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

2024-03-01 16:16:51
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.

Abstract (translated)

本研究利用图论和深度学习评估阿尔茨海默病(AD)的病理变化,重点关注经典(CAD)和快速(rpAD)进展形式。它分析死后脑组织中斑块和tau tangles的分布。病理图像转换为基于tau病理的图,用于统计分析和机器学习分类器的指标。这些分类器包括SHAP值可解释性,以区分CAD和rpAD。图神经网络(GNNs)显示比传统CNN方法更有效的分析数据,保留空间病理上下文。此外,GNNs通过可解释性AI技术提供了显著的见解。分析显示,rpAD中网络密度较高,且对大脑皮层层的中层有显著影响;而CAD影响同一皮层区域的表面和深层。这些结果表明,每个AD变体的神经病理网络组织都是独特的。



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