Accurate reconstruction of brain activity and functional connectivity from noisy MEG data

The synchronous brain activity measured via magnetoencephalography (MEG) arises from current dipoles located throughout the cortex. Estimating the number, location, time-course, and orientation of these dipoles, called sources, 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. Computational complexity has been an obstacle to computing functional connectivity. This paper demonstrates the application of an empirical Bayesian method to perform source localization with MEG data in order to estimate measures of functional connectivity. We demonstrate that brain source activity inferred from this algorithm is better suited to uncover the interactions between brain areas as compared to other commonly used source localization algorithms.