On a Geometry of Interbrain Networks

2025-09-12

AI Summary

Conventional social neuroscience relies on static correlation metrics to analyze inter-brain synchrony, yielding limited mechanistic interpretability of neural interactions. Method: This paper introduces a dynamic cross-brain network analysis framework grounded in discrete differential geometry, pioneering the integration of network curvature modeling and curvature distribution entropy into hyperscanning research. By quantifying the temporal evolution of network curvature, the method precisely identifies critical state transitions in inter-brain connectivity during social interaction. Contribution/Results: Evaluated across multiple hyperscanning paradigms, the approach significantly enhances both the interpretability and detection sensitivity of neural synchrony phenomena. It establishes a novel computational paradigm for uncovering the dynamic neural underpinnings of social cognition and provides a generalizable analytical toolkit for network-level neurodynamic investigation.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Social media analysis through the lenses of networks
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Developing geometric framework for interbrain network analysis
• Overcoming limitations of correlation-based synchrony metrics
• Enhancing hyperscanning to uncover neural interaction mechanisms

Innovation

• Leveraging discrete geometry for neural network analysis
• Using entropy metrics from curvature distributions
• Enhancing hyperscanning with geometric framework