Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease
Description: We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer’s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). These are the probabilistic atrophy maps for the temporal, subcortical and cortical factors as described in the paper.
Related article: http://doi.org/10.1073/pnas.1611073113
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