期刊
NEUROINFORMATICS
卷 11, 期 3, 页码 319-337出版社
HUMANA PRESS INC
DOI: 10.1007/s12021-013-9178-1
关键词
Neuroimaging software; Pattern recognition; Machine learning; Image analysis; MVPA
资金
- Department of Computer Science, University College London
- Wellcome Trust [WT086565/Z/08/Z]
- PASCAL2
- HARVEST programme
- Fonds de la Recherche Scientifique-FNRS, Belgium
- Portuguese Foundation for Science and Technology, Portugal
- Swiss National Science Foundation [PP00P2-123438]
- Center for Biomedical Imaging (CIBM) of the EPFL
- University and Hospital of Lausanne
- University and Hospital Geneva
- King's College London Centre of Excellence in Medical Engineering
- Wellcome Trust
- EPSRC [WT088641/Z/09/Z]
- Marie Curie Actions [299500]
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.
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