A multi-layer network approach to MEG connectivity analysis

Brookes, Matthew J. and Tewarie, Prejaas K. and Hunt, Benjamin A. E. and Robson, Siân E. and Gascoyne, Lauren E. and Liddle, Elizabeth B. and Liddle, Peter F. and Morris, Peter G. (2016) A multi-layer network approach to MEG connectivity analysis. NeuroImage, 132 . pp. 425-438. ISSN 1053-8119

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Available under Licence Creative Commons Attribution.
Download (3MB) | Preview

Abstract

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.

Item Type: Article
Keywords: Multi-layer networks; Magnetoencephalography; MEG; Functional connectivity; Neural oscillations; Schizophrenia; Visual cortex; Motor cortex
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Physics and Astronomy
Identification Number: 10.1016/j.neuroimage.2016.02.045
Depositing User: Brookes, Matthew
Date Deposited: 20 Apr 2017 10:37
Last Modified: 21 Apr 2017 15:36
URI: http://eprints.nottingham.ac.uk/id/eprint/42020

Actions (Archive Staff Only)

Edit View Edit View