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

2015/03/04 by Natalia Y. Bilenko, Bilenko, Natalia Y., Jack L. Gallant +1 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Gene expression and cancer classification #Machine Learning (stat.ML) #Neural Networks and Applications #Quantitative Methods (q-bio.QM)

paper · pdf · doi:10.48550/arxiv.1503.01538

openalex publication_date 2015/03/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Canonical correlation analysis (CCA) is a valuable method for interpreting\ncross-covariance across related datasets of different dimensionality. There are\nmany potential applications of CCA to neuroimaging data analysis. For instance,\nCCA can be used for finding functional similarities across fMRI datasets\ncollected from multiple subjects without resampling individual datasets to a\ntemplate anatomy. In this paper, we introduce Pyrcca, an open-source Python\nmodule for executing CCA between two or more datasets. Pyrcca can be used to\nimplement CCA with or without regularization, and with or without linear or a\nGaussian kernelization of the datasets. We demonstrate an application of CCA\nimplemented with Pyrcca to neuroimaging data analysis. We use CCA to find a\ndata-driven set of functional response patterns that are similar across\nindividual subjects in a natural movie experiment. We then demonstrate how this\nset of response patterns discovered by CCA can be used to accurately predict\nsubject responses to novel natural movie stimuli.\n

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