Influence of a silastic ECoG grid on EEG/ECoG based source analysis.

The simultaneous evaluation of the local electrocorticogram (ECoG) and the more broadly distributed electroencephalogram (EEG) from humans undergoing evaluation for epilepsy surgery has been shown to further the understanding of how pathologies give rise to spontaneous seizures. However, a well-known problem is that the disruption of the conducting properties of the brain coverings can render simultaneous scalp and intracranial recordings unrepresentative of the habitual EEG. The ECoG electrodes for measuring the potential on the surface of the cortex are commonly embedded into one or more sheets of a silastic material. These highly resistive silastic sheets influence the volume conduction and might therefore also influence the scalp EEG and ECoG measurements. We carried out a computer simulation study to examine how the scalp EEG and the ECoG, as well as the source reconstruction therefrom, employing equivalent current dipole estimation methods, are affected by the insulating ECoG grids. The finite element method with high quality tetrahedral meshes, generated using a constrained Delaunay tetrahedralization meshing approach, was used to model the volume conductor that incorporates the very thin ECoG sheets. It is shown that the insulating silastic substrate of the ECoG grids can have a large impact on the scalp potential and on source reconstruction from scalp EEG data measured in the presence of the grids. The reconstruction errors are characterized with regard to the location of the source in the brain and the mislocalization tendency. In addition, we found a non-negligible influence of the insulating grids on ECoG based source analysis. We conclude, that the thin insulating ECoG sheets should be taken into account, when performing source analysis of simultaneously measured ECoG and scalp EEG data.

Validating EEG source imaging using intracranial electrical stimulation.

Electrical source imaging is used in presurgical epilepsy evaluation and in cognitive neurosciences to localize neuronal sources of brain potentials recorded on EEG. This study evaluates the spatial accuracy of electrical source imaging for known sources, using electrical stimulation potentials recorded on simultaneous stereo-EEG and 37-electrode scalp EEG, and identifies factors determining the localization error. In 11 patients undergoing simultaneous stereo-EEG and 37-electrode scalp EEG recordings, sequential series of 99-110 biphasic pulses (2 ms pulse width) were applied by bipolar electrical stimulation on adjacent contacts of implanted stereo-EEG electrodes. The scalp EEG correlates of stimulation potentials were recorded with a sampling rate of 30 kHz. Electrical source imaging of averaged stimulation potentials was calculated utilizing a dipole source model of peak stimulation potentials based on individual four-compartment finite element method head models with various skull conductivities (range from 0.0413 to 0.001 S/m). Fitted dipoles with a goodness of fit of ≥80% were included in the analysis. The localization error was calculated using the Euclidean distance between the estimated dipoles and the centre point of adjacent stimulating contacts. A total of 3619 stimulation locations, respectively, dipole localizations, were included in the evaluation. Mean localization errors ranged from 10.3 to 26 mm, depending on source depth and selected skull conductivity. The mean localization error increased with an increase in source depth (r(3617) = [0.19], P = 0.000) and decreased with an increase in skull conductivity (r(3617) = [-0.26], P = 0.000). High skull conductivities (0.0413-0.0118 S/m) yielded significantly lower localization errors for all source depths. For superficial sources (<20 mm from the inner skull), all skull conductivities yielded insignificantly different localization errors. However, for deeper sources, in particular >40 mm, high skull conductivities of 0.0413 and 0.0206 S/m yielded significantly lower localization errors. In relation to stimulation locations, the majority of estimated dipoles moved outward-forward-downward to inward-forward-downward with a decrease in source depth and an increase in skull conductivity. Multivariate analysis revealed that an increase in source depth, number of skull holes and white matter volume, while a decrease in skull conductivity independently led to higher localization error. This evaluation of electrical source imaging accuracy using artificial patterns with a high signal-to-noise ratio supports its application in presurgical epilepsy evaluation and cognitive neurosciences. In our artificial potential model, optimizing the selected skull conductivity minimized the localization error. Future studies should examine if this accounts for true neural signals.

Modeling of the human skull in EEG source analysis.

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 modeled 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 taken into account. We considered sources in the entire brain and determined errors both in the forward calculation and the reconstructed dipole position. Our results show that accounting for the local variations over the skull surface is important, whereas assuming isotropic or anisotropic skull conductivity has little influence. Moreover, we showed that, if using an isotropic and homogeneous skull model, the ratio between skin/brain and skull conductivities should be considerably lower than the commonly used 80:1. For skull modeling, we recommend (1) Local models: if compact and spongy bone can be identified with sufficient accuracy (e.g., from MRI) and their conductivities can be assumed to be known (e.g., from measurements), one should model these explicitly by assigning each voxel to one of the two conductivities, (2) Global models: if the conditions of (1) are not met, one should model the skull as either homogeneous and isotropic, but with considerably higher skull conductivity than the usual 0.0042 S/m, or as homogeneous and anisotropic, but with higher radial skull conductivity than the usual 0.0042 S/m and a considerably lower radial:tangential conductivity anisotropy than the usual 1:10.

LORETA With Cortical Constraint: Choosing an Adequate Surface Laplacian Operator.

Low resolution electromagnetic tomography (LORETA) is a well-known method for the solution of the l2-based minimization problem for EEG/MEG source reconstruction. LORETA with a volume-based source space is widely used and much effort has been invested in the theory and the application of the method in an experimental context. However, it is especially interesting to use anatomical prior knowledge and constrain the LORETA's solution to the cortical surface. This strongly reduces the number of unknowns in the inverse approach. Unlike the Laplace operator in the volume case with a rectangular and regular grid, the mesh is triangulated and highly irregular in the surface case. Thus, it is not trivial to choose or construct a Laplace operator (termed Laplace-Beltrami operator when applied to surfaces) that has the desired properties and takes into account the geometry of the mesh. In this paper, the basic methodology behind cortical LORETA is discussed and the method is applied for source reconstruction of simulated data using different Laplace-Beltrami operators in the smoothing term. The results achieved with the different operators are compared with respect to their accuracy using various measures. Conclusions about the choice of an appropriate operator are deduced from the results.

Ictal EEG source imaging in presurgical evaluation: High agreement between analysis methods.

PURPOSE: To determine the agreement between five different methods of ictal EEG source imaging, and to assess their accuracy in presurgical evaluation of patients with focal epilepsy. It was hypothesized that high agreement between methods was associated with higher localization-accuracy. METHODS: EEGs were recorded with a 64-electrode array. Thirty-eight seizures from 22 patients were analyzed using five different methods phase mapping, dipole fitting, CLARA, cortical-CLARA and minimum norm. Localization accuracy was determined at sub-lobar level. Reference standard was the final decision of the multidisciplinary epilepsy surgery team, and, for the operated patients, outcome one year after surgery. RESULTS: Agreement between all methods was obtained in 13 patients (59%) and between all but one methods in additional six patients (27%). There was a trend for minimum norm being less accurate than phase mapping, but none of the comparisons reached significance. Source imaging in cases with agreement between all methods was not more accurate than in the other cases. Ictal source imaging achieved an accuracy of 73% (for operated patients: 86%). CONCLUSION: There was good agreement between different methods of ictal source imaging. However, good inter-method agreement did not necessarily imply accurate source localization, since all methods faced the limitations of the inverse solution.

Simulating transcranial direct current stimulation with a detailed anisotropic human head model.

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique able to induce long-lasting changes in cortical excitability that can benefit cognitive functioning and clinical treatment. In order to both better understand the mechanisms behind tDCS and possibly improve the technique, finite element models are used to simulate tDCS of the human brain. With the detailed anisotropic head model presented in this study, we provide accurate predictions of tDCS in the human brain for six of the practically most-used setups in clinical and cognitive research, targeting the primary motor cortex, dorsolateral prefrontal cortex, inferior frontal gyrus, occipital cortex, and cerebellum. We present the resulting electric field strengths in the complete brain and introduce new methods to evaluate the effectivity in the target area specifically, where we have analyzed both the strength and direction of the field. For all cerebral targets studied, the currently accepted configurations produced sub-optimal field strengths. The configuration for cerebellum stimulation produced relatively high field strengths in its target area, but it needs higher input currents than cerebral stimulation does. This study suggests that improvements in the effects of transcranial direct current stimulation are achievable.