Prefrontal cortex activity is reduced in gambling and nongambling substance users during decision-making

Article (PDF Available)inHuman Brain Mapping 28(12):1276-86 · December 2007with47 Reads
DOI: 10.1002/hbm.20344 · Source: PubMed
Abstract
Poor decision-making is a hallmark of addiction, whether to substances or activities. Performance on a widely used test of decision-making, the Iowa Gambling Task (IGT), can discriminate controls from persons with ventral medial frontal lesions, substance-dependence, and pathological gambling. Positron emission tomography (PET) studies indicate that substance-dependent individuals show altered prefrontal activity on the task. Here we adapted the IGT to an fMRI setting to test the hypothesis that defects in ventral medial and prefrontal processing are associated with impaired decisions that involve risk but may differ depending on whether substance dependence is comorbid with gambling problems. 18 controls, 14 substance-dependent individuals (SD), and 16 SD with gambling problems (SDPG) underwent fMRI while performing a modified version of the IGT. Group differences were observed in ventral medial frontal, right frontopolar, and superior frontal cortex during decision-making. Controls showed the greatest activity, followed by SDPG, followed by SD. Our results support a hypothesis that defects in ventral medial frontal processing lead to impaired decisions that involve risk. Reductions in right prefrontal activity during decision-making appear to be modulated by the presence of gambling problems and may reflect impaired working memory, stimulus reward valuation, or cue reactivity in substance-dependent individuals.
Prefrontal Cor tex Activity is Reduced in Gambling
and Nongambling Substance Users During
Decision-Making
Jody Tanabe,
1,2
*
Laetitia Thompson,
2
Eric Claus,
3
Manish Dalwani,
2
Kent Hutchison,
3
and Marie T. Banich
2,3
1
Department of Radiology, University of Colorado at Denver and Health Sciences Center,
Denver, Colorado
AQ2
2
Department of Psychiatry, University of Colorado at Denver and Health Sciences Center,
Denver, Colorado
3
Department of Psychology, University of Colorado Boulder, Colorado
Abstract: Objective: Poor decision-making is a hallmark of addiction, whether to substances or activities.
Performance on a widely used test of decision-making, the Iowa Gambling Task (IGT), can discriminate
controls from persons with ventral medial frontal lesions, substance-dependence, and pathological
gambling. Positron emission tomography (PET) studies indicate that substance-dependent individuals
show altered prefrontal activity on the task. Here we adapted the IGT to an fMRI setting to test the hy-
pothesis that defects in ventral medial and prefrontal processing are associated with impaired decisions
that involve risk but may differ depending on whether substance dependence is comorbid with gam-
bling problems. Method: About 18 controls, 14 substance-dependent individuals (SD), and 16 SD with
gambling problems (SDPG) underwent fMRI while performing a modified version of the IGT. Result:
Group differences were observed in ventral medial frontal, right frontopolar, and superior frontal cor-
tex during decision-making. Controls showed the greatest activity, followed by SDPG, followed by SD.
Conclusion: Our results support a hypothesis that defects in ventral medial frontal processing lead to
impaired decisions that involve risk. Reductions in right prefrontal activity during decision-making
appear to be modulated by the presence of gambling problems and may reflect impaired working
memory, stimulus reward valuation, or cue reactivity in substance-dependent individuals. Hum Brain
Mapp 00:000000, 2007.
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Key words: substance abuse; pathological gambling; decision-making; fMRI; prefrontal cortex
INTRODUCTION
The Iowa Gambling Task (IGT) is a widely used instru-
ment for assessing decision-making under uncertainty
[Bechara et al., 1994]. On this task, patients with medial
and orbitofrontal lesions, pathological gamblers, and a
subset of substance dependent individuals demonstrate
preferences for short-term gains at the risk of larger net
losses [Bechara et al., 1994, 2000; Manes et al., 2002; Petry,
2001a,b]. Positron emission tomography (PET) studies dur-
ing decision-making using the IGT [Ernst et al., 2002] have
yielded increased cerebral blood flow in ventral medial
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Presented at the Organization for Human Brain Mapping 2006,
Florence, Italy.
Contract grant sponsor: Institute for Research on Pathological
Gambling and Related Disorders, Harvard Medical School, Divi-
sion of Addictions; Contract grant sponsor: USPHS; Contract grant
number: K08DA1505.
*Correspondence to: Jody Tanabe, M.D.; Box A034, 4200 East 9th
Avenue, Denver, CO 80262. E-mail: jody.tanabe@uchsc.edu
Received for publication 19 April 2006; Revision 16 September
2006; Accepted 18 September 2006
DOI: 10.1002/hbm.20344
Published online in Wiley InterScience (www.interscience.wiley.
com).
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frontal, orbitofrontal, and cingulate cortex, areas frequently
implicated in risky decision making. Compared with that
of controls, these regions are also differentially affected in
substance dependent individuals. Bolla et al. found dose-
related reductions in right orbitofrontal and dorsolateral
prefrontal cortex in abstinent marijuana users compared
with that of controls [Bolla et al., 2005]. Reductions in right
dorsolateral prefrontal cortex were also found in abstinent
cocaine users [Bolla et al., 2003]. Overall, these studies sug-
gest that dysfunction of the right prefrontal neural net-
work may underlie the poor decisions characteristic of
substance users.
The extent to which separate brain regions can be linked
to different components of the decision-making process
such as stimulus, decision, motor action, anticipation, and
feedback response is limited by the temporal resolution of
PET (1 min). In contrast, higher temporal resolution of
functional MRI (fMRI) allows modeling of these components
separately as recently demonstrated by Fukui et al. Medial
frontal activity was observed during risky anticipation
(high-risk minus low-risk selections) [Fukui et al., 2005].
While the paradigm in this fMRI study mimicked the origi-
nal IGT, the lack of baseline scans where no decision was
made but the task was identical in all other respects pre-
cluded evaluation of the decision-making process per se.
Previous studies have also been limited by potentially
confounding effects of the gambling nature of the task. In-
herent in many decision-making tasks that involve risk,
including the IGT, are sensory gambling cues that, by
themselves, may induce prefrontal activity in the absence
of a required decision [Crockford et al., 2005]. This is
important to consider because pathological gambling is
highly comorbid with substance abuse. A survey of nearly
35,000 respondents revealed an odds ratio ranging from
3 to 6 between pathological gambling and drug abuse
[Petry et al., 2005]. Thus, it is not clear whether the altered
pattern of prefrontal activity observed in substance
dependence individuals is related to substance abuse or
reflects comorbid gambling problems.
In the current study, we address several of these limita-
tions. First, we utilize fMRI to achieve higher temporal re-
solution than earlier PET studies using the IGT. Second, we
carefully characterize our subject population to differentiate
between individuals with substance dependence only when
compared with those with substance dependence and gam-
bling problems. Although projections from ventral tegmen-
tal area to the nucleus accumbens are well known to
underlie acute positive drug reinforcement, research sug-
gests that other structures such as the prefrontal cortex are
likely to be critical in the development of drug addiction.
Thus, our a priori hypothesis was that compared to
controls, substance-dependent individuals would show
decreased ventral medial prefrontal activity during decision
making that involved risk. We also predicted that, among
substance-dependent individuals, presence of gambling
problems would be associated with greater prefrontal activ-
ity as a result of the gambling cues inherent in the IGT.
METHODS
Participants
About 56 participants were recruited (16 controls, 20
participants with substance dependence without pathologi-
cal gambling (SD), and 20 participants with substance de-
pendence and pathological gambling (SDPG)). Groups
were matched for age, gender, and ethnicity. SD and
SDPG were recruited from the Addiction Research and
Treatment Service at UCDHSC, a long-term residential
treatment service. Substance dependent individuals had
experienced serious substance abuse problems in the past
and were required to be abstinent a minimum of 2 months.
Urine toxicology screening for opiates, stimulants, and
marijuana was conducted at the time of MR scanning.
SDPG were defined as having a South Oaks Gambling
Screen (SOGS) scores 5 [Lesieur and Blume, 1987]. SD
and controls had SOGS scores 1. All participants pro-
vided written informed consent approved by the Colorado
Multiple Institutional Review Board.
Tests and Interviews
The following were administered to all participants:
a. Barratt Impulsivity Scale [Patton et al., 1995].
b. Zuckerman’s Sensation Seeking Questionnaire.
c. Composite International Diagnostic Interview—Sub-
stance Abuse Module (CIDI-SAM) [Cottler et al., 1995].
d. Wechsler Abbreviated Scale of Intelligence (WASI)
(Psychological Corporation, 1999
AQ3
).
Iowa Gambling Task Modified for fMRI
We used a modified version of the Iowa Gambling Task
(IGT) [Bechara et al., 1994] appropriate for an fMRI setting,
which avoids some confounds of the original task. As in the
IGT, there were four decks of cards. Two of the decks (‘‘good
decks’’) were associated with low payouts and low penalties,
resulting in an overall net gain. The other two decks (‘bad
decks’’) were associated with large payouts and large penal-
ties resulting in an overall net loss. For each trial, participants
were shown four decks, along with the instructions ‘Play or
Pass’ printed under one of the four decks. Participants
decided whether to Play or Pass on each trial and pressed a
button to indicate the response. If the subject chose Play, a
monetary outcome was shown on the screen, and this num-
ber was added to the running total. Thus, in contrast to the
IGT, the computer selected the card, rather than the subject,
ensuring that differences in search strategy across the decks
did not confound performance. In addition, participants
received a single monetary outcome on all trials in which
‘Play’ was chosen (e.g., þ$100 or $50), rather than receiv-
ing a constant reward and occasional punishment.
Participants began the task with a hypothetical $2,000
and were told they could earn $10.00 real dollars if they
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had more than $2,000 at the end of the task. Participants
completed 64 ‘learning’ trials while anatomical images
were acquired, followed by 96 trials during functional
scanning. Each trial was 14 s (4 s decision, 4 s outcome,
6 s during which participants viewed a þ to allow the
hemodynamic response to return close to baseline before
the next trial). The first two scans (4 s) corresponding to
the decision component comprised an event.
Decision condition
The subject was asked to decide whether to Play or Pass
the card by pressing the appropriate button, followed by
the outcome associated with that deck.
No decision condition
The task was identical to the decision condition except that
participants were explicitly told which button to press (e.g.,
press Play), followed by the outcome associated with that
deck. Participants were informed that although they would
continue to win and lose money even if they did not want to
make a Play response, they would still be able to learn about
the payoff schedule of the decks to benefit later trials.
MR Image Acquisition
Images were acquired with a 3T GE whole body MR sys-
tem (General Electric, Milwaukee, WI) using a standard
quadrature head coil. A high-resolution 3D T1-weighted an-
atomical scan was acquired, followed by the functional
scans using gradient-echo echo-planar imaging (EPI) (TR ¼
2,000, TE ¼ 35, FA ¼ 77, 64
2
matrix, 240 mm
2
FOV, axial
slices angled parallel to the AC-PC line, 4 mm thick, 0 mm
gap). Two runs of 337 scans per run were acquired. A set
of inversion recovery images (IR-EPI) (TR ¼ 3,000, TE ¼ 35,
TI ¼ 505, FA ¼ 90) acquired at the same resolution and
position as the functional images was used as an intermedi-
ate step in the spatial normalization process described later.
fMRI Data Analysis
Motion correction, spatial normalization, model specifi-
cation and estimation, and statistical inference were per-
formed with SPM2.
Preprocessing
After excluding the first seven volumes for saturation
effects, images were motion-corrected and resliced. Eight
participants were excluded for head motion exceeding
2 mm, resulting in good quality data on 48 participants (14
control, 16 SD, 18 SDPG). EPI images were normalized to
the Montreal Neurological Institute (MNI) template using
a three-part method that included an intermediate IR-EPI
dataset. EPI images were coregistered to the IR-EPI series.
The coregistered IR-EPI images were segmented into gray
matter, white matter, and CSF. The segmented gray matter
images were normalized to the MNI gray matter template.
Transformation parameters were then applied to the core-
gistered EPI series. Compared with normalizing functional
gradient echo EPI directly to the EPI template, the three-
part method resulted in better normalization particularly
in the orbitofrontal regions. A 6-mm FWHM Gaussian
smoothing kernel was applied to the normalized images.
First level fixed-effects model specification
Data were analyzed using two-stage mixed effects model.
At the first level, two predictors were used to model fMRI
data. The first predictor sought to ascertain which brain
regions were activated when an individual made a decision
as compared to when no decision was required. A hybrid
block-event related design was used in which the first two
scans corresponding to the decision component of each trial
comprised an event. The second predictor sought to ascer-
tain which brain regions were more sensitive to wins or
losses. This was a pure event-related model that retrospec-
tively extracted the fMRI response immediately following
either a win or loss outcome for each trial. Trials of wins
were compared with that of losses and vice versa, regard-
less of whether the individual made the decision. The third
predictor was similar to the one used by previous investiga-
tors [Fukui et al., 2005] which sought to ascertain whether
the modeled fMRI signal was convolved with the hemody-
namic response function. Low frequency noise was removed
using a high pass filter with cutoff of 128 s.
Brain activity for predictors 1 and 2
(2nd level mixed effects)
Predictor 1 (decision > no decision): A 1 sample t-test of
the parameter estimate was performed on all participants.
Statistical maps were corrected for multiple comparisons
using family-wise error correction, and set at a threshold of
P < 0.05 and minimum 50 contiguous voxels (0.5 cm
3
).
Brain regions, Brodmann area, MNI coordinate, t-value, and
P-values were reported for local maxima.
Predictor 2 (wins > losses or losses > wins): Because the
total number of events for each subject was relatively low
(80), we lacked power to analyze this condition with
multiple comparisons correction. Therefore, statistical maps
were set at a lower threshold of P < 0.001 and a spatial
extent of 30 voxels.
Group difference in brain activity for predictors 1
and 2 (2nd level mixed effects)
Predictor 1. Differences among the three groups for pre-
dictor 1 (decision > no decision) were analyzed using a sec-
ond level one-way ANCOVA with education and IQ as cova-
riates. Maps were set at a threshold of P < 0.005 (F > 6, df ¼ 2,
43) uncorrected for multiple comparisons and a spatial extent
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of 30 voxels. Brain regions, Brodmann area, MNI coordinate,
and size of fMRI effect (b) was reported for each local maxima.
Post-hoc comparisons were made using Tukey’s test.
Predictor 2. We did not perform a second level
ANOVA for the second predictor due to insufficient power.
Post-hoc test of group differences in risky
versus sa fe decisions
Regions identified as significantly different across
groups for predictor 1 were selected for subsequent analy-
sis using an event-related model that compared the deci-
sion to play cards from risky (A, B) versus safe (C,D)
decks. IQ and education were entered as covariates.
Analysis of Behavioral Data
Performance
In our version of the IGT, 160 trials were presented to the
participant, half of which consisted of Decision and the other
half passive or No decision. The 80 Decision trials were di-
vided into five time periods. Therefore, each deck was viewed
four times per time period (4 decks 4views 5timeperi-
ods ¼ 80) for the Decision condition. The net score (difference
between number of times good versus bad cards were
played) was calculated for each time period. A 3 (group) 5
(time) repeated measures ANOVA on the net score was ana-
lyzed for main effects of group, time, and group by time inter-
action, after covarying for IQ and education.
RESULTS
Subject Characteristics
There was no difference between SDPG and SD in the
number of substances on which they met criteria for de-
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Figure 1.
Performance on modified Iowa Gambling Task. All participants
increased net score over time consistent with learning. Controls
and SDPG had higher net scores than SD, but the difference was
not statistically significant (data are mean 6 SEM).
Figure 2.
Second level analysis of brain regions
active during decision-making com-
pared with no-decision making. Statis-
tical maps were corrected for multiple
comparisons using family-wise-error and
were set at a threshold P < 0.005.
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pendence (CIDI-SAM 3) (SDPG vs. SD, 3.8 (1.7) vs. 3.0
(1.0)). There was a trend for higher tobacco dependence
among SDPG compared with SD and controls (P ¼ 0.06,
Pearson w
2
). Controls were more educated, had a higher
IQ, and scored lower on measurements of impulsivity and
sensation-seeking compared with SD and SDPG (Table
T1
I).
Although the groups were slightly unbalanced for gender,
this was not significant (w
2
¼ 2.98, 2 df).
In-Magnet Behavioral Data
Performance. Repeated measures ANOVA on net
score revealed a significant main effect of time (F ¼ 3.6, df
¼ 4, P ¼ 0.01). An increase in net score over time was con-
sistent with learning. There was no main effect of group or
interaction between group and time. Compared to SD, con-
trols and SDPG had a higher net score on all but the second
time block, but this was not significant. A partial Z
2
which
describes the proportion of variability attributed to the fac-
tor time (linear model) for each group was 0.21, 0.14, 0.08
for control, SDPG, and SD, respectively (see Fig.
F1
1).
Predictor 1: Brain Activity During Decision-Making
Group average. Second-level analysis of all partici-
pants (n ¼ 48) revealed significant activity in right orbitofron-
tal, bilateral ventral lateral frontal/anterior insula, anterior
cingulate, ventral medial frontal (BA 25), ventral striatum,
parietal, and occipital lobes during decision-making (Table
T2,F2
II,
Fig. 2).
Group differences. After controlling for IQ and edu-
cation, brain activity differed across groups in the ventral
medial frontal (BA 25/11) (F
2,43
¼ 11.3, P < 0.001), right
frontopolar (BA 10) (F
2,43
¼ 9.2, P < 0.001), and right supe-
rior frontal gyri (BA 9/10) (F
2,45
¼ 12.4, P < 0.001) (see
Fig.
F3
3). Post-hoc comparisons showed that controls had
greater ventral medial prefrontal activity than all substance
dependent persons. Controls and SDPG had greater right
superior frontal and frontopolar activity compared with
SD (Table
T3,F4
III, Fig. 4).
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TABLE II. Regions of brain activity during ‘decision > no decision’ across all participants (n 5 48)
Region MNI coord tP
Right orbitofrontal (BA10) 25 58 8 6.2 0.002
Right anterior cingulate (BA 32) 6 36 26 8.6 0.000
Left anterior cingulate (BA 32) 6 32 36 8.9 0.000
Right ventral lateral frontal/anterior insula (BA 47) 34 22 4 7.5 0.000
44 18 2 6.8 0.000
Left ventral lateral frontal/anterior insula (BA 47) 30 24 8 6.5 0.001
Left ventral medial prefrontal (BA 25) 12 16 16 7.1 0.000
Left nucleus accumbens 8108 6.6 0.001
Right subgenual anterior cingulate (BA 25/11) 12 20 10 6.0 0.005
Left parietal (BA 7) 28 66 44 6.1 0.003
Right parietal (BA 7) 34 64 40 6.1 0.003
Left occipital (BA 18) 26 90 4 6.8 0.000
24 88 12 6.2 0.002
P-values are corrected for multiple comparisons.
TABLE I. Characteristics of the study population
SDPG
(n ¼ 20)
SD
(n ¼ 20)
Control
(n ¼ 16)
Age 35 (7) 35 (7) 37 (9)
Gender (M/F) 12/8 9/9 5/11
Education 11 (2) 11 (3) *14 (2)
Ethnicity
Caucasian 14 16 12
African American 3 0 2
Hispanic 3 2 2
SOGS
a
*10.7 (4.4) 0.2 (0.4) 0.1 (0.3)
Mean number
b
3.8 (1.7) 3.1 (1.0) na
% Substance dep
c
Cocaine 75% 75% na
Alcohol 70% 70% na
Amphetamine 70% 60% na
Cannabis 55% 60% na
Opiates 40% 25% na
Hallucinogens 25% 5% na
Sedative 15% 5% na
Club drugs 0% 10% na
Inhalants 0% 0% na
Phencyclidine 0% 0% na
Tobacco ***30% 0% 6.3%
IQ 100 (11) 103 (12) **113 (9)
Impulsivity 76 (15) 73 (12) *55 (8)
IMP-SSd 13 (6) 12 (5) ***7 (5)
Data are mean (SD).
a
South Oaks Gambling Screen (SOGS) (Lesieur and Blume, 1987).
b
Mean number ¼ Mean number of substances on which part-
icipants met criteria for dependence (CIDI-SAM 3).
c
Substance dep, % of participants who met criteria for depend-
ence for that drug.
d
Impulsivity and sensation seeking (IMP-SS).
**** P ¼ 0.06, Pearson w
2
.
*P < 0.005, control vs. SDPG, control vs. SD.
** P < 0.01, control vs. SDPG, control vs. SD.
***P < 0.05, control vs. SDPG, control vs. SD.
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Predictor 2: Brain Activity During Wins Versus
Losses and Losses Versus Wins
Group average. For the condition of wins greater
than losses, medial frontal lobe regions were activated. For
the condition of losses greater than wins, lateral frontal
regions showed greater activity (Fig.
F5
5, P < 0.001, uncor-
rected for multiple comparisons). There was insufficient
power to detect brain activity after correcting for multiple
comparisons (total events 80 per subject).
Group differences. There were no significant group
differences for predictor 2.
Post-Hoc Test of Group Differences in Risky
Versus Safe Decisions
There was a significant group difference in ventral medial
frontal activity when deciding to play risky as compared to
safe decks (F ¼ 4.5, df ¼ 2, P ¼ 0.02). Post-hoc analysis
showed that SDPG activated this region more than controls
(parameter estimates: 64 6 0.66, 0.27 6 0.58, 0.16 6 0.65,
mean 6 SD for controls, SD, SDPG, respectively). There
were no group differences in right prefrontal regions.
DISCUSSION
Brain Activity During Decision-Making Using the
Modified IGT
Our study indicated that the brain regions activated dur-
ing decision-making involving risk, when compared with
passively watching the computer make decisions on our
modified version of the IGT, included orbitofrontal cortex,
ventral medial frontal, ventrolateral frontal/anterior insula,
anterior cingulate, ventral striatum, parietal, and occipital
lobes. As such, our results are consistent with lesion
[Bechara et al., 1994; Clark et al., 2003; Manes et al., 2002;
Saver and Damasio, 1991], PET [Bolla et al., 2003, 2005;
Ernst et al., 2002; Fishbein et al., 2005; London et al., 2000;
Rogers et al., 1999a,b], and fMRI [Cohen et al., 2005; Elliott
et al., 2000] studies implicating orbitofrontal cortex in
reward-related decision-making processes. Our version of
the modified gambling task appears to engage the same
general neural circuitry as the standard gambling task.
It should be acknowledged that because in our modified
IGT, the computer determines the deck that will be played,
rather than the participant doing so, anticipation precedes
as well as follows the decision. As such, the two compo-
nents are not easily separated in our paradigm. Our pri-
mary objective was to examine the process in total, rather
than specifically isolate anticipation from other compo-
nents of decision making and to avoid having participants
feel pressured to make a ‘split-second’ decision, which
would introduce an element of randomness.
Orbitofrontal and ventral medial prefrontal cortex
Our results are consistent with previous PET studies of
the IGT [Bolla et al., 2003, 2005; Ernst et al., 2002] in yielding
prefrontal, anterior cingulate, and parietal lobe activity.
fMRI studies have shown orbitofrontal cortex (OFC) activity
during decision-making involving guesswork [Elliott et al.,
1999], uncertainty and risk [Cohen et al., 2005], and encod-
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Figure 3.
Second level ANCOVA statistical maps
showing brain regions significantly differ-
ent across three groups during decision-
making were ventral medial prefrontal,
right superior frontal, right frontopolar
and were set at threshold F > 6(P <
0.005). Images are shown in radiological
convention (right is on the left). Education
and IQ were entered as covariates.
Figure 4.
Size of fMRI effect during decision-making for ventral medial frontal
and right orbitofrontal regions (MNI coordinates shown) was larg-
est in controls, followed by SDPG, followed by SD. Data represent
size of fMRI effect (b) for individuals. Data were analyzed using
ANOVA and post-hoc Tukeys, controlling for education and IQ.
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ing stimulus reward value [O’Doherty, 2004; O’Doherty
et al., 2001b]. The OFC activity we observed is within BA10
and more dorsal than that reported in most imaging studies
of decision-making where it lies closer to BA 10/11. Our
local maxima are similar, however, to that of Rogers et al.,
who also used a decision-making task invoking risk and
conflict [Rogers et al., 1999b]. In this context, it is interesting
to note that impairment on the IGT was, in fact, greater for
patients with dorsal prefrontal lesions compared with orbito-
frontal lesions [Manes et al., 2002], suggesting a prominent
role of more dorsal areas of prefrontal cortex in decision
making. The activity was most prominent on the right side
which is consistent with previous work showing preferential
right orbitofrontal prefrontal activity during decision-making
[Elliott et al., 1999; Ernst et al., 2002] and a right laterality
effect in lesion studies [Clark et al., 2003]. Implications of
ventral medial prefrontal cortex vmPFC activity are dis-
cussed below in the section on group differences.
Anterior cingulate cortex
We observed activity in anterior cingulate cortex (ACC),
consistent with previous PET studies using the IGT [Ernst
et al., 2002], but in contrast to studies using the Rogers
Decision-making task (RDMT) which involves choosing
between likely, but small risk and reward, or unlikely, but
large risk and reward [Rogers et al., 1999a]. A major differ-
ence between the tasks is that the IGT involves learning.
These findings support the notion that ACC is involved in
stimulus-reinforcement learning and performance monitor-
ing, especially when predictability is low and rate of errors
is high. From the viewpoint of a naı
¨
ve subject, outcomes
in the RDMT are more predictable than in the IGT because
the probability of reward is explicitly given on every trial.
ACC has also been implicated in reward expectancy [Shidara
and Richmond, 2002], high risk decision making [Cohen
et al., 2005], and self-selected responses [Elliott et al., 1999].
Ventral lateral frontal/anterior insula
Activity in the ventral lateral frontal regions reported
here (BA 47) is almost identical in location to that observed
by Rogers et al. in their PET study [Rogers et al., 1999b]. It
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Figure 5.
Second level event-related analysis of brain activity during the expe-
rience of winning or losing. Medial inferior orbitofrontal regions (BA
25 (10, 14, 20) and BA 32 (16, 34, 6))weremoreactiveduring
wins versus losses, whereas lateral inferior orbitofrontal regions (BA
47/12 (34, 20, 16) were more active during losses versus wins.
Superior medial fr ontal (BA 9 (0, 52, 38)) was also more active dur-
ing losses vs. wins. Color bar corresponds to t-value.
TABLE III. Brain regions where activity differed across group during decision-making
Region MNI F (df 2,43)
fMRI effect size (b)
Control SDPG SD
Ventral medial frontal (BA 25/11) 2248 11.3 0.78* 0.35 0.33
Right superior frontal (BA 9/10) 28 48 30 12.4 0.47 0.39 0.65***
Right frontopolar (BA 10) 18 58 14 9.2 0.28 0.32 0.52**
SD, substance-dependent; SDPG, substance-dependent with gambling problems; BA, Brodmann area. IQ and education were entered as
covariates.
*Control vs. SD, control vs. SDPG, P < 0.005.
***SD vs. control, SD vs. SDPG, P < 0.001.
**SD vs. control, SD vs. SDPG, P < 0.005.
C
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L
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has been postulated that this region mediates retrieval or is
involved in inhibitory function.
Nucleus accumbens
The nucleus accumbens within the ventral striatum has
long been implicated in drug reinforcement and reward
anticipation. In animals trained in cocaine self-administra-
tion, neurons in the nucleus accumbens fire prior to the le-
ver press and are thought to reflect anticipation [Carelli and
Deadwyler, 1996]. Knutson et al. has shown that anticipation
of increasing rewards activates ventral striatum in healthy
participants [Knutson et al., 2001]. Given that our paradigm
examined anticipation along with decision, it is possible that
the activity we observed in the nucleus accumbens was
related to the anticipation or unpredictability of reward.
Occipital/parietal
Increased activation of occipital and parietal activation
during decision-making could reflect greater visual atten-
tion or resource allocation [Banich, 2004] necessitated by
having to actively make a decision rather than passively
watching the computer do so. Superior parietal activity
has been reported in previous studies of decision-making
[Rogers et al., 1999b].
Group Differences in Brain Activity During
Decision-Making
The key finding of this study was that, compared with
controls, substance-dependent individuals showed lower
ventral medial frontal activity, supporting the a priori hy-
pothesis that this sample group would show altered ventral
medial frontal activity during decisions involving risk. In
addition, we observed less right prefrontal activity during
decision-making in nongambling substance abusers com-
pared with gambling substance abusers or controls. Later,
we discuss the group differences observed for these regions.
Ventral medial prefrontal cortex/infragenual ACC
We found that compared with controls both groups with
substance dependence showed less activation of this region
with the maximal group difference at the border of BA 25
and 11. The ventral medial prefrontal cortex (VMPFC),
which consists of infra- and prelimbic cortex, is not a clearly
defined structure [Milad and Quirk, 2002] and has been con-
sidered to include both BA 11 (Paulus et al., 2002; Schnyer
et al., 2005) and BA 25 (also called infragenual anterior cin-
gulate) [London et al., 2004]. Hence, our findings are consist-
ent with impairment on the IGT found in patients with bilat-
eral VMPFC lesions [Bechara et al., 1994] and support a hy-
pothesis that defects in ventral medial frontal processing
lead to impaired decisions that involve risk.
BA 25 and 11 are both implicated in drug addiction.
Medial BA 11 may mediate motivation, reward salience,
and impulse control, whereas BA 25 may mediate affective
state, emotional reactivity, visceromotor function, and
drug craving [Volkow et al., 2005]. Also consistent with
our findings, Bolla et al. found lower medial frontal activ-
ity in heavy compared with light marijuana users and in
both groups compared with controls, although the differ-
ences compared with controls were more lateral within BA
11 [Bolla et al., 2005]. Further evidence of abnormal func-
tion in BA 25 in substance users is lower glucose metabo-
lism observed during a continuous performance task
[London et al., 2004]. It is unlikely that group differences
in brain activity were due to differences in performance,
since any group differences in behavior were slight, at
best. The fact that substance-dependent individuals (gam-
bling and non-gambling) had lower VMPFC activity dur-
ing decision-making, despite similar performance, suggests
that fMRI may be a more sensitive probe of VMPFC dys-
function in this group that, by definition, takes greater
risks despite adverse consequences.
Retrospective, event-related modeling of the decision to
play risky compared with safe decks revealed greater ven-
tral medial/infragenual activity in gamblers with SD com-
pared with that in controls. The finding would be consist-
ent with a heightened visceral reaction during the selection
and anticipation of risky cards as would be expected in a
gambler. Further studies will be necessary to confirm the
finding given the overall low power.
Right anterior prefrontal (frontopolar BA 10,
superior frontal BA 9/10)
In this region there was less activity for nongambling
substance abusers than the other two groups. For the pur-
poses of this discussion, we will consider the two peaks
(frontopolar and superior frontal) together as they are near
each other and credibly localize to BA 10 since the activity
was at the border of superior BA 10. Consistent with our
findings, Paulus has reported reduced right prefrontal and
dorsolateral frontal activity during a two-choice prediction
task in methamphetamine users compared with that in
controls [Paulus et al., 2002, 2003]. Using PET imaging,
Bolla et al. reported lower activity in right prefrontal cor-
tex in abstinent marijuana users compared with that in
controls during decision making using the IGT. The local
maximum in their study (20, 40, 33) is very close to ours
(28, 48, 30). Northoff et al. found that performance on the
IGT could be predicted by activity during an affective
judgment task at a peak (16, 66, 8) close to the one at which
we observe group differences (18, 58, 14), suggesting that
this region in our task may be involved in affective judg-
ments [Northoff et al., 2006]. The altered prefrontal activity
in substance dependent individuals that we observed was
generally more anterior and dorsal than the orbitofrontal
cortex, which has been implicated in drug addiction. Several
studies suggest that orbitofrontal cortex dysfunction in drug
users may reflect deficits in reward and risk assessment,
motivation, emotion, craving, and goal-directed behavior
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[Kalivas and Volkow, 2005; London et al., 2000]. It is uncer-
tain why the local maximum in our study was more dorsal
than OFC, although our results are consistent with a study
showing that patients with dorsal prefrontal lesions were
more impaired on the IGT than those with orbitofrontal
lesions, independent of lesion volume [Manes et al., 2002].
In general, less consideration has been given to defining the
function of the rostral prefrontal (frontopolar) as compared
with orbitofrontal. In contrast to these studies, increased right
prefrontal activity was seen in cocaine addicts compared to
controls performing the IGT [Bolla et al., 2003]. Further
work will be needed to explain these differences.
Among substance users, the presence of gambling prob-
lems was associated with greater right prefrontal activity,
possibly reflecting hypersensitivity to gambling cues. Path-
ological gamblers showed greater orbitofrontal and right
DLPFC activity compared with controls during visual
gambling-related cues [Crockford et al., 2005]. Another
possibility is that the higher right prefrontal activity
reflects better stimulus-reinforcement learning by the
SDPG group. In fact, similar to controls, they learned to
differentiate the good and bad decks slightly faster than
nongambling SD. A third possible explanation of the dif-
ference among substance users is that prefrontal cortex
may act to bias responses based on previously experienced
rewards and punishments [Frank and Claus, 2006]. That
activity was lowest in the SD suggests that this group is
less able to maintain reward and/or punishment informa-
tion from trial to trial, and thus may be less sensitive to
that particular type of information during decisions.
Feedback Response to Wins Compared
With Losses
Kringelbach and Rolls suggest two distinct functional pat-
terns within the orbitofrontal cortex: a medial-lateral trend
representing value of the reinforcers and a posterior-anterior
trend representing complexity. Medial regions appear to be
more sensitive to reward while lateral regions more sensitive
to punishment [Kringelbach and Rolls, 2004]. Abstract rein-
forcers (e.g., monetary contingencies) tended to activate an-
terior orbitofrontal cortex while primary reinforces (e.g.,
taste or smell) tended to activate posterior orbitofrontal cor-
tex. Consistent with the medial-lateral distinction of rein-
forcement value, we observed a nonsignificant trend of infe-
rior medial activity in ‘Win > loss’ and lateral activity in ‘loss
> win’ [Kringelbach and Rolls, 2004; O’Doherty et al., 2001].
We did not find significant activation in ventral striatum
during reward as has been shown by others [Breiter et al.,
2001; Knutson et al., 2000]. There are several possible rea-
sons. First, there were relatively few punishment trials
which may have led to poor estimates of the model and
low power. Second, in the analysis of wins > losses, we
included wins and losses in both the Decision and No Deci-
sion condition. Thus, it is conceivable that expectations
were different during the No Decision trials, and activity in
the striatum was washed out. Third, since our trial onset
time was not jittered with respect to the TR, it is possible
that the hemodynamic response function was not modeled
precisely at the correct time with regard to the outcome
response, thus reducing the sensitivity for small activations.
As our primary aim was to investigate group differences in
neural processes during decision-making not necessarily the
response to outcome, our findings of no activity in nucleus
accumbens are not necessarily contrary to previous studies.
Behavioral Performance on Modified IGT
There were no differences in the net score (difference in
choices from good versus bad decks) between controls and
substance-dependent individuals. Although most studies
find lower performance in substance-dependent individu-
als compared with that in controls we did not. As shown
by Bolla et al., such differences in behavioral performance
are not necessary to observe differences in brain activation.
In that study, individuals addicted to cocaine had differen-
ces in prefrontal activity compared to controls in the ab-
sence of significant differences in performance on IGT
[Bolla et al., 2003]. The ability of IGT to discriminate
between substance users and controls in adolescents has
not been as consistent as in the adult population [Aklin
et al., 2005; Ernst et al., 2003; Lejuez et al., 2003].
Our modified version differs from the original IGT in
several ways, which may help to explain why we did not
observe significant group differences in performance. In
the original task, the subject is free to select any of four
decks of cards, whereas in the modified version a deck is
presented to the subject who then decides to play or pass,
thus assuring that participants make a decision about each
deck an equal number of times. Second, to provide a suita-
ble baseline for fMRI analysis, a No decision block was
included in which the subject is told whether to Play or
Pass. Third, in the IGT, trials are self-paced and presented
in quick succession whereas in the fMRI version, cards
were presented at a fixed rate and each trial was followed
by 6 s of fixation. The effect sizes for the behavioral data
suggests that SD performed worst, while controls and
SDPG performed similarly well. In spite of these differen-
ces, we feel the task is comparable to the original IGT and
the fMRI analysis benefits from the added constraints. The
performance mimics those of the classic IGT task. Over
time, participants learn to select cards from the low
reward but good decks as compared to high reward but
bad decks. In the IGT, it is reasonable to assume that
within every trial, participants consider (not necessarily
consciously) potential gains and losses (or an overall
expected valence [Busemeyer and Stout, 2002]) associated
with each card, and that cards with higher expected va-
lence are more likely to be chosen. Similarly, when partici-
pants are required to make a Play/Pass decision in our
task, the cognitive process of comparing the previous gains
and losses is likely engaged. The decision is made if a par-
ticular threshold for expected valence is exceeded. One
could argue that the decks in the IGT are approached in a
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similar fashion, where each expected valence is assessed
and a Play/Pass (Go/NoGo; Approach/Avoid) choice is
made for each deck. Thus, although the pace is different,
the decision process is likely to be very similar in the origi-
nal and modified IGT. The modified task remains complex
(no subject was able to articulate a strategy for deck selec-
tion) with low predictability, features thought to be essen-
tial for eliciting emotional input into the decision. Present-
ing a single outcome rather than allowing a free choice
eliminates the problem of not knowing whether the subject
is more focused on gain or loss. Using this modification,
Peters and Slovic found that affective reactivity correlated
in predicted ways with performance (e.g., positive reactiv-
ity was associated with attraction to high-gain decks while
negative reactivity was associated with avoidance of high-
risk decks) [Peters and Slovic, 2003]. Thus, while the modi-
fied tasks is not the same as the original, there is evidence
that affectivity is involved in the decision.
Our finding that SDPG was similar to or better than SD
is inconsistent with that of Petry et al., who found that
gambling substance users were more impaired on IGT
compared with nongambling substance users [Petry,
2001b]. In that study, the degree of impairment was not
overwhelming, with the gambling substance users show-
ing only a mild preference for deck B. Small samples and
a nonsignificant difference in females may explain the be-
havioral findings. In a previous study, female controls
showed lower performance compared with female sub-
stance users, whereas male controls showed better per-
formance compared with male substance users [Bechara
and Martin, 2004; Stout et al., 2005]. Therefore, the propor-
tionately large number of female controls may contribute
to the lack of significant group differences in behavior.
Limitations
A limitation of this study was the difference in educa-
tion and IQ between the controls and substance dependent
individuals. It was difficult to recruit controls of similar IQ
and education level because the potential controls often
turned out to have a history of drug use. Group effects
were significant after controlling for education, IQ, and
gender using ANCOVA. The lack of neuropsychological
tests of affect, memory, and attention is a limitation as
group differences in brain activity may reflect group dif-
ferences in these functions. Regarding the diagnosis of
pathological gambling, the SOGS [Lesieur and Blume,
1987] is a screening instrument and not a diagnostic
assessment. Nonetheless, a score of five or greater identi-
fies the presence of gambling problems.
CONCLUSION
Despite these limitations, the present study suggests that
impaired decision-making in chronic substance users may be
mediated by abnormalities in ventral medial frontal process-
ing. The decrease in right prefrontal activity in substance de-
pendent individuals as compared to those whose substance
dependence is comorbid with pathological gambling may
reflect differences in stimulus reward valuation, cue-reactivity,
or the ability to maintain information on recent gain or loss.
ACKNOWLEDGMENTS
The authors wish to thank Ken Gaipa and Julie Miller
from the Addiction Research Treatment Service for their
support and Nicole Johnson for assistance in recruitment.
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    • "Behaviorally , patients were slower than healthy control subjects , although equally as accurate. Impulsive-choice-related behavior has been studied using the Iowa Gambling Task (Tanabe et al. 2007 ). Research shows that in decision-making tasks involving risk, the presence of gambling problems is related to altered VMPFC activity. "
    [Show abstract] [Hide abstract] ABSTRACT: In light of the upcoming eleventh edition of the International Classification of Diseases (ICD-11), the question arises as to the most appropriate classification of 'Pathological Gambling' ('PG'). Some academic opinion favors leaving PG in the 'Impulse Control Disorder' ('ICD') category, as in ICD-10, whereas others argue that new data especially from the neurobiological area favor allocating it to the category of 'Substance-related and Addictive Disorders' ('SADs'), following the decision in the fifth revision of the Diagnostic and Statistical Manual of Mental Disorders. The current review examines important findings in relation to PG, with the aim of enabling a well-informed decision to be made with respect to the classification of PG as a SAD or ICD in ICD-11. Particular attention is given to cognitive deficits and underlying neurobiological mechanisms that play a role in SADs and ICDs. These processes are impulsivity, compulsivity, reward/punishment processing and decision-making. In summary, the strongest arguments for subsuming PG under a larger SAD category relate to the existence of similar diagnostic characteristics; the high co-morbidity rates between the disorders; their common core features including reward-related aspects (positive reinforcement: behaviors are pleasurable at the beginning which is not the case for ICDs); the findings that the same brain structures are involved in PG and SADs, including the ventral striatum. Research on compulsivity suggests a relationship with PG and SAD, particularly in later stages of the disorders. Although research is limited for ICDs, current data do not support continuing to classify PG as an ICD.
    Article · Mar 2016
    • "For example, it has been demonstrated that increased distress leads to an increased framing effect (Druckman and McDermott, 2008 ) while successful cognitive reappraisal of emotions associated with decision frames reduces the susceptibility to framing (Miu and Cris¸anCris¸an, 2011). In this study, our results demonstrated that the right (but not the left) OFC–bilateral amygdala connectivity mediated the gene–behavior association, which was consistent with previous studies showing preferential right OFC activity during decision-making (Elliott et al., 1999; Ernst et al., 2002; De Martino et al., 2006; Tanabe et al., 2007) and a right laterality effect in lesion studies on decision-making, emotional processing, and other purported OFC functions (for a review, see Happaney et al., 2004; see also Rolls et al., 1994; Stuss and Alexander, 1999; Manes et al., 2002; Tranel et al., 2002 ). Several possible reasons might contribute to this laterality effect (for a review, see Happaney et al., 2004 ), such as the differential involvement of the right and the left hemispheres in avoidance (negative affect) and approach (positive affect), respectively (Bechara, 2004; see also Davidson and Irwin, 1999; Davidson et al., 2000). "
    [Show abstract] [Hide abstract] ABSTRACT: Individuals tend to avoid risk in a gain frame, in which options are presented in a positive way, but seek risk in a loss frame, in which the same options are presented negatively. Previous studies suggest that emotional responses play a critical role in this "framing effect." Given that the Met allele of COMT Val158Met polymorphism (rs4680) is associated with the negativity bias during emotional processing, this study investigated whether this polymorphism is associated with individual susceptibility to framing and which brain areas mediate this gene-behavior association. Participants were genotyped, scanned in resting state, and completed a monetary gambling task with options (sure vs risky) presented as potential gains or losses. The Met allele carriers showed a greater framing effect than the Val/Val homozygotes as the former gambled more than the latter in the loss frame. Moreover, the gene-behavior association was mediated by resting-state functional connectivity (RSFC) between orbitofrontal cortex (OFC) and bilateral amygdala. Met allele carriers showed decreased RSFC, thereby demonstrating higher susceptibility to framing than Val allele carriers. These findings demonstrate the involvement of COMT Val158Met polymorphism in the framing effect in decision-making and suggest RSFC between OFC and amygdala as a neural mediator underlying this gene-behavior association. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.
    Full-text · Article · Feb 2016
    • "Performance of risk-taking and decision-making tasks involves evaluation of risky features and avoidance of disadvantageous choices (Rothman and Salovey, 1997). In pathological gambling and substance-use disorders, reduced activation of reward-related circuitry is observed during gambling-related decision-making (de Ruiter et al., 2012; Tanabe et al., 2007). Thus, in the current study, we hypothesized that IGD relative to healthy control subjects (HCs) would show disadvantageous decision-making that would relate to diminished activation of reward-related fronto-striatal brain regions. "
    [Show abstract] [Hide abstract] ABSTRACT: Individuals with Internet gaming disorder (IGD) continue gaming despite adverse consequences. However, the precise mechanism underlying this behavior remains unknown. In this study, data from 20 IGD subjects and 16 otherwise comparable healthy control subjects (HCs) were recorded and compared when they were undergoing risk-taking and risky decision-making during functional magnetic resonance imaging (fMRI). During risk-taking and as compared to HCs, IGD subjects selected more risk-disadvantageous trials and demonstrated less activation of the anterior cingulate, posterior cingulate and middle temporal gyrus. During risky decision-making and as compared to HCs, IGD subjects showed shorter response times and less activations of the inferior frontal and superior temporal gyri. Taken together, data suggest that IGD subjects show impaired executive control in selecting risk-disadvantageous choices, and they make risky decisions more hastily and with less recruitment of regions implicated in impulse control. These results suggest a possible neurobiological underpinning for why IGD subjects may exhibit poor control over their game-seeking behaviors even when encountering negative consequences and provide possible therapeutic targets for interventions in this population.
    Full-text · Article · Nov 2015
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