  Brain function relies on a precisely coordinated and dynamic balance between
the functional integration and segregation of distinct neural systems.
Characterizing the way in which neural systems reconfigure their interactions
to give rise to distinct but hidden brain states remains an open challenge. In
this paper, we propose a Bayesian model-based characterization of latent brain
states and showcase a novel method based on posterior predictive discrepancy
using the latent block model to detect transitions between latent brain states
in blood oxygen level-dependent (BOLD) time series. The set of estimated
parameters in the model includes a latent label vector that assigns network
nodes to communities, and also block model parameters that reflect the weighted
connectivity within and between communities. Besides extensive in-silico model
evaluation, we also provide empirical validation (and replication) using the
Human Connectome Project (HCP) dataset of 100 healthy adults. Our results
obtained through an analysis of task-fMRI data during working memory
performance show appropriate lags between external task demands and
change-points between brain states, with distinctive community patterns
distinguishing fixation, low-demand and high-demand task conditions.
