We present a novel Bayesian method for identifying the transition of the brain states in working memory
task fMRI data via model fitness assessment. Specifically, we detect dynamic community structure
change-point(s) based on overlapped sliding window applied to multivariate time series. We use the
weighted stochastic block model to quantify the likelihood of a network configuration and develop a novel scoring criterion that we call posterior predictive discrepancy by evaluating the goodness of fit between
model and observations within the sliding window. The parameters for this model include the latent label
vector assigning network nodes to interacting communities, and the block model parameters determining
the weighted connectivity within and between communities. The GLM analyses were conducted in both
subject level and group level and the contrast between 2-back, 0-back and baseline were used to localise the regions of interest in task fMRI data.