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Canonical correlation analysis (CCA) is a valuable method for interpretingcross-covariance across related datasets of different dimensionality. There aremany potential applications of CCA to neuroimaging data analysis. For instance,CCA can be used for finding functional similarities across fMRI datasetscollected from multiple subjects without resampling individual datasets to atemplate anatomy. In this paper, we introduce Pyrcca, an open-source Pythonmodule for executing CCA between two or more datasets. Pyrcca can be used toimplement CCA with or without regularization, and with or without linear or aGaussian kernelization of the datasets. We demonstrate an application of CCAimplemented with Pyrcca to neuroimaging data analysis. We use CCA to find adata-driven set of functional response patterns that are similar acrossindividual subjects in a natural movie experiment. We then demonstrate how thisset of response patterns discovered by CCA can be used to accurately predictsubject responses to novel natural movie stimuli.

Pyrcca: regularized kernel canonical correlation analysis in Python and its applications to neuroimaging