Influence of tissue resistivities on neuromagnetic fields and electric potentials studied with a finite element model of the head

Haueisen, Jens; Ramon, Ceon; Eiselt, M.; Brauer, Hartmut; Nowak, H.

doi:10.1109/10.605429

Cited by 274 publications

(214 citation statements)

References 24 publications

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“…A second potential improvement to our results could be obtained by accounting for conductivity inhomogeneities and anisotropies within the layers of the head model (Peters and De Munck, 1991;Rudy et al, 1979), using e.g. finite-element methods (Haueisen et al, 1997). Each subject's conductivity tensor could be calculated from an additional, diffusion tensor MRI scan (Tuch et al, 2001).…”

Section: Limitations Of the Current Studymentioning

confidence: 99%

The advantage of combining MEG and EEG: Comparison to fMRI in focally stimulated visual cortex

et al. 2007

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To exploit the high (millisecond) temporal resolution of magnetoencephalography (MEG) and electroencephalography (EEG) for measuring neuronal dynamics within well-defined brain regions, it is important to quantitatively assess their localizing ability. Previous modeling studies and empirical data suggest that a combination of MEG and EEG signals should yield the most accurate localization, due to their complementary sensitivities. However, these two modalities have rarely been explicitly combined for source estimation in studies of recorded brain activity, and a quantitative empirical assessment of their abilities, combined and separate, is currently lacking. Here we studied early visual responses to focal Gabor patches flashed during subject fixation. MEG and EEG data were collected simultaneously, and compared with the functional MRI (fMRI) localization produced by identical stimuli, in the same subjects. This allowed direct evaluation of the localization accuracy of separate and combined MEG/EEG inverse solutions. We found that the localization accuracy of the combined MEG+EEG solution was consistently better than that of either modality alone, using three different source estimation approaches. Further analysis suggests that this improved localization is due to the different properties of the two imaging modalities, rather than simply due to increased total channel number. Thus, combining MEG and EEG data is important for highresolution spatiotemporal studies of the human brain.

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Section: Limitations Of the Current Studymentioning

confidence: 99%

The advantage of combining MEG and EEG: Comparison to fMRI in focally stimulated visual cortex

et al. 2007

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“…About 7,000 boundary elements and 3,500 surface nodes were generated from each subject's T 1 -weighted MR images. The relative conductivity values of the brain, skull, and scalp were assumed to be 1, 1/16, and 1, respectively (Haueisen et al, 1997;Oostendorp et al, 2000). The electrode locations were fitted to the boundary elements using anatomical landmarks (nasion and two auricular points) (de Munck et al, 1991) and adjusted manually in the CURRY5 software platform.…”

Section: Eeg Cortical Source Imagingmentioning

confidence: 99%

Spatial resolution of EEG cortical source imaging revealed by localization of retinotopic organization in human primary visual cortex

Gururajan

Zhang

et al. 2007

Journal of Neuroscience Methods

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The aim of the present study is to investigate the spatial resolution of electroencephalography (EEG) cortical source imaging by localizing the retinotopic organization in the human primary visual cortex (V1). Retinotopic characteristics in V1 obtained from functional magnetic resonance imaging (fMRI) study were used as reference to assess the spatial resolution of EEG since fMRI can discriminate small changes in activation in visual field. It is well-known that the activation of the early C1 component in the visual evoked potential (VEP) elicited by pattern onset stimuli coincides well with the activation in the striate cortex localized by fMRI. In the present experiments, we moved small circular checkerboard stimuli along horizontal meridian and compared the activations localized by EEG cortical source imaging with those from fMRI. Both fMRI and EEG cortical source imaging identified spatially correlated activity within V1 in each subject studied. The mean location error, between the fMRI-determined activation centers in V1 and the EEG source imaging activation peak estimated at equivalent C1 components (peak latency: 74.8 ± 10.6 ms), was 7 mm (25% and 75% percentiles are 6.45 mm and 8.4 mm, respectively), which is less than the change in fMRI activation map by a 3° visual field change (7.8 mm). Moreover, the source estimates at the earliest major VEP component showed statistically good correlation with those obtained by fMRI. The present results suggest that the spatial resolution of the EEG cortical source imaging is can correctly discriminate cortical activation changes in V1 corresponding to less than 3° visual field changes.

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“…It is also known that the brain white matter has an anisotropic conductivity with a ratio of about 1 to 10 5,6,10,11 . WM has the mean resistivity 700 Ωcm with 350 Ωcm and 1050 Ωcm values for lower and upper bounds, respectively, having the variation of ±50% 23 . Therefore, the skull and WM exhibit the inhomogeneous and anisotropy properties.…”

Section: Introductionmentioning

confidence: 99%

Influence of white matter inhomogeneous anisotropy on EEG forward computing

Bashar

Wen

2008

Australas. Phys. Eng. Sci. Med.

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In this paper, we model the human head using the Volume and Wang's constraint methods, and study the inhomogeneous anisotropic conductivity for white matter (WM) using finite element method (FEM). To represent the WM accurately, the conductivity ratio approximation (CRA) and statistical conductivity approximation (SCA) techniques are applied to assign inhomogeneous anisotropic conductivity. This model is evaluated and compared with a homogeneous isotropic model and a homogeneous anisotropic model. The results show that the effects of inhomogeneous anisotropic conductivity of WM on the scalp EEG are significant.

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Influence of tissue resistivities on neuromagnetic fields and electric potentials studied with a finite element model of the head

Cited by 274 publications

References 24 publications

The advantage of combining MEG and EEG: Comparison to fMRI in focally stimulated visual cortex

The advantage of combining MEG and EEG: Comparison to fMRI in focally stimulated visual cortex

Spatial resolution of EEG cortical source imaging revealed by localization of retinotopic organization in human primary visual cortex

Influence of white matter inhomogeneous anisotropy on EEG forward computing

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