Sleep homeostasis during daytime food entrainment in mice

24h rhythms of physiology and behavior are driven by the environment and an internal endogenous timing system. Daily restricted feeding (RF) in nocturnal rodents during their inactive phase initiates food anticipatory activity (FAA) and a reorganisation of the typical 24h sleep-wake structure. Here, we investigate the effects of daytime feeding, where food access was restricted to 4h during the light period ZT4-8 (Zeitgeber time; ZT0 is lights on), on sleep-wake architecture and sleep homeostasis in mice. Following 10 days of RF, mice were returned to ad libitum feeding. To mimic the spontaneous wakefulness associated with FAA and daytime feeding, mice were then sleep deprived between ZT3-6. While the amount of wake increased during FAA and subsequent feeding, total wake time over 24h remained stable as the loss of sleep in the light phase was compensated for by an increase in sleep in the dark phase. Interestingly, sleep which followed spontaneous wake episodes during the dark period and the extended period of wake associated with FAA, exhibited lower levels of slow-wave activity (SWA) when compared to baseline or after sleep deprivation, despite a similar duration of waking. This suggests an evolutionary mechanism of reducing sleep drive during negative energy balance to enable greater arousal for food seeking behaviors. However, the total amount of sleep and SWA accumulated during the 24h was similar between baseline and RF. In summary, our study suggests that despite substantial changes in the daily distribution and quality of wake induced by RF, sleep homeostasis is maintained.</p

Continuous and non-invasive thermography of mouse skin accurately describes core body temperature patterns, but not absolute core temperature

Body temperature is an important physiological parameter in many studies of laboratory mice. Continuous assessment of body temperature has traditionally required surgical implantation of a telemeter, but this invasive procedure adversely impacts animal welfare. Near-infrared thermography provides a non-invasive alternative by continuously measuring the highest temperature on the outside of the body (Tskin), but the reliability of these recordings as a proxy for continuous core body temperature (Tcore) measurements has not been assessed. Here, Tcore (30 s resolution) and Tskin (1 s resolution) were continuously measured for three days in mice exposed to ad libitum and restricted feeding conditions. We subsequently developed an algorithm that optimised the reliability of a Tskin-derived estimate of Tcore. This identified the average of the maximum Tskin per minute over a 30-min interval as the optimal way to estimate Tcore. Subsequent validation analyses did however demonstrate that this Tskin-derived proxy did not provide a reliable estimate of the absolute Tcore due to the high between-animal variability in the relationship between Tskin and Tcore. Conversely, validation showed that Tskin-derived estimates of Tcore reliably describe temporal patterns in physiologically-relevant Tcore changes and provide an excellent measure to perform within-animal comparisons of relative changes in Tcore

Somnotate: a probabilistic sleep stage classifier for studying vigilance state transitions

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"—a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics

Absent sleep EEG spindle activity in GluA1 (Gria1) knockout mice:relevance to neuropsychiatric disorders

Sleep EEG spindles have been implicated in attention, sensory processing, synaptic plasticity and memory consolidation. In humans, deficits in sleep spindles have been reported in a wide range of neurological and psychiatric disorders, including schizophrenia. Genome-wide association studies have suggested a link between schizophrenia and genes associated with synaptic plasticity, including the Gria1 gene which codes for the GluA1 subunit of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor. Gria1−/− mice exhibit a phenotype relevant for neuropsychiatric disorders, including reduced synaptic plasticity and, at the behavioural level, attentional deficits leading to aberrant salience. In this study we report a striking reduction of EEG power density including the spindle-frequency range (10–15 Hz) during sleep in Gria1−/− mice. The reduction of spindle-activity in Gria1−/− mice was accompanied by longer REM sleep episodes, increased EEG slow-wave activity in the occipital derivation during baseline sleep, and a reduced rate of decline of EEG slow wave activity (0.5–4 Hz) during NREM sleep after sleep deprivation. These data provide a novel link between glutamatergic dysfunction and sleep abnormalities in a schizophrenia-relevant mouse model

Neonatal Androgenization Exacerbates Alcohol-Induced Liver Injury in Adult Rats, an Effect Abrogated by Estrogen

Alcoholic liver disease (ALD) affects millions of people worldwide and is a major cause of morbidity and mortality. However, fewer than 10% of heavy drinkers progress to later stages of injury, suggesting other factors in ALD development, including environmental exposures and genetics. Females display greater susceptibility to the early damaging effects of ethanol. Estrogen (E2) and ethanol metabolizing enzymes (cytochrome P450, CYP450) are implicated in sex differences of ALD. Sex steroid hormones are developmentally regulated by the hypothalamic-pituitary-gonadal (HPG) axis, which controls sex-specific cycling of gonadal steroid production and expression of hepatic enzymes. The aim of this study was to determine if early postnatal inhibition of adult cyclic E2 alters ethanol metabolizing enzyme expression contributing to the development of ALD in adulthood. An androgenized rat model was used to inhibit cyclic E2 production. Control females (Ctrl), androgenized females (Andro) and Andro females with E2 implants were administered either an ethanol or isocalorically-matched control Lieber-DeCarli diet for four weeks and liver injury and CYP450 expression assessed. Androgenization exacerbated the deleterious effects of ethanol demonstrated by increased steatosis, lipid peroxidation, profibrotic gene expression and decreased antioxidant defenses compared to Ctrl. Additionally, CYP2E1 expression was down-regulated in Andro animals on both diets. No change was observed in CYP1A2 protein expression. Further, continuous exogenous administration of E2 to Andro in adulthood attenuated these effects, suggesting that E2 has protective effects in the androgenized animal. Therefore, early postnatal inhibition of cyclic E2 modulates development and progression of ALD in adulthood

Global sleep homeostasis reflects temporally and spatially integrated local cortical neuronal activity

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

Cortical neuronal activity determines the dynamics of local sleep homeostasis

The homeostatic regulation of sleep manifests as a relative constancy of its total daily amount, and the compensation of sleep loss by an increase in its subsequent duration and intensity. Theoretical descriptions of this phenomenon define “Process S”, a variable with dynamics dependent only on sleep-wake history and whose levels are reflected in EEG slow wave activity. While numerous hypotheses have been advanced regarding the substrate and role of Process S, such as synaptic or energy homeostasis, it remains unclear whether these dynamics are fundamentally driven by a need to homeostatically regulate specific variables, or by an unknown innate process which enforces that a certain daily sleep quota is obtained. Sleep is typically defined based on brain-derived criteria, such as behaviour or EEG power spectra, and variation in brain activity during wakefulness has been linked to variation in Process S accumulation. We therefore hypothesised that Process S dynamics might be related to the quantity and characteristics of spiking activity in cortical neurones. Specifically, we assumed that Process S changes as a function of the deviation of neuronal firing rate from a locally defined set point. To relate these dynamics explicitly to patterns of spiking activity, we incorporated the occurrence of network spiking off periods as both the defining measure of Process S and as the determinant of its rate of decay. This approach was able to describe the time course of Process S, crucially without explicit knowledge of the animal’s global sleep-wake state. This work provides a conceptual advance in our understanding of the substrate of sleep homeostasis and provides important links between local and global aspects of sleep regulation

Cortical region–specific sleep homeostasis in mice: effects of time of day and waking experience

Sleep-wake history, wake behaviours, lighting conditions and circadian time influence sleep, but neither their relative contribution, nor the underlying mechanisms are fully understood. The dynamics of EEG slow-wave activity (SWA) during sleep can be described using the two-process model, whereby the parameters of homeostatic Process S are estimated using empirical EEG SWA (0.5-4 Hz) in non-rapid eye movement sleep (NREM), and the 24-h distribution of vigilance states. We hypothesised that the influence of extrinsic factors on sleep homeostasis, such as the time of day or wake behaviour, would manifest in systematic deviations between empirical SWA and model predictions. To test this hypothesis, we performed parameter estimation and tested model predictions using NREM SWA derived from continuous EEG recordings from the frontal and occipital cortex in mice. The animals showed prolonged wake periods, followed by consolidated sleep, both during the dark and light phases, and wakefulness primarily consisted of voluntary wheel running, learning a new motor skill or novel object exploration. Simulated SWA matched empirical levels well across conditions, and neither waking experience nor time of day had a significant influence on the fit between data and simulation. However, we consistently observed that Process S declined during sleep significantly faster in the frontal than in the occipital area of the neocortex. The striking resilience of the model to specific wake behaviours, lighting conditions and time of day suggests that intrinsic factors underpinning the dynamics of Process S are robust to extrinsic influences, despite their major role in shaping the overall amount and distribution of vigilance states across 24 h

Cortical region-specific sleep homeostasis in mice: effects of time of day and waking experience

Sleep-wake history, wake behaviors, lighting conditions, and circadian time influence sleep, but neither their relative contribution nor the underlying mechanisms are fully understood. The dynamics of electroencephalogram (EEG) slow-wave activity (SWA) during sleep can be described using the two-process model, whereby the parameters of homeostatic Process S are estimated using empirical EEG SWA (0.5-4 Hz) in nonrapid eye movement sleep (NREMS), and the 24 hr distribution of vigilance states. We hypothesized that the influence of extrinsic factors on sleep homeostasis, such as the time of day or wake behavior, would manifest in systematic deviations between empirical SWA and model predictions. To test this hypothesis, we performed parameter estimation and tested model predictions using NREMS SWA derived from continuous EEG recordings from the frontal and occipital cortex in mice. The animals showed prolonged wake periods, followed by consolidated sleep, both during the dark and light phases, and wakefulness primarily consisted of voluntary wheel running, learning a new motor skill or novel object exploration. Simulated SWA matched empirical levels well across conditions, and neither waking experience nor time of day had a significant influence on the fit between data and simulation. However, we consistently observed that Process S declined during sleep significantly faster in the frontal than in the occipital area of the neocortex. The striking resilience of the model to specific wake behaviors, lighting conditions, and time of day suggests that intrinsic factors underpinning the dynamics of Process S are robust to extrinsic influences, despite their major role in shaping the overall amount and distribution of vigilance states across 24 hr