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Sleep is crucial for daytime functioning, cognitive performance and general well-being. These aspects of daily life are known to be impaired after extended wake, yet, the underlying neuronal correlates have been difficult to identify. Accumulating evidence suggests that normal functioning of the brain is characterized by long-range temporal correlations (LRTCs) in cortex, which are supportive for decision-making and working memory tasks. Here we assess LRTCs in resting state human EEG data during a 40-hour sleep deprivation experiment by evaluating the decay in autocorrelation and the scaling exponent of the detrended fluctuation analysis from EEG amplitude fluctuations. We find with both measures that LRTCs decline as sleep deprivation progresses. This decline becomes evident when taking changes in signal power into appropriate consideration. In contrast, the presence of strong signal power increases in some frequency bands over the course of sleep deprivation may falsely indicate LRTC changes that do not reflect the underlying long-range temporal correlation structure. Our results demonstrate the importance of sleep to maintain LRTCs in the human brain. In complex networks, LRTCs naturally emerge in the vicinity of a critical state. The observation of declining LRTCs during wake thus provides additional support for our hypothesis that sleep reorganizes cortical networks towards critical dynamics for optimal functioning.

Pathological changes in excitability of cortical tissue commonly underlie the initiation and spread of seizure activity in patients suffering from epilepsy. Accordingly, monitoring excitability and controlling its degree using antiepileptic drugs (AEDs) is of prime importance for clinical care and treatment. To date, adequate measures of excitability and action of AEDs have been difficult to identify. Recent insights into ongoing cortical activity have identified global levels of phase synchronization as measures that characterize normal levels of excitability and quantify any deviation therefrom. Here, we explore the usefulness of these intrinsic measures to quantify cortical excitability in humans. First, we observe a correlation of such markers with stimulation-evoked responses suggesting them to be viable excitability measures based on ongoing activity. Second, we report a significant covariation with the level of AED load and a wake-dependent modulation. Our results indicate that excitability in epileptic networks is effectively reduced by AEDs and suggest the proposed markers as useful candidates to quantify excitability in routine clinical conditions overcoming the limitations of electrical or magnetic stimulation. The wake-dependent time course of these metrics suggests a homeostatic role of sleep, to rebalance cortical excitability.

Sleep is regulated by a homeostatic process which in the two-process model of human sleep regulation is represented by electroencephalogram slow-wave activity (SWA). Many studies of acute manipulation of wake duration have confirmed the precise homeostatic regulation of SWA in rodents and humans. However, some chronic sleep restriction studies in rodents show that the sleep homeostatic response, as indexed by SWA, is absent or diminishes suggesting adaptation occurs. Here, we investigate the response to 7 days of sleep restriction (6 hr time in bed) and extension (10 hr time in bed) as well as the response to subsequent total sleep deprivation in 35 healthy participants in a cross-over design. The homeostatic response was quantified by analyzing sleep structure and SWA measures. Sleep restriction resulted primarily in a reduction of rapid eye movement (REM) sleep. SWA and accumulated SWA (slow-wave energy, SWE) were not much affected by sleep extension/restriction. The SWA responses did not diminish significantly in the course of the intervention and did not deviate significantly from the predictions of the two-process model. The response to total sleep deprivation consisted of an increase in SWA and rise rate of SWA and SWE and did not differ between the two conditions. The data show that changes in sleep duration within an ecologically relevant range have a marked effect on REM sleep and that SWA responds in accordance with predictions based on a saturating exponential increase during wake and an exponential decline in sleep of homeostatic sleep pressure during both chronic sleep restriction and extension.

The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. We tested our methods in healthy subjects and in patients. Most algorithms yielded good results comparable to human interrater agreement. Our study revealed that deep neural networks (DNNs) working with raw data performed better than feature-based methods. We also demonstrated that taking the local temporal structure of sleep into account a priori is important. Our results demonstrate the utility of neural network architectures for the classification of sleep.

Abstract Study objectives The wake-sleep transition zone represents a poorly defined borderland, containing, for example, microsleep episodes (MSEs), which are of potential relevance for diagnosis and may have consequences while driving. Yet, the scoring guidelines of the American Academy of Sleep Medicine (AASM) completely neglect it. We aimed to explore the borderland between wakefulness and sleep by developing the Bern continuous and high-resolution wake-sleep (BERN) criteria for visual scoring, focusing on MSEs visible in the electroencephalography (EEG), as opposed to purely behavior- or performance-defined MSEs. Methods Maintenance of Wakefulness Test (MWT) trials of 76 randomly selected patients were retrospectively scored according to both the AASM and the newly developed BERN scoring criteria. The visual scoring was compared with spectral analysis of the EEG. The quantitative EEG analysis enabled a reliable objectification of the visually scored MSEs. For less distinct episodes within the borderland, either ambiguous or no quantitative patterns were found. Results As expected, the latency to the first MSE was significantly shorter in comparison to the sleep latency, defined according to the AASM criteria. In certain cases, a large difference between the two latencies was observed and a substantial number of MSEs occurred between the first MSE and sleep. Series of MSEs were more frequent in patients with shorter sleep latencies, while isolated MSEs were more frequent in patients who did not reach sleep. Conclusion The BERN criteria extend the AASM criteria and represent a valuable tool for in-depth analysis of the wake-sleep transition zone, particularly important in the MWT.

Background: Multisensor fitness trackers offer the ability to longitudinally estimate sleep quality in a home environment with the potential to outperform traditional actigraphy. To benefit from these new tools for objectively assessing sleep for clinical and research purposes, multisensor wearable devices require careful validation against the gold standard of sleep polysomnography (PSG). Naturalistic studies favor validation. Objective: This study aims to validate the Fitbit Charge 2 against portable home PSG in a shift-work population composed of 59 first responder police officers and paramedics undergoing shift work. Methods: A reliable comparison between the two measurements was ensured through the data-driven alignment of a PSG and Fitbit time series that was recorded at night. Epoch-by-epoch analyses and Bland-Altman plots were used to assess sensitivity, specificity, accuracy, the Matthews correlation coefficient, bias, and limits of agreement. Results: Sleep onset and offset, total sleep time, and the durations of rapid eye movement (REM) sleep and non–rapid-eye movement sleep stages N1+N2 and N3 displayed unbiased estimates with nonnegligible limits of agreement. In contrast, the proprietary Fitbit algorithm overestimated REM sleep latency by 29.4 minutes and wakefulness after sleep onset (WASO) by 37.1 minutes. Epoch-by-epoch analyses indicated better specificity than sensitivity, with higher accuracies for WASO (0.82) and REM sleep (0.86) than those for N1+N2 (0.55) and N3 (0.78) sleep. Fitbit heart rate (HR) displayed a small underestimation of 0.9 beats per minute (bpm) and a limited capability to capture sudden HR changes because of the lower time resolution compared to that of PSG. The underestimation was smaller in N2, N3, and REM sleep (0.6-0.7 bpm) than in N1 sleep (1.2 bpm) and wakefulness (1.9 bpm), indicating a state-specific bias. Finally, Fitbit suggested a distribution of all sleep episode durations that was different from that derived from PSG and showed nonbiological discontinuities, indicating the potential limitations of the staging algorithm. Conclusions: We conclude that by following careful data processing processes, the Fitbit Charge 2 can provide reasonably accurate mean values of sleep and HR estimates in shift workers under naturalistic conditions. Nevertheless, the generally wide limits of agreement hamper the precision of quantifying individual sleep episodes. The value of this consumer-grade multisensor wearable in terms of tackling clinical and research questions could be enhanced with open-source algorithms, raw data access, and the ability to blind participants to their own sleep data.

The topographic distribution of sleep EEG power is a reflection of brain structure and function. The goal of this study was to examine the degree to which genes contribute to sleep EEG topography during adolescence, a period of brain restructuring and maturation. We recorded high-density sleep EEG in monozygotic (MZ; n = 28) and dizygotic (DZ; n = 22) adolescent twins (mean age = 13.2 ± 1.1 years) at two time points 6 months apart. The topographic distribution of normalized sleep EEG power was examined for the frequency bands delta (1–4.6 Hz) to gamma 2 (34.2–44 Hz) during NREM and REM sleep. We found highest heritability values in the beta band for NREM and REM sleep (0.44 ≤ h 2  ≤ 0.57), while environmental factors shared amongst twin siblings accounted for the variance in the delta to sigma bands (0.59 ≤ c 2  ≤ 0.83). Given that both genetic and environmental factors are reflected in sleep EEG topography, our results suggest that topography may provide a rich metric by which to understand brain function. Furthermore, the frequency specific parsing of the influence of genetic from environmental factors on topography suggests functionally distinct networks and reveals the mechanisms that shape these networks.

•First investigation of 5 G RF-EMF effects on NREM sleep spindles in genetic context.•Variant rs7304986 of CACNA1C modulates 5 G effects on spindle center frequency.•Exposure to 3.6 GHz 5 G RF-EMF accelerates spindle frequency in T/C allele carriers.•Spindle frequency in T/C carriers accelerated over widespread cortical areas.•Studies elucidating biological mechanisms underlying 5 G RF-EMF effects warranted. The introduction of 5G technology as the latest standard in mobile telecommunications has raised concerns about its potential health effects. Prior studies of earlier generations of radiofrequency electromagnetic fields (RF-EMF) demonstrated narrowband spectral increases in the electroencephalographic (EEG) spindle frequency range (11–16 Hz) in non-rapid-eye-movement (NREM) sleep. However, the impact of 5G RF-EMF on sleep remains unexplored. Additionally, RF-EMF can activate l-type voltage-gated calcium channels (LTCC), which have been linked to sleep quality and EEG oscillatory activity. This study investigates whether the allelic variant rs7304986 in the CACNA1C gene, encoding the α1C subunit of LTCC, modulates 5G RF-EMF effects on EEG spindle activity in NREM sleep. Thirty-four participants, genotyped for rs7304986 (15 T/C and 19 matched T/T carriers), underwent a double-blind, sham-controlled study with standardized left-hemisphere exposure to two 5G RF-EMF signals (3.6 GHz and 700 MHz) for 30 min before sleep. Sleep spindle activity was analyzed using high-density EEG and the Fitting Oscillations & One Over f (FOOOF) algorithm. T/C carriers reported longer sleep latency compared to T/T carriers. A significant interaction between RF-EMF exposure and rs7304986 genotype was observed, with only 3.6 GHz exposure in T/C carriers inducing a faster spindle center frequency in the central, parietal, and occipital cortex compared to sham. These findings suggest that 3.6 GHz 5G RF-EMF modulates spindle center frequency in NREM sleep in a CACNA1C genotype-dependent manner, implicating LTCC in the physiological response to RF-EMF and underscoring the need for further research into 5G effects on brain health.

Sleep homeostasis manifests as a relative constancy of its daily amount and intensity. Theoretical descriptions define ‘Process S’, a variable with dynamics dependent on global sleep-wake history, and reflected in electroencephalogram (EEG) slow wave activity (SWA, 0.5–4 Hz) during sleep. The notion of sleep as a local, activity-dependent process suggests that activity history must be integrated to determine the dynamics of global Process S. Here, we developed novel mathematical models of Process S based on cortical activity recorded in freely behaving mice, describing local Process S as a function of the deviation of neuronal firing rates from a locally defined set-point, independent of global sleep-wake state. Averaging locally derived Processes S and their rate parameters yielded values resembling those obtained from EEG SWA and global vigilance states. We conclude that local Process S dynamics reflects neuronal activity integrated over time, and global Process S reflects local processes integrated over space.

Microsleep episodes (MSEs) are short fragments of sleep (1-15 s) that can cause dangerous situations with potentially fatal outcomes. In the diagnostic sleep-wake and fitness-to-drive assessment, accurate and early identification of sleepiness is essential. However, in the absence of a standardised definition and a time-efficient scoring method of MSEs, these short fragments are not assessed in clinical routine. Based on data of moderately sleepy patients, we recently developed the Bern continuous and high-resolution wake-sleep (BERN) criteria for visual scoring of MSEs and corresponding machine learning algorithms for automatic MSE detection, both mainly based on the electroencephalogram (EEG). The present study aimed to investigate the relationship between automatically detected MSEs and driving performance in a driving simulator, recorded in parallel with EEG, and to assess algorithm performance for MSE detection in severely sleepy participants. Maintenance of wakefulness test (MWT) and driving simulator recordings of 18 healthy participants, before and after a full night of sleep deprivation, were retrospectively analysed. Performance of automatic detection was compared with visual MSE scoring, following the BERN criteria, in MWT recordings of 10 participants. Driving performance was measured by the standard deviation of lateral position and the occurrence of off-road events. In comparison to visual scoring, automatic detection of MSEs in participants with severe sleepiness showed good performance (Cohen's kappa = 0.66). The MSE rate in the MWT correlated with the latency to the first MSE in the driving simulator ( = -0.54, < 0.05) and with the cumulative MSE duration in the driving simulator ( = 0.62, < 0.01). No correlations between MSE measures in the MWT and driving performance measures were found. In the driving simulator, multiple correlations between MSEs and driving performance variables were observed. Automatic MSE detection worked well, independent of the degree of sleepiness. The rate and the cumulative duration of MSEs could be promising sleepiness measures in both the MWT and the driving simulator. The correlations between MSEs in the driving simulator and driving performance might reflect a close and time-critical relationship between sleepiness and performance, potentially valuable for the fitness-to-drive assessment.