{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:24:05Z","timestamp":1766067845806,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031439063"},{"type":"electronic","value":"9783031439070"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43907-0_60","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"626-636","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Graph Convolutional Network with Morphometric Similarity Networks for Schizophrenia Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3322-1323","authenticated-orcid":false,"given":"Hye Won","family":"Park","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8537-5045","authenticated-orcid":false,"given":"Seo Yeong","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3991-3870","authenticated-orcid":false,"given":"Won Hee","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"60_CR1","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/s12021-017-9338-9","volume":"15","author":"C Aine","year":"2017","unstructured":"Aine, C., Bockholt, H.J., Bustillo, J.R., et al.: Multimodal neuroimaging in schizophrenia: description and dissemination. Neuroinformatics 15, 343\u2013364 (2017)","journal-title":"Neuroinformatics"},{"doi-asserted-by":"crossref","unstructured":"Bessadok, A., Mahjoub, M.A., Rekik, I.: Graph neural networks in network neuroscience. IEEE Trans. Pattern Anal. Mach. Intell. 45(5), 5833\u22125848 (2022)","key":"60_CR2","DOI":"10.1109\/TPAMI.2022.3209686"},{"unstructured":"Bilder, R., Poldrack, R., Cannon, T., et al.: UCLA consortium for neuropsychiatric phenomics LA5c Study. OpenNeuro (2020)","key":"60_CR3"},{"key":"60_CR4","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1093\/schbul\/sby058","volume":"44","author":"FJ Charlson","year":"2018","unstructured":"Charlson, F.J., Ferrari, A.J., Santomauro, D.F., et al.: Global epidemiology and burden of schizophrenia: findings from the global burden of disease study 2016. Schizophr. Bull. 44, 1195\u20131203 (2018)","journal-title":"Schizophr. Bull."},{"unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp. 3844\u20133852. (2016)","key":"60_CR5"},{"key":"60_CR6","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl, B.: FreeSurfer. Neuroimage 62, 774\u2013781 (2012)","journal-title":"Neuroimage"},{"key":"60_CR7","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s12021-013-9184-3","volume":"11","author":"RL Gollub","year":"2013","unstructured":"Gollub, R.L., Shoemaker, J.M., King, M.D., et al.: The MCIC collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia. Neuroinformatics 11, 367\u2013388 (2013)","journal-title":"Neuroinformatics"},{"doi-asserted-by":"crossref","unstructured":"Huang, Y., Albert, C.: Semi-supervised multimodality learning with graph convolutional neural networks for disease diagnosis. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 2451\u20132455 (2020)","key":"60_CR8","DOI":"10.1109\/ICIP40778.2020.9191172"},{"key":"60_CR9","series-title":"Lima, Peru, October 4\u20138, 2020, Proceedings, Part VII 23","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1007\/978-3-030-59728-3_55","volume-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference","author":"Y Huang","year":"2020","unstructured":"Huang, Y., Chung, A.C.: Edge-variational graph convolutional networks for uncertainty-aware disease prediction. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference. Lima, Peru, October 4\u20138, 2020, Proceedings, Part VII 23, pp. 562\u2013572. Springer, Cham (2020)"},{"key":"60_CR10","doi-asserted-by":"publisher","first-page":"104096","DOI":"10.1016\/j.compbiomed.2020.104096","volume":"127","author":"H Jiang","year":"2020","unstructured":"Jiang, H., Cao, P., Xu, M., Yang, J., Zaiane, O.: Hi-GCN: a hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction. Comput. Biol. Med. 127, 104096 (2020)","journal-title":"Comput. Biol. Med."},{"key":"60_CR11","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/s12021-011-9133-y","volume":"10","author":"DN Kennedy","year":"2012","unstructured":"Kennedy, D.N., Haselgrove, C., Hodge, S.M., et al.: CANDIShare: a resource for pediatric neuroimaging data. Neuroinformatics 10, 319\u2013322 (2012)","journal-title":"Neuroinformatics"},{"key":"60_CR12","doi-asserted-by":"publisher","first-page":"104949","DOI":"10.1016\/j.compbiomed.2021.104949","volume":"139","author":"M Khodatars","year":"2021","unstructured":"Khodatars, M., Shoeibi, A., Sadeghi, D., et al.: Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review. Comput. Biol. Med. 139, 104949 (2021)","journal-title":"Comput. Biol. Med."},{"key":"60_CR13","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1093\/cercor\/bhx317","volume":"29","author":"BS Khundrakpam","year":"2019","unstructured":"Khundrakpam, B.S., Lewis, J.D., Jeon, S., et al.: Exploring individual brain variability during development based on patterns of maturational coupling of cortical thickness: a longitudinal MRI study. Cereb. Cortex 29, 178\u2013188 (2019)","journal-title":"Cereb. Cortex"},{"key":"60_CR14","doi-asserted-by":"publisher","first-page":"2533","DOI":"10.1093\/cercor\/bhy123","volume":"29","author":"R Kong","year":"2019","unstructured":"Kong, R., Li, J., Orban, C., et al.: Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion. Cereb. Cortex 29, 2533\u20132551 (2019)","journal-title":"Cereb. Cortex"},{"key":"60_CR15","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1586\/ern.10.93","volume":"10","author":"MK Larson","year":"2010","unstructured":"Larson, M.K., Walker, E.F., Compton, M.T.: Early signs, diagnosis and therapeutics of the prodromal phase of schizophrenia and related psychotic disorders. Expert Rev. Neurother. 10, 1347\u20131359 (2010)","journal-title":"Expert Rev. Neurother."},{"key":"60_CR16","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1093\/schbul\/sbac047","volume":"48","author":"D Lei","year":"2022","unstructured":"Lei, D., Qin, K., Pinaya, W.H., et al.: Graph convolutional networks reveal network-level functional dysconnectivity in schizophrenia. Schizophr. Bull. 48, 881\u2013892 (2022)","journal-title":"Schizophr. Bull."},{"key":"60_CR17","doi-asserted-by":"publisher","first-page":"9604","DOI":"10.1073\/pnas.1820754116","volume":"116","author":"SE Morgan","year":"2019","unstructured":"Morgan, S.E., Seidlitz, J., Whitaker, K.J., et al.: Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes. Proc. Natl. Acad. Sci. 116, 9604\u20139609 (2019)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"60_CR18","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.media.2018.06.001","volume":"48","author":"S Parisot","year":"2018","unstructured":"Parisot, S., Ktena, S.I., Ferrante, E., et al.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer\u2019s disease. Med. Image Anal. 48, 117\u2013130 (2018)","journal-title":"Med. Image Anal."},{"key":"60_CR19","doi-asserted-by":"publisher","first-page":"103977","DOI":"10.1016\/j.ebiom.2022.103977","volume":"78","author":"K Qin","year":"2022","unstructured":"Qin, K., Lei, D., Pinaya, W.H., et al.: Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites. EBioMedicine 78, 103977 (2022)","journal-title":"EBioMedicine"},{"key":"60_CR20","doi-asserted-by":"publisher","first-page":"137","DOI":"10.3389\/fnhum.2012.00137","volume":"6","author":"G Repov\u0161","year":"2012","unstructured":"Repov\u0161, G., Barch, D.M.: Working memory related brain network connectivity in individuals with schizophrenia and their siblings. Front. Hum. Neurosci. 6, 137 (2012)","journal-title":"Front. Hum. Neurosci."},{"key":"60_CR21","doi-asserted-by":"publisher","first-page":"3522","DOI":"10.1016\/j.neuroimage.2011.10.086","volume":"59","author":"R Romero-Garcia","year":"2012","unstructured":"Romero-Garcia, R., Atienza, M., Clemmensen, L.H., Cantero, J.L.: Effects of network resolution on topological properties of human neocortex. Neuroimage 59, 3522\u20133532 (2012)","journal-title":"Neuroimage"},{"key":"60_CR22","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","volume":"52","author":"M Rubinov","year":"2010","unstructured":"Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059\u20131069 (2010)","journal-title":"Neuroimage"},{"doi-asserted-by":"crossref","unstructured":"Sadeghi, D., Shoeibi, A., Ghassemi, N., et al.: An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: methods, challenges, and future works. Comput. Biol. Med. 105554 (2022)","key":"60_CR23","DOI":"10.1016\/j.compbiomed.2022.105554"},{"key":"60_CR24","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.neuron.2017.11.039","volume":"97","author":"J Seidlitz","year":"2018","unstructured":"Seidlitz, J., V\u00e1\u0161a, F., Shinn, M., et al.: Morphometric similarity networks detect microscale cortical organization and predict inter-individual cognitive variation. Neuron 97, 231\u2013247 (2018)","journal-title":"Neuron"},{"key":"60_CR25","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1038\/s41597-021-01004-8","volume":"8","author":"SC Tanaka","year":"2021","unstructured":"Tanaka, S.C., Yamashita, A., Yahata, N., et al.: A multi-site, multi-disorder resting-state magnetic resonance image database. Scientific data 8, 227 (2021)","journal-title":"Scientific data"},{"key":"60_CR26","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1093\/cercor\/bhx249","volume":"28","author":"F V\u00e1\u0161a","year":"2018","unstructured":"V\u00e1\u0161a, F., Seidlitz, J., Romero-Garcia, R., et al.: Adolescent tuning of association cortex in human structural brain networks. Cereb. Cortex 28, 281\u2013294 (2018)","journal-title":"Cereb. Cortex"},{"key":"60_CR27","doi-asserted-by":"publisher","first-page":"105239","DOI":"10.1016\/j.compbiomed.2022.105239","volume":"142","author":"G Wen","year":"2022","unstructured":"Wen, G., Cao, P., Bao, H., et al.: MVS-GCN: A prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis. Comput. Biol. Med. 142, 105239 (2022)","journal-title":"Comput. Biol. Med."},{"key":"60_CR28","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.schres.2017.11.038","volume":"214","author":"JL Winterburn","year":"2019","unstructured":"Winterburn, J.L., Voineskos, A.N., Devenyi, G.A., et al.: Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? a multi-method and multi-dataset study. Schizophr. Res. 214, 3\u201310 (2019)","journal-title":"Schizophr. Res."},{"unstructured":"Ying, Z., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: Gnnexplainer: generating explanations for graph neural networks. Adv. Neural Inf. Process. Syst. 9244\u20139255 (2019)","key":"60_CR29"},{"key":"60_CR30","doi-asserted-by":"publisher","first-page":"184","DOI":"10.3389\/fnhum.2018.00184","volume":"12","author":"F Zhao","year":"2018","unstructured":"Zhao, F., Zhang, H., Rekik, I., An, Z., Shen, D.: Diagnosis of autism spectrum disorders using multi-level high-order functional networks derived from resting-state functional MRI. Front. Hum. Neurosci. 12, 184 (2018)","journal-title":"Front. Hum. Neurosci."},{"key":"60_CR31","doi-asserted-by":"publisher","first-page":"732","DOI":"10.1109\/TAFFC.2018.2890597","volume":"12","author":"W Zheng","year":"2019","unstructured":"Zheng, W., Eilam-Stock, T., Wu, T., et al.: Multi-feature based network revealing the structural abnormalities in autism spectrum disorder. IEEE Trans. Affect. Comput. 12, 732\u2013742 (2019)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"60_CR32","first-page":"887","volume":"3","author":"W Zheng","year":"2018","unstructured":"Zheng, W., Yao, Z., Xie, Y., Fan, J., Hu, B.: Identification of Alzheimer\u2019s disease and mild cognitive impairment using networks constructed based on multiple morphological brain features. Biol. Psychiatry: Cogn. Neurosci. Neuroimaging 3, 887\u2013897 (2018)","journal-title":"Biol. Psychiatry: Cogn. Neurosci. Neuroimaging"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43907-0_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T18:30:37Z","timestamp":1709836237000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43907-0_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439063","9783031439070"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43907-0_60","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","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":"2250","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":"730","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":"32% - 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":"3","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":"5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}