New Bayesian Joint Detection and Estimation in Functional MRI

The functional MRI (Magnetic Resonance Imaging), fMRI, is a new imaging tool to study and evaluate the brain from a functional point of view. The bloodoxygenation-level-dependent (BOLD) signal is currently used to detect the activation of brain regions with a stimulus application, e.g., visual or auditive. In a block design approach the stimuli (called paradigm in the fMRI scope) are designed to detect activated and non activated brain regions with maximized certainty. However, corrupting noise in MRI volumes acquisition, patient motion and the normal brain activity interference makes this detection a difficult task. The most used activation detection fMRI algorithm, here called SPM-GLM [1] uses a conventional statistical inference methodology based on the t-statistics, where it assumes a rather rigid shape on the BOLD Hemodynamic Response Function (HRF), constant for the whole region of interest (ROI). A different perspective is presented in this paper; a new Bayesian method, here called SPM-MAP, for the joint detection of brain activated regions and estimation of the underlying HRF. This approach presents two main advantages: 1) the activity detection benefits from the method’s high flexibility toward the HRF shape; 2) it provides local HRF estimations. Monte Carlo tests were performed with synthetic data, first comparing the activity detection section of SPMMAP with the standard SPM-GLM method and second analysing the whole SPM-MAP performance. Finally, detection analysis results on real fMRI block-designed data are presented.