Many functional magnetic resonance imaging (fMRI) studies rely on estimates of hierarchically organised brain networks whose segregation and integration reflect the dynamic transitions of latent cognitive states. However, most existing methods for estimating the community structure of networks from both individual and group-level analysis neglect the variability between subjects and lack validation. In this paper, we develop a new multilayer community detection method based on Bayesian latent block modelling. The method can robustly detect the group-level community structure of weighted functional networks that give rise to hidden brain states with an unknown number of communities and retain the variability of individual networks. For validation, we propose a new community structure-based multivariate Gaussian generative model to simulate synthetic signal. Our result shows that the inferred community memberships using hierarchical Bayesian analysis are consistent with the predefined node labels in the generative model. The method is also tested using real working memory task-fMRI data of 100 unrelated healthy subjects from the Human Connectome Project. The results show distinctive community structure patterns between 2-back, 0-back, and fixation conditions, which may reflect cognitive and behavioural states under working memory task conditions.

翻译：许多功能磁共振成像（fMRI）研究依赖于对层次化组织的脑网络进行估计，这些网络的分离与整合反映了潜在认知状态的动态转换。然而，现有大多数从个体与群体层面分析中估计网络社区结构的方法忽略了被试间的变异性，且缺乏验证。本文提出了一种基于贝叶斯潜在区块建模的新型多层社区检测方法。该方法能够稳健地检测加权功能网络的群体层面社区结构，这些网络产生具有未知社区数量的隐藏脑状态，同时保留个体网络的变异性。为进行验证，我们提出了一种新的基于社区结构的多变量高斯生成模型来模拟合成信号。结果表明，通过层次贝叶斯分析推断出的社区成员关系与生成模型中预定义的节点标签一致。该方法还使用来自人类连接组计划的100名无关联健康被试的真实工作记忆任务fMRI数据进行了测试。结果显示，2-back、0-back与注视条件之间存在明显的社区结构模式差异，这可能反映了工作记忆任务条件下的认知与行为状态。