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

ABSTRACT: 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 haemodyna

SUBMITTER: Hindriks R 

PROVIDER: S-EPMC10024695 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature