Chronic dietary supplementation with nicotinamide riboside reduces sleep need in the laboratory mouse.

Non-rapid eye movement sleep (NREMS) is accompanied by a reduction in cerebral glucose utilization. Enabling this metabolic change may be a central function of sleep. Since the reduction in glucose metabolism is inevitably accompanied by deceleration of downstream oxidation/reduction reactions involving nicotinamide adenine dinucleotide (NAD), we hypothesized a role for NAD in regulating the homeostatic dynamics of sleep at the biochemical level. We applied dietary nicotinamide riboside (NR), a NAD precursor, in a protocol known to improve neurological outcome measures in mice. Long-term (6-10 weeks) dietary supplementation with NR reduced the time that mice spent in NREMS by 17 percent and accelerated the rate of discharge of sleep need according to a mathematical model of sleep homeostasis (Process S). These findings suggest that increasing redox capacity by increasing nicotinamide availability reduces sleep need and increases the cortical capacity for energetically demanding high-frequency oscillations. In turn, this work demonstrates the impact of redox substrates on cortical circuit properties related to fatigue and sleep drive, implicating redox reactions in the homeostatic dynamics of cortical network events across sleep-wake cycles.

Pilot In-Flight Sleep During Long-Range and Ultra-Long Range Commercial Airline Flights.

INTRODUCTION: In commercial aviation, pilot fatigue is a major threat to safety. One key fatigue mitigation strategy on long-range (LR; 8-16 h) and ultra-long range (ULR; 16+ h on at least 10% of trips) routes is allotting in-flight rest breaks for the pilots. Since sleep is a strong predictor of performance, it is important to quantify total in-flight sleep (TIFS) and determine rest scheme schedules that optimize sleep opportunity and subsequent performance. Here we quantify in-flight sleep and characterize rest schemes by type and efficiency.METHODS: Between 2015 and 2019, we collected data on in-flight sleep on 3 LR and 5 ULR routes totaling 231 pilots flying over 1200 flight duty periods. Data were collected using a combination of actigraphy and logbooks.RESULTS: Over all combinations of flight direction, crew and LR vs. ULR, average TIFS ranged from 3.4 h to 5.2 h with some ULR pilots getting over 8 h. Most crews made use of simple two- or three-break rest schemes and the complex four-break rest schemes were used almost exclusively on the three longest ULR routes. The complex schemes were less efficient than simple schemes, although this effect was small. Complex schemes resulted in no more TIFS compared to simple schemes on the same routes.DISCUSSION: Overall, we find that crews are getting more sleep on these routes than previously reported on similar routes. Most crews use simple rest schemes and these simple schemes are more efficient than complex schemes.Rempe MJ, Basiarz E, Rasmussen I, Belenky G, Lamp A. Pilot in-flight sleep during long-range and ultra-long range commercial airline flights. Aerosp Med Hum Perform. 2022; 93(4):368-375.

Flight Crew Alertness and Sleep Relative to Timing of In-Flight Rest Periods in Long-Haul Flights.

BACKGROUND: In-flight breaks are used during augmented long-haul flight operations, allowing pilots a sleep opportunity. The U.S. Federal Aviation Administration duty and rest regulations restrict the pilot flying the landing to using the third rest break. It is unclear how effective these restrictions are on pilots ability to obtain sleep. We hypothesized there would be no difference in self-reported sleep, alertness, and fatigue between pilots taking the second vs. third rest breaks.METHODS: Pilots flying augmented operations in two U.S.-based commercial airlines were eligible for the study. Volunteers completed a survey at top-of-descent (TOD), including self-reported in-flight sleep duration, and Samn-Perelli fatigue and Karolinska Sleepiness Scale ratings. We compared the second to third rest break using noninferiority analysis. The influence of time of day (home-base time; HBT) was evaluated in 4-h blocks using repeated measures ANOVA.RESULTS: From 787 flights 500 pilots provided complete data. The second rest break was noninferior to the third break for self-reported sleep duration (1.5 0.7 h vs. 1.4 0.7 h), fatigue (2.0 1.0 vs. 2.9 1.3), and sleepiness (2.6 1.4 vs. 3.8 1.8) at TOD for landing pilots. Measures of sleep duration, fatigue, and sleepiness were influenced by HBT circadian time of day.DISCUSSION: We conclude that self-reported in-flight sleep, fatigue, and sleepiness from landing pilots taking the second in-flight rest break are equivalent to or better than pilots taking the third break. Our findings support providing pilots with choice in taking the second or third in-flight rest break during augmented operations.Gregory KB, Soriano-Smith RN, Lamp ACM, Hilditch CJ, Rempe MJ, Flynn-Evans EE, Belenky GL. Flight crew alertness and sleep relative to timing of in-flight rest periods in long-haul flights. Aerosp Med Hum Perform. 2021; 92(2):8391.

Delta: The Value That Matters in Fatigue Risk Management.

INTRODUCTION: Noninferiority or equivalence testing are often used when comparing a novel pharmaceutical, operation, or procedure to the current standard designated as safe. Noninferiority and equivalence testing require estimates of a metric called delta: the margin of meaningful difference. Inappropriate delta margins can lead to invalid conclusions, thereby creating uncertainty about a studys scientific credibility. We recommend that a working group be convened with the following goals: 1) to evaluate delta values currently in use in aviation; 2) to determine if it is possible to develop a systematic, evidence-based, and replicable process to derive delta values based on statistical properties from population data, rather than a mixture of evidence- and opinion-based processes; and 3) based on the findings of the second goal, update the current delta values in use in aviation. This working group should include, at a minimum, government agencies and other key stakeholders using these values within operational settings.Lamp ACM, Rempe MJ, Belenky GL. Delta: the value that matters in fatigue risk management. Aerosp Med Hum Perform. 2021; 92(2):127128.

A Role for Astroglial Calcium in Mammalian Sleep and Sleep Regulation.

Mammalian sleep expression and regulation have historically been thought to reflect the activity of neurons. Changes in other brain cells (glia) across the sleep-wake cycle and their role in sleep regulation are comparatively unexplored. We show that sleep and wakefulness are accompanied by state-dependent changes in astroglial activity. Using a miniature microscope in freely behaving mice and a two-photon microscope in head-fixed, unanesthetized mice, we show that astroglial calcium signals are highest in wake and lowest in sleep and are most pronounced in astroglial processes. We also find that astroglial calcium signals during non-rapid eye movement sleep change in proportion to sleep need. In contrast to neurons, astrocytes become less synchronized during non-rapid eye movement sleep after sleep deprivation at the network and single-cell level. Finally, we show that conditionally reducing intracellular calcium in astrocytes impairs the homeostatic response to sleep deprivation. Thus, astroglial calcium activity changes dynamically across vigilance states, is proportional to sleep need, and is a component of the sleep homeostat.

Mathematical modeling of sleep state dynamics in a rodent model of shift work.

Millions of people worldwide are required to work when their physiology is tuned for sleep. By forcing wakefulness out of the body's normal schedule, shift workers face numerous adverse health consequences, including gastrointestinal problems, sleep problems, and higher rates of some diseases, including cancers. Recent studies have developed protocols to simulate shift work in rodents with the intention of assessing the effects of night-shift work on subsequent sleep (Grønli et al., 2017). These studies have already provided important contributions to the understanding of the metabolic consequences of shift work (Arble et al., 2015; Marti et al., 2016; Opperhuizen et al., 2015) and sleep-wake-specific impacts of night-shift work (Grønli et al., 2017). However, our understanding of the causal mechanisms underlying night-shift-related sleep disturbances is limited. In order to advance toward a mechanistic understanding of sleep disruption in shift work, we model these data with two different approaches. First we apply a simple homeostatic model to quantify differences in the rates at which sleep need, as measured by slow wave activity during slow wave sleep (SWS) rises and falls. Second, we develop a simple and novel mathematical model of rodent sleep and use it to investigate the timing of sleep in a simulated shift work protocol (Grønli et al., 2017). This mathematical framework includes the circadian and homeostatic processes of the two-process model, but additionally incorporates a stochastic process to model the polyphasic nature of rodent sleep. By changing only the time at which the rodents are forced to be awake, the model reproduces some key experimental results from the previous study, including correct proportions of time spent in each stage of sleep as a function of circadian time and the differences in total wake time and SWS bout durations in the rodents representing night-shift workers and those representing day-shift workers. Importantly, the model allows for deeper insight into circadian and homeostatic influences on sleep timing, as it demonstrates that the differences in SWS bout duration between rodents in the two shifts is largely a circadian effect. Our study shows the importance of mathematical modeling in uncovering mechanisms behind shift work sleep disturbances and it begins to lay a foundation for future mathematical modeling of sleep in rodents.

A Rodent Model of Night-Shift Work Induces Short-Term and Enduring Sleep and Electroencephalographic Disturbances.

Millions of people worldwide are working at times that overlap with the normal time for sleep. Sleep problems related to the work schedule may mediate the well-established relationship between shift work and increased risk for disease, occupational errors and accidents. Yet, our understanding of causality and the underlying mechanisms that explain this relationship is limited. We aimed to assess the consequences of night-shift work for sleep and to examine whether night-shift work-induced sleep disturbances may yield electrophysiological markers of impaired maintenance of the waking brain state. An experimental model developed in rats simulated a 4-day protocol of night-work in humans. Two groups of rats underwent 8-h sessions of enforced ambulation, either at the circadian time when the animal was physiologically primed for wakefulness (active-workers, mimicking day-shift) or for sleep (rest-workers, mimicking night-shift). The 4-day rest-work schedule induced a pronounced redistribution of sleep to the endogenous active phase. Rest-work also led to higher electroencephalogram (EEG) slow-wave (1-4 Hz) energy in quiet wakefulness during work-sessions, suggesting a degraded waking state. After the daily work-sessions, being in their endogenous active phase, rest-workers slept less and had higher gamma (80-90 Hz) activity during wake than active-workers. Finally, rest-work induced an enduring shift in the main sleep period and attenuated the accumulation of slow-wave energy during NREM sleep. A comparison of recovery data from 12:12 LD and constant dark conditions suggests that reduced time in NREM sleep throughout the recorded 7-day recovery phase induced by rest-work may be modulated by circadian factors. Our data in rats show that enforced night-work-like activity during the normal resting phase has pronounced acute and persistent effects on sleep and waking behavior. The study also underscores the potential importance of animal models for future studies on the health consequences of night-shift work and the mechanisms underlying increased risk for diseases.

Beta EEG reflects sensory processing in active wakefulness and homeostatic sleep drive in quiet wakefulness.

Markers of sleep drive (<10 Hz; slow-wave activity and theta) have been identified in the course of slow-wave sleep and wakefulness. So far, higher frequencies in the waking electroencephalogram have not been examined thoroughly as a function of sleep drive. Here, electroencephalogram dynamics were measured in epochs of active wake (wake characterized by high muscle tone) or quiet wake (wake characterized by low muscle tone). It was hypothesized that the higher beta oscillations (15-35 Hz, measured by local field potential and electroencephalography) represent fundamentally different processes in active wake and quiet wake. In active wake, sensory stimulation elevated beta activity in parallel with gamma (80-90 Hz) activity, indicative of cognitive processing. In quiet wake, beta activity paralleled slow-wave activity (1-4 Hz) and theta (5-8 Hz) in tracking sleep need. Cerebral lactate concentration, a measure of cerebral glucose utilization, increased during active wake whereas it declined during quiet wake. Mathematical modelling of state-dependent dynamics of cortical lactate concentration was more precisely predictive when quiet wake and active wake were included as two distinct substates rather than a uniform state of wakefulness. The extent to which lactate concentration declined in quiet wake and increased in active wake was proportionate to the amount of beta activity. These data distinguish quiet wake from active wake. Quiet wake, particularly when characterized by beta activity, is permissive to metabolic and electrophysiological changes that occur in slow-wave sleep. These data urge further studies on state-dependent beta oscillations across species.

Sleep Homeostatic and Waking Behavioral Phenotypes in Egr3-Deficient Mice Associated with Serotonin Receptor 5-HT2 Deficits.

STUDY OBJECTIVE: The expression of the immediate early gene early growth response 3 (Egr3) is a functional marker of brain activity including responses to novelty, sustained wakefulness, and sleep. We examined the role of this gene in regulating wakefulness and sleep. METHODS: Electroencephalogram/electromyogram (EEG/EMG) were recorded in Egr3-/- and wild-type (WT) mice during 24 h baseline, 6 h sleep disruption and 6 h recovery. Serotonergic signaling was assessed with 6 h EEG/EMG recordings after injections of nonselective 5-HT2 antagonist (clozapine), selective 5-HT2 antagonists (5-HT2A; MDL100907 and 5-HT2BC; SB206553) and a cocktail of both selective antagonists, administered in a randomized order to each animal. RESULTS: Egr3-/- mice did not exhibit abnormalities in the timing of wakefulness and slow wave sleep (SWS); however, EEG dynamics in SWS (suppressed 1-3 Hz power) and in quiet wakefulness (elevated 3-8 Hz and 15-35 Hz power) differed in comparison to WT-mice. Egr3-/- mice showed an exaggerated response to sleep disruption as measured by active wakefulness, but with a blunted increase in homeostatic sleep drive (elevated 1-4 Hz power) relative to WT-mice. Egr3-/-mice exhibit greatly reduced sedative effects of clozapine at the electroencephalographic level. In addition, clozapine induced a previously undescribed dissociated state (low amplitude, low frequency EEG and a stable, low muscle tone) lasting up to 2 h in WT-mice. Egr3-/- mice did not exhibit this phenomenon. Selective 5-HT2A antagonist, alone or in combination with selective 5-HT2BC antagonist, caused EEG slowing coincident with behavioral quiescence in WT-mice but not in Egr3-/- mice. CONCLUSION: Egr3 has an essential role in regulating cortical arousal, wakefulness, and sleep, presumably by its regulation of 5-HT2 receptors.

An automated sleep-state classification algorithm for quantifying sleep timing and sleep-dependent dynamics of electroencephalographic and cerebral metabolic parameters.

INTRODUCTION: Rodent sleep research uses electroencephalography (EEG) and electromyography (EMG) to determine the sleep state of an animal at any given time. EEG and EMG signals, typically sampled at >100 Hz, are segmented arbitrarily into epochs of equal duration (usually 2-10 seconds), and each epoch is scored as wake, slow-wave sleep (SWS), or rapid-eye-movement sleep (REMS), on the basis of visual inspection. Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations. METHODS: We developed a semiautomated state-scoring procedure that uses a combination of principal component analysis and naïve Bayes classification, with the EEG and EMG as inputs. We validated this algorithm against human-scored sleep-state scoring of data from C57BL/6J and BALB/CJ mice. We then applied a general homeostatic model to characterize the state-dependent dynamics of sleep slow-wave activity and cerebral glycolytic flux, measured as lactate concentration. RESULTS: More than 89% of epochs scored as wake or SWS by the human were scored as the same state by the machine, whether scoring in 2-second or 10-second epochs. The majority of epochs scored as REMS by the human were also scored as REMS by the machine. However, of epochs scored as REMS by the human, more than 10% were scored as SWS by the machine and 18 (10-second epochs) to 28% (2-second epochs) were scored as wake. These biases were not strain-specific, as strain differences in sleep-state timing relative to the light/dark cycle, EEG power spectral profiles, and the homeostatic dynamics of both slow waves and lactate were detected equally effectively with the automated method or the manual scoring method. Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state scoring relative to manual classification. CONCLUSIONS: Machine scoring is as effective as human scoring in detecting experimental effects in rodent sleep studies. Automated scoring is an efficient alternative to visual inspection in studies of strain differences in sleep and the temporal dynamics of sleep-related physiological parameters.

Cerebral lactate dynamics across sleep/wake cycles.

Cerebral metabolism varies dramatically as a function of sleep state. Brain concentration of lactate, the end product of glucose utilization via glycolysis, varies as a function of sleep state, and like slow wave activity (SWA) in the electroencephalogram (EEG), increases as a function of time spent awake or in rapid eye movement sleep and declines as a function of time spent in slow wave sleep (SWS). We sought to determine whether lactate concentration exhibits homeostatic dynamics akin to those of SWA in SWS. Lactate concentration in the cerebral cortex was measured by indwelling enzymatic biosensors. A set of equations based conceptually on Process S (previously used to quantify the homeostatic dynamics of SWA) was used to predict the sleep/wake state-dependent dynamics of lactate concentration in the cerebral cortex. Additionally, we applied an iterative parameter space-restricting algorithm (the Nelder-Mead method) to reduce computational time to find the optimal values of the free parameters. Compared to an exhaustive search, this algorithm reduced the computation time required by orders of magnitude. We show that state-dependent lactate concentration dynamics can be described by a homeostatic model, but that the optimal time constants for describing lactate dynamics are much smaller than those of SWA. This disconnect between lactate dynamics and SWA dynamics does not support the concept that lactate concentration is a biochemical mediator of sleep homeostasis. However, lactate synthesis in the cerebral cortex may nonetheless be informative with regard to sleep function, since the impact of glycolysis on sleep slow wave regulation is only just now being investigated.

Sleep slow-wave activity regulates cerebral glycolytic metabolism.

Non-rapid eye movement sleep (NREMS) onset is characterized by a reduction in cerebral metabolism and an increase in slow waves, 1-4-Hz oscillations between relatively depolarized and hyperpolarized states in the cerebral cortex. The metabolic consequences of slow-wave activity (SWA) at the cellular level remain uncertain. We sought to determine whether SWA modulates the rate of glycolysis within the cerebral cortex. The real-time measurement of lactate concentration in the mouse cerebral cortex demonstrates that it increases during enforced wakefulness. In spontaneous sleep/wake cycles, lactate concentration builds during wakefulness and rapid eye movement sleep and declines during NREMS. The rate at which lactate concentration declines during NREMS is proportional to the magnitude of electroencephalographic (EEG) activity at frequencies of <10 Hz. The induction of 1-Hz oscillations, but not 10-Hz oscillations, in the electroencephalogram by optogenetic stimulation of cortical pyramidal cells during wakefulness triggers a decline in lactate concentration. We conclude that cerebral SWA promotes a decline in the rate of glycolysis in the cerebral cortex. These results demonstrate a cellular energetic function for sleep SWA, which may contribute to its restorative effects on brain function.

A mathematical model of the sleep/wake cycle.

We present a biologically-based mathematical model that accounts for several features of the human sleep/wake cycle. These features include the timing of sleep and wakefulness under normal and sleep-deprived conditions, ultradian rhythms, more frequent switching between sleep and wakefulness due to the loss of orexin and the circadian dependence of several sleep measures. The model demonstrates how these features depend on interactions between a circadian pacemaker and a sleep homeostat and provides a biological basis for the two-process model for sleep regulation. The model is based on previous "flip-flop" conceptual models for sleep/wake and REM/NREM and we explore whether the neuronal components in these flip-flop models, with the inclusion of a sleep-homeostatic process and the circadian pacemaker, are sufficient to account for the features of the sleep/wake cycle listed above. The model is minimal in the sense that, besides the sleep homeostat and constant cortical drives, the model includes only those nuclei described in the flip-flop models. Each of the cell groups is modeled by at most two differential equations for the evolution of the total population activity, and the synaptic connections are consistent with those described in the flip-flop models. A detailed analysis of the model leads to an understanding of the mathematical mechanisms, as well as insights into the biological mechanisms, underlying sleep/wake dynamics.

Compartmental neural simulations with spatial adaptivity.

Since their inception, computational models have become increasingly complex and useful counterparts to laboratory experiments within the field of neuroscience. Today several software programs exist to solve the underlying mathematical system of equations, but such programs typically solve these equations in all parts of a cell (or network of cells) simultaneously, regardless of whether or not all of the cell is active. This approach can be inefficient if only part of the cell is active and many simulations must be performed. We have previously developed a numerical method that provides a framework for spatial adaptivity by making the computations local to individual branches rather than entire cells (Rempe and Chopp, SIAM Journal on Scientific Computing, 28: 2139-2161, 2006). Once the computation is reduced to the level of branches instead of cells, spatial adaptivity is straightforward: the active regions of the cell are detected and computational effort is focused there, while saving computations in other regions of the cell that are at or near rest. Here we apply the adaptive method to four realistic neuronal simulation scenarios and demonstrate its improved efficiency over non-adaptive methods. We find that the computational cost of the method scales with the amount of activity present in the simulation, rather than the physical size of the system being simulated. For certain problems spatial adaptivity reduces the computation time by up to 80%.