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.

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.

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.

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.

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.

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.

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.

Olfactory decline is a well-established aspect of Parkinsons disease (PD) and is considered one of its earliest signs, often preceding motor symptoms by years to decades. However, because olfactory impairment is also common in healthy aging and other medical conditions, current olfactory tests that score performance (odor detection, discrimination, and identification) lack disease specificity. In contrast to performance scores, olfactory perceptual fingerprints are derived from odor ratings and sniffing behavior, and provide a stable measure of how the world smells to an individual. To test the hypothesis that olfactory perceptual fingerprints may provide a disease-specific marker, we obtained them in three cohorts: Individuals with PD (n=33), healthy age-matched controls (n=33), and critically, in participants with non-PD olfactory dysfunction (n=28). Consistent with previous results, a standard clinical olfactory test detected impairment in both PD and non-PD olfactory dysfunction, but failed to distinguish between these two groups. In contrast, olfactory perceptual fingerprints detected impairment, and distinguished PD from non-PD olfactory dysfunction at 88% accuracy (SVM classification, leave-one-out cross validation, 90% sensitivity, 85% specificity, P=3.2x10-4), or 94% accuracy after matching age and sex (SVM classification, leave-one-out cross-validation, 100% sensitivity, 88% specificity, P=0.0047). The difference between PD related and unrelated olfactory decline was particularly evident in sniffing behavior: Whereas both healthy participants and non-PD olfactory decline groups decreased sniff duration in response to unpleasant odors (-12.5% and -11.36% respectively), individuals with PD paradoxically increased sniff duration (+1.69%; P=4.5x10-5). Thus, PD was marked not by loss of olfactory performance, but by a distinct shift in olfactory perception. These findings imply that olfactory perceptual fingerprints provide for a disease-specific marker in PD.

Sleep regularity is an important but under-measured dimension of sleep health. Objective indices from actigraphy or wearables are robust but resource-intensive. The Sleep Regularity Questionnaire (SRQ) offers a brief subjective tool, but its validity against objective and diary-based indices in healthy adults is unclear. In Part 1, 31 adults wore a smart ring continuously for 21 nights. Device-derived regularity metrics included the Sleep Regularity Index (SRI), interdaily stability (IS), social jetlag (SJL), composite phase deviation (CPD), and the standard deviation of sleep onset and wake time. In Part 2, 52 adults completed a one-week sleep diary, from which variability in sleep timing, total sleep time (TST), SJL and nightly perceived sleep quality were derived. All participants completed the SRQ and Brief Pittsburgh Sleep Quality Index (B-PSQI). In Part 1, associations between SRQ scores and device-derived SRI, IS, SJL, CPD and timing variability were small (absolute r [&le;] 0.36). Higher SRQ Global and Sleep Continuity scores were moderately associated with better B-PSQI global scores (r -0.37 to -0.44). In Part 2, SRQ Global and Circadian Regularity showed small-to-moderate associations with higher diary-rated sleep quality and lower bedtime variability (r {approx} 0.40 and -0.32 to -0.34), while correlations with other diary metrics and B-PSQI were weak (absolute r [&le;] 0.25). The SRQ shows modest convergent validity with diary-based timing variability and perceived sleep quality, but only weak correspondence with smart ring-based sleep regularity indices. It is likely to complement, rather than replace, objective monitoring in healthy adults with relatively regular sleep-wake patterns.

BackgroundAs sleep data from wearable devices are increasingly available in health research, there are new opportunities to understand sleep regulation behaviors as modifiable risk factors for disease. At such a large scale (tens of thousands of people over millions of day-level observations), prioritizing and interpreting sleep behaviors is challenging while maintaining biological relevance and modifiability. In this work, we aim to address this challenge by proposing a framework to interpret Fitbit data through a well-known neurobiological framing of sleep regulation, the two-process model. MethodsWe use data from the All of Us Research Program, a national biobank with passively collected Fitbit data for 32,292 people across 15,754,893 total days. We map Fitbit behaviors (b) to either circadian (C) or homeostatic (S) processes. Using iterative exploratory factor analysis to obtain weights, the Fitbit Cb and Sb are then weighted at the level of each day to create Cb and Sb scores. FindingsCb and Sb scores were found to align with expected real-world relationships with age, seasonality, shift work, and napping. Cb and Sb scores were interpreted with relation to depression, where it was found that Sb scores are highly associated with likelihood of diagnosis (OR = 1.5, p < 2e-16) while Cb and Sb scores are equally associated with severity (Sb score {beta} = 0.2, Cb score {beta} = 0.21, p < 2e-16). InterpretationCb and Sb scores support longitudinal interpretation (e.g., changes in Sb around treatment), aggregation (e.g., differences in Cb between two groups), and actionable modification (e.g., reduce naps to improve poor Sb). Overall, our behavior scores allow for interpretation of wearables sleep data and can be utilized across many disease contexts to better understand how sleep influences health. FundingThis work was supported by NIH training grant T32GM145734 and NIH R21HL172038.

BackgroundEmerging research indicates that light exposure may influence sleep quality. Identifying key light-exposure behaviours associated with poor sleep quality in athletes may allow practitioners to efficiently screen for sleep difficulties and prioritise athletes for further assessment. Translating these findings into a practical screening tool could enhance willingness of high-performance professionals to monitor sleep and light exposure in athletes. HypothesisKey predictor variables identified by feature reduction techniques will lead to higher predictive accuracy in determining which light behaviours are associated with poor sleep quality in athletes. Study DesignCross-sectional study. Level of EvidenceLevel 3. Methods121 athletes from varying competitive levels completed questionnaires, including the Light Exposure Behaviour Assessment (LEBA) and Pittsburgh Sleep Quality Index (PSQI). Poor sleep quality was defined using the PSQI cut-off >5. Least absolute shrinkage and selection operator (LASSO) regression identified light exposure variables from the LEBA questionnaire most strongly associated with good and poor sleep quality in athletes. Three models were compared: a full-variable model (23 items), a factor-specific model (Factor 3: screen/device use), and a feature-reduced model (LASSO-selected items). ResultsPhone use before bed, checking phone/watch during the night, were identified as variables of greatest association with poor sleep quality and used for reduced feature set modelling. On an independent test set, the feature-reduced model achieved area under the curve (AUC) 0.83, sensitivity 0.70, and specificity 0.92. ConclusionsOur findings report that phone-related behaviours before and in bed are associated with a higher likelihood of poor sleep quality. These behaviours, combined with the developed nomogram, provide a preliminary, low-burden screening tool to identify athletes who may be experiencing sleep difficulties. The high specificity indicates that athletes flagged by the tool are likely to have genuine poor sleep quality, warranting further assessment to identify underlying causes and appropriate interventions. Clinical RelevanceEducation and interventions focused on light exposure factors were identified as most influencing sleep quality in a multifaceted athletic population and could be prioritised to optimise sleep quality. The developed sleep quality nomogram may be useful as a decision-making tool to improve sleep monitoring practice among practitioners.

PurposeTo develop and validate a prediction model for sleep apnea syndrome (SAS) treated with continuous positive airway pressure (CPAP) in the general population. MethodsUsing claims and health checkup data held by JMDC Inc., linked to personal health records (Pep Up), we developed and internally validated a prediction model for SAS treated with CPAP, defined as a diagnosis of SAS and reimbursement records of CPAP. Every three months from January 1, 2022 to July 1, 2024 (i.e., 11 timepoints), we identified eligible individuals with available data both 1 year before and 1 year after that timepoint to define the presence/absence of SAS treated with CPAP, as well as 279 predictor variables. We developed a LightGBM model for the training and tuning datasets and evaluated its performance on the validation dataset. ResultsAmong 18,692,873 observations (mean age 44.8{+/-}11.3 years, women 37.5%) obtained from 1,858,566 people, 300,868 (1.6%) had SAS treated with CPAP. The area under the receiver operating characteristic curve was 0.898 (95% confidence interval 0.895-0.901). The positive predictive values among people with the top 1% and 10% prediction scores were 28.3% and 10.3%, respectively. According to the SHapley Additive exPlanations plot, male sex was the most important predictor, followed by age, body mass index, and waist circumference. We also demonstrated that personal health records significantly improved the predictive performance. ConclusionWe developed a prediction model to identify people at high risk of SAS and encourage them to undergo polysomnography or related tests.

We describe the design of the first non-pharmacological prevention trials of Parkinsons Disease worldwide: the randomised controlled Slow-SPEED trials. The three trials examine the feasibility and preliminary efficacy of a gamified, remotely administered exercise intervention vs. active control program over 18-36 months in the Netherlands (n=110), United Kingdom (n=110) and United States (n=600). Each trial focuses on a complementary prodromal subgroup: isolated/idiopathic REM sleep behavioural disorder, hyposmia, or LRRK2/GBA1 mutation carriers. These trials will provide unique insights for large-scale Parkinsons Disease prevention studies.

Gait impairment (GI) and freezing of gait (FOG) affect 80% of patients with advanced Parkinsons disease. Continuous deep brain stimulation (cDBS) provides limited adaptability to address the episodic nature of FOG due to fixed parameters. Neural biomarkers for adaptive DBS are limited by signal artifacts and poor FOG classification. Wearable inertial measurement units (IMUs) offer a promising alternative by directly measuring signatures of GI&FOG. We developed Kinematic adaptive DBS (KaDBS), the first intelligent system to dynamically modulate stimulation in response to real-time gait metrics. KaDBS integrates bilateral shank-mounted IMUs with an investigational neurostimulator through a wireless architecture enabling step-detection, arrhythmicity calculation, and probabilistic FOG classification. Two control algorithms were implemented: an arrhythmicity model based on stride variability, and a P(FOG) classifier implementing tri-state control based on stepwise freezing probabilities. In the largest KaDBS cohort to date (n=8), we compared OFF, cDBS, KaDBS, and intermittent DBS during harnessed stepping and free walking. KaDBS was safe and well tolerated with no serious adverse events; symptom-free reports were 87.5% and 71.4% for arrhythmicity and P(FOG) models respectively, compared to 50.0% for cDBS. All symptoms were mild, transient, and resolved without intervention. KaDBS significantly reduced percent time freezing versus OFF during stepping-in-place (35.8%, P= 4.80 x 10-3) and free walking (33.4%, p = 9.00 x 10-). Therapeutic effects concentrated in baseline freezers: two participants with 100% time freezing during OFF achieved complete resolution with KaDBS, while non-freezers maintained stable gait. These findings establish KaDBS as a safe, effective approach to personalized neuromodulation for PD.

ObjectiveWe developed and validated a detection-guided artifact removal framework for clinical electroencephalography (EEG). It corrects only the contaminated segments and preserves artifact-free data. ApproachThe framework employs convolutional neural network (CNN) detectors trained on the Temple University Hospital (TUH) Artifact Corpus of 150 recordings from 105 patients. For eye movement artifacts (20 second windows), it uses independent component analysis (ICA) and canonical correlation analysis (CCA). For muscle artifacts (5 second windows), it employs wavelet thresholding and empirical mode decomposition (EMD). For non-physiological artifacts (1 second windows), it utilizes spherical spline interpolation and artifact subspace reconstruction (ASR). Removal is applied exclusively to detector-flagged windows, and unflagged windows remain unchanged. In a held-out test set of 21 patients and 30 recordings, we compared selective and global removal using correlation, root mean squared error (RMSE), and peak signal-to-noise ratio (PSNR). Main resultsSelective removal outperformed global removal in all six methods and 18 metric comparisons, with a p-value of less than 10-105. Selective processing maintained a clean-segment correlation above 0.987, whereas global removal reduced the correlation to values as low as 0.39 for CCA and 0.47 for ASR. CCA removed 74.6% of the eye movement artifact amplitude, EMD removed 99.8% of the high-frequency (30-40 Hz) muscle contamination, and ASR reduced the non-physiological artifact amplitude by 37.1%. The preservation of artifact-free windows remained high for all methods and indicated minimal distortion of the clean EEG. SignificanceDetection-guided selective removal addresses a significant limitation of global correction pipelines that can remove clean EEG signals. This framework automates artifact removal without manual review and preserves the signal fidelity for clinical interpretation. Its modular design facilitates integration into real-time monitoring systems for acute and perioperative care.

This study compared the effects of a 25-min nap opportunity and a 10-min non-sleep deep rest (NSDR) condition on perceptual, cognitive, and physical performance in physically active young adults. Sixty participants (26 female, 34 male; 22 {+/-} 4 years) were randomly assigned to one of three groups (nap, NSDR, control; n = 20 each). All groups completed identical assessments immediately, 20 min, and 40 min post-intervention. Mixed-effects models, adjusted for sex, prior-night sleep, and weekly physical activity, revealed a significant Group x Time interaction for sleepiness, fatigue, readiness to perform, and handgrip strength (p < 0.05). At 40 min post-intervention, the nap group reported lower fatigue than control and higher readiness to perform than both control and NSDR (p < 0.05). No significant effects were observed for the NSDR condition on perceptual, cognitive, or physical outcomes (p > 0.05). These findings indicate that a short nap can enhance perceived readiness and reduce fatigue after a brief latency period, whereas NSDR did not elicit significant effects under the present conditions.