SUPPLEMENTARY METHODS
PARTICIPANT RECRUITMENT AND SCREENING
22 BD participants were recruited from local mental health trusts and specialist affective
disorder clinics within Greater Manchester, United Kingdom. Data from 2 BD participants
were not analysed (one did not complete all runs, and one due to neuroradiological
abnormality) as were the data from two HC participants (one did not complete all runs, one
due to technical failure). The final sample consisted of 20 participants in each group (see
Table 1). The study was approved by the National Health Service Research Ethics Committee
in the United Kingdom (NREC) and the University of Manchester Senate Ethics Committee.
Participants were included if they met the criteria for BD-1 or BD-2, but did not meet the
criteria for a mood episode (in the case of the BD group) or a psychiatric disorder, in the case
of healthy controls. To ensure a representative sample, anxiety, and alcohol and substance use
disorders, were not exclusion criteria. However, participants were excluded if they had used
substances in the last four months or alcohol in the past 24 hours.
TASK
In addition to the task description (see main methods), we emphasise that the four options in
each trial did not differ in value or probability, ensuring that all participants had sufficient
numbers of trials in each condition, and that there were no between-groups differences in trial
numbers. Hence there was equivalent signal-to-noise ratio in all between-group comparisons.
The duration of the selection phase was tapered over the first trials of each run (5 sec for
trials 1-2, 3 sec for trials 3-4 and 2 sec thereafter) to ensure that participants had time to
acclimatise and respond. Participants were instructed to respond in this time, and informed
that if no timely response was issued, a random choice would be made. The colour chosen
was displayed in the centre of the Roulette wheel during anticipation and outcome. The
duration of both of these phases were separately jittered between 3-4 seconds. The relatively
short duration of the anticipation and outcome events was selected for the following, largely
pragmatic, reasons. First, to promote attentional focus on the task as far as possible in an
impulsive clinical population that may find sustained attention difficult. Second, we required
a high number of total trials to yield a sufficient number of trials in the low-probability
conditions within an acceptable total scan duration. The crosshair was either 1.5 sec (75% of
trials) or 4 sec (25%) to facilitate a strong baseline in the model, with which to compare
conditions of interest.
FMRI ACQUISITION, PREPROCESSING AND MODELLING
Echo planar images were acquired over eight runs of 150 volumes, using a Phillips 1.5 T
scanner (TR = 2.45 s, TE = 47 ms; flip angle = 90°). Volumes comprised 30 slices (4mm, no
gap; in-plane voxel dimensions 1.5 x 1.5 mm), collected in ascending order, and with
standard field of view.
SPM8 (Wellcome Department of Cognitive Neurology, University College London) was
used to pre-process and analyse the images. Each participant’s functional images were
motion-corrected with realignment to the mean image and time-corrected to the middle slice.
The functional images were then coregistered with the structural image, spatially normalised
to MNI space, and smoothed with an 8 mm gaussian kernel. The data for all participants were
analysed at the first-level using the general linear model, as implemented in SPM8 (Friston et
al., 1994). Regressors were convolved with a haemodynamic response function, and contrast
images computed. The realignment parameters were included as additional regressors at the
first level to reduce residual effects of motion.
REPRESENTATION OF EXPECTED VALUE AND PREDICTION ERROR:
In a separate model, “Net” expected value (EV) and prediction error (PE) were additionally
entered, as parametric modulators of the anticipation (EV only) and outcome (PE only) event
regressors. Net EV was calculated trial-wise as the arithmetic product of reward probability
and magnitude minus the arithmetic product of loss probability and magnitude (i.e. EV =
EVGain - EVLoss). PE was calculated trial-wise as the difference between EV and the actual
outcome (outcome minus net EV). This allowed the model to take into consideration the
pattern of neural activity that would be expected based on the relative differences between the
parameters of EV and PE terms.
SUPPLEMENTARY RESULTS
STATE EFFECTS OF AFFECTIVE SYMPTOMS
To examine the effects of residual mood symptoms on activity in the 3 ROIs, the main
analyses were repeated with Hamilton Depression Rating Scale (HRSD) and Bech-Rafaelsen
Mania Scale (MAS) scores entered as covariates. Interactions with these covariates were
separately explored by means of partial correlations that controlled for the effect of the other
covariate not being inspected. Analyses focussed on the bipolar disorder (BD) group because
controls did not exhibit a sufficient range of HRSD and MAS scores (Table 1).
ANTICIPATION:
Activity in dlPFC ROI showed a non-significant trend for a Magnitude-MAS interaction
[F(1, 17) = 3.55, p= .077]. Controlling for HRSD, MAS score was positively correlated with
the difference between small and large magnitude trials [r(17) = .416, p=.077]. In the main
analysis, an overall effect of magnitude signalled that trials with large stake were perceived
as more relevant than those with smaller amounts at stake. The positive correlation with
manic symptoms therefore suggests that the perceived relevance of large-stake prospects is
amplified with increasing manic symptoms.
For the NAcc, there was a probability-HRSD interaction [F(1, 17) = 5.25, p = .035], with no
other significant effects or interactions (p≥.14). HRSD was negatively correlated with the
difference between high minus low probability trials, controlling for MAS [r(17)= -.486,
p=.035]. In the main analyses, the BD group showed a greater increase in NAcc for highprobability rewards (compared to low), consistent with a greater drive to obtain rewards when
they are on offer. Hence the negative association between this effect and depressive
symptoms suggests that state depression is associated with a reduced drive to obtain reward.
The vmPFC ROI showed a main effect of MAS [F(1, 17) = 7.07, p = .017], such that higher
MAS scores were associated with greater vmPFC activity. This is consistent with state mania
driving up the perceived value of prospects related to reward in vmPFC, regardless of their
probability.
OUTCOME:
For dlPFC, there were no effects of state symptoms (p≥.1).
For NAcc, there was a main effect of HRSD [F(1, 17) = 3.99, p=.052], consistent with state
depression reducing activations for rewards and potentiating the deactivation observed for
losses (see Supplementary Figure 1). There were also trends for MAS-magnitude [F(1, 17) =
3.74, p=.07] and MAS-probability-magnitude [F(1, 17) = 3.37, p=.084] interactions, although
these did not reach significance. The MAS-probability-magnitude interaction was explored
by repeating the ANCOVA separately for low and high probability outcomes. The MASmagnitude interaction was significant for low-probability (p=.04) but not high-probability
(p=.88) outcomes. Controlling for HRSD, MAS was positively correlated with the difference
between low-probability outcomes of small and large magnitude [r(17) = .474, p=.04]. In the
primary analysis (see main results), NAcc activation was greater for large relative to small
magnitude outcomes. Collectively these findings suggest that mania is associated with a
greater increase in NAcc activation for large relative to small outcomes, particularly when
they are unexpected.
For vmPFC, there was a Probability-MAS [F(1, 17) = 5.45, p = .03] interaction. Controlling
for HRSD, the size of the difference between outcomes of different probabilities (low- minus
high- probability) was negatively correlated with MAS [r(17)=-.49, p=.03]). In the main
analysis, the effect of probability on vmPFC was dependent on valence: the response was
strongest to unexpected rewards relative to expected, with a greater deactivation in response
to losses. Together, these findings indicate that state mania is associated with a reduction in
both the vmPFC activation in response to unexpected rewards and its deactivation in response
to unexpected losses (relative to expected rewards and losses respectively).
PARAMETRIC ANALYSIS OF EXPECTED VALUE AND PREDICTION ERROR
ANTICIPATION:
Parametric modulation by EV was observed in R. dlPFC [F(1, 38)=9.0, p=.005] but not NAcc
or vmPFC (p ≥ .414). There were no group effects or interactions (p ≥ .155).
OUTCOME:
Parametric modulation by PE was observed in bilateral NAcc [F(1, 38)=51.4, p<.001],
bilateral dlPFC [F(1, 38)=17.7, p<.001] and vmPFC [t(39)=4.91, p<.001]. The BDR group
evidenced greater PE-modulated activity in vmPFC [t(38)=2.07, p=.046] but no difference in
NAcc or dlPFC (p≥ .3).
SUPPLEMENTARY DISCUSSION
The supplementary results reported here extend the main analyses by exploring the influence
of affective state on the neural processing of motivational information. In this way, these
findings provide insight into how these processes are affected during episodes of depression
and mania. In addition, we performed parametric analyses to isolate neuronal populations
responding to expected value (EV) and prediction error (PE).
During anticipation, all three regions of interest showed modulation by affective state. In the
main analyses, the dorsolateral prefrontal cortex (dlPFC) activity differentiated gambles
based on their relevance, with increased activation when anticipating large and likely rewards
(relative to small and unlikely rewards, respectively). This indicates that there is greater
attentional tracking of prospects when the potential reward is high, as these were most
consistent with the long-term goal of maximising winnings. This attentional bias was
positively associated with manic symptoms indicating that highly rewarding behaviours may
be particularly enticing during mania. Mania was also associated with greater overall
activation of the vmPFC, indicating an indiscriminate inflation of the perceived value of
prospects related to reward. Importantly, both the dlPFC and vmPFC effects occurred
irrespective of the likelihood of reward. Hence mania may be associated with an inability to
devalue highly rewarding behaviours even when the chances of negative repercussions are
high. This offers a plausible explanation for the elevated levels of risk-taking seen during
manic episodes (APA, 2000).
In contrast, anticipatory activity in NAcc was modulated by depressive symptoms. Main
analyses showed that this region preferentially activated when rewards were likely as
opposed to unlikely, and that this effect was stronger for BD patients. This was interpreted as
signifying a greater drive to obtain reward, when rewards are likely to be available. This
effect was negatively associated with depressive symptoms, indicating that state depression
reduces the drive to obtain reward. This fits with clinical features of depression, including
reduced interest in pursuing previously enjoyed activities and reduced motivation to strive for
goals (APA, 2000).
Outcome-locked NAcc activity was also separately modulated by affective symptoms. On the
one hand, state depression was associated with a blunted response to rewards and a
potentiated response to losses (Supplementary Figure 1), consistent with anhedonia and
hypersensitivity to punishment being cardinal features of depression (APA, 2000, Eisner et
al., 2008). On the other hand, manic symptoms were associated a trend for greater increase in
NAcc activation elicited by large relative to small outcomes, particularly when unexpected.
This indicates that mania is associated either with an increased valuation of large magnitude
outcomes, or a reduced valuation of small magnitude outcomes. Both scenarios could explain
the increased risk-taking evident during mania. Indeed the seeking out of more potent
rewards may either be driven directly, through increased valuation of these large rewards, or
indirectly by reducing the intrinsic value of everyday activities associated with smaller
reward.
Activity in regions of NAcc, dlPFC and vmPFC was modulated by PE, consistent with these
regions being involved in outcome evaluation and reinforcement learning (e.g. Corlett et al.,
2004, Pessiglione et al., 2006). Greater PE-modulated activity in vmPFC in the BD group
tallies with the overall higher outcome-locked activity in this region (main results). Together,
these findings suggest that BD patients may track motivational outcomes more intently,
consistent with the inflated importance of goal-attainment in BD (Johnson, 2005) and our
previous fMRI findings in non-clinical hypomania (O'Sullivan et al., 2011). The steeper
parametric response may be driven by an increased response to unexpected and large rewards
(relative to controls), conceivably amplifying the subjective value of novel rewards over
those from everyday behaviours.
In summary, affective symptoms separately modulate how rewards are processed in BD, via
somewhat distinct neural mechanisms. Depression was associated with an attenuation of the
ventral striatal reward system, culminating in a reduced drive to obtain rewards and a
dampening of the hedonic impact when rewards were received. Mania, on the other hand,
modulated cortical attention and valuation systems, and was associated with greater tracking
and valuation of potentially highly rewarding options, regardless of the likelihood of success
or failure. BD patients do not show marked deficits in encoding the expected value or
prediction error signals, and instead show an increased tracking of motivational outcomes in
an integrated valuation system.
REFERENCES
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Washington, DC: APA; 2000.
Corlett PR, Aitken MRF, Dickinson A, Shanks DR, Honey GD, Honey RAE, et al. Prediction error during
retrospective revaluation of causal associations in humans: fMRI evidence in favor of an associative
model of learning. Neuron. 2004;44(5):877-88.
Eisner LR, Johnson SL, Carver CS. Cognitive responses to failure and success relate uniquely to
bipolar depression versus mania. Journal of Abnormal Psychology. 2008;117(1):154.
Friston KJ, Holmes AP, Worsley KJ, Poline JP, Frith CD, Frackowiak RSJ. Statistical parametric maps in
functional imaging: a general linear approach. Human Brain Mapping. 1994;2(4):189-210.
Hare TA, Camerer CF, Rangel A. Self-control in decision-making involves modulation of the vmPFC
valuation system. Science. 2009;324(5927):646-8.
Johnson SL. Mania and dysregulation in goal pursuit: a review. Clinical Psychology Review.
2005;25(2):241-62.
O'Sullivan N, Szczepanowski R, El-Deredy W, Mason L, Bentall RP. fMRI evidence of a relationship
between hypomania and both increased goal-sensitivity and positive outcome-expectancy bias.
Neuropsychologia. 2011;49(10):2825-35.
Pessiglione M, Seymour B, Flandin G, Dolan RJ, Frith CD. Dopamine-dependent prediction errors
underpin reward-seeking behaviour in humans. Nature. 2006;442(7106):1042-5.
Supplementary Figure 1. Processing of gain and loss outcomes in nucleus accumbens
(NAcc) is modulated by depressive symptoms. Within euthymic bipolar disorder patients, a
subgroup exhibiting relatively high levels of depressive symptoms evidenced both a
dampened response to reward as well as an enhanced response to loss, compared to those
exhibiting low levels of depressive symptoms. Note that mean scores on the Hamilton
Depression Rating Scale were in the ‘remission’ range for both the low (1.25; SD = 1.03) and
high (6.40; SD = 1.91) subgroups. ‘fMRI signal’ represents the change from baseline in the
blood-oxygen-level-dependent signal averaged across left and right NAcc.
Supplementary Table 1. Activated foci for whole-brain contrasts of main effects greater than rest (crosshair) during anticipation of outcome.
Anticipation
p
threshold
Cluster
threshold
Probability
(High > Low)
0.05 FWE
8
Region
Rt Somatosensory Association Cortex
/ Extrastriate cortex
Rt Inferior frontal gyrus (opercularis)
Rt Inferior frontal gyrus (triangularis)
Rt Frontal eye field
Magnitude
(Large > Small)
0.001
Rt Inferior frontal gyrus (opercularis)
Rt Lateral orbitofrontal cortex
0.001
Coordinates
X
Y
Z
Cluster
size
Z
score
7/19
44
45
8
30
46
46
32
-66
10
40
10
38
32
20
60
423
44
13
13
5.74
5.23
4.83
4.92
44
47
47
8
48/44
44
44
-34
4
-50
12
26
22
32
16
32
0
-2
48
28
317
152
102
160
44
4.38
4.18
4.14
3.73
3.66
7
18
48/45
40
37
26
30
46
-38
56
-64
-88
26
-40
-56
48
4
28
38
-6
1043
95
224
86
58
5.07
4.31
4.16
3.93
3.92
44/6
6
-48
-34
8
2
34
60
98
23
3.77
3.62
20
Rt Frontal eye field
Lt Inferior frontal gyrus (opercularis)
Parametric EV
Brodmann
Area
20
Rt Precuneus
Rt V2
Rt Inferior frontal gyrus (triangularis)
Lt Supramarginal Gyrus
Rt Fusiform
Lt Inferior frontal gyrus (opercularis) /
Premotor cortex
Lt Premotor cortex
Supplementary Table 2. Activated foci for whole-brain contrasts of main effects greater than rest (crosshair) at outcome.
p
threshold
Cluster
threshold
Valence
(Gain > Loss)
0.05 FWE
9
Probability
(High > Low)
0.001
Magnitude
(Large > Small)
0.001
Parametric PE
0.05 FWE
Outcome
Region
Brodmann
Area
Coordinates
X
Y
Z
Cluster
z
size
score
Lt Ventral striatum
Rt Ventral striatum
Lt Ventromedial prefrontal cortex
Lt Lateral orbitofrontal cortex
Rt Inferior frontal gyrus (triangularis)
Rt Inferior frontal gyrus (opercularis)
Lt dorsolateral prefrontal cortex
25
25
10
47
45
44
9
9
-10
10
-8
-40
44
52
-24
-16
12
14
44
46
42
12
32
32
-10
-8
-2
0
-2
20
42
52
502
370
670
52
36
175
19
10
7.34
7.06
6.23
5.90
5.81
5.30
4.94
4.68
Rt Middle / Superior temporal gyrus
Lt Lateral orbitofrontal cortex
Lt Retrosplenial cingulate cortex
22/21
47
29
62
-40
-4
-50
22
-40
16
-6
10
38
64
41
3.82
3.62
3.50
Rt Lateral orbitofrontal cortex
Rt Inferior frontal gyrus (opercularis)
Lt Lateral orbitofrontal cortex
Rt Precuneus
Lt Inferior frontal gyrus (opercularis)
47
44
47
7
48/44
44
46
-34
32
-48
26
14
20
-62
14
0
34
-2
44
28
96
82
83
39
33
3.90
3.81
3.71
3.54
3.51
25
25
-10
8
12
14
-12
-8
153
184
6.12
5.91
8/9/32
25
-16
-4
32
32
54
6
45
8
5.29
5.08
26
26
6
Lt Ventral striatum
Rt Ventral striatum
Lt dorsolateral prefrontal
cortex/dorsal anterior cingulate
Lt Subgenual cortex
Supplementary Table 3. Activated foci for whole-brain contrasts of interactive effects greater than rest (crosshair) during anticipation and outcome.
Anticipation
P
threshold
Cluster
threshold
Probability x
Magnitude
0.005
43
Brodmann
Area
Coordinates
X
Y
Z
Cluster
size
z
score
47
38/47
30
42
38
16
4
-12
71
54
3.57
3.25
Rt Precuneus
Lt Posterior cingulate
7
23
36
-8
-62
-50
44
28
177
39
4.77
3.66
Lt Fusiform gyrus
Lt Inferior temporal gyrus
Rt Posterior cingulate
37
20
23
-48
-38
2
-54
-26
-36
4
-4
34
86
28
56
3.85
3.72
3.51
Lt Inferior Temporal Gyrus
Lt Posterior cingulate
20
23
-42
-2
-24
-36
-4
32
68
80
3.43
3.11
Region
Rt Lateral orbitofrontal cortex
Rt Temporal pole / orbitofrontal cortex
Outcome
Valence x
Probability
Valence x
Magnitude
Valence x Probablity
x Magnitude
0.001
0.001
0.005
26
26
42