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Activity Number: 141 - Recent Advances in High-Dimensional Bayesian Model Selection
Type: Invited
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: International Indian Statistical Association
Abstract #326506 Presentation
Title: Fully Bayesian Spectral Methods for Imaging Data
Author(s): Brian Reich and Joseph Guinness* and Simon Vandekar and Russell T Shinohara and Ana-Maria Staicu
Companies: North Carolina State University and NC State University and University of Pennsylvania and University of Pennsylvania and NC State University
Keywords: sphere; neuroimaging; Gaussian process; spatial regression

Medical imaging data with thousands of spatially-correlated data points are common in many fields. Methods that account for spatial correlation often require cumbersome matrix evaluations which are prohibitive for data of this size, and thus current work has either used low-rank approximations or analyzed data in blocks. We propose a method that accounts for nonstationarity, functional connectivity of distant regions of interest, and local signals, and can be applied to large multi-subject datasets using spectral methods combined with Markov Chain Monte Carlo sampling. We illustrate using simulated data that properly accounting for spatial dependence improves precision of estimates and yields valid statistical inference. We apply the new approach to study associations between cortical thickness and Alzheimer's disease, and find several regions of the cortex where patients with Alzheimer's disease are thinner on average than healthy controls.

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

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