Download Altered neural reward and loss processing and

doi:10.1093/scan/nsu158
SCAN (2015) 10,1102^1112
Altered neural reward and loss processing and prediction
error signalling in depression
Bettina Ubl,1 Christine Kuehner,2 Peter Kirsch,3 Michaela Ruttorf,4 Carsten Diener,1,5,* and Herta Flor1,*
1
Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim,
Germany, 2Research Group Longitudinal and Intervention Research, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health,
Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 3Department of Clinical Psychology, Central Institute of Mental Health,
Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 4Computer Assisted Clinical Medicine, Medical Faculty Mannheim,
Heidelberg University, Mannheim, Germany, and 5School of Applied Psychology, SRH University of Applied Sciences, Heidelberg, Germany
Dysfunctional processing of reward and punishment may play an important role in depression. However, functional magnetic resonance imaging (fMRI)
studies have shown heterogeneous results for reward processing in fronto-striatal regions. We examined neural responsivity associated with the
processing of reward and loss during anticipation and receipt of incentives and related prediction error (PE) signalling in depressed individuals.
Thirty medication-free depressed persons and 28 healthy controls performed an fMRI reward paradigm. Regions of interest analyses focused on
neural responses during anticipation and receipt of gains and losses and related PE-signals. Additionally, we assessed the relationship between
neural responsivity during gain/loss processing and hedonic capacity. When compared with healthy controls, depressed individuals showed reduced
fronto-striatal activity during anticipation of gains and losses. The groups did not significantly differ in response to reward and loss outcomes. In
depressed individuals, activity increases in the orbitofrontal cortex and nucleus accumbens during reward anticipation were associated with hedonic
capacity. Depressed individuals showed an absence of reward-related PEs but encoded loss-related PEs in the ventral striatum. Depression seems to be
linked to blunted responsivity in fronto-striatal regions associated with limited motivational responses for rewards and losses. Alterations in PE encoding
might mirror blunted reward- and enhanced loss-related associative learning in depression.
Keywords: fMRI; reward; loss; prediction error; depression
INTRODUCTION
Alterations in the processing of reward and loss have been proposed to
characterise depression (Martin-Soelch, 2009; Eshel and Roiser, 2010).
Maladaptive responsivity to rewards and losses has been linked to
functional alterations in brain regions involved in appetitive and aversive associative learning (Elliott et al., 2000; Robinson et al., 2011) such
as prefrontal and striatal structures [nucleus accumbens (NAcc), caudate, putamen] (Pizzagalli et al., 2009; Nikolova et al., 2012). The
investigation of neural activation during the anticipation and outcome
phases of reward and loss permits a more detailed examination of
differences in the neural processing of reward and loss (Knutson
et al., 2008; Pizzagalli et al., 2009; Rademacher et al., 2010). Some
studies suggest that motivational processes are linked to outcome predicting incentive cues during anticipation, while affective responses
might dominate the receipt of reward and loss (Dillon et al., 2008,
2011). The dissociation between motivation and pleasurable consumption has been gaining increased appreciation, thereby mirroring results
of animal and human studies that distinguish between ‘wanting’
during anticipation and hedonic ‘liking’ at the outcome level of
reward and loss (Berridge and Robinson, 1998; Berridge and
Kringelbach, 2008; Dillon et al., 2008, 2011). Moreover, anhedonia,
the inability to experience pleasure and to respond affectively to
Received 21 November 2013; Revised 10 July 2014; Accepted 14 October 2014
Advance Access publication 6 January 2015
We gratefully acknowledge the valuable help of our research assistants in data collection. We thank our
participants for the generous cooperation and use of their time. The work was supported by grants of the German
Research Foundation (Deutsche Forschungsgemeinschaft, DFG; SFB636 project D4 and SFB636 project Z3).
*These authors equally contributed to this work.
Correspondence should be addressed to Carsten Diener, SRH University of Applied Sciences, School of Applied
Psychology, Maria-Probst-Strasse 3, 69123 Heidelberg, Germany. E-mail: [email protected]
pleasure-predicting cues, is a core symptom of depression that seems
to be substantially associated with altered learning from both positive
and negative outcomes (Chase et al., 2010). Several studies reported
anhedonia to be linked to reduced ventral striatal (VS) activity during
reward processing that includes components such as sensitisation towards rewarding stimuli as well as expectation and motivation to
obtain reward (Keedwell et al., 2005; Epstein et al., 2006; DerAvakian and Markou, 2012).
Dysfunctions in reward processing, particularly in fronto-striatal
regions, have been suggested as an important psychophysiological
marker of depression (Hasler et al., 2004; Dunlop and Nemeroff,
2007). In line with this, depressed individuals showed reduced
fronto-striatal activity during reward anticipation (Knutson et al.,
2008; Smoski et al., 2009; Stoy et al., 2012) and outcome (Knutson
et al., 2008; Pizzagalli et al., 2009). However, intact responsivity in the
VS, including the NAcc (Pizzagalli et al., 2009), or enhanced activity in
the anterior cingulate cortex (ACC) during reward anticipation
(Knutson et al., 2008) have also been reported. Furthermore, increased
activation in the dorsolateral and medial prefrontal cortex (dlPFC,
mPFC) but reduced activity in the caudate nucleus was found
during the anticipation and receipt of reward in medication-free depressed adolescents (Forbes et al., 2009). A similar activation pattern
was reported for euthymic patients indicating hyperactivation in the
ACC, midfrontal gyrus and cerebellum during reward anticipation, but
hypoactivation in the orbitofrontal cortex (OFC), ventromedial prefrontal cortex (vmPFC) and insula during reward outcome (Dichter
et al., 2012). McCabe et al. (2009) employed a functional magnetic
resonance imaging (fMRI) paradigm in which remitted depressed individuals and healthy controls received appetitive and aversive flavours
and pictures as well as their respective combinations. During reward
processing, recovered depressed individuals showed reduced responses
in the VS to the rewarding flavour and in the cingulate cortex and OFC
to the combined appetitive stimuli. The authors replicated the study in
ß The Author (2015). Published by Oxford University Press. For Permissions, please email: [email protected]
Altered neural reward and loss processing
Table 1 Overview of neural activation in response to reward and loss anticipation and
outcome when comparing depressed individuals with healthy controls
Significant region
Finding of activation
DS vs HC
ACC
Reward: hyperactivation in DS (Knutson et al., 2008),
hypoactivation in DS (Smoski et al., 2009, 2011)
Loss: hypoactivation in DS (Knutson et al., 2008)
mPFC
Reward: hypoactivation in DS (Knutson et al., 2008)
OFC
Reward: hypoactivation in DS (Smoski et al., 2011)
dlPFC
Reward: hyperactivation in DS (Smoski et al., 2009)
Reward: hypoactivation in DS (Pizzagalli et al., 2009;
Putamena
Stoy et al., 2012)
Loss: hypoactivation in DS (Stoy et al., 2012)
a
Reward: hypoactivation in DS (Pizzagalli et al., 2009;
Caudate nucleus
Smoski et al., 2009; Stoy et al., 2012)
Loss: hypoactivation in DS (Stoy et al., 2012)
Hippocampus
Reward: hypoactivation in DS (Smoski et al., 2009, 2011)
Thalamus
Reward: hypoactivation in DS (Smoski et al., 2009),
hyperactivation in DS (Smoski et al., 2009)
adolescent DS vs HC (Forbes et al., 2009)
mPFC
Reward: hyperactivation in DS
dlPFC
Reward: hyperactivation in DS
a
Reward: hypoactivation in DS
Caudate nucleus
high-risk individuals vs HC
ACC
Reward: hypoactivation in high risk individuals (Gotlib et al., 2010;
McCabe et al., 2012)
Loss: hyperactivation in high risk individuals (Gotlib et al., 2010),
hypoactivation in high risk individuals (McCabe et al., 2012)
mPFC
Reward: hypoactivation in high risk individuals (Gotlib et al., 2010)
a
Reward: hypoactivation in high risk individuals (Gotlib et al., 2010)
Putamen
Loss: hypoactivation in high risk individuals (Gotlib et al., 2010)
Caudate nucleusa
Pallidus
Loss: hypoactivation in high risk individuals (Gotlib et al., 2010)
Thalamus
Reward: hypoactivation in high risk individuals (Gotlib et al., 2010)
Insula
Reward: hypoactivation in high risk individuals (Gotlib et al., 2010),
hyperactivation in high risk individuals (Gotlib et al., 2010)
Loss: hyperactivation in high risk individuals (McCabe et al., 2012)
OFC
Reward: hypoactivation (McCabe et al., 2012)
Loss: hyperactivation in high risk individuals (McCabe et al., 2012)
recovered DS vs HC
ACC
Reward: hyperactivation in DS (Dichter et al., 2012),
hypoactivation in DS (McCabe et al., 2009)
vmPFC
Reward: hypoactivation in DS (Dichter et al., 2012)
OFC
Reward: hypoactivation in DS (McCabe et al., 2009;
Dichter et al., 2012)
Loss: hypoactivation in DS (McCabe et al., 2009)
Insula
Reward: hypoactivation in DS (Dichter et al., 2012)
Mid frontal gyrus
Reward: hyperactivation in DS (Dichter et al., 2012)
Cerebellum
Reward: hyperactivation in DS (Dichter et al., 2012)
Reward: hypoactivation in DS (McCabe et al., 2009)
Putamena
Reward: hypoactivation in DS (McCabe et al., 2009)
Caudate nucleusa
Loss: hyperactivation in DS (McCabe et al., 2009)
Note: aStriatal region which is part oft the NAcc.
a sample of young never-depressed individuals at familial risk for developing major depression and found blunted neural responses in the
ACC and OFC to rewarding stimuli (McCabe et al., 2012). Table 1
summarises findings on altered reward processing in depressed and
high risk samples.
This heterogeneity of findings challenges the assumption of generally
reduced fronto-striatal activity as the major neural correlate of altered
reward processing in depression. Moreover, hypoactivity during
anticipation and receipt of reward has been found in limbic regions
(Smoski et al., 2009, 2011; Dichter et al., 2012). So far, only few studies
investigated the processing of loss. Reduced dorsal striatal activity
during loss outcomes was shown both for medication-free acutely
depressed adults (Pizzagalli et al., 2009) and female adolescents at
high risk for depression (Gotlib et al., 2010). Young at-risk individuals
SCAN (2015)
1103
showed hyperactivation in the OFC and insula and hypoactivation in
the ACC to aversive stimuli (McCabe et al., 2012). In remitted
depressed individuals, McCabe et al. (2009) found decreased OFC activity to combined aversive stimuli and enhanced caudate activity to an
aversive picture. During the anticipation of losses, Stoy et al. (2012)
reported blunted activity in the VS in depressed patients. For an overview of findings related to altered loss processing in depressed and high
risk samples see also Table 1.
Notably, most of these studies used secondary punishments such as
monetary losses and not primary punishments such as painful stimulation (e.g. Diener et al., 2009a,b; Kuehner et al., 2011) or aversive taste
(e.g. McCabe et al., 2012). In addition, monetary loss constitutes a
punishment by removal of an appetitive stimulus (type II punishment)
and not a direct (type I) punishment (i.e. the administration of an
aversive stimulus) (Metereau and Dreher, 2013). Thus, monetary loss
and physical punishment may be processed differently, and neural responses to monetary losses may be considered to reflect neural reward
rather than punishment processing.
In appetitive and aversive associative learning, the prediction error
(PE) is an index that brings the anticipation and receipt of incentives
together (Schultz et al., 1997). PEs covary with the probability of
reward and loss and reflect the deviation of actual outcomes from
their expectations (Schultz et al., 1997). They are assumed to underlie
adaptive outcome predictions to gain future rewards (Montague et al.,
2004) and to avoid potential losses (Boksem et al., 2008). There is
evidence that PEs are encoded particularly in the mesocorticolimbic
system, whose (sub-)regions are involved in the coding of reward and
punishment (Reynolds and Berridge, 2002; Seymour et al., 2005). It
has been widely assumed that there is a positive PE when the outcome
is better than expected (e.g. delivery of unexpected rewards) and a
negative PE when the outcome is worse than expected (e.g. omission
of predicted rewards) (e.g. Tobler et al., 2006; Yacubian et al., 2006).
PEs are maximal at highest uncertainty (P ¼ 0.5) in cue-outcome contingencies. If uncertainty is low, the salience of a cue declines, and PEsignals proportionally diminish towards zero. Dopaminergic (DA)
neurons in the prefrontal cortex (PFC) including the ACC, VS and
other midbrain structures such as the ventral tegmental area (VTA)
appear to code PE-signals most significantly for reward, and less dominantly for loss and punishments (Abler et al., 2006; Yacubian et al.,
2006; Schultz, 2010; Garrison et al., 2013). In addition, PE-signals have
been detected in non-dopamine rich brain systems including the insula
and amygdala (Seymour et al., 2004, 2005; Gradin et al., 2011). Few
studies have investigated PE encoding during reward learning in depression. These studies found enhanced PE-signals in the VTA and
prefrontal areas (Steele et al., 2004; Kumar et al., 2008; Gradin et al.,
2011), but also marked reductions in reward-related PE-signal encoding over time in striatal structures, thalamus, hippocampus and the
rostral and dorsal cingulate cortex (Kumar et al., 2008; Gradin et al.,
2011). So far, loss-related PE signalling has not been investigated in
depressed individuals, thereby hampering conclusions about the
specificity of results.
Therefore, this study aimed to extend findings on the neural correlates of altered incentive processing in a large sample of medication-free
depressed individuals by investigating the anticipation and receipt of
both reward and loss and altered reward- and loss-related PE signalling
in brain regions of interest within a novel integrative approach. First,
we aimed to investigate whether depressed individuals show aberrant
neural activation in fronto-striatal regions during the anticipation of
reward and loss. Furthermore, we were interested in determining
whether the processing of neural outcome would also be associated
with fronto-striatal alterations in depression. In addition, we expected
that fronto-striatal activation during the anticipation and actual outcome of reward and loss would be associated with self-rated hedonic
1104
SCAN (2015)
capacity, i.e. the ability to experience pleasure. In concordance with
previous studies demonstrating that reward- and loss-related brain
regions are more activated in case of higher gains and losses (Abler
et al., 2005; Knutson et al., 2005; Yacubian et al., 2006), we used a
reward paradigm with low and high monetary gains and losses
(cf. Kirsch et al., 2003; Knutson et al., 2008; Pizzagalli et al., 2009;
Plichta et al., 2012). Here, we expected larger activation differences
between groups for higher magnitudes of rewards and losses.
Importantly, we focussed our investigation on brain regions of interest
that have previously been suggested as relevant for altered reward and
loss processing in depression in at least two of the studies presented in
Table 1.
METHODS AND MATERIALS
Participants
Thirty medication-free individuals with a diagnosis of major depressive
disorder and/or dysthymia aged 18–60 were recruited by public announcements. Twenty-eight age-, education- and gender-matched
healthy controls were recruited by random selection from the local
census bureau of the city of Mannheim, Germany. Participants were
examined using the Structured Clinical Interview for DSM-IV Axis I
Disorders (SCID-I) (First et al., 1996; German version Wittchen et al.,
1997). Control participants were excluded if they met criteria for a
current DSM-IV Axis I disorder or lifetime criteria for any affective
disorder. General exclusion criteria were current alcohol or drug abuse,
current use of psychotropic medication and current or lifetime psychotic symptoms and neurological diseases. Participants completed the
Beck Depression Inventory II (BDI-II) (Beck et al., 1996) and were
evaluated for interviewer-rated depression severity using the Hamilton
Rating Scale for Depression (HAM-D) (Hamilton, 1960). Hedonic
capacity was captured by the Snaith Hamilton Pleasure Scale
(SHAPS), a 14-item scale with adequate psychometric properties developed for the self-reported assessment of hedonic capacity (Snaith
et al., 1995; Nakonezny et al., 2010; Sherdell et al., 2012). All subjects
were right handed. Three depressed individuals (10%) met criteria for
a comorbid anxiety disorder (n ¼ 2 with panic disorder with agoraphobia, n ¼ 1 with specific phobia) according to DSM-IV as assessed
by SCID-I (Wittchen et al., 1997). Further sample characteristics are
provided in Table 2. This study was in accordance with the declaration
of Helsinki and was approved by the ethics committee of the Medical
Faculty Mannheim, Heidelberg University. All participants gave written informed consent to participate.
fMRI paradigm
Participants completed a modified version of the monetary reward
paradigm by Kirsch et al. (2003) and Plichta et al. (2012) (Figure 1).
Trials began with the visual presentation of incentive cues (6 s)
predicting potential monetary gains (upward arrows) or losses (downward arrows) with either low ( 0.2 E) or high ( 2.0 E) magnitudes.
Horizontally oriented arrows indicated the control condition, which
did not result in monetary outcomes. After the offset of the incentive
cue, a flash light was presented for 100 ms indicating that the participant had to press a button on the response device with the right index
finger as quickly as possible in order to gain money or not to lose
money, respectively. Subsequently, participants received visual performance feedback (1.5 s) and were informed about their current
balance for 1.5 s. During the control condition, only feedback about
the button press was presented [‘button (not) pushed’]. Reaction time
(RT) thresholds were adaptively determined depending on subjects’
performance in the previous trial, varying from 300 to 1.500 ms. The
adaptive algorithm resulted in a decrease of 10% of the threshold after
a fast response and an increase of 5% after a slow response. This was
B. Ubl et al.
Table 2 Sample characteristics
Measure
Depressed individuals
(n ¼ 30)
Healthy controls
(n ¼ 28)
P value
Age in years (SD)a
Education in years (SD)a
BDI II (SD)a
HAM-D 21 (SD)a
SHAPS (SD)a
Female (%)b
Affective diagnosis
Major depression (%)b
Dystymia (%)b
Double depression (%)b/c
46 (11.85)
15.56 (2.12)
25.50 (7.54)
18.40 (5.02)
42.93 (6.80)
16 (53.3)
43.96 (12.85)
14.92 (2.18)
2.00 (3.09)
1.07 (1.41)
49.29 (4.27)
15 (53.6)
P ¼ 0.53
P ¼ 0.26
P < 0.001
P < 0.001
P < 0.001
P ¼ 0.60
13 (43.3)
4 (13.3)
13 (43.3)
Note: aData expressed as mean, standard deviation (SD) and P value resulting from two-sample
t-test.
b
Data expressed as number, percentage and P value resulting from 2 test.
c
DSM-IV TR criteria of major depression and dysthymia.
BDI II, Beck’s Depression Inventory Second Edition 21 Items; HAM-D 21, Hamilton Depression Scale
21 Items; SHAPS, Snaith Hamilton Pleasure Scale.
done in order to achieve comparable wins and losses across subjects, to
ensure positive and negative PEs, and to update predictions. Each
condition was presented in 20 trials in randomised order. The experiment was run using the Presentation software package version 14.2
(Neurobehavioral Systems, Albany, CA, http://www.neurobs.com).
fMRI data acquisition
Before the fMRI session, all participants completed a practice session of
the task. Whole-brain fMRI images of the participants were acquired
using a 3T Magnetom TRIO whole body MR-scanner (Siemens
Medical Solutions, Erlangen, Germany) equipped with a standard
12-channel head coil. A gradient-echo echo planar imaging (EPI) sequence (protocol parameters: TR ¼ 2700 ms; TE ¼ 27 ms; matrix
size ¼ 96 96; field of view ¼ 220 220 mm2; flip angle ¼ 908,
GRAPPA PAT 2) was used to record 658 functional volumes. Each
volume consisted of 40 axial slices (slice thickness ¼ 2.3 mm;
gap ¼ 0.7 mm) measured in descending slice order and positioned
along the line from the anterior to the posterior commissure
(AC–PC orientation). An automated high-order shimming technique
was used to maximise magnetic field homogeneity.
Data analysis
Reaction times
Our adaptive algorithm for reaction thresholds allowed habitually slow
participants to achieve success during the task although their RTs may
have exceeded RTs of comparably fast participants. To test for differences in RTs with regard to group and condition, RTs were analysed
using repeated measures analyses of variance (RM ANOVA) with
group (depressed vs healthy) as between-subjects and condition
(high gains, low gains, high losses, low losses and control condition)
as within-subjects factor. Significant main or interaction effects were
analysed by post hoc t-tests. Moreover, two-sample t-tests were applied
to properly test for group differences in the final thresholds for sufficiently fast reactions towards gains (low and high gains combined) and
losses (low and high losses combined). To test whether the adaptive
algorithm was effective, performance measures other than RTs and
thresholds were analysed by running two sample t-tests for the
number of fast/slow reactions, reward trials ending in gains/no gains,
loss trials ending in losses/no losses, gross money won or lost during
the task and total money won. We additionally tested for group differences in response omissions. Statistical significance was accepted at
P < 0.05, two-tailed. In case of violation of sphericity, which was tested
Altered neural reward and loss processing
SCAN (2015)
1105
Fig. 1 Reward paradigm. Trials began with the visual presentation of different incentive cues which predicted potential monetary outcomes (gains/losses) with either low ( 0.2 E) or high ( 2.0 E)
magnitudes. Trial outcome depended on the subject’s response (button press) to a flash light that appeared after cue offset. The individual response time threshold was adaptively determined.
by Mauchly’s test, we used the Greenhouse-Geisser corrections.
Levene’s test was used to assess the equality of variances between
samples. Analyses were performed using SPSS (Vs. 18; SPSS Inc.,
Chicago, IL).
fMRI data processing
fMRI volumes were analysed using SPM5 (http://www.fil.ion.ucl.ac.uk/
spm/software/spm5/) implemented in MATLAB R2006b (The
MathWorks Inc., Natick, MA). After discarding the first four volumes
to account for T1-saturation effects, images were realigned to the fifth
volume by minimising the mean square error (rigid body transformation). Participants with motion estimates exceeding 3.0 mm and 28
were excluded from the analyses. Images were slice time corrected to
reference slice 20 and normalised to the standard space of the Montreal
Neurological Institute using the EPI template provided by SPM5. The
voxel size was set to 3.0 mm3. To reduce spatial noise (and allow for
corrected statistical inference), the volumes were smoothed with a 6.0
mm3 Gaussian kernel.
Pre-processed data were subjected to a first level fixed effects analysis
to separately determine gain- and loss-related neural responses for each
participant. An event-related model-based analysis was implemented
using the general linear model to estimate parameters for the different
conditions. BOLD responses were modelled as a canonical haemodynamic response function and convolved with the stimulus onset
resulting in 17 regressors for all conditions. Additionally, one task
reaction parameter, which describes the onsets of the flash light, and
six realignment motion parameters (3 translations/rotations) were
included as condition-specific nuisance covariates, removing flash
light- and movement-related signal changes that might be correlated
with the experimental design. For statistical analyses, the fMRI time
series were high-pass filtered (temporal cut off: 128 s) to remove
baseline drifts and corrected for serial autocorrelations using firstorder autoregressive functions AR (1). Contrast images for each magnitude of reward and loss during anticipation and outcome were calculated for each voxel, including high-reward anticipation vs control
condition, low-reward anticipation vs control condition, high loss anticipation vs control condition, low loss anticipation vs control condition, high-reward outcome vs control condition, low-reward outcome
vs control condition, high loss outcome vs control condition and low
loss outcome vs control condition.
In a next step, second-level random effects analyses were conducted.
First, to check whether the paradigm had activated reward and lossassociated brain regions, individual contrast images of the parameter
estimates for healthy controls and depressed subjects were submitted to
group-level random-effects analyses. Therefore, we used one-sample
t-tests, in which whole-brain comparisons were computed to identify
task-specific responses that should match brain regions as established
in previous studies (e.g. Pizzagalli et al., 2009; Smoski et al., 2011). All
tests were set to a threshold of P < 0.05, accounting for multiple comparisons (family-wise error rate corrected; FWE). Activation clusters
were classified as significant activations with minimum values of
z > 2.58 (cf. Lieberman and Cunningham, 2009). Second, comparisons
between groups were performed using two-sample t-tests for contrast
images involving high-/low-reward anticipation vs control condition,
high/low loss anticipation vs control condition, high-/low-reward
outcome vs control condition and high/low loss outcome vs control
condition. We created a frontal and a striatal mask which we applied
for voxel-wise FWE-corrected region of interest (ROI) analyses
(cf. Poldrack, 2007). These masks were based on repeated findings
from neuroimaging studies on reward and loss processing in depressed
individuals, remitted depressed individuals and individuals at high risk
for depression. The masks comprised frontal and striatal regions
that reached significance in at least two of the studies summarised in
1106
SCAN (2015)
B. Ubl et al.
Fig. 2 Panels A–C show figures of axial (A), coronal (B) and sagittal (C) brain sections that correspond to the shape and location of the frontal mask (blue coloured), including the dlPFC, OFC, mPFC and ACC,
and striatal mask (pink coloured), including the caudate nucleus and putamen. Regions of interest were specified by mask files supplied by the Wake Forest University PickAtlas v2.0 (Tzourio-Mazoyer et al.,
2002; Maldjian et al., 2003). The masks were overlaid on a structural template image provided by MarsBaR (Matthew et al., 2002).
Table 1. The frontal mask included four frontal ROIs, i.e. the dlPFC
(Forbes et al., 2009; Smoski et al., 2009), mPFC (Knutson et al., 2008;
Forbes et al., 2009; Gotlib et al., 2010), OFC (McCabe et al., 2009,
2012; Smoski et al. 2011; Dichter et al., 2012) and ACC (Knutson et al.,
2008; Smoski et al., 2009, 2011; McCabe et al., 2009, 2012; Gotlib et al.,
2010; Smoski et al., 2011; Dichter et al., 2012). The striatal mask
comprised two basal ganglia structures, i.e. the caudate nucleus
(Forbes et al., 2009; McCabe et al., 2009; Pizzagalli et al., 2009;
Smoski et al., 2009; Gotlib et al., 2010; Stoy et al., 2012) and putamen
(including the NAcc) (McCabe et al., 2009; Pizzagalli et al., 2009;
Gotlib et al., 2010; Stoy et al., 2012). Figure 2 depicts the ROIs
which were combined into the frontal and striatal masks. The significance level was set at P < 0.05 (FWE-corrected). ROIs were specified by
mask files derived from the automated anatomical labelling in the
Wake Forest University PickAtlas v2.0 (Tzourio-Mazoyer et al., 2002;
Maldjian et al., 2003). Moreover, peak activations were extracted from
ROIs that reached significance. Maximum peak activations were then
compared using follow-up group-by-condition ANOVA with magnitude (E0.20, E2.00) as within- and diagnostic group (healthy controls,
depressed individuals) as the between-subjects factor.
In order not to miss regions emerging outside our frontal and striatal ROIs whole brain corrected analyses with P < 0.05 FWE were performed for all contrasts of each magnitude of reward and loss
anticipation and outcome vs control condition. These results are
reported in the Supplementary Material.
Brain activation and hedonic capacity
At the single-subject level, we extracted beta weights from ROIs that
reached significance during second-level analyses of the anticipation
and outcome conditions, to test for the degree of association between
hedonic capacity measured by the SHAPS and reward- and loss-related
brain activation. The extracted beta weights represent the magnitude of
activation for each significant ROI. Here, we calculated group-wise
Pearson’s partial correlation coefficients separately for each group to
allow for statistical non-independence (P < 0.05; two-tailed) (Poldrack
and Mumford, 2009). We controlled for depression severity measured
by the HAM-D.
Prediction error
In a second single-subject model, 4 regressors of interest and 7 additional regressors (1 task reaction parameter and 6 motion parameters)
were defined including the anticipation phase for all conditions, all
gain and all loss outcomes combined, and the control condition as 0th
order regressors. For each outcome regressor (gains and losses), a respective first order parameter was inserted modelling gain- and lossrelated outcomes in a parametric linear trend by using PE values as
parameter inputs (Abler et al., 2006). To keep orthogonality to the
main outcome regressors, modulation regressors were mean-corrected
by SPM5.
Gain- and loss-related expected values (EVs) were calculated as
EV ¼ m*P (m ¼ magnitude, P ¼ probability) to estimate positive and
negative gain- and loss-related PE values (PE ¼ R – EV; with R ¼ actual
outcome) for each subject (Staudinger et al., 2009). EVs were modelled
with individual trial-by-trial probabilities (P), which followed from the
RT windows adaptively tailored to the individual response times
during the task. For this purpose, we started with a probability of
P ¼ 0.50 for the first event for all participants equally. As a function
of subjects’ performance after the first trial, the probability for the next
trial was set up with a 10% decrease when the subject responded fast
and with a 5% increase when he or she performed poorly. This algorithm was applied to all trials, terminating at the final trial of the task.
We obtained two parameters, one for low and high gains and one for
low and high losses. These parameters were included in the statistical
model in order to modulate the data of combined gains and losses at
the time of their reception (cf. Metereau and Dreher, 2013). ROI analyses focussed on amygdala, hippocampus, thalamus, VTA, striatum,
especially the VS [caudate nucleus, olfactory tubercle (OT), ventromedial parts of the caudate nucleus and putamen], ACC and PFC
(OFC and dlPFC), derived from the Wake Forest University
PickAtlas v2.0 (Tzourio-Mazoyer et al., 2002; Maldjian et al., 2003).
By using one-sample t-tests, ROI analyses determined regions that
encode PEs for each group separately. We additionally tested ROIs
for group differences using two-sample t-tests; P < 0.05, FWEcorrected.
RESULTS
Behavioural data: RTs
Two sample t-tests showed that the adaptive algorithm of the reward
paradigm performed adequately since our groups did not significantly
differ in the percentages of fast/slow reactions and gains and losses
(Supplementary Table S1).
The RM ANOVA revealed a significant effect of condition
[F(1,56) ¼ 27.72, P < 0.001] and a marginally significant effect of
group [F(1,56) ¼ 3.45, P ¼ 0.07]. The interaction of group x condition
Altered neural reward and loss processing
[F(1,56) ¼ 0.63, P ¼ 0.56] was not significant. Paired t-tests showed
that the participants were faster during all gain (Mgain ¼ 225.05,
SD ¼ 46.29) and loss conditions (Mloss ¼ 225.02, SD ¼ 40.42) compared with the control condition (Mcontrol ¼ 303.21, SD ¼ 90.92);
tgain vs control(57) ¼ 6.79, P < 0.001 and tloss vs control(57) ¼ 7.22,
P < 0.001, and during the high (Mhigh gain ¼ 212.85, SD ¼ 51.00) compared with the low-gain condition (Mlow gain ¼ 237.25, SD ¼ 53.36);
thigh vs low gain(57) ¼ 3.85, P < 0.001. Depressed individuals
(Mdepressed ¼ 22.96, SD ¼ 55.25) compared with healthy controls
(Mhealthy controls ¼ 209.39, SD ¼ 33.34) showed marginally slower RTs
in the high-loss condition; t(56) ¼ 1.70, P ¼ 0.09.
Moreover, there were no significant group differences in the final
thresholds for sufficiently fast reactions for gains (Mdepressed ¼ 214.68,
SD ¼ 45.67, Min ¼ 150.73, Max ¼ 348.99; Mhealthy controls ¼ 205.04,
SD ¼ 22.53, Min ¼ 164.78, Max ¼ 265.09); t(42.96) ¼ 1.03, P ¼ 0.31,
SD ¼ 46.93,
Min ¼ 150.55,
and
losses
(Mdepressed ¼ 212.13,
Max ¼ 347.37; Mhealthy controls ¼ 202.10, SD ¼ 22.48, Min ¼ 165.28,
Max ¼ 260.78); t(42.27) ¼ 1.05, P ¼ 0.30.
fMRI data: neural activation in ROIs for depressed
individuals vs healthy controls
Reward/loss anticipation and outcome in the healthy control group
revealed the typical pattern usually observed in reward tasks including
all ROIs which are known to be involved in the processing of reward
and loss (Supplementary Table S2). Depressed individuals showed activation in fewer ROIs typically related to the processing of reward and
loss, especially during reward and loss anticipation (Supplementary
Table S3). Within group results of PE encoding are shown in the
Supplementary Table S4. In the following we report results for the
between group comparisons for the frontal and striatal mask.
Anticipation: high/low reward vs control condition
We identified fronto-striatal regions reflecting significantly decreased
activity during high reward anticipation in depressed subjects, including the right VS (i.e. NAcc) (x ¼ 9, y ¼ 18, z ¼ 6, P ¼ 0.045, z ¼ 3.52),
right middle OFC (x ¼ 33, y ¼ 51, z ¼ 3, P ¼ 0.046, z ¼ 3.88) and left
rostral ACC (rACC) (x ¼ 9, y ¼ 33, z ¼ 9, P ¼ 0.048, z ¼ 3.82). No
significant group differences in neural activation were found during
low monetary reward anticipation (Figure 3).
In the right NAcc, the ANOVA revealed significant main effects of
magnitude [F(1,56) ¼ 103.56, P < 0.001] and group [F(1,56) ¼ 16.05,
P < 0.001], and a significant group by magnitude interaction
[F(1,56) ¼ 5.46, P < 0.03]. This interaction was due to significantly
decreased activation in depressed individuals vs healthy controls in
response to high-gain anticipation but not to low-gain anticipation
(Figure 3).
In the left OFC, significant main effects of magnitude
[F(1,56) ¼ 44.59, P < 0.001] and group [F(1,56) ¼ 4.55, P < 0.04] were
qualified by a significant group by magnitude interaction
[F(1,56) ¼ 4.27, P < 0.05]. Compared with healthy controls, depressed
subjects showed significantly weaker responses to anticipated high
gains but not to low gains.
In the left rACC, the ANOVA yielded significant main effects of
magnitude [F(1,56) ¼ 41.30, P < 0.001, Z2p ¼ 0.42] and group
[F(1,56) ¼ 7.27, P < 0.01] but no significant interaction of group by
magnitude [F(1,56) ¼ 1.81, P ¼ 0.18].
Anticipation: high/low loss vs control condition
Group contrasts for high-loss anticipation revealed significantly
reduced activity in depressed subjects compared with healthy controls
in the left rACC (x ¼ -6, y ¼ 36, z ¼ 6, P ¼ 0.038, z ¼ 3.94). Again,
SCAN (2015)
1107
there were no significant group differences in activation during lowloss anticipation.
In the left rACC, the ANOVA revealed a significant main effect of
magnitude [F(1,56) ¼ 14.09, P < 0.001], no significant main effect of
group [F(1,56) ¼ 1.93, P ¼ 0.17], and a significant group by magnitude
interaction [F(1,56) ¼ 4.88, P < 0.03] (Figure 3). Hence, group differences in the left rACC were specific to high loss but not to low loss
incentives.
Outcome: high/low reward vs control condition
No significant group differences in activations during high and low
gain outcomes were identified.
Outcome: high/low loss vs control condition
No significant group differences in activations were found during high
and low loss outcomes.
Brain activation and hedonic capacity
Correlational analyses of individual beta weights from statistically significant ROIs with hedonic capacity (SHAPS) revealed that in depressed individuals, hedonic capacity was positively correlated with
activity in the OFC (rpart ¼ 0.48, df ¼ 27, P < 0.01) and marginally positively correlated with NAcc activity (rpart ¼ 0.32, df ¼ 27, P ¼ 0.07)
during high reward anticipation. In healthy controls, SHAPS scores
were positively correlated with activity in the NAcc (rpart ¼ 0.46,
df ¼ 22, P < 0.05) during high reward anticipation (Figure 3).
PE signalling: between group results
Group comparisons showed significantly increased activation for
reward-related PE-signals in the right rACC (x ¼ 9, y ¼ 45, z ¼ 24,
P ¼ 0.010, z ¼ 4.22) and left amygdala (x ¼ 21, y ¼ 6, z ¼ 12,
P ¼ 0.011, z ¼ 3.50) in healthy controls compared with depressed individuals. For loss-related PE-signal encoding, depressed individuals
compared with healthy controls showed significantly increased brain
activation in the right VS, specifically in the right OT (x ¼ 6, y ¼ 24,
z ¼ 3, P ¼ 0.028, z ¼ 3.21).
DISCUSSION
By investigating a large sample of medication-free depressed individuals, this study provides evidence for a rather homogenous pattern of
blunted fronto-striatal activity during anticipatory processing of
reward and loss in depression. In contrast, behavioural performances
RTs and final thresholds for fast reactions of depressed and healthy
participants were similar, except that depressed individuals were
slightly, but not significantly slower than healthy controls, which was
moreover restricted to responses towards high loss cues. A similar lack
of group differences in behavioural performance has been reported
before (Knutson et al., 2008; Pizzagalli et al., 2009; Smoski et al.,
2009; Stoy et al., 2012). Hypoactivation was apparent only for high
incentive magnitudes, potentially mirroring the effect that reward- and
loss-related brain regions need substantial stimulation to respond
(Abler et al., 2005; Knutson et al., 2005; Yacubian et al., 2006). Our
results also show an absence of reward-related PE encoding in depressed individuals, whereas loss-related PEs were associated with
increased neural activity in the right VS in this group.
Brain activity during reward and loss anticipation
Depressed individuals showed reduced neural activity during both
reward and loss anticipation. Reduced activity in the NAcc (as part
of the VS), OFC and rACC underpin previous findings for frontostriatal hypoactivation during reward anticipation in depressed
1108
SCAN (2015)
B. Ubl et al.
Fig. 3 Panels A and C depict significantly reduced activation of fronto-striatal regions in depressed individuals compared with healthy controls during high gain anticipation (A) and in the rostral part of the
anterior cingulate cortex during high loss anticipation (C). The colour scale represents t-scores. Panel B depicts the partial correlation between the SHAPS (higher total SHAPS scores indicate higher levels of
hedonic capacity) and the maximal peak activation (beta weights) for the orbitofrontal cortex (R2¼0.184) and the nucleus accumbens (R2¼0.023), adjusted for depression severity measured by the HAM-D
scale.
individuals and individuals in high risk for depression (Forbes et al.,
2009; Pizzagalli et al., 2009; Smoski et al., 2009; Tung et al., 2009;
Gotlib et al., 2010; Smoski et al., 2011; Stoy et al., 2012; McCabe
et al., 2012). In our study, hypoactivation in the NAcc and OFC was
specific for high incentive rewards but not for low rewards, whereas
results of the ANOVA indicate that reduced activity of the rACC in
depressed subjects applied to both reward magnitudes. Notably, several
fMRI studies report blunted VS activation in depressed compared with
non-depressed individuals during processing of reward incentives
(Forbes et al., 2009; Smoski et al., 2011; Stoy et al., 2012). Therefore,
our results give further evidence of dysfunctional VS activity during
anticipatory reward processing in depression, which appears to represent a rather robust finding (Forbes et al., 2009; Tung et al., 2009; Stoy
et al., 2012). The striatum and the OFC, particularly the medial OFC,
are presumed key structures in a complex neural reward network
mediating various aspects of reward processing, which can be summarised as translation of reward information into suitable goal-directed
behaviour (Kohls et al., 2012). According to incentive salience theory,
the NAcc represents a motor-limbic interface, which is linked to
reward-related motivation by attributing incentive salience to reward
incentive cues (Berridge, 2007). Thereby, incentive salience describes
the intrinsic motivation to achieve reward and has been referred to as
‘wanting’. In this context, ‘wanting’ is perceived as an active state
during reward processing in which an incoming stimulus is attached
with attractiveness and the importance for pursuit (Kohls et al., 2012).
Moreover, animal studies identified separate opioid mechanisms
within the medial shell of the NAcc controlling ‘wanting’ and ‘liking’
in rats. Whereas a cubic-millimetre in the rostrodorsal quadrant of the
medial NAcc shell has been found to contain an opioid hedonic hotspot that contributes to ‘liking’, other subregions of the medial NAcc
shell are reported to generate ‘wanting’ (Peciña and Berridge, 2005;
Castro and Berridge, 2014). However, taking these studies into consideration, it has to be acknowledged that, with the spatial resolution
(3 mm) in our study, we cannot rule out that our significant NAcc
cluster also involves neurons coding for ‘liking’ (see Castro and
Berridge, 2014). Nevertheless, during anticipatory reward processing,
the NAcc together with the OFC is most likely involved in motivation
regulation, and activation of the NAcc sustains appetitive motivation
for gaining reward (Delgado et al., 2000; Der-Avakian and Markou,
2012; Warner-Schmidt et al., 2012; Kumar et al., 2014). The OFC
specifically plays a critical role in computational and cognitive
processing by signalling the relative and affective value of reinforcers
Altered neural reward and loss processing
during reward anticipation (Schoenbaum et al., 2011; Der-Avakian and
Markou, 2012). Embedded in the neural reward network, the ACC
receives information from the OFC about the reward value and computes the effort that is required for goal-directed behaviour. The NAcc
then provides motivational ‘wanting’ and appropriately regulates motivation to initiate goal-related actions processed in prefrontal areas.
Our finding that depressed individuals show a significant positive association of hedonic capacity with increased incentive responsivity
within the OFC and a marginally significant positive association with
activity in the NAcc support the assumption that the reduced motivational ‘wanting’ in depressed individuals is related to deficits in hedonic processing (cf. Sherdell et al., 2012). Moreover, associations
between subjective hedonic capacity and reward-related brain activity
could not be explained by concurrent depression levels. These findings
are largely in line with results of a neuroimaging study in which anhedonia severity, but not depressive symptomatology in general, was
negatively related to VS activity during the processing of monetary
reward (Wacker et al., 2009).
Significant activity decreases during loss anticipation in depressed
individuals were restricted to the rACC. The rACC comprises the anterior and ventral parts of the ACC and is involved in executive and
emotional functions (Bush et al., 2000). It is regarded as central node
in a cognitive-affective neural network that is involved in assessing the
need for behavioural adaptations, especially after expectations have
been violated (Walton et al., 2002; Eisenberger et al., 2003; Walton
et al., 2003; Luu and Peterson, 2004). In particular, key functions of the
rACC include the allocation of attention to emotional information and
to regulate affective responses related to internal and external stimuli
(Bush et al., 2000; Rushworth, 2008; Pizzagalli et al., 2011).
Hypoactivity of the rACC in depression has been found before
(Diener et al., 2012), e.g. in tasks addressing the appraisal of emotional
stimuli (Gotlib et al., 2005; Guyer et al., 2012). Thus, reduced activation of this region in depressed individuals might point to lower affective regulation and attention allocation, probably resulting in
dysfunctional behavioural adaptation in situations where the delivery
of a loss is expected.
Therefore, our finding of blunted anticipatory neural activity in
depressed individuals highlights the importance of fronto-striatal
structures for approach motivation (‘wanting’) in depression. In this
context, depressed individuals might have a reduced potential to compute the value of reward stimuli and to initiate goal-directed behaviour
to cues that are predictive for reward-related outcomes. Moreover,
depressed individuals also seem to regulate affective responses to
anticipated losses differently compared with non-depressed individuals. These alterations in the processing of incentive salience and affective response regulation fit well with the pathological reactivity to
incentive stimuli mirrored by depressive symptoms such as loss of
motivation and decreased approach behaviour (Sherrat and Macleod,
2013).
Brain activity during reward and loss outcome
We did not identify altered fronto-striatal activity during reward and
loss outcome in depressed individuals. This result is in line with a
study of Stoy et al. (2012) who found similar dysfunctions during
the anticipation but not during the receipt of reward. However, several
studies using reward paradigms identified alterations in fronto-striatal
regions in depressed individuals during the outcome phase (Knutson
et al., 2008; Forbes et al., 2009; Pizzagalli et al., 2009; Smoski et al.,
2009). Differences in paradigms might play a role for the heterogeneous results. For example, Forbes et al. (2009) employed a modified
version of a card guessing task and Smoski et al. (2009) used the Wheel
of Fortune (Shad et al., 2011). Both tasks have been established in
SCAN (2015)
1109
investigating reward- and loss-related decision making, and chances
of gaining and losing are based on probabilities that are computed
before choice selection. In contrast, gaining and losing money in our
task depended on the RT of the participants. More similar to our
reward task is the Monetary Incentive Delay task (MID) (e.g.
Pizzagalli et al., 2009), in which chances of winning and losing are
based on the participants’ performance in the task. The MID was
used in the study of Pizzagalli et al. (2009) in which monetary gains
ranged from $1.96 to $2.34 and monetary losses from $1.81 to
$2.19. In this investigation, incentive cues during the anticipation
phase did not signal different outcome magnitude but only outcome
valence, and the participants were not informed about the varying
magnitudes until performance feedback was presented. Thus, the
MID task in the study by Pizzagalli et al. (2009) may have been
more sensitive in mapping outcome processing whereas our task has
been shown to be particularly reliable in assessing neural processing of
reward anticipation (Plichta et al., 2012). Furthermore, in the study by
Pizzagalli et al. (2009) the probability of winning or avoiding to lose
money was balanced out by chance (i.e. 50%). In contrast, the probability of successful trials in our study was 65% (Supplementary
Table S1). For outcome predictability, it was found that striatal regions
are maximally responsive to unpredictable rewards, i.e. when reward is
delivered in 50% of the trials (Berns et al., 2001; Tricomi et al., 2004).
Taken together, the present body of evidence suggests that depressed
individuals show dysfunctions both during reward anticipation and
outcome. The observed heterogeneity in study results may possibly
be traced back to the fact that different experimental reward paradigms
vary in their sensitivity to map different phases of neural reward
processing.
Reward- and loss-related PE signals
The attribution of incentive salience to outcome-related cues has been
suggested as a crucial co-process of associative learning (Berridge,
2007; Esber and Haselgrove, 2011). Accordingly, impaired hedonic
capacity and motivational ‘wanting’ in depressed individuals may
negatively affect the processing of cue-outcome associations as well
as the coding of PE-signals (Kumar et al., 2008).
In our study, modelling of neural activity during reward and loss
outcomes as a linear function of transient reward and loss PE-signals
revealed blunted or absent (Supplementary Table S4) reward-related
PE-signals and enhanced loss-related PE-signals in depressed individuals. Our results on reward-related PE encoding corroborate previous
findings of abnormal appetitive PE-signal coding in depression, where
reward-related PE-signals were also associated with reduced rACC activity in depressed individuals (Kumar et al., 2008). Our findings of
increased neural responses to loss-related PEs provide new insights on
associative learning in depression suggesting that, in depressed individuals, reward- and loss-related PEs may be represented in an opposite manner. This is in line with the literature providing striking
evidence for reduced responsiveness to rewards and an enhanced sensitivity to punishments in depression (Diener et al., 2009a,b; Eshel and
Roiser, 2010).
When compared with healthy controls, depressed individuals coded
loss-related PE signals in the right VS, namely the right OT. Although
the OT is functionally heterogeneous, the medial OT is conceptualised
as part of the VS, thus playing a prominent role in the DA reward
circuitry (Ikemoto, 2007). Research on the OT has often focussed on
its function in motivation and reward-guided behaviour (Heimer,
2003; Ikemoto, 2007). However, the VS has been shown to be involved
in PE computation regardless of the valence and nature of stimuli (cf.
Seymour et al., 2007) suggesting its more general role for processing
PEs that are assigned to salience (Berridge, 2007; Garrison et al., 2013).
1110
SCAN (2015)
As salience describes the state of a stimulus outbalancing competing
stimuli, thereby reflecting motivational processes, our results suggest
that loss-related learning of stimulus-response-outcome associations in
depression might be biased by increased salience attribution to stimuli
indicating type II punishments (Berridge, 2007; Jensen et al., 2007).
Finally, increased PE signalling may manifest itself as an enhanced
ability to bias action selections and avoidance behaviour in loss-related
events (Garrison et al., 2013). However, our finding on loss-related PE
encoding in depression needs replication.
A recent study has shown that apart from the striatum, the ACC and
amygdala seem to be cardinal nodes of a saliency network that considers appetitive (reward) and aversive (loss) PEs in a similar vein to
motivationally salient stimuli, albeit predominantly for other types of
reinforcers (Metereau and Dreher, 2013). Whereas ACC activity specifically has been found to signal the need for attention during learning
(Bryden et al., 2011), and attention is primarily attracted by salient (or
alerting) stimuli, several studies emphasise the role of the amygdala in
the detection and evaluation of stimuli that are motivationally significant for behaviour (Sander et al., 2003; Herbert et al., 2009;
Cunningham and Kirkland, 2013). Accordingly, impaired neural
coding of reward-related PEs by depressed individuals in this study
might reflect limited neural resources for processing reward learning
signals that can be traced back to blunted attention and salience processing of appetitive stimuli in depression. Our results furthermore
point to a reduced ability to learn stimulus-reward associations, possibly leading to significant alterations in approach behaviour in depression (McClure et al., 2004; den Ouden et al., 2012).
Limitations
A limitation of this study is that our paradigm targets brain regions
involved in the processing of reward and type II punishments but not
primary type I punishments. To examine punishment processing and
associated PE signalling in depression, future studies should also consider the application of aversive stimuli (e.g. pain).
CONCLUSIONS
This study provides evidence for blunted neural activation in frontal
and striatal regions during reward and loss anticipation in depression,
which is largely in line with other studies investigating medication-free
acutely depressed individuals. A novel finding of our study is a homogenous pattern of hypoactivity in the NAcc and OFC structures indicating limited motivational (‘wanting’) responses to reward cues.
Moreover, reduced activity in these regions was associated with a
lack of subjective hedonic capacity. During loss processing, we found
decreased rACC activation suggesting that depressed individuals show
dysfunctions in regulating affective responses to anticipated loss.
Regarding PE encoding, we identified reduced reward-related PE-signals in the amygdala and rACC and increased loss PE-signals within
the VS in depressed individuals. These results point to blunted rewardand enhanced loss-related associative learning in depression.
SUPPLEMENTARY DATA
Supplementary data are available at SCAN online.
Conflict of Interest
None declared.
REFERENCES
Abler, B., Walter, H., Erk, S. (2005). Neural correlates of frustration. Neuroreport, 16(7),
669–72.
B. Ubl et al.
Abler, B., Walter, H., Erk, S., Kammerer, H., Spitzer, M. (2006). Prediction error as a linear
function of reward probability is coded in human nucleus accumbens. Neuroimage,
31(2), 790–5.
Beck, A.T., Steer, R.A., Ball, R., Ranieri, W. (1996). Comparison of Beck Depression
Inventories -IA and -II in psychiatric outpatients. Journal of Personality Assessment,
67(3), 588–97.
Berns, G.S., McClure, S.M., Pagnoni, G., Montague, P.R. (2001). Predictability modulates
human brain response to reward. Journal of Neuroscience, 21(8), 2793–8.
Berridge, K.C. (2007). The debate over dopamine’s role in reward: the case for incentive
salience. Psychopharmacology, 191(3), 391–431.
Berridge, K.C., Kringelbach, M.L. (2008). Affective neuroscience of pleasure: reward in
humans and animals. Psychopharmacology, 199(3), 457–80.
Berridge, K.C., Robinson, T.E. (1998). What is the role of dopamine in reward: hedonic
impact, reward learning, or incentive salience? Brain Research. Brain Research Reviews,
28(3), 309–69.
Boksem, M.A., Tops, M., Kostermans, E., De Cremer, D. (2008). Sensitivity to punishment
and reward omission: evidence from error-related ERP components. Biological
Psychology, 79(2), 185–92.
Bryden, D.W., Johnson, E.E., Tobia, S.C., Kashtelyan, V., Roesch, M.R. (2011). Attention
for learning signals in anterior cingulate cortex. Journal of Neuroscience, 31(50),
18266–74.
Bush, G., Luu, P., Posner, M.I. (2000). Cognitive and emotional influences in anterior
cingulate cortex. Trends in Cognitive Sciences, 4(6), 215–22.
Castro, D.C., Berridge, K.C. (2014). Opioid hedonic hotspot in nucleus accumbens shell:
mu, delta, and kappa maps for enhancement of sweetness “liking” and “wanting”.
Journal of Neuroscience, 34(12), 4239–50.
Chase, H.W., Frank, M.J., Michael, A., Bullmore, E.T., Sahakian, B.J., Robbins, T.W.
(2010). Approach and avoidance learning in patients with major depression and healthy
controls: relation to anhedonia. Psychological Medicine, 40(3), 433–440.
Cunningham, W.A., Kirkland, T. (2013). The joyful, yet balanced, amygdala: moderated
responses to positive but not negative stimuli in trait happiness. Social Cognitive and
Affective Neuroscience, 9(6), 760–6.
Delgado, M.R., Nystrom, L.E., Fissell, C., Noll, D.C., Fiez, J.A. (2000). Tracking the hemodynamic responses to reward and punishment in the striatum. Journal of
Neurophysiology, 84(6), 3072–7.
den Ouden, H.E., Kok, P., de Lange, F.P. (2012). How prediction errors shape perception,
attention, and motivation. Frontiers in Psychology, 3, 548.
Der-Avakian, A., Markou, A. (2012). The neurobiology of anhedonia and other rewardrelated deficits. Trends in Neurosciences, 35(1), 68–77.
Dichter, G.S., Kozink, R.V., McClernon, F.J., Smoski, M.J. (2012). Remitted major depression is characterized by reward network hyperactivation during reward anticipation and
hypoactivation during reward outcomes. Journal of Affective Disorders, 136(3), 1126–34.
Diener, C., Kuehner, C., Brusniak, W., Struve, M., Flor, H. (2009a). Effects of stressor
controllability on psychophysiological, cognitive and behavioural responses in patients
with major depression and dysthymia. Psychological Medicine, 39(1), 77–86.
Diener, C., Kuehner, C., Brusniak, W., Ubl, B., Wessa, M., Flor, H. (2012). A meta-analysis
of neurofunctional imaging studies of emotion and cognition in major depression.
Neuroimage, 61(3), 677–85.
Diener, C., Struve, M., Balz, N., Kuehner, C., Flor, H. (2009b). Exposure to uncontrollable
stress and the postimperative negative variation (PINV): prior control matters. Biological
Psychology, 80(2), 189–95.
Dillon, D.G., Deveney, C.M., Pizzagalli, D.A. (2011). From basic processes to real-world
problems: how research on emotion and emotion regulation can inform understanding
of psychopathology, and vice versa. Emotion Review, 3(1), 74–82.
Dillon, D.G., Holmes, A.J., Jahn, A.L., Bogdan, R., Wald, L.L., Pizzagalli, D.A. (2008).
Dissociation of neural regions associated with anticipatory versus consummatory
phases of incentive processing. Psychophysiology, 45(1), 36–49.
Dunlop, B.W., Nemeroff, C.B. (2007). The role of dopamine in the pathophysiology of
depression. Archives of General Psychiatry, 64(3), 327–37.
Eisenberger, N.I., Lieberman, M.D., Williams, K.D. (2003). Does rejection hurt? An FMRI
study of social exclusion. Science, 302(5643), 290–2.
Elliott, R., Friston, K.J., Dolan, R.J. (2000). Dissociable neural responses in human reward
systems. Journal of Neuroscience, 20, 6159–65.
Epstein, J., Pan, H., Kocsis, J.H., et al. (2006). Lack of ventral striatal response to positive
stimuli in depressed versus normal subjects. American Journal of Psychiatry, 163(10),
1784–90.
Esber, G.R., Haselgrove, M. (2011). Reconciling the influence of predictiveness and uncertainty on stimulus salience: a model of attention in associative learning. Proceedings of
the Royal Society B: Biological Sciences, 278(1718), 2553–61.
Eshel, N., Roiser, J.P. (2010). Reward and punishment processing in depression. Biological
Psychiatry, 68(2), 118–24.
First, M.B., Spitzer, R.L., Gibbon, M., Williams, J.B.W. (1996). Structured Clinical Interview
for DSM-IV Axis I Disorders, Research Version (SCID-I). Washington, DC: American
Psychiatric Press.
Forbes, E.E., Hariri, A.R., Martin, S.L., et al. (2009). Altered striatal activation predicting
real-world positive affect in adolescent major depressive disorder. American Journal of
Psychiatry, 166(1), 64–73.
Altered neural reward and loss processing
Garrison, J., Erdeniz, B., Done, J. (2013). Prediction error in reinforcement learning: a
meta-analysis of neuroimaging studies. Neuroscience and Biobehavioral Reviews, 37(7),
1297–310.
Gotlib, I.H., Hamilton, J.P., Cooney, R.E., Singh, M.K., Henry, M.L., Joormann, J. (2010).
Neural processing of reward and loss in girls at risk for major depression. Archives of
General Psychiatry, 67(4), 380–7.
Gotlib, I.H., Sivers, H., Gabrieli, J.D., et al. (2005). Subgenual anterior cingulate activation
to valenced emotional stimuli in major depression. Neuroreport, 16(16), 1731–4.
Gradin, V.B., Kumar, P., Waiter, G., et al. (2011). Expected value and prediction error
abnormalities in depression and schizophrenia. Brain, 134(Pt 6), 1751–64.
Guyer, A.E., Choate, V.R., Pine, D.S., Nelson, E.E. (2012). Neural circuitry underlying
affective response to peer feedback in adolescence. Social Cognitive and Affective
Neuroscience, 7(1), 81–92.
Hamilton, M. (1960). A rating scale for depression. The Journal of Neurology, Neurosurgery,
and Psychiatry, 23, 56–62.
Hasler, G., Drevets, W.C., Manji, H.K., Charney, D.S. (2004). Discovering endophenotypes
for major depression. Neuropsychopharmacology, 29(10), 1765–81.
Heimer, L. (2003). A new anatomical framework for neuropsychiatric disorders and drug
abuse. American Journal of Psychiatry, 160(10), 1726–39.
Herbert, C., Ethofer, T., Anders, S., et al. (2009). Amygdala activation during reading of
emotional adjectives–an advantage for pleasant content. Social Cognitive and Affective
Neuroscience, 4(1), 35–49.
Ikemoto, S. (2007). Dopamine reward circuitry: two projection systems from the ventral
midbrain to the nucleus accumbens-olfactory tubercle complex. Brain Research Reviews,
56(1), 27–78.
Jensen, J., Smith, A.J., Willeit, M., et al. (2007). Separate brain regions code for salience vs.
valence during reward prediction in humans. Human Brain Mapping, 28(4), 294–302.
Keedwell, P.A., Andrew, C., Williams, S.C., Brammer, M.J., Phillips, M.L. (2005). The
neural correlates of anhedonia in major depressive disorder. Biological Psychiatry,
58(11), 843–53.
Kirsch, P., Schienle, A., Stark, R., et al. (2003). Anticipation of reward in a nonaversive
differential conditioning paradigm and the brain reward system: an event-related fMRI
study. Neuroimage, 20(2), 1086–95.
Knutson, B., Bhanji, J.P., Cooney, R.E., Atlas, L.Y., Gotlib, I.H. (2008). Neural responses to
monetary incentives in major depression. Biological Psychiatry, 63(7), 686–92.
Knutson, B., Taylor, J., Kaufman, M., Peterson, R., Glover, G. (2005). Distributed neural
representation of expected value. Journal of Neuroscience, 25(19), 4806–12.
Kohls, G., Chevallier, C., Troiani, V., Schultz, R.T. (2012). Social ‘wanting’ dysfunction in
autism: neurobiological underpinnings and treatment implications. Journal of
Neurodevelopmental Disorders, 4(1), 10.
Kuehner, C., Diener, C., Ubl, B., Flor, H. (2011). Reproducibility and predictive value of
the post-imperative negative variation during aversive instrumental learning in depression. Psychological Medicine, 41(4), 890–2.
Kumar, P., Berghorst, L.H., Nickerson, L.D., et al. (2014). Differential effects of acute stress
on anticipatory and consummatory phases of reward processing. Neuroscience, 266,
1–12.
Kumar, P., Waiter, G., Ahearn, T., Milders, M., Reid, I., Steele, J.D. (2008). Abnormal
temporal difference reward-learning signals in major depression. Brain, 131(Pt 8),
2084–93.
Lieberman, M.D., Cunningham, W.A. (2009). Type I and Type II error concerns in fMRI
research: re-balancing the scale. Social Cognitive and Affective Neuroscience, 4(4), 423–8.
Luu, P., Peterson, M.S. (2004). The anterior cingulate cortex: regulating actions in contex.
In: Posner, M.I., editor. Cognitive Neuroscience of Attention. New York: Guilford Press.
Maldjian, J.A., Laurienti, P.J., Kraft, R.A., Burdette, J.H. (2003). An automated method for
neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets.
Neuroimage, 19(3), 1233–9.
Martin-Soelch, C. (2009). Is depression associated with dysfunction of the central reward
system? Biochemical Society Transactions, 37(1), 313–17.
Matthew, B., Jean-Luc Anton, R., Valabregue, J.P. (2002). Region of interest analysis using
an SPM toolbox [abstract]. Presented at the 8th International Conference on Functional
Mapping of the Human Brain, June 2–6, Sendai, Japan. Available on CD-ROM in
Neuroimage, 16(2), abstract 497.
McCabe, C., Cowen, P.J., Harmer, C.J. (2009). Neural representation of reward in recovered depressed patients. Psychopharmacology, 205(4), 667–77.
McCabe, C., Woffindale, C., Harmer, C.J., Cowen, P.J. (2012). Neural processing of reward
and punishment in young people at increased familial risk of depression. Biological
Psychiatry, 72(7), 588–94.
McClure, S.M., York, M.K., Montague, P.R. (2004). The neural substrates of reward processing in humans: the modern role of FMRI. Neuroscientist, 10(3), 260–8.
Metereau, E., Dreher, J.C. (2013). Cerebral correlates of salient prediction error for different rewards and punishments. Cerebral Cortex, 23(2), 477–87.
Montague, P.R., McClure, S.M., Baldwin, P.R., et al. (2004). Dynamic gain control of
dopamine delivery in freely moving animals. Journal of Neuroscience, 24(7), 1754–9.
Nakonezny, P.A., Carmody, T.J., Morris, D.W., Kurian, B.T., Trivedi, M.H. (2010).
Psychometric evaluation of the Snaith-Hamilton pleasure scale in adult outpatients
with major depressive disorder. International Clinical Psychopharmacology, 25(6),
328–33.
SCAN (2015)
1111
Nikolova, Y.S., Bogdan, R., Brigidi, B.D., Hariri, A.R. (2012). Ventral striatum reactivity to
reward and recent life stress interact to predict positive affect. Biological Psychiatry,
72(2), 157–63.
Peciña, S., Berridge, K.C. (2005). Hedonic hot spot in nucleus accumbens shell: where do
mu-opioids cause increased hedonic impact of sweetness? Journal of Neuroscience,
25(50), 11777–86.
Pizzagalli, D.A., Dillon, D.G., Bogdan, R., Holmes, A. (2011). Reward and punishment
processing in the human brain: clues from affective neuroscience and implications for
depression research. In: Vartanian, O., Mandel, D., editors. Neuroscience of Decision
Making. New York: Psychology Press, pp. 199–220.
Pizzagalli, D.A., Holmes, A.J., Dillon, D.G., et al. (2009). Reduced caudate and nucleus
accumbens response to rewards in unmedicated individuals with major depressive disorder. American Journal of Psychiatry, 166(6), 702–10.
Plichta, M.M., Schwarz, A.J., Grimm, O., et al. (2012). Test-retest reliability of
evoked BOLD signals from a cognitive-emotive fMRI test battery. Neuroimage, 60(3),
1746–58.
Poldrack, R.A. (2007). Region of interest analysis for fMRI. Social Cognitive and Affective
Neuroscience, 2(1), 67–70.
Poldrack, R.A., Mumford, J.A. (2009). Independence in ROI analysis: where is the voodoo?
Social Cognitive and Affective Neuroscience, 4(2), 208–13.
Rademacher, L., Krach, S., Kohls, G., Irmak, A., Grunder, G., Spreckelmeyer, K.N. (2010).
Dissociation of neural networks for anticipation and consumption of monetary and
social rewards. Neuroimage, 49(4), 3276–85.
Reynolds, S.M., Berridge, K.C. (2002). Positive and negative motivation in nucleus accumbens shell: bivalent rostrocaudal gradients for GABA-elicited eating, taste “liking”/
“disliking” reactions, place preference/avoidance, and fear. Journal of Neuroscience,
22(16), 7308–20.
Robinson, L., Platt, B., Riedel, G. (2011). Involvement of the cholinergic system in conditioning and perceptual memory. Behavioural Brain Research, 221(2), 443–65.
Rushworth, M.F. (2008). Intention, choice, and the medial frontal cortex. Annals of the
New York Academy of Sciences, 1124, 181–207.
Sander, D., Grafman, J., Zalla, T. (2003). The human amygdala: an evolved system for
relevance detection. Nature Reviews Neuroscience, 14(4), 303–16.
Schoenbaum, G., Takahashi, Y., Liu, T.L., McDannald, M.A. (2011). Does the orbitofrontal
cortex signal value? Annals of the New York Academy of Sciences, 1239, 87–99.
Schultz, W. (2010). Dopamine signals for reward value and risk: basic and recent data.
Behavioral and Brain Functions, 6, 24.
Schultz, W., Dayan, P., Montague, P.R. (1997). A neural substrate of prediction and
reward. Science, 275(5306), 1593–9.
Seymour, B., Daw, N., Dayan, P., Singer, T., Dolan, R. (2007). Differential encoding
of losses and gains in the human striatum. The Journal of Neuroscience, 27(18),
4826–31.
Seymour, B., O’Doherty, J.P., Dayan, P., et al. (2004). Temporal difference models describe
higher-order learning in humans. Nature, 429(6992), 664–7.
Seymour, B., O’Doherty, J.P., Koltzenburg, M., et al. (2005). Opponent appetitive-aversive
neural processes underlie predictive learning of pain relief. Nature Neuroscience, 8(9),
1234–40.
Shad, M.U., Bidesi, A.P., Chen, L.A., Ernst, M., Rao, U. (2011). Neurobiology of
decision making in depressed adolescents: a functional magnetic resonance imaging
study. Journal of the American Academy of Child and Adolescent Psychiatry, 50(6),
612–21 e612.
Sherrat, K.A., MacLeod, A.K. (2013). Underlying motivation in the approach and avoidance goals of depressed and non-depressed individuals. Cognition and Emotion, 27(8),
1432–40.
Sherdell, L., Waugh, C.E., Gotlib, I.H. (2012). Anticipatory pleasure predicts motivation for
reward in major depression. Journal of Abnormal Psychology, 121(1), 51–60.
Smoski, M.J., Felder, J., Bizzell, J., et al. (2009). fMRI of alterations in reward selection,
anticipation, and feedback in major depressive disorder. Journal of Affective Disorders,
118(1–3), 69–78.
Smoski, M.J., Rittenberg, A., Dichter, G.S. (2011). Major depressive disorder is characterized by greater reward network activation to monetary than pleasant image rewards.
Psychiatry Research, 194(3), 263–70.
Snaith, R.P., Hamilton, M., Morley, S., Humayan, A., Hargreaves, D., Trigwell, P. (1995). A
scale for the assessment of hedonic tone the Snaith-Hamilton Pleasure Scale. The British
Journal of Psychiatry, 167(1), 99–103.
Staudinger, M.R., Erk, S., Abler, B., Walter, H. (2009). Cognitive reappraisal modulates
expected value and prediction error encoding in the ventral striatum. Neuroimage, 47(2),
713–21.
Steele, J.D., Meyer, M., Ebmeier, K.P. (2004). Neural predictive error signal correlates with
depressive illness severity in a game paradigm. Neuroimage, 23(1), 269–80.
Stoy, M., Schlagenhauf, F., Sterzer, P., et al. (2012). Hyporeactivity of ventral striatum
towards incentive stimuli in unmedicated depressed patients normalizes after treatment
with escitalopram. Journal of Pharmacology, 16(5), 677–88.
Tobler, P.N., O’Doherty, J.P., Dolan, R.J., Schultz, W. (2006). Human neural learning
depends on reward prediction errors in the blocking paradigm. Journal of
Neurophysiology, 95(1), 301–10.
1112
SCAN (2015)
Tricomi, E.M., Delgado, M.R., Fiez, J.A. (2004). Modulation of caudate activity by action
contingency. Neuron, 41(2), 281–92.
Tung, P., Wiviott, S.D., Cannon, C.P., Murphy, S.A., McCabe, C.H., Gibson, C.M. (2009).
Seasonal variation in lipids in patients following acute coronary syndrome on fixed
doses of Pravastatin (40 mg) or Atorvastatin (80 mg) (from the Pravastatin or
Atorvastatin Evaluation and Infection Therapy-Thrombolysis In Myocardial Infarction
22 [PROVE IT-TIMI 22] Study). American Journal of Cardiology, 103(8), 1056–60.
Tzourio-Mazoyer, N., Landeau, B., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI singlesubject brain. Neuroimage, 15(1), 273–89.
Wacker, J., Dillon, D.G., Pizzagalli, D.A. (2009). The role of the nucleus accumbens and
rostral anterior cingulate cortex in anhedonia: integration of resting EEG, fMRI, and
volumetric techniques. Neuroimage, 46(1), 327–37.
B. Ubl et al.
Walton, M.E., Bannerman, D.M., Alterescu, K., Rushworth, M.F. (2003). Functional specialization within medial frontal cortex of the anterior cingulate for evaluating effortrelated decisions. The Journal of Neuroscience, 23(16), 6475–9.
Walton, M.E., Bannerman, D.M., Rushworth, M.F. (2002). The role of rat medial frontal
cortex in effort-based decision making. The Journal of Neuroscience, 22(24), 10996–1003.
Warner-Schmidt, J.L., Schmidt, E.F., Marshall, J.J., et al. (2012). Cholinergic interneurons
in the nucleus accumbens regulate depression-like behavior. Proceedings of the National
Academy of Sciences of the United States of America, 109(28), 11360–5.
Wittchen, H.-U., Zaudig, M., Fydrich, T. (1997). Strukturiertes Klinisches Interview für
DSM-IV. Hogrefe: Göttingen.
Yacubian, J., Glascher, J., Schroeder, K., Sommer, T., Braus, D.F., Buchel, C. (2006).
Dissociable systems for gain- and loss-related value predictions and errors of prediction
in the human brain. The Journal of Neuroscience, 26(37), 9530–7.