Skull Defects in Finite Element Head Models for Source Reconstruction from Magnetoencephalography Signals

Abstract

Magnetoencephalography (MEG) signals are influenced by skull defects. However, there is a lack of evidence of this influence during source reconstruction. Our objectives are to characterize errors in source reconstruction from MEG signals due to ignoring skull defects and to assess the ability of an exact finite element head model to eliminate such errors. A detailed finite element model of the head of a rabbit used in a physical experiment was constructed from magnetic resonance and co-registered computer tomography imaging that differentiated nine tissue types. Sources of the MEG measurements above intact skull and above skull defects respectively were reconstructed using a finite element model with the intact skull and one incorporating the skull defects. The forward simulation of the MEG signals reproduced the experimentally observed characteristic magnitude and topography changes due to skull defects. Sources reconstructed from measured MEG signals above intact skull matched the known physical locations and orientations. Ignoring skull defects in the head model during reconstruction displaced sources under a skull defect away from that defect. Sources next to a defect were reoriented. When skull defects, with their physical conductivity, were incorporated in the head model, the location and orientation errors were mostly eliminated. The conductivity of the skull defect material non-uniformly modulated the influence on MEG signals. We propose concrete guidelines for taking into account conducting skull defects during MEG coil placement and modeling. Exact finite element head models can improve localization of brain function, specifically after surgery.

Variation in reported human head tissue electrical conductivity values

“…The influence of skull conductivity and segmentation inaccuracies has been explored extensively, revealing overwhelming source localisation errors for EEG (Lanfer et al, 2012;Montes-Restrepo et al, 2014;Wolters et al, 2006) and MEG (Cho, Vorwerk, Wolters, & Knösche, 2015;Lau et al, 2016) of up to 2cm. Moreover, variations in skull conductivity have been found to impact transcranial electric stimulation focality and dose (Santos et al, 2016;Schmidt, Wagner, Burger, van Rienen, & Wolters, 2015;Wenger, Salvador, Basser, & Miranda, 2015), with one study revealing an error of 8% in dose (Fernández-Corazza et al, 2017).…”

A Mixed Finite Element Method to Solve the EEG Forward Problem

“…The dipole can be modeled either in the "current space" (6a) or in the "potential space" (6b) (sometimes also called "pressure space" due to the origin of Mixed-FEM in reservoir simulations [38]). The first option corresponds to an evaluation of the functional l in the discrete space RT 0 as it was defined in (12). For j p = mδ x0 , i.e., a current dipole with moment m at position x 0 , we have…”

Three‐dimensional FDTD modeling of neurons to solve EEG and MEG forward problem

“…4,5 In this method, the electric fields and the potential difference between the probes are measured on the head so that the body and head characteristics have a considerable impact. [6][7][8] Another method for electric field measurement is to directly use the platinum-iridium electrodes in the brain in order to remove the effect of the skull, which is called electrocorticography. 9 In addition, the tiny magnetic fields produced by the currents of neurons activity are measured by removing the effects of electromagnetic characteristics.…”