Inferring multi-scale neural mechanisms with brain network modelling
- Charité - Universitätsmedizin Berlin, Germany
- University of Toronto, Canada
- Aix-Marseille Université, France
- 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
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Hybrid Brain Model dataAvailable at Open Science Framework Repository under a CC0 1.0 Universal license.
Article and author information
Author details
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.
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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Inferring multi-scale neural mechanisms with brain network modelling
eLife 7:e28927.
https://doi.org/10.7554/eLife.28927
