Measuring Large-Scale Synchronization with Human MEG and EEG: Challenges and Solutions

  • Felix Siebenhühner
  • Muriel Lobier
  • Sheng H. Wang
  • Satu Palva
  • J. Matias PalvaEmail author


Specific kinds of neuronal interactions, such as phase coupling of neuronal oscillations, are likely to be essential systems-level mechanisms for coordinating neuronal communication, integration, and segregation. The functional roles of these interactions during cognitive tasks in healthy humans can be investigated with magneto- and electroencephalography (MEG/EEG), the only means for noninvasive electrophysiological recordings of human cortical activity. While advances in source modeling have opened new avenues for assessing inter-areal interactions with MEG/EEG, several factors limit the accuracy and inferential value of such analyses. In this chapter, we provide an overview of common source analysis strategies for mapping inter-areal interactions with MEG/EEG. Linear mixing between sources, as caused by volume conduction and signal mixing, is the principal confounder in connectivity analysis and always leads to false positive observations. We discuss the sensitivity of different interaction metrics to directly and indirectly caused false positives and conclude with approaches to mitigate these problems. In conclusion, MEG and EEG are becoming increasingly useful for assessing inter-areal neuronal interaction in humans.


Independent Component Analysis Volume Conduction Neuronal Oscillation True Interaction Source Reconstruction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Antiqueira L, Rodrigues FA, van Wijk BCM, Costa LF, Daffertshofer A (2010) Estimating complex cortical networks via surface recordings—a critical note. Neuroimage 53:439–449CrossRefPubMedGoogle Scholar
  2. Bassett DS, Bullmore E, Verchinski BA, Mattay VS, Weinberger DR, Meyer-Lindenberg A (2008) Hierarchical organization of human cortical networks in health and schizophrenia. J Neurosci 28:9239–9248CrossRefPubMedPubMedCentralGoogle Scholar
  3. Bassett DS, Nelson BG, Mueller BA, Camchong J, Lim KO (2011) Altered resting state complexity in schizophrenia. Neuroimage 59:2196–2207CrossRefPubMedPubMedCentralGoogle Scholar
  4. Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7:1129–1159CrossRefPubMedGoogle Scholar
  5. Brookes MJ, Woolrich MW, Barnes GR (2012) Measuring functional connectivity in MEG: a multivariate approach insensitive to linear source leakage. Neuroimage 63:910–920CrossRefPubMedPubMedCentralGoogle Scholar
  6. Bruns A (2004) Fourier-, hilbert- and wavelet-based signal analysis: are they really different approaches? J Neurosci Methods 137:321–332CrossRefPubMedGoogle Scholar
  7. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198CrossRefPubMedGoogle Scholar
  8. Buzsaki G, Draguhn A (2004) Neuronal oscillations in cortical networks. Science 304:1926–1929CrossRefPubMedGoogle Scholar
  9. Cammoun L, Gigandet X, Meskaldji D, Thiran JP, Sporns O, Do KQ, Maeder P, Meuli R, Hagmann P (2012) Mapping the human connectome at multiple scales with diffusion spectrum MRI. J Neurosci Methods 203:386–397CrossRefPubMedGoogle Scholar
  10. Daffertshofer A, van Wijk BCM (2011) On the influence of amplitude on the connectivity between phases. Front Neuroinform 5:6CrossRefPubMedPubMedCentralGoogle Scholar
  11. Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179–194CrossRefPubMedGoogle Scholar
  12. Drakesmith M, El-Deredy W, Welbourne S (2013) Reconstructing coherent networks from electroencephalography and magnetoencephalography with reduced contamination from volume conduction or magnetic field spread. PLoS One 8, e81553CrossRefPubMedPubMedCentralGoogle Scholar
  13. Fischl B, Sereno MI, Dale AM (1999) Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage 9:195–207CrossRefPubMedGoogle Scholar
  14. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33:341–355CrossRefPubMedGoogle Scholar
  15. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V, Makris N, Rosen B, Dale AM (2004) Automatically parcellating the human cerebral cortex. Cereb Cortex 14:11–22CrossRefPubMedGoogle Scholar
  16. Fries P (2015) Rhythms for cognition: communication through coherence. Neuron 88:220–235CrossRefPubMedGoogle Scholar
  17. Gross J, Baillet S, Barnes GR, Henson RN, Hillebrand A, Jensen O, Jerbi K, Litvak V, Maess B, Oostenveld R, Parkkonen L, Taylor JR, van Wassenhove V, Wibral M, Schoffelen JM (2013) Good practice for conducting and reporting MEG research. Neuroimage 65:349–363CrossRefPubMedPubMedCentralGoogle Scholar
  18. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6, e159CrossRefPubMedPubMedCentralGoogle Scholar
  19. Hamalainen MS, Sarvas J (1989) Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data. IEEE Trans Biomed Eng 36:165–171CrossRefPubMedGoogle Scholar
  20. Hillebrand A, Barnes GR, Bosboom JL, Berendse HW, Stam CJ (2012) Frequency-dependent functional connectivity within resting-state networks: an atlas-based MEG beamformer solution. Neuroimage 59:3909–3921CrossRefPubMedPubMedCentralGoogle Scholar
  21. Hipp JF, Hawellek DJ, Corbetta M, Siegel M, Engel AK (2012) Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat Neurosci 15(6):884–890CrossRefPubMedGoogle Scholar
  22. Ioannidis JP (2005) Why most published research findings are false. PLoS Med 2, e124CrossRefPubMedPubMedCentralGoogle Scholar
  23. Korhonen O, Palva S, Palva JM (2014) Sparse weightings for collapsing inverse solutions to cortical parcellations optimize M/EEG source reconstruction accuracy. J Neurosci Methods 226C:147–160CrossRefGoogle Scholar
  24. Kriegeskorte N, Simmons WK, Bellgowan PS, Baker CI (2009) Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci 12:535–540CrossRefPubMedPubMedCentralGoogle Scholar
  25. Lachaux JP, Rodriguez E, Martinerie J, Varela FJ (1999) Measuring phase synchrony in brain signals. Hum Brain Mapp 8:194–208CrossRefPubMedGoogle Scholar
  26. Li X, Yao X, Fox J, Jefferys JG (2007) Interaction dynamics of neuronal oscillations analysed using wavelet transforms. J Neurosci Methods 160:178–185CrossRefPubMedGoogle Scholar
  27. Lin FH, Witzel T, Ahlfors SP, Stufflebeam SM, Belliveau JW, Hamalainen MS (2006) Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates. Neuroimage 31:160–171CrossRefPubMedGoogle Scholar
  28. Micheloyannis S, Pachou E, Stam CJ, Breakspear M, Bitsios P, Vourkas M, Erimaki S, Zervakis M (2006) Small-world networks and disturbed functional connectivity in schizophrenia. Schizophr Res 87:60–66CrossRefPubMedGoogle Scholar
  29. Mosher JC, Leahy RM, Lewis PS (1999) EEG and MEG: forward solutions for inverse methods. IEEE Trans Biomed Eng 46:245–259CrossRefPubMedGoogle Scholar
  30. Newman M (2003) The structure and function of complex networks. SIAM Rev 45:167–256CrossRefGoogle Scholar
  31. Nikulin VV, Linkenkaer-Hansen K, Nolte G, Lemm S, Muller KR, Ilmoniemi RJ, Curio G (2007) A novel mechanism for evoked responses in the human brain. Eur J Neurosci 25:3146–3154CrossRefPubMedGoogle Scholar
  32. Palva JM, Palva S, Kaila K (2005) Phase synchrony among neuronal oscillations in the human cortex. J Neurosci 25:3962–3972CrossRefPubMedGoogle Scholar
  33. Palva S, Palva JM (2012) Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs. Trends Cogn Sci 16:219–230CrossRefPubMedGoogle Scholar
  34. Palva JM, Monto S, Kulashekhar S, Palva S (2010) Neuronal synchrony reveals working memory networks and predicts individual memory capacity. Proc Natl Acad Sci USA 107:7580–7585CrossRefPubMedPubMedCentralGoogle Scholar
  35. Palva S, Kulashekhar S, Hamalainen M, Palva JM (2011) Localization of cortical phase and amplitude dynamics during visual working memory encoding and retention. J Neurosci 31:5013–5025CrossRefPubMedPubMedCentralGoogle Scholar
  36. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069CrossRefPubMedGoogle Scholar
  37. Schoffelen JM, Gross J (2009) Source connectivity analysis with MEG and EEG. Hum Brain Mapp 30:1857–1865CrossRefPubMedGoogle Scholar
  38. Sharon D, Hamalainen MS, Tootell RB, Halgren E, Belliveau JW (2007) The advantage of combining MEG and EEG: comparison to fMRI in focally stimulated visual cortex. Neuroimage 36:1225–1235CrossRefPubMedPubMedCentralGoogle Scholar
  39. Siebenhühner F, Weiss SA, Coppola R, Weinberger DR, Bassett DS (2013) Intra- and inter-frequency brain network structure in health and schizophrenia. PLoS One 8, e72351CrossRefPubMedPubMedCentralGoogle Scholar
  40. Singer W (1999) Neuronal synchrony: a versatile code for the definition of relations? Neuron 24(49–65):111–125Google Scholar
  41. Singer W (2009) Distributed processing and temporal codes in neuronal networks. Cogn Neurodyn 3:189–196CrossRefPubMedPubMedCentralGoogle Scholar
  42. Sinkkonen J, Tiitinen H, Naatanen R (1995) Gabor filters: an informative way for analysing event-related brain activity. J Neurosci Methods 56:99–104CrossRefPubMedGoogle Scholar
  43. Stam CJ, van Straaten ECW (2012) Go with the flow: use of a directed phase lag index (dPLI) to characterize patterns of phase relations in a large-scale model of brain dynamics. Neuroimage 62:1415–1428CrossRefPubMedGoogle Scholar
  44. Stam CJ, Nolte G, Daffertshofer A (2007) Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 28:1178–1193CrossRefPubMedGoogle Scholar
  45. Taulu S, Simola J, Kajola M (2005) Applications of the signal space separation method. IEEE Trans Signal Process 53:3359–3372CrossRefGoogle Scholar
  46. Van Veen BD, van Drongelen W, Yuchtman M, Suzuki A (1997) Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans Biomed Eng 44:867–880CrossRefPubMedGoogle Scholar
  47. van Wijk Bernadette CM, Stam CJ, Daffertshofer A (2010) Comparing brain networks of different size and connectivity density using graph theory. PLoS One 5, e13701CrossRefPubMedPubMedCentralGoogle Scholar
  48. Vinck M, Oostenveld R, van Wingerden M, Battaglia F, Pennartz CM (2011) An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage 55:1548–1565CrossRefPubMedGoogle Scholar
  49. Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, Roffman JL, Smoller JW, Zollei L, Polimeni JR, Fischl B, Liu H, Buckner RL (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106:1125–1165CrossRefPubMedGoogle Scholar
  50. Watts DJ (2004) Small worlds. Princeton University Press, Princeton, NJGoogle Scholar
  51. Whalen C, Maclin EL, Fabiani M, Gratton G (2008) Validation of a method for coregistering scalp recording locations with 3D structural MR images. Hum Brain Mapp 29:1288–1301CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Felix Siebenhühner
    • 1
  • Muriel Lobier
    • 1
  • Sheng H. Wang
    • 1
  • Satu Palva
    • 1
  • J. Matias Palva
    • 1
    Email author
  1. 1.Neuroscience CenterUniversity of HelsinkiHelsinkiFinland

Personalised recommendations