论文标题
多频EEG网络中的社区检测
Community Detection in Multi-frequency EEG Networks
论文作者
论文摘要
目的：近年来，已经使用图理论工具研究了人脑的功能连通性。一种方法是社区检测，这对于揭示本地化网络至关重要。现有方法集中于由单个频段构建的网络，同时忽略功能连接性的多频性质。因此，有必要研究多频功能连接性，以便能够捕获神经元连接的完整视图。方法：在本文中，我们使用多层网络来建模多频功能连接。在建议的模型中，每层对应于不同的频带。然后，我们将模块化的定义扩展到多层网络，以开发新的社区检测算法。结果}所提出的方法应用于在人脑中错误监测的研究期间收集的脑电图数据。研究了两种响应类型的不同频段内部和跨不同频段之间的差异，即错误和正确。结论：结果表明，在发生错误响应之后，大脑组织自己以跨越频率形成社区，尤其是在theta和伽马频段之间，而没有观察到类似的跨频社区形成以进行正确的响应。此外，与正确响应的社区结构相比，针对错误响应所检测到的社区结构更加一致。意义：多频功能连接网络模型与多层社区检测算法结合使用，可以揭示跨不同任务和响应类型跨跨频功能连接网络形成的变化。
Objective: In recent years, the functional connectivity of the human brain has been studied with graph theoretical tools. One such approach is community detection which is fundamental for uncovering the localized networks. Existing methods focus on networks constructed from a single frequency band while ignoring multi-frequency nature of functional connectivity. Therefore, there is a need to study multi-frequency functional connectivity to be able to capture the full view of neuronal connectivity. Methods: In this paper, we use multilayer networks to model multi-frequency functional connectivity. In the proposed model, each layer corresponds to a different frequency band. We then extend the definition of modularity to multilayer networks to develop a new community detection algorithm. Results} The proposed approach is applied to electroencephalogram data collected during a study of error monitoring in the human brain. The differences between the community structures within and across different frequency bands for two response types, i.e. error and correct, are studied. Conclusion: The results indicate that following an error response, the brain organizes itself to form communities across frequencies, in particular between theta and gamma bands while a similar cross-frequency community formation is not observed for the correct response. Moreover, the community structures detected for the error response were more consistent across subjects compared to the community structures for correct response. Significance: The multi-frequency functional connectivity network models combined with multilayer community detection algorithms can reveal changes in cross-frequency functional connectivity network formation across different tasks and response types.
