Approches bayésiennes variationnelles pour la détection-estimation des activations cérébrales en IRM fonctionnelle

English

Séminaire Probabilités & Statistique

23/02/2012 - 14:00 Lotfi CHAARI (LJK / Mistis) Salle 1 - Tour IRMA

In standard clinical 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 propose in this work a joint detection-estimation procedure. 
We adopt the so-called 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 modelling. 
In contrast to previous works that use Markov Chain Monte Carlo (MCMC) techniques  to approximate 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 and allow fine automatic tunig of spatial regularization parameters. 
It follows a new algorithm that exhibits interesting properties compared to the previously used MCMC-based approach. Experiments on artificial and real data show that VEM-JDE is robust to model mis-specification and provides computational gain while maintaining good performance in terms of activation detection and hemodynamic shape recovery.