2024/03/01 by Gabriel Jiménez, Jimenez, Gabriel, Leopold Hebert-Stevens +7
Biochemistry, Genetics and Molecular Biology · Computer Science · #Bioinformatics and Genomic Networks #Biomedical Text Mining and Ontologies #Computational Drug Discovery Methods #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2403.00636
openalex publication_date 2024/03/01 · openalex created_date 2024/03/05 · openalex updated_date 2026/07/28
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.