Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts

Mathematical analysis of empirical magnetoencephalography data in combination with biophysical simulations shed light on the complementary nature of power correlation networks to phase coupling networks in the human brain. Well-known haemodynamic resting-state networks are better mirrored in power correlation networks than phase coupling networks in electrophysiological data. However, what do these power correlation networks reflect? We address this long-outstanding question in neuroscience using rigorous mathematical analysis, biophysical simulations with ground truth and application of these mathematical concepts to empirical magnetoencephalography (MEG) data. Our mathematical derivations show that for two non-Gaussian electrophysiological signals, their power correlation depends on their coherence, cokurtosis and conjugate-coherence. Only coherence and cokurtosis contribute to power correlation networks in MEG data, but cokurtosis is less affected by artefactual signal leakage and better mirrors haemodynamic resting-state networks. Simulations and MEG data show that cokurtosis may reflect co-occurrent bursting events. Our findings shed light on the origin of the complementary nature of power correlation networks to phase coupling networks and suggests that the origin of resting-state networks is partly reflected in co-occurent bursts in neuronal activity.

Automated summary

Mathematical analysis and simulations of empirical magnetoencephalography (MEG) data show that power correlation networks better mirror haemodynamic resting-state networks than phase coupling networks in electrophysiological data. The power correlation of non-Gaussian electrophysiological signals depends on their coherence, cokurtosis, and conjugate-coherence. Cokurtosis contributes to power correlation networks and may reflect co-occurrent bursting events in neuronal activity.