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. Author manuscript; available in PMC: 2026 Sep 25.
Published in final edited form as: Biol Psychol. 2026 Sep 6;210:109381. doi: 10.1016/j.biopsycho.2026.109381

Striatal Social Reward Sensitivity Predicts Trust-Related Brain Responses Depending on Closeness and Depression

Shenghan Wang a, Yi Yang a, Cooper J Sharp a, Dominic Fareri b, Jason Chein a, David V Smith a
PMCID: PMC13601942  NIHMSID: NIHMS2211513  PMID: 42702279

Abstract

Aim.

Depression is associated with social dysfunction, but the mechanisms linking affective symptoms to disrupted close relationships remain understudied.

It is also unclear whether neural sensitivity to shared reward predicts social valuation during more complex interactions, with the possibility of depression altering the link between how people experience rewards shared with and trust reciprocated by close others. Thus, this preregistered fMRI study tested whether neural sensitivity to shared reward cross-task predicts trust-related behavior and brain responses, and whether these associations depend on perceived closeness and depressive symptoms.

Methods.

Participants completed a reward-sharing task and a Trust Game with a close friend, a stranger, and a computer. We measured striatal shared reward sensitivity (SRS; friend > computer) and tested whether it related to investment behavior and brain responses to trust reciprocation. Depressive symptoms and perceived closeness were assessed via self-report.

Results.

In a final sample of n = 123, participants reporting more depressive symptoms invested more in their friend than in the computer. Striatal SRS predicted temporoparietal junction responses to reciprocated trust, but this association depended jointly on social closeness and depression — with depression reversing the expected pattern among individuals reporting closer relationships. Striatal SRS was also inversely associated with connectivity between the default mode network and cerebellum during reciprocity.

Conclusions.

These findings suggest that closeness calibrates the striatal SRS’ link to regional activity and network-level responses during social exchange, while depression alters how striatal SRS relates to regional activity, potentially disrupting how individuals interpret and respond to close others.

Keywords: Depression, Default Mode Network, Social Closeness, Shared Reward, Trust Game, Ventral Striatum

1. Introduction

Real-life decisions are often made in socially salient contexts, where choices are shaped by the actions of and effects on others. A question of broad interest is how the social environment affects reward valuation signals that guide choice behavior (1), and whether perceived social connectedness with those involved modulates this influence — potentially leading to adaptive or maladaptive decision making. These interactions may be further shaped by internal states that influence how individuals represent both reward value and social meaning. Depression, in particular, is known to disrupt both reward circuitry (2) and social information processing (3,4), with effects that can persist even after clinical remission (5,6). Further, reward and social dysfunction may intermingle in close relationships, where diminished feelings of social closeness have been reported in depression (7).

Although reward valuation, social context processing, and representations of social closeness are related, converging evidence suggests they rely on partially distinct neural systems: social reward valuation is closely tied to ventral striatal circuitry, whereas representations of social closeness are more consistently associated with the default mode network (DMN, 8,9). Consistent with this distinction, the ventral striatum (VS) preferentially activates during rewarding experiences involving close others, such as adolescent decision making in the presence of peers (10), sharing monetary rewards with a close friend (11), and when a trusted friend reciprocates monetary investment (12). In contrast, social closeness is more typically reflected in activity within core DMN regions in social perception and social valuation tasks (13), particularly where the DMN overlaps with the “social brain” (14), including medial prefrontal cortex (mPFC, 13) and temporoparietal junction (TPJ, 14). It remains unknown, however, whether VS responses to social valuation generalize across contexts and predict the neural substrates of social closeness perception in other tasks.

Despite these dissociations, disruptions in both reward processing and social functioning in depression suggest that the link between social closeness representations and reward valuation may be altered. Depression is thought to be rooted in aberrant reward valuation and motivational processes though recent work has revealed additional nuance (18,19), with blunted striatal responding long documented as a key neural underpinning of anhedonia (20–22). Social-domain specific anhedonia and social withdrawal are frequently reported symptoms as well (23–25), and major nodes in DMN failed to deactivate during the processing of negative emotional stimuli in depressive versus healthy participants (26), while reduced DMN intrinsic connectivity has been observed in those with greater social dysfunction (27). Consistent with this observation, studies of maternal depression report altered motivation and neural responses to infant cues, including reduced striatal and mPFC activation (28–30).

How social context processing is altered in depression remains inconclusive, with comparable numbers of studies reporting blunting (31–33) or sensitization (34–38) to social outcomes in depression. This inconsistency may reflect varied experimental manipulations of social context and uncontrolled salience of social relationships in studies where perceived closeness has not been explicitly measured. Studies also vary in whether they assess passive responses to social feedback versus valuation processes that guide behavior under uncertainty. Behavioral economic games offer a useful way to probe these issues, linking social valuation to observable decisions while allowing rigorous experimental control over social outcomes (39).

This study examines whether VS responses to social context and closeness during a reward task predict cross-task behavior and brain function, and whether depression alters that link. Participants completed a card-guessing task involving reward sharing and a Trust Game involving investment under the risk of defection, with the same partners across both tasks. Prior work has operationalized social closeness in different ways—comparing social partners at varying levels of perceived closeness (9) or social partners with nonsocial agents or objects (40). Notably, these contrasts isolate overlapping but distinct aspects of social reward processing: a friend–computer contrast captures the combined influence of social presence and relational closeness, whereas a friend–stranger contrast holds social presence constant to isolate closeness-specific variation (17,41). Thus, we preregistered analyses focusing on the friend–computer contrast as a test of socially shared reward relative to a nonsocial control, with friend–stranger contrasts as a secondary test of how relational closeness modulates reward sensitivity. Because representations of social closeness and self–other information are distributed across multiple DMN regions, we used a whole-network DMN seed; mPFC and TPJ remained a priori activation ROIs, and mPFC and VS remained preregistered connectivity targets. We tested activation and connectivity patterns involving VS, mPFC, TPJ, and DMN, asking three key questions. First, does striatal SRS predict friend-versus-computer differences in investment behavior and mPFC/TPJ activation during reciprocated-versus-defected outcomes in the Trust Game? Second, are these effects moderated by perceived closeness and depressive symptoms? Finally, do these relationships extend to functional connectivity between the DMN and key social valuation regions (mPFC and VS)?

2. Methods

2.1. Participants

We recruited 225 participants from the greater Philadelphia area (preregistration: https://osf.io/p654k). All provided written informed consent under Temple University IRB-approved protocol. Data were collected as part of a larger project (42) and are publicly available on OpenNeuro (https://openneuro.org/datasets/ds005123/versions/1.1.3). After preregistered exclusions (detailed in Supplementary Methods), final samples of n = 123 were retained for all analyses (demographic details in Supplementary Table 1). A G*Power sensitivity analysis (α=.05, 1–β=.80) indicated that with n=123 our preregistered interaction test was calibrated to detect effects of f2≥0.09 with 80% power (smaller effects would have lower power).

2.2. Experimental tasks

Participants performed two tasks in PsychoPy (43) in counterbalanced order: a Shared Reward Task and a Trust Game, each involving social and nonsocial partners (friend, stranger, or computer). Participants provided a real-life friend’s photo and were told their friend would serve as one of their partners. To ensure incentive compatibility, participants were informed that bonus payments were determined by outcomes from one randomly selected trial per task.

In the Shared Reward Task (Figure 1a), participants guessed whether a hidden number was greater or less than five. Correct guesses yielded a $10 gain for both the participant and the partner; incorrect guesses yielded a $5 loss for both. In the Trust Game (Figure 1b), participants received an $8 endowment and chose how much to invest with their partner; invested amounts were tripled, and partners either reciprocated (returning half) or defected (keeping all). All partners had a 50% reciprocation rate. For full task details, see the Supplementary Methods and the accompanying data descriptor (42).

Figure 1. Study overview and task design.

Figure 1.

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A) Shared Reward Task: On each trial, participants viewed one of three partners (friend, stranger, or computer) and guessed whether a randomly generated number (1–9) would be above or below 5. Correct guesses resulted in both the participant and partner winning $10; incorrect guesses resulted in a $5 loss for both; trials landing on 5 yielded no change. Striatal responses during reward > loss outcomes with a friend were extracted as the index of striatal shared reward sensitivity (SRS). B) Trust Game: Participants played with the same three partners. On each trial, they decided how much of an $8 endowment to invest. Investments were tripled, and the partner either reciprocated by returning half or defected by keeping the full amount. striatal SRS was used to predict neural responses during the outcome phase of trials in which a friend reciprocated trust. Figures adapted from Smith et al. (2024) with permission.

2.3. Individual difference measures

Depression symptoms were assessed at the first appointment using the “Down” subscale of the 7 Up 7 Down Inventory (44), a seven-item, four-point questionnaire measuring the frequency of depressive behaviors and feelings (e.g., “There have been times when you really got down on yourself and felt worthless”). Perceived social closeness toward each partner was assessed using the Inclusion of Other in Self (IOS) scale (45). Participants viewed a seven-point series of overlapping circles depicting increasing self–other overlap and selected the image that best represented their relationship with each partner (friend, stranger, and computer).

2.4. Imaging data acquisition and preprocessing

Imaging data were collected at the Temple University Brain Imaging and Research Center using a 3T Siemens PRISMA scanner and a 20-channel head coil. Functional images were collected using a multi-echo echo-planar imaging (EPI) sequence with four echoes (TEs = 13.8, 31.54, 49.28, 67.02 ms), a TR of 1615 ms, voxel size of 2.7 mm3, multiband acceleration (factor = 3), and GRAPPA = 2. A total of 51 axial slices were acquired with a 10% inter-slice gap. To support motion correction and registration, single-band reference images were collected for each run. For co-registration of the functional images, high-resolution T1-weighted anatomical images were acquired using an MPRAGE sequence (TR = 2400 ms, TE = 2.17 ms, flip angle = 8°, voxel size = 1 mm3). Additional MRI acquisition and preprocessing procedures are detailed in the supplement and the technical details of acquisition, including variations in flip angle and the inclusion of phase images, are described in Smith et al. (42).

Before further processing, structural and functional data were converted to BIDS format using HeuDiConv (46). All data were then preprocessed using the default settings in fMRIPrep (version 24.1.1, 46), including skull stripping, spatial normalization to MNI space, slice-timing correction, and motion correction (details in Supplemental Methods). Field maps were created from the available phase images using the MEDIC algorithm (48) and used for susceptibility distortion correction (Supplemental Methods). Following fMRIPrep, functional data were smoothed using a 5 mm full-width at half-maximum (FWHM) Gaussian kernel in FSL, and grand-mean intensity normalization was applied using a single multiplicative factor.

2.5. Neuroimaging Analyses

Neuroimaging analyses including activation and connectivity were performed on FSL version 6.0.7 (49,50) using general linear models with local autocorrelation (51). First-level models for each task included outcome and decision phase regressors differentiated by partner identity, convolved with a double-gamma hemodynamic response function. To assess task-dependent functional connectivity, we used the spatially distributed DMN map from Smith et al. (52) as the seed network. Participant-specific DMN time series were derived using dual regression and entered into network-based psychophysiological interaction models examining coupling with mPFC and VS during Trust Game outcomes. Full model specifications are provided in the Supplementary Methods.

After combining runs with a fixed-effects model, group-level analyses used FSL FLAME Stage 1 (53). Analyses followed a preregistered ROI-first approach before proceeding to whole-brain analyses. ROIs were defined a priori: mPFC from a Neurosynth association map (keyword “mPFC,” z > 8), TPJ from the Mars connectivity atlas, and ventral striatum from the Oxford–GSK–Imanova Striatal Connectivity Atlas. We defined striatal SRS as VS activation in the contrast of reward > loss sharing outcomes with a friend vs. a computer. Whole-brain z-statistic maps were thresholded at z > 3.1 with cluster-extent correction at p < 0.05 (54). Brain imaging results are displayed using MRIcroGL (55).

2.6. Deviations from Pre-registration

Our pre-registered analyses focused on the contrast between close friends and computer partners (Friend > Computer) for testing hypotheses about activation in mPFC and TPJ and connectivity between the ventral striatum (VS) or mPFC and the default mode network (DMN) during trust reciprocation. One section of the preregistration refers to the contrast as Friend > Stranger, though all other sections clearly specify Friend > Computer. For consistency with our stated hypotheses and analytic plan, we interpret this as an isolated inconsistency and focus on the Friend > Computer contrast in the main text, with Friend > Stranger results reported in the Supplement. After the primary model investigating closeness’s effect on striatal SRS returned null effects, we conducted follow-up analyses not specified in the preregistration, specifically through reduced models without depression terms.

3. Results

3.1. Does Striatal SRS Predict Trust Behavior?

We first confirmed that participants rated their friend as significantly closer than the computer partner (β = −3.65, SE = 0.13, t(244) = −28.13, p < .0001; Figure 2a), and invested more in their friend than in the computer (β = 1.206, SE = 0.09, t(244) = 12.96, p < .0001; Figure 2b). We then tested our preregistered hypothesis that greater closeness to the friend would amplify VS shared-reward sensitivity — the rationale being that a stronger social bond might enhance the striatal SRS. In a model including closeness, depressive symptoms, and their interaction, VS shared-reward sensitivity was not significantly associated with closeness (β = −0.012, SE = 0.053, t = −0.23, p = .82), and this association was not moderated by depression (β = −0.003, SE = 0.008, t = −0.39, p = .70). Results were similar when using an alternative friend–stranger contrast (see Supplement). These findings suggest that VS shared-reward sensitivity operates independently of perceived relationship strength, reflecting a more general signal for social reward value.

Figure 2. Partner closeness and depressive symptoms shape trust behavior.

Figure 2.

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A) Participants rated their friend as significantly closer than the stranger or computer on the Inclusion of Other in Self scale. B) Participants invested more in their friend than in other partners. C) Higher depressive symptoms were associated with greater investment in the friend relative to the computer. Panels A and B show fixed-effect estimates from mixed-effects models with random intercepts, overlaid with raw data points. Panel C shows the association between depressive symptoms and partner-selective investment, controlling for age and gender. ***p < .001, *p < .05; error bars and shaded areas reflect standard error.

We next examined whether VS shared-reward sensitivity predicts trust behavior, operationalized as relative investment in the friend versus computer. VS shared-reward sensitivity was not associated with investment differences (β = −0.110, SE = 0.180, t = −0.61, p = .54), and this relationship was not moderated by closeness (β = 0.110, SE = 0.118, t = 0.93, p = .35) or predicted by closeness alone (β = 0.074, SE = 0.072, t = 1.02, p = .31). In contrast, individuals reporting more depressive symptoms invested more in their friend than in the computer (β = 0.051, SE = 0.022, t = 2.29, p = .024; Figure 2c). Together, these results indicate that VS shared-reward sensitivity did not account for individual differences in trust behavior, whereas depressive symptoms were associated with greater trusting investment to close partners.

3.2. Does Striatal SRS Predict Activation During Reciprocity?

To establish that our paradigm captured the expected social modulation of striatal reward responses, we first examined whether VS activation varied as a function of partner closeness across both tasks. As predicted, VS activation was strongest when outcomes were shared with or reciprocated by a close friend (Figure 3; see Supplementary Results S1 for full statistics), replicating prior work (11,12). We then tested our preregistered hypothesis that individuals with greater VS shared-reward sensitivity would show increased activation in cortical regions implicated in social valuation and inference — specifically mPFC and TPJ — when a close friend reciprocated trust. Using preregistered ROI models, we tested whether striatal SRS predicted activation during reciprocate > defect outcomes with the friend relative to the computer. Across both regions, there were no significant associations with striatal SRS (mPFC: β = −0.404, SE = 0.302, t = −1.34, p = .18; TPJ: β = 0.035, SE = 0.240, t = 0.15, p = .89), and no evidence of moderation by closeness or depression. In a follow-up model including all interaction terms, a three-way interaction emerged in right TPJ (β = −0.027, SE = 0.013, t = −2.14, p = .035): greater striatal SRS was associated with increased TPJ activation during a friend’s reciprocation, but only among participants reporting high depressive symptoms and low closeness.

Figure 3. Ventral striatal activation is strongest during reward sharing and reciprocation with a friend, but does not differentiate partners during loss or defection.

Figure 3.

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A) We conducted two 2 (outcome valences) x 3 (partners) ANOVA on average VS activation during the outcome phases of the Shared Reward task and Trust Game. B) Average VS activation (Z-statistics) during reward and loss sharing with each partner in the Shared Reward Task. VS activation was greatest during reward sharing with a friend but did not differ across partners during loss sharing. C) Average VS activation (Z-statistics) during reciprocation and defection from each partner in the Trust Game. VS activation was greatest when reciprocated by a friend but did not differ across partners during defection. Error bars reflect standard error; n = 123.

To identify additional regions where striatal SRS related to activity during trust reciprocity, we conducted whole-brain voxelwise analyses using the same models. Greater VS shared-reward sensitivity was associated with stronger activation to trust reciprocity in the superior parietal lobule (SPL, MNI = 33, −48, 41; cluster = 27 voxels, p = .013), and showed a closeness-dependent effect in retrosplenial cortex (RSC, MNI = −2, −53, 8; cluster = 24 voxels, p = .020), such that the association was positive for close friends and negative for distant friends. Whole-brain three-way interaction models revealed a broader pattern, including effects in TPJ, posterior cingulate/precuneus, dorsal lateral prefrontal cortex, and dorsal anterior cingulate cortex (Supplementary Table 2; Figure 3). In each of these regions, greater VS shared-reward sensitivity predicted weaker activation when participants high in depressive symptoms were reciprocated by a close friend, but stronger activation when reciprocation came from a more distant friend. The reverse pattern emerged among participants low in depressive symptoms. Supplementary analyses using the friend–stranger contrast revealed different activation patterns and interaction effects than those observed in the friend–computer analyses (see Supplement).

3.3. Does Striatal SRS Predict DMN Connectivity During Reciprocity?

We next tested whether individual differences in VS shared-reward sensitivity (Friend > Computer) predicted connectivity within the social valuation network during reciprocity. Specifically, we examined connectivity between a default mode network (DMN) seed and two preregistered targets—mPFC and VS—during reciprocate > defect outcomes from a friend relative to a computer partner. In separate models including either self-reported closeness or depressive symptoms as moderators, VS shared-reward sensitivity did not predict DMN–mPFC connectivity (closeness model: β = −0.120, SE = 0.225, t = −0.53, p = .59; depression model: β = −0.001, SE = 0.120, t = −0.01, p = .99) or DMN–VS connectivity (closeness model: β = 0.158, SE = 0.233, t = 0.68, p = .50; depression model: β = 0.057, SE = 0.124, t = 0.46, p = .65). Neither closeness nor depressive symptoms significantly moderated these associations. Exploratory models using the Friend > Stranger contrast yielded a similar pattern of null results (see Supplement).

Although ROI analyses showed no reliable associations between striatal SRS and DMN connectivity, whole-brain analyses revealed reduced DMN connectivity with the cerebellum in individuals with greater VS shared-reward sensitivity. Specifically, in models that separately examined closeness and depressive symptoms as moderators, greater striatal SRS was associated with reduced DMN connectivity in a cluster spanning cerebellar lobule V/VI (closeness model: MNI = 25, −45, −27; 33 voxels, p = .005; depression model: MNI = 25, −53, −27; 25 voxels, p = .019). Exploratory friend–stranger analyses further revealed a striatal SRS x closeness x depression three-way interaction in DMN–VS connectivity and a depression-related striatal SRS effect on DMN’s coupling with the dorsolateral prefrontal cortex (see Supplemental Figure 2). Together, these findings suggest that VS shared-reward sensitivity may relate to network-level responses during reciprocity, with aspects of this relationship varying as a function of depressive symptoms (see Supplement).

4. Discussion

It is widely understood that the VS tracks reward value and demonstrates sensitivity to social context. It has remained unclear, however, whether this neural correlate of social value is indicative of variations in behavior and brain responses across disparate social processing domains. Despite evidence that depression influences social reward processing, it remains unknown how altered VS signaling may shape engagement of specific brain systems and consequent behavior in social tasks. In this study, we used two tasks — the Shared Reward Task and the Trust Game — in which participants shared monetary outcomes with a close partner and had their trust either reciprocated or violated by that same partner. We considered whether an index of striatal sensitivity to social reward sharing (the striatal SRS) could predict behavior, regional activation, and/or interregional connectivity during the Trust Game.

Replicating earlier work, we found that participants reported greater closeness with friends, invested more in them, and displayed stronger striatal activity when sharing rewards with them. We also found that depression predicted relative investment in the friend, with greater depression coinciding with larger investment. We further observed that striatal SRS predicted closeness-dependent activation in SPL and RSC, regions implicated in social processing (56,57). Further, jointly moderated by closeness and depression, striatal SRS predicted TPJ activation during friend’s (versus computer’s) reciprocity — a region known for mentalizing (58), intent inference (59), and impression updating in close relationships (60). This last finding suggests that perceived closeness calibrates how striatal shared-reward sensitivity maps onto TPJ responses during reciprocity, whereas depressive symptoms alter the direction of this association.

We also observed an inverse relationship between striatal SRS and functional connectivity between the cerebellum and DMN during a friend’s (vs. computer’s) reciprocity. Prior resting-state studies have reported altered connectivity between cerebellar regions and DMN nodes across psychiatric and neurodevelopmental conditions, although the direction and localization of these effects vary across studies (61–64). Because this literature is largely based on resting-state connectivity and typically uses individual DMN nodes rather than a whole-network seed, it provides only indirect context for the present task-based finding. More broadly, recent work demonstrates that the cerebellar circuit supports domain-general prediction and supervised learning in addition to the traditional motor control role (65,66), and that specific subregions are fundamental to social information processing (67,68). This finding also accords with evidence that the cerebellum is anatomically (69) and functionally (70) coupled with cortico-striatal reward circuitry, particularly during social processing (71). Together, these results suggest that stronger striatal SRS with a friend was associated with reduced task-dependent coupling between DMN and cerebellar social learning circuitry during that friend’s reciprocation — perhaps because familiar partners don’t demand the same dynamic updating of social impressions. Confirmation will require follow-up studies examining how experimentally controlled variations in striatal signaling affect DMN-cerebellar connectivity and social valuation learning.

Together, these results suggest that the striatal social reward signal calibrates neural resources devoted to evaluating partner trustworthiness in accordance with perceived social closeness, aligning with an adaptive regulatory role of VS documented previously (e.g., 72), but that depression alters this association. In the case of higher depression, we observe elevated investment in closer friends, and an amplified striatal SRS that predicts TPJ hypo-activation in response to a close friend’s reciprocation but TPJ hyper-activation in response to a more distant friend’s reciprocation. This suggests that a depressed individual’s heightened valuation of shared reward with a close friend also dampens the neural response underlying evaluation of that friend’s trustworthiness. This complex pattern of amplified and dampened regional activity offers an explanation for the inconsistent observations of social blunting (31–33) and sensitization (34,36–38) under depression, showing that the two effects can coexist. It would be interesting to test whether this juxtaposition extends to striatal signaling in non-social value-based processes as well.

We acknowledge two key limitations. First, contrary to our predictions, neither continuous ratings of social closeness nor striatal SRS signal intensity directly predicted investment behavior. This absence may reflect learning-driven updates to perceived closeness and trust during the game, such that initial ratings are revised on the basis of actual outcomes (73), obscuring any prior association with regional activation. Future experiments could assess closeness intermittently during gameplay or experimentally manipulate reciprocation rate to tease apart learning and initial valuation. Second, we assessed depressive symptomatology using the 7-up-7-down scale in a non-clinical sample, which may limit sensitivity to clinical-range depression and the ability to disentangle symptom domains such as anhedonia and depressive mood.

Despite these limitations, the current study provides the first evidence that VS reward sensitivity in one social context (reward sharing) predicts activation and connectivity in regions involved in social valuation in another social context (trust reciprocation). This cross-task prediction approach could be extended to other value-based social processes to test the generalizability and trait-like nature of striatal social sensitivity. Additionally, the current study provides neural evidence that apparent social blunting and sensitization may coexist among individuals reporting greater depressive symptoms, with the pattern varying as a function of perceived closeness. Together, these findings suggest that social dysfunction in depression may be nuanced, with sensitization in one social domain acting as a marker for blunting in another.

Supplementary Material

1

Figure 4. Depression reverses the association between striatal responsivity and TPJ activation during trust reciprocation.

Figure 4.

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A) Whole-brain analysis of the friend–computer reciprocate > defect contrast revealed a significant three-way interaction in right TPJ, with striatal SRS as predictor and closeness and depressive symptoms as joint moderators. Clusters were thresholded at Z > 3.1 and corrected for multiple comparisons at p < .05. Thresholded and non-thresholded images are available on Neurovault (identifiers.org/neurovault.collection:23155) B) At low levels of depression (mean – 1 SD), striatal SRS positively predicted TPJ activation during reciprocation from close friends and negatively for distant friends. C) At high levels of depression (mean + 1 SD), this pattern reversed. Johnson–Neyman plots illustrate the conditional effect of striatal SRS on TPJ activation across closeness values; shaded cyan areas indicate significant effects (p < .05) and pink areas non-significant effects. Depression was modeled continuously in all analyses. Rec–Def = reciprocate > defect contrast.

Figure 5. Striatal SRS negatively predicts DMN–cerebellum connectivity during trust reciprocation.

Figure 5.

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A) Whole-brain network-based psychophysiological interaction analysis with DMN as the seed network revealed a cerebellar cluster in which connectivity with the DMN was negatively predicted by striatal SRS, based on the friend > computer reciprocate > defect contrast. Results were thresholded at Z > 3.1 and corrected for multiple comparisons at p < .05. Thresholded and non-thresholded images are available on Neurovault (identifiers.org/neurovault.collection:23155). B) Scatterplot illustrates the negative association between striatal SRS and DMN–cerebellum connectivity. F-C Rec–Def = friend > computer reciprocate > defect contrast; DMN = default mode network.

Highlights:

  • Striatal sensitivity to shared reward (SRS) cross-task predicts trust game responses.

  • Depressive symptoms predict greater investment in friends vs computers

  • Low depression links striatal SRS to higher TPJ responses to close friend reciprocity

  • High depression links striatal SRS to lower TPJ responses to close friend reciprocity

Striatal SRS predicts lower DMN-cerebellar connectivity at reciprocity of friend

Acknowledgments

This work was supported in part by a grant from the National Institute on Aging (R01-AG067011 to DVS). This research includes calculations carried out on HPC resources supported by the US Army Research Laboratory under contract number ARL W911NF212007. We thank Abraham Dachs, Arun Lakshmanan, Ashley Hawk, Derrick Dwamena, Hannah van Heerden, Ishika Kohli, Jamie-Nicole Luistro, Jenelle Scholl, Matthew McCormick, Melanie Kos, and Ryan Gephart for their contributions to data collection. A preprint version of this work is available on bioRxiv.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Disclosure Statement

The authors have no conflicts of interest to disclose.

Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used Chat-GPT for debugging and text editing. The authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Informed consent and patient details

Written informed consent to take part in the study and to publish the article has been obtained from all participants or their legal representatives. The privacy rights of participants have been observed.

Studies in Human

This study was performed in compliance with relevant laws, regulatory frameworks and guidelines where the research took place.

This study was approved by the Temple University IRB.

(Approval No. 28455)

Data and code availability

Analysis code and de-identified data related to this project can be found on GitHub (https://github.com/DVS-Lab/r01-soi) and OpenNeuro (https://openneuro.org/datasets/ds005123/).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS2211513-supplement-1.docx (370.5KB, docx)

Data Availability Statement

Analysis code and de-identified data related to this project can be found on GitHub (https://github.com/DVS-Lab/r01-soi) and OpenNeuro (https://openneuro.org/datasets/ds005123/).

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