Decoding semi-constrained brain activity from FMRI using support vector machines and gaussian processes.

Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental paradigms characterized by a series of distinct states induced by a temporally constrained experime...

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Autores principales:	Jessica Schrouff, Caroline Kussé, Louis Wehenkel, Pierre Maquet, Christophe Phillips
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d83cc1c1b96f4748b88600c402066af3
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id	oai:doaj.org-article:d83cc1c1b96f4748b88600c402066af3
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spelling	oai:doaj.org-article:d83cc1c1b96f4748b88600c402066af32021-11-18T07:20:47ZDecoding semi-constrained brain activity from FMRI using support vector machines and gaussian processes.1932-620310.1371/journal.pone.0035860https://doaj.org/article/d83cc1c1b96f4748b88600c402066af32012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22563410/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental paradigms characterized by a series of distinct states induced by a temporally constrained experimental design. In more realistic conditions, the number, sequence and duration of mental states are unpredictably generated by the individual, resulting in complex and imbalanced fMRI data sets. This study tests the classification of brain activity, acquired on 16 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. To deal with these issues, two classification techniques were considered (Support Vector Machines, SVM, and Gaussian Processes, GP), as well as different feature extraction methods (General Linear Model, GLM and SVM). These techniques were combined in order to identify the procedures leading to the highest accuracy measures. Our results showed that 12 data sets out of 16 could be significantly modeled by either SVM or GP. Model accuracies tended to be related to the degree of imbalance between classes and to task performance of the volunteers. We also conclude that the GP technique tends to be more robust than SVM to model unbalanced data sets.Jessica SchrouffCaroline KusséLouis WehenkelPierre MaquetChristophe PhillipsPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 4, p e35860 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jessica Schrouff Caroline Kussé Louis Wehenkel Pierre Maquet Christophe Phillips Decoding semi-constrained brain activity from FMRI using support vector machines and gaussian processes.
description	Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental paradigms characterized by a series of distinct states induced by a temporally constrained experimental design. In more realistic conditions, the number, sequence and duration of mental states are unpredictably generated by the individual, resulting in complex and imbalanced fMRI data sets. This study tests the classification of brain activity, acquired on 16 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. To deal with these issues, two classification techniques were considered (Support Vector Machines, SVM, and Gaussian Processes, GP), as well as different feature extraction methods (General Linear Model, GLM and SVM). These techniques were combined in order to identify the procedures leading to the highest accuracy measures. Our results showed that 12 data sets out of 16 could be significantly modeled by either SVM or GP. Model accuracies tended to be related to the degree of imbalance between classes and to task performance of the volunteers. We also conclude that the GP technique tends to be more robust than SVM to model unbalanced data sets.
format	article
author	Jessica Schrouff Caroline Kussé Louis Wehenkel Pierre Maquet Christophe Phillips
author_facet	Jessica Schrouff Caroline Kussé Louis Wehenkel Pierre Maquet Christophe Phillips
author_sort	Jessica Schrouff
title	Decoding semi-constrained brain activity from FMRI using support vector machines and gaussian processes.
title_short	Decoding semi-constrained brain activity from FMRI using support vector machines and gaussian processes.
title_full	Decoding semi-constrained brain activity from FMRI using support vector machines and gaussian processes.
title_fullStr	Decoding semi-constrained brain activity from FMRI using support vector machines and gaussian processes.
title_full_unstemmed	Decoding semi-constrained brain activity from FMRI using support vector machines and gaussian processes.
title_sort	decoding semi-constrained brain activity from fmri using support vector machines and gaussian processes.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/d83cc1c1b96f4748b88600c402066af3
work_keys_str_mv	AT jessicaschrouff decodingsemiconstrainedbrainactivityfromfmriusingsupportvectormachinesandgaussianprocesses AT carolinekusse decodingsemiconstrainedbrainactivityfromfmriusingsupportvectormachinesandgaussianprocesses AT louiswehenkel decodingsemiconstrainedbrainactivityfromfmriusingsupportvectormachinesandgaussianprocesses AT pierremaquet decodingsemiconstrainedbrainactivityfromfmriusingsupportvectormachinesandgaussianprocesses AT christophephillips decodingsemiconstrainedbrainactivityfromfmriusingsupportvectormachinesandgaussianprocesses
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Decoding semi-constrained brain activity from FMRI using support vector machines and gaussian processes.

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