biostudies-literatureKim MNSFNIBIB NIH HHSNIA NIH HHSNIANIMH NIH HHSNational Research Foundation of KoreaNational Institutes of HealthNIBIBNLMMcDonnell Center for Systems NeuroscienceNIH Blueprint for Neuroscience ResearchNLM NIH HHSNIH HHSNational Science Foundation102297https://www.ebi.ac.uk/biostudies/studies/S-EPMC8792314biostudies-literatureUnknown76The advances in technologies for acquiring brain imaging and high-throughput genetic data allow the researcher to access a large amount of multi-modal data. Although the sparse canonical correlation analysis is a powerful bi-multivariate association analysis technique for feature selection, we are still facing major challenges in integrating multi-modal imaging genetic data and yielding biologically meaningful interpretation of imaging genetic findings. In this study, we propose a novel multi-task learning based structured sparse canonical correlation analysis (MTS2CCA) to deliver interpretable results and improve integration in imaging genetics studies. We perform comparative studies with state-of-the-art competing methods on both simulation and real imaging genetic data. On the simulatioMedical image analysisMulti-task learning based structured sparse canonical correlation analysis for brain imaging genetics.PMC8792314S10 OD023495RF1 AG063481IIS 1837964U01 AG068057R01 LM013463U54 MH091657R01 EB022574R01 AG058854NRF-2020R1A6A3A03038525Saykin AJShen LMoore JHLiu KYan JMin EJLong QKim MfalseMulti-task learning based structured sparse canonical correlation analysis for brain imaging genetics.The advances in technologies for acquiring brain imaging and high-throughput genetic data allow the researcher to access a large amount of multi-modal data. Although the sparse canonical correlation analysis is a powerful bi-multivariate association analysis technique for feature selection, we are still facing major challenges in integrating multi-modal imaging genetic data and yielding biologically meaningful interpretation of imaging genetic findings. In this study, we propose a novel multi-task learning based structured sparse canonical correlation analysis (MTS2CCA) to deliver interpretable results and improve integration in imaging genetics studies. We perform comparative studies with state-of-the-art competing methods on both simulation and real imaging genetic data. On the simulatio2022-01-01T00:00:00Z2022 Feb2026-04-08T10:46:05.525Z2025-04-21T15:51:45.081ZS-EPMC87923143487192910.1016/j.media.2021.102297