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.

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.

Metrics

  • 9,817
    views
  • 1,516
    downloads
  • 145
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Share this article

https://doi.org/10.7554/eLife.28927

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Fangluo Chen, Dylan C Sarver ... G William Wong
    Research Article

    Obesity is a major risk factor for type 2 diabetes, dyslipidemia, cardiovascular disease, and hypertension. Intriguingly, there is a subset of metabolically healthy obese (MHO) individuals who are seemingly able to maintain a healthy metabolic profile free of metabolic syndrome. The molecular underpinnings of MHO, however, are not well understood. Here, we report that CTRP10/C1QL2-deficient mice represent a unique female model of MHO. CTRP10 modulates weight gain in a striking and sexually dimorphic manner. Female, but not male, mice lacking CTRP10 develop obesity with age on a low-fat diet while maintaining an otherwise healthy metabolic profile. When fed an obesogenic diet, female Ctrp10 knockout (KO) mice show rapid weight gain. Despite pronounced obesity, Ctrp10 KO female mice do not develop steatosis, dyslipidemia, glucose intolerance, insulin resistance, oxidative stress, or low-grade inflammation. Obesity is largely uncoupled from metabolic dysregulation in female KO mice. Multi-tissue transcriptomic analyses highlighted gene expression changes and pathways associated with insulin-sensitive obesity. Transcriptional correlation of the differentially expressed gene (DEG) orthologs in humans also shows sex differences in gene connectivity within and across metabolic tissues, underscoring the conserved sex-dependent function of CTRP10. Collectively, our findings suggest that CTRP10 negatively regulates body weight in females, and that loss of CTRP10 results in benign obesity with largely preserved insulin sensitivity and metabolic health. This female MHO mouse model is valuable for understanding sex-biased mechanisms that uncouple obesity from metabolic dysfunction.

    1. Computational and Systems Biology
    Huiyong Cheng, Dawson Miller ... Qiuying Chen
    Research Article

    Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.