An-Najah Staff

Electroencephalography (EEG) is an indispensable neurological diagnostic tool in terms of the fast ‎time scale, porta-bility and cost efficiency. Improved spatial res-olution of EEG measures would ‎greatly bene-fit multiple clinical and research applications, including stroke, epilepsy and cognitive ‎stud-ies. The recent advances in dense arrays elec-trode application have made feasible EEG brain ‎imaging for both rapid application and long-term monitoring.1 It has been shown that reliable ‎inverse solutions can be obtained and dense array sampling (128, 256 and 512 chan-nels) on the ‎scalp can be projected back to the cortex providing a unique opportunity for monitoring brain ‎activity both in space and time (Figure 1). However, the spatial accuracy of EEG will remain limited ‎because i) mostly simplistic models of  the human head (like multi-shell spheres) are commonly ‎used in the inverse procedure of  back-to-cortex projec-tion, and ii) the regional conductivities of ‎the human head tissues are largely unknown. This is true not only in each measurement case but in ‎general.  Several imaging modalities have been proposed so far to quantitatively measure the ‎electrical conductivity of  tissue noninva-sively, but none of them is free from some lim-itations ‎and shortcomings.  Magnetoacoustic Hall effect imaging2 relies on propagation of ultrasound into ‎the tissue, and is not quantita-tive.

Magnetic resonance current density imaging3 requires ‎applying rather high level of external currents to make produced magnetic field contrast visible by ‎MRI.  The electrical conductivity tensor of tissue can be quantita-tively inferred from the water ‎self-diffusion tensor as measured by diffusion tensor mag-netic resonance imaging (DTI).4 It can ‎be suc-cessful in extracting anisotropic conductivities of   the brain tissue,  but more problematic ‎regards bone (skull) tissues where the water content is much smaller. The lack of  accurate skull ‎conductivity (most resistive tissue) is particularly problem-atic given the developmental variations ‎in the human skull from infancy through adoles-cence. Without an accurate forward model of the ‎skull (specifying the volume conduction from cortex to scalp) even advanced inverse efforts ‎cannot achieve precision with EEG data as the error of source localisation due to conductivity ‎uncertainty may reach a few cen-timetres.5 Several authors addressed this prob-lem by using the ‎noninvasive electrical impedance tomography (EIT) approach.  A similar approach was used by ‎Hoekema et al6 in the semi in-vivo conductivity measure-ments of the skull parts temporarily ‎removed during epilepsy surgery, - fitting for only one unknown parameter was performed,  ‎though. Goncalves et al7,8 applied spherical and a three-layer boundary element (BEM) models to ‎fit their EIT measurements for six subjects.9 However, since in such models skull thickness and ‎conduc-tivity are interchangeable to some extent,  more accurate geometry representation is ‎needed. Recently we have shown in our group10,11 that using the parameterised EIT ‎measurements procedure and realistically shaped high-resolution finite dif-ference models (FDM) ‎of the human head based on the subject specific co-registered CT and MRI scans, as is shown in ‎Figure 2, it was possible to extract three and four tissues conductivities (Table 1) both in ‎simulations with synthetic and real experimental data with good accuracy using the multi-start ‎downhill simplex algorithm.

Our current focus is to further improve con-ductivity information for ‎the benefit of high-res-olution EEG source localisation. We are conduct-ing measurements ‎repeatedly on individual sub-jects to prove the method’s robustness and show individual variability ‎of head tissues conductivity across individuals. Electrical Geodesics, Inc. (EGI) has developed a data ‎acquisition system that pro-vides current injection between selected electrode pairs (at very safe ‎current levels) and simultane-ous acquisition of  return potentials from the dense sensor array of  ‎the Geodesic Sensor Net. Our current work will further refine a methodol-ogy for constructing ‎accurate forward models of electrical conductance for the human head through incorporating the ‎high-resolution struc-tural details of  the human head from MRI/CT scans and providing the non-‎invasive procedure for estimation of  the major tissues conductivity parameters. The latter is based ‎on parameterised EIT inverse solution for the data collected at EGI. In the next stage of the ‎project, a refined forward solver will incorporate anisotropies of  the head tissues, in particular ‎skull and white matter.

The advanced simulation annealing algorithm has been proved to show ‎better performance in the inverse procedure in terms of  finding the global minima of the cost ‎function with larger number of unknowns. This will allow us to extend the pro-cedure to a ‎parcellated skull (10-12 anatomically relevant bone plates) and include the skull con-ductivity ‎inhomogeneities information into the forward solver for the EEG inverse problem. These tasks ‎require extensive computational resources. At Neuroinformatics Center, University of Oregon we ‎have access to a multi-cluster (SGI, Dell,  IBM p650; IBM p690;  IBM BladeServer) high performance ‎parallel computing system dedicated to analysis of  human EEG and MEG data. The first and ‎primary application of  these computing resources is to solve the conductivity problem of the ‎human head.