Dynamics of large-scale electrophysiological networks: a technical review

O'Neill, George C., Tewarie, Prejaas K., Vidaurre, Diego, Liuzzi, Lucrezia, Woolrich, Mark W. and Brookes, Matthew J. (2017) Dynamics of large-scale electrophysiological networks: a technical review. NeuroImage . ISSN 1095-9572 (In Press)

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/886491
Keywords: Dynamic functional connectivity; Magnetoencephalography; Dynamic functional networks; Electroencephalography; MEG; EEG
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Physics and Astronomy
Identification Number: https://doi.org/10.1016/j.neuroimage.2017.10.003
Depositing User: O'Neill, George
Date Deposited: 27 Nov 2017 14:27
Last Modified: 04 May 2020 19:10
URI: https://eprints.nottingham.ac.uk/id/eprint/48365

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