Robust methods for reconstructing brain activity and functional connectivity between brain sourceswith MEG/EEG data

The synchronous brain activity measured via magentoencephalography (MEG) or electroencephalography (EEG) arises from current dipoles located throughout the cortex. The number, location, time-course, and orientation of these dipoles, called sources, are estimated using a source localization algorithm. Source localization remains a challenging task, one that is significantly compounded by the effects of source correlations and interference from spontaneous brain activity and sensor noise. Likewise, assessing the interactions between the individual sources, known as functional connectivity, is also confounded by noise and correlations in the sensor recordings. In addition, computational complexity has been an obstacle to computing functional connectivity. This paper derives an empirical Bayesian method for performing source localization with MEG and EEG data that includes noise and interference suppression. We demonstrate that this method surpasses standard methods of localization. In addition, we demonstrate that brain source activity inferred from this algorithm is better suited to uncover the interactions between brain areas.