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.

翻译：本研究提出并评估了一种基于图的框架，用于评估阿尔茨海默病神经病理学的变异，重点关注经典进展型和快速进展型。组织病理学图像被转换为基于tau病理（即淀粉样斑块和tau缠结）的图，并将导出的度量指标用于机器学习分类器。该分类器结合了SHAP值可解释性以区分经典进展型和快速进展型。此外，我们测试了图神经网络从图中提取拓扑嵌入，并将其用于阿尔茨海默病进展形式的分类。分析表明，快速进展型具有更密集的网络，并对大脑皮层各层产生独特影响：快速进展型主要影响中间层，而经典进展型则影响相同皮层区域的浅层和深层。这些结果表明，每种阿尔茨海默病变体都具有独特的神经病理学网络组织。