Pattern Recognition for Neuroimaging Toolbox (PRoNTo)
PRoNTo (Pattern Recognition for Neuroimaging Toolbox) is a software toolbox based on pattern recognition techniques for the analysis of neuroimaging data. Statistical pattern recognition is a field within the area of machine learning which is concerned with automatic discovery of regularities in data through the use of computer algorithms, and with the use of these regularities to take actions such as classifying the data into different categories. In PRoNTo, brain scans are treated as spatial patterns and statistical learning models are used to identify statistical properties of the data that can be used to discriminate between experimental conditions or groups of subjects (classification models) or to predict a continuous measure (regression models).
PRoNTo aims to facilitate the interaction between machine learning and neuroimaging communities. On one hand, the machine learning community can contribute to the toolbox with novel machine learning models. On the other hand, the toolbox provides a variety of tools for the neuroscience and clinical neuroscience communities, enabling them to ask new questions that cannot be easily investigated using existing software and analysis tools.
PRoNTo is distributed for free as copyright software under the terms of the GNU General Public License as published by the Free Software Foundation. The development of the toolbox has been supported by the PASCAL Harvest framework and The Wellcome Trust.
- 23/03/2017 - Registration for the next PRoNTo course (1st-2nd June 2017) is now open! Please see HERE for programme and how to register.
- 02/09/2016 - Check out our new list of frequently asked questions (FAQ) HERE.
- 12/03/2016 - Registration for the next PRoNTo course (23rd-24th May 2016) now open! Please see HERE for programme and how to register.
- 27/04/2015 - PRoNTo v2.0 is now available for download HERE. Please note that this is a beta version. Any bug reports or comments/suggestions are welcome on our mailing list.
- 16/04/2015 - The next PRoNTo course will take place in London (18-19 May 2015). For more information, please click here.
- 31/01/2014 - The next PRoNTo course will take place in London (19-20 May 2014). For more information, please click here.
- 23/07/2013 - PRoNTo v1.1 is now available for download HERE. Any bug reports or comments/suggestions are welcome on our mailing list.
- 22/02/2013 - Next PRoNTo's courses have been announced: please see here for more information.
- 19/02/2013 - PRoNTo's PAPER has been published in NEUROINFORMATICS!! Click here to download.
- 28/08/2012 - PRoNTo's manual and datasets page have been updated. The manual now includes a tutorial chapter on how to analyse an fMRI block design dataset with PRoNTo (the data can be downloaded from here).
- 12/06/2012 - PRoNTo v1.1 beta is now available for download HERE. Please note this is still a beta version! The final version of PRoNTo v1.1 will be released soon. Any bug reports or comments/suggestions are welcome on our mailing list.
- 17/05/2012 - PRoNTo mailing list is available for subscription subcribe here
- 09/05/2012 - PRoNTo has a poster at HBM2012: "391MT - PRoNTo: Pattern Recognition for Neuroimaging Toolbox"
The latest version of the software can be downloaded here. For a description of all of PRoNTo's functionalities, please consult the manual, here. Several PRoNTo courses will take place throughout the year. These courses provide an introduction to pattern recognition methods in the context of neuroimaging and an introduction to the toolbox. For more information on the next courses and to download the slides from previous ones, see here.
- Janaina Mourao-Miranda (University College London)
- Jessica Schrouff (University of Liege)
- Christophe Phillips (University of Liege)
- Maria Joao Rosa (University College London)
- Jane Rondina (University of São Paulo)
- Andre Marquand (Kings College London)
- John Ashburner (University College London)
- Jonas Richiardi (University of Geneva)
- Carlton Chu (NIH)
- Joao Matos Monteiro (University College London)
- Anil Rao (University College London)