PRoNTo: Pattern Recognition for Neuroimaging Toolbox
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ORIGINAL ARTICLE
PRoNTo: Pattern Recognition for Neuroimaging Toolbox J. Schrouff & M. J. Rosa & J. M. Rondina & A. F. Marquand & C. Chu & J. Ashburner & C. Phillips & J. Richiardi & J. Mourão-Miranda Published online: 16 February 2013 # The Author(s) 2013. This article is published with open access at Springerlink.com
Abstract 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
J. Schrouff and M.J. Rosa contributed equally to this work J. Schrouff : C. Phillips Cyclotron Research Centre, University of Liège, Liège, Belgium M. J. Rosa (*) : J. M. Rondina : J. Mourão-Miranda Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, Gower Street, WC1E 6BT London, UK e-mail: [email protected] A. F. Marquand : J. Mourão-Miranda Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London, London, UK C. Chu Section on Functional Imaging Methods, Laboratory of Brain and Cognition, NIMH, NIH, Bethesda, USA J. Ashburner Wellcome Trust Centre for NeuroImaging, University College London, London, UK C. Phillips Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium J. Richiardi Functional Imaging in Neuropsychiatric Disorders Lab, Department of Neurology and Neurological Sciences, Stanford University, Stanford, USA J. Richiardi Laboratory for Neurology & Imaging of Cognition, Departments of Neurosciences and Clinical Neurology, University of Geneva, Geneva, Switzerland J. M. Rondina Neuroimaging Laboratory, Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
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. Keywords Neuroimaging software . Pattern recognition . Machine learning . Image analysis . MVPA
Introduction Two of the most fundamental questions in the field of neurosciences are how information is represented in the different brain structures, and how this information evolves wit
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