Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia
Open accessAbstract
By exploiting cross-information among multiple imaging data, multimodal fusion has often been used to better understand brain diseases. However, most current fusion approaches are blind, without adopting any prior information. There is increasing interest to uncover the neurocognitive mapping of specific clinical measurements on enriched brain imaging data; hence, a supervised, goal-directed model that employs prior information as a reference to guide multimodal data fusion is much needed and becomes a natural option. Here, we proposed a fusion with reference model called "multi-site canonical correlation analysis with reference + joint-independent component analysis" (MCCAR+jICA), which can precisely identify co-varying multimodal imaging patterns closely related to the reference, such as cognitive scores. In a three-way fusion simulation, the proposed method was compared with its alternatives on multiple facets; MCCAR+jICA outperforms others with higher estimation precision and high accuracy on identifying a target component with the right correspondence. In human imaging data, working memory performance was utilized as a reference to investigate the co-varying working memory-associated brain patterns among three modalities and how they are impaired in schizophrenia. Two independent cohorts (294 and 83 subjects respectively) were used. Similar brain maps were identified between the two cohorts along with substantial overlaps in the central executive network in fMRI, salience network in sMRI, and major white matter tracts in dMRI. These regions have been linked with working memory deficits in schizophrenia in multiple reports and MCCAR+jICA further verified them in a repeatable, joint manner, demonstrating the ability of the proposed method to identify potential neuromarkers for mental disorders.
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mCCAR+jICA can identify co-varying multimodal brain patterns significantly associated with cognition measures in psychiatry.
“In particular, the supervised fusion model of mCCA with reference + joint independent components analysis (mCCAR+jICA), can identify co-varying multimodal brain patterns significantly associated with cognition measures in psychiatry [37, 38].”
From Confound Controlled Multimodal Neuroimaging Data Fusion and Its Application to Developmental Disorders (2025) · cites this paper as [37]
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“In particular, the supervised fusion model of mCCA with reference + joint independent components analysis (mCCAR + jICA), can identify co-varying multimodal brain patterns significantly associated with cognition measures in psychiatry [37], [38].”
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- Authors
- Qi, S., Calhoun, V. D., van Erp, T. G. M., Bustillo, J., Damaraju, E., Turner, J. A., Du, Y., Yang, J., Chen, J., Yu, Q., Mathalon, D. H., Ford, J. M., Voyvodic, J., Mueller, B. A., Belger, A., McEwen, S., Potkin, S. G., Preda, A., Jiang, T., & Sui, J.
- Affiliations
- Chinese Academy of Sciences (CN)
- Mind Research Network (US)
- University of New Mexico (US)
- Beijing Institute of Technology (CN)
- San Francisco VA Medical Center (US)
- Duke University (US)
- University of Minnesota (US)
- University of California, Los Angeles (US)
- University of California, Irvine (US)
- ORCID
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2018-01
- Volume
- 37
- Issue
- 1
- Pages
- 93-105
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- PMC
- PMC5750081
- OpenAlex
- W2735221233
- Semantic Scholar
- 4b502a75b9149728c2807686e2dd2f1e018f0d1f
- Cited by
- 99
- References
- 66
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 11 · 2023: 10 · 2022: 16 · 2021: 11 · 2020: 13 · 2019: 9 · 2018: 6 · 2017: 1
- FWCI
- 4.55
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- UNC Libraries
- Funders
- National High-Tech Development Plan (863 plan) (2015AA020513)
- Chinese National Science Foundation (81471367)
- Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (XDB02060005)
- 100 Talents Plan of CAS
- NIH (P20GM103472, R01EB005846, 1R01EB006841)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Mathematics › Computational Mathematics › Tensor decomposition and applications
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Keywords
- multimodal fusion, canonical correlation analysis, joint independent component analysis, working memory deficits, schizophrenia, neuromarkers, frontoparietal network, salience network, white matter tracts, functional MRI, cross-site validation
- MeSH terms
- Supervised Machine Learning, Adult, Algorithms, Brain Mapping, Computer Simulation, Humans, Image Interpretation, Computer-Assisted, Middle Aged, Schizophrenia, Cohort Studies, Multimodal Imaging
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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