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An EEG Study of Brain Connectivity Dynamics at the Resting State

机译：静息状态下脑连通动力学的脑电图研究

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摘要

We investigated the dynamical behavior of resting state functional connectivity using EEG signals. Employing a recently introduced methodology that considers the time variations of phase coupling among signals from different channels, a sequence of functional connectivity graphs (FCGs) was constructed for different frequency bands and analyzed based on graph theoretic tools. In the first stage of analysis, hubs were detected in the FCGs based on local and global efficiency. The probability of each node to be identified as a hub was estimated. This defined a topographic function that showed widespread distribution with prominence over the frontal brain regions for both local and global efficiency. Hubs consistent across time were identified via a summarization technique and found to locate over forehead. In the second stage of analysis, the modular structure of each single FCG was delineated. The derived time-dependent signatures of functional structure were compared in a systematic way revealing fluctuations modulated by frequency. Interestingly, the evolution of functional connectivity can be described via abrupt transitions between states, best described as short-lasting bimodal functional segregations. Based on a distance function that compares clusterings, we discovered that these segregations are recurrent. Entropic measures further revealed that the apparent fluctuations are subject to intrinsic constraints and that order emerges from spatially extended interactions.
机译：我们调查了使用脑电信号的静态功能连接的动力学行为。使用最近引入的一种方法，该方法考虑了来自不同通道的信号之间的相位耦合的时间变化，针对不同的频段构建了一系列功能连接图（FCG），并基于图论工具进行了分析。在分析的第一阶段，根据本地和全球效率在FCG中检测到枢纽。估计每个节点被识别为集线器的可能性。这就定义了一种地形功能，该功能在局部和全局效率方面均表现出广泛分布，分布在额脑区域。通过汇总技术确定了时间一致的轮毂，并发现其位于额头上方。在分析的第二阶段，描述了每个单个FCG的模块化结构。以系统的方式比较了导出的功能结构的时间依赖性签名，揭示了受频率调制的波动。有趣的是，功能连通性的演变可以通过状态之间的突然转变来描述，最好形容为持久的双峰功能分离。基于比较聚类的距离函数，我们发现这些隔离是经常发生的。熵测进一步表明，表观波动受到内在约束，并且其顺序是由空间扩展的相互作用产生的。

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