PRoNTo: Pattern Recognition for Neuroimaging Toolbox

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 univ...

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Published inNeuroinformatics (Totowa, N.J.) Vol. 11; no. 3; pp. 319 - 337
Main Authors Schrouff, J., Rosa, M. J., Rondina, J. M., Marquand, A. F., Chu, C., Ashburner, J., Phillips, C., Richiardi, J., Mourão-Miranda, J.
Format Journal Article Web Resource
LanguageEnglish
Published Boston Springer US 01.07.2013
Springer
Springer Nature B.V
Humana Press
Subjects
Age Factors
Algorithms
Applied sciences
Artificial intelligence
Bioinformatics
Biological and medical sciences
Biomedical and Life Sciences
Biomedicine
Brain - physiology
Brain Mapping
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer science; control theory; systems
Computer Simulation
Engineering, computing & technology
Exact sciences and technology
Human health sciences
Humans
image analysis
Image Processing, Computer-Assisted
Ingénierie, informatique & technologie
Investigative techniques, diagnostic techniques (general aspects)
Learning and adaptive systems
Likelihood Functions
machine learning
Medical sciences
Multivariate Analysis
multivariate pattern analysis
Nervous system
Neuroimaging
Neurology
Neurosciences
Original
Original Article
Pattern Recognition, Automated
Pattern recognition. Digital image processing. Computational geometry
Predictive Value of Tests
Radiodiagnosis. Nmr imagery. Nmr spectrometry
Radionuclide investigations
Sciences de la santé humaine
Software
toolbox
Image analysis
Neuroimaging software
Pattern recognition
MVPA
Machine learning
Computer vision
Data analysis
Statistical analysis
Software development
Image processing
Cognition
Multivariate analysis
Modeling
Open source software
Encephalon
Statistical data
Cognitive theory
Neuroscience
Medical imagery
Pattern analysis
Artificial intelligence
Medical application
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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 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.
AbstractList 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.[PUBLICATION 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 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.
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.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.
Author Chu, C.
Rondina, J. M.
Phillips, C.
Marquand, A. F.
Mourão-Miranda, J.
Schrouff, J.
Rosa, M. J.
Ashburner, J.
Richiardi, J.
Author_xml – sequence: 1
  givenname: J.
  surname: Schrouff
  fullname: Schrouff, J.
  organization: Cyclotron Research Centre, University of Liège
– sequence: 2
  givenname: M. J.
  surname: Rosa
  fullname: Rosa, M. J.
  email: m.rosa@ucl.ac.uk
  organization: Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London
– sequence: 3
  givenname: J. M.
  surname: Rondina
  fullname: Rondina, J. M.
  organization: Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, Neuroimaging Laboratory, Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo
– sequence: 4
  givenname: A. F.
  surname: Marquand
  fullname: Marquand, A. F.
  organization: Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London
– sequence: 5
  givenname: C.
  surname: Chu
  fullname: Chu, C.
  organization: Section on Functional Imaging Methods, Laboratory of Brain and Cognition, NIMH, NIH
– sequence: 6
  givenname: J.
  surname: Ashburner
  fullname: Ashburner, J.
  organization: Wellcome Trust Centre for NeuroImaging, University College London
– sequence: 7
  givenname: C.
  surname: Phillips
  fullname: Phillips, C.
  organization: Cyclotron Research Centre, University of Liège, Department of Electrical Engineering and Computer Science, University of Liège
– sequence: 8
  givenname: J.
  surname: Richiardi
  fullname: Richiardi, J.
  organization: Functional Imaging in Neuropsychiatric Disorders Lab, Department of Neurology and Neurological Sciences, Stanford University, Laboratory for Neurology & Imaging of Cognition, Departments of Neurosciences and Clinical Neurology, University of Geneva
– sequence: 9
  givenname: J.
  surname: Mourão-Miranda
  fullname: Mourão-Miranda, J.
  organization: Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London
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GIGA CRC (Cyclotron Research Center) In vivo Imaging-Aging & Memory - ULiège
Wellcome Trust, London
Computer Science Department, University College London
Ecole Polytechnique Fédérale de Lausanne
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Issue 3
Keywords Image analysis
Neuroimaging software
Pattern recognition
MVPA
Machine learning
Computer vision
Data analysis
Statistical analysis
Software development
Image processing
Cognition
Multivariate analysis
Modeling
Open source software
Encephalon
Statistical data
Cognitive theory
Neuroscience
Medical imagery
Pattern analysis
Artificial intelligence
Medical application
Language English
License http://creativecommons.org/licenses/by-nc/2.0
CC BY 4.0
Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
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scopus-id:2-s2.0-84881091929
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PublicationTitle Neuroinformatics (Totowa, N.J.)
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Snippet In the past years, mass univariate statistical analyses of neuroimaging data have been complemented by the use of multivariate pattern analyses, especially...
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SubjectTerms Age Factors
Algorithms
Applied sciences
Artificial intelligence
Bioinformatics
Biological and medical sciences
Biomedical and Life Sciences
Biomedicine
Brain - physiology
Brain Mapping
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer science; control theory; systems
Computer Simulation
Engineering, computing & technology
Exact sciences and technology
Human health sciences
Humans
image analysis
Image Processing, Computer-Assisted
Ingénierie, informatique & technologie
Investigative techniques, diagnostic techniques (general aspects)
Learning and adaptive systems
Likelihood Functions
machine learning
Medical sciences
Multivariate Analysis
multivariate pattern analysis
Nervous system
Neuroimaging
Neurology
Neurosciences
Original
Original Article
Pattern Recognition, Automated
Pattern recognition. Digital image processing. Computational geometry
Predictive Value of Tests
Radiodiagnosis. Nmr imagery. Nmr spectrometry
Radionuclide investigations
Sciences de la santé humaine
Software
toolbox
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Title PRoNTo: Pattern Recognition for Neuroimaging Toolbox
URI https://link.springer.com/article/10.1007/s12021-013-9178-1
https://www.ncbi.nlm.nih.gov/pubmed/23417655
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http://orbi.ulg.ac.be/handle/2268/140242
https://pubmed.ncbi.nlm.nih.gov/PMC3722452
Volume 11
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