{"ID":2857450,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.09175","arxiv_id":"2510.09175","title":"Beyond Pairwise Connections: Extracting High-Order Functional Brain Network Structures under Global Constraints","abstract":"Functional brain network (FBN) modeling often relies on local pairwise interactions, whose limitation in capturing high-order dependencies is theoretically analyzed in this paper. Meanwhile, the computational burden and heuristic nature of current hypergraph modeling approaches hinder end-to-end learning of FBN structures directly from data distributions. To address this, we propose to extract high-order FBN structures under global constraints, and implement this as a Global Constraints oriented Multi-resolution (GCM) FBN structure learning framework. It incorporates 4 types of global constraint (signal synchronization, subject identity, expected edge numbers, and data labels) to enable learning FBN structures for 4 distinct levels (sample/subject/group/project) of modeling resolution. Experimental results demonstrate that GCM achieves up to a 30.6% improvement in relative accuracy and a 96.3% reduction in computational time across 5 datasets and 2 task settings, compared to 9 baselines and 10 state-of-the-art methods. Extensive experiments validate the contributions of individual components and highlight the interpretability of GCM. This work offers a novel perspective on FBN structure learning and provides a foundation for interdisciplinary applications in cognitive neuroscience. Code is publicly available on https://github.com/lzhan94swu/GCM.","short_abstract":"Functional brain network (FBN) modeling often relies on local pairwise interactions, whose limitation in capturing high-order dependencies is theoretically analyzed in this paper. Meanwhile, the computational burden and heuristic nature of current hypergraph modeling approaches hinder end-to-end learning of FBN structu...","url_abs":"https://arxiv.org/abs/2510.09175","url_pdf":"https://arxiv.org/pdf/2510.09175v1","authors":"[\"Ling Zhan\",\"Junjie Huang\",\"Xiaoyao Yu\",\"Wenyu Chen\",\"Tao Jia\"]","published":"2025-10-10T09:17:21Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":608452,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2857450,"paper_url":"https://arxiv.org/abs/2510.09175","paper_title":"Beyond Pairwise Connections: Extracting High-Order Functional Brain Network Structures under Global Constraints","repo_url":"https://github.com/lzhan94swu/GCM","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}