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Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers

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Cite this article as: eLife 2020;9:e54055 doi: 10.7554/eLife.54055


Electrophysiological methods, i.e., M/EEG provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability. Here, we propose a multimodal model to robustly combine MEG, MRI and fMRI for prediction. We focus on age prediction as a surrogate biomarker in 674 subjects from the Cam-CAN dataset. Strikingly, MEG, fMRI and MRI showed additive effects supporting distinct brain-behavior associations. Moreover, the contribution of MEG was best explained by cortical power spectra between 8 and 30 Hz. Finally, we demonstrate that the model preserves benefits of stacking when some data is missing. The proposed framework, hence, enables multimodal learning for a wide range of biomarkers from diverse types of brain signals.

Data availability

We used the publicly available Cam-CAN dataset. All software and code necessary to obtain the derivative data is shared on github: https://github.com/dengemann/meg-mri-surrogate-biomarkers-aging-2020

The following previously published data sets were used

Article and author information

Author details

  1. Denis Alexander Engemann

    Parietal, Inria Saclay, Palaiseau, France
    For correspondence
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7223-1014
  2. Oleh Kozynets

    Parietal, Inria Saclay, Palaiseau, France
    Competing interests
    No competing interests declared.
  3. David Sabbagh

    Parietal, Inria Saclay, Palaiseau, France
    Competing interests
    No competing interests declared.
  4. Guillaume Lemaître

    Parietal, Inria Saclay, Palaiseau, France
    Competing interests
    No competing interests declared.
  5. Gaël Varoquaux

    Parietal, Inria Saclay, Palaiseau, France
    Competing interests
    Gaël Varoquaux, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1076-5122
  6. Franziskus Liem

    Dynamics of Healthy Aging, University of Zürich, Zürich, Switzerland
    Competing interests
    No competing interests declared.
  7. Alexandre Gramfort

    Parietal, Inria Saclay, Palaiseau, France
    Competing interests
    No competing interests declared.


H2020 European Research Council (SLAB ERC-YStG-676943)

  • Alexandre Gramfort

French National Institute of Computer Science (Medecine Numerique)

  • Denis Alexander Engemann

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.


Human subjects: This study is conducted in compliance with the Helsinki Declaration. No experiments on living beings were performed for this study. The data that we used was acquired by the Cam-CAN consortium and has been approved by the local ethics committee, Cambridgeshire 2 Research Ethics Committee (reference: 10/H0308/50).

Reviewing Editor

  1. Alexander Shackman, University of Maryland, United States

Publication history

  1. Received: November 29, 2019
  2. Accepted: May 9, 2020
  3. Accepted Manuscript published: May 19, 2020 (version 1)
  4. Version of Record published: June 22, 2020 (version 2)


© 2020, Engemann et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.


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