Short communcationOrganization of intrinsic functional brain connectivity predicts decisions to reciprocate social behavior

- Functional connectivity predicted 20% of the variance in reciprocity behavior.
- rs fMRI, moral development and age explained 49% of reciprocity behavior variance.
- Brain imaging may be valuable to refine models of human behavior.

Reciprocation of trust exchanges is central to the development of interpersonal relationships and societal well-being. Understanding how humans make pro-social and self-centered decisions in dyadic interactions and how to predict these choices has been an area of great interest in social neuroscience. A functional magnetic resonance imaging (fMRI) based technology with potential clinical application is the study of resting state brain connectivity. We tested if resting state connectivity may predict choice behavior in a social context. Twenty-nine healthy adults underwent resting state fMRI before performing the Trust Game, a two person monetary exchange game. We assessed the ability of patterns of resting-state functional brain organization, demographic characteristics and a measure of moral development, the Defining Issues Test (DIT-2), to predict individuals' decisions to reciprocate money during the Trust Game. Subjects reciprocated in 74.9% of the trials. Independent component analysis identified canonical resting-state networks. Increased functional connectivity between the salience (bilateral insula/anterior cingulate) and central executive (dorsolateral prefrontal cortex/ posterior parietal cortex) networks significantly predicted the choice to reciprocate pro-social behavior (R2 = 0.20, p = 0.015). Stepwise linear regression analysis showed that functional connectivity between these two networks (p = 0.002), age (p = 0.007) and DIT-2 personal interest schema score (p = 0.032) significantly predicted reciprocity behavior (R2 = 0.498, p = 0.001). Intrinsic functional connectivity between neural networks in conjunction with other individual characteristics may be a valuable tool for predicting performance during social interactions. Future replication and temporal extension of these findings may bolster the understanding of decision making in clinical, financial and marketing settings.

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