|
Activity Number:
|
204
- Bayesian Methods for the Analysis of Complex Brain Imaging Data
|
|
Type:
|
Topic-Contributed
|
|
Date/Time:
|
Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
|
|
Sponsor:
|
Section on Bayesian Statistical Science
|
|
Abstract #317045
|
|
|
Title:
|
A Hierarchical Bayesian Approach to Predicting Time-to-Conversion to Alzheimer's Disease Using a Longitudinal Map of Cortical Thickness
|
|
Author(s):
|
Mark Fiecas* and Ning Dai and Hakmook Kang and Galin Jones
|
|
Companies:
|
University of Minnesota and University of Minnesota and Vanderbilt University and University of Minnesota
|
|
Keywords:
|
Alzheimer's disease;
Survival analysis;
Longitudinal data analysis;
Neuroimaging
|
|
Abstract:
|
Prior studies have shown that atrophy in vulnerable cortical regions is associated with an increased risk of progression to clinical dementia. In this work, we utilize the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to investigate the relationship between the temporally changing spatial topography of cortical thickness and conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). We develop a novel Bayesian latent spatial model that employs the spatial information underlying the thickness effects across the cortical surface. The proposed method facilitates the development of imaging markers by reliably quantifying and mapping the regional vulnerability to AD progression across the cortical surface. Simulation results showed substantial gains in statistical power and estimation performance by accounting for the spatial structure of the association. Using MRI data from ADNI, we examined the topographic patterns of anatomic regions where cortical thinning is associated with an increased risk of developing AD.
|
Authors who are presenting talks have a * after their name.
