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Activity Number: 243 - Brain Structural and Functional Connectivity Analysis
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #328475 Presentation
Title: The Spatial Wishart Process and Its Applications to Diffusion Tensor Images
Author(s): Zhou Lan* and Brian Reich and Joseph Guinness and Dipankar Bandyopadhyay
Companies: North Carolina State University and North Carolina State University and NC State University and Virginia Commonwealth University
Keywords: Diffusion Tensor Imaing; Spatial Wishart Process; Cholesky Decomposition; Nearest Neighbor Gaussian Processes; Cocaine Use
Abstract:

Diffusion Tensor Imaging (DTI) is an MRI-based neuroimaging technique used to measure the diffusion process of water molecules in the brain. Data from DTI scan are often summarized by a 3x3 positive definite matrix for each voxel. To make full use of the matrix-valued data, we propose a spatial Wishart process which captures spatial dependence between nearby matrices while assuming diffusion tensors marginally follow a Wishart distribution. We propose a spatial Wishart process with varying coefficients to model spatial dependence and test for covariate effects. Because the spatial Wishart process has a complicated density function, we develop approximations based on Cholesky decomposition. Due to the computational problem caused by massive MRI data, we further adopt Nearest Neighbor Gaussian Processes (NNGP) approximation for fast computation. In simulations, we demonstrate the improved performance compared to standard methods for detecting regions of the brain affected by covariates. We also apply our method to cocaine users data and controls to detect regions of difference.


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

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