Reference : PRoNTo: Pattern Recognition for Neuroimaging Toolbox
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
Human health sciences : Multidisciplinary, general & others
http://hdl.handle.net/2268/140242
PRoNTo: Pattern Recognition for Neuroimaging Toolbox
English
Schrouff, Jessica mailto [Université de Liège - ULg > > Centre de recherches du cyclotron >]
Rosa, Maria Joao []
Rondina, Jane []
Marquand, Andre []
Chu, Carlton []
Ashburner, John []
Phillips, Christophe [Université de Liège - ULg > > Centre de recherches du cyclotron >]
Richiardi, Jonas []
Mourão-Miranda, Janaina []
Feb-2013
Neuroinformatics
11
3
319-337
Yes (verified by ORBi)
International
1539-2791
1559-0089
[en] machine learning ; neuroimaging ; toolbox ; image analysis ; multivariate pattern analysis
[en] 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 formultivariate 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.
Computer Science Department, University College London ; Institute of Psychology, King's College, London ; Wellcome Trust, London ; Centre de Recherches du Cyclotron - CRC ; Ecole Polytechnique Fédérale de Lausanne
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS ; Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture (Communauté française de Belgique) - FRIA ; PASCAL2 and its Harvest Project ; Department of Computer Science, University College London ; Wellcome Trust ; Portuguese Foundation for Science and Technology , Ministry of Science, Portugal ; Swiss National Science Foundation (PP00P2-123438) and Center for Biomedical Imaging (CIBM) of the EPFL and Universities and Hospitals of Lausanne and Geneva ; The King’s College London Centre of Excellence in Medical Engineering, funded by the Wellcome Trust and EPSRC under grant no. WT088641/Z/09/Z
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/140242
10.1007/s12021-013-9178-1
http://link.springer.com/article/10.1007%2Fs12021-013-9178-1

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