title: Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging.
abstract: In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. We first use an abstract example to describe Pyrcca functionality. We then demonstrate how Pyrcca can be used to analyze neuroimaging data. Specifically, we use Pyrcca to implement cross-subject comparison in a natural movie functional magnetic resonance imaging (fMRI) experiment by finding a data-driven set of functional response patterns that are similar across individuals. We validate this cross-subject comparison method in Pyrcca by predicting responses to novel natural movies across subjects. Finally, we show how Pyrcca can reveal retinotopic organization in brain responses to natural movies without the need for an explicit model.
authors.affiliation: Helen Wills Neuroscience Institute, University of California, Berkeley Berkeley, CA, USA.
authors.forename: Natalia Y
authors.initials: NY
authors.lastname: Bilenko
authors.affiliation: Helen Wills Neuroscience Institute, University of California, BerkeleyBerkeley, CA, USA; Department of Psychology, University of California, BerkeleyBerkeley, CA, USA.
authors.forename: Jack L
authors.initials: JL
authors.lastname: Gallant
keywords: Python; canonical correlation analysis; covariance analysis; cross-subject alignment; fMRI; partial least squares regression