Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging

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, polyn...

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Vydáno v:	Frontiers in neuroinformatics Ročník 10; s. 49
Hlavní autoři:	Bilenko, Natalia Y., Gallant, Jack L.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland Frontiers Research Foundation 22.11.2016 Frontiers Media S.A
Témata:	Analysis of covariance Bioinformatics Brain architecture Brain mapping canonical correlation analysis Correlation analysis covariance analysis cross-subject alignment Datasets Eigenvalues fMRI Functional magnetic resonance imaging Medical imaging Methods Multivariate analysis Neuroimaging Neuroscience partial least squares regression Principal components analysis python Registration Retina United States > US California fMRI covariance analysis cross-subject alignment partial least squares regression canonical correlation analysis Python
ISSN:	1662-5196, 1662-5196
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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.
AbstractList	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. 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.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.
Author	Gallant, Jack L. Bilenko, Natalia Y.
AuthorAffiliation	1 Helen Wills Neuroscience Institute, University of California, Berkeley Berkeley, CA, USA 2 Department of Psychology, University of California, Berkeley Berkeley, CA, USA
AuthorAffiliation_xml	– name: 1 Helen Wills Neuroscience Institute, University of California, Berkeley Berkeley, CA, USA – name: 2 Department of Psychology, University of California, Berkeley Berkeley, CA, USA
Author_xml	– sequence: 1 givenname: Natalia Y. surname: Bilenko fullname: Bilenko, Natalia Y. – sequence: 2 givenname: Jack L. surname: Gallant fullname: Gallant, Jack L.
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Copyright	2016. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2016 Bilenko and Gallant. 2016 Bilenko and Gallant
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Keywords	fMRI covariance analysis cross-subject alignment partial least squares regression canonical correlation analysis Python
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SubjectTerms	Analysis of covariance Bioinformatics Brain architecture Brain mapping canonical correlation analysis Correlation analysis covariance analysis cross-subject alignment Datasets Eigenvalues fMRI Functional magnetic resonance imaging Medical imaging Methods Multivariate analysis Neuroimaging Neuroscience partial least squares regression Principal components analysis python Registration Retina
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Title	Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging
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