Inferring functional interaction and transition patterns via dynamic Bayesian variable partition models.

ABSTRACT: Multivariate connectivity and functional dynamics have been of wide interest in the neuroimaging field, and a variety of methods have been developed to study functional interactions and dynamics. In contrast, the temporal dynamic transitions of multivariate functional interactions among brain networks, in particular, in resting state, have been much less explored. This article presents a novel dynamic Bayesian variable partition model (DBVPM) that simultaneously considers and models multivariate functional interactions and their dynamics via a unified Bayesian framework. The basic idea is to detect the temporal boundaries of piecewise quasi-stable functional interaction patterns, which are then modeled by representative signature patterns and whose temporal transitions are characterized by

SUBMITTER: Zhang J 

PROVIDER: S-EPMC4018439 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature