Inferring multi-scale neural mechanisms with brain network modelling

  1. Michael Schirner
  2. Anthony Randal McIntosh
  3. Viktor Jirsa
  4. Gustavo Deco
  5. Petra Ritter  Is a corresponding author
  1. Charité - Universitätsmedizin Berlin, Germany
  2. University of Toronto, Canada
  3. Aix-Marseille Université, France
  4. Universitat Pompeu Fabra, Spain

Abstract

The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.

Data availability

The following data sets were generated
    1. Schirner M
    2. McIntosh AR
    3. Jirsa V
    4. Deco G
    5. Ritter P
    (2017) Hybrid Brain Model data
    Available at Open Science Framework Repository under a CC0 1.0 Universal license.

Article and author information

Author details

  1. Michael Schirner

    Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8227-8476
  2. Anthony Randal McIntosh

    Rotman Research Institute of Baycrest Centre, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
  3. Viktor Jirsa

    Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
    Competing interests
    The authors declare that no competing interests exist.
  4. Gustavo Deco

    Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
    Competing interests
    The authors declare that no competing interests exist.
  5. Petra Ritter

    Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
    For correspondence
    petra.ritter@charite.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4643-4782

Funding

James S. McDonnell Foundation (Brain Network Recovery Group JSMF22002082)

  • Anthony Randal McIntosh
  • Viktor Jirsa
  • Gustavo Deco
  • Petra Ritter

Bundesministerium für Bildung und Forschung (Bernstein Focus State Dependencies of Learning 01GQ0971-5)

  • Petra Ritter

European Union Horizon2020 (ERC Consolidator grant BrainModes 683049)

  • Petra Ritter

Bundesministerium für Bildung und Forschung (US-German Collaboration in Computational Neuroscience 01GQ1504A)

  • Petra Ritter

Bundesministerium für Bildung und Forschung (Max-Planck Society)

  • Petra Ritter

John von Neumann Institute for Computing at Jülich Supercomputing Centre (Grant NIC#8344 & NIC#10276)

  • Petra Ritter

Stiftung Charité/Private Exzellenzinitiative Johanna Quandt and Berlin Institute of Health

  • Petra Ritter

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

Ethics

Human subjects: Research was performed in compliance with the Code of Ethics of the World Medical Association (Declaration of Helsinki). Written informed consent was provided by all subjects with an understanding of the study prior to data collection, and was approved by the local ethics committee in accordance with the institutional guidelines at Charité Hospital Berlin.

Reviewing Editor

  1. Charles E Schroeder, Columbia University College of Physicians and Surgeons, United States

Publication history

  1. Received: May 23, 2017
  2. Accepted: January 4, 2018
  3. Accepted Manuscript published: January 8, 2018 (version 1)
  4. Version of Record published: February 7, 2018 (version 2)

Copyright

© 2018, Schirner 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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  1. Michael Schirner
  2. Anthony Randal McIntosh
  3. Viktor Jirsa
  4. Gustavo Deco
  5. Petra Ritter
(2018)
Inferring multi-scale neural mechanisms with brain network modelling
eLife 7:e28927.
https://doi.org/10.7554/eLife.28927

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