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
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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.
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Brain
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–
–
–
–
–
–
–
–
–
–
’
–
/
20
What is Addiction?
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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
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Huettel, S. A., Song, A. W. & McCarthy, G. (2008).
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Pagliaccio, D., Barch, D. M., Bogdan, R., (2015). Shared predisposition
in the association between cannabis use and subcortical brain
structure.
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Purcell, E. M., Torrey, H. C. & Pound, R. V. (1946). Resonance absorption
by nuclear moments in a solid.
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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.
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Schacht, J. P., Hutchison, K. E. & Filbey, F. M. (2012). Associations between
cannabinoid receptor-1 (CNR1) variation and hippocampus and
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Whitford, T. J., Savadjiev, P., Kubicki, M., (2011). Fiber geometry in
the corpus callosum in schizophrenia: evidence for transcallosal
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Resonance Imaging
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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.
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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
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Neuropsychopharmacology
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Prog Neuropsychopharmacol Biol Psychiatry
Proc Natl Acad Sci U S A
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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.
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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.
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Motivation to Initiate Drug Use to Recreational Drug Use
Büchel, C., Peters, J., Banaschewski, T., et al. (2017). Blunted ventral striatal
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Carelli, R. M., Ijames, S. G. & Crumling, A. J. (2000). Evidence that separate
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Kalivas, P. W. & Volkow, N. D. (2005). The neural basis of addiction: a
pathology of motivation and choice. Am J Psychiatry, 162(8),
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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),
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/
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.
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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.
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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
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et al.
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Drug Alcohol Depend
et al.
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Alcohol Clin Exp Res
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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
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3932 –3939. doi:10.1523/JNEUROSCI.0433-05.2005
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alcohol substantially decrease glucose metabolism in the human brain.
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Volkow, N. D., Kim, S. W., Wang, G. J., et al. (2013). Acute alcohol
intoxication decreases glucose metabolism but increases acetate
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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
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(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
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/
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
<0.05).
(THC) metabolites
P
A quantitative meta-analysis of fi ndings from cue-reactivity neuroima-
ging studies was conducted by Kuhn and Gallinat (2011). They performed
activation likelihood estimation to determine overlaps in brain mechanisms elicited by cue-induced craving paradigms in nicotine, alcohol and
cocaine users. Their results found a consistent ventral striatum response,
and to a lesser degree, anterior cingulate and amygdala responses, to drug
cues. These regions may therefore refl ect the core circuit of drug craving.
Importantly, these brain responses are correlated with the subjective
experience of craving. Additionally, they are also correlated with addiction severity, such that the greater the response in these areas to cues, the
/
Neurophysiological Underpinnings of Craving
101
Please rate your urge to use marijuana right now
OR
No urge
0 1 2 3 4
5 6 7
8
9
10
at all
Extremely
high urge
+
Cue exposure
Rate
20 s
Figure 7.1
Washout
5 s
20 s
Cue-elicited craving paradigm using tactile cannabis cue paraphernalia, a
neutral object (pencil) and appetitive non-drug reward cues (fruit, not shown).
(From Filbey et al., 2016.)
greater the severity of symptoms related to addiction. For example, in
alcohol users, Myrick
viduals
et al.
(2004) reported that alcohol-dependent indi-
demonstrated a greater blood
oxygenated level-dependent
(BOLD) response in the PFC and anterior limbic areas after a sip of
alcohol and exposure to visual alcohol cues relative to non-dependent
alcohol users. Similarly, in cannabis users, the pattern of activation was
significantly positively correlated with drug-related problems as measured
by the Marijuana Problem Scale (MPS) (Filbey
et al.,
2009).
Neurophysiological Underpinnings of Craving
EEG has also been used to investigate cue-elicited craving in addiction. In cocaine users, EEG studies have found high
in response to cocaine-related cues (Liu et
These
al.,
β
spectral power
1998; Reid et
al.,
2003).
β states are the states associated with normal waking consciousβ power have also been associated with
ness. These increases in
greater subjective craving (Herning et
β
al.,
1997). Similar increases in
spectral power have been reported in nicotine users in response to
cigarette-related cues (Knott
et
al.,
2008a, 2008b). Event-related
potential (ERP) studies also report higher cortical activation in
/
102
Craving
response to drug cues such as increased P300 amplitude has been
reported in response to drug cues in alcohol (Herrmann
, 2000)
and nicotine (Warren & McDonough, 1999) users. P300 is a positive
de flection in voltage that occurs between 250 and 500 ms following the
onset of a stimulus and has been associated with engagement of
attention (such as orienting) to stimuli. Increased late positive potential (LPP) amplitudes have also been reported in response to drugrelated pictures compared with neutral pictures in individuals addicted
to alcohol (Heinze , 2007; Herrmann , 2001; Namkoong ,
2004), cocaine (Dunning , 2011; Franken , 2003; van de Laar
, 2004) and heroin (Franken
, 2003). LPPs have a latency
(delay between stimulus and response) of 400–500 ms after the onset
of a stimulus and have been suggested to facilitate attention to emotional stimuli. Taken together, EEG studies of cue-elicited craving in
addiction suggest that greater cortical arousal – in the form of
increased β, P300 and LPP amplitudes during drug cues – is linked
to greater subjective craving.
et al.
et al.
et al.
et al.
et al.
et al.
et al.
et al.
Contextual Cues
In addition to drug cues as described above, environmental or contextual
cues that have been associated with drug use can also trigger drug
craving. The brain mechanisms that underlie the response to contextual
cues appear to involve a more distributed neural network from that
underlying craving in response to drug cues. This network includes brain
regions that subserve emotional and cognitive aspects of memory in the
link between environmental cues and craving. Paradigms that use contextual cues utilize individual evocative scripts that ask participants to
imagine themselves in a setting where they would have been using
cocaine. In addition, neutral scripts, such as those that ask participants
to imagine themselves making art, are also presented. The scripts
included vivid descriptions of emotions and sensations of the activities.
In one such study, Bonson
(2002) reported that the presentation of
“evocative scripts” that described the context where drug use occurs in
the individuals, in combination with videos and paraphernalia related to
cocaine, elicited activation of the lateral amygdala, an important area for
emotion regulation. These findings replicated earlier reports of the
involvement of areas in the limbic system important for processing
emotion and memory in response to cocaine cues (Childress
,
1999). Taken together, cue-elicited studies of cocaine show that limbic
cortex activation is a component of cue-induced craving.
et al.
et
al.
/
Do Drugs Hijack the Reward Circuitry of the Brain?
103
Do Drugs Hijack the Reward Circuitry of the Brain?
As described above, the literature suggests that subjective craving is
correlated to the brain’s response in the reward circuitry (described in
Chapter 4). The question then becomes whether these increased brain
responses to drug cues are due to general hypersensitivity to salient
stimuli, as would be suggested by the reward deficiency syndrome, or
whether this hyper-responsivity is specifi c to drug and alcohol cues.
Early cue-elicited craving paradigms compared drug cues with neutral
cues. For instance, early studies in alcohol craving compared alcohol
taste relative to neutral tastes such as water or artificial saliva. Thus,
whether a differential response in the brain to alcohol tastes relative to
neutral tastes were driven by alcohol-specific craving processes or by the
general appetitiveness of the alcohol taste relative to water or artificial
saliva remained unknown.
Subsequent studies, such as those
by Filbey
et al.
(2008), integrated
control cues of equal appetitiveness to address this concern. For
example, one such study delivered small amounts of alcohol to heavydrinking adults and compared the brain ’s response relative to a sweet yet
unfamiliar taste, such as litchi juice (Filbey
et al.,
2008). The results
showed that the taste of an alcoholic beverage is a very powerful cue,
producing a signifi cant BOLD response in the striatum, ventral tegmen-
tal area (VTA) and PFC, above and beyond that of an appetitive and
novel cue. Other studies have also reported similar
findings
of drug-
related activation in similar pathways for natural rewards. For example,
Childress
et
al.
(2008) compared cocaine cues with sexual cues (in
addition to neutral and aversive cues) in male cocaine patients, and
found increased activity encompassing the ventral pallidum/amygdala
in response to cocaine cues relative to sexual cues (Figure 7.2). These
findings
suggest that cocaine leads to greater activation in a primordial
brain circuitry that encodes evocative stimuli. A similar approach was
also applied in cannabis use where tactile and visual cues for cannabis
cues were compared with neutral cues as well as appetitive non-drugreward cues (Figure 7.1) (Filbey
et al.,
2016). For the appetitive cues,
participants were presented with their preferred fruit. The authors found
that exposure to cannabis cues in long-term daily cannabis users elicited
a greater response in the orbitofrontal cortex, striatum, anterior cingu-
late gyrus and VTA relative to that in non-users. These findings demon-
strate hyper-responsivity and specifi city of the brain response to
cannabis cues in long-term cannabis users that are above the response
to natural reward cues. These observations
are concordant
with
/
104
Craving
“Unseen” cue
+
paradigm
Null
Sexual
Neutral
Aversive
Cocaine
Figure 7.2
Cue-elicited craving paradigm. A study by Childresset al. (2008) found a greater
response to cocaine than to sexual (also aversive and neutral) cues.
incentive-sensitization models, suggesting sensitization of the mesocorticolimbic regions and disruption of the natural reward processes
following drug use.
According to Daglish and colleagues, the brain networks involved in
drug craving are the same networks as for various cognitive processes
such as emotion, attention and memory, in addition to reward processing
(Daglish & Nutt, 2003; Daglish , 2003). However, in the case of
addiction, these networks become hypersensitive to drug-related cues. In
other words, the brain is “hijacked” by drugs, which is in line with the
incentive-sensitization model (see Chapter 3). This idea stems from
fi ndings that illustrate that the difference in users and non-users is not
the involvement of these various cognitive networks but the degree to
which they are engaged in the users (e.g. heroin users in the study by Sell
, 2000). As mentioned in the previous section, studies illustrate that
subjective craving is correlated strongly with activation increases in the
reward pathway (orbitofrontal cortex and striatum), as well as areas
et al.
et al.
/
Greater Craving or Greater Attention?
105
Fixation cross
+
5
0
0
Target stimulus
m
s
Masking stimulus
3
3
m
s
Fixation cross
4
2
0
0
0
6
7
m
s
m
s
1
Figure 7.3
0
+
0
0
m
s
Representative trial from the backward-masked cue task. In each trial,
participants were presented with the following visual stimuli in immediate succession:
crosshair (500 ms); target stimulus (33 ms); masking stimulus (467 ms); crosshair (1000 ms).
Target images were presented from one of four categories: cocaine (shown), neutral, sexual
and aversive.
(From Young et al., 2014. © 2014 Society for Neuroscience, USA.)
related to memory (hippocampus, PFC), emotion (amygdala) and attention (anterior cingulate gyrus, PFC). Functional connectivity between
these regions has been shown to reflect the ability of drug cues to
activate attentional and memory circuits to a greater degree than nondrug cues.
Greater Craving or Greater Attention?
The idea presented by Daglish (2003) that the ability of drug cues to
activate attentional and memory circuits to a greater degree than nondrug cues underlines craving suggests that craving may simply be attention. Studies by Childress
(2008) and Young
(2014) that used
masked cues have shed some light on this topic and support the notion
that craving is implicit, i.e. occurs subconsciously and only occasionally
intrudes into conscious awareness (Tiffany & Wray, 2012). These studies
utilized backward-masked images of cocaine, sexual, aversive and neutral cues and presented them rapidly (i.e. 33 ms) (Figure 7.3). Backward
masking presents a masked stimulus immediately after another brief
et al.
et al.
et al.
/
106
Craving
target stimulus, which often leads to a failure to perceive the masked
stimulus, in order to examine pre-attentive processes. These studies
found evidence for involvement of the limbic cortex during the masked
or subconscious exposure to the drug and sexual cues that correlated
with positive affect to the visible versions of the same cues.
Neuromolecular Mechanisms
The idea that craving occurs after the drug is consumed suggests the
occurrence of neural adaptations following drug exposure. One of the
cellular changes triggered by drug use is increased dendritic structure via
increased dendritic spine density in the nucleus accumbens and PFC.
Nestler and colleagues
suggested that these dendritic alterations are
mediated by transformation of FBJ murine osteosarcoma viral oncogene
homolog B (FosB) to
Δ FosB
(Figure 7.4) (Nestler, 2001; Nestleret
al.,
2001). FosB is a transcription factor in the brain, which, together with
other molecules, is involved in signal transduction that conveys genetic
information between the cells and also determines activation of certain
genes. This transformation is
initiated by increases in dopamine
following drug exposure, which increases with continued drug exposure
(i.e. chronic use). In terms of transduction,
ΔFosB
inactivates the
Repeated drug
exposure
(e.g. via
neurotrophic
factors, FosB,
CDK5?)
Normal responses to drugs
Figure 7.4
Use-dependent plasticity leading
to sensitized responses to drug
and environmental cues
Regulation of the dendritic structure by drugs of abuse. Expansion of a neuron
’s
dendritic tree and spine density occurs after chronic exposure to a drug of abuse in the
nucleus accumbens and PFC, mediated by
ΔFosB and the consequent induction of CDK5.
(From Nestler et al., 2001. © 2001 Springer Nature, USA.)
/
Review Questions
107
dynorphin gene (encoding dynorphins, which are endogenous opioids)
and activates the cyclin-dependent kinase 5 gene (CDK5) that encodes
cell division protein CDK5, a protein involved in neuronal maturation
and migration. The CDK5 protein stimulates dendritic spine growth in
the nucleus accumbens, which leads to craving and drug sensitivity.
ΔFosB in fluences growth factors and structural changes (neuronal plasti-
city) in the brain – approximately in the region where memory is formed.
The fact that these mechanisms resemble those in some learning models
(e.g. long-term potentiation) suggests that
ΔFosB
may mediate cue-
elicited craving. ΔFosB is stable, therefore initiating and sustaining these
changes in gene expression long after drug use ceases. Transgenic mice
ΔFosB have
Consequently, ΔFosB has
studies have shown that animals with overexpression of
increased sensitivity to the effects of drugs.
been posited as a “ molecular switch” that converts the acute response to
drugs into long-term responses, such as craving. See Spotlight for a
description of how post-mortem
ΔFosB may
indicate the persistence of
physiological craving.
Summary Points
•
The conceptualization
of craving
has been
advanced
by neuroimaging
techniques.
•
Neuroimaging studies demonstrate a heightened brain response in wide
brain networks encompassing reward, attention, emotion and memory
systems.
•
Patterns of brain response to drug cues are greater than those to natural
rewards and are correlated with subjective craving as well as with indices of
addiction severity.
•
•
EEG studies show heightened arousal in response to drug cues.
Backward masking provides evidence for the subconscious awareness of
drug cues.
• ΔFosB mediates the neural changes, including craving, that occur following
drug exposure.
Review Questions
•
•
What were the criticisms in the conceptualization of craving?
What are the wider systems that integrate to underlie craving in response
to drug cues?
/
108
•
•
•
Craving
What is the primary finding of EEG studies during cue-elicited craving?
Describe the process of backward masking and what this has approach
answered in terms of drug craving?
How can ΔFosB be a marker of addiction?
Further Reading
Ekhtiari, H., Nasseri, P., Yavari, F., Mokri, A. & Monterosso, J. (2016). Neuroscience of drug craving for addiction medicine: from circuits to therapies.
, 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.
, 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
Grant, S., London, E. D., Newlin, D. B.,
(1996). Activation of memory
circuits during cue-elicited cocaine craving.
,
93(21), 12040–12045.
Gu, X. & Filbey, F. (2017). A Bayesian observer model of drug craving.
, 74(4), 419–420. doi:10.1001/jamapsychiatry.2016.3823
Myrick, H., Anton, R. F., Li, X.,
(2004). Differential brain activity in
alcoholics and social drinkers to alcohol cues: relationship to craving.
, 29(2), 393–402. doi:10.1038/sj.npp.1300295
Robinson, T. E. & Berridge, K. C. (1993). The neural basis of drug craving: an
incentive-sensitization theory of addiction.
, 18(3),
247–291.
Tiffany, S. T., Carter, B. L. & Singleton, E. G. (2000). Challenges in the
manipulation, assessment and interpretation of craving relevant variables.
, 95, Suppl. 2, S177–S187.
Tiffany, S. T. & Wray, J. M. (2012). The clinical significance of drug craving.
, 1248, 1–17. doi:10.1111/j.1749-6632.2011.06298.x
Prog Brain Res
Prog Neuropsychopharmacol Biol Psychiatry
Proc Natl Acad Sci U S A
et al.
Proc Natl Acad Sci U S A
JAMA
Psychiatry
et al.
Neuropsychopharmacology
Brain Res Brain Res Rev
Addiction
Ann N Y Acad Sci
Spotlight
Drug Cravings Persist in Death
The presence of mutated ΔFosB protein weeks after the drug-use event
suggests that craving persists for weeks, even after cessation of use.
/
Spotlight
109
A group of scientists led by Monika Seltenhammer from MedUni in Vienna,
fi
Austria, made headlines in 2016 when they published their research ndings
on evidence that drug craving persists in the dead (Seltenhammer et al. ,
2016). In their study, they examined tissue samples from the nucleus accumbens of
fifteen
deceased heroin addicts and
measured levels of
ΔFosB
fifteen
non-drug users. They
and found that accumulation of the protein was
still detectable 9 days after death. The scientists referred to this effect as
“dependence
” From this finding,
memory.
the scientists inferred that
ΔFosB
persists even longer in living individuals, perhaps as long as months. This
supports existing animal
findings
of protein differences in live substance-
exposed animals relative to non-exposed animals, although lasting far longer
in post-mortem human brain tissue.
(a)
(b)
DGsp
(c)
DGip
(d)
(e)
(f)
(g)
Figure S7.1
Measuring
ΔFosB. Image thresholding analysis of raw FosB/ΔFosB
immunoreactivity (a) involves selecting regions of interest (b), then thresholding (b) and
fi
–
magni cation (d g), DGip, infrapyramidal blade of the dentate gyrus; DGsp,
suprapyramidal blade of the dentate gyrus. (From Nishijimaet 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 importance of this discovery is in providing evidence of physiological
craving that could be used as a marker of addiction severity, independent of
/
110
Craving
toxicology. Furthermore, this research underlines the importance of postmortem studies in informing potential mechanisms and targets for treatment
Δ
for addiction (Figure S7.1). The scientists suggest that activation of FosB can
be prevented, and future studies are needed to determine how targeting
ΔFosB can be used to treat the onset of addictive behavior.
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/
C H A PT E R E I G H T
Impulsivity
Learning Objectives
•
fi
Be able to explain the challenges in de ning impulsivity as a unitary
construct.
•
Be able to describe the literature on whether impulsivity is a cause or a
consequence of addiction.
•
•
•
Be able to discuss the concepts of risky decision making.
Be able to understand inhibitory control and delay discounting.
Be able to outline the networks and neurotransmitter mechanisms related
to impulsivity.
Introduction
Impulsivity is a multifaceted construct that encompasses a number of
concepts bound together by an inability to control one ’s behavior. These
concepts include, but are not limited to, risk taking, disinhibition and
delay discounting (Figure 8.1). Whether aspects of impulsivity are the
cause or effect of substance use remains to be determined. Advances in
research show that there may be underlying risks for addiction related to
the tendency to be impulsive that may then be further exacerbated by
substance use. Innovative research techniques aimed at disentangling the
various aspects of impulsivity have noted that different types of impulsivity are associated with different types of substance use.
Broadly speaking, impulsivity is the propensity to respond without
foresight. Because impulsive behavior can occur as a result of defi cits
at any stage of response production
–
response selection, response
preparation, response initiation or response execution
–
de fi ning impul-
sivity as a unitary concept has been a challenge in empirical research.
Dissociable cognitive processes (behavioral and neurobiological) underlie impulsive behavior and differentially contribute toward producing a
response. In general, the behavioral tasks used to measure impulsivity
/
Introduction
115
determine: 1) the perseverance of a response despite negative consequences; 2) the preference for a small immediate reward over a larger
delayed reward; and 3) the ability to withhold a pre-potent response.
Although extensive research has focused on understanding the ontology
of impulsivity, there continuous to be debate in this
Gerbing
et al.
field.
Work by
(1987) using a factor analysis on eleven self-report meas-
ures and four behavioral tasks revealed three impulsivity factors, which
they referred to as spontaneity, persistence and care free. Principal
components analysis of a widely used self-report questionnaire, the
Barratt Impulsiveness Scale (BSS-11), revealed a three-factor model of
impulsivity that includes greater motor activation, less attention and less
planning. Overall, these models include the following elements: 1) a
decreased sensitivity to
negative
consequences (risk-taking); 2) rapid,
unplanned reactions to stimuli before complete processing of information (impaired inhibitory control); and 3) a lack of regard for long-term
consequences (delay discounting). Overall, it is evident that impulsivity,
measured in a number of ways, is associated with some forms of drug
abuse and seems likely to result from multiple dysfunctions in corticostriatal pathways associated with diverse forms of impulsivity (Figure 8.2).
This chapter will review the vast literature that aims to understand the
bond between impulsivity and addiction. Emphasis will be given toward
Figure 8.1
Impulsivity leads to risky behavior.
/
116
Impulsivity
Visual
loop
Motor
loop
Executive
loop
Motivational
loop
Figure 8.2
Corticostriatal pathways. Disruptions in these pathways underlying executive,
motivational, motor and visual function contribute toward impulsivity.
(From Seger et al., 2011.)
clarifying the different concepts that underlie the broad construct of
impulsivity and the distinct methods used to study each one.
Neuropharmacology of Impulsivity
Research in attention defi cit/hyperactivity disorder (ADHD) has provided insight into the neuropharmacological basis of impulsivity. Methylphenidate (Ritalin) and amphetamines are the primary medications for
ADHD. Both block the reuptake of dopamine and norepinephrine into
pre-synaptic neurons, which leads to an increase in post-synaptic levels
of dopamine and norepinephrine. The increased availability of dopamine is considered a primary mechanism for the relief of ADHD symptoms. Thus, low dopaminergic tone has been suggested as one of the
underlying neuropharmacological mechanisms of impulsive behavior.
Similarly, increased noradrenaline has been shown to reduce impulsivity
/
Is Impulsivity Pre-existing or Drug Induced?
117
in widely utilized tasks of decision making such as the five-choice serial
reaction time task (5CSRTT) and delay discounting tasks (Robinson
, 2008). Some suggest that this may be an indirect effect that is
based largely on downstream effects of noradrenaline on dopamine.
Others, however, suggest a role of serotonin or 5-hydroxytryptamine
(5-HT) levels in subcortical regions, such as the nucleus accumbens. This
notion is based on studies demonstrating that an impulsive response to
tasks such as the 5CSRTT is negatively correlated with 5-HT turnover in
the nucleus accumbens (Moreno , 2010).
et al.
et al.
Is Impulsivity Pre-existing or Drug Induced?
Many consider impulsivity to be a continuous spectrum, and thus simply
being impulsive does not, on its own, indicate pathology. However,
impulsivity is more likely to be present in individuals with certain psychiatric disorders, such as addiction. Most studies that use self-reported
measures of impulsivity find higher levels of impulsivity in substancedependent individuals than in healthy comparison subjects (Crews &
Boettiger, 2009; Rodriguez-Cintas
, 2016). Among substancedependent individuals, those who are dependent on multiple substances
are more impulsive than those who are dependent on a single substance.
Some of the most widely utilized self-report questionnaires are the
Barratt Impulsiveness Scale (BIS-11), the UPPS-P Impulsive Behavior
Scale (IBS) and the Kirby test of delay discounting, which yield three
major subscales of impulsivity: “attentional,” “motor” and “non-planning.” The UPPS-P IBS is a fifty-nine-item self-reported scale with five
distinct subscales (positive urgency, negative urgency, lack of premeditation, lack of perseverance and sensation seeking).
The idea that impulsivity may be a pre-existing vulnerability for addiction comes from work demonstrating the heritability of impulsivity as a
stable trait (Kreek
, 2005). One such study used a family study
approach to determine the heritability of impulsivity. Ersche
(2010)
examined impulsivity and sensation seeking in a large group of stimulant
abusers and their siblings, as well as in age- and IQ-matched controls. As
seen in Figure 8.3, impulsivity, but not sensation seeking, was significantly
elevated in the siblings compared with controls, suggesting heritability of
impulsivity. The stimulant-using individuals exhibited the highest levels of
both sensation seeking and impulsivity. This is concordant with findings by
de Wit (2009) showing that siblings of chronic stimulant users had higher
levels of trait impulsivity than control volunteers, but did not differ from
control volunteers with regard to sensation-seeking traits. Candidate gene
et
al.
et al.
et al.
/
118
Impulsivity
(a)
(b)
Impulsivity
)ES 1 ± nae m( ero c s la to t V-SSS
)ES 1± nae m( ero c s la to t 11-S IB
100
*
*
*
80
60
40
20
Sensation seeking
30
*
*
25
20
15
10
5
0
0
Controls
Siblings
Drug users
(c)
Controls
Siblings
Drug users
(d)
40
10
*
*
*
20
10
)ES 1 ±( ero c s nae M
)ES 1 ± ero c s nae M
*
30
*
8
6
4
2
0
0
BIS
BIS
BIS non-
attention
motor
planning
Thrill and Experience Disinhibition Boredom
adventure
seeking
susceptibility
seeking
Controls
Figure 8.3
Siblings
Drug users
Study in stimulant-dependent individuals, their non-using siblings and non-
using controls demonstrating that impulsivity traits (but not sensation seeking) may be a
predisposing factor for stimulant dependence. The results show measurement of impulsivity
traits using BIS-11 (a, c) and sensation-seeking personality traits using the Sensation-
<
fi
Seeking Scale Form V (SSS-V) (b, d). SE, standard error; *, signi cant difference at P
0.05.
(From Ersche et al., 2010.)
studies have also found associations between genes that regulate the serotonergic system (tryptophan hydroxylase 1 and 2, serotonin transporter),
the dopaminergic system (dopamine transporter, monoamine metabolism
pathway) and the noradrenergic system (dopamine
β -hydroxylase)
and
impulsive personality. Together, these studies suggest that impulsivity is
heritable and could be an endophenotype for addiction.
Notably, the study by Ersche et
al.
(2010) also reported that those with
stimulant abuse had impulsivity even greater than their siblings, suggesting that exposure to drugs may exacerbate an already elevated level of
/
Is Impulsivity Pre-existing or Drug Induced?
Stimulus
Duration
119
Trial
Response
Condition
1
X
Go
700 ms
+
300 ms
2
Y
Go
700 ms
+
300 ms
X
3
Go
700 ms
+
300 ms
X
4
No go
700 ms
+
300 ms
312
Figure 8.4 Illustration of a go/no go test. A response is made for every go condition (i.e.
each visual presentation of
presentations of
“X ”
and
“ Y” )
but not for no go conditions (i.e. consecutive
“X”).
impulsivity. The notion that impulsivity may be drug induced comes
from drug administration and neuroimaging studies. For example, there
is considerable evidence that acute alcohol exposure increases impulsive
responding in tasks such as the go/no go test and stop-signal reaction
time (SSRT) task (Figure 8.4) (Dougherty
et al.,
2008). These widely
/
120
Impulsivity
used tasks of response inhibition measure one ’ s ability to inhibit a motor
response. Neuroimaging studies also demonstrate that chronic substance
abuse is associated with structural, functional and metabolic changes in
brain areas that underlie processes related to impulsivity, including the
lateral prefrontal cortex (PFC) and orbitofrontal cortex. In sum, the
neurotoxic effects of drugs on brain regions may underlie the impaired
inhibitory processes observed in addiction.
Given the mounting
evidence suggesting impulsivity as a pre-
existing risk factor as well as a consequence of drug use, it is possible
that these two etiologies both contribute to addiction, although at
different stages of the process. Specifically, the existing relationship
between impulsivity and other drug abuse vulnerability factors, such
as sex, hormonal status, reactivity to non-drug rewards and early
environmental experiences, may impact drug intake during all phases
of addiction.
Risky Decision Making
Another aspect of impulsivity is acting without regard for consequences. Interestingly, while impulsivity often involves risks, the risks
associated with impulsive behavior are often unrelated to sensation
seeking, highlighting how impulsivity and sensation seeking are dissociable constructs (as described above in the study by Ersche
2010). Support
et al.,
for this also exists in the animal literature. For
example, in a study on rats differentially characterized on impulsivity
and sensation seeking, it was found that high-sensation-seeking rats
were
more
sensitive
to
cocaine
and
acquired
cocaine
self-
administration more rapidly compared with the high-impulsive rats
that did not acquire cocaine self-administration as rapidly. However,
the high-impulsive rats exhibited greater cocaine-seeking behavior
despite mild foot-shock punishment (Belin
et
al.,
2008). This drug-
seeking behavior despite negative consequences, in this case foot
shock, is considered risky decision making.
A widely utilized task to evaluate risky decision making in humans is
the Iowa gambling task (IGT) (Bechara
et al.,
1994). The IGT is a
computerized card game that measures sensitivity to rewards and losses.
During the IGT, participants must weigh expected but uncertain rewards
and penalties, for example taking bigger risks for greater rewards or
smaller risks for lesser rewards. Using the IGT, neuroimaging studies
have shown that the right ventromedial PFC is engaged during decision
making, although activation in the left ventromedial PFC is associated
/
Inhibitory Control
121
with successful IGT performance. Lesion studies corroborate these fi ndings, showing that those with ventromedial PFC lesions exhibit poor
decision making. Other studies have also reported specificity of the
ventromedial PFC’s role in decision making. A study by Clark
et al.
(2008) in lesion patients, for example, found dissociable roles for the
ventromedial PFC and insula where the ventromedial PFC played a role
in the regulation of decision making during trials with known outcome
probabilities (see Figure 8.5), while the insula had a specifi c role only at
more unfavorable odds, confirming the specificity of the insula during
affective decision making.
Inhibitory Control
Another aspect of impulsivity is the ability to stop an action that has
either already been initiated or is in the choice selection phase. Imagine
the effort required to release the gas pedal when driving through a
stoplight that has just turned from green to yellow. This action requires
a similar process of inhibiting a pre-potent response (i.e. stepping on the
gas pedal). As introduced earlier, some of the widely used tasks to
measure inhibitory control are the SSRT task and the go/no go test.
Whereas the SSRT involves the cancellation of an already selected
response (“ action cancellation” ) the go/no go test implicates action
restraint. An animal analogue of this paradigm is the 5CSRTT, where
animals are trained to detect brief visual targets to earn food. Anticipatory responses that occur prior to the onset of the visual signals are
considered premature responses.
The circuit that underlies inhibitory control includes the right inferior
frontal
gyrus,
the
anterior
cingulate
cortex,
and
the
pre-
supplementary and motor cortex, as well as the basal ganglia and
projections to the subthalamic nucleus (Aron
et al.,
2007) (Figure 8.6).
Critics of this right-lateralized model argue for the additional contributions of left hemispheric regions. Some also suggest that, given that
response inhibition during the SSRT task is in response to an external
cue, the described processes may be predominantly attention driven.
Last, despite the prevailing argument that inhibitory control is exerted
top-down by cortical mechanisms, there is growing evidence that
neural circuitry involving both cortical and subcortical mechanisms
are implicated, particularly within
the
basal ganglia. Moreover,
the possibility exists for impulsivity to be caused by chemical dysmodulation, not only of cortical processes but also at the level of the
striatum.
/
122
Impulsivity
3
2
1
1
2
3
IN
VMPF
1
VMPF
2
1
2
3
4
IN
3
5 >
# 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.
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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.
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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.
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Ersche, K. D., Turton, A. J., Pradhan, S., Bullmore, E. T. & Robbins, T. W.
(2010). Drug addiction endophenotypes: impulsive versus sensationPsychol Bull
J Neurosci
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et al.
Brain
Pharmacol Biochem Behav
Neuron
Addict Biol
Drug Alcohol Depend
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Gerbing, D. W., Ahadi, S. A. & Patton, J. H. (1987). Toward a
conceptualization of impulsivity: components across the behavioral
and self-report domains.
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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),
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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
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, 5, 66. doi:10.3389/
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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-
<
based therapeutic interventions. Threshold for conjunction:
P
3
0.005 uncorrected and a
minimum cluster size of 100 mm . Threshold for difference contrast:P
3
<
0.05 false discovery
rate-corrected and a minimum cluster size of 100 mm . A, anterior; ACC, anterior cingulate
cortex; IFG, inferior frontal gyrus; L, left; MFG, middle frontal gyrus; OFC, orbitofrontal
cortex; P, posterior; Prec, precuneus; R, right; VS, ventral striatum. (From Konovaet al.,
fi
2013. © 2013 Elsevier, USA.) (A black and white version of this gure will appear in some
formats. For the color version, please refer to the plate section.)
/
140
Impacts of Brain-Based Discoveries
have a clear, linear relationship with the duration of abstinence. For
example, underactivation of
the inhibitory
control
network may
worsen during the early stages of withdrawal before it rebounds
during protracted abstinence. This makes timing of treatment strategies, such as bolstering inhibitory control, critical, given that weakness in prefrontal control systems during early withdrawal poses a
high risk for relapse.
In general, cognitive impairments are associated with poorer adherence to treatment. For example, cocaine users who failed to complete
CBT
had
significantly
worse performance on
tests of
attention,
memory, spatial ability, speed, accuracy, global functioning and cogni-
tive pro ficiency compared with those who completed the CBT regimen
(Aharonovich
et
al.,
2006). Similar
findings
were found in cannabis
users who did not complete treatment, i.e. poorer abstract reasoning
and processing accuracy (Aharonovich
et
al.,
2008). In addition to
cognitive performance predicting treatment adherence, performance
on measures of risk taking and sustained attention has been found to
predict CBT outcomes in terms of negative drug screens in cocaine
users. Notably, overall cognitive performances as indexed by a compos-
ite score did not predict treatment response, suggesting a specificity of
the effects of cognitive domains on the clinical course of drug treatment
outcomes (Carroll
et al.,
2011). In general, impairments in inhibitory
control tend to be associated with poorer outcomes
et al.,
(Verdejo-Garcia
2012).
Long-term relapse prevention is the biggest challenge in addiction
intervention.
Studies
only
show
modest
effect
sizes
of
current
approaches because of the heterogeneity of patient samples. Given
the individual variability of addiction in terms of risks and manifestations,
“one
size does not
fit
all. ” Identifying effective treatment has
shown promise when biologically de fi ned endophenotypes
(versus
behavioral symptoms) are used. For example, naltrexone treatment
has been found to be more effective in carriers of a specific variant of
the
μ-opioid
receptor gene (Chen
et al.,
2013). Similar genetic effects
may be present for the response to acamprosate, specifi cally in genes
associated
with
glutamatergic/GABAergic negative
system (Ooteman
et
al.,
reinforcement
2009). Very recently, biological differences
between patient groups are also being identi fied using functional neuro-
imaging. Naltrexone is suggested to work better in a subgroup of
patients with higher cue reactivity when shown appetitive alcohol pictures. Magnetic resonance spectroscopy of brain glutamate levels may
detect potential acamprosate responders.
/
Further Reading
141
Summary Points
• Studies demonstrate that a combination of pharmacological and cognitive
approaches lead to better treatment success.
• There are three stages to the recovery from addiction: detoxification, initial
recovery and relapse prevention.
• The synergistic mechanisms in combined pharmacological and behavioral
therapies may be a combination of “top-down” mechanisms through
behavioral intervention with “bottom-up” processes in pharmacological
approaches.
Review Questions
What are the common targets of pharmacological and cognitive therapies?
How can neuroimaging methods lead to individualized treatment?
What are the three primary stages of addiction intervention?
How could behavioral and pharmacological treatment mechanisms complement each other?
• What biological pathways do behavioral and pharmacological treatments
both target?
•
•
•
•
Further Reading
Bickel, W. K., Christensen, D. R. & Marsch, L. A. (2011). A review of
computer-based interventions used in the assessment, treatment, and
research of drug addiction.
, 46(1), 4–9. doi:10.3109/
10826084.2011.521066
Chung, T., Noronha, A., Carroll, K. M.,
(2016). Brain mechanisms of
change in addictions treatment: models, methods, and emergingfindings.
, 3(3), 332–342. doi:10.1007/s40429-016-0113-z
Feldstein Ewing, S. W., Filbey, F. M., Hendershot, C. S., McEachern, A. D. &
Hutchison, K. E. (2011). Proposed model of the neurobiological mechanisms underlying psychosocial alcohol interventions: the example of motivational interviewing.
, 72(6), 903–916.
Feldstein Ewing, S. W., Filbey, F. M., Sabbineni, A., Chandler, L. D. & Hutchison, K. E. (2011). How psychosocial alcohol interventions work: a preliminary look at what FMRI can tell us.
, 35(4), 643–651.
doi:10.1111/j.1530-0277.2010.01382.x
Subst Use Misuse
et al.
Curr Addict Rep
J Stud Alcohol Drugs
Alcohol Clin Exp Res
/
142
Impacts of Brain-Based Discoveries
Feldstein Ewing, S. W., Houck, J. M., Yezhuvath, U.,et al. (2016). The impact
of therapists’ words on the adolescent brain: in the context of addiction
treatment. Behav Brain Res, 297, 359–369. doi:10.1016/j.bbr.2015.09.041
Feldstein Ewing, S. W., McEachern, A. D., Yezhuvath, U.,et al. (2013).
Integrating brain and behavior: evaluating adolescents’ response to a cannabis intervention. Psychol Addict Behav, 27(2), 510–525. doi:10.1037/
a0029767
Gilfillan, K. V., Dannatt, L., Stein, D. J. & Vythilingum, B. (2018). Heroin
detoxification during pregnancy: a systematic review and retrospective
study of the management of heroin addiction in pregnancy.S Afr Med J,
108(2), 111–117. doi:10.7196/SAMJ.2017.v108i2.7801
Glasner-Edwards, S. & Rawson, R. (2010). Evidence-based practices in addiction treatment: review and recommendations for public policy. Health
Policy, 97(2–3), 93–104. doi:10.1016/j.healthpol.2010.05.013
Gorsane, M. A., Kebir, O., Hache, G.,et al. (2012). Is baclofen a revolutionary
medication in alcohol addiction management? Review and recent updates.
Subst Abus, 33(4), 336
–349. doi:10.1080/08897077.2012.663326
Liu, J., Nie, J. & Wang, Y. (2017). Effects of group counseling programs,
cognitive behavioral therapy, and sports intervention on internet addiction
in East Asia: a systematic review and meta-analysis. Int J Environ Res Public
Health , 14(12). doi:10.3390/ijerph14121470
Spotlight 1
fl
The astounding rise in rates of addiction in the USA has led to a high need for
addiction treatment specialists. Some areas such as Lehigh Valley in Pennsylvania have addressed this rising rate of addiction by relying on certified
recovery specialists. Certi fied recovery specialists are individuals who themselves are in long-term recovery from addiction. After completion of over 50 h
of intensive training related to recovery management, certified recovery specialists can then help others in need by providing support in a similar way to
their own recovery. Pennsylvania’s training program was established in 2008,
and today, peer counseling programs exist nationwide in the USA.
Peer recovery specialists support clients’ recovery from addiction alongside
healthcare specialists who provide the necessary treatment. Peer recovery
specialists leverage their own experience living in recovery and assist clients
during the transition from treatment back to society (Figure S9.1). They guide
on practical matters such as finding employment, housing and education.
Leveraging the Power of Peer In uence
/
Spotlight 2
Figure S9.1
143
Peer addiction recovery specialists bring different perspective to treatment.
In the case of Lehigh Valley, each peer recovery specialist supports up to thirty
clients.
The benefits of peer counseling programs are reciprocal. The process of
providing support and managing the functional needs of others encourages
peer recovery specialists to maintain the same level of expectations for themselves. In short, as peer counselors encourage their clients to resist the urge to
use substances, so do they. Witnessing others overcome their addiction
through the program also keeps the peer counselors motivated and encouraged to continue down their path.
Spotlight 2
The Balance of Legislation and Cost of Addiction Treatment
The US Department of Health and Human Services estimated that, in 2015,
the opioid epidemic cost $55 billion in health and social services and $20
billion in emergency department and inpatient care for opioid poisonings.
Given the upward trend in rates of opioid-related deaths in the USA (e.g. 8%
/
144
Impacts of Brain-Based Discoveries
in 2010 to 25% in 2015, according to the Centers for Disease Control and
Prevention), the costs for treatment programs are expected to rise, contributing toward growing economic challenges in healthcare. For example, the
budget cuts in the Affordable Care Act’s requirement for addiction services
under Medicaid have led to a 2018 ban on drug toxicology tests that verify
adherence to treatment and abstinence during addiction treatment in Maryland. The Maryland Medicaid program claimed to have spent 23% of its $315
million budget for substance use treatment. Most legislators acknowledge the
opioid epidemic and advocate for more drug treatment centers but are
hindered by the associated costs. As an alternative approach, legislative
leaders, such as those in Indiana, have reached out to private foundations
to help fund more centers. Additionally, a Senate committee is considering a
bill that allows tougher penalties against drug dealers if one of their customers dies of an overdose.
Despite these costs, changes in legislation have been put in place to maximize treatment opportunities. In 2017, Jessie’s Law was passed by the Senate
ensuring that clinical providers have information on patients’ substance abuse
history1. House-passed bills would make drug treatment available in jail to
people charged with misdemeanors and would make it easier for drug counselors to be licensed, to fund overdose rescue medications such as naloxone
and to study whether office-based treatment programs should be licensed.
References
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predict low treatment retention in cocaine dependent patients.
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Jessie s Law was named after Jessica Grubb who was in recovery from opioid abuse when
she underwent surgery. Her discharging physician did not receive the information about
her history of opioid use and erroneously discharged her with a prescription forfifty
oxycodone tablets. Jessie overdosed and died the same night.
’
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/
C H A PT E R T EN
Conclusions
Learning Objectives
•
fi
Be able to summarize how neuroscienti c research has advanced our
understanding of addiction.
•
Be able to appreciate how identifying risk factors can advance prediction
and intervention strategies.
•
Be able to describe endophenotypes that lead to individual differences in
susceptibility to psychoactive substances.
•
Be able to understand differences in manifestations of addiction across
males and females.
•
Be able to explain the limitations and future needs of neuroscience research
in addiction.
Introduction
As Chapters 1 and 9 discussed, the social implications of addiction have
led to the stigma that addiction is a social problem. This general public
opinion may originate from the more evident societal burden of addiction relative to the personal burden that is usually minimized by the
sufferer. For example, approximately $67 billion is spent in the USA due
to crime, lost work productivity and social support related to addiction.
This stigma of addiction as a non-medical disorder has perpetuated in
medical settings where the training curricula continue to place little to no
emphasis on programs related to the treatment of addiction. As a result,
medical practices rarely evaluate potential substance-related problems,
which, in turn, leads to poor prognosis. The preceding chapters discussed
how the operational de fi nition of addiction has been validated by neuroscientifi c research in the absence of diagnostic laboratory tests or
biomarkers for substance use disorder or addiction (see Chapter 1 for
diagnostic criteria). Indeed, neuroscience research, especially with the
advancements of in vivo human imaging techniques, has provided us
/
Risk Factors Inform Better Prevention and Intervention
149
with knowledge of the neurobiological foundations for the observable
symptoms of addiction. It has provided us with mechanisms by which we
can develop effective treatment and make predictions of outcomes. In
sum, neuroscientific research has shed light on the very complex neurobiological framework that parallels the complex behavioral sequelae of
addiction. The neuroscience of addiction will continue to evolve as our
understanding of these intricate neural processes deepens. Equally
important will be an understanding of the interactions between these
neurobiological processes and the myriad factors that modulate them.
Not everyone who consumes drugs and alcohol becomes addicted. In
fact, the prevalence of addiction relative to the total number of individuals who use drugs and alcohol is relatively modest. For example, among
those who have tried cocaine, only about 17% become addicted; about
15% of those who drink become dependent; and for nicotine, 30% of
those who try smoking become addicted smokers. What makes some
individuals more vulnerable than others? What are the mechanisms that
increase their brain s sensitivity to the psychoactive effects of substances? Behavioral and genetic studies provide some information about
these morbidities. The individual factors contributing to vulnerability to
addiction are complex and have not yet been fully elucidated. This
chapter will discuss neuroscientific discoveries on how these factors
modulate the response to substances.
’
Risk Factors Inform Better Prevention and Intervention
Risk factors are defined as characteristics that heighten one s likelihood
for addiction. These factors could be biological, psychological, social or
environmental. It is widely accepted that one of the primary risk factors
associated with the development of addiction is adolescent onset of use.
Developmental neuroscience studies posit that the rapid brain maturation of prefrontal network connections responsible for decision making
and inhibitory control during adolescence makes the adolescent brain
more vulnerable to the effects of psychoactive substances. Important
neuromaturational processes during adolescence through to young
adulthood are believed to bring about improved higher-order cognition
by refining neural systems locally and globally through white and gray
matter developments (Casey , 2005). In general, gray matter
reductions and cortical thinning coincide with increased white matter
volume and organization throughout adolescence and young adulthood,
suggestive of synaptic pruning and axonal myelination (see Chapter 1)
(Giorgio , 2010; Gogtay , 2004; Hasan , 2007; Lebel ,
’
et
et al.
et al.
al.
et al.
et al.
/
150
Conclusions
2010; Shaw , 2008). Exposure to psychoactive substances during
adolescence is thought to disrupt the strengthening of connections
between higher-order association areas such as the corticostriatal network (Wierenga , 2016).
Early life stress during this critical period for neurodevelopment has
also been associated with a greater risk for later development of addiction. Stress induces the release of central corticotropin-releasing factor
from the hypothalamus that binds to corticotropin-releasing factor
receptors in the pituitary. This interaction in the pituitary stimulates
the production of active peptides, including β-endorphin and adrenocorticotropic hormone, which is carried via blood to the adrenal glands
where it induces the secretion of glucocorticoids. The glucocorticoids
are then transported by the blood to the brain, where they act on
numerous signaling systems including the dopaminergic reward system,
in addition to systems involved in physiological stress responses (e.g.
increases in blood glucose levels and blood pressure) (see Chapter 6 for
more information on neuroadaptations related to stress). This stressrelated modulation of the reward system during neurodevelopment
may therefore disrupt the maturational process of the reward system.
Indeed, pre-clinical studies in rats show that early life stress is associated
with dysregulation in midbrain circuitry (Chocyk , 2015), linked to
dysfunctions in reward-related behavior (see Spotlight 1 for more on the
interaction between stress and addiction).
et al.
et al.
et al.
Addiction Endophenotypes
There is strong evidence from family, adoption and twin studies of the
role of genetic factors in the development of addictions (Ducci & Goldman, 2012). Figure 10.1 illustrates heritability across ten addictions,
demonstrating that while almost all have at least 40% heritability, it is
lowest for hallucinogens (39%) and highest for cocaine (72%). A specific
area of neuroscientific research referred to as imaging genetics leverages
knowledge from addiction genetics studies in order to determine the
source of variability in neural signaling pathways associated with addiction. Speci fically, genetic variability is associated with neurobiological
processes gleaned from human in vivo neuroimaging methods, such as
blood oxygenated level-dependent (BOLD) functional magnetic resonance imaging (fMRI), pharmacological fMRI and multimodal positron
emission tomography (PET)/fMRI. A study by Hariri (2009) illustrated
how linking genetic variability with the neurobiology of complex traits
such as personality and temperament can identify individual variability
/
Addiction Endophenotypes
151
1
0.8
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0.6
2
h
0.4
0.2
Mean
0
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9
9
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g
u
ll
a
H
Addictive agents (number of twin pairs)
Heritability ( 2; weighted means and ranges) of ten addictions based on a
large survey of adult twins.
Figure 10.1
h
(From Ducci & Goldman, 2012, adapted from Goldman
, 2005. © 2005 Springer Nature, USA.)
et al.
of risk, which can serve as an important predictor of vulnerability to
addiction (Figure 10.2). In this example, the link between the genetic risk
for depression ( HTR1A-1019 G allele), whose functional signifi cance is
heightened serotonin signaling, and trait anxiety, which predicts depression, is amygdala reactivity. This link or intermediate expression
between the genetic mechanism and the behavioral manifestation is
referred to as an endophenotype.
The concept of endophenotypes in psychiatric genetics was introduced
by Gottesman and Shields (1972) to address the poor reproducibility of
genetic
findings
and challenges in determining underlying etiologies
based on diagnostic criteria in schizophrenia. They de fined the concept
as internal phenotypes that lie on the pathway between genes and
disease and whose variation depend on variation in fewer genes than
the more complex disease phenotype, as illustrated in Figure 10.3. In
essence, endophenotypes should be more tractable to genetic analyses.
Neuroscientific research has therefore focused on identification of endophenotypes that predispose individuals to compulsive drug use to allow
/
152
Conclusions
60
n o it u b i rts i d dev res bO
(e)
(a)
50
Variability in measures of temperament and
personality (e.g. trait anxiety) may predict risk
40
for neuropsychiatric disease (e.g. depression),
especially in the context of environmental
30
stressors
20
Personality measure
60
(b)
e r usaem yt i la n os reP
50
Variability in behaviorally relevant brain circuit
function (e.g. threat-related amygdala reactivity)
40
may represent a disease-related bias in processing
specific types of information (e.g. attentional bias
to threat)
30
20
–0.5
0.5
0
1.0
1.5
Brain circuit function
1.5
n o itc n uf t i uc r ic n ia r B
(c)
1.0
Variability in molecular signaling pathways (e.g.
increased 5-HT
1A
0.5
autoreceptors assayed with PET)
predicting this brain circuit function may represent
a specific pathophysiological mechanism and
therapeutic target (e.g. 5-HT
0
1A
autoreceptor
antagonism)
–0.5
1.0
2.0
3.0
4.0
5.0
6.0
7.0
Molecular signaling pathway
yaw hta p g n i la n g is ra l uce l o M
7.0
(d)
6.0
Functional genetic polymorphisms (e.g. HTR1A-1019G
5.0
allele) efficiently represent emergent variability in the
entire biological cascade from (c) to (a) and may
4.0
represent predictive markers of specific disease
3.0
processes that can lead to personalized medicine (e.g.
administering 5-HT
1A
2.0
antagonists to only depressed
patients possessing the1019 G allele)
1.0
AA
AB
BB
Functional genetic polymorphism
Figure 10.2
Integration of complementary technologies (e) can be used to reveal the
fi
neurobiology of individual differences in complex behavioral traits. Specically, trait anxiety
(a) associated with depression can be linked with amygdala reactivity (via fMRI) (b), which
can then be associated with serotonin signaling (via PET) (c) and tied to variability in the
HTR1A-1019
G allele (d).
(From Hariri, 2009. © 2009 Annual Reviews, USA.)
/
Addiction Endophenotypes
153
s isyl a n a c it e n e g d n a
e pyt o n e h p f o yt ixe l pm oC
Number of genes
Figure 10.3 The concept of endophenotypes is that they lie in the causal pathway between
the genetic mechanisms and observable behavior.
(Redrawn by author, from Gottesman & Gould, 2003.)
better identifi cation of genetic mechanisms and thus biological pathways,
and to determine the functional consequences of risk-associated genes.
As illustrated in Figure 10.4, Rangaswamy and Porjesz (2008) suggested that brain electroencephalography (EEG) oscillations are valuable
endophenotypes for alcohol use disorders. Speci fi cally, they found that
θ (3–7 Hz) event-related oscillations underlying the P3 response are associated with individuals with alcohol use disorders and their unaffected
relatives, and are linked with GABAergic, cholinergic and glutamatergic
genes (GABRA2,
CHRM2
and
GRM8 ,
respectively). These oscillations
refl ect a link between associative and integrative brain functions.
Further associations between the inhibitory
γ-aminobutyric
acid
(GABA) α2 receptor subunit ( GABRA2) gene and alcohol use disorder
have been reported using fMRI. Specifically, Villafuerte
et al.
(2011)
found that increased activation in the insula cortex activation during
anticipation of monetary rewards was correlated with impulsivity measures and the risk markers for alcohol use disorders. Brain structure may
also be a useful endophenotype, as demonstrated by Schacht
et al.
(2012)
(Figure 10.5). Their research showed an interaction between cannabinoid receptor 1 (CNR1) genes, hippocampal volume and cannabis use,
whereby cannabis users with the risk genes (CNR1 G carriers) had
smaller hippocampal volumes than controls.
These endophenotypes can then be used to inform preventative
approaches, which may include pro-social and cognitive support to
/
154
Conclusions
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
2YPN
8381S7D
376S7D
926S7D
2081S7D
315S7D
0971S7D
20
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
5
gene
22.6 kb
6
Coding
5’ -UTR
3’ -UTR
Sequence
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
7409977 sr
5-4nortn i
4204281 sr
4711602 sr
6468731 sr
8585541 sr
5692877 sr
0710087 sr
587 c c
8121 c c
4-3nortn i
734879 sr
0870532 sr
9654241 sr
7834241 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.
(From Rangaswamy & Porjesz, 2008.) (A black and white version of thisfigure will appear in
some formats. For the color version, please refer to the plate section.)
Figure 10.4
develop decision making and inhibitory control process that would lead
to better avoidance
of risk-taking behavior. These strategies may
be
particularly useful in high-risk individuals, such as adolescents with a
family history of addiction, peer drug in
fluences, externalizing
and risk-
/
Sex Differences in Addiction
155
5000
***
**
***
4500
4000
3500
)3 m m( e muloV
3000
2500
2000
1500
1000
500
0
L hippocampus*
R hippocampus*
Controls A/A
Cannabis A/A
Controls A/A and G/G
Cannabis A/A and G/G
Changes in brain volume may be an endophenotype for cannabis use disorder.
Figure 10.5
fi
The graph shows a signi cant difference in bilateral hippocampal volumes for cannabis users
and matched healthy controls according to genotype. *P
group and genotype; **
P
±
0.05; ***P
±
±
0.05 for interaction between
0.001. L, left; R, right.
(From Schacht et al., 2012.)
taking behaviors, psychiatric disorders, etc. In terms of treatment, risk
factors
could exacerbate
the
symptoms
of
addiction;
thus,
treatment
approaches should place emphasis on identifying and managing these
vulnerability
mechanisms.
fi cant
identify signi
Comprehensive
cognitive
cognitive
impairments from
assessments
help
risk factors that com-
pound the presentation of addiction. Knowing each individual ’s cognitive
pro
file
could
better
facilitate
targeted
strategies
that
support
fi
treatment in those with speci c risk factors.
Sex Differences in Addiction
There is an emergent need to better understand the mechanisms by
which
the
females.
response
to
Understanding
substances
these
might
differences
differ
can
between
help
to
males
provide
and
more
effective treatment, as well as develop treatments that could modulate
/
156
Conclusions
the effects of hormones on treatment outcomes. Behaviorally, there are
sex differences in terms of the development of addiction where females
escalate more quickly and experience greater withdrawal symptoms than
males. For example, female rats develop conditioned place preference at
a lower threshold than males and are much more responsive to drugconditioned stimuli. Sex effects have also been observed in brain function. fMRI during cue reactivity showed a greater response to cues in the
striatum, hippocampus, amygdala and lateral orbitofrontal cortex in
females than in males (Wetherill , 2015). These results highlight
differential reward processing in males and females.
Beyond sex differences, the impact of hormones on the response to
substances has also been noted. In women, subjective feelings fluctuate
during the menstrual cycle whereby a greater response to drugs (e.g.
cocaine) has been observed during the follicular phase but is reduced
during the luteal phase. Pre-clinical studies have also shown greater
reinstatement as a function of estradiol levels but are attenuated by
progesterone. The estrous cycle also influences the effects of stimulants
on psychomotor behavior (Bobzean , 2014).
Research has suggested that the primary mechanism for sex differences in addiction is likely due to the interaction between hormone and
dopamine function. First, there are basal differences. Females are
reported to have lower levels of dopamine than males, which is likely
to contribute to greater impulsivity and vulnerability toward addiction.
Males have up to 10% more striatal dopamine receptors than females
and have more dopamine release in the striatum relative to females.
There is also sexual dimorphism on the effects of estradiol. Estradiol
directly stimulates dopamine release in the striatum, but estradiol downregulates dopamine receptor D2 binding in females but not in males.
et al.
et al.
The Question of Causality
An important question in terms of the neuroscience of addiction is
whether neural abnormalities are precursors to addiction that place
individuals at heightened vulnerability to the effects of substances, or
are the direct effects of substances on the brain. To address this important question, studies ideally should evaluate these key brain processes
before and after exposure to substances. However, such studies are
dif ficult and expensive. Thus, there are currently only a few longitudinal
studies that we can draw from. One such study is the Dunedin Multidisciplinary Health and Development Study (often referred to as the
Dunedin Longitudinal Study), which has been evaluating a long-standing
/
General Conclusions
157
birth cohort of 1037 people born between April 1972 and March 1973 in
Dunedin, New Zealand. The results of this study reported that daily
cannabis users who initiated use during adolescence had elevated risk for
psychosis as well as cognitive declines, such as a loss of 8 IQ points as
assessed from age 11 to age 38 (Figure 10.6) (Meieret
al.,
2012).
General Conclusions
Neuroscientific research has advanced our knowledge of addiction as a
brain disease by translating important
fi ndings
from animal models of
drug addiction in order to provide the foundations for studying the
neurobiological basis of human drug addiction. These studies have
Figure 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
/
158
Conclusions
(c)
0.4
One diagnosis
Two diagnoses
Three or more diagnoses
0.2
QI el a c s-lluf ni egn ah C
) stinu noit ai v ed d r adn at s ni(
0
–0.2
–0.4
–0.6
P = 0.44
–0.8
P = 0.02
Not cannabis
Cannabis
Not cannabis
Cannabis
dependent
dependent
dependent
dependent
dependent
dependent
before age 18
before age 18
before age 18
before age 18
before age 18
before age 18
n = 17)
(
Figure 10.6
P = 0.09
Cannabis
n = 57)
(
n = 12)
(
n = 21)
(
n = 23)
(
Not cannabis
n = 14)
(
( cont.)
Study found changes in full-scale IQ (in standard deviation units) from childhood
to adulthood. Individuals who initiated cannabis use during adolescence (black
bars) showed greater decrements in IQ relative to those who began use in
adulthood (gray bars).
(From: (b) https://pixabay.com/en/weed-smoke-drug-marijuana-joint-837125/; (c) Meier
et al., 2012.)
provided empirical evidence of the neurobiological framework to sup-
port concepts gleaned from behavioral studies. Neuroscientific research
has provided multiple entry points for consideration in terms of preven-
tion and intervention strategies through identi fication of the biological
pathways that regulate the reward processes that underlie reward, motiv-
ation and inhibitory control. Neuroscienti fic research has also disentan-
gled the processes that underlie the behavioral symptoms of craving and
withdrawal. Through these studies, we have discovered the neuroadaptations that underlie the persistence of addiction and the wide brain
/
Review Questions
159
networks implicated in these changes, particularly the mesocorticolimbic
network, which is innervated by dopaminergic projections. These studies
have also helped us understand the dynamic changes throughout the
course of the addiction cycle that lead to the positive reinforcing effects
of drugs and the negative reinforcing effects of drug abstinence. Interventions can be designed based on this neuroscienti
fic
speci
fic knowledge so that
brain pathways can be targeted and remediated by behavioral
and pharmacological approaches that have been shown to be bene
Finally, through neuroscienti
fic
ficial.
research, we are able to triangulate the
events that occur between the genetic mechanisms and the expression of
addiction to better understand factors that increase risk for, but also
factors that might protect against, addiction.
There is still a long road ahead as our understanding of these processes emphasizes the gaps in current knowledge. The Spotlight section
throughout this book highlight some of these gaps that have current
ficance
signi
in society. See Spotlight 2 for an example of how advocacy
can help change the face of and eliminate the stigma related to addiction.
Summary Points
•
Advancements in neuroscience techniques have paved the way for our
understanding of addiction as 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
•
Understanding
structure and function and behavioral symptoms of addiction.
addiction
is
neural
important
mechanisms
in
underlying
identifying
potential
behavioral
targets
symptoms
for
of
therapeutic
interventions.
•
Dopamine dysregulation
in
fl
substance abuse disorders is in uenced
by
biological sex and hormone levels.
Review Questions
•
•
•
How do risk factors leave the brain vulnerable to addiction?
fi
What is the bene t of identifying endophenotypes for addiction?
What
are
the
underlying
mechanisms
that
underlie
the
difference
in
response to drugs between males and females?
/
160
Conclusions
Further Reading
Abasi, I. & Mohammadkhani, P. (2016). Family risk factors among women
with addiction-related problems: an integrative review.
, 5(2), e27071. doi:10.5812/ijhrba.27071
Buckland, P. R. (2008). Will we everfind the genes for addiction?
,
103(11), 1768–1776. doi:10.1111/j.1360-0443.2008.02285.x
Ducci, F. & Goldman, D. (2008). Genetic approaches to addiction: genes
and alcohol.
, 103(9), 1414–1428. doi:10.1111/j.13600443.2008.02203.x
Feldstein Ewing, S. W., Filbey, F. M., Loughran, T. A., Chassin, L. & Piquero,
A. R. (2015). Which matters most? Demographic, neuropsychological,
personality, and situational factors in long-term marijuana and alcohol
trajectories for justice-involved male youth.
, 29(3),
603–612. doi:10.1037/adb0000076
Filbey, F. M., Schacht, J. P., Myers, U. S., Chavez, R. S. & Hutchison, K. E.
(2010). Individual and additive effects of the
and
genes on
brain response to marijuana cues.
, 35(4),
967–975. doi:10.1038/npp.2009.200
Ketcherside, A., Baine, J. & Filbey, F. (2016). Sex effects of marijuana on brain
structure and function.
, 3, 323–331. doi:10.1007/s40429016-0114-y
Konova, A. B., Moeller, S. J., Parvaz, M. A.,
(2016). Converging effects of
cocaine addiction and sex on neural responses to monetary rewards.
, 248, 110–118. doi:10.1016/j.pscychresns.2016.01.001
McCrory, E. J. & Mayes, L. (2015). Understanding addiction as a developmental
disorder: an argument for a developmentally informed multilevel approach.
, 2(4), 326–330. doi:10.1007/s40429-015-0079-2
Morrow, J. D. & Flagel, S. B. (2016). Neuroscience of resilience and vulnerability for addiction medicine: from genes to behavior.
, 223,
3–18. doi:10.1016/bs.pbr.2015.09.004
Prashad, S., Milligan, A. L., Cousijn, J. & Filbey, F. M. (2017). Cross-cultural
effects of cannabis use disorder: evidence to support a cultural neuroscience approach.
, 4(2), 100–109. doi:10.1007/s40429-0170145-z
Puetz, V. B. & McCrory, E. (2015). Exploring the relationship between childhood maltreatment and addiction: a review of the neurocognitive evidence.
, 2(4), 318–325. doi:10.1007/s40429-015-0073-8
Int J High
Risk
Behav Addict
Addiction
Addiction
Psychol Addict Behav
CNR1
FAAH
Neuropsychopharmacology
Curr Addict Rep
et al.
Psychiatry Res
Curr Addict Rep
Prog Brain Res
Curr Addict Rep
Curr Addict Rep
/
Spotlight 1
161
Spotlight 1
The Relationship Between Stress and Addiction
Seamus McDonald was just 2.5 years old when he witnessed both of his
parents being shot to death. This traumatic event not only changed his life
instantly in that moment but also changed its course dramatically. McDonald
was a responsible citizen and father; however, when he became involved with
an organization that assisted victims of violence, the experience triggered the
deeply rooted trauma from his early childhood. He began using cannabis to
treat his post-traumatic stress disorder (PTSD) from the murder of his parents.
The American
mediating
Academy of Pediatrics now
mechanism
between
behavioral
recognizes toxic stress as a
problems
and
stress/trauma
endured during childhood. Toxic stress leads to changes in multiple biological
systems that contribute to vast alterations in behavioral and health problems
in childhood and into adulthood, such as PTSD and addiction (Figure S10.1).
Figure S10.1
Post-traumatic stress disorder (PTSD).
(From www.pexels.com/photo/adult-alone-black-and-white-dark-551588/.)
Patients with PTSD have reported that cannabis provides relief from their
symptoms with fewer side effects than prescribed medications. To date, most
of what is known is based on anecdotal evidence. Research into the therapeutic effects of cannabis is hampered by US federal policies, especially the
classi
fication
hurdles
are
of cannabis as a Schedule I drug. For some researchers, these
worth
overcoming
so
that
much-needed
questions
can
be
answered.
/
162
Conclusions
Spotlight 2
A Rocker s Fight Against Addiction
’
In February 2018, the musician Flea disclosed his struggles with addiction in a
Time
editorial,
fl
“The
temptation
of
drugs
is
a
bitch”
(http://time.com/
5168435/ ea-temptation-drug-addiction-opioid-crisis/). Flea, who is the lead
fi
bassist for the rock band Red Hot Chili Peppers, candidly described his rsthand life experiences that contributed to his substance abuse and addiction,
and that eventually led him back to good health. Stating that drugs have been
fixture in his life since infancy, he also described witnessing loved ones’ lives
end tragically due to addiction. He details how ful
filling responsibilities as a
father was challenging yet infl uential in his fi ght against the disease and
a
would later help him defeat it. Alongside his personal motivation, he ascribes
his success to a number of support systems that included counseling, meditation, exercise and spiritual guidance. In the end, he claims that recognizing
and accepting the challenges of addiction“helped [him] stay away from the
temptation of drugs.” Alluding to the chronic nature of the disease, he adds,
“It’s always there, seducing you to come on in and get your head right,
” as he
describes repeatedly dealing with severe anxieties that challenge his sobriety.
In light of the current opioid epidemic in the USA, he recalls his own
experience with opioids and is forthright about the role that the medical
community played in this crisis (see Spotlight sections in Chapter 9 to learn
how legislation is addressing the opioid crisis). He cited that, following a
broken arm, his physician overprescribed oxycodone (OxyContin), sending
him home with a 2-month supply with instructions to take as many as four
pills per day. He described how Oxycontin removed his physical pain but also
diminished his ability to function personally and professionally. Although Flea
discontinued his use of Oxycontin before his 2-month supply was depleted,
his
first-hand experience has given him insight into how little we know about
pain management and how our current approaches need to be improved.
References
Bobzean, S. A., DeNobrega, A. K. & Perrotti, L. I. (2014). Sex differences in
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Chocyk, A., Majcher-Maslanka, I., Przyborowska, A., Mackowiak, M. &
Wedzony, K. (2015). Early-life stress increases the survival of midbrain
/
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/
Glossary
Accuracy – the ability of an experimental result to conform to an actual, true
or correct value or representation.
Acetate – a salt that is produced by acetic acid and metabolized by glial cells
in the brain. Molecular formula: CH3CO2
À
Activation likelihood estimation – an algorithm used to determine
coordinate-based activation of specific brain regions from neuroimaging
data across multiple studies and subjects. Particularly useful in assessing
the convergence of results across many different experiments.
Agonist – a molecule or ligand that activates a particular cellular receptor.
Allosteric – indirect modulation or regulation via a non-active site.
Amotivation – a lack of motivation stemming from detachment or decreased
emotion or drive.
Anhedonia – a decreased ability to experience pleasure.
Antagonist – a molecule or ligand that blocks receptor activation, partially,
completely or irreversibly.
Appetitiveness – the extent to which a stimuli, object or event elicits an
appealing response.
Backward masking – a stimulus paradigm in which a stimulus is presented
and then almost immediately covered or hidden. This conceptual model
is useful for investigating spatiotemporal processing, motion perception,
reaction time, etc.
Behavior sensitization – an increased motor-stimulant response to a
substance that occurs after repeated use and exposure to that substance.
β spectral power – the strength of β (frequencies of approximately 13–30 Hz)
power contained in the EEG signal.
Biomarkers – a wide subcategory of biological or medical signs that can be
examined objectively and quanti fied to indicate normal, pathological or
pharmacological effects on biological functioning. They may also
indicate disease outcomes, effects of treatment, or environmental
exposure to chemicals or nutrients.
Cannabinoids – naturally occurring or synthetic compounds that modulate
the endocannabinoid system, activating CB1 and CB2 receptors within
the body. They may be plant derived (e.g. tetrahydrocannabinol and
cannabidiol) or produced by the human body (e.g. anandamide and
2-arachidonoylglycerol).
Choline – a molecular precursor to acetylcholine, commonly utilized in
magnetic resonance spectroscopy (MRS) to identify the presence of
/
166
Glossary
brain tumors. It also serves many other functions throughout the body
including neurotransmitter synthesis, cell membrane signaling, liquid
transport and methyl group metabolism.
Classical conditioning
–
a mechanism of learning and memory, in which
one associates a relevant stimulus with an otherwise, non-relevant
stimulus. Typically occurs after repeated exposure to the two stimuli
together.
Cognitive behavioral model
– a theory based on the assumption that
mental
processes can influence emotional and behavioral (physiological)
responses.
Cognitive behavioral therapy (CBT)
–
a type of therapy that seeks to help
patients recognize, avoid and cope with the situations in which they are
most likely to abuse drugs.
Computed tomography (CT)
–
a type of computerized X-ray imaging that
constructs a three-dimensional image from many individual crosssectional X-ray images, taken in succession, of an anatomical region.
Used primarily in neuroscience for structural measurements of the
nervous system.
Contingency management (CM)
– a method that uses positive reinforcement
such as providing rewards or privileges for remaining drug free, for
attending and participating in counseling sessions, or for taking
treatment medications as prescribed.
Craving
–
the intense desire to use or obtain a substance. May be
continuous, or may occur randomly or after presentation of drugrelated cues.
Creatine
– an amino acid that
is utilized by cells under high-energy demand.
This metabolite is commonly targeted in magnetic resonance
spectroscopy (MRS) to examine metabolic activity in neurons of the
human brain.
Cue reactivity
–
a conditioned response (craving) to various stimuli that are
associated (either naturally or through repeated exposure) with drugseeking and drug-taking behaviors.
Delay discounting
– the tendency to undervalue a reward or punishment that
is received after a delayed time period. This concept is thought to be the
underlying principle of the tendency of individuals to choose smaller,
more immediate rewards over bigger rewards that require a waiting time
for receipt.
Depressant
– a substance that
slows the activity of the central nervous
system, typically through activation of GABAergic neurons. This
category includes sedatives, tranquilizers and alcohol.
Diffusivity – the pattern and nature of a substance’s ability to spread (or
diffuse) throughout a system.
/
Glossary
167
Dopamine
–
a neurotransmitter that is prevalent in brain regions that
regulate movement, emotion, motivation and reward.
Drug expectancy
–
the cognitive and perceptual outcomes that occur from
the anticipated drug effects of the user. Examining this phenomenon can
provide insights into drug initiation, reinforcement and sustained use.
Drug half-life – the time required for the concentration or amount of drug in
the plasma to be reduced by one-half.
Dysphoria – the inability to derive pleasure from common non-drug-related
rewards.
Ecological validity
– the extent
to which experimental results reflect real-
world scenarios or phenomenon. This indicates the relevance of a study
to generalize, inform and predict actual, real-world events.
Effort–reward calculation
– the mental
calculation in making a decision of
the energetic cost of an action (effort) compared with the benefit of the
resulting outcome (reward).
Electroencephalography (EEG)
–
an electrophysiological technique that
records electrical conductance of cortical neurons in the brain. This
technique is favorable because it is able to obtain this information with
high temporal resolution.
Emotion regulation
– the ability of
a person to regulate and modify their
emotional experiences and expression.
Endophenotype – genetic factors that are determined through genetic testing
and are prevalent in association with specific behaviors, illnesses or
other psychophysiological factors. The examination of endophenotypes
is utilized to better assess gene–environment interactions of psychiatric
illnesses.
Etiology
–
the medical pursuit of the cause and origin of a disease.
Excitatory post-synaptic potential – the change in electrical conductance of a
neuronal membrane at the synapse that
increases
the likelihood of an
action potential.
FBJ murine osteosarcoma viral oncogene homolog B (FosB) – an important
transcription factor in neural plasticity. This gene is thought to play a
vital role in the transition into addiction. It is consider to be the
biological mechanism behind the concept of the metaphorical
that is permanently “turned on” in addictive disorders.
“switch”
Fetal alcohol syndrome – a condition that affects the developing embryo and
fetus of alcohol-using mothers. It is characterized by distinct facial
features and developmental problems. These characteristics include
abnormal eye shape, underdeveloped maxillary bones, joint and palmar
crease anomalies, cardiac defects, post-natal growth retardation,
developmental delay, mental de ficiency and central nervous system
dysfunction.
/
168
Glossary
Final common pathway
– the mesolimbic dopamine system,
the primary
neural circuit responsible for reward processing, which is often referred
to as the “final common pathway ” as all substances of abuse
pharmacologically in fluence this neurological pathway. It is
hypothesized to be the key system effected in reward system dysfunction
seen in addiction.
Fractional anisotropy
– a method for
evaluating white matter tracts and
calculating the magnitude of directionality of diffusion of these tracts
throughout the brain.
Glucose metabolism
–
glucose, the primary energy source for the brain, is
processed by the mitochondria inside neurons and other cells in the
central nervous system to produce ATP. ATP is then used throughout
the cell to carry out many cellular functions.
Hallucinogens
–
typically referred to as psychedelics. These psychoactive
substances alter perception, mood and other cognitive functions.
Hedonic set point
– neurological
alterations that occur after repeated
substance use and continue down a cyclical path, resulting in a reduced
“set
point” of reward processing, meaning that everyday rewarding
experiences are no longer as pleasurable as they once were, leading to
continued substance use in the attempt to get back to the original “set
point” of reward and pleasure.
Heritability – an estimate of the degree of variation in a phenotypic trait in a
population that is due to genetic variation between individuals in that
population.
Homeostasis
–
the biological concept that an organism will self-regulate in
order to maintain stability within its biological systems.
Incentive salience
– a theory that distinguishes motivation, or“ wanting,” from
liking or the memory of a rewarding experience of a substance. It proposes
that motivation is a critical component of addiction and is essentially
responsible for assigning importance and incentive to obtain a drug.
Incentive sensitization
–
a theory of addiction that posits that drug-induced
neurological alterations in the reward system cause increased arousal to
the drug and motivation to receive and use the drug. This results in a
pathological
“wanting”
to use and obtain the drug, even though the
pleasurable effects of the drug remain unchanged.
Inhalants
– the volatile substances
(gases or vapors) that are found in many
common household products (gases, liquids, aerosols and some solids).
Inhalation is often known as “sniffing, ”
“spraying.”
“huffing,” “ bagging ”
or
Inhibitory post-synaptic potential – change in electrical conductance of a
neuronal membrane at the synapse that
decreases
the likelihood of an
action potential.
/
Glossary
169
Interoception – the brain’s ability to construct a sense of self by processing
awareness of bodily sensations, behavior and cognition.
Intoxication – includes the behavioral, physiological, and cognitive effects or
alterations produced after a significant amount of a substance is
consumed.
Intracranial self-stimulation – an experimental method used in laboratory
animals to mimic the reinforcing effects of drug administration and
produce dopamine signaling. A stimulating electrode is surgically placed
in the animal ’s brain, specifically in the median forebrain bundle. The
animal is given the option to pull a lever/press a button and receive a
small electrical stimulation to that area of the brain.
Ionic gradient
– a concept
of biochemistry in which cellular membranes
+
+
2+
separate electrically charged ions (Na , K , Ca
À
, Cl ) through proteins
called active transporters. As ionic receptors open, these ionsflow across
the membrane and down the concentration gradient, causing a change in
the electrical charge of the cell. This physiological mechanism is a critical
component of many major biological functions at the cellular level.
Late positive potential (LPP) – a slow (300 –700 ms) positive event-related
potential that is thought to measure attention to emotionally salient stimuli.
Magnetic resonance imaging (MRI)
magnetic
fields and radio waves
–
a scanning technique that utilizes
to generate images of internal
structures.
Magnetic resonance spectroscopy (MRS) – a complimentary technique to
magnetic resonance imaging (MRI). This method measures the
attachment of hydrogen protons to various molecules, allowing the
measurement of different tissues (to assess the mass and region of brain
tumors) and various concentrations of brain metabolites.
Motivational enhancement therapy (MET) – a therapy that uses strategies to
evoke rapid and internally motivated behavior change to stop drug use
and facilitate treatment entry.
N-Acetylaspartate (NAA) – this molecule is the most reliable metabolic
target in magnetic resonance spectroscopy (MRS) and is extremely
concentrated throughout the central nervous system.
Narcotics
– opium, opium derivatives and their partially synthetic substitutes.
Derived from the Greek word for “ stupor,” narcotics dull the senses and
are commonly prescribed for pain relief.
Neonatal abstinence syndrome – occurs in babies after in utero exposure to
opioids. It is a drug-withdrawal syndrome that includes symptoms of
autonomic instability, spastic movements, irritability, poor sucking
reflex, impaired weight gain and, in some cases, seizures.
Opponent-process theory – a mechanism of homeostasis. For every
emotionally responsive event, the brain produces a counteracting,
/
170
Glossary
opposite emotional response, drawing the net emotional reaction closer
to neutral. If a positive stimulus or event is removed abruptly, the
contracting negative response will continue.
Pavlovian conditioning
–
a learning mechanism through the paired
association of two stimuli that leads to a new learned response, first
described by Ivan Pavlov; also referred to as classical conditioning.
Pharmacodynamics
– the biomedical
study of the interaction between drug
concentration, site of action, behavioral and biological effects, time
course of action and intensity of effects. Understanding these
components is critical in determining dose effects, toxicity and clinical
outcomes.
–
Place preference
an experimental protocol to non-invasively measure
perceived drug reward in laboratory animals. It is assumed that the more
time the animal spends in an area in which it had previously received
drug administration, the greater the reward response to that drug.
Positron emission tomography (PET) – a non-invasive technique that
enables the measurement of physiological functioning in the brain
through the utilization of radioactive tracers that measure cerebral
blood
flow, metabolism, neurotransmitter
binding and levels of
radiolabeled drugs.
Pre-potent response – the most immediate and automatic response that
arises in the face of new or relevant stimuli. In many situations, these
foremost and immediate responses are inhibited depending on context,
environment or the consideration of other information.
Probability discounting
– the tendency to assign less
value to a gain that is
received under probabilistic conditions than the same gain received
under certain gain. Probability and value become associated, whereby
the perceived value of a gain goes down as the probability of receiving it
goes down.
Pyramidal cell
–
a type of neuron that is characterized by distinct apical and
basal dendritic trees and a pyramid-shaped nucleus. These cells are
abundant throughout the central nervous system, particularly in the
cortex, hippocampus and amygdala. Because of their complex structure,
they are able to adapt to many diverse and specialized functions.
P300
– a positive (P) deflection of voltage and approximately 300 ms latency
of stimulus presentation to electrical change in the brain. This neural
change in electrical conductance is thought to be elicited by the
participant’s cognitive reaction, rather than by a physiological response
to a stimulus.
Radionucleotides
– nucleotides
that have been tagged with a radioactive
tracer.
/
Glossary
171
– a chemical
Radiotracer
compound that binds to a particular biological
molecule and emits a radioactive signal. This enables the measurement
of physiological properties (e.g. receptor binding, diffusion of
molecules) of a radiolabeled molecule in living subjects.
– any condition that
Reinforcer
increases the probability of a particular
behavior. In the context of addition, it is any cue, situation or object that
increases the likelihood of substance use or reinstatement.
Reinstatement
–
a return to substance use after a period of sustained
abstinence or extinction of use.
Reliability
–
the consistency of experimental results across measures and/or
studies. The importance of reliability is in producing results that are
accurate, dependable and reproducible.
Resting-state functional connectivity (rsFC) – a type of functional magnetic
resonance imaging (fMRI) analysis that examines blood flow between
regions of the brain. This method allows researchers to examine how
various cortical regions send signals, communicate and ultimately work
with other neural regions during a period of rest.
Reward de ficiency syndrome
DRD2 gene,
– a genetic disorder
primarily affecting the
causing impairment in the functioning of the dopamine D2
receptor and resulting in hypodopaminergic function. These cellular
defects lead to impaired reward processing and may predispose
individuals to addictive behaviors.
Risk factors – characteristics at the biological, psychological, family,
community or cultural level that precede and are associated with a
higher likelihood of a negative outcome.
Single-photon emission computed tomography (SPECT) – a neuroimaging
technique that utilizes nuclear medicine and a γ-ray camera to construct
a three-dimensional image from multiple two-dimensional images of
radioactive distribution throughout the brain.
Stimulant
–
a substance that causes increased arousal and cognitive
enhancement through neurochemical effects on monoamines, a class of
neurotransmitter that includes norepinephrine and dopamine.
Stimulants also stimulate other physiological systems, causing increased
heart rate, blood pressure, glucose production and respiration.
Superconducting quantum interference device (SQUID) – an extremely
sensitive magnetometer, capable of measuring small changes in the
magnetic
fields of neurons.
This method provides high temporal
resolution and allows real-time tracking of neuronal firing sequences.
Sympathomimetic
– producing physiological
effects characteristic of the
sympathetic nervous system by promoting the stimulation of
sympathetic nerves.
/
172
Glossary
Tesla (T) – a measure of the strength or intensity of a magnetic field,
typically used to assign magnetic force of a magnetic resonance imaging
(MRI) machine: the higher the Tesla value, the higher the resolution of
the MRI image.
Tolerance
– a condition that
occurs after repeated substance use, in which
more of the substance is required to produce the same level of effect that
was experienced at the initial time of use.
Tractography
–
a method of measuring anatomical connections between
brain regions that facilitate information transfer and processing across
the central nervous system. This imaging tool utilizes magnetic
resonance imaging (MRI) technology to map white matter tracts
throughout the brain.
Transduction
– the cellular
process of sending or receiving chemical and
electrical signals, transferred through the cellular membrane at the
synapse, to initiate internal cellular processes inherently and of
neighboring cells.
Validity
– the ability of
an assessment or result to accurately measure or
represent the intended concept, variable or phenomenon. Validity is
dependent on reliability.
Withdrawal
–
a pattern of physical and psychological symptoms that occurs
after abrupt cessation of substance use. These symptoms are typically
negatively perceived by the user and contribute to the difficulty in
remaining abstinent.
/
Index
acamprosate, 134–135
and dopamine, 53
activation likelihood estimation (ALE), 100
electrophysiological markers, 89
acute withdrawal, 85–86
and endophenotypes, 153–154
addiction
intoxication symptoms, 64–65
behavioral definition of, 4, 12–14
late positive potential (LPP), 102
behavioral progression of, 9–10
pharmacological interventions,
and causality, 156–157
chemical, 6 –12
as chronic brain disease, 130–132
classification systems of, 6–9
133–135
and social class, 41
stigma of, 5
withdrawal symptoms, 83
clinical de finition/diagnosis of, 2, 6– 9
allostatic theory, 36– 38, 90 –91
demography of, 5
α power, 88–89
mental disorders and, 4
American Psychiatric Association (APA),
dark side of, 90– 91
phenomenology of, 4
allosteric potentiator, 134
6
rates of, 1, 149
amotivation, 88
stigma of, 5–6
amphetamine use
addiction theories
allostatic, 36 –38
brain disease model, 9–12
action areas of, 66
behavioral addiction of, 9– 10
amygdala volume
cue-elicited craving, 40
and alcohol use, 73
future of, 40–41
and cannabis use, 28
impaired response inhibition and salience
and the cue-elicited craving model,
attribution syndrome (iRISA), 38–40
incentive sensitization, 34–36
40
and emotion regulation, 102
adolescence, 127, 149– 150
Anagnostaras, S. C., 35
Adolescent Brain Cognitive Development
anhedonia, 88
(ABCD) study, 32
antagonists, 66
agonists, 66
antireward system, 12
Aharonovich, E., 140
anxiety
Ahmed, S. H., 12
and cannabis use, 41
alcohol use
and high
action areas of, 68
and anhedonia in protracted withdrawal,
87
β activity, 88 –89
internet/video game addiction,
94
appetitiveness, 103–105
appetitiveness, 103
arterial spin labeling, 100
behavioral effects of, 10
attention
brain mechanisms of, 71–73
craving studies, 98–99, 102
demographics of, 5
and cognitive behavior therapy (CBT),
135
and craving, 105 –106
/
174
Index
attention deficit/hyperactivity disorder
(ADHD), 116–117
behavioral effects of, 10
craving, 100 –101
endocannabinoid system, 53
Babor, T. F., 5
and endophenotypes, 153
backward masking, 106
and genetics, 29
Balleine, B. W., 137
longitudinal study of, 156–157
Barratt Impulsiveness Scale, 115, 117
and perceived stress, mood, 40–41
Bauer, L. O., 89
and stress, 161
Begleiter, H., 69
treatment outcomes, 140
behavior prediction, 32
withdrawal symptoms, 83–84
behavior sensitizing experiments, 9
Carroll, K. M., 134, 140
behavioral addiction, 12–14
Casey, B. J., 149
behavioral drug treatment interventions,
Centers for Disease Control and Prevention
135–137
Berridge, K. C., 35
β
β
(CDC), 43
cerebral blood flow (CBF), 86
power
chemical addiction, 6 –12
and anxiety, 89–90
Childress, A. R., 102–103, 104 –105
and craving, 101
Chocyk, A., 150
spectral power, 101
choline, 27
Bickel, W. K., 136
Cicero, T. J., 5
biochemical imaging, 27 –28
Clark, L., 121, 123
biomarkers, 28
classical conditioning experiments, 9
blood oxygenated level dependent (BOLD)
cocaine
signal, 25
action areas of, 68
Bobzean, S. A., 156
acute withdrawal, 86
Boeijinga, P. H., 135
appetitiveness, 103
Boileau, L., 36
craving studies, 100
Bonson, K. R., 102
and dopamine, 52
brain
during protracted withdrawal, 88
adolescent, 149–150
drug effects on mesocorticolimbic reward
system, 11
brain disease model (addiction), 2, 9–12,
130–132
brain function
during protracted withdrawal, 88
electrophysiological markers,
88– 90
and the iRISA theory, 38
late positive potential (LPP), 102
pharmacological interventions, 134
treatment outcomes, 140
withdrawal symptoms, 83
during withdrawal, 86
cognitive behavioral models, 135–137
hijacking by drugs, 104–105
cognitive behavioral therapy (CBT), 136,
and impulsivity, 123
and intoxication, 68–73
and love, 30
measurement of, 22–24
bupropion, 134
138, 140
cognitive impairment and addiction,
12
compulsive disorders, 12– 13
Conklin, C. A., 98
contingency management, 136
cannabis use
action areas of, 68
Corbit, J. D., 36
Costello, M. R., 137
/
Index
craving
175
and reward learning mechanisms, 51–53
and the allostatic theory, 38
and the incentive-sensitization model, 35
and attention, 105–106
and the iRISA theory, 38
contextual cues, 102
during protracted withdrawal, 87–88
and the cue-elicited craving model, 40
dopamine-depletion hypothesis, 86
cue-reactivity paradigms, 99–101
drug classification, 66
after death, 110
Drug Enforcement Administration
defined and research history, 98–99
(DEA), 2
neural mechanisms of, 101
drug expectations, 75
neurological underpinnings of, 101–102
drug treatment interventions
neuromolecular mechanisms, 106–107
behavioral, 12, 135 –137
and reward system hijacking, 103–105
combined approaches to, 137–139
creatine, 27
legislation versus cost, 143–144
cue-elicited craving theory, 40
outcomes, 138–140
cue-reactivity approach
and craving, 99
and methadone, 133
paradigms, 99–101
peer in fluence on, 142–143
pharmacological, 132 –135
drug treatment protocol, 131–132
drugs (DEA schedule), 3
Drummond, D. C., 98
Dackis, C. A., 86
Ducci, F., 150, 151
Dagher, A., 13
Dunedin Multidisciplinary Health and
Daglish, M. R., 104 –105
Development Study, 156–157
Decade of the Brain, 130
Dunning, J. P., 102
delay discounting, 115, 123–125, 137
dysphoria, 82
demographics
and drug use, 5
ecological validity (craving), 99 –100
and impulsivity, 127
ecstasy.
See MDMA
demography of addiction, 5
effort– reward calculation, 56
dendritic alterations (brain), 106–107
electroencephalography (EEG)
depression
and alcohol endophenotypes, 153–154
and cannabis use, 41
and brain mechanism, 69
genetic risk for, 151
and craving, 101 –102
DeWitt, S., 40
performance of, 22– 24
diagnosis of addiction, 6–7
and withdrawal, 88–90
Diagnostic and Statistical Manual of Mental
Disorders (DSM), 2, 6
environment, 102
diffusivity, 25
enzyme-linked receptors, 66
diffusion tensor imaging (DTI), 24
Ersche, K. D., 117, 118, 120, 125
endophenotype, 118, 150–155
disulfiram, 135
etiology of addiction, 6
Domino, E. F., 69, 88
Evoy, K. E., 134
dopamine
excitatory post-synaptic potential, 22
and ADHD, 116–117
in behavioral activation and effort, 56
FBJ murine osteosarcoma viral oncogene
and craving, 100
homolog B (FosB), 106– 107,
and hedonistic response, 10–11
110
and hormones, 156
Fehr, C., 85
/
176
Index
Feldstein Ewing, S. W., 136
Gooding, D. C., 89
fetal alcohol syndrome, 4
Gould, K. L., 86
Filbey, F. M., 5, 13, 40 –41, 100, 101,
G protein-coupled receptor, 66
103–104
Gritz, E. R., 89
final common pathway, 53–54
five-choice serial reaction time task
(5CSRTT), 121, 125
food addiction, 12–13
half-life (substance), 82–85
Hariri, A. R., 150, 152
Hasan, K. M., 1, 149
fractional anisotropy, 25
hedonistic set point, 35
Franken, I. H., 89, 102
Heinze, M., 102
Franklin, T. R., 100
Hendriks, V. M., 102
functional MRI (fMRI)
heritability, 150
and adolescence, 60
Herning, R. I., 101
and backward masking, 105–106
heroin use
and brain mechanism, 71–73
and cognitive behavioral therapy (CBT),
136
electrophysiological markers, 88
hijacking the brain, 105
late positive potential (LPP), 102
craving studies, 99, 133–134
description of, 25
withdrawal symptoms, 83
Herrmann, M. J., 102
and sex in addiction, 153
Holden, C., 12
homeostasis, 36–38
Gallinat, J., 13, 100
Hommer, D. W., 40
gambling addiction, 12
hormones and dopamine, 156
γ-aminobutyric acid
hypersensitization, 35
(GABA)
and acamprosate, 134–135
and acute withdrawal symptoms, 86
hypothalamic–pituitary–adrenal axis
(HPA), 90
and alcohol use, 68, 153
and hedonistic response, 10
gender and addiction, 5, 155–156
impaired response inhibition and salience
attribution syndrome (iRISA), 38–40
gene expression receptors, 66
Impulse Behavior Scale (IBS), 117
genetics
impulsivity
and addiction, 5, 55–56, 150 –155
in adolescence, 127 –128
and drug expectancy, 75
defined, 114–116
and impulsivity, 118
and delaying discounting of reward,
and limitations to neuroimaging, 29
ΔFosB, 106–107
123 –125
and inhibitory control, 121
George, O., 90, 100
nature of, 117– 120
Gerbing, D. W., 115
neuropharmacology of, 116–117
Giorgio, A., 1, 149
and risky decision making, 120–121
Glenn, S. W., 90
incentive salience, 35, 47
glucose metabolism, 70–71
incentive-sensitization theory, 34–36,
go/no go test, 119
Gogtay, N., 1, 2, 149
Gold, M. S., 85
Goldman, D., 150, 151
Goldstein, R. Z., 38, 39, 139
103 –104
inhibitory control, 121, 140
inhibitory post-synaptic potential, 22
International Classification of Diseases
(ICD), 2, 6
/
Index
internet/video game addiction
177
LSD (lysergic acid diethylamide), 15, 66
as behavioral addiction, 14
separation anxiety, 94
interoceptive processes, 40
intoxication (drug)
action areas of, 66–68
magnetic resonance imaging (MRI), 12,
24–27
magnetic resonance spectroscopy (MRS),
27
brain mechanisms of, 68–73
magnetoencephalography (MEG), 22–23
defined, 64 –65
Marijuana Problem Scale (MPS), 101
modulators of, 73–75
Martinotti, G., 87
pharmacodynamics of, 66
masked cue task, 105–106
intracranial self-stimulation experiments, 9,
48
ion channel receptors, 66
ionic gradients, 22
Iowa gambling task (IGT), 120–121
McDonough, B. E., 102
MDMA (3,4methylenedioxymethamphetamine),
15, 66
mechanisms of addiction, 9–12
memory and addiction, 56–58
Jarvis, M. J., 5
mental disorders and addiction, 4
Jessie’s Law, 144
mesolimbic reward system (brain)
Johnson, T. S., 134
and behavioral addiction, 13–14
changes during addiction, 10–12
Kalivas, P. W., 53
and the cue-elicited craving model, 40
Ketcherside, A., 41
as reward system, 49
Kim, J. E., 13
metabolites (brain tissue), 27
King, D. E., 88
methadone, 133
Kish, S., 16
methamphetamine use, 53
Knott, V. J., 88, 101
monetary incentive delay task, 60
Kober, H., 136
morphine, 9
Konova, A. B., 137, 139
motivation
Koob, G. F., 12, 35, 36, 37, 90 –91
and future drug use prediction, 60
Kourosh, A. S., 13
and reward learning mechanisms, 47–58
Kuczenski, R., 9
Kuhn, S., 13, 100
motivational enhancement therapy (MET),
136
motivational interviewing (MI), 136, 138
Landes, R. D., 136
late positive potential (LPP), 102
Le Moal, M., 35, 36, 37, 90 –91
Myrick, H., 100, 101
N -acetylaspartate (NAA), 27
Lebel, C., 1, 149
Namkoong, K., 102
Leith, N. J., 9
National Institutes of Health (NIH), 31
Lenoir, M., 12
natural reinforcers, 12
Lewis, C. C., 136
neonatal abstinence syndrome, 4, 93
ligands, 66
Nestler, E. J., 106
limbic cortex activation, 102
neuroimaging studies
Littel, M., 89
and addiction activity, 12
Liu, X., 101
and behavior prediction, 32
Loughead, J., 133
craving, 99– 101
love and brain function, 30
diffusion tensor imaging (DTI), 24
/
178
Index
neuroimaging studies (cont.)
electroencephalography (EEG), 22–24,
69, 88–90, 101–102, 153 –154
functional MRI (fMRI), 25, 60, 71–73, 99,
105–106, 133 –134, 136, 153
and impulsivity, 119
and the opioid system, 53
pharmacological interventions, 133
as public health concern, 43–45, 162
treatment cost, 143–144
opponent-process theory, 36, 90– 91
Orsini, C., 82
limitations of, 28–29
magnetic resonance imaging (MRI),
24–27
magnetic resonance spectroscopy (MRS),
27
P300, 101 – 102
Pagliaccio, D., 28
Papageorgiou, C. C., 89
Pavlovian conditioning, 98–99
magnetoencephalography (MEG), 22–23
peer recovery specialists, 142–143
of behavioral addiction, 13–14
pharmacodynamics, 66
of combined drug interventions, 137–138
pharmacological interventions, 132–135
positron emission tomography (PET), 12,
phencyclidine (PCP), 68
26–28, 52 –53, 69–71, 100
single-photon emission computed
tomography (SPECT), 26, 27–28
structural MRI, 24
Niaura, R. S., 98
nicotine use
phenomenology of addiction, 4
place preference, 9–10
pleasure molecule.
See dopamine
Porjesz, B., 69, 89, 153, 154
positron emission tomography (PET)
and brain mechanism, 69–71
action areas of, 66–68
craving studies, 100
and brain mechanism, 68–70
dopamine studies, 27–28, 53
and craving, 100, 101–102
and the cholinergic system, 53
delay discounting, 124
post-acute withdrawal syndrome, 87–88
post-traumatic stress disorder (PTSD),
15– 16, 161
demographics of, 5
Potenza, M. N., 137 –138
pharmacological interventions, 133–134
prefrontal cortex
and social class, 41
and craving, 100, 106–107
withdrawal symptoms, 83, 87
and decision making, 120–121
nucleus accumbens
and dopamine, 51–53
and acute withdrawal symptoms, 86
during withdrawal, 86
and ADHD, 116–117
dysfunction and relapse, 85–86
as common addiction pathway, 54
and the iRISA theory, 38– 39
and craving, 106–107, 109
and reinstatement, 54
and dopamine, 49–52
Nutt, D. J., 104
pre-potent response, 115
probability discounting, 124
Probst, C. C., 12
O’ Brien, C. P., 13
protracted withdrawal symptoms, 87–88
Ogawa, S., 25
psychedelic drug therapeutic benefits,
opioid use
15– 16
action areas of, 68
psychiatric disorders and addiction, 5
addiction from birth, 93
pyramidal cells, 22
behavioral effects of, 10
demographics of, 5
radionucleotides, 27
and hedonistic response, 11
radiotracer, 100
/
Index
179
Rangaswamy, M., 153, 154
social class
receptors, 66
and addiction, 5
Reid, M. S., 101
and drug use, 41
reinstatement experiments
and drug relapse, 9
and
final common pathway, 53–54
relapse prediction
for drug-addicted patients, 131
electrophysical makers for, 89–90
prefrontal cortex and, 85–86
reinstatement experiments, 10
Sofuoglu, M., 134, 137
Solomon, R. L., 36
stimulant use
behavioral effects of, 10
demographics of, 5
stop signal reaction time (SSRT), 119, 121,
123
stress (adolescent), 150, 161
relapse prevention, 140
structural MRI, 24
resting-state functional connectivity (rsFC),
substance use disorder (SUD)
71
reward deficiency syndrome, 55
reward system
addiction as, 2
behavioral symptoms of, 12–13
classi fication systems of, 6–7
and addiction, 10–12
sugar addiction, 12–13
and behavioral-drug treatment
superconducting quantum interference
interventions, 137
device (SQUID), 22
and craving, 103–105
Surwillo, W. W., 88
and incentive salience, 35
sympathomimetic action, 86
and motivation, 47– 58
risk factors, 5, 149–150
Tanabe, J., 86, 87
risky decision making, 120–121
tanning addiction, 12, 13
Robinson, T. E., 35, 50, 99
Teklezgi, B. G., 133
Roemer, R. A., 88
tetrahydrocannabinol (THC), 68
thalamus, 86
Salamone, J. D., 56, 57
Tiffany, S. T., 98–99, 105
Schacht, J. P., 28, 153, 155
tolerance
Schedule I drugs, 2–3
and the allostatic theory, 36–38
Schneider, F., 99
brain adaptation and, 11
school dropout rate and addiction, 4
and substance use, 4
Sell, L. A., 104
sugar, 12– 13
Seltenhammer, M., 109
tractography, 25
serotonin, 68
transduction, 66
sex addiction
appetitiveness, 103–104
unemployment rate and addiction, 4
as behavioral addiction, 12–13
Shaw, P., 1, 150
Vaituzis, A. C., 1, 150
shopping addiction, 13
van de Laar, M. C., 102
Shufman, E., 88
van Eimeren, T., 12
single-photon emission computed
Venables, P. H., 88
spectroscopy (SPECT), 27–28,
69
Sinha, R., 86
Skinner, M. D., 135
Verdejo-Garcia, A., 140
Vogeler, T., 134
Volkow, N. D., 13, 38, 39, 40, 52, 53 –54, 71,
75, 85, 86, 88
/
180
Index
wait circuit, 124
and incentive sensitization model, 35
waiting, 125
and the iRISA theory, 39
–
Wang, G. J., 71
protracted, 87 88
Warren, C. A., 102
and substance use, 4
Weeks, J. R., 9
sugar, 12
Wexler, B. E., 136
symptoms and classi cation of,
fi
– 84
Wierenga, L. M., 150
82
Winterer, G., 89
Wong, D. F., 100
withdrawal
Worhunsky, P. D., 13
– 86
acute, 85
World Health Organization, 2, 6
and the allostatic theory, 36
Wrase, J., 99
–
between systems adaptations, 90 91
Wray, J. M., 105
brain adaptation and, 11
See adolescence
brain function during, 86
young adult.
and dark side of addiction, 11
Young, K. A., 105
fi
–
de ned, 81 82
–
electrophysiological mechanisms of, 88 90
Zubieta, J. K., 86
/
HA B
K
J
I C
N
D
Q M
P L
O
5
E
F
G
G
1.0
C
B
A
H
0.9
Age
I
0.8
0.7
20
K
r ett am y ar G
0.6
J
0.5
0.4
0.3
0.2
0.1
0.0
Plate 1.1
A longitudinal study demonstrating neuromaturational processes from 5 to 20
years of age.
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)
(
Plate 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
fi
water pointing in the direction of the ber bundle.
/
(a) 2-FA PET imaging of nAChR occupancy from cigarette smoke exposure
kBq/mL
9
0.0 Cigarette 0.1 Cigarette 0.3 Cigarette 1.0 Cigarette 3.0 Cigarette
(b)
MRI
0
(c)
V s/fp
10
kBq
10
MRI
No smoking Q-3
Q-1
(0.0 ng/ml) (0.4 ng/ml) (2.6 ng/ml)
0
0
T1-weighted
MRI
Plate 5.3 PET studies to determine the effects of nicotine administration.
Control
Second-hand
smoke
/
CSD/BEM topographic map
of fast β power
Relapse-prone
group
Current density
2
[uAmm/mm ]
0.00597
Left hem.
0.00490
0.00398
Right hem.
0.00299
0.00199
0.000996
0
Abstinence-prone
group
Plate 6.3 Fast
β power can
be a predictor of relapse in polysubstance users during a 3-month abstinence.
/
(a)
DGsp
(b)
(c)
DGip
(d)
(e)
(f)
(g)
Plate S7.1
Measuring
ΔFosB.
/
3
2
1
1
2
3
IN
VMPF
1
2
VMPF
IN
3
1 2 3 4 5>
# of overlaps
Healthy controls
Lesion controls
VMPF
Insula
80
70
60
50
teB %
40
30
20
10
0
9 to 1
8 to 2
7 to 3
6 to 4
Chance of winning
Plate 8.5
Ventromedial PFC lesions lead to risky decision making.
/
-12R
L
-9
-6
+3
+6
T value
-3.20
+9
+24
Plate 9.3
+12
+15
+18
+21
+39
+42
+45
+48
-5.00
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.
/
(a)
Pharmacological
interventions
MFG
L
VS
MFG
Cognitive-based
interventions
MFG
MFG
VS
Conjunction
MFG
MFG
VS
Y=13
Y=23
R
OFC
IFG
OFC
Prec
A
IFG
OFC
Prec
IFG
Prec
P
X=–3
Pharmacological
interventions
(b)
A
Cognitive-based
interventions
Cognitive-based >
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.
/