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Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

View ORCID ProfilePaul Triebkorn, Joelle Zimmermann, Leon Stefanovski, View ORCID ProfileDipanjan Roy, Ana Solodkin, View ORCID ProfileViktor Jirsa, Gustavo Deco, Michael Breakspear, View ORCID ProfileAnthony Randal McIntosh, Petra Ritter
doi: https://doi.org/10.1101/2020.03.26.009795
Paul Triebkorn
1Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, Berlin, Germany
2Bernstein Center for Computational Neuroscience, Berlin, Germany
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  • For correspondence: jan-paul.triebkorn@charite.de petra.ritter@charite.de
Joelle Zimmermann
3Baycrest Health Sciences, Rotman Research Institute, Toronto, Canada
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Leon Stefanovski
1Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, Berlin, Germany
2Bernstein Center for Computational Neuroscience, Berlin, Germany
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Dipanjan Roy
4National Brain Research Centre, Manesar, India
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Ana Solodkin
5Anatomy & Neurobiology and Neurology, UC Irvine Health, Irvine, USA
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Viktor Jirsa
6Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
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Gustavo Deco
7Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
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Michael Breakspear
8QIMR Berghofer Medical Research Institute, Herston, Australia
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Anthony Randal McIntosh
3Baycrest Health Sciences, Rotman Research Institute, Toronto, Canada
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Petra Ritter
1Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, Berlin, Germany
2Bernstein Center for Computational Neuroscience, Berlin, Germany
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  • For correspondence: jan-paul.triebkorn@charite.de petra.ritter@charite.de
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Abstract

Using The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters: conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.

With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain.

Author Summary In order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 26, 2020.
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Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study
Paul Triebkorn, Joelle Zimmermann, Leon Stefanovski, Dipanjan Roy, Ana Solodkin, Viktor Jirsa, Gustavo Deco, Michael Breakspear, Anthony Randal McIntosh, Petra Ritter
bioRxiv 2020.03.26.009795; doi: https://doi.org/10.1101/2020.03.26.009795
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Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study
Paul Triebkorn, Joelle Zimmermann, Leon Stefanovski, Dipanjan Roy, Ana Solodkin, Viktor Jirsa, Gustavo Deco, Michael Breakspear, Anthony Randal McIntosh, Petra Ritter
bioRxiv 2020.03.26.009795; doi: https://doi.org/10.1101/2020.03.26.009795

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