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Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data

Mourão-Miranda, Janaina ; Bokde, Arun L.W. ; Born, Christine ; Hampel, Harald ; Stetter, Martin

NeuroImage (Orlando, Fla.), 2005-12, Vol.28 (4), p.980-995 [Periódico revisado por pares]

United States: Elsevier Inc

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Título:
Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data
Autor: Mourão-Miranda, Janaina ; Bokde, Arun L.W. ; Born, Christine ; Hampel, Harald ; Stetter, Martin
Assuntos: Aged ; Algorithms ; Artificial Intelligence ; Brain ; Brain - physiology ; Brain Mapping ; Brain research ; Classifiers ; Data Interpretation, Statistical ; Discrimination (Psychology) - physiology ; Face ; Female ; Functional magnetic resonance imaging data analysis ; Humans ; Image Processing, Computer-Assisted - statistics & numerical data ; Machine learning methods ; Magnetic Resonance Imaging - classification ; Male ; NMR ; Nuclear magnetic resonance ; Photic Stimulation ; Probability Theory ; Recognition (Psychology) - physiology ; Reproducibility of Results ; Space Perception - physiology ; Studies ; Support Vector Machine
É parte de: NeuroImage (Orlando, Fla.), 2005-12, Vol.28 (4), p.980-995
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
Descrição: In the present study, we applied the Support Vector Machine (SVM) algorithm to perform multivariate classification of brain states from whole functional magnetic resonance imaging (fMRI) volumes without prior selection of spatial features. In addition, we did a comparative analysis between the SVM and the Fisher Linear Discriminant (FLD) classifier. We applied the methods to two multisubject attention experiments: a face matching and a location matching task. We demonstrate that SVM outperforms FLD in classification performance as well as in robustness of the spatial maps obtained (i.e. discriminating volumes). In addition, the SVM discrimination maps had greater overlap with the general linear model (GLM) analysis compared to the FLD. The analysis presents two phases: during the training, the classifier algorithm finds the set of regions by which the two brain states can be best distinguished from each other. In the next phase, the test phase, given an fMRI volume from a new subject, the classifier predicts the subject's instantaneous brain state.
Editor: United States: Elsevier Inc
Idioma: Inglês

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Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data

Mourão-Miranda, Janaina ; Bokde, Arun L.W. ; Born, Christine ; Hampel, Harald ; Stetter, Martin

United States: Elsevier Inc

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