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Exploring the Correlation Between M/EEG Source–Space and fMRI Networks at Rest

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Abstract

Magneto/electro-encephalography (M/EEG) source connectivity is an emerging approach to estimate brain networks with high temporal and spatial resolutions. Here, we aim to evaluate the effect of functional connectivity (FC) methods on the correlation between M/EEG source–space and fMRI networks at rest. Two main FC families are tested: (i) FC methods that do not remove zero-lag connectivity including Phase Locking Value (PLV) and Amplitude Envelope Correlation (AEC) and (ii) FC methods that remove zero-lag connections such as Phase Lag Index (PLI) and two orthogonalisation approaches combined with PLV (PLVCol, PLVPas) and AEC (AECCol, AECPas). Methods are evaluated on resting state M/EEG signals recorded from healthy participants at rest (N = 74). Networks obtained by each FC method are compared with fMRI networks (obtained from the Human Connectome Project). Results show low correlations for all FC methods, however PLV and AEC networks are significantly correlated with fMRI networks (ρ = 0.12, p = 1.93 × 10–8 and ρ = 0.06, p = 0.007, respectively), while other methods are not. These observations are consistent for all M/EEG frequency bands and for different FC matrices threshold. Our main message is to be careful in selecting FC methods when comparing or combining M/EEG with fMRI. We consider that more comparative studies based on simulation and real data and at different levels (node, module or sub networks) are still needed in order to improve our understanding on the relationships between M/EEG source–space networks and fMRI networks at rest.

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Acknowledgements

This study was supported by the Future Emerging Technologies (H2020-FETOPEN-2014-2015-RIA under agreement No. 686764) as part of the European Union’s Horizon 2020 research and training program 2014–2018. This work has received a French government support granted to the CominLabs excellence laboratory and managed by the National Research Agency in the “Investing for the Future” program under reference ANR-10-LABX-07-01. This work was also financed by the AZM and SAADE Association, Tripoli, Lebanon and by the National Council for Scientific Research (CNRS) in Lebanon. Authors would like to thank Campus France, Programme Hubert Curien CEDRE (PROJECT No. 42257YA), for supporting this study. HCP data was provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. Authors would like to thank Olivier Dufor for collecting the EEG data and Martijn Van Den Heuvel for providing the fMRI connectivity matrices from the human connectome project.

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Authors and Affiliations

  1. Univ Rennes, LTSI, 35000, Rennes, France

    Jennifer Rizkallah, Fabrice Wendling & Mahmoud Hassan

  2. Azm Center for Research in Biotechnology and Its Application, EDST, Lebanese University, Tripoli, Lebanon

    Jennifer Rizkallah & Hassan Amoud

  3. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza D’armi, 09123, Cagliari, Italy

    Matteo Fraschini

  4. NEUROCORT Technologies, R&D Department, Rennes, France

    Mahmoud Hassan

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Correspondence to Jennifer Rizkallah.

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Rizkallah, J., Amoud, H., Fraschini, M. et al. Exploring the Correlation Between M/EEG Source–Space and fMRI Networks at Rest. Brain Topogr 33, 151–160 (2020). https://doi.org/10.1007/s10548-020-00753-w

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