Motility phenotype in a zebrafish vmat2 mutant

In the present study, we characterize a novel zebrafish mutant of solute carrier 18A2 (slc18a2), also known as vesicular monoamine transporter 2 (vmat2), that exhibits a behavioural phenotype partially consistent with human Parkinson´s disease. At six days-post-fertilization, behaviour was analysed and demonstrated that vmat2 homozygous mutant larvae, relative to wild types, show changes in motility in a photomotor assay, altered sleep parameters, and reduced dopamine cell number. Following an abrupt lights-off stimulus mutant larvae initiate larger movements but subsequently inhibit them to a lesser extent in comparison to wild-type larvae. Conversely, during a lights-on period, the mutant larvae are hypomotile. Thigmotaxis, a preference to avoid the centre of a behavioural arena, was increased in homozygotes over heterozygotes and wild types, as was daytime sleep ratio. Furthermore, incubating mutant larvae in pramipexole or L-Dopa partially rescued the motor phenotypes, as did injecting glial cell-derived neurotrophic factor (GDNF) into their brains. This novel vmat2 model represents a tool for high throughput pharmaceutical screens for novel therapeutics, in particular those that increase monoamine transport, and for studies of the function of monoamine transporters.

How reliable is measurement of posture during sleep: real-world measurement of body posture and movement during sleep using accelerometers

Objective Understanding sleeping behaviours could improve prevention and treatment of sleep problems and associated health conditions. This study aimed to evaluate a method to assess body posture and movement during sleep using trunk-worn accelerometers for 28 days. Approach Participants (50 adults with low back pain (66% female); aged 32(±9) years) wore two activPAL-micro sensors (thigh, trunk) during their normal daily life for 28 consecutive days. Parameters related to body posture (e.g., time spent lying supine or prone) and movement (e.g., number of turns) during sleep were calculated for each night. Average values for each parameter were identified for different periods, the Spearman-Brown Prophecy Formula was used to estimate the minimum number of nights required to obtain a reliable estimate of each parameter, and repeatability of measures between different weeks was calculated. Main Results Participants spent 8.1(±0.8) hours asleep and most time (44%) was spent in a supine posture. The minimum number of nights required for reliable estimates varied between sleep parameters, range 4-21 nights. The most stable parameters (i.e., requiring less than seven nights) were “average activity”, “no. of turns”, “time spent prone”, and “posture changes in the first hour”. Some measures differed substantially between weeks. Significance Most sleep parameters related to body posture and movement require a week or more of monitoring to provide reliable estimates of behaviour over one month. Notably, one week may not reflect behaviour in another week, and the time varying nature of sleep needs to be considered.

Influence of Pair-housing on Sleep Parameters Evaluated with Actigraphy in Female Rhesus Monkeys

Rhesus monkeys are naturally social animals, and behavioral management strategies have focused on promoting pairhousingin laboratory settings as an alternative to individual or group housing. In humans, co-sleeping can have a major impact on bed partners’ sleep, raising the possibility that pair-housing also may influence sleep parameters in monkeys. In the present study, we investigated if pair-housing would impact home-cage partner’s sleep in female rhesus monkeys, and if nighttime separation using socialization panels would alter this pattern. Sleep parameters of 10 experimentally naïve adult female rhesus monkeys (5 pairs) were evaluated for 7 consecutive days using actigraphy monitors attached to primate collars. Paired animals then were separated by socialization panels during the night, and sleep-associated measures were evaluated for 7 consecutive days. The data showed that sleep efficiency was significantly lower when monkeys were pairhoused as compared with when they were separated. On the nights when subjects were pair-housed, a positive correlation was detected for sleep measures (both sleep latency and efficiency) of both members of a pair (R2’s = 0.16–0.5), suggesting that pair-housing influences sleep quality. On nights when subjects were separated, no correlations were observed for sleep measures between members of the pairs (R2’s = 0.004–0.01), suggesting that when separated, the home-cage partner’s sleep no longer influenced the partner’s sleep. Our results indicate that pair-housing has a strong impact on the home-cage partner’s sleep, and that this pattern can be prevented by nighttime separation using socialization panels. Studies evaluating sleep in pair-housed monkeys should consider the effects that the partner’s sleep may have on the subject’s sleep. Sleep is a biologic phenomenon and experimental outcome that affects physical and behavioral health and altered sleep due to pair-housing may affect a range of research outcomes.

Sleep-Wake Pattern, Sleep Quality and Daytime Status in Fixed Day-Shift Hospital Workers

Objectives: Sleep issues are more prevalent in healthcare workers compared to workers in other industries. This study investigated sleep-wake pattern, sleep quality, and daytime status in hospital workers using a Galaxy Watch3 (GW3), a wrist-worn device that uses an accelerometer and heart rate sensor to distinguish sleep and wakefulness.Methods: Multiple sleep parameters including total sleep time (TST) were obtained using a GW3. The Epworth sleepiness scale (ESS), insomnia severity index (ISI), Pittsburgh sleep quality index (PSQI), and bedtime procrastination scale (BPS) were used to assess participants’ status.Results: A total of 70 daytime hospital workers (male, 45.7%; mean age, 35.66±7.79 yr) participated in the monitoring of their sleep-wake patterns for 30 consecutive days. Participants had a mean ESS of 8.14±3.62, ISI of 6.13±3.83, and PSQI of 4.86±2.14. The mean TST was 5.75±0.74 hr (range: 3.42–6.88) during workdays and 5.92±0.92 hr (range: 2.87–8.25) during free days. Chronotype (mid-sleep on freedays corrected for sleep debt accumulated over the work week) was 3.60±1.03 clock hr (range: 1.84–6.69). BPS was negatively correlated with age (rho=-0.27, p=0.022), TST of workdays (rho=-0.53, p<0.001), and TST of free days (rho=-0.43, p<0.001). A higher BPS was associated with larger social jetlag (rho=0.28, p=0.018) and later chronotype (rho=0.41, p<0.001).Conclusions: In this study, 91.5% of daytime hospital workers suffered from chronic sleep insufficiency (<7 hr during both workdays and free days) although their daytime sleepiness or subjective sleep were not poor. Individuals with a later chronotype had poorer sleep quality and worse sleep procrastination behavior.

The “Sleep Well, Lincolnshire” Project: Evaluation of an Online Sleep Practitioner Clinic

Objectives: Poor sleep is associated with adverse outcomes during childhood. Behavioral insomnia is the most common sleep difficulty experienced by children. The coronavirus disease 2019 (COVID-19) global pandemic in 2020 has profoundly affected children’s sleep patterns. This project aimed to evaluate a one-toone sleep service delivered via online clinics by community sleep practitioners in the UK.Methods: This was an observational pre- and post-evaluation study over a 12-month period. The intervention derived from aspects of cognitive-behavioral therapy for insomnia. The evaluation was questionnaire-based and assessed sleep parameters and well-being.Results: 104 parents returned completed questionnaires. The average time of sleep onset was 1 hour and 39 minutes pre-intervention and 20 minutes post-intervention. The average number of nights per week that children woke up was 3.9 pre-intervention and 0.9 post-intervention; the number of night awakenings fell from 1.9 to 0.5 and the time that children were awake after sleep onset fell from 66.8 minutes to 5.8 minutes. The average time that children were asleep was 8.0 hours per night pre-intervention and 10.2 hours post-intervention. The improvement in all sleep parameters was statistically significant (p<0.05). All parameters of parental and children’s well-being improved significantly (p<0.05), except for perceived ability to drive (p=0.07). All parents stated that they would recommend sleep support and 20% already had done so.Conclusions: The COVID-19 pandemic has accelerated the development of remote health care solutions, and in the case of children’s sleep clinics, the online mode of intervention delivery that is as effective, acceptable, and accessible as face-to-face delivery.

The Effects of Concussion on Quantity and Quality of Sleep in Football Athletes

ObjectiveThe objective was to observe the quantity and quality of sleep of collegiate athletes following a concussion.BackgroundPatients diagnosed with a concussion report a disruption or change in their sleep with 46% of patients still having sleep disturbances 3 months after the event. Research is lacking on the sleep disruption or sleep changes in athletes who have experienced a concussion.Design/MethodsThis IRB-approved convenient cohort study involved athletes from 2 local universities. 27 (20 non-concussed and 7 concussed) male collegiate football players (19.93 ± 1.14 years old, 1.82 ± 0.08 m, and 96.42 ± 21.26 kg) wore a Readiband device for 7–10 days or throughout concussion recovery. Concussed participants completed a symptom score sheet each day. Participants returned the Readiband device and completed the Pittsburgh Sleep Quality Index Questionnaire (PSQIQ) after 7–10 days or on return to play. The sleep parameters, and PSQIQ scores were analyzed using non-parametric & independent t-tests with the alpha level set at 0.05.ResultsThe t-tests indicated a difference between the total minutes in bed at the initial measurement (F = 11.839, df = 1, p = 0.037) between the concussed (353.29 ± 110.48 minutes) and non-concussed (471.5 ± 125.09 minutes) groups. There was also a difference between the total minutes asleep at the initial measurement (F = 12.662, df = 1, p = 0.032) between the concussed (286.43 ± 86.73) and non-concussed groups (383.7 ± 104.86). The last measurement that indicated a difference was the calculated minutes in bed at the initial measurement (F = 11.916, df = 1, p = 0.023) between the concussed (326.4 3 ± 97.01) and non-concussed groups (441.60 ± 110.55).ConclusionsThe study results indicate that concussion affects the quantity of sleep, with concussed athletes spending less time in bed and fewer minutes asleep. Changes in sleep occur post-concussion, which may delay concussion recovery.

Non-rapid eye movement sleep and wake neurophysiology in schizophrenia

Motivated by the potential of objective neurophysiological markers to index thalamocortical function in patients with severe psychiatric illnesses, we comprehensively characterized key NREM sleep parameters across multiple domains, their interdependencies, and their relationship to waking event-related potentials and symptom severity. In 130 schizophrenia (SCZ) patients and controls, we confirmed a marked reduction in sleep spindle density in SCZ and extended these findings to show that only slow spindles predicted symptom severity, and that fast and slow spindle properties were largely uncorrelated. We also describe a novel measure of slow oscillation and spindle interaction that was attenuated in SCZ. The main sleep findings were replicated in a demographically distinct sample, and a joint model, based on multiple NREM components, predicted disease status in the replication cohort. Although also altered in patients, auditory event-related potentials elicited during wake were unrelated to NREM metrics. Consistent with a growing literature implicating thalamocortical dysfunction in SCZ, our characterization identifies independent NREM and wake EEG biomarkers that may index distinct aspects of SCZ pathophysiology and point to multiple neural mechanisms underlying disease heterogeneity. This study lays the groundwork for evaluating these neurophysiological markers, individually or in combination, to guide efforts at treatment and prevention as well as identifying individuals most likely to benefit from specific interventions.

PHASE: A MATLAB Based Program for the Analysis of Drosophila Phase, Activity, and Sleep under Entrainment

The problem of entrainment is central to circadian biology. In this regard, Drosophila has been an important model system. Owing to the simplicity of its nervous system and the availability of powerful genetic tools, the system has shed significant light on the molecular and neural underpinnings of entrainment. However, much remains to be learned regarding the molecular and physiological mechanisms underlying this important phenomenon. Under cyclic light/dark conditions, Drosophila melanogaster displays crepuscular patterns of locomotor activity with one peak anticipating dawn and the other anticipating dusk. These peaks are characterized through an estimation of their phase relative to the environmental light cycle and the extent of their anticipation of light transitions. In Drosophila chronobiology, estimations of phases are often subjective, and anticipation indices vary significantly between studies. Though there is increasing interest in building flexible analysis software tools in the field, none incorporates objective measures of Drosophila activity peaks in combination with the analysis of fly activity/sleep in the same program. To this end, we have developed PHASE, a MATLAB-based program that is simple and easy to use and (i) supports the visualization and analysis of activity and sleep under entrainment, (ii) allows analysis of both activity and sleep parameters within user-defined windows within a diurnal cycle, (iii) uses a smoothing filter for the objective identification of peaks of activity (and therefore can be used to quantitatively characterize them), and (iv) offers a series of analyses for the assessment of behavioral anticipation of environmental transitions.