Over the past two decades, tools from network science have been leveraged to characterize the organization of both structural and functional networks of the brain. One such measure of network organization is hub node identification. Hubs are specialized nodes within a network that link distinct brain units corresponding to specialized functional processes. Conventional methods for identifying hub nodes utilize different types of centrality measures and participation coefficient to profile various aspects of nodal importance. These methods solely rely on the functional connectivity networks constructed from functional magnetic resonance imaging (fMRI), ignoring the structure-function coupling in the brain. In this paper, we introduce a graph signal processing (GSP) based hub detection framework that utilizes both the structural connectivity and the functional activation to identify hub nodes. The proposed framework models functional activity as graph signals on the structural connectivity. Hub nodes are then detected based on the premise that hub nodes are sparse, have higher level of activity compared to their neighbors, and the non-hub nodes' activity can be modeled as the output of a graph-based filter. Based on these assumptions, an optimization framework, GraFHub, is formulated to learn the coefficients of the optimal polynomial graph filter and detect the hub nodes. The proposed framework is evaluated on both simulated data and resting state fMRI (rs-fMRI) data from Human Connectome Project (HCP).

翻译：在过去的二十年中，网络科学的工具已被用于刻画大脑结构和功能网络的组织方式。网络组织的一种度量是枢纽节点识别。枢纽是网络中专化的节点，它们连接对应特定功能过程的不同大脑单元。识别枢纽节点的传统方法利用不同类型的中心性度量和参与系数来描述节点重要性的各个方面。这些方法完全依赖于从功能磁共振成像数据构建的功能连接网络，忽略了大脑中的结构-功能耦合。本文介绍了一种基于图信号处理的枢纽检测框架，该框架利用结构连接性和功能激活两者来识别枢纽节点。所提出的框架将功能活动建模为结构连接上的图信号。然后基于以下前提检测枢纽节点：枢纽节点是稀疏的，与其邻居相比具有更高的活动水平，并且非枢纽节点的活动可以建模为基于图的滤波器的输出。基于这些假设，我们构建了一个优化框架 GraFHub，以学习最优多项式图滤波器的系数并检测枢纽节点。所提出的框架在模拟数据和来自人类连接组计划的静息态功能磁共振成像数据上进行了评估。