Distributed current estimates using cortical orientation constraints

Distributed source models of magnetoencephalographic (MEG) and electroencephalographic (EEG) data employ dense distributions of current sources in a volume or on a surface. Previously, anatomical magnetic resonance imaging (MRI) data have been used to constrain locations and orientations based on cortical geometry extracted from anatomical MRI data. We extended this approach by first calculating cortical patch statistics (CPS), which for each patch corresponding to a current source location on the cortex comprise the area of the patch, the average normal direction, and the average deviation of the surface normal from its average. The patch areas were then incorporated in the forward model to yield estimates of the surface current density instead of dipole amplitudes at the current locations. The surface normal data were employed in a loose orientation constraint (LOC), which allows some variation of the current direction from the average normal. We employed this approach both in the l(2) minimum-norm estimates (MNE) and in the more focal l(1) minimum-norm solutions, the minimum-current estimate (MCE). Simulations in auditory and somatosensory areas with current dipoles and 10- or 20-mm diameter cortical patches as test sources showed that applying the LOC can increase localization accuracy. We also applied the method to in vivo auditory and somatosensory data.