Skip to main content
SpringerLink
Log in

New horizons in clinical electric source imaging

Neue Perspektiven in der klinischen EEG-Quellenlokalisation

  • Review
  • Published:
Zeitschrift für Epileptologie Aims and scope Submit manuscript

Abstract

The clinical usefulness of electric source imaging (ESI) in presurgical epilepsy evaluation has now been demonstrated; however, its use in clinical routine remains somewhat limited. Here, we discuss the added clinical value of ESI and how its integration in the presurgical work-up of epilepsy patients can be optimized. We present methodological differences of ESI on interictal and ictal epileptic activity as well as their evaluation compared with intracranial EEG data. Beyond the choice of patterns used for ESI, the choice of inverse methods can have tremendous impact on source imaging results. It is of great clinical interest that during recent years several inverse methods have been developed that are sensitive to the spatial extent of the generators of epileptic activity. In this regard, we discuss the performance of different approaches. Advanced inverse methods that consider the temporal evolution of the EEG signal even allow for localizing deep generators with good spatial accuracy. In summary, recent methodological advances in ESI foster its application in clinical practice.

Zusammenfassung

Der Nutzen der Elektroenzephalogramm(EEG)-Quellenlokalisation („electric source imaging“, ESI) in der prächirurgischen Epilepsiediagnostik ist belegt, klinisch wird ESI allerdings weiterhin nur begrenzt eingesetzt. Innerhalb dieses Artikels werden Vorteile der ESI und ihre Integration in den klinischen Alltag vorgestellt. Es werden methodische Unterschiede zwischen ESI auf der Basis interiktaler und iktaler epileptischer Aktivität und die Validierung mit dem intrakraniellen EEG erörtert. Über die Auswahl der EEG-Muster hinaus kann die Wahl der inversen Methode entscheidenden Einfluss auf das ESI-Ergebnis haben. Von großem klinischem Interesse ist, dass in letzter Zeit inverse Methoden entwickelt wurden, die sensitiv für die Ausdehnung von Generatoren epileptischer Aktivität sind. In diesem Zusammenhang wird die Leistungsfähigkeit unterschiedlicher Methoden verglichen. Fortgeschrittene inverse Methoden sind zudem in der Lage, den zeitlichen Verlauf von EEG-Signalen zu berücksichtigen und auch von der Oberfläche weit entfernte, tiefe Generatoren epileptischer Aktivität mit guter räumlicher Auflösung zu lokalisieren. Zusammenfassend unterstützen neue methodische Entwicklungen die weitere klinische Anwendung von ESI.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Michel CM, Murray MM, Lantz G, Gonzalez S, Spinelli L, Grave de Peralta R (2004) EEG source imaging. Clin Neurophysiol 115(10):2195–2222

    Article  Google Scholar 

  2. Vulliemoz S, Lemieux L, Daunizeau J, Michel CM, Duncan JS (2010) The combination of EEG source imaging and EEG-correlated functional MRI to map epileptic networks. Epilepsia 51(4):491–505. https://doi.org/10.1111/j.1528-1167.2009.02342.x

    Article  PubMed  Google Scholar 

  3. Heers M, Hedrich T, An D et al (2014) Spatial correlation of hemodynamic changes related to interictal epileptic discharges with electric and magnetic source imaging. Hum Brain Mapp 35:4396–4414. https://doi.org/10.1002/hbm.22482

    Article  PubMed  Google Scholar 

  4. Mégevand P, Seeck M (2018) Electroencephalography, magnetoencephalography and source localization: their value in epilepsy. Curr Opin Neurol 31(2):176–183. https://doi.org/10.1097/WCO.0000000000000545

    Article  PubMed  Google Scholar 

  5. Mouthaan BE, Rados M, Barsi P et al (2016) Current use of imaging and electromagnetic source localization procedures in epilepsy surgery centers across Europe. Epilepsia 57:770–776. https://doi.org/10.1111/epi.13347

    Article  PubMed  Google Scholar 

  6. Plummer C, Harvey AS, Cook M (2008) EEG source localization in focal epilepsy: Where are we now? Epilepsia 49:201–218. https://doi.org/10.1111/j.1528-1167.2007.01381.x

    Article  PubMed  Google Scholar 

  7. Feng R, Hu J, Pan L et al (2016) Application of 256-channel dense array electroencephalographic source imaging in presurgical workup of temporal lobe epilepsy. Clin Neurophysiol. https://doi.org/10.1016/j.clinph.2015.03.009

    Article  PubMed  Google Scholar 

  8. Englot DJ, Nagarajan SS, Imber BS et al (2015) Epileptogenic zone localization using magnetoencephalography predicts seizure freedom in epilepsy surgery. Epilepsia. https://doi.org/10.1111/epi.13002

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lascano AM, Perneger T, Vulliemoz S et al (2016) Yield of MRI, high-density electric source imaging (HD-ESI), SPECT and PET in epilepsy surgery candidates. Clin Neurophysiol 127:150–155. https://doi.org/10.1016/j.clinph.2015.03.025

    Article  PubMed  Google Scholar 

  10. Knowlton RC, Elgavish RA, Bartolucci A et al (2008) Functional imaging: II. Prediction of epilepsy surgery outcome. Ann Neurol 64:35–41. https://doi.org/10.1002/ana.21419

    Article  PubMed  Google Scholar 

  11. Brodbeck V, Spinelli L, Lascano AM et al (2011) Electroencephalographic source imaging: a prospective study of 152 operated epileptic patients. Brain 134:2887–2897. https://doi.org/10.1093/brain/awr243

    Article  PubMed  PubMed Central  Google Scholar 

  12. Abdallah C, Maillard LG, Rikir E et al (2017) Localizing value of electrical source imaging: frontal lobe, malformations of cortical development and negative MRI related epilepsies are the best candidates. Neuroimage Clin 16:319–329. https://doi.org/10.1016/J.NICL.2017.08.009

    Article  PubMed  PubMed Central  Google Scholar 

  13. Mégevand P, Spinelli L, Genetti M et al (2014) Electric source imaging of interictal activity accurately localises the seizure onset zone. J Neurol Neurosurg Psychiatry 85:38–43. https://doi.org/10.1136/jnnp-2013-305515

    Article  PubMed  Google Scholar 

  14. Brodbeck V, Lascano AM, Spinelli L et al (2009) Accuracy of EEG source imaging of epileptic spikes in patients with large brain lesions. Clin Neurophysiol. https://doi.org/10.1016/j.clinph.2009.01.011

    Article  PubMed  Google Scholar 

  15. Brodbeck V, Spinelli L, Lascano AM et al (2010) Electrical source imaging for presurgical focus localization in epilepsy patients with normal MRI. Epilepsia. https://doi.org/10.1111/j.1528-1167.2010.02521.x

    Article  PubMed  Google Scholar 

  16. Lantz G, Grave de Peralta R, Spinelli L et al (2003) Epileptic source localization with high density EEG: How many electrodes are needed? Clin Neurophysiol 114:63–69. https://doi.org/10.1016/S1388-2457(02)00337-1

    Article  CAS  Google Scholar 

  17. Song J, Davey C, Poulsen C et al (2015) EEG source localization: sensor density and head surface coverage. J Neurosci Methods 256:9–21. https://doi.org/10.1016/j.jneumeth.2015.08.015

    Article  PubMed  Google Scholar 

  18. Bach Justesen A, Eskelund Johansen AB, Martinussen NI et al (2018) Added clinical value of the inferior temporal EEG electrode chain. Clin Neurophysiol. https://doi.org/10.1016/j.clinph.2017.09.113

    Article  PubMed  Google Scholar 

  19. Seeck M, Koessler L, Bast T et al (2017) The standardized EEG electrode array of the IFCN. Clin Neurophysiol 128:2070–2077. https://doi.org/10.1016/j.clinph.2017.06.254

    Article  PubMed  Google Scholar 

  20. Lantz G, Spinelli L, Seeck M et al (2003) Propagation of interictal epileptiform activity can lead to erroneous source localizations: a 128-channel EEG mapping study. J Clin Neurophysiol 20:311–319

    Article  PubMed  Google Scholar 

  21. Mălîia MD, Meritam P, Scherg M et al (2016) Epileptiform discharge propagation: analyzing spikes from the onset to the peak. Clin Neurophysiol 127:2127–2133. https://doi.org/10.1016/j.clinph.2015.12.021

    Article  PubMed  Google Scholar 

  22. Scheuer ML, Bagic A, Wilson SB (2017) Spike detection: Inter-reader agreement and a statistical Turing test on a large data set. Clin Neurophysiol 128:243–250. https://doi.org/10.1016/j.clinph.2016.11.005

    Article  PubMed  Google Scholar 

  23. Birot G, Spinelli L, Vulliémoz S et al (2014) Head model and electrical source imaging: a study of 38 epileptic patients. Neuroimage Clin 5:77–83. https://doi.org/10.1016/j.nicl.2014.06.005

    Article  PubMed  PubMed Central  Google Scholar 

  24. Vorwerk J, Cho JH, Rampp S et al (2014) A guideline for head volume conductor modeling in EEG and MEG. Neuroimage 100:590–607. https://doi.org/10.1016/j.neuroimage.2014.06.040

    Article  PubMed  Google Scholar 

  25. Brunet D, Murray MM, Michel CM (2011) Spatiotemporal analysis of multichannel EEG: CARTOOL. Comput Intell Neurosci. https://doi.org/10.1155/2011/813870

    Article  PubMed  PubMed Central  Google Scholar 

  26. Oostenveld R, Fries P, Maris E, Schoffelen JM (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci. https://doi.org/10.1155/2011/156869

    Article  PubMed  PubMed Central  Google Scholar 

  27. Tadel F, Baillet S, Mosher JC et al (2011) Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci. https://doi.org/10.1155/2011/879716

    Article  PubMed  PubMed Central  Google Scholar 

  28. van Mierlo P, Strobbe G, Keereman V et al (2017) Automated long-term EEG analysis to localize the epileptogenic zone. Epilepsia Open. https://doi.org/10.1002/epi4.12066

    Article  PubMed  PubMed Central  Google Scholar 

  29. Koessler L, Benar C, Maillard L et al (2010) Source localization of ictal epileptic activity investigated by high resolution EEG and validated by SEEG. Neuroimage. https://doi.org/10.1016/j.neuroimage.2010.02.067

    Article  PubMed  Google Scholar 

  30. Beniczky S, Lantz G, Rosenzweig I et al (2013) Source localization of rhythmic ictal EEG activity: a study of diagnostic accuracy following STARD criteria. Epilepsia. https://doi.org/10.1111/epi.12339

    Article  PubMed  PubMed Central  Google Scholar 

  31. Nemtsas P, Birot G, Pittau F et al (2017) Source localization of ictal epileptic activity based on high-density scalp EEG data. Epilepsia. https://doi.org/10.1111/epi.13749

    Article  PubMed  Google Scholar 

  32. Lantz G, Michel CM, Seeck M et al (1999) Frequency domain EEG source localization of ictal epileptiform activity in patients with partial complex epilepsy of temporal lobe origin. Clin Neurophysiol. https://doi.org/10.1016/S0013-4694(98)00117-5

    Article  PubMed  Google Scholar 

  33. Pellegrino G, Hedrich T, Chowdhury R et al (2016) Source localization of the seizure onset zone from ictal EEG/MEG data. Hum Brain Mapp. https://doi.org/10.1002/hbm.23191

    Article  PubMed  Google Scholar 

  34. Mosher JC, Baillet S, Leahy RM (1999) EEG source localization and imaging using multiple signal classification approaches. J Clin Neurophysiol 16:225–238

    Article  CAS  PubMed  Google Scholar 

  35. Beniczky S, Oturai PS, Alving J et al (2006) Source analysis of epileptic discharges using multiple signal classification analysis. Neuroreport. https://doi.org/10.1097/01.wnr.0000230517.93714.f6

    Article  PubMed  Google Scholar 

  36. Gross J, Kujala J, Hamalainen M et al (2001) Dynamic imaging of coherent sources: studying neural interactions in the human brain. Proc Natl Acad Sci U S A 98:694–699. https://doi.org/10.1073/pnas.98.2.694

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Elshoff L, Muthuraman M, Anwar AR et al (2013) Dynamic imaging of coherent sources reveals different network connectivity underlying the generation and perpetuation of epileptic seizures. PLoS ONE 8:1–11. https://doi.org/10.1371/journal.pone.0078422

    Article  CAS  Google Scholar 

  38. Heers M, Hirschmann J, Jacobs J et al (2014) Frequency domain beamforming of magnetoencephalographic beta band activity in epilepsy patients with focal cortical dysplasia. Epilepsy Res 108:1195–1203. https://doi.org/10.1016/j.eplepsyres.2014.05.003

    Article  PubMed  Google Scholar 

  39. Pascual-Marqui RD (2002) Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol 24(Suppl D):5–12

    PubMed  Google Scholar 

  40. Epitashvili N, Schulze-Bonhage A, Dümpelmann M (2016) Source localization of ictal epileptic activity in patients with focal epilepsy validated with invasive EEG. Epilepsia 57(Suppl. 2):135. https://doi.org/10.1111/epi.13609

    Article  Google Scholar 

  41. Cosandier-Rimélé D, Ramantani G, Zentner J et al (2017) A realistic multimodal modeling approach for the evaluation of distributed source analysis: application to sLORETA. J Neural Eng 14:56008. https://doi.org/10.1088/1741-2552/aa7db1

    Article  PubMed  Google Scholar 

  42. Ramantani G, Cosandier-Rimélé D, Schulze-Bonhage A et al (2013) Source reconstruction based on subdural EEG recordings adds to the presurgical evaluation in refractory frontal lobe epilepsy. Clin Neurophysiol. https://doi.org/10.1016/j.clinph.2012.09.001

    Article  PubMed  Google Scholar 

  43. Lanfer B, Scherg M, Dannhauer M et al (2012) Influences of skull segmentation inaccuracies on EEG source analysis. Neuroimage. https://doi.org/10.1016/j.neuroimage.2012.05.006

    Article  PubMed  Google Scholar 

  44. Lanfer B, Röer C, Scherg M et al (2013) Influence of a silastic ECoG grid on EEG/ECoG based source analysis. Brain Topogr. https://doi.org/10.1007/s10548-012-0251-0

    Article  PubMed  Google Scholar 

  45. von Ellenrieder N, Beltrachini L, Muravchik CH, Gotman J (2014) Extent of cortical generators visible on the scalp: effect of a subdural grid. Neuroimage. https://doi.org/10.1016/j.neuroimage.2014.08.009

    Article  Google Scholar 

  46. von Ellenrieder N, Beltrachini L, Perucca P, Gotman J (2014) Size of cortical generators of epileptic interictal events and visibility on scalp EEG. Neuroimage. https://doi.org/10.1016/j.neuroimage.2014.02.032

    Article  Google Scholar 

  47. Tao JX, Ray A, Hawes-Ebersole S, Ebersole JS (2005) Intracranial EEG substrates of scalp EEG interictal spikes. Epilepsia 46(5):669–676

    Article  PubMed  Google Scholar 

  48. Ramantani G, Dümpelmann M, Koessler L et al (2014) Simultaneous subdural and scalp EEG correlates of frontal lobe epileptic sources. Epilepsia. https://doi.org/10.1111/epi.12512

    Article  PubMed  Google Scholar 

  49. Kobayashi K, Yoshinaga H, Ohtsuka Y, Gotman J (2005) Dipole modeling of epileptic spikes can be accurate or misleading. Epilepsia. https://doi.org/10.1111/j.0013-9580.2005.31404.x

    Article  PubMed  Google Scholar 

  50. Lapalme E, Lina JM, Mattout J (2006) Data-driven parceling and entropic inference in MEG. Neuroimage. https://doi.org/10.1016/j.neuroimage.2005.08.067

    Article  PubMed  Google Scholar 

  51. Amblard C, Lapalme E, Lina JM (2004) Biomagnetic source detection by maximum entropy and graphical models. Ieee Trans Biomed Eng. https://doi.org/10.1109/TBME.2003.820999

    Article  PubMed  Google Scholar 

  52. Hedrich T, Pellegrino G, Kobayashi E et al (2017) Comparison of the spatial resolution of source imaging techniques in high-density EEG and MEG. Neuroimage. https://doi.org/10.1016/j.neuroimage.2017.06.022

    Article  PubMed  Google Scholar 

  53. Molins A, Stufflebeam SM, Brown EN, Hämäläinen MS (2008) Quantification of the benefit from integrating MEG and EEG data in minimum ℓ2-norm estimation. Neuroimage. https://doi.org/10.1016/j.neuroimage.2008.05.064

    Article  PubMed  Google Scholar 

  54. Grova C, Daunizeau J, Lina JM et al (2006) Evaluation of EEG localization methods using realistic simulations of interictal spikes. Neuroimage. https://doi.org/10.1016/j.neuroimage.2005.08.053

    Article  PubMed  Google Scholar 

  55. Metz CE (1986) ROC methodology in radiologic imaging. Invest Radiol 21:720–733

    Article  CAS  PubMed  Google Scholar 

  56. Birot G, Albera L, Wendling F, Merlet I (2011) Localization of extended brain sources from EEG/MEG: The ExSo-MUSIC approach. Neuroimage. https://doi.org/10.1016/j.neuroimage.2011.01.054

    Article  PubMed  Google Scholar 

  57. Mosher JC, Lewis PS, Leahy RM (1992) Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans Biomed Eng. https://doi.org/10.1109/10.141192

    Article  PubMed  Google Scholar 

  58. Chowdhury RA, Merlet I, Birot G et al (2016) Complex patterns of spatially extended generators of epileptic activity: comparison of source localization methods cMEM and 4‑exso-MUSIC on high resolution EEG and MEG data. Neuroimage. https://doi.org/10.1016/j.neuroimage.2016.08.044

    Article  PubMed  PubMed Central  Google Scholar 

  59. Friston K, Harrison L, Daunizeau J et al (2008) Multiple sparse priors for the M/EEG inverse problem. Neuroimage. https://doi.org/10.1016/j.neuroimage.2007.09.048

    Article  PubMed  PubMed Central  Google Scholar 

  60. Pascual-Marqui RD, Michel CM, Lehmann D (1994) Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int J Psychophysiol. https://doi.org/10.1016/0167-8760(84)90014-X

    Article  PubMed  Google Scholar 

  61. Hämäläinen MS, Ilmoniemi RJ (1994) Interpreting magnetic fields of the brain: minimum norm estimates. Med Biol Eng Comput. https://doi.org/10.1007/BF02512476

    Article  PubMed  Google Scholar 

  62. Heers M, Chowdhury RA, Hedrich T et al (2016) Localization accuracy of distributed inverse solutions for electric and magnetic source imaging of Interictal epileptic discharges in patients with focal epilepsy. Brain Topogr. https://doi.org/10.1007/s10548-014-0423-1

    Article  PubMed  Google Scholar 

  63. Sohrabpour A, Lu Y, Worrell G, He B (2016) Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy. Neuroimage 142:27–42. https://doi.org/10.1016/j.neuroimage.2016.05.064

    Article  PubMed  PubMed Central  Google Scholar 

  64. Pellegrino G, Hedrich T, Chowdhury RA et al (2018) Clinical yield of magnetoencephalography distributed source imaging in epilepsy: A comparison with equivalent current dipole method. Hum Brain Mapp 39:218–231. https://doi.org/10.1002/hbm.23837

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Laith Hamid acknowledges the support by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the Collaborative Research Center (Sonderforschungsbereich) SFB1261 “Magnetoelectric Electrodes: From Composite Materials to Biomagnetic Diagnostics” and the support by the European Union’s Seventh Framework Programme for research, technological development and demonstration through the project DESIRE “Development Epilepsy” (Grant Agreement no: 602531), WP2 & WP4, http://epilepsydesireproject.eu/. Pierre Mégevand acknowledges the support of the Swiss National Science Foundation (grant 167836).

Author information

Authors and Affiliations

  1. Department of Basic Neuroscience, Faculty of Medicine, University of Geneva, Geneva, Switzerland

    Pierre Mégevand MD, Ph.D.

  2. Division of Neurology, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, 1205, Geneva, Switzerland

    Pierre Mégevand MD, Ph.D.

  3. Institute of Medical Psychology and Medical Sociology, University Medical Center of Schleswig Holstein, Faculty of Medicine, University of Kiel, Kiel, Germany

    Laith Hamid

  4. Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Str. 64, 79100, Freiburg i. Br., Germany

    Matthias Dümpelmann &  Marcel Heers

Authors
  1. Pierre Mégevand MD, Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laith Hamid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthias Dümpelmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marcel Heers
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Pierre Mégevand MD, Ph.D. or Marcel Heers.

Ethics declarations

Conflict of interest

P. Mégevand, L. Hamid, M. Dümpelmann, and M. Heers declare that they have no competing interests.

For this article no studies with human participants or animals were performed by any of the authors. All studies performed were in accordance with the ethical standards indicated in each case.

Additional information

Disclaimer

Some of the methods reported in this article are not certified for clinical use yet.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mégevand, P., Hamid, L., Dümpelmann, M. et al. New horizons in clinical electric source imaging. Z. Epileptol. 32, 187–193 (2019). https://doi.org/10.1007/s10309-019-0258-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10309-019-0258-6

Keywords

Schlüsselwörter

Search

Navigation