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A Bayesian hierarchical framework for modeling brain connectivity for neuroimaging data
Author(s) -
Chen Shuo,
Bowman F. DuBois,
Mayberg Helen S.
Publication year - 2016
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12433
Subject(s) - neuroimaging , bayesian probability , computer science , bayesian hierarchical modeling , hierarchical database model , artificial intelligence , bayesian inference , machine learning , data mining , psychology , neuroscience
Summary 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.