{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:41:53Z","timestamp":1775324513331,"version":"3.50.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2017,7,12]],"date-time":"2017-07-12T00:00:00Z","timestamp":1499817600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61422204, 61473149, 61501230"],"award-info":[{"award-number":["61422204, 61473149, 61501230"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000005","name":"United States Department of Defense","doi-asserted-by":"publisher","award":["W81XWH-14-2-0151, W81XWH-13-1-0259, and W81XWH-12-2-0012"],"award-info":[{"award-number":["W81XWH-14-2-0151, W81XWH-13-1-0259, and W81XWH-12-2-0012"]}],"id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Neuroimaging genetics identifies the relationships between genetic variants (i.e., the single nucleotide polymorphisms) and brain imaging data to reveal the associations from genotypes to phenotypes. So far, most existing machine-learning approaches are widely used to detect the effective associations between genetic variants and brain imaging data at one time-point. However, those associations are based on static phenotypes and ignore the temporal dynamics of the phenotypical changes. The phenotypes across multiple time-points may exhibit temporal patterns that can be used to facilitate the understanding of the degenerative process. In this article, we propose a novel temporally constrained group sparse canonical correlation analysis (TGSCCA) framework to identify genetic associations with longitudinal phenotypic markers.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The proposed TGSCCA method is able to capture the temporal changes in brain from longitudinal phenotypes by incorporating the fused penalty, which requires that the differences between two consecutive canonical weight vectors from adjacent time-points should be small. A new efficient optimization algorithm is designed to solve the objective function. Furthermore, we demonstrate the effectiveness of our algorithm on both synthetic and real data (i.e., the Alzheimer\u2019s Disease Neuroimaging Initiative cohort, including progressive mild cognitive impairment, stable MCI and Normal Control participants). In comparison with conventional SCCA, our proposed method can achieve strong associations and discover phenotypic biomarkers across multiple time-points to guide disease-progressive interpretation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The Matlab code is available at https:\/\/sourceforge.net\/projects\/ibrain-cn\/files\/.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx245","type":"journal-article","created":{"date-parts":[[2017,4,17]],"date-time":"2017-04-17T19:11:50Z","timestamp":1492456310000},"page":"i341-i349","source":"Crossref","is-referenced-by-count":52,"title":["Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis"],"prefix":"10.1093","volume":"33","author":[{"given":"Xiaoke","family":"Hao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chanxiu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwen","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA"},{"name":"School of Informatics and Computing, Indiana University, Indianapolis, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohui","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA"},{"name":"School of Informatics and Computing, Indiana University, Indianapolis, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shannon L","family":"Risacher","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew J","family":"Saykin","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA"},{"name":"School of Informatics and Computing, Indiana University, Indianapolis, IN, 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