Papers by Carsten Wolters
The results of brain connectivity analysis using reconstructed source time courses derived from E... more The results of brain connectivity analysis using reconstructed source time courses derived from EEG and MEG data depend on a number of algorithmic choices. While previous studies have investigated the influence of the choice of source estimation method or connectivity measure, the effects of the head modeling errors or simplifications have not been studied sufficiently. In the present simulation study, we investigated the influence of particular properties of the head model on the reconstructed source time courses as well as on source connectivity analysis in EEG and MEG. Therefore, we constructed a realistic head model and applied the finite element method to solve the EEG and MEG forward problem. We considered the distinction between white and gray matter, the distinction between compact and spongy bone, the inclusion of a cerebrospinal fluid (CSF) compartment, and the reduction to a simple 3layer model comprising only skin, skull, and brain. Source time courses were reconstructed using a beamforming approach and the source connectivity was estimated by the imaginary coherence (ICoh) and the generalized partial directed coherence (GPDC). Our results show that in both EEG and MEG, neglecting the white and gray matter distinction or the CSF causes considerable errors in reconstructed source time courses and connectivity analysis, while the distinction between spongy and compact bone is just of minor relevance, provided that an adequate skull conductivity value is used. Large inverse and connectivity errors are found in the same regions that show large topography errors in the forward solution.
F1000Research, Mar 11, 2016
Biomedizinische Technik, Jan 30, 2012
It is known that incorporating cerebrospinal fluid (CSF) into realistic volume conductor models a... more It is known that incorporating cerebrospinal fluid (CSF) into realistic volume conductor models adds precision for source analysis. However, modeling interior CSF compartments like ventricles or deep sulci creates a complex source space with many deep cavities. Such a fragmented source space can cause problems for dipole fitting and other inverse methods. A solution could be to use a simplified head model, where only the superficial CSF layer between the brain surface and the inner skull boundary is included, while interior CSF compartments are ignored. The present paper aims at investigating if simplified CSF models are sufficiently accurate for forward and inverse solutions. A simulation study using realistic volume conductor models was performed. First, a detailed and anatomically plausible reference model was created. Then, two test models were derived. Test model A ignored CSF completely, while test model B only ignored CSF in deep sulci and the ventricles. Forward computation errors were assessed by directly comparing the potentials computed in the reference and test models. Inverse errors were investigated by performing source reconstruction of single sources in the test models to reconstruct the potentials simulated in the reference model. Large topography (relative difference measure (RDM) > 0.1) and localization errors (> 4mm) were found for superficial sources and sources close to internal CSF compartments for model A. In model B, RDM errors > 0.1 were found for only few sources close to the ventricles and close to sulci of larger extent. Localization errors in model B were below 2 mm for nearly all reference sources. The results suggest that ignoring CSF when creating a head model leads to substantial localization errors. Ignoring only internal CSFfilled compartments, while modeling superficial CSF layers allows source localization with relatively high precision. Thus, avoiding a fragmented source space by not modeling internal CSF compartments is acceptable.
Brain communications, Dec 29, 2022
Electrical source imaging is used in presurgical epilepsy evaluation and in cognitive neuroscienc... more 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 fourcompartment 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.
Biomedizinische Technik, Jan 6, 2012
Biomedizinische Technik, Jan 6, 2012
The effect of volume conduction on brain source reconstruction using beamformer techniques – in p... more The effect of volume conduction on brain source reconstruction using beamformer techniques – in particular Synthetic Aperture Magnetometry (SAM) – is investigated. The sphere model that is commonly used in the analysis of brain activity provides only a very rough representation of the real head geometry, whereas Finite Element (FE) models can model realistic head shapes and head tissue conductivity distributions very accurately. The effect of inaccurate geometry approximation on the beamformer is examined in simulations. We show that the effects are especially large in those regions where the sphere model differs the most from the true head geometry. It is also common practice to model the head volume to be entirely isotropic. In reality the head has anisotropic compartments as well. We have investigated the effects of skull anisotropy in a realistically shaped FE model. Simulations show that by disregarding the skull anisotropy the depth localization is inaccurate, especially in re...
BioMedical Engineering OnLine, 2018
Background: Accurately solving the electroencephalography (EEG) forward problem is crucial for pr... more Background: Accurately solving the electroencephalography (EEG) forward problem is crucial for precise EEG source analysis. Previous studies have shown that the use of multicompartment head models in combination with the finite element method (FEM) can yield high accuracies both numerically and with regard to the geometrical approximation of the human head. However, the workload for the generation of multicompartment head models has often been too high and the use of publicly available FEM implementations too complicated for a wider application of FEM in research studies. In this paper, we present a MATLAB-based pipeline that aims to resolve this lack of easy-to-use integrated software solutions. The presented pipeline allows for the easy application of five-compartment head models with the FEM within the FieldTrip toolbox for EEG source analysis. Methods: The FEM from the SimBio toolbox, more specifically the St. Venant approach, was integrated into the FieldTrip toolbox. We give a short sketch of the implementation and its application, and we perform a source localization of somatosensory evoked potentials (SEPs) using this pipeline. We then evaluate the accuracy that can be achieved using the automatically generated five-compartment hexahedral head model [skin, skull, cerebrospinal fluid (CSF), gray matter, white matter] in comparison to a highly accurate tetrahedral head model that was generated on the basis of a semiautomatic segmentation with very careful and time-consuming manual corrections. Results: The source analysis of the SEP data correctly localizes the P20 component and achieves a high goodness of fit. The subsequent comparison to the highly detailed tetrahedral head model shows that the automatically generated five-compartment head model performs about as well as a highly detailed four-compartment head model (skin, skull, CSF, brain). This is a significant improvement in comparison to a three-compartment head model, which is frequently used in praxis, since the importance of modeling the CSF compartment has been shown in a variety of studies. Conclusion: The presented pipeline facilitates the use of five-compartment head models with the FEM for EEG source analysis. The accuracy with which the EEG forward problem can thereby be solved is increased compared to the commonly used threecompartment head models, and more reliable EEG source reconstruction results can be obtained.
Frontiers in neuroscience, 2016
Magnetoencephalography (MEG) signals are influenced by skull defects. However, there is a lack of... more Magnetoencephalography (MEG) signals are influenced by skull defects. However, there is a lack of evidence of this influence during source reconstruction. Our objectives are to characterize errors in source reconstruction from MEG signals due to ignoring skull defects and to assess the ability of an exact finite element head model to eliminate such errors. A detailed finite element model of the head of a rabbit used in a physical experiment was constructed from magnetic resonance and co-registered computer tomography imaging that differentiated nine tissue types. Sources of the MEG measurements above intact skull and above skull defects respectively were reconstructed using a finite element model with the intact skull and one incorporating the skull defects. The forward simulation of the MEG signals reproduced the experimentally observed characteristic magnitude and topography changes due to skull defects. Sources reconstructed from measured MEG signals above intact skull matched th...
Epilepsy Research, 2016
This article appeared in a journal published by Elsevier. The attached copy is furnished to the a... more This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are
The inverse problem in Electro-and Magneto-EncephaloGraphy (EEG/MEG) aims at reconstructing the u... more The inverse problem in Electro-and Magneto-EncephaloGraphy (EEG/MEG) aims at reconstructing the underlying current distribution in the human brain using potential differences and/or magnetic fluxes that are measured non-invasively directly, or at a close distance, from the head surface. The solution requires repeated computation of the forward problem, i.e., the simulation of EEG and MEG fields for a given dipolar source in the brain using a volume-conduction model of the head. The associated differential equations are derived from the Maxwell equations. Not only do various head tissues exhibit different conductivities, some of them are also anisotropic conductors as, e.g., skull and brain white matter. To our knowledge, previous work has not extensively investigated the impact of modeling tissue anisotropy on source reconstruction. Currently, there are no readily available methods that allow direct conductivity measurements. Furthermore, there is still a lack of sufficiently powerful software packages that would yield significant reduction of the computation time involved in such complex models hence satisfying the time-restrictions for the solution of the inverse problem. In this dissertation, techniques of multimodal Magnetic Resonance Imaging (MRI) are presented in order to generate high-resolution realistically shaped anisotropic volume conductor models. One focus is the presentation of an improved segmentation of the skull by means of a bimodal T1/PD-MRI approach. The eigenvectors of the conductivity tensors in anisotropic white matter are determined using whole head Diffusion-Tensor-MRI. The Finite Element (FE) method in combination with a parallel algebraic multigrid solver yields a highly efficient solution of the forward problem. After giving an overview of state-of-the-art inverse methods, new regularization concepts are presented. Next, the sensitivity of inverse methods to tissue anisotropy is tested. The results show that skull anisotropy affects significantly EEG source reconstruction whereas white matter anisotropy affects both EEG and MEG source reconstructions. Therefore, highresolution FE forward modeling is crucial for an accurate solution of the inverse problem in EEG and MEG.
PLoS ONE, 2014
To increase the reliability for the non-invasive determination of the irritative zone in presurgi... more To increase the reliability for the non-invasive determination of the irritative zone in presurgical epilepsy diagnosis, we introduce here a new experimental and methodological source analysis pipeline that combines the complementary information in EEG and MEG, and apply it to data from a patient, suffering from refractory focal epilepsy. Skull conductivity parameters in a six compartment finite element head model with brain anisotropy, constructed from individual MRI data, are estimated in a calibration procedure using somatosensory evoked potential (SEP) and field (SEF) data. These data are measured in a single run before acquisition of further runs of spontaneous epileptic activity. Our results show that even for single interictal spikes, volume conduction effects dominate over noise and need to be taken into account for accurate source analysis. While cerebrospinal fluid and brain anisotropy influence both modalities, only EEG is sensitive to skull conductivity and conductivity calibration significantly reduces the difference in especially depth localization of both modalities, emphasizing its importance for combining EEG and MEG source analysis. On the other hand, localization differences which are due to the distinct sensitivity profiles of EEG and MEG persist. In case of a moderate error in skull conductivity, combined source analysis results can still profit from the different sensitivity profiles of EEG and MEG to accurately determine location, orientation and strength of the underlying sources. On the other side, significant errors in skull modeling are reflected in EEG reconstruction errors and could reduce the goodness of fit to combined datasets. For combined EEG and MEG source analysis, we therefore recommend calibrating skull conductivity using additionally acquired SEP/SEF data.
PloS one, 2015
We investigated two important means for improving source reconstruction in presurgical epilepsy d... more We investigated two important means for improving source reconstruction in presurgical epilepsy diagnosis. The first investigation is about the optimal choice of the number of epileptic spikes in averaging to (1) sufficiently reduce the noise bias for an accurate determination of the center of gravity of the epileptic activity and (2) still get an estimation of the extent of the irritative zone. The second study focuses on the differences in single modality EEG (80-electrodes) or MEG (275-gradiometers) and especially on the benefits of combined EEG/MEG (EMEG) source analysis. Both investigations were validated with simultaneous stereo-EEG (sEEG) (167-contacts) and low-density EEG (ldEEG) (21-electrodes). To account for the different sensitivity profiles of EEG and MEG, we constructed a six-compartment finite element head model with anisotropic white matter conductivity, and calibrated the skull conductivity via somatosensory evoked responses. Our results show that, unlike single mod...
IFMBE Proceedings, 2010
Free MEG and EEG data analysis software packages springing from academic research are now widely ... more Free MEG and EEG data analysis software packages springing from academic research are now widely used in published work. These toolboxes and applications are typically developed by or in close contact with researchers addressing cognitive or clinical neuroscience questions. Thus they often contain the latest methodological developments from the research community. It is therefore vital to educate MEG researchers and make them aware of the new possibilities offered by these toolboxes. The aim of this paper is to ...
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Papers by Carsten Wolters