A Bayesian hierarchical framework for modeling brain connectivity for neuroimaging data
Corresponding Author
Shuo Chen
Department of Epidemiology and Biostatistics, University of Maryland, College Park, Maryland 20742, U.S.A.
email: [email protected]Search for more papers by this authorF. DuBois Bowman
Department of Biostatistics, Columbia University, Manhattan, New York 10032, U.S.A.
Search for more papers by this authorHelen S. Mayberg
School of Medicine, Emory University, Atlanta, Georgia 30322, U.S.A.
Search for more papers by this authorCorresponding Author
Shuo Chen
Department of Epidemiology and Biostatistics, University of Maryland, College Park, Maryland 20742, U.S.A.
email: [email protected]Search for more papers by this authorF. DuBois Bowman
Department of Biostatistics, Columbia University, Manhattan, New York 10032, U.S.A.
Search for more papers by this authorHelen S. Mayberg
School of Medicine, Emory University, Atlanta, Georgia 30322, U.S.A.
Search for more papers by this authorSummary
We propose a novel Bayesian hierarchical model for brain imaging data that unifies voxel-level (the most localized unit of measure) and region-level brain connectivity analyses, and yields population-level inferences. Functional connectivity generally refers to associations in brain activity between distinct locations. The first level of our model summarizes brain connectivity for cross-region voxel pairs using a two-component mixture model consisting of connected and nonconnected voxels. We use the proportion of connected voxel pairs to define a new measure of connectivity strength, which reflects the breadth of between-region connectivity. Furthermore, we evaluate the impact of clinical covariates on connectivity between region-pairs at a population level. We perform parameter estimation using Markov chain Monte Carlo (MCMC) techniques, which can be executed quickly relative to the number of model parameters. We apply our method to resting-state functional magnetic resonance imaging (fMRI) data from 32 subjects with major depression and simulated data to demonstrate the properties of our method.
Supporting Information
Additional Supporting Information may be found in the online version of this article.
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