Face perception arises from a collective activation of brain regions in the occipital, parietal and temporal cortices. Despite the wide acknowledgment that these regions act in an intertwined network, the network behavior itself is poorly understood. Here we present a study in which time‐varying connectivity estimated from EEG activity elicited by facial expressions presentation was characterized using graph‐theoretical measures of node centrality and global network topology. Results revealed that face perception results from a dynamic reshaping of the network architecture, characterized by the emergence of hubs located in the occipital and temporal regions of the scalp. The importance of these nodes can be observed from the early stages of visual processing and reaches a climax in the same time‐window in which the face‐sensitive N170 is observed. Furthermore, using Granger causality, we found that the time‐evolving centrality of these nodes is associated with ERP amplitude, providing a direct link between the network state and local neural response. Additionally, investigating global network topology by means of small‐worldness and modularity, we found that face processing requires a functional network with a strong small‐world organization that maximizes integration, at the cost of segregated subdivisions. Interestingly, we found that this architecture is not static, but instead, it is implemented by the network from stimulus onset to ~200 ms. Altogether, this study reveals the event‐related changes underlying face processing at the network level, suggesting that a distributed processing mechanism operates through dynamically weighting the contribution of the cortical regions involved.

中文翻译：

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事件相关的网络变化揭示了面部处理过程中皮层整合的动态

面部感知源于枕叶、顶叶和颞叶皮质中大脑区域的集体激活。尽管人们普遍承认这些区域在一个相互交织的网络中发挥作用，但对网络行为本身却知之甚少。在这里，我们提出了一项研究，其中使用节点中心性和全局网络拓扑的图论测量来表征从面部表情呈现引起的 EEG 活动估计的时变连接性。结果表明，人脸感知是网络架构动态重塑的结果，其特征是位于头皮枕骨和颞叶区域的枢纽出现。这些节点的重要性可以从视觉处理的早期阶段观察到，并在观察面敏 N170 的同一时间窗口中达到高潮。此外，使用格兰杰因果关系，我们发现这些节点的时间演化中心性与 ERP 幅度相关，提供了网络状态和局部神经响应之间的直接联系。此外，通过小世界和模块化研究全局网络拓扑，我们发现人脸处理需要一个功能网络，该网络具有强大的小世界组织，以最大限度地集成，代价是分离的细分。有趣的是，我们发现这种架构不是静态的，而是由网络从刺激开始到约 200 ms 实现的。总而言之，这项研究揭示了网络层面人脸处理背后的事件相关变化，表明分布式处理机制通过动态加权相关皮层区域的贡献来运作。