Graph Theory and GNNs to Unravel the Topographical Organization of Brain Lesions in Variants of Alzheimer's Disease Progression

This study utilizes graph theory and deep learning to assess variations in Alzheimer's disease (AD) neuropathologies, focusing on classic (cAD) and rapid (rpAD) progression forms. It analyses the distribution of amyloid plaques and tau tangles in postmortem brain tissues. Histopathological images are converted into tau-pathology-based graphs, and derived metrics are used for statistical analysis and in machine learning classifiers. These classifiers incorporate SHAP value explainability to differentiate between cAD and rpAD. Graph neural networks (GNNs) demonstrate greater efficiency than traditional CNN methods in analyzing this data, preserving spatial pathology context. Additionally, GNNs provide significant insights through explainable AI techniques. The analysis shows 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.