Graph Theory and GNNs to Unravel the Topographical Organization of Brain Lesions in Variants of Alzheimer's Disease Progression
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Abstract (arXiv)
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.
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References · 6
- 2018How Powerful are Graph Neural Networks?cited by 4,355
- 2020Principal Neighbourhood Aggregation for Graph Netscited by 477
- 2019CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Imagescited by 57
- 2018Deep learning for comprehensive forecasting of Alzheimer's Disease progressioncited by 11
- 2021ADiag: Graph Neural Network Based Diagnosis of Alzheimer's Diseasecited by 3
- 2021Comparisons of Graph Neural Networks on Cancer Classification Leveraging a Joint of Phenotypic and Genetic Featurescited by 2
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