Oscillation-based connectivity architecture is dominated by an intrinsic spatial organization, not cognitive state or frequency

Abstract

Functional connectivity (FC) of neural oscillations (~1-150Hz) is thought to facilitate neural information exchange across brain areas by forming malleable neural ensembles in the service of cognitive processes. However, neural oscillations and their FC are not restricted to certain cognitive demands and continuously unfold in all cognitive states. To what degree is the spatial organization of oscillation-based FC affected by cognitive state or governed by an intrinsic architecture? And what is the impact of oscillation frequency and FC mode (phase-versus amplitude coupling)? Using ECoG recordings of 18 presurgical patients, we quantified the state-dependency of oscillation-based FC in five canonical frequency bands and across an array of 6 task states. For both phase- and amplitude coupling, static FC analysis revealed a spatially largely state-invariant (i.e. intrinsic) component in all frequency bands. Further, the observed intrinsic FC pattern was spatially similar across all frequency bands. However, temporally independent FC dynamics in each frequency band allow for frequency-specific malleability in information exchange. In conclusion, the spatial organization of oscillation-based FC is largely stable over cognitive states, i.e. primarily intrinsic in nature, and shared across frequency bands. The state-invariance is in line with prior findings at the other temporal extreme of brain activity, the infraslow range (~<0.1Hz) observed in fMRI. Our observations have implications for conceptual frameworks of oscillation-based FC and the analysis of task-related FC changes.

Genetic polymorphisms in COMT and BDNF influence synchronization dynamics of human neuronal oscillations

“…BDNF Val homozygotes also had stronger q synchronization within DAN and stronger left-hemispheric b synchronization between various subsystems, particularly involving DAN and DMN. This robust modulation of synchronization dynamics by BDNF polymorphism could contribute to significant differences across individuals in behavior and cognition, which have been shown to be strongly influenced by spatial and spectral characteristics of oscillatory synchronization (Betti et al, 2018;Mostame and Sadaghiani, 2020;Siebenhu ¨hner et al, 2020). However, large-scale synchronization was not influenced by COMT Val 158 Met polymorphism, although it has been shown that neuropharmacologically elevated levels of catecholamines are related to fMRI-based changes in functional connectivity (van den Brink et al, 2018).…”

“…This finding implies that the strongest effects of BDNF polymorphisms on global synchronization might not be due to a shifts in global excitation, but to other mechanisms such as the influence of BDNF on maintenance, maturation, and formation of neuronal networks (Park and Poo, 2013;Zagrebelsky and Korte, 2014) putatively via receptor TrkB (neurotrophic receptor tyrosine kinase 2) signaling (Castre ´n and Monteggia, 2021; Winkel et al, 2021). These could impact large-scale network synchronization via changes in structural connectivity (Mostame and Sadaghiani, 2020) or via changes in brain gray matter whose density has been shown to be associated with neuroreceptor and neurotransporter availabilities (Manninen et al, 2021) and whose thickness is associated with the structure of oscillatory networks (Mahjoory et al, 2020).…”

“…The intrinsic spatial organization of spontaneous resting-state functional connectivity (FC) observed with MEG (Betti et al, 2018;Oswald et al, 2017;Siebenhu ¨hner et al, 2020), intra-cranial EEG (Arnulfo et al, 2020), and functional magnetic resonance imaging (fMRI) (Hahn et al, 2020;Vidaurre et al, 2021) predicts individual variability in task performance. Resting-state FC networks reflect individual trait-like behavior (Seitzmana et al, 2019) that could arise from the underlying individual brain structural organization (Cabral et al, 2014;Mostame and Sadaghiani, 2020) and neuromodulation (McCormick et al, 2020;Pfeffer et al, 2021;van den Brink et al, 2018;van den Brink et al, 2019). Amplitude envelopes of fast (>1 Hz) oscillations are also characterized by slow (0.1-1 Hz) and infra-slow (0.01-0.1 Hz) fluctuations (Hiltunen et al, 2014;Hipp and Siegel, 2015;Monto et al, 2008).…”