On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations.
Commun Biol
; 7(1): 217, 2024 Feb 21.
Article
em En
| MEDLINE
| ID: mdl-38383808
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Análise de Correlação Canônica
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article