SynthesisHuman brain mapping2020
A technical review of canonical correlation analysis for neuroscience applications.
Synthesis in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 2 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
92 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Multi-omics time-series analysis in microbiome research: a systematic review.Briefings in bioinformatics · 2025Pooled it
- A technical review of canonical correlation analysis for neuroscience applications.Human brain mapping · 2020Pooled it
- A Canonical Correlation Approach Towards Identifying Latent Neurofeedback Responses in Patients with Schizophrenia.Applied psychophysiology and biofeedback · 2025Trial
- A Multiscale Spatiotemporal Causal Mapping Algorithm for Revealing Neural Network Mechanisms of Transcutaneous Auricular Vagus Nerve Stimulation.Human brain mapping · 2026Article
- Cedalion tutorial: a Python-based framework for comprehensive analysis of multimodal fNIRS and DOT from the lab to the everyday world.Neurophotonics · 2026Article
- Linking the exposome to the brain-behaviour phenotype.Nature reviews. Neuroscience · 2026Review
- Predicting pain location from resting-state brain fMRI.bioRxiv : the preprint server for biology · 2026Article
- Molecular and cellular correlates of human cortical lateralization.Communications biology · 2026Article
- The Default Mode Network and Behavior: a Model to Analyse Psycho-Physiological Interactions in Resting State fMRI.Brain topography · 2026Article
- Brain regions with gestational age differences mediate cognition in adolescents born very premature.Communications biology · 2026Article
- Whole-brain mapping of TMS-induced discomfort with large-scale concurrent TMS-fMRI.bioRxiv : the preprint server for biology · 2026Article
- Remote regulation of hepatic lipid secretion in the intestine by metabolic interaction of dietary arginine with ornithine.Scientific reports · 2026Article
- Article
- Generative Principal Component Regression via Variational Inference.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2026Article
- Toward zero-calibration MEG brain-computer interfaces based on event-related fields.Biomedical engineering letters · 2026Article
- Multimodal Fusion of Structural and Diffusion MRI for Intelligence Prediction.Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation · 2026Article
- Clinical profile impacts the replicability of multivariate brain-behavioural associations.bioRxiv : the preprint server for biology · 2025Article
- Leveraging multivariate approaches to advance the science of early-life adversity.Child abuse & neglect · 2025Review
- Large-scale comparative analysis reveals top graph signal processing features for subject identification.bioRxiv : the preprint server for biology · 2025Article
- Cortical myelin mapping in antipsychotic medication-naïve, first-episode psychosis patients.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Article
32 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
3 authors.
Funding
Abstract
Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools to jointly investigate relationships among multiple data sets, which can uncover disease or environmental effects in various modalities simultaneously and characterize changes during development, aging, and disease progressions comprehensively. In the past 10 years, despite an increasing number of studies have utilized CCA in multivariate analysis, simple conventional CCA dominates these applications. Multiple CCA-variant techniques have been proposed to improve the model performance; however, the complicated multivariate formulations and not well-known capabilities have delayed their wide applications. Therefore, in this study, a comprehensive review of CCA and its variant techniques is provided. Detailed technical formulation with analytical and numerical solutions, current applications in neuroscience research, and advantages and limitations of each CCA-related technique are discussed. Finally, a general guideline in how to select the most appropriate CCA-related technique based on the properties of available data sets and particularly targeted neuroscience questions is provided.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.