| Title: | On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations |
| Journal: | Communications Biology |
| Published: | 21 Feb 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38383808/ |
| DOI: | https://doi.org/10.1038/s42003-024-05869-4 |
| URL: | https://www.nature.com/articles/s42003-024-05869-4.pdf |
| Citations: | 10 (10 in last 2 years) as of 8 Aug 2024 |
Publication 8703
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Abstract
Associations between datasets can be discovered through multivariate methods like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property for interpretability and generalizability of CCA/PLS associations is stability of their feature patterns. However, stability of CCA/PLS in high-dimensional datasets is questionable, as found in empirical characterizations. To study these issues systematically, we developed a generative modeling framework to simulate synthetic datasets. We found that when sample size is relatively small, but comparable to typical studies, CCA/PLS associations are highly unstable and inaccurate; both in their magnitude and importantly in the feature pattern underlying the association. We confirmed these trends across two neuroimaging modalities and in independent datasets with n ≈ 1000 and n = 20,000, and found that only the latter comprised sufficient observations for stable mappings between imaging-derived and behavioral features. We further developed a power calculator to provide sample sizes required for stability and reliability of multivariate analyses. Collectively, we characterize how to limit detrimental effects of overfitting on CCA/PLS stability, and provide recommendations for future studies.</p>
9 Authors
- Markus Helmer
- Shaun Warrington
- Ali-Reza Mohammadi-Nejad
- Jie Lisa Ji
- Amber Howell
- Benjamin Rosand
- Alan Anticevic
- Stamatios N. Sotiropoulos
- John D. Murray
1 Application
| Application ID | Title |
|---|---|
| 43822 | Multi-Modal Analysis of the UK Biobank Neuroimaging Data |
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