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.

翻译：神经科学中的有效分析极大受益于稳健的概念框架。社会神经科学中传统的脑间同步性度量通常依赖于固定的、基于相关性的方法，这将其解释能力限制在描述性观察层面。受网络科学中几何见解成功整合的启发，我们提出利用离散几何来研究社交互动过程中神经相互作用的动态重构。与传统的同步性方法不同，我们的方法通过神经网络不断演化的几何结构来解释脑间连接性的变化。这一几何框架通过一个流程得以实现，该流程利用从曲率分布导出的熵度量来识别网络连接中的关键转变。通过这种方式，我们显著增强了超扫描方法在揭示交互性社会行为背后神经机制方面的能力。