IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 44, NO. 8, AUGUST 1997 727 Influence of Tissue Resistivities on Neuromagnetic Fields and Electric Potentials Studied with a Finite Element Model of the Head Jens Haueisen,* Ceon Ramon, Member, IEEE, Michael Eiselt, Hartmut Brauer, Member, IEEE, and Hannes Nowak Abstract— Modeling in magnetoencephalography (MEG) and electroencephalography (EEG) requires knowledge of the in vivo tissue resistivities of the head. The aim of this paper is to examine the influence of tissue resistivity changes on the neuromagnetic field and the electric scalp potential. A high-resolution finite ele- ment method (FEM) model (452 162 elements, 2-mm resolution) of the human head with 13 different tissue types is employed for this purpose. Our main finding was that the magnetic fields are sensitive to changes in the tissue resistivity in the vicinity of the source. In comparison, the electric surface potentials are sensitive to changes in the tissue resistivity in the vicinity of the source and in the vicinity of the position of the electrodes. The magnitude (strength) of magnetic fields and electric surface potentials is strongly influenced by tissue resistivity changes, while the topography is not as strongly influenced. Therefore, an accurate modeling of magnetic field and electric potential strength requires accurate knowledge of tissue resistivities, while for source localization procedures this knowledge might not be a necessity. Index Terms—Biological tissues, conductivity, electroenceph- alography, finite element methods, magnetoencephalography. I. INTRODUCTION T HE in vivo resistivity values of the different tissues of the human head are needed for forward and inverse modeling in magnetoencephalography (MEG) and electroen- cephalography (EEG). There are studies which show that even within simple spherical volume conductors inhomogeneities close to sources can significantly affect the measured MEG and EEG [27], [28]. Other studies investigated the ratio of conductivities with the help of spherical models (see e.g., [1], [25]) and with compartmental boundary element method Manuscript received January 26, 1996, revised April 2, 1997. This work was partly supported by the German ministry of education and research, (BMFT) under project 01ZZ9104. The work of C. Ramon was supported by the National Science Foundation under Grant BCS-9 209 938 and the University of Washington under the Graduate School Research Fund. Asterisk indicates corresponding author. *J. Haueisen is with Biomagnetisches Zentrum, Friedrich-Schiller- Universit¨ at Jena, Philosophenweg 3, Jena 07740 Germany (e-mail: haueisen@biomag.uni-jena.de). C. Ramon is with the Center for Bioengineering, University of Washington, Seattle, WA 98195 USA. M. Eiselt is with the Institut f¨ ur Pathologische Physiologie, Friedrich- Schiller-Universit¨ at Jena, Jena 07740 Germany. H. Brauer is with the Technische Universit¨ at Ilmenau, Institut f¨ ur Allgemeine und Theoretische Elektrotechnik, Ilmenau 98684 Germany. H. Nowak is with Biomagnetisches Zentrum, Friedrich-Schiller-Universit¨ at Jena, Jena 07740 Germany. Publisher Item Identifier S 0018-9294(97)05352-4. (BEM) models (see, e.g., [13]). A more recent systematic investigation of conductivity changes using BEM models and simplified geometries for certain brain inhomogeneities also found significant effects on the magnitude of magnetic fields and electric potentials [14]. A major drawback of all these studies is the limited inclusion of realistic inhomogeneities as they are present within the human head. Also the number of compartments and subsequently the number of conductivities used is usually very limited. Some studies do also use relative conductivity values (compartment ratios) only, since pure MEG and pure EEG modeling with compartmental models (BEM or spheres) does not need absolute conductivity val- ues. For combined MEG/EEG analysis absolute conductivity values are necessary. The aim of this study is to quantify how different tissue resistivities change the solution of the forward problem in MEG and EEG modeling. We employ a high-resolution FEM model (2 2 2 mm) of the human head with 13 tissue types which has been used and validated previously [12]. Recently, studies have been published on the influence of tissue resistivities and on estimating tissue resistivities for the human torso using FEM [5], [6], [15], [16], but to our knowledge no such studies have been performed so far for the human head. II. METHODS A. FEM Volume Conductor Modeling Our FEM head model construction and solution technique is described in detail elsewhere [12]. A brief summary is given here. The human head model was constructed out of 128 sagittal magnetic resonance imaging (MRI) scans with a slice thickness of 2 mm. Based on these scans a semiautomatic tissue classifier was used to distinguish between the different tissue types in the head. The 13 different tissue types which were used to describe the conductivities in the human head are given in Table I. Fig. 1 shows the upper part of a MRI scan and the corresponding FEM model cross section (classified slice). The model used for the computations presented here extended to the chin [12]. A large scale finite element mesh was generated by connecting all slices. This leads to a uniform grid of 452 162 hexahedral elements with a voxel resolution of 2 2 2 mm. Based on this grid a linear system of equations was set up and solved iteratively by means of the successive over-relaxation (SOR) method. The relaxation 0018–9294/97$10.00  1997 IEEE