On the role of skull parcellation in the computational modeling of human head conductivity (original) (raw)
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EEG Analysis on Skull Conductivity Perturbations Using Realistic Head Model
Lecture Notes in Computer Science, 2009
Measurement of electroencephalogram (EEG) requires accurate estimation of tissue conductivity. Among the head tissues, skull compartment has less conductivity due to compacta and spongiosa, which impacts on EEG measurement. Therefore, skull conductivity plays a vital role in head modeling, forward computation and source localization. In this study, we have investigated the effects of scalp potentials due to skull conductivity perturbations in realistic head models using different skull to brain and/or scalp conductivity ratio (σ ratio ). Several studies used this σ ratio as 1/80, however, other studies found the values of σ ratio between 1/20 and 1/72. Each head model constructed from the values of different σ ratio ranging from 1/20 to 1/72 is compared to the head model constructed from σ ratio = 1/80. The obtained results demonstrated that the skull conductivity perturbations have effects on EEG and the head model constructed from less σ ratio generates larger errors due to higher potential differences.
Modeling of the human skull in EEG source analysis
Human Brain Mapping, 2011
We used computer simulations to investigate finite element models of the layered structure of the human skull in EEG source analysis. Local models, where each skull location was modelled differently, and global models, where the skull was assumed to be homogeneous, were compared to a reference model, in which spongy and compact bone were explicitly accounted for. In both cases, isotropic and anisotropic conductivity assumptions were considered. We considered sources in the entire brain and determined errors both in the forward calculation and the reconstructed dipole position.
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014
We investigate influence of different conductivity models within a framework of electroencephalogram (EEG) source localization on white matter and skull areas. Particularly, we investigate five different spherical models having either isotropic or anisotropic conductivity for both considered areas. To this end, the anisotropic finite difference reciprocity method is used in solving the EEG forward problem. We evaluate a numeric skull conductivity modeling, in terms of the minimum dipole localization/orientation error. As a result, two skull models reach the lowest dipole localization error (less than 6 mm), namely, single anisotropic layer and three isotropic layers (hard bone/spongy bone/hard bone). Additionally, two different electrode configurations (10 − 20 and 10 − 10 electrodes) are tested showing that the error decreases almost twice for the latter one, although computational burden significantly increases.
2008 11th International Conference on Computer and Information Technology, 2008
The aim of this study is to investigate the effects of white matter (WM) and skull inhomogeneous anisotropic tissue conductivities on human head modeling. The inhomogeneity of WM and skull is included using fractional anisotropy (FA) method and the anisotropy is included according to Volume constraint in the head model construction. A fivelayered spherical head model implemented using finite element method (FEM) is used as a volume conductor with a known current source to measure the electroencephalogram (EEG) on the head surface. Statistical measurement techniques are applied to analyze the EEGs obtained from inhomogeneous anisotropic head models and a homogeneous isotropic model. This study finds that the effects of WM and skull inhomogeneous anisotropy on EEG are significant.
A guideline for head volume conductor modeling in EEG and MEG
NeuroImage, 2014
For accurate EEG/MEG source analysis it is necessary to model the head volume conductor as realistic as possible. This includes the distinction of the different conductive compartments in the human head. In this study, we investigated the influence of modeling/not modeling the conductive compartments skull spongiosa, skull compacta, cerebrospinal fluid (CSF), gray matter, and white matter and of the inclusion of white matter anisotropy on the EEG/MEG forward solution. Therefore, we created a highly realistic 6-compartment head model with white matter anisotropy and used a state-of-the-art finite element approach. Starting from a 3-compartment scenario (skin, skull, and brain), we subsequently refined our head model by distinguishing one further of the abovementioned compartments. For each of the generated five head models, we measured the effect on the signal topography and signal magnitude both in relation to a highly resolved reference model and to the model generated in the previous refinement step. We evaluated the results of these simulations using a variety of visualization methods, allowing us to gain a general overview of effect strength, of the most important source parameters triggering these effects, and of the most affected brain regions. Thereby, starting from the 3-compartment approach, we identified the most important additional refinement steps in head volume conductor modeling. We were able to show that the inclusion of the highly conductive CSF compartment, whose conductivity value is well known, has the strongest influence on both signal topography and magnitude in both modalities. We found the effect of gray/white matter distinction to be nearly as big as that of the CSF inclusion, and for both of these steps we identified a clear pattern in the spatial distribution of effects. In comparison to these two steps, the introduction of white matter anisotropy led to a clearly weaker, but still strong, effect.
IEEE Transactions on Biomedical Engineering, 2003
In vivo measurements of equivalent resistivities of skull (skull) and brain (brain) are performed for six subjects using an electric impedance tomography (EIT)-based method and realistic models for the head. The classical boundary element method (BEM) formulation for EIT is very time consuming. However, the application of the Sherman-Morrison formula reduces the computation time by a factor of 5. Using an optimal point distribution in the BEM model to optimize its accuracy, decreasing systematic errors of numerical origin, is important because cost functions are shallow. Results demonstrate that skull brain is more likely to be within 20 and 50 rather than equal to the commonly accepted value of 80. The variation in brain (average = 301 cm SD = 13%) and skull (average = 12230 cm SD = 18%) is decreased by half, when compared with the results using the sphere model, showing that the correction for geometry errors is essential to obtain realistic estimations. However, a factor of 2.4 may still exist between values of skull brain corresponding to different subjects. Earlier results show the necessity of calibrating brain and skull by measuring them in vivo for each subject, in order to decrease errors associated with the electroencephalogram inverse problem. We show that the proposed method is suited to this goal. Index Terms-Electric impedance tomography (EIT), electrical resistivities, electroencephalogram inverse problem (EEG IP), realistic models. I. INTRODUCTION T HE INVERSE problem (IP) of electroencephalogram (EEG) aims to determine the sources inside the brain that best explain the electrical potentials measured on the surface of the scalp [4]. The determination of the sources is made through the use of mathematical models ([5]-[8]) which describe the head as an electrical conductor. In this way, the knowledge of the electrical resistivities of the tissues of the
Inter-Subject Variability of Skull Conductivity and Thickness in Calibrated Realistic Head Models
NeuroImage, 2020
Skull conductivity has a substantial influence on EEG and combined EEG and MEG source analysis as well as on optimized transcranial electric stimulation. To overcome the use of standard literature values, we propose a noninvasive two-level calibration procedure to estimate skull conductivity individually in a group study with twenty healthy adults. Our procedure requires only an additional run of combined somatosensory evoked potential and field data, which can be easily integrated in EEG/MEG experiments. The calibration procedure uses the P20/N20 topographies and subject-specific realistic head models from MRI. We investigate the inter-subject variability of skull conductivity and relate it to skull thickness, age and gender of the subjects, to the individual scalp P20/N20 surface distance between the P20 potential peak and the N20 potential trough as well as to the individual source depth of the P20/N20 source. We found a considerable inter-subject variability for (calibrated) skull conductivity (8.44 ± 4.84 mS/m) and skull thickness (5.97 ± 1.19 mm) with a statistically significant correlation between them (rho = 0.52). Age showed a statistically significant negative correlation with skull conductivity (rho =-0.5). Furthermore, P20/N20 surface distance and source depth showed large inter-subject variability of 12.08 ± 3.21 cm and 15.45 ± 4.54 mm, respectively, but there was no significant correlation between them. We also found no significant differences among gender subgroups for the investigated measures. It is thus important to take the inter-subject variability of skull conductivity and thickness into account by means of using subject-specific calibrated realistic head modeling.
2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
In this paper we present results of the equivalent brain and skull resistivities (ρ ρ ρ ρ brain and ρ ρ ρ ρ skull) for 6 different subjects using 2 different and independent procedures: an EIT based method and the combined analysis of SEF/SEP data. With the EIT based method known currents are injected into the head and the resulting potential distributions are recorded from scalp electrodes. The conductivities are estimated by fitting the conductivity parameters of a 3-sphere head model onto the measured potentials. With the combined SEP/SEF method, a current source is activated inside the brain using a nervous medianus stimulation. The MEG data is used to determine dipole position and tangential orientation, whereas the simultaneously recorded EEG data is used to find the dipole radial component and the electrical conductivities of the brain and the skull. The results show a large variability in the ratio of skull and brain conductivities ρ ρ ρ ρ skull /ρ ρ ρ ρ brain over subjects. However, a strong agreement was found between the results of EIT and SEF/SEP methods even though they are quite different, both in theoretical and technical terms. These results indicate that generic conductivity values will result in large systematic errors of EEG inverse modelling. However, the good agreement between the EIT and the SEP/SEF method indicates that the individual's ρ ρ ρ ρ skull /ρ ρ ρ ρ brain ratio can reliably be determined using the EIT method.