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Oxytocin modulates social value representations in the amygdala

Nature Neuroscience volume 22, pages 633–641 (2019)Cite this article

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Abstract

Humans exhibit considerable variation in how they value their own interest relative to the interests of others. Deciphering the neural codes representing potential rewards for self and others is crucial for understanding social decision-making. Here we integrate computational modeling with functional magnetic resonance imaging to investigate the neural representation of social value and the modulation by oxytocin, a nine-amino acid neuropeptide, in participants evaluating monetary allocations to self and other (self–other allocations). We found that an individual’s preferred self–other allocation serves as a reference point for computing the value of potential self–other allocations. In more prosocial participants, amygdala activity encoded a social-value-distance signal; that is, the value dissimilarity between potential and preferred allocations. Intranasal oxytocin administration amplified this amygdala representation and increased prosocial behavior in more individualistic participants but not in more prosocial ones. Our results reveal a neurocomputational mechanism underlying social-value representations and suggest that oxytocin may promote prosociality by modulating social-value representations in the amygdala.

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Fig. 1: Experimental design.
Fig. 2: Oxytocin boosts prosociality selectively in individualists.
Fig. 3: Amygdala activity in prosocials (n = 30 males) and right lOFC activity in individualists (n = 30 males) encode social value distance relative to an individual-specific reference point.
Fig. 4: Oxytocin promotes amygdala activity in representing social values in individualists (nplacebo  = 30, noxytocin  = 26 in individualists, nplacebo = 30, noxytocin = 30 in prosocials).

Code availability

Analysis code to model the social value representation based on preference rating data is provided in the Supplementary Software.

Data availability

The data that support the findings of this study and the analysis code are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank H. Zhang, D. Wang, X. Zou and H. Wang for their assistance in data collection. This work was supported by the National Natural Science Foundation of China (Project nos. 31722026, 91632118, 31771204 and 31661143039 to Y.M.), the Fundamental Research Funds for the Central Universities (grant nos. 2017XTCX04 and 2018EYT04 to Y.M.), the Open Research Fund of the State Key Laboratory of Cognitive Neuroscience, Beijing Normal University (to Y.M.), startup funding from the State Key Laboratory of Cognitive Neuroscience and Learning, the IDG/McGovern Institute for Brain Research, Beijing Normal University (to Y.M.), by the International Max Planck Research School on Computational Methods in Psychiatry and Ageing Research (IMPRS COMP2PSYCH to Y.L.), and by the Max Planck Society and a MRC Career Development Award (no. MR/N02401X/1 to R.B.R.)

Author information

Author notes

  1. These authors contributed equally: Shiyi Li, Wanjun Lin and Wenxin Li.

Authors and Affiliations

  1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

    Yunzhe Liu, Shiyi Li, Wanjun Lin, Xinyuan Yan & Yina Ma

  2. IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

    Yunzhe Liu, Shiyi Li, Wanjun Lin, Xinyuan Yan & Yina Ma

  3. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China

    Yunzhe Liu, Shiyi Li, Wanjun Lin, Xinyuan Yan & Yina Ma

  4. School of Psychological and Cognitive Sciences, Peking University, Beijing, China

    Wenxin Li, Xuena Wang & Xinyue Pan

  5. Wellcome Centre for Human Neuroimaging, University College London, London, UK

    Robb B. Rutledge

  6. Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK

    Robb B. Rutledge

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  1. Yunzhe Liu
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Contributions

Y.M. conceived and designed the project and designed the fMRI experiment. Y.M. and Y.L. designed the replication experiments. W.Li., X.W., X.P. and Y.M. performed the fMRI experiment. S.L. and X.Y. performed the replication experiments. Y.L., S.L., W.Lin. and Y.M. analyzed the data and interpreted the results of the fMRI and behavioral experiments. Y.L., W.Lin., R.B.R. and Y.M. wrote the paper.

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Correspondence to Yina Ma.

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Journal peer review information: Nature Neuroscience thanks Bruno Averbeck, Andreas Meyer-Lindenberg, and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Integrated supplementary information

Supplementary Figure 1 General experimental procedure in the fMRI study.

There were two sessions: one behavioral session where participants (n = 282) completed two economic games to measure social disposition and were checked for eligibility for the fMRI study; the other fMRI session where prosocials (n = 66) and individualists (n = 61) were randomly assigned to the placebo and oxytocin treatment in a double-blind, placebo-controlled, between-subjects design, and underwent fMRI scanning 35 min later after spray administration. After the fMRI scanning, participants were asked to perform a similar behavioral task in a social competitive context. Note: PANAS, Positive and Negative Affect Schedule to monitor mood (Watson et al.61); TD, triple dominance (van Lange. 5) and SVO, social value orientation (Murphy et al. 28) to measure individual’s disposition of social value orientation. In the fMRI study, we first invited participants (n = 282) to a behavioral session to identify their dispositions in social value orientation. In the behavioral session, all participants provided demographic information and completed the triple dominance (TD) and social value orientation (SVO) tasks, which were conventional measurements of one’s stable disposition in social value orientation (Haruno & Frith. 3; Hilbig et al.62). To incentivize authentic responses during social interactions, participants were recruited in groups of 8–10 individuals (all were strangers to each other). For each economic game, participants were paired with a new, mutually anonymous partner and were asked to make monetary allocation decisions between the self and the partner.

Supplementary Figure 2 Model comparison analysis based on variational free energy.

a) fMRI during-scan behavioral data (n = 125 males, 180 trials for each participant); b) post-scan behavioral data (n = 125 males, 90 trials for each participant); c) independent online-replication data (n = 315 subjects, 82 trials for each participant); d) independent oxytocin behavioral replication data (n = 80 males, 82 trials for each participant). The social reference model (M4) consistently outperformed other models. Error bars represented standard error of the mean. In the behavioral analysis, we modeled trial-by-trial preference ratings using 8 different models. Model fits were performed on z-scored preference ratings. The first one was based on the value of monetary outcome to the self ($Self) and to the partner ($Other). The second and third models considered inequality aversion, in addition to the value of $Self and $Other, the third model made a difference between advantage and disadvantage inequality. The fourth model (i.e., M4) is the social reference model, considering the cosine similarity between the current offer and the most preferred one. Note that the equation of M4 was equivalent to a model of a * cos (θ- φ) based on trigonometry, where φ was the reference point, i.e., the angle between the most preferred allocation. The cos (θ- φ) was actually used in the fMRI analysis as the parametric regressor given it is a more compact measure (we used the distance to this cosine similarity, i.e., 1- cos (θ- φ)). The fifth and sixth models built up the inequality aversion in a compact way of summarizing the offer (similar to the social reference model), i.e., similarity between the potential offer (θ) and equal offer (45°) described as cosine (θ − 45°). The sixth model also considered fixed reference to the pure egocentric (cos (θ−0°), i.e., cos(θ)) and allocentric (cos (90°-θ), i.e., sin (θ)) offer. The last two models, seventh and eighth models, considered loss aversion with the same or different free parameters for loss aversion to self and to the partner. Given that BIC tends to overestimate model complexity in the trade-off of a growing number of free parameters and goodness-of-fit, we employed the variational free energy as the model selection criteria, which was insensitive to additional model complexity induced by adding covariance components (Friston et al.63) and has shown with better model selection ability relative to AIC/BIC (Rigoux et al.64; Penny.65). Among these 8 models, the best model for the current design was the social reference model (i.e., M4), according to model selection using free energy. Below listed all the 8 models for behavioral analysis (with a, b, c, and d as potential free parameters): M1: a * $Self + b * $Other M2: a * $Self + b * $Other + c * abs ($Self - $Other) M3: a * $Self + b * $Other + c * max (0, $Self - $Other) + d * max (0, $Other - $Self) M4: a * cos (θ) + b * sin (θ) M5: a * cos (θ - 45°) M6: a * cos (θ) + b * sin (θ) + c * cos (θ - 45°) M7: a * max (0, $Self) + b * max (0, $Other) + c * min (0, $Self) + c * min (0, $Other) M8: a * max (0, $Self) + b * max (0, $Other) + c * min (0, $Self) + d * min (0, $Other).

Supplementary Figure 3 Results of decision time.

The positive correlation between the social value distance (i.e., deviation from reference point for each allocation) and the decision time was higher in individualists (n = 30 males) than prosocials (n = 31 males) under placebo (revealed by independent-samples t-test on the Fisher z-scored correlation coefficients, t59 = 2.33, p = 0.023) in the original study (a) and in the additional oxytocin experiment (b, n = 80 males, 40 individualists and 40 prosocials, revealed by independent-samples t-test, t78 = 1.86, p = 0.067). The greater the dissimilarity between potential and preferred allocations, the longer individualists took to evaluate their preference. Error bars represented standard error of the mean. * p = 0.023, and + p = 0.067.

Supplementary Figure 4 Baseline saliva oxytocin assessment.

a) Individual’s baseline salivary oxytocin level was measured before the treatment and experiment and did not differ between the prosocials (n = 63 males) and individualists (n = 51 males) or between oxytocin (n = 57 males) and placebo (n = 57 males) groups in the result of Treatment x Social Disposition ANOVA. b) Pearson correlation analysis showed that the pre-experiment measure of baseline salivary oxytocin level was not related to the reference point in the social value representation (n = 114 males). Error bars represented standard error of the mean (n.s, not significant). The saliva samples were immediately stored at −20 °C until the batch assay. The samples were assayed using standard procedures with a commercially available enzyme immunoassay (EIA) kit (ADI-900–153, Enzo Life Science, Plymouth Meeting, PA). Before the assay, the reagents and the samples were balanced at room temperature 20–28 °C. Then, the standard and treated samples were added to a row of wells at 50 μl per well in turn and marked. Then, 25 μl of the enzyme conjugation solution was added to the wells with the standard, and the wells with the samples were assayed and fully mixed. The liquid in the wells and residual liquid were removed after a 60-min incubation reaction at 37 °C. The plates were washed with prediluted cleaning liquid 5 times. A 50 μl aliquot of substrate I and substrate II was later added to each well in turn, mixed fully, and kept from light at room temperature for a 15-min reaction. Then, 50μl of stop solution was added to each well and mixed fully to stop the reaction. The oxytocin extraction efficiency was 90% (114 out of 127 participants), as determined by spiking with a known amount of hormone and extracting this known amount along with the samples. Oxytocin levels in extracted saliva were then quantified using the oxytocin EIA, in which the salivary oxytocin hormone competed with exogenously added alkaline phosphatase-linked oxytocin, for binding sites on the oxytocin antibody. The optical density (OD) was measured on a Sunrise plate reader (Tecan, Research Triangle Park, NC) at 405 nm after 30 min. The hormone content (in pg/ml) was determined by plotting the OD of each sample against a standard curve.

Supplementary Figure 5 Effects of social perceptions of the partner.

participants’ social perceptions of their partner, we asked participants to rate their partner on 3 aspects: the first impression, likeability, and attractiveness, on a scale from 0 (lowest) to 10 (highest). There was no significant difference across all groups on any of these measures (n = 125 males). The rating data were analyzed using Treatment x Social Disposition ANOVAs followed by planned two-tailed t tests. Additionally, we asked participants to talk about only their names, hometown, etc. and made sure no topics related to any decision-making, payoff, or task-related information were raised: thus, participants would not be aware of each other’s social preferences. Data were plotted as boxplots for each group with boxes indicating 25–75% interquartile range, the inside horizontal lines indicating median values and whiskers indicating the minimum and maximum values. The horizontal line in the attractiveness panel was overlaid with the lower bound of box.

Supplementary Figure 6 Effects of scanning order and partner type on social reference point.

a) No effect of scanning order on the social reference point (n = 125 males). b) No effect of order and partner type on the social reference point. We conducted ANOVA with scanning order or partner type as between-subjects factors on individual-specific social preference point (φ). There was no effect of Scanning-order (a), or effect of partner type (e.g., prosocial-prosocial; prosocial-individualist; individualist-prosocial; individualist-individualist), i.e., no main effect of Order or interaction effect between Order and Social Disposition on the prosociality index (b). Error bars represented standard error of the mean (n.s, not significant).

Supplementary Figure 7 Amygdala responses predicted inequality aversion in prosocials.

a) Coronal view of bilateral anatomically defined amygdala ROI (red). Pearson correlation analyses showed that amygdala activity was positively correlated with inequality aversion across all participants (n = 116 males, b) but was only significant in prosocials (n = 30 males under placebo, c, and n = 30 males under oxytocin, d) and not in individualists (n = 30 males under placebo, e; n = 26 males under oxytocin, f).

Supplementary Figure 8 Other brain regions showing the interaction effect of treatment and social disposition in coding social-value distance.

Whole-brain analysis revealed that (a) right temporoparietal junction (rTPJ) and (b) ventral striatum (VST) also showed distinct effects of oxytocin in prosocials (n = 30 males under placebo and n = 30 males under oxytocin) and individualists (n = 30 males under placebo, n = 26 males under oxytocin) (P < 0.05, FWE-corrected at the cluster level after voxel-wise thresholding at P < 0.001).

Supplementary Figure 9 Amygdala responses to different parametric regressors.

Amygdala responses to different parametric regressors (divided into bins), including the deviations from the individual-specific reference point (a-d); the deviations from allocentric reference point (e-h); and the absolute value difference (i-l) in prosocials under placebo (the first column, i.e., a/e/i, n = 30 males) or oxytocin (the second column, i.e., b/f/j, n = 30 males), and in individualists under placebo (the third column, i.e., c/g/k, n = 30 males) or oxytocin (the fourth column, i.e., d/h/l, n = 26 males). The amygdala activity increased as a function of deviations from individual-specific reference point in prosocials under placebo (slope estimate of the linear fit = 0.222, p = 0.001, a) and this pattern was diminished under oxytocin (slope estimate = 0.010, p = 0.88, b). In contrast, oxytocin increased amygdala responses to deviations from individual-specific reference-point in individualists (slope estimate = 0.232, p = 0.003, c), and this pattern was not found under placebo (slope estimate = 0.042, p = 0.50, d). Monotonically increasing patterns were not present for amygdala responses for absolute value differences or for deviations from allocentric reference (all p > 0.5, e-l). Error bars represented standard error of the mean.

Supplementary Figure 10 Neural activity in the mPFC and lOFC for encoding the preference rating.

Neural activity in the mPFC (a) and lOFC (b), encoding subjective preference ratings on social allocations (height threshold p < 0.001, cluster-based FWE correction, p < 0.05), was modulated by Social Disposition and oxytocin treatment (n = 116 males). Error bars represented standard error of the mean (*P < 0.05, **P < 0.01, and ***P < 0.001).

Supplementary Figure 11 Functional connectivity between the amygdala and vmPFC.

Functional connectivity between the amygdala and vmPFC was stronger in individualists (n = 56 males) than in prosocials (n = 60 males) (voxel-wise p < 0.001, uncorrected). Oxytocin increased the amygdala-vmPFC coupling individualists but not prosocials. Further ROI analysis (functionally defined vmPFC cluster showing different coupling with amygdala between individualists and prosocial under placebo, height threshold p < 0.001, suggested that oxytocin increased the strength of functional connectivity between amygdala and vmPFC in encoding social value distance in individualists (one-sample t-test, t54 = 2.69, p = 0.009) but not in prosocials (t58 = 0.067, p = 0.95). Error bars represented standard error of the mean (**P < 0.01; n.s, not significant).

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Liu, Y., Li, S., Lin, W. et al. Oxytocin modulates social value representations in the amygdala. Nat Neurosci 22, 633–641 (2019). https://doi.org/10.1038/s41593-019-0351-1

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