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  Inferring multi-scale neural mechanisms with brain network modelling

Schirner, M., McIntosh, A. R., Jirsa, V., Deco, G., & Ritter, P. (2018). Inferring multi-scale neural mechanisms with brain network modelling. eLife, 7: e28927. doi:10.7554/eLife.28927.001.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-ACA6-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-A343-9
Genre: Journal Article

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 Creators:
Schirner, Michael1, 2, 3, Author
McIntosh, Anthony Randal4, Author
Jirsa, Viktor5, Author
Deco, Gustavo6, 7, 8, 9, Author              
Ritter, Petra1, 2, 3, 10, Author              
Affiliations:
1Charité University Medicine Berlin, Germany, ou_persistent22              
2Berlin Institute of Health (BIH), Germany, ou_persistent22              
3Bernstein Center for Computational Neuroscience, Berlin, Germany, ou_persistent22              
4Rotman Research Institute, University of Toronto, ON, Canada, ou_persistent22              
5Institut de Neurosciences des Systèmes, Aix-Marseille Université Faculté de Médecine, France, ou_persistent22              
6Center for Brain and Cognition, University Pompeu Fabra. Barcelona, Spain, ou_persistent22              
7Catalan Institution for Research and Advanced Studies (ICREA), University Pompeu Fabra. Barcelona, Spain, ou_persistent22              
8Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634551              
9School of Psychological Sciences, Monash University, Melbourne, Australia, ou_persistent22              
10Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              

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Free keywords: Brain modeling; EEG; Alpha rhythm; Computational biology; Connectomics; fMRI; Human; Neuroscience; Resting-state networks; Systems biology
 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.

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Language(s): eng - English
 Dates: 2017-05-232018-01-042018-01-08
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.7554/eLife.28927.001
PMID: 29308767
PMC: PMC5802851
PII: e28927
 Degree: -

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Project name : -
Grant ID : JSMF22002082
Funding program : -
Funding organization : James S. McDonnell Foundation
Project name : Human Brain Project Specific Grant Agreement 1 / HBP SGA1
Grant ID : 720270
Funding program : Horizon 2020
Funding organization : European Commission (EC)
Project name : Bernstein Focus State Dependencies of Learning
Grant ID : 01GQ0971-5
Funding program : -
Funding organization : Bundesministerium für Bildung und Forschung (BMBF)
Project name : Personalized whole brain simulations: linking connectomics and dynamics in the human brain / BrainModes
Grant ID : 683049
Funding program : Horizon 2020
Funding organization : European Commission (EC)
Project name : US-German Collaboration in Computational Neuroscience
Grant ID : 01GQ1504A
Funding program : -
Funding organization : Bundesministerium für Bildung und Forschung (BMBF)
Project name : -
Grant ID : -
Funding program : -
Funding organization : Max-Planck Society
Project name : -
Grant ID : NIC#8344 ; NIC#10276
Funding program : -
Funding organization : John von Neumann Institute for Computing
Project name : -
Grant ID : -
Funding program : Private Exzellenzinitiative Johanna Quandt
Funding organization : Stiftung Charité

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Title: eLife
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Cambridge : eLife Sciences Publications
Pages: - Volume / Issue: 7 Sequence Number: e28927 Start / End Page: - Identifier: ISSN: 2050-084X
CoNE: https://pure.mpg.de/cone/journals/resource/2050-084X