Pyrcca: regularized kernel canonical correlation analysis in Python and its applications to neuroimaging
This provides a tool for neuroimaging researchers to analyze cross-subject data without anatomical alignment, but it is incremental as it packages existing CCA methods.
The authors introduced Pyrcca, an open-source Python module for regularized kernel canonical correlation analysis (CCA), and applied it to neuroimaging data to find functional response patterns similar across subjects in a natural movie experiment, enabling accurate prediction of responses to novel stimuli.
Canonical correlation analysis (CCA) is a valuable method for interpreting cross-covariance across related datasets of different dimensionality. There are many potential applications of CCA to neuroimaging data analysis. For instance, CCA can be used for finding functional similarities across fMRI datasets collected from multiple subjects without resampling individual datasets to a template anatomy. In this paper, we introduce Pyrcca, an open-source Python module for executing CCA between two or more datasets. Pyrcca can be used to implement CCA with or without regularization, and with or without linear or a Gaussian kernelization of the datasets. We demonstrate an application of CCA implemented with Pyrcca to neuroimaging data analysis. We use CCA to find a data-driven set of functional response patterns that are similar across individual subjects in a natural movie experiment. We then demonstrate how this set of response patterns discovered by CCA can be used to accurately predict subject responses to novel natural movie stimuli.