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A multi-layer network approach to MEG connectivity analysis

Authors

Prejaas K. Tewarie

Benjamin A. E. Hunt

Robson

Peter F. Liddle

Peter G. Morris



Abstract

© 2016 The Authors. Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia.

Citation

Brookes, M. J., Tewarie, P. K., Hunt, B. A. E., Robson, S. E., Gascoyne, L. E., Liddle, E. B., …Morris, P. G. (2016). A multi-layer network approach to MEG connectivity analysis. NeuroImage, 132, 425-438. https://doi.org/10.1016/j.neuroimage.2016.02.045

Journal Article Type Article
Acceptance Date Feb 15, 2016
Online Publication Date Feb 22, 2016
Publication Date May 15, 2016
Deposit Date Apr 20, 2017
Publicly Available Date Apr 20, 2017
Journal NeuroImage
Print ISSN 1053-8119
Electronic ISSN 1095-9572
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 132
Pages 425-438
DOI https://doi.org/10.1016/j.neuroimage.2016.02.045
Keywords Multi-layer networks; Magnetoencephalography; MEG; Functional connectivity; Neural oscillations; Schizophrenia; Visual cortex; Motor cortex
Public URL https://nottingham-repository.worktribe.com/output/789936
Publisher URL http://www.sciencedirect.com/science/article/pii/S1053811916001543

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