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

@article{Wolters2006InfluenceOT,
  title={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},
  author={Carsten H. Wolters and Alfred Anwander and Xavier M. Tricoche and David M. Weinstein and Martin A. Koch and Rob Macleod},
  journal={NeuroImage},
  year={2006},
  volume={30},
  pages={813-826}
}
The influence of forward model conductivities on EEG/MEG source reconstruction
  • J. Haueisen
  • Geology
    2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging
  • 2007
In order to reconstruct the neuronal activity underlying measured EEG and MEG data both the forward problem (computing the electromagnetic field due to given sources) and the inverse problem (finding
Improved EEG source analysis using low‐resolution conductivity estimation in a four‐compartment finite element head model
TLDR
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.
Influence of Modeling Errors in the Boundary Element Analysis of EEG Forward Problems upon the Solution Accuracy
lectroencephalography (EEG) and magnetoencephal- ography (MEG) are noninvasive human brain imaging devices to estimate neural electrical activities in human cerebral cortex using electromagnetic
Influence of anisotropic conductivity on EEG source reconstruction: investigations in a rabbit model
TLDR
The results indicate that the expected average source localization error due to anisotropic white matter conductivity is within the principal accuracy limits of current inverse procedures, however, larger localization errors might occur in certain cases.
The Finite Element Method in EEG/MEG Source Analysis
Electro- and magnetoencephalography (EEG/MEG)-based source reconstruction of cerebral activity (the EEG/MEG inverse problem) is an important tool both in clinical practice and research and in
Combining EEG and MEG for the Reconstruction of Epileptic Activity Using a Calibrated Realistic Volume Conductor Model
TLDR
A new experimental and methodological source analysis pipeline is introduced that combines the complementary information in EEG and MEG, and applied to data from a patient, suffering from refractory focal epilepsy, to increase the reliability for the non-invasive determination of the irritative zone in presurgical epilepsy diagnosis.
A Finite-Difference Solution for the EEG Forward Problem in Inhomogeneous Anisotropic Media
TLDR
This work develops a computationally efficient FDM solution that can flexibly integrate voxel-wise conductivity and anisotropy information, and applies the developed FDM tool to high-resolution MR images from a real experimental subject, to demonstrate the potential added value.
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References

SHOWING 1-10 OF 69 REFERENCES
Influence of tissue conductivity inhomogeneity and anisotropy on EEG/MEG based source localization in the human brain
The inverse problem in Electroand Magneto-EncephaloGraphy (EEG/MEG) aims at reconstructing the underlying current distribution in the human brain using potential differences and/or magnetic fluxes
Improved tissue modeling and fast solver methods for high resolution FE-modeling in EEG/MEG-source localization
TLDR
This paper outlines how individualized high resolution finite element (FE) models, exploiting multimodal MR-imaging protocols, are automatically constructed and presents an improved segmentation of the skull through a combination of T1and PD-MRI.
Efficient Computation of Lead Field Bases and Influence Matrix for the FEM-based EEG and MEG Inverse Problem. Part I: Complexity Considerations
TLDR
The lead field approach will speed up the state-of-the-art forward approach by a factor of more than 100 for a realistic choice of the number of sensors and sources and leads to a highly efficient solution of FE-based source reconstruction problems.
The Influence of Brain Tissue Anisotropy on Human EEG and MEG
TLDR
It is expected that inclusion of tissue anisotropy information will improve source estimation procedures and find a major influence on the amplitude of EEG and MEG due to the change in conductivity and the inclusion of anisotropic volume conduction.
Influence of skull anisotropy for the forward and inverse problem in EEG: Simulation studies using FEM on realistic head models
TLDR
The influence of skull anisotropy on the forward and inverse problems in brain functional imaging with EEG is investigated for realism in the description of conduction from primary neural currents to scalp potentials.
Effects of local skull inhomogeneities on EEG source estimation.
Referenced EEG and head volume conductor model: geometry and parametrical setting
TLDR
The limits are shownBesides which model reduction is possible preserving EEG simulation accuracy according to the two competitive definitions for skull conductivity, which involve a proper choice for the EEG reference as well as dipole equivalent source characteristics (position and orientation).
Volume conduction effects in EEG and MEG.
The influence of skull-conductivity misspecification on inverse source localization in realistically shaped finite element head models
TLDR
An error in conductivity of lower than 20% appears to be acceptable for fine finite element head models with average discretization errors down to 3mm to estimate the error in source localization due to errors in assumed conductivity values.
Conductivity tensor mapping of the human brain using diffusion tensor MRI
TLDR
The effective medium model indicates a strong linear relationship between the conductivity and diffusion tensor eigenvalues (respectively, σ and d) in agreement with theoretical bounds and experimental measurements presented here.
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