Sleep Transitions Research Articles

Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI.

Understanding the function of sleep requires studying the dynamics of brain activity across whole-night sleep and their transitions. However, current gold standard polysomnography (PSG) has limited spatial resolution to track brain activity. Additionally, previous fMRI studies were too short to capture full sleep stages and their cycling. To study whole-brain dynamics and transitions across whole-night sleep, we used an unsupervised learning approach, the Hidden Markov model (HMM), on two-night, 16 hr fMRI recordings of 12 non-sleep-deprived participants who reached all PSG-based sleep stages. This method identified 21 recurring brain states and their transition probabilities, beyond PSG-defined sleep stages. The HMM trained on one night accurately predicted the other, demonstrating unprecedented reproducibility. We also found functionally relevant subdivisions within rapid eye movement (REM) and within non-REM 2 stages. This study provides new insights into brain dynamics and transitions during sleep, aiding our understanding of sleep disorders that impact sleep transitions.

Profiling the sleep architecture of ageing adults using a seven-state continuous-time Markov model.

Sleep is a complex biological process regulated by networks of neurons and environmental factors. As one falls asleep, neurotransmitters from sleep-wake regulating neurones work in synergy to control the switching of different sleep states throughout the night. As sleep disorders or underlying neuropathology can manifest as irregular switching, analysing these patterns is crucial in sleep medicine and neuroscience. While hypnograms represent the switching of sleep states well, current analyses of hypnograms often rely on oversimplified temporal descriptive statistics (TDS, e.g., total time spent in a sleep state), which miss the opportunity to study the sleep state switching by overlooking the complex structures of hypnograms. In this paper, we propose analysing sleep hypnograms using a seven-state continuous-time Markov model (CTMM). This proposed model leverages the CTMM to depict the time-varying sleep-state transitions, and probes three types of insomnia by distinguishing three types of wake states. Fitting the proposed model to data from 2056 ageing adults in the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study, we profiled sleep architectures in this population and identified the various associations between the sleep state transitions and demographic factors and subjective sleep questions. Ageing, sex, and race all show distinctive patterns of sleep state transitions. Furthermore, we also found that the perception of insomnia and restless sleep are significantly associated with critical transitions in the sleep architecture. By incorporating three wake states in a continuous-time Markov model, our proposed method reveals interesting insights into the relationships between objective hypnogram data and subjective sleep quality assessments.

The Kir channel in the nucleus tractus solitarius integrates the chemosensory system with REM sleep executive machinery for homeostatic balance

The locus coeruleus (LC), nucleus tractus solitarius (NTS), and retrotrapezoid nucleus (RTN) are critical chemosensory regions in the brainstem. In the LC, acid-sensing ion channels and proton pumps serve as H+ sensors and facilitate the transition from non-rapid eye movement (NREM) to rapid eye movement (REM) sleep. Interestingly, the potassium inward rectifier (KIR) channels in the LC, NTS, and RTN also act as H+-sensors and are a primary target for improving sleep in obstructive sleep apnea and Rett syndrome patients. However, the role of Kir channels in NREM to REM sleep transition for H+ homeostasis is not known. Male Wistar rats were surgically prepared for chronic sleep–wake recording and drug delivery into the LC, NTS, and RTN. In different animal cohorts, microinjections of the Kir channel inhibitor, barium chloride (BaCl2), at concentrations of 1 mM (low dose) and 2 mM (high dose) in the LC and RTN significantly increased wakefulness and decreased NREM sleep. However, BaCl2 microinjection into the LC notably reduced REM sleep, whereas it didn’t change in the RTN-injected group. Interestingly, BaCl2 microinjections into the NTS significantly decreased wakefulness and increased the percent amount of NREM and REM sleep. Additionally, with the infusion of BaCl2 into the NTS, the mean REM sleep episode numbers significantly increased, but the length of the REM sleep episode didn’t change. These findings suggest that the Kir channels in the NTS, but not in the LC and RTN, modulate state transition from NREM to REM sleep.

Multiscale neural dynamics in sleep transition volatility across age scales: a multimodal EEG-EMG-EOG analysis of temazepam effects.

Recent advances in computational modeling techniques have facilitated a more nuanced understanding of sleep neural dynamics across the lifespan. In this study, we tensorize multiscale multimodal electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals and apply Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling to quantify interactions between age scales and the use of pharmacological sleep aids on sleep stage transitions. Our cohort consists of 22 subjects in a crossover design study, where each subject received both a sleep aid and a placebo in different sessions. To understand these effects across the lifespan, three evenly distributed age groups were formed: 18-29, 30-49, and 50-66years. The methodological framework implemented here employs tensor-based machine learning techniques to compute continuous wavelet transform time-frequency features and utilizes a GARCH model to quantify sleep signal volatility across age scales. Support Vector Machines are used for feature ranking, and our analysis captures interactions between signal entropy, age, and sleep aid status across frequency bands, sleep transitions, and sleep stages. GARCH model results reveal statistically significant volatility clustering in EEG, EMG, and EOG signals, particularly during transitions between REM and non-REM sleep. Notably, volatility was higher in the 50-66 age group compared to the 18-29 age group, with marked fluctuations during transitions from deep sleep to REM sleep (standard deviation of 0.35 in the older group vs. 0.30 in the 18-29 age group, p < 0.05). Statistical comparisons of volatility across frequency bands, age scales, and sleep stages highlight significant differences attributable to sleep aid use. Mean conditional volatility parameterization of the GARCH model reveals directional influences, with a causality index of 0.75 from frontal to occipital regions during REM sleep transition periods. Our methodological framework identifies distinct neural behavior patterns across age groups associated with each sleep stage and transition, offering insights into the development of targeted interventions for sleep regularity across the lifespan.

Nocturnal Sleep Dynamics Alterations in the Early Stages of Behavioral Variant Frontotemporal Dementia.

Sleep disorders have been recognized as an integral component of the clinical syndrome in several neurodegenerative diseases, including Alzheimer's Disease (AD). However, limited data exists for rarer types of neurodegenerative diseases, such as behavioral variant Frontotemporal Dementia (bvFTD). This study aims to analyze EEG power spectra and sleep stage transitions in bvFTD patients, hypothesizing that bvFTD may show distinctive sleep stage transitions compared to patients with Alzheimer's Disease (AD). Eighteen probable bvFTD patients and eighteen age- and sex-matched probable AD patients underwent overnight polysomnography (PSG) and completed sleep disorders questionnaires. Sleep questionnaires, full-night EEG spectra, and sleep stage transitions indexes were compared between groups. bvFTD patients had higher Insomnia Severity Index (ISI) scores (95%CI: 0, 5) and reported poorer sleep quality than AD patients (p<0.01). Compared to AD, bvFTD patients showed higher N1 percentage (95%CI: 0.1, 6), lower N3 percentage (95%CI: -13.6, -0.6), higher sleep-wake transitions (95%CI: 1.49, 8.86) and N1 sleep-wake transitions (95%CI: 0.32, 6.1). EEG spectral analysis revealed higher spectral power in bvFTD compared to AD patients in faster rhythms, especially sigma rhythm, across all sleep stages. In bvFTD patients, sleep-wake transitions were positively associated with ISI. Patients with bvFTD present higher rates of transitions between wake and sleep than AD patients. The increased frequency of sleep transitions indicates a higher degree of sleep instability in bvFTD, which may reflect an imbalance in sleep-wake promoting systems. Sleep stage transitions analysis may provide novel insights into the sleep alterations of bvFTD patients.

SleepGCN: A transition rule learning model based on Graph Convolutional Network for sleep staging

Background and Objective:Automatic sleep staging is essential for assessing and diagnosing sleep disorders, serving millions of people who suffer from them. Numerous sleep staging models have been proposed recently, but most of them have not fully explored the sleep transition rules that are essential for sleep experts to identify sleep stages. Therefore, one objective of this paper is to develop an automatic sleep staging model to capture the transition rules between sleep stages. Methods:In this paper, we propose a novel sleep staging model named SleepGCN. It utilizes the deep features of electroencephalogram (EEG) and electrooculogram (EOG) signals extracted by the sleep representation learning (SRL) module, in conjunction with the transition rules learned by the sleep transition rule learning (STRL) module to identify sleep stages. Specifically, the SRL module utilizes the residual network (ResNet) and Long Short Term Memory (LSTM) structure to capture the deep time-invariant features and temporal information of each sleep stage from the two-channel EEG-EOG, and then applies a feature enhancement block to obtain the refined features. The STRL module employs a Graph Convolutional Network (GCN) and a transition rule matrix to capture transition rules between sleep stages based on the sequence labels of the input signals. Results:We evaluate SleepGCN on five public datasets: SleepEDF-20, SleepEDF-78, SHHS, DOD-H and DOD-O. Overall, SleepGCN achieves an accuracy of 89.70%, 87.70%, 86.16%, 82.07%, and 81.20%, alongside a macro-average F1-score of 85.20%, 82.70%, 77.69%, 72.44%, and 72.93% across these datasets, respectively. Conclusions:The results achieved by our proposed model are much better than those of all other compared models. The ablation study validates the contributions of the SRL and STRL modules proposed in SleepGCN to the sleep staging tasks. Additionally, it shows that the sleep staging model using two-channel EEG-EOG outperforms the model using single-channel EEG or EOG. Overall, SleepGCN is an effective solution for sleep staging using two-channel EEG-EOG.

Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI.

Understanding the function of sleep requires studying the dynamics of brain activity across whole-night sleep and their transitions. However, current gold standard polysomnography (PSG) has limited spatial resolution to track brain activity. Additionally, previous fMRI studies were too short to capture full sleep stages and their cycling. To study whole-brain dynamics and transitions across whole-night sleep, we used an unsupervised learning approach, the Hidden Markov model (HMM), on two-night, 16-hour fMRI recordings of 12 non-sleep-deprived participants who reached all PSG-based sleep stages. This method identified 21 recurring brain states and their transition probabilities, beyond PSG-defined sleep stages. The HMM trained on one night accurately predicted the other, demonstrating unprecedented reproducibility. We also found functionally relevant subdivisions within rapid eye movement (REM) and within non-REM 2 stages. This study provides new insights into brain dynamics and transitions during sleep, aiding our understanding of sleep disorders that impact sleep transitions.

Agile C-states: A Core C-state Architecture for Latency Critical Applications Optimizing both Transition and Cold-Start Latency

Latency critical applications running in modern datacenters exhibit irregular request arrival patterns and are implemented using multiple services with strict latency requirements (30 μs –250 μs ). These characteristics render existing energy saving idle CPU sleep states ineffective due to the performance overhead caused by the state’s transition latency. Besides the state transition latency, another important contributor to the performance overhead of sleep states is the cold-start latency, or in other words, the time required to warm-up microarchitectural state (e.g., cache contents, branch predictor metadata) that is flushed or discarded when transitioning to a lower-power state. Both the transition latency and cold-start latency can be particularly detrimental to the performance of latency critical applications with short execution times. While prior work focuses on mitigating the effects of transition and cold-start latency by optimizing request scheduling, in this work, we propose a redesign of the Core C-state architecture for latency-critical applications. In particular, we introduce C6Awarm a new Agile Core C-state that drastically reduces the performance overhead caused by idle sleep state transition latency and cold-start latency, while maintaining significant energy savings. C6Awarm achieves its goals by implementing 1) medium-grained power gating, 2) preserving the microarchitectural state of the core and 3) by keeping the clock generator and PLL active and locked. Our analysis for a set of microservices based on an Intel Skylake server, shows that C6Awarm manages to reduce the energy consumption by up to \(70\% \) with limited performance degradation (at-most \(2\% \) ).

Advancing mental health predictions through sleep posture analysis: a stacking ensemble learning approach

Sleep posture is closely related to sleep quality, and can offer insights into an individual’s health. This correlation can potentially aid in the early detection of mental health disorders such as depression and anxiety. Current research focuses on embedding pressure sensors in bedsheets, attaching accelerometers on a subject’s chest, and installing cameras in bedrooms for sleep posture monitoring. However, such solutions sacrifice either the user's sleep comfort or privacy. This study explores the effectiveness of using contactless ultra-wideband (UWB) sensors for sleep posture monitoring. We employed a UWB dataset that is composed of the measurements from 12 volunteers during sleep. A stacking ensemble learning method is introduced for the monitoring of sleep postural transitions, which constitute two levels of learning. At the base-learner level, six transfer learning models (VGG16, ResNet50V2, MobileNet50V2, DenseNet121, VGG19, and ResNet101V2) are trained on the training dataset for initial predictions. Then, the logistic regression is employed as a meta-learner which is trained on the predictions gained from the base-learner to obtain final sleep postural transitions. In addition, a sleep posture monitoring algorithm is presented that can give accurate statistics of total sleep postural transitions. Extensive experiments are conducted, achieving the highest accuracy rate of 86.7% for the classification of sleep postural transitions. Moreover, time-series data augmentation is employed, which improves the accuracy by 13%. The privacy-preserving sleep monitoring solution presented in this paper holds promise for applications in mental health research.

Data-driven mathematical modeling of sleep consolidation in early childhood

Across early childhood development, sleep behavior transitions from a biphasic pattern (a daytime nap and nighttime sleep) to a monophasic pattern (only nighttime sleep). The transition to consolidated nighttime sleep, which occurs in most children between 2- and 5-years-old, is a major developmental milestone and reflects interactions between the developing homeostatic sleep drive and circadian system. Using a physiologically-based mathematical model of the sleep-wake regulatory network constrained by observational and experimental data from preschool-aged participants, we analyze how developmentally-mediated changes in the homeostatic sleep drive may contribute to the transition from napping to non-napping sleep patterns. We establish baseline behavior by identifying parameter sets that model typical 2-year-old napping behavior and 5-year-old non-napping behavior. Then we vary six model parameters associated with the dynamics of and sensitivity to the homeostatic sleep drive between the 2-year-old and 5-year-old parameter values to induce the transition from biphasic to monophasic sleep. We analyze the individual contributions of these parameters to sleep patterning by independently varying their age-dependent developmental trajectories. Parameters vary according to distinct evolution curves and produce bifurcation sequences representing various ages of transition onset, transition durations, and transitional sleep patterns. Finally, we consider the ability of napping and non-napping light schedules to reinforce napping or promote a transition to consolidated sleep, respectively. These modeling results provide insight into the role of the homeostatic sleep drive in promoting interindividual variability in developmentally-mediated transitions in sleep behavior and lay foundations for the identification of light- or behavior-based interventions that promote healthy sleep consolidation in early childhood.

The Hidden Impact of ADHD Symptoms in Preschool Children with Autism: Is There a Link to Somatic and Sleep Disorders?

Neurodevelopmental disorders, such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), do often present in comorbidity among them and with other medical conditions, including sleep and gastrointestinal (GI) disorders and somatic complaints. An anonymous online survey based on standardized questionnaires (SDSC, KL-ASD, APSI, ROME V CRITERIA, CPRS, CBCL) was completed by the parents of 46 preschoolers diagnosed with ASD. A high prevalence (47%) of ADHD symptoms in this population was found, surpassing previous estimates. Sleep disturbances, especially difficulties in initiating and maintaining sleep and sleep-wake transition, are more pronounced in ASD patients with comorbid ADHD. Additionally, in patients over 4 years old, there is a high prevalence of functional GI symptoms (Rome V criteria: 38%). Parental stress is significantly associated with ADHD symptoms, sleep disorders, and GI symptoms. Comprehensive assessments considering both core symptoms of neurodevelopmental disorders and associated comorbidities are crucial for more effective treatment strategies and improved wellbeing in affected individuals and their families.

Sex differences in body temperature and neural power spectra in response to repeated restraint stress

Repeated stress is associated with an increased risk of developing psychiatric illnesses such as post-traumatic stress disorder (PTSD), which is more common in women, yet the neurobiology behind this sex difference is unknown. Habituation to repeated stress is impaired in PTSD, and recent preclinical studies have shown that female rats do not habituate as fully as male rats to repeated stress, which leads to impairments in cognition and sleep. Further research should examine sex differences after repeated stress in other relevant measures, such as body temperature and neural activity. In this study, we analyzed core body temperature and EEG power spectra in adult male and female rats during restraint, as well as during sleep transitions following stress. We found that core body temperature of male rats habituated to repeated restraint more fully than female rats. Additionally, we found that females had a higher average beta band power than males on both days of restraint, indicating higher levels of arousal. Lastly, we observed that females had lower delta band power than males during sleep transitions on Day 1 of restraint, however, females demonstrated higher delta band power than males by Day 5 of restraint. This suggests that it may take females longer to initiate sleep recovery compared with males. These findings indicate that there are differences in the physiological and neural processes of males and females after repeated stress. Understanding the way that the stress response is regulated in both sexes can provide insight into individualized treatment for stress-related disorders.

Sleep physiology in patients with epilepsy: Influence of seizures on rapid eye movement (REM) latency and REM duration.

A well-established bidirectional relationship exists between sleep and epilepsy. Patients with epilepsy tend to have less efficient sleep and shorter rapid eye movement (REM) sleep. Seizures are far more likely to arise from sleep transitions and non-REM sleep compared to REM sleep. Delay in REM onset or reduction in REM duration may have reciprocal interactions with seizure occurrence. Greater insight into the relationship between REM sleep and seizure occurrence is essential to our understanding of circadian patterns and predictability of seizure activity. We assessed a cohort of adults undergoing evaluation of drug-resistant epilepsy to examine whether REM sleep prior to or following seizures is delayed in latency or reduced in quantity. We used a spectrogram-guided approach to review the video-electroencephalograms of patients' epilepsy monitoring unit admissions for sleep scoring to determine sleep variables. In our cohort of patients, we found group- and individual-level delay of REM latency and reduced REM duration when patients experienced a seizure before the primary sleep period (PSP) of interest or during the PSP of interest. A significant increase in REM latency and decrease in REM quantity were observed on nights where a seizure occurred within 4 h of sleep onset. No change in REM variables was found when investigating seizures that occurred the day after the PSP of interest. Our study is the first to provide insight about a perisleep period, which we defined as 4-h periods before and after the PSP. Our results demonstrate a significant relationship between seizures occurring prior to the PSP, during the PSP, and in the 4-h perisleep period and a delay in REM latency. These findings have implications for developing a biomarker of seizure detection as well as longer term seizure risk monitoring.

Characterizing the power spectrum dynamics of the nrem to rem sleep transition

Characterizing the power spectrum dynamics of the nrem to rem sleep transition

Early and consistent safe sleep practices in the neonatal intensive care unit: a sustained regional quality improvement initiative.

To increase compliance with standardized safe sleep recommendations for patients in a cohort of regional level III/IV neonatal intensive care units (NICUs) in accordance with recently revised guidelines issued by the American Academy of Pediatrics (AAP). A regional quality improvement (QI) initiative led by a multidisciplinary task force standardized safe sleep criteria across participating NICU sites. Universal and unit-specific interventions were implemented via Plan-Do-Study-Act (PDSA) cycles with evaluation of compliance through routine crib audits, run chart completion, and Pareto chart analysis. Following QI implementation, compliance with safe sleep guidelines for eligible NICU infants improved from 34% to 90% from October 2019 through September 2022. Compliance with early, consistent modeling of safe sleep practices nearly tripled in this cohort of regional NICUs. A standardized, timely approach to safe sleep transition demonstrated dramatic and sustained improvement in the practice and modeling of safe sleep behaviors in the NICU.

Analysing the impact of body position shift on sleep architecture and stage transition: A comprehensive multidimensional study using event-synchronised polysomnography data.

Although understanding the physiological mechanisms of obstructive sleep apnea (OSA) is important for treating OSA, limited studies have examined OSA patients' sleep architecture at the epoch-by-epoch level and analysed the impact of sleep position and stage on OSA pathogenesis. The epoch-labelled polysomnogram was analysed multidimensionally to investigate the effect of sleep position on the sleep architecture and risk factors of apnea in patients with OSA. This retrospective multicentric case-control study reviewed full-night diagnostic polysomnography of 6983 participants. The difference in the proportion of time spent supine during non-rapid eye movement (NREM) and REM stages, and the mean duration of respiratory events per body position were evaluated. The frequency of sleep stage transition per body position shift type was computed. Further subgroup analysis was performed based on OSA severity and positional dependency. Supine time in patients with OSA varied across sleep stages, with lower proportions in N3 and REM, and shorter durations with severity. Patients with OSA spent less time in supine positions during N3 and REM, and experienced longer apnea events in both positions compared to the control group. The frequency of all sleep stage transitions increased with OSA severity and was higher among non-positional OSA than positional OSA and the control group, regardless of body position shift type. The sleep stage transition from N3 and REM to wakefulness was notably heightened during position shift. Understanding the sleep architecture of patients with OSA requires analysing various sleep characteristics including sleep position simultaneously, with future studies focusing on position detection to predict sleep stages and respiratory events.

Sequence signal reconstruction based multi-task deep learning for sleep staging on single-channel EEG

The temporal context information between sleep stage sequence contains sleep transition rules, which is important for improving sleep staging performance. Existing multi-task learning methods reconstruct EEG signals from one sleep stage, ignoring the importance of sequential temporal context in capturing long-term dependencies to enhance representation learning. To address these issues, we propose a multi-task deep learning model to jointly reconstruct sequence signal and segment time series. The model enhances the ability of time series segmentation task to capture sequential temporal context and improves the performance of single-channel EEG by optimizing the common encoder for sequence signal reconstruction task. In addition, we design a one-dimensional channel attention module to enhance the feature representation extracted for sleep sequence signal. The experimental results on four datasets show that the multi-task deep learning model can improve the generalization using sequence signal reconstruction. Compared with other state-of-the-art methods, the method proposed in this study obtained competitive performance in terms of metrics such as accuracy, which is 85.6% on the 2013 version of Sleep-EDF Database Expanded, 83.4% on the 2018 version of Sleep-EDF Database Expanded, 85.6% on Sleep Heart Health Study, and 77.4% on CAP Sleep Database.

Sleep Stage Transitions and Sleep-Dependent Memory Consolidation in Children with Narcolepsy-Cataplexy.

Electroencephalographic sleep stage transitions and altered first REM sleep period transitions have been identified as biomarkers of type 1 narcolepsy in adults, but not in children. Studies on memory complaints in narcolepsy have not yet investigated sleep-dependent memory consolidation. We aimed to explore stage transitions; more specifically altered REM sleep transition and its relationship with sleep-dependent memory consolidation in children with narcolepsy. Twenty-one children with narcolepsy-cataplexy and twenty-three healthy control children completed overnight polysomnography and sleep-dependent memory consolidation tests. Overnight transition rates (number of transitions per hour), global relative transition frequencies (number of transitions between a stage and all other stages/total number of transitions × 100), overnight transitions to REM sleep (transition from a given stage to REM/total REM transitions × 100), and altered first REM sleep period transitions (transitions from wake or N1 to the first REM period) were computed. Narcoleptic children had a significantly higher overnight transition rate with a higher global relative transition frequencies to wake. A lower sleep-dependent memory consolidation score found in children with narcolepsy was associated with a higher overnight transition frequency. As observed in narcoleptic adults, 90.48% of narcoleptic children exhibited an altered first REM sleep transition. As in adults, the altered sleep stage transition is also present in children with narcolepsy-cataplexy, and a higher transition rate could have an impact on sleep-dependent memory consolidation. These potential biomarkers could help diagnose type 1 narcolepsy in children more quickly; however, further studies with larger cohorts, including of those with type 2 narcolepsy and hypersomnia, are needed.

Deficiency of orexin signaling during sleep is involved in abnormal REM sleep architecture in narcolepsy

Narcolepsy is a sleep disorder caused by deficiency of orexin signaling. However, the neural mechanisms by which deficient orexin signaling causes the abnormal rapid eye movement (REM) sleep characteristics of narcolepsy, such as cataplexy and frequent transitions to REM states, are not fully understood. Here, we determined the activity dynamics of orexin neurons during sleep that suppress the abnormal REM sleep architecture of narcolepsy. Orexin neurons were highly active during wakefulness, showed intermittent synchronous activity during non-REM (NREM) sleep, were quiescent prior to the transition from NREM to REM sleep, and a small subpopulation of these cells was active during REM sleep. Orexin neurons that lacked orexin peptides were less active during REM sleep and were mostly silent during cataplexy. Optogenetic inhibition of orexin neurons established that the activity dynamics of these cells during NREM sleep regulate NREM-REM sleep transitions. Inhibition of orexin neurons during REM sleep increased subsequent REM sleep in "orexin intact" mice and subsequent cataplexy in mice lacking orexin peptides, indicating that the activity of a subpopulation of orexin neurons during the preceding REM sleep suppresses subsequent REM sleep and cataplexy. Thus, these results identify how deficient orexin signaling during sleep results in the abnormal REM sleep architecture characteristic of narcolepsy.

Nocturnal selective pressures on the evolution of human musicality as a missing piece of the adaptationist puzzle.

Human musicality exhibits the necessary hallmarks for biological adaptations. Evolutionary explanations focus on recurrent adaptive problems that human musicality possibly solved in ancestral environments, such as mate selection and competition, social bonding/cohesion and social grooming, perceptual and motor skill development, conflict reduction, safe time-passing, transgenerational communication, mood regulation and synchronization, and credible signaling of coalition and territorial/predator defense. Although not mutually exclusive, these different hypotheses are still not conceptually integrated nor clearly derived from independent principles. I propose The Nocturnal Evolution of Human Musicality and Performativity Theory in which the night-time is the missing piece of the adaptationist puzzle of human musicality and performing arts. The expansion of nocturnal activities throughout human evolution, which is tied to tree-to-ground sleep transition and habitual use of fire, might help (i) explain the evolution of musicality from independent principles, (ii) explain various seemingly unrelated music features and functions, and (iii) integrate many ancestral adaptive values proposed. The expansion into the nocturnal niche posed recurrent ancestral adaptive challenges/opportunities: lack of luminosity, regrouping to cook before sleep, imminent dangerousness, low temperatures, peak tiredness, and concealment of identity. These crucial night-time features might have selected evening-oriented individuals who were prone to acoustic communication, more alert and imaginative, gregarious, risk-taking and novelty-seeking, prone to anxiety modulation, hedonistic, promiscuous, and disinhibited. Those night-time selected dispositions may have converged and enhanced protomusicality into human musicality by facilitating it to assume many survival- and reproduction-enhancing roles (social cohesion and coordination, signaling of coalitions, territorial defense, antipredatorial defense, knowledge transference, safe passage of time, children lullabies, and sexual selection) that are correspondent to the co-occurring night-time adaptive challenges/opportunities. The nocturnal dynamic may help explain musical features (sound, loudness, repetitiveness, call and response, song, elaboration/virtuosity, and duetting/chorusing). Across vertebrates, acoustic communication mostly occurs in nocturnal species. The eveningness chronotype is common among musicians and composers. Adolescents, who are the most evening-oriented humans, enjoy more music. Contemporary tribal nocturnal activities around the campfire involve eating, singing/dancing, storytelling, and rituals. I discuss the nocturnal integration of musicality's many roles and conclude that musicality is probably a multifunctional mental adaptation that evolved along with the night-time adaptive landscape.