Realistic head model preparation for EEG forward problem

The real head model has been used for the accurate scalp potential modeling. The realistic shapes of head tissues were derived from a set of 2-D magnetic resonance images (MRI) by extracting surface boundaries for the major tissues such as the scalp, the skull, the cerebrospinal fluid (CSF), the white matter, and the gray matter. From boundary data a 3-D volume generic head model has been constructed and a mesh for an arbitrary complexity head shape has been generated for finite-element method (FEM) modeling. This paper first addresses the use of this realistic finite elements head model to solve the EEG forward problem. The accuracy and computational time of the potential modeling are then examined.

Objective. In electroencephalography (EEG) measurements, the signal of each recording electrode is contrasted with a reference electrode or a combination of electrodes. The estimation of a neutral reference is a long-standing issue in EEG data analysis, which has motivated the proposal of different re-referencing methods, among which linked-mastoid re-referencing (LMR), average re-referencing (AR) and reference electrode standardization technique (REST). In this study we quantitatively assessed the extent to which the use of a high-density montage and a realistic head model can impact on the optimal estimation of a neutral reference for EEG recordings. Approach. Using simulated recordings generated by projecting specific source activity over the sensors, we assessed to what extent AR, REST and LMR may distort the scalp topography. We examined the impact electrode coverage has on AR and REST, and how accurate the REST reconstruction is for realistic and less realistic (three-layer and single-layer spherical) head models, and with possible uncertainty in the electrode positions. We assessed LMR, AR and REST also in the presence of typical EEG artifacts that are mixed in the recordings. Finally, we applied them to real EEG data collected in a target detection experiment to corroborate our findings on simulated data. Main results. Both AR and REST have relatively low reconstruction errors compared to LMR, and that REST is less sensitive than AR and LMR to artifacts mixed in the EEG data. For both AR and REST, high electrode density yields low re-referencing reconstruction errors. A realistic head model is critical for REST, leading to a more accurate estimate of a neutral reference compared to spherical head models. With a low-density montage, REST shows a more reliable reconstruction than AR either with a realistic or a three-layer spherical head model. Conversely, with a high-density montage AR yields better results unless precise information on electrode positions is available. Significance. Our study is the first to quantitatively assess the performance of EEG re-referencing techniques in relation to the use of a high-density montage and a realistic head model. We hope our study will help researchers in the choice of the most effective re-referencing approach for their EEG studies.

Neonatal EEG at scalp is focal and implies high skull conductivity in realistic neonatal head models

Heat transfer due to electroconvulsive therapy: Influence of anisotropic thermal and electrical skull conductivity

Computational head and brain volume conductor modeling is a practical and non-invasive method to investigate neuroelectrical activity in the brain. Anatomical structures included in a model affect the flow of volume currents and the resulting scalp surface potentials. The influence of different tissues within the head on scalp surface potentials was investigated by constructing five highly detailed, realistic head models from segmented and processed Visible Human Man digital images. The models were: (1) model with 20 different tissues, that is, skin, dense connective tissue (fat), aponeurosis (muscle), outer, middle and inner tables of the scalp, dura matter, arachnoid layer (including cerebrospinal fluid), pia matter, six cortical layers, eye tissue, muscle around the eye, optic nerve, temporal muscle, white matter and internal air, (2) model with three main inhomogeneities, that is, scalp, skull, brain, (3) model with homogeneous scalp and remaining inhomogeneities, (4) model with homogeneous skull and remaining inhomogeneities, and (5) model with homogeneous brain matter and remaining inhomogeneities. Scalp potentials because of three different dipolar sources in the parietal-occipital lobe were computed for all five models. Results of a forward solution revealed that tissues included in the model and the dipole source location directly affect the simulated scalp surface potentials. The major finding indicates that significant change in the scalp surface potentials is observed when the brain's distinctions are removed. The other modifications, for example, layers of the scalp and skull are important too, but they have less effect on the overall results.

Various source localization techniques have indicated the generators of each identifiable component of movement-related cortical potentials, since the discovery of the surface negative potential prior to self-paced movement by Kornhuber and Decke. Readiness potentials and fields preceding self-paced finger movements were recorded simultaneously using multichannel electroencephalography (EEG) and magnetoencephalography (MEG) from five healthy subjects. The cortical areas involved in this paradigm are the supplementary motor area (SMA) (bilateral), pre-SMA (bilateral), and contralateral motor area of the moving finger. This hypothesis is tested in this paper using the dipole source analysis independently for only EEG, only MEG, and both combined. To localize the sources, the forward problem is first solved by using the boundary-element method for realistic head models and by using a locally-fitted-sphere approach for spherical head models consisting of a set of connected volumes, typically representing the scalp, skull, and brain. In the source reconstruction it is to be expected that EEG predominantly localizes radially oriented sources while MEG localizes tangential sources at the desired region of the cortex. The effect of MEG on EEG is also observed when analyzing both combined data. When comparing the two head models, the spherical and the realistic head models showed similar results. The significant points for this study are comparing the source analysis between the two modalities (EEG and MEG) so as to assure that EEG is sensitive to mostly radially orientated sources while MEG is only sensitive to only tangential sources, and comparing the spherical and individual head models.

Caudal cingulate cortex involvement in pain processing: an inter-individual laser evoked potential source localisation study using realistic head models

Neuroelectromagnetic Forward Head Modeling Toolbox

This paper presents finite-difference-time-domain (FDTD) calculations of the specific absorption rate (SAR) in realistic head models from exposure to a generic handset working as 1750 Mhz. The head models with different sizes were obtained from the same whole-body model. The purpose of this work is to study whether there is a variation of SAR absorption in the same brain, but the sizes of head models are different. The obtained peak SARs in each of the tissues were averaged over 10 grams of tissue in the shape of cube. It was found that the SAR absorption in human brain is dependent on the size of the head model. The induced SAR in brain tissues in smaller head model is larger than that in larger head model. It suggested that the mobile phone dosimetric analysis in most of the previous literatures which only considered head model in the simulation may overestimate the brain exposure compared to the practical situation that the whole-body exposed to the fields radiated by the mobile phone.

By the use of three different head models in EEG dipole analysis, we tried to model the origin of interictal and ictal epileptic activity as precisely as possible. Further, as a control, a second evaluation was made by an independent group to control for interindividual reliability of the dipole source analysis. With the realistic head model (CURRY) considering cortex, skull, and skin segmentation, the spike source was located. In five patients with mesial temporal epileptogenesis, confirmed by successful epilepsy surgery, the spike source was close to the hippocampus, with a mean distance of the dipole source from the hippocampus of 13.6 mm (range, 9-17.2 mm). In one case the ictal EEG also could be analyzed and resulted in a dipole-source localization comparable to the interictal source. In both head models using either pure cortex segmentation only or a concentric three-shell model, the dipole source was systematically dislocated in a more superior position. Data analysis by a second group with independently chosen EEG samples and identical individual head model resulted in deviations of <5.3 mm. Data analysis using independently selected spikes and independently segmented head models resulted in deviations < or =16.7 mm. In four cases of extratemporal epileptogenesis, the origin of interictal epileptiform discharges was localized to the suspected primary epileptogenic zone.

The purpose of this study is to evaluate the difference between anatomical locations of mismatch negativity (MMN) generators using a realistic head model and the Talairach coordinate system. This was performed by dipole source analysis by using a high density 128 channel electroencephalography (EEG) acquisition system and the subjects' individual 3D magnetic resonance images (MRI) for the realistic head model, in 24 healthy subjects. For dipole source localization, both the Talairach coordinate system and the individual MRI realistic head models were used and location results were compared. The MMN generators were clearly localized in the superior temporal gyri, especially in Heschl's gyrus, according to each individual's structural MRI. Only 37.5% of subjects showed the same anatomical locations of the MMN generator in both hemispheres in the realistic head model and in Talairach coordinate system, but fifteen subjects (62.5%) didn't. This result indicates that individually registered functional locations are desirable for the precise localization of activated areas in functional imaging studies and that a brain coordinate system is needed which adequately accounts for ethnic differences.

To improve the EEG source localization in the brain, the conductivities used in the head model play a very important role. In this study, we focus on the modeling of the anisotropic conductivity of the white matter. The anisotropic conductivity profile can be derived from diffusion weighted magnetic resonance images (DW-MRI). However, deriving these anisotropic conductivities from diffusion weighted MR images of the white matter is not straightforward. In the literature, two methods can be found for calculating the conductivity from the diffusion weighted images. One method uses a fixed value for the ratio of the conductivity in different directions, while the other method uses a conductivity profile obtained from a linear scaling of the diffusion ellipsoid. We propose a model which can be used to derive the conductivity profile from the diffusion tensor images. This model is based on the variable anisotropic ratio throughout the white matter and is a combination of the linear relationship as stated in the literature, with a constraint on the magnitude of the conductivity tensor (also known as the volume constraint). This approach is stated in the paper as approach A. In our study we want to investigate dipole estimation differences due to using a more simplified model for white matter anisotropy (approach B), while the electrode potentials are derived using a head model with a more realistic approach for the white matter anisotropy (approach A). We used a realistic head model, in which the forward problem was solved using a finite difference method that can incorporate anisotropic conductivities. As error measures we considered the dipole location error and the dipole orientation error. The results show that the dipole location errors are all below 10 mm and have an average of 4 mm in gray matter regions. The dipole orientation errors ranged up to 66.4°, and had a mean of, on average, 11.6° in gray matter regions. In a qualitative manner, the results show that the orientation and location error is dependent on the orientation of the test dipole. The location error is larger when the orientation of the test dipole is similar to the orientation of the anisotropy, while the orientation error is larger when the orientation of the test dipole deviates from the orientation of the anisotropy. From these results, we can conclude that the modeling of white matter anisotropy plays an important role in EEG source localization. More specifically, accurate source localization will require an accurate modeling of the white matter conductivity profile in each voxel.

Kinect-driven Patient-specific Head, Skull, and Muscle Network Modelling for Facial Palsy Patients

In this work, a methodology is developed to solve the forward problem of electromagnetic source imaging using realistic head models. For this purpose, first segmentation of the 3 dimensional MR head images is performed. Then triangular, quadratic meshes are formed for the interfaces of the tissues. Thus, realistic meshes, representing scalp, skull, CSF, brain and eye tissues, are formed. At least 2000 nodes for the scalp and 5000 for the cortex are needed to obtain reasonable geometrical approximation. Solution of the forward problem using our previous Boundary Element Method (BEM) formulation with quadratic elements remains to be made.

Uncertainty quantification and sensitivity analysis of transcranial electric stimulation for 9-subdomain human head model