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Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-st... Full description

Journal Title: Magnetic Resonance Imaging 2008, Vol.26(7), pp.921-934
Main Author: Formisano, Elia
Other Authors: De Martino, Federico , Valente, Giancarlo
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0730-725X ; E-ISSN: 1873-5894 ; DOI: 10.1016/j.mri.2008.01.052
Link: http://dx.doi.org/10.1016/j.mri.2008.01.052
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recordid: elsevier_sdoi_10_1016_j_mri_2008_01_052
title: Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning
format: Article
creator:
  • Formisano, Elia
  • De Martino, Federico
  • Valente, Giancarlo
subjects:
  • Functional Mri
  • Machine Learning
  • Pattern Recognition
  • Multivariate Classification
  • Multivariate Regression
  • Functional Mri
  • Machine Learning
  • Pattern Recognition
  • Multivariate Classification
  • Multivariate Regression
  • Medicine
ispartof: Magnetic Resonance Imaging, 2008, Vol.26(7), pp.921-934
description: Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms “learn” a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete ( classification) or continuous ( regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set (“brain reading”). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and...
language: eng
source:
identifier: ISSN: 0730-725X ; E-ISSN: 1873-5894 ; DOI: 10.1016/j.mri.2008.01.052
fulltext: fulltext
issn:
  • 0730-725X
  • 0730725X
  • 1873-5894
  • 18735894
url: Link


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subjectFunctional Mri ; Machine Learning ; Pattern Recognition ; Multivariate Classification ; Multivariate Regression ; Functional Mri ; Machine Learning ; Pattern Recognition ; Multivariate Classification ; Multivariate Regression ; Medicine
descriptionMachine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms “learn” a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete ( classification) or continuous ( regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set (“brain reading”). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and...
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Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms “learn” a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete ( classification) or continuous ( regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set (“brain reading”). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and...

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