{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T11:34:59Z","timestamp":1758281699381,"version":"3.44.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051615","type":"print"},{"value":"9783032051622","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-05162-2_34","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T23:26:16Z","timestamp":1758237976000},"page":"352-361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Graph Disentanglement Learning for\u00a0fMRI Analysis: Decoupling Disease, Covariates, and\u00a0Individual Variability"],"prefix":"10.1007","author":[{"given":"Shengjie","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Zhuangzhuang","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Ziqi","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xiao-Yong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yuan","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"issue":"5","key":"34_CR1","doi-asserted-by":"publisher","first-page":"5833","DOI":"10.1109\/TPAMI.2022.3209686","volume":"45","author":"A Bessadok","year":"2022","unstructured":"Bessadok, A., Mahjoub, M.A., Rekik, I.: Graph neural networks in network neuroscience. IEEE Trans. Pattern Anal. Mach. Intell. 45(5), 5833\u20135848 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"34_CR2","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1177\/1971400917697342","volume":"30","author":"KA Smitha","year":"2017","unstructured":"Smitha, K.A., et al.: Resting state fMRI: a review on methods in resting state connectivity analysis and resting state networks. Neuroradiol. J. 30(4), 305\u2013317 (2017)","journal-title":"Neuroradiol. J."},{"issue":"1","key":"34_CR3","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.neuroimage.2007.01.054","volume":"36","author":"H Yang","year":"2007","unstructured":"Yang, H., et al.: Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. Neuroimage 36(1), 144\u2013152 (2007)","journal-title":"Neuroimage"},{"issue":"1","key":"34_CR4","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.neuroimage.2003.12.030","volume":"22","author":"Y Zang","year":"2004","unstructured":"Zang, Y., et al.: Regional homogeneity approach to fMRI data analysis. Neuroimage 22(1), 394\u2013400 (2004)","journal-title":"Neuroimage"},{"issue":"1","key":"34_CR5","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., et al.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"key":"34_CR6","doi-asserted-by":"crossref","unstructured":"Luo, X., et al.: An interpretable brain graph contrastive learning framework for brain disorder analysis. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining, pp. 1074\u2013 1077 (2024)","DOI":"10.1145\/3616855.3635695"},{"issue":"2","key":"34_CR7","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1109\/TMI.2022.3201974","volume":"42","author":"L Peng","year":"2022","unstructured":"Peng, L., et al.: GATE: graph CCA for temporal self-supervised learning for label-efficient fMRI analysis. IEEE Trans. Med. Imaging 42(2), 391\u2013402 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"34_CR8","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. NeurIPS 27 (2014)"},{"issue":"8","key":"34_CR9","doi-asserted-by":"publisher","first-page":"4154","DOI":"10.1109\/JBHI.2023.3274531","volume":"27","author":"G Wen","year":"2023","unstructured":"Wen, G., et al.: Graph self-supervised learning with application to brain networks analysis. IEEE J. Biomed. Health Inform. 27(8), 4154\u20134165 (2023)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Xiaowei Yu et al. \u201cLongitudinal infant functional connectivity prediction via conditional intensive triplet network\u201d. In: MICCAI. Springer. 2022, pp. 255\u2013264","DOI":"10.1007\/978-3-031-16452-1_25"},{"issue":"6597","key":"34_CR11","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1126\/science.abm2461","volume":"376","author":"A Aglinskas","year":"2022","unstructured":"Aglinskas, A., Hartshorne, J.K., Anzellotti, S.: Contrastive machine learning reveals the structure of neuroanatomical variation within autism. Science 376(6597), 1070\u20131074 (2022)","journal-title":"Science"},{"key":"34_CR12","unstructured":"Xu, K., et al.: How powerful are graph neural networks?\u201d arXiv preprint arXiv:1810.00826 (2018)"},{"issue":"1","key":"34_CR13","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1038\/s41592-018-0235-4","volume":"16","author":"O Esteban","year":"2019","unstructured":"Esteban, O., et al.: fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16(1), 111\u2013116 (2019)","journal-title":"Nat. Methods"},{"issue":"1","key":"34_CR14","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1006\/nimg.2001.0978","volume":"15","author":"N Tzourio-Mazoyer","year":"2002","unstructured":"Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273\u2013289 (2002)","journal-title":"Neuroimage"},{"issue":"4","key":"34_CR15","doi-asserted-by":"publisher","first-page":"2096","DOI":"10.1016\/j.neuroimage.2012.02.001","volume":"60","author":"A Zalesky","year":"2012","unstructured":"Zalesky, A., Fornito, A., Bullmore, E.: On the use of correlation as a measure of network connectivity. Neuroimage 60(4), 2096\u20132106 (2012)","journal-title":"Neuroimage"},{"key":"34_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102233","volume":"74","author":"X Li","year":"2021","unstructured":"Li, X., et al.: Braingnn: interpretable brain graph neural network for fmri analysis. Med. Image Anal. 74, 102233 (2021)","journal-title":"Med. Image Anal."},{"key":"34_CR17","unstructured":"Chen, Y., et al.: Adversarial learning based node-edge graph attention networks for autism spectrum disorder identification. IEEE Trans. Neural Netw. Learn. Syst. (2022)"},{"key":"34_CR18","first-page":"5812","volume":"33","author":"Y You","year":"2020","unstructured":"You, Y., et al.: Graph contrastive learning with augmentations. NeurIPS 33, 5812\u20135823 (2020)","journal-title":"NeurIPS"},{"key":"34_CR19","unstructured":"You, Y., et al.: Graph contrastive learning automated. In: ICML, pp. 12121\u201312132. PMLR (2021)"},{"key":"34_CR20","unstructured":"Xie, Y., Xu, Z., Ji, S.: Self-supervised representation learning via latent graph prediction. In: ICML, pp. 24460\u2013 24477. PMLR (2022)"},{"key":"34_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102932","volume":"90","author":"S Zhang","year":"2023","unstructured":"Zhang, S., et al.: A-GCL: adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders. Med. Image Anal. 90, 102932 (2023)","journal-title":"Med. Image Anal."},{"key":"34_CR22","unstructured":"Mo, Y., et al.: Disentangled multiplex graph representation learning. In: ICML, pp. 24983\u201325005. PMLR (2023)"},{"key":"34_CR23","first-page":"1871","volume":"9","author":"R-E Fan","year":"2008","unstructured":"Fan, R.-E., et al.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871\u20131874 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"34_CR24","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1023\/A:1010920819831","volume":"45","author":"DJ Hand","year":"2001","unstructured":"Hand, D.J., Till, R.J.: A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach. Learn. 45, 171\u2013186 (2001)","journal-title":"Mach. Learn."},{"issue":"7","key":"34_CR25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0068910","volume":"8","author":"M Xia","year":"2013","unstructured":"Xia, M., Wang, J., He, Y.: BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE 8(7), e68910 (2013)","journal-title":"PLoS ONE"},{"issue":"11","key":"34_CR26","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1016\/j.biopsych.2004.11.019","volume":"57","author":"LJ Seidman","year":"2005","unstructured":"Seidman, L.J., Valera, E.M., Makris, N.: Structural brain imaging of attention-deficit\/hyperactivity disorder. Biol. Psychiat. 57(11), 1263\u20131272 (2005)","journal-title":"Biol. Psychiat."},{"issue":"6","key":"34_CR27","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1002\/hbm.21058","volume":"31","author":"K Konrad","year":"2010","unstructured":"Konrad, K., Eickhoff, S.B.: Is the ADHD brain wired differently? a review on structural and functional connectivity in attention deficit hyperactivity disorder. Hum. Brain Mapp. 31(6), 904\u2013916 (2010)","journal-title":"Hum. Brain Mapp."},{"issue":"2","key":"34_CR28","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.pscychresns.2004.11.007","volume":"138","author":"J Riffkin","year":"2005","unstructured":"Riffkin, J., et al.: A manual and automated MRI study of anterior cingulate and orbito-frontal cortices, and caudate nucleus in obsessive-compulsive disorder: comparison with healthy controls and patients with schizophrenia. Psychiatry Res. Neuroimaging 138(2), 99\u2013113 (2005)","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"34_CR29","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.neubiorev.2015.01.013","volume":"54","author":"G Pergola","year":"2015","unstructured":"Pergola, G., et al.: The role of the thalamus in schizophrenia from a neuroimaging perspective. Neurosci. Biobehav. Rev. 54, 57\u201375 (2015)","journal-title":"Neurosci. Biobehav. Rev."},{"key":"34_CR30","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3389\/fnagi.2019.00097","volume":"11","author":"C Belkhiria","year":"2019","unstructured":"Belkhiria, C., et al.: Cingulate cortex atrophy is associated with hearing loss in presbycusis with cochlear amplifier dysfunction. Front. Aging Neurosci. 11, 97 (2019)","journal-title":"Front. Aging Neurosci."},{"issue":"35","key":"34_CR31","doi-asserted-by":"publisher","first-page":"12638","DOI":"10.1523\/JNEUROSCI.2559-11.2011","volume":"31","author":"JE Peelle","year":"2011","unstructured":"Peelle, J.E., et al.: Hearing loss in older adults affects neural systems supporting speech comprehension. J. Neurosci. 31(35), 12638\u201312643 (2011)","journal-title":"J. Neurosci."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05162-2_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T23:26:23Z","timestamp":1758237983000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05162-2_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032051615","9783032051622"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05162-2_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,19]]},"assertion":[{"value":"19 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}