A Variational Bayesian approach for the Joint Detection Estimation of Brain Activity in functional MRI
نویسندگان
چکیده
Nous abordons dans cet article le problème de la détection-estimation jointe de l’activité cérébrale en IRM fonctionnelle. Pour ce faire, nous adoptons le cadre JDE développé dans [1] et étendu dans [2] avec un modèle de champ de Markov caché afin de considérer les dépendances spatiales entre les voxels. Cette extension est essentielle mais induit une grande complexité opératoire qui a été contournée dans [2] en utilisant des méthodes de simulation stochastique (MCMC) qui sont très coûteuses en temps de calcul. Nous proposons ici une alternative pour lever cette limitation en reformulant le cadre JDE en un problème à données manquantes en utilisant pour l’inférence un algorithme EM dans lequel nous mettons en œuvre des techniques d’approximation variationnelle. Des illustrations sur des données artificielles réalistes montrent que l’algorithme EM variationnel permet de dépasser les performances de l’approche MCMC.
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