Macroscopic cortical dynamics: Spatially uncorrelated but temporally coherent rich-club organisations in source-space resting-state EEG

TL;DR

This study uses singular value decomposition on source-space EEG to reveal that resting-state cortical activity exhibits complex, temporally coherent rich-club organizations with distinct spectral dynamics, advancing understanding of cortical network behavior.

Contribution

It introduces a novel application of SVD to source-reconstructed EEG to characterize the spatiotemporal dynamics of rich-club organizations in resting-state networks.

Findings

Rich-club topology varies across different brain networks.

Distinct spectral regimes with alpha-beta oscillations and <0.1 Hz envelope dynamics.

Reliable method for analyzing cortical activity dynamics using EEG.

Abstract

Synchronous oscillations of neuronal populations support resting-state cortical activity. Recent studies indicate that resting-state functional connectivity is not static, but exhibits complex dynamics. The mechanisms underlying the complex dynamics of cortical activity have not been well characterised. Here, we directly apply singular value decomposition (SVD) in source-reconstructed electroencephalography (EEG) in order to characterise the dynamics of spatiotemporal patterns of resting-state functional connectivity. We found that changes in resting-state functional connectivity were associated with distinct complex topological features, "Rich-Club organisation", of the default mode network, salience network, and motor network. Rich-club topology of the salience network revealed greater functional connectivity between ventrolateral prefrontal cortex and anterior insula, whereas Rich-club topologies of the default mode networks revealed bilateral functional connectivity between fronto-parietal and posterior cortices. Spectral analysis of the dynamics underlying Rich-club organisations of these source-space network patterns revealed that resting-state cortical activity exhibit distinct dynamical regimes whose intrinsic expressions contain fast oscillations in the alpha-beta band and with the envelope-signal in the timescale of $<0.1$ Hz. Our findings thus demonstrated that multivariate eigen-decomposition of source-reconstructed EEG is a reliable computational technique to explore how dynamics of spatiotemporal features of the resting-state cortical activity occur that oscillate at distinct frequencies.