基于相位的边耦合：建模 EEG 网络中的重叠功能架构与高阶边对边交互
Phase-based Edge Coupling: Modeling Overlapping Functional Architectures and High-Order Edge-to-Edge Interactions in EEG Networks
arXiv:2610.32286v1 公告类型：新摘要：网络神经科学传统上依赖于以节点为中心的方法，这些方法在捕捉大脑重叠的功能架构和更高阶的边对边交互方面存在固有局限。新兴的以边为中心的范式通过将分析单元从节点转向边来弥补这一空白。然而，尽管以边为中心的模型已在 DTI 和 fMRI 中建立，但可比的 EEG 方法尚未开发。在本研究中，我们提出了一种基于相位的边中心耦合（PEC）算法用于 EEG 网络建模，其中边时间序列由 EEG 信号的瞬时相位差导出，边对边耦合通过归一化内积量化。仿真测试证明了 PEC 算法的稳健性，对虚假耦合达到高准确率（0.7）。在提供足够数据长度（>=16 个周期）时，PEC 在个体可识别性和认知状态可识别性上均优于传统的相位节点中心耦合（PNC），能够更优地表征稳定的个体神经特征和动态的任务依赖功能配置。此外，与 PNC 的硬划分模块化不同，PEC 捕捉到了高度重叠但功能专门化的模块结构和连接枢纽，与任务参与的认知过程相一致。应用于二元互动时，PEC 识别出编码对称和非对称神经协调的独特跨脑模块。这些发现确立了 PEC 作为 EEG 网络建模的有效且强大的工具，为大脑在脑内和脑间网络中复杂的、多功能组织提供了新见解。
把边中心网络分析从 DTI 和 fMRI 扩展到 EEG，为研究重叠功能模块和脑间耦合提供了可用的算法。
arXiv:2610.32286v1 Announce Type: new Abstract: Network neuroscience has traditionally relied on node-centric approaches, which are inherently limited in capturing the brain's overlapping functional architectures and higher-order edge-to-edge interactions. The emerging edge-centric paradigm addresses this gap by shifting the analytical unit from nodes to edges. However, while edge-centric models have been established for DTI and fMRI, comparable EEG methodologies have yet to be developed. In this study, we proposed a Phase-based Edge-centric Coupling (PEC) algorithm for EEG network modelling, where edge time series are derived from instantaneous phase differences of EEG signals and edge-to-edge coupling is quantified via normalized inner product. Simulation tests demonstrated the robustness of PEC algorithm, achieving high accuracy ( 0.7) against spurious coupling.When provided sufficient data length(>=16 cycles), PEC outperformed traditional Phase Node-centric Coupling (PNC) in both individual identifiability and cognitive-state identifiability, enabling superior characterization of stable individual neural signatures and dynamic task-dependent functional configurations. Furthermore, unlike PNC's hard-partitioned modularity, PEC captured highly overlapping yet functionally specialized modular structures and connector hubs, aligning with task-engaged cognitive processes. Applied to dyadic interactions, PEC identified distinct cross-brain modules encoding symmetric and asymmetric neural coordination. These findings establish PEC as a valid and powerful tool for EEG network modelling, providing new insights into the brain's complex, multifunctional organization at both intra-brain and inter-brain networks.
中文翻译还没有生成，先显示原文。
来源：arXiv q-bio.NC 预印本 · arxiv.org