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


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.



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.


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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

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