Fast joint detection-estimation of evoked brain activity in event-related fMRI using a variational approach

Lotfi Chaari 1, 2, * Thomas Vincent 1, 2, * Florence Forbes 2, * Michel Dojat 3, * Philippe Ciuciu 1
* Auteur correspondant
1 LNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
3 ANTE-INSERM U836, équipe 5 - Neuro-imagerie fonctionnelle et métabolique
GIN - Grenoble Institut des Neurosciences
Abstract : In standard within-subject analyses of event-related fMRI data, two steps are usually performed separately: detection of brain activity and estimation of the hemodynamic response. Because these two steps are inherently linked, we adopt the socalled region-based Joint Detection-Estimation (JDE) framework that addresses this joint issue using a multivariate inference for detection and estimation. JDE is built by making use of a regional bilinear generative model of the BOLD response and constraining the parameter estimation by physiological priors using temporal and spatial information in a Markovian model. In contrast to previous works that use Markov Chain Monte Carlo (MCMC) techniques to sample the resulting intractable posterior distribution, we recast the JDE into a missing data framework and derive a Variational Expectation-Maximization (VEM) algorithm for its inference. A variational approximation is used to approximate the Markovian model in the unsupervised spatially adaptive JDE inference, which allows automatic fine-tuning of spatial regularization parameters. It provides a new algorithm that exhibits interesting properties in terms of estimation error and computational cost compared to the previously used MCMC-based approach. Experiments on artificial and real data show that VEM-JDE is robust to model misspecification and provides computational gain while maintaining good performance in terms of activation detection and hemodynamic shape recovery.
Keywords : Biomedical signal detection-estimation functional MRI brain imaging Joint Detection-Estimation Markov random field EM algorithm Variational approximation fMRI VEM Mean-field
Type de document :
Article dans une revue
IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2013, 32 (5), pp.821-837. 〈10.1109/TMI.2012.2225636〉
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Lotfi Chaari, Thomas Vincent, Florence Forbes, Michel Dojat, Philippe Ciuciu. Fast joint detection-estimation of evoked brain activity in event-related fMRI using a variational approach. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2013, 32 (5), pp.821-837. 〈10.1109/TMI.2012.2225636〉. 〈inserm-00753873〉

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