{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T08:07:32Z","timestamp":1758874052783,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":11,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,12,5]],"date-time":"2020-12-05T00:00:00Z","timestamp":1607126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities","award":["No. N2016001"],"award-info":[{"award-number":["No. N2016001"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62076059"],"award-info":[{"award-number":["No.62076059"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,12,5]]},"DOI":"10.1145\/3451421.3451432","type":"proceedings-article","created":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T14:38:41Z","timestamp":1630075121000},"page":"46-48","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["S-GCN: A siamese spectral graph convolutions on brain connectivity networks"],"prefix":"10.1145","author":[{"given":"Xiaoli","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Chemical and Biomolecular Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiali","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Chemical and Biomolecular Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Cao","sequence":"additional","affiliation":[{"name":"Northeastern University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,8,27]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trci.2019.05.008"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(18)30403-4"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"S. Qi S. Meesters K. Nicolay B. M. ter Haar Romeny P. Ossenblok The influence of construction methodology on structural brain network measures: A review Journal of neuroscience methods 253 (2015) 170\u2013182.  S. Qi S. Meesters K. Nicolay B. M. ter Haar Romeny P. Ossenblok The influence of construction methodology on structural brain network measures: A review Journal of neuroscience methods 253 (2015) 170\u2013182.","DOI":"10.1016\/j.jneumeth.2015.06.016"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Y. Zhu S. Qi B. Zhang D. He Y. Teng J. Hu X. Wei Connectome-based biomarkers predict subclinical depression and identify abnormal brain connections with the thalamus and lateral habenula Frontiers in psychiatry 10 (2019) 371  Y. Zhu S. Qi B. Zhang D. He Y. Teng J. Hu X. Wei Connectome-based biomarkers predict subclinical depression and identify abnormal brain connections with the thalamus and lateral habenula Frontiers in psychiatry 10 (2019) 371","DOI":"10.3389\/fpsyt.2019.00371"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Khazaee A. Ebrahimzadeh A. Babajani-Feremi Application of advanced machine learning methods on resting-state fmri network for identification of mild cognitive impairment and alzheimers disease Brain imaging and behavior 10 (3) (2016) 799\u2013817.  Khazaee A. Ebrahimzadeh A. Babajani-Feremi Application of advanced machine learning methods on resting-state fmri network for identification of mild cognitive impairment and alzheimers disease Brain imaging and behavior 10 (3) (2016) 799\u2013817.","DOI":"10.1007\/s11682-015-9448-7"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"J. Wang X. Zuo Y. He Graph-based network analysis of resting-state functional mri Frontiers in systems neuroscience 4 (2010) 16  J. Wang X. Zuo Y. He Graph-based network analysis of resting-state functional mri Frontiers in systems neuroscience 4 (2010) 16","DOI":"10.3389\/fnsys.2010.00016"},{"key":"e_1_3_2_1_7_1","first-page":"185","volume-title":"International conference on medical image computing and computer-assisted intervention","author":"Parisot S. I.","year":"2017","unstructured":"S. Parisot , S. I. Ktena , E. Ferrante , M. Lee , R. G. Moreno , B. Glocker , D. Rueckert, Spectral graph convolutions for population-based disease prediction , in: International conference on medical image computing and computer-assisted intervention , Springer , 2017 , pp. 177\u2013 185 . S. Parisot, S. I. Ktena, E. Ferrante, M. Lee, R. G. Moreno, B. Glocker, D. Rueckert, Spectral graph convolutions for population-based disease prediction, in: International conference on medical image computing and computer-assisted intervention, Springer, 2017, pp. 177\u2013185."},{"key":"e_1_3_2_1_8_1","first-page":"493","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Li N. C.","year":"2019","unstructured":"X. Li , N. C. Dvornek , Y. Zhou , J. Zhuang , P. Ventola , J. S. Duncan, Graph neural network for interpreting task-fmri biomarkers , in: International Conference on Medical Image Computing and Computer-Assisted Intervention , Springer , 2019 , pp. 485\u2013 493 . X. Li, N. C. Dvornek, Y. Zhou, J. Zhuang, P. Ventola, J. S. Duncan, Graph neural network for interpreting task-fmri biomarkers, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2019, pp. 485\u2013493."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.12.052"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"S. Parisot S. I. Ktena E. Ferrante M. Lee R. Guerrero B. Glocker D. Rueckert Disease prediction using graph convolutional networks: application to autism spectrum disorder and alzheimers disease Medical image analysis 48 (2018) 117\u2013130  S. Parisot S. I. Ktena E. Ferrante M. Lee R. Guerrero B. Glocker D. Rueckert Disease prediction using graph convolutional networks: application to autism spectrum disorder and alzheimers disease Medical image analysis 48 (2018) 117\u2013130","DOI":"10.1016\/j.media.2018.06.001"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.02.062"}],"event":{"name":"ISICDM 2020: The Fourth International Symposium on Image Computing and Digital Medicine","acronym":"ISICDM 2020","location":"Shenyang China"},"container-title":["The Fourth International Symposium on Image Computing and Digital Medicine"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3451421.3451432","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3451421.3451432","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:17:31Z","timestamp":1750191451000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3451421.3451432"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,5]]},"references-count":11,"alternative-id":["10.1145\/3451421.3451432","10.1145\/3451421"],"URL":"https:\/\/doi.org\/10.1145\/3451421.3451432","relation":{},"subject":[],"published":{"date-parts":[[2020,12,5]]},"assertion":[{"value":"2021-08-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}