{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T03:24:48Z","timestamp":1768447488739,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T00:00:00Z","timestamp":1685923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 MH118695"],"award-info":[{"award-number":["R01 MH118695"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 MH123610"],"award-info":[{"award-number":["R01 MH123610"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 AG073949"],"award-info":[{"award-number":["R01 AG073949"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010801","name":"Xunta de Galicia","doi-asserted-by":"publisher","award":["ED481B 2022\/012"],"award-info":[{"award-number":["ED481B 2022\/012"]}],"id":[{"id":"10.13039\/501100010801","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fulbright Commission Spain","award":["ED481B2022\/012"],"award-info":[{"award-number":["ED481B2022\/012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data\u2019s true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the \u201cshared\u201d subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs.<\/jats:p>","DOI":"10.3390\/s23115333","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T02:57:47Z","timestamp":1685933867000},"page":"5333","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis"],"prefix":"10.3390","volume":"23","author":[{"given":"Mingyu","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ben","family":"Gabrielson","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0826-453X","authenticated-orcid":false,"given":"Mohammad Abu Baker Siddique","family":"Akhonda","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanlu","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6543-8236","authenticated-orcid":false,"given":"Francisco","family":"Laport","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA"},{"name":"CITIC Research Center, University of A Coru\u00f1a, 15008 A Coru\u00f1a, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vince","family":"Calhoun","sequence":"additional","affiliation":[{"name":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0594-2796","authenticated-orcid":false,"given":"T\u00fclay","family":"Adali","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,5]]},"reference":[{"key":"ref_1","unstructured":"Comon, P., and Jutten, C. 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