Brain Topography

, Volume 26, Issue 2, pp 212–228 | Cite as

Influence of a Silastic ECoG Grid on EEG/ECoG Based Source Analysis

  • Benjamin Lanfer
  • Christian Röer
  • Michael Scherg
  • Stefan Rampp
  • Christoph Kellinghaus
  • Carsten Wolters
Original Paper

Abstract

The simultaneous evaluation of the local electrocorticogram (ECoG) and the more broadly distributed electroencephalogram (EEG) from humans undergoing evaluation for epilepsy surgery has been shown to further the understanding of how pathologies give rise to spontaneous seizures. However, a well-known problem is that the disruption of the conducting properties of the brain coverings can render simultaneous scalp and intracranial recordings unrepresentative of the habitual EEG. The ECoG electrodes for measuring the potential on the surface of the cortex are commonly embedded into one or more sheets of a silastic material. These highly resistive silastic sheets influence the volume conduction and might therefore also influence the scalp EEG and ECoG measurements. We carried out a computer simulation study to examine how the scalp EEG and the ECoG, as well as the source reconstruction therefrom, employing equivalent current dipole estimation methods, are affected by the insulating ECoG grids. The finite element method with high quality tetrahedral meshes, generated using a constrained Delaunay tetrahedralization meshing approach, was used to model the volume conductor that incorporates the very thin ECoG sheets. It is shown that the insulating silastic substrate of the ECoG grids can have a large impact on the scalp potential and on source reconstruction from scalp EEG data measured in the presence of the grids. The reconstruction errors are characterized with regard to the location of the source in the brain and the mislocalization tendency. In addition, we found a non-negligible influence of the insulating grids on ECoG based source analysis. We conclude, that the thin insulating ECoG sheets should be taken into account, when performing source analysis of simultaneously measured ECoG and scalp EEG data.

Keywords

Finite element method FEM ECoG Presurgical epilepsy diagnosis Simultaneous EEG Constrained Delaunay tetrahedralization Dipole fitting method 

Notes

Acknowledgments

This work was supported by the Deutsche Forschungsgemeinschaft (WO1425/2-1, STE380/14-1). The authors would like to thank Chris Johnson, Tolga Tasdizen and Darby J. Van Uitert from the SCI Institute, University of Utah, Salt Lake City, USA, Gregory A. Worrell from the Department of Neurology and Division of Epilepsy, Mayo Clinic, Rochester, Minnesota, USA, and Scott Makeig from the Swartz Center for Computational Neuroscience, University of California San Diego, USA, for providing the necessary data for model construction and for their valuable help and the fruitful discussions with regard to this study. We would also like to thank the anonymous reviewers for their helpful critics and comments that significantly improved our manuscript.

References

  1. Akhtari M, Bryant H, Mamelak A, Flynn E, Heller L, Shih J, Mandelkem M, Matlachov A, Ranken D, Best E, et al (2002) Conductivities of three-layer live human skull. Brain Topogr 14(3):151–167PubMedCrossRefGoogle Scholar
  2. Alarcon G, Kissani N, Dad M, Elwes R, Ekanayake J, Hennessy M, Koutroumanidis M, Binnie C, Polkey C (2001) Lateralizing and localizing values of ictal onset recorded on the scalp: evidence from simultaneous recordings with intracranial foramen ovale electrodes. Epilepsia 42(11):1426–1437PubMedCrossRefGoogle Scholar
  3. Bast T, Oezkan O, Rona S, Stippich C, Seitz A, Rupp A, Fauser S, Zentner J, Rating D, Scherg M (2004) EEG and MEG source analysis of single and averaged interictal spikes reveals intrinsic epileptogenicity in focal cortical dysplasia. Epilepsia 45(6):621–631PubMedCrossRefGoogle Scholar
  4. Bast T, Boppel T, Rupp A, Harting I, Hoechstetter K, Fauser S, Schulze-Bonhage A, Rating D, Scherg M (2006) Noninvasive source localization of interictal EEG spikes: effects of signal-to-noise ratio and averaging. J Clin Neurophysiol 23(6):487–497PubMedCrossRefGoogle Scholar
  5. Baumann S, Wozny D, Kelly S, Meno F (1997) The electrical conductivity of human cerebrospinal fluid at body temperature. IEEE Trans Biomed Eng 44(3):220–223PubMedCrossRefGoogle Scholar
  6. Baumgartner C, Lindinger G, Ebner A, Aull S, Serles W, Olbrich A, Lurger S, Czech T, Burgess R, Luders H (1995) Propagation of interictal epileptic activity in temporal lobe epilepsy. Neurology 45(1):118–122PubMedCrossRefGoogle Scholar
  7. Bertrand O, Thévenet M, Perrin F (1991) 3D finite element method in brain electrical activity studies. In: Nenonen J, Rajala H, Katila T (eds) Biomagnetic localization and 3D modelling. Report of the Dep. of Tech. Physics, Helsinki University of Technology, pp 154–171Google Scholar
  8. Braess D (2007) Finite elements: theory, fast solvers and applications in solid mechanics. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  9. Buchner H, Knoll G, Fuchs M, Rienäcker A, Beckmann R, Wagner M, Silny J, Pesch J (1997) Inverse localization of electric dipole current sources in finite element models of the human head. Electroencephalogr Clin Neurophysiol 102:267–278PubMedCrossRefGoogle Scholar
  10. Cook M, Koles Z (2006) A high-resolution anisotropic finite-volume head model for EEG source analysis. In: Proceedings of the 28th annual international conference of the IEEE engineering in medicine and biology society, pp 4536–4539Google Scholar
  11. Dannhauer M, Lanfer B, Wolters C, Knösche T (2011) Modeling of the human skull in EEG source analysis. Hum Brain Mapp 32(9):1383–1399. doi: 10.1002/hbm.21114
  12. de Munck J, Peters M (1993) A fast method to compute the potential in the multisphere model. IEEE Trans Biomed Eng 40(11):1166–74PubMedCrossRefGoogle Scholar
  13. Dümpelmann M, Fell J, Wellmer J, Urbach H, Elger C (2009) 3D source localization derived from subdural strip and grid electrodes: a simulation study. Clin Neurophysiol 120:1061–1069PubMedCrossRefGoogle Scholar
  14. Ebersole J (1999) Non-invasive pre-surgical evaluation with EEG/MEG source analysis. Electroencephalogr Clin Neurophysiol Suppl 50:167–174PubMedGoogle Scholar
  15. Fuchs M, Wagner M, Kastner J (2007) Development of volume conductor and source models to localize epileptic foci. J Clin Neurophysiol 24(2):101–119 doi: 10.1097/WNP.0b013e318038fb3e PubMedCrossRefGoogle Scholar
  16. Güllmar D, Haueisen J, Reichenbach J (2010) Influence of anisotropic electrical conductivity in white matter tissue on the EEG/MEG forward and inverse solution. a high-resolution whole head simulation study. NeuroImage. doi: 10.1016/j.neuroimage.2010.02.014
  17. Hackbusch W (1992) Elliptic differential equations. Springer, BerlinCrossRefGoogle Scholar
  18. Hallez H, Vanrumste B, Hese PV, D’Asseler Y, Lemahieu I, de Walle RV (2005) A finite difference method with reciprocity used to incorporate anisotropy in electroencephalogram dipole source localization. Phys Med Biol 50:3787–3806PubMedCrossRefGoogle Scholar
  19. Hämäläinen M, Ilmoniemi R (1994) Interpreting magnetic fields of the brain: minimum norm estimates. Med Biol Eng Comp 32:35–42CrossRefGoogle Scholar
  20. Hämäläinen M, Hari R, Ilmoniemi R, Knuutila J, Lounasmaa O (1993) Magnetoencephalography: theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 65:413–497CrossRefGoogle Scholar
  21. Huiskamp G, Maintz J, Wieneke G, Viergever M, van Huffelen A (1997) The influence of the use of realistic head geometry in the dipole localization of interictal spike activity in MTLE patients. Biomed Tech 42:84–87Google Scholar
  22. Huiskamp G, Vroeijenstijn M, van Dijk R, Wieneke G, van Huffelen A (1999) The need for correct realistic geometry in the inverse EEG problem. IEEE Trans Biomed Eng 46(11):1281–1287PubMedCrossRefGoogle Scholar
  23. Huiskamp G, Oostendorp T, Hoekema R, Leijten F (2000) Simultaneous eeg/meg and ecog source characterization of interictal spikes. In: BIOMAG2000, Proceedings of the 12th international conference on biomagnetism. http://biomag2000.hut.fi
  24. Knösche T (1997) Solutions of the neuroelectromagnetic inverse problem. Ph.D. thesis, University of Twente, The NetherlandsGoogle Scholar
  25. Kobayashi K, Merlet I, Gotman J (2001) Separation of spikes from background by independent component analysis with dipole modeling and comparison to intracranial recordings. Clin Neurophysiol 112(3):405–413PubMedCrossRefGoogle Scholar
  26. Kybic J, Clerc M, Abboud T, Faugeras O, Keriven R, Papadopoulo T (2005) A common formalism for the integral formulations of the forward EEG problem. IEEE Trans Med Imag 24(1):12–18CrossRefGoogle Scholar
  27. Lai Y, van Drongelen W, Ding L, Hecox K, Towle V, Frim D, He B (2005) Estimation of in vivo human brain-to-skull conductivity ratio from simultaneous extra- and intra-cranial electrical potential recordings. Clin Neurophysiol 116:456–465PubMedCrossRefGoogle Scholar
  28. Lantz G, Holub H, Ryding E, Rosen I (1996) Simultaneous intracranial and extracranial recordings of interictal epileptiform activity in patients with drug resistent partial epilepsy: patterns of conduction and results from dipole reconstructions. Electroencephalogr Clin Neurophysiol 99:69–78PubMedCrossRefGoogle Scholar
  29. Lantz G, de Peralta MG, Gonzalez A, Michel C (2001) Noninvasive localization of electromagnetic epileptic activity. II. Demonstration of sublobar accuracy in patients with simultaneous surface and depth recordings. Brain Topogr 14(2):139–147PubMedCrossRefGoogle Scholar
  30. Law S (1993) Thickness and resistivity variations over the upper surface of the human skull. Brain Topogr 6(2):99–109PubMedCrossRefGoogle Scholar
  31. Leeman B, Cole A (2008) Advancements in the treatment of epilepsy. Ann Rev Med 59(1), 503–523. doi: 10.1146/annurev.med.58.071105.110848. http://arjournals.annualreviews.org/doi/abs/10.1146/annurev.med.58.071105.110848 Google Scholar
  32. Lew S, Wolters C, Anwander A, Makeig S, MacLeod R (2009) Improved EEG source analysis using low resolution conductivity estimation in a four-compartment finite element head model. Hum Brain Mapp 30(9), 2862–2878. http://dx.doi.org/10.1002/hbm.20714
  33. Lew S, Wolters C, Dierkes T, Röer C, MacLeod R (2009) Accuracy and run-time comparison for different potential approaches and iterative solvers in finite element method based EEG source analysis. Applied Numerical Mathematics 59(8):1970–1988. doi: 10.1016/j.apnum.2009.02.006
  34. Maes F, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imag 16(2):187–198CrossRefGoogle Scholar
  35. Meijs J, Weier O, Peters M, van Oosterom A (1989) On the numerical accuracy of the boundary element method. IEEE Trans Biomed Eng 36(10):1038–1049PubMedCrossRefGoogle Scholar
  36. Merlet I, Gotman J (1999) Reliability of dipole models of epileptic spikes. Clin Neurophysiol 110(6):1013–1028PubMedCrossRefGoogle Scholar
  37. Michel C, Murray M, Lantz G, Gonzalez S, Spinelli L, de Peralta R (2004) EEG source imaging. Clin Neurophysiol 115: 2195–2222. Invited reviewGoogle Scholar
  38. Mikuni N, Nagamine T, Ikeda A, Terada K, Taki W, Kimura J, Kikuchi H, Shibasaki H (1997) Simultaneous recording of epileptiform discharges by MEG and subdural electrodes in temporal lobe epilepsy. NeuroImage 5(4):298–306. doi: 10.1006/nimg.1997.0272. http://www.sciencedirect.com/science/article/pii/S105381199790272X Google Scholar
  39. Mosher J, Lewis P, Leahy R (1992) Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans Biomed Eng 39(6):541–557PubMedCrossRefGoogle Scholar
  40. Neuroscan: CURRY. CURrent Reconstruction and Imaging (2009)Google Scholar
  41. Pataraia E, Lindinger G, Deecke L, Mayer D, Baumgartner C (2005) Combined MEG/EEG analysis of the interictal spike complex in mesial temporal lobe epilepsy. NeuroImage 24:607–614PubMedCrossRefGoogle Scholar
  42. Penfield W (1950) The surgical therapy of temporal lobe seizures. Trans Am Neurol Assoc 51:146–149PubMedGoogle Scholar
  43. Pham D, Prince J (1998) An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recognit Lett 20:57–68CrossRefGoogle Scholar
  44. Plummer C, Harvey A, Cook M (2008) EEG source localization in focal epilepsy: where are we now?. Epilepsia 49(2):201–218PubMedCrossRefGoogle Scholar
  45. Pursiainen S, Sorrentino A, Campi C, Piana M (2011) Forward simulation and inverse dipole localization with the lowest order raviart-thomas elements for electroencephalography. Inverse Problems 27(4). doi: 10.1088/0266-5611/27/4/045003
  46. Ramon C, Schimpf P, Haueisen J, Holmes M, Ishimaru A (2004) Role of soft bone, CSF and gray matter in EEG simulations. Brain Topogr 16(4):245–248PubMedCrossRefGoogle Scholar
  47. Ray A, Tao J, Hawes-Ebersole S, Ebersole J (2007) Localizing value of scalp EEG spikes: a simultaneous scalp and intracranial study. J Clin Neurophysiol 118(1):69–79CrossRefGoogle Scholar
  48. Röer C (2008) Source analysis of simultaneous EEG and ECoG mesurements in presurgical epilepsy diagnosis. Diplomarbeit in physik, Institut für Biomagnetismus und Biosignalanalyse, Universitätsklinikum MünsterGoogle Scholar
  49. Rosenow F, Luders H (2001) Presurgical evaluation of epilepsy. Brain Behav Evol 124(Pt 9):1683–1700PubMedCrossRefGoogle Scholar
  50. Roth B, Ko D, von Albertini-Carletti I, Scaffidi D, Sato S (1997) Dipole localization in patients with epilepsy using the realistically shaped head model. Electroencephalogr Clin Neurophysiol 102:159–166PubMedCrossRefGoogle Scholar
  51. Rullmann M, Anwander A, Dannhauer M, Warfield S, Duffy F, Wolters C (2009) EEG source analysis of epileptiform activity using a 1mm anisotropic hexahedra finite element head model. NeuroImage 44(2):399–410. doi: 10.1016/j.neuroimage.2008.09.009. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2642992/
  52. Salayev K, Nakasato N, Ishitobi M, Shamoto H, Kanno A, Iinuma K (2006) Spike orientation may predict epileptogenic side across cerebral sulci containing the estimated equivalent dipole. Clin Neurophysiol 117:1836–43PubMedCrossRefGoogle Scholar
  53. Sarvas J (1987) Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Phys Med Biol 32(1):11–22PubMedCrossRefGoogle Scholar
  54. Scherg M, Von Cramon D (1986) Evoked dipole source potentials of the human auditory cortex. Electroencephalogr Clin Neurophysiol 65(5):344–60PubMedCrossRefGoogle Scholar
  55. Scherg M, Bast T, Berg P (1999) Multiple source analysis of interictal spikes: goals, requirements, and clinical value. J Clin Neurophysiol 16(3):214–224PubMedCrossRefGoogle Scholar
  56. Schimpf P, Haynor D, Kim Y (1996) Object-free adaptive meshing in highly heterogeneous 3-D domains. Int J Biomed Comput 40(3):209–225. doi: 10.1016/0020-7101(95)01146-3. http://www.sciencedirect.com/science/article/pii/0020710195011463 Google Scholar
  57. Schwarz H (1991) Methode der finiten Elemente. B.G.Teubner, StuttgartCrossRefGoogle Scholar
  58. Scientific Computing and Imaging Institute (SCI): SCIRun: A scientific computing problem solving environment. http://www.scirun.org
  59. Si H (2008) Adaptive tetrahedral mesh generation by constrained Delaunay refinement. Int J Numer Methods Eng 75(7):856–880. doi: 10.1002/nme.2318 Google Scholar
  60. Si H (2009) TetGen—a quality tetrahedral mesh generator and three-dimensional Delaunay triangulator, user’s manual. Tech. rep., Weierstra\({\ss}\)-Institut für Angewandte Analysis und Stochastik, Berlin. http://tetgen.berlios.de
  61. Si H, Gärtner K (2005) Meshing piecewise linear complexes by constrained Delaunay tetrahedralizations. In: Proceedings of the 14th international meshing roundtable, pp 147–163. Sandia National LaboratoriesGoogle Scholar
  62. SimBio Development Group: SimBio: A generic environment for bio-numerical simulations. online, http://www.mrt.uni-jena.de/simbio. Accessed 15 June 2012
  63. Soza G (2005) Registration and simulation for the analysis of intraoperative brain shift. Ph.D. thesis, Faculty of Computer Science, Friedrich-Alexander-Universität Erlangen-NürnbergGoogle Scholar
  64. Stefan H, Hummel C, Scheler G, Genow A, Druschky K, Tilz C, Kaltenhauser M, Hopfengartner R, Buchfelder M, Romstock J (2003) Magnetic brain source imaging of focal epileptic activity: a synopsis of 455 cases. Brain Behav Evol 126(Pt 11):2396–2405PubMedCrossRefGoogle Scholar
  65. Tao J, Baldwin M, Hawes-Ebersole S, Ebersole J (2007a) Cortical substrates of scalp EEG epileptiform discharges. J Clin Neurophysiol 24(2):96–100PubMedCrossRefGoogle Scholar
  66. Tao J, Baldwin M, Ray A, Hawes-Ebersole S, Ebersole J (2007b) The impact of cerebral source area and synchrony on recording scalp electroencephalographyictal patterns. Epilepsia 48(11):2167–2176PubMedCrossRefGoogle Scholar
  67. Vallaghe S, Papadopoulo T (2010) A trilinear immersed finite element method for solving the electroencephalography forward problem. SIAM J Sci Comput 32(4):2379 doi: 10.1137/09075038X CrossRefGoogle Scholar
  68. van den Broek S, Reinders F, Donderwinkel M, Peters M (1998) Volume conduction effects in EEG and MEG. Electroencephalogr Clin Neurophysiol 106:522–534PubMedCrossRefGoogle Scholar
  69. Waberski T, Gobbele R, Herrendorf G, Steinhoff B, Kolle R, Fuchs M, Paulus W, Buchner H (2000) Source reconstruction of mesial-temporal epileptiform activity: comparison of inverse techniques. Epilepsia 41(12):1574–583PubMedCrossRefGoogle Scholar
  70. Weinstein D, Zhukov L, Johnson C (2000) Lead-field bases for electroencephalography source imaging. Ann Biomed Eng 28(9):1059–1066PubMedCrossRefGoogle Scholar
  71. Wiebe S, Blume W, Girvin J, Eliasziw M (2001) A randomized, controlled trial of surgery for temporal-lobe epilepsy. New England J Med 345(5):311–318CrossRefGoogle Scholar
  72. Wolters C (2008) Finite element method based electro- and magnetoencephalography source analysis in the human brain. Habilitation in mathematics, Faculty of Mathematics and Natural Sciences, University of Münster, GermanyGoogle Scholar
  73. Wolters C, Grasedyck L, Hackbusch W (2004) Efficient computation of lead field bases and influence matrix for the FEM-based EEG and MEG inverse problem. Inverse Probl 20(4):1099–1116. doi: 10.1088/0266-5611/20/4/007 CrossRefGoogle Scholar
  74. Wolters C, Anwander A, Weinstein D, Koch M, Tricoche X, MacLeod R (2006) 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 30(3):813–826. doi: 10.1016/j.neuroimage.2005.10.014 PubMedCrossRefGoogle Scholar
  75. Wolters C, Anwander A, Berti G, Hartmann U (2007a) Geometry-adapted hexahedral meshes improve accuracy of finite element method based EEG source analysis. IEEE Trans Biomed Eng 54(8):1446–1453. doi: 10.1109/TBME.2007.890736 PubMedCrossRefGoogle Scholar
  76. Wolters C, Köstler H, Möller C, Härtlein J, Grasedyck L, Hackbusch W (2007b) Numerical mathematics of the subtraction method for the modeling of a current dipole in EEG source reconstruction using finite element head models. SIAM J Sci Comput 30(1):24–45. doi: 10.1137/060659053 CrossRefGoogle Scholar
  77. Zhang Y, Ding L, van Drongelen W, Hecox K, Frim D, He B (2006) A cortical potential imaging study from simultaneous extra- and intracranial electrical recordings by means of the finite element method. Neuroimage 31(4):1513–1524PubMedCrossRefGoogle Scholar
  78. Zhang Y, van Drongelen W, Kohrman M, He B (2008) Three-dimensional brain current source reconstruction from intra-cranial ECoG recordings. NeuroImage 42(2):683–695. doi: 10.1016/j.neuroimage.2008.04.263. http://www.sciencedirect.com/science/article/B6WNP-4SGKBB4-3/2/be9c5075b633a57b111b633e3203b58b
  79. Zienkiewicz OC, Taylor RL, Zhu JZ (2005) The finite element method. Its basis and fundamentals. Elsevier, Butterworth-HeinemannGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Benjamin Lanfer
    • 1
  • Christian Röer
    • 1
  • Michael Scherg
    • 2
  • Stefan Rampp
    • 3
  • Christoph Kellinghaus
    • 4
  • Carsten Wolters
    • 1
  1. 1.Institute for Biomagnetism and BiosignalanalysisWestfälische Wilhelms-Universität MünsterMünsterGermany
  2. 2.BESA GmbHGräfelfingGermany
  3. 3.Department of Neurology, Epilepsy CenterUniversity Hospital ErlangenErlangenGermany
  4. 4.Department of NeurologyKlinikum OsnabrückOsnabrückGermany

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