Title: A Bayesian latent spatial model for mapping the cortical signature of progression to Alzheimer's disease
Authors: Ning Dai - University of Minnesota (United States) [presenting]
Hakmook Kang - Vanderbilt University (United States)
Galin Jones - University of Minnesota (United States)
Mark Fiecas - University of Minnesota (United States)
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 conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD), and the dynamically changing cortical thickness over time and across the cortex of the brain. 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. We apply the proposed method to the longitudinal structural MRI data from ADNI to examine the topographic patterns of anatomic regions where atrophy is associated with conversion to AD. A simulation study is conducted to show the substantial gains in estimation performance and statistical efficiency by accounting for the spatial structure of cortical atrophy associated with AD.