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Dynamic recruitment of resting state sub-networks

O'Neill, George C.; Bauer, Markus; Woolrich, Mark W.; Morris, Peter G.; Barnes, Gareth R.; Brookes, Matthew Jon


George C. O'Neill

Mark W. Woolrich

Gareth R. Barnes


Resting state networks (RSNs) are of fundamental importance in human systems neuroscience with evidence suggesting that they are integral to healthy brain function and perturbed in pathology. Despite rapid progress in this area, the temporal dynamics governing the functional connectivities that underlie RSN structure remain poorly understood. Here, we present a framework to help further our understanding of RSN dynamics. We describe a methodology which exploits the direct nature and high temporal resolution of magnetoencephalography (MEG). This technique, which builds on previous work, extends from solving fundamental confounds in MEG (source leakage) to multivariate modelling of transient connectivity. The resulting processing pipeline facilitates direct (electrophysiological) measurement of dynamic functional networks. Our results show that, when functional connectivity is assessed in small time windows, the canonical sensorimotor network can be decomposed into a number of transiently synchronising sub-networks, recruitment of which depends on current mental state. These rapidly changing sub-networks are spatially focal with, for example, bilateral primary sensory and motor areas resolved into two separate sub-networks. The likely interpretation is that the larger canonical sensorimotor network most often seen in neuroimaging studies reflects only a temporal aggregate of these transient sub-networks. Our approach opens new frontiers to study RSN dynamics, showing that MEG is capable of revealing the spatial, temporal and spectral signature of the human connectome in health and disease.


O'Neill, G. C., Bauer, M., Woolrich, M. W., Morris, P. G., Barnes, G. R., & Brookes, M. J. (2015). Dynamic recruitment of resting state sub-networks. NeuroImage, 115,

Journal Article Type Article
Acceptance Date Apr 11, 2015
Online Publication Date Apr 18, 2015
Publication Date Jul 15, 2015
Deposit Date Jul 10, 2015
Publicly Available Date Jul 10, 2015
Journal NeuroImage
Print ISSN 1053-8119
Electronic ISSN 1053-8119
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 115
Public URL
Publisher URL
Copyright Statement Copyright information regarding this work can be found at the following address:


1-s2.0-S1053811915003213-main.pdf (1.8 Mb)

Copyright Statement
Copyright information regarding this work can be found at the following address:

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