Publication details

Getting into sync: Data-driven analyses reveal patterns of neural coupling that distinguish among different social exchanges

Authors

ŠPILÁKOVÁ Beáta SHAW Daniel Joel CZEKÓOVÁ Kristína MAREČEK Radek BRÁZDIL Milan

Year of publication 2020
Type Article in Periodical
Magazine / Source Human Brain mapping
MU Faculty or unit

Central European Institute of Technology

Citation
Doi http://dx.doi.org/10.1002/hbm.24861
Keywords competition; co-operation; hyperscanning; interaction structure; inter-subject correlation; neural coupling; social interaction
Description In social interactions, each individual's brain drives an action that, in turn, elicits systematic neural responses in their partner that drive a reaction. Consequently, the brain responses of both interactants become temporally contingent upon one another through the actions they generate, and different interaction dynamics will be underpinned by distinct forms of between-brain coupling. In this study, we investigated this by "performing functional magnetic resonance imaging on two individuals simultaneously (dual-fMRI) while they competed or cooperated with one another in a turn-based or concurrent fashion." To assess whether distinct patterns of neural coupling were associated with these different interactions, we combined two data-driven, model-free analytical techniques: group-independent component analysis and inter-subject correlation. This revealed four distinct patterns of brain responses that were temporally aligned between interactants: one emerged during co-operative exchanges and encompassed brain regions involved in social cognitive processing, such as the temporo-parietal cortex. The other three were associated with competitive exchanges and comprised brain systems implicated in visuo-motor processing and social decision-making, including the cerebellum and anterior cingulate cortex. Interestingly, neural coupling was significantly stronger in concurrent relative to turn-based exchanges. These results demonstrate the utility of data-driven approaches applied to "dual-fMRI" data in elucidating the interpersonal neural processes that give rise to the two-in-one dynamic characterizing social interaction.
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