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Biophysical network models of phase-synchronization in MEG resting-state

View ORCID ProfileN Williams, B Toselli, F Siebenhühner, S Palva, G Arnulfo, S Kaski, JM Palva
doi: https://doi.org/10.1101/2021.08.04.455014
N Williams
1Helsinki Institute of Information Technology, Department of Computer Science, Aalto University, Finland
2Department of Neuroscience & Biomedical Engineering, Aalto University, Finland
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  • For correspondence: nitin.williams@aalto.fi
B Toselli
3Department of Informatics, Bioengineering, Robotics & Systems Engineering, University of Genoa, Italy
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F Siebenhühner
4Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland
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S Palva
4Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland
5Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, United Kingdom
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G Arnulfo
3Department of Informatics, Bioengineering, Robotics & Systems Engineering, University of Genoa, Italy
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S Kaski
1Helsinki Institute of Information Technology, Department of Computer Science, Aalto University, Finland
6Department of Computer Science, University of Manchester, United Kingdom
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JM Palva
2Department of Neuroscience & Biomedical Engineering, Aalto University, Finland
4Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland
5Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, United Kingdom
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Abstract

Magnetoencephalography (MEG) is used extensively to study functional connectivity (FC) networks of phase-synchronization, but the relationship of these networks to their biophysical substrates is poorly understood. Biophysical Network Models (BNMs) have been used to produce networks corresponding to MEG-derived networks of phase-synchronization, but the roles of inter-regional conduction delays, the structural connectome and dynamics of model of individual brain regions, in obtaining this correspondence remain unknown. In this study, we investigated the roles of conduction delays, the structural connectome, and dynamics of models of individual regions, in obtaining a correspondence between model-generated and MEG-derived networks between left-hemispheric regions. To do this, we compared three BNMs, respectively comprising Wilson-Cowan oscillators interacting with diffusion Magnetic Resonance Imaging (MRI)-based patterns of structural connections through zero delays, constant delays and distance-dependent delays respectively. For the BNM whose networks corresponded most closely to the MEG-derived network, we used comparisons against null models to identify specific features of each model component, e.g. the pattern of connections in the structure connectome, that contributed to the observed correspondence. The Wilson-Cowan zero delays model produced networks with a closer correspondence to the MEG-derived network than those produced by the constant delays model, and the same as those produced by the distance-dependent delays model. Hence, there is no evidence that including conduction delays improves the correspondence between model-generated and MEG-derived networks. Given this, we chose the Wilson-Cowan zero delays model for further investigation. Comparing the Wilson-Cowan zero delays model against null models revealed that both the pattern of structural connections and Wilson-Cowan oscillatory dynamics contribute to the correspondence between model-generated and MEG-derived networks. Our investigations yield insight into the roles of conduction delays, the structural connectome and dynamics of models of individual brain regions, in obtaining a correspondence between model-generated and MEG-derived networks. These findings result in a parsimonious BNM that produces networks corresponding closely to MEG-derived networks of phase-synchronization.

Highlights

  • Simple biophysical model produces close match (ρ=0.49) between model and MEG networks

  • No evidence for conduction delays improving match between model and MEG networks

  • Pattern of structural connections contributes to match between model and MEG networks

  • Wilson-Cowan oscillatory dynamics contribute to match between model and MEG networks

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://data.mendeley.com/datasets/bgs7w9z24h/1

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-NC-ND 4.0 International license.
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Posted August 05, 2021.
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Biophysical network models of phase-synchronization in MEG resting-state
N Williams, B Toselli, F Siebenhühner, S Palva, G Arnulfo, S Kaski, JM Palva
bioRxiv 2021.08.04.455014; doi: https://doi.org/10.1101/2021.08.04.455014
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Biophysical network models of phase-synchronization in MEG resting-state
N Williams, B Toselli, F Siebenhühner, S Palva, G Arnulfo, S Kaski, JM Palva
bioRxiv 2021.08.04.455014; doi: https://doi.org/10.1101/2021.08.04.455014

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