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

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.