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3 - Imaging the electric neuronal generators of EEG/MEG

Published online by Cambridge University Press:  15 December 2009

Christoph M. Michel
Affiliation:
Université de Genève
Thomas Koenig
Affiliation:
University Hospital of Psychiatry, Berne, Switzerland
Daniel Brandeis
Affiliation:
Department of Child and Adolescent Psychiatry, University of Zurich, Switzerland and Central Institute of Mental Health, Mannheim, Grmany
Lorena R. R. Gianotti
Affiliation:
Universität Zürich
Jiří Wackermann
Affiliation:
Institute for Frontier Areas of Psychology and Mental Health, Freiburg im Breisgau, Germany
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Summary

Introduction

In order to try to understand “how the brain works,” one must make measurements of brain function. And ideally, the measurements should be as noninvasive as possible, i.e. the brain should be disturbed as little as possible during the measurement of its functions. One of the first types of noninvasive measurements reported in the literature, by Hans Berger, that directly tapped brain function was the human electroencephalogram (EEG), consisting of scalp electric potential differences as a function of time. In fact, Berger saw the EEG as a “window into the brain.” One of Berger's first observations that showed compelling evidence of having tapped brain function was the alpha rhythm. This oscillatory activity, at around 10–12 Hz, is optimally recorded from a posterior electrode with an anterior reference. The activity is very pronounced when the human subject is with eyes closed, awake, alert, resting. By simply being instructed to perform a mental task such as overtly subtracting the number seven serially, starting at 500, the alpha activity disorganizes and almost disappears.

The main subject matter addressed in this chapter is the use of noninvasive extracranial measurements, i.e. the EEG and the magnetoencephalogram (MEG), for the estimation of the distribution in the brain of their electric neuronal generators. This can be seen as an extension of Berger's initial efforts towards developing a window into the brain.

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Publisher: Cambridge University Press
Print publication year: 2009

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References

Berger, H.Über das Elektroenkephalogramm des Menschen. Archiv für Psychiatrie und Nervenkrankheiten 1929;87:527–570.CrossRefGoogle Scholar
Martin, JH. The collective electrical behavior of cortical neurons: the electroencephalogram and the mechanisms of epilepsy. In Kandel, ER, Schwartz, JH, Jessell, TM, eds. Principles of Neural Science. London: Prentice Hall International; 1991, pp. 777–791.Google Scholar
Hämäläinen, MS, Hari, R, Ilmoniemi, RJ, Knuutila, JE, Lounasmaa, OV. Magnetoencephalography – theory, instrumentation, and applications to noninvasive studies of the working human brain. Review of Modern Physics 1993;65:413–497.CrossRefGoogle Scholar
Mitzdorf, U.Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. Physiology Review 1985;65:37–100.CrossRefGoogle ScholarPubMed
Dale, AM, Liu, AK, Fischl, BRet al. Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron 2000;26:55–67.CrossRefGoogle ScholarPubMed
Baillet, S, Mosher, JC, Leahy, RM. Electromagnetic brain mapping. IEEE Signal Processing Magazine 2001;18:14–30.CrossRefGoogle Scholar
Llinas, RR. The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. Science 1988;242:1654–1664.CrossRefGoogle ScholarPubMed
Haalman, I, Vaadia, E.Dynamics of neuronal interactions: relation to behavior, firing rates, and distance between neurons. Human Brain Mapping 1997;5:249–253.3.0.CO;2-3>CrossRefGoogle ScholarPubMed
Sukov, W, Barth, DS. Three-dimensional analysis of spontaneous and thalamically evoked gamma oscillations in auditory cortex. Journal of Neurophysiology 1998;79:2875–2884.CrossRefGoogle ScholarPubMed
Sarvas, J.Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Physics in Medicine and Biology 1987;32:11–22.CrossRefGoogle ScholarPubMed
Brazier, MAB. A study of the electrical fields at the surface of the head. Electroencephalography and Clinical Neurophysiology Supplement 1949;2:38–52.Google Scholar
Wilson, FN, Bayley, RH. The electric field of an eccentric dipole in a homogeneous spherical conducting medium. Circulation 1950;1:84–92.CrossRefGoogle Scholar
Frank, E.Electric potential produced by 2 point current sources in a homogeneous conducting sphere. Journal of Applied Physics 1952;23:1225–1228.CrossRefGoogle Scholar
Geisler, CD, Gerstein, GL. The surface EEG in relation to its sources. Electroencephalography and Clinical Neurophysiology 1961;13:927–934.CrossRefGoogle Scholar
Lehmann, D, Kavanagh, RH, Fender, DH. Field studies of averaged visually evoked EEG potentials in a patient with a split chiasm. Electroencephalography and Clinical Neurophysiology 1969;26:193–199.CrossRefGoogle Scholar
Henderson, CJ, Butler, SR, Glass, A.The localization of equivalent dipoles of EEG sources by the application of electrical field theory. Electroencephalography and Clinical Neurophysiology 1975;39:117–130.CrossRefGoogle ScholarPubMed
Scherg, M, Cramon, D. A new interpretation of the generators of BAEP waves I-V: Results of a spatio-temporal dipole model. Electroencephalography and Clinical Neurophysiology 1985;62:290–299.CrossRefGoogle ScholarPubMed
Mosher, JC, Leahy, RM, Lewis, PS. EEG and MEG: forward solutions for inverse methods. IEEE Transactions on Biomedical Engineering 1999;46:245–259.CrossRefGoogle ScholarPubMed
Wolters, CH, Anwander, A, Tricoche, Xet 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. Neuroimage 2006;30:813–826.CrossRefGoogle Scholar
Cormack, AM. Representation of a function by its line integrals with some radiological applications. Journal of Applied Physics 1963;34:2722.CrossRefGoogle Scholar
Hounsfield, GN. Computerized transverse axial scanning (tomography). 1. Description of system. British Journal of Radiology 1973;46:1016–1022.CrossRefGoogle Scholar
Gordon, R, Herman, GT. 3-Dimensional reconstruction from projections – review of algorithms. International Review of Cytology – a Survey of Cell Biology 1974;38:111–151.Google Scholar
Helmholtz, H.Ueber einige Gesetze der Vertheilung elektrischer Ströme in körperlichen Leitern, mit Anwendung auf die thierisch-elektrischen Versuche. Annalen der Physikalischen Chemie 1853;89:211–233; 353–357.CrossRefGoogle Scholar
Tikhonov, A, Arsenin, V.Solutions to Ill-Posed Problems. Washington, DC: Winston; 1977.Google Scholar
Green, PJ, Silverman, BW. Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach. London: Chapman and Hall; 1994.CrossRefGoogle Scholar
Schmidt, DM, George, JS, Wood, CC. Bayesian inference applied to the electromagnetic inverse problem. Human Brain Mapping 1999;7:195–212.3.0.CO;2-F>CrossRefGoogle ScholarPubMed
Hämälainen, MS. Interpreting Measured Magnetic Fields of the Brain: Estimates of Current Distributions. Tech. Rep. TKK-F-A559. Espoo, Finland: Helsinki University of Technology; 1984.Google Scholar
Rao, CR, Mitra, SK. Theory and application of constrained inverse of matrices. Siam Journal on Applied Mathematics 1973;24:473–488.CrossRefGoogle Scholar
Pascual-Marqui, RD. Review of methods for solving the EEG inverse problem. International Journal of Bioelectromagnetism 1999;1:75–86.Google Scholar
Axler, S, Bourdon, P, Ramey, W.Harmonic Function Theory. New York: SpringerVerlag; 1992.CrossRefGoogle Scholar
Lin, F-H, Witzel, T, Ahlfors, SPet al. Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates. Neuroimage 2006;31:160–171.CrossRefGoogle ScholarPubMed
Greenblatt, RE, Ossadtchi, A, Pflieger, ME. Local linear estimators for the bioelectromagnetic inverse problem. IEEE Transactions on Signal Processing 2005;53:3403–3412.CrossRefGoogle Scholar
Fuchs, M, Wagner, M, Köhler, T, Wischmann, H-A. Linear and nonlinear current density reconstructions. Journal of Clinical Neurophysiology 1999;16:267–295.CrossRefGoogle ScholarPubMed
Gorodnitsky, IF, George, JS, Rao, BD. Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm. Electroencephalography and Clinical Neurophysiology 1995;95:231–251.CrossRefGoogle ScholarPubMed
Grave, PeraltaMenendez, R, Gonzalez Andino, SL. A critical analysis of linear inverse solutions. IEEE Transactions on Biomedical Engineering 1998;45:440–448.Google Scholar
Pascual-Marqui, RD, Michel, CM, Lehmann, D.Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology 1994;18:49–65.CrossRefGoogle Scholar
Titterington, DM. Common structure of smoothing techniques in statistics. International Statistical Review 1985;53:141–170.CrossRefGoogle Scholar
Wahba, G.Spline Models for Observational Data. Philadelphia, PA: SIAM; 1990.CrossRefGoogle Scholar
Grave, PeraltaMenendez, R, Murray, MM, Michel, CM, Martuzzi, R, Gonzalez Andino, SL. Electrical neuroimaging based on biophysical constraints. Neuroimage 2004;21:527–539.CrossRefGoogle Scholar
Phillips, JW, Leahy, RM, Mosher, JC. MEG-based imaging of focal neuronal current sources. IEEE Transactions on Medical Imaging 1997;16:338–348.CrossRefGoogle ScholarPubMed
Schmidt, DM, George, JS, Wood, CC. Bayesian inference applied to the electromagnetic inverse problem. Human Brain Mapping 1999;7:195–212.3.0.CO;2-F>CrossRefGoogle ScholarPubMed
Baillet, S, Garnero, L.A Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem. IEEE Transactions on Biomedical Engineering 1997;44:374–385.CrossRefGoogle ScholarPubMed
Grave, PeraltaMenendez, R, Gonzalez Andino, SL, Hauk, O, Spinelli, L, Michel, CM. A linear inverse solution with optimal resolution properties: WROP. Biomedical Engineering (Biomedizinische Technik) 1997;42:53–56.Google Scholar
Sekihara, K, Scholz, B.Average-intensity reconstruction and Wiener reconstruction of bioelectric current distribution based on its estimated covariance matrix. IEEE Transactions on Biomedical Engineering 1995;42:149–157.CrossRefGoogle ScholarPubMed
Michel, CM, Grave de Peralta, R, Lantz, Get al. Spatio-temporal EEG analysis and distributed source estimation in presurgical epilepsy evaluation. Journal of Clinical Neurophysiology 1999;16:225–238.CrossRefGoogle Scholar
Baillet, S, Mosher, JC, Leahy, RM. Electromagnetic brain mapping. IEEE Signal Processing Magazine 2001;18:14–30.CrossRefGoogle Scholar
He, B, Lian, J.High-resolution spatio-temporal functional neuroimaging of brain activity. Critical Reviews in Biomedical Engineering 2002;30:283–306.CrossRefGoogle ScholarPubMed
He, B, Lian, J.Electrophysiological neuroimaging: solving the EEG inverse problem. In He, B, ed. Neuronal Engineering. Norwell, USA: Kluwer Academic Publishers; 2005, pp. 221–261.CrossRefGoogle Scholar
Michel, CM, Murray, MM, Lantz, Get al. EEG source imaging. Clinical Neurophysiology 2004;115:2195–2222.CrossRefGoogle ScholarPubMed
Plummer, C, Harvey, AS, Cook, M.EEG source localization in focal epilepsy: where are we now? Epilepsia 2008;49:201–218.CrossRefGoogle ScholarPubMed
Lantz, G, Michel, CM, Pascual-Marqui, RDet al. Extracranial localization of intracranial interictal epileptiform activity using LORETA (low resolution electromagnetic tomography). Electroencephalography and Clinical Neurophysiology 1997;102:414–422.CrossRefGoogle Scholar
Lantz, G, Michel, CM, Seeck, Met al. Frequency domain EEG source localization of ictal epileptiform activity in patients with partial complex epilepsy of temporal lobe origin. Clinical Neurophysiology 1999;110:176–184.CrossRefGoogle ScholarPubMed
Lantz, G, Michel, CM, Seeck, Met al. Space-oriented segmentation and 3-dimensional source reconstruction of ictal EEG patterns. Clinical Neurophysiology 2001;112:688–697.CrossRefGoogle ScholarPubMed
Zumsteg, D, Friedman, A, Wennberg, RA, Wieser, HG. Source localization of mesial temporal interictal epileptiform discharges: correlation with intracranial foramen ovale electrode recordings. Clinical Neurophysiology 2005;116:2810–2818.CrossRefGoogle ScholarPubMed
Zumsteg, D, Andrade, DM, Wennberg, RA. Source localization of small sharp spikes: low resolution electromagnetic tomography (LORETA) reveals two distinct cortical sources. Clinical Neurophysiology 2006;117:1380–1387.CrossRefGoogle ScholarPubMed
Zumsteg, D, Friedman, A, Wieser, HG, Wennberg, RA. Source localization of interictal epileptiform discharges: comparison of three different techniques to improve signal to noise ratio. Clinical Neurophysiology 2006;117:562–571.CrossRefGoogle ScholarPubMed
Sperli, F, Spinelli, L, Seeck, Met al. EEG source imaging in paediatric epilepsy surgery: a new perspective in presurgical workup. Epilepsia 2006;47:981–990.CrossRefGoogle Scholar
Lantz, G, Grave de Peralta, R, Spinelli, L, Seeck, M, Michel, CM. Epileptic source localization with high density EEG: how many electrodes are needed? Clinical Neurophysiology 2003;114:63–69.CrossRefGoogle ScholarPubMed
Lantz, G, Spinelli, L, Seeck, Met al. Propagation of interictal epileptiform activity can lead to erroneous source localizations: A 128 channel EEG mapping study. Journal of Clinical Neurophysiology 2003;20:311–319.CrossRefGoogle ScholarPubMed
Michel, CM, Lantz, G, Spinelli, Let al. 128-channel EEG source imaging in epilepsy: clinical yield and localization precision. Journal of Clinical Neurophysiology 2004;21:71–83.CrossRefGoogle ScholarPubMed
Holmes, MD, Brown, M, Tucker, DM. Are “generalized” seizures truly generalized? Evidence of localized mesial frontal and frontopolar discharges in absence. Epilepsia 2004;45:1568–1579.CrossRefGoogle ScholarPubMed
Holmes, MD. Dense array EEG: methodology and new hypothesis on epilepsy syndromes. Epilepsia 2008;49:3–14.CrossRefGoogle ScholarPubMed
Spinelli, L, Andino, SG, Lantz, G, Seeck, M, Michel, CM. Electromagnetic inverse solutions in anatomically constrained spherical head models. Brain Topography 2000;13:115–125.CrossRefGoogle ScholarPubMed
Grave de Peralta, R, Gonzalez, S, Lantz, G, Michel, CM, Landis, T.Noninvasive localization of electromagnetic epileptic activity. I. Method descriptions and simulations. Brain Topography 2001;14:131–137.Google Scholar
Seeck, M, Lazeyras, F, Michel, CMet al. Non invasive epileptic focus localization using EEG-triggered functional MRI and electromagnetic tomography. Electroencephalography and Clinical Neurophysiology 1998;106:508–512.CrossRefGoogle ScholarPubMed
Vitacco, D, Brandeis, D, Pascual-Marqui, R, Martin, E.Correspondence of event-related potential tomography and functional magnetic resonance imaging during language processing. Hum Brain Mapping 2002;17:4–12.CrossRefGoogle ScholarPubMed
Mulert, C, Jager, L, Schmitt, Ret al. Integration of fMRI and simultaneous EEG: towards a comprehensive understanding of localization and time-course of brain activity in target detection. Neuroimage 2004;22:83–94.CrossRefGoogle ScholarPubMed
Schulz, E, Maurer, U, Van, Mark, Set al. Impaired semantic processing during sentence reading in children with dyslexia: combined fMRI and ERP evidence. Neuroimage 2008;41:153–168.CrossRefGoogle ScholarPubMed
Mardia, KV, Kent, JT, Bibby, JM. Multivariate Analysis. London: Academic Press; 1979.Google Scholar
Pascual-Marqui, RD. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods and Findings in Experimental and Clinical Pharmacology 2002:24 Suppl C:5–12.Google ScholarPubMed
Sekihara, K, Sahani, M, Nagarajan, SS. Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction. Neuroimage 2005;25:1056–1067.CrossRefGoogle ScholarPubMed
Michel, CM, Seeck, M, Murray, MM. The speed of visual cognition. Supplement in Clinical Neurophysiology 2004;57:617–627.CrossRefGoogle ScholarPubMed
Santis, L, Clarke, S, Murray, MM. Automatic and intrinsic auditory “what” and “where” processing in humans revealed by electrical neuroimaging. Cerebral Cortex 2007;17:9–17.CrossRefGoogle Scholar
Murray, MM, Camen, C, Gonzalez Andino, SL, Bovet, P, Clarke, S.Rapid brain discrimination of sounds of objects. Journal of Neuroscience 2006;26:1293–1302.CrossRefGoogle Scholar
Spierer, L, Tardif, E, Sperdin, H, Murray, MM, Clarke, S.Learning-induced plasticity in auditory spatial representations revealed by electrical neuroimaging. Journal of Neuroscience 2007;27:5474–5483.CrossRefGoogle ScholarPubMed
Meylan, RV, Murray, MM. Auditory-visual multisensory interactions attenuate subsequent visual responses in humans. Neuroimage 2007;35:244–254.CrossRefGoogle ScholarPubMed
Pascual-Marqui, RD. Reply to comments made by R. Grave De Peralta Menendez and S.I. Gozalez Andino; Appendix II. (http://ijbem.k.hosei.ac.jp/2006-/volume1/number2/html/pas-app2.htm). International Journal of Bioelectromagnetism (online journal) 1999;1.Google Scholar
Veen, BD, KM, Buckley. Beamforming: a versatile approach to spatial filtering. IEEE ASSP Magazine 1988;5:4–24.CrossRefGoogle Scholar
Lutkepohl, H.Handbook of Matrices. New York, NY: John Wiley & Sons Ltd.; 1996.Google Scholar
Robinson, SE, Vrba, J.Functional neuroimaging by synthetic aperture magnetometry (SAM). In Yoshimoto, T, Kotani, M, Kuriki, S, Karibe, H, Nakasoto, N, eds. Recent Advances in Biomagnetism. Sendai: Tohoku University Press; 1999, pp. 302–305.Google Scholar
Veen, BD, Drongelen, W, Yuchtman, M, Suzuki, A.Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Transactions on Biomedical Engineering 1997;44:867–880.CrossRefGoogle ScholarPubMed
Sekihara, K, Scholz, B.Generalized Wiener estimation of three-dimensional current distribution from biomagnetic measurements. IEEE Transactions on Biomedical Engineering 1996;43:281–291.CrossRefGoogle ScholarPubMed
Sekihara, K, Nagarajan, SS. Neuromagnetic source reconstruction and inverse modeling. In He, B, ed. Modeling and Imaging of Bioelectric Activity – Principles and Applications. New York, NY: Kluwer Academic/Plenum Publishers; 2004, pp. 213–250.Google Scholar
Sekihara, K, Nagarajan, SS, Poeppel, D, Marantz, A, Miyashita, Y.Application of an MEG eigenspace beamformer to reconstructing spatio-temporal activities of neural sources. Human Brain Mapping 2002;15:199–215.CrossRefGoogle ScholarPubMed
Rush, S, Driscoll, DA. EEG electrode sensitivity – an application of reciprocity. IEEE Transactions on Biomedical Engineering 1969;16:15–22.CrossRefGoogle ScholarPubMed
Ryynanen, OR, Hyttinen, JA, Malmivuo, JA. Effect of measurement noise and electrode density on the spatial resolution of cortical potential distribution with different resistivity values for the skull. IEEE Transactions on Biomedical Engineering 2006;53:1851–1858.CrossRefGoogle ScholarPubMed
Bertrand, O, Thevenet, M, Perrin, F.3D finite element method in brain electrical activity studies. In Nenonen, J, Rajala, HM, Katila, T, eds. Biomagnetic Localization and 3D Modelling. Technical Report TKK-F-A689. Helsinki: Helsinki University of Technology; 1991, pp. 154–171.Google Scholar
Hämäläinen, M, Sarvas, J.Realistic conductor geometry model of the human head for interpretation of neuromagnetic data. IEEE Transactions on Biomedical Engineering 1989;36:165–171.CrossRefGoogle ScholarPubMed
Fuchs, M, Wagner, M, Kastner, J.Development of volume conductor and source models to localize epileptic foci. Journal of Clinical Neurophysiology 2007;24:101–119.CrossRefGoogle ScholarPubMed

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