On the EEG/MEG forward problem solution for distributed cortical sources

In studies of EEG/MEG problems involving cortical sources, the cortex may be modeled by a 2-D manifold inside the brain. In such cases the primary or impressed current density over this manifold is usually approximated by a set of dipolar sources located at the vertices of the cortical surface tessellation. In this study, we analyze the different errors induced by this approximation on the EEG/MEG forward problem. Our results show that in order to obtain more accurate solutions of the forward problems with the multiple dipoles approximation, the moments of the dipoles should be weighted by the area of the surrounding triangles, or using an alternative approximation of the primary current as a constant or linearly varying current density over plane triangular elements of the cortical surface tessellation. This should be taken into account when computing the lead field matrix for solving the EEG/MEG inverse problem in brain imaging methods.

[1]  D. Geselowitz.  On bioelectric potentials in an inhomogeneous volume conductor. , 1967, Biophysical journal.

[2]  P. S. Lewis,et al.  Anatomical constraints for neuromagnetic source models , 1991, Medical Imaging.

[3]  Karl J. Friston,et al.  Statistical parametric mapping , 2013 .

[4]  Jean Gotman,et al.  The influence of electrode location errors on EEG dipole source localization with a realistic head model , 2001, Clinical Neurophysiology.

[5]  Thom F. Oostendorp,et al.  The conductivity of the human skull: results of in vivo and in vitro measurements , 2000, IEEE Transactions on Biomedical Engineering.

[6]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[7]  Katrina Wendel,et al.  Correlation between Live and Post Mortem Skull Conductivity Measurements , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  A. S. Ferguson,et al.  A complete linear discretization for calculating the magnetic field using the boundary element method , 1994, IEEE Transactions on Biomedical Engineering.

[9]  Imam Samil Yetik,et al.  Surface-Source Modeling and Estimation Using Biomagnetic Measurements , 2006, IEEE Transactions on Biomedical Engineering.

[10]  Karl J. Friston,et al.  Statistical parametric mapping , 2013 .

[11]  J.C. de Munck,et al.  A random dipole model for spontaneous brain activity , 1992, IEEE Transactions on Biomedical Engineering.

[12]  L. Hörmander.  The analysis of linear partial differential operators , 1990 .

[13]  A. van Oosterom,et al.  The Solid Angle of a Plane Triangle , 1983, IEEE Transactions on Biomedical Engineering.

[14]  D. Lehmann,et al.  Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[15]  C. Davatzikos,et al.  Using a deformable surface model to obtain a shape representation of the cortex , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[16]  Zoltan J. Koles,et al.  A computationally efficient method for accurately solving the EEG forward problem in a finely discretized head model , 2005, Clinical Neurophysiology.

[17]  Carlos H. Muravchik,et al.  Effects of geometric head model perturbations on the EEG forward and inverse problems , 2006, IEEE Transactions on Biomedical Engineering.

[18]  Onno W. Weier,et al.  On the numerical accuracy of the boundary element method (EEG application) , 1989, IEEE Transactions on Biomedical Engineering.

[19]  S. Baillet,et al.  Localization of realistic cortical activity in MEG using current multipoles , 2004, NeuroImage.

[20]  Xavier Tricoche,et al.  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 , 2006, NeuroImage.

[21]  Robert Plonsey,et al.  Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields , 1995 .

[22]  Bin He,et al.  Spatial resolution of EEG cortical source imaging revealed by localization of retinotopic organization in human primary visual cortex , 2007, Journal of Neuroscience Methods.

[23]  M. Hämäläinen,et al.  Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data , 1989, IEEE Transactions on Biomedical Engineering.

[24]  W. van Drongelen,et al.  Estimation of in vivo brain-to-skull conductivity ratio in humans. , 2006, Applied physics letters.

[25]  A. Dale,et al.  Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach , 1993, Journal of Cognitive Neuroscience.

[26]  J. D. Munck.  A linear discretization of the volume conductor boundary integral equation using analytically integrated elements (electrophysiology application) , 1992 .

[27]  J. D. Munck,et al.  A fast method to compute the potential in the multisphere model (EEG application) , 1993, IEEE Transactions on Biomedical Engineering.

[28]  J.C. Mosher,et al.  Recursive MUSIC: A framework for EEG and MEG source localization , 1998, IEEE Transactions on Biomedical Engineering.

[29]  Jerry L. Prince,et al.  Reconstruction of the human cerebral cortex from magnetic resonance images , 1999, IEEE Transactions on Medical Imaging.

[30]  D. Geselowitz.  On the magnetic field generated outside an inhomogeneous volume conductor by internal current sources , 1970 .

[31]  R. Ilmoniemi,et al.  Interpreting magnetic fields of the brain: minimum norm estimates , 2006, Medical and Biological Engineering and Computing.

[32]  A. Dale,et al.  Distributed current estimates using cortical orientation constraints , 2006, Human brain mapping.

[33]  A. Dale,et al.  Conductivity tensor mapping of the human brain using diffusion tensor MRI , 2001, Proceedings of the National Academy of Sciences of the United States of America.