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Reindl, V.

Publications and source records attributed to Reindl, V..

2 recordsLinked to original sources

Synchrony of mind and body are distinct in mother-child dyads

Hyperscanning studies have begun to unravel the brain mechanisms underlying social interaction, indicating a functional role for interpersonal neural synchronization (INS), yet the mechanisms that drive INS are poorly understood. While interpersonal synchrony is considered a multimodal phenomenon, it is not clear how different biological and behavioral synchrony markers are related to each other. The current study, thus, addresses whether INS is functionally-distinct from synchrony in other systems - specifically the autonomic nervous system (ANS) and motor behavior. To test this, we used a novel methodological approach, based on concurrent functional near-infrared spectroscopy-electrocardiography, recorded while N = 34 mother-child and stranger-child dyads (child mean age 14 years) engaged in cooperative and competitive tasks. Results showed a marked differentiation between neural, ANS and behavioral synchrony. Importantly, only in the neural domain was higher synchrony for mother-child compared to stranger-child dyads observed. Further, ANS and neural synchrony were positively related during competition but not during cooperation. These results suggest that synchrony in different behavioral and biological systems may reflect distinct processes. Mother-child INS may arise due to neural processes related to social affiliation, which go beyond shared arousal and similarities in behavior.

neuroscience

Interacting brains revisited: A cross-brain network neuroscience perspective

Elucidating the neural basis of social behavior is a long-standing challenge in neuroscience. Such endeavors are driven by attempts to extend the isolated perspective on the human brain by considering interacting persons brain activities, but a theoretical and computational framework for this purpose is still in its infancy. Here, we posit a comprehensive framework based on bipartite graphs for interbrain networks and address whether they provide meaningful insights into the neural underpinnings of social interactions. First, we show that the nodal density of such graphs exhibits nonrandom properties. While the current analyses mostly rely on global metrics, we encode the regions roles via matrix decomposition to obtain an interpretable network representation yielding both global and local insights. With Bayesian modeling, we reveal how synchrony patterns seeded in specific brain regions contribute to global effects. Beyond inferential inquiries, we demonstrate that graph representations can be used to predict individual social characteristics, outperforming functional connectivity estimators for this purpose. In the future, this may provide a means of characterizing individual variations in social behavior or identifying biomarkers for social interaction and disorders.

neuroscience