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Activity Number: 25
Type: Topic Contributed
Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #311916 View Presentation
Title: Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection
Author(s): Jeff Goldsmith*+ and Lei Huang and Ciprian Crainiceanu
Companies: Columbia University and Johns Hopkins University and Johns Hopkins University
Keywords: Markov random field ; Gibbs sampler ; Ising prior
Abstract:

We develop scalar-on-image regression models when images are registered multidimensional manifolds. We propose a fast and scalable Bayes inferential procedure to estimate the image coefficient. The central idea is the combination of an Ising prior distribution, which controls a latent binary indicator map, and an intrinsic Gaussian Markov random field, which controls the smoothness of the nonzero coefficients. The model is fit using a single-site Gibbs sampler, which allows fitting within minutes for hundreds of subjects with predictor images containing thousands of locations. We apply this method to a neuroimaging study where cognitive outcomes are regressed on measures of white matter microstructure at every voxel of the corpus callosum for hundreds of subjects.


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