Cerebral Cortex May 2009;19:1134--1143
Advance Access publication September 11, 2008
Distinct Roles of Prefrontal Cortical
Subregions in the Iowa Gambling Task
Natalia S. Lawrence1,2,3, Fabrice Jollant1,4, Owen O’Daly2,
Fernando Zelaya2and Mary L. Phillips1,5
1King’s College London, Section of Neuroscience and Emotion,
Division of Psychological Medicine and Psychiatry,
2Department of Clinical Neuroscience, Institute of Psychiatry,
De Crespigny Park, London SE5 8AF, UK,3Wales Institute of
Cognitive Neuroscience, School of Psychology, Cardiff
University, Cardiff, UK,4Universite ´ Montpellier 1, Inserm U888,
Montpellier, CHU Montpellier, Montpellier France and
5Department of Psychiatry, University of Pittsburgh, Pittsburgh,
Natalia S. Lawrence and Fabrice Jollant contributed equally to
The Iowa Gambling Task (IGT) assesses decision-making under
initially ambiguous conditions. Neuropsychological and neuroimag-
ing data suggest, albeit inconsistently, the involvement of numerous
prefrontal cortical regions in task performance. To clarify the
contributions of different prefrontal regions, we developed and
validated a version of the IGT specifically modified for event-related
functional magnetic resonance imaging. General decision-making in
healthy males elicited activation in the ventromedial prefrontal
cortex. Choices from disadvantageous versus advantageous card
decks produced activation in the medial frontal gyrus, lateral
orbitofrontal cortex (OFC), and insula. Moreover, activation in these
regions, along with the pre-supplementary motor area (pre-SMA)
and secondary somatosensory cortex, was positively associated
with task performance. Lateral OFC and pre-SMA activation also
showed a significant modulation over time, suggesting a role in
learning. Striato-thalamic regions responded to wins more than
losses. These results both replicate and add to previous findings
and help to reconcile inconsistencies in neuropsychological data.
They reveal that deciding advantageously under initially ambiguous
conditions may require both continuous and dynamic processes
involving both the ventral and dorsal prefrontal cortex.
Keywords: ambiguity, decision-making, medial frontal gyrus, orbitofrontal
cortex, pre-supplementary motor area, wins
The Iowa Gambling Task (IGT; Bechara et al. 1994) is an
extremely widely and frequently used neuropsychological test
of decision-making ability under initially ambiguous conditions
(Brand et al. 2007). The test simulates real-life decision making
by testing the ability of participants to learn to sacrifice
immediate rewards in favor of long-term gain. Deficits in task
performance have been demonstrated in diverse psychiatric
groups, including pathological gamblers (Cavedini et al. 2002a),
patients with Obsessive-Compulsive Disorder (Cavedini et al.
2002b, Cavallaro et al. 2003, Lawrence et al. 2006), patients
with eating disorders (Cavedini et al. 2004), psychopathic
(Mitchell et al. 2002), and substance-dependent individuals
(Grant et al. 2000; Bechara and Damasio 2002) and suicide
attempters (Jollant et al. 2005).
Evidence for the neurobiological basis of the IGT originates
in the pioneering studies of Bechara et al. (Bechara et al. 1994,
1999), which demonstrated task deficits in patients with
ventromedial prefrontal cortex (VM PFC) and amygdala lesions.
Subsequent studies revealed a more specific role for the right
lateral orbitofrontal cortex (OFC) in IGT performance (Tranel
et al. 2002) although some contest this functional specificity
and suggest that more widespread prefrontal lesions in
dorsolateral (Manes et al. 2002; Clark et al. 2003; Fellows and
Farah 2005) and dorsomedial (Manes et al. 2002) regions also
impair performance, albeit via different underlying mechanisms
(Manes et al. 2002; Bechara 2004; Fellows and Farah 2005).
Neuroimaging studies, primarily using positron emission
tomography (PET), have confirmed activation of the VM PFC
(Brodmann area [BA] 11, 47) during IGT performance and have
additionally implicated a wider network of brain areas typically
associated with working memory and visual attention, in-
cluding the dorsolateral PFC, right anterior cingulate gyrus,
right parietal cortex, thalamus, anterior insula, and cerebellum
(Ernst et al. 2002). PET data further revealed that men and
women show differences in regional activation during IGT
performance (Bolla et al. 2004), with men activating the right
lateral OFC (BA 10, 47) more than women, and women
showing greater recruitment of left dorsolateral PFC, left
medial frontal gyrus and temporal lobe than men. These sex
differences in task-related activation could help to explain the
sometimes reported male advantage on the IGT (Reavis and
Overman 2001; Bolla et al. 2004; Overman et al. 2004; Lawrence
et al. 2006). A key role for the right OFC in mediating
successful IGT performance was also suggested by a positive
correlation between activation in this region and performance,
in a PET study in healthy controls and abstinent cocaine users
(Bolla et al. 2003).
Whilst PET studies have been crucial in supporting the
results of lesion studies regarding the neurobiological basis of
the IGT, they have been constrained by the limited temporal
resolution of PET, preventing clarification of regional activa-
tion associated with temporally distinct elements of IGT
performance, such as responses during disadvantageous (or
‘‘risky’’) versus advantageous (or ‘‘safe’’) decisions, early versus
late trials and the processing of wins versus losses. These
elements of the IGT are associated with different skin
conductance responses (Bechara et al. 1997, 1999; Lawrence
et al. 2006), indicating that they are separable and may be
related to distinct underlying neural mechanisms. In addition,
PET studies have provided evidence that different brain
regions are associated with improved performance and
learning on the IGT, compared with task performance per
se (Ernst et al. 2002), further suggesting that it is important to
distinguish between the different task components. Func-
tional magnetic resonance imaging (fMRI), with its superior
? The Author 2008. Published by Oxford University Press. All rights reserved.
For permissions, please e-mail: firstname.lastname@example.org
by guest on June 10, 2013
temporal and spatial resolution is ideally placed to overcome
the limitations of PET and permit a more detailed examination
of task-related functional neuroanatomy. To date, however,
only 2 fMRI studies have examined blood oxygenation level--
dependent (BOLD) activity during the original IGT, with one
focusing on the decision-making phase (Fukui et al. 2005) and
the other on the outcome phase (Windmann et al. 2006).
Neither of these studies separated out all the task components
described above, limiting their contrasts to comparing
choices from risky versus safe decks (Fukui et al. 2005) and
rewarding versus punishing outcomes (Windmann et al.
2006). Both studies confirmed a role for the medial frontal
gyrus (BA 10) in the IGT and Fukui et al. (2005) revealed that
this activation was positively related to task performance.
Fukui et al. (2005) also suggested that poor signal recovery
due to susceptibility artifacts in the OFC may have prevented
a role for this more ventral PFC brain region from being
observed in their study. A third fMRI study (Tanabe et al.
2007) used a heavily modified version of the IGT, in which
a computer rather than the participant selected the cards, and
revealed greater activation in healthy controls than in
substance-dependent individuals in ventral medial frontal
(BA 25/11) and right anterior prefrontal (BA 10) regions
during active versus passive decision-making.
A large number of fMRI studies have used a range of
gambling tasks similar to, but simpler than, the IGT to dissect
the neural mechanisms of decision-making under conditions
of risk or ambiguity, and during the processing of positive and
negative reinforcement (see Ernst and Paulus 2005; Krain
et al. 2006 for reviews). These studies have also suggested
a key role for brain areas such as the OFC and medial frontal
gyrus in making choices under risky conditions. However,
whilst these simpler gambling tasks offer greater functional
specificity for fMRI, many of them (often deliberately) fail to
model the strategic learning dimension involved in the IGT,
and this may be a key factor explaining poor task performance
in groups with psycho- or neuropathology. For example,
patients with focal dorsolateral and dorsomedial PFC lesions
show a deficit on the IGT but not on the Cambridge Gamble
and Risk tasks (which do not involve learning over trials)
(Manes et al. 2002). Furthermore, these simpler gambling
tasks may not model real-life decision-making as well as the
In the present study, we report an fMRI study of a novel
version of the IGT, specifically adapted for use with event-
related fMRI, which enables examination of the neural
correlates associated with different task components, in-
cluding decision making per se, choices from risky versus safe
decks, successful task performance, learning over time, and
receipt of wins versus losses. This study therefore builds upon
previous fMRI studies of the IGT, which were limited to
examining one aspect of the task. The current task promises
to be a useful tool for exploring the specific neuroanatomical
basis for decision-making problems in clinical populations.
Current data are somewhat inconsistent but allow us to
1) Decision-making, that is, task performance per se, may be
associated with VM PFC (and ventrolateral PFC) activity, in
addition to other regions including dorsal PFC, right
anterior cingulate gyrus, right parietal cortex, thalamus,
anterior insula and cerebellum (confirming PET studies).
2) Choices from risky versus safe decks may be associated with
VM PFC and, in particular, BA 10 activity (confirming Fukui
et al. 2005).
3) Existing fMRI and PET data associating BA 10 and right OFC
activation with successful task performance allow us to
hypothesize that activation in both regions may be
correlated with performance and additionally involved in
task learning, in addition to being associated with risky
4) In additional analyses, we also wished to examine brain
activity related to wins versus losses in the IGT. We predict
that wins will be associated with greater activation in
ventral and dorsal striatum, and VM PFC (Delgado et al.
2000; Breiter et al. 2001; Rogers et al. 2004).
In view of the reported sex differences in IGT performance,
we included only men in the current study.
Materials and Methods
Seventeen, right-handed (Oldfield 1971) male volunteers participated
in this study. Data from 2 subjects were excluded from analysis; 1 due
to technical problems and 1 because of insufficient cards chosen (3)
from the high-risk decks (A and B). The remaining 15 subjects were
aged between 22 and 57 years (mean age = 32.7 years, SD = 10.1), with
a mean education of 17.1 years (SD = 1.8) and a mean estimated verbal
IQ (estimated using the National Adult Reading Test, Nelson 1982) of
118.76 (SD = 5.33). Subjects were all task-naı¨ve, reported no history of
any neurological or psychiatric disorder and no drug dependence by
clinical screen (MINI 5.0.0; Sheehan et al. 1998) and were unmedicated
at the time of the study. The Ethical Committee (Research) of the
Maudsley Hospital and Institute of Psychiatry approved the study
protocol and all subjects signed an informed consent form prior to their
participation. Participants were paid £30 for their participation.
Subjects performed the computerized version of the IGT (Bechara et al.
2000) that was modified for rapid event-related fMRI (see below). The
IGT (Bechara et al. 1994) simulates real-life decision making by testing
the ability of participants to sacrifice immediate rewards in favor of
long-term gain. Briefly, participants must select 100 cards, one at a time,
from 4 identical-looking decks of cards, labeled A, B, C, and D.
Participants are told that when they pick a card they will win some
money but occasionally they will lose some money. They can select
cards from any deck and the goal of the game is to win as much money
as possible. Participants in our study (as in Bechara et al. 2000) were
also told that ‘‘It is important to know that the computer does not make
you lose money at random. However, there is no way for you to figure
out when or why you lose money. You may find yourself losing money
on all of the decks, but some decks will make you lose more than
others. Even if you lost a lot of money, you can still win if you stay away
from the worst decks.’’ All participants began with a £2000 loan to play
the game and wins and losses (in pounds Sterling) were tracked on
screen using a green bar at the bottom of the screen. A red bar under
the green bar reminded participants of how much money they had
borrowed to play the game. Decks A and B were associated with large
gains (£190, £200, and £210) and large losses (£240, £250, £260),
whereas decks C and D provided smaller gains (£90, £100, and £110)
and small losses (£40, £50, and £60). Decks A and B are ‘‘disadvanta-
geous’’ (also referred to here as ‘‘risky’’) as they lead to a net loss over
time, whereas decks C and D are ‘‘advantageous’’ (also referred to here
as ‘‘safe’’) because they lead to a net gain. Task performance is measured
by calculating the number of cards picked from advantageous decks
(C + D) minus the number of cards picked from disadvantageous decks
(A + B) in each block of 20 card selections. Healthy controls usually
show an improvement in performance over the 5 blocks of 20 card
selections, indicating that over time they are learning to avoid the
Cerebral Cortex May 2009, V 19 N 5 1135
by guest on June 10, 2013
disadvantageous decks associated with larger losses. Participants were
not paid any extra real money depending on their performance in this
The task was modified from the original computerized version to
increase the number of similar trials in each condition (e.g., choices
from risky and safe decks, trials resulting in wins and losses) in order to
optimize statistical power for fMRI analysis. The modifications made
1) Reinforcement contingencies for decks A and B were similar to the
original deck A (including a net loss of £250 over 10 cards), and
those for decks C and D similar to the original deck C (a net gain of
£250 over 10 cards). This change resulted in all 4 decks having the
same mean frequency of wins to losses (50:50), which is equivalent
to the frequency of punishment in the original IGT decks A and C.
Decks B and D from the original task, which have infrequent very
large losses, were therefore not represented in this version of the
task. This modification resulted in roughly equal numbers of win
and loss trials, optimizing the statistical comparison between these
2 conditions. The schedules of reinforcement for decks A and B, and
C and D were equivalent but trials were presented in a different
order. As choices from risky decks A and B (and safe decks C and D)
were equivalent, this enabled us to collapse across each deck type
and doubled our power when analyzing fMRI data from trials
involving risky versus safe choices. Debriefing of subjects indicated
that they were not aware that decks A and B, and C and D, were the
same—many reported different subjective feelings toward the
decks within each pair;
2) Trials resulting in a win and a loss were separated. In the original
version of the task, subjects either win money or occasionally win
and then immediately lose money within the same trial, but we
separated these into pure win or loss trials to optimize the contrast
between wins and losses. The reinforcement schedule was carefully
constructed so that the variable amounts won were about double
and those lost were similar to those in the original task, resulting in
similar net wins and losses, for example, where in the original task
a choice from deck A resulted in a win of £90 and a loss of £300 (a
net loss of £210), in our version there was simply a loss of £240-
£260. We ‘‘created’’ these numbers de novo to ensure the same net
gain or loss as in the original IGT (+ or – £250 per 10 cards selected
from each deck), whilst retaining reinforcements of similar value to
those used in the original task;
3) Variable intertrial intervals of at least 3--5 s, where just a fixation
cross was presented in the center of a blank screen were imposed.
These periods served as an implicit baseline for fMRI analysis. At the
beginning of the next trial, the cards reappeared accompanied by
the command ‘‘wait’’ and then, after 2 s, subjects were instructed to
‘‘Pick a card’’. The 2-s ‘‘wait’’ period alerted subjects that a decision
would soon be required to ensure that they were able to make their
decision with the allocated 3-s time window;
4) Subjects had to make their response within 3 s of the ‘‘Pick a card’’
prompt appearing or else they received a ‘‘Too Late’’ message and
the task progressed to the next trial.
Trials lasted 10--12 s (mean 11 s) and the timing for each trial was as
follows: Cards appear on screen and subject told to ‘‘wait’’ (2 s); ‘‘Pick
a card’’ prompt is displayed under the cards and subjects must select
one of the 4 decks using a 4-key button box in their right hand (up to 3
s); immediately after a response has been made the reinforcement is
displayed (e.g., ‘‘You have won £X’’) and the green bar moves up or
down (2 s); a fixation cross is then presented in the center of the
screen for a variable period (of at least 3--5 s) until the next trial begins.
The duration of the fixation cross on each trial was adjusted for the
subject’s reaction time, so that the task lasted exactly 1321 s (22.02
min) for each subject.
The same instructions were read to all participants. They then
practiced a control task (5 trials) inside the MRI scanner prior to the
scan to familiarize themselves with task requirements. The task began
with 10 trials of a visuomotor control task followed by 100 trials of the
experimental task, and ended with another 10 trials of the control task.
During the control task, subjects were presented with 4 decks of cards
labeled E, F, G, and H and were instructed to pick a card from a specific
deck by pressing one of 4 buttons using their right hand. Each trial
therefore began with, for example, ‘‘Pick card E’’ rather than ‘‘Pick
a card.’’ Subjects were encouraged to be accurate and were informed
that a correct response would be recorded for up to 3 s after the
command. As ‘‘reinforcement,’’ subjects saw the message ‘‘You picked
a card,’’ displayed for 2 s after a response had been made. The green bar
at the bottom of the screen did not move up or down during the
control task. The decks E, F, G, and H were a different color than A, B, C,
and D. The screen appearance and trial structure was otherwise
identical in the control and experimental tasks.
Following task completion, participants were asked to describe any
strategy they had used, and then they were asked whether they had
picked more cards from any particular deck(s), or whether they had
avoided any particular deck(s), and why they had done so. Responses
were graded 1 if participants explained that cards from decks A (and/or
B) resulted in a net loss, whereas cards from decks C (and/or) D
produced a net gain and 0 if they gave another (incorrect) response.
These questions aimed to determine which subjects had developed
explicit awareness of the rules for winning the IGT.
Our modified version of the IGT was first tested outside the MRI
environment in a separate group of 9 male subjects to ensure that the
above modifications did not alter task performance relative to the
original computerized version of the IGT. Results were compared with
those obtained in a different group of 19 male controls who had
completed the original computerized version of the IGT as part of
another study (Lawrence et al. 2006). Repeated-measures ANOVA for
net score ([C + D] – [A + B]) over the 5 blocks of 20 cards selections,
with group as a between-subjects factor, indicated no significant
differences between the 2 groups/tasks (F1,26= 0.18, P = 0.67). There
was a significant main effect of block (F4,23= 7.5, P < 0.001), but no
interaction of group 3 block (F4,23= 0.75, P = 0.57), showing that
similar levels of learning were observed in both tasks (see also Fig. 1).
None of the pilot subjects participated in the scanning experiment.
Gradient echo echoplanar imaging (EPI) data were acquired on a GE
Signa 1.5 T system (General Electric, Milwaukee, WI) at the Maudsley
Hospital, London. 440 T2*-weighted whole-brain volumes depicting
BOLD contrast (Ogawa et al. 1990) were acquired over 22 min at each
of 42 noncontiguous planes (3 mm thickness, 0.3 mm interslice gap)
parallel to the intercommissural (AC--PC) line; echo time 40 ms,
Figure 1. Behavioral validation of the IGT modified for fMRI. Performance in healthy
male controls in the original and modified version of the task. Graph shows the mean
(±SEM) net score per block of 20 card selections in a group (n 5 19) performing the
original computerized version of the task (taken from Lawrence et al. 2006; solid line,
circles), a group (n 5 9) performing the modified version in a pilot experiment (dotted
line, triangles), and a group (n 5 15) performing the modified version during the
present fMRI experiment (dashed line, squares). There were no differences in
performance between these groups.
fMRI Study of Iowa Gambling Task
Lawrence et al.
by guest on June 10, 2013
repetition time 3 s, flip angle 90?, field of view 24 cm, 64 3 64 matrix,
3.75 3 3.75 mm in-plane resolution. This EPI dataset provided almost
complete brain coverage (Simmons et al. 1999) and had sufficiently
high spatial resolution to be used for conversion of subject’s data into
standard space (Montreal Neurological Institute [MNI] template) during
analysis. Stimuli were back-projected onto a screen at the subject’s feet
and were viewed with the aid of prism glasses attached to the inside of
the radio-frequency head-coil.
Data were analyzed using SPM2 (Wellcome Department of Imaging
Neuroscience, London, UK) implemented in Matlab 6.5 (Mathworks,
Inc, Natick, MA) using an event-related model (Josephs et al. 1997). EPI
data were first corrected for head movements, unwarped (Andersson
et al. 2001), spatially normalized (Friston et al. 1995a), and smoothed
using an isotropic 8-mm full-width half-maximum Gaussian kernel to
minimize noise and residual differences in gyral anatomy. Each
normalized image set was also band pass filtered with a 128-s temporal
high-pass filter to remove low-frequency noise. Inspection of motion
correction parameters for all 15 subjects revealed that none had moved
further than 2 mm or rotated more than 1.5?, enabling all data to be
included in the data analysis. A multiple regression analysis was applied
to the EPI data, with regressors corresponding to the trials where
‘‘risky’’ decisions were made (selections from decks A and B), trials
where ‘‘safe’’ decisions were made (selections from decks C and D), and
trials in the control task. Regressors consisted of trial reaction times
timed from the beginning of the scan. When no card selection was
made (on average < 1 (0.67)), these trials were excluded from the
analysis. Each condition was modeled by convolving delta functions of
relevant decision times with a canonical hemodynamic response
function. Contrast images were calculated by applying linear contrasts
to the parameter estimates for the regressor of each event. The contrast
images were then entered into one-sample t-tests, to instantiate
random-effects group analyses (Friston et al. 1995b). The t-test maps
were thresholded using a voxel-wise P value of 0.001 (uncorrected)
followed by an adjustment for minimum cluster volume (P < 0.05,
corrected for whole-brain volume). Reported voxels correspond to
standardized MNI coordinate space. Conversion to coordinates in the
Talairach and Tournoux atlas (Talairach and Tournoux 1988) was also
carried out using Matthew Brett’s mni2tal tool (see http://imaging.mrc-
cbu.cam.ac.uk/imaging/MniTalairach) to enable approximate labeling
of cortical BAs.
A second multiple regression analysis examined responses to
reinforcement. For this analysis, events were recoded as win or loss
trials and 2-s long events were counted from the time of the onset of
reinforcement (when the subject pressed a button to select a card).
Contrast maps were generated to examine our main hypotheses.
1) Decision-making per se. Choices from all experimental decks (A, B,
C, D) versus directed responding in the control task (‘‘general
2) Choices from risky (A + B) decks versus safe (C + D) decks (‘‘risky
3) Successful task learning: To examine whether neural activity in the
above contrasts was related to task performance we used whole-
brain correlations with the total net score. In addition we explored
whether activation in clusters showing a significant correlation with
task performance (i.e., using this as a mask) changed over time on
the task, by examining linear modulation of activation in these
regions. For this analysis, all decision events (choices from decks A
to D) were weighted by a linear trend over the session (i.e., starting
at –1, going through zero and ending with the final trial weighted as
1) and this was compared with mean activation during the control
events on a subject-by-subject basis. The resulting beta value for
each subject was entered into a standard one-sample t-test to
determine (masked) regions showing a significant effect of the
linear trend on the decision (ABCD) events, relative to the control
task. We suggest that regions showing both a positive association
with performance and a modulation over time may be related to
4) Choices that resulted in wins versus losses.
Statistical analyses of behavioral data were carried out with the
Statistical Package for the Social Sciences (SPSS) version 12.0 for
Windows (SPSS, Inc, Chicago, IL).
Performance on the Gambling Task
Subjects performed the visuomotor control task at near ceiling
levels, with all subjects choosing the deck as instructed on at
least 95% of trials (mean accuracy 99%). Performance on the
IGT modified for fMRI varied between participants, with total
net scores ranging from –31 to +69 (mean 26.27 ± [SD] 31.53)
out of a total possible range of –100 to +100. The substantial
individual differences in performance aided the regression
analysis of performance against the BOLD data. There was no
relationship between-subjects’ IGT score and their mean
reaction time (RT; group average; 655.6 ± [SD] 122.4 ms), and
no difference in RTs for choices from risky versus safe decks, so
RT data were not explored further.
Repeated-measures ANOVA was carried out with the net
score per 5 blocks of 20 card selections as the repeated
measures to assess learning. Results indicated a significant
effect of block, F4,56 = 4.37, P
performance improved over time as expected. Within-subjects
contrasts further revealed that a linear function explained the
change in score over blocks (F1,14 = 8.43, P < 0.05). The
majority of subjects (n = 9; 60%) showed learning on the IGT
and obtained a net score of at least 20. The remaining 40% (n =
6) failed to show robust learning and obtained net scores of 4
and below. Debriefing of subjects indicated that 10 subjects
(67%) had developed explicit awareness of the correct strategy
to win the IGT, and these individuals performed significantly
better (mean net score 39.9 ± 30) than the 5 subjects who
failed to develop such an awareness (mean net score = –1 ± 6.8;
F1,14= 8.69, P = 0.01). All good performers were therefore able
to report the correct strategy for winning the IGT, in addition
to one poor performer.
The above distribution of performance and explicit un-
derstanding is similar to that we observed previously in 19
healthy males using the original computerized version of the
IGT as part of another study (Lawrence et al. 2006). Indeed,
there were no differences in overall performance between
these 2 groups (F1,32= 0.004, P = 0.95) and no differences in
learning over the task (nonsignificant block 3 group in-
teraction, F4,29= 1.99, P = 0.1). In addition, the learning curve
observed in this study was very similar to that reported in
controls using the computerized task (with decks A#B#C#D#) in
Bechara et al. (2000). This second behavioral validation
confirms that our version of the IGT adapted for event-related
fMRI results in similar levels of performance to the original
computerized version. Figure 1 shows the change in score over
the 100 experimental trials in the 15 male controls scanned in
this study, the 9 male controls in the pilot study and the 19
male controls from our previous study performing the original
computerized version of the IGT.
< 0.01, indicating that
BOLD Activity during Decision Making versus Control
Task and Relationship to Task Performance
Comparison of BOLD activity during card selection in the
decision-making versus control task revealed only one cluster,
Cerebral Cortex May 2009, V 19 N 5 1137
by guest on June 10, 2013
in the VM PFC that showed significantly more activation in the
decision-making condition (Table 1). This cluster was located
bilaterally in the medial OFC (BA 11)/ventral anterior cingulate
cortex (BA 24/32), extending into the caudate (see Fig. 2,
We used a whole-brain correlation with the total net score
to examine whether activation during decision-making per se
was related to IGT performance. Activation in one small cluster
showed a significant positive correlation with net score; this
was located in the precentral gyrus, BA 6/4 (Table 1), just
dorsolateral to the similar cluster identified in the risky versus
safe correlation analysis described below.
BOLD Activity during Choices from Risky versus Safe
Decks and Relationship to Task Performance
The contrast of choices from risky (A + B) versus safe (C + D)
decisions, albeitina moredorsalcluster (BA24/32/10/9)relative
from risky decks was also associated with increased activation in
lateral OFC (BA 47), extending into the insula, bilaterally, and in
the visual (occipital) cortex (Table 2, Fig. 2, orange clusters).
There were no regions showing significantly greater activation to
choices from safe relative to risky decks.
We examined whether neural activity in the key contrast of
choices from risky versus safe decks was related to task
performance using a whole-brain correlation with the total net
score. This analysis revealed 4 clusters showing a significant
positive correlation with performance (Table 2, Fig. 2, blue
clusters). The most significant correlated cluster (r = 0.902)
was located in the medial frontal gyrus, BA 10, in a region
anterior to, but overlapping with, the medial frontal gyrus
cluster identified in the main risky versus safe contrast detailed
above. Activity in another region identified in the main
contrast, the left lateral OFC (BA 47), was also significantly
related to task performance (r = 0.82, see overlapping blue and
orange clusters, Fig. 2). Finally, activity in more posterior
frontal regions, the pre-supplementary motor area (pre-SMA)
(BA 6/8) and lateral sulcus (secondary somatosensory cortex,
SII), was positively correlated with task performance (r = 0.86
and r = 0.84, respectively).
Brain regions from the active decision-making versus control task contrast showing significant
activation and a significant relationship with task performance
Regional activations SideBAVolume
(x, y, z)
Decision making per se
Anterior cingulate/medial OFC
24/32/11120 9, 36, ?3
?3, 36, ?3
Note: L 5 left, R 5 right. Coordinates refer to the cluster peak voxel in mm (MNI) and
coordinates in italics refer to 2 local maxima more than 8 mm apart, in mm (MNI). BA estimated
from mni2tal conversion to Talairach and Tournoux (1988) with positive 5 right (x), anterior (y),
and superior (z).
Figure 2. BOLD activity during the IGT. Clusters showing significant activation during decision-making versus the control task (green) and during choices from risky versus safe
decks (orange). Regions in the risky versus safe contrast showing a positive correlation with task performance are shown in light blue. Activations are displayed on axial sections
starting at z 5 ?8 (top row), and a midline sagittal section (bottom) in neurological orientation (left is left). Graph shows the correlation between net score on the IGT and
activation in BA 10 (coordinates ?3, 66, 15). Actual neural responses in the 15 participants are represented by red points and idealized responses on a line of best fit are shown
fMRI Study of Iowa Gambling Task
Lawrence et al.
by guest on June 10, 2013
Finally, we examined which clusters of performance-related
activation varied over time. Our behavioral data indicated that
performance showed a linear change (improvement) over time
so we explored linear increases and decreases in task-related
activity (relative to the control task) within performance-
correlated areas. No regions showed a linear increase in
activation over time. However, activation in part of the left
lateral OFC (BA 47)/ insula cluster showed a linear decrease in
activation across all 15 participants (Table 2, Fig. 2, blue left BA
47 cluster). When only the 9 participants who had shown
robust learning (total score > 20) were examined, part of the
pre-SMA (BA 6/8) cluster that correlated with performance
showed a linear decrease in activation over time (Table 2).
BOLD Activity during Wins versus Losses in the Gambling
Comparison of BOLD responses during all periods of wins
versus losses (independent of deck chosen) revealed a large
network of brain regions responding more to wins (Table 3,
Fig. 3). Clusters were located in traditional reward-related brain
areas in the ventral striatum and thalamus, along with activation
in posterior parieto-occipital regions related to visual attention.
In addition, win-related activity was observed in the cerebellum
and supplementary motor cortex (BA 6). There were no
significant increases in response to losses relative to wins.
This study disentangles, for the first time, the contributions of
different PFC regions to performance of a modified version of
the IGT. The findings support a key role for the medial frontal
gyrus, BA 10, in risky decision-making and successful task
performance, providing a powerful replication of the one
previous fMRI study of decision-making in the IGT (Fukui et al.
2005). Further contrasts suggest equally important roles for the
left lateral OFC (BA 47) and dorsal (pre-supplementary motor)
cortex in learning to win on the task, whereas pointing to
a more general (performance-unrelated) role for the ventro-
medial OFC. These findings help to reconcile inconsistencies
from neuropsychological studies concerning the role of these
ventral and dorsal prefrontal brain areas in the IGT (e.g.,
Bechara et al. 1994, 1999; Tranel et al. 2002 vs. Manes et al.
2002; Clark et al. 2003; Fellows and Farah 2005), inform the
results of PET studies of this task, and are consistent with recent
frameworks describing neural networks underlying decision-
making (Bechara and Damasio 2005; Ernst and Paulus 2005).
We predicted that decision-making in the context of risk,
that is, task performance per se, would be associated with VM
PFC and ventrolateral PFC activity, in addition to several other
regions. Our contrast of active decision-making versus the
control task, however, only revealed activity in one large VM
PFC cluster encompassing the bilateral subgenual anterior
cingulate (BA 24/32) and the medial OFC (BA 11). Activation in
this region was predicted from neuropsychological and PET
studies of the IGT (Bechara et al. 1994, 1999; Ernst et al. 2002;
Bolla et al. 2003, 2004). In our study, VM PFC activation is likely
to have been related to more general task components such as
the monitoring of rewards and response-reinforcement con-
tingencies across trials (Elliott et al. 1999; O’Doherty et al. 2001;
Kringelbach and Rolls 2004; Rogers et al. 2004; Windmann et al.
2006), as there was no reinforcement in the control task. It is
interesting to note that the VM PFC activation observed here for
task performance per se borders onto both the more rostro-
dorsal medial frontal gyrus activation related to risky versus safe
decisions and the more ventro-caudal right caudate activation
related to processing of wins versus losses (see below and also
Rogers et al. 2004). Taken together, these findings suggest that
the role of this VM PFC cluster is to integrate information from
the processing of rewards to guide decisions; perhaps by
transferring relevant reinforcement-related information to the
Brain regions showing significant activation during choices from risky (A, B) versus safe (C, D) decks and showing a correlation with task performance
Regional activationsSideBA Volume (voxels)Coordinate (x, y, z) Voxel (Z-value)
Choices from risky versus safe decks
Middle/superior occipital cortex
Anterior cingulate gyrus/
Superior medial frontal cortex
Inferior frontal operculum
Positive correlation with score
Superior medial frontal gyrus/
Pre-SMAL 6/8 74
9Lateral sulcus (SII)L
32 47 21, ?9
15, ?6Inferior OFC/insula
Decreases in activation over time (linear)
In good performers only (n 5 9)
Note: L 5 left, R 5 right. Coordinates refer to the cluster peak voxel in mm (MNI) and coordinates in italics refer to 2 local maxima more than 8 mm apart, in mm (MNI). BA estimated from mni2tal
conversion to Talairach and Tournoux (1988) with positive 5 right (x), anterior (y), and superior (z).
Cerebral Cortex May 2009, V 19 N 5 1139
by guest on June 10, 2013
more dorsal BA 10 cluster that is specifically related to risky
decision-making and successful task performance (see below).
This suggested role for the VM PFC fits well with its
demonstrated role in integrating reward-related autonomic
input (Critchley et al. 2000), and its functional connectivity
with dorsomedial PFC during high-risk decisions (Cohen et al.
Selection of cards from risky versus safe decks yielded
activation in a large cluster of medial frontal gyrus, stretching
from the (pregenual) anterior cingulate gyrus (BA 24/32) to
the superior frontal cortex (BA 10/9). This region is anterior
and dorsal to the VM PFC cluster described above and overlaps
with the slightly more dorsal region (coordinates –2, 57, 21)
identified using a similar contrast in the study by Fukui et al.
(2005). Activation in the more anterior portion of this cluster
was also strongly positively correlated with task performance
in both this and the Fukui et al. study (Fukui et al. 2005),
suggesting that it plays a key role in successful IGT
performance. Furthermore, activation in this same anterior
BA10 region (coordinates 4, 54, 4 and 16, 66, 8) during affective
judgments of emotional pictures was positively related to IGT
performance (Northoff et al. 2006). We found no evidence for
a linear decrease in activation over time in this region, further
replicating previous findings (Fukui et al. 2005), and suggesting
that BA 10 is recruited during risky decisions throughout the
task. In fact, activation in this region may be associated with the
development of anticipatory arousal during risky decisions
(Critchley et al. 2000), which builds throughout the task and is
believed to be important for learning to avoid the risky decks
(Bechara et al. 1997). Studies of neurological patients suggest
that lesions in both ventral and/or dorsal medial PFC result in
fairly specific neuropsychological impairments affecting IGT
performance (Bechara et al. 1998; Manes et al. 2002). We
suggest that this anterior medial frontal gyrus (BA 10) cluster is
crucial for IGT performance and that previous neuropsycho-
logical data are consistent if one assumes that lesions showed
overlap within this functional region (Bechara et al. 1998;
Manes et al. 2002). Recent accounts of the role of medial BA10
in general cognition suggest that it operates as a ‘‘gateway,’’
maximizing attention to environmental stimuli (as opposed to
self-generated, stimulus-independent thought) in the pursuit of
goals in ‘‘ill-structured’’ tasks (Burgess et al. 2007). Others
suggest that this region is implicated in making decisions about
emotional material in the context of goal-directed behavior,
that depend upon affective (as opposed to cognitive) evalua-
tion of that material (Rogers et al. 2004; Northoff et al. 2006).
Such affective evaluation requires directing attention to
stimulus-related internal states, which are putatively repre-
sented by autonomic signals or ‘‘somatic markers’’ (Bechara and
Damasio 2005). In the context of the IGT, BA 10 activation is
associated with using internal signals (generated by external
cues/reinforcements and communicated via the ventromedial
and lateral OFC) to select goal-directed responses.
Another key region identified by the risky versus safe
decision contrast was the bilateral ventrolateral PFC/lateral
OFC (BA 47), extending into the insula. Moreover, activation in
the left lateral OFC was positively associated with task
performance and showed a linear decrease in activation over
time across the whole group. These findings suggest that the
lateral OFC may play an important role early on in task
performance, during the learning phase (particularly in those
Brain regions showing significant activation in response to wins versus losses (all decks)
Caudate (includes nucleus accumbens)
Inferior Parietal lobe
Superior frontal gyrus
Superior parietal/middle occipital cortex
Coordinate (x, y, z)
36 ?75 ?45
?21, ?21, 27
6 18, 63
Note: L 5 left, R 5 right. Coordinates refer to the cluster peak voxel in mm (MNI). We have omitted the 2 local maxima for the sake of brevity; these data are available on request. BA estimated from
mni2tal conversion to Talairach and Tournoux (1988) with positive 5 right (x), anterior (y), and superior (z).
Figure 3. BOLD activity during wins versus losses (all decks). Activations are
displayed on coronal (top) and axial (bottom) sections in neurological orientation (left
is left). Cross-hairs are centered on the nucleus accumbens and thalamus/caudate,
rather than on cluster peak coordinates (which are respectively, ?18, 18, 12, and 3,
fMRI Study of Iowa Gambling Task
Lawrence et al.
by guest on June 10, 2013
who perform well), but then shows habituation in all
participants as the task progresses. The lateral OFC and
ventrolateral PFC have repeatedly been implicated in learning
tasks involving reinforcement, particularly punishment or
unsteady outcome processing (Windmann et al. 2006), and in
particular, appear to underlie error-dependent response
switching in reversal-learning tasks (Cools et al. 2002;
Kringelbach and Rolls 2004). This cognitive function is crucial
in the early stages of the IGT, when the initially high-gain decks
A and B begin to lose large sums of money and participants
must learn to shift their choices to decks C and D. Indeed, IGT
deficits in patients with predominantly left OFC damage have
been related to a reversal-learning impairment (Fellows and
Related (more dorso-caudal) activations to risky versus safe
decisions in the bilateral anterior insula were most likely
associated with risk-tasking/anticipation of punishment in our
study. fMRI studies support the role of the insula in represent-
ing negative somatic states, including those associated with
risk-taking and punishment, and further reveal that insula
activation is associated with subsequent behavior modification
(Paulus et al. 2003; Wrase et al. 2007). Furthermore, anterior
insula activation is related to explicit awareness of interocep-
tive signals (Critchley et al. 2004), supporting the idea that it
plays a key role in the generation/processing of somatic
markers, which are believed to be important for successful IGT
performance (Bechara et al. 1999). In our study, activation in
the left anterior insula and functionally related secondary
somatosensory cortex (SII) was positively associated with task
performance, reinforcing this idea. Connected activation peaks
in the left lateral OFC and insula that are related to successful
task performance and diminish over time, reflect the related
roles of these brain regions in the IGT; the insula signaling the
anticipation and receipt of larger losses during risky decisions
and the lateral OFC translating this signal into response shifting,
particularly during the early stages of the task.
Whilst IGT performance is particularly sensitive to VM PFC
dysfunction (Bechara et al. 1994, 1998; Cavallaro et al. 2003),
neuropsychological and PET studies indicate some dependence
on working memory functions associated with more dorsal
sectors of the PFC (Ernst et al. 2002; Manes et al. 2002; Clark
et al. 2003; Bechara, 2004; Fellows and Farah 2005). In
agreement with a previous PET study (Bolla et al. 2004), we
did not find any specific task-related activation in the
dorsolateral PFC in the current male sample, arguably because
our contrasted conditions were matched in terms of their
working memory demands, but we did find that activations in
lateral and medial motor-related areas (precentral gyrus and
pre-SMA, respectively) were positively related to task perfor-
mance. Interestingly, activation in the pre-SMA, BA 6/8, also
showed a linear decrease over time in our sub-set of good
performers. These data suggest that, similar to the ventrolateral
PFC, the pre-SMA may be recruited early on in task
performance during the learning phase of the task. Indeed,
activation in BA 6/8, in addition to the ventrolateral PFC, is
observed during reversal learning (Cools et al. 2002). The pre-
SMA is involved in planning motor responses under internal
control in more complex tasks (Vorobiev et al. 1998), and
activation in BA 8/6 is associated with uncertainty and decision
conflict (Ullsperger and von Cramon 2003; Volz et al. 2005).
Our data may therefore indicate that participants who show
a greater sensitivity to uncertainty early on in the task and
respond with more careful planning of motor responses—es-
pecially during risky trials—learn the winning strategy more
effectively. As explicit awareness of task strategy develops (as it
did in all our good performers) and uncertainty diminishes,
there is a corresponding decrease in activation in BA 6/8 in
these good performers. In contrast, activation in the more
ventro-anterior cluster in BA 10/32 is maintained during risky
decisions throughout the task, supporting the idea that BA 32
mediates response conflict in well-defined tasks where rules
are known (Volz et al. 2005).
Neural responses in regions associated with visual attention
(in occipital, temporal and parietal cortices) were greater
during risky decisions and in response to wins versus losses.
These findings reflect predictable increases in visual attention
to the more salient/arousing aspects of the task (Critchley et al.
2000); that is, participants showed heightened attention during
choices from the risky decks with their larger reinforcements
and in response to wins generally. There were no clusters in
visual attention areas that showed a positive correlation with
task performance, indicating that differences in performance
were not related to varying levels of attention paid by
participants. This reinforces data from neuropsychological
studies showing no relationship between IGT performance
Finally, in addition to the above, we observed greater brain
responses to wins than losses in several brain regions typically
associated with the processing and anticipation of rewards,
including the ventral striatum, thalamus, cerebellum and
superior frontal gyrus (Breiter et al. 2001; Knutson et al.
2001; Rogers et al. 2004; Windmann et al. 2006). Importantly,
these clusters did not overlap with those identified in the
various decision-making contrasts described above, suggesting
that the above activations are related to aspects of risky
decision-making, rather than to processing the different levels
of reward from the risky versus safe decks. These findings are
similar to those of Rogers et al. (2004), showing largely
separable activations in the pregenual versus subcallosal
anterior cingulate cortex for the processing of reward-related
information in, respectively, decision versus outcome phases of
a gambling task. We did not find any regions showing greater
activation to losses than wins in our modified version of the
IGT. However, as BOLD responses to punishment can be
manifested as deactivations in reward-related regions (Delgado
et al. 2000; Breiter et al. 2001) we examined our win > loss
clusters for evidence of this. Consistent with previous studies,
there was significant loss-related deactivation (relative to
a fixation cross baseline) in the left ventral striatum, suggesting
that deactivation to loss outcomes was driving some of the wins
versus loss activation in this region (data available on request).
Due to reported sex differences in IGT performance and brain
activation, the current study examined men only. Future
studies should examine whether the neural basis of advanta-
geous decision-making as revealed by fMRI is similar in women.
We did not compare fMRI data between participants who had
and had not developed explicit awareness of the rules for
winning in the IGT due to the small and inhomogeneous
sample sizes obtained (n = 10 and 5, respectively). This would
be an interesting question to address in future fMRI studies
carried out in a larger sample. We were unable to obtain good
quality skin conductance data in this study so could not
Cerebral Cortex May 2009, V 19 N 5 1141
by guest on June 10, 2013
directly examine the relationship between regional brain
activation, somatic signals and performance. Further fMRI
studies involving careful online measurement of autonomic
arousal during performance of the present task would clarify
the relationship between these variables.
In conclusion and consistent with recent theoretical
accounts (Bechara and Damasio 2005; Ernst and Paulus
2005), decision-making in the IGT requires the rostral medial
PFC (BA 10), which is believed to integrate information from
neural systems involved in executive functions (dorsolateral
PFC) and outcome processing (insula, SII, medial and lateral
OFC, ventral striatum) in order to affectively evaluate stimuli.
Feedback from the medial OFC tracks reward value and may
maintain an advantageous strategy, whereas lateral OFC/insula
activation increases when reinforcements are inconsistent and
the subject needs to suppress the previously rewarded
response and change strategy (Windmann et al. 2006). The
affective evaluation carried out by BA 10 involves both the
generation of expectancy and the assessment of feedback, and
leads to the selection and implementation of a motor response,
which is associated with pre-SMA activation. Deciding advan-
tageously under initially ambiguous conditions may require an
adequate interplay of these continuous and dynamic processes.
University of London Central Research Fund; and Royal Society,
We would like to thank Jeff Dalton for programming this version of the
IGT, Prof. Mick Brammer and Dr Vincent Giampietro for technical
advice and Dr Dominique Drapier, Dr Nick Walsh, and Dr Paul
Keedwell for their practical assistance. Conflict of Interest: None
Address correspondence to Dr Natalia Lawrence, Wales Institute of
Cognitve Neuroscience, School of Psychology, Tower Building, Cardiff
University, Park Place, Cardiff CF10 3AT, UK. Email: LawrenceNS@
Andersson JLR, Hutton C, Ashburner J, Turner R, Friston K. 2001.
Modelling geometric deformations in EPI time series. Neuroimage.
Bechara A. 2004. The role of emotion in decision-making: evidence
from neurological patients with orbitofrontal damage. Brain Cogn.
Bechara A, Damasio AR, Damasio H, Anderson SW. 1994. Insensitivity to
future consequences following damage to human prefrontal cortex.
Bechara A, Damasio H, Damasio AR, Lee GP. 1999. Different
contributions of the human amygdala and ventromedial prefrontal
cortex to decision-making. J Neurosci. 19:5473--5481.
Bechara A, Damasio H, Tranel D, Anderson SW. 1998. Dissociation of
working memory from decision making within the human pre-
frontal cortex. J Neurosci. 18:428--437.
Bechara A, Damasio H, Tranel D, Damasio AR. 1997. Deciding
advantageously before knowing the advantageous strategy. Science.
Bechara A, Tranel D, Damasio H. 2000. Characterization of the decision-
making deficit of patients with ventromedial prefrontal cortex
lesions. Brain. 123:2189--2202.
Bechara A, Damasio H. 2002. Decision-making and addiction (part I):
impaired activation of somatic states in substance dependent
individuals when pondering decisions with negative future con-
sequences. Neuropsychologia. 40:1675--1689.
Bechara A, Damasio AR. 2005. The somatic marker hypothesis: a neural
theory of economic decision. Games Econ Behav. 52:336--372.
Bolla KI, Eldreth DA, London ED, Kiehl KA, Mouratidis M, Contoreggi C,
et al. 2003. Orbitofrontal cortex dysfunction in abstinent cocaine
abusers performinga decision-making
Bolla KI, Eldreth DA, Matochik JA, Cadet JL. 2004. Sex-related
differences in a gambling task and its neurological correlates. Cereb
Brand M, Recknor EC, Grabenhorst F, Bechara A. 2007. Decisions
under ambiguity and decisions under risk: correlations with
executive functions and comparisons of two different gambling
tasks with implicit and explicit rules. J Clin Exp Neuropsychol.
Breiter HC, Aharon I, Kahneman D, Dale A, Shizgal P. 2001. Functional
imaging of neural responses to expectancy and experience of
monetary gains and losses. Neuron. 30:619--639.
Burgess PW, Dumontheil I, Gilbert SJ. 2007. The gateway hypothesis of
rostral prefrontal cortex (area 10) function. Trends Cogn Sci.
Cavallaro R, Cavedini P, Mistretta P, Bassi T, Angelone SM, Ubbiali A,
et al. 2003. Basal-corticofrontal circuits in schizophrenia and
obsessive-compulsive disorder: a controlled, double dissociation
study. Biol Psychiatry. 54:437--443.
Cavedini P, Bassi T, Ubbiali A, Casolari A, Giordani S, Zorzi C, et al. 2004.
Neuropsychological investigation of decision-making in anorexia
nervosa. Psychiatry Res. 127:259--266.
Cavedini P, Riboldi G, D’Annucci A, Belotti P, Cisima M, Bellodi L. 2002b.
Decision-making heterogeneity in obsessive-compulsive disorder:
ventromedial prefrontal cortex function predicts different treat-
ment outcomes. Neuropsychologia. 40:205--211.
Cavedini P, Riboldi G, Keller R, D’Annucci A, Bellodi L. 2002a. Frontal
lobe dysfunction in pathological gambling patients. Biol Psychiatry.
Clark L, Iversen SD, Goodwin GM. 2001. A neuropsychological
investigation of prefrontal cortex involvement in acute mania. Am
J Psychiatry. 158:1605--1611.
Clark L, Manes F, Antoun N, Sahakian BJ, Robbins TW. 2003. The
contributions of lesion laterality and lesion volume to decision-
making impairment following frontal lobe damage. Neuropsycholo-
Cools R, Clark L, Owen AM, Robbins TW. 2002. Defining the neural
mechanisms of probabilistic reversal learning using event-related
functional magnetic resonance imaging. J Neurosci. 22:4563--4567.
Cohen MX, Heller AS, Ranganath C. 2005. Functional connectivity with
anterior cingulate and orbitofrontal cortices during decision-
making. Brain Res Cogn Brain Res. 23:61--70.
Critchley HD, Elliott R, Mathias CJ, Dolan RJ. 2000. Neural activity
relating to generation and representation of galvanic skin conduc-
tance responses: a functional magnetic resonance imaging study.
J Neurosci. 20:3033--3040.
Critchley HD, Wiens S, Rotshtein P, Ohman A, Dolan RJ. 2004. Neural
systems supporting interoceptive
Delgado MR, Nystrom LE, Fissell C, Noll DC, Fiez JA. 2000. Tracking the
hemodynamic responses to reward and punishment in the striatum.
J Neurophysiol. 84:3072--3077.
Elliott R, Rees G, Dolan RJ. 1999. Ventromedial prefrontal cortex
mediates guessing. Neuropsychologia. 37:403--411.
Ernst M, Bolla K, Mouratidis M, Contoreggi C, Matochik JA, Kurian V,
et al. 2002. Decision-making in a risk-taking task: a PET study.
Ernst M, Paulus MP. 2005. Neurobiology of decision making: a selective
review from a neurocognitive and clinical perspective. Biol
Fellows LK, Farah MJ. 2005. Different underlying impairments in
decision-making following ventromedial and dorsolateral frontal
lobe damage in humans. Cereb Cortex. 15:58--63.
Friston KJ, Ashburner J, Poline JB, Frith CD, Heather JD, Frackowiak RS.
1995a. Spatial registration and normalization of images. Hum Brain
fMRI Study of Iowa Gambling Task
Lawrence et al.
by guest on June 10, 2013
Friston KJ, Holmes AP, Worsley KJ, Poline JB, Frith CD, Frackowiak RS. Download full-text
1995b. Statistical parametric maps in functional imaging: a general
linear approach. Hum. Brain Mapp. 2:189--210.
Fukui H, Murai T, Fukuyama H, Hayashi T, Hanakawa T. 2005.
Functional activity related to risk anticipation during performance
of the Iowa Gambling Task. Neuroimage. 24:253--259.
Grant S, Contoreggi C, London ED. 2000. Drug abusers show impaired
performance in a laboratory test of decision-making. Neuropsycho-
Jollant F, Bellivier F, Leboyer M, Astruc B, Torres S, Verdier R, et al. 2005.
Impaired decision making in suicide attempters. Am J Psychiatry.
Josephs O, Turner R, Friston K. 1997. Event-related fMRI. Hum Brain
Knutson B, Fong GW, Adams CM, Varner JL, Hommer D. 2001.
Dissociation of reward anticipation and outcome with event-related
fMRI. Neuroreport. 12:3683--3687.
Krain AL, Wilson AM, Arbuckle R, Castellanos FX, Milham MP. 2006.
Distinct neural mechanisms of risk and ambiguity: a meta-analysis of
decision-making. Neuroimage. 32:477--484.
Kringelbach ML, Rolls ET. 2004. The functional neuroanatomy of the
human orbitofrontal cortex: evidence from neuroimaging and
neuropsychology. Prog Neurobiol. 72:341--372.
Lawrence NS, Wooderson S, Mataix-Cols DM, David R, Speckens A,
Phillips ML. 2006. Decision making and set shifting impairments are
associated with distinct symptom dimensions in obsessive-compul-
sive disorder. Neuropsychology. 20:409--419.
Manes F, Sahakian B, Clark L, Rogers R, Antoun N, Aitken M, Robbins T.
2002. Decision-making processes following damage to the pre-
frontal cortex. Brain. 125:624--639.
Mitchell DG, Colledge E, Leonard A, Blair RJ. 2002. Risky decisions and
response reversal: is there evidence of orbitofrontal cortex
Nelson HE. 1982. National Adult Reading Test (NART) test manual.
Northoff G, Grimm S, Boeker H, Schmidt C, Bermpohl F, Heinzel A,
Hell D, Boesiger P. 2006. Affective judgment and beneficial decision
making: ventromedial prefrontal activity correlates with perfor-
mance in the Iowa Gambling Task. Hum Brain Mapp. 27:572--587.
O’Doherty J, Kringelbach ML, Rolls ET, Hornak J, Andrews C. 2001.
Abstract reward and punishment representations in the human
orbitofrontal cortex. Nat Neurosci. 4:95--102.
Ogawa S, Lee TM, Kay AR, Tank DW. 1990. Brain magnetic resonance
imaging with contrast dependent blood oxygenation. Proc Natl
Acad Sci USA. 87:8868--8872.
Oldfield RC. 1971. The assessment and analysis of handedness: the
Edinburgh Inventory. Neuropsychologia. 102:97--113.
Overman WH, Frassrand K, Ansel S, Trawalter S, Bies B, Redmond A.
2004. Performance on the IOWA card task by adolescents and
adults. Neuropsychologia. 42:1838--1851.
Paulus MP, Rogalsky C, Simmons A, Feinstein JS, Stein MB. 2003.
Increased activation in the right insula during risk-taking decision
making is related to harm avoidance and neuroticism. Neuroimage.
Reavis R, Overman WH. 2001. Adult sex differences on a decision-
making task previously shown to depend on the orbital prefrontal
cortex. Behav Neurosci. 115:196--206.
Rogers RD, Ramnani N, Mackay C, Wilson JL, Jezzard P, Carter CS, et al.
2004. Distinct portions of anterior cingulate cortex and medial
prefrontal cortex are activated by reward processing in separable
phases of decision-making cognition. Biol Psychiatry. 55:594--602.
Simmons A, Moore E, Williams SCR. 1999. Quality control for functional
magnetic resonance imaging using automated data analysis and
Thwart charting. Magn Reson Med. 41:1927--1931.
Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E,
Hergueta T, Baker R, Dunbar GC. 1998. The Mini-International
Neuropsychiatric Interview (M.I.N.I.): the development and valida-
tion of a structured diagnostic psychiatric interview for DSM-IV and
ICD-10. J Clin Psychiatry. 59(Suppl. 20):22--33; quiz 34--57.
Talairach J, Tournoux P. 1988. Co-planar stereotactic atlas of the human
brain. Stuttgart: Thieme.
Tanabe J, Thompson L, Claus E, Dalwani M, Hutchison K, Banich MT.
2007. Prefrontal cortex activity is reduced in gambling and
nongambling substance users during decision-making. Hum Brain
Tranel D, Bechara A, Denburg NL. 2002. Asymmetric functional roles of
right and left ventromedial prefrontal cortices in social conduct,
decision-making, and emotional processing. Cortex. 38:589--612.
Ullsperger M, von Cramon DY. 2003. Error monitoring using external
feedback: specific roles of the habenular complex, the reward
system, and the cingulate motor area revealed by functional
magnetic resonance imaging. J Neurosci. 23:4308--4314.
Volz KG, Schubotz RI, von Cramon DY. 2005. Variants of uncertainty in
decision-making and their neural correlates. Brain Res Bull.
Vorobiev V, Govoni P, Rizzolatti G, Matelli M, Luppino G. 1998.
Parcellation of human mesial area 6: cytoarchitectonic evidence for
three separate areas. Eur J Neurosci. 10:2199--2203.
Windmann S, Kirsch P, Mier D, Stark R, Walter B, Gu ¨ ntu ¨ rku ¨ n O, Vaitl D.
2006. On framing effects in decision making: linking lateral versus
medial orbitofrontal cortex activation to choice outcome process-
ing. J Cogn Neurosci. 18:1198--1211.
Wrase J, Kahnt T, Schlagenhauf F, Beck A, Cohen MX, Knutson B,
Heinz A. 2007. Different neural systems adjust motor behavior in
response to reward and punishment. Neuroimage. 36:1253--1262.
Cerebral Cortex May 2009, V 19 N 5 1143
by guest on June 10, 2013