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Express"],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Brain disorder (BD) classification based on population graph learning has shown promising performance by jointly modeling resting-state functional magnetic resonance imaging data and phenotypic information. Existing graph Transformer\u2013based population graph encoders suffer from high computational cost on densely connected subject graphs and lack an efficient mechanism for global contextual modeling across distant subjects. To address these limitations, we propose HGMA-Net, a hierarchical graph\u2013Mamba hybrid framework for multi-modal BD classification. Specifically, the proposed HGMA module redesigns the shallow encoder stage using a dual-path architecture that combines local graph aggregation with global sequence modeling. A degree-guided node serialization strategy provides a topology-aware ordering of graph nodes, enabling bidirectional Mamba state space models to efficiently capture global contextual information with linear sequence complexity. An adaptive gating mechanism is further introduced to dynamically fuse global sequential features and local graph-structural features. To balance computational efficiency and representational capacity, HGMA is applied only at the first encoder layer, while deeper layers retain TransformerConv for local topology refinement. Experiments on the ABIDE and ADHD-200 datasets demonstrate that HGMA-Net achieves superior classification performance, obtaining AUC scores of 89.14% and 91.70%, respectively. HGMA-Net also retains efficient inference when global contextual modeling is introduced at the shallow encoder stage.<\/jats:p>","DOI":"10.1088\/2057-1976\/aeaaec","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T22:47:42Z","timestamp":1790117262000},"page":"055040","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["HGMA-Net: a hierarchical graph\u2013mamba encoder for multi-modal brain disorder classification on population graphs"],"prefix":"10.1088","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3837-8563","authenticated-orcid":true,"given":"Wanting","family":"Lai","sequence":"first","affiliation":[{"name":"Guangdong Provincial Engineering and Technology Research Center of Light and Health, Guangdong Pharmaceutical University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Kehong","family":"Zheng","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Engineering and Technology Research Center of Light and Health, Guangdong Pharmaceutical University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Yongling","family":"He","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Engineering and Technology Research Center of Light and Health, Guangdong Pharmaceutical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaozhen","family":"Lin","sequence":"additional","affiliation":[{"name":"Shenzhen Bao\u2019an District Songgang People\u2019s Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Engineering and Technology Research Center of Light and Health, Guangdong Pharmaceutical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuejun","family":"Qiu","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Engineering and Technology Research Center of Light and Health, Guangdong Pharmaceutical University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,10,6]]},"reference":[{"key":"bpexaeaaecbib1","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/S1474-4422(24)00038-3","type":"journal-article","article-title":"Global, regional, and national burden of disorders affecting the nervous system, 1990\u20132021: a systematic analysis for the global burden of disease study 2021","volume":"23","author":"Steinmetz","year":"2024","journal-title":"Lancet Neurol."},{"key":"bpexaeaaecbib2","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1001\/jama.2022.23661","type":"journal-article","article-title":"Autism spectrum disorder: a review","volume":"329","author":"Hirota","year":"2023","journal-title":"JAMA"},{"key":"bpexaeaaecbib3","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1038\/s41572-024-00495-0","type":"journal-article","article-title":"Attention-deficit\/hyperactivity disorder","volume":"10","author":"Faraone","year":"2024","journal-title":"Nat. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-06-03","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-22","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-10-06","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}