Dynamics of large-scale electrophysiological networks: A technical review

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

Research output: Contribution to journalReview articleAcademicpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)559-576
JournalNeuroImage
Volume180
DOIs
Publication statusPublished - 2018
Externally publishedYes

Cite this

O'Neill, G. C., Tewarie, P., Vidaurre, D., Liuzzi, L., Woolrich, M. W., & Brookes, M. J. (2018). Dynamics of large-scale electrophysiological networks: A technical review. NeuroImage, 180, 559-576. https://doi.org/10.1016/j.neuroimage.2017.10.003
O'Neill, George C. ; Tewarie, Prejaas ; Vidaurre, Diego ; Liuzzi, Lucrezia ; Woolrich, Mark W. ; Brookes, Matthew J. / Dynamics of large-scale electrophysiological networks: A technical review. In: NeuroImage. 2018 ; Vol. 180. pp. 559-576.
@article{cf1b19486d2c4a47a78d4bf4c961369b,
title = "Dynamics of large-scale electrophysiological networks: A technical review",
abstract = "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.",
author = "O'Neill, {George C.} and Prejaas Tewarie and Diego Vidaurre and Lucrezia Liuzzi and Woolrich, {Mark W.} and Brookes, {Matthew J.}",
year = "2018",
doi = "10.1016/j.neuroimage.2017.10.003",
language = "English",
volume = "180",
pages = "559--576",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",

}

O'Neill, GC, Tewarie, P, Vidaurre, D, Liuzzi, L, Woolrich, MW & Brookes, MJ 2018, 'Dynamics of large-scale electrophysiological networks: A technical review' NeuroImage, vol. 180, pp. 559-576. https://doi.org/10.1016/j.neuroimage.2017.10.003

Dynamics of large-scale electrophysiological networks: A technical review. / O'Neill, George C.; Tewarie, Prejaas; Vidaurre, Diego; Liuzzi, Lucrezia; Woolrich, Mark W.; Brookes, Matthew J.

In: NeuroImage, Vol. 180, 2018, p. 559-576.

Research output: Contribution to journalReview articleAcademicpeer-review

TY - JOUR

T1 - Dynamics of large-scale electrophysiological networks: A technical review

AU - O'Neill, George C.

AU - Tewarie, Prejaas

AU - Vidaurre, Diego

AU - Liuzzi, Lucrezia

AU - Woolrich, Mark W.

AU - Brookes, Matthew J.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85032958811&origin=inward

UR - https://www.ncbi.nlm.nih.gov/pubmed/28988134

U2 - 10.1016/j.neuroimage.2017.10.003

DO - 10.1016/j.neuroimage.2017.10.003

M3 - Review article

VL - 180

SP - 559

EP - 576

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -