Alpha Oscillations Reduce Temporal Long-Range Dependence in Spontaneous Human Brain Activity

Abstract

Ongoing neural dynamics comprise both frequency-specific oscillations and broadband-features, such as long-range dependence (LRD). Despite both being behaviorally relevant, little is known about their potential interactions. In humans, 8-12 Hz α oscillations constitute the strongest deviation from 1/f power-law scaling, the signature of LRD. We postulated that α oscillations, believed to exert active inhibitory gating, downmodulate the temporal width of LRD in slower ongoing brain activity. In two independent "resting-state" datasets (electroencephalography surface recordings and magnetoencephalography source reconstructions), both across space and dynamically over time, power of α activity covaried with the power slope <5 Hz (i.e., greater α activity shortened LRD). Causality of α activity dynamics was implied by its temporal precedence over changes of slope. A model where power-law fluctuations of the α envelope inhibit baseline activity closely replicated our results. Thus, α oscillations may provide an active control mechanism to adaptively regulate LRD of brain activity at slow temporal scales, thereby shaping internal states and cognitive processes. The two prominent features of ongoing brain activity are oscillations and temporal long-range dependence. Both shape behavioral performance, but little is known about their interaction. Here, we demonstrate such an interaction in EEG and MEG recordings of task-free human brain activity. Specifically, we show that spontaneous dynamics in alpha activity explain ensuing variations of dependence in the low and ultra-low-frequency range. In modeling, two features of alpha oscillations are critical to account for the observed effects on long-range dependence, scale-free properties of alpha oscillations themselves, and a modulation of baseline levels, presumably inhibitory. Both these properties have been observed empirically, and our study hence establishes alpha oscillations as a regulatory mechanism governing long-range dependence or "memory" in slow ongoing brain activity.

On the relevance of EEG resting theta activity for the neurophysiological dynamics underlying motor inhibitory control

“…While this is increasingly recognized in the literature (Becker et al, 2018;Haag et al, 2015;Mahjoory et al, 2019;Tavor et al, 2016), the current study is the first to show this for the theta band oscillations in the context of inhibitory control. Notably, the data analysis of the PLF in Nogo trials revealed no differential effects between the high baseline group and the low baseline group, which was confirmed by Bayesian statistics.…”

“…In line with overarching theoretical conceptions concerning the function of the medial frontal cortex and theta band activity (Cavanagh & Frank, 2014), the increase in theta band power in Nogo trials has been suggested to reflect an "alarm" or "surprise signal," which is crucial for increasing cognitive control Dippel et al, 2017;Wessel, 2018). On the other hand, it is increasingly recognized that there are similarities between resting dynamics and activity related to cognitive control (Becker, Van de Ville, & Kleinschmidt, 2018;Haag et al, 2015;Mahjoory, Cesnaite, Hohlefeld, Villringer, & Nikulin, 2019;Tavor et al, 2016). Based on these findings, it could be assumed that the individual ability to increase theta activity beyond a certain resting state activity is essential for successful response inhibition performance.…”

“…Based on these findings, it could be assumed that the individual ability to increase theta activity beyond a certain resting state activity is essential for successful response inhibition performance. On the other hand, it is increasingly recognized that there are similarities between resting dynamics and activity related to cognitive control (Becker, Van de Ville, & Kleinschmidt, 2018;Haag et al, 2015;Mahjoory, Cesnaite, Hohlefeld, Villringer, & Nikulin, 2019;Tavor et al, 2016). Some findings suggest that resting state EEG activity can predict cognitive control (Nakao, Bai, Nashiwa, & Northoff, 2013) and that resting theta activity in the middle frontal gyrus is associated with modulations in the topography of EEG activity during response inhibition (Lansbergen, Schutter, & Kenemans, 2007;Schiller, Gianotti, Nash, & Knoch, 2014).…”

A sound-sensitive source of alpha oscillations in human non-primary auditory cortex

“…Finally, we wanted to establish whether the suppressed low frequency activity in alHG reflected a reduction in the strength of a narrowband oscillation that is present before stimulus onset, or could be better characterized as a change in the exponent χ ("slope") of the scale-free 1/f χ component of the power spectrum. The latter may reflect properties of neural circuits distinct from those that generate narrowband oscillations (He, 2014;Gao, 2015;Podvalny et al, 2015;Voytek et al, 2015;Becker et al, 2018). The analyses described so far, in which power at each frequency was normalized by its value during a pre-stimulus baseline, cannot differentiate between these two contributions to neural power spectra.…”

Spatially regularized wavelet leader scale-free analysis of fMRI data

“…Indeed, scale-free temporal dynamics were observed across several modalities, both at rest or during Work supported by ANR-16-CE33-0020 MultiFracs, France task performance, and under various conditions or pathologies [1][2][3][4][5][6][7][8][9][10][11][12]. It has also been documented that scale-free dynamics are functionally associated with neural excitability [3,13] and negatively correlates with power fluctuation in alpha-band (8−12 Hz) [14], hence explaining the modulation of scaling exponents with task engagement or pathologies.…”