Progress in Neuro-Psychopharmacology & Biological Psychiatry 64 (2016) 52–59

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Diminished caudate and superior temporal gyrus responses to effort-based decision making in patients with first-episode major depressive disorder Xin-hua Yang a,b, Jia Huang c, Yong Lan b, Cui-ying Zhu b, Xiao-qun Liu d, Ye-fei Wang a, Eric F.C. Cheung e, Guang-rong Xie a,⁎⁎, Raymond C.K. Chan c,⁎ a Mental Health Institute, The Second Xiangya Hospital, National Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Changsha, Hunan 410011, China b College of Business, Hunan Agricultural University, Changsha, China c Neuropsychology and Applied Cognitive Neuroscience Laboratory, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China d School of Public Health, Central South University, Changsha, China e Castle Peak Hospital, Hong Kong Special Administrative Region, China

a r t i c l e

i n f o

Article history: Received 29 May 2015 Received in revised form 6 July 2015 Accepted 12 July 2015 Available online 18 July 2015 Keywords: Anhedonia Depression Effort-based decision-making Motivation Reward processing

a b s t r a c t Background: Anhedonia, the loss of interest or pleasure in reward processing, is a hallmark feature of major depressive disorder (MDD), but its underlying neurobiological mechanism is largely unknown. The present study aimed to examine the underlying neural mechanism of reward-related decision-making in patients with MDD. Method: We examined behavioral and neural responses to rewards in patients with first-episode MDD (N = 25) and healthy controls (N = 25) using the Effort-Expenditure for Rewards Task (EEfRT). The task involved choices about possible rewards of varying magnitude and probability. We tested the hypothesis that individuals with MDD would exhibit a reduced neural response in reward-related brain structures involved in cost–benefit decision-making. Results: Compared with healthy controls, patients with MDD showed significantly weaker responses in the left caudate nucleus when contrasting the ‘high reward’–‘low reward’ condition, and blunted responses in the left superior temporal gyrus and the right caudate nucleus when contrasting high and low probabilities. In addition, hard tasks chosen during high probability trials were negatively correlated with superior temporal gyrus activity in MDD patients, while the same choices were negatively correlated with caudate nucleus activity in healthy controls. Conclusions: These results indicate that reduced caudate nucleus and superior temporal gyrus activation may underpin abnormal cost–benefit decision-making in MDD. © 2015 Elsevier Inc. All rights reserved.

1. Introduction Anhedonia, or markedly diminished pleasure in everyday life, is a core feature of major depressive disorder (MDD). Past findings have

Abbreviations: ACC, anterior cingulate cortex; BDI, Beck Depression Inventory; DA, dopamine; DSM-IV, Diagnostic and Statistical Manual of Mental Disorders IV; EEfRT, Effort-Expenditure for Rewards Task; EPI, echoplanar imaging; GEE, Generalized Estimating Equation; HAMD, Hamilton Rating Scale for Depression; HC/HR, high cost/ high reward; LC/LR, low cost/low reward; MDD, major depressive disorder; PFC, medial prefrontal cortex; SCID-NP, Structured Clinical Interview for DSM: Non-patients; SHAPS, Snaith–Hamilton Pleasure Scale; SPM, Statistical Parametric Mapping software; TEPS, Temporal Experience of Pleasure Scale; VS, ventral striatum. ⁎ Correspondence to: R. Chan, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, China. ⁎⁎ Correspondence to: G. Xie, Mental Health Institute, the Second Xiangya Hospital, Central South University, Changsha, China. E-mail addresses: [email protected] (G. Xie), [email protected] (R.C.K. Chan).

http://dx.doi.org/10.1016/j.pnpbp.2015.07.006 0278-5846/© 2015 Elsevier Inc. All rights reserved.

focused on affective ratings of positively-valenced stimuli (Berlin et al., 1998; Dichter et al., 2010), while recent studies indicate that reward deficits in anhedonia are much broader than hedonic responses (Treadway and Zald, 2011). Preclinical studies suggest that dopamine (DA) signaling may be selectively involved in reward motivation (Salamone et al., 2007), while others have emphasized the role of the striatum, a key region involved in the processing of reward-relevant information (Haber and Knutson, 2010). Neuroimaging studies indicate that motivation deficits may reflect dysfunction in the striatum (nucleus accumbens, caudate, putamen) (Kurniawan et al., 2010; Pizzagalli et al., 2009; Salamone et al., 2007; Treadway et al., 2012b). However, previous neuroimaging studies had seldom focused on anhedonic symptomatology in MDD. In particularly, whether striatal abnormalities are more closely associated with deficits in cost–benefit decision-making in reward processing is unclear. Key evidence for the role of mesolimbic DA in motivation is provided by studies in effort-based decision-making in rodents (Denk et al., 2005;

X. Yang et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 64 (2016) 52–59

Salamone et al., 2007). In these experiments, DA depletion—especially in the ventral striatum (VS)—resulted in a behavioral shift towards low effort options (Salamone et al., 2012). Recent studies in humans reported that administration of the DA agonist D-amphetamine produced a dose-dependent increase in the willingness to work for rewards as assessed by the Effort-Expenditure for Rewards Task (Wardle et al., 2011). Similar effects of DA enhancement with the DA precursor Ldopa have been observed in measures of reward anticipation and optimism bias (Sharot et al., 2009, 2012). In particular, a recent PET study exploring dopaminergic mechanisms of individual differences in effort-related behavior found that the magnitude of DA release in the striatum positively predicted the proportion of high effort choices that subjects made in low probability trials. Consistent with this idea, functional magnetic resonance imaging (fMRI) studies in cost–benefit decision-making in humans have shown activation at the VS when encoding action and its outcomes (Croxson et al., 2009; Treadway et al., 2012b). For instance, the study by Kurniawan et al. (2010) showed higher VS activation when participants chose an option that required less physical effort compared with an option that required more physical effort. Schouppe et al. (2014) found that the striatum plays a central role in the integration and computation of cognitive effort-based choices. Taken together, these studies provide preliminary evidence to suggest that the striatum may play an important role in determining whether an individual is willing to overcome effort costs. More importantly, using the Effort-Expenditure for Rewards Task (EEfRT) and related tasks, clinical findings regarding the role of DA in human motivation parallel preclinical findings. Depressed patients and individuals with self-reported motivational anhedonia show reduced willingness to expend effort for monetary rewards, which is consistent with the hypothesis that depression is associated with a core motivational deficit (Kurniawan et al., 2010; Treadway et al., 2012a). The failure of reward incentives to modulate effort expenditure was also reported by Clery-Melin et al. (2011), in which patients with MDD both exerted less force than controls and failed to modulate their effort expenditure on the basis of monetary incentives. Sherdell et al. (2012) also found evidence for reduced sensitivity to reward predicting cues. These studies point towards a failure to modulate effort production as a function of reward information in depressed patients. In contrast, no study has examined the neural representation of effort to choose an action in individuals with MDD. Although considerable evidence suggests that the VS is a key structure in a neural system that mediates effortbased decision making, recent studies have revealed strong rewardrelated neural activity in the dorsal striatum, indicating that the dorsal striatum may also be heavily involved in reward and value processing (Daw and Doya, 2006; Samejima et al., 2005). For instance, firing of neurons in the dorsomedial striatum changes according to the flexibility of choice behavior (Braun and Hauber, 2011). Notably, a recent study revealed that choice, reward and chosen value signals are all stronger in the dorsal striatum than the VS, suggesting that the dorsal striatum might play a more prominent role in updating value (Kim et al., 2009). In light of growing evidence for the involvement of the dorsal striatum in value-based decision-making, we hypothesized that deficits in effort-based behavior in MDD patients may be associated with the dorsal striatum. To investigate this, we conducted a functional magnetic resonance imaging (fMRI) study using the EEfRT (Treadway et al., 2009) in a group of first episode drug-naive depressed individuals (N = 25) and healthy controls (N = 25) to test whether MDD was associated with a reduced neural response in structures involved in cost–benefit decision-making. Given that the relationship between anhedonia and effort-based decision making was moderated by variables also known to influence striatal DA release (Croxson et al., 2009; Kurniawan et al., 2010), we hypothesized that blunted responses, particularly at the caudate nucleus, in depressed individuals would be associated with biasing choices away from actions that entail greater effort.

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2. Material and methods 2.1. Participants Patients with first-episode, drug-naive MDD were recruited from the outpatient clinic of the Second Xiangya Hospital in Central South University in China. All the patients met the diagnostic criteria for MDD according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) (APA, 1994). Inclusion criteria were 1) psychotropic-free and were having their first episode of depression; 2) currently experiencing an episode of depression with HAMD total score ≥ 20 on the 24-item Hamilton Rating Scale for Depression (HAMD; mean score = 27.58, SD = 4.62) (Williams, 1988); 3) a duration of illness of N2 weeks but b 60 weeks; and 4) age between 18 and 45. Exclusion criteria were any psychotropic medication in the past two weeks, fluoxetine in the past six weeks, or dopaminergic drugs or antipsychotics in the past six months; a current or past history of MDD with psychotic features; and the presence of other axis I diagnoses (including lifetime substance dependence and any substance use disorder in the past year), with the exception of anxiety disorders. Healthy controls reported no medical or neurological illness, no current or past psychopathology, and no use of psychotropic medications assessed by the Structured Clinical Interview for DSM-IV: Non-patient edition (SCID-NP) (First, 2012). The final sample included 25 psychotropic-free patients with first-episode MDD and 25 demographically-matched healthy controls. All participants were right handed as assessed by the Annett Handedness Scale (Annett, 1970). This study was approved by the Central South University Institutional Review Board. A complete description of the study was provided to all participants, who gave written informed consent. 2.2. Effort-based decision-making task The task used in the present study was a modified version of the Effort Expenditure for Rewards Task (Treadway et al., 2009). Briefly, this was a multi-trial task in which participants were given an opportunity in each trial to choose between a high cost/high reward (HC/HR) and a low cost/low reward (LC/LR) option to obtain monetary rewards. We modified the task with fixed reward magnitude and reduced the effects of fatigue by reducing the number of button presses. Each trial presented the participant with a choice between a ‘hard task’ (HC/HR option), requiring 20 button presses with the non-dominant pinky finger within 4 s, and an ‘easy task’ (LC/LR option), requiring 10 button presses with the dominant index finger within 4 s. For the easy-task, participants were eligible to win ¥0.5 for each successfully completed trial. For the hard task, participants were eligible to win two possible higher monetary rewards (¥0.8 and ¥5.0) (‘reward magnitude’). Participants were informed that not every completed task could earn them money, and the reward amount was given only when trials were scheduled. In order to help participants determine which trials were more likely to be win-trials, they were provided with information regarding the probability of receiving reward from the hard tasks. Trials had three levels of probability: ‘high’ 80%, ‘medium’ 50% and ‘low’ 20%, making the stimuli conditions 2 × 3 factors, with each of these factors appearing six times, in a total of 36 trials. To provide stable estimations, the fMRI task was conducted in three runs (9 min 4 s for each run). 2.3. Subjective measures of depression and anhedonia The Beck Depression Inventory (BDI) (Beck et al., 1961) is a 21-item scale that evaluates the severity of depression. The Chinese version used for the present study has been validated in Chinese samples (Wang et al., 1999). The alpha in the current sample was 0.83. Anhedonia was assessed by the Chinese versions of the Snaith– Hamilton Pleasure Scale (SHAPS) (Liu et al., 2012) and the Temporal

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Experience of Pleasure Scale (TEPS) (Chan et al., 2010). The SHAPS was used to measure the state of anhedonia, whereas the TEPS was used to measure the anticipatory and the consummatory component of pleasure experience. The Chinese versions of these scales have been shown to possess adequate reliability in a previous study (Chan et al., 2012). 2.4. Procedure Data collection occurred prior to the start of treatment. After the participants completed all self-report measures, they were provided with a series of task instructions so that they understood the task and its contingencies. They were allowed to complete as many practice trials of the reward task as they felt necessary. The fMRI task was conducted in an event-related design in three runs (9 min 4 s for each run). Participants were compensated 50 RMB for their time and ‘earned’ 10–70 RMB from the task. 2.5. Image acquisition Functional and structural MRIs were performed with a Siemens 3 T (Siemens 3 T-Trio a Tim, Erlangen, Germany) MRI whole body scanner using a 32-channel head coil. Functional images were obtained using a T2-weighted single-shot gradient echoplanar imaging (EPI) sequence (TR: 2000, TE: 25, 80° flip angle, FOV: 240 mm, matrix: 64 × 64, voxel size: 3.3 × 3.3 × 4 mm). Each EPI volume contained 32 axial slices (thickness 4.5 mm, 0 mm gap), acquired in interleaved order, covering the whole brain. Each run contained 272 functional images. The first three slices of each run were discarded to allow for T1 equilibration. In addition, a high-resolution T1-weighted magnetization-prepared rapid gradient-echo imaging (MP-RAGE) 3D MRI sequence was obtained from each subject (TR: 2000 ms, TE: 2.26 ms, 80° flip angle, FOV: 256 × 256, matrix: 256 × 256, voxel size: 1 × 1 × 1 mm3).

second-level group analysis using an independent sample t-test. The resulting activation peaks were superimposed on standard highresolution anatomical images. Two independent sample t-tests were conducted between the patients and the controls. To further examine the performance of each group in each condition, activations that were associated with reward processing were selected. ROI of the left caudate was defined by the contrasts of (healthy controls N patients) and (high N low). ROIs of the right caudate and the left superior temporal gyrus were defined by the contrast of (healthy controls N patients) and (80% N 20%). These ROIs were constructed by the Marsbar software (Brett et al., 2002) using a box with a peak activation center and 10 widths. A corrected threshold of p b 0.05 (two-tailed) was derived from a combined threshold of p b 0.001 for each voxel and a cluster size of N15 voxels was determined using the AlphaSim program in the REST software (parameters: single voxel p b 0.001, 1000 iterations, FWHM = 8 mm, with gray matter mask; Song et al., 2011). The EEfRT behavioral data was analyzed using Generalized Estimating Equation (GEE) models according to previous studies (Treadway et al., 2009; Yang et al., 2014). Trial number was used as a covariate to control for possible fatigue. For within-group individual difference models, gender was added as a covariate. 3. Results 3.1. Demographic information Table 1 summarizes the characteristics and self-reported measures of the participants. There was no significant difference between the patient and the control groups in gender, age, years of education and IQ. The patient group showed higher levels of state anhedonia (measured by the SHAPS), and higher levels of trait anticipatory and consummatory anhedonia (measured by the TEPS) than healthy controls. In addition, the MDD group showed a higher level of depressive symptoms (measured by the BDI) (all p b 0.001).

2.6. Imaging preprocessing and analyses 3.2. Behavioral results The study employed a 3 × 2 × 2 factorial design: probability (80% vs. 50%vs. 20%), reward magnitude (high vs. low) and group (healthy controls vs. patient group). Data were analyzed using the Statistical Parametric Mapping software (SPM8; Wellcome Department of Imaging Neuroscience, London, UK) implemented in Matlab 2012a (Mathworks Inc., Sherborn, MA, USA). Functional images were realigned by affine registration to correct for scan head motions. Data for seven participants were lost because of excessive motion (N 4 mm), leaving 25 individuals with MDD and 25 controls for the fMRI analysis. The mean functional image was subsequently co-registered to the 3D high resolution structural image of each subject. Each subject's structural image was normalized to the T1 template provided by SPM, and the normalization parameters were then applied to all the functional images. Images were re-sampled at a 2 × 2 × 2 mm 3 voxel size in the normalization step, and then spatially smoothed using an 8 mm full width at half maximum Gaussian Kernel. Functional data were analyzed using the general linear model. Parameter estimates were subsequently calculated for each voxel using weighted least squares to provide maximum likelihood estimates based on the non-sphericity assumption of the data to obtain identical and independently distributed error terms. To highlight activity correlating with hard task choices under reward and probability conditions, we computed a set of contrasts testing the main effects of reward magnitude, reward probability, and the interaction. For each subject, 13 contrasts were computed. Main effects were calculated by contrasts (high 80% + high 50% + high 20%) − (low 80% + low 50% + low 20%) for reward magnitude, contrasts (high 80% + low 80%) − (high 20% + low 20%) for probability, and contrasts (high 80% + low 20%) − (high 20% + low 80%) for reward magnitude and probability interaction. These first-level individual contrast maps were fed into a

The participants had to make a decision between HC/HR and LC/LR within 4 s according to reward magnitude and probability. A total of 97.7% (± 2%) of the healthy controls successfully made their own choices within 4 s, while 95.1% (± 7%) of patients with MDD did (t(48) = 1.57, p = 0.12). All participants chose a mixture of HC/HR trials and LC/LR trials, and there was no difference in the percentage of trials successfully completed between the patient group (M = 86.91%, SD = 13.78%) and the control group (M = 77.67%, SD = 23.89%) (t(48) = 1.67, p = 0.10), suggesting that fMRI findings were not confounded by task difficulty. Moreover, the mean choice reaction time Table 1 Demographic and self-reported measures for the participants. Healthy controls Depressed patients T/χ2 value (df = 48) (n = 25) (n = 25) Gender (Male/Female) Age (years) Education (years) Estimated I.Q. SHAPS TEPS-ANT TEPS-CON TEPS total BDI total HAMD (24-item)

10/15 28.36 ± 7.87 13.40 ± 3.12 113.68 ± 17.95 21.56 ± 6.41 37.00 ± 6.26 44.00 ± 7.27 81.00 ± 12.83 11.08 ± 8.8 –

12/13 28.96 ± 7.00 13.64 ± 3.04 109.96 ± 22.42 34.36 ± 6.13 30.44 ± 6.97 33.08 ± 6.84 63.52 ± 12.7 33.04 ± 9.93 27.58 ± 4.62

χ2 = 0.33, df = 1 −0.28 −0.28 0.65 −7.22⁎⁎⁎ 3.50⁎⁎⁎ 5.47⁎⁎⁎ 4.84⁎⁎⁎ −8.28⁎⁎⁎ –

Notes: IQ: Chinese version of the Wechsler Adult Intelligence Scale—Revised; SHAPS: Snaith–Hamilton Pleasure Scale; TEPS-ANT: Temporal Experience of Pleasure Scale– Anticipatory Pleasure Subscale; TEPS-CON: Temporal Experience of Pleasure Scale– Consummatory Pleasure Subscale; BDI: Beck Depression Inventory; HAMD: The Hamilton Rating Scale for Depression. ⁎⁎⁎ p b 0.001.

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during the task did not differ between the MDD group (M = 1130.50, SD = 331.61) and the healthy controls (M = 1238.28, SD = 338.98, t(48) = 1.14, p = 0.26). The GEE model showed that, compared with the control group, the patient group was significantly less likely to make HC/HR choices (b = − 0.17, p = 0.042). Consistent with our prior results using the EEfRT (Yang et al., 2014), there was no interaction between group and reward magnitude, group and probability, and group and expected value. 3.3. fMRI results 3.3.1. Reward magnitude A complete list of regions showing group differences is provided in Table 2. Relative to controls, MDD patients showed weaker activation at the left middle frontal gyrus during high reward magnitude trials (Fig. 1A) and decreased responses in the left caudate in high minus low reward trials (Fig. 1B). To further investigate this finding, a group and reward interaction at different probability levels (high 80%–low 80%, high 50%–low 50%, and high 20%–low 20%) was performed. We found that MDD patients showed decreased activation at the right caudate and the left middle temporal gyrus in response to high minus low reward during high probability trials (80%), but no significant difference was evident for reward magnitude during low (20%) and medium (50%) probability trials. 3.3.2. Probability For the probability main effect, MDD patients showed relatively weaker activation at the left medial frontal gyrus, the right thalamus and the cingulate gyrus in response to choices for high probability (80%) than controls (Fig. 1C). We did not find any difference in activity for low (20%) and medium (50%) probability trials. For the group and probability interaction, relative to controls, MDD patients showed significantly weaker responses to 80% minus 20% condition at the right caudate and the left superior temporal gyrus (Fig. 1D). Moreover, in the MDD group, the same deactivation pattern in the right caudate was also observed in the 80% minus 20% with high reward condition, but not in the 80% minus 20% with low reward condition. No significant finding was observed for the 80% minus 50% and the 50% minus 20% conditions between the two groups. Finally, we did not find any suprathreshold activity for reward magnitude and probability interaction between the two groups. Follow-up analyses on beta weights extracted from the caudate, middle frontal gyrus, and superior temporal gyrus regions were entered into the group × condition (probability and reward magnitude) ANOVAs and no difference was observed. 3.4. Relationship between EEfRT performance and brain region activities To test whether hard tasks chosen and brain region activities were correlated, mean beta weights were extracted from each activity cluster and entered into Pearson's correlation analyses for both groups (Table 3). For the MDD patient group, activation in the left superior temporal gyrus in high probability (80%) conditions was negatively correlated with hard task choices. In contrast, for the control group, activation in the right caudate in high probability (80%) conditions was negatively correlated with hard task choices. Moreover, during low reward magnitude condition, activation in the left caudate was negatively correlated with choices for medium (50%) and high probability (80%) trials, while activation in the left medial frontal gyrus was also negatively correlated with high probability (80%) trials. There was no correlation between activation in the left superior temporal gyrus and the caudate and pleasure experience and depressive symptoms in the MDD group, but activation in the left caudate in low reward conditions was negatively correlated with the anticipatory subscale, the consummatory subscale and the total score on the TEPS (r = −0.52, p b 0.01; r = −0.42, p = 0.036; r = −0.51, p b 0.01) in healthy controls.

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4. Discussion This study investigated behavioral and brain activations involved in choosing an action based on effort in patients with MDD. Behaviorally, patients with MDD showed evidence of motivational anhedonia and were less willing to expend effort for the opportunity to earn larger monetary rewards as compared with controls. These behavioral differences were mirrored by group differences in caudate nucleus activation to reward-based decision-making, with MDD patients showing weaker responses in the left caudate nucleus when contrasting the ‘high reward’–‘low reward’ condition, and decreased activation at the left superior temporal gyrus and the right caudate nucleus during the contrast of high reward probabilities and low reward probabilities. Secondly, we also found that, during high-magnitude reward conditions, patients with MDD showed less deactivation at the middle frontal gyrus and blunted response at the left medial frontal gyrus, the right thalamus and the cingulate gyrus during high reward probability conditions (80%), but there was no difference in reward and probability interaction. Finally, negative correlation between EEfRT performance and activation at the superior temporal gyrus was observed in MDD patients. In contrast, negative correlation between EEfRT performance and activation at the caudate was observed in controls. These findings extend from previous reports on motivation deficits in MDD (Clery-Melin et al., 2011; Sherdell et al., 2012; Treadway et al., 2012a), suggesting that the neural basis underlying cost–benefit decision-making appears to be the dysfunction of the caudate nucleus and superior temporal gyrus. Our results suggest that there is a motivation deficit manifesting in a preference away from the high-effort/high reward option in patients with MDD. It is possible that weaker DA signaling at the caudate nucleus may explain this phenomenon. The caudate nucleus and the VS are important structures in the brain reward system, which consists of a set of dopaminergic neural pathways that is strongly associated with reward behaviors and pleasurable experiences (Koob, 1996). In the present study, MDD patients showed a decrease in left caudate activation when choosing high compared to low-reward options. This is consistent with reduced striatal response to rewards that has been found in patients with anhedonia (Knutson et al., 2008; Kumar et al., 2008; Pizzagalli et al., 2009). Recent fMRI studies in the healthy population also provide support for the importance of the striatum in effortrelated processes. For instance, Kurniawan et al. (2010) reported higher dorsolateral striatal activity for choosing low compared to high-effort options in a physical effort task. Croxson et al. (2009) found that activity in the striatum correlates with both anticipated costs and anticipated reward of effortful actions. Samejima et al. (2005) reported that striatal activity correlated with the value of an action, which provided input information for action selection. In effort-based decision making, linking a chosen action to its outcome is central for optimal goal-directed behavior. Healthy people can make an action choice based on an integration of action and goal values while patients with deficits in hedonic capacity fail to do so. Dopamine release in the caudate nucleus may be suppressed in patients with MDD, which may prevent them from recognizing an advantageous context to adapt their actions accordingly; or there may be a failure to translate reward cues into appropriate action selection and execution, leading to a failure to exert effortful action for higher monetary reward. Furthermore, the same caudate nucleus hyperresponsivity was found during high reward probability (80%) trials compared to low reward probability (20%) trials. Disrupted dopamine signaling in the mesolimbic system, especially in the caudate nucleus, may underlie motivational anhedonia observed in patients with MDD. In contrast, the role of the temporal lobe has received relatively less attention. In comparison to controls, we found that MDD patients exhibited weaker responses in the left superior temporal gyrus during the 80% minus 20% condition. The temporal lobe has differential connections to the medial and the orbital prefrontal regions (Kondo et al., 2003), areas commonly implicated in MDD. Some evidence suggests

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Table 2 fMRI activation patterns in each condition. Contrast Healthy controls N depressed patients High vs. low reward High reward vs. rest Prob8 vs. rest

Prob8 vs. prob2

Prob8-2 vs. high_reward

Depressed patients High vs. low reward

High_reward

Prob8 vs. rest

K

X

Y

20 17 14 19 36 18 36 54 32 14 33 18

0 −21 −24 −45 −18 9 3 24 −45 9 9 3

−60 −18 −3 −36 −6 −36 3 −30 −33 15 15 −18

63 22 62 1296 441 30 39 30 1113 589 32

0 −33 −27 −30 −54 −39 6 15 −30 −42 6

5853 37 41 2912 492 55 27 26 24 2795 398 92 43 23 18 38 17 633 195 271 126 49 60 57 72 16 33 14 14 26 448 41 32 35 28 24 15 22

27 0 −30 −36 −30 −3 12 30 −9 −3 −57 −36 −9 12 9 −3 −6 −45 −18 24 18 −33 −18 18 0 54 9 −12 −21 24 −36 −18 42 45 −39 24 −63 −6

Z

T

Brain region

−4 32 48 40 52 4 40 16 12 8 8 16

4.31 3.88 3.92 3.68 4.06 3.98 3.98 4.92 4.16 3.83 4.05 4

L anterior lobe L caudate L frontal lobe middle frontal gyrus L parietal lobe supramarginal gyrus L frontal lobe medial frontal gyrus R sub-lobar thalamus R limbic lobe cingulate gyrus R sub-lobar caudate caudate tail L temporal lobe superior temporal gyrus R sub-lobar caudate caudate body R caudate body caudate R medial dorsal nucleus thalamus

−63 −24 −93 −72 21 −3 54 −84 −72 24 54

−40 48 −12 −16 12 44 −8 −40 −12 28 −8

4.34 4.19 4.03 7.47 6.19 4.8 4.75 3.83 7.35 6.78 4.8

L posterior lobe L parietal lobe postcentral gyrus L occipital lobe inferior occipital gyrus L posterior lobe L frontal lobe inferior frontal gyrus L claustrum R frontal lobe medial frontal gyrus R posterior lobe inferior semi-lunar lobule L posterior lobe declive L frontal lobe middle frontal gyrus R frontal lobe medial frontal gyrus N.S. N.S.

−87 −12 36 −78 0 9 18 −54 15 −87 15 0 15 18 3 9 57 −33 −30 −30 −81 −78 27 −45 −15 −15 15 −90 −75 −30 −66 −33 −18 −51 −33 −81 −39 −48

−20 12 28 −20 48 48 0 40 0 −4 20 44 0 0 12 48 0 12 52 16 −36 −40 0 48 16 8 4 −16 −24 20 28 52 12 20 16 −36 16 36

6.85 4.67 4.3 7.73 6.42 4.81 4.49 4.11 4.09 7.11 6.99 6.2 4.54 4.41 4.32 4.32 4.02 5.52 5.46 5.1 4.74 4.72 4.65 4.57 4.36 4.03 3.99 3.96 3.76 4.73 4.69 4.54 4.29 4 3.93 3.8 3.79 3.58

R posterior lobe L thalamus L frontal lobe middle frontal gyrus L posterior lobe declive L frontal lobe middle frontal gyrus L limbic lobe cingulate gyrus R sub-lobar caudate caudate body R parietal lobe angular gyrus L sub-lobar caudate caudate head L occipital lobe lingual gyrus L frontal lobe inferior frontal gyrus L frontal lobe middle frontal gyrus L sub-lobar caudate caudate head R sub-lobar caudate caudate head R sub-lobar caudate caudate body L limbic lobe cingulate gyrus L frontal lobe medial frontal gyrus L temporal lobe superior temporal gyrus L frontal lobe paracentral lobule R sub-lobar caudate caudate tail R posterior lobe uvula L sub-lobar insula L sub-lobar caudate caudate head R parietal lobe precuneus L sub-lobar thalamus R temporal lobe superior temporal gyrus R sub-lobar caudate caudate body L posterior lobe declive L posterior lobe uvula R sub-lobar caudate L temporal lobe middle temporal gyrus L frontal lobe paracentral lobule R sub-lobar insula R temporal lobe superior temporal gyrus L temporal lobe superior temporal gyrus R posterior lobe pyramis L temporal lobe superior temporal gyrus L parietal lobe precuneus

Prob8-2 Prob8-2_high_reward Healthy controls High vs. low reward

High vs. rest reward

Prob8 vs. rest

Prob8-2

Prob8-2_high_reward

Note: coordinates of the maximal point of activation and the associated T-values are shown in MNI spaces. L = left; R = right. The activations in these brain regions are primarily significant at cluster level p b 0.001, AlphaSim corrected p b 0.05; cluster threshold size k = 14. prob2: probability 20%; prob8: probability 80%; prob8-2: probability 80% vs. 20% interaction; prob82_high_reward: probability 80% vs. 20% interaction under high reward.

that the temporal lobe may be linked to the concept of incentive salience, as well as motivated goal-directed behavior by associations with reinforcing events (Berridge, 2007; McClure et al., 2003; Robbins

and Everitt, 2007). Abnormally reduced temporal lobe reward-learning signals in MDD patients would imply reduced salience to rewarding events, such as the case in individuals with anhedonia (Kumar et al.,

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Fig. 1. Reward-related activation in participants with depression (N = 25) and healthy controls (N = 25). Saggital, axial and coronal slices showing the making of choice activity in corticostriatal regions which is displayed for both groups as well as for the random-effects analyses comparing the two groups. In panel A, the major depression group shows weaker activation in the middle frontal gyrus during high reward magnitude in the MDD group. Panel B shows significantly reduced activation in the left caudate nucleus during high minus low reward relative to the healthy controls. In panel C, MDD patients exhibited weaker activation in the left medial frontal gyrus, the right thalamus and cingulate gyrus responses to choices during high probability (80%). Panel D indicated weaker responses to 80% minus 20% in the right caudate and the left superior temporal gyrus. All contrasts are at primarily threshold p b 0.001, at AlphaSim corrected p b 0.05; size k = 14. Left hemisphere is displayed on the right.

2008). Moreover, gray matter reduction has also been reported with voxel-based morphometry studies of the temporal pole in first-episode MDD (Peng et al., 2011). Studies have pointed to the crucial role of cortico-subcortical networks for cost–benefit decision-making, and cortical regions (including the temporal and frontal lobes) are required to calculate effort and reward value information and modulate subcortical activities (the striatum and the anterior cingulate cortex) in a top-down manner (Kurniawan et al., 2011). Our findings suggest that dysfunction

Table 3 Correlations between fMRI activation and proportion of hard-task choices. Group

Contrast

Depressed patients Prob2

Healthy controls

Brain region

Caudate_r Temporal_l Prob8 Caudate_r Temporal_l Low reward Caudate_l Middle_frontal_l High reward Caudate_l Middle_frontal_l Prob2 Caudate_r Temporal_l Prob8 Caudate_r Temporal_l Low reward Caudate_l Middle_frontal_l High reward Caudate_l Middle_frontal_l

20%

50%

80%

0.04 −0.04 −0.09 −0.27 0.29 0.10 0.08 −0.07 0.17 −0.14 −0.08 −0.25 −0.18 −0.14 0.30 0.14

0.08 −0.02 −0.13 −0.33 0.08 −0.10 −0.01 −0.10 −0.24 −0.27 −0.34 −0.32 −0.46⁎⁎

−0.19 −0.11 −0.24 −0.45⁎⁎ 0.07 0.09 0.24 0.02 −0.26 −0.28 −0.55⁎ −0.39 −0.54⁎ −0.44⁎⁎

−0.37 0.37 0.27

0.30 0.17

Note: probability: 20%, 50%, and 80% in EEfRT; prob8: probability 80%; prob2: probability 20%; Middle_frontal: middle frontal gyrus; temporal: superior temporal gyrus; L: left hemisphere; R: right hemisphere. ⁎ p b 0.05. ⁎⁎ p b 0.01.

at the superior temporal gyrus in MDD patients might have caused a shift away from HC/HR options due to this mechanism. We found an association between choosing hard tasks and activity in some brain regions. In the MDD group, activation at the left superior temporal gyrus in high probability (80%) conditions was negatively associated with hard task choices. In contrast, for the control group, activation at the right caudate in high probability (80%) conditions was negatively associated with hard task choices. These findings suggest that hard task choices during high reward probability trials are related to superior temporal gyrus activity in MDD patients, while the same choices were more related to caudate nucleus activity in healthy controls. It is proposed that the striatum is less reactive to reward, and increased cortical function (such as the medial prefrontal cortex) reflects signaling to enhance neural response or elicit dopamine release in MDD (Keedwell et al., 2005). It is possible that the increase in temporal lobe activity is a result of compensation for caudate nucleus deactivation to cost–benefit decision-making in patients with MDD. Future research is needed to address this issue. In addition, compared with controls, we also found that MDD patients exhibited blunted responses in the middle frontal gyrus during high reward value conditions, and deactivation in the left medial frontal gyrus, the right thalamus and the cingulate gyrus during high reward probability (80%) conditions. The medial prefrontal cortex (PFC) may play a role in maintaining effortful responses to rewards (Walton et al., 2002) and modulate activities at the striatum and the anterior cingulate cortex (ACC) in a top-down manner (Wager et al., 2008). The ACC is primarily involved in integrating information about reward benefits and effort costs of an action (Floresco et al., 2008). After the initial cost–benefit assessment at the PFC and the ACC, the striatum facilitates willingness to execute effortful actions by emphasizing the value of potential benefits (Schouppe et al., 2014). These interconnected areas may

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constitute a brain system for the evaluation of effort-related cost–benefit decisions and the value of persisting with a course of action given the expected rewards. Impairment of this dopaminergic circuit would result in abnormal reward valuation, failure in effort calculation of multiple choices, or deficits in making an optimal action. We thus inferred that failure to appropriately engage reward-processing in the corticostriatal network may contribute to reduced motivation towards effortful actions in patients with MDD. Although there were motivation deficits in reward-based decisionmaking, no differences were observed between MDD patients and controls in reward network activation to expected value (reward magnitude × probability). In the present study, MDD patients seemingly retained their capacities for integrating information to a similar degree as controls, but they failed to respond optimally. Moreover, although activation in the left caudate in low reward conditions was negatively associated with pleasure experience on the TEPS, no relationship was found between brain region activities, pleasure experience and depressive symptoms in MDD patients. These findings are different from previous studies which reported reduced ACC activation when MDD patients calculated costs and benefits (Smoski et al., 2008; Treadway et al., 2012a). However, given the small sample size of our study, negative findings should be interpreted with caution. In terms of clinical implication, our findings suggest that a treatment regimen which attempts to increase cortico-striatal network activities (especially the caudate) in MDD patients may ameliorate anhedonic symptoms. Because activation in these regions has been linked to reward and motivation, training MDD patients to sustain engagement with tasks which may activate motivation may be useful in clinical practice. In conclusion, our findings suggest that individuals with depression suffer from an inability to exert a large effort to receive a high reward that is reflected in cortico-striatal connectivity deactivation, particularly at the caudate nucleus and the superior temporal gyrus. These results suggest that motivation impairment is a core deficit of MDD, which renders everyday tasks abnormally effortful for patients with the disorder. Further studies should examine whether the task could be used to predict clinical outcome of anti-depressant treatment.

Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgements This study was supported by grants from the National Science Fund China (81371492), and the Chinese Society of Academic Degrees and Graduate Education (2013Y13) to GRX; grants from the ‘Strategic Priority Research Program (B)’ of the Chinese Academy of Sciences (XDB02030002), the National Science Fund China (81088001), the Beijing Training Project for the Leading Talents in S & T (Z151100000315020), and the Key Laboratory of Mental Health, Institute of Psychology to RCKC; a grant from the National Science Fund China (31100747) to JH; and a grant from the China Postdoctoral Science Foundation (2015M571149) to XHY.

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Diminished caudate and superior temporal gyrus responses to effort-based decision making in patients with first-episode major depressive disorder.

Anhedonia, the loss of interest or pleasure in reward processing, is a hallmark feature of major depressive disorder (MDD), but its underlying neurobi...
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