Research Repository

See what's under the surface

How do spatially distinct frequency specific MEG networks emerge from one underlying structural connectome? The role of the structural eigenmodes

Tewarie, Prejaas; Abeysuriya, Romesh; Byrne, Áine; O'Neill, George C.; Sotioropoulos, Stamatios N.; Brookes, Matthew J.; Coombes, Stephen

Authors

Stamatios Sotiropoulos

Prejaas Tewarie

Romesh Abeysuriya

Áine Byrne

George C. O'Neill

Stamatios N. Sotiropoulos

Matthew J. Brookes

Stephen Coombes

Abstract

Functional networks obtained from magnetoencephalography (MEG) from different frequency bands show distinct spatial patterns. It remains to be elucidated how distinct spatial patterns in MEG networks emerge given a single underlying structural network. Recent work has suggested that the eigenmodes of the structural network might serve as a basis set for functional network patterns in the case of functional MRI. Here, we take this notion further in the context of frequency band specific MEG networks. We show that a selected set of eigenmodes of the structural network can predict different frequency band specific networks in the resting state, ranging from delta (1–4 Hz) to the high gamma band (40–70 Hz). These predictions outperform predictions based from surrogate data, suggesting a genuine relationship between eigenmodes of the structural network and frequency specific MEG networks. We then show that the relevant set of eigenmodes can be excited in a network of neural mass models using linear stability analysis only by including delays. Excitation of an eigenmode in this context refers to a dynamic instability of a network steady state to a spatial pattern with a corresponding coherent temporal oscillation. Simulations verify the results from linear stability analysis and suggest that theta, alpha and beta band networks emerge very near to the bifurcation. The delta and gamma bands in the resting state emerges further away from the bifurcation. These results show for the first time how delayed interactions can excite the relevant set of eigenmodes that give rise to frequency specific functional connectivity patterns.

Journal Article Type Article
Publication Date Feb 1, 2019
Print ISSN 1053-8119
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 186
Pages 211-220
DOI https://doi.org/10.1016/j.neuroimage.2018.10.079
Keywords Functional connectivity; Magnetoencephalography; Neural mass; Neural mass bifurcation; Eigenmodes; MEG
Publisher URL https://www.sciencedirect.com/science/article/pii/S1053811918320603

This output is not freely accessible at this point due to copyright restrictions.




Downloadable Citations