On a Geometry of Interbrain Networks

Nicolás Hinrichs, Noah Guzmán, Melanie Weber

Keywords: Discrete Geometry, Graph Curvature, Inter-brain Networks, Hyperscanning, Social Neuroscience, Network Dynamics

TL;DR: This paper replaces static correlation metrics with a discrete-geometry framework using graph Ricci curvatures, enabling detection of dynamic phase transitions and routing shifts that reveal mechanisms of social brain interactions.

Abstract: Effective analysis in neuroscience benefits significantly from robust conceptual frameworks. Traditional metrics of interbrain synchrony in social neuroscience typically depend on fixed, correlation-based approaches, restricting their explanatory capacity to descriptive observations. Inspired by the successful integration of geometric insights in network science, we propose leveraging discrete geometry to examine the dynamic reconfigurations in neural interactions during social exchanges. Unlike conventional synchrony approaches, our method interprets inter-brain connectivity changes through the evolving geometric structures of neural networks. This geometric framework is realized through a pipeline that identifies critical transitions in network connectivity using entropy metrics derived from curvature distributions. By doing so, we significantly enhance the capacity of hyperscanning methodologies to uncover underlying neural mechanisms in interactive social behavior.

Submission Number: 40