Abstracts of Presentations at the International Conference on Basic and Clinical Multimodal Imaging (BaCI), a Joint Conference of the International Society for Neuroimaging in Psychiatry (ISNIP), the International Society for Functional Source Imaging (ISFSI), the International Society for Bioele... (original) (raw)
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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.
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.
Development of Volume Conductor and Source Models to Localize Epileptic Foci
Journal of Clinical Neurophysiology, 2007
T he boundary element method (BEM) improves the localization accuracy of bioelectric source reconstruction results, as compared with simple spherical shell models, by approximating the volume conductor properties of realistically shaped compartments of isotropic and homogeneous conductivities. The anisotropy and fine structure of the real tissue surrounding the electrical sources to be reconstructed are purposefully neglected. These factors can in principle be treated by finite element method (FEM) models, but they suffer from large computational effort and individual in vivo conductivity and anisotropy parameters that are mostly unknown. The BEM is a compromise between oversimplified, spherically symmetric models, which reflect only mean conductivities but not the shape of the compartments, and overly complex models for which detailed real tissue data are not FIGURE 1. Averaged MRI dataset (Montreal Neurologic Institute) with (extended) skin, skull, and brain compartments.
On the role of skull parcellation in the computational modeling of human head conductivity
In this paper we propose a method to include skull inhomogeneities in the EEG forward/inverse calculation by parcellating the skull based on a combination of skull anatomy and thickness. We show that it is enough to determine a few skull parameters (4 - 6) in the inverse search to capture the skull inhomogeneities if we also include the thickness. The validity of the linear relation between skull conductivities and skull thickness is investigated, our results show that this relation applies. The effect of skull inhomogeneities on the conductivity reconstruction of the human head tissues is presented. Our simulation results show here that the models approximating the skull as a homogenous tissue could produce an or- der of magnitude error in the retrieved conductivities in comparison with the models based on parcellated skull.
Influences of skull segmentation inaccuracies on EEG source analysis
NeuroImage, 2012
The low-conducting human skull is known to have an especially large influence on electroencephalography (EEG) source analysis. Because of difficulties segmenting the complex skull geometry out of magnetic resonance images, volume conductor models for EEG source analysis might contain inaccuracies and simplifications regarding the geometry of the skull. The computer simulation study presented here investigated the influences of a variety of skull geometry deficiencies on EEG forward simulations and source reconstruction from EEG data.
Influence of the head model on EEG and MEG source connectivity analysis
2013
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.
Influence of the head model on EEG and MEG source connectivity analyses
NeuroImage, 2015
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 problems. 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 3-layer model comprising only the skin, skull, and brain. Source time courses were reconst...
Human Brain Mapping, 2009
Bioelectric source analysis in the human brain from scalp electroencephalography (EEG) signals is sensitive to geometry and conductivity properties of the different head tissues. We propose a low-resolution conductivity estimation (LRCE) method using simulated annealing optimization on high-resolution finite element models that individually optimizes a realistically shaped four-layer volume conductor with regard to the brain and skull compartment conductivities. As input data, the method needs T1-and PD-weighted magnetic resonance images for an improved modeling of the skull and the cerebrospinal fluid compartment and evoked potential data with high signal-to-noise ratio (SNR). Our simulation studies showed that for EEG data with realistic SNR, the LRCE method was able to simultaneously reconstruct both the brain and the skull conductivity together with the underlying dipole source and provided an improved source analysis result. We have also demonstrated the feasibility and applicability of the new method to simultaneously estimate brain and skull conductivity and a somatosensory source from measured tactile somatosensory-evoked potentials of a human subject. Our results show the viability of an approach that computes its own conductivity values and thus reduces the dependence on assigning values from the literature and likely produces a more robust estimate of current sources. Using the LRCE method, the individually optimized four-compartment volume conductor model can, in a second step, be used for the analysis of clinical or cognitive data acquired from the same subject. Hum Brain Mapp 30: [2862][2863][2864][2865][2866][2867][2868][2869][2870][2871][2872][2873][2874][2875][2876][2877][2878] 2009. V V C 2008 Wiley-Liss, Inc.
IEEE Transactions on Biomedical Engineering, 2000
Mesh generation in finite-element-(FE) methodbased electroencephalography (EEG) source analysis generally influences greatly the accuracy of the results. It is thus important to determine a meshing strategy well adopted to achieve both acceptable accuracy for potential distributions and reasonable computation times and memory usage. In this paper, we propose to achieve this goal by smoothing regular hexahedral finite elements at material interfaces using a node-shift approach. We first present the underlying theory for two different techniques for modeling a current dipole in FE volume conductors, a subtraction and a direct potential method. We then evaluate regular and smoothed elements in a four-layer sphere model for both potential approaches and compare their accuracy. We finally compute and visualize potential distributions for a tangentially and a radially oriented source in the somatosensory cortex in regular and geometry-adapted three-compartment hexahedra FE volume conductor models of the human head using both the subtraction and the direct potential method. On the average, node-shifting reduces both topography and magnitude errors by more than a factor of 2 for tangential and 1.5 for radial sources for both potential approaches. Nevertheless, node-shifting has to be carried out with caution for sources located within or close to irregular hexahedra, because especially for the subtraction method extreme deformations might lead to larger overall errors. With regard to realistic volume conductor modeling, node-shifted hexahedra should thus be used for the skin and skull compartments while we would not recommend deforming elements at the grey and white matter surfaces.