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Sleep disruption is common in pediatric intensive care unit (PICU) patients, but measuring sleep in this population is challenging. We aimed to evaluate the utility of actigraphy for estimating circadian rhythmicity in mechanically ventilated PICU patients and its accuracy for measuring sleep by comparing it to polysomnogram (PSG). We conducted a single-center prospective observational study of children 6 months - 17 years of age receiving mechanical ventilation and standard, protocolized sedation for acute respiratory failure, excluding children with acute or historical neurologic injury. We enrolled 16 children and monitored them with up to 14 days of actigraphy and 24 hours of simultaneous limited (10 channel) PSG. Daily actigraphy-based activity profiles demonstrated that patients had a high level of nighttime activity (30-41% of total activity), suggesting disrupted circadian activity cycles. Among n = 12 patients with sufficient actigraphy and PSG data overlap, actigraphy-based sleep estimation showed poor agreement with PSG-identified sleep states, with good sensitivity (94%) but poor specificity (28%), low accuracy (70%,) and low agreement (Cohen's kappa = 0.2, 95% CI = 0.08-0.31). Using univariate linear regression, we identified that Cornell Assessment of Pediatric Delirium scores were associated with accuracy of actigraphy but that other clinical factors including sedative medication doses, activity levels, and restraint use were not. In this population, actigraphy did not reliably discern between sleep and wake states. However, in select patients, actigraphy was able to distinguish diurnal variation in activity patterns, and therefore may be useful for evaluating patients' response to circadian-oriented interventions.

Large scale single cell omics profiling is revolutionising our understanding of cell types, especially in complex organs like the brain. This presents both an opportunity and a challenge for cell ontologies. Annotation of cell types in single cell 9omics data typically uses unstructured free text, making comparison and mapping of annotation between datasets challenging. Annotation with cell ontologies is key to overcoming this challenge, but this will require meeting the challenge of extending cell ontologies representing classically defined cell types by defining and classifying cell types directly from data. Here we present the Brain Data Standards Ontology (BDSO), a data driven ontology that is built as an extension to the Cell Ontology (CL). It supports two major use cases: cell type annotation, and navigation, search, and organisation of a web application integrating single cell omics datasets for the mammalian primary motor cortex. The ontology is built using a semi-automated pipeline that interlinks cell type taxonomies and necessary and sufficient marker genes, and imports relevant ontology modules derived from external ontologies. Overall, the BDS ontology provides an underlying structure that supports these use cases, while remaining sustainable and extensible through automation as our knowledge of brain cell type expands.

A recent article by Gizowski and Bourque shows that vasopressinergic (VP) neurons within the suprachiasmatic nucleus (SCN) master circadian clock have the ability of encoding afferent input from osmosensors and generating appropriate homeostatic responses, suggesting that SCN neurons can integrate internal circadian time and acute changes in homeostatic markers.

Nearly all mammals display robust daily rhythms of physiology and behavior. These approximately 24-h cycles, known as circadian rhythms, are driven by a master clock in the suprachiasmatic nucleus (SCN) of the hypothalamus and affect biological processes ranging from metabolism to immune function. Perhaps the most overt output of the circadian clock is the sleep-wake cycle, the integrity of which is critical for health and homeostasis of the organism. In this review, we summarize our current understanding of the circadian regulation of sleep. We discuss the neural circuitry and molecular mechanisms underlying daily sleep timing, and the trajectory of circadian regulation of sleep across development. We conclude by proposing future research priorities for the field that will significantly advance our mechanistic understanding of the circadian regulation of sleep.

STUDY OBJECTIVES: Sleep disturbances are common co-morbidities of epileptic disorders. Dravet syndrome (DS) is an intractable epilepsy accompanied by disturbed sleep. While there is evidence that daily sleep timing is disrupted in DS, the difficulty of chronically recording polysomnographic sleep from patients has left our understanding of the effect of DS on circadian sleep regulation incomplete. We aim to characterize circadian sleep regulation in a mouse model of DS. METHODS: Here we exploit long-term electrocorticographic recordings of sleep in a mouse model of DS in which one copy of the Scn1a gene is deleted. This model both genocopies and phenocopies the disease in humans. We test the hypothesis that the deletion of Scn1a in DS mice is associated with impaired circadian regulation of sleep. RESULTS: We find that DS mice show impairments in circadian sleep regulation, including a fragmented rhythm of non-rapid eye movement (NREM) sleep and an elongated circadian period of sleep. Next, we characterize re-entrainment of sleep stages and siesta following jet lag in the mouse. Strikingly, we find that re-entrainment of sleep following jet lag is normal in DS mice, in contrast to previous demonstrations of slowed re-entrainment of wheel-running activity. Finally, we report that DS mice are more likely to have an absent or altered daily “siesta”. CONCLUSIONS: Our findings support the hypothesis that the circadian regulation of sleep is altered in DS and highlight the value of long-term chronic polysomnographic recording in studying the role of the circadian clock on sleep/wake cycles in pre-clinical models of disease.

The temporal distribution of sleep stages is critical for the study of sleep function, regulation, and disorders in higher vertebrates. This temporal distribution is typically determined polysomnographically. In laboratory rodents, scoring of electrocorticography (ECoG) and electromyography (EMG) recordings is usually performed manually, where 5-10 second epochs are categorized as one of three specific stages: wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep. This process is laborious, time-consuming, and particularly impractical for large experimental cohorts with recordings lasting longer than 24 hours.To circumvent this problem, we developed an open-source Python toolkit, Sleep Identification Enabled by Supervised Training Algorithms (SIESTA), that automates the detection of these three main behavioral stages in mice. Our supervised machine learning algorithm extracts features from the ECoG and EMG signals, then automatically scores recordings with a hierarchical classifier based on Bagging Random Forest approaches. We evaluated this approach on data collected from wild-type mice housed under both normal and different lighting conditions, as well as from a mutant mouse line with abnormal sleep phenotypes. To validate its performance on test data, we compared SIESTA with manually scored data and obtained F1 scores of 0.92 for wakefulness, 0.81 for REM, and 0.93 for NREM.SIESTA has a user-friendly interface that can be used without coding expertise. To our knowledge, this is the first time that such a strategy has been developed using all open-source and freely available resources, and our aim is that SIESTA becomes a useful tool that facilitates further research of sleep in rodent models.

Study Objectives Sleep disturbances are common co-morbidities of epileptic disorders. Dravet syndrome (DS) is an intractable epilepsy accompanied by disturbed sleep. While there is evidence that daily sleep timing is disrupted in DS, the difficulty of chronically recording polysomnographic sleep from patients has left our understanding of the effect of DS on circadian sleep regulation incomplete. We aim to characterize circadian sleep regulation in a mouse model of DS. Methods Here we exploit long-term electrocorticographic recordings of sleep in a mouse model of DS in which one copy of the Scn1a gene is deleted. This model both genocopies and phenocopies the disease in humans. We test the hypothesis that the deletion of Scn1a in DS mice is associated with impaired circadian regulation of sleep. Results We find that DS mice show impairments in circadian sleep regulation, including a fragmented rhythm of non-rapid eye movement (NREM) sleep and an elongated circadian period of sleep. Next, we characterize re-entrainment of sleep stages and siesta following jet lag in the mouse. Strikingly, we find that re-entrainment of sleep following jet lag is normal in DS mice, in contrast to previous demonstrations of slowed re-entrainment of wheel-running activity. Finally, we report that DS mice are more likely to have an absent or altered daily “siesta”. Conclusions Our findings support the hypothesis that the circadian regulation of sleep is altered in DS and highlight the value of long-term chronic polysomnographic recording in studying the role of the circadian clock on sleep/wake cycles in pre-clinical models of disease.

Summary Successful reproduction in female mammals is precisely timed and must be able to withstand the metabolic demand of pregnancy and lactation. We show that kisspeptin-expressing neurons in the arcuate hypothalamus (Kiss1ARH) of female mice control the daily timing of food intake, along with the circadian regulation of locomotor activity, sleep, and core body temperature. Toxin-induced silencing of Kiss1ARH neurons shifts wakefulness and food consumption to the light phase and induces weight gain. Toxin-silenced mice are less physically active and have attenuated temperature rhythms. Because the rhythm of the master clock in the suprachiasmatic nucleus (SCN) appears to be intact, we hypothesize that Kiss1ARH neurons signal to neurons downstream of the master clock to modulate the output of the SCN. We conclude that, in addition to their well-established role in regulating fertility, Kiss1ARH neurons are a critical component of the hypothalamic circadian oscillator network that times overt rhythms of physiology and behavior.