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While conventional multivariate approaches often simplify statistical assumptions and estimate one-dimensional independent sources shared across modalities, the true relationships between latent sources are likely more complex\u2014statistical dependence may exist both within and between modalities and span more than one dimension per modality. Here, we introduce Multimodal Subspace Independent Vector Analysis (MSIVA), a method for capturing both joint and unique vector sources from multiple data modalities by defining cross-modal and unimodal subspaces with variable dimensions. MSIVA enables flexible estimation of varying-size independent subspaces within modalities and their one-to-one linkage to corresponding subspaces across modalities. Crucially, it captures subject-level variability at the voxel level within independent subspaces, in contrast to traditional methods that share identical independent components across subjects. We evaluated three initialization workflows with five candidate subspace structures in multiple synthetic datasets and two large multimodal neuroimaging datasets, including structural MRI (sMRI) and functional MRI (fMRI). After confirming that MSIVA successfully recovered ground-truth subspace structures in synthetic data, we applied MSIVA to identify latent subspace structures in neuroimaging data. Subsequent subspace-specific canonical correlation analysis, brain-phenotype prediction, and voxelwise brain-age delta analysis revealed that MSIVA sources were strongly associated with multiple phenotype variables, including age, sex, schizophrenia, lifestyle factors, and cognitive functions. Further, we identified modality- and group-specific brain regions related to age (for example, cerebellum, precentral gyrus, and cingulate gyrus in sMRI; occipital lobe and superior frontal gyrus in fMRI), sex (for example, cerebellum in sMRI, frontal lobe in fMRI, and precuneus in both sMRI and fMRI), and schizophrenia (for example, cerebellar, frontal, and insular cortices in sMRI; occipital pole, lingual gyrus, and precuneus in fMRI), shedding light on linked phenotypic and neuropsychiatric biomarkers of brain structure and function.<\/jats:p>","DOI":"10.1162\/imag.a.1266","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T21:00:20Z","timestamp":1779224420000},"update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal subspace independent vector analysis effectively captures latent relationships between brain structure and function"],"prefix":"10.1162","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5604-9493","authenticated-orcid":false,"given":"Xinhui","family":"Li","sequence":"first","affiliation":[{"name":"Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States"},{"name":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Kochunov","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0594-2796","authenticated-orcid":false,"given":"Tulay","family":"Adali","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7271-1288","authenticated-orcid":false,"given":"Rogers F.","family":"Silva","sequence":"additional","affiliation":[{"name":"Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9058-0747","authenticated-orcid":false,"given":"Vince D.","family":"Calhoun","sequence":"additional","affiliation":[{"name":"Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States"},{"name":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2026,6,18]]},"reference":[{"issue":"3","key":"2026061815554473700_IMAG.a.1266-b1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MSP.2014.2300511","article-title":"Diversity in independent component and vector analyses: Identifiability, algorithms, and applications in medical imaging","volume":"31","author":"Adali","year":"2014","journal-title":"IEEE Signal Processing Magazine"},{"issue":"9","key":"2026061815554473700_IMAG.a.1266-b2","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1109\/JPROC.2015.2461601","article-title":"Multimodal data fusion using source separation: Application to medical imaging","volume":"103","author":"Adali","year":"2015","journal-title":"Proceedings of the IEEE"},{"issue":"9","key":"2026061815554473700_IMAG.a.1266-b3","doi-asserted-by":"crossref","first-page":"1478","DOI":"10.1109\/JPROC.2015.2461624","article-title":"Multimodal data fusion using source separation: Two effective models based on ICA and IVA and their properties","volume":"103","author":"Adali","year":"2015","journal-title":"Proceedings of the IEEE"},{"issue":"4","key":"2026061815554473700_IMAG.a.1266-b4","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s12021-017-9338-9","article-title":"Multimodal neuroimaging in schizophrenia: Description and dissemination","volume":"15","author":"Aine","year":"2017","journal-title":"Neuroinformatics"},{"issue":"4","key":"2026061815554473700_IMAG.a.1266-b5","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.neuron.2012.05.001","article-title":"Data visualization in the neurosciences: Overcoming the curse of dimensionality","volume":"74","author":"Allen","year":"2012","journal-title":"Neuron"},{"key":"2026061815554473700_IMAG.a.1266-b6","unstructured":"Amari, S., Cichocki, A., & Yang, H. 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