Interlayer connectivity reconstruction for multilayer brain networks using phase oscillator models

Tewarie, Prejaas; Prasse, Bastian; Meier, Jil M.; Byrne, Áine; De Domenico, Manlio; Stam, Cornelis Jan (Kees); Brookes, Matthew J.; Hillebrand, Arjan; Daffertshofer, Andreas; Coombes, Stephen; Van Mieghem, Piet

Abstract

Large-scale neurophysiological networks are often reconstructed from band-pass filtered time series derived from magnetoencephalography (MEG) data. Common practice is to reconstruct these networks separately for different frequency bands and to treat them independently. Recent evidence suggests that this separation may be inadequate, as there can be significant coupling between frequency bands (interlayer connectivity). A multilayer network approach offers a solution to analyze frequency-specific networks in one framework. We propose to use a recently developed network reconstruction method in conjunction with phase oscillator models to estimate interlayer connectivity that optimally fits the empirical data. This approach determines interlayer connectivity based on observed frequency-specific time series of the phase and a connectome derived from diffusion weighted imaging. The performance of this interlayer reconstruction method was evaluated in-silico. Our reconstruction of the underlying interlayer connectivity agreed to very high degree with the ground truth. Subsequently, we applied our method to empirical resting-state MEG data obtained from healthy subjects and reconstructed two-layered networks consisting of either alpha-to-beta or theta-to-gamma band connectivity. Our analysis revealed that interlayer connectivity is dominated by a multiplex structure, i.e. by one-to-one interactions for both alpha-to-beta band and theta-to-gamma band networks. For theta-gamma band networks, we also found a plenitude of interlayer connections between distant nodes, though weaker connectivity relative to the one-to-one connections. Our work is an stepping stone towards the identification of interdependencies across frequency-specific networks. Our results lay the ground for the use of the promising multilayer framework in this field with more-informed and justified interlayer connections.

Citation

Tewarie, P., Prasse, B., Meier, J. M., Byrne, Á., De Domenico, M., Stam, C. J. (., …Van Mieghem, P. (2021). Interlayer connectivity reconstruction for multilayer brain networks using phase oscillator models. New Journal of Physics, 23(6), Article 063065. https://doi.org/10.1088/1367-2630/ac066d

• Journal Article Type: Article
• Acceptance Date: May 28, 2021
• Online Publication Date: May 28, 2021
• Publication Date: Jun 1, 2021
• Deposit Date: Jun 10, 2021
• Publicly Available Date: Jun 10, 2021
• Journal: New Journal of Physics
• Electronic ISSN: 1367-2630
• Publisher: IOP Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 6
• Article Number: 063065
• DOI: https://doi.org/10.1088/1367-2630/ac066d
• Keywords: General Physics and Astronomy
• Public URL: https://nottingham-repository.worktribe.com/output/5653769
• Publisher URL: https://iopscience.iop.org/article/10.1088/1367-2630/ac066d/meta