Skip to Main content Skip to Navigation
Journal articles

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 
* Corresponding author
2 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
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.
Complete list of metadata

Cited literature [52 references]  Display  Hide  Download
Contributor : Michel Dojat Connect in order to contact the contributor
Submitted on : Monday, November 19, 2012 - 5:16:02 PM
Last modification on : Thursday, January 20, 2022 - 5:30:19 PM
Long-term archiving on: : Thursday, February 21, 2013 - 11:40:50 AM


Files produced by the author(s)




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⟩



Record views


Files downloads