Abstract
Previous studies have shown that neurodegenerative diseases, specifically Alzheimer’s disease (AD), primarily affect brain network function due to neuropathological burdens that spread throughout the network, similar to prion-like propagation. Therefore, identifying brain network alterations is crucial in understanding the pathophysiological mechanism of AD progression. Although recent graph neural network (GNN) analyses have provided promising results for early AD diagnosis, current methods do not account for the unique topological properties and high-order information in complex brain networks. To address this, we propose a brain network-tailored hypergraph neural network (BrainHGNN) to identify the propagation patterns of neuropathological events in AD. Our BrainHGNN approach constructs a hypergraph using region of interest (ROI) identity encoding and random-walk-based sampling strategy, preserving the unique identities of brain regions and characterizing the intrinsic properties of the brain-network organization. We then propose a self-learned weighted hypergraph convolution to iteratively update node and hyperedge messages and identify AD-related propagation patterns. We conducted extensive experiments on ADNI data, demonstrating that our BrainHGNN outperforms other state-of-the-art methods in classification performance and identifies significant propagation patterns with discriminative differences in group comparisons.
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Acknowledgements
This work was supported in part by the National Key Research and Development Program of China (2022YFE0112200), the National Natural Science Foundation of China (U21A20520,62102153), the Science and Technology Project of Guangdong Province (2022A0505050014), the Guangdong Key Laboratory of Human Digital Twin Technology (2022B1212010004), Natural Science Foundation of Guangdong Province of China (2022A1515011162), Key-Area Research and Development Program of Guangzhou City (202206030009), and the China Postdoctoral Science Foundation (2021M691062, 2023T160226). The neuroimaging datasets used in this study were supported by the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
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Cai, H., Zhou, Z., Yang, D., Wu, G., Chen, J. (2023). Discovering Brain Network Dysfunction in Alzheimer’s Disease Using Brain Hypergraph Neural Network. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14224. Springer, Cham. https://doi.org/10.1007/978-3-031-43904-9_23
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