用几何方法分析大脑间动态连接，揭示社交互动中的神经机制。
On a Geometry of Interbrain Networks
- 通过曲率分布熵识别脑间连接的关键转变点。
- 相比传统相关性方法，能捕捉动态网络结构变化。
- 适合研究社交交互神经机制的科研人员。
神经科学的有效分析依赖于稳健的概念框架。传统社会神经科学中的脑间同步度量多基于固定的相关性方法，限制了其解释能力，仅能提供描述性观察。受网络科学中几何洞察成功的启发，我们提出利用离散几何来研究社交互动过程中神经连接的动态重组。与传统同步方法不同，该方法通过神经网络演化的几何结构来解读脑间连接的变化。这一几何框架通过一个流程实现：利用源自曲率分布的熵度量，识别网络连接中的关键转变。由此，显著提升了高精度扫描（hyperscanning）方法在揭示互动社交行为潜在神经机制方面的能力。
原文摘要 · Abstract (English)
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.
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