Pattern Recognition for Neuroimaging Toolbox

author: Jessica Schrouff, Cyclotron Research Center, University of Liège
published: Aug. 6, 2013,   recorded: April 2013,   views: 2663


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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 and MATLAB-based, 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.

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Reviews and comments:

Comment1 Mark Lense, December 27, 2021 at 12:49 p.m.:

But what if there are some changes during the project that make the team think that a different model would be more effective? Can I change the model while the project is running? The answer to this question is almost always yes, but this should be done taking into account the possible consequences of such changes for the project, read more at . As a last resort, it is better to change the model than to try to use one that is not suitable enough to meet the needs of the project.

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