Hierarchical Bayesian inference of brain activity

Abstract

Magnetoencephalography (MEG) can measure brain activity with millisecond-order temporal resolution, but its spatial resolution is poor, due to the ill-posed nature of the inverse problem, for estimating source currents from the electromagnetic measurement. Therefore, prior information on the source currents is essential to solve the inverse problem. We have proposed a new hierarchical Bayesian method to combine several sources of information. In our method, the variance of the source current at each source location is considered an unknown parameter and estimated from the observed MEG data and prior information by using variational Bayes method. The fMRI information can be imposed as prior distribution rather than the variance itself so that it gives a soft constraint on the variance. It is shown that the hierarchical Bayesian method has better accuracy and spatial resolution than conventional linear inverse methods by evaluating the resolution curve. The proposed method also demonstrated good spatial and temporal resolution for estimating current activity in early visual area evoked by a stimulus in a quadrant of the visual field. © 2008 Springer-Verlag Berlin Heidelberg.