A central goal of basic and clinical human neuroscience is to understand how individual
differences in phenotypic features relate to variation in neural features, which is
vital to parse patient heterogeneity within and across diagnostic categories. Data-driven
discovery of linear latent brain-phenotype relationships can be achieved through Canonical
Correlation Analysis (CCA) and Partial Least Squares (PLS). Analysis in the modern
“big data” regime, with very high-dimensional feature spaces, raises complex computational
issues such as overfitting of statistical models. It is therefore essential to establish
under which circumstances CCA/PLS methods yield reliable and interpretable brain-phenotype
relationships.
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© 2020 Published by Elsevier Inc.
