Sensitivity of EEG and MEG measurements to tissue conductivity

Abstract

Monitoring the electrical activity inside the human brain using electrical and magnetic field measurements requires a mathematical head model. Using this model the potential distribution in the head and magnetic fields outside the head are computed for a given source distribution. This is called the forward problem of the electro-magnetic source imaging. Accurate representation of the source distribution requires a realistic geometry and an accurate conductivity model. Deviation from the actual head is one of the reasons for the localization errors. In this study, the mathematical basis for the sensitivity of voltage and magnetic field measurements to perturbations from the actual conductivity model is investigated. Two mathematical expressions are derived relating the changes in the potentials and magnetic fields to conductivity perturbations. These equations show that measurements change due to secondary sources at the perturbation points. A finite element method (FEM) based formulation is developed for computing the sensitivity of measurements to tissue conductivities efficiently. The sensitivity matrices are calculated for both a concentric spheres model of the head and a realistic head model. The rows of the sensitivity matrix show that the sensitivity of a voltage measurement is greater to conductivity perturbations on the brain tissue in the vicinity of the dipole, the skull and the scalp beneath the electrodes. The sensitivity values for perturbations in the skull and brain conductivity are comparable and they are, in general, greater than the sensitivity for the scalp conductivity. The effects of the perturbations on the skull are more pronounced for shallow dipoles, whereas, for deep dipoles, the measurements are more sensitive to the conductivity of the brain tissue near the dipole. The magnetic measurements are found to be more sensitive to perturbations near the dipole location. The sensitivity to perturbations in the brain tissue is much greater when the primary source is tangential and it decreases as the dipole depth increases. The resultant linear system of equations can be used to update the initially assumed conductivity distribution for the head. They may be further exploited to image the conductivity distribution of the head from EEG and/or MEG measurements. This may be a fast and promising new imaging modality.

Data-driven forward model inference for EEG brain imaging

“…There is a broad consensus that forward model uncertainty is an important limiting factor for EEG imaging by source reconstruction [17,24,55,56,57,58,59]. Our results add quantitative evidence to this view, both in terms of the tissue conductivity ratio, which is the main source of uncertainty if the brain topography is correct, and more broadly when both anatomy and conductivities are unknown.…”

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

“…For the class of superficial neocortical sources, we chose two dipoles in the right somatosensory cortex, one of them approximately tangentially oriented (in the posterior -anterior direction) and the other approximately radially oriented (in the inferior -superior direction). Because it is known that both EEG and MEG are especially sensitive to conductivity changes in the vicinity of the dipole (Haueisen et al, 2000;Gencer and Acar, 2004), we checked the environment of the superficial somatosensory sources and found that only 15% of the surrounding finite elements were labeled as white matter and 0% as skull. The representative of the second class, deep sources, was chosen in the left thalamus, where the source orientation is approximately radial.…”

“…One impediment to using the FE method -and to this type of modeling in general -has been the high computational cost of carrying out the simulations. The use of recently developed advances in the FE method in EEG/MEG inverse problems (Weinstein et al, 2000;Wolters et al, 2002;Gencer and Acar, 2004;Wolters et al, 2004b) dramatically reduces the complexity of the computations, so that the main disadvantage of FE modeling no longer exists. In realistic FE models, sensitivity studies have been carried out for the influence of skull anisotropy on EEG and MEG (van den Broek et al, 1998;Marin et al, 1998;Wolters, 2003), while, to our knowledge, only a few studies have investigated the influence of realistic white matter anisotropy Wolters, 2003).…”

Skull Conductivity Estimation for EEG Source Localization

“…Most of the source localization techniques use a default value for this parameter. However, this value can affect the reconstruction considerably [3], [6]- [8] and has been shown to vary significantly across different subjects [2], [9]. To increase the reconstruction quality without measuring the skull conductivity directly, several methods try to estimate it jointly with the brain activity directly from the M/EEG measurements [10]- [17].…”