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Activity Number: 183 - SPEED: Bayesian Methods Student Awards
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 11:15 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #325122
Title: A Bayesian Approach to Dynamic Functional Connectivity in fMRI Data
Author(s): Ryan Warnick* and Marina Vannucci and Michele Guindani and Erik Erhardt and Elena Allen and Vince Calhoun
Companies: Rice University and Rice University and University of California, Irvine and University of New Mexico and Medici Technologies and University of New Mexico
Keywords: Graphical Models ; Neuroimaging ; Hidden Markov Model ; fMRI ; Variable Selection
Abstract:

Dynamic functional connectivity, i.e., the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently re-ceived wide interest in the neuroimaging literature. Current approaches for studying dynamic connectivity often rely on ad-hoc approaches for inference, with the fMRI time courses segmented by a sequence of sliding windows. We propose a principled Bayesian approach for estimating time varying functional connectivity networks. Our method utilizes a hidden Markov model for classification of latent neurological states, achieving estimation of the connectivity networks in an integrated framework that bor- rows 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. We apply our method to simulated task-based fMRI data, where we show how our approach allows the decoupling of the task-related activations and the functional connectivity states. We also analyze data from an fMRI sensorimotor task experiment on a control.


Authors who are presenting talks have a * after their name.

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