ReviewJournal of neuroscience methods2012
A review of multivariate methods for multimodal fusion of brain imaging data.
Review in Journal of neuroscience methods, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 179 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
179 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- AI-based recognition of facial and micro-expressions for the diagnosis of mental and neurological disorders: a systematic review.BMC psychiatry · 2025Pooled it
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- A technical review of canonical correlation analysis for neuroscience applications.Human brain mapping · 2020Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Discovering hierarchical common brain networks via multimodal deep belief network.Medical image analysis · 2019Pooled it
- Trial
- From form to concept: a dynamic spatiotemporal brain network cascade supports language comprehension.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- A spatially discretized convolutional neural mass model for studying meso-scale spatio-temporal transformations in the rat hippocampus.Journal of computational neuroscience · 2026Article
- Lucid Dreaming Frequency Associated With Grey-White Matter Networks: An Exploratory Multimodal MRI Study.Journal of sleep research · 2026Article
- Brain network correlates of fatigue, depression, and anxiety in patients with Crohn's Disease in different disease states.BMC gastroenterology · 2026Article
- Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence.Science advances · 2026Article
- Aberrant fronto-limbic network in adolescents with attention-deficit/hyperactivity disorder: a multimodal MRI study using parallel ICA.European archives of psychiatry and clinical neuroscience · 2026Article
- Shared representations in brains and models reveal a two-route cortical organization during scene perception.Communications biology · 2026Article
- A spatially discretized convolutional neural mass model for studying meso-scale spatio-temporal transformations in the rat hippocampus.Research square · 2026Article
- Combining multimodal fatigue fracture surface images for analysis with a CNN.Scientific reports · 2026Article
- Parsing Reward Processing Substages With Multimodal EEG-fMRI.Human brain mapping · 2026Article
- Generalizable structure-function covariation predictive of antidepressant response revealed by target-oriented multimodal fusion.Nature. Mental health · 2026Article
- Parallel multilink group joint ICA (pmg-jICA): Fusion of 3D structural and 4D functional data across multiple resting fMRI networks.Network neuroscience (Cambridge, Mass.) · 2026Article
- Multiplex functional connectome graph transformer for cognitive vulnerability in dialysis-treated chronic kidney disease.Frontiers in psychiatry · 2026Article
- Morphometric features enhance phenotype discrimination in frontotemporal lobar degeneration.Brain communications · 2026Article
119 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The development of various neuroimaging techniques is rapidly improving the measurements of brain function/structure. However, despite improvements in individual modalities, it is becoming increasingly clear that the most effective research approaches will utilize multi-modal fusion, which takes advantage of the fact that each modality provides a limited view of the brain. The goal of multi-modal fusion is to capitalize on the strength of each modality in a joint analysis, rather than a separate analysis of each. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions from high dimensional data with a limited number of subjects. Numerous research efforts have been reported in the field based on various statistical approaches, e.g. independent component analysis (ICA), canonical correlation analysis (CCA) and partial least squares (PLS). In this review paper, we survey a number of multivariate methods appearing in previous multimodal fusion reports, mostly fMRI with other modality, which were performed with or without prior information. A table for comparing optimization assumptions, purpose of the analysis, the need of priors, dimension reduction strategies and input data types is provided, which may serve as a valuable reference that helps readers understand the trade-offs of the 7 methods comprehensively. Finally, we evaluate 3 representative methods via simulation and give some suggestions on how to select an appropriate method based on a given research.
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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.