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

BioRxiv : the Preprint Server for Biology
Xiuming Zhang


We employed a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural magnetic resonance imaging (MRI) 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). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid-positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, while the cortical ...Continue Reading

Related Concepts

Alzheimer's Disease
Amygdaloid Structure
Cerebral Cortex
Corpus Striatum Structure
Hippocampus (Brain)
Magnetic Resonance Imaging
Occipital Lobe

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Related Papers

Proceedings of the National Academy of Sciences of the United States of America
Xiuming ZhangAlzheimer’s Disease Neuroimaging Initiative
Neurobiology of Aging
Marilyn S Albert
Neurobiology of Aging
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