Graph Theory and GNNs to Unravel the Topographical Organization of Brain
  Lesions in Variants of Alzheimer's Disease Progression
Leopold Hebert-Stevens Gabriel Jimenez Benoit Delatour Lev Stimmer Daniel Racoceanu
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.