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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. 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