Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease

Contributed by yeoyeo02

If you use data from this collection please cite:
http://doi.org/10.1073/pnas.1611073113

View ID Name Type
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AuthorsXiuming Zhang, Elizabeth C. Mormino, Nanbo Sun, Reisa A. Sperling, Mert R. Sabuncu and B. T. Thomas Yeo
DescriptionWe 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.
JournalProceedings of the National Academy of Sciences
Contributors
DOI10.1073/pnas.1611073113
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id1917
Add DateOct. 13, 2016, 5:54 a.m.