Graph Theory and GNNs to Unravel the Topographical Organization of Brain Lesions in Variants of Alzheimer's Disease Progression (2403.00636v2)
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.
- “A meta-graph approach for analyzing whole slide histopathological images of human brain tissue with Alzheimer’s disease biomarkers,” in Medical Imaging 2023: Digital and Computational Pathology, John E. Tomaszewski and Aaron D. Ward, Eds. International Society for Optics and Photonics, 2023, vol. 12471, p. 124711T, SPIE.
- “Graph neural networks and their current applications in bioinformatics,” Frontiers in genetics, vol. 12, pp. 690049, 2021.
- “Graph convolutional neural networks for alzheimer’s disease classification,” in 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE, 2019, pp. 414–417.
- Vishnu Ram Sampathkumar, “Adiag: Graph neural network based diagnosis of alzheimer’s disease,” arXiv preprint arXiv:2101.02870, 2021.
- So Yeon Kim, “Personalized explanations for early diagnosis of alzheimer’s disease using explainable graph neural networks with population graphs,” Bioengineering, vol. 10, no. 6, 2023.
- “Eeg-based graph neural network classification of alzheimer’s disease: An empirical evaluation of functional connectivity methods,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 2651–2660, 2022.
- “Identifying patients with alzheimer’s disease using resting-state fmri and graph theory,” Clinical Neurophysiology, vol. 126, no. 11, pp. 2132–2141, 2015.
- “Artificial intelligence for alzheimer’s disease: promise or challenge?,” Diagnostics, vol. 11, no. 8, pp. 1473, 2021.
- “Machine learning for comprehensive forecasting of alzheimer’s disease progression,” Scientific reports, vol. 9, no. 1, pp. 13622, 2019.
- “How powerful are graph neural networks?,” arXiv preprint arXiv:1810.00826, 2018.
- “Principal neighbourhood aggregation for graph nets,” Advances in Neural Information Processing Systems, vol. 33, pp. 13260–13271, 2020.
- “Comparisons of graph neural networks on cancer classification leveraging a joint of phenotypic and genetic features,” arXiv preprint arXiv:2101.05866, 2021.
- “Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images,” in Proceedings of the IEEE/CVF international conference on computer vision workshops, 2019, pp. 0–0.
- “Topological feature extraction and visualization of whole slide images using graph neural networks,” in BIOCOMPUTING 2021: Proceedings of the Pacific Symposium. World Scientific, 2020, pp. 285–296.
- “A graph neural network framework for mapping histological topology in oral mucosal tissue,” BMC bioinformatics, vol. 23, no. 1, pp. 506, 2022.
- “Point set morphological filtering and semantic spatial configuration modeling: Application to microscopic image and bio-structure analysis,” Pattern Recognition, vol. 45, no. 8, pp. 2894–2911, 2012.
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