{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T20:58:38Z","timestamp":1758401918621,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031721199"},{"type":"electronic","value":"9783031721205"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72120-5_57","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:02:53Z","timestamp":1727870573000},"page":"612-622","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Self-supervised Learning with\u00a0Adaptive Graph Structure and\u00a0Function Representation for Cross-Dataset Brain Disorder Diagnosis"],"prefix":"10.1007","author":[{"given":"Dongdong","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linlin","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengjun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenrong","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqi","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyun","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lichi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"issue":"3","key":"57_CR1","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1073\/pnas.1418031112","volume":"112","author":"P Barttfeld","year":"2015","unstructured":"Barttfeld, P., Uhrig, L., Sitt, J.D., Sigman, M., Jarraya, B., Dehaene, S.: Signature of consciousness in the dynamics of resting-state brain activity. Proceedings of the National Academy of Sciences 112(3), 887\u2013892 (2015)","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"57_CR2","doi-asserted-by":"crossref","unstructured":"Benesty, J., Chen, J., Huang, Y., Cohen, I.: Pearson correlation coefficient. In: Noise reduction in speech processing, pp.\u00a01\u20134. Springer (2009)","DOI":"10.1007\/978-3-642-00296-0_5"},{"key":"57_CR3","doi-asserted-by":"crossref","unstructured":"Chen, D., Liu, M., Shen, Z., Zhao, X., Wang, Q., Zhang, L.: Learnable subdivision graph neural network for functional brain network analysis and interpretable cognitive disorder diagnosis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 56\u201366. Springer (2023)","DOI":"10.1007\/978-3-031-43993-3_6"},{"key":"57_CR4","doi-asserted-by":"crossref","unstructured":"Chen, D., Zhang, L.: Fe-stgnn: Spatio-temporal graph neural network with functional and effective connectivity fusion for mci diagnosis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 67\u201376. Springer (2023)","DOI":"10.1007\/978-3-031-43993-3_7"},{"key":"57_CR5","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3389\/fnins.2017.00125","volume":"11","author":"S Chen","year":"2017","unstructured":"Chen, S., Xing, Y., Kang, J.: Latent and abnormal functional connectivity circuits in autism spectrum disorder. Frontiers in neuroscience 11, \u00a0125 (2017)","journal-title":"Frontiers in neuroscience"},{"key":"57_CR6","doi-asserted-by":"crossref","unstructured":"Consortium, A.: The adhd-200 consortium: A model to advance the translational potential of neuroimaging in clinical neuroscience. Frontiers in Systems Neuroscience 6, \u00a062 (2012)","DOI":"10.3389\/fnsys.2012.00062"},{"key":"57_CR7","first-page":"27","volume":"7","author":"C Craddock","year":"2013","unstructured":"Craddock, C., Benhajali, Y., Chu, C., Chouinard, F., Evans, A., Jakab, A., Khundrakpam, B.S., Lewis, J.D., Li, Q., Milham, M., et\u00a0al.: The neuro bureau preprocessing initiative: Open sharing of preprocessed neuroimaging data and derivatives. Frontiers in Neuroinformatics 7, \u00a027 (2013)","journal-title":"Frontiers in Neuroinformatics"},{"issue":"2","key":"57_CR8","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1109\/TMI.2022.3218745","volume":"42","author":"H Cui","year":"2023","unstructured":"Cui, H., Dai, W., Zhu, Y., Kan, X., Gu, A.A.C., Lukemire, J., Zhan, L., He, L., Guo, Y., Yang, C.: Braingb: a benchmark for brain network analysis with graph neural networks. IEEE Transactions on Medical Imaging 42(2), 493\u2013506 (2023)","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"57_CR9","doi-asserted-by":"publisher","first-page":"585","DOI":"10.3389\/fnins.2019.00585","volume":"13","author":"FV Farahani","year":"2019","unstructured":"Farahani, F.V., Karwowski, W., Lighthall, N.R.: Application of graph theory for identifying connectivity patterns in human brain networks: A systematic review. Frontiers in Neuroscience 13, \u00a0585 (2019)","journal-title":"Frontiers in Neuroscience"},{"key":"57_CR10","doi-asserted-by":"crossref","unstructured":"Jaiswal, A., Babu, A.R., Zadeh, M.Z., Banerjee, D., Makedon, F.: A survey on contrastive self-supervised learning. Technologies 9(1) (2021)","DOI":"10.3390\/technologies9010002"},{"issue":"17","key":"57_CR11","doi-asserted-by":"publisher","first-page":"4997","DOI":"10.1002\/hbm.25175","volume":"41","author":"E Jun","year":"2020","unstructured":"Jun, E., Na, K.S., Kang, W., Lee, J., Suk, H.I., Ham, B.J.: Identifying resting-state effective connectivity abnormalities in drug-na\u00efve major depressive disorder diagnosis via graph convolutional networks. Human Brain Mapping 41(17), 4997\u20135014 (2020)","journal-title":"Human Brain Mapping"},{"issue":"12","key":"57_CR12","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1038\/s41551-022-00914-1","volume":"6","author":"R Krishnan","year":"2022","unstructured":"Krishnan, R., Rajpurkar, P., Topol, E.J.: Self-supervised learning in medicine and healthcare. Nature Biomedical Engineering 6(12), 1346\u20131352 (2022)","journal-title":"Nature Biomedical Engineering"},{"key":"57_CR13","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1007\/s11682-013-9279-3","volume":"8","author":"P Lin","year":"2014","unstructured":"Lin, P., Sun, J., Yu, G., Wu, Y., Yang, Y., Liang, M., Liu, X.: Global and local brain network reorganization in attention-deficit\/hyperactivity disorder. Brain imaging and behavior 8, 558\u2013569 (2014)","journal-title":"Brain imaging and behavior"},{"key":"57_CR14","doi-asserted-by":"crossref","unstructured":"Linus, E., Henry, G., Chen, C., Loy, Timothy, M.H.: Self-supervised representation learning: Introduction, advances, and challenges. IEEE Signal Processing Magazine 39(3), 42\u201362 (2022)","DOI":"10.1109\/MSP.2021.3134634"},{"key":"57_CR15","doi-asserted-by":"crossref","unstructured":"Liu, M., Zhang, H., Liu, M., Chen, D., Zhuang, Z., Wang, X., Zhang, L., Peng, D., Wang, Q.: Randomizing human brain function representation for brain disease diagnosis. IEEE Transactions on Medical Imaging pp.\u00a01\u20131 (2024)","DOI":"10.1109\/TMI.2024.3368064"},{"issue":"4","key":"57_CR16","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1016\/j.neuroimage.2008.10.031","volume":"44","author":"C Misra","year":"2009","unstructured":"Misra, C., Fan, Y., Davatzikos, C.: Baseline and longitudinal patterns of brain atrophy in mci patients, and their use in prediction of short-term conversion to ad: results from adni. Neuroimage 44(4), 1415\u20131422 (2009)","journal-title":"Neuroimage"},{"issue":"5","key":"57_CR17","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.96.052410","volume":"96","author":"K Mukta","year":"2017","unstructured":"Mukta, K., MacLaurin, J., Robinson, P.: Theory of corticothalamic brain activity in a spherical geometry: Spectra, coherence, and correlation. Physical Review E 96(5), 052410 (2017)","journal-title":"Physical Review E"},{"key":"57_CR18","doi-asserted-by":"publisher","first-page":"1001848","DOI":"10.3389\/fnhum.2023.1001848","volume":"17","author":"S Nag","year":"2023","unstructured":"Nag, S., Uludag, K.: Dynamic effective connectivity using physiologically informed dynamic causal model with recurrent units: A functional magnetic resonance imaging simulation study. Frontiers in Human Neuroscience 17, 1001848 (2023)","journal-title":"Frontiers in Human Neuroscience"},{"key":"57_CR19","doi-asserted-by":"crossref","unstructured":"Pribram, K.H.: The brain, cognitive commodities, and the enfolded order. In: The optimum utilization of knowledge, pp. 29\u201340 (2019)","DOI":"10.4324\/9780429313301-3"},{"issue":"1","key":"57_CR20","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., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: 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"},{"key":"57_CR21","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s10827-010-0262-3","volume":"30","author":"R Vicente","year":"2011","unstructured":"Vicente, R., Wibral, M., Lindner, M., Pipa, G.: Transfer entropy-a model-free measure of effective connectivity for the neurosciences. Journal of Computational Neuroscience 30, 45\u201367 (2011)","journal-title":"Journal of Computational Neuroscience"},{"issue":"17","key":"57_CR22","doi-asserted-by":"publisher","first-page":"5672","DOI":"10.1002\/hbm.26469","volume":"44","author":"X Wang","year":"2023","unstructured":"Wang, X., Chu, Y., Wang, Q., Cao, L., Qiao, L., Zhang, L., Liu, M.: Unsupervised contrastive graph learning for resting-state functional mri analysis and brain disorder detection. Human Brain Mapping 44(17), 5672\u20135692 (2023)","journal-title":"Human Brain Mapping"},{"issue":"2","key":"57_CR23","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1007\/s11682-015-9408-2","volume":"10","author":"CY Wee","year":"2016","unstructured":"Wee, C.Y., Yang, S., Yap, P.T., Shen, D.: Sparse temporally dynamic resting-state functional connectivity networks for early mci identification. Brain imaging and behavior 10(2), 342\u2013356 (2016)","journal-title":"Brain imaging and behavior"},{"key":"57_CR24","doi-asserted-by":"crossref","unstructured":"Wen, G., Cao, P., Liu, L., Yang, J., Zhang, X., Wang, F., Zaiane, O.R.: Graph self-supervised learning with application to brain networks analysis. IEEE Journal of Biomedical and Health Informatics (2023)","DOI":"10.1109\/JBHI.2023.3274531"},{"key":"57_CR25","doi-asserted-by":"crossref","unstructured":"Xing, X., Jin, L., Li, Q., Chen, L., Xue, Z., Peng, Z., Shi, F., Shen, D.: Detection of discriminative neurological circuits using hierarchical graph convolutional networks in fmri sequences. In: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis, pp. 121\u2013130 (2020)","DOI":"10.1007\/978-3-030-60365-6_12"},{"key":"57_CR26","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: International Conference on Learning Representations, pp. 1\u201317 (2019)"},{"key":"57_CR27","doi-asserted-by":"crossref","unstructured":"Yang, Y., Cui, H., Yang, C.: Ptgb: Pre-train graph neural networks for brain network analysis. In: Conference on Health, Inference, and Learning (2023)","DOI":"10.1109\/BigData55660.2022.10020314"},{"key":"57_CR28","doi-asserted-by":"crossref","unstructured":"You, Y., Chen, T., Wang, Z., Shen, Y.: Bringing your own view: Graph contrastive learning without prefabricated data augmentations. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. pp. 1300\u20131309 (2022)","DOI":"10.1145\/3488560.3498416"},{"key":"57_CR29","doi-asserted-by":"crossref","unstructured":"Zhao, S., Fang, L., Wu, L., Yang, Y., Han, J.: Decoding task sub-type states with group deep bidirectional recurrent neural network. Medical Image Computing and Computer Assisted Intervention pp. 241\u2013250 (2022)","DOI":"10.1007\/978-3-031-16431-6_23"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72120-5_57","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:28:49Z","timestamp":1727872129000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72120-5_57"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721199","9783031721205"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72120-5_57","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests.","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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}