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.

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Add DateOct. 13, 2016, 5:54 a.m.
Uploaded byyeoyeo02
Related article DOI10.1073/pnas.1611073113
Related article authorsXiuming Zhang, Elizabeth C. Mormino, Nanbo Sun, Reisa A. Sperling, Mert R. Sabuncu and B. T. Thomas Yeo
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