Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach.

Autor: Tewarie P; Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom. Electronic address: prejaas.tewarie@nottingham.ac.uk., Hillebrand A; Department of Clinical Neurophysiology, MEG Center, VU University Medical Centre, Amsterdam, The Netherlands., van Dijk BW; Department of Clinical Neurophysiology, MEG Center, VU University Medical Centre, Amsterdam, The Netherlands., Stam CJ; Department of Clinical Neurophysiology, MEG Center, VU University Medical Centre, Amsterdam, The Netherlands., O'Neill GC; Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom., Van Mieghem P; Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft, The Netherlands., Meier JM; Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft, The Netherlands., Woolrich MW; Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom; Centre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom., Morris PG; Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom., Brookes MJ; Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom.
Jazyk: angličtina
Zdroj: NeuroImage [Neuroimage] 2016 Nov 15; Vol. 142, pp. 324-336. Date of Electronic Publication: 2016 Aug 03.
DOI: 10.1016/j.neuroimage.2016.07.057
Abstrakt: Neuronal oscillations exist across a broad frequency spectrum, and are thought to provide a mechanism of interaction between spatially separated brain regions. Since ongoing mental activity necessitates the simultaneous formation of multiple networks, it seems likely that the brain employs interactions within multiple frequency bands, as well as cross-frequency coupling, to support such networks. Here, we propose a multi-layer network framework that elucidates this pan-spectral picture of network interactions. Our network consists of multiple layers (frequency-band specific networks) that influence each other via inter-layer (cross-frequency) coupling. Applying this model to MEG resting-state data and using envelope correlations as connectivity metric, we demonstrate strong dependency between within layer structure and inter-layer coupling, indicating that networks obtained in different frequency bands do not act as independent entities. More specifically, our results suggest that frequency band specific networks are characterised by a common structure seen across all layers, superimposed by layer specific connectivity, and inter-layer coupling is most strongly associated with this common mode. Finally, using a biophysical model, we demonstrate that there are two regimes of multi-layer network behaviour; one in which different layers are independent and a second in which they operate highly dependent. Results suggest that the healthy human brain operates at the transition point between these regimes, allowing for integration and segregation between layers. Overall, our observations show that a complete picture of global brain network connectivity requires integration of connectivity patterns across the full frequency spectrum.
(Copyright © 2016. Published by Elsevier Inc.)
Databáze: MEDLINE
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