We present a unified statistical framework for characterizing community
structure of brain functional networks that captures variation across
individuals and evolution over time. Existing methods for community detection
focus only on single-subject analysis of dynamic networks; while recent
extensions to multiple-subjects analysis are limited to static networks. To
overcome these limitations, we propose a multi-subject, Markov-switching
stochastic block model (MSS-SBM) to identify state-related changes in brain
community organization over a group of individuals. We first formulate a
multilayer extension of SBM to describe the time-dependent, multi-subject brain
networks. We develop a novel procedure for fitting the multilayer SBM that
builds on multislice modularity maximization which can uncover a common
community partition of all layers (subjects) simultaneously. By augmenting with
a dynamic Markov switching process, our proposed method is able to capture a
set of distinct, recurring temporal states with respect to inter-community
interactions over subjects and the change points between them. Simulation shows
accurate community recovery and tracking of dynamic community regimes over
multilayer networks by the MSS-SBM. Application to task fMRI reveals meaningful
non-assortative brain community motifs, e.g., core-periphery structure at the
group level, that are associated with language comprehension and motor
functions suggesting their putative role in complex information integration.
Our approach detected dynamic reconfiguration of modular connectivity elicited
by varying task demands and identified unique profiles of intra and
inter-community connectivity across different task conditions. The proposed
multilayer network representation provides a principled way of detecting
synchronous, dynamic modularity in brain networks across subjects.

使用统一的统计框架，提出了 MSS-SBM 方法，可以跨个体并能捕捉时间上的演化，用于大量 multi-subject 的人脑功能网络的群体分析，应用于任务 fMRI 的分析中，探测了动态重构特征和不同任务条件下的 community connectivity 独特性。