Effects of skull thickness, anisotropy, and inhomogeneity on forward EEG/ERP computations using a spherical three‐dimensional resistor mesh model

Abstract

Bone thickness, anisotropy, and inhomogeneity have been reported to induce important variations in electroencephalogram (EEG) scalp potentials. To study this effect, we used an original three-dimensional (3-D) resistor mesh model described in spherical coordinates, consisting of 67,464 elements and 22,105 nodes arranged in 36 different concentric layers. After validation of the model by comparison with the analytic solution, potential variations induced by geometric and electrical skull modifications were investigated at the surface in the dipole plane and along the dipole axis, for several eccentricities and bone thicknesses. The resistor mesh permits one to obtain various configurations, as local modifications are introduced very easily. This has allowed several head models to be designed to study the effects of skull properties (thickness, anisotropy, and heterogeneity) on scalp surface potentials. Results show a decrease of potentials in bone, depending on bone thickness, and a very small decrease through the scalp layer. Nevertheless, similar scalp potentials can be obtained using either a thick scalp layer and a thin skull layer, and vice versa. It is thus important to take into account skull and scalp thicknesses, because the drop of potential in bone depends on both. The use of three different layers for skull instead of one leads to small differences in potential values and patterns. In contrast, the introduction of a hole in the skull highly increases the maximum potential value (by a factor of 11.5 in our case), because of the absence of potential drop in the corresponding volume. The inverse solution without any a priori knowledge indicates that the model with the hole gives the largest errors in both position and dipolar moment. Our results indicate that the resistor mesh model can be used as a robust and user-friendly simulation tool in EEG or event-related potentials. It makes it possible to build up real head models directly from anatomic magnetic resonance imaging without tessellation, and is able to take into account head heterogeneities very simply by changing volume elements conductivity. Hum. Brain Mapping 21:84-95, 2004.

Modeling of the human skull in EEG source analysis

“…The isotropic homogeneous (IH) model is the one used in the vast majority of studies in conjunction with the analytic sphere model [e.g., Buchner et al, 1995], the boundary element method 1 [e.g., Fuchs et al, 2007;Hämä-läinen et al, 1993;Kybic et al, 2005], and also the finite element method (Bertrand et al, 1991;van den Broek et al, 1998a;Wolters et al, 2006]. The anisotropic homogeneous (AH) model has been proposed by some authors as an alternative to better account for the layering of the skull [Bertrand et al, 1991;Chauveau et al, 2004;Fuchs et al, 2007;Marin et al, 1998;Rullmann et al, 2009;van den Broek et al, 1998a;Wolters et al, 2006]. Both models deviate from the real structure of the skull in two ways: (1) they assume that the properties of the skull are the same at every location and (2) they approximate the layered skull structure by assuming a single isotropic or anisotropic compartment, respectively.…”

“…Regarding question (2), in many applications, it is assumed that the tangential conductivity is about 10 times higher than the radial one [Chauveau et al, 2004;Marin et al, 1998;Rush and Driscoll, 1968;van den Broek et al, 1998a;Wolters et al, 2006]. This goes back to the work of Rush and Driscoll [1968], who measured the radial and tangential conductivities in soaked dried skull material, but give no further details on how these measurements were actually performed.…”

Functional and time-course changes in single word production from childhood to adulthood

“…A decrease in the amplitudes of VEP with age has been repeatedly described using different kinds of visual stimuli (Allison et al, 1984;Brecelj et al, 2002;Hoffmann et al, 2001;Holcomb, Coffey and Neville, 1992;Mahajan and McArtur, 2012;Taylor et al, 2004). Larger amplitudes in children have been attributed to less thick skulls or to generators being closer to the surface electrode in smaller brains (Chauveau et al, 2004;Picton and Taylor, 2007). Alternatively, amplitude decreases may result from increasing automaticity; the same processes require fewer resources, leading to a decrease in cerebral activation, a phenomenon that has been described in many developmental brain imaging studies (Durston and Casey, 2006).…”

Neurofeedback fMRI‐mediated learning and consolidation of regional brain activation during motor imagery

“…These EEG-mediated approaches, however, lack the spatial specificity required to characterize regional brain activity since detected EEG signals reflect the activation of a widespread network of cortical/subcortical function. Topographic EEG source mapping or MEG (magneto-encephalography) [Wang et al, 2001;Chauveau et al, 2004] still are deficient of the spatial resolution necessary to resolve localized brain activities. Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) are alternative neuroimaging modalities, but both require the injection of radioactive contrast material, which make their routine application less desirable.…”