Sleep

Isolated rapid eye movement sleep behavior disorder is a strong clinical marker of future alpha-synucleinopathy, but earlier stages of this risk pathway remain insufficiently characterized. Rapid eye movement sleep without atonia is the polysomnographic substrate of this disorder and may also be detected in individuals without clinical dream-enactment behavior. Whether isolated rapid eye movement sleep without atonia is a benign finding or an early risk state for future rapid eye movement sleep behavior disorder and neurodegeneration remains unknown. DREAMER is a multicenter, prospective, observational cohort protocol designed to identify adults without clinical rapid eye movement sleep behavior disorder who show isolated rapid eye movement sleep without atonia during full-night laboratory video-polysomnography. Four Italian sleep centers will use harmonized eligibility criteria, standardized clinical and sleep assessment, quantitative REM Atonia Index scoring, secure web-based data capture, and planned longitudinal follow-up. Adults aged 40 years or older undergoing video-polysomnography will be screened. Participants with prior rapid eye movement sleep behavior disorder or technically inadequate REM sleep/chin electromyographic data will be excluded. Isolated rapid eye movement sleep without atonia will be defined in participants without clinical rapid eye movement sleep behavior disorder using a REM Atonia Index threshold of <0.85. The target recruitment is more than 500 participants over 18 months, with an expected enriched subgroup of approximately 85 individuals with isolated rapid eye movement sleep without atonia. Ancillary neurophysiological assessments and blood sampling for future biomarker studies will be obtained when feasible. DREAMER is intended to create a harmonized, trial-ready cohort for evaluating isolated rapid eye movement sleep without atonia as a potential early risk marker for incident rapid eye movement sleep behavior disorder and subsequent neurodegenerative outcomes. The study is registered at ClinicalTrials.gov as DREAMER, ClinicalTrials.gov Identifier NCT06140511.

Objectives: Self-applied, low-density EEG offers opportunities to examine sleep in the home environment, yet its feasibility during behavioural sleep interventions remains unexplored. This pilot study aimed to evaluate the feasibility and acceptability of a self-applied, low-density EEG device during sleep restriction therapy (SRT) and explore effects on sleep and affect. Methods: Seventeen adults with insomnia and depressive symptoms completed a 2-week baseline and 4 weeks of SRT. The primary outcome was the proportion of expected EEG recordings completed and scoreable. Secondary outcomes included clinical measures, sleep continuity (sleep diary, actigraphy), sleep architecture (low-density EEG for 9 nights), power spectral density, and affect. Data were analysed with linear mixed models. Cohen's d and 95% confidence intervals were reported. Results: Feasibility was demonstrated (92% of expected EEG nights completed). SRT was associated with reductions in insomnia severity, depressive symptoms, negative affect, and increases in positive affect. Robust improvements were observed across treatment in sleep continuity (SOL, WASO, SE) from diary, which were paralleled by actigraphy. EEG revealed reduced TIB, TST, N1, N2, REM sleep, and REM latency during week one. Reductions in EEG-derived TIB and N1 sleep were maintained at night 28. There were no reliable differences for spectral or spindle measures. Conclusions: These findings suggest that self-applied, low-density EEG during SRT is feasible, acceptable, and may capture sleep changes during treatment. They highlight the potential for multi-night monitoring of sleep interventions at home and elucidating mechanisms underlying therapeutic change.

Background: Circadian rest-activity rhythms weaken with age, but whether sleep disorders modify this trajectory is unknown. Methods: We analyzed wrist accelerometry data from 4,386 participants aged 6-80 years in the 2011-2012 National Health and Nutrition Examination Survey (NHANES). Circadian features were extracted using cosinor analysis and nonparametric methods; a Circadian Disruption Index (CDI) was constructed from five standardized components. Survey-weighted regression with natural cubic splines and Wald F-tests tested age-by-sleep-disorder interactions using Taylor series linearization for variance estimation. Results: Doctor-diagnosed sleep disorder (N = 360, 8.2%) was associated with significantly different age-related trajectories of amplitude (F(2,17) = 11.24, p = 0.0008) and MESOR (F(2,17) = 8.22, p = 0.0032), both surviving Bonferroni correction (p < 0.006). CDI was higher in those with a sleep disorder (0.290 vs. 0.131, p < 0.001) and was independently associated with higher BMI (beta = 1.33 kg/m2, p < 0.001), higher HbA1c (beta = 0.089%, p = 0.004), greater diabetes prevalence (beta = 3.8 percentage points, p < 0.001), and worse depressive symptoms (beta = 0.43 PHQ-9 points, p = 0.020). Sensitivity analyses using a broader sleep problem exposure did not replicate these interactions. Conclusions: Doctor-diagnosed sleep disorders are associated with an altered age-related decline in circadian amplitude and mean activity level. CDI was independently linked to cardiometabolic and depressive outcomes, supporting a mechanistic connection between clinically significant sleep pathology and circadian disruption across the lifespan.

BackgroundSleep-onset insomnia (SOI) is characterized by difficulty initiating sleep and is frequently associated with psycho-affective disorders. Despite its high prevalence and clinical impact, pathophysiological biomarkers and clear nosological frameworks remain lacking. Conventional polysomnographic (PSG) measures provide limited insight into the continuous dynamics of vigilance during the transition from wakefulness to sleep and across the night. MethodsWe retrospectively analyzed PSG recordings from 2 952 individuals using fine-grained EEG markers of vigilance, including theta/alpha ratio dynamics, micro-sleep episodes, and probability-of-wakefulness metrics. Individuals with and without SOI were compared, and SOI subgroups with and without depressive or anxiety symptoms were further examined. ResultsIndividuals with SOI exhibited a persistent state of elevated EEG-defined vigilance extending from wakefulness through the sleep onset period (SOP) and across all sleep stages, including N2, N3, and REM sleep. This hypervigilance was associated with vigilance instability during the SOP and a delayed accumulation of deep sleep over the night. Importantly, hypervigilance was more pronounced in isolated SOI than in SOI comorbid with psycho-affective symptoms, particularly depressive symptoms, and remained largely undetected by conventional PSG macrostructure measures. ConclusionsThese findings support a reconceptualization of SOI as a disorder of sustained vigilance dysregulation and reveal heterogeneity in hypervigilance across insomnia phenotypes. This dissociation from psycho-affective symptoms challenges current nosological frameworks at the interface of sleep and psychiatric disorders. By capturing microstructural alterations in vigilance invisible to standard scoring, continuous EEG-based markers provide mechanistic insight into insomnia heterogeneity and may enable biologically informed phenotyping across psychiatric conditions.

Sleep is fundamental to metabolic regulation, cognitive performance, immune function, and cardiovascular health, and evening screen exposure is widely proposed as a behavioural contributor whose adult evidence base remains thin. Here we analyze 350,600 paired screen-day and following-night observations from 3,086 Ultrahuman Ring AIR adult users. Sleep quality was assessed via the rings composite sleep score, derived from heart-rate variability, nightly movement, and skin temperature. At the user level, users in the highest screen-time quintile had lower sleep scores (Cohens d = -0.30), shorter sleep duration (d = -0.25), and lower sleep efficiency (d = -0.14) than the lowest screen-time quintile (all q [&le;] 0.005). Further, 45+ min of screen use in the last hour before bed was associated with mean sleep scores at the bottom of the cohort range, whereas the same dose 4-5 hours earlier showed no detectable cost, so the timing of screen use, not just its total, mattered. We next asked whether the way users distribute their screen time across the 24 hours, independent of total dose, separates users by sleep outcome. K-means clustering on 24-hour screen-use profiles identified three phenotypes: Daytime Peakers (DP), Late-Night Users (LNU), and Round-the-Clock Users (RCU), distinguished primarily by their nighttime share of 24-h screen use (DP 8.2%, LNU 16.9%, RCU 29.3%). Despite comparable total daily screen time, the phenotype gap in mean sleep score between DP (75.2 {+/-} 0.3 SEM) and RCU (66.7 {+/-} 0.6) was 8.5 points. We further identified users who transitioned phenotypes across four sequential quarters of follow-up; in this longitudinal subcohort, the magnitude of sleep-score change tracked the magnitude of the behavioural shift, with DP [-&gt;] LNU transitioners declining by 5.16 {+/-} 0.94 points and LNU [-&gt;] RCU transitioners by 4.79 {+/-} 1.87 points (both p < 0.05). Together, these findings position the temporal distribution of screen use, alongside its daily total and its concentration immediately before bed, as a behavioural correlate of objectively measured sleep quality in adults.

2)BackgroundCircadian rhythm desynchrony (CD) occurs when there is a mismatch between the circadian clock and local time, such as shift work. Mouse models are commonly employed to study CD, but may have significant shortcomings such as environmental masking, a focus only on sleep physiology, and significant variability between study designs. ObjectiveThis study used in vivo telemetry for simultaneous, real-time monitoring of locomotor activity (LA), core body temperature (CBT), and brain activity (EEG) in freely moving C57BL/6J mice to assess CD effects. MethodsFour-month-old C57BL/6J mice (n=11) were surgically implanted with telemeters enabling simultaneous real-time recording of LA, CBT, EEG.: Mice were sequentially exposed to a control condition standard 12:12h light-dark cycle (T24) then 4, 8-day CD paradigms: 10:10 h short day (T20), social jet lag (SJL), repeated 6h phase advances (6A2), and a 3:3 h ultradian cycle (T6)For each paradigm, the final 48h of data (250 Hz) were analyzed. ResultsWe found clear differences in the severity of the effects of each CD paradigm on sleep and circadian fitness, where T20[~]T6>SJL>6A2. CBT revealed broader disruption, but EEG outputs proved the most sensitive indicators of internal desynchrony. ConclusionsEach CD paradigm produced a unique profile across behavioral, physiological, and neural domains. We have also identified Gamma CV as a novel, sensitive metric of CD. These results highlight the necessity of multimodal monitoring to accurately characterize the impact of ecologically relevant stressors on circadian and sleep physiology. Statement of SignificanceCircadian rhythm desynchrony (CD), driven by shift work, jet lag, and modern irregular light exposure, is a major health burden linked to metabolic, neurodegenerative, and neuropsychiatric diseases. However, standard methods for measuring CD in laboratory models often rely on simple locomotor activity, which can "mask" the true extent of internal circadian stress. In this study, we simultaneously monitored brain EEG activity, core body temperature, and motion across four distinct models of circadian stress. We discovered that locomotor activity is a deceptive indicator of health; while mice appeared to show no alterations under several stress paradigms, their brain waves and body temperatures revealed the underlying impact of CD. Specifically, we identified "Gamma CV" as a highly sensitive new brain-wave marker that detects early circuit instability even when behavior appears normal and sleep quantity is preserved. These findings provide a marker for identifying early neurological vulnerability to irregular light schedules, offering a potential bridge to understanding similar gamma brain-wave alterations seen in addiction, early-stage Alzheimers disease, and other disorders.

Background Insomnia symptoms are common in older adults. While observational studies suggest physical activity (PA) timing affects health outcomes, its effect on sleep remains unclear. We compared morning versus evening PA effects on insomnia severity and sleep quality in older adults with insomnia symptoms. Methods Eligible participants were aged 60 to 80 years with (sub)clinical insomnia (Insomnia Severity Index [ISI] score [&ge;]10). In a randomized cross-over trial, participants engaged in coached PA in the morning (10:00 - 11:00) or evening (19:30 - 20:30) for 14 days each. ISI scores were assessed post-intervention. Objective sleep parameters; duration, latency, efficiency, and timing, were assessed with a Withings Sleep Analyzer under the mattress. Subjective sleep quality was reported daily via smartphone app. Salivary dim light melatonin onset (DLMO) was measured on the final day of each intervention. Results Of 37 participants (mean ISI 14.3 {+/-} 3.3), 27 completed the study (mean age 69.8 {+/-} 5; 63% women). ISI scores improved after both morning ({Delta} - 2.5; 95% CI: - 1.14, - 3.83) and evening ({Delta} - 2.0; 95% CI: - 0.63, - 3.38) activity relative to baseline, but were not different between interventions. Compared to evening activity, sleep midpoint occurred earlier with morning activity (03:40 vs 04:00; {Delta} - 20 min; 95% CI: - 31, - 8). No differences in subjective sleep quality or DLMO were found. Exploratory analyses suggested insomnia scores improved specifically in late chronotypes following morning activity. Conclusions While morning vs. evening PA timing did not impact most sleep quality measures, it influenced sleep timing. Larger studies are needed to define optimal and personalized PA timing for improving sleep.

The precise coordination of slow oscillations (SO) and sleep spindles during non-rapid eye movement (NREM) sleep supports memory consolidation and may serve as a sensitive marker of cognitive aging. However, longitudinal changes in their oscillatory dynamics in midlife and older age remain poorly understood. Using polysomnography with high-density EEG at two timepoints over [~]2.5 years, we examined changes in local NREM slow wave (SW), sleep spindle (occurring in the 11-16 Hz sigma range), and SO-sigma coupling strength in cognitively unimpaired middle-aged to older adults at risk for Alzheimers disease. Fronto-central SO-sigma power coupling strength significantly declined over time, independent of changes in multiple measures of SW and sleep spindle expression. Local declines in multiple sleep spindle measures were also observed. Greater baseline levels of cerebrospinal fluid (CSF) neurogranin, a postsynaptic protein abundantly expressed in the dendritic spines of the hippocampus and cerebral cortex and implicated in calcium-dependent synaptic plasticity, predicted the magnitude of longitudinal decline in SO-fast sigma coupling strength, which in turn predicted episodic memory performance changes. These findings suggest that longitudinal changes in local sleep oscillatory dynamics are related to decreased synaptic integrity and may serve as an early indicator of memory decline in older adults at risk for Alzheimers disease.

Young people with major depressive disorder (MDD) exhibit altered thermoregulation, which has also been linked to vigilance and sustained attention. However, whether peripheral skin temperature is associated with cognitive vulnerability around sleep onset is unknown. We examined the relationship between the distal-proximal skin temperature gradient (DPG) and vigilance in 38 young people with MDD (20.1{+/-}3.7 years, 65.9% female) using an in-laboratory protocol spanning 4h before, to 2h after, habitual sleep time. Participants were classified into DPGwarm and DPGcold subgroups based on being above or below median DPG before sleep onset. Linear mixed models adjusted for age and sex examined psychomotor vigilance task performance across timepoints. The DPGwarm subgroup (n=19) showed significantly worse performance than DPGcold (n=19) across the evening for mean reaction time (RT), reciprocal reaction time, number of lapses, and fastest 10% of RT (all p[&le;]0.003). Significant GroupxTime interactions were observed for mean RT (F(3,90.4)=5.00, p=0.003) and lapses (F(3,93.6)=6.73, p<0.001), with DPGwarm participants showing progressively worse performance approaching sleep onset. At 2h post-habitual sleep onset, DPGwarm participants exhibited slower RT ({Delta}=129ms, p<0.001) and nearly four times more lapses (14.9 vs 4.1, p<0.001). Performance decrements were not accompanied by differences in melatonin timing, subjective sleepiness or mood, suggesting DPG may index cognitive vulnerability independently. Of note, younger age was associated with greater vigilance decrements. These findings demonstrate that elevated peripheral skin temperature before sleep onset is associated with reduced vigilance in young people with MDD, and may therefore have potential utility as a non-invasive thermoregulatory biomarker of cognitive vulnerability.

The hippocampus is essential for memory consolidation, a process mediated by high-frequency oscillations known as ripples during non-rapid eye movement (NREM) sleep. Ramelteon, a selective MT1/MT2 receptor agonist, has been reported to possess cognitive-enhancing properties; however, its impact on the fine-scale dynamics of hippocampal ripples remains unclear. We performed chronic local field potential recordings from the dorsal hippocampus and prefrontal cortex in mice. Following the intraperitoneal administration of either vehicle or ramelteon, we evaluated sleep architecture and characterized ripple properties, including occurrence rate, amplitude, instantaneous frequency, and duration during NREM sleep. Ramelteon administration significantly increased NREM sleep occupancy. Notably, we found that ramelteon significantly enhanced both the occurrence rate and amplitude of hippocampal ripples compared to the control group. While a slight increase in intra-ripple frequency was observed, other structural features, such as ripple duration and asymmetry index, remained unaffected. Our findings demonstrate that ramelteon facilitates hippocampal ripple dynamics by increasing their occurrence and synchrony during NREM sleep. Given the critical role of ripples in memory consolidation, these neurophysiological changes may underlie the procognitive effects of ramelteon. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=55 SRC="FIGDIR/small/723673v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@c798c7org.highwire.dtl.DTLVardef@1ff616eorg.highwire.dtl.DTLVardef@1557dc8org.highwire.dtl.DTLVardef@1b4e89e_HPS_FORMAT_FIGEXP M_FIG C_FIG

Rationale: Conventional measures of obstructive sleep apnea severity, particularly the apnea-hypopnea index, do not adequately capture event-level neurophysiologic responses to respiratory events. Whether post-apnea/hypopnea arousal dynamics provide prognostic information beyond established metrics remains unknown. Objectives: To determine whether post-apnea/hypopnea arousal dynamics are associated with all-cause and cardiovascular mortality. Methods: We conducted a retrospective analysis of in-home polysomnography data from 8,053 adults across four community-based cohorts. Peak time (PT; latency to maximal arousal probability), peak height (PH; maximal arousal probability), and area under the curve (AUC; cumulative arousal probability) were derived from peri-stimulus time histograms aligned to event termination. Associations with mortality were examined using multivariable Cox models and random-effects meta-analysis. Measurements and Main Results: PT, but not PH or AUC, was associated with mortality. In pooled analyses, each 1-second delay in PT was associated with higher all-cause mortality in males (hazard ratio [HR], 1.04; 95% confidence interval [CI], 1.02-1.06) and females (HR, 1.03; 95% CI, 1.00-1.06). For cardiovascular mortality, each 1-second delay in PT was associated with higher risk in males (HR, 1.05; 95% CI, 1.02-1.08) but not females (HR, 1.04; 95% CI, 0.99-1.10). Associations were driven primarily by non-rapid eye movement sleep and remained materially unchanged after additional adjustment for apnea-hypopnea index, arousal index, and hypoxic burden. Conclusions: Delayed arousal timing after apnea/hypopnea termination was associated with increased mortality risk independent of conventional measures of obstructive sleep apnea severity. Event-level arousal timing may provide prognostic information beyond count-based and hypoxemia-based metrics.

Study ObjectivesSleep spindles are implicated in memory consolidation. Yet direct evidence linking spindle dynamics to declarative memory outcomes remains limited. We thus tested whether targeted memory reactivation (TMR) time-locked to sleep spindles enhances declarative memory, and whether the temporal organization of stimulated spindles-trains versus isolated events-is selectively associated with distinct memory outcomes. MethodsTwenty-eight healthy young adults learned image locations from two categories (animals, clothing) in a grid, each paired with a distinct auditory cue. During overnight NREM sleep, one cue was replayed time-locked to spindles detected in real-time using a closed-loop system (TMR condition); the other served as the non-reactivated control (No-TMR condition). Category-cue assignment was counterbalanced. Post-sleep recall, recognition accuracy, and movement time were assessed. ResultsRecall accuracy was significantly higher in the TMR than the No-TMR condition (93.96% vs. 90.61%, p = .024), whereas recognition accuracy (p = .139) and movement time (p = .651) did not differ. Stimulation intensity within spindle trains correlated with the TMR effect on recall (Spearman {rho} = .531, p = .004), whereas the proportion of isolated spindle stimulations correlated with the TMR effect on recognition ({rho} = .563, p = .002). Cross-associations were not significant. ConclusionsSpindle-locked TMR enhances recall-based declarative memory retention. The selective association between spindle temporal clustering and memory outcomes suggests that train-embedded and isolated spindles support different aspects of memory consolidation, highlighting spindle temporal context as a functionally relevant dimension of sleep-dependent memory processing.

Animals sleep more when they are sick. In C. elegans, stress-induced sleep (SIS) follows cellular injury such as exposure to ultraviolet (UV) light. The genetic regulators of SIS remain incompletely defined. Using a worm-picking robot, multi-well WorMotel imaging, and association analysis we performed a semi-automated screen of 941 whole-genome-sequenced Million Mutation Project (MMP) strains. We quantified behavioral activity and quiescence before and after ultraviolet (UV) radiation. We applied the Sequence Kernel Association Test (SKAT) to this behavioral data to prioritize 6,663 genes and observed significant enrichment of known SIS genetic regulators. Based on these results, we conducted a candidate validation screen for additional genes regulating SIS. We identified three genes (strd-1, egl-8, cla-1), mutations in which reproducibly influence SIS. Further exploration of these genes holds potential for enhancing our understanding of the molecular basis of SIS. These findings establish a pipeline for automated behavioral phenotyping coupled with gene-based association to accelerate studies of C. elegans neurogenetics.

Background: Improvements in health technology offer opportunities for remote disease screening, diagnosis and monitoring. The Withings Sleep Analyzer (WSA), an under mattress ballistocardiograph sensor able to detect body movement, breathing, and cardiac ejection is a promising technology for the non-invasive detection and monitoring of neurodegenerative diseases. InSleep46 aims to evaluate whether the WSA is able to detect preclinical Alzheimer's disease in members of the 1946 British Birth cohort, now in their late 70s. Objectives: To assess feasibility of deployment of a remote sleep, circadian and physiological monitoring device in a population of older adults. Participants: 356 participants from the Insight 46 neuroimaging sub-study (1946 British Birth Cohort), all born in one week in March 1946. Methods: We describe remote recruitment, device installation, and troubleshooting protocols. Feasibility analysis examined participant characteristics associated with recruitment and successful device set-up using logistic regression. Troubleshooting events for device installation and maintenance were recorded over a mean 14-month follow-up period. Results: During the feasibility analysis period, 263 (74%) participants, mean (SD) age 77 years (0.47) agreed to take part, of whom 245 (93%) successfully set up the WSA. Recruitment and successful set up of the WSA were not dependent on cognitive ability, socioeconomic position, or educational attainment. 162 (62%) of recruited individuals required [&ge;]1 troubleshooting call (mean 2.3 per participant, range 0-16). 603 calls were required in total. Conclusion: Deployment of a remote sleep and physiological monitoring device in an older adult population is feasible. Most participants required individualised assistance to set up the device. For the technology to be widely implemented, the set up must be accessible, with dedicated support available.

Background Postoperative sleep disorder, a frequently observed complication, is associated with heightened pain sensitivity, exacerbated inflammatory reactions, and compromised tissue repair. Sufentanil, a highly selective -opioid receptor agonist, is widely used in patient-controlled intravenous analgesia (PCIA) and has been associated with reduced sleep efficiency. Oxycodone, as a /{kappa} dual receptor agonist, has shown a lower incidence of adverse effects in clinical practice. Despite these pharmacological differences, the comparative effects of oxycodone- versus sufentanil-based PCIA on postoperative sleep remain poorly characterized. Recent advances in wearable devices demonstrate strong agreement with polysomnography (PSG) in intergroup comparisons of sleep efficiency and total sleep time, enabling continuous, non-invasive, multi-night sleep monitoring and offering a viable alternative for clinical postoperative sleep research. Hence, we design this clinical trial to compare postoperative sleep efficiency between patients receiving oxycodone-based versus sufentanil-based PCIA under wearable sleep monitoring. Methods This study is a randomized, double-blind, placebo-controlled trial that was conducted at a single center. A sample size of 68 patients was determined through calculation, and these patients will be randomly assigned to either the oxycodone group or the sufentanil group. Sleep monitoring was initiated using a wristband device one day before surgery after recruitment. The sleep quality data at different setting time will be monitored. All patients will be followed up by blinded evaluators at baseline and 1, 2, and 30 days after the intervention. The follow-up included pain scores, postoperative complications and adverse events, etc. Discussion By integrating a modern photoelectric device with first-line analgesics, we hope the result of the study will inform perioperative sleep management, guide clinical analgesic selection, and improve patient recovery quality.

Background: Sleep debt and irregular sleep patterns are highly prevalent amongst adolescents. However, whether the absence of these sleep behaviours protects against subsequent depression remains unclear. Here, we examined the association of sleep debt, weekend catch-up sleep (WCS), and social jetlag (SJL) in adolescence with depression in young adulthood and identified underlying biopsychosocial mechanisms. Methods: Secondary data analyses were conducted using the Avon Longitudinal Study of Parents and Children. Bedtimes and wake-up times on school days and weekends (i.e., sleep duration) and sleep need were self-reported at 15 years. This was used to generate sleep debt (sleep need minus school day sleep duration), WCS (weekend sleep duration minus school day sleep duration), and SJL (absolute difference in the midpoint of sleep times between school days and weekends). Depression was assessed at 24 years with the Clinical Interview Schedule-Revised. Common mental health symptoms, biological, and school-related factors at 17 years were the mediators. Results: Logistic regression analyses revealed that greater WCS (adjusted odds ratio [AOR]=0.90; 95% CI=0.84-0.97; p=0.004) and lower sleep debt (AOR=1.10; 95% confidence interval [CI]=1.03-1.18; p=0.005) at age 15 reduced the likelihood of depression at 24 years. Irritability at 17 years partially mediated the relationship between sleep debt and depression (bias-corrected estimate=0.003; 95% CI=0.002-0.004; p<0.001). Conclusions: Adolescents who experience less sleep debt (i.e., less discrepancies between their actual sleep and their perceived sleep need) and those who extend their sleep duration on weekends are at reduced risk for depression in young adulthood. These findings underscore the need for greater opportunities for adolescents to obtain more hours of sleep to protect them against later poor mental health outcomes, such as depression. Keywords: Sleep; longitudinal studies; depression; ALSPAC

Background. Poor sleep quality is common in people with multiple sclerosis (pwMS) and reduces quality of life. Objectives. To examine associations between modifiable factors and sleep quality in pwMS. Methods. In a prospective clinic cohort (2017-2023), we evaluated whether baseline measures of disability, depression, fatigue, and pain were associated with poor sleep quality (Pittsburgh Sleep Quality Index, PSQI) cross-sectionally using covariate-adjusted linear regression, structural equation modeling (SEM), and LASSO logistic regression, and longitudinally using mixed-effects models. Results. In this cohort (n=750; mean age 48.9 years; 80.3% women, 88.7% relapsing type), higher body mass index ({beta} [95% CI]: 0.06 [0.01, 0.12], p=.001) and area deprivation index (6.78 [2.17, 11.39], p<.001) were associated with worse baseline PSQI scores. In adjusted analyses (n=730), disability, depression, fatigue, and pain were each associated with worse sleep. In SEM, pain had a moderate direct effect on sleep ({beta} [95% CI]: 0.56 [0.48, 0.64], p<.001). LASSO models that included pain outperformed the benchmark (AUROC 0.741 vs 0.517). Longitudinally (n=382), time and higher baseline pain predicted worse sleep ({beta} [95% CI]: time in months 0.04 [0.02, 0.06], p<.001; pain 0.36 [0.31, 0.41], p<.001). Conclusion. Pain is a key, potentially modifiable driver of poor sleep quality in pwMS.

Sleep disturbances are common among individuals with schizophrenia and can exacerbate disruptions in cognitive processes like learning and memory. Elucidating pharmacologically targetable molecular pathways perturbed by schizophrenia genes may uncover new treatment avenues. Here, we investigated the relationship of the schizophrenia-associated gene znf804a with sleep and circadian pathways. Using multi-day behavior tracking, we showed that znf804a zebrafish mutants displayed changes in sleep and circadian behaviors when light cues were removed. Through bulk RNA sequencing of fish raised under normal light cycling and dark-only conditions, we identified altered gene expression in the core and auxiliary pathways controlling circadian rhythms. Expression of fbxl3a, which encodes a modulator of the core negative feedback regulator of the clock, decreased in a dose-dependent manner as znf804a mutant copy number increased. Further analysis also revealed shifts in the relative abundance of specific transcripts, including idh1, suggesting znf804a could influence transcript processing or stability. Together, these findings link a ZNF804A ortholog to sleep and circadian behaviors and identify the regulation of fbxl3a and transcript processing as candidate mechanisms through which this schizophrenia risk gene may influence circadian biology.

Sleep posture is known to be relevant to various sleep disorders, such as sleep apnea, but is not often quantified in sleep monitoring systems. We address this with a novel vision-based approach, which is robust to the challenging conditions (variable lighting, partial occlusions, variable geometry) of inbed monitoring. This paper proposes a novel, attention-driven deep learning framework for the robust classification of head and body pose from infrared (IR) video streams during sleep of older people and people living with Alzheimers. Our architecture integrates a pre-trained convolutional backbone with a novel Multi-Head Channel-Spatial Attention (MH-CSA) module. The MH-CSA mechanism hierarchically identifies salient features by first capturing multi-scale spatial context using parallel heads with varied dilation rates, and then adaptively recalibrating feature importance via integrated Squeeze-and-Excitation blocks. To specifically address class imbalance, the model is optimized using a Dynamic Class-Balanced Focal Loss, which forces the network to focus on hard-to-classify examples from underrepresented classes. Whilst most prior sleep analysis work is developed using data from healthy younger participants, our system was developed and validated on a nocturnal sleep dataset of older adults and people living with Alzheimers disease, with IR video synchronized to clinical video-Polysomnography (vPSG). For head position classification, the system achieved an F1-score of 91% for older adults and 90% for people living with Alzheimers; for body pose prediction, the scores were 91% and 89% for the respective cohorts. These results demonstrate significant potential for application in understanding sleep behavior and informing appropriate sleep interventions.

Introduction: Actigraphy sleep-wake classification methods increasingly seek to leverage raw acceleration data and machine-learning-based classification, but performance evaluation in pediatrics is limited. We trained machine-learning models using pediatric data and compared their sleep-wake classification performance with existing algorithms for children. Methods: Sixty-five children (46% female, ages 5.3 to 17.7 years) completed in-lab overnight polysomnography and wore a GENEActiv device on their non-dominant wrist. The acceleration data were converted into 30-second epochs and aligned with physician-scored sleep-wake data from electroencephalography. Seven machine-learning models were trained using leave-one-subject-out cross-validation. Epoch-by-epoch analyses generated performance metrics (e.g., balanced accuracy [BA]) and discrepancy analyses provided overall sleep duration bias estimates. The combination of highest performance and least bias was used to rank using Euclidean distance scores - where a lower score represents closer to perfect performance and zero bias. For benchmarking, we included GGIR sleep scoring algorithms and an adult trained random forest classifier. Results: Overall, 560.1 hours of polysomnography and actigraphy data were collected (74.4% of epochs were scored as sleep). The pediatric-trained local-global long-short term memory (LSTM) classifier had the most optimal epoch-by-epoch performance (e.g., BA=0.85, sensitivity=0.88, specificity=0.83, ROC-AUC=0.95, and Cohen kappa=0.67). These metrics exceeded that of an adult-trained random forest classifier and GGIR-based algorithms. Discrepancy analyses revealed that overall sleep duration was underestimated by an average of 25 minutes using the LSTM classifier with no proportional bias. Conclusion: We trained seven pediatric sleep-wake classifiers that had strong ability to detect sleep and wake, with the LSTM classifier being most optimal.