  In this study, we proposed and evaluated a graph-based framework to assess
variations in Alzheimer's disease (AD) neuropathologies, focusing on classic
(cAD) and rapid (rpAD) progression forms. Histopathological images are
converted into tau-pathology-based (i.e., amyloid plaques and tau tangles)
graphs, and derived metrics are used in a machine-learning classifier. This
classifier incorporates SHAP value explainability to differentiate between cAD
and rpAD. Furthermore, we tested graph neural networks (GNNs) to extract
topological embeddings from the graphs and use them in classifying the
progression forms of AD. The analysis demonstrated denser networks in rpAD and
a distinctive impact on brain cortical layers: rpAD predominantly affects
middle layers, whereas cAD influences both superficial and deep layers of the
same cortical regions. These results suggest a unique neuropathological network
organization for each AD variant.
