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The Journal of Neuroscience, October 15, 1999, 19(20):9029-9038
Choosing between Small, Likely Rewards and Large, Unlikely
Rewards Activates Inferior and Orbital Prefrontal Cortex
Robert D.
Rogers1, 2,
Adrian M.
Owen2,
Hugh C.
Middleton3,
Emma J.
Williams4,
John D.
Pickard5,
Barbara J.
Sahakian6, and
Trevor W.
Robbins1
1 Department of Experimental Psychology, University of
Cambridge, Cambridge CB2 3EB, United Kingdom, 2 Medical
Research Council Cognition and Brain Sciences Unit, Cambridge CB2 2EF,
United Kingdom, 3 Nottingham University Division of
Psychiatry, Nottingham NG3 6AA, United Kingdom,
4 Wolfson Brain Imaging Centre, 5 Department of
Neurosurgery, and 6 Department of Psychiatry,
Addenbrooke's Hospital, Cambridge CB2 2QQ, United Kingdom
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ABSTRACT |
Patients sustaining lesions of the orbital prefrontal cortex (PFC)
exhibit marked impairments in the performance of laboratory-based gambling, or risk-taking, tasks, suggesting that this part of the human
PFC contributes to decision-making cognition. However, to date,
little is known about the particular regions of the orbital cortex that
participate in this function. In the present study, eight healthy
volunteers were scanned, using H2150 PET
technology, while performing a novel computerized risk-taking task. The
task involved predicting which of two mutually exclusive outcomes would
occur, but critically, the larger reward (and penalty) was associated
with choice of the least likely outcome, whereas the smallest reward
(and penalty) was associated with choice of the most likely outcome.
Resolving these "conflicting" decisions was associated with three
distinct foci of regional cerebral blood flow increase within
the right inferior and orbital PFC: laterally, in the anterior part of
the middle frontal gyrus [Brodmann area 10 (BA 10)], medially, in the
orbital gyrus (BA 11), and posteriorly, in the anterior portion of the
inferior frontal gyrus (BA 47). By contrast, increases in the degree of
conflict inherent in these decisions was associated with only limited
changes in activity within orbital PFC and the anterior cingulate
cortex. These results suggest that decision making recruits neural
activity from multiple regions of the inferior PFC that receive
information from a diverse set of cortical and limbic inputs, and that
the contribution of the orbitofrontal regions may involve processing
changes in reward-related information.
Key words:
H2150 PET; decision
making; orbitolateral PFC; orbitomedial PFC; frontal lobes; executive
function
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INTRODUCTION |
Human patients with damage to
orbital regions of prefrontal cortex (PFC) are more likely to exhibit
personality change and difficulties with social interactions than
patients with damage to more dorsal regions of PFC (Stuss and Benson,
1986 ; Damasio, 1994; Rolls et al., 1994). However, understanding these
behavioral changes in terms of compromised cognitive functions
supported by orbital PFC has been complicated by clinical evidence that such difficulties in social cognition and real-life decision making are
frequently not accompanied by marked changes in many important forms of
cognitive function (Eslinger and Damasio, 1985; Saver and Damasio,
1991). Indeed, several of these other cognitive functions, different
types of working memory (Goldman-Rakic, 1987, 1994, 1996; Petrides,
1994, 1995), the control of attention (Dias et al., 1996), and
behavioral flexibility (Milner, 1964; Berman et al., 1995), have each
been proposed to involve dorsolateral regions of PFC, highlighting the
possibility that orbital sectors mediate distinctive mechanisms of
particular importance to social cognition (Damasio et al.,
1990).
Research into these issues has been advanced significantly by the
demonstration that patients exhibiting such "acquired sociopathy" after orbital PFC damage also show consistent deficits on a gambling task involving choices between actions that differ in terms of the size
and probabilities of their associated punishments and rewards (for
review, see Bechara et al., 1994, 1996; Damasio, 1996a). Furthermore,
work published recently in this journal (Bechara et al., 1998) found
that orbital PFC patients exhibited difficulties with such decision
making in the absence of consistent deficits on a modified delayed
response task. Because dorsolateral PFC patients showed the opposite
pattern of impairment, i.e., deficient delayed response performance but
normal decision making, these data appear to confirm the relatively
independent contributions made by the orbital and dorsolateral PFC to
decision-making and working memory cognition, respectively. Overall,
the trend of the clinical and experimental evidence suggests that the
orbital PFC, presumably through its rich interconnections with limbic cortices and other neural stations deeply implicated in processes of
incentive motivation and reinforcement (Damasio, 1994), represents an
important site of contact between emotional or affective information and mechanisms of action selection (for review, see Rolls, 1996).
Although studies with neurological patients have highlighted the role
of orbital PFC in decision-making cognition, functional imaging
techniques offer the opportunity for specifying more closely which
areas of the orbital PFC are particularly involved. The orbital cortex
is relatively differentiated in terms of its cytoarchitecture and
patterns of interconnectivity (Carmichael et al., 1994, 1995a,b). Moreover, it is likely to be functionally heterogeneous (Rolls, 1996).
To address these issues, we used the slow bolus infusion method of
water activation (H215O)
to study a novel decision-making task in which subjects were asked to
gamble accumulated reward on predictions about which of two mutually
exclusive outcomes would occur. Critically, the largest reward was
always associated with the least likely of the two outcomes, ensuring
that the element of conflict inherent in risk taking was preserved.
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MATERIALS AND METHODS |
Subjects. Eight right-handed volunteers, all males,
participated. None had a history of psychiatric or neurological
illness. Their mean age was 31.9 ± 2.0 (SE) years, whereas
their mean verbal IQ, estimated with the National Adult Reading Test
(Nelson, 1982), was in the above average range at 120.9 ± 1. Each
subject underwent 12 positron emission tomography (PET) scans and one
magnetic resonance imaging (MRI) scan within a single session. All
subjects gave informed, written consent for participation in the study
after its nature and possible consequences had been explained to them. The study was approved by the Local Research and Ethics Committee.
Task design. Two typical displays from the decision-making
task are shown in Figure 1, A
and B. The subject was told that the computer had hidden a
yellow token inside one of the red or blue boxes arrayed at the top of
the screen and that he had to decide whether this token was hidden
inside a red box or a blue box. However, this decision involved
gambling a certain number of points associated with each choice. In
these examples, if the subject chose red, then he gained 30 points if
the yellow token was indeed hidden inside a red box, but lost 30 points
if the token was hidden inside a blue box. On the other hand, if the subject chose blue, then he gained 70 points if the token was hidden
inside a blue box, but lost 70 points if it was hidden inside a red
box. The subject was told that there was an equal probability that the
token would be hidden inside any of the six boxes. The subject
indicated his decision by touching one of the two square response
panels, located at the bottom of the display, containing the associated
"stake" written in either red or blue ink. Immediately after a
selection, one of the boxes opened to reveal the location of the token,
accompanied by either a "You win!" or a "You lose!" message
(written in large yellow helvetica font). If the subject chose the
correct color, the stake associated with that color was added to the
total points score; if the subject chose the wrong color, the same
stake was subtracted. No monetary significance was attached to the
points accumulated by the end of the task.
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Figure 1.
Typical displays from the decision-making task,
and associated behavioral data across the present study.
A, C, E, Example decision
from the 4:2 condition, percentage of choice of the most likely outcome
and mean deliberation times as a function of the balance of reward
associated with the two outcomes. B, D,
F, Example decision from the 5:1 condition, percentage
of choice of the most likely outcome and mean deliberation times as a
function of the balance of reward associated with the two
outcomes.
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At the start of each sequence, the subject was given 100 points and
instructed to make whatever choices thought necessary to increase this
score by as much as possible. It was emphasized that these choices
might involve either conservative or risk-taking behavior. The ratio of
colored boxes (5:1, 4:2, and 3:3) and the balance between the
associated rewards (10 vs 90, 20 vs 80, 30 vs 70, 40 vs 60, and 50 vs
50) varied independently from trial to trial according to a fixed
pseudorandom sequence. This sequence ensured that each balance of
reward and each ratio of colored boxes co-occurred an equal number of
times, with the restriction that on all trials with an unequal
ratio of red and blue boxes (i.e., 5:1 or 4:2), the larger reward was
always associated with the least likely outcome (i.e., the
color with the fewest number of boxes; see Fig.
1A,B), thus capturing the conflict inherent in
risk-taking situations.
The data analyses centered around two main measures: (1) speed of
decision making, i.e., how long it took the subject to decide which
color of box was hiding the yellow token as measured by the mean
deliberation time (measured in milliseconds), and (2) choice of the
most likely outcome (associated with the smaller reward).
Design. For 8 of the 12 scans, the subject began working
through sequences of decisions 1 min before the scan commenced.
However, at the start of the scan window, i.e., when the "head
count" began to rise, the experimenter advanced the subject to one of
two conditions involving concealed runs of particular ratios of red and
blue boxes (see below). After completing this concealed run, the
subject was returned to his or her original place in the entire
sequence that was then completed. Preliminary pilot tests had shown
that each of these hidden runs occupied the typical subject for ~1 min. Because most of the regional cerebral blood flow (rCBF)
arising from the cognitive activity associated with any scan window
coincides with the steepest increase in head counts ( 30 sec;
Silbersweig et al., 1993), hidden runs of 1 min were sufficient to
ensure that the rCBF data reflected the mental activity associated with the different conditions. On the remaining four scans, the subjects performed a purpose-designed visuomotor control task (see below).
Earlier work had shown that subjects appear to be more sensitive to the
balance of reward associated with the two outcomes when the ratio of
the colored boxes was 4:2 compared to when it was 5:1. For this reason,
our design involved two conditions that allowed us to assess decision
making with these different ratios. Thus, in the 4:2 choice conditions,
the subject was scanned while making decisions that involved ratios of
either 4 red:2 blue or 2 red:4 blue (e.g., Fig. 1A),
and in which the reward associated with the two outcomes was always one
of 30 vs 70, 20 vs 80, and 10 vs 90. As noted above, these choices tend
to be particularly associated with reduced choice of the most likely
outcome, as well as increased deliberation times, as a function of the
balance of reward associated with the two possible outcomes.
In the 5:1 choice conditions, the subject was scanned while making
decisions involving ratios of only 5 red:1 blue or 1 red:5 blue (e.g.,
Fig. 1B). Although the rewards associated with the two outcomes were the same as in the 4:2 choice conditions, i.e., 30 vs
70, 20 vs 80, and 10 vs 90, these decisions tend to be associated with
more consistent choice of the most likely outcome, as well as
relatively constant deliberation times. To control for possible differences in the amount of visual and motor processing associated with the 4:2 and 5:1 choice conditions, the presentation rate of trials
in each of the 5:1 choice conditions was "yoked" to the latencies
of choices in an earlier 4:2 condition. Additionally, because recent
evidence has suggested that rCBF changes within orbital PFC and
associated limbic circuitry can be seen with changes in reinforcement
rate (Elliott et al., 1999), the frequency of reward within the scan
windows of the 5:1 choice conditions was also yoked to earlier 4:2
choice conditions. This was achieved by having the computer select the
location of the yellow token after the subject had made a response in
the 5:1 conditions and thereby permitting the number of wins and losses
to be balanced with the 4:2 conditions. In this way, differences in the
rCBF in the 4:2 and 5:1 conditions cannot be attributed to gross
differences in motor activity or rate of positive or negative feedback
across conditions. The subject was not informed about this feature of the study design.
In the control condition, alternative displays showed only all red or
all blue boxes with the yellow token already revealed at onset, thus
ensuring that subjects were not able to covertly predict which color of
box was hiding the yellow token. Moreover, all features of the displays
that had previously indicated reward-based information, i.e., the total
points score and the size of the rewards associated with two outcomes,
were now marked with Xs. The subject was required to monitor the
displays until one of the response panels brightened with a white
border before touching that panel, the precise delay corresponding to
the time required to make earlier decisions in a yoked 4:2 choice condition.
The twelve scans were divided into four runs of three scans each. The
first scan in each run was always a 4:2 choice condition, whereas the
second and third scans were always either a 5:1 choice condition or a
control condition; the order of these two conditions was
counterbalanced across scans within and between subjects. To remove
linear time effects associated with earlier versus later scans, scan
order was entered as a covariate (of no interest) in all analyses of
the rCBF data. Before the first scan, but after the subject had been
positioned in the scanner, the nature of the task and the task displays
were explained to the subject, who was allowed to complete just one
sample decision as training.
Scanning procedure and statistical analysis. Each subject
was scanned in the presence of low background noise and dimmed ambient lighting. The task displays were presented on a MicroTouch 20C touch-sensitive screen controlled by a Pentium microcomputer. The
screen was mounted at a viewing distance of ~50 cm so that the
subject could touch all areas of the screen with the index finger of
the dominant hand, which was rested on the chest between responses.
PET scans were obtained with the General Electrics Advance system,
which produces 35 image slices at an intrinsic resolution of
~5.0 × 5.0 × 5.0 mm. Using the bolus
H215O methodology, rCBF
was measured during four separate scans for each of the three
experimental and control conditions (total = 12 scans). For each
scan, subjects received a 20 sec intravenous bolus of
H215O through a forearm
cannula at a concentration of 300 MBq/ml 1 and a flow rate of 10 ml/min1. With this method, each scan
provided an image of rCBF integrated over a period of 90 sec from when
the tracer first entered the cerebral circulation. The twelve PET scans
were initially realigned using the first scan as a reference and then
again using the mean of the scans as a reference, normalized to a
standard brain template that forms part of the Statistical Parametric
Mapping 98 (SPM98) software, corrected for global CBF value, and
averaged across the eight subjects for each activation state. Then the
images were smoothed using an isotropic Gaussian kernel at 16 mm
full-width half-maximum (FWHM). Finally, blood flow changes between
conditions were estimated for each voxel according to the general
linear model, as implemented by Statistical Parametric Mapping (SPM 96; provided by the Wellcome Department of Cognitive Neurology, London, UK).
For each subject, a three-dimensional MRI volume (1.5 × 1.5 × 3.0 mm) was acquired using a 0.5 T system and Bruker console and
resliced to be coregistered with the PET data. Composite stereotaxic MRI and PET volumes were merged to allow direct anatomical localization of regions with statistically significant rCBF change between conditions. Effects at each and every voxel were estimated according to
the general linear model (Friston et al., 1995). Condition effects at
each voxel were compared using linear contrasts. The resulting set of
voxel t statistics constitute a statistical parametric map
(SPM {t}). SPM {t} maps were transformed to the unit normal distribution SPM {Z} for display and thresholded at 3.09. The resulting foci were characterized in terms of spatial extent
(k) and peak height (u). The significance of each
region was estimated using distributional approximations from the
theory of Gaussian fields. This characterization is in terms of the
probability that the peak height observed (or higher) could occur by
chance [PZmax > u] over the entire volume
analyzed (i.e., a corrected p value).
In the case of comparisons between the decision-making and control
scans, all predicted increases in rCBF were tested against a threshold
of p < 0.05 corrected for multiple comparisons within a volume approximating the size of the orbital PFC. The technique for
calculating such a threshold has been described elsewhere (Worsley et
al., 1996). Predicted peaks were confined to orbital PFC in view of the
considerable neuropsychological evidence that altered decision making
is associated specifically with lesions in these cortical fields
(Bechara et al., 1994, 1998; Rogers et al., 1999). To anticipate the
results, decision making was exclusively associated with highly
significant activations in the orbital PFC, with no evidence of
increased activity in other parts of the PFC at either corrected or
indeed uncorrected thresholds. Activations (and relative deactivations)
beyond the frontal cortex (none of which were predicted a priori) are
detailed in the tables and reported only briefly in the text if they
survived the additional threshold of p < 0.05 corrected for multiple comparisons across the whole brain. As noted
above, task-unrelated changes in rCBF associated with linear time
effects associated with earlier versus later scans were removed by
entering scan position as a covariate (of no interest) in all analyses.
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RESULTS |
Task performance
The behavioral data associated with each sequence of decisions
(i.e., percentage of choice of the most likely outcome and mean
deliberation times) were subject to multifactorial, repeated-measures ANOVA with the following within-subject factors: run (first,
second, third, or fourth); ratio (4:2 or 5:1), and balance of reward
(50 vs 50, 40 vs 60, 30 vs 70, 20 vs 80, or 10 vs 90). The proportions of trials on which subjects chose the most likely outcome were arcsine-transformed as is appropriate whenever variance is proportional to the mean (Howell, 1987). However, the data shown in the tables and
figures represent untransformed values. In those instances in which the
additional assumption of homogeneity of covariance in repeated-measures
ANOVA was violated, as assessed using the Mauchly sphericity test, the
degrees of freedom against which the F term was tested were
reduced by the value of the Greenhouse-Geisser epsilon (Howell, 1987).
Additional analyses were performed on the mean deliberation times and
percentage of choice of the most likely outcome specifically associated
with the concealed runs of decisions manipulated in the 4:2 and 5:1
conditions, to check that subjects' behavior during the scan windows
was similar to that seen over the entire set of decision-making sequences.
In general, subjects' decision making was markedly influenced by the
balance of rewards associated with the most and the least likely
outcomes. Specifically, subjects' choice of the most likely outcome
was significantly reduced as the size of its reward was diminished in
comparison with that of the least likely outcome (Table
1;
F(4,28) = 6.97; p = 0.001), whereas the time required to make these choices was
significantly increased (F(4,28) = 3.05; p < 0.05). Moreover, as predicted, the extent to
which the balance of rewards influenced subjects' decisions tended to
be greater when the ratio of red and blue boxes was 4:2 compared to
when it was 5:1, both in terms of choice of the most likely outcome (F(4,28) = 3.43; p < 0.05) and time required to make decisions (F(4,
28) = 2.89; p < 0.05). Further analysis of
simple effects demonstrated that deliberation times were significantly
influenced by the balance of rewards with ratios of 4:2 (Fig.
1E; F(4,28) = 3.73;
p < 0.05) but not with ratios of 5:1 (Fig.
1F; F(4,28) = 1.85).
Choice of the most likely outcome was reduced by the changing balance
of rewards with both ratios (Fig. 1C,D;
F(4,28) = 7.96; p < 0.001; F(4,28) = 3.58;
p < 0.05). Finally, subjects took significantly longer
to make their choices with ratios of 4:2 compared to 5:1 (2505 ± 170 msec vs 2263 ± 164 msec;
F(1,7) = 29.30; p = 0.001), especially in the earlier compared to later runs
(F(3,21) = 3.99; p < 0.05). In general, deliberation times were increased in the earlier
runs (F(3,21) = 30.81;
p < 0.0001).
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Table 1.
Decision-making performance (i.e. percentage of choice of
the most likely outcome and mean deliberation times, plus SEs) as a
function of the balance of rewards
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Additional analyses were performed on the decisions of the concealed
runs constituting the 4:2 and 5:1 conditions (see above). These data
were collected during a period beginning at the start of the scan
windows and ending 60 sec later. The within-subject factors were
unchanged except that the balance of rewards had only three levels
instead of five (i.e., 30 vs 70, 20 vs 80, or 10 vs 90). Despite the
reduced power available with this much restricted data set,
decision-making performance within the scan windows of the 4:2 and 5:1
conditions was typical of the complete sequences. Thus, choice of the
most likely outcome was significantly reduced as the size of its
associated reward was diminished relative to that associated with the
least likely outcome (Table 1;
F(1.19,14) = 9.47; p < 0.01). The time required to make decisions also increased, although
not significantly (F(2,14) = 1.3).
Additionally, the balance of rewards influenced subjects' choices more
in the 4:2 condition than in the 5:1 condition
(F(2,14) = 4.46; p < 0.05). Finally, as with the complete sequences, subjects took
significantly longer to make their choices in the 4:2 compared to the
5:1 condition (2662 ± 200 msec vs 2373 ± 208 msec;
F(1,7) = 20.53; p < 0.005), especially within the earlier runs of the study
(F(3,20) = 4.94; p = 0.01). Deliberation times were significantly increased in the earlier
compared to the later runs (F(3,21) = 18.25; p < 0.0001).
Regional cerebral blood flow changes
Decision-making versus control conditions
Subtraction of the rCBF associated with the visuomotor control
conditions from that associated with the 4:2 and 5:1 conditions combined isolated significant and distinct activations in ventral, but
not dorsolateral, sectors of the right PFC (Table
2). Specifically, there was a highly
significant peak positioned along the orbital frontal gyrus [Brodmann
area 11 (BA 11); z score = 4.14; Fig. 2A], another
positioned more laterally along the most anterior and ventral portion
of the middle frontal gyrus (BA 10/11; z score = 4.51;
Fig. 2B), and a third significant peak positioned
just anterior to the insular cortex, along the ventral part of the inferior frontal gyrus (BA 47; z score = 4.48; Fig.
2C). There were no rCBF increases associated with decision
making in other PFC areas.
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Table 2.
Comparison of the combined rCBF from the 4:2 and 5:1
conditions with the rCBF associated with performance of the visuomotor
control task
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Figure 2.
Peaks of activity-associated performance of the
decision-making task compared to the visuomotor control task rendered
onto the averaged MRI scans of the eight volunteer subjects used in the
current study (threshold, p < 0.01).
A, Peak of activation in orbitomedial PFC (BA 11);
B, peak of activity within orbitolateral PFC (BA 10);
C, activation within the inferior convexity (BA
47).
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Additional activations not predicted a priori included a significant
rCBF increase along the right fusiform gyrus (Table 2; BA 18;
z score = 5.21). There was also a marked activation
within the superior parietal lobule on the left (BA 7; z
score = 5.48), as well two distinct activations in the same area
on the right (BA 7/40; z scores = 5.78 and 5.41).
Finally, there were significant peaks within the lateral cerebellum on
the left (z score = 4.66) and the medial cerebellum on
the right (z score = 5.23).
Subtraction of the combined rCBF of the 4:2 and 5:1 conditions from
that of the control conditions also revealed evidence of relatively
reduced activation associated with decision making within left anterior
PFC; specifically, along the left medial frontal gyrus (Table 2; BA 10;
z scores = 4.71 and 4.12). Additional unpredicted areas
of reduced rCBF in the decision-making compared to control conditions
were concentrated within predominantly temporal lobe areas (Table 2)
and included the left middle temporal gyrus (BA 39; z
score = 5.22) and left uncus (BA 28/36; z score = 4.69), the right middle and superior temporal gyri (BA 21, z
score = 4.70; BA 22, z score = 4.84), as well as
the left precentral gyrus (BA 4; z score = 4.37) and
lateral cerebellum on the right (z score = 4.87).
In general, separate comparisons involving each of the decision-making
conditions with the control condition reflected similar patterns of
activation in the inferior and orbital PFC, as well as posterior
temporal and parietal areas (Tables 3,
4). In particular, decision making in the
4:2 condition (Fig. 3A) and
the 5:1 condition (Fig. 3B) activated roughly the same three
sites in right orbital PFC: laterally, along the anterior part of the
middle frontal gyrus (BA 10/11; z scores = 4.24 in the
4:2 condition, 3.92 in the 5:1 condition); posteriorly, along the
inferior frontal gyrus (BA 47; z scores = 4.07 in the
4:2 condition, 3.89 in the 5:1 condition); and, medially, in the region
of the orbital frontal gyrus (BA 11; z score = 4.42 in
the 4:2 condition; z score = 3.81 in the 5:1
condition).
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Table 3.
Comparison of the rCBF associated with the 4:2 conditions
only with the rCBF associated with performance of the visuomotor
control task
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Table 4.
Comparison of the rCBF associated with the 5:1 conditions
only with the rCBF associated with performance of the visuomotor
control task
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Figure 3.
Increased rCBF from the two decision-making
conditions compared with the visuomotor control task rendered onto a
representative brain (threshold, p < 0.01).
A, 4:2 condition  control task; B, 5:1
condition control task. Note the lack of activity within
dorsolateral areas of the PFC.
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The two decision-making conditions showed more limited distributions of
reduced rCBF in comparison with the control conditions (Tables 3, 4).
Specifically, in orbital PFC areas, only the 4:2 condition showed
reduced significantly reduced activity within the left orbital gyrus
(BA 11; z score = 4.54). However, both conditions were
associated with marked deactivations in temporal areas: along the right
inferior and middle temporal gyri in the 4:2 conditions (BA 21, z score = 4.32; BA 20, z score = 4.35), and along the left middle temporal gyrus (BA 39; z
score = 4.45), left inferior temporal gyrus (BA 20; z
score = 4.44) and left uncus (BA 28; z score = 4.65) in the 5:1 conditions. Additional rCBF deactivations were evident
along the precentral gyrus on the left in the 4:2 condition (BA 4/6;
z score = 5.01) and along the superior temporal gyrus
on the right in the 5:1 condition (BA 22; z score = 4.35).
4:2 condition minus 5:1 condition
Direct subtraction of the rCBF associated with the 5:1 conditions
from that associated with the 4:2 conditions isolated only modest
changes in regional neural activity. Specifically, there was only a
limited activation along the orbital frontal gyrus on the left (BA 11;
z score = 3.28; Table 5),
as well as a more extensive peak positioned along the anterior
cingulate gyrus (BA 24; z score = 3.62). There was also
some evidence of relatively increased rCBF in the area of the ventral
striatum, just adjacent to the nucleus accumbens and putamen
(z score = 3.92). However, none of these predicted or
unpredicted rCBF changes survived correction for multiple comparisons.
Subtraction of the rCBF in the 4:2 conditions from the rCBF in the 5:1
conditions revealed only a single area of changed rCBF along the left
middle frontal gyrus (BA 6; z score = 4.26).
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Table 5.
Direct comparison of the rCBF associated with the 4:2
conditions with the rCBF associated with the 5:1 conditions
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Covariates of interest
Further analyses, collapsed across the 4:2 and 5:1 conditions,
failed to find any significant association between rCBF in any cortical
area and the principal performance measures associated with the scanned
sequences: percentage of choice of the most likely outcome, mean
deliberation time, mean number of points earned during the scans, and
total reward at the end of the scans. Activity within the anterior
portion of the right orbital gyrus (BA 11; x = 14;
y = 56; z = 20; z
score = 3.66) did show a positive relationship with the total
change in points, i.e., summed losses or wins, from the start of the
scan windows through to their completion. However, this increase did
not survive correction the threshold set for multiple comparisons
within the orbital PFC using the Worsley formula (see above).
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DISCUSSION |
The behavior of our subjects, across both the entire set of
decision-making sequences completed during the study and the restricted sequences completed within the scan windows of the 5:1 and 4:2 conditions, indicated that the choice of the most likely outcome was
significantly reduced when its associated reward was decreased in
comparison with that associated with the least likely outcome. Deliberation times associated with these choices were also
significantly increased. Thus, these behavioral data (Fig.
1C-F) reflect the conflict inherent in "risky
choices" in which the probability of relevant outcomes is pitted
against the balance of their associated reinforcers. We have shown
that, in a sample of healthy young adult males of relatively high
intelligence, resolving this conflict in favor of one choice over
another is associated with at least three distinct foci of rCBF
increase within the inferior and orbital PFC: laterally, in the
anterior part of the middle frontal gyrus (BA 10), medially, in the
orbital gyrus (BA 11), and posteriorly, in the anterior portion
of the inferior frontal gyrus (BA 47).
The multiple activations associated here with choices differing in the
likelihood and size of their rewards help to explain the apparently
greater incidence of deficient decision making in neurological patients
sustaining damage to the orbital PFC compared to those sustaining
damage in more dorsolateral and dorsomedial areas (Bechara et al.,
1996, 1998, 1999; Rogers et al., 1999). In view of the current results,
it seems that focal lesions of the orbital cortex, as the result of
surgery or stroke (Damasio et al., 1996b), are likely to affect
cortical areas encompassing the rCBF changes seen here, increasing the
probability of deficits in resolving between competing actions on the
basis of ambiguous or conflicting information (Bechara et al., 1994,
1996, 1998, 1999; Eslinger and Damasio, 1985).
Choices in this study were not associated with any significant changes
in neural activity within those dorsolateral prefrontal areas that have
repeatedly been shown to mediate important aspects of the executive
control of behavior such as working memory, planning, and attention
(Goldman-Rakic, 1987, 1996; Petrides, 1994, 1995; Dias et al., 1996;
Roberts et al., 1996). Thus, these results complement both experimental
data indicating that impairments in decision making are dissociable
from impairments in spatial memory (Bechara et al., 1998) and clinical
assessments that ineffective decision making in real-life contexts can
be accompanied by relatively normal performance on standard tests of
frontal lobe function and measures of visuospatial performance,
language, and memory (Eslinger and Damasio, 1985; Saver and Damasio,
1991; Rahman et al., 1999).
Given the intrinsic connectivity within orbital PFC (Barbas and Pandya,
1989; Carmichael and Price, 1995b), the activations of the present
study are not likely to be functionally independent. Nevertheless,
their distribution within the inferior and orbital cortex reflects the
diversity of cell types and connectivity extrinsic to the PFC. Thus,
the strong activations around the orbital frontal gyrus fell within an
area that, in the primate brain, has a distinctive granular
cytoarchitecture (Carmichael and Price, 1994) and receives rich
innervation from all major stations of limbic-hippocampal circuitry (Morecroft et al., 1992; Carmichael and Price, 1995a). By
contrast, the peaks around the inferior frontal and middle frontal gyri
(BA 47 and 10/11) were located in areas that have a relatively
agranular composition (Carmichael and Price, 1994) and receive more
pronounced input from distinct sensory association cortices (Jones and
Powell, 1970; Barbas, 1988; Morecroft et al., 1992; Carmichael and
Price, 1995b). Thus, decision making in this study activated distinct
areas of inferior and orbital PFC that have access to heteromodal
sources of information and are ideally positioned to integrate sensory
and object-based processing of exteroceptive stimuli with processing of
their associated reward-punishment valence. Moreover, in addition to
its reciprocal connections with medial temporal systems (Jones and
Powell, 1970), the orbitomedial and orbitolateral PFC provide important
output pathways into the ventral striatum (Haber et al., 1995) and are
able to interface such "affective" information with mechanisms of
action selection routed through corticostriatal loops (Rolls,
1996).
The orbital PFC is also a prominent target of the monoamine
neuromodulatory projections (Thierry et al., 1973). Indeed, the orbital
PFC is just one station in an extensive circuitry, incorporating the
ventral striatum and amygdala, implicated in processes of reinforcement
and incentive motivation and under strong influence from
mesocorticolimbic dopamine input (DiChiara and Imperato, 1988;
Koob and Bloom, 1988; Wise and Rompré, 1989).
Consequently, recent findings that subjects with a history of chronic
amphetamine abuse show a pattern of decision-making deficits that
closely resembles that shown selectively by patients sustaining damage to orbital PFC suggests that decision-making cognition may be susceptible to altered neuromodulation, perhaps affecting orbital PFC
function (Rogers et al., 1999). Converging evidence that this is the
case can be seen in the demonstration of marked impairments in the
decision making of normal volunteers after acute plasma tryptophan
depletion (Rogers et al., 1999), raising the further possibility that
reduction in central 5-hydroxytryptamine, itself strongly associated
with disorganized, impulsive, and aggressive behavior (Linnoila et al.,
1983), is associated with altered decision making in laboratory settings.
The contributions of the orbital PFC to decision making are poorly
understood; resolving choices between small, likely rewards and larger,
unlikely rewards must recruit several, as yet unspecified, cognitive
operations (Bechara et al., 1997; Rogers et al., 1999). However, the
proposal that the orbital PFC is involved in the representation of
stimulus-reward relationships (for review, see Iversen and Mishkin,
1970; Jones and Mishkin, 1972; Dias et al., 1996; Rolls, 1996)
seems especially pertinent because effective real-life decision making
must require accurate information about the current reward valence of
relevant exteroceptive stimuli. However, the nature of this information
remains controversial. On the one hand, the orbital PFC may help to
mediate decision making by providing action selection mechanisms with
direct information about the reinforcing properties of all types of
unconditioned and conditioned stimuli (Rolls, 1996); on the other hand,
the orbital PFC may reactivate somatic states previously conditioned to
salient features of the choice confronting the subject (Damasio, 1994).
In this context, it is notable that although the reward offered to our
subjects, experimenter-defined "points" having no monetary significance, was rather abstract and arbitrary in character, it is
clear that the decision making of our subjects was sensitive to the
combination of size and probability of rewards associated with the two
response options (Fig. 1C-F). Moreover, decision making per se over this kind of reward, although effective in activating extensive parts of orbital PFC, did not activate other stations in the circuitry associated with processes of reinforcement such as the ventral striatum and amygdala. Although the detection of
rCBF changes in these smaller structures may have been hampered by the
width of smoothing filter applied to our data (FWHM = 16 mm),
the present results suggest that the orbital PFC is particularly implicated in mediating decision making over "secondary"
reinforcement (i.e., reinforcement conditioned to stimuli associated
with "primary" reward; see also Bechara et al., 1999). Exploring
whether the orbital PFC participates in a wider network mediating
primary reinforcement requires manipulating the type of reinforcement available to subjects in similar tasks.
The strong activations seen in the orbital PFC during the
decision-making conditions compared to the control conditions contrasts with the more restricted activity apparent in the direct comparisons between the 4:2 and 5:1 conditions. In general, the decision of the 4:2
conditions were more affected by the balance of reinforcers than those
of the 5:1 conditions and were associated with marked increases in
deliberation times (Fig. 1). Although the limited increase in rCBF seen
within the anterior cingulate gyrus is entirely consistent with its
proposed role in response selection mechanisms in coordination with
interconnected limbic circuitry and orbital PFC (Vogt et al., 1992),
the absence of large activations in the orbital PFC itself suggests
that this area makes a necessary contribution to decision making that
does not depend to any great extent on the degree of conflict inherent
in the choice. However, our design deliberately matched reinforcement
density across the 4:2 and 5:1 conditions. Recent studies suggest that
the activity of the orbital PFC is sensitive to changes in acquired
reward (Elliott et al., 1999) and violations of expectations (Nobre et
al., 1999). Thus, research into the relationship between decision
making, orbital PFC activity, and magnitude of reward also seems warranted.
Finally, appropriate deliberation about the available options in our
decision-making task may also have required the temporary suppression
of activated or primed responses, for example, those directed toward
actions associated with larger but less probable rewards, and this
suppression may have been reflected in the activations seen in the
inferior convexity during the 4:2 and 5:1 conditions (Kawashima et al.,
1996; Konishi et al., 1998; Krams et al., 1998). However, our peaks
within the inferior convexity are somewhat ventral to those most
recently associated with this inhibitory function (cf. Konishi et al.,
1998) and are closer to activations previously seen in working memory
studies (Owen et al., 1996; Smith et al., 1996; Courtney et al.,
1998). Because it has been proposed that the inferior convexity is
involved in the retrieval of information from posterior cortical areas
(Petrides, 1994, 1995, 1996; Owen et al., 1996), it is possible
that this area contributes to decision making, not by mediating some
generic inhibitory function, but by mediating retrieval and/or
comparator operations, e.g., over recent reinforcing events, needed for
effective choices. Converging evidence that this is the case can be
seen in a significant association (n = 84;
r = 0.39; p < 0.001) between deliberation times in a decision-making task similar to the one used
here and performance on a spatial span task (E. Bazanis, R. D. Rogers, J. H. Dowson, T. W. Robbins, and B. J. Sahakian, unpublished observations) that has previously been shown to activate the same area of ventrolateral PFC as activated in the current study
(Owen et al., 1996). Finally, the decision-making deficits of orbital
PFC patients do not take the form of impulsive or disinhibited responding (Bechara et al., 1996), but rather slow and ineffective deliberation about the conflicting options for action (Rogers et al.,
1999), again suggesting that the contribution of the orbital PFC to
decision-making cognition is not the provision of a simple inhibitory mechanism.
| |
FOOTNOTES |
Received March 29, 1999; revised July 14, 1999; accepted August 3, 1999.
This work was supported by a Programme Grant from the Wellcome Trust to
T.W.R., B.J.E., A.C.R., and B.J.S., and by Technology Foresight
(J.D.P.). This is a publication of the Medical Research Council
Cooperative on Brain, Behavior, and Neuropsychiatry. We thank Matthew
Brett for his advice and help with data analysis.
Correspondence should be addressed to Robert D. Rogers, Department of
Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK.
| |
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D. Knoch, L. R. R. Gianotti, A. Pascual-Leone, V. Treyer, M. Regard, M. Hohmann, and P. Brugger
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M. K. Hunt, D. R. Hopko, R. Bare, C. W. Lejuez, and E. V. Robinson
Construct Validity of the Balloon Analog Risk Task (BART): Associations With Psychopathy and Impulsivity
Assessment,
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[Abstract]
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S. M. Strakowski, C. M. Adler, S. K. Holland, N. P. Mills, M. P. DelBello, and J. C. Eliassen
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H. Bowden-Jones, M. McPhillips, R. Rogers, S. Hutton, and E. Joyce
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F. X. Vollenweider, M. E. Liechti, and M. P. Paulus
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F. Jollant, F. Bellivier, M. Leboyer, B. Astruc, S. Torres, R. Verdier, D. Castelnau, A. Malafosse, and P. Courtet
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H. F. Clarke, S. C. Walker, H. S. Crofts, J. W. Dalley, T. W. Robbins, and A. C. Roberts
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L. K. Fellows and M. J. Farah
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L. K. Fellows
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F. Sotres-Bayon, D. E.A. Bush, and J. E. LeDoux
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M. Grossman, C. McMillan, P. Moore, L. Ding, G. Glosser, M. Work, and J. Gee
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I. Bohn, C. Giertler, and W. Hauber
NMDA Receptors in the Rat Orbital Prefrontal Cortex are Involved in Guidance of Instrumental Behaviour under Reversal Conditions
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L. K. Fellows and M. J. Farah
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M. Ernst, A. S. Kimes, E. D. London, J. A. Matochik, D. Eldreth, S. Tata, C. Contoreggi, M. Leff, and K. Bolla
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I. Bohn, C. Giertler, and W. Hauber
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M. Piefke, P. H. Weiss, K. Zilles, H. J. Markowitsch, and G. R. Fink
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M. E. Walton, D. M. Bannerman, and M. F. S. Rushworth
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A. L. Brody, M. A. Mandelkern, E. D. London, A. R. Childress, G. S. Lee, R. G. Bota, M. L. Ho, S. Saxena, L. R. Baxter Jr, D. Madsen, et al.
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D. R. Royall, E. C. Lauterbach, J. L. Cummings, A. Reeve, T. A. Rummans, D. I. Kaufer, W. C. LaFrance Jr., and C. E. Coffey
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R. Z. Goldstein and N. D. Volkow
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M. J. Thieben, A. J. Duggins, C. D. Good, L. Gomes, N. Mahant, F. Richards, E. McCusker, and R. S. J. Frackowiak
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F. Manes, B. Sahakian, L. Clark, R. Rogers, N. Antoun, M. Aitken, and T. Robbins
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J. S. Rubinsztein, P. C. Fletcher, R. D. Rogers, L. W. Ho, F. I. Aigbirhio, E. S. Paykel, T. W. Robbins, and B. J. Sahakian
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D. M. Small, R. J. Zatorre, A. Dagher, A. C. Evans, and M. Jones-Gotman
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G. S. Berns, S. M. McClure, G. Pagnoni, and P. R. Montague
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C. R. Savage, T. Deckersbach, S. Heckers, A. D. Wagner, D. L. Schacter, N. M. Alpert, A. J. Fischman, and S. L. Rauch
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N. Mavaddat, P. J. Kirkpatrick, R. D. Rogers, and B. J. Sahakian
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M. G. Baxter, A. Parker, C. C. C. Lindner, A. D. Izquierdo, and E. A. Murray
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TOP
ABSTRACT
INTRODUCTION
