Advertisement

Methods to Estimate Functional and Effective Brain Connectivity from MEG Data Robust to Artifacts of Volume Conduction

  • Guido NolteEmail author
  • Laura Marzetti
Reference work entry

Abstract

Due to the high temporal resolution of MEG data, they are well suited to study brain dynamics, while the limited spatial resolution constitutes a major confounder when one wants to estimate brain connectivity. To a very large extent, functional relationships between MEG sensors and estimated sources are caused by incomplete demixing of the brain sources. Many measures of functional and effective connectivity are highly sensitive to such mixing artifacts. In this chapter, we review methods that address this problem. They are all based on the insight that the imaginary part of the cross-spectra cannot be explained as a mixing artifact. Several variants of this idea will be presented. We will present three different methods adapted to localize source interactions: (a) minimum overlap component analysis (MOCA) decomposes linear estimates of the P most relevant singular vectors of the imaginary parts of the cross-spectra, (b) the MUSIC algorithm can be applied to this same subspace, and (c) the estimated sources can be analyzed further using multivariate generalizations of the imaginary part of coherency. Finally, a causal relation between these sources can be estimated using the phase slope index (PSI). The methods will be illustrated for empirical MEG data of a single subject under resting state condition.

Notes

Acknowledgments

This work was supported by grants from the EU (ERC-2010-AdG-269716), the DFG (SFB 936/A3), the BMBF (031A130), and from the Human Connectome Project (1U54MH091657-01) funded by the 16 National Institutes of Health Institutes and Centers that support the NIH Blueprint for Neuroscience Research.

References

  1. Brookes MJ, Hale JR, Zumer JM, Stevenson CM, Francis ST, Barnes GR, Owen JP, Morris PG, Nagarajan SS (2011a) Measuring functional connectivity using MEG: methodology and comparison with fcMRI. NeuroImage 56:1082–1104PubMedPubMedCentralCrossRefGoogle Scholar
  2. Brookes MJ, Woolrich M, Luckhoo H, Price D, Hale JR, Stephenson MC, Barnes GR, Smith SM, Morris PG (2011b) Investigating the electrophysiological basis of resting state networks using MEG. Proc Natl Acad Sci USA 108:16783–16788PubMedCrossRefPubMedCentralGoogle Scholar
  3. Brookes MJ, Woolrich M, Barnes GR (2012) Measuring functional connectivity in MEG: a multivariate approach insensitive to linear source leakage. NeuroImage 63:910–920PubMedPubMedCentralCrossRefGoogle Scholar
  4. Buckner RL, Vincent JL (2007) Unrest at rest: default activity and spontaneous network correlations. NeuroImage 37:1091–1096PubMedCrossRefGoogle Scholar
  5. Cole DM, Smith SM, Beckmann CF (2010) Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front Syst Neurosci 4:8PubMedPubMedCentralGoogle Scholar
  6. Daglish M, Lingford-Hughes A, Nutt D (2005) Human functional neuroimaging connectivity research in dependence. Rev Neurosci 16(2):151–157PubMedCrossRefGoogle Scholar
  7. Damoiseaux JS, Greicius MD (2009) Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity. Brain Struct Funct 213(6):525–533PubMedCrossRefGoogle Scholar
  8. de Pasquale F, Della Penna S, Snyder AZ, Marzetti L, Pizzella V, Romani GL, Corbetta M (2012) A cortical core for dynamic integration of functional networks in the resting human brain. Neuron 74:753–764PubMedPubMedCentralCrossRefGoogle Scholar
  9. Deco G, Corbetta M (2010) The dynamical balance of the brain at rest. Neuroscientist 17:107–123PubMedPubMedCentralCrossRefGoogle Scholar
  10. Ewald A, Marzetti L, Zappasodi F, Meinecke FC, Nolte G (2012) Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space. NeuroImage 60(1):476–488PubMedCrossRefGoogle Scholar
  11. Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8:700–711PubMedGoogle Scholar
  12. Fries P (2009) Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annu Rev Neurosci 32:209–224PubMedCrossRefPubMedCentralGoogle Scholar
  13. Gow DW, Segawa JA, Ahlfors SP, Lin FH (2008) Lexical influences on speech perception: a granger causality analysis of MEG and EEG source estimates. NeuroImage 43:614–623PubMedPubMedCentralCrossRefGoogle Scholar
  14. Gross J, Timmermann L, Kujala J, Dirks M, Schmitz F, Salmelin R, Schnitzler A (2002) The neural basis of intermittent motor control in humans. Proc Natl Acad Sci USA 99:2299–2302PubMedCrossRefGoogle Scholar
  15. Gross J, Schmitz F, Schnitzler I, Kessler K, Shapiro K, Schnitzler A (2006) Anticipatory control of long range phase synchronization. Eur J Neurosci 24:2057–2060PubMedCrossRefPubMedCentralGoogle Scholar
  16. Hari R, Salmelin R (2012) Magnetoencephalography: from SQUIDs to neuroscience. NeuroImage 61:386–396PubMedPubMedCentralCrossRefGoogle Scholar
  17. Hipp JF, Hawellek DJ, Corbetta M, Siegel M, Engel AK (2012) Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat Neurosci 15:884–890PubMedPubMedCentralCrossRefGoogle Scholar
  18. Ioannides AA, Liu LC, Kwapien J, Drozdz S, Streit M (2000) Coupling of regional activations in a human brain during an object and face affect recognition task. Hum Brain Mapp 11:77–92PubMedCrossRefPubMedCentralGoogle Scholar
  19. Jerbi K, Lachaux JP, N’Diaye K, Pantazis D, Leahy RM, Garnero L, Baillet S (2007) Coherent neural representation of hand speed in humans revealed by MEG imaging. Proc Natl Acad Sci USA 104:7676–7681PubMedCrossRefPubMedCentralGoogle Scholar
  20. Liu Z, Fukunaga M, de Zwart JA, Duyn JH (2010) Large-scale spontaneous fluctuations and correlations in brain electrical activity observed with magnetoencephalography. NeuroImage 51:102–111PubMedPubMedCentralCrossRefGoogle Scholar
  21. Marzetti L, Del Gratta C, Nolte G (2008) Understanding brain connectivity from EEG data by identifying systems composed of interacting sources. NeuroImage 42:87–98PubMedCrossRefGoogle Scholar
  22. Marzetti L, Della Penna S, Snyder AZ, Pizzella V, Nolte G, de Pasquale F, Romani GL, Corbetta M (2013) Frequency specific interactions of MEG resting state activity within and across brain networks as revealed by the multivariate interaction measure. NeuroImage 79:172–183PubMedPubMedCentralCrossRefGoogle Scholar
  23. Matsuda Y, Yamaguchi K (2004) Semi-invariant function of Jacobi algorithm in independent component analysis. In: Proceedings of the international joint conference on neural networksGoogle Scholar
  24. McKeown MJ, Sejnowski TJ (1998) Independent component analysis of fMRI data: examining the assumptions. Hum Brain Mapp 6:368–372PubMedCrossRefGoogle Scholar
  25. Meinecke F, Ziehe A, Kurths J, Müller KR (2005) Measuring phase synchronization of superimposed signals. Phys Rev Lett 94:084102PubMedCrossRefGoogle Scholar
  26. Miller KJ, Weaver KE, Ojemann JG (2009) Direct electrophysiological measurement of human default network areas. Proc Natl Acad Sci USA 106:12174–12177PubMedCrossRefGoogle Scholar
  27. Mosher JC, Baillet S, Leahy RM (1999) EEG source localization and imaging using multiple signal classification approaches. J Clin Neurophysiol 16(3):225–238PubMedPubMedCentralCrossRefGoogle Scholar
  28. Nolte G (2003) The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors. Phys Med Biol 48(22):3637–3652PubMedCrossRefGoogle Scholar
  29. Nolte G, Bai U, Weathon L, Mari Z, Vorbach S, Hallet M (2004) Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin Neurophysiol 115:2294–2307CrossRefGoogle Scholar
  30. Nolte G, Meinecke FC, Ziehe A, Müller KR (2006) Identifying interactions in mixed and noisy complex systems. Phys Rev E 73:051913CrossRefGoogle Scholar
  31. Nolte G, Ziehe A, Nikulin VV, Schlögl A, Krämer N, Brismar T, Müller KR (2008) Robustly estimating the flow direction of information in complex physical systems. Phys Rev Lett 100:234101PubMedPubMedCentralCrossRefGoogle Scholar
  32. Nolte G, Marzetti L, Valdes Sosa P (2009) Minimum overlap component analysis (MOCA) of EEG/MEG data for more than two sources. J Neurosci Methods 183:72–76PubMedCrossRefGoogle Scholar
  33. 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 2011:Article ID 156869CrossRefGoogle Scholar
  34. Pascual-Marqui RD, Lehmann D, Koukkou M, Kochi K, Anderer P, Saletu B, Tanaka H, Hirata K, John ER, Prichep L, Biscay-Lirio R, Kinoshita T (2011) Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philos Transact A Math Phys Eng Sci 369(1952):3768–3784CrossRefGoogle Scholar
  35. Pereda E, Quiroga RQ, Bhattacharya J (2005) Nonlinear multivariate analysis of neurophysiological signals. Prog Neurobiol 77(1–2):1–37PubMedCrossRefGoogle Scholar
  36. Schnitzler A, Gross J (2005) Normal and pathological oscillatory communication in the brain. Nat Rev Neurosci 6(4):285–296PubMedPubMedCentralCrossRefGoogle Scholar
  37. Schoffelen JM, Gross J (2009) Source connectivity analysis with MEG and EEG. Hum Brain Mapp 30:1857–1865PubMedPubMedCentralCrossRefGoogle Scholar
  38. Sekihara K, Owen JP, Trisno S, Nagarajan SS (2011) Removal of spurious coherence in MEG source-space coherence analysis. IEEE Trans Biomed Eng 58:3121–3129PubMedPubMedCentralCrossRefGoogle Scholar
  39. Shahbazi Avarvand F, Ewald A, Nolte G (2012) Localizing true brain interactions from EEG and MEG data with subspace methods and modified beamformers. Comput Math Methods Med 2012:402341PubMedPubMedCentralCrossRefGoogle Scholar
  40. Siegel M, Donner TH, Oostenveld R, Fries P, Engel AK (2008) Neuronal synchronization along the dorsal visual pathway reflects the focus of spatial attention. Neuron 60(4):709–719PubMedPubMedCentralCrossRefGoogle Scholar
  41. Singer W (1999) Neuronal synchrony: a versatile code for the definition of relations? Neuron 24:49–65PubMedCrossRefGoogle Scholar
  42. 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 28:1178–1193PubMedPubMedCentralCrossRefGoogle Scholar
  43. Stinstra JG, Peters MJ (1998) The volume conductor may act as a temporal filter on the ECG and EEG. Med Biol Eng Comput 36:711–716PubMedCrossRefGoogle Scholar
  44. Varela F, Lachaux J, Rodriguez E, Martinerie J (2001) The brain web: phase synchronization and large-scale integration. Nat Rev Neurosci 2:229–239CrossRefGoogle Scholar
  45. 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 55:1548–1565PubMedPubMedCentralCrossRefGoogle Scholar
  46. Womelsdorf T, Fries P (2006) Neuronal coherence during selective attentional processing and sensory-motor integration. J Physiol Paris 100:182–193PubMedCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Neurophysiology and PathophysiologyUniversity Medical Center Hamburg-EppendorfHamburgGermany
  2. 2.Department of Neuroscience, Imaging and Clinical Sciences“G. d’Annunzio” University Chieti-PescaraChietiItaly
  3. 3.Institute for Advanced Biomedical Technologies“G. d’Annunzio” University FoundationChietiItaly

Section editors and affiliations

  • Matthew J. Brookes
    • 1
  1. 1.Sir Peter Mansfield Magnetic Resonance CentreSchool of Physics, University of NottinghamNottinghamUK

Personalised recommendations