Bayesian Joint Detection-Estimation of Brain Activity Using MCMC With a Gamma-Gaussian Mixture Prior Model

The classical approach of within-subject analysis in event-related functional magnetic resonance imaging (fMRI) first relies on (i) a detection step to localize which parts of the brain are activated by a given stimulus type, and then on (ii) an estimation step to recover the temporal dynamics of the brain response. To date, specially in region-based analysis, the two questions have been addressed separately while intrinsically connected to each other. This situation motivates the need for new methods in neuroimaging that go beyond this unsatisfactory trade-off. In this paper, we propose a generalization of a region based Bayesian detection-estimation approach that addresses (i)-(ii) simultaneously as a bilinear inverse problem. The proposed extension relies on a 2-class Gamma-Gaussian prior mixture modeling to classify the voxels of the brain region either as activated or unactivated. Our approach provides both a spatial activity map and a HRF estimation using Monte Carlo Markov chain (MCMC) techniques. Results show that this novel mixture model yields lower false positive rates and a better sensitivity in comparison with a 2-class Gaussian mixture