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Brain function relies on a precisely coordinated and dynamic balance betweenthe functional integration and segregation of distinct neural systems.Characterizing the way in which neural systems reconfigure their interactionsto give rise to distinct but hidden brain states remains an open challenge. Inthis paper, we propose a Bayesian model-based characterization of latent brainstates and showcase a novel method based on posterior predictive discrepancyusing the latent block model to detect transitions between latent brain statesin blood oxygen level-dependent (BOLD) time series. The set of estimatedparameters in the model includes a latent label vector that assigns networknodes to communities, and also block model parameters that reflect the weightedconnectivity within and between communities. Besides extensive in-silico modelevaluation, we also provide empirical validation (and replication) using theHuman Connectome Project (HCP) dataset of 100 healthy adults. Our resultsobtained through an analysis of task-fMRI data during working memoryperformance show appropriate lags between external task demands andchange-points between brain states, with distinctive community patternsdistinguishing fixation, low-demand and high-demand task conditions.

Identification of brain states, transitions, and communities using functional MRI