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Functional Brain Networks (FBNs) have become crucial biomarkers for the diagnosis of MDD, and Graph Convolutional Networks (GCNs) have demonstrated great potential in extracting FBN features. However, existing GCN methods typically depend on large\u2010scale labeled datasets and lack clinical interpretability in MDD identification, which limits their application.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We introduce Universum Inspired Graph Contrast Learning (Uni\u2010GCL), a novel graph\u2010based self\u2010supervised learning framework to relieve the demand for training sample size. Additionally, to enhance the clinical interpretability of GCNs, we optimized the Gradient\u2010weighted Class Activation Mapping (Grad\u2010CAM) method to enable it to effectively pinpoint the crucial nodes within the GCN.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The Uni\u2010GCL method outperformed previous state\u2010of\u2010the\u2010art approaches. It improved accuracy by 3% at Site 21 and 5% at Site 1. Additionally, Uni\u2010GCL identified that brain regions FGoperc. R, CAU. L, FFG. R, AMYG. L, PUT. L, AMYG. R, PUT. R, THA. L, IFGtriang. R, FFG. L, INS. L, INS. R, HIP. L and HIP. R make significant contributions to the diagnosis of MDD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Uni\u2010GCL can effectively reduce the sample size for model training and improve the diagnostic performance of MDD. Meanwhile, the adapted Grad\u2010CAM provides an effective tool to enhance the interpretability of GCN.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cns.71158","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T13:45:41Z","timestamp":1789998341000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Universum Inspired Graph Contrast Learning for Major Depressive Disorder Identification: In Functional Brain Network View"],"prefix":"10.1002","volume":"32","author":[{"given":"Hao","family":"Qin","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering Chongqing Jiaotong University  Chongqing China"},{"name":"Department of Radiology and Nuclear Medicine, School of Medicine, Tongji Hospital Tongji University  Shanghai China"},{"name":"Institute of Medical Imaging Artificial Intelligence Tongji University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanghui","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery The First Affiliated Hospital of Fujian Medical University  Fuzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8545-9997","authenticated-orcid":false,"given":"Di","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology &amp; Artificial Intelligence Nanjing Forestry University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering Chongqing Jiaotong University  Chongqing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leina","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics Chongqing Jiaotong University  Chongqing China"},{"name":"Key Laboratory of Complex Systems Optimization and Intelligent Control of Chongqing Municipal Education Commission Chongqing Jiaotong University  Chongqing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5114-9660","authenticated-orcid":false,"given":"Weikai","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence Shandong Jianzhu University  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7914-6015","authenticated-orcid":false,"given":"Xiaowen","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine, School of Medicine, Tongji Hospital Tongji University  Shanghai China"},{"name":"Institute of Medical Imaging Artificial Intelligence Tongji University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1136\/gpsych-2021-100527"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijbiomac.2021.05.172"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41572-023-00454-1"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1136\/gpsych-2021-100527"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/wps.20050"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/cns.13048"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2020.00284"},{"issue":"7","key":"e_1_2_11_9_1","first-page":"1912","article-title":"Estimating Functional Connectivity Networks via Low\u2010Rank Tensor Approximation With Applications to MCI Identification","volume":"67","author":"Jiang X.","year":"2019","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.4103\/1673-5374.233433"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1515\/tnsci-2022-0234"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2903332"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2928631"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104096"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102272"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3153322"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhae065"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108749"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2025.111559"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1136\/gpsych-2023-101436"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.11.006"},{"key":"e_1_2_11_22_1","article-title":"Mixup: Beyond Empirical Risk Minimization","author":"Zhang H.","year":"2017","journal-title":"arXiv Preprint arXiv:171009412"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-03335-4_31"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2015.2403573"},{"issue":"1","key":"e_1_2_11_25_1","first-page":"857","article-title":"Self\u2010Supervised Learning: Generative or Contrastive","volume":"35","author":"Liu X.","year":"2021","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_2_11_26_1","volume-title":"paper presented at: Proceedings of the IEEE International Conference on Computer Vision","author":"Selvaraju R. 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