SLEEP

ObjectiveK-complexes (KCs) are large-amplitude EEG events that represent N2 sleep stage and have been linked to sensory gating, sleep protection, and memory consolidation. Their detection remains limited by inter-rater variability in visual scoring and by the reliance of detectors on features that discard temporal information. We propose a two-stage detector that combines a rule-based candidate localization algorithm with a Support Vector Machine (SVM) classifier operating directly on the raw 2-seconds waveform, and we evaluate it against an adjudicated expert consensus of two different datasets. MethodsPolysomnographic recordings from 10 healthy adults (Dataset 1) were independently annotated by two human scorers; discordant events were adjudicated by a senior expert, yielding 240 consensus KCs. The automatic classifier was evaluated using subject-level 10-fold Group K-Fold cross-validation and compared directly against the two human scorers under identical conditions. Cross-dataset generalization was further assessed on the public DREAMS database (Dataset 2) under both external and internal training criteria. ResultsThe SVM classifier achieved the highest F1-score (79.4%) and accuracy (78.8%) among all scorers, with balanced recall (81.7%) and specificity (75.8%). Of the 58 false positives, 42 originated from events both experts had rejected yet displayed canonical KC morphology and received high classifier confidence (P(KC)>0.7 in 45.2% of cases). This pattern was replicated on Dataset 2. ConclusionA waveform-based classifier matches expert performance and systematically flags morphologically valid KCs that fall outside conventional visual-scoring criteria. SignificanceThese findings question the existence of an unambiguous ground truth for KC detection and support a data-driven redefinition of the event boundary, with implications for sleep staging and memory-consolidation research.

Abstract Background Prediction of long-term neurodevelopmental outcomes remains challenging after perinatal asphyxia. Here, we studied whether computational metrics of brain function derived from neonatal EEG are associated with long-term neurodevelopment in infants with perinatal asphyxia. Methods Total of 36 term-born infants with perinatal asphyxia with or without hypoxic-ischemic encephalopathy were studied with neonatal multichannel electroencephalography (EEG). We computed local EEG amplitudes and phase-amplitude coupling (PAC), as well as large-scale functional cortical networks estimated using amplitude-amplitude correlations (AAC) and phase-phase correlations (PPC). These EEG-derived markers were tested for associations with neurodevelopmental outcomes at two years, assessed using the Griffiths Scales of Child Development, 3rd edition (GMDS-III). Results EEG amplitudes showed positive associations with GMDS-III Foundations of Learning and General Development scores across most electrodes during quiet sleep, with the strongest effects observed at frontal and central regions (r = 0.44-0.66). PAC showed negative associations with the same scores mainly over parietal and temporal regions (r = -0.45 to -0.55). Cortical AAC networks demonstrated the most robust and widespread negative associations in all frequency bands during quiet sleep (r = -0.47 to -0.54), with 70-72% of connections significant in high delta frequency. In turn, PPC networks showed frequency-selective and more spatially constrained negative associations during quiet sleep (r = -0.48 to -0.53), involving 5-12% of the network. Conclusions Both local and network-based metrics in the newborn brain show significant association with neurodevelopmental outcome at 2 years after perinatal asphyxia.

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.

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.

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.

Sleep spindles are rhythmic electroencephalographic signatures of non-rapid-eye-movement sleep. Their dysregulation has been implicated in several neuropsychiatric illnesses. Spindles have a characteristic waxing and waning shape, but the cellular and circuit mechanisms controlling their shape are not well understood. Recent but sparse research has implied that sleep spindle shape becomes abnormal in post-traumatic stress disorder (PTSD). PTSD patients have dysfunctional GABAA receptors in midline thalamic regions, areas involved in the orchestration of sleep spindles. We modelled this GABAA dysfunction within thalamocortical (TC) neurons using localized CRISPR-Cas9 technology to test the hypothesis that GABA dysfunction would dysregulate sleep spindle shape and cause symptoms of PTSD, in mouse model behavioral evaluations. We found sleep spindles were shorter and abnormally shaped, having lost their characteristic waxing and waning shape, in mice with GABAA receptor knock-down in TC neurons (TC-1KD). TC-1KD mice failed to recover from learned fearful reactions following an aversive stimulus. We tested this with a contextual fear conditioning paradigm using electric foot shocks. A control group with intact GABAA receptors successfully habituated to the fear conditioned location in subsequent visits to that context without foot shocks. In contrast, TC-1KD mice never habituated, suggesting abnormally extended fearful memories. The number of inhibitory post synaptic currents in TC neurons were significantly decreased in vitro, confirming an effective knock-down. Our results imply that abnormally shaped sleep spindles may serve as a biomarker of GABAA receptor dysfunction in TC neurons which may be involved in abnormal fear processing in PTSD. We postulate GABAA receptor dysfunction in TC neurons may be underlying pathophysiology of PTSD and our findings here may inspire the development of screens, diagnostics and objective characteristics of stress related disorders, including PTSD.

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.

Sleep abnormalities and dysfunction of gamma band (30-80 Hz) activity generated by parvalbumin (PV) interneurons are early characteristics of Alzheimers disease (AD) which correlate with the severity of amyloid-{beta} deposition (A{beta}) and cognitive impairment. However, the timing of these alterations in vivo with respect to disease progression is unclear. Here, in longitudinal recordings from APP/PS1/PV-cre (AD mice) from 3-6 months, we found reduced sleep slow-wave power (0.5-4 Hz) in hippocampus and medial prefrontal cortex in AD mice as young as 3 months old, compared to non-AD (PV-cre) mice, well before overt pathology. This finding was primarily due to reductions in the NREM delta range (1.5-4 Hz), a hallmark of restorative functions of sleep. In contrast, beta (15-30 Hz) power linked to insomnia was significantly higher across all sleep-wake states. Loss of deep NREM sleep was not compensated by an increase in NREM sleep time, instead NREM sleep during the dark (active) phase was slightly but significantly lower in AD mice. 40-Hz auditory steady-state responses and associated evoked calcium responses of hippocampal PV neurons recorded using fiber photometry were also impaired by 3 months old. However, Y-maze performance in 3- and 6-month-old AD mice was not significantly different from non-AD mice. These results reveal reduced deep sleep and PV-associated 40-Hz activity as very early changes amenable to early intervention occurring prior to cognitive deficits. Furthermore, they establish APP/PS1 mice as a good model to causally test the relationship between sleep, PV neuronal activity and amyloid-mediated pathology.

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.

Abstract Importance: Irregular sleep-wake patterns have been associated with poor health and cognitive outcomes, yet evidence linking 24-hour sleep-wake regularity to cognitive decline or dementia remains inconsistent. Particularly, regularity can be measured as regularity of rest-wake, sleep-wake or overall 24-hour activity, but it is unclear which aspects are most relevant for cognitive aging. Objective: To assess associations of rest-wake, sleep-wake, and 24-hour activity regularity with cognitive decline and dementia risk. Design: Observational prospective study comprised of six US and European cohorts: MrOS (sleep study between 2003-2005, mean follow-up: 7.1 years), Rotterdam Study (2004-2007, 11.6 years), MESA (2010-2013, 8.2 years), MAP (2005-2018, 7.2 years), Whitehall II (2012-2013, 6.9 years), and UKB (2013-2015, 7.9 years). Setting: Cohort-specific estimates were pooled using random-effects meta-analysis. Analyses were done between June 2025 and March 2026. Participants 74,733 dementia-free adults with multi-day actigraphy were included across cohorts: MrOS (age: 67-96 years, female:0%), MESA (54-95y, female:54.6%), Rotterdam Study (46-98y, female:55.0%), MAP (56-100y, female:77.1%), Whitehall II (59-83y, female:25.9%), and UKB (55-78y, female:55.5%). Exposure: Day-to-day rest-wake regularity (Rest Regularity Index, RRI), day-to-day sleep-wake regularity (Sleep Regularity Index, SRI), and 24-hour activity regularity (Interdaily Stability, IS) were derived from multi-day actigraphy. Main Outcome: Outcomes were risk of dementia and changes in global cognition. Results: Across six cohorts, 1,906 dementia cases occurred among 74,733 participants. After adjusting for demographics, health behaviors, depressive symptoms and cardiovascular comorbidities, each 1-SD higher regularity score was associated with an 9-14% lower dementia risk (pooled hazard ratios: RRI 0.86 95%CI: [0.79-0.95]; SRI 0.87[0.79-0.97]; IS: 0.91[0.88-0.95]). Associations were approximately linear. Age-stratified analyses showed directionally stronger associations among adults aged < 65, although meta-regression did not support an interaction(p > 0.55). Greater regularity was associated with modestly slower decline in global cognition (pooled {beta} per 1-SD higher score of RRI per year: 0.003, 95%CI [0.001-0.006]). Conclusions & Relevance: Greater regularity of rest-wake, sleep-wake, and 24-hour activity rhythms was associated with lower dementia risk and modestly slower global cognitive decline. These findings suggest that 24-hour sleep-wake regularity is a relevant behavioral marker of cognitive aging and may inform future efforts to identify or intervene on early risk.

Dream recall varies substantially both between individuals and from night to night within the same individual. Although nocturnal awakenings are thought to facilitate the encoding and later retrieval of dream experiences, it remains unclear whether dream recall is shaped primarily by awakening frequency or by more specific awakening characteristics, including duration, sleep stage, and timing within the night. Here, we analyzed two cohorts: cohort 1 consisted of 708 adults spanning the full range of dream recall frequency, assessed across three waves with home sleep recordings and questionnaire-based dream recall frequency measures; cohort 2 consisted of 124 adults with high dream recall frequency, assessed across multiple nights with home sleep recordings and daily dream reports. Using multilevel models with within-between decomposition, we examined trait-like and state-like associations between awakening measures and dream recall outcomes. At the trait level, both questionnaire-based dream recall frequency in cohort 1 and daily dream recall (i.e., a sense of having dreamed) in cohort 2 were associated with a specific nocturnal awakening profile: more habitual long REM awakenings and short NREM awakenings, with REM awakening effects remaining robust after adjustment for sleep duration. At the state level, in cohort 2, nights with more short and medium REM awakenings than usual increased the likelihood of morning dream recall, whereas nights with more long REM awakenings than usual increased the likelihood of morning dream content recall (i.e., remembering dream content). These findings support the arousal-retrieval and functional state-shift models, while highlighting important nuances in the associations between nocturnal awakenings and different dream recall outcomes.

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.

Background: Sleep fragmentation and reduced sleep efficiency are markers of disrupted sleep architecture linked to cognitive and age-related decline. Current assessments rely on subjective reports prone to recall bias, limiting their effectiveness for longitudinal monitoring. Data-driven analysis of sleep using physiological signals such as EEG and EMG remains underutilised, particularly in mid-to-older adults. Objective: We present a deep learning pipeline for automated sleep staging and label-free abnormality scoring, with the primary objective of quantifying deviations in sleep architecture to capture progressive sleep disruption and longitudinal change. Methods: Temporal and attention-based models were benchmarked using datasets from the National Sleep Research Resource and PhysioBank. To improve class-specific performance, we introduce a stacking-based ensemble of sleep stage classifiers, each trained to specialise in a different stage. For longitudinal scoring, we develop a reconstruction loss-based abnormality metric using a temporal convolutional autoencoder trained on hypnograms generated by the sleep staging models. Results: Attention-based models, particularly AttnSleep, achieved the highest performance in both multimodal and single-channel settings (accuracy: 0.85 and 0.83; F1: 0.79 and 0.74, respectively). The encoder-decoder ensemble model improved overall classification accuracy by 3% compared to the best-performing biased base classifier, with a modest gain in N1-stage F1 score (0.444). The proposed abnormality score correlated with Pittsburgh Sleep Quality Index components and showed sensitivity to synthetic hypnogram degradation, highlighting its potential as a label-free indicator of sleep disruption. Conclusion: Automated classification and annotation-free scoring enable an end-to-end multimodal pipeline that supports scalable, objective sleep health monitoring, with relevance for future clinical deployment.

Wearable EEG devices such as the Zmax headband offer scalable alternatives to laboratory polysomnography (PSG) for sleep monitoring, but their real-world performance in home settings remains poorly characterised. This study presents a systematic validation of automated sleep staging on the Wearanize+ dataset; a unique multimodal resource providing synchronised full PSG, bilateral Zmax EEG (F7-Fpz/F8-Fpz), and psychiatric phenotyping from 100 participants recorded at home. We first developed and applied an automated signal quality screening framework, revealing that 10% of recordings failed completely due to signal dropout and a further 16% showed partial degradation. We then evaluated two automated staging algorithms; Dreamento and YASA against PSG manual scoring, stratified by signal quality. In technically adequate recordings (N=74), YASA achieved significantly higher agreement than Dreamento (mean {kappa}=0.450 vs 0.371; {Delta}{kappa}=+0.079, p=0.0005), primarily through substantially improved N2 detection (recall: 0.64 vs 0.36). Both algorithms showed a systematic N2/N3 boundary confusion, however in opposite directions: Dreamento over-called N3 (37% of N2 epochs mis-staged as N3), while YASA over-called N2 (35% of N3 epochs mis-staged as N2). Critically, Dreamento showed greater robustness than YASA in degraded-quality recordings (WARN group: {kappa}=0.414 vs 0.330), consistent with its training on Zmax-specific data. Signal quality metrics did not predict staging performance within adequate recordings, indicating that channel topology is the primary limiting factor for frontal single-channel staging. These findings establish the Wearanize+ dataset as a benchmark for wearable sleep staging and motivate the use of PSG manual stage labels for downstream physiological analyses.

Wearable EEG devices enable home sleep monitoring but require systematic spectral validation before their physiological outputs can serve as proxies for polysomnographic features. This study provides comprehensive spectral validation of the Zmax EEG headband against concurrent PSG using the Wearanize+ dataset. Seventy-one participants with adequate signal quality underwent simultaneous home PSG and Zmax recording. Bandpower correspondence, calibration robustness, within-subject reliability, lateralisation, and spindle detection were evaluated across all sleep stages. Zmax systematically underestimates bandpower across all frequency bands (bias -0.41 to -0.74 log units), attributable to the active Fpz reference electrode. A per-subject N2-referenced calibration eliminates this bias; N2 calibration outperformed N3 and REM alternatives (mean post-calibration r=0.601 vs 0.479 and 0.489). Post-calibration spectral correspondence was strong for alpha (N3: r=0.806) and sigma (N3: r=0.752). Within-subject reliability was excellent (split-half r>0.99). Demographic factors explained less than 4% of offset variance. Lateralisation analysis was underpowered (36-39% power; N=194 required for 80% power). Spindle under-detection was traced to YASA's relative sigma power pre-filter; lowering this threshold recovered PSG-equivalent counts with near-zero bias. These findings establish a validated calibration framework and evidence-based feature selection recommendations for Zmax-based sleep biomarker research.

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.

The temporal coupling between cortical blood-oxygen-level-dependent (BOLD) activity and CSF inflow has recently been proposed as a non-invasive marker of glymphatic function, a brain-wide clearance system closely linked to sleep, neuromodulatory regulation and neurodegeneration. Reduced BOLD-CSF coupling has been previously reported in Parkinsons disease but its characterization in dementia with Lewy bodies, regional specificity and relevance to shared neuropsychiatric symptoms remain unclear. Using resting-state functional MRI, we quantified global and regional BOLD-CSF coupling in 39 participants, including 17 with Parkinsons disease (mean age 61.4 years), 10 with dementia with Lewy bodies (mean age 72.8 years) and 12 healthy controls (mean age 66.2 years), and examined the relationship with clinical and cognitive measures, as well as volumetric measures of the subcortical ascending arousal network. Parkinsons disease and dementia with Lewy bodies patients both demonstrated weaker global BOLD-CSF coupling compared to controls, with no detectable difference between patient groups. Coupling reductions were most pronounced within the unimodal and attentional networks, encompassing regions that are particularly vulnerable in Lewy body disease. Weaker coupling was associated with the severity of hallucinations and cognitive fluctuations, poorer nocturnal sleep quality and impaired attentional working memory, but not overall motor symptom burden. Associations between BOLD-CSF coupling and basal forebrain and brainstem volumes were observed, though partially age-dependent, suggesting a complex interaction between neuromodulatory system degeneration, ageing and brain-fluid dynamics. Our results provide preliminary evidence that disrupted temporal coordination between cerebrovascular activity and CSF inflow may contribute to the fluctuating neuropsychiatric features of Lewy body disease and highlight the utility of BOLD-CSF coupling as a dynamic in vivo proxy of glymphatic function. Replication in larger cohorts incorporating multimodal imaging and biomarkers of pathology will be essential to validate these findings and determine whether brain-fluid dysregulation represents a potentially modifiable therapeutic target.

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

The Empatica E4 wristband provides continuous multi-modal physiological monitoring including blood volume pulse (BVP), electrodermal activity (EDA) and skin temperature (TEMP) but its validity for sleep-stage-specific autonomic and thermoregulatory monitoring has not been systematically evaluated against concurrent polysomnography (PSG). Using the Wearanize+ dataset which provides synchronised PSG, Empatica E4, and Zmax EEG recordings from 100 home-recorded participants; a systematic validation of Empatica E4 physiological signals against PSG ground truth across five sleep stages was conducted. Of 100 participants, 92 had Empatica data; 69 met Zmax EEG signal quality criteria and formed the analysis sample. Heart rate (HR) from the pre-computed Empatica HR channel showed valid stage-specific patterns (Wake: 70.9 bpm, N3: 61.2 bpm) and moderate inter-device MeanNN correspondence with PSG ECG (Spearman r=0.35-0.42 across stages). Skin temperature showed the expected thermoregulatory pattern (Wake: 33.92C, N3: 35.48C) and is recommended for downstream analyses. Tonic EDA showed an inverted stage pattern attributable to wrist sweat accumulation during deep sleep, representing a known confound for wrist-worn EDA during sleep. Phasic EDA showed plausible patterns and may be used with caution. These findings establish a validated feature set for Empatica E4 sleep research and directly inform multimodal psychiatric biomarker studies using the Wearanize+ dataset.

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.