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Activity Number: 249 - Multivariate Methods for Neuroimaging Data
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Imaging
Abstract #322488
Title: Spatial Auto-Regressive Model for Von-Mises Fisher Distributed Directional Data
Author(s): Zhou Lan* and Arkaprava Roy
Companies: Yale School of Medicine and University of Florida
Keywords: Alzheimer's disease; autoregressive model; diffusion tensor imaging; directional data; von-Mises Fisher distribution

Diffusion tensor imaging (DTI) is a prevalent neuroimaging tool, and diffusion directions are one of the most important information derived from DTI as they depict the anatomical structures of fiber tracts. This work studies the possible association between diffusion directions and Alzheimer's disease progression via an image-on-scalar type regression model. The spatial dependence further motivates us to assume the residuals and the regression effects of age, gender, and disease status are correlated when they are both spatially close and on the same fiber tract. To characterize the diffusion directions, we propose a von-Mises Fisher distribution-based error model with mean direction, dependent on the subjects' covariates through a link function. The link function allows us to model the regression coefficients in the Euclidean space and eases our prior specification. This allows us to characterize their dependence along the fiber tract. The model's key statistical properties and a comprehensive toolbox for Bayesian inference of the directional data are provided. The numerical studies based on synthetic data and ADNI data demonstrate that our model performs overwhelmingly better.

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

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