Neurocognitive Risk and Protective Factors
in Addictive Disorders
Dana G. Smith
King’s College
A dissertation submitted for the degree of Doctor of Philosophy at the
University of Cambridge
Declaration
This dissertation is the result of my own work and includes nothing that is the outcome of
work done in collaboration, except where specifically indicated in the text. This
dissertation does not exceed the word limit specified by the School of Biological Sciences
and has not been submitted in whole or in part for any other degree or qualification at any
other university.
The research reported in this dissertation has been presented in the following
publications:
Smith DG, Jones PS, Bullmore ET, Robbins TW, & Ersche KD. (2013). Enhanced
orbitofrontal cortex function and lack of attentional bias to cocaine cues in
recreational stimulant users. Biological Psychiatry. Published online.
Smith DG, Jones PS, Williams GB, Bullmore ET, Robbins TW, & Ersche KD. (2013).
Overlapping decline in orbitofrontal gray matter volume related to cocaine use
and body mass index. Addiction Biology. Published online.
Smith DG, Jones PS, Bullmore ET, Robbins TW, & Ersche KD. (2013). Cognitive
control dysfunction and abnormal frontal cortex activation in stimulant drug users
and their siblings. Translational Psychiatry. 3: e257.
Ersche KD, Jones PS, Williams GB, Smith DG, Bullmore ET, & Robbins TW. (2013).
Distinctive personality traits and neural correlates associated with stimulant drug
use versus familial risk of stimulant dependence. Biological Psychiatry. 74: 137144.
3
Smith DG & Robbins TW. (2012). The neurobiological underpinnings of obesity and
binge eating: A rationale for adopting the food addiction model. Biological
Psychiatry. 73: 804-810.
Robbins TW, Gillan CM, Smith DG, de Wit S, & Ersche KD. (2012). Neurocognitive
endophenotypes of impulsivity and compulsivity: Towards dimensional
psychiatry. Trends in Cognitive Science. 16: 81-91.
4
Acknowledgments
Thank you, first and foremost, to Trevor Robbins and Karen Ersche for giving me this
opportunity and for all of their guidance, knowledge and support throughout the PhD. I
have learned so much from you both.
Thank you to Annemieke Apergis-Schoute, Ed Bullmore, Gonzalo Urcelay, Mercedes
Arroyo, Naresh Subramaniam, Sanja Abbot, Sharon Morein-Zamir and the other
members of the BCNI for their patience and advice. Special thanks to Simon Jones for his
invaluable help with fMRI analyses.
Thank you to Abigail Turton, Claire Whitelock, Rania Christoforou and Heatha
Agyepong for their assistance with data collection and organization during the ICCAM
project.
Thank you to Claire Gillan, Yasemin Yazar and Jane Garrison for sharing their time,
space and chocolate with me.
Thank you to all the participants who took part in these studies for sharing their stories.
Thank you to the Cambridge Overseas Trust for providing the funding for my PhD.
Thank you to the King’s College Graduate community for providing much-needed
distraction, commiseration, humour and friendship throughout these three years. Thank
you especially to Ruth Watkinson and Sam Greenbury
Thank you to my parents, without whose love and support I could not have done this.
5
6
Abstract
Cognitive impairments and changes in the structure and function of related brain regions,
namely the prefrontal cortex and striatum, have long been implicated in drug addiction.
However, it is unknown whether these abnormalities predate substance abuse, potentially
serving as risk factors for dependence, or if they are the consequence of protracted use.
To address this question, endophenotype research using stimulant-dependent individuals’
biological siblings has been used to investigate traits implicated in the pathology of
addiction. Impairments present in both groups suggest an underlying risk-state for
dependence, while additional abnormalities present only in stimulant-dependent
individuals reflect potential effects of the drugs themselves. Contrastingly, there are also
individuals who use stimulant drugs in a controlled manner without developing
dependence. These ‘recreational users’ may lack the underlying traits that comprise a
greater risk for dependence, or they might maintain additional protective factors against
the development of addiction. Experiments in the first half of this dissertation used
functional magnetic resonance imaging to investigate neurocognitive similarities and
differences between dependent stimulant users, their non-dependent siblings, recreational
users of cocaine, and unrelated healthy control volunteers. In Chapter 2, performance on
a colour-word Stroop task was impaired in both stimulant-dependent individuals and their
siblings, suggesting an endophenotype of cognitive inefficiency. However, neural activity
significantly differed between the groups, indicating additional changes specific to the use
of stimulant drugs. In Chapter 3, dependent users showed significant attentional bias to
salient stimuli on a cocaine-word Stroop task, with a concurrent increase in prefrontal
activation. Conversely, recreational users showed resilience in the face of cocaine cues
and a decrease in arousal. Finally, Chapter 4 explored differences in reward sensitivity to
7
both generic and drug-specific reinforcers, comparing the effects of personal and family
history of stimulant exposure on a monetary incentive delay task.
It is also under debate whether the neurocognitive differences seen in stimulantdependent individuals are unique to substance abuse, or if parallel changes in behaviour
and neurobiology are present in similar addiction-spectrum disorders, such as binge
eating leading to obesity. In Chapter 5, stimulant-dependent and obese individuals with
binge-eating behaviours showed differences in their substance-specific and general
reward responsivity on a novel reward-valuation task. However, in Chapter 6 a similar
decline in orbitofrontal cortex grey matter volume in relation to both years of stimulant
use and body mass index was identified, implicating an overlap in this area between both
conditions. These findings are integrated in Chapter 7, discussing the neurocognitive risk
and protective factors that underlie an individual’s vulnerability for addiction, not only to
stimulant drugs, but also potentially for other addictive behaviours.
8
Table of Contents
Chapter 1. General Introduction
1. Definition of Addiction .............................................................................................. 13
2. Striatal Dopamine System and Bottom-Up Motivation ............................................. 14
2.1 Incentive Salience ................................................................................................. 15
2.2 Transition from Goal-Directed to Habitual Drug Behaviour ............................... 16
3. Dopamine System Abnormalities in Stimulant Dependence ..................................... 17
3.1 Genetic Variants Implicated in Addiction ............................................................ 19
4. Impulsivity ................................................................................................................. 19
5. Prefrontal Cortex and Top-Down Control ................................................................. 22
5.1 Cognitive Impairment ........................................................................................... 24
6. Orbitofrontal Cortex ................................................................................................... 25
6.1 OFC Abnormalities in Stimulant Dependence ..................................................... 26
6.2 Reward Valuation ................................................................................................. 26
6.3 Attentional Bias .................................................................................................... 28
6.4 Compulsive Behaviour ......................................................................................... 29
7. Reward System Abnormalities ................................................................................... 30
8. Endophenotype Research ........................................................................................... 32
9. Recreational Stimulant Use ........................................................................................ 34
10. Food Addiction ......................................................................................................... 36
11. Aims and Objectives ................................................................................................ 38
Chapter 2. Cognitive Control Dysfunction and Abnormal Frontal Cortex Activation
in Stimulant Drug Users and their Biological Siblings
1. Introduction ................................................................................................................ 41
2. Materials and Methods ............................................................................................... 43
2.1 Participants ........................................................................................................... 43
2.2 Measures ............................................................................................................... 46
2.3 Procedure .............................................................................................................. 46
2.4 Imaging Acquisition ............................................................................................. 47
2.5 Data Analysis ........................................................................................................ 47
3. Results ........................................................................................................................ 49
9
3.1 Stroop Performance .............................................................................................. 50
3.2 Neuroimaging Analysis ........................................................................................ 51
3.3 Regression of Stroop Performance to Neuroimaging Findings ............................ 56
3.4 Relationship of Structural Changes to Stroop Performance ................................. 56
3.5 Effects of Drug Use History on Performance ....................................................... 58
4. Discussion .................................................................................................................. 58
Chapter 3. Enhanced Orbitofrontal Cortex Function and Lack of Attentional Bias to
Cocaine Cues in Recreational Stimulant Users
1. Introduction ................................................................................................................ 63
2. Materials and Methods ............................................................................................... 66
2.1 Participants ........................................................................................................... 66
2.2 Behavioural Measures .......................................................................................... 68
2.3 Behavioural Analysis ............................................................................................ 69
2.4 Imaging Analysis .................................................................................................. 69
3. Results ........................................................................................................................ 71
3.1 Behavioural Performance ..................................................................................... 71
3.2 Functional Imaging ............................................................................................... 72
4. Discussion .................................................................................................................. 77
Chapter 4. A Comparison of Endophenotype and Stimulant Exposure Effects in
Reward Sensitivity
1. Introduction ................................................................................................................ 82
2. Materials and Methods ............................................................................................... 85
2.1 Participants ........................................................................................................... 85
2.2 Behavioural Task .................................................................................................. 85
2.3 Behavioural Analysis ............................................................................................ 88
2.4 Imaging Analysis .................................................................................................. 88
3. Results ........................................................................................................................ 90
3.1. Demographic ....................................................................................................... 90
3.2 Behavioural ........................................................................................................... 91
3.3 Neuroimaging ....................................................................................................... 93
4. Discussion ................................................................................................................ 100
10
Chapter 5. Reward Valuation and Incentive Motivation in Addictive Disorders
1. Introduction .............................................................................................................. 105
2. Materials and Methods ............................................................................................. 110
2.1 Participants ......................................................................................................... 110
2.2 Behavioural Task ................................................................................................ 111
2.3 Self-Report Measures ......................................................................................... 113
2.4 Analysis .............................................................................................................. 113
3. Results ...................................................................................................................... 114
3.1 Self-Report Data ................................................................................................. 114
3.2 Response Time Behavioural Analysis ................................................................ 116
3.3 Subjective Ratings .............................................................................................. 118
3.4 Correlation Analyses .......................................................................................... 118
4. Discussion ................................................................................................................ 120
Chapter 6. Overlapping Decline in Orbitofrontal Grey Matter Volume Related to
Cocaine Use and Body Mass Index
1. Introduction .............................................................................................................. 124
2. Materials and Methods ............................................................................................. 125
2.1 Participants ......................................................................................................... 125
2.2 Imaging Procedures ............................................................................................ 127
3. Results ...................................................................................................................... 128
4. Discussion ................................................................................................................ 132
Chapter 7. General Discussion
1. Summary .................................................................................................................. 135
2. Links to Personality Traits ....................................................................................... 143
3. Synthesis................................................................................................................... 148
4. Future Directions ...................................................................................................... 153
5. Conclusions and Implications .................................................................................. 155
References ....................................................................................................................... 157
Appendices ...................................................................................................................... 182
11
12
Chapter 1. Introduction
Chapter 1. General Introduction
Addictive disorders are a major mental health concern, with roughly 300,000 individuals
in the UK currently dependent on an illicit substance (National Treatment Agency for
Substance Misuse, 2012). The economic costs of this disease are estimated to reach
fifteen billion pounds every year in health care, treatment services, law enforcement, lost
productivity, and drug-related crime (Gordon, 2006). However, with almost three million
people reporting having used an illicit substance in the last year, it appears that only ten
percent of individuals who try drugs will become dependent upon them. The question
thus arises, what are the risk and protective factors involved in these odds? How can nine
out of ten individuals try drugs of abuse without developing a dependence upon them, and
what is different in those one in ten who do become addicted?
1. Definition of Addiction
Drug addiction is a relapsing and remitting disorder characterized by the compulsive
seeking and taking of a substance, despite severe negative consequences such as poor
health, legal and financial problems, and the damaging of social relationships. This
includes a loss of control over use of the drug, consuming greater quantities and using
more frequently than intended or despite a desire to stop, and a strong feeling of negative
affect, including distress, anxiety and dysphoria when experiencing withdrawal
(American Psychiatric Association, 2000). Intense feelings of craving consisting of a
powerful urge and desire to use frequently accompany physical and psychological
dependence, and are often triggered by associated environmental stimuli, such as certain
people or places, or drug-related paraphernalia.
13
Chapter 1. Introduction
Originally begun as a hedonic choice, drug taking can devolve into a compulsive habit
where the behaviour is maintained despite a loss of the rewarding sensations first
associated with the action. The initial positive reinforcement for drug taking comes from
the pleasure derived from the euphoric ‘high’ effects of the stimulant itself, while
negative reinforcement maintains continued drug-seeking behaviours, attempting to stave
off withdrawal and the associated negative affect that can follow (Koob & Le Moal,
2001; Solomon, 1980). Positive reinforcers from the drug producing pleasurable
experiences are followed by disparate effects in an opponent-process model, generating
contrasting negative feelings in a simple dynamic control system (Solomon, 1980). These
withdrawal symptoms typically manifest as opposing physiological and psychological
reactions to those experienced during the initial high, motivating users to take more of the
drug to alleviate the unpleasant symptoms.
2. Striatal Dopamine System and Bottom-Up Motivation
All drugs of abuse act on the mesolimbic dopamine system, impacting it either directly or
indirectly (Di Chiara, 1999; Koob & Nestler, 1997; Nutt, 1996). Stimulant drugs like
cocaine and amphetamine directly alter ventral striatal dopamine levels by interfering
with monoamine transporters, inhibiting the reuptake of endogenous dopamine from the
synapses, or by increasing dopamine concentration and release in presynaptic vesicles.
Psychostimulants also significantly elevate dopamine levels in the prefrontal cortex via
their action on noradrenergic neurons (Di Chiara, 1999; Tanda, Pontieri, Frau, &
DiChiara, 1997). Other substances like cannabis and opiates affect the system indirectly,
with cannabinoid receptors and opioid peptides synapsing onto dopaminergic neurons in
the nucleus accumbens shell and ventral tegmental area, causing a cascade of dopamine
release throughout the mesolimbic system (Tanda, Pontieri, & DiChiara, 1997).
14
Chapter 1. Introduction
Dopamine is crucially involved in reward valuation and reinforcement learning, as well as
the development of habits (Baldo & Kelley, 2007; Di Chiara, 1999; Wise & Bozarth,
1985; Wise, 2004). As such, the mesolimbic dopamine system is not limited to the
rewarding sensations associated with drugs of abuse, but also plays a reinforcing role in
all pleasurable experiences. This includes anticipation and receipt of food, sex and money
(Deadwyler, 2010; Everitt, 1990; Hernandez & Hoebel, 1988; Schott et al., 2008; Wise &
Rompre, 1989), with dopaminergic activity underlying the positive reinforcing properties
of both intrinsically rewarding and conditioned stimuli (Di Chiara, 1999; Wise, 2004).
Dopamine agonists such as amphetamine magnify the reinforcing effects of conditioned
stimuli that already depend on dopamine activity, and this enhancement makes stimulant
drugs particularly susceptible to abuse and the development of compulsive tendencies.
2.1 Incentive Salience
Elevation of dopamine levels in the striatum can also create associations between drug
cues and the ‘high’ experienced upon administration. This enhances the substance’s abuse
potential, potentiating relevant stimulus-reward associations. The anticipation of
receiving the drug can then cause similar increases in dopamine release, even before the
substance’s excitatory properties are experienced (Berridge & Robinson, 1998; Di Chiara,
1999; Ito, Dalley, Howes, Robbins, & Everitt, 2000; Koob & Le Moal, 2001). A formerly
neutral accompaniment can thus be rendered desirable or rewarding through its
association with the drug and its acute effects, initiating a transfer of incentive salience
and phasic dopamine release to the associated cue (Ito, Dalley, Robbins, & Everitt, 2002;
Schultz, Dayan, & Montague, 1997). Administration of psychostimulants can increase
acquisition of conditioned reinforcement learning, potentiating responding for a
conditioned reinforcer, as well as enhancing pavlovian-instrumental transfer and certain
forms of conditioning (Everitt & Robbins, 2005; Taylor & Robbins, 1986; Wise, 2004;
15
Chapter 1. Introduction
Wyvell & Berridge, 2001). Conversely, dopamine receptor antagonists diminish
incentive-motivation and may impede conditioning (Koob & Le Moal, 2001; Wise,
Spindler, DeWit, & Gerberg, 1978; Wise, 2004). Thus, the reinforcing qualities of drugs
of abuse are thought to be facilitated through both direct and indirect activation of the
mesolimbic dopamine circuit, eliciting positive feelings of reward and incentivising
future use, instilling the substance and its associated cues with particular potency (Di
Chiara, 1999; Koob & Le Moal, 2001).
Learned drug associations can increase the likelihood for relapse long after acute
withdrawal effects have dissipated. This occurs by triggering anticipatory or craving
sensations upon encounter with environmental cues that were previously associated with
drug administration. These stimuli have been engendered with greater incentive salience
by associative learning, granting them many of the same reinforcing properties of the
drug itself. These secondary drug cues can then cause a release of striatal dopamine
themselves, independent of the presence of the original substance, thus hypothetically
instigating anticipation and desire for the subsequent effects of the drug that typically
follow (Berridge & Robinson, 1998). Feelings of craving precipitated by exposure to
drug-related cues and memories have been shown to be accompanied by greater
activation in the prefrontal cortex, dorsal striatum and regions implicated in the limbic
system (Childress et al., 1999; Daglish et al., 2001; Garavan et al., 2000; Grant et al.,
1996; Volkow et al., 2006, 2010).
2.2 Transition from Goal-Directed to Habitual Drug Behaviour
There are also changes in dopamine firing within the basal ganglia that mediate the
transition from initially goal-directed or hedonically motivated drug taking to more
compulsive drug-related habits. This is theorized to involve a signalling transfer shift
16
Chapter 1. Introduction
from ventral to dorsal striatal control, which subserves a devolution from action-outcome
to stimulus-response behaviours (Belin & Everitt, 2008; Everitt & Robbins, 2005; Everitt
et al., 2008; Robbins, Gillan, Smith, de Wit, & Ersche, 2012). As drug taking is devalued
through a loss of the initial subjective pleasure experienced upon administration, drug
seeking can become compulsive and habitual, triggered by salient cues like environment
or drug-related paraphernalia. These behaviours are thought to be perpetuated by
signalling from the lateral caudate and putamen, which contribute to sustained movement
and habitual actions (Everitt et al., 2008; Yin, Knowlton, & Balleine, 2004; Yin, Ostlund,
& Balleine, 2008).
3. Dopamine System Abnormalities in Stimulant Dependence
Stimulant-dependent individuals have known impairments in fronto-striatal functioning,
which are potentially exacerbated by stimulant drugs’ manipulation of the dopamine
system. Alterations in cortico-striatal circuitry include reduced baseline levels of
endogenous dopamine, lower dopamine D2 receptor availability, diminished dopamine
transporter activity, and eventually a decline in dopamine receptor sensitivity through a
down-regulation of the system via over-stimulation from prolonged drug administration
(Dalley et al., 2007; Lee et al., 2009; Martinez et al., 2004, 2009; Porrino, Daunais,
Smith, & Nader, 2004; Volkow et al., 1997). These can all contribute to dysfunctional
patterns of responding and impulsive behaviour typically seen in stimulant-dependent
individuals.
For example, diminished dopamine release in the nucleus accumbens in response to
methylphenidate has been seen in cocaine-dependent individuals as compared with
healthy controls, and decreased dopamine receptor binding has been reported in
stimulant-dependent patients (Martinez et al., 2004, 2009; Volkow et al., 1997, 2001,
17
Chapter 1. Introduction
2007). Additionally, lower endogenous dopamine receptor density in healthy individuals
make them more likely to rate the stimulating effects of amphetamine as pleasant,
compared with reports of noxious or over-arousing and anxiety-inducing effects in those
with naturally higher levels (Thanos et al., 2001; Volkow et al., 1999). Performanceenhancing stimulant drugs are also most effective in individuals with higher baseline rates
of impulsivity, which has been linked to reduced dopamine receptor availability (Cools,
Sheridan, Jacobs, & D’Esposito, 2007). Thus, subjective pleasure experienced from
stimulant drugs, as well as their abuse potential, can largely depend on an individual’s
underlying dopamine activity (Volkow et al., 2006). This has led to the self-medication
hypothesis for the initiation and eventual compulsive continuation of stimulant use in
dependent individuals, presumably subconsciously ‘treating’ their low endogenous
dopamine activity with psychostimulant drugs.
There have also been reports of structural abnormalities in the basal ganglia in stimulantdependent individuals, specifically increased grey matter volume in the caudate, putamen
and pallidum (Chang et al., 2005; Ersche et al., 2011; Jacobsen, Giedd, Gottschalk,
Kosten, & Krystal, 2001). Differences in basal ganglia volume have also been linked to
attention deficit/hyperactivity disorder (ADHD) (Castellanos et al., 1996), with decreases
in striatal volume thought to potentially underlie the pathological impulsive responding.
The finding of similar rates of impulsivity in patients with ADHD as in those with
stimulant-dependence, as well as similar treatment options using dopamine and
noradrenaline agonists, provides further support for the self-medication hypothesis in
stimulant dependence (Vaidya et al., 1998).
18
Chapter 1. Introduction
3.1 Genetic Variants Implicated in Addiction
These findings may be modified by a genetic profile affecting dopamine pathways,
placing individuals at a greater susceptibility for reward system dysfunction. Due to its
role in dopamine receptor availability, the A1 allele of the TaqIA gene has historically
been a target for drug and alcohol research over the last 20 years (Hall, Drgonova, Jain, &
Uhl, 2013; Noble, 2000). Presence of the A1 allele can cause a 30-40% reduction in
striatal dopamine D2 receptors (Jonsson et al., 1999), and has been implicated in
corresponding deficits in glucose metabolism in the orbitofrontal cortex (OFC), frontal
and temporal gyri, insula, hippocampus, and dorsal and ventral striatum (Noble,
Gottschalk, Fallon, Ritchie, & Wu, 1997). As such, some studies have reported that
chronic drug users show a greater preponderance of A1 compared to the general
population, and its presence is thought to underlie some of the structural and behavioural
abnormalities seen in dependent individuals (Comings, Muhleman, Ahn, Gysin, &
Flanagan, 1994; Noble, 2000). Another possible genetic target for the dopamine system’s
role in addiction that has received significant attention is the dopamine transporter gene
(DAT) (Guindalini et al., 2006; Uhl, Hall, & Sora, 2002). However, it should be noted
that research has veered away from dopamine gene expression theories in recent years,
and critiques about prior genetic research in addiction have been raised (Hall et al., 2013).
4. Impulsivity
Decreased striatal D2 receptor availability is frequently cited as a risk factor in stimulant
dependence, as it has been empirically linked to an increased tendency for impulsive
behaviours (Dalley, Everitt, & Robbins, 2011; Dalley et al., 2007; Kirby & Petry, 2004;
Lee et al., 2009; Perry & Carrol, 2008; Volkow et al., 1993, 1996, 2001). Impulsivity is
the tendency to act prematurely, without foresight, and despite adverse consequences
19
Chapter 1. Introduction
(Robbins et al., 2012). Conversely, sensation-seeking has been defined as an interest in
searching out new experiences, even if they are risky or potentially dangerous. Trait
impulsivity is highly implicated in addictive tendencies, often divided into difficulties
with waiting or delaying gratification, and problems with stopping a prepotent response
(Dalley et al., 2011; Perry & Carrol, 2008). This places impulsive individuals at a
disadvantage in inhibiting maintained actions or urges, as well as potentially increasing
their susceptibility to the immediate rewarding properties of drugs of abuse over
consideration of long-term consequences. Increases in impulsivity are therefore
particularly associated with the acquisition of drug-taking behaviours, as well as the loss
of control seen during the transition from goal-directed to compulsive drug use (Perry &
Carrol, 2008; Everitt et al., 2008).
In animal models, Dalley and others have shown a greater preference for to cocaine and
an increase in self-administration rates in highly impulsive animals, thought to correspond
with a decrease in D2 receptor binding availability in the ventral striatum (Dalley et al.,
2007). Increases in sensation- or novelty-seeking in rats has also been linked to a higher
proclivity for initial stimulant administration, whereas impulsivity as a distinct trait is
more associated with the development of compulsive drug behaviours (Belin, Mar,
Dalley, Robbins, & Everitt, 2008). High impulsivity has also been linked to greater risk
for relapse after drug abstinence (Everitt et al., 2008).
Similarly, high levels of impulsivity and impairments in inhibitory control, as measured
by self-report assessments and behavioural tasks, are associated with increased severity in
human drug dependence (Clark, Robbins, Ersche, & Sahakian, 2006; Ersche, Roiser,
Robbins, & Sahakian, 2008). The most commonly administered self-report measure for
impulsivity is the Barratt Impulsivity Scale (BIS-11), a 30-item questionnaire assessing
20
Chapter 1. Introduction
subjective tendencies towards impulsive-related actions, such as frequent moves or job
changes, and self-control abilities like paying attention, concentrating and future planning
(Patton, Stanford, & Barratt, 1995). Items are divided into three subscales: attention,
motor behaviour and non-planning.
Behaviourally, impulsivity can be assessed with either ‘stopping’ or ‘waiting’ paradigms.
This is typically done using the Stop Signal Reaction Time Task (SSRTT), a behavioural
test assessing stopping ability as an indicator of impulsivity and poor motor inhibitory
control (Ersche et al., 2011; Logan, Schachar, & Tannock, 1997; Robbins et al., 2012). In
the task, the participant must respond rapidly to a repeated visual stimulus (i.e., pressing a
button every time an arrow appears on the screen); however, they must inhibit this
ongoing response when they are presented with a stop signal, such as a loud beep.
Successful stops indicate adequate inhibitory control functioning, whereas impairments
are suggestive of ‘stopping impulsivity’.
Similarly, cognitive control is often measured using a Stroop task. Here, participants must
name the font colour of a target word that spells out either the same or a different colourword as the font (MacDonald, Cohen, Stenger, & Carter, 2000; Stroop, 1935). Responses
to incongruent colour-word combinations present a greater cognitive demand than
congruent pairings because of the interference of the prepotent tendency to read a word
rather than determine its colour. The interference score indexes how well a person exerts
cognitive control over this automatic behaviour (word-reading) in favour of a more
unusual behaviour (colour-naming). The task can also be adapted to measure emotional
interference causing attentional bias (i.e., for phobias or drug-related material in
substance users).
21
Chapter 1. Introduction
Waiting or choice impulsivity can be assessed with delay discounting measures, in which
the participant must choose between a small immediate reward and a larger delayed
option (Kirby & Petry, 2004). Impulsive individuals often over-value the smaller sooner
option, unable to wait and devaluing the larger delayed reward. Stopping and waiting are
thought to be two distinct aspects of the generalised impulsivity umbrella, and both of
these constructs only correlate mildly with self-report ratings (Reynolds, Ortengren,
Richards, & de Wit, 2006).
An illuminating study by Lee et al. (2009) successfully synthesised these concepts of
underlying biological and personality risk-traits for dependence with the consequential
effects of stimulant
use. Elevated BIS-11 self-report impulsivity scores in
methamphetamine users were related to decreased dopamine D2/D3 receptor binding in
the caudate and putamen, and both measures were significantly influenced by lifetime
exposure to methamphetamine (Lee et al., 2009). Additionally, poor behavioural
inhibitory performance on the SSRTT has been identified as a predictive factor for drug
and alcohol related problems in adolescents (Nigg et al., 2006; Whelan et al., 2012).
Together, this evidence suggests that there are potentially both predating differences in
dopamine system functioning, with related elevations in impulsivity in those who become
dependent on stimulants, as well as potential additional exacerbatory effects on the
dopamine system and impulsive behaviour from prolonged use of the drug itself (Ersche,
Bullmore, et al., 2010).
5. Prefrontal Cortex and Top-Down Control
In addition to dopamine-related abnormalities in the striatum, structural and functional
aberrations in the prefrontal cortex (PFC) have been implicated in stimulant dependence.
Decreases in D2 receptor availability in the PFC have been reported in stimulant22
Chapter 1. Introduction
dependent individuals, with correlations seen between striatal and prefrontal dopamine
functioning (Schoenbaum & Shaham, 2008; Volkow et al., 1993, 2001). This suggests an
important feedback loop between the striatum and PFC, modulated by dopamine and
potentially malfunctioning in stimulant-dependent individuals.
Porrino and colleagues have shown metabolic changes in both cortical and subcortical
limbic regions accompanying acute cocaine exposure in non-human primates (Porrino,
Smith, Nader, & Beveridge, 2007). This includes decreases in ventral striatal and
ventromedial prefrontal cortex glucose metabolism (Porrino et al., 2002). These effects
were exacerbated by prolonged stimulant administration, with both more severe and more
widespread disruption seen throughout these regions in association with chronic use
(Beveridge, Smith, Daunais, Nader, & Porrino, 2006). Advancing changes into the dorsal
striatum with long-term exposure was also evident, providing support for the proposed
shift in dopamine signalling control from the ventral to the dorsal striatum, as previously
suggested (Everitt & Robbins, 2005).
Abnormalities in prefrontal structural integrity have also been associated with prolonged
stimulant use, including significant reductions in both grey matter volume and white
matter connectivity. This finding has been reliably reproduced in numerous samples of
both abstinent and currently dependent stimulant users from a variety of different groups
(Ersche et al., 2011; Franklin et al., 2002; Lim, Choi, Pomara, Wolkin, & Rotrosen, 2002;
Liu, Matochik, Cadet, & London, 1998; Matochik, London, Eldreth, Cadet, & Bolla,
2003; Tanabe et al., 2009). The cognitive ramifications of these structural differences are
listed below.
23
Chapter 1. Introduction
5.1 Cognitive Impairment
The prefrontal cortex is critically involved in self-control and impulsivity, as well as goal
representation, making dysfunction in this area especially significant. Thus, these
structural changes can have considerable consequences, and the PFC abnormalities cited
above are thought to be at the root of many of the cognitive difficulties seen in drugdependent individuals.
Several decades of work have reported on a wide-range of cognitive deficits observed in
stimulant-dependent individuals (Ersche & Robbins, 2011; Goldstein & Volkow, 2011;
Rogers & Robbins, 2001; Verdejo-Garcia, Bechara, Recknor, & Perez-Garcia, 2006;
Verdejo-García & Pérez-García, 2007). This includes crucial difficulties with response
inhibition and self-control (Goldstein, Volkow, Wang, Fowler, & Rajaram, 2001; Hester
& Garavan, 2004; Kaufman, Ross, Stein, & Garavan, 2003; Monterosso, Aron, Cordova,
Xu, & London, 2005; Verdejo-Garcia, Perales, & Perez-Garcia, 2007), as well as
detriments in working memory (Ersche, Clark, London, Robbins, & Sahakian, 2006;
Tomasi et al., 2007), decision-making (Bechara & Damasio, 2002; Bechara, 2005;
Bechara et al., 2001; Ersche et al., 2005; Ersche, Fletcher, et al., 2006), sustained
attention (Gooding, Burroughs, & Boutros, 2008; Horner, 1999; Levine et al., 2006;
London et al., 2005), task-switching (Ersche et al., 2008), and affective responding and
emotion regulation (Fox, Axelrod, Paliwal, Sleeper, & Sinha, 2007). These impairments
often correlate with years of substance use, implicating prolonged exposure to stimulant
drugs in more severe dysfunction. However, other studies have shown executive function
difficulties in the drug-naïve relatives of dependent users, including both biological
siblings and offspring, suggesting that these impairments may also be underlying risk
factors for dependence (Ersche, Turton, et al., 2012; Silveri, Rogowska, McCaffrey, &
Yurgelun-Todd, 2011).
24
Chapter 1. Introduction
6. Orbitofrontal Cortex
A ventromedial component of the PFC, encompassing medial areas of the orbitofrontal
cortex (OFC) and stretching back into the anterior cingulate cortex (ACC), has been
particularly implicated in stimulant dependence.
The OFC is defined as the anterior portion of Brodmann areas 10 and 11, wrapping
medial to lateral; areas 12 and 25 more medially and caudally; and laterally extending
posteriorly into area 47 (Elliott, Dolan, & Frith, 2000). The medial OFC – often
synonymous with the ventromedial prefrontal cortex, an area overlapping both
structurally and functionally (Noonan, Mars, & Rushworth, 2011; Rushworth, Noonan,
Boorman, Walton, & Behrens, 2011) – is critical in reward valuation, affective decisionmaking and goal-directed behaviour (Gottfried, O’Doherty, & Dolan, 2003; Rolls, 2000;
Schoenbaum, Roesch, & Stalnaker, 2006; Schoenbaum & Shaham, 2008; Volkow &
Fowler, 2000). This is thought to be due in part to the region’s projections to and from
limbic regions, including the ventral striatum and basolateral amygdala (Elliott, Dolan, &
Frith, 2000; Rushworth, Behrens, Rudebeck, & Walton, 2007; Schoenbaum et al., 2006).
This is distinct from the executive control functions and ‘colder’ cognitive processes of
the more lateral, posterior and superior regions of the prefrontal cortex. Indeed, the more
lateral regions of the OFC are typically associated with suppressing previously initiated
responses, as in the case of reversal learning (Elliott et al., 2000). The lateral OFC has
also been linked to the processing of both positive and negative feedback, whereas the
medial OFC is most strongly implicated in reward learning (Noonan et al., 2011). There
is also evidence that the lateral OFC is involved in compulsive behaviours, such as in
obsessive-compulsive disorder (Joel, Doljansky, Roz, & Rehavi, 2005).
25
Chapter 1. Introduction
In the current thesis, I am most concerned with the ventromedial prefrontal cortex/medial
orbitofrontal cortex and this region’s role in reward processing and affective decisionmaking; and in discussion of the OFC, this is the region being referred to unless otherwise
specified.
6.1 OFC Abnormalities in Stimulant Dependence
Similar structural abnormalities as those seen in the wider prefrontal cortex in dependent
stimulant users have been reported in the OFC, including a reduction in glucose
metabolism relating to dopamine receptor availability in the striatum, as well as
significant decreases in medial OFC grey matter volume in chronic drug users (Nader,
Czoty, Gould, & Riddick, 2008; Porrino et al., 2007; Volkow et al., 1997, 2001). Using
voxel-based morphometry analysis, these decreases in OFC grey matter volume and
connectivity have been linked to increases in drug-related compulsivity, as determined by
the Obsessive-Compulsive Drug Use Scale (OCDUS) (Franken, Hendriksa, & van den
Brink, 2002), with greater disruption in the OFC associated with higher compulsivity
ratings (Ersche et al., 2011; Meunier et al., 2011). Conversely, enlarged basal ganglia
volume, particularly in the left caudate nucleus, was related to elevated impulsivity levels
in these individuals (Ersche et al., 2011).
6.2 Reward Valuation
Activation of the OFC is associated with the expected value of an outcome, and neurons
in the OFC exhibit greater firing for a preferred reward, with the amplitude of the cell’s
response dependent on the subjective value of the anticipated receipt (Gottfried et al.,
2003; Schoenbaum et al., 2006; Tremblay & Schultz, 1999). This subjective reward
valuation includes a comparison of outcomes of different natures and over different timecourses, and is a changing and transient response dependent on an individual’s current
26
Chapter 1. Introduction
state and motivations (Levy & Glimcher, 2012; Rolls, 2000). These anticipatory reward
valuations are learned over time through past experiences and, combined with feedback
processing via the anterior cingulate cortex, are crucial in the process of affective
decision-making (Elliott et al., 2000; Rushworth et al., 2007; Rushworth & Behrens,
2008). Consequently, abnormal or reduced medial OFC functioning has been linked to
impaired decision-making in a variety of paradigms and patient groups (Bechara,
Damasio, Damasio, & Anderson, 1994; Bechara, Damasio, & Damasio, 2000; Clark et
al., 2008; Grant, Contoreggi, & London, 2000). In dependent drug users this effect is
particularly evident, and it is thought to partially underlie the known risky behaviours that
often accompany chronic drug taking (Bechara & Damasio, 2002; Ersche & Robbins,
2011). This includes needle sharing, unsafe sexual behaviour and driving while
intoxicated (Aitken, Kerger, & Crofts, 2000; Albery, Strang, Gossop, & Griffiths, 2000;
Cavazos-Rehg et al., 2009; Hwang et al., 2000), actions that are indicative of potential
decision-making impairments.
In addition to the structural and metabolic reductions seen in the OFC in drug-dependent
individuals, decreased functional activation in response to cognitive tasks has also been
reported in a number of studies (Bolla et al., 2003; Ersche et al., 2011; London, Ernst,
Grant, Bonson, & Weinstein, 2000; Matochik et al., 2003; Schoenbaum & Shaham, 2008;
Volkow et al., 1993). This includes dampened responding to universal reinforcers like
food, money and sex (Garavan et al., 2000; Goldstein & Volkow, 2011). In one study,
cocaine-dependent individuals showed reduced functional magnetic resonance imaging
(fMRI) blood oxygen level dependent (BOLD) responding in the OFC in response to a
monetary reward compared with control participants, and they also demonstrated a lack
of sensitivity to different reward magnitudes (Goldstein, Tomasi, Alia-Klein, et al., 2007;
Goldstein et al., 2007). However, increased activation in this same region is also present
27
Chapter 1. Introduction
in response to drug-related stimuli in dependent users, which is often implicated in
maintaining or rekindling drug-seeking behaviour, even despite a desire to stop (Childress
et al., 1999; Schoenbaum & Shaham, 2008; Tiffany & Carter, 1998; Volkow & Fowler,
2000). This heightened response is suggestive of drug craving and a ‘hijacking’ of the
reward system by prolonged stimulant use in dependent individuals (Ehrman, Robbins,
Childress, & O’Brien, 1992; Garavan et al., 2000; Volkow & Fowler, 2000). Supporting
this theory, in a forced choice task cocaine-addicted animals consistently chose drug
rewards over a food option (Aigner & Balster, 1978; Woolverton & Anderson, 2006), and
a separate study has shown elevated OFC and striatal responses following the
administration of cocaine compared with receipt of a juice reward in non-human primates
(Opris, Hampson, & Deadwyler, 2009).
6.3 Attentional Bias
In human drug users, heightened drug-related salience is often assessed through
attentional bias tasks, measuring emotional interference from associated cues. As
mentioned above, an emotional Stroop task can be used to assess drug-related distraction
and cognitive interference. In previous studies, greater impairment on the cocaine-word
Stroop has been related to abnormal responding in the medial OFC and ACC, areas
implicated in craving, reward and attention (Ersche, Bullmore, et al., 2010; Franken,
2003; Goldstein, Tomasi, Rajaram, et al., 2007). Franken (2003) posits that this biased
attention network in drug users stems from dysfunctional involuntary reactions to drugrelated cues. Due to the limited nature of the brain’s attentional capacity, attention is
typically allocated to only a subset of external stimuli to avoid overstimulation. However,
when a stimulus is particularly potent it can ‘hijack’ this system and assume a greater
proportion of the attentional resources. Modelled in an emotional Stroop task, this results
in higher response latencies for salient words, as too much attention is paid to the content
28
Chapter 1. Introduction
of the word rather than the colour of the font, distracting the individual from the task athand (Field & Cox, 2008; Hester, Dixon, & Garavan, 2006).
Elevated activation in limbic regions, including the striatum, amygdala, ACC and OFC, is
also associated with increased craving in response to drug-related cues (Childress et al.,
1999; Daglish et al., 2001; Daglish & Nutt, 2003; Garavan et al., 2000; Grant et al., 1996;
Volkow et al., 2006). However, purposeful cognitive inhibition of craving upon cue
exposure can result in a decrease in glucose metabolic activity in these regions (Volkow
et al., 2010). Therefore, although these responses are automatic and unintentional –
similar to the prepotent urge to read the content of a word rather than name the colour of
the font – they appear to be able to be controlled at times through cognitive effort and
resistance.
6.4 Compulsive Behaviour
The lateral OFC is also central in obsessive-compulsive disorder and is theorized to be
linked to the compulsive nature of substance dependence. This is due to its possible
involvement in habit development and updating goal-directed information; impairments
in these processes are thought to be present in individuals with greater compulsive
tendencies (Balleine & O’Doherty, 2010). This is most often demonstrated through
reversal-learning or set-shifting tasks, where a previously rewarded response is devalued
while the alternate option becomes the target. Individuals with OFC damage or
dysfunction are frequently impaired on reversal-learning assessments, perseverating on
the previously reinforced behaviour and failing to update their responses (Cools, Clark,
Owen, & Robbins, 2002; Izquierdo, Suda, & Murray, 2004). Devaluation or aversive
learning appears to be similarly impaired in animals with OFC lesions, with a failure to
devaluate a stimulus-response association even after a punishment or noxious pairing
(Izquierdo et al., 2004; Schoenbaum et al., 2006). These effects have been related to
29
Chapter 1. Introduction
prolonged drug exposure in animal models (Jentsch, Olausson, De La Garza, & Taylor,
2002; Schoenbaum & Shaham, 2008; Schoenbaum & Setlow, 2005), and are also evident
in human substance users (Ersche et al., 2008), with dependent individuals continuing to
use even in the face of severe negative consequences.
7. Reward System Abnormalities
As discussed above, in addition to the cognitive impairments seen in drug-dependent
individuals, there is also dysfunction present in reward valuation caused by abnormalities
in striatal dopamine system functioning, as well as in the ventromedial prefrontal cortex /
orbitofrontal cortex. The brain’s reward system synthesizes both the bottom-up and topdown inputs from the ventral striatum, orbitofrontal cortex and anterior cingulate cortex
to create an integrated view of the subjective value of various stimuli. This evaluation is
often based on previous experiences, forming both explicit and implicit recollections and
shaping future decisions, resulting in reinforcement-guided choices (Rushworth &
Behrens, 2008). However, when this evaluative process is disrupted, as through
prolonged exposure to stimulant drugs, decision-making can become impaired (Bechara,
2005; Schoenbaum et al., 2006).
There are currently two well-accepted, yet conflicting theories of potential abnormal
reward processing in addiction involving the responses to organically salient rewards.
One, the reward deficiency theory (Bjork, Smith, & Hommer, 2008; Blum et al., 2000),
premises that individuals at risk for substance abuse maintain a generally underactive
reward system due to a pre-morbid reduction in the number of D2 dopamine receptors
available in the mesolimbic circuit, particularly in the striatum. At-risk individuals then
‘self-medicate’, seeking out drugs and other stimulating activities to offset this imbalance
and artificially elevate their naturally low levels of striatal dopamine. Additional down30
Chapter 1. Introduction
regulation of these dopamine receptors due to increased dopamine turnover in the
mesolimbic reward pathway after prolonged drug exposure further compacts this problem
by de-sensitizing the individual and lowering the homeostatic threshold, thereby requiring
greater reward salience to achieve activation (Bjork et al., 2008; Koob et al., 2004;
Volkow, Wang, Fowler, & Telang, 2008; Wang et al., 2001; Wang, Volkow, Thanos, &
Fowler, 2004). This further diminishes the already reduced effect of naturally rewarding
stimuli to signal the release of dopamine, causing the system involving both the OFC and
striatum to be under-responsive to non-drug rewards (Bjork et al., 2008; Koob & Le
Moal, 2005). This decrease in D2 receptor availability is also associated with diminished
OFC metabolism (Volkow et al., 2001; Volkow, Wang, Telang, et al., 2008), which is
linked to both objective and subjective reward valuation and decision-making, and where
impairment can result in impulsive actions (Bechara, 2005; Goldstein, Tomasi, AliaKlein, et al., 2007; Goldstein et al., 2007; Knutson, Westdorp, Kaiser, & Hommer, 2000;
Robbins, Ersche, & Everitt, 2008).
Conversely, the opponent-process, or incentive sensitization, theory proposes that drug
users are overly sensitive to rewards, manifested in impulsive and sensation-seeking
behaviour (Robinson & Berridge, 1993). Immediate rewards are disproportionately
valued via delay discounting, and the ventral striatum is overly activated in response to
salient stimuli. These individuals may also experience an up-regulation in striatal
dopamine receptor sensitivity after excess activation of the reward pathways as a
consequence of drug use, rendering them increasingly more responsive to both
anticipation and receipt of reward, regardless of type. Additionally, these individuals
typically have deficient or under-utilized prefrontal cortices, making self-control and
impulse restriction even more challenging (Bechara, 2005; Bjork et al., 2008; Goldstein
& Volkow, 2002).
31
Chapter 1. Introduction
8. Endophenotype Research
For all of these abnormalities, there is an ongoing debate as to whether the cortical and
subcortical aberrations present in stimulant dependence are predating risk factors for
addiction or are the consequence of protracted stimulant use, resulting in potential
neurotoxic effects on the brain. This includes both the alterations in reward sensitivity
subserved by the dopamine system and the suboptimal frontal cortex control circuitry.
Research from animal models provides strong evidence for both the chicken and the egg
argument, with underlying predefined personality traits, such as impulsivity and
sensation-seeking, implicated in a greater risk for both the initiation and development of
compulsive or addictive drug behaviours (Belin et al., 2008; Dalley et al., 2007).
However, there is also strong evidence for further disruption in the fronto-striatal
dopamine circuitry, particularly involving structural abnormalities in the prefrontal
cortex, that are associated with prolonged drug abuse and the devolution into compulsive
drug-seeking and taking (Porrino et al., 2007).
In human participants, one of the few means to investigate this chicken-or-egg debate is
through endophenotype research. Endophenotypes are stable quantifiable variables
associated with the genetic risk for a disorder and that are abnormal both in patients and
their relatives (Gottesman & Gould, 2003; Robbins et al., 2012). This includes both the
structural and behavioural abnormalities typically associated with the phenotype for a
pathology, such as the increased impulsivity and reduced frontal cortical volume
implicated in stimulant dependence. If there is evidence of impairment in both the
patients and their non-drug using biological relatives, it suggests an underlying risk factor
for dependence. However, greater dysfunction present only in the patient group is
indicative of potentially exacerbated abnormalities from the chronic stimulant use itself,
or perhaps an additional predisposing element unique to this group.
32
Chapter 1. Introduction
A new line of research from our group has attempted to address this question by studying
stimulant-dependent individuals, their non-dependent biological siblings and unrelated
healthy control volunteers on a variety of cognitive, personality and structural imaging
assessments (Ersche, Jones, et al., 2012; Ersche, Turton, et al., 2012; Ersche, Turton,
Pradhan, Bullmore, & Robbins, 2010). In an initial report, higher levels of trait
impulsivity, as measured by the BIS-11, and sensation-seeking, assessed using the
Zuckerman Sensation Seeking Scale (SSS-V) (Zuckerman, Eysenck, & Eysenck, 1978),
were evident in the stimulant-dependent individuals compared with controls (Ersche,
Turton, et al., 2010). However, their non-dependent siblings also showed elevated
impulsivity as compared to controls, but no difference in sensation-seeking. This suggests
that impulsivity may be an underlying trait predating stimulant dependence, which would
indicate potential genetic or childhood environmental effects on dopamine levels in both
members of the sibling pair, such as presence of the TaqIA allele. However, sensationseeking, which has been linked to the initiation of drug use and experimentation, was
unique to the stimulant-dependent individuals, likely spurring their initial drug-seeking
behaviour (Belin et al., 2008; Ersche, Turton, et al., 2010). An absence of this trait in the
sibling participants may therefore have protected them from initiating drug use, and
subsequently from following the same path as their dependent brothers and sisters.
In addition to self-report trait impulsivity, performance on the SSRTT was equally poor in
both stimulant-dependent individuals and their siblings as compared with healthy
controls. These behavioural impairments corresponded to abnormalities in cortical and
subcortical brain structure, including decreases in grey matter volume in the posterior
postcentral gyrus, superior temporal gyrus and posterior insula, as well as increases in the
medial temporal lobe and basal ganglia (Ersche, Jones, et al., 2012). Notably,
performance on the SSRTT was also directly related to reductions in white matter
33
Chapter 1. Introduction
integrity, particularly in tracts adjacent to the inferior frontal gyrus, an area important for
self-control. Specifically, white matter fractional anisotropy (FA) values significantly
negatively correlated with SSRTT ability, such that a decrease in FA integrity
corresponded to increased time needed to stop a prepotent response. These structural
impairments were evident in the stimulant-dependent and sibling participants and were
significantly higher in both groups compared with control participants. These findings
persuasively demonstrate shared structural and behavioural abnormalities in both
stimulant-dependent individuals and their non-dependent biological siblings, providing
evidence for underlying dysfunction in the self-control circuitry that predates and
potentially predisposes an individual for substance abuse. However, there was also
evidence of crucial differences that might have served as additional risk factors in the
dependent individuals, or, conversely, protective factors in the siblings.
9. Recreational Stimulant Use
It is important to note that the vast majority of individuals who use drugs do not become
addicted, and of the estimated 16.2 million people who use cocaine worldwide, only one
out of every eight is thought to develop dependence (United Nations Office on Drugs and
Crime, 2012). Furthermore, there appears to be a unique subset of individuals who are
able to maintain stable, controlled, occasional use of stimulant drugs without developing
dependency or abuse. These ‘recreational users’ are thought to possess greater selfcontrol over their drug-taking behaviours. They are also potentially lacking some of the
crucial risk factors involved in the development of dependence, including increases in
impulsivity and compulsivity (Ersche, Jones, Williams, Smith, et al., 2013). Recent
research has suggested that there are inherent differences in the brains of individuals who
are able to use drugs recreationally, most notably increased volume in the orbitofrontal
34
Chapter 1. Introduction
cortex compared with dependent stimulant users, as well as healthy control volunteers
(Ersche, Jones, Williams, Smith, et al., 2013). Differences in key personality traits also
emerged between these groups, with recreational users reporting significantly lower
levels of impulsivity and compulsivity than both dependent individuals and their nondependent siblings, providing further support that these characteristics are underlying
elements. However, the recreational users did crucially share elevated levels of sensationseeking with the dependent individuals, suggesting that this trait is implicated in the
tendency towards initial drug experimentation, yet not in the development of dependence
(Ersche, Jones, Williams, Smith, et al., 2013).
Previous research into non-dependent stimulant users has been limited and the results are
somewhat conflicting. This is thought to largely be due to varying definitions of the
quality and quantity of use classifying an individual as ‘recreational’. Some prior studies
have shown evidence of impairment on tasks of executive function in recreational users
compared with controls (Colzato, Huizinga, & Hommel, 2009; Colzato, van den
Wildenberg, & Hommel, 2007; Soar, Mason, Potton, & Dawkins, 2012), while other
assessments showed no difference in performance (Colzato et al., 2009).
If the structural and functional abnormalities reported in stimulant-dependent individuals
were largely due to the effects of cocaine exposure on the brain, recreational users would
be expected to show similar, though not as severe, changes in structure and function.
However, they would not possess the attributes associated with increased pre-morbid risk
for dependence. Furthermore, there may be additional changes in individuals who can use
stimulant drugs recreationally that serve as protective factors against the development of
dependence. Other key dissociations between dependent cocaine users, their nondependent biological siblings and recreational users of the drug, similar to that of
impulsivity and sensation-seeking, could help elucidate the distinction between
35
Chapter 1. Introduction
underlying neurobiological abnormalities associated with risk for dependence, and those
traits that are consequential from the effects of cocaine on the brain.
10. Food Addiction
It has been suggested that the behavioural patterns and neurochemical changes implicated
in heightened risk for addiction do not just apply to drugs of abuse. Similar
disadvantageous habits have been associated with problem gambling behaviour and, more
recently, compulsive overeating leading to obesity (Avena, Rada, & Hoebel, 2008; Davis
& Carter, 2009; Ifland et al., 2009; Parylak, Koob, & Zorrilla, 2011; Pelchat, 2002).
Binge eating is defined as: “recurring episodes of eating, in a discrete period of time, an
amount of food that is definitely larger than most people would eat during a similar
period of time [with a]…lack of control during the episodes” (American Psychiatric
Association, 2000), criteria that closely match those used to describe drug dependence
(Table 1.1). Specifically, addicted individuals experience a lack of control in the face of
food or drugs of abuse, have a continuation of over-use despite severe health, social, legal
and financial problems, and are unsuccessful at attempts to cut back or reduce their
consumption. These behaviours are typically accompanied by feelings of guilt, remorse
and distress.
36
Chapter 1. Introduction
Table 1.1. A categorical comparison of the DSM-IV definitions of substance
dependence and criteria for binge eating behaviour
Co-Morbid Symptom
Substance Dependence
Binge Eating Disorder
Escalation of use
The substance is taken in larger
amounts or over a longer period
than intended
Eating large amounts of food
when not feeling physically
hungry
Loss of control
There is a persistent desire or
unsuccessful effort to cut down
or control substance use
A sense of lack of control
during the episodes, e.g., a
feeling that one can’t stop
eating or control what or how
much one is eating
Social consequences
Important social, occupational
or recreational activities are
given up or reduced because of
use
Eating alone because of being
embarrassed by how much one
is eating
Personal distress
The substance use is continued
despite knowledge of having a
persistent physical or
psychological problem that is
likely to have been caused or
exacerbated by the substance
Feeling disgusted with oneself,
depressed, or feeling very guilty
after overeating; Marked
distress regarding binge eating;
Eating until feeling
uncomfortably full
In addition to analogous behavioural traits, there are also similarities in the brain structure
and neurochemical profile of substance-dependent and obese individuals. These include
abnormalities in the dopamine and opioid neurotransmitter systems, changes in frontostriatal circuitry, and associated dysfunctional impulsive and compulsive behaviours
(Avena, Rada, Bocarsly, & Hoebel, 2005; Avena, Rada, & Hoebel, 2009; Volkow, Wang,
Fowler, et al., 2008; Wang et al., 2004). Obese individuals show a decrease in striatal
dopamine D2 receptor availability, similar to those with problem drug use (Wang et al.,
2001, 2004). Highly palatable foods also directly affect the mesolimbic dopamine and
opioid pathways, and cue-induced anticipation for foods high in fat or sugar can elevate
37
Chapter 1. Introduction
activity in the fronto-limbic circuitry, thought to correspond with striatal dopamine
release (Rada, Avena, & Hoebel, 2005; Small, Jones-Gotman, & Dagher, 2003). The
reward deficiency hypothesis has thus similarly been advanced for obese individuals to
explain how a baseline hypo-functioning dopamine system might lead to self-medication
through over-consumption of high-fat/high-sugar foods (Davis & Fox, 2008; Davis,
Strachan, & Berkson, 2004).
Although the premise is still controversial (Ziauddeen & Fletcher, 2013; Ziauddeen,
Farooqi, & Fletcher, 2012), it has been proposed that in instances of uncontrollable binge
eating behaviour leading to obesity there are several behavioural and neurobiological
parallels with drug addiction, and that using an addiction model (and similar
pharmacological treatments) may help when addressing this behaviour (Cambridge et al.,
2013; Giuliano, Robbins, Nathan, Bullmore, & Everitt, 2012).
11. Aims and Objectives
The neurobiological underpinnings of key traits involved in drug addiction, namely
impulsivity, cognitive control and reward valuation, will be compared in dependent
stimulant users, their non-dependent biological siblings, recreational cocaine users, and
non-drug using healthy control volunteers in an attempt to parse out the underlying risk
and protective factors involved in susceptibility for stimulant dependence. If additional
similarities were seen between dependent individuals and their biological siblings, it
would provide further support for the notion of endophenotypes for drug dependence,
with traits and behaviours implicated in addiction predating drug initiation and creating a
greater risk for dependence. Conversely, greater similarities between the two drug-using
groups – dependent and recreational – would suggest shared abnormalities as a result of
stimulant drug use. Alternatively, unique differences in the siblings and recreational
38
Chapter 1. Introduction
participants from the dependent users, and even from healthy control individuals, could
indicate potential protective factors in these groups that prevented them from developing
drug dependence.
In Chapter 2, this will be investigated using the classic colour-word Stroop task to assess
cognitive control ability and its neurobiological correlates in dependent stimulant users,
their non-dependent biological siblings and healthy control volunteers. This will help
determine whether impairments in cognitive control serve as an endophenotype for
stimulant dependence, as difficulties with impulsivity and motor control have been
previously shown to do.
In Chapter 3, these cognitive control abilities will be assessed in the face of salient cues,
using an emotional drug-word Stroop task during fMRI to measure attentional bias and
distraction to drug cues. This will be administered to the same group of dependent
stimulant users and healthy control volunteers, as well as to a group of recreational users
of cocaine to determine if recreational users experience similar symptoms of craving and
attentional bias as dependent individuals.
In Chapter 4, responses to both drug rewards and a universal reinforcer, money, will be
compared between all four groups to assess differences in behavioural and functional
reward responding, and to determine its role as a potential risk or protective factor in
stimulant dependence.
I will also explore these traits in another potential addiction spectrum disorder, binge
eating leading to obesity, comparing obese and stimulant-dependent individuals to assess
the wider dimensionality and application of these neurobiological behaviours. Similar
tendencies between the two groups would provide support for the notion of ‘addiction
spectrum disorders’, classifying over-eating as a type of food addiction – a currently
39
Chapter 1. Introduction
contentious proposal. Additionally, comparisons between these groups – both potentially
types of behavioural and chemical dependencies – could provide further insight into
whether the differences seen in addicted individuals are a result of the use of stimulants or
are a maladaptive pattern of behaviour that go beyond drugs of abuse.
In Chapter 5 the work on reward responding from Chapter 4 will be expanded to explore
responding to a variety of different reward conditions in a separate group of dependent
stimulant users and healthy controls, as well as compared to obese adults with binge
eating disorder.
In Chapter 6 grey matter volume in the orbitofrontal cortex, an area crucial for the
previously explored behaviours of reward valuation, will be compared between cocainedependent individuals and healthy control participants, investigating changes in cortical
volume relating to both years of stimulant use and increases in body mass index.
Finally, in Chapter 7, the findings reported in the preceding chapters will be summarized
and contextualized in a general discussion, remarking on the significance of these
findings and the impact and possibilities for future research.
40
Chapter 2. Cognitive control endophenotype in drug dependence
Chapter 2. Cognitive Control Dysfunction and Abnormal Frontal
Cortex Activation in Stimulant Drug Users and their Biological Siblings
1. Introduction
As outlined in the General Introduction, disability in drug-dependent individuals is
commonly marked by impairments in cognitive function and concomitant neural
biomarkers. This includes abnormalities in the orbitofrontal cortex (OFC) and loss of
prefrontal cortex grey matter volume (Ersche et al., 2011; Franklin et al., 2002; Matochik
et al., 2003; Schoenbaum & Shaham, 2008; Tanabe et al., 2009; Volkow et al., 2001),
along with increases in impulsivity and poor inhibitory control (Garavan et al., 2000;
Jentsch & Taylor, 1999). These deficits are typically viewed as the consequence of
protracted stimulant use (Koob & Volkow, 2010), however, evidence suggests they may
also predate heavy drug taking and facilitate the transition from recreational to
compulsive use (Belin et al., 2008; Dalley et al., 2011; Ersche, Turton, et al., 2010;
Ersche, Jones, et al., 2012). As such, these behavioural and neurological traits may serve
as endophenotypes for dependence, potentially predisposing an individual for addiction.
An endophenotypic trait is a stable, quantifiable biological or behavioural characteristic
that is intrinsically associated with the pathology of a disorder. Importantly, it is present
not only in the patient group, but also in their unaffected relatives. This suggests that the
trait is heritable and possibly tied to a genetic risk for the condition, however, it is also an
intermediate variable – involved in the disorder but not necessarily deterministic as it also
appears in the unaffected relatives.
The colour-word Stroop is a well-known test of cognitive inhibition (MacDonald et al.,
2000; Stroop, 1935), assessing executive control over an automatic behaviour (word41
Chapter 2. Cognitive control endophenotype in drug dependence
reading) in favour of a more unusual behaviour (colour-naming). Interference represents a
conflict between the two sources of information (font colour and word meaning), and
cognitive control is needed to overcome this conflict. Performance is measured by
response latencies and interference scores, derived from the difference between congruent
and incongruent trial response times (RTs). Greater discrepancy between these conditions
indicates increased impairment.
Previous research investigating Stroop performance in stimulant-dependent individuals
(SDIs) using functional magnetic resonance imaging (fMRI) has shown impaired
inhibition on the task, with corresponding abnormalities in brain activation (Nestor,
Ghahremani, Monterosso, & London, 2011; Salo et al., 2002; Verdejo-Garcia et al.,
2007). Performance has been used to successfully predict drug treatment outcomes
(Brewer, Worhunsky, Carroll, Rounsaville, & Potenza, 2008; Carpenter, Schreiber,
Church, & McDowell, 2006; Streeter et al., 2008), with increased activity in the
prefrontal cortex (PFC), anterior cingulate cortex (ACC) and dorsal striatum positively
correlated with self-reported abstinence and time spent in rehabilitation facilities (Brewer
et al., 2008; Nestor et al., 2011). Other studies of the Stroop have reported no differences
in behavioural performance between SDIs and controls, but have shown significantly less
activation in the OFC, inferior frontal gyrus (IFG), ACC, parietal lobe, thalamus and
caudate nucleus (Barros-Loscertales et al., 2011; Bolla et al., 2004; Brewer et al., 2008;
Ersche, Bullmore, et al., 2010; Goldstein et al., 2001; Salo, Fassbender, Buonocore, &
Ursu, 2012).
However, as with most cognitive impairments in stimulant dependence, it is unknown
whether these behavioural differences and corresponding neural abnormalities are due to
pre- or post-morbid factors – i.e. whether they precede or are the result of long-term drug
42
Chapter 2. Cognitive control endophenotype in drug dependence
taking. Comparing SDIs and their unaffected biological siblings on the colour-word
Stroop could provide insight into whether the present impairments in inhibitory control
are precursory or consequential to drug abuse via exploration of endophenotypes for
stimulant dependence. Shared abnormalities in behavioural performance and brain
activation during the task would suggest an endophenotype of impaired cognitive control
and increased risk for stimulant dependence, whereas greater disability in the SDIs could
indicate direct consequences of chronic stimulant use.
On an assessment of Stroop performance using fMRI in SDIs, their biological siblings
and unrelated healthy control volunteers, we predicted: 1) that both SDIs and their
siblings would be impaired compared with controls, suggesting an endophenotype of poor
cognitive control; 2) that SDIs would be more impaired than their siblings, indicating
further drug-induced disability; and 3) that interference scores would correlate with
abnormalities in IFG activation on the task, a region previously implicated in inhibitory
control (Bunge, Ochsner, Desmond, Glover, & Gabrieli, 2001; Nestor et al., 2011; Zysset,
Muller, Lohmann, & von Cramon, 2001).
2. Materials and Methods
2.1 Participants
Three equal groups of 50 stimulant-dependent individuals, 50 of their non-dependent
biological siblings and 50 unrelated healthy control volunteers were tested according to
protocol approved by the Cambridge Research Ethics Committee (Ersche, Jones, et al.,
2012; Ersche, Turton, et al., 2012, 2010). SDIs were recruited through drug-treatment
centres and word-of-mouth; sibling participants were recruited through the initial contact
with their dependent brother or sister. Healthy control volunteers were recruited through
the community using local advertisements.
43
Chapter 2. Cognitive control endophenotype in drug dependence
All participants were between the ages of 18-55, had no history of psychotic or
neurodevelopmental disorder, neurological illness or traumatic head injury, and were
fluent in English. Participants were screened for mental illness using the Structured
Clinical Interview for DSM-IV-TR Axis I Disorders (SCID) (First, Spitzer, Gibbon, &
Williams, 2002), semi-structured interview for drug history, and checked for current
physical health, colour-blindness and demographic information. Written informed consent
was obtained prior to enrolment.
SDIs were included in the study if they satisfied DSM-IV-TR criteria for cocaine (94%)
or amphetamine (6%) dependence, had a first-degree sibling who had no personal history
of drug abuse (with the exception of nicotine), shared both biological parents, and was
able to take part. Control volunteers had no personal or family history of drug or alcohol
dependence and were matched for age, gender and education levels. Severity of drug use
in SDIs was measured using the Obsessive Compulsive Drug Use Scale (OCDUS)
(Franken, Hendriksa, & van den Brink, 2002), age of onset and years of use. Drug abuse
tendencies in control and sibling participants were assessed using the Drug Abuse
Screening Test (DAST-20) (Skinner, 1982). Alcohol use and depression levels were
measured in all participants using the Alcohol-Use Disorder Identification Test (AUDIT)
(Saunders, Aasland, Babor, Delafuente, & Grant, 1993) and Beck Depression Inventory
(BDI-II) (Beck, Steer, & Garbin, 1988); IQ levels were estimated with the National Adult
Reading Test (NART) (Nelson, 1982). See Table 2.1 for demographic information.
Twelve individuals were excluded due to head movement (greater than 1.5 mm in any
direction, or enough to cause inter-slice variance), the presence of a clinically significant
structural abnormality, or inadequate task performance in which blocks of trials had to be
discarded. This resulted in 138 total participants: control=47, SDI=42, sibling=49.
44
Chapter 2. Cognitive control endophenotype in drug dependence
Table 2.1. Demographic information and ANOVA group comparisons for 42
stimulant-dependent individuals, 49 of their biological siblings and 47 unrelated
healthy control volunteers
Control
Mean (SD)
Sibling
Mean (SD)
SDI
Mean (SD)
Demographics
Age
32.34 (8.63)
32.63 (8.35)
34.24 (7.39)
Sex (% male)
63.8%
49.0%
95.2%
IQ (NART)
112.67 (8.09)
108.91 (8.88)
110.64 (7.46)
Education (years)
12.70 (1.92)
12.12 (2.00)
11.69 (1.70)
Depression (BDI-II)
2.21 (2.56)
5.22 (6.19)
18.43 (12.18)
Smoking status (%
10.6%
55.1%
92.9%
smoker)
Average daily cigarettes
2.47 (4.79)
5.08 (7.82)
15.92 (13.02)
Alcohol use (AUDIT
3.32 (2.28)
3.86 (4.50)
12.51 (11.53)
score)
Drug Use
DAST-20
0.00 (0.00)
0.51 (1.10)
OCDUS
23.86 (9.07)
Age onset stimulant use
16.45 (2.86)
Years stimulant use
15.74 (6.44)
Urine stimulants (%)
92.9%
Last stimulant use (days)
2.17 (2.41)
Bonferroni group post-hoc comparisons
Control vs. Control vs.
SDIs vs.
Demographics
F/χ2
P
SDI
Sibling
Sibling
Age
0.68
0.506
0.828
1.00
1.00
Sex (% male)
21.45
<0.001
<0.001
0.205
<0.001
IQ (NART)
2.42
0.093
0.770
0.089
1.00
Education (years)
3.22
0.043
0.039
0.406
0.837
Depression (BDI-II)
53.45
<0.001
<0.001
0.183
<0.001
Smoking status
68.16
<0.001
<0.001
<0.001
<0.001
(% smoker)
AUDIT
23.53
<0.001
<0.001
1.00
<0.001
SDI: Stimulant-dependent individuals; NART: National Adult Reading Test; BDI-II:
Beck Depression Inventory; AUDIT: Alcohol-Use Disorder Identification Test; DAST20: Drug Abuse Screening Test; OCDUS: Obsessive Compulsive Drug Use Scale.
45
Chapter 2. Cognitive control endophenotype in drug dependence
2.2 Measures
The Stroop task was administered during fMRI scanning. Both the colour-word and drugword Stroop (discussed in Chapter 3) were administered in succession during the
scanning session, counter-balanced for the order of the two tasks. The tasks were
presented in a block design in an attempt to prevent any spill over effects of activation
incurred from the salient drug words to the colour-word trials.
In the current task, participants were presented with one of four colour words displayed in
one of the four font colours – red, blue, green, yellow. They were asked to identify the
font colour of the word using a four-button box, each button corresponding to a colour.
Participants were trained on button-colour allocation before entering the scanner. During
the congruent condition, the word was identical to the font colour in which it was
presented; for the incongruent condition the word was displayed in one of the other three
colours. Performance was measured by accuracy and response latencies. Interference
scores were calculated by subtracting the difference in median RTs on correct trials
between the challenge and control conditions (incongruent–congruent).
2.3 Procedure
The task was administered as a blocked paradigm to prevent interfering carry-over effects
between trial conditions (Ersche, Bullmore, et al., 2010; Nestor et al., 2011). Two blocks
were presented containing 16 trials of either congruent or incongruent colour words; the
order of words within each block was randomized and the order of blocks was
counterbalanced across participants. Each trial lasted 2.2 seconds, the stimulus word
presented for 1.9 seconds followed by an inter-trial fixation cross for 0.3 seconds. The
allocation of font colours to words was randomized; each of the four colours occurred
equally often in each condition and no two identical colours ever followed one another. A
46
Chapter 2. Cognitive control endophenotype in drug dependence
cigarette break was allowed no less than one hour before scan time to prevent either
nicotine withdrawal or acute nicotine effects from influencing performance.
2.4 Imaging Acquisition
Identical imaging acquisition methods were used for all fMRI studies included in this
dissertation. Thus, imaging procedures will be described in full detail here, and the reader
will be referred to this section for imaging parameters in subsequent chapters.
Whole-brain fMRI data were acquired at the Wolfson Brain Imaging Centre, University
of Cambridge, Cambridge, England, using a Siemens Magnetom TIM Trio scanner
operating 3T (Siemens Medical Solutions, Erlangen, Germany). During the task, 32
transaxial sections of gradient echo, echoplanar imaging (EPI) data depicting blood
oxygen level dependent (BOLD) contrast were acquired parallel to the intercommissural
line with the following parameters: repetition time (TR)=2000 ms, echo time (TE)=30
ms, flip angle=78°, slice thickness=3 mm plus 0.75 mm, matrix of 64x64 with field of
view (FOV)=192x192 mm giving 3x3 mm in-plane resolution. Prior to data analysis, the
first five images were discarded for T1 equilibration.
T1 structural scans were collected using magnetization-prepared rapid acquisition
gradient-echo (MPRAGE) sequence: 176 slices of 1 mm thickness, with TR=2300 ms,
TE=2.98 ms, inversion time (TI)=900 ms, flip angle=9°, FOV=240x256 mm.
2.5 Data Analysis
Behavioural data were analysed using Statistical Package for Social Science (SPSS v.18;
IBM SPSS Statistics, Chicago, Illinois). Analysis of variance (ANOVA) and chi-square
tests assessed differences in demographic information. A general linear model (GLM)
multivariate analysis in a 2x3 condition by group design with Bonferroni post-hoc
corrections was used to compare RTs, as well as interference scores and error rates
47
Chapter 2. Cognitive control endophenotype in drug dependence
between groups. Paired samples t-tests assessed RTs within groups. Median scores on
correct trials only were used in response latency analyses, and significance levels were set
at p<0.05. In preparation for parametric analyses, BDI-II data were square-root
transformed to reduce skew. As gender, education, smoking status, AUDIT and BDI-II
scores differed between participant groups, multivariate analyses were conducted both
with and without these variables as covariates (see Appendix A, Table S2.1).
FMRI analysis was conducted using Cambridge Brain Analysis software (CamBA)
(http://www-bmu.psychiatry.cam.ac.uk/software/;
Cambridge,
UK).
Data
were
preprocessed to correct for motion, differential slice-timing and smoothed in plane by 0.5
voxels (Bullmore et al., 1999, 2001; Suckling et al., 2006). Cluster significance levels
were set for all imaging analyses using family-wise error (FWE) correction for multiple
comparisons p<0.05. Significant cluster values from CamBA fMRI contrast analyses
were exported and further evaluated comparing group activation means using GLM
multivariate analyses with Bonferroni corrections in SPSS. Correlations between
behavioural data and group fMRI contrast activations were also conducted using Pearson
correlations in SPSS.
First-level whole brain analysis measured activation among all participants contrasting
incongruent–congruent conditions on successful trials. A design matrix composed of trial
onset and response times was convolved with hemodynamic response function (Glover,
1999), producing statistical maps of voxelwise responses. These maps were normalized to
Montreal Neurological Institute (MNI) standard space by affine transformation to an
echo-planar imaging template (http://www.fil.ion.ucl.ac.uk/spm) to obtain a group
activation map of the contrast.
48
Chapter 2. Cognitive control endophenotype in drug dependence
A three-way GLM omnibus analysis in CamBA assessed group differences in activation
on the incongruent–congruent contrast. In accordance with previous studies with the
Stroop (Bolla et al., 2004; Brewer et al., 2008; Ersche, Bullmore, et al., 2010), as well as
our a priori hypothesis, this group analysis was repeated with restricted search volume
masks of the IFG and ACC, taken from Hammer’s probabilistic atlas (Hammers et al.,
2003).
Behavioural interference scores were regressed onto first-level contrast clusters, and
within-groups GLM was processed amongst all participants in CamBA. This resulted in a
group activation map of significant clusters from the incongruent–congruent contrast that
directly correlated with behavioural interference scores in all participants. This analysis
was repeated with restriction to the IFG.
A grey matter voxel-based morphometry (VBM) analysis previously conducted in these
individuals (Ersche, Jones, et al., 2012) was employed with post-hoc application of the
IFG mask to focus differences in cortical volume to this region. The VBM analysis was
conducted by P. S. Jones using FSL (http://www.fmrib.ox.ac.uk/fsl/fslvbm/index.html,
Oxford, UK) on T1-weighted images collected during the same session as the functional
data, and then compared for group differences using CamBA software for permutation
testing (Smith & Nichols, 2009). In the current study, IFG grey matter volume was
compared between groups using ANOVA and correlated with behavioural performance
using Pearson coefficients.
3. Results
Stimulant-dependent individuals had been on drugs for an average of 15.7 years (±6.4
SD), beginning use at the age of 16.5 (±2.9 SD). Ninety-three percent (n=39) tested
49
Chapter 2. Cognitive control endophenotype in drug dependence
positive for stimulants at the time of testing using urinalysis drug screen, with a mean
time since last use of 2.2 days (±2.4 SD). SDIs had significantly higher depression and
alcohol abuse scores than both sibling and control participants. There were also
significantly more males in the stimulant-dependent group and higher rates of cigarette
smoking. Control and sibling participants did not differ in terms of sex, depression rates
or alcohol use, though there were differences between the two groups in smoking status
(Table 2.1). There were no differences in age or IQ between any of the three groups, and
only SDIs and controls differed in terms of educational attainment.
3.1 Stroop Performance
Using GLM multivariate analyses, significant differences arose between groups on
response
latencies
for
congruent
(F(2,135)=7.243,
p=0.001)
and
incongruent
(F(2,135)=4.452, p=0.013) trials (Table 2.2). These values remained significant after
controlling for gender, education, smoking status, alcohol use and BDI-II depression
scores in the model. As there were no differences in any of the results when using
covariates or not, we report the rest of the results without covariates in the model. See
Supplemental Information (Appendix A, Table S2.1) for a full set of results with
covariates. In the congruent condition SDIs had significantly slower responses than
controls (Bonferroni post-hoc p=0.001), while the siblings were slower than controls at
p<0.10. On incongruent trials, both the SDIs (p=0.031) and their siblings (p=0.035) were
significantly slower than controls. Stimulant-dependent and sibling groups did not differ
from one another in either condition.
There were no differences between groups on interference scores contrasting congruent
from incongruent trials (F(2,135)=0.797, p=0.45). However, in a paired samples t-test all
three groups were significantly slower on the incongruent than congruent condition
50
Chapter 2. Cognitive control endophenotype in drug dependence
within their own cohorts (controls: t(46)=7.319, p<0.001; SDI: t(41)=5.372, p<0.001;
siblings: t(48)=7.327, p<0.001). This confirms the additional cognitive load of the
incongruent trials, regardless of group. Groups did not differ in the number of errors made
on the task (F(2,135)=1.915, p=0.151).
Table 2.2. Behavioural results and group comparisons on the colour-word Stroop
Control
Mean (SD)
Sibling
Mean (SD)
SDI
Mean (SD)
F/χ2
P
Congruent med RT
653.17(125.99)
723.14(137.50)
768.99(171.36)
7.24
0.001
Incongruent med RT
802.72(181.25)
904.85(214.90)
910.76(188.10)
4.45
0.013
Interference med RT
149.55(140.08)
181.70(173.58)
141.77(171.05)
0.80
0.454
2.30 (7.2%)
3.37 (10.5%)
3.43 (10.7%)
1.92
0.151
Behavioural Results
Total mean errors
Congruent and incongruent trial results represented via median response latencies
(ms); interference scores reported as the difference in response times between the
two trial conditions (ms); total mean errors and error percentage are given.
3.2 Neuroimaging Analysis
In a first-level analysis, four clusters emerged that significantly differed in BOLD
activation on the incongruent–congruent contrast among all participants. Increases in
activity on incongruent compared with congruent trials were seen in the left IFG,
stretching into the dorsolateral prefrontal cortex and precentral/middle frontal gyrus,
while decreases in activation on the incongruent compared with congruent trials were
present in the right rolandic operculum and caudate (Figure 2.1, Table 2.3). These
differences in BOLD signal represent greater (or less) relative activation in all
participants during performance of incongruent word trials, as compared to activity
present during performance of congruent word trials. Follow-up analyses using a GLM
51
Chapter 2. Cognitive control endophenotype in drug dependence
multivariate analysis conducted in SPSS comparing group activation in these four clusters
did not reveal any differences in activity levels between groups.
A)
X= -46
Y=6
Z=38
X=50
Y= -24
Z=12
B)
Figure 2.1. Whole-brain fMRI activation among stimulant-dependent individuals,
their non-dependent siblings and healthy control volunteers on the Stroop,
contrasting congruent from incongruent colour word median response latencies.
Four clusters were identified as having significantly different activation on
incongruent than congruent conditions, with peak values in: A) the left precentral
gyrus (Montreal Neurological Institute coordinates (x, y, z): -44, 4, 34) and left
inferior frontal gyrus (-50, 14, 28); and B) the right caudate (4, 4, 12) and right
rolandic operculum (60, -22, 12). Activation in these regions did not differ between
groups.
52
Chapter 2. Cognitive control endophenotype in drug dependence
Table 2.3. Mean activation cluster voxels for imaging contrasts amongst all groups
Brodmann Cluster size Peak value
Contrast activation areas
Area
(voxels)
coordinates
Whole-brain contrast (incongruent-congruent) analysis among all participants
Cluster 1
Right rolandic operculum, right superior temporal 13, 22, 40,
105
60, -22, 12
gyrus, right heschl gyrus, right insula, right
41, 42, 43
postcentral gyrus, right supramarginal gyrus
Cluster 2
Right caudate
125
59
4, 4, 12
Cluster 3
Left inferior frontal gyrus – triangularis,
9, 45, 46
114
-50, 14, 28
opercularis, left precentral gyrus
Cluster 4
Left precentral gyrus, left middle frontal gyrus,
6, 8, 9
126
-44, 4, 34
left postcentral gyrus
Between group contrast comparison
Cluster 1
Right rolandic operculum, right insula, right
13
232
40, -26, 28
caudate, right supramarginal gyrus
Cluster 2
Left medial superior frontal gyrus, left middle
8, 24, 32
110
-28, 14, 34
frontal gyrus, left superior frontal gyrus, left
inferior frontal gyrus – opercularis
Between group comparison with IFG Mask
Cluster 1
Left inferior frontal gyrus – opercularis,
13
43
-42, 10, 20
triangularis, left rolandic operculum, left insula
Interference regression activation with IFG Mask
Cluster 1
Left inferior frontal gyrus – triangularis, left
13, 45, 47
25
-38, 28, 4
insula
Cluster 2
Right inferior frontal gyrus – triangularis,
13, 45
30
42, 24, 12
operculum
Contrasts represent activation on incongruent compared to congruent trials, first
amongst all participants and then comparing contrast activations between groups.
Interference scores are regressed onto contrast activations in the fourth analysis,
using all participants. Significance set a p<0.05 family-wise error correction for
multiple comparisons. Coordinates listed are in MNI standard space.
53
Chapter 2. Cognitive control endophenotype in drug dependence
In a whole-brain omnibus group comparison, two clusters emerged that significantly
differed between groups during the incongruent–congruent contrast: right insula/rolandic
operculum (F(2,135)=18.373, p<0.001) and left medial/superior frontal gyrus
(F(2,135)=12.094, p<0.001; Table 2.3). In these regions, siblings registered a significant
relative decrease in activation compared to control and stimulant-dependent participants
(Bonferroni p<0.001 for all comparisons). SDIs and controls did not differ from one
another.
After restriction of the analysis to the IFG, an additional cluster was revealed in the left
hemisphere that significantly differed between groups during the incongruent–congruent
contrast (F(2,135)=11.981, p<0.001). The siblings again had significantly lower activity
than both SDIs (p<0.001) and controls (p=0.006), whereas SDIs and controls did not
significantly differ (Figure 2.2, Table 2.3). Application of a mask of the ACC did not
result in any significant clusters emerging that differed in activation in this region
between the three groups.
54
Chapter 2. Cognitive control endophenotype in drug dependence
A)
SDIs
B)
P
A R
LR
Y=10
X= -38
L
Z=20
Figure 2.2. Activation differences in the IFG between groups during Stroop
performance, with application of an IFG mask. Siblings demonstrated a significant
decrease in activation in the left IFG compared with both SDIs and controls,
whereas SDIs and controls did not differ from one another in this region. A) Group
differences in fMRI activation in the left IFG, representing a decrease in activation
in the siblings. B) Left IFG cluster.
55
Chapter 2. Cognitive control endophenotype in drug dependence
3.3 Regression of Stroop Performance to Neuroimaging Findings
When behavioural interference scores were regressed onto the incongruent–congruent
imaging contrast in CamBA no significant areas were found. However, upon application
of the IFG mask two bilateral clusters arose that were significantly deactivated in
association with interference scores among all participants.
Follow-up analyses in SPSS confirmed that activity in these areas negatively correlated
with behavioural performance in all participants (left: r=-0.351, p<0.001; right: r=0-.398,
p<0.001), with greater deactivation during the contrast signifying greater interference.
When each group was analysed individually, the correlation with interference scores
during the incongruent–congruent interference regression contrast remained significant in
the left (r=-0.454, p=0.001) but not right IFG for control participants. Among SDIs,
interference scores negatively correlated with activity in both the left and right IFG
during the incongruent–congruent interference regression contrast (left: r=-0.366,
p=0.017; right: r=-0.580, p<0.001). In sibling participants only right IFG activation in the
incongruent–congruent interference regression contrast negatively correlated with
interference scores (r=-0.377, p=0.008).
3.4 Relationship of Structural Changes to Stroop Performance
A structural analysis comparing grey matter volume between these groups revealed
further differences in performance based on cortical volume. Upon application of the IFG
mask onto the VBM analysis, we discovered differences in grey matter volume bilaterally
in the IFG in both SDIs and their siblings as compared to controls (left: F(2,135)=11.749,
p<0.001; right: F(2,135)=9.384, p<0.001). These structural changes related to behavioural
performance, such that bilateral decreases in IFG grey matter volume were correlated
with RTs on both congruent and incongruent trials among all participants (congruent: left
56
Chapter 2. Cognitive control endophenotype in drug dependence
r=-0.337, p<0.001; right r=-0.320, p<0.001; incongruent: left: r=-0.253, p=0.003; right
r=-0.224, p=0.008; Figure 2.3). However, interference scores did not correlate with
cortical volume. Additionally, IFG volume and BOLD activity levels did not correlate.
A)
Differences in IFG Grey Matter Volume Between Groups
L inferior frontal gyrus
Mean Grey Matter Volume
R inferior frontal gyrus
SDIs
B)
Correlation Between Response Times and IFG Grey Matter
L inferior frontal gyrus
Mean Grey Matter Volume
R inferior frontal gyrus
Mean Response Time
57
Chapter 2. Cognitive control endophenotype in drug dependence
Figure 2.3. Structural differences in the IFG between stimulant-dependent
individuals (SDIs), their siblings and control participants. A) SDIs and siblings both
demonstrated a significant decrease in grey matter volume in the bilateral IFG as
compared to controls. There was no difference in volume between SDIs and their
siblings. B) IFG grey matter volume decreases significantly correlated bilaterally
with an increase in response times on both conditions of the colour-word Stroop
task, with a decrease in volume relating to an increase in response latencies.
3.5 Effects of Drug Use History on Performance
Among the SDIs, there was a significant correlation between years of stimulant use and
errors made on the task (r=0.388, p=0.011), such that the longer an individual had used
stimulant drugs, the greater number of errors were committed. There were no other
significant relationships between drug use history and either colour-word Stroop
performance or neuroimaging activation. This includes time since last stimulant use,
which did not correlate with any measure of task ability, including reaction times,
interference scores and errors committed, or functional activation on the Stroop. A
median split for time since last use (≥ 2 days) also did not reveal any significant
differences in either behavioural or functional performance.
4. Discussion
Stimulant-dependent individuals and their non-dependent biological siblings were
significantly more impaired on the colour-word Stroop task than unrelated healthy control
volunteers, as demonstrated by longer response latencies during the task. However, the
sibling-pairs did not differ from one another on any variable, and there were no
differences between any of the groups in interference scores. In terms of BOLD activity,
58
Chapter 2. Cognitive control endophenotype in drug dependence
significant differences were evident between the sibling participants and both stimulantdependent and control individuals, with the siblings comparatively under-activating the
bilateral IFG and left superior/middle frontal gyrus. However, the SDIs and controls did
not differ in activity levels in any region. Two primary questions arise from these results:
why was there no difference in activity between SDIs and controls despite differences in
behavioural performance?; and why did the siblings significantly under-activate frontal
gyral regions compared with the other groups?
The lack of differentiation between groups on interference scores suggests that, despite
slowing in the SDIs and siblings, there was no greater dysfunction on the challenging
incongruent condition. Instead, there appeared to be globalised slowing or inefficiency
rather than task-specific impairment. This absence of differences in interference scores
was not unexpected, as previous studies investigating colour-word Stroop performance in
SDIs have shown no impairments in behavioural performance compared with control
individuals (Barros-Loscertales et al., 2011; Bolla et al., 2004; Ersche, Bullmore, et al.,
2010; Goldstein et al., 2001). Cognitive slowing corresponded to structural differences in
the IFG in both SDIs and their siblings, with decreases in grey matter volume correlating
with increased response times on both conditions. This suggests that these structural
changes affected cognitive efficiency, but did not cause a condition-specific impairment.
Earlier findings in these groups support these results, with decreases in white matter
connectivity adjacent to the IFG associated with increased slowing on a motor control
task (Ersche, Jones, et al., 2012).
The question remains as to why SDIs, despite their similar patterns of activation, had such
a significant increase in activity compared with their siblings. It is possible that the hypoactivation exhibited by the siblings was the endophenotype-like response, and that SDIs
59
Chapter 2. Cognitive control endophenotype in drug dependence
prior to drug abuse would have demonstrated similar decreases in activation during
inhibitory control. However, the effect of stimulant use, whether chronic or acute, may
have altered this original response. An assessment of motor inhibition in adolescents
showed similar results, with a relative over-activation in the same right IFG region in
adolescents who had experimented with drugs compared with those who had not (Whelan
et al., 2012). This suggests that the increase in neural activation during cognitive control
(including inhibitory response) occurs relatively early as a consequence of drug abuse.
Conversely, the decreased activation in the siblings could be representative of a protective
factor against drug abuse, despite similar manifestations of behavioural impairments in
inhibitory control. This account suggests that SDIs and siblings may experience similar
baseline activation, but that the effects of stimulants could have elevated stimulant users’
BOLD activity to that of controls.
Alternatively, as 93% of SDIs tested positive for stimulants, their surprising relative
hyper-activation compared with their siblings could be an acute effect of stimulant drugs
on the brain. Cocaine can increase neural activity (Breiter et al., 1997), elevating low
baseline levels in abstinent users to those of non-drug-using controls (Garavan, Kaufman,
& Hester, 2008). This may explain the lack of difference in BOLD activation between
SDIs and control participants in the current study, which is in conflict with previous
investigations of the colour-word Stroop in dependent drug users (Barrós-Loscertales et
al., 2011; Bolla et al., 2004; Brewer et al., 2008; Nestor et al., 2011). This phenomenon
has also been proposed to support the self-medication hypothesis in individuals with
decreased dopaminergic activity. However, in the current investigation time since last use
did not correlate with any behavioural or functional imaging results in the SDIs.
Moreover, a median split conducted on time since last use did not reveal any significant
differences in performance or activation between individuals with shorter or longer
60
Chapter 2. Cognitive control endophenotype in drug dependence
periods of abstinence. Thus, we do not believe that either the acute effect of stimulants or
stimulant withdrawal significantly affected SDIs’ behavioural performance or functional
activation on the task.
Finally, as the colour-word Stroop task was administered in succession with the cocaineword Stroop task, it is possible that participants who completed the cocaine-word Stroop
task first could have experienced a carry-over effect of heightened BOLD activation that
was initially in response to the salient cocaine cue words. This may have resulted in an
unintentional increase in activation during subsequent non-salient trials. However, the
task was administered as a counter-balanced block design in an attempt to prevent this
spill over from occurring, and only half of the SDIs would have completed the task in this
order. Thus, we do not believe that the results in the current study were significantly
affected by this task design.
The absence of activation in the ACC, a key area in Stroop performance (Bolla et al.,
2004; Brewer et al., 2008; Kerns et al., 2004; MacDonald et al., 2000; Salo et al., 2002)
was perhaps surprising. However, absence of cingulate activation on the Stroop has also
been noted in other studies with drug using participants (Ersche, Bullmore, et al., 2010;
Zysset et al., 2001). The ACC is thought to control conflict monitoring, and it is most
activated during changing task demands (such as switching from congruent to
incongruent stimuli), resulting in greater cognitive conflict (Kerns et al., 2004). However,
the present study used a block design, circumventing the change in task demands. This
adjustment of task structure may explain the lack of ACC activation, as there was no
conflict between task demands within each block. Additionally, greater ACC activation
on error trials is commonly cited, with incorrect responses requiring behavioural
adjustments on subsequent trials (Kerns et al., 2004). As only correct responses were
61
Chapter 2. Cognitive control endophenotype in drug dependence
included in the current model, it is possible this region was not significantly recruited
during correct responding. Finally, dissociation between IFG and ACC activation during
Stroop performance has been suggested, the ACC compensating for diminished IFG
control (MacDonald et al., 2000). Given the significant increase in IFG activity,
particularly in SDIs and controls, it is possible that the ACC was not requisitely recruited
by participants during the current task.
To further explore the different effects of acute and chronic stimulant use, as well as the
presence or absence of an underlying risk for dependence, research into an alternative
group of cocaine users – individuals who use the drug recreationally without developing
dependence – will be discussed in the next chapter to help parse out the impact of these
different variables. Additionally, the colour-word Stroop task is a measure of ‘cold’
cognitive function, assessing inhibitory control in a neutral valence condition. However,
as stimulant-dependent individuals are known to also have impairments in their responses
to more salient stimuli, particularly in regard to drug-related cues, we explored
differences in inhibitory processes in the face of emotional stimuli in the next chapter
using a drug-word Stroop task.
62
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
Chapter 3. Enhanced Orbitofrontal Cortex Function and Lack of
Attentional Bias to Cocaine Cues in Recreational Stimulant Users
1. Introduction
In addition to the cognitive dysfunction present in dependent stimulant users discussed in
the previous chapter, there is profound evidence of a disruption in affective system
processing, thought to stem from abnormalities in the frontostriatal reward circuitry. This
is particularly evident in the face of salient drug stimuli, where the associated cues are
thought to ‘hijack’ the reward system, emphasizing drug rewards over other priorities.
This can lead to significant drug craving, which can in turn cause unplanned or undesired
use.
Attentional bias to drug-related cues can elicit feelings of craving and, combined with the
poor decision-making and inhibitory control characteristic of stimulant-dependent
individuals (SDIs), can precipitate relapse (Bechara, 2005; Copersino et al., 2004; Field &
Cox, 2008; Garavan et al., 2000). These experiences are thought to be subserved by
dysfunction in the prefrontal cortex (PFC) (Everitt et al., 2007; London et al., 2000;
Schoenbaum & Shaham, 2008), where, as discussed previously, dependent stimulant
users have been shown to have decreased grey matter volume compared with healthy
control individuals (Alia-Klein et al., 2011; Ersche et al., 2011; Franklin et al., 2002;
Hanlon, Dufault, Wesley, & Porrino, 2011; Sim et al., 2007). Additionally, SDIs typically
exhibit a significant decrease in PFC activation on executive function tasks, often
accompanied by behavioural impairments in self-control, inhibition and working memory
(Barros-Loscertales et al., 2011; Bolla et al., 2004; Nestor et al., 2011; Tomasi et al.,
2007; Verdejo-Garcia et al., 2006).
63
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
However, it is important to note that although these cognitive impairments can be serious,
they do not afflict all drug users. In fact, the vast majority of individuals who try
stimulant drugs do not become addicted to them (United Nations Office on Drugs and
Crime, 2012). Moreover, there seems to be a select subset of the population who are able
to use cocaine recreationally in a controlled manner without developing dependence
(Ersche, Jones, Williams, Smith, et al., 2013). These individuals report consistent,
occasional, social use of cocaine without experiencing a loss of control or exhibiting
symptoms of dependence or abuse (Ersche, Jones, Williams, Smith, et al., 2013). They
also do not report feeling cravings for cocaine, and their use is planned rather than
impulsive.
These individuals who have used cocaine in a stable manner for an extended period of
time without developing a dependency are an intermediary group that can be used to
assess potential cocaine-induced abnormalities and distinguish them from traits involved
in compulsive use and dependence. Recreational users would be expected to show
similar, though not as severe, changes in structure and function attributed to prolonged
stimulant use, but not the abnormalities associated with increased premorbid risk for
dependence. Furthermore, there may be additional differences between the brains of
dependent and recreational stimulant users that serve as protective factors in these nondependent individuals (Ersche, Jones, Williams, Smith, et al., 2013). However, it should
be noted that inherent differences in cocaine exposure between the dependent and
recreational users may create potential confounds when comparing cognitive function and
attentional bias to drug cues.
The emotional cocaine-word Stroop is a well-established measure of cognitive control
and attentional bias to cocaine-related stimuli (Field & Cox, 2008). Like the classic
64
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
version of the Stroop task, participants are asked to ignore the content of a target word,
responding instead to the colour of the font. Performance is assessed via a relative
increase in response latencies to cocaine cues compared to those for matched neutral
words. Impairment stems from a problem with attention allocation and selective
processing of more salient cues, as stimulant users become distracted by the cocaine
words (Carpenter et al., 2006; Copersino et al., 2004; Ersche, Bullmore, et al., 2010;
Hester et al., 2006). Preoccupation with cocaine stimuli is thought to interfere with the
normal cognitive processes required to name the font colour of the word, resulting in
higher reaction times than on emotionally neutral words (Cox, Fadardi, & Pothos, 2006;
Field & Cox, 2008). In several studies this bias has been linked to cocaine craving scores,
with longer response latencies correlating with greater self-report levels of craving
(Copersino et al., 2004; Franken, Kroon, & Hendriks, 2000).
In the current study, we investigated selective bias to cocaine-cue words on an emotional
Stroop task using a functional magnetic resonance imaging (fMRI) paradigm. We
assessed the same groups of 50 stimulant-dependent individuals and 50 healthy control
volunteers from the previous chapter, with the addition of a new group of participants –
27 recreational users of cocaine – in an attempt to determine the effect of drug use
severity on attentional bias to cocaine-related cues. Data was collected from the
recreational users after the original study was completed; however, an identical testing
battery and study procedures were used in these individuals. There were no differences in
the scanning facility or parameters used during the experiment, ensuring consistency
between the groups.
For the cocaine Stroop task, the sibling individuals from the previous chapter were
excluded from the contrast as the current investigation was specifically testing the effects
65
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
of cocaine exposure on responses to drug-related cues, not endophenotype effects. This
means that the primary contrast was between the SDIs, recreational users of cocaine and
unrelated control volunteers. The sibling individuals could not be used as control subjects
in the current assessment as they had greater than normal experience with stimulantrelated stimuli via their dependent brothers and sisters (though no direct exposure to
stimulant drug-taking themselves), which can still result in salient drug associations
(Barnard, 2005). However, as they had no personal direct stimulant exposure they could
not provide any insight into mechanisms regarding automatic drug-cue associations, or
the resistance to such salient connections.
We compared the groups on both their behavioural and neural responses to cocaine versus
neutral stimuli during the Stroop. To the best of our knowledge, this is the first
investigation into the functional neural activity of recreational cocaine users, as well as
the first to assess responses to cocaine-related stimuli in this group. We predicted that
recreational users would fall between stimulant-dependent and control individuals in
terms of both behavioural and neurocognitive responses, given their greater familiarity
with cocaine cues but non-dependent patterns of use.
2. Materials and Methods
2.1 Participants
As in the previous chapter, 50 stimulant-dependent individuals were recruited through
drug-treatment centres and word-of-mouth; 27 recreational cocaine users and 52 healthy
control volunteers were recruited from the community using local advertisements. Two
additional control volunteers were added to the sample from the previous study in an
attempt to better match the groups, taking into account the new cohort of recreational
users. All SDIs met DSM-IV-TR criteria for stimulant dependence; control volunteers
66
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
had no personal or family history of drug abuse or dependence. Recreational users had
been using cocaine for a minimum of two years without displaying any DSM-IV-TR
symptoms of physical or psychological dependence. Cocaine non-dependence was
screened for in these individuals focusing not only on frequency and amount of use, but
also on patterns of use and associated cognitive and emotional perceptions. For example,
recreational participants reported no feelings of guilt or remorse about using and stated
that their occasional use did not interfere with work, school, family or social obligations.
They also reported no feelings of cravings or urges to use the drug and could “take it or
leave it”. The Obsessive Compulsive Drug Use Scale (OCDUS) was also administered to
assess drug use severity, measuring thoughts and obsessions regarding cocaine use, as
well as success at resisting these urges (Franken et al., 2002). Drug urinalysis was
collected from all participants, and control and recreational users tested negative for all
substances. Conversely, 93% of SDIs tested positive for stimulants. Members from all
three groups also reported either current or prior use of tobacco and cannabis. As in the
previous chapter, depression scores, alcohol use tendencies and verbal IQ were assessed
using the Beck Depression Inventory (BDI-II) (Beck et al., 1988), Alcohol-Use Disorder
Identification Test (AUDIT) (Saunders et al., 1993), and National Adult Reading Test
(NART) (Nelson, 1982), respectively.
In the present task, 15 individuals were excluded from analysis, resulting in a total of 114
participants. Reasons for exclusion included incomplete dataset, excessive head motion
during scanning, poor understanding of task requirements, and neurological abnormalities
discovered post-hoc. An updated table with demographic information for the present
study is included below (Table 3.1).
67
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
Table 3.1. Demographic information and group differences for 41 dependent and 26
recreational stimulant users, and 47 healthy control volunteers
Control
Mean (SD)
Recreational
Mean (SD)
Dependent
Mean (SD)
F/χ2
P
32.52 (8.86)
33 (63.5%)
112.61 (8.18)
12.69 (1.92)a
2.27 (2.54)a
3.35 (2.25)a
30 (57.7%)ab
11 (21.2%)ab
29.15 (7.56)c
14 (51.9%)c
115.64 (5.39)c
13.41 (1.74)c
3.78 (4.32)c
5.74 (0.30)c
23 (88.9%)bc
26 (96.3%)b
34.28 (7.25)c
44 (88.0%)c
110.64 (7.52)c
11.58 (1.74)ac
18.08 (11.91)ac
11.14 (1.59)ac
49 (98.0%)ac
50 (100%)a
3.609
13.127
3.609
9.917
58.405
15.703
23.640
85.116
0.030
0.001
0.030
<0.001
<0.001
<0.001
<0.001
<0.001
100.35 (96.86)
21.26 (5.10)
7.85 (5.87)
1.19 (1.62)
11 (40.7%)
2.78 (4.70)
19.56 (4.81)
12.64 (6.51)
23.66 (9.35)
33 (66.0%)
-7.08
-1.44
3.145
12.35
4.57
<0.001
0.155
0.002
<0.001
0.033
Demographics
Age*
Gender (n male:%)*
NART (score)*
Education (years)*
BDI-II (total score)*
AUDIT (total score)*
Tobacco use history (%)*
Cannabis use history (%)*
Drug Use
Last stimulant use (days)*
Age of use onset (years)
Duration of use (years)*
OCDUS (total score)*
Cannabis use (n:%)*
*Significant group difference at p<0.05. a Significant difference between Dependent
and Control groups at Bonferroni post-hoc p<0.01.
Recreational and Control.
c
b
Significance between
Significance between Dependent and Recreational.
NART: National Adult Reading Test; BDI-II: Beck Depression Inventory; AUDIT:
Alcohol-Use Disorder Identification Test; OCDUS: Obsessive Compulsive Drug Use
Scale.
2.2 Behavioural Measures
All participants completed the cocaine-word Stroop task during fMRI scanning. The task
consisted of two 16-trial blocks of either common cocaine-related words (i.e. coke, line,
snort) or neutral words that were matched for length and frequency of use (i.e. song,
band, piano). Each trial lasted 2.2 seconds, the target word presented for 1.9 seconds
68
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
followed by an inter-trial interval of 0.3 seconds. Word order was randomised within each
block and blocks were counter-balanced between participants. Individuals were asked to
respond to the font colour of the word using a four-button box, with each button
corresponding to one of the four font colours (red, blue, green, yellow). Participants were
trained on the task and button-colour allocation before entering the scanner. Performance
was measured using response latencies and accuracy. Interference scores were calculated
as the difference between response times on cocaine and neutral trials.
2.3 Behavioural Analysis
As before, data were analysed using the Statistical Package for Social Science (SPSS
v.18). Demographic information and error scores were analysed using analysis of
variance (ANOVA), chi-square and independent samples t-tests. Response times and
interference scores were compared between groups using general linear model (GLM)
multivariate and repeated-measures analyses, controlling for age, gender, education,
smoking status, BDI-II and AUDIT scores. Post-hoc analyses were conducted using
Bonferroni tests. Pearson correlation coefficients were used to assess relations between
variables, and paired samples t-tests compared categorical differences in response times
within groups. Median values were used for response latency analyses and only correct
trials were included; significance levels were set at p<0.05.
2.4 Imaging Analysis
Imaging acquisition procedures were identical to those discussed in the previous chapter;
see Chapter 2, Section 2.4 for detailed information.
FMRI data were analysed using Cambridge Brain Analysis software (CamBA)
(http://www-bmu.psychiatry.cam.ac.uk/software/; Cambridge, UK). Images were motioncorrected and registered to Montreal Neurological Institute (MNI) standard stereotactic
69
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
space using affine transformation (http://www.fil.ion.ucl.ac.uk/spm), and were spatially
smoothed in-plane with a Gaussian kernel of 0.5 voxels FWHM.
The presentation time of each word was modelled as an event onset, with the duration as
time until response. Each event was convolved with a canonical blood oxygen level
dependent (BOLD) hemodynamic response function in CamBA to assess brain activation
up to the time of response on all trials. A comparison of BOLD responses during cocaine
versus neutral word trials was conducted, first among all participants and then comparing
contrast activation between groups. Analyses were repeated using the inferior frontal
gyrus (IFG) as a region of interest, given our significant findings from the previous
chapter, as well as reports from other studies identifying the IFG as a region involved in
Stroop task performance (Ersche, Bullmore, et al., 2010) and an area commonly found to
be abnormal in stimulant-dependent individuals (Bolla et al., 2004). Significance levels
were set at family-wise error (FWE) correction p<0.05 for all analyses.
Whole-brain imaging analysis compared activation during cocaine and neutral word
conditions on successful trials among all participants, creating a group contrast map of
significant voxel-wise responses of the cocaine–neutral contrast. Significant clusters were
then exported to SPSS and compared between groups using GLM multivariate analysis
and Bonferroni post-hoc corrections, including the demographic variables listed above as
covariates in the model.
Between-group activation differences were also assessed using a three-way GLM
omnibus analysis in CamBA, identifying clusters that significantly differed between
groups on the cocaine versus neutral contrast. These results were then exported into SPSS
for further analysis, comparing group differences in BOLD response in the identified
clusters using GLM multivariate analysis.
70
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
A within-group GLM was conducted in CamBA, regressing behavioural interference
scores onto the group activation contrast map. This identified regions in which BOLD
contrast activity levels directly correlated with cocaine interference scores. These regions
were further analysed in SPSS using Pearson correlation and GLM multivariate analyses.
Within-group GLM and between-group GLM omnibus analyses were repeated after
application of a restricted small volume mask of the IFG taken from Hammer’s
probabilistic atlas (Hammers et al., 2003).
3. Results
The three groups significantly differed in terms of age and gender, with recreational users
being younger than stimulant-dependent individuals and more females present in the
control and recreational groups. SDIs also had significantly lower verbal IQ scores than
recreational users and fewer years of education than both control and recreational
participants. There were higher rates of tobacco and cannabis use in the stimulantdependent and recreational users, as well as higher alcohol and depression scores in SDIs.
As such, age, gender, education, smoking status, BDI-II and AUDIT scores were
controlled for in all analyses.
3.1 Behavioural Performance
Stimulant-dependent individuals were more impaired than both recreational and control
participants on the cocaine-word Stroop task, demonstrated via a greater number of errors
committed on both cocaine (Kruskal-Wallis χ2=23.01, p<0.001) and neutral trials
(χ2=6.62, p<0.036), and increased interference scores (F(2,105)=3.017, p=0.053), though
this lost significance after controlling for demographic variables. Conversely, recreational
users did not differ from controls on any behavioural measure (see Appendix B, Table
S3.1 for full behavioural results). In a mixed-model ANOVA with participant group and
71
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
word type (cocaine versus neutral), SDIs had significantly slower response times
compared with the other two groups (F(2,105)=3.787, p=0.026). Within the groups, SDIs
also demonstrated attentional bias to cocaine cues, with significantly increased response
latencies to cocaine over neutral stimuli (t(40)=3.665, p=0.001). Neither recreational
users nor controls had an increase in response times from cocaine to neutral words
(control: t(46)=1.238, p=0.222; recreational: t(25)=-1.561, p=0.131).
3.2 Functional Imaging
Six clusters emerged that demonstrated significantly different activation during cocaine
versus neutral trials amongst all participants (Figure 3.1; Appendix B, Table S3.2). This
included increases in left orbitofrontal and inferior frontal gyrus activity, as well as
bilaterally in the superior medial frontal lobe and anterior cingulate cortex. A significant
decrease in activation was also seen in the right cuneus/precuneus and superior and
inferior parietal lobe. Activity in these regions did not differ between groups.
72
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
A)
X=2
Y=5
0
Z= -20
X= -46
Y=26
0
Z= -8
B)
Figure 3.1. Significant clusters identified during the first-level fMRI contrast,
comparing activation in response to cocaine versus neutral words amongst all
participants. Six significant clusters were identified; regions shown include increases
in activation on cocaine compared to neutral trials in the A) left orbitofrontal cortex,
bilateral superior medial frontal gyrus and bilateral anterior cingulate cortex, and
B) left inferior frontal gyrus and left angular gyrus. Activation in these areas did not
differ between participant groups. Significance was set at p<0.05 family-wise error
correction for multiple comparisons. Coordinates listed are in MNI standard space.
Two clusters in the right orbitofrontal/anterior cingulate cortex and right angular
gyrus/posterior cingulate cortex significantly differed between groups during a secondlevel GLM omnibus contrast comparing activation to cocaine–neutral trials between
participants. In both of these areas, recreational users demonstrated a significant decrease
in activity compared with SDIs and control participants. There were no differences in
activation levels between controls and SDIs in these regions (Figure 3.2; Appendix B,
Table S3.3).
73
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
A)
Cluster 1 – R OFC/ACC
Cluster 2 – R angular gyrus
B)
X=16
Y=48
Z= -4
Figure 3.2. Second-level group contrast, comparing activation between groups
during the cocaine–neutral word contrast. Significant differences emerged in two
clusters: the right orbitofrontal cortex and anterior cingulate cortex, and the right
angular gyrus and posterior cingulate cortex. Recreational cocaine users
significantly under-activated these two regions in comparison with the other two
groups. Coordinates listed are in MNI standard space. Cluster significance set at
74
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
p<0.05 family-wise error correction for multiple comparisons. Group comparisons
made using ANCOVA models, controlling for age, gender, years of education,
smoking status, BDI-II depression and AUDIT alcohol scores, with Bonferroni posthoc correction p<0.05.
After restricting analysis to the inferior frontal gyrus, three additional clusters emerged
that significantly differed between participants in the cocaine–neutral word GLM
omnibus contrast (Appendix B, Table S3.3). These three areas spanned across the right
IFG, including the insula and operculum. Control participants showed a significant underactivation in the first cluster compared with both dependent and recreational users; these
two groups did not differ from each other in this area. In the second cluster, there was
again a significant decrease in activity in the recreational participants compared with both
stimulant-dependent and control individuals, whereas in the third cluster the dependent
stimulant users had significantly greater activation compared to recreational users but not
controls (Figure 3.3).
75
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
A)
IFG cluster 1
IFG cluster 2
IFG cluster 3
B)
X=36
Y=14
Z=10
Figure 3.3. Between-group differences in contrast activation in the lateral inferior
frontal gyrus (IFG) upon application of a small-volume correction mask. Stimulantdependent individuals demonstrated an increase in activity throughout the IFG,
whereas recreational users showed a relative decrease in activity in two of the three
clusters, but had equal levels to dependent users in the third. Control participants
76
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
typically fell between the two stimulant using groups in terms of activation.
Coordinates listed are in MNI standard space. Cluster significance set at p<0.05
family-wise error correction for multiple comparisons. Group comparisons made
using ANCOVA models controlling for age, gender, years of education, smoking
status, BDI-II depression and AUDIT alcohol scores, with Bonferroni post-hoc
correction p<0.05.
When interference scores were regressed against activation levels among all groups on
the cocaine–neutral contrast, no significant clusters arose. However, upon application of a
mask of the IFG, three areas in the right IFG emerged where activity significantly
correlated with cocaine interference scores in all participants. These were centred in the
right IFG pars triangularis and pars orbitalis. The trend again remained of recreational
users showing a comparative under-activation of these regions, while dependent stimulant
users exhibited a relative over-activation (Appendix B, Figure S3.1); however, these
differences were only significant in the cluster centred on the right IFG pars orbitalis.
Activity levels in control participants fell between the other two groups, and did not differ
from either recreational users or controls. Activation in all three regions significantly
negatively correlated with interference scores in all groups, with greater impairment
associated with greater activity on cocaine versus neutral trials (r=0.377, 0.405, 0.381;
p<0.001 all correlations).
4. Discussion
Recreational cocaine users were unimpaired on a cognitive control task measuring
attentional bias to cocaine-related stimuli, performing on par with non-drug using control
participants. Conversely, stimulant-dependent individuals were significantly impaired
77
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
compared to the other two groups, registering longer response times, higher interference
scores and more errors committed. Recreational users also demonstrated very different
patterns of activation during task performance, significantly under-activating prefrontal
and orbitofrontal regions compared with both stimulant-dependent and control
individuals.
The absence of a significant increase in response times among recreational users to
cocaine-related stimuli indicates that they do not share an attentional bias to these words
with dependent users. Drug-related attentional bias has been linked to increased
motivation to obtain the substance, as well as heightened emotional salience for these
cues (Field & Cox, 2008; Goldstein & Volkow, 2002). The absence of preferential
attention to cocaine cues in the recreational users suggests an inherent difference in
automatic drug-related stimulus processing between the two participant groups,
underlying an intrinsic difference in the pattern and type of drug use. Similar results have
been reported in dependent and recreational cannabis users, with more severe users
reporting greater drug craving and increased attentional bias on a cannabis-modified
Stroop task (Field, 2005). Similarly, light social drinkers had less attentional bias to
alcohol cues than heavy drinkers (Field, Christiansen, Cole, & Goudie, 2007; Townshend
& Duka, 2001). In the current assessment, SDIs were impaired on all aspects of the task,
registering significantly slower responses on both trial conditions. This is suggestive of a
more globalized inefficiency in these individuals that is not unique to the cocaine-related
stimuli, potentially relating to increased age, lower IQ or general cognitive impairment
from prolonged stimulant use. However, the higher interference scores in these
participants, demonstrating significantly slower responses on cocaine versus neutral trials,
are indicative of greater content-specific impairment as well.
78
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
The decrease in activation in the IFG and OFC seen in the recreational users also suggests
that these words do not hold the same salience as they do for the dependent users. The
IFG is thus not equivalently recruited in the recreational users, as the need to inhibit an
initial attentional bias is less prevalent in these individuals. Increased activation in the
SDIs compared with recreational users could reflect greater effort being exerted to resist
the distracting cocaine words, represented via increased interference scores on the task.
Higher prefrontal activity in these participants could also be due to the increased salience
of the words for these individuals, resulting in greater neuronal excitation. However, it
should be noted that in only one cluster in the IFG did SDIs show greater BOLD
activation than control individuals. Thus, the significant contrast was primarily between
the dependent and recreational users of cocaine, rather than between dependent
individuals and control participants. This result is surprising given the significant
attentional bias to these cues in the SDIs compared with the control group, and previous
evidence showing heightened BOLD response in response to drug-related cues in
dependent individuals (Ersche, Bullmore, et al., 2010).
Resistance to drug-related attentional bias as measured by the Stroop has previously been
cited as an indicator of successful abstinence efforts in treatment seekers (Brewer et al.,
2008; Carpenter et al., 2006; Marissen et al., 2006). In the brain, impairments in response
inhibition manifested as abnormal activation in the frontal pole and cingulate cortex,
thought to correspond with the increased cognitive demand required to override an initial
emotional reaction to the drug words (Ersche, Bullmore, et al., 2010; Goldstein et al.,
2009; Goldstein, Tomasi, Rajaram, et al., 2007; Wexler et al., 2001). The inferior frontal
gyrus in particular has been found to exhibit increased activation in response to drug cues,
and is known to be associated with cognitive control and the inhibition of prepotent
responses (Bunge et al., 2001; Ersche, Bullmore, et al., 2010).
79
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
Previous work with the current participant groups has shown differences in cortical
structure, with recreational users having significantly greater volume in the OFC, while
dependent users showed a significant reduction in OFC grey matter, as well as in the
posterior insula (Ersche, Jones, Williams, Smith, et al., 2013; Ersche, Jones, et al., 2012).
Similar dissociation in OFC activation during the cocaine-word Stroop task between
dependent and recreational users of cocaine may reflect the structural differences in this
area, which could be serving as a protective factor against the development of dependence
in recreational users. In the previous chapter, increased frontal volume was associated
with more efficient cognitive processing, and it is possible that the enlarged OFC in the
recreational users compared with control and SDIs resulted in them having to recruit less
cortical activity during task performance, rendering the task less cognitively demanding.
Rather than being at an earlier stage in the development towards dependence, we believe
that these individuals are able to maintain a recreational pattern of cocaine use without
devolving into full-fledged dependency. The cognitive, behavioural, structural and
functional data all point to a unique pattern of use in these individuals that is distinct from
those who are dependent on cocaine (Ersche, Jones, Williams, Smith, et al., 2013).
Additionally, data from the biological siblings of SDIs show very different results,
displaying cognitive, personality and structural traits that are most similar to those of their
dependent siblings, despite never having been dependent on drugs (Ersche, Jones, et al.,
2012; Ersche, Turton, et al., 2012, 2010). Thus, these findings suggest two possible
important qualitative differences between dependent and recreational stimulant users.
Underlying and potentially predisposing factors for stimulant dependence, such as
decreased cognitive efficiency and impaired impulse control, may be present in the
former, as well as in their non-drug abusing siblings, while these potentially predisposing
factors are not present in recreational users. Conversely, recreational users may exhibit
80
Chapter 3. Absence of cocaine cue bias in recreational stimulant users
resilience against the development of dependence, including neurobiological changes
such as increased orbitofrontal cortex size and efficiency that are not evident in dependent
users or their siblings. This pattern of qualitative differences would seem to make it
unlikely that recreational users are merely at an earlier stage on an ultimate trajectory to
dependence.
In the current cocaine-word Stroop task, it is difficult to parse out whether the differences
between recreational and dependent users are due to impaired executive control in the
SDIs in response to cocaine stimuli, or a global increase in cognitive control in the
recreational users. Future studies should include a non-drug related evocative word
condition to test whether the difference is specific to cocaine-related words or is also
present for other salient emotional stimuli. Additionally, the Stroop paradigm has been
shown to have poor internal reliability (Cools et al., 2007), and studies employing
objective indices of attentional bias, such as eye-tracking, should be administered to test
the reproducibility of this effect.
The findings reported here focus on the responses of qualitatively different groups of
cocaine users – dependent and recreational individuals – to drug-related stimuli. Given
the known abnormalities in the mesolimbic dopamine circuitry and corresponding
differences in reward system responding associated with heavy stimulant use, comparing
reactions to a more general reinforcer could help illuminate how wide-spread these
differences in reward responsivity are in dependent and recreational users. Additionally,
reintroducing the sibling participants to this analysis would enable a comprehensive
comparison of predating and consequential differences in the brain and behaviour
involved in stimulant use and dependence. This analysis is pursued in the following
chapter.
81
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
Chapter 4. A Comparison of Endophenotype and Stimulant Exposure
Effects in Reward Sensitivity
1. Introduction
In addition to the ‘top-down’ cognitive impairments seen in dependent stimulant users, there
are also known dysfunctions in ‘bottom-up’ processes, such as atypical reward responding
associated with altered dopamine activity in the striatum (Schott et al., 2008). A growing
body of research has suggested that abnormalities in the mesolimbic dopamine reward
circuitry may both underlie and be perpetuated by the effects of stimulant drugs acting
directly on this system (Blum et al., 2000; Volkow, Wang, Telang, et al., 2008). In the
previous section, neural responses to cocaine cues were measured using functional
neuroimaging to assess cognitive bias to drug-related stimuli. In the current study, we
examined blood oxygen level dependent (BOLD) activation during functional magnetic
resonance imaging (fMRI) in response to a more universal (though secondary) reinforcer –
money – to determine if changes in general reward responsivity are linked to increased risk
for drug dependence, or if abnormalities in reward circuitry are observed only in response to
drug cues.
The Monetary Incentive Delay task (MID) is a well-validated paradigm that assesses
sensitivity to differing magnitudes of financial rewards (Knutson et al., 2000). Larger
rewards typically elicit greater activation in the ventral and dorsal striatum during
anticipation to respond, as well as the medial orbitofrontal cortex (OFC) (Knutson, Adams,
Fong, & Hommer, 2001; Knutson, Fong, Adams, Varner, & Hommer, 2001; Schott et al.,
2008). The striatum has long been implicated in reward responding and, as discussed
82
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
previously, is also a key target for addiction research, with stimulant drugs acting directly on
the dopamine-rich circuitry (Berridge & Robinson, 1998; Everitt & Robbins, 2005; Wise &
Bozarth, 1985; Wise & Rompre, 1989). Manipulation of the striatal dopamine system
through the use of illicit substances is thought to ‘hijack’ normal reward processing in
stimulant-dependent individuals (SDIs), with stimulant drugs becoming over-valued while
other rewards, such as food or sex, lose their reinforcing properties (Bjork et al., 2008;
Garavan et al., 2000; Goldstein et al., 2007; Jia et al., 2011). Likewise, decreases in grey
matter volume and BOLD activation in the OFC, an area involved in reward valuation and
decision-making (Bechara et al., 2000; Levy & Glimcher, 2012) have also been observed in
addicted individuals, and are potentially a consequence of prolonged stimulant use (Bolla et
al., 2004; Ersche et al., 2011; Franklin et al., 2002; Matochik et al., 2003).
Previous studies administering the MID to substance-dependent individuals have reported
somewhat conflicting results. Studies in stimulant- and cannabis-dependent individuals have
shown a hyper-activation of the BOLD response, particularly in the ventral striatum, medial
frontal lobes, insula and anterior cingulate cortex (Bjork et al., 2008; Jia et al., 2011; Nestor,
Hester, & Garavan, 2010). Conversely, alcohol-dependent individuals exhibit a hypoactivation to reward relative to healthy control volunteers (Beck et al., 2009; Wrase et al.,
2007).
As with most impairments associated with stimulant dependence, it is unclear whether the
differences in activation are an underlying issue, predating and potentially predisposing an
individual to find stimulant drugs more rewarding and thus making them more susceptible to
the development of dependence, or if the disruption is a result of the effects of stimulant
drugs on the brain. In the current study, we compared behavioural performance and neural
83
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
responses between all four of the same participant groups from the previous two chapters –
dependent stimulant users, their non-dependent biological siblings, recreational users of
cocaine, and healthy control volunteers – on a modified MID task during fMRI scanning. We
compared BOLD responses to different magnitudes of monetary rewards in a priori
designated regions of the striatum and OFC, and additionally completed an exploratory
whole-brain analysis. We also adapted the task to include drug-related stimuli in an attempt
to determine if responses to drug-specific rewards mirror those seen to money, a more
general reinforcer.
If abnormalities in reward processing represent underlying risk factors for dependence, we
would expect the siblings and SDIs to display similar responses to the money cues.
Conversely, if alterations in reward processing are a consequence of the effects of stimulant
drugs or an indicator of a willingness to use drugs, we would expect dependent and
recreational users to show similar responses, and for both to differ from the control and
sibling participants (though due to greater exposure the SDIs might show a greater effect than
the recreational users).
In the current investigation, we hypothesised that stimulant-dependent individuals would
exhibit heightened sensitivity to both monetary and drug stimuli via increases in BOLD
activation in the striatum and OFC compared with the other participant groups, as shown in
previous studies (Bjork et al., 2008; Jia et al., 2011). We predicted recreational users to also
show an increase in activation to reward anticipation compared with control and sibling
participants, but not to the same extent as the dependent users due to differences in exposure
and drug use severity. Finally, we hypothesised that sibling and control participants would
not differ in their responses to either condition. Thus, we predict that sensitivity to reward
84
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
will not reflect an endophenotypic trait, but will instead be more suggestive of a potential
consequence of stimulant use on the brain’s reward circuitry.
2. Materials and Methods
2.1 Participants
Recruitment, screening and demographic information for the four participant groups have
been described in the preceding two chapters. Four groups of 50 stimulant-dependent
individuals (SDIs), 50 of their non-dependent biological siblings, 27 recreational cocaine
users, and 52 unrelated healthy control volunteers took part in the study.
In the current investigation, 39 individuals were excluded from the analysis due to excessive
head movement, presence of a clinically significant neurological abnormality, or poor
performance on the task in which a condition had to be discarded due to no correct responses.
This resulted in 140 total participants (control=43, SDI=35, sibling=40, recreational=22).
Demographic information for this subset of participants is provided in Appendix C (Table
S4.1).
2.2 Behavioural Task
Participants completed a modified version of the Monetary Incentive Delay task (Knutson et
al., 2000) during fMRI scanning using two types of motivationally salient outcomes:
monetary rewards – similar to those used in the original task – and images of drug stimuli.
The task was administered in two runs (money, drug), presented in randomised order.
Participants were told that they would be paid according to their performance, i.e. the money
that they earned during the monetary condition plus an equivalent financial bonus reflecting
their performance on the drug condition.
85
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
In the task, participants were presented with one of five different cues signalling the trial
type. In the money condition, the cue was associated with a large (50 pence) or small (10
pence) reward, or a neutral (no reward) outcome (see Figure 4.1). As in the original MID
task, the neutral cue was an empty circle, the small money cue was a circle transected by a
single horizontal line, and the large money cue was a circle transected by two horizontal
lines. In the drug condition, the rewarding cue consisted of an image of the stimulant-using
individual’s drug of choice (either powder cocaine, crack cocaine or amphetamine), while a
picture of a water bottle constituted the neutral cue. The rewarding feedback slides depicted
an image of an individual self-administering the drug of choice or drinking water for the
neutral trial.
Cues were presented for 250 ms, followed by a jittered blank anticipation screen for 30005000 ms with a mean of 4000 ms. A target stimulus (white square) was then presented for
between 100-400 ms, during which time the participant had to respond in order to receive
reward. If the participant responded before the target disappeared, a feedback screen
depicting the amount of money won, or an image of a person using their drug of choice, was
presented for 1650 ms. If the respondent was too slow, an image of the neutral feedback slide
was presented representing a non-win trial – i.e. either the blank circle depicting 0 pence or
the image of a person drinking from a water bottle.
At the end of the experiment, participants were asked to rate their willingness to pick up
either a 10 pence or 50 pence coin off the floor on a visual analogue scale (Always – Never)
as a proxy for subjective value of each monetary reward.
86
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
Cue 250 ms
Anticipation
3000-5000 ms
Target 100-400 ms
Feedback 1650 ms
Figure 4.1. Image displays for the modified Monetary Incentive Delay task for the
money and drug conditions. Participants were primed with a cue designating the type of
87
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
trial that was about to take place, followed by a jittered anticipation slide and then the
target to which they must respond. A feedback slide signifying either a successful or
unsuccessful trial was then displayed. Unsuccessful trials were represented by the
neutral image conditions.
2.3 Behavioural Analysis
Mean response times for each condition were compared between groups using general linear
model (GLM) multivariate analyses, with Bonferroni post-hoc corrections for multiple
comparisons, in the Statistical Package for Social Sciences (SPSS v.19). A repeated measures
GLM was conducted for the drug and money conditions separately, with group (four levels:
SDIs, siblings, recreational users and healthy volunteers) as the between-subjects factor and
cue type (two levels: reward, collapsed across high and low value stimuli for the money
condition, and neutral) as the within-subjects factor. For post-hoc analysis, simple main
effects were calculated using relevant variations of this GLM. Subjective monetary
valuations of 10 pence and 50 pence, as well as the total amount of money earned, were
compared between groups using GLM multivariate analyses with Bonferroni post-hoc
corrections.
2.4 Imaging Analysis
See image acquisition parameters described in Chapter 2, Section 2.4.
FMRI data were analyzed using Statistical Parametric Mapping software (SPM v.8)
(http://www.fil.ion.ucl.ac.uk/spm/software/spm8/; London, UK). The first five images were
discarded to account for T1 equilibration. Images underwent motion and slice-timing
correction and were registered to Montreal Neurological Institute (MNI) standard stereotactic
88
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
space. Images were spatially smoothed using a 5 mm Gaussian kernel full-width at half
maximum. Scans with movement greater than 1.5 mm in any direction, or enough to cause
inter-slice variance, were excluded from analysis. Other scan exclusion criteria included large
dropout in the key regions of the basal ganglia and OFC.
At the first-level, design matrices modelling BOLD hemodynamic response functions at the
time of anticipation (cue onset) and receipt of feedback (outcome) were created for each
stimulus type (money large, money small, money neutral, drug, drug neutral). Large and
small money trials were combined in contrasts to compare responses across the reward
conditions with the money neutral trials. Comparisons between anticipation of reward
(money/drug) and neutral trials were conducted, as were comparisons for receipt of reward
for successful (win) compared with unsuccessful (no win) outcomes. In total, four contrasts
were computed: anticipation of money minus anticipation of neutral; anticipation of drug
minus anticipation of neutral; feedback during successful money trials minus feedback during
unsuccessful money trials; and feedback during successful drug trials minus feedback during
unsuccessful drug trials.
In second level analyses, the main effect of each contrast (collapsing across group) was
computed using one-sample t-tests. The groups were then compared on each of the four
contrasts using analysis of variance (ANOVA) F-statistics. Further contrasts between the
participant groups were also run, enabling us to better answer our a priori question of which
traits in stimulant dependence are due to potential endophenotype effects and which may be
attributed to stimulant use. This included a contrast of those with a personal or family history
of drug dependence (SDIs and siblings) to those without (control individuals and recreational
89
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
users), as well as an investigation into the effects of stimulant exposure (SDIs and
recreational users versus control and sibling participants).
Analyses for all contrasts were repeated applying a priori specified regions of interest (ROIs:
ventral
and
dorsal
striatum;
orbitofrontal
cortex),
defined
using
PickAtlas
(http://www.nitrc.org/projects/wfu_pickatlas/) with Anatomic Automatic Labelling (AAL)
coordinates. We corrected for multiple comparisons, controlling the family-wise error rate
(FWE) at the voxel-level.
Principal eigenvariates for significant clusters identified during the second-level analyses
comparing groups were extracted from SPM and compared post-hoc in SPSS using ANOVA.
This was used to determine the direction of differences in BOLD response between groups,
but not to make formal statistical interference. These eigenvariates were also used for
comparisons with other behavioural variables to determine relationships between functional
and behavioural performance, using Pearson correlation coefficients.
3. Results
3.1. Demographic
As in the previous analyses with these groups, the participants significantly differed on
several measures, including age, gender, education levels and smoking status (Appendix C,
Table S4.1). As such, these variables were included as covariates in group comparisons of
behavioural and functional imaging variables in SPSS in an attempt to control for these
differences.
90
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
3.2 Behavioural
There was no significant main effect of task condition on reaction times (F(3,136)=1.098,
p=0.350; Table 4.1). However, we detected a significant group-by-condition interaction
(F(3,9)=2.400, p=0.012). Analyses of simple main effects revealed that within the dependent
and recreational groups, responses to the money trials were significantly faster than to neutral
trials (dependent: F(1,34)=13.119, p=0.001; recreational: F(1,21)=5.799, p=0.025). This
effect was also present in the control group at trend level (control: F(1,42)=3.350, p=0.074),
but not in the sibling group (F(1,39)=1.296, p=0.262). There were no significant differences
in response latencies among any of the groups on the drug versus drug neutral trials, and
there was no main effect of group on global response times.
There was a significant difference in the total money earned between the four groups
(F(3,136)=2.741, p=0.011), with SDIs obtaining significantly more money than recreational
users at Bonferroni corrected p<0.05. The four groups also trended towards a difference in
their self-reported likelihood of picking up a 10 pence coin off the ground (F(3,136)=1.993,
p=0.061), used as a subjective measurement of the value attributed to the two rewards
presented in the task (Figure S4.1). This effect was driven by the sibling participants rating
themselves as less likely to pick up 10 pence off the floor than the other three groups;
however, this did not survive Bonferroni post-hoc comparisons. There was no difference
between groups in subjective valuation for the 50 pence reward (F(3,136)=0.891, p=0.516).
91
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
Table 4.1. Behavioural performance differences on the Monetary Incentive Delay task between groups
Behavioural Results
Money cue mean RT (ms)
Money neutral cue mean RT (ms)
Drug cue mean RT (ms)
Drug neutral cue mean RT (ms)
Money won (£)*
10 pence value (% pick up)
50 pence value
Control n=43
Mean (SD)
Sibling n=40
Mean (SD)
Recreational n=22
Mean (SD)
Dependent n=35
Mean (SD)
F
P
205.52 (17.05)
209.17 (21.75)
208.28 (18.14)
205.54 (17.21)
9.30 (1.08)
65.05 (29.64)
76.70 (23.80)
209.49 (22.40)
211.59 (24.51)
215.74 (27.18)
217.48 (27.11)
9.32 (0.91)
49.35 (35.38)
67.42 (31.98)
201.38 (20.32)
206.64 (23.48)
203.30 (21.53)
203.54 (23.23)
9.08 (1.16)
55.36 (32.20)
75.59 (22.18)
206.92 (26.10)
217.83 (30.91)
213.47 (32.06)
217.43 (31.20)
9.89 (1.03)
70.54 (28.88)
79.89 (25.77)
0.365
1.065
1.322
1.539
2.741
1.993
0.891
0.921
0.389
0.245
0.160
0.011
0.061
0.516
*Significant group difference at p<0.05. In Bonferroni post-hoc tests, dependent individuals won significantly more money than
recreational users, and sibling participants trended towards ranking a 10 pence coin as less desirable. Analysis corrected for age,
gender, education levels and smoking status.
92
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
3.3 Neuroimaging
Anticipation of Monetary Reward
Analysis of the monetary anticipation contrast (money minus no money trials) revealed
significant activation in the right medial OFC and putamen using ROI analyses among all
participants (both p<0.05; Figure 4.2; Table 4.2). However, testing the group-by-condition
interaction, no significant differences emerged between the four groups in activation during
anticipation of the monetary reward in the OFC or striatum. There were also no significant
group effects at the whole brain level that survived FWE correction. Additionally, no
significant differences arose that survived FWE correction when concentrating on the a priori
contrasts testing potential endophenotype effects (those with a personal or family history of
dependence versus those without) or the effects of stimulant drug use (stimulant users versus
non-users).
In the sibling participants, reward-related speeding (i.e. the difference in reaction time
between the neutral and money conditions) significantly negatively correlated with activation
in the striatum (right putamen: r=0.381, p=0.015; left putamen: r=0.348, p=0.028), such that
heightened anticipatory putamen activity corresponded to relatively slower responses on the
money compared with the neutral trials. This effect did not emerge in any other participant
group, and no other behavioural measures correlated with BOLD activity.
93
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
Figure 4.2. Striatal activation amongst all participants during anticipation of money versus
neutral trials using region of interest analysis (MNI coordinates (x,y,z) mm: -8, 12, 7). There
were no significant differences in activation in either cluster between participant groups.
Receipt of Monetary Reward
Analysis of the money feedback contrast (successful minus unsuccessful trials) revealed
significant activation in the left caudate, bilateral putamen and medial OFC across groups
using ROI analyses (all p<0.05; Table 4.2). Across all participants, activation in the caudate
was significantly correlated with the amount of money won on the task (r=0.171, p=0.043);
no other behavioural measures correlated with BOLD activity. No regions survived FWE
correction for the group-by-condition interaction. Likewise, there was no significant
activation supporting an endophenotype effect during feedback on money trials. However,
there was a significant difference in BOLD response in the OFC ROI (FWE p<0.05) between
those with and without a personal history of stimulant exposure: the control and sibling
participants exhibited a significant relative increase in activation bilaterally in the medial
OFC compared with SDIs and recreational users (Figure S4.2). However, during post-hoc
analysis no differences survived Bonferroni post-hoc correction.
94
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
Table 4.2. Significant cluster activation among participants during money conditions:
anticipation of money compared to anticipation of neutral trials, and feedback for successful
versus unsuccessful money trials.
Anticipation Money – Neutral
All participants ROI striatum
FWE p<0.05
Right putamen
Left putamen
All participants ROI OFC
FWE p<0.05
Right medial orbitofrontal cortex
Feedback Money
Successful – Unsuccessful
All participants ROI striatum
FWE p<0.05
Right putamen
Left putamen
Left caudate
All participants ROI OFC
FWE p<0.05
Left medial orbitofrontal cortex
Cluster
Size
Peak
Voxel
T
Post-hoc
ANOVA F
Post-hoc
ANOVA P
552
521
30, 17, 4
-27, 14, 4
10.36
9.51
1.70
1.68
0.113
0.120
170
12, 35, -11
6.56
0.58
0.769
Cluster
Size
Peak
Voxel
T
Post-hoc
ANOVA F
Post-hoc
ANOVA P
381
301
69
27, -10, 10
-30, -10, 1
-18, -16, 22
7.48
8.55
6.63
1.09
1.20
0.63
0.374
0.305
0.728
183
-3, 41, -14
6.86
0.31
0.950
Anticipation of Drug Reward
Analysis of the drug anticipation contrasts revealed a significant cluster in the right medial
OFC (FWE p<0.05), but no activation in the striatum with ROI analyses among all
participants. However, in the group-by-condition interaction, there was significantly different
activation in the right putamen (F(3,136)=4.042, p<0.001) but no interaction in the OFC.
This effect was largely driven by the SDIs, who exhibited an increase in activation in this
region compared with their siblings and recreational users, while the sibling participants
demonstrated a decrease in activity compared to SDIs and controls (Figure 4.3). There was
also a significant group-by-condition interaction in the left middle cingulum, stretching into
95
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
the left supplementary motor area, and vermis at the whole-brain level (FWE p<0.05), with
effects evident in the same direction (drug users over-activating and siblings under-activating
compared with all of the other groups). There was no correlation between behavioural
performance and BOLD activation in any cluster during the anticipation condition.
Examining the contrasts focused on our hypotheses, the combined stimulant-using group
(recreational and dependent) showed a significant increase in activation in the right OFC ROI
compared with the combined sibling and control group (Figure S3). This effect was strongest
compared with the siblings (recreational: p=0.08; SDI: p=0.020, Bonferroni corrected); the
controls did not differ from either of the drug-using groups in post-hoc analyses. There was
no effect of stimulant exposure in the striatum. At the whole-brain level, an effect in the same
direction was also evident in the left angular gyrus, with the stimulant-using participants
demonstrating greater activation than
the combined sibling and control group
(F(3,136)=3.17; FWE p=0.004). In post-hoc analyses, the recreational users had significantly
greater BOLD activity than the siblings (p=0.006, Bonferroni corrected); no other post-hoc
comparisons reached significance. There were no differences relating to an endophenotype
effect.
96
Chapter 4. Decreased reward sensitivity protects against stimulant dependence
A)
B)
97
Chapter 4. Decreased Reward Sensitivity Protects Against Stimulant Dependence
Figure 4.3. A) Activity in the left middle cingulum (MNI coordinates (x,y,z) mm: -6,
11, 40), which significantly differed between participant groups during anticipation
of drug–neutral trials at the whole-brain level. This was driven by dependent
individuals having greater activity in this area than all of the other three groups, as
well as an increase in control participants compared to the sibling individuals. Other
regions that showed a significant group effect during the drug anticipation contrast
were the left precentral gyrus/supplementary motor area and in the vermis. B) After
a region of interest analysis focusing on the striatum, an additional cluster emerged
in the right putamen (MNI coordinates: 30, 17, 4) that significantly differed between
participants during the contrast. Again, dependent individuals showed greater
activity compared with their siblings and recreational users. Controls also displayed
greater activity than the sibling participants.
Receipt of Drug Reward
Analysis of the drug feedback contrast (successful minus unsuccessful trials) revealed
significant activation in the left and right putamen and left OFC using ROI analyses
across all participants (all p<0.05; Table 4.3). There were no significant group-bycondition interactions and no significant effects in the planned comparisons relating to
either an exposure effect or an endophenotype effect. Activation during drug feedback did
not correlate with any behavioural measures.
98
Chapter 4. Decreased Reward Sensitivity Protects Against Stimulant Dependence
Table 4.3. Significant cluster activation among participants during drug conditions:
anticipation of drug compared to anticipation of neutral trials; and feedback for successful
versus unsuccessful drug trials.
Anticipation Drug – Neutral
Cluster
Size
Peak
Voxel
T
Post-hoc
ANOVA F
Post-hoc
ANOVA P
-6, 11, 40
12.38
5.56
<0.001
-30, 5, 40
4.90
5.11
<0.001
6, -58, -11
4.69
6.05
<0.001
30, 17, 4
9.33
4.04
<0.001
12, 50, -14
3.33
1.52
0.164
Cluster
Size
Peak
Voxel
T
Post-hoc
ANOVA F
Post-hoc
ANOVA P
407
381
-21, 8, -5
21, 11, -5
9.20
8.90
0.58
0.61
0.768
0.746
120
-3, 44, -14
6.22
0.46
0.862
ANOVA
FWE p<0.05
Left middle cingulum*
65
Dependent > Control: p=0.025
Dependent > Sibling: p<0.001
Dependent > Recreational: p=0.010
Control > Sibling: p=0.011
Left supplementary motor area*
2
Dependent > Sibling: p<0.001
Control > Sibling: p=0.013
Recreational > Sibling: p=0.001
Vermis*
14
Dependent > Sibling: p<0.001
Dependent > Recreational: p=0.012
Control > Sibling: p=0.008
All participants ROI striatum
FWE p<0.05
11
Right putamen
Dependent > Sibling: p<0.001
Dependent > Recreational: p=0.075
Control > Sibling: p=0.010
All participants ROI OFC
FWE p<0.05
Right medial orbitofrontal cortex*
1
No significant group differences
Feedback Drug
Successful – Unsuccessful
All participants ROI striatum
FWE p<0.05
Left putamen
Right putamen
All participants ROI OFC
FWE p<0.05
Left medial orbitofrontal cortex
*Significant group difference at p<0.05.
99
Chapter 4. Decreased Reward Sensitivity Protects Against Stimulant Dependence
4. Discussion
A test of reward-responsivity was administered to stimulant-dependent individuals, their
non-dependent biological siblings, recreational users of cocaine, and unrelated healthy
control volunteers in an attempt to determine whether abnormalities in reward system
functioning that commonly accompany stimulant dependence are due to underlying
impairments or are the result of long-term stimulant use. Stimulant-dependent individuals
and recreational users of cocaine both showed a relative increase in activation during
anticipation of stimulant cues compared with non-drug-using control and sibling
participants, particularly in the OFC. SDIs also demonstrated an additional increase in
striatal activation in response to these stimulant cues. However, both drug-using groups
exhibited a relative under-activation compared with that of non-drug-using participants in
response to the receipt of a monetary reward. These findings suggest a potential effect of
stimulant drugs like cocaine on the mesolimbic dopamine circuitry, and might support the
theory of a ‘hijacking’ of the reward system by drugs of abuse, rendering other non-drug
reinforcers less effective.
4.1 Effects of Stimulant Use
Differences in activity in reward circuitry in the stimulant-using participants – with an
increase in activation in response to drug cues, but a decrease in activity upon receipt of a
monetary reward – may be a consequence of their drug use, resulting in a disruption of
the mesolimbic striatal dopamine circuitry. Alternatively, these varying sensitivities to
reward may be a contributor to the initiation of stimulant use, present only in those who
are more likely to try drugs.
It is notable that the primary group differences between drug-using and non-using
participants in response to both types of reward stimuli were in the OFC. This was a
100
Chapter 4. Decreased Reward Sensitivity Protects Against Stimulant Dependence
primary region of interest due to its involvement in value (Levy & Glimcher, 2012) and
being an area known to be particularly affected by stimulant use (Ersche, Williams,
Robbins, & Bullmore, 2013). However, it is somewhat perplexing that the dependent and
recreational users demonstrated similar activation in this region on the current task given
the significant differences in activation in the area these same individuals displayed in the
previous chapter in response to the cocaine Stroop task. Additionally, these same
participant groups have also shown significant differences in grey matter volume in this
region, with SDIs having a decrease in cortical volume while the recreational users
showed an increase in OFC grey matter (Ersche, Jones, Williams, Smith, et al., 2013).
While both drug-using groups demonstrated increased OFC activation during anticipation
of the drug trials compared with the non-drug users, further differences in activity
occurred in the dependent individuals; SDIs showed greater activation in the striatum, left
middle cingulum and vermis compared with recreational users. This may be suggestive of
a potential dose effect in the SDIs given their greater exposure to cocaine, or this may be
indicative of a ‘dependence effect’, reflecting the different patterns of stimulant use in the
two drug using groups. This finding is supported by the recreational users’ notable lack of
a significant response to cocaine cues in the emotional Stroop task reported in Chapter 3.
Curiously, there was no difference in activation between the groups during feedback for
drug rewards. It is possible that after prolonged stimulant use, it is only the anticipation
of reward that is arousing for stimulant users, while the notice of receipt no longer elicits
a significant response. Supporting this, it was only during the feedback condition for
money that there was a difference between the stimulant using and non-using groups,
with the sibling and control individuals exhibiting a higher BOLD signal. This finding
supports the incentive salience model of reward processing, where the cues predicting a
101
Chapter 4. Decreased Reward Sensitivity Protects Against Stimulant Dependence
reward come to be over-valued, taking on the reinforcing properties of the stimulus itself
(Robinson & Berridge, 1993). Thus, it is potentially only in anticipation of a reward,
prompted by the associated cue, that objects are valued, particularly for drug-associated
stimuli. However, previous investigations of the MID in stimulant users have reported
conflicting results, with SDIs exhibiting a relative increase in activation to monetary
reward receipt compared with control individuals (Bjork et al., 2008; Jia et al., 2011).
4.2 Evidence for an Endophenotype
Notably, there were no behavioral or functional responses that were uniquely present in
both the sibling and stimulant-dependent groups that might have been suggestive of an
endophenotype trait. This is in striking contrast to the results reported in Chapter 2, as
well as previous investigations in these participant groups in which they displayed
evidence of similarities suggestive of an endophenotype, including impulsivity and poor
motor control (Ersche, Jones, et al., 2012). However, the different nature of the task used
in the current investigation – targeting a reward-related motivation to respond – may
explain this unique finding.
Interestingly, during anticipation for the drug cues the sibling individuals not only
displayed a decrease in activation compared with the SDIs, they also showed a relative
decrease in activity compared with the control participants. This may be due to an
aversion to the drug-related stimuli due to their experiences with their stimulantdependent brothers and sisters (Barnard, 2005). Supporting this specificity, during the
money trials striatal activation in the siblings was no different from that of control
volunteers. Prior research in individuals with a family history of alcohol dependence has
shown a relative increase in striatal dopamine receptor availability in family-history
positive participants (Volkow et al., 2006). This increase was proposed to potentially lead
102
Chapter 4. Decreased Reward Sensitivity Protects Against Stimulant Dependence
to a reduced sensitivity for drug reinforcement, acting as a potential protective factor
against addiction. In the current study, there do not appear to be any general abnormalities
in striatal activity in the sibling individuals, however, any potential striatal dopamine
effects similar to those listed above may be limited to drug-specific cues.
4.3 All Group Comparisons
Among all participant groups, there was significant activation in the striatum and
orbitofrontal cortex in response to rewarding stimuli. This supports the general notion that
these areas are crucial for reward processing; it also importantly demonstrates that the
current version of the MID accesses the same circuitry as previous investigations using
variants of the task (Beck et al., 2009b; Bjork et al., 2008; Knutson, Adams, et al., 2001;
Schott et al., 2008). However, there were few differences in activation between groups in
this area, limited to the drug anticipatory condition only. The lack of differences in striatal
activation was surprising and somewhat conflicting with previous studies in stimulantdependent individuals (Bjork et al., 2008; Jia et al., 2011). The wider and more diverse
spread of participant groups in the current investigation may have washed out this effect
during the four-group comparisons, particularly as the recreational users fell between the
control and dependent participants in their activation during the money anticipation
condition, not differing from either group.
Shortcomings of this investigation include the ranging demographic differences between
the participant groups. Although we included these demographic data as covariates in the
analysis, better matching for these variables during recruitment could be improved in
future studies. Furthermore, it was not possible to directly compare responses to drug and
money rewards within the participant groups due to inherent differences in task design
103
Chapter 4. Decreased Reward Sensitivity Protects Against Stimulant Dependence
between the two conditions, thus preventing a direct contrast of responsivity to these two
types of reinforcers.
Prior research has suggested that with chronic use, dependent stimulant users become
increasingly more sensitive to the reinforcing properties of their drug of choice, but
relatively less sensitive to other more generic rewards, such as money or food (Aigner &
Balster, 1978; Opris et al., 2009; Woolverton & Anderson, 2006). Our results tentatively
support this hypothesis, however, going forward, it would be illuminating to test this
theory more directly by comparing BOLD response to drug and non-drug rewards in
these groups. Furthermore, it is possible that money cues may also have attained drugrelated salience for dependent individuals through the continual purchasing and
procurement of illicit substances. Thus, it would be beneficial to look at primary
unconditioned reinforcers, such as food or erotic images, which do not suffer from this
confound.
104
Chapter 5. Reward valuation in addictive disorders
Chapter 5. Reward Valuation and Incentive Motivation in Addictive
Disorders
1. Introduction
In the previous chapter, responses to a universal conditioned reinforcer, money, and drugspecific cues were examined separately. Stimulant-dependent individuals (SDIs) exhibited an
increase in activation to both types of stimuli, suggesting a potentially hypersensitive reward
system. This heightened responsivity may place them at a greater susceptibility to first seek
out rewarding drug-related experiences and later contribute to maintained use despite
negative consequences, as is characteristic of dependence. However, these two different
reinforcers were examined separately, preventing a direct comparison of responses to each
type of reward.
Prior research has suggested that with chronic use, dependent drug users become increasingly
more sensitive to the reinforcing properties of their drug of choice, but relatively less
sensitive to other more generic rewards, such as money or food (Aigner & Balster, 1978;
Opris et al., 2009; Woolverton & Anderson, 2006). This distinction has been used in the
debate over the two different reward dysfunction theories involved in stimulant-dependence,
the reward deficiency argument (Bjork et al., 2008; Blum et al., 2000) and the theory of
incentive sensitization (Robinson & Berridge, 1993). As reported in the General Introduction,
in the former, individuals at risk for substance dependence are thought to have a generally
under-active reward system, underpinned by a decrease in dopamine D2 receptor availability
in the fronto-striatal circuitry. This can then lead to ‘self-medication’ with drugs or other
types of dopamine-boosting reward substances, such as high-caloric foods or sex. Further
105
Chapter 5. Reward valuation in addictive disorders
down-regulation of dopamine receptors in the striatum and prefrontal cortex due to the
chronic effects of drug use can exacerbate the problem, resulting in greater amounts and
more potent substances being required to activate the mesolimbic reward system. If this were
the case, SDIs would be thought to show a general decrease in responding to universal
reinforcers, both compared with control individuals and in contrast to drug-related cues.
In incentive sensitization, individuals at risk for substance dependence are thought to have a
general over-arousal to rewarding stimuli, leaving them particularly susceptible to the
reinforcing properties of activities that tap into the mesolimbic dopamine circuitry. These
individuals are generally more impulsive and are drawn towards acutely rewarding stimuli,
exhibiting traits of ‘waiting’ impulsivity (Dalley et al., 2011), including steeper delayed
reward discounting. If this were the case, SDIs would demonstrate heightened responsivity to
all reinforcing cues compared to control individuals. Results from the previous chapter
provide support for this theory, as the dependent participants exhibited an increase in
responding to both types of reward cues – drugs and money – particularly compared with
their biological siblings and recreational users of cocaine. However, a potential caveat of that
prior study is the possibility that the money cues may also have attained drug-related salience
for dependent individuals through the continual purchasing and procurement of illicit
substances. Thus, it is also important to look at more primary unconditioned reinforcers that
have no associations with drug use.
The question then arises as to how responding to various different types of rewards – drug
and non-drug – might vary within an individual with stimulant dependence, and how this
might differ from the responses of non-dependent individuals.
106
Chapter 5. Reward valuation in addictive disorders
The majority of prior research on incentive motivation in SDIs has either examined responses
only to drug-related cues (Goldstein, Tomasi, Rajaram, et al., 2007; Volkow, Wang, Fowler,
et al., 2008; Volkow et al., 2006), or they have explored SDIs’ reactivity to a single other
type of salient image (i.e., money, erotic cues or non-specific affective images) (Aguilar de
Arcos et al., 2008; Aguilar de Arcos, Verdejo-Garcia, Peralta-Ramirez, Sanchez-Barrera, &
Perez-Garcia, 2005; Asensio et al., 2010; Beck et al., 2009; Bjork et al., 2008; Gerra et al.,
2003; Goldstein, Tomasi, Alia-Klein, et al., 2007; Goldstein et al., 2007; Wexler et al., 2001;
Wrase et al., 2007). Very few of these studies have directly compared responding to drugrelated cues with that of other types of more organically rewarding stimuli (Garavan et al.,
2000; Goldstein et al., 2009). However, one good example of such research is Garavan and
colleagues’ study comparing neural responses to an explicit sex film and a film depicting
cocaine use (Garavan et al., 2000). Cocaine-dependent individuals exhibited significantly
greater activation for the cocaine film, particularly in reward processing areas such as the
anterior cingulate cortex and ventral striatum. Conversely, healthy control participants were
significantly more aroused by the sex film, both in contrast to the drug film and compared
with the cocaine users’ reactions. While the areas of activation in response to the sex film
were similar in both subject groups, as well as overlapping with the activated areas in
cocaine-dependent individuals viewing the drug film, the control individuals demonstrated
more widespread global activity, as well as significantly greater levels of activation in these
regions. This suggests that ordinarily salient stimuli are still excitatory for SDIs and activate
the same reward system as cocaine cues, but to a lesser degree and not to the same spatial
extent as for drug-naïve controls. Therefore, this study suggests that reward processing in
SDIs, though similar for both cocaine and other salient stimuli, appears to be blunted in
response to non-drug reward cues.
107
Chapter 5. Reward valuation in addictive disorders
Still, it is difficult to discern whether stimulant-dependent individuals’ abnormal reward
responses are due to a pre-morbid system abnormality or are a direct result of the effects of
stimulant use on the mesolimbic dopamine system. In the previous chapters, we aimed to
address this question by testing the non-dependent biological siblings of SDIs and
recreational users of cocaine in an attempt to identify possible risk and protective factors
involved in dependence. In the current section we will explore this question by comparing
SDIs’ responses to different types of rewards with those of a group of individuals who
potentially qualify as having another kind of addictive disorder, but without the external
elevation of striatal dopamine levels caused by stimulant drugs: obese individuals with binge
eating behaviours (OBE).
Parallels between the diagnostic pathology, as well as underlying neurobiological
abnormalities, have been made between SDIs and OBEs, suggesting that the latter may have
a type of ‘food addiction’ (Davis & Carter, 2009). Binge eating is defined as: “recurring
episodes of eating, in a discrete period of time, an amount of food that is definitely larger
than most people would eat during a similar period of time [with a]…lack of control during
the episodes” (American Psychiatric Association, 2000). The Diagnostic and Statistical
Manual of Mental Disorders’ criteria for binge eating disorder are reminiscent of those for
drug dependence, including an escalation and loss of control over consumption of the
substance, and a continuation of the behaviour despite personal distress, and health, social,
legal or financial problems. Neurocognitive similarities also abound, with shared dysfunction
identified in fronto-striatal circuitry involving dopamine and opioid neurotransmitter systems
in both groups (Baldo & Kelley, 2007; Volkow, Wang, Fowler, et al., 2008; Wang et al.,
2004). Highly palatable foods, such as those high in fat and sugar, also tap into the same
108
Chapter 5. Reward valuation in addictive disorders
mesolimbic dopamine system as drugs of abuse, implicating dysfunction in this area in both
conditions (Rada et al., 2005; Small et al., 2003).
By examining this separate set of individuals who theoretically have an alternative type of
addictive disorder, it will be possible to control for the potential neurotoxic effects of longterm stimulant use on the mesolimbic dopamine reward circuitry. Additionally, measuring
responses to a variety of different types of incentive stimuli, including general and drugspecific ones, will enable a differentiation between the two theories of reward processing by
testing whether SDIs and OBEs show a general over-activation and arousal in response to
any kind of rewarding stimuli, or if they demonstrate atypical responding only to their
particular substance of choice.
In the current study, a novel reward incentive task was administered to assess responses to a
variety of different salient stimuli, including cocaine and alcohol drug cues, money, sweet
and savoury high caloric foods, erotic images and pleasant nature scenes. If a system-wide
down-regulation in the mesolimbic dopamine circuitry were present, we would predict that
SDIs and OBEs would exhibit diminished responses for conventionally rewarding stimuli,
represented by slower reaction times, but have faster responses to cues of their ‘drug of
choice’. Conversely, an up-regulation of the dopamine receptors would hypothetically result
in all salient stimuli carrying greater incentive value and eliciting quicker responses.
Additionally, by testing OBEs’ responses and comparing their reactions to food cues with
SDIs’ responses to cocaine stimuli, we would be able to more confidently establish whether
these impairments were due to the dopaminergic effects of the drugs themselves. Finally,
similar decreases in arousal to classically motivating images, both compared with healthy
109
Chapter 5. Reward valuation in addictive disorders
controls and against each population’s substance of choice, would further support the notion
of addiction spectrum disorders.
2. Materials and Methods
2.1 Participants
Fifty-four individuals from three different participant groups were recruited: cocainedependent individuals (n=11), obese persons with binge eating behaviours (n=23), and
healthy control volunteers (n=20). All participants were between the ages of 20 and 65, able
to read and write in English, and provided written informed consent for participation.
Stimulant-dependent participants were recruited from a concurrently running study on
cocaine-dependent men (NREC10/H0306/69, PI: KD Ersche). At the end of this initial
testing session, the men were offered the opportunity to stay for an additional half hour to
complete another computerized test (the reward valuation task) and some follow-up
questionnaires. Eleven out of the thirty men approached agreed to stay and take part. These
men all met DSM-IV-TR criteria for cocaine dependence and all tested positive for stimulant
drugs at the onset of the study testing day, confirming use within the last 72 hours; none were
actively seeking treatment for cocaine dependence.
Participants with binge eating behaviours were recruited from a subject pool at
GlaxoSmithKline from a previous investigation on obese individuals with binge eating
behaviours. Binge eating was initially assessed using the Binge Eating Scale (BES)
(Gormally, Black, Daston, & Rardin, 1982) for inclusion in the original GlaxoSmithKline
study and participant pool. This questionnaire, along with other measures of binge and
disordered eating behaviours, was re-administered upon enrolment in the current
110
Chapter 5. Reward valuation in addictive disorders
investigation to confirm presence of binge eating. Twenty-three individuals from the
participant pool took part in the study; however, five were excluded from analyses as their
current BES scores did not meet criteria for binge eating disorder.
Control participants were recruited from the local community and had no history of drug or
alcohol abuse, or disordered eating. One individual was excluded from the analysis for
having a BMI over 30, which placed them into the obese range.
2.2 Behavioural Task
The reward valuation task is a novel computerized test modelled on the Monetary Incentive
Delay task (Chapter 4) (Knutson et al., 2000). Participants were first primed for 3000 ms with
one of six different types of salient images (alcohol, cocaine, erotic pictures, high caloric
foods, money, pleasant nature scenes) or a neutral control image (furniture). After the prime,
a short jittered delay period from 500-1500 ms was presented, followed by a separate distinct
target cue to which the participants had to respond as quickly as possible by pressing the
‘Enter’ key (see Figure 5.1). Participants were given feedback for 1000 ms after each trial
and were rewarded based on their reaction times (RT). Responses under their mean RT
resulted in a reward of 20 pence; responses faster than one standard deviation below their
personal mean won them one pound. If their response was above the mean RT they won
nothing and were urged to try again. Each image category contained six different pictures and
presentation was randomized throughout the task. The entire image set was repeated four
times for a total of 168 trials. Participants were trained on the task with neutral cues before
beginning testing, from which their mean reaction time was determined to compare
responding during task performance.
111
Chapter 5. Reward valuation in addictive disorders
Prime
3000 ms
Anticipation
500-1500 ms
Cue
1000 ms
Blank
1000 ms
Feedback
1000 ms
You Win 1 Pound!!!
You Win 20 Pence!
Try Again!
Figure 5.1. Reward valuation task experiment design. In the task, participants were
first primed by a rewarding salient picture (alcohol, cocaine, erotic, food, money,
pleasant nature scenes) or a neutral image (furniture). They were instructed to respond
only when they saw the target response cue, which was presented after a jittered
anticipation phase ranging from 500-1500 ms. After the target cue was presented, they
had to press the ‘Enter’ key as fast as possible, and they were told that the faster they
responded the more money they could win.
112
Chapter 5. Reward valuation in addictive disorders
2.3 Self-Report Measures
After the task, participants were asked to rate one image from each of the different categories
in regards to pleasantness, arousal, wanting and liking using a series of visual analogue scales
– i.e. ranging from ‘Very pleasant’ to ‘Very unpleasant’. Participants completed a series of
questionnaires to assess impulsive and sensation-seeking personality traits, as these variables
have been linked to greater risk for addictive tendencies and are affected by the fronto-striatal
dopamine system (Barratt Impulsivity Scale (BIS-11); Zuckerman Sensation-Seeking Scale
(SSS-V)) (Patton, Stanford, & Barratt, 1995; Zuckerman, Eysenck, & Eysenck, 1978).
Participants were also administered several questionnaires asking about eating behaviours
and attitudes towards food (Eating Disorder Examination-Questionnaire (EDE-Q); Binge
Eating Scale (BES)) (Fairburn & Beglin, 1994; Gormally et al., 1982). The Beck Depression
Inventory (BDI-II) (Beck, Steer, Ball, & Ranieri, 1996) was administered to judge current
mood; education levels and the National Adult Reading Test (NART) (Nelson, 1982) were
used as a measure of IQ. Questions asking about the participants’ eating patterns and
preferred foods, drug and alcohol use, gambling behaviour and disposable income were also
included.
2.4 Analysis
Between-groups analyses for response times and subjective ratings were conducted using
general linear model (GLM) multivariate analyses. Age and gender were controlled for in
GLM analyses on response latencies, as these variables differed between groups and
significantly affected group performances on reaction times. Within-group repeated measures
analyses were conducted to compare responses to the different variables within each group.
Analysis of variance (ANOVA) was used to compare demographic and questionnaire data
between groups. Pearson’s correlation analyses were used to determine relationships between
113
Chapter 5. Reward valuation in addictive disorders
task performance and self-report data. Significance levels were set at p<0.05 and Tukey tests
were used in post-hoc analyses.
3. Results
3.1 Self-Report Data
Participants significantly differed on several demographic variables, including age, sex,
education levels and IQ scores (Table 5.1). Groups also differed on self-report assessments,
including measures of disordered eating behaviour, impulsivity and sensation-seeking.
Specifically, obese individuals with binge eating behaviours had significantly higher scores
on disordered eating measures than either of the other two groups. Both stimulant-dependent
and obese participants also had higher ratings of depression and impulsivity than controls;
however, obese individuals had significantly lower sensation-seeking scores than stimulantdependent and control participants.
114
Chapter 5. Reward valuation in addictive disorders
Table 5.1. Demographic information and self-report responses for 19 control
participants, 18 obese individuals with binge eating behaviour, and 11 stimulantdependent men
Demographics
Ageac
Sex (% male)bc
Educationab
NARTab
BMIac
Disposable
Income
Smoker (% yes)
THC (% yes)
Gamble (% yes)
Questionnaires
EDE-Qac
BESac
BDI-IIab
BIS-11ab
SSS-Vac
Control
Mean (SD)
OBE
Mean (SD)
SDI
Mean (SD)
28.47 (8.54)
47.4%
17.56 (2.43)
118.65 (5.70)
22.54 (2.46)
46.06 (10.92)
50%
12.89 (1.88)
111.18 (9.10)
35.47 (3.95)
35.11 (8.77)
100%
11.64 (2.25)
106.91 (10.02)
24.87 (3.06)
15.82
9.18
31.77
7.42
80.16
<0.001
0.010
<0.001
0.002
<0.001
£365.56
£670.59
£1030.00
3.04
0.058
36.8%
36.8%
15.8%
61.1%
27.8%
27.8%
81.8%
81.8%
40.0%
2.91
2.02
2.05
0.233
0.364
0.358
27.06 (20.36)
7.33 (3.72)
6.22 (4.52)
63.78 (10.29)
21.50 (5.86)
107.35 (46.66)
22.00 (6.62)
14.18 (8.78)
69.59 (7.97)
13.82 (5.81)
43.56 (48.70)
10.11 (10.32)
16.33 (13.22)
67.44 (8.78)
24.00 (9.32)
12.00
14.27
5.08
3.15
7.48
<0.001
<0.001
0.002
0.024
<0.001
F/χ2
P
a
Significant difference between control and obese individuals (OBE)
b
Significant difference between control and stimulant-dependent individuals (SDI)
c
Significant difference between OBE and SDI
National Adult Reading Test (NART), Body Mass Index (BMI), Eating Disorder
Examination-Questionnaire (EDE-Q), Binge Eating Scale (BES), Beck Depression
Inventory (BDI-II), Barratt Impulsivity Scale (BIS-11), Zuckerman Sensation-Seeking
Scale (SSS-V).
115
Chapter 5. Reward valuation in addictive disorders
3.2 Response Time Behavioural Analysis
Response times for each of the different conditions were compared between groups using a
general linear model multivariate analysis, controlling for age and gender as these variables
significantly related to response time differences on the task (Table 5.2; Figure 5.1). After
taking into account these variables, there was a significant difference between groups for
alcohol (F(2,43)=3.217, p=0.022), money (F(2,43)=3.340, p=0.018), and pleasant nature
scenes (F(2,43)=2.806, p=0.038). Reaction times for food stimuli differed at p<0.10
(F(2,43)=2.324, p=0.072). However, in post-hoc analyses, significant differences only
emerged between groups for the food condition, with OBE participants responding
significantly slower than both controls (p=0.036) and SDIs (p=0.046).
Response times for the different stimuli were also compared within each participant group
using GLM repeated measures analyses. For controls, there were no significant differences in
response times between the different conditions at p<0.05, however, several trended towards
significance at p<0.10. This primarily consisted of slower response times for the neutral
images (compared to alcohol p=0.083, food p=0.058 and money p=0.064). There were also
slower times for the cocaine compared with the food condition (p=0.080). Stimulantdependent participants were significantly faster on the money condition than for all other
reward images (compared to cocaine p=0.003, erotic p=0.012, nature p=0.006 and neutral
p=0.007; trending at p<0.10 for alcohol p=0.058 and food p=0.085). There was also a
significant effect between food and nature images (p=0.048), food and cocaine images
(p=0.038), and food and neutral images at p<0.10, with participants responding significantly
faster on food images in both contrasts. For the OBE group there were no differences
between response times in any of the conditions, except for a difference at p<0.10 between
nature and neutral images.
116
Chapter 5. Reward valuation in addictive disorders
Figure 5.1. Mean response times for each condition by group. Across the board, obese
individuals with binge eating behaviour (OBE) were slower at responding than controls
and stimulant-dependent individuals (SDI). There were no differences between controls
and SDIs on their response times for any condition. Within the groups, SDIs were
significantly faster on the money condition and to a lesser degree in response to food
cues, while the control participants were slower overall to the neutral images. The OBE
participants did not differ within their responses to the different stimuli.
117
Chapter 5. Reward valuation in addictive disorders
Table 5.2. Reaction times for reward image conditions and differences between groups
controlling for age and gender
Control
Mean (SD)
Behavioural Results
Alcohol*
237.58 (36.36)
Cocaine
244.94 (41.66)
Erotic
238.18 (37.93)
Food
234.34 (33.60)
Money*
237.29 (31.43)
Nature*
242.93 (35.28)
Neutral
247.65 (37.15)
OBE
Mean (SD)
SDI
Mean (SD)
F
P
263.64 (32.97)
260.38 (33.91)
261.74 (28.39)
268.86 (41.01)
263.41 (44.45)
265.77 (41.05)
256.66 (30.31)
241.81 (30.67)
250.48 (33.38)
247.15 (39.75)
239.32 (32.88)
230.59 (32.33)
250.01 (28.76)
251.21 (40.84)
3.22
1.53
1.70
2.32
3.34
2.81
0.75
0.022
0.211
0.168
0.072
0.018
0.038
0.562
* Response times significantly differed between groups at p<0.05
3.3 Subjective Ratings
On the subjective ratings, there were group differences for cocaine (F(2,42)=20.451,
p<0.001), erotic (F(2,42)=3.981, p=0.008), sweet food (F(2,42)=3.378, p=0.017), and money
images (F(2,42)=3.336, p=0.018). The differences for cocaine and erotic trials were driven
by the SDIs judging the images significantly more positively than the other two groups
(cocaine p<0.001; erotic p=0.002). However, for the sweet food images the group differences
were driven by the SDIs rating the images less highly than the other groups (p=0.039). There
were no differences between groups for savoury foods. Finally, ratings for the money
condition also significantly differed, this time with the control participants reporting that they
liked the images less than the other two groups (p=0.034).
3.4 Correlation Analyses
Age significantly correlated with reaction times for all conditions except for food and neutral
images. Self-reported discretionary income did not correlate with responses to any condition
118
Chapter 5. Reward valuation in addictive disorders
and most notably did not correlate with response times for monetary rewards. BMI and
ratings of disordered eating behaviour did significantly positively correlate with reaction
times on all task conditions, which was most likely driven by the obese participants having
significantly slower responses across the board compared to the other two groups. Sensationseeking scores significantly negatively correlated with response times on alcohol, erotic,
food, money and nature images. However, sensation-seeking also significantly negatively
correlated with age, with younger individuals having higher sensation-seeking scores as well
as faster response times overall, so this may have been driven by an age effect.
Within each group individually, age correlated with all response times in control participants.
BMI also significantly correlated with reaction times on cocaine, food, money and nature
scenes in this group. Notably, age and BMI did not correlate in these participants, suggesting
that it is not a combined effect of age influencing BMI and response times. Sensation-seeking
did not correlate with responses in these individuals. In the OBE group, age did not correlate
with response times, however, BMI did still positively correlate with reactions to cocaine,
erotic, nature and neutral images. Disordered eating only significantly correlated with
responses to food cues, with greater dysfunction relating to slower responses. Interestingly,
neither eating disorder nor binge eating scales correlated with BMI, though the two measures
did highly correlate with one another. Binge eating scores also positively correlated with
alcohol and erotic reaction times in this group. BIS-11 impulsivity scores correlated with
reaction times for the food images, but not with any other condition. Sensation-seeking total
scores did not correlate with reaction times. In stimulant-dependent men, the only significant
correlation that existed was between responses to erotic images and sensation-seeking total
scores.
119
Chapter 5. Reward valuation in addictive disorders
4. Discussion
In the current investigation, it was predicted that stimulus cues that were judged as more
rewarding would elicit faster response times due to either an increase in incentive salience for
those stimuli or because of a more generalised decrease in responsiveness to other rewarding
cues. Specifically, we hypothesised that participants dependent on cocaine would respond
faster for cocaine cues compared to other types of rewarding images, as well as compared
with other participant groups, and that obese individuals with binge-eating behaviours would
respond relatively faster for food cues. Instead, there were few differences either between or
within the groups, and those that did emerge seemed to adhere more to a general pattern of
group differences in response times, potentially influenced by age and gender, rather than to
the groups’ particular reward preferences.
For example, money and food cues elicited the fastest times within SDIs, rather than cocaine
trials, and there were no significant differences between groups during the drug condition.
Conversely, OBE participants were slower on the majority of trials compared with the other
groups, and particularly in response to food cues. Overall, there were trends for both age and
BMI to be positively correlated with reaction times on all conditions, while sensation-seeking
scores corresponded to faster response times on most task conditions, though this effect may
also have been influenced by differences in age on the measure.
A potential alternative explanation for results on the task is that rather than being incentivised
by the targeted rewarding stimuli, the two clinical participant groups were actually distracted
by these specific images, resulting in slower reactions. This would be similar to the cocaineword Stroop effect seen in Chapter 3, with dependent individuals responding significantly
slower in the face of cocaine cues. However, within these groups responses to cocaine and
120
Chapter 5. Reward valuation in addictive disorders
food trials, respectively, did not differ from the reaction times for other reinforcers,
regardless of direction, though higher eating disorder scores in the OBE group did correspond
specifically to slower reaction times on food trials.
Interestingly, within the groups there were no correlations between subjective ratings for
each of the different stimuli and the reaction times elicited in response to them. This could
have one of two implications: 1) there is a ‘disconnection’ or uncoupling between the
participants’ subjective experiences and their behavioural responses, or 2) the current task
may not be sufficiently sensitive to allow a behavioural correlate of individuals’ subjective
experiences. The Monetary Incentive Delay task (MID), on which this paradigm is based,
rarely produces behavioural performance effects between groups as there is both a
hypothetical ceiling and floor effect for response times – i.e. an individual can only respond
so quickly on a measure of reaction times, and if a participant is too slow on the task they
will not win any money, so they must respond on most trials within a certain amount of time.
As such, the MID was designed to be administered using functional magnetic resonance
imaging (fMRI), as reported in the previous chapter. Whilst there are typically no
behavioural effects present in the task, activation in various reward regions, most notably the
ventral striatum and orbitofrontal cortex, often show differences in responding between task
conditions, as well as between participant groups. Therefore, it would be interesting to
develop the current reward valuation task into an imaging paradigm to determine if there
were differences in functional responses to the various stimuli, both within and between the
participant groups.
The poor matching between the different participant groups in key demographic variables,
particularly age and gender, which can significantly affect response times (Tun & Lachman,
121
Chapter 5. Reward valuation in addictive disorders
2008) places a major caveat on the conclusions of this study. Thus, the most stable group
effect seen on the task of OBEs responding significantly slower than the other groups may
have been driven by the significant increase in age and greater number of female members in
this group. However, it should be noted that after controlling statistically for both of these
variables there were still significant differences between the groups, with the OBEs again
responding more slowly. Additionally, within the control group, BMI significantly correlated
with response times on the task. It is possible that this trend could be indicative of an overall
flattening of reward responsivity corresponding to increased BMI, similar to the suggested
reward deficiency theory. Previous studies investigating responding to a food reward in obese
individuals demonstrated an increase in neural activation in fronto-striatal circuitry during
anticipation of a food reward, but a subsequent decrease in striatal activation upon receipt
(Stice, Spoor, Bohon, & Small, 2008; Stice, Yokum, Blum, & Bohon, 2010). It is possible
that the slower response times seen in the OBE group reflect a decrease in motivation for
general rewards; however, the absence of a difference in anticipatory excitation to the food
rewards somewhat refutes this theory. Again, it would be illuminating to administer the
reward valuation task during fMRI scanning to determine if there were any differences in
neural activation to the different reward stimuli.
On the other hand, the stimulant-dependent individuals were quicker to respond to the money
cues and subjectively rated some of the images more highly than the other two groups. This
finding is similar to that of the previous chapter, with stimulant-dependent individuals
showing a heightened arousal to both types of rewarding cues, potentially reflective of the
incentive sensitization theory. Thus, in the obese individuals there appeared to be a general
flattening of responses, suggestive of the reward deficiency theory, while the drug users
demonstrated behaviours perhaps more characteristic of incentive sensitization. It could be
122
Chapter 5. Reward valuation in addictive disorders
that the effect of protracted stimulant use on the dopamine reward system resulted in this
sensitization in the cocaine-dependent men, or these differences could still reflect underlying
disparities between the two groups.
These differences in response patterns between the SDI and OBE groups, the two potential
‘addiction spectrum’ cohorts, tends to contradict the notion of shared abnormalities or
underlying risk traits in reward responding in these individuals. However, there are still other
potential similarities between the two groups that provide a case for classifying them as
similar disorders along a spectrum. This includes shared structural and functional
abnormalities in self-control and reward processing regions, such as the lateral and medial
frontal cortices (Franklin et al., 2002; Pannacciulli et al., 2006; Volkow, Wang, Fowler, et al.,
2008; Volkow et al., 2001; Volkow, Wang, Telang, et al., 2008). In the following chapter we
explore this concept through an investigation of structural similarities between the groups.
123
Chapter 6. OFC loss with BMI and cocaine use
Chapter 6. Overlapping Decline in Orbitofrontal Grey Matter Volume
Related to Cocaine Use and Body Mass Index
1. Introduction
Although there was no evidence of behavioural similarities between stimulant-dependent and
obese individuals with binge eating behaviours in the previous chapter, there are still shared
structural brain abnormalities that may both underlie and be exacerbated by these two
conditions. Specifically, grey matter reductions in the OFC have been seen in obese (Gunstad
et al., 2008; Pannacciulli et al., 2006; Taki et al., 2008; Walther, Birdsill, Glisky, & Ryan,
2010) and cocaine-dependent individuals (Ersche et al., 2011; Franklin et al., 2002; Liu et al.,
1998; Matochik et al., 2003), and deficiencies in OFC glucose metabolism have been
reported in both groups (Volkow et al., 1991, 1993, 2001, 2009; Volkow, Wang, Telang, et
al., 2008). Additionally, corresponding impairments in associated cognitive processes, such
as executive functioning and affective decision-making, are also present in both conditions
(Davis, Levitan, Muglia, Bewell, & Kennedy, 2004; Ersche, Clark, et al., 2006; Gunstad et
al., 2007; Verdejo-Garcia et al., 2006).
As discussed in the General Introduction, the orbitofrontal cortex (OFC) is critically involved
in integrating emotion and behaviour, as in cases of reward valuation (Bechara et al., 2000;
Levy & Glimcher, 2012). These abilities comprise goal-directed behaviour, actions carried
out to achieve a certain objective (Balleine & O’Doherty, 2010). Dysfunction in the OFC can
impair these abilities, resulting in behaviour that becomes compulsive, maintained despite
attempts to stop or a shift in desired outcome. The OFC is also crucially implicated in the
gustatory cortex, providing evaluative judgments on taste (Rolls, 2000). This overlap
124
Chapter 6. OFC loss with BMI and cocaine use
between reward valuation and motivated behaviour makes the OFC a key target for
impulsive/compulsive disorders, like drug addiction and, more recently, compulsive overeating leading to obesity, as in cases of binge eating. Both conditions can be thought of as
behaviours that began as hedonically motivated actions but devolved into undesirable habits,
and as such, both have been linked to variations in this region.
These neurobiological commonalities, particularly in this key area of the OFC, have led to
the discussion of classifying over-eating leading to obesity, as in instances of binge eating, as
an addictive disorder (Davis & Carter, 2009) – though this is still considered to be a
contentious proposal (Ziauddeen & Fletcher, 2013). However, despite the reported overlap
between these behavioural and structural deficits, no direct comparisons of cortical volume
between the two conditions have been made. In the current investigation, we used voxelbased morphometry to contrast structure volume in the OFC relating to both body mass index
(BMI) and years of cocaine use in a previously collected dataset of stimulant-dependent and
matched control participants with BMIs ranging up to obese. We hypothesised that there
would be overlapping regions of OFC volume decline corresponding to both years of cocaine
use and BMI, both within the stimulant-dependent group, as well as between stimulantdependent and control individuals.
2. Materials and Methods
2.1 Participants
We compared structural MRI scans of a previously published sample of 60 healthy control
volunteers with BMIs ranging from normal weight to obese (17.8–33.5, mean=25.5), and 60
cocaine-dependent individuals with a similar range of BMIs (16.6–32.7, mean=24.2) and a
span of 31 years of stimulant use (mean=9.8 years) (Ersche, Jones, Williams, Robbins, &
125
Chapter 6. OFC loss with BMI and cocaine use
Bullmore, 2013; Ersche et al., 2011). Cocaine-dependent individuals were recruited from
local drug treatment services and satisfied criteria for stimulant dependence according to the
DSM-IV-TR (American Psychiatric Association, 2000). Control participants were free from
any neuropsychiatric clinical history and were recruited from the community. Control and
cocaine-dependent participants did not differ in terms of age, gender or mean BMI, though
there was a significant difference in years of education (Table 6.1). Body-weight from two
controls was missing, resulting in 118 total participants.
Table 6.1. Demographic information and group differences for 60 cocaine-dependent
individuals and 58 healthy control volunteers
Demographics
Gender (n male: %)
Age (years)
NART (score)
Education (years)*
Body mass index
Control
Mean (SD)
Cocaine Dependent
Mean (SD)
T/χ2
P
46 (76.7%)
32.3 (8.3)
110.0 (7.0)
12.3 (1.6)
25.53 (3.3)
53 (88.3%)
32.5 (8.5)
109.5 (6.9)
11.5 (1.7)
24.22 (4.3)
2.83
-0.12
0.36
2.78
1.83
0.148
0.905
0.716
0.006
0.070
To address the current hypothesis, grey matter data were newly processed by P.S. Jones using
voxel-based morphometry (VBM) analysis to take into account the effects of BMI and
stimulant use on brain volume. Groups were analysed separately to account for previously
discovered differences in brain structure (Ersche et al., 2011), and analyses were focused on
the a priori designated region of the OFC. BMI scores were regressed onto OFC volume in
all participants, and years of cocaine use were regressed onto OFC grey matter in the cocaine
users. We compared affected areas, identifying shared regions of decline associated with both
years of cocaine use and BMI, as well as comparing OFC volume between the two groups.
126
Chapter 6. OFC loss with BMI and cocaine use
2.2 Imaging Procedures
T1 structural MRI data were obtained using a 3T Siemens Magnetom Tim Trio scanner
(Siemens Medical Solutions, Erlangen, Germany) at the Wolfson Brain Imaging Centre,
University of Cambridge, UK. One hundred seventy-six scans were collected using a
magnetically prepared rapid acquisition gradient echo sequence (MPRAGE) with the
following parameters: thickness=1 mm, repetition time=2.30 ms, echo time=2.98 ms,
inversion time=900 ms, flip angle=9˚, field of view=240 x 256.
Grey matter volume maps from each participant were processed using FSL VBM 1.1
(http://www.fmrib.ox.ac.uk/fsl/fslvbm/index.html). Images were tissue-segmented using
FAST (Zhang, Brady, & Smith, 2001), and resulting grey matter volumes were aligned to
standard MNI stereotactic space using FLIRT affine registration, followed by non-linear
registration using FNIRT (Andersson, Jenkinson, & Smith, 2007a, 2007b; Jenkinson,
Bannister, Brady, & Smith, 2002; Jenkinson & Smith, 2001). Images were averaged to create
a grey matter template, and each image was then non-linearly re-registered to the group map.
Images were smoothed using a full-width half-maximum (FWHM) 2.3 mm Gaussian kernel.
Images were analysed in CamBA 2.3.0 (http://www-bmu.psychiatry.cam.ac.uk/software/;
Cambridge, UK) using a restricted search volume mask of the orbitofrontal cortex taken from
Hammer’s probabilistic atlas (Hammers et al., 2003). Body mass index values for each
participant were regressed onto the grey matter volume maps, searching for voxels that were
significantly associated with BMI. Years of cocaine use were similarly regressed on grey
matter maps in cocaine-dependent individuals. Association was determined using clusterlevel spatial permutation testing (Suckling & Bullmore, 2004), and significance levels were
set at p~0.0002, controlling for multiple comparisons. These results were exported to
127
Chapter 6. OFC loss with BMI and cocaine use
Statistical Package for Social Sciences (SPSS v.19) and compared post-hoc using Pearson
correlation coefficients and independent samples t-tests for group comparisons of grey matter
volume loss; significance levels were set at p<0.05.
3. Results
In control participants four significant clusters negatively correlated with BMI bilaterally
throughout the OFC (r=-0.688, p<0.001; Figures 6.1 and 6.2), while in cocaine users two
regions in the right OFC negatively correlated with BMI (r=-0.624, p<0.001; see Appendix
D, Table S6.1 for coordinates). These areas overlapped in the right middle orbitofrontal
gyrus, superior orbitofrontal gyrus and gyrus rectus (Brodmann Areas 10, 11, 25).
Additionally, OFC volume negatively correlated with years of cocaine use in cocainedependent individuals in two bilateral clusters (r=-0.705, p<0.001). The cocaine-related
decline in grey matter overlapped with BMI-related reductions bilaterally in the superior
orbitofrontal gyrus, middle orbitofrontal gyrus and left gyrus rectus (Brodmann Area 11).
128
Chapter 6. OFC loss with BMI and cocaine use
-28
-24
-20
-16
-12
-8
Figure 6.1 Overlapping regions of orbitofrontal cortex (OFC) grey matter volume
decline among healthy control and cocaine-dependent individuals, corresponding to
increased body mass index (BMI) and years of cocaine use. Magenta – clusters for
reduced grey matter with BMI in controls; Blue – clusters for reduced grey matter with
BMI in cocaine users; Green – clusters for reduced grey matter with years of cocaine
use; Cyan – overlapping voxels from magenta and green.
129
Chapter 6. OFC loss with BMI and cocaine use
Grey matter volume related to body mass index
A)
Grey matter volume related to years of use
B)
Figure 6.2. A) Negative correlation between OFC grey matter volume and BMI in
control and cocaine-dependent individuals. Groups were analysed separately and then
overlaid to demonstrate similar decreases in OFC volume in association with increased
BMI. B) Correlation between OFC volume reductions and years of cocaine use.
130
Chapter 6. OFC loss with BMI and cocaine use
In cocaine users, we observed a significant correlation between grey matter volume decline
associated with increased BMI and with years of cocaine use (r=0.354, p=0.006; Figure 6.3);
however, cocaine use and BMI itself did not correlate significantly (r=-0.127, p=0.338). This
suggests an overlapping reduction related to both increased BMI and prolonged cocaine use,
their effects on the brain potentially impacting the same regions.
Figure 6.3. Correlation between OFC grey matter volume associated with years of
cocaine use and OFC volume related to BMI in cocaine-dependent individuals. As there
was no direct correlation between years of cocaine use and BMI, a significant positive
correlation suggests there is a relationship between the effect these two variables (years
of cocaine use and BMI) have on the OFC independently of one another.
131
Chapter 6. OFC loss with BMI and cocaine use
When directly comparing total OFC volume between the two groups, cocaine-dependent
individuals had significantly less grey matter than controls (t=7.694, p<0.001). This effect
remained significant after including only overweight controls (n=34) and lean cocainedependents (n=37; t=4.097, p<0.001). However, the steepness of the linear regressions
between the key variables (grey matter and BMI, and grey matter and cocaine years) did not
significantly differ. This suggests that while the most pronounced effects on orbitofrontal
structure were associated with cocaine use, this is perhaps more indicative of premorbid
differences rather than an accumulated effect of stimulant use, though other studies have
shown evidence to the contrary (Ersche, Williams, Robbins, & Bullmore, 2013; Porrino et
al., 2007).
4. Discussion
Years of cocaine use and increased BMI both negatively correlated with grey matter volume
in the OFC in healthy control and cocaine-dependent individuals. Additionally, select OFC
regions showed decreases relating to both variables, suggesting an overlap between volume
reductions associated with over-consumption of food and drugs of abuse. This decline may
reflect underlying differences in the OFC’s role in goal-directed behaviour present in both
groups, as both begin as motivated behaviours (taking drugs or eating high-caloric foods for
hedonic reasons), but in some instances can become compulsive, habitually maintained after
the behaviour is no longer desired (Balleine & O’Doherty, 2010). Both cues for high-caloric
foods and drugs of abuse activate similar regions in dependent drug users and obese
individuals (Tang, Fellows, Small, & Dagher, 2012), and the OFC is activated in terms of
reward responding to multiple types of stimuli, including monetary reward, food and drugs of
abuse during experiences of craving (FitzGerald, Seymour, & Dolan, 2009; Garavan et al.,
132
Chapter 6. OFC loss with BMI and cocaine use
2000; Kim, Shimojo, & O’Doherty, 2011; Levy & Glimcher, 2012). Associated impairments
in inhibitory control and decision-making may also be implicated as consequences of OFC
volume reduction.
It should be noted that while many studies show similar BMI-related declines in grey matter
volume, other investigations report no differences in brain structure associated with obesity.
For example, one longitudinal study of middle-aged to elderly adults found no additional
changes in grey matter volume associated with an increase in BMI (Driscoll et al., 2012).
Another investigation actually reported an increase in grey matter in the medial orbitofrontal
cortex and ventral striatum in obese individuals (Horstmann et al., 2011). Other research has
suggested that the changes seen in grey matter volume are related to fat-free mass, rather than
body fat percentage (Weise, Thiyyagura, Reiman, Chen, & Krakoff, 2013). There are also
notable gender differences seen in prior research on the topic, with some studies showing
associations between BMI and brain volume only in male participants (Taki et al., 2008),
while other investigations report these results solely in women (Walther et al., 2010). Finally,
many of these studies have been conducted in elderly individuals, particularly those already
showing evidence of cognitive impairment or Alzheimer’s disease (Ho et al., 2010; Raji et
al., 2010). This is a confounding variable and suggests that the effect of BMI on the brain
may be greatest in patients already experiencing cognitive and structural decline; however,
recent investigations into obese children and adolescents have also reported similar grey
matter volume decline (Alosco et al., 2014; Yokum, Ng, & Stice, 2012). Thus, with the
recent proliferation of research into the topic, there are conflicting reports regarding the
extent and potential mechanisms behind these findings, and more research is needed on the
topic to better examine these findings.
133
Chapter 6. OFC loss with BMI and cocaine use
Importantly, the participants in the current study were not recruited on the basis of their
BMIs, and their range of normal weight to obese was representative of the general
population. They were also healthy individuals without any pre-existing physical,
psychological or cognitive impairments. This demonstrates that the relationship with grey
matter volume is present even in persons of normal or slightly elevated body weight, and is
not limited to obese individuals.
While these findings provide some support for the argument that some individuals who
overeat may display characteristics similar to those of addictive disorders, this comparison
does have its limitations and is currently under debate (Ziauddeen et al., 2012).
134
Chapter 7. Discussion
Chapter 7. General Discussion
1. Summary
The research presented in this thesis provides evidence for both neurocognitive risk and
protective factors involved in addictive disorders, focusing on the conditions of stimulant
dependence and obesity resulting from binge eating behaviour. These include differences in
inhibitory control and reward valuation in the face of both general and substance-specific
stimuli. Behavioural abnormalities also corresponded to variations in structure and function
in regions of the brain implicated in cognitive control and reward processing, particularly in
areas of the prefrontal cortex, such as the inferior frontal gyrus (IFG) and medial
orbitofrontal cortex (OFC).
The first half of this thesis centred on stimulant addiction, researching both dependent and
recreational users of cocaine, as well as the non-dependent biological siblings of addicted
individuals using endophenotype research. These studies were conducted in an attempt to
identify underlying risk factors for dependence, compared with more long-term effects of
drugs on the brain and behaviour, as well as potential protective or resilience traits against its
development. This research was aimed at answering the chicken-or-egg question of which
abnormalities typically seen in cases of substance dependence are predating neurocognitive
differences that can place an individual at a heightened risk for dependence, and which are
the result of chronic drug use.
The study presented in Chapter 2 investigated executive control and correlative neural
activation in stimulant-dependent individuals (SDIs), their non-dependent biological siblings
135
Chapter 7. Discussion
and unrelated healthy controls using the classic colour-word Stroop task. A decline in
cognitive efficiency resulting in significantly slower global response times and relating to
decreased volume in the IFG was seen in both SDIs and their siblings, suggesting that these
impairments are not the result of drug abuse but are instead underlying risk factors for
addiction. However, there was an additional decrease in cortical volume in the dependent
individuals, indicating a potential exacerbating effect of stimulant use on this pre-existing
trait.
The lack of difference in activation during the task between SDIs and controls was
surprising, particularly when compared with the significant decrease in activity seen in the
sibling participants. One possible explanation is that the SDIs may have potentially initially
experienced decreases in activation similar to their non-dependent siblings, but that either the
acute or the chronic effects of stimulant drugs on the brain have affected their neural
responding. This would imply that the decreased IFG activation was, in fact, an
endophenotype of dependence, perhaps reflecting poor cognitive efficiency. However, this
interpretation is speculative and should be taken with caution.
In Chapter 3, a study of dependent and recreational stimulant users and healthy control
volunteers revealed that stimulant-dependent individuals were significantly more impaired on
the cocaine-word Stroop – a task of cognitive inhibition involving cocaine cues –
demonstrating significant attentional bias to the salient cocaine words, while recreational
users showed no signs of impairment on the task. Additionally, recreational individuals
displayed significantly different patterns of activation during performance, with a decrease in
prefrontal and orbitofrontal cortical activity compared with SDIs. One possible interpretation
for these findings is that cognitive control was more efficient in recreational users, with
136
Chapter 7. Discussion
resistance to distraction by cocaine cues being less effortful in these individuals. Meanwhile
in the SDIs, performance of the task may have required greater recruitment of neural
resources, as the objective was significantly more difficult to execute.
The relative decrease in OFC activity in the recreational users could also be linked to an
absence of craving in these individuals. The OFC has been implicated in reward valuation
and motivation for a stimulus (O’Doherty, 2002), and craving for cocaine cues has been
linked to increased OFC activity (London et al., 2000; Wang et al., 1999). A higher value
placed upon cocaine for dependent users could result in greater activation in this region, and
activity in the area has been correlated with self-report craving scores in dependent users
(Grant et al., 1996; London et al., 2000; Volkow et al., 1991). The relative decrease in OFC
activity in recreational users suggests they are not experiencing cue-induced craving for
cocaine in a similar manner as the SDIs and instead may have some resilience against these
types of addictive behaviours.
Decision-making abilities have also been linked to the OFC, with patients with ventromedial
prefrontal cortex lesions exhibiting severe impairment on a decision-making task (Bechara et
al., 1994). Dependent drug users have shown similar dysfunction on decision-making
assessments, potentially due to drug-induced abnormalities in this region (Bechara et al.,
2000; Ersche et al., 2005). The under-activation of recreational users in this area, combined
with larger structural volumes, suggests that they may exert greater self-control and
demonstrate improved decision-making abilities, particularly compared to dependent
individuals. This could be particularly applicable in situations involving ‘myopia for the
future’ where long-term consequences must be weighed against short-term rewards. Drug
users are typically impaired when making these types of decisions (Kirby, Petry, & Bickel,
137
Chapter 7. Discussion
1999), particularly when drug use is involved. However, the recreational users’ anecdotal
ability to prioritize work or school above drug taking suggests they may have improved selfcontrol and future planning.
Finally, the OFC is commonly implicated in habitual repetitive behaviours characteristic of
obsessive-compulsive disorder (OCD), and impairment in this region in drug-dependent
individuals is thought to contribute to the compulsive drug-seeking and taking behaviours
present in addiction (London et al., 2000). Several studies investigating patients with OCD
have demonstrated an increase in glucose metabolism in the OFC, indicating hyperactivity in
this region (Baxter et al., 1988; Menzies et al., 2008). The contrasting decrease in activity in
the recreational users in this area may act as a potential protective factor against compulsive
behaviours (Ersche, Jones, Williams, Smith, et al., 2013), thus making them less likely to
develop drug dependence.
Building on these findings, in Chapter 4, we investigated all four participant groups – SDIs,
their non-dependent biological siblings, recreational users of cocaine and unrelated healthy
control volunteers – on a separate neurobiological construct, assessing differences in
responding to both universal (money) and drug-specific reward cues. We examined how
reactions to these two stimuli related to both stimulant use and a risk for dependence. Rather
than finding a potential endophenotype for dependence, as reported in previous investigations
with these groups (Ersche, Jones, et al., 2012; Ersche, Turton, et al., 2012), the results of the
current study pointed towards consequential effects of drug exposure on brain responses to
reward, particularly in the OFC and striatum. Both SDIs and recreational users displayed
increased BOLD activation in response to drug cues compared with the sibling and control
participants; however, there were additional increases in striatal activity in the SDIs that were
138
Chapter 7. Discussion
not present in the recreational users. This may be indicative of an increased exposure effect in
the SDIs resulting in greater anticipatory excitation, reflective of the findings presented in
Chapter 3 of greater attentional bias to cocaine cues.
Curiously, there was a difference between the recreational and dependent individuals in the
area that was most activated in response to anticipation for drug reward. The SDIs showed a
greater response in the striatum compared with all the other participant groups, including the
recreational individuals, whereas the recreational users showed the greatest difference in the
OFC. This may be suggestive of a difference between the quality of drug use between the
two groups, or this may be representative of the difference in exposure between these
participants. One possibility is that increased activation in the striatum in response to drug
cues is reflective of greater compulsive-like tendencies in regards to drug taking. The
putamen is strongly implicated in habit formation (Yin & Knowlton, 2006), and SDIs
showed significantly increased activation in this area in response to anticipation for drug
reward, suggesting they have a compulsive or habitual response to these stimuli that is not
present in the recreational cocaine users. Conversely, activation in the OFC in response to
drug cues may be reflecting an evaluative response to these stimuli (Levy & Glimcher, 2012),
which both groups of drug users would presumably rate more highly.
In addition to the differences in responding to drug cues, there was also an exposure effect in
regard to monetary rewards. The sibling and control individuals displayed greater activation
in the OFC in response to successful feedback on the money trials compared with both
dependent and recreational users. This suggests there may be a partial flattening to non-drug
rewards in the stimulant users, potentially reflective of the reward deficiency theory of
addiction, with a ‘hijacking’ of the reward system by drugs of abuse. However, this exposure
139
Chapter 7. Discussion
effect was only present in response to monetary reward receipt, not anticipation. This is
perhaps suggestive of the incentive salience theory of reward processing (Robinson &
Berridge, 1993), with anticipatory cues becoming ‘more valuable’ and eliciting a heightened
response compared with obtaining the reward itself.
One potential explanation for these differences in sensitivity to reward is an alteration in
dopamine receptor density in the mesolimbic system. A decrease in dopamine receptor
availability has been linked to a greater proclivity for stimulant drug use, with individuals
with fewer dopamine D2 receptors finding the acute effects of stimulants more enjoyable,
and thus reinforcing repeated use (Volkow et al., 1996, 1999). A previous investigation into
the relatives of alcohol-dependent individuals showed an increase in striatal dopamine
receptor density compared with control individuals without a family history of dependence
(Volkow et al., 2006). Thus it is possible that the sibling individuals in the current study have
a relative increase in striatal dopamine receptor density compared with their dependent
brothers and sisters, protecting them potential reinforcing properties of drugs of abuse.
However, we did not see any increases in striatal responding in the sibling participants, so
there does not appear to be support for this theory in the current findings.
In the second half of this thesis, research was extended to include an additional group of
individuals who, it has been argued, also fall within the spectrum of addictive disorders –
obese individuals with binge eating behaviours (Davis & Carter, 2009; Ifland et al., 2009;
Parylak, Koob, & Zorrilla, 2011; Pelchat, 2002). This cohort, who show some of the same
behavioural and neurobiological tendencies as individuals with substance dependence (Avena
et al., 2008; Volkow, Wang, Fowler, et al., 2008; Wang et al., 2004), was considered as a
potential comparison group for dependent stimulant users, as they maintain similar traits of
140
Chapter 7. Discussion
impulsivity, compulsivity and decreased executive function, but do not have any of the
additional changes in the fronto-striatal dopamine system caused by the use of stimulant
drugs. Thus, this group could potentially provide further insight into possible shared
underlying risk traits for maladaptive addictive behaviours and help to distinguish them from
the consequences of long-term drug use on the brain.
In Chapter 5, responding to cues of reward was again investigated, this time with a novel
group of cocaine-dependent men, as well as obese individuals with binge eating behaviours
(OBEs). Behavioural responses to a variety of rewarding stimuli were studied to provide a
more thorough investigation of differences in sensitivity to both generic and substancespecific reinforcers. This enabled us to address the two theories of disordered reward
responding in substance-dependent individuals: an overall under-active reward system
causing drugs of abuse to be deemed disproportionately reinforcing (Blum et al., 2000), or a
general increase in responsivity to rewarding stimuli, which is then transferred particularly to
drug-related cues and can hijack this system (Robinson & Berridge, 1993).
There were few significant differences in response times both between and within participant
groups to the different stimuli. Overall, the OBE participants trended towards displaying
slower reactions to the cues, particularly in response to food stimuli. Conversely, the SDIs
showed quicker response times, especially for the money cues. From these results, rather than
sharing similar abnormalities in reward processing systems, it appears that the two addiction
spectrum groups might each potentially fulfil one of the different theories on dysfunctional
reward valuation. The overall depressed responding to reward cues in the OBE group is
suggestive of the reward deficiency theory (Bjork, Smith, & Hommer, 2008; Blum et al.,
2000; Blum, Cull, Braverman, & Comings, 1996), which has been proposed previously to
141
Chapter 7. Discussion
explain some of the behavioural traits specifically involved in obesity (Davis & Fox, 2008;
Davis, Strachan, & Berkson, 2004). Conversely, based on results from Chapters 4 and 5, it
appears that stimulant-dependent individuals adhere more to the incentive sensitization
theory, displaying heightened sensitivity to several types of rewarding stimuli, including
those associated with drug use (such as stimulants and potentially monetary rewards) as well
as more general reinforcers.
However, it should be cautioned that the results from Chapter 5 are only suggestive, and
significant demographic differences between the groups, particularly age and gender, may
have accounted for the group effects. This included an overall increase in age in the OBE
group compared both to SDIs and controls, which was strongly associated with globalized
slower responding on the task.
In Chapter 6, these group comparisons were further explored by investigating how structural
differences in the key region of the orbitofrontal cortex – implicated in reward valuation and
decision-making – were associated with both cocaine use and body mass index (BMI). A
decline in OFC volume was associated with years of stimulant use, as reported in previous
investigations (Ersche et al., 2011; Franklin et al., 2002; Matochik et al., 2003; Sim et al.,
2007), as well as an increase in BMI in both cocaine-dependent men and healthy control
volunteers. Moreover, there was an overlap in the regions and specific voxels in which
declines were seen both between participant groups and associated with each condition.
This finding is the first to identify a specific overlap in areas affected by stimulant use and
weight gain, which potentially provides fodder for the argument of overeating leading to
obesity in some instances qualifying as an addictive disorder. However, it should be noted
that an overall greater decline was seen in cocaine-dependent men compared to the healthy
142
Chapter 7. Discussion
control volunteers, regardless of their BMI. This indicates that although there was an overlap
in brain volume trends between the two conditions, greater structural dysfunction was
associated with stimulant use.
While a decrease in grey matter volume has been shown to directly result from drug use
(Porrino et al., 2004, 2007), and despite the several studies showing a decrease in structural
integrity in the brains of obese individuals (Gunstad et al., 2008; Maayan, Hoogendoorn,
Sweat, & Convit, 2011; Pannacciulli et al., 2006; Taki et al., 2008; Walther et al., 2010), it is
as yet unknown whether these differences are caused by an increase in BMI or are underlying
abnormalities contributing to the condition. It is possible that predating decreases in
orbitofrontal volume in both groups led to a pattern of behaviour associated with heightened
risk for addictive behaviours, such as low self-control or a greater tendency towards habit
formation. Additionally, evidence of cognitive disruption in obese individuals, which would
be hypothesised to mirror that of substance-dependent individuals given some of their similar
structural abnormalities, has been conflicting. Some studies provided evidence for deficits in
executive functioning in obese individuals, such as decreased working memory or decisionmaking abilities; however, other investigations have shown no such dysfunction, or even an
improvement in performance on other cognitive constructs (Elias, Elias, Sullivan, Wolf, &
D’Agostino, 2003; Gunstad et al., 2008; Gunstad, Lhotsky, Wendell, Ferrucci, & Zonderman,
2010; Stanek et al., 2013). Thus, the functional impact of these structural differences, as well
as their origin, is still unknown.
2. Links to Personality Traits
Other research that has been referred to throughout this dissertation, and that I have
contributed to, reports on key personality traits involved in addiction in the four participant
143
Chapter 7. Discussion
groups examined in Chapters 2-4 (Ersche, Jones, Williams, Smith, et al., 2013). This paper is
included as Appendix E. Briefly, it was discovered that the SDIs and their biological siblings
had significantly higher rates of impulsivity and compulsivity than healthy control
volunteers, suggesting that these personality traits may serve as endophenotypes for stimulant
dependence. However, there was an additional increase in these qualities in the SDIs
compared with their siblings, implying an additional drug effect on these characteristics.
Conversely, sensation-seeking scores were elevated only in the SDIs and recreational users of
cocaine, suggesting that this trait may correspond to an increased likelihood to initiate drug
use. Recreational users were no different in rates of impulsivity and compulsivity than
control volunteers, potentially protecting them from transitioning to drug dependence (Figure
7.1).
These findings led us to believe that impulsivity and compulsivity are endophenotypic traits,
acting as underlying risk factors for stimulant dependence. Conversely, sensation-seeking
appears to be specific to the initiation of drug use, possibly representing a motivating quality
to first seek out experiences with stimulant drugs. The decrease in impulsive and compulsive
traits in the recreational users may have protected them from the development of dependence
from their casual drug-taking, while low sensation-seeking may have prevented the biological
siblings of dependent drug users from ever initiating drug use, thus protecting them against
the potential increased likelihood for a development of dependence had they started taking
drugs.
144
Chapter 7. Discussion
Figure 7.1. Group differences in the personality traits of impulsivity, compulsivity and sensation-seeking in the four groups of
stimulant-dependent individuals, their non-dependent biological siblings, recreational users of cocaine, and unrelated control
volunteers, as reported on in Chapters 2-4. Reproduced from (Ersche, Jones, Williams, Smith, et al., 2013).
145
Chapter 7. Discussion
These personality traits were also linked to the behavioural and neurobiological
characteristics examined in this dissertation. Decreases in grey matter volume in the inferior
frontal gyrus, as reported in Chapter 2, was associated with an increase cocaine-specific
compulsivity as measured by the OCDUS (right IFG: r=-0.297, p=0.056; left IFG: r=-0.308,
p=0.047), as well as compulsivity scores on the PADUA (Burns, Keortge, Formea, &
Sternberger, 1996), an assessment of general obsessive-compulsive behavioural traits (right
IFG: r=-0.237, p=0.05; left IFG: r=-0.297, p<0.01). Impulsivity, as measured by the BIS-11,
correlated with response times on the congruent condition of the colour-word Stroop
(r=0.249, p=0.003); and scores on the PADUA, BIS-11 and SSS-V sensation-seeking scale
all negatively correlated with activation in the right IFG during the group contrast on the
colour-word Stroop (r=-0.228, p=0.007; r=-0.271, p=0.001; r=-0.196, p=0.022, respectively),
such that higher scores on these measures was associated with a decrease in right IFG
activation on the task.
On the cocaine Stroop task, BIS-11 impulsivity scores were positively correlated with
response times on the cocaine word trials (r=0.280, p=0.002), as well as with interference
scores (r=0.193, p=0.040) and errors committed on the task (r=0.344, p<0.001). Impulsivity
scores were also correlated with activation during the cocaine Stroop task in the orbitofrontal
cortex/anterior cingulate cortex (r=0.218, p=0.019) and two clusters in the inferior frontal
gyrus (r=0.220, p=0.018; r=0.277, p=0.003). Sensation-seeking scores did not relate to
behavioural performance on the task, but there was a significant negative correlation between
sensation-seeking and activation in the right angular gyrus during the group performance
comparison (r=-0.205, p=0.028).
146
Chapter 7. Discussion
On the Monetary Incentive Delay task, anticipatory activation for the drug rewards in the left
middle cingulum significantly correlated with impulsivity and sensation-seeking (r=0.210,
p=0.013; r=0.213, p=0.011, respectively). BIS-11 impulsivity scores also related to increased
activity in the striatum during anticipation for drug rewards (r=0.174, p=0.040).
Behaviourally, there were no correlations between personality traits and performance on the
task, except for an increase in money won on the task relating to increased impulsivity
(r=0.260, p=0.002) and OCDUS cocaine compulsivity scores (r=0.272, p=0.040).
From this research it appears there are three crucial components in the development of
substance dependence: sensation-seeking and high reward sensitivity to facilitate and spur
initial experimentation with the drug; impulsivity and poor cognitive control resulting in the
continued and escalated use of the substance, despite potentially disadvantageous
consequences or a desire not to; and following from that, compulsivity leading to habitual use
of the drug even in the face of punishment, attempts to stop, and loss of the acutely rewarding
sensations first experienced. Accordingly, stimulant-dependent individuals met all of these
criteria, with high levels of sensation-seeking, impulsivity and compulsivity, as well as low
cognitive control. Their non-dependent biological siblings shared similar levels of
impulsivity, compulsivity and poor self-control, but crucially did not demonstrate the
sensitivity to reward or sensation-seeking traits to cause them to take the first step and initiate
drug use. Conversely, recreational users of cocaine had high levels of sensation-seeking and
in similar tendencies towards reward valuation as the dependent individuals, particularly
during anticipation for drug rewards. However, these individuals lacked the critical increases
in impulsivity and compulsivity implicated in dependence, and had much higher levels of
cognitive control compared with the dependent individuals and their siblings, and even in
some instances compared with healthy control participants.
147
Chapter 7. Discussion
These patterns of behaviour appear to be stable traits in these participant groups, manifested
in several different types of behavioural and neurobiological assessments, including a neutral
cognitive control test (colour-word Stroop), an assessment of attentional bias and distraction
to cocaine stimuli (cocaine-word Stroop), and responsivity to both general and drug-related
reward cues (Monetary Incentive Delay). Thus, these tendencies seem to be robust findings
strongly suggestive of predating abnormalities in the brains and behaviours of individuals
who maintain a greater risk for stimulant dependence, as well as crucial unique protective
factors in those who have remained resilient to it.
3. Synthesis
Despite the evidence for endophenotypic traits present in both the dependent participants and
their biological siblings, there were also additional changes in the brains of dependent
individuals compared to their siblings. These differences may have been the result of chronic
stimulant use on the brain, though they were largely unseen in the recreational users, or they
could represent additional deficits that placed these individuals at a heightened risk for
dependence. Conversely, the additional differences in the recreational and sibling participants
could be protective traits that were present in these individuals and kept them safe from the
development of drug dependence, either preventing initial use or enabling them to maintain
control over their behaviours if they did take drugs.
In the studies involving the recreational participants, the finding of critical differences
between recreational and dependent users of cocaine, including a lack of distraction,
emotional salience and sensitivity to reward in the face of cocaine-related stimuli, indicates
that there may be inherent behavioural and neurobiological differences between those who
148
Chapter 7. Discussion
can use a class A drug such as cocaine recreationally without making the transition to
dependence, and those who have become dependent upon the substance.
In the second half the thesis, a separate cohort of individuals potentially on the addiction
spectrum – obese individuals with binge eating behaviours – was investigated for similar
traits of reward valuation and orbitofrontal cortex functioning as those of stimulantdependent individuals. However, in the few tests conducted on these participants, there was
only a small overlap in their characteristics compared to the stimulant-dependent individuals.
Instead, the greatest similarity appeared to be between OBEs and the sibling participants
tested in the earlier studies. This included tendencies towards impulsive behaviours, but no
increase in sensation-seeking, as well as flattened reward responding to both general and
specific reinforcers. For example, while these participant groups were not tested on the same
battery and were not initially planned on being compared, it is interesting to note that in posthoc analyses, the sibling and OBE participants did not differ on self-report measures of
impulsivity using the BIS-11, but both groups were significantly higher than control and
recreational participants on this measure (see Figure 7.2). Additionally, both groups also
showed lower sensation-seeking scores than the dependent and recreational users, with the
OBE participants scoring even lower than the controls and sibling participants. Thus, these
obese individuals appear to maintain impulsive traits potentially placing them at a higher risk
for addictive tendencies, but not the reward and sensation-seeking characteristics that would
incite them to initiate drug use. Instead, their maladaptive patterns of behaviour manifested
onto the more traditional and omnipresent reinforcer of high-caloric foods.
149
Chapter 7. Discussion
Figure 7.2. Group differences in impulsivity (BIS-11) and sensation-seeking (SSS-V)
scores between control participants, obese individuals with binge-eating behaviours,
recreational cocaine users, dependent stimulant users and their non-dependent
biological siblings. Obese binge-eating individuals were most similar to the sibling
150
Chapter 7. Discussion
participants, with impulsivity scores higher than controls and recreational users, but
lower than dependent individuals. On a measure of sensation-seeking, obese individuals
expressed the lowest scores, most similar to sibling and control participants. This
provides evidence for a strong similarity between obese individuals with binge-eating
tendencies and the siblings of stimulant-dependent individuals, possibly showing
attributes suggestive of dependent behaviours, but without the high sensation-seeking
that is most strongly linked to the initiation of drug experimentation. Thus, these
individuals’ addictive tendencies may manifest in a different manner or to another type
of substance, like food, rather than through drug use. Personality measures taken from.
Several genetic links have been explored to potentially explain some of the overlap between
addictive and eating disorders. This has primarily focused on differences in genes, such as
TaqIA, involved in dopamine transmission due to the neurotransmitter’s role in motivated
behaviours and reward system functioning (Blum et al., 1990; Comings et al., 1993;
Comings, Muhleman, Ahn, Gysin, & Flanagan, 1994; Davis et al., 2009; Felsted, Ren,
Chouinard-Decorte, & Small, 2010; Jonsson et al., 1999; Noble, 2000; Noble et al., 1993;
Stice, Spoor, Bohon, & Small, 2008). Specifically, alleles resulting in reduced dopamine
receptor density or decreased dopamine transporter availability have been linked to a greater
risk for impulsive disorders and an increase in both the expected and experienced pleasure
derived from rewarding substances, such as high-caloric foods or drugs of abuse (Comings et
al., 1993, 1994; Felsted et al., 2010; Spitz et al., 2000; Stice et al., 2008; Thanos et al., 2001).
An alternative genetic explanation involves the monoamine oxidase A (MAOA) gene, which
is implicated in the metabolism of monoamine neurotransmitters, subsequently affecting
cerebral levels of dopamine, noradrenaline and serotonin (Ducci et al., 2006). A
151
Chapter 7. Discussion
polymorphism in the gene transcription results in a decrease in monoamine oxidase A
activity, reducing the turnover of monoamines and thus leading to an increase of these
neurotransmitters in the synapse. This sequencing has subsequently been associated with
heightened aggression, impulsivity and increased risk for addiction (Caspi et al., 2002;
Philibert, Gunter, Beach, Brody, & Madan, 2008; Saito et al., 2002), although other
investigations have found no such link between the polymorphism and these behaviours
(Koller, Bondy, Preuss, Bottlender, & Soyka, 2003). Due to dopamine’s known involvement
in eating behaviours and reward valuation (Baldo & Kelley, 2007; Wang et al., 2001), the
MAOA gene has also been investigated as a potential factor in over-eating leading to obesity
(Camarena et al., 2004; Ducci et al., 2006). As such, the presence of the polymorphism
resulting in low MAOA activity was found to be associated with elevated dopamine
metabolites and an increase in body weight and body mass index among both alcoholdependent and healthy control individuals (Ducci et al., 2006). Finally, the MAOA gene is
also implicated in cortical volume, particularly in the OFC, and presence of the
polymorphism resulting in low monoamine oxidase A activity has been linked to decreased
functioning in this area (Meyer-Lindenberg et al., 2006). Furthermore, when combined with
stimulant use, presence of the low MAOA expression type can lead to a significant
exacerbation of the effects of stimulant drugs on cortical volume, resulting in escalated grey
matter decline, particularly in the OFC (Alia-Klein et al., 2011). Thus, there is a potential
genetic link between elevated BMI, substance dependence and decline in OFC grey matter
volume, as presented in the findings reported in Chapter 6.
152
Chapter 7. Discussion
4. Future Directions
A primary concern regarding the investigations presented in this thesis is the ranging
differences in demographics between the participant groups. These include age, gender
distribution, educational attainment, and the co-morbid presence of depression and alcohol
abuse in the SDIs. Although we attempted to control for these variables in statistical analyses,
better matching for these variables and other demographics during recruitment could be
improved in future studies. However, it should be noted that some of these demographic
differences might be intrinsically linked to the participant groups, such as higher education
acting as a protective factor in recreational users, or the presence of polysubstance abuse in
the dependent individuals.
Another possible point of contention in these investigations is that ‘recreational cocaine use’
is not a precisely defined categorization. In the studies presented in this thesis, criteria based
on the quantity and quality of use, as well as an absence of any family history of substance
dependence, were used to make these classifications. However, research from other
laboratories has focused solely on the quantity and frequency of cocaine used when making
their evaluations. As such, previous investigations have included individuals who our
classifications would have designated as ‘abusive’ rather than ‘recreational’ users. These
discrepancies in inclusion criteria may help to explain the difference in findings reported here
in comparison to prior studies of non-dependent stimulant users (Colzato et al., 2007; Soar et
al., 2012). A universal definition for this type of use would be highly beneficial when making
these sorts of distinctions between recreational, abusive and dependent behaviours in future
research.
153
Chapter 7. Discussion
The relatively short abstinence period of the stimulant-dependent individuals should also be
better controlled for, with a minimum abstinence requirement to ensure results are not effects
of either acute stimulant use or withdrawal, but are stable traits of this group.
Subsequent studies could employ genetic analyses to elucidate whether similarities in
performance between the stimulant-dependent and sibling groups were due to inherited traits
or shared environmental experiences during development. As the sibling pairs were raised in
the same households, it is difficult to discern the extent to which environmental effects, such
as low socioeconomic status, might have affected these individuals. These experiences could
cause them to differ in performance from control and recreational participants, who were less
likely to be exposed to these challenges growing up. Conversely, inherited polymorphisms
involving the TaqAI or MAOA genes, among others, may have contributed to their
differences in performance and heightened risk for dependence.
Future investigations should use positron emission tomography (PET) to measure dopamine
receptor availability and binding, particularly in recreational users of cocaine and the nondependent siblings of addicted individuals, given the neurotransmitter’s known involvement
in addictive disorders. This would be particularly informative in regards to aberrations in
reward sensitivity, as stated in Chapter 4, and could help provide a neurobiological
explanation for some of the behavioural and functional differences seen between these
participant groups.
In order to determine other potential similarities between substance dependence and binge
eating disorder, assessments traditionally used to measure impairments associated with
stimulant dependence – such as impulsivity using the Stop Signal Reaction Time task, or the
Monetary Incentive Delay task as a more standardised assessment of reward sensitivity –
154
Chapter 7. Discussion
should be administered. Additionally, an assessment of attentional bias to food-related
stimuli, such as a food-related Stroop task, should be implemented. Another assessment, the
delay discounting task, could also be used to measure reward-related impulsivity to both
general (money) and substance-specific (food or drugs) stimuli.
5. Conclusions and Implications
The chicken-or-egg debate of substance dependence has dominated much of the field of
addiction research over the last decade – whether behavioural and neurobiological
abnormalities commonly seen in drug-dependent individuals are underlying traits that foster
an increased vulnerability for dependence, or if they are the consequence of long-term use.
Here, evidence is presented regarding predating risk factors that can place an individual at a
greater susceptibility for experimentation with illicit substances, as well as an additional
heightened risk for the subsequent development of dependence. However, evidence is also
presented for resilience traits that can potentially protect against the initiation of drug use, as
well as the slide into addiction.
The findings presented in this thesis provide support for the notion that drug addiction is an
underlying neurobiological disorder, rather than a volitional choice made by those who are
dependent on a substance (Nutt, 2013). These studies could be used as evidence in the
argument for using rehabilitative treatments rather than more draconian tactics when dealing
with drug-related crimes. This research could also help in the development of addiction
prevention programs that are specifically targeted towards individuals who maintain a higher
risk for dependence. By identifying these individuals earlier on – i.e. during adolescence and
before they have begun experimenting with drugs – and with simple and easily administered
questionnaires or neurocognitive tests – more focused prevention or harm reduction methods
155
Chapter 7. Discussion
could be implemented. Additionally, the protective factors identified in the sibling and
recreational participants, particularly of decreased reward valuation, could be utilized to
encourage those at risk to indulge in other sorts of less harmful rewarding stimuli instead of
drugs of abuse or over-eating.
Finally, while there was only marginal support for the classification of binge eating leading
to obesity as an addictive disorder, the findings from these investigations could still be used
to identify and target those individuals who might be at a greater susceptibility to this type of
behaviour as well. This also includes those individuals with cognitive control difficulties,
increases in impulsivity, and potentially greater tendencies towards habitual or compulsive
behaviour stemming from abnormalities in the orbitofrontal cortex, but without the key trait
of sensation-seeking.
While these studies cannot yet answer the question of how to prevent unhealthy
dependencies, they can help bring us one step closer to identifying and potentially treating
those who are at the highest risk for addiction.
156
References
References
Aguilar de Arcos, F., Verdejo-García, A., Ceverino, A., Montañez-Pareja, M., LópezJuárez, E., Sánchez-Barrera, M., … Pérez-García, M. (2008). Dysregulation of
emotional response in current and abstinent heroin users: Negative heightening and
positive blunting. Psychopharmacology, 198(2), 159–166.
Aguilar de Arcos, F., Verdejo-Garcia, A., Peralta-Ramirez, M. I., Sanchez-Barrera, M., &
Perez-Garcia, M. (2005). Experience of emotions in substance abusers exposed to
images containing neutral, positive, and negative affective stimuli. Drug and Alcohol
Dependence, 78(2), 159–167.
Aigner, T., & Balster, R. (1978). Choice behavior in rhesus monkeys: cocaine versus
food. Science, 201(4355), 534–535.
Aitken, C., Kerger, M., & Crofts, N. (2000). Drivers who use illicit drugs: behaviour and
perceived risks. Drugs: Education, Prevention, and Policy, 7(1), 39–50.
Albery, I. P., Strang, J., Gossop, M., & Griffiths, P. (2000). Illicit drugs and driving:
prevalence, beliefs and accident involvement among a cohort of current out-oftreatment drug users. Drug and Alcohol Dependence, 58(1-2), 197–204.
Alia-Klein, N., Parvaz, M. A., Woicik, P. A., Konova, A. B., Maloney, T., Shumay, E.,
… Goldstein, R. Z. (2011). Gene x disease interaction on orbitofrontal gray matter in
cocaine addiction. Archives of General Psychiatry, 68(3), 283–294.
Alosco, M. L., Stanek, K. M., Galioto, R., Korgaonkar, M. S., Grieve, S. M., Brickman,
A. M., … Gunstad, J. (2014). Body mass index and brain structure in healthy
children and adolescents. The International Journal of Neuroscience, 124(1), 49–55.
American Psychiatric Association. (2000). Diagnostic and Statistical Manual of Mental
Disorders (4th ed.). Washington, D.C.: American Psychiatric Association.
Andersson, J., Jenkinson, M., & Smith, S. (2007a). Non-linear optimisation. In FMRIB
technical report TR07JA1.
Andersson, J., Jenkinson, M., & Smith, S. (2007b). Non-linear registration, aka Spatial
normalisation. In FMRIB technical report TR07JA2.
Asensio, S., Romero, M. J., Palau, C., Sanchez, A., Senabre, I., Morales, J. L., …
Romero, F. J. (2010). Altered neural response of the appetitive emotional system in
cocaine addiction: an fMRI Study. Addiction Biology, 15(4), 504–516.
Avena, N. M., Rada, P., Bocarsly, M., & Hoebel, B. G. (2005). Sugar bingeing produces
effects on opioid and dopamine systems: Possible roles in drug abuse.
Neuropsychopharmacology, 30, S30–S30.
157
References
Avena, N. M., Rada, P., & Hoebel, B. G. (2008). Evidence for sugar addiction: behavioral
and neurochemical effects of intermittent, excessive sugar intake. Neuroscience and
Biobehavioral Review, 32(1), 20–39.
Avena, N. M., Rada, P., & Hoebel, B. G. (2009). Sugar and fat bingeing have notable
differences in addictive-like behavior. Journal of Nutrition, 139(3), 623–628.
Baldo, B. A., & Kelley, A. E. (2007). Discrete neurochemical coding of distinguishable
motivational processes: insights from nucleus accumbens control of feeding.
Psychopharmacology, 191(3), 439–459.
Balleine, B. W., & O’Doherty, J. P. (2010). Human and rodent homologies in action
control: corticostriatal determinants of goal-directed and habitual action.
Neuropsychopharmacology, 35(1), 48–69.
Barnard, M. (2005). Drugs in the family: The impact on parents and siblings. London:
Joseph Rowntree Foundation.
Barrós-Loscertales, A., Bustamante, J.-C. C., Ventura-Campos, N., Llopis, J.-J. J., Parcet,
M.-A. A., Avila, C., & Barros-Loscertales, A. (2011). Lower activation in the right
frontoparietal network during a counting Stroop task in a cocaine-dependent group.
Psychiatry Research, 194(2), 111–118.
Baxter, L. R., Schwartz, J. M., Mazziotta, J. C., Phelps, M. E., Pahl, J. J., Guze, B. H., &
Fairbanks, L. (1988). Cerebral glucose metabolic rates in nondepressed patients with
obsessive-compulsive disorder. American Journal of Psychiatry, 145(12), 1560–
1563.
Bechara, A. (2005). Decision making, impulse control and loss of willpower to resist
drugs: a neurocognitive perspective. Nature Neuroscience, 8(11), 1458–1463.
Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to
future consequences following damage to human prefrontal cortex. Cognition, 50(13), 7–15.
Bechara, A., & Damasio, H. (2002). Decision-making and addiction (part I): impaired
activation of somatic states in substance dependent individuals when pondering
decisions with negative future consequences. Neuropsychologia, 40(10), 1675–1689.
Bechara, A., Damasio, H., & Damasio, A. R. (2000). Emotion, decision making and the
orbitofrontal cortex. Cerebral Cortex, 10(3), 295–307.
Bechara, A., Dolan, S., Denburg, N., Hindes, A., Anderson, S. W., & Nathan, P. E.
(2001). Decision-making deficits, linked to a dysfunctional ventromedial prefrontal
cortex, revealed in alcohol and stimulant abusers. Neuropsychologia, 39(4), 376–
389.
Beck, A., Schlagenhauf, F., Wüstenberg, T., Hein, J., Kienast, T., Kahnt, T., … Wrase, J.
(2009). Ventral striatal activation during reward anticipation correlates with
impulsivity in alcoholics. Biological Psychiatry, 66(8), 734–742.
158
References
Beck, A., Steer, R. A., Ball, R., & Ranieri, W. (1996). Comparison of Beck Depression
Inventories -IA and -II in psychiatric outpatients. Journal of Personality Assessment,
67(3), 588–597.
Beck, A. T., Steer, R. A., & Garbin, M. G. (1988). Psychometric Properties of the Beck
Depression Inventory - 25 Years of Evaluation. Clinical Psychology Review, 8(1),
77–100.
Belin, D., & Everitt, B. J. (2008). Cocaine seeking habits depend upon dopaminedependent serial connectivity linking the ventral with the dorsal striatum. Neuron,
57(3), 432–441.
Belin, D., Mar, A. C., Dalley, J. W., Robbins, T. W., & Everitt, B. J. (2008). High
impulsivity predicts the switch to compulsive cocaine-taking. Science, 320(5881),
1352–1355.
Berridge, K. C., & Robinson, T. E. (1998). What is the role of dopamine in reward:
hedonic impact, reward learning, or incentive salience? Brain Research Reviews,
28(3), 309–369.
Beveridge, T. J. R., Smith, H. R., Daunais, J. B., Nader, M. A., & Porrino, L. J. (2006).
Chronic cocaine self-administration is associated with altered functional activity in
the temporal lobes of non human primates. European Journal of Neuroscience,
23(11), 3109–3118.
Bjork, J. M., Smith, A. R., & Hommer, D. W. (2008). Striatal sensitivity to reward
deliveries and omissions in substance dependent patients. NeuroImage, 42(4), 1609–
1621.
Blum, K., Braverman, E. R., Holder, J. M., Lubar, J. F., Monastra, V. J., Miller, D., …
Comings, D. E. (2000). Reward deficiency syndrome: A biogenetic model for the
diagnosis and treatment of impulsive, addictive, and compulsive behaviors. Journal
of Psychoactive Drugs, 32, Supplement.
Blum, K., Cull, J. G., Braverman, E. R., & Comings, D. E. (1996). Reward deficiency
syndrome. American Scientist, 84(2), 132–145.
Blum, K., Noble, E. P., Sheridan, P. J., Montgomery, A., Ritchie, T., Jagadeeswaran, P.,
… Cohn, J. B. (1990). Allelic association of human dopamine D2 receptor gene in
alcoholism. Journal of the American Medical Association, 263(15), 2055–2060.
Bolla, K. I., Eldreth, D. A., London, E. D., Kiehl, K. A., Mouratidis, M., Contoreggi, C.,
… Ernst, M. (2003). Orbitofrontal cortex dysfunction in abstinent cocaine abusers
performing a decision-making task. NeuroImage, 19(3), 1085–1094.
Bolla, K. I., Ernst, M., Kiehl, K., Mouratidis, M., Eldreth, D., Contoreggi, C., … London,
E. (2004). Prefrontal cortical dysfunction in abstinent cocaine abusers. Journal of
Neuropsychiatry and Clinical Neuroscience, 16(4), 456–464.
159
References
Breiter, H. C., Gollub, R. L., Weisskoff, R. M., Kennedy, D. N., Makris, N., Berke, J. D.,
… Hyman, S. E. (1997). Acute effects of cocaine on human brain activity and
emotion. Neuron, 19(3), 591–611.
Brewer, J. A., Worhunsky, P. D., Carroll, K. M., Rounsaville, B. J., & Potenza, M. N.
(2008). Pretreatment brain activation during stroop task is associated with outcomes
in cocaine-dependent patients. Biological Psychiatry, 64(11), 998–1004.
Bullmore, E. T., Brammer, M. J., Rabe-Hesketh, S., Curtis, V. A., Morris, R. G.,
Williams, S. C., … McGuire, P. K. (1999). Methods for diagnosis and treatment of
stimulus-correlated motion in generic brain activation studies using fMRI. Human
Brain Mapping, 7(1), 38–48.
Bullmore, E. T., Long, C., Suckling, J., Fadili, J., Calvert, G., Zelaya, F., … Brammer, M.
(2001). Colored noise and computational inference in neurophysiological (fMRI)
time series analysis: resampling methods in time and wavelet domains. Human Brain
Mapping, 12(2), 61–78.
Bunge, S. A., Ochsner, K. N., Desmond, J. E., Glover, G. H., & Gabrieli, J. D. E. (2001).
Prefrontal regions involved in keeping information in and out of mind. Brain, 124,
2074–2086.
Burns, G. L., Keortge, S. G., Formea, G. M., & Sternberger, L. G. (1996). Revision of the
Padua Inventory of obsessive compulsive disorder symptoms: Distinctions between
worry, obsessions, and compulsions. Behaviour Research and Therapy.
Camarena, B., Santiago, H., Aguilar, A., Ruvinskis, E., González-Barranco, J., &
Nicolini, H. (2004). Family-based association study between the monoamine oxidase
A gene and obesity: implications for psychopharmacogenetic studies.
Neuropsychobiology, 49(3), 126–129.
Cambridge, V. C., Ziauddeen, H., Nathan, P. J., Subramaniam, N., Dodds, C.,
Chamberlain, S. R., … Fletcher, P. C. (2013). Neural and behavioral effects of a
novel mu opioid receptor antagonist in binge-eating obese people. Biological
Psychiatry, 73(9), 887–894.
Carpenter, K. M., Schreiber, E., Church, S., & McDowell, D. (2006). Drug Stroop
performance: relationships with primary substance of use and treatment outcome in a
drug-dependent outpatient sample. Addictive Behaviors, 31(1), 174–181.
Carver, C. S., & White, T. L. (1994). Behavioral-inhibition, behavioral activation, and
affective responses to impending reward and punishment - the Bis Bas scales.
Journal of Personality and Social Psychology, 67(2), 319–333.
Caspi, A., McClay, J., Moffitt, T. E., Mill, J., Martin, J., Craig, I. W., … Poulton, R.
(2002). Role of genotype in the cycle of violence in maltreated children. Science,
297(5582), 851–854.
Castellanos, F. X., Giedd, J. N., Marsh, W. L., Hamburger, S. D., Vaituzis, A. C.,
Dickstein, D. P., … Rapoport, J. L. (1996). Quantitative brain magnetic resonance
160
References
imaging in attention-deficit hyperactivity disorder. Archives of General Psychiatry,
53(7), 607–616.
Cavazos-Rehg, P. A., Spitznagel, E. L., Schootman, M., Strickland, J. R., Afful, S. E.,
Cottler, L. B., & Bierut, L. J. (2009). Risky sexual behaviors and sexually
transmitted diseases: a comparison study of cocaine-dependent individuals in
treatment versus a community-matched sample. AIDS Patient Care and STDs, 23(9),
727–734.
Chang, L., Cloak, C., Patterson, K., Grob, C., Miller, E. N., & Ernst, T. (2005). Enlarged
striatum in abstinent methamphetamine abusers: a possible compensatory response.
Biological Psychiatry, 57(9), 967–974.
Childress, A. R., Mozley, P. D., McElgin, W., Fitzgerald, J., Reivich, M., & O’Brien, C.
P. (1999). Limbic activation during cue-induced cocaine craving. American Journal
of Psychiatry, 156(1), 11–18.
Clark, L., Bechara, A., Damasio, H., Aitken, M. R. F., Sahakian, B. J., & Robbins, T. W.
(2008). Differential effects of insular and ventromedial prefrontal cortex lesions on
risky decision-making. Brain, 131(Pt 5), 1311–1322.
Clark, L., Robbins, T. W., Ersche, K. D., & Sahakian, B. J. (2006). Reflection impulsivity
in current and former substance users. Biological Psychiatry, 60(5), 515–522.
Colzato, L. S., Huizinga, M., & Hommel, B. (2009). Recreational cocaine polydrug use
impairs cognitive flexibility but not working memory. Psychopharmacology, 207(2),
225–234.
Colzato, L. S., van den Wildenberg, W. P. M., & Hommel, B. (2007). Impaired inhibitory
control in recreational cocaine users. Plos One, 2(11), e1143.
Comings, D. E., Flanagan, S. D., Dietz, G., Muhleman, D., Knell, E., & Gysin, R. (1993).
The dopamine-D(2) receptor (Drd2) as a major gene in obesity and height.
Biochemical Medicine and Metabolic Biology, 50(2), 176–185.
Comings, D. E., Muhleman, D., Ahn, C., Gysin, R., & Flanagan, S. D. (1994). The
dopamine-D(2) receptor gene - a genetic risk factor in substance-abuse. Drug and
Alcohol Dependence, 34(3), 175–180.
Cools, R., Clark, L., Owen, A. M., & Robbins, T. W. (2002). Defining the neural
mechanisms of probabilistic reversal learning using event-related functional
magnetic resonance imaging. Journal of Neuroscience, 22(11), 4563–4567.
Cools, R., Sheridan, M., Jacobs, E., & D’Esposito, M. (2007). Impulsive personality
predicts dopamine-dependent changes in frontostriatal activity during component
processes of working memory. Journal of Neuroscience, 27(20), 5506–5514.
Copersino, M. L., Serper, M. R., Vadhan, N., Goldberg, B. R., Richarme, D., Chou, J. C.,
… Cancro, R. (2004). Cocaine craving and attentional bias in cocaine-dependent
schizophrenic patients. Psychiatry Research, 128(3), 209–218.
161
References
Cox, W. M., Fadardi, J. S., & Pothos, E. M. (2006). The addiction-stroop test: theoretical
considerations and procedural recommendations. Psychological Bulletin, 132(3),
443–476.
Daglish, M. R. C., & Nutt, D. J. (2003). Brain imaging studies in human addicts.
European Neuropsychopharmacology, 13(6), 453–458.
Daglish, M. R. C., Weinstein, A., Malizia, A. L., Wilson, S., Melichar, J. K., Britten, S.,
… Nutt, D. J. (2001). Changes in regional cerebral blood flow elicited by craving
memories in abstinent opiate-dependent subjects. American Journal of Psychiatry,
158(10), 1680–1686.
Dalley, J. W., Everitt, B. J., & Robbins, T. W. (2011). Impulsivity, compulsivity, and topdown cognitive control. Neuron, 69(4), 680–694.
Dalley, J. W., Fryer, T. D., Brichard, L., Robinson, E. S. J., Theobald, D. E. H., Lääne,
K., … Robbins, T. W. (2007). Nucleus accumbens D2/3 receptors predict trait
impulsivity and cocaine reinforcement. Science, 315(5816), 1267–1270.
Davis, C., & Carter, J. C. (2009). Compulsive overeating as an addiction disorder. A
review of theory and evidence. Appetite, 53(1), 1–8.
Davis, C., & Fox, J. (2008). Sensitivity to reward and body mass index (BMI): evidence
for a non-linear relationship. Appetite, 50(1), 43–49.
Davis, C., Levitan, R. D., Muglia, P., Bewell, C., & Kennedy, J. L. (2004). Decisionmaking deficits and overeating: a risk model for obesity. Obesity Research, 12(6),
929–935.
Davis, C., Levitan, R. D., Reid, C., Carter, J. C., Kaplan, A. S., Patte, K. A., … Kennedy,
J. L. (2009). Dopamine for “wanting” and opioids for “liking”: a comparison of
obese adults with and without binge eating. Obesity, 17(6), 1220–1225.
Davis, C., Strachan, S., & Berkson, M. (2004). Sensitivity to reward: implications for
overeating and overweight. Appetite, 42(2), 131–138.
Deadwyler, S. A. (2010). Electrophysiological correlates of abused drugs: relation to
natural rewards. Annals of the New York Academy of Sciences, 1187(1), 140–147.
Di Chiara, G. (1999). Drug addiction as dopamine-dependent associative learning
disorder. European Journal of Pharmacology, 375(1-3), 13–30.
Driscoll, I., Beydoun, M. A., An, Y., Davatzikos, C., Ferrucci, L., Zonderman, A. B., &
Resnick, S. M. (2012). Midlife obesity and trajectories of brain volume changes in
older adults. Human Brain Mapping, 33(9), 2204–2210.
Ducci, F., Newman, T. K., Funt, S., Brown, G. L., Virkkunen, M., & Goldman, D.
(2006). A functional polymorphism in the MAOA gene promoter (MAOA-LPR)
162
References
predicts central dopamine function and body mass index. Molecular Psychiatry,
11(9), 858–866.
Ehrman, R. N., Robbins, S. J., Childress, A. R., & O’Brien, C. P. (1992). Conditioned
responses to cocaine-related stimuli in cocaine abuse patients. Psychopharmacology,
107(4), 523–529.
Elias, M. F., Elias, P. K., Sullivan, L. M., Wolf, P. A., & D’Agostino, R. B. (2003).
Lower cognitive function in the presence of obesity and hypertension: the
Framingham heart study. International Journal of Obesity, 27(2), 260–268.
Elliott, R., Dolan, R. J., & Frith, C. D. (2000). Dissociable functions in the medial and
lateral orbitofrontal cortex: Evidence from human neuroimaging studies. Cerebral
Cortex, 10(3), 308–317.
Ersche, K. D., Barnes, A., Jones, P. S., Morein-Zamir, S., Robbins, T. W., & Bullmore, E.
T. (2011). Abnormal structure of frontostriatal brain systems is associated with
aspects of impulsivity and compulsivity in cocaine dependence. Brain, 134, 2013–
2024.
Ersche, K. D., Bullmore, E. T., Craig, K. J., Shabbir, S. S., Abbott, S., Müller, U., …
Robbins, T. W. (2010). Influence of compulsivity of drug abuse on dopaminergic
modulation of attentional bias in stimulant dependence. Archives of General
Psychiatry, 67(6), 632–644.
Ersche, K. D., Clark, L., London, M., Robbins, T. W., & Sahakian, B. J. (2006). Profile of
executive and memory function associated with amphetamine and opiate
dependence. Neuropsychopharmacology, 31(5), 1036–1047.
Ersche, K. D., Fletcher, P. C., Lewis, S. J. G., Clark, L., Stocks-Gee, G., London, M., …
Sahakian, B. J. (2005). Abnormal frontal activations related to decision-making in
current and former amphetamine and opiate dependent individuals.
Psychopharmacology, 180(4), 612–623.
Ersche, K. D., Fletcher, P. C., Roiser, J. P., Fryer, T. D., London, M., Robbins, T. W., &
Sahakian, B. J. (2006). Differences in orbitofrontal activation during decisionmaking between methadone-maintained opiate users, heroin users and healthy
volunteers. Psychopharmacology, 188(3), 364–73.
Ersche, K. D., Jones, P. S., Williams, G. B., Robbins, T. W., & Bullmore, E. T. (2013).
Cocaine dependence: a fast-track for brain ageing? Molecular Psychiatry, 18(2),
134–135.
Ersche, K. D., Jones, P. S., Williams, G. B., Smith, D. G., Bullmore, E. T., & Robbins, T.
W. (2013). Distinctive personality traits and neural correlates associated with
stimulant drug use versus familial risk of stimulant dependence. Biological
Psychiatry, 74(2), 137–144.
163
References
Ersche, K. D., Jones, P. S., Williams, G. B., Turton, A. J., Robbins, T. W., & Bullmore,
E. T. (2012). Abnormal brain structure implicated in stimulant drug addiction.
Science, 335(6068), 601–604.
Ersche, K. D., & Robbins, T. W. (2011). An integrated framework for human
neuroimaging studies of addiction from a preclinical perspective. In B. Adinoff & E.
A. Stein (Eds.), Neuroimaging in Addiction (pp. 7–35). Chichester, UK: John Wiley
& Sons, Ltd.
Ersche, K. D., Roiser, J. P., Robbins, T. W., & Sahakian, B. J. (2008). Chronic cocaine
but not chronic amphetamine use is associated with perseverative responding in
humans. Psychopharmacology, 197(3), 421–431.
Ersche, K. D., Turton, A. J., Chamberlain, S. R., Muller, U., Bullmore, E. T., & Robbins,
T. W. (2012). Cognitive dysfunction and anxious-impulsive personality traits are
endophenotypes for drug dependence. American Journal of Psychiatry, 169(9), 926–
936.
Ersche, K. D., Turton, A. J., Pradhan, S., Bullmore, E. T., & Robbins, T. W. (2010). Drug
addiction endophenotypes: impulsive versus sensation-seeking personality traits.
Biological Psychiatry, 68(8), 770–773.
Ersche, K. D., Williams, G. B., Robbins, T. W., & Bullmore, E. T. (2013). Meta-analysis
of structural brain abnormalities associated with stimulant drug dependence and
neuroimaging of addiction vulnerability and resilience. Current Opinion in
Neurobiology, 23(4), 615–624.
Everitt, B. J. (1990). Sexual motivation: A neural and behavioural analysis of the
mechanisms underlying appetitive and copulatory responses of male rats.
Neuroscience and Biobehavioral Reviews, 14(2), 217–232.
Everitt, B. J., Belin, D., Economidou, D., Pelloux, Y., Dalley, J. W., & Robbins, T. W.
(2008). Neural mechanisms underlying the vulnerability to develop compulsive
drug-seeking habits and addiction. Philosophical Transactions of the Royal Society,
Series B Biological Sciences, 363(1507), 3125–3135.
Everitt, B. J., Hutcheson, D. M., Ersche, K. D., Pelloux, Y., Dalley, J. W., & Robbins, T.
W. (2007). The orbital prefrontal cortex and drug addiction in laboratory animals
and humans. Annals of the New York Academy of Sciences, 1121, 576–597.
Everitt, B. J., & Robbins, T. W. (2005). Neural systems of reinforcement for drug
addiction: from actions to habits to compulsion. Nature Neuroscience, 8(11), 1481–
1489.
Fairburn, C. G., & Beglin, S. J. (1994). Assessment of eating disorders: interview or selfreport questionnaire? The International Journal of Eating Disorders, 16(4), 363–
370.
164
References
Felsted, J. A., Ren, X., Chouinard-Decorte, F., & Small, D. M. (2010). Genetically
determined differences in brain response to a primary food reward. Journal of
Neuroscience, 30(7), 2428–2432.
Field, M. (2005). Cannabis “dependence” and attentional bias for cannabis-related words.
Behavioural Pharmacology, 16(5-6), 473–476.
Field, M., Christiansen, P., Cole, J., & Goudie, A. (2007). Delay discounting and the
alcohol Stroop in heavy drinking adolescents. Addiction, 102(4), 579–586.
Field, M., & Cox, W. M. (2008). Attentional bias in addictive behaviors: a review of its
development, causes, and consequences. Drug and Alcohol Dependence, 97(1-2), 1–
20.
First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (2002). Structured Clinical
Interview for DSM-IV-TR Axis I Disorders (Research V.). New York: New York
State Psychiatric Institute, Biometrics Research.
FitzGerald, T. H. B., Seymour, B., & Dolan, R. J. (2009). The role of human orbitofrontal
cortex in value comparison for incommensurable objects. Journal of Neuroscience,
29(26), 8388–8395.
Fox, H. C., Axelrod, S. R., Paliwal, P., Sleeper, J., & Sinha, R. (2007). Difficulties in
emotion regulation and impulse control during cocaine abstinence. Drug and
Alcohol Dependence, 89(2-3), 298–301.
Franken, I. (2003). Drug craving and addiction: integrating psychological and
neuropsychopharmacological approaches. Progress in Neuro-Psychopharmacology
and Biological Psychiatry, 27(4), 563–579.
Franken, I. H. A., Hendriksa, V. M., & van den Brink, W. (2002). Initial validation of two
opiate craving questionnaires the obsessive compulsive drug use scale and the
desires for drug questionnaire. Addictive Behaviors, 27(5), 675–685.
Franken, I. H. A., Kroon, L. Y., & Hendriks, V. M. (2000). Influence of individual
differences in craving and obsessive cocaine thoughts on attentional processes in
cocaine abuse patients. Addictive Behaviors, 25(1), 99–102.
Franklin, T. R., Acton, P. D., Maldjian, J. A., Gray, J. D., Croft, J. R., Dackis, C. A., …
Childress, A. R. (2002). Decreased gray matter concentration in the insular,
orbitofrontal, cingulate, and temporal cortices of cocaine patients. Biological
Psychiatry, 51(2), 134–142.
Garavan, H., Kaufman, J. N., & Hester, R. (2008). Acute effects of cocaine on the
neurobiology of cognitive control. Philosophical Transactions of the Royal Society
of London. Series B, Biological Sciences, 363(1507), 3267–3276.
Garavan, H., Pankiewicz, J., Bloom, A., Cho, J. K., Sperry, L., Ross, T. J., … Stein, E. A.
(2000). Cue-induced cocaine craving: neuroanatomical specificity for drug users and
drug stimuli. American Journal of Psychiatry, 157(11), 1789–1798.
165
References
Gerra, G., Baldaro, B., Zaimovic, A., Moi, G., Bussandri, M., Raggi, M. A., & Brambilla,
F. (2003). Neuroendocrine responses to experimentally-induced emotions among
abstinent opioid-dependent subjects. Drug and Alcohol Dependence, 71(1), 25–35.
Giuliano, C., Robbins, T. W., Nathan, P. J., Bullmore, E. T., & Everitt, B. J. (2012).
Inhibition of opioid transmission at the μ-opioid receptor prevents both food seeking
and binge-like eating. Neuropsychopharmacology, 37(12), 2643–2652.
Glover, G. H. (1999). Deconvolution of impulse response in event-related BOLD fMRI.
NeuroImage, 9(4), 416–429.
Goldstein, R. Z., Alia-Klein, N., Tomasi, D., Carrillo, J. H., Maloney, T., Woicik, P. A.,
… Volkow, N. D. (2009). Anterior cingulate cortex hypoactivations to an
emotionally salient task in cocaine addiction. Proceedings of the National Academy
of Sciences of the United States of America, 106(23), 9453–9458.
Goldstein, R. Z., Alia-Klein, N., Tomasi, D., Zhang, L., Cottone, L. A., Maloney, T., …
Volkow, N. D. (2007). Is decreased prefrontal cortical sensitivity to monetary
reward associated with impaired motivation and self-control in cocaine addiction?
American Journal of Psychiatry, 164(1), 43–51.
Goldstein, R. Z., Tomasi, D., Alia-Klein, N., Cottone, L. A., Zhang, L., Telang, F., &
Volkow, N. D. (2007). Subjective sensitivity to monetary gradients is associated
with frontolimbic activation to reward in cocaine abusers. Drug and Alcohol
Dependence, 87(2-3), 233–240.
Goldstein, R. Z., Tomasi, D., Rajaram, S., Cottone, L. A., Zhang, L., Maloney, T., …
Volkow, N. D. (2007). Role of the anterior cingulate and medial orbitofrontal cortex
in processing drug cues in cocaine addiction. Neuroscience, 144(4), 1153–1159.
Goldstein, R. Z., & Volkow, N. D. (2002). Drug addiction and its underlying
neurobiological basis: Neuroimaging evidence for the involvement of the frontal
cortex. American Journal of Psychiatry, 159(10), 1642–1652.
Goldstein, R. Z., & Volkow, N. D. (2011). Dysfunction of the prefrontal cortex in
addiction: neuroimaging findings and clinical implications. Nature Reviews:
Neuroscience, 12(11), 652–669.
Goldstein, R. Z., Volkow, N. D., Wang, G. J., Fowler, J. S., & Rajaram, S. (2001).
Addiction changes orbitofrontal gyrus function: Involvement in response inhibition.
NeuroReport, 12(11), 2595–2599.
Gooding, D. C., Burroughs, S., & Boutros, N. N. (2008). Attentional deficits in cocainedependent patients: converging behavioral and electrophysiological evidence.
Psychiatry Research, 160(2), 145–154.
Goodman, W. K., Price, L. H., Rasmussen, S. A., Mazure, C., Delgado, P., Heninger, G.
R., & Charney, D. S. (1989). The Yale-Brown Obsessive Compulsive Scale. II.
Validity. Archives of General Psychiatry, 46(11), 1012–1016.
166
References
Gordon, L. (2006). The economic and social costs of Class A drug use in England and
Wales. London.
Gormally, J., Black, S., Daston, S., & Rardin, D. (1982). The assessment of binge eating
severity among obese persons. Addictive Behaviors, 7(1), 47–55.
Gottesman, I. I., & Gould, T. D. (2003). The endophenotype concept in psychiatry:
etymology and strategic intentions. American Journal of Psychiatry, 160(4), 636–
645.
Gottfried, J. A., O’Doherty, J., & Dolan, R. J. (2003). Encoding predictive reward value
in human amygdala and orbitofrontal cortex. Science, 301(5636), 1104–1107.
Grant, S., Contoreggi, C., & London, E. D. (2000). Drug abusers show impaired
performance in a laboratory test of decision making. Neuropsychologia, 38(8),
1180–1187.
Grant, S., London, E. D., Newlin, D. B., Villemagne, V. L., Liu, X., Contoreggi, C., …
Margolin, A. (1996). Activation of memory circuits during cue-elicited cocaine
craving. Proceedings of the National Academy of Sciences of the United States of
America, 93(21), 12040–12045.
Guindalini, C., Howard, M., Haddley, K., Laranjeira, R., Collier, D., Ammar, N., …
Breen, G. (2006). A dopamine transporter gene functional variant associated with
cocaine abuse in a Brazilian sample. Proceedings of the National Academy of
Sciences of the United States of America, 103(12), 4552–4557.
Gunstad, J., Lhotsky, A., Wendell, C. R., Ferrucci, L., & Zonderman, A. B. (2010).
Longitudinal examination of obesity and cognitive function: results from the
Baltimore longitudinal study of aging. Neuroepidemiology, 34(4), 222–229.
Gunstad, J., Paul, R. H., Cohen, R. A., Tate, D. F., Spitznagel, M. B., & Gordon, E.
(2007). Elevated body mass index is associated with executive dysfunction in
otherwise healthy adults. Comprehensive Psychiatry, 48(1), 57–61.
Gunstad, J., Paul, R. H., Cohen, R. A., Tate, D. F., Spitznagel, M. B., Grieve, S., &
Gordon, E. (2008). Relationship between body mass index and brain volume in
healthy adults. The International Journal of Neuroscience, 118(11), 1582–1593.
Hall, F. S., Drgonova, J., Jain, S., & Uhl, G. R. (2013). Implications of genome wide
association studies for addiction: Are our a priori assumptions all wrong?
Pharmacology & Therapeutics, 140(3), 267–279.
Hammers, A., Allom, R., Koepp, M. J., Free, S. L., Myers, R., Lemieux, L., … Duncan, J.
S. (2003). Three-dimensional maximum probability atlas of the human brain, with
particular reference to the temporal lobe. Human Brain Mapping, 19(4), 224–247.
Hanlon, C. A., Dufault, D. L., Wesley, M. J., & Porrino, L. J. (2011). Elevated gray and
white matter densities in cocaine abstainers compared to current users.
Psychopharmacology, 218(4), 681–692.
167
References
Hernandez, L., & Hoebel, B. G. (1988). Food reward and cocaine increase extracellular
dopamine in the nucleus accumbens as measured by microdialysis. Life Sciences,
42(18), 1705–1712.
Hester, R., Dixon, V., & Garavan, H. (2006). A consistent attentional bias for drugrelated material in active cocaine users across word and picture versions of the
emotional Stroop task. Drug and Alcohol Dependence, 81(3), 251–257.
Hester, R., & Garavan, H. (2004). Executive dysfunction in cocaine addiction: evidence
for discordant frontal, cingulate, and cerebellar activity. Journal of Neuroscience,
24(49), 11017–11022.
Ho, A. J., Raji, C. A., Becker, J. T., Lopez, O. L., Kuller, L. H., Hua, X., … Thompson,
P. M. (2010). Obesity is linked with lower brain volume in 700 AD and MCI
patients. Neurobiology of Aging, 31(8), 1326–1339.
Horner, M. D. (1999). Attentional functioning in abstinent cocaine abusers. Drug and
Alcohol Dependence, 54(1), 19–33.
Horstmann, A., Busse, F. P., Mathar, D., Müller, K., Lepsien, J., Schlögl, H., … Pleger,
B. (2011). Obesity-related differences between women and men in brain structure
and goal-directed behavior. Frontiers in Human Neuroscience, 5, 58.
Hwang, L. Y., Ross, M. W., Zack, C., Bull, L., Rickman, K., & Holleman, M. (2000).
Prevalence of sexually transmitted infections and associated risk factors among
populations of drug abusers. Clinical Infectious Diseases, 31(4), 920–926.
Ifland, J. R., Preuss, H. G., Marcus, M. T., Rourke, K. M., Taylor, W. C., Burau, K., …
Manso, G. (2009). Refined food addiction: a classic substance use disorder. Medical
Hypotheses, 72(5), 518–526.
Ito, R., Dalley, J. W., Howes, S. R., Robbins, T. W., & Everitt, B. J. (2000). Dissociation
in conditioned dopamine release in the nucleus accumbens core and shell in response
to cocaine cues and during cocaine-seeking behavior in rats. Journal of
Neuroscience, 20(19), 7489–7495.
Ito, R., Dalley, J. W., Robbins, T. W., & Everitt, B. J. (2002). Dopamine release in the
dorsal striatum during cocaine-seeking behavior under the control of a drugassociated cue. Journal of Neuroscience, 22(14), 6247–6253.
Izquierdo, A., Suda, R. K., & Murray, E. A. (2004). Bilateral orbital prefrontal cortex
lesions in rhesus monkeys disrupt choices guided by both reward value and reward
contingency. Journal of Neuroscience, 24(34), 7540–7548.
Jacobsen, L. K., Giedd, J. N., Gottschalk, C., Kosten, T. R., & Krystal, J. H. (2001).
Quantitative morphology of the caudate and putamen in patients with cocaine
dependence. American Journal of Psychiatry, 158(3), 486–489.
168
References
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for
the robust and accurate linear registration and motion correction of brain images.
NeuroImage, 17(2), 825–841.
Jenkinson, M., & Smith, S. (2001). A global optimisation method for robust affine
registration of brain images. Medical Image Analysis, 5(2), 143–156.
Jentsch, D., Olausson, P., De La Garza, R., & Taylor, J. R. (2002). Impairments of
reversal learning and response perseveration after repeated, intermittent cocaine
administrations to monkeys. Neuropsychopharmacology, 26(2), 183–190.
Jentsch, D., & Taylor, J. R. (1999). Impulsivity resulting from frontostriatal dysfunction
in drug abuse: implications for the control of behavior by reward-related stimuli.
Psychopharmacology, 146(4), 373–390.
Jia, Z., Worhunsky, P. D., Carroll, K. M., Rounsaville, B. J., Stevens, M. C., Pearlson, G.
D., & Potenza, M. N. (2011). An initial study of neural responses to monetary
incentives as related to treatment outcome in cocaine dependence. Biological
Psychiatry, 70(6), 553–560.
Joel, D., Doljansky, J., Roz, N., & Rehavi, M. (2005). Role of the orbital cortex and of
the serotonergic system in a rat model of obsessive compulsive disorder.
Neuroscience, 130(1), 25–36.
Jonsson, E. G., Nothen, M. M., Grunhage, F., Farde, L., Nakashima, Y., Propping, P., &
Sedvall, G. C. (1999). Polymorphisms in the dopamine D2 receptor gene and their
relationships to striatal dopamine receptor density of healthy volunteers. Molecular
Psychiatry, 4(3), 290–296.
Kaufman, J. N., Ross, T. J., Stein, E. A., & Garavan, H. (2003). Cingulate hypoactivity in
cocaine users during a GO-NOGO task as revealed by event-related functional
magnetic resonance imaging. Journal of Neuroscience, 23(21), 7839–7843.
Kerns, J. G., Cohen, J. D., MacDonald 3rd, A. W., Cho, R. Y., Stenger, V. A., & Carter,
C. S. (2004). Anterior cingulate conflict monitoring and adjustments in control.
Science, 303(5660), 1023–1026.
Kim, H., Shimojo, S., & O’Doherty, J. P. (2011). Overlapping responses for the
expectation of juice and money rewards in human ventromedial prefrontal cortex.
Cerebral Cortex, 21(4), 769–776.
Kirby, K. N., & Petry, N. M. (2004). Heroin and cocaine abusers have higher discount
rates for delayed rewards than alcoholics or non-drug-using controls. Addiction, 99,
461–471.
Kirby, K. N., Petry, N. M., & Bickel, W. K. (1999). Heroin addicts have higher discount
rates for delayed rewards than non-drug-using controls. Journal of Experimental
Psychology, 128(1), 78–87.
169
References
Knutson, B., Adams, C. M., Fong, G. W., & Hommer, D. (2001). Anticipation of
increasing monetary reward selectively recruits nucleus accumbens. Journal of
Neuroscience, 21(16), RC159.
Knutson, B., Fong, G. W., Adams, C. M., Varner, J. L., & Hommer, D. (2001).
Dissociation of reward anticipation and outcome with event-related fMRI.
NeuroReport, 12(17), 3683–3687.
Knutson, B., Westdorp, A., Kaiser, E., & Hommer, D. (2000). FMRI visualization of
brain activity during a monetary incentive delay task. NeuroImage, 12(1), 20–27.
Koller, G., Bondy, B., Preuss, U. W., Bottlender, M., & Soyka, M. (2003). No association
between a polymorphism in the promoter region of the MAOA gene with antisocial
personality traits in alcoholics. Alcohol and Alcoholism, 38(1), 31–34.
Koob, G. F., Ahmed, S. H., Boutrel, B., Chen, S. A., Kenny, P. J., Markou, A., … Sanna,
P. P. (2004). Neurobiological mechanisms in the transition from drug use to drug
dependence. Neuroscience and Biobehavioral Reviews, 27(8), 739–749.
Koob, G. F., & Le Moal, M. (2001). Drug addiction, dysregulation of reward, and
allostasis. Neuropsychopharmacology, 24(2), 97–129.
Koob, G. F., & Le Moal, M. (2005). Plasticity of reward neurocircuitry and the “dark
side” of drug addiction. Nature Neuroscience, 8(11), 1442–1444.
Koob, G. F., & Nestler, E. J. (1997). The neurobiology of drug addiction. Journal of
Neuropsychiatry and Clinical Neurosciences, 9(3), 482–497.
Koob, G. F., & Volkow, N. D. (2010).
Neuropsychopharmacology, 35(1), 217–238.
Neurocircuitry
of
addiction.
Lee, B., London, E. D., Poldrack, R. A., Farahi, J., Nacca, A., Monterosso, J. R., …
Mandelkern, M. A. (2009). Striatal dopamine d2/d3 receptor availability is reduced
in methamphetamine dependence and is linked to impulsivity. Journal of
Neuroscience, 29(47), 14734–40.
Levine, A. J., Hardy, D. J., Miller, E., Castellon, S. A., Longshore, D., & Hinkin, C. H.
(2006). The effect of recent stimulant use on sustained attention in HIV-infected
adults. Journal of Clinical and Experimental Neuropsychology, 28(1), 29–42.
Levy, D. J., & Glimcher, P. W. (2012). The root of all value: a neural common currency
for choice. Current Opinion in Neurobiology, 22(6), 1027–1038.
Lim, K. O., Choi, S. J., Pomara, N., Wolkin, A., & Rotrosen, J. P. (2002). Reduced
frontal white matter integrity in cocaine dependence: a controlled diffusion tensor
imaging study. Biological Psychiatry, 51(11), 890–895.
170
References
Liu, X., Matochik, J. A., Cadet, J. L., & London, E. D. (1998). Smaller volume of
prefrontal lobe in polysubstance abusers: A magnetic resonance imaging study.
Neuropsychopharmacology, 18(4), 243–252.
Logan, G. D., Schachar, R. J., & Tannock, R. (1997). Impulsivity and inhibitory control.
Psychological Science, 8(1), 60–64.
London, E. D., Berman, S. M., Voytek, B., Simon, S. L., Mandelkern, M. A., Monterosso,
J., … Ling, W. (2005). Cerebral metabolic dysfunction and impaired vigilance in
recently abstinent methamphetamine abusers. Biological Psychiatry, 58(10), 770–
778.
London, E. D., Ernst, M., Grant, S., Bonson, K., & Weinstein, A. (2000). Orbitofrontal
cortex and human drug abuse: functional imaging. Cerebral Cortex, 10(3), 334–342.
Maayan, L., Hoogendoorn, C., Sweat, V., & Convit, A. (2011). Disinhibited eating in
obese adolescents is associated with orbitofrontal volume reductions and executive
dysfunction. Obesity, 19(7), 1382–1387.
MacDonald, A. W., Cohen, J. D., Stenger, V. A., & Carter, C. S. (2000). Dissociating the
role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control.
Science, 288(5472), 1835–1838.
Marissen, M. A. E., Franken, I. H. A., Waters, A. J., Blanken, P., van den Brink, W., &
Hendriks, V. M. (2006). Attentional bias predicts heroin relapse following treatment.
Addiction, 101(9), 1306–1312.
Martinez, D., Broft, A., Foltin, R. W., Slifstein, M., Hwang, D. R., Huang, Y., …
Laruelle, M. (2004). Cocaine dependence and D2 receptor availability in the
functional subdivisions of the striatum: relationship with cocaine-seeking behavior.
Neuropsychopharmacology, 29(6), 1190–1202.
Martinez, D., Greene, K., Broft, A., Kumar, D., Liu, F., Narendran, R., … Kleber, H. D.
(2009). Lower level of endogenous dopamine in patients with cocaine dependence:
findings from PET imaging of D(2)/D(3) receptors following acute dopamine
depletion. American Journal of Psychiatry, 166(10), 1170–1177.
Matochik, J. A., London, E. D., Eldreth, D. A., Cadet, J. L., & Bolla, K. I. (2003). Frontal
cortical tissue composition in abstinent cocaine abusers: a magnetic resonance
imaging study. NeuroImage, 19(3), 1095–1102.
Menzies, L., Chamberlain, S. R., Laird, A. R., Thelen, S. M., Sahakian, B. J., &
Bullmore, E. T. (2008). Integrating evidence from neuroimaging and
neuropsychological studies of obsessive-compulsive disorder: The orbitofrontostriatal model revisited. Neuroscience and Biobehavioral Reviews, 32(3), 525–549.
Meunier, D., Ersche, K. D., Craig, K. J., Fornito, A., Merlo-Pich, E., Fineberg, N. A., …
Bullmore, E. T. (2011). Brain functional connectivity in stimulant drug dependence
and obsessive-compulsive disorder. NeuroImage.
171
References
Meyer-Lindenberg, A., Buckholtz, J. W., Kolachana, B., R Hariri, A., Pezawas, L., Blasi,
G., … Weinberger, D. R. (2006). Neural mechanisms of genetic risk for impulsivity
and violence in humans. Proceedings of the National Academy of Sciences of the
United States of America, 103(16), 6269–6274.
Miller, G. A., & Chapman, J. P. (2001). Misunderstanding analysis of covariance.
Journal of Abnormal Psychology, 110(1), 40–48.
Monterosso, J. R., Aron, A. R., Cordova, X., Xu, J., & London, E. D. (2005). Deficits in
response inhibition associated with chronic methamphetamine abuse. Drug and
Alcohol Dependence, 79(2), 273–277.
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.
Philosophical Transactions of the Royal Society of London Series B, Biological
Sciences, 363(1507), 3223–3232.
National Treatment Agency for Substance Misuse. (2012). Drug Treatment 2012:
Progress Made, Challenges Ahead. London.
Nelson, H. E. (1982). The National Adult Reading Test Manual. Windsor: NFER-Nelson.
Nestor, L., Hester, R., & Garavan, H. (2010). Increased ventral striatal BOLD activity
during non-drug reward anticipation in cannabis users. NeuroImage, 49(1), 1133–
1143.
Nestor, L. J., Ghahremani, D. G., Monterosso, J., & London, E. D. (2011). Prefrontal
hypoactivation during cognitive control in early abstinent methamphetaminedependent subjects. Psychiatry Research: Neuroimaging, 194(3), 287–295.
Nigg, J. T., Wong, M. M., Martel, M. M., Jester, J. M., Puttler, L. I., Glass, J. M., …
Zucker, R. A. (2006). Poor response inhibition as a predictor of problem drinking
and illicit drug use in adolescents at risk for alcoholism and other substance use
disorders. Journal of the American Academy of Child and Adolescent Psychiatry,
45(4), 468–475.
Noble, E. P. (2000). Addiction and its reward process through polymorphisms of the D2
dopamine receptor gene: a review. European Psychiatry, 15(2), 79–89.
Noble, E. P., Blum, K., Khalsa, M. E., Ritchie, T., Montgomery, A., Wood, R. C., …
Anglin, M. D. (1993). Allelic association of the D2 dopamine receptor gene with
cocaine dependence. Drug and Alcohol Dependence, 33(3), 271–285.
Noble, E. P., Gottschalk, L. A., Fallon, J. H., Ritchie, T. L., & Wu, J. C. (1997). D2
dopamine receptor polymorphism and brain regional glucose metabolism. American
Journal of Medical Genetics, 74(2), 162–166.
Noonan, M. P., Mars, R. B., & Rushworth, M. F. S. (2011). Distinct roles of three frontal
cortical areas in reward-guided behavior. Journal of Neuroscience, 31(40), 14399–
14412.
172
References
Nutt, D. J. (1996). Addiction: brain mechanisms and their treatment implications. The
Lancet, 347(8993), 31–36.
Nutt, D. J. (2013). Addiction: lifestyle choice or medical diagnosis? Journal of
Evaluation in Clinical Practice, 19(3), 6269–6274.
O’Doherty, J. (2002). Role of human orbitofrontal cortex in representing stimulus-reward
value. Psychophysiology, 39, S4–S4.
Opris, I., Hampson, R. E., & Deadwyler, S. A. (2009). The encoding of cocaine vs.
natural rewards in the striatum of nonhuman primates: categories with different
activations. Neuroscience, 163(1), 40–54.
Pannacciulli, N., Del Parigi, A., Chen, K., Le, D. S., Reiman, E. M., & Tataranni, P. A.
(2006). Brain abnormalities in human obesity: a voxel-based morphometric study.
NeuroImage, 31(4), 1419–1425.
Parylak, S. L., Koob, G. F., & Zorrilla, E. P. (2011). The dark side of food addiction.
Physiology and Behavior, 104(1), 149–156.
Patton, J. H., Stanford, M. S., & Barratt, E. S. (1995). Factor structure of the Barratt
impulsiveness scale. Journal of Clinical Psychology, 51(6), 768–774.
Pelchat, M. L. (2002). Of human bondage: food craving, obsession, compulsion, and
addiction. Physiology and Behavior, 76(3), 347–352.
Perry, J. L., & Carrol, M. E. (2008). The role of impulsive behavior in drug abuse.
Psychopharmacology, 200(1), 1–26.
Philibert, R. A., Gunter, T. D., Beach, S. R. H., Brody, G. H., & Madan, A. (2008).
MAOA methylation is associated with nicotine and alcohol dependence in women.
American Journal of Medical Genetics, 147B(5), 565–570.
Porrino, L. J., Daunais, J. B., Smith, H. R., & Nader, M. A. (2004). The expanding effects
of cocaine: studies in a nonhuman primate model of cocaine self-administration.
Neuroscience and Biobehavioral Review, 27(8), 813–820.
Porrino, L. J., Lyons, D., Miller, M. D., Smith, H. R., Friedman, D. P., Daunais, J. B., &
Nader, M. A. (2002). Metabolic mapping of the effects of cocaine during the initial
phases of self-administration in the nonhuman primate. Journal of Neuroscience,
22(17), 7687–7694.
Porrino, L. J., Smith, H. R., Nader, M. A., & Beveridge, T. J. R. (2007). The effects of
cocaine: a shifting target over the course of addiction. Progress in NeuroPsychopharmacology & Biological Psychiatry, 31(8), 1593–600.
Rada, P., Avena, N. M., & Hoebel, B. G. (2005). Daily bingeing on sugar repeatedly
releases dopamine in the accumbens shell. Neuroscience, 134(3), 737–744.
173
References
Raji, C. A., Ho, A. J., Parikshak, N. N., Becker, J. T., Lopez, O. L., Kuller, L. H., …
Thompson, P. M. (2010). Brain structure and obesity. Human Brain Mapping, 31(3),
353–364.
Reynolds, B., Ortengren, A., Richards, J. B., & de Wit, H. (2006). Dimensions of
impulsive behavior: personality and behavioral measures. Personality and Individual
Differences, 40(2), 305–315.
Robbins, T. W., Ersche, K. D., & Everitt, B. J. (2008). Drug addiction and the memory
systems of the brain. Annals of the New York Academy of Sciences, 1141, 1–21.
Robbins, T. W., Gillan, C. M., Smith, D. G., de Wit, S., & Ersche, K. D. (2012).
Neurocognitive endophenotypes of impulsivity and compulsivity: towards
dimensional psychiatry. Trends in Cognitive Sciences, 16(1), 81–91.
Robinson, T. E., & Berridge, K. C. (1993). The neural basis of drug craving: An
incentive-sensitization theory of addiction. Brain Research Reviews, 18, 247–291.
Rogers, R. D., & Robbins, T. W. (2001). Investigating the neurocognitive deficits
associated with chronic drug misuse. Current Opinion in Neurobiology, 11(2), 250–
257.
Rolls, E. T. (2000). The orbitofrontal cortex and reward. Cereb Cortex, 10(3), 284–294.
Rushworth, M. F. S., & Behrens, T. E. J. (2008). Choice, uncertainty and value in
prefrontal and cingulate cortex. Nature Neuroscience, 11(4), 389–397.
Rushworth, M. F. S., Behrens, T. E. J., Rudebeck, P. H., & Walton, M. E. (2007).
Contrasting roles for cingulate and orbitofrontal cortex in decisions and social
behaviour. Trends in Cognitive Sciences, 11(4), 168–176.
Rushworth, M. F. S., Noonan, M. P., Boorman, E. D., Walton, M. E., & Behrens, T. E.
(2011). Frontal cortex and reward-guided learning and decision-making. Neuron,
70(6), 1054–1069.
Saito, T., Lachman, H. M., Diaz, L., Hallikainen, T., Kauhanen, J., Salonen, J. T., …
Tiihonen, J. (2002). Analysis of monoamine oxidase A (MAOA) promoter
polymorphism in Finnish male alcoholics. Psychiatry Research, 109(2), 113–119.
Salo, R., Fassbender, C., Buonocore, M. H., & Ursu, S. (2013). Behavioral regulation in
methamphetamine abusers: An fMRI study. Psychiatry Research: Neuroimaging,
211(3), 234–238.
Salo, R., Nordahl, T. E., Possin, K., Leamon, M., Gibson, D. R., Galloway, G. P., …
Sullivan, E. V. (2002). Preliminary evidence of reduced cognitive inhibition in
methamphetamine-dependent individuals. Psychiatry Research, 111(1), 65–74.
Saunders, J. B., Aasland, O. G., Babor, T. F., Delafuente, J. R., & Grant, M. (1993).
Development of the Alcohol-Use Disorders Identification Test (AUDIT) - WHO
174
References
collaborative project on early detection of persons with harmful alcoholconsumption-II. Addiction, 88(6), 791–804.
Schoenbaum, G., Roesch, M. R., & Stalnaker, T. A. (2006). Orbitofrontal cortex,
decision-making and drug addiction. Trends in Neurosciences, 29(2), 116–124.
Schoenbaum, G., & Setlow, B. (2005). Cocaine makes actions insensitive to outcomes but
not extinction: implications for altered orbitofrontal-amygdalar function. Cerebral
Cortex, 15(8), 1162–1169.
Schoenbaum, G., & Shaham, Y. (2008). The role of orbitofrontal cortex in drug
addiction: A review of preclinical studies. Biological Psychiatry, 63(3), 256–262.
Schott, B. H., Minuzzi, L., Krebs, R. M., Elmenhorst, D., Lang, M., Winz, O. H., …
Bauer, A. (2008). Mesolimbic functional magnetic resonance imaging activations
during reward anticipation correlate with reward-related ventral striatal dopamine
release. Journal of Neuroscience, 28(52), 14311–14319.
Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and
reward. Science, 275(5306), 1593–1599.
Silveri, M. M., Rogowska, J., McCaffrey, A., & Yurgelun-Todd, D. A. (2011).
Adolescents at risk for alcohol abuse demonstrate altered frontal lobe activation
during stroop performance. Alcoholism-Clinical and Experimental Research, 35(2),
218–228.
Sim, M. E., Lyoo, I. K., Streeter, C. C., Covell, J., Sarid-Segal, O., Ciraulo, D. A., …
Renshaw, P. F. (2007). Cerebellar gray matter volume correlates with duration of
cocaine use in cocaine-dependent subjects. Neuropsychopharmacology, 32(10),
2229–2237.
Skinner, H. A. (1982). The drug abuse screening test. Addictive Behaviors, 7(4), 363–371.
Small, D. M., Jones-Gotman, M., & Dagher, A. (2003). Feeding-induced dopamine
release in dorsal striatum correlates with meal pleasantness ratings in healthy human
volunteers. NeuroImage, 19(4), 1709–1715.
Smith, S. M., & Nichols, T. E. (2009). Threshold-free cluster enhancement: Addressing
problems of smoothing, threshold dependence and localisation in cluster inference.
NeuroImage, 44(1), 83–98.
Soar, K., Mason, C., Potton, A., & Dawkins, L. (2012). Neuropsychological effects
associated with recreational cocaine use. Psychopharmacology, 222(4), 633–643.
Solomon, R. L. (1980). The opponent-process theory of acquired motivation: the costs of
pleasure and the benefits of pain. American Psychologist, 35(8), 691–712.
175
References
Spitz, M. R., Detry, M. A., Pillow, P., Hu, Y. H., Amos, C. I., Hong, W. K., & Wu, X. F.
(2000). Variant alleles of the D2 dopamine receptor gene and obesity. Nutrition
Research, 20(3), 371–380.
Stanek, K. M., Strain, G., Devlin, M., Cohen, R., Paul, R., Crosby, R. D., … Gunstad, J.
(2013). Body mass index and neurocognitive functioning across the adult lifespan.
Neuropsychology, 27(2), 141–151.
Stice, E., Spoor, S., Bohon, C., & Small, D. M. (2008). Relation between obesity and
blunted striatal response to food is moderated by TaqIA A1 allele. Science,
322(5900), 449–452.
Stice, E., Yokum, S., Blum, K., & Bohon, C. (2010). Weight gain Is associated with
reduced striatal response to palatable food. Journal of Neuroscience, 30(39), 13105–
13109.
Streeter, C. C., Terhune, D. B., Whitfield, T. H., Gruber, S., Sarid-Segal, O., Silveri, M.
M., … Yurgelun-Todd, D. A. (2008). Performance on the Stroop predicts treatment
compliance in cocaine-dependent individuals. Neuropsychopharmacology, 33(4),
827–836.
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of
Experimental Psychology, 18, 643–662.
Suckling, J., & Bullmore, E. T. (2004). Permutation tests for factorially designed
neuroimaging experiments. Human Brain Mapping, 22(3), 193–205.
Suckling, J., Davis, M. H., Ooi, C., Wink, A. M., Fadili, J., Salvador, R., … Bullmore, E.
T. (2006). Permutation testing of orthogonal factorial effects in a languageprocessing experiment using fMRI. Human Brain Mapping, 27(5), 425–433.
Taki, Y., Kinomura, S., Sato, K., Inoue, K., Goto, R., Okada, K., … Fukuda, H. (2008).
Relationship between body mass index and gray matter volume in 1,428 healthy
individuals. Obesity, 16(1), 119–124.
Tanabe, J., Tregellas, J. R., Dalwani, M., Thompson, L., Owens, E., Crowley, T., &
Banich, M. (2009). Medial orbitofrontal cortex gray matter is reduced in abstinent
substance-dependent individuals. Biological Psychiatry, 65(2), 160–164.
Tanda, G., Pontieri, F. E., & DiChiara, G. (1997). Cannabinoid and heroin activation of
mesolimbic dopamine transmission by a common mu 1 opioid receptor mechanism.
Science, 276(5321), 2048–2050.
Tanda, G., Pontieri, F. E., Frau, R., & DiChiara, G. (1997). Contribution of blockade of
the noradrenaline carrier to the increase of extracellular dopamine in the rat
prefrontal cortex by amphetamine and cocaine. European Journal of Neuroscience,
9(10), 2077–2085.
176
References
Tang, D. W., Fellows, L. K., Small, D. M., & Dagher, A. (2012). Food and drug cues
activate similar brain regions: a meta-analysis of functional MRI studies. Physiology
& Behavior, 106(3), 317–324.
Taylor, J. R., & Robbins, T. W. (1986). 6-Hydroxydopamine lesions of the nucleus
accumbens, but not of the caudate nucleus, attenuate enhanced responding with
reward-related
stimuli
produced
by
intra-accumbens
d-amphetamine.
Psychopharmacology, 90(3), 390–397.
Thanos, P. K., Volkow, N. D., Freimuth, P., Umegaki, H., Ikari, H., Roth, G., …
Hitzemann, R. (2001). Overexpression of dopamine D2 receptors reduces alcohol
self-administration. Journal of Neurochemistry, 78(5), 1094–1103.
Tiffany, S. T., & Carter, B. L. (1998). Is craving the source of compulsive drug use?
Journal of Psychopharmacology, 12(1), 23–30.
Tomasi, D., Goldstein, R. Z., Telang, F., Maloney, T., Alia-Klein, N., Caparelli, E. C., &
Volkow, N. D. (2007). Widespread disruption in brain activation patterns to a
working memory task during cocaine abstinence. Brain Research, 1171, 83–92.
Townshend, J., & Duka, T. (2001). Attentional bias associated with alcohol cues:
differences between heavy and occasional social drinkers. Psychopharmacology,
157(1), 67–74.
Tremblay, L., & Schultz, W. (1999). Relative reward preference in primate orbitofrontal
cortex. Nature, 398(6729), 704–708.
Tun, P. A., & Lachman, M. E. (2008). Age differences in reaction time and attention in a
national telephone sample of adults: education, sex, and task complexity matter.
Developmental Psychology, 44(5), 1421–1429.
Uhl, G. R., Hall, F. S., & Sora, I. (2002). Cocaine, reward, movement and monoamine
transporters. Molecular Psychiatry, 7(1), 21–26.
United Nations Office on Drugs and Crime. (2012). World Drug Report 2012. Vienna:
United Nations.
Vaidya, C. J., Austin, G., Kirkorian, G., Ridlehuber, H. W., Desmond, J. E., Glover, G.
H., & Gabrieli, J. D. E. (1998). Selective effects of methylphenidate in attention
deficit hyperactivity disorder: A functional magnetic resonance study. Proceedings
of the National Academy of Sciences, 95(24), 14494–14499.
Verdejo-García, A, & Pérez-García, M. (2007). Ecological assessment of executive
functions in substance dependent individuals. Drug and Alcohol Dependence, 90(1),
48–55.
Verdejo-Garcia, A. J., Perales, J. C., & Perez-Garcia, M. (2007). Cognitive impulsivity in
cocaine and heroin polysubstance abusers. Addictive Behaviors, 32(5), 950–966.
177
References
Verdejo-García, Antonio, Bechara, A., Recknor, E. C., & Pérez-García, M. (2006).
Executive dysfunction in substance dependent individuals during drug use and
abstinence: an examination of the behavioral, cognitive and emotional correlates of
addiction. Journal of the International Neuropsychological Society, 12(3), 405–415.
Volkow, N. D., Chang, L., Wang, G. J., Fowler, J. S., Ding, Y. S., Sedler, M., … Pappas,
N. (2001). Low level of brain dopamine D2 receptors in methamphetamine abusers:
association with metabolism in the orbitofrontal cortex. American Journal of
Psychiatry, 158(12), 2015–2021.
Volkow, N. D., & Fowler, J. S. (2000). Addiction, a disease of compulsion and drive:
involvement of the orbitofrontal cortex. Cerebral Cortex, 10(3), 318–325.
Volkow, N. D., Fowler, J. S., Wang, G. J., Hitzemann, R., Logan, J., Schlyer, D. J., …
Wolf, A. P. (1993). Decreased dopamine D2 receptor availability is associated with
reduced frontal metabolism in cocaine abusers. Synapse, 14(2), 169–177.
Volkow, N. D., Fowler, J. S., Wang, G. J., Telang, F., Logan, J., Jayne, M., … Swanson,
J. M. (2010). Cognitive control of drug craving inhibits brain reward regions in
cocaine abusers. NeuroImage, 49(3), 2536–2543.
Volkow, N. D., Fowler, J. S., Wolf, A. P., Hitzemann, R., Dewey, S., Bendriem, B., …
Hoff, A. (1991). Changes in brain glucose-metabolism in cocaine dependence and
withdrawal. American Journal of Psychiatry, 148(5), 621–626.
Volkow, N. D., Wang, G. J., Begleiter, H., Porjesz, B., Fowler, J. S., Telang, F., …
Thanos, P. K. (2006). High levels of dopamine D2 receptors in unaffected members
of alcoholic families: possible protective factors. Archives of General Psychiatry,
63(9), 999–1008.
Volkow, N. D., Wang, G. J., Fowler, J. S., Gatley, S. J., Ding, Y. S., Logan, J., …
Lieberman, J. (1996). Relationship between psychostimulant-induced “high” and
dopamine transporter occupancy. Proceedings of the National Academy of Sciences
of the United States of America, 93(19), 10388–10392.
Volkow, N. D., Wang, G. J., Fowler, J. S., Logan, J., Gatley, S. J., Gifford, A., … Pappas,
N. (1999). Prediction of reinforcing responses to psychostimulants in humans by
brain dopamine D2 receptor levels. American Journal of Psychiatry, 156(9), 1440–
1443.
Volkow, N. D., Wang, G. J., Fowler, J. S., Logan, J., Gatley, S. J., Hitzemann, R., …
Pappas, N. (1997). Decreased striatal dopaminergic responsiveness in detoxified
cocaine-dependent subjects. Nature, 386(6627), 830–833.
Volkow, N. D., Wang, G. J., Fowler, J. S., & Telang, F. (2008). Overlapping neuronal
circuits in addiction and obesity: evidence of systems pathology. Philosophical
Transactions of the Royal Society of London. Series B, Biological Sciences,
363(1507), 3191–3200.
178
References
Volkow, N. D., Wang, G. J., Telang, F., Fowler, J. S., Goldstein, R. Z., Alia-Klein, N., …
Pradhan, K. (2009). Inverse association between BMI and prefrontal metabolic
activity in healthy adults. Obesity, 17(1), 60–65.
Volkow, N. D., Wang, G. J., Telang, F., Fowler, J. S., Logan, J., Childress, A. R., …
Wong, C. (2006). Cocaine cues and dopamine in dorsal striatum: mechanism of
craving in cocaine addiction. Journal of Neuroscience, 26(24), 6583–6588.
Volkow, N. D., Wang, G. J., Telang, F., Fowler, J. S., Logan, J., Jayne, M., … Wong, C.
(2007). Profound decreases in dopamine release in striatum in detoxified alcoholics:
possible orbitofrontal involvement. Journal of Neuroscience, 27(46), 12700–12706.
Volkow, N. D., Wang, G. J., Telang, F., Fowler, J. S., Thanos, P. K., Logan, J., …
Pradhan, K. (2008). Low dopamine striatal D2 receptors are associated with
prefrontal metabolism in obese subjects: possible contributing factors. NeuroImage,
42(4), 1537–1543.
Walther, K., Birdsill, A. C., Glisky, E. L., & Ryan, L. (2010). Structural brain differences
and cognitive functioning related to body mass index in older females. Human Brain
Mapping, 31(7), 1052–1064.
Wang, G. J., Volkow, N. D., Fowler, J. S., Cervany, P., Hitzemann, R. J., Pappas, N. R.,
… Felder, C. (1999). Regional brain metabolic activation during craving elicited by
recall of previous drug experiences. Life Sciences, 64(9), 775–784.
Wang, G. J., Volkow, N. D., Logan, J., Pappas, N. R., Wong, C. T., Zhu, W., … Fowler,
J. S. (2001). Brain dopamine and obesity. Lancet, 357(9253), 354–357.
Wang, G. J., Volkow, N. D., Thanos, P. K., & Fowler, J. S. (2004). Similarity between
obesity and drug addiction as assessed by neurofunctional imaging: a concept
review. Journal of Addictive Diseases, 23(3), 39–53.
Weise, C. M., Thiyyagura, P., Reiman, E. M., Chen, K., & Krakoff, J. (2013). Fat-free
body mass but not fat mass is associated with reduced gray matter volume of cortical
brain regions implicated in autonomic and homeostatic regulation. NeuroImage, 64,
712–721.
Wexler, B. E., Gottschalk, C. H., Fulbright, R. K., Prohovnik, I., Lacadie, C. M.,
Rounsaville, B. J., & Gore, J. C. (2001). Functional magnetic resonance imaging of
cocaine craving. American Journal of Psychiatry, 158(1), 86–95.
Whelan, R., Conrod, P. J., Poline, J. B., Lourdusamy, A., Banaschewski, T., Barker, G. J.,
… Garavan, H. (2012). Adolescent impulsivity phenotypes characterized by distinct
brain networks. Nature Neuroscience, 15(6), 920–925.
Wise, R. A. (2004). Dopamine, learning and motivation. Nature Reviews: Neuroscience,
5(6), 483–494.
Wise, R. A., & Bozarth, M. A. (1985). Brain mechanisms of drug reward and euphoria.
Psychiatic Medicine, 3(4), 445–460.
179
References
Wise, R. A., & Rompre, P. P. (1989). Brain dopamine and reward. Annual Review of
Psychology, 40, 191–225.
Wise, R. A., Spindler, J., DeWit, H., & Gerberg, G. J. (1978). Neuroleptic-induced
“anhedonia” in rats: pimozide blocks reward quality of food. Science, 201(4352),
262–264.
Woolverton, W. L., & Anderson, K. G. (2006). Effects of delay to reinforcement on the
choice between cocaine and food in rhesus monkeys. Psychopharmacology, 186(1),
99–106.
Wrase, J., Schlagenhauf, F., Kienast, T., Wüstenberg, T., Bermpohl, F., Kahnt, T., …
Heinz, A. (2007). Dysfunction of reward processing correlates with alcohol craving
in detoxified alcoholics. NeuroImage, 35(2), 787–794.
Wyvell, C. L., & Berridge, K. C. (2001). Incentive sensitization by previous amphetamine
exposure: increased cue-triggered “wanting” for sucrose reward. Journal of
Neuroscience, 21(19), 7831–7840.
Yin, H. H., & Knowlton, B. J. (2006). The role of the basal ganglia in habit formation.
Nature Reviews: Neuroscience, 7(6), 464–476.
Yin, H. H., Knowlton, B. J., & Balleine, B. W. (2004). Lesions of dorsolateral striatum
preserve outcome expectancy but disrupt habit formation in instrumental learning.
European Journal of Neuroscience, 19(1), 181–189.
Yin, H. H., Ostlund, S. B., & Balleine, B. W. (2008). Reward-guided learning beyond
dopamine in the nucleus accumbens: the integrative functions of cortico-basal
ganglia networks. European Journal of Neuroscience, 28(8), 1437–1448.
Yokum, S., Ng, J., & Stice, E. (2012). Relation of regional gray and white matter volumes
to current BMI and future increases in BMI: a prospective MRI study. International
journal of obesity (2005), 36(5), 656–64.
Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a
hidden Markov random field model and the expectation-maximization algorithm.
IEEE transactions on medical imaging, 20(1), 45–57.
Ziauddeen, H., Farooqi, S., & Fletcher, P. C. (2012). Obesity and the brain: how
convincing is the addiction model? Nature Reviews. Neuroscience, 13(4), 279–286.
Ziauddeen, H., & Fletcher, P. C. (2013). Is food addiction a valid and useful concept?
Obesity Reviews, 14(1), 19–28.
Zuckerman, M., Eysenck, S., & Eysenck, H. J. (1978). Sensation seeking in England and
America: cross-cultural, age, and sex comparisons. Journal of Consulting and
Clinical Psychology, 46(1), 139–149.
180
References
Zysset, S., Muller, K., Lohmann, G., & von Cramon, D. Y. (2001). Color-word matching
stroop task: Separating interference and response conflict. NeuroImage, 13(1), 29–
36.
181
Appendices
Appendices
Appendix A. Supplemental Information for Chapter 2
Table S2.1. As sex, education, smoking status, depression scores and alcohol use all
significantly differed between participant groups, largely driven by the stimulantdependent individuals, we included these as covariates in the general linear model
multivariate regression analysis. However, there were no differences in the results both
with and without these covariates (Miller & Chapman, 2001). As these variables seem to
be somewhat intrinsically linked to drug addiction — nicotine dependence, alcohol abuse
and depression commonly occurring as co-morbidities in the disorder — we thought it
prudent to report the results without using these variables as covariates. However, we
report the results including covariates in the model, as there were slight discrepancies.
Covariates included
F(2,129)
P
Covariates excluded
F(2,135)
P
Behavioural analysis
Congruent RT
3.74
0.026*
7.243
Incongruent RT
3.86
0.024*
4.452
Interference
0.96
0.387
0.797
Errors
2.92
0.057
1.915
Imaging analysis: First level
Left precentral / middle frontal gyrus
0.14
0.873
0.330
Left inferior frontal gyrus
0.74
0.482
0.261
Right caudate
0.86
0.424
0.542
Right rolandic operculum
0.16
0.851
0.001
Behavioural analysis: Second level
Right insula / rolandic operculum
16.94
<0.001*
18.373
Left medial superior frontal gyrus
11.85
<0.001*
12.094
Behavioural analysis: Second level: Inferior frontal gyrus mask
Left inferior frontal gyrus
8.96
<0.001*
11.981
*Significant group difference at p<0.05
182
0.001*
0.013*
0.450
0.151
0.719
0.771
0.583
0.999
<0.001*
<0.001*
<0.001*
Appendices
Appendix B. Supplemental Information for Chapter 3
Table S3.1. Behavioural results for the cocaine-word Stroop, comparing recreational
cocaine users, stimulant-dependent individuals (SDI) and healthy control volunteers using
ANCOVA models with Bonferroni post-hoc corrections. Dependent stimulant users were
significantly more impaired on the task, demonstrated via greater interference scores,
higher response latencies and more errors committed. Recreational users and controls did
not differ behaviourally on any measure. Covariates in the analysis include age, gender,
education, smoking status, BDI-II depression and AUDIT alcohol scores.
Control
Recreational
Median (SD)
Median (SD)
Behavioural Results
Cocaine RT*
687.05(144.35)a 655.00(85.25)c
Neutral RT*
669.37(138.45)a 677.94(101.40)
Interference
17.68(97.90)
-22.94(74.96)
Cocaine word
0.70(0.91)a
0.61(0.70)c
errors*
Neutral word
errors*
1.41(3.26)a
1.31(1.26)
SDI
Median (SD)
F/X2
P
874.44(222.20)ac
784.11(187.27)a
90.32(161.07)
6.57
3.67
3.02
0.002
0.029
0.053
2.32(2.25)ac
23.01
<0.001
2.53(3.27)a
6.62
0.036
*Significant group difference at p<0.05 X2 Kruskal-Wallis Test
a
c
Significance between SDI and Control groups in Bonferroni post-hoc test at p<0.01.
Significance between SDI and Recreational groups in Bonferroni post-hoc test at
p<0.01.
183
Appendices
Table S3.2. Peak activation voxels for clusters identified during a first-level whole-brain
imaging analysis amongst all participants, contrasting differences in activation during
cocaine versus neutral word trials. Significance set a p<0.05 family-wise error correction
for multiple comparisons. Coordinates listed are in MNI standard space.
First-level analysis
Contrast activation areas
Cluster 1
Left gyrus rectus, left middle
frontal orbital, right gyrus rectus
Cluster 2
Left inferior frontal gyrus – pars
triangularis, left inferior frontal
gyrus – pars orbitalis, left insula
Cluster 3
Left superior medial frontal
gyrus, right superior medial
frontal gyrus, left anterior
cingulum, right anterior cingulum
Cluster 4
Left angular gyrus, left middle
temporal gyrus
Cluster 5
Right precuneus, right cuneus,
right superior parietal lobe, right
superior occipital lobe
Cluster 6
Right inferior parietal lobe, right
postcentral sulcus, right
supramarignal gyrus, right
superior parietal lobe
Brodmann
Area
Cluster size
(voxels)
Max F
Peak MNI
coordinates
11
83
3.51
-6, 50, -16
13, 45, 47
153
2.70
-50, 36, -2
9, 10, 32
80
2.68
6, 54, 26
39
93
2.33
-52, -58, 24
7
98
-2.78
10, -74, 48
1, 2, 40
84
-2.59
50, -48, 48
184
Appendices
Table S3.3. Mean activation levels for second-level fMRI general linear model omnibus
contrasts comparing brain activation between groups during the cocaine–neutral word
contrast, both with and without a small-volume correction mask of the inferior frontal
gyrus. Coordinates listed are in MNI standard space. Cluster significance set a p<0.05
family-wise error correction for multiple comparisons. Post-hoc group comparisons made
using ANCOVA models, controlling for age, gender, years of education, smoking status,
BDI-II depression and AUDIT alcohol scores, with Bonferroni correction p<0.05.
Mean Activation Levels
Control Recreational
SDI
F
P
GLM activation areas
Cluster1 – 169 voxels*
BA 10, 11, 32
R middle orbitofrontal cortex
R anterior cingulate cortex
R superior med frontal lobe
R superior orbitofrontal cortex
Cluster 2 – 111 voxels*
BA 31
R angular gyrus
R posterior cingulate cortex
GLM IFG activation areas
Cluster1 – 27 voxels*
BA 44
R IFG pars opercularis
R rolandic operculum
Cluster 2 – 21 voxels
R IFG pars triangularis
R insula
Cluster 2 – 31 voxels*
BA 13, 44
R IFG pars opercularis
R insula
R rolandic operculum
4.32b
-126.03bc
102.43c
10.297
<.001
0.89b
-111.23bc
37.93c
10.649
<.001
-15.64ab
7.56b
11.24a
9.761
<0.001
-0.48b
-19.36bc
16.60c
8.129
<0.001
-11.79
-27.77c
11.58c
5.604
0.005
*Significant group difference at FWE p<0.05
a
Significance between SDI and Control groups in Bonferroni post-hoc test at p<0.01
b
Significance between Recreational and Control in Bonferroni post-hoc test at p<0.01
c
Significance between SDI and Recreational in Bonferroni post-hoc test at p<0.01
185
Appendices
A)
IFG Interference Cluster 1
IFG Interference Cluster 2
IFG Interference Cluster 3
B)
186
Appendices
Figure S3.1. A) A regression of behavioural interference scores onto fMRI imaging
contrasts comparing cocaine and neutral words resulted in a positive correlation between
interference on the task and activation in three clusters in the right inferior frontal gyrus
(IFG). Greater activity in the right IFG related to increased impairment on the task, with
greater distraction to the cocaine-salient words. This effect was largely driven by the
stimulant-dependent individuals, who had significantly more activation in this region
compared with recreational cocaine users. B) Between group comparison of BOLD
activation in clusters in the right IFG that correlated with interference scores on the
cocaine Stroop task. Recreational cocaine users again showed less activation than
dependent users, though this was only significant in one cluster using ANCOVA models,
controlling for age, gender, years of education, smoking status, BDI-II depression and
AUDIT alcohol scores, with Bonferroni post-hoc correction p<0.05.
187
Appendices
Appendix C. Supplemental Information for Chapter 4
Table S4.1. Demographic information for sample used in the Monetary Incentive Delay task of 35 stimulant-dependent individuals, 40 of their
non-dependent biological siblings, 22 recreational users of cocaine, and 43 unrelated healthy control volunteers.
Demographics
Age*
Gender (n male:%)*
NART (score)*
Education (years)*
BDI-II (total score)*
AUDIT (total score)*
Tobacco use (%)*
Cannabis use (%)*
Control
Mean (SD)
Sibling
Mean (SD)
Recreational
Mean (SD)
Dependent
Mean SD)
F / χ2
P
32.44 (8.90)
28 (65.1%)
111.60 (1.29)
12.60 (1.94)
2.05 (2.21)
3.32 (2.36)
26 (60.5%)
10 (23.3%)
31.77 (8.48)
19 (47.5%)
108.89 (8.33)
12.00 (1.95)
4.98 (6.09)
4.12 (4.78)
37 (92.5%)
30 (75.0%)
27.50 (6.16)
11 (50.0%)
115.75 (5.40)
13.41 (1.79)
3.68 (4.42)
5.73 (1.61)
19(86.4%)
21(95.5%)
34.48 (7.83)
33 (94.3%)
110.48 (7.85)
11.63 (1.75)
17.91 (12.07)
13.76 (11.82)
34(97.1%)
35(100%)
3.38
20.61
3.43
4.78
36.36
19.16
32.90
64.69
0.020
<0.001
0.019
0.003
<0.001
<0.001
<0.001
<0.001
National Adult Reading Test (NART), Beck Depression Inventory (BDI-II), Alcohol-Use Disorders Identification Test (AUDIT)
188
Appendices
Figure S4.1. Differences in subjective ratings for a 10 pence piece, judged by selfreported likelihood to pick the coin up off the ground. Sibling participants reported
themselves as less likely to pick up the coin compared with their stimulant-dependent
brothers and sisters, though this did not survive more stringent Bonferroni post-hoc
corrections. This decrease in reward valuation was later reflected in their neural activation
during the Monetary Incentive Delay task, particularly in functional activity in the right
supplementary motor area during anticipation of a money trial, which significantly
correlated with this subjective 10p rating.
189
Appendices
Figure S2. Difference between non-drug-using and drug-using participant groups in
BOLD activity in the right orbitofrontal gyrus during feedback for successful versus
unsuccessful trials during the money condition. The sibling and control participants
displayed significantly more activation than both the dependent and recreational cocaine
users in this area at FWE p<0.05, though the groups did not differ with Bonferroni posthoc tests for directionality.
190
Appendices
Figure S3. Difference between drug-using and non-drug-using groups in BOLD activity
in the right orbitofrontal gyrus during anticipation of drug versus neutral cues. The sibling
participants displayed significantly less activation than both the dependent and
recreational cocaine users. Control individuals did not differ from either group.
191
Appendices
Appendix D. Supplemental Information for Chapter 6
Table S6.1. Voxel coordinates of orbitofrontal clusters associated with either body mass
index (BMI) or years of cocaine use in control and cocaine-dependent individuals.
Regions overlapping, or nearly overlapping – no more than four voxels away in any
direction, are highlighted as follows:
Control (con) BMI and cocaine-dependent (coc) BMI
Control BMI and cocaine-dependent years of use (YOU)
Cocaine-dependent BMI and YOU
192
Appendices
Cluster
21
22
23
24
25
26
Con BMI 1
56 voxels
-4,40,-30
-4,40,-28
-6,56,-28
-2,42,-26
4,44,-26
-2,52,-26
-6,56,-26
-4,32,-24
2,34,-24
6,36,-24
-4,40,-24
2,40,-24
-8,56,-24
2,-46,-24
8,54,-24
-2,28,-22
2,34,-22
6,36,-22
2,44,-22
-4,42,-22
-2,46,-22
14,48,-22
4,52,-22
6,58,-22
-6,56,-22
8,62,-20
22,42,-20
-4,42,-20
16,48,-20
Con BMI 2
223 voxels
Con BMI 3
45 voxels
Con BMI 4
23 voxels
Coc BMI 1
93 voxels
Coc BMI 2
81 voxels
Coc YOU 1
269 voxels
Coc YOU 2
192 voxels
-6,42,-28
-2,42,-26
4,44,-26
-2,52,-26
-6,56,-26
-4,32,-24
2,34,-24
6,36,-24
-4,40,-24
2,40,-24
-8,56,-24
2,-46,-24
8,54,-24
-2,28,-22
2,34,-22
6,36,-22
2,44,-22
-4,42,-22
-2,46,-22
14,48,-22
4,52,-22
6,58,-22
-6,56,-22
8,62,-20
22,42,-20
-4,42,-20
16,48,-20
193
-2,22,-26
16,24,-26
-8,42,-26
16,24,-26
-8,42,-26
0,26,-24
0,38,-24
18,24,-24
-12,42,-24
-14,46,-24
18,24,-24
-12,42,-24
-14,46,-24
4,34,-22
2,38,-22
2,42,-22
16,60,-22
4,34,-22
2,38,-22
2,42,-22
16,60,-22
24,26,-22
28,32,-22
-20,32,-22
18,34,-22
-24,34,-22
-12,42,-22
-16,46,-22
-12,50,-22
-12,56,-22
24,26,-22
28,32,-22
-20,32,-22
18,34,-22
-24,34,-22
-12,42,-22
-16,46,-22
-12,50,-22
-12,56,-22
4,40,-20
12,60,-20
4,40,-20
12,60,-20
24,26,-20
-20,26,-20
30,32,-20
-24,34,-20
24,26,-20
-20,26,-20
30,32,-20
-24,34,-20
Appendices
-2,46,-20
-6,56,-20
-2,46,-20
-6,56,-20
27
2,28,-18
4,46,-18
14,54,-18
-28,54,-18
4,54,-18
-4,56,-18
-12,60,-18
10,62,-18
-20,64,-18
2,28,-18
4,46,-18
14,54,-18
-28,54,-18
4,54,-18
-4,56,-18
-12,60,-18
10,62,-18
-20,64,-18
2,28,-18
4,46,-18
14,54,-18
-28,54,-18
4,54,-18
-4,56,-18
-12,60,-18
10,62,-18
-20,64,-18
4,34,-18
16,56,-18
4,34,-18
16,56,-18
28
-2,52,-16
24,44,-16
18,54,-16
-28,54,-16
-18,52,-16
-20,62,-16
4,60,-16
-2,52,-16
24,44,-16
18,54,-16
-28,54,-16
-18,52,-16
-20,62,-16
4,60,-16
-2,52,-16
24,44,-16
18,54,-16
-28,54,-16
-18,52,-16
-20,62,-16
4,60,-16
4,28,-16
4,44,-16
24,48,-16
12,60,-16
4,28,-16
4,44,-16
24,48,-16
12,60,-16
29
-8,46,-14
-18,48,-14
18,54,-14
-2,48,-14
4,52,-14
-20,62,-14
-8,46,-14
-18,48,-14
18,54,-14
-2,48,-14
4,52,-14
-20,62,-14
-8,46,-14
-18,48,-14
18,54,-14
-2,48,-14
4,52,-14
-20,62,-14
4,28,-14
24,48,-14
22,56,-14
2,56,-14
12,60,-14
4,28,-14
24,48,-14
22,56,-14
2,56,-14
12,60,-14
194
20,40,-20
-16,46,-20
-16,52,-20
22,18,-18
30,24,-18
-22,26,-18
30,32,-18
18,32,-18
-30,38,-18
20,36,-18
28,42,-18
-18,44,-18
-16,52,-18
-12,60,-18
24,24,-16
-22,26,-16
26,42,-16
-16,30,-16
16,32,-16
40,36,-16
-26,40,-16
-22,50,-16
-28,48,-16
24,24,-14
-40,26,-14
-24,30,-14
20,34,-14
-28,32,-14
40,36,-14
-28,36,-14
-34,36,-14
20,40,-20
-16,46,-20
-16,52,-20
22,18,-18
30,24,-18
-22,26,-18
30,32,-18
18,32,-18
-30,38,-18
20,36,-18
28,42,-18
-18,44,-18
-16,52,-18
-12,60,-18
24,24,-16
-22,26,-16
26,42,-16
-16,30,-16
16,32,-16
40,36,-16
-26,40,-16
-22,50,-16
-28,48,-16
24,24,-14
-40,26,-14
-24,30,-14
20,34,-14
-28,32,-14
40,36,-14
-28,36,-14
-34,36,-14
Appendices
30
20,52,-12
-18,48,-12
-24,46,-12
-28,56,-12
-18,60,-12
20,52,-12
-18,48,-12
-24,46,-12
-28,56,-12
-18,60,-12
4,32,-12
24,56,-12
12,58,-12
4,32,-12
24,56,-12
12,58,-12
31
-16,48,-10
20,52,-10
24,62,-10
-16,48,-10
20,52,-10
24,62,-10
4,28,-10
8,34,-10
16,58,-10
26,60,-10
4,28,-10
8,34,-10
16,58,-10
26,60,-10
32
22,58,-8
6,36,-8
24,60,-8
6,36,-8
24,60,-8
33
6,40,-6
34
4,42,-4
195
-20,56,-14
24,44,-14
-38,28,-12
-20,28,12
40,32,-12
20,34,-12
-28,32,-12
30,32,-12
36,34,-12
-26,44,-12
38,38,-12
30,38,-12
-18,44,-12
-30,42,-10
40,30,-10
28,34,-10
36,40,-10
-28,36,-10
30,38,-10
-16,48,-10
-30,52,-10
36,36,-8
-34,38,-8
-30,38,-8
38,42,-8
38,30,-6
-30,40,-6
-32,36,-4
-20,56,-14
24,44,-14
-38,28,-12
-20,28,12
40,32,-12
20,34,-12
-28,32,-12
30,32,-12
36,34,-12
-26,44,-12
38,38,-12
30,38,-12
-18,44,-12
-30,42,-10
40,30,-10
28,34,-10
36,40,-10
-28,36,-10
30,38,-10
-16,48,-10
-30,52,-10
36,36,-8
-34,38,-8
-30,38,-8
38,42,-8
38,30,-6
-30,40,-6
Appendices
Appendix E. Ersche KD, Jones PS, Williams GB, Smith DG, Bullmore ET, Robbins
TW. (2013). Distinctive personality traits and neural correlates associated with stimulant
drug use versus risk of stimulant dependence. Biological Psychiatry, 74, 137-144.
196