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Inferring multi-scale neural mechanisms with brain network modelling

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dc.contributor.author Schirner, Michael
dc.contributor.author Randal McIntosh, Anthony
dc.contributor.author Jirsa, Viktor K.
dc.contributor.author Deco, Gustavo
dc.contributor.author Ritter, Petra
dc.date.accessioned 2019-04-08T08:45:54Z
dc.date.available 2019-04-08T08:45:54Z
dc.date.issued 2018
dc.identifier.citation Schirner M, Randal McIntosh A, Jirsa V, Deco G, Ritter P. Inferring multi-scale neural mechanisms with brain network modelling. Elife. 2018 Jan 8;7:e28927. DOI: 10.7554/eLife.28927.001
dc.identifier.issn 2050-084X
dc.identifier.uri http://hdl.handle.net/10230/37059
dc.description.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.
dc.description.sponsorship The authors gratefully acknowledge the computing time granted by the John von Neumann Institute for Computing (NIC) provided on the supercomputer JURECA (Krause and Thörnig, 2016) at Jülich Supercomputing Centre (www.fz-juelich.de, Grant NIC#8344 and NIC#10276 to PR). The authors acknowledge the support of the James S McDonnell Foundation (Brain Network Recovery Group JSMF22002082) to ARM, VJ, GD, and PR, and funding granted by the German Ministry of Education and Research (US-German Collaboration in Computational Neuroscience 01GQ1504A, Bernstein Focus State Dependencies of Learning 01GQ0971-5, the Max-Planck Society Minerva Program), the European Union Horizon2020 (ERC Consolidator Grant BrainModes 683049), Stiftung Charité/Private Exzellenzinitiative Johanna Quandt and Berlin Institute of Health (BIH Johanna Quandt Professorship for Brain Simulation) to PR. This publication is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No BrainModes 683049 (ERC Consolidator grant to PR). The authors thank Olaf Sporns, Jochen Braun and Andreas Daffertshofer for their helpful comments on the manuscript. The authors declare no competing financial interests.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher eLife
dc.relation.ispartof e-Life. 2018 Jan 8;7:e28927.
dc.rights © Schirner M, Randal McIntosh A, Jirsa V, Deco G, Ritter P. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title Inferring multi-scale neural mechanisms with brain network modelling
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://dx.doi.org/10.7554/eLife.28927
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/683049
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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