Increasing the accuracy of electromagnetic inverses using functional area source correlation constraints

Estimating cortical current distributions from electroencephalographic (EEG) or magnetoencephalographic data is a difficult inverse problem whose solution can be improved by the addition of priors on the associated neural responses. In the context of visual activation studies, we propose a new approach that uses a functional area constrained estimator (FACE) to increase the accuracy of the reconstructions. It derives the source correlation matrix from a segmentation of the cortex into areas defined by retinotopic maps of the visual field or by functional localizers obtained independently by fMRI. These areas are computed once for each individual subject and the associated estimators can therefore be reused for any new study on the same participant. The resulting FACE reconstructions emphasize the activity of sources within these areas or enforce their intercorrelations. We used realistic Monte-Carlo simulations to demonstrate that this approach improved our estimates of a diverse set of source configurations. Reconstructions obtained from a real EEG dataset demonstrate that our priors improve the localization of the cortical areas involved in horizontal disparity processing.