SLEEP

Sleep arousals trigger rapid autonomic shifts, yet their specific sympathetic signatures remain poorly characterized due to the mixed sympathetic-parasympathetic nature of traditional cardiovascular markers. Electrodermal activity (EDA), driven exclusively by sympathetic sudomotor pathways, offers a more direct opportunity to characterize arousal-related autonomic responses during sleep. This study quantifies the evolution of EDA-based features associated with arousal events in 100 adults using polysomnography and high-resolution EDA recordings. We implemented a time-varying frequency decomposition framework to isolate sleep-specific sympathetic components, extracting statistical and peak-based features from arousal segments and matched stable-sleep controls. Compared to controls, arousal segments exhibited robust sympathetic modulation in EDA persisting 40 seconds post-arousal. While long arousals produced robust responses, short arousals showed negligible sudomotor responses. REM and NREM sleep showed consistent feature trajectories, with greater variability during REM. The observed activation is primarily driven by clustered sympathetic bursts and amplitude enhancement rather than shifts in peak frequency. These findings establish EDA as a highly sensitive marker of sleep-related autonomic activation and provide a quantitative baseline for characterizing sympathetic responses to sleep arousals.

Background/ObjectivesElectromyography (EMG), video-polysomnography (vPSG), and wrist actigraphy are each used to develop diagnostic algorithms for Rapid eye movement sleep behavior disorder (RBD). However, the extent to which they capture overlapping versus distinct motor phenomena remains unknown. We evaluated the respective contributions of actigraphy, EMG and vPSG to the measurement of REM-sleep motor activity. MethodsSeventeen adults with RBD (Mount Sinai n = 9; Stanford n = 8) and eight control participants from an open Newcastle dataset underwent vPSG and concomitant wrist actigraphy. Flexor digitorum superficialis EMG activity and video-detected movements were manually scored in 3-second mini epochs. Actigraphy was quantified using an acceleration-magnitude-based activity count model. Statistical and agreement analyses were performed to assess the motor events captured by all three, any two, or by each modality independently during REM sleep. ResultsIn participants with RBD, actigraphy-derived movement load was significantly higher during REM sleep than during non-REM stages, a pattern not observed in control participants. Across 12,941 3-second mini epochs, EMG, actigraphy, and video detected 1,703, 1,613, and 811 motor events, of which 413 were detected concurrently by all three modalities. Pairwise agreement was moderate and increased from EMG-actigraphy ({kappa} = 0.27 {+/-} 0.10) to actigraphy-video ({kappa} = 0.41 {+/-} 0.12) and EMG-video ({kappa} = 0.45 {+/-} 0.15). Of EMG-detected events, 49.0% were also detected by actigraphy; of actigraphy-detected events, 37.2% were detected by EMG and 34.9% by video. Actigraphy activity counts were highest for events detected by all three modalities and lowest for actigraphy-only events. ConclusionActigraphy-measured REM-related motor activity was elevated in RBD but not in controls. EMG, actigraphy, and video captured partially overlapping motor events in RBD patient, with actigraphy showing the highest sensitivity and manually scored video the lowest.

BackgroundWhile accurate sleep measurement is vital for older adults, the validity of actigraphy (AG) in free-living environments remains controversial, particularly given the flexible sleep-wake schedules common in this demographic. To address this uncertainty, we assessed the accuracy of wrist AG against in-home portable electroencephalography (EEG) among community-dwelling older adults. MethodsCommunity-dwelling older adults underwent concurrent sleep monitoring using a portable EEG device and a wrist-worn AG for five consecutive nights whenever possible, with monitoring extended to up to seven nights when feasible. Key sleep parameters, including total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency, were derived from both devices. Measurement agreement was assessed using Bland-Altman plots and multilevel modeling, while reliability and accuracy were quantified via intraclass correlation coefficients (ICCs) and mean absolute percentage error (MAPE). ResultsForty-nine adults contributed 217 nights of recordings. On average, AG slightly overestimated TST and sleep efficiency and underestimated SOL and WASO compared with EEG. Single-measure ICCs were 0.73 for TST and 0.38 for WASO (0.84 and 0.55 for averages across nights), and the MAPE was 11% for TST but exceeded 50% for SOL and WASO, indicating poor accuracy for these indices. ConclusionIn community-dwelling older adults, wrist AG yielded acceptably accurate estimates of average TST, supporting its use in epidemiological monitoring of sleep duration. However, large errors for SOL and WASO indicate that portable EEG- or polysomnography-based assessment remains indispensable when precise evaluation of sleep initiation and nocturnal wakefulness is required.

Sleep architecture is essential for metabolic and cardiovascular health, yet the impact of day-to-day dietary variation on objective sleep physiology remains unclear. Using 4.8 thousand person-nights with real-time dietary logs and multi-stage wearable sleep recordings, we examined how prior-day nutrition relates to next-night sleep under free-living conditions. Higher fiber density was associated with increased restorative sleep, including +0.59 pp deep sleep, +0.76 pp REM sleep, -1.35 pp light sleep, and -1.14 bpm lower mean nocturnal heart rate. Greater plant diversity and higher whole-plant food intake were similarly associated with lower nocturnal heart rate (-0.72 to -0.94 bpm). Meal-timing behaviors primarily influenced sleep duration, sleep-onset latency, and autonomic tone: heavier evening meals were associated with +7.7 min longer total sleep time and +0.73 bpm higher nocturnal heart rate. In contrast, short-term variation in macronutrient energy distribution and micronutrient consumption showed no robust associations with sleep outcomes. When analyses were restricted to more extreme dietary contrasts, effect magnitudes increased while remaining directionally consistent. These findings indicate that routine daily dietary choices, particularly plant-forward composition and meal timing, have immediate and measurable effects on objective sleep architecture.

Background and ObjectivesSleep has been associated with cognition and risk of dementia. However, sleep is a highly complex and multi-dimensional state, and there is uncertainty about which aspects of sleep are most relevant to cognitive performance and dementia risk. We applied a data-driven approach to identify clusters of sleep variables that reflect meaningful sleep composites and examined their association with cognitive performance and dementia risk. MethodsData from the Sleep and Dementia Consortium, consisting of 5 US population-based cohorts were utilized. Participants had methodologically consistent, home-based polysomnography, self-report habitual sleep, neuropsychological assessments, and dementia risk surveillance. The pooled cognitive analysis included 5,958 participants aged [≥]45 years, and the incident dementia analysis included 5,471 participants aged [≥]60 years. A cluster around latent variables analysis was used to derive 9 latent sleep composites from 44 sleep metrics. Global cognitive composite z-scores were derived from principal component analysis. Linear regression models were used to assess associations between sleep composites and cognitive performance. Cox proportional hazard models assessed associations between sleep composites and incident dementia. ResultsMean (SD) age was 70 {+/-} 11 and 74 {+/-} 12 years for the cognitive and dementia analysis, respectively. There were 1,134 incident dementia cases (median follow-up time of 5-19 years). 9 sleep composites were identified, together explaining 49% of the total variance in the original 44 sleep metrics: Sleep quantity and efficiency, sleep fragmentation, light NREM predominance, N3 predominance, spindle number and duration, REM sleep bouts, respiratory disturbances, slow oscillation-spindle coupling and spindle amplitude. Of these, composites reflecting greater sleep quantity and efficiency (i.e., longer and more consolidated sleep; pooled {beta} per one-unit change in composite, 0.03; 95% CI: 0.004 - 0.06; p=0.033) and stronger slow oscillation-spindle coupling (pooled {beta}, 0.04; 95% CI: 0.003 - 0.07; p=0.039) were associated with better global cognition. However, no significant associations were identified between the 9 sleep composites and dementia risk. DiscussionOur data-driven approach identified longer, more consolidated sleep and stronger slow oscillation-spindle coupling as the composites of sleep most strongly related to cognitive performance. These composites may be useful in guiding further investigations of sleep-brain health relationships.

INTRODUCTIONAgitation is a common and burdensome neuropsychiatric symptom in dementia that fluctuates from day to day, but objective tools for short-term risk stratification are limited. We examined whether nocturnal physiological signals from unobtrusive under-mattress sensors predict next-day daytime agitation and whether associations differ for agitation occurrence versus severity. METHODSWe extracted cardiorespiratory, movement, and sleep-proxy features from two long-term care cohorts (N=55; 333 nights) and one external home-monitoring cohort (N=18; 803 nights). A two-part mixed-effects framework was used to model next-day agitation episodes. RESULTSLower nocturnal respiratory rate and greater activity instability independently predicted higher odds of next-day agitation occurrence. Associations were stronger for motor than verbal agitation. Respiration-related predictors were validated externally. Conversely, no nocturnal features significantly predicted agitation severity. DISCUSSIONPassive sleep monitoring identified reproducible, physiologically interpretable markers of next-day agitation occurrence, supporting the potential of under-mattress sensing for short-term risk stratification and more proactive dementia care.

Study ObjectivesTo develop machine-learning models for sleep stage classification, arousal detection, and respiratory event detection from overnight polysomnography, and to evaluate their performance relative to expert scorers. MethodsOvernight polysomnography recordings were obtained from healthy participants and participants referred for suspected sleep-disordered breathing. Four certified scorers completed calibration sessions and generated reference annotations for sleep stages, arousals, and respiratory events. A subset of recordings was independently annotated by all scorers to support consensus analyses, enabling direct comparison between model outputs and human inter-scorer agreement. Gradient-boosted decision tree models were trained using hand-crafted features derived from standard physiological signals. ResultsSleep stage classification achieved accuracy 0.840, Cohens kappa 0.791, and F1-score 0.841, with limits of agreement for total sleep time of approximately {+/-}0.5 h. Arousal detection achieved an F1-score of 0.733, with limits of agreement for the arousal index of approximately {+/-}15 events/h. Respiratory event detection achieved an F1-score of 0.818, with limits of agreement for the apnea-hypopnea index also within approximately {+/-}15 events/h. In consensus analyses, model performance was comparable to human inter-scorer agreement for sleep stages and arousals, while remaining below human inter-scorer agreement for respiratory events, despite high absolute performance relative to prior studies. ConclusionsThe proposed models achieved performance approaching human-level agreement across major sleep scoring tasks. These findings indicate that high consistency in expert annotations is a key factor underlying robust model performance and support the use of quality-controlled annotations for developing reliable automated sleep analysis systems. Statement of significanceManual scoring of overnight sleep studies remains a major bottleneck in sleep medicine, limiting efficiency, consistency, and large-scale research. This study demonstrates that interpretable automated analysis can achieve performance approaching human-level agreement for core sleep scoring tasks when reference annotations are highly consistent. By directly comparing model outputs with calibrated inter-scorer agreement, the results show that annotation quality is a key determinant of attainable accuracy, rather than model complexity alone. Such systems may provide stable and reproducible reference outputs that support clinical decision making, scorer training, and standardization across centers. Important remaining challenges include validation across institutions and populations, robustness to real-world signal artifacts, and extension to clinically meaningful subtypes of respiratory events.

Type 1 narcolepsy (NT1), a disorder caused by the loss of hypocretin/orexin transmission, is characterized by daytime sleepiness and symptoms where Rapid Eye Movement (REM) sleep, a state normally occurring from middle to late in the night, can intermingle with wakefulness. This results in cataplexy and sleep paralysis, episodes of muscle paralysis when awake, or in the generation of dream-like hallucinations and vivid dreaming, periods of visual imagery or sensory experiences that occur while awake, notably when falling asleep (hypnagogic hallucinations) or lingering dreams with over-realistic recall. Using deep learning of nocturnal sleep polysomnography (PSG) signals (EEG, EMG and EOG) applied to sleep stage scoring, we found that NT1 shows abnormally short wake to REM sleep transitions and occurrences of abnormal sleep stages probabilities of wake, REM sleep and N1 (very light NREM) sleep abnormally co-occurs (sleep stage mixing). Interestingly, although presence of these during sleep enables NT1 diagnosis with performances similar to gold standard diagnostic procedure, the multiple sleep latency test (MSLT), the cortical localization of these dissociations remains unclear. In this work, we used electrode specific predictions of sleep stages to explore if these are global or observed at the local cortical level. Surprisingly, although sleep stage mixing was preeminent between REM sleep, N1 and wake across all electrodes, it was found to fluctuate across locations, with stronger fluctuations found in frontal and central locations, notably in the dominant (left) hemisphere. The strongest single discriminator for NT1 was N1-REM stage mixing across central electrodes (C3-C4), showing 4.3-fold higher dissociation in NT1 patients (Cohens d = 0.61). Analysis of sleep stage dissociations across varying time scales revealed that windows lasting several minutes were most predictive of NT1 status, aligning with the duration of clinically reported symptoms of dissociated REM sleep in narcolepsy. Local N1-W-REM sleep dissociations correlated with CSF orexin/hypocretin levels and severity as measured using MSLT. The predominance of stage mixing in frontal and central regions, areas typically associated with executive and motor control, may contribute to the partial preservation of awareness during dissociated REM phenomena. Further, self-reports of hypnagogic hallucinations correlated best with dissociations involving occipital locations, in agreement with its usual visual content. Coherence analysis was also conducted but did not reveal additional insight. These results suggest that orexin deficiency destabilizes REM sleep organization across cortical projection area contributing both to REM sleep dissociation and to abnormal state transitions observed in NT1.

Recent methodological advances have enabled minimally invasive, convenient self-assessment of central circadian phase in home-based settings [1]. In this report, we describe salivary melatonin onset secretion profiles in 261 participants (36% male; age range, 9-83 years) who were recruited from 18 clinics in North America or by self-referral. All participants received a standardized, at-home central circadian phase assessment kit by mail or directly through their provider, along with written and video sample collection instructions. The standard protocol consisted of 7 or 9 saliva samples collected at 1-hour intervals in dim light on a single occasion, starting up to 7 hours prior to an individuals habitual bedtime and proceeding until 1-5 hours past bedtime. The number of samples and the start and end times were highly dependent on the nature and timing of the individuals primary concern and were dictated by the referring sleep-health provider. Samples were frozen immediately after collection and then returned to a central laboratory via 2-day shipping after an additional 48 hours of home freezing. Samples were subsequently kept frozen until the day of melatonin assay. Of the 261 participants, 91 (34.9%) exhibited DLMOs within the Predicted Onset Window (POW). The remaining 170 participants (65.2%) showed profiles that did not meet criteria for a predicted, aligned phase onset, including 162 (62.1% of the total sample) that could be classified into atypical phenotypes, distributed as follows: 28 showed a Delayed Melatonin Onset (10.7%), 28 showed an Advanced Melatonin Onset (10.7%), 27 exhibited Hypermelatoninemia (10.3%), 36 exhibited Hypomelatoninemia (13.8%), and 43 presented with Irregular/Multipeak profiles (16.5%). The remaining 8 participants (3.1%) exhibited profiles that could not be reliably classified. These results highlight the unexpectedly high proportion of non-predicted melatonin patterns and demonstrate that over 80% of profiles can be reliably assigned to clinically meaningful circadian phenotypes. The Discussion explores how real-world melatonin profiling can identify relevant circadian contributors to sleep disruption that symptom reports alone may fail to detect. We also describe and discuss these phenotypes in detail, considering their biological contexts and potential clinical relevance.

Background and ObjectivesPhysical activity and sleep are potential modifiable risk factors for the development of Alzheimers disease and related disorders (ADRD), but few studies have objectively measured both domains in participants across the cognitive continuum. Research Design and MethodsStandard clinical assessment, accelerometry, and at-home EEG sleep data were obtained from older controls (n=9) and adults who met consensus diagnostic criteria for mild cognitive impairment (MCI; n=7), Alzheimers disease (AD; n=10), and dementia with Lewy bodies (DLB; n=11). Given these sample sizes, descriptive statistics are presented rather than formal statistical testing. ResultsThe MCI group had the most surprising findings--although they were cognitively similar to the control group, they were less physically active than the AD group and had the worst sleep efficiency. The DLB group had the most severe motor and neuropsychiatric symptoms, were the least physically active, spent the least amount of time in rapid eye movement (REM) sleep, and spent the highest amount of time in non-REM sleep with hypotonia (NRH). The AD group had physical activity counts that fell between the DLB and control groups; REM sleep and NRH levels that were similar to the control and MCI groups; and autonomic activation index (AAI) and sleep spindle durations that were higher than the MCI and DLB groups. Discussion and ImplicationsThese findings highlight interesting physical activity and sleep patterns between groups, but larger samples are needed to investigate how objectively measured physical activity and sleep might serve as disease-specific digital biomarkers of neurodegenerative disorders. Translational SignificanceThis study uses wearable technologies to measure physical activity and sleep in adults with and without cognitive impairment. The study found that adults with mild cognitive impairment had physical activity and sleep patterns that resembled people with dementia despite having cognitive scores that were closer to cognitively healthy controls. Sleep and activity patterns were distinct when comparing participants with Alzheimers and dementia with Lewy bodies. Larger studies are needed to validate these findings, but mobile health devices may be an accessible way to detect early cognitive decline and help differentiate dementia subtypes, resulting in earlier, targeted clinical care.

Study ObjectivesNarcolepsy type 1 (NT1) is characterized by excessive daytime sleepiness and cataplexy. Previous studies have implicated the amygdala, thalamus, brainstem and hippocampus in the pathophysiology of NT1. We here aimed to examine more detailed subregional case-control differences in MRI-based segmentations of these brain regions to gain deeper insights. MethodsWe obtained 3T MRI brain scans from 54 NT1 patients (39 females, mean age 21.8 {+/-} 11.0 years, 51 with confirmed hypocretin-deficiency and three patients that had not performed this measure) and 114 healthy controls (77 females, mean age 23.2 {+/-} 9.0 years). Automated segmentation of the hippocampus, amygdala, thalamus, and brainstem was performed on T1-weighted MRI data using FreeSurfer. Case-control volume differences were tested using general linear models and permutation testing. The false discovery rate was controlled at 5% with the Benjamini-Hochberg procedure. ResultsThe analysis revealed no significant case-control differences for any of the subregions in the hippocampus, thalamus, amygdala and brainstem after correction for multiple testing. ConclusionsBased on a detailed automated MRI-based segmentation analyses in a relatively large national sample, NT1 patients had no significant changes in any amygdala, thalamus, brainstem or hippocampus subregions compared to controls. In the future large multi-site studies could be performed to achieve sufficient power to detect more subtle group differences.

BackgroundNocturnal hypoglycaemia (NH) is a common and challenging complication in Type 1 Diabetes (T1D), disrupting blood glucose control and sleep physiology. Its real-world impact on sleep architecture remains poorly characterised. Consumer wearables offer a way to examine these associations under free-living conditions, providing detailed insight into behavioural and physiological responses to nocturnal blood glucose fluctuations. This study aims to assess how wearable-derived sleep metrics and physiological features could be used as indicators of NH, including the effects of how low blood glucose levels fall during hypoglycaemic events and the associated pre-event changes. MethodsWe conducted a comparative observational analysis of paired continuous glucose monitoring (CGM) and Garmin smartwatch data collected over 12 weeks from 17 adults with T1D. Nights were categorised as normoglycaemia, hyperglycaemia, or hypoglycaemia Level 1 ([&ge;]3.1 and <3.9 mmol/L), and hypoglycaemia Level 2 (<3.0 mmol/L). Thirteen sleep metrics, including total sleep time, wake after sleep onset (WASO), sleep-stage proportions, fragmentation indices, and physiological features such as heart rate, were compared using non-parametric tests. Pre-hypoglycaemic event analyses examined 60-minute and 15-minute windows preceding hypoglycaemia to identify early deviations in sleep and physiological metrics. ResultsAcross 573 nights, 17.5% involved Level 1 and 7.3% Level 2 hypoglycaemia. Level 2 hypoglycaemia was associated with 31 minutes less wakefulness, 17-25 minutes more REM, and up to 74% more deep sleep compared with normo-glycaemic nights. Sleep efficiency increased during hypoglycaemic events despite greater fragmentation. Pre-hypoglycaemic episode analyses revealed shorter awake and light-sleep bouts, as well as a 9.8% higher heart rate, preceding Level 2 episodes. ConclusionsWearable-derived sleep and physiological signals reveal clear intraindividual changes both before and during NH. Our findings indicate that Level 2 episodes are associated with deeper sleep and reduced behavioural arousal, suggesting that CGM alarms may be less effective at waking individuals during level2 NH. By characterising pre-hypoglycaemic changes that differ based on hypoglycaemia level, this work provides preliminary evidence for personalised, wearable-based early-warning systems. Such approaches could help distinguish nocturnal hypoglycaemic events and support more effective alerting, particularly in settings with limited or no access to CGM. Author SummaryO_ST_ABSWhy was this study done?C_ST_ABSPeople with Type 1 Diabetes (T1D) frequently experience nocturnal hypoglycaemia (low blood glucose at night), a dangerous event that often goes unnoticed because individuals are less able to recognise symptoms or wake up during sleep. These events also disrupt sleep in ways that are not well characterised under real-world conditions. Limited access to continuous glucose monitoring (CGM), especially in low- and middle-income countries, highlights the need for affordable alternatives to ensure nighttime safety. What did we do and find?Using more than 500 nights of paired smartwatch and CGM data, we investigated how sleep features change when blood glucose levels fall overnight. We found that hypoglycaemic nights show distinct alterations in sleep architecture, including increased REM and deep sleep, and greater micro-fragmentation. A key finding was that Level 2 hypoglycaemia was associated with deeper sleep and reduced wakefulness. This pattern indicates that individuals may be less likely to awaken during more severe events, even when alarms are present. Pre-hypoglycaemic episode analysis revealed additional early-warning signals, such as shorter awake and light-sleep bouts and elevated heart rate, before level 2 hypoglycaemia occurred. What do these findings mean?Smartwatches can capture sleep-based changes that appear before and during nocturnal hypoglycaemia. Because deeper sleep during Level 2 episodes may reduce responsiveness to CGM alerts, these results suggest that current alarm approaches could be improved by incorporating sleep features alongside glucose data. Such sleep-informed detection may enhance the reliability of hypoglycaemia alerts, reduce missed events during deep sleep, and provide a foundation for low-cost early-warning systems in settings where CGM is unavailable or unaffordable. Further research is needed in larger and more diverse populations, but this work provides early evidence that wearable-derived sleep features can meaningfully strengthen nocturnal hypoglycaemia detection.

Nocturnal glucose regulation is modulated by autonomic and circadian mechanisms, yet their dynamic interplay in apparently healthy, free-living populations remains poorly studied. Here, we assessed 227,860 nights of concurrent sleep data from Ultrahuman AIR ring and M1 continuous glucose monitoring (CGM) system across 5849 adults globally to examine nocturnal cardio-metabolic coupling. We found that higher sleep consistency was inversely associated with glucose variability, and vice versa. Unsupervised clustering of metrics characterizing nightly sleep quality and demographic factors revealed phenotypes corresponding to better vs poorer metabolic management. Clustering on aggregated sleep scores differentiated users on metabolic metrics with larger effect sizes, rather than on base sleep metrics. A subgroup analysis of sleep sessions in the upper and lower quartiles of the sleep-metabolic spectrum, revealed an asymmetric coupling between metabolic and sleep factors in determining phenotype. Nights corresponding to poorer sleep-metabolic management displayed greater shape similarity between nightly heart rate (HR) and glucose curves, compared to sleep sessions with better sleep-metabolic management. These findings demonstrate that multi-sensor digital phenotyping can improve the profiling of sleep and metabolic alignment in largely healthy adults, with simple sleep/wake regularity emerging as a behaviorally tractable determinant of cardio-metabolic homeostasis.

Actigraphy is a popular behavioral sleep assessment tool in research and clinical practice. Hierarchical hand-scoring approaches remain the standard for actigraphy rest interval estimation, but can be impractical for large cohort studies and suffer from reproducibility problems. We developed a semi-automated pipeline (actiSleep) to set rest intervals consistent with best-practice hand-scoring algorithms incorporating event marker, diary, light, and activity data. To evaluate actiSleep performance, we used data from an observational study of 51 adolescents (14-19yr), with and without family history of bipolar disorder. Participants completed 2 weeks of wrist actigraphy and daily sleep diary. We first hand-scored records using a standardized hierarchical algorithm incorporating event marker, diary, light, and activity data. We then compared the hand-scored rest intervals to those from actiSleep and two automated activity-based algorithms (Activity-Merged, Activity-Only). Activity-Only used activity-based sleep estimation and Activity-Merged joined closely adjacent rest intervals. For rest onset, rest offset, and rest duration, all algorithms had strong mean agreement with hand-scoring: actiSleep estimates were within 1-3 minutes, Activity-Merged within 2-4 minutes, and Activity-Only within 7-14 minutes. However, actiSleep had notably better (narrower) margins of agreement with hand-scoring, as evidenced by Bland-Altman plots, and greater positive predictive value and true positive rates for rest detection, especially in the 60 minutes surrounding the onset and offset of the rest interval. The actiSleep algorithm successfully estimates actigraphy rest intervals comparable to hand-scoring while avoiding pitfalls of activity-only algorithms. actiSleep has potential to replace hand-scoring for research in adolescents but requires further testing and validation in other samples.

BackgroundSleep disturbance is a core feature of bipolar disorder (BD) and often precedes mood recurrence, particularly in youth. Although actigraphy provides objective sleep measurement, it is limited by cost and monitoring duration. Passive smartphone-based mobile sensing offers a scalable alternative, but its validity in youths with BD is unclear. MethodsAnalyses included adolescents and young adults (ages 14-25) with BD-I/II from the PROMPT-BD study with at least four days of concurrent actigraphy and mobile sensing. Actigraphy-derived sleep metrics (total sleep time [TST], sleep onset, sleep offset, midsleep, wake after sleep onset [WASO]) were compared with smartphone-derived proxies (total offline time [TOT], onset, offset, midsleep, phone use after sleep onset [PASO]). Agreement was evaluated using root mean squared error (RMSE) and mixed-effects models. Zero-inflated negative binomial models examined associations between WASO and PASO. Sensitivity analyses tested robustness to missing data, smartphone use patterns, sleep window definitions, operating system, presence vs. absence of mood symptoms and anxiety, and weekend effects. ResultsMobile sensing showed strong convergence with actigraphy for sleep timing and duration (standardized {beta} = 0.54-0.75, all p < .0001). RMSEs were <21 minutes for onset, offset, midsleep, and TST, with strongest agreement for midsleep (RMSE = 14.8 minutes). Mobile sensing slightly overestimated sleep duration and estimated earlier timing. PASO underestimated WASO (RMSE = 48.8 minutes), but greater WASO significantly increased the odds of detecting any PASO (OR per 15 minutes = 1.35, p < .0001). Findings were robust across sensitivity analyses. ConclusionsPassive smartphone-derived sleep metrics approximated actigraphy-based estimates of sleep timing and duration in youth with BD. Given the widespread availability of smartphones in this population, this supports their potential as scalable tools for monitoring circadian disruption and informing early intervention.

BackgroundObstructive sleep apnea (OSA), as measured by the Apnea Hypopnea Index (AHI), is associated with adverse outcomes. Measures that characterize the temporal variability in events may provide information over and beyond a simple summary of event frequency as measured by the AHI. Research QuestionTo assess whether temporal variability in the occurrence of obstructive apnea/hypopneas during the night is associated with all-cause mortality or incident cardiovascular disease (CVD). Study Design and MethodsData from the Sleep Heart Health Study (SHHS), a prospective multi-site community-based cohort were analyzed. For each person, the intervals between apnea/hypopnea events (inter-event interval; IEI) were used to calculate a coefficient of variation for their IEIs (IEI_CV). Risk for mortality (n=5,701) and incident CVD (n=4,373) were estimated by adjusted Cox proportional hazard models. Sensitivity analyses were conducted to test potential explanatory variables such as hypoxic burden and duration of uninterrupted sleep. ResultsIn 11.8 years of follow-up (median, IQR 10.6-12.2), 1,287 deaths occurred. After adjusting for potential confounders, including OSA severity, participants in the lowest quartile of IEI_CV (Q1) had a 40% higher risk of all-cause mortality compared with those in the highest quartile (Q4) (hazard ratio [HR] = 1.40; 95% confidence interval [CI], 1.20-1.64). In 11.5 years of follow-up (IQR 7.9-12.7), 867 CVD events occurred. The adjusted hazard rate for CVD was 29% higher (HR=1.29 [1.06-1.56]) for those with less variable IEI. Minimal reductions in effects sizes were observed after additional adjustment for hypoxic burden and additional novel and traditional covariates. In sensitivity analyses, adjusting for the longest bout of uninterrupted sleep without respiratory events attenuated the association for CVD incidence (HR=1.15 [0.89-1.50]). InterpretationThe temporal distribution of respiratory events - specifically, less variability in inter-event intervals (more regular event occurrences) - is associated with higher mortality and incident CVD.

Isolated/idiopathic rapid-eye-movement (REM)-Sleep Behavior Disorder (iRBD) is characterized by dream enactment behaviors associated with loss of REM atonia. iRBD is in most cases a prodromal synucleinopathy, and emerging evidence suggests associations between RBD and other neurological and psychiatric conditions. In this study, we performed pathway-based polygenic risk score (PRS) and rare variant burden analyses to examine these potential associations. Pathway-specific PRS were constructed from genome-wide association study summary statistics of five neurodegenerative and seven psychiatric traits across 10 biologically relevant pathway categories, including a total of 279 pathways, in 1,573 iRBD cases and 16,022 controls from the International RBD Study Group and UK Biobank. Rare variant burden tests were performed in 1,264 iRBD cases and 2,581 controls. We identified multiple potential pathways indicating shared polygenic risk between RBD and both neurodegenerative and psychiatric disorders. Lewy body diseases and post-traumatic stress disorder had the most shared polygenic risk pathways in neurological and psychiatric disorders, respectively. Two pathways, the serotonin transport pathway and the chaperone-mediated autophagy pathway, showed the strongest association with iRBD, and gene-based rare variants analyses revealed five genes associated with iRBD: GBA1, PLEKHM1, LRP2, P2RX1, and HAP1. Subsequent analysis of these genes in Parkinsons disease and dementia with Lewy bodies replicated several associations. Together, these findings provide novel insights into the shared genetic architecture underlying iRBD, neurodegenerative disorders, and psychiatric traits, with implications for early identification and mechanistic understanding.

Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal state for Lewy body disorders, with the highest likelihood of long-term conversion to a clinical diagnosis of either Parkinsons disease (PD) or dementia with Lewy bodies (DLB). There is heterogeneity in the neuropathophysiology of iRBD that may have prognostic significance regarding the ultimate clinical features, and previous research has not focused on iRBD biologically defined as having neuronal synuclein disease (NSD) present. Parkinsons Progression Markers Initiative (PPMI) is a longitudinal, observational, multi-center natural history study. PPMI participants with recently-diagnosed, polysomnogram-confirmed iRBD and who were cerebrospinal fluid neuronal -synuclein seed amplification assay positive without a clinical diagnosis of PD or DLB, were examined for the clinical characteristics of prodromal PD and DLB, including mild cognitive impairment (MCI), subthreshold parkinsonism, and a range of neuropsychiatric, autonomic and sensory symptoms, and compared with a group of internal, robust healthy controls (HCs). iRBD participants (N=197) performed worse cognitively than the HC group (N=136), including on a cognitive summary score (p<0.0003, effect size = 0.41). In addition, the iRBD group was more likely to have subthreshold parkinsonism (odds ratio = 24.5, p<0.0001), neuropsychiatric symptoms (odds ratio = 3.5, p<0.0001), autonomic symptoms (odds ratio = 7.2, p<0.0001) and sensory symptoms (odds ratio = 13.2, p<0.0001) compared with the HCs. In the iRBD group, the most common symptoms or features were hyposmia (75%), pain (54%), urinary problems (52%), constipation (49%), lightheadedness (40%) and anxiety (36%). In contrast, rates of mild cognitive impairment (MCI; 32%), subthreshold parkinsonism (27%) and psychosis (7%) were lower. iRBD participants with an abnormal dopamine transporter SPECT scan (DaTscan) had higher anxiety scores and more frequent antidepressant use than those with a normal DaTscan. Only 10% of iRBD participants met diagnostic criteria for prodromal DLB criteria due to the requirement for MCI as a defining feature. However, treating MCI as just one of five possible clinical domains, multi-domain impairment in wide-ranging combinations affected the majority of iRBD participants. In summary, persons with iRBD and positive CSF neuronal -synuclein testing, but without a clinically-diagnosed neurodegenerative disorder, have cognitive deficits of moderate effect size, and also have elevated rates of subthreshold parkinsonism and symptoms across neuropsychiatric, autonomic, and sensory domains, compared with healthy controls. These findings highlight the importance of assessing multiple clinical domains and symptoms in early biologically-defined synuclein disease without a formal neurodegenerative disease diagnosis, and not anchoring diagnostic criteria solely to motor symptoms or cognitive impairment.

IntroductionSleep spindles are electroencephalographic elements characteristic of non-rapid eye movement sleep generated by thalamo-cortical interactions. Spindles have been linked to some of the cognitive benefits afforded by sleep and high spindle activity is associated with increased arousal threshold (deeper sleep). Here, we demonstrate that targeting the thalamus with Transcranial Electrical Stimulation with Temporal Interference (TES-TI) can enhance spindle activity. Methods24 participants (25.5 {+/-} 9.5 years; 69.6% F) underwent thalamic TES-TI stimulation during daytime naps. Three stimulation protocols were tested during stage 2 of non-rapid eye movement sleep (N2): fixed difference frequency of 10 Hz (TES15kHz-TI10Hz), difference frequency matched to individual spindle peak (TES15kHz-TIPeak), and carrier frequency only (TES15kHz). Spectral power in the spindle (sigma) band and integrated spindle activity (ISA) were compared before and during the stimulation, and across stimulation protocols. ResultsTES15kHz-TI10Hz stimulation was associated with a significant increase in sigma band power ({Delta}[x]STIM-PRE = 0.49 log10{micro}V2, p = 0.021) and ISA ({Delta}[x]STIM-PRE = 7.48 {micro}V/s, p = 0.042). Cluster-based analysis localized the increase in sigma power over the frontal and centro-parietal areas (p = 0.022). Linear mixed effects models showed that both sigma band power and ISA during stimulation increased significantly in TES15kHz-TI10Hz compared to the TES15kHz protocol ({beta} = 0.67 log10{micro}V2, p = 0.018; {beta} = 14.70 {micro}V/s, p = 0.0077), while the TES15kHz-TIPeak did not show the same effect. ConclusionsThis study provides evidence supporting the successful use of TES-TI targeting the thalamus to enhance sleep spindle activity. Stimulation at a fixed difference frequency of 10 Hz increased sigma band power and ISA, whereas neither stimulation matched to individual sigma band peak nor TES alone produced comparable effects. These promising results warrant further investigations into the cognitive and clinical impact of TES-TI, a non-invasive neuromodulation tool that can reach deep brain regions. Statement of significanceThis study provides evidence that thalamo-cortical networks, which are central to many physiological and pathological brain activities, can be modulated non-invasively in humans. More specifically, the findings show that transcranial electrical stimulation with temporal interference targeting the thalamus can selectively enhance sleep spindle activity. This work introduces a new strategy for precisely targeting sleep-generating mechanisms regulated by deep brain circuits without surgery or medication. Key next steps include determining how this increase in spindle activity can positively impact cognition and assessing the translational potential of this approach for clinical populations.

IntroductionObstructive sleep apnea (OSA) is characterized by recurrent upper airway obstruction during sleep, leading to intermittent hypoxia, sleep fragmentation, and autonomic dysregulation. These disturbances contribute to nocturnal blood pressure (BP) surges and increased cardiovascular risk. While sleep-stage-dependent BP modulation is well established, high-resolution data on sleep-stage- specific systolic BP variability (BPV) in OSA are limited. This study examined beat-to-beat systolic BPV during N2 and rapid eye movement (REM) sleep and its relationship with sleep fragmentation indices in patients with moderate-to-severe OSA. MethodsClinically suspected OSA patients aged 18-65 years underwent overnight level I video polysomnography. Patients with apnea-hypopnea index (AHI) <15 events/h, central sleep apnea >35%, beta-blocker use, or excessive artifacts were excluded. Continuous systolic BP was estimated using a validated pulse transit time (PTT) method calibrated against cuff BP. Artifact-free 20-minute continuous segments were extracted separately from N2 and REM sleep. Sleep fragmentation metrics included number and duration of respiratory events, arousals, and area under the curve of oxygen saturation (AUC SpO2). BPV indices included mean systolic BP, standard deviation (SD), and coefficient of variation (COV). ResultsSixteen patients contributed 16 N2 and 16 REM segments. N2 sleep showed a higher number of respiratory events (p = 0.005) and arousals (p = 0.01) than REM sleep, while event duration and AUC SpO2 were comparable. Mean systolic BP was 126 {+/-} 12.5 mmHg during N2 and 130 {+/-} 14.9 mmHg during REM, with REM significantly higher than N2 (mean difference -3.62 mmHg; p = 0.01). BP variability was highest during REM (SD 7.12 [4.91-9.25] mmHg; COV 5.95 [3.89-6.84%), intermediate during N2 (SD 5.25 [4.02-6.75] mmHg; COV 4.42 [3.17-4.83%), and lowest during wake (p < 0.001). In N2 sleep, arousal duration predicted mean systolic BP (R{superscript 2} = 0.48, p = 0.0025), while AUC SpO2 strongly predicted SD and COV (R{superscript 2} = 0.74-0.79, p < 0.0001). REM-stage correlations were weaker and not predictive. ConclusionSystolic BP variability in OSA is strongly sleep-stage dependent, with REM sleep exhibiting exaggerated BP instability despite fewer respiratory events. Stage specific mechanisms linking arousals and hypoxia to BP regulation may underlie cardiovascular vulnerability in OSA.