ArticleHuman brain mapping2021
Stability test of canonical correlation analysis for studying brain-behavior relationships: The effects of subject-to-variable ratios and correlation strengths.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Spontaneous brain regional dynamics contribute to generalizable brain-behaviour associations.Nature human behaviour · 2026Article
- Deciphering the Heterogeneity of Schizophrenia: A Multimodal and Multivariate Neuroimaging Framework for Unveiling Brain-Symptom Relationships and Underlying Subtypes.Schizophrenia bulletin · 2026Article
- Multivariate patterns among multimodal neuroimaging and clinical, cognitive, and daily functioning characteristics in bipolar disorder.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Article
- Interpretable and integrative deep learning for discovering brain-behaviour associations.Scientific reports · 2025Article
- On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations.Communications biology · 2024Article
- Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study.Human brain mapping · 2023Article
- Stability test of canonical correlation analysis for studying brain-behavior relationships: The effects of subject-to-variable ratios and correlation strengths.Human brain mapping · 2021Article
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Abstract
Canonical correlation analysis (CCA), a multivariate approach to identifying correlations between two sets of variables, is becoming increasingly popular in neuroimaging studies on brain-behavior relationships. However, the CCA stability in neuroimaging applications has not been systematically investigated. Although it is known that the number of subjects should be greater than the number of variables due to the curse of dimensionality, it is unclear at what subject-to-variable ratios (SVR) and at what correlation strengths the CCA stability can be maintained. Here, we systematically assessed the CCA stability, in the context of investigating the relationship between the brain structural/functional imaging measures and the behavioral measures, by measuring the similarity of the first-mode canonical variables across randomly sampled subgroups of subjects from a large set of 936 healthy subjects. Specifically, we tested how the CCA stability changes with SVR under two different brain-behavior correlation strengths. The same tests were repeated using an independent data set (n = 700) for validation. The results confirmed that both SVR and correlation strength affect greatly the CCA stability-the CCA stability cannot be guaranteed if the SVR is not sufficiently high or the brain-behavior relationship is not sufficiently strong. Based on our quantitative characterization of CCA stability, we provided a practical guideline to help correct interpretation of CCA results and proper applications of CCA in neuroimaging studies on brain-behavior relationships.
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