  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.
