A Probabilistic Approach to Discovering Dynamic Full-Brain Functional Connectivity Patterns



Recent work indicates that the covariance structure of functional magnetic resonance imaging (fMRI) data -- commonly described as functional connectivity -- can change as a function of the participant's cognitive state (for review see Turk-Browne et al., 2013). Here we present a technique, termed hierarchical topographic factor analysis (HTFA), for efficiently discovering full-brain networks in large multi-subject neuroimaging datasets...