Premium
A Bayesian latent spatial model for mapping the cortical signature of progression to Alzheimer's disease
Author(s) -
Dai Ning,
Kang Hakmook,
Jones Galin L.,
Fiecas Mark B.
Publication year - 2021
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11588
Subject(s) - neuroimaging , magnetic resonance imaging , dementia , alzheimer's disease neuroimaging initiative , neuroscience , bayesian probability , atrophy , statistical power , disease , computer science , medicine , psychology , artificial intelligence , pathology , radiology , mathematics , statistics
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 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.