Outlier detection for robust region-based estimation of the hemodynamic response function in event-related fMRI

In functional magnetic resonance imaging (fMRI), the hemodynamic response function (HRF) represents the impulse response of the neurovascular system. Its identification is essential for a deeper understanding of the dynamics of cerebral activity. In previous papers, we developed a voxelwise approach, i.e. based on a single time-course. In this paper, we propose an extension to cope with region-based HRF estimation. We introduce a spatial homogeneous model that assumes the same HRF shape for a majority of voxels within a given region-of-interest (ROI). A least trimmed squares estimator is employed to select those voxels. A Bayesian HRF estimation is then performed with the corresponding time courses. Our approach is tested on real fMRI data to illustrate the gain in robustness achieved with the region-based estimate.