A JOURNAL OF NEUROLOGY
Abnormal structure of frontostriatal brain
systems is associated with aspects of impulsivity
and compulsivity in cocaine dependence
Karen D. Ersche,1,2Anna Barnes,1,2P. Simon Jones,1,2Sharon Morein-Zamir,1,2
Trevor W. Robbins2,3and Edward T. Bullmore1,2,4
1 Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
2 Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
3 Department of Experimental Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
4 GlaxoSmithKline, Clinical Unit Cambridge, Cambridge, CB2 2GG, UK
Correspondence to: Dr Karen D. Ersche,
University of Cambridge,
Department of Psychiatry,
Herchel Smith Building for Brain and Mind Sciences,
Cambridge Biomedical Campus,
Cambridge CB2 0SZ, UK
A growing body of preclinical evidence indicates that addiction to cocaine is associated with neuroadaptive changes in frontos-
triatal brain systems. Human studies in cocaine-dependent individuals have shown alterations in brain structure, but it is less
clear how these changes may be related to the clinical phenotype of cocaine dependence characterized by impulsive behaviours
and compulsive drug-taking. Here we compared self-report, behavioural and structural magnetic resonance imaging data on a
relatively large sample of cocaine-dependent individuals (n = 60) with data on healthy volunteers (n = 60); and we investigated
the relationships between grey matter volume variation, duration of cocaine use, and measures of impulsivity and compulsivity
in the cocaine-dependent group. Cocaine dependence was associated with an extensive system of abnormally decreased grey
matter volume in orbitofrontal, cingulate, insular, temporoparietal and cerebellar cortex, and with a more localized increase in
grey matter volume in the basal ganglia. Greater duration of cocaine dependence was correlated with greater grey matter volume
reduction in orbitofrontal, cingulate and insular cortex. Greater impairment of attentional control was associated with reduced
volume in insular cortex and increased volume of caudate nucleus. Greater compulsivity of drug use was associated with
reduced volume in orbitofrontal cortex. Cocaine-dependent individuals had abnormal structure of corticostriatal systems,
and variability in the extent of anatomical changes in orbitofrontal, insular and striatal structures was related to individual
differences in duration of dependence, inattention and compulsivity of cocaine consumption.
Keywords: orbitofrontal; insula; caudate; grey matter; cocaine dependence; sustained attention; stop signal; impulsivity; compulsivity
Abbreviations: OCDUS = Obsessive–Compulsive Drug Use Scale; RVIP = Rapid Visual Information Processing Task
doi:10.1093/brain/awr138 Brain 2011: 134; 2013–2024 |
Received February 21, 2011. Revised April 3, 2011. Accepted April 26, 2011
? The Author (2011). Published by Oxford University Press on behalf of the Guarantors of Brain.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5),
which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
According to the European Monitoring Centre for Drugs and Drug
Addiction (EMCDDA), cocaine is the second most widely used illicit
drug in Europe (after cannabis; EMCDDA, 2010). Approximately 14
million Europeans are believed to have used cocaine at least once in
their lifetime (EMCDDA, 2010), but not everybody who uses co-
caine becomes addicted to it. It has been estimated that ?20% of
cocaine users develop dependence (Wagner and Anthony, 2002).
Impulsive individuals, i.e. people who tend to show behaviour that
is premature, poorly planned and often inappropriate for the con-
text (Moeller et al., 2001a), seem to be particularly vulnerable to
making the transition from recreational to compulsive cocaine use
(Verdejo-Garcia et al., 2008; Potenza and Taylor, 2009). Impulsivity,
as assessed by self-report, has been shown to further increase with
chronic cocaine exposure (Ersche et al., 2010). This is of concern
because in drug-dependent individuals, impulsivity also increases the
risk of adverse life events (Hayaki et al., 2005) and the likelihood of
early treatment drop-out (Moeller et al., 2001b). Impulsivity can
also be assessed by behavioural tasks; but, in healthy individuals,
self-report and behavioural measures of impulsivity are only weakly
correlated (Reynolds et al., 2006; Meda et al., 2009).
Here we investigate impulsivity in individuals who have become
dependent on cocaine using both self-report and behavioural
measures. There is convincing preclinical evidence indicating
that addiction is associated with neuroadaptive changes in the
frontostriatal networks, which may influence both impulsivity
and drug-related compulsivity
Schoenbaum and Shaham, 2008). Compulsivity of drug use is
defined as a maladaptive tendency to repeat or perseverate in a
previously rewarded behaviour (e.g. cocaine-seeking or consump-
tion) even in the face of significant aversive or disadvantageous
consequences (e.g. failure of relationships, loss of employment,
imprisonment, etc). Previous studies in humans with cocaine
dependence have found significant changes in grey matter in pre-
frontal and striatal brain regions (Jacobsen et al., 2001; Fein et al.,
2002; Franklin et al., 2002; Matochik et al., 2003; Sim et al.,
2007). Morerecently, neuroimaging
computational techniques in order to relate aspects of impulsivity
to the structural MRI scans of psychiatric patients (Matsuo et al.,
2009; Schiffer et al., 2010; Schwartz et al., 2010). The present
study aims to build on this work by investigating the relationship
between individual differences in impulsivity and cocaine-related
compulsivity and grey matter volume variation in large-scale brain
systems, in a sizeable sample of cocaine-dependent individuals and
healthy volunteers. We hypothesized that the increased levels of
impulsivity and compulsivity in cocaine users would be associated
with anatomical changes in frontostriatal brain systems.
Materials and methods
Sixty individuals with a history of chronic cocaine abuse, satisfying the
DSM-IV-TR (Diagnostic and Statistical Manual of Mental Disorders,
4th Edition Revised, American Psychiatric Association, 2000) criteria
for cocaine dependence, and 60 healthy control volunteers without
a history of drug abuse took part in the study. All participants were
aged 18–50 years and in good physical health. Participants were psy-
chiatrically evaluated using the structured clinical interview for DSM-IV
(First et al., 2002). Exclusion criteria included a major medical or
neurological illness, a lifetime history of a psychotic disorder, a history
of a traumatic head injury or any contra-indications to MRI scanning.
The cocaine users were non-treatment seeking and recruited from the
local community by advertisements and word-of-mouth. All cocaine
users were actively using cocaine, as verified by positive urine screens
for cocaine on the day of scanning. On average, cocaine users had
been using cocaine for 10 years ? 7.1 standard deviation (SD), start-
ing at the age of 21 years ? 5.7 SD. On the Obsessive–Compulsive
Drug Use Scale (OCDUS; Franken et al., 2002; Ersche et al., 2010),
the cocaine users typically reported moderate levels of cocaine-related
compulsivity (OCDUS mean = 21.3 ? 8.5 SD; range 5–40). One
cocaine user was prescribed mirtazapine, two were prescribed benzo-
diazepines and one regularly used over-the-counter paracetamol. Fifty
cocaine users also met DSM-IV criteria for nicotine dependence, 16 for
alcohol dependence, 11 for cannabis dependence and four for heroin
dependence. The majority of the cocaine users were smoking cannabis
regularly (68%) and many also consumed other drugs (ecstasy 28%,
amphetamines 18%, hallucinogens 15%, benzodiazepines 11% and
GlaxoSmithKline healthy volunteer panel, and partly by advertisement
in the local community. They did not satisfy criteria for alcohol abuse
or dependence, and were not taking prescribed or illicit drugs on a
regular basis. Urine samples provided on the testing day were negative
for all illicit substances tested. Seventeen per cent of this sample re-
ported recreational cannabis use in the past, 7% were occasional to-
bacco smokers and 36% had smoked tobacco in the past. All
participants completed the National Adult Reading Test (NART;
Nelson, 1982), as an estimate of verbal IQ and the Beck Depression
Inventory (BDI-II, Beck et al., 1996) to assess depressive mood.
The study protocol received ethical approval from the Cambridge
Research Ethics Committee and written informed consent was ob-
tained from all volunteers prior to study enrolment.
Impulsivity was assessed using both self-report questionnaire measures
and behavioural tasks. The two self-report measures comprised: (i) the
Barratt Impulsiveness Scale (Patton et al., 1995), a 30-item question-
naire, which assesses impulsive personality traits in three dimensions:
attention (inattention and cognitive instability), motor behaviour
(spontaneous actions) and non-planning (lack of forethought); and
(ii) the Behavioural Inhibition/Activation System scale (Carver and
White, 1994), a 20-item questionnaire that measures both inhibitory
and excitatory tendencies in behaviour. The behavioural inhibition
system subscale assesses the individual’s behaviour in the anticipation
of punishment; the behavioural activation system subscale assesses
behaviour in the anticipation of rewarding outcomes, i.e. the tendency
to respond with heightened energy and positive affect in the context
of rewarding events (reward responsiveness), the pursuit of rewarding
goals (drive) and the impulsive approach towards potential rewards
For the behavioural assessment of impulsivity, we focused on those
aspects of impulsivity that have been classified as impulsive actions
(Schachar et al., 2007), i.e. behaviours involving either the cancellation
of an ongoing response or the inhibition of inappropriate actions.
Brain 2011: 134; 2013–2024K. D. Ersche et al.
Impulsive actions are believed to be particularly relevant for the de-
velopment of compulsive patterns of cocaine abuse (Belin et al., 2008;
Winstanley et al., 2010), and in the present study were measured by
two computerized tests: (i) The Stop-Signal task (Logan et al., 1997),
which measures the time that an individual needs to withhold an on-
stop-signal reaction time is based on the assumption that go and
stop processes are independent; reaction time on successful go-trials
and on unsuccessful stop-trials was recorded in addition to the out-
come of each stop trial; additionally, we also computed relative slow-
ing on the go trials after a stop trial; and (ii) the Rapid Visual
Information Processing Task (RVIP) (www.camcog.com) is a test of
sustained attention equivalent to the Continuous Performance Test;
it measures a person’s capacity to discriminate between targets and
non-targets (target sensitivity, A0) and evaluates their tendency to re-
spond irrespective of the presence of a target (response bias, B00).
Errors are calculated either by the number of targets missed (omission
errors) or by responses to non-targets (commission errors). Impulsivity
is reflected by an increased number of commission errors paired with
short response latencies.
Behavioural data on four individuals were lost due to technical prob-
lems (Stop-Signal task: one control, two cocaine users; and RVIP task:
one control, one cocaine user); and the Behavioural Inhibition/
Activation System scale scores for one cocaine user were incomplete,
and not included in the analysis.
Statistical analysis of demographic and
Non-imaging data were analysed using the Statistical Packages for the
Social Sciences version 13 (SPSS Inc.). All tests were two-tailed and an
effect was deemed significant at P50.05. Independent-sample t-tests
were used to explore group differences in demographic variables,
including measures of mood. Chi-square or Fisher’s exact tests were
used, as appropriate, for the analyses of categorical data. An explora-
tory factor analysis with principal components extraction was per-
formed to identify a few major components of variation/covariation
underlying all (self-report and behavioural) impulsivity measures in all
participants. As healthy volunteers had no cocaine-taking experiences,
the OCDUS scores were not available in this group, and therefore the
OCDUS score was not included in the principal component analysis.
The participants’ scores on each impulsivity component were then
subject to group comparisons using analysis of covariance with years
of education and depressive mood (Beck Depression Inventory,
Version 2; BDI-II) scores included as covariates. Pearson correlations
were estimated between each of the impulsivity component scores and
the duration of cocaine abuse.
Magnetic resonance imaging data
acquisition and preprocessing
The MRI data were acquired at the Wolfson Brain Imaging Centre,
University of Cambridge, UK, using a Siemens Magentom Trio Tim
scanner operating at 3 Tesla (www.medical.siemens.com). For the
T1-weighted MRI scans, a magnetically prepared rapid acquisition
gradient echo sequence (MPRAGE) was used (176 slices of 1mm
thickness, repetition time = 2300ms, echo time = 2.98ms, inversion
time = 900ms, flip angle = 9?, field of view = 240 ? 256). All magnetic
resonance images were screened for normal radiological appearance
by a specialist in neuroradiology.
The grey matter volume maps were constructed from each partici-
pant’s image using FSLVBM 1.1 (http://www.fmrib.ox.ac.uk/fsl/
fslvbm/index.html). First, structural images were skull-stripped using
the brain extraction tool (Smith, 2002) and tissue-type segmentation
was conducted using FAST (Zhang et al., 2001). The resulting grey
matter partial volume images were aligned to MNI standard space
using the affine registration tool FLIRT (Jenkinson and Smith 2001;
Jenkinson et al., 2002), followed by a non-linear registration using
FNIRT (Andersson et al., 2007a, b) implementing a b-spline represen-
tation of the registration warp field (Rueckert et al., 1999). The images
were averaged to create a study-specific template, to which the native
grey matter images were then non-linearly re-registered. The regis-
tered partial volume images were modulated (to correct for local ex-
pansion or contraction) by dividing by the Jacobian of the warp field,
and smoothed with an isotropic Gaussian kernel with full-width
half-maximum = 2.3mm to minimize slight misregistration errors.
Magnetic resonance imaging
The smoothed grey matter maps were statistically analysed using
CamBA software, version 2.3.0 (http://www-bmu.psychiatry.cam.ac.
uk/software/). For statistical inference, we used permutation methods
and spatially extended statistics with nominal type I error control and
greater sensitivity than voxel-based metrics (Suckling and Bullmore
2004). First, we performed a whole-brain analysis of group differences
in grey matter volume using the general linear model with a
single-factor two independent groups ANOVA design. This resulted
in a map of brain areas that demonstrated significant differences in
grey matter volume in cocaine users compared with healthy volun-
teers. Secondly, we explored variations in abnormal brain anatomy
that were associated with individual differences in impulsivity, compul-
sivity and duration of cocaine use. For these analyses, we focused on
the data from cocaine users only and tested associations with behav-
ioural, clinical and cognitive variables in those brain regions that were
significantly abnormal in the cocaine user group compared with
healthy volunteers. In other words, the map of between-group differ-
ences in brain anatomy obtained by the first analysis was used as an
inclusive mask to define a restricted search volume for the secondary
analyses, which entailed regressing grey matter volume at each voxel
within the mask on the following variables: principal component scores
for inattention and impulsive reward-seeking dimensions of impulsivity;
duration of cocaine use; and compulsivity of cocaine use (OCDUS
scores). Regional mean grey matter volumes for those regions that
showed significant association with any of the behavioural or clinical
variables were graphically examined to evaluate the possible effects of
For both whole-brain analysis of between-group differences, and
the masked analysis of associations between grey matter and clinical
or behavioural variables, statistical inference was by permutation test-
ing at the level of spatially contiguous voxel clusters (Suckling and
Bullmore, 2004). The P-value for significance was adjusted to control
for multiple comparisons so that the expected number of false positive
clusters in each analysis was less than one. Thus the cluster-wise prob-
ability threshold for significance in the whole-brain analysis was
P = 0.001 and the corresponding threshold for each of the masked
analyses was P?0.002. The slightly more lenient threshold for signifi-
cance in the masked analysis reflects the smaller search volume
(number of voxel clusters) tested.
Frontostriatal systems, impulsivity and cocaineBrain 2011: 134; 2013–2024 |
Demographics and group differences
The two groups were reasonably well matched in terms of age,
gender and verbal intelligence (Table 1). Eighty-three per cent of
the cocaine users had a high school education; although this level
is comparable with other studies in cocaine dependence (e.g.
Buchanan et al., 2006), it is falling behind education levels in
the control group, in which 98% of volunteers had a high
school degree (Fisher’s exact P = 0.008). The cocaine users also
reported more dysphoric mood compared with the healthy volun-
teers, which is not unusual for substance-dependent individuals
(Buckley et al., 2001). In keeping with previous research, the co-
caine users reported increased trait-impulsivity and appetitive
motivation (Moeller et al., 2004; 2005; Franken and Muris
2006; Ersche et al., 2010). However, these high levels of
self-reported impulsivity were not reflected in their behavioural
performance. On the Stop-Signal task, cocaine users showed an
overall slowing of responses that was not limited to the stop-signal
reaction time; latencies on both stop- and go-trials were pro-
longed. Their poor target detection accuracy on a test of sustained
attention was due to the fact that cocaine users missed significant-
ly more targets than controls. We found evidence for generally
impaired attentional control rather than the more specific pattern
of an increased rate of false alarms and speeded-up responding,
which has traditionally been considered to be a marker of impul-
sive behaviour. Statistical details of all self-report and behavioural
measures are shown in Table 1.
We used principal component analysis to examine how the dif-
ferent task measures were related to each other in all participants.
A five-component solution, comprising all components with
Table 1 Demographic and impulsivity measures for healthy volunteers and cocaine-dependent individuals
Group characteristics Healthy volunteersCocaine dependentt-value or ?2
Depressive mood (BDI-II total score)
Education (years of formal education)
Trait-impulsivity (BIS-11 scale, total score)
Anxiety-avoidance (BIS/BAS scale)
Reward-approach (BIS/BAS scale)
BAS reward responsiveness
Sustained attention (CANTAB-RVIP)
Signal detection (A’)
Response bias (B00)
Commission errors (number)
Omission errors (number)
Correct responses/hits (number)
Mean reaction time (ms)
Response inhibition (stop-signal task)
Percentage of successful stops
Mean reaction time on successful Go-trials (ms)
Mean reaction time on unsuccessful Stop-trials (ms)
Stop-signal reaction time (ms)
Post-stop slowing (ms)
n = 60
n = 60
32.3 ? 8.3
110.0 ? 7.0
2.1 ? 3.2
12.3 ? 1.6
60.8 ? 7.5
14.3 ? 2.8
22.8 ? 3.3
23.8 ? 4.0
32.5 ? 8.5
109.5 ? 6.9
13.2 ? 11.6
11.5 ? 1.7
76.4 ? 9.6
18.6 ? 3.9
27.5 ? 5.4
31.3 ? 4.3
18.6 ? 3.419.4 ? 3.7
11.0 ? 1.9
12.0 ? 1.8
16.4 ? 1.9
12.1 ? 2.6
13.6 ? 1.8
16.9 ? 2.2
0.92 ? 0.05
0.9 ? 0.3
1.5 ? 2.1
8.5 ? 4.8
18.5 ? 4.8
407.8 ? 96.3
0.89 ? 0.04
1.0 ? 0.0
1.0 ? 1.2
11.5 ? 4.7
15.5 ? 4.7
439.4 ? 85.2
54.4 ? 3.1
481.3 ? 61.6
447.1 ? 51.2
234.9 ? 46.2
485.7 ? 99.2
53.4 ? 4.8
532.9 ? 87.7
476.7 ? 56.0
263.2 ? 55.2
560.1 ? 253.1
? 0.51 ? 0.81
? 0.40 ? 0.89
0.00 ? 0.92
0.27 ? 1.00
?0.03 ? 1.09
0.54 ? 0.90
0.42 ? 0.94
0.00 ? 1.09
?0.28 ? 0.92
0.03 ? 0.91
BDI-II = Beck Depression Inventory, Version 2; BIS-11 = Barratt Impulsiveness Scale; BIS/BAS = Behavioural Inhibition/Activation System scale; CANTAB = Cambridge
Neuropsychological Test Automated Battery; NART = National Adult Reading Test.
Brain 2011: 134; 2013–2024K. D. Ersche et al.
standardized eigenvalues 41, accounted for 71% of the total
variance/covariance. As shown in Table 2, the first component,
labelled tentatively ‘inattention’, loaded on the behavioural meas-
ures of target detection during sustained attention (RVIP) and re-
sponse latencies during the response inhibition task. The second
self-report measures reflecting reward-driven behaviours (behav-
ioural activation system items) as well as motor and cognitive im-
pulsivity [Barratt Impulsiveness Scale-11 (BIS-11)]. The third
component, ‘response slowing’, loaded strongly on prolonged re-
sponse times on the stop-signal task. The fourth component, ‘im-
pulsive responding’, summarized
anticipation of reward [Behavioural Activation System (BAS)] and
lack of forward thinking (BIS-11). The fifth component, ‘anxious
responding’, loaded highly on self-reported avoidance behaviour
(Behavioural Inhibition/Activation System scale) and also on im-
pulsive errors on the RVIP.
Group comparisons on the five components were controlled
for the between-group differences in years of education and dys-
phoric mood ratings. The analyses revealed significant group
[F(1,111) = 20.46,
reward-seeking [F(1,111) = 16.48, P50.001]; statistical details
of the group comparisons are shown in Table 1.
Group differences in grey matter volume
There were significant differences in grey matter volume between
the two groups (Fig. 1). There was widespread significant loss of
grey matter in orbitofrontal cortex bilaterally in the cocaine user
group. Grey matter volume was also abnormally reduced in the
insula, the medial frontal and anterior cingulate cortex, temporo-
parietal cortex and the cerebellum. In contrast to this extensive
system of decreased cortical grey matter volume, cocaine users
also showed a significant increase of grey matter volume mainly
localized to basal ganglia structures (including putamen, caudate
nucleus and pallidum), and cerebellum.
Individual differences in impulsivity,
compulsivity and grey matter volume
To investigate how the significant components of impulsivity were
associated with the abnormal grey matter systems in the
cocaine-dependent group, we separately regressed cocaine users’
individual scores on each of the two abnormal components (in-
attention and impulsive reward-seeking) on grey matter volume in
each voxel of the corticostriatal system that was abnormal in the
cocaine-dependent individuals group compared with healthy par-
ticipants. This procedure identified a set of voxels where grey
matter volume was significantly positively correlated with the
first impulsivity component (inattention) in the cocaine users
(coloured in red in Fig. 2) in the left caudate nucleus [Montreal
Neurological Institute coordinates (x, y, z; mm): ?18, 18, 8], and
negatively correlated with grey matter volume in the insula
bilaterally [coloured in blue (38, ?8, 18) and (?36, 0, 8)], and
in the right middle temporal gyrus (56, 0, ?18). The second com-
ponent (impulsive reward-seeking) was not significantly correlated
with grey matter volume variation in the cocaine-dependent
We also regressed the OCDUS score of cocaine-related compul-
sivity on those grey matter systems in the cocaine group that
differed from control volunteers. As shown in Fig. 2, drug-related
Table 2 The eigenvector matrices of the principal component analysis including 16 impulsivity variables in all participants
Per cent variance (cumulative variance), %
BIS score (BIS/BAS)
BAS reward responsiveness
RVIP A0(response accuracy)
RVIP B00(response bias)
RVIP mean RT correct responses
RVIP commission errors
RVIP omission errors
RVIP correct responses
Stop-Signal mean successful go-RT
Stop-Signal mean unsuccessful stop-RT
Stop-Signal reaction time
Component loadings of 50.5 were considered significant and are given in bold.
BIS-11 = Barratt Impulsiveness Scale; BIS/BAS = Behavioural Inhibition/Activation System scale; RT = reaction time.
Frontostriatal systems, impulsivity and cocaineBrain 2011: 134; 2013–2024 |
compulsivity was significantly associated with grey matter loss in
the orbitofrontal cortex (?2, 32, ?18). The OCDUS score was
correlated with the inattention score (r = 0.31, P50.05) (Table 3),
and the OCDUS-related decline in grey matter in the orbitofrontal
cortex was correlated with the inattention-related decline in grey
matter in the insula and middle temporal gyrus (r = 0.36,
Relationship between duration of
cocaine dependence and grey matter
To investigate whether or not altered grey matter volume was
related to the duration of cocaine abuse, we regressed the
number of years of cocaine abuse of each cocaine user on the
map of grey matter volume differences. We found that the indi-
viduals who had been using cocaine for longer periods of time,
had greater extent of grey matter volume reduction in the anterior
and middle cingulate gyrus, middle frontal cortex (orbital part),
rectus gyrus, supplementary motor area, superior temporal
gyrus, insula, cerebellum and in the left caudate (r = ?0.75,
P40.001; Fig. 2).
By comparing grey matter volume between chronic cocaine users
and healthy volunteers, we confirmed findings from previous stu-
dies of significant grey matter loss in large parts of frontal and
parietal cortices and the enlargement of striatal structures in co-
caine dependence (Jacobsen et al., 2001; Fein et al., 2002;
Franklin et al., 2002; Matochik et al., 2003; Sim et al., 2007).
We further found that the caudate enlargement in cocaine users
was associated with significant attentional impairments, whereas
the reduction in grey matter in the orbitofrontal cortex was asso-
ciated with cocaine-related compulsivity. The abnormal changes in
grey matter in the striatum and in the orbitofrontal cortex were
both related to the duration of cocaine abuse, i.e. the longer co-
caine users have been using cocaine, the greater the loss of grey
matter. Our observations are in keeping with the findings from
preclinical studies indicating that neuroadaptive changes in fron-
tostriatal networks are associated with cocaine dependence
(Jentsch and Taylor 1999; Everitt and Robbins, 2005; Koob and
Le Moal, 2005). More specifically they show that individual dif-
ferences in the duration of cocaine dependence, attentional im-
pairment and compulsivity of drug use are correlated with each
-40 -34-28 -22 -16
-10 -428 14
20 26 32 3844
Figure 1 Whole-brain maps of significant differences in grey matter volume between healthy volunteers and cocaine users. Voxels
coloured blue indicate brain areas in which cocaine users have reduced grey matter volume compared with healthy volunteers, and voxels
coloured red indicate brain areas in which cocaine users have abnormally increased grey matter volume. These results were generated by
permutation testing of voxel cluster statistics with cluster-wise P50.001, at which level we expect less than one false positive cluster per
map. The statistical results are overlaid on the FSL MNI152 standard T1image and the numbers beneath each section of the image refer to
its position (mm) relative to the intercommissural plane in standard stereotactic space. L = left; R = right.
Brain 2011: 134; 2013–2024 K. D. Ersche et al.
other and with the extent of grey matter volume abnormality in
orbitofrontal cortex, insula and the caudate nucleus.
Relationships between behaviour and
related brain structure
As hypothesized, and consistent with previous studies, the cocaine
users perceived themselves as highly impulsive, scoring significant-
ly higher on the impulsivity questionnaires compared with healthy
volunteers (Moeller et al., 2004; 2005; Franken and Muris, 2006;
Ersche et al., 2010). However, their behavioural performance was
not impulsive in the sense of being premature, which might reflect
a ceiling effect of task performance, but did confirm the significant
attentional problems that have been previously reported in chronic
cocaine users (Horner, 1999; Aharonovich et al., 2003, 2006;
Jovanovski et al., 2005; Goldstein et al., 2007; Tomasi et al.,
2007; Gooding et al., 2008). The fact that the cocaine users
had prolonged (not speeded) response latencies in both tasks
may reflect a failure in attentional processing (Sarter et al.,
2001). Successful performance on both tasks requires sustained
attention and performance monitoring, which involves both
prefrontal and subcortical structures including the insula and the
caudate nucleus (Coull et al., 1996; Lawrence et al., 2003; Ray Li
et al., 2008). Specifically, increased functional activation of the
caudate has been associated with improved performance on the
RVIP task (Lawrence et al., 2002) and the Stop-Signal task (i.e.
shortened stop-signal reaction times) (Ray Li et al., 2008). Indeed,
the inattentive performance profile in our cocaine users was sig-
nificantly correlated with grey matter volume changes in the insula
and the caudate. Both brain areas have been associated with the
acute effects of cocaine in humans (Breiter et al., 1997) and
chronic cocaine use in experimental monkeys (Porrino et al.,
2007). It is thus conceivable that cocaine-induced structural
changes in cortical organization cause abnormalities of sustained
attention and attentional control in cocaine-dependent individuals.
A similar inattentive performance profile was observed on an
analogous test of sustained attention and response inhibition in
rats with lesions to the dorsomedial striatum (which corresponds
to the caudate nucleus in humans) (Rogers et al., 2001; Eagle and
Robbins 2003), and following direct local infusion of dopamine D2
receptor antagonists into this structure (Eagle et al., 2011), sup-
porting the notion of caudate neuropathology as well as reduced
dopamine D2 receptor functioning in our cocaine-dependent
Figure 2 Maps of brain regions demonstrating significant association between grey matter volume and measures of duration of cocaine
use, compulsivity and impulsivity in the group of cocaine users. Regions where grey matter volume correlated significantly with the
duration of cocaine use in drug users are indicated in orange. Regions that correlated significantly with compulsive cocaine-taking (as
assessed by the OCDUS) are coloured in green. Regions where grey matter volume correlated significantly with the inattention component
of impulsivity in cocaine users are indicated in red (if the correlation was positive) and blue (if the correlation was negative). The scatter
plots beneath each section of the brain image show the correlation between these measures and the total grey matter volume for each
drug user in those regions found to be significantly correlated by permutation testing of cluster-level statistics in the restricted search
volume or mask defined by the areas of significant between-group difference in grey matter anatomy (Fig. 1). The probability threshold for
significance was P?0.002 for each analysis, at which level we expect less than one false positive cluster per map. The statistical results are
overlaid on the FSL MNI152 standard T1 image and the numbers above each section of the image refer to its plane position (mm) relative
to the origin in MNI stereotactic space. L = left; R = right.
Frontostriatal systems, impulsivity and cocaineBrain 2011: 134; 2013–2024 |
Relationships between brain structure
and duration of cocaine use
We found that overall, compared with healthy volunteers, cocaine
users had significantly increased grey matter volume in subcortical
structures including the caudate nucleus. However, we also found
a strong, negative correlation between duration of cocaine use
and grey matter volume in frontal and cingulate cortex, insula
and caudate. In other words, greater duration of cocaine use
was associated with relatively reduced grey matter volume in
Like most drugs of abuse, cocaine exerts its pharmacological
effects in the ventral striatum (Di Chiara and Imperato, 1988).
Enlarged striatal structures have been reported previously in
chronic cocaine users (Jacobsen et al., 2001) and methampheta-
mine users (Chang et al., 2005; Jernigan et al., 2005), but also in
individuals with autism and fragile X syndrome (Voelbel et al.,
2006; Langen et al., 2007; Hallahan et al., 2011). However, the
neuropathology underlying this enlargement is not fully under-
stood. Blockade of dopamine D2 receptors by antipsychotic
drugs has been shown to increase the volume of basal ganglia
structures in both animals and humans (Benes et al., 1985;
Keshavan et al., 1994; Chakos et al., 1998; Corson et al., 1999;
Scherk and Falkai 2006), possibly indicating that striatal enlarge-
ment is associated with an under-active dopamine system. It has
recently been shown in humans that variation in grey matter
volume correlates both positively and negatively with individual
differences in the expression of D2-like receptors in various brain
regions, including the caudate (Woodward et al., 2009). Cocaine
dependence has also been associated with significant reduction in
striatal dopamine D2 receptor density (Volkow et al., 1997;
Martinez et al., 2004), along with significant reduction in dopa-
mine transmission [i.e. reduced endogenous dopamine release and
presynaptic re-uptake (Wu et al., 1997; Martinez et al., 2009)].
Thus there is reasonable prior evidence, consistent with the
between-group difference observed in these data, to suggest
that striatal enlargement is an imaging marker of cocaine depend-
ence, which may reflect reduced dopamine neurotransmission and
could indeed be a predisposing factor rather than a consequence
of cocaine use. In this context, the relatively reduced striatal and
cortical volumes associated with greater duration of cocaine use
could conceivably represent a ‘normalization’ of striatal volume
due to repeated exposure to the dopamine-enhancing effects of
cocaine. In support of this hypothesis, we note a similar inverse
association between striatal volume and cumulative methampheta-
mine use has previously been reported by Chang et al. (2005). It
is also notable that caudate volume is smaller in children with
attention deficit hyperactivity disorder who have been treated
with methylphenidate compared with unmedicated children with
attention deficit hyperactivity disorder (Bussing et al., 2002). As
methylphenidate is pharmacologically very similar to cocaine
(Volkow et al., 1995), it has been speculated that the reduced
caudate volume might be related to a methylphenidate-induced
increase in dopamine neurotransmission, reflecting an opposite
effect to the volume change observed in patients with schizophre-
nia following treatment with dopamine antagonists (Bussing et al.,
2002). However, the literature regarding caudate volume in child-
hood attention deficithyperactivity
(Castellanos et al., 2002) and the exact mechanisms underlying
the striatal volume changes over time in both treated attention
Table 3 Correlation matrix of impulsivity, compulsivity and duration of cocaine use in cocaine-dependent individuals
Duration of cocaine abuse
Brain 2011: 134; 2013–2024 K. D. Ersche et al.
deficit hyperactivity disorder and cocaine dependence require fur-
ther investigation to test causal explanatory models.
Impulsivity is thought to be a vulnerability factor for substance
abuse and dependence (Dalley et al., 2007; de Wit, 2009). Yet,
the high levels of self-reported impulsivity were only weakly
related to the behavioural measures of impulsivity in the present
study and also in previous studies (Reynolds et al., 2006; Meda
et al., 2009). However, one has to bear in mind that impulsivity is
a multifaceted construct (Evenden, 1999). As can be seen in
Table 2, the self-report and the behavioural measures loaded on
different components, suggesting that they are not measuring the
same aspects of impulsivity. While performance in the two behav-
ioural tasks was associated with altered grey matter volume, the
questionnaire measures were unrelated to brain structure. Several
lines of research have shown significant associations between
Barratt Impulsiveness Scale-11 impulsivity and responses to dopa-
minergic drugs (Cools et al., 2007; Clatworthy et al., 2009; Lee
et al., 2009; Buckholtz et al., 2010). It is thus conceivable that
self-reported impulsivity, as measured by the Barratt Impulsiveness
Scale, indexes the functional integrity of the brain dopamine
system rather than the structure of the corticostriatal networks it
Relationships between brain structure
Frontostriatal dysfunction in cocaine dependence is thought to
underlie the compulsive pattern of drug consumption and behav-
ioural rigidity in the face of negative consequences (Jentsch and
Taylor, 1999; Robbins and Everitt, 1999; Volkow and Fowler,
2000; Schoenbaum and Shaham, 2008). In the present study,
cocaine-related compulsivity was associated with a significant
loss of grey matter in the orbitofrontal cortex, which may reflect
the shift in the control of behaviour from the prefrontal cortex to
the striatum that has been demonstrated by preclinical research
(Everitt and Robbins 2005; Porrino et al., 2007). Neuroimaging
studies using PET have also shown that hypometabolic activity in
the orbitofrontal cortex of cocaine users is associated with reduced
dopamine receptor density in the striatum (Volkow et al., 1993).
Presumably, reduction in grey matter in the orbitofrontal cortex
may reflect the lack of top-down control that reduces drug users’
ability to optimally guide their behaviour. This lack of orbitofrontal
control may result in drug-craving and disinhibition when faced
with drug-related cues (Volkow and Fowler, 2000). The OCDUS
scale measures the subjective interference and distress caused by
drug-related thoughts and compulsive behaviour patterns. Indeed,
those cocaine users reporting high levels of cocaine-related com-
pulsivity, as indexed by high scores on the OCDUS scale, showed
the greatest reduction in grey matter volume in the orbitofrontal
Methodological limitations and
The study sample was sizeable in comparison with previous neuro-
imaging studies of cocaine dependence. However, the cocaine
users were somewhat heterogeneous in terms of their exposure
to alcohol, nicotine and other illicit drugs; and the study was not
designed powerfully to investigate possible differences in brain
anatomy between subgroups of the cocaine user group defined
by their concurrent use of alcohol and other drugs. Larger studies
will be required in future to address this issue although it will likely
always prove challenging to identify cocaine-dependent individuals
who are not also dependent on one or more other substances. To
conduct statistical testing we used permutation-based methods
that have been previously described and validated in terms of
nominal type 1 error control (Bullmore et al., 1999; Suckling
and Bullmore, 2004). This non-parametric approach to voxel
cluster-level analysis offers considerable advantages in terms of
sensitivity compared with mass univariate analysis of individual
voxels, or parametric testing of voxel clusters (Bullmore et al.,
1999). However, it does entail some assumptions, including the
assumption that the spatial covariance or smoothness of the voxel
statistic maps is homogeneous. This assumption is unlikely to be
entirely justified in analysis of ‘raw’ MRI data, which typically
demonstrate local inhomogeneities of spatial covariance, e.g. in
subcortical structures and at the boundaries between grey and
white matter (Flitney and Jenkinson, 2000). To address this pos-
sible concern, we applied a Gaussian filter to the statistic maps
before significance testing (which will have rendered the spatial
covariance more homogeneous than in the raw data). We have
also corroborated the results of cluster-level mapping by graphical
and statistical examination of grey matter volume at a regional
level for key structures such as the striatum.
We acknowledge that impulsivity is not a unitary construct
(Evenden, 1999), and we have not investigated all aspects of
this construct. In particular, we did not examine impulsive
choice, which may possibly involve more cognitive aspects of im-
pulsivity than the ‘impulsive actions’ investigated in this study
(Winstanley et al., 2010). Not only impulsivity but also compulsiv-
ity may be a multifaceted construct. We only used the OCDUS
scale as a measure of cocaine-related compulsivity but novel ex-
perimental tasks are now needed to quantify compulsive behav-
iour. Further studies will be required to investigate the neural
substrates of compulsivity in addiction. It will also be necessary
to expand the investigation to other drugs of abuse, as well as
to analysing the effects of stimulants, such as those of cocaine we
have reported here.
In summary, we identified extensive significantly decreased grey
matter volume in orbitofrontal and other cortical regions, and a
significant increase in grey matter volume of the basal ganglia, in
cocaine-dependent individuals. We also showed that the changes
in grey matter volume within this frontostriatal circuitry were
associated with cocaine-related compulsivity and attentional im-
pairments, suggesting that they may reflect the shift in the control
of behaviour from the frontal cortex to the striatum that has pre-
viously been predicted by preclinical research. Finally, behavioural
Frontostriatal systems, impulsivity and cocaineBrain 2011: 134; 2013–2024 |
and brain imaging markers were significantly correlated with the
duration of cocaine abuse, suggesting (but not proving) that
changes in brain systems controlling attention and compulsive
of prolonged cocaine
The authors would like to thank all the volunteers for their par-
ticipation in this study, and staff at the GlaxoSmithKline Clinical
Unit Cambridge and the Wolfson Brain Imaging Centre for their
dedicated support. We are also grateful to Dr Sanja Abbot for her
assistance with data extraction and to Dr John Suckling for advice
on statistical analysis.
This work was funded and sponsored by GlaxoSmithKline
(RG45422) and conducted within the GlaxoSmithKline (GSK)
Clinical Unit Cambridge, UK. The Behavioural and Clinical
Neuroscience Institute (BCNI) at the University of Cambridge is
jointly funded by the Medical Research Council and The
Wellcome Trust. K.D.E. and P.S.J. are supported by a grant from
the Medical Research Council (MRC). A.B. is a recipient of the
NARSAD Young Investigator Award. S.M.-Z. is funded by a
Wellcome Trust Program Grant to T.W.R. T.W.R. consults for
Cambridge Cognition and various pharmaceutical companies,
including GlaxoSmithKline. E.T.B. is employed part-time by
GlaxoSmithKline and part-time by the University of Cambridge.
Funding to pay the Open Access publication charges for this
article was provided by GlaxoSmithKline.
Aharonovich E, Nunes E, Hasin D. Cognitive impairment, retention and
abstinence among cocaine abusers in cognitive-behavioral treatment.
Drug Alcohol Depend 2003; 71: 207–11.
Aharonovich E, Hasin DS, Brooks AC, Liu X, Bisaga A, Nunes EV.
Cognitive deficits predict low treatment retention in cocaine depend-
ent patients. Drug Alcohol Depend 2006; 81: 313–22.
American Psychiatric Association. Diagnostic and statistical manual of
mental disorders. 4th edn., text revision. Washington, DC: American
Psychiatric Association; 2000.
Andersson JLR, Jenkinson M, Smith S. Non-linear optimisation. FMRIB
Andersson JLR, Jenkinson M, Smith S. Non-linear registration, aka Spatial
normalisation. FMRIB technical report TR07JA2, 2007b. www.fmrib
Beck AT, Steer RA, Brown GK. Manual for Beck Depression Inventory-II.
San Antonio, TX: Psychological Corporation; 1996.
Belin D, Mar AC, Dalley JW, Robbins TW, Everitt BJ. High impulsivity
predicts the switch to compulsive cocaine-taking. Science 2008; 320:
Benes FM, Paskevich PA, Davidson J, Domesick VB. The effects of halo-
peridol on synaptic patterns in the rat striatum. Brain Research 1985;
Breiter HC, Gollub RL, Weisskoff RM, Kennedy DN, Makris N, Berke JD,
et al. Acute effects of cocaine on human brain activity and emotion.
Neuron 1997; 19: 591–611.
Buchanan D, Tooze JA, Shaw S, Kinzly M, Heimer R, Singer M.
Demographic, HIV risk behavior, and health status characteristics
of “crack” cocaine injectors compared to other injection drug
users in three New England cities. Drug Alcohol Depend 2006; 81:
Buckholtz JW, Treadway MT, Cowan RL, Woodward ND, Li R,
Ansari MS, et al. Dopaminergic network differences in human impul-
sivity. Science 2010; 329: 532.
Buckley TC, Parker JD, Heggie J. A psychometric evaluation of the BDI-II
in treatment-seeking substance abusers. J Subst Abuse Treat 2001; 20:
Bullmore ET, Suckling J, Overmeyer S, Rabe-Hesketh S, Taylor E,
Brammer MJ. Global, voxel, and cluster tests, by theory and permu-
tation, for a difference between two groups of structural MR images
of the brain. IEEE Tans Med Imaging 1999; 18: 32–42.
Bussing R, Grudnik J, Mason D, Wasiak M, Leonard C. ADHD and con-
duct disorder: an MRI study in a community sample. World J Biol
Psychiatry 2002; 3: 216–220.
Carver CS, White TL. Behavioral-inhibition, behavioral activation, and
affective responses to impending reward and punishment - the BIS/
BAS scales. J Pers Soc Psychol 1994; 67: 319–33.
Castellanos FX, Lee PP, Sharp W, Jeffries NO, Greenstein DK, Clasen LS,
et al. Developmental Trajectories of Brain Volume Abnormalities in
Disorder. JAMA 2002; 288: 1740–1748.
Chakos MH, Shirakawa O, Lieberman J, Lee H, Bilder R, Tamminga CA.
Striatal enlargement in rats chronically treated with neuroleptic. Biol
Psychiatry 1998; 44: 675–684.
Chang L, Cloak C, Patterson K, Grob C, Miller EN, Ernst T. Enlarged
striatum in abstinent methamphetamine abusers: A possible compen-
satory response. Biol Psychiat 2005; 57: 967–974.
Clatworthy PL, Lewis SJG, Brichard L, Hong YT, Izquierdo D, Clark L,
et al. Dopamine release in dissociable striatal subregions predicts the
different effects of oral methylphenidate on reversal learning and spa-
tial working memory. J Neurosci 2009; 29: 4690–6.
Cools R, Sheridan M, Jacobs E, D’Esposito M. Impulsive personality pre-
dicts dopamine-dependent changes in frontostriatal activity during
component processes of working memory. J Neurosci 2007; 27:
Corson PW, Nopoulos P, Miller DD, Arndt S, Andreasen NC. Change in
basal ganglia volume over 2 years in patients with schizophrenia:
typical versus atypical neuroleptics. Am J Psychiatry 1999; 156:
Coull JT, Frith CD, Frackowiak RSJ, Grasby PM. A fronto-parietal
network for rapid visual information processing: a PET study of sus-
tained attention and working memory. Neuropsychologia 1996; 34:
Dalley JW, Fryer TD, Brichard L, Robinson ESJ, Theobald DEH, Laane K,
et al. Nucleus accumbens D2/3 receptors predict trait impulsivity and
cocaine reinforcement. Science 2007; 315: 1267–70.
de Wit H. Impulsivity as a determinant and consequence of drug use: a
review of underlying processes. Addict Biol 2009; 14: 22–31.
Di Chiara G, Imperato A. Drugs abused by humans preferentially increase
synaptic dopamine concentrations in the mesolimbic system of freely
moving rats. Proc Natl Acad Sci USA 1988; 85: 5274–8.
Eagle DM, Robbins TW. Inhibitory control in rats performing a
stop-signal reaction-time task: Effects of lesions of the medial striatum
and d-amphetamine. Behav Neurosci 2003; 117: 1302–17.
Eagle DM, Wong JCK, Allan ME, Mar AC, Theobald DEH, Robbins TW.
Contrasting roles for dopamine D1- and D2-receptor subtypes in the
dorsomedial striatum but not the nucleus accumbens core during be-
havioural inhibition in the stop-signal task in rats. J Neurosci 2011; 31:
Ersche KD, Bullmore ET, Craig KJ, Shabbir SS, Abbott S, Muller U, et al.
Influence of compulsivity of drug abuse on dopaminergic modulation
Brain 2011: 134; 2013–2024K. D. Ersche et al.
of attentional bias in stimulant dependence. Arch Gen Psychiatry
2010; 67: 632–644.
Ersche KD, Turton AJ, Pradhan S, Bullmore ET, Robbins TW. Drug ad-
diction endophenotypes: impulsive versus sensation-seeking personal-
ity traits. Biol Psychiatry 2010; 68: 770–3.
European Monitoring Centre for Drugs and Drug Addiction. Annual
report 2010: the state of the drugs problem in Europe. Lisbon,
Portugal:Office for Official
Evenden JL. Varieties of impulsivity. Psychopharmacology 1999; 146:
Everitt BJ, Robbins TW. Neural systems of reinforcement for drug addic-
tion: from actions to habits to compulsion. Nat Neurosci 2005; 8:
Fein G, Di Sclafani V, Meyerhoff DJ. Prefrontal cortical volume reduction
associated with frontal cortex function deficit in 6-week abstinent
crack-cocaine dependent men. Drug Alcohol Depend 2002; 68:
First MB, Spitzer RL, Gibbon M, Williams JBW. Structured clinical inter-
view for DSM-IV-TR axis I disorders, research version, non-patient
edition. (SCID-I/NP). New York: Biometrics Research, New York
State Psychiatric Institute; 2002.
Flitney DE, Jenkinson M. Technical report TR00DF1 - cluster analysis
revisited. Oxford, UK: Oxford Centre for Functional Magnetic
Resonance Imaging of the Brain, University of Oxford; 2000.
Franken IHA, Hendriks VM, Van den Brink W. Initial validation of two
opiate craving questionnaires: The Obsessive Compulsive Drug Use
Scale and the Desires for Drug Questionnaire. Addict Behav 2002;
FrankenIHA, MurisP. BIS/BAS
college students’substance use.
Franklin TR, Acton PD, Maldjian JA, Gray JD, Croft JR, Dackis CA, et al.
Decreased gray matter concentration in the insular, orbitofrontal, cin-
gulate, and temporal cortices of cocaine patients. Biol Psychiat 2002;
Goldstein RZ, Tomasi D, Alia-Klein N, Zhang L, Telang F, Volkow ND.
The effect of practice on a sustained attention task in cocaine abusers.
NeuroImage 2007; 35: 194–206.
GoodingDC, BurroughsS, Boutros
cocaine-dependent patients: converging behavioral and electrophysio-
logical evidence. Psychiatry Res 2008; 160: 145–54.
Hallahan BP, Craig MC, Toal F, Daly EM, Moore CJ, Ambikapathy A,
et al. In vivo brain anatomy of adult males with Fragile X syndrome:
an MRI study. NeuroImage 2011; 54: 16–24.
Hayaki J, Stein MD, Lassor JA, Herman DS, Anderson BJ. Adversity
among drug users: relationship to impulsivity. Drug Alcohol Depend
2005; 78: 65–71.
Horner MD. Attentional functioning in abstinent cocaine abusers. Drug
Alcohol Depend 1999; 54: 19–33.
Jacobsen LK, Giedd JN, Gottschalk C, Kosten TR, Krystal JH. Quantitative
morphology of the caudate and putamen in patients with cocaine
dependence. Am J Psychiatry 2001; 158: 486–9.
Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for
the robust and accurate linear registration and motion correction of
brain images. NeuroImage 2002; 17: 825–41.
Jenkinson M, Smith S. A global optimisation method for robust affine
registration of brain images. Med Image Anal 2001; 5: 143–56.
Jentsch JD, Taylor JR. Impulsivity resulting from frontostriatal dysfunction
in drug abuse: implications for the control of behavior by reward-
related stimuli. Psychopharmacology 1999; 146: 373–90.
Jernigan TL, Gamst AC, Archibald SL, Fennema-Notestine C, Mindt MR,
Marcotte TL, et al. Effects of methamphetamine dependence and HIV
infection on cerebral morphology. Am J Psychiatry 2005; 162:
Jovanovski D, Erb S, Zakzanis KK. Neurocognitive deficits in cocaine
Neuropsychol 2005; 27: 189–204.
NN. Attentionaldeficits in
of the evidence.J Clin Exper
Keshavan M, Bagwell W, Haas G, Sweeney J, Schooler N, Pettegrew J.
Changes in caudate volume with neuroleptic treatment. Lancet 1994;
Koob GF, Le Moal M. Plasticity of reward neurocircuitry and the ‘dark
side’ of drug addiction. Nat Neurosci 2005; 8: 1442–4.
Langen M, Durston S, Staal WG, Palmen SJMC, van Engeland H.
Caudate nucleus is enlarged in high-functioning medication-naive sub-
jects with autism. Biol Psychiatry 2007; 62: 262–6.
Lawrence NS, Ross TJ, Hoffmann R, Garavan H, Stein EA. Multiple neur-
onal networks mediate sustained attention. J Cogn Neurosci 2003; 15:
Lawrence NS, Ross TJ, Stein EA. Cognitive mechanisms of nicotine on
visual attention. Neuron 2002; 36: 539–48.
Lee B, London ED, Poldrack RA, Farahi J, Nacca A, Monterosso JR, et al.
Striatal dopamine D2/D3 receptor availability is reduced in metham-
phetamine dependence and is linked to impulsivity. J Neurosci 2009;
Logan GD, Schachar RJ, Tannock R. Impulsivity and inhibitory control.
Psychol Sci 1997; 8: 60–4.
Martinez D, Broft A, Foltin RW, Slifstein M, Hwang DR, Huang YY, et al.
Cocaine dependence and D-2 receptor availability in the functional
subdivisions of the striatum: relationship with cocaine-seeking behav-
ior. Neuropsychopharmacology 2004; 29: 1190–1202.
Martinez D, Greene K, Broft A, Kumar D, Liu F, Narendran R, et al.
Lower level of endogenous dopamine in patients with cocaine
following acute dopamine depletion. Am J Psychiatry 2009; 166:
Matochik JA, London ED, Eldreth DA, Cadet JL, Bolla KI. Frontal cortical
tissue composition in abstinent cocaine abusers: a magnetic resonance
imaging study. Neuroimage 2003; 19: 1095–102.
Matsuo K, NicolettiMA, Peluso
Watanabe Y, et al. Anterior cingulate volumes associated with trait
impulsivity in individuals with bipolar disorder. Bipolar Disord 2009;
Meda SA, Stevens MC, Potenza MN, Pittman B, Gueorguieva R,
Andrews MM, et al. Investigating the behavioral and self-report con-
structs of impulsivity domains using principal component analysis.
Behav Pharmacol 2009; 20: 390–9.
Moeller FG, Barratt ES, Dougherty DM, Schmitz JM, Swann AC.
Psychiatric aspects of impulsivity. Am J Psychiatry 2001a; 158:
MoellerFG, BarrattES, Fischer
Mathias CW, et al. P300 event-related potential amplitude and impul-
sivity in cocaine-dependent subjects. Neuropsychobiology 2004; 50:
Moeller FG, Dougherty DM, Barratt ES, Schmitz JM, Swann AC,
Grabowski J. The impact of impulsivity on cocaine use and retention
in treatment. J Subst Abuse Treat 2001b; 21: 193–8.
Moeller FG, Hasan KM, Steinberg JL, Kramer LA, Dougherty DM,
Santos RM, et al. Reduced anterior corpus callosum white matter
integrity is related to increased impulsivity and reduced discriminability
Neuropsychopharmacology 2005; 30: 610–7.
Patton JH, Stanford MS, Barratt ES. Factor structure of the Barratt
Impulsiveness Scale. J Clin Psychol 1995; 51: 768–74.
Porrino LJ, Lyons D, Miller MD, Smith HR, Friedman DP, Daunais JB,
et al. Metabolic mapping of the effects of cocaine during the initial
phases of self-administration in the nonhuman primate. J Neurosci
2002; 22: 7687–94.
Porrino LJ, Smith HR, Nader MA, Beveridge TJR. The effects of cocaine:
a shifting target overthe course
Psychopharmacol Biol Psych 2007; 31: 1593–600.
Potenza MN, Taylor JR. Found in translation: understanding impulsivity
and related constructs through integrative preclinical and clinical re-
search. Biol Psychiatry 2009; 66: 714–6.
MAM, Hatch JP, NemotoK,
CJ, Dougherty DM, Reilly EL,
test manual.Windsor, UK:
Frontostriatal systems, impulsivity and cocaineBrain 2011: 134; 2013–2024 |
Ray Li CS, Yan P, Sinha R, Lee TW. Subcortical processes of motor Download full-text
response inhibition during a stop signal task. NeuroImage 2008; 41:
Reynolds B, Ortengren A, Richards JB, de Wit H. Dimensions of impul-
sive behavior: personality and behavioral measures. Pers Indiv Differ
2006; 40: 305–15.
Robbins TW, Everitt BJ. Drug addiction: bad habits add up. Nature 1999;
Rogers RD, Baunez C, Everitt BJ, Robbins TW. Lesions of the medial and
lateral striatum in the rat produce differential deficits in attentional
performance. Behav Neurosci 2001; 115: 799–811.
Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ.
Nonrigid registration using free-form deformations: application to
breast MR images. IEEE Tans Med Imaging 1999; 18: 712–21.
Sarter M, Givens B, Bruno JP. The cognitive neuroscience of sustained
attention: where top-down meets bottom-up. Brain Res Rev 2001; 35:
Schachar R, Logan GD, Robaey P, Chen S, Ickowicz A, Barr C.
Restraint and cancellation: multiple inhibition deficits in attention
deficit hyperactivity disorder. J Abnorm Child Psychol 2007; 35:
Scherk H, Falkai P. Effects of antipsychotics on brain structure. Curr Opin
Psychiatry 2006; 19: 145–50.
Schiffer B, Muller BW, Scherbaum N, Forsting M, Wiltfang J, Leygraf N,
et al. Impulsivity-related brain volume deficits in schizophrenia-
addiction comorbidity. Brain 2010; doi: 10.1093/brain/awq153.
Schoenbaum G, Shaham Y. The role of orbitofrontal cortex in drug ad-
diction: a review of preclinical studies. Biol Psychiatry 2008; 63:
Schwartz DL, Mitchell AD, Lahna DL, Luber HS, Huckans MS,
Mitchell SH, et al. Global and local morphometric differences in
recently abstinent methamphetamine-dependent
NeuroImage 2010; 50: 1392–401.
Sim ME, Lyoo IK, Streeter CC, Covell J, Sarid-Segal O, Ciraulo DA,
et al. Cerebellar gray matter volume correlates with duration of co-
caine use in cocaine-dependent subjects. Neuropsychopharmacology
2007; 32: 2229–37.
Smith SM. Fast robust automated brain extraction. Hum Brain Mapp
2002; 17: 143–55.
Suckling J, Bullmore E. Permutation tests for factorially designed neuroi-
maging experiments. Hum Brain Mapp 2004; 22: 193–205.
Tomasi D, Goldstein RZ, Telang F, Maloney T, Alia-Klein N, Caparelli EC,
et al. Thalamo-cortical dysfunction in cocaine abusers: implications in
attention and perception. Psychiatry Res Neuroimaging 2007; 155:
Verdejo-Garcia A, Lawrence AJ, Clark L. Impulsivity as a vulnerability
marker for substance-use disorders: review of findings from high-risk
research, problem gamblers and genetic association studies. Neurosci
Biobehavioral Rev 2008; 32: 777–810.
Voelbel GT, Bates ME, Buckman JF, Pandina G, Hendren RL. Caudate
nucleus volume and cognitive performance: are they related in child-
hood psychopathology? Biol Psychiatry 2006; 60: 942–50.
Volkow ND, Ding YS, Fowler JS, Wang GJ, Logan J, Gatley JS, et al. Is
methylphenidate like cocaine - studies on their pharmacokinetics and
distribution in the human brain. Arch Gen Psychiatry 1995; 52:
Volkow ND, Fowler JS. Addiction, a disease of compulsion and drive:
involvement of the orbitofrontal cortex. Cereb Cortex 2000; 10:
Volkow ND, Fowler JS, Wang GJ, Hitzemann R, Logan J, Schlyer DJ,
et al. Decreased dopamine-D(2) receptor availability is associated with
reduced frontal metabolism in cocaine abusers. Synapse 1993; 14:
Volkow ND, Wang GJ, Fowler JS, Logan J, Gatley SJ, Hitzemann R, et al.
cocaine-dependent subjects. Nature 1997; 386: 830–3.
Wagner FA, Anthony JC. From first drug use to drug dependence: de-
velopmental periods of risk for dependence upon marijuana, cocaine,
and alcohol. Neuropsychopharmacology 2002; 26: 479–88.
Winstanley CA, Olausson P, Taylor JR, Jentsch JD. Insight into the rela-
tionship between impulsivity and substance abuse from studies using
animal models. Alcohol Clin Exp Res 2010; 34: 1306–18.
Woodward ND, Zald DH, Ding Z, Riccardi P, Ansari MS, Baldwin RM,
et al. Cerebral morphology and dopamine D2/D3 receptor distribution
in humans: a combined [18F]fallypride and voxel-based morphometry
study. NeuroImage 2009; 46: 31–8.
Wu JC, Bell K, Najafi A, Widmark C, Keator D, Tang C, et al. Decreasing
striatal 6-FDOPA uptake with increasing duration of cocaine with-
drawal. Neuropsychopharmacology 1997; 17: 402–9.
Zhang YY, Brady M, Smith S. Segmentation of brain MR images through
a hidden Markovrandomfield
maximization algorithm. IEEE Tans Med Imaging 2001; 20: 45–57.
modeland the expectation-
Brain 2011: 134; 2013–2024K. D. Ersche et al.