Nocturnal sodium oxybate increases the anterior cingulate cortex magnetic resonance glutamate signal upon awakening.

Clinical guidelines recommend sodium oxybate (SXB; the sodium salt of γ-hydroxybutyrate) for the treatment of disturbed sleep and excessive daytime sleepiness in narcolepsy, yet the underlying mode of action is elusive. In a randomised controlled trial in 20 healthy volunteers, we aimed at establishing neurochemical changes in the anterior cingulate cortex (ACC) following SXB-enhanced sleep. The ACC is a core neural hub regulating vigilance in humans. At 2:30 a.m., we administered in a double-blind cross-over manner an oral dose of 50 mg/kg SXB or placebo, to enhance electroencephalography-defined sleep intensity in the second half of nocturnal sleep (11:00 p.m. to 7:00 a.m.). Upon scheduled awakening, we assessed subjective sleepiness, tiredness and mood and measured two-dimensional, J-resolved, point-resolved magnetic resonance spectroscopy (PRESS) localisation at 3-Tesla field strength. Following brain scanning, we used validated tools to quantify psychomotor vigilance test (PVT) performance and executive functioning. We analysed the data with independent t tests, false discovery rate (FDR) corrected for multiple comparisons. The morning glutamate signal (at 8:30 a.m.) in the ACC was specifically increased after SXB-enhanced sleep in all participants in whom good-quality spectroscopy data were available (n = 16; pFDR < 0.002). Further, global vigilance (10th-90th inter-percentile range on the PVT) was improved (pFDR < 0.04) and median PVT response time was shorter (pFDR < 0.04) compared to placebo. The data indicate that elevated glutamate in the ACC could provide a neurochemical mechanism underlying SXB's pro-vigilant efficacy in disorders of hypersomnolence.

Validation of Fitbit Charge 2 Sleep and Heart Rate Estimates Against Polysomnographic Measures in Shift Workers: Naturalistic Study.

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.

Diurnal variations in multi-sensor wearable-derived sleep characteristics in morning- and evening-type shift workers under naturalistic conditions.

Consumer-grade, multi-sensor, rest-activity trackers may be powerful tools, to help optimize rest-activity management in shiftwork populations undergoing circadian misalignment. Nevertheless, performance testing of such devices under field conditions is scarce. We previously validated Fitbit Charge 2TM against home polysomnography and now evaluated the potential of this device to document differences in rest-activity behavior, including sleep macrostructure, in first-responder shift workers in an operational setting. We continuously monitored 89 individuals (54% females; mean age: 33.9 ± 7.7 years) for 32.5 ± 9.3 days and collected 2,974 individual sleep episodes scattered around the clock. We stratified the study participants according to their self-reported circadian preference on the reduced Horne-Östberg Morningness-Evening Questionnaire (rMEQ; the scores from 4 participants were missing). Fitbit estimates of sleep duration, wakefulness after sleep onset (WASO), REM sleep percentage in the first NREM-REM sleep cycle, and REM sleep latency formed approximately sinusoidal oscillations across 24 hours. Generalized additive mixed model analyses revealed that the phase position of sleep duration minimum was delayed by 2.8 h in evening types (ET; rMEQ ≤ 11; n = 20) and by 2.6 h in intermediate types (IT; 11 < rMEQ < 18; n = 45) when compared to morning types (MT; rMEQ ≥ 18; n = 20). Similarly, the phase position of WASO was delayed by 2.7 h in ET compared to MT. While nocturnal sleep duration did not differ among the three groups, sleep episodes during the biological day decreased in duration from ET to IT to MT. Together, the findings support the notion that a consumer-grade, rest-activity tracker allows estimation of behavioral sleep/wake cycles and sleep macrostructure in shift workers under naturalistic conditions that are consistent with their self-reported chronotype.

Oscillatory patterns in the electroencephalogram at sleep onset.

Falling asleep is a gradually unfolding process. We investigated the role of various oscillatory activities including sleep spindles and alpha and delta oscillations at sleep onset (SO) by automatically detecting oscillatory events. We used two datasets of healthy young males, eight with four baseline recordings, and eight with a baseline and recovery sleep after 40 h of sustained wakefulness. We analyzed the 2-min interval before SO (stage 2) and the five consecutive 2-min intervals after SO. The incidence of delta/theta events reached its maximum in the first 2-min episode after SO, while the frequency of them was continuously decreasing from stage 1 onwards, continuing over SO and further into deeper sleep. Interestingly, this decrease of the frequencies of the oscillations were not affected by increased sleep pressure, in contrast to the incidence which increased. We observed an increasing number of alpha events after SO, predominantly frontally, with their prevalence varying strongly across individuals. Sleep spindles started to occur after SO, with first an increasing then a decreasing incidence and a continuous decrease in their frequency. Again, the frequency of the spindles was not altered after sleep deprivation. Oscillatory events revealed derivation dependent aspects. However, these regional aspects were not specific of the process of SO but rather reflect a general sleep related phenomenon. No individual traits of SO features (incidence and frequency of oscillations) and their dynamics were observed. Delta/theta events are important features for the analysis of SO in addition to slow waves.

Unraveling the genetic underpinnings of sleep deprivation-induced impairments in human cognition.

The biobehavioral phenomena of sleep and cognition involve complex phenotype-genotype associations, i.e., complex relationships between observable traits and the genetic variants that contribute to the expression of those traits. There is a general belief that investigating such relationships requires large sample sizes. However, sleep- and cognition-related phenotype-genotype associations may be strengthened through carefully controlled laboratory studies that amplify a given cognitive phenotype by perturbing the biobehavioral system through sleep deprivation and/or pharmacogenetic interventions. Utilization of performance tasks that dissociate cognitive processes allows for cognitive endophenotyping, that is, making precise measurements that capture the essence of a cognitive phenotype. This enables assessment of the genetic underpinnings of cognitive impairment due to sleep deprivation without necessarily requiring large samples. Theory-driven gene selection, selective population sampling techniques to avoid underrepresentation of rare genetic variants, and modern statistical techniques informed by prior knowledge further enhance statistical power. Here we illustrate these approaches on the basis of recent findings, supplemented with some new results, as well as a discussion of modern regression methods for statistical analysis. Ongoing research employing these methods is driving advancements in the understanding of the genetic underpinnings of cognitive impairment associated with sleep loss.

Neurophysiological signature of gamma-hydroxybutyrate augmented sleep in male healthy volunteers may reflect biomimetic sleep enhancement: a randomized controlled trial.

Gamma-hydroxybutyrate (GHB) is an endogenous GHB/GABAB receptor agonist, which has demonstrated potency in consolidating sleep and reducing excessive daytime sleepiness in narcolepsy. Little is known whether GHB's efficacy reflects the promotion of physiological sleep mechanisms and no study has investigated its sleep consolidating effects under low sleep pressure. GHB (50 mg/kg p.o.) and placebo were administered in 20 young male volunteers at 2:30 a.m., the time when GHB is typically given in narcolepsy, in a randomized, double-blinded, crossover manner. Drug effects on sleep architecture and electroencephalographic (EEG) sleep spectra were analyzed. In addition, current source density (CSD) analysis was employed to identify the effects of GHB on the brain electrical sources of neuronal oscillations. Moreover, lagged-phase synchronization (LPS) analysis was applied to quantify the functional connectivity among sleep-relevant brain regions. GHB prolonged slow-wave sleep (stage N3) at the cost of rapid eye movement (REM) sleep. Furthermore, it enhanced delta-theta (0.5-8 Hz) activity in NREM and REM sleep, while reducing activity in the spindle frequency range (13-15 Hz) in sleep stage N2. The increase in delta power predominated in medial prefrontal cortex, parahippocampal and fusiform gyri, and posterior cingulate cortex. Theta power was particularly increased in the prefrontal cortex and both temporal poles. Moreover, the brain areas that showed increased theta power after GHB also exhibited increased lagged-phase synchronization among each other. Our study in healthy men revealed distinct similarities between GHB-augmented sleep and physiologically augmented sleep as seen in recovery sleep after prolonged wakefulness. The promotion of the sleep neurophysiological mechanisms by GHB may thus provide a rationale for GHB-induced sleep and waking quality in neuropsychiatric disorders beyond narcolepsy.