标题： 超图重叠社区检测在脑网络中的应用 显示英文标题

标题： Hypergraph Overlapping Community Detection for Brain Networks

摘要： 功能性磁共振成像（fMRI）已被广泛用于构建人类大脑的功能连接网络（FCNs）。TFCNs主要局限于量化ROI之间的成对关系，忽略了多个脑区之间的高阶依赖关系。最近，从fMRI时间序列数据中提出了超图构建方法，以描述多个ROI之间的高阶关系。尽管已有多种从fMRI时间序列构建超图的方法，但如何表征这些超图的拓扑结构仍然是一个开放问题。在本文中，我们对脑超网络中的社区检测领域做出了两个关键贡献。首先，我们为每个受试者构建了一个超图，捕捉区域之间的高阶依赖关系。其次，我们在超图上引入了一种基于谱聚类的方法来检测重叠的社区结构。最后，所提出的方法被用于检测多个受试者之间的共识社区结构。所提出的方法被应用于人类连接组计划的静息态fMRI数据，以总结一群健康年轻成年人之间的重叠社区结构。 显示英文摘要

摘要： Functional magnetic resonance imaging (fMRI) has been commonly used to construct functional connectivity networks (FCNs) of the human brain. TFCNs are primarily limited to quantifying pairwise relationships between ROIs ignoring higher order dependencies between multiple brain regions. Recently, hypergraph construction methods from fMRI time series data have been proposed to characterize the high-order relations among multiple ROIs. While there have been multiple methods for constructing hypergraphs from fMRI time series, the question of how to characterize the topology of these hypergraphs remains open. In this paper, we make two key contributions to the field of community detection in brain hypernetworks. First, we construct a hypergraph for each subject capturing high order dependencies between regions. Second, we introduce a spectral clustering based approach on hypergraphs to detect overlapping community structure. Finally, the proposed method is implemented to detect the consensus community structure across multiple subjects. The proposed method is applied to resting state fMRI data from Human Connectome Project to summarize the overlapping community structure across a group of healthy young adults.