Function–structure associations of the brain: Evidence from multimodal connectivity and covariance studies
Open accessAbstract
Despite significant advances in multimodal imaging techniques and analysis approaches, unimodal studies are still the predominant way to investigate brain changes or group differences, including structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI) and electroencephalography (EEG). Multimodal brain studies can be used to understand the complex interplay of anatomical, functional and physiological brain alterations or development, and to better comprehend the biological significance of multiple imaging measures. To examine the function-structure associations of the brain in a more comprehensive and integrated manner, we reviewed a number of multimodal studies that combined two or more functional (fMRI and/or EEG) and structural (sMRI and/or DTI) modalities. In this review paper, we specifically focused on multimodal neuroimaging studies on cognition, aging, disease and behavior. We also compared multiple analysis approaches, including univariate and multivariate methods. The possible strengths and limitations of each method are highlighted, which can guide readers when selecting a method based on a given research question. In particular, we believe that multimodal fusion approaches will shed further light on the neuronal mechanisms underlying the major structural and functional pathophysiological features of both the healthy brain (e.g. development) or the diseased brain (e.g. mental illness) and, in the latter case, may provide a more sensitive measure than unimodal imaging for disease classification, e.g. multimodal biomarkers, which potentially can be used to support clinical diagnosis based on neuroimaging techniques.
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Supervised fusion can capture multimodal co-varying patterns related to specific clinical measures.
“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 [7]
The Introduction describes fusion as finding joint relationships across imaging modalities, often through cross-modal covariance. The Three-way Fusion section also discusses multimodal patterns relevant to distinguishing clinical groups. However, the passages do not establish the more specific claim that supervised fusion captures co-varying patterns tied to particular clinical measures (searched: supervised fusion, clinical measures). (LLM, conf=0.82)
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Research works printed but not linked: 1
- Smieskova R Allen P Simon A Aston J Bendfeldt K Drewe J Gruber K Gschwandtner U Klarhoefer M Lenz C Scheffler K Stieglitz RD Radue EW McGuire P Riecher-Rossler A Borgwardt SJ 2012 Different duration of at-risk mental state associated with neurofunctional abnormalities. A multimodal imaging study Hum Brain Mapp 33 2281 2294 21922599 PMC6870404
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Jiang, R., Calhoun, V. D., Fan, L., Zuo, N., Jung, R., Qi, S., Lin, D., Li, J., Zhuo, C., Song, M., et al. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhz134 · Cited by 126 · see citing works
Self-citationQi, S., Calhoun, V. D., van Erp, T. G. M., Bustillo, J., Damaraju, E., Turner, J. A., Du, Y., Yang, J., Chen, J., Yu, Q., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2725306 · Cited by 99 · see citing works
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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 · see citing works
“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-citation
- Authors
- Sui, J., Huster, R., Yu, Q., Segall, J. M., & Calhoun, V. D.
- Affiliations
- Carl von Ossietzky Universität Oldenburg (DE)
- Mind Research Network (US)
- Corresponding author
- Calhoun, Vince D.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-11
- Volume
- 102
- Pages
- 11-23
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3969780
- OpenAlex
- W2166367991
- Cited by
- 162
- References
- 143
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 14 · 2023: 14 · 2022: 10 · 2021: 20 · 2020: 7 · 2019: 11 · 2018: 17 · 2017: 18 · 2016: 14 · 2015: 11 · 2014: 8
- FWCI
- 1.56
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- “100 Talents Plan” of Chinese Academy of Sciences
- National Institutes of Health (R01EB 006841, R01EB005846)
- National Sciences Foundation (1016619)
- National Key Basic Research and Development Program of China (973) (2011CB707800)
- Strategic Priority Research Program of the Chinese Academy of Sciences (XDB02030300)
- German Research Foundation: DFG (HU 1729/2-1)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- multimodal neuroimaging, multimodal fusion, covariance analysis, structural MRI, functional MRI, diffusion tensor imaging, EEG, multimodal biomarkers, brain aging, cognitive function, mental illness, disease classification, brain development
- MeSH terms
- Brain, Electroencephalography, Humans, Magnetic Resonance Imaging, Nerve Net, Diffusion Tensor Imaging, Neuroimaging, Multimodal Imaging
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