Abstract / Summary
Abstract Amyloid beta (Aβ) deposition and tau accumulation are neuropathological hallmarks of Alzheimer’s disease (AD). While Aβ deposits present with a wide morphological variety, a comprehensive, cross-regional overview of Aβ plaque composition in AD and associations with clinicopathological traits are missing. In this study, we systematically documented the abundance and morphological diversity of Aβ plaques in up to 23 brain regions of 84 advanced AD cases. Deposits were segmented with a random forest pixel classifier in 4G8 diaminobenzidine stains and classified into six classes using a convolutional neural network (cored, diffuse light, diffuse dense, compact, small dense plaques, cerebral amyloid angiopathy (CAA)). Deposit counts were correlated between classes, regions, and tau load using Pearson correlation. Associations between plaque densities and co-pathologies, sex, ApoE, familial vs. sporadic cases, age at clinical onset, disease duration, and age at death were examined. Our classification model achieved a recall of 81.5% and a precision of 82.4% in the test set. Cortical brain regions showed the highest Aβ plaque loads, predominantly consisting of diffuse light and small dense plaques. Plaque densities correlated within and across the hippocampal and cortical regions. Abundances of diffuse dense, small dense, and compact plaques were positively correlated with one another, while densities of cored plaques and CAA did not correlate with other plaque classes. Only cored plaques were positively correlated with the entorhinal tau load, while other plaque classes, except CAA, showed negative correlations. Notably, female sex was associated with more diffuse plaques, and ApoE4 was associated with diffuse dense and small dense plaques. Earlier age at onset and death were associated with higher densities of diffuse light plaques. Overall, associations varied across brain regions and plaque classes. Cored plaques and CAA stand out statistically from other Aβ deposits, suggesting distinct association patterns in the course of the disease.