Automatic relevance determination based hierarchical Bayesian MEG inversion in practice.

In recent simulation studies, a hierarchical Variational Bayesian (VB) method, which can be seen as a generalisation of the traditional minimum-norm estimate (MNE), was introduced for reconstructing distributed MEG sources. Here, we studied how nonlinearities in the estimation process and hyperparameter selection affect the inverse solutions, the feasibility of a full Bayesian treatment of the hyperparameters, and multimodality of the true posterior, in an empirical dataset wherein a male subject was presented with pure tone and checkerboard reversal stimuli, alone and in combination. An MRI-based cortical surface model was employed. Our results show, with a comparison to the basic MNE, that the hierarchical VB approach yields robust and physiologically plausible estimates of distributed sources underlying MEG measurements, in a rather automated fashion.