Dipole models for the EEG and MEG

Schimpf, P.H.; Ramon, Ceon; Haueisen, Jens

doi:10.1109/10.995679

Cited by 140 publications

(118 citation statements)

References 31 publications

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“…Although there are several different approaches in common use for this type of problem the finite element (FE) method is able to treat both realistic geometries and inhomogeneous and anisotropic material parameters (Haueisen, 1996;Buchner et al, 1997;van den Broek et al, 1998;Marin et al, 1998;Schimpf et al, 2002) and so is the approach we employed. Previous work has not sufficiently investigated the impact of tissue anisotropy on EEG and MEG.…”

Section: Introductionmentioning

confidence: 99%

Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling

et al. 2006

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To achieve a deeper understanding of the brain, scientists, and clinicians use electroencephalography (EEG) and magnetoencephalography (MEG) inverse methods to reconstruct sources in the cortical sheet of the human brain. The influence of structural and electrical anisotropy in both the skull and the white matter on the EEG and MEG source reconstruction is not well understood.In this paper, we report on a study of the sensitivity to tissue anisotropy of the EEG/MEG forward problem for deep and superficial neocortical sources with differing orientation components in an anatomically accurate model of the human head.The goal of the study was to gain insight into the effect of anisotropy of skull and white matter conductivity through the visualization of field distributions, isopotential surfaces, and return current flow and through statistical error measures. One implicit premise of the study is that factors that affect the accuracy of the forward solution will have at least as strong an influence over solutions to the associated inverse problem.Major findings of the study include (1) anisotropic white matter conductivity causes return currents to flow in directions parallel to the white matter fiber tracts; (2) skull anisotropy has a smearing effect on the forward potential computation; and (3) the deeper a source lies and the more it is surrounded by anisotropic tissue, the larger the influence of this anisotropy on the resulting electric and magnetic fields. Therefore, for the EEG, the presence of tissue anisotropy both for the skull and white matter compartment substantially compromises the forward potential computation and as a consequence, the inverse source reconstruction. In contrast, for the MEG, only the anisotropy of the white matter compartment has a significant effect. Finally, return currents with high amplitudes were found in the highly conducting cerebrospinal fluid compartment, underscoring the need for accurate modeling of this space. D

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Section: Introductionmentioning

confidence: 99%

Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling

et al. 2006

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“…890736 the determination of surface potentials from current sources in the volume. Because of its ability to treat volume conductors of arbitrary complexity and model inhomogeneous and anisotropic tissue conductivity, the finite-element method (FEM) has become popular to solve the forward problem [1], [2], [4], [5], [14], [19], [25]- [27]. An essential prerequisite for FE modeling is the generation of a mesh which represents the geometric and electric properties of the volume conductor.…”

Section: Introductionmentioning

confidence: 99%

Geometry-Adapted Hexahedral Meshes Improve Accuracy of Finite-Element-Method-Based EEG Source Analysis

Wolters

Anwander

Berti³

et al. 2007

IEEE Trans. Biomed. Eng.

106

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Abstract-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.

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“…Due to the high mesh complexity, the typical direct method for computing the leadfield matrix becomes unreasonably slow for finite element models. To overcome this, alternative methods for calculating the leadfield matrix, such as the adjoint [18], subtraction [19,20], and reciprocity [21,22] approaches have been devised. The approach taken here was derived from Helmholtz's principle of reciprocity and first applied to EEG by Rush and Driscoll [1,21].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2014

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We present a finite element modeling (FEM) implementation for solving the forward problem in electroencephalography (EEG). The solution is based on Helmholtz's principle of reciprocity which allows for dramatically reduced computational time when constructing the leadfield matrix. The approach was validated using a 4-shell spherical model and shown to perform comparably with two current state-of-the-art alternatives (OpenMEEG for boundary element modeling and SimBio for finite element modeling).We applied the method to real human brain MRI data and created a model with five tissue types: white matter, grey matter, cerebrospinal fluid, skull, and scalp. By calculating conductivity tensors from diffusion-weighted MR images, we also demonstrate one of the main benefits of FEM: the ability to include anisotropic conductivities within the head model. Root-mean square deviation between the standard leadfield and the leadfield including white-matter anisotropy showed that ignoring the directional conductivity of white matter fiber tracts leads to orientation-specific errors in the forward model. Realistic head models are necessary for precise source localization in individuals. Our approach is fast, accurate, open-source and freely available online.

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Dipole models for the EEG and MEG

Cited by 140 publications

References 31 publications

Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling

Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling

Geometry-Adapted Hexahedral Meshes Improve Accuracy of Finite-Element-Method-Based EEG Source Analysis

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

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