TY - JOUR T1 - Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease JF - bioRxiv DO - 10.1101/064295 SP - 064295 AU - Xiuming Zhang AU - Elizabeth C. Mormino AU - Nanbo Sun AU - Reisa A. Sperling AU - Mert R. Sabuncu AU - B.T. Thomas Yeo AU - for the Alzheimer’s Disease Neuroimaging Initiative Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/07/17/064295.abstract N2 - 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 factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared to temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Code from this manuscript is publicly available at link_to_be_added. ER -