Detecting genetic associations with brain imaging phenotypes in Alzheimer’s disease via a novel structured SCCA approach

dc.contributor.authorDu, Lei
dc.contributor.authorLiu, Kefei
dc.contributor.authorYao, Xiaohui
dc.contributor.authorRisacher, Shannon L.
dc.contributor.authorHan, Junwei
dc.contributor.authorSaykin, Andrew J.
dc.contributor.authorGuo, Lei
dc.contributor.authorShen, Li
dc.contributor.departmentRadiology and Imaging Sciences, School of Medicineen_US
dc.date.accessioned2022-08-16T11:51:59Z
dc.date.available2022-08-16T11:51:59Z
dc.date.issued2020-04
dc.description.abstractBrain imaging genetics becomes an important research topic since it can reveal complex associations between genetic factors and the structures or functions of the human brain. Sparse canonical correlation analysis (SCCA) is a popular bi-multivariate association identification method. To mine the complex genetic basis of brain imaging phenotypes, there arise many SCCA methods with a variety of norms for incorporating different structures of interest. They often use the group lasso penalty, the fused lasso or the graph/network guided fused lasso ones. However, the group lasso methods have limited capability because of the incomplete or unavailable prior knowledge in real applications. The fused lasso and graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. In this paper, we introduce two new penalties to improve the fused lasso and the graph/network guided lasso penalties in structured sparse learning. We impose both penalties to the SCCA model and propose an optimization algorithm to solve it. The proposed SCCA method has a strong upper bound of grouping effects for both positively and negatively highly correlated variables. We show that, on both synthetic and real neuroimaging genetics data, the proposed SCCA method performs better than or equally to the conventional methods using fused lasso or graph/network guided fused lasso. In particular, the proposed method identifies higher canonical correlation coefficients and captures clearer canonical weight patterns, demonstrating its promising capability in revealing biologically meaningful imaging genetic associations.en_US
dc.eprint.versionAuthor's manuscripten_US
dc.identifier.citationDu L, Liu K, Yao X, et al. Detecting genetic associations with brain imaging phenotypes in Alzheimer's disease via a novel structured SCCA approach. Med Image Anal. 2020;61:101656. doi:10.1016/j.media.2020.101656en_US
dc.identifier.urihttps://hdl.handle.net/1805/29772
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.isversionof10.1016/j.media.2020.101656en_US
dc.relation.journalMedical Image Analysisen_US
dc.rightsPublisher Policyen_US
dc.sourcePMCen_US
dc.subjectBrain imaging geneticsen_US
dc.subjectSparse canonical correlation analysis (SCCA)en_US
dc.subjectFused pairwise group Lassoen_US
dc.titleDetecting genetic associations with brain imaging phenotypes in Alzheimer’s disease via a novel structured SCCA approachen_US
dc.typeArticleen_US
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