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Activity Number: 437 - Novel Bayesian Methods and Their Impacts on Scientific Applications
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #300043
Title: Bayesian Tensor Regression for Neuroimaging Data
Author(s): Montserrat Fuentes and Hossein Moradi*
Companies: Virginia Commonwealth University and South Dakota State University
Keywords: spatial dependence; high dimensional data; Bayesian; Neuroimaging; Addiction studies; tensor regression

In Neurosciences, magnetic resonance imaging (MRI) is a primary modality for studying brain structure and activity. Modeling spatial dependence of MRI data at different scales is one of the main challenges of contemporary neuroimaging. Methods that account for spatial correlation often require very cumbersome matrix evaluations which are prohibitive for data of this size, and thus current methods typically reduce dimensionality by modeling covariance among regions of interest rather than among voxels. However, ignoring spatial dependence at different scales could drastically reduce our ability to detect important activation patterns in the brain. To overcome these problems, we introduce a novel Bayesian Tensor approach, treating the brain image as response and having a vector of predictors. Our method provides estimates of the parameters of interest using a generalized sparsity principle. We demonstrate posterior consistency and develop a computational efficient algorithm. The effectiveness of our approach is illustrated through simulation studies and the analysis of the effects of cocaine addiction on the brain structure.

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

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