Partial sparse canonical correlation analysis (PSCCA) for population studies in medical imaging

We propose a new multivariate method, partial sparse canonical correlation analysis (PSCCA), for computing the statistical comparisons needed by population studies in medical imaging. PSCCA is a multivariate generalization of linear regression that allows one to statistically parameterize imaging studies in terms of multiple views of the population (e.g., the full collection of measurements taken from an image set along with batteries of cognitive or genetic data) while controlling for nuisance variables. This paper develops the theory of PSCCA, provides an algorithm and illustrates PSCCA performance on both simulated and real datasets. We show, as a first application and evaluation of this new methodology, that PSCCA can improve detection power over mass univariate approaches while retaining the interpretability and biological plausibility of the estimated effects. We also discuss the strengths, limitations and future potential of this methodology.