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Dynamics of large-scale electrophysiological networks: a technical review

O'Neill, George C.; Tewarie, Prejaas K.; Vidaurre, Diego; Liuzzi, Lucrezia; Woolrich, Mark W.; Brookes, Matthew J.


George C. O'Neill

Prejaas K. Tewarie

Diego Vidaurre

Lucrezia Liuzzi

Mark W. Woolrich


For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography / electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity.


O'Neill, G. C., Tewarie, P. K., Vidaurre, D., Liuzzi, L., Woolrich, M. W., & Brookes, M. J. (2017). Dynamics of large-scale electrophysiological networks: a technical review. NeuroImage, doi:10.1016/j.neuroimage.2017.10.003. ISSN 1053-8119

Journal Article Type Article
Acceptance Date Oct 2, 2017
Online Publication Date Oct 4, 2017
Publication Date Oct 4, 2017
Deposit Date Nov 27, 2017
Publicly Available Date Oct 5, 2018
Journal NeuroImage
Print ISSN 1053-8119
Electronic ISSN 1095-9572
Publisher Elsevier
Peer Reviewed Peer Reviewed
Keywords Dynamic functional connectivity; Magnetoencephalography; Dynamic functional networks; Electroencephalography; MEG; EEG
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Copyright Statement Copyright information regarding this work can be found at the following address:
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Dynamic_conectivity_meg_V3.pdf (1.3 Mb)

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

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