The Neuroscience of Addiction
The Neuroscience of Addiction This book addresses a growing need for accessible information on the fi neuros
Views 157 Downloads 5 File size 6MB
Recommend stories
- Author / Uploaded
- Brujoucm
Citation preview
The Neuroscience of Addiction
This
book
addresses
a
growing
need
for
accessible
information
on
the
fi
neuroscience of addiction. In the past decade, neuroscienti c research has greatly advanced our understanding of the brain mechanisms of addiction;
fi
fic
however, this information remains largely con ned to scienti
outlets. As
legislation continues to evolve and the stigma surrounding addiction persists, new
findings
on the impact of substances on the brain are an important
public health issue. Francesca Mapua Filbey gives readers an overview of research on addiction including classic theories as well as current neuroscienti
fic studies. A variety of textual supports – including a glossary, learning
objectives and review questions
– help students better reinforce their reading
and make the text a ready-made complement to undergraduate and graduate courses on addiction.
Francesca Mapua Filbey is
a Professor of Cognition and Neuroscience and
Bert Moore Endowed Chair of BrainHealth for the School of Behavioral and
Brain
Sciences
at
the
University
of
Texas
at
Dallas.
She
conducts
research aimed at understanding the biobehavioral mechanisms of addictive disorders for the improvement of early detection and intervention.
/
Cambridge Fundamentals of Neuroscience in Psychology Developed in response to a growing need to make neuroscience accessible to students and other non-specialist readers, the
Neuroscience in Psychology
Cambridge Fundamentals of
series provides brief introductions to key areas
of neuroscience research across major domains of psychology. Written by experts in cognitive, social, affective, developmental, clinical and applied neuroscience, these books will serve as ideal primers for students and other readers seeking an entry point to the challenging world of neuroscience.
Books in the Series
The Neuroscience of Expertise The Neuroscience of Intelligence Cognitive Neuroscience of Memory The Neuroscience of Adolescence The Neuroscience of Suicidal Behavior The Neuroscience of Creativity Cognitive and Social Neuroscience of Aging The Neuroscience of Sleep and Dreams The Neuroscience of Addiction
by Merim Bilali
ć
by Richard J. Haier by Scott D. Slotnick
by Adriana Galván by Kees van Heeringen
by Anna Abraham by Angela Gutchess
by Patrick McNamara
by Francesca Mapua Filbey
/
The Neuroscience of Addiction Francesca Mapua Filbey University of Texas at Dallas
/
University Printing House, Cambridge CB2 8BS, United Kingdom One Liberty Plaza, 20th Floor, New York, NY 10006, USA 477 Williamstown Road, Port Melbourne, VIC 3207, Australia 314 –321, 3rd Floor, Plot 3, Splendor Forum, Jasola District Centre, New Delhi – 110025, India 79 Anson Road, #06– 04/06, Singapore 079906 Cambridge University Press is part of the University of Cambridge. It furthers the University’s mission by disseminating knowledge in the pursuit of education, learning and research at the highest international levels of excellence. www.cambridge.org Information on this title: www.cambridge.org/9781107127982 DOI: 10.1017/9781316412640 © Francesca Mapua Filbey 2019 This publication is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 2019 Printed in the United Kingdom by TJ International Ltd, Padstow Cornwall A catalogue record for this publication is available from the British Library. Library of Congress Cataloging-in-Publication Data
Names: Filbey, Francesca M., 1972- author. Title: The neuroscience of addiction / Francesca Mapua Filbey. Other titles: Cambridge fundamentals of neuroscience in psychology. Description: Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2019. | Series: Cambridge fundamentals of neuroscience in psychology | Includes bibliographical references and index. Identifiers: LCCN 2018049853 | ISBN 9781107127982 (hardback : alk. paper) | ISBN 9781107567337 (paperback : alk. paper) Subjects: | MESH: Behavior, Addictive | Substance-Related Disorders | Brain–physiopathology | Neurosciences | Risk Factors Classification: LCC RC564 | NLM WM 176 | DDC 616.86 –dc23 LC record available at https://lccn.loc.gov/2018049853 ISBN 978-1-107-12798-2 Hardback ISBN 978-1-107-56733-7 Paperback Cambridge University Press has no responsibility for the persistence or accuracy of URLs for external or third-party internet websites referred to in this publication and does not guarantee that any content on such websites is, or will remain, accurate or appropriate.
/
To David: thank you for your love and support. To Colin: thank you for nourishing my mind. To Alastair: thank you for nourishing my spirit. To Juan and Georgina Mapua: thank you for always believing in me. To Felipe and Emerita Canlas: thank you for being my example of dedication.
/
/
Table of Contents List of Plates List of Figures List of Tables Preface 1 What is Addiction?
page xi xii xvi xvii
1
Learning Objectives
1
Introduction
1
The Phenomenology of Substance Use Disorders
4
The Demography of Addiction
5
The Stigma of Addiction
5
The Diagnosis of Addiction
6
A Brain Disease Model of Addiction Non-Drug Addictions
9 12
Summary Points
14
Review Questions
14
Further Reading
14
Spotlight
15
References
16
2 Human Neuroscience Approaches Toward the Understanding of Addiction
21
Learning Objectives
21
Introduction
21
Measuring the Brain’s Electrical Activity
22
Visualizing the Brain’s Structure and Function
24
Biochemical Imaging
27
Limitations of Neuroimaging Research
28
Summary Points
29
Review Questions
29
Further Reading
29
Spotlight 1
30
Spotlight 2
32
References
32
/
viii
Table of Contents
3 Brain-Behavior Theories of Addiction
Learning Objectives
34 34
Introduction
34
The Incentive-Sensitization Theory
35
The Allostatic Model: Dysregulation in Homeostasis
36
The Impaired Response Inhibition and Salience Attribution (iRISA) Syndrome Model
38
The Future of Brain-Behavior Theories of Addiction
40
Summary Points
42
Review Questions
42
Further Reading
42
Spotlight
43
References
45
4 From the Motivation to Initiate Drug Use to Recreational Drug Use: Reward and Motivational Systems
47
Learning Objectives
47
Introduction
47
Reward and Motivational Systems Guide the Direction of Behavior
48
Predicting Rewards: Evidence for the Primary Role of Dopamine
51
Final Common Pathway: All Drugs Lead to One
53
Corticostriatal Circuitry and Effort–Reward Imbalance
56
Is Addiction a Reward Deficiency Syndrome?
55
Role of Memory Systems
56
Summary Points
58
Review Questions
58
Further Reading
59
Spotlight
60
References
61
5 Intoxication
64
Learning Objectives
64
Introduction
64
Drug Pharmacodynamics
66
Actions of Addictive Drugs
66
Brain Mechanisms of Intoxication: Evidence From Neuroimaging Pharmacological Studies
68
Modulators of Intoxication: Challenges in Human Research
73
Summary Points
75
Review Questions
76
Further Reading
76
/
Table of Contents
ix
Spotlight
76
References
78
6 Withdrawal
81
Learning Objectives
81
Introduction
81
What Does Withdrawal Look Like?
82
Acute Withdrawal Symptoms and Associated Neural Mechanisms
85
Protracted Withdrawal Symptoms and Associated Neural Mechanisms
87
Electrophysiological Mechanisms of Withdrawal
88
A Model of Opposing Mechanisms: Between-System Response to Drugs
90
Summary Points
91
Review Questions
92
Further Reading
92
Spotlight 1
93
Spotlight 2
94
References
94
7 Craving
98
Learning Objectives
98
Introduction
98
Cue-Elicited Craving Paradigms and Associated Neural Mechanisms
99
Neurophysiological Underpinnings of Craving
101
Contextual Cues
102
Do Drugs Hijack the Reward Circuitry of the Brain?
103
Greater Craving or Greater Attention?
105
Neuromolecular Mechanisms
106
Summary Points
107
Review Questions
107
Further Reading
108
Spotlight
108
References
110
8 Impulsivity
114
Learning Objectives
114
Introduction
114
Neuropharmacology of Impulsivity
116
Is Impulsivity Pre-existing or Drug Induced?
117
Risky Decision Making
120
/
x
Table of Contents
Inhibitory Control Delay Discounting of Reward
Summary Points Review Questions Further Reading Spotlight References
121 123
125 125 126 127 128
9 Impacts of Brain-Based Discoveries on Prevention and Intervention Approaches
Learning Objectives Introduction Pharmacological Approaches Behavioral Approaches Combined Approaches Treatment Outcomes Summary Points Review Questions Further Reading Spotlight 1 Spotlight 2 References
130 130 130 132 135 137 138 141 141 141 142 143 144
Summary Points Review Questions Further Reading Spotlight 1 Spotlight 2 References
148 148 148 149 150 155 156 157 159 159 160 161 162 162
Glossary Index
165 173
10 Conclusions
Learning Objectives
Introduction Risk Factors Inform Better Prevention and Intervention Addiction Endophenotypes Sex Differences in Addiction The Question of Causality General Conclusions
Color plate section found between pages 172 and 173
/
List of Plates
1.1
A longitudinal study demonstrating neuromaturational processes from 5 to 20 years of age.
2.4
Gray matter has predominantly isotropic (soccer ball-shaped) water diffusion, while dense white matter tracks have highly anisotropic (rugby ball-shaped) diffusion of water pointing in the direction of the
5.3 6.3
fi ber bundle.
PET studies to determine the effects of nicotine administration. Fast
β power can be a predictor of relapse in polysubstance users
during a 3-month abstinence. S7.1 Measuring
ΔFosB.
8.5
Ventromedial PFC lesions lead to risky decision making.
9.3
Following methadone-assisted therapy (MAT), long-term abstinent heroin users (mean length of abstinence, 193 days) had a greater decreased response in striatal areas compared with shortterm abstinent heroin users (mean length of abstinence, 23 days) during a cue-induced craving task.
9.5
Common (a) and distinct (b) neural targets of pharmacological and cognitive-based therapeutic interventions.
10.4 Brain EEG oscillations may be useful endophenotypes for alcohol use disorders.
/
List of Figures
1.1
A longitudinal study demonstrating neuromaturational processes from 5 to 20 years of age.
1.2
Animal behavioral paradigms in addiction studies.
1.3
Sites of action of various drugs on the mesocorticolimbic
page 2
8
reward system.
11
S1.1 Magic mushrooms.
16
2.1
Magnetoencephalography scanner with patient.
23
2.2
Mechanisms of MRI.
24
2.3
A patient going through a magnetic resonance imaging machine.
2.4
25
Gray matter has predominantly isotropic (soccer ball-shaped) water diffusion, while dense white matter tracks have highly anisotropic (rugby ball-shaped) diffusion of water pointing in the direction of the
2.5
fi ber bundle.
26
MRS image of a 34-year-old man with human
immunodefi ciency virus (HIV) infection and alcohol dependence.
27
S2.1 What does 45 years of love look like in the brain? S2.2 Associating the brain with behavior began with the
field of
phrenology. 3.1
31 32
Diagram describing the addiction cycle – preoccupation/
anticipation (“ craving”), binge/intoxication and withdrawal/
negative affect – with the different criteria for substance dependence incorporated from the 3.2 3.3
Diagnostic and Statistical
Manual of Mental Disorders , 4th edn.
37
PFC and subcortical regions in drug users and non-users.
39
The iRISA model depicting the interactions between the
Daily smoking, risky alcohol consumption and illicit drug use by people with the lowest and highest socioeconomic status (SES), in Australians aged 14 years or older, in 2013.
S3.1 The modern opioid epidemic. 4.1
41 44
Lever press (a) and intracranial self-stimulation (ICSS) (b) are two examples of experimental paradigms used to study reward and motivation in animals.
48
/
List of Figures 4.2
xiii
The brain’ s reward system lies in the mesocorticolimbic pathway, which is regulated by dopamine.
4.3
49
Camera lucida drawings of medium spiny neurons in the shell (top) and core (bottom) regions of the nucleus accumbens of saline- and amphetamine-pretreated rats.
50
4.4
The release of dopamine signals reward.
52
4.5
According to Kalivas and Volkow (2005), the projection from the PFC to the nucleus accumbens core to the ventral pallidum is a
fi nal common pathway for drug seeking
by
increases in dopamine release (via stress, a drug-associated cue or the drug itself) in the PFC. 4.6
54
Experiments on the effects of dopamine depletion on effort.
57
S4.1 (a) Sensation and novelty seeking are characteristic of adolescence. (b) Schematic of the monetary incentive delay task.
61
5.1
Alcohol intoxication may impact sensorimotor skills.
65
5.2
Mechanisms of drug action.
67
5.3
PET studies to determine the effects of nicotine administration.
70
5.4
Example of a virtual reality driving simulator device.
72
5.5
(a) Position of the amygdala (arrow). (b). Response in brain regions to emotional faces during alcohol intoxication.
73
S5.1 Law enforcement challenges during changes in cannabis 6.1 6.2
legislation.
77
The severity of cannabis withdrawal symptoms across time.
84
Change in CBF in the thalamus from baseline to overnight abstinence and subjective withdrawal from nicotine as measured by the Minnesota withdrawal score from baseline to withdrawal.
β power can be a predictor of relapse in
6.3
Fast
6.4
Neuroadaptations between the reward and stress
polysubstance users during a 3-month abstinence. systems during withdrawal.
87 89 91
S6.1 Babies have to be weaned from opiates when born from opiate-using mothers. S6.2 Can Facebook be addictive? 7.1
93 94
Cue-elicited craving paradigm using tactile cannabis cue paraphernalia, a neutral object (pencil) and appetitive
7.2
non-drug reward cues (fruit, not shown).
101
Cue-elicited craving paradigm.
104
/
xiv
List of Figures
7.3
Representative trial from the backward-masked cue task.
105
7.4
Regulation of the dendritic structure by drugs of abuse.
106
S7.1 Measuring
ΔFosB.
109
8.1
Impulsivity leads to risky behavior.
115
8.2
Corticostriatal pathways.
116
8.3
Study in stimulant-dependent individuals, their non-using siblings and non-using controls demonstrating that impulsivity traits (but not sensation seeking) may be a
8.4
predisposing factor for stimulant dependence.
118
Illustration of a go/no go test.
119
8.5
Ventromedial PFC lesions lead to risky decision making.
122
8.6
Schematic of the stop circuit.
123
8.7
Illustration of a delay discounting task.
124
8.8
Schematic of the wait circuit.
124
S8.1 Adolescence is a critical neurodevelopmental period. 9.1
127
Relapse rates for drug-addicted patients compared with those suffering from diabetes, hypertension and asthma.
131
9.2
Components of comprehensive drug addiction treatment.
132
9.3
Following methadone-assisted therapy (MAT), long-term abstinent heroin users (mean length of abstinence, 193 days) had a greater decreased response in striatal areas compared with short-term abstinent heroin users (mean length of abstinence, 23 days) during a cue-induced craving task.
9.4
134
Proposed model illustrating synergistic mechanisms between behavioral and pharmacological treatment approaches for addiction.
9.5
138
Common (a) and distinct (b) neural targets of pharmacological and cognitive-based therapeutic interventions.
139
S9.1 Peer addiction recovery specialists bring different perspective to treatment.
10.1 Heritability (h ; weighted means and ranges) of
143
2
ten addictions based on a large survey of adult twins.
151
10.2 Integration of complementary technologies can be used to reveal the neurobiology of individual differences in complex behavioral traits.
152
10.3 The concept of endophenotypes is that they lie in the causal pathway between the genetic mechanisms and observable behavior.
153
10.4 Brain EEG oscillations may be useful endophenotypes for alcohol use disorders.
154
/
List of Figures
xv
10.5 Changes in brain volume may be an endophenotype for cannabis use disorder.
155
10.6 (a) Birth cohort design. (b) The prospective study included initiation alcohol and drug use. (c) Using a prospective, longitudinal design on a birth cohort, the Dunedin Study found changes in full-scale IQ (in standard deviation units) from childhood to adulthood. S10.1 Post-traumatic stress disorder (PTSD).
157 161
/
List of Tables
1.1 2017 Schedule of Drugs according to the US Drug Enforcement Administration (DEA). page 3 1.2 Modi fications to addiction diagnosis from DSM-IV to DSM-5. 7 1.3 Outline of overlapping behavioral symptoms between SUDs and compulsive overeating (Volkow & O Brien, 2007). 13 6.1 Drug specificity and timing of acute withdrawal symptoms. 83 ’
/
Preface The concerted effort by the US government to determine underlying brain mechanisms for diseases during the
“Decade
” in
of the Brain
the
1990s has led to greater attention on the role of the brain in addiction.
fi
Neuroscience research has made signi cant progress toward our understanding of the antecedents as well as the consequences of addiction, which, in turn, has helped de-stigmatize addiction and get help to those
fi
who need it. However, to date, this information remains largely con ned
fic outlets resulting in a lag in dissemination to students and the
to scienti
general community. This may
contribute
to the
lack of emphasis on
addiction in most training programs, including clinical programs, despite the prevalence of addiction and its high co-morbidity with other diseases and disorders. The need for this book is further highlighted by the recent public health issues surrounding two substances: cannabis and opioids. Hence, there is a growing need for accessible information on the neuroscience of addiction that caters to both students and the general public.
Approach This book has been written to
fill
a
void in the areas of behavioral
neuroscience and neuropsychopharmacology. To date, the single most relevant textbook on this topic is one focused on the use of neuroimaging tools to study addiction, rather than to explain it. It is also written for a
fic
scienti
audience, not undergraduate students or lay people. As scien-
fic inquiry and public interest in the addicted brain have grown, so too
ti
has the need for a comprehensive and accessible textbook that communicates extant neuroscience research on this topic. This book will serve as an educational tool for neuroscience and pre-med students and trainees at all levels. Undergraduate students in upper-division courses, graduate students and educated lay people are the target audience for this book. It is
written at
a
level
appropriate
for
individuals
with minimal
to no
background in neuroscience so as to be accessible for scientists in other disciplines,
including
public
policy,
public
health
and developmental
psychology, with interest in the adolescent brain. This book can serve as
a
supplemental
textbook in upper-level
college/university
courses
such as Brain and Behavior, Psychopharmacology, Neuropsychology, Behavioral Neuroscience and as a trade book for educated lay people
/
xviii
Preface
(as it has been written in an accessible style), and/or as a main textbook in a college/university course or seminar at the advanced undergraduate
level or the graduate level (along with supplemental scientific articles). It is written in language that is accessible to students, non-specialists and educated lay people alike.
This book is included in the Cambridge Fundamentals of Neuroscience in Psychology series published by Cambridge University Press. The goal of this series is to introduce readers to the use of neuroscience methods and research to inform psychological questions.
Coverage and Organization This book has been written and organized to cover the neuroscienti fi c research that supports the most widely reported stages of addiction. I wrote the
fi rst
three chapters to lay the groundwork for the more in-
depth topics covered in the later chapters. The introductory chapter serves to provide a general foundation for the clinical and behavioral features of addiction. This is followed by a chapter that then describes the approaches used by neuroscience research, which are also consequently referred to throughout the rest of the book. This chapter, then, should provide a very
basic familiarity with current scientific techniques as used to study addiction. The last of the foundational chapters describes the various theories that stimulate the investigative research described in subsequent chapters. The goal of these foundational chapters is to broadly set out the current thinking in the
fi eld as well as provide the necessary background knowledge
to be able to integrate information from the subsequent chapters. The later chapters starting with Chapter 4 each focus on the important constructs related to addiction and are organized to follow a somewhat ecological order of the progression of addiction stemming from acute intoxication and rewarding effects of substance use to withdrawal symptoms and addiction interventions. These chapters cover the basic research that supports the understanding of these constructs as well as issues related to the understanding of these constructs. The concluding chapter discusses auxiliary topics relevant to these processes such as individual variability. It then provides a cohesive overview of the neuroscience of addiction zeitgeist.
Features Each chapter contains comprehensive cepts
or
challenging
topics.
Each
figures that best illustrate figure is referred to in
conthe
/
Preface
xix
corresponding text. Summary Points are provided at the end each chapter to help focus the reader on the most important points and to reinforce the gist of each chapter. Review Questions are also provided to challenge the reader’ s understanding of each chapter. These questions are related to the important points of the chapter. The chapters also have a Further Reading section that directs readers to supplemental materials that could facilitate further learning. The Spotlight sections take current issues and integrate these timely topics with constructs from the chapter. These spotlights help put constructs into a real-world perspective that is aimed to stimulate critical thinking in readers.
/
/
C H A P T ER O N E What is Addiction?
Learning Objectives • • • • •
fi
Be able to describe the clinical de nition of addiction. Be able to recognize the phenomenology of addiction.
fi
Be able to explain how psychoactive substances are classi ed. Be able to characterize the brain disease model of addiction. Be able to understand the concept of behavioral addiction.
Introduction
According to the World Health Organization, there were 2 billion alcohol users, 1.3 billion smokers and 185 million drug users in the year 2000. This figure contributed to 12.4% of all deaths worldwide that year. Addiction does not discriminate. It affects both sexes, all races and all ages. However, the highest rate of addiction is in the adolescent to emerging-adult populations (ages 12 29 years) (UNODC, 2012). The high rate of substance use initiation during this period has the potential to change the tone for how the brain develops, given that this age period is when the brain undergoes critical maturation processes. Figure 1.1 illustrates brain development as a process consisting of gray matter reductions and cortical thinning that is then followed by increased white matter volume, connectivity and organization through adolescence and young adulthood (Giorgio , 2010; Gogtay , 2004; Hasan , 2007; Lebel , 2010; Shaw , 2008). Guided by multidisciplinary research in neuroscience, epidemiology, brain imaging and genetics, addiction is now understood to be a brain disease due to the changes it exerts on the brain. Like other brain diseases, addiction is best described using the three Ps: pervasive, persistent and pathological. Addiction is as it affects all aspects of the individual s life. Addiction is as its effects persevere despite efforts by the individual. Last, addiction is because the –
et al.
et al.
et al.
et al.
et al.
pervasive
’
persistent
pathological
/
2
What is Addiction?
HA B K
J
I C
N Q M P L O 5
D
E F G
G 1.0
B A H
C
0.9
Age
I
0.8 0.7
20
K
rettam ya rG
J
0.6 0.5 0.4 0.3 0.2 0.1 0.0
Figure 1.1
A longitudinal study demonstrating neuromaturational processes from 5 to
20 years of age. (From Gogtay et al., 2004. © 2004 National Academy of Sciences, USA.)
fi
(A black and white version of this gure will appear in some formats. For the color version, please refer to the plate section.)
effects are uncontrollable . Thus, compulsive drug seeking and continued use despite negative consequences broadly characterize addiction.
From a clinical perspective, addiction is officially diagnosed via clinical
interview using guidelines such as the Mental Disorders ,
Diagnostic and Statistical Manual of
currently in its 5th edition (DSM-5) by the American
Psychiatric Association or the International Classification of Diseases (ICD) published by the World Health Organization. According to the DSM-5,
addiction is a chronic progressive disease with behavioral patterns that fall within a spectrum of severity. Thus, the DSM-5, implemented in 2014, refers to this broad spectrum as “ substance use disorders” (SUDs). In the USA, the Drug Enforcement Administration (DEA) organizes drugs within a schedule of drug classes that are based on risk for abuse and harm as well as acceptable medical use (Table 1.1). Schedule I drugs have the highest risk for abuse and harm and little to no medical bene fi t, while
schedule V drugs have low potential for abuse. Schedule I drugs include heroin, lysergic acid diethylamide (LSD), cannabis, peyote, methaqualone, and 3,4-methylenedioxymethamphetamine (ecstasy). Furthermore, drugs of abuse are classified into categories based on their mechanism of
/
Introduction
3
Table 1.1 2017 Schedule of Drugs according to the US DEA. The DEA
fi
classi es drugs into
five
distinct categories or schedules depending on the
drug ’ s acceptable medical use and the drug ’s abuse or dependency potential. Schedule I drugs have the highest potential for abuse and the potential to create severe psychological and/or physical dependence. Schedule V drugs represent the least potential for abuse.
fi
fi
Drug
Classi cation meaning (de ned by the
Drugs, substances,
schedule
DEA)
chemicals
No currently accepted medical use
Heroin
High potential for abuse
LSD
Schedule I
Cannabis Ecstasy Methaqualone Peyote Schedule II
High potential for abuse
Vicodin
Severe dependence risk
Cocaine Methamphetamine Methadone Dilaudid Demerol OxyContin Fentanyl Dexedrine Adderall Ritalin
Schedule III
Moderate to low potential for abuse
Codeine
Moderate to low dependence risk
Ketamine Anabolic Steroids Testosterone
Schedule IV
Low potential risk for abuse
Xanax
Low potential for dependence
Darvocet Valium Ativan Ambien Tramadol
Schedule V
Lower potential risk for abuse
Robitussin
Lower potential risk for dependence
Lyrica
/
4
What is Addiction?
action and behavioral effects: narcotics, cannabinoids, depressants, stimulants, hallucinogens and inhalants. For instance, some target specific receptors (e.g. cannabinoids) whereas others target multiple receptor systems (e.g. stimulants). The Phenomenology of Substance Use Disorders
Addiction is often de fined as compulsive drug seeking despite the negative consequences related with the substance use. Although the criteria for the clinical diagnosis of drug abuse and dependence has been and will continue to be modified based on scientific research, the behavioral sequelae associated with addiction revolve around a heightened response to rewarding stimuli and the uncontrollable behavior that individuals present in order to consume the rewarding stimuli. Various models of addiction suggest several stages and processes that contribute to addiction (discussed in Chapter 3). However, they all begin with the initial hedonic or pleasurable response to substances that lends itself to increased motivation to acquire and consume substances, as well as impulsivity and loss of control over their use. Tolerance and withdrawal are also vital processes that contribute to the maintenance of substance use despite a desire to quit. What makes addiction so complex is the multidimensional processes that lead to a cascade of neural and biological events. These events increase the risk for other illnesses such as AIDS, cancer, and cardiovascular and respiratory diseases, as well as mental disorders including psychosis. Use of substances can also transmit harmful effects to unborn fetuses such as in the case of fetal alcohol syndrome, premature birth and neonatal abstinence syndrome. Individuals with addiction are also at risk for failing to meet their responsibilities. For example, substance abuse increases the risk for dropping out of school (27% of high-school dropouts smoked cannabis, 10% abused prescription drugs, 42% consumed alcohol; US Substance Abuse and Mental Health Administration, www .samhsa.gov/data), one in six unemployed individuals use substances (www.samhsa.gov/data) and ~70% of incarcerated offenders regularly used drugs prior to their incarceration (US Dept. of Justice Report, www.bjs.gov/content/dcf/duc.cfm). Most of these consequences persist despite discontinuation from drug use. Thus, prevention and treatment strategies should focus on modifying behaviors that promote protracted abstinence. Current research in SUD intervention is focusing on more targeted treatment, given that current programs have very poor success rates, with~70% relapse within the first year. /
The Stigma of Addiction
5
The Demography of Addiction Epidemiological studies make sense of connections between demographic factors and substance use. These studies demonstrate associations between certain demographics and prevalence of substance use. For instance, stimulant users in developed countries have been found to be typically lower-class, 20– 25-year-old males (Babor, 1994). US national survey data also show that alcohol use varies by age, sex and ethnic background. For instance, young males tend to drink alcohol more than females and older individuals. Similar associations are also found in nicotine use such that higher rates of smoking are found in those of lower social class (Jarvis et
al.,
2008). Dynamic factors, however,
change the trends in substance users. For example, while opioid use was historically most prevalent in urban 18–25-year-old males in the USA, there has been a shift toward more widespread use that includes a greater number of female users in the last few years (Cicero et
al.,
2014). There are also commonalities in the demographic characteristics of users across different substances. In general, substance-abusing individuals tend to be male, young and have low socioeconomic status. Notably, accessibility of substances also plays a large role in these associations, contributing to alcohol and nicotine use being the most prevalent of all substance use. However, of all of these characteristics, age appears to be the most important demographic correlate. Several factors contribute to the abuse potential within certain demographic populations. Interactions of the drug with other disorders can
infl uence its likelihood for abuse and dependence. For instance, popula-
tions characterized as being high in risk-taking behavior are more likely to abuse substances. Psychiatric disorders that are associated with an increased risk of abuse include schizophrenia, bipolar disorder, depres-
sion and attention de fi cit/hyperactivity disorder (ADHD). Genetic factors also play an important role in the risk for addiction. Implicated
genes are typically those that regulate dopaminergic functioning, such as the dopamine receptor D4 gene (Filbey et
al.,
2008).
The Stigma of Addiction Historically, addiction has been and, to some extent, continues to be viewed as a “ disorder of free will.” Such perception implies that addiction is a social issue that should be handled by social solutions. These putative social issues include failings in childhood upbringing including the home and school environment, aversive conditions including neglect
/
6
What is Addiction?
and abuse, cultural acceptance, absence of positive influences and role models, unstructured environments, and negative peer and societal influences. While some of these social factors may contribute toward the initiation of substance use, growing empirical evidence does not support social issues as the core basis of addiction. Let us take the example of alcohol. The large majority of the population consumes alcohol on a regular basis (52% of American adults are current regular drinkers); however, only 10% of the drinking population develops an addiction (Blackwell , 2014). This demonstrates that there is more to the equation than “free will.” Social solutions have also largely failed to remediate those who are addicted, primarily because they do not address the underlying etiology. Because of the stigma of addiction, those with addiction: 1) do not seek the necessary treatment; 2) do not receive the necessary social support; or 3) receive largely ineffective treatment that does not address the underlying mechanisms of addiction. et al.
The Diagnosis of Addiction
The clinical diagnoses of mental health disorders rely on classification systems that have been developed over centuries. These classification systems differ based on their purpose for classification (clinical, research or administrative objectives), as well as emphasis on discerning features of diagnostic categories (phenomenology versus etiology). The two most prominent systems are the (DSM) and the fi (ICD). The ICD, developed by the World Health Organization, published the fi rst section for mental health disorders in 1949 within its 6th edition. Based on this, the American Psychiatric Association Committee on Nomenclature and Statistics developed the 1st edition of the DSM in 1952. The DSM then became the first official manual of mental disorders to focus on clinical use. The DSM-5, which was published in 2013 and implemented in 2014, is the most recent version. In terms of the diagnosis of addiction, the DSM-5 classifies the diagnosis of SUDs based on evidence of impaired control, social impairment, risky use and pharmacological criteria. The major modification from DSM-IV to DSM-5 is the combination of the categorical symptoms in DSM-IV into a continuum in DSM-5 (Table 1.2). Thus, rather than dimorphic diagnoses of substance abuse and dependence, a unidimensional diagnosis of SUD is evaluated on a scale from mild to severe depending on the number of symptoms presented. This decision was Diagnostic and Statistical Manual of Mental
Disorders
International Classi
cation of Diseases
/
The Diagnosis of Addiction
7
fi
Table 1.2 Modi cations to addiction diagnosis from DSM-IV to DSM-5.
Criterion
DSM-IV
DSM-IV
substance
substance
DSM-5
abuse
dependence
SUD
Tolerance
X
X
Withdrawal
X
X
Taken more/longer than intended
X
X
Desire/unsuccessful efforts to quit use
X
X
Great deal of time taken by activities
X
X
X
X
X
X
involved with use Use despite knowledge of problems associated with use Important activities given up because of use Recurrent use resulting in a failure to
X
X
X
X
X
X
fi
ful ll important role obligations Recurrent use resulting in physically hazardous behavior (e.g. driving) Continued use despite recurrent social problems associated with use Craving for the substance
X
based on evidence showing that symptoms of abuse and dependence were not independent of each other and formed a single dimension. As a result, two to three symptoms would classify as “mild SUD” , four to
five
symptoms as “ moderate SUD” and six to eleven symptoms as “severe SUD.” Since the inception of this new classi fication system for addiction
diagnosis, opponents of this system have argued that the unidimensional
classification does not re fl ect the discrete nature of the features of addiction, namely, withdrawal, tolerance and craving. Indeed, these constructs have been viewed as conceptually and empirically distinct, and subsequent chapters will discuss the neuroscienti fic foundations of each of these constructs.
Another modi fication is the overarching criteria for SUDs independ-
ent of substance, as well as the inclusion of behavioral addictions (e.g. gambling disorder). Incidentally, DSM-5 includes a section with tools to
/
8
What is Addiction?
Electric stimulator
Pump dispensing drug or saline
Computer Lever
(a) Drug
1 2
4
3
5
6
8
7
9
10
11
12
Saline 3
1 2
4
5
7
6
8
9
10
11
12
? (b) Figure 1.2
Drug-tested mouse prefers chamber in which drug was given
Animal behavioral paradigms in addiction studies. (a) In self-administration
models, animals continuously perform an action (e.g. pressing a lever) in order to receive a
/
A Brain Disease Model of Addiction
9
improve the diagnosis of personality disorders, and incorporates diagnoses that may be considered for future iterations of the DSM. This section (section III) includes internet gaming disorder and caffeine use disorder. A Brain Disease Model of Addiction
As mentioned earlier, the view that addiction is a social issue overlooks the role of the brain in the behavioral symptoms related to addiction. By doing so, interventions attempt to modify behavior that may not be directly related to the underlying mechanisms. What are these underlying mechanisms of addiction? Much of what we know about addiction as a brain disease originates from seminal animal research that began ~30 years ago. For instance, animal experiments utilizing intracranial self-stimulation demonstrated how animals will readily self-administer drugs of abuse and how these drugs alter the animal s reward threshold (Figure 1.2a). In a classic study of the positive reinforcing effects of morphine, Weeks and colleagues trained rats to self-deliver morphine intravenously (Weeks, 1962). They discovered that the unrestrained rats self-injected morphine and that the greater the dose, the less they selfinjected. Classical conditioning models, such as conditioned place preference, show the development of paired associations between the rewarding properties of drugs and the cue that signals exposure to the drug, suggesting adaptations in reward learning mechanisms (Figure 1.2b). Behavior sensitization models assess the result of repeated drug exposure and suggest an augmented response following continued use. These models demonstrate the progression of addiction from the initial hedonic response to the drug ( liking the drug) to that of yearning or craving ( wanting the drug). For example, behavior sensitization has been described in terms of locomotor activity in rats sensitized to higher doses of amphetamine (e.g. 2.0 mg/kg intraperitoneally) where an initial slowing is later followed by an increase (Leith & Kuczenski, 1982). Another example is the reinstatement model, which also assesses how repeated drug exposure impacts behavior but is used to test ’
“
“
”
”
reward or receive intracranial current in brain-rewarding loci (self-stimulation). (b) In placepreference models, animals spend more time in an environment where they had repeatedly received a drug, demonstrating positive reinforcing mechanisms of drugs. (From Camí & Farré, 2003. © 2003 Massachusetts Medical Society, USA.)
/
10
What is Addiction?
mechanisms of drug relapse. In these models, an established operant response for the drug such as lever pressing that has been extinguished re-emerges or reinstates. For example, place preference to previously drug-paired environments can be reinstated following extinction in animals. These animal models have been translated into human models (discussed in Chapter 2), and with advanced technologies (discussed in
Chapter 2) and focused scientific research, there is now a growing understanding of the key role of neurobiological mechanisms underlying processes related to addiction. These processes are discussed individually in subsequent chapters. The initial effects of substances on behavior widely vary because each drug’ s mechanism of action on the brain is unique. Opioids bind to
μ
receptors in the brain, which results in feelings of euphoria,
sedation and tranquility. The importance of
μ
receptors is demon-
strated in studies where mice lacking this receptor do not exhibit these behavioral effects, and also do not become physically addicted. Cannabis also causes relaxation but exerts its effects by binding to cannabinoid (CB1) receptors in the brain. The effects of cannabis also include a sense of well-being, as well as slowing of cognitive functions. Slowing of cognitive functions also results from alcohol, although alcohol modulates activity in several receptors including serotonin (5-hydroxytryptamine, 5HT), nicotinic,
γ-aminobutyric
acid (GABA) and
N-methyl-d-aspartate
(NMDA) receptors. Unlike depressants, such as alcohol, psychostimulants, in general, result in opposite effects such as increased alertness, arousal, concentration and motor activity by blocking the reuptake of dopamine, norepinephrine and serotonin. This results in a rapid release and accumulation of neurotransmitters in the synaptic cleft. However, despite this wide range of mechanisms and effects, virtually all addictive substances target brain regions in the medial portion of the limbic and frontal lobes. These regions form a neural pathway that is innervated primarily by dopaminergic projections that originate from the ventral tegmental area (VTA) in the midbrain and project to the amygdala and the nucleus accumbens. Because of dopamine’s role in the hedonic response, this neural pathway is referred to as the dopaminergic reward pathway due to its role in processing rewarding drug and nondrug stimuli (illustrated in Figure 1.3). In addition to dopamine, this pathway is also modulated by opioids, GABA and endocannabinoids, and also processes emotion and motivation. This pathway is, therefore, important in the conscious experience of taking a drug, drug craving and compulsion. It is within this pathway that substances exert their effects. Thus, brain regions within this pathway are likely to endure pervasive
/
A Brain Disease Model of Addiction
11
Amphetamines, cocaine, opioids, cannabinoids, phencyclidine
5-HT
Amygdala DA DA
Opioid GABA
GLU
Opioid
GLU GABA
Prefrontal cortex
GABA DA
GABA 5-HT
DA
Locus NE ceruleus
Opioid
Nucleus accumbens
Raphe nucleus Ventral tegmental area
Opioids, ethanol, barbiturates, benzodiazepines
Sites of action of various drugs on the mesocorticolimbic reward system. Although the pathways primary neurotransmitter is dopamine (DA), this circuit is innervated by glutamatergic (GLU) projections,γ-aminobutyric acid (GABA) norepinephrine (NE) and serotonergic (5-HT) projections. Figure 1.3
’
(From Camí & Farré, 2003. © 2003 Massachusetts Medical Society, USA.)
and potentially permanent changes. Some of the symptoms of addiction, such as tolerance and withdrawal, are examples of this adaptation. Thus, drugs of abuse alter the neural transmission and functioning of the reward pathway from its evolutionary role of sustaining the organism (i.e. via natural, non-drug rewards). The result of this dysregulated reward network is a decreased responsivity to natural rewards. This neural adaptation or
“hijacking” of
the brain is what classifies addiction
as a brain disease. Changes in neural transmission in the mesolimbic reward pathway also lead to a cascade of events that occurs in other neurochemical systems, such as the stress system. Indeed, studies have found that chronic drug use leads to dysregulation in stress hormones such as corticotropinreleasing factor in the hypothalamic –pituitary– adrenal (HPA) axis. George Koob has described this
“antireward
system” as the dysregula-
tion of the stress system that contributes to the negative emotional state occurring during abstinence from drugs (Koob, 2006). Koob has referred to this negative state as
“the
dark side of addiction ” and it is often
associated with withdrawal symptoms. Lastly, the compulsive drug seeking associated with addiction is associated with cognitive impairment such as poor decision making, inhibitory control, learning and memory,
/
12
What is Addiction?
which are cognitive functions within areas of the prefrontal cortex (PFC). Some of these changes in the brain are long term, which contributes to the relapsing nature of the disease despite protracted periods of abstinence. Neuroimaging studies in humans have supported the involvement of these systems in addiction. For instance, techniques such as positron emission tomography (PET) and magnetic resonance imaging (MRI) scans have shown that regions within the mesocorticolimbic pathway that include the orbitofrontal cortex, PFC, anterior cingulate gyrus, amygdala and nucleus accumbens are activated during drug intoxication. Although PET and MRI only measure neural activity indirectly, these results are likely due to increased dopamine levels in this pathway during drug consumption. Interestingly, during withdrawal, the reverse effect is observed (i.e. decreased activity). Non-Drug Addictions
So far, this chapter has focused on addiction in terms of response to substances of abuse, sometimes referred to as “chemical addiction.” However, a growing area of research has found similar behavioral symptoms (tolerance, withdrawal, compulsion) that occur as a consequence of non-substance or “behavioral addictions. ” These have been evidenced in compulsive activities such as eating, sex/pornography, exercising, gambling, video gaming and tanning, among others (Holden, 2010). These compulsive disorders were previously categorized as “substance-related disorders, ” “impulse control disorders, not otherwise specified” or “eating disorders”. However, emergent neuroimaging studies suggest that these behavioral addictions may have overlapping mechanisms with substance addictions (Table 1.3) (Holden, 2001; Probst & van Eimeren, 2013). Non-drug addictions have also been observed in animal models. Forexample, during intravenous self-administration experiments, rats trained to press a lever for highly palatable foods such as sugar and saccharin were shown to reduce self-administration of cocaine and heroin (Lenoir & Ahmed, 2008). This unexpected finding suggests that these natural reinforcers (i.e. sweet foods) have a higher reinforcing value than cocaine, even in animals with an extensive history of drug intake. Studies by Hoebel (2009) have also demonstrated behavioral plasticity following a history of intermittent sugar access, supporting the notion that sugar consumption meets the criteria for addiction. Tolerance has also been noted whereby an increase in intake is observed et al.
/
Non-Drug Addictions
13
Table 1.3 Outline of overlapping behavioral symptoms between SUDs and compulsive overeating (Volkow & O Brien, 2007). ’
SUDs
Compulsive overeating
Tolerance
Increasing amounts of food to maintain satiety Distress and dysphoria during dieting Larger amounts eaten than intended Persistent desire to curtail amount eaten Great deal of time spent eating
Withdrawal symptoms Larger amounts used than intended Persistent desire to quit Great deal of time spent using or obtaining Decreased social activities Continued use despite physical or psychological problems
Activities given up from fear of rejection or due to physical limitations Overeating despite adverse physical and psychological consequences
during sugar self-administration (Colantuoni , 2001). Interestingly, withdrawal symptoms such as anxiety and depression were observed following removal of sugar or fat access (Colantuoni , 2002). In humans, neuroimaging studies demonstrate a neural response in the mesocorticolimbic reward system similar to drug addiction in individuals with problems with gambling (Worhunsky , 2014) , sex (Kuhn & Gallinat, 2014), internet/video games (Kim , 2014), food (Filbey , 2012), shopping (Dagher, 2007) and tanning (Kourosh , 2010). These studies suggest that the reward system is responsible for neural adaptations as a consequence of these compulsive behaviors. Pitchers (2010) reported neural adaptations in the form of increased dendrites and dendritic spines within the nucleus accumbens in rats during “withdrawal” from sexual experience. Additionally, like drugs of abuse and other natural rewards, exercise in rodents has been shown to be associated with increased dopamine signaling in the nucleus accumbens and striatum (Freed & Yamamoto, 1985; Hattori , 1994). Notably, despite the overlap in brain regions, single-unit recordings have suggested that different cell populations are responsible for the response to self-administration of natural rewards and drugs of abuse such as cocaine or ethanol (Bowman , 1996; Carelli, 2002; Carelli , 2000; Robinson & Carelli, 2008). Importantly, emerging clinical evidence suggests that pharmacotherapies used to treat drug addiction may be a et al.
et al.
et al.
et al.
et al.
et al.
et al.
et
et al.
al.
et al.
/
14
What is Addiction?
successful approach to treating non-drug addictions. For example, naltrexone, nalmefene, -acetylcysteine and modafinil have all been reported to reduce craving in pathological gamblers (Grant , 2006; Kim , 2001; Leung & Cottler, 2009). N
et al.
et al.
Summary Points •
•
•
• •
Both the animal and human literature support the notion that addiction is a brain disorder stemming from the positive reinforcing mechanisms in the mesolimbic pathway. Chronic use leads to neuroadaptation, primarily within this pathway, that results in the behavioral symptoms of addiction. These adaptations also lead to changes in other brain systems, including the stress system. Through this cycle, addiction becomes a chronic, relapsing disorder. More recently, non-drug addictions have been identified, with evidence showing parallel neural mechanisms to those of substance-based addictions.
Review Questions How are the five categories in the DEA s classification of substances delineated? What are the current (i.e. DSM-5) primary symptoms of SUD according to clinical guidelines? What are the seminal animal studies that have helped shape our understanding of addiction as a brain disease? How is dopamine critical in the processes related to addiction?
•
’
•
•
•
Further Reading Babor, T. F. (2011). Substance, not semantics, is the issue: comments on the proposed addiction criteria for DSM-V. Addiction, 106(5), 870–872; discussion 895–877. doi:10.1111/j.1360-0443.2010.03313.x Barnett, A. I., Hall, W., Fry, C. L., Dilkes-Frayne, E. & Carter, A. (2017). Drug and alcohol treatment providers’ views about the disease model of
/
Spotlight
15
addiction and its impact on clinical practice: a systematic review. Drug Alcohol Rev, 37(6), 697–720. doi:10.1111/dar.12632 Burrows, T., Kay-Lambkin, F., Pursey, K., Skinner, J. & Dayas, C. (2018). Food addiction and associations with mental health symptoms: a systematic review with meta-analysis. J Hum Nutr Diet , 31(4), 544–572. doi:10.1111/ jhn.12532 Diana, M. (2011). The dopamine hypothesis of drug addiction and its potential therapeutic value. Front Psychiatry , 2, 64. doi:10.3389/ fpsyt.2011.00064 Grant, J. E. & Chamberlain, S. R. (2016). Expanding the definition of addiction: DSM-5 vs. ICD-11. CNS Spectr, 21(4), 300–303. doi:10.1017/ S1092852916000183 Hou, H., Wang, C., Jia, S., Hu, S. & Tian, M. (2014). Brain dopaminergic system changes in drug addiction: a review of positron emission tomography findings. Neurosci Bull, 30(5), 765–776. doi:10.1007/s12264-0141469-5 Lewis, M. D. (2011). Dopamine and the neural“now”: essay and review of addiction: a disorder of choice. Perspect Psychol Sci, 6(2), 150–155. doi:10.1177/1745691611400235 Singer, M. (2012). Anthropology and addiction: an historical review.Addiction, 107(10), 1747 –1755. doi:10.1111/j.1360-0443.2012.03879.x
Spotlight The magic in the mushrooms remains unknown
A 2015 report published by theCanadian Medical Association Journalpointed to several small studies demonstrating that psychedelic drugs such as LSD and 3,4-methylenedioxymethamphetamine (MDMA) may be effective in reducing symptoms of post-traumatic stress disorder (PTSD) anxiety, as well as addiction (Tupper et al., 2015). A small 2014 Swiss study, for instance, found that people with terminal illness treated with a combination of LSD and psychotherapy had lower rates of anxiety (Gasser et al., 2014). A US study involving a small group of patients also found that MDMA, more commonly known as ecstasy, can greatly reduce symptoms of PTSD. However, many caution of the negative side effects of psychedelics on mood and cognition, as well as sensory processing and perception. For instance, LSD, psilocybin (obtained from magic mushrooms) and mescaline can cause psychosis and/or hallucinations (Figure S1.1).
/
16
What is Addiction?
Figure S1.1
Magic mushrooms.
(From https://pixabay.com/en/alone-autumn-background-britain-1239208/. Reproduced under Creative Commons CC0 license.)
fi
Since the 1950s, the therapeutic bene ts of psychedelics have always been argued. However, how psychedelics affect the brain remains unknown. Furthermore, it remains to be determined for what purposes psychedelics should
fi
be used in addition to the risks and bene ts associated. Stephen Kish, who studies the use of ecstasy in the treatment of PTSD, suggests that it increases a person’s sociability, which may foster patients’ interactions with their therapists (Kish et al., 2010). However, he also notes that psychedelics cause hallucinations and, in some cases, psychosis. The biggest concern in these studies is the risk that people would selfmedicate with psychedelic drugs. The fact remains that the forms available on the street are unlikely to be pure and could lead to serious health problems and even death.
References
Babor, T. F. (1994). Overview: demography, epidemiology and psychopharmacology –making sense of the connections. 89(11), 1391–1396. Blackwell, D. L., Lucas, J. W. & Clarke, T. C. (2014).
Addiction
,
Summary Health
. Vital Health Statistics, Series 10, No. 260. Hyattsville, MD: National Center For Health Statistics. Bowman, E. M., Aigner, T. G. & Richmond, B. J. (1996). Neural signals in the monkey ventral striatum related to motivation for juice and Statistics for U.S. Adults: National Health Interview Survey, 2012
/
References
17
cocaine rewards. , 75(3), 1061–1073. doi:10.1152/ jn.1996.75.3.1061 Camí, J. & Farré, M. (2003). Drug addiction. , 349(10), 975–986. doi:10.1056/NEJMra023160 Carelli, R. M. (2002). The nucleus accumbens and reward: neurophysiological investigations in behaving animals. , 1(4): 281– 296. doi:10.1177/1534582302238338 Carelli, R. M., Ijames, S. & Crumling, A. (2000). Evidence that separate neural circuits in the nucleus accumbens encode cocaine versus “natural” (water and food) reward. , 20(11), 4255–4266. doi:10.1523/JNEUROSCI.20-11-04255.2000 Cicero, T. J., Ellis, M. S., Surratt, H. L. & Kurtz, S. P. (2014). The changing face of heroin use in the United States: a retrospective analysis of the past 50 years. , 71(7), 821–826. doi:10.1001/ jamapsychiatry.2014.366 Colantuoni, C., Schwenker, J., McCarthy, J., (2001). Excessive sugar intake alters binding to dopamine and mu-opioid receptors in the brain. , 12(16), 3549–3552. Colantuoni, C., Rada, P., McCarthy, J., (2002). Evidence that intermittent, excessive sugar intake causes endogenous opioid dependence. , 10(6), 478–488. doi:10.1038/oby.2002.66 Dagher, A. (2007). Shopping centers in the brain. , 53(1), 7–8. doi:10.1016/j.neuron.2006.12.014 Filbey, F. M., Ray, L., Smolen, A., (2008). Differential neural response to alcohol priming and alcohol taste cues is associated with DRD4 VNTR and OPRM1 genotypes. , 32(7), 1113 –1123. doi:10.1111/j.1530-0277.2008.00692.x Filbey, F. M., Myers, U. S. & Dewitt, S. (2012). Reward circuit function in high BMI individuals with compulsive overeating: similarities with addiction. , 63(4), 1800– 1806. doi:10.1016/j. neuroimage.2012.08.073 Freed, C. R. & Yamamoto, B. K. (1985). Regional brain dopamine metabolism: a marker for the speed, direction, and posture of moving animals. , 229(4708), 62–65. doi:10.1126/science.4012312 Gasser, P., Holstein, D., Michel, Y., (2014). Safety and efficacy of lysergic acid diethylamide-assisted psychotherapy in subjects with anxiety associated with life-threatening diseases: a randomized active placebo-controlled phase 2 pilot study. , 202(7), 513 –520. doi:10.1097/NMD.0000000000000113 Giorgio, A., Watkins, K. E., Chadwick, M., (2010). Longitudinal changes in grey and white matter during adolescence. , 49(1), 94– 103. doi:10.1016/j.neuroimage.2009.08.003 J Neurophysiol
N Engl Med
Behav Cogn
Neurosci Rev
J Neurosci
JAMA Psychiatry
et al.
Neuroreport
et al.
Obes Res
Neuron
et al.
Alcohol Clin Exp Res
Neuroimage
Science
et al.
J Nerv Ment Dis
et al.
Neuroimage
/
18
What is Addiction?
Gogtay, N., Giedd, J. N., Lusk, L., (2004). Dynamic mapping of human cortical development during childhood through early adulthood. , 101(21), 8174 8179. doi:10.1073/pnas.0402680101 Grant, J. E., Potenza, M. N., Hollander, E., (2006). Multicenter investigation of the opioid antagonist nalmefene in the treatment of pathological gambling. , 163(2), 303 312. doi:10.1176/ appi.ajp.163.2.303 Hasan, K. M., Sankar, A., Halphen, C., (2007). Development and organization of the human brain tissue compartments across the lifespan using diffusion tensor imaging. , 18(16), 1735 1739. Hattori, S., Naoi, M. & Nishino, H. (1994). Striatal dopamine turnover during treadmill running in the rat: relation to the speed of running. , 35(1), 41 49. doi:10.1016/0361-9230(94)90214-3 Hoebel, B. G., Avena, N. M., Bocarsly, M. E. & Rada, P. (2009). Natural addiction: a behavioral and circuit model based on sugar addiction in rats. , 3(1), 33 41. doi:10.1097/ ADM.0b013e31819aa621 Holden, C. (2001). Behavioral addictions: do they exist? , 294(5544), 980 982. doi:10.1126/science.294.5544.980 (2010). Behavioral addictions debut in proposed DSM-V. , 327 (5968), 935. doi:10.1126/science.327.5968.935 Jarvis, M. J., Fidler, J., Mindell, J., Feyerabend, C. & West, R. (2008). Assessing smoking status in children, adolescents and adults: cotinine cut-points revisited. , 103(9), 1553 1561. doi:10.1111/j.13600443.2008.02297.x Kim, J. E., Son, J. W., Choi, W. H., (2014). Neural responses to various rewards and feedback in the brains of adolescent Internet addicts detected by functional magnetic resonance imaging. , 68(6), 463 470. doi:10.1111/pcn.12154 Kim, S. W., Grant, J. E., Adson, D. E. & Shin, Y. C. (2001). Double-blind naltrexone and placebo comparison study in the treatment of pathological gambling. , 49, 914 921. doi:10.1016/ S0006-3223(01)01079-4 Kish, S. J., Lerch, J., Furukawa, Y., (2010). Decreased cerebral cortical serotonin transporter binding in ecstasy users: a positron emission tomography/[11C]DASB and structural brain imaging study. , 133 (6), 1779 1797. Koob, G. F. (2006). The neurobiology of addiction: a neuroadaptational view relevant for diagnosis. , 101, Suppl. 1, 23 30. doi:10.1111/ j.1360-0443.2006.01586.x et al.
Proc
–
Natl Acad Sci U S A
et al.
–
Am J Psychiatry
et al.
Neuroreport
–
–
Brain Res Bull
J Addict Med
‘
–
’
Science
–
Science
Addiction
–
et al.
Psychiatry Clin
–
Neurosci
Biol Psychiatry
–
et al.
Brain
–
Addiction
–
/
References
19
Kourosh, A. S., Harrington, C. R. & Adinoff, B. (2010). Tanning as a behavioral addiction. Am J Drug Alcohol Abuse, 36(5), 284 290. doi:10.3109/00952990.2010.491883 Kuhn, S. & Gallinat, J. (2014). Brain structure and functional connectivity associated with pornography consumption: the brain on porn. JAMA Psychiatry, 71(7), 827 834. doi:10.1001/jamapsychiatry.2014.93 Lebel, C., Caverhill-Godkewitsch, S. & Beaulieu, C. (2010). Age-related variations of white matter tracts. Neuroimage, 52(1), 20 31. doi:10.1016/j.neuroimage.2010.03.072 Leith, N. J. & Kuczenski, R. (1982). Two dissociable components of behavioral sensitization following repeated amphetamine administration. Psychopharmacology (Berl), 76(4), 310 315. Lenoir, M. & Ahmed, S. H. (2008). Supply of a nondrug substitute reduces escalated heroin consumption. Neuropsychopharmacology , 33(9), 2272 2282. doi:10.1038/sj.npp.1301602 Leung, K. S. & Cottler, L. B. (2009). Treatment of pathological gambling. Curr Opin Psychiatry, 22(1), 69 74. doi:10.1097/ YCO.0b013e32831575d9 Pitchers, K. K., Balfour, M. E., Lehman, M. N.,et al. (2010). Neuroplasticity in the mesolimbic system induced by natural reward and subsequent reward abstinence. Biol Psychiatry 67(9), 872 879. doi:10.1016/j. biopsych.2009.09.036 Probst, C. C. & van Eimeren, T. (2013). The functional anatomy of impulse control disorders. Curr Neurol Neurosci Rep, 13(10), 386. doi:10.1007/ s11910-013-0386-8 Robinson, D. L. & Carelli, R. M. (2008). Distinct subsets of nucleus accumbens neurons encode operant responding for ethanol versus water. Eur J Neurosci, 28(9), 1887 1894. doi: 10.1111/j.14609568.2008.06464.x Shaw, P., Kabani, N. J., Lerch, J. P., et al. (2008). Neurodevelopmental trajectories of the human cerebral cortex. J Neurosci , 28(14), 3586 3594. doi:10.1523/JNEUROSCI.5309-07.2008 Tupper, K. W., Wood, E., Yensen, R. & Johnson, M. W. (2015). Psychedelic medicine: a re-emerging therapeutic paradigm. CMAJ, 187(14), 1054 1059. doi:10.1503/cmaj.141124 UNODC. (2012). World Drug Report 2012. Vienna, Austria: United Nations. Available at: www.unodc.org/documents/data-and-analysis/WDR2012/ WDR_2012_web_small.pdf Volkow, N. D. & O Brien, C. P. (2007). Issues for DSM-V: should obesity be included as a brain disorder? Am J Psychiatry, 164(5), 708 710. doi:10.1176/appi.ajp.164.5.708 –
–
–
–
–
–
–
–
–
–
’
–
/
20
What is Addiction?
Weeks, J. R. (1962). Experimental morphine addiction: method for automatic intravenous injections in unrestrained rats.Science, 138 (3537), 143–144. Worhunsky, P. D., Malison, R. T., Rogers, R. D. & Potenza, M. N. (2014). Altered neural correlates of reward and loss processing during simulated slot-machine fMRI in pathological gambling and cocaine dependence. Drug Alcohol Depend, 145, 77–86. doi:10.1016/j. drugalcdep.2014.09.013
/
C H A P T ER TW O
Human Neuroscience Approaches Toward the Understanding of Addiction
Learning Objectives •
Be able to identify current neuroimaging techniques used to study addiction in humans.
• •
Be able to understand the current limitations of neuroimaging research. Be able to describe the fundamental features of each neuroimaging technique.
•
Be able to understand the various brain mechanisms that neuroimaging techniques can examine.
•
Be able to appreciate how neuroimaging techniques can be applied in clinical and research practice.
Introduction Our understanding of addiction as a brain disease can be attributed largely to the recent advancements in brain imaging techniques. While issues in methodological differences within human neuroimaging studies
can
add
approaches behavioral
complexity
integrating studies,
to
this
picture,
neuroscience
genetics
and
the
use
with other
pharmacology,
understanding of the mechanisms underlying
of
multivariate
disciplines, provides
a
such
as
deeper
addiction. In addition,
translational studies that apply lessons gained from non-human studies for
testing
within
humans
have
enriched
our
understanding
of
the
overall mechanisms of addictive processes. How have these neuroimaging techniques advanced over the years? And what kind of information do they provide above and beyond what
fi ndings from neuroimaging
we can glean clinically? How can we harness research
in
order
to
improve
the
lives
of
those
who
suffer
from
addiction?
/
22
Human Neuroscience Approaches
Measuring the Brain ’s Electrical Activity
Introduced in the 1920s, the technique of electroencephalography (EEG) takes advantage of the electrophysiological properties of the brain. By measuring these electrophysiological signals or brain waves, we are able to determine patterns of electrical charges (frequency, voltage, morphology and topography) from large representative samples of cortical neurons largely pyramidal cells. Brain function can then be inferred from these patterns that reflect neuronal factors and activities including ionic gradients from neuronal membranes, and excitatory and inhibitory post-synaptic potentials. EEG recordings are measured by electrodes placed either extracranially (on the scalp) or intracranially (via surgical placement directly on the surface of the brain) to record the electrical voltage fluctuations generated by the brain. Currently, a minimum of twenty-one electrodes is considered ideal for a clinical study, although higher density array EEG systems of up to 256 electrodes are available. Currently, while electrical signals provide high temporal resolution data regarding brain activity, the poor spatial resolution of the two-dimensional EEG representation of a threedimensional brain poses limitations in interpretation of the data. Thus, source localization is limited in extracranial EEG recordings. Furthermore, EEG recordings are the result of synchronous activity from large samples of neurons, which conceals small activity or activity from smaller samples of neurons. The net effect of electrophysiological activity in the brain also generates a magnetic field that can be detected. Magnetoencephalography (MEG) is a technique that measures the magnetic fields emitted by electrical activity in the brain (Figure 2.1). The magnetic field emitted by neurons passes through brain tissue and the skull with little distortion, thereby generating better spatial localization relative to EEG, as the scalp distorts magnetic fields less than electrical signals. Although the brain s magnetic field is 10 15 Tesla (T; i.e. 100 million times smaller than the Earth s magnetic field), superconducting sensors called superconducting quantum interference devices (SQUIDs) are able to detect and record this signal. More than 300 fixed SQUID sensors are embedded within the MEG helmet. SQUID sensors amplify the magneticfields generated by intracellular currents within the dendrites of pyramidal cells. These cells are perpendicular to the cortical surface. While MEG has the advantage of measuring neural activity directly, it is not sensitive beyond the first few centimeters of the cortex, as the signals from internal neurons decay quickly over distance (Cohen & Cuf fin, 1991; “
”
–
–
’
’
/
Measuring the Brain’s Electrical Activity
Figure 2.1
23
Magnetoencephalography scanner with patient.
(From https://images.nimh.nih.gov/public_il/image_details.cfm?id=80. © National Institute of Mental Health, National Institutes of Health, Department of Health and Human Services.)
Huettel
et
al.,
2008). The MEG signal is also highly susceptible to
magnetic interference such as a car driving by or other electrical sources; therefore, MEG scanners have to be in magnetically shielded rooms. Both EEG and MEG are considered direct measures of brain function
to study event-related potentials/fields, or in the time-frequency domain,
to study oscillatory activity. They provide very high temporal resolution in the order of milliseconds. These techniques can be conducted extracranially and are therefore non-invasive and do not require injection or exposure to X-rays. Thus, these techniques can be used in virtually all populations. Lastly, due to the passive nature of these techniques, recordings can be conducted in most settings, especially for EEG.
/
24
Human Neuroscience Approaches
Visualizing the Brain ’s Structure and Function
First utilized in the 1970s, magnetic resonance imaging (MRI) is one of the most widely used neuroimaging techniques today. MRI is still considered “state of the art” given its
flexibility and sensitivity as a diagnos-
tic imaging modality that is capable of characterizing a wide range of parameters. The fundamental concept of MRI lies in the discovery of nuclear magnetic resonance of protons in water molecules and its interaction with a magnetic
fi eld.
Bloch and Purcell then measured the
effective precessional spin properties of protons within a given magnetic
fi eld,
thereby yielding an MRI signal (Block
et al.,
1946; Purcell et
al.,
1946). During an MRI scan, a radiofrequency pulse is delivered that causes protons to spin in a different direction. When the radiofrequency pulse is turned off, the protons return back to their low-energy state and their normal alignment within the magnetic field. This return to the low-
energy state or relaxation causes release of stored energy in the form of light, which is detected by the magnetic resonance scanner and is converted to the images that we see (Figure 2.2). MRI yields high-resolution images of brain macro- and microstructure, function and neurochemical composition (Figure 2.3). Structural MRI scans provide static images of the brain’s anatomy. From these
images, quantification of the structural dimensions of brain regions (e.g. volume), shape and tissue composition can be determined.
On a microstructural level, diffusion tensor imaging (DTI) detects the movement of water molecules through tissue, thereby providing information on the architecture and integrity of white matter fi bers in the brain.
Precess ion
Applied magnetic field
Figure 2.2
Mechanisms of MRI. The MRI signal stems from the circling or precession of the
fi
spinning protons around the axis of the magnetic eld (center arrow).
/
Visualizing the Brain’s Structure and Function
Figure 2.3
25
A patient going through a magnetic resonance imaging machine.
(From https://commons.wikimedia.org/wiki/File:US_Navy_030819-N-9593R-228_Civilian_technician,_ Jose_Araujo_watches_as_a_patient_goes_through_a_Magnetic_Resonance_Imaging,_(MRI)_ machine.jpg. CC-PD National Naval Medical Center, Bethesda, MD, 2003)
DTI indexes can quantify the length of fi ber bundles (e.g. tractography), as well as the directionality (e.g. fractional anisotropy) and diffusivity (e.g. trace) of water molecules through brain tissue. High fractional anisotropy
and low diffusivity re flect healthy white matter (Figure 2.4).
In addition to structural information, MRI also enables functional imaging that offers dynamic physiological information of the brain. Functional MRI (fMRI) paradigms provide near real-time information regarding task-induced as well as resting baseline state neural activation. The fundamental element of fMRI scans is the blood oxygenated leveldependent (BOLD) signal. Originally discovered by Seiji Ogawa in 1990, the BOLD signal refers to the in vivo change of blood oxygenation that leads to variation in the magnetic signal detectable with MRI. The BOLD signal therefore provides information on brain regions that have increased oxygenation as the result of being active and requiring more energy. It is therefore anindirect measure of neural function and relies on assumptions regarding underlying neuronal activity. fMRI also includes perfusion techniques (with or without endogenous or exogenous contrast), regional cerebral blood
flow
and cerebrospinal
fl uid
measurements, as well as phase contrastflow measurements.
pulsation
/
26
Human Neuroscience Approaches
l1 l1
l2
l2
l3
l3
Isotropic
Anisotropic
l1 = longitudinal (axial) diffusivity (AD) l2 + l3 )/2 = radial diffusivity (RD)
(
l1 + l2 + l3 )/3 = mean diffusivity (MD)
(
Figure 2.4
Gray matter has predominantly isotropic (soccer ball-shaped) water
diffusion, while dense white matter tracks have highly anisotropic (rugby ball-shaped)
fi
diffusion of water pointing in the direction of the ber bundle. The measure most commonly used to characterize directional diffusion is fractional anisotropy (FA). This measure gives a value of between 0 and 1 to indicate the fraction of diffusion that is in the longitudinal direction compared with the proportion of diffusion in both transverse directions. Other measures that can be used are axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD). (From Whitfordet al., 2011.) (A black and white version of this
figure
will appear in some formats. For the color version, please refer to the plate
section.)
Innovations in both scanner hardware and scan sequences continue to provide advancements in diagnostic MRI techniques. These improvements include higher
field
imaging up to 11.75 T (standard hospital
MRIs are 1.5 or 3 T), multiband imaging via advanced coil technology, shorter
echo time imaging and simultaneous
scanning modalities
including PET-MRI, SPECT-MRI and EEG-MRI, as well as the development of novel molecular MRI agents. Thus, continued advancements in our understanding of brain mechanisms via MRI techniques are still to come. Computed tomography (CT) and positron emission tomography (PET) also provide visualization of brain structure and function, respectively. However, with the advent of MRI, PET is now more widely used for detection of brain molecules and is discussed in greater detail in the following section.
/
Biochemical Imaging
27
Biochemical Imaging
Other imaging techniques provide quantifi cation of brain molecules. These include magnetic resonance spectroscopy (MRS) (Figure 2.5), PET and single-photon emission computed tomography (SPECT). MRS is conducted using an MRI scanner and is based on radiofrequency signals or peaks within a spectrum that are unique to metabolites such as
N-acetylaspartate (NAA), choline and creatine in brain tissue.
Unlike MRS that does not use radioactive isotopes, PET and SPECT use
radionucleotides that are injected into the individual. The advantages of PET and SPECT techniques include their ability to provide information
15000
15000
Control NAA
10000
NAA 10000
Cr Cho
Cr Cho
5000
5000
0
0 5.0
4.0
15000
3.0
2.0
1.0
0.0
5.0 15000
Alcohol NAA
10000
4.0
3.0
Cr Cho
2.0
1.0
0.0
1.0
0.0
HIV+Alcohol NAA
10000 Cr Cho
5000
5000
0
0 5.0
Figure 2.5
HIV
4.0
3.0
2.0
1.0
0.0
5.0
4.0
3.0
2.0
fi
MRS image of a 34-year-old man with human immunodeciency virus (HIV)
infection and alcohol dependence. The brain images show the parietal-occipital cortical
fi
region (in white) sampled by MRS for metabolite quantication. The graphs below show the MRS spectra of various brain metabolites in people with HIV infection alone, alcoholism alone, co-morbid HIV infection and alcoholism, and control subjects with neither condition.
fi
fi
The peak representing the metaboliteN -acetylaspartate (NAA) shows a signi cant de cit in the HIV plus alcoholism group compared with the other groups. Cho, choline; Cr, creatine. (From Rosenbloom et al., 2010. © 2010 Alcohol Research: Current Reviews, USA.)
/
28
Human Neuroscience Approaches
on biochemistry. PET ligands can bind to molecules or neuroreceptors of interest such as glucose, dopamine, serotonin and opioid receptors. In this way, studies can quantify changes in glucose metabolism and receptors of interest. Both PET and SPECT detect
γ-rays
emitted from the
decay of the radioactive tracer and convert these into images. However, they differ in that PET has better sensitivity for detecting γ-rays (up to 1000 times), the radiotracers have a shorter half-life and there is higher
image quality relative to SPECT. In conclusion, the benefi t of biochem-
ical imaging is not only in informing mechanisms and potential biomarkers of disease states but also in establishing diagnoses and drug effects on neurotransmission and metabolism.
Limitations of Neuroimaging Research Our current understanding of brain changes associated with addiction is limited by the feasibility of conducting these types of studies in humans.
Speci fically, while
findings
from association studies suggest potential
mechanisms whereby addiction may relate to brain alterations, causality (i.e. that addiction led to brain changes or vice versa) can only be inferred rather than tested directly. In other words, are these brain alterations the chicken or the egg? The two possible scenarios to be considered are: 1) observed alterations are the direct result of exposure to substances; and 2) observed alterations existed prior to exposure to substances and are the risk factors that contribute to substance abuse and dependence. Without a prospective, longitudinal study that examines the brain before and after exposure to substances, the chicken or the egg debate may never be fully answered. However, there are various approaches that attempt to provide some information that could suggest causation. Each one makes an attempt to advance our understanding; however, the vast majority of these studies contradict each other due to differences in approach. For instance, genetic, family, sibling and twin studies attempt to disentangle brain changes that may be associated with genetic factors versus exposure to substance. Our own work in cannabis users found an interaction between cannabinoid receptor genes and cannabis use on amygdala volumes, suggesting that the effect of cannabis interacts with genetic predisposition in determining the size of the amygdala (Schacht 2012). However, a recent publication by Pagliaccioet
al.
et al.,
(2015) reported
no effect of cannabis use on amygdala volumes. Specifi cally, while the
authors reported smaller amygdala volumes in cannabis users compared with non-users, there was no difference in amygdala volume between cannabis users and their siblings. These
findings suggest that previously
/
Further Reading
29
reported brain volume differences between users and non-users may not be due to cannabis, but rather be a genetically pre-determined brain alteration that puts one at risk for cannabis use. In short, much work remains to be done in this area, but the existing literature points to a very complicated picture likely involving a recursive function and involving several moderating and mediating variables.
Summary Points • • • • •
Advancements in neuroscience techniques have paved the way for the understanding that addiction is a brain disorder. Neuroimaging techniques provide the ability to measure the electrophysiological, functional, structural and biochemical composition of the brain. Brain imaging techniques provide evidence for associations between brain structure and function and behavioral symptoms of addiction. Understanding neural mechanisms underlying behavioral symptoms of addiction is important in identifying potential targets for therapeutic interventions. Future research should focus on determining the exact relationship between changes in the brain and exposure to substances.
Review Questions • How have neuroimaging advancements informed our understanding of addiction? • How is EEG different from MEG? • What are the various techniques that can be used during MRI? • What can PET tell us that is different from MRI? • What chemicals can we measure using MRS? • What is the definition of “resting state ” in neuroimaging terms? • What are the limitations should we keep in mind when interpreting neuroimaging findings?
Further Reading Garrison, K. A. & Potenza, M. N. (2014). Neuroimaging and biomarkers in addiction treatment. Curr Psychiatry Rep, 16(12), 513. doi:10.1007/ s11920-014-0513-5
/
30
Human Neuroscience Approaches
Liu, P., Lu, H., Filbey, F. M.,et al. (2014). MRI assessment of cerebral oxygen metabolism in cocaine-addicted individuals: hypoactivity and dose dependence. NMR Biomed, 27(6), 726–732. doi:10.1002/nbm.3114 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), 62–78. doi:10.1111/nyas.12561 Mello, N. K. (1973). A review of methods to induce alcohol addiction in animals. Pharmacol Biochem Behav, 1(1), 89–101. Morgenstern, J., Naqvi, N. H., Debellis, R. & Breiter, H. C. (2013). The contributions of cognitive neuroscience and neuroimaging to understanding mechanisms of behavior change in addiction.Psychol Addict Behav, 27(2), 336–350. doi:10.1037/a0032435 Myers, K. M. & Carlezon, W. A., Jr. (2010). Extinction of drug- and withdrawal-paired cues in animal models: relevance to the treatment of addiction. Neurosci Biobehav Rev, 35(2), 285–302. doi:10.1016/j. neubiorev.2010.01.011 Nader, M. A., Czoty, P. W., Gould, R. W. & Riddick, N. V. (2008). Positron emission tomography imaging studies of dopamine receptors in primate models of addiction. Philos Trans R Soc Lond B Biol Sci, 363(1507), 3223–3232. doi:10.1098/rstb.2008.0092 Parvaz, M. A., Alia-Klein, N., Woicik, P. A., Volkow, N. D. & Goldstein, R. Z. (2011). Neuroimaging for drug addiction and related behaviors.Rev Neurosci , 22(6), 609–624. doi:10.1515/RNS.2011.055 Stapleton, J., West, R., Marsden, J. & Hall, W. (2012). Research methods and statistical techniques in addiction. Addiction, 107(10), 1724–1725. doi:10.1111/j.1360-0443.2012.03969.x Yalachkov, Y., Kaiser, J. & Naumer, M. J. (2012). Functional neuroimaging studies in addiction: multisensory drug stimuli and neural cue reactivity. Neurosci Biobehav Rev, 36(2), 825–835. doi:10.1016/j.neubiorev.2011.12.004
Spotlight 1 Love on the brain
Advancements in neuroimaging technology have demonstrated that the brain functions via well-orchestrated, interconnected networks of brain regions. These intrinsically linked brain networks simultaneously activate when we are “at rest ” or not performing a specific task. There is growing research in how these “resting-state ” networks may be related to individual factors.
/
Spotlight 1
Figure S2.1
31
What does 45 years of love look like in the brain?
Research has widely accepted that feelings of love are rewarding and are therefore also subserved by the reward network. It is therefore expected that as our feelings of love changes, so do the brain regions that underlie these processes (Figure S2.1). Recently, a group of researchers examined how changes in feelings of love may in
fluence resting-state networks. They found that functional connectivity
(i.e. how temporally synchronized neural responses are between regions) within the reward, motivation and emotion regulation network (dorsal anterior cingulate cortex, insula, caudate, amygdala and nucleus accumbens) was greater in a group of participants who self-reported being
“in
”
love
com-
pared with those who were not in love (ended romantic relationship recently/ never been in love).
/
32
Human Neuroscience Approaches
Spotlight 2
Can we use neuroimaging to predict future behavior? Imagine
if we
could
predict the
later development of mental
disorders,
including addiction, in children (Figure S2.2). Can information gathered today be used to support the individual in order to prevent (or delay) the potential onset of mental disorders? Current research is capitalizing on neuroimaging techniques in order to make the ability to predict and prevent disorders a reality. Recently, the National Institutes of Health (NIH) funded a historic study called the Adolescent Brain Cognitive Development or ABCD Study (https://abcdstudy.org/) that has the ultimate goal of using advanced brain imaging to map brain development in order to
find predictors of mental health issues and addiction. –
This nationwide study on 10,000 9 10-year-olds will collect information on mental health, addiction, education, culture, environment and genetics to determine how these factors may be associated with how the brain develops. Children from this study will be tested yearly over a 10-year period to identify risk factors and protective factors, mental health issues and addiction. The ability to predict will ultimately lead to better outcomes for our children.
Figure S2.2
fi
Associating the brain with behavior began with the eld of phrenology.
From www.pexels.com/photo/photo-of-head-bust-print-artwork-724994/.
References
Bloch, F., Hansen, W. W. & Packard, M. (1946). Nuclear induction. Phys Rev , 69(3–4), 127. doi:10.1103/PhysRev.69.127 Cohen, D. & Cuffin, B. N. (1991). EEG versus MEG localization accuracy: theory and experiment. Brain Topogr, 4(2), 95–103. doi:10.1007/ BF01132766 /
References
33
Huettel, S. A., Song, A. W. & McCarthy, G. (2008). , 2nd edn. Sunderland, MA: Sinauer Associates. Ogawa, S., Lee, T. M., Kay, A. R. & Tank, D. W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. , 87(24): 9868– 9872. doi:9868–9872. 10.1073/ pnas.87.24.9868 Pagliaccio, D., Barch, D. M., Bogdan, R., (2015). Shared predisposition in the association between cannabis use and subcortical brain structure. , 72(10), 994– 1001. doi:10.1001/ jamapsychiatry.2015.1054 Purcell, E. M., Torrey, H. C. & Pound, R. V. (1946). Resonance absorption by nuclear moments in a solid. , 69(1–2), 37–38. doi:10.1103/ PhysRev.69.37 Rosenbloom, M. J., Sullivan, E. V. & Pfefferbaum, A. (2010). Focus on the brain: HIV infection and alcoholism: comorbidity effects on brain structure and function. , 33(3), 247–257. Schacht, J. P., Hutchison, K. E. & Filbey, F. M. (2012). Associations between cannabinoid receptor-1 (CNR1) variation and hippocampus and amygdala volumes in heavy cannabis users. , 37(11), 2368–2376. doi:10.1038/ npp.2012.92 Whitford, T. J., Savadjiev, P., Kubicki, M., (2011). Fiber geometry in the corpus callosum in schizophrenia: evidence for transcallosal misconnection. , 132(1), 69–74. doi:10.1016/ j.schres.2011.07.010 Functional Magnetic
Resonance Imaging
Proc Natl Acad Sci U S A
et al.
JAMA Psychiatry
Phys Rev
Alcohol Res Health
Neuropsychopharmacology
et al.
Schizophrenia Res
/
C H A PT E R T H R E E Brain-Behavior Theories of Addiction
Learning Objectives
• •
Be able to identify different brain-based models of addiction.
“
” and “liking” in
Be able to explain the difference between wanting
the
context of incentive sensitization.
• •
Be able to discuss the process of opponent processes in addiction.
•
Be able to explain the mechanisms behind the cue-elicited craving model.
Be able to describe the role of the prefrontal cortex in the behavioral manifestations of addiction as presented by the iRISA syndrome model.
Introduction
The National Institute of Drug Abuse (NIDA) in the USA defines drug addiction as a
“chronic,
often relapsing
brain
disease.” In this vein,
multiple models of addiction have been proposed to explain the link between brain mechanisms and observable behavioral symptoms of addiction. These conceptual or theoretical models have advanced neuroscience research in addiction by providing a working framework that can be tested and elaborated upon. This chapter will describe some of the predominant models including the incentive-sensitization theory, the allostasis theory, the impaired response inhibition and salience attribution (iRISA) syndrome model and the cue-elicited craving model. Initial theories on substance use assumed that the pleasurable effects of the drug instigate drug consumption, and that dependence develops out of a persistent drive to obtain these positive effects. These initial theories, however, failed to incorporate other aspects that occur during the progression of the disorder, such as tolerance and withdrawal. The idea of withdrawal during addiction suggests a shift in the progression of the disorder from one that is initially driven by positive incentives to one that is negatively reinforced, such as to avoid the withdrawal symptoms following cessation of drug use. Such a shift would suggest neural
/
The Incentive-Sensitization Theory
35
adaptations during the progression toward addiction. In 1993, Robinson and Berridge proposed an
“ incentive-sensitization”
model in which
drugs of abuse cause alterations in a number of neural systems, specifically in areas that control motivation and reward. Koob and Le Moal
(1997, 2008) proposed a neurobiological model stemming from motivation theories that described a pathological shift in the
“ hedonic
set
point ” resulting in a loss of control over drug intake. Prior to the 21st century, most of the neurobiological models focused largely on subcortical processes that do not capture behavioral, cognitive and emotional factors that are also crucial to the development of addiction. Addressing these gaps, emerging theories integrate cortical aspects of drug-induced neuroadaptations, and provide testable hypotheses and contribute unique perspectives of addiction.
The Incentive-Sensitization Theory
Developed by Robinson and Berridge (1993), this is the first neuroadaptationist model, which suggests that neural changes that occur during repeated substance use impact neural substrates underlying reinforcement and motivation. According to this theory, addiction develops from hypersensitization to the drug ’s effects in mesocorticolimbic regions, which leads to the drug ’s increased incentive salience. Incentive salience is a reward-based motivational state driven by a strong, subconscious association between the drug and the feelings of reward, thereby resulting in pathological motivation for drugs (compulsive opposed to
“liking ” ).
“ wanting ”
as
This model proposes that incentive sensitization of
drug stimuli stems from changes in memory and learning systems that direct motivation to specifi c and appropriate stimuli. Specifically, asso-
ciative learning processes modulate neural sensitization that manifests as behavioral sensitivity in conditioned (previously learned) environments (Anagnostaras et
al.,
2002).
Dopamine-related pathways are implicated in the “ wanting ” (dopamine and glutamate in corticolimbic regions) aspect of this model, which is different from “liking ” (dopaminergic, GABAergic, endocannabinoid and opioid signaling associated with the dorsal striatum). In this manner, drug acquisition
“ short
circuits” the normal relationship between behav-
ior and its resulting hedonic value that would otherwise allow for the encoding of important survival information, such as food consumption and sex. Although limited, potential mechanisms for sensitization have been demonstrated in both pre-clinical and clinical studies. In sensitized
animals, increased firing is frequently observed in mesolimbic neurons.
/
36
Brain-Behavior Theories of Addiction
Similar findings have been11 observed in humans using positron emission tomography (PET) and [ C]raclopride. Boileau (2006) reported greater psychomotor response and increased dopamine release (i.e. a 11 greater reduction in [ C]raclopride binding) in the ventral striatum in amphetamine-sensitized men and this effect was still present at the 1-year follow-up. et al.
The Allostatic Model: Dysregulation in Homeostasis
This model was developed to explain the motivational mechanisms that drive excessive drug seeking and loss of control over drug use, and is founded largely on the opponent-process motivation theory of emotions proposed by Solomon and Corbit (1974). The opponent-process theory states that the expression of one emotion (e.g. pleasure) suppresses the opposite emotion (e.g. pain). Speci fically, in response to a stimulus, the initial response is of heightened arousal, which is short lived and intense. This positive response is followed by a gradual dip toward the opposite, negative affective response that decays back into normal equilibrium or homeostasis, i.e. a stable state of moderate arousal. Solomon and Corbit (1974) referred to the negative affect component as the opponent process. From an addiction perspective, the opponent-process theory of motivation suggests that the initial pleasurable feelings (euphoria, relief from anxiety) from drug use are followed by the opponent process of negative emotional experiences, such as withdrawal symptoms (e.g. headache, nausea). In other words, the acute hedonic state produced by drug use is opposed by the brain s mechanisms to return to homeostasis. This process is complicated by the fact that, with repeated drug use, tolerance develops whereby greater amounts of the drug are needed in order to achieve the same hedonic state. Interestingly, however, according to the opponent-process theory, tolerance is not the result of habituation to the positive effects but rather a sensitization to the negative effects. Thus, the opponent-process theory suggests that repeated drug use leads to larger effects of the opponent process, while the hedonic state becomes smaller. Continued drug use is therefore motivated by the need to avoid these negative states (see Chapter 6). Koob & Le Moal (1997) extended this model to incorporate the neurobiological adaptations that underlie this dysregulation in homeostasis (Figure 3.1). They described three stages of addiction: 1) binge intoxication, followed by 2) the withdrawal/negative affect, and then by 3) preoccupation/anticipation that would be likely to resume the cycle. ’
/
The Allostatic Model: Dysregulation in Homeostasis
37
cu p ati on eoc / Pr i cip a ti on an t Persistent desire
Preoccupation with
Larger amounts taken
obtaining persistent physical/
than expected
psychological problems
l/
c
no it
ac ix o tn n iB i
eg
n ge i ta ev t iW h a ff rd a e w a
ADDICTION
t
Tolerance withdrawal compromised social, occupational or recreational activities
Diagram describing the addiction cycle – preoccupation/anticipation (“craving”), binge/intoxication and withdrawal/negative affect – with the different criteria for substance dependence incorporated from theDiagnostic and Statistical Manual of Mental Disorders, 4th edn.
Figure 3.1
(Adapted from Koob & Le Moal, 2008.)
Neurobiologically, the sensation of reward during the
first phase
occurs
as a result of excitatory dopaminergic signaling in the nucleus accumbens. This intense pleasure is encoded as a highly salient and rewarding memory. However, while this positive memory may encourage substance seeking, on a cellular level this heightened reward signaling refl ects two
states of imbalance: within systems, whereby receptors triggered by specifi c substances are downregulated to maintain homeostasis in the presence of
the
substance, and
between systems, which
reflects
heightened connectivity between reward regions and decreased connectivity from inhibitory regions such as the prefrontal cortex (PFC) to reward regions. The second stage, withdrawal, is characterized by downregulation of the relevant receptor in an effort to maintain homeostasis in the presence of the substance (e.g. dopamine in the case of cocaine, opioid receptors in the case of heroin, GABA receptors in the case of alcohol). Additionally, in this paradigm, the experience of tolerance
refl ects the general decrease in excitatory dopaminergic signaling in the substance-adapted state. However, without reward circuitry is
“underwhelmed,”
the substance, the
manifesting as symptoms of
/
38
Brain-Behavior Theories of Addiction
negative affect, physical discomfort and dysphoria. This perpetuates until the individual alleviates this negative state with substance use, which initiates both a new high and a subsequent low. The third stage consists of preoccupation, anticipation or craving. This is characterized by the individual ’s drive to avoid discomfort, whereby substances are used in an effort to
“ feel
normal” and to prevent withdrawal symptoms
(versus feeling pleasure). This state reflects long-term changes in neural networks that place individuals at high risk for relapse after a period of cessation.
The Impaired Response Inhibition and Salience Attribution (iRISA) Syndrome Model
In 2002, Goldstein and Volkow proposed one of the
first
models that
integrate the behavioral, cognitive and emotional features in existing models of addiction. Their model, the iRISA syndrome model, is based predominantly on neuroimaging
findings
in cocaine-using populations
and highlights the important role of the PFC neurocircuitry in moderating clusters of interconnected behaviors (Figure 3.2): drug intoxication,
drug
craving,
compulsive
drug
administration
and
drug
withdrawal. The specific PFC regions include dorsal PFC subregions
(the dorsolateral PFC, dorsal anterior cingulate cortex and inferior frontal gyrus) that are involved in higher-order control or
“ cold ”
pro-
cesses. Ventral PFC subregions (the medial orbitofrontal cortex, ventromedial PFC
and
rostroventral anterior
cingulate
cortex) are
involved in more automatic, emotion-related processes or
“hot ”
processes. The iRISA model proposes that drug intoxication, which is traditionally viewed as the result of neural changes in subcortical regions, is also accompanied by increased dopamine levels in frontal regions as well as activation in the PFC and anterior cingulate gyrus. Furthermore, the patterns of activation are associated with the subjective perception of intoxication, the reinforcing effects of the drug or enhanced mood. Drug craving
–
cesses
is also suggested to be associated with activation in the orbito-
–
a conditioned response to drugs that involves memory pro-
frontal and anterior cingulate cortices. Greater activation in these regions has been demonstrated across different substance-abusing populations and via different drug cue modalities (e.g. visual, tactile, gustatory). Similar to intoxication, activation in these prefrontal regions also correlates with self-reports of craving. Compulsive drug administration that occurs during the shift from the hedonic state to the negative state
/
The iRISA Syndrome Model
a) Healthy state
39
b) Craving
c) Intoxication
and withdrawal
and bingeing
Dorsal PFC (“cold” functions)
Ventral PFC (“hot” functions)
STOP!
Drug-related functions
STOP?
GO!
Non-drug-related functions
The iRISA model depicting the interactions between the PFC and subcortical regions in drug users and non-users. Drug-related neuropsychological functions (e.g. incentive salience, drug wanting, attention bias and drug seeking) that are regulated by these subregions are represented by darker shades and non-drug-related functions (e.g. sustained effort) are represented by lighter shades. Thick arrows depict increases in input and the sizes of circles demonstrate the balance between drug- and non-drug-related functions. Figure 3.2
(Adapted from Goldstein & Volkow, 2011.)
(similar to the opponent process described earlier) is associated with loss of control that is subserved by prefrontal control regions including the thalamo-orbitofrontal circuitry and the anterior cingulate gyrus. Finally, drug withdrawal symptoms are thought to be the result of disruptions in frontal cortical circuits that underlie the release of neurotransmitters such as dopamine, serotonin and corticotropin-releasing factors. Whereas PFC activation underlies craving, withdrawal is suggested to be due to deactivation of the PFC. An elaboration of the iRISA model proposed in 2011 (Goldstein & Volkow, 2011) detailed the interactions between the PFC and subcortical regions during behaviors related to addiction. Relative to a healthy, non-drug-abusing state, PFC connectivity creates a conflict during craving and withdrawal states such that drug-related cognitive functions, emotions and behaviors predominate over the non-drug-related
/
40
Brain-Behavior Theories of Addiction
functions. These decreased non-drug-related functions (e.g. attention) lead to reduced self-control, anhedonia, stress reactivity and anxiety. During intoxication and bingeing, higher-order non-drug-related cognitive functions are suppressed by increased input from the regions that regulate drug-related, “hot ” functions, i.e. there is decreased input from higher-order cognitive control areas, and the “hot” regions come to dominate the higher-order cognitive input. Thus, attention narrows to focus on drug-related cues over all other reinforcers, impulsivity increases and basic emotions – such as fear, anger or love – are unrestrained. The result is that automatic, stimulus-driven behaviors, such as compulsive drug consumption, predominate. The Cue-Elicited Craving Model
As characterized by Kalivas and Volkow (2005), craving plays a key role in maintaining addiction. Concretely, this team found that substancerelated cues induce the same neurochemical and behavioral responses as the substance itself. Empirically, neuroimaging studies indicate that craving for these substances occurs within the reward circuitry (Filbey & DeWitt, 2012; Filbey , 2009, 2012; Hommer, 1999; Volkow , 2002). Specifically, the cue or conditioned stimulus may begin to gain salience within the anterior cingulate (motivation) and the amygdala (emotion). Interoceptive and memory processes may then catalyze activation within the insula and hippocampus, respectively. This subsequently triggers dopamine release from the ventral tegmental area (VTA) to the basal ganglia and cortex, which encodes the learned association between the substance and its salient environmental cues (Filbey & DeWitt, 2012). Finally, the cue-elicited connection is then observed in relevant mesocorticolimbic pathways (e.g. Filbey , 2008). et al.
et al.
et
al.
The Future of Brain-Behavior Theories of Addiction
As with all conceptual models, validation of the theories behind the models is an important step. Consequently, current scientific research is focused largely on these important scientific goals. Challenging the tenet of these models is important in order to continue to make scientific discoveries toward understanding the underpinnings of addiction. As with most disorders that affect behavior, the picture is complex and consists of several factors beyond those that involve the brain. For instance, it is well known that individual differences significantly
/
The Future of Brain-Behavior Theories of Addiction
41
Daily smoking Abstainers Lifetime risky drinkers Single occasion risk (monthly) Any illicit Cannabis Lowest SES Ecstasy Highest SES Meth/amphetamines Cocaine 0
5
10
15
20
25
30
35
Percentage
Figure 3.3 Daily smoking, risky alcohol consumption and illicit drug use by people with the lowest and highest socioeconomic status (SES), in Australians aged 14 years or older, in 2013. (Adapted from Australian Institute of Health and Welfare, 2014.)
influence susceptibility to addiction. This is clearly highlighted by the fact that, although drugs induce changes in the brain, only a small fraction of substance users develop an addiction (~10%). Those who become addicted typically have co-occurring disorders such as mood disorders. A study by Ketcherside and Filbey (2015) addressed this issue by testing the relationship between perceived stress, mood (i.e. depression and anxiety) and problems related to cannabis use. They found that having symptoms of depression and anxiety mediated the relationship between perceived stress and problems with cannabis use. In other words, the mechanism by which the experience of stress then leads to problems with cannabis use is through having symptoms related to depression or anxiety. The implication of this finding is that treatment focused on depression and anxiety symptoms in those with cannabis use problems may prove to be effective, as it is through this pathway that cannabis use problems develop. Beyond biological or psychological factors, it is also important to consider environmental factors. Environmental factors, such as socioeconomic status or peer use, have been shown to influence the development of drug addiction (Figure 3.3). To conclude, taking these non-neurobiological factors into consideration in an evidence-based approach would strengthen current models of addiction that would lead to identifying and, therefore, tackling, these determinants that lead to drug-related problems in the first place. /
42
Brain-Behavior Theories of Addiction
Summary Points
• • • • •
Neurobiological models have evolved to explain the neural adaptations that occur during the progression of addiction from drug intoxication to compulsive drug seeking. The incentive-sensitization model explains behaviors related to the transition from “liking” to “wanting” a drug. The allostatic model proposes a framework that takes into account the opponent processes of positive and negative states in addiction. The iRISA syndrome model integrates higher-order functions in the PFC toward a better understanding of how the complicated behavioral, cognitive and emotional landscape of addiction is modulated by the PFC. The cue-elicited craving model focuses on the heterogeneity in cognitive processes that underlie continued drug seeking.
Review Questions
How do the different models of addiction differ? What is the difference between “wanting” and “liking” a drug? What is the primary focus of the allostasis model and what behavioral theory is it based on? • What brain region and associated process does the iRISA model integrate into its framework? • What different cognitive processes does the cue-elicited craving model incorporate?
• • •
Further Reading
Bickel, W. K., Mellis, A. M., Snider, S. E.,et al. (2018). 21st century neurobehavioral theories of decision making in addiction: review and evaluation. Pharmacol Biochem Behav, 164, 4–21. doi:10.1016/j.pbb.2017.09.009 Carey, R. J., Carrera, M. P. & Damianopoulos, E. N. (2014). A new proposal for drug conditioning with implications for drug addiction: the Pavlovian twostep from delay to trace conditioning. Behav Brain Res, 275, 150–156. doi:10.1016/j.bbr.2014.08.053 Dayan, P. (2009). Dopamine, reinforcement learning, and addiction.Pharmacopsychiatry, 42, Suppl. 1, S56 –S65. doi:10.1055/s-0028-1124107
/
Spotlight
43
DeWitt, S. J., Ketcherside, A., McQueeny, T. M., Dunlop, J. P. & Filbey, F. M. (2015). The hyper-sentient addict: an exteroception model of addiction. , 41(5), 374 381. doi:10.3109/ 00952990.2015.1049701 Di Chiara, G., Bassareo, V., Fenu, S., (2004). Dopamine and drug addiction: the nucleus accumbens shell connection. , 47, Suppl. 1, 227 241. doi:10.1016/j.neuropharm.2004.06.032 Garcia Pardo, M. P., Roger Sanchez, C., de la Rubia Orti, J. E. & Aguilar Calpe, M. A. (2017). Animal models of drug addiction. , 29(4), 278 292. doi:10.20882/adicciones.862 Lewis, M. D. (2011). Dopamine and the neural now : essay and review of addiction: a disorder of choice. , 6(2), 150 155. doi:10.1177/1745691611400235 O Brien, C. P., Childress, A. R., McLellan, A. T. & Ehrman, R. (1992). A learning model of addiction. , 70, 157 177. Robinson, T. E. & Berridge, K. C. (1993). The neural basis of drug craving: an incentive-sensitization theory of addiction. , 18(3), 247 291. doi:10.1016/0165-0173(93)90013-P Weiss, F. (2010). Advances in animal models of relapse for addiction research. In C. M. Kuhn & G. F. Koob, eds., , 2nd edn. Boca Raton, FL: CRC Press, pp. 126. Am
J
Drug
Alcohol
Abuse
–
et al.
Neuropharmacology
–
Adicciones
–
“
Perspect
”
Psychol
Sci
–
’
Res Publ Assoc Res Nerv Ment Dis
–
Brain Res Brain Res Rev
–
Advances in
Addiction
the Neuroscience of –
Spotlight Is addiction a moral failing?
An alarming report from the Centers for Disease Control and Prevention (CDC) in 2016 stated that ninety-one Americans die from opioid over dose every day. This figure is higher than deaths from car accidents or gun homicides. In opioid addiction, which has now reached epidemic proportions (i.e. six out of ten drug overdose deaths are due to opioids), 80% developed their addiction after being prescribed opioid medication for pain (Figure S3.1). In other words, in these cases, addiction began with a medical prescription. In turn, the opioid epidemic in the USA has led to muchfinger pointing, with blame put on pharmaceutical companies for creating and aggressively marketing these highly addictive drugs and on physicians who have heavily prescribed the drugs (perhaps not knowing the high risk of addiction). However, the public health response to the opioid epidemic is unlike that of past drug epidemics. Specifically, treatment rather than criminal justice options are provided to those who have opioid addiction. This humane
/
44
Brain-Behavior Theories of Addiction
Figure S3.1 The modern opioid epidemic. (Adapted from NC Department of Health and Human Services, 2016.)
/
References
45
approach to addiction as a public health concern rather than a criminal issue is the approach taken in Poland under their“treat rather than punish” principle. Poland’s National Program for Counteracting Drug Addiction (2011 –2016) placed greater emphasis on improving the quality of drug-prevention programs and the quality of life of those undergoing treatment, harm reduction and social reintegration measures. The response to the opioid epidemic in the USA can hopefully lead a change in how addiction is addressed, i.e. by making sure that those with an addiction have access to effective treatment. As important, it is critical that we remove the stigma of addiction and accept that addiction can happen to anyone.
References
Australian Institute of Health and Welfare. (2014). National Drug Strategy Household Survey detailed report: 2013. Drug statistics series no. 28. Canberra, Australia: Australian Institute of Health and Welfare. Anagnostaras, S. G., Schallert, T. & Robinson, T. E. (2002). Memory processes governing amphetamine-induced psychomotor sensitization. , 26(6), 703–715. doi:10.1016/S0893-133X (01)00402-X Boileau, I., Dagher, A., Leyton, M., (2006). Modeling sensitization to stimulants in humans: an [11C]raclopride/positron emission tomography study in healthy men. , 63(12), 1386 –1395. doi:10.1001/archpsyc.63.12.1386 Filbey, F. M., Claus, E., Audette, A. R., (2008). Exposure to the taste of alcohol elicits activation of the mesocorticolimbic neurocircuitry. , 33(6), 1391– 1401. doi:10.1038/sj. npp.1301513 Filbey, F. M., Claus, E. D., Morgan, M., Forester, G. R. & Hutchison, K. (2012). Dopaminergic genes modulate response inhibition in alcohol abusing adults. , 17(6), 1046–1056. doi:10.1111/j.13691600.2011.00328.x Filbey, F. M. & DeWitt, S. J. (2012). Cannabis cue-elicited craving and the reward neurocircuitry. , 38(1), 30– 35. doi:10.1016/j.pnpbp.2011.11.001 Filbey, F. M., Schacht, J. P., Myers, U. S., Chavez, R. S. & Hutchison, K. E. (2009). Marijuana craving in the brain. , 106(31), 13016 –13021. doi:10.1073/pnas.0903863106 Goldstein, R. Z. & Volkow, N. D. (2002). Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of Neuropsychopharmacology
et al.
Arch Gen Psychiatry
et al.
Neuropsychopharmacology
Addict Biol
Prog Neuropsychopharmacol Biol Psychiatry
Proc Natl Acad Sci U S A
/
46
Brain-Behavior Theories of Addiction
the frontal cortex. Am J Psychiatry, 159(10), 1642–1652. doi:10.1176/ appi.ajp.159.10.1642 (2011). Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat Rev Neurosci, 12(11), 652–669. doi:10.1038/nrn3119 Hommer, D. W. (1999). Functional imaging of craving. Alcohol Res Health , 23(3), 187– 196. Kalivas, P. W. & Volkow, N. D. (2005). The neural basis of addiction: a pathology of motivation and choice. Am J Psychiatry, 162(8), 1403 –1413. doi:10.1176/appi.ajp.162.8.1403 Ketcherside, A. & Filbey, F. M. (2015). Mediating processes between stress and problematic marijuana use. Addict Behav , 45, 113–118. doi:10.1016/j.addbeh.2015.01.015 Koob, G. F. & Le Moal, M. (1997). Drug abuse: hedonic homeostatic dysregulation. Science, 278(5335), 52 –58. (2008). Neurobiological mechanisms for opponent motivational processes in addiction. Philos Trans R Soc Lond B Biol Sci, 363(1507), 3113 –3123. doi:10.1098/rstb.2008.0094 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), 247– 291. NC Department of Health and Human Services (2016). Jan. 19 task force meeting documents. Available at: www.ncdhhs.gov/document/jan-19task-force-meeting-documents (accessed August 1, 2017). Solomon, R. L. & Corbit, J. D. (1974). An opponent-process theory of motivation. I. Temporal dynamics of affect. Psychol Rev, 81(2), 119 –145. doi:10.1037/h0036128 Volkow, N. D., Fowler, J. S., Wang, G. J. & Goldstein, R. Z. (2002). Role of dopamine, the frontal cortex and memory circuits in drug addiction: insight from imaging studies. Neurobiol Learn Mem, 78(3), 610–624. doi:10.1006/nlme.2002.4099.
/
C H A P T ER F O U R From the Motivation to Initiate Drug Use to Recreational Drug Use: Reward and Motivational Systems
Learning Objectives
• • • • •
Be able to describe the regions within the mesocorticolimbic pathway. Be able to explain the role of dopamine in reward and motivation. Be able to identify the
final common
pathway.
fi
Be able to understand the notion of the reward de ciency syndrome. Be able to discuss the role of memory systems in reward and motivation.
Introduction
As introduced in Chapter 3, the development of addiction hinges on increased (drug “wanting”) placed on the substance of abuse. In other words, compulsive drug-taking behavior occurs at the expense of other activities, whether recreational or occupational. The acquisition of greater incentive salience for drugs (versus other rewarding stimuli) suggests alterations in reward-motivation systems in the brain. In the 1950s, two Canadian physiologists implanted electrodes in specific brain regions of rats (Olds & Milner, 1954). The rats were then given the opportunity to stimulate these brain regions, later termed “reward centers,” by pressing a button. Once they started pressing the stimulation button, they stopped doing anything else, which was the first hint of a strong behavioral reinforcing mechanism (Figure 4.1; see also Figure 2.1). Since then, researchers have shown that this reward center of the brain – the nucleus accumbens – is also involved in drug addiction. Just showing people drug-related pictures led to strong activation in parts of the brain related to craving for the drugs (Filbey , 2011). This chapter will describe the first ecological stage of the progression of addiction: initial motivation to use drugs. This chapter will explain the cliché that “drugs hijack the brain” by discussing the various neuroimaging studies that demonstrate this phenomenon. incentive salience
et al.
/
48
Motivation to Initiate Drug Use to Recreational Drug Use
(a)
Speaker Signal lights Pellet dispenser Lever Dispenser tube Food cup Electric grid
To shock generator Suspending elastic band
(b)
Lever
Lever press activates stimulator Stimulator Figure 4.1
Lever press (a) and intracranial self-stimulation (ICSS) (b) are two examples of
experimental paradigms used to study reward and motivation in animals. (a) Animals learn to press a lever to receive rewards (e.g. food, water, sexual mates, drugs). (b) In ICSS, animals receive electrical stimulation directly into reward areas of the brain, without the in
fluence
fi
of speci c incentives. These animal paradigms have implicated a role of the
mesolimbic dopamine system and its connections with motivational systems.
Reward and Motivational Systems Guide the Direction of Behavior The reward and motivational systems contribute toward goal-directed action,
allowing
environmental
organisms
events.
to
These
encode values
the
relative
provide
the
values basis
fi
of
speci c
for
choice,
/
Reward and Motivational Systems
49
Orbitofrontal cortex Nucleus accumbens Mesolimbic dopamine pathway Mesocortical dopamine pathway
Figure 4.2
Ventral tegmental area (VTA)
The brain’s reward system lies in the mesocorticolimbic pathway, which is
regulated by dopamine. This pathway has dopamine cell bodies in the ventral tegmental area and projects to the nucleus accumbens and areas in the prefrontal cortex, particularly the orbitofrontal cortex.
allowing organisms to select actions based on prior knowledge of the consequences of an action, as well as the value of those consequences (see Spotlight for an example of research that examined these systems to identify the risk for addiction). Reward (i.e. feelings of pleasure) and motivation mechanisms that guide directed behavior include anticipation, stimulus evaluation and prediction of reward. Reward and motivation processes occur within a neural circuitry encompassing prefrontal and striatal areas. The key structures within this reward-motivation circuitry are the anterior cingulate cortex, the orbital prefrontal cortex (PFC), the ventral striatum and midbrain dopamine neurons (Figure 4.2). Together, the connections among these areas form a complex neural network that underlies incentive-based or reinforcement learning that leads to goal-directed behaviors and habit formation. The nucleus accumbens encodes the relationship between stimuli and behavioral responses. As such, it is the key region through which salient stimuli exert their reinforcing actions. Evidence for this exists in studies demonstrating increases in dopamine levels in the nucleus accumbens during rewarding behaviors such as eating, drinking and sexual activity. The nucleus accumbens contains two functionally distinct
/
50
Motivation to Initiate Drug Use to Recreational Drug Use
Saline
Amphetamine
Figure 4.3 Camera lucida drawings of medium spiny neurons in the shell (top) and core (bottom) regions of the nucleus accumbens of saline- and amphetamine-pretreated rats. These cells were selected for illustration because their values were closest to the group average of any cells studied. The drawing to the right of each cell represents a dendritic segment used to calculate spine density.
(From Robinson & Kolb, 1997, adapted from Paxinos & Watson, 1997. © 1997 Society for Neuroscience, USA.)
subregions – the shell and the core. The shell is interconnected with the hypothalamus and ventral tegmental area (VTA), while the core has innervations with the anterior cingulate and orbitofrontal cortex. An interesting finding in animal studies was that different subsets of neurons in the nucleus accumbens respond differentially to encoding “natural” rewards such as water versus cocaine (Carelli , 2000). Given the limitations of current techniques for visualization of in vivo responses and the small size of the nucleus accumbens, this finding has not yet been tested in humans. Studies have also demonstrated dendritic changes in the nucleus accumbens following repeated activation that may reflect learning (Figure 4.3) (Robinson & Kolb, 1997). It is therefore likely that these morphological changes in addition to other reported intracellular changes within the nucleus accumbens play a role in the development of addiction. The reciprocal connections between the shell of the nucleus accumbens and the VTA are thought to be important in modulating motivational salience and reinforcement learning. Specifically, when a salient et al.
/
Predicting Rewards
51
event occurs, projections from the VTA release dopamine, which triggers a behavioral response to the motivational event. This process leads to cellular changes that establish learned associations for highly desirable stimuli. Over time, repeated exposure to the same motivational event no longer leads to the same level of dopamine released in response to the event; however, the conditioned stimuli predicting the event continue to trigger the release of dopamine (see Chapter 7 for further details). Unlike the shell of the nucleus accumbens, its core has projections to PFC areas including the anterior cingulate and orbitofrontal cortex. These connections
underlie
the motivation for rewarding stimuli,
thereby contributing to response selection and adaptive learning. Studies have illustrated that the magnitude of change in metabolic activity in both the orbitofrontal and anterior cingulate cortices correlates with the
intensity of the self-reported cue-induced craving. Drug specifi city of increased prefrontal activity is illustrated by studies demonstrating reduced prefrontal activity during biologically relevant rewards, such as sexually evocative cues and also during decision-making tasks that typically elicit a prefrontal response (Garavan et
al.,
2000). Thus, dysregula-
tion in the anterior cingulate and orbitofrontal cortex is not only critical for cue-elicited motivation but also in decision making (i.e. cognitive control) over drug seeking (discussed in Chapter 8).
Predicting Rewards: Evidence for the Primary Role of Dopamine Based on the circuitry described above, it can be surmised that dopamine plays a key role in reward-motivation processes. Given the brain regions involved during these processes, dopamine can be seen as serving two functions in the circuit: 1) to alert the organism to novel salient stimuli, and thereby promote neuroplasticity (learning); and 2) to alert the organism to an upcoming familiar motivationally relevant event, on the basis of learned associations made with environmental stimuli predicting the event. This is how dopamine has become known as the
“ pleasure
molecule.” Early evidence of dopamine ’s role came from cellular recording studies in animals. These studies demonstrated that dopamine neurons
fi re
when an unexpected reward is anticipated but not during
the reward itself (Figure 4.4). Dopamine neurons were also inhibited during expected rewards. Based on these studies, it was suggested that dopamine signals aid in learning motivated behavior. In other words, dopamine draws our attention to unexpected positive outcomes for the purpose of promoting rewarded behaviors.
/
52
Motivation to Initiate Drug Use to Recreational Drug Use
Dopamine transporter blocked by cocaine Dopamine
Transmitting neuron
Dopamine receptor
Intensity of effect Receiving neuron Figure 4.4
Cocaine
The release of dopamine signals reward. This illustrates mechanisms by which
dopamine is released following exposure to cocaine. Cocaine blocks dopamine transporters. Thus, reuptake of dopamine is inhibited, leading to increased levels of dopamine in the synaptic cleft.
Human research has also provided evidence for the important role of dopamine during reward and motivation. These studies showed that large and fast increases in dopamine levels that are longer in duration and more intense than those induced by dopamine cell firing to other salient events underlie the development of drug addiction. Higher and longer dopamine release potentiates the threshold required for motivational events to activate dopamine neurons, thereby requiring more potent stimuli to reach the prior levels of dopaminergic signaling. Decreases in dopamine release and in dopamine D2 receptors in the striatum also occur following drug use. For example, positron emission tomography (PET) with the D2/3 dopamine receptor ligand antagonist 11 [ C]raclopride in combination with methylphenidate (a dopamine reuptake inhibitor, the same as cocaine) showed that methamphetamine abusers had 24% lower levels of dopamine transporters in the striatum compared with people who never used the drug (Volkow , 2001). These reductions in striatal extracellular dopamine levels are associated with reduced activity of the orbitofrontal cortex and the cingulate gyrus. et al.
/
Final Common Pathway: All Drugs Lead to One
53
11
Interestingly, PET [ C]raclopride studies have also shown that, in response to drug-related stimuli (drug cues), these hypoactive prefrontal regions become hyperactive proportionally to the subjective desire for the drug or craving, and may be the mechanism by which “drugs hijack
the brain” (discussed further in Chapter 7). Specifically, dopamine release was related to increased motivation, despite the absence of a reward (Volkow
et al.,
2001).
So far, we have discussed how dopamine is critical for acute reward and reinforced learning that leads to addiction. Although, in general, dysfunction of the dopaminergic circuitry may be the neural substrate for the development and maintenance of addiction, an important note is that endstage addiction is primarily due to neural adaptations in glutamatergic projections from the PFC to the nucleus accumbens. Alterations in excitatory input lead to a reduction in the capacity of the PFC to initiate behaviors in response to natural rewards and to provide executive control over drug seeking (lack of control or impulsivity is discussed further in Chapter 8). The hyper-responsivity of the PFC to rewarding stimuli leads to increased glutamatergic input in the nucleus accumbens, where excitatory synapses have a reduced capacity to regulate neurotransmission.
Final Common Pathway: All Drugs Lead to One As discussed in the previous section, dopamine is implicated in the initiation and development of drug and alcohol addiction. So how is this possible given the varied neuropharmacological effects of different drugs and alcohol? While cocaine and methamphetamines target dopamine receptors directly, other substances disrupt different parts of the rewardmotivation circuitry. For example, nicotine disrupts the cholingergic system, cannabis disrupts the endocannabinoid system and opiates dis-
rupt the opioid system (see Chapter 5 for a list of specific drug targets). In other words, how do the adaptations in different neural systems disrupt
dopamine
signaling manifested in addiction?
Volkow (2005) proposed a
“final
Kalivas and
common pathway” to answer this
question (Figure 4.5). Kalivas and Volkow (2005) proposed that the glutamatergic projection from the PFC to the nucleus accumbens core and ventral pallidum constitute the
fi nal
common pathway (top path in Figure 4.5) for initi-
ation of drug seeking. This notion was based on experiments showing overlapping yet distinct neurocircuitry underlying cue-, drug- and stressinduced reinstatement of drug-seeking behavior. Reinstatement refers to the resumption of a previously drug-reinforced behavior by exposure to
/
54
Motivation to Initiate Drug Use to Recreational Drug Use
Ventral pallidum
Nucleus
Prefrontal
accumbens
cortex
core
Ventral Basolateral
tegmental
amygdala
area
Extended amygdala Final common pathway
Central amygdala nucleus, bed nucleus
Cue
of the stria terminalis, nucleus accumbens shell
Stress
Figure 4.5
According to Kalivas and Volkow (2005), the projection from the PFC to the
fi
nucleus accumbens core to the ventral pallidum is a nal common pathway for drug seeking by increases in dopamine release (via stress, a drug-associated cue or the drug itself) in the PFC.
different types of drug cues (cue-induced), drugs (drug-induced) or stressors (stress-induced) after the drug-reinforced behavior has been extinguished. Drug-induced reinstatement involves prefrontal (i.e. dorsomedial) glutamatergic projections to the nucleus accumbens core and dopaminergic projections from the dorsomedial PFC to the nucleus accumbens shell. Cue-induced reinstatement occurs primarily via dopamine and glutamate projections from the VTA, basolateral amygdala, dorsomedial PFC and nucleus accumbens core. Stress-induced reinstatement involves noradrenergic and corticotropin-releasing factor inputs to the central amygdala and bed nucleus of the stria terminalis and nucleus accumbens shell that serially project to the dorsomedial PFC and VTA. In sum, projections from the VTA (all forms of reinstatement), basolateral amygdala (cue reinstatement) and extended amygdala (stress reinstatement) converge on motor pathways involving the dorsomedial PFC and nucleus accumbens core that represents a pathway.”
“fi nal
common
/
Is Addiction a Reward Deficiency Syndrome?
55
Is Addiction a Reward Deficiency Syndrome?
As discussed above, the addiction literature largely supports the notion that dysfunction in the dopaminergic system leads to reduced dopamine levels. This reduction in dopamine levels underlies the compulsion to seek more potent stimuli, such as drugs. The interesting question then becomes, why do only a fraction (i.e. ~10%) of individuals who consume substances become addicted? If highly potent substances, such as drugs and alcohol, lead to the same cascade of events, yet only some individuals develop hypersensitivity to its effects, there are likely risk factors that make some more vulnerable to these effects than others. One of the most-studied risk factors is a potential genetic mechanism, particularly dopaminergic genes. Of the dopaminergic genes, the allele of the dopamine D2 receptor gene ( ), which leads to compromised D2 receptors, has been associated with a higher risk for multiple addictive, impulsive and compulsive behavioral propensities, such as severe alcohol, cocaine, heroin, cannabis and nicotine use, glucose bingeing, pathological gambling, sex addiction, attention deficit/hyperactivity disorder (ADHD), Tourette s syndrome, autism, chronic violence, post-traumatic stress disorder, schizoid/avoidant cluster, conduct disorder and antisocial behavior (Blum , 2000). Blum explained the effects of reduced dopamine levels across these various clinical presentations as a reward deficiency syndrome. The reward deficiency syndrome provides a framework by which a breakdown of the reward cascade occurs as a result of both genetic and environmental factors (Blum , 2012). The reward deficiency syndrome hypothesis emerged from findings that therapies that increase dopamine levels such as dopamine D2 agonists such as bromocriptine or induction of D 2-directed mRNA significantly reduce symptoms associated with substance use (e.g. craving, self-administration). Thus, stimulation of D2 receptors resolved the effects of dopamine depletion. Blum and colleagues proposed that D2 receptor stimulation signals a negative feedback mechanism in the mesolimbic system to induce mRNA expression causing proliferation of D2 receptors (Blum , 2012). Along with genetic studies demonstrating that dopaminergic polymorphisms of the and dopamine transporter ( ) alleles are associated with behaviors related to dopaminergic depletion (addictive, obsessive, compulsive and impulsive tendencies), the reward deficiency syndrome is proposed as an important phenotype for addiction. A1
DRD2
’
et al.
et al.
et al.
DRD2
DAT
/
56
Motivation to Initiate Drug Use to Recreational Drug Use
Corticostriatal Circuitry and Effort –Reward Imbalance While much attention has been placed on the reward-inducing effects of dopamine transmission, there are other aspects of dopaminergic signaling that do not mediate reward processes. For example, studies have also found evidence for the role of dopamine during effort (i.e. lever pressing) but not with the amount of reward. Thus, it is equally important to consider the role of dopamine in behavioral activation and effort. Salamone
et al.
(2007) postulated that dopamine’s role is to overcome
work-related response expenditure. This idea comes from animal research showing how the effects of reduced dopamine in the nucleus accumbens on food-seeking behavior is contingent on how much work
is required to accomplish the task. Specifically, in rats, when minimal work was required, lever pressing for
food rewards was largely
unaffected by dopamine depletions in the nucleus accumbens. In contrast, when the required level of work was high, lever pressing for food rewards was substantially impaired by dopamine depletions in the nucleus accumbens. Interestingly, when dopamine transmission was modulated, rats with reduced dopamine in the nucleus accumbens reallocated their instrumental behavior away from food-reinforced tasks that had high response requirements and instead selected a less effortful type of food-seeking behavior (Figure 4.6). Likewise, dopamine antagonists that block dopamine release, therefore preventing striatal activation, have been found to induce fatigue and reduce motivation. Blocking striatal response leads to a dysregulation of perceived effort vs. perceived gain, referred to as effort–reward calculation (Dobryakova
et al.,
2013).
Role of Memory Systems The research on reward and motivation and how these processes relate to addiction continues to evolve from a model of incentive salience encoding to a functionally more complex model that includes externally and internally driven attention and reward expectancy, as well as prediction errors. This more complicated network suggests an integral role of memory systems, which attempts to resolve the unanswered question of how salient stimuli act on the neural mechanisms of learning and memory underlying reinforcement learning. In other words, how
does
stored
information
(i.e.
memory)
about
reinforcing
stimuli drive addictive behavior? Animal studies suggest that such
/
Role of Memory Systems
57
(b)
(a)
Palatable food /FR 5
?? Lab chow / free access
(c)
(d)
Dopamine-depleted rat
Control rat
Figure 4.6
Experiments on the effects of dopamine depletion on effort. In these studies,
animals select between high-effort conditions where highly palatable food reward is
fi
accessible through lever pressing (with xed ratios) or low-effort conditions where less preferred food reward (lab chow) is freely available (a, b). Untreated rats prefer the highly palatable food and lever press, and eat little of the freely available chow (c). This demonstrates preference for high effort/high reward during normal dopamine levels. In contrast, dopamine-depleted rats (through dopamine antagonists) shift their choice from the high-effort condition (lever pressing) to the low-effort condition (freely available chow) (d). This demonstrates the importance of dopamine on effort expenditure. (From Salamone et al., 2007. © 2007 Springer-Verlag, USA.)
information is processed in several independent learning and memory systems. Rewarding stimuli interact with these systems in three ways: 1) they activate neural substrates of observable approach or escape responses; 2) they produce
unobservable internal states that can
be perceived as rewarding or aversive; and 3) they modulate or enhance the
information stored in each of the memory systems
(White, 1996). It is suggested that each addictive drug maintains its
/
58
Motivation to Initiate Drug Use to Recreational Drug Use
own self-administration by mimicking some subset of these actions. Evidence demonstrating actions of drugs on multiple neural substrates of reinforcement suggests that no single factor is likely to explain either addictive behavior in general or self-administration in particular. Thus, the basic mechanisms that underlie reward and motivation are similar to those that underlie learning and memory. The dopaminergic and glutamatergic neurotransmitter systems play integrative roles in motivation,
learning
and
memory, thereby
modulating
adaptive
behavior (Kelley, 2004a, 2004b).
Summary Points •
The
mesocorticolimbic
pathway
underlies
reward
and
motivation
processes.
•
Dopamine is the primary neurotransmitter in the reward-signaling pathway and underlies processes related to the acquisition of positively reinforced behavior.
•
The
final
common pathway involves glutamatergic projections from the
PFC to striatal regions.
•
Alterations in the
DRD2 gene lead to a reward deficiency syndrome, such as
addiction.
•
Dopamine depletion also leads to changes in perceived effort required for perceived gain.
•
Reward and motivation systems share mechanisms that underlie learning and memory.
Review Questions •
What are the brain regions within the mesocorticolimbic pathway and what processes do they underlie?
• •
What has been referred to as the primary reward center of the brain? What is the evidence suggesting that dopamine is the primary neurotransmitter for reward and motivation?
• • • •
Explain the
final common
pathway.
What are the three ways that induce drug reinstatement in animals?
fi
What is the premise behind the theory of reward de ciency syndrome? What is the role of memory systems in reward and motivation?
/
Further Reading
59
Further Reading Ekhtiari, H., Nasseri, P., Yavari, F., Mokri, A. & Monterosso, J. (2016). Neuroscience of drug craving for addiction medicine: from circuits to therapies. Prog Brain Res, 223, 115–141. doi:10.1016/bs.pbr.2015.10.002 Filbey, F. M. & DeWitt, S. J. (2012). Cannabis cue-elicited craving and the reward neurocircuitry. Prog Neuropsychopharmacol Biol Psychiatry, 38(1), 30–35. doi:10.1016/j.pnpbp.2011.11.001 Filbey, F. M. & Dunlop, J. (2014). Differential reward network functional connectivity in cannabis dependent and non-dependent users. Drug Alcohol Depend, 140, 101–111. doi:10.1016/j.drugalcdep.2014.04.002 Filbey, F. M., Schacht, J. P., Myers, U. S., Chavez, R. S. & Hutchison, K. E. (2009). Marijuana craving in the brain.Proc Natl Acad Sci U S A, 106(31), 13016–13021. doi:10.1073/pnas.0903863106 Filbey, F. M., Dunlop, J., Ketcherside, A., et al. (2016). fMRI study of neural sensitization to hedonic stimuli in long-term, daily cannabis users. Hum Brain Mapp, 37(10), 3431–3443. doi:10.1002/hbm.23250 Franken, I. H. (2003). Drug craving and addiction: integrating psychological and neuropsychopharmacological approaches.Prog Neuropsychopharmacol Biol Psychiatry , 27(4), 563–579. doi:10.1016/S0278-5846(03) 00081-2 Gu, X. & Filbey, F. (2017). A Bayesian observer model of drug craving.JAMA Psychiatry, 74(4), 419–420. doi:10.1001/jamapsychiatry.2016.3823 Heinz, A., Beck, A., Mir, J., et al. (2010). Alcohol craving and relapse prediction: imaging studies. In C. M. Kuhn & G. F. Koob, eds.,Advances in the Neuroscience of Addiction, 2nd edn. Boca Raton, FL: CRC Press, pp. 137–162. 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), 247–291. doi:10.1016/0165-0173(93)90013-P Sinha, R. (2009). Modeling stress and drug craving in the laboratory: implications for addiction treatment development. Addict Biol, 14(1), 84–98. doi:10.1111/j.1369-1600.2008.00134.x Wise, R. A. (1988). The neurobiology of craving: implications for the understanding and treatment of addiction. J Abnorm Psychol, 97(2), 118–132. doi:10.1037/0021-843X.97.2.118
/
60
Motivation to Initiate Drug Use to Recreational Drug Use
Spotlight
Motivated to predict future drug abuse Early intervention for substance use disorder is key to treatment success and is the reason why much research is dedicated toward identifying ways to predict risk for developing addiction. If a person ’s susceptibility to addiction was known, effective preventative strategies could be applied. Knowledge of the risk for addiction can inform targeted treatment. For example, knowing the mechanisms that led to the disorder can lead to timely and effective interventions. A group of scientists from Stanford aimed to determine whether risk for
fi
drug addiction could be identi ed using brain response patterns in 14-yearolds with high novelty seeking. Novelty seeking is an attribute that promotes
fi
independence and is therefore bene cial during adolescence. This is why although novelty seeking has also been associated with later development of drug addiction, not everyone who is novelty seeking becomes addicted to drugs. The question then becomes, what makes novelty seeking in adolescence a risk for drug addiction? To answer this question, Büchelet al. (2017) used functional magnetic resonance imaging (fMRI; see Chapter 2) to test whether brain responses in the brain’s motivational areas in 144 14-year-olds predicted drug abuse at age 16. Using the monetary incentive delay task (Figure S4.1), which measures the response to monetary gains, the researchers found that the 14-year-old adolescents who showed reduced motivational activity during monetary gain were more likely to abuse drugs by the time
fi
they were 16 years old. In other words, insuf cient activation in motivation areas in novelty-seeking adolescents may be a predictor of later drug abuse.
(a)
/
References
61
(b)
Magnitude cue shapes:
250 ms 1.75–14 s 160–260 ms
+
Magnitude cue
~2–14 s
+ Target
250 ms
1.75–14 s
Magnitude cue
+
160–260 ms ~2–14 s
+ Target
$0
Win
$1
$10
1s
+ Hit? + Hit/win cue
2.12 s
+
Hit/win cue
0–12 s
(+$0.00) $10.00 Feedback
1s
$ Win? $
1s
2.12 s
+
1s
+ ITI ITI 0–12 s
(+$1.00) $11.00 Feedback
+ ITI
(a) Sensation and novelty seeking are characteristic of adolescence. (b) Schematic of the monetary incentive delay task. This is a widely utilized task to measure brain responses during motivated behavior. In this task, participants win or avoid losing money if they are able to press a button while the target (the white square in this illustration) is present. The task not only provides researchers with the ability to measure responses during monetary wins and losses but is also able to determine if the magnitude of the reward (i.e. different amounts of money: $0, $1 or $10 in this illustration) influences response. ITI, intertrial interval. Figure S4.1
References
Blum, K., Braverman, E. R., Holder, J. M., (2000). Reward deficiency syndrome: a biogenetic model for the diagnosis and treatment of impulsive, addictive, and compulsive behaviors. , 32, Suppl. 1, p. i-iv, 1– 112112. Blum, K., Gardner, E., Oscar-Berman, M. & Gold, M. (2012).“Liking” and “wanting” linked to Reward Deficiency Syndrome (RDS): hypothesizing differential responsivity in brain reward circuitry. , 18(1), 113–118. doi:10.2174/138161212798919110 et al.
J Psychoactive Drugs
et al.
Curr
Pharm Des
/
62
Motivation to Initiate Drug Use to Recreational Drug Use
Büchel, 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. doi:10.1038/ ncomms14140 Carelli, R. M., Ijames, S. G. & Crumling, A. J. (2000). Evidence that separate neural circuits in the nucleus accumbens encode cocaine versus “natural” (water and food) reward. J Neurosci , 20(11), 4255–4266. doi:10.1523/JNEUROSCI.20-11-04255.2000 Dobryakova, E., DeLuca, J., Genova, H. M. & Wylie, G. R. (2013). Neural correlates of cognitive fatigue: cortico-striatal circuitry and effortreward imbalance. J Int Neuropsychol Soc, 19(8), p. 849–853. doi:10.1017/S1355617713000684 Filbey, F. M., Claus, E. D. & Hutchison, K. E. (2011). A neuroimaging approach to the study of craving. In: Adinoff, A. & Stein, E., eds. Neuroimaging in Addiction. London: Wiley-Blackwell, pp. 133 –156. Garavan, H., Pankiewicz, J., Bloom, A., et al. (2000). Cue-induced cocaine craving: neuroanatomical speci ficity for drug users and drug stimuli. Am J Psychiatry, 157(11), 1789– 1798. doi:10.1176/appi. ajp.157.11.1789 Kalivas, P. W. & Volkow, N. D. (2005). The neural basis of addiction: a pathology of motivation and choice. Am J Psychiatry, 162(8), 1403 –1413. doi:10.1176/appi.ajp.162.8.1403 Kelley, A. E. (2004a). Memory and addiction: shared neural circuitry and molecular mechanisms. Neuron, 44(1), 161–179. doi:10.1016/j. neuron.2004.09.016 (2004b). Ventral striatal control of appetitive motivation: role in ingestive behavior and reward-related learning. Neurosci Biobehav Rev , 27(8), 765 –776. doi:10.1016/j.neubiorev.2003.11.015 Olds, J. & Milner, P. (1954). Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain.J Comp Physiol Psychol, 47(6), 419–427. doi:10.1037/h0058775 Paxinos, G. & Watson, C. (1997). The Rat Brain in Stereotaxic Coordinates , 3rd edn. New York, NY: Academic Press. Robinson, T. E. & Kolb, B. (1997). Persistent structural modifications in nucleus accumbens and prefrontal cortex neurons produced by previous experience with amphetamine. J Neurosci, 17(21), 8491 –8497. doi:10.1523/JNEUROSCI.17-21-08491.1997 Salamone, J. D., Correa, M., Farrar, A. & Mingote, S. M. (2007). Effortrelated functions of nucleus accumbens dopamine and associated forebrain circuits. Psychopharmacology (Berl), 191(3), 461–482. doi:10.1007/s00213-006-0668-9
/
References
63
Volkow, N. D., Chang, L., Wang, G. J. et
al.
(2001). Loss of dopamine
transporters in methamphetamine abusers recovers with protracted abstinence.
J Neurosci ,
21(23), 9414–9418. doi:10.1523/
JNEUROSCI.21-23-09414.2001 White, N. M. (1996). Addictive drugs as reinforcers: multiple partial actions on memory systems. Addiction, 91(7), 921–949; discussion 951– 65. doi:10.1046/j.1360-0443.1996.9179212.x
/
CHAPTER F IVE Intoxication
Learning Objectives
• • • •
Be able to explain the concept of intoxication. Be able to understand the principles of pharmacodynamics. Be able to discuss the actions of each drug class. Be able to summarize the effects of intoxication on glucose metabolism, cerebral blood
•
flow, brain
function and electrophysiology.
Be able to describe the modulators of intoxication effects.
Introduction
Drug intoxication refers to the immediate effects of the drug and occurs during consumption of a drug in a large enough dose to produce signi
fi-
cant behavioral, physiological or cognitive impairments. It is these intoxicating effects that drive initial drug use. When drugs and alcohol are consumed, a cascade of short- and long-term effects follows. Although some of the effects of intoxication are pleasant and desired, other effects can be aversive (Figure 5.1). For example, alcohol intoxication or the state of being “drunk ” mani-
fests as facial flushing, slurred speech, unsteady gait, euphoria, increased activity, volubility, disorderly conduct, slowed reactions, impaired judgement and motor incoordination, insensibility and stupefaction. Understanding the effects of intoxication on the brain can inform how this process contributes to drug addiction. This chapter will discuss the mechanisms that underlie these intense feelings of pleasure that occur while taking some of the most common substances of abuse including alcohol, nicotine, cannabis and cocaine. According to the ICD-10, “ intoxication is a condition that follows the administration of a psychoactive substance and results in disturbances in the level of consciousness, cognition, perception, judgement, affect, or behavior, or other psychophysiological functions and responses ” (World
/
Introduction
Figure 5.1
65
Alcohol intoxication may impact sensorimotor skills.
Health Organization, 2004). Disturbances result from the direct pharmacological effects of the drug, as well as through learned experiences. Acute intoxication is transient and is positively correlated with dose levels. The intensity of intoxication lessens with time, and the effects eventually disappear in the absence of further use of the substance.
Symptoms of intoxication are not always reflective of the primary actions of the substance. For instance, depressant drugs may lead to symptoms of agitation or hyperactivity, and stimulant drugs may lead to socially withdrawn and introverted behavior. Some drugs, such as cannabis and hallucinogens, may lead to unpredictable effects, while many psychoactive substances can produce different types of effects at different levels of intoxication. A clear example of this latter effect is during alcohol intoxication, which is associated with energetic effects at low dose levels that could lead to agitation at medium dose levels and at sedation at higher levels.
/
66
Intoxication
Drug Pharmacodynamics To begin to understand the speci fic effects of addictive drugs on the brain and behavior, it is
fi rst
important to understand the principles of
pharmacodynamics. Pharmacodynamics refers to the mechanisms of drugs at both organ and cellular levels. It also refers to dose– effect relationships, as well as interactive effects of drugs. The majority of drugs interact with target biomolecules, such as enzymes, ion channels and transporters through receptor binding. Receptors are macromolecules located on the cell surface whose function is to recognize drug
signals and initiate a response (i.e. transduction). Drugs can be classified
based on the receptor ’s response to them (see Figure 5.2): agonists activate receptors; antagonists block the action of an agonist on the receptor; inverse agonists activate receptors to produce an effect in the opposite direction of an agonist; partial agonists activate a receptor but only at a submaximum level while also blocking the action of a full
agonist; and ligands have selective binding to specifi c receptors or sites.
There are four classes of receptor that can transduce a signal to a response: G protein-coupled receptors, ion-channel receptors, enzymelinked receptors and receptors of gene expression.
Actions of Addictive Drugs Although the feeling of a
“ high”
or
“ rush ”
immediately following drug
consumption is associated with increases in extracellular dopamine in the striatum, particularly the nucleus accumbens (see Chapter 4), different substances have discrete mechanisms of action. Stimulants target different molecules. For example, amphetamines, cocaine, lysergic acid diethylamide (LSD) and 3,4-methylenedioxymethamphetamine (MDMA) can increase dopamine by triggering dopamine release or blocking dopamine transporters. Dopamine transporters are the main mechanism for recycling dopamine back into the nerve terminals. Elevated levels of dopamine lead to feelings of alertness and happiness, and reduce feelings of hunger (see Chapter 4 on cocaine’s action on dopamine transporters). Amphetamines, cocaine and LSD also increase serotonin levels. Increased levels of serotonin result in feelings of happiness and fullness. Increased serotonin also provides pain relief. Finally, amphetamines and cocaine also act as norepinephrine receptor agonists, triggering increased heart rate, alertness and happiness, and decreasing blood circulation and pain. Nicotine is also a stimulant that acts as a receptor
agonist
at
nicotinic
acetylcholine
receptors
(nAChRs),
/
Agonistic drug effects
Antagonistic drug effects
Drug increases the synthesis of
Drug blocks the synthesis
neurotransmitter molecules
of neurotransmitters
Drug increases the number of
Drug causes neurotransmitter
neurotransmitter molecules by
to leak from the vesicles and
destroying degrading enzymes
be destroyed by degrading enzymes
Drug increases release of
Drug blocks the release of
neurotransmitters from terminal
the neurotransmitter from
buttons
terminal buttons
Drug binds to autoreceptors
Drug activates autoreceptors
and blocks their inhibitory effect
and inhibits neurotransmitter
on neurotransmitter release
release
Drug binds to postsynaptic receptors
Drug is a receptor blocker;
and either activates them or increases
it binds to the postsynaptic
the effect on them of neurotransmitters
receptors and blocks the effect of the neurotransmitter
Drug blocks the deactivation of neurotransmitters by blocking degradation or reuptake
Figure 5.2
Mechanisms of drug action.
/
68 particularly
Intoxication
α4β2
but not
receptor antagonist.
α4β 2
α4β9
and
α4β10
receptors, where it acts as a
receptors are present on dopamine neurons,
and may be the mechanism through which nicotine exerts its reinforcing effects. Activation of nAChRs leads to increased acetylcholine, which modulates other neurotransmitter functions and is associated with increased memory, muscle contractions, sweat and saliva secretions, and decrease heart rate. Sedatives or depressants, such as alcohol, barbiturates and benzodiazepines, increase dopamine indirectly through
their effects on γ -aminobutyric acid (GABA) receptors, which decrease
the excitability of neurons. This action promotes decreased brain function, inducing sleepiness and reducing anxiety, alertness, memory and muscle tension. Sedative-anesthetic drugs such as phencyclidine (PCP) and ketamine are
N-methyl-d-aspartate (NMDA) receptor (a type of
glutamatergic receptor) antagonists. The primary effect is increased excitatory transmission, which leads to visual and auditory distortions (hallucinations), as well as perceptual changes at higher doses (dissociations or feelings of detachment). Opiates such as morphine, heroin and hydrocodone bind to
μ -opioid
receptors present on dopamine and
GABA neurons, thus regulating dopamine function.
μ -Opioid receptor
binding leads to sedation, increasing sleepiness and reducing anxiety and pain. Tetrahydrocannabinol (THC) in cannabis is a partial agonist at cannabinoid 1 (CB1) receptors that modulate dopamine cells and postsynaptic dopamine signaling. The effects of THC on CB1 receptors include increased hunger, happiness and calmness, but it can also lead to unusual thoughts and feelings. Moreover, the modulatory role of CB1 receptors on dopamine functioning provides a possible mechanism through which THC may increase the reinforcing effects of other drugs of abuse, such as alcohol, nicotine, cocaine and opioids.
Brain Mechanisms of Intoxication: Evidence From Neuroimaging Pharmacological Studies Neuroimaging approaches (described in Chapter 2) have advanced our understanding of the brain mechanisms that underlie the intoxicating effects of addictive drugs in humans. These paradigms typically involve a single-dose
administration
and
combine
functional
neuroimaging
approaches with self-reports (questionnaires or clinical interviews) to track brain function with subjective experience related to acute intoxication. Thus, although animal studies have been able to provide extensive evidence that drug intoxication is related to disruption of dopamine levels, only human neuroimaging studies can integrate these
findings
/
Brain Mechanisms of Intoxication
69
with the behavioral manifestations of drug intoxication (e.g. highs and craving). The biggest challenge for human neuroimaging research involves the temporal issues surrounding acute pharmacological effects. This is one of the reasons why substances such as nicotine and alcohol that pervade the brain quickly and have a short duration of effect relative to other substances have been widely studied. Some of the first in vivo studies illustrating the acute effects of drugs in the human brain utilized electroencephalography (EEG) techniques. These studies provided evidence for the diverse mechanisms by which substances of abuse target the brain. Alterations in different eventrelated potential (ERP) components have been observed following acute administration of cannabis, alcohol and cocaine (Porjesz & Begleiter, 1981; Roth , 1977). EEG recordings during nicotine administration have indicated shifts from low to high frequencies. Specifically, Domino (2003) administered an average nicotine yield cigarette to overnightabstinent smokers and found decreased EEG α1, δ and θ frequencies but increased α2 and β frequency amplitudes, indicating increased arousal and alertness after nicotine exposure. EEG studies on alcohol, however, found opposite effects, with alterations primarily in lowerfrequency bands. For example, low doses of ethanol (0.75 mg/kg) at 90 min post-consumption in young adult males increased power in the θ (4–7 Hz) and α (7.5–9 Hz) frequency bands (Ehlers , 1989). Interestingly, those with high amounts of fast α activity prior to ethanol administration reported having fewer feelings of intoxication after ethanol than those with lower amounts of pre-drug fast α waves (9–12 Hz). In sum, it appears that increases in α frequency may underlie the feelings of euphoria during acute intoxication (Lukas 1995). In addition to EEG, positron emission tomography (PET) and singlephoton emission computed tomography (SPECT) techniques have allowed the visualization of acute drug effects at the neuronal receptor level. These studies have provided information on displacement of labeled, receptor-specific ligands, allowing visualization of receptor regulation in affected circuits. Several studies have shown the acute effects of alcohol on dopamine levels. In smokers, PET studies have demonstrated a dose-dependent effect on nAChR binding. For example, Brody 18 (2006) used 2-[ F]fluoro-3-(2( )-azetidinylmethoxy) pyridine as a ligand for nAChRs during PET to determine β2* nAChRs (nAChRs containing the β2* subunit, where * represents other subunits that may also be part of the receptor) occupancy following varying amounts of nicotine (none, one puff, three puffs, one full cigarette, or to satiety [two and a half to three cigarettes]). They found a linear relationship between the amount et al.
et al.
et al.,
et al.
S
/
70
Intoxication
2-FA PET imaging of nAChR occupancy from cigarette smoke exposure (a) kBq/mL
MRI
9
0.0 Cigarette
0.1 Cigarette
0.3 Cigarette
1.0 Cigarette
(b)
3.0 Cigarette
0
(c)
V /f
kB q
s
10
10
0
MRI
No smoking
Q-3
Q-1
(0.0 ng/ml) (0.4 ng/ml) (2.6 ng/ml)
p
0 T1- weight ed
C ont rol
Second- hand
MR I
smoke
PET studies to determine the effects of nicotine administration. (a) Nicotine intake leads to dose-dependent occupancy ofα4β2* nAChRs (noted by progressively decreasing nAChR binding in blue with increased dose). (b) Low-nicotine cigarettes result in 26% and 79% α4β2* nAChR occupancies. (c) Moderate second-hand smoke exposure results in 19% occupancy of α4β2* nAChRs in smokers (shown) and non-smokers (not shown). 2-FA, 2-[18F]fluoro-3-(2( )-azetidinylmethoxy) pyridine; MRI, magnetic resonance imaging. (From Jasinska , 2014.) (A black and white version of thisfigure will appear in some formats. For the color version, please refer to the plate section.) Figure 5.3
S
et al.
of cigarette smoke exposure and β2* nAChR occupancy (Figure 5.3). They further noted that β2* nAChR binding lasted for up to 3.1 h after exposure, suggesting long-lasting saturation of β2* nAChRs. Similar prolonged effects123on β2* nAChR occupancy has been reported using the chemical 5-[ I]iodo-85380 to quantify nAChRs during SPECT (Esterlis , 2010). They found 67Æ9% (range 55 80%) receptor occupancy after subjects had smoked to satiety (~2.4 cigarettes). Of note, these studies were conducted in experienced smokers, and thus findings may be different in naïve users. However, studies of second-hand smoke have reported similar nAChR occupancy in both smokers and nonsmokers (Figure 5.3c). PET can also inform on how substances affect the brain s energy utilization or glucose metabolism (the brain s primary energy source). et
–
al.
’
’
/
Brain Mechanisms of Intoxication
71
In cocaine abusers, acute cocaine administration, and in heavy drinkers (and controls) acute alcohol administration decreases brain glucose metabolism (Volkow , 1990). Many studies have shown that low to moderate doses of alcohol (0.25–0.75 g/kg) significantly reduce glucose metabolism in the brain, from 10% to 30%, especially in the occipital cortex (for visual processing) and cerebellum (for movement and balance) (Volkow 2006; Wang , 2000). Interestingly, this change in glucose metabolism is network specific, such that moderate doses of alcohol (0.75 g/kg) decreased absolute whole-brain metabolism but increased metabolism in reward-motivation regions such as the striatum (including the nucleus accumbens) and the amygdala. Given this decrease in glucose metabolism following acute alcohol intake (hypoglycemia), what does the brain use for energy? Research has suggested that acetate may be an alternative brain energy source to glucose during acute alcohol intoxication (Volkow , 2013). This was discovered during an alcohol challenge study, where the brain areas showing the largest decreases in [18F]fluorodeoxyglucose had the largest increases in [1–11C]acetate brain uptake. In addition to changes in glucose metabolism, PET has also provided information on the effects of addictive drugs on brain blood flow. PET studies have shown that these effects do not involve the entire brain but are regionally specific. Studies in alcohol have shown increases in cerebral blood flow after varying doses of alcohol in prefrontal and temporal regions (Sano , 1993; Tolentino , 2011). In contrast, cerebral blood flow appears to decrease in the cerebellum (Ingvar , 1998). Another way to measure brain activity besides cerebral blood flow changes is through fluctuations in functional connectivity via functional magnetic resonance imaging (fMRI). More specifically, resting-state functional connectivity (rsFC) during fMRI is a technique whereby functional connectivity during the resting state (rather than during performance of a task), also referred to as intrinsic connectivity, is inferred as the temporal correlation between activated regions in the brain. rsFC studies following acute intravenous alcohol infusion have shown increased intrinsic connectivity in an auditory network (temporal lobe and anterior cingulate cortex), as well as in the visual cortex network (Esposito , 2010). These studies took into consideration the vascular effects of the drugs, which can confound cerebral blood flow. For example, the vasoconstricting properties of cocaine could decrease cerebral blood flow. fMRI studies can also evaluate how acute intoxication can affect brain function during tasks as opposed to during the resting state, as discussed et al.
et al.,
et al.
et al.
et al.
et al.
et al.
et al.
/
72
Intoxication
Figure 5.4
Example of a virtual reality driving simulator device.
(From Fan et al., 2018.)
above. Some of the earliest studies examined the brain s response to simple visual and auditory stimulation following alcohol administration (Levin , 1998; Seifritz , 2000) and reported brain activation reductions (via the BOLD response; see Chapter 2) in respective visual and auditory cortices following alcohol administration. Later studies have also reported similar decreases in neural response effects during cognitive or emotional tasks after alcohol consumption. For example, alcohol intake increased the time it took to respond to an attention task and increased commission and omission errors (Anderson , 2011). Dose-dependent reductions in brain response were also noted across several brain regions including the insula, lateral prefrontal cortex and parietal lobe. Similar dose-related decreases in neural activation in driving-associated brain regions that correlated with driving performance have also been reported (Meda , 2009). An example of a virtual reality driving simulator device is shown in Figure 5.4. Meda (2009) tested driving performance using such a device during fMRI at different blood alcohol concentrations. The findings revealed dosedependent disruptions in the spatiotemporal (superior, middle and orbitofrontal gyri, anterior cingulate, primary/supplementary motor areas, basal ganglia and cerebellum) neural response during driving, especially at high doses (0.10% blood alcohol concentration). In terms of driving performance, white line crossings and mean speed also demonstrated significant dose-dependent changes. Altogether, these task-activation fMRI studies suggest that alcohol reduces brain activity through significant functional alterations in brain regions involved in attention, perception, and motor planning and control. ’
et al.
et al.
et al.
et al.
et al.
/
Modulators of Intoxication: Challenges in Human Research
73
In terms of emotional processing during intoxicated states, alcohol fMRI studies indicate that alcohol blunts the brain s response to emotional stimuli. For example, Gilman (2008) reported that a blood alcohol content of 0.08% (following ethanol infusion) led to an undifferentiated response during viewing of fearful or neutral faces in regions important for emotional processing (amygdala, insula and parahippocampal gyrus) (Figure 5.5). There has also been evidence for lack of amygdala response a critical area for emotion recognition while viewing threatening faces (e.g. angry, fearful) (Sripada , 2011). ’
et al.
–
–
et al.
Modulators of Intoxication: Challenges in Human Research
It is important to note that there is wide individual variability in the presentation of intoxicating effects of drugs and alcohol. This is due to (a)
Insula Claustrum Putamen Caudate nucleus Internal capsule Globus pallidus Thalamus Corpus callosum Lateral ventricle Choroid plexus Fornix Third ventricle Medial medullary lamina Intermediate mass Third ventricle Optic tract Corpora mamillaria Amygdaloid nucleus
Figure 5.5
(a) Position of the amygdala (arrow). (b). Response in brain regions to
fi
emotional faces during alcohol intoxication. Asterisks indicate statistically signi cant differences in the level of activation. (Part (b) from Gilman et al., 2008. © 2008 Society for Neuroscience, USA.)
/
74
Intoxication
(b) Striatal areas of interest 0.12
Alcohol fearful
e n i l es a b ot ev it a l er
e g n a hc l a n g is e g at n ecr eP
0.1 0.08
* ***
*
*
*
Alcohol neutral
* ***
Placebo fearful
0.06
Placebo neutral
0.04
*
0.2 0
–0.02 –0.04 –0.06 –0.08 –0.1 Nucleus accumbens (left)
(right)
Putamen (left)
Caudate
(right)
(left)
(right)
Visual–emotional areas of interest 0.35 Alcohol fearful
e n i l es a b ot ev it a l er
e g n a hc l a n g is e g at n ecr eP
0.3 0.25 0.02
**
**
**
Alcohol neutral Placebo fearful Placebo neutral
0.15 0.1 0.05 0
–0.05 –0.1 Amygdala (left)
Figure 5.5
(right)
Fusiform gyrus (left)
(right)
Lingual gyrus (left)
(right)
( cont.)
several factors that interact with the mechanisms that underlie intoxication. These factors could be: 1) context dependent, e.g. rate of consumption, concentration or potency of the drug; 2) individual characteristics, e.g. sex, age or genetics; or 3) state dependent, e.g. expectancies, or adaptations to substance use (e.g. tolerance) (see Spotlight on how these factors pose challenges for drug policies). The speed with which a drug acts depends on the dose taken, the mode of administration, and the rate of clearance to and from the brain. Intravenous
/
Summary Points
75
delivery leads to the fastest drug effects because the drug reaches the brain more quickly. The response to drugs is also related to previous drug experiences. For example, the magnitude of intoxication (i.e. the increase in dopamine) attenuates with greater severity of substance use. Acute administration of methylphenidate, for example, increased levels of glucose metabolism in prefrontal-striatal areas in active cocaine et
al.,
et al.,
abusers
with
low
D2
receptor
availability
(Volkow
1999) but decreased it in non-addicted individuals (Volkow 2005). Individual differences in personality traits as well as drug
–
expectancies
the expected effect of a drug
–
can also in fluence
intoxicated behavior and may interfere with the pharmacodynamic properties of drugs. Females are also typically more sensitive to intoxicating effects of drugs, perhaps due to general differences in body weight, percentage body
fat or rate of renal clearance of
unchanged drug (which is decreased in females due to a lower glomerular
filtration
rate or
flow
rate of
fl uid
through the kidney). Similar
age effects may be due to a reduction in renal and hepatic clearance with increasing age. Last, dopamine sensitivity based on underlying genetic factors can also influence the response to the intoxicating
effects of drugs. This notion suggests that genetic variations in the dopamine D2 receptor gene (DRD2) allele may lead to hypersensitivity of dopamine release, leading to increased likelihood of relapse (Blum
et al.,
2009). In other words, dopaminergic agonists may result
in stronger activation of brain reward circuitry in those who carry the DRD2 A1
with the
allele compared with the
A1
DRD2 A2
allele because those
allele have signifi cantly lower D2 receptor density (see
reward deficiency syndrome in Chapter 4).
Summary Points • • •
fi
The speci city of drug targets lead to the varied intoxication effects. Brain blood
flow
fi
during intoxication is region speci c.
There is a reduction
in
glucose metabolism during
intoxication
that is
correlated with increases in acetate in the same regions.
•
Levels of intoxication are due to many factors that are: 1) context depend-
•
Intoxicated driving is due to dose-related decreases in neural activation that
ent; 2) individual dependent; or 3) state dependent.
are correlated with driving performance, especially at high doses.
/
76
Intoxication
Review Questions Describe the specific mechanisms leading to various intoxicating effects of each drug class type. What can factors that influence differences in intoxication effects be categorized into? In general, what do EEG studies show in terms of changes in brain electrophysiology during intoxication? How is cerebral blood flow impacted during intoxication? What happens to glucose and acetate during intoxicated states? Describe the neural underpinnings of intoxicated driving. What mechanisms underlie the emotional symptoms during intoxication?
•
•
•
•
•
•
•
Further Reading Calhoun, V. D., Pekar, J. J. & Pearlson, G. D. (2004). Alcohol intoxication effects on simulated driving: exploring alcohol-dose effects on brain activation using functional MRI. , 29(11), 2097 2017. doi:10.1038/sj.npp.1300543 Hsieh, Y. J., Wu, L. C., Ke, C. C., (2018). Effects of the acute and chronic ethanol intoxication on acetate metabolism and kinetics in the rat brain. , 42(2), 329 337. doi:10.1111/acer.13573 Mathew, R. J., Wilson, W. H., Coleman, R. E., Turkington, T. G. & DeGrado, T. R. (1997). Marijuana intoxication and brain activation in marijuana smokers. , 60(23), 2075 2089. doi:10.1016/S0024-3205(97)00195-1 Volkow, N. D., Kim, S. W., Wang, G. J., (2013). Acute alcohol intoxication decreases glucose metabolism but increases acetate uptake in the human brain. , 64, 277 283. doi:10.1016/j.neuroimage.2012.08.057 Volkow, N. D., Wang, G. J., Fowler, J. S., (2000). Cocaine abusers show a blunted response to alcohol intoxication in limbic brain regions. , 66(12), PL161 167. doi:10.1016/S0024-3205(00)00421-5 Neuropsychopharmacology
–
et al.
Alcohol Clin Exp Res
–
Life Sci
–
et al.
Neuroimage
–
et al.
Life Sci
–
Spotlight Buzz Kill
The legalization of cannabis for recreational use in California made the state the world’s largest cannabis market. One of the challenges this brings is to
/
Spotlight
77
law enforcement, which has the responsibility of ensuring the safety of Californian roads from intoxicated drivers (Figure S5.1). Californian police are now trained on how to identify cannabis-impaired drivers without the help of objective measures, because, unlike a quantifiable marker of legal
Figure S5.1
Law enforcement challenges during changes in cannabis legislation.
(From https://www.pexels.com/photo/auto-automobile-blur-buildings-532001/.)
limits such as blood alcohol level (0.08% in California), there is no presumed level of intoxication in California, and intoxication and cognitive and motor impairment are highly variable among individuals. Although some Californian police departments are using saliva tests, a blood sample is the only method that provides quantification of THC in the system. Blood testing is currently a voluntary test in California that drivers can refuse. All of these efforts may be in vain, given the number of factors that contribute toward measurable levels, which consequently diminish the meaningfulness of these tests. These factors include how the cannabis was consumed and metabolized. In the end, the best current method is to train law enforcement officers to spot signs of impairment. Drugged driving screening looks for cognitive changes among twelve different steps. For instance, suspects are told to tip back their heads and estimate when 30 s have passed; some drugs make time seem to slow down, while other drugs produce the sensation that time has accelerated, affecting the user s perception. The California Highway Patrol and other agencies also are cooperating with the Center for Medicinal Cannabis ’
/
78
Intoxication
Research at the University of California, San Diego. The center is analyzing and trying to improve both the human drug-recognition experts and the saliva testing as part of a 2-year, $1.8 million study. Researchers are giving 180 volunteers cannabis with varying levels of potency, and then measuring both their performance in a driving simulator and ways of spotting any impairment. They also are trying to learn whether there is a particular presumptive level of cannabis intoxication that impairs driving.
References
Anderson, B. M., Stevens, M. C., Meda, S. A., (2011). Functional imaging of cognitive control during acute alcohol intoxication. , 35(1), 156–165. doi:10.1111/j.1530-0277.2010.01332.x Blum, K., Chen, T. J., Downs, B. W., (2009). Neurogenetics of dopaminergic receptor supersensitivity in activation of brain reward circuitry and relapse: proposing “ deprivation-amplification relapse therapy” (DART). , 121(6), 176–196. doi:10.3810/ pgm.2009.11.2087 Brody, A. L., Mandelkern, M. A., London, E. D., (2006). Cigarette smoking saturates brain α4β2 nicotinic acetylcholine receptors. , 63(8), 907– 915. doi:10.1001/archpsyc.63.8.907 Domino, E. F. (2003). Effects of tobacco smoking on electroencephalographic, auditory evoked and event related potentials. , 53(1), 66–74. doi:10.1016/S0278-2626(03) 00204-5 Ehlers, C. L., Wall, T. L. & Schuckit, M. A. (1989). EEG spectral characteristics following ethanol administration in young men. , 73(3), 179–187. doi:10.1016/ 0013-4694(89)90118-1 Esposito, F., Pignataro, G., Di Renzo, G., (2010). Alcohol increases spontaneous BOLD signal fluctuations in the visual network. , 53(2), 534–543. doi:10.1016/j.neuroimage.2010.06.061 Esterlis, I., Cosgrove, K. P., Batis, J. C., (2010). Quantification of smoking-induced occupancy of β2-nicotinic acetylcholine receptors: estimation of nondisplaceable binding. , 51(8), 1226–1233. doi:10.2967/jnumed.109.072447 Fan, J., Chen, S., Liang, M. & Wang, F. (2018). Research on visual physiological characteristics via virtual driving platform. , 10(1), 1687814017717664. doi:10.1177/1687814017717664 Gilman, J. M., Ramchandani, V. A., Davis, M. B., Bjork, J. M. & Hommer, D. W. (2008). Why we like to drink: a functional magnetic resonance et al.
Alcohol
Clin Exp Res
et al.
Postgrad Med
et al.
Arch
Gen Psychiatry
Brain Cogn
Electroencephalogr Clin Neurophysiol
et al.
Neuroimage
et al.
J Nucl Med
Adv Mech
Eng
/
References
79
imaging study of the rewarding and anxiolytic effects of alcohol. , 28(18), 4583–4591. doi:10.1523/JNEUROSCI. 0086-08.2008 Ingvar, M., Ghatan, P. H., Wirsén-Meurling, A., (1998). Alcohol activates the cerebral reward system in man. , 59(3), 258 –269. doi:10.15288/jsa.1998.59.258 Jasinska, A.J., Zorick, T., Brody, A. L. & Stein, E. A. (2014). Dual role of nicotine in addiction and cognition: a review of neuroimaging studies in humans. , 84, 111–122. doi:10.1016/j. neuropharm.2013.02.015 Levin, J. M., Ross, M. H., Mendelson, J. H., (1998). Reduction in BOLD fMRI response to primary visual stimulation following alcohol ingestion. , 82(3), 135–146. doi:10.1016/S0925-4927(98) 00022-5 Lukas, S. E., Mendelson, J. H. & Benedikt, R. (1995). Electroencephalographic correlates of marihuana-induced euphoria. , 37(2), 131–140. doi:10.1016/0376-8716(94) 01067-U Meda, S. A., Calhoun, V. D., Astur, R. S., (2009) Alcohol dose effects on brain circuits during simulated driving: an fMRI study. , 30(4), 1257–1270. doi:10.1002/hbm.20591 Porjesz, B. & Begleiter, H. (1981). Human evoked brain potentials and alcohol. , 5(2), 304–317. doi:10.1111/j.15300277.1981.tb04904.x Roth, W. T., Tinklenberg, J. R. & Kopell, B. S. (1977). Ethanol and marihuana effects on event-related potentials in a memory retrieval paradigm. , 42(3), 381–388. doi:10.1016/0013-4694(77)90174-2 Sano, M., Wendt, P. E., Wirsén, A., (1993). Acute effects of alcohol on regional cerebral blood flow in man. , 54(3), 369–376. doi:10.15288/jsa.1993.54.369 Seifritz, E., Bilecen, D., Hänggi, D., (2000). Effect of ethanol on BOLD response to acoustic stimulation: implications for neuropharmacological fMRI. , 99(1), 1–13. doi:10.1016/ S0925-4927(00)00054-8 Sripada, C. S., Angstadt, M., McNamara, P., King, A. C. & Phan, K. L. (2011). Effects of alcohol on brain responses to social signals of threat in humans. , 55(1), 371–380. doi:10.1016/j. neuroimage.2010.11.062 Tolentino, N. J., Wierenga, C. E., Hall, S., (2011). Alcohol effects on cerebral blood flow in subjects with low and high responses to alcohol. , 35(6), 1034–1040. doi:10.1111/j.15300277.2011.01435.x J Neurosci
et al.
J Stud Alcohol
Neuropharmacology
et al.
Psychiatry Res
Drug Alcohol Depend
et al.
Hum Brain
Mapp
Alcohol Clin Exp Res
Electroencephalogr Clin Neurophysiol
et al.
J Stud Alcohol
et al.
Psychiatry Res
Neuroimage
et al.
Alcohol Clin Exp Res
/
80
Intoxication
Volkow, N. D., Hitzemann, R., Wolf, A. P., et al. (1990). Acute effects of ethanol on regional brain glucose metabolism and transport. Psychiatry Res, 35(1), 39– 48. doi:10.1016/0925-4927(90)90007-S
Volkow, N. D., Wang, G. J., Fowler, J. S., et al. (1999). Blockade of striatal dopamine transporters by intravenous methylphenidate is not
sufficient to induce self-reports of “high ”. J Pharmacol Exp Ther , 288(1), 14– 20.
Volkow, N. D., Wang, G. J., Ma, Y., et al. (2005). Activation of orbital and medial prefrontal cortex by methylphenidate in cocaine-addicted subjects but not in controls: relevance to addiction. J Neurosci, 25(15), 3932 –3939. doi:10.1523/JNEUROSCI.0433-05.2005 Volkow, N. D., Wang, G. J., Franceschi, D., et al. (2006). Low doses of alcohol substantially decrease glucose metabolism in the human brain. Neuroimage , 29(1), 295–301. doi:10.1016/j.neuroimage.2005.07.004
Volkow, N. D., Kim, S. W., Wang, G. J., et al. (2013). Acute alcohol intoxication decreases glucose metabolism but increases acetate uptake in the human brain. Neuroimage, 64, 277– 283. doi:10.1016/j. neuroimage.2012.08.057 Wang, G. J., Volkow, N. D., Franceschi, D., et al. (2000). Regional brain metabolism during alcohol intoxication. Alcohol Clin Exp Res , 24(6), 822 –829. World Health Organization (2004). ICD-10, 2nd edn. Geneva: World Health Organization.
/
C H A P T ER SI X Withdrawal
Learning Objectives
• •
Be able to explain the concept of withdrawal. Be able to describe the various factors that lead to different manifestations of withdrawal.
•
Be able to understand the mechanisms that lead to symptoms of withdrawal.
•
Be able to decipher the different neurobiological mechanisms of acute versus protracted withdrawal symptoms.
•
Be able to summarize molecular targets that can be used to alleviate withdrawal symptoms.
Introduction
Withdrawal is a negative state that occurs following cessation from use of a drug that has caused physical dependence. In other words, withdrawal most often occurs in those who have used a drug on a regular basis rather than occasionally. The symptoms of withdrawal often include irritability, insomnia, changes in appetite, restlessness, headaches, nausea and nervousness. Much like other drug effects (i.e. intoxication), withdrawal symptoms vary depending on the type of drug and are infl uenced by
various individual factors, such as frequency and quantity of drug use. Withdrawal symptoms in chronic users of certain drugs such as opiates, alcohol and sedatives can be severe, and sometimes fatal. Withdrawal symptoms also vary throughout the course of abstinence, suggesting different neurobiological mechanisms in acute and protracted abstinence, although both contribute toward the risk for relapse. Why does the brain exhibit these intense symptoms when a drug is no longer in the body? What have we learned about the state of withdrawal that can be used to promote protracted abstinence? Current evidence
suggests that withdrawal is the brain’s attempt to adapt to the influx of
/
82
Withdrawal
potent substances. Neural adaptations include the downregulation (or decrease) of receptors (e.g. dopamine in the case of cocaine, opioid receptors in the case of heroin, and γ -aminobutyric acid [GABA] receptors in the case of alcohol). All of these adaptations are in an effort to maintain a balance or homeostasis in the presence of the substance. This chapter will discuss current knowledge on the neurobiological underpinnings of the withdrawal syndrome. The various brain mechanisms underlying the varied withdrawal symptoms will be discussed in addition to the factors that contribute to withdrawal symptoms.
What Does Withdrawal Look Like? Just like intoxication symptoms (see Chapter 5), withdrawing from substances can lead to varied presentations depending on the pharmacological
mechanisms of
the
substance
(see Table
6.1). However,
withdrawal typically manifests in behaviorally opposing ways to the intoxicating effects of a substance. For example, while pupils constrict during opioid intoxication, they dilate during withdrawal. Other somatic
disturbances include dif fi culties with sleep, sweating, tremors, muscle
aches and seizures. In general, withdrawal symptoms from all drugs also lead to mood disturbances, although the extent of the disturbances varies depending on the type of drug (see Spotlight 1 for a description of neonatal abstinence syndrome). Negative emotional states (e.g. dysphoria) are characterized by an inability to derive pleasure from common non-drug-related rewards (e.g. food, personal relationships) (see Spotlight 2 for potential negative emotional states following discon-
tinued use of the internet). There are also drug-specifi c withdrawal effects as outlined in Table 6.1, such as fatigue, decreased mood and psychomotor retardation during psychostimulant withdrawal, whereas amphetamine withdrawal is associated with decreased motivation, such as attenuated responding on a progressive ratio schedule for a sweet solution (Orsini
et al.,
2001). Withdrawal symptoms also vary by length
of abstinence from the drug, and can be classifi ed in terms of whether
they are associated with short-term (acute) or long-term (protracted) abstinence from the drug. Acute withdrawal symptoms are those that begin within hours or days after last use of the substance, while protracted withdrawal symptoms are those that go beyond this initial response to the absence of the drug and can persist for months, and sometimes even years. The timeline of withdrawal symptoms is based primarily on each drug ’s half-life. The term half-life is a pharmacokinetic parameter
/
What Does Withdrawal Look Like?
83
ficity and timing of acute
Table 6.1 Drug speci
withdrawal symptoms.
Physical and Drug
Cocaine
Alcohol
Onset
Duration
Characteristics
psychiatric issues
Depends on
3–4
Sleeplessness
Stroke
administration
days
or excessive
Cardiovascular
methods – can
restlessness
collapse
begin within
Increased
Myocardial
hours of last use
appetite
infarction
Depression
Organ infarction
Paranoia
Violence
Reduced
Severe depression
energy
Suicide
24–48 h after
5–7
Increased
Almost all organ
drop in blood
days
blood
systems are affected:
pressure,
cardiomyopathy,
heart rate and
liver disease,
temperature.
esophageal and
Nausea,
rectal varices,
vomiting and
Korsakoff’ s
diarrhea
syndrome
Seizures
Fetal alcohol
Delirium
syndrome
alcohol content
Death Heroin
Within 24 h of
4–7
Nausea
Dehydration
last use
days
Vomiting
Neonatal abstinence
Diarrhea
syndrome
Goose bumps Runny nose Teary eyes Yawning Cannabis
3–5 days
Up to 28
Irritability
days
Appetite disturbance Sleep disturbance Nausea Dif
ficulty
concentrating Nystagmus Diarrhea
/
84
Withdrawal
Table 6.1 (cont.)
Physical and Drug
Onset
Nicotine
1
–2
Duration
Characteristics
psychiatric issues
1 10
Irritability
Insomnia
weeks
Anxiety
Constipation
Depression
Dizziness
–
days
fi
Dif culty
Nausea
concentrating
Sore throat
Increased
Tremors
appetite
Increased heart rate
sm ot pmys f o yt ir ev eS 0
1
2
3
4
5
6
7
8
9
10
11
12
13
Irritability
Insomnia
Anger
Dysphoria
Nervousness
Craving
Tension
Stomach pain
Restlessness
Shakiness
Lack of appetite
Sweating
14
15
16
17
18
19
20
Days since last use
Figure 6.1
The severity of cannabis withdrawal symptoms across time.
de fined by the time it takes for the concentration of the drug in the plasma or the total amount in the body to be reduced by 50%. In other words, after one half-life, the concentration of the drug in the body will be half of the starting dose. For example, as illustrated in Figure 6.1, research suggests that, although the half-life of cannabis is highly variable, it is typically ~3–4 days. Unlike cannabis, other drugs have shorter half-lives, leading to faster onset of withdrawal symptoms following discontinued use, e.g. the half-life of heroin is 12 h, opiates is 8 h, alcohol
/
Acute Withdrawal Symptoms and Associated Neural Mechanisms
85
is 8 h and benzodiazepines is 24 h. However, as mentioned earlier, the individual experience of withdrawal symptoms varies in severity and duration based on factors such as duration, frequency and quantity of use, metabolism, sex, age, weight, method of intake (e.g. inhaling, injecting, swallowing, snorting), medical and mental health factors, genetic predisposition and the presence of other substances. For example, research suggests that alcohol s effect on dopamine release is greater for males than for females, which may account for the greater number of men with alcohol use disorder (~10% of the general population) than women (3 5%) (National Institute on Alcohol Abuse and Alcoholism, 2006). ’
–
Acute Withdrawal Symptoms and Associated Neural Mechanisms
The American Society of Addiction Medicine (ASAM) defines acute withdrawal as the onset of a predictable constellation of signs and symptoms following the abrupt discontinuation of, or rapid decrease in, dosage of a psychoactive substance. These acute symptoms following discontinuation of drug use have been attributed to uncompensated adaptive changes specific to each drug s molecular mechanisms and the associated neural adaptations that occur. For example, neuroadaptations in cocaine and stimulant use include dopamine transporter expression increases that result in decreases in the number of post-synaptic dopamine receptors, which then deplete pre-synaptic dopamine (Dackis & Gold, 1985). This dopamine-depleted state following discontinuation of drug use leads to the discomfort associated with withdrawal that drives drug-seeking behavior aimed at restoring dopamine levels. Indeed, empirical studies have found reduced dopamine levels in the nucleus accumbens (an important region in the dopaminergic reward system; see Chapter 4) in those withdrawing from cocaine, morphine, amphetamine and alcohol. Additionally, lower striatal dopamine D 2 receptor binding during withdrawal has been found in chronic cocaine (Volkow , 1993), alcohol (Volkow , 1996), methamphetamine (Volkow , 2001) and nicotine users (Fehr , 2008). Dopaminergic adaptations likely lead to dysfunction in areas within the dopaminergic reward system, such as prefrontal cortical (PFC) areas (i.e. orbitofrontonal cortex, dorsolateral PFC, anterior cingulate cortex). PFC dysfunction could lead to symptoms that resemble those of major depressive disorder. Indeed, studies in patients with depression show similar de ficits in “
”
’
et al.
et al.
et al.
et al.
/
86
Withdrawal
PFC function. Dysfunction in PFC areas leads to impaired emotion regulation, which is especially relevant for inhibitory control and coping with stress, and is therefore a strong predictor of relapse (see Sinha & Li, 2007, for a review). In addition to the dopamine-depletion hypothesis, de ficiencies in other neurotransmitter systems also play a role in the homeostatic process during withdrawal. Related to the dopamine-depletion hypothesis, because dopamine signals are transferred through GABA pathways, enhanced sensitivity to the effects (e.g. sleepiness) of GABA-enhancing drugs such as lorazepam has also been observed in the first few days of cocaine withdrawal in chronic cocaine users. This may be due to the downregulation of GABA during chronic cocaine use (Volkow , 1998). In addition to dopamine and GABA, other studies have also shown decreases in μ-opioid receptor binding during cocaine withdrawal (Zubieta , 1996). In terms of brain function, drug withdrawal is found to be associated with neural responsivity. For example, Volkow (1991) reported that, within 1 week of cocaine withdrawal, cocaine users had higher levels of global brain metabolism (determined by positron emission tomography [PET]) and regional brain metabolism in the basal ganglia and orbitofrontal cortex relative to non-using participants. This increase in metabolism in areas within the dopaminergic reward pathway can therefore also be attributed to dopamine depletion. Reductions in cerebral blood flow (CBF) in the PFC have also been observed in cocaine users during early withdrawal (10 days) relative to healthy controls (Volkow , 1988). The authors suggested that this reduction in CBF may be reflective of the effects of vasospasm in cerebral arteries exposed chronically to the sympathomimetic actions of cocaine. In nicotine users, no changes in CBF were noted before and after overnight abstinence; however, subjective withdrawal symptoms were inversely related to CBF in the thalamus (Tanabe , 2008). This inverse correlation, as illustrated in Figure 6.2, suggests that the greater the withdrawal symptoms, the less the reduction in thalamic CBF following overnight abstinence. Because it has been shown that individuals with low-grade nicotine withdrawal are more likely to relapse than those with greater withdrawal symptoms that abate quickly, the findings by Tanabe (2008) of a greater magnitude of CBF change may be the mechanism that underlies the risk for nicotine addiction relapse. Withdrawal from alcohol has also been associated with reductions in glucose metabolism in the striatal-thalamo-orbitofrontal cortex circuit (Volkow , 1996). et al.
et al.
et al.
et al.
et al.
et al.
et al.
/
Protracted Withdrawal Symptoms
87
30
) g/ n im/ lm( FBC c im a l a ht n i e g n a hC
15
0
–15 –1
0
1
Less withdrawal
Figure 6.2
2
3
More withdrawal
Change in CBF in the thalamus from baseline to overnight abstinence and
subjective withdrawal from nicotine as measured by the Minnesota withdrawal score from baseline to withdrawal. (From Tanabe et al., 2008. © 2007 Springer Nature, USA.)
Protracted Withdrawal Symptoms and Associated Neural Mechanisms
As opposed to acute withdrawal, protracted withdrawal persists beyond the timeframe of acute withdrawal symptoms and has broader effects. Protracted withdrawal is also referred to as long-term, chronic or postacute withdrawal syndrome and has never been formally accepted by the American Psychological Association (APA). To date, less is known about the mechanisms of protracted withdrawal relative to acute withdrawal. Protracted withdrawal symptoms have been most studied following alcohol abstinence. Anhedonia, which is the decreased ability to experience pleasure, is one of the most common withdrawal symptoms during protracted abstinence and has been observed during withdrawal from alcohol, opioids and other drugs. Martinotti
et al.
(2008) reported the presence of anhedonia
in those abstinent from alcohol for up to 1 year, suggesting the relevance
/
88
Withdrawal
of protracted withdrawal in alcohol users. Other symptoms of protracted withdrawal include anxiety, sleep difficulties, short-term memory impairment, fatigue, executive functioning deficits (decision making, inhibitory control) and craving. Symptoms are wide ranging and can include anxiety, hostility, irritability, depression, mood changes, fatigue and insomnia, and have been suggested to last 2 years or longer following cessation of alcohol use. Similar to acute withdrawal, neuroimaging correlates of protracted withdrawal appear also to be hypofunction in dopamine pathways such as decreases in D2 receptor expression and decreases in dopamine release. This reduction in dopamine activity may underlie anhedonia and amotivation during protracted withdrawal. This decreased dopamine activity in the presence of reward persists beyond the presence of acute physical withdrawal from alcohol. Brain function is also reduced during protracted withdrawal in PFC areas such as the dorsolateral prefrontal regions, cingulate gyrus and orbitofrontal cortex, which are important in inhibitory control. Interestingly, the enhanced brain metabolism reported by Volkow (1991) in cocaine patients with less than 1 week s abstinence described above was not observed in those within 2 4 weeks after discontinued cocaine use. This suggests a time-dependent attenuation in metabolic activity associated with withdrawal symptoms. et al.
’
–
Electrophysiological Mechanisms of Withdrawal
Electrophysiology studies have advanced our understanding of drug withdrawal and its associated behaviors by quantifying reduced cortical sensitivity through EEG frequency band measures and event-related potential (ERPs). Withdrawal from cocaine has been shown to demonstrate reduced low-frequency waves (i.e. δ and θ), which are correlated with drowsiness (Roemer , 1995), but increasedα and β frequencies, which are important for alert states (King , 2000). Increased α frequency has also been reported during early withdrawal in heroinaddicted individuals, although this attenuated over time (Shufman , 1996). In contrast to the pattern observed during cocaine abstinence, nicotine withdrawal was associated with increased θ frequency, while high-frequency waves such as α and β frequencies were decreased (Domino, 2003). Decreases in α frequency has been associated with a slow reaction time (Surwillo, 1963), diminished arousal and decreased vigilance (Knott & Venables, 1977). These deficits in α activity appear to reverse with protracted abstinence, suggesting that they may be et al.
et al.
et al.
/
Electrophysiological Mechanisms of Withdrawal
89
CSD/BEM topographic map of fast β power
Relapse-prone group
Current density 2
[uAmm/mm ]
Left hem.
0.00597 0.00490 0.00398
Right hem.
0.00299 0.00199 0.000996 0
Abstinence-prone group
Figure 6.3
Fast
β power can be a predictor of relapse in polysubstance users during a
3-month abstinence. BEM, boundary element method; CSD, current source density. (From
fi
Bauer, 2001. © 2001 Springer Nature, USA.) (A black and white version of this gure will appear in some formats. For the color version, please refer to the plate section.)
measuring the acute effects of drug withdrawal (Gritz , 1975). In terms of ERP measurements during withdrawal, increases in N200 and P300 latencies and decreases in N100 and P300 amplitudes have been reported in those with alcohol use disorder (Porjesz , 1987). A reduced P300 amplitude is a consistent finding during cocaine (Gooding , 2008), heroin (Papageorgiou , 2004) and nicotine (Littel & Franken, 2007) abstinence. These electrophysiological markers could be used to predict relapse, and could therefore play a crucial role in treatment development of addiction. For instance, classification methods based on α and β activity have distinguished with 83–85% accuracy abstinent alcohol users who relapsed from those who remained abstinent (Winterer , 1998). In a large prospective study by Bauer (2001), EEG power spectral density during a 3-month abstinence from polysubstance use revealed that an enhanced amount of high-frequency (19.5–39.8 Hz) β activity distinguished patients who would later relapse from those who remained abstinent (Figure 6.3). High β activity reflects hyperarousal and has previously been linked to high anxiety. Furthermore, source localization density analysis localized the fast β activity to deep, anterior regions of the frontal brain, such as the orbitofrontal cortex – an area important for et al.
et
et al.
al.
et al.
et al.
/
90
Withdrawal
emotion regulation. ERP studies have also distinguished abstainers from relapsers using N200 latency with an overall predictive rate of 71% in alcohol users (Glenn , 1993), and P300 amplitude in cocaineaddicted individuals (Bauer, 1997). et
al.
A Model of Opposing Mechanisms: Between-System Response to Drugs
Chapter 3 described models of addiction that were based on opposing processes (i.e. an allostatic model) whereby the initial pleasurable feelings (euphoria, relief from anxiety) from drug use are followed by the opponent process of negative emotional experiences or affective changes such as anxiety, depression and dysphoria. Based on the opponent process theory, withdrawal symptoms are the opposing processes of the acute positively reinforcing actions of drugs. These between-system neuroadaptations (Figure 6.4) occur as a mechanism by which stress modulates both the brain stress and aversive systems to restore normal function despite the presence of drug. Specifically, withdrawal from substances activates both the hypothalamic–pituitary–adrenal (HPA) axis (stress modulation system) and the brain stress/aversive system. The HPA axis is composed of three major structures: the paraventricular nucleus of the hypothalamus, the anterior lobe of the pituitary gland and the adrenal gland. This interaction results in elevated adrenocorticotropic hormone, corticosterone and amygdala corticotropin-releasing factor during acute withdrawal (Koob & Le Moal, 2008). This notion suggests that brain stress systems respond rapidly to changes in homeostasis but are slow to habituate or disengage in this compensatory process (Koob & Le Moal, 2008). It is the prolonged habituation that may lead to the pathological negative states associated with addiction withdrawal (Koob & Le Moal, 2001). This is what has been referred to as the “dark side of addiction. ” Evidence to support this comes from studies demonstrating that corticotropin-releasing factor antagonists, delivered intracerebroventricularly or systemically, reverse the anxiogenic-like response during cocaine, nicotine and alcohol withdrawal (George , 2007; Koob & Le Moal, 2008). In sum, the negative emotional symptoms during drug withdrawal are associated with between-system changes reflected by a decrease in dopaminergic activity in the mesolimbic dopamine system and with between-system recruitment of neurotransmitter systems that convey stress and anxiety-like effects. Other neurotransmitter systems involved in emotional dysregulation of the motivational effects of drug et al.
/
Summary Points
Stimulus value Action value/cost Anticipation/availability Context Action inhibition Emotion control Outcome valuation Drug subjective value
91
ACC Craving
+
dlPFC
Thal
DS
GP NAC Craving vmPFC Action B NS – T
vlPFC
+
HPC CeA
+ OFC Craving
–
Insula
External context Internal context
Stress Incentive to action
Affective state
Neuroadaptations between the reward and stress systems during withdrawal. ACC, anterior cingulate cortex; BNST, bed nucleus of the stria terminalis; CeA, central nucleus of the amygdala; DS, dorsal striatum; dlPFC, dorsolateral PFC; GP, globus pallidus; HPC, hippocampus; NAC, nucleus accumbens; OFC, orbitofrontal cortex; Thal, thalamus; vIPFC, ventrolateral PFC; vmPFC, ventromedial PFC. Figure 6.4
(Modified from George & Koob, 2013.)
withdrawal include norepinephrine, substance P, vasopressin, neuropeptide Y, endocannabinoids and nociception (Koob & Le Moal, 2008). Summary Points
•
Acute withdrawal symptoms begin within hours or days after last use of the substance, while protracted withdrawal symptoms can persist for months, and sometimes even years.
/
92
Withdrawal
• •
Decreases in dopamine tone in the nucleus accumbens occur during acute drug withdrawal from all major drugs of abuse. Neural adaptations that contribute toward withdrawal symptoms include downregulation (or the decrease) of receptors.
Review Questions • What are the individual differences that contribute to the highly variable presentation of withdrawal symptoms? • What is the primary determinant of the timeline of drug withdrawal effects? • How does dopamine depletion result in withdrawal symptoms? • How do between-system changes contribute to withdrawal?
Further Reading De Biasi, M. & Dani, J. A. (2011). Reward, addiction, withdrawal to nicotine. Annu Rev Neurosci, 34, 105–130. doi:10.1146/annurev-neuro-061010113734 Filbey, F. M., Dunlop, J. & Myers, U. S. (2013). Neural effects of positive and negative incentives during marijuana withdrawal.PLoS One, 8(5), e61470. doi:10.1371/journal.pone.0061470 George, O., Koob, G. F. & Vendruscolo, L. F. (2014). Negative reinforcement via motivational withdrawal is the driving force behind the transition to addiction. Psychopharmacology (Berl), 231(19), 3911–3917. doi:10.1007/ s00213-014-3623-1 Myers, K. M. & Carlezon, W. A., Jr. (2010). Extinction of drug- and withdrawal-paired cues in animal models: relevance to the treatment of addiction. Neurosci Biobehav Rev, 35(2), 285–302. doi:10.1016/j. neubiorev.2010.01.011 Negus, S. S. & Banks, M. L. (2018). Modulation of drug choice by extended drug access and withdrawal in rhesus monkeys: implications for negative reinforcement as a driver of addiction and target for medications development. Pharmacol Biochem Behav, 164, 32–39. doi:10.1016/j .pbb.2017.04.006 Piper, M. E. (2015). Withdrawal: expanding a key addiction construct.Nicotine Tob Res, 17(12), 1405 –1415. doi:10.1093/ntr/ntv048
/
Spotlight 1
93
Spotlight 1
Withdrawn From Birth
The opiate epidemic in the USA also impacts unborn infants of opiate-using mothers. Opiate addiction is often initiated through prescription opiates for pain that, left unresolved, can develop into heroin addiction. Heroin, which is cheaper and with longer-lasting effects than prescription opiates, therefore provides an attractive alternative for those with chronic pain, including childbearing women. Weaning from heroin is challenging. In pregnant women, withdrawal symptoms can endanger their pregnancy. However, pregnant women who undergo medication-assisted therapies (i.e. methadone or buprenorphine) endure a condemning stigma.
Figure S6.1
Babies have to be weaned from opiates when born from opiate-using
mothers. (From https://pixabay.com/en/baby-crying-cry-crying-baby-cute-2387661/.)
While the circumstances that lead to opiate addiction for these women vary, the effects of exposure to drugs in the womb are the same. Most of these infants are born prematurely and suffering from withdrawal, a condition called neonatal abstinence syndrome (NAS). Withdrawal symptoms in babies with NAS are similar to those experienced by adults. These include excessive crying, vomiting, diarrhea, muscle twitches and seizures (Figure S6.1). Fortunately, there is awareness of this problem, and programs have been developed to provide support for these women. Such programs provide women with clinicians to help manage their medication-assisted therapy, and support so that they are able to take care of their families through childcare and education. Such programs have led to reductions in the infants length of stay in neonatal intensive care units, e.g. by 33% in Texas (Cleveland , 2015). ’
et al.
/
94
Withdrawal
Spotlight 2
Internet Separation Anxiety With the majority of the population on their electronic devices more hours than not, researchers have begun to ask whether addictive processes may be involved in the use of these devices and their applications (Figure S6.2). A study by Reed et al. (2017) examined the behavioral symptoms of being away from internet use, and found similarities with withdrawal symptoms from drug addiction. They discovered that people who spend an extended amount of time on the internet experience increased heart rate and rises in blood pressure after they stop using the internet. The study was based on
–
144 adults aged 18 33. The authors warn that these physiological changes may lead to anxiety as well as to hormonal imbalances. Only time will tell what the long-term effects of excessive electronic device use on public health and society will be, but government organizations are already feeling the pressure to create policies. For example, the Ethiopian government recently shut down internet access across the entire country to support students studying for their national examinations. This is in addition to the goal of preventing examination questions being leaked online.
Figure S6.2
Can Facebook be addictive?
(From https://pixabay.com/en/facebook-social-media-addiction-2387089/.)
References
Bauer, L. O. (1997). Frontal P300 decrements, childhood conduct disorder, family history, and the prediction of relapse among abstinent cocaine abusers.
Drug Alcohol Depend, 44(1), 1–10. doi:10.1016/S0376-8716
(96)01311-7
/
References
95
(2001). Predicting relapse to alcohol and drug abuse via quantitative electroencephalography. , 25(3), 332–340. doi:10.1016/S0893-133X(01)00236-6 Cleveland, L., Paradise, K., Borsuk, C., Coutois, B. & Ramirez, L. (2015). Neuropsychopharmacology
The Mommies Toolkit: Improving Outcomes for Families Impacted by
. Austin, TX: Texas Department of State Health Services. Available at: www.dshs.texas.gov/sa/NAS/ Mommies_Toolkit.pdf Dackis, C. A. & Gold, M. S. (1985). New concepts in cocaine addiction: the dopamine depletion hypothesis. , 9(3), 469–477. doi:10.1016/0149-7634(85)90022-3 Domino, E. F. (2003). Effects of tobacco smoking on electroencephalographic, auditory evoked and event related potentials. , 53(1), 66– 74. doi:10.1016/S0278-2626(03) 00204-5 Fehr, C., Yakushev, I., Hohmann, N., (2008). Association of low striatal dopamine D2 receptor availability with nicotine dependence similar to that seen with other drugs of abuse. , 165(4), 507–514. doi:10.1176/appi.ajp.2007.07020352 George, O. & Koob, G. F. (2013). Control of craving by the prefrontal cortex. , 110(11), 4165–4166. doi:10.1073/ pnas.1301245110 George, O., Ghozland, S., Azar, M. R., (2007). CRF–CRF 1 system activation mediates withdrawal-induced increases in nicotine selfadministration in nicotine-dependent rats. , 104(43), 17198 –17203. doi:10.1073/pnas.0707585104 Glenn, S. W., Sinha, R. & Parsons, O. A. (1993). Electrophysiological indices predict resumption of drinking in sober alcoholics. , 10(2), 89 –95. doi:10.1016/0741-8329(93)90086-4 Gooding, D. C., Burroughs, S. & Boutros, N. N. (2008). Attentional deficits in cocaine-dependent patients: converging behavioral and electrophysiological evidence. , 160(2), 145– 154. doi:10.1016/j.psychres.2007.11.019 Gritz, E. R., Shiffman, S. M., Jarvik, M. E., (1975). Physiological and psychological effects of methadone in man. , 32(2), 237–242. doi:10.1001/archpsyc.1975.01760200101010 King, D. E., Herning, R. I., Gorelick, D. A. & Cadet, J. L. (2000). Gender differences in the EEG of abstinent cocaine abusers. , 42(2), 93–98. doi:10.1159/000026678 Knott, V. J. & Venables, P. H. (1977). EEG alpha correlates of non-smokers, smokers, smoking, and smoking deprivation. , 14(2), 150 –156. doi:10.1111/j.1469-8986.1977.tb03367.x Neonatal Abstinence Syndrome
Neurosci Biobehav Rev
Brain Cogn
et al.
Am J Psychiatry
Proc Natl Acad Sci U S A
et al.
Proc Natl Acad Sci U S A
Alcohol
Psychiatry Res
et al.
Arch Gen Psychiatry
Neuropsychobiology
Psychophysiology
/
96
Withdrawal
Koob, G. F. & Le Moal, M. (2001). Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology , 24(2), 97–129. doi:10.1016/ S0893-133X(00)00195-0 (2008). Neurobiological mechanisms for opponent motivational processes in addiction. Philos Trans R Soc Lond B Biol Sci, 363(1507), 3113 –3123. doi:10.1098/rstb.2008.0094 Littel, M. & Franken, I. H. (2007). The effects of prolonged abstinence on the processing of smoking cues: an ERP study among smokers, ex-smokers and never-smokers. J Psychopharmacol , 21(8), 873–882. doi:10.1177/0269881107078494 Martinotti, G., Nicola, M. D., Reina, D.,et al. (2008). Alcohol protracted withdrawal syndrome: the role of anhedonia. Subst Use Misuse, 43(3–4), 271–284. doi:10.1080/10826080701202429 National Institute on Alcohol Abuse and Alcoholism (2006). Alcohol Use and Alcohol Use Disorders in the United States: Main Findings From
the 2001 – 2002 National Epidemiologic Survey on Alcohol and Related
Conditions (NESARC). Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health. Orsini, C., Koob, G. F. & Pulvirenti, L. (2001). Dopamine partial agonist reverses amphetamine withdrawal in rats. Neuropsychopharmacology , 25(5), 789– 792. doi:10.1016/S0893-133X(01)00270-6 Papageorgiou, C. C., Liappas, I. A., Ventouras, E. M.,et al. (2004). Longterm abstinence syndrome in heroin addicts: indices of P300 alterations associated with a short memory task. Prog Neuropsychopharmacol Biol Psychiatry, 28(7), 1109–1115. doi:10.1016/j.pnpbp.2004.05.049 Porjesz, B., Begleiter, H., Bihari, B. & Kissin, B. (1987). Event-related brain potentials to high incentive stimuli in abstinent alcoholics. Alcohol, 4(4), 283–287. doi:10.1016/0741-8329(87)90024-3 Reed, P., Romano, M., Re, F., et al. (2017). Differential physiological changes following internet exposure in higher and lower problematic internet users. PLoS One, 12(5), e0178480. doi:10.1371/journal. pone.0178480 Roemer, R. A., Cornwell, A., Dewart, D., Jackson, P. & Ercegovac, D. V. (1995). Quantitative electroencephalographic analyses in cocainepreferring polysubstance abusers during abstinence. Psychiatry Res, 58(3), 247– 257. doi:10.1016/0165-1781(95)02474-B Shufman, E., Perl, E., Cohen, M., et al. (1996). Electro-encephalography spectral analysis of heroin addicts compared with abstainers and normal controls. Isr J Psychiatry Relat Sci, 33(3), 196–206. 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), 25–31. doi:10.1080/09595230601036960
/
References
97
Surwillo, W. W. (1963). The relation of simple response time to brain-wave frequency and the effects of age. , 15, 105–114. doi:10.1016/0013-4694(63)90043-9 Tanabe, J., Crowley, T., Hutchison, K., (2008). Ventral striatal blood flow is altered by acute nicotine but not withdrawal from nicotine. , 33(3), 627–633. doi:10.1038/sj. npp.1301428 Volkow, N. D., Mullani, N., Gould, K. L., Adler, S. & Krajewski, K. (1988). Cerebral blood flow in chronic cocaine users: a study with positron emission tomography. , 152(5), 641– 648. doi:10.1192/ bjp.152.5.641 Volkow, N. D., Fowler, J. S., Wolf, A. P., (1991). Changes in brain glucose metabolism in cocaine dependence and withdrawal. , 148(5), 621–626. doi:10.1176/ajp.148.5.621 Volkow, N. D., Fowler, J. S., Wang, G. J., (1993). Decreased dopamine D2 receptor availability is associated with reduced frontal metabolism in cocaine abusers. , 14(2), 169–177. doi:10.1002/syn.890140210 Volkow, N. D., Wang, G. J., Fowler, J. S., (1996). Decreases in dopamine receptors but not in dopamine transporters in alcoholics. , 20(9), 1594–1598. doi:10.1111/j.1530-0277.1996. tb05936.x (1998). Enhanced sensitivity to benzodiazepines in active cocaineabusing subjects: a PET study. , 155(2), 200–206. doi:10.1176/ajp.155.2.200 Volkow, N. D., Chang, L., Wang, G. J., (2001). Low level of brain dopamine D2 receptors in methamphetamine abusers: association with metabolism in the orbitofrontal cortex. , 158(12), 2015 –2021. doi:10.1176/appi.ajp.158.12.2015 Winterer, G., Kloppel, B., Heinz, A., (1998). Quantitative EEG (QEEG) predicts relapse in patients with chronic alcoholism and points to a frontally pronounced cerebral disturbance. , 78(1– 2), 101–113. doi:10.1016/S0165-1781(97)00148-0 Zubieta, J. K., Gorelick, D. A., Stauffer, R., (1996). Increased mu opioid receptor binding detected by PET in cocaine-dependent men is associated with cocaine craving. , 2(11), 1225– 1229. doi:10.1007/s00213-008-1225-5. Electroencephalogr Clin
Neurophysiol
et al.
Neuropsychopharmacology
Br J Psychiatry
et al.
Am
J Psychiatry
et al.
Synapse
et al.
Alcohol Clin Exp Res
et al.
Am J Psychiatry
et al.
Am J Psychiatry
et al.
Psychiatry Res
et al.
Nat Med
/
C H A PT E R S EV EN
Craving
Learning Objectives • •
Be able to understand the problems in the conceptualization of craving. Be able to describe neuroimaging approaches to examine cue-elicited craving.
•
“
”
Be able to explain what is meant by the statement that drugs hijack the brain.
•
Be able to discuss studies demonstrating how craving and attention are separate processes.
•
Be able to summarize the role of
ΔFosB in craving.
Introduction
Craving is often defined as a strong subjective desire to use alcohol or drugs. Historically, there has been debate with regard to the conceptualization and measurement of craving (see reviews by Tiffany & Conklin, 2000; Tiffany , 2000). Craving can be measured in terms of physical manifestations or psychological experiences. Craving can therefore be viewed as a multidimensional construct involving subjective, behavioral or physiological responses. Research stemming from the 1980s advanced the study of craving by incorporating a cue-reactivity approach. During cue reactivity, individuals are exposed to drug cues (e.g. the sight of drug paraphernalia or the smell of alcohol), which are linked to self-report measures of craving. The measurement of craving in this context of cue reactivity is grounded on theoretical learning theory frameworks, such as Pavlovian conditioning. Cue-reactivity research has also emphasized experimental control in an attempt to improve reliability and validity in the measurement of craving (Drummond, 2000; Niaura , 1988). The notion of craving has historically been criticized for its subjective nature, which does not prospectively predict drug-use behavior (Tiffany , 2000). There were et al.
et al.
et al.
/
Cue-Elicited Craving Paradigms
99
also concerns with regard to the ecological validity of the use of subjective measures in laboratory settings that question their accuracy, reliability and validity. Translating cue-elicited craving to classical approaches from animal models has also been a challenge. For example, subjective craving is not easily discerned in animals; thus, direct translation of the multidimensional construct of craving may not be possible from animal models to humans. As discussed in Chapter 4, findings from the animal literature have shown that the motivation to use drugs is linked to the actions of drugs on the mesocorticolimbic pathways in the brain, which are the neural substrates that putatively underlie the attribution of incentive salience to alcohol and other drugs of abuse (Berridge & Robinson, 1998; Robinson & Berridge, 1993; Wise, 1988). Recently, scientists have begun to use neuroimaging approaches to focus on the neurobiology of craving in humans. The use of more objective neuroimaging techniques alleviates the burden of proof on subjective responses, thus addressing some of the limitations of accuracy and validity in behavioral investigations. Neuroimaging approaches also allow greater consistency between animal and human models because of the focus on neurobiology. This chapter will focus on the various techniques that demonstrate the presence of cue-elicited craving across different substances and that led to the addition of craving as a primary symptom for the diagnosis of a substance use disorder (SUD) in the DSM-5. Cue-Elicited Craving Paradigms and Associated Neural Mechanisms
Cue-elicited craving paradigms entail exposing individuals to substancerelated cues and linking the event to a subjective measure of craving. Cue-elicited craving paradigms consist of a variety of sensory modalities including visual, olfactory, auditory and tactile presentations. One of the earliest studies used ethanol odor to elicit subjective craving in alcohol users (Schneider , 2001). The functional magnetic resonance imaging (fMRI) results showed that increases in the neural response in the cerebellum and amygdala during the smell of ethanol were positively correlated with subjective craving for alcohol. Visual cue modalities are the most widely used. These paradigms involve visual presentations of cue images, such as drug paraphernalia. For example, a study by Wrase (2002) presenting visual alcohol stimuli to participants showed significant activation in the fusiform gyrus, basal ganglia and orbitofrontal gyrus compared with abstract control et
al.
et al.
/
100
Craving
pictures. Videos have also been utilized to study craving (Wrase
et al.,
2002). For example, using positron emission tomography (PET), current cocaine users were exposed to a 10 min videotape of persons using cocaine as well as a 45 min audiotape of pleasurable experiences from cocaine use (taken from actual interviews with cocaine abusers) (Wong et al.,
2006). The results of this study found that displacement of the 11
radiotracer [ C]raclopride, which is a measure of occupancy at D2-like receptors, increased in the putamen of participants who reported cueelicited craving compared with those who did not. Furthermore, the intensity of the self-reported craving was positively correlated with the increase in dopamine receptor occupancy, suggesting increased release of intrasynaptic dopamine in the putamen. These results provide support for the role of dopamine in the dorsal striatum during the subjective experience of craving. To address the issue of ecological validity, some cue-elicited craving paradigms have also used a combination of modalities to mimic realworld scenarios. For instance, simultaneous presentation of taste (sip of alcohol) and visual (picture of alcohol stimuli) cues revealed that alcohol cues increase activation in the prefrontal cortex (PFC) (George et 2001) and limbic areas (Myrick et
al.,
2004). A study by Franklin
al.,
et al.
(2007) presented tactile cues (cigarettes) in conjunction with smoking cue-related videos during arterial spin labeling. They found greater activation compared with a neutral cue in the amygdala, ventral striatum, hippocampus, insula, orbitofrontal cortex and thalamus (Franklin 2007). Studies by Filbey
et al.
et al.,
(2016) using fMRI used simultaneous
presentation of tactile and visual cannabis cues (cannabis paraphernalia) (Figure 7.1). This study also found positive brain behavior correlations between the neural response to cannabis cues in frontostriatal– temporal regions and subjective craving, cannabis-related problems, withdrawal symptoms and levels of (cluster-threshold
z
= 2.3,
tetrahydrocannabinol
# of overlaps
Healthy controls Lesion controls
80
VMPF 70
Insula
60
50 teB %
40
30
20
10
0 9 to 1
8 to 2
7 to 3
6 to 4
Chance of winning
Figure 8.5
Ventromedial PFC lesions lead to risky decision making. A studies found that
twenty patients with ventromedial PFC (VMPF) lesions (left side) exhibited greater betting behavior compared with forty-one non-lesion controls, thirteen patients with insula lesions
/
Delay Discounting of Reward
123
“Stopping” impulsivity
PFC
SNc
RIFG/OFC
Caudateputamen
Raphe
ACC GP
Th
LC
dPM SMA/pre-SMA
STN
Figure 8.6
M1
Schematic of the stop circuit. Inhibitory control depends on the interactions
between PFC areas (cortical motor areas: M1, primary motor cortex; SMA/pre-SMA, supplementary motor area; dPM, dorsal pre-motor area), the right inferior frontal gyrus (RIFG), the anterior cingulate cortex (ACC), the orbitofrontal cortex (OFC), and striatal regions including the dorsal striatum (caudate-putamen), globus pallidus (GP) and subthalamic nucleus (STN), which project via the thalamus (Th) to the PFC. The PFC and striatal networks are modulated by midbrain dopaminergic neurons in the substantia nigra pars compacta (SNc)/ventral tegmental area, serotonergic neurons in the raphé nuclei (Raphe) and noradrenergic neurons in the locus coeruleus (LC). (From Dalley et al., 2011. © 2011 Elsevier, USA.)
Delay Discounting of Reward
Preference of an immediately available small reward over experiencing a delay for a larger one is another facet of impulsivity referred to as delay discounting (Figure 8.7). Delay discounting can be modeled as hyperbolic discounting, originally described in pigeons that displayed a switch to selection of the smaller of the two rewards as their values decreased
Figure 8.5
(cont.)
and twelve lesion controls (with mainly dorsolateral and/or ventrolateral PFC damage). IN, insula cortex. (From Clark et of this
figure will appear in
al.,
2008.) (A black and white version
some formats. For the color version, please refer to
the plate section.)
/
124
Impulsivity
Delay discounting task
Now
Later
Smaller reward
Larger reward
(immediate)
(delayed)
E.g. $2 in 5 s
$
E.g. $5 in 10 s
$ $
Figure 8.7
$
Illustration of a delay discounting task.
“Waiting” impulsivity
PFC
HC
AMG
ACC
VTA PLd NAcb core PLv Raphe
IL LC
Figure 8.8
NAcb shell
Schematic of the wait circuit. Delay discounting of reward depends on top-down
PFC interactions with the hippocampus (HC), amygdala (AMG) and structures in the ventral striatum, including the nucleus accumbens core (NAcb core) and shell (NAcb shell). The anterior cingulate cortex (ACC), dorsal and ventral prelimbic cortex (PLd and PLv), and infralimbic cortex (IL) make distinct contributions to waiting via topographically organized inputs to the NAcb. VTA, ventral tegmental area; LC, locus coeruleus. (From Dalley et al., 2011. © 2011 Elsevier, USA.)
over time (Ainslie, 1975). Current delay discounting paradigms have measured choice after short temporal delays as well as probability discounting of reward where the dimension of temporal delay is replaced by reinforcer uncertainty.
/
Review Questions
125
In contrast to inhibitory control as described above as the process of stopping a response, delay discounting can be viewed in terms of the action of waiting. A dissociation between inhibitory control ( “stopping”) and delay discounting (“waiting”) is demonstrated in high-impulsive rats who exhibited delay discounting in the 5CSRTT although they had intact inhibitory control in the SSRT task. These findings suggest potentially two distinct neural substrates governing these impulsivity domains of stopping (e.g. dorsal striatum) versus waiting (e.g. ventral striatum) (Figure 8.8). One of the earliest relevant studies of delay discounting was the finding that rats that preferentially (75% of trials) chose small (two food pellets) immediate rewards over large (twelve pellets) rewards delivered after a delay of 15s subsequently consumed significantly more of a 12% alcohol solution than the less-impulsive subgroups (Poulos , 1995). In terms of addiction, rats that demonstrate delay discounting acquire drug self-administration more quickly than rats that do not. et al.
Summary Points •
Impulsivity is a heterogeneous construct consisting of independent processes that lead to poor decision making.
•
Although there is agreement that impulsive behavior is related to addiction, whether impulsivity is a cause or a consequence of addiction remains to be answered. It is also likely that, while impulsivity may be a risk factor that leads to addiction, drug exposure further exacerbates impulsive behavior, which leads to continued drug use.
•
fi
Risky decision making is de ned as persistence despite the potential for negative consequences.
• •
Inhibitory control is the ability to inhibit a premature response. Delay discounting of reward is preferential selection of immediate yet small rewards rather than waiting for delayed, larger rewards.
•
Corticostriatal
networks
underlie
the
various
processes
related
to
impulsivity.
•
Dopamine is the primary neurotransmitter that regulates impulsive behaviors, although both noradrenaline and serotonin also play a role.
Review Questions •
How can studies such as the one described by Erscheet al. (2010) decipher the chronicity of impulsive behavior in addiction?
/
126
• • • • •
Impulsivity
What is the definition of risky decision making? What are the most widely utilized paradigms to assess response inhibition? What does delay discounting refer to? How do corticostriatal regions interact to control behavior? How do noradrenaline and serotonin contribute toward impulsive behavior?
Further Reading Beaton, D., Abdi, H. & Filbey, F. M. (2014). Unique aspects of impulsive traits in substance use and overeating: specific contributions of common assessments of impulsivity. , 40(6), 463–475. doi:10.3109/00952990.2014.937490 Crews, F. T. & Boettiger, C. A. (2009). Impulsivity, frontal lobes and risk for addiction. , 93(3), 237–247. doi:10.1016/j. pbb.2009.04.018 Ding, W. N., Sun, J. H., Sun, Y. W., (2014). Trait impulsivity and impaired prefrontal impulse inhibition function in adolescents with internet gaming addiction revealed by a Go/No-Go fMRI study. , 10, 20. doi:10.1186/1744-9081-10-20 Filbey, F. M. & Yezhuvath, U. S. (2017). A multimodal study of impulsivity and body weight: integrating behavioral, cognitive, and neuroimaging approaches. , 25(1), 147–154. doi:10.1002/oby.21713 Filbey, F. M., Claus, E. D., Morgan, M., Forester, G. R. & Hutchison, K. (2012). Dopaminergic genes modulate response inhibition in alcohol abusing adults. , 17(6), 1046–1056. doi:10.1111/j.1369-1600.2011.00328.x Hu, Y., Salmeron, B. J., Gu, H., Stein, E. A. & Yang, Y. (2015). Impaired functional connectivity within and between frontostriatal circuits and its association with compulsive drug use and trait impulsivity in cocaine addiction. , 72(6), 584–592. doi:10.1001/jamapsychiatry.2015.1 Jupp, B. & Dalley, J. W. (2014). Convergent pharmacological mechanisms in impulsivity and addiction: insights from rodent models. , 171(20), 4729–4766. doi:10.1111/bph.12787 McHugh, M. J., Demers, C. H., Braud, J., (2013). Striatal-insula circuits in cocaine addiction: implications for impulsivity and relapse risk. , 39(6), 424–432. doi:10.3109/00952990.2013.847446 Pivarunas, B. & Conner, B. T. (2015). Impulsivity and emotion dysregulation as predictors of food addiction. , 19, 9–14. doi:10.1016/j. eatbeh.2015.06.007 Am
Pharmacol
J
Biochem
Drug
Alcohol
Abuse
Behav
et al.
Behav Brain Funct
Obesity (Silver Spring)
Addict Biol
JAMA Psychiatry
Br J Pharmacol
et al.
Am J Drug
Alcohol Abuse
Eat
Behav
/
Spotlight
127
Stevens, L., Verdejo-Garcia, A., Goudriaan, A. E.,et al. (2014). Impulsivity as a vulnerability factor for poor addiction treatment outcomes: a review of neurocognitive
findings
among individuals with substance use disorders.
–
J Subst Abuse Treat, 47(1), 58 72. doi:10.1016/j.jsat.2014.01.008 Winstanley, C. A. (2007). The orbitofrontal cortex, impulsivity, and addiction: probing orbitofrontal dysfunction at the neural, neurochemical, and molecu-
–
lar level. Ann N Y Acad Sci, 1121, 639 655. doi:10.1196/annals.1401.024
Spotlight
Why So Impulsive? Teenagers are universally viewed as an impulsive population. Before the advent of imaging technology, it was thought that, following puberty, individuals (and their brains) are more or less how they will be for the rest of their lives. However, research has shown that the teenage brain is still developing, with areas for impulse control and decision making
– the PFC – being the last to develop
(Figure S8.1). The brain, in essence, develops from the back to the front.
Figure S8.1
Adolescence is a critical neurodevelopmental period and is associated with
highly impulsive behavior.
/
128
Impulsivity
These longitudinal studies collecting structural brain data on individuals across multiple years during adolescent development noted that the brain continues to develop into the mid- to late-20s before it is considered fully
“mature” or
fully myelinated to adult levels. The critical neurodevelopment during this period
occur within
the white matter tracts that connect different brain
regions. Thus, these frontal control areas are not accessed as rapidly. This leads to greater risk-taking behavior, including substance use.
References
Ainslie, G. (1975). Specious reward: a behavioral theory of impulsiveness and impulse control. , 82(4), 463–496. doi:10.1037/h0076860 Aron, A. R., Behrens, T. E., Smith, S., Frank, M. J. & Poldrack, R. A. (2007). Triangulating a cognitive control network using diffusion-weighted magnetic resonance imaging (MRI) and functional MRI. , 27(14), 3743–3752. doi:10.1016/0010-0277(94)90018-3 Bechara, A., Damasio, A. R., Damasio, H. & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. , 50(1–3), 7–15. Belin, D., Mar, A. C., Dalley, J. W., Robbins, T. W. & Everitt, B. J. (2008). High impulsivity predicts the switch to compulsive cocaine-taking. , 320(5881), 1352 –1355. doi:10.1126/science.1158136 Clark, L., Bechara, A., Damasio, H., (2008). Differential effects of insular and ventromedial prefrontal cortex lesions on risky decisionmaking. , 131(5), 1311–1322. doi: 10.1093/brain/awn066 Crews, F. T. & Boettiger, C. A. (2009). Impulsivity, frontal lobes and risk for addiction. , 93(3), 237–247. doi:10.1016/j. pbb.2009.04.018 Dalley, J. W., Everitt, B. J., & Robbins, T. W. (2011). Impulsivity, compulsivity, and top-down cognitive control. , 69(4), 680–694. doi:10.1016/j.neuron.2011.01.020 de Wit, H. (2009). Impulsivity as a determinant and consequence of drug use: a review of underlying processes. , 14(1), 22–31. doi:10.1111/j.1369-1600.2008.00129.x 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. , 96(1–2), 111 –120. doi:10.1016/j.drugalcdep.2008.02.002 Ersche, K. D., Turton, A. J., Pradhan, S., Bullmore, E. T. & Robbins, T. W. (2010). Drug addiction endophenotypes: impulsive versus sensationPsychol Bull
J Neurosci
Cognition
Science
et al.
Brain
Pharmacol Biochem Behav
Neuron
Addict Biol
Drug Alcohol Depend
/
References
129
seeking personality traits. , 68(8), 770–773. doi:10.1016/ j.biopsych.2010.06.015 Gerbing, D. W., Ahadi, S. A. & Patton, J. H. (1987). Toward a conceptualization of impulsivity: components across the behavioral and self-report domains. , 22(3), 357–379. doi:10.1207/s15327906mbr2203_6 Kreek, M.J., Nielsen, D. A., Butelman, E. R. & LaForge, K. S. (2005). Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction. , 8(11), 1450 –1457. doi:10.1038/nn1583 Moreno, M., Cardona, D., Gómez, M. J., (2010). Impulsivity characterization in the Roman high- and low-avoidance rat strains: behavioral and neurochemical differences. , 35(5), 1198–208. doi:10.1038/npp.2009.224 Poulos, C. X., Le, A. D. & Parker, J. L. (1995). Impulsivity predicts individual susceptibility to high levels of alcohol self-administration. , 6(8), 810–814. doi:10.1097/00008877-19951200000006 Robinson, E. S., Eagle, D. M., Mar, A. C., (2008). Similar effects of the selective noradrenaline reuptake inhibitor atomoxetine on three distinct forms of impulsivity in the rat. , 33(5), 1028–1037. doi:10.1038/sj.npp.1301487 Rodriguez-Cintas, L., Daigre, C., Grau-López, L., (2016). Impulsivity and addiction severity in cocaine and opioid dependent patients. , 58, 104–109. doi:10.1016/j.addbeh.2016.02.029 Seger, C. A. & Spiering, B. J. (2011). A critical review of habit learning and the basal ganglia. , 5, 66. doi:10.3389/ fnsys.2011.00066 Biol Psychiatry
Multivariate Behav Res
Nat Neurosci
et al.
Neuropsychopharmacology
Behav Pharmacol
et al.
Neuropsychopharmacology
et al.
Addict Behav
Front Syst Neurosci
/
CHAPTER NINE Impacts of Brain-Based Discoveries on Prevention and Intervention Approaches
Learning Objectives
• • •
Be able to understand how addiction is a chronic brain disease. Be familiar with pharmacological targets for addiction. Be able to describe the cognitive mechanisms supported by behavioral treatment.
•
Be able to characterize the synergy between pharmacological and behavioral approaches.
•
Be able to identify the biological pathways targeted by interventions.
Introduction
Because the effects of addiction have such high social implications, it has historically been viewed primarily as a social problem (i.e. “ disordered will ”) rather than a medical/health problem. This misconception has contributed to the current lack of successful approaches to the prevention and intervention of addiction. Over the last two decades, and partly due to the
“Decade
of the Brain” in 1990– 2000, a greater scientifi c
understanding and public awareness of addiction as a chronic brain disease emerged. Thus, current effective treatment programs are based on the understanding that addiction is a treatable disease that affects brain function, and that treatment must be individualized and address other possible mental disorders. As discussed in Chapter 5, although
drugs of abuse have different mechanisms of action, neuroscientifi c research, particularly in vivo human neuroimaging studies, has provided evidence that they all alter the brain’s dopaminergic signaling in the mesolimbic reward system. Dysfunction in this system leads to alterations in reward-processing, motivational and goal-directed behaviors as well as inhibitory control, as discussed throughout this book. These are therefore key brain regions and processes that can be targeted in therapeutic interventions.
/
Introduction
131
Relapse rates at 1 year post-discharge
80 70 60 50 40 30 20 10 0 Type 1 diabetes
Figure 9.1
Hypertension
Asthma
Alcohol dependence
Relapse rates for drug-addicted patients compared with those suffering from
diabetes, hypertension and asthma. Relapse is common and similar across these illnesses (as is adherence to medication). Thus, drug addiction should be treated like any other chronic illness, with relapse serving as a trigger for renewed intervention. (Data from McLellan et al., 2000.).
Addiction is a lifelong, chronic brain disease. The term chronic reflects its enduring pathology, which suggests a high likelihood that symptoms of addiction will recur despite abstinence from the substance (i.e. relapse). To put this into perspective, the rate of relapse for addiction is similar to that of other chronic diseases such as diabetes, hypertension and asthma, all of which have physiological as well as psychological components,
and
have
the
same rate
of
medication adherence
(Figure 9.1). Current intervention strategies focus on supporting abstinence by alleviating withdrawal symptoms, promoting treatment adherence and supporting
protracted abstinence through prevention of
relapse. There are several treatment approaches including pharmacological as well as behavioral/neurocognitive methods. Research shows that a combination of approaches facilitates greater outcomes, which is in line with the high complexity of addiction and the recovery process. Indeed, treatment strategies must take into account that the disruptions caused by addiction are widespread, affecting, among others, medical, psychological, social and occupational aspects of the individual. Thus, treatment programs incorporate comprehensive rehabilitation services to meet these varied needs (Figure 9.2). See Spotlight 1 for a description of the socio-occupational support provided by peer counseling programs.
So, how has current scienti fic knowledge of addiction as a brain
disorder been translated into clinical applications that bene fit those who need it most? What are novel entry points that can be exploited
/
132
Impacts of Brain-Based Discoveries
Vocational services Mental
Family
health
services
services Assessment Evidence-Based treatment Substance use monitoring Clinical/case management
Legal
Medical
Recovery support programs
services
Continuing care
services
HIV/AIDS
Educational
services
services
Figure 9.2 Components of comprehensive drug addiction treatment. The best treatment programs provide a combination of therapies and other services to meet the needs of the individual patient. (From National Institute on Drug Abuse, 2018.)
for the development of more effective treatment? Exciting progress in neuroscience research is in the translation of these neuroimaging
fi nd-
ings into clinical applications that promise to improve the status quo of clinical practice. A typical drug treatment protocol involves several steps, including: 1) detoxifi cation (the process by which the body rids
itself of a drug); 2) initial recovery where the focus is on sustaining motivation; and 3) relapse prevention, which may include treatment for co-occurring mental health issues such as depression and anxiety. This chapter will focus on how neuroscience research has advanced our informed addiction prevention and intervention strategies. Translational neuroscience research has: 1) advanced our understanding of risk factors that could facilitate early intervention; 2) facilitated improvement of standard treatment programs; 3) provided information on who, what and how intervention will be effective; and 4) fostered the development of novel and more targeted interventions.
Pharmacological Approaches
Pharmacological interventions are an important part of treatment, especially when combined with behavioral therapies. Medications can be
/
Pharmacological Approaches
133
used to manage withdrawal symptoms, prevent relapse and treat co-
occurring conditions by targeting specifi c receptors, either activating or blocking their mechanism of action, thereby interrupting how substances of abuse interact with brain receptors. There are a number of pharmacotherapies currently used for treatment of opioid, tobacco and alcohol addiction. Studies are underway to develop similar pharmacotherapies for stimulant and cannabis addiction. Opioid receptor medications include both opioid receptor agonists and antagonists. Currently, methadone and buprenorphine are the only opioid agonists approved for drug treatment in the USA (see Spotlight 2 to understand how legislation balances the costs related to opioid addiction). Opioid agonist therapy is effective in managing opioid withdrawal
and in reducing craving. Methadone, specifi cally, is a μ-opioid agonist as well as an
d -aspartate
N -methyl-
(NMDA) receptor antagonist. Func-
tional magnetic resonance imaging (fMRI) studies show that reductions in craving as a result of methadone treatment are associated with decreased activation in the limbic system (Li et
al.,
2013). Mass spec-
trometry imaging studies confirm that methadone is distributed in the
striatal and hippocampal regions, including the nucleus caudate, putamen and upper cortex in in vivo rat brains (Teklezgi et
findings
al.,
2018). These
suggest that mitigation of cue-induced craving may be the pri-
mary effect of methadone that may be key in long-term abstinence (Figure 9.3) (Li
et al.,
2013). The NMDA antagonist effect involves
modulation of the glutamatergic system, which is thought to mediate
the development of tolerance. Naltrexone is a μ -opioid,
κ-opioid and δ-
opioid antagonist and is approved for the treatment of opioid and alcohol use disorder. Studies show that naltrexone leads to good outcomes in decreasing subjective craving, which has been associated with decreases in the neural response to alcohol cues during fMRI in orbital and cingulate gyri, and inferior frontal and middle frontal gyri
–
areas
important for emotion, cognition, reward, punishment and learning/ memory. This attenuation of salience of alcohol cues may be the primary mechanism for the prevention of relapse. Cholinergic medications modulate the cholinergic system and are used primarily during tobacco smoking cessation. Bupropion is a nicotinic acetylcholine receptor (nAChR) antagonist and inhibits neuronal reuptake of dopamine. In effect, bupropion reduces craving. In contrast, varenicline is a partial agonist of the the
α4β2
subtype and full agonist of
α7 nAChR subtype, therefore leading to enhancement of cholinergic
transmission. Studies have shown that it reduces nicotine withdrawal symptoms and improves cognitive performance through increased activation of the prefrontal cortex (PFC) (Loughead et
al.,
2010). Because of
/
134
-12R
Impacts of Brain-Based Discoveries
L
-9
-6
+3
+6
T value -3.20 +9
+24
+12
+15
+18
+21
+39
+42
+45
+48
-5.00
Figure 9.3 Following methadone-assisted therapy (MAT), long-term abstinent heroin users (mean length of abstinence, 193 days) had a greater decreased response in striatal areas compared with short-term abstinent heroin users (mean length of abstinence, 23 days) during a cue-induced craving task. (From Liet al. , 2013.) (A black and white version of this
figure
will appear in some formats. For the color version, please refer to the plate section.)
the cognitive-enhancing effects of nAChR agonists, these medications have also been examined for the improvement of cognitive impairment in other types of addiction. For example, galantamine is an acetylcholinesterase inhibitor as well as an allosteric potentiator of the nAChR, and has been found to improve cognitive performance – sustained attention and working memory function – contributing toward decreased drug use (tested via a urine screen) in cocaine users (Sofuoglu & Carroll, 2011). Studies comparing bupropion with varenicline have reported greater rates of cessation with varenicline at 3 and 12 months post-detoxification, which highlights the important role of cognitive functioning in promoting behaviors necessary to maintain abstinence (Johnson, 2010). Similarly, the combination therapy of varenicline and bupropion yields greater efficacy than monotherapy (Vogeler , 2016). Acamprosate has a chemical structure similar to that ofγ-aminobutyric acid (GABA) and acts primarily by restoring normal NMDA receptor tone in the glutamate system. Acamprosate is thought to also suppress excitation-induced calcium entry that results from chronic alcohol exposure, thereby altering the conformation of the NMDA et al.
/
Behavioral Approaches
135
receptors. The balance of GABA and glutamate tone may be the mechanism that leads to its therapeutic effects. Acamprosate has been shown to reduce craving, leading to dose-dependent effects on decreasing alcohol consumption, increasing rate of treatment completion and maintaining abstinence. Using magnetoencephalography (see Chapter 2) in alcohol-dependent participants, it was found that acamprosate decreased the arousal level during alcohol withdrawal, as indicated by
α slow-wave
index measurement, in the parietotemporal regions (Boeijinga
et al.,
2004). This finding is in line with the notion that acamprosate modulates
neuronal hyperexcitability of acute alcohol withdrawal, acting through glutamatergic neurotransmission. The aldehyde dehydrogenase inhibitor
disul fi ram is an alcohol-
aversive agent that has also been used to treat alcohol use disorder as a deterrent. Disulfiram markedly alters the metabolism of alcohol, which
leads to increased blood acetaldehyde concentrations. This accumulation
of acetaldehyde leads to aversive effects such asflushing, systemic vasodilation, respiratory diffi culties, nausea, hypotension and other symp-
toms
(i.e.
acetaldehyde
syndrome).
In
contrast
to
anti-craving
medications, disulfiram does not modulate neurobiological reward mech-
anisms but rather works by producing an aversive reaction to alcohol. As
a deterrent, the therapeutic effect of disulfiram in supporting abstinence
is mediated through its psychological effects, i.e. the expectancy effect due to anticipation of the aversive reaction. Evidence for this comes from a meta-analysis, which showed that the significant therapeutic
effects of disulfiram are greater in open-label trials (Skinner et al., 2014).
Behavioral Approaches Behavioral approaches are designed to enhance the cognitive deficits linked to addiction, particularly prefrontal lobe functioning. Prefrontal areas such as the orbitofrontal, dorsolateral prefrontal and anterior cingulate cortices mediate executive functioning
such as attention,
working memory, decision making, set shifting and inhibitory control, among others. Cognitive behavioral models provide cognitive strategies and training that increase self-control and awareness of triggers for drug use. For example, cognitive behavioral therapy (CBT) may be utilized for the reduction of a cue-elicited craving response. The “ active ingredi-
ents” of CBT may exert their effects via strengthening aspects of executive control over behavior. Although the neural mechanisms by which CBT exerts its therapeutic effects are still unclear, neuroimaging studies have begun to understand that improvement of brain network function is
/
136
Impacts of Brain-Based Discoveries
involved. For example, CBT has been shown to strengthen the network connectivity that underlies executive functioning, such as attention (Lewis
et
al.,
2009). Additionally, an fMRI study investigating cue-
induced craving and using instructions based on CBT strategies to focus on long-term consequences of tobacco use rather than short-term pleasurable tobacco associations found that dorsolateral PFC regions exerted control over ventral striatal activation in the regulation of craving (Kober
et al.,
2010).
Cognitive rehabilitation strategies provide intensive exposure to computerized exercises that strengthen memory, attention, planning and other executive functioning. Improvement of these cognitive skills should therefore result in: 1) greater cognitive control over learned behavior related to substance use; 2) decreased impulsivity; 3) improved decision making; and 4) awareness of cognitions associated with drug use. Neuroimaging studies suggest that cognitive rehabilitation may normalize regional brain activation in the PFC (Wexler Bickel
et al.
et al.,
2000).
(2011) demonstrated that focused training on computerized
memory tasks resulted in significant reductions in an aspect of impulsiv-
ity, delay discounting (i.e. preference for immediate versus delayed rewards), among stimulant users. Psychosocial interventions such as motivational enhancement therapy (MET) and motivational interviewing (MI) are brief and focused interventions that aim to increase one’ s motivation to change. Research suggests that the effi cacy of these approaches depends on age, type of drug addiction and the goal of the intervention. For example, MET has shown treatment success in cannabis-using adults but not consistently in adolescents or in those using cocaine, heroin or nicotine. Feldstein Ewing
et al.
(2011) suggested that MI supports a reduction in substance
use by attenuation of the response in regions in the reward pathway,
which suggest that the effi cacy of MI is in reducing the salience of drug cues. Furthermore, they found that the active ingredient in MI, i.e. client change talk, elicited activation in areas that underlie self-awareness– the left inferior frontal gyrus/anterior insula and superior temporal gyri (Feldstein
Ewing
et
al.,
2014). Contingency
management (CM)
approaches have shown strong empirical support in randomized clinical
trials. CM corrects the amplified valuation of immediate reward and the discounted value of delayed rewards (delay discounting) by reinforcing targeted outcomes with positive incentives. Delay discounting has been associated with poor treatment outcome for addiction and has been shown to involve cortical and subcortical systems involved in decision
making (Balleine
et al.,
2007). Subcortical reward regions such as the
/
Combined Approaches
137
ventral striatum are highly sensitive to small immediate rewards, whereas cortical regions in the PFC are more engaged during larger but delayed rewards (Kable & Glimcher, 2007). Combined Approaches
The theory behind combined approaches is that neural alterations induced by pharmacotherapy may complement the cognitive mechanisms that behavioral approaches target. For instance, the reduced sensitivity to drug cues obtained by an anti-craving medication could be augmented by better cognitive control skills developed through CBT. Such a combined approach would maximize treatment success, particularly if implemented in early recovery when these skills are still developing. There is evidence to support the notion that combined pharmacological and behavioral therapies lead to better treatment outcomes than monotherapies. In one example, bupropion together with group counseling in nicotine users showed a reduction in glucose metabolism in the posterior cingulate cortex, an important region for goaldirected behavior, relative to monotherapy (Costello , 2010). Sofuoglu (2013) combined galantamine and CBT intervention to leverage the enhancing benefits of galantamine for improved memory and attention, which could then facilitate learning of CBT skills and strategies. Combined treatment boosts the efficacy of each individual approach, especially during a critical period when the greatest opportunities for improvements can be made (i.e. early recovery). These studies suggest that synergistic mechanisms occur in pharmacological and behavioral therapies. Potenza (2011) proposed a model by which brain mechanisms may mediate the effects of combined behavioral and pharmacological treatments for the treatment of addiction (Figure 9.4). They proposed that behavioral approaches are more efficacious in targeting “top-down ” PFC functions, such as inhibitory control, whereas pharmacological treatments are more targeted toward subcortical or“bottomup” processes, such as the reward–craving response. Konova (2013) reviewed the neuroimaging literature on the brain response to addiction interventions to determine the mechanisms by which these distinct interventions work independently and synergistically. Specifically, using a meta-analysis, they examined the distinct and common neural patterns associated with pharmacological and behavioral monotherapies. Overall, they found significant overlaps in the mechanisms between pharmacological and behavioral approaches in the dopaminergic reward pathway, i.e. the ventral striatum, inferior frontal gyrus et al.
et al.
et al.
et al.
/
138
Impacts of Brain-Based Discoveries
Cognitive
Behavioral
enhancement
treatments (CBT,
treatments
CM, MI and other)
Prefrontal cortex Partial nAChR
Executive functions, response
agonists
inhibition to drug cues, inhibition
DAT inhibitors
of drug-seeking behavior
α
2
agonists and
NET inhibitors DA
Glutamate
Glu
medications L. Cer.
Nac
NE neurons
VTA
Drug
DA neurons
withdrawal
Drug reward
NE
Dysphoria
DA
DA agonists Opioid agonist and antagonists
GABA
and antagonists
medications
Figure 9.4 Proposed model illustrating synergistic mechanisms between behavioral and pharmacological treatment approaches for addiction. DA, dopamine; DAT, dopamine transporter; Nac, nucleus accumbens; Glu, glutamate; VTA, ventral tegmental area; L. Cer., locus coeruleus; NE, norepinephrine; NET, norepinephrine transporter. (From Potenza et al., 2011. © 2011 Elsevier, USA.)
and orbitofrontal cortex (Figure 9.5). They also noted that, while there were overlaps, behavioral interventions were more likely to modulate the response in the anterior cingulate, middle frontal gyrus and precuneus/posterior cingulate cortex relative to pharmacological interventions, con firming the
“top-down”
notion of behavioral interventions as
suggested by the model of Potenza
et al.
(2011). Overall, these findings
suggest a potential mechanism by which the combined use of pharmacological and cognitive-based strategies may produce synergistic (due to their common targets) or complementary (due to their distinct targets)
therapeutic effects. The infl uences of behavioral interventions on pre-
frontal and parietal cortical regions may be important for treatment adherence.
Treatment Outcomes
To date, prognosis following treatment is diffi cult to assess given the lack of knowledge with regard to the extent to which cognitive and
neurobiological
impairments
recover
with
abstinence.
As
mentioned earlier, recovery is complex, and improvements do not
/
Treatment Outcomes
(a)
139
Pharmacological
Cognitive-based
interventions
interventions
MFG
MFG
MFG
VS
Conjunction
MFG
MFG
MFG VS
VS
Y = 13
L
R
IFG
IFG
Y = 23
IFG
OFC
OFC
Prec
OFC
Prec
Prec
A
P
X = –3
(b)
Pharmacological
Cognitive-based
Cognitive-based >
interventions
interventions
pharmacological
ACC
ACC
A
P
X =8
MFG
MFG R
L
Prec
Prec
Z = 40
Figure 9.5
Common (a) and distinct (b) neural targets of pharmacological and cognitive-
pharmacological
P
ACC
ACC X=8
MFG
L Prec
MFG
R
Prec
Z=40 Plate 9.5
Common (a) and distinct (b) neural targets of pharmacological and cognitive-
based therapeutic interventions.
/
Controls (N=100) ERO
TOT
12
Head plot
θ
power µv
Fz
Alcoholics (N=100) ERO
TOT
θ
2
Fz
12
40 30
Brain
20
oscillations
10 0
0 0
0
700 Power µv
0
20
40
Power µv 0
60
20
40
2
60
Chromosome 7
3.5
0
700
2
θ
Fz, Max LOD=3.16 at 161 cM
3
Cz, Max LOD=3.6 at 164 cM Pz, Max LOD=2.29 at 162 cM
2.5
2
DOL
Genetic
1.5
linkage 1 2MRHC
8 M RG
0.5
0
497S7D
5081S7D
905S7D
160
4281S7D
140
4081S7D
9881S7D 894S7D 7482S7D
120
7181S7D
128S7D
9971S7D
100
6971S7D
028S7D
7971S7D
80
0781S7D
6403S7D
566S7D 976S7D
60
0381S7D
874S7D
40
196S7D 125S7D
718S7D
6482S7D
20
2YPN
8381S7D 376S7D 926S7D
2081S7D
315S7D
0971S7D
0
180
Chromosome position (cM) CHRM2
81.7 kb
3
no xe
2
no xe
no xe
no xe
no xe
1
Candidate
41.1 kb
4
gene
5 22.6 kb
6 Coding
5’ -UTR
3’ -UTR
Sequence
Plate 10.4
656423 sr
0568731 sr
8454241 sr 6no xe
maetsnwod
3991918 sr RTU ’3
156423 sr
2991918 sr
056423 sr
046423 sr 6-5nortn i
4508496 sr
6870532 sr
5 xe2mrh c 5no xe
4711602 sr
7409977 sr 5-4nortn i
4204281 sr
6468731 sr
8585541 sr
0710087 sr
5692877 sr
587 c c
8121 c c 4-3nortn i
734879 sr
0870532 sr
7834241 sr
9654241 sr
06274231 sr
8554241 sr
4754241 sr 1no xe
maertspu
8554241 sr
SNPs
Genetic association
Brain EEG oscillations may be useful endophenotypes for alcohol use disorders.
/