ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2016
Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 228 papers, 5 of them syntheses that pooled it.
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Who cites it
228 citing papers in PubMed, 5 syntheses or guidelines pooled it, 472 citations in OpenAlex.
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- Physiological influences on neurovascular coupling: A systematic review of multimodal imaging approaches and recommendations for future study designs.Experimental physiology · 2025Pooled it
- Multivariate brain-behaviour associations in psychiatric disorders.Translational psychiatry · 2024Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
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- Group Joint ICA (gjICA): A Method for Multimodal Fusion of Concurrent EEG and fMRI Data.Human brain mapping · 2026Article
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- From Structure to Function and Back Again: A GAN-Guided Diffusion Framework for Generating Clinically Meaningful Multimodal Neuroimaging Data.Research square · 2026Article
- Variations of structural-functional coupling in post-traumatic stress disorder are associated with underlying molecular and transcriptional features.Psychological medicine · 2026Article
- An interpretable cross-attentive multi-modal MRI fusion framework for schizophrenia identification.Neuroimage. Reports · 2026Article
- Multimodal fusion of brain imaging and proteomics reveals a brain-body pathway linking depression and metabolic dysfunction.Psychological medicine · 2026Article
- Benchmarking Multimodal Deep Fusion Strategies for Heterogeneous Neuroimaging and Cognitive Data Using a Controlled Sex Classification Task.Brain sciences · 2026Article
- Six artificial intelligence innovation strategies applied to autism spectrum disorder research: A narrative review.Pediatric investigation · 2026Review
- Anomaly Detection for Structural and Functional Connectivity in Glioma Patients.NMR in biomedicine · 2026Article
168 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
It is becoming increasingly clear that combining multi-modal brain imaging data is able to provide more information for individual subjects by exploiting the rich multimodal information that exists. However, the number of studies that do true multimodal fusion (i.e. capitalizing on joint information among modalities) is still remarkably small given the known benefits. In part, this is because multi-modal studies require broader expertise in collecting, analyzing, and interpreting the results than do unimodal studies. In this paper, we start by introducing the basic reasons why multimodal data fusion is important and what it can do, and importantly how it can help us avoid wrong conclusions and help compensate for imperfect brain imaging studies. We also discuss the challenges that need to be confronted for such approaches to be more widely applied by the community. We then provide a review of the diverse studies that have used multimodal data fusion (primarily focused on psychosis) as well as provide an introduction to some of the existing analytic approaches. Finally, we discuss some up-and-coming approaches to multi-modal fusion including deep learning and multimodal classification which show considerable promise. Our conclusion is that multimodal data fusion is rapidly growing, but it is still underutilized. The complexity of the human brain coupled with the incomplete measurement provided by existing imaging technology makes multimodal fusion essential in order to mitigate against misdirection and hopefully provide a key to finding the missing link(s) in complex mental illness.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.