{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T10:03:45Z","timestamp":1766484225236,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031210136"},{"type":"electronic","value":"9783031210143"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21014-3_1","type":"book-chapter","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T13:43:40Z","timestamp":1671111820000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Function MRI Representation Learning via\u00a0Self-supervised Transformer for\u00a0Automated Brain Disorder Analysis"],"prefix":"10.1007","author":[{"given":"Qianqian","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lishan","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"1_CR1","unstructured":"Organization, W.H., et al.: Depression and other common mental disorders: global health estimates. World Health Organization, Technical report (2017)"},{"key":"1_CR2","unstructured":"Bains, N., Abdijadid, S.: Major depressive disorder. In: StatPearls [Internet]. StatPearls Publishing (2021)"},{"issue":"6","key":"1_CR3","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1001\/archpsyc.62.6.593","volume":"62","author":"RC Kessler","year":"2005","unstructured":"Kessler, R.C., Berglund, P., Demler, O., Jin, R., Merikangas, K.R., Walters, E.E.: Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch. General Psychiatry 62(6), 593\u2013602 (2005)","journal-title":"Arch. General Psychiatry"},{"issue":"1","key":"1_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/nrdp.2016.65","volume":"2","author":"C Otte","year":"2016","unstructured":"Otte, C., et al.: Major depressive disorder. Nat. Rev. Dis. Primers 2(1), 1\u201320 (2016)","journal-title":"Nat. Rev. Dis. Primers"},{"issue":"9475","key":"1_CR5","doi-asserted-by":"publisher","first-page":"1961","DOI":"10.1016\/S0140-6736(05)66665-2","volume":"365","author":"GS Alexopoulos","year":"2005","unstructured":"Alexopoulos, G.S.: Depression in the elderly. The Lancet 365(9475), 1961\u20131970 (2005)","journal-title":"The Lancet"},{"key":"1_CR6","first-page":"591","volume":"21","author":"F Edition","year":"2013","unstructured":"Edition, F., et al.: Diagnostic and statistical manual of mental disorders. Am. Psychiatr. Assoc. 21, 591\u2013643 (2013)","journal-title":"Am. Psychiatr. Assoc."},{"issue":"suppl 6","key":"1_CR7","doi-asserted-by":"publisher","first-page":"11183","DOI":"10.4088\/JCP.8133su1c.03","volume":"70","author":"GI Papakostas","year":"2009","unstructured":"Papakostas, G.I.: Managing partial response or nonresponse: switching, augmentation, and combination strategies for major depressive disorder. J. Clin. Psychiatry 70(suppl 6), 11183 (2009)","journal-title":"J. Clin. Psychiatry"},{"issue":"7","key":"1_CR8","doi-asserted-by":"publisher","first-page":"1399","DOI":"10.1038\/npp.2017.36","volume":"42","author":"C B\u00fcrger","year":"2017","unstructured":"B\u00fcrger, C., et al.: Differential abnormal pattern of anterior cingulate gyrus activation in unipolar and bipolar depression: an fMRI and pattern classification approach. Neuropsychopharmacology 42(7), 1399\u20131408 (2017)","journal-title":"Neuropsychopharmacology"},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.neuroimage.2017.12.052","volume":"169","author":"SI Ktena","year":"2018","unstructured":"Ktena, S.I., et al.: Metric learning with spectral graph convolutions on brain connectivity networks. NeuroImage 169, 431\u2013442 (2018)","journal-title":"NeuroImage"},{"key":"1_CR10","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.neucom.2018.05.084","volume":"312","author":"L Qiao","year":"2018","unstructured":"Qiao, L., Zhang, L., Chen, S., Shen, D.: Data-driven graph construction and graph learning: a review. Neurocomputing 312, 336\u2013351 (2018)","journal-title":"Neurocomputing"},{"issue":"1","key":"1_CR11","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1007\/s11682-018-9846-8","volume":"13","author":"B Cheng","year":"2019","unstructured":"Cheng, B., Liu, M., Zhang, D., Shen, D.: Robust multi-label transfer feature learning for early diagnosis of Alzheimer\u2019s disease. Brain Imaging Behav. 13(1), 138\u2013153 (2019). https:\/\/doi.org\/10.1007\/s11682-018-9846-8","journal-title":"Brain Imaging Behav."},{"key":"1_CR12","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","volume":"69","author":"H Guan","year":"2022","unstructured":"Guan, H., Liu, M.: Domain adaptation for medical image analysis: a survey. IEEE Trans. Biomed. Eng. 69, 1173\u20131185 (2022)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"1","key":"1_CR13","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4\u201324 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1_CR14","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57\u201381 (2020)","journal-title":"AI Open"},{"key":"1_CR15","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"issue":"3","key":"1_CR16","first-page":"1","volume":"14","author":"WL Hamilton","year":"2020","unstructured":"Hamilton, W.L.: Graph representation learning. Synth. Lect. Artif. Intell. Mach. Learn. 14(3), 1\u2013159 (2020)","journal-title":"Synth. Lect. Artif. Intell. Mach. Learn."},{"issue":"4","key":"1_CR17","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1109\/TMI.2021.3051604","volume":"40","author":"D Yao","year":"2021","unstructured":"Yao, D., et al.: A mutual multi-scale triplet graph convolutional network for classification of brain disorders using functional or structural connectivity. IEEE Trans. Med. Imaging 40(4), 1279\u20131289 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"18","key":"1_CR18","doi-asserted-by":"publisher","first-page":"9078","DOI":"10.1073\/pnas.1900390116","volume":"116","author":"CC Yan","year":"2019","unstructured":"Yan, C.C., et al.: Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc. Natl. Acad. Sci. 116(18), 9078\u20139083 (2019)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Yan, C., Zang, Y.: DPARSF: a MATLAB toolbox for \u201cpipeline\u201d data analysis of resting-state fMRI. Front. Syst. Neurosci. 4, 13 (2010)","DOI":"10.3389\/fnsys.2010.00013"},{"issue":"3","key":"1_CR20","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/s12021-016-9299-4","volume":"14","author":"CG Yan","year":"2016","unstructured":"Yan, C.G., Wang, X.D., Zuo, X.N., Zang, Y.F.: DPABI: data processing & analysis for (resting-state) brain imaging. Neuroinformatics 14(3), 339\u2013351 (2016). https:\/\/doi.org\/10.1007\/s12021-016-9299-4","journal-title":"Neuroinformatics"},{"key":"1_CR21","unstructured":"Sporns, O.: Graph theory methods: applications in brain networks. Dialogues Clin. Neurosci. (2022)"},{"key":"1_CR22","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"issue":"3","key":"1_CR23","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1016\/j.neuroimage.2011.10.015","volume":"59","author":"CY Wee","year":"2012","unstructured":"Wee, C.Y., et al.: Identification of MCI individuals using structural and functional connectivity networks. NeuroImage 59(3), 2045\u20132056 (2012)","journal-title":"NeuroImage"},{"key":"1_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.H., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Cham (2009). https:\/\/doi.org\/10.1007\/978-0-387-21606-5"},{"key":"1_CR25","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1016\/j.neuroimage.2016.09.046","volume":"146","author":"J Kawahara","year":"2017","unstructured":"Kawahara, J., et al.: BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146, 1038\u20131049 (2017)","journal-title":"NeuroImage"},{"key":"1_CR26","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"issue":"5","key":"1_CR27","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.3349\/ymj.2017.58.5.1018","volume":"58","author":"EK Kang","year":"2017","unstructured":"Kang, E.K., Lee, K.S., Lee, S.H.: Reduced cortical thickness in the temporal pole, insula, and pars triangularis in patients with panic disorder. Yonsei Med. J. 58(5), 1018\u20131024 (2017)","journal-title":"Yonsei Med. J."},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Yang, Z., Guo, H., Ji, S., Li, S., Fu, Y., Guo, M., Yao, Z.: Reduced dynamics in multivariate regression-based dynamic connectivity of depressive disorder. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1197\u20131201. IEEE (2020)","DOI":"10.1109\/BIBM49941.2020.9313228"},{"key":"1_CR29","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.pnpbp.2015.07.006","volume":"64","author":"XH Yang","year":"2016","unstructured":"Yang, X.H., et al.: Diminished caudate and superior temporal gyrus responses to effort-based decision making in patients with first-episode major depressive disorder. Progress Neuro-Psychopharmacol. Biol. Psychiatry 64, 52\u201359 (2016)","journal-title":"Progress Neuro-Psychopharmacol. Biol. Psychiatry"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21014-3_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T13:44:01Z","timestamp":1671111841000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21014-3_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031210136","9783031210143"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21014-3_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"64","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"75% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}