Influence of Skull Modeling Approaches on EEG Source Localization

Abstract

Electroencephalographic source localization (ESL) relies on an accurate model representing the human head for the computation of the forward solution. In this head model, the skull is of utmost importance due to its complex geometry and low conductivity compared to the other tissues inside the head. We investigated the influence of using different skull modeling approaches on ESL. These approaches, consisting in skull conductivity and geometry modeling simplifications, make use of X-ray computed tomography (CT) and magnetic resonance (MR) images to generate seven different head models. A head model with an accurately segmented skull from CT images, including spongy and compact bone compartments as well as some air-filled cavities, was used as the reference model. EEG simulations were performed for a configuration of 32 and 128 electrodes, and for both noiseless and noisy data. The results show that skull geometry simplifications have a larger effect on ESL than those of the conductivity modeling. This suggests that accurate skull modeling is important in order to achieve reliable results for ESL that are useful in a clinical environment. We recommend the following guidelines to be taken into account for skull modeling in the generation of subject-specific head models: (i) If CT images are available, i.e., if the geometry of the skull and its different tissue types can be accurately segmented, the conductivity should be modeled as isotropic heterogeneous. The spongy bone might be segmented as an erosion of the compact bone; (ii) when only MR images are available, the skull base should be represented as accurately as possible and the conductivity can be modeled as isotropic heterogeneous, segmenting the spongy bone directly from the MR image; (iii) a large number of EEG electrodes should be used to obtain high spatial sampling, which reduces the localization errors at realistic noise levels.

Skull Modeling Effects in Conductivity Estimates Using Parametric Electrical Impedance Tomography

“…Initially, simplified three layered spherical models [16] were used, but technological advances have enabled progressively more realistic models that employ anatomically realistic three or four nested layers [17], models that include marrow bone (MB) [18], small foramina in the skull [10], [19], and even blood vessels [20]. The quality of these models depends on both accuracy of characterizing numerous head tissues and their electrical properties, including anisotropy.…”

A finite-element reciprocity solution for EEG forward modeling with realistic individual head models

“…It is also well known that the skull conducts faster radially than tangentially, leading to a smearing effect on the recorded scalp EEG [48]. It has recently been shown, however, that geometrical modeling errors of the skull have a larger effect on electromagnetic source localization than the conductivity model assigned to the skull region(s) [49]. At present our implementation uses a simple single-shell skull with an isotropic conductivity of 0.0042 S/m (roughly 1/80 th that of the grey matter) [48].…”

Multiple sparse volumetric priors for distributed EEG source reconstruction

“…Also future studies including more realistic head models should be introduced in the framework. For example, with accurate modeling of the skull including compact bone and spongy bone (Montes-Restrepo et al, 2013) or tissue anisotropy (Hallez et al, 2007). An important issue here is to assign the conductivity values to the different modeled tissues in the head model.…”

“…The skull conductivity for example is strongly debated. Assuming a lower conductivity, for example 0.0041 S/m assumed in the Fieldtrip software package (Oostenveld & Oostendorp, 2002), would cause differences in source depths (Dannhauer et al, 2011;Montes-Restrepo et al, 2013). Again the Bayesian framework is a useful tool to evaluate different modeling options based on realistic data.…”