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

TL;DR

This paper introduces a graph-based machine learning framework utilizing GNNs and explainability techniques to distinguish between classic and rapid Alzheimer's disease progression by analyzing neuropathological brain graphs.

Contribution

It presents a novel graph neural network approach with explainability to differentiate AD progression variants based on topological brain lesion organization.

Findings

Denser brain networks in rapid AD

Distinct impact on cortical layers between AD variants

GNNs effectively classify AD progression types

Abstract

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.