{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:04:11Z","timestamp":1791911051833,"version":"4.3.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>Brain functional connectivity analysis on fMRI data could improve the understanding of human brain function. However, due to the influence of the inter-subject variability and the heterogeneity across subjects, previous methods of functional connectivity analysis are often insufficient in capturing disease-related representation so that decreasing disease diagnosis performance.\n\nIn this paper, we first propose a new multi-graph fusion framework to fine-tune the original representation derived from Pearson correlation analysis, and then employ L1-SVM on fine-tuned representations to conduct joint brain region selection and disease diagnosis for avoiding the issue of the curse of dimensionality on high-dimensional data. The multi-graph fusion framework automatically learns the connectivity number for every node (i.e., brain region) and integrates all subjects in a unified framework to output homogenous and discriminative representations of all subjects. Experimental results on two real data sets, i.e., fronto-temporal dementia (FTD) and obsessive-compulsive disorder (OCD), verified the effectiveness of our proposed framework, compared to state-of-the-art methods.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/81","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T08:12:10Z","timestamp":1594195930000},"page":"580-586","source":"Crossref","is-referenced-by-count":6,"title":["Multi-graph Fusion for Functional Neuroimaging Biomarker Detection"],"prefix":"10.24963","author":[{"given":"Jiangzhang","family":"Gan","sequence":"first","affiliation":[{"name":"Center for Future Media and School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731\uff0cChina"},{"name":"School of Natural and Computational Science, Massey University Auckland Campus, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Center for Future Media and School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu , 611371\uff0cChina"},{"name":"School of Natural and Computational Science, Massey University Auckland Campus, New Zealand"},{"name":"Sichuan Artificial Intelligence Research Institute, Yibin 644000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongyao","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Natural and Computational Science, Massey University Auckland Campus, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonghua","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Natural and Computational Science, Massey University Auckland Campus, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junbo","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Medicine and Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziwen","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Psychology and Sociology, Shenzhen University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guorong","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Medicine and Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T22:13:11Z","timestamp":1594246391000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/81"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/81","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}