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Tabelow, Karsten [Author]; Clayden, Jon D. [Author]; Lafaye de Micheaux, Pierre [Author]; Polzehl, Jörg [Author]; Schmid, Volker J. [Author]; Whitcher, Brandon [Author]

Image analysis and statistical inference in neuroimaging with R

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  • Media type: Text; Report; E-Book
  • Title: Image analysis and statistical inference in neuroimaging with R
  • Contributor: Tabelow, Karsten [Author]; Clayden, Jon D. [Author]; Lafaye de Micheaux, Pierre [Author]; Polzehl, Jörg [Author]; Schmid, Volker J. [Author]; Whitcher, Brandon [Author]
  • imprint: Weierstrass Institute for Applied Analysis and Stochastics publication server, 2010
  • Language: English
  • DOI: https://doi.org/10.20347/WIAS.PREPRINT.1578
  • Keywords: 62G05 ; 62P10 ; article ; diffusion weighted imaging -- functional magnetic resonance imaging -- dynamic contrast-enhanced magnetic resonance imaging -- computational neuroscience ; 62H35 ; 92C55
  • Origination:
  • Footnote: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
  • Description: R is a language and environment for statistical computing and graphics. It can be considered an alternative implementation of the S language developed in the 1970s and 1980s for data analysis and graphics (Becker and Chambers, 1984; Becker et al., 1988). The R language is part of the GNU project and offers versions that compile and run on almost every major operating system currently available. We highlight several R packages built specifically for the analysis of neuroimaging data in the context of functional MRI, diffusion tensor imaging, and dynamic contrast-enhanced MRI. We review their methodology and give an overview of their capabilities for neuroimaging. In addition we summarize some of the current activities in the area of neuroimaging software development in R.