Journal article
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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 1.8MB, Terms of use)
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- Publisher copy:
- 10.1016/j.neuroimage.2017.10.003
Authors
- Publisher:
- Elsevier
- Journal:
- Neuroimage More from this journal
- Volume:
- 180
- Issue:
- B
- Pages:
- 559-576
- Publication date:
- 2017-10-04
- Acceptance date:
- 2017-10-02
- DOI:
- EISSN:
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1095-9572
- ISSN:
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1053-8119
- Keywords:
- Pubs id:
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pubs:736326
- UUID:
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uuid:449e90ac-4288-4d10-9fb7-8e6e567335ac
- Local pid:
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pubs:736326
- Source identifiers:
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736326
- Deposit date:
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2017-10-16
Terms of use
- Copyright holder:
- Elsevier Inc
- Copyright date:
- 2017
- Notes:
- Copyright © 2017 Elsevier Inc. This is the accepted manuscript version of the article. The final version is available online from Elsevier at: https://doi.org/10.1016/j.neuroimage.2017.10.003
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