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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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Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects
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Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.079 · Cited by 912
Self-citationCalhoun, V. D., & Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2015.12.005 · Cited by 472
Self-citationMachine learning in major depression: from classification to treatment outcome prediction
Gao, S., Calhoun, V. D., & Sui, J. (2018) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.13048 · Cited by 370
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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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