Changes in EEG multiscale entropy and power‐law frequency scaling during the human sleep cycle

Abstract

We explored changes in multiscale brain signal complexity and power‐law scaling exponents of electroencephalogram (EEG) frequency spectra across several distinct global states of consciousness induced in the natural physiological context of the human sleep cycle. We specifically aimed to link EEG complexity to a statistically unified representation of the neural power spectrum. Further, by utilizing surrogate‐based tests of nonlinearity we also examined whether any of the sleep stage‐dependent changes in entropy were separable from the linear stochastic effects contained in the power spectrum. Our results indicate that changes of brain signal entropy throughout the sleep cycle are strongly time‐scale dependent. Slow wave sleep was characterized by reduced entropy at short time scales and increased entropy at long time scales. Temporal signal complexity (at short time scales) and the slope of EEG power spectra appear, to a large extent, to capture a common phenomenon of neuronal noise, putatively reflecting cortical balance between excitation and inhibition. Nonlinear dynamical properties of brain signals accounted for a smaller portion of entropy changes, especially in stage 2 sleep.

Dynamic changes in rhythmic and arrhythmic neural signatures in the subthalamic nucleus induced by anaesthesia and intubation

“…1A). In addition, there was also significant decrease in power in the high beta (22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40), and gamma (40-80 Hz) frequency bands in the STN at LOC (Fig. 1A).…”

“…Increasing evidence has emerged suggesting that the scale-free features can uncover the functional involvement of neuronal activity in specific states. The power-low exponent has been shown to be modulated by sleep 26 , neurological and psychiatric disorders 27,28 . The changes of power-low exponent during propofol-induced anaesthesia were also observed from human EEG and macaque electrocorticography (ECoG) 29,30 .…”

Slow oscillation-spindle coupling predicts sequence-based language learning

“…To address whether changes in frequency-band activity reflect task-related changes in underlying neural populations or non-oscillatory activity (Dave, Brothers, & Swaab, 2018), we examined whether the 1/ƒ exponent predicted changes in task-evoked oscillatory power from Baseline to Delayed sessions between the Sleep and Wake conditions. This analysis was performed to distinguish task-related oscillatory activity from broadband neural activity (i.e., changes in 1/ƒ slope) induced by a period of sleep relative to wake (Miskovic, MacDonald, Rhodes, & Cote, 2019;Wen & Liu, 2016). Figure 3 shows the three-way interaction effects for the three models, as well as a decomposition of the fractal versus the oscillatory components in the task-related EEG.…”

Ictal recruitment of anterior nucleus of thalamus in human focal epilepsy

“…We calculated multiscale entropy (MSE) to characterize the temporal predictability of a time series across several time scales in the thalamus, serving as an index of its capacity for processing information (Miskovic et al, 2019). While estimating the MSE, the maximum template length (M) was set to 3, and the matching threshold was set to 0.5, scaled to 10.…”

“…The epileptogenic zone (EZ), as viewed by Bancaud and Talairach, is the site of the initiation and early propagation of the seizure (Talairach and Bancaud, 1966). Second, using an established seizure detection algorithm (line length) (Esteller et al, 2004) (Miskovic et al, 2019). In sum, using qualitative and quantitative metrics, we have characterized the recruitment of ANT in drug-resistant TLE syndrome.…”