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Title: Bayesian approaches for dynamic brain connectivity Authors:  Michele Guindani - University of California, Irvine (United States) [presenting]
Abstract: A Bayesian framework for estimating time-varying functional connectivity networks from brain fMRI data will be discussed. Dynamic functional connectivity, i.e., the study of how interactions among brain regions change dynamically over the course of a fMRI experiment, has recently received wide interest in the neuroimaging literature. The method utilizes state space models for classification of latent neurological states, achieving estimation of the connectivity networks in an integrated framework that borrows strength over the entire time course of the experiment. Furthermore, we assume that the graph structures, which define the connectivity states at each time point, are related within a super-graph, to encourage the selection of the same edges among related graphs.