PRoNTo: Pattern Recognition for Neuroimaging Toolbox
By Jessica Schrouff, Maria João Duarte Rosa, Jane Maryam Rondina, Andre F. Marquand, Carlton Chu, John Ashburner, Christophe L. M. Phillips, Jonas Richiardi, Janaina Mourão Miranda. Neuroinformatics, 2013
439 citations recorded by the source corpus.
In the past years, mass univariate statistical analyses of neuroimaging data have been complemented by the use of multivariate pattern analyses, especially based on machine learning models. While these allow an increased sensitivity for the detection of spatially distributed effects compared to univariate techniques, they lack an established and accessible software framework. The goal of this work was to build a toolbox comprising all the necessary functionalities for multivariate analyses of neuroimaging data, based on machine learning models. The “Pattern Recognition for Neuroimaging Toolbox” (PRoNTo) is open-source, cross-platform, MATLAB-based and SPM compatible, therefore being suitable for both cognitive and clinical neuroscience research. In addition, it is designed to facilitate novel contributions from developers, aiming to improve the interaction between the neuroimaging and machine learning communities. Here, we introduce PRoNTo by presenting examples of possible research questions that can be addressed with the machine learning framework implemented in PRoNTo, and cannot be easily investigated with mass univariate statistical analysis.
Subfields: Biophysics, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/1b286f0984301793f711899aa3294974f60ffed9
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In Biophysics
In Cognitive Neuroscience
- Gaël Varoquaux
- James V. Haxby
- Andrew C. Connolly
- John-Dylan Haynes
- Martin N. Hebart
- Nikolaas N. Oosterhof
- Bertrand Thirion
- Kai Görgen
Citations in the corpus
- The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data (2015, background): “A more recent development is PRoNTo (Pattern Recognition of Neuroimaging Toolbox, Schrouff et al., 2013) which has the benefit of a graphical user interface and which also comes with an SPM interface.”
- Machine learning for neuroimaging with scikit-learn (2014, methodology): “PRoNTo (Schrouff et al., 2013) is written in Matlab and can easily interface with SPM but does not propose many machine learning algorithms.”
- CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave (2016, methodology): “CoSMoMVPA is not the first MVPA toolbox for cognitive neuroscience; indeed, since the Princeton toolbox (Detre et al., 2006), several other toolboxes have been released, both for the Python lan-guage—PyMVPA (Hanke, Halchenko, Sederberg, Hanson, et al., 2009; Hanke, Halchenko, Sederberg, Olivetti, et”