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But they usually suffer from significant inter\u2010site heterogeneity caused by the use of different scanners and\/or scanning protocols and fail to build generalizable models that can well adapt to multiple target domains. In this article, we propose a dual\u2010expert fMRI harmonization (DFH) framework for automated MDD diagnosis. Our DFH is designed to simultaneously exploit data from a single labeled source domain\/site and two unlabeled target domains for mitigating data distribution differences across domains. Specifically, the DFH consists of a\n                    <jats:italic>domain\u2010generic student model<\/jats:italic>\n                    and two\n                    <jats:italic>domain\u2010specific teacher\/expert models<\/jats:italic>\n                    that are jointly trained to perform knowledge distillation through a\n                    <jats:italic>deep collaborative learning module<\/jats:italic>\n                    . A student model with strong generalizability is finally derived, which can be well adapted to unseen target domains and analysis of other brain diseases. To the best of our knowledge, this is among the first attempts to investigate\n                    <jats:italic>multi\u2010target fMRI harmonization<\/jats:italic>\n                    for MDD diagnosis. Comprehensive experiments on 836 subjects with rs\u2010fMRI data from 3 different sites show the superiority of our method. The discriminative brain functional connectivities identified by our method could be regarded as potential biomarkers for fMRI\u2010related MDD diagnosis.\n                  <\/jats:p>","DOI":"10.1002\/hbm.26343","type":"journal-article","created":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T07:15:57Z","timestamp":1684998957000},"page":"4256-4271","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Addressing multi\u2010site functional\n                    <scp>MRI<\/scp>\n                    heterogeneity through dual\u2010expert collaborative learning for brain disease identification"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8769-496X","authenticated-orcid":false,"given":"Yuqi","family":"Fang","sequence":"first","affiliation":[{"name":"Department of Radiology and BRIC University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guy G.","family":"Potter","sequence":"additional","affiliation":[{"name":"Departments of Psychiatry and Behavioral Sciences Duke University Medical Center  Durham North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongtu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingxia","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology and BRIC University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,5,25]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119330"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/ajmg.b.32633"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neulet.2009.05.022"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2014.09.028"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/brb3.1502"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.11.024"},{"key":"e_1_2_10_8_1","doi-asserted-by":"crossref","unstructured":"Gadgil S. Zhao Q. Pfefferbaum A. Sullivan E. V. Adeli E. &Pohl K. M.(2020).Spatio\u2010temporal graph convolution for resting\u2010state fMRI analysis. In International Conference on Medical Image Computing and Computer\u2010Assisted Intervention. 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