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Part 2: Data from: Clinical and pathologic correlations of machine learning quantification of Aβ deposits across three brain Regions of decedents with Alzheimer's disease

Rai Sharma, Shivam Rajendra1; Garcia, David2; Saito, Naomi1; Beckett, Laurel1; Sevilla, Louise C.2; Vasquez, La Rissa2; DeCarli, Charles S.1; Gutman, David3; Vizcarra, Juan3; Coughlin, David G.4; Teich, Andrew F.5; Garcia, Lorena1; Mungas, Dan M.1; Chuah, Chen-Nee1; Dugger, Brittany12

Published Jan 20, 2026 on Dryad. https://doi.org/10.5061/dryad.wstqjq30t

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Abstract

The advent of machine learning enables scalable quantification of neuropathology, offering deeper phenotyping of Alzheimer's disease (AD). In this study, we quantified amyloid-beta (Aβ) deposits across multiple brain regions and examined their associations with clinical, demographic, and genetic factors in persons pathologically diagnosed with AD. We analyzed densities (#/mm2) of cored plaques, diffuse plaques, and cerebral amyloid angiopathy (CAA) in 273 individuals from three Alzheimer’s Disease Research Centers. Formalin-fixed paraffin-embedded sections of frontal, temporal, and parietal cortices were immunostained and digitized, generating 799 whole slide images (WSIs). Following log transformation, mixed-effects modeling revealed that the parietal cortex had the highest cored plaque densities (p < 0.001), while the temporal cortex had the highest diffuse plaque densities (p < 0.001); CAA showed no regional differences. Wilcoxon rank-sum tests and covariate-adjusted linear models showed ApoE ε4− status was associated with higher cored plaque densities in the temporal lobe (p = 0.04), while ApoE ε4+ status was associated with higher diffuse plaque densities in the temporal lobe (p = 0.001) and increased CAA in the frontal lobe (p = 0.004). These findings support deeper phenotyping to define generalizable patterns of disease heterogeneity and neuroanatomical distribution in AD, providing insights that may guide precision medicine–based approaches.