Tracking changes in functional connectivity of brain networks from resting-state fMRI using particle filters

Recent empirical research has discovered that linkages among fMRI signals of the brain in resting-state have meaningful temporal variations. Most current studies of brain networks assume that these linkages are constant. We propose a model and an accompanying algorithm to infer and track changes in these interaction strengths, thus providing a more comprehensive way to study brain dynamics. The stochastic model employed is akin to one used for neuronal states (DCM) and a Rao-Blackwellized filtering algorithm is set up for tracking purposes. Our results show that time-varying interactions among brain regions can be successfully found which have the potential of providing great clinical value.