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Analyse parcimonieuse des données d'IRM fonctionnelle dans un cadre bayésien variationnel

Christine Bakhous 1, * Florence Forbes 1, * Farida Enikeeva 1, * Thomas Vincent 1, * Michel Dojat 2 Philippe Ciuciu 3
* Corresponding author
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
2 Neuroimagerie fonctionnelle et perfusion cérébrale
GIN - Grenoble Institut des Neurosciences
3 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
Abstract : Analysing functional Magnetic Resonance Imaging (fMRI) data is mainly done using the general linear model (GLM) in which the activation of a brain area is supposed to depend on all delivered stimuli (e.g. motor, visual, etc.) although activation is likely to be induced by only some of them in specific brain areas. Inclusion of irrelevant events may degrade the results, particularly when the Hemodynamic Response Function (HRF) is jointly estimated. In addition, a prior selection of relevant condition for each brain region is not always possible (e.g. pathology). To face this issue, we propose an efficient variational procedure that automatically selects the conditions according to the brain activity they elicit. It follows an improved activation detection and local HRF estimation that we illustrate on real fMRI data.
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Submitted on : Monday, January 20, 2014 - 11:07:40 PM
Last modification on : Monday, December 13, 2021 - 9:16:03 AM


  • HAL Id : hal-00933701, version 1



Christine Bakhous, Florence Forbes, Farida Enikeeva, Thomas Vincent, Michel Dojat, et al.. Analyse parcimonieuse des données d'IRM fonctionnelle dans un cadre bayésien variationnel. 45èmes Journées de Statistique, Société Française de Statistique, May 2013, Toulouse, France. ⟨hal-00933701⟩



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