Dipole estimation errors due to differences in modeling anisotropic conductivities in realistic head models for EEG source analysis

EEG source analysis is a valuable tool for brain functionality research and for diagnosing neurological disorders, such as epilepsy. It requires a geometrical representation of the human head or a head model, which is often modeled as an isotropic conductor. However, it is known that some brain tissues, such as the skull or white matter, have an anisotropic conductivity. Many studies reported that the anisotropic conductivities have an influence on the calculated electrode potentials. However, few studies have assessed the influence of anisotropic conductivities on the dipole estimations. In this study, we want to determine the dipole estimation errors due to not taking into account the anisotropic conductivities of the skull and/or brain tissues. Therefore, head models are constructed with the same geometry, but with an anisotropically conducting skull and/or brain tissue compartment. These head models are used in simulation studies where the dipole location and orientation error is calculated due to neglecting anisotropic conductivities of the skull and brain tissue. Results show that not taking into account the anisotropic conductivities of the skull yields a dipole location error between 2 and 25 mm, with an average of 10 mm. When the anisotropic conductivities of the brain tissues are neglected, the dipole location error ranges between 0 and 5 mm. In this case, the average dipole location error was 2.3 mm. In all simulations, the dipole orientation error was smaller than 10°. We can conclude that the anisotropic conductivities of the skull have to be incorporated to improve the accuracy of EEG source analysis. The results of the simulation, as presented here, also suggest that incorporation of the anisotropic conductivities of brain tissues is not necessary. However, more studies are needed to confirm these suggestions.

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

Electroencephalogram (EEG) source analysis is a noninvasive technique used in the presurgical of epilepsy. In this study, the dipole location and orientation errors due to skull conductivity perturbations were investigated in two groups of three-dimensional head models: A spherical head model and a realistic head model. In each group, the head model had a brain-to-skull conductivity ratio (Rsigma) within the range of 10-40. Solving the forward problem in the head model with skull conductivity perturbations along with the inverse problem in the baseline head model with Rsigma=20 permitted the derivation of the dipole estimation errors. Perturbations in the skull conductivity generated dipole location and orientation errors: The larger the perturbations, the larger the errors and the error ranges. The dipole orientation error due to skull conductivity perturbations was not great (maximal mean of < 5 degrees) in this study, but the dipole location error was notable (maximal mean of > 5 mm). Therefore, the influence of skull conductivity perturbations on EEG dipole source analysis cannot be neglected. This study suggests that it is necessary to measure the skull conductivity of the individual patients in order to achieve accurate EEG source analysis.

Although the use of transcranial direct current stimulation (tDCS), a noninvasive brain stimulation technique, has become popular in cognitive neuroscience research and clinical applications, there still exists limited knowledge on how to optimally use tDCS for the brain stimulation. To understand the underlying principles and effects of tDCS, finite element analysis (FEA) has been successfully applied due to its truly volumetric analysis and capability of incorporating the anisotropic tissue property. However, there are still many factors to be considered in stimulation: the influence of the white matter (WM) anisotropy is one of them which has not been considered in tDCS. In this study, we have examined the influence of the WM anisotropic conductivities on tDCS via high-resolution FE head models of the whole head. The effects of the WM anisotropy has been assessed by comparing the current density maps computed from the anisotropic FE model against those of the isotropic model using the similarity measures. The results show that there are significant differences caused by the tissue anisotropy, indicating that the anisotropic electrical conductivity is one of the critical factors to be considered during tDCS for accurate and effective stimulation of the brain.KeywordsTranscranial direct current stimulationFinite element analysisWhite matter anisotropic conductivity

Spatial mislocalization of EEG electrodes – effects on accuracy of dipole estimation

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

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.

The performance of the three-shell spherical head model versus the performance of the realistic head model is investigated when solving the inverse problem with a single dipole, in the presence of noise. This is evaluated by inspecting the average dipole location error when applying a spherical and a realistic head model, for 1000 noisy scalp potentials, originating from the same test dipole and having the same noise level. The location errors are obtained utilizing a local linearization, which is validated with a Monte Carlo simulation. For 27 electrodes, an EEG epoch of one time sample and spatially white Gaussian noise we found that the importance of the realistic head model over the spherical head model reduces by increasing the noise level.

BackgroundThe aim of electroencephalogram (EEG) source localization is to find the brain areas responsible for EEG waves of interest. It consists of solving forward and inverse problems. The forward problem is solved by starting from a given electrical source and calculating the potentials at the electrodes. These evaluations are necessary to solve the inverse problem which is defined as finding brain sources which are responsible for the measured potentials at the EEG electrodes.MethodsWhile other reviews give an extensive summary of the both forward and inverse problem, this review article focuses on different aspects of solving the forward problem and it is intended for newcomers in this research field.ResultsIt starts with focusing on the generators of the EEG: the post-synaptic potentials in the apical dendrites of pyramidal neurons. These cells generate an extracellular current which can be modeled by Poisson's differential equation, and Neumann and Dirichlet boundary conditions. The compartments in which these currents flow can be anisotropic (e.g. skull and white matter). In a three-shell spherical head model an analytical expression exists to solve the forward problem. During the last two decades researchers have tried to solve Poisson's equation in a realistically shaped head model obtained from 3D medical images, which requires numerical methods. The following methods are compared with each other: the boundary element method (BEM), the finite element method (FEM) and the finite difference method (FDM). In the last two methods anisotropic conducting compartments can conveniently be introduced. Then the focus will be set on the use of reciprocity in EEG source localization. It is introduced to speed up the forward calculations which are here performed for each electrode position rather than for each dipole position. Solving Poisson's equation utilizing FEM and FDM corresponds to solving a large sparse linear system. Iterative methods are required to solve these sparse linear systems. The following iterative methods are discussed: successive over-relaxation, conjugate gradients method and algebraic multigrid method.ConclusionSolving the forward problem has been well documented in the past decades. In the past simplified spherical head models are used, whereas nowadays a combination of imaging modalities are used to accurately describe the geometry of the head model. Efforts have been done on realistically describing the shape of the head model, as well as the heterogenity of the tissue types and realistically determining the conductivity. However, the determination and validation of the in vivo conductivity values is still an important topic in this field. In addition, more studies have to be done on the influence of all the parameters of the head model and of the numerical techniques on the solution of the forward problem.

Magnetization transfer and T2 quantitation in normal appearing cortical gray matter and white matter adjacent to focal abnormality in patients with traumatic brain injury.

Background: This study aimed to establish normal regional Magnetization Transfer Ratio (MTR) values in white and grey matter using 3-Tesla MRI in healthy adult subjects, with a particular focus on cortical and deep brain structures. It also aims to assess the variability of these values and provide a baseline for further pathological studies. Materials and Methods: Seventy healthy volunteers (28 females, 42 males, mean age 28 years) were included. MRI scans were performed using a 3.0 Tesla MRI scanner. Conventional cranial MRI sequences were acquired, followed by Magnetization Transfer Imaging (MTI) with off-resonance pulses. MTR maps were generated from proton density-weighted images obtained with and without magnetization transfer pre-pulses. Measurements were performed in 31 white matter and 9 grey matter regions, with additional assessments of CSF for noise control. Statistical analysis was carried out to compare MTR values across different brain regions. Results: The mean MTR value was significantly higher in white matter (23.9 ± 0.21) compared to grey matter (17.3 ± 0.77). The corpus callosum had the highest MTR values within the white matter, particularly in the splenium, while the thalamus exhibited the highest MTR values in the grey matter. Regional variations were observed, with higher MTR values in the occipital and temporal lobes and lower values in the frontal and parietal lobes. MTR measurements showed excellent intra-observer (ICC &amp;gt; 0.9) and good inter-observer reliability (ICC 0.80–0.90). Conclusions: This study provides detailed MTR mapping of normal brain tissue, highlighting significant regional dif-ferences between white and grey matter. The findings offer valuable baseline data for assessing structural changes in CNS diseases and for evaluating the efficacy of therapeutic interventions. MTR measurements demonstrate high reproducibility, making this technique a reliable tool in clinical and research applications for monitoring pathological alterations in the central nervous system.

Reduced glucose uptake and Aβ in brain regions with hyperintensities in connected white matter

The influence of head tissue conductivity on magnetoencephalography (MEG) was investigated by comparing the normal component of the magnetic field calculated at 61 detectors and the localization accuracy of realistic head finite element method (FEM) models using dipolar sources and containing altered scalp, skull, cerebrospinal fluid, gray, and white matter conductivities to the results obtained using a FEM realistic head model with the same dipolar sources but containing published baseline conductivity values. In the models containing altered conductivity values, the tissue conductivity values were varied, one at a time, between 10% and 200% of their baseline values, and then varied simultaneously. Although changes in conductivity values for a single tissue layer often altered the calculated magnetic field and source localization accuracy only slightly, varying multiple conductivity layers simultaneously caused significant discrepancies in calculated results. The conductivity of scalp, and to a lesser extent that of white and gray matter, appears especially influential in determining the magnetic field. Comparing the results obtained from models containing the baseline conductivity values to the results obtained using other published conductivity values suggests that inaccuracies can occur depending upon which tissue conductivity values are employed. We show the importance of accurate head tissue conductivities for MEG source localization in human brain, especially for deep dipole sources or when an accuracy greater than 1.4 cm is needed.

Anisotropic water diffusion in white and gray matter of the neonatal piglet brain before and after transient hypoxia-ischaemia

Assessment of white matter integrity of autistic preschool children with diffusion weighted MR imaging