Maximum a Posteriori Maximum Entropy HRF Estimation in Event Related fMRI

Abstract

Functional magnetic resonance imaging (fMRI) is an important technique for neuroimaging. The conventional system identification methods used in fMRI data analysis model the whole brain as a stationary linear system characterized by its impulse response defined by the hemodynamic response function (HFR). Modeling and estimating the HFR in fMRI experiments is an important aspect of the analysis of functional neuroimages. This work uses a Bayesian approach to linear systems analysis with Gaussian noise to make inferences about the HRF. A new method based on maximum entropy considerations is proposed to define the prior involved in estimating the posterior distribution characterizing the HRF. Us..