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Three-way (N-way) fusion of brain imaging data based on mCCA+jICA and its application to discriminating schizophrenia
Open accessAbstract
Multi-modal fusion is an effective approach to better understand brain diseases. However, most such instances have been limited to pair-wise fusion; because there are often more than two imaging modalities available per subject, there is a need for approaches that can combine multiple datasets optimally. In this paper, we extended our previous two-way fusion model called “multimodal CCA +joint ICA”, to three or N way fusion, that enables robust identification of correspondence among N data types and allows one to investigate the important question of whether certain disease risk factors are shared or distinct across multiple modalities. We compared “mCCA+jICA” with its alternatives in a 3-way fusion simulation and verified its advantages in both decomposition accuracy and modal linkage detection. We also applied it to real functional Magnetic Resonance Imaging (fMRI)-Diffusion Tensor Imaging (DTI) and structural MRI fusion to elucidate the abnormal architecture underlying schizophrenia (n=97) relative to healthy controls (n=116). Both modality-common and modality-unique abnormal regions were identified in schizophrenia. Specifically, the visual cortex in fMRI, the anterior thalamic radiation (ATR) and forceps minor in DTI, and the parietal lobule, cuneus and thalamus in sMRI were linked and discriminated between patients and controls. One fMRI component with regions of activity in motor cortex and superior temporal gyrus individually discriminated schizophrenia from controls. Finally, three components showed significant correlation with duration of illness (DOI), suggesting that lower gray matter volumes in parietal, frontal, temporal lobe and cerebellum are associated with increased DOI, along with white matter disruption in ATR and cortico-spinal tracts. Findings suggest that the identified fractional anisotropy changes may relate to the corresponding functional/structural changes in brain that are thought to play a role in the clinical expression of schizophrenia. The proposed “mCCA +jICA” method showed promise for elucidating the joint or coupled neuronal abnormalities underlying mental illnesses and improves our understanding of the disease process.
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“Multimodal fusion is focused on jointly identifying links between multimodal data, and can help identify the neural mechanisms underlying complex brain disorders, especially supervised fusion that can capture multimodal co-varying patterns related to specific clinical measures [6, 7].”
From Confound Controlled Multimodal Neuroimaging Data Fusion and Its Application to Developmental Disorders (2025) · cites this paper as [6]
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Cites
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Ashburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150
GPOWER: A general power analysis program
Erdfelder, E., Faul, F., & Buchner, A. (1996) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03203630 · Cited by 4,864
Spatial registration and normalization of images
Friston, K. J., Ashburner, J., Frith, C., Poline, J.-B., Heather, J. D., & Frackowiak, R. S. J. (1995) · Human Brain Mapping · doi:10.1002/hbm.460030303 · Cited by 3,744
Structured Clinical Interview for DSM-IV Axis I Disorders
Spitzer, R. L., First, M. B., & Gibbon, M. (1997) · Medical Entomology and Zoology · Cited by 3,582
Scale for Assessment of Positive Symptoms (SAPS) 1984
Andreasen, N. C. (1995) · Open Scholarship Institutional Repository (Washington University in St. Louis) · Cited by 2,423
Self-citationNegative v Positive Schizophrenia
Andreasen, N. C. (1982) · Archives of General Psychiatry · doi:10.1001/archpsyc.1982.04290070025006 · Cited by 2,008
Self-citationScale for the Assessment of Negative Symptoms
Andreasen, N. C. (1982) · PsycTESTS Dataset · doi:10.1037/t12696-000 · Cited by 1,888
Self-citationThe Scale for the Assessment of Negative Symptoms (SANS): Conceptual and Theoretical Foundations
Andreasen, N. C. (1989) · The British Journal of Psychiatry · doi:10.1192/s0007125000291496 · Cited by 1,532
Self-citation
Cited by
Sorted by citations
Items 101–143 of 143
Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter
Colmenares, A. M., Hefner, M. B., Calhoun, V. D., Salerno, E. A., Fanning, J. T., Gothe, N. P., McAuley, E., Kramer, A. F., & Burzynska, A. Z. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1094313 · Cited by 6
Self-citationChen, H., Guo, Y., Ke, J., Qiu, J., Zhang, L., Xu, Q., Zhong, Y., Lu, G., Qin, H., Qi, R., et al. (2023) · Neuroscience · doi:10.1016/j.neuroscience.2023.11.018 · Cited by 6
Qi, S., Calhoun, V. D., van Erp, T. G. M., Damaraju, E., Bustillo, J., Du, Y., Turner, J. A., Mathalon, D. H., Ford, J. M., Voyvodic, J. T., et al. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591609 · Cited by 6
Self-citationVieira, S., Pinaya, W. H. L., Garcia‐Dias, R., & Mechelli, A. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815739-8.00016-x · Cited by 6
Kubera, K. M., Rashidi, M., Schmitgen, M. M., Barth, A., Hirjak, D., Otte, M., Sambataro, F., Calhoun, V. D., & Wolf, R. C. (2023) · Schizophrenia Research · doi:10.1016/j.schres.2023.03.001 · Cited by 5
Self-citationLiu, Y., Wang, H., Sha, G., Cao, Y., Chen, Y., Chen, Y., Zhang, J., Chai, C., Fan, Q., & Xia, S. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1374948 · Cited by 5
Feature-based MRI data fusion for cardiac arrhythmia studies
Magtibay, K., Beheshti, M., Foomany, F. H., Massé, S., Lai, P. F. H., Zamiri, N., Asta, J. A., Nanthakumar, K., Jaffray, D. A., Krishnan, S., et al. (2016) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2016.02.006 · Cited by 5
Duda, M., Faghiri, A., Belger, A., Bustillo, J., Ford, J. M., Mathalon, D. H., Mueller, B. A., Pearlson, G. D., Potkin, S. G., Preda, A., et al. (2025) · Neuroinformatics · doi:10.1007/s12021-025-09728-3 · Cited by 4
Self-citationDuda, M., Faghiri, A., & Calhoun, V. D. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995415 · Cited by 4
Self-citationMohammadi‐Nejad, A., Hossein‐Zadeh, G., & Hamid Soltanian-zadeh, A. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493392 · Cited by 4
Stout, D. M., Simmons, A. N., Nievergelt, C. M., Minassian, A., Biswas, N., Maihofer, A. X., Risbrough, V. B., & Baker, D. G. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-022-01303-7 · Cited by 6
Inter-individual single-trial classification of MEG data using M-CCA
Michalke, L., Dreyer, A. M., Borst, J. P., & Rieger, J. W. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120079 · Cited by 4
Liu, C., Peng, Y., Yang, Y., Li, P., Chen, D., Nie, D., Liu, H., & Liu, P. (2023) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.15739 · Cited by 4
Zhang, W., Shi, J., Yu, J., Zhan, L., Thompson, P. M., & Wang, Y. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950574 · Cited by 4
Cao, Q., Li, B., & Fan, L. (2017) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/siprocess.2017.8124533 · Cited by 4
Mirjalili, M., & Hossein‐Zadeh, G. (2017) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke.2017.8167925 · Cited by 4
Multimodal Fusion of Structural and Functional Brain Imaging Data
Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., & Calhoun, V. D. (2016) · Neuromethods · doi:10.1007/978-1-4939-5611-1_28 · Cited by 4
Self-citationLocal Discriminant Hyperalignment for Multi-Subject fMRI Data Alignment
Yousefnezhad, M., & Zhang, D. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10506 · Cited by 4
Liang, C., Silva, R. F., Adali, T., Jiang, R., Zhang, D., Qi, S., & Calhoun, V. D. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3597045 · Cited by 3
“Multimodal fusion is focused on jointly identifying links between multimodal data, and can help identify the neural mechanisms underlying complex brain disorders, especially supervised fusion that can capture multimodal co-varying patterns related to specific clinical measures [6], [7].”
Self-citationSaha, R., Saha, D. K., Fu, Z., Duda, M., Silva, R. F., Wilson, T. W., Wang, Y., Stephen, J. M., & Calhoun, V. D. (2025) · Human Brain Mapping · doi:10.1002/hbm.70241 · Cited by 3
Self-citationGou, M., Li, W., Tong, J., Zhou, Y., Xie, T., Yu, T. Y., Feng, W., Li, Y., Chen, S., Tian, B., et al. (2023) · Molecular Neurobiology · doi:10.1007/s12035-023-03694-0 · Cited by 3
Saha, R., Saha, D. K., Fu, Z., Silva, R. F., & Calhoun, V. D. (2023) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi58575.2023.10313489 · Cited by 3
Self-citationWang, K., Fu, Y., Li, L., Zhang, L., Huang, M., Yan, W., Shan, X., Yan, Z., & Lü, Y. (2024) · Neuroendocrinology · doi:10.1159/000539097 · Cited by 4
Duda, M., Faghiri, A., Belger, A., Bustillo, J., Ford, J. M., Mathalon, D. H., Mueller, B. A., Pearlson, G. D., Potkin, S. G., Preda, A., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.05.547840 · Cited by 3
Self-citationFunctionally Adaptive Structural Basis Sets of the Brain: A Dynamic Fusion ApproachPreprint
Duda, M., Chen, J., Belger, A., Ford, J. M., Mathalon, D. H., Preda, A., Turner, J. A., van Erp, T. G. M., Pearlson, G. D., & Calhoun, V. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.18.599621 · Cited by 3
Self-citationLiu, C., Wei, Z., Tang, J., Liu, Y., Lu, J., Li, Y., Liu, X., Liu, P., & Chen, F. (2025) · BMC Medicine · doi:10.1186/s12916-025-04179-8 · Cited by 2
Cui, F., Zhao, L., Lü, M., Liu, R., Lv, Q., Lin, D., Li, K., Zhang, Y., Wang, Y., Wang, Y., et al. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad295 · Cited by 2
Neuroimaging: Diagnostic Boundaries and Biomarkers
Galderisi, S., Giordano, G. M., & DeLisi, L. E. (2019) · Springer eBooks · doi:10.1007/978-3-319-97307-4_1 · Cited by 3
Buchsbaum, M. S., Mitelman, S. A., Christian, B. T., Merrill, B., Buchsbaum, B. R., Mitelman, D., Mukherjee, J., & Lehrer, D. S. (2021) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2021.111428 · Cited by 2
Functionally Adaptive Structural Basis Sets of the Brain: A Dynamic Fusion Approach
Duda, M., Chen, J., Belger, A., Ford, J. M., Mathalon, D. H., Preda, A., Turner, J. A., van Erp, T. G. M., Pearlson, G. D., & Calhoun, V. D. (2025) · Human Brain Mapping · doi:10.1002/hbm.70302 · Cited by 1
Self-citationCao, P., Han, Y., Zou, L., Gao, S., Xu, Q., Xiao, C., & Xu, X. (2026) · Schizophrenia Research · doi:10.1016/j.schres.2026.01.008 · Cited by 1
Supervised Sparse Components Analysis with Application to Brain Imaging Data
Kawaguchi, A. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.80531 · Cited by 1
Duda, M., Agcaoglu, O., & Calhoun, V. D. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635669 · Cited by 1
Self-citationTensor Kernel Learning for Classification of Alzheimer’s Conditions using Multimodal Data
Thanh, V. D., Thanh, L. T., Tuấn, P. M., Trung, N. L., Abed‐Meraim, K., Adel, M., Nguyen, V.-D., Trung, N. T., Long, D. D., & Chén, O. Y. (2024) · International Conference on Multimedia Analysis and Pattern Recognition (MAPR) · doi:10.1109/mapr63514.2024.10661014 · Cited by 1
Duda, M., & Calhoun, V. D. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782492 · Cited by 1
Self-citationFeature-based approach to fuse fMRI and DTI in epilepsy using joint independent component analysis
Riazi, A. H., Hamid Soltanian-zadeh, A., & Hossein‐Zadeh, G. (2012) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme.2012.6519682 · Cited by 1
Mohammadi‐Nejad, A., Hossein‐Zadeh, G., Ananloo, E. S., & Hamid Soltanian-zadeh, A. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102698 · Cited by 1
The Mind Research Network - Mental Illness Neuroscience Discovery Grant
Roberts, J., & Calhoun, V. D. (2013) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.2172/1111123
Self-citationRole of diversity in ICA and IVA: theory and applications
Adalı, T. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2235353
Cao, S., Liang, C., Zhu, Q., Jiang, R., Zhang, D., Calhoun, V. D., & Qi, S. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4001-0_18
Self-citationThe Co-varying Multimodal Pattern in Treatment-Resistant and Non-treatment-Resistant Schizophrenia
Cao, S., Gao, S., Liang, C., Calhoun, V. D., Wen, X., Fu, Z., Wu, L., Jiang, R., Zhang, D., Qi, S., et al. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4001-0_8
Self-citationFan, Y., Chen, J., Liu, L., Zhang, C., Guo, J., Chen, H., & Yang, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.06.704308
An interpretable cross-attentive multi-modal MRI fusion framework for schizophrenia identification
Zhou, Z., Orlichenko, A., Qu, G., Fu, Z., Ding, Z., Stephen, J., Wilson, T., Calhoun, V. D., & Wang, Y. (2026) · Neuroimage Reports · doi:10.1016/j.ynirp.2026.100338 · Cited by 6
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Metadata
- Authors
- Sui, J., He, H., Pearlson, G. D., Adali, T., Kiehl, K. A., Yu, Q., Clark, V. P., Castro, E., White, T., Mueller, B. A., Ho, B. C., Andreasen, N. C., & Calhoun, V. D.
- Affiliations
- Mind Research Network (US)
- Yale University (US)
- University of Minnesota (US)
- University of Iowa (US)
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 66
- Pages
- 119-132
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3897558
- OpenAlex
- W2128042129
- Semantic Scholar
- 00f4386941282969de8ee224e46b80c6e021e5ba
- Cited by
- 143
- References
- 71
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 15 · 2023: 14 · 2022: 9 · 2021: 16 · 2020: 9 · 2019: 11 · 2018: 9 · 2017: 14 · 2016: 14 · 2015: 7 · 2014: 9 · 2013: 4
- FWCI
- 3.44
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Funders
- National Institutes of Health (R01EB 006841, R01EB 005846, R01MH072681-01, R01MH43775, R01MH074797, R01MH077945)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Mathematics › Computational Mathematics › Tensor decomposition and applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- schizophrenia, anterior thalamic radiation, forceps minor, fractional anisotropy, disease duration, gray matter volume, white matter disruption, thalamus
- MeSH terms
- Brain, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Schizophrenia, Anisotropy, Diffusion Tensor Imaging
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