Multivariate model specification for fMRI data.

Détails

ID Serval
serval:BIB_C8789C673286
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Multivariate model specification for fMRI data.
Périodique
Neuroimage
Auteur⸱e⸱s
Kherif F., Poline J.B., Flandin G., Benali H., Simon O., Dehaene S., Worsley K.J.
ISSN
1053-8119 (Print)
ISSN-L
1053-8119
Statut éditorial
Publié
Date de publication
2002
Volume
16
Numéro
4
Pages
1068-1083
Langue
anglais
Notes
Publication types: Journal ArticlePublication Status: ppublish
Résumé
We present a general method-denoted MoDef-to help specify (or define) the model used to analyze brain imaging data. This method is based on the use of the multivariate linear model on a training data set. We show that when the a priori knowledge about the expected brain response is not too precise, the method allows for the specification of a model that yields a better sensitivity in the statistical results. This obviously relies on the validity of the a priori information, in our case the representativity of the training set, an issue addressed using a cross-validation technique. We propose a fast implementation that allows the use of the method on large data sets as found with functional Magnetic Resonance Images. An example of application is given on an experimental fMRI data set that includes nine subjects who performed a mental computation task. Results show that the method increases the statistical sensitivity of fMRI analyses.
Mots-clé
Brain/physiology, Cognition/physiology, Humans, Linear Models, Magnetic Resonance Imaging, Mathematics, Mental Processes/physiology, Models, Neurological, Multivariate Analysis
Pubmed
Web of science
Création de la notice
11/09/2011 13:33
Dernière modification de la notice
20/08/2019 15:43
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