Joint Bayesian detection of brain activated regions and local HRF estimation in functional MRI
The blood-oxygenation-level-dependent (BOLD) signal, measured with the magnetic resonance imaging (MRI), 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. In this paper a new Bayesian method, called SPM-MAP, is proposed where a joint detection of brain activated regions and estimation of the underlying hemodynamic impulse response function (HRF) is proposed. Monte Carlo tests on its error probability and HRF estimation with synthetic data are performed and presented.