Functional MRI-derived priors for solving the EEG/MEG inverse problem

Abstract

Introduction Because of their excellent temporal accuracy (of the order of 1 ms), electroencephalography (EEG) and magnetoencephalography (MEG) provide the most relevant data for studying the temporal dynamics of brain activity. However, difficulties arise when trying to localize the electromagnetic sources of this activity from EEG/MEG scalp recordings. This mathematical inverse problem is indeed ill-posed and largely underdetermined. An efficient way of constraining the problem and thereby reducing the solution space is to perform a regularization. By taking some anatomical and/or functional a priori knowledge into account, the regularization process may yield a more consistent localization of the electromagnetic sources. Anatomical priors have already been used (e.g., [1]) but only few regularization methods (e.g., [2]) have introduced functional information so far. In this study, we propose a new multimodal approach for solving the EEG/MEG inverse problem. This method involves