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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction. MHNet has two branches: the Euclidean Space Features Extraction (ESFE) module and the Non-Euclidean Space Features Extraction (Non-ESFE) module, followed by a Feature Fusion-based Classification (FFC) module for NDD identification. ESFE includes a Functional Connectivity Generation (FCG) module and a High-order Convolutional Neural Network (HCNN) module to extract local and high-order features from BFNs in Euclidean space. Non-ESFE comprises a Generic Internet-like Brain Hierarchical Network Generation (G-IBHN-G) module and a High-order Graph Neural Network (HGNN) module to capture topological and high-order features in non-Euclidean space. Experiments on three public datasets show that MHNet outperforms state-of-the-art methods using both AAL1 and Brainnetome Atlas templates. Extensive ablation studies confirm the superiority of MHNet and the effectiveness of using multi-view fMRI information and high-order features. Our study also offers atlas options for constructing more sophisticated hierarchical networks and explains the association between key brain regions and NDD. MHNet leverages multi-view feature learning from both Euclidean and non-Euclidean spaces, incorporating high-order information from BFNs to enhance NDD classification performance.<\/jats:p>","DOI":"10.1007\/s10278-025-01399-5","type":"journal-article","created":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T12:52:56Z","timestamp":1738068776000},"page":"2994-3014","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MHNet: Multi-view High-Order Network for Diagnosing Neurodevelopmental Disorders Using Resting-State fMRI"],"prefix":"10.1007","volume":"38","author":[{"given":"Yueyang","family":"Li","sequence":"first","affiliation":[]},{"given":"Weiming","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Wenhao","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Luhui","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Hongyu","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Hongjie","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Lingbin","family":"Bian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9701-2918","authenticated-orcid":false,"given":"Nizhuan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,28]]},"reference":[{"issue":"1","key":"1399_CR1","doi-asserted-by":"crossref","first-page":"11254","DOI":"10.1038\/ncomms11254","volume":"7","author":"N Yahata","year":"2016","unstructured":"Yahata, N., Morimoto, J., Hashimoto, R., Lisi, G., Shibata, K., Kawakubo, Y., Kuwabara, H., Kuroda, M., Yamada, T., Megumi, F., et al.: A small number of abnormal brain connections predicts adult autism spectrum disorder. 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