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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Vydané v:Frontiers in neuroinformatics Ročník 10; s. 49
Hlavní autori: Bilenko, Natalia Y., Gallant, Jack L.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland Frontiers Research Foundation 22.11.2016
Frontiers Media S.A
Predmet:
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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Reviewed by: Sergey M. Plis, Mind Research Network, USA; Li Dong, University of Science and Technology of China, China
Edited by: Pedro Antonio Valdes-Sosa, Joint China Cuba Lab for Frontiers Research in Translational Neurotechnology, Cuba
ISSN:1662-5196
1662-5196
DOI:10.3389/fninf.2016.00049