Synchrony, metastability, dynamic integration, and competition in the spontaneous functional connectivity of the human brain

Vincent Wens*, Mathieu Bourguignon, Marc Vander Ghinst, Alison Mary, Brice Marty, Nicolas Coquelet, Gilles Naeije, Philippe Peigneux, Serge Goldman, Xavier De Tiège

*Corresponding author for this work

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4 Citations (Scopus)

Abstract

The human brain is functionally organized into large-scale neural networks that are dynamically interconnected. Multiple short-lived states of resting-state functional connectivity (rsFC) identified transiently synchronized networks and cross-network integration. However, little is known about the way brain couplings covary as rsFC states wax and wane. In this magnetoencephalography study, we explore the synchronization structure among the spontaneous interactions of well-known resting-state networks (RSNs). To do so, we extracted modes of dynamic coupling that reflect rsFC synchrony and analyzed their spatio-temporal features. These modes identified transient, sporadic rsFC changes characterized by the widespread integration of RSNs across the brain, most prominently in the β band. This is in line with the metastable rsFC state model of resting-state dynamics, wherein our modes fit as state transition processes. Furthermore, the default-mode network (DMN) stood out as being structured into competitive cross-network couplings with widespread DMN-RSN interactions, especially among the β-band modes. These results substantiate the theory that the DMN is a core network enabling dynamic global brain integration in the β band.

Original languageEnglish
Pages (from-to)313-324
Number of pages12
JournalNeuroImage
Volume199
DOIs
Publication statusPublished - 1 Oct 2019
Externally publishedYes

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Wens, V., Bourguignon, M., Vander Ghinst, M., Mary, A., Marty, B., Coquelet, N., ... De Tiège, X. (2019). Synchrony, metastability, dynamic integration, and competition in the spontaneous functional connectivity of the human brain. NeuroImage, 199, 313-324. https://doi.org/10.1016/j.neuroimage.2019.05.081