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 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.

The dissociative role of bursting and non-bursting neural activity in the oscillatory nature of functional brain networks

“…Previous work established a key connection between amplitude connectivity at rest and transient oscillatory bursts (Seedat et al, 2020). These bursts emerge naturally in neurocomputational models near criticality, i.e., close to bifurcations of limit cycles, and indicate the presence of metastable brain oscillations (Freyer et al, 2011;Hindriks and Tewarie, 2023). On the other hand, the debate remains open on whether oscillatory bursts really differ from sustained brain rhythms, both from the electrophysiological and the functional perspectives (van Ede et al, 2018).…”

“…This suggests that spontaneous brain activity contains two functionally distinct classes of spectrally similar neural oscillations: transient oscillatory bursts subtending amplitude connectivity (Seedat et al, 2020), and non-bursting background oscillations not involved in the process of amplitude coupling. The idea of considering brain rhythms as being composed of transient bursts has gained significant weight over the last years (Jones et al, 2016;van Ede et al, 2018) and its functional implications received a lot of attention, be it in relation to motor control (Bonaiuto et al, 2021;Feingold et al, 2015;Sherman et al, 2016), working memory (Higgins et al, 2021;Lundqvist et al, 2016), or resting-state functional connectivity (Baker et al, 2014;Coquelet et al, 2022;Hindriks and Tewarie, 2023;Seedat et al, 2020;Vidaurre et al, 2018b). On the other hand, the possibility and functional implications of non-bursting, possibly sustained, brain oscillations remain largely unexplored.…”

“…The electrophysiology of intrinsic brain networks is strongly constrained by the amplitude envelope correlation (AEC) (O’Neill et al, 2015a; Sadaghiani et al, 2022; Siegel et al, 2012). The AEC is a functional connectivity measure that focuses on the oscillation amplitude of neural assemblies, tracks the spontaneous rise and fall of transient brain rhythms (Hari and Salmelin, 1997; van Ede et al, 2018), and quantifies to what extent these rhythms tend to burst simultaneously in distinct brain areas (Baker et al, 2014; Hindriks and Tewarie, 2023; Seedat et al, 2020). The co-occurrence of transient oscillatory bursts is actually thought to underlie electrophysiological network connectivity.…”

Real-time low latency estimation of brain rhythms with deep neural networks

“…This follows from the observation reported in [4,29] that the phase locking value (a measure of phase synchrony between a pair of narrow-band random processes) and the coherence between these processes are deterministically connected. At the same time, the presence of non-Gaussian components significantly complicates this relation [17]. Artificial NNs are capable of representing sufficiently complex (theoretically arbitrary) mappings and in principle may take advantage of the relationships between instantaneous amplitude and phase inherent to the data.…”

Causal Interactions between Phase- and Amplitude-Coupling in Cortical Networks

“…Both connectivity modes have been associated with a broad variety of brain disorders (5) and cognitive processes (6,7) and for many years the studies of FC have been focused on one or the other coupling mode. Whether both types of connectivity are equivalent or perhaps redundant has been addressed recently in studies showing that these coupling modes can differentiate strongly, especially during presence of stimulation (7)(8)(9). Furthermore, patterns of both connectivity modes differ across cortical areas and frequency bands.…”