohol and substance addictions Chapter | 7

NeurocogniƟve FuncƟons

Psychopathology Dimension

97

Observed Symptoms ADHD

SelecƟve aƩenƟon and working memory

Response InhibiƟon

Conduct Disorder

-

Externalizing problems

OpposiƟonal Defiant Disorder (…) SƟmulant Substance Abuse

Binge Drinking Delay DiscounƟng

General Psychopathology (P) factor

-

Drinking problems Cannabis Abuse Other Drug Abuse

Reward-based decision making

+

(…) Substance Misuse

General IQ

Depression General Anxiety Phobias Obsessive Compulsive Disorder

(…) FIGURE 7.1 Schemata of the associations between cognitive factors and patterns of substance use and comorbid psychopathology reviewed; a negative sign (“
”) denotes a negative association (i.e., poor performance on cognitive task associated with increased substance use and/or psychopathology) and a positive sign (“þ”) denotes a positive association (i.e., good performance on cognitive task associated with increased substance use). Adapted from Castellanos-Ryan, N., Briére, F.N., O’Leary-Barrett, M., et al., 2016. The structure of psychopathology in adolescence and its common personality and cognitive correlates. J. Abnorm. Psychol. 125, 1039e1052 and Conrod, P.J., Nikolaou, K., 2016. Annual research review: on the developmental neuropsychology of substance use disorders. J. Child Psychol. Psychiatry 57 (3), 371e394.

might be explained by age-related variability across studies and the developmental stage at which cognitive processes are assessed. Cognitive functions and substance use can both be modeled at between- and within-person levels, meaning that individuals might score stably high or low on these variables (between-person effects), but biologic and contextual factors can also cause them to vary over time (within-person variability). Finally, these models can also be used to examine temporal precedence in the relationship between two variables (e.g., change in one precedes changes in the other). This type of data modeling requires more than two observations per variable and large data sets. Therefore, only a few studies have been published examining the relationship between cognition and substance use in this way. One recent study used data from a large longitudinal cohort of students as they entered the seventh grade and were followed annually on cognitive and substance use outcomes until the 11th grade. Measures of performance IQ (perceptual reasoning), delayed memory recall, WM and response inhibition were assessed using a computerized battery from the school computer laboratory during a supervised group testing session. Multilevel modeling first evaluated the relationship between overall

tendency toward higher levels of substance use and cognitive functioning in substance naïve adolescents. This study showed that lower perceptual reasoning, recall memory, WM, and response inhibition all predicted greater likelihood of becoming a more frequent alcohol and cannabis consumer throughout middle and high school. Both common and specific cognitive risk profiles were revealed: accounting for the fact that alcohol and cannabis co-occur and co-evolve, risk for alcohol misuse appeared specifically related to low levels of perceptual reasoning (performance IQ) throughout adolescence, and risk for cannabis misuse was specifically related to poor response inhibition. The longitudinal design of this study permitted the estimation of within-person relationships between cognitive and substance use variables, allowing for an examination of hypotheses on the causal influence of substance use on cognitive development. This study showed that over and above general common vulnerability between low cognitive functioning and substance misuse in adolescence, if a child showed an increase in cannabis use in a given year over and above their mean level of use over the course of adolescence, they reliably showed a decrease in functioning across all four domains of cognition

98

Cognition and Addiction

assessed. Furthermore, two cognitive domains (response inhibition and WM) were shown to be impacted over the longer term, even if substance use returned to baseline levels of use. This study also highlighted the additive effects of these multilevel relationships, showing that the combination of premorbid cognitive risk factors with the more subtle concurrent and lagged effects of adolescent substance use on cognitive functioning resulted in significant cognitive lag (3 years) for adolescent cannabis users compared with their nonusing peers on some cognitive outcomes. Another longitudinal study using a similar multilevel analytic strategy further delved into potential mediators of these long-term effects of substance use on cognitive outcomes and found that some of the effects of cannabis use on global cognition (verbal IQ) were mediated or accounted for by academic achievement (i.e., high school graduation) (Castellanos-Ryan et al., 2017), suggesting that some effects of cannabis on global cognitive outcomes might be characterized by a cascading series of cognitive and academic failures, with severe social consequences for the individual. The findings from this new wave of studies that capitalize on the availability of large longitudinal data sets and new computational opportunities suggest that the relationship between cognition and substance use risk is bidirectional or reciprocal, which might explain the rapid escalation of substance-related problems and academic and cognitive decline in young substance users (Grant and Dawson, 1998; Rioux et al., 2017; Castellanos-Ryan et al., 2017). In recent years, there has been much attention placed on the consequences of substance use, particularly during adolescence, and the findings reviewed above suggest that some of the focus and concern about the neurocognitive consequences of adolescent cannabis and other substance use exposure should be directed toward identifying and intervening with adolescents who display neurocognitive impairment before the initiation of cannabis or other substance use and reducing their risk for early onset substance misuse. Understanding the neuropsychological functions that predate substance use initiation is crucial to preventing further neurocognitive impairments resulting from substance use.

References Acheson, A., Vincent, A.S., Sorocco, K.H., Lovallo, W.R., 2011. Greater discounting of delayed rewards in young adults with family histories of alcohol and drug use disorders: studies from the Oklahoma family health patterns project. Alcohol Clin. Exp. Res. 35 (9), 1607e1613. Afzali, M.H., Sunderland, M., Stewart, S., et al., 2019. Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation. Addiction 114 (4), 662e671.

Aron, A.R., Robbins, T.W., Poldrack, R.A., 2014. Inhibition and the right inferior frontal cortex: one decade on. Trends Cognit. Sci. 18 (4), 177e185. Aronen, E.T., Vuontela, V., Steenari, M.R., Salmi, J., Carlson, S., 2005. Working memory, psychiatric symptoms, and academic performance at school. Neurobiol. Learn. Mem. 83 (1), 33e42. Audrain-McGovern, J., Rodriguez, D., Epstein, L.H., Cuevas, J., Rodgers, K., Wileyto, E.P., 2009. Does delay discounting play an etiological role in smoking or is it a consequence of smoking? Drug Alcohol Depend. 103 (3), 99e106. Aytaclar, S., Tarter, R.E., Kirisci, L., Lu, S., 1999. Association between hyperactivity and executive cognitive functioning in childhood and substance use in early adolescence. J. Am. Acad. Child Adolesc. Psychiatry 38 (2), 172e178. Baker, F., Johnson, M.W., Bickel, W.K., 2003. Delay discounting in current and never-before cigarette smokers: similarities and differences across commodity, sign, and magnitude. J. Abnorm. Psychol. 112, 382e392. Barkley, R.A., 1997. Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychol. Bull. 121 (1), 65e94. Barrett, L.F., Tugade, M.M., Engle, R.W., 2004. Individual differences in working memory capacity and dual-process theories of the mind. Psychol. Bull. 130 (4), 553e573. Bauer, L.O., Hesselbrock, V.M., 1999. Subtypes of family history and conduct disorder: effects on P300 during the stroop test. Neuropsychopharmacology 21 (1), 51e62. Bechara, A., 2005. Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nat. Neurosci. 8 (11), 1458e1463. Bechara, A., Dolan, S., Hindes, A., 2002. Decision-making and addiction (part II): myopia for the future or hypersensitivity to reward? Neuropsychologia 40, 1690e1705. Bickel, W.K., Marsch, L.A., 2001. Toward a behavioral economic understanding of drug dependence: delay discounting processes. Addiction 96 (1), 73e86. Bickel, W.K., Jarmolowicz, D.P., Mueller, E.T., Koffarnus, M.N., Gatchalian, K.M., 2012. Excessive discounting of delayed reinforcers as a trans-disease process contributing to addiction and other diseaserelated vulnerabilities: emerging evidence. Pharmacol. Ther. 134 (3), 287e297. Bickel, W.K., Koffarnus, M.N., Moody, L., Wilson, A.G., 2014. The behavioral- and neuro-economic process of temporal discounting: a candidate behavioral marker of addiction. Neuropharmacology 76 Pt B, 518e527. Bobova, L., Finn, P.R., Rickert, M.E., Lucas, J., 2009. Disinhibitory psychopathology and delay discounting in alcohol dependence: personality and cognitive correlates. Exp. Clin. Psychopharmacol. 17 (1), 51e61. Bolla, K.I., Eldreth, D.A., London, E.D., et al., 2003. Orbitofrontal cortex dysfunction in abstinent cocaine abusers performing a decisionmaking task. NeuroImage 19 (3), 1085e1094. Bolla, K.I., Eldreth, D.A., Matochik, J.A., Cadet, J.L., 2005. Neural substrates of faulty decision-making in abstinent marijuana users. NeuroImage 26 (2), 480e492. Carlson, S.R., McLarnon, M.E., Iacono, W.G., 2007. P300 amplitude, externalizing psychopathology, and earlier- versus later-onset substance-use disorder. J. Abnorm. Psychol. 116, 565e577.

Cognitive risk factors for alcohol and substance addictions Chapter | 7

Carlson, S.M., Zelazo, P.D., Faja, S., 2013. Executive function. In: Zelazo, P.D. (Ed.), The Oxford Handbook of Developmental Psychology. Vol 1: Body and Mind. Oxford University Press, New York, pp. 706e743. Castellanos, F.X., Sonuga-Barke, E.J.S., Milham, M.P., Tannock, R., 2006. Characterizing cognition in ADHD: beyond executive dysfunction. Trends Cognit. Sci. 10, 117e123. Castellanos-Ryan, N., Rubia, K., Conrod, P.J., 2011. Response inhibition and reward response bias mediate the predictive relationships between impulsivity and sensation seeking and common and unique variance in conduct disorder and substance misuse. Alcohol Clin. Exp. Res. 35 (1), 140e155. Castellanos-Ryan, N., Seguin, J.R., Vitaro, F., Parent, S., Tremblay, R.E., 2013a. Impact of a 2-year multimodal intervention for disruptive 6-year-olds on substance use in adolescence: randomised controlled trial. Br. J. Psychiatry 203 (3), 188e195. Castellanos-Ryan, N., Séguin, J.R., Vitaro, F., Parent, S., Tremblay, R.E., 2013b. A multimodal intervention for disruptive kindergarten children reduces substance-use across adolescence: a randomized control trial. Br. J. Psychiatry 203, 188e195. Castellanos-Ryan, N., Struve, M., Whelan, R., et al., 2014. Neural and cognitive correlates of the common and specific variance across externalizing problems in young adolescence. Am. J. Psychiatry 171 (12), 1310e1319. Castellanos-Ryan, N., Briére, F.N., O’Leary-Barrett, M., et al., 2016. The structure of psychopathology in adolescence and its common personality and cognitive correlates. J. Abnorm. Psychol. 125, 1039e1052. Castellanos-Ryan, N., Pingault, J.B., Parent, S., Vitaro, F., Tremblay, R.E., Seguin, J.R., 2017. Adolescent cannabis use, change in neurocognitive function, and high-school graduation: a longitudinal study from early adolescence to young adulthood. Dev. Psychopathol. 29 (4), 1253e1266. Chambers, C.D., Garavan, H., Bellgrove, M.A., 2009. Insights into the neural basis of response inhibition from cognitive and clinical neuroscience. Neurosci. Biobehav. Rev. 33 (5), 631e646. Chassin, L., Dmitrieva, J., Modecki, K., et al., 2010. Does adolescent alcohol and marijuana use predict suppressed growth in psychosocial maturity among male juvenile offenders? Psychol. Addict. Behav. 24 (1), 48e60. Conrod, P.J., Nikolaou, K., 2016. Annual research review: on the developmental neuropsychology of substance use disorders. J. Child Psychol. Psychiatry 57 (3), 371e394. Conrod, P.J., Castellanos-Ryan, N., Strang, J., 2010. Brief, personalitytargeted coping skills interventions and survival as a non-drug user over a 2-year period during adolescence. Arch. Gen. Psychiatry 67 (1), 85e93. Conrod, P.J., O’Leary-Barrett, M., Newton, N., et al., 2013. Effectiveness of a selective, personality-targeted prevention program for adolescent alcohol use and misuse: a cluster randomized controlled trial. JAMA Psychiatry 70 (3), 334e342. Corral, M.M., Holguín, S.R., Cadaveira, F., 1999. Neuropsychological characteristics in children of alcoholics: familial density. J. Stud. Alcohol 60, 509e513. Corral, M., Holguín, S.R., Cadaveira, F., 2003. Neuropsychological characteristics of young children from high-density alcoholism families: a three-year follow-up. J. Stud. Alcohol 64, 195e199.

99

Courtney, K.E., Arellano, R., Barkley-Levenson, E., et al., 2012. The relationship between measures of impulsivity and alcohol misuse: an integrative structural equation modeling approach. Alcohol Clin. Exp. Res. 36 (6), 923e931. Cousijn, J., Vingerhoets, W.A., Koenders, L., et al., 2014. Relationship between working-memory network function and substance use: a 3year longitudinal fMRI study in heavy cannabis users and controls. Addict. Biol. 19 (2), 282e293. Deckel, A.W., Bauer, L., Hesselbrock, V., 1995. Anterior brain dysfunctioning as a risk factor in alcoholic behaviors. Addiction 90 (10), 1323e1334. Degenhardt, L., Hall, W., 2012. Extent of illicit drug use and dependence, and their contribution to the global burden of disease. Lancet 379, 55e70. Dom, G., Sabbe, B., Hulstijn, W., van den Brink, W., 2005. Substance use disorders and the orbitofrontal cortex: systematic review of behavioural decision-making and neuroimaging studies. Br. J. Psychiatry 187, 209e220. Dubé, G., Bordeleau, M., Cazale, L., et al., 2009. Enquête québécoise sur le tabac, l’alcool, la drogue et le jeu chez les élèves du secondaire, 2008. Québec. Ellingson, J.M., Fleming, K.A., Verges, A., Bartholow, B.D., Sher, K.J., 2014. Working memory as a moderator of impulsivity and alcohol involvement: testing the cognitive-motivational theory of alcohol use with prospective and working memory updating data. Addict. Behav. 39 (11), 1622e1631. Ensminger, M.E., Juon, H.S., Fothergill, K.E., 2002. Childhood and adolescent antecedents of substance use in adulthood. Addiction 97 (7), 833e844. Fergusson, D.M., Horwood, L.J., Ridder, E.M., 2005. Show me the child at seven II: childhood intelligence and later outcomes in adolescence and young adulthood. J. Child Psychol. Psychiatry 46 (8), 850e858. Finn, P.R., 2002. Motivation, working memory, and decision making: a cognitive-motivational theory of personality vulnerability to alcoholism. Behav. Cognit. Neurosci. Rev. 1, 183e205. Finn, P.R., Hall, J., 2004. Cognitive ability and risk for alcoholism: shortterm memory capacity and intelligence moderate personality risk for alcohol problems. J. Abnorm. Psychol. 113, 569e581. Finn, P.R., Justus, a, Mazas, C., Steinmetz, J.E., 1999. Working memory, executive processes and the effects of alcohol on Go/No-Go learning: testing a model of behavioral regulation and impulsivity. Psychopharmacology (Berl.) 146, 465e472. Fried, P.A., Watkinson, B., Gray, R., 2005. Neurocognitive consequences of marihuana–a comparison with pre-drug performance. Neurotoxicol. Teratol. 27 (2), 231e239. Friedman, N.P., Miyake, A., 2004. The relations among inhibition and interference control functions: a latent-variable analysis. J. Exp. Psychol. Gen. 133 (1), 101e135. Giancola, P.R., Mezzich, A.C., 2000. Neuropsychological deficits in female adolescents with a substance use disorder: better accounted for by conduct disorder? J. Stud. Alcohol 61 (6), 809e817. Giancola, P.R., Moss, H.B., 1998. Executive cognitive functioning in alcohol use disorders. Recent Dev. Alcohol 14, 227e251. Gillen, R., Hesselbrock, V., 1992. Cognitive functioning, ASP, and family history of alcoholism in young men at risk for alcoholism. Alcohol Clin. Exp. Res. 16 (2), 206e214.

100 Cognition and Addiction

Goudriaan, A.E., Grekin, E.R., Sher, K.J., 2007. Decision making and binge drinking: a longitudinal study. Alcohol Clin. Exp. Res. 31 (6), 928e938. Grant, B.F., Dawson, D.A., 1998. Age of onset of drug use and its association with DSM-IV drug abuse and dependence: results from the national longitudinal alcohol epidemiologic survey. J. Subst. Abus. 10 (2), 163e173. Grant, J.E., Chamberlain, S.R., Schreiber, L., Odlaug, B.L., 2012. Neuropsychological deficits associated with cannabis use in young adults. Drug Alcohol Depend. 121 (1e2), 159e162. Green, L., Myerson, J., 2004. A discounting framework for choice with delayed and probabilistic rewards. Psychol. Bull. 130, 769e792. Grenard, J.L., Ames, S.L., Wiers, R.W., Thush, C., Sussman, S., Stacy, A.W., 2008. Working memory capacity moderates the predictive effects of drug-related associations on substance use. Psychol. Addict. Behav. 22 (3), 426e432. Gruber, S.A., Sagar, K.A., Dahlgren, M.K., Racine, M., Lukas, S.E., 2012. Age of onset of marijuana use and executive function. Psychol. Addict. Behav. 26 (3), 496e506. Hanson, K.L., Winward, J.L., Schweinsburg, A.D., Medina, K.L., Brown, S.A., Tapert, S.F., 2010. Longitudinal study of cognition among adolescent marijuana users over three weeks of abstinence. Addict. Behav. 35 (11), 970e976. Hanson, K.L., Medina, K.L., Padula, C.B., Tapert, S.F., Brown, S.A., 2011. Impact of adolescent alcohol and drug use on neuropsychological functioning in young adulthood: 10-year outcomes. J. Child Adolesc. Subst. Abus. 20 (2), 135e154. Harvey, M.A., Sellman, J.D., Porter, R.J., Frampton, C.M., 2007. The relationship between non-acute adolescent cannabis use and cognition. Drug Alcohol Rev. 26 (3), 309e319. Hester, R., Garavan, H., 2004. Executive dysfunction in cocaine addiction: evidence for discordant frontal, cingulate, and cerebellar activity. J. Neurosci. 24 (49), 11017e11022. Hill, S.Y., Yuan, H., Locke, J., 1999. Path analysis of P300 amplitude of individuals from families at high and low risk for developing alcoholism. Biol. Psychiatry 45 (3), 346e359. Hobson, C.W., Scott, S., Rubia, K., 2011. Investigation of cool and hot executive function in ODD/CD independently of ADHD. J. Child Psychol. Psychiatry 52 (10), 1035e1043. Hofmann, W., Schmeichel, B.J., Baddeley, A.D., 2012. Executive functions and self-regulation. Trends Cognit. Sci. 16 (3), 174e180. Hyman, S.E., Malenka, R.C., Nestler, E.J., 2006. Neural mechanisms of addiction: the role of reward-related learning and memory. Annu. Rev. Neurosci. 29, 565e598. Jacobus, J., Tapert, S.F., 2014. Effects of cannabis on the adolescent brain. Curr. Pharm. Des. 20 (13), 2186e2193. Jacobus, J., Tapert, S.F., 2013. Neurotoxic effects of alcohol in adolescence. In: NolenHoeksema, S. (Ed.), Annual Review of Clinical Psychology, vol. 9, pp. 703e721. Johnson, M.W., Bickel, W.K., Baker, F., 2007. Moderate drug use and delay discounting: a comparison of heavy, light, and never smokers. Exp. Clin. Psychopharmacol. 15 (2), 187e194. Johnson, W., Hicks, B.M., McGue, M., Iacono, W.G., 2009. How intelligence and education contribute to substance use: hints from the Minnesota twin family study. Intelligence 37 (6), 613e624. Johnston, L.D., O’Malley, P.M., Miech, R.A., Bachman, J.G., Schulenberg, J.E., 2016. Monitoring the Future National Survey Results on Drug Use, 1975e2015: Overview, Key Findings on

Adolescent Drug Use. Institute for Social Research, The University of Michigan., Ann Arbor. Kamarajan, C., Porjesz, B., Jones, K.A., et al., 2005. Alcoholism is a disinhibitory disorder: neurophysiological evidence from a Go/No-Go task. Biol. Psychol. 69 (3), 353e373. Kane, M.J., Engle, R.W., 2002. The role of prefrontal cortex in workingmemory capacity, executive attention, and general fluid intelligence: an individual-differences perspective. Psychonomic Bull. Rev. 9 (4), 637e671. Kaufman, J.N., Ross, T.J., Stein, E.A., Garavan, H., 2003. Cingulate hypoactivity in cocaine users during a GO-NOGO task as revealed by event-related functional magnetic resonance imaging. J. Neurosci. 23 (21), 7839e7843. Khurana, A., Romer, D., Betancourt, L.M., Brodsky, N.L., Giannetta, J.M., Hurt, H., 2013. Working memory ability predicts trajectories of early alcohol use in adolescents: the mediational role of impulsivity. Addiction 108 (3), 506e515. King, K.M., Meehan, B.T., Trim, R.S., Chassin, L., 2006. Substance use and academic outcomes: synthesizing findings and future directions. Addiction 101 (12), 1688e1689. Kirby, K.N., Petry, N.M., Bickel, W.K., 1999. Heroin addicts have higher discount rates for delayed rewards than non-drug-using controls. J. Exp. Psychol. Gen. 128, 78e87. Lawrence, A.J., Luty, J., Bogdan, N.A., Sahakian, B.J., Clark, L., 2009. Impulsivity and response inhibition in alcohol dependence and problem gambling. Psychopharmacology (Berl.) 207 (1), 163e172. Leonard, K.E., Das Eiden, R., 2002. Cognitive functioning among infants of alcoholic fathers. Drug Alcohol Depend. 67, 139e147. Li, C.-S.R., Milivojevic, V., Kemp, K., Hong, K., Sinha, R., 2006. Performance monitoring and stop signal inhibition in abstinent patients with cocaine dependence. Drug Alcohol Depend. 85, 205e212. Lijffijt, M., Kenemans, J.L., Verbaten, M.N., van Engeland, H., 2005. A meta-analytic review of stopping performance in attention-deficit/ hyperactivity disorder: deficient inhibitory motor control? J. Abnorm. Psychol. 114 (2), 216e222. Lisdahl, K.M., Price, J.S., 2012. Increased marijuana use and gender predict poorer cognitive functioning in adolescents and emerging adults. J. Int. Neuropsychol. Soc. 18 (4), 678e688. Lubman, D.I., Cheetham, A., Yucel, M., 2015. Cannabis and adolescent brain development. Pharmacol. Ther. 148, 1e16. Luria, A.R., 1966. Higher Cortical Functions in Man, second ed. Basic Books, New York. Mathias, C.W., Blumenthal, T.D., Dawes, M.A., et al., 2011. Failure to sustain prepulse inhibition in adolescent marijuana users. Drug Alcohol Depend. 116 (1e3), 110e116. Medina, K.L., Hanson, K.L., Schweinsburg, A.D., Cohen-Zion, M., Nagel, B.J., Tapert, S.F., 2007. Neuropsychological functioning in adolescent marijuana users: subtle deficits detectable after a month of abstinence. J. Int. Neuropsychol. Soc. 13 (5), 807e820. Meier, M.H., Caspi, A., Danese, A., et al., 2018. Associations between adolescent cannabis use and neuropsychological decline: a longitudinal co-twin control study. Addiction 113 (2), 257e265. Middleton, F.A., Strick, P.L., 2001. Cerebellar projections to the prefrontal cortex of the primate. J. Neurosci. 21 (2), 700e712. Miyake, A., Friedman, N.P., Emerson, M.J., Witzki, A.H., Howerter, A., Wager, T.D., 2000. The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: a latent variable analysis. Cognit. Psychol. 41 (1), 49e100.

Cognitive risk factors for alcohol and substance addictions Chapter | 7

Morgan, M.J., Impallomeni, L.C., Pirona, A., Rogers, R.D., 2006. Elevated impulsivity and impaired decision-making in abstinent Ecstasy (MDMA) users compared to polydrug and drug-naive controls. Neuropsychopharmacology 31 (7), 1562e1573. Morin, J.G., Afzali, M.H., Bourque, J., et al., 2019. A population-based analysis of the relationship between substance use and adolescent cognitive development. Am. J. Psychiatry. https://doi.org/10.1176/ appi.ajp.2018.18020202. Mortensen, L.H., Sorensen, T.I., Gronbaek, M., 2005. Intelligence in relation to later beverage preference and alcohol intake. Addiction 100 (10), 1445e1452. Moss, H.B., Kirisci, L., Gordon, H.W., Tarter, R.E., 1994. A neuropsychologic profile of adolescent alcoholics. Alcohol Clin. Exp. Res. 18 (1), 159e163. Nigg, J.T., Glass, J.M., Wong, M.M., et al., 2004. Neuropsychological executive functioning in children at elevated risk for alcoholism: findings in early adolescence. J. Abnorm. Psychol. 113, 302e314. Nigg, J.T., Wong, M.M., Martel, M.M., et al., 2006. Poor response inhibition as a predictor of problem drinking and illicit drug use in adolescents at risk for alcoholism and other substance use disorders. J. Am. Acad. Child Adolesc. Psychiatry 45, 468e475. Norman, A.L., Pulido, C., Squeglia, L.M., Spadoni, A.D., Paulus, M.P., Tapert, S.F., 2011. Neural activation during inhibition predicts initiation of substance use in adolescence. Drug Alcohol Depend. 119 (3), 216e223. Odgers, C.L., Caspi, A., Nagin, D.S., et al., 2008. Is it important to prevent early exposure to drugs and alcohol among adolescents? Psychol. Sci. 19 (10), 1037e1044. Ohmura, Y., Takahashi, T., Kitamura, N., 2005. Discounting delayed and probabilistic monetary gains and losses by smokers of cigarettes. Psychopharmacology (Berl.) 182 (4), 508e515. Oosterlaan, J., Sergeant, J.A., 1998. Response inhibition and response reengagement in attention-deficit/hyperactivity disorder, disruptive, anxious and normal children. Behav. Brain Res. 94 (1), 33e43. Osler, M., Nordentoft, M., Andersen, A.M., 2006. Childhood social environment and risk of drug and alcohol abuse in a cohort of Danish men born in 1953. Am. J. Epidemiol. 163 (7), 654e661. Ozkaragoz, T., Satz, P., Noble, E.P., 1997. Neuropsychological functioning in sons of active alcoholic, recovering alcoholic, and social drinking fathers. Alcohol 14 (1), 31e37. Patrick, M.E., Wightman, P., Schoeni, R.F., Schulenberg, J.E., 2012. Socioeconomic status and substance use among young adults: a comparison across constructs and drugs. J. Stud. Alcohol Drugs 73 (5), 772e782. Peeters, M., Monshouwer, K., Janssen, T., Wiers, R.W., Vollebergh, W.A., 2014. Working memory and alcohol use in at-risk adolescents: a 2-year follow-up. Alcohol Clin. Exp. Res. 38 (4), 1176e1183. Pennington, B.F., Ozonoff, S., 1996. Executive functions and developmental psychopathology. J. Child Psychol. Psychiatry 37, 51e88. Petry, N.M., 2001. Substance abuse, pathological gambling, and impulsiveness. Drug Alcohol Depend. 63 (1), 29e38. Poon, E., Ellis, D.A., Fitzgerald, H.E., Zucker, R.A., 2000. Intellectual, cognitive, and academic performance among sons of alcoholics, during the early school years: differences related to subtypes of familial alcoholism. Addiction 24, 1020e1027. Rachlin, H., 1997. Self and self-control. Ann. N.Y. Acad. Sci. 818, 84e97.

101

Resing, W., Drenth, P., 2007. Intelligence: Knowing and Measuring. Publisher Nieuwezijds, Amsterdam. Rioux, C., Castellanos-Ryan, N., Parent, S., Vitaro, F., Tremblay, R.E., Séguin, J.R., 2017. Age of cannabis use onset and adult drug abuse symptoms: a prospective study of common risk factors and indirect effects. Can. J. Psychiatry 63 (7), 457e464. Rogers, R.D., Everitt, B.J., Baldacchino, A., et al., 1999. Dissociable deficits in the decision-making cognition of chronic amphetamine abusers, opiate abusers, patients with focal damage to prefrontal cortex, and tryptophan-depleted normal volunteers: evidence for monoaminergic mechanisms. Neuropsychopharmacology 20 (4), 322e339. Rubino, T., Parolaro, D., 2016. The impact of exposure to cannabinoids in adolescence: insights from animal models. Biol. Psychiatry 79 (7), 578e585. Rypma, B., Berger, J.S., D’Esposito, M., 2002. The influence of workingmemory demand and subject performance on prefrontal cortical activity. J. Cognit. Neurosci. 14 (5), 721e731. Schilt, T., Goudriaan, A.E., Koeter, M.W., van den Brink, W., Schmand, B., 2009. Decision making as a predictor of first ecstasy use: a prospective study. Psychopharmacology (Berl.) 203 (3), 519e527. Scholes-Balog, K.E., Hemphill, S.A., Evans-Whipp, T.J., Toumbourou, J.W., Patton, G.C., 2016. Developmental trajectories of adolescent cannabis use and their relationship to young adult social and behavioural adjustment: a longitudinal study of Australian youth. Addict. Behav. 53, 11e18. Schulenberg, J.E., Maggs, J.L., 2002. A developmental perspective on alcohol use and heavy drinking during adolescence and the transition to young adulthood. J. Stud. Alcohol Suppl. (14), 54e70. Séguin, J.R., Pihl, R.O., Harden, P.W., Tremblay, R.E., Boulerice, B., 1995. Cognitive and neuropsychological characteristics of physically aggressive boys. J. Abnorm. Psychol. 104 (4), 614e624. Séguin, J.R., Nagin, D., Assaad, J.M., Tremblay, R.E., 2004. Cognitiveneuropsychological function in chronic physical aggression and hyperactivity. J. Abnorm. Psychol. 113 (4), 603e613. Sher, K., Walitzer, K., Wood, P., Brent, E., 1991. Characteristics of children of alcoholics: putative risk factors, substance use and abuse, and psychopathology. J. Abnorm. Psychol. 100 (4), 427e448. Single, E., Rehm, J., Robson, L., Truong, M.V., 2000. The relative risks and etiologic fractions of different causes of death and disease attributable to alcohol, tobacco and illicit drug use in Canada. CMAJ 162 (12), 1669e1675. Smith, J.L., Mattick, R.P., Jamadar, S.D., Iredale, J.M., 2014. Deficits in behavioural inhibition in substance abuse and addiction: a metaanalysis. Drug Alcohol Depend. 145, 1e33. Sonuga-Barke, E.J., Dalen, L., Remington, B., 2003. Do executive deficits and delay aversion make independent contributions to preschool attention-deficit/hyperactivity disorder symptoms? J. Am. Acad. Child Adolesc. Psychiatry 42 (11), 1335e1342. Spreen, O., Strauss, E., 1998. A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary, second ed. Oxford University Press, New York, NY, US. Squeglia, L.M., Spadoni, A.D., Infante, M.A., Myers, M.G., Tapert, S.F., 2009. Initiating moderate to heavy alcohol use predicts changes in neuropsychological functioning for adolescent girls and boys. Psychol. Addict. Behav. 23 (4), 715e722.

102 Cognition and Addiction

Squeglia, L.M., Jacobus, J., Nguyen-Louie, T.T., Tapert, S.F., 2014. Inhibition during early adolescence predicts alcohol and marijuana use by late adolescence. Neuropsychology 28 (5), 782e790. Statistics Team NHS Digital, 2017. Statistics on Drugs Misuse: England, 2017. https://digital.nhs.uk/data-and-information/publications/statistical/statistics-on-drug-misuse/2017. Stuss, D.T., 2011. Functions of the frontal lobes: relation to executive functions. J. Int. Neuropsychol. Soc. 17 (5), 759e765. Takahashi, T., Ohmura, Y., Oono, H., Radford, M., 2009. Alcohol use and discounting of delayed and probabilistic gain and loss. Neuroendocrinol. Lett. 30 (6), 749e752. Tapert, S.F., Baratta, M.V., Abrantes, A.M., Brown, S.A., 2002a. Attention dysfunction predicts substance involvement in community youths. J. Am. Acad. Child Adolesc. Psychiatry 41 (6). Tapert, S.F., Granholm, E., Leedy, N.G., Brown, S.A., 2002b. Substance use and withdrawal: neuropsychological functioning over 8 years in youth. J. Int. Neuropsychol. Soc. 8 (7), 873e883. Tarter, R.E., Jacob, T., Bremer, D.L., 1989. Specific cognitive impairment in sons of early onset alcoholics. Alcohol Clin. Exp. Res. 13 (6), 786e789. Tarter, R., Kirisci, L., Habeych, M., Reynolds, M., 2004. Neurobehavior disinhibition in childhood predisposes boys to substance use disorder by young adulthood: direct and mediated etiologic pathways. Drug Alcohol 73, 121e132. Trim, R.S., Chassin, L., 2008. Neighborhood socioeconomic status effects on adolescent alcohol outcomes using growth models: exploring the role of parental alcoholism. J. Stud. Alcohol Drugs 69 (5), 639e648. Uekermann, J., Daum, I., Schlebusch, P., Wiebel, B., Trenckmann, U., 2003. Depression and cognitive functioning in alcoholism. Addiction 98 (11), 1521e1529. United Nations, 2018. World Drug Report 2018. Geneva. Vassileva, J., Conrod, P.J., 2018. Impulsivities and addictions: a multidimensional integrative framework informing assessment and novel interventions for substance use disorders. Phil. Trans. R. Soc. B 374 (1766). https://doi.org/10.1098/rstb.2018.0137. Verbruggen, F., Logan, G.D., 2009. Models of response inhibition in the stop-signal and stop-change paradigms. Neurosci. Biobehav. Rev. 33 (5), 647e661. Verrico, C.D., Gu, H., Peterson, M.L., Sampson, A.R., Lewis, D.A., 2014. Repeated D9-tetrahydrocannabinol exposure in adolescent monkeys:

persistent effects selective for spatial working memory. Am. J. Psychiatry 171 (4), 416e425. Vuchinich, R., Simpson, C., 1998. Hyperbolic temporal discounting in social drinkers and problem drinkers. Exp. Clin. Psychopharmacol. 6, 292e305. Wetherill, R.R., Squeglia, L.M., Yang, T.T., Tapert, S.F., 2013. A longitudinal examination of adolescent response inhibition: neural differences before and after the initiation of heavy drinking. Psychopharmacology (Berl.) 230 (4), 663e671. Whelan, R., Watts, R., Orr, C.A., et al., 2014. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature 512 (7513), 185e189. Whipple, S.C., Parker, E.S., Noble, E.P., 1988. An atypical neurocognitive profile in alcoholic fathers and their sons. J. Stud. Alcohol 49 (3), 240e244. White, J., Batty, G.D., 2012. Intelligence across childhood in relation to illegal drug use in adulthood: 1970 British Cohort Study. J. Epidemiol. Community Health 66 (9), 767e774. White, J.W., Gale, C.R., Batty, G.D., 2012. Intelligence quotient in childhood and the risk of illegal drug use in middle-age: the 1958 National Child Development Survey. Ann. Epidemiol. 22 (9), 654e657. Wiers, R.W., Gunning, B.W., Sergeant, J.A., 1998. Is a mild deficit in executive functions in boys related to childhood ADHD or to parental multigenerational alcoholism. J. Abnorm. Child Psychol. 26, 415e430. Wiers, R.W., Bartholow, B.D., van den Wildenberg, E., et al., 2007. Automatic and controlled processes and the development of addictive behaviors in adolescents: a review and a model. Pharmacol. Biochem. Behav. 86 (2), 263e283. Yechiam, E., Stout, J.C., Busemeyer, J.R., Rock, S.L., Finn, P.R., 2005. Individual differences in the response to forgone payoffs: an examination of high functioning drug abusers. J. Behav. Decis. Mak. 18 (2), 97e110. Young, S.E., Friedman, N.P., Miyake, A., et al., 2009. Behavioral disinhibition: liability for externalizing spectrum disorders and its genetic and environmental relation to response inhibition across adolescence. J. Abnorm. Psychol. 118 (1), 117e130. Zelazo, P.D., Craik, F.I., Booth, L., 2004. Executive function across the life span. Acta Psychol. 115 (2e3), 167e183.

Chapter 8

Neuropsychological deficits in alcohol use disorder: impact on treatment Ange´line Maillard, Nicolas Cabe´, Fausto Viader and Anne Lise Pitel Normandie Univ, UNICAEN, PSL Université de Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, Caen, France

Introduction Alcohol use disorder (AUD) is defined as a chronic, relapsing disease of the brain, which is characterized by a high rate of relapse (Koob and Volkow, 2016). Acute alcohol-induced intoxication transiently alters the brain functioning while ethanol is still present in the blood, whereas the effects of chronic alcohol misuse affect the brain in enduring ways even after withdrawals. In 2013, the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5, American Psychiatric Association, 2013) has proposed to consider excessive alcohol drinking no more as a categorical phenotype (dependence vs. abuse, DSM-IV, American Psychiatric Association, 2000) but as an AUD lying along a continuum of severity, from mild to moderate to severe according to the number of criteria (out of 11) presented by the patient. AUD is characterized by a 12-month prevalence of 13.9% in the worldwide population, whereas lifetime prevalence is 29.1% (Grant et al., 2015). Every year, 3.3 million deaths are partially attributable to excessive alcohol consumption. Life expectancy is reduced by 20 years for an alcohol-dependent person (John et al., 2013). Indeed, alcohol is the direct cause of more than 60 diseases from fetal alcohol syndrome to hepatic cirrhosis and psychotic manifestations. In addition, alcohol contributes to the development and the course of more than 200 diseases such as cancers, neuropsychiatric conditions, cardiovascular or neurological diseases, infectious diseases, etc. (World Health Organisation, 2014). For example, Schwarzinger et al. (2018) indicated that AUD is a major risk factor for early onset of all types of dementia. Even in absence of ostensible alcohol-related disease, chronic alcohol consumption can result in an invisible disability: AUD is not only associated with motor dysfunctions (gait and

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00008-3 Copyright © 2020 Elsevier Inc. All rights reserved.

balance) but also with a variety of cognitive disorders. After alcohol withdrawal, 50%e80% of the recently detoxified patients exhibit neuropsychological impairments (Ihara et al., 2000; Oscar-Berman et al., 2014 for review). The DSM-5 classification introduced a diagnosis of “alcohol-induced neurocognitive disorders” to describe these neuropsychological deficits observed in AUD patients even in absence of any neurological complications. Alcohol-induced neurocognitive impairments are also considered along a continuum of severity from mild to major deficits, comparable with those observed in patients with Korsakoff’s syndrome (KS), depending on how they interfere with independence in everyday activities. Despite their prevalence and their potential harmful effects on social and occupational integration, as well as rehabilitation, these cognitive impairments remain frequently undiagnosed because neuropsychological abilities are not systematically assessed in AUD patients. In this chapter, we will describe the cognitive impairments and brain abnormalities in AUD patients and the reversibility of these deficits with abstinence. We will then focus on the clinical implications of the cognitive deficits. And finally, we will provide some recommendations for clinicians and researchers who work in the field of alcohol addiction.

Altered brain structure and function in alcohol use disorder During the last decades, many studies have shown that chronic alcohol consumption results in brain damage and associated heterogeneous cognitive deficits (Pitel et al., 2011), including impairments of executive functions, memory, visuospatial abilities, difficulties in emotional

103

104 Cognition and Addiction

processing, and theory of mind (ToM) abilities (Le Berre et al., 2017 for review). Alcohol-related brain damage is characterized by a reduction of brain volume, an enlargement of the ventricles and sulci, and an increased cerebrospinal fluid quantity. Several brain regions, including the cerebellum, corpus callosum, hippocampus, thalamus, amygdala, and frontal cortices, are especially vulnerable (Oscar-Berman and Marinkovic, 2003; Sullivan, 2003). Thus, two brain networks seem particularly affected in AUD patients: the circuit of Papez (PC) and the frontocerebellar circuit (FCC), which share the thalamus as a key node (Fig. 8.1) (Pitel et al., 2015).

Attention, working memory, and executive functions It is now well-known that attention, working memory, and executive functions rely notably on the prefrontal cortex. Indeed, patients with a frontal lobe lesion frequently have difficulties in behavioral control and regulation, potentially with harmful consequences in their daily life. Postmortem analyses have revealed decreased neuronal density in the frontal cortex of AUD patients (Harper and Matsumoto, 2005). Moreover, in vivo studies revealed alcohol-related gray matter volume deficits (Chanraud et al., 2007; Pitel et al., 2012), functional abnormalities during a spatial working memory task (Tapert et al., 2001), decreased cerebral blood flow (Gansler et al., 2000), and lower glucose metabolism (Dao-Castellana et al., 1998; Ritz et al., 2016b) in the frontal cortices. Not only is the frontal cortex implicated in executive functioning but also other brain regions connected to the

frontal cortex. The cerebellum seems essential to the neural circuitry subserving cognition, particularly executive function and working memory. The cerebellum and the frontal cortex are connected through the pons (feedforward loop) and thalamus (feedback loop) within the FCC (Ritz et al., 2016b). The different nodes of the FCC are affected by heavy and chronic alcohol consumption (Fig. 8.1). MRI studies have shown atrophy in AUD patients compared with controls in the cerebellum (Antunez et al., 1998; Sullivan, 2003), pons (Chanraud et al., 2009b; Sullivan, 2003) and thalamus (Le Berre et al., 2014; Pitel et al., 2012). These regional volumes have been related to executive abilities in AUD patients (Chanraud et al., 2007; Sullivan, 2003; Zahr et al., 2010). Regarding white matter volume, brain abnormalities have been found in AUD patients in the cerebellum and midbrain (Mechtcheriakov et al., 2007; Pitel et al., 2012; Sullivan, 2003). An alteration of the white matter tracts within the midbrain and pons, characterized by 18% fewer fibers in AUD than in healthy controls, indicates a disconnection within the FCC (Chanraud et al., 2009b). The authors also found a correlation between these altered white matter fibers integrity and impaired results on a flexibility task (part B of Trail Making Test). Brain alterations in nodes and connections of the FCC seem to be better predictors of executive dysfunction than damage of the prefrontal regions solely (Chanraud et al., 2007; Sullivan, 2003). Attention is defined by Mesulam (1999) as “a preferential allocation of limited processing resources to events that have become behaviorally relevant.” Usually, three main higher order attentional processes are distinguished: (a) selective attention, the ability to focus cognitive set on relevant information and inhibit distracting stimuli;

Fronto-cerebellar circuit

Papez’s circuit

FRONTAL CORTEX

CINGULATE GYRUS HIPPOCAMPAL FORMATION

THALAMUS

PONS CEREBELLUM

MAMMILLARY BODIES

FIGURE 8.1 The two brain circuits mainly affected in alcohol use disorder (AUD). From Pitel, A.L., Segobin, S.H., Ritz, L., Eustache, F., Beaunieux, H., 2015. Thalamic abnormalities are a cardinal feature of alcohol-related brain dysfunction. Neurosci. Biobehav. Rev. 54, 38e45. https://doi.org/10. 1016/j.neubiorev.2014.07.023.

Neuropsychological deficits in alcohol use disorder: impact on treatment Chapter | 8

(b) sustained attention, the aptitude to maintain a consistent response for a long time; and (c) divided attention, the capacity to treat simultaneously two tasks. Selective and sustained attention seems to be preserved in AUD patients, while divided attention abilities are impaired (Tedstone and Coyle, 2004). Regarding processing speed, sometimes considered as reflecting low-level attention, results are more heterogeneous. Noël et al. (2001) reported that recently detoxified AUD patients had preserved performance on parts A of the Trail Making Test and Hayling Test, or color-naming part of the Stroop Task, and presented normal latency time on the Tower of London test. On the contrary, Nowakowska-Domagała et al. (2017) found that AUD patients were slower than healthy controls on parts A of the Trail Making test. Working memory is a short-term memory system that allows temporary storage and manipulation of the information necessary for complex cognitive tasks such as language comprehension, learning, and reasoning. Working memory, which requires the simultaneous storage and processing of information, is composed of three slave systems under the control of a central executive (Baddeley, 2000; Baddeley and Hitch, 1974). The slave systems are short-term storage systems comprising the phonological loop, which processes verbal information, the visuospatial sketchpad, which processes visuospatial information, and the episodic buffer, which links information across domains and maintains such multimodal information. The storage of both verbal and nonverbal components can be impaired in AUD patients (Beatty et al., 1996; Kopera et al., 2012; Pitel et al., 2007b), although the nonverbal working memory component is typically observed as more severely affected than the verbal one (Sullivan et al., 2000). The episodic buffer was also found to be impaired in AUD (Pitel et al., 2007b). Finally, the central executive, which is regarded as being similar to executive functions, is classically described as compromised in AUD patients. Executive functions are cognitive abilities that control and regulate the cognitive system to coordinate thoughts and actions toward a goal. They enable us to face complex and nonroutine situations (Alvarez and Emory, 2006). These functions permit a behavioral adaptation to environmental changes. Executive functions are not a unitary construct, they are a multifactorial system composed of several components, presenting specific characteristics, which are interacting with each other (Hull et al., 2008; Jurado and Rosselli, 2007). Executive functions include mental flexibility, abstraction, planning, problem-solving, shifting of mental states, monitoring and updating of working memory representations, organization, rules deduction, and categorization. While two-thirds of AUD patients exhibit executive function impairments (Ihara et al., 2000), there is heterogeneity in the profile of executive dysfunction. Several

105

studies have shown that AUD patients perform worse than healthy controls on part B of Trail Making Test, which assesses set shifting and mental flexibility (Ihara et al., 2000; Loeber et al., 2009; Moriyama et al., 2002; Noël et al., 2001). The Wisconsin Card Sorting Test is frequently used to evaluate executive functions in AUD patients. It indicates that recently abstinent patients present an inability to conceptualize, be flexible, and consider feedback information from the experimenter (Chanraud et al., 2007; Ratti et al., 2002; Salgado et al., 2009). The number of categories found and error rate are especially sensitive to the effects of chronic alcohol consumption (Stephan et al., 2017). AUD patients also present a deficit of inhibition when evaluated with the Stroop Task (Konrad et al., 2012; Pitel et al., 2007a; Schulte et al., 2012; Tedstone and Coyle, 2004) and the part B of the Hayling Test (Noël et al., 2001). Stephan et al. (2017), in their metaanalyses, indicated that the Hayling Test is very sensitive to the effects of alcohol. AUD patients also presented organization difficulties and deficits in selfgeneration of strategies as revealed by verbal fluency and Ruff Figural Fluency Tests (Oscar-Berman et al., 2009). Updating abilities, assessed with n-back tasks, are impaired in AUD patients (Pitel et al., 2007a, 2009). The use of the Tower of London test suggests that planning abilities are impaired as well (Goudriaan et al., 2006), but such finding could also be related to the deficits of flexibility and inhibition. In impulsivity tests such as in a go/no-go task, AUD patients responded too quickly and did not inhibit responses when a stop signal appeared (Pandey et al., 2012). Executive functions are also impaired when examined with the Behavioral Assessment of Dysexecutive Syndrome (BADS), a battery of executive tests designed to have an ecologic validity (Ihara et al., 2000). In this battery, the temporal judgment and the modified six elements subtests seemed particularly affected. All together, these data suggest that chronic and excessive alcohol consumption results in executive dysfunction. Despite the variety of executive deficits observed in AUD, several studies (Kamarajan et al., 2005; Noël et al., 2007) suggested that an impairment of inhibition could be a central feature in the neuropsychological profile of the patients. In accordance, Brion et al. (2017) investigated whether the impurity and multidetermined nature of the executive tasks previously used could explain the variety of the deficits observed. They explored the three main executive components (shifting of mental sets, monitoring and updating of working memory representations, and inhibition of prepotent responses) described by Miyake et al. (2000) and conducted specific tasks to selectively evaluate these components in AUD patients. For each task, they used accuracy and reaction time indexes as dependent variables, and they found that reaction time was relatively preserved, whereas AUD patients were

106 Cognition and Addiction

significantly less accurate than the healthy control participants. They also found a moderate deficit of inhibition, while shifting and updating were more severely impaired. The authors concluded that alcohol-related executive deficits did not include only an inhibition deficit but also other executive alterations.

Episodic memory Mnemonic functions and notably episodic memory mainly rely on PC. PC involves gray matter nodes of the limbic system including the hippocampus, thalamus, mammillary bodies, and cingulate cortex, interconnected by bundles of white matter fibers (Fig. 8.1). The anterior thalamus receives inputs from the mammillary bodies via the mammillothalamic tract and projects to the cingulate cortex via the internal capsule. Then, the cingulum bundle connects the cingulate cortex to the entorhinal cortex and hippocampus, which projects to the mammillary bodies through the fornix. Studies in AUD patients reported volume loss in mammillary bodies (Pitel et al., 2012; Sheedy et al., 1999; Sullivan et al., 1999), hippocampus (Sullivan et al., 1995), thalamus (Cardenas et al., 2007; Chanraud et al., 2007), and cingulate cortex (Pitel et al., 2012) but failed to show any correlation between gray matter macrostructural abnormalities and episodic memory impairments. Rather, episodic memory disorder may be associated with alteration of gray matter microstructure in the medial temporal lobes (Chanraud et al., 2009a) or damage of white matter bundles and tracts, in particular, the cingulum and the fornix (Pfefferbaum et al., 2009; Schulte et al., 2010; Trivedi et al., 2013), leading to a disruption of the PC. Segobin et al. (2015) found lower episodic memory performance in AUD patients with the most severe alterations of the microstructure within the cingulum and fornix. Episodic memory is currently described as the memory system in charge of the encoding, storage, and retrieval of personally experienced events, associated with a precise spatial and temporal context of encoding. Episodic memory allows the conscious recollection of personal events from one’s past and the mental projection of anticipated events into one’s subjective future (Wheeler et al., 1997). Recollection of episodic events requires autonoetic awareness, which is the impression of reexperiencing or reliving the past and mentally traveling back in subjective time (Tulving, 2001). Episodic memory is not only hierarchically the most sophisticated memory system but also the most sensitive to pathology, trauma, and toxicity. Most studies investigated episodic memory in AUD with classical learning tasks such as learning a list of words (Sherer, 1992), faceename associations (Beatty et al., 1995), or delayed recall of a complex figure (Sullivan et al., 1992). Learning abilities were impaired for both verbal and nonverbal information. Although AUD patients performed

lower than healthy controls on the Free and Cued Selective Reminding Test (Pitel et al., 2007a), they seemed to improve their performance at the same rate. They can indeed show evidence of some learning over trials (Ryan and Butters, 1980). Pitel et al. (2007a) investigated episodic memory in accordance with the current and comprehensive definition of this skill: encoding, storage, and retrieval of factual information located in a precise space-time context associated with autonoetic recollection. AUD patients showed impairment on a recognition task test after a spontaneous encoding as well as on a free recall task after a deep encoding. These results suggest an impairment of both encoding and retrieval abilities in AUD. However, authors did not find any storage impairment in AUD patients, in accordance with a previous research (Sherer, 1992). Moreover, the spatiotemporal context of encoding was also altered, with a deficit in spatial and temporal contexts (Pitel et al., 2007a; Salmon et al., 1986). Patients tended not to recall complete episodes, i.e., correct factual information associated with the correct spatiotemporal context of encoding, suggesting incomplete episodic memories. AUD patients also present difficulties identifying the source of remembered information (Schwartz et al., 2002) and a deficit of autonoetic consciousness (Pitel et al., 2007a). Noel et al. (2012) indicated that patients perform better on cued-recall and recognition testing conditions, which are less dependent on strategic retrieval operations. In AUD patients, impaired learning abilities could be related to executive dysfunctions and notably impoverished generation of spontaneous strategies. However, another study found very little relationship between episodic memory performance and executive results, and suggested rather a genuine episodic memory impairment that could not be interpreted solely as the consequence of executive dysfunctions (Pitel et al., 2007a). Another component of episodic memory is prospective memory, which is the ability of remembering to carry out an intended action at some future point in time (Brandimonte et al., 1996). The Prospective Memory Questionnaire, based on self-report measures, revealed prospective memory complaints in AUD (Heffernan et al., 2002; Ling et al., 2003), suggesting that prospective memory may be impaired in AUD patients (Heffernan, 2008 for a review). The severity of the complaints was associated with the total amount of alcohol consumption (Ling et al., 2003). Moreover, patients who reported prospective memory difficulties also complained about impaired executive functioning (Heffernan et al., 2005). They did not appear to use sufficient internal or external memory strategies to compensate for prospective memory deficits (Heffernan et al., 2002). Autobiographical memory (AM) refers to remote memory, comprising the specific personal events (episodic

Neuropsychological deficits in alcohol use disorder: impact on treatment Chapter | 8

component) as well as general knowledge about one-self (semantic component) (Conway, 2001). Compared with healthy controls, AUD patients recalled specific memories less frequently and general memories more frequently, which is a phenomenon of overgenerality (D’Argembeau et al., 2006). However, when a specific past event was provided, AUD patients subjectively experienced as many sensory and contextual details as controls. AUD patients may encode and/or access fewer episodic memories than controls, but when they do, the richness of the memories seems qualitatively equivalent to that of controls. Nandrino et al. (2016) compared semantic and episodic dimensions of AM in AUD patients after a short-term (STA, nearly 5 weeks) and long-term (LTA, at least 6 months) abstinence and healthy controls. On the overall, the two groups of AUD patients were especially impaired for recall of both episodic and semantic recent events and knowledge, corresponding to the drinking period. However, no significant differences were observed between the AUD and control groups for childhood semantic events. Concerning episodic events from childhood, STA provided fewer memories than healthy controls and LTA. First, these results suggest encoding alteration during the drinking period. Second, the semantic component of AM may be less affected by heavy chronic drinking than the episodic component. Third, the preservation of episodic memories from childhood may be preserved in LTA because of cognitive and brain recovery with sobriety. Although AUD patients are impaired on most of the episodic memory components, they seem to present a limited awareness of those deficits. AUD patients may thus exhibit a deficit of metamemory, which refers to personal knowledge about one’s own memory abilities (Flavell, 1971). Metamemory is related to monitoring and control processes. Indeed, to improve performance during a memory task, it is necessary to adjust strategies according to this one. Monitoring concerns the capacity to assess future performance before a memory task and the skills to evaluate performance retrospectively (Nelson and Narens, 1990). The most frequently used measure of metamemory is the feeling-of-knowing (FOK) (Hart, 1965), characterized by the ability to accurately predict the future performance on tasks requiring recognition of newly learned information. The FOK judgment is recorded on a Likerttype scale (from 0% “definitely will not recall” to 100% “definitely will recall”). A FOK accuracy index is calculated to evaluate the agreement between predictions of the future recognition performance and real recognition performance (GoodmaneKruskal Gamma statistic; Nelson, 1984). Le Berre et al. (2010) found that AUD patients were impaired in this task as they obtained a FOK index significantly lower than that of the control group (not better than chance level). Patients had a tendency to overestimate their memory skills: they predicted that they would be

107

capable of recognizing the correct word while they actually failed to do so. An explanation of this metamemory deficit is that AUD patients fail to update information about their level of memory and, as a consequence, assess their memory skills regarding earlier functioning in life (Le Berre and Sullivan, 2016). This metamemory impairment may be considered as a mild form of anosognosia, a lack of insight of the disease frequently observed in KS.

Semantic memory Semantic memory is sustained by relatively preserved lateral temporal lobes in AUD. Semantic memory refers to the memory of meaning, understanding, general knowledge about the world, and other concept-based knowledge unrelated to specific experiences. The level of consciousness associated with semantic memory is noetic (giving rise to feelings of familiarity or knowing) because it is independent of encoding context (Tulving, 1985, 2001). Fama et al. (2011) studied remote semantic memory processes in three clinical groups: AUD group, patients infected with the human immunodeficiency virus (HIV), and patients comorbid for both conditions (AUD þ HIV group), compared with healthy controls. AUD and HIV groups exhibited performance below healthy controls, but these differences were not statistically significant, whereas AUD þ HIV group appeared impaired compared with healthy controls. Although remote semantic memory has been found preserved in AUD patients (Fama et al., 2011), recently detoxified patients may experience difficulties to acquire new semantic information. Pitel et al. (2007b) studied the ability to acquire new semantic concepts including, for each concept, a label, a superordinate category, and three features associated with a picture. The learning protocol comprised eight daily sessions. AUD patients were able to acquire the category and features of the semantic concepts, albeit slowly, but they presented impaired label learning. AUD patients invoked different and inefficient cognitive strategies to attempt to compensate for impaired episodic and working memory. The use of errorless learning may be relevant for AUD patients with cognitive deficits to learn new complex semantic knowledge, and more particularly, new labels (Pitel et al., 2010). Moreover, information acquired with errorless learning was flexible, i.e., it may be generalized and or transferred to other situations. This learning condition allows preventing that patients repeat their errors in the course of the acquisition, learning them instead of the correct answers, and leading to learning impairments.

Procedural memory Hubert et al. (2007) highlighted a specific brain network involved in procedural learning and memory. Procedural

108 Cognition and Addiction

learning is a dynamic process involving different phases (cognitive, association, and autonomous) and resulting in the automation of the procedure that underlie motor, visuospatial, or cognitive skills (Anderson, 1992). During the cognitive phase, brain structures such as the prefrontal cortex, anterior cingulate cortex, right angular cortex, and posterior cerebellar regions are activated. The associative phase is mainly underlined by caudate nucleus and occipital regions. The posterior brain is also found activated during the autonomous phase with the anterior cerebellum (Hubert et al., 2007). Although remote procedural memory seems to be preserved, the acquisition of a new cognitive procedure may be affected by chronic alcohol consumption. Pitel et al. (2007b) tested procedural learning with the Tower of Toronto task (TT task) during four daily sessions. AUD patients and healthy controls performed 10 trials in each learning sessions (40 trials in total). The TT task consists of a rectangular base with three pegs and four colored disks on the leftmost peg. Participants are required to rebuild the initial disk configuration on the rightmost peg, following some rules. Early in abstinence, AUD patients were slower than controls and made more moves to achieve the task, but they managed to reach the same level of performance as controls at the end of the 40 trials (Pitel et al., 2007b). The between-group difference regarding the learning dynamics may be related to the fact that cognitive procedural learning requires episodic memory and working memory in the initial stage of learning (Beaunieux et al., 2006). Alcoholrelated episodic and working memory deficits may have prevented patients from completing the cognitive and associative stages at the same pace as controls, making it difficult to automate the new cognitive procedure. An MRI investigation reinforced this hypothesis as it indicated that procedural learning performance correlated with gray matter volume of the angular gyrus and caudate nucleus, not only during the first learning trials but also after 40 trials of the TT task (Ritz et al., 2014). Another explanation of these altered cognitive procedural learning abilities may be related to the visuospatial deficits frequently reported in AUD (Beatty et al., 1996; Fama et al., 2004).

Perceptive memory and visuospatial abilities There is no specific brain region involved in perceptive memory but several neural networks including sensory areas. Perceptive memory is in charge of the encoding and the storage of perceptual features of physical objects. This memory system includes both conscious and nonconscious processing of sensoriperceptual information (Tulving and Schacter, 1990). Perceptual memory is assumed to be involved in perceptual priming, which refers to the effect in which exposure to the form of a stimulus influences a response to a later stimulus. It can be considered as the root

of human memory because it is through perceptual memory that information is subsequently and progressively transferred into the different representation memory systems. This memory component depends on sensory modalities, notably on sight. In this vein, perceptual memory is linked to three visuospatial abilities: visuoperceptual skills, which concern abilities to classify stimuli such as objects or faces; visuospatial skills, which include localization in space, navigation, and the conceptualization of the distance; and visuoconstruction, which is the ability to organize elements into correct spatial relationships. In AUD, several structural brain abnormalities have been related to visuospatial deficits. A decreased volume in the parietal lobes has been observed (Chanraud et al., 2007; Fein et al., 2002) and associated with poor performance in spatial processing (Fein et al., 2009). However, cerebellar hemispheric white matter may be a better predictor of visuospatial abilities than parietal lobes volume (Sullivan, 2003). Impairments in perceptual abilities have been reported in AUD patients by many studies using the embedded figures test (Sullivan et al., 2002; Fama et al., 2004), mental rotation test (Beatty et al., 1996), block design subtest from Weschler Adult Intelligence Scale (Beatty et al., 1996; Oscar-Berman et al., 2009; Sullivan et al., 2002), and ReyeOsterrieth Complex Figure Test (Beatty et al., 1996; Sullivan et al., 2002). All these tasks are complex, they require different visuospatial components and the integrity of other cognitive functions. For example, poor performance in ReyeOsterrieth Complex Figure copy could be explained by a deficit in visuoperceptual skills, visuoconstruction, or executive functioning. When using an implicit perceptual learning paradigm (assessed with a picture fragment completion task, for example), AUD patients were impaired on the primary components of visuoperception and explicit memory for visuospatial stimuli but obtained preserved results on the perceptual learning task (Fama et al., 2004). These findings suggest that visuospatial perception is impaired in AUD but patients can take advantage of prior exposure to enhance performance based on preserved implicit memory. Although AUD patients performed at the same level as healthy controls on the perceptual learning task, groups used different strategies: visuoperceptual abilities predicted perceptual learning performance in the control group, whereas in the AUD group, performance was predicted by executive abilities.

Emotional processes and theory of mind Emotions The amygdala plays a key role in emotional regulation and behavioral control (McBride, 2002 for review). Wrase et al. (2002) found a reduction of gray matter volume in the

Neuropsychological deficits in alcohol use disorder: impact on treatment Chapter | 8

limbic system of AUD patients, notably in amygdala. In addition, Marinkovic et al. (2009) identified abnormal activation of the amygdala and hippocampus during a task of facial emotions identification in AUD. Chronic and heavy alcohol consumption alters emotional processing. AUD patients present a tendency to alexithymia, i.e., they have difficulties to experiment, characterize, and express their own internal emotional state (Maurage et al., 2017; Uzun et al., 2003). They also exhibit deficits in detection and interpretation of others’ emotions (de Timary et al., 2010). Several studies have shown that AUD patients do not succeed in identifying emotions of faces (Kornreich et al., 2002; Philippot et al., 1999), prosody (Brion et al., 2018; Maurage et al., 2009; Monnot et al., 2001), and body postures (Maurage et al., 2009). Moreover, AUD patients do not seem aware of their difficulties in interpreting facial emotions (Philippot et al., 1999). D’Hondt et al. (2015) indicates that AUD patients would need higher intensity in emotion expressing to make efficient identification. Comorbidity between AUD and mood disorders is well established. Moreover, most AUD patients exhibit heightened sensitivity to negative emotions during early withdrawal, especially when they present anxious or depressed symptoms (Schuckit, 2006). Maurage et al. (2017) conducted a cluster analysis in AUD patients, taking into consideration two types of highly prevalent socioemotional difficulties: alexithymia (three subscales’ scores of Toronto Alexithymia Scale-II) and interpersonal problems (six subscales’ scores of the Inventory of Interpersonal Problem). They identified five distinct subgroups of patients showing different specific patterns of emotional and interpersonal difficulties. These findings are in line with the idea that cognitive deficits observed in AUD are heterogeneous and suggest that classical group comparisons can be misleading and should be completed by subgroup explorations.

Social cognition Social cognition concerns the cognitive processes, such as emotion decoding, ToM, and empathy, that enable individuals to take advantage of being part of a social group (Frith, 2008). Several regions, including notably the prefrontal cortex, anterior cingulate cortex, the temporal pole, and the striatum, have been found to be involved in ToM. Abu-Akel and Shamay-Tsoory (2011) presented a model in which these cortical and subcortical regions are subdivided and functionally organized into networks that subserve the ability to represent cognitive and affective mental states to both self and others. A few studies have examined brain dysfunction related to ToM. They report that in AUD, social cognition deficits are related to prefrontal (Uekermann and Daum, 2008) and temporoparietal dysfunction (Samson, 2009).

109

ToM is defined as the capacity to infer mental states from others’ social signals to predict their behaviors, desires, intentions, and beliefs. Several studies have reported ToM impairments in AUD patients (Bosco et al., 2014; Maurage et al., 2015; Onuoha et al., 2016; Thoma et al., 2013). Maurage et al. (2015) specified that 50% of the AUD patients may present ToM impairments, and in most cases, the deficit concerns the tracking of other people’s mental states. Maurage et al. (2016) highlighted a dissociation between impaired affective ToM (i.e., the ability to understand and experience others’ feelings and emotions) and relatively preserved cognitive ToM (i.e., the ability to identify others’ intentions and thoughts). Empathy is defined as the ability to understand and share others’ feelings and emotions. AUD patients may present a dissociation between an impaired emotional component of empathy (capacity to feel other people’s emotions) and a preserved cognitive empathy (capacity to understand other people’s mental states such as though and opinions) (Maurage et al., 2011). Overall, deficits of social cognition observed in AUD patients (Kornreich et al., 2002; Maurage et al., 2017) could disturb interpersonal relationships within the context of a vicious cycle with alcohol consumption as a coping mechanism to overcome social isolation.

Reversibility of cognitive deficits and cerebral damage with abstinence Many studies provided evidence of the brain and cognitive recovery after drinking cessation (Mulhauser et al., 2018; Rosenbloom et al., 2004; van Eijk et al., 2013) even in the absence of any stimulation. Goldman (1990) refers to timedependent recovery to describe this phenomenon; the phrase “spontaneous recovery” is also used. Recovery of the brain structure and function has been reported in cross-sectional investigations that compare groups of patients with different length of sobriety or in longitudinal studies of a single AUD group to assess within-subject changes in the course of abstinence.

Brain recovery Improvement of brain structural integrity (Stavro et al., 2013) is related to the length of abstinence and varies according to the cerebral regions. After a long abstinence period (4 years of sobriety), the blood flow in the frontal lobe seems to increase and even return to normal (Gansler et al., 2000). One year of abstinence is also associated with improved fractional anisotropy of the corpus callosum (Alhassoon et al., 2012). Cardenas et al. (2007) highlighted that recovery of temporal lobes, cerebellum, and anterior cingulate among others brain structures was more limited in relapser than in abstainer AUD patients at 8-month

110 Cognition and Addiction

follow-up. A short period of sobriety (1 month) has been found to result in increased white matter volume and decreased cerebrospinal fluid (Agartz et al., 2003). Interestingly, even a short-term period without alcohol induces noticeable changes in gray matter volume (20 days in Pfefferbaum et al., 1995; 2 weeks in van Eijk et al., 2013). More recently, Segobin et al. (2014) evaluated brain recovery within 6 months with an original and novel method. In this longitudinal study, patients examined at follow-up were not classified into relapsers versus abstainers. The authors analyzed the relationship between regional brain changes and the total amount of alcohol consumed over the 6-month follow-up. They found that interim drinking correlated with the volume of different brain regions (cerebellum, striatum, and cingulate gyrus notably): heavy interim drinking was related to lower recovery. In addition, the degree of recovery was not the same for the entire brain, indicating that the dynamics of neural plasticity may be regionally specific. Interestingly, the findings also revealed that very limited alcohol consumption (50 mg) cause hypotension, bradycardia, dyspnea, loss of consciousness, seizure, coma, and death (Mayer, 2014; Kim and Baum, 2015). Nicotine acutely agonizes nAChRs on dopamine (DA) neurons in the ventral tegmental area and nucleus accumbens, which stimulates DA release in the mesolimbic pathway. Nicotine also indirectly facilitates DA release by enhancing glutamate release (Benowitz, 2009). Nicotine administration releases other neurotransmitters, including ACh, norepinephrine, and gamma-aminobutyric acid (GABA) (Wonnacott, 1997). At nicotine concentrations typical for daily smokers, a4b2* receptors are nearly saturated (Brody et al., 2006) and are likely in a desensitized state (i.e., have a reduced effect). nAChRs return to a sensitized state following overnight abstinence. Chronic daily smoking upregulates nAChRs (Mukhin et al., 2008), thought to be due to the prolonged desensitization, and also downregulates DA receptors in the striatum (Dagher et al., 2001). Nicotine has a half-life of about 2 h (Benowitz et al., 1982), and withdrawal symptoms may begin as little as an hour after the last cigarette. Symptoms include cigarette craving, irritability, anxiety, difficulty concentrating, increased appetite, restlessness, depressed mood, and insomnia (APA, 2013). Withdrawal symptoms peak within the first week of abstinence and last 2e4 weeks (Hughes, 2007), although cigarette cue-induced cravings can continue and even escalate over time (Bedi et al., 2011). Smoking cessation results in readaptations in nAChR; the density of b2 subunit normalizes to nonsmoker levels after 6e12 weeks of abstinence (Cosgrove et al., 2009).

Addiction liability Tobacco has a higher rate of dependence among users than other drugs of abuse (Lopez-Quintero et al., 2011), which appears incongruent with the modest, acute effects of low nicotine doses. An important mediator between initial

132 Cognition and Addiction

smoking and addiction may be the age of acquisition. Approximately 90% of adult daily smokers initiated use before the age of 18, and nearly all began before the age of 25 (DHHS, 2014). Adolescence is a critical period for brain development and is marked by increased plasticity and rapid growth of neural circuits that underlie social, emotional, and motivational processes. Many of these processes are regulated by the prefrontal cortex, which continues developing into young adulthood, up to 25 years of age. The combination of immature prefrontal cognitive control and increased reactivity of subcortical rewarde related processes may lead to a greater susceptibility to addiction (DHHS, 2016). In fact, symptoms of nicotine dependence can precede daily smoking among adolescents (Gervais et al., 2006). This, in combination with tobacco availability and popularity, may partly account for high rates of tobacco addiction.

Cognitive effects of nicotine and tobacco Short-term effects Nicotine influences a range of cognitive processes, such as reaction time, attention, learning, and memory. nAChRs have a role in encoding new memories; agonists enhance encoding of new information and antagonists produce memory deficits (Felix and Levin, 1997; Prickaerts et al., 2012). However, the effects on memory performance have been demonstrated in rodents more than in humans (Levin et al., 2006). In nonabstinent smokers and nonsmokers, nicotine has primarily been shown to reduce reaction times on tests of attention and memory (Levin et al., 2006; Swan and Lessov-Schlaggar, 2007; Heishman et al., 2010). It is possible that the broad array of nicotine effects on cognition are indirectly due to its effects on attentional performance: nicotine could simply improve the ability to focus attention and maintain task engagement and reduce the influence of distracting irrelevant stimuli (Evans and Drobes, 2009). In addition, nicotine’s effects on cognitive performance may depend on the level of effort required by the task, the individual’s baseline level of performance, or their underlying cholinergic function. Nicotine improves attention in people with poor baseline performance, such as schizophrenia and attention deficit/hyperactivity disorder, which may explain why certain populations with poor attentional performance are at higher risk of tobacco dependence (Evans and Drobes, 2009). Furthermore, genetic variations in ACh and DA receptors could contribute to individual differences in the attentional effects of nicotine (Ahrens et al., 2015). Functional neuroimaging measures how nicotine and tobacco withdrawal affect brain activity. Recent neuroimaging work focuses on the functional organization of the

brain into networks, identified by their synchronous coupling of spontaneous activity fluctuations at rest and when engaged in a cognitive task. This is referred to as “functional connectivity” and represents the efficiency of neural communication within and between brain networks. Three major networks include the executive control network, which is active during cognitive task performance, the default mode network, which is active during rest, and the salience network, which switches between them (Seeley et al., 2007; Buckner et al., 2008; Goulden et al., 2014). Nicotine and nAChR agonists may diminish default mode network activity and/or enhance executive control network activity, thus improving cognition by shifting brain network activity from internally directed to externally directed processes (Sutherland et al. 2012, 2015). For example, in nonsmokers, nicotine suppressed activity in the default mode network and increased activity in the visual attention network (Tanabe et al., 2011).

Long-term effects Long-term tobacco smoke exposure has been associated with cognitive deficits across the life span. Secondhand smoke exposure in children and fetuses produces cognitive deficits later in life (Swan and Lessov-Schlaggar, 2007). For example, tobacco smoke extract (compared with nicotine alone) administered to pregnant rats in doses equivalent to secondhand smoke produced hyperactivity, working memory deficits, and impaired emotional processing in their adolescent and adult offspring (Hall et al., 2016). Smoking during adolescence produces both acute and long-term impairments in cognition and attention, and nicotine exposure in adolescent rats produces long-lasting synaptic changes in prefrontal cortical regions that may underlie cognition and attention (DHHS, 2016). These cognitive deficits may be partly due to smoking-related smaller gray matter volume and lower gray matter density, especially in the prefrontal cortex (Brody et al., 2004; Gallinat et al., 2006; Vnukova et al., 2017). Smoking is also associated with cognitive deficits and decline in late life, and it may increase the risk of neurodegeneration in Alzheimer’s disease or other dementias possibly via oxidative stress, inflammation, or atherosclerosis (Swan and LessovSchlaggar, 2007).

Withdrawal effects Nicotine withdrawal impairs response inhibition, attention, reaction time, and working memory (McClernon et al., 2015). Although, the most consistent effects of overnight smoking abstinence are on subjective withdrawal symptoms such as craving, negative mood, self-reported difficulty concentrating, and increased hunger. Abstinence

Tobacco addiction: cognition, reinforcement, and mood Chapter | 9

effects on cognitive performance are generally of a smaller magnitude but reliably show deficits such as slower reaction times (Leventhal et al., 2010). Neuroimaging studies suggest that these cognitive deficits relate to dysfunction in frontal cortical areas (McClernon et al., 2015). One of the most consistent effects of withdrawal is on diminished sustained attention, which may indirectly contribute to other cognitive deficits (Evans and Drobes, 2009). Smoking, or nicotine administration, can improve these deficits in withdrawn smokers. For example, nicotine improved cued spatial attentional orienting among withdrawn smokers who had slower reaction time (RT)s at baseline; which supports the idea of baseline performance dependency of nicotine’s attentional effects (Hammersley et al., 2016). In another study, individual differences in cognitive withdrawal symptom improvement during nicotine replacement were associated with inverse coupling between the executive control network and the default mode network (Cole et al., 2010). This suggests that the therapeutic effects of nicotine replacement are related to modulation of brain processes involved in cognitive control. Cognitive improvement may be a form of smoking reinforcement, especially when it reverses the effects of withdrawal. Although there is some quantitative evidence for improvements in cognition, nicotine could indirectly influence cognitive performance by improving mood and the motivation to maintain attention and exert cognitive effort. This may be a small improvement above smokers’ baseline function or a return to baseline during withdrawal (Evans and Drobes, 2009).

Nicotine reinforcement Reinforcement enhancement Nicotine is a weak primary reinforcer compared with other drugs of abuse. Rats that have been trained to selfadminister cocaine will work harder for the drug as the schedule of reinforcement becomes more challenging (Richardson and Roberts, 1996), but rats tend to not work harder for nicotine (Rupprecht et al., 2015). Nicotine alone does support modest levels of rat self-administration, but nicotine self-administration is facilitated by having a paired sensory stimulus cue, such as a sound or light, even if the stimulus itself has little to no reinforcing value on its own (Sorge et al., 2009). Interestingly, pairing nicotine with another weak, unconditioned reinforcer produces a synergistic effect on motivation to obtain both (Donny et al., 2003). This synergy is referred to as “reinforcement enhancement” (Caggiula et al., 2009), and this may be a critical mechanism for understanding the discrepancy between the modest abuse liability of nicotine and the tenacity

133

of tobacco addiction. There are a couple different hypotheses about how this mechanism works: (1) Nicotine may be enhancing the reinforcement strength of other primary reinforcers or amplifying their incentive salience (Chaudhri et al., 2006; Rupprecht et al., 2015). Specifically, rats’ response rates to obtain both a nondrug reinforcer and nicotine are twice the response rate produced for either reinforcer alone (Donny et al., 2003). This couse of nicotine and other reinforcers appears to parallel some human behavior, such as nondaily smokers using tobacco while socializing, attending parties, and/or drinking alcohol (Nguyen and Zhu, 2009; Shiffman et al., 2009). Another example is that taking a break from school/work to relax and socialize with friends/coworkers can elicit tobacco cravings if frequently paired with smoking (i.e., a “smoke break”). The repeated pairing of smoking with other reinforcers may be an important phase in the transition from initial use to tobacco addiction. (2) Neutral stimuli consistently paired with nicotine may become conditioned stimuli that develop their own secondary reinforcing properties (Palmatier et al., 2007). For example, when initially experienced, the sensorimotor aspects of smoking (e.g., sight, smell, flavor, throat sensation, and hand to mouth motion) may be subjectively aversive, but over time, these aspects can become reinforcing on their own. In fact, replacing smokers’ cigarettes with denicotinized cigarettes will maintain smoking behavior (Donny and Jones, 2009). Outside the laboratory, the subjective effects of nicotine may play a role in the initial use and acquisition of smoking, but over time, tobacco use becomes associated with contextual cues, such as one’s mood and surrounding environment, in addition to the nonnicotine sensorimotor aspects of smoking. Habitual cigarette smoking is maintained by this conditioning, as many smokers reach for a cigarette while driving their car, with a cup of coffee or glass of beer. Likewise, withdrawal symptoms such as irritability and anxiety can be alleviated by smoking a cigarette, thus smokers can become conditioned to regard stress and frustration derived from any source as a cue for smoking (Benowitz, 2009).

Neural mechanisms Although classical conditioning paradigms based on animal models may not be fully reproducible in humans because of the amount of time involved, neuroimaging studies can provide insights into neural mechanisms of how nicotine interacts with nondrug reinforcers (usually money) in the absence of behavioral effects.

134 Cognition and Addiction

An important concept in reinforcement learning is prediction errordthe difference between the cue-predicted outcome and the actual outcome (i.e., what was expected vs. what was received). Prediction errors may serve to alert and orient the individual’s attention to the discrepancy. The orienting of attention can then drive additional learning and memory about the cueeoutcome relationship, ultimately helping the individual adapt its behavior to changes in the environment. DA signaling in the mesolimbic pathway appears to code prediction errors. There is a phasic DA signal following unexpected natural rewards, and this signal shifts to a predictive cue after classical conditioning (Schultz and Dickinson, 2000). Nicotine helps boost DA transmission and could potentially enhance DA signaling of prediction errors, thereby amplifying the saliency of reward cues or outcomes, or improve attention, learning, and memory for these events. Perhaps by strengthening the cueeoutcome relationship, nicotine enhances learning and memory for its own use. Conversely, if withdrawal dampens prediction errors, perhaps this is related to anhedonic effects of withdrawal (i.e., loss of interest in alternative reinforcers). Alternatively, nicotine and tobacco withdrawal may affect tonic DA tone or both tonic and phasic signaling (Zhang et al., 2012). There have been a few neuroimaging studies on how nicotine/smoking affects prediction error signaling during classical conditioning, although results have been mixed. Compared with nonsmokers, smokers had reduced prediction error signaling in the striatum and medial prefrontal cortex, which was related to the duration of smoking in years (Rose et al., 2012). Although these smokers were not withdrawn, this may inform the anhedonic consequences of smoking cessation. In nonsmokers, both unexpected outcomes and acute nicotine increased activation in the anterior insula, which is part of the salience network, and may subserve how nicotine amplifies the salience of nondrug reinforcers (Addicott et al., 2017). Another study reported that smoking withdrawal decreased the signal associated with phasic DA signals across both expected and unexpected outcomes, suggesting that changes in phasic DA are unlikely contributing to reward processing deficits (Oliver et al., 2016). An interesting twist in this line of research is the influence of drug expectations. Smokers who believed they were smoking nicotine-free cigarettes had less neural responses in the striatum to reward prediction errors, compared with when they were told they were smoking nicotine cigarettes. These effects were not observed in other brain regions activated by the task. This suggests that beliefs about the presence of a neuroactive substance such as nicotine can override its physical presence. Evidently, drug expectation is an important cognitive mechanism in addiction (Gu et al., 2015). A related type of neuroimaging paradigm used to understand the function of the mesolimbic DA pathway uses

an anticipatory money-reward cue phase, followed by a feedback phase indicating whether any money was won on that trial based on the individual’s performance. This type of paradigm typically elicits activation in the striatum and prefrontal cortex as well as other regions (e.g., Knutson et al., 2001) and has been used in a number of studies to investigate the acute and chronic effects of nicotine and tobacco. This research tends to show that nicotine increases activation to anticipatory cues (Fedota et al., 2015; Moran et al., 2018) and reward feedback (Addicott et al., 2019) (although one study reported acute nicotine reduced anticipatory activation in satiated smokers) (Rose et al., 2013), and smoking withdrawal reduces activation to reward feedback (Sweitzer et al., 2014; Addicott et al., 2019). However, smokers’ anticipation-related activation for cigarette reward is greater than for money reward during withdrawal (Sweitzer et al., 2014). This could account for the withdrawal-induced bias toward anticipation of smoking rewards at the expense of other, nondrug rewards, which motivates smoking behavior and interferes with cessation success.

The emotionesmoking relationship Smoking as a maladaptive response to negative mood Tobacco addiction disproportionately affects individuals with mood disorders. Compared with never smokers, smokers are 1.85 times more likely to have depression, 1.71 times more likely to have anxiety, and 1.69 times more likely to experience psychological distress (Taylor et al., 2014a). Nicotine and other nAChR agonists have antidepressant effects (Gandelman et al., 2018), and smoking could potentially mitigate symptoms of depression while nicotine withdrawal exacerbates them. A smoker’s propensity to relieve stress and negative mood by smoking parsimoniously explains emotion-smoking comorbidity (Leventhal and Zvolensky, 2015). Several important concepts related to the emotione smoking relationship are anhedonia, anxiety sensitivity, and distress tolerance. Anhedonia is the loss of an ability to feel pleasure and may be a symptom of tobacco withdrawal (Cook et al., 2017). High levels of anhedonia have been negatively associated with smokers’ time to relapse (Cook et al., 2010). Anxiety sensitivity is the belief that symptoms of anxiety are intolerable or have harmful consequences, and it is related to more severe nicotine withdrawal symptoms (Zvolensky et al., 2004). During a quit attempt, smokers with high anxiety sensitivity had a greater risk of smoking on days when they experienced increased negative affect (Langdon et al., 2016). Distress tolerance is the ability to pursue a goal (e.g., smoking cessation) in spite of physical or psychological distress (e.g., withdrawal

Tobacco addiction: cognition, reinforcement, and mood Chapter | 9

symptoms or cigarette craving). Laboratory-based behavioral measures of distress tolerance have been positively associated with smokers’ time to lapse/relapse (Brown et al., 2009; Kahler et al., 2013), and low distress tolerance may be related to diminished top-down cognitive control of behavior during stress (Daughters et al., 2016). Leventhal and Zvolensky wrote a comprehensive review and analysis of research linking anhedonia, anxiety sensitivity, and distress tolerance to smoking behaviors, including the initiation of smoking, progression to regular smoking, tobacco addiction/dependence, cessation, and lapse/relapse. To summarize, smoking is particularly reinforcing for individuals with poor emotional regulation because smoking can enhance positive affect, relieve anxiety, and terminate distress. Individuals with anhedonia, anxiety sensitivity, and low distress tolerance may be hypermotivated to react to emotional disturbance with smoking behavior. They may also be more sensitive to the effects of smoking on affective state. Likewise, these three emotional vulnerabilities amplify the effects of tobacco withdrawal on loss of reward, anxiogenesis, and distress exacerbation (Leventhal and Zvolensky, 2015).

Neural mechanisms Common to many mental illnesses, including addictions, is an increased sensitivity to stress or elevated stress levels (Esch et al., 2002). Across different drug addictions, stress often provokes craving and relapse (Mantsch et al., 2016), and chronic stress is an important trigger for relapse during a smoking cessation attempt (McKee et al., 2003). In fact, acute stress, which can reinstate extinguished drug-seeking behavior, is an animal model for relapse (Shaham et al., 2003). Several different neural mechanisms may underlie the stressesmoking relationship. To begin with, cholinergic signaling in the hippocampus, amygdala, prefrontal cortex, and striatum modulates behavioral responses to stressors (Higley and Picciotto, 2014). In addition, stress-related drug-taking behaviors are associated with amygdala function (Sharp, 2017), and noradrenergic and cholinergic signaling in the amygdala regulate anxiety- and depressionrelated behaviors (Mineur et al., 2018). Another neural mechanism is the extrahypothalamic corticotropine releasing factor receptor system, which elicits anxietyrelated behaviors and is thought to be related to negative mood states associated with withdrawal from nicotine or other drugs (George et al., 2007). This system is also implicated in stress-induced reinstatement of nicotineseeking behavior (Zislis et al., 2007). Lastly, the medial habenulaeinterpeduncular axis has a high density of nAChRs, and accumulating evidence suggests that this axis relates to fear/anxiety-related responses. Animal studies have shown nicotine withdrawal increases glutamatergic

135

signaling in the interpeduncular nucleus, and overactivation of neurons in the interpeduncular nucleus intermediate subregion may be responsible for anxiogenic effects of withdrawal (Molas et al., 2017).

The role of the insular cortex The insular cortex, folded deeply within the lateral sulcus, plays a special role in the emotionesmoking relationship. The insula is integral to interoception (i.e., the conscious awareness of the internal state of one’s body) and its subjective emotional interpretation (Craig, 2009). The insula is also connected to cognitive control brain regions that subserve goal-directed behavior (Nelson et al., 2010; Chang et al., 2013). This is highly relevant to addiction because the insula may link the physical and emotional awareness of drug withdrawal and cravings to volitional drug-taking behavior (e.g., smoking a cigarette in response to craving) (Garavan, 2010; Naqvi et al., 2014). A landmark study reported that smokers with strokeinduced insula lesions were more likely to quit smoking easily, notably with a sudden loss of the urge to smoke or “disruption of smoking addiction” (Naqvi et al., 2007). Although this retrospective study was limited by potentially inaccurate recollection of smokers’ behavior, several prospective studies have reported similar results. One study found that smokers with insula lesions were more likely to have quit smoking 1-year poststroke and had less difficulty quitting (Suner-Soler et al., 2012), although a follow-up investigation showed insula lesions no longer predicted abstinence at 6-years poststroke (Suner-Soler et al., 2018). Other prospective studies have shown that strokes affecting the basal ganglia, and the basal ganglia and the insula, were more likely to result in smoking cessation at 12-months poststroke (Gaznick et al., 2014), and smokers with insula lesions had less withdrawal symptom severity during hospitalization (Abdolahi et al., 2015). With one exception (Bienkowski et al., 2010) these studies support the role of the insula in tobacco addiction. Neuroimaging studies of neurologically intact smokers also support a role for the insula. At rest, smokers have weaker functional connectivity between the insula and other brain regions than nonsmokers (Bi et al., 2017; Zhou et al., 2017). Weaker insula connectivity among smokers has also been associated with an increased likelihood of lapse and relapse (Janes et al., 2010; Addicott et al., 2015; Zelle et al., 2017). Alternatively, while viewing cigaretterelated images, stronger insula connectivity was associated with the magnitude of smokers’ craving (Maria et al., 2015) and with increased pleasantness ratings for smoking images during withdrawal (Avery et al., 2017). Although an insula lesion may lessen the interoceptive awareness of withdrawal or cravings, in an intact brain, the insula coordinates with other brain regions to respond, or inhibit a

136 Cognition and Addiction

response, to cravings according to one’s goal to smoke or remain abstinent. Thus, both stronger and weaker connectivity between the insula and other brain regions can support tobacco addiction or smoking cessation.

Cause, consequence, or shared underlying mechanism Is smoking a cause or consequence of mood disorders? The “self-medication” hypothesis postulates that symptoms of mood disorders precede smoking and smoking helps alleviate these symptoms, suggesting that tobacco addiction is a consequence of these disorders. Smoking may provide temporary relief of negative mood symptoms and improve arousal and motivation, while nicotine withdrawal could exacerbate negative mood symptoms and interfere with tobacco cessation efforts. As smokers learn to modulate their mood with tobacco, they may lose the ability to engage alternative coping mechanisms, thus becoming more and more dependent on smoking to provide stress relief. Alternatively, smoking may increase the risk of depression or anxiety. Chronic nicotine dysregulates the hypothalamicepituitaryeadrenal axis, which leads to hypersecretion of cortisol and changes in associated monoamine neurotransmitters (Markou et al., 1998). Ultimately, this can change the response to stress and exacerbate symptoms of depression and anxiety over time, suggesting that tobacco addiction can cause mood disorders. However, recent genetic research does not suggest a causal role for smoking heaviness in the development of depression and anxiety (Taylor et al., 2014a; Skov-Ettrup et al., 2017). A review of longitudinal studies reported that about 50% of studies provided evidence that smoking was a consequence of depression or anxiety, and about 33% of studies provided evidence that smoking caused depression and anxiety. However, there was substantial heterogeneity across studies in populations, design, and diagnostic measures (Fluharty et al., 2017). It is also possible that smoking and mood disorders have a shared etiology or develop in conjunction with one another. There may be shared genetic vulnerability, or early life stress may precipitate the onset and progression of both.

Smoking cessation and mood Despite the interaction between smoking and mood, smokers with mental illness desire to quit smoking similar to smokers in the general population (Prochaska et al., 2017). However, their actual rates of smoking cessation are lower (RCP, 2013). This could be due to an exacerbation of mood symptoms during withdrawal. Among smokers with mood disorders, anxiety and depression may initially worsen during a quit attempt, especially if the quit attempt

failed (Berlin et al., 2010). However, another study reported that successful quitters did not show significant changes in depression or anxiety over a 1-month period, nor did quitting contribute to adverse mental health outcomes (Capron et al., 2014). Ultimately, long-term cessation is associated with improvements in depression, anxiety, stress, and mood, both in the general population and clinical populations. This is perhaps due to breaking the cycle of recurring withdrawal symptoms. Effect sizes on the improvements in mood due to smoking cessation are equal or larger than those of antidepressant treatment for mood and anxiety disorders (Taylor et al., 2014b). Long-term cessation may even lead to a reduced incidence of depression (Shahab et al., 2014; Bakhshaie et al., 2015). Potentially, quitting smoking improves mental health, or improving mental health assists cessation, or there is a common underlying factor. However, existing studies cannot determine causality (Taylor et al., 2014b). Smokers with mood disorders and other mental illnesses face additional barriers to smoking cessation. The most commonly cited barriers are the management of mental illness symptoms (i.e., smoking to improve attention/ cognition/motivation, reduce negative affect, cope with stress) and social barriers (i.e., smoking is a way to fit in, smoking with peers) (Trainor and Leavey, 2017). The efficacy of pharmacotherapy is similar between smokers with and without mental illness (West et al., 2018), but the high rates of smoking suggest that tobacco cessation programs designed for the general population are poorly integrated, less effective, or not addressing the additional barriers faced by individuals with mental illness (Cook et al., 2014). Research is needed on the development and implementation of effective cessation interventions for this group (Metse et al., 2017).

Recommendations for clinicians and researchers Given its broad range of negative health effects, clinicians in all fields of medicine should discuss tobacco use with their patients, especially e-cig use with adolescent patients. Unfortunately, many clinicians and health-care systems do not treat tobacco use consistently and effectively (Fiore et al., 2008b), possibly because clinicians lack training in tobacco intervention strategies, or health-care systems lack policies for routine tobacco screening and intervention. However, many resources are available to help guide discussions about patients’ smoking (e.g., Fiore et al., 2008a). Additionally, the National Institutes of Health provides a free toolbox (nihtoolbox.org) of standardized measures of smoking and tobacco use, such as emotional and health expectations for smoking, motivations for smoking, and

Tobacco addiction: cognition, reinforcement, and mood Chapter | 9

nicotine dependence severity. These measures can help improve communication between clinicians and patients. The research reviewed in this chapter brings together the cognitive, affective, and reinforcing effects of nicotine and tobacco. There is an ongoing need for smoking cessation therapies that target where and how these three areas overlap. In particular, we need cessation interventions tailored to specific populations, such as people with schizophrenia, depression, type II diabetes, or substance use disorders. Each group has its own set of challenges and standard of care. Smoking cessation interventions should be custom fit for each population. As described in this chapter, smoking has a high rate of comorbidity with other psychiatric disorders, especially substance use disorders. If a patient is undergoing treatment for a serious substance use disorder, his/her smoking may not be considered a priority. However, evidence suggests that smoking can act synergistically with other drugs of abuse, and treatment for tobacco dependence does not interfere with treatment for other substance use disorders (e.g., alcoholism) (Kalman et al., 2010). Clinicians should consider providing concurrent treatment for tobacco and other substance use disorders. Importantly, research should investigate if quitting smoking improves overall well-being and therapeutic outcomes for other physical and mental health problems.

Summary and conclusions In summary, tobacco remains a widely used drug. Although cigarettes are the most commonly used form of tobacco, ecig use is on the rise. The popularity of e-cigs among adolescents and young adults is especially troubling because the risk of developing nicotine addiction and then transitioning to cigarette usage could undermine the decline in smoking rates. Now that the FDA has the power to regulate tobacco, additional policies could be implemented to reduce nicotine content and flavorants in tobacco and e-cigs to help prevent such a reversal. While smoking rates have generally been in decline, certain populations, such as those with mental illness, have persistently high rates of smoking. One explanation for this is the relationship between smoking and mood regulation. Many smokers, especially those experiencing anhedonia and anxiety, smoke to relieve negative mood and stress. Withdrawal symptoms can exacerbate negative moods and create an additional barrier to cessation. Evidence suggests that quitting smoking can improve mood in the long term, but additional research is needed to improve the efficacy of smoking cessation treatments for individuals with mental illness and affective distress. There may be some overlap in the effects of nicotine and tobacco on mood and on cognitive performance. As mood is improved by smoking, there may be increased motivation to concentrate and perform well. Nicotine

137

improves attention, especially in populations with poor baseline performance characteristic of some mental illnesses. Neuroimaging studies suggest that these cognitive effects may relate to enhancement of the executive control network (engaged by externally driven processes, e.g., a cognitive task), suppression of the default mode network (engaged by internally driven processes, e.g., while daydreaming), or a combination of both. Likewise, tobacco withdrawal may change the relationship between these two networks. If true, future research in this area ought to investigate how these networks can be manipulated to support tobacco cessation, via pharmacological or nonpharmacological (e.g., neurofeedback, transcranial magnetic stimulation) methods. It is difficult to understand why tobacco is so highly addictive, given its modest acute effects. The extent of classical conditioning (i.e., the number of times cigarette smoking is paired with another stimulus or experience) and the age of initiation play critical roles in the transition from occasional to daily smoking. Nicotine appears to enhance the motivation for other reinforcers as well, which is evident in the amount of effort expended to obtain other reinforcers when nicotine is physiologically present and in DA-rich mesolimbic brain activation when anticipating or receiving other reinforcers. Research in this area is important to understanding withdrawal-related anhedonia and the loss of motivation for nondrug rewards. As the smoking rates in the general population decline, there may be less concern or funding for tobacco addiction research. This may leave many vulnerable populations understudied and underserved. As tobacco addiction is a pervasive disease that interacts with and exacerbates other physical and mental illnesses, treatment for tobacco addiction should be an integral part of primary care medicine.

References Abdolahi, A., Williams, G.C., Benesch, C.G., Wang, H.Z., Spitzer, E.M., et al., 2015. Damage to the insula leads to decreased nicotine withdrawal during abstinence. Addiction 110, 1994e2003. Addicott, M.A., Oliver, J.A., Joseph McClernon, F., 2017. Nicotine increases anterior insula activation to expected and unexpected outcomes among nonsmokers. Psychopharmacology (Berl.) 234, 1145e1154. Addicott, M.A., Sweitzer, M., McClernon, F.J., 2019. The effects of nicotine and tobacco use on brain reward function: interaction with nicotine dependence severity. Nicotine Tob. Res. 21, 764e771. Addicott, M.A., Sweitzer, M.M., Froeliger, B., Rose, J.E., McClernon, F.J., 2015. Increased functional connectivity in an insulabased network is associated with improved smoking cessation outcomes. Neuropsychopharmacology 40, 2648e2656. Ahrens, S., Markett, S., Breckel, T.P.K., Behler, O., Reuter, M., et al., 2015. Modulation of nicotine effects on selective attention by DRD2 and CHRNA4 gene polymorphisms. Psychopharmacology 232, 2323e2331.

138 Cognition and Addiction

APA, 2013. Diagnostic and Statistical Manual of Mental Disorders: DSM5. American Psychiatric Association, Washington, DC. Avery, J.A., Burrows, K., Kerr, K.L., Bodurka, J., Khalsa, S.S., et al., 2017. How the brain wants what the body needs: the neural basis of positive alliesthesia. Neuropsychopharmacology 42, 822e830. Babb, S., Malarcher, A., Schauer, G., Asman, K., Jamal, A., 2017. Quitting smoking among adults - United States, 2000-2015. Morb. Mortal. Wkly. Rep. 65, 1457e1464. Bakhshaie, J., Zvolensky, M.J., Goodwin, R.D., 2015. Cigarette smoking and the onset and persistence of depression among adults in the United States: 1994e2005. Compr. Psychiatry 60, 142e148. Bedi, G., Preston, K.L., Epstein, D.H., Heishman, S.J., Marrone, G.F., et al., 2011. Incubation of cue-induced cigarette craving during abstinence in human smokers. Biol. Psychiatry 69, 708e711. Benowitz, N.L., 2009. Pharmacology of nicotine: addiction, smokinginduced disease, and therapeutics. Annu. Rev. Pharmacol. Toxicol. 49, 57e71. Benowitz, N.L., Jacob, P., Jones, R.T., Rosenberg, J., 1982. Interindividual variability in the metabolism and cardiovascular effects of nicotine in man. J. Pharmacol. Exp. Ther. 221, 368e372. Berlin, I., Chen, H.N., Covey, L.S., 2010. Depressive mood, suicide ideation and anxiety in smokers who do and smokers who do not manage to stop smoking after a target quit day. Addiction 105, 2209e2216. Bero, L., 2003. Implications of the tobacco industry documents for public health and policy. Annu. Rev. Public Health 24, 267e288. Bi, Y.Z., Yuan, K., Guan, Y.Y., Cheng, J.D., Zhang, Y.J., et al., 2017. Altered resting state functional connectivity of anterior insula in young smokers. Brain Imaging Behav. 11, 155e165. Bienkowski, P., Zatorski, P., Baranowska, A., Ryglewicz, D., Sienkiewicz-Jarosz, H., 2010. Insular lesions and smoking cessation after first-ever ischemic stroke: a 3-month follow-up. Neurosci. Lett. 478, 161e164. Brody, A.L., Mandelkern, M.A., Jarvik, M.E., Lee, G.S., Smith, E.C., et al., 2004. Differences between smokers and nonsmokers in regional gray matter volumes and densities. Biol. Psychiatry 55, 77e84. Brody, A.L., Mandelkern, M.A., London, E.D., Olmstead, R.E., Farahi, J., et al., 2006. Cigarette smoking saturates brain alpha(4)beta(2) nicotinic acetylcholine receptors. Arch. Gen. Psychiatry 63, 907e915. Brown, R.A., Lejuez, C.W., Strong, D.R., Kahler, C.W., Zvolensky, M.J., et al., 2009. A prospective examination of distress tolerance and early smoking lapse in adult self-quitters. Nicotine Tob. Res. 11, 493e502. Buckner, R.L., Andrews-Hanna, J.R., Schacter, D.L., 2008. The brain’s default network - anatomy, function, and relevance to disease. In: Year in Cognitive Neuroscience 2008, vol. 1124, pp. 1e38. Caggiula, A.R., Donny, E.C., Palmatier, M.I., Liu, X., Chaudhri, N., et al., 2009. The role of nicotine in smoking: a dual-reinforcement model. Nebr. Symp. Motiv. 55, 91e109. Capron, D.W., Allan, N.P., Norr, A.M., Zvolensky, M.J., Schmidt, N.B., 2014. The effect of successful and unsuccessful smoking cessation on short-term anxiety, depression, and suicidality. Addict. Behav. 39, 782e788. Chang, L.J., Yarkoni, T., Khaw, M.W., Sanfey, A.G., 2013. Decoding the role of the insula in human cognition: functional parcellation and large-scale reverse inference. Cerebr. Cortex 23, 739e749.

Chaudhri, N., Caggiula, A.R., Donny, E.C., Palmatier, M.I., Liu, X., et al., 2006. Complex interactions between nicotine and nonpharmacological stimuli reveal multiple roles for nicotine in reinforcement. Psychopharmacology 184, 353e366. Cole, D.M., Beckmann, C.F., Long, C.J., Matthews, P.M., Durcan, M.J., et al., 2010. Nicotine replacement in abstinent smokers improves cognitive withdrawal symptoms with modulation of resting brain network dynamics. Neuroimage 52, 590e599. Colton, C.W., Manderscheid, R.W., 2006. Congruencies in increased mortality rates, years of potential life lost, and causes of death among public mental health clients in eight states. Prev. Chronic Dis. 3, A42. Cook, B.L., Wayne, G.F., Kafali, E.N., Liu, Z., Shu, C., et al., 2014. Trends in smoking among adults with mental illness and association between mental health treatment and smoking cessation. JAMA 311, 172e182. Cook, J., Spring, B., McChargue, D., Doran, N., 2010. Effects of anhedonia on days to relapse among smokers with a history of depression: a brief report. Nicotine Tob. Res. 12, 978e982. Cook, J.W., Lanza, S.T., Chu, W.H., Baker, T.B., Piper, M.E., 2017. Anhedonia: its dynamic relations with craving, negative affect, and treatment during a quit smoking attempt. Nicotine Tob. Res. 19, 703e709. Cosgrove, K.P., Batis, J., Bois, F., Maciejewski, P.K., Esterlis, I., et al., 2009. beta(2)-Nicotinic acetylcholine receptor availability during acute and prolonged abstinence from tobacco smoking. Arch. Gen. Psychiatry 66, 666e676. Craig, A.D., 2009. How do you feel - now? The anterior insula and human awareness. Nat. Rev. Neurosci. 10, 59e70. Dagher, A., Bleicher, C., Aston, J.A., Gunn, R.N., Clarke, P.B., et al., 2001. Reduced dopamine D1 receptor binding in the ventral striatum of cigarette smokers. Synapse 42, 48e53. Daughters, S.B., Ross, T.J., Bell, R.P., Yi, J.Y., Ryan, J., et al., 2016. Distress tolerance among substance users is associated with functional connectivity between prefrontal regions during a distress tolerance task. Addict. Biol. 22, 1378e1390. DHHS, 2014. The Health Consequences of Smoking - 50 Years of Progress: A Report of the Surgeon General. Retrieved from: https:// www.surgeongeneral.gov/library/reports/50-years-of-progress/. DHHS, 2016. E-Cigarette Use Among Youth and Young Adults: A Report of the Surgeon General. Retrieved from: https://e-cigarettes.surgeongeneral.gov/documents/2016_sgr_full_report_non-508.pdf. Donny, E.C., Chaudhri, N., Caggiula, A.R., Evans-Martin, F.F., Booth, S., et al., 2003. Operant responding for a visual reinforcer in rats is enhanced by noncontingent nicotine: implications for nicotine selfadministration and reinforcement. Psychopharmacology 169, 68e76. Donny, E.C., Jones, M., 2009. Prolonged exposure to denicotinized cigarettes with or without transdermal nicotine. Drug Alcohol Depend. 104, 23e33. Drope, J., Liber, A.C., Cahn, Z., Stoklosa, M., Kennedy, R., et al., 2018. Who’s still smoking? Disparities in adult cigarette smoking prevalence in the United States. CA Cancer J. Clin. 68, 106e115. Esch, T., Stefano, G.B., Fricchione, G.L., Benson, H., 2002. The role of stress in neurodegenerative diseases and mental disorders. Neuroendocrinol. Lett. 23, 199e208. Etter, J.F., Bullen, C., 2011. Electronic cigarette: users profile, utilization, satisfaction and perceived efficacy. Addiction 106, 2017e2028.

Tobacco addiction: cognition, reinforcement, and mood Chapter | 9

Evans, D.E., Drobes, D.J., 2009. Nicotine self-medication of cognitiveattentional processing. Addict. Biol. 14, 32e42. FDA, 2012. Harmful and Potentially Harmful Constituents in Tobacco Products and Tobacco Smoke: Established List. Retrieved from: www.fda.gov/TobaccoProducts/ GuidanceComplianceRegulatoryInformation/ucm297786.htm. FDA, 2017. FDA’s Plan for Tobacco and Nicotine Regulation. Retrieved from: https://www.fda.gov/TobaccoProducts/NewsEvents/ ucm568425.htm. Fedota, J.R., Sutherland, M.T., Salmeron, B.J., Ross, T.J., Hong, L.E., et al., 2015. Reward anticipation is differentially modulated by varenicline and nicotine in smokers. Neuropsychopharmacology 40, 2038e2046. Felix, R., Levin, E.D., 1997. Nicotinic antagonist administration into the ventral hippocampus and spatial working memory in rats. Neuroscience 81, 1009e1017. Fiore, M.C., Jaen, C.R., Baker, T.B., Bailey, W.C., Bennett, G., et al., 2008a. A clinical practice guideline for treating tobacco use and dependence: 2008 update - a US Public Health Service report. Am. J. Prev. Med. 35, 158e176. Fiore, M.C., Jaen, C.R., Baker, T.B., Bailey, W.C., Benowitz, N.L., et al., 2008b. Treating Tobacco Use and Dependence: 2008 Update. Department of Health and Human Services. Public Health Service, Rockville, MD, US. Fluharty, M., Taylor, A.E., Grabski, M., Munafo, M.R., 2017. The association of cigarette smoking with depression and anxiety: a systematic review. Nicotine Tob. Res. 19, 3e13. Gallinat, J., Meisenzahl, E., Jacobsen, L.K., Kalus, P., Bierbrauer, J., et al., 2006. Smoking and structural brain deficits: a volumetric MR investigation. Eur. J. Neurosci. 24, 1744e1750. Gandelman, J.A., Newhouse, P., Taylor, W.D., 2018. Nicotine and networks: potential for enhancement of mood and cognition in late-life depression. Neurosci. Biobehav. Rev. 84, 289e298. Garavan, H., 2010. Insula and drug cravings. Brain Struct. Funct. 214, 593e601. Gaznick, N., Tranel, D., McNutt, A., Bechara, A., 2014. Basal ganglia plus insula damage yields stronger disruption of smoking addiction than basal ganglia damage alone. Nicotine Tob. Res. 16, 445e453. George, O., Ghozland, S., Azar, M.R., Cottone, P., Zorrilla, E.P., et al., 2007. CRF-CRF1 system activation mediates withdrawal-induced increases in nicotine self-administration in nicotine-dependent rats. Proc. Natl. Acad. Sci. U.S.A. 104, 17198e17203. Gervais, A., O’Loughlin, J., Meshefedjian, G., Bancej, C., Tremblay, M., 2006. Milestones in the natural course of onset of cigarette use among adolescents. Can. Med. Assoc. J. 175, 255e261. Goodman, J., 2005. Tobacco in History and Culture: An Encyclopedia. Charles Scribner’s Sons, Thomson Gale, Farmington Hills, MI. Gostin, L.O., 2009. FDA regulation of tobacco politics, law, and the public’s health. JAMA 302, 1459e1460. Goulden, N., Khusnulina, A., Davis, N.J., Bracewell, R.M., Bokde, A.L., et al., 2014. The salience network is responsible for switching between the default mode network and the central executive network: replication from DCM. Neuroimage 99, 180e190. Grant, J.E., Potenza, M.N., 2005. Tobacco use and pathological gambling. Ann. Clin. Psychiatry 17, 237e241.

139

Gu, X.S., Lohrenz, T., Salas, R., Baldwin, P.R., Soltani, A., et al., 2015. Belief about nicotine selectively modulates value and reward prediction error signals in smokers. Proc. Natl. Acad. Sci. U.S.A. 112, 2539e2544. Hackshaw, A., Morris, J.K., Boniface, S., Tang, J.L., Milenkovic, D., 2018. Low cigarette consumption and risk of coronary heart disease and stroke: meta-analysis of 141 cohort studies in 55 study reports. BMJ Br. Med. J. 360, j5855, 361. Hall, B.J., Cauley, M., Burke, D.A., Kiany, A., Slotkin, T.A., et al., 2016. Cognitive and behavioral impairments evoked by low-level exposure to tobacco smoke components: comparison with nicotine alone. Toxicol. Sci. 151, 236e244. Hammersley, J.J., Gilbert, D.G., Rzetelny, A., Rabinovich, N.E., 2016. Moderation of nicotine effects on covert orienting of attention tasks by poor placebo performance and cue validity. Pharmacol. Biochem. Behav. 149, 9e16. Heishman, S.J., Kleykamp, B.A., Singleton, E.G., 2010. Meta-analysis of the acute effects of nicotine and smoking on human performance. Psychopharmacology 210, 453e469. Higley, M.J., Picciotto, M.R., 2014. Neuromodulation by acetylcholine: examples from schizophrenia and depression. Curr. Opin. Neurobiol. 29, 88e95. Hoffman, A.C., Evans, S.E., 2013. Abuse potential of non-nicotine tobacco smoke components: acetaldehyde, nornicotine, cotinine, and anabasine. Nicotine Tob. Res. 15, 622e632. Hughes, J.R., 2007. Effects of abstinence from tobacco: valid symptoms and time course. Nicotine Tob. Res. 9, 315e327. Jamal, A., King, B.A., Neff, L.J., Whitmill, J., Babb, S.D., et al., 2016. Current cigarette smoking among adults - United States, 2005e2015. Morb. Mortal. Wkly. Rep. 65, 1205e1211. Janes, A.C., Pizzagalli, D.A., Richardt, S., Frederick, B.D., Chuzi, S., et al., 2010. Brain reactivity to smoking cues prior to smoking cessation predicts ability to maintain tobacco abstinence. Biol. Psychiatry 67, 722e729. Kahler, C.W., McHugh, R.K., Metrik, J., Spillane, N.S., Rohsenow, D.J., 2013. Breath holding duration and self-reported smoking abstinence intolerance as predictors of smoking lapse behavior in a laboratory analog task. Nicotine Tob. Res. 15, 1151e1154. Kalman, D., 2002. The subjective effects of nicotine: methodological issues, a review of experimental studies, and recommendations for future research. Nicotine Tob. Res. 4, 25e70. Kalman, D., Kim, S., DiGirolamo, G., Smelson, D., Ziedonis, D., 2010. Addressing tobacco use disorder in smokers in early remission from alcohol dependence: the case for integrating smoking cessation services in substance use disorder treatment programs. Clin. Psychol. Rev. 30, 12e24. Kim, J.W., Baum, C.R., 2015. Liquid nicotine toxicity. Pediatr. Emerg. Care 31, 517e521. Knutson, B., Adams, C.M., Fong, G.W., Hommer, D., 2001. Anticipation of increasing monetary reward selectively recruits nucleus accumbens. J. Neurosci. 21, RC159. Langdon, K.J., Farris, S.G., Overup, C.S., Zvolensky, M.J., 2016. Associations between anxiety sensitivity, negative affect, and smoking during a self-guided smoking cessation attempt. Nicotine Tob. Res. 18, 1188e1195.

140 Cognition and Addiction

Leventhal, A.M., Waters, A.J., Moolchan, E.T., Heishman, S.J., Pickworth, W.B., 2010. A quantitative analysis of subjective, cognitive, and physiological manifestations of the acute tobacco abstinence syndrome. Addict. Behav. 35, 1120e1130. Leventhal, A.M., Zvolensky, M.J., 2015. Anxiety, depression, and cigarette smoking: a transdiagnostic vulnerability framework to understanding emotion-smoking comorbidity. Psychol. Bull. 141, 176e212. Levin, E.D., McClernon, F.J., Rezvani, A.H., 2006. Nicotinic effects on cognitive function: behavioral characterization, pharmacological specification, and anatomic localization. Psychopharmacology 184, 523e539. Lopez-Quintero, C., de los Cobos, J.P., Hasin, D.S., Okuda, M., Wang, S., et al., 2011. Probability and predictors of transition from first use to dependence on nicotine, alcohol, cannabis, and cocaine: results of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Drug Alcohol Depend. 115, 120e130. Lopez, A.D., Mathers, C.D., Ezzati, M., Jamison, D.T., Murray, C.J.L., 2006. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet 367, 1747e1757. Mantsch, J.R., Baker, D.A., Funk, D., Le, A.D., Shaham, Y., 2016. Stressinduced reinstatement of drug seeking: 20 years of progress. Neuropsychopharmacology 41, 335e356. Maria, M.M.M.S., Hartwell, K.J., Hanlon, C.A., Canterberry, M., Lematty, T., et al., 2015. Right anterior insula connectivity is important for cue-induced craving in nicotine-dependent smokers. Addict. Biol. 20, 407e414. Markou, A., Kosten, T.R., Koob, G.F., 1998. Neurobiological similarities in depression and drug dependence: a self-medication hypothesis. Neuropsychopharmacology 18, 135e174. Mayer, B., 2014. How much nicotine kills a human? Tracing back the generally accepted lethal dose to dubious self-experiments in the nineteenth century. Arch. Toxicol. 88, 5e7. McClernon, F.J., Addicott, M.A., Sweitzer, M.M., 2015. Smoking abstinence and neurocognition: implications for cessation and relapse. Curr. Top. Behav. Neurosci. 23, 193e227. McKee, S.A., Maciejewski, P.K., Falba, T., Mazure, C.M., 2003. Sex differences in the effects of stressful life events on changes in smoking status. Addiction 98, 847e855. Metse, A.P., Wiggers, J.H., Wye, P.M., Wolfenden, L., Prochaska, J.J., et al., 2017. Smoking and mental illness: a bibliometric analysis of research output over time. Nicotine Tob. Res. 19, 24e31. Mineur, Y.S., Cahuzac, E.L., Mose, T.N., Bentham, M.P., Plantenga, M.E., et al., 2018. Interaction between noradrenergic and cholinergic signaling in amygdala regulates anxiety- and depressionrelated behaviors in mice. Neuropsychopharmacology 43, 2118e2125. Molas, S., DeGroot, S.R., Zhao-Shea, R., Tapper, A.R., 2017. Anxiety and nicotine dependence: emerging role of the habenulo-interpeduncular axis. Trends Pharmacol. Sci. 38, 169e180. Moran, L.V., Stoeckel, L.E., Wang, K., Caine, C.E., Villafuerte, R., et al., 2018. Nicotine increases activation to anticipatory valence cues in anterior insula and striatum. Nicotine Tob. Res. 20, 851e858. Mukhin, A.G., Kimes, A.S., Chefer, S.I., Matochikl, J.A., Contoreggi, C.S., et al., 2008. Greater nicotinic acetylcholine receptor density in smokers than in nonsmokers: a PET study with 2-F-18-FA85380. J. Nucl. Med. 49, 1628e1635.

Naqvi, N.H., Gaznick, N., Tranel, D., Bechara, A., 2014. The insula: a critical neural substrate for craving and drug seeking under conflict and risk. In: Year in Cognitive Neuroscience, vol. 1316, pp. 53e70. Naqvi, N.H., Rudrauf, D., Damasio, H., Bechara, A., 2007. Damage to the insula disrupts addiction to cigarette smoking. Science 315, 531e534. Nelson, S.M., Dosenbach, N.U.F., Cohen, A.L., Wheeler, M.E., Schlaggar, B.L., et al., 2010. Role of the anterior insula in task-level control and focal attention. Brain Struct. Funct. 214, 669e680. Ng, M., Freeman, M.K., Fleming, T.D., Robinson, M., DwyerLindgren, L., et al., 2014. Smoking prevalence and cigarette consumption in 187 countries, 1980-2012. JAMA 311, 183e192. Nguyen, Q.B., Zhu, S.H., 2009. Intermittent smokers who used to smoke daily: a preliminary study on smoking situations. Nicotine Tob. Res. 11, 164e170. Oliver, J.A., Evans, D.E., Addicott, M.A., Brandon, T.H., Drobes, D.J., 2016. Nicotine Withdrawal Induces Neural and Behavioral Deficits in Reward Processing. Pacek, L.R., Cioe, P.A., 2015. Tobacco use, use disorders, and smoking cessation interventions in persons living with HIV. Curr. HIV AIDS Rep. 12, 413e420. Palmatier, M.I., Liu, X., Matteson, G.L., Donny, E.C., Caggiula, A.R., et al., 2007. Conditioned reinforcement in rats established with selfadministered nicotine and enhanced by noncontingent nicotine. Psychopharmacology 195, 235e243. Phillips, E., Wang, T.W., Husten, C.G., Corey, C.G., Apelberg, B.J., et al., 2017. Tobacco product use among adults - United States, 2015. Morb. Mortal. Wkly. Rep. 66, 1209e1215. Picciotto, M.R., Higley, M.J., Mineur, Y.S., 2012. Acetylcholine as a neuromodulator: cholinergic signaling shapes nervous system function and behavior. Neuron 76, 116e129. Pokhrel, P., Herzog, T.A., Muranaka, N., Regmi, S., Fagan, P., 2015. Contexts of cigarette and e-cigarette use among dual users: a qualitative study. BMC Public Health 15. Prickaerts, J., van Goethem, N.P., Chesworth, R., Shapiro, G., Boess, F.G., et al., 2012. EVP-6124, a novel and selective alpha 7 nicotinic acetylcholine receptor partial agonist, improves memory performance by potentiating the acetylcholine response of alpha 7 nicotinic acetylcholine receptors. Neuropharmacology 62, 1099e1110. Prochaska, J.J., Das, S., Young-Wolff, K.C., 2017. Smoking, mental illness, and public health. Annu. Rev. Public Health 38 38, 165e185. Raw, M., Ayo-Yusuf, O., Chaloupka, F., Fiore, M., Glynn, T., et al., 2017. Recommendations for the implementation of WHO framework convention on tobacco control article 14 on tobacco cessation support. Addiction 112, 1703e1708. RCP, 2013. Smoking and Mental Health. Royal College of Physicians, Royal College of Psychiatrists, London. Richardson, N.R., Roberts, D.C., 1996. Progressive ratio schedules in drug self-administration studies in rats: a method to evaluate reinforcing efficacy. J. Neurosci. Methods 66, 1e11. Rodgman, A., Perfetti, T.A., 2009. The Chemical Components of Tobacco and Tobacco Smoke. CRC Press, Taylor & Francis Group, Boca Raton, FL. Rose, E.J., Ross, T.J., Salmeron, B.J., Lee, M., Shakleya, D.M., et al., 2012. Chronic exposure to nicotine is associated with reduced rewardrelated activity in the striatum but not the midbrain. Biol. Psychiatry 71, 206e213.

Tobacco addiction: cognition, reinforcement, and mood Chapter | 9

Rose, E.J., Ross, T.J., Salmeron, B.J., Lee, M., Shakleya, D.M., et al., 2013. Acute nicotine differentially impacts anticipatory valence- and magnitude-related striatal activity. Biol. Psychiatry 73, 280e288. Rostron, B.L., Chang, C.M., Pechacek, T.F., 2014. Estimation of cigarette smoking-attributable morbidity in the United States. JAMA Intern. Med. 174, 1922e1928. Rupprecht, L.E., Smith, T.T., Schassburger, R.L., Buffalari, D.M., Sved, A.F., et al., 2015. Behavioral mechanisms underlying nicotine reinforcement. Curr. Top. Behav. Neurosci. 24, 19e53. Schliebs, R., Arendt, T., 2006. The significance of the cholinergic system in the brain during aging and in Alzheimer’s disease. J. Neural Transm. 113, 1625e1644. Schultz, W., Dickinson, A., 2000. Neuronal coding of prediction errors. Annu. Rev. Neurosci. 23, 473e500. Seeley, W.W., Menon, V., Schatzberg, A.F., Keller, J., Glover, G.H., et al., 2007. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27, 2349e2356. Shahab, L., Andrew, S., West, R., 2014. Changes in prevalence of depression and anxiety following smoking cessation: results from an international cohort study (ATTEMPT). Psychol. Med. 44, 127e141. Shaham, Y., Shalev, U., Lu, L., de Wit, H., Stewart, J., 2003. The reinstatement model of drug relapse: history, methodology and major findings. Psychopharmacology 168, 3e20. Sharp, B.M., 2017. Basolateral amygdala and stress-induced hyperexcitability affect motivated behaviors and addiction. Transl. Psychiatry 7. Shiffman, S., Kirchner, T.R., Ferguson, S.G., Scharf, D.M., 2009. Patterns of intermittent smoking: an analysis using ecological momentary assessment. Addict. Behav. 34, 514e519. Siegel, M.B., Tanwar, K.L., Wood, K.S., 2011. Electronic cigarettes as a smoking-cessation: tool results from an online survey. Am. J. Prev. Med. 40, 472e475. Skov-Ettrup, L.S., Nordestgaard, B.G., Petersen, C.B., Tolstrup, J.S., 2017. Does high tobacco consumption cause psychological distress? A Mendelian randomization study. Nicotine Tob. Res. 19, 32e38. Sorge, R.E., Pierre, V.J., Clarke, P.B.S., 2009. Facilitation of intravenous nicotine self-administration in rats by a motivationally neutral sensory stimulus. Psychopharmacology 207, 191e200. Suner-Soler, R., Grau-Martin, A., Terceno, M., Silva, Y., Davalos, A., et al., 2018. Biological and psychological factors associated with smoking abstinence six years post-stroke. Nicotine Tob. Res. 20, 1182e1188. Suner-Soler, R., Grau, A., Gras, M.E., Font-Mayolas, S., Silva, Y., et al., 2012. Smoking cessation 1 Year poststroke and damage to the insular cortex. Stroke 43, 131e136. Sutherland, M.T., McHugh, M.J., Pariyadath, V., Stein, E.A., 2012. Resting state functional connectivity in addiction: lessons learned and a road ahead. Neuroimage 62, 2281e2295. Sutherland, M.T., Ray, K.L., Riedel, M.C., Yanes, J.A., Stein, E.A., et al., 2015. Neurobiological impact of nicotinic acetylcholine receptor agonists: an activation likelihood estimation meta-analysis of pharmacologic neuroimaging studies. Biol. Psychiatry 78, 711e720. Swan, G.E., Lessov-Schlaggar, C.N., 2007. The effects of tobacco smoke and nicotine on cognition and the brain. Neuropsychol. Rev. 17, 259e273.

141

Sweitzer, M.M., Geier, C.F., Joel, D.L., McGurrin, P., Denlinger, R.L., et al., 2014. Dissociated effects of anticipating smoking versus monetary reward in the caudate as a function of smoking abstinence. Biol. Psychiatry 76, 681e688. Tanabe, J., Nyberg, E., Martin, L.F., Martin, J., Cordes, D., et al., 2011. Nicotine effects on default mode network during resting state. Psychopharmacology 216, 287e295. Taylor, A.E., Fluharty, M.E., Munafo, M.R., 2014a. Investigating the possible causal association between smoking and depression and anxiety using Mendelian randomisation meta-analysis: the carta consortium. J. Epidemiol. Community Health 68. A45-A45. Taylor, G., McNeill, A., Girling, A., Farley, A., Lindson-Hawley, N., et al., 2014b. Change in mental health after smoking cessation: systematic review and meta-analysis. Br. Med. J. 348. Trainor, K., Leavey, G., 2017. Barriers and facilitators to smoking cessation among people with severe mental illness: a critical appraisal of qualitative studies. Nicotine Tob. Res. 19, 14e23. Vnukova, M., Ptacek, R., Raboch, J., Stefano, G., 2017. Central nervous system grey matter decreases in volume in smokers impacting cognitive abilities: a systematic review. Eur. Psychiatry 41. S883S883. Weinberger, A.H., Funk, A.P., Goodwin, R.D., 2016. A review of epidemiologic research on smoking behavior among persons with alcohol and illicit substance use disorders. Prev. Med. 92, 148e159. West, R., Evins, A.E., Benowitz, N.L., Russ, C., McRae, T., et al., 2018. Factors associated with the efficacy of smoking cessation treatments and predictors of smoking abstinence in EAGLES. Addiction 113, 1507e1516. WHO, 2018. Global progress report on implementation of the WHO framework convention on tobacco control. Retrieved from: http:// www.who.int/fctc/reporting/WHO-FCTC-2018_global_progress_ report.pdf. Wonnacott, S., 1997. Presynaptic nicotinic ACh receptors. Trends Neurosci. 20, 92e98. Zelle, S.L., Gates, K.M., Fiez, J.A., Sayette, M.A., Wilson, S.J., 2017. The first day is always the hardest: functional connectivity during cue exposure and the ability to resist smoking in the initial hours of a quit attempt. Neuroimage 151, 24e32. Zhang, L.F., Dong, Y., Doyon, W.M., Dani, J.A., 2012. Withdrawal from chronic nicotine exposure alters dopamine signaling dynamics in the nucleus accumbens. Biol. Psychiatry 71, 184e191. Zhou, S., Xiao, D., Peng, P., Wang, S.K., Liu, Z., et al., 2017. Effect of smoking on resting-state functional connectivity in smokers: an fMRI study. Respirology 22, 1118e1124. Zislis, G., Desai, T.V., Prado, M., Shah, H.P., Bruijnzeel, A.W., 2007. Effects of the CRF receptor antagonist D-Phe CRF(12-41) and the alpha 2-adrenergic receptor agonist clonidine on stress-induced reinstatement of nicotine-seeking behavior in rats. Neuropharmacology 53, 958e966. Zvolensky, M.J., Feldner, M.T., Leen-Feldner, E., Bonn-Miller, M.O., McLeish, A.C., et al., 2004. Evaluating the role of anxiety sensitivity in smoking outcome expectancies among regular smokers. Cognit. Ther. Res. 28, 473e486.

Chapter 10

Cognitive sequelae of cannabis use Ileana Pacheco-Colo´n and Raul Gonzalez Center for Children and Families, Department of Psychology, Florida International University, Miami, United States

Introduction Cannabis use is prevalent. In 2016, approximately 44% of Americans over the age of 12 reported having tried cannabis at least once (Center for Behavioral Health Statistics and Quality, 2017). Additionally, 45% of 12th graders reported having used cannabis, with 36% reporting use in the past year (Johnston et al., 2016). Although these figures represent an increase in the annual prevalence of use since 1991, they remain significantly lower than estimates from 1977 to 1980, which neared 50% (Johnston et al., 2016). At the same time, public opinion toward cannabis legalization has become more favorable. While only 12% of Americans supported the legalization of cannabis in 1969, more recent surveys indicate that 57% support legalization of cannabis (Pew Research Center, 2016). Consistent with these trends, 29 US states and the District of Columbia have passed medical marijuana legislation, and 9 have legalized recreational use for adults 21 and older. These trends can also be observed internationally, with countries such as Uruguay legalizing recreational use and countries such as Germany, Canada, Argentina, Czech Republic, Italy, and Mexico passing medical marijuana laws. A recent evidence-based consensus report from the National Academies of Sciences concluded that there is moderate evidence for acute effects of cannabis on cognition, but limited evidence for cannabis-associated cognitive sequelae after prolonged abstinence (National Academies of Sciences, 2017). Indeed, the majority of the extant literature on the nonacute cognitive sequelae of cannabis use consists of cross-sectional studies and modestly sized samples (e.g., N < 100) with varying levels of cannabis use. Several meta-analyses have helped to synthesize results from studies examining nonacute effects of cannabis use on cognition. However, an increasing number of longitudinal studies have allowed for causal inferences regarding the effects of cannabis use on cognitive functioning. This chapter aims to summarize the evidence for acute and nonacute effects of cannabis on cognitive

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00010-1 Copyright © 2020 Elsevier Inc. All rights reserved.

functioning. First, we provide a brief overview of the neuropharmacology of cannabis and its constituents. We then synthesize available evidence from neurobehavioral studies on acute and nonacute effects of cannabis on cognition. An exhaustive review of all published studies would be beyond the scope of this chapter; thus, we highlight recent and/or notable studies. Finally, we discuss the clinical significance and implications of these findings and make recommendations for future cannabis research.

Neuropharmacology of cannabis The cannabis plant contains 426 known active chemical compounds, approximately 60 of which are cannabinoids. Of these, the two most researched cannabinoids are delta-9tetrahydrocannabinol (THC) and cannabidiol (CBD). There are also two main cannabis strains: Cannabis indica and Cannabis sativa. Indica-dominant strains are primarily enjoyed for relaxation, pain relief, and sleep, whereas sativadominant strains are preferred for euphoria and energy enhancement (Erkelens and Hazekamp, 2014; Pearce et al., 2014). However, the extent to which these subjective reports correlate with pharmacological distinctions between these strains is not yet clear (Erkelens and Hazekamp, 2014). THC has been identified as the primary psychoactive constituent in cannabis. Both animal and human research have demonstrated that THC exerts its effects on the central nervous system primarily through activity at cannabinoid receptor type 1 (CB1; Pertwee, 2006, 2008). CB1 receptors are located throughout the cortex, with dense concentrations in brain regions relevant to cognitive and psychomotor functioning, including the hippocampus, amygdala, basal ganglia, and cerebellum (Burns et al., 2007; Glass et al., 1997). They can also be found in peripheral nervous tissue, liver, thyroid, uterus, and testicles (Pertwee, 2006). CB1 receptors mediate inhibitory action on the release of several neurotransmitters, including serotonin, acetylcholine, dopamine, and glutamate (Atakan, 2012). Thus, when cannabis is

143

144 Cognition and Addiction

used, THC functions as a partial agonist at CB1 receptors and inhibits the release of neurotransmitters normally modulated by endocannabinoids, thereby influencing cognition. Although THC shows an affinity for CB1 receptors, it is also a partial agonist at a second type of receptor, cannabinoid receptor type 2 (CB2; Pertwee, 2008). CB2 receptors are mostly expressed in immune cells, spleen, and the gastrointestinal tissue (Pertwee, 2006). Indeed, THC exerts its immunosuppressive effects through its activity at CB2 receptors (Onaivi et al., 2012). Nonetheless, recent research has shown that CB2 receptors can also be found in neuronal, glial, and endothelial cells in the brain (Onaivi et al., 2012). CB1 and CB2 receptors may work both independently and cooperatively across different cell populations to regulate important physiological activities (Onaivi et al., 2012). However, more research is needed to understand the effects of THC activity at CB2 receptors in the central nervous system and the nature of the complex interactions between receptor types. Several other non-CB1 and non-CB2 receptors have been suggested as potential members of the cannabinoid receptor family (Atakan, 2012). These include G proteine coupled receptor (GPR) 3, GPR6, GPR12, GPR19, and, most notably, GPR18 and GPR55 (Morales and Reggio, 2017). However, findings have been largely inconsistent, thus precluding complete characterization of these receptors and their relationships with the endocannabinoid system. Thus, the existence of a CB3 receptor has not yet been confirmed (Morales and Reggio, 2017). Recently, there has been increased interest in CBD as a potentially therapeutic agent. CBD appears to exert its effects through activity on several different types of receptors. For instance, although CBD has low affinity for both CB1 and CB2 receptors, it can antagonize CB1 and CB2 receptor agonists or serve as an inverse agonist even at low concentrations (Bergamaschi et al., 2011). Research has also identified other potential mechanisms of action for CBD, including GPR55 (Bergamaschi et al., 2011). Furthermore, findings from both animal and human studies suggest that CBD is associated with a variety of therapeutic effects, including anxiolytic, antipsychotic, antiepileptic, sedative, antiinflammatory, and neuroprotective properties (Bergamaschi et al., 2011; Maroon and Bost, 2018). CBD and THC have similar effects in some domains and differing or opposite effects in others. For instance, both THC and CBD have antiemetic and immunomodulatory effects (Atakan, 2012). However, unlike THC, CBD is not associated with psychoactive effects or cognitive impairment (Russo and Guy, 2006). Some studies have found that CBD potentiates the effects of THC through its activity at CB1 receptors (Hayakawa et al., 2008; Klein et al., 2011), whereas others have found that CBD can antagonize THC effects via other receptors such as GPR55

(Zuardi et al., 2012). A recent review article concluded that although exposure to high THC or low CBD cannabis is associated with greater cognitive impairment, it is still not clear whether increased CBD concentrations may offset detrimental cognitive effects of THC (Colizzi and Bhattacharyya, 2017). Therefore, more carefully characterizing the time between CBD and THC intake and/or the precise CBD to THC ratio would help elucidate the complex interactions between these cannabinoids (Zuardi et al., 2012). This research will enable scientists to determine how to maximize potentially therapeutic effects of CBD while minimizing deleterious consequences associated with THC, which could make cannabis a more viable treatment option for many. In addition to the cannabinoids, the cannabis plant contains many other types of compounds that may interact synergistically to produce therapeutic effects. This interactive synergy between active and “inactive” components is referred to as the cannabis entourage effect (Ben-Shabat et al., 1998). Terpenoids, for instance, are essential oil components responsible for the aroma of cannabis. These are “generally recognized as safe” by the US Food and Drug Administration, but are considered “of pharmacological interest” when present at high concentrations (Russo, 2011). It is plausible that terpenoids with pain-relieving, antianxiety, or sedative effects could supplement effects of cannabinoids such as THC and CBD on sleep, pain, anxiety, and other clinical conditions, thereby enhancing the efficacy of cannabis as a therapeutic agent (Russo, 2011). However, the lack of scientific rigor in existing studies and the dearth of randomized controlled trials make it difficult to draw conclusions regarding the extent to which other cannabis compounds are relevant to the cognitive and/or therapeutic effects of cannabis (Russo, 2011).

Cognitive deficits associated with cannabis Acute effects of cannabis intoxication on cognition Reviews A large body of work has examined the effects of acute cannabis intoxication on various aspects of cognitive functioning. For instance, Gonzalez (2007) conducted a review of findings from studies published between the 1970s and 2007 and concluded that acute cannabis intoxication is consistently linked to retrieval-based memory problems. Specifically, when individuals are shown information while they are intoxicated, they show deficits in their ability to recall that information. However, intoxicated participants can recall information that was presented before cannabis intoxication. Acute cannabis intoxication

Cognitive sequelae of cannabis use Chapter | 10

was also frequently linked to increased regional cerebral blood flow and metabolism in frontal, limbic, and cerebellar regions (Gonzalez, 2007). Crean et al. (2011) conducted an evidence-based review of the acute effects of cannabis on executive functions in adults. They concluded that while cannabis effects on memory are well-established, evidence of effects of acute cannabis intoxication on other domains of executive functioning was mixed. Among studies focusing on attention and information processing, some have found improved performance among acutely intoxicated heavy cannabis users, whereas others have found poorer performance among acutely intoxicated light users. This suggests that the impairing effects of cannabis intoxication may be stronger among inexperienced than regular users because of tolerance and/or neuroadaptive effects associated with cannabis (Crean et al., 2011). Acute cannabis use was also linked to impairments in decision-making, although studies varied as to whether they examined response speed, latency, or accuracy. In general, higher doses of THC were associated with poorer cognitive performance than lower doses of THC (Crean et al., 2011). More recently, a systematic review by Broyd et al. (2016) reported on findings from 38 studies examining acute effects of cannabis on cognition. Consistent with other reviews, acute cannabis effects were most often reported in the domain of verbal learning and memory, albeit less consistently for working memory. Acute cannabis intoxication was also linked to impairments in focused, divided, and sustained attention, psychomotor functioning, and inhibition. However, reported effects on executive functioning domains, such as planning, reasoning, interference control, and decision-making, were mixed. Inconsistencies across acute administration studies could be explained by a variety of factors. First, these studies differ in the route of administration of cannabis they employed. Specifically, studies that used smoked, vaporized, or intravenous cannabis administration are likely to see more immediate yet shorter-lasting effects than studies using oral or sublingual cannabis administration. These differences in route of administration could also explain why dose-dependent effects are not consistently reported. Second, cannabis effects on cognition may vary across types of cannabinoids. For instance, CBD may moderate the effects of THC such that greater CBD content may protect THC-induced impairments in verbal learning and memory (Englund et al., 2013; Morgan et al., 2010). Third, although sex differences in cannabinoid metabolism and action are well-established, these differences are not sufficiently accounted for in these studies (Crane et al., 2013). Finally, participants’ histories of use could result in differential cognitive effects of acute cannabis intoxication. Several studies have found that cannabis intoxication affects cognitive performance differently for heavy, regular cannabis

145

users than for light, infrequent users such that heavier users show lesser impairments. There is preclinical evidence to support the development of tolerance among frequent cannabis users, although evidence of tolerance in cognitive domains among humans is limited (Broyd et al., 2016).

Notable cross-sectional studies Several recent cannabis administration studies have made significant contributions to our understanding of the acute effects of cannabis. In the first study to administer cannabis to users under the age of 18, Mokrysz et al. (2016) conducted a double-blind placebo-controlled study to compare the acute effects of cannabis use in adolescents with adult males. Adolescents and adults were matched on premorbid IQ, anxiety, depression, impulsivity, and schizotypy. After receiving either active or placebo cannabis, participants completed a prose recall task measuring episodic memory, a spatial N-back task measuring working memory, and a stop signal task measuring response inhibition. When intoxicated with cannabis, adults showed greater impairment in delayed recall of prose and had longer reaction times on the spatial N-back task than adolescents. Cannabis intoxication also led to impaired response inhibition accuracy in adolescents but not adults. Importantly, although all participants in this study were “regular” users, adolescents reported greater frequency of cannabis use per month than adults. Thus, the reduced impairment seen in adolescents relative to adults may reflect tolerance effects. Alternatively, adolescents have a higher basal metabolism and a lower percentage body fat than adults, which could lead adolescents to metabolize THC more quickly, potentially resulting in reduced memory effects. Finally, adolescents reported more frequent and heavier use of cigarettes than adults, which may offset cannabis effects on working memory (Schuster et al., 2015; Schuster et al., 2016b), as well as less frequent alcohol use. Therefore, it is possible that group differences in use of other substances may have influenced these findings. Ramaekers et al. (2016a) examined acute effects of cannabis as a function of participants’ drug use history in a sample of 132 adult users of cannabis and cocaine. In this double-blind placebo-controlled study, participants received a dose of cannabis, cocaine, or placebo. Cannabis use in this sample ranged from infrequent to daily. Participants completed a neurocognitive battery assessing executive functioning (Tower of London task), impulse control (stop signal task), attention (divided attention task), and psychomotor performance (critical tracking task). Results indicated a main effect of cannabis intoxication across all measures such that the cannabis group showed worse performance relative to those who received placebo. Although there was no main effect of cannabis use history, there was an interaction between cannabis use history and

146 Cognition and Addiction

psychomotor performance such that cannabis-induced impairment decreased with increasing frequency of use, which would suggest tolerance effects. However, these effects may have been driven by worsening psychomotor performance over time in the placebo group, rather than in any of the drug conditions, for which psychomotor performance remained stable. Together, these results suggest that acute cannabis-induced neurocognitive impairment may not depend on cannabis use history and that tolerance to these acute effects is generally absent in regular users. Other studies have examined the effects of different cannabis compositions on cognition. Notably, Lawn et al. (2016) examined the acute effects of cannabis with and without CBD on effort-related decision-making in a sample of 17 occasional cannabis users (3 times/week and 4 times in the past year). Using repeated measures, placebocontrolled double-blind design, participants received vaporized cannabis with CBD, cannabis without CBD, and placebo. They completed an effort expenditure for rewards task, in which they decided between a low-effort choice, which yielded a small amount of money, and a high-effort choice, which could yield larger amounts of money. Results indicated that administration of placebo predicted higher effort relative to cannabis without CBD, suggesting that acute cannabis intoxication is associated with lower effort. Because the difference between cannabis with and without CBD was not significant, there is no evidence to suggest that CBD can reduce the negative impact of THC on effortrelated decision-making. Some studies have examined the moderating influence of genetic expression on the association between cannabis intoxication and cognitive performance. Notably, Ramaekers et al. (2016b) examined levels of the enzyme dopamine b-hydroxylase (DbH), which transforms dopamine to noradrenaline, and tonic dopamine levels in 122 regular users of cannabis (i.e., used at least twice over 3 months) and cocaine (i.e., used at least 5 times in past year). Individuals were identified as having either low-activity or high-activity DbH genotypes. All participants received acute doses of cannabis, cocaine, or placebo and completed the Matching Familiar Figures Test to assess cognitive impulse control. Users with the low DbH genotype under acute intoxication of cannabis or cocaine showed increased cognitive impulsivity on the Matching Familiar Figures Test. These results suggest that certain cannabis users who also use cocaine may be at risk for experiencing hyperdopaminergic cognitive states influencing substance-driven behaviors especially among users with high-risk DbH genotypes. Although participants in this study used both cocaine and cannabis, the results suggest that genetic differences may impact cannabis effects on cognition.

Nonacute or residual/long-term effects of cannabis use on cognition Reviews and meta-analyses Several recent reviews have examined the long-term effects of cannabis use on cognition. Ganzer et al. (2016) conducted a systematic review of 38 studies between 2004 and 2015 examining the residual neurocognitive effects of cannabis use in adolescents and adults after a prolonged period of abstinence. Overall, the findings regarding neurocognition were heterogeneous. Most studies reported some deficits in attention or concentration in abstinent cannabis users, as well as in different aspects of memory. Findings in the domains of inhibition, impulsivity, visuospatial functioning, and decision-making were mixed. Although not many studies examined motor function, most of those that did found worse performance in cannabis users relative to nonusing controls even after prolonged abstinence. Furthermore, results suggested that neuropsychological functioning in individuals who initiated cannabis use at an earlier age was not significantly different from that of individuals with a later age of onset (Ganzer et al., 2016). On the other hand, a review by Curran et al. (2016) identified episodic memory impairments as the most consistently reported long-term effects of cannabis use, while findings for working memory, attention, and impulsivity were mixed. Somewhat similarly, reviews by Broyd et al. (2016) and Nader and Sanchez (2018) identified verbal learning and memory and executive functions as the domains most consistently impaired with long-term cannabis use. These reviews concluded that neurocognitive impairments may persist for at least 1 week when cannabis use is chronic, but are often resolved with long periods of abstinence (e.g.,  4 weeks; Broyd et al., 2016; Nader and Sanchez, 2018). Three meta-analyses have synthesized findings from studies examining associations between cannabis use and various aspects of neuropsychological functioning, focusing on nonacute use (i.e., when participants are not acutely intoxicated). Grant et al. (2003) conducted a meta-analysis on 15 studies, with a total of 704 cannabis users and 484 nonusing controls. Results suggested a small but significant “residual cannabis effect” on cognition, suggesting that cannabis users’ overall neuropsychological performance was worse than that of controls by approximately one-fifth of a standard deviation. Specifically, cannabis users showed significantly worse performance than nonusers in the domains of learning and forgetting. Similarly, the metaanalysis by Schreiner and Dunn (2012) included 33 studies, independent from those in the Grant et al. meta-analysis,

Cognitive sequelae of cannabis use Chapter | 10

which yielded 1010 cannabis users and 839 controls. Results indicated a modest negative association between cannabis use and global neuropsychological functioning. Significant negative effects were also observed in the domains of learning, forgetting/retrieval, abstraction/executive function, attention, motor skills, and verbal language. The magnitude of these effects approximated one-third of a standard deviation. More recently, Scott et al. (2018) conducted a metaanalysis examining the nonacute effects of cannabis on cognition among adolescents and young adults. This analysis included 69 studies for a total of 2152 cannabis users and 6575 controls with minimal cannabis exposure. Results indicated a small negative association between frequent or heavy cannabis use and overall cognitive functioning. Effect sizes were significant in the domains of learning, executive functioning (abstraction)/shifting, speed of information processing, delayed memory, executive functioning (inhibition, updating)/working memory, and attention. Effect sizes were modest and did not vary by sample age or age of onset of cannabis use. Importantly, studies with eligibility criteria requiring abstinence longer than 72 h had a very small nonsignificant overall effect of cannabis use on cognition relative to studies requiring abstinence of 72 h or less. Together, results from reviews and meta-analyses suggest that frequent or heavy cannabis use is associated with small negative effects on overall cognition, as well as specific domains, including learning and memory, executive functioning, and attention. However, these reductions in cognitive functioning may reflect residual effects from acute use, as they significantly diminish with prolonged abstinence. Across most studies, age of onset of cannabis use did not have a significant effect on the degree of observed cognitive impairment.

Longitudinal studies Although meta-analyses have been valuable in synthesizing and advancing cannabis research, they preclude making causal inferences regarding the links between cannabis use and cognitive functioning. Longitudinal designs which assess how changes in cannabis use prospectively influence cognitive functioning are better equipped to address questions of causality. Recently, Gonzalez et al. (2017) reviewed seven longitudinal studies published between 2005 and 2016, which examined nonacute effects of cannabis use on neuropsychological functioning. Four of these studies compared neuropsychological data before and after initiation of cannabis use. Across studies, IQ and episodic memory were most often reported to be impacted by cannabis use, with the magnitude of effects ranging from 1/5 to 1/2 of a standard deviation. Importantly, cannabis use was associated with neuropsychological decline only at the highest levels of cannabis use, and this effect was often

147

attenuated after controlling for confounding variables, such as use of other substances. Since this review was published, several longitudinal studies have continued to find links between cannabis use and poorer neuropsychological functioning. For instance, Castellanos-Ryan et al. (2017) followed a sample of 294 male participants from ages 13 to 20. Substance use was assessed annually until age 17, and again at age 20, and neurocognition was assessed twicedin early adolescence and early adulthood. Results indicated that before initiation of cannabis use, poor short-term and working memory and high verbal IQ were prospectively associated with earlier age of onset of cannabis use. Earlier age of onset and higher frequency of cannabis use throughout adolescence were, in turn, associated with neurocognitive decline in verbal IQ and worsening performance on executive functioning tasks assessing reward learning and processing in young adulthood. Although the link between cannabis use frequency and verbal IQ was mediated by lower rates of high school graduation among cannabis users, the association between cannabis use and poorer executive functioning persisted even after controlling for graduation, other substance use, and externalizing symptoms. These findings suggest that the link between cannabis use and cognition can be bidirectional such that certain cognitive profiles can lead to earlier age of onset, which can, in turn, adversely impact cognitive functioning and later life outcomes. Becker et al. (2018) examined a sample of 26 heavy cannabis users who initiated cannabis use before age 17 and 31 age- and sex-matched controls. Participants completed neuropsychological assessments at ages 19e20 and 21e22, which included measures of psychomotor function, speeded attention, verbal fluency, memory, planning, and decision-making. After controlling for age, sex, and alcohol use, cannabis users showed relative impairments in working memory, planning, and verbal memory at both baseline and follow-up; however, cannabis use was not associated with declines in cognitive performance, which remained stable over time. Rather, results suggest that these deficits represent enduring vulnerabilities, especially among chronic heavy users. Importantly, earlier age of onset of cannabis use was associated with poorer performance in the domains of verbal learning and memory and planning over time. This finding suggests that early onset cannabis use may have a neurotoxic effect on underlying brain circuitry, which results in decreased retention of information over time. Similarly, Boccio and Beaver (2017) assessed a large sample of adolescents at four different time points: ages 12e21 (Wave I), 13e22 (Wave II), and 18e26 (Wave III). This study examined associations between cannabis use and verbal intelligence after controlling for age, sex, race, and socioeconomic status. Results revealed that having tried cannabis at Waves II or III was significantly associated with declines in verbal IQ from Waves I to III, with

148 Cognition and Addiction

effect magnitude ranging from 1 to 2 IQ points. However, cumulative cannabis frequency was not associated with changes in IQ. In short, although initiation of cannabis use was associated with neuropsychological decline, effects of cannabis were not dose-dependent, suggesting that the relationship between cannabis and cognition may be explained by other factors. Interestingly, findings from a small, preliminary longitudinal analysis by Gruber et al. (2016) suggest that medical marijuana might have positive effects on cognition. This study examined the impact of medical marijuana on executive functioning in a sample of 11 adults who were either cannabis-naïve or abstinent for at least 10 years before study entry. All participants had a valid certification for medical marijuana as prescribed for a variety of conditions, including anxiety, depression, sleep problems, and chronic pain. Participants completed executive functioning assessments at baseline and 3-month follow-up. Results indicated that, in general, patients experienced significant improvement in measures of executive functioning at the follow-up visit, namely the Stroop Color Word Test and the Trail Making Test, such that they were faster but equally accurate. There were also trends suggesting slight improvements in tasks such as the Wisconsin Card Sorting Test or the LetterNumber Sequencing Task. Although the influence of practice effects could not be ruled out, practice effects are typically observed only with more frequent administration and alternate versions of these tasks were used at follow-up. Gruber et al. (2016) proposed that improvements may have been a result of participants experiencing amelioration of their clinical symptoms, differences in the active ingredients in medical (e.g., high CBD, low THC) versus recreational marijuana (e.g., high THC, low CBD), or reduction in use of other medications with known neurocognitive side effects (e.g., benzodiazepines). Similar results have been reported with a larger sample during an executive functioning fMRI task (Gruber et al., 2018). Of all longitudinal studies, co-twin designs with large samples, which can control for genetic and shared family factors, are best positioned to make strong causal inferences regarding the nonacute effects of cannabis on cognitive functioning over time. One such study by Jackson et al. (2016) used data from 3066 twins from two longitudinal cohorts: the Risk Factors for Antisocial Behavior (RFAB) study and the Minnesota Twin Family Study (MTFS). Participants in the RFAB study underwent IQ testing at ages 9e10 and 19e20, and those in the MTFS underwent IQ testing at ages 11e12 and ages 17e19. Participants were classified as nonusers or users, with users further divided into those who had ever used cannabis, those who had used cannabis 30 or more times, and those who were ever daily users for a period greater than 6 months. Across both cohorts, users showed a significant decline in performance in the Vocabulary and Information subtests. After controlling for covariates (i.e., age, sex, zygosity,

socioeconomic status), this difference remained significant for the RFAB, but not the MTFS cohort. Changes in neuropsychological performance did not vary as a function of cannabis use frequency. Furthermore, co-twin control analyses indicated no significant differences in test performance between mono- or dizygotic twin pairs discordant for cannabis use history. Another longitudinal co-twin study by Meier et al. (2018) found similar results. This study followed a sample of 1989 twins from the Environmental Risk Longitudinal Twin Study. IQ was assessed at ages 5, 12, and 18, with substance use and executive functions assessed at age 18. Results revealed that cannabis-dependent adolescents had lower IQs at all ages than nondependent adolescents. Relative to nonusers, adolescent cannabis users had lower IQ at ages 12 and 18 and showed greater decline from ages 12 to 18. However, there was little evidence that these differences were associated with cannabis use, as differences in IQ were not significant between discordant twin pairs. Twins who used cannabis more frequently did, however, perform more poorly on a working memory test than their co-twins. Together, results from longitudinal co-twin studies suggest cannabis use does not appear to cause decline in neuropsychological functioning. Rather, family background factors may better explain why cannabis users perform worse on measures of neuropsychological functioning.

Notable cross-sectional studies Recent cross-sectional studies have examined the impact of age of cannabis use initiation on adult neurocognitive performance. For instance, Schuster et al. (2016a) examined the association between age of onset and learning impairments in a sample of 48 young adults who reported using cannabis at least once a week, as compared with 48 age- and gender-matched nonusers. Users were classified as early onset (use at or before age 16) or late onset (use after age 16). Cannabis users with early onset showed lower overall learning and worse delayed recall performance on the California Verbal Learning Test-II than late-onset users and controls. However, there were no significant betweengroup differences in delayed recall after controlling for performance on the learning trials. Early onset users also evidenced significantly less semantic clustering than controls, though this difference in learning strategy use did not mediate the association between onset of cannabis use and delayed recall. Thus, these results suggest that the poor memory performance typically associated with cannabis use may be explained by factors such as age of onset and learning inefficiencies. Similarly, Dahlgren et al. (2016) assessed the impact of different patterns of cannabis use on executive functioning, as measured by the Stroop Color Word Test and the Wisconsin Card Sorting Task. Participants were 44 chronic

Cognitive sequelae of cannabis use Chapter | 10

heavy adult cannabis users (used 5 times per week) and 32 nonusers. Users were subdivided into early onset (regular use before age 16) and late onset (regular use at or after age 16), with regular use broadly defined as cannabis use on a routine, expected, and consistent basis. Cannabis users showed poorer performance on both tasks relative to nonusers. These differences, however, were driven by the performance of the early onset cannabis users; late onset users’ performance was similar to that of nonusers. The association between early onset and poorer performance on the Wisconsin Card Sorting Task was still present after accounting for frequency and amount of weekly cannabis use, suggesting that age of onset of regular cannabis use uniquely contributed to executive functioning impairments. Together, findings from these cross-sectional studies suggest that poor neurocognitive performance by cannabis users may be at least partially explained by age of onset. Some studies have also examined the moderating influence of genetic factors on the long-term cognitive effects of cannabis use, although most have done so in the context of psychosis. Verdejo-García et al. (2013) examined whether two common genetic polymorphismsdthe catechol-O-methyltransferase (COMT) gene val 158met polymorphism and the SLC6A4 gene 5-HTTLPR polymorphismdmoderated the effects of cannabis on executive functioning. Participants were 86 daily cannabis users who met criteria for cannabis abuse or dependence and 58 nonusers, with groups matched for genetic makeup, sex, age, IQ, and education. Although there were no significant between-group differences in performance of any executive function task, a genotype X group interaction revealed more nuanced results. Cannabis users with the val/val genotype showed lower accuracy of sustained attention in the CANTAB Rapid Visual Information Processing Test than nonusers with this genotype. Cannabis users carrying the COMT val allele committed more response monitoring and set shifting errors in the CANTAB intradimensional/extradimensional set shifting task than cannabis users with the met/met genotype. Also, cannabis users with the 5-HTTLPR s/s genotype performed worse than s/s nonusers on the Iowa Gambling Task such that s/s cannabis users take longer to learn the task than their nonuser counterparts. Thus, results from this study suggest that genetic factors may make some cannabis users more vulnerable to negative long-term neurocognitive effects, particularly in the realm of executive functioning.

Clinical significance of cognitive deficits associated with cannabis Although most of the studies described have found statistically significant effects of cannabis on cognition, it is important to consider the difference between statistical and

149

clinical significance. Most of the reported effects range from approximately 1/5 to 1/2 of a standard deviation. Therefore, these effects fall short of the criteria typically used by clinicians to determine whether observed impairment is significant (i.e., 1e1.5 standard deviations below average.) Thus, although effects of cannabis use on cognition are statistically significant, they may not be clinically meaningful and may represent relative rather than absolute impairments. However, this is not to suggest that cannabis use has no effect on neurocognitive functioning; rather, more detailed studies are needed to better understand the functional impact of its documented adverse effects on neurocognition. Moreover, preexisting individual differences in cognition, family background factors, and genetics may make some individuals more vulnerable than others to cannabis-associated neurocognitive deficits.

Recommendations for researchers/ clinicians interested in cognitive profiling in the context of cannabis As can be seen in Table 10.1, it can be concluded that heavy cannabis users (e.g., daily or almost daily) are likely to exhibit lower neuropsychological performance than nonusers across studies. These relative impairments are most likely to be reported on measures of learning, memory, and IQ. Impairments are also frequently reported in the domain of executive function, albeit less consistently. However, these deficits are likely to remit with prolonged abstinence (4 weeks). Deficits that persist may represent preexisting vulnerabilities. Furthermore, earlier cannabis use onset seems to be associated with poorer outcomes, although results across studies are not wholly consistent with this finding, and the link between cannabis use and cognitive function may not be dose-dependent. Thus, we recommend that researchers or clinicians interested in cognitive profiling assess neurocognitive functioning in the domains of IQ, learning and memory, and executive functioning, as well as substance use characteristics including level of cannabis use, length of abstinence, other substance use, and age of onset of cannabis use.

Conclusion Cannabis is a complex drug with numerous components, of which THC and CBD are the most studied. THC is the main psychoactive constituent in cannabis, and it exerts its effects on the central nervous system primarily through its activity at CB1 receptors. Because these receptors are densely distributed throughout brain regions including hippocampal and limbic areas, exposure to cannabis results in a variety of acute and nonacute effects on cognition. These findings are summarized in Table 10.1. Specifically,

150 Cognition and Addiction

TABLE 10.1 Integrated and summarized key findings for each discussed study approach. Studies on acute effects

Key findings

Reviews

l

Cannabis intoxication is consistently linked to impairments in learning and memory and less consistently linked to problems with attention and executive functioning (e.g., decision-making)

Cross-sectional studies

l

Acute effects of cannabis on cognition may be moderated by factors such as cannabis composition (e.g., THC:CBD ratio), genetics, and cannabis use history

Studies on nonacute effects

Key findings

Reviews and meta-analyses

l

l l

Longitudinal and co-twin studies

l

l

l

Cross-sectional studies

l

Frequent and/or heavy cannabis use leads to small negative effects on overall cognition, as well as learning and memory, executive functioning, and attention These residual effects diminish with prolonged abstinence Age of onset may not impact degree of impairment Small residual effects of cannabis use are most often observed in the domains of episodic memory and IQ Observed cognitive decline among cannabis users may be better explained by factors other than cannabis use, such as family background and other substance use Earlier age of onset may negatively impact cognition Residual effects of cannabis use on cognition may be moderated by factors such as age of onset and genetics

acute intoxication with cannabis has been most consistently linked to impairments in learning, memory, and psychomotor function, with some studies also reporting impairments in attention and executive functioning. With regard to nonacute effects, evidence suggests that heavy or chronic cannabis use may be associated with declines in IQ, episodic memory, and executive functioning. However, these effects are small in magnitude and likely to diminish with prolonged abstinence. Furthermore, longitudinal co-twin studies suggest that family background factors, not cannabis, may explain poorer cognitive performance among cannabis users (Gonzalez et al., 2017). Several important factors may account for cross-study variability in results. First, the level of cannabis use in the sample may influence the extent or magnitude of observed cognitive impairments. Studies suggest that acute cannabis intoxication may differentially impact cognition among heavy and light cannabis users such that heavy users are less impaired. This could reflect potential tolerance effects or neuroadaptation to chronic exposure. On the other hand, results from studies examining nonacute effects most often report impairments only for participants with the highest levels of cannabis use, although many studies have failed to find a dose-dependent effect (Boccio and Beaver, 2017; Jackson et al., 2016). However, there is a great deal of inconsistency across studies in the operationalization of terms such as “regular” cannabis use, which has ranged from daily to monthly use, as well as in the quantification and assessment of prior cannabis use. For instance, some studies measure cannabis use in grams, while others measure number of joints, and others measure number of days

or times used. Some rely on lifetime use, while others focus on past month or past week use. A more careful characterization of participants’ patterns and histories of cannabis use will facilitate reconciliation of results across studies. Age of onset of cannabis use has also been inconsistently linked to cognitive function. Longitudinal and crosssectional studies have found that earlier age of onset of cannabis use predicts poorer cognitive functioning in adulthood (Becker et al., 2018; Boccio and Beaver, 2017; Castellanos-Ryan et al., 2017; Dahlgren et al., 2016; Schuster et al., 2016a). Some studies suggest a bidirectional relationship between age of onset and cognition, such that certain preexisting cognitive profiles may predict an earlier age of onset, which, in turn, predicts greater impairment, perhaps because of cannabis adversely impacting neurodevelopment during adolescence (Becker et al., 2018; Castellanos-Ryan et al., 2017). However, other studies have found that observed cannabis-related cognitive impairments did not vary as a function of age of onset (Ganzer et al., 2016; Scott et al., 2018). To further complicate this issue, there is also a great deal of variability in studies’ definitions of “early” age of onset. Therefore, although it is neurobiologically plausible, more research is needed to unequivocally determine whether earlier age of onset is associated with poorer cognitive outcomes later in life. Future studies should also continue to examine the cognitive effects of different cannabis compositions (e.g., different THC to CBD ratios). Studies examining acute effects have yielded mixed results; high THC or low CBD cannabis has been consistently linked to poorer cognitive performance, but the effects of the interaction

Cognitive sequelae of cannabis use Chapter | 10

between THC and CBD are still unclear. A more precise characterization of the CBD to THC ratio and of the time between CBD and THC intake would help advance research in this area (Zuardi et al., 2012). Studies focusing on nonacute effects of cannabis have rarely examined effects of cannabis potency or composition. Research in this area will enable scientists to determine how to leverage potentially therapeutic effects of CBD while minimizing the possibility of THC-related harm. Understanding the effects of different cannabis potencies or THC concentrations is also increasingly urgent, as cannabis potency has continued to rise and use of more potent products (e.g., waxes) is becoming more prevalent (Smart et al., 2017). These challenges are compounded by growing recognition that additional compounds found in the cannabis plant (e.g., terpenes) might have their own psychoactive effects or modify the effects of THC at cannabinoid receptors (Russo, 2011). The field requires transdisciplinary, translational, observational, and experimental studies at varying levels of analysis to better understand for whom and under what conditions cannabis may be most harmful to the brain and cognition. Tightly controlled experimental studies with nonhuman subjects that administer and compare effects of specific cannabis preparations of known composition may shed the most light in this area. Providing human research participants with cannabis may be feasible for acute administration studies, but too burdensome and problematic from a legal and ethical standpoint when considering longer-term use. In observational studies of human cannabis users, questions on effects of cannabis constituents and potency may be addressed by having users bring in samples of their cannabis for chemical analysis. Unfortunately, this approach bring its own challenges because of laws and regulations that may impede the collection and processing of cannabis at universities, as well as the challenges of growing cannabis strains with the same chemical composition (de Meijer et al., 1992; Jikomes and Zoorob, 2018; Vandrey et al., 2015). Other critical avenues for strengthening observational studies on the effects of cannabis on cognition come from “big science” approaches that include very large sample sizes, transdisciplinary protocols, and prospective longitudinal designs. An example of such an undertaking is the Adolescent Brain and Cognitive Development (ABCD) study (www.abcd-study.org) recently launched by the NIH, which follows a nationally representative sample of more than 11,000 9- and 10-year-old substance-naïve children for a period of 10 years, assessing a variety of domains including cognition, brain structure and function, genetics, substance use, mental health, family, and cultural environment. The study’s large sample size will allow for sufficient power to detect small effects and complex interactions

151

among variables, allow for subgroup analyses, and facilitate the use of powerful data mining techniques. Because it is a nationally representative sample, it will also allow for generalizability of findings to the population at large. Furthermore, the prospective longitudinal design that begins at an age young enough to track effects of puberty, adolescent development, and onset and course of cannabis use will better enable us to disentangle risk factors from consequences of cannabis use. Finally, the use of an “open science” framework facilitates transparency, reproducibility, and access to the broader scientific community. In conclusion, findings from studies reviewed in this chapter have made significant contributions to our understanding of the neuropharmacology of cannabis, as well as its acute and nonacute effects on cognition. Several questions remain unanswered, such as the acute and longterm effects of different cannabis compositions, the cognitive sequelae of lower levels of cannabis use, the impact of earlier age of onset, and long-term adverse consequences attributable specifically to cannabis. With the rapid commercialization and deregulation of cannabis, along with heightened interest in its medical use, it is now more important than ever for science to inform the public on the potential benefits and risks of cannabis consumption.

References Atakan, Z., 2012. Cannabis, a complex plant: different compounds and different effects on individuals. Ther. Adv. Psychopharmacol. 2 (6), 241e254. https://doi.org/10.1177/2045125312457586. Becker, M.P., Collins, P.F., Schultz, A., Urosevic, S., Schmaling, B., Luciana, M., 2018. Longitudinal changes in cognition in young adult cannabis users. J. Clin. Exp. Neuropsychol. 40 (6), 529e543. https://doi.org/10.1080/13803395.2017.1385729. Ben-Shabat, S., Fride, E., Sheskin, T., Tamiri, T., Rhee, M.H., Vogel, Z., et al., 1998. An entourage effect: inactive endogenous fatty acid glycerol esters enhance 2-arachidonoyl-glycerol cannabinoid activity. Eur. J. Pharmacol. 353 (1), 23e31. Bergamaschi, M.M., Queiroz, R.H.C., Zuardi, A.W., Crippa, J.A.S., 2011. Safety and side effects of cannabidiol, a Cannabis sativa constituent. Curr. Drug Saf. 6 (4), 237e249. Boccio, C.M., Beaver, K.M., 2017. Examining the influence of adolescent marijuana use on adult intelligence: further evidence in the causation versus spuriousness debate. Drug Alcohol Depend. 177, 199e206. https://doi.org/10.1016/j.drugalcdep.2017.04.007. Broyd, S.J., van Hell, H.H., Beale, C., Yücel, M., Solowij, N., 2016. Acute and chronic effects of cannabinoids on human cognitionda systematic review. Biol. Psychiatry 79 (7), 557e567. https://doi.org/10.1016/ j.biopsych.2015.12.002. Burns, H.D., Van Laere, K., Sanabria-Bohórquez, S., Hamill, T.G., Bormans, G., Eng, W., et al., 2007. [18F]MK-9470, a positron emission tomography (PET) tracer for in vivo human PET brain imaging of the cannabinoid-1 receptor. Proc. Natl. Acad. Sci. U.S.A. 104 (23), 9800e9805. https://doi.org/10.1073/pnas.0703472104.

152 Cognition and Addiction

Castellanos-Ryan, N., Pingault, J.-B., Parent, S., Vitaro, F., Tremblay, R.E., Séguin, J.R., 2017. Adolescent cannabis use, change in neurocognitive function, and high-school graduation: a longitudinal study from early adolescence to young adulthood. Dev. Psychopathol. 29 (4), 1253e1266. https://doi.org/10.1017/ S0954579416001280. Center for Behavioral Health Statistics and Quality, 2017. 2016 National Survey on Drug Use and Health: Detailed Tables. Substance Abuse and Mental Health Services Administration. Colizzi, M., Bhattacharyya, S., 2017. Does cannabis composition matter? Differential effects of delta-9-tetrahydrocannabinol and cannabidiol on human cognition. Curr. Addict. Rep. 4 (2), 62e74. https://doi.org/10. 1007/s40429-017-0142-2. Crane, N.A., Schuster, R.M., Fusar-Poli, P., Gonzalez, R., 2013. Effects of cannabis on neurocognitive functioning: recent advances, neurodevelopmental influences, and sex differences. Neuropsychol. Rev. 23 (2), 117e137. https://doi.org/10.1007/s11065-012-9222-1. Crean, R.D., Crane, N.A., Mason, B.J., 2011. An evidence based review of acute and long-term effects of cannabis use on executive cognitive functions. J. Addict. Med. 5 (1), 1e8. https://doi.org/10.1097/ADM. 0b013e31820c23fa. Curran, H.V., Freeman, T.P., Mokrysz, C., Lewis, D.A., Morgan, C.J.A., Parsons, L.H., 2016. Keep off the grass? Cannabis, cognition and addiction. Nat. Rev. Neurosci. 17 (5), 293e306. https://doi.org/10. 1038/nrn.2016.28. Dahlgren, M.K., Sagar, K.A., Racine, M.T., Dreman, M.W., Gruber, S.A., 2016. Marijuana use predicts cognitive performance on tasks of executive function. J. Stud. Alcohol Drugs. https://doi.org/10.15288/ jsad.2016.77.298. de Meijer, E.P.M., van der Kamp, H.J., van Eeuwijk, F.A., 1992. Characterisation of Cannabis accessions with regard to cannabinoid content in relation to other plant characters. Euphytica 62 (3), 187e200. https://doi.org/10.1007/BF00041753. Englund, A., Morrison, P.D., Nottage, J., Hague, D., Kane, F., Bonaccorso, S., et al., 2013. Cannabidiol inhibits THC-elicited paranoid symptoms and hippocampal-dependent memory impairment. J. Psychopharmacol. (Oxford, Engl.) 27 (1), 19e27. https://doi.org/10.1177/0269881112460109. Erkelens, J.L., Hazekamp, A., 2014. That which we call Indica, by any other name would smell as sweet. Cannabinoids 9 (1), 9e15. Ganzer, F., Bröning, S., Kraft, S., Sack, P.-M., Thomasius, R., 2016. Weighing the evidence: a systematic review on long-term neurocognitive effects of cannabis use in abstinent adolescents and adults. Neuropsychol. Rev. 26 (2), 186e222. https://doi.org/10.1007/s11065016-9316-2. Glass, M., Faull, R.L.M., Dragunow, M., 1997. Cannabinoid receptors in the human brain: a detailed anatomical and quantitative autoradiographic study in the fetal, neonatal and adult human brain. Neuroscience 77 (2), 299e318. https://doi.org/10.1016/S03064522(96)00428-9. Gonzalez, R., 2007. Acute and non-acute effects of cannabis on brain functioning and neuropsychological performance. Neuropsychol. Rev. 17 (3), 347e361. https://doi.org/10.1007/s11065-007-9036-8. Gonzalez, R., Pacheco-Colón, I., Duperrouzel, J.C., Hawes, S.W., 2017. Does cannabis use cause declines in neuropsychological functioning? A review of longitudinal studies. J. Int. Neuropsychol. Soc. 23 (9e10), 893e902. https://doi.org/10.1017/S1355617717000789.

Grant, I., Gonzalez, R., Carey, C.L., Natarajan, L., Wolfson, T., 2003. Non-acute (residual) neurocognitive effects of cannabis use: a meta-analytic study. J. Int. Neuropsychol. Soc. 9 (5), 679e689. https://doi.org/10.1017/S1355617703950016. Gruber, S.A., Sagar, K.A., Dahlgren, M.K., Gonenc, A., Smith, R.T., Lambros, A.M., et al., 2018. The grass might Be greener: medical marijuana patients exhibit altered brain activity and improved executive function after 3 months of treatment. Front. Pharmacol. 8. https://doi.org/10.3389/fphar.2017.00983. Gruber, S.A., Sagar, K.A., Dahlgren, M.K., Racine, M.T., Smith, R.T., Lukas, S.E., 2016. Splendor in the grass? A pilot study assessing the impact of medical marijuana on executive function. Front. Pharmacol. 7, 355. https://doi.org/10.3389/fphar.2016.00355. Hayakawa, K., Mishima, K., Hazekawa, M., Sano, K., Irie, K., Orito, K., et al., 2008. Cannabidiol potentiates pharmacological effects of D9tetrahydrocannabinol via CB1 receptor-dependent mechanism. Brain Res. 1188, 157e164. https://doi.org/10.1016/j.brainres.2007.09.090. Jackson, N.J., Isen, J.D., Khoddam, R., Irons, D., Tuvblad, C., Iacono, W.G., et al., 2016. Impact of adolescent marijuana use on intelligence: results from two longitudinal twin studies. Proc. Natl. Acad. Sci. U.S.A. 113 (5), E500eE508. https://doi.org/10.1073/pnas. 1516648113. Jikomes, N., Zoorob, M., 2018. The cannabinoid content of legal cannabis in Washington state varies systematically across testing facilities and popular consumer products. Sci. Rep. 8 (1), 4519. https://doi.org/10. 1038/s41598-018-22755-2. Johnston, L.D., Miech, R.A., O’Malley, P.M., Bachman, J.G., Schulenberg, J.E., 2016. Teen Use of Any Illicit Drug Other than Marijuana at New Low, Same True for Alcohol. Klein, C., Karanges, E., Spiro, A., Wong, A., Spencer, J., Huynh, T., et al., 2011. Cannabidiol potentiates D9-tetrahydrocannabinol (THC) behavioural effects and alters THC pharmacokinetics during acute and chronic treatment in adolescent rats. Psychopharmacology 218 (2), 443e457. https://doi.org/10.1007/s00213-011-2342-0. Lawn, W., Freeman, T.P., Pope, R.A., Joye, A., Harvey, L., Hindocha, C., et al., 2016. Acute and chronic effects of cannabinoids on effortrelated decision-making and reward learning: an evaluation of the cannabis “amotivational” hypotheses. Psychopharmacology 233 (19e20), 3537e3552. https://doi.org/10.1007/s00213-016-4383-x. Maroon, J., Bost, J., 2018. Review of the neurological benefits of phytocannabinoids. Surg. Neurol. Int. 9, 91. https://doi.org/10.4103/sni.sni_ 45_18. Meier, M.H., Caspi, A., Danese, A., Fisher, H.L., Houts, R., Arseneault, L., Moffitt, T.E., 2018. Associations between adolescent cannabis use and neuropsychological decline: a longitudinal co-twin control study. Addiction 113 (2), 257e265. https://doi.org/10.1111/ add.13946. Mokrysz, C., Freeman, T.P., Korkki, S., Griffiths, K., Curran, H.V., 2016. Are adolescents more vulnerable to the harmful effects of cannabis than adults? A placebo-controlled study in human males. Transl. Psychiatry 6 (11), e961. https://doi.org/10.1038/tp.2016.225. Morales, P., Reggio, P.H., 2017. An update on non-CB1, non-CB2 cannabinoid related G-protein-coupled receptors. Cannabis Cannabinoid Res. 2 (1), 265. https://doi.org/10.1089/can.2017.0036.

Cognitive sequelae of cannabis use Chapter | 10

Morgan, C.J.A., Schafer, G., Freeman, T.P., Curran, H.V., 2010. Impact of cannabidiol on the acute memory and psychotomimetic effects of smoked cannabis: naturalistic study. Br. J. Psychiatry 197 (4), 285e290. https://doi.org/10.1192/bjp.bp.110.077503. Nader, D.A., Sanchez, Z.M., 2018. Effects of regular cannabis use on neurocognition, brain structure, and function: a systematic review of findings in adults. Am. J. Drug Alcohol Abuse 44 (1), 4e18. https:// doi.org/10.1080/00952990.2017.1306746. National Academies of Sciences, E., & Medicine, 2017. The Health Effects of Cannabis and Cannabinoids: The Current State of Evidence and Recommendations for Research. The National Academies Press, Washington, DC. Onaivi, E.S., Ishiguro, H., Gu, S., Liu, Q.-R., 2012. CNS effects of CB2 cannabinoid receptors: beyond neuro-immuno-cannabinoid activity. J. Psychopharmacol. 26 (1), 92e103. https://doi.org/10.1177/ 0269881111400652. Pearce, D.D., Mitsouras, K., Irizarry, K.J., 2014. Discriminating the effects of cannabis sativa and cannabis indica: a web survey of medical cannabis users. J. Altern. Complement. Med. 20 (10), 787e791. https://doi.org/10.1089/acm.2013.0190. Pertwee, R.G., 2006. Cannabinoid pharmacology: the first 66 years. Br. J. Pharmacol. 147 (S1), S163eS171. https://doi.org/10.1038/sj.bjp. 0706406. Pertwee, R.G., 2008. Ligands that target cannabinoid receptors in the brain: from THC to anandamide and beyond. Addict. Biol. 13 (2), 147e159. https://doi.org/10.1111/j.1369-1600.2008.00108.x. Pew Research Center, 2016. Support for Marijuana Legalization Continues to Rise. Ramaekers, J.G., van Wel, J.H., Spronk, D.B., Toennes, S.W., Kuypers, K.P.C., Theunissen, E.L., Verkes, R.J., 2016a. Cannabis and tolerance: acute drug impairment as a function of cannabis use history. Sci. Rep. 6. https://doi.org/10.1038/srep26843. Ramaekers, J.G., van Wel, J.H., Spronk, D., Franke, B., Kenis, G., Toennes, S.W., et al., 2016b. Cannabis and cocaine decrease cognitive impulse control and functional corticostriatal connectivity in drug users with low activity DBH genotypes. Brain Imaging Behav. 10 (4), 1254e1263. https://doi.org/10.1007/s11682-015-9488-z. Russo, E.B., 2011. Taming THC: potential cannabis synergy and phytocannabinoid-terpenoid entourage effects. Br. J. Pharmacol. 163 (7), 1344e1364. https://doi.org/10.1111/j.1476-5381.2011.01238.x. Russo, E., Guy, G.W., 2006. A tale of two cannabinoids: the therapeutic rationale for combining tetrahydrocannabinol and cannabidiol. Med.

153

Hypotheses 66 (2), 234e246. https://doi.org/10.1016/j.mehy.2005.08. 026. Schreiner, A.M., Dunn, M.E., 2012. Residual effects of cannabis use on neurocognitive performance after prolonged abstinence: a metaanalysis. Exp. Clin. Psychopharmacol. 20 (5), 420e429. https://doi. org/10.1037/a0029117. Schuster, R.M., Crane, N.A., Mermelstein, R., Gonzalez, R., 2015. Tobacco may mask poorer episodic memory among young adult cannabis users. Neuropsychology 29 (5), 759e766. https://doi.org/10. 1037/neu0000173. Schuster, R.M., Hoeppner, S.S., Evins, A.E., Gilman, J.M., 2016a. Early onset marijuana use is associated with learning inefficiencies. Neuropsychology 30 (4), 405e415. https://doi.org/10.1037/ neu0000281. Schuster, R.M., Mermelstein, R.J., Hedeker, D., 2016b. Ecological momentary assessment of working memory under conditions of simultaneous marijuana and tobacco use. Addiction (Abingdon, Engl.) 111 (8), 1466e1476. https://doi.org/10.1111/add.13342. Scott, J.C., Slomiak, S.T., Jones, J.D., Rosen, A.F.G., Moore, T.M., Gur, R.C., 2018. Association of cannabis with cognitive functioning in adolescents and young adults: a systematic review and metaanalysis. JAMA Psychiatry. https://doi.org/10.1001/jamapsychiatry. 2018.0335. Smart, R., Caulkins, J.P., Kilmer, B., Davenport, S., Midgette, G., 2017. Variation in cannabis potency and prices in a newly legal market: evidence from 30 million cannabis sales in Washington state. Addiction (Abingdon, Engl.) 112 (12), 2167e2177. https://doi.org/10. 1111/add.13886. Vandrey, R., Raber, J.C., Raber, M.E., Douglass, B., Miller, C., BonnMiller, M.O., 2015. Cannabinoid dose and label accuracy in edible medical cannabis products. JAMA 313 (24), 2491e2493. https://doi. org/10.1001/jama.2015.6613. Verdejo-García, A., Fagundo, A.B., Cuenca, A., Rodriguez, J., Cuyás, E., Langohr, K., et al., 2013. COMT val158met and 5-HTTLPR genetic polymorphisms moderate executive control in cannabis users. Neuropsychopharmacology 38 (8), 1598. https://doi.org/10.1038/npp. 2013.59. Zuardi, A.W., Hallak, J.E.C., Crippa, J.A.S., 2012. Interaction between cannabidiol (CBD) and D(9)-tetrahydrocannabinol (THC): influence of administration interval and dose ratio between the cannabinoids. Psychopharmacology 219 (1), 247e249. https://doi.org/10.1007/ s00213-011-2495-x.

Chapter 11

Cognitive deficits in people with stimulant use disorders Antonio Verdejo-Garcia1 and Adam J. Rubenis2 1

School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia; 2Turning Point

Alcohol and Drug Centre, Melbourne, VIC, Australia

State of the problem Amphetamines and cocaine are the most used illicit stimulants globally. Amphetamines are the second most used illicit drug after cannabis, and cocaine ranks number four after opioids (Peacock et al., 2018). The mean annual prevalence of use is 0.77 for amphetamines and 0.35 for cocaine (Peacock et al., 2018). However, there is substantial variation as a function of geographical location and age. For example, the mean rate of amphetamines use is up to 4.9% in Australia, while mean rates of cocaine use are up to 4% in the United Kingdom and 3% in the United States (Mounteney et al., 2016; Peacock et al., 2018). Moreover “last year” and “lifetime” use prevalence rates are substantially higher among people aged 15e34 years (Mounteney et al., 2016; Peacock et al., 2018). Repeated use of cocaine and amphetamines (particularly the strongest, most harmful form “methamphetamine”) can result in stimulant use disorder, or stimulant addiction, as well as other physical and mental health conditions. The burden of disease associated with the use of illicit stimulants is mainly linked to substance use disorders and other psychiatric comorbidities (e.g., mood disorders, borderline personality disorder), as well as cirrhosis, liver cancer, and HIV (mostly in relation to injecting methamphetamine) (Grant et al., 2016; Peacock et al., 2018). This burden of disease has been estimated at 37.6 disability-adjusted life years (DALYs) per 100,000 population for amphetamine addiction and 15.9 DALYs per 100,000 population for cocaine addiction (Degenhardt et al., 2014). Chronic use of cocaine and (meth)amphetamine is associated with long-term harmful effects in the brain and cognition, which in turn contribute to the development and maintenance of addiction (Koob and Volkow, 2010) and have a significant impact on quality of life and related

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00011-3 Copyright © 2020 Elsevier Inc. All rights reserved.

disability (Rubenis et al., 2018; Tiffany et al., 2012). This chapter reviews the cognitive deficits associated with cocaine and methamphetamine addiction, starting with an overview of neuropharmacological and neuroadaptive effects of these drugs and then moving on to their acute and long-term cognitive effects and related clinical implications.

Neuroadaptive effects of stimulants Cocaine produces its psychoactive effects primarily by blocking the reuptake of dopamine into presynaptic neurons, thus increasing dopamine availability in the frontalstriatal circuits (i.e., ventral tegmental area, striatum, amygdala, prefrontal cortex). Repeated cocaine use results in long-term neurophysiological changes, or neuroadaptive effects, which are often opposite to the acute effects of the drug. To illustrate these changes, we draw on evidence from animal models designed to mimic the long-term effects of cocaine use in humans (e.g., extended cocaine self-administration). Based on these models, the long-term neuroadaptive effects of cocaine include (1) reduction of dopamine and glutamate receptors in frontostriatal circuits, in which these neurotransmitters are densely expressed (Luscher, 2016; Spencer et al., 2016); (2) changes in gene expression via transcriptional factors (e.g., deltaFOSB, CREB, BDNF) or epigenetic mechanisms (Li and Wolf, 2015; Nestler, 2014; Robison and Nestler, 2011); (3) suppression of adult neurogenesis (Noonan et al., 2010; Sudai et al., 2011); and (4) persistent upregulation of the stress neuroendocrine systems (Koob and Kreek, 2007; McReynolds et al., 2014). Animal models have shown that repeated cocaine use produces long-term depletion in metabotropic glutamate receptors and dopamine D2 receptors in the medial prefrontal and orbitofrontal cortices

155

156 Cognition and Addiction

(Ben-Shahar et al., 2012, 2013; Briand et al., 2008; Kasanetz et al., 2013). Further neuroadaptations in glutamate and dopamine systems encompass upregulation of AMPA-type glutamate receptors, changes in intrinsic membrane excitability, and decreased extracellular neurotransmitter levels, all having a profound impact on synaptic connections between the striatum, the limbic system, and the prefrontal cortex (Bonci et al., 2003; Wolf, 2010). Another well-established neuroadaptive mechanism involves cocaine-induced mobilization of gene transcription factors; for example, cocaine-induced overexpression of the deltaFOSB is associated with orbitofrontal dysfunction and poor impulse control in cocaine-treated rats (Winstanley et al., 2009). There is also evidence for more stable cocaine-induced epigenetic changes in DNA methylation and histone modification causing global brain and hippocampal attrition and deficits in cognitive tests of attention and working memory (He et al., 2006; Novikova et al., 2008; Zhao et al., 2015). Cocaine can also block cell proliferation and neurogenesis in the dentate gyrus of the hippocampus, negatively affecting working memory (Sudai et al., 2011), and persistently elevate cortisol and corticotropin-releasing hormone levels (Smith et al., 2004) contributing to memory and executive dysfunctions (Fox et al., 2009). Methamphetamine exerts its psychoactive effects via release of large quantities of monoamine neurotransmitters, especially dopamine, and by preventing their reuptake, resulting in an abnormally high concentration of these neurotransmitters at the receiving synapse (Panenka et al., 2013). Over time, neuroadaptive processes include (1) reduced neurotransmitter availability and (2) transcriptional and epigenetic alterations. Methamphetamine primarily appears to result in neuroadaptation of dopaminerelated function. Indeed, dopamine availability and function in the striatum is reduced (Chang et al., 2007; Panenka et al., 2013) and appears to be underpinned by downregulation of D2-type receptors and dopamine transporter availability (Ashok et al., 2017). Concentrations of monoamines are also diminished in other regions of the brain associated with higher-order cognitive processes and emotion regulation. For example, low levels of norepinephrine in prefrontal cortex regions can result in memory and executive function problems and increased levels of anxiety (Freye, 2009; Wang et al., 2000) as well as low levels of serotonin in the orbitofrontal and occipital cortices (Kish et al., 2009; Scott et al., 2007). Methamphetamine use is similarly associated with reduced frontal cortex volumes and diminished connectivity between the prefrontal cortex and the parietal cortex (Oh et al., 2005; Thompson et al., 2004; Tobias et al., 2010) and may underpin executive dysfunctions among users (Dean et al., 2013). Additionally, methamphetamine use can result in increased acetylated histone H3, contributing to enhance

drug-related motivation (i.e., conditioned place preference) (Shibasaki et al., 2011), while histone H4 hypoacetylation is associated with decreased glutamate receptor expression in the striatum (Cadet and Jayanthi, 2013; Jayanthi et al., 2014), which impacts frontal-striatal circuits and executive functions (McClure and Bickel, 2014).

Cognitive profiles Acute effects In this section, we rely on evidence from placebocontrolled acute administration studies in humans. Cocaine and amphetamines have cognitive enhancing effects on processing speed, attention, and response inhibition in psychomotor tasks after acute administration (reviewed in Spronk et al., 2013). Interestingly, these effects are associated with drug-induced activations in brain regions implicated in cognitive control and performance monitoring, including the dorsolateral prefrontal cortex, the inferior frontal gyrus, and the insula (Garavan et al., 2008). Acute administration of methamphetamine is also associated with better episodic memory encoding, particularly for positive affective material, in adequate sleep conditions (the drug has the opposite effect for poor sleepers) (Ballard et al., 2015). There are no studies on the acute effects of stimulant drugs on executive functions such as working memory, cognitive flexibility, and decision-making.

Long-term effects In this section, we rely mostly on meta-analytic studies on cognitive differences between people with stimulant use disorders versus healthy controls. People with cocaine use disorder and intermediate abstinence (3 months), compared with controls, have moderate cognitive deficits (Cohen’s d  0.5) in attention, verbal learning and memory, working memory, and impulsivity (Potvin et al., 2014; Jovanovski et al., 2005). Impulsivity deficits include attention and motor inhibition problems (Czermainski et al., 2017; Smith et al., 2014). After 6e12 months of abstinence, general cognitive function recovers and deficits become largely negligible, although the few studies available do not allow reliable conclusions about the recovery of specific cognitive domains (Potvin et al., 2014). For example, a comprehensive review of cocaine-related cognitive deficits observed strong, consistent deficits in reward-based decision-making (Spronk et al., 2013), and these particular deficits seem to persist during long-term abstinence in cocaine users (Verdejo-Garcia et al., 2007b). People with cocaine use disorder also show social cognition problems, including poorer emotion recognition (Castellano et al., 2015) and blunted social-affective valuation (Preller et al., 2014), and these deficits are still

Cognitive deficits in people with stimulant use disorders Chapter | 11

observable in participants with long-term abstinence (Fernandez-Serrano et al., 2011). People with methamphetamine use disorder, compared with controls, have moderate cognitive deficits in verbal learning/memory, working memory, impulsivity/executive functions, and social cognition (Dean et al., 2013; Potvin et al., 2018). These deficits were observed during abstinence, although there is not sufficient evidence to analyze the specific impact of short-term versus long-term abstinence. The strongest and most clinically significant deficits (Cohen’s d  0.9) are related to impulsivity and social cognition (Potvin et al., 2018), although the social cognition findings are based on a limited number of studies. A comparison between the cognitive deficits of people with cocaine versus methamphetamine use disorders indicates that cocaine use is distinctively associated with deficits in working memory, whereas methamphetamine use is distinctively associated with delayed episodic memory (Hall et al., 2017).

Recovery Recent longitudinal evidence in cocaine users suggests that attention and episodic memory deficits recover to normal levels after long-term abstinence (12 months) (Vonmoos et al., 2014). Conversely, working memory and executive functions (including tests of fluency and cognitive flexibility) seem to be more stable across intermediate and long-term abstinence (Almeida et al., 2017; Vonmoos et al., 2014). Cross-sectional studies including participants with large ranges of abstinence have also supported the notion that executive deficits, and specifically response inhibition, cognitive flexibility, and affective-based decision-making deficits, are less sensitive to individual variation in abstinence duration (Crocker et al., 2017; Fernandez-Serrano et al., 2011). The limited longitudinal evidence in methamphetamine users suggests that long-term abstinence is associated with general cognitive recovery, but not with significant recovery of specific cognitive domains, including attention, impulsivity, and executive functions (Iudicello et al., 2010; Schulte et al., 2014).

Moderators Age of onset Stimulant use during adolescence can generate disproportionate cognitive deficits because of interference with maturational processes (Paus et al., 2008). Accordingly, cognitive studies have shown that people with cocaine use disorder who started stimulant use during adolescence (18 years old) have significantly greater general cognitive deficits than those with later age of onset (Vonmoos et al.,

157

2013). Although there is limited evidence on which specific domains are most affected by early use, initial evidence suggests that episodic memory and working memory can be particularly impacted (Lopes et al., 2017; Vonmoos et al., 2014). This finding is consistent with the notion of stimulant-induced disruption of the maturation of dorsolateral prefrontal cortex regions, which are responsible for memory encoding and working memory processes (Tendilla-Beltran et al., 2016).

Cumulative exposure Higher levels of stimulant use (greater exposure to their neuroadaptive effects) should be associated with poorer cognitive performance. However, there are important limitations in the measurement of cumulative drug exposure. Self-report measures are limited by memory biases and demand characteristics, whereas biological assays have inherent limitations in the time window that they can cover (e.g., urine analyses can only cover hours to a few days after last use) and/or their sensitivity and specificity (e.g., hair analyses can be potentially confounded by hair color and external exposure) (Donovan et al., 2012). Notwithstanding these limitations, several studies have shown significant correlations between amount of cocaine use and response inhibition and decision-making deficits (Bolla et al., 2004; Fernandez-Serrano et al., 2010; Verdejo-Garcia et al., 2007a; Verdejo-Garcıa et al., 2005). Moreover, research comparing chronic and recreational cocaine users has shown that long-term cocaine exposure, indicated by hair analyses, significantly increases the risk of cognitive deficits in attention, memory, working memory, and executive functions (Vonmoos et al., 2013). With regard to methamphetamine use, the findings on the impact of cumulative exposure are still inconclusive (Dean et al., 2013). Self-report of drug use can be especially problematic in methamphetamine users, as they have particularly strong episodic memory deficits (Hall et al., 2017), and there is a lack of cognitive research using biological assays. Therefore, future studies should incorporate reliable biological assays. According to emerging evidence, amount/frequency of methamphetamine use and recent use (vs. duration/ chronicity) seem to be more closely related with cognitive deficits, particularly memory and executive functions (Dean et al., 2013).

Route of administration Smoking and injecting routes of administration bring more drug to the brain more quickly (Stahl, 2013), and hence these patterns should be theoretically associated with greater cognitive impairment. However, no studies have specifically and systematically examined the impact of route of administration on cognitive performance among

158 Cognition and Addiction

stimulant users. Moreover, post hoc analyses of subgroups of methamphetamine users with and without cognitive impairment have not shown significant differences as a function of route of administration (Cherner et al., 2010). There is a need for more research in this area, as there is a clear neurobiological basis to expect differences in cognition, and differences in route of administration are clinically meaningful, e.g., cocaine smokers versus snorters have poorer addiction treatment prognosis (Kiluk et al., 2013).

studies did not find any support for the role of verbal learning and memory in either treatment abstinence and retention (Aharonovich et al., 2003; Turner et al., 2009) or adherence defined as appointment attendance and/or compliance with medication (Fagan et al., 2015). However, the role of learning and memory might have been masked by small sample sizes and low power. In summary, there is suggestive evidence of a meaningful link between learning and memory and treatment outcomes in stimulant users.

Attention

Clinical significance of cognitive deficits associated with stimulants use The cognitive deficits identified in the previous section (i.e., attention, memory, impulsivity, and executive functions) can hinder the ability to benefit from addiction treatment (Dominguez-Salas et al., 2016). Attention and memory problems can compromise understanding of the contents of “talking therapies” (Shoptaw, 2014). Heightened impulsivity and reduced executive functions may also affect the ability to engage with treatment activities (Carroll et al., 2011) and to remain in treatment long enough to achieve beneficial effects (Vergara-Moragues et al., 2017; Washio et al., 2011). The importance of understanding these links is reinforced by high vulnerability to relapse among individuals with stimulant use disorders (Brecht and Herbeck, 2014). This section examines the longitudinal predictive link between cognitive function in early treatment and subsequent treatment outcomes. In these studies, participants are tested in early treatment and then followed up to confirm abstinence/completion of a treatment program or their level of engagement. Because people with cocaine and methamphetamine use disorders have overlapping deficits in memory, attention, impulsivity, and executive functions and enroll in similar treatment modalities, we organize the section by cognitive domains and discuss together findings in users of both stimulants.

There is strong support for the role of attentional function in predicting treatment outcomes. Five studies have found significant support for this link (Aharonovich et al., 2003, 2006; Fagan et al., 2015; Harris et al., 2014; Streeter et al., 2008), while two found partial support (Carroll et al., 2011; Chen et al., 2015). Sustained or selective attention predicted attendance or number of sessions completed in psychologically based treatments (e.g., cognitive behavioral therapy; CBT) in several studies (Aharonovich et al., 2003, 2006; Carroll et al., 2011; Fagan et al., 2015; Harris et al., 2014; Streeter et al., 2008). One study found that attention was predictive of relapse but not dropout in a sample of individuals with methamphetamine addiction (Chen et al., 2015), while another study found that sustained attention predicted the number of psychological treatment modules completed and days in treatment, but not abstinence (Carroll et al., 2011). Although these findings primarily relate to cocaine (Aharonovich et al., 2003, 2006; Carroll et al., 2011; Fagan et al., 2015; Streeter et al., 2008), all available research provides either complete or partial support for a link between attentional function and psychological treatment outcomes and suggests a consistent link between these variables. The association between attentional function and drug use/relapse is less conclusive, with one study finding a significant relationship (Chen et al., 2015) and another finding no relationship (Carroll et al., 2011), and suggests that further well-controlled research is required.

Memory Although there are no studies on people with methamphetamine use disorders, five studies have examined the link between learning/memory and cocaine addiction treatment outcomes (Aharonovich et al., 2003; Aharonovich et al., 2006; Fagan et al., 2015; Fox et al., 2009; Turner et al., 2009). Deficits in visual and verbal memory were significantly greater in those who dropped out of treatment when compared with individuals completing the program (Aharonovich et al., 2006), while verbal and auditory declarative memory have been significantly associated with higher levels of cocaine use after inpatient treatment (Fox et al., 2009). However, three

Working memory and executive functions Two studies have directly examined the predictive value of working memory on stimulant addiction treatment outcomes (Dean et al., 2009; Patterson et al., 2010). In participants with methamphetamine use disorder, working memory performance significantly predicted completion of a 12-week treatment program, although this relationship was no longer significant after adjusting for baseline methamphetamine use (Dean et al., 2009). Poorer working memory performance (slower reaction time under highest working memory load) was also significantly negatively associated with days to nicotine (an alkaloid

Cognitive deficits in people with stimulant use disorders Chapter | 11

stimulant) relapse (Patterson et al., 2010). Other measures of cognitive control have significantly predicted stimulant treatment outcomes in eight studies, including relapse (Adinoff et al., 2016; Carroll et al., 2011; Powell et al., 2010), treatment retention/completion (Aharonovich et al., 2003, 2006; Streeter et al., 2008; Turner et al., 2009), and adherence to a treatment program (Fagan et al., 2015). However, three of these studies found only partial support for the relationship, where not all measures of executive functions were significantly predictive (Aharonovich et al., 2006), abstinence was predicted at only one of three time points (Powell et al., 2010), or executive functions did not predict other treatment-related outcomes (treatment sessions completed; Carroll et al., 2011). Furthermore, two studies found no significant relationship between executive functions and treatment outcomes (Chen et al., 2015; Harris et al., 2014). However, relationships may have been masked by liberal inclusion criteria (i.e., participants who met DSM-IV criteria for methamphetamine abuse rather than dependence; Chen et al., 2015) or a small sample size (Harris et al., 2014). Overall, the available evidence supports a relationship between working memory/executive functions and a range of treatment outcomes (relapse, treatment completion, and adherence).

Impulsivity and decision-making Several forms of impulsivity have been examined in the context of stimulant addiction treatment outcomes. Two studies have supported a link between motor response inhibition and treatment outcomes, predicting nicotine abstinence at 1 week, 1 month, and 3 months (Powell et al., 2010), and relapse in participants with cocaine addiction, but not days in treatment or sessions completed (Carroll et al., 2011). Delay discounting significantly predicted treatment retention (Stevens et al., 2014) and abstinence in primarily stimulant-addicted individuals (Sheffer et al., 2012; Washio et al., 2011). However, Harris et al. (2014) found that delay discounting was not a significant predictor of reduction in nicotine use, but may be partly explained by the young age of the sample (adolescents) and the tendency for delay discounting to be higher and less variable in younger individuals and therefore less useful as a predictor. Decision-making has been examined in stimulantdependent individuals in five studies and has predicted relapse at 3 months (Verdejo-Garcia et al., 2014), proportion of positive drug tests up to 20 weeks (Nejtek et al., 2013), dropout from methamphetamine treatment (Chen et al., 2015), and psychological sessions/modules completed and days of abstinence (Carroll et al., 2011). However, other studies found that decision-making did not

159

predict days in treatment (Carroll et al., 2011) or relapse (Adinoff et al., 2016; Chen et al., 2015). Overall, these findings are mixed and suggest that while there is evidence that impulsive action and delay discounting frequently predict relapse, it is unclear what role these constructs play in predicting retention and adherence to treatment. Furthermore, decision-making shows a relationship with treatment outcomes broadly but does not show a consistent pattern (i.e., there is conflicting evidence for relapse and treatment retention, while one study supports a link with treatment engagement).

Summary There is evidence of specific domains of cognition predicting treatment outcomes in individuals with stimulant use disorders. While evidence for the role of memory is inconclusive, there is strong evidence linking attentional function with outcomes in psychological treatments (e.g., CBT), but not relapse or drug use. Findings relating to working memory and executive functions suggest an association with lower treatment retention and relapse, and impulsivity and decision-making deficits predict relapse. These findings collectively suggest that higher-order cognitive skills are predictive of treatment outcomes in a range of domains. However, it appears that these relationships are complex, where different cognitive domains influence different treatment outcomes and interact with each other in outcome prediction (Rubenis et al., 2017), which highlights the importance of a nuanced approach to cognitive prediction of outcomes.

Recommendations for researchers and clinicians interested in cognitive assessment in the context of stimulants use The cognitive assessment of people with cocaine and methamphetamine use disorders should focus on the domains of attention, verbal memory, working memory and executive functions, and impulsivity/decision-making. Assessment of attention, verbal memory, working memory, and executive functions can be particularly useful to characterize long-term cognitive deficits (Vonmoos et al., 2013), whereas assessment of working memory/executive functions and impulsivity/decision-making is especially relevant when predicting treatment outcomes and relapse (Dominguez-Salas et al., 2016; Stevens et al., 2014). Cognitive assessment in stimulant users should carefully control for potential moderators and confounders, including background characteristics (i.e., education, IQ, trait features, and clinical diagnoses such as ADHD and

160 Cognition and Addiction

personality disorders) and drug use patterns, particularly age of onset and cumulative amount of drug use indicated with reliable assessments (cue-guided self-report or adequate biological assays). Although there is a solid body of evidence on the cognitive domains that we should assess in the context of stimulant use disorders, there are no common, harmonized, or standardized tools to perform these assessments, and this is a question for future research. The development of a harmonized cognitive assessment tool/battery should be guided by the purpose of the assessment (e.g., characterization of deficits vs. prediction of outcomes), the evidence base (i.e., the assessment should focus on the most relevant domains identified in this chapter), and evidences of reliability, especially testeretest reliability to enable multiple assessments during abstinence and/or monitoring of treatment-related recovery and construct ecological and predictive validity including positive and negative predictive values (Verdejo-Garcia, 2017).

References Ben-Shahar, O.M., Szumlinski, K.K., Lominac, K.D., Cohen, A., Gordon, E., Ploense, K.L., et al., 2012. Extended access to cocaine self-administration results in reduced glutamate function within the medial prefrontal cortex. Addict. Biol. 17 (4), 746e757. Adinoff, B., Carmody, T.J., Walker, R., Donovan, D.M., Brigham, G.S., Winhusen, T.M., 2016. Decision-making processes as predictors of relapse and subsequent use in stimulant-dependent patients. Am. J. Drug Alcohol Abuse 42 (1), 88e97. Aharonovich, E., Nunes, E., Hasin, D., 2003. Cognitive impairment, retention and abstinence among cocaine abusers in cognitivebehavioral treatment. Drug Alcohol Depend. 71 (2), 207e211. Aharonovich, E., Hasin, D.S., Brooks, A.C., Liu, X., Bisaga, A., Nunes, E.V., 2006. Cognitive deficits predict low treatment retention in cocaine dependent patients. Drug Alcohol Depend. 81 (3), 313e322. Almeida, P.P., de Araujo Filho, G.M., Malta, S.M., Laranjeira, R.R., Marques, A.C.R., Bressan, R.A., Lacerda, A.L., 2017. Attention and memory deficits in crack-cocaine users persist over four weeks of abstinence. J. Subst. Abus. Treat. 81, 73e78. Ashok, A.H., Mizuno, Y., Volkow, N.D., Howes, O.D., 2017. Association of stimulant use with dopaminergic alterations in users of cocaine, amphetamine, or methamphetamine: a systematic review and metaanalysis. JAMA Psychiatry 74 (5), 511e519. Ballard, M.E., Weafer, J., Gallo, D.A., de Wit, H., 2015. Effects of acute methamphetamine on emotional memory formation in humans: encoding vs consolidation. PLoS One 10 (2), e0117062. Ben-Shahar, O., Sacramento, A.D., Miller, B.W., Webb, S.M., Wroten, M.G., Silva, H.E., et al., 2013. Deficits in ventromedial prefrontal cortex group 1 metabotropic glutamate receptor function mediate resistance to extinction during protracted withdrawal from an extensive history of cocaine self-administration. J. Neurosci. 33 (2), 495e506a.

Bolla, K., Ernst, M., Kiehl, K., Mouratidis, M., Eldreth, D., Contoreggi, C., et al., 2004. Prefrontal cortical dysfunction in abstinent cocaine abusers. J. Neuropsychiatry Clin. Neurosci. 16 (4), 456e464. Bonci, A., Bernardi, G., Grillner, P., Mercuri, N.B., 2003. The dopaminecontaining neuron: maestro or simple musician in the orchestra of addiction? Trends Pharmacol. Sci. 24 (4), 172e177. Brecht, M., Herbeck, D., 2014. Time to relapse following treatment for methamphetamine use: a long-term perspective on patterns and predictors. Drug Alcohol Depend. 139, 18e25. Briand, L.A., Flagel, S.B., Garcia-Fuster, M.J., Watson, S.J., Akil, H., Sarter, M., Robinson, T.E., 2008. Persistent alterations in cognitive function and prefrontal dopamine D2 receptors following extended, but not limited, access to self-administered cocaine. Neuropsychopharmacology 33 (12), 2969e2980. Cadet, J.L., Jayanthi, S., 2013. Epigenetics of methamphetamine-induced changes in glutamate function. Neuropsychopharmacology 38 (1), 248e249. Carroll, K.M., Kiluk, B.D., Nich, C., Babuscio, T.A., Brewer, J.A., Potenza, M.N., et al., 2011. Cognitive function and treatment response in a randomized clinical trial of computer-based training in cognitivebehavioral therapy. Subst. Use Misuse 46 (1), 23e34. Castellano, F., Bartoli, F., Crocamo, C., Gamba, G., Tremolada, M., Santambrogio, J., et al., 2015. Facial emotion recognition in alcohol and substance use disorders: a meta-analysis. Neurosci. Biobehav. Rev. 59, 147e154. Chang, L., Alicata, D., Ernst, T., Volkow, N., 2007. Structural and metabolic brain changes in the striatum associated with methamphetamine abuse. Addiction 102 (s1), 16e32. Chen, Y.C., Chen, C.K., Wang, L.J., 2015. Predictors of relapse and dropout during a 12-week relapse prevention program for methamphetamine users. J. Psychoact. Drugs 47 (4), 317e324. Cherner, M., Suarez, P., Casey, C., Deiss, R., Letendre, S., Marcotte, T., et al., 2010. Methamphetamine use parameters do not predict neuropsychological impairment in currently abstinent dependent adults. Drug Alcohol Depend. 106 (2), 154e163. Crocker, C.E., Purdon, S.E., Hanstock, C.C., Lakusta, B., Seres, P., Tibbo, P.G., 2017. Enduring changes in brain metabolites and executive functioning in abstinent cocaine users. Drug Alcohol Depend. 178, 435e442. Czermainski, F.R., Willhelm, A.R., Santos, Á.Z., Pachado, M.P., de Almeida, R.M.M., 2017. Assessment of inhibitory control in crack and/or cocaine users: a systematic review. Trends Psychiatry Psychother. 39 (3), 216e225. Dean, A.C., London, E.D., Sugar, C.A., Kitchen, C.M.R., Swanson, A.-N., Heinzerling, K.G., et al., 2009. Predicting adherence to treatment for methamphetamine dependence from neuropsychological and drug use variables. Drug Alcohol Depend. 105 (1e2), 48e55. Dean, A.C., Groman, S.M., Morales, A.M., London, E.D., 2013. An evaluation of the evidence that methamphetamine abuse causes cognitive decline in humans. Neuropsychopharmacology 38 (2), 259e274. Degenhardt, L., Baxter, A.J., Lee, Y.Y., Hall, W., Sara, G.E., Johns, N., et al., 2014. The global epidemiology and burden of psychostimulant dependence: findings from the Global Burden of Disease Study 2010. Drug Alcohol Depend. 137 (1), 36e47.

Cognitive deficits in people with stimulant use disorders Chapter | 11

Domínguez-Salas, S., Díaz-Batanero, C., Lozano-Rojas, O.M., VerdejoGarcía, A., 2016. Impact of general cognition and executive function deficits on addiction treatment outcomes: systematic review and discussion of neurocognitive pathways. Neurosci. Biobehav. Rev. 71, 772e801. Donovan, D.M., Bigelow, G.E., Brigham, G.S., Carroll, K.M., Cohen, A.J., Gardin, J.G., et al., 2012. Primary outcome indices in illicit drug dependence treatment research: systematic approach to selection and measurement of drug use end-points in clinical trials. Addiction 107 (4), 694e708. Fagan, C.S., Carmody, T.J., McClintock, S.M., Suris, A., Nakamura, A., Jeon-Slaughter, H., et al., 2015. The effect of cognitive functioning on treatment attendance and adherence in comorbid bipolar disorder and cocaine dependence. J. Subst. Abus. Treat. 49, 15e20. Fernandez-Serrano, M.J., Pérez-García, M., Schmidt Río-Valle, J., Verdejo-Garcia, A., 2010. Neuropsychological consequences of alcohol and drug abuse on different components of executive functions. J. Psychopharmacol. 24 (9), 1317e1332. Fernández-Serrano, M.J., Pérez-García, M., Verdejo-García, A., 2011. What are the specific vs. generalized effects of drugs of abuse on neuropsychological performance? Neurosci. Biobehav. Rev. 35 (3), 377e406. Fox, H.C., Jackson, E.D., Sinha, R., 2009. Elevated cortisol and learning and memory deficits in cocaine-dependent individuals: relationship to relapse outcomes. Psychoneuroendocrinology 34 (8), 1198e1207. Freye, E., 2009. Pharmacology and Abuse of Cocaine, Amphetamines, Ecstasy and Related Designer Drugs. Springer Netherlands, Dordrecht, Netherlands. Garavan, H., Kaufman, J.N., Hester, R., 2008. Acute effects of cocaine on the neurobiology of cognitive control. Phil. Trans. Biol. Sci. 363 (1507), 3267e3276. Grant, B.F., Saha, T.D., Ruan, W.J., Goldstein, R.B., Chou, S.P., Jung, J., et al., 2016. Epidemiology of DSM-5 drug use disorder: results from the national epidemiologic survey on alcohol and related conditionseIII. JAMA Psychiatry 73 (1), 39e47. Hall, M.G., Hauson, A.O., Wollman, S.C., Allen, K.E., Connors, E.J., Stern, M.J., et al., 2017. Neuropsychological comparisons of cocaine versus methamphetamine users: a research synthesis and metaanalysis. Am. J. Drug Alcohol Abuse 44 (3), 1e17. Harris, M., Penfold, R.B., Hawkins, A., Maccombs, J., Wallace, B., Reynolds, B., 2014. Dimensions of impulsive behavior and treatment outcomes for adolescent smokers. Exp. Clin. Psychopharmacol 22 (1), 57e64. He, F., Lidow, I.A., Lidow, M.S., 2006. Consequences of paternal cocaine exposure in mice. Neurotoxicol. Teratol. 28 (2), 198e209. Iudicello, J.E., Woods, S.P., Vigil, O., Cobb Scott, J., Cherner, M., Heaton, R.K., et al., 2010. Longer term improvement in neurocognitive functioning and affective distress among methamphetamine users who achieve stable abstinence. J. Clin. Exp. Neuropsychol. 32 (7), 704e718. Jayanthi, S., McCoy, M.T., Chen, B., Britt, J.P., Kourrich, S., Yau, H.J., et al., 2014. Methamphetamine downregulates striatal glutamate receptors via diverse epigenetic mechanisms. Biol. Psychiatry 76 (1), 47e56.

161

Jovanovski, D., Erb, S., Zakzanis, K.K., 2005. Neurocognitive deficits in cocaine users: a quantitative review of the evidence. J. Clin. Exp. Neuropsychol. 27 (2), 189e204. Kasanetz, F., Lafourcade, M., Deroche-Gamonet, V., Revest, J.M., Berson, N., Balado, E., et al., 2013. Prefrontal synaptic markers of cocaine addiction-like behavior in rats. Mol. Psychiatry 18 (6), 729e737. Kiluk, B.D., Babuscio, T.A., Nich, C., Carroll, K.M., 2013. Smokers versus snorters: do treatment outcomes differ according to route of cocaine administration? Exp. Clin. Psychopharmacol 21 (6), 490e498. Kish, S.J., Fitzmaurice, P.S., Boileau, I., Schmunk, G.A., Ang, L.C., Furukawa, Y., et al., 2009. Brain serotonin transporter in human methamphetamine users. Psychopharmacology 202 (4), 649e661. Koob, G., Kreek, M.J., 2007. Stress, dysregulation of drug reward pathways, and the transition to drug dependence. Am. J. Psychiatry 164 (8), 1149e1159. Koob, G.F., Volkow, N.D., 2010. Neurocircuitry of addiction. Neuropsychopharmacology 35 (1), 217e238. Li, X., Wolf, M.E., 2015. Multiple faces of BDNF in cocaine addiction. Behav. Brain Res. 279, 240e254. Lopes, B.M., Gonçalves, P.D., Ometto, M., dos Santos, B., Cavallet, M., Chaim-Avancini, T.M., et al., 2017. Distinct cognitive performance and patterns of drug use among early and late onset cocaine users. Addict. Behav. 73, 41e47. Luscher, C., 2016. The emergence of a circuit model for addiction. Annu. Rev. Neurosci. 39, 257e276. McClure, S.M., Bickel, W.K., 2014. A dual-systems perspective on addiction: contributions from neuroimaging and cognitive training. Ann. N. Y. Acad. Sci. 1327 (1), 62e78. McReynolds, J.R., Vranjkovic, O., Thao, M., Baker, D.A., Makky, K., Lim, Y., Mantsch, J.R., 2014. Beta-2 adrenergic receptors mediate stress-evoked reinstatement of cocaine-induced conditioned place preference and increases in CRF mRNA in the bed nucleus of the stria terminalis in mice. Psychopharmacology 231 (20), 3953e3963. Mounteney, J., Griffiths, P., Sedefov, R., Noor, A., Vicente, J., Simon, R., 2016. The drug situation in Europe: an overview of data available on illicit drugs and new psychoactive substances from European monitoring in 2015. Addiction 111 (1), 34e48. Nejtek, V.A., Kaiser, K.A., Zhang, B., Djokovic, M., 2013. Iowa Gambling Task scores predict future drug use in bipolar disorder outpatients with stimulant dependence. Psychiatr. Res. 210 (3), 871e879. Nestler, E.J., 2014. Epigenetic mechanisms of drug addiction. Neuropharmacology 76 (Part B), 259e268. Noonan, M.A., Bulin, S.E., Fuller, D.C., Eisch, A.J., 2010. Reduction of adult hippocampal neurogenesis confers vulnerability in an animal model of cocaine addiction. J. Neurosci. 30 (1), 304e315. Novikova, S.I., He, F., Bai, J., Cutrufello, N.J., Lidow, M.S., Undieh, A.S., 2008. Maternal cocaine administration in mice alters DNA methylation and gene expression in hippocampal neurons of neonatal and prepubertal offspring. PLoS One 3 (4), e1919. Oh, J.S., Lyoo, I.K., Sung, Y.H., Hwang, J., Kim, J., Chung, A., et al., 2005. Shape changes of the corpus callosum in abstinent methamphetamine users. Neurosci. Lett. 384 (1), 76e81.

162 Cognition and Addiction

Panenka, W.J., Procyshyn, R.M., Lecomte, T., MacEwan, G.W., Flynn, S.W., Honer, W.G., Barr, A.M., 2013. Methamphetamine use: a comprehensive review of molecular, preclinical and clinical findings. Drug Alcohol Depend. 129 (3), 167e179. Patterson, F., Jepson, C., Loughead, J., Perkins, K., Strasser, A.A., Siegel, S., et al., 2010. Working memory deficits predict short-term smoking resumption following brief abstinence. Drug Alcohol Depend. 106 (1), 61e64. Paus, T., Keshavan, M., Giedd, J.N., 2008. Why do many psychiatric disorders emerge during adolescence? Nat. Rev. Neurosci. 9 (12), 947e957. Peacock, A., Leung, J., Larney, S., Colledge, S., Hickman, M., Rehm, J., et al., 2018. Global statistics on alcohol, tobacco and illicit drug use: 2017 status report. Addiction. https://doi.org/10.1111/add.14234. Potvin, S., Stavro, K., Rizkallah, É., Pelletier, J., 2014. Cocaine and cognition: a systematic quantitative review. J. Addict. Med. 8 (5), 368e376. Potvin, S., Pelletier, J., Grot, S., Hébert, C., Barr, A., Lecomte, T., 2018. Cognitive deficits in individuals with methamphetamine use disorder: a meta-analysis. Addict. Behav. 80, 154e160. Powell, J., Dawkins, L., West, R., Powell, J., Pickering, A., 2010. Relapse to smoking during unaided cessation: clinical, cognitive and motivational predictors. Psychopharmacology 212 (4), 537e549. Preller, K.H., Herdener, M., Schilbach, L., Stämpfli, P., Hulka, L.M., Vonmoos, M., et al., 2014. Functional changes of the reward system underlie blunted response to social gaze in cocaine users. Proc. Natl. Acad. Sci. U.S.A. 111 (7), 2842e2847. Robison, A.J., Nestler, E.J., 2011. Transcriptional and epigenetic mechanisms of addiction. Nat. Rev. Neurosci. 12 (11), 623e637. Rubenis, A.J., Fitzpatrick, R.E., Lubman, D.I., Verdejo-Garcia, A., 2017. Working memory predicts methamphetamine hair concentration over the course of treatment: moderating effect of impulsivity and implications for dual-systems model. Addict. Biol. https://doi.org/10.1111/ adb.12575. Rubenis, A.J., Fitzpatrick, R.E., Lubman, D.I., Verdejo-Garcia, A., 2018. Impulsivity predicts poorer improvement in quality of life during early treatment for people with methamphetamine dependence. Addiction 113 (4), 668e676. Schulte, M.H., Cousijn, J., den Uyl, T.E., Goudriaan, A.E., van den Brink, W., Veltman, D.J., et al., 2014. Recovery of neurocognitive functions following sustained abstinence after substance dependence and implications for treatment. Clin. Psychol. Rev. 34 (7), 531e550. Scott, J.C., Woods, S.P., Matt, G.E., Meyer, R.A., Heaton, R.K., Atkinson, J.H., Grant, I., 2007. Neurocognitive effects of methamphetamine: a critical review and meta-analysis. Neuropsychol. Rev. 17 (3), 275e297. Sheffer, C., MacKillop, J., McGeary, J., Landes, R., Carter, L., Yi, R., et al., 2012. Delay discounting, locus of control, and cognitive impulsiveness independently predict tobacco dependence treatment outcomes in a highly dependent, lower socioeconomic group of smokers. Am. J. Addict. 21 (3), 221e232. Shibasaki, M., Mizuno, K., Kurokawa, K., Ohkuma, S., 2011. L-type voltage-dependent calcium channels facilitate acetylation of histone H3 through PKCg phosphorylation in mice with methamphetamineinduced place preference. J. Neurochem. 118 (6), 1056e1066. Shoptaw, S., 2014. Commentary on Gowin et al. (2014): brain is behaviordmethamphetamine dependence and recovery. Addiction 109 (2), 248e249.

Smith, S.M., Vaughan, J.M., Donaldson, C.J., Rivier, J., Li, C., Chen, A., Vale, W.W., 2004. Cocaine-and amphetamine-regulated transcript activates the hypothalamic-pituitary-adrenal axis through a corticotropin-releasing factor receptor-dependent mechanism. Endocrinology 145 (11), 5202e5209. Smith, J.L., Mattick, R.P., Jamadar, S.D., Iredale, J.M., 2014. Deficits in behavioural inhibition in substance abuse and addiction: a metaanalysis. Drug Alcohol Depend. 145, 1e33. Spencer, S., Scofield, M., Kalivas, P.W., 2016. The good and bad news about glutamate in drug addiction. J. Psychopharmacol. 30 (11), 1095e1098. Spronk, D.B., van Wel, J.H., Ramaekers, J.G., Verkes, R.J., 2013. Characterizing the cognitive effects of cocaine: a comprehensive review. Neurosci. Biobehav. Rev. 37 (8), 1838e1859. Stahl, S.M., 2013. Stahl’s Essential Psychopharmacology: Neuroscientific Basis and Practical Applications, fourth ed. Cambridge University Press, Cambridge, UK. Stevens, L., Verdejo-García, A., Goudriaan, A.E., Roeyers, H., Dom, G., Vanderplasschen, W., 2014. Impulsivity as a vulnerability factor for poor addiction treatment outcomes: a review of neurocognitive findings among individuals with substance use disorders. J. Subst. Abus. Treat. 47 (1), 58e72. Streeter, C.C., Terhune, D.B., Whitfield, T.H., Gruber, S., Sarid-Segal, O., Silveri, M.M., et al., 2008. Performance on the Stroop predicts treatment compliance in cocaine-dependent individuals. Neuropsychopharmacology 33 (4), 827e836. Sudai, E., Croitoru, O., Shaldubina, A., Abraham, L., Gispan, I., Flaumenhaft, Y., et al., 2011. High cocaine dosage decreases neurogenesis in the hippocampus and impairs working memory. Addict. Biol. 16 (2), 251e260. Tendilla-Beltrán, H., Arroyo-García, L.E., Diaz, A., Camacho-Abrego, I., de la Cruz, F., Rodríguez-Moreno, A., Flores, G., 2016. The effects of amphetamine exposure on juvenile rats on the neuronal morphology of the limbic system at prepubertal, pubertal and postpubertal ages. J. Chem. Neuroanat. 77, 68e77. Thompson, P.M., Hayashi, K.M., Simon, S.L., Geaga, J.A., Hong, M.S., Sui, Y., et al., 2004. Structural abnormalities in the brains of human subjects who use methamphetamine. J. Neurosci. 24 (26), 6028e6036. Tiffany, S.T., Friedman, L., Greenfield, S.F., Hasin, D.S., Jackson, R., 2012. Beyond drug use: a systematic consideration of other outcomes in evaluations of treatments for substance use disorders. Addiction 107 (4), 709e718. Tobias, M.C., O’Neill, J., Hudkins, M., Bartzokis, G., Dean, A.C., London, E.D., 2010. White-matter abnormalities in brain during early abstinence from methamphetamine abuse. Psychopharmacology 209 (1), 13e24. Turner, T.H., LaRowe, S., Horner, M.D., Herron, J., Malcolm, R., 2009. Measures of cognitive functioning as predictors of treatment outcome for cocaine dependence. J. Subst. Abus. Treat. 37 (4), 328e334. Verdejo-Garcıa, A.J., López-Torrecillas, F., de Arcos, F.A., PérezGarcıa, M., 2005. Differential effects of MDMA, cocaine, and cannabis use severity on distinctive components of the executive functions in polysubstance users: a multiple regression analysis. Addict. Behav. 30 (1), 89e101. Verdejo-Garcia, A., 2017. Neuroclinical assessment of addiction needs to incorporate decision-making measures and ecological validity. Biol. Psychiatry 81 (7), e53ee54.

Cognitive deficits in people with stimulant use disorders Chapter | 11

Verdejo-Garcia, A., Benbrook, A., Funderburk, F., David, P., Cadet, J.L., Bolla, K.I., 2007a. The differential relationship between cocaine use and marijuana use on decision-making performance over repeat testing with the Iowa Gambling Task. Drug Alcohol Depend. 90 (1), 2e11. Verdejo-García, A., Rivas-Pérez, C., Vilar-López, R., Pérez-García, M., 2007b. Strategic self-regulation, decision-making and emotion processing in poly-substance abusers in their first year of abstinence. Drug Alcohol Depend. 86 (2e3), 139e146. Verdejo-Garcia, A., Albein-Urios, N., Martinez-Gonzalez, J.M., Civit, E., De la Torre, R., Lozano, O., 2014. Decision-making impairment predicts 3-month hair-indexed cocaine relapse. Psychopharmacology 231 (21), 4179e4187. Vergara-Moragues, E., Verdejo-García, A., Lozano, O.M., SantiagoRamajo, S., González-Saiz, F., Espinosa, P.B., García, M.P., 2017. Association between executive function and outcome measure of treatment in therapeutic community among cocaine dependent individuals. J. Subst. Abus. Treat. 78, 48e55. Vonmoos, M., Hulka, L.M., Preller, K.H., Jenni, D., Schulz, C., Baumgartner, M.R., Quednow, B.B., 2013. Differences in selfreported and behavioral measures of impulsivity in recreational and dependent cocaine users. Drug Alcohol Depend. 133 (1), 61e70. Vonmoos, M., Hulka, L.M., Preller, K.H., Minder, F., Baumgartner, M.R., Quednow, B.B., 2014. Cognitive impairment in cocaine users is drug-

163

induced but partially reversible: evidence from a longitudinal study. Neuropsychopharmacology 39 (9), 2200e2210. Wang, Y., Chou, J., Jeng, C.H., Morales, M., Wang, J.Y., 2000. Chronic methamphetamine exposure decreases high affinity uptake function in norepinephrine afferents in the cerebellar cortex: an electrophysiological and electrochemical study. Neuropharmacology 39 (11), 2112e2123. Washio, Y., Higgins, S.T., Heil, S.H., McKerchar, T.L., Badger, G.J., Skelly, J.M., Dantona, R.L., 2011. Delay discounting is associated with treatment response among cocaine-dependent outpatients. Exp. Clin. Psychopharmacol 19 (3), 243e248. Winstanley, C.A., Green, T.A., Theobald, D.E., Renthal, W., LaPlant, Q., DiLeone, R.J., et al., 2009. DFosB induction in orbitofrontal cortex potentiates locomotor sensitization despite attenuating the cognitive dysfunction caused by cocaine. Pharmacol. Biochem. Behav. 93 (3), 278e284. Wolf, M.E., 2010. The Bermuda Triangle of cocaine-induced neuroadaptations. Trends Neurosci. 33 (9), 391e398. Zhao, Q., Hou, J., Chen, B., Shao, X., Zhu, R., Bu, Q., et al., 2015. Prenatal cocaine exposure impairs cognitive function of progeny via insulin growth factor II epigenetic regulation. Neurobiol. Dis. 82, 54e65.

Chapter 12

Cognitive consequences of 3,4-methylenedioxymethamphetamine use Catharine Montgomery1 and Carl A. Roberts2 1

School of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool, United Kingdom; 2Institute of Psychology, Health and

Society, University of Liverpool, Liverpool, United Kingdom

Introductiondepidemiology of 3,4-methylenedioxymethamphetamine use 3,4-Methylenedioxymethamphetamine commonly referred to as MDMA or ecstasy is a ring-substituted phenethylamine with empathogenic properties (Dumont and Verkes, 2006). While MDMA was originally patented by the German pharmaceutical company Merck in 1914, it did not really come to prominence until it was resynthesized by Alexander Shulgin in 1965 (Shulgin and Shulgin, 1991). After this time, MDMA was used as an aid to psychological therapy in the United States (Beck and Rosenbaum, 1994), showing promising effects for increasing openness in marriage and relationship problems (Greer and Tolbert, 1986). It went on to become popular as a club drug during the 1980s, particularly at raves and dance music events (Parrott, 2001), and increases in use continued for the next 20 years (Schuster et al., 1998). MDMA use was made illegal in the United Kingdom in 1977 under a modification of the Misuse of Drugs Act (1977), making it a Class A drug and placing it under schedule I, which contains drugs that are believed to have no therapeutic value and are thus illegal to possess, supply, or prescribe (White et al., 1997). Similarly, it was banned in the United States in 1985 and placed on the schedule I list of prohibited substances (Kraner et al., 2001) and in Australia in 1986, where it was placed in schedule 9. Despite prohibition of MDMA, according to the World Drug Report (WDR) (United Nations Office on Drugs and Crime, UNODC, 2017), globally in 2017, it was estimated that there were 21,650,000 MDMA users, representing 0.45% of adults aged 16e54. After Oceania (2.42%), Europe has the

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00012-5 Copyright © 2020 Elsevier Inc. All rights reserved.

highest estimated use (0.69%), followed by the Americas (0.51%), Asia (0.43%), and Africa (0.22%). Household population estimate surveys in various countries indicate that use is highest in the 16e25 age group (e.g., Broadfield, 2017). The global market for substances containing MDMA is smaller than that of other amphetamine-type drugs such as methamphetamine, but it is becoming more complex, with various forms and types of substances available. The WDR (UNODC, 2017) highlights that there are currently three main types of MDMA available: (i) ecstasy in tablet form, which contains little or no MDMA; (ii) ecstasy in tablet form with very high MDMA content; and (iii) ecstasy which is sold in powder or crystal form of varying purities and under a range of names, e.g., Molly, Mandy, Magic. After the increases in use in the 1980s and 1990s, use peaked around early 2000s (Parrott, 2013) at about the same time purity of tablets was also at its peak. In the late 2000s, purity dropped significantly. At this time, there was a subsequent rise in the use of Novel Psychoactive Substances (NPS) and a drop in MDMA use. One purported reason for this is a decrease in MDMA supply driven by a number of seizures of MDMA precursors such as Safrole oil (Mounteney et al., 2018). During this low purity period, many tablets contained NPS and other adulterants such as paramethoxymethamphetamine, which lead to increases in risk of harm from using MDMA (Nicol et al., 2015; EMCDDA, 2003). Anecdotally, reports suggest that users believe that powdered MDMA is of higher quality and thus safer than tablets, leading to increased popularity of powdered MDMA in recent years, particularly in the United States (Palamar, 2017). Recently, the ecstasy market has recovered, with a decrease in adulterants reported in

165

166 Cognition and Addiction

drug seizures over the 2010e16 period and fewer NPS and stimulant adulterants (UNODC, 2017). Use of MDMA is generally recreational, and while individuals can become habituated to use as with any substance, addictive potential is low (O’Brien, 1996). It is thus not clear what might predispose one to use MDMA as opposed to another recreational drug, although the desired empathogenic effects are the most likely cause of use. Some studies have proposed a role for self-medication (Khantzian, 1997) with depression predating MDMA. One Dutch cohort study found that abnormal scores on anxiety and depression at age 10 could predict future ecstasy use at age 25 (Huizink et al., 2006), with two further studies implicating a role for preexisting phobias, somatoform disorders and syndromes, dysthymia, and panic disorders in future ecstasy use (Falck et al., 2006; Lieb et al., 2002). In addition to these psychiatric abnormalities, a range of sociodemographic variables including education, employment status, relationship status, and social class were all correlated with future use of ecstasy suggesting that use is multifaceted. In retrospective studies, such mood differences have been observed in MDMA users, with some studies reporting higher rates of depression, especially in the short-term after use (e.g., McGuire et al., 1994; MacInnes et al., 2001; Parrott and Lasky, 1998), although polysubstance abuse is believed to contribute to these effects (Roiser and Sahakian, 2004). MDMA users have also exhibited differences on personality scales, which could contribute to drug initiation and continuation of drug use, as seen in other substance users. For example, MDMA users report higher levels of extraversion on the Big Five personality scale (Ter Bogt et al., 2006), a trait which has been linked to reward sensitivity and frequency of intoxication (Depue and Collins, 1999). Users of MDMA also exhibit higher scores on measures of novelty seeking (Dughiero et al., 2001) and impulsivity (Morgan, 1998; Morgan et al., 2006), although the latter is not a consistent finding (Gouzoulis-Mayfrank et al., 2003).

Neuropharmacological/neuroadaptive effects of 3,4methylenedioxymethamphetamine Pharmacokinetics and pharmacodynamics Recreationally, MDMA is usually administered orally and enters circulation via the liver. Peak plasma concentrations are seen 1.5e3.0 h after oral administration (de la Torre et al., 2000), with a 100 mg dose producing an elimination half-life of around 9 h (de la Torre et al., 2000b). For a discussion of pharmacokinetic time course and related effects, see de la Torre et al. (2000).

In the brain, MDMA is a monoamine reuptake inhibitor, causing the release of serotonin, dopamine, and norepinephrine, in addition to blocking their reuptake (Berger et al., 1992; Nichols et al., 1982). Increased levels of serotonin in the synapse are responsible for the majority of the primary subjective effects, with MDMA administration purported to release up to 80% of stored serotonin (Green et al., 2003). It has been suggested that use of MDMA can damage the serotonin system, and the remainder of this section discusses research investigating MDMA-related changes in brain function.

Animal research There are a number of factors that might contribute to MDMA-related serotonergic damage in the brains of both animals and humans. One of the most prominent theories of axonal damage is The Integrated Hypothesis proposed by Sprague et al. (1998), in which the authors propose a clear role for dopamine in the damage sustained to serotonin axons. This hypothesis is based on animal studies which have observed that manipulating levels of dopamine causes changes in levels of neurotoxicity following MDMA administration (see, for example, Aguirre et al. (1998); Nash and Nichols, 1991; Stone et al. (1988)). However, the exact mechanism of MDMA-related neurotoxicity in animal models is not fully understood. Studies in both animals and humans have implicated external factors which may play a role, for example, increased ambient temperature leading to hyperthermia can increase the level of axonal loss (Capela et al., 2006; Green et al., 2003). Furthermore, when housed at higher temperatures to induce hyperthermia, rats do still sustain serotonergic damage even if administered a-methyl-p-tyrosine to inhibit dopamine synthesis (Yuan et al., 2002). Thus the mechanism of neurotoxicity must be more complex than previously thought. One likely explanation is that a neurotoxic metabolite of MDMA itself causes axonal loss. For example, methylenedioxyethylamphetamine (MDA), which is both a metabolite of MDMA (de la Torre et al., 2004) and an adulterant in MDMA tablets/powder (ecstasydata.org, 2018), has been shown to cause similar axonal loss in rodent studies (Battaglia et al., 1987; Stone et al., 1986). Moreover, metabolites of both MDMA and MDA have also been found to cause degeneration of serotonin axons in rodents, particularly under higher ambient temperatures (Capela et al., 2006). Thus it is likely that metabolites of MDMA and related compounds, potentially in conjunction with dopamine, are the cause of MDMA-related serotonergic axon loss in animals. Animal research has been useful for elucidating a potential mechanism of neurotoxicity and testing this theory in a range of paradigms. Early studies in rodents, using various dosing

Cognitive consequences of 3,4-methylenedioxymethamphetamine use Chapter | 12

regimens, showed loss of serotonergic uptake sites following MDMA administration (Broening et al., 1994b, 1995; Schmidt et al., 1994), in addition to reductions in brain serotonin (Schmidt et al., 1986; Stone et al., 1987). It is unclear why MDMA-related damage appears to be restricted to axon terminals in rodent studies, but this could be related to dose. Studies in nonhuman primates have similarly provided evidence of the neurotoxicity of MDMA. Rhesus monkeys have shown significant reductions in levels of brain 5-HT and 5-HIAA 14 weeks following administration, coupled with a significant loss of cortical serotonin, but not dopamine uptake sites (Insel et al., 1989). Rhesus monkeys have also shown significant decreases in hippocampal 5-HT 30 days after MDMA administration (Ali et al., 1993). For reviews of animal literature, see Battaglia et al. (1998) and Ricaurte et al. (2000). While studies in rodents and nonhuman primates have shown that MDMA has the potential to be a neurotoxin and have suggested a mechanism for this neurotoxicity, it is sometimes difficult to extrapolate these findings from animal studies to humans. However, it is believed that interspecies scaling paradigms are relatively robust (Nair and Jacob, 2016). For a discussion of the comparable doses in humans and rodents, see Baumann et al. (2009).

Human imaging Various paradigms and techniques of neuroimaging have been used to retrospectively investigate changes to brain structure and function in human MDMA users. For brevity, this section focuses primarily on studies using magnetic resonance imaging (MRI) and positron emission tomography (PET). For functional imaging during cognitive performance, see Section 3.2. MRI has been used extensively in human MDMA users to assess changes of the neural correlates that might underlie changes in behavior and cognition. Using functional MRI (fMRI), Cowan et al. (2003) observed reduced gray matter density in ecstasy polydrug users in areas of the neocortex that have been linked to executive functions (Miyake et al., 2000), namely Brodmann’s areas 18, 21, and 45 (secondary visual cortex, middle temporal gyrus, and inferior frontal gyrus, respectively). Decreases in gray matter density were also observed in the bilateral cerebellum and the midline brainstem. In a later study, Cowan and co-workers (Cowan et al., 2006) investigated the blood oxygen leveledependent (BOLD) response to visual cortex activation in ecstasy users; however, no between-group differences were observed in visual cortex activation. Subsequent within-group analyses revealed that MDMA exposure was correlated with number of activated pixels for photic stimulation. However, MDMA exposure was not correlated with BOLD signal change; lifetime use of other

167

substances (alcohol, hallucinogens, sedatives, cannabis) was inversely related to this measure. MDMA-related decreases in cerebral blood flow (CBF) in response to pharmacological challenge have also been observed using MRI. Schouw et al. (2012) found that such differences were most pronounced in the left thalamus, although significant decreases were also observed in the right occipital cortex and the right frontal cortex. However, increases in CBF were observed in the left globus pallidus and left frontal cortex. Using diffusion tensor imaging (DTI) in MRI, which allows imaging of tissue at a microscopic level, de Win et al. (2007) found that significant differences in white matter following MDMA use were reduced to below statistical significance following control for multiple comparisons. However, it is worthy of note that the reductions in the apparent diffusion coefficient (ADC) observed in MDMA users in the thalamus remained significant. The authors suggest that this could reflect sustained vasoconstriction of cerebral blood vessels following even moderate use of MDMA. Furthermore, at follow-up (de Win et al., 2008), MDMA users still exhibited significant decreases in fractional anisotropy (FA) in the thalamus and white matter in frontoparietal areas. These changes were again coupled with changes in ADC in the thalamus, which the authors propose reflect axonal damage related to MDMA use. Furthermore, changes in ADC proposed to reflect axon loss have also been reported by Reneman and co-workers (Reneman et al., 2001) in the globus pallidus. However, these decreases in FA in the thalamus have not been consistently shown in other studies, with Liu et al. (2011) showing significant increases in FA in the bilateral thalami. MDMA users did, however, show similar changes in ADCs, with decreases observed in the thalamus bilaterally and increases in the bilateral anterior internal capsule, the bilateral superior longitudinal fasciculus, and the splenium and genu of the corpus callosum. Moeller et al. (2007) also observed reduced diffusivities in MDMA users relative to controls in the corpus callosum, although no differences were observed in FA as in previous studies. Taken together, the findings in DTI suggest that human users of MDMA sustain damage to serotonergic axons, with some studies suggesting recovery with abstinence and a role of the use of other drugs. PET studies have also variably been used to investigate MDMA-related changes in CBF and to measure changes in serotonin transporter (SERT) density when using radioligands that label the SERT during the scan. An early study by McCann et al. (1998) used the latter technique and observed a significant global decrease in the distribution volume ratios (DVRs) of SERT in MDMA users relative to controls. These differences were also related to extent of previous MDMA exposure, suggesting a doseeresponse relationship. These results were supported by Buchert et al.

168 Cognition and Addiction

(2003), who found ecstasy-related reductions in DVRs of SERT in the mesencephalon, caudate, and thalamus. The effects were not present in former users, suggesting possible recovery of function with extending abstinence. Another study replicated this, showing significantly reduced DVRs in the mesencephalon, with typical number of ecstasy exposures predicting DVRs in the thalamus and caudate nucleus and lifetime dose (tablets) predicting DVRs in the mesencephalon (Thomasius et al., 2003). A longitudinal study has also shown that there may be some recovery of function with decreases in level of use (Thomasius et al., 2006). Ecstasy-related differences in DVRs of SERT were observed at baseline in the mesencephalon, although these effects were nonsignificant at follow-up, where the current MDMA users indicated that they were using at lower levels compared with baseline. Interestingly, in a study comparing only former users with polydrug users and nondrug users, no differences were observed between any group in DVRs (Sudhakar et al., 2009). In addition, a follow-up of original participants by Buchert et al. (2003) (Buchert et al., 2004) observed significant reductions in DVRs in current users in the posterior cingulate gyrus, left caudate, thalamus, occipital cortex, medial temporal lobes, and the mesencephalon. Moreover, abstinence was positively correlated with DVRs, further supporting the notion that SERT damage can be reversed after cessation of use. However, the reduced binding was more pronounced in female users than in male users. This was explored further in a study looking solely at female MDMA users, where increases in 5-HT2a binding were observed in occipital-parietal, temporal, occipito-temporalparietal, frontal, and frontoparietal regions (Di lorio et al., 2012). The effects appear to be attributable to previous ecstasy use within the sample, with lifetime dose correlating with 5-HT2a binding in frontoparietal, occipitotemporal, frontolimbic, and frontal regions. Urban et al. (2012) observed similar upregulation of 5-HT2a receptor binding, with decreased regional SERT availability in various cortical, but not subcortical, regions. McCann and co-workers have also shown significantly reduced SERT binding in multiple brain regions (occipital cortex, parietal cortex, temporal cortex, anterior cingulate cortex, posterior cingulate cortex, dorsolateral prefrontal cortex [DLPFC], and hippocampus) (McCann et al., 2008), with DVRs of SERT correlating with duration of abstinence and typical monthly dose of MDMA (McCann et al., 2005). This is supported by Kish et al. (2010), reporting region-specific decreases in SERT binding in ecstasy users in the entire cerebral cortex, with the occipital cortex reductions being most pronounced. In summary, both MRI and PET have been useful in providing evidence for changes to the structure of the brain in human MDMA users. However, it should be noted that many studies suggest recovery, at least in part, after

prolonged abstinence, a role for the concomitant use of other drugs and a role for level of previous ecstasy use. In addition, the use of some radioligands in PET studies has been criticized in relation to their selectivity for SERT. However, a recent metaanalysis of imaging studies has provided a good discussion of the specificity of ligands used in such studies and provides evidence that these techniques can be useful for reducing polydrug effects in subsequent analyses (see Roberts et al., 2016a).

Potential adverse effects and pharmacologically confounding factors Cognitive consequences of use are discussed in the Section Cognitive deficits associated with MDMA, while negative physiological consequences and potential confounding factors are discussed here. The acute effects of MDMA range from mild empathogen and entactogen qualities to potential systemic toxicity (Rietjens et al., 2012). The most frequently reported adverse effects following administration are hypertension (Harris et al., 2002), hyperthermia (Ridpath et al., 2014), arrhythmia (Badon et al., 2002), lack of appetite (Gamma et al., 2000), nausea (Liechti et al., 2000), and, in more severe cases, loss of consciousness (Le Roux et al., 2015). For prevalence of adverse effects, see Green et al. (2003) or Devlin & Henry (2008). There are a number of factors, both physiological and environmental, which could exacerbate the toxic effects of MDMA, through their interaction with individual differences in pharmacokinetics (Capela et al., 2009). For example, gender differences have been observed, with females more likely to experience adverse effects, possibly because of the effects of hormones on pharmacokinetics (Liechti et al., 2001; Simmler et al., 2011). Genetic differences in the levels of enzymes used in the metabolism of MDMA might also play a role. Poor metabolizers (PM) with low activity of the main enzyme used to metabolize MDMAdCYP2D6dhave been shown to experience heightened subjective effects acutely because of higher plasma levels of the drug (Tucker et al., 1994). Sustained higher plasma levels could also increase the adverse effects as rodent studies have shown that CYP2D6 status determines hyperthermia-related toxicity (Colado et al., 1995), although this has been difficult to replicate in humans (Farré et al., 2007). Nonetheless, poorer CYP2D6 function has been shown to result in a longer plasma half-life in humans, which results in sustained acute effects and thus possible toxicity (Hysek et al., 2012). The effects of CYP2D6 status are further confounded by MDMA-related inhibition of enzyme activity (see Rietjens et al., 2012 for review). Other enzymes implicated in the breakdown of monoamines have also been implicated in heightened MDMA toxicity. For example, decreases in catechol-O-methyl-transferase (COMT) have been shown to increase systemic (Capela et al., 2009) and hepatotoxicity (Antolino-Lobo et al., 2010). SERT

Cognitive consequences of 3,4-methylenedioxymethamphetamine use Chapter | 12

polymorphisms, such as the SERT genotype 5-HTTLPR, which indicates decreased SERT and therefore results in lower levels of 5-HT uptake, have been implicated in a number of studies of MDMA users. MDMA users with this genotype have been shown to have a higher susceptibility to mood disorders (Martín-Santos et al., 2010), although there is limited evidence for increased severity of cognitive deficits in human users with this polymorphism (Reneman et al., 2006). Polydrug use increases the risk associated with MDMA use because of interactions between drugs and their metabolites. Different drug combinations can increase or decrease toxicity; for example, co-use of an Selective Serotonin Reuptake Inhibitor (SSRI) at the same time as MDMA, either as medically recommended or recreationally, can affect the metabolism of any drugs taken at the same time via induction of enzymes in the liver (Farré et al., 2007; Segura et al., 2005). Polydrug use is very common among recreational ecstasy users, with relatively few studies utilizing samples of ecstasy-only users (e.g., Halpern et al., 2004). Thus it is likely that any observed neurotoxic effects in human users are related to combined drug effects. This is problematic as some drugs have been suggested to increase the neurotoxic potential of MDMA; for example, the co-use of ecstasy with cocaine could increase circulating levels of dopamine, and thus increase neurotoxicity, while co-use of ecstasy and cannabis could reduce ecstasy-related hyperthermia and thus reduce any adverse effects (Sarne and Keren, 2004). For a review of polydrug use and effects of MDMA, see Carvalho et al., 2012).

Cognitive deficits associated with 3,4-methylenedioxymethamphetamine Research on cognitive deficits in ecstasy polydrug users is borne out of preclinical psychopharmacology work showing dense innervation of serotonin receptors in prefrontal brain regions necessary for performing many higher-order cognitive tasks. Unfortunately data on longterm abstinence from use are generally lacking (>5 years abstinence). However, there are many studies investigating short/medium-term cognitive effects. The initial paragraphs in this section are a brief overview of the most robust effects observed with recreational MDMA polydrug users, which are in explicit memory domains. Following this, we discuss higher-order “executive” functions which are a set of general-purpose control processes (Miyake and Friedman, 2012) which underpin and contribute more broadly to our overall cognitive functioning. Ecstasy users show consistent deficits relative to controls in the declarative memory, i.e., conscious memory recall. This appears to be true for immediate, as well as delayed word (Bolla et al. 1998; Downey et al., 2015; Parrott et al. 1998; Parrott and Lasky, 1998; Reneman et al.,

169

2001; Thomasius et al., 2003) and prose (Bhattachary and Powell, 2001; Krystal et al., 1992; Morgan, 1999; Morgan et al., 2002) recall tasks. Further to this, individual reports suggest that poor recall is associated with increased monthly (Bolla et al., 1998), past year (Price et al., 2014), and lifetime dose of ecstasy (Gouzoulis-Mayfrank et al., 2000; Downey et al., 2015). Schilt et al. (2007) suggest that novice users with low ecstasy exposure show significant immediate and delayed recall deficits compared with controls and that verbal recall is particularly sensitive to ecstasy effects. However, Thomasius et al. (2003) suggest that deficits in prose recall do not improve with abstinence. Prospective memory (PRM) is a “real-world” memory process which involves remembering to carry out a future intended action, e.g., remembering to post a birthday card. Ecstasy users appear to be poor at this type of memory. This has been shown by Heffernan et al. (2001a; 2001b), who report ecstasy-related impairment on short-term habitual memory subscales of the Prospective Memory Questionnaire (PMQ). Rendell et al. (2007) observe ecstasy users to be significantly impaired relative to nonusers on prospective memory measures in an ecologically valid “virtual week” paradigm, a pattern which remained unchanged after controlling for cannabis use. Furthermore, Rendell et al. (2007) suggest that greater PRM deficits are apparent in more frequent ecstasy users. Gallagher et al. (2014) also suggest that ecstasy users have generalized PRM problems which may interfere with everyday functioning, with ecstasy users displaying PRM problems that are associated with increased average session dose (i.e., higher nightly doses), rather than cumulative lifetime dose. Executive functioning in the broader context of working memory has been examined extensively in ecstasy polydrug users. This is due to the executive functions relying on recruitment of the prefrontal cortex (PFC) for adequate performance. The PFC contains a large number of 5-HT2A receptors, as such it is thought that executive performance deficits may be the result of serotonergic neurodegeneration. Miyake et al. (2000) suggested that the central executive of working memory was not a unified construct and that the executive functions comprise 3 separable subfunctions, including mental set switching, inhibitory control, and memory updating. Fisk and Sharp (2004) added access to long-term memory as a further component. Findings have been mixed in terms of ecstasy users’ performance on executive tasks. For example, many studies examining switching (the ability to switch attention between tasks) suggest there are limited performance differences between ecstasy users and controls (see, for example, Back-Madruga et al., 2003; Dafters et al., 2004; Fox et al., 2001; Hoshi et al., 2007; McCardle et al., 2004; Montgomery et al., 2005; Reneman et al., 2006; Zakzanis and Young, 2001), yet using pooled data in a metaanalysis, Roberts et al. (2016b) observe

170 Cognition and Addiction

ecstasy users to be significantly impaired relative to nonuser control groups. Inhibitory control, the ability to withhold or inhibit a dominant response when it is not necessary, seems generally unaffected in ecstasy users compared with controls whether it is assessed using the Stroop task (Back-Madruga et al., 2003; Croft et al., 2001; Gouzoulis-Mayfrank et al., 2000; Halpern et al., 2011; Morgan et al., 2002; Wagner et al., 2012), Random Letter Generation (Fisk et al., 2004; Fisk and Montgomery, 2009; Montgomery et al., 2005; Murphy et al., 2011), or Go/No Go (Gouzoulis-Mayfrank et al., 2003; Hanson and Luciana, 2010; Roberts and Garavan, 2010). However, memory updating (involving continuous monitoring and filtering of information to update the contents of working memory) shows more consistent impairment following ecstasy use. Ecstasy users perform consistently worse than controls at letter updating (Montgomery and Fisk, 2008; Montgomery et al., 2005), whereby increased MDMA use leads to poorer performance. Conversely, studies using the backward digit span measure of memory updating frequently report no observable deficits in ecstasy users (Bedi and Redman, 2008; Bhattachary and Powell, 2001; Croft et al., 2001; Gouzoulis-Mayfrank et al., 2003; Nulsen et al., 2011; Reay et al., 2006; Thomasius et al., 2006). Spatial working memory deficits are associated with MDMA dose intensity (Hanson and Luciana, 2010) and frequency of use (Montgomery and Fisk, 2008). Spatial working memory effects may also persist after prolonged abstinence (>6 months) and remain after controlling for cannabis use (Montgomery and Fisk, 2008). Similarly users are poorer at computation span than controls, and this also persists after cessation of use (Wareing et al., 2004, 2005). However, low-level difficulty n-back tasks rarely yield ecstasy-related cognitive deficits (Daumann et al., 2003a,b; Daumann et al., 2004; Gouzoulis-Mayfrank et al., 2003) probably because of the low-level cognitive demand. Finally, access to semantic/long-term memory (retrieval of words and ability to access long-term memory) is usually assessed using word fluency tasks such as the Chicago Word Fluency Task (CWFT) and Controlled Oral Word Association Task (COWAT). Ability to perform this function accurately relies on areas of the DLPFC (Stuss et al., 1998). Ecstasy-related deficits are apparent in written word fluency using the CWFT (Fisk and Montgomery, 2009; Montgomery et al., 2005, 2007), yet this seems to be less problematic in its oral format (FAS task or COWAT), perhaps because of the oral version being much shorter and not placing sustained load on the DLPFC (e.g., Semple et al., 1999).

Functional imaging A number of fMRI studies from Daumann and co-workers have investigated brain activity during cognitive performance in MDMA users. Daumann et al. (2003a)

investigated differences in activation between heavy MDMA users, moderate MDMA users, and nonuser controls during n-back performance. Deficits were seen in heavy users and BOLD responses were observed in the left frontal and temporal regions relative to moderate users and controls. In another study, to attempt to control for the effects of concomitant use of other drugs in the ecstasyusing group, Daumann et al. (2003b) compared pure ecstasy users, polyvalent ecstasy users (concomitant use of ecstasy and amphetamines and cannabis), and nonuser controls on the n-back task. Only pure MDMA users showed reduced BOLD signaling in the temporal and angular gyri during the 1-back condition. A similar pattern was seen in the 2-back condition, where pure MDMA users exhibited lower BOLD activation in the angular gyrus. In another fMRI study, Daumann et al. (2005) report hippocampal dysfunction in ecstasy/polydrug users relative to cannabis-only users. While there were no performance deficits on a paired associate learning task, ecstasy users exhibited less activity in the left hippocampus compared with the control group. Ecstasy users have also displayed greater BOLD activation during delayed, but not immediate, recall tasks in three discreet brain areas (Moeller et al., 2004): the left medial and superior frontal gyri; the left thalamus, caudate, and putamen; the right hippocampal formation. However, after controlling for cannabis use in the sample, the effects in the frontal cortex were no longer significant, suggesting that concomitant use of cannabis is a confounding factor. In line with the preceding findings of hippocampal dysfunction, Jacobsen et al. (2004) found that adolescent MDMA users displayed significantly lower hippocampal activity relative to controls during a working memory task. Further analyses revealed that abstinence period was significantly negatively correlated with left hippocampal activity, but that this may recover after prolonged abstinence. Raj et al. (2010) used fMRI during a semantic recognition task and observed reduced BOLD signal in MDMA polydrug users. In addition, there were a number of significant correlations between MDMA use and BOLD signal change in left BA 9 (DLPFC), 18 (secondary visual cortex), and 21/22 (inferior/middle temporal gyrus), but not BA 45 (inferior frontal gyrus). Indices of MDMA use (lifetime episodes and lifetime dose) were correlated inversely with %BOLD signal change at BA 9, in the DLPFC. While there were also a number of significant correlations between activation and indices of cannabis and cocaine use, after controlling for these, the relationship between MDMA use and activation in BA9 remained significant. Roberts and Garavan (2010) observed MDMA-related changes in activity during a response inhibition task, with users displaying greater activity in right middle and inferior frontal gyri, right middle frontal gyrus, and right inferior parietal lobule, compared with controls. There were also significant differences in the right middle and inferior temporal gyri, with MDMA users displaying greater activation after errors. Conversely, nonusers displayed greater

Cognitive consequences of 3,4-methylenedioxymethamphetamine use Chapter | 12

deactivation after errors in the left medial frontal gyrus and left posterior cingulate. Taken together, the differences in brain activation, despite the nonsignificant behavioral differences, show that MDMA users are working harder to obtain the same behavioral output. Such findings are supported in functional near-infrared spectroscopy studies, where changes in oxygenated and deoxygenated hemoglobin have been observed in MDMA users in areas of the DLPFC in the absence of any behavioral differences on a range of cognitive tasks (Montgomery et al., 2017; Roberts and Montgomery, 2015a,b; Roberts et al., 2015). fMRI has not always elucidated dysfunction during working memory tasks. Jager et al. (2008) found no significant differences in activation between MDMA users and nonusers on the Sternberg task or an attention task. However, in an associative learning task, ecstasy use was related to lower left DLPFC activity relative to controls, in addition to higher activation of the right middle occipital gyrus.

Clinical significance of cognitive deficits associated with 3,4methylenedioxymethamphetamine It is difficult to estimate the clinical significance of deficits associated with MDMA use. It is clear from the preceding sections that MDMA users do exhibit some differences in cognition relative to nonusers, but relatively few studies assess how this will impact everyday function. A systematic review of cognitive studies in 2009 suggested that while MDMA users are impaired in various facets of cognitive function (particularly immediate and delayed recall), the clinical implications of such deficits are likely to be small (Rogers et al., 2009). More recently, a pooled data analysis looking at light MDMA polydrug users investigated the clinical relevance of verbal memory deficits by computing a clinical memory impairment quotient (Kuypers et al., 2016). The analysis concluded that there were clinically relevant verbal memory impairments present during intoxication, but not during abstinence. Thus it seems that deficits in verbal memory in MDMA users, while well-documented in the literature, may not be of clinical relevance. However, a recent metaanalysis looking at executive functioning only, and not verbal memory, found robust evidence for MDMA-related deficits in three executive functions (updating, attention switching, and access to semantic memory) (Roberts et al., 2016b). While a number of studies have also reported mood differences between users and nonusers subacutely (Curran and Travill, 1997; Parrott and Lasky, 1998; Verheyden et al., 2002) and long-term (MacInnes et al., 2001; Thomasius et al., 2003), a metaanalysis of available studies suggests that these differences are unlikely to be of clinical significance (Sumnall and Cole, 2005).

171

Recommendations for researchers/ clinicians interested in cognitive profiling in the context of 3,4methylenedioxymethamphetamine It is clear that users of ecstasy exhibit cognitive deficits and/or changes in indices of brain activity. Retrospective cognitive profiling of ecstasy users in the domains of executive functioning and working memory would allow researchers to characterize cognitive deficits and tailor harm reduction advice using salient strategies for individuals who may exhibit impaired decision-making (Panagopoulos and Ricciardelli, 2005). While recreational ecstasy use is not classically associated with dependency in the same way that alcohol, cannabis, cocaine, methamphetamine, and opioids are, there is evidence that a minority of users are concerned about their use and seek treatment. As with other studies of hallucinogenic drugs, Degenhardt et al. (2010) have suggested problematic MDMA use is best defined by, and described using, the terms “compulsive use” and “escalating use.” Future research could investigate cognitive phenotyping, functional connectome phenotyping, or identification of neurobiomarkers which predict susceptibility to escalating MDMA use. This would require experimental and brain imaging methods for neurocognitive profiling before initiation of drug use to observe (neuro)cognitive predictors of initiation of use, as well as tracking changes over time. There are already large neurocognitive and brain imaging consortium projects (e.g., IMAGEN and ENIGMA) which are conducting research in this area. However, such research must seek to carefully control the many confounding variables in the drug research field (indices and patterns of drug use, drug purity, personality, IQ, socioeconomic factors, state and trait psychological factors), as mentioned earlier in the chapter. Most of the knowledge of MDMA-associated cognitive deficits comes from retrospective studies of recreational polydrug users. However, there has been recent interest in the therapeutic potential of MDMA as an adjunct to talking therapy in treatment-resistant psychological disorders. The Multidisciplinary Association for Psychedelic Studies (MAPS) has recently began recruitment for the first phase III clinical trial of MDMA-assisted psychotherapy for the treatment of Post Traumatic Stress Disorder (PTSD). It may be necessary to better understand the cognitive effects of a controlled use of clinical-grade MDMA. Isolation of MDMA from extrapsychopharmacological factors, polydrug use, and drug purity issues may be an avenue for cognitive profiling, which may result in better informed harm reduction strategies for recreational use. Pharmacological cognitive profiling of recreational MDMA is problematic, not least because of the vast

172 Cognition and Addiction

differences in drug purity. While some administration studies investigating adverse effects have shown changes indicative of neurotoxicity in human users (e.g., Gamma et al., 2000), recent controlled trials using clinical-grade MDMA have investigated acute changes in brain activity that could indicate utility of MDMA in treatment-resistant psychological conditions (e.g., Carhart-Harris et al., 2015). More research is needed to elucidate the acute potential benefits of MDMA on psychological function, and distinctions should be made in the literature between recreational ecstasy use and controlled medical MDMA administration.

Key points and conclusion The recreational use of MDMA/ecstasy is prevalent throughout the world. While there have been year-by-year changes in levels of use associated with various factors (e.g., availability, purity, current trends in use), ecstasy remains a popular drug of choice for many individuals, particularly those between the ages of 16e24. Given the research summarized herewith regarding the potential to affect both the structure and function of the central nervous system, health information targeted at users of the drug should focus on harm reduction techniques. Such techniques could be related to reducing acute effects that users may exacerbate with environmental factors (e.g., increased temperature), and also approaching drug testing services at events and festivals to confirm the presence of the intended chemicals in the substance they are using. Prohibition of MDMA use has been in force for over 30 years, yet use continues at a steady rate, so it is unlikely that continued prohibition will change this. Drug policymakers should consider how best to protect substance users from risks of harm when reforming drug policies and consider that prohibition and criminalization of use may not be the most appropriate response.

References Aguirre, N., Barrionuevo, M., Lasheras, B., DelRio, J., 1998. The role of dopaminergic systems in the perinatal sensitivity to 3,4methylenedioxymethamphetamine-induced neurotoxicity in rats. J. Pharmacol. Exp. Ther. 286, 1159e1165. Ali, S.F., Newport, G.D., Scallet, A.C., Biniedna, Z., Ferguston, S.A., BailyJR, Paul, M.G., Slikker Jr., W., 1993. Oral administration of 3,4methylenedioxymethamphetamine (MDMA) produces selectiveserotonergic depletion in the nonhuman primate. Neurotoxicol.Teratol. 15, 91e96. Antolino-Lobo, I., Meulenbelt, J., Nijmeijer, S.M., Scherpenisse, P., van den Berg, M., van Duursen, M.B., 2010. Differential roles of phase I and phase II enzymes in 3,4-methylendioxymethamphetamineinduced cytotoxicity. Drug Metab. Dispos. 38, 1105e1112. Badon, L.A., Hicks, A., Lord, K., Ogden, B.A., Meleg-Smith, S., Varner, K.J., 2002. Changes in cardiovascular responsiveness and

cardiotoxicity elicited during binge administration of Ecstasy. J. Pharmacol. Exp. Ther. 302 (3), 898e907. Back-Madruga, C., Boone, K.B., Chang, L., Grob, C.S., Lee, A., Nations, H., Poland, R.E., 2003. Neuropsychological effects of 3,4methylenedioxymethamphetamine (MDMA or ecstasy) in recreational users. Clin. Neuropsychol. 17 (4), 446e459. Battaglia, G., Yeh, S.Y., O’Hearn, E., Molliver, M.E., Kuhar, M.J., De Souza, E.B., 1987. 3,4-Methylenedioxymethamphetamine and 3,4methylenedioxyamphetamine destroy serotonin terminals in rat brain: quantification of neurodegeneration by measurement of [3H] paroxetine-labeled serotonin uptake sites. J. Pharmacol. Exp. Ther. 242, 911e916. Battaglia, G., Yeh, S.Y., De Souza, E.B., 1988. MDMA-induced neurotoxicity: parameters of degeneration and recovery of brain serotonin neurons. Pharmacol. Biochem. Behav. 29 (2), 269e274. Baumann, M.H., Zolkowska, D., Kim, I., Scheidweiler, K.B., Rothman, R.B., Huestis, M.A., 2009. Effects of dose and route of administration on pharmacokinetics of (þ or -)-3,4-methylenedioxymethamphetamine in the rat. Drug Metab. Dispos. 37, 2163e2170. Beck, J., Rosenbaum, M., 1994. The Pursuit of Ecstasy: The MDMA Experience. State University of New York Press, Albany, NY. Bedi, G., Redman, J., 2008. Ecstasy use and higher-level cognitive functions: weak effects of ecstasy after control for potential confounds. Psychol. Med. 38 (9), 1319e1330. Berger, U.V., Gu, X.F., Azmitia, E.C., 1992. The substituted amphetamines 3,4-methylenedioxymethamphetamine, methamphetamine, pchloroamphetamine and fenfluramine induce 5-hydroxytryptamine release via a common mechanism blocked by fluoxetine and cocaine. Eur. J. Pharmacol. 215, 153e160. Bhattachary, S., Powell, J.H., 2001. Recreational use of 3,4- methylenedioxymethamphetamine (MDMA) or “ecstasy”: evidence for cognitive impairment. Psychol. Med. 31 (4), 647e658. Bolla, K.I., McCann, U.D., Ricaurte, G.A., 1998. Memory impairment in abstinent MDMA (“ecstasy”) users. Neurology 51, 1532e1537. Broadfield, D. (Ed.), 2017. Statistical Bulletin 07/17: Drug Misuse: Findings from the 2016/17 Crime Survey for England and Wales. Home Office, London. Available at: https://www.gov.uk/government/ uploads/system/uploads/attachment_data/file/633349/drug-misuse2017-hosb1117.pdf. Broening, H.W., Bowyer, J.F., Slikker Jr., W., 1995. Age-dependent sensitivity of rats to the long-term effects of the serotonergic neurotoxicant (þ/-)-3,4-methylenedioxymethamphetamine (MDMA) correlates with the magnitude of the MDMA-induced thermal response. J. Pharmacol. Exp. Ther. 275, 325e333. Broening, H.W., Bacon, L., Slikker, W., 1994. Age modulates the longterm but not the acute effects of the serotonergic neurotoxicant 3,4methylenedioxymethamphetamine. J. Pharmacol. Exp. Ther. 271 (1), 285e293. Buchert, R., Thomasius, R., Nebeling, B., Petersen, K., Obrocki, J., Jenicke, L., Wilke, F., Wartberg, L., Zapletalova, P., Clausen, M., 2003. Long-term effects of “ecstasy” use on serotonin transporters of the brain investigated by PET. J. Nucl. Med. 44 (3), 375e384. Buchert, R., Thomasius, R., Wilke, F., Petersen, K., Nebeling, B., Obrocki, J., Clausen, M., 2004. A voxel-based PET investigation of the long-term effects of “ecstasy” consumption on brain serotonin transporters. Am. J. Psychiatry 161 (7), 1181e1189.

Cognitive consequences of 3,4-methylenedioxymethamphetamine use Chapter | 12

Capela, J.P., Meisel, A., Abreu, A.R., Branco, P.S., Ferreira, L.M., Lobo, A.M., et al., 2006. Neurotoxicity of ecstasy metabolites in rat cortical neurons, and influence of hyperthermia. J. Pharmacol. Exp. Ther. 316 (1), 53e61. Capela, J.P., Carmo, H., Remião, F., Bastos, M.L., Meisel, A., Carvalho, F., 2009. Molecular and cellular mechanisms of ecstasyinduced neurotoxicity: an overview. Mol. Neurobiol. 39, 210e271. Carhart-Harris, R.L., Murphy, K., Leech, R., Erritzoe, D., Wall, M.B., Ferguson, B., et al., 2015. The effects of acutely administered 3, 4methylenedioxymethamphetamine on spontaneous brain function in healthy volunteers measured with arterial spin labeling and blood oxygen leveledependent resting state functional connectivity. Biol. Psychiatry 78, 554e562. Carvalho, M., Carmo, H., Costa, V.M., Capela, J.P., Pontes, H., Remião, F., Carvalho, F., Bastos Mde, L., 2012. Toxicity of amphetamines: an update. Arch. Toxicol. 86, 1167e1231. Colado, M.I., Williams, J.L., Green, A.R., 1995. The hyperthermic and neurotoxic effects of “Ecstasy” (MDMA) and 3,4 methylenedioxyamphetamine (MDA) in the Dark Agouti (DA) rat, a model of the CYP2D6 poor metabolizer phenotype. Br. J. Pharmacol. 115, 1281e1289. Cowan, R.L., Haga, E., Frederick, B.B., Dietrich, M.S., Vimal, R.L.P., Lukas, S.E., Renshaw, P.F., 2006. MDMA use is associated with increased spatial BOLD fMRI visual cortex activation in human MDMA users. Psychopharmacol. Biochem. Behav. 84, 219e228. Cowan, R.L., Lyoo, I.K., Sung, S.M., Ahn, K.H., Kim, M.J., Hwang, J., Haga, E., Vimal, R.L.P., Lukas, S.E., Renshaw, P.F., 2003. Reduced cortical gray matter density in human MDMA (Ecstasy) users: a voxel-based morphometry study. Drug Alcohol Depend. 72 (3), 225e235. Croft, R.J., Mackay, A.J., Mills, A.T.D., Gruzelier, J.G.H., 2001. The relative contributions of ecstasy and cannabis to cognitive impairment. Psychopharmacology 153, 373e379. Curran, H.V., Travill, R.A., 1997. Mood and cognitive effects of 3,4methylenedioxymethamphetamine (MDMA, “Ecstasy”): weekend “high” followed by mid-week “low”. Addiction 92 (7), 821e831. Dafters, R.I., Hoshi, R., Talbot, A.C., 2004. Contribution of cannabis and MDMA (“ecstasy”) to cognitive changes in long-term polydrug users. Psychopharmacology 173, 405e410. Daumann, J., Fimm, B., Willmes, K., Thron, A., Gouzoulis-Mayfrank, E., 2003a. Cerebral activation in abstinent ecstasy (MDMA) users during a working memory task: a functional magnetic resonance imaging (fMRI) study. Cogn. Brain Res. 16 (3), 479e487. Daumann, J., Schnitker, R., Weidemann, J., Schnell, K., Thron, A., Gouzoulis-Mayfrank, 2003b. Neural correlates of working memory in pure and polyvalent ecstasy (MDMA) users. Neuroreport 14 (15), 1983e1987. Daumann, J., Fischermann, T., Heekeren, K., Thron, A., GouzoulisMayfrank, E., 2004. Neural mechanisms of working memory in ecstasy (MDMA) users who continue or discontinue ecstasy and amphetamine use: evidence from an 18-month longitudinal functional magnetic resonance imaging study. Biol. Psychiatry 56 (5), 349e355. Daumann, J., Fischermann, T., Heekeren, K., Henke, K., Thron, A., Gouzoulis-Mayfrank, E., 2005. Memory-related hippocampal dysfunction in poly-drug ecstasy (3,4-methylenedioxymethamphetamine) users. Psychopharmacology 180, 607e611. Degenhardt, K., Bruno, R., Topp, L., 2010. Is ecstasy a drug of dependence? Drug Alcohol Depend. 107 (1), 1e10.

173

de la Torre, R., Farré, M., Roset, P.N., Hernández López, C., Mas, M., Ortuño, J., et al., 2000. Pharmacology of MDMA in humans. Ann. N. Y. Acad. Sci. 914 (1), 225e237. de La Torre, R., Farré, M., Ortuño, J., Mas, M., Brenneisen, R., Roset, P.N., et al., 2000b. Non-linear pharmacokinetics of MDMA (“ecstasy”) in humans. Ann. N. Y. Acad. Sci. 49 (2), 104e109. de la Torre, R., Farre, M., Roset, P.N., Pizzaro, N., Abanades, S., Segura, M., Camí, J., 2004. Human pharmacology of MDMA: pharmacokinetics, metabolism, and disposition. Ther. Drug Monit. 26, 137e144. de Win, M.M., Reneman, L., Jager, G., Vlieger, E.J., Olabarriaga, S.D., Lavini, C., et al., 2007. A prospective cohort study on sustained effects of low-dose ecstasy use on the brain in new ecstasy users. Neuropsychopharmacology 32, 458e470. de Win, M.M., Jager, G., Booij, J., Reneman, L., Schilt, T., Lavini, C., Olabarriaga, S.D., Ramsey, N.F., den Heeten, G.J., van den Brink, W., 2008. Neurotoxic effects of ecstasy on the thalamus. Br. J. Psychiatry 193, 289e296. Depue, R.A., Collins, P.F., 1999. Neurobiology of the structure of personality: dopamine, facilitation of incentive motivation, and extraversion. Behav. Brain Sci. 22 (3), 491e517. Devlin, R.J., Henry, J.A., 2008. Clinical review: major consequences of illicit drug consumption. Crit. Care 12, 202. Di lorio, C.R., Watkins, T.J., Dietrich, M.S., Cao, A., Blackford, J.U., Rogers, B., Ansari, M.S., Baldwin, R.M., Li, R., Kessler, R.M., Salomon, R.M., Benningfield, M., Cowan, R.L., 2012. Evidence for chronically altered cortical serotonin function in human female recreational ecstasy (MDMA) polydrug users. Arch. Gen. Psychiatr. 69 (4), 399e409. Downey, L.A., Sands, H., Jones, L., Clow, A., Evans, P., Stalder, T., Parrott, A.C., 2015. Reduced memory skills and increased hair cortisol levels in recent Ecstasy/MDMA users: significant but independent neurocognitive and neurohormonal deficits. Hum. Psychopharmacol. Clin. Exp. 30 (3), 199e207. Dughiero, G., Schifano, F., Forza, G., 2001. Personality dimensions and psychopathological profiles of Ecstasy users. Hum. Psychopharmacol. 16, 635e639. Dumont, G.J., Verkes, R.J., 2006. A review of acute effects of 3,4methylenedioxymethamphetamine in healthy volunteers. J. Psychopharmacol. 20, 176e187. EcstasyData.org, 2018. EcstasyData.org: Test Result Statistics: Substances by Year. www.ecstasydata.org. European Monitoring Centre for Drugs and Drug Addiction (EMCDDA), 2003. Report on the Risk Assessment of PMMA in the Framework of the Joint Action on New Synthetic Drugs. EMCDDA Risk Assessments Series, vol. 5. Lisbon, March 2003. Falck, R.S., Wang, J., Carlson, R.G., Siegal, H.A., 2006. Prevalence and correlates of current depressive symptomatology among a community sample of MDMA users in Ohio. Addict. Behav. 31, 90e101. Farré, M., Abanades, S., Roset, P.N., Peiró, A.M., Torrens, M., O’Mathúna, B., Segura, M., de la Torre, R., 2007. Pharmacological interaction between 3,4-methylenedioxymethamphetamine (ecstasy) and paroxetine: pharmacological effects and pharmacokinetics. J. Pharmacol. Exp. Ther. 323, 954e962. Fisk, J.E., Montgomery, C., Murphy, P., Wareing, M., 2004. Evidence for executive deficits among users of MDMA (Ecstasy). B. J. Psychol. 95 (4), 457e466.

174 Cognition and Addiction

Fisk, J.E., Montgomery, C., 2009. Evidence for selective executive function deficits in ecstasy/polydrug users. J. Psychopharmacol. 23 (1), 40e50. Fisk, J.E., Sharp, C.A., 2004. Age-related impairment in executive functioning: updating, inhibition, shifting and access. J. Clin. Exp. Neuropsychol. 26 (7), 874e890. Fox, H.C., Parrott, A.C., Turner, J.J.D., 2001. Ecstasy use: cognitive deficits related to doseage rather than self reported problematic use of the drug. J. Psychopharmacol. 15 (4), 273e281. Gallagher, D.T., Hadjiefthyvoulou, F., Fisk, J.E., Montgomery, C., Robinson, S., Judge, J., 2014. Prospective memory deficits in in illicit polydrug users are associated with the average long-term typical dose of ecstasy typically consumed in a single session. Neuropsychology 28 (1), 43e54. Gamma, A., Buck, A., Berthold, T., Liechti, M.E., Vollenweider, F.X., Hell, D., 2000. 3,4-Methylenedioxymethamphetamine (MDMA) modulates cortical and limbic brain activity as measured by [H(2)(15) O]-PET in healthy humans. Neuropsychopharmacology 23 (4), 388e395. Gouzoulis-Mayfrank, E., Daumann, J., Tuchtenhagen, F., Pelz, S., Becker, S., Kunert, H.-J., Fimm, B., Sass, H., 2000. Impaired cognitive performance in drug free users of recreational ecstasy (MDMA). J. Neurol. Neurosurg. Psychiatry 68 (6), 719e725. Gouzoulis-Mayfrank, E., Thimm, B., Rezk, M., Hensen, G., Daumann, J., 2003. Memory impairment suggests hippocampal dysfunction in abstinent ecstasy users. Prog. Neuropsychopharmacol. Biol. Psychiatry 27, 819e827. Green, A.R., Mechan, A.O., Elliott, J.M., O’Shea, E., Colado, M.I., 2003. The pharmacology and clinical pharmacology of 3,4methylenedioxymethamphetamine (MDMA, “ecstasy”). Pharmacol. Rev. 55, 463e508. Greer, G., Tolbert, R., 1986. Subjective reports of the effects of MDMA in a clinical setting. J. Psychoact. Drugs 18, 319e327. Hanson, K., Luciana, M., 2010. Neurocognitive impairments in MDMA and other drug users: MDMA alone may not be a cognitive risk factor. J. Clin. Exp. Neuropsychopharmacol. 32 (4), 337e349. Halpern, J.H., Pope, H.r., Sherwood, A.R., Barry, S., Hudson, J.I., Yurgelun-Todd, D., 2004. Residual neuropsychological effects of illicit 3,4-methylenedioxymethamphetamine (MDMA) in individuals with minimal exposure to other drugs. Drug Alcohol Depend. 75 (2), 135e147. Halpern, J.H., Sherwood, A.R., Hudson, J.I., Gruber, S., Kozin, D., Pope Jr., H.G., 2011. Residual neurocognitive features of long-term ecstasy users with minimal exposure to other drugs. Addiction 106 (4), 777e786. Harris, D.S., Baggott, M., Mendelson, J.H., Mendelson, J.E., Jones, R.T., 2002. Subjective and hormonal effects of 3,4methylenedioxymethamphetamine (MDMA) in humans. Psychopharmacology 162 (4), 396e405. Heffernan, T.M., Jarvis, H., Rodgers, J., Scholey, A.B., Ling, J., 2001a. Prospective memory, everyday cognitive failure and central executive function in recreational users of ecstasy. Hum. Psychopharmacol. Clin. Exp. 16, 607e612. Heffernan, T.M., Ling, J., Scholey, A.B., 2001b. Subjective ratings of prospective memory deficits in MDMA (“ecstasy”) users. Hum. Psychopharmacol. Clin. Exp. 16, 339e344. Hoshi, R., Mullins, K., Boundy, C., Brignell, C., Piccini, P., Curran, H.V., 2007. Neurocognitive function in current and ex-users of ecstasy in

comparison to both matched polydrug-using controls and drug-naive controls. Psychopharmacology 194, 371e379. Huizink, A.C., Ferdinand, R.F., van der Ende, J., Verhulst, F.C., 2006. Symptoms of anxiety and depression in childhood and use of MDMA: prospective, population-based study. Br. Med. J. 332, 825e827. Hysek, C.M., Simmler, L.D., Nicola, V.G., Vischer, N., Donzelli, M., Krähenbühl, S., Grouzmann, E., Huwyler, J., Hoener, M.C., Liechti, M.E., 2012. Duloxetine inhibits effects of MDMA (“ecstasy”) in vitro and in humans in a randomized placebo-controlled laboratory study. PLoS One 7, e36476. Insel, T.R., Battaglia, G., Johannessen, J.N., Marra, S., De Souza, E.B., 1989. 4-Methylenedioxymethamphetamine (“ecstasy”) selectively destroys brain serotonin terminals in rhesus monkeys. J. Pharmacol. Exp. Ther. 249 (3), 713e720. Jacobsen, L.K., Mencl, W.E., Pugh, K.R., Skudlarski, P., Krystal, J.H., 2004. Preliminary evidence of hippocampal dysfunction in adolescent MDMA (“ecstasy”) users: possible relationship to neurotoxic effects. Psychopharmacology 173, 383e390. Jager, G., de Win, M.M.L., van der Tweel, I., Schilt, T., Kahn, R.S., van den Brink, W., van Ree, J.M., Ramsey, N.F., 2008. Assessment of cognitive brain function in ecstasy users and contributions of other drugs of abuse: results from an fMRI study. Neuropsychopharmacology 33, 247e258. Khantzian, E.J., 1997. The self-medication hypothesis of substance use disorders: a reconsideration and recent applications. Harv. Rev. Psychiatry 4 (5), 231e244. Kish, S.J., Lerch, J., Furukawa, Y., Tong, J., McCluskey, T., Wilkins, D., Houle, S., Meyer, J., Mundo, E., Wilson, A.A., Rusjan, P.M., SaintCyr, J.A., Guttman, M., Collins, D.L., Shapiro, C., Warsh, J.J., Boileau, I., 2010. Decreased cerebral cortical serotonin transporter binding in ecstasy users: a positron emission tomography/[11C]DASB and structural brain imaging study. Brain J. Neurol. 133, 1779e1797. Kraner, J.C., McCoy, D.J., Evans, M.A., Evans, L.E., Sweeney, B.J., 2001. Fatalities caused by the MDMA-related drug paramethoxyamphetamine (PMA). J. Anal. Toxicol. 25, 645e648. Krystal, J.H., Price, L.H., Opsahl, C., Ricaurte, G.A., Heninger, G.R., 1992. Chronic 3,4-Methylenedioxymethamphetamine (MDMA) use: effects of mood and neurophysiological function. Am. J. Drug Alcohol Abuse 18 (3), 331e341. Kuypers, K.P.C., Theunissen, E.L., van Wel, J.H.P., de Sousa Fernandes Perna, E.B., Linssen, A., Sambeth, A., et al., 2016. Verbal memory impairment in polydrug ecstasy users: a clinical perspective. PLoS One 11 (2), e0149438. Le Roux, G., Bruneau, C., Lelièvre, B., Bretaudeau-Deguigne, M., Turcant, A., Harry, P., Boels, D., 2015. Recreational phenethylamine poisonings reported to a French poison control center. Drug Alcohol Depend. 154, 46e53. Lieb, R., Schuetz, C.G., Pfister, H., Sydow, K., von Wittchen, H.U., 2002. Mental disorders in ecstasy users: a prospective longitudinal investigation. Drug Alcohol Depend. 68, 195e207. Liechti, M.E., Gamma, A., Vollenweider, F.X., 2001. Gender differences in the subjective effects of MDMA. Psychopharmacology 154 (2), 161e168. Liechti, M.E., Baumann, C., Gamma, A., Vollenweider, F.X., 2000. Acute psychological effects of 3,4-methylenedioxymethamphetamine (MDMA, ecstasy) are attenuated by the serotonin uptake inhibitor citalopram. Neuropsychopharmacology 22, 513e521.

Cognitive consequences of 3,4-methylenedioxymethamphetamine use Chapter | 12

Liu, H.S., Chou, M.C., Chung, H.W., Cho, N.Y., Chiang, S.W., Wang, C.Y., Kao, H.W., Huang, G.S., Chen, C.Y., 2011. Potential long-term effects of MDMA on the basalganglia-thalamocortical circuit: a proton MR spectroscopy and diffusion-tensorimaging study. Radiology 260, 531e540. MacInnes, N., Handley, S.L., Harding, G.F.A., 2001. Former chronic methylenedioxymethamphetamine (MDMA or ecstasy) users report mild depressive symptoms. J. Psychopharmacol. 15 (3), 181e186. Martín-Santos, R., Torrens, M., Poudevida, S., Langohr, K., Cuyás, E., Pacifici, R., Farré, M., Pichini, S., de la Torre, R., 2010. 5-HTTLPR polymorphism, mood disorders and MDMA use in a 3-year followup study. Addict. Biol. 15, 15e22. McCann, U.D., Szabo, Z., Scheffel, U., Dannals, R.F., Ricaurte, G.A., 1998. Positron emission tomogpaphic evidence of toxic effect of MDMA on brain serotonin neurons in human beings. Lancet 352, 1433e1437. McCann, U., Szabo, Z., Seckin, E., Rosenblatt, P., Mathews, W.B., Ravert, H.T., Dannals, R.F., Ricaurte, G.A., 2005. Quantitative PET studies of the serotonin transporter in MDMA users and controls using [11C]McN5652 and [11C]DASB. Neuropsychopharmacology 30, 1741e1750. McCann, U.D., Szabo, Z., Vranesic, M., Palermo, M., Mathews, W.B., Ravert, H.T., Dannals, R.F., Ricaurte, G.A., 2008. Positron emission tomographic studies of brain dopamine and serotonin transporters in abstinent () 3,4-methylenedioxymethamphetamine (“ecstasy”) users: relationship to cognitive performance. Psychopharmacology 200 (3), 439e450. McCardle, K., Luebbers, S., Carter, J.D., Croft, R.J., Stough, C., 2004. Chronic MDMA (ecstasy) use, cognition and mood. Psychopharmacology 173, 434e439. McGuire, P.K., Cope, H., Fahy, T.A., 1994. Diversity of psychopathology associated with use of 3,4-methylenedioxymethamphetamine (Ecstasy). Br. J. Psychiatry 165, 391e395. Miyake, A., Friedman, N.P., 2012. The nature and organization of individual differences in executive functions: four general conclusions. Curr. Dir. Psychol. Sci. 21 (1), 8e14. Miyake, A., Friedman, N.P., Emerson, M.J., Witzki Howerter, A., Wager, T.D., 2000. The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: a latent variable analysis. Cogn. Psychol. 41, 49e100. Moeller, F.G., Steinberg, J.L., Dougherty, D.M., Narayana, P.A., Kramer, L.A., Renshaw, P.F., 2004. Functional MRI study of working memory in MDMA users. Psychopharmacology (Berl.) 177, 185e194. Moeller, F.G., Steinberg, J.L., Lane, S.D., Buzby, M., Swann, A.C., Hasan, K.M., Kramer, L.A., Narayana, P.A., 2007. Diffusion tensor imaging in MDMA users and controls: association with decision making. Am. J. Drug Alcohol Abuse 33, 777e789. Morgan, M.J., 1998. Recreational use of “ecstasy” (MDMA) is associated with elevated impulsivity. Neuropsychopharmacology 19 (4), 252e264. Morgan, M.J., 1999. Memory deficits associated with recreational use of “ecstasy” (MDMA). Psychopharmacology 141, 30e36. Morgan, M.J., McFie, L., Fleetwood, L.H., Robinson, J.A., 2002. Ecstasy (MDMA): are the psychological problems associated with its use reversed by prolonged abstinence? Psychopharmacology 159, 294e303.

175

Morgan, M.J., Impallomeni, L.C., Pirona, A., Rogers, R.D., 2006. Elevated impulsivity and impaired decision-making in abstinent ecstasy (MDMA) users compared to polydrug and drug naïve controls. Neuropsychopharmacology 31, 1562e1573. Montgomery, C., Fisk, J.E., 2008. Ecstasy-related deficits in the updating component of executive processes. Hum. Psychopharmacol. 23, 495e511. Montgomery, C., Fisk, J.E., Newcombe, R., Murphy, P.N., 2005. The differential effects of ecstasy/polydrug use on executive components: shifting, inhibition, updating and access to semantic memory. Psychopharmacology 182, 262e276. Montgomery, C., Fisk, J.E., Wareing, M., Murphy, P., 2007. Self-reported sleep quality and cognitive performance in ecstasy users. Hum. Psychopharmacol. 22, 537e548. Montgomery, C., Fisk, J.E., Roberts, C.A., 2017. Updating of working memory in ecstasy polydrug users: findings from fNIRS. Hum. Psychopharmacol. Clin. Exp. 32, e32609. Mounteney, J., Griffiths, P., Bo, A., Cunningham, A., Matias, J., Pirona, A., 2018. Nine reasons why ecstasy is not quite what it used to be. Int. J. Drug Policy 51, 36e41. Murphy, P.N., Erwin, P.G., Maciver, L., Fisk, J.E., Larkin, D., Wareing, M., Montgomery, C., Hilton, J., Tames, F.J., Bradley, B., Yanulevitch, K., Ralley, R., 2011. The relationships of “ecstasy” (MDMA) and cannabis use to impaired executive inhibition and access to semantic long-term memory. Hum. Psychopharmacol. 26, 460e469. Nair, A.B., Jacob, S., 2016. A simple practice guide for dose conversion between animals and human. J. Basic Clin. Pharm. 7 (2), 27e31. Nash, J.F., Nichols, D.E., 1991. Microdialysis studies on 3,4methylenedioxy-amphetamine and structurally related analogues. Eur. J. Pharmacol. 200, 53e58. Nicol, J.J.E., Yarema, M.C., Jones, G.R., Martz, W., Purssell, R.A., MacDonald, J.C., Wishart, I., Durigon, M., Tzemis, D., Buxton, J.A., 2015. Deaths from exposure to paramethoxymethamphetamine in Alberta and British Columbia, Canada: a case series. CMAJ Open 3 (1), E83eE90. Nichols, D., Lloyd, D.H., Hoffman, A.J., Nichols, M.B., Yim, G.K.W., 1982. Effects of certain hallucinogenic amphetamine analogues on the release of 3H-serotonin from the rat whole brain synaptosomes. J. Med. Chem. 25, 530e535. Nulsen, C., Fox, A., Hammond, G., 2011. Electrophysiological indices of altered working memory processes in long-term ecstasy users. Hum. Psychopharmacol. 26, 488e497. O’Brien, 1996. Drug addiction and drug abuse. In: Hardman, J.G., Goodman Gilman, A., Limbird, L.E. (Eds.), Goodman and Gilman’s: The Pharmacological Basis of Therapeutics, ninth ed. McGraw-Hill, New York, pp. 557e577. Palamar, J.J., 2017. There’s something about Molly: the underresearched yet popular powder form of ecstasy in the United States. Subst. Abuse 38 (1), 15e17. Panagopoulos, I., Ricciardelli, L.A., 2005. Harm reduction and decision making among recreational ecstasy users. Int. J. Drug Policy 16 (1), 54e64. Parrott, A.C., 2001. Human psychopharmacology of Ecstasy (MDMA): a review of 15 years of empirical research. Hum. Psychopharmacol. Clin. Exp. 16, 557e577.

176 Cognition and Addiction

Parrott, A.C., 2013. Human psychobiology of MDMA or “Ecstasy”: an overview of 25 years of empirical research. Hum. Psychopharmacol. Clin. Exp. 28, 289e307. Parrott, A.C., Lees, A., Garnham, N.J., Jones, M., Wesnes, K., 1998. Cognitive performance in recreational users of MDMA or “ecstasy”: evidence for memory deficits. J. Psychopharmacol. 12, 79e83. Parrott, A.C., Lasky, J., 1998. Ecstasy (MDMA) effects upon mood and cognition: before, during and after a Saturday night dance. Psychopharmacology 139 (3), 261e268. Price, J.S., Shear, P., Lisdahl, K.M., 2014. Ecstasy exposure and gender: examining components of verbal memory functioning. PLoS One 9 (12), e115645. Raj, V., Liang, H.C., Woodward, N.D., Bauernfeind, A.L., Lee, J., Dietrich, M.S., Park, S., Cowan, R.L., 2010. MDMA (ecstasy) use is associated with reduced BOLD signal change during semantic recognition in abstinent human polydrug users: a preliminary fMRI study. J. Psychopharmacol. 24 (2), 187e201. Reay, J.L., Hamilton, C., Kennedy, D.O., Scholey, A.B., 2006. MDMA polydrug users show process-specific central executive impairments coupled with impaired social and emotional judgement processes. J. Psychopharmacol. 20 (3), 385e388. Rendell, P.G., Gray, T.J., Henry, J.D., Tolan, A., 2007. Prospective memory impairment in ecstasy (MDMA) users. Psychopharmacology 194, 497e504. Reneman, L., Lavalaye, J., Schmand, B., de Wolff, F.A., van den Brink, W., den Heeten, G.J., Booij, J., 2001. Cortical serotonin transporter density and verbal memory in individuals who stopped using 3,4-Methylenedioxymethamphetamine (MDMA or “Ecstasy”). Arch. Gen. Psychiatr. 58 (10), 901e906. Reneman, L., Schilt, T., de Win, M.M., Booij, J., Schmand, B., van den Brink, W., Bakker, O., 2006. Memory function and serotonin transporter promoter gene polymorphism in ecstasy (MDMA) users. J. Psychopharmacol. 20, 389e399. Ricaurte, G.A., Jie, Y., McCann, U.D., 2000. (þ/-)3,4methylenedioxymethamphetamine (“ecstasy”)-induced serotonin neurotoxicity: studies in animals. Neuropsychobiology 42 (1), 5e10. Rietjens, S.K., Hondebrink, L., Westerink, R.H.S., Meulenbelt, J., 2012. Pharmacokinetics and pharmacodynamics of 3,4methylenedioxymethamphetamine (MDMA): interindividual differences due to polymorphisms and drugedrug interactions. Crit. Rev. Toxicol. 42 (10), 854e876. Ridpath, A., Driver, C.R., Nolan, M.L., et al., 2014. Illnesses and deaths among persons attending an electronic dance-music festival e New York City, 2013. MMWR Morb. Mortal. Wkly. Rep. 63 (50), 1195e1198. Roiser, J.P., Sahakian, B.J., 2004. Relationship between ecstasy use and depression: a study controlling for poly-drug use. Psychopharmacology 173, 411e417. Roberts, G.M., Garavan, H., 2010. Evidence of increased activation underlying cognitive control in ecstasy and cannabis users. Neuroimage 52 (2), 429e435. Roberts, C.A., Wetherell, M.A., Fisk, J.E., Montgomery, C., 2015. Differences in prefrontal blood oxygenation during an acute multitasking stressor in ecstasy polydrug users. Psychol. Med. 45, 395e406. Roberts, C.A., Montgomery, C., 2015a. Cortical oxygenation suggests increased effort during cognitive inhibition in ecstasy polydrug users. J. Psychopharmacol. 29, 1170e1181.

Roberts, C.A., Montgomery, C., 2015b. fNIRS suggests increased effort during executive access in ecstasy polydrug users. Psychopharmacology (Berl.) 232, 1571e1582. Roberts, C.A., Jones, A., Montgomery, C., 2016b. Meta-analysis of executive functioning in ecstasy/polydrug users. Psychol. Med. 46 (8), 1581e1596. Roberts, C.A., Jones, A., Montgomery, C., 2016a. Meta-analysis of molecular imaging of serotonin transporters in ecstasy/polydrug users. Neurosci. Biobehav. Rev. 63, 158e167. Rogers, G., Elston, J., Garside, R., Roome, C., Taylor, R., Younger, P., et al., 2009. The harmful health effects of recreational ecstasy: a systematic review of observational evidence. Health Technol. Assess. 13 (6), 354. Sarne, Y., Keren, O., 2004. Are cannabinoid drugs neurotoxic or neuroprotective? Med. Hypotheses 63, 187e192. Schilt T, de Win, T.M.M.L., Koeter, M., Jager, G., Korf, D.J., van den Brink, W., Schmand, B., 2007. Cognition in novice ecstasy users with minimal exposure to other drugs e a prospective cohort study. JAMA Psychiatr. 64 (6), 728e736. Schmidt, C.J., Sullivan, C.K., Fadayel, G.N., 1994. Blockade of striatal 5hydroxytryptamine2 receptors reduced the increase in extracellular concentrations of dopamine produced by the amphetamine analogue 3,4-methylenedioxymethamphetamine. J. Neurochem. 62, 1382e1389. Schmidt, C.J., Wu, L., Lovenberg, W., 1986. Methylenedioxy methamphetamine: a potentially neurotoxic amphetamine analogue. Eur. J. Pharmacol. 124, 175e178. Schouw, M.L.J., Gevers, S., Caan, M.W.A., Majoie, C.B.L.M., Booij, J., Nederveen, A.J., Reneman, L., 2012. Mapping serotonergic dysfunction in MDMA (ecstasy) users using pharmacological MRI. Eur. Neuropsychopharmacol. 22 (8), 537e545. Schuster, P., Lieb, R., Lamertz, C., Wittchen, H.U., 1998. Is the use of ecstasy and hallucinogens increasing? Eur. Addict. Res. 4, 75e82. Segura, M., Farré, M., Pichini, S., Peiró, A.M., Roset, P.N., Ramírez, A., Ortuño, J., Pacifici, R., Zuccaro, P., Segura, J., de la Torre, R., 2005. Contribution of cytochrome P450 2D6 to 3,4methylenedioxymethamphetamine disposition in humans: use of paroxetine as a metabolic inhibitor probe. Clin. Pharmacokinet. 44, 649e660. Semple, D.M., Ebmeier, K.P., Glabus, M.F., O’Carroll, R.E., Johnstone, E.C., 1999. Reduced in vivo binding to the serotonin transporter in the cerebral cortex of MDMA (‘ecstasy’) users. Br. J. Psychiatr. 175, 63e69. Shulgin, A.T., Shulgin, 1991. PHIKAL. Transform Press, Berkeley, CA. Simmler, L.D., Hysek, C.M., Liechti, M.E., 2011. Sex differences in the effects of MDMA (ecstasy) on plasma copeptin in healthy subjects. J. Clin. Endocrinol. Metab. 96, 2844e2850. Sprague, J., Everman, S., Nichols, D., 1998. An integrated hypothesis for the serotonergic axonal loss induced by 3,4methylenedioxymethamphetamine. Neurotoxicology 19, 427e441. Stone, D.M., Stahl, D.C., Hanson, G.R., Gibb, J.W., 1986. The effects of 3,4-methylenedioxymethamphetamine (MDMA) and 3,4methylenedioxyamphetamine (MDA) on monoaminergic systems in the rat brain. Eur. J. Pharmacol. 128, 41e48. Stone, D.M., Merchant, K.M., Hanson, G.R., Gibb, J.W., 1987. Immediate and long-term effects of 3,4-methylenedioxymethamphetamine on serotonin pathways in brain of rat. Neuropharmacology 26 (12), 1677e1683.

Cognitive consequences of 3,4-methylenedioxymethamphetamine use Chapter | 12

Stone, D.M., Johnson, M., Hanson, G.R., 1988. Role of endogenous dopamine in the central serotonergic deficits induced by 3,4methylenedioxymethamphetainine. J. Pharmacol. Exp. Ther. 247, 79e87. Stuss, D.T., Alexander, M.P., Hamer, L., Palumbo, C., Dempster, R., Binns, M., Levine, B., Izukawa, D., 1998. The effects of focal anterior and posterior brain lesions on verbal fluency. J. Int. Neuropsychol. Soc. 4, 265e278. Sudhakar, S., Hoshi, R., Bhagwagar, Z., Murthy, N.V., Hinz, R., Cowen, P., Curran, H.V., Grasby, P., 2009. Brain serotonin transporter binding in former users of MDMA (“ecstasy”). Br. J. Psychiatry 194, 355e359. Sumnall, H.R., Cole, J.C., 2005. Self-reported depressive symptomatology in community samples of polysubstance misusers who report Ecstasy use: a meta-analysis. J. Psychopharmacol. 19 (1), 84e92. Ter Bogt, T.F.M., Engels, R.C.M.E., Dubas, J.S., 2006. Party people: personality and MDMA use of house party visitors. Addict. Behav. 31, 1240e1244. Thomasius, R., Petersen, K., Buchert, R., Andersen, B., Zapletalova, P., Wartberg, L., Nebeling, B., Schmoldt, A., 2003. Mood, cognition and serotonin transporter availability in current and former ecstasy (MDMA) users. Psychopharmacology 167, 85e96. Thomasius, R., Zapletalova, P., Petersen, K., Buchert, R., Andersend, B., Wartberg, L., Nebeling, B., Schmoldt, A., 2006. Mood, cognition and serotonin transporter availability in current and former ecstasy (MDMA) users: the longitudinal perspective. J. Psychopharmacol. 20 (2), 211e225. Tucker, G.T., Lennard, M.S., Ellis, S.W., Woods, H.F., Cho, A.K., Lin, L.Y., Hiratsuka, A., Schmitz, D.A., Chu, T.Y., 1994. The demethylenation of methylenedioxymethamphetamine (“ecstasy”) by debrisoquine hydroxylase (CYP2D6). Biochem. Pharmacol. 47, 1151e1156. United Nations Office on Drugs and Crime, UNODC, 2017. World Drug Report 2017. Available at. https://www.unodc.org/wdr2017/index. html. Urban, N.B.L., Girgis, R.R., Talbot, P.S., Kegeles, L.S., Xu, X., Frankle, W.G., Hart, C.L., Slifstein, M., Abi-Dargham, A., Laruelle, M., 2012. Sustained recreational use of ecstasy is associated with altered pre and postsynaptic markers of serotonin transmission in neocortical areas: a PET study with [11C]DASB and [11C]MDL 100907. Neuropsychopharmacology 37, 1465e1473. Verheyden, S.L., Hadfield, J., Calin, T., Curran, H.V., 2002. Sub-acute effects of MDMA (3,4-methylenedioxymethamphetamine, “ecstasy”) on mood: evidence of gender differences. Psychopharmacology 161, 23e31. Wagner, D., Becker, B., Koester, P., Gouzoulis-Mayfrank, E., Daumann, J., 2012. A prospective study of learning, memory, and executive function in new MDMA users. Addiction 108, 136e145. Wareing, M., Fisk, J.E., Murphy, P., Montgomery, C., 2004. Verbal working memory deficits in current and previous users of MDMA. Hum. Psychopharmacol. 19, 225e234.

177

Wareing, M., Fisk, J.E., Murphy, P., Montgomery, C., 2005. Visuo-spatial working memory deficits in current and former users of MDMA (“ecstasy”). Hum. Psychopharmacol. 20, 115e123. White, J.M., Bochner, F., Irvine, R.J., 1997. The agony of ecstasy. Med. J. Aust. 166, 117. Yuan, J., Cord, B.J., McCann, U.D., Callahan, B.T., Ricaurte, G.A., 2002. Effect of depleting vesicular and cytoplasmic dopamine on methylenedioxymethamphetamine neurotoxicity. J. Neurochem. 80 (6), 960e969. Zakzanis, K.K., Young, D.A., 2001. Memory impairment in abstinent MDMA (“ecstasy”) users: a longitudinal investigation. Neurology 56, 966e969.

Further reading Benzenhöfer, U., Passie, T., 2010. Rediscovering MDMA (ecstasy): the role of the American chemist Alexander T. Shulgin. Addiction 105, 1355e1361. Broening, H.W., 1994a. Developmental Age Modulates Sensitivity to the Serotonergic Neurotoxicant 3,4-Methylenedioxymethamphetamine. Dissertation. University of Arkansas for Medical Sciences. Chang, L., Grob, C., Ernst, T., Itti, L., Mishkin, F.S., Jose-Melchor, R., Poland, R.E., 2000. Effect of ecstasy [3,4methylenedioxymethamphetamine (MDMA)] on cerebral blood flow: a co-registered SPECT and MRI study. Psychiatr. Res. Neuroimaging Section 98, 15e28. Commins, D.L., Vosmer, G., Virus, R., Woolverton, W., Schuster, C., Seiden, L., 1987. Biochemical and histological evidence that methylenedioxymethylamphetamine (MDMA) is toxic to neurons in the rat brain. J. Pharmacol. Exp. Ther. 241, 338e345. Hatzidimitriou, G., McCann, U.D., Ricaurte, G.A., 1999. Altered serotonin innervation patterns in the forebrain of monkeys treated with (þ/-)3,4methylenedioxymethamphetamine seven years previously: factors influencing abnormal recovery. J. Neurosci. 19, 5096e5107. O’Hearn, E., Battaglia, G., De Souza, E.B., Kuhar, M.J., Molliver, M.E., 1988. Methylenedioxyamphetamine (MDA) and methylenedioxymethamphetamine (MDMA) cause selective ablation of serotonergic axon terminals in forebrain: immunocytochemical evidence for neurotoxicity. J. Neurosci. 8 (8), 2788e2803. Parrott, A.C., 2011. Residual neurocognitive features of ecstasy use: a reinterpretation of Halpern et al. (2011) consistent with serotonergic neurotoxicity. Addiction 106, 1365e1372. Salzmann, J., Marie-Claire, C., Noble, F., 2004. Acute and long-term effects of ecstasy. Presse Med. 33 (18 Suppl), 24e32. Schmidt, C.J., Black, C.K., Taylor, V.L., 1990. Antagonism of the neurotoxicity due to a single administration of methylenedioxymethamphetamine. Eur. J. Pharmacol. 181, 59e70.

Chapter 13

Cognitive consequences of opioid use Alex Baldacchino1, Douglas Steele2, Fleur Davey3 and Serenella Tolomeo1 1

Division of Populations and Health Science, St Andrews Medical School, University of St Andrews, St Andrews, Fife, United Kingdom; 2Institute of

Neuroscience, Ninewells Hospital Medical School, University of Dundee, Dundee, Tayside, United Kingdom; 3Research and Development Department, NHS Fife, Dunfermline, Fife, United Kingdom

Introduction Opioids are widely administered in many countries because of their ability to treat mild to severe pain, suppress coughs, treat diarrhea, and alleviate the physical withdrawal symptoms associated with opioid dependency. According to data from the 2015 North American National Survey on Drug Use and Health, 91.8 million (37.8%) of the US adults over the age of 18 had been prescribed opioids in the previous year (Han et al., 2017). This accounts for approximately one third of all US adults at that time. To further consider the duration of the prescription, a study by Hudson et al. (2008) using the 2000e01 Healthcare for Communities survey showed 2% of the 7909 respondents reported regular medicinal opioid use (at least 5 days opioid consumption per week for at least 4 weeks). Kelly et al. (2008) who surveyed 19,150 US adults between 1998 and 2006 also found 2% of responders (equating to 4.3 million nationwide) reported regular opioid use, nearly half of whom had been taking opioids for two or more years, and nearly 20% for over 5 years. Studies in Finland from 1998 to 99 (Pitkala et al., 2002) and Denmark in 2000 (Eriksen et al., 2006) showed a similar prevalence of 2.8% and 3% regular opioid use, respectively. Other studies considering general US population surveys have reported a range of opioid prescription prevalence from 3% of the population up to 6.3% depending on the particular study population or the specific measurement/definition of opioid use (Paulose-Ram et al., 2003; Olsen et al., 2006). In 2012, a large population-based Canadian study suggested a level of opioid abuse of about 5% of the population (Shield et al., 2013). The US National Surveys on Drug Use and Health (NSDUH) estimated that almost 12.5 million Americans over the age of 12 abused prescription opioids in 2012 (SAMH, 2013). In 2014, 11.5 million adults abused prescription opioids, with almost two million individuals with a prescription opioid use disorder defined according to DSM-IV diagnostic criteria (Han et al., 2017). Additionally, the 2015 NSDUH found 22.2% of

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00013-7 Copyright © 2020 Elsevier Inc. All rights reserved.

individuals misusing prescribed opioids were using larger amounts than directed, 14.6% used them more frequently, and 13.1% used them for longer than recommended (Han et al., 2017). Furthermore, a study by Shei et al. (2015), considering prescription opioid abuse in European countries, estimated a prevalence per 10,000 of about 13.7 in France, 11.0 in Germany, and 10.7 in the United Kingdom but less than 1 per 10,000 in Spain and Italy. This is not just a consideration in the adult population; a survey of American 10e18 year olds from 2008 to 11 (Osborne et al., 2017) reported that 4.8% had used prescription opiates in the previous 30 days (two-thirds of whom had abused prescription opioids and one-third had been prescribed them). This is a particular cohort of concern due to the risk of opioid use continuing into adulthood and the unclear potential detrimental effects of opioids on the developing nervous system of these younger individuals. In addition to this legitimate medical source of opiates, which may be further misused by the recipient, or diverted to the illegal market, the prevalence of the illicit opiate heroin is also widespread in many countries. This availability of opioids from potentially multiple sources contributes to a widespread global, regional, and local pattern of opioid use. According to the 2017 European Drug Report, published by the European Monitoring Center for Drugs and Drug Addiction, heroin is the most commonly used illicit opiate in Europe although other licit synthetic opioids are being increasingly misused (EMCDDA, 2017). In 2015, 0.4% or 1.3 million European adults (15e64 years of age) were estimated to be high-risk opioid users. Germany, Spain, France, Italy, and the United Kingdom accounted for 76% of these users. In the United States, it has been estimated that between 2007 and 2011, the number of US citizens having used heroin rose from 373,000 to 620,000 (SAMH, 2013). It is difficult to accurately calculate the overall level of opioid consumption due to the multiple legal and illicit sources of these substances. This calculation is further

179

180 Cognition and Addiction

complicated by the illegal nature of prescription misuse, prescription diversion, and illicit drug use, hindering accurate self-reporting for epidemiological monitoring to authorities due to fear of adverse repercussions following disclosure. To further understand the prevalence of opioid dependence globally, Degenhardt et al. (2014) undertook a systematic review of epidemiology of opioid dependence as part of the 2010 global burden of disease project publications. Between 1990 and 2010, the estimation of opioiddependent individuals aged over 15 increased from 10.4 million to 15.5 million with the United Kingdom and Australia having the highest estimated prevalence (0.48% and 0.46%, respectively) followed by western Europe and North America. In 2010, 9.2 million disability-adjusted life years, a summary measure of overall disease burden, were attributable to opioid dependence, a figure that has increased over time. This increase in disease burden has been driven by an increased prevalence of dependence, rather than changes in global age profiles or population size.

Long-term cognitive deficits associated with opioids In the following sections, we will review the cognitive literature on several different groups of opioid users including those (1) taking illicit heroin and prescribed methadone and buprenorphine for the treatment of opioid dependence, (2) other opioids for treatment of chronic pain, and (3) abstinent populations with a history of opioid dependence or abuse. A large number of studies reported the effects of chronic opioid use on a variety of neuropsychological skills. The population studied was either from the opioiddependent or chronic pain clinical settings. Most studies have attempted to target one opioid such as prescribed methadone or illicit heroin use. In addition to the literature on the chronic effects of heroin and methadone, some studies have examined neuropsychological functioning in the more general group of “opioid addicts” by combining participants with current methadone, heroin, and/or other opioid use. The progress in each of these areas will be examined in turn.

Neuropsychological functioning in mixed opioid using and dependent populations The results of such studies are difficult to interpret as these groups often contain individuals who abuse various different opioids, and it is therefore not possible to attribute observed deficits to the effects of any specific type of opioid. However as many individuals in this population will use a variety of different opioids in their lifetime, these

studies may help to examine whether there is a neuropsychological profile characteristic of this population. An early example of this type of research was conducted by Rounsaville et al. (1982), who assessed a group of 72 opioid addicts on entering treatment, using a brief neuropsychological battery. This group consisted of individuals who were still using illicit heroin, those who had recently commenced on a prescribed methadone dose, and those who had recently been detoxified from all opioids. Many of these individuals could be classified as poly drug users, reporting regular use of other substances including amphetamines, cocaine, sedatives, cannabis, and alcohol. The group varied widely in terms of the length and nature of their drug abuse. The authors found that although the opioid group’s intellectual functioning scores were in the normal range, their performance in a number of areas of neuropsychological functioning was at the mildly impaired range. These included tasks of attention, cognitive flexibility, and motor impulsivity. When the opioid group was compared with a control group of nonsubstance using participants matched for sociodemographic variables, the former did not perform significantly below the latter group on any of the measures included. Six months after the initial assessment, the authors noted that the improvements observed could not be attributed to the effects of detoxification from opioids as more than half of the sample had positive urine samples for opioids. Instead it was suggested that these improvements were associated to an overall change in clinical status of this group compared with their initial presentation. Although this study failed to find any significant differences between opioid users and nonsubstance using controls, it did show that following a period of relative stability in treatment, improvements were seen in some areas of functioning. This suggested that on entering treatment, opioid-dependent individuals were performing at a level below their actual optimal ability on several indices of neuropsychological functioning. Ersche et al. (2006a and 2005) compared a group of opioid-dependent individuals with a group of amphetamine-dependent individuals across a number of neuropsychological domains. The opioid-dependent group consisted largely of methadone maintenance patients and current illicit heroin users, as well as participants receiving prescribed buprenorphine, dihydrocodeine, diamorphine, and morphine sulfate. Urine analysis showed recent use of other substances in around half of the opioid group. Control groups included drug-free controls, drug-free (abstinent) previously opioid users, and drug-free (abstinent) examphetamine users. All participants were assessed using three measures of executive functioning (impulsivity, planning, and cognitive flexibility tests) and two measures of visual memory. On the planning task, both the current

Cognitive consequences of opioid use Chapter | 13

and former poly drug users performed significantly worse than the nonsubstance use healthy controls. Amphetamine users’ performance was poorer than opioid users, and there was no difference between current and former substance users. Performance on cognitive flexibility (attentional set-shifting task) was comparable for all groups. On both visual memory tests, current and former substance users performed at a level that was significantly poorer than controls. These results contradicted previous and subsequent studies as they failed to find any difference between current and former heroin users. Instead, these results supported the notion that the neuropsychological deficits observed in chronic opioid users were not a direct result of the opioid itself but rather were a consequence of the factors associated with long-term drug abuse (Darke et al., 2000). This is in contrast with more recent studies which had provided evidence for impairments in current opioid users above and beyond those observed in abstinent ex-opioid addicts (e.g., Mintzer et al., 2005; Verdejo-Garcia et al., 2005a,b). However, there were a number of limitations to the study by Ersche et al. (2006a and 2005), the most obvious of these being the heterogeneous nature of the opioid group in terms of the type of opioid used, whether opioid use was illicit or prescribed opioid, and whether other illicit drugs were used concurrently. In addition, the former amphetamine and opioid users were combined into one group for comparison, with some reporting a history of previous amphetamine use or previous opioid use and others reporting a history of both amphetamine and opioid use. Given these limitations, the results of this study should be treated with caution. Ornstein et al. (2000) conducted a study that aimed to clarify the notion that there exists a distinct profile of neuropsychological impairment that is common to heroindependent individuals. In this study, a group of participants whose primary drug of abuse was heroin and most also treated with methadone was compared with a group who primarily used amphetamine. A third group of substance-free participants was matched to the other two groups for age and premorbid intellectual functioning. The assessment consisted of a number of subtests chosen from the Cambridge Neuropsychological Test Automated Battery (CANTAB)ecomputerized test battery (Sahakian et al., 1988), as well as a test of verbal fluency. CANTAB tests included measures of visual and visuospatial recognition memory, spatial working memory, attentional rule shifting, and spatial planning. This study found that, relative to controls, the heroin group generated fewer words (but not significantly) on the verbal fluency task and showed no improvement following practice trials on the test of visuospatial strategy. In addition, significant impairments were found in visual and visuospatial recognition memory, attentional set-shifting, and spatial planning. These results pointed to the existence of a diverse pattern of neuropsychological

181

impairment in heroin-dependent individuals who were still using heroin. Overall chronic opioid effects in cognition were summarized in a metaanalysis by Baldacchino et al. (2012), which suggested that chronic opioid exposure is associated with impairments across a range of different neuropsychological domains. However, the only domains that the analysis suggests robust impairments with medium effect sizes according to Cohen were those of verbal memory, cognitive impulsivity, and flexibility (Table 13.1).

Neuropsychological functioning in illicit heroin using and dependent populations Heroin (diamorphine) is a semisynthetic opioid that is derived from morphine. It is most often used medically as an analgesic or illicitly for recreation. Illicit heroin users often start by “smoking” this substance (burning the heroin and inhaling the fumes), but many will quickly progress to intravenous use. Injecting heroin allows it to pass quickly through the bloodebrain barrier where it is broken down into monoacetylmorphine and morphine. These bind to muopioid receptors and result in intense analgesic, euphoric, and anxiolytic effects (Jaffe, 1990). A review by Lundqvist (2005) discussed the research evidence for neuropsychological impairments as a result of different types of substance use. This paper concluded that “there is a consensus that all drugs cause a disharmony in the neuropsychological network, causing a decrease in activity in areas responsible for short term memory, attention and executive functioning, with the possible exception of heroin.” However, the literature described in this section paints a different picture. Although some studies have failed to find any evidence for significant neuropsychological decline associated with heroin abuse, others have shown that individuals with current heroin use displayed impairments in a variety of neuropsychological domains. However, the evidence did not support a link between the amount of heroin used and/or duration of heroin use and level of impairment (Prosser et al., 2006). In a test by Stevens et al. (2007) for assessing various executive functions, memory and learning were administered to 25 male heroin-dependent individuals and compared with 26 poly drug abusers abstinent for more than 3 months and another 26 nonsubstance-using healthy male controls. There was significant impairment in cognitive flexibility, working memory, and sustained attention in the heroin group (Stevens et al., 2007). The heroin-dependent group attending a chronic pain clinic in McNairy et al. (1984) was significantly impaired in verbal learning but not in memory, cognitive flexibility, and sustained attention. The study suggested that the neuropsychological impairment could have been caused by the chronic use of opioids and compounded by the “slowed,

182 Cognition and Addiction

TABLE 13.1 Summary of the evidence presented in this review for an association between chronic and dependent opioid use and neuropsychological impairment. Neuropsychological domain

Chronic opioid dependence studies*

Standardized Effect size d

Cognitive impulsivity

Clark et al. (2006)Y Rogers et al. (1999)Y Ersche et al. (2005)4

0.78 0.68 0.46 0.31

Motor impulsivity

n/a

n/a

Nonplanning impulsivity

Ersche et al. (2006a)Y

0.95 (TOL)

Ornstein et al. (2000)Y

1.18 (TOL)

Clark et al. (2006)Y

0.78 (IGT)

Ersche et al. (2006a)4

0.18 (IED)

Ornstein et al. (2000)Y

0.55 (IED)

Ersche et al. (2006a)Y

0.89 (PAL) and 0.64 (PRM)

Ornstein et al. (2000)Y

0.87 (SRM) and 0.53 (SWM)

Ersche et al. (2006a)Y

0.98 (PRM)

Ornstein et al. (2000)Y

0.80 (PRM)

Cognitive flexibility

Short-term memory

Long-term memory

(IGT) (BIS) (CGT) (CGT)

*, P < .05; 4, no significant difference in neuropsychological performance; Y, significant neuropsychological deficits present; [, significant improvement in neuropsychological performance when compared with healthy controls; d, Cohen’s effect size defined as the difference between two means divided by a standard deviation for the data. Standardized effect sizes are reported regardless of the statistical significance (P-value) of the results reported in the original studies. n/a, data not available. BIS, Barratt Impulsivity Scale; CANTAB, Cambridge Neuropsychological Test Automated Battery; CGT, Cambridge Gambling Task; DMS, Delayed Matching to Sample Test; IED, Intra/Extra Dimensional Set-Shifting Task; IGT, Iowa Gambling Task; PAL, Paired Associate Learning Task; PRM, Pattern Recognition Memory; SRM, Spatial Recognition Memory; SWM, Spatial Working Memory; TOL, Tower of London; VFT, Verbal Fluency Test.

disorganized or inappropriate responses to environmental demands for adaptive and stressful behavior such as chronic pain and the iatrogenic prescription of opioids.” Finally in an empirical study by Tolomeo et al. (2016), illicit heroin use (n ¼ 24) presented with significant impairments in cognitive and motor impulsivity and strategic planning when compared with stable methadone (n ¼ 29) users and healthy controls (n ¼ 28).

Neuropsychological functioning in abstinent former heroin-dependent populations A number of the studies of methadone use and neuropsychological functioning included a control group of abstinent ex-heroin users (Mintzer et al., 2005; Prosser et al., 2006; Clark et al., 2006), and these have generally indicated that this group may be impaired in some areas relative to controls with no history of opioid use but may be less impaired than current methadone users. Several further studies that focused on abstinent ex-heroin users contributed to the research in this area. Two studies by Verdejo-Garcia et al. (2007a); VerdejoGarcía and Pérez-García (2007b) focused on the effects of substance misuse on executive functions. Specifically, these studies set out to examine executive function in

abstinent polysubstance users whose primary addiction was heroin and in those whose primary addiction was cocaine. A third group of healthy, substance-free controls was also included in the study. All participants in the heroin and cocaine groups had been abstinent for a minimum of 2 weeks with no history of mood disorder, head injury, or neurological disorder. The results showed that the heroin polysubstance users displayed significant impairment in motor impulsivity, cognitive impulsivity, and cognitive flexibility relative to controls. Fishbein et al. (2007) contrasted the cognitive performance of four groups of participants, pure users of heroin, co-users of heroin and alcohol, pure alcohol users, and nonusers, on measures of visual memory and different components of executive functions including, nonplanning impulsivity, cognitive flexibility, and cognitive impulsivity. Substance users were evaluated after 3 weeks of abstinence. The data suggested that heroin users had significantly impaired performance on cognitive impulsivity and cognitive flexibility, taking more risk even though they had more time to make a decision. However, performance on visual memory and problem-solving tasks (nonplanning impulsivity) by heroin users did better than the other two cohorts, suggesting that these tasks were more closely linked to chronic alcohol rather than heroin use.

Cognitive consequences of opioid use Chapter | 13

The results of this study added further support to the idea that long-term heroin use cause deficits in at least some areas of executive functioning. A similar study by the same author (Fishbein et al., 2005) observed similar impairments in decision-making with a group of heroin users who have been abstinent for more than 12 weeks. This heroin group selected significantly more risky choices, particularly riskiest scenarios despite repeated penalties incurred (i.e., they were less likely to employ a more cautious strategy in response to improbable options). The group’s choice did not appear to be due to motor impulsivity as they had ample time to think about their next move and could have a willingness to accept the likelihood of negative consequences even in unfavorable circumstances. Such significant increase in cognitive (risk-taking) impulsivity in heroin-dependent individuals was suggested as a cognitive marker of substance dependence that does not recover with prolonged abstinence (Clark et al., 2006). In Brand et al. (2008), 18 inpatients from an addiction unit were tested after a 2 week opioid detoxification period from heroin. These opioid-dependent individuals significantly chose the risky alternatives more frequently but performing no different in nonplanning impulsivity and problem-solving from the healthy nonsubstance user control group. Although the research in this area was limited, it seemed to point to a general improvement in at least some areas of neuropsychological functioning after at least 2 weeks of abstinence from heroin use. This suggested that some of the deficits observed in the active opioid users were either transient effects from the acute intoxication of the drug itself. It is therefore important to test if there is an effect in neuropsychological performance with the same individuals prospectively at different stages of duration of abstinence. In two observational studies by Baldacchino et al., (2016) and Tolomeo et al. (2018), abstinent, previously opioid-dependent individuals showed significantly impaired cognitive and nonplanning impulsivity and flexibility when compared with healthy controls (Table 13.2).

Neuropsychological functioning in methadone users Early studies (conducted between 1970 and 1982) failed to detect any difference in neuropsychological functioning. However, systematic reviews of these early studies concluded several methodological limitations (Lombardo et al. (1976), Gordon (1970), Gordon and Appel (1995), Appel (1982), Rothenberg et al. (1977), Mintzer and Stitzer (2002), Zacny (1995). Starting a new generation of better-controlled studies, Darke et al. (2000) compared neuropsychological performance in methadone-maintained individuals with opioidfree controls matched for age, gender, and education. This

183

study examined a number of areas of neuropsychological functioning, including information processing, attention, short- and long-term verbal and nonverbal memory and cognitive flexibility. The authors reported that despite being matched to the control group in terms of their premorbid level of intellectual functioning, the methadone maintenance groups’ performance was significantly poorer than the control group in all domains tested. There was no significant effect of methadone dose on performance in any of the domains. However, the authors pointed out that the methadone-maintained group reported a significantly higher incidence of alcohol dependence and nonfatal overdose, both of which were found to be independent predictors of poorer performance in each neuropsychological domain. The methadone maintenance group also had a significantly higher prevalence of head injury than the control group, another common cause of neuropsychological impairment. This study demonstrated the potential difficulties in identifying neuropsychological impairment that can unequivocally be attributed to opioid abuse rather than to the range of conditions that are frequently comorbid with opioid dependency. In addition, the results of this study are limited by the fact that the methadone maintenance group reported a high incidence of other substance use, and recent illicit substance use was not objectively verified using urine analysis. The authors suggested that the neuropsychological impairments seen in opioid users are likely to be a consequence of factors associated with substance abuse lifestyle, rather than the direct effects of the opioids or other substance. In the same year, a study by Specka et al. (2000) also compared methadone-maintained participants with matched substance-free controls on a number of measures of neuropsychological functions relevant specifically to driving ability. The methadone maintained group in this study demonstrated significant impairments in attention and tachistoscopic perception, they were faster but less accurate on a response time task (motor impulsivity deficits), and they were more accurate than controls but slower on a visual tracking test (reduced reaction time). Although this study had implications in further understanding the association between methadone and neuropsychological skills relative to driving, it was limited by the fact that participants who tested positive for other substances in their urine were not excluded. Rotheram-Fuller et al. (2004) compared methadonemaintained patients with substance-free controls matched for premorbid intellectual functioning on cognitive impulsivity (risk-taking) and cognitive flexibility (perseveration). In addition, the authors divided both groups into smokers and nonsmokers to determine whether smoking had any impact on performance of these tasks. This study showed that the methadone-maintained group who smoked displayed significant impairments in cognitive impulsivity

TABLE 13.2 Summary of the evidence presented in this review for an association between illicit and dependent heroin use and neuropsychological impairment at two stages of opioid receptor occupancy (use and abstinent). Neuropsychological domain

Illicit chronic heroin use*

Standardized effect size d (neuropsychological test)

Abstinent exheroin use*

Standardized effect size d (neuropsychological test)

Cognitive impulsivity (reflection impulsivity)

Baldacchino et al. (2014)Y Ersche et al. (2005)4

0.84 (CGT) 0.31 (CGT)

Mintzer et al. (2005)Y

0.30 (IGT)

Verdejo-Garcia et al. (2007a)Y

0.54 (IGT)

Fishbein et al. (2007)Y

0.38 (RDMT)

Fishbein et al. (2005)Y

1.00 (RDMT)

Clark et al. (2006)Y

0.68 (BIS)

Tolomeo et al. (2016)Y

0.70 (CGT)

Verdejo-Garcia et al. (2007a)4

0.66 (go/no-go)

Verdejo-Garcia et al. (2007b)Y

0.87 (go/no-go)

Fishbein et al. (2007)4

1.05 (SOC)

Brand et al. (2008)4

0.19 (TOH)

Tolomeo et al. (2016)

(SOC)

Motor impulsivity

Nonplanning impulsivity

Cognitive flexibility

Baldacchino et al. (2014)Y

Baldacchino et al. (2014) Y

Short-term memory

Long-term memory

0.80 (SOC)

Hill andand Mikhael (1979)Y

0.44 (CT)

Verdejo-Garcia et al. (2007a)Y

0.67 (ST) andand 0.29 (WCST)

Stevens et al. (2007)Y

0.40 (TMT)

Fishbein et al. (2007)Y

0.27 (ST)

Brand et al. (2008)Y

1.42 (MCST)

Tolomeo et al. (2018)Y

3.30 (IED)

Prosser et al. (2006)4

0.33(COWAT)

McNairy et al. (1984)4

Sustained attention

1.00(AGN)

0.18 (TMT)

Tolomeo et al. (2018)Y

0.70 (IED)

McNairy et al. (1984)4

0.20 (WAIS II)

Verdejo-Garcia et al. (2007a)Y

0.34 (5DT)

Stevens et al. (2007)Y

0.32 (SRTT)

Mintzer et al. (2005)Y

0.52 (DSST)

McNairy et al. (1984)4

0.21 (AVLT)

Verdejo-Garcia et al. (2007a)Y

0.81 (CBT)

Stevens et al. (2007)4

0.39 (DMS)

Fishbein et al. (2007)4

0.59 (PAL)

Stevens et al. (2007)4

0.22 (WMSR)

Overall, the literature suggested limited but significant deficits in attention and cognitive flexibility in the heroin-dependent population and significant deficits in attention, impulsivity, and cognitive flexibility in the abstinent heroin cohorts (Ferna´ndez-Serrano et al., 2010a,b). *, P < 0.05; 4, no difference in neuropsychological performance; Y, neuropsychological deficits present; [, improvement in neuropsychological performance when compared with healthy controls; d, Cohen’s effect size defined as the difference between two means divided by a standard deviation for the data. Standardized effect sizes are reported regardless of the statistical significance (P-value) of the results reported in the original studies. n/c, controls not healthy controls or not enough information to calculate effect size. AGN, Affective go/no-go; AVLT, Auditory Verbal Learning Test; BIS, Barratt Impulsivity Scale; CANTAB, Cambridge Neuropsychological Test Automated Battery; CBT, Cognitive Bias Test; CGT/RDMT, Cambridge Gambling Task/Roger’s Decision-Making Test, Halstead Reitan Neuropsychological Test Battery; COWAT/FAS, Controlled Oral Word Association Test/ Phonological Fluency Test; CT, Category Test; 5DT, Five Digit Test; DMS, Delayed Matching to Sample Test; DSST, Digital Symbol Substitution Test; Go/no-go, Go/no-go test; IED, Intra/Extra Dimensional Shift; IGT, Iowa Gambling Task; MCST, Maudsley Card Sorting Test; PAL, Paired Associate Learning Task; RFFT, Ruff Figural Fluency Test; SRTT, Serial Reaction Time Task; ST, Stroop Test; TOH, Tower of Hanoi; TMT, Trail Making Test; TOL/SOC, Tower of London/Stockings of Cambridge; WAIS II, Wechsler Adult Intelligence Scale Second Edition; WCST, Wisconsin Card Sorting Test; WMSR, Wechsler Memory Scale Revised.

Cognitive consequences of opioid use Chapter | 13

relative to controls and the nonsmoking methadonemaintained group. There were no differences between groups in cognitive flexibility. These results suggest that in addition to the numerous other risk factors for neuropsychological impairment associated with substance use, smoking may be related to impairment in cognitive impulsivity and possibly in other neuropsychological domains. Research conducted by Mintzer and Stitzer (2002) provided evidence for the presence of impaired neuropsychological functioning as a result of methadone-maintenance therapy. Their study compared the performance of a group of methadone-maintained participants with matched drug-free controls across a range of neuropsychological domains. Urine testing before assessment provided objective evidence of recent abstinence from other substances. The authors suggested that the methadone maintenance group showed significant impairments relative to controls in the areas of psychomotor speed, short-term memory, and cognitive flexibility. In 2005, Mintzer et al. developed their earlier study by comparing the results of a new group of opioid-free ex-heroin users on the same battery of neuropsychological tests retrospectively with their initial two groups. The new group was matched to the earlier two groups demographically and matched to the methadone maintenance group in terms of history of substance use. The authors found that in general, the new group’s scores fell between that of the methadone maintenance group and the controls on most tests, although they only performed significantly better than the methadone group on a test of cognitive flexibility and significantly below the control group on the task of psychomotor speed. The results of this study supported to the notion that the significant impairments seen in methadone-maintained patients may be related to the direct effects of opioids rather than factors other than those associated with substance abuse (e.g., history of head injury overdose etc.), as it suggested that some recovery of function may occur with detoxification from all opioids. In a similar study, Prosser et al. (2006) compared methadone-maintained ex-heroinedependent group with a group of abstinent heroin-dependents who had been detoxified from methadone. Both groups were matched for substance using history. A group of healthy nonsubstance using controls was also included in this study. The authors hypothesized that abstinent heroin-dependent individuals should perform better than methadone-maintained participants on tests of various neuropsychological skills. However, the results of this study showed that both methadone maintenance and abstinent heroin-dependent groups performed significantly worse than controls but, at a similar level to one another, on attention and cognitive flexibility (perseveration). The only significant difference between methadone maintenance and abstinent heroin-dependent

185

groups was on a test of visuospatial memory, with the abstinent heroin group performing more poorly. No effect of length or level of prior heroin use on neuropsychological functioning was found. Although this study is useful as it compared the effects of current methadone use with the possible residual effects of long-term opioid use, caution should be used in comparing the results of the two heroindependent groups with the nonsubstance using healthy control group. This is because both the former groups had fewer years of formal education than controls, and their scores on a test of verbal functioning were lower than those of the controls. As the authors explained, this measure is often used as an estimate of an individual’s premorbid level of intellectual functioning, suggesting that the two heroin groups had lower levels of premorbid intellectual functioning than the control group. If this is the case, then it would be expected that their performance on other measures of neuropsychological functioning would also be lower, consistent with their estimated premorbid level of functioning. Passetti et al. (2008) tested 37 opioid-dependent heroin users 6 weeks after starting a community methadone treatment program and subsequently followed up 3 months after. They were tested on measures of nonplanning impulsivity, motor impulsivity, and cognitive impulsivity (risk-taking). Three months after initiation of methadone treatment, 10 individuals had become abstinent for heroin while another 24 were taking at least heroin on a weekly basis on top of their methadone medication. The study stated that performance on cognitive impulsivity (Cambridge Gambling Task [CGT] and Iowa Gambling Task) at baseline could predict clinical outcome. There were no significant deficits observed in strategic planning and motor impulsivity. Finally Gradin et al. (2013) followed by Tolomeo et al. (2016, 2018) and Baldacchino et al. (2014) from the same research team presented a group of stable methadone users with significantly impaired impulsivity-related domains but no impairments in compulsivity domains (Table 13.3). From the range of studies that have examined the impact of methadone on neuropsychological functioning, there seems to be an evidence base describing impairment in methadone users in a number of neuropsychological domains. There is conflicting evidence regarding the possible impact of the dose of methadone on level of impairment with some research pointing to no effect of dose on performance, and some research reporting a doserelated impact on the specific domains of delayed verbal memory and reaction time. Furthermore, other studies suggested that some recovery of functioning takes places with time in methadone-maintained ex-heroin users. Finally, those studies that have compared methadone users with abstinent ex-heroinedependent and substance-free healthy controls have indicated that the abstinent ex-heroinedependent group performed at a superior level

186 Cognition and Addiction

TABLE 13.3 Summary of the evidence presented in this review for an association between methadone use and neuropsychological impairments. Neuropsychological domain

Methadone use*

Cohen’s d test (Neuropsychological)

Cognitive impulsivity

Pirastu et al. (2006)Y

3.02 (IGT)

Rotheram-Fuller et al. (2004)Y

0.89 (IGT)

Passetti et al. (2008)Y

n/c

Tolomeo et al. (2016)Y

0.06 (CGT)

Clark et al. (2006)Y

0.78 (IGT)

Ersche et al. (2006b)4

0.31 (CGT)

Mintzer et al. (2005)Y

0.30 (IGT)

Yin et al. (2012)Y

0.7 (WCST)

Baldacchino et al. (2014)4

0.99 (CGT)

Passetti et al. (2008)4

n/c

Prosser et al. (2006)4

0.78

Baldacchino et al. (2014)4

0.87 (AGN)

Clark et al. (2006)Y

0.68 (BIS)

Fadardi and Ziaee (2010)

n/c

Liao et al. (2014)4

0.03 (SSRT)

Mintzer et al. (2005)Y

0.30 (IGT)

Wang et al. (2014)4

5.18 (MT)

Passetti et al. (2008)4

0.98 (TOL)

Tolomeo et al. (2016)

0.12 (SOC)

Clark et al. (2006)Y

0.78 (IGT)

Motor impulsivity

Nonplanning impulsivity

Cognitive flexibility

Sustained attention

Ornstein et al. (2000)Y

1.18 (TOL)

Darke et al. (2000)Y

0.65 (WCST); 0.57 (COWAT)

Mintzer et al. (2005)Y

0.87 (TMT)

Rotheram-Fuller et al. (2004)4

0.61 (WCST)

Pirastu et al. (2006)Y

7.27 (WCST)

Prosser et al. (2006)Y

0.65 (ST)

Soyka et al. (2008)Y

0.81 (TMT); 0.54 (RWT)

Tolomeo et al. (2018)4

0.05 (IED)

Ersche et al. (2006b)Y

0.95 (TOL)

Gupta et al. (2014)Y

0.4 (ST)

Ornstein et al. (2000)Y

0.55 (IED)

Yates (2009)

0.49 (ST)

Darke et al. (2000)Y

0.76 (WAIS II)

Specka et al. (2000)Y

0.45 (DR2) and 0.64 (Q1)

Mintzer et al. (2005)Y

1.00 (DSST)

Soyka et al. (2008)Y

0.77 (DR2) Continued

Cognitive consequences of opioid use Chapter | 13

187

TABLE 13.3 Summary of the evidence presented in this review for an association between methadone use and neuropsychological impairments.dcont’d Neuropsychological domain

Short-term memory

Long-term memory

Methadone use*

Cohen’s d test (Neuropsychological)

Wang et al. (2014)4

8.79 (CPT)

Lin et al. (2012)

0.03 (PASAT)

Yates (2009)

0.6 (WCST)

Darke et al. (2000)Y

0.80 (WMSR) and 0.55 (RCFT)

Mintzer et al. (2005)Y

0.70 (2BT)

Prosser et al. (2006)Y

0.97 (BVRT)

Pirastu et al. (2006)Y

7.82 (BVRT)

Soyka et al. (2008)Y

0.83 (AVLT)

Ersche et al. (2006b)Y

7.82 (BVRT), 0.83 (AVLT) 0.89 (PAL) and 0.64 (PRM)

Gupta et al. (2014)4

0.01 (BVMT)

Ornstein et al. (2000)Y

0.87 (SRM) and 0.53 (SWM)

Wang et al. (2014)4

0.8 (WMSR)

Yates (2009)Y

0.7 (WMS)

Darke et al. (2000)Y; Gupta et al. (2014)

1.40 (WMSR)

Wang et al. (2014)4

0.3 (WMSR)

Yates (2009)Y

0.68 (WMS)

*P < 0.05; 4, no difference in neuropsychological performance; Y, neuropsychological deficits present; [, improvement in neuropsychological performance when compared with healthy controls; d, Cohen’s effect size defined as the difference between two means divided by a standard deviation for the data. Standardized effect sizes are reported regardless of the statistical significance (P-value) of the results reported in the original studies. n/c, controls not healthy controls or not enough information to calculate effect size. AVLT, Auditory Verbal Learning Test; 2BT, Two Back Task; BVRT, Benton Visual Retention Test; CANTAB, Cambridge Neuropsychological Test Automated Battery; COWAT/FAS, Controlled Oral Word Association Test/Phonological Fluency Test; DR2, Simple Choice Reaction; DSST, Digital Symbol Substitution Test; IGT, Iowa Gambling Task; Q1, Attention under Monotonous Circumstances; RCFT, Rey Osterreith Complex Figure Test; RWT, Regensburger Word Fluency Test; ST, Stroop Test; TMT, Trail Making Test, Act React Test Systems (ART 90/2020); TOL, Tower of London, Halstead Reitan Neuropsychological Test Battery; WAIS II, Weschler Adult Intelligence Scale Second Edition; WCST, Wisconsin Card Sorting Test; d, Weschler Memory Scale Revised.

to methadone users but below the level of substance-free and healthy controls. A metaanalysis of data from a total cohort of 1063 methadone users, 412 abstinent (boys) in performance but girls took longer to respond than boys. Continued

Peer-reviewed working memory training: is it an effective intervention for addiction? Chapter | 18

251

TABLE 18.1 Details of peer-reviewed working memory training programs and their supporting studies.dcont’d N-back (n [ 32) Thompson et al. (2016)

Intensive Working Memory Training Produces Functional Changes in Large-scale Frontoparietal Networks.

20 sessions of adaptive training

39 participants in active training group; between 18 and 45-year-old adults, R handed, good health, and not taking psychoactive medication

Training resulted in taskspecific expansion of dual n-back abilities. Training differentially affected activations in two large-scale frontoparietal networks thought to underlie WM: the executive control network and the dorsal attention network. Activations in both networks linearly scaled with WM load before training, but training dissociated the role of the two networks and eliminated this relationship in the executive control network. Load-dependent functional connectivity both within and between these two networks increased following training, and the magnitudes of increased connectivity were positively correlated with improvements in task performance. These results provide insight into the adaptive neural systems that underlie large gains in WM capacity through training.

Lindeløv et al., 2016

Training and transfer effects of N-back training for brain-injured and healthy subjects.

20 sessions

17 patients and 18 healthy subjects

Neither group demonstrated transfer to untrained tasks; computerized training facilitates improvement of specific skills rather than high-level cognition in healthy and ABI subjects. The acquisition of these specific skills seems to be impaired by brain injury.

LawlorSavage and Goghari, 2016

Dual N-Back Working Memory Training in Healthy Adults: A Randomized Comparison to Processing Speed Training.

20e30 min training session in 1 sitting, 5 days a week, for 5 weeks at a location of their convenience. Participants completed a log indicating dates and times trained across the 5week period

57 healthy adults aged 30e60 years

Findings suggest that dual n-back WM training may not benefit WM or fluid intelligence in healthy adults. Further investigation is necessary to clarify if other forms of WMT may be beneficial and what factors impact training-related benefits, should they occur, in this population.

Heinzel et al., 2016

Neural correlates of training and transfer effects in working memory in older adults.

12 sessions (45 min each)

32 healthy older participants (60e75 years)

WM performance improved with training and behavioral transfer to tests measuring executive functions, processing speed, and fluid intelligence was found. MRI findings indicate a training-related increase in processing efficiency of WM networks, potentially related to the process of WM updating. Performance gains in untrained tasks suggest that transfer to other cognitive tasks remains possible in aging. Continued

252 Cognition and Addiction

TABLE 18.1 Details of peer-reviewed working memory training programs and their supporting studies.dcont’d N-back (n [ 32) Minear et al., 2016

A simultaneous examination of two forms of working memory training: Evidence for near transfer only.

4 weeks of training

31 undergraduates

Evidence for near transfer from the spatial n-back training to new forms of n-back.

Heinzel et al., 2017

Transfer Effects to a Multimodal Dual-Task after Working Memory Training and Associated Neural Correlates in Older Adults e A Pilot Study.

4 weeks of 12 training sessions of an adaptive n-back training (approximately 45 min each)

18 participants (11 females; mean  SD age ¼ 65.78  3.04) in the training group

The results indicate that 12 sessions of numerical n-back training can improve the performance in the trained task. Moreover, a transfer to the performance in a dual-task was found.

Jungle Memory (n [ 2) Author

Title

Length of working memory training

Study population

Main findings

Nelwan and Kroesbergen (2016)

Limited Near and Far Transfer Effects of Jungle Memory Working Memory Training on Learning Mathematics in Children with Attentional and Mathematical Difficulties.

Three different groups: (1) the experimental group (first Jungle Memory (JM) for 8 weeks then MT (math training) for 8 weeks); (2) the first control group (MT first then JM); (3) a second control (usual education first, then MT). Assessment occurred three times: before training, after 8 weeks, and posttraining.

64 school-aged children between 9 and 12 years old with difficulties in mathematics, attention, and WM

Some possible short-term effects on near transfer measures of verbal WM. Improvements in mathematics but unknown if mediated by gains in WM. Furthermore, it remains unclear whether the effects found on improving mathematics were actually mediated by gains in WM. It is argued that JM does not train the components of WM involved in maths enough, and this can be because of the training’s lack of adaptivity, failing to provide children with tailored instruction, and feedback.

Alloway et al. (2013)

Computerized working memory training: Can it lead to gains in cognitive skills in students?

Active control: complete the training program once a week over an 8-week period. They completed 24 sessions on average for all three memory games (eight sessions per game).Training group used the program 4 times a week and completed 84 sessions on average for all three memory games (28 sessions per game) over an 8-week period.

94 students classified as having learning difficulties, allocated to 3 different groups (nonactive control, active control, and training group)

Evidence that children with JM first performed better after MT than children who did not follow JM first or did not train with JM at all.

PssCogRehab (n [ 1) Author

Title

Length of working memory training

Study population

Main findings

Bickel et al. (2011)

Remember the future: working memory training decreases delay discounting among stimulant addicts

All participants completed 1 pretraining session, 4 to 15 training sessions, and 1 posttraining session. The range of time lapsed between pre- and posttraining sessions was 9

27 adults in treatment for stimulant use were randomly assigned to receive either WM training or control training

Discount rates were positively correlated with memory training performance measures. These results offer further evidence of a functional relationship between delay discounting and working memory.

Continued

Peer-reviewed working memory training: is it an effective intervention for addiction? Chapter | 18

253

TABLE 18.1 Details of peer-reviewed working memory training programs and their supporting studies.dcont’d N-back (n [ 32) e44 days, with a mean of 25 days. Cogmed (n [ 30) Author

Title

Length of working memory training

Study Population

Main Findings

Aasvik et al., 2016

Effectiveness of Working Memory Training among Subjects Currently on Sick Leave Due to Complex Symptoms.

1 training session (30e45 min) every day for 5 weeks.

Patients currently on sick leave due to symptoms of pain, insomnia, fatigue, depression, and anxiety

Working memory training (WMT) does not improve general WM capacity per se in addition to no added effects in targeting and improving selfperceived memory functioning. But evidence suggests that inhibitory control is accessible and susceptible to modification by adaptive WMT.

Sadeghi et al., 2017

Feasibility of computerized working memory training in individuals with Huntington disease

The patients underwent 25 sessions of Cogmed in total (5 day/week for 5 weeks).

9 early stage patients with Huntington’s (26 e62 years)

Improvement on Cogmed tasks; patients found training helpful and reported memory improvement.

Fuentes and Kerr, 2016

Maintenance effects of working memory intervention (Cogmed) in children with symptomatic epilepsy

The WM intervention was the Cogmed “RoboMemo” program and consisted of five 30e45min sessions per week over a 5e7-week period. Participants were exposed to 12 WM exercises (7 visual espatial, 2 visual everbal, and 3 auditory with visual responses) in all over the duration of training, with each session consisting of 8 exercises.

28 children with epilepsy (between 6.5 and 15.5 years)

WMT possibly improves related skills and these effects are maintained for 3 months. No transfer to fluid reasoning.

Hitchcock & Westwell (2017)

A cluster-randomised, controlled trial of the impact of Cogmed Working Memory Training on both academic performance and regulation of social, emotional and behavioural challenges.

45min/every school day for 5 weeks.

Primary school children

CWMT did not improve control of attention in the classroom or regulation of social, emotional, and behavioral difficulties.

Lee et al. (2016)

Effects of working memory training on children born preterm.

15 min a day, 5 days a week for 5 weeks.

Preterm children aged between 4 and 6 years

Results revealed that significant improvements in verbal WM was emerging in preterm children at 5-week follow-up, while significant gains in visuospatial working memory was found posttraining and at 5week follow-up in agematched term-born children. These results indicated that WMT has benefits for preterm children. Continued

254 Cognition and Addiction

TABLE 18.1 Details of peer-reviewed working memory training programs and their supporting studies.dcont’d N-back (n [ 32) Hardy et al. (2016)

Feasibility of HomeBased Computerized Working Memory Training With Children and Adolescents With Sickle Cell Disease.

Phillips et al. (2016)

Computerized Working Memory Training for Children with Moderate to Severe Traumatic Brain Injury: A DoubleBlind, Randomized, Placebo-Controlled Trial.

Wayne et al. (2016)

Working Memory Training and Speech in Noise Comprehension in Older Adults.

Roberts et al. (2016)

5 days each week for 5 weeks (25 training sessions).

Youth with SCD between the age of 7 and 16 years

Children who completed Cogmed exhibited improvements in verbal WM, visuospatial STM, and visuospatial WM. Suggestive that Cogmed is associated with WM improvement in youth with SCD.

27 children with moderate to severe TBI

Study provides evidence of near and far transfer of training to WM and academic skills for children with TBI.

25 sessions (30me1h/ 5 sessions per week) of adaptive working memory training and placebo training over 10 weeks in crossover design.

26 healthy subjects (13 male, 13 female) between 59 and 73 years

Scores on the adaptive WMT tasks improved as a result of training. However, training did not transfer to other WM tasks, nor to tasks recruiting other cognitive domains. No training-related improvement in speech-in-noise performance. The Reading Span Test significantly correlated only with a test of visual episodic memory, suggesting that the Reading Span Test is not a pure test of working memory, as is commonly assumed.

Academic Outcomes 2 Years After Working Memory Training for Children With Low Working Memory: A Randomized Clinical Trial.

20e25 training sessions of 45 min duration at school.

First graders from 44 schools in Melbourne, Australia

WM screening of children 6e7 years of age is feasible, and an adaptive working memory training program may temporarily improve visuospatial short-term memory.

van der Donk et al. (2016)

Predictors and Moderators of Treatment Outcome in Cognitive Training for Children With ADHD.

25 sessions (standard)

A total of 98 children (aged 8e12 years) with ADHD and comorbid LDs and/or ODD and on medication

Cognitive training can be beneficial for certain subgroups of children with ADHD; individual differences should be taken into account in future trials.

Bigorra et al. (2016)

Long-term far transfer effects of working memory training in children with ADHD: a randomized controlled trial.

25 sessions of 30e35 min: 5 sessions per week over 5 weeks.

66 children with combined-type ADHD (7 e12 years) from child and adolescent psychiatric unit

CWMT had a significant impact on ADHD deficits by achieving long-term far transfer effects. Based on the results obtained in the present study, CWMT may be a recommended intervention.

Grunewaldt et al. (2016)

Computerized working memory training has positive long-term effect in very low birth weight preschool children.

The children in the intervention group trained 10e15 min per day, 5 days a week for 5 weeks (25 sessions).

20 VLBW (very low birth weight) preschool children (5e6 years)

Computerized working memory training seems to have positive and persisting effects on working memory, and visual and verbal learning, at 7-month follow-up in VLBW preschool children. Continued

Peer-reviewed working memory training: is it an effective intervention for addiction? Chapter | 18

255

TABLE 18.1 Details of peer-reviewed working memory training programs and their supporting studies.dcont’d N-back (n [ 32) Steeger et al. (2016)

Combined cognitive and parent training interventions for adolescents with ADHD and their mothers: A randomized controlled trial.

Over 5w, completed a high- or low-dose version of Cogmed-RM (25 sessions).

91 adolescents (ages 11 e15 years)

Individual intervention effects showed that treatment CWMT significantly improved WM spans.

Cox et al. (2015)

Feasibility and acceptability of a remotely administered computerized intervention to address cognitive late effects among childhood cancer survivors.

25 WMT sessions over 5e9 weeks at home with weekly phonebased coaching.

Survivors of childhood cancers between 8 and 16 years of age, spoke English as a primary language, and had been off treatment for at least 1 year with no evidence of recurrent disease.

Cogmed is a feasible and acceptable intervention for childhood cancer survivors.

Mawjee et al. (2015)

Working Memory Training in PostSecondary Students with ADHD: A Randomized Controlled Study.

5 training sessions per week for 5 weeks (1 session/day of specified length: standard length vs. shortened length training group).

97 postsecondary students 18e35 years old with ADHD (59.8% females).

This study failed to find robust evidence of benefits of standard-length CWMT for improving WM in college students with ADHD, and the overall pattern of findings raise questions about the specificity of training effects.

So¨derqvist and Bergman Nutley (2015)

Working Memory Training is Associated with Long Term Attainments in Math and Reading.

The CMWT group underwent training as part of their curriculum for 5 weeks.

All 20 students in a grade 4 classroom (9e10 years)

The results suggest that WM training can help optimize the academic potential of high performers.

Kerr and Blackwell (2015)

Near transfer effects following working memory intervention (Cogmed) in children with symptomatic epilepsy: An open randomized clinical trial.

5 sessions per week for 5e7 weeks.

77 children with symptomatic epilepsy (ages 6.5e15.5 years; 100% taking medication)

This is the first study to evaluate the effectiveness of intervention to ameliorate WM deficits commonly experienced by children with symptomatic epilepsy. Results support group improvement on some untrained tasks immediately postintervention, demonstrating preliminary usefulness of Cogmed as a treatment option.

Van der Donk et al., 2015

Cognitive training for children with ADHD: a randomized controlled trial of cogmed working memory training and ’paying attention in class’.

Children followed the standard CWMT protocol, which means following the computer training program for 5 weeks, 5 times a week, w45 min a day.

102 children with ADHD between 8 and 12 years both medicated and medication naı¨ve

Results showed an effect of time on verbal WM, attention, inhibition, planning, parent, and teacher ratings of executive functioning and ADHDrelated behavior.

Holmes et al. (2015)

Improving working memory in children with low language abilities.

20 sessions of Cogmed Working Memory Training (45min).

19 children aged 8e10 years with LLA (low language abilities) (n ¼ 12, after dropout and others)

Preliminary evidence that intensive working memory training may be effective for enhancing the weakest aspects of STM in children with low verbal abilities and may also be of value in developing compensatory strategies. Continued

256 Cognition and Addiction

TABLE 18.1 Details of peer-reviewed working memory training programs and their supporting studies.dcont’d N-back (n [ 32) Mawjee et al., 2014

Working Memory Training in ADHD: Controlling for Engagement, Motivation, and Expectancy of Improvement (Pilot Study).

Participants randomized into 25 sessions of standard (45 min) or shortened (15 min) sessions.

38 postsecondary students with ADHD

There was no significant difference in completion rate or training index score between the standard- and shortenedlength groups, indicating that both groups showed improvement and put forth good effort during training.

Au et al. (2014)

A feasibility trial of Cogmed working memory training in fragile X syndrome.

1 session a day, 5 days a week for 5 weeks (session: 15e30 min).

8 participants with Fragile X Syndrome

Cogmed Jungle Memory is a feasible intervention in FXS, though a certain baseline level of ability is required.

Chacko et al. (2014)

A randomized clinical trial of Cogmed Working Memory Training in school-age children with ADHD: a replication in a diverse sample using a control condition.

30e45 min increments over 5 days per week (25 training days total).

85 school children with ADHD (7e11 years) randomized to either standard CWMT or CWMT placebo condition

CWMT demonstrates effects on certain aspects of working memory in children with ADHD; however, CWMT does not appear to foster treatment generalization to other domains of functioning. As such, CWMT should not be considered a viable treatment for children with ADHD.

DongenBoomsma et al. (2014)

Working memory training in young children with ADHD: a randomized placebocontrolled trial.

25 sessions of 15 min, 5 days a week.

47 children (5e7 years) with ADHD (w/o psychotropic medication) RA to active (adaptive) or placebo (nonadaptive).

This study failed to find robust evidence for benefits of CWMT over the placebo training on behavioral symptoms, neurocognitive, daily executive, and global clinical functioning in young children with ADHD.

Bjo¨rkdahl et al., 2013

A randomized study of computerized working memory training and effects on functioning in everyday life for patients with brain injury.

30e45 min per session, 5 days a week for 5 weeks (25 sessions).

20 outpatients with WM deficits (22-63yoa) were randomized into intervention group.

The WM training seems to have a generalized effect on functional activity and lessens fatigue; the intervention group improved on digit span, FIS (Fatigue Impact Scale) and WM questionnaire. Improved in motor skills and process skills (AMPS).

Akerlund et al. (2013)

Can computerized working memory training improve impaired working memory, cognition and psychological health?

30e45 min, 5 days/ week for 5 weeks.

Randomized study (n ¼ 47) with an intervention group and a control group (mean age ¼ 47.7y)

Results indicated that computerized WM training can improve working memory, cognition, and psychological health.

Grunewaldt et al. (2013)

Working memory training improves cognitive function in VLBW preschoolers.

10e15 min a day, 5 days a week over a 5week period.

20 VLBW preschoolers aged 5e6 years.

This study shows that VLBW preschoolers benefit from a computerized working memory training program. It is speculated that such training before starting school may prevent or reduce cognitive problems that impact educational achievement. Continued

Peer-reviewed working memory training: is it an effective intervention for addiction? Chapter | 18

257

TABLE 18.1 Details of peer-reviewed working memory training programs and their supporting studies.dcont’d N-back (n [ 32) Gibson et al. (2013)

Exploration of an adaptive training regimen that can target the secondary memory component of working memory capacity.

25 days of WM training within 5 weeks.

20 undergraduates randomly assigned to standard exercise (n ¼ 10) or modified exercise (n ¼ 10) condition.

The main findings suggested that the SM component could be enhanced by span-based exercises when a more lenient recall accuracy threshold was used; manipulation of exercise type (complex span vs. simple span) showed little effect on the SM component of WM capacity.

Gibson et al. (2012)

Component Analysis of Simple Span vs. Complex Span Adaptive Working Memory Exercises: A Randomized, Controlled Trial.

25 days of CWMT within 5 weeks; but required to complete at least 20 days to be included.

74 adolescents (9e16 years) randomly assigned to either the standard exercise (N ¼ 36) or the modified exercise (N ¼ 38) training condition.

The main findings showed that SM capacity did not improve, even in the modified training condition. Hence, the potency of span-based WM interventions cannot be increased simply by converting simple span exercises into complex span exercises.

Gray et al. (2012)

Effects of a computerized working memory training program on working memory, attention, and academics in adolescents with severe LD and comorbid ADHD: a randomized controlled trial.

12 different WMT exercises with an average training time per day of 45 min approximately (excluding breaks).

60 teenagers with LD/ ADHD (12e17 years)

Adolescents in the WM training group showed greater improvements in a subset of WM criterion measures compared with those in the math training group, but no training effects were observed on the near or far measures. Results suggest that WM training may enhance some aspects of WM in youths with LD/ADHD.

Johansson and Tornmalm, 2012

Working memory training for patients with acquired brain injury: effects in daily life.

Training for 30e45 min, three times a week during a period of 7 e8 weeks. To this, 30 min per training day was scheduled to exchange experiences of WM deficits, training experiences, and strategies.

18 patients, aged 17e64, who had difficulties in daily life pertaining to working memory deficits

The computerized training showed a significant improvement on trained working memory tasks. Patients starting at a low training level improved the most. Self-rating measurements and interviews indicated that patients experienced fewer cognitive problems in daily life after training. The effect was maintained at the 6 month follow-up.

Author

Title

Length of working memory training

Study Population

Main findings

Toril et al. (2016)

Video Game Training Enhances Visuospatial Working Memory and Episodic Memory in Older Adults.

15 1-h video game training sessions with a series of video games selected from a commercial package (Lumosity).

Participants were 19 volunteer older adults

Suggests that video game training might be an effective intervention tool to improve WM and other cognitive functions in older adults.

Lumosity (n [ 4)

Continued

258 Cognition and Addiction

TABLE 18.1 Details of peer-reviewed working memory training programs and their supporting studies.dcont’d N-back (n [ 32) Wentink et al. (2016)

The effects of an 8week computer-based brain training programme on cognitive functioning, QoL and self-efficacy after stroke.

The training consisted of gaming at home during a period of 8 weeks, at least 5 days per week, approximately 15e20 min per day, resulting in a requested play time of 600 min. The control group received general information about the brain weekly. The total duration of the control intervention was on average 70 min per person.

107 participants between 45 and 75 years, diagnosed with stroke 12 e36 months ago, having self-perceived cognitive impairments

The effects found in the study on the WM tests were smaller than other studies using CBCR training primarily focused on one cognitive function among stroke patient indicating that this was attempting to test too many components rather than targeted WM training. There was also no evidence of far transfer effects.

Ballesteros et al., 2014

Brain training with nonaction video games enhances aspects of cognition in older adults: a randomized controlled trial.

20  1 h nonaction video game training sessions selected from the Lumosity package.

40 healthy older volunteers (age range 57e80 years)

Visuospatial WM and executive control (shifting strategy) did not improve.

Thompson et al. (2016)

Optimizing memory function in temporal lobe epilepsy.

Memory training was provided on an individual basis in up to 2 sessions and totaled a maximum of 4 h.

70 people with TLE, complaining of memory difficulties. Aged from 19 to 67 years. 40 with left TLE

Lumosity use was not associated with changes in the memory outcome measures versus conventional memory tests. So, the study indicates traditional memory rehabilitation techniques can help reduce the burden of memory impairment in TLE and so no evidence that Lumosity has specific advantages.

Neuroracer (n [ 1); Project Evo (n [ 2) Authors

Title

Length of working memory training

Study population

Main findings

Anguera et al. (2013)

Video game training enhances cognitive control in older adults.

1 h a day, three times a week, for 4 weeks

46 participants (60e85 years)

Working memory and sustained attention improved and persisted for 6 months.

Anguera et al., 2017

Improving late life depression and cognitive control through the use of therapeutic video game technology: A proof-of-concept randomized trial.

20 min five times a week, for 4 weeks

12 participants, 60 years and over

Mood, self-reported functioning, working memory. and attention improved.

Arean et al., 2016

The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial.

Participants were randomly assigned to one of the three groups: (1) Project:EVO (experimental group) (2) iPST (active control), and (3) Health Tips (passive control).

626 participants with mild to moderate depression as determined by a 9-item Patient Health Questionnaire

The study found that both the Project:EVO and iPST groups had greater improvements in mood than the control group. This was particularly true for participants with moderate levels of depression.

Length of training

Study Population

Main Findings

Neuronation (n [ 1) Author

Title

Continued

Peer-reviewed working memory training: is it an effective intervention for addiction? Chapter | 18

259

TABLE 18.1 Details of peer-reviewed working memory training programs and their supporting studies.dcont’d N-back (n [ 32) Niedeggen et al.

A computer-based cognitive training based on NeuroNation

25 min, 4 weeks

Experimental group: n ¼ 13 (8 women; 55 e80 years) Control group: n ¼ 11 (8 women; 55e78 years)

Although both groups demonstrated improvements in the nontrained visuospatial working memory (WM) task, it was higher for the experimental group. Both groups also showed improvements in unrelated tasks measuring mental flexibility, with improvements greatest for the experimental group. Neither group demonstrated a generalized transfer effect (memory; selective and divided attention; processing speed) and neither group showed a change in subjective well-being or the estimation of their own cognitive capacities.

Author

Title

Length of WM training

Study population

Main findings

Brooks et al. (2017a)

The impact of cognitive training in substance use disorder: the effect of working memory training on impulse control in methamphetamine users.

4 weeks, 5 days per week, half an hour per day, 5  5 min sessions with 1 min break in between

Methamphetamine use disorder male inpatients in Cape Town South Africa versus healthy matched controls

Feelings of self-control and mood were significantly higher in the MUD group who had WMT compared with those who had only standard treatment. Also, total impulsivity scores and lack of planning significantly improved in the WMT group compared with the treatment only group. Finally, selfregulation questionnaire scores were also significantly improved in patients who engaged in WMT versus those who did not.

Brooks et al. (2016)

Psychological intervention with working memory training increases basal ganglia volume: A VBM study of inpatient treatment for methamphetamine use

4 weeks, 5 days per week, half an hour per day, 5  5 min sessions with 1 min break in between

Methamphetamine use disorder male inpatients in Cape Town South Africa versus healthy matched controls

Treatment as usual was associated with increased bilateral striatal volume, whereas those who engaged in WMT had more widespread bilateral basal ganglia volume increase, extending to the amygdala and hippocampus. Reduced bilateral cerebellar volume was associated with reduced impulsivity scores.

C-ya (n [ 2)

total of 17 half-hour sessions, found that single n-back was as effective as dual n-back in terms of temporary near transfer to measures of general intelligence. They also found that n-back training appears domain free in that verbal n-back training had near transfer improvements to visuospatial reasoning (Jaeggi et al., 2014). In line with this, earlier studies have shown near transfer of effects of n-back training on measures of general intelligence and matrix reasoning (see Jaeggi, Buschkuehl, Jonides and

Shah, 2011). However, more recent studies have not found these effects, often commenting that a major problem with n-back training is its limit of effect solely to the task trained (Thompson et al., 2013; Lindeløv et al., 2016). Although far transfer effects on attention and intelligence, as well as skills transfer to demonstrate long-term improvement over months are not usually demonstrated with n-back, earlier tests have demonstrated longevity of skills transfer illustrated by n-back training effects for over

260 Cognition and Addiction

3 months (see Jaeggi et al., 2011). However, a later study (Jaeggi et al., 2014) did not replicate this finding after a 3-month follow-up. Thus, while there is some indication that n-back WMT improves far transfer effects on other cognitive modalities such as attention and intelligence in the short term, there is not yet convincing evidence that the improvement effects are long-lasting. However, it is not yet known whether far transfer effects of n-back WMT will be observed for measures other than attention and intelligence, such as self-regulation and impulse control, which may be more relevant to the treatment of cognitive deficits in addiction.

Jungle memory (n [ 2); Alloway (2009) JM is a WMT program designed for children (aged from 7 to 16 years old) and uses games that are colorful and stimulating to motivate children to engage. Given that the focus of JM is on children’s cognitive performance, this could explain why it has not been used for people with addictive disorders. JM involves three training programmes: quicksand, codebreaker, and river crossing. Quicksand involves remembering the location of a letter or a string of letters. Codebreaker involves spatial awareness and recognition of letters in different positions. River crossing involves a WM task that encourages children to complete increasingly difficult mathematical problems. Typically, children complete all of these tasks over 8 weeks, and the authors report sustained improvement to WM after the completion of the program. In a study by Alloway et al. (2013), participants with learning difficulties were sorted into three groups: the controls who received no training and continued with regular classroom activities, a low-frequency WMT group that performed training once a week (total of 24 sessions over 8 weeks), and a third high-frequency WMT group that performed training four times a week (total of 84 sessions over 8 weeks). Findings indicated that there were improvements in verbal and nonverbal, as well as visuospatial, WM tasks for the high-frequency WMT group compared with the low frequency group, and significant improvements in spelling were also noted in the highfrequency group. Additionally, with high-frequency training, it was evidenced that JM led to longevity far transfer of skills transfer, with students exhibiting improvements that were still evident up to 8 months posttraining. Specifically, the data indicated a longevity of skills effect in both verbal and visuospatial WM, verbal ability, and spelling, but no convincing far transfer effects of improvement in other cognitive domains. Another study using JM, conducted by Nelwan and Kroesbergen (2016), found no evidence of far transfer effects, at least in mathematical skills (which is all that was tested in this study). Indeed, the study explored the

potential far transfer benefits of WMT on mathematical skills, as WM may be an essential component for mathematical reasoning, although its exact role and mechanisms underlying mathematical abilities remain unclear. The study compared three groups of Dutch children between the ages of 9 and 12, with mathematical and attentional difficulties after they received JM and maths training (Math Garden). The overall findings provided a limited contribution to WMT literature and its near and far transfer effects. The study found that the computerized mathematics training had beneficial effects on children’s mathematical abilities, whereas JM WMT training did not exhibit a positive or added effect. However, it is mentioned that perhaps increasing the amount of effort during WMT could improve the outcomes on JM and therefore mathematics. Unfortunately, the study presented with limitations such as lack of students’ investment and effort and therefore claims that more research is needed before attempting to link improved cognitive performance to WMT, at least in children.

PSSCogRehab (n [ 1) Bracy (1994) PSSCogRehab is a Cognitive Rehabilitation Therapy System packaged as a computerized program that runs on computers. The WMT element consists of 8 modules that include 67 computerized tasks. WMT is completed in 6e12 months depending on the level of impairment or commitment, and the program aims to retrain cognitive skills that are deficient due to brain degeneration or brain damage, caused by substance use for example. The training mostly focuses on attention, problem-solving, memory recall, and visuospatial exercises. The memory training includes using sequenced recall (e.g., words, digits, and graphics, involving auditory and visual tasks) and nonsequenced recall. The problem-solving skills development includes tasks such as completing additions and subtractions, creating pyramids by placing smaller disks on larger ones using as little movements as possible, or arranging marbles in specific color patterns. Finally, visuospatial training includes exercises such as line and angle discrimination tasks, design completions, block counting, or deciding which pattern is different to the others. In addition to these exercises, participants are advised to complete the PSSCogRehab exercises at home in their own time, but must also attend face-to-face sessions with a clinician to track progress and adjust the therapy exercises accordingly. The exercises are challenging but not impossible, and accordingly, participants are expected to perform the right amount of exercise daily, which matches their level of ability. The participant completes these exercises daily, between the weekly face-to-face sessions at the center. This “homework” concept allows the participant to incorporate and practice compensation skills discussed

Peer-reviewed working memory training: is it an effective intervention for addiction? Chapter | 18

during the face-to-face session. However, it is important to note that the computer program by itself is not used as a monotherapy and has been shown in the publication to be most effective when used in parallel with other processes. For example, during teaching and using compensatory skills developed in face-to-face sessions, psychological counseling, and environment restructuring and following outpatients when they have reintegrated back into their work, school, or home environment. Additionally, the PSSCogRehab program has demonstrated statistically significant improvements in cognitive functioning in patients on completion. Only one study to date has published research using PSSCogRehab, to the authors’ knowledge, in patients with substance use disorder. Bickel et al. (2011) examined 27 participants with stimulant use disorder (mainly cocaine, some with methamphetamine use disorder), who were randomly assigned to either PSSCogRehab training or to an active control condition. Participants completed 1 pretraining session, 4e15 training sessions, and 1 posttraining session. The training sessions involved completing the four memory training programs twice. The number of sessions completed was due to each participant’s progress. For example, three consecutive sessions without improved performance on any two programs resulted in the conclusion of training, with a minimum of 4 and a maximum of 15 training sessions. By comparison, the active control session was almost identical but for the fact that participants were given the answers during the session, so they did not engage WM. Bickel and colleagues reported that the stimulant users who engaged in WMT had a significant decrease in delay discounting (a measure of decision impulsivity, based on whether immediate or future rewards are chosen), and so they suggest that WM plays a vital role in the control of impulsivity. However, follow-up studies to test far transfer effects have yet to be conducted with PSSCogRehab.

CogMed (n [ 31) Klingberg et al., (2002) CogMed is a computerized WM training program first developed to improve WM in children with ADHD. CogMed has showed promising results in this and other populations, such as autism spectrum disorders, Huntington’s disease, epilepsy, and also AUD. However, the majority of the research is still conducted in children, mostly with conditions characterized by poor WM, such as learning disabilities (LDs), ADHD, dyslexia, children with genetic conditions like sickle cell disease (SCD), and children born preterm, in other words, with very low birth weight (VLBW). The other clinical populations include adults with clinical conditions (e.g., symptoms of pain, insomnia, fatigue, depression, and anxiety), who are HIV positive or with recognized WM deficits.

261

It is, however, the CogMed research conducted on children and adolescents that has yielded the most significant results, with evidence of verbal, visuospatial WM, and short-term memory (STM) improvements in children with SCD and those born preterm (Lee et al., 2016; Hardy et al., 2016). In neurotypical first graders, there was evidence of improvements in visuospatial STM, although these were temporary (Roberts et al., 2016). Additionally, there was evidence of a significant impact on WM deficits and a longterm far transfer effect in children with ADHD, and as such, CogMed was a recommended intervention by Bigorra et al. (2016). Another study by Grunewaldt et al. (2016) found CogMed to have long-lasting effects on WM in VLBW preschool children (e.g., over many months). Similarly, Gray et al. (2012) found that adolescents with ADHD and LDs showed great improvements in WM (specifically mathematical reasoning), although with no evidence of longterm skill transfer. On the other hand, Hitchock and Westwell (2017) found that CogMed had no impact on neurologically intact children, and Chacko et al. (2014) deemed CogMed to be an incomplete and inefficient treatment by itself; however, limited findings could be due to variations in the number of sessions provided across studies. Klingberg and colleagues suggest that sessions of CogMed should typically last between 30 and 45 min, with training extending to between 5 weeks (minimum) and 8 weeks. Moreover, Klingberg and colleagues suggest that these training sessions should occur at least three times a week to ensure and maintain gradual increase in performance, which may strengthen the effects on WM performance and also far transfer effects to other cognitive domains. In line with these suggestions, a recent pilot study conducted by Sadegh et al. (2017) explored the effects of CogMed on nine early stage patients with Huntington’s disease (26e62 years of age). The patients underwent 25 sessions of CogMed in total (5 days/week for 5 weeks). The patients who successfully adhered to the training showed both improvements on CogMed tasks and noted that they found the training helpful. These findings serve to highlight the feasibility of CogMed in this population. However, because of the small sample size, a wider and more controlled intervention is necessary to understand the efficacy of CogMed and the reliability of findings (Sadegh et al., 2017). In terms of far transfer effects of CogMed, Fuentes and Kerr (2016) studied the effects on 28 epileptic children and were interested in the maintenance effects of the training (3-month follow-up). The children underwent the training five times a week over 5e7 weeks (each session lasting between 30 and 45 min). The children showed improvements in auditory and visual attention as well as WM immediately after the training, and these improvements were sustained at a 3 month follow-up. However, no transfer to other WM areas (e.g., visual/verbal WM, fluid reasoning) was noted. This study, albeit with a small

262 Cognition and Addiction

sample size, indicates that there is evidence of WM improvement, particularly in attention measures that can sustain after the training (Fuentes and Kerr, 2016). In terms of those with addictive disorders, CogMed has been used to examine the effect of WMT in those with AUDs. For example, Houben and colleagues, in 2011, examined 48 problem drinkers who performed a visuospatial WM task, a backward digit span task, and a letter span task that was adapted from the CogMed training paradigm. Participants also took part in control tasks, during 25 sessions over at least 25 days. Before and after training, the authors measured WM and levels of drinking behavior. It was reported that WMT improved WM performance and also reduced alcohol intake for more than 1 month after the training (e.g., longevity of skills transfer and far transfer effects). Interestingly, the authors reported that increased WM ability following the WMT reduced alcohol consumption, but particularly in participants with relatively strong automatic preferences for alcohol (Houben et al., 2011). In another study by Houben and colleagues in 2016, improvement in overeating (which is considered by some to be a form of food addiction, with deficits in self-regulation) was examined using CogMed-inspired WMT. In the study, 50 overweight participants performed 20e25 sessions of WMT or control/sham training (Houben et al., 2016). The authors reported that relative to the control condition, CogMed reduced psychopathological eating-related thoughts and emotional eating (but not external eating). At 1 month later, the effects were still present, suggesting a longevity of skills transfer effect. Food intake and body weight was not significantly influenced by WMT, although it did reduce food intake among highly restrained participants. In a similar study by the same group, but this time with a gamified (e.g., computer gameelike) version of the CogMed-inspired WMT intervention, the authors examined weight, food intake, executive functioning, self-control, eating style, eating psychopathology, and healthy eating in a group of 91 overweight individuals with a desire to lose weight (Dassen et al., 2018b). The authors found that WM span was higher than control from pretest to posttest. Furthermore, there was some evidence of longevity of skills transfer (e.g., WM span increase) at 1, but not 6, month. However, no far transfer effects to other executive functioning measures were found. In a study by the Higgs group using a CogMed-inspired WMT paradigm, dietary self-care in people with Type 2 diabetes was examined for its effects on cognition, food intake, glycemic control (HbA1c), cholesterol, and also self-reports of the experience of the training (Whitelock et al., 2018). The authors used a double-blind, parallel group, randomized control trial to examine 45 participants in the active WMT group versus 36 in the control WMT group. They found improved WM updating ability after

WMT, but no overall effects of training on other measures of cognition, food intake, HbA1c, cholesterol, food cravings, and dietary self-efficacy and self-care. In post hoc analyses, the authors reported that participants who scored highly on dietary restraint in the active training group showed a more significant reduction in fat intake pre- to posttest compared with controls. The most recent study using CogMed was by Khemiri and colleagues in 2019, and it examined participants with AUD with 5 weeks of either active WMT or a control training (Khemiri et al., 2019). The authors report that the AUD group demonstrated significantly greater verbal WM compared with the control group after 5 weeks. Additionally, there was a trend for WMT to reduce the number of drinks consumed per drinking occasion, but had no significant influence on any other neuropsychological measure. This studydwith measures of executive function and impulse control (number of drinks consumed), as opposed to the common measures of attention and global intelligencedgoes some way to support the hypothesis that WMT improves processes of executive function and selfregulation/impulse control (in this case, for drinking per occasion).

Lumosity (n [ 4) Lumos Labs (2005) Lumosity is an online cognitive training program with games claiming to improve memory, attention, speed of processing, and problem-solving. However, in 2016, Lumosity became an example for the need to engage in the robust peer-review process, as it was fined $2K by the Federal State Commission, for its claims that the training helps users perform better at work and in school, reducing or delaying cognitive impairment associated with age and other serious health conditions (Federal Trade Commission, 2016). Studies using Lumosity as a WMT have demonstrated mixed results. A study conducted by Toril et al. (2016) on neurologically intact older adults found significant WM enhancements in the trained group when assessed on visuospatial WM tasks (Corsi blocks and Jigsaw puzzle task), compared with the control group who demonstrated no changes. Improvements in episodic and STM were also noted, and these were maintained for 3 months. This led Toril et al. (2016) to conclude that video game training could be an effective intervention tool to improve WM and other cognitive functions in older adults. On the other hand, Wentink et al. (2016) who examined stroke patients found Lumosity to have limited effects on WM and speed of processing. Thompson et al. (2016), in a study on WM in individuals with temporal lobe epilepsy, also failed to find significant links between the use of Lumosity and WM improvements. Furthermore, although Ballesteros et al. (2014) (who examined neurologically intact older adults) found significant improvements in the Lumosity-trained

Peer-reviewed working memory training: is it an effective intervention for addiction? Chapter | 18

groups in processing speed, attention, and immediate and delayed visual recognition memory, it was also found that visuospatial WM did not improve. Thus, concluding that video game training can enhance some cognitive abilities but perhaps not WM in a neurotypical sample. Unfortunately, neither the duration of Lumosity training sessions, nor the frequency of training, was noted. As such, given the lack of research using Lumosity in addiction populations at present, it is not yet clear whether this intervention would be useful for those with various addictive behaviors.

Neuroracer (n [ 1); Project:EVO (n [ 2); both versions of the same product founded by Gazzaley and Akili Interactive Neuroracer is an adaptive video game (Project:EVO is the newer version of the game) challenging players on two tasks, navigating and responding to signs while steering a car. One peer-reviewed publication was found, which tests this version of the training. The task engages multiple skills, particularly aspects of attention, task switching, focusing, and WM. Anguera and colleagues comment that even though Neuroracer is not designed specifically to improve WM, the multitasking nature of it may put strain on the cognitive system, improving all of its subcomponents, including aspects of attention and WM (Anguera et al., 2013). A study by Anguera et al. (2013) on 46 participants (60e85 years) found that those who completed the multitasking training condition (n ¼ 16) for 1 hour a day, three times a week, for 4 weeks, found improvement in WM and sustained attention. These results indicate achievement “beyond that of untrained 20 year olds” (Anguera et al., 2013) and persisted for 6 months after the training concluded. The newly branded form of the game (Project:EVO) is currently being tested as a diagnostic tool for Alzheimer’s disease as well as a treatment for depression, autism, traumatic brain injury, cerebrovascular dementia, and ADHD (http://www.akiliinteractive.com/). Two peerreviewed publications were found for Project:EVO, which involves guiding a character through a virtual environment while engaging with certain targets (Anguera et al., 2017). It is adaptive to the ability of the user while remaining challenging, which helps to encourage cognitive improvement in the user. Project:EVO aims to improve the symptoms of WM, inattention, and executive function deficits as well as mood. In a pilot trial by Anguera et al. (2017), of 22 participants (60 years and over) with late-life depression, 12 participants were assigned to play Project:EVO for 20 min, five times a week, for 4 weeks, while the other participants receiving problem-solving therapy (PST) (n ¼ 10). Both groups showed improvements in mood and self-reported cognitive functions. However, the Project:EVO participants also demonstrated gains in areas of

263

attention and WM, which were not observed in the PST participants. Furthermore, in another study of 209 adults (18 years and older) with mild to moderate depression, symptoms of depression were lessened after Project:EVO training compared with controls (Arean et al., 2016). Neuroracer is not available commercially, but Project:EVO will be available on mobile phones and the iPad in the near future. Given that the authors report significant findings in adultsdparticularly those with affect regulation (e.g., higher levels of anxiety/depression) and cognitive difficulties, which are often comorbidities in those with addictiondthere appears to be support for the notion that WM training may be useful in reducing cognitive-affective difficulties in those being treated for addiction.

NeuroNation (n [ 1); Ahmadi and Futorjanski (2011) NeuroNation is an online brain-training platform attesting to improve cognitive skills such as numeracy, language (verbal fluency), reasoning, memory (WM specifically), and perception (behavioral control required for flexible thinking, concentration, multitasking, and willpower). One peer-reviewed publication demonstrates the effects of NeuroNation on processing speed, set-shifting, inhibition, reasoning, and self-reported cognitive failures, as well as improvements in the WMT itself. Training was completely administered at home in 176 healthy control participants (mean age 50%, 62% female) with 82 participants in the training group and 94 participants in the control group (Strobach and Huestegge, 2017). The authors found that in contrast to the active control training group, NeuroNation improved processing speed and set-shifting tasks (i.e., far transfer effects), but these improvements were not as conclusive as the improvements found for WM near transfer effects. Moreover, performance improvements using NeuroNation were more pronounced for highperforming participants (i.e., magnification effects), suggesting an added effect of participant motivation. NeuroNation is an adaptive program, adjusting to the user’s abilities. Progress is charted and can be compared with others also playing. It claims that only 15 min of brain training is required per day and is available on the Internet or as an App for phones and tablets/iPads. In 2013, NeuroNation received recognition from the German Federal Ministry of Health, and their website claims collaborations with numerous hospitals, universities, and outpatient rehabilitative programs. The collaborations address areas such as improving symptomatology of schizophrenia, anorexia nervosa, stroke, cognitive decline, Parkinson’s, Huntington’s, and chemotherapy’s aversive effects, depression as well as contributing to cognitive and athletic enhancement. These studies and programs appear to be ongoing, and no information regarding their published material is given on

264 Cognition and Addiction

NeuroNation’s website, barring the one study described above (Strobach and Huestegge, 2017).

application of WMT interventions for addiction treatment will be further discussed below.

Curb Your Addiction (n [ 2); Brooks (2016).

Discussion

C-Ya is the newest peer-reviewed WMT App at time of writing, according to the authors’ knowledge, and was first published in 2016 to examine the effects on inpatients being treated for methamphetamine use disorder (Brooks et al., 2016). The background research to the App is based on neuroimaging data (structural and functional) in patients with chronic anorexia nervosa (for review see Brooks et al., 2017a,b), who appear to have maladaptive WM processes underlying cognitions and ruminations pertaining to excessive appetite control. The authors suggest that improved (e.g., broadened, strengthened) appetite/impulse control may be achieved by harnessing WM processes using the C-Ya App. The C-Ya App is based on the traditional n-back WM task (see 3.1), but it differs in that it includes stimulation up to 8-back with peripheral and supraliminal distraction with images of food and drugs for example. The first published study of C-Ya demonstrated that 4 weeks (20 sessions) of half-hour standard (without subliminal stimulation) C-Ya sessions appeared to increase brain volume in a brain region associated with appetite and impulsivity (bilateral basal ganglia). A second publication followed, demonstrating that the level of C-Ya engagement in the first publication appeared to improve self-reported impulse control, mood, and anxiety in these patients (Brooks et al., 2017a,b). C-Ya training follows the classic n-back task, where single letters are presented consecutively on the screen of the smartphone, and the screen is pressed when a target letter appears. The target is determined by the “n-back” level, for example, 0-back represents the target letter “X”; 1-back is when the targetdthe current letter on the screendis the same as one shown previously; 2-backdtwo previously; 3-backdthree previously, and so on, up to 8-back. The rationale for the supraliminal distraction categories is that they stimulate the mesolimbic salience/arousal regions of the brain, placing additional attentional processing demands on the user during the completion of the WMT. As such, if players can improve their competency during the supraliminal categories, in line with a specific stimulus that is particularly relevant to the user, the author suggests that players will strengthen neural pathways that enable an ecologically valid application of the training to exert cognitive control in real life (e.g., peripheral distractions are constant in everyday life). To date, according to the authors’ knowledge, C-Ya, PSSCogRehab, and most recently CogMed are the only WMT interventions that have examined their effects in people with addiction (methamphetamine use, cocaine use disorder and AUD, and overeating as a food addiction). The

The aim of this systematic review of WMT paradigms was to examine the effects of currently peer-reviewed WMT interventions on various populations, in terms of the measures used and the near versus far transfer effects reported. Primarily, this was to critically consider whether there is utility in pursuing WMT as an intervention option for the treatment of addiction. Additionally, the review sought to determine a) WMT Apps that have been peer-reviewed in scientific journals, b) near and far transfer effects, c) the type of human populations (clinical and/or nonclinical) studied, d) the nature of the differences between WMT paradigms, f) existing limitations of the delivery of WMT, and g) whether there is utility in using WMT for the treatment of addiction. By addressing these aims, it might be easier to evaluate the claims laid by the most robust (e.g., scientific peer-reviewed and published) WMT apps available, which will better support the choices made by researchers and clinicians when considering the use of these interventions for addiction. The findings of this review are explained in relation to these aims below.

Peer-reviewed working memory training paradigms Eight peer-reviewed WMT paradigms were found, namely (in chronological order of creation) (i) Classic n-back tasks; (ii) PSSCogRehab; (iii) Jungle Memory; (iv) CogMed (v) Lumosity, (vi) Neuroracer/Project:EVO; (vii) Neuronation, and (viii) Curb Your Addiction (C-Ya). The most popular WMT paradigm is n-back, whereas the most published/ peer-reviewed App/intervention, including extensive reviews, is CogMed All of the WMT paradigms reviewed here purport to alter neurocognitive processes, which are likely associated with changes in top-down prefrontal cortex and bottom-up basal ganglia function. For example, recent studies of WMT paradigms, such as CogMed, C-Ya, n-back, have demonstrated neural effects in children, adults with AUD, overeating, Type 2 diabetes, and methamphetamine addiction, and older adults (Klingberg, 2010; Brooks et al., 2016; Pergher et al., 2018), in line with neurocognitive performance benefits to various domains, such as attention, intelligence, and most recently, self-regulation and impulse control (e.g., reduced drinking of alcohol and improved self-report measures of impulse control). It is suggested that with adaptive WMT (e.g., updating the difficulty based on the participant’s fluctuating performance), by multitasking (paradigms such as CogMed), or via progression through increasingly difficult levels (paradigms such as C-Ya for

Peer-reviewed working memory training: is it an effective intervention for addiction? Chapter | 18

impulse control training using supraliminal distractions), synaptic plasticity in the prefrontal cortex and basal ganglia can lead to far transfer and longevity of skills transfer effects. And as the field currently reports inconsistent findings, the skepticism for WMT may be fueled by measures that do not adequately assess the underlying neural mechanisms associated with the neurocognitive process that is actually being altered (e.g., attention versus self-regulation). To test whether neurocognitive processes are altered, WMT studies to date have focused their measures on attention, global intelligence, mathematical ability, and the transfer to other cognitive skills such as visuospatial acuity; only a few studies have measured self-regulation/impulse control and control of eating or alcohol drinking (e.g., Bickel et al., 2011; Houben et al., 2011; Brooks et al., 2016; Houben et al., 2016; Khemiri et al., 2019). Related studies of overeating suggest a small improvement over control of eating following WMT (Houben et al., 2016; Dassen et al., 2018a; Whitelock et al., 2018). Most studies of WMT have been conducted in children and adolescents, with the forerunner in this research being CogMed, demonstrating improvements to inattention, visual, and verbal WM (Spencer-Smith and Klingberg, 2015). At the end of 2018, an update in the field of WMT for children and adolescents has been provided, with a review of WMT paradigms and their neural (e.g., neuroplasticity) and cognitive (e.g., near versus far transfer) effects was published (Rossignoli-Palomeque et al., 2018). This review concluded that only 14% (n ¼ 10) of the WMT studies included scientific data to support the theory that WMT is useful to alter neuroplasticity. This is due to the fact that many studies make claims without conducting brain imaging studies, while those studies that do report some imaging data demonstrate changes in frontostriatal circuitry (Klingberg, 2010; Brooks et al., 2016; Pergher et al., 2018). Unfortunately, of the peer-reviewed WMT studies to date, 68% (n ¼ 40) were not randomized or controlled, and of those that were randomized, only 13% (n ¼ 9) of studies were double-blinded. Finally, only 19% (n ¼ 13) WMT studies included an active control group. As such, it can be clearly established that skepticism and inconsistency in the findings of WMT studies to date may be due not only to insignificant data and/or false positive data but also to lack of robust testing and false negative data.

Near and far transfer effects of peer-reviewed working memory training paradigms Visuospatial and verbal WM, processing speed, attention, memory, fluid intelligence, and mood were the neurocognitive areas where researchers reported the most improvement after a period of WMT (Bickel et al., 2011; Alloway et al., 2013; Lilienthal et al., 2013; Schweizer

265

et al., 2013; Jaeggi et al., 2014; Arean et al., 2016). Most recently, improved verbal WM in line with a trend for less alcoholic drinks consumed per drinking occasion was reported after 5 weeks of CogMed training (Khemiri et al., 2019). There is an understanding that these WMT paradigms influence other cognitive abilities (besides simply allowing for improved ability in the paradigm practiced) as they activate common neural networks shared by other cognitive areas, such as those prefrontal cortex networks involved in future planning and reward evaluation (Jaeggi et al., 2014; Beatty and Vartanian, 2015; Brooks et al., 2016). Furthermore, pushing participants to their limits was seen to have the most influence on positive outcomes, particularly if participants were required to do two or more activities at once (Jaeggi et al., 2008). The frequency of practice was also stressed as imperative to the success of the training, with more practice producing better and more lasting results (Alloway et al., 2013). WMT, like other forms of (e.g., physical) training, appears to require continuous practice to produce positive cognitive and behavioral change in the individual, and this may be something that is not currently adhered to in practice or in studies of WMT. Yet, it should be noted that even a minimum training period of 3e4 weeks was found to produce lasting benefits 6e12 months later by some studies reviewed. In line with this, the motivation and investment of the participant was also frequently mentioned by studies as an integral part of the success of any cognitive training program (Nelwan and Kroesbergen; 2016). However, somewhat more optimistically, the latest review of WMT by Rossignoli-Palomeque and colleagues in 2018 concluded that 51% of studies (n ¼ 36) demonstrated far transfer effects (e.g., to other cognitive domains besides WM during the experiments) in child and adolescent populations, although only 16% (n ¼ 11) reported that these effects remained at follow-up (e.g., at least 1 month after testing).

Previous research into working memory training and implications for addiction The first systematic review of WMT was by Takeuchi (2010), which explored the effects of WMT on cognitive function and neural systems in adults, providing a good theoretical background on the history of WMT. The review explored facets of WMT that were beyond the scope of this systematic review, such as (i) the combination of WMT with other cognitive training (which might be difficult for those with addiction who have compromised executive functioning), (ii) how other cognitive functions, such as creativity (which is difficult to assess), are influenced by WMT, (iii) the neural networks involved in WMT, and (iv) the genetic mechanisms of WMT. Furthermore, the paper also expanded on the mechanisms of action in various clinical

266 Cognition and Addiction

populations that this systematic review did not cover (e.g., an emphasis on stroke [very briefly covered in Lumosity] and multiple sclerosis patients). Similarly, the current systematic review took the approach of summarizing the different components of the training process: in terms of training content (the tasks to be performed), the type of WM that was assessed (e.g., visuospatial), the duration of the training session (in minutes), the intensity of the training regime (how often to train a week and for how many weeks), and the mode of WM training delivery (e.g., on a smartphone or a website via a PC). Factors found to affect the training process were also looked at including adaptive WMT and participant motivation. As the previous review found, the current review discovered that many WMT paradigms provide feedback on completion of the task/game, with an option for a more detailed feedback and the opportunity to rely on a coach or clinician. These aspects appear to demonstrate that feedback is an integral part of the WMT motivation process. C-Ya made use of immediate feedback during the task by providing alerts when correct or incorrect. Additionally, making use of colorful and stimulating games also nurtured motivation in participants (Brooks et al., 2016). However, in general, it is noted that some clinical populations struggle with remaining attentive or motivated to perform the tasks (notably ADHD children and children with LDs). Some of these factors may also be pertinent to those with addiction; for example, participant motivation is a factor, particularly given that most substance use disorders tend to damage the prefrontal cortex function that is involved in goal-oriented skills. Given that few WMT studies to date have been conducted in those with addiction, including stimulant use, alcohol use, and food addiction/ overeating (Bickel et al., 2011; Houben et al., 2011, 2016; Dassen et al., 2018a; Brooks et al., 2016, 2017; Whitelock et al., 2018), it is not yet possible to comment on the factors that may help to improve symptoms for addiction, and more exploration of WMT in addiction is needed. A second review by the founder of the WMT program CogMed Klingberg (2010) focuses on neuroplasticity occurring during WMT. The results yielded by Klingberg’s research were supported by the findings of the current review, in that there is likely an increase in WM capacity after training, which may be sustained in line with frontostriatal structural and functional changes (e.g., Brooks et al., 2016, 2017). There is some evidence of transfer effects, such as improvements in attention and verbal WM and in inhibition and reasoning. Klingberg’s review also focused on aspects such as the underlying neural correlates of WMT, which have been more recently reviewed again (Constantinidis and Klingberg, 2016). It is suggested that the neural machinery of WM is limited in function to the frontoparietal and insular cortex circuitry (see Rottschy et al., 2012) but can be expanded with WMT, which may mean better global versus local neurocircuitry synchrony

between these regions. The review documents how a wealth of modern neuroimaging data, both from primates and humans, as well as computer-modeling techniques, has been able to pinpoint more precisely the brain regions that may be influenced by WMT. Constantinidis and Klingberg summarize in the review that improved neural connectivity (e.g., via a process of neuroplasticity) occurs following a course of WMT, between prefrontal cortex neuronal clusters specifically within the dorsolateral prefrontal cortex and the parietal cortex. Moreover, the role of dopamine as a facilitator within the corticolimbic and parietal networks during WMT is largely implied by the various reviews to date (e.g., Constantinidis and Klingberg, 2016; Brooks et al., 2017a,b; Parr and Friston, 2017). The findings of the current and previous reviews of WMT, particularly those that examine neural processes, are pertinent for those with addiction, as the frontostriatal dopaminergic system is largely implicated in maladaptive decision-making (valuation), lack of control over drug taking, and addictive behaviors (Guttman et al., 2018). For example, those with substance use disorder have greater preference for risky choices, favoring appetitive choices for immediate versus future rewards (temporal/delay discounting), and are more likely to experience reinforcement learning from reward than from punishment (VerdejoGarcia et al., 2018). It is suggested that adaptive, progressively difficult ecologically valid WMT (e.g., that motivates the participant and includes peripheral distractors mimicking real life), which measures self-regulation and impulse control as opposed to attention and intelligence, may be most beneficial, not only to improve treatment for addiction but also to improve randomized controlled double-blind studies on the effects of WMT.

Limitations Various limitations of WMT research need to be considered for this field to be evaluated as a viable avenue for addiction research. The most problematic issue is the concern over how far the benefits of using the WMT paradigms extend. In other words, there were mixed results as to whether participants just became better at using the WMT paradigms (e.g., “near transfer effects” or longevity of skills transfer ¼ />1 month) or whether this benefit extended to real-life applications of WM and other cognitive areas such as fluid intelligence (e.g., “far transfer effects”) (Alloway et al., 2013; Thompson et al., 2013; Anguera et al., 2017; Lindeløv et al., 2016; Nelwan and Kroesbergen, 2016). There were also mixed results regarding how long the effects of the training lasted posttraining, ranging from 3 to 8 months (Jaeggi et al., 2011; Alloway et al., 2013; Anguera et al., 2013; Toril et al., 2016). Another limitation was the reliance on participants’ effort, investment, and motivation for the paradigms to work (Nelwan and

Peer-reviewed working memory training: is it an effective intervention for addiction? Chapter | 18

Kroesbergen, 2016). Participant dropout, boredom, and fatigue are the major concerns, and thus there is pressure on these paradigms to provide services that will entice their users to use the WMT paradigms on a continuous basis, as this is seen to bring about the best results (Alloway et al., 2013). Finally, it could be argued that lack of far transfer and longevity of skills transfer directly after training and during follow-up may be due to testing/measurement bias and also that WMTdlike physical exercisedneeds to be continued for beneficial effects to be observed long term, and most studies of WMT rely on measures of attention and intelligence. These measures that may not be sufficient to identify ecologically valid improvements in addiction populations following WMT, and other measures might be better at teasing out effects, such as those using decisionmaking, self-regulation, or impulse control.

Supplementary table of nonepeer-reviewed paradigms.

Conclusions Peer-reviewed computerized WMT programs are few (n ¼ 8), by comparison to the number of programs available on the Internet to date. Lack of peer review invites higher levels of skepticism, diminishing the impact of legitimate, rigorous research conducted by researchers in the WMT field. A gold standard for WMT research specifying how training should be administered to people with addiction and how best to measure potential effects would reduce the inconsistencies in the field and aid identification of the existence of far transfer and longevity of skills effects. Currently, it appears that progressively difficult, adaptive WMT that requires users to reach their current limit of WM capacity is most beneficial, but maintaining user motivation to engage can be difficult. From the peerreviewed publications to date, which comprise mostly of studies using the multitasked CogMed and the n-back WM task, it seems that those with learning difficulties and attention deficit disorders, especially children and adolescents, benefit the most over the long term from WMT. However, there is currently a paucity of WMT research examining the potential beneficial effects in people with addiction (Bickel et al., 2011; Houben et al., 2011, 2016; Brooks et al., 2016, 2017a,b; Dassen et al., 2018b; Whitelock et al., 2018). This is striking, given that common pre- and comorbid traits of those with addictions are linked to WM deficits, including risky decision-making, reward reinforcement learning, and impulse control disorders (Verdejo-Garcia et al., 2018). Furthermore, WMT studies of the benefits in those with addiction should ensure to measure, not only attention and global intelligence, as is common in current studies, but also cognitions underlying addiction-related deficits. Finally, WMT studies of addiction should be randomized controlled, double-blind studies, if the field is to understand the link between the neural processes of WM and training.

267

Name

Source

Study Blue

https://www.studyblue.com/

Vismory

http://mermoz.net/portfolio/vismory

Memory!

https://www.neurodevelop.com/Memory

Monster Hunt

https://www.neurodevelop.com/ Monster_Hunt

Brain HQ

https://www.brainhq.com/

Activate

http://www.c8home.com/

Elevate

https://www.elevateapp.com/

MyHAPPYNeuron

http://www.happy-neuron.com/

Brain Fitness Pro

http://www.mindsparke.com/brain_ fitness_training.php

Brain Trainer Special

https://play.google.com/store/apps/ details?id¼com.fit.brain.trainer. special&hl¼en

Cognifit

https://www.cognifit.com/

Peak Brain Training

http://www.peak.net/

Fit Brains Trainer

https://www.neurodevelop.com/Fit_ Brain_Trainer

References Aasvik, J.K., Woodhouse, A., Stiles, T.C., Jacobsen, H.B., Landmark, T., Glette, M., et al., 2016. Effectiveness of working memory training among subjects currently on sick leave due to complex symptoms. Front. Psychol. 7. Ahmadi, Futorjanski, 2011. https://www.neuronation.de/. Åkerlund, E., Esbjörnsson, E., Sunnerhagen, K.S., Björkdahl, A., 2013. Can computerized working memory training improve impaired working memory, cognition and psychological health? Brain Inj. 27 (13e14), 1649e1657. Alloway, T.P., Alloway, R., 2009. The efficacy of working memory training in improving crystallized intelligence. Nat. Proced. Alloway, T.P., Bibile, V., Lau, G., 2013. Computerized working memory training: can it lead to gains in cognitive skills in students? Comput. Hum. Behav. 29 (3), 632e638. Anderson, M.C., Bunce, J.G., Barbas, H., 2016. Prefrontal-hippocampal pathways underlying inhibitory control over memory. Neurobiol. Learn. Mem. 134, 145e161. Anguera, J.A., Boccanfuso, J., Rintoul, J.L., Al-Hashimi, O., Faraji, F., Janowich, J., Kong, E., Larraburo, Y., Rolle, C., Johnston, E., Gazzaley, A., 2013. Video game training enhances cognitive control in older adults. Nature 501 (7465), 97e101. Anguera, J.A., Gunning, F.M., Arean, P.A., 2017. Improving late life depression and cognitive control through the use of therapeutic video game technology: a proof-of-concept randomized trial. Depress. Anxiety 34 (6), 508e517. Arean, P.A., Hallgren, K.A., Jordan, J.T., Gazzaley, A., Atkins, D.C., Heagerty, P.J., et al., 2016. The use and effectiveness of mobile apps for depression: results from a fully remote clinical trial. J. Med. Internet Res. 18 (12), e330.

268 Cognition and Addiction

Au, J., Berkowitz-Sutherland, L., Schneider, A., Schweitzer, J.B., Hessl, D., Hagerman, R., 2014. A feasibility trial of CogMedÔ working memory training in fragile X syndrome. J. Pediatr. Genet. 3 (03), 147e156. Ballesteros, S., Prieto, A., Mayas, J., Toril, P., Pita, C., de León, L.P., et al., 2014. Brain training with non-action video games enhances aspects of cognition in older adults: a randomized controlled trial. Front. Aging Neurosci. 6. Beatty, E.L., Vartanian, O., 2015. The prospects of working memory training for improving deductive reasoning. Front. Hum. Neurosci. 9. Bickel, W.K., Yi, R., Landes, R.D., Hill, P.F., Baxter, C., 2011. Remember the future: WM training decreases delay discounting among stimulant addicts. Biol. Psychiatry 69 (3), 260e265. Bigorra, A., Garolera, M., Guijarro, S., Hervás, A., 2016. Long-term far transfer effects of working memory training in children with ADHD: a randomized controlled trial. Eur. Child Adolesc. Psychiatry 25 (8), 853e867. Bjorkdahl, A., Akerlund, E., Svensson, S., Esbjornsson, E., 2013. A randomized study of computerized working memory training and effects on functioning in everyday life for patients with brain injury. Brain Inj. 27 (13e14), 1658e1665. Bock, J., Poeggel, G., Gruss, M., Wingenfeld, K., Braun, K., 2014. Infant cognitive training preshapes learning- relevant prefrontal circuits for adult learning: learning-induced tagging of dendritic spines. Cerebr. Cortex 24, 2920e2930. Bracy, O.L., 1994. Psychological software services cognitive rehabilitation: technical manual Indianapolis. In: PSSCogReHab. Brooks, S.J., Burch, K.H., Maiorana, S.A., Cocolas, E., Schioth, H.B., Nilsson, E.K., Kamaloodien, K., Stein, D.J., 2016. Psychological intervention with working memory training increases basal ganglia volume: a VBM study of inpatient treatment for methamphetamine use. Neuroimage Clinic 12, 478e491. Brooks, S.J., Funk, S.G., Young, S.Y., Schiöth, H.B., 2017b. The role of working memory for cognitive control in anorexia nervosa versus substance use disorder. Front. Psychol. 8, 1651. Brooks, S.J., January 16, 2016. A debate on working memory and cognitive control: can we learn about the treatment of substance use disorders from the neural correlates of anorexia nervosa? BMC Psychiatry 16, 10. Brooks, S.J., Wiemerslage, L., Burch, K.H., Maiorana, S.A., Cocolas, E., Schiöth, H.B., Kamaloodien, K., Stein, D.J., 2017a. The impact of cognitive training in substance use disorder: the effect of working memory training on impulse control in methamphetamine users. Psychopharmacology 234 (12), 1911e1921. Buschkuehl, M., Hernandez-Garcia, L., Jaeggi, S.M., Bernard, J.A., Jonides, J., 2014. Neural effects of short-term training on working memory. Cogn. Affect. Behav. Neurosci. 14 (1), 147e160. Cassanelli, P.M., Cladouchos, M.L., Fernández Macedo, G., Sifonios, L., Giaccardi, L.I., Gutiérrez, M.L., et al., 2015. Working memory training triggers delayed chromatin remodeling in the mouse corticostriatothalamic circuit. Prog. Neuropsychopharmacol. Biol. Psychiatry 60, 93e103. Chacko, A., Bedard, A.C., Marks, D.J., Feirsen, N., Uderman, J.Z., Chimiklis, A., et al., 2014. A randomized clinical trial of CogMedÔ CogMedÔ Ô working memory training in school-age children with ADHD: a replication in a diverse sample using a control condition. J. Child Psychol. Psychiatry 55 (3), 247e255.

Chan, J.S., Wu, Q., Liang, D., Yan, J.H., 2015. Visuospatial working memory training facilitates visually-aided explicit sequence learning. Acta Psychol. 161, 145e153. Chooi, W.-T., Thompson, L.A., 2012. Working memory training does not improve intelligence in healthy young adults. Intelligence 40, 531e542. Constantinidis, C., Klingberg, T., 2016. The neuroscience of working memory capacity and training. Nat. Rev. Neurosci. 17 (7), 438e449. Cox, L.E., Ashford, J.M., Clark, K.N., Martin-Elbahesh, K., Hardy, K.K., Merchant, T.E., Zhang, H., 2015. Feasibility and acceptability of a remotely administered computerized intervention to address cognitive late effects among childhood cancer survivors. Neuro oncol. Prac. 2 (2), 78e87. Dassen, F.C.M., Houben, K., Allom, V., Jansen, A., 2018a. Self-regulation and obesity: the role of executive function and delay discounting in the prediction of weight loss. J Behav Med. 41 (6), 806e818. Dassen, F.C.M., Houben, K., Van Breukelen, G.J.P., Jansen, A., 2018b. Gamified working memory training in overweight individuals reduces food intake but not body weight. Appetite 124, 89e98. Dongen-Boomsma, M., Vollebregt, M.A., Buitelaar, J.K., SlaatsWillemse, D., 2014. Working memory training in young children with ADHD: a randomized placebo-controlled trial. J. Child Psychol. Psychiatry 55 (8), 886e896. Federal Trade Commission, 2016. Lumosity to Pay $2 Million to Settle FTC Deceptive Advertising Charges for its “Brain Training” Program Company Claimed Program Would Sharpen Performance in Everyday Life and Protect Against Cognitive Decline. https://www.ftc.gov/ news-events/press-releases/2016/01/lumosity-pay-2-million-settle-ftcdeceptive-advertising-charges. Fuentes, A., Kerr, E.N., 2016. Maintenance effects of working memory intervention (Cogmed) in children with symptomatic epilepsy. Epilepsy Behav. 67, 51e59. Gibson, B.S., Gondoli, D.M., Kronenberger, W.G., Johnson, A.C., Steeger, C.M., Morrissey, R.A., 2013. Exploration of an adaptive training regimen that can target the secondary memory component of working memory capacity. Mem. Cogn. 41 (5), 726e737. Gibson, B.S., Kronenberger, W.G., Gondoli, D.M., Johnson, A.C., Morrissey, R.A., Steeger, C.M., 2012. Component analysis of simple span vs. complex span adaptive working memory exercises: a randomized, controlled trial. J. Appl. Res. Memory Cogn. 1 (3), 179e184. Gray, S.A., Chaban, P., Martinussen, R., Goldberg, R., Gotlieb, H., Kronitz, R., Tannock, R., 2012. Effects of a computerized working memory training program on working memory, attention, and academics in adolescents with severe LD and comorbid ADHD: a randomized controlled trial. J. Child Psychol. Psychiatry 53 (12), 1277e1284. Grunewaldt, K.H., Løhaugen, G.C.C., Austeng, D., Brubakk, A.M., Skranes, J., 2013. Working memory training improves cognitive function in VLBW preschoolers. Pediatrics 131 (3), e747ee754. Grunewaldt, K.H., Skranes, J., Brubakk, A.M., Lähaugen, G.C., 2016. Computerized working memory training has positive long-term effect in very low birthweight preschool children. Dev. Med. Child Neurol. 58 (2), 195e201. Guttman, Z., Moeller, S.J., London, E.D., 2018. Neural underpinnings of maladaptive decision-making in addictions. Pharmacol. Biochem. Behav. 164, 84e98.

Peer-reviewed working memory training: is it an effective intervention for addiction? Chapter | 18

Hardy, S.J., Hardy, K.K., Schatz, J.C., Thompson, A.L., Meier, E.R., 2016. Feasibility of home-based computerized working memory training with children and adolescents with Sickle Cell disease. Pediatr. Blood Canc. 63 (9), 1578e1585. Heinzel, S., Lorenz, R.C., Pelz, P., Heinz, A., Walter, H., Kathmann, N., et al., 2016. Neural correlates of training and transfer effects in working memory in older adults. Neuroimage 134, 236e249. Heinzel, S., Rimpel, J., Stelzel, C., Rapp, M.A., 2017. Transfer effects to a multimodal dual-task after working memory training and associated neural correlates in older adults e a pilot study. Front. Hum. Neurosci. 11, 85. Heinzel, S., Schulte, S., Onken, J., Duong, Q.L., Riemer, T.G., Heinz, A., et al., 2014. Working memory training improvements and gains in non-trained cognitive tasks in young and older adults. Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn. 21 (2), 146e173. Hitchcock, C., Westwell, M.S., 2017. A cluster-randomised, controlled trial of the impact of CogMedÔ Working Memory Training on both academic performance and regulation of social, emotional and behavioural challenges. J. Child Psychol. Psychiatry 58 (2), 140e150. Holmes, J., Butterfield, S., Cormack, F., van Loenhoud, A., Ruggero, L., Kashikar, L., Gathercole, S., 2015. Improving working memory in children with low language abilities. Front. Psychol. 6. Houben, K., Dassen, F.C., Jansen, A., 2016. Taking control: working memory training in overweight individuals increases self-regulation of food intake. Appetite 105, 567e574. Houben, K., Wiers, R.W., Jansen, A., 2011. Getting a grip on drinking behavior: training working memory to reduce alcohol abuse. Psychol. Sci. 22 (7), 968e975. Jaeggi, S.M., Buschkuehl, M., Jonides, J., Perrig, W.J., 2008. Improving fluid intelligence with training on working memory. Proc. Natl. Acad. Sci. U. S. A. 105 (19), 6829e6833. Jaeggi, S.M., Buschkuehl, M., Jonides, J., Shah, P., 2011. Short- and longterm benefits of cognitive training. Proc. Natl. Acad. Sci. U. S. A. 108 (25), 10081e10086. Jaeggi, S.M., Buschkuehl, M., Shah, P., Jonides, J., 2014. The role of individual differences in cognitive training and transfer. Mem. Cognit. 42 (3), 464e480. Johansson, B., Tornmalm, M., 2012. Working memory training for patients with acquired brain injury: effects in daily life. Scand. J. Occup. Ther. 19 (2), 176e183. Katz, B., Jaeggi, S., Buschkuehl, M., Stegman, A., Shah, P., 2014. Differential effect of motivational features on training improvements in school-based cognitive training. Front. Hum. Neurosci. 8, 242. Kerr, E.N., Blackwell, M.C., 2015. Near-transfer effects following working memory intervention (CogMedÔ CogMedÔ Ô ) in children with symptomatic epilepsy: an open randomized clinical trial. Epilepsia 56 (11), 1784e1792. Khemiri, L., Brynte, C., Stunkel, A., Klingberg, T., Jayaram-Lindström, N., 2019. Working memory training in alcohol use disorder: a randomized controlled trial. Alcohol Clin. Exp. Res. 43 (1), 135e146. Kirchner, W.K., 1958. Age differences in short-term retention of rapidly changing information. J. Exp. Psychol. 55, 352e358. Klingberg, T., 2010. Training and plasticity of working memory. Trends Cognit. Sci. 14 (7), 317e324. Klingberg, T., Forssberg, H., Westerberg, H., 2002. Training of working memory in children with ADHD. J. Clin. Exp. Neuropsychol. 24 (6), 781e791.

269

Labs, 2005. https://www.lumosity.com/en/. Lawlor-Savage, L., Goghari, V.M., 2016. Dual N-back working memory training in healthy adults: a randomized Comparison to Processing Speed Training. PLoS One 11 (4), e0151817. Lee, C.S., Pei, J., Andrew, G., A Kerns, K., Rasmussen, C., 2016. Effects of working memory training on children born preterm. Appl. Neuropsychol.: Child 1e16. Levenson, J.M., O’Riordan, K.J., Brown, K.D., Trinh, M.A., Molfese, D.L., Sweatt, J.D., 2004. Regulation of histone acetylation during memory formation in the hippocampus. J. Biol. Chem. 279, 405450e405459. Lilienthal, L., Tamez, E., Shelton, J.T., Myerson, J., Hale, S., 2013. Dual n-back training increases the capacity of the focus of attention. Psychon. Bull. Rev. 20 (1), 135e141. Lindelov, J.K., Dall, J.O., Kristensen, C.D., Aagesen, M.H., Olsen, S.A., Snuggerud, T.R., et al., 2016. Training and transfer effects of N-back training for brain-injured and healthy subjects. Neuropsychol. Rehabil. 26 (5e6), 895e909. Martin, D.M., Liu, R., Alonzo, A., Green, M., Player, M.J., Sachdev, P., et al., 2014. Can transcranial direct current stimulation enhance outcomes from cognitive training? A randomized controlled trial in healthy participants. Int. J. Neuropsychopharmacol. 16 (9), 1927e1936. Mawjee, K., Woltering, S., Lai, N., Gotlieb, H., Kronitz, R., Tannock, R., 2017. Working Memory Training in ADHD: Controlling for Engagement, Motivation, and Expectancy of Improvement (pilot study). J. Atten. Disord. 21 (11), 956e968. Mawjee, K., Woltering, S., Tannock, R., 2015. Working memory training in post secondary students with ADHD: a randomized controlled study. PLoS One 10 (9), e0137173. Minear, M., Brasher, F., Guerrero, C.B., Brasher, M., Moore, A., Sukeena, J., 2016. A simultaneous examination of two forms of working memory training: evidence for near transfer only. Mem Cognit 44 (7), 1014e1037. Morra, S., Borella, E., 2015. Working memory training: from metaphors to models. Front. Psychol. 6, 1097. Morrison, A.B., Chein, J.M., 2011. Does working memory training work? The promise and challenges of enhancing cognition by training working memory. Psychon. Bull. Rev. 18 (1), 46e60. Nelwan, M., Kroesbergen, E.H., 2016. Limited near and far transfer effects of Jungle Memory working memory training on learning mathematics in children with attentional and mathematical difficulties. Front. Psychol. 7. Oelhafen, S., Nikolaidis, A., Padovani, T., Blaser, D., Koenig, T., Perrig, W.J., 2013. Increased parietal activity after training of interference control. Neuropsychologia 51 (13), 2781e2790. Owens, M., Koster, E.H., Derakshan, N., 2013. Improving attention control in dysphoria through cognitive training: transfer effects on working memory capacity and filtering efficiency. Psychophysiology 50 (3), 297e307. Parr, T., Friston, K.J., 2017. Working memory, attention, and salience in active inference. Sci. Rep. 7 (1), 14678. Pelegrina, S., Lechuga, M.T., Garcia-Madruga, J.A., Elosua, M.R., Macizo, P., Carreiras, M., et al., 2015. Normative data on the n-back task for children and young adolescents. Front. Psychol. 6, 1544. Pergher, V., Wittevrongel, B., Tournoy, J., Schoenmakers, B., Van Hulle, M.M., 2018. N-back training and transfer effects revealed by behavioral responses and EEG. Brain Behav. 8 (11), e01136.

270 Cognition and Addiction

Phillips, N.L., Mandalis, A., Benson, S., Parry, L., Epps, A., Morrow, A., Lah, S., 2016. Computerized working memory training for children with moderate to severe traumatic brain injury: a double-blind, randomized, placebo-controlled trial. J. Neurotrauma 33 (23), 2097e2104. Pugin, F., Metz, A.J., Stauffer, M., Wolf, M., Jenni, O.G., Huber, R., 2014. Working memory training shows immediate and long-term effects on cognitive performance in children. F1000Res 3, 82. Ramey, T., Regier, P.S., 2018. Cognitive impairment in substance use disorders. CNS Spectr. 2018, 1e12. Redick, T.S., Shipstead, Z., Harrison, T.L., Hicks, K.L., Fried, D.E., Hambrick, D.Z., et al., 2013. No evidence of intelligence improvement after working memory training: a randomized, placebocontrolled study. J. Exp. Psychol. Gen. 142 (2), 359e379. Roberts, G., Quach, J., Spencer-Smith, M., Anderson, P.J., Gathercole, S., Gold, L., et al., 2016. Academic outcomes 2 years after working memory training for children with low working memory: a randomized clinical trial. JAMA Pediatr. 170 (5), e154568. Rossignoli-Palomeque, T., Perez-Hernandez, E., González-Marqués, J., 2018. Brain training in children and adolescents: is it scientifically valid? Front. Psychol. 9, 565. Rottschy, C., Langner, R., Dogan, I., Reetz, K., Laird, A.R., Schulz, J.B., Fox, P.T., Eickhoff, S.B., 2012. Modelling neural correlates of working memory: a coordinate-based meta-analysis. Neuroimage 60 (1), 830e846. Rudebeck, S.R., Bor, D., Ormond, A., O’Reilly, J.X., Lee, A.C., 2012. A potential spatial working memory training task to improve both episodic memory and fluid intelligence. PLoS One 7 (11), e50431. Sadeghi, M., Barlow-Krelina, E., Gibbons, C., Shaikh, K.T., Fung, W.L.A., Meschino, W.S., et al., 2017. Feasibility of computerized working memory training in individuals with Huntington disease. PLoS One 12 (4), e0176429. Salminen, T., Frensch, P., Strobach, T., Schubert, T., 2016. Age-specific differences of dual n-back training. Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn. 23 (1), 18e39. Salminen, T., Strobach, T., Schubert, T., 2012. On the impacts of working memory training on executive functioning. Front. Hum. Neurosci. 6, 166. Sari, B.A., Koster, E.H., Pourtois, G., Derakshan, N., 2016. Training working memory to improve attentional control in anxiety: a proof-ofprinciple study using behavioral and electrophysiological measures. Biol. Psychol. 121 (Pt B), 203e212. Schneiders, J.A., Opitz, B., Tang, H., Deng, Y., Xie, C., Li, H., et al., 2012. The impact of auditory working memory training on the frontoparietal working memory network. Front. Hum. Neurosci. 6, 173. Schwarb, H., Nail, J., Schumacher, E.H., 2016. Working memory training improves visual short-term memory capacity. Psychol. Res. 80 (1), 128e148. Schweizer, S., Grahn, J., Hampshire, A., Mobbs, D., Dalgleish, T., 2013. Training the emotional brain: improving affective control through emotional working memory training. J. Neurosci. 33 (12), 5301e5311. Söderqvist, S., Nutley, S.B., 2015. Working memory training is associated with long term attainments in math and reading. Front. Psychol. 6. Spencer-Smith, M., Klingberg, T., 2015. Benefits of a working memory training program for inattention in daily life: a systematic review and meta-analysis. PLoS One 10 (3), e0119522. Steeger, C.M., Gondoli, D.M., Gibson, B.S., Morrissey, R.A., 2016. Combined cognitive and parent training interventions for adolescents

with ADHD and their mothers: a randomized controlled trial. Child Neuropsychol. 22 (4), 394e419. Stepankova, H., Lukavsky, J., Buschkuehl, M., Kopecek, M., Ripova, D., Jaeggi, S.M., 2014. The malleability of working memory and visuospatial skills: a randomized controlled study in older adults. Dev. Psychol. 50 (4), 1049e1059. Strobach, T., Huestegge, L., 2017. Evaluating the effectiveness of commercial brain game training with working memory tasks. J. Cogn. Enhance. 1 (4), 539e558. Sun, Y., Taya, F., Chen, Y., Delgado Martinez, I., Thakor, N., Bezerianos, A., 2014. Topological changes of the effective connectivity during the working memory training. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2014, 6242e6245. Swank, M.W., Sweatt, J.D., 2001. Increased histone acetyltransferase and lysine acetyltransferase activity and biphasic activation of the ERK/ RSK cascasde in insular cortex during novel taste learning. J. Neurosci. 21, 3383e3391. Takeuchi, H., Taki, Y., Kawashima, R., 2010. Effects of working memory training on cognitive functions and neural systems. Rev. Neurosci. 21 (6), 427e449. Thompson, P.J., Conn, H., Baxendale, S.A., Donnachie, E., McGrath, K., Geraldi, C., Duncan, J.S., 2016. Optimizing memory function in temporal lobe epilepsy. Seizure 38, 68e74. Thompson, T.W., Waskom, M.L., Garel, K.L., Cardenas-Iniguez, C., Reynolds, G.O., Winter, R., et al., 2013. Failure of working memory training to enhance cognition or intelligence. PLoS One 8 (5), e63614. Toril, P., Reales, J.M., Mayas, J., Ballesteros, S., 2016. Video game training enhances visuospatial working memory and episodic memory in older adults. Front. Hum. Neurosci. 10. van der Donk, M., Hiemstra-Beernink, A.C., Tjeenk-Kalff, A., van der Leij, A., Lindauer, R., 2015. Cognitive training for children with ADHD: a randomized controlled trial of Cogmed working memory training and ‘paying attention in class’. Front. Psychol. 6, 1081. Van der Donk, M.L., Hiemstra-Beernink, A.C., Tjeenk-Kalff, A.C., van der Leij, A., Lindauer, R.J., 2016. Predictors and moderators of treatment outcome in cognitive training for children with ADHD. J. Atten. Disord. 1087054716632876. Verdejo-Garcia, A., Chong, T.T., Stout, J.C., Yücel, M., London, E.D., 2018. Stages of dysfunctional decision-making in addiction. Pharmacol. Biochem. Behav. 164, 99e105. Waris, O., Soveri, A., Laine, M., 2015. Transfer after Working Memory Updating Training. PLoS One 10 (9), e0138734. Wayne, R.V., Hamilton, C., Huyck, J.J., Johnsrude, I.S., 2016. Working memory training and speech in noise comprehension in older adults. Front. Aging Neurosci. 8. Wentink, M.M., Berger, M.A.M., de Kloet, A.J., Meesters, J., Band, G.P.H., Wolterbeek, R., Vliet Vlieland, T.P.M., 2016. The effects of an 8-week computer-based brain training programme on cognitive functioning, QoL and self-efficacy after stroke. Neuropsychol. Rehabil. 26 (5e6), 847e865. Whitelock, V., Nouwen, A., Houben, K., van den Akker, O., Rosenthal, M., Higgs, S., 2018. Does working memory training improve dietary self-care in type 2 diabetes mellitus? Results of a double blind randomised controlled trial. Diabetes Res. Clin. Pract. 143, 204e214.

Chapter 19

Inhibitory control training Andrew Jones1 and Matt Field2 1

Department of Psychological Sciences, University of Liverpool, Liverpool, Merseyside, United Kingdom; 2Department of Psychology, University of

Sheffield, Sheffield, South Yorkshire, United Kingdom

Introduction: alignment between the training and cognitive changes that characterize addiction Inhibitory control refers to the ability to stop, change, or delay a response that is no longer appropriate in the circumstances (Logan et al., 1984). A prototypical example is when one is driving toward an intersection and a traffic signal changes from red to green: the red light serves as a cue to inhibit the act of pressing the accelerator, and failure to do so can have negative consequences. Inhibitory control can be measured in the laboratory using computerized tasks such as the Stop Signal and Go/No-Go tasks (Diamond, 2013). These tasks require participants to respond to environmental stimuli as quickly and accurately as possible, unless a different stimulus (the “Stop Signal” or “No-Go cue”) is presented, in which case the participant should refrain from responding. Difficulty exercising inhibitory control can be inferred from the number of inhibition errors or by modeling the latency to execute a stopping response based on reaction times and inhibition errors (Band et al., 2003). Individual differences in inhibitory control contribute to broader constructs including executive functions and impulsivity (Bickel et al., 2012), and inhibitory control may also underlie more fuzzy constructs such as “self-control” (Baumeister, 2014); see (Fujita, 2011). Deficits in inhibitory control (and executive functions, impulsivity, and self-control more broadly) are theorized to play an important causal role in the onset and persistence of substance use disorders (SUDs) (De Wit, 2009; Dick et al., 2010; Goldstein and Volkow, 2011). These claims are founded on observations that a variety of SUDs and behavioral addictions (such as problem gambling) are characterized by impaired inhibitory control (Smith et al., 2014), that inhibitory deficits may occur premorbid to substance involvement (Fernie et al., 2013; Tarter et al., 2004), and that severe inhibitory control impairments

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00019-8 Copyright © 2020 Elsevier Inc. All rights reserved.

may confer increased risk of relapse to substance use after treatment (Petit et al., 2014; Rupp et al., 2016). Substance users’ deficits in inhibitory control are exacerbated when they are in the presence of substancerelated cues. This has been demonstrated for alcohol-, tobacco-, and cocaine-related cues (Luijten et al., 2011; Pike et al., 2013), and this is a very general feature of motivation because food-related cues also lead to impairments in inhibitory control (Jones et al., 2018). These transient state fluctuations in inhibitory control during exposure to substance-related cues may partially account for the influence of those cues on subjective craving and relapse to substance use after a period of abstinence (De Wit, 2009; Jones et al., 2013a). Indeed, a recent study demonstrated that the effects of alcohol cues on alcohol consumption in the laboratory were partially mediated by the influence of those cues on inhibitory control (Field and Jones, 2017).

Description of the training and proposed mechanisms Two forms of inhibitory control training (ICT) can be distinguished: “general” and “cue-specific.” The goal of general ICT is to improve global inhibitory control capacity or the motivation to engage inhibitory control, whereas cuespecific ICT works on associative learning principles with the goal to train participants to engage inhibitory control whenever specific types of environmental cues are encountered in the future. During general ICT, participants might practice inhibitory control tasks (such as the Stop Signal task) several times over several days, weeks, or months (Spierer et al., 2013). To facilitate robust improvements in inhibitory control, the inhibitory control task could escalate in

271

272 Cognition and Addiction

difficulty over time. For example, Berkman et al. (2014) demonstrated progressive improvements in inhibitory control in participants who completed multiple sessions of a modified Stop Signal task that became progressively more difficult in line with participants’ performance. During cue-specific ICT, participants repeatedly perform a modified inhibitory control task (such as the Go/No-Go or Stop Signal task) that has substance-related cues embedded into it. The contingency between substance-related cues and inhibition signals can be manipulated so that participants who receive active ICT form an association between substance-related cues and inhibition of behavior. See Fig. 19.1 for an example. On each trial, participants would be instructed to press one of the two keys to indicate whether the picture that is presented depicts an alcohol-related or a soda-related object. On some trials, the signal to inhibit (the “No-Go” or “Stop Signal”) would be presented alongside the picture. Over a number of trials, if the contingency between the alcohol-related pictures and the occurrence of inhibitory signals is high (typically 80%e100%), and the contingency between the soda-related pictures and the occurrence of inhibitory signals is low (typically 0%e20%), then participants should form an association between alcohol-related cues and engagement of inhibitory control.

The two forms of ICT are thought to have distinct mechanisms of action. General ICT rests on the assumption that practice or training of a specific task will result in improvements in the target construct, which generalize or transfer to other tasks that measure the same or similar constructs. Although it is possible to train participants to improve their performance on a specific task through practice, the existence of transfer effects is a matter of some debate (Diamond and Ling, 2016; Enge et al., 2014; Owen et al., 2010). Regarding cue-specific ICT, there is evidence that the training changes associations between substancerelated cues and engagement of inhibitory control: after training, participants make fewer inhibition errors during exposure to substance-related cues, and they are slower to respond to substance-related cues when required to do so (Jones and Field, 2012), with similar findings after ICT with food-related cues reported by Lawrence et al. (2015). There is also some evidence that these effects may be underpinned by changes in evaluation of substance-related cues because repeated exercise of inhibitory control during exposure to substance-related cues results in devaluation of those cues, which in turn blunts the influence of those cues on motivated behavior (Houben et al., 2011). However, as discussed in Section 4, the mechanisms of action of cue-specific ICT are still under investigation.

Evidence for the efficacy of inhibitory control training

FIGURE 19.1 Panels represent the sequence of events during a single trial of alcohol-related cue-specific inhibitory control training. Participants are instructed to categorize pictures according to their content (alcohol- or stationery-related, in this example), unless a “Stop Signal” (“¼” in this example) is presented. If Stop Signals are consistently presented on alcohol picture trials, participants should form associations between alcohol and engagement of inhibitory control.

To date, the majority of studies that have investigated the influence of ICT on substance use can be characterized as translational, proof-of-concept laboratory studies with healthy participants. As with other forms of cognitive bias modification, these types of studies should be distinguished from randomized controlled trials (RCTs) of interventions with clinical populations (Wiers et al., 2018). Most of these laboratory studies administered a single brief session of ICT in the laboratory before measuring how much alcohol participants would voluntarily consume in the context of a bogus “taste test” or similar. The rationale for these proof-of-concept studies is to investigate if direct manipulation of a target construct (inhibitory control, in this case) has a causal influence on a target behavior (alcohol consumption, in this case). Regarding general ICT, several studies manipulated task instructions before participants completed a Stop Signal task, with the intention to manipulate participants’ motivation to exercise inhibitory control (rather than their inhibitory control capacity per se). For example, Jones et al. (2011a,b) gave participants a Stop Signal task with instructions to either focus on inhibiting to stop signals or to respond as quickly as they could. In both studies, participants who were prompted to focus on inhibition made fewer inhibition errors during the task and, crucially, they consumed less alcohol during a

Inhibitory control training Chapter | 19

bogus taste test immediately afterward, compared to control groups of participants who were instructed to prioritize rapid responding (however, see “null” findings in Jones et al., 2013b; Smith et al., 2017). Although these findings provide important support for the general principle that short-term changes in inhibitory control might have a causal influence on alcohol consumption, they do not establish that inhibitory control capacity can be improved through training and that this could have beneficial effects on alcohol consumption or other substance use. Indeed, a study that administered multiple sessions of general ICT to healthy volunteers detected no improvement in inhibitory control capacity after training and no reduction in self-reported alcohol consumption (Bartsch et al., 2016). Similarly, after initially promising demonstrations of general ICT effects on gambling behaviors in the laboratory (Verbruggen et al., 2012), multiple ICT sessions delivered over an extended period of time failed to influence gambling behaviors (Verbruggen et al., 2013). Turning to cue-specific ICT, numerous studies have demonstrated that, compared to a control intervention, a single brief session of alcohol cue-specific ICT results in a robust reduction in alcohol consumption in the laboratory (Jones et al., 2016b). Some studies reported that these beneficial effects of cue-specific ICT also influenced selfreported alcohol consumption for up to 1 week after receiving cue-specific ICT (Houben et al., 2011), although this finding has not always been replicated (Bowley et al., 2013; Jones and Field, 2012; Smith et al., 2017). One recent study applied a similar cue-specific ICT paradigm to tobacco smokers and reported suggestive evidence that cue-specific ICT may increase the resistance to smoke a cigarette in the laboratory after overnight abstinence, although this effect was not statistically significant (Adams et al., 2017). The literature on food cue-specific ICT is also relevant here: compared to a control intervention, a single session of food cue-specific ICT leads to reductions in food consumption and changes in food choice in laboratory settings (Allom et al., 2015; Jones et al., 2016b), and multiple sessions of food cuespecific ICT result in weight loss in people who are trying to lose weight (Lawrence et al., 2015; Stice et al., 2016; Van Koningsbruggen et al., 2014). Despite this overall optimistic picture, it is noteworthy that a number of recent studies have failed to replicate the effects of a single session of cue-specific ICT on alcohol or food consumption in the laboratory (Adams et al., 2017; Bongers et al., 2018; Smith et al., 2017). Furthermore, it is important not to overinterpret findings from laboratory studies, given that such studies are typically conducted with healthy volunteers (rather than people with SUDs) using proxy measures of alcohol or food consumption that may bear little resemblance to alcohol or food consumption, or other substance use, outside of the laboratory (Field et al., 2018; Jones et al., 2016a).

273

Efficacy in people with substance use disorder In a recent RCT, we randomized heavy drinkers to complete multiple sessions of different forms of ICT over the Internet (Jones et al., 2018). Although not formally diagnosed with alcohol use disorder, participants were recruited on the basis of their heavy drinking and a desire to “cut down,” and therefore they would likely have met diagnostic criteria for (at least) mild alcohol use disorder (American Psychiatric Association, 2013). After receiving a brief intervention, participants completed online general or alcohol cue-specific ICT sessions (or an active control intervention, in which participants responded quickly to alcohol and neutral images but were never required to inhibit their behavior) every other day for 4 weeks (up to 14 ICT sessions in total). Participants self-reported their alcohol consumption every day during this 4-week intervention period and again at 6-week follow-up. Results indicated that all participants reported substantial reductions in alcohol consumption over the course of the intervention period (approximately 13 UK units, or 104 g of alcohol, per week, on average). However, there were no between-group differences, i.e., no beneficial effects of general or cue-specific ICT relative to each other or relative to an active control. These findings raise questions about the efficacy of other related interventions such as the Alcohol Attention Control Training Program (AACTP), the reported beneficial effects of which (Fadardi and Cox, 2009) tend to disappear when it is compared with an active rather than a passive control condition (Wiers et al., 2015). These disappointing findings suggest that despite promising findings from proof-of-concept laboratory studies, when administered to people with SUDs symptoms in real-world settings, any beneficial effects of either general or cuespecific ICT may be obscured by nonspecific factors such as regular self-monitoring of alcohol consumption and receipt of a brief intervention (Jenkins et al., 2009). This echoes a recent observation from a metaanalysis of “self-control training” (such as repeatedly squeezing a handgrip over several weeks), which demonstrated that the effects of self-control training are typically fairly small compared with the effects of commonly used interventions on symptoms of psychological disorders (Friese et al., 2017). As ICT is investigated for other populations (e.g., Alcorn et al., 2017), it is important to ensure that nonspecific treatment effects are adequately controlled for when evaluating effects on substance use. We await findings from additional RCTs in participants with SUDs or behavioral addictions.

Mechanisms of action of inhibitory control training As previously noted, it is a matter of some debate whether general ICT (and other “cognitive training” interventions)

274 Cognition and Addiction

can yield generalizable and transferable improvements in the target psychological construct (Diamond and Ling, 2016; Owen et al., 2010). Both studies that investigated the effects of general ICT in substance using populations found no improvement in inhibitory control capacity after multiple sessions of training (Bartsch et al., 2016; Jones et al., 2018), which is consistent with the broader literature on transfer effects after cognitive training. The mechanisms of action of cue-specific ICT have been more intensively studied. The importance of associative learning to inhibitory control is well known (Verbruggen et al., 2014; Verbruggen and Logan, 2008), and the rationale behind cue-specific ICT is to exploit associative learning principles to train participants to form associations between substance-related cues and inhibition of behavior such that inhibitory control is automatically evoked whenever substance-related cues are encountered in the future. Findings from laboratory studies demonstrate that such learning does indeed take place. For example, Jones and Field (2012) demonstrated that over the course of alcohol cue-specific ICT, participants made fewer inhibition errors to alcohol-related cues and they were slower to respond to alcohol-related cues when required to respond to them, although this was not replicated in a more recent study (Di Lemma and Field, 2017). However, in a metaanalysis, we demonstrated that the extent to which participants formed associations between appetitive cues and inhibitory control (as inferred from the number of inhibitory failures during exposure to those cues after ICT) was correlated with the effect of ICT on appetitive behavior in the laboratory (Jones et al., 2016b). This implicates changes in cue inhibition associations as an important mechanism of action of ICT on behavior. A second potential mechanism of action of cue-specific ICT is a reduction in the value of cues that are paired with inhibition. According to Behavior Stimulus Interaction (BSI) theory (Veling et al., 2008), the act of inhibiting behavior to appetitive cues creates a response conflict. During cue-specific ICT, this conflict is resolved by devaluing those cues such that they no longer elicit appetitive approach behaviors. Indeed,

some studies have demonstrated that alcohol cue-specific ICT results in changes in implicit evaluations of alcohol-related cues (Houben et al., 2012). However, this has not been consistently replicated (Bowley et al., 2013; Di Lemma and Field, 2017), and metaanalysis of the alcohol- and food cuespecific ICT literature suggests that these devaluation effects may not be robust (Jones et al., 2016b). Finally, we note that we comprehensively tested the mechanisms of action of general and cue-specific ICT in our recent RCT (Jones et al., 2018), and we found no evidence of changes in inhibitory control capacity, inhibitory control in response to alcohol cues, or devaluation of alcohol cues after multiple sessions of ICT delivered via the Internet. Similarly, no support for these mechanisms was found after multiple sessions of food cue-specific ICT, compared with a control group who received health information, over the course of 1 week (Poppelaars et al., 2018). This complicates interpretation of the null effects of ICT on self-reported alcohol consumption and calorie intake because one should not expect ICT to lead to changes in substance use if it does not cause robust changes in its candidate mechanisms of action (see Wiers et al., 2018). While there are considerable benefits to online delivery of interventions (Griffiths et al., 2006), limitations such as poor participant retention (White et al., 2010) and lack of contact with health care professionals (Riper et al., 2011) may partially account for these disappointing findings (Table 19.1).

Conclusions and recommendations The available evidence provides important “proof-ofconcept” support for ICT because it demonstrates that brief sessions of ICT can prompt reductions in alcohol consumption in the laboratory. Furthermore, a comparable literature on food-specific ICT and food consumption in the laboratory suggests that ICT operates through fairly general motivational mechanisms that are not unique to addiction. However, based on the (very limited) evidence conducted outside of laboratory settings, our strong recommendation

TABLE 19.1 Summary of the evidence for effectiveness of inhibitory control training (ICT) on key outcomes in different types of studies. Outcome

Translational lab studies

Studies performed in real-world settings

Change in inhibitory control or other candidate mechanisms General ICT

Generally positive findings

Generally null findings

Cue-specific ICT

Mixed findings

Generally null findings

General ICT

Mixed findings

Generally null findings

Cue-specific ICT

Mixed findings

Generally null findings

Change in drinking behavior

Inhibitory control training Chapter | 19

is that ICT is not ready to implement as a standalone or adjunct treatment for SUDs or behavioral addictions. Further research is required to clarify the psychological mechanisms of action of ICT and to confirm that the parameters and contexts in which ICT is likely to be delivered in clinical, naturalistic, or online settings are likely to yield robust changes in these psychological mechanisms of action. At this point, but not before, it would be appropriate to evaluate the effectiveness of ICT as a standalone or adjunct intervention for SUDs.

References Adams, R.C., Lawrence, N.S., Verbruggen, F., Chambers, C.D., 2017. Training response inhibition to reduce food consumption: mechanisms, stimulus specificity and appropriate training protocols. Appetite 109, 11e23. https://doi.org/10.1016/j.appet.2016.11.014. Adams, S., Mokrysz, C., Attwood, A.S., Munafò, M.R., 2017. Resisting the urge to smoke: inhibitory control training in cigarette smokers. R. Soc. Open Sci. 4 (8). Alcorn 3rd, J.L., Pike, E., Stoops, W.S., Lile, J.A., Rush, C.R., 2017. A pilot investigation of acute inhibitory control training in cocaine users. Drug Alcohol Depend. 174, 145e149. https://doi.org/10.1016/ j.drugalcdep.2017.01.014. Allom, V., Mullan, B., Hagger, M.S., 2015. Does inhibitory control training improve health behaviour? A meta-analysis. Health Psychol. Rev. 10, 168e186. American Psychiatric Association, 2013. Diagnostic and statistical manual of mental disorders, fifth ed. Washington, DC. Band, G.P.H., van der Molen, M.W., Logan, G.D., 2003. Horse-race model simulations of the stop-signal procedure. Acta Psychol. 112 (2), 105e142. Bartsch, A.L., Kothe, E., Allom, V., Mullan, B., Houben, K., 2016. The effect of non-specific response inhibition training on alcohol consumption: an intervention. J. Addict. Res. Ther. 7 (1), 260. Baumeister, R.F., 2014. Self-regulation, ego depletion, and inhibition. Neuropsychologia 65, 313e319. https://doi.org/10.1016/ j.neuropsychologia.2014.08.012. Berkman, E.T., Kahn, L.E., Merchant, J.S., 2014. Training-induced changes in inhibitory control network activity. J. Neurosci. 34 (1), 149e157. Bickel, W.K., Jarmolowicz, D.P., Mueller, E.T., Gatchalian, K.M., McClure, S.M., 2012. Are executive function and impulsivity antipodes? A conceptual reconstruction with special reference to addiction. Psychopharmacology 221 (3), 361e387. Bongers, P., Houben, K., Jansen, A., 2018. Double up! Examining the effects of adding inhibition training to food cue exposure in chocolateloving female students. Appetite 121, 154e162. https://doi.org/10. 1016/j.appet.2017.11.096. Bowley, C., Faricy, C., Hegarty, B., Johnstone, S.J., Smith, J., Kelly, P., Rushby, J., 2013. The effects of inhibitory control training on alcohol consumption, implicit alcohol-related cognitions and brain electrical activity. Int. J. Psychophysiol. 89, 342e348. De Wit, H., 2009. Impulsivity as a determinant and consequence of drug use: a review of underlying processes. Addict. Biol. 14 (1), 22e31. Diamond, A., 2013. Executive functions. Annu. Rev. Psychol. 64, 135e168.

275

Diamond, A., Ling, D.S., 2016. Conclusions about interventions, programs, and approaches for improving executive functions that appear justified and those that, despite much hype, do not. Dev. Cogn. Neurosci. 18, 34e48. https://doi.org/10.1016/j.dcn.2015.11.005. Dick, D.M., Smith, G., Olausson, P., Mitchell, S.H., Leeman, R.F., O’Malley, S.S., Sher, K., 2010. Understanding the construct of impulsivity and its relationship to alcohol use disorders. Addict. Biol. 15 (2), 217e226. Di Lemma, L.C.G., Field, M., 2017. Cue avoidance training and inhibitory control training for the reduction of alcohol consumption: a comparison of effectiveness and investigation of their mechanisms of action. Psychopharm 234, 2489e2498. Enge, S., Behnke, A., Fleischhauer, M., Küttler, L., Kliegel, M., Strobel, A., 2014. No evidence for true training and transfer effects after inhibitory control training in young healthy adults. J. Exp. Psychol. Learn. Mem. Cogn. 40 (4), 987e1001. Fadardi, J.S., Cox, W.M., 2009. Reversing the sequence: reducing alcohol consumption by overcoming alcohol attentional bias. Drug Alcohol Depend. 101, 137e145. https://doi.org/10.1016/ j.drugalcdep.2008.11.015. Fernie, G., Peeters, P., Gullo, M.J., Christiansen, P., Cole, J., Sumnall, H., Field, M., 2013. Multiple components of impulsivity predict prospective alcohol involvement in adolescents. Addiction 108, 1916e1923. Field, M., Jones, A., 2017. Elevated alcohol consumption following alcohol cue exposure is partially mediated by reduced inhibitory control and increased craving. Psychopharm 234, 2979e2988. Field, M., Jones, A., Kersbergen, I., Robinson, E., 2018. Experimental research requires valid and sensitive measures of alcohol intake, and this is a step in the right direction: commentary on Leeman and colleagues (2018). Alcohol Clin. Exp. Res. 42 (6), 1019e1021. https:// doi.org/10.1111/acer.13641. Friese, M., Frankenbach, J., Job, V., Loschelder, D.D., 2017. Does selfcontrol training improve self-control? A meta-analysis. Perspect. Psychol. Sci. 12 (6), 1077e1099. https://doi.org/10.1177/ 1745691617697076. Fujita, K., 2011. On conceptualizing self-control as more than the effortful inhibition of impulses. Pers. Soc. Psychol. Rev. 15 (4), 352e366. Goldstein, R.Z., Volkow, N., 2011. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 12, 652e669. Griffiths, F., Lindenmeyer, A., Powell, J., Lowe, P., Thorogood, M., 2006. Why are health care interventions delivered over the internet? A systematic review of the published literature. J. Med. Internet Res. 8 (2), e10. https://doi.org/10.2196/jmir.8.2.e10. Houben, K., Havermans, R.C., Nederkoorn, C., Jansen, A., 2012. Beer à no-go: learning to stop responding to alcohol cues reduces alcohol intake via reduced affective associations rather than increased response inhibition. Addiction 107 (7), 1280e1287. Houben, K., Nederkoorn, C., Wiers, R.W., Jansen, A., 2011. Resisting temptation: decreasing alcohol-related affect and drinking behavior by training response inhibition. Drug Alcohol Depend. 116, 132e136. https://doi.org/10.1016/j.drugalcdep.2010.12.011. Jenkins, R.J., McAlaney, J., McCambridge, J., 2009. Change over time in alcohol consumption in control groups in brief intervention studies: systematic review and meta-regression study. Drug Alcohol Depend. 100 (1e2), 107e114. https://doi.org/10.1016/ j.drugalcdep.2008.09.016.

276 Cognition and Addiction

Jones, A., Button, E., Rose, A.K., Robinson, E., Christiansen, P., Di Lemma, L., Field, M., 2016a. The ad-libitum alcohol “taste test”: secondary analyses of potential confounds and construct validity. Psychopharmacology 233 (5), 917e924. https://doi.org/10.1007/ s00213-015-4171-z. Jones, A., McGrath, E., Robinson, E., Houben, K., Nederkoorn, C., Field, M., 2018. A randomized controlled trial of inhibitory control training for the reduction of alcohol consumption in problem drinkers. J Consult Clin Psychol 86, 991e1004. Jones, A., Christiansen, P., Nederkoorn, C., Houben, K., Field, M., 2013a. Fluctuating disinhibition: implications for the understanding and treatment of alcohol and other substance use disorders. Front. Psychiatry 22 (4). Jones, A., Cole, J., Goudie, A., Field, M., 2011a. Priming a restrained mental set reduces alcohol-seeking independently of mood. Psychopharmacology 218 (3), 557e565. Jones, A., Di Lemma, L.C.G., Robinson, E., Christiansen, P., Nolan, S., Tudur-Smith, C., Field, M., 2016b. Inhibitory control training for appetitive behaviour change: a meta-analytic investigation of mechanisms of action and moderators of effectiveness. Appetite 97, 16e28. https://doi.org/10.1016/j.appet.2015.11.013. Jones, A., Field, M., 2012. The effects of cue-specific inhibition training on alcohol consumption in heavy social drinkers. Exp. Clin. Psychopharmacol 21, 8e16. https://doi.org/10.1037/a0030683. Jones, A., Field, M., Christiansen, P., Stancak, A., 2013b. P300 during response inhibition is associated with ad-lib alcohol consumption in social drinkers. J. Psychopharmacol. 27 (6), 507e514. https://doi.org/ 10.1177/0269881113485142. Jones, A., Guerrieri, R., Fernie, G., Cole, J., Goudie, A., Field, M., 2011b. The effects of priming restrained versus disinhibited behaviour on alcohol-seeking in social drinkers. Drug Alcohol Depend. 113 (1), 55e61. Lawrence, N.S., Verbruggen, F., Morrison, S., Adams, R.C., Chambers, C.D., 2015. Stopping to food can reduce intake. Effects of stimulus-specificity and individual differences in dietary restraint. Appetite 85, 91e103. Logan, G.D., Cowan, W.B., Davis, K.A., 1984. On the ability to inhibit simple and choice reaction time responses: a model and a method. J. Exp. Psychol. Hum. Percept. Perform. 10 (2), 276e291. Luijten, M., Littel, M., Franken, I.H.A., 2011. Deficits in inhibitory control in smokers during a Go/Nogo task: an investigation using eventrelated brain potentials. PLoS One 6 (4). Owen, A.M., Hampshire, A., Grahn, J.A., Stenton, R., Dajani, S., Burns, A.S., et al., 2010. Putting brain training to the test. Nature 465 (7299), 775e778. https://doi.org/10.1038/nature09042. Petit, G., Cimochowska, A., Kornreich, C., Hanak, C., Verbanck, P., Campanella, S., 2014. Neurophysiological correlates of response inhibition predict relapse in detoxified alcoholic patients: some preliminary evidence from event-related potentials. Neuropsychiatric Dis. Treat. 10, 1025e1037. https://doi.org/10.2147/NDT.S61475. Pike, E., Stoops, W.W., Fillmore, M.T., Rush, C.R., 2013. Drug-related stimuli impair inhibitory control in cocaine abusers. Drug Alcohol Depend. 133 (2), 768e771. Poppelaars, A., Scholten, H., Granic, I., Veling, H., JohnsonGlenberg, M.C., Luijten, M., 2018. When winning is losing: a randomized controlled trial testing a video game to train food-specific inhibitory control. Appetite 129, 143e154. https://doi.org/10.1016/j. appet.2018.06.039. Riper, H., Spek, V., Boon, B., Conijn, B., Kramer, J., Martin-Abello, K., Smit, F., 2011. Effectiveness of E-Self-help interventions for

curbing adult problem drinking: a meta-analysis. J. Med. Internet Res. 13 (2). Rupp, C.I., Beck, J.K., Heinz, A., Kemmler, G., Manz, S., Tempel, K., Fleischhacker, W.W., 2016. Impulsivity and alcohol dependence treatment completion: is there a neurocognitive risk factor at treatment entry? Alcohol Clin. Exp. Res. 40 (1), 152e160. https://doi.org/ 10.1111/acer.12924. Smith, J., Mattick, R., Jamadar, S., Iredale, J., 2014. Deficits in behavioural inhibition in substance abuse and addiction: a meta-analysis. Drug Alcohol Depend. 145, 1e33. Smith, J.L., Dash, N.J., Johnstone, S.J., Houben, K., Field, M., 2017. Current forms of inhibitory training produce no greater reduction in drinking than simple assessment: a preliminary study. Drug Alcohol Depend. 173, 47e58. https://doi.org/10.1016/j.drugalcdep.2016. 12.018. Spierer, L., Chavan, C.F., Manuel, A.L., 2013. Training-induced behavioral and brain plasticity in inhibitory control. Front. Hum. Neurosci. 7, 427. Stice, E., Lawrence, N.S., Kemps, E., Veling, H., 2016. Training motor responses to food: a novel treatment for obesity targeting implicit processes. Clin. Psychol. Rev. 49, 16e27. https://doi.org/10.1016/ j.cpr.2016.06.005. Tarter, R.E., Kirisci, L., Habeych, M., Reynolds, M., Vanyukov, M., 2004. Neurobehavior disinhibition in childhood predisposes boys to substance use disorder by young adulthood: direct and mediated etiologic pathways. Drug Alcohol Depend. 73 (2), 121e132. Van Koningsbruggen, G.M., Veling, H., Stroebe, W., Aarts, H., 2014. Comparing two psychological interventions in reducing impulsive processes of eating behaviour: effects on self-selected portion size. Br. J. Health Psychol. 19 (4), 767e782. Veling, H., Holland, R.W., van Knippenberg, A., 2008. When approach motivation and behavioral inhibition collide: behavior regulation through stimulus devaluation. J. Exp. Soc. Psychol. 44 (4), 1013e1019. Verbruggen, F., Adams, R., Chambers, C.D., 2012. Proactive motor control reduces monetary risk taking in gambling. Psychol. Sci. 23 (7), 805e815. Verbruggen, F., Adams, R.C., van ’t Wout, F., Stevens, T., McLaren, I.P.L., Chambers, C.D., 2013. Are the effects of response inhibition on gambling long-lasting? PLoS One 8 (7). Verbruggen, F., Best, M., Bowditch, W.A., Stevens, T., McLaren, I.P.L., 2014. The inhibitory control reflex. Neuropsychologia 65, 263e278. https://doi.org/10.1016/j.neuropsychologia.2014.08.014. Verbruggen, F., Logan, G.D., 2008. Automatic and controlled response inhibition: associative learning in the go/No-go and stop-signal paradigms. J. Exp. Psychol. Gen. 137 (4), 649e672. White, A., Kavanagh, D., Stallman, H., Klein, B., Kay-Lambkin, F., Proudfoot, J., et al., 2010. Online alcohol interventions: a systematic review. J. Med. Internet Res. 12 (5), e62. https://doi.org/10.2196/ jmir.1479. Wiers, R.W., Boffo, M., Field, M., 2018. What’s in a trial? On the importance of distinguishing between experimental lab studies and randomized controlled trials: the case of cognitive bias modification and alcohol use disorders. J. Stud. Alcohol Drugs 79 (3), 333e343. Wiers, R.W., Houben, K., Fadardi, J.S., van Beek, P., Rhemtulla, M., Cox, W.M., 2015. Alcohol cognitive bias modification training for problem drinkers over the web. Addict. Behav. 40, 21e26. https:// doi.org/10.1016/j.addbeh.2014.08.010.

Chapter 20

Goal-based interventions for executive dysfunction in addiction treatment Antonio Verdejo-Garcia School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia

Goal-based interventions for cognitive deficits associated with addiction People with substance use disorders often have deficits in executive functions, including working memory, response inhibition, cognitive flexibility, and decision-making skills (Fernández-Serrano et al., 2010; Fernández-Serrano et al., 2011; Verdejo-García et al., 2018). Crucially, executive deficits are strong predictors of clinical outcomes in the context of addiction treatment. Reduced performance in tests of response inhibition/impulsivity and cognitive flexibility is associated with lower treatment retention (Stevens et al., 2015; Streeter et al., 2008; Turner et al., 2009) and poor performance in tests of working memory and decision-making linked to greater risk of drug relapse (Domínguez-Salas et al., 2016; Rubenis et al., 2018; Stevens et al., 2014; Verdejo-García et al., 2014). Goalbased interventions were originally designed to address self-regulation deficits in people with brain injuries that damaged executive functions, but spared more basic cognitive skills, such as language and memory (Hart and Evans, 2006; Levine et al., 2000). They utilize interactive training tasks, strategy learning, and real-life examples to enable participants to control prepotent responses and align behavior with goals (Hart and Evans, 2006; Levine et al., 2011). Although the executive and self-regulation deficits of people with substance use disorders are subtler than those found in the brain injury presentations described above (Caracuel et al., 2008), the rationale of goal-based interventions and the “hands-on” approach they utilize seem optimal for addiction treatment (Verdejo-García, 2016). On the one hand, they are designed to train the executive skills needed to achieve complex goals (e.g., abstinence, return to work). Standard addiction treatments are also based on these goals, but they may not explicitly train participants on how to achieve them. On the

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00020-4 Copyright © 2020 Elsevier Inc. All rights reserved.

other hand, goal-based interventions use interactive exercises and personal projects to practice, apply, and correct goal-directed actions and decisions, and hence they foster engagement, rehearsal in a controlled environment, and transfer to real-life goals. Supervised practice and feedback contributes to overcoming one of the main challenges of addiction treatment, which is the generalization of selfregulation skills to exert control over drug use in real-life scenarios. The best-validated goal-based intervention, which has been applied in people with substance use disorders, is called Goal Management Training (GMT) (Levine et al., 2007, 2011) (first applied in substance users by Alfonso et al., 2011). Originally designed for patients with brain injuries causing self-regulation deficits, GMT is a manualized group intervention that involves instruction and practice on response inhibition, mindfulness, goal setting, strategy application, and decision-making, as well as an overarching strategy to link these trainings (Levine et al., 2011). GMT practice is applied to real-life examples from participants, which facilitates transfer to tangible treatment goals such as drug abstinence (Verdejo-García, 2016). In addition to GMT, there are at least two other experimental interventions that utilize goal-directed approaches and have been applied in the context of addiction treatment. One is based on a combination of GMT and other programs for rehabilitation of executive functions and training on implementation of intentions (Gollwitzer and Sheeran, 2006; Prestwich et al., 2006), which was created “ad hoc” for Cognitive Remediation (CR) in people with substance use disorders (Marceau et al., 2017). The other one is an ecological intervention based on chess, i.e., it uses the game as a platform to train goal-related strategies, including response inhibition and planning (“stop and think”) and strategic decision-making (“analysis of short- and longterm consequences”) during the preparation of chess

277

278 Cognition and Addiction

movements (Gonçalves et al., 2014). Chess training was supplemented with a motivational intervention to facilitate treatment engagement. In the following sections, the rationale and the contents of these interventions are discussed, as well as their main findings and proposed mechanisms, with special emphasis on GMT and specific mentions to CR and chess when relevant.

Intervention approaches and mechanisms GMT uses therapist-guided instruction, practice, and strategy learning. It contains seven to nine modules that follow a progressive structure; that is, more basic abilities are trained first, and more complex abilities and strategies rest on initial trainings. The executive function exercises include training of response inhibition (“stop”), mindfulness (“focus on the goal”), working memory (“goal setting”), and decision-making (“alignment between goals and subsequent action selection”). These exercises/trained skills are progressively combined in the strategy “Stope State goaleCheck” that is applied to multitasking exercises and everyday projects. The strategy enables participants to overcome action slips (e.g., impulsive responses, habits), navigate complex decisions, and ultimately achieve longterm goals. The GMT program applied in the context of substance use disorders contains seven to eight modules of 90 min each, plus w3 h per week homework in the form of projects (Alfonso et al., 2011; Valls-Serrano et al., 2016). In modules 1e2, participants are trained on mindfulness and response inhibition skills. Response inhibition training is applied to control impulsive and habitual responses and mindfulness exercises to train present-mindedness and attentional control (or goal focus). In modules 3e4, participants are trained to use working memory (“the mental blackboard”) to maintain their goals “on line” and make them resistant to distraction and habits. Working memory and goal setting exercises are followed by association strategies that teach them how to link the “Stop” and mindfulness techniques with goal setting in the mental blackboard (“State goal”). In modules 5e8, participants are trained on decision-making skills, including managing competing goals, goal-based prioritization of action selection, and monitoring of decision-making outcomes (“Check”). They are also trained on the overarching strategy, i.e., “StopeState goaleCheck,” and instructed to apply this strategy in real-life activities and personal projects.

Evidence of the efficacy of the training Three studies have examined the effects of GMT on executive functions and self-regulation deficits in people

with substance use disorders (Alfonso et al., 2011; Casaletto et al., 2016; Valls-Serrano et al., 2016). Alfonso et al. (2011) applied the standard version of GMT (Levine et al., 2011) combined with mindfulness meditation (Kabat-Zinn, 2003) in alcohol and stimulant polysubstance users enrolled in community-based outpatient treatment. Participants chose to enroll in GMT as an adjunctive to treatment as usual (i.e., counseling and relapse prevention) or standard treatment alone. Although participants were not randomized, the two groups did not significantly differ on clinical characteristics. Seven GMT modules were applied across 14 weeks, together with 14 values-based mindfulness meditation sessions. The value-based mindfulness component was added to GMT, which already contains mindfulness practice, to facilitate the switch between prepotent responses (i.e., impulsive and habitual actions) and goal setting (via mindfulness training on goal-related values such as “willpower” and “endurance”) in the context of the “StopeStateeCheck” strategy. The training, compared to treatment as usual, was associated with significant improvements in working memory indicated by the Letter Number Sequencing test (Wechsler, 1997), response inhibition indicated by the Stroop test (Delis et al., 2001), and decision-making measured with the Iowa Gambling Task (Bechara et al., 2000). In a subsequent randomized trial, Valls-Serrano et al. (2016) applied a similar GMT plus mindfulness protocol in polysubstance users enrolled in residential treatment. Eight GMT modules and eight mindfulness sessions, applied weekly, were compared to treatment as usual (therapeutic community). Findings showed that GMT, compared to treatment as usual, was associated with significant improvements in working memory measured via Letter Number Sequencing, reflection impulsivity (a tendency to gather less information before making a decision) measured with the Information Sampling Task (Clark et al., 2006), and strategy application in a real-life planning task (the Multiple Errands Test) (Burgess et al., 2006). GMT was also associated with a significant reduction of subjective stress levels. Casaletto et al. (2016) tested an abbreviated version of GMT, i.e., a single session focused on learning of the strategy: “Stope StateeCheck” in a three-group randomized design comparing (1) GMT, (2) GMT plus metacognitive strategies (i.e., additional instruction on the link between GMT training and executive dysfunction), and (3) no-treatment control in a sample of polysubstance users with HIV. Findings showed that the two GMT interventions, compared with no-treatment, resulted in significant improvement on an Everyday Multitasking Test (Scott et al., 2011) (similar to the Multiple Errands Test used in Valls-Serrano et al. (2016)). There were no differences between the two versions of GMT. The analysis of moderators showed that the participants with poorer

Goal-based interventions for executive dysfunction in addiction treatment Chapter | 20

baseline executive functions, methamphetamine use (vs. other substance use) disorders, and depression were the ones who got more benefit from GMT. With regard to other goal-based interventions, two studies have applied goal-oriented cognitive rehabilitation in the context of substance use disorders (Gonçalves et al., 2014; Marceau et al., 2017). Marceau et al. (2017) applied a miscellaneous CR program incorporating aspects of GMT and other goal interventions among a mixed sample of primarily methamphetamine and alcohol users following residential treatment. They applied 12 2-h sessions across 4 weeks and compared the goal intervention with treatment as usual (therapeutic community). Findings showed that the CR program, compared to treatment as usual, was associated with significant improvements in response inhibition indicated by the Stroop inhibition index (Delis et al., 2001), as well as behavioral indices of executive functions and self-regulation, measured with well-validated questionnaires. The CR program did not achieve significant effects on working memory indicated by the working memory index of the Wechsler Adult Intelligence Scale (Wechsler, 2008) or flexibility indicated by the Stroop shifting index (Delis et al., 2001) and the Trail Making Test (Strauss et al., 2006). Moving beyond traditional approaches, Gonçalves et al. (2014) applied an innovative goal-oriented training via chess exercises to strengthen goal-based planning and action selection in cocaine-dependent inpatients following a 4-week inpatient treatment. This training consisted of 10 90 min therapist-assisted group sessions that instructed participants on chess rules (to train goal-directed behavior) and chess strategy (to train “stop and think” and decisionmaking strategies, i.e., adequate consideration of the consequences of different moves/decisions). In addition, they incorporated motivational enhancement techniques to pair chess exercises with real-life goals and strategies. The chess training approach is theoretically relevant to address real-life self-regulation deficits, as it has been proposed that goal-based training is more effective in the context of meaningful everyday activities (Diamond and Ling, 2015). The training was associated with improvement of working memory span, but no significant improvements were found in other executive tasks or an impulsivity questionnaire (i.e., the Barratt Impulsivity Scale (Patton et al., 1995)). In summary, preliminary studies of GMT in the context of addiction treatment have shown beneficial effects of the training on executive functions, including working memory, response inhibition/impulsivity, and strategy application in planning and multitasking tests. These benefits are theoretically consistent with the active ingredients of GMT, i.e., the “StopeStateeCheck” strategy (Levine et al., 2011), and similar to the cognitive gains observed in clinical trials using GMT in brain injury patients with selfregulation deficits (Novakovic-Agopian et al., 2011; Stubberud et al., 2013; Tornås et al., 2016). Stress

279

reduction and improvement of affective-based decisionmaking are consistent with the findings of mindfulness interventions in the context of addiction treatment (Garland et al., 2014), and GMT also teaches strategies to manage conflictive goals and stress during decision-making (Verdejo-García, 2016). The results from other goalrelated interventions support the notion that goal-based trainings can significantly improve executive functions, although their active ingredients and specific outcomes are still unclear, as different approaches have shown benefits on different executive components (i.e., CR on response inhibition vs. chess on working memory).

Discussion of the neurocognitive mechanisms in light of evidence GMT is the best-examined goal-based intervention both in the general context of executive function/self-regulation deficits and in the specific context of substance use disorders (Stamenova and Levine, 2018; Verdejo-García, 2016). The overarching “StopeState goaleCheck” strategy trained by GMT improves response inhibition and attentional control (i.e., goal focus) and promotes a more cautious approach to action selection (Levine et al., 2011). Through consolidation of this strategy, GMT places specific emphasis on goal-based strategies that can override automatic responses including reward-driven impulsive behaviors and automatic habits (Verdejo-García et al., 2019). The strategy can also strengthen representations of action-outcome relationships during decision-making (Verdejo-García, 2016). In addition to specific mechanisms, Hart and Evans (2006) defined more general advantages of structured goal-based interventions, including fostering of self-direction, energization, (goal-related) persistence, and knowledge of novel strategies via rehearsal and practice. These aspects are particularly relevant for people with substance use disorders, who often have problems with directing attention to goals, apathy, and lack of perseverance symptoms and rigid sets of habits and routines (Caracuel et al., 2008; VerdejoGarcía et al., 2007). Also noteworthy, GMT uses the basic principles of associative learning to train therapeutic strategies, which is one of the cognitive abilities that is preserved and even enhanced in people with substance use disorders (Leland et al., 2008), making them more suitable to benefit from it. CR for substance users and chess-based training are less structured interventions, still in need of further evidence, but they have shown relatively consistent findings in terms of improvement of executive functions (although the executive gains vary across the two interventions) and behavioral self-regulation. They also offer interesting additions to standard goal-based interventions. The chess intervention is an example of how executive functions can

280 Cognition and Addiction

be trained within the context of real-life meaningful activities, provided that they have executive demands, such as ambiguity and complexity. The miscellaneous CR program applied by Marceau et al. (2017) combines classic goal-based approaches based on neuropsychological principles with implementation of intentions, which is a goal-oriented intervention stemming from a different tradition of cognitive psychology. Implementation of intentions acts through formation of intentions (detailed plans about when and how to act) and associations between opportunities and planned responses, in the form of an “if, then” structure (Forcano, Mata, de la Torre and VerdejoGarcía, 2019; Webb et al., 2010). It adds new aspects to the active ingredients of GMT, such as the strengthening of future-based representations (intentions) and the “if, then” strategy, similar to a selective cueing for goals, also used in earlier versions of GMT (Levine et al., 2000). The neurobiological mechanisms that underpin goalbased intervention effects in substance use disorders remain untested. In patients with brain injuries that have overlapping neurobiology with substance use disorders (frontal-striatal deficits), Chen et al. (2011) examined the impact of GMT on brain activity in a goal-based selective attention task. Findings showed that GMT, compared to control, enhanced goal-oriented neural activation in the dorsolateral prefrontal cortex (DLPFC) and the extrastriate cortex during this task. Importantly, individual differences in DLPFC activation were associated with treatment response. Most of the studies that have applied goal-based interventions in substance users have also incorporated aspects of mindfulness and motivational interventions (Alfonso et al., 2011; Gonçalves et al., 2014; Valls-Serrano et al., 2016). Hypothetically, mindfulness and motivational trainings aim to improve the integration of goal representations, coded in the DLPFC, with feedback representations, coded in the medial prefrontal cortex, the anterior cingulate cortex, and the insula (Verdejo-Garcia et al., 2015). That is, mindfulness and motivational strategies would speculatively boost GMT effects on cortical networks relevant to attentional control and decisionmaking (McConnell and Froeliger, 2015).

Recommendations for researchers and clinicians interested in using goal-based interventions Goal-oriented trainings have been successfully tested in “proof-of-concept” studies and in randomized observational studies with small samples, but they are in need of randomized controlled studies. Future studies would benefit from more rigorous clinical trial designs that are adequately powered and aligned with the current SPIRIT recommendations (Chan et al., 2013). Ideally, these trials should incorporate mechanistic accounts and determine if the trainings have significant effects on alcohol and drug use

outcomes, and if these clinical outcomes are mediated by the proposed neurocognitive and neurobiological mechanisms. Neurocognitive assessment tools and relevant biomarkers such as functional neuroimaging should assist in this line of research. Another common limitation in all studies is that goal-based trainings are typically tested within the context of treatment as usual, and thus it is not clear if the observed effects are because of the training itself or to synergistic effects of the standard treatment and the training. Three-group designs including training, sham control, and treatment as usual are required to upgrade the quality of cognitive training research in substance use disorders (Wykes and Spaulding, 2011). In addition, goalbased intervention studies are in need of more thorough investigation of mediation and moderation mechanisms, similar to what has been done in other cognitive trainings (Eberl et al., 2013; Gladwin et al., 2015; Houben et al., 2012). More research is needed to determine if improvement in goal-based self-regulation is the mechanism relevant to GMT and if such mechanism is relevant to prevent relapse. More research is also needed to determine the differential contribution of goal management “cognitive” exercises versus mindfulness meditation “affective” exercises into GMT therapeutic and neurobiological pathways (Garland et al., 2014; McConnell and Froeliger, 2015). Therefore, to circumvent the limitations of the existing literature, future studies could benefit from applying the following recommendations: (1) align with current SPIRIT guidelines for the design of randomized controlled trials; (2) apply goal-based interventions with specific active ingredients (i.e., mechanisms) and analogous “sham” conditions that control for nonspecific effects of the cognitive training, along with treatment as usual as a third arm; (3) apply treatment fidelity strategies to ensure standardized delivery of goal-based trainings; (4) conduct intention to treat analyses of the targeted cognitive outcomes plus meaningful clinical outcomes, such as alcohol and/or drug use during and after the intervention, craving, and quality of life; and (5) allow power to conduct mediation and moderation analyses to establish the pathways of therapeutic effects. Goal-based interventions seem to be theoretically relevant and practically useful for populations with substance use disorders, but clinically oriented research is needed to determine its efficacy and efficiency in the context of addiction treatment.

References Alfonso, J.P., Caracuel, A., Delgado-Pastor, L.C., Verdejo-García, A., 2011. Combined goal management training and mindfulness meditation improve executive functions and decision-making performance in abstinent polysubstance abusers. Drug Alcohol Depend. 117 (1), 78e81. Bechara, A., Tranel, D., Damasio, H., 2000. Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain 123 (11), 2189e2202.

Goal-based interventions for executive dysfunction in addiction treatment Chapter | 20

Burgess, P.W., Alderman, N., Forbes, C., Costello, A., Coates, L.M.-A., Dawson, D.R., et al., 2006. The case for the development and use of “ecologically valid” measures of executive function in experimental and clinical neuropsychology. J. Int. Neuropsychol. Soc. 12 (2), 194e209. Caracuel, A., Verdejo-García, A., Vilar-Lopez, R., Perez-Garcia, M., Salinas, I., Cuberos, G., et al., 2008. Frontal behavioral and emotional symptoms in Spanish individuals with acquired brain injury and substance use disorders. Arch. Clin. Neuropsychol. 23 (4), 447e454. Casaletto, K.B., Moore, D.J., Woods, S.P., Umlauf, A., Scott, J., Heaton, R.K., 2016. Abbreviated goal management training shows preliminary evidence as a neurorehabilitation tool for HIV-associated neurocognitive disorders among substance users. Clin. Neuropsychol. 30 (1), 107e130. Chan, A.-W., Tetzlaff, J.M., Altman, D.G., Dickersin, K., Moher, D., 2013. SPIRIT 2013: new guidance for content of clinical trial protocols. Lancet 381 (9861), 91e92. Chen, A.J.-W., Novakovic-Agopian, T., Nycum, T.J., Song, S., Turner, G.R., Hills, N.K., et al., 2011. Training of goal-directed attention regulation enhances control over neural processing for individuals with brain injury. Brain 134 (5), 1541e1554. Clark, L., Robbins, T.W., Ersche, K.D., Sahakian, B.J., 2006. Reflection impulsivity in current and former substance users. Biol. Psychiatry 60 (5), 515e522. Delis, D.C., Kaplan, E., Kramer, J.H., 2001. Delis-Kaplan Executive Function SystemÒ(D-KEFSÒ): Examiner’s Manual: Flexibility of Thinking, Concept Formation, Problem Solving, Planning, Creativity, Impluse Control, Inhibition. Pearson. Diamond, A., Ling, D.S., 2015. Conclusions about interventions, programs, and approaches for improving executive functions that appear justified and those that, despite much hype, do not. Dev. Cogn. Neurosci. https://doi.org/10.1016/j.dcn.2015.11.005. Domínguez-Salas, S., Díaz-Batanero, C., Lozano-Rojas, O.M., VerdejoGarcía, A., 2016. Impact of general cognition and executive function deficits on addiction treatment outcomes: systematic review and discussion of neurocognitive pathways. Neurosci. Biobehav. Rev. 71, 772e801. Eberl, C., Wiers, R.W., Pawelczack, S., Rinck, M., Becker, E.S., Lindenmeyer, J., 2013. Approach bias modification in alcohol dependence: do clinical effects replicate and for whom does it work best? Dev. Cogn. Neurosci. 4, 38e51. Fernández-Serrano, M.J., Pérez-García, M., Perales, J.C., VerdejoGarcía, A., 2010. Prevalence of executive dysfunction in cocaine, heroin and alcohol users enrolled in therapeutic communities. Eur. J. Pharmacol. 626 (1), 104e112. Fernández-Serrano, M.J., Pérez-García, M., Verdejo-García, A., 2011. What are the specific vs. generalized effects of drugs of abuse on neuropsychological performance? Neurosci. Biobehav. Rev. 35 (3), 377e406. Forcano, L., Mata, F., de la Torre, R., Verdejo-García, A., 2018. Cognitive and neuromodulation strategies for unhealthy eating and obesity: systematic review and discussion of neurocognitive mechanisms. Neurosci. Biobehav. Rev. 87, 161e191. https://doi.org/10.1016/ j.neubiorev.2018.02.003. Garland, E., Froeliger, B., Howard, M., 2014. Mindfulness training targets neurocognitive mechanisms of addiction at the attention-appraisalemotion interface. Front. Psychiatry 4, 173.

281

Gladwin, T.E., Rinck, M., Eberl, C., Becker, E.S., Lindenmeyer, J., Wiers, R.W., 2015. Mediation of cognitive bias modification for alcohol addiction via stimulus-specific alcohol avoidance association. Alcohol Clin. Exp. Res. 39 (1), 101e107. Gollwitzer, P.M., Sheeran, P., 2006. Implementation intentions and goal achievement: a meta-analysis of effects and processes. Adv. Exp. Soc. Psychol. 38, 69e119. Gonçalves, P.D., Ometto, M., Bechara, A., Malbergier, A., Amaral, R., Nicastri, S., et al., 2014. Motivational interviewing combined with chess accelerates improvement in executive functions in cocaine dependent patients: a one-month prospective study. Drug Alcohol Depend. 141, 79e84. Hart, T., Evans, J., 2006. Self-regulation and goal theories in brain injury rehabilitation. J. Head Trauma Rehabil. 21 (2), 142e155. Houben, K., Havermans, R.C., Nederkoorn, C., Jansen, A., 2012. Beer à No-Go: learning to stop responding to alcohol cues reduces alcohol intake via reduced affective associations rather than increased response inhibition. Addiction 107 (7), 1280e1287. Kabat-Zinn, J., 2003. Mindfulness-based interventions in context: past, present, and future. Clin. Psychol. Sci. Pract. 10 (2), 144e156. Leland, D.S., Arce, E., Miller, D.A., Paulus, M.P., 2008. Anterior cingulate cortex and benefit of predictive cueing on response inhibition in stimulant dependent individuals. Biol. Psychiatry 63 (2), 184e190. Levine, B., Robertson, I.H., Clare, L., Carter, G., Hong, J., Wilson, B.A., et al., 2000. Rehabilitation of executive functioning: an experimentaleclinical validation of goal management training. J. Int. Neuropsychol. Soc. 6 (3), 299e312. Levine, B., Schweizer, T.A., O’Connor, C., Turner, G., Gillingham, S., Stuss, D.T., et al., 2011. Rehabilitation of executive functioning in patients with frontal lobe brain damage with goal management training. Front. Hum. Neurosci. 5. Levine, B., Stuss, D.T., Winocur, G., Binns, M.A., Fahy, L., Mandic, M., et al., 2007. Cognitive rehabilitation in the elderly: effects on strategic behavior in relation to goal management. J. Int. Neuropsychol. Soc. 13 (1), 143e152. Marceau, E.M., Berry, J., Lunn, J., Kelly, P.J., Solowij, N., 2017. Cognitive remediation improves executive functions, self-regulation and quality of life in residents of a substance use disorder therapeutic community. Drug Alcohol Depend. 178, 150e158. McConnell, P.A., Froeliger, B., 2015. Mindfulness, mechanisms and meaning: perspectives from the cognitive neuroscience of addiction. Psychol. Inq. 26 (4), 349e357. Novakovic-Agopian, T., Chen, A.J.-W., Rome, S., Abrams, G., Castelli, H., Rossi, A., et al., 2011. Rehabilitation of executive functioning with training in attention regulation applied to individually defined goals: a pilot study bridging theory, assessment, and treatment. J. Head Trauma Rehabil. 26 (5), 325e338. Patton, J.H., Stanford, M.S., Barratt, E.S., 1995. Factor structure of the Barratt impulsiveness scale. J. Clin. Psychol. 51 (6), 768e774. Prestwich, A., Conner, M., Lawton, R.J., 2006. Implementation intentions: can they be used to prevent and treat addiction. In: Wiers, R.W., Stacy, A.W. (Eds.), Handbook of Implicit Cognition and Addiction. Sage Publications, pp. 455e469. Rubenis, A.J., Fitzpatrick, R.E., Lubman, D.I., Verdejo-García, A., 2018. Impulsivity predicts poorer improvement in quality of life during early treatment for people with methamphetamine dependence. Addiction 113 (4), 668e676.

282 Cognition and Addiction

Scott, J.C., Woods, S.P., Vigil, O., Heaton, R.K., Schweinsburg, B.C., Ellis, R.J., et al., 2011. A neuropsychological investigation of multitasking in HIV infection: implications for everyday functioning. Neuropsychology 25 (4), 511. Stamenova, V., Levine, B., 2018. Effectiveness of goal management trainingÒ in improving executive functions: a meta-analysis. Neuropsychol. Rehabil. 1e31. Stevens, L., Goudriaan, A., Verdejo-García, A., Dom, G., Roeyers, H., Vanderplasschen, W., 2015. Impulsive choice predicts short-term relapse in substance-dependent individuals attending an in-patient detoxification programme. Psychol. Med. 45 (10), 2083e2093. Stevens, L., Verdejo-García, A., Goudriaan, A.E., Roeyers, H., Dom, G., Vanderplasschen, W., 2014. Impulsivity as a vulnerability factor for poor addiction treatment outcomes: a review of neurocognitive findings among individuals with substance use disorders. J. Subst. Abus. Treat. 47 (1), 58e72. Strauss, E., Sherman, E.M., Spreen, O., 2006. A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary. American Chemical Society. Streeter, C.C., Terhune, D.B., Whitfield, T.H., Gruber, S., Sarid-Segal, O., Silveri, M.M., et al., 2008. Performance on the Stroop predicts treatment compliance in cocaine-dependent individuals. Neuropsychopharmacology 33 (4), 827. Stubberud, J., Langenbahn, D., Levine, B., Stanghelle, J., Schanke, A.-K., 2013. Goal management training of executive functions in patients with spina bifida: a randomized controlled trial. J. Int. Neuropsychol. Soc. 19 (6), 672e685. Tornås, S., Løvstad, M., Solbakk, A.-K., Evans, J., Endestad, T., Hol, P.K., et al., 2016. Rehabilitation of executive functions in patients with chronic acquired brain injury with goal management training, external cuing, and emotional regulation: a randomized controlled trial. J. Int. Neuropsychol. Soc. 22 (4), 436e452. Turner, T.H., LaRowe, S., Horner, M.D., Herron, J., Malcolm, R., 2009. Measures of cognitive functioning as predictors of treatment outcome for cocaine dependence. J. Subst. Abus. Treat. 37 (4), 328e334.

Valls-Serrano, C., Caracuel, A., Verdejo-García, A., 2016. Goal management training and mindfulness meditation improve executive functions and transfer to ecological tasks of daily life in polysubstance users enrolled in therapeutic community treatment. Drug Alcohol Depend. 165, 9e14. Verdejo-García, A., 2016. Cognitive training for substance use disorders: neuroscientific mechanisms. Neurosci. Biobehav. Rev. 68, 270e281. https://doi.org/10.1016/j.neubiorev.2016.05.018. Verdejo-García, A., Albein-Urios, N., Martinez-Gonzalez, J.M., Civit, E., De la Torre, R., Lozano, O., 2014. Decision-making impairment predicts 3-month hair-indexed cocaine relapse. Psychopharmacology 231 (21), 4179e4187. Verdejo-García, A., Alcázar-Córcoles, M.A., Albein-Urios, N., 2019. Neuropsychological Interventions for Decision-Making in Addiction: a Systematic Review. Neuropsychol Rev. 29 (1), 79e92. https:// doi.org/10.1007/s11065-018-9384-6. Verdejo-García, A., Bechara, A., Recknor, E.C., Pérez-García, M., 2007. Negative emotion-driven impulsivity predicts substance dependence problems. Drug Alcohol Depend. 91 (2), 213e219. Verdejo-García, A., Chong, T.T.-J., Stout, J.C., Yücel, M., London, E.D., 2018. Stages of dysfunctional decision-making in addiction. Pharmacol. Biochem. Behav. 164, 99e105. Verdejo-Garcia, A., Clark, L., Verdejo-Román, J., Albein-Urios, N., Martinez-Gonzalez, J.M., Gutierrez, B., Soriano-Mas, C., 2015. Neural substrates of cognitive flexibility in cocaine and gambling addictions. Br. J. Psychiatry 207 (2), 158e164. Webb, T.L., Sniehotta, F.F., Michie, S., 2010. Using theories of behaviour change to inform interventions for addictive behaviours. Addiction 105 (11), 1879e1892. Wechsler, D., 1997. Wechsler Memory Scale (WMS-III), vol. 14. Psychological corporation San Antonio, TX. Wechsler, D., 2008. Wechsler Adult Intelligence ScaleeFourth Edition (WAISeIV). Psychological Corporation, San Antonio, Texas. Wykes, T., Spaulding, W.D., 2011. Thinking about the future cognitive remediation therapydwhat works and could we do better? Schizophr. Bull. 37 (Suppl. l_2), S80eS90.

Chapter 21

Neurocognitive mechanisms of mindfulness-based interventions for addiction Eric L. Garland1, M. Aryana Bryan1, Adam W. Hanley1 and Matthew O. Howard2 1

Center on Mindfulness and Integrative Health Intervention Development, University of Utah, Salt Lake City, UT, United States; 2University of North

Carolina at Chapel Hill, NC, United States

Introduction In recent decades, the cross-fertilization of cognitive science and neuroscience has deepened insights into mechanisms underlying addiction. Prevailing models depict addiction as a disease process of reward dysregulation caused by exaggerated incentive salience and habit formation coupled with natural reward deficits and excessive stressdmaladaptive processes exacerbated by impairments in executive function (Koob and Volkow, 2016). These processes are thought to result in the canonical, core behavioral feature of addiction: a pattern of compulsive, maladaptive drug seeking and use despite negative consequences (Robinson and Berridge, 1993). Although neurobiological research elucidated new treatment targets for pharmacotherapeutic interventions, the development of novel behavioral treatments has lagged behind these discoveries. Contemporary behavioral treatments are mostly limited to psychotherapeutic methods developed decades before current neuroscientific models of addiction, such as motivational interviewing, cognitive behavioral therapy, and dialectical behavioral therapy. While breakthroughs at the time of their inception, the efficacy of these first- and second-wave behavioral approaches in treating many facets of addictive disorders is limited to modest effect sizes overall (Lundahl et al., 2010; Magill and Ray, 2009), and many patients remain nonresponsive to treatmentdparticularly those with neurocognitive deficits (Stevens et al., 2014). Yet, a third wave of behavioral interventions may hold promise for specifically targeting dysregulated neurocognitive processes underlying addiction. Mindfulness meditation is one promising third-wave treatment. In the past two decades, there has been a

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00021-6 Copyright © 2020 Elsevier Inc. All rights reserved.

revival of mindfulness meditation in treating stress-related conditions, including addiction, concurrent with advances in neuroscience. Early mindfulness-based interventions (MBIs) such as Mindfulness-Based Stress Reduction (MBSR) (Kabat-Zinn, 1990) and Mindfulness-Based Cognitive Therapy (MBCT) (Segal et al., 2002) have demonstrated efficacy in treating myriad psychological disorders (Goldberg et al., 2018). More recently, MBIs have been developed as extensions of these earlier programs, adapted to specifically target biobehavioral mechanisms underlying addictions. These treatments include Mindfulness-Based Relapse Prevention (MBRP) (Bowen et al., 2010) and Mindfulness-Oriented Recovery Enhancement (MORE) (Garland, 2013). MBRP and MORE are similar in their shared 8-week group structure, modeled after first-generation MBIs. Each group treatment session includes clinician-guided mindfulness exercises, such as body scans and mindful breathing, followed by a debriefing group process and psychoeducational information. However, MORE and MBRP differ significantly with respect to (1) the techniques and psychoeducational topics addressed and (2) the way in which mindfulness practices are debriefed and processed. First, while both interventions target addictive behaviors, craving, and automaticity with mindfulness, MORE specifically leverages mindfulness training to foster negative emotion regulation via cognitive reappraisal and amplify natural reward processing via savoring. Thus, MORE is an integrative treatment, integrating traditional mindfulness practices with techniques drawn from cognitive behavioral therapy and positive psychology. Comparatively, MBRP is more exclusively oriented around traditional mindfulness skill training and does not teach participants to engage in reappraisal or savoring techniques.

283

284 Cognition and Addiction

Second, MORE uses a structured, directive form of processing in which therapists are explicitly taught to elicit phenomenological descriptions of practice experiences, positively reinforce therapeutic practice outcomes while reframing practice challenges, and explore how in-session practice experiences could be translated into daily life. In that regard, MORE draws upon behavioral change theory principles of selective reinforcement and successive approximation to shape participant responses to maximize therapeutic experiences and reduce addictive behavior. Comparatively, MBRP engages participants through a more nondirective, Rogerian, client-centered approach to processing mindfulness experiences that strongly emphasizes qualities of acceptance and nonjudgment. A wealth of controlled research studies have supported these MBIs as having significant clinical benefits to individuals struggling with use of reinforcing substances such as alcohol, cocaine, nicotine, and opioids (Li et al., 2017). This chapter operationalizes mindfulness in the treatment process as a means of training neurocognitive processes and then details evidence for neurocognitive mechanisms underlying the effectiveness of MBIs.

Mindfulness as a means of targeting mechanisms of addiction Mindfulness as a construct is derived from ancient IndoSino-Tibetan contemplative practices and philosophies focused on liberation from suffering via training the mind to gain insight into the nature of reality. Contemporary scientific literature, however, has examined and operationalized mindfulness as a discrete state, trait, and practice. As behavioral treatments, MBIs train practitioners to cultivate a metacognitive state of awareness known as state mindfulness. This state is characterized by present moment attention and nonreactive, nonjudgmental observation of thoughts, feelings, physical sensations, and perceptions (Kabat-Zinn, 2003). The practice of mindfulness is comprised of two core elements: focused attention and open monitoring (Lutz et al., 2008; Vago and Silbersweig, 2012). Focused attention involves concentration on a sensory object while gently acknowledging and then disengaging from emotional or cognitive elaboration. The focus of attention may be on interoceptive and proprioceptive body sensations such as the sensation of one’s breathing or contact of one’s legs with a chair; however, external visual foci can also be used. Typically, focused attention transitions into the practice of open monitoring. This practice continues to include observation and disengagement from emerging thoughts and feelings while also reflexively turning attention back on itself to attend to the field of awareness in which mental contents arise (Lutz et al., 2008). From a

cognitive neuroscience perspective, this field might be considered the entirety of the adaptive global workspace of consciousness (Raffone and Srinivasan, 2009). Open monitoring is a metacognitive state of awareness in the sense that it involves observing the content of conscious experience while simultaneously appraising the process or quality of consciousness itself. Such mindfulness practices are thought to reduce cognitive, emotional, and behavioral reactivity through revealing the relative impermanence and insubstantiality of any particular thought or feeling (Hanh, 2002). Neuroimaging studies have found that mindfulness practice is associated with enhanced activity and connectivity among the prefrontal cortex (PFC) and anterior cingulate cortex (ACC) with the striatum (Tang et al., 2015). Importantly, these circuits, which become dysregulated in addiction, modulate attention, automaticity, and reward, suggesting that mindfulness practice drives enduring changes in how the brain encodes motivational salience, habit behavior, and self-control. Neurocognitive models have linked mindfulness practice to persistent changes in neural networks involved in executive monitoring of working memory, response inhibition, and emotion regulation (Vago and Silbersweig, 2012). These models propose that mindfulness practice involves a cyclic series of mental operations in which one (1) orients and sustains attention on an object, (2) monitors working memory for instances of mind wandering, (3) engages inhibitory control when habitual emotional associations and behavioral impulses arise, and (4) reorients attention back to the initial meditative object. These and other key neural alterations are discussed below. Importantly, structural and functional neural changes following mindfulness practice appear to be lasting (Tang, 2015). Durable neuroplastic changes induced via mindfulness practice may support dispositional or trait mindfulnessdthe proclivity to exhibit mindful attitudes and behavioral tendencies in everyday life and outside the context of meditation sessions (Baer and Krietemeyer, 2006). Data indicate that the development of trait mindfulness is a linear function of the trajectory of state mindfulness induced over repeated mindfulness practice sessions (Kiken et al., 2015). The accrual of state mindfulness into dispositional mindfulness is of note, as increases in sustained dispositional mindfulness appear to mediate many of the therapeutic outcomes of MBIs in clinical settings (Gu et al., 2015). Importantly, core attributes of dispositional or trait mindfulness are the ability to remain nonreactive to and observant of interoceptive and exteroceptive stimuli, with nonjudgmental acceptance and self-awareness of automatic thoughts and behavioral tendencies (Baer et al., 2006). These capacities are associated with increased cognitive emotion regulation (Anicha et al., 2012; Hanley and Garland, 2014). Dispositional mindfulness is antithetical to

Neurocognitive mechanisms of mindfulness-based interventions for addiction Chapter | 21

impulsivity and compulsivity, traits that characterize and drive addictive behaviors; thus, it is logical that developing trait mindfulness could mitigate compulsive drug-seeking and drug-taking behaviors. In fact, addiction could be described as mindlessness (Langer, 1992) in that addictive behaviors are facilitated by an “autopilot”-like drive, compromising the inhibitory power that would typically be exerted by conscious volition in the context of negative consequences that await the individual. The acquisition of dispositional mindfulness may buffer against the compulsive, automatized behaviors hallmarking addiction. In that regard, trait mindfulness is significantly inversely correlated with substance use (Karyadi et al., 2014), substance misuse (Priddy et al., 2018), and craving (Garland et al., 2014). Additionally, trait mindfulness practice is positively associated with the ability to attentionally disengage and recover autonomic functioning after exposure to substance-related cues (Baker and Garland, 2018; Garland, 2011; Garland et al., 2011). Given that attentional and autonomic cue reactivity predicts relapse to substance use (Garland et al., 2012; Sinha et al., 2003), dispositional mindfulness may serve an important protective effect in individuals with substance use disorders. While drug seeking does frequently require novel and flexible behaviors in the seeking, acquisition, and use of drugs (Singer et al., 2017), automaticity plays a large role in appetitive and consummatory actions present in substance use disorders (Tiffany, 1990). Thus, mindfulness of one’s automatized behavioral and emotional reactions may allow for greater self-regulation of habitual addictive behavior. From this perspective, mindfulness practice may evoke the state of mindfulness, which accrues into a durable propensity to exhibit the trait of mindfulness in everyday life, thereby acting as a buffer against addictive behaviors.

Clinical format and efficacy of mindfulness-based interventions for addiction MBIs for addiction provide instruction in standard focused attention and open monitoring meditation practices found in other MBIs (e.g., MBSR, MBCT) in addition to training in tailored mindfulness techniques designed to target neurocognitive mechanisms underpinning craving, attentional bias, and addictive automaticity. MBIs for addiction also teach mindfulness skills to mitigate relapse risk factors, including maladaptive beliefs and negative affective states. These intervention models (e.g., MBRP, MORE) typically follow an 8e10-week group therapy format led by trained clinicians (Bowen et al., 2010; Garland, 2013). In each session, participants are led through formal mindfulness meditation practices, followed by structured debriefing and

285

psychoeducation. Additionally, participants are instructed to complete at-home therapeutic exercises of formal and informal mindfulness practices along with assignments to self-monitor symptoms associated with addiction, such as craving and negative affect. Metaanalyses support the efficacy of MBIs. Randomized control trials (RCTs) consistently demonstrate significant reductions in severity and frequency of substance misuse (d ¼ 0.33, 95% CI [0.88, 0.14]) and craving (d ¼ 0.68, 95% CI [1.11, 0.025]) following MBI (Li et al., 2017). Moreover, RCTs show that MBIs can significantly alleviate affective and physical symptoms frequently co-occurring with addiction, including stress, negative affect, and pain (Goyal et al., 2014). In a collective assessment of the effects of MBIs across a host of psychiatric disorders (including substance use disorders), MBIs were found to be superior to minimal treatment and nonspecific active control conditions, while showing comparable effects to other bona fide evidence-based treatments (Goldberg et al., 2018).

Neurocognitive mechanisms of mindfulness as a treatment for addiction Here we review findings concerning neurocognitive mechanisms through which MBIs support recovery from addiction. A neurocognitive model of mindfulnesscentered regulation of addictive behavior by Garland et al. (2014b) (see Fig. 21.1) hypothesizes that MBIs exert their effects by (1) strengthening functional connectivity within a top-down metacognitive control network integral to attention and inhibitory control and (2) strengthening functional connectivity between the metacognitive control network and bottom-up brain regions implicated in reward, habit behavior, and emotional experience. According to this model, MBIs are mental training programs designed to exercise and strengthen prefrontally mediated cognitive control networks that have become weak during the process of addiction. Remediation of functional and structural atrophy in brain circuits instantiating cognitive control allows for regulation of impulses originating in subcortical brain networks, providing the neural substrate through which mindfulness training can lead to deautomatization of addictive habits and restructuring of valuation processes to motivate adaptive, goaldirected behavior. We now discuss evidence derived from early-stage studies (e.g., proof of principle / stage II RCTs) demonstrating mindfulness’ efficacy in regulating these neurocognitive processes in both naïve/advanced meditators and healthy/subclinical/clinical populations.

286 Cognition and Addiction

FIGURE 21.1 Neurocognitive model of mindfulness-centered regulation of addictive behavior. This adaptation of Garland et al., (2014) model of mindfulness-centered regulation posits that mindfulness-based interventions ameliorate the craving, negative affective states, and automatic habit behaviors underpinning addiction by enhancing functional connectivity (1) within a “top-down” metacognitive brain network integral to attentional and inhibitory control (DLPFC, dACC, parietal cortex) and (2) between this metacognitive attentional/inhibitory control network and “bottom-up” brain regions implicated in reward, habit behavior, and emotional experience. Enhanced functional connectivity within and between these neural circuits may facilitate deautomatization of addictive habits and restructuring of valuation processes to motivate adaptive, goal-directed behavior. DLPFC, dorsolateral prefrontal cortex; dACC, dorsal anterior cingulate cortex; PCC, posterior cingulate cortex; DS, dorsal striatum; VS, ventral striatum; Thal, thalamus; Hipp, hippocampus; Amy, amygdala; OFC, orbitofrontal cortex; MFC, medial prefrontal cortex.

Effects of mindfulness on “top-down” mechanisms of cognitive control Attentional control Mindfulness meditation practices and MBIs that emphasize focused attention have been shown to improve various indices of attentional control. Intensive mindfulness meditation training produces durable increases in sustained attention capacity (Zanesco et al., 2018). Brief mindfulness practice also has positive effects on sustained attention (Wenk-Sormaz, 2005; Zeidan et al., 2010). Examination of attentional subsystems found that intensive mindfulness practice enhanced alerting and attentional orienting, as well as selective attention/conflict monitoring (Jha et al., 2007)dan attentional capacity that is stronger among regular mindfulness meditators than among meditationnaïve individuals (Hodgins and Adair, 2010). Similarly, mindfulness training attenuates the attentional blink, and concurrent EEG data suggest this phenomenon is mediated by flexible reallocation of attentional resources (Slagter et al., 2007). Neuroimaging studies have demonstrated that mindfulness training is associated with increased activity in various PFC subregions underlying attentional control (Chan and Woollacott, 2007; Tomasino and Fabbro, 2016). Furthermore, mindfulness training changes activation and connectivity in brain regions governing attention to

interoceptive sensations and integration of interoceptive and exteroceptive inputs (Farb et al., 2013). Taken together, these findings suggest that overall improvements in attentional control via mindfulness are mediated by enhancements in various subcomponents of attentional processing and changes in neural resource allocation. Mindfulness-based improvements in attentional control may result in decreases in addiction attentional bias (Field and Cox, 2008). In that regard, mindfulness training through MORE was associated with significant effects on both alcohol (Garland et al., 2010) and opioid attentional bias (Garland et al., 2017), the latter of which predicted decreases in opioid misuse by follow-up.

Regulation of automaticity Mindfulness also appears to facilitate deautomatization of habitual behaviors through practices of both open monitoring and focused attention. Reviews suggest that mindfulness-induced metaawareness of one’s internal and external experiences and improvements in cognitive flexibility lead to enhanced regulation of automatic habits (Kang et al., 2013; Lao et al., 2016). Deautomatization appears to occur through the process of decentering (i.e., shifting from immersion in internal eventsdsuch as thought, emotions, and physical

Neurocognitive mechanisms of mindfulness-based interventions for addiction Chapter | 21

sensationsdto a third-person perspective in which internal events are viewed from a psychological distance), which in turn creates opportunity for conscious responding in place of automatic reaction (Segal et al., 2002). This metacognitive insight promotes reallocation of attention from the automatic fixation to the intended stimuli (Teasdale, 1999). Such regulation of automaticity is evident in multiple studies demonstrating effects of mindfulness meditation on reducing automatic cognitive interference during the Stroop task (Fan et al., 2014; Moore and Malinowski, 2009; Wenk-Sormaz, 2005). Mindfulness training appears to promote cognitive flexibility on semantic tasks requiring switching from overlearned habitual associations (Wenk-Sormaz, 2005; Whitmarsh et al., 2013). Heeren et al. (2009) also assessed cognitive flexibility via the Verbal Fluency Task and found that mindfulness supported reduced rigidity in responses. Similarly, a study employing the Water Jug Task showed that mindfulness contributed to increased problem solving, which requires cognitive flexibility, allowing for interruptions in automatic cognition and responding (Greenberg et al., 2012). Mindfulness is also associated with enhanced behavioral flexibility, indicating a reduced reliance on scripted, habitual responses. Focused attention practice (i.e., mindful eating) among novices facilitated reversal learning, an indicator of behavioral flexibility (Janssen et al., 2018). Similarly, mindfulness training decreased classically conditioned behavior by delaying onset of first conditioned response and decreasing conditioned response frequency during an eyeblink conditioning task (Hanley and Garland, 2019). Among advanced meditators, open monitoring meditation improved metrics of behavioral flexibility on the flanker task (Tsai and Chou, 2016). Neuroimaging research complements these findings by demonstrating effects of mindfulness training on functional connectivity among frontoparietal regions integral to top-down control of bottom-up reactions (Taren et al., 2017).

Inhibitory control A growing body of evidence demonstrates that mindfulness training strengthens inhibitory control capacity, likely through overlapping mechanisms as those involved in modulating automaticity. MBIs reduce self-reported impulsivity and improve inhibitory control performance on the Go/No-Go task and two choice impulsivity paradigm (Soler et al., 2016). Similarly, intensive mindfulness meditation training improves response inhibition with consequent effects on self-regulatory capacity (Sahdra et al., 2011); these effects were partially maintained several years later (Sahdra et al., 2011). Such inhibitory control benefits do not necessarily require long-term training. Brief mindfulness meditation has been shown to restore inhibitory control capacity following self-control depletion

287

(Friese et al., 2012). These behavioral improvements have been paralleled by effects of mindfulness training on eventrelated potentials during Go/No-Go task performance among individuals with attention deficit/hyperactivity disorder (Schoenberg et al., 2014) and addictive behaviors (Andreu et al., 2018). In addition, in a sample of opioid users, 8 weeks of MORE improved inhibitory control during negative affective interference on an emotional Go/No-Go task (Garland et al., 2019). Similar effects of mindfulness have been observed using functional magnetic resonance imaging (fMRI). MBI participants exhibited reduced errors on a response inhibition task coupled with concurrent increases in dorsolateral PFC responses (Allen et al., 2012). MBIs have also been shown to increase functional connectivity in circuits mediating intentional inhibition (Sevinc et al., 2018). Participation in an MBI was associated with increased white matter integrity and functional connectivity in brain regions implicated in inhibitory control networks, specifically the ACC and striatum (Tang et al., 2012; Tang et al., 2010; Xue et al., 2011).

Effects of mindfulness on enhancing cognitive regulation of reward, negative emotion, and cue reactivity Amplifying reward and positive affect Mindfulness training can amplify positive affective experience in clinical and healthy populations (Garland et al., 2015). The positive emotion regulatory effects of mindfulness are explicated in the Mindfulness-to-Meaning Theory (MMT), which proposes that mindfulness training propels an upward spiral of positive cognitioneemotion interactions involving broadening of attention to positive information that informs subsequent positive reappraisals, resulting in positive emotions even in the face of adversity (Garland et al., 2015). Outside the context of addiction, the MMT is supported by longitudinal empirical research (Garland et al., 2017c). Attention to positive information may also be a key means by which mindfulness ameliorates addiction. The restructuring reward hypothesis (Garland, 2016; Garland et al., 2014b) proposes that mindfulness training may facilitate a shift in the relative salience of drug and natural rewards, thereby reducing addictive behavior. Specifically, this shift occurs through mindfulness techniques aimed at downregulating the heightened or sensitized valuation of drug-related reward while simultaneously upregulating the perceived value of natural rewardsdi.e., natural reinforcers and socially constructed rewards that were pleasurable and reinforcing before the development of addiction (Garland, 2016). Simply put, MBIs may increase the pleasure and meaning derived from natural rewards and thereby decrease wanting for drugs, restoring function in key hedonic

288 Cognition and Addiction

systems of the brain that have become dysregulated during addiction. MBIs may facilitate this restructuring of reward through savoring, a technique that involves sustained attention on the pleasurable sensory features of a naturally rewarding stimulus coupled with metacognitive awareness of pleasant somatic and affective responses to that stimulus. Oscillating between these exteroceptive and interoceptive forms of attention to positive information is thought to overcome the “hedonic treadmill effect” to magnify and sustain the pleasure derived from the rewarding object or event (Garland, 2016). A wealth of evidence supports the restructuring reward hypothesis. Practicing mindfulness has been shown to increase the hedonic experience of food consumption (Arch et al., 2016; Hong et al., 2014; Hong et al., 2011), and participation in MBCT facilitated increases in subjective reward valuation of daily living activities (Geschwind et al., 2011). An RCT of MORE as a treatment for prescription opioid misuse demonstrated significant increases in autonomic and electrocortical responses to natural rewards that were associated with decreases in opioid craving (Garland et al., 2014a, 2014c). In direct support of the restructuring reward hypothesis, MORE increased autonomic responses to naturally rewarding stimuli relative to opioid-related stimuli, and increases in this measure of relative cardiac responsiveness predicted reduced opioid misuse at 3-month follow-up (Garland et al., 2017d). Ecological momentary assessment data collected during this trial bolstered these autonomic results by demonstrating that participation in MORE led to increases in momentary positive affect that predicted decreases in opioid misuse (Garland et al., 2017b). Finally, in a small sample of nicotine-dependent smokers participating in a pilot fMRI study of MORE, increased activation in brain regions encoding reward (ventral striatum and rostral anterior cingulate cortex [rACC]) was observed during savoring practice. These neural reward-based activations significantly predicted reductions in cigarette smoking over the course of treatment (Froeliger et al., 2017). Positive-affect-enhancing effects of MBIs have been demonstrated across multiple studies in myriad contexts. The generalizability of the effects of mindfulness on positive affect makes such interventions valuable methods for treating the reward-related pathology and anhedonia undergirding addiction.

Dampening negative affect and stress In addition to enhancing reward processing and positive affect, MBIs appear to facilitate the regulation of stress and negative affect. Some candidate cognitive emotion regulatory mechanisms include acceptance (Lindsay and Creswell, 2017), attentional control (Malinowski, 2013), decentering (Bernstein et al., 2015), disruption of rumination (Gu et al., 2015), and reappraisal (Garland et al., 2015).

These mechanisms are likely interconnected and reciprocally energizing (cf., the MMT). Independently or interactively, these mechanisms may be implicated in the significant decreases in negative affect (Goyal et al., 2014) and stress (Li et al., 2017) observed in metaanalyses of MBIs. The effects of MBIs on negative affect and stress are evident in autonomic responses. In that regard, heart rate variability (HRV) has been widely used in mindfulness studies as a parasympathetically mediated indicator of selfregulation of stress and negative emotions (Holzman and Bridgett, 2017; Thayer and Lane, 2000). In that regard, participation in MBRP was associated with greater increases in HRV during stress exposure compared to treatment-as usual and control conditions for individuals diagnosed with substance use disorders (Carroll and Lustyk, 2017). Similarly, in a sample of nicotine-deprived smokers, mindfulness training was associated with significant increases in HRV during stress exposure (Paz et al., 2017). In a pilot RCT of MORE as a treatment for alcohol use disorder, participants demonstrated significantly greater HRV recovery from stress-primed alcohol cues following the MORE intervention (Garland et al., 2010). Finally, in a Stage 2 RCT of MORE treatment for prescription opioid misuse, participants receiving MORE exhibited significant increases in HRV during attention to negative emotional stimuli presented in dot probe (Garland et al., 2014a) and affective picture viewing tasks (Garland et al., 2017d). A growing body of neuroimaging studies further elucidates mechanisms underlying mindfulness practice in improving negative emotion regulation. In that regard, MBIs attenuate amygdala and insula activation in response to stressful stimuli (Kober et al., 2017; Taren et al., 2015). Similarly, mindfulness training increased functional connectivity and white matter changes in cognitive control networks (e.g., the mPFC-ACC circuit) that were associated with significant improvements in self-reported emotion regulation capacity (Tang et al., 2016). These findings are of particular importance as functional connectivity between these two regions has been hypothesized to drive symptoms of addiction and propensity to relapse (Droutman et al., 2015; Xie et al., 2011).

Regulating craving and cue reactivity Although MBIs appear to enhance pleasure derived from naturally rewarding experiences, pleasure or “liking” alone has been shown insufficient to drive addiction (Robinson and Berridge, 2008). Similarly, while anhedonia and negative affect support relapse to drug use, motivation to carry out drug-seeking behavior is necessary (Koob and Moal, 2008; Robinson and Berridge, 1993). Thus, to be efficacious in treating addiction, MBIs must disrupt negative affect and amplify liking responses to natural rewards and also attenuate the wanting of drug rewards, preventing

Neurocognitive mechanisms of mindfulness-based interventions for addiction Chapter | 21

them from supplanting natural and prosocial reinforcers. Craving, manifested by a subjective wanting for addictive substances and heightened incentive salience of substancerelated cues, is an important driver in escalating compulsive drug seeking and drug use, as well as relapse to substance use after periods of abstinence (Robinson and Berridge, 1993). As previously discussed, metaanalysis indicates that MBIs result in moderate effect size decreases in craving (Li et al., 2017). These reductions in craving might be explained by the effects of MBIs on attentional and physiological reactivity to substance cues. As previously discussed, MORE has been shown to significantly modify addiction attentional bias in samples of alcohol-dependent individuals (Garland et al., 2010) and opioid misusers (Garland et al., 2017). In the same vein, MORE significantly modulates autonomic markers of drug cue reactivity (Garland et al., 2014; Garland et al., 2017d). The effects of MBIs on attentional and autonomic indices of cue reactivity have been paralleled by changes in neural markers of incentive salience. In a lab-based brief mindfulness induction, mindful attention to nicotine cues decreased activity in the subgenual ACC that was associated with attenuated craving (Westbrook et al., 2011). Similarly, smokers viewing cigarette images during a cue reactivity task showed significant posttreatment reductions in ventral striatal activity following treatment with MORE (Froeliger et al., 2017). Collectively, these findings suggest that MBIs are capable of decreasing subjective, attentional, and physiological markers of drug-related wanting. Insofar as sensitized physiological and behavioral responding to drugrelated cues drives the cycle of addiction, the ability of MBIs to modulate and perhaps even reverse this phenomenon appears highly clinically significant. Returning to the restructuring reward hypothesis, because liking or pleasure is embedded within the larger neural network subserving wanting or motivation (Olney et al., 2018), it is possible that the effects of MBIs on enhancing responsivity to natural reward might attenuate the wanting or craving of drug reward governed by dysregulation in this broader reward system. Ultimately, the ability of MBIs to restore responding to natural rewards while dampening maladaptive appetitive motivations may have robust therapeutic value.

Hypothesized roles of core mindfulness elements in addiction treatment While accumulating evidence suggests MBIs are efficacious addiction treatment options that target a variety of therapeutic mechanisms, optimally tailoring MBIs for addiction treatment will require a better understanding of how the core mindfulness elements (focused attention and open monitoring) individually and synergistically exert therapeutic effects. Many mindfulness training programs

289

initially emphasize the focused attention element, designed to encourage “top-down” mechanisms of cognitive control, before introducing the more advanced practice of open monitoring. The rationale behind this graduated approach is that attentional stability developed through focused attention practice will promote the capacity to engage metacognitive awareness during open monitoring practice, which in turn will facilitate regulation of maladaptive behaviors and destructive emotions while promoting wellbeing. Furthermore, stabilizing subjective experience through focused attention is likely to allow one to gain insight into the transitory and nonveridical nature of experience (e.g., “thoughts and cravings are unsubstantial mental experiences that continually come and go, so I do not have to react to them”), an insight that can be more directly explored with open monitoring practice. Thus, the synergy of these two elements provide (1) the selfmonitoring skills to more quickly notice when attention is captured by a maladaptive object (e.g., craving) and to gain awareness of automaticity; (2) the cognitive capacity to shift attention from maladaptive objects onto an intended object (e.g., the breath) while inhibiting behavioral impulses to disrupt automatic appetitive habits; (3) the ability to expand awareness to include naturally rewarding aspects of the social and natural environment (e.g., the feel of the sun on the skin, spending time with a loved one); and (4) a means of sustaining attention on salutary objects and events to augment (i.e., savor) the healthy sense of pleasure and meaning that can be derived from them, thereby diminishing the need for substance use to obtain hedonic equilibrium. Over time, these elements of mindfulness may work in combination to reshape subjective experience and adaptively reorganize neural pathways integral to addiction recovery.

Future directions for mindfulness-based interventions and addiction Although a substantial body of evidence has accumulated supporting the efficacy of MBIs for addiction treatment (Black, 2014; Brewer et al., 2014; Garland, 2016; McConnell and Froeliger, 2015), Stage III and IV clinical trials with larger samples and longer follow-up periods are needed to determine the durability of therapeutic effects. In addition, there is a need for additional mechanistic studies among individuals with substance use disorders at various stages of recovery. Well-controlled, longitudinal functional, and structural neuroimaging (e.g., fMRI) studies can elucidate effects of MBIs on top-down cognitive regulation of bottom-up limbic and striatal responses integral to addictive behavior. Imaging techniques with fine-grained temporal resolution (e.g., EEG, MEG) may determine at which stage of neurocognitive processing MBIs exert their effects. For instance, MBIs might target initial attentional

290 Cognition and Addiction

orienting and automatic behavioral processes arising within the first few hundred milliseconds after the presentation of a substance-related cue. Conversely, MBIs might modify later cognitive and emotional elaboration occurring around 1000 ms or later after onset of a substance cue or potentially foster physiological recovery from cue exposure. Molecular neuroimaging (e.g., PET) studies may also provide important insights into the effects of MBIs on a synaptic level to explore the key neurotransmitters underlying mindfulness efficacy. In light of the apparent effects of MBIs on neurocognition, and given the important role of acetylcholine in modulating prefrontal cortical activity (Klinkenberg et al., 2011), investigation of this neurotransmitter has valuable exploratory potential. Furthermore, in view of the role of dopamine in attention, learning, and reward, MBIs might produce dopaminergic effects, and indeed, data from early meditation research suggest this may be the case (Kjaer et al., 2002). Finally, MBIs such as MORE enhance hedonic function, suggesting a role for endogenous opioids or endocannabinoids in mediating these effects. Cognitive and affective neuroscience can test these hypotheses and elucidate malleable mechanisms of mindfulness to inform future iterations of MBI treatment optimization. Addiction neuroscience suggests that escalation to compulsive drug use develops via usurpation of reward learning circuitry by increasing the salience of drug-related cues, driving the overlearned, maladaptive drug-seeking behaviors that characterize addiction (Hyman et al., 2006). Recent data show that despite multiple treatment episodes, approximately half of those with substance use disorders do not achieve sustained remission (Fleury et al., 2016). Yet, extant addiction treatments have often been delivered within an acute care model, resulting in remission of symptoms during the course of treatment, followed by successive relapses after a series of acute care episodes (McLellan, 2002). Given that the chronically relapsing condition of addiction involves a process of overlearning (Hyman et al., 2006), it is highly plausible that mindfulness itself must be overlearned to sustainably treat symptoms of this recalcitrant malady. Because most studies have focused on MBIs approximately 8 weeks in duration, it is unknown whether mindfulness is most effective when practiced regularly over one’s lifetime to support long-term recovery from addiction. Plausibly, long-term practice of MBIs may be required to produce enduring neural plasticity capable of counteracting neurocognitive deficits underlying addiction, but to answer this question doseeresponse studies are needed. Future studies could also examine to what extent mindful regulatory strategies must be activated consciously versus through a process of automatic self-regulation (c.f., Mauss et al., 2007). Altogether, contemporary models regard addiction as largely mediated by loss of cognitive control over automaticity and driven by dysregulation of hedonic brain circuitry. This form of behavioral escalation may be especially

tractable to therapeutic strategies like mindfulness that appear to enhance top-down cognitive regulation over bottom-up appetitive habits. To the extent that MBIs effectively target maladaptive neurocognitive processes underlying addiction, they hold promise as key elements in the armamentarium of addictions treatment.

Funding E.L.G. was supported by NIDA grant R01DA042033 (PI: Garland) and NCCIH grant R61AT009296 (PI: Garland) during the preparation of this manuscript.

References Allen, M., Dietz, M., Blair, K.S., van Beek, M., Rees, G., VestergaardPoulsen, P., et al., 2012. Cognitive-affective neural plasticity following active-controlled mindfulness intervention. J. Neurosci. 32 (44), 15601e15610. https://doi.org/10.1523/JNEUROSCI.295712.2012. Andreu, C.I., Cosmelli, D., Slagter, H.A., Franken, I.H.A., 2018. Effects of a brief mindfulness-meditation intervention on neural measures of response inhibition in cigarette smokers. PLoS One 13 (1), e0191661. https://doi.org/10.1371/journal.pone.0191661. Anicha, C.L., Ode, S., Moeller, S.K., Robinson, M.D., 2012. Toward a cognitive view of trait mindfulness: distinct cognitive skills predict its observing and nonreactivity Facets. J. Personal. 80 (2), 255e285. https://doi.org/10.1111/j.1467-6494.2011.00722.x. Arch, J.J., Brown, K.W., Goodman, R.J., Della Porta, M.D., Kiken, L.G., Tillman, S., 2016. Enjoying food without caloric cost: the impact of brief mindfulness on laboratory eating outcomes. Behav. Res. Ther. 79, 23e34. https://doi.org/10.1016/j.brat.2016.02.002. Baer, R.A., Krietemeyer, J., 2006. Overview of mindfulness- and acceptance-based treatment approaches. In: Mindfulness-Based Treatment Approaches: Clinician’s Guide to Evidence Base and Applications. Elsevier, Boston, MA, pp. 1e27. Baer, R.A., Smith, G.T., Hopkins, J., Krietemeyer, J., Toney, L., 2006. Using self-report assessment methods to explore facets of mindfulness. Assessment 13, 27e45. Bernstein, A., Hadash, Y., Lichtash, Y., Tanay, G., Shepherd, K., Fresco, D.M., 2015. Decentering and related constructs: a critical review and metacognitive processes model. Perspect. Psychol. Sci. 10 (5), 599e617. https://doi.org/10.1177/1745691615594577. Black, D.S., 2014. Mindfulness-based interventions: an antidote to suffering in the context of substance use, misuse, and addiction. Subst. Use Misuse 49 (5), 487e491. https://doi.org/10.3109/10826084.2014. 860749. Bowen, S., Chawla, N., Marlatt, G.A., 2010. Mindfulness-Based Relapse Prevention for Addictive Behaviors. Guilford Press, New York. Brewer, J., Judson, A., Elwafi, H., Davis, J., 2014. Craving to quit: psychological models and neurobiological mechanisms of mindfulness training as treatment for addictions. Transl. Issues Psychol. Sci. 1, 70e90. S. https://doi.org/10.1037/2332-2136.1.S.70. Carroll, H., Lustyk, M.K.B., 2017. Mindfulness-based relapse prevention for substance use disorders: effects on cardiac vagal control and craving under stress. Mindfulness 1e12. https://doi.org/10.1007/ s12671-017-0791-1. Chan, D., Woollacott, M., 2007. T. J. Altern. Complement. Med. 13, 651e657.

Neurocognitive mechanisms of mindfulness-based interventions for addiction Chapter | 21

Droutman, V., Read, S.J., Bechara, A., 2015. Revisiting the role of the insula in addiction. Trends Cognit. Sci. 19 (7), 414e420. https://doi. org/10.1016/j.tics.2015.05.005. Fan, Y., Tang, Y.-Y., Tang, R., Posner, M.I., 2014. Short term integrative meditation improves resting alpha activity and stroop performance. Appl. Psychophysiol. Biofeedback 39 (3e4), 213e217. https://doi. org/10.1007/s10484-014-9258-5. Farb, N.A., Segal, Z.V., Anderson, A.K., 2013. Mindfulness meditation training alters cortical representations of interoceptive attention. Soc. Cognit. Affect Neurosci. 8 (1), 15e26. Field, M., Cox, W.M., 2008. Attentional bias in addictive behaviors: a review of its development, causes, and consequences. Drug Alcohol Depend. 97, 1e20. Fleury, M.-J., Djouini, A., Huỳnh, C., Tremblay, J., Ferland, F., Ménard, J.M., Belleville, G., 2016. Remission from substance use disorders: a systematic review and meta-analysis. Drug Alcohol Depend. 168, 293e306. https://doi.org/10.1016/j.drugalcdep.2016.08.625. Friese, M., Messner, C., Schaffner, Y., 2012. Mindfulness meditation counteracts self-control depletion. Conscious. Cognit. 21 (2), 1016e1022. https://doi.org/10.1016/j.concog.2012.01.008. Froeliger, B., Mathew, A.R., Mcconnell, P.A., Eichberg, C., Saladin, M.E., Carpenter, M.J., Garland, E.L., 2017. Restructuring reward mechanisms in nicotine addiction: a pilot fMRI study of mindfulnessoriented recovery enhancement for cigarette smokers. Evid. Based Complement Altern. Med. 2017. https://doi.org/10.1155/2017/ 7018014. Garland, E.L., 2013. Mindfulness-Oriented Recovery Enhancement for Addiction, Stress, and Pain. NASW Press, Washington, D.C. Garland, E.L., Gaylord, S.A., Boettiger, C.A., Howard, M.O., 2010. Mindfulness training modifies cognitive, affective, and physiological mechanisms implicated in alcohol dependence: results of a randomized controlled pilot trial. J. Psychoact. Drugs 42 (2), 177e192. Garland, E.L., 2011. Trait mindfulness predicts attentional and autonomic regulation of alcohol cue-reactivity. J. Psychophysiol. 25 (4), 180e189. https://doi.org/10.1027/0269-8803/a000060. Garland, E.L., 2016. Restructuring reward processing with mindfulnessoriented recovery enhancement: novel therapeutic mechanisms to remediate hedonic dysregulation in addiction, stress, and pain. Ann. N. Y. Acad. Sci. 1373 (1), 25e37. Garland, E.L., Baker, A.K., Howard, M.O., 2017. Mindfulness-oriented recovery enhancement reduces opioid attentional bias among prescription opioid-treated chronic pain patients. J. Soc. Soc. Work. Res. 8 (4), 493e509. https://doi.org/10.1086/694324. Garland, E.L., Boettiger, C.A., Howard, M.O., 2011. Targeting cognitiveaffective risk mechanisms in stress-precipitated alcohol dependence: an integrated, biopsychosocial model of automaticity, allostasis, and addiction. Med. Hypotheses 76 (5), 745e754. Garland, E.L., Bryan, C.J., Finan, P.H., Thomas, E.A., Priddy, S.E., Riquino, M.R., Howard, M.O., 2017b. Pain, hedonic regulation, and opioid misuse: modulation of momentary experience by mindfulnessoriented recovery enhancement in opioid-treated chronic pain patients. Drug Alcohol Depend. 173, S65eS72. Garland, E.L., Bryan, M.A., Priddy, S.E., Riquino, M.R., Froeliger, B., Howard, M.O., 2019. Effects of mindfulness-oriented recovery enhancement versus social support on negative affective interference during inhibitory control among opioid-treated chronic pain patients: a pilot mechanistic study. Ann. Behav. Med.

291

Garland, E.L., Farb, N.A., Goldin, P.R., Fredrickson, B.L., 2015. Mindfulness broadens awareness and builds eudaimonic meaning: a process model of mindful positive emotion regulation. Psychol. Inq. 26 (4), 293e314. https://doi.org/10.1080/1047840X.2015.1064294. Garland, E.L., Franken, I.H., Howard, M.O., 2012. Cue-elicited heart rate variability and attentional bias predict alcohol relapse following treatment. Psychopharmacology 222 (1), 17e26. Garland, E.L., Froeliger, B., Howard, M.O., 2014a. Effects of mindfulness-oriented recovery enhancement on reward responsiveness and opioid cue-reactivity. Psychopharmacology 231 (16), 3229e3238. https://doi.org/10.1007/s00213-014-3504-7. Garland, E.L., Froeliger, B., Howard, M.O., 2014b. Mindfulness training targets neurocognitive mechanisms of addiction at the attentionappraisal-emotion interface. Front. Psychiatry 4. https://doi.org/10. 3389/fpsyt.2013.00173. Garland, E.L., Froeliger, B., Howard, M.O., 2014c. Neurophysiological evidence for remediation of reward processing deficits in chronic pain and opioid misuse following treatment with mindfulness-oriented recovery enhancement: exploratory ERP findings from a pilot RCT. J. Behav. Med. 38 (2), 327e336. https://doi.org/10.1007/s10865-0149607-0. Garland, E.L., Hanley, A.W., Goldin, P.R., Gross, J.J., 2017c. Testing the mindfulness-to-meaning theory: evidence for mindful positive emotion regulation from a reanalysis of longitudinal data. PLoS One 12 (12), e0187727. https://doi.org/10.1371/journal.pone.0187727. Garland, E.L., Howard, M.O., Zubieta, J.-K., Froeliger, B., 2017d. Restructuring hedonic dysregulation in chronic pain and prescription opioid misuse: effects of mindfulness-oriented recovery enhancement on responsiveness to drug cues and natural rewards. Psychother. Psychosom. 86 (2), 111e112. Garland, E.L., Roberts-Lewis, A., Kelley, K., Tronnier, C., Hanley, A., 2014. Cognitive and affective mechanisms linking trait mindfulness to craving among individuals in addiction recovery. Subst. Use Misuse 49 (5), 525e535. https://doi.org/10.3109/10826084.2014.850309. Geschwind, N., Peeters, F., Drukker, M., van Os, J., Wichers, M., 2011. Mindfulness training increases momentary positive emotions and reward experience in adults vulnerable to depression: a randomized controlled trial. J. Consult. Clin. Psychol. 79 (5), 618. Goldberg, S.B., Tucker, R.P., Greene, P.A., Davidson, R.J., Wampold, B.E., Kearney, D.J., Simpson, T.L., 2018. Mindfulnessbased interventions for psychiatric disorders: a systematic review and meta-analysis. Clin. Psychol. Rev. 59, 52e60. https://doi.org/10. 1016/j.cpr.2017.10.011. Goyal, M., Singh, S., Sibinga, E.M., Gould, N.F., Rowland-Seymour, A., Sharma, R., 2014. Meditation programs for psychological stress and well-being: a systematic review and meta-analysis. others JAMA Intern. Med. 174 (3), 357e368. Greenberg, J., Reiner, K., Meiran, N., 2012. “Mind the trap”: mindfulness practice reduces cognitive rigidity. PLoS One 7 (5), e36206. Gu, J., Strauss, C., Bond, R., Cavanagh, K., 2015. How do mindfulnessbased cognitive therapy and mindfulness-based stress reduction improve mental health and wellbeing? A systematic review and metaanalysis of mediation studies. Clin. Psychol. Rev. 37, 1e12. https:// doi.org/10.1016/j.cpr.2015.01.006. Hanh, T.N., 2002. Transformation and Healing: Sutra on the Four Establishments of Mindfulness, second ed. Parallax Press.

292 Cognition and Addiction

Hanley, A., Garland, E.L., 2019. Mindfulness training disrupts Pavlovian conditioning. Physiol. Behav. 204, 151e154. https://doi.org/10.1016/ j.physbeh.2019.02.028. Hanley, A.W., Garland, E.L., 2014. Dispositional mindfulness co-varies with self-reported positive reappraisal. Pers. Indiv. Differ. 66, 146e152. Heeren, A., Van Broeck, N., Philippot, P., 2009. The effects of mindfulness on executive processes and autobiographical memory specificity. Behav. Res. Ther. 47 (5), 403e409. Hodgins, H.S., Adair, K.C., 2010. Attentional processes and meditation. Conscious. Cognit. 19 (4), 872e878. https://doi.org/10.1016/j.concog. 2010.04.002. Holzman, J.B., Bridgett, D.J., 2017. Heart rate variability indices as biomarkers of top-down self-regulatory mechanisms: a meta-analytic review. Neurosci. Biobehav. Rev. 74, 233e255. Hong, P.Y., Lishner, D.A., Han, K.H., 2014. Mindfulness and eating: an experiment examining the effect of mindful raisin eating on the enjoyment of sampled food. Mindfulness 5 (1), 80e87. Hong, P.Y., Lishner, D.A., Han, K.H., Huss, E.A., 2011. The positive impact of mindful eating on expectations of food liking. Mindfulness 2 (2), 103e113. Hyman, S.E., Malenka, R.C., Nestler, E.J., 2006. Neural mechanisms of addiction: the role of reward-related learning and memory. Annu. Rev. Neurosci. 29 (1), 565e598. https://doi.org/10.1146/annurev.neuro.29. 051605.113009. Janssen, L.K., Duif, I., van Loon, I., de Vries, J.H.M., Speckens, A.E.M., Cools, R., Aarts, E., 2018. Greater mindful eating practice is associated with better reversal learning. Sci. Rep. 8 (1), 5702. https://doi.org/ 10.1038/s41598-018-24001-1. Jha, A.P., Krompinger, J., Baime, M.J., 2007. Mindfulness training modifies subsystems of attention. Cognit. Affect Behav. Neurosci. 7 (2), 109e119. https://doi.org/10.3758/CABN.7.2.109. Kabat-Zinn, J., 2003. Mindfulness-based interventions in context: past, present, and future. Clin. Psychol. Sci. Pract. 10, 144e156. Kabat-Zinn, J., 1990. Full Catastrophe Living. Delacorte Press, NY. Kang, Y., Gruber, J., Gray, J.R., 2013. Mindfulness and de-automatization. Emotion Rev. 5 (2), 192e201. https://doi.org/10.1177/ 1754073912451629. Karyadi, K.A., VanderVeen, J.D., Cyders, M.A., 2014. A meta-analysis of the relationship between trait mindfulness and substance use behaviors. Drug Alcohol Depend. 143 (Suppl. C), 1e10. https://doi.org/10. 1016/j.drugalcdep.2014.07.014. Kiken, L.G., Garland, E.L., Bluth, K., Palsson, O.S., Gaylord, S.A., 2015. From a state to a trait: trajectories of state mindfulness in meditation during intervention predict changes in trait mindfulness. Pers. Indiv. Differ. 81, 41e46. https://doi.org/10.1016/j.paid.2014.12.044. Kjaer, T.W., Bertelsen, C., Piccini, P., Brooks, D., Alving, J., Lou, H.C., 2002. Increased dopamine tone during meditation-induced change of consciousness. Cogn. Brain Res. 13 (2), 255e259. Klinkenberg, I., Sambeth, A., Blokland, A., 2011. Acetylcholine and attention. Behav. Brain Res. 221 (2), 430e442. Kober, H., Brewer, J.A., Height, K.L., Sinha, R., 2017. Neural stress reactivity relates to smoking outcomes and differentiates between mindfulness and cognitive-behavioral treatments. Neuroimage 1 (151(Suppl. C)), 4e13. Koob, G.F., Moal, M.L., 2008. Addiction and the brain antireward system. Annu. Rev. Psychol. 59 (1), 29e53. https://doi.org/10.1146/annurev. psych.59.103006.093548. Koob, G.F., Volkow, N.D., 2016. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiatry 3 (8), 760e773. https://doi.org/ 10.1016/S2215-0366(16)00104-8.

Langer, E.J., 1992. Matters of mind: mindfulness/mindlessness in perspective. Conscious. Cognit. 1 (3), 289e305. https://doi.org/10. 1016/1053-8100(92)90066-J. Lao, S.-A., Kissane, D., Meadows, G., 2016. Cognitive effects of MBSR/ MBCT: a systematic review of neuropsychological outcomes. Conscious. Cognit. 45, 109e123. https://doi.org/10.1016/j.concog. 2016.08.017. Li, W., Howard, M.O., Garland, E.L., McGovern, P., Lazar, M., 2017. Mindfulness treatment for substance misuse: a systematic review and meta-analysis. J. Subst. Abus. Treat. 75, 62e96. https://doi.org/10. 1016/j.jsat.2017.01.008. Lindsay, E.K., Creswell, J.D., 2017. Mechanisms of mindfulness training: monitor and acceptance theory (MAT). Clin. Psychol. Rev. 51, 48e59. Lundahl, B.W., Kunz, C., Brownell, C., Tollefson, D., Burke, B.L., 2010. A meta-analysis of motivational interviewing: twenty-five years of empirical studies. Res. Soc. Work. Pract. 20 (2), 137e160. https://doi. org/10.1177/1049731509347850. Lutz, A., Slagter, H.A., Dunne, J.D., Davidson, R.J., 2008. Attention regulation and monitoring in meditation. Trends Cognit. Sci. 12, 163e169. Magill, M., Ray, L.A., 2009. Cognitive-behavioral treatment with adult alcohol and illicit drug users: a meta-analysis of randomized controlled trials. J. Stud. Alcohol Drugs 70 (4), 516e527. https://doi. org/10.15288/jsad.2009.70.516. Malinowski, P., 2013. Neural mechanisms of attentional control in mindfulness meditation. Front. Neurosci. 7. https://doi.org/10.3389/ fnins.2013.00008. Mauss, I.B., Bunge, S.A., Gross, J.J., 2007. Automatic emotion regulation. Soc. Personal. Psychol. Compass 1 (1), 146e167. McConnell, P.A., Froeliger, B., 2015. Mindfulness, mechanisms and meaning: perspectives from the cognitive neuroscience of addiction. Psychol. Inq. 26 (4), 349e357. McLellan, A.T., 2002. Have we evaluated addiction treatment correctly? Implications from a chronic care perspective. Addiction 97 (3), 249e252. https://doi.org/10.1046/j.1360-0443.2002.00127.x. Moore, A., Malinowski, P., 2009. Meditation, mindfulness and cognitive flexibility. Conscious. Cognit. 18, 176e186. Olney, J.J., Warlow, S.M., Naffziger, E.E., Berridge, K.C., 2018. Current perspectives on incentive salience and applications to clinical disorders. Curr. Opin. Behav. Sci. 22, 59e69. https://doi.org/10.1016/j. cobeha.2018.01.007. Paz, R., Zvielli, A., Goldstein, P., Bernstein, A., 2017. Brief mindfulness training de-couples the anxiogenic effects of distress intolerance on reactivity to and recovery from stress among deprived smokers. Behav. Res. Ther. 95 (Suppl. C), 117e127. https://doi.org/10.1016/j. brat.2017.05.017. Priddy, S.E., Hanley, A.W., Riquino, M.R., Platt, K.A., Baker, A.K., Garland, E.L., 2018. Dispositional mindfulness and prescription opioid misuse among chronic pain patients: craving and attention to positive information as mediating mechanisms. Drug Alcohol Depend. 188, 86e93. https://doi.org/10.1016/j.drugalcdep.2018.03.040. Raffone, A., Srinivasan, N., 2009. An adaptive workspace hypothesis about the neural correlates of consciousness: insights from neuroscience and meditation studies. In: Srinivasan, N. (Ed.), Progress in Brain Research, vol. 176. Elsevier, pp. 161e180. https://doi.org/10.1016/ S0079-6123(09)17620-3. Robinson, T.E., Berridge, K.C., 1993. The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res. Brain Res. Rev. 18 (3), 247e291.

Neurocognitive mechanisms of mindfulness-based interventions for addiction Chapter | 21

Robinson, T.E., Berridge, K.C., 2008. Review. The incentive sensitization theory of addiction: some current issues. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 3137e3146. Sahdra, B., Maclean, K., Ferrer, E., Shaver, P., Rosenberg, E., Jacobs, T., et al., 2011. Enhanced response inhibition during intensive meditation training predicts improvements in self-reported adaptive socioemotional functioning. Emotion (Washington, D.C.) 11, 299e312. https:// doi.org/10.1037/a0022764. Schoenberg, P.L.A., Hepark, S., Kan, C.C., Barendregt, H.P., Buitelaar, J.K., Speckens, A.E.M., 2014. Effects of mindfulness-based cognitive therapy on neurophysiological correlates of performance monitoring in adult attention-deficit/hyperactivity disorder. Clin. Neurophysiol. 125 (7), 1407e1416. https://doi.org/10.1016/j.clinph. 2013.11.031. Segal, Z., Williams, J.M., Teasdale, J.D., 2002. Mindfulness-Based Cognitive Therapy for Depression. The Guilford Press, New York. Sevinc, G., Holzel, B., Hashmi, J., Greenberg, J., McCallister, A., Treadway, M., et al., 2018. Common and dissociable neural activity following mindfulness-based stress reduction and relaxation response programs. Psychosom. Med. 1. https://doi.org/10.1097/PSY. 0000000000000590. Singer, B.F., Fadanelli, M., Kawa, A.B., Robinson, T.E., 2018. Are cocaine-seeking “habits” necessary for the development of addictionlike behavior in rats? J. Neurosci. 38 (1), 60e73, 2458e17. https:// doi.org/10.1523/JNEUROSCI.2458-17.2017. Sinha, R., Talih, M., Malison, R., Cooney, N., Anderson, G., Jeanne Kreek, M., 2003. Hypothalamic-pituitary-adrenal axis and sympathoadreno-medullary responses during stress-induced and drug cueinduced cocaine craving states. Psychopharmacology 170, 62e72. https://doi.org/10.1007/s00213-003-1525-8. Slagter, H.A., Lutz, A., Greischar, L.L., Francis, A.D., Nieuwenhuis, S., Davis, J.M., Davidson, R.J., 2007. Mental training affects distribution of limited brain resources. PLoS Biol. 5, e138. Soler, J., Elices, M., Pascual, J.C., Martin-Blanco, A., Feliu-Soler, A., Carmona, C., Portella, M.J., 2016. Effects of Mindfulness Training on Different Components of Impulsivity in Borderline Personality Disorder: Results from a Pilot Randomized Study. Borderline Personality Disorder and Emotion Dysregulation; London, vol. 3. Retrieved from. http://search.proquest.com/docview/1767700696/abstract/ DEAE59DCE9224444PQ/1. Stevens, L., Verdejo-García, A., Goudriaan, A.E., Roeyers, H., Dom, G., Vanderplasschen, W., 2014. Impulsivity as a vulnerability factor for poor addiction treatment outcomes: a review of neurocognitive findings among individuals with substance use disorders. J. Subst. Abus. Treat. 47 (1), 58e72. https://doi.org/10.1016/j.jsat.2014.01.008. Tang, Y.-Y., Hölzel, B.K., Posner, M.I., 2015. The neuroscience of mindfulness meditation. Nat. Rev. Neurosci. 16 (4), 213e225. Tang, Y.-Y., Lu, Q., Fan, M., Yang, Y., Posner, M.I., 2012. Mechanisms of white matter changes induced by meditation. Proc. Natl. Acad. Sci. U.S.A. 109 (26), 10570e10574. Tang, Y.-Y., Lu, Q., Geng, X., Stein, E.A., Yang, Y., Posner, M.I., 2010. Short-term meditation induces white matter changes in the anterior cingulate. Proc. Natl. Acad. Sci. U.S.A. 107 (35), 15649e15652. Tang, Y.-Y., Tang, R., Posner, M.I., 2016. Mindfulness meditation improves emotion regulation and reduces drug abuse. Drug Alcohol Depend. 163, S13eS18. https://doi.org/10.1016/j.drugalcdep.2015.11. 041.

293

Taren, A.A., Gianaros, P.J., Greco, C.M., Lindsay, E.K., Fairgrieve, A., Brown, K.W., et al., 2015. Mindfulness meditation training alters stress-related amygdala resting state functional connectivity: a randomized controlled trial. Soc. Cognit. Affect Neurosci. 10 (12), 1758e1768. https://doi.org/10.1093/scan/nsv066. Taren, A.A., Gianaros, P.J., Greco, C.M., Lindsay, E.K., Fairgrieve, A., Brown, K.W., et al., 2017. Mindfulness meditation training and executive control network resting state functional connectivity: a randomized controlled trial. Psychosom. Med. 79 (6), 674e683. https:// doi.org/10.1097/PSY.0000000000000466. Teasdale, J.D., 1999. Metacognition, mindfulness, and the modification of mood disorders. Clin. Psychol. Psychother. 6, 146e155. Thayer, J.F., Lane, R.D., 2000. A model of neurovisceral integration in emotion regulation and dysregulation. J. Affect. Disord. 61, 201e216. Tiffany, S.T., 1990. A cognitive model of drug urges and drug-use behavior: role of automatic and nonautomatic processes. Psychol. Rev. 97, 147e168. Tomasino, B., Fabbro, F., 2016. Increases in the right dorsolateral prefrontal cortex and decreases the rostral prefrontal cortex activation after-8 weeks of focused attention based mindfulness meditation. Brain Cogn. 102, 46e54. https://doi.org/10.1016/j.bandc.2015.12. 004. Tsai, M.-H., Chou, W.-L., 2016. Attentional orienting and executive control are affected by different types of meditation practice. Conscious. Cognit. 46, 110e126. https://doi.org/10.1016/j.concog. 2016.09.020. Vago, D.R., Silbersweig, D.A., 2012. Self-awareness, self-regulation, and self-transcendence (S-ART): a framework for understanding the neurobiological mechanisms of mindfulness. Front. Hum. Neurosci. 6. Retrieved from. https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC3480633/. Wenk-Sormaz, H., 2005. Meditation can reduce habitual responding. Altern. Ther. Health Med. 11, 42e58. Westbrook, C., Creswell, J.D., Tabibnia, G., Julson, E., Kober, H., Tindle, H.A., 2011. Mindful attention reduces neural and self-reported cue-induced craving in smokers. Soc. Cognit. Affect Neurosci. 8 (1), 73e84. Whitmarsh, S., Uddén, J., Barendregt, H., Petersson, K.M., 2013. Mindfulness reduces habitual responding based on implicit knowledge: evidence from artificial grammar learning. Conscious. Cognit. 22 (3), 833e845. https://doi.org/10.1016/j.concog.2013.05.007. Xie, C., Li, S.-J., Shao, Y., Fu, L., Goveas, J., Ye, E., et al., 2011. Identification of hyperactive intrinsic amygdala network connectivity associated with impulsivity in abstinent heroin addicts. Behav. Brain Res. 216 (2), 639e646. https://doi.org/10.1016/j.bbr.2010.09.004. Xue, S., Tang, Y.-Y., Posner, M.I., 2011. Short-term meditation increases network efficiency of the anterior cingulate cortex. Neuroreport 22 (12), 570. https://doi.org/10.1097/WNR.0b013e328348c750. Zanesco, A.P., King, B.G., MacLean, K.A., Saron, C.D., 2018. Cognitive aging and long-term maintenance of attentional improvements following meditation training. J. Cognit. Enhanc. 1e17. https://doi. org/10.1007/s41465-018-0068-1. Zeidan, F., Johnson, S.K., Diamond, B.J., David, Z., Goolkasian, P., 2010. Mindfulness meditation improves cognition: evidence of brief mental training. Conscious. Cognit. 19 (2), 597e605.

Chapter 22

Brain stimulation as an emerging treatment for addiction Colleen A. Hanlon, Logan T. Dowdle, Daniel H. Lench and Tonisha Kearney Ramos Department of Psychiatry and Behavioral Sciences, College of Medicine, Medical University of South Carolina. Charleston, SC, United States of America

Noninvasive modulation of neural circuitry in humans

that optogenetics or designer receptors allow in preclinical research.

Through decades of preclinical research, it is now wellunderstood that drug-taking behavior can be modulated by altering activity in frontal-striatal circuits. Additionally, altered functional and structural integrity in these circuits has been related to substance abuse chronicity and resilience to relapse. The challenge now is to translate these basic science discoveries into a safe and effective treatment for our patients. This chapter will introduce transcranial magnetic stimulation (TMS) as a noninvasive brain stimulation tool that is currently being used to advance therapeutic options for patients with various substance use disorders.

Moving to the clinic

Preclinical foundation Through direct cortical stimulation and optogenetic stimulation, it is possible to change drug self-administration in a causal manner (Bass et al., 2013; Cassataro et al., 2014). The prefrontal cortex in rodents is typically divided into an infralimbic and a prelimbic domain. The infralimbic cortex is functionally and anatomically similar to the ventral medial prefrontal cortex (PFC) (also referred to as the orbitomedial prefrontal cortex) in primates (Barbas, 1995; Barbas, 2000; Groenewegen and Uylings, 2000), whereas the prelimbic cortex is functionally similar to more dorsal and lateral aspects of the human prefrontal cortex (Vertes, 2004). Optogenetic stimulation of these areas can alter cocaine seeking in a direction-specific manner (Chen et al., 2013; Stefanik et al., 2013). These data provide a foundation for developing a brain stimulation intervention in clinical populations. Until recently, however, we have not had the ability to selectively modulate limbic or executive control circuits in human clinical research in the manner

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00022-8 Copyright © 2020 Elsevier Inc. All rights reserved.

The earliest treatments for addiction were behavioral interventions, many of which are still used frequently including contingency management and cognitive behavioral therapy (CBT). Many of these interventions are discussed in other chapters of this book. To date, there are no FDA-approved pharmacological treatments for either cocaine or methamphetamine; however, some studies have shown modest reductions in substance use. In this section, we will discuss a growing new domain of research in addiction, which is utilizing TMS as a tool to decrease drug use and associated behaviors (e.g., craving) through longterm potentiation (LTP) and/or depression of the frontalstriatal circuits, which have been discussed in the previous sections. Although this line of research is still in its infancy, the development of innovative, biologically based brain stimulation therapies for substance dependence is among the best examples of translating decades of functional neuroimaging research into a clinically meaningful treatment.

What is transcranial magnetic stimulation? The majority of our knowledge regarding the basic electrophysiological effects of TMS on the brain is from studies in the motor system. When applied over the hand areas of the primary motor cortex, a single pulse of TMS induces a contraction of the contralateral hand. The strength of this contraction (e.g., motor evoked potential [MEP]) is dose-dependently related to the strength of the induced electrical field (Barker et al., 1986). The MEP can be manipulated by pharmaceutical agents that effect

295

296 Cognition and Addiction

glutamate and voltage-gated sodium channels (DiLazzarro et al., 2008; Ziemann and Rothwell, 2000). Nearly 70% of the variance in motor threshold (defined as the minimum TMS intensity required to generate an MEP on 50% of the trials) is accounted for by variability in scalp to cortex distance. The effects of a single TMS pulse decay exponentially with distance and are spatially restricted to cortical areas directly exposed to the TMS-induced field (typically 2e4 cm from the center of the coil). When the depolarizing current from TMS is strong enough, however, it leads to a cascade of neurotransmitter release, excitatory postsynaptic synaptic potentials, and eventually action potentials in neurons receiving monosynaptic inputs from the neurons depolarized by the TMS pulse. This polysynaptic modulation via TMS underscores its utility as a tool to modulate frontal-striatal circuits in substance use disorder patients. This has been documented using interleaved TMS/BOLD imaging, wherein a single pulse of TMS induces an elevation in the blood oxygen leveledependent (BOLD) signal in the vicinity of the TMS coil and in monosynaptic target regions (Bohning et al., 1998). The amplitude of the BOLD signal induced by a single pulse of TMS to the primary motor cortex is similar to the amplitude of the BOLD signal induced by an intentional contraction (Denslow et al., 2005). From the perspective of addiction, it is possible to differentially activate frontostriatal circuits involved in limbic control from those involved in executive control through stimulating the MPFC and dorsolateral prefrontal cortex (DLPFC), respectively (Hanlon et al., 2013). Cocaine users, for example, have a lower ventral striatal BOLD response to MPFC stimulation than healthy controls (Hanlon et al., 2016). In this study, TMS was applied to the MPFC (Brodmann area [BA] 10) and the DLPFC (lateral BA 9) of 36 individualsd18 cocaine-dependent individuals with a history of failed quit attempts and 18 age-matched controls. Cocaine users had a lower ventral striatal BOLD response to MPFC stimulation but no difference in dorsal striatal response to DLPFC stimulation. Among controls, DLPFC stimulation led to a reciprocal attenuation of MPFC activity (BA 10), but this pattern did not exist in cocaine users. No relationship was found between regional diffusion metrics and functional activity. Considered together, these data suggest that, when engaged, cocaine users can mobilize their executive control system similar to controls but that the “set point” for mobilizing their limbic arousal system has been elevated; an interpretation consistent with opponent process theories of addiction.

Using repetitive transcranial magnetic stimulation to modulate cortical-striatal connectivity While single pulses of TMS coupled with neuroimaging provide a controlled method to probe function in a neural

circuit, repetitive pulses of TMS can be used to induce LTP or long-term depression (LTD) in a given neural region as well as its monosynaptic afferents (Denslow et al., 2005; Bestmann et al., 2004; Nahas et al., 2001; Bohning et al., 2000). An LTD-like effect can be achieved through TMS by either using a low-frequency stimulation (typically 1 Hz) or through a bursting frequency, such as continuous theta burst (Huang et al., 2005). In preclinical literature, theta burst stimulation is a well-known form of electrical stimulation which can induce LTP or LTD of synaptic activity in a given brain region (Malenka and Bear, 2004; Bear and Malenka, 1994). Human theta burst stimulation protocols use repetitive transcranial magnetic stimulation (rTMS) to induce similar forms of LTP and LTD by using intermittent or continuous bursts, respectively (Huang et al., 2005; Di Lazzaro et al., 2005). With continuous theta burst stimulation (cTBS), bursts of three pulses at 50 Hz are applied at a frequency of 5 Hz at amplitude that is typically determined by the active motor threshold. When performed over the primary motor cortex, a lower amplitude of cTBS for 40 s leads to an attenuation of motor-evoked potentials that is comparable to a higher amplitude to 1Hz singlefrequency stimulation for 20 min. Stagg and colleagues have demonstrated that this attenuating effect of cTBS is likely due to an increase in g-aminobutyric acid (GABA) at the area of stimulation (Stagg et al., 2009) rather than a change in glutamate. The use of rTMS to modulate neural circuits will be the topic of the remainder of this article, as that it is the technique most likely to induce a sustainable change in neural circuitry of substance-dependent individuals.

Applications to substance use disorders The potential of rTMS as a new tool for modulating craving among substance-dependent populations has garnered significant attention in the literature (see reviews: Bellamoli et al., 2014, Gorelick et al., 2014, Wing et al., 2013, Barr et al., 2011). As this field develops, the primary questions will likely be as follows: (1) what cortical location should we target to maximally affect the circuitry we are interested in changing? and (2) what stimulation frequency should we choose? There will likely not be a single “optimal” protocol for all individuals or all classes of drugs. For example, some individuals may benefit the most of a treatment strategy that amplifies their executive control circuitry (e.g., 10 Hz DLPFC stimulation), while others may benefit the most from a strategy that attenuates limbic circuitry involved in drug craving (e.g., 1 Hz medial prefrontal/ frontal pole stimulation). Before moving forward with expensive and slow multisite clinical trials investigating the efficacy of rTMS as a viable treatment tool for addiction, however, it is useful to explore these combinations of frequencies and cortical targets to maximize our potential impact on the patients.

Brain stimulation as an emerging treatment for addiction Chapter | 22

To date, nearly all of the rTMS studies in addiction have targeted the same neural regiondthe DLPFC (Eichhammer et al., 2003; Li et al., 2013a,b; Pripfl et al., 2014; Herremans et al., 2012; Hoppner et al., 2011; Mishra et al., 2010; Camprodon et al., 2007; Politi et al., 2008). While many of these studies demonstrated that high-frequency (LTP-like) rTMS stimulation to the DLPFC can result in a significant reduction of craving, the neurobiological mechanism through which this might happen is not clear. In a comprehensive review of the literature on the efficacy of rTMS as a treatment tool for smoking, Wing and colleagues (Wing et al., 2013) present a model in which the beneficial effects of LTP-like TMS on the DLPFC are associated with a release of dopamine in the nucleus accumbens. This model is supported by important work from Strafella and colleagues which used positron emission tomography to demonstrate that DLPFC stimulation was associated with an increase in dopamine binding in the caudate (Strafella et al., 2001). The primary cortical inputs to the nucleus accumbens, however, are the medial and orbital prefrontal cortices, not the DLPFC. Given that the nucleus accumbens is one of the primary brain regions involved in craving, it seems that targeting the MPFC would be a more direct method to modulate nucleus accumbens activity among substancedependent populations. Given that craving for cocaine is associated with an increase in dopamine in the striatum, it is reasonable to pursue an LTD-like rTMS protocol over the MPFC to attenuate activity in this neural circuit. Prior data from our laboratory demonstrates that a single pulse of TMS to the MPFC in healthy individuals leads to an increase in BOLD signal in the ventral striatum (Hanlon et al., 2013). Recent extension of that work to cocainedependent population demonstrated that the cocaine users have a hyperactive BOLD response in the dorsal and ventral striatum relative to controls (Hanlon et al., 2016). This elevated ventral striatal sensitivity following MPFC stimulation, a frontostriatal circuit involved in the limbic aspects of craving, may be a prime circuit to attenuate these individuals vulnerability to drug-related cues.

Applications to smoking Since 2003, 12 studies have investigated rTMS and cigarette smoking. All but one of these studies exclusively used high-frequency stimulation (10e20 Hz), with most choosing the conventional left hemisphere as the target for stimulation. Early results were somewhat mixed, finding that a single session of high-frequency left DLPFC stimulation reduced smoking, but not craving (Eichhammer et al., 2003), while another study found that it was effective in reducing craving (Li et al., 2013a). Following the publication of a study that suggested that 10 sessions were sufficient to alter both craving and consumption (Amiaz et al., 2009), Dinur-Klein and colleagues reported the

297

largest TMS addiction clinical trial to date, delivering 13 sessions to 115 subjects. They found that high-frequency stimulation, delivered the lateral prefrontal cortex and insula, was effective at reducing the number of cigarettes smoked. Additionally, this effect was larger in the group randomized to have cue exposure before stimulation sessions (Dinur-Klein et al., 2014). This cue exposure represents one method of context- or state-dependent modification of treatment effectiveness. Put simply, it may be easier to modify an active or engaged circuit. While Dinur-Klein used cue exposure, this idea can easily be extended into many other paradigms. One alternative possibility to be discussed in further detail in Applications to substance use disorders section is the engagement of executive control circuitry. Briefly, Sheffer and colleagues combined eight sessions of self-help therapy with active or sham rTMS, finding that the combination of active rTMS and therapy was better than therapy alone, leading to greater smoking abstinence (Sheffer et al., 2018).

Application to alcohol The number of completed studies in the field of alcohol addiction is similar to that of nicotine, with 10 studies currently published. Seven of these studies have used high-frequency stimulation at the DLPFC, though most have targeted the right side, rather than the left. This is likely based on an early and promising study which found reductions in craving compared to sham, after 10 sessions of active 10 Hz stimulation (Mishra et al., 2010). Unlike early nicotine work, reductions in craving were not found after a single session (Herremans et al., 2012), though this does not invalidate the use of rTMS in alcohol. For example, despite Class I evidence for the treatment of depression, a single session of rTMS likely has no effect on mood (Remue et al., 2016). For the left DLPFC, an additional study failed to find an effect of 10 sessions of 20 Hz stimulation on craving (Hoppner et al., 2011). The diversity of these studies brings to the forefront the number of choices that are available to a TMS researcher. While we will discuss alternative TMS targets at a later point, the investigator must also decide on the specific frequency or pattern of stimulation, the number of sessions, simultaneous task (if any), and specific outcome of interest, just to name a few. The importance of this last component is shown by a recent study which found that changes in cue reactivity depend on the level of cue reactivity before the delivery of rTMS, that is, there were rate-dependent effects (Herremans et al., 2016). The alcohol literature supports the need of delivering multiple rTMS sessions for larger, measurable effects.

Application to cocaine There have been eight reports of the effectiveness of rTMS for cocaine use disorder; of these, half targeted the DLPFC

298 Cognition and Addiction

with high-frequency stimulation. The earliest report found that right but not left high-frequency stimulation of the DLPFC was able to transiently reduce craving (Camprodon et al., 2007). Latter work exploring more sessions matched common depression protocols and targeted the left DLPFC. The most recent example of this, from 2016, found that eight sessions of rTMS were more effective that treatment as usual (pharmacotherapy), with the rTMS group having a higher number of cocaine-free urine drug screens (Terraneo et al., 2016). This finding builds on earlier work, which reported that cocaine craving reduced over 10 sessions of rTMS (Politi et al., 2008). While these two studies support the use of high-frequency left DLPFC stimulation as a promising target, there is still a need for larger shamcontrolled studies to confirm these preliminary findings.

Application to other substance using populations For other substances of abuse, such as opiates (including heroin), methamphetamine, and marijuana, there has been limited research on the effectiveness of rTMS. Opiates may be the most promising target, as there is growing evidence that DLPFC stimulation is able to reduce pain (Taylor et al., 2013; Brighina et al., 2011; Borckardt et al., 2007; Borckardt et al., 2008), which can lead to the initiation or maintenance of opiate use disorders. Currently, there is only one published research study examining rTMS for heroin use, finding that a single session of high-frequency stimulation was able to reduce cue-induced craving in heroin users Shen and Cao (Shen et al., 2016). It is important to note that is likely to change rapidly, as there are a number of ongoing trials in the United States based on the studies registered in clinicaltrials.gov. For marijuana, there is evidence that rTMS can be safely delivered to this population, but in the sample studied there were no significant changes in craving, relative to sham (Sahlem et al., 2018). Among those with methamphetamine use disorder, there is currently no consensus on the most effective treatment. Early work found that 1 Hz stimulation of the DLPFC increased cue-induced craving (Li et al., 2013b), though another group found that both 10 and 1 Hz reduced craving (Liu et al., 2017). Of these, 10 Hz stimulation, as in other substance use disorders, appears to be the most promising. Recently, five sessions of 10 Hz DLPFC stimulation reduced methamphetamine cue-induced craving relative to sham (Su et al., 2017).

Application to compulsive eating and gambling In parallel with the increasing application of rTMS to substance use disorders, other groups have explored rTMS as a tool to treat other disorders. Among eating disorders, researchers have sought to use rTMS for both anorexia and

bulimia nervosa. Many case reports, some occurring incidentally during rTMS for depression, were encouraging, finding that rTMS reduced bingeing and purging (Hausmann et al., 2004; Downar et al., 2012; McClelland et al., 2013). There were also pilot studies that found reduced food craving after a single session (Van den Eynde et al., 2010), although well-matched sham stimulation had similar effects (Barth et al., 2011). Recent larger randomized trials have also failed to find these positive effects (McClelland et al., 2016; Gay et al., 2016). The development of rTMS for pathological gambling has followed a similar trajectory. Early studies found that the DLPFC may be a reasonable target, with 1Hz stimulation, often thought of as decreasing circuit activity, increasing risk-taking behavior (Knoch et al., 2006). One sham-controlled crossover study found that 10 Hz stimulation at the left DLPFC was able to reduce gambling cueinduced craving (Gay et al., 2017). This is in contrast to more recent work using 1 Hz stimulation at the right DLPFC, which found reductions in craving from both active and sham stimulation (Sauvaget et al., 2018). As with other conditions, it is important to consider that these behavioral addictions are heterogenous, and in many of these studies, the sample sizes remain relatively small. As is the case in substance use disorders, there is a need for further research to determine the optimal target and stimulation frequency.

Integration of neuromodulation with cognitive and pharmacotherapies Repetitive transcranial magnetic stimulation with cognitive therapy In the first applications of rTMS for neuropsychiatric disorders, treatment was performed at rest and in the absence of other forms of intervention (Tsagaris et al., 2016; George et al., 2010). Today, an increasing interest in how to improve the therapeutic effects of rTMS is being explored. The principle that a neural circuit is more “plastic” or primed when it is engaged during stimulation has offered the field of rTMS a potential solution to increase treatment efficacy and even durability (Vedeniapin et al., 2010). In clinical depression and posttraumatic stress disorder studies, combining modified CBT protocols with highfrequency DLPFC stimulation has shown both feasibility and promise (Donse et al., 2018; Vedeniapin et al., 2010; Kozel et al., 2018). Combined rTMS and behavioral therapy is now in the early stages of being evaluated in addiction and substance abuse disorders (Sheffer et al., 2018). Independently, CBT has developed as a therapeutic approach to prevent relapse in drug abuse and addiction by having patients identify and correct problematic behaviors. This form of therapy has been shown to be effective for

Brain stimulation as an emerging treatment for addiction Chapter | 22

alcohol use disorder, marijuana-dependent, and cocainedependent individuals (Carroll and Onken, 2005). Cognitive therapies such as CBT engage executive and inhibitory control circuitry, which includes brain regions like the DLPFC and anterior cingulate cortex (Beauregard, 2014). Although several other behavioral interventions have been explored for substance use disorders, cognitive therapies are unique in their ability to enhance the engagement of these networks, which are also being explored as rTMS targets (Hanlon et al., 2015). In a study of smoking relapse prevention, participants were given eight FF (Forever Free) relapse prevention booklets to read during and between sessions of 20Hz DLPFC stimulation (Sheffer et al., 2018). Results showed that this combined evidence-based self-help and rTMS treatment can significantly increase abstinence rates and reduce relapse. One question that remains to be answered in studies combining cognitive therapies with rTMS treatment is the impact of their temporal relationship. The timing of the therapies can be delivered concurrently, sequentially, or interleaved, although a consensus on the best ordering is yet to be determined (Huerta and Volpe, 2009).

Repetitive transcranial magnetic stimulation and pharmacotherapy A full discussion of this can be found in a recent manuscript in Pharmacological Reviews (Hanlon et al., 2018), but we will highlight a few components of the idea here. Much of the promise of rTMS stems from characterization as a circuit-based tool, in contrast to the effective but systemic nature of pharmacological agents. Furthermore, rTMS is being developed as a treatment specifically for disorders with no FDA-approved medications. Despite this posture, there may be untapped potential in the combination of these tools. Blending the circuit-specific effects of TMS with the historical knowledge of pharmacological agents may lead to larger or more durable effects on plasticity in the circuit of interest. While much of this chapter focused on standard rTMS protocols, in the study of pharmacological TMS, the bulk of the findings have been derived from other methods. A common technique used in pharmacological TMS studies to induce plasticity changes involves pairing a peripheral stimulation (a paired associative stimulus [PAS]) at the median nerve of the arm with a TMS pulse to the contralateral M1 region. If the median nerve stimulus is paired with a 10-ms delay, subsequent TMS-induced MEPs are reduced. This effect is known as PAS-LTD (Wolters et al., 2003). In contrast, a 25-ms delay leads to larger MEPs, a method known as PAS-LTP (Stefan et al., 2000). These techniques can be used to measure plasticity changes and provide insight into the mechanisms that underlie rTMS outcomes. For example, amphetamine (Nitsche et al., 2004)

299

has been shown to enhance PAS-LTP, as has acute nicotine (Thirugnanasambandam et al., 2011). While the preceding paragraph related possible benefits, there is also a note of caution. Commonly used medications that antagonize NMDA receptors, such as dextromethorphan (Liebetanz et al., 2002; Nitsche et al., 2003), or have GABAergic effects such as diazepam (Heidegger et al., 2010) and baclofen (McDonnell et al., 2007) may block LTP- or LTD-like effects. Still other drugs, such as D-cycloserine, may reverse the direction of plasticity in an rTMS protocol (Teo et al., 2007). Thus far, no studies have combined standard rTMS protocols, such as 10 Hz stimulation, with an unrelated pharmacological agent to affect behavioral changes in a clinical population. It is clear, however, that the effects of rTMS protocols can also be altered by concurrent drug administration. For example, both lorazepam, a GABA facilitator, and dextromethorphan can attenuate the effects of 1 Hz stimulation (Fitzgerald et al., 2005). Findings such as this highlight both the promise of combinatorial therapies and the importance of considering the pharmacological history of patients-seeking treatment. It is critical that the fields extend these results beyond the motor system and seek out treatments that combine the systemic and powerful effects of pharmacological agents with the specificity of TMS in a beneficial way.

Summary Selective modulation of frontal-striatal circuits involved in limbic and executive control may be an innovative and useful treatment strategy to prevent cue-associated relapse in substance-dependent individuals. rTMS is an FDAapproved treatment for depression and is growing in clinical use and acceptance, with >700 machines in the United States and emerging insurance reimbursement. As the field of addiction moves forward with pursuing rTMS as a new tool to modulate craving and the frontal-striatal circuits that contribute to chronic use and relapse, it will be important to consider the optimal site, frequency, and patient population to target. The data presented in this chapter demonstrate that while most of the efforts for rTMS in addiction have been focused on increasing activity in the DLPFC, decreasing activity in the MPFC and ventral striatum may also be a feasible and fruitful target to consider. It seems plausible that either increasing neural firing in the executive control circuit (perhaps via high-frequency TMS in the DLPFC) or decreasing firing in the limbic circuit in the presence of cues (perhaps via low-frequency TMS in the MPFC) may be valuable strategies for decreasing vulnerability to drug-related cues among our patients. Before moving forward with slow and expensive clinical trials, however, it is important to have a comprehensive understanding of limbic and executive circuit functioning

300 Cognition and Addiction

in a diverse cross section of substance-dependent individuals. With this knowledge we will be able to develop circuit-specific treatment strategies for these populations. Before pursuing large expensive clinical trials of DLPFC stimulation or MPFC stimulation as a potential treatment adjuvant to CBT among substance-dependent individuals, however, it is critical to determine whether it is tolerable, is feasible, effects craving, and improves outcomes to treatment-engaged individuals. In summary, this chapter hopes to have planted several seeds in the reader’s mind regarding the utility of dopaminergic frontal-striatal systems as useful targets for TMS treatment development. Many of the challenges facing development of efficacious pharmacotherapies for substance dependence could be ameliorated by delivering the drugs to specific neural circuits in a functionally relevant manner. Through the continued refinement of noninvasive brain stimulation as a tool to modulate neural circuits, in the next decade, we will be rigorously evaluating these tools to enhance and refine pharmacotherapeutic development for neurologic and psychiatric disorders, including substance use disorder.

References Amiaz, R., et al., 2009. Repeated high-frequency transcranial magnetic stimulation over the dorsolateral prefrontal cortex reduces cigarette craving and consumption. Addiction 104 (4), 653e660. Barbas, H., 1995 Mar-Apr. Pattern in the cortical distribution of prefrontally directedneurons with divergent axons in the rhesus monkey. Cereb Cortex 5 (2), 158e165. PubMed PMID: 7620292. Barbas, H., 2000. Complementary roles of prefrontal cortical regions in cognition,memory, and emotion in primates. Adv Neurol 84, 87e110. Review. PubMed PMID:11091860. Barker, A.T., et al., 1986. Clinical evaluation of conduction time measurements in central motor pathways using magnetic stimulation of human brain. Lancet 1 (8493), 1325e1326. Barr, M.S., Farzan, F., Wing, V.C., George, T.P., Fitzgerald, P.B., Daskalakis, Z.J., 2011 Oct. Repetitive transcranial magnetic stimulation and drug addiction. Int RevPsychiatry 23 (5), 454e466. https://doi.org/ 10.3109/09540261.2011.618827. Review. PubMed PMID: 22200135. Barth, K.S., et al., 2011. Food cravings and the effects of left prefrontal repetitive transcranial magnetic stimulation using an improved sham condition. Front. Psychiatry 2, 9. Bass, C.E., Grinevich, V.P., Gioia, D., Day-Brown, J.D., Bonin, K.D., Stuber, G.D., Weiner, J.L., Budygin, E.A., 2013 Nov 26. Optogenetic stimulation of VTA dopamine neurons reveals that tonicbut not phasic patterns of dopamine transmission reduce ethanolself-administration. Front Behav Neurosci 7, 173. https://doi.org/10.3389/ fnbeh.2013.00173 eCollection 2013. PubMed PMID: 24324415; PubMed CentralPMCID: PMC3840465. Bear, M.F., Malenka, R.C., 1994. Synaptic plasticity: LTP and LTD. Curr. Opin. Neurobiol. 4 (3), 389e399. Beauregard, M., 2014. Functional neuroimaging studies of the effects of psychotherapy. Dialogues Clin. Neurosci. 16 (1), 75e81.

Bestmann, S., et al., 2004. Functional MRI of the immediate impact of transcranial magnetic stimulation on cortical and subcortical motor circuits. Eur. J. Neurosci. 19 (7), 1950e1962. Bellamoli, E., Manganotti, P., Schwartz, R.P., Rimondo, C., Gomma, M., Serpelloni, G., 2014. rTMS in the treatment of drug addiction: an update about human studies. BehavNeurol 2014, 815215. https:// doi.org/10.1155/2014/815215. Epub 2014 Jan 23. Review.PubMed PMID: 24803733; PubMed Central PMCID: PMC4006612. Bohning, D.E., et al., 1998. Echoplanar BOLD fMRI of brain activation induced by concurrent transcranial magnetic stimulation. Investig. Radiol. 33 (6), 336e340. Bohning, D.E., et al., 2000. BOLD-f MRI response to single-pulse transcranial magnetic stimulation (TMS). J. Magn. Reson. Imaging 11 (6), 569e574. Borckardt, J.J., et al., 2007. Fifteen minutes of left prefrontal repetitive transcranial magnetic stimulation acutely increases thermal pain thresholds in healthy adults. Pain Res. Manag. 12 (4), 287e290. Borckardt, J.J., et al., 2008. Significant analgesic effects of one session of postoperative left prefrontal cortex repetitive transcranial magnetic stimulation: a replication study. Brain Stimul. 1 (2), 122e127. Brighina, F., et al., 2011. Modulation of pain perception by transcranial magnetic stimulation of left prefrontal cortex. J. Headache Pain 12 (2), 185e191. Camprodon, J.A., et al., 2007. One session of high frequency repetitive transcranial magnetic stimulation (rTMS) to the right prefrontal cortex transiently reduces cocaine craving. Drug Alcohol Depend. 86 (1), 91e94. Carroll, K.M., Onken, L.S., 2005. Behavioral therapies for drug abuse. Am. J. Psychiatry 162 (8), 1452e1460. Cassataro, D., Bergfeldt, D., Malekian, C., Van Snellenberg, J.X., Thanos, P.K., FishellG, Sjulson, L., 2014 Jan. Reverse pharmacogenetic modulation of the nucleus accumbens reducesethanol consumption in a limited access paradigm. Neuropsychopharmacology 39 (2), 283e290. https://doi.org/10.1038/npp.2013.184. Epub 2013 Aug 1. PubMed PMID:23903031; PubMed Central PMCID: PMC3870771. Chen, B.T., Yau, H.J., Hatch, C., Kusumoto-Yoshida, I., Cho, S.L., Hopf, F.W., Bonci, A., 2013 Apr 18. Rescuing cocaine-induced prefrontal cortex hypoactivity prevents compulsivecocaine seeking. Nature 496 (7445), 359e362. https://doi.org/10.1038/nature12024. Epub 2013 Apr 3. PubMed PMID: 23552889. Denslow, S., et al., 2005. Cortical and subcortical brain effects of transcranial magnetic stimulation (TMS)-induced movement: an interleaved TMS/functional magnetic resonance imaging study. Biol. Psychiatry 57 (7), 752e760. Di Lazzaro, V., et al., 2005. Theta-burst repetitive transcranial magnetic stimulation suppresses specific excitatory circuits in the human motor cortex. J. Physiol. 565 (Pt 3), 945e950. Di Lazzaro, V., Ziemann, U., Lemon, R.N., 2008 Oct. State of the art: Physiology oftranscranial motor cortex stimulation. Brain Stimul 1 (4), 345e362. https://doi.org/10.1016/j.brs.2008.07.004. Epub 2008 Oct 7. Review PubMed PMID: 20633393. Dinur-Klein, L., et al., 2014. Smoking cessation induced by deep repetitive transcranial magnetic stimulation of the prefrontal and insular cortices: a prospective, randomized controlled trial. Biol. Psychiatry 76 (9), 742e749.

Brain stimulation as an emerging treatment for addiction Chapter | 22

Donse, L., et al., 2018. Simultaneous rTMS and psychotherapy in major depressive disorder: clinical outcomes and predictors from a large naturalistic study. Brain Stimulation 11 (2), 337e345. Downar, J., et al., 2012. Unanticipated rapid remission of refractory bulimia nervosa, during high-dose repetitive transcranial magnetic stimulation of the dorsomedial prefrontal cortex: a case report. Front. Psychiatry 3, 30. Eichhammer, P., et al., 2003. High-frequency repetitive transcranial magnetic stimulation decreases cigarette smoking. J. Clin. Psychiatry 64 (8), 951e953. Fitzgerald, P.B., et al., 2005. A study of the effects of lorazepam and dextromethorphan on the response to cortical 1 Hz repetitive transcranial magnetic stimulation. Neuroreport 16 (13), 1525e1528. Gay, A., et al., 2016. A lack of clinical effect of high-frequency rTMS to dorsolateral prefrontal cortex on bulimic symptoms: a randomised, double-blind trial. Eur. Eat. Disord. Rev. 24 (6), 474e481. Gay, A., et al., 2017. A single session of repetitive transcranial magnetic stimulation of the prefrontal cortex reduces cue-induced craving in patients with gambling disorder. Eur. Psychiatry 41, 68e74. George, M.S., et al., 2010. Daily left prefrontal transcranial magnetic stimulation therapy for major depressive disorder: a sham-controlled randomized trial. Arch. Gen. Psychiatry 67 (5), 507e516. Gorelick, D.A., Zangen, A., George, M.S., 2014 Oct. Transcranial magnetic stimulation in thetreatment of substance addiction. Ann N Y Acad Sci 1327, 79e93. https://doi.org/10.1111/nyas.12479. Epub 2014 Jul 28. Review. PubMed PMID: 25069523; PubMedCentral PMCID: PMC4206564. Groenewegen, H.J., Uylings, H.B., 2000. The prefrontal cortex and the integration ofsensory, limbic and autonomic information. Prog Brain Res 126, 3e28. Review.PubMed PMID: 11105636. Hanlon, C.A., et al., 2013. Probing the frontostriatal loops involved in executive and limbic processing via interleaved TMS and functional MRI at two prefrontal locations: a pilot study. PLoS One 8 (7), e67917. Hanlon, C.A., et al., 2015. What goes up, can come down: novel brain stimulation paradigms may attenuate craving and craving-related neural circuitry in substance dependent individuals. Brain Res. 1628 (Pt A), 199e209. Hanlon, C.A., Dowdle, L.T., Henderson, J.S., 2018 Jul. Modulating Neural Circuits withTranscranial Magnetic Stimulation: Implications for Addiction TreatmentDevelopment. Pharmacol Rev 70 (3), 661e683. https://doi.org/10.1124/pr.116.013649. Review. PubMed PMID: 29945899; PubMed Central PMCID: PMC6020107. Hausmann, A., et al., 2004. Repetitive transcranial magnetic stimulation (rTMS) in the double-blind treatment of a depressed patient suffering from bulimia nervosa: a case report. Int. J. Neuropsychopharmacol. 7 (3), 371e373. Hanlon, C.A., Dowdle, L.T., Moss, H., Canterberry, M., George, M.S., 2016 Dec. Mobilization ofMedial and Lateral Frontal-Striatal Circuits in Cocaine Users and Controls: AnInterleaved TMS/BOLD Functional Connectivity Study. Neuropsychopharmacology 41 (13), 3032e3041. https://doi.org/10.1038/npp.2016.114. Epub 2016 Jul 4. PubMed PMID:27374278; PubMed Central PMCID: PMC5101551. Heidegger, T., Krakow, K., Ziemann, U., 2010. Effects of antiepileptic drugs on associative LTP-like plasticity in human motor cortex. Eur. J. Neurosci. 32 (7), 1215e1222. Herremans, S.C., et al., 2012. No influence of one right-sided prefrontal HF-rTMS session on alcohol craving in recently detoxified alcohol-

301

dependent patients: results of a naturalistic study. Drug Alcohol Depend. 120 (1e3), 209e213. Herremans, S.C., et al., 2016. Accelerated HF-rTMS protocol has a ratedependent effect on dACC activation in alcohol-dependent patients: an open-label feasibility study. Alcohol Clin. Exp. Res. 40 (1), 196e205. Hoppner, J., et al., 2011. Repetitive transcranial magnetic stimulation (rTMS) for treatment of alcohol dependence. World J. Biol. Psychiatr. 12 (Suppl. 1), 57e62. Huang, Y.Z., et al., 2005. Theta burst stimulation of the human motor cortex. Neuron 45 (2), 201e206. Huerta, P.T., Volpe, B.T., 2009. Transcranial magnetic stimulation, synaptic plasticity and network oscillations. J. NeuroEng. Rehabil. 6, 7. Knoch, D., et al., 2006. Disruption of right prefrontal cortex by low-frequency repetitive transcranial magnetic stimulation induces risk-taking behavior. J. Neurosci. 26 (24), 6469e6472. Kozel, F.A., et al., 2018. Repetitive TMS to augment cognitive processing therapy in combat veterans of recent conflicts with PTSD: a randomized clinical trial. J. Affect. Disord. 229, 506e514. Li, X., et al., 2013a. Repetitive transcranial magnetic stimulation of the dorsolateral prefrontal cortex reduces nicotine cue craving. Biol. Psychiatry 73 (8), 714e720. Li, X., et al., 2013b. Low frequency repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex transiently increases cue-induced craving for methamphetamine: a preliminary study. Drug Alcohol Depend. 133 (2), 641e646. Liebetanz, D., et al., 2002. Pharmacological approach to the mechanisms of transcranial DC-stimulation-induced after-effects of human motor cortex excitability. Brain 125 (10), 2238e2247. Liu, Q., et al., 2017. Either at left or right, both high and low frequency rTMS of dorsolateral prefrontal cortex decreases cue induced craving for methamphetamine. Am. J. Addict. 26 (8), 776e779. Malenka, R.C., Bear, M.F., 2004. LTP and LTD: an embarrassment of riches. Neuron 44 (1), 5e21. McClelland, J., et al., 2013. Improvements in symptoms following neuronavigated repetitive transcranial magnetic stimulation (rTMS) in severe and enduring anorexia nervosa: findings from two case studies. Eur. Eat. Disord. Rev. 21 (6), 500e506. McClelland, J., et al., 2016. A randomised controlled trial of neuronavigated repetitive transcranial magnetic stimulation (rTMS) in anorexia nervosa. PLoS One 11 (3), e0148606. McDonnell, M.N., Orekhov, Y., Ziemann, U., 2007. Suppression of LTPlike plasticity in human motor cortex by the GABAB receptor agonist baclofen. Exp. Brain Res. 180 (1), 181e186. Mishra, B.R., et al., 2010. Efficacy of repetitive transcranial magnetic stimulation in alcohol dependence: a sham-controlled study. Addiction 105 (1), 49e55. Nahas, Z., et al., 2001. Unilateral left prefrontal transcranial magnetic stimulation (TMS) produces intensity-dependent bilateral effects as measured by interleaved BOLD fMRI. Biol. Psychiatry 50 (9), 712e720. Nitsche, M.A., et al., 2003. Pharmacological modulation of cortical excitability shifts induced by transcranial direct current stimulation in humans. J. Physiol. 553 (Pt 1), 293e301. Nitsche, M.A., et al., 2004. Catecholaminergic consolidation of motor cortical neuroplasticity in humans. Cerebr. Cortex 14 (11), 1240e1245.

302 Cognition and Addiction

Politi, E., et al., 2008. Daily sessions of transcranial magnetic stimulation to the left prefrontal cortex gradually reduce cocaine craving. Am. J. Addict. 17 (4), 345e346. Pripfl, J., et al., 2014. Transcranial magnetic stimulation of the left dorsolateral prefrontal cortex decreases cue-induced nicotine craving and EEG delta power. Brain Stimul. 7 (2), 226e233. Remue, J., Baeken, C., De Raedt, R., 2016. Does a single neurostimulation session really affect mood in healthy individuals? A systematic review. Neuropsychologia 85, 184e198. Sahlem, G.L., et al., 2018. Repetitive transcranial magnetic stimulation (rTMS) administration to heavy cannabis users. Am. J. Drug Alcohol Abuse 44 (1), 47e55. Sauvaget, A., et al., 2018. Both active and sham low-frequency rTMS single sessions over the right DLPFC decrease cue-induced cravings among pathological gamblers seeking treatment: a randomized, double-blind, sham-controlled crossover trial. J. Behav. Addict. 7 (1), 126e136. Sheffer, C.E., et al., 2018. Preventing relapse to smoking with transcranial magnetic stimulation: feasibility and potential efficacy. Drug Alcohol Depend. 182, 8e18. Shen, Y., et al., 2016. 10-Hz repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex reduces heroin cue craving in long-term addicts. Biol. Psychiatry 80 (3), e13ee14. Stagg, C.J., et al., 2009. Neurochemical effects of theta burst stimulation as assessed by magnetic resonance spectroscopy. J. Neurophysiol. 101 (6), 2872e2877. Stefan, K., et al., 2000. Induction of plasticity in the human motor cortex by paired associative stimulation. Brain 123 (3), 572e584. Stefanik, M.T., Kupchik, Y.M., Brown, R.M., Kalivas, P.W., 2013 Aug21. Optogenetic evidence thatpallidal projections, not nigral projections, from the nucleus accumbens core arenecessary for reinstating cocaine seeking. J Neurosci 33 (34), 13654e13662. https://doi.org/10.1523/ JNEUROSCI.1570-13.2013. PubMed PMID: 23966687;PubMed Central PMCID: PMC3755713. Strafella, A.P., et al., 2001. Repetitive transcranial magnetic stimulation of the human prefrontal cortex induces dopamine release in the caudate nucleus. J. Neurosci. 21 (15), RC157.

Su, H., et al., 2017. High frequency repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex for methamphetamine use disorders: a randomised clinical trial. Drug Alcohol Depend. 175, 84e91. Taylor, J.J., et al., 2013. Naloxone-reversible modulation of pain circuitry by left prefrontal rTMS. Neuropsychopharmacology 38 (7), 1189e1197. Teo, J.T., Swayne, O.B., Rothwell, J.C., 2007. Further evidence for NMDA-dependence of the after-effects of human theta burst stimulation. Clin. Neurophysiol. 118 (7), 1649e1651. Terraneo, A., et al., 2016. Transcranial magnetic stimulation of dorsolateral prefrontal cortex reduces cocaine use: a pilot study. Eur. Neuropsychopharmacol. 26 (1), 37e44. Thirugnanasambandam, N., et al., 2011. Nicotinergic impact on focal and non-focal neuroplasticity induced by non-invasive brain stimulation in non-smoking humans. Neuropsychopharmacology 36 (4), 879e886. Tsagaris, K.Z., Labar, D.R., Edwards, D.J., 2016. A framework for combining rTMS with behavioral therapy. Front. Syst. Neurosci. 10, 82. Van den Eynde, F., et al., 2010. Repetitive transcranial magnetic stimulation reduces cue-induced food craving in bulimic disorders. Biol. Psychiatry 67 (8), 793e795. Vedeniapin, A., Cheng, L., George, M.S., 2010a. Feasibility of simultaneous cognitive behavioral therapy and left prefrontal rTMS for treatment resistant depression. Brain Stimul. 3 (4), 207e210. Vertes, R.P., 2004 Jan. Differential projections of the infralimbic and prelimbic cortex inthe rat. Synapse 51 (1), 32e58. PubMed PMID: 14579424. Wing, V.C., et al., 2013. Brain stimulation methods to treat tobacco addiction. Brain Stimul. 6 (3), 221e230. Wolters, A., et al., 2003. A temporally asymmetric Hebbian rule governing plasticity in the human motor cortex. J. Neurophysiol. 89 (5), 2339e2345. Ziemann, U., Rothwell, J.C., 2000 Jul. I-waves in motor cortex. J Clin Neurophysiol 17 (4), 397e405. PubMed PMID: 11012042.

Chapter 23

Pharmacological cognitive enhancers MacKenzie R. Peltier1, 2 and Mehmet Sofuoglu1, 2 1

Yale School of Medicine, Department of Psychiatry, New Haven, United States; 2VA Connecticut Healthcare System, West Haven, United States

Introduction

Executive functioning

Substance use disorders (SUDs) remain a significant public health problem with over 250 million current illicit drug users worldwide (United Nations Office on Drug and Crime, 2017). Despite recent developments in pharmacological interventions, no medications are available for the treatment of methamphetamine, cocaine, or cannabis use disorders (Sofuoglu and Kosten, 2005; Hill and Sofuoglu, 2007; Sofuoglu et al., 2010). Additionally, while there are several effective pharmacological treatments available for nicotine, alcohol, and opioid use disorders, relapse rates remain high. This highlights the imperative need for the development of new pharmacological interventions to treat SUDs. This chapter will first provide a brief overview of cognitive functioning as it relates to SUD and the rationale for targeting cognitive enhancement in the treatment of SUDs. It will present a summary of potential target mechanisms to address cognitive deficits related to drug use. This will be followed by discussion of candidate medications for cognitive enhancement for substance use.

Executive control is related to the dorsolateral/medial, superior frontal, and orbitofrontal networks of the prefrontal cortex (Abernathy et al., 2010; McGuire and Botvinick, 2010). These networks regulate the majority of an individual’s goal-directed behavior and conflict resolution, as well as determine the importance of environmental information. Disruptions in executive control have been postulated to play a role in compulsive drug use (Everitt et al., 2008; Goldstein and Volkow, 2011; Robbins and Arnsten, 2009; Sofuoglu et al., 2013). Monoamines, including dopamine, serotonin, and norepinephrine, as well as orexin and acetylcholine, impact these functions (Robbins and Arnsten, 2009). Executive functioning consists of numerous cognitive functions including working memory, sustained attention, problem solving, decision-making, response inhibition, and cognitive flexibility (Friedman et al., 2008). Of note, working memory, sustained attention, and response inhibition have been previously identified as potential target mechanisms for the treatment of SUDs and will be further described below (Sofuoglu et al., 2013; de Wit, 2009; Eagle et al., 2008a,b; Grégoire et al., 2012). Response inhibition is described as an individual’s capacity to voluntarily inhibit an automatic process. Response inhibition is often probed through tasks including the Stop-Signal Task (SST) and Go/No-Go. These tests are timed choice response tasks, which assess inhibition of a response that has already begun (Eagle et al., 2008a,b; Littman and Takács, 2017). The SST requires individuals to respond as quickly as possible to a series of choices (e.g., press corresponding button for the presented direction of the arrow), with a stop signal presented after the choice in some instances, which requires the individual to withhold the response (Band and van Boxtel, 1999). Similarly, during the Go/No-Go, the individual responds to the “go” or “no-go” choice as quickly as possible; however, a stop signal is presented with the initial

Cognitive function within the context of substance use disorder Cognitive function is broadly defined as one’s mental processing and includes domains of learning, memory, emotions, executive function, language, and sensory motor processing (Sachdev et al., 2014). A cognitive model of SUD is proposed in the dual-process theory. Accordingly, SUD can be viewed as a conflict between automatic or implicit cognitions, which generally enhance the risk of drug taking and relapses, and executive or explicit cognitions, which generally inhibit the automatic cognitions as well as the risk of drug use or relapse (Sava et al., 2009; Sofuoglu et al., 2013). These processes are also relevant when discussing drug use and subsequent treatment.

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00023-X Copyright © 2020 Elsevier Inc. All rights reserved.

303

304 Cognition and Addiction

signal, requiring the individual to withhold the choice response (Eagle et al., 2008a,b). Impaired response inhibition function, as measured by either task, has been associated with substance use. Specifically, individuals with cocaine and methamphetamine dependence have shown worse response inhibition performance than healthy controls, and poor response inhibition performance has been a predictor of substance use among at-risk adolescents (Li et al., 2006; Fernández-Serrano et al., 2012; Monterosso et al., 2005; Nigg et al., 2006). The brain’s norepinephrine system contributes to the response inhibition function, with research indicating that the dorsomedial prefrontal cortical areas are important for inhibiting the initiated response (Aston-Jones and Gold, 2009; Friedman et al., 2008a,b; Bari et al., 2011). Using pharmacological probes, increases in synaptic norepinephrine levels have been associated with improved response inhibition performance (Aston-Jones and Gold, 2009). For instance, atomoxetine, a norepinephrine transporter inhibitor, improves response inhibition, likely through increases of synaptic dopamine and norepinephrine levels in the prefrontal cortex (Aston-Jones and Gold, 2009; Bari et al., 2011). This finding may translate to improved ability to resist drug craving and urges by atomoxetine treatment (Bari et al., 2011). Working memory is defined as remembering an event or retrieving an event from long-term storage, to regulate behavior, including drug seeking and use (Arnsten et al., 2015). Laboratory measures of working memory have historically consisted of auditory or visuospatial span tasks, during which individuals are asked to change the provided information, by updating or manipulating it while the information is being held (Sofuoglu et al., 2013). Given that dopamine and norepinephrine are the primary neuromodulators associated with working memory function, monoamine transport inhibitors (e.g., atomoxetine, modafinil, and methylphenidate) and alpha2-adrenergic agonists (e.g., guanfacine) have been used in studies to probe and enhance working memory function (Marquand et al., 2011; Minzenberg and Carter, 2007; Swartz et al., 2008). Impaired working memory function has been associated with chronic cocaine and methamphetamine use, as it may be related to deficits in response inhibition, which have been suggested to facilitate substance use craving or relapse (Jovanovski et al., 2005; Scott et al., 2007; Chambers et al., 2009). Thus, working memory may be a target for novel SUD interventions. Sustained attention refers to a controlled process rooted in external stimuli, as well as executive attention. Laboratory studies often probe sustained attention through continuous performance tasks (e.g., Rapid Visual Information Processing Task; RVIP). The RVIP instructs individuals to engage with visual stimuli, which are presented rapidly, and respond to infrequently presented stimuli (Turner et al., 2005). Lapses in attention, often measured by this task, have been proposed as a precursor to drug-seeking behavior and substance use,

including methamphetamine, cocaine, cannabis, and nicotine use, which have been associated with poor sustained attention (de Wit, 2009; Jovanovski et al., 2005; Scott et al., 2007; Sofuoglu et al., 2012; Bolla et al., 2002; Simon et al., 2010; Durazzo et al., 2012). These processes are modulated by acetylcholine, as well as dopamine, norepinephrine, glutamate, and gamma-aminobutyric acid (GABA) (Levin et al., 2011). It is thought that acetylcholine release in the prefrontal cortex mediates such processes, and enhancement of dopamine, norepinephrine, and acetylcholine may prove to be pharmacological targets to improve sustained attention (Levin et al., 2011; Sofuoglu et al., 2013).

Automatic cognitive processes Automatic or implicit cognitive processes have been associated with craving, as well as substance use and subsequent relapse (Carpenter et al., 2006; Cox et al., 2006; Field et al., 2009; Rooke et al., 2008). These processes are rapid and often bypass conscious awareness. Incentive sensitization theory within the context of SUDs asserts that chronic drug use “rewires” the brain to pathologically incentivize motivation for drugs (Robinson and Berridge, 2008; Waters et al., 2009). Attentional bias describes the implicit attention to or on specific stimuli (e.g., to drugrelated stimuli), reflecting the incentive value of drug cues (Cox et al., 2014; Colzato et al., 2007; Dougherty et al., 2008; Wagner et al., 2013). Measures of attentional bias include modified Stroop tasks, during which individuals are presented with words written in a colored ink and then asked to name that ink color, and dot probe tasks, which measure the speed one visually attends to neutral stimuli when it is with other drug-related cues (Wiers and Stacy, 2006; Sofuoglu et al., 2013). Attentional bias is related to addiction as it refers to the effect of decreased or slowed performance for drugrelated cues (e.g., visual cues) and thus has been related to increased drug cue exposure, provocation of craving, and subsequent use (Sofuoglu et al., 2013). It is important to note that attentional bias is a dynamic process that is not necessarily pathological; it is not always a consequence of substance use, as it can also be impacted by such use. In fact, healthy individuals are also able to quickly learn associations between arbitrary stimuli and reward outcomes (Anderson, 2016). In light of this learned relationship between stimuli and rewards, several reviews have asserted that attentional bias is an important cognitive mechanism in the treatment of SUDs (Field and Cox, 2008; Franken, 2003). It has been suggested that addiction-related attentional bias describes the learning in which reward is paired with drug cues and those drug-associated stimuli become salient (Anderson, 2016; Leeman et al., 2014). Targeting the underlying mechanisms of this learned relationship in SUD treatment

Pharmacological cognitive enhancers Chapter | 23

may improve treatment outcomes and decrease cravings (Anderson, 2016; Leeman et al., 2014). It has been proposed that attentional bias is attenuated by several pharmacological interventions including dopamine antagonists (e.g., haloperidol), glutamatergic medications (e.g., n-acetylcysteine), and monoamine transporters (e.g., atomoxetine) (Levi Bolin et al., 2017; Passamonti et al., 2017; Franken et al., 2004). Additionally, psychological interventions such as attentional bias modification (ABM) have been associated with decreased attentional bias among alcohol and cigarette users, and cognitive behavioral therapy (CBT) has also shown promise in attenuating attentional bias in cocaine users (Leeman et al., 2014; DeVito et al., 2018). Given the relevance to addictive disorders, these mechanisms may serve as potential treatment targets for cognitive enhancement approaches.

Cognitive deficits in substance use disorders A large body of literature has established that chronic substance use is associated with significant cognitive deficits, with impairments spanning decision-making, attentional functioning, working memory, response inhibition, and other executive functions (for review, see Sofuoglu et al. (2013)). This relationship has often been postulated to be dose-related with greater use associated with greater deficits (Bolla et al., 2002; Colzato et al., 2007; Dougherty et al., 2008). However, it is important to note that cognitive deficits and drug use may not be causally linked; this association is also complicated by other moderating factors (Sofuoglu et al., 2013). Cognitive deficits may be a preexisting feature, which may place individuals at increased risk for initiating and escalating drug use (Wagner et al., 2013; Grant et al., 2012; Squeglia et al., 2014; Aharonovich et al., 2017). For instance, one study demonstrated that tobacco smokers were more likely to have poor performance on tests of visual attention and impulsivity than nonsmokers; these differences were not related to lifetime cigarette exposure (Wagner et al., 2013). This indicates that the cognitive impairment observed may have been present before onset of cigarette use (Wagner et al., 2013). Furthermore, when comparing cognitive function and personality traits associated with drug use, in stimulant-dependent individuals and their siblings without a drug use history, cognitive deficits were found in both siblings using substances and those who were not drug-dependent (Ersche et al., 2012). Conversely, there is also evidence for drug-related cognitive deficits. A recent longitudinal study demonstrated that moderate cocaine use was related to cognitive impairment among 57 cocaine users. After 12 months, participants who reduced cocaine use improved on cognitive tasks, while those increase their cocaine use exhibited poorer

305

performance on tests of working memory (Vonmoos et al., 2014). Similarly, previously naive 3,4-methylenedioxymethamphetamine (MDMA) users were followed over 1 year and those using more than 10 MDMA pills had poorer performance on a test of learning, as compared to controls, despite no previous cognitive differences between the groups (Wagner et al., 2012). Furthermore, many psychiatric disorders are also associated with cognitive impairments and thus may make an individual more vulnerable to abuse substances (Sofuoglu et al., 2013; Bakhshaie et al., 2015; Carrà et al., 2017). For instance, in a study of cocaine-dependent adults, those with attention-deficit/hyperactivity disorder (ADHD) demonstrated greater cognitive impairments than those without an ADHD comorbidity (Cunha et al., 2013). This demonstrates that preexisting psychiatric symptoms may impact underlying cognitive functions, thus increasing an individual’s vulnerability for drug use. Cognitive impairments have also been generally linked to poorer treatment retention for SUDs; studies of individuals using alcohol, cocaine, cannabis, and methamphetamines demonstrate that those who do not complete SUD treatment have significantly worse performance on measures assessing a range of cognitive functions and domains (e.g., attention, memory, abstract reasoning) compared with individuals completing SUD treatment (Carroll et al., 2014; Dean et al., 2009; Verdejo-García et al., 2007; Copersino et al., 2012; Bates et al., 2013). Furthermore, it has been consistently established that cognitive impairments, including executive function and inhibitory control, are associated with increased treatment attrition (Verdejo-García et al., 2007; Copersino et al., 2012; Brewer et al., 2008; Streeter et al., 2007; Turner et al., 2009). Thus, it is postulated that cognitive impairments observed in executive and implicit mechanisms may be predictors of SUD treatment engagement and positive treatment outcomes. These deficits also hinder the ability of individuals in SUD treatment to learn new coping skills as part of their recovery. Many psychological interventions, including CBT, strive to improve cognitive control and enhance executive functioning through cognitively demanding treatment tasks (DeVito et al., 2018; Sofuoglu et al., 2016). Cognitive deficits are often associated with decreased ability to maintain daily living activities (e.g., working and independent living) and impaired social capacity, among individuals with schizophrenia (Eack et al., 2007; Thaker and Carpenter Jr, 2001; Tripathi et al., 2018). Evidence within the context of SUDs supports this hypothesis, as it has been shown that the cognitive deficits may interfere with the ability of the individual to appropriately engage in the intervention’s tasks, thus potentially mediating poorer treatment outcomes (Bates et al., 2013). Given this information, further investigation of these cognitive deficits as targets for pharmacological intervention is warranted to

306 Cognition and Addiction

improve treatment retention and outcomes. To date, cholinergic medications, monoamine transporters, dopamine antagonists (e.g., antipsychotics), glutamatergic medications, GABAergic medications, and sex steroids have been explored as underlying mechanisms and possible interventions. The recent relevant findings are discussed below.

demonstrated that among detoxified individuals with alcohol use disorder, they consumed less alcohol per day than those receiving placebo, although there was no effect on time until first severe relapse (Mann et al., 2006). Thus, galantamine has demonstrated promising results in these studies for the treatment of SUDs, including alcohol, cocaine, and nicotine use.

Target mechanisms

Rivastigmine

Table 23.1 summarizes the most recent evidence of cognitive functioning enhancement in each of the following targets within the context of SUDs.

Rivastigmine is an acetylcholinesterase inhibitor, which has demonstrated efficacy for many of the cognitive and functional problems related to Alzheimer’s disease (Corey-Bloom et al., 1998; Winblad et al., 2007). In a recent study of occasional cannabis users, 3 mg/day pretreatment of rivastigmine attenuated effects of delayed recalls and there was a trend of improvement on immediate recall (Theunissen et al., 2015). Additionally in another study, cocaine-dependent, nonetreatment-seeking participants demonstrated improved working memory span following administration of rivastigmine (3 mg or 6 mg/day) (Mahoney et al., 2014). In terms of substance use outcomes, rivastigmine demonstrated reductions in tobacco cravings and consumption (30% decrease in cigarettes per day over 12-week study) in alcohol-dependent daily smokers who were randomized to receive either 6 mg/day of rivastigmine or placebo (Diehl et al., 2009). Evidence suggests that further investigation of acetylcholinesterase inhibitors is warranted.

Cholinergic medications Pharmacological interventions targeting the cholinergic system have been well-established to improve cognitive functions. Cholinesterase inhibitors have been shown to increase concentration of acetylcholine at the synapse and thus increase acetylcholine transmission. Acetylcholine has been associated with improved attention, working memory, motivation, and reward (Mooney et al., 2009; Sarter et al., 2014; Mark et al., 2011). Accordingly, cholinesterase inhibitors have been approved for the treatment of Alzheimer’s disease and are currently being explored as novel treatments for schizophrenia or traumatic brain injury (Sofuoglu et al., 2016; Pae, 2013; Bengtsson and Godbolt, 2016). Recent interest in their utility to improve cognitive function in the treatment of SUDs is explored below.

Galantamine Galantamine is not only an acetylcholinesterase inhibitor but it is also an allosteric potentiator of the nicotinic acetylcholine receptor, notably in the a7 and a4b2 subtypes (Schilström et al., 2006). Several studies have demonstrated the potential use of galantamine as a treatment for SUDs. For example, the use of galantamine (8 mg/day) in a double-blinded study of methadone-maintained cocaine-dependent participants resulted in a reduction in frequency of cocaine use over time. However, it did not demonstrate improvement in cognitive functioning, including in sustained attention (Carroll et al., 2018). Another placebo-controlled study of galantamine (8 mg/day) improved sustained attention and response inhibition among abstinent cigarette smokers. It was also found to attenuate the positive subjective effects of intravenous nicotine (Sofuoglu et al., 2012). Similar results were reported by Ashare et al. (2016), demonstrating its potential utility for smoking cessation. Following 2 weeks of daily galantamine administration (8 mg/day week one; 16 mg/day week two), decreased cigarettes smoked per day, as well as positive subjective effects of smoking as compared to placebo, were observed (Ashare et al., 2016). In addition to the treatment of cocaine and tobacco use disorders, galantamine has shown promise in the treatment of alcohol use disorder. One study

Donepezil Donepezil is another acetylcholinesterase inhibitor utilized to increase cortical acetylcholine for the treatment of Alzheimer’s disease (Repantis et al., 2010). Following 3 days of daily administration of 5 mg of donepezil in cocainedependent participants, donepezil increased subjective ratings, including “any” and “good” drug effects, following low-dose intravenous cocaine administration (Grasing et al., 2010). Looking across intravenous cocaine doses, donepezil decreased dysphoric and somatic symptoms, but did not affect the number of cocaine injections participants selfadministered within the laboratory paradigm (Grasing et al., 2010). Additionally, daily doses of donepezil (10 mg) for 10 weeks were associated with decreases in cocaine severity scores (as measured by self- and study physician-rated Clinical Global Impression scales) and self-reported cocaine use (Winhusen et al., 2005). However, to date, no study has explored donepezil’s effect on cognitive functioning within the context of substance use. Given the limited data, it is unclear if donepezil is a potential pharmacological intervention for SUDs.

Varenicline Varenicline is a partial agonist of the a4b2, as well as a full agonist of a7, which has demonstrated efficacy for

TABLE 23.1 Studies of pharmacological enhancers in the context of substance use. Target Acetylcholine

Medication Galantamine

Rivastigmine

Dose/Design

Enhanced Cognitive Domain

Cognitive Measure 0

Participants

Results

Carroll et al., 2018

8 mg/day for 12 weeks; between-subjects

Memory and sustained attention

RVP A ; intra/extradimensional set shifting

120 methadonemaintained, cocainedependent participants (28 galantamine þ CBT4CBT; 27 galantamine þ TAU; 38 placebo þ CBT4CBT; 27 placebo þTAU)

Reduction in frequency of cocaine use over time; no improvement in cognitive functioning

Sofuoglu et al., 2012

Two 4-day treatments of 8 mg/day; within-subject

Sustained attention and response inhibition

SART

12 nonetreatment-seeking smokers

Attenuated subjective response to nicotine; showed improved performance on No-Go trials

Theunissen et al., 2015

20 mg pretreatment; withinsubject

Memory, perceptual motor control, attention, and motor impulsivity

Visual verbal learning task; prospective memory test; Sternberg memory test; critical tracking task; divided attention task; Stop-Signal Task

15 occasional cannabis users

Attenuated effects on delayed recall; trended toward improvement on immediate recall

Mahoney et al., 2014

3 or 6 mg for 7 days; betweensubjects

Attention, verbal/ episodic memory, and working memory

CPT-II; HVLT-R; Dual N-Back task

41 cocaine-dependent, nonetreatment-seeking participants

Improved working memory span; no substance use outcome measures included

Roberts and McKee, 2018

1 or 2 mg/day; between-subjects

Working memory, sustained attention, and response inhibition

Digit span backward; CPT

55 heavy drinkers, meeting criteria for AUD

Reduced alcohol use; dose-dependent increase in working memory; faster reaction time; no improvement of inhibitory control or sustained attention

Verplaetse et al., 2016

1 or 2 mg/day; between-subjects

Sustained attention, response inhibition, working memory, processing speed, and perceptional motor performance

CPT; DSST; NBack; Pursuit Rotor task

44 participants meeting criteria for AUD

Attenuated increases in subjective intoxication, perceptual motor response; attenuated alcohol-related executive functioning decreases

307

Continued

Pharmacological cognitive enhancers Chapter | 23

Varenicline

Authors

Enhanced Cognitive Domain

Cognitive Measure

Participants

Results

200 mg/study session; within-subject

Cognitive control and cognitive performance

Stroop task; withinnetwork and between-network functional connectivity (fMRI)

31 participants (15 AUD; 16 control)

Improved cognitive control; induced changes in betweennetwork functional connectivity in AUD indicating enhanced cognitive performance

Kalechstein et al., 2010

200 mg/daily; between-subjects

Attention/information processing, episodic memory, and working memory

CPT-II; HVLT-R; Dual n-back task

61 cocaine-dependent individuals (16 modafinil; 16 escitalopram; 15 modafinil þ escitalopram; 14 placebo)

Improved working memory span; trendlevel improvements in visual working memory, and sustained attention; no effect on processing speed or episodic memory

Methylphenidate

Li et al., 2010

0.5 mg/kg of body weight, IV injection; withinsubjects

Inhibitory control

Stop-Signal Task (fMRI)

10 nonetreatmentseeking, cocainedependent participants

Improved inhibitory control; evoked changes in prefrontal brain activation

Methamphetamine/ D-amphetamine

Reed and Evans, 2016

10 or 20 mg/study session; withinsubject

Impulsivity and risktaking

IMT/DMT, GoStop task, DDT, Balloon Analogue Risk Task

34 cocaine users (13 intranasal; 21 smoked)

Did not attenuate impulsive responding

Atomoxetine

Passamonti et al., 2017

50 mg/single dose; between subject

Attentional bias

Line-counting task; Go/No-Go task

28 cocaine-dependent participants; 28 healthy controls

Reduced attentional bias to drug-related cues

Antipsychotic

Haloperidol

Franken et al., 2004

2 mg/ single dose; within-subject

Attentional bias

Emotional Stroop Task

18 heroin dependent males

Improved performance on task

Alpha2adrenergic

Guanfacine

McKee et al., 2015

3 mg/day for 21 days before study sessions; between-subject

Self-control

fMRI Stroop Color Word Interference Task

33 nicotine-deprived smokers

Increased ability to resist smoking in laboratory; increased ventromedial prefrontal activity during task; did not affect Stroop results

Target Monoamine transporters

Medication

Authors

Dose/Design

Modafinil

Schmaal et al., 2013

308 Cognition and Addiction

TABLE 23.1 Studies of pharmacological enhancers in the context of substance use.dcont’d

Glutamate

Memantine

D-Cycloserine

Minocycline

40 mg; betweensubjects

Sustained attention

Word recall, RVIP, DSST PAL, SRM, PAL, AGNG

60 heavy regulareheavy smokers (smoked at least 10 CPD)

No effect on smoking behavior; failed to show any cognitive effects

Krupitsky et al., 2007

20 mg or 40 mg on study day; between-subjects

Verbal fluency and immediate/delayed recall

Verbal Fluency Test; HVLT

38 male inpatients meeting criteria for alcohol dependence

Attenuated alcohol craving in dose-related trend; no significant effects on cognitive functioning

Kalechstein et al., 2012

50 mg on each study day; between-subjects

Attention/information processing speed, episodic memory, and executive/frontal lobe functioning

CPT-II; HVLT-R; Dual N-back task;

27 concurrent cocaineand nicotine-dependent participants (15 D-cycloserine; 12 placebo)

Did not modulate neurocognitive functioning

Kamboj et al., 2011

125 mg; betweensubjects

Attentional basis

Dot probe task

36 heavy social drinkers (19 D-cycloserine; 17 placebo)

Reduction in attentional bias to alcohol cues over time; did not enhance habituation of alcohol cue reactivity

Sofuoglu et al., 2011

200 mg/day for 5 days and then assigned to either 20 mg/70 DAMP or placebo/DAMP; within-subject

Sustained attention

SART

10 healthy participants

Attenuated DAMPinduced subjective reward; increased reaction times on Go/NoGo task

Sofuoglu et al., 2009

200 mg/day for 5day treatment periods; withinsubject

Sustained attention

SART

12 nonetreatment-seeking smokers

No effect on smoking SA, withdrawal or cognitive performance

Levi Bolin et al., 2017

2400 mg/day for four practice days and then seven maintenance days; within-subject

Attentional bias

Visual probe task

14 individuals meeting criteria for cocaine abuse or dependence (DSM-TRIV)

Attenuated attentional bias

Continued

Pharmacological cognitive enhancers Chapter | 23

N-acetylcysteine

Jackson et al., 2009

309

Enhanced Cognitive Domain

Cognitive Measure

Participants

Results

12 mg on two study days over 12day experiment; between-subjects

Motor learning and impulsivity

CPT-AX; Motor Sequence Task

6 nonetreatment-seeking, cocaine-dependent subjects

Did not differ from placebo in cue responses and false alarm rates; positive correlation between motor learning and total sleep time

Sofuoglu et al., 2005

4 or 8 mg single dose; withinsubjects

Response inhibition

Stroop test

12 nonetreatment-seeking smokers

Attenuated cigarette craving and enhanced performances on Stroop test

Fox et al., 2013

400 mg/day for 7 days; betweensubjects

Inhibitory control

Stroop Color Word Task

42 abstinent, treatmentseeking cocainedependent individuals

Attenuated drug craving; cognitive performance improved across conditions

Sofuoglu et al., 2011

200 or 400 mg/ study day; withinsubjects

Sustained attention, response speed, visuomotor coordination, and response inhibition

DSST; Stroop test

64 male and female abstinent smokers

Improved cognitive performance and reduced urge to smoke

Target

Medication

Authors

Dose/Design

GABA

Tiagabine

Morgan and Malison, 2008

Sex steroids

Progesterone

AGNG, Affective Go/No-Go; AUD, alcohol use disorder; BID, twice a day; CPD, cigarettes per day; CPT, continuous performance task; DAMP, dextroamphetamine; DDT, Delay-Discounting Task; DSST, Digit Symbol Substitution Task; HVLT-R, Hopkins Verbal Learning Test-Revised; IMT/DMT, Immediate Memory Task/Delayed Memory Task; PAL, Paired Associates Learning; RVIP, Rapid Visual Information Processing Task; RVP A’, Rapid Visual Information Processing task; SA, self-administration; SART, Sustained Attention to Response Test; SRM, spatial recognition memory.

310 Cognition and Addiction

TABLE 23.1 Studies of pharmacological enhancers in the context of substance use.dcont’d

Pharmacological cognitive enhancers Chapter | 23

smoking cessation. Recent studies have suggested that varenicline may improve drinking outcomes through the enhancement of cognitive functioning (Roberts and McKee, 2018; Verplaetse et al., 2016). Roberts and McKee (2018) randomized adult heavy drinkers to receive varenicline (1 mg/day; 2 mg/day) or placebo. Among those participants receiving 2 mg/day, improvements in working memory were associated with decreased drinking during an ad-lib drinking task (Roberts and McKee, 2018). Comparable results were observed in a similar study, in which varenicline attenuated increases in subjective intoxication, as well as perceptual motor responses (Verplaetse et al., 2016). In addition to effects on drinking outcomes, varenicline has also shown improvements in reaction time to both visual and auditory stimuli among methamphetamine-dependent participants, receiving oral varenicline (titrated up to 1 mg), as well as improvements in working memory and attention during nicotine withdrawal (Kalechstein et al., 2014; Patterson et al., 2009). These promising results are indicative of the potential utility of varenicline to improve cognitive functioning and subsequently positively impact substance use outcomes.

Monoamine transporter inhibitors Modafinil Modafinil is a weak inhibitor of dopamine and norepinephrine transporters and it has been shown to also affect GABA, glutamate, and orexin (Minzenberg and Carter, 2007; Sofuoglu et al., 2016). To date, modafinil has been used as a wakefulness promoter and its cognitive-enhancing effects have been used to treat various neuropsychiatric disorders, including several SUDs (Minzenberg and Carter, 2007). With regard to methamphetamine use disorder, 7 days of 200 mg/day modafinil was associated with improved immediate verbal function among individuals following detoxification from methamphetamine (Hester et al., 2010). Higher doses of modafinil (400 mg/day for 3 days) have also been associated with improved working memory in this population (Kalechstein et al., 2010). Consistent with these findings, modafinil has also been shown to attenuate positive subjective ratings of intravenous cocaine (Verrico et al., 2014; Hart et al., 2007). Additionally, among individuals meeting criteria for alcohol dependence, those receiving a single dose of modafinil (200 mg) demonstrated improved response inhibition among those participants who had poor baseline levels of response inhibition. This finding was likely mediated through the greater observed activation in the thalamus and supplementary motor area (Schmaal et al., 2013). Furthermore, another study administrating modafinil 300 mg/day for 10 weeks to individuals with alcohol use disorder demonstrated similar results; improvements were

311

observed in self-reported state impulsivity, and among those with poor baseline response inhibition, modafinil increased the percentage of abstinent days and increased the time to relapse. Of note, in individuals with better baseline response inhibition, modafinil increased drinking behavior (Joos et al., 2013). Results warrant further exploration of the role of response inhibition to substance use and the utility of modafinil to augment this cognitive function.

Methylphenidate Methylphenidate is also an inhibitor of dopamine and norepinephrine transporters and has been shown to be an effective intervention for improving response inhibition in ADHD (DeVito et al., 2009). There was initial promise in methylphenidate’s ability to reduce cocaine use. One study of a single 20 mg dose of methylphenidate increased responses to rewarded drug cue reactivity task, which in turn was associated with reduced behavioral measure of impulsivity (Goldstein et al., 2010). Consistent with these findings, other studies have demonstrated improvements in response inhibition, as well as decreased cocaine use (Levin et al., 2007; Li et al., 2010). However, a recent review (see Dürsteler et al. (2015)) describes overall inconsistent findings, highlighting several negative clinical trials. Considering this, the clinical utility of methylphenidate remains to be uncertain.

Oral methamphetamine/D-amphetamine Oral methamphetamine and D-amphetamine (the latter is an enantiomer of amphetamine) are utilized to treat ADHD through indirectly increasing dopaminergic functioning (Mooney et al., 2009; Reed and Evans, 2016). Previous limited evidence showed that oral D-amphetamine increases impulsivity, as measured by inhibitory responding, among smoked cocaine users (Fillmore et al., 2002, 2003). However, a recent study demonstrated that those receiving the 10 mg oral D-amphetamine demonstrated a blunted response to the positive subjective drug effects, while those in the 20 mg group endorsed higher ratings of cocaine craving. Across dose groups, there was little effect on measures of impulsivity (Reed and Evans, 2016). Conversely, cocaine users receiving sustained release oral methamphetamine (30 mg) exhibited a decreased proportion of cocaine-positive urine drug screens and reported less cocaine cravings (Mooney et al., 2009). In a randomized control trial of D-amphetamine, among individuals with methamphetamine use disorder, 110 mg/day of sustained release D-amphetamine for 12 weeks was associated with increased length in treatment stay, general reduction in self-reported and biologically confirmed methamphetamine use, as well as decreased severity in addiction measures (Longo et al., 2010). These findings indicate that these substances may have promise in the treatment of SUD and

312 Cognition and Addiction

there is continued need to evaluate them in clinical trials for stimulant addiction.

Atomoxetine Atomoxetine is an inhibitor of norepinephrine transporters and has also been shown to increase norepinephrine and dopamine levels in the prefrontal cortex (Bymaster et al., 2002; Swanson et al., 2006). Among healthy volunteers, atomoxetine (40 mg) improved inhibitory control, likely through the modulation of increased activation in the right inferior frontal gyrus (Chamberlain et al., 2009). Furthermore, the same research group studying a 60 mg dose of atomoxetine demonstrated similar improvements in inhibitory control among individuals diagnosed with ADHD (Chamberlain et al., 2007). To date, only two studies have explored the utility of atomoxetine for the treatment of SUDs. Passamonti et al. (2017) administered a single 40 mg oral dose of atomoxetine to 28 cocaine-dependent individuals and 28 healthy controls (Passamonti et al., 2017). Atomoxetine, when compared to placebo, attenuated attentional bias, as measured by linecounting task with cocaine-related and neutral pictures, in the cocaine-dependent individuals (Passamonti et al., 2017). Additionally, daily administration of 80 mg of atomoxetine among individuals with opioid/amphetamine dependence, who were maintained on buprenorphine/naloxone, was shown to decrease depressive symptoms and also the use of amphetamine-based stimulants (e.g., combinations of methamphetamine and amphetamine), as measured by urine toxicology (Schottenfeld et al., 2018). Thus, illustrating atomoxetine’s promise to improve inhibitory control in this population and potentially augment established SUD treatments.

Antipsychotic Haloperidol It has been hypothesized, primarily based on preclinical studies, that increases in dopaminergic activity in the corticostriatal reward circuit following drug-related cues may contribute to enhanced attentional bias toward these salient drug-related cues, thus leading to continued drug use (Franken et al., 2004; Robinson and Berridge, 1993). Haloperidol is a first-generation antipsychotic, traditionally used in the treatment of schizophrenia spectrum disorders, by blocking dopaminergic D2 receptors throughout the central nervous system (Risch, 1996; Richelson, 1999; Jibson, 2018). To date, few studies have explored the utility of haloperidol for the treatment of SUDs (Franken et al., 2004; Berger et al., 1996). It has been demonstrated that an oral 4 mg dose of haloperidol decreased cue-elicited cocaine cravings in individuals meeting criteria for cocaine dependence (Berger et al., 1996); however, an oral 2 mg dose did not attenuate opioid cravings among detoxified individuals with opioid use disorder (Franken et al., 2004). In the same

study of abstinent individuals with opioid use disorder, a single dose of haloperidol was found to improve performance on measures of attentional bias, thus indicating that dopaminergic mechanisms may mediate cognitive functioning in substance-using populations (Franken et al., 2004). Further research is needed to elucidate the role of haloperidol in improving attentional biases in SUD populations.

Alpha2-adrenergic agonist Guanfacine Guanfacine is an alpha2-adrenergic agonist that reduces norepinephrine activity via stimulation of presynaptic alpha2-adrenergic agonist receptors. It has been utilized in the treatment of hypertension and ADHD (Sofuoglu et al., 2013). One study of nicotine-deprived smokers receiving guanfacine (3 mg/day) demonstrated altered prefrontal activity during the Stroop cognitive control task and reduced cigarette use (McKee et al., 2015). It has also been shown to improve working memory among healthy individuals, as well as those with ADHD or schizophrenia (Swartz et al., 2008; Friedman et al., 2001; Scahill et al., 2001). Further study of guanfacine among participants with SUDs is needed to determine its utility to assist in cessation treatments.

Glutamatergic medications Memantine Memantine is a noncompetitive NMDA antagonist that is used to enhance cognitive functioning in Alzheimer’s disease (Hashimoto, 2009; Sofuoglu et al., 2013). Regarding the utility of memantine in the treatment of addictions, Krishnan-Sarin et al. (2015) reported that among individuals with higher baseline levels of impulsivity, those receiving 20 mg/day (titrated for 8 days) of memantine reported not only reduced alcohol craving, as previously observed (Krupitsky et al., 2007), but also increased alcohol drinking (Krishnan-Sarin et al., 2015). These findings are similar to previously negative clinical trials, in which memantine did not improve alcohol, nicotine, or cocaine dependence (Bisaga et al., 2011; Evans et al., 2007; Jackson et al., 2008). Conversely, in a study of opioid-dependent young adults, 30 mg memantine, in combination with buprenorphine/naloxone, showed promise in the short-term treatment opioid use disorder, reducing relapse and subsequent opioid use after individuals stopped taking buprenorphine/naloxone (Gonzalez et al., 2015), thus warranting further exploration of memantine’s utility to augment opioid use disorder treatments. D-Cycloserine D-Cycloserine (DCS) is a partial agonist of the NMDA-type glutamate receptor, at the glycine site (Sofuoglu et al., 2013). It has demonstrated utility as an augmentation for

Pharmacological cognitive enhancers Chapter | 23

behavioral interventions for anxiety disorders (McNally, 2007; Ressler et al., 2004; Wilhelm et al., 2008). Furthermore, among healthy individuals, it is has been shown to improve declarative memory (Onur et al., 2010). While a recent study of a 50 mg dose of DCS did not attenuate cocaine cue reactivity, as measured through subjective craving and physiological reactivity, it has been shown to reduce smoking urges and physiological reactivity in response to smoking cues among nicotine-dependent participants (Santa Ana et al., 2015; Santa Ana et al., 2009). Additionally, evidence suggests that 50 mg of DCS enhanced reductions in alcohol cravings during an extinction therapy session, an effect that was observed throughout all subsequent extinction sessions (MacKillop et al., 2015). However, enhancement of neurocognitive function among substance users remains mixed (Kalechstein et al., 2014; Kamboj et al., 2011). This indicates that DCS may have promise as a treatment for specific substances of abuse, but further research is needed.

Minocycline Minocycline, an antibiotic that treats acne, has been explored for its potential cognitive-enhancing effects in the treatment of neurodegenerative and neuropsychiatric disorders. For instance, there is preliminary evidence that minocycline improves the negative symptoms of schizophrenia and attention (as measured by the continuous performance test, identical pairs), when added to an atypical antipsychotic treatment protocol (Liu et al., 2014). There is additional evidence that minocycline improves performance on measures of working memory, as well as avolition and anxiety/depressive symptomology in treatment refractory patients with schizophrenia or schizoaffective disorder (Kelly et al., 2015). Regarding its use for treatment of SUDs, research has shown that, among healthy controls, 4e5 days of 200 mg/day minocycline improved response inhibition and attenuates subjective reward effects in response to dextroamphetamine (Sofuoglu et al., 2011a). Additionally, minocycline demonstrated a greater reduction in craving for cigarettes within an IV nicotine paradigm, among smokers receiving 4 days of 200 mg/day minocycline treatment (Sofuoglu et al., 2009a). This illustrates its potential use in the treatment of nicotine and exploration of its use in the treatment of other stimulant use disorders.

N-Acetylcysteine N-acetylcysteine (NAC) is an agent that modulates glutamatergic pathways, which has begun to be studied in psychiatric disorders (for review, see Dean et al. (2011)). Of note, it has also been shown to normalize glutamate levels among cocaine-dependent patients, and thus, it is currently being explored as an intervention for SUDs (Schmaal et al., 2012). It has begun to be studied specifically as a treatment for adolescent cannabis use disorder. A

313

recent clinical trial has established that treatment-seeking adolescents with cannabis use disorder receiving 1200 mg twice per day had 2.4 times the odds of having negative urine test for cannabis during treatment (Gray et al., 2012). NAC has also demonstrated utility in the treatment of cocaine use disorder. A recent study administering 2400 mg/day over four practice days and seven maintenance days demonstrated that NAC attenuated salience of cocaine-related cues (Levi Bolin et al., 2017). Furthermore, secondary analysis demonstrated that among more impulsive patients who were medication adherent, NAC had increased abstinence rates (Bentzley et al., 2016). In terms of smoking abstinence, NAC has also shown promise. One study of frontal striatal resting-state functional connectivity demonstrated that participants receiving 1200 mg twice per day reported less nicotine craving and increased positive affect; additionally, they exhibited strong frontal striatal resting-state functional connectivity and continued abstinence (Froeliger et al., 2015). This evidence suggests that NAC has potential utility as an intervention for SUDs via its modulation of glutamatergic pathways.

GABAergic medications Tiagabine Several studies have demonstrated that tiagabine treatment attenuates the subjective ratings of stimulant administration (cocaine and nicotine). One study showed that following two oral doses of 4 mg of tiagabine participants reported attenuated subjective ratings of “stimulated” and “cocaine craving,” after intravenous cocaine administration (Sofuoglu and Kosten, 2005). Another study of cocaine-dependent participants demonstrated decreased levels of the inactive cocaine metabolite, benzoylecgonine, following daily doses of tiagabine (20 mg/day) and weekly CBT for 10 weeks (Winhusen et al., 2005). Additionally, among overnight abstinent smokers in a double-blind, placebo-controlled cross-over study, 8 mg of tiagabine demonstrated that it attenuated positive subject ratings and craving in response to intravenous nicotine, as well as improved performance in the Stroop test (Sofuoglu et al., 2005). Of note, one study demonstrated that tiagabine did not differ from placebo responding on a vigilance task (Morgan and Malison, 2008). Accordingly, tiagabine shows promising results for reducing drug craving and positive drug effects through improvement of cognitive functioning.

Exogenous sex steroids Estradiol Accumulating evidence suggests that estradiol modulates cognitive functioning, with higher levels of estradiol associated with improvement on performances on tests of verbal fluency, fine motor skills, verbal memory, spatial abilities, and

314 Cognition and Addiction

perceptual speed (Luine, 2014). Despite these cognitive improvements, high levels of estradiol are generally associated with increased drug craving and response, likely through increasing levels of dopamine in the brain (Becker and Hu, 2008). In the preclinical literature, administration of estradiol has been associated with enhanced positive drug effects, motivation for drug use, and enhanced behavioral response to drugs (for review, see Becker and Hu (2008); Moran-Santa Maria et al. (2014)). Additionally, among clinical populations, increased levels of endogenous estradiol have been associated with heightened reinforcing drug effects and enhanced subjective, positive drug responses, most notably nicotine and cocaine (for review, see Moran-Santa Maria et al. (2014)). Thus, despite estradiol being associated with improved cognitive functioning, it is unclear if it has utility in the treatment of SUDs.

Progesterone Evidence has emerged that exogenous progesterone may be utilized as a pharmacological treatment for SUDs, as it has a wide range of effect of the brain including modulating cognitive functioning. Administering micronized, exogenous progesterone, which interacts with multiple neurotransmitter receptors (e.g., sigma, glutamate, GABAA, and nicotinic), has been shown to have positive effects on cognitive inhibition in individuals abusing nicotine and cocaine (Baulieu, 1998; Turkmen et al., 2011; Lynch and Sofuoglu, 2010; Milivojevic et al., 2016; Fox et al., 2013; Sofuoglu et al., 2011a). Men and women abstaining from cocaine demonstrated enhanced performance on the Stroop Color Word Task following 7 days of administration of 400 mg/day progesterone (Fox et al., 2013). Similarly, a dose of 200 mg progesterone improved inhibitory control performance (measured by the Stroop Test) in women tobacco smokers (Sofuoglu et al., 2011b). Compared with placebo, progesterone has also been shown to enhance cognitive performance on the Digit Symbol Substitution Test in abstinent tobacco smokers and improve scores on the Thought Facilitation Task scale on the Mayer-Salovey-Caruso Emotional Intelligence Test among cocaine users who also abuse alcohol (Sofuoglu et al., 2011b; Milivojevic et al., 2014). In addition to enhancing cognitive effects, exogenous progesterone has been shown to also reduce drug craving/ urges and attenuate subjective positive effects of substances, including nicotine and cocaine (for review, see Lynch and Sofuoglu (2010); Evans (2007)). This promising evidence suggests that progesterone may modulate effects of drugs of abuse, at least in part, through cognitive enhancement.

Conclusions There is a large body of literature that associates long-term drug use with significant cognitive impairments, which negatively impact SUDs treatment retention and outcomes.

Attenuating these impairments may prove to be a novel area for the development of pharmacological interventions to improve treatment of SUDs. Review of the current literature indicates that many cognitive-enhancing pharmacological therapeutics are available; however, only limited studies to date have explored their utility in the treatment of SUDs. Among these limited studies, preliminary evidence suggests that minocycline may be one promising candidate for the future treatment of SUDs. Minocycline has generally decreased craving and other drug-related positive subjective effects across several stimulants in preliminary studies (Sofuoglu et al., 2009a, 2011a). Given the evidence that minocycline also improves cognitive functions such as attention, response inhibition, and working memory, as well as targets negative symptoms associated with schizophrenia, exploration of the treatment utility of minocycline across substances use of abuse is promising (Liu et al., 2014; Kelly et al., 2015; Sofuoglu et al., 2011a). Use of progesterone in the treatment of SUDs also warrants further investigation, as current research demonstrates that it improves cognitive functions important in SUD treatment, such as response inhibition and information processing, while also decreasing urges/cravings for substances and positive subjective effects (Lynch and Sofuoglu, 2010; Fox et al., 2013; Evans, 2007; Sofuoglu et al., 2009b). Additionally, oral methamphetamine and dextroamphetamine, as well as derivatives of these substances, have shown promise in generally lowering drug cravings and reducing use of some stimulants (Mooney et al., 2009; Reed and Evans, 2016; Longo et al., 2010); however, future research is needed to investigate the abuse potential of these substances and determine novel directions for their development in SUDs treatment. Based on the current literature, it is difficult to draw overarching conclusions regarding the utility of these treatments to modulate underlying cognitive mechanisms to treat SUDs. There is a clear need for standardized measurements to assess the modulation of cognitive functioning in substance use treatment outcomes, as well as identify what clinical populations are most likely to benefit from these interventions. Kwako et al. (2016) have proposed a framework for assessing key cognitive domains in substance use, the Addictions Neuroclinical Assessment (ANA), which may address this limitation (Kwako et al., 2016). The ANA includes focused assessment of three functional domains relevant to SUDs, executive functioning, incentive salience, and negative emotionality, paired with assessment of biological (e.g., neuroimaging) and psychosocial (e.g., substance use history) variables (Kwako et al., 2016). It has been postulated that the ANA will assist in the important integration of research with clinical practice, as it directly compliments the efforts of the Research Domain Criteria (RDoC) to provide a systematic framework to assess/treat SUDs as an

Pharmacological cognitive enhancers Chapter | 23

endophenotype (Kwako et al., 2016; DeVito et al., 2016). It should also be noted that ANA has been criticized for not including a decision-making measures, a relevant cognitive domain for the assessment and treatment addiction, as well as not including neurocognitive measures with established ecological validity, to improve translation between research and clinical practices (Verdejo-Garcia, 2017). Despite these potential limitations, ANA is one possible tool to elucidate the role of cognitive functioning in the etiology and development of SUDs and how modulation of addiction-relevant domains of cognitive functioning can be utilized in SUDs treatment, thus advancing clinical care (DeVito et al., 2016). Future research efforts should also focus on delineating the individual differences regarding response to pharmacological cognitive enhancers, which may affect what clinical populations would most benefit from these treatments. For instance, it is unclear based on the current evidence if individuals with mild versus more severe cognitive impairments, or those with comorbid psychiatric disorders, would be more likely to benefit from these pharmacological approaches. Additionally, it is currently unclear at what point during SUDs treatment these medications should be implemented (e.g., following abstinence) or if they may be utilized to augment current psychological interventions, such as CBT. The implementation of a standardized measurement, such as the ANA, would allow for important clarification regarding who would most likely benefit from these interventions and the way they should be implemented in an individual’s treatment plan. Overall, the present review indicates that pharmacological cognitive enhancement is a promising treatment for SUDs; however, significant future research is needed to elucidate the utility of such treatments across different substances and populations.

Acknowledgments This work was supported by the Veterans Administration Mental Illness Research, Education and Clinical Center (MIRECC), and National Institute of Drug Abuse (NIDA training grant T32DA007238).

References Abernathy, K., Chandler, L.J., Woodward, J.J., 2010. Alcohol and the prefrontal cortex. Int. Rev. Neurobiol. 91, 289e320. Aharonovich, E., Shmulewitz, D., Wall Melanie, M., Grant Bridget, F., Hasin Deborah, S., 2017. Self-reported cognitive scales in a US National Survey: reliability, validity, and preliminary evidence for associations with alcohol and drug use. Addiction 112 (12), 2132e2143. Anderson, B.A., 2016. What is abnormal about addiction-related attentional biases? Drug Alcohol Depend. 167, 8e14.

315

Arnsten, A.F.T., Wang, M., Paspalas, C.D., 2015. Dopamine’s actions in primate prefrontal cortex: challenges for treating cognitive disorders. Pharmacol. Rev. 67 (3), 681e696. Ashare, R.L., Kimmey, B.A., Rupprecht, L.E., Bowers, M.E., Hayes, M.R., Schmidt, H.D., 2016. Repeated administration of an acetylcholinesterase inhibitor attenuates nicotine taking in rats and smoking behavior in human smokers. Transl. Psychiatry 6 (1), e713. Aston-Jones, G., Gold, J.I., 2009. How we say no: norepinephrine, inferior frontal gyrus, and response inhibition. Biol. Psychiatry 65 (7), 548e549. Bakhshaie, J., Zvolensky, M.J., Goodwin, R.D., 2015. Cigarette smoking and the onset and persistence of depression among adults in the United States: 1994e2005. Compr. Psychiatry 60, 142e148. Band, G.P.H., van Boxtel, G.J.M., 1999. Inhibitory motor control in stop paradigms: review and reinterpretation of neural mechanisms. Acta Psychol. 101 (2), 179e211. Bari, A., Mar, A.C., Theobald, D.E., et al., 2011. Prefrontal and monoaminergic contributions to stop-signal task performance in rats. J. Neurosci. 31 (25), 9254e9263. Bates, M.E., Buckman, J.F., Nguyen, T.T., 2013. A role for cognitive rehabilitation in increasing the effectiveness of treatment for alcohol use disorders. Neuropsychol. Rev. 23 (1), 27e47. Baulieu, E.E., 1998. Neurosteroids: a novel function of the brain. Psychoneuroendocrinology 23 (8), 963e987. Becker, J.B., Hu, M., 2008. Sex differences in drug abuse. Front. Neuroendocrinol. 29 (1), 36e47. Bengtsson, M., Godbolt, A.K., 2016. Effects of acetylcholinesterase inhibitors on cognitive function in patients with chronic traumatic brain injury: a systematic review. J. Rehabil. Med. 48 (1), 1e5. Bentzley, J.P., Tomko, R.L., Gray, K.M., 2016. Low pretreatment impulsivity and high medication adherence increase the odds of abstinence in a trial of N-acetylcysteine in adolescents with cannabis use disorder. J. Subst. Abus. Treat. 63, 72e77. Berger, S.P., Reid, M.S., Delucchi, K., et al., 1996. Haloperidol antagonism of cue-elicited cocaine craving. Lancet 347 (9000), 504e508. Bisaga, A., Sullivan, M.A., Cheng, W.Y., et al., 2011. A placebo controlled trial of memantine as an adjunct to oral naltrexone for opioid dependence. Drug Alcohol Depend. 119 (1e2), e23ee29. Bolla, K.I., Brown, K., Eldreth, D., Tate, K., Cadet, J.L., 2002. Doserelated neurocognitive effects of marijuana use. Neurology 59 (9), 1337. Brewer, J.A., Worhunsky, P.D., Carroll, K.M., Rounsaville, B.J., Potenza, M.N., 2008. Pre-treatment brain activation during Stroop task is associated with outcomes in cocaine dependent patients. Biol. Psychiatry 64 (11), 998e1004. Bymaster, F.P., Katner, J.S., Nelson, D.L., et al., 2002. Atomoxetine increases extracellular levels of norepinephrine and dopamine in prefrontal cortex of rat: a potential mechanism for efficacy in attention deficit/hyperactivity disorder. Neuropsychopharmacology 27, 699. Carpenter, K.M., Schreiber, E., Church, S., McDowell, D., 2006. Drug Stroop performance: relationships with primary substance of use and treatment outcome in a drug-dependent outpatient sample. Addict. Behav. 31 (1), 174e181. Carrà, G., Nicolini, G., Crocamo, C., et al., 2017. Executive control in schizophrenia: a preliminary study on the moderating role of COMT Val158Met for comorbid alcohol and substance use disorders. Nord. J. Psychiatr. 71 (5), 332e339.

316 Cognition and Addiction

Carroll, K.M., Kiluk, B.D., Nich, C., et al., 2014. Computer-assisted delivery of cognitive-behavioral therapy: efficacy and durability of CBT4CBT among cocaine-dependent individuals maintained on methadone. Am. J. Psychiatry 171 (4), 436e444. Carroll, K.M., Charla, N., DeVito, E.E., Shi, J.M., Sofuoglu, M., 2018. Galantamine and computerized cognitive behavioral therapy for cocaine dependence: a randomized clinical trial. J. Clin. Psychiatry 79 (1), 17m11669. Chamberlain, S.R., del Campo, N., Dowson, J., et al., 2007. Atomoxetine improved response inhibition in adults with attention deficit/hyperactivity disorder. Biol. Psychiatry 62 (9), 977e984. Chamberlain, S.R., Hampshire, A., Müller, U., et al., 2009. Atomoxetine modulates right inferior frontal activation during inhibitory control: a pharmacological functional magnetic resonance imaging study. Biol. Psychiatry 65 (7), 550e555. Chambers, C.D., Garavan, H., Bellgrove, M.A., 2009. Insights into the neural basis of response inhibition from cognitive and clinical neuroscience. Neurosci. Biobehav. Rev. 33 (5), 631e646. Colzato, L.S., van den Wildenberg, W.P.M., Hommel, B., 2007. Impaired inhibitory control in recreational cocaine users. PLoS One 2 (11), e1143. Copersino, M.L., Schretlen, D.J., Fitzmaurice, G.M., et al., 2012. Effects of cognitive impairment on substance abuse treatment attendance: predictive validation of a brief cognitive screening measure. Am. J. Drug Alcohol Abuse 38 (3), 246e250. Corey-Bloom, J.R., Anand, J.V., Veach, J., et al., 1998. A randomized trial evaluating the efficacy and safety of ENA 713 (rivastigmine tartrate), a new acetylcholinesterase inhibitor, in patients with mild to moderately severe Alzheimer’s disease. Int. J. Geriatr. Psychopharmacol. 1, 55e65. Cox, W., Fadardi, J., Pothos, E., 2006. The addiction-Stroop test: theoretical considerations and procedural recommendations. Psychol. Bull. 132 (3), 443e476. Cox, W.M., Fadardi, J.S., Intriligator, J.M., Klinger, E., 2014. Attentional bias modification for addictive behaviors: clinical implications. CNS Spectr. 19 (3), 215e224. Cunha, P.J., Gonçalves, P.D., Ometto, M., et al., 2013. Executive cognitive dysfunction and ADHD in cocaine dependence: searching for a common cognitive endophenotype for addictive disorders. Front. Psychiatry 4, 126. de Wit, H., 2009. Impulsivity as a determinant and consequence of drug use: a review of underlying processes. Addict. Biol. 14 (1), 22e31. Dean, A.C., London, E.D., Sugar, C.A., et al., 2009. Predicting adherence to treatment for methamphetamine dependence from neuropsychological and drug use variables. Drug Alcohol Depend. 105 (1e2), 48e55. Dean, O., Giorlando, F., Berk, M., 2011. N-acetylcysteine in psychiatry: current therapeutic evidence and potential mechanisms of action. J. Psychiatry Neurosci. 36 (2), 78e86. DeVito, E.E., Blackwell, A.D., Clark, L., et al., 2009. Methylphenidate improves response inhibition but not reflectioneimpulsivity in children with attention deficit hyperactivity disorder (ADHD). Psychopharmacology 202 (1e3), 531e539. DeVito, E.E., Carroll, K.M., Sofuoglu, M., 2016. Toward refinement of our understanding of the fundamental nature of addiction. Biol. Psychiatry 80 (3), 172e173. DeVito, E.E., Kiluk, B.D., Nich, C., Mouratidis, M., Carroll, K.M., 2018. Drug Stroop: mechanisms of response to computerized cognitive

behavioral therapy for cocaine dependence in a randomized clinical trial. Drug Alcohol Depend. 183, 162e168. Diehl, A., Nakovics, H., Mutschler, J., Hermann, D., Kiefer, F., 2009. Rivastigmine reduces tobacco craving in alcohol-dependent smokers. Pharmacopsychiatry 42 (03), 89e94. Dougherty, D.M., Marsh-Richard, D.M., Hatzis, E.S., Nouvion, S.O., Mathias, C.W., 2008. A test of alcohol dose effects on multiple behavioral measures of impulsivity. Drug Alcohol Depend. 96 (1e2), 111e120. Durazzo, T.C., Meyerhoff, D.J., Nixon, S.J., 2012. A comprehensive assessment of neurocognition in middle-aged chronic cigarette smokers. Drug Alcohol Depend. 122 (1e2), 105e111. Dürsteler, K.M., Berger, E.-M., Strasser, J., et al., 2015. Clinical potential of methylphenidate in the treatment of cocaine addiction: a review of the current evidence. Subst. Abuse Rehabil. 6, 61e74. Eack, S.M., Hogarty, G.E., Greenwald, D.P., Hogarty, S.S., Keshavan, M.S., 2007. Cognitive enhancement therapy improves emotional intelligence in early course schizophrenia: preliminary effects. Schizophr. Res. 89 (1e3), 308e311. Eagle, D.M., Bari, A., Robbins, T.W., 2008a. The neuropsychopharmacology of action inhibition: cross-species translation of the stop-signal and go/no-go tasks. Psychopharmacology 199 (3), 439e456. Eagle, D.M., Baunez, C., Hutcheson, D.M., Lehmann, O., Shah, A.P., Robbins, T.W., 2008b. Stop-signal reaction-time task performance: role of prefrontal cortex and subthalamic nucleus. Cerebr. Cortex 18 (1), 178e188. Ersche, K.D., Turton, A.J., Chamberlain, S.R., Müller, U., Bullmore, E.T., Robbins, T.W., 2012. Cognitive dysfunction and anxious-impulsive personality traits are endophenotypes for drug dependence. Am. J. Psychiatry 169 (9), 926e936. Evans, S.M., 2007. The role of estradiol and progesterone in modulating the subjective effects of stimulants in humans. Exp. Clin. Psychopharmacol 15 (5), 418e426. Evans, S.M., Levin, F.R., Brooks, D.J., Garawi, F., 2007. A pilot doubleblind treatment trial of memantine for alcohol dependence. Alcohol Clin. Exp. Res. 31 (5), 775e782. Everitt, B.J., Belin, D., Economidou, D., Pelloux, Y., Dalley, J.W., Robbins, T.W., 2008. Neural mechanisms underlying the vulnerability to develop compulsive drug-seeking habits and addiction. Phil. Trans. Biol. Sci. 363 (1507), 3125e3135. Fernández-Serrano, M.J., Perales, J.C., Moreno-López, L., PérezGarcía, M., Verdejo-García, A., 2012. Neuropsychological profiling of impulsivity and compulsivity in cocaine dependent individuals. Psychopharmacology 219 (2), 673e683. Field, M., Cox, W.M., 2008. Attentional bias in addictive behaviors: a review of its development, causes, and consequences. Drug Alcohol Depend. 97 (1), 1e20. Field, M., Munafò, M.R., Franken, I.H.A., 2009. A meta-analytic investigation of the relationship between attentional bias and subjective craving in substance abuse. Psychol. Bull. 135 (4), 589e607. Fillmore, M.T., Rush, C.R., Hays, L., 2002. Acute effects of oral cocaine on inhibitory control of behavior in humans. Drug Alcohol Depend. 67 (2), 157e167. Fillmore, M.T., Rush, C.R., Marczinski, C.A., 2003. Effects of damphetamine on behavioral control in stimulant abusers: the role of prepotent response tendencies. Drug Alcohol Depend. 71 (2), 143e152.

Pharmacological cognitive enhancers Chapter | 23

Fox, H.C., Sofuoglu, M., Morgan, P.T., Tuit, K.L., Sinha, R., 2013. The effects of exogenous progesterone on drug craving and stress arousal in cocaine dependence: impact of gender and cue type. Psychoneuroendocrinology 38 (9), 1532e1544. Franken, I.H.A., 2003. Drug craving and addiction: integrating psychological and neuropsychopharmacological approaches. Prog. Neuro Psychopharmacol. Biol. Psychiatry 27 (4), 563e579. Franken, I.H.A., Hendriks, V.M., Stam, C.J., Van den Brink, W., 2004. A role for dopamine in the processing of drug cues in heroin dependent patients. Eur. Neuropsychopharmacol. 14 (6), 503e508. Friedman, J.I., Adler, D.N., Temporini, H.D., et al., 2001. Guanfacine treatment of cognitive impairment in schizophrenia. Neuropsychopharmacology 25 (3), 402e409. Friedman, N.P., Miyake, A., Young, S.E., DeFries, J.C., Corley, R.P., Hewitt, J.K., 2008. Individual differences in executive functions are almost entirely genetic in origin. J. Exp. Psychol. Gen. 137 (2), 201e225. Froeliger, B., McConnell, P.A., Stankeviciute, N., McClure, E.A., Kalivas, P.W., Gray, K.M., 2015. The effects of N-Acetylcysteine on frontostriatal resting-state functional connectivity, withdrawal symptoms and smoking abstinence: a double-blind, placebo-controlled fMRI pilot study. Drug Alcohol Depend. 156, 234e242. Goldstein, R.Z., Volkow, N.D., 2011. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 12 (11), 652e669. Goldstein, R.Z., Woicik, P.A., Maloney, T., et al., 2010. Oral methylphenidate normalizes cingulate activity in cocaine addiction during a salient cognitive task. Proc. Natl. Acad. Sci. U.S.A. 107 (38), 16667e16672. Gonzalez, G., DiGirolamo, G., Romero-Gonzalez, M., Smelson, D., Ziedonis, D., Kolodziej, M., 2015. Memantine improves buprenorphine/naloxone treatment for opioid dependent young adults. Drug Alcohol Depend. 156, 243e253. Grant, J.E., Chamberlain, S.R., Schreiber, L., Odlaug, B.L., 2012. Neuropsychological deficits associated with cannabis use in young adults. Drug Alcohol Depend. 121 (1e2), 159e162. Grasing, K., Mathur, D., Newton, T.F., DeSouza, C., 2010. Donepezil treatment and the subjective effects of intravenous cocaine in dependent individuals. Drug Alcohol Depend. 107 (1), 69e75. Gray, K.M., Carpenter, M.J., Baker, N.L., et al., 2012. A double-blind randomized controlled trial of N-acetylcysteine in cannabisdependent adolescents. Am. J. Psychiatry 169 (8), 805e812. Grégoire, S., Rivalan, M., Le Moine, C., Dellu-Hagedorn, F., 2012. The synergy of working memory and inhibitory control: behavioral, pharmacological and neural functional evidences. Neurobiol. Learn. Mem. 97 (2), 202e212. Hart, C.L., Haney, M., Vosburg, S.K., Rubin, E., Foltin, R.W., 2007. Smoked cocaine self-administration is decreased by modafinil. Neuropsychopharmacology 33, 761. Hashimoto, K., 2009. Emerging role of glutamate in the pathophysiology of major depressive disorder. Brain Res. Rev. 61 (2), 105e123. Hester, R., Lee, N., Pennay, A., Nielsen, S., Ferris, J., 2010. The effects of modafinil treatment on neuropsychological and attentional bias performance during 7-day inpatient withdrawal from methamphetamine dependence. Exp. Clin. Psychopharmacol 18 (6), 489e497. Hill, K.P., Sofuoglu, M., 2007. Biological treatments for amphetamine dependence: recent progress. CNS Drugs 21 (10), 851.

317

Jackson, A., Nesic, J., Groombridge, C., Clowry, O., Rusted, J., Duka, T., 2009. Differential involvement of glutamatergic mechanisms in the cognitive and subjective effects of smoking. Neuropsychopharmacology 34, 257e265. Jibson, MD., 2018. First-generation antipsychotic medications: Pharmacology, administration, and comparative side effects. UptoDate. https://www.uptodate.com/contents/first-generation-antipsychoticmedications-pharmacology-administration-and-comparative-sideeffects. Joos, L., Goudriaan, A.E., Schmaal, L., et al., 2013. Effect of modafinil on impulsivity and relapse in alcohol dependent patients: a randomized, placebo-controlled trial. Eur. Neuropsychopharmacol. 23 (8), 948e955. Jovanovski, D., Erb, S., Zakzanis, K.K., 2005. Neurocognitive deficits in cocaine users: a quantitative review of the evidence. J. Clin. Exp. Neuropsychol. 27 (2), 189e204. Kalechstein, A.D., De La Garza, R., Newton, T.F., 2010. Modafinil administration improves working memory in methamphetaminedependent individuals who demonstrate baseline impairment. Am. J. Addict. 19 (4), 340e344. Kalechstein, AD., Yoon, JH., Mahoney 3rd, JJ., Newton, TF., Chang, L., De La Garza 2nd, R., 2012. d-Cycloserine administration does not affect neurocognition in concurrent cocaine- and nicotine-dependent volunteers. Pharmacol Biochem Behav 103 (2), 403e407. Kalechstein, A.D., Mahoney, J.J., Verrico, C.D., De La Garza, R., 2014. Short-term, low-dose varenicline administration enhances information processing speed in methamphetamine-dependent users. Neuropharmacology 85, 493e498. Kamboj, S.K., Massey-Chase, R., Rodney, L., et al., 2011. Changes in cue reactivity and attentional bias following experimental cue exposure and response prevention: a laboratory study of the effects of dcycloserine in heavy drinkers. Psychopharmacology 217 (1), 25e37. Kelly, D.L., Sullivan, K.M., McEvoy, J.P., et al., 2015. Adjunctive minocycline in clozapine treated schizophrenia patients with persistent symptoms. J. Clin. Psychopharmacol. 35 (4), 374e381. Krishnan-Sarin, S., O’Malley, S.S., Franco, N., et al., 2015. NMDA receptor antagonism has differential effects on alcohol craving and drinking in heavy drinkers. Alcohol Clin. Exp. Res. 39 (2), 300e307. Krupitsky, E.M., Neznanova, O., Masalov, D., et al., 2007. Effect of memantine on cue-induced alcohol craving in recovering alcoholdependent patients. Am. J. Psychiatry 164 (3), 519e523. Kwako, L.E., Momenan, R., Litten, R.Z., Koob, G.F., Goldman, D., 2016. Addictions neuroclinical assessment: a neuroscience-based framework for addictive disorders. Biol. Psychiatry 80 (3), 179e189. Leeman, R.F., Robinson, C.D., Waters, A.J., Sofuoglu, M., 2014. A critical review of the literature on attentional bias in cocaine use disorder and suggestions for future research. Exp. Clin. Psychopharmacol 22 (6), 469e483. Levi Bolin, B., Alcorn III, J.L., Lile, J.A., et al., 2017. N-Acetylcysteine reduces cocaine-cue attentional bias and differentially alters cocaine self-administration based on dosing order. Drug Alcohol Depend. 178, 452e460. Levin, F.R., Evans, S.M., Brooks, D.J., Garawi, F., 2007. Treatment of cocaine dependent treatment seekers with adult ADHD: double-blind comparison of methylphenidate and placebo. Drug Alcohol Depend. 87 (1), 20e29.

318 Cognition and Addiction

Levin, E.D., Bushnell, P.J., Rezvani, A.H., 2011. Attention-modulating effects of cognitive enhancers. Pharmacol. Biochem. Behav. 99 (2), 146e154. Li, C.-S.R., Morgan, P.T., Matuskey, D., et al., 2010. Biological markers of the effects of intravenous methylphenidate on improving inhibitory control in cocaine-dependent patients. Proc. Natl. Acad. Sci. U.S.A. 107 (32), 14455e14459. Littman, R., Takács, Á., 2017. Do all inhibitions act alike? A study of go/ no-go and stop-signal paradigms. PLoS One 12 (10), e0186774. Liu, F., Guo, X., Wu, R., et al., 2014. Minocycline supplementation for treatment of negative symptoms in early-phase schizophrenia: a double blind, randomized, controlled trial. Schizophr. Res. 153 (1), 169e176. Longo, M., Wickes, W., Smout, M., Harrison, S., Cahill, S., White, J.M., 2010. Randomized controlled trial of dexamphetamine maintenance for the treatment of methamphetamine dependence. Addiction 105 (1), 146e154. Luine, V.N., 2014. Estradiol and cognitive function: past, present and future. Horm. Behav. 66 (4), 602e618. Lynch, W.J., Sofuoglu, M., 2010. Role of progesterone in nicotine addiction: evidence from initiation to relapse. Exp. Clin. Psychopharmacol 18 (6), 451e461. MacKillop, J., Few, L.R., Stojek, M.K., et al., 2015. D-cycloserine to enhance extinction of cue-elicited craving for alcohol: a translational approach. Transl. Psychiatry 5, e544. Mahoney, J.J., Kalechstein, A.D., Verrico, C.D., Arnoudse, N.M., Shapiro, B.A., De La Garza, R., 2014. Preliminary findings of the effects of rivastigmine, an acetylcholinesterase inhibitor, on working memory in cocaine-dependent volunteers. Progress NeuroPsychopharmacol. Biol. Psychiatry 50, 137e142. Mann, K., Ackermann, K., Diehl, A., et al., 2006. Galantamine: a cholinergic patch in the treatment of alcoholism: a randomized, placebo-controlled trial. Psychopharmacology 184 (1), 115e121. Mark, G.P., Shabani, S., Dobbs, L.K., Hansen, S.T., 2011. Cholinergic modulation of mesolimbic dopamine function and reward. Physiol. Behav. 104 (1), 76e81. Marquand, A.F., De Simoni, S., O’Daly, O.G., Williams, S.C.R., MourãoMiranda, J., Mehta, M.A., 2011. Pattern classification of working memory networks reveals differential effects of methylphenidate, atomoxetine, and placebo in healthy volunteers. Neuropsychopharmacology 36, 1237. McGuire, J.T., Botvinick, M.M., 2010. Prefrontal cortex, cognitive control, and the registration of decision costs. Proc. Natl. Acad. Sci. U.S.A. 107 (17), 7922e7926. McKee, S.A., Potenza, M.N., Kober, H., et al., 2015. A translational investigation targeting stress-reactivity and pre-frontal cognitive control with guanfacine for smoking cessation. J. Psychopharmacol. (Oxford, England) 29 (3), 300e311. McNally, R.J., 2007. Mechanisms of exposure therapy: how neuroscience can improve psychological treatments for anxiety disorders. Clin. Psychol. Rev. 27 (6), 750e759. Milivojevic, V., Sinha, R., Morgan, P.T., Sofuoglu, M., Fox, H.C., 2014. Effects of endogenous and exogenous progesterone on emotional intelligence in cocaine dependent men and women who also abuse alcohol. Hum. Psychopharmacol. 29 (6), 589e598. Milivojevic, V., Fox, H.C., Sofuoglu, M., Covault, J., Sinha, R., 2016. Effects of progesterone stimulated allopregnanolone on craving and stress response in cocaine dependent men and women. Psychoneuroendocrinology 65, 44e53.

Minzenberg, M.J., Carter, C.S., 2007. Modafinil: a review of neurochemical actions and effects on cognition. Neuropsychopharmacology 33, 1477. Monterosso, J.R., Aron, A.R., Cordova, X., Xu, J., London, E.D., 2005. Deficits in response inhibition associated with chronic methamphetamine abuse. Drug Alcohol Depend. 79 (2), 273e277. Mooney, M.E., Herin, D.V., Schmitz, J.M., Moukaddam, N., Green, C., Grabowski, J., 2009. Effects of oral methamphetamine on cocaine use: a randomized, double-blind, placebo-controlled trial. Drug Alcohol Depend. 101 (1e2), 34e41. Moran-Santa Maria, M.M., Flanagan, J., Brady, K., 2014. Ovarian hormones and drug abuse. Curr. Psychiatr. Rep. 16 (11), 511e511. Morgan, P.T., Malison, R.T., 2008. Pilot study of Lorazepam and tiagabine effects on sleep, motor learning, and impulsivity in cocaine abstinence. Am. J. Drug Alcohol Abuse 34 (6), 692e702. Nigg, J.T., Wong, M.M., Martel, M.M., et al., 2006. Poor response inhibition as a predictor of problem drinking and illicit drug use in adolescents at risk for alcoholism and other substance use disorders. J. Am. Acad. Child Adolesc. Psychiatry 45 (4), 468e475. Onur, O.A., Schlaepfer, T.E., Kukolja, J., et al., 2010. The N-Methyl-DAspartate receptor Co-agonist D-cycloserine facilitates declarative learning and hippocampal activity in humans. Biol. Psychiatry 67 (12), 1205e1211. Pae, C.-U., 2013. Role of the cholinesterase inhibitors in the treatment of schizophrenia. Expert Opin. Investig. Drugs 22 (3), 293e298. Passamonti, L., Luijten, M., Ziauddeen, H., et al., 2017. Atomoxetine effects on attentional bias to drug-related cues in cocaine dependent individuals. Psychopharmacology 234 (15), 2289e2297. Patterson, F., Jepson, C., Strasser, A.A., et al., 2009. Varenicline improves mood and cognition during smoking abstinence. Biol. Psychiatry 65 (2), 144e149. Ray Li, C.-S., Huang, C., Constable, R.T., Sinha, R., 2006. Imaging response inhibition in a stop-signal task: neural correlates independent of signal monitoring and post-response processing. J. Neurosci. 26 (1), 186e192. Reed, S.C., Evans, S.M., 2016. The effects of oral d-amphetamine on impulsivity in smoked and intranasal cocaine users. Drug Alcohol Depend. 163, 141e152. Repantis, D., Laisney, O., Heuser, I., 2010. Acetylcholinesterase inhibitors and memantine for neuroenhancement in healthy individuals: a systematic review. Pharmacol. Res. 61 (6), 473e481. Ressler, K.J., Rothbaum, B.O., Tannenbaum, L., et al., 2004. Cognitive enhancers as adjuncts to psychotherapy: use of d-cycloserine in phobic individuals to facilitate extinctionof fear. Arch. Gen. Psychiatry 61 (11), 1136e1144. Richelson, E., 1999. Receptor pharmacology of neuroleptics: relation to clinical effects. J. Clin. Psychiatry 60 (Suppl. 10), 5e14. Risch, C.S., 1996. Pathophysiology of schizophrenia and the role of newer antipsychotics. Pharmacotherapy 16 (1), 11e14. Robbins, T.W., Amnsten, A.F.T., 2009. The neuropsychopharmacology of fronto-executive function: monoaminergic modulation. Annu. Rev. Neurosci. 32, 267e287. Roberts, W., McKee, S., 2018. Effects of varenicline on cognitive performance in heavy drinkers: dose-response effects and associations with drinking outcomes. Exp. Clin. Psychopharmacol 26 (1), 49e57. Robinson, T.E., Berridge, K.C., 1993. The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res. Rev. 18 (3), 247e291.

Pharmacological cognitive enhancers Chapter | 23

Robinson, T.E., Berridge, K.C., 2008. The incentive sensitization theory of addiction: some current issues. Phil. Trans. Biol. Sci. 363 (1507), 3137e3146. Rooke, S.E., Hine, D.W., Thorsteinsson, E.B., 2008. Implicit cognition and substance use: a meta-analysis. Addict. Behav. 33 (10), 1314e1328. Sachdev, P.S., Blacker, D., Blazer, D.G., et al., 2014. Classifying neurocognitive disorders: the DSM-5 approach. Nat. Rev. Neurol. 10, 634. Santa Ana, E.J., Prisciandaro, J.J., Saladin, M.E., et al., 2015. D-cycloserine combined with cue exposure therapy fails to attenuate subjective and physiological craving in cocaine dependence. Am. J. Addict. 24 (3), 217e224. Santa Ana, E.J., Rounsaville, B.J., Frankforter, T.L., et al., 2009. DCycloserine attenuates reactivity to smoking cues in nicotine dependent smokers: a pilot investigation. Drug Alcohol Depend. 104 (3), 220e227. Sarter, M., Lustig, C., Howe, W.M., Gritton, H., Berry, A.S., 2014. Deterministic functions of cortical acetylcholine. Eur. J. Neurosci. 39 (11), 1912e1920. Sava, S., McCaffrey, A., Yurgelun-Todd, Deborah, A., 2009. Gender, cognition, and addiction. In: Brady Kathleen, T., Back Sudie, E., Greenfield, S.F. (Eds.), Women & Addiction A Comprehensive Handbook. The Guilford Press, New York, NY, pp. 133e146. Scahill, L., Chappell, P.B., Kim, Y.S., et al., 2001. A placebo-controlled study of guanfacine in the treatment of children with tic disorders and attention deficit hyperactivity disorder. Am. J. Psychiatry 158 (7), 1067e1074. Schilström, B., Ivanov, V.B., Wiker, C., Svensson, T.H., 2006. Galantamine enhances dopaminergic neurotransmission in vivo via allosteric potentiation of nicotinic acetylcholine receptors. Neuropsychopharmacology 32, 43e53. Schmaal, L., Veltman, D.J., Nederveen, A., van den Brink, W., Goudriaan, A.E., 2012. N-acetylcysteine normalizes glutamate levels in cocaine-dependent patients: a randomized crossover magnetic resonance spectroscopy study. Neuropsychopharmacology 37 (9), 2143e2152. Schmaal, L., Joos, L., Koeleman, M., Veltman, D.J., van den Brink, W., Goudriaan, A.E., 2013. Effects of modafinil on neural correlates of response inhibition in alcohol-dependent patients. Biol. Psychiatry 73 (3), 211e218. Schottenfeld, R.S., Chawarski, M.C., Sofuoglu, M., et al., 2018. Atomoxetine for amphetamine-type stimulant dependence during buprenorphine treatment: a randomized controlled trial. Drug Alcohol Depend. 186, 130e137. Scott, J.C., Woods, S.P., Matt, G.E., et al., 2007. Neurocognitive effects of methamphetamine: a critical review and meta-analysis. Neuropsychol. Rev. 17 (3), 275e297. Simon, S.L., Dean, A.C., Cordova, X., Monterosso, J.R., London, E.D., 2010. Methamphetamine dependence and neuropsychological functioning: evaluating change during early abstinence. J. Stud. Alcohol Drugs 71 (3), 335e344. Sofuoglu, M., Kosten, T.R., 2005. Novel approaches to the treatment of cocaine addiction. CNS Drugs 19 (1), 13e25. Sofuoglu, M., Mouratidis, M., Yoo, S., Culligan, K., Kosten, T., 2005. Effects of tiagabine in combination with intravenous nicotine in overnight abstinent smokers. Psychopharmacology 181 (3), 504e510. Sofuoglu, M., Waters, A.J., Mooney, M., O’Malley, S.S., 2009a. Minocycline reduced craving for cigarettes but did not affect smoking or

319

intravenous nicotine responses in humans. Pharmacol. Biochem.Behav. 92 (1), 135e140. Sofuoglu, M., Mitchell, E., Mooney, M., 2009b. Progesterone effects on subjective and physiological responses to intravenous nicotine in male and female smokers. Hum. Psychopharmacol. 24 (7), 559e564. Sofuoglu, M., Sugarman, D.E., Carroll, K.M., 2010. Cognitive function as an emerging treatment target for marijuana addiction. Exp. Clin. Psychopharmacol 18 (2), 109e119. Sofuoglu, M., Mooney, M., Kosten, T., Waters, A., Hashimoto, K., 2011a. Minocycline attenuates subjective-rewarding effects of dextroamphetamine in humans. Psychopharmacology 213 (1), 61e68. Sofuoglu, M., Mouratidis, M., Mooney, M., 2011b. Progesterone improves cognitive performance and attenuates smoking urges in abstinent smokers. Psychoneuroendocrinology 36 (1), 123e132. Sofuoglu, M., Herman, A.I., Li, Y., Waters, A.J., 2012. Galantamine attenuates some of the subjective effects of intravenous nicotine and improves performance on a Go No-Go task in abstinent cigarette smokers: a preliminary report. Psychopharmacology 224 (3), 413e420. Sofuoglu, M., DeVito, E.E., Waters, A.J., Carroll, K.M., 2013. Cognitive enhancement as a treatment for drug addictions. Neuropharmacology 64 (1), 452e463. Sofuoglu, M., DeVito, E.E., Waters, A.J., Carroll, K.M., 2016. Cognitive function as a trans-diagnostic treatment target in stimulant use disorders. J. Dual Diagnosis 12 (1), 90e106. Squeglia, L.M., Jacobus, J., Nguyen-Louie, T.T., Tapert, S.F., 2014. Inhibition during early adolescence predicts alcohol and marijuana use by late adolescence. Neuropsychology 28 (5), 782e790. Streeter, C.C., Terhune, D.B., Whitfield, T.H., et al., 2007. Performance on the Stroop predicts treatment compliance in cocaine-dependent individuals. Neuropsychopharmacology 33, 827. Swanson, C.J., Perry, K.W., Koch-Krueger, S., Katner, J., Svensson, K.A., Bymaster, F.P., 2006. Effect of the attention deficit/hyperactivity disorder drug atomoxetine on extracellular concentrations of norepinephrine and dopamine in several brain regions of the rat. Neuropharmacology 50 (6), 755e760. Swartz, B.E., McDonald, C.R., Patel, A., Torgersen, D., 2008. The effects of guanfacine on working memory performance in patients with localization-related epilepsy and healthy controls. Clin. Neuropharmacol. 31 (5), 251e260. Thaker, G.K., Carpenter Jr., W.T., 2001. Advances in schizophrenia. Nat. Med. 7, 667. Theunissen, E., Heckman, P., Sousa Fernandes Perna, E., et al., 2015. Rivastigmine but not vardenafil reverses cannabis-induced impairment of verbal memory in healthy humans. Psychopharmacology 232 (2), 343e353. Tripathi, A., Kar, S.K., Shukla, R., 2018. Cognitive deficits in schizophrenia: understanding the biological correlates and remediation strategies. Clin. Psychopharmacol. Neurosci. 16 (1), 7e17. Turkmen, S., Backstrom, T., Wahlstrom, G., Andreen, L., Johansson, I.M., 2011. Tolerance to allopregnanolone with focus on the GABA-A receptor. Br. J. Pharmacol. 162 (2), 311e327. Turner, D.C., Blackwell, A.D., Dowson, J.H., McLean, A., Sahakian, B.J., 2005. Neurocognitive effects of methylphenidate in adult attentiondeficit/hyperactivity disorder. Psychopharmacology 178 (2), 286e295.

320 Cognition and Addiction

Turner, T.H., LaRowe, S., Horner, M.D., Herron, J., Malcolm, R., 2009. Measures of cognitive functioning as predictors of treatment outcome for cocaine dependence. J. Subst. Abus. Treat. 37 (4), 328e334. United Nations Office on Drug & Crime, 2017. Fact Sheet on Statistics and Trends in Illicit Drugs. Verdejo-Garcia, A., 2017. Neuroclinical assessment of addiction needs to incorporate decision-making measures and ecological validity. Biol. Psychiatry 81 (7), e53ee54. Verdejo-García, A.J., Perales, J.C., Pérez-García, M., 2007. Cognitive impulsivity in cocaine and heroin polysubstance abusers. Addict. Behav. 32 (5), 950e966. Verplaetse, T.L., Pittman, B.P., Shi, J.M., Tetrault, J.M., Coppola, S., McKee, S.A., 2016. Effect of varenicline combined with high-dose alcohol on craving, subjective intoxication, perceptual motor response, and executive cognitive function in adults with alcohol use disorders: preliminary findings. Alcohol Clin. Exp. Res. 40 (7), 1567e1576. Verrico, C.D., Haile, C.N., Mahoney, J.J., Thompson-Lake, D.G.Y., Newton, T.F., De La Garza, R., 2014. Treatment with modafinil and escitalopram, alone and in combination, on cocaine-induced effects: a randomized, double blind, placebo-controlled human laboratory study. Drug Alcohol Depend. 141, 72e78. Vonmoos, M., Hulka, L.M., Preller, K.H., Minder, F., Baumgartner, M.R., Quednow, B.B., 2014. Cognitive impairment in cocaine users is druginduced but partially reversible: evidence from a longitudinal study. Neuropsychopharmacology 39 (9), 2200e2210.

Wagner, D., Becker, B., Koester, P., Gouzoulis-Mayfrank, E., Daumann, J., 2012. A prospective study of learning, memory, and executive function in new MDMA users. Addiction 108 (1), 136e145. Wagner, M., Schulze-Rauschenbach, S., Petrovsky, N., et al., 2013. Neurocognitive impairments in non-deprived smokersdresults from a population-based multi-center study on smoking-related behavior. Addict. Biol. 18 (4), 752e761. Waters, A.J., Carter, B.L., Robinson, J.D., et al., 2009. Attentional bias is associated with incentive-related physiological and subjective measures. Exp. Clin. Psychopharmacol 17 (4), 247e257. Wiers, R.W., Stacy, A.W., 2006. Handbook of Implicit Cognition and Addiction. Sage Publications, Thousand Oaks. Wilhelm, S., Buhlmann, U., Tolin, D.F., et al., 2008. Augmentation of behavior therapy with d -cycloserine for obsessive-compulsive disorder. Am. J. Psychiatry 165 (3), 335e341. Winblad, B., Cummings, J., Andreasen, N., et al., 2007. A six-month double-blind, randomized, placebo-controlled study of a transdermal patch in Alzheimer’s diseaseee rivastigmine patch versus capsule. Int. J. Geriatr. Psychiatry 22 (5), 456e467. Winhusen, T.M., Somoza, E.C., Harrer, J.M., et al., 2005. A placebocontrolled screening trial of tiagabine, sertraline and donepezil as cocaine dependence treatments. Addiction 100, 68e77.

Chapter 24

Cognitive research on addiction in a changing policy landscape Andrew Dawson1, Wayne Hall2, 3, 4 and Adrian Carter1, 2 1

School of Psychological Sciences, Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, VIC, Australia; 2UQ

Centre for Clinical Research, University of Queensland, Brisbane, QLD, Australia; 3Centre for Youth Substance Abuse Research, University of Queensland, Brisbane, QLD, Australia; 4National Addiction Centre, Kings College London, London, WC2R 2LS, United Kingdom

Introduction Since the 1970s, cognitive research on addiction1 has advanced at an impressive rate. Pioneering developments in precise and robust cognitive assessment (e.g., the Cambridge Neuropsychological Test Automated Battery; Robbins et al., 1994) and task-based imaging technologies have fueled this advance. There is now a growing literature on the cognitive processes that are perturbed in people addicted to various substances, both licit (e.g., alcohol and nicotine) and illicit (e.g., opioids, stimulants, and psychedelics), and some forms of addictive behaviors (e.g., gambling, gaming). During this period of scientific growth, the policy landscape around the use of addictive substances and behaviors has undergone major changes. For example, we have witnessed a tentative and often contested embrace of harm reduction measures, such as supervised injecting centers in some cities (e.g., Vancouver, Barcelona, Sydney, Melbourne) and trials of prescribed injectable opioids (e.g., United Kingdom, Switzerland, Canada, Spain) (March et al., 2006). Recreational cannabis use has been legalized in a growing number of jurisdictions (e.g., several US states, Canada, and Uruguay) and there is a growing skepticism about the justification and utility of the “war on drugs” (e.g., British Broadcasting Corporation, 2015). Not 1. We define “cognitive research on addiction” as peer-reviewed quantitative research that has employed neuropsychological or cognitive (“decision-making”) paradigms (with or without neuroimaging) in casee control studies of addicted individuals (both substance and gambling addiction) and healthy controls. We do not include findings from attention-based paradigms (e.g., cue reactivity and attentional bias modification) in this chapter, mostly for theoretical reasons (unconscious desires or “wants” are distinct from cognitive desires; Berridge et al., 2009) and also for brevity.

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00024-1 Copyright © 2020 Elsevier Inc. All rights reserved.

all policy changes have been in a more liberal direction. Some governments, such as those in Australia, Brazil, and Singapore, have banned the sale and criminalized the use of less hazardous ways of consuming some drugs, such as electronic cigarettes. Some low- and middle-income countries, such as the Philippines, have instituted widespread extrajudicial murder as national drug control policy. In this chapter we address the following questions: How much have advances in cognitive research on addiction influenced policies toward drugs, mental health, and criminal justice in high-income countries? And how might they exert greater influence in the future? In contrast, how much have shifts in drug policy affected the sort of cognitive research that is being conducted around the globe? Are there looming shifts on the policy landscape for which cognitive researchers of addiction should prepare? We begin by briefly summarizing research insights into addicted individuals’ cognitive functioning. We then discuss cognitive research’s limited impact on policy toward addictive drugs, mental health, and criminal justice policy to date, before describing how cognitive science research may influence policy in the future. We then argue that top-down changes to the policy landscape can suddenly and dramatically influence cognitive research on addictions. To illustrate this, we discuss the plausible downstream effects on cognitive research of policies that loosen restrictions on the use of psychedelic drugs in clinical research and the legalization of recreational cannabis use.

Cognitive research on addiction Addiction is commonly understood as an impaired ability to control one’s use of an addictive substance or one’s

321

322 Cognition and Addiction

engagement in addictive forms of behavior. This impaired decision-making is often described somewhat loosely as a compulsion or loss of control. Cognitive research on addiction attempts to uncover in detail the specific decisionmaking processes that are affected when someone becomes addicted to using a drug or engaging in a specific behavior in a way that adversely affects their life.

Aberrant learning Addiction is often referred to as a "disorder of learning" (Hyman et al., 2006; Lewis, 2015). Addictive behaviors, on this view, are overlearned habits; people who have developed addictions are initially goal-directed (or "modelbased") in their use of drugs or engagement in behavior (i.e., they aim to experience hedonic or motivational effects from using the substance or behavior). Over time, however, their need to use drugs or engage in the behavior comes to be habitual or "model-free" (Everitt and Robbins, 2016). Most of the empirical support for this account stems from work in rodents, although supportive evidence has emerged from cross-sectional research on people with polysubstance-, stimulant-, or alcohol-dependent use (Ersche et al., 2016; McKim et al., 2016; Sebold et al., 2014; Sebold et al., 2017; Sjoerds et al., 2013; Voon et al., 2015; cf. Hogarth et al., 2018). Longitudinal studies with human subjects are still required to track this potential transition from goal-directed to habitual drug-related behaviors in addicted individuals.

Impulsivity to compulsivity Another conceptually overlapping cognitive account of addiction focuses on a transition from “impulsivity” to “compulsivity” (Fineberg et al., 2014). In humans, impulsivity and compulsivity are multidimensional constructs. Two dimensions of impulsivity are choice and motor impulsivity (Hamilton et al., 2015a,b). Choice impulsivity (also referred to as excessive delay or temporal discounting) refers to a consistent tendency in most people to prefer to receive smaller rewards sooner rather than larger rewards later. Choice impulsivity is a potential behavioral marker, or cognitive endophenotype, of addiction (Bickel et al., 2014; MacKillop, 2013). Although not generally considered to be a dimension of impulsivity (cf. Fineberg et al., 2014), risky decision-making under uncertainty seems to be common in addicted individuals (Verdejo-Garcia et al., 2018), along with the heightened discounting of later, larger rewards. Motor impulsivity, or impaired response inhibition, is an impaired ability to refrain from initiating a response or difficulty in stopping an on-going response. It also appears to be a defining cognitive feature of addicted individuals (Morein-Zamir and Robbins, 2015).

Subdomains of compulsivity are far from established, but there is some agreement that cognitive inflexibility, attentional inflexibility, and habit learning (see above) are crucial processes in persons with compulsive tendencies (Fineberg et al., 2014). Relevant human evidence is sparse, however, as there are few available cognitive paradigms capable of adequately capturing these processes. There is also some uncertainty about how to demarcate “habits” from “compulsions” (Sjoerds et al., 2014). Sjoerds et al. (2014) point out that while negative motivational habits, or “compulsions,” are distinct from motor habits, both are likely to play a role in addiction. A thorough attempt to map the compulsivity elements involved in gambling addiction was a recent systematic review and metaanalysis from Van Timmeren et al. (2018). They found individuals with gambling addiction demonstrate performance deficits on behavioral tasks requiring (1) cognitive flexibility, (2) cognitive control of intuitive, but incorrect, responses, and (3) the ability to shift one’s prior response pattern or attention.

Impaired impulse inhibition A third line of cognitive research on addiction emphasizes the importance of loss of top-down control as much as bottom-up habit domination or compulsion (Goldstein and Volkow, 2011). The “brake failure” perspective emphasized in this line of research suggests that people with addictions have difficulties on tasks that tap into response inhibition (see “motor impulsivity” above) and executive function. Establishing the dimensions of “executive function” has proven challenging, but cognitive control, planning, task-and-rule shifting, and working memory often feature in these models (Snyder et al., 2015). There is evidence of deficits in these domains in individuals who are alcohol-, cannabis-, cocaine-, methamphetamine-, and opioid-dependent (Baldacchino et al., 2012; Broyd et al., 2016; Le Berre et al., 2017; Potvin et al., 2018; Potvin et al., 2014; Stephan et al., 2017; cf. Frazer et al., 2018; Hart et al., 2012). In sum, there are various competing and overlapping theories of cognition in addictive behavior. However, there is no overarching consensus as to which model captures all crucial features of cognition in addiction for all individuals. Next, we look at the implications of this research for policy.

Cognitive research on addiction and its (so far) limited impact on policy Cognitive research on addiction and chronic drug use has been used to argue that addicted individuals have significant impairments in decision-making capacity. These

Cognitive research on addiction in a changing policy landscape Chapter | 24

findings are supported by human neuroimaging and animal studies that identify neurobiological changes in brain structure and function, which suggest that addicted individuals have cognitive impairments. In the United States, this research has been used to argue that addiction is a chronic and relapsing brain disease (Leshner, 1997; Volkow et al., 2016). This view has been heavily promoted by the National Institute on Drug Addiction (NIDA), the body responsible for 85% of the world’s addiction research (Hall et al., 2015). It is also a view that has been adopted by prominent addiction agencies such as the American Society of Addiction Medicine (2011). An explicit promise of the “brain disease model of addiction” (BDMA) is that it will improve addiction treatment by providing more effective treatments, reducing the use of coercive and punitive responses to addictive behavior, and reducing the stigma and discrimination experienced by people with addictions (Volkow et al., 2016). There has been significant criticism of both the evidence supporting a BDMA and its assumed positive impact on society (e.g., Heather, 2017; Lewis, 2015; Satel and Lilienfeld, 2017). For a detailed review of the evidence, see Hall et al. (2015). The view that addiction is a chronic brain disease that “hijacks” users’ ability to control their drug use is inconsistent with observational evidence that the majority of people who meet lifetime diagnostic criteria for addiction in epidemiological surveys have matured out of addiction, usually in the absence of treatment (Heyman, 2009). The fact that even severely dependent individuals are able to titrate or stop using drugs in response to small financial rewards or punitive responses is also difficult to reconcile with the chronic relapsing brain disease view of addiction (see Hall et al., 2015). There is also very little evidence that the BDMA has produced the benefits that Leshner and colleagues promised. While it is plausible that ascribing addiction to neurocognitive changes outside a person’s control may reduce the stigma of addiction, there has been no evidence that this has proved to be the case. Longitudinal studies suggest that neurobiological explanations of mental disorders may have in fact increased stigma (Pescosolido, 2010). Advances in neuroscience have also failed to increase treatment seeking or treatment access, with 85% of people with addictions not accessing any treatment, let alone more effective forms of treatment (Hall et al., 2015).

Potential policy impacts of cognitive accounts of addiction One policy implication of work on aberrant learning and compulsivity is that severe penalties (e.g., imprisonment) imposed after long delays (as typically happens in the criminal justice system) will fail to reduce drug use (Ersche

323

et al., 2016). It would also be ethically unjustifiable to punish people for behavior that they lack the cognitive capacity to control. These two cognitive models imply a strong domain-general degree of automaticity in the behavior of people with addictions. It suggests that people who are addicted would also be resistant to positive treatment incentives (e.g., contingency management) and environment improvements (e.g., reduced gambling advertising, secure accommodation, alcohol price increases, and so on). We know, however, that positive treatment incentives and environment improvements can both reduce drug consumption and enable people with addictions to move out of drug use. The impaired impulse inhibition account is potentially more optimistic about addicted individuals’ agency and prospects for recovery than habit- and compulsivityfocused accounts. It implies that much of the suffering that characterizes addiction could be reduced through effective executive function training (e.g., tablet-based working memory training or therapist-based goal management rehabilitation; Verdejo-Garcia, 2016), targeted environmental approaches to reduce the likelihood of drug use or harm (e.g., syringe dispensing machines that alleviate the need to plan ahead and make it easier for people to make better decisions about drug consumption), or being encouraged to “outsource” their higher-order thinking through “implementation intentions” or precommitment strategies (e.g., deciding to avoid people or places associated with drug use) (Brandstätter et al., 2001; Gollwitzer, 1999). A potent example is the attempt to introduce mandatory or voluntary precommitment cards that enable people with gambling addictions to set a maximum amount of money they can lose before beginning gambling, before cognitive distortions caused by gambling drastically impair their ability to stop gambling (Ladouceur et al., 2012). Unfortunately, many attempts to introduce precommitment approaches have been met with significant political and commercial resistance, despite strong cognitive evidence to support their trialling or introduction. Keith Humphreys et al. (2017) recently argued that “research on the brain and its interactions with the environment . has only occasionally been applied in public policy [emphasis added]” (p. 1237). This should come as no surprise to those who recognize that, unfortunately, scientific evidence is seldom the determining, or even a major, factor in actual policy decisions (Australian Academy of Science, 2017; Humphreys and Piot, 2012). This also holds true for cognitive research on addiction.

Drug policy Cognitive research on addiction has had negligible impact on drug policy in large part because most policies and regulations governing the sale and production of addictive

324 Cognition and Addiction

drugs emerged at the beginning of the 20th century, well before various types of addiction became the subject of systematic scientific investigation (Nutt and McLellan, 2014). The major national agreements (such as the 1914 Harrison Act in the United States) that formed the basis of modern drug control policies were in response to the growing consumption of drugs, such as opium, laudanum, and cocaine and the social harms and nuisance that they caused (Courtwright, 2001). The changing attitudes toward different types of drug use over the past century and regulations governing what we can drink or consume have been driven by complex social and cultural concerns, such as gender, class, commercial trade, and race (Berridge, 2013). The “war on drugs” that emerged in the 1970s under President Nixon was in response to the cultural upheavals of the late 1960s and a perceived epidemic of heroin use by US soldiers in Vietnam. The drivers of subsequent shifts in more liberal directions (e.g., medicinal cannabis in the United States) are similarly difficult to disentangle. Public support for medicinal and legalized cannabis probably grew in response to repeated exposure to claimed medical benefits of cannabis use, which have often gone beyond what the evidence supports (Grant et al., 2003; Sznitman and BrettevilleJensen, 2015). Other political, social, and cultural factors have also played a role that remains to be elucidated (Cruz et al., 2016, 2018). Cognitive evidence has had little impact on the classification of substances as legal or illegal (Kalant, 2010; Nutt et al., 2010). Cognitive factors have not played a major role in attempts to construct more “rational” scales of the harms caused by different drugs of abuse (Nutt et al., 2007). Nutt and colleagues’ more “rational” classification of drugs was based primarily on the estimated individual and societal harm that each drug produces. Concepts such as “intensity of pleasure” and “psychological dependence” were the only parameters of relevance to the neurocognitive changes described above. The general point is illustrated by the very minor role played by neurocognitive evidence in the debates around legalization of recreational cannabis use in the United States (Cressey, 2015). The loosening of regulations of cannabis, whether as medicalized, decriminalized, or legalized recreational use, has arguably been the most significant shift in drug policy in the United States in recent years. We take no particular stance on whether and how this evidence should have influenced cannabis policy debates. It seems that public attitudes have become more supportive of legalization primarily on the basis of growing familiarity with the alleged benefits of medicinal uses of cannabis and political preferences, rather than on the neurocognitive literature (Cressey, 2015; Cruz et al., 2018). These policy changes occurred despite the NIDA Director, Nora Volkow, using neuroscientific evidence to vigorously advocate against cannabis legalization in the United States

(Freyer, 2018). The BDMA has similarly been employed to support the continued prohibition of and “war” on drugs (Courtwright, 2010; Vrecko, 2010).

Addiction treatment policy It is difficult to find evidence that cognitive research on addiction has had a positive effect on mental health policy. Nora Volkow has claimed that advances in addiction cognitive neuroscience paved the way for substance addiction treatment to be covered under medical insurance (“Obamacare”) in the Mental Health Parity and Addiction Equity Act 2008 (Volkow and Koob, 2015). We were unable to find any convincing support for this claim, even in the document Volkow cites (Busch et al., 2014) or in other relevant documents (e.g., Botticelli, 2014). It is possible that this policy change may have come from subtle shifts in public opinion encouraged by the public embrace of some neurocognitive findings on addiction. Yet evidence for any uptake of addiction science is absent and it is difficult to reconcile with longitudinal survey data showing that public attitudes toward addicted individuals remain steadfastly negative (Pescosolido et al., 2010). Nor has the BDMA increased the use of effective harm reduction policies in the United States (e.g., needle and syringe programs [NSPs]; supervised injection centers; opioid substitution treatment [OST] programs; Mattick et al., 2014). If addiction was widely accepted to be a brain disease that hijacked people’s brains, as the BMDA claims, then one might expect to see more public support for interventions that reduce the harm caused by these disorders. However, access to harm reduction programs (e.g., NSPs, OST, injecting centers) in the United States is minimal, and where they are provided, they are usually done in a punitive way, such as expelling patients from programs for failing to recover, as indicated by a positive urine test (Nadelmann and LaSalle, 2017).

Criminal justice policy Volkow et al. (2016) have also claimed that addiction neuroscience has facilitated the passage of US legislation to reduce prison sentences for nonviolent drug-related offending. Again, we cannot find support for this claim in the documents cited for the claim. Increased treatment for addicted offenders is more likely to be the result of politicians, police, and judges realizing that mass incarceration of drug offenders is economically unsustainable (Williams, 2015). For example, in California, the reduced imprisonment of nonviolent criminal offenders was driven by the economic unsustainability of the state prison system. Lobbying by the pharmaceutical industry and politicians’ searching for ways to reduce the economic burden of prisons have encouraged courts’ to use legal coercion and

Cognitive research on addiction in a changing policy landscape Chapter | 24

compulsory treatment to divert offenders from prisons (Carter and Hall, 2018; Hall and Carter, 2013).

An avenue for a greater impact on mental health and criminal justice policy Cognitive science does offer some support for more effective criminal justice responses to addictive drug use, such as Sobriety 24/7 (Kleiman, 2009). These approaches aim to provide “swift, certain, and fair” punishment (SCFP) by ensuring quick delivery of less severe penalties for infringements that are well-defined and articulated (e.g., testing positive for alcohol or drugs) (Curtis et al., 2018). SCFP programs were introduced in Hawaii (Kleiman, 2009) and showed that addicted offenders reduced their drug use and offending in response to modest incentives delivered swiftly and surely (e.g., avoiding short periods in jail) after testing positive for drugs. Kleiman reported observational evidence on the effectiveness of what he describes as “coerced abstinence” in a court in Hawaii in which drug use and recidivism in methamphetamine-using offenders was substantially reduced by requiring them to undergo random weekly urinalyses and punishing positive urine tests with 24 h of immediate and certain imprisonment. Kleiman argued that court-supervised addiction treatment should be reserved for offenders who fail to respond to this type of coerced abstinence program. Similar programs for repeat drinkdriving offenders, such as Sobriety 24/7, have been shown to significantly reduce alcohol use and drink driving (Midgette, 2016). The use of incentives to assist people with addictions to control their drug use is consistent with neuroeconomic theories of addiction (e.g., Ainslie, 2001). These predict that addicted individuals will be insensitive to large disincentives that uncertainly occur in the distant future (e.g., a long prison sentence following a protracted trial process) because they heavily discount future punishment against the immediate benefits of drug use. While many of these programs were developed without the direct influence of cognitive research, SCFP programs are consistent with what we know about the impact of addictive drug use on cognition. Cognitive neuroscience could be used to support their introduction (Curtis et al., 2018; Hall and Carter, 2013) and optimize the design of the schedules of reinforcement. While these programs are not couched explicitly in the language of cognitive science, their features overcome the cognitive limitations of people with addictions in that penalties are delivered quickly and with certainty and predictability and that the programs are simple to understand and require no elaborate planning or prospective memory to complete (Curtis et al., 2018). Research on SCFP programs is preliminary but promising (Curtis et al., 2018). Future evaluations are warranted.

325

Cognitive science also provides support for the use of financial incentives (e.g., vouchers or money) to encourage individuals to refrain from using drugs (Higgins and Petry, 1999). There is good evidence that paying small amounts of money to people with addictions to refrain from using drugs can significantly reduce their drug use (cf. Washio et al., 2011). A Scottish study of pregnant smokers, for example, found that small financial rewards that accumulated over time reduced the number of women who smoked during pregnancy, increasing the length of their pregnancy and their baby’s birth weight (Higgins et al., 2012). Cognitive science could be used to inform the design of reward schedules in financial incentive programs. These programs are particularly effective for initiatives that only require adherence for short, defined periods of time (e.g., during pregnancy, receiving Hepatitis vaccinations, blood-borne virus treatment). Unfortunately, programs that reward or pay people with substance use disorders for not using drugs are often unpopular with the public and policymakers who believe that refraining from using drugs is something that they should be doing without any reward. As we saw with the BDMA, moral attitudes toward people with addictions are difficult to shift by providing mechanistic explanations of people’s behavior.

Public policy can powerfully affect cognitive research The changing regulation of substances can affect the type of cognitive research that can be conducted. Regulatory barriers may be removed that make the use of a drug in research difficult. Changes in drug policy may also create new social environments that allow researchers to better study the cognitive effects of a drug. We briefly outline below two examples of how potential and recent changes in drug policy are affecting the type of cognitive research that is now possible.

Loosening of restrictions on use of psychedelics in clinical research There is growing momentum to reduce restrictions on studies of the therapeutic use of psychedelics (i.e., lysergic acid diethylamide (LSD), psilocybin/psilocin, dipropyltryptamine, ibogaine, ayahuasca, mescaline, and ketamine) in the treatment of addiction and other clinical disorders (Belouin and Henningfield, 2018). Concerns that people with addictions will abuse these substances are beginning to decrease as evidence confirms that psychedelics have a low abuse potential and toxicity and have potential therapeutic benefits in conditions that are not responsive to treatment (Morgan et al., 2017).

326 Cognition and Addiction

The history of research on psychedelics as addiction treatments provides a nice illustration of how drug policy can affect research. During the 1950s and 60s, research on LSD as a treatment for alcohol addiction flourished. The findings from this early research were promising, if limited by small samples and short follow-up periods. The state of the research was equivalent to phase 1 or 2 clinical studies in current terminology (Belouin and Henningfield, 2018; Bogenschutz and Johnson, 2016). A metaanalysis of six randomized controlled trials concluded that LSD reduced drinking after 6 months (Krebs and Johansen, 2012). This research was abruptly halted in the 1970s because of fears that LSD would be misused by young people and produce dramatic cultural changes. The recent loosening of restrictions on clinical research is enabling a new wave of trials of LSD treatment for alcohol and other addictions, such as heroin (Savage and McCabe, 1973). Growing disenchantment and frustration with current treatments for addictive disorders, together with their enormous human and economic burden, has prompted this willingness to reexplore psychedelics’ therapeutic potential (Bogenschutz and Johnson, 2016). Cultural practices (e.g., ayahuasca and ibogaine use at addiction retreats in Latin America, Canada, and New Zealand) and the potential benefits of ketamine in managing depression (DeWilde et al., 2015) may also have been influential. Randomized controlled trials of psychedelics could include measures to assess whether the cognitive deficits seen in alcohol-dependent individuals improve with LSD and psilocybin (Bogenschutz et al., 2015; Perry et al., 2007). Deficits in executive function in alcohol-dependent individuals (e.g., cognitive control, planning) may be amendable to recovery if psychedelic drugs’ can increase “self-efficacy” and “self-reflection” and reduce cravings and negative affect (Bogenschutz et al., 2015; Jones et al., 2018).

Legalization of recreational cannabis At the time of writing, recreational cannabis is legal in nine states in the United States (Alaska, California, Colorado, Massachusetts, Maine, Nevada, Oregon, Vermont, and Washington) and Uruguay. It will become legal in Canada in October 2018. Legalization of recreational cannabis in these jurisdictions will make cannabis more readily accessible at a lower price and allow it to be used in the absence of criminal penalties. These changes are likely to increase the frequency of use among current cannabis users, possibly increase their duration of use, and, in the longer term, probably increase the number of cannabis users (Hall and Lynskey, 2016). These policy changes provide a number of natural experiments that will allow researchers to compare the effects of increased cannabis use on cognition (Cressey, 2015).

They will permit researchers to study larger samples of people who are regularly using highly potent forms of cannabis, thereby increasing the statistical power and scientific quality of studies on the cognitive effects of cannabis use. To date, most work in cannabis and cognition has been cross-sectional and therefore unable to distinguish between causation and correlation. It has also not been able to quantify the amount of cannabis used or the amounts of cannabinoids administered. Cannabis is often described as a single substance or at best two: tetrahydrocannabinol (THC) and cannabidiol (CBD). It is in fact made up of over 200 ingredients that may have a yet-unknown impact on an individual’s cognition and behavior (Atakan, 2012). Most laboratory research relies on pharmaceutically controlled cannabinoid products that contain a very limited number of the psychoactive cannabinoids. Recent research suggests that the psychotogenic effects of cannabis may be due to the high concentrations of THC and low concentrations of CBD in modern hybridized plants (Murray et al., 2016). High concentrations of CBD may have a neuroprotective effect, and CBD may also be useful in treating epilepsy and psychoses (Englund et al., 2013; Perucca, 2017). It is not clear what the relative effects of THC and CBD concentrations may be on cognition and mental health in humans. Very preliminary evidence suggests that coadministration of THC and CBD can reduce THC-induced time perception errors, emotional blunting, and immediate and delayed recall deficits (Englund et al., 2017), although much more work in humans is needed. The legalization of cannabis may thus provide an opportunity to better understand whether CBD can offset the adverse effects of THC (Hall and Lynskey, 2016). Studies showing that CBD can reduce some THC-induced cognitive impairments require independent replication (Colizzi and Bhattacharyya, 2017). If replicated, any policy change (e.g., requiring minimum CBD levels in legal cannabis products) will also need to be rigorously evaluated.

Conclusion Cognitive research on the addictions has grown at an impressive rate and is providing a comprehensive account of different types of compromised cognition in people who are addicted. There is increasing potential for cognitive research to influence addiction treatment and criminal justice policy if moralistic resistance to innovative approaches among the public and policymakers can be reduced. Its tools will also prove extremely useful in assessing the therapeutic effects of currently illicit drugs if policymakers open up new opportunities for cognitive researchers (e.g., by reducing barriers to research on psychedelics). These opportunities might, in turn, provide cognitive researchers armed with new data a novel way to advocate for

Cognitive research on addiction in a changing policy landscape Chapter | 24

drug policy reform, although history would suggest that optimism about the impact of evidence should be tempered.

References Ainslie, G., 2001. Breakdown of Will. Cambridge University Press, Cambridge. American Society of Addiction Medicine, 2011. Public Policy Statement: Definition of Addiction (Long Version). American Society of Addiction Medicine, Chevy Chase, MD. Atakan, Z., 2012. Cannabis, a complex plant: different compounds and different effects on individuals. Ther. Adv. Psychopharmacol. 2 (6), 241e254. Australian Academy of Science, 2017. An Interdisciplinary Approach to Living in a Risky World. Australian Academy of Science, Canberra. Baldacchino, A., Balfour, D.J.K., Passetti, F., Humphris, G., Matthews, K., 2012. Neuropsychological consequences of chronic opioid use: a quantitative review and meta-analysis. Neurosci. Biobehav. Rev. 36, 2056e2068. Belouin, S.J., Henningfield, J.E., February 21, 2018. Psychedelics: where we are now, why we got here, what we must do. Neuropharmacology [Epub ahead of print]. Berridge, K.C., Robinson, T.E., Aldridge, J.W., 2009. Dissecting components of reward: ‘Liking’, ‘wanting’, and learning. Curr. Opin. Pharmacol. 9 (1), 65e73. Berridge, V., 2013. Demons: Our Changing Attitudes to Alcohol, Tobacco and Drugs. Oxford University Press, Oxford. Bickel, W.K., Koffarnus, M.N., Moody, L., Wilson, A.G., 2014. The behavioural- and neuro-economic process of temporal discounting: a candidate behavioral marker of addiction. Neuropharmacology 76 (Pt B), 518e527. British Broadcasting Corporation, 2015. Has the War on Drugs Been Lost? http://www.bbc.com/news/health-31922609. Bogenschutz, M.P., Johnson, M.W., 2016. Classic hallucinogens in the treatment of addictions. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 64, 250e258. Bogenschutz, M.P., Forcehimes, A.A., Pommy, J.A., Wilcox, C.E., Barbosa, P., Strassman, R.J., 2015. Psilocybin-assisted treatment for alcohol dependence: a proof-of-concept study. J. Psychopharmacol. 29 (3), 289e299. Botticelli, M., 2014. National Blueprint for Drug Policy Reform Released Today in Roanoake, VA. https://obamawhitehouse.archives.gov/blog/ 2014/07/09/national-blueprint-drug-policy-reform-released-todayroanoke-va. Brandstätter, V., Lengfelder, A., Gollwitzer, P.M., 2001. Implementation intentions and efficient action initiation. J. Personal. Soc. Psychol. 81 (5), 946e960. Broyd, S.J., van Hell, H.H., Beale, C., Yücel, M., Solowij, N., 2016. Acute and chronic effects of cannabinoids on human cognition e a systematic review. Biol. Psychiatry 79 (7), 557e567. Busch, S.H., Epstein, A.J., Harhay, M.O., Fiellin, D.A., Un, H., Barry, C.L., 2014. The effects of federal parity on substance use disorder treatment. Am. J. Manag. Care 20 (1), 76e82. Carter, A., Hall, W., 2018. From coerced to compulsory treatment of addiction in the patient’s best interests: is it supported by the evidence? In: Spivakovsky, C., Seear, K., Carter, A. (Eds.), Critical Perspectives on Coercive Interventions. Routledge, London.

327

Colizzi, M., Bhattacharyya, S., 2017. Does cannabis composition matter? Differential effects of delta-9-tetrahydrocannabinol and cannabidiol on human cognition. Curr. Addict. Rep. 4 (2), 62e74. Courtwright, D.T., 2001. Forces of Habit: Drugs and the Making of the Modern World. Harvard University Press, Cambridge, MA. Courtwright, D., 2010. The NIDA brain-disease paradigm: history, resistance, and spinoffs. BioSocieties 5 (1), 137e147. Cressey, D., 2015. The cannabis experiment. Nature 524 (7565), 280e283. Cruz, J.M., Boidi, M.F., Queirolo, R., 2018. Saying no to weed: public opinion towards cannabis legalisation in Uruguay. Drugs Educ. Prev. Policy 25 (1), 67e76. Cruz, J.M., Queirolo, R., Boidi, M.F., 2016. Determinants of public support for marijuana legalisation in Uruguay, the United States, and El Salvador. J. Drug Issues 46 (4), 308e325. Curtis, A., Youssef, G., Guadagno, B., Manning, V., Enticott, P.G., et al., 2018. Swift, certain and fair justice: insights from behavioural learning and neurocognitive research. Drug Alcohol Rev. 37 (Suppl. 1), s240es245. DeWilde, K.E., Levitch, C.F., Murrough, J.W., Mathew, S.J., Iosifescu, D.V., 2015. The promise of ketamine for treatment-resistant depression: current evidence and future directions. Ann. N. Y. Acad. Sci. 1345 (1), 47e58. Englund, A., Freeman, T.P., Murray, R.M., McGuire, P., 2017. Can we make cannabis safer? Lancet Psychiatry 4, 643e648. Englund, A., Morrison, P.D., Nottage, J., Hague, D., Kane, F., Kapur, S., 2013. Cannabidiol inhibits THC-elicited paranoid symptoms and hippocampal-dependent memory impairment. J. Psychopharmacol. 27 (1), 19e27. Ersche, K.D., Gillan, C.M., Jones, P.S., Williams, G.B., Ward, L.H., Robbins, T.W., 2016. Carrots and sticks fail to change behaviour in cocaine addiction. Science 352 (6292), 1468e1471. Everitt, B.J., Robbins, T.W., 2016. Drug addiction: updating actions to habits to compulsions ten years on. Annu. Rev. Psychol. 67, 23e50. Fineberg, N.A., Chamberlain, S.R., Goudriaan, A.E., Stein, D.J., Vanderschuren, L.J., et al., 2014. New developments in human neurocognition: clinical, genetic, and brain imaging correlates of impulsivity and compulsivity. CNS Spectr. 19 (1), 69e89. Frazer, K.M., Richards, Q., Keith, D.R., 2018. The long-term effects of cocaine use on cognitive functioning: a systematic critical review. Behav. Brain Res. 348 (1), 241e262. Freyer, F.J., 2018. Director of Drug Abuse Institute Offers Words of Caution on Marijuana. https://www.bostonglobe.com/metro/2018/05/ 03/words-caution-marijuana/mvdlHOrEjmtiQXFG0U66MJ/story. html. Goldstein, R.Z., Volkow, N.D., 2011. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 12 (11), 652e669. Gollwitzer, P.M., 1999. Implementation intentions: strong effects of simple plans. Am. Psychol. 54, 493e503. Grant, I., Gonzalez, R., Carey, C.L., Natarajan, L., Wolfson, T., 2003. Non-acute (residual) neurocognitive effects of cannabis use: a metaanalytic study. J. Int. Neuropsychol. Soc. 9 (5), 679e689. Hall, W., Carter, A., 2013. How may neuroscience affect the way that the criminal courts deal with addicted offenders? In: Vincent, N.A. (Ed.), Neuroscience and Legal Responsibility. Oxford University Press, Oxford, New York.

328 Cognition and Addiction

Hall, W., Carter, A., Forlini, C., 2015. The brain disease model of addiction: is it supported by the evidence and has it delivered on its promises? Lancet Psychiatry 2 (1), 105e110. Hall, W., Lynskey, M., 2016. Evaluating the public health impacts of legalizing recreational cannabis use in the United States. Addiction 111 (10), 1764e1773. Hamilton, K.R., Littlefield, A.K., Anastasio, N.C., Cunningham, K.A., Fink, L.H., et al., 2015a. Rapid-response impulsivity: definitions, measurement issues, and clinical implications. Personal. Disord. 6 (2), 168e181. Hamilton, K.R., Mitchell, M.R., Wing, V.C., Balodis, I.M., Bickel, W.K., Fillmore, M., et al., 2015b. Choice impulsivity: definitions, measurement issues, and clinical implications. Personal. Disord. 6 (2), 182e198. Hart, C.L., Marvin, C.B., Silver, R., Smith, E.E., 2012. Is cognitive functioning impaired in methamphetamine users? A critical review. Neuropsychopharmacology 37, 586e608. Heather, N., 2017. Q: is addiction a brain disease or a moral failing? A: neither. Neuroethics 10 (1), 115e124. Heyman, G., 2009. Addiction: A Disorder of Choice. Harvard University Press, Cambridge, MA. Higgins, S.T., Petry, N.M., 1999. Contingency management: incentives for sobriety. Alcohol Res. Health 23 (2), 122e127. Higgins, S.T., Washio, Y., Heil, S.H., Solomon, L.J., Gaalema, D.E., Higgins, T.M., Bernstein, I.M., 2012. Financial incentives for smoking cessation among pregnant and newly postpartum women. Prev. Med. 55, s33es40/. Hogarth, L., Lam-Cassettari, C., Pacitti, H., Currah, T., Mahlberg, J., et al., May 22, 2018. Intact goal-directed control in treatment-seeking drug users indexed by outcome-devaluation and Pavlovian to instrumental transfer: critique of habit theory. Eur. J. Neurosci. [Epub ahead of print]. http://doi.10.1111/ejn.13961. Humphreys, K., Malenka, R.C., Knutson, B., MacCoun, R.J., 2017. Brains, environments, and policy responses to addiction. Science 356 (6344), 1237e1238. Humphreys, K., Piot, P., 2012. Scientific evidence alone is not sufficient basis for health policy. BMJ 344, e1316. Hyman, S., Malenka, R.C., Nestler, E.J., 2006. Neural mechanisms of addiction: the role of reward-related learning and memory. Annu. Rev. Neurosci. 29, 565e598. Jones, J.L., Mateus, C.F., Malcolm, R.J., Brady, K.T., Back, S.E., 2018. Efficacy of ketamine in the treatment of substance use disorders: a systematic review. Front. Psychiatry 9, 277. Kalant, H., 2010. Drug classification: science, politics, both or neither? Addiction 105 (7), 1146e1149. Kleiman, M., 2009. When Brute Force Fails: How to Have Less Crime and Less Punishment. Princeton University Press, Princeton. Krebs, T.S., Johansen, P.O., 2012. Lysergic acid diethylamide (LSD) for alcoholism: meta-analysis of randomized controlled trials. J. Psychopharmacol. 26 (7), 994e1002. Ladouceur, R., Blaszczynski, A., Lalande, D.R., 2012. Pre-commitment in gambling: a review of the empirical evidence. Int. Gambl. Stud. 12 (2), 215e230. Le Berre, A.-P., Fama, R., Sullivan, E.V., 2017. Executive functions, memory, and social cognitive deficits and recovery in chronic alcoholism: a critical review to inform future research. Alcohol Clin. Exp. Res. 41 (8), 1432e1443. Leshner, A.I., 1997. Addiction is a brain disease, and it matters. Science 278 (5335), 45e47.

Lewis, M., 2015. The Biology of Desire: Why Addiction Is Not a Disease. Scribe, Melbourne. MacKillop, J., 2013. Integrating behavioral economics and behavioral genetics: delayed reward discounting as an endophenotype for addictive disorders. J. Exp. Anal. Behav. 99 (1), 14e31. March, J.C., Oviedo-Joekes, E., Perea-Milla, E., Carrasco, F., PEPSA team, 2006. Controlled trial of prescribed heroin in the treatment of opioid addiction. J. Subst. Abus. Treat. 31 (2), 203e211. Mattick, R.P., Breen, C., Kimber, J., Davoli, M., 2014. Buprenorphine maintenance versus placebo or methadone maintenance for opioid dependence. Cochrane Database Syst. Rev. 2, CD002207. McKim, T.H., Bauer, D.J., Boettiger, C.A., 2016. Addiction history associates with propensity to form habits. J. Cogn. Neurosci. 28, 1024e1038. Midgette, G., 2016. A New Approach to Reducing Heavy Drinking and Alcohol-Involved Crime? Insights from RAND Research on 24/7 Sobriety Programs. RAND Corporation, Santa Monica. Morein-Zamir, S., Robbins, T.W., 2015. Fronto-striatal circuits in response-inhibition: relevance to addiction. Brain Res. 1628 (Pt A), 117e129. Morgan, C., McAndrew, A., Stevens, T., Nutt, D., Lawn, W., 2017. Tripping up addiction: the use of psychedelic drugs in the treatment of problematic drug and alcohol use. Curr. Opin. Behav. Sci. 13, 71e76. Murray, R.M., Quigley, H., Quattrone, D., Englund, A., Di Forti, M., 2016. Traditional marijuana, high-potency cannabis and synthetic cannabinoids: increasing risk for psychosis. World Psychiatry 15 (3), 195e204. Nadelmann, E., LaSalle, L., 2017. Two steps forward, one step back: current harm reduction policy and politics in the United States. Harm Reduct. J. 14, 37. Nutt, D.J., King, L.A., Phillips, L.D., 2010. Drug harms in the UK: a multicriteria decision analysis. Lancet 376 (9752), 1558e1565. Nutt, D., King, L.A., Saulsbury, W., Blakemore, C., 2007. Development of a rational scale to assess the harm of drugs of potential misuse. Lancet 369 (9566), 1047e1053. Nutt, D., McLellan, A.T., 2014. Can neuroscience improve addiction treatment and policies? Publ. Health Rev. 35 (2), 1e12. Perry Jr., E.B., Cramer, J.A., Cho, H.-S., Petrakis, I.L., Karper, L.P., Yale Ketamine Study Group, 2007. Psychiatric safety of ketamine in psychopharmacology research. Psychopharmacology 192 (2), 253e260. Perucca, E., 2017. Cannabinoids in the treatment of epilepsy: hard evidence at last? J. Epilepsy Res. 7 (2), 61e76. Pescosolido, B.A., Martin, J.K., Long, J.S., Medina, T.R., Phelan, J., Link, B., 2010. “A disease like any other?”: a decade of change in public reactions to schizophrenia, depression, and alcohol dependence. Am. J. Psychiatry 167, 1321e1330. Potvin, S., Pelletier, J., Grot, S., Hébert, C., Barr, A.M., Lecomte, T., 2018. Cognitive deficits in individuals with methamphetamine use disorder: a meta-analysis. Addict. Behav. 80, 154e160. Potvin, S., Stavro, K., Rizkallah, E., Pelletier, J., 2014. Cocaine and cognition: a systematic quantitative review. J. Addict. Med. 8 (5), 368e376. Robbins, T.W., James, M., Owen, A.M., Sahakian, B.J., McInnes, L., Rabbitt, P., 1994. Cambridge Neuropsychological Test Automated Battery (CANTAB): a factor analytic study of a large sample of normal elderly volunteers. Dementia 5 (5), 266e281.

Cognitive research on addiction in a changing policy landscape Chapter | 24

Satel, S.L., Lilienfeld, S.O., 2017. If addiction is not best conceptualised a brain disease, then what kind of disease is it? Neuroethics 10 (1), 19e24. Savage, C., McCabe, O.L., 1973. Residential psychedelic (LSD) therapy for the narcotic addict: a controlled study. Arch. Gen. Psychiatry 28, 808e814. Sebold, M., Deserno, L., Nebe, S., Schad, D.J., Garbusow, M., et al., 2014. Model-based and model-free decisions in alcohol dependence. Neuropsychobiology 70 (2), 122e131. Sebold, M., Nebe, S., Garbusow, M., Guggenmos, M., Schad, D.J., Heinz, A., 2017. When habits are dangerous: alcohol expectancies and habitual decision making predict relapse in alcohol dependence. Biol. Psychiatry 82 (11), 847e856. Sjoerds, Z., de Wit, S., van den Brink, W., Robbins, T.W., Beekman, A.T., Veltman, D.J., 2013. Behavioral and neuroimaging evidence for overreliance on habit learning in alcohol-dependent patients. Transl. Psychiatry 3, e337. Sjoerds, Z., Luigjes, J., van den Brink, W., Denys, D., Yücel, M., 2014. The role of habits and motivation in human drug addiction: a reflection. Front. Psychiatry 5, 8. Snyder, H.R., Miyake, A., Hankin, B.L., 2015. Advancing understanding of executive function impairments and psychopathology: bridging the gap between clinical and cognitive approaches. Front. Psychol. 6, 328. Stephan, R.A., Alhassoon, O.M., Allen, K.E., Wollman, S.C., Hall, M., Grant, I., 2017. Meta-analyses of clinical neuropsychological tests of executive dysfunction and impulsivity in alcohol use disorder. Am. J. Drug Alcohol Abuse 43 (1), 24e43. Sznitman, S.R., Bretteville-Jensen, A.L., 2015. Public opinion and medical cannabis policies: examining the role of underlying beliefs and national medical cannabis policies. Harm Reduct. J. 12, 46.

329

Van Timmeren, T., Daams, J.G., van Holst, R.J., Goudriaan, A.E., 2018. Compulsivity-related neurocognitive performance deficits in gambling disorder: a systematic review and meta-analysis. Neurosci. Biobehav. Rev. 84, 204e217. Verdejo-Garcia, A., 2016. Cognitive training for substance use disorders: neuroscientific mechanisms. Neurosci. Biobehav. Rev. 68, 270e281. Verdejo-Garcia, A., Chong, T.J., Stout, J.C., Yücel, M., London, E.D., 2018. Stages of dysfunctional decision-making in addiction. Pharmacol. Biochem. Behav. 164, 99e105. Volkow, N.D., Koob, G., 2015. Brain disease model of addiction: why is it so controversial? Lancet Psychiatry 2 (8), 677e679. Volkow, N.D., Koob, G.F., McLellan, A.T., 2016. Neurobiologic advances from the brain disease model of addiction. N. Engl. J. Med. 374, 363e371. Voon, V., Derbyshire, K., Ruck, C., Irvine, M.A., Worbe, Y., Bullmore, E.T., 2015. Disorders of compulsivity: a common bias towards learning habits. Mol. Psychiatry 20 (3), 345e352. Vrecko, S., 2010. Birth of a brain disease: science, the state and addiction neuropolitics. Hist. Hum. Sci. 23 (4), 52e67. Washio, Y., Higgins, S.T., Heil, S.H., McKerchar, T.L., Badger, G.J., Skelly, J.M., Dantona, R.L., 2011. Delay discounting is associated with treatment response among cocaine-dependent outpatients. Exp. Clin. Psychopharmacol 19 (3), 243e248. Williams, T., 2015. Police Leaders Join Call to Cut Prison Rosters. https:// www.nytimes.com/2015/10/21/us/police-leaders-join-call-to-cutprison-rosters.html.

Chapter 25

Population neuroscience in addiction research Toma´s Paus Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada; Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada

This chapter provides an introduction to population neuroscience, a combination of epidemiology, genetics, and neuroscience, and its use in addiction research. We start with a general overview of population neuroscience, its goals, and approaches. We then identify motivations for applying this approach in the context of addiction research and describe the design of two ongoing cohorts focusing on adolescence and youth: the Saguenay Youth Study and the IMAGEN Study. After a brief overview of the findings generated by these two studies, we focus on challenges associated with the interpretation of such observations. We conclude with an outlook for future research in this area.

Population neuroscience: an overview Let us begin by stating the obvious: human behavior is complex and so are the forces that shape it. The same of course applies to the organ generating our behavior: the human brain. Behavioral and brain traits provide complementary explanatory levels, focusing on one or the other reflects questions being asked. The main goal of population neuroscience is to gain understanding of the forces shaping the human brain from conception forward (Paus, 2010, 2013, 2016). As pointed out elsewhere (Paus, 2016), “practitioners of population neuroscience are cognizant of three key challenges inherent in their pursuits: (1) An infinite combination of factors influencing the brain from within (genes and their regulation) and the outside (social and physical environment); (2) Presence of developmental cascades that carry such influences from one time point to the next (e.g., prenatal to postnatal), from one organ to another (e.g., cardiometabolic to brain), and from one level of

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00025-3 Copyright © 2020 Elsevier Inc. All rights reserved.

organization to a different one (e.g., behavior to gene regulation and vice versa); (3) Structural and functional complexity of the human brain.” The three challenges can be met by conducting our pursuits in large samples of participants drawn from the general population and evaluated with state-of-the-art tools for assessing (a) genes and their regulation; (b) external and internal environments; and (c) brain properties, all done in an integrative fashion and across life span (Fig. 25.1). Next, I will describe briefly basic concepts pertinent for the assessment of (a) genes and their regulation; (b) physical and social environment; and (c) the brain structure and function. For a detailed treatment of these topics, see Paus, 2016.

Genes and gene regulation We differ from each other by thousands to millions of variants in the DNA sequence (Manolio et al., 2009). These variants include single-nucleotide polymorphisms (SNPs), copy number variants, as well as copy number neutral inversions and translocations. It is not surprising that discovering new associations between common SNPs and complex traits requires large samples. For example, recent efforts by the CHARGE and ENIGMA Consortia, and their mutual replications, identified molecular architecture underlying cortical thickness and surface area of the human cerebral cortex by metaanalyzing data obtained in over 40,000 individuals (BioRxiv 399402, 404558). Similarly, a genome-wide association study (GWAS) carried out by the Psychiatric Genomics Consortium identified 128 SNPs (108 independent loci) associated with schizophrenia by comparing 36,989 cases with 113,075 controls (Biological insights from 108 schizophrenia-associated genetic loci,

331

332 Cognition and Addiction

FIGURE 25.1 Population Neuroscience: assessing (A) genes and their regulation; (B) external and internal environments; and (C) the brain properties across the life span and generations. From (Paus, 2016).

2014). As expected, however, the amount of phenotypic variance explained by these SNPs is small. Polygenic scores calculated from the 108 genome-wide significant loci explained 3.4% variation on the liability scale to schizophrenia (Biological insights from 108 schizophreniaassociated genetic loci, 2014). This (low) number is consistent with the highly polygenic nature of complex traits. Would considering all common SNPs explain more variance in a given phenotype? This is indeed the case, as demonstrated for 49 different traits using the genome-wide complex trait analysis (Yang et al., 2011, 2013). We have shown that an overall pattern of genotypic variations across w500,000 SNPs explains up to 50% of phenotypic variations in the brain structure (Toro et al., 2015) and function (Dickie et al., 2014). It is unlikely, however, that each of the 500,000 SNPs contributes equally, each adding 0.0001% to the total of 50% of variance explained; not all genomic locations are created equal. This brings us to genome biology and the knowledge gained from projects such as the Encyclopedia of DNA Elements, ENCODE (Kellis et al., 2014). Genomes can be partitioned into a number of functional domains. For example, all SNPs included in the 1000 Genomes dataset can be annotated (classified) as belonging to one of the following six partitions: coding region (0.9% of all SNPs), untranslated region (0.9%), promoter (2.6%), DNase I hypersensitivity site (DHS; 16.4%), intron (28.6%), and intergenic region (50.5%)dsee Table S4 in Gusev et al., 2014. Genetic variants located in the different partitions may be over- or underrepresented among SNPs associated with a given trait. For example, across 11 common diseases, the top hits are highly enriched in the coding regions (13.8-fold enrichment) and less so in the

DHS regions (5.1
enrichment). At the same time, SNPbased heritability estimates are the highest for the DHS partition (79% SNP heritability), as compared with the coding regions (8% SNP heritability) (Gusev et al., 2014). Thus, knowledge of genome biology is beginning to guide the interpretation of GWAS-based findings. The key word here is regulation of gene expression, either globally or in a tissue-specific manner. One can use the classical GWAS approach to search for genetic variants associated with interindividual variations in gene expression: expression Quantitative Trait Loci, eQTLs (Albert and Kruglyak, 2015). Here, the level of gene expression represents a trait, with expression levels of 20,000þ genes measured in each sample either through expression microarrays or RNA sequencing. A GWAS approach is then used to identify associations between genetic variations (SNPs) and expression levels, thus identifying eQTLs. Such GWASbased analyses of gene expression have been carried out in a number of different tissues, including the human brain (Ramasamy et al., 2014). In the human cerebral cortex, the regional pattern of their expression isdfor some genesdhighly consistent across individuals. As we describe below, this allows us to use resources such as the Allen Human Brain Atlas (Hawrylycz et al., 2012) to facilitate interpretations of various MRI-derived phenotypes (see “Challenges and outlook”).

Built and social environment We are both recipients and creators of our environment (Kendler et al., 2003). There are countless variations and permutations of physical, built, and social environments that surround us in space and time. The physical and social

Population neuroscience in addiction research Chapter | 25

ecology contributesdtogether with genesdto many environments that exist in an individual’s body: on the body surfaces (e.g., microbes on our skin and in the gut) and in the circulating blood (e.g., micronutrients, toxins, inflammatory molecules). Measuring these “body” environments is straightforward: one needs a biological sample (stool, urine, blood) and an appropriate high-throughput assay for assessing a particular part of the metabolome, a catalog containing at least w8500 endogenous and w40,000 exogenous compounds (Wishart, 2011). Assessing individual’s ecosystem is more challenging. Asking a series of questions using a standard survey is the most common way for collecting information about the individual’s physical, built, and social environment. Although valuable, there are two main disadvantages of the survey-based approach: (1) participant’s time (many hours required to cover multiple domains) and (2) self-reported nature of the collected information and, therefore, possible biases and errors. Alternative approaches are being developed by data scientists for extracting information about built and social environment from various digital sources (See “Where do we go next”). Smartphones and other mobile technologies equipped with a GPS device, accelerometers, heart rate monitors, or even sweat-based monitors of metabolites and electrolytes (Gao et al., 2016) provide new opportunities for sampling individual’s behavior and physiology in time and space, evaluating this individual-based metric against physical and social context using aggregate data. For example, Hurvitz and colleagues used mobile technology (accelerometers, GPS devices, and/or a multimodal sensor) to create “LifeLogs” containing data derived from these devices that are, in turn, visualized in geospace with “LifeLog Views” (Hurvitz et al., 2014). In summary, technological advances are expanding our abilities to assess the individual’s internal and external environments with an increasing breadth and precision. Together with aggregate data mapped in well-defined geospatial units with high granularity, these tools allow us to relate the individual’s enviroment to his/her phenome.

333

Brain structure and function Over the past 30þ years, MRI has become the method of choice for studying both structure and function of the human brain across the life span. This is due to its versatility (same scanner used for measuring a variety of brain phenotypes), availability (e.g., w11,000 MRI units in the United States), and the noninvasive nature of MRI technology, namely no exposure to ionizing radiation. The noninvasiveness makes MRI particularly suitable for research studies of brain development and maturation in general (nonclinical) population. As pointed out above, the majority of MRI scanners come equipped with basic hardware and software allowing one to acquire a wide variety of brain “images” using a particular temporal sequence of radiofrequency pulses, gradients, and readouts - a “scan” (Moore and Chung, 2017). These scans allow one to capture different types of MR signal, which is based on electromagnetic energy emitted by precessing nuclei of hydrogen; the most common types of images acquired in population-based studies include T1-weighted and T2-weighted images, diffusion tensor images (DTI), and functional MRI. Multimodal imaging of the brain structure, that is, a combination of multiple “scans” of the person’s brain during a single session, is particularly helpful for deriving multiple brain phenotypes and triangulating their neurobiological underpinnings. Fig. 25.2 shows a particular example of such a multimodal protocol, as employed in one of our population-based studies of the human brain (Bjornholm et al., 2017). Using these images, one can derive a number of characteristics informative with regard to normal and abnormal brain development and maturation. Thus, owing to the high contrast between gray and white matter, T1-weighted images are particularly useful for estimatingdusing automatic image-processing toolsdthe size of the cerebral cortex (thickness, surface area) and its subdivisions (Fischl and Dale, 2000) and volumes of various subcortical structures, such as the hippocampus (Pipitone et al., 2014).

FIGURE 25.2 Coronal slices of multimodal images of the brain structure acquired in members of a birth cohort when they reached 20 years of age. T1W, T1-weighted image; qT1, T1 relaxation time; qT2, T2 relaxation time; MWF, myelin water fraction; FA, fractional anisotropy; MD, mean diffusivity; MTR, magnetization transfer ratio. From Lerch, J.P., van der Kouwe, A.J., Raznahan, A., et al., 2017. Studying neuroanatomy using MRI. Nat. Neurosci. 20, 314e326.

334 Cognition and Addiction

Quantitative estimates of T1 and T2 relaxation times provide valuable information about tissue properties of various gray-matter (e.g., cerebral cortex) and white-matter (e.g., corpus callosum) structures. Images of myelin water fraction and magnetization transfer ratio (MTR) captured in a complementary fashiondrelative amount of myelin in a given 3D voxel; relative to other types of tissue occupying the same voxel, such as the axon (Bjornholm et al., 2017). Finally, DTI-derived estimates of mean diffusivity and fractional anisotropy reflect constraints imposed on water diffusion by the geometry (and biological properties) of cellular elements that make up a given voxel of imaged tissue. Taking advantage of this feature when modeling spatial patterns of water motion across voxels, the so-called tractography is used commonly to identify bundles of fibers (Jbabdi et al., 2015); note that this approach works well for long-range fibers that constitute, however, only w4% of all axons (Schüz and Braitenberg, 2002). The above MRI-derived metrics provide a toolbox with which one can embark on mapping the development and maturation of the brain structure in vivo. When mapping brain function, the most common MR signal used as an indirect index of neuronal activity, most likely excitatory postsynaptic potentials (Logothetis et al., 2001), is the blood oxygenation leveledependent (BOLD) contrast; it arises from the disproportionate increase of blood flow into the region engaged functionally at a given moment. In most fMRI studies, the degree of engagement of a particular brain region is measured simply by tracking the BOLD signal over time and calculating the difference between time segments that differ from each other with regard to the presence or absence of a particular stimulus ordin more general termsda difference in a particular well-defined behavioral state (e.g., anticipation of reward). This calculation is carried out throughout the brain voxelby-voxel, typically after a number of preprocessing steps; the main outputs of such analyses are statistical maps that indicate in which voxels (regions) the BOLD signal measured during one state differs from that in another state. Although voxel-wise analyses of fMRI data allow for an unbiased search for (group) differences throughout the brain, it can be useful to restrict the “search space” to a set of specific brain regions in some instances. In particular, such a region-of-interest (ROI) approach is suitable for testing specific hypothesis in large population-based studies in which one models a brain response in the context of a variety of genetic and environmental influences. There are various ways in which one can define a priori such ROIs, both functionally and anatomically. The former can be achieved through metaanalyses of published reports (Eickhoff et al., 2009) or by constructing probabilistic maps using large datasets (Tahmasebi et al., 2012), while the latter can take advantage of various anatomical parcellations of the human brain (e.g., the Automatic Anatomical

Labeling atlas (Tzourio-Mazoyer et al., 2002)), probabilistic maps of cytoarchitecture (Eickhoff et al., 2007), or metaanalyses of structural imaging studies (Cheung et al., 2010). The network structure of our brains implies constant functional interactions across various spatially segregated modules. Functional imaging studies provide a unique opportunity to quantify such interregional interactions by virtue of measuring BOLD signals across the entire brain simultaneously. The primary tool for exploring the so-called functional connectivity has been a statistical analysis of concurrent fluctuations in the BOLD signal over time, with or without the engagement of a participant in a particular paradigm. Various methods have been introduced to measure functional and effective connectivity, from simple interregional correlations to multivariate techniques (reviewed in Friston, 2011).

Population neuroscience: addiction research A growing number of studies are asking questions about the human brain and addiction; a total of 7376 items are identified when searching PubMed (brain AND addiction, filtered for “humans,” April 2, 2019), increasing from 30 items in 1990 to 449 items in 2018. What motivates this work? Substance use and substance use disorders are among the greatest contributors to preventable morbidity and mortality worldwide (Rehm et al., 2009; Whiteford et al., 2013; The Health Consequences of Smoking e 50 Years of Progress: A Report of the Surgeon General, 2014). A person’s liability to develop and sustain a substance use disorder is unfolding during two periods: (1) adolescence, when substance use emerges (Johnston et al., 2011) and (2) young adulthood, when substance use peaks (Johnston et al., 2009; Sussman and Arnett, 2014; Chen and Jacobson, 2012). Substance use disorders contribute to about 5% of years lived with disability (Whiteford et al., 2013); they also represent one of the most common comorbid conditions associated with other psychiatric disorders (Krishnan, 2005). This public health imperative motivates research on interindividual variations in the risk for developing substance use (and its progression), as well as research on possible consequences of substance use on mental and physical health. Population neuroscience represents a useful framework for (1) bringing together “external” (environment) and “internal” (genes) factors thatdin combinationdmay influence person’s liability for developing substance use disorder and (2) providing a phenotype (brain structure and function) that may help us understand the nature of such influences. Note, however, that observational studies cannot infer causality and directionality in the relationship between environment (or genes)

Population neuroscience in addiction research Chapter | 25

and brain phenotypes. Given the emergence of substance use during adolescence, this period of human development represents the main focus of several ongoing studies. I will now describe two such studies: the Saguenay Youth Study (SYS) and the IMAGEN Study.

The Saguenay Youth Study The SYS cohort includes 1029 adolescents and their 962 parents (at first assessment). This single-site cohort was recruited via adolescents attending high schools in the SaguenayeLac-Saint-Jean region of Quebec, Canada. This region is home to the largest genetic founder population in North America (Peltonen et al., 2000; De Braekeleer, 1991; Grompe et al., 1994; De Braekeleer et al., 1998). Both maternal and paternal grandparents of the adolescents were required to be of French-Canadian ancestry born in the region; as such, all adolescents and their parents are of a single ethnicity (European [French] ancestry). Half of the adolescents were exposed prenatally to maternal cigarette smoking; the other half (“nonexposed”) were matched to the “exposed” by maternal education and school attended. The cohort is family-based (481 families), including only adolescents who have one or more siblings of similar age

335

(12e18 years) and both biological parents of the FrenchCanadian origin born in the region. In Wave 1 (November 2003eFebruary 2012), phenotyping of the adolescents took place over several sessions (15 h in total) and included a number of brain and cardiometabolic domains detailed in Table 25.1 (further details in Pausova et al., 2007; Pausova et al., 2017 and www.saguenayyouth-study.org). During this Wave, we asked the parents to fill out a number of questionnaires about the family environment, their mental health, and substance use and provide a blood sample. Between 2012 and 2015, we have carried out full phenotyping of the parents using virtually the same protocol used in adolescents in Wave 1. Fasting (morning) blood samples were drawn on the day of the hospital visit during which we acquired MRIs, complete cardiovascular and metabolic assessments, cognitive testing, and psychiatric interviews (Pausova et al., 2016). In 2018, we initiated a follow-up assessment of the young people (Wave 2). The follow-up consists of the following elements: (1) an Internet-based self-assessment of substance use, mental health, life events, and other health-related domains; (2) a face-to-face structured psychiatric interview, a follow-back assessment of cannabis use and computer-based assessment of cognition;

TABLE 25.1 Saguenay Youth Study (Adolescents; Wave 1)dPhenotypes. Domain

Tool

Phenotypes

Brain

MRI

Global and regional volumes; cortical surface and thickness; MTR

Cognition

6-h battery

PIQ and VIQ; memory; executive functioning, phonological and motor skills; social cognition

Mental health

DPS, GRIP

Epidemiological diagnoses; symptom counts

Substance use

GRIPado

Cigarette smoking, cannabis, alcohol use, drug experimentation (age of initiation, lifetime history, last 30 days, binge drinking)

Personality

NEO-PI-R

Neuroticism, extroversion, openness, agreeableness, conscientiousness

Sexual maturation

PDS

Tanner stages

Lifestyle

Lerner

Sleep, nutrient intake, physical activity, extracurricular activities, sexuality, vocational aspirations

Family environment

FamEnvi

Stressful life events, financial difficulties, SES (family income, parental education)

Body

MRI, bioimpedance

Subcutaneous and visceral fat; total body fat and muscle mass

Cardiovascular

Finometer

Beat-by-beat systolic and diastolic blood pressure; heart rate; sympathetic and parasympathetic tone

Hormones

Blood

Testosterone, estrogen, cortisol

Biochemistry

Blood

Glucose, insulin, cholesterol, triglycerides, HDL, leptin, CRP, free fatty acids

Lipidomics

LC-ESI-MS

w750 lipid species (in progress)

CRP, C-reactive protein; DPS, DISC predictive scales; GRIP, Groupe de Recherche sur l’Inadaptation Psychosociale, adolescent self-assessment of mental health and substance use developed for the SYS by J. Se´guin based on validated National Longitudinal Survey of Children and Youth (NLSCY) and Quebec Longitudinal Study of Child Development (QLSCD) protocols; HDL, high-density lipoprotein; LC-ESI-MS, liquid chromatography electrospray ionization mass spectrometry; Lerner, adolescent self-assessment developed by Richard Lerner. MTR, magnetization transfer ratio; NEO-PI, neuroticism, extraversion, opennessdpersonality inventory; PDS, Puberty Development Scale; PIQ, performance IQ; VIQ, verbal IQ.

336 Cognition and Addiction

TABLE 25.2 Assessment of cannabis, alcohol, and cigarette smoking in young adulthood. Lifetime

Last 12 months

Last 30 days

Instruments

Substance use disorders

Instruments

Cannabis

Number of uses

Number of use days

Number of use days

Mental health and addiction, GRIP, ESPAD, CUPIT

Cannabis use disorder

Mini Plus, CUPIT

Alcohol

Number of drinks

Number of drinking days

Number of drinking days

Mental health and addiction, ESPA, ASR

Alcohol use disorder

Mini Plus, AUDIT

Cigarettes

Number of smokes

Number of smoking days

Number of smoking days

Mental health and addiction, GRIP, ASR

Tobacco use disorder

FTND, SSAGA

ASR, Adult Self Report; AUDIT, Alcohol Use Disorders Identification Test; CUPIT, Cannabis Use Problems Identification Test; ESPAD, European School Survey Project on Alcohol and Other Drug; FTND, Fagerstrom Test for Nicotine Dependence; GRIP, Groupe de recherche sur l’inadaptation psychosociale chez l’enfant; MINI Plus, Mini-International Neuropsychiatric Interview; SRE, Subjective Response to Ethanol; SSAGA, Semi-Structured Assessment for the Genetics of Alcoholism. Instruments compiled by the SYS team.

(3) cardiometabolic assessment; (4) magnetic resonance imaging; and (5) a blood sample. The key substance use variables and instruments are summarized in Table 25.2. Given the long initial period of phenotyping (November 2003eFebruary 2012), we are able to stagger these assessments so that participants’ age (at follow-up) will vary between 25 and 35 years (median age: 30 years). In Wave 2, we are readministering (on line) family environment, lifestyle, and mental health/substance use (GRIP, life habits) questionnaires, as well as several other questionnaires that were administered to the parents. Note in particular the use of instruments relevant for the assessment of cannabis (Cannabis Use Problems Identification Test, GRIPado) and alcohol use (Alcohol Use Disorders Identification Test [AUDIT], Subjective Response to Ethanol, GRIPado), as well as other illegal substances (European School Survey Project on Alcohol and Other Drug [ESPAD], Drug Abuse Screening Test [DAST]). During a visit, we draw a blood sample (after overnight fasting) for future “omics” analyses (e.g., genomics, transcriptomics, metabolomics), carry out a structured psychiatric interview (Mini-International Neuropsychiatric Interview (Sheehan et al., 1997)) and the University of California, Los Angeles (UCLA) Natural History Interview (cannabis section only; Murphy et al., 2010), assess cognitive abilities using a validated computer-based battery comprising 12 tests of executive function, memory, learning, and attention (www. cambridgebrainscience.com), and acquire a series of MRI scans. The latter include T1-weighted images, MTR, DTI, and abdominal scans (1.5T, Siemens Avanto).

assessments have been carried out, each including an MRI session, a cognitive assessment, and blood draw. In addition, adolescents and their parents completed internet-based self-assessments of their substance use, mental health, family environment, and stressful life events (https:// imagen-europe.com/). These visits took place at the following ages: Visit 1 at 14 years of age (n ¼ 2000), Visit 2 at 19 years of age (n ¼ 1500), and Visit 3 at 23 years of age (n ¼ 1,100, ongoing). In addition, an internet-based self-assessment of substance use took place between Visits 1 and 2 at 16 years of age (n ¼ 1700). Cognitive assessments were carried out with the Cambridge Neuropsychological Test Automated Battery (CANTAB) and six subtests of WISC (Visit 1 only), mental health was assessed with the Development and Well-Being Assessment interview (Goodman et al., 2000) and Strengths and Difficulties Questionnaire (Goodman, 1997), and substance use with a number of questionnaires (e.g., AUDIT, ESPAD, FTND; see Table 25.2 for abbreviations). At all sites, the MRI session included a series of structural (T1-weighted images, DTI) and functional (Monetary Incentive Delay [MID] task, Stop-Signal Task [SST], Face Task) scans. The three functional paradigms have been selected to engage brain networks relevant for reward anticipation and processing (MID task; Knutson et al., 2001), cognitive control and impulsivity (SST; Logan, 1994), and processing of social signals from biological motion (Face task; Grosbras and Paus, 2006). In addition, resting-state scans were acquired at the majority of sites, while MTR and abdominal scans were acquired at a single site.

IMAGEN study The IMAGEN cohort is a multisite study that includes 2000 adolescents (at first assessment) recruited via high schools in eight European cities including Berlin, Dresden, Hamburg, Mannheim (Germany), London, Nottingham (United Kingdom), Paris (France), and Dublin (Ireland) (Schumann et al., 2010). Three waves of face-to-face

Findings Over the past 10 years, the two studies have reported a number of findings about brain maturation during adolescence in general, as well as observations that speak to the relationship between genes, environment, and substance use/addiction. Table 25.3 provides an overview of these

Population neuroscience in addiction research Chapter | 25

337

TABLE 25.3 Summary of relevant publications from the Saguenay Youth Study and the IMAGEN Study. MTR, Magnetization Transfer Ratio; PEMCS, Prenatal Exposure to Maternal Cigarette Smoking; VBM, Voxel Based Morphometry; OFC, orbitofrontal cortex; ACC, anterior cingulate cortex; DL-FC, dorsolateral frontal cortex; PFC, prefrontal cortex; PAG, periaqueductal grey; BMI, body mass index; ICV, intracranial volume First Author

Journal

Pub year

PMID

Volume of white matter, MTR

Perrin

Journal of neuroscience

2008

18799683

PEMCS

Cortical thickness, Positive youth development

Toro

Neuropsychopharmacology

2008

17609681

PEMCS

Corpus callosum (volume, MTR)

Paus

Neuroimage

2008

18221892

Peer influence

Cortical thickness, Positive youth development, IQ

Grosbras

Social Neuroscience

2008

18979383

Androgens, Puberty

Fiber-tract structural properties

Herve

Human Brain Mapping

2009

19235881

Puberty

Volume of white matter, MTR

Perrin

NeuroImage

2009

19349224

PEMCS

Cognitive abilities

Kafouri

Int J Epidemiology

2009

19039007

PEMCS

Visceral fat

Syme

Obesity

2010

19851308

PEMCS

Orbitofrontal cortex (thickness), substance use

Lotfipour

Arch Gen Psychiatry

2009

19884612

PEMCS

Striatum (volume), drug use

Lotfipour

Molecular Psychiatry

2010

20029407

PEMCS

DNA methylation (BDNF)

ToledoRodriguez

Am J Med Genet B Neuropsychiatr Genet

2010

20583129

Video gaming

Striatum (volume, response to feedback), Cambridge Gambling Task

Kuhn

Transl Psychiatry

2011

22833208

Cigarette smoking

Striatum (reward anticipation), impulsivity, novelty seeking

Peters

Am J Psychiatry

2011

21362742

Risk taking

Striatum (volume, reward anticipation), Cambridge Gambling Task

Schneider

Am J Psychiatry

2012

21955931

RASGRF2 haplotype

Striatum (reward anticipation), drinking

Stacey

PNAS

2012

23223532

Initiation of early drinking

Brain response to reward anticipation

Nees

Neuropsychopharmacology

2012

22113088

PEMCS

Cortical surface area and folding

Paus

Cerebral Cortex

2012

22156575

Impulsivity

Neural correlates of inhibitory control (Stop-Signal Task [SST])

Whelan

Nature Neuroscience

2012

22544311

Impulsiveness

Cortical thickness (“prefrontal cortex")

Schilling

Molecular Psychiatry

2013

22665261

Impulsiveness

Brain structure (VBM)

Schilling

Human Brain Mapping

2013

22076840

PEMCS

Amygdala (volume), fat intake

Haghighi

JAMA Psychiatry

2013

22945562

Breastfeeding

Cortical thickness

Kafouri

Int J Epidemiology

2013

23175518

Exposures

Outcomes

Androgens

Continued

338 Cognition and Addiction

TABLE 25.3 Summary of relevant publications from the Saguenay Youth Study and the IMAGEN Study. MTR, Magnetization Transfer Ratio; PEMCS, Prenatal Exposure to Maternal Cigarette Smoking; VBM, Voxel Based Morphometry; OFC, orbitofrontal cortex; ACC, anterior cingulate cortex; DL-FC, dorsolateral frontal cortex; PFC, prefrontal cortex; PAG, periaqueductal grey; BMI, body mass index; ICV, intracranial volumedcont’d First Author

Journal

Pub year

PMID

Impulsivity, Brain response in SST (in OFC)

Heinrich

Eur J Neurosci

2013

23551272

ODZ4

Amygdala (response to reward)

Heinrich

Bipolar Disord

2013

23611537

CHRNA5-CHRNA3-CHRNB4 cluster

Striatum, OFC, ACC (response to reward), anxiety sensitivity

Nees

Neuropsychopharmacology

2013

23689675

PEMCS

Striatum (reward anticipation)

Muller

JAMA Psychiatry

2013

23784668

Visceral fat

Executive functions, memory

Schwartz

Int J Obesity

2013

23797144

PEMCS

Fetal organ volumes (Brain, kidney, lungs, liver)

Anblagan

PLOS One

2013

23843995

Compulsive behavior

OFC, DL-FC, striatum (volumes)

Montigny

PLOS One

2013

24244633

OPRM1 (opioid receptor mu 1)

Fat intake, amygdala (volume), Obesity

Haghighi

Molecular Psychiatry

2014

23337944

Puberty, sex hormones

Pituitary volume

Wong

NeuroImage

2014

24632090

Video gaming

Cortical thickness

Kuhn

PLOS One

2014

24633348

COMT

Brain response in SST (in PFC)

White

Neuropsychopharmacology

2014

24820538

PEMCS

Drug use, externalizing behavior

Lotfipour

Addiction

2014

24942256

Exposures

Outcomes

AMBRA1

Cigarette smoking

Anxiety, depression

Taylor

BMJ Open

2014

25293386

Neuropsychosocial profiles

Alcohol misuse

Whelan

Nature

2014

25043041

Family history of alcoholism

Striatum (reward anticipation)

Muller

Addict Biol

2015

24903627

PEMCS

Fat dietary preference

Lee

J Psychiatry Neurosci

2015

25266401

PEMCS

DNA methylation (genome wide)

Lee

Environ Heatlh Perspect

2015

25325234

BDNF

Alcohol use, striatum (response to reward anticipation)

Nees

Alcohol

2015

25650137

DNA methylation (PPM1G)

Alcohol use disorder, Brain response (Stop Tasks) in subthalamic nucleus

Ruggeri

Am J Psychiatry

2015

25982659

RSU1

Alcohol use, Brain response to reward anticipation (striatum)

Ojelade

PNAS

2015

26170296

Cannabis, PRSsch

Cortical thickness

French

JAMA Psychiatry

2015

26308966

Cannabis

Brain response to faces (amygdala)

Spechler

Dev Cogn Neurosci

2015

26347227

Personality (SURPS)

Substance use (alcohol use, smoking, and cannabis use)

Jurk

Alcohol Clin exp Res

2015

26463560

Population neuroscience in addiction research Chapter | 25

339

TABLE 25.3 Summary of relevant publications from the Saguenay Youth Study and the IMAGEN Study. MTR, Magnetization Transfer Ratio; PEMCS, Prenatal Exposure to Maternal Cigarette Smoking; VBM, Voxel Based Morphometry; OFC, orbitofrontal cortex; ACC, anterior cingulate cortex; DL-FC, dorsolateral frontal cortex; PFC, prefrontal cortex; PAG, periaqueductal grey; BMI, body mass index; ICV, intracranial volumedcont’d Exposures

Outcomes

First Author

Journal

Pub year

PMID

OFC (gyrification)

Drinking behavior

Kuhn

Addiction Biology

2016

25913102

CNR1

Brain response to faces

Ewald

Eur J Neurosci

2016

26527537

Accumbens genes (e.g.,EHD4)

Binge drinking, Brain response to reward (striatum)

Stacey

J Psychiatry Neurosci

2016

26679926

Reward processing “nodes” (striatal and cortical), VPS4A

Hyperactivity, alcohol use

Jia

PNAS

2016

27001827

GWAS

Lifetime cannabis use

Stringer

Transl Psychiatry

2016

27023175

Striatum (reward anticipation): functional connectivity

Cigarette smoking

Jollans

Dev Neuropsychol

2016

27074029

KALRN

Brain response to reward (striatum), Binge drinking

PenaOliver

Front Genet

2016

27092175

Personality (TCI), Cambridge Gambling Task, Brain response to reward (MID), ANKK1, HOMER1

Alcohol use

Heinrich

Biol Psychol

2016

27180911

Polygenic Risk Score (schizophrenia)

Brain response to reward, IQ, impulsivity

Lancaster

JAMA Psychiatry

2016

27384424

PEMCS

Cognitive abilities (WAIS subtests, CANTAB subtests)

Ramsay

BMC Psychiatry

2016

27908296

Sex, age

Disordered eating Behaviour

Bartholdy

Eur Child Adolesc Psychiatry

2017

28050706

OPRM1

Brain response to reward (PAG, striatum), pain complaints

Nees

Pain

2017

28092323

Brain response to reward (striatum, PFC), personality

Problematic drug use (defined as the intake of increased amounts of licit and/or illicit drugs; cigs: 1þ cigs per days, alcohol: 20þ drinks per month [last 30 days], cannabis: 39þ lifetime occasions, other drugs: 3þ lifetime occasions)

Buchel

Nat Commun

2017

28221370

GABRB1

Brain response to reward (MID) and cognition control (SST)

Duka

Front Behav Neurosci

2017

28261068

Sex hormones

Cortical thickness and thinning

Wong

Cerebral Cortex

2018

28334178

EFHD2 (Swiprosin-1)

Alcohol use, anxiety

Mielenz

Mol Psychiatry

2018

28397836

PSD3

Alcohol use, Binge drinking, BOLD response in SST (in PFC)

Gonzalez

Mol Psychiatry

2018

28607459

OPRL1 (opioid Receptorelike 1) methylation status

Binge drinking, Brain response to reward anticipation (striatum)

Ruggeri

J Child Psychol Psychiatry

2018

29197086

Continued

340 Cognition and Addiction

TABLE 25.3 Summary of relevant publications from the Saguenay Youth Study and the IMAGEN Study. MTR, Magnetization Transfer Ratio; PEMCS, Prenatal Exposure to Maternal Cigarette Smoking; VBM, Voxel Based Morphometry; OFC, orbitofrontal cortex; ACC, anterior cingulate cortex; DL-FC, dorsolateral frontal cortex; PFC, prefrontal cortex; PAG, periaqueductal grey; BMI, body mass index; ICV, intracranial volumedcont’d Exposures

Outcomes

First Author

Journal

Pub year

PMID

PEMCS

Offspring overweight

Albers

Int J Obesity

2018

29717267

Psychosocial, brain, and genetic features

Initiation of cannabis use

Spechler

Eur J Neurosci

2018

29889330

DRD1, DRD2

Alcohol misuse, brain response to reward (striatum, OFC)

Baker

Psychol Med

2019

29909784

Ventromedial PFC (volume)

Hyperactive/inattention symptoms

Albaugh

Cerebral Cortex

2018

29912404

GWAS

Age at first cannabis use

Minica

Addiction

2018

30003630

PEMCS

Offspring BMI

Albers

Obes Rev

2018

30035359

TTC12-ANKK1-DRD2

Cigarette smoking, Brain response to reward (striatum)

Macare

Eur Neuropsychopharmacol

2018

30104163

Brain structure

Symptoms of attention deficit hyperactivity disorder or conduct disorder

Bayard

Mol Psychiatry

2018

30108313

COMT

Attention deficit hyperactivity symptoms

Millenet

Front Genet

2018

30108607

White matter (FA, MD)

Concurrent subthreshold depression and depression at 2-yr follow-up

Vulser

Am J Psychiatry

2018

30111185

GWAS

Cannabis use

Pasman

Nature Neuroscience

2018

30150663

DRD2 methylation status

IQ, gray matter (striatum), brain response to reward (striatum)

Kaminski

Transl Psychiatry

2018

30166545

Age, delta age, cell-specific gene expression

Cortical MTR

Patel

Cerebral Cortex

2018

30169567

Visceral fat, glycerophosphocholines

White matter (T1W signal intensity, MTR), Processing speed

Syme

Int J Obesity

2018

30206338

Functional connectivity

Reward anticipation and receipt (MID task)

Cao

Human Brain Mapping

2018

30240509

Polygenic Risk Score (psychosis), Brain response to faces (at 14)

Psychotic experiences (at 18)

Velthorts

Transl Psychiatry

2018

30258131

16p11.2 distal CNV

ICV, subcortical volumes, IQ

Sonderby

Molecular Psychiatry

2018

30283035

Psychosocial stress, alcohol use, smoking

DNA methylation (EWAS)

Tay

Am J Psychiatry

2019

30525907

Breastfeeding

IQ

Hartwig

IJE

2018

30541029

Peer victimization

Psychopathology

Quinlan

Molecular Psychiatry

2018

30542059

Cannabis (extremely low levels of use)

Gray matter

Orr

Journal of Neuroscience

2019

30643026

Population neuroscience in addiction research Chapter | 25

341

TABLE 25.3 Summary of relevant publications from the Saguenay Youth Study and the IMAGEN Study. MTR, Magnetization Transfer Ratio; PEMCS, Prenatal Exposure to Maternal Cigarette Smoking; VBM, Voxel Based Morphometry; OFC, orbitofrontal cortex; ACC, anterior cingulate cortex; DL-FC, dorsolateral frontal cortex; PFC, prefrontal cortex; PAG, periaqueductal grey; BMI, body mass index; ICV, intracranial volumedcont’d Exposures

Outcomes

First Author

Journal

Pub year

PMID

GWAS

Gray matter (voxel wise)

Luo

JAMA Psychiatry

2019

30649180

TANK gene

Brain response to reward (MID), cognition control (SST), and social cues (Face)

Muller

Cerebral Cortex

2019

30721969

Puberty

Resting-state connectivity, psychopathology

Ernst

Transl Psychiatry

2019

30804326

reports ordered by their year of publication. In this table, the first two columns identify the key “exposures” and “outcomes” addressed in a given report. These termsdand their equivalents (e.g., “independent” and “dependent” variables, as used in experimental studies)dshould be interpreted against the background of observational studies and their limitations: even if one can be certain about temporal order in some instances (e.g., exposure to maternal smoking during pregnancy, substance use during adolescence), one cannot infer causality from the mere presence of (statistical) associations between “exposures” and “outcomes.” Furthermore, in cases of brainebehavior relationshipsdsuch as cannabis use and cortical thickness measured at the same time pointdassignment of the two variables as “exposures” and “outcomes” is arbitrary, as we cannot establish directionality in their relationships. Let us now highlight a few of these findings. Given the puberty-associated changes in hormonal environment, it is not surprising to see a number of structural correlates of sexual maturation, including variations in cortical graymatter (Paus et al., 2010), the overall volume and structural properties of white matter (Perrin et al., 2008, 2009), and the volume of the pituitary gland (Wong et al., 2014). Furthermore, we took advantage of the head MRIs to quantify a number of nonbrain phenotypes, namely morphology of the face (Mareckova et al., 2011) and the voice box (Markova et al., 2016), and relate these to pubertal stages (and hormone levels). The multidomain nature of the Saguenay Youth Study allowed us to evaluate sex differences in the adolescent brain and body. Table 25.4 provides a summary of effects sizes of these differences for a total of 66 traits; 59 of these traits showed sex differences (at a nominal P < 0.05), with small (32), medium (13), and large (11) effects (Paus et al., 2017). A number of observations have been made with regard to the prenatal exposure to maternal smoking including

associations with lower (exposed vs. nonexposed) cortical thickness (Lotfipour et al., 2009; Toro et al., 2008), smaller corpus callosum (Paus et al., 2008), higher adiposity (Syme et al., 2010), and its relationship to fat intake and the amygdala volume (Haghighi et al., 2013), or differences in DNA methylation (Lee et al., 2015; Toledo-Rodriguez et al., 2010). Some of our findings suggest that prenatal exposure to maternal cigarette smoking is associated with reward deficiency rather than higher sensitivity to reward (Lotfipour et al., 2009; Muller et al., 2013). Again, we need to keep in mind that no causality can be inferred from these associations. The concurrent assessment of substance use and functional engagement of neural circuits involved in reward and cognitive control provided initial insights into relevant brainebehavior relationships. For example, a differential BOLD response to anticipated reward in (ventral) striatum, as assessed by the MID task, is one of the most consistent findings across a number of traits including cigarette smoking (Peters et al., 2011; Jollans et al., 2016), risk taking (Schneider et al., 2012), video gaming (Kuhn et al., 2011), compulsive behavior (Montigny et al., 2013), and problematic drug use (Buchel et al., 2017). At the same time, a number of genetic variations are associated with the brain response to anticipated reward assessed by the same task (RASGRF276, CHRNA5-CHRNA3-CHRNB4 cluster (Nees et al., 2013), BDNF (Nees et al., 2015), RSU1 (Ojelade et al., 2015), EHD4 (Stacey et al., 2016), KALRN (Pena-Oliver et al., 2016), DRD1, DRD2, TTC12-ANKK1DRD2 (Baker et al., 2019; Macare C et al., 2018). This series of observations made over the years awaits a synthesis that, together with parallel work in experimental models, promises to integrate the role of the genetic and environmental factors in shaping the reward system of the human brain and their significance for the risk of addiction. Our work in the Saguenay Youth Study and the IMAGEN Study, together with that in other cohorts such as

Sex (d; M eF)

Sex 3 Age (P)

r Female

n Male

n Female

0.01

0.08

479

509

4.86Ee01

0.03

0.01

476

509

6.69Ee03

0.29

0.16

476

509

1.18

1.85Ee03

0.37

0.24

476

509

1.28Ee56

1.08

5.12Ee25

0.62

0.32

467

512

Body water (log)

1.99Ee55

1.07

1.22Ee24

0.61

0.31

471

511

Visceral fat to total body fat (ratio, log)

2.70Ee40

0.90

1.19Ee01

0.15

0.28

453

502

Voice

Formant position

3.73Ee10

0.87

1.18Ee01

0.22

0.03

94

144

Anthropometry

Height

6.46Ee36

0.81

5.98Ee29

0.67

0.33

495

531

Face

Perceived maleness

1.28Ee29

0.81

1.46Ee03

0.33

0.07

407

431

Food intake

Food recall: Energy intake (log)

3.56Ee28

0.72

1.42Ee03

0.22

0.04

478

509

Food intake

Food recall: Protein (log)

4.22Ee24

0.66

8.82Ee02

0.17

0.08

479

512

Food intake

Food recall: Carbohydrates (log)

3.25Ee20

0.60

5.01Ee02

0.15

0.03

468

504

Food intake

Food recall: Fat (log)

8.83Ee19

0.57

3.51Ee02

0.18

0.07

481

514

Metabolic

Food recall: Polyunsaturated FAs (log)

5.52Ee14

0.49

2.24Ee01

0.15

0.09

477

513

Metabolic

Food recall: Saturated FAs (log)

9.92Ee14

0.48

1.46Ee02

0.19

0.05

481

513

Body image

Body esteem: Weight (log)

1.02Ee13

0.48

9.53Ee02

0.04

0.13

494

502

Body image

Body esteem: Appearance (log)

9.13Ee11

0.42

8.04Ee01

0.09

0.10

486

500

Cardiovascular

Diastolic blood pressure (averaged time series)

1.74Ee09

0.40

3.24Ee01

0.07

0.01

444

478

Cardiovascular

Diastolic blood pressure (clinical)

8.62Ee10

0.39

1.03Ee01

0.09

0.01

482

511

Anthropometry

Waist circumference (log)

1.28Ee09

0.39

5.43Ee03

0.30

0.14

482

529

Cardiovascular

Systolic blood pressure (clinical)

2.60Ee09

0.38

8.10Ee02

0.18

0.09

485

515

Metabolic

Glucose (log)

2.94Ee09

0.38

1.52Ee04

0.03

0.27

488

519

Cardiovascular

Systolic blood pressure (averaged time series)

3.18Ee07

0.34

2.31Ee01

0.17

0.13

444

478

Physical activity

Exercise (log)

4.94Ee07

0.32

2.01Ee01

0.10

0.19

477

518

Domain

Variable

Sex (P)

Brain

Cortical area

4.59E e100

1.52

1.76Ee01

Brain

Brain volume

7.25Ee95

1.47

Brain

White matter volume (lobar)

2.81Ee87

1.40

Brain

Gray matter volume (lobar)

2.74Ee66

Anthropometry

Lean body mass (log)

Anthropometry Anthropometry

r Male

342 Cognition and Addiction

TABLE 25.4 Sex differences in phenotypes assessed across a number of brain and body domains in the Saguenay Youth Study. Phenotypes are ordered by their effect size, from the largest positive values (indicating mean values higher in males than females) to the largest negative values (indicating mean values higher in females than males). d, Cohen’s d. Threshold for corrected P value: P [ 7.50Ee04.

Anthropometry

Weight (log)

1.96Ee06

0.30

4.19Ee07

0.52

0.35

490

529

Cardiovascular

Parasympathetic tone (high-frequency interbeat interval, log)

3.36Ee06

0.29

4.26Ee01

0.11

0.07

489

525

Substance use

Binge drinking (5þ drinks on same occasion in last 30 days)

4.60Ee02

0.21

1.21Ee02

0.34

0.08

158

199

Autonomic

Sympathetic tone (low-frequency diastolic blood pressure, log)

1.89Ee03

0.20

5.68Ee01

0.05

0.01

490

525

Substance use

Smoking: Negative life history

1.97Ee03

0.19

7.56Ee01

0.26

0.27

491

523

Face

PC1

1.55Ee01

0.15

5.35Ee01

0.14

0.07

183

199

Food intake

Food recall: Fat percentage (%)

6.95Ee02

0.12

4.21Ee01

0.08

0.04

482

517

Voice

Vocal tract length (log)

1.28Ee01

0.10

4.38Ee03

0.28

0.11

411

460

Food intake

Food recall: Protein percentage (log)

3.00Ee01

0.07

6.80Ee01

0.02

0.05

479

514

Body mass index (log)

4.45Ee01

0.05

4.94Ee01

0.26

0.25

488

524

Marijuana (ever, never)

1.58Ee01

0.09

7.41Ee01

0.42

0.39

488

524

Body image

Body esteem: Attribution

1.12Ee01

0.10

2.82Ee01

0.05

0.12

494

533

Food intake

Food recall: Carbohydrate percentage (%)

4.28Ee02

0.13

5.46Ee01

0.07

0.05

482

517

Anthropometry

Visceral fat volume (log)

3.46Ee02

0.14

8.41Ee01

0.12

0.14

466

519

Sleep

Nap during day

1.29Ee02

0.16

7.47Ee01

0.29

0.31

495

532

Personality

Conscientiousness

1.11Ee03

0.21

4.72Ee01

0.00

0.05

488

522

Substance use

Alcohol (ever tried)

6.75Ee04

0.21

2.31Ee02

0.58

0.53

491

524

Metabolic

Triglycerides (log)

4.11Ee04

0.22

7.85Ee01

0.18

0.18

487

525

Metabolic

Low-density lipoprotein (log)

2.77Ee04

0.23

5.98Ee01

0.10

0.07

485

522

Metabolic

Insulin resistance (HOMA index, log)

7.71Ee05

0.25

9.31Ee02

0.06

0.19

479

516

Brain

Cortical thickness

5.28Ee05

0.26

6.44Ee06

0.53

0.33

479

509

Sleep

Sleeping quality

3.21Ee05

0.26

7.19Ee01

0.12

0.10

495

532

Metabolic

C-reactive protein (log)

4.14Ee05

0.26

3.23Ee08

0.09

0.40

479

513

Voice

Vocal fold length

8.32Ee04

0.27

1.08Ee07

0.45

0.29

237

411

Sleep

Tired during day

6.98Ee07

0.31

1.57Ee01

0.13

0.23

495

532

Metabolic

Insulin (log)

3.36Ee07

0.32

1.53Ee01

0.04

0.14

481

523

Sleep

Night wake

1.13Ee07

0.33

9.22Ee01

0.01

0.01

494

530

Personality

Resistance to peer influence (log)

6.66Ee08

0.37

2.82Ee01

0.14

0.25

399

464

Cardiovascular

Heart rate (averaged time series)

2.02Ee09

0.38

8.14Ee03

0.32

0.19

491

525

343

Continued

Population neuroscience in addiction research Chapter | 25

Anthropometry Substance use

Domain

Variable

Sex (P)

Sex (d; M eF)

Sex 3 Age (P)

r Male

r Female

Personality

Neuroticism

2.69Ee11

0.42

1.78Ee03

0.11

Metabolic

Cholesterol (log)

9.96Ee12

0.43

1.82Ee01

Personality

Extroversion

1.61Ee12

0.45

1.95Ee01

n Male

n Female

0.09

488

519

0.04

0.13

490

521

0.14

0.07

489

522

Brain

Corticospinal tract: MTR

4.86Ee12

0.51

1.23Ee02

0.09

0.08

361

409

Metabolic

High-density lipoprotein (log)

8.56Ee16

0.51

2.36Ee07

0.19

0.13

492

522

Metabolic

Insulin resistance (HOMA-B, log)

8.73Ee21

0.61

1.40Ee01

0.02

0.08

478

516

Personality

Openness

3.61Ee22

0.62

8.30Ee01

0.26

0.23

488

522

Personality

Agreeableness

5.86Ee23

0.64

6.27Ee01

0.05

0.08

488

522

Brain

Pituitary volume (log)

6.13Ee23

0.65

3.57Ee05

0.48

0.31

472

506

Anthropometry

Total body fat (log)

6.73Ee24

0.67

5.03Ee01

0.22

0.32

452

506

Anthropometry

Subcutaneous abdominal fat volume (log)

3.89Ee25

0.67

4.82Ee02

0.06

0.23

481

521

Voice

Fundamental frequency (log)

2.20Ee71

1.87

1.03Ee40

0.71

0.29

204

328

From Paus, T., Wong, A.P., Syme, C., Pausova, Z., 2017. Sex differences in the adolescent brain and body: findings from the saguenay youth study. J. Neurosci. Res. 95, 362e370.

344 Cognition and Addiction

TABLE 25.4 Sex differences in phenotypes assessed across a number of brain and body domains in the Saguenay Youth Study. Phenotypes are ordered by their effect size, from the largest positive values (indicating mean values higher in males than females) to the largest negative values (indicating mean values higher in females than males). d, Cohen’s d. Threshold for corrected P value: P [ 7.50Ee04.dcont’d

Population neuroscience in addiction research Chapter | 25

the Avon Longitudinal Study of Parents and Children (ALSPAC; Boyd et al., 2013), allowed us to discover and replicate a subtle interactive relationship between genes and environment. We close this section by describing a report in which we asked whether the use of cannabis during early adolescence (by age 16) is associated with variations in brain maturation as a function of genetic risk of schizophrenia (French et al., 2015). We addressed this question in three samples of typically developing youth, namely SYS (n ¼ 949; both sexes), ALSPAC (n ¼ 295; males only), and IMAGEN (n ¼ 333; both sexes), for whom we obtained (1) information about their cannabis use during adolescence; (2) structural images of their brains; and (3) their polygenic risk score of schizophrenia (PRSSCH). The polygenic risk scores were calculated from 114 SNPs identified by the Psychiatric Genomics Consortium in a genome-wide comparison of 36,989 patients with schizophrenia and 113,075 controls (Biological insights from 108 schizophrenia-associated genetic loci, 2014). Note that this score is based on common genetic variants and, as such, it has a normal distribution in general population. In SYS males, we observed an interaction between cannabis use and the risk score on age-adjusted cortical thickness; higher risk score is associated with lower cortical thickness in cannabis “ever users” but not in “nonusers.” This was not the case in SYS females. In ALSPAC and IMAGEN, we assessed relationships between cumulative cannabis frequency and cortical thickness in individuals with highor low-risk score. In the ALSPAC high-risk group, “heavy users” (61 occasions) had lower cortical thickness than both the “never users” and “light users.” No such differences were observed in the ALSPAC low-risk group. In the IMAGEN high-risk group of males, “heavy users” (20 occasions) showed a larger decrease in cortical thickness (between 14 and 19 years of age) than “never users”; this was also true for the comparison of “medium users” (3e19 occasions) with “never users” (Fig. 25.3). No such differences were found in IMAGEN females. Across the three samples, the relationship between cannabis use and cortical thickness was highly significant in male adolescents belonging to the high-risk groups (the Stouffer’s meta P-value ¼ 2.3E-6). This was not the case for low-risk males, or either the low-risk or high-risk female groups (meta P-value > 0.05). Finally, we showed that regional variations in the magnitude of a group difference (never vs. ever users) in cortical thickness vary as a function of regional differences in the expression of cannabinoid receptor 1 (CNR1) gene, as provided by the Allen Human Brain Atlas (Hawrylycz et al., 2012) and calculated for each cortical region using an approach developed in our laboratory (French and Paus, 2015). This finding supports the possibility that cannabinoids indeed play a role in the observed relationship between cannabis use, PRSSCH, and cortical thickness.

345

Challenges and outlook As any other approach, population neuroscience comes with its own challenges. In addition to the time and effort it takes to recruit and assess large (>1000) numbers of participants, as well as the cost of deep phenotyping and omics-based assays, the key challenges lie in the interpretation of the findings. These exist at two different levels. At a design level, the majority of research taking this approach is based on data collected in observational studies. As detailed elsewhere (Paus, 2013), the way participants are recruited represents a possible source of ascertainment biases and, consequently, deviations from the composition of general population. For example, selecting adolescents who used cannabis from a large birth cohort by reviewing answers to questionnaires administered to all cohort members would yield a more representative sample than when recruiting from a community by a targeted (cannabis-related) advertisement. Other less obvious but more common biases relate to self-selection when volunteering in time-demanding longitudinal studies (e.g., family income, education). Furthermore, and as discussed above, observational studies do not allow us to infer causality and, with a few exceptions, directionality of statistical associations between “exposures” and “outcomes”. Nonetheless, when combined with molecular genetics, one can strengthen interpretations along these lines. For example, we showed thatdin male adolescentsdthe relationship between testosterone levels and white matter is moderated by a functional polymorphism in androgen receptor gene (i.e., gene x environment interaction), thus supporting our conclusion that testosterone indeed drives the growth of white-matter volume (Perrin et al., 2008). A subsequent experimental study (in rats) confirmed this conclusion (Pesaresi et al., 2015). In recent years, the use of genetic variations as “instrumental variables” has gained popularity in a framework of Mendelian randomization (Smith et al., 2008). In the context of addition research, a rather surprising reinterpretation of the directionality in a cannabiseschizophrenia relationship has emerged, namely evidence for a causal positive influence of schizophrenia risk on cannabis use (Pasman et al., 2018). At a level of specific brain phenotypes, the field is also beginning to take advantage of advances in molecular genetics, and publicly available resources, to facilitate interpretations of MRI-based findings. In our work, for example, we use interregional profiles of gene expression in the human cerebral cortex, provided in the Allen Human Brain Atlas (Hawrylycz et al., 2012) and remapped to the Desikan-Killany parcellation of FreeSurfer (French and Paus, 2015), to asses which genes relate to a phenotype of interest. As mentioned above, we showed that differences between cannabis “users” and “nonusers” in regional values of cortical thickness varieddacross 34 “FreeSurfer”

346 Cognition and Addiction

FIGURE 25.3 Dot plots of mean cortical thickness for different groups of male cannabis users at high and low risk. Thickness values are binned and stacked horizontally within each grouping. Mean thickness values are marked with thick black lines. Significant group differences are marked with lines and Cohen d statistics. (A), Age-adjusted cortical thickness is presented in male participants who ever and never used cannabis. (B), Change in cortical thickness (Time 2  Time 1) by number of occasions of use. (C), Age-adjusted cortical thickness is presented by number of occasions of use. ALSPAC, Avon Longitudinal Study of Parents and Children; SYS, Saguenay Youth Study. Cortical thickness is presented in arbitrary units (residuals). a, P < 0.005, t test. b, P < 0.05, t test. From French, L., Gray, C., Leonard, G., et al., 2005. Early cannabis use, polygenic risk score for schizophrenia and brain maturation in adolescence. JAMA Psychiatry 7, 1002e1011.

Population neuroscience in addiction research Chapter | 25

regions (left hemisphere)das a function of regional values of expression CNR1, a gene coding for a cannabinoid receptor 1; cortical regions with higher CNR1 expression show a bigger difference in thickness (French et al., 2015). Similarly, we used NR3C1 gene (coding for glucocorticoid receptor) to facilitate interpretations of age-related changes in cortical thickness during adolescence (Wong et al., 2018; Parker et al., 2017) and the relationship between stressful life events and brain response to faces (Lieslehto et al., 2017). We have extended this approach into the so-called virtual histology, whereby cell-specific gene markers are used to identify cellular correlates of MRI-based metrics (Shin et al., 2018; Patel et al., 2018). Finally, a number of reports listed in Table 25.3 combined population and experimental neuroscience to advance our understanding of causal mechanisms underlying addiction, in particular, in relation to alcohol use (Stacey et al., 2012, 2016; PenaOliver et al., 2016; Mielenz et al., 2018).

Where do we go next? First, in addition to the ongoing cohort studies, such as the Saguenay Youth Study and IMAGEN Study, and longitudinal birth cohorts, such as ALSPAC (Boyd et al., 2013), the Northern Finland Birth Cohort (Jarvelin et al., 1993), and the Generation R Study (Jaddoe et al., 2012), new population-based studies with a strong neuroimaging component have been initiated in recent years. Most notably, the Adolescent Brain Cognitive Development (ABCD) Study has recruited over 11,000 children (9e10 years old) to participate in longitudinal (every 2 years) assessments of their brains and behavior throughout adolescence (Garavan et al., 2018). Similar to the IMAGEN Study, the ABCD Study will provide new knowledge about brain characteristics that precede (possible risks) or follow (possible consequences) the onset of substance use in general population. Second, we need to enhance assessment of the environment surrounding participants in our studies throughout their lives. As pointed out above, the developing human being is under a myriad of influences related to his/her physical environment (e.g., air, trees), built environment (e.g., transportation) and, most importantly, social environment (e.g., family, peers, neighbors) (Ruiz Jdel et al., 2016). There is a growing number of examples whereby data science in general, and “population informatics” in particular (Kum et al., 2014), show the power of extracting information about social and built environment from various digital sources (e.g., satellite images, social media, patterns of mobile phone use) and relating it to phenomena such as brain maturation (Parker et al., 2017), well-being (Kardan et al., 2015), obesity (Maharana and Okanyene Nsoesie, 2018), health (Abnousi et al., 2019), and social relationships (David-Barrett et al., 2016; Gruzd and

347

Haythornthwaite, 2013). Twitter-based studies of social behavior include, for example, a cyclic nature of coordinated social activity (Morales et al., 2017) or public attention and temporal patterns of tweets on specific social issues (Peng et al., 2017). To start, such informationd mapped in space and timedcan be considered at an aggregate level (e.g., neighborhood, census tract) and related to the individual member of a cohort through a geospatial information system (Paus, 2016). Third, as mentioned above, there is a great desire to uncover causal influences shaping the developing brain. Some would argue that the ultimate test of a causal hypothesis is an experiment. In studies of human development, interventions represent unique opportunities for examining causal effects of a variety of influences tested by randomizing individuals (or groups of individuals) into “experimental” and “control” arms and measuring outcomes before and after the intervention. Randomized control trials and quasiexperimental designs have been used, for example, to evaluate effects of breastfeeding on cognitive development (Kramer et al., 2008) or effectiveness of various psychosocial interventions on mental health in children and adolescents (Sandler et al., 2014). Incorporating state-of-the-art assessment of brain and behavior in future interventions has the potential to provide insights relevant for understanding brain development as well as mechanisms underlying the success (or failure) of a given intervention. Clearly, interventions aimed at reducing the risk of substance use disorders represent an ideal meeting point between science and public health.

Acknowledgments The work described in this chapter was made possible by our funders, including the Canadian Institutes of Health Research and the National Institutes of Health (USA). I am grateful to my students, fellows, and colleagues for their contributions made in the course of our studies of the adolescent brain. I very much appreciate the collaborative spirit of my academic colleagues associated with a number of cohorts, including the IMAGEN Study, ALSPAC, Northern Finland Birth Cohort, and the Porto Alegre-São Paulo High Risk Cohort Study for the Development of Childhood Psychiatric Disorders. My work on population neuroscience would not be possible without Dr. Zdenka Pausova. Over more than 20 years, Zdenka has provided me the inspiration and knowledge necessary for embarking on studies in genetics and epigenetics. Together, we built the Saguenay Youth Study, which provides the template for most of the ideas and concepts described here.

References Abnousi, F., Rumsfeld, J.S., Krumholz, H.M., 2019. Social determinants of health in the digital age: determining the source code for nurture. JAMA 321, 247e248. Albert, F.W., Kruglyak, L., 2015. The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. 16, 197e212.

348 Cognition and Addiction

Baker, T.E., Castellanos-Ryan, N., Schumann, G., et al., 2019. Modulation of orbitofrontal-striatal reward activity by dopaminergic functional polymorphisms contributes to a predisposition to alcohol misuse in early adolescence. Psychol. Med. 49, 801e810. Schizophrenia Working Group of the Psychiatric Genomics C, 2014. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421e427. Bjornholm, L., Nikkinen, J., Kiviniemi, V., et al., 2017. Structural properties of the human corpus callosum: multimodal assessment and sex differences. Neuroimage 152, 108e118. Boyd, A., Golding, J., Macleod, J., et al., 2013. Cohort profile: the “children of the 90s”dthe index offspring of the Avon longitudinal study of parents and children. Int. J. Epidemiol. 42, 111e127. Buchel, C., Peters, J., Banaschewski, T., et al., 2017. Blunted ventral striatal responses to anticipated rewards foreshadow problematic drug use in novelty-seeking adolescents. Nat. Commun. 8, 14140. Chen, P., Jacobson, K.C., 2012. Developmental trajectories of substance use from early adolescence to young adulthood: gender and racial/ ethnic differences. J. Adolesc. Health 50, 154e163. Cheung, C., Yu, K., Fung, G., et al., 2010. Autistic disorders and schizophrenia: related or remote? An anatomical likelihood estimation. PLoS One 5, e12233. David-Barrett, T., Kertesz, J., Rotkirch, A., et al., 2016. Communication with family and friends across the life course. PLoS One 11, e0165687. De Braekeleer, M., 1991. Hereditary disorders in saguenay-Lac-St-Jean (Quebec, Canada). Hum. Hered. 41, 141e146. De Braekeleer, M., Mari, C., Verlingue, C., et al., 1998. Complete identification of cystic fibrosis transmembrane conductance regulator mutations in the CF population of Saguenay Lac-Saint-Jean (Quebec, Canada). Clin. Genet. 53, 44e46. Dickie, E.W., Tahmasebi, A., French, L., et al., 2014. Global genetic variations predict brain response to faces. PLoS Genet. 10, e1004523. Eickhoff, S.B., Paus, T., Caspers, S., et al., 2007. Assignment of functional activations to probabilistic cytoarchitectonic areas revisited. Neuroimage 36, 511e521. Eickhoff, S.B., Laird, A.R., Grefkes, C., Wang, L.E., Zilles, K., Fox, P.T., 2009. Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty. Hum. Brain Mapp. 30, 2907e2926. Fischl, B., Dale, A.M., 2000. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U.S.A. 97, 11050e11055. French, L., Paus, T., 2015. A FreeSurfer view of the cortical transcriptome generated from the allen human brain atlas. Front. Neurosci. 9, 323. French, L., Gray, C., Leonard, G., et al., 2015. Early cannabis use, polygenic risk score for schizophrenia and brain maturation in adolescence. JAMA Psychiatry 72, 1002e1011. Friston, K.J., 2011. Functional and effective connectivity: a review. Brain Connect. 1, 13e36. Gao, W., Emaminejad, S., Nyein, H.Y., et al., 2016. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature 529, 509e514. Garavan, H., Bartsch, H., Conway, K., et al., 2018. Recruiting the ABCD sample: design considerations and procedures. Dev. Cognit. Neurosci. 32, 16e22. Goodman, R., 1997. The strengths and difficulties questionnaire: a research note. J. Child Psychol. Psychiatry 38, 581e586.

Goodman, R., Ford, T., Richards, H., Gatward, R., Meltzer, H., 2000. The development and well-being assessment: description and initial validation of an integrated assessment of child and adolescent psychopathology. J. Child Psychol. Psychiatry 41, 645e655. Grompe, M., St-Louis, M., Demers, S.I., al-Dhalimy, M., Leclerc, B., Tanguay, R.M., 1994. A single mutation of the fumarylacetoacetate hydrolase gene in French Canadians with hereditary tyrosinemia type I. N. Engl. J. Med. 331, 353e357. Grosbras, M.H., Paus, T., 2006. Brain networks involved in viewing angry hands or faces. Cerebr. Cortex 16, 1087e1096. Gruzd, A., Haythornthwaite, C., 2013. Enabling community through social media. J. Med. Internet Res. 15, e248. Gusev, A., Lee, S.H., Trynka, G., et al., 2014. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535e552. Haghighi, A., Schwartz, D.H., Abrahamowicz, M., et al., 2013. Prenatal exposure to maternal cigarette smoking, amygdala volume, and fat intake in adolescence. JAMA Psychiatry 70, 98e105. Hawrylycz, M.J., Lein, E.S., Guillozet-Bongaarts, A.L., et al., 2012. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391e399. Hurvitz, P.M., Moudon, A.V., Kang, B., Saelens, B.E., Duncan, G.E., 2014. Emerging technologies for assessing physical activity behaviors in space and time. Front. Public health 2, 2. Jaddoe, V.W., van Duijn, C.M., Franco, O.H., et al., 2012. The Generation R Study: design and cohort update 2012. Eur. J. Epidemiol. 27, 739e756. Jarvelin, M.R., Hartikainen-Sorri, A.L., Rantakallio, P., 1993. Labour induction policy in hospitals of different levels of specialisation. Br. J. Obstet. Gynaecol. 100, 310e315. Jbabdi, S., Sotiropoulos, S.N., Haber, S.N., Van Essen, D.C., Behrens, T.E., 2015. Measuring macroscopic brain connections in vivo. Nat. Neurosci. 18, 1546e1555. Johnston, L.D.O.M.P., Bachman, J.G., Schulenberg, J.E., December 14, 2011. Marijuana Continues to Rise Among U.S. Teens, While Alcohol Use Hits Historic Lws. University of Michigen News Service, Ann Arbor, MI, p. 2011. Johnston, L.D.O.M.P., Bachman, J.G., Schulenberg, J.E., 2009. Monitoring the Future National Survey Results on Drug Use, 1975e2007. Bethesda, MD. Jollans, L., Zhipeng, C., Icke, I., et al., 2016. Ventral striatum connectivity during reward anticipation in adolescent smokers. Dev. Neuropsychol. 41, 6e21. Kardan, O., Gozdyra, P., Misic, B., et al., 2015. Neighborhood greenspace and health in a large urban center. Sci. Rep. 5, 11610. Kellis, M., Wold, B., Snyder, M.P., et al., 2014. Defining functional DNA elements in the human genome. Proc. Natl. Acad. Sci. U.S.A. 111, 6131e6138. Kendler, K.S., Gardner, C.O., Prescott, C.A., 2003. Personality and the experience of environmental adversity. Psychol. Med. 33, 1193e1202. Knutson, B., Fong, G.W., Adams, C.M., Varner, J.L., Hommer, D., 2001. Dissociation of reward anticipation and outcome with event-related fMRI. Neuroreport 12, 3683e3687. Kramer, M.S., Aboud, F., Mironova, E., et al., 2008. Breastfeeding and child cognitive development: new evidence from a large randomized trial. Arch. Gen. Psychiatry 65, 578e584.

Population neuroscience in addiction research Chapter | 25

Krishnan, K.R., 2005. Psychiatric and medical comorbidities of bipolar disorder. Psychosom. Med. 67, 1e8. Kuhn, S., Romanowski, A., Schilling, C., et al., 2011. The neural basis of video gaming. Transl. Psychiatry 1, e53. Kum, H.C., Krishnamurthy, A., Machanavajjhala, A., Ahalt, S., 2014. Social Genome: Putting Big Data to Work for Population Informatics. IEEE Computer Special Outlook Issue, pp. 56e63. Lee, K.W., Richmond, R., Hu, P., et al., 2015. Prenatal exposure to maternal cigarette smoking and DNA methylation: epigenome-wide association in a discovery sample of adolescents and replication in an independent cohort at birth through 17 years of age. Environ. Health Perspect. 123, 193e199. Lerch, J.P., van der Kouwe, A.J., Raznahan, A., et al., 2017. Studying neuroanatomy using MRI. Nat. Neurosci. 20, 314e326. Lieslehto, J., Kiviniemi, V., Maki, P., et al., 2017. Early adversity and brain response to faces in young adulthood. Hum. Brain Mapp. 38, 4470e4478. Logan, G., 1994. On the ability to inhibit thought and action: a user’s guide to the stop signal paradigm. In: Dagenbach, D.C.T. (Ed.), Inhibitory Processes in Attention, Memory and Language. Academic Press, San Diego, pp. 189e236. Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., Oeltermann, A., 2001. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150e157. Lotfipour, S., Ferguson, E., Leonard, G., et al., 2009. Orbitofrontal cortex and drug use during adolescence: role of prenatal exposure to maternal smoking and BDNF genotype. Arch. Gen. Psychiatry 66, 1244e1252. Macare, C., Ducci, F., Zhang, Y., et al., 2018. A neurobiological pathway to smoking in adolescence: TTC12-ANKK1-DRD2 variants and reward response. Eur. Neuropsychopharmacol. 28, 1103e1114. Maharana, A., Okanyene Nsoesie, E., 2018. Use of deep learning to examine the association of the built environment with prevalence of neighborhood adult obesity. JAMA Netw. Open 1, e181535. Manolio, T.A., Collins, F.S., Cox, N.J., et al., 2009. Finding the missing heritability of complex diseases. Nature 461, 747e753. Mareckova, K., Weinbrand, Z., Chakravarty, M.M., et al., 2011. Testosterone-mediated sex differences in the face shape during adolescence: subjective impressions and objective features. Horm. Behav. 60, 681e690. Markova, D., Richer, L., Pangelinan, M., et al., 2016. Age- and sex-related variations in vocal-tract morphology and voice acoustics during adolescence. Horm. Behav. 81, 84e96. Mielenz, D., Reichel, M., Jia, T., et al., 2018. EFhd2/Swiprosin-1 is a common genetic determinator for sensation-seeking/low anxiety and alcohol addiction. Mol. Psychiatry 23, 1303e1319. Montigny, C., Castellanos-Ryan, N., Whelan, R., et al., 2013. A phenotypic structure and neural correlates of compulsive behaviors in adolescents. PLoS One 8, e80151. Moore, M.M., Chung, T., 2017. Review of key concepts in magnetic resonance physics. Pediatr. Radiol. 47, 497e506. Morales, A.J., Vavilala, V., Benito, R.M., Bar-Yam, Y., 2017. Global patterns of synchronization in human communications. J. R. Soc. Interface 14. Muller, K.U., Mennigen, E., Ripke, S., et al., 2013. Altered reward processing in adolescents with prenatal exposure to maternal cigarette smoking. JAMA Psychiatry 70, 847e856. Murphy, D.A., Hser, Y.I., Huang, D., Brecht, M.L., Herbeck, D.M., 2010. Self-report of longitudinal substance use: a comparison of the UCLA

349

natural history interview and the addiction severity index. J. Drug Issues 40, 495e516. Nees, F., Witt, S.H., Lourdusamy, A., et al., 2013. Genetic risk for nicotine dependence in the cholinergic system and activation of the brain reward system in healthy adolescents. Neuropsychopharmacology 38, 2081e2089. Nees, F., Witt, S.H., Dinu-Biringer, R., et al., 2015. BDNF Val66Met and reward-related brain function in adolescents: role for early alcohol consumption. Alcohol 49, 103e110. Ojelade, S.A., Jia, T., Rodan, A.R., et al., 2015. Rsu1 regulates ethanol consumption in Drosophila and humans. Proc. Natl. Acad. Sci. U.S.A. 112, E4085eE4093. Parker, N., Wong, A.P., Leonard, G., et al., 2017. Income inequality, gene expression, and brain maturation during adolescence. Sci. Rep. 7, 7397. Pasman, J.A., Verweij, K.J.H., Gerring, Z., et al., 2018. GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nat. Neurosci. 21, 1161e1170. Patel, Y., Shin, J., Gowland, P.A., Pausova, Z., Paus, T., consortium, I., 2018. Maturation of the human cerebral cortex during adolescence: myelin or dendritic arbor? Cerebr. Cortex. Paus, T., 2010. Population neuroscience: why and how. Hum. Brain Mapp. 31, 891e903. Paus, T., 2013. Population Neuroscience. Springer-Verlag, Berlin Heidelberg. Paus, T., 2016. Population neuroscience. Handb. Clin. Neurol. 138, 17e37. Paus, T., Nawazkhan, I., Leonard, G., et al., 2008. Corpus callosum in adolescent offspring exposed prenatally to maternal cigarette smoking. Neuroimage 40, 435e441. Paus, T., Nawaz-Khan, I., Leonard, G., et al., 2010. Sexual dimorphism in the adolescent brain: role of testosterone and androgen receptor in global and local volumes of grey and white matter. Horm. Behav. 57, 63e75. Paus, T., Wong, A.P., Syme, C., Pausova, Z., 2017. Sex differences in the adolescent brain and body: findings from the saguenay youth study. J. Neurosci. Res. 95, 362e370. Pausova, Z., Paus, T., Abrahamowicz, M., et al., 2007. Genes, maternal smoking, and the offspring brain and body during adolescence: design of the Saguenay youth study. Hum. Brain Mapp. 28, 502e518. Pausova, Z., Paus, T., Abrahamowicz, M., et al., 2017. Cohort profile: the saguenay youth study (SYS). Int. J. Epidemiol. 46, e19. Peltonen, L., Palotie, A., Lange, K., 2000. Use of population isolates for mapping complex traits. Nat. Rev. Genet. 1, 182e190. Pena-Oliver, Y., Carvalho, F.M., Sanchez-Roige, S., et al., 2016. Mouse and human genetic analyses associate Kalirin with ventral striatal activation during impulsivity and with alcohol misuse. Front. Genet. 7, 52. Peng, T.Q., Sun, G., Wu, Y., 2017. Interplay between public attention and public emotion toward multiple social issues on twitter. PLoS One 12, e0167896. Perrin, J.S., Herve, P.Y., Leonard, G., et al., 2008. Growth of white matter in the adolescent brain: role of testosterone and androgen receptor. J. Neurosci. 28, 9519e9524. Perrin, J.S., Leonard, G., Perron, M., et al., 2009. Sex differences in the growth of white matter during adolescence. Neuroimage 45, 1055e1066.

350 Cognition and Addiction

Pesaresi, M., Soon-Shiong, R., French, L., Kaplan, D.R., Miller, F.D., Paus, T., 2015. Axon diameter and axonal transport: in vivo and in vitro effects of androgens. Neuroimage 115, 191e201. Peters, J., Bromberg, U., Schneider, S., et al., 2011. Lower ventral striatal activation during reward anticipation in adolescent smokers. Am. J. Psychiatry 168, 540e549. Pipitone, J., Park, M.T., Winterburn, J., et al., 2014. Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage 101, 494e512. Ramasamy, A., Trabzuni, D., Guelfi, S., et al., 2014. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418e1428. Rehm, J., Mathers, C., Popova, S., Thavorncharoensap, M., Teerawattananon, Y., Patra, J., 2009. Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. Lancet 373, 2223e2233. Ruiz Jdel, C., Quackenboss, J.J., Tulve, N.S., 2016. Contributions of a child’s built, natural, and social environments to their general cognitive ability: a systematic scoping review. PLoS One 11, e0147741. Sandler, I., Wolchik, S.A., Cruden, G., et al., 2014. Overview of metaanalyses of the prevention of mental health, substance use, and conduct problems. Annu. Rev. Clin. Psychol. 10, 243e273. Schneider, S., Peters, J., Bromberg, U., et al., 2012. Risk taking and the adolescent reward system: a potential common link to substance abuse. Am. J. Psychiatry 169, 39e46. Schumann, G., Loth, E., Banaschewski, T., et al., 2010. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol. Psychiatry 15, 1128e1139. Schüz, A., Braitenberg, V., 2002. The Human Cortical White Matter: Quantitative Aspects of Cortico-Cortical Long-Range Connectivity. Taylor & Francis, London and New York. Sheehan, D.V., Lecrubier, Y., Harnett-Sheehan, K., Janavs, J., Weiller, E., Bonara, L.I., Keskiner, A., Schinka, J., Knapp, E., Sheehan, M.F., Dunbar, G.C., 1997. Reliability and validity of the MINI international neuropsychiatric interview (M.I.N.I.): according to the SCID-P. Eur. Psychiatry 12. Shin, J., French, L., Xu, T., et al., 2018. Cell-specific gene-expression profiles and cortical thickness in the human brain. Cerebr. Cortex 28, 3267e3277. Smith, G.D., Timpson, N., Ebrahim, S., 2008. Strengthening causal inference in cardiovascular epidemiology through Mendelian randomization. Ann. Med. 40, 524e541. Stacey, D., Bilbao, A., Maroteaux, M., et al., 2012. RASGRF2 regulates alcohol-induced reinforcement by influencing mesolimbic dopamine neuron activity and dopamine release. Proc. Natl. Acad. Sci. U.S.A. 109, 21128e21133. Stacey, D., Lourdusamy, A., Ruggeri, B., et al., 2016. A translational systems biology approach in both animals and humans identifies a functionally related module of accumbal genes involved in the regulation of reward processing and binge drinking in males. J. Psychiatry Neurosci. 41, 192e202.

Sussman, S., Arnett, J.J., 2014. Emerging adulthood: developmental period facilitative of the addictions. Eval. Health Prof. 37, 147e155. Syme, C., Abrahamowicz, M., Mahboubi, A., et al., 2010. Prenatal exposure to maternal cigarette smoking and accumulation of intraabdominal fat during adolescence. Obesity (SilverSpring) 18, 1021e1025. Tahmasebi, A.M., Artiges, E., Banaschewski, T., et al., 2012. Creating probabilistic maps of the face network in the adolescent brain: a multicentre functional MRI study. Hum. Brain Mapp. 33, 938e957. U.S. Department of Health and Human Services, 2014. The Health Consequences of Smoking e 50 Years of Progress: A Report of the Surgeon General. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, Atlanta. Toledo-Rodriguez, M., Lotfipour, S., Leonard, G., et al., 2010. Maternal smoking during pregnancy is associated with epigenetic modifications of the brain-derived neurotrophic factor-6 exon in adolescent offspring. Am. J. Med. Genet. B. Neuropsychiatry Genet. 153B, 1350e1354. Toro, R., Leonard, G., Lerner, J.V., et al., 2008. Prenatal exposure to maternal cigarette smoking and the adolescent cerebral cortex. Neuropsychopharmacology 33, 1019e1027. Toro, R., Poline, J.B., Huguet, G., et al., 2015. Genomic architecture of human neuroanatomical diversity. Mol. Psychiatry 20, 1011e1016. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., et al., 2002. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273e289. Whiteford, H.A., Degenhardt, L., Rehm, J., et al., 2013. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 382, 1575e1586. Wishart, D.S., 2011. Advances in metabolite identification. Bioanalysis 3, 1769e1782. Wong, A.P., Pipitone, J., Park, M.T., et al., 2014. Estimating volumes of the pituitary gland from T1-weighted magnetic-resonance images: effects of age, puberty, testosterone, and estradiol. Neuroimage 94C, 216e221. Wong, A.P., French, L., Leonard, G., et al., 2018. Inter-regional variations in gene expression and age-related cortical thinning in the adolescent brain. Cerebr. Cortex 28, 1272e1281. Yang, J., Lee, S.H., Goddard, M.E., Visscher, P.M., 2011. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76e82. Yang, J., Lee, T., Kim, J., et al., 2013. Ubiquitous polygenicity of human complex traits: genome-wide analysis of 49 traits in Koreans. PLoS Genet. 9, e1003355.

Chapter 26

Drug use and self-awareness of treatment need: an exemplar of how population-based survey studies can address questions relevant to the neuroscience of insight Scott J. Moeller1, Renee D. Goodwin2, 3, Ryan M. Sullivan1, 4 and Antonio Verdejo-Garcia5 1

Department of Psychiatry, Stony Brook University School of Medicine, Stony Brook, NY, United States; 2Department of Epidemiology and

Biostatistics, School of Public Health, The City University of New York, New York, NY, United States; 3Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States; 4Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, United States; 5School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia

Introduction Substance use disorders (SUDs) contribute to global morbidity and mortality (Degenhardt and Hall, 2012). In the United States, 3.9% and 9.9% of the population meet criteria for 12-month and lifetime SUD, respectively, and these prevalences may be increasing (Grant et al., 2016). SUDs are also highly comorbid with other forms of psychopathology (Lai et al., 2015) and co-occurring polysubstance use (McCabe et al., 2017). Yet, despite the effectiveness, at least in the short-term, of behavioral (Benishek et al., 2014; Magill and Ray, 2009; Oikonomou et al., 2017) and pharmacological (Donoghue et al., 2015; Nosyk et al., 2015) therapies, only a minority of individuals with an SUD ultimately seek treatment (Blanco et al., 2015; Compton et al., 2007; Grant et al., 2016; Hedden and Gfroerer, 2011; Mojtabai and Crum, 2013). These low treatment prevalences may reflect, among many factors, pessimistic efficacy beliefs (Mojtabai and Crum, 2013), lack of adequate health insurance for substance use treatment (Ilgen et al., 2011), cultural norms (Gopalkrishnan and Babacan, 2015), the potential for stigmatization (Kulesza et al., 2014), and/or a paucity of resources (Bobrova et al., 2006; McLellan and Meyers, 2004). Beyond these well-recognized treatment barriers, we and others have suggested that another mechanism that may help to explain low rates of treatment seeking for SUDs

Cognition and Addiction. https://doi.org/10.1016/B978-0-12-815298-0.00026-5 Copyright © 2020 Elsevier Inc. All rights reserved.

may involve impaired insight into illness severity, such that individuals with SUDs may have decreased metacognitive access to the severity of their drug-related problems or need for treatment (Goldstein et al., 2009; Moeller and Goldstein, 2014; Verdejo-Garcia et al., 2013; Williams et al., 2015). Supporting this perspective, laboratory studies have shown individuals with SUDs have a decreased capacity to selfmonitor ongoing task performance (Hester et al., 2007, 2009; Moeller et al., 2016) or interoceptive experiences (Naqvi et al., 2007; Stewart et al., 2014); individuals with SUDs also exhibit discrepancies between self-reports and objective behavior (Moeller et al., 2010, 2014) and between self-reports and informant reports of behavioral symptoms linked to addiction (Moreno-Lopez et al., 2017; Verdejo-Garcia and Perez-Garcia, 2008). In some of these same laboratory studies, the observed behavioral deficits have correlated with functional and/or structural abnormalities in the anterior cingulate cortex (ACC), ventromedial prefrontal cortex (vmPFC), insula, and dorsal striatum. Involvement of these regions is consistent with studies conducted in populations with other psychiatric disorders more classically characterized by impaired insight, including schizophrenia (van der Meer et al., 2013), Alzheimer’s disease (Amanzio et al., 2011), frontotemporal dementia (Shany-Ur et al., 2014), and traumatic brain injury (Ham et al., 2014).

351

352 Cognition and Addiction

However, prior clinical studies primarily have included individuals who are dependent on a single substance (e.g., stimulants or alcohol). When study inclusion is opened to polysubstance dependence or substance-related comorbidities, there is usually insufficient statistical power to dissect the contributions of individual drugs to the insight-related impairments. Therefore, in the clinical neuroscience literature, a question persists with respect to which substances (and their respective patterns of use) are associated with the highest propensity for denying a need for SUD treatment and/or exhibiting other insight-related impairments. The question regarding the association between drug use and treatment need awareness is perhaps best addressed using a large-scale, population-based approach. Indeed, epidemiology has a longstanding interest in documenting the various predictors of treatment need awareness, including predictors related to sociodemographics, mental health comorbidities, and/or legal factors (Booth et al., 2013, 2014; Borders et al., 2015; Edlund et al., 2006; Grella et al., 2009; Mojtabai and Crum, 2013; Mojtabai et al., 2002; Oleski et al., 2010), as well predictors related to select substance use variables (Edlund et al., 2009; Falck et al., 2007; Glass et al., 2015; Wu and Ringwalt, 2004). Thus, the idea explored in this chapter is that epidemiological measures can be used to inform cognitive questions that otherwise would be intractable in the small sample sizes typically seen in clinical neuroscience studies. In the current study, we examined a nationally representative, multiyear sample of individuals with at least one SUD, testing whether the use of certain substances would be associated with reduced treatment need awareness, one component (among others) of clinical insight impairment (Amador and David, 1998). In our analyses, we also statistically adjusted for relevant sociodemographics and for proxies of physical health, mental health, legal problems, and the general tendency to seek treatment of any kind, which together could influence one’s decision or prompt a court mandate to seek SUD treatment. Compared with prior studies that examined drug use predictors specifically (Edlund et al., 2009; Falck et al., 2007; Glass et al., 2015; Wu and Ringwalt, 2004), the current study used a larger sample of respondents that allowed us to examine a more comprehensive list of drug use predictors for a more comprehensive list of substances. For example, it is currently unknown whether recency of substance use, independently of its overall frequency and use disorder, predict treatment need unawareness; such a relationship would present an important translational parallel to prior clinical work (Martinez-Gonzalez et al., 2016; Moeller et al., 2010, 2016; Verdejo-Garcia and Perez-Garcia, 2008). The present study can contribute knowledge to the epidemiological links between drug use and treatment need awareness and simultaneously provide a potentially interesting, albeit still speculative at this stage, link to an important outstanding question in the clinical neuroscience

of insight. Using a publicly available epidemiological dataset, we are unable to reach the richness of clinical research data, but we are able to derive answers to circumscribed questions while testing large numbers of individuals efficiently and at low cost. In doing so, we can explore questions that are currently unanswerable in clinical neuroscience research, including whether treatment need awareness differs by the substance that is misused (e.g., alcohol vs. marijuana). Our study echoes the approach of “population neuroscience” (Falk et al., 2013; Paus, 2010), leveraging interdisciplinary capabilities at the intersection of neuroscience (which emphasizes the fundamental mechanisms underlying complex behavior) and population science (which emphasizes large, representative samples to make generalizable conclusions about a phenomenon of interest).

Methods Sample Data were drawn from the National Survey on Drug Use and Health (NSDUH), sponsored by the Substance Abuse and Mental Health Service Administration, spanning 2004e13 (see Appendix for citations); this multiyear approach ensured a large number of individuals with SUD. The NSDUH uses a combination of audio computerassisted self-interview, computer-assisted personal interview, and computer-assisted self-interview techniques to measure the prevalences and correlates of substance use and SUDs (as defined by the Diagnostic and Statistical Manual of Mental Disorders, fourth edition [DSM-IV] [American Psychiatric Association, 2000]) among the general population of the United States; relevant sociodemographic information was also collected. The target population was noninstitutionalized respondents 12 years or older, incorporating a multistage area probability sample for all 50 states and the District of Columbia. Respondents gave verbal consent in accordance with the RTI International (formerly: Research Triangle Institute) Institutional Review Board (IRB) and received $30 for participation. To obtain our final analytical sample, we filtered the dataset by removing respondents without an SUD, duplicate identifiers, minors below age 18 (whose sociodemographic information would not be comparable with adults), and individuals for whom treatment-seeking data were unavailable (for SUD, but also for nonsubstance mental health treatment) (Fig. 26.1).

Outcome variable The NSDUH asks respondents: “During the past 12 months, did you need treatment or counseling for your alcohol or drug use?” and “During the past 12 months, that

Drug use and self-awareness of treatment need Chapter | 26

353

FIGURE 26.1 Derivation of the National Survey on Drug Use and Health (NSDUH) analytical sample, with data collected from 2004 through 2013 inclusive.

is since [DATEFILL], have you received treatment or counseling for your use of alcohol or any drug, not counting cigarettes?” From these two variables, we derived three levels of treatment need awareness: no perception of treatment need (TxUnaware), perception of treatment need but no treatment sought (TxAware), and treatment sought (TxSought), respectively.

Drug use variables For a given substance, we analyzed the presence (yes/no) of past-year dependence (e.g., the presence of withdrawal symptoms when use is discontinued) and (separately) pastyear abuse (e.g., use in hazardous situations, such as driving while impaired). DSM-IV criteria established abuse and dependence for all drugs except cigarettes, for which Fagerstrom criteria were used (i.e., nicotine dependence was recorded if the respondent reported smoking cigarettes in the past month and if the first cigarette was smoked

within 30 min of waking). In addition to diagnostic criteria, we analyzed past-year number of days used (use frequency; categorical levels), past-month use (use recency; yes/no), and substance use before age 18 (early initiation; yes/no). We conducted these analyses for alcohol, cigarettes, and marijuana; we also conducted these analyses for a fourth category we created, labeled as “other drugs,” which collapsed across cocaine/crack, heroin, hallucinogens, analgesics, tranquilizers, and noncocaine stimulants; these drugs were less frequently used, and preliminary analyses with the illicit drugs considered separately (e.g., heroin vs. cocaine) were underpowered.

Sociodemographic and general health covariates Sociodemographic covariates included sex, race/ethnicity, age, marital status, education, employment status, income, and

354 Cognition and Addiction

whether the individual had been arrested (with the latter potentially being relevant to court-mandated treatment). We also included a variable assessing presence of dysphoric mood symptoms (i.e., feeling sad/blue much of the day and/or losing pleasure in enjoyable activities) and respondents’ selfreported overall health, ranging from excellent to poor. Finally, we included variables relevant to the receipt of mental health treatment (i.e., apart from SUD treatment): whether individuals sought nonsubstance mental health treatment and (separately) whether individuals were found to need, but not receive, mental health treatment. Prior treatment seeking has been associated with greater odds of future treatment seeking (Blanco et al., 2015), and comorbid mood/anxiety disorders have been associated with greater odds of reporting an unmet need for SUD treatment (Melchior et al., 2014).

Statistical analyses We obtained relative risk ratios (RRRs) from two sets of multinomial regression analyses, which estimated the associations between each substance use variable and treatment need perception. Analyses were performed using Stata/MP 14, which accommodates the complex survey design of the NSDUH. The first set of RRRs examined the relationship between drug use predictors and reporting no awareness of treatment need (TxUnaware) (N ¼ 36,466; 89.1% of the sample). The second set of RRRs examined the relationship between drug use predictors, reporting an awareness of treatment need but without seeking treatment (TxAware) (N ¼ 1755; 4.3% of the sample). Individuals who sought treatment (TxSought) (N ¼ 2711; 6.6% of the sample) were considered the reference group in both sets of RRRs. For all analyses, each substance (alcohol, nicotine, marijuana, and “other drugs”) was considered separately. Each analysis was conducted across the entire available sample, not just within a particular substance (e.g., marijuana use could predict treatment need awareness among individuals with alcohol problems). All analyses also adjusted for the sociodemographic/health characteristics included in Table 26.1, all other substances use variables examined for each substance, and the survey year as a categorical variable. We also conducted the following supporting analyses to bolster conclusions and lessen the influence of potential confounds. First, to protect against issues associated with unbalanced cell counts (i.e., most of the sample belonged to the TxUnaware subgroup), we repeated all analyses using a randomly selected subsample of 3050 TxUnaware respondents (approximately 1.75 times the size of the smallest group). Second, to lessen the possibility that effects of recent drug use represent artifacts of treatment-seeking individuals reducing their recent drug use, we repeated all analyses after excluding individuals who reported no attempt to reduce or stop their drug use over the past year. Only effects that reached significance in these supporting analyses, as marked with a

cross in all tables, are reported and interpreted in the text. Finally, for variables that showed similar effects in the TxUnaware and TxAware subgroups (i.e., significant and in the same direction, consistent with proportional odds), follow-up ordinal logistical regressions examined whether the particular variable exerted graded effects on the extent of treatment need perception (with TxSought, TxAware, and TxUnaware, respectively, reflecting decreasing treatment need perceptions). Significant effects for these ordinal logistic regressions are indicated by a double cross in all tables.

Results Table 26.1 displays the sociodemographics of the sample. Tables 26.2e26.3 display the frequencies, percentages, and RRRs for all drug-related variables (Table 26.2: substance diagnoses; Table 26.3: substance use) by study group (TxUnaware: not perceiving a need for treatment, TxAware: perceiving a need for treatment but not seeking it, and TxSought [the reference group in the analyses]: seeking treatment).

Drug use predictors of TxUnaware status Diagnostic status: Individuals who abused alcohol, but not individuals who were dependent on alcohol, were more likely to perceive no need for treatment (i.e., to belong to the TxUnaware group vs. the TxSought group). In contrast, individuals who were dependent on nicotine or illicit drugs were less likely to perceive no need for treatment. Substance use initiation: When examining initiation into substance use, individuals who used any substances before age 18 were less likely to belong to the TxUnaware group. Substance use frequency: Individuals who used all substances frequently (vs. no use within the past year) were also less likely to belong to the TxUnaware group, with such relationships reaching significance for moderate use of alcohol (50e99 days) and marijuana (12e49 days) and heavy use of “other drugs” (100e299 and 300e365 days). An exception to this pattern, however, was observed for the heaviest use of marijuana (300e365 days), in which the direction of association was reversed. Substance use recency: Independent of frequency, individuals who used substances recently (i.e., within the last month) were more likely to belong to the TxUnaware group (note that recency analyses could not be conducted for cigarettes due to data collinearity).

Drug use predictors of TxAware status Diagnostic status: Individuals with nicotine dependence and individuals with marijuana abuse were less likely to belong to the TxAware group (i.e., perceive a need for treatment but not seek it, compared with seeking treatment).

Drug use and self-awareness of treatment need Chapter | 26

355

TABLE 26.1 Sociodemographic and wellness characteristics and perceived need for treatment. Sought treatment (N [ 2711) (reference category) N(%)

Perceived need, did not seek treatment (N [ 1755) N(%)

No perceived need for treatment (N [ 36466) N(%)

Male

1,693 (62.4)

1,001 (57.0)

22,206 (60.9)

Female

1,018 (37.6)

754 (43.0)

14,260 (39.1)

1,891 (69.8)

1072 (61.1)

24,556 (67.3)

African American

293 (10.8)

238 (13.6)

3,654 (10.0)

Hispanic

281 (10.4)

282 (16.1)

5,213 (14.3)

20 (0.7)

16 (0.9)

840 (2.3)

9 (0.3)

10 (0.6)

232 (0.6)

Native American

100 (3.7)

73 (4.2)

817 (2.2)

More than one race (non-Hispanic)

117 (4.3)

64 (3.6)

1,154 (3.2)

50

104 (3.8)

107 (6.1)

1,412 (3.9)

35e49

504 (18.6)

344 (19.6)

4,561 (12.5)

26e34

397 (14.6)

284 (16.2)

4,902 (13.4)

18e25

1706 (62.9)

1,020 (58.1)

25,591 (70.2)

333 (12.3)

323 (18.4)

6,107 (16.7)

19 (0.7)

25 (1.4)

189 (0.5)

417 (15.4)

260 (14.8)

1942 (71.6)

1,147 (65.4)

27,460 (75.3)

801 (29.5)

487 (27.7)

6,541 (17.9)

Characteristic Sex

Race/ethnicity Caucasian

Asian Pacific Islander

Age, y

Marital status Married Widowed Divorced/separated Never married

2,710 (7.4)

Education 6 weeks) can significantly improve executive functions and particularly inhibitory control (Xue et al., 2019). The benefits of high-intensity exercise on cognition or addiction treatment outcomes are comparatively less clear at this stage (Browne et al., 2017; Colledge et al., 2018). Overall, there is emerging evidence that suggests that the combination of aerobic exercise combined and inhibitory control training can have synergistic effects on cognitive recovery and related clinical outcomes. More generally, acute bouts of aerobic exercise could be scheduled before cognitively demanding addiction therapies (e.g., CBT) to optimize delivery of therapeutic contents. The third approach consisting on combining bottom-up and top-down cognitive trainings sounds immediately

Synergistic opportunities in combined interventions for addiction treatment Chapter | 30

intuitive. However, its application is not without challenges. For example, we applied a combination of CBM (bottom-up, efficacious in several independent randomized trials, see Chapter 17) and working memory training (top-down, mixed evidence, see Chapter 18) in people with alcohol use disorders and found that the combination training did not improve cognitive performance or clinical outcomes (Manning et al., 2019). Working memory training even reduced the magnitude of the benefit that we had previously achieved in similar populations with CBM alone (e.g., Manning et al., 2016). We reasoned that one of the factors explaining lack of success might be the risk of overwhelming cognitive abilities and generating frustration. Therefore, this approach should carefully consider the timing and the intensity of the “combination training,” for example, by alternating different trainings on different days and ensuring that difficulty is progressive. Timing is another important factor, as bottom-up interventions can be more adequate during early treatment (or even pretreatment) to build the necessary prerequisite skills to later benefit from more complex top-down interventions such as working memory training. It is also possible that other combinations of bottom-up and top-down trainings hold greater potential and thus yield better results in the future. For example, there are ongoing trials combining CBM and extinction training in alcohol users (Chapter 17), and GMT has successfully incorporated mindfulness practice in polysubstance users (Chapter 20). In both cases, the combined trainings are implemented as an integrated treatment approach, enabling researchers and treatment providers to synergize and progressively pace the therapeutic mechanisms of the two interventions. Integrating rather than simply adding two cognitive training interventions is a more promising strategy to develop feasible and cost-effective training protocols that can be incorporated to the treatment of substance use disorders in clinical settings.

Interventions tapping into decisionmaking We make multiple decisions every single day. For people with addiction problems, many of these decisions involve some degree of conflict between drug-related inputs (drug craving is a key symptom that does not subside with abstinence) and conflicting goal-related inputs (e.g., abstinence, recovery, health, family) (Noël et al., 2013). Vulnerabilities in this decision-making process are a hallmark of addiction (Redish et al., 2008; Verdejo-García and Bechara, 2009). In this context, we need interventions that (1) empower patients to resolve these decisions advantageously and in a continuous manner and/or (2) modify the environment to reduce the conflict between competing inputs.

407

If we focus on patients’ everyday decision-making, an exciting new avenue consists on overlapping cognitive neuroscience informed decision-making interventions, such as episodic future thinking, with increasingly available immersive technologies, such as augmented and mixed reality, which will be routinely embedded in mobile phone devices within the next 10 years. Episodic future thinking is a cognitive exercise/training that aims to promote a futureoriented approach to decision-making by increasing the tangibility, personal relevance, and positive value of future events (Schacter et al., 2017). Emerging evidence shows that episodic future thinking training interventions significantly reduce delay discounting (preference for immediate over delayed rewards) among people with different addictions (Chiou and Wu, 2016; Snider et al., 2016; Stein et al., 2016) and ad libitum tobacco consumption in smokers (Stein et al., 2018). This evidence suggests that this training promotes long-termebased decision-making and control of drug use. Combining episodic future thinking and immersive technology would enable patients to engage in concrete future-oriented visualization exercises, assisted by augmented or mixed reality environments (e.g., when thinking about health and family, they could visualize future, “healthy versions” of themselves and their loved ones), to promote more personalized and realistic forecasting outputs and facilitate long-termebased decisionmaking. Most importantly, this training could occur at any time, as needed, fitting with the continuous flow of decisions of daily life and providing an “ad hoc” tool for risky situations (e.g., drug priming and stress situations, entering “the zone” as described in gambling disorder). It is important to note that immersive technology could also be incorporated to other interventions such as CBM that do not specifically target decision-making skills but can be customized to adapt their therapeutic mechanisms to foster future-oriented choicesdfor example, by visualizing the negative consequences of approaching drug-related stimuli and the positive consequences of approaching alternative stimuli. Goal-related interventions, such as GMT or motivational interview, could also benefit from immersive technology-aided future visualization exercises. Environment modification approaches should leverage on evidence-based policies that reduce the subjective value of drug-related behaviors and increase the value of competitive reinforcers (see Chapter 24). In addition, it would be interesting to explore the potential of nudging initiatives, namely, implicit and minimally invasive environmental interventions that can have a significant impact on behavior change. Nudging strategies can be potentially applied to shift decision-making tendencies around drug use in the general community and to facilitate abstinence maintenance among drug users in recovery. Specific nudging strategies for patients in recovery may include environmental interventions in their own homes. For

408 Cognition and Addiction

example, smart lights can be used to regulate light type and intensity to promote circadian alignment, given the impact of circadian disruption on motivation-based decisionmaking and drug relapse (Cain et al., 2008; Doyle et al., 2015). Similarly, home-based remote monitoring devices could trigger personalized reminders compatible with future-oriented goals to strengthen goal representations during everyday decision-making (Malek et al., 2018). Another avenue to explore is that of increasing the perceived value of treatment among patients, applying knowledge of the behavioral economics of addiction. For example, people with substance use disorders have proven to be exquisitely sensitive to the value of uncertain (vs. certain) commodities (Vosburg et al., 2010). Thus, it is tempting to speculate that we could increase the subjective value of (and adherence to) treatment by keeping the content of therapeutic packages uncertain. Although these are still speculative ideas that require more elaboration and proper testing for safety and efficacy, the bottom line is we could explore nudging-inspired interventions for behavior change in the context of compensation of the decisionmaking deficits of people with addiction.

Conclusion In this final chapter, we adopt an intentionally hypothetical and futuristic view to exploiting synergistic opportunities for addiction treatment derived from cognitive neuroscience knowledge. We tentatively conclude that combined interventions blending top-down and bottom-up approaches, when meaningfully integrated, can provide more comprehensive coverage of the faulty neurocognitive systems involved in addiction pathophysiology and thus improve clinical outcomes. Combinations of “top-down cognitive control trainings þ contingency management” and “inhibitory control training þ aerobic exercise” seem particularly promising, with well-established effects of “GMT þ mindfulness.” The combination of CBM (bottomup) and traditional CBT (top-down) is also well founded and under current evaluation. Promising combined interventions should eventually progress toward fully blended interventions. We need more research to identify the key therapeutic ingredients and parameters of such integrated therapies. In addition, the advent of portable immersive technologies opens an exciting new avenue for implementation of “online/real-time” decision-making trainings with a focus on strengthening a goal-based future thinking strategy. Finally, we speculate about novel ways in which we could modify macro- and microenvironments (e.g., social policies, patient’s houses, treatment packages) to test the effects of minimally invasive nudging interventions on patients’ decision-making abilities.

References Alfonso, J.P., Caracuel, A., Delgado-Pastor, L.C., Verdejo-García, A., 2011. Combined goal management training and mindfulness meditation improve executive functions and decision-making performance in abstinent polysubstance abusers. Drug Alcohol Depend. 117 (1), 78e81. Bickel, W.K., Yi, R., Landes, R.D., Hill, P.F., Baxter, C., 2011. Remember the future: working memory training decreases delay discounting among stimulant addicts. Biol. Psychiatry 69 (3), 260e265. Blume, A.W., Alan Marlatt, G., 2009. The role of executive cognitive functions in changing substance use: what we know and what we need to know. Ann. Behav. Med. 37 (2), 117e125. Browne, S.E., Flynn, M.J., O’Neill, B.V., Howatson, G., Bell, P.G., Haskell-Ramsay, C.F., 2017. Effects of acute high-intensity exercise on cognitive performance in trained individuals: a systematic review. In: Progress in Brain Research, 234. Elsevier, pp. 161e187. Cain, S.W., McDonald, R.J., Ralph, M.R., 2008. Time stamp in conditioned place avoidance can be set to different circadian phases. Neurobiol. Learn. Mem. 89 (4), 591e594. Chiou, W.-B., Wu, W.-H., 2016. Episodic future thinking involving the nonsmoking self can induce lower discounting and cigarette consumption. J. Stud. Alcohol Drugs 78 (1), 106e112. Colledge, F., Gerber, M., Pühse, U., Ludyga, S., 2018. Anaerobic exercise training in the therapy of substance use disorders: a systematic review. Front. Psychiatry 9, 644. De Crescenzo, F., Ciabattini, M., D’Alò, G.L., De Giorgi, R., Del Giovane, C., Cassar, C., Cipriani, A., 2018. Comparative efficacy and acceptability of psychosocial interventions for individuals with cocaine and amphetamine addiction: a systematic review and network meta-analysis. PLoS Med. 15 (12), e1002715. De Giorgi, R., D’Alò, G.L., De Crescenzo, F., 2017. Psychosocial interventions in stimulant use disorders: a focus on women. Curr. Opin. Psychiatr. 30 (4), 275e282. Doyle, S.E., Feng, H., Garber, G., Menaker, M., Lynch, W.J., 2015. Effects of circadian disruption on methamphetamine consumption in methamphetamine-exposed rats. Psychopharmacology 232 (12), 2169e2179. Heitmann, J., van Hemel-Ruiter, M.E., Vermeulen, K.M., Ostafin, B.D., MacLeod, C., Wiers, R.W., de Jong, P.J., 2017. Internet-based attentional bias modification training as add-on to regular treatment in alcohol and cannabis dependent outpatients: a study protocol of a randomized control trial. BMC Psychiatry 17 (1), 193. Hötting, K., Röder, B., 2013. Beneficial effects of physical exercise on neuroplasticity and cognition. Neurosci. Biobehav. Rev. 37 (9), 2243e2257. Kiluk, B.D., Buck, M.B., Devore, K.A., Babuscio, T.A., Nich, C., Carroll, K.M., 2017. Performance-based contingency management in cognitive remediation training: a pilot study. J. Subst. Abus. Treat. 72, 80e88. Lynch, W.J., Peterson, A.B., Sanchez, V., Abel, J., Smith, M.A., 2013. Exercise as a novel treatment for drug addiction: a neurobiological and stage-dependent hypothesis. Neurosci. Biobehav. Rev. 37 (8), 1622e1644. Malek, H.B., Berna, F., D’Argembeau, A., 2018. Envisioning the times of future events: the role of personal goals. Conscious. Cognit. 63, 198e205.

Synergistic opportunities in combined interventions for addiction treatment Chapter | 30

Manning, V., Mroz, K., Garfield, J.B., Staiger, P.K., Hall, K., Lubman, D.I., Verdejo-García, A., 2019. Combining Approach Bias Modification with Working Memory Training during Inpatient Alcohol Withdrawal: An Open-Label Pilot Trial of Feasibility and Acceptability. Substance Abuse Treatment, Prevention, and Policy in submission. Manning, V., Staiger, P.K., Hall, K., Garfield, J.B., Flaks, G., Leung, D., Verdejo-Garcia, A., 2016. Cognitive bias modification training during inpatient alcohol detoxification reduces early relapse: a randomized controlled trial. Alcohol Clin. Exp. Res. 40 (9), 2011e2019. McPherson, S.M., Burduli, E., Smith, C.L., Herron, J., Oluwoye, O., Hirchak, K., Roll, J.M., 2018. A review of contingency management for the treatment of substance-use disorders: adaptation for underserved populations, use of experimental technologies, and personalized optimization strategies. Subst. Abuse Rehabil. 9, 43. McSween, M.P., Coombes, J.S., MacKay, C.P., Rodriguez, A.D., Erickson, K.I., Copland, D.A., McMahon, K.L., 2019. The immediate effects of acute aerobic exercise on cognition in healthy older adults: a systematic review. Sports Med. 49 (1), 67e82. https://doi.org/ 10.1007/s40279-018-01039-9. Mooney, L.J., Cooper, C., London, E.D., Chudzynski, J., Dolezal, B., Dickerson, D., Rawson, R.A., 2014. Exercise for methamphetamine dependence: rationale, design, and methodology. Contemp. Clin. Trials 37 (1), 139e147. National Institute on Drug Abuse, 2015. NIDA research report: therapeutic communities (series 15-4877). Retrieved from: https://www. drugabuse.gov/publications/research-reports/therapeutic-communities. Noël, X., Brevers, D., Bechara, A., 2013. A neurocognitive approach to understanding the neurobiology of addiction. Curr. Opin. Neurobiol. 23 (4), 632e638. Rash, C.J., Stitzer, M., Weinstock, J., 2017. Contingency management: new directions and remaining challenges for an evidence-based intervention. J. Subst. Abus. Treat. 72, 10e18. Redish, A.D., Jensen, S., Johnson, A., 2008. Addiction as vulnerabilities in the decision process. Behav. Brain Sci. 31 (4), 461e487. Robbins, T., Ersche, K., Everitt, B., 2008. Drug addiction and the memory systems of the brain. Ann. N. Y. Acad. Sci. 1141 (1), 1e21. Robertson, C.L., Ishibashi, K., Chudzynski, J., Mooney, L.J., Rawson, R.A., Dolezal, B.A., London, E.D., 2016. Effect of exercise training on striatal dopamine D2/D3 receptors in methamphetamine users during behavioral treatment. Neuropsychopharmacology 41 (6), 1629.

409

Schacter, D.L., Benoit, R.G., Szpunar, K.K., 2017. Episodic future thinking: mechanisms and functions. Curr. Opin. Behav. Sci. 17, 41e50. Snider, S.E., LaConte, S.M., Bickel, W.K., 2016. Episodic future thinking: expansion of the temporal window in individuals with alcohol dependence. Alcohol Clin. Exp. Res. 40 (7), 1558e1566. Stanger, C., Budney, A.J., Bickel, W.K., 2013. A developmental perspective on neuroeconomic mechanisms of contingency management. Psychol. Addict. Behav. 27 (2), 403. Stein, J.S., Tegge, A.N., Turner, J.K., Bickel, W.K., 2018. Episodic future thinking reduces delay discounting and cigarette demand: an investigation of the good-subject effect. J. Behav. Med. 41 (2), 269e276. Stein, J.S., Wilson, A.G., Koffarnus, M.N., Daniel, T.O., Epstein, L.H., Bickel, W.K., 2016. Unstuck in time: episodic future thinking reduces delay discounting and cigarette smoking. Psychopharmacology 233 (21e22), 3771e3778. Tardelli, V.S., do Lago, M.P.P., Mendez, M., Bisaga, A., Fidalgo, T.M., 2018. Contingency management with pharmacologic treatment for stimulant use disorders: a review. Behav. Res. Ther. 111, 57e63. Valls-Serrano, C., Caracuel, A., Verdejo-García, A., 2016. Goal Management Training and Mindfulness Meditation improve executive functions and transfer to ecological tasks of daily life in polysubstance users enrolled in therapeutic community treatment. Drug Alcohol Depend. 165, 9e14. Verdejo-García, A., Alcázar-Córcoles, M., Albein-Urios, N., 2018a. Neuropsychological interventions for decision-making in addiction. Neuropsychology Review invited submission. Verdejo-García, A., Bechara, A., 2009. A somatic marker theory of addiction. Neuropharmacology 56, 48e62. Verdejo-García, A., Chong, T.T.-J., Stout, J.C., Yücel, M., London, E.D., 2018b. Stages of dysfunctional decision-making in addiction. Pharmacol. Biochem. Behav. 164, 99e105. Vosburg, S.K., Haney, M., Rubin, E., Foltin, R.W., 2010. Using a novel alternative to drug choice in a human laboratory model of a cocaine binge: a game of chance. Drug Alcohol Depend. 110 (1e2), 144e150. Voss, M.W., Soto, C., Yoo, S., Sodoma, M., Vivar, C., van Praag, H., 2019. Exercise and hippocampal memory systems. Trends Cognit. Sci. 23 (4), 318e333. https://doi.org/10.1016/j.tics.2019.01.006. Xue, Y., Yang, Y., Huang, T., 2019. Effects of chronic exercise interventions on executive function among children and adolescents: a systematic review with meta-analysis. Br. J. Sports Med. https:// doi.org/10.1136/bjsports-2018-099825.

Index Note: ‘Page numbers followed by “f ” indicate figures and “t” indicate table’.

A Aberrant learning, 1, 322 Acetylcholine (ACh) system, 131 Acquired hepatocerebral degeneration, 114 Action-outcome (A-O) mechanisms, 9e10 Active inference models, 41e42 Addiction treatment policy, 324 ADHD. See Attention-deficit/hyperactivity disorder (ADHD) Adolescence, 91 Adolescent Brain Cognitive Development (ABCD) study, 13, 368 Affective empathy. See Emotional empathy Affect recognition, 64 Ageealcohol use disorder interaction, 117 Alcohol, 379 applications, 297 emotional empathy, 66 emotion recognition and cognitive empathy, 65e66 moral decision-making, 67 perspective-taking and theory of mind (ToM), 66e67 social decision-making, 67 Alcohol Attention Control Training Program (AACTP), 232e233, 273 Alcohol dehydrogenase (ADH), 371 Alcohol dependence (AD), 63, 65, 67 Alcohol-induced neurocognitive impairments, 103 Alcohol use disorder (AUD), 202, 222e223, 273, 379 altered brain structure and function, 103e109 attention, working memory, and executive functions, 104e106 brain circuits, 104f characteristics, 104 emotions, 108e109 episodic memory, 106e107 perceptive memory and visuospatial abilities, 108 procedural memory, 107e108 semantic memory, 107 social cognition, 109 characteristics, 103 clinical implication and relapse factors, 111e113 decision-making, 111e112 interpersonal relationships, 112e113

motivation, 111 new complex learning, 112 treatment compliance and quality of life, 111, 112f cognitive deficits reversibility and cerebral damage with abstinence, 109e111 brain recovery, 109e110 factors influencing, 111 neuropsychological recovery, 110e111 life expectancy, 103 neurocognitive complications, 113e116 central pontine myelinolysis (CPM), 115e116 hepatic encephalopathy (HE), 114e115 Korsakoff’s syndrome (KS), 114 MarchiafavaeBignami disease (MBD), 114 Wernicke’s encephalopathy (WE), 113e114 researchers and clinicians recommendations, 116e120 differential diagnosis, 117e118 neuropsychological profile heterogeneity, 117, 118f neuropsychological rehabilitation, 118e120 screening and assessment modalities, 116e117 treatment modifications, 117, 119f Aldehyde dehydrogenase (ALDH), 371 Alpha2-adrenergic agonist, 312 Alzheimer’s disease (AD), 117e118, 132, 306 Amphetamines acute effects, 156 long-term effects, 156e157 prevalence, 155 Amygdata, 108e109 Animal models devolving from prefrontal to striatal control, 11e12 drug-seeking habits, 9e10 transitioning from ventral to dorsal striatum, 10e11 Anterior cingulate cortex (ACC), 66, 69, 284 Anthropomorphism, 212e213 Antipsychotic, 312 Antisocial personality disorders (ASPD), 79, 82 Apparent diffusion coefficient (ADC), 167 Approach bias modification, 233e235 Assessment paradigms, in decision-making dysfunctions, 26

behavioral task, 35e41, 39te40t Balloon Analogue Risk Task (BART), 36 Beads/Box Task (BT), 38 Cambridge gambling task (CGT), 37 Delay Discounting Task (DDT), 35e36 Effort Expenditure for Reward Task (EEfRT), 37e38 Game of Dice Task (GDT), 37 Iowa Gambling Task (IGT), 36e37 Risk Gains Task (RGT), 38e39 computational modeling, 41e43 Bayesian-Expected Utility Model, 43 classification, 44f data-driven approaches, 41 earning to maximize approach, 42 Expectancy-Valence Learning (EVL) model, 43 Prospective Valence Learning (PVL), 43 reinforcement learning (RL), 42 strategy-switching heuristic choice model, 42 theory-driven approaches, 41 neuroimaging, 44e47 fMRI, 44 model-based fMRI approaches, 46 task-based fMRI evidence, 45e46 self-reports, 26e33, 34te35t Barratt Impulsivity Scale (BIS), 27 Consideration of future consequences scale (CFCS), 32 Effect expectancy questionnaire, 30e31 Eysenck impulsiveness scale, 29 Monetary choice questionnaire (MCQ), 27e28 Reinforcement Survey Schedule (RSS), 31e32 Rewarding events inventory (REI), 31 Sensation seeking scales (SSS), 29 Sensitivity to Reinforcement of Addictive and other Primary Rewards (STRAP-R), 32e33 Substance Use Risk Profile Scale (SURPS), 33 Temporal experience of pleasure scale (TEPS), 29e30 UPPS impulsive behavior scale, 28 three-dimensional matrix, 49, 49f Atomoxetine, 312 Attention alcohol use disorder (AUD), 104e106 cognitive deficits, 158

411

412 Index

Attentional bias modification, 231e233 Attention-deficit/hyperactivity disorder (ADHD), 68e69, 95, 200 AUD. See Alcohol use disorder (AUD) Autobiographical memory (AM), 106e107 Awareness of Social Inference Test (TASIT), 70e71

B Balloon Analogue Risk Task (BART), 36 Barratt Impulsivity Scale (BIS), 27 Beads/box task (BT), 38 BEARNI. See Brief Evaluation of AlcoholRelated Neuropsychological Impairment (BEARNI) Behavior Stimulus Interaction (BSI), 274 Big Data approaches, 365 genetics, 371e373 neuroimaging, 368e371 online-based research, 365e367 BIS. See Barratt Impulsivity Scale (BIS) Blood oxygen level dependent (BOLD), 44, 167 Borderline personality disorders (BPD), 79e82 Brain disease model of addiction (BDMA), 323 Brain education, 398 Brain exercises, 399 Brain planner, 399 Brain recovery, 109e110 Brain stimulation tool noninvasive modulation of neural circuitry, 295e296 moving to clinic, 295 preclinical foundation, 295 transcranial magnetic stimulation (TMS), 295e296 repetitive transcranial magnetic stimulation (rTMS) with cognitive therapy, 298e299 pharmacotherapy, 299 Brief Evaluation of Alcohol-Related Neuropsychological Impairment (BEARNI), 116 Brisbane Longitudinal Twin study, 203

C Cambridge gambling task (CGT), 37 Cambridge Neuropsychological Test Automated Battery (CANTAB), 181 Cannabidiol (CBD), 143e144, 326 Cannabinoid receptor type 1 (CB1), 143e144 Cannabinoid receptor type 2 (CB2), 144 Cannabis, 143 clinical significance, 149 cognitive deficits associated with intoxication effects, 144e146 residual/long-term effects, 146e149 emotional empathy, 67 emotion recognition and cognitive empathy, 67

neuropharmacology, 143e144 perspective-taking and theory of mind (ToM), 68 researchers/clinicians recommendations, 149 social decision-making, 68 social reward, 68 Catechol-O-methyltransferase (COMT), 149, 168e169 CBM. See Cognitive bias modification (CBM) Central pontine myelinolysis (CPM), 115e116 CFCS. See Consideration of future consequences scale (CFCS) CGT. See Cambridge gambling task (CGT) Chicago Word Fluency Task (CWFT), 170 Cholinergic medications, 306e311 Chronicity, 192 Cirrhotic portosystemic shunting, 115 Cluster B personality disorders, 80e82 Cocaine acute effects, 156 applications, 297e298 chronic use, 155 long-term effects, 156e157 long-term neuroadaptive effects, 155e156 prevalence, 155 recovery, 157 Cocaine users, 5 Cognition, 1 addiction vulnerability and consequences dependent vs. recreational users, 4e5 endophenotype studies, 4 longitudinal studies, 3e4 neurotoxicity-controlled studies, 4 stimulant users vs. gamblers, 5 interface between nature and nurture, 5e6 neurobiological models, 2e3 social accounts of addiction, 2e3 Cognitive behavioral therapy (CBT), 214e215, 405 Cognitive bias, 224 attentional biases, 224e225 dysfunctional cognitions, 225 Cognitive bias modification (CBM), 231, 367, 405 approach bias modification, 233e235 attentional bias modification, 231e233 clinical applications, 236e237 memory bias modification, 235e236 neurocognitive effects, 236 Cognitive deficits, 222e224 decision-making and related processes, 223e224 goal-based interventions, 277e278 inhibitory control and other executive functions, 222e223 Cognitive empathy, 64 Cognitive function, 92 Cognitive Remediation (CR), 277e278 Cognitive research on addiction, 321 aberrant learning, 322 impaired impulse inhibition, 322 impulsivity, 322

policy impact, 322e325 addiction treatment policy, 324 criminal justice policy, 324e325 drug policy, 323e324 implication, 323 mental health, 325 public policy, 325e326 legalization of recreational cannabis, 326 psychedelics use, 325e326 Cognitive training interventions, 231 Combining top-down and bottom-up approaches, 406e407 Comorbidity, 79 and executive functioning, 84 Compensatory strategies, 399 Composite International Diagnostic Interview (CIDI), 193e194 Comprehensive Affect Test System (CATS-A), 64 Compulsions, 9e10 Compulsive eating, 298 Compulsive Internet use scale (CIUS), 203 Computational modeling, in decision-making dysfunctions (DMDs), 41e43 Bayesian-Expected Utility Model, 43 classification, 44f data-driven approaches, 41 earning to maximize approach, 42 Expectancy-Valence Learning (EVL) model, 43 Prospective Valence Learning (PVL), 43 reinforcement learning (RL), 42 strategy-switching heuristic choice model, 42 theory-driven approaches, 41 Conduct disorder (CD), 94 Consideration of future consequences scale (CFCS), 32 Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA), 368 Contemporary models, 1 Controlled Oral Word Association Task (COWAT), 170 Control-related deficit theory, 111e112 CPM. See Central pontine myelinolysis (CPM) Criminal justice policy, 324e325 CYP2D6, 168e169

D D-amphetamine,

311e312 (DCS), 312e313 Decision-making dysfunctions (DMDs), 25 assessment paradigms, 26 behavioral task, 35e41, 39te40t computational modeling, 41e43 neuroimaging, 44e47 self-reports, 26e33, 34te35t three-dimensional matrix, 49, 49f cognitive deficits, 159, 223e224 cognitive functions learning, 26 reward/value, 26

D-Cycloserine

Index

risk/probability, 26 temporality, 25e26 three-dimensional matrix, 49, 49f dimensions, 25 interventions tapping, 407e408 levels of evidence case-control studies, 47e48 cohort studies, 48 cross-sectional studies, 47 metaanalyses, 48 randomized controlled studies, 48 systematic review, 48 three-dimensional matrix, 49, 49f personality disorders (PDs), 82 Decision-making models, 2 Delay discounting, 94e95, 224 Delay discounting task (DDT), 35e36 Delta-9-tetrahydrocannabinol (THC), 143e144 Dependent vs. recreational users, 4e5 Diffusion tensor imaging (DTI), 167 Distribution volume ratios (DVRs), 167e168 DMDs. See Decision-making dysfunctions (DMDs) Donepezil, 306 Dopamine b-hydroxylase (DbH), 146 Dorsolateral prefrontal cortex (DLPFC), 2, 5, 11, 236, 280 Dorsolateral striatum (DLS), 10e11 Dorsomedial striatum (DMS), 10e11 Drug policy, 323e324 Drug reinforcers, 10 Drug use predictors of TxAware status, 354e356 of TxUnaware status, 354 Drug use variables, 353 Dual models of addiction, 17, 18f neuroimaging evidence, 18e20 Dynamic craving model (DCM), 394e395, 394f

E Ecstasy use, 170e171 EEfRT. See Effort expenditure for reward task (EEfRT) Effect expectancy questionnaire, 30e31 Effort expenditure for reward task (EEfRT), 37e38 Electronic gaming machines (EGMs), 209e210, 212 Emotional empathy, 64 Emotional Facial Expression Decoding Test, 70e71 Emotion perception, 64 Emotion recognition, 64 Empathy, 109 Endophenotype studies, 4 Entactogenes, 70 Episodic memory, 106e107 Estradiol, 313e314 Executive functions (EF), 4e5, 92e94 alcohol use disorder (AUD), 104e106 cognitive deficits, 158e159

Executive network, 19e20 Exogenous sex steroids, 313e314 Expectancy-Valence Learning (EVL) model, 43 Eysenck impulsiveness scale, 29

F Far transfer, 243 Fractional anisotropy (FA), 167 Framework Convention on Tobacco Control (FCTC), 130e131 Frontotemporal lobar degeneration (FTLD), 118 Functional magnetic resonance imaging (fMRI), 44, 287, 368 Future time perspective (FTP), 32

G GABAergic medications, 313 Galantamine, 306 Gamblers Anonymous (GA), 214 Gambling applications, 298 cognitions, 201e202 cognitive distortions anthropomorphism, 212e213 illusion of control, 211e212 cognitive model, 210e211 electronic gaming machines (EGMs), 209e210 immersion, 213 individual risk factors (neuro)cognitive factors, 201e202 genetic risk, 202e203 personality, 200e201 neurocognitive correlates, 211 personal vulnerabilities, 209e210 research, 199e200 social and individual predictors, 203e204 treatment and intervention, 214e215 Game of dice task (GDT), 37, 223e224 Gaming disorder cognitive factors, 222e225 cognitive bias, 224e225 cognitive deficit, 222e224 definition, 222 internet addiction, 221 from internet addiction, 221e222 recognition, 221e222 smartphone addiction, 221 GDT. See Game of dice task (GDT) Genome-wide association studies (GWAS) approaches, 371 Glutamatergic medications, 312e313 GMT. See Goal Management Training (GMT) Goal-based interventions for cognitive deficits, 277e278 efficacy evidence, 278e279 intervention approaches and mechanisms, 278 neurocognitive mechanisms, 279e280 researchers and clinicians recommendations, 280

413

Goal Management Training (GMT), 277e278, 405 G proteinecoupled receptor (GPR), 144 Guanfacine, 312

H Habit network, 20 Haloperidol, 312 HE. See Hepatic encephalopathy (HE) Heart rate variability (HRV), 288 Hepatic encephalopathy (HE), 114e115 Hierarchical Taxonomy of Psychopathology (HiTOP), 80e81 5-HTTLPR, 149

I

ICT. See Inhibitory control training (ICT) IGT. See Iowa gambling task (IGT) Illusion of control, 211e212 IMAGEN, 336, 368 Imbalanced system models, 405e406 Immediate Memory Task (IMT), 83 Impaired Response Inhibition and Salience Attribution (iRISA) model, 17, 18f Implicit Association Test (IAT), 233e234 Impulsive personality traits, 80e81 Impulsivity, 13, 81, 200 cognitive deficits, 159 cognitive research on addiction, 322 Incentive-sensitization theory, 17 Inferior frontal cortex (IFC), 66 Information and Communication Technologies (ICTs), 221 Inhibitory control, 18e20 Inhibitory control training (ICT) definition, 271 efficacy, 272e273 individual differences, 271 mechanisms of action, 273e274, 274t proposed mechanisms, 271e272 substance use disorder (SUD), 273 substance users’ deficit, 271 Insular cortex, 135e136 The Integrated Hypothesis, 166 Integrative cognitive interventions, 398e399 Intelligence quotient (IQ), 3, 95e96 International Classification of Diseases (ICD), 199e200 International Youth Development Study, 203e204 Internet addiction, 221 Internet-based interventions, 367 Internet gaming disorder, 202 Internet Gaming Disorder (IGD), 222e223 Iowa gambling task (IGT), 36e37, 45e46 iRISA. See Impaired Response Inhibition and Salience Attribution (iRISA) model

K Korsakoff’s syndrome (KS), 114

414 Index

L Learning theories, 1 Leisure Interest Checklist (LIC), 31 Levels of evidence, in decision-making dysfunctions case-control studies, 47e48 cohort studies, 48 cross-sectional studies, 47 metaanalyses, 48 randomized controlled studies, 48 systematic review, 48 three-dimensional matrix, 49, 49f LIC. See Leisure Interest Checklist (LIC) Longitudinal studies, 3e4 Lysergic acid diethylamide (LSD), 325e326

M Magnetic resonance imaging (MRI), 167 MarchiafavaeBignami disease (MBD), 114 Marijuana, 298 MBIs. See Mindfulness-based interventions (MBIs) MCQ. See Monetary choice questionnaire (MCQ) Memantine, 312 Memory bias modification, 235e236 cognitive deficits, 158 network, 20 Mental and emotional perspective-taking, 64e65 Metacognitive training, 399 Methamphetamine, 298 Methylenedioxyethylamphetamine (MDA), 166 Methylenedioxymethamphetamine (MDMA), 63e64, 72, 95 clinical significance, 171 cognitive deficits associated with, 169e170 epidemiology, 165e166 neuropharmacological/neuroadaptive effects animal research, 166e167 functional imaging, 170e171 human imaging, 167e168 pharmacokinetics and pharmacodynamics, 166 pharmacologically confounding factors, 168e169 potential adverse effects, 168e169 researchers/clinicians recommendations, 171e172 Methylphenidate, 311 Mindfulness-based interventions (MBIs), 283e284 addiction treatment, 289 clinical format and efficacy, 285 mechanisms, 284e285 neurocognitive mechanisms, 285e289, 286f reward, negative emotion and cue reactivity amplifying reward and positive affect, 287e288 craving and cue reactivity regulation, 288e289 dampening negative affect and stress, 288

“top-down” mechanisms attentional control, 286 automaticity regulation, 286e287 inhibitory control, 287 Mindfulness-Based Relapse Prevention (MBRP), 283e284 Mindfulness-Based Stress Reduction (MBSR), 283e284 Mindfulness-Oriented Recovery Enhancement (MORE), 283e284 Minnesota Twin Family Study (MTFS), 148 Minocycline, 313 MoCA. See Montreal Cognitive Assessment (MoCA) Modafinil, 311 Model-based (MB) learning system, 42 Model-free (MF) learning system, 42 Molecular neuroimaging, 290 Monetary choice questionnaire (MCQ), 27e28 Monoamine transporter inhibitors, 311e312 Montreal Cognitive Assessment (MoCA), 116 Moral decision-making/moral judgment, 65 Movie for the Assessment of Social Cognition (MASC), 64e65 MTFS. See Minnesota Twin Family Study (MTFS) Multifaceted Empathy Task (MET), 64 Myelinolysis, 115e116

N N-acetylcysteine (NAC), 313 National Institute on Drug Abuse (NIDA), 322e323 National Survey on Drug Use and Health (NSDUH), 352 drug use variables, 353 general health covariates, 353e354 outcome variable, 352e353 sample, 352, 353f sociodemographic covariates, 353e354, 355te356t statistical analyses, 354 National Surveys on Drug Use and Health (NSDUH), 179 Neurobiological models of drug addiction, 17 Neurobiological recovery, 379 Neurocognitive deficits, 379e385 abstinence, 380e385 addiction, 379e385 Neurocognitive dysfunction, 379 Neuroimaging, in decision-making dysfunctions (DMDs), 44 model-based fMRI approaches, 46 task-based fMRI evidence balloon analogue risk task (BART), 45 Cambridge gambling task (CGT), 46 delay discounting tasks (DDT), 45 Iowa gambling task (IGT), 45e46 Neuropsychological recovery apparent discrepancies, 110 episodic memory, 110 executive functions, 110 visuospatial process, 110

Neuroscience-based cognitive interventions, 393e398 cognitive modifications, 394e397 attention bias interventions, 395 inhibitory control interventions, 397 interoceptive-based interventions, 396e397 memory-based interventions, 395e396 saliency-based interventions, 395 neurocognitive rehabilitation, 397e398 psychoeducation and metacognitive training, 394 Neurotoxicity-controlled studies, 4 Nicotine, 372 cognitive effects long-term effects, 132 short-term effects, 132 withdrawal effects, 132e133 neural effects, 131 reinforcement neural mechanisms, 133e134 reinforcement enhancement, 133 Nonemedical prescription opioid users (NMPOU), 70e71 Novel Psychoactive Substances (NPS), 165e166

O Online-based research, 365e367 Opioids, 70e71, 179 applications, 298 combinations, 188e189 long-term cognitive de?cits, 180e189 methodological issues, 189e194 context, 190e191 data analysis, 194 data gathering, 193e194 population studied, 191e192 substance misuse and dependence, 192e193 neuropsychological functioning abstinent former heroin-dependent populations, 182e183 buprenorphine, 187e188 illicit heroin, 181e182 methadone users, 183e187 mixed opioid, 180e181 Optimal decision-making, 94e95 Oral methamphetamine, 311e312 Orbitofrontal cortex (OFC), 11e12

P Peer-reviewed working memory training (WMT), 264e265 CogMed, 261e262 Curb Your (C-Ya) Addition, 264 jungle memory (JM), 260 lumosity, 262e263 N-back (n ¼ 32), 245e260 near and far transfer effects, 265 NeuroNation, 263e264 neuroracer, 263 PSSCogRehab, 260e261

Index

Perceptive memory, 108 Personality, 5 Personality disorders (PDs) broad symptoms dimensions, 80e81 clusters, 79 cross-sectional and longitudinal evidence, 80 executive functioning, 82 impulsive personality traits, 80e81 neurocognitive functioning, 81e82 preliminary neurocognitive model, 84e85 PES. See Pleasure Events Schedule (PES) Pharmacological cognitive enhancement Pleasure Events Schedule (PES), 31 Polydrug use, 169 Polysubstance users (PSU), 71, 192, 379e380 Population neuroscience addiction research, 334e345 IMAGEN study, 336 Saguenay youth study (SYS), 335e336, 335te336t built and social environment, 332e334 brain structure and function, 333e334, 333f challenges, 331, 345e347 genes and gene regulation, 331e332 Positron emission tomography (PET), 167e168 Practice of mindfulness, 284 Prefrontal cortex (PFC), 11, 169e170, 284 Preliminary neurocognitive model, 84e85 Procedural memory, 107e108 Progesterone, 314 Prospective memory (PRM), 169 Prospective Memory Questionnaire (PMQ), 169 Prospective Valence Learning (PVL), 43 Psychoeducational therapy, 112

R Reading the Mind in the Eyes Task (RMET), 64e65 REI. See Rewarding events inventory (REI) Reinforcement learning (RL), 42 Reinforcement Survey Schedule (RSS), 31e32 Relative risk ratios (RRRs), 354 Repetitive transcranial magnetic stimulation (rTMS), 13 with cognitive therapy, 298e299 pharmacotherapy, 299 Response inhibition, 5, 94 Restructuring reward hypothesis, 287e288 Reward-based decision-making, 95 Reward deficit theory, 112 Rewarding events inventory (REI), 31 Reward network, 18, 20 Reward system, 17 Risk Factors for Antisocial Behavior (RFAB), 148 Risk gains task (RGT), 38e39 Rivastigmine, 306 RSS. See Reinforcement Survey Schedule (RSS)

S Saguenay youth study (SYS), 335e336, 335te336t Salience network, 18e20 Selective Serotonin Reuptake Inhibitor (SSRI), 169 Self-directed network, 20 Self-regulation, 277 Semantic memory, 107 Sensation seeking scales (SSS), 29 Sensitivity to Reinforcement of Addictive and other Primary Rewards (STRAP-R), 32e33 Serotonin transporter (SERT), 167e168 Smoking applications, 297 cessation, 130 cessation and mood, 136 morbidity and mortality, 129e130 prevalence, 129 Social cognition, 63, 109 alcohol use disorder (AUD), 109 definitions, 64e65 drug-related changes, 63 interaction, 64f relevance for treatment, 73 substance use disorders (SUDs) alcohol, 65e67 cannabis, 67e68 entactogenes, 70 opioids, 70e71 polysubstance use, 71 stimulants, 68e70 Social cognition processes, 5e6 Social decision-making, 65 Social reward, 65 Social theories, 1 South Oaks Gambling Screen (SOGS), 200 Spatial working memory deficits, 170 SSS. See Sensation seeking scales (SSS) State mindfulness, 284 Stimulants emotional empathy, 68e69 emotion recognition and cognitive empathy, 68 moral decision-making, 69 perspective-taking and theory of mind (ToM), 69 social decision-making, 69 social reward, 69e70 Stimulant use disorders, 155 acute effects, 156 clinical significance, 158e159 attention, 158 impulsivity and decision-making, 159 memory, 158 working memory (WM) and executive functions (EF), 158e159 long-term effects, 156e157

415

moderators age of onset, 157 cumulative exposure, 157 route of administration, 157e158 recovery, 157 researchers and clinicians recommendations, 159e160 state of problem, 155 stimulants neuroadaptive effects, 155e156 Stimulant users vs. gamblers, 5 Stimuluseresponse (S-R) habit process, 9e10 Stop-Signal Task, 4 STRAP-R. See Sensitivity to Reinforcement of Addictive and other Primary Rewards (STRAP-R) Stress models, 2 Substance misuse, 192e193 cognitive function structure, 92 Substance use disorders (SUDs), 25, 63, 91, 209, 303, 351, 379. See also Decision-making dysfunctions (DMDs) alcohol, 65e67 applications, 296e298 broad symptoms dimensions, 80e81 cannabis, 67e68 clinical studies, 352 clusters, 79 cognitive deficits, 305e306 cognitive function automatic cognitive processes, 304e305 executive functioning, 303e304 cross-sectional and longitudinal evidence, 80 current study, 352 drug use predictors of TxAware status, 354e356 of TxUnaware status, 354 entactogenes, 70 and executive functioning, 82e84 impulsive personality traits, 80e81 inhibitory control training (ICT), 273 neurocognitive functioning, 81e82 opioids, 70e71 polysubstance use, 71 preliminary neurocognitive model, 84e85 stimulants, 68e70 target mechanisms, 306e314, 307te310t alpha2-adrenergic agonist, 312 antipsychotic, 312 cholinergic medications, 306e311 exogenous sex steroids, 313e314 GABAergic medications, 313 glutamatergic medications, 312e313 monoamine transporter inhibitors, 311e312 treatment need awareness, 356 Substance Use Risk Profile Scale (SURPS), 33 SURPS. See Substance Use Risk Profile Scale (SURPS) Swift, certain, and fair punishment (SCFP), 325

416 Index

T Taiwan National Health Insurance Research Database, 80 Temporal experience of pleasure scale (TEPS), 29e30 Terpenoids, 144 Tetrahydrocannabinol (THC), 326 Theory of Mind (ToM), 63 Theory of mind (ToM), 109 Tiagabine, 313 Tobacco addiction clinicians and researchers, 136e137 electronic cigarettes, 130 emotionesmoking relationship insular cortex, 135e136 maladaptive response to negative mood, 134e135 neural mechanisms, 135 shared underlying mechanism, 136 smoking cessation and mood, 136 nicotine cognitive effects

long-term effects, 132 short-term effects, 132 withdrawal effects, 132e133 nicotine reinforcement neural mechanisms, 133e134 reinforcement enhancement, 133 pharmacology acetylcholine (ACh) system, 131 chemicals, 131 liability, 131e132 nicotine neural effects, 131 policy, in United States and world, 130e131 smoking cessation, 130 smoking prevalence, 129 smoking-related morbidity and mortality, 129e130 ToM. See Theory of mind (ToM) Transcranial magnetic stimulation (TMS) basic electrophysiological effects, 295e296 modulate cortical-striatal connectivity, 296

U UPPS impulsive behavior scale, 28

V Varenicline, 306e311 Ventrolateral prefrontal cortex (VLPFC), 5 Ventromedial prefrontal cortex (VMPFC), 67, 69e70 Visuospatial abilities, 108

W Wernicke’s encephalopathy (WE), 113e114 Working memory (WM), 92e94, 243 alcohol use disorder (AUD), 104e106 cognitive deficits, 158e159 Working memory training (WMT), 243 limitations, 266e267 training and implications, 265e266 World Drug Report (WDR), 165e166