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Then, a hypergraph convolutional neural network with a weighted fusion layer is constructed to integrate age, sex features, and the topological features of the sparsified brain functional network for accurate classification of different disease stages. Finally, gradient backpropagation-based localization is used to identify disease-related key brain regions. Experimental results demonstrate that the Euler characteristic-based sparsification method can objectively simplify and preserve critical information in brain functional networks, achieving an accuracy of 90.22% in the AD versus normal control classification task. Moreover, the identified key regions, the identified key regions, including the hippocampus and precuneus, show strong consistency with known clinical neuropathological findings, improving the interpretability of classification results.<\/jats:p>","DOI":"10.1007\/s44443-025-00231-y","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T16:09:21Z","timestamp":1758730161000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A sparse brain network reconstruction based on euler characteristics for alzheimer\u2019s disease classification"],"prefix":"10.1007","volume":"37","author":[{"given":"Xu","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Tiejun","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Heng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jianyu","family":"Miao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,24]]},"reference":[{"issue":"9","key":"231_CR1","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1038\/s41591-022-01981-2","volume":"28","author":"JN Acosta","year":"2022","unstructured":"Acosta JN et al (2022) Multimodal biomedical AI. 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