Journal of Biological Rhythms

The circadian system evolved under natural light-dark cycles, while modern humans spend much of their time indoors under electric lighting that differs substantially from daylight in intensity, spectral composition, and temporal structure. How such lighting environments influence circadian system has not been systematically examined in a diurnal animal model under ecologically relevant conditions. In this study, we used the diurnal Nile grass rat (Arvicanthis niloticus) to assess daily locomotor rhythms across four lighting conditions designed to approximate common human exposure scenarios: rectangular daylight (D65-R; [~]5,600 lux), semi-sigmoidal daylight mimicking natural intensity dynamics (D65-S; matched peak intensity with [~]50% lower cumulative energy), fluorescent indoor light (F12; [~]150 lux), and fluorescent light supplemented with a one-hour midday daylight pulse (F12+D65-P). Using a within-subject design (n = 8), male grass rats were housed under each condition for two weeks. D65-R produced the highest daytime activity levels and the strongest day/night activity ratio, consistent with robust circadian entrainment. Despite matching peak intensity, D65-S did not yield comparable circadian outcomes, indicating that cumulative photon exposure, rather than peak intensity alone, contributes to entrainment strength. Notably, the addition of a one-hour midday daylight pulse (D65-P) partially increased circadian amplitude under otherwise fluorescent conditions, with higher periodogram amplitude relative to F12 alone. A separate cohort of males was exposed to D65-R or F12 for six weeks (n = 10/condition) to assess physiological outcomes, including metabolic and reproductive measures. Compared with the D65-R group, F12 group showed higher diabetic rate (10% vs. 40%) and reduced sperm mobility (45{+/-}6 vs. 19{+/-}1 %), consistent with potential downstream correlates of circadian rhythm disruption. Together, these findings demonstrate that lighting conditions characteristic of indoor environments produce weaker circadian organization than daylight-equivalent lighting in a diurnal rodent, which underscore the importance of light quality in shaping circadian rhythms and downstream physiological processes.

Circadian rhythms are crucial to biological functions, and cognitive functions such as attention, choice, and preference-related behaviors are modulated by circadian rhythms and disrupted in mood disorders such as Seasonal Affective Disorder (SAD) and Major Depressive Disorder (MDD). These neuropsychiatric diseases can be induced or worsened by alterations to daily light patterns and can also be treated with circadian-timed bright-light therapy, suggesting modulatory effects of light brightness on mood and behavior. While most laboratory rodents are nocturnal, the Nile grass rat (Arvicanthis niloticus) is diurnal, offering a unique model to study light modulation effects relevant to humans. In this work, we track daily activity in male and female grass rats under varied lighting for several weeks, revealing sex-specific circadian patterns and responses. These findings establish a foundation for mechanistic studies of light effects on mood-related brain circuits in diurnal animals.

The circadian clock system drives rhythms in gene expression, tailoring an organisms behavior and physiology to the [~]24-hour day-night environmental cycle. The mechanisms underlying this system are believed to be largely the same between adult males and females, but recent findings are starting to challenge this notion. Menstrual/estrous cycles (e-cycles) in females are known to modulate a variety of circadian-controlled behaviors. However, their interaction with circadian rhythmicity at the transcriptional level remains unknown. To assess the interaction between e-cycles and the circadian clock, we explored densely collected mouse liver circadian transcriptomes across all four phases of the e-cycle. Surprisingly, we found that the circadian rhythmicity in female livers was strikingly dependent on e-cycle phase, with the largest differences aligning with pre- and post-ovulation. The differential rhythmicity followed prominent yet distinct patterns, which extend and diversify overall sex differences in rhythmic gene expression. Our data also predict that sex and e-cycle may modulate how core circadian transcription factors may regulate expression of some output genes, but other mechanisms appear complex and potentially multifaceted. Nonetheless, the differences in rhythmicity impact broad aspects of liver function, making this panoramic dataset a novel resource for identifying and exploring novel interactions of the estrous cycle on gene expression and overall liver functions.

Study Objectives To develop and initially validate the Circadian Disruption Index (CDI), a self-report measure of circadian disruption, and obtain preliminary evidence of its responsiveness to circadian health education. Methods In Study 1, 244 participants completed a 22-item CDI version and external measures. The sample was randomly divided for exploratory and confirmatory factor analyses. Internal consistency, external associations, and discrimination of poor sleep quality were examined. In Study 2, 72 postgraduate students completed the CDI before and 1 week after a 16-hour circadian health education program in an uncontrolled pre-post design. Results Analyses yielded a 15-item, three-factor structure comprising rhythm stability and light exposure, behavioral habits and diet, and sleep quality and subjective complaints. Total-score internal consistency was acceptable (Cronbach's = 0.871). Confirmatory factor analysis showed a comparative fit index of 0.902 and a root mean square error of approximation of 0.072, although the Tucker-Lewis index was 0.882. CDI scores correlated with sleep quality, chronotype, corrected midsleep on free days, depression, and anxiety, but not social jetlag. The area under the curve for poor sleep quality was 0.807 (95% confidence interval, 0.753-0.862), with an exploratory cutoff of [≤] 23. In Study 2, CDI scores decreased from 22.26 to 19.88 (p = 0.002; Cohen's dz = 0.36). Conclusions The CDI demonstrated satisfactory internal consistency, a meaningful multidimensional structure, and responsiveness to short-term changes following circadian health education, supporting its potential utility for assessing circadian disruption and monitoring circadian-related behavioral changes.

Background: Female chronic pelvic pain disorders (CPPDs) are highly prevalent and frequently accompanied by sleep disturbance and autonomic nervous system (ANS) dysregulation. Heart rate variability (HRV), a non-invasive index of ANS function, may provide an objective, physiological correlate of sleep health and can be monitored using wearable devices, enabling a continuous, scalable approach. Objectives: This study examined whether wearable-derived daily HRV metrics are associated with self-reported sleep disturbance in women with CPPD(s) compared with healthy controls, using epoch-level data and generalized additive models. Methods: We conducted a retrospective observational study using up to 90 days of data from a mobile health research app. Participants were 128 women with CPPD(s) and 63 demographically matched healthy controls, who completed a daily PROMIS-based 3-item sleep disturbance questionnaire and wore Fitbit devices that provided 5-minute HRV epochs. Primary predictors were high frequency (HF) and low frequency (LF) power and root mean square of successive differences (RMSSD), with group (CPPD vs control), daily pain severity, and menstrual status as covariates. We fit separate generalized additive mixed models (GAMMs) for each HRV metric with a nonlinear smooth term and an HRV x Group interaction. Results: Higher HF and RMSSD were associated with lower sleep disturbance scores, and these associations were stronger in controls than in the CPPD group (HF x group B {approx} -1.59, p < 0.00010; RMSSD x group B {approx} -0.58, p < 0.0001). LF showed a more complex pattern but also differed by group (B {approx} -0.531, p < 0.0001). HRV smooth terms were highly nonlinear, and models explained ~8-9% of deviance in sleep disturbances. Pain severity and menstrual bleeding were strongly associated with worse sleep. Conclusion: These findings indicate small but consistent associations between wearable-derived HRV metrics and daily sleep disturbances in women with CPPD(s) and healthy controls, with weaker associations in CPPD(s). Integrating continuous HRV with symptom tracking could support low-burden and multimodal monitoring of sleep health in chronic pelvic pain, but prospective validation is needed before HRV can be used for diagnostic or treatment response decision making.

The cortisol awakening response (CAR) marks the transition from rest to wake phase by a sharp increase in cortisol levels upon awakening in the morning. This increase may assist in cognitive and behavioral awakening, but its function is not fully understood yet. In this pilot study we aimed to provide first data on the influence of immune system activity on the CAR. Thereby, a within-subject analysis approach was applied to avoid confounding by between-subject bias and improve inference of the results. Three healthy subjects collected their overnight urine for analysis of neopterin (Th1 immune activation marker) and sampled saliva at 0, 30, and 45 minutes after awakening in the morning for cortisol determination and CAR estimation. Additionally, subjects wore an EEG-headband overnight for objective determination of the awakening timepoint. Random-effects models were computed to estimate the effect of overnight neopterin on the CAR. There was a significant positive effect of overnight neopterin levels on the CAR, indicating that overnight Th1 immune activation may predict the dynamic increase of cortisol in the morning, with higher immune levels leading to a stronger CAR. These results provide first evidence for the immunological awakening hypothesis and a potential role of the CAR in the maintenance of circadian immune rhythms, but given the small number of participants, findings should be interpreted as exploratory.

Sleep is a ubiquitous, yet highly variable, behavior across species. The duration and timing of sleep are influenced by ecological demands and dietary context. In the mosquito Aedes aegypti, a blood-feeding insect with specialized nutritional requirements, the relationship between feeding and sleep remains poorly understood. Here, we investigated how blood-derived dietary protein influences sleep regulation. Using postural analysis, videography, and arousal-threshold assays, we established that immobility bouts of greater than 10 minutes reliably define sleep in Ae. aegypti. Mosquitoes lacking the circadian clock gene cycle still maintained daily sleep rhythms but exhibited reduced sleep duration and heightened overall activity. Infrared activity monitoring revealed that blood-fed females showed a marked increase in sleep beginning immediately after feeding and persisting for several days, accompanied by reduced locomotor activity. Notably, this sleep elevation lasted well beyond the cessation of previously reported host-seeking phases, raising the possibility of distinct phases of opportunistic versus targeted host pursuit. To determine the dietary basis of this effect, we tested mosquitoes fed a bovine serum albumin (BSA)-based diet. BSA feeding alone was sufficient to mimic the sleep-promoting and activity-reducing effects of blood, suggesting dietary protein is a major nutritional regulator. Moreover, RNAi-mediated knockdown of the leucokinin receptor (Lkr), which has previously been associated with fluid homeostasis and feeding behavior, resulted in enhanced sleep and reduced activity, implicating mosquito LK signaling in the modulation of postprandial sleep. Together, these findings demonstrate that blood-derived proteins drive sustained increases in sleep and reductions in locomotor activity in Ae. aegypti. This work positions Ae. aegypti as a model for dissecting nutrient-specific regulation of sleep and highlights potential adaptive functions of protein-induced quiescence, such as energy conservation and predator avoidance. More broadly, it provides insight into how specialized diets shape the neural and behavioral architecture of sleep.

Objectives. To test whether one week of self-administered morning bright light therapy (BLT) improves sleep, daytime sleepiness and alertness, mood, and objective cognition in healthy university students. Methods. Thirty-three healthy students completed a two-week randomized within-subject crossover trial comparing one week of morning BLT (30 min of 10,000 lx; melanopic equivalent daylight illuminance of approximately 8,989 lx) with one week of usual-light control in counterbalanced order, with no washout. Sleep was assessed with wrist-worn Fitbit sleep tracking and daily diaries; daytime sleepiness (Karolinska and Stanford Sleepiness Scales), positive and negative affect (PANAS), mood (POMS), and a cognitive battery (Stroop, Flanker, Corsi, verbal span) were also assessed, alongside post-trial semi-structured interviews. Outcomes were analyzed with linear mixed-effects models, with Holm correction across five primary outcomes. Results. BLT reduced daytime sleepiness in a time-of-day-specific manner (condition x time-of-day interaction; largest reduction at 12:00, dz = -0.58, with a smaller but still significant reduction at 15:00), reduced night-to-night variability in sleep duration (dz = -0.52), increased Fitbit sleep efficiency (dz = 0.81), and increased PANAS positive affect (dz = 0.41). Objective cognition was unchanged across all measures. Interviews indicated that participants experienced BLT primarily as a sleep and alertness intervention, with minor tolerability issues. Conclusions. Brief morning BLT improved alertness, sleep regularity and efficiency, and positive affect, but not objective cognition, in healthy students, supporting morning light as a low-burden strategy for daytime functioning while cautioning against overstating cognitive benefits.

Chronic partial and acute total sleep loss have a distinct impact on sleep architecture. Namely, acute sleep deprivation primarily leads to a strong rebound of slow wave sleep, while chronic sleep restriction results in an increased propensity of REM sleep. The aim of this work was to examine whether these different effects would translate into quantifiable changes in sleep state boundaries and dynamics using a model-based method. Besides conventional sleep stage scoring, we applied an EEG model (state space approach) for dynamic analysis of nocturnal EEG recordings in 14 healthy subjects under experimental chronic sleep restriction (last of 7 nights with 5 hours of time in bed) and after acute sleep deprivation (sleep following 40 hours of wakefulness), in comparison to baseline sleep. Subjects under chronic sleep restriction revealed increased similarities in the frequency composition of REM sleep and wakefulness and thus, a decreased differentiation of state boundaries between the two behavioral states. Contrarily, acute sleep deprivation affected the spectral composition of NREM sleep. Only acute sleep deprivation resulted in more stable slow wave sleep. Our explorative study confirmed that the distinct effects of increased REM sleep and slow wave sleep propensity following acute total and chronic partial sleep loss are reflected in differential changes of behavioral state boundaries and sleep dynamics. This suggests that these sleep structure characteristics are state dependent, which may allow using such measures in the future to track treatment effects in clinical populations characterized by sleep behavioral state dysregulation.

Dietary methionine restriction has been shown to improve metabolic health and treat multiple diseases. Methionine metabolism regulates transmethylation reactions, including N6-methyladenosine (m6A) RNA methylation, by modulating the availability of S-adenosyl methionine (SAM). Both m6A RNA methylation and methionine metabolism are involved in the regulation of the circadian clock. However, it remains unclear whether dietary methionine influences circadian rhythms through the regulation of m6A RNA modification. In this study, we investigated the effects of short-term methionine deprivation on the diurnal oscillations of m6A RNA methylation in the mouse liver. We found that a methionine-deficient (MD) diet reprogrammed the cyclic expression patterns of m6A writers, erasers, and readers. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) revealed that the MD diet induced de novo diurnal m6A oscillations in genes associated with RNA processing, protein translation, protein ubiquitination, and mTORC1 signaling pathways. RNA-seq and quantitative proteomics analyses demonstrated that MD-induced changes in m6A RNA levels were linked to alterations in mRNA and protein abundance. We observed that dynamic m6A RNA methylation of the transcripts encoding two key enzymes, MAT2A and CBS, helps maintain methionine homeostasis in response to methionine starvation. These findings identify m6A RNA methylation as a key mechanism linking methionine metabolism to circadian regulation.

Background: Automatic sleep staging algorithms are increasingly applied in clinical and home-based recordings. However, their performance may degrade when transferred to new montages and clinical populations. This is particularly relevant in reduced-channel portable PSG and in disorders such as REM sleep behaviour disorder (RBD), where altered sleep architecture may challenge pretrained models. Objective: To evaluate and compare multiple open-source sleep staging algorithms on a minimal portable PSG setup in controls and patients with and without RBD, and to assess the impact of fine-tuning on clinic-ascertained data. Methods: Six open-source models were applied to 76 subjects recruited from three clinical sleep medicine sites. Performance was assessed using accuracy, F1 scores, and Cohen's kappa, both overall and per sleep stage. Each model was evaluated out-of-the-box and after fine-tuning on clinical data. Results: Out-of-the-box performance varied substantially across models (Cohen's kappa 0.21-0.54). Fine-tuning consistently improved agreement, with the best-performing model (GSSC) reaching Cohen's kappa = 0.58 indicating moderate to good agreement. Performance was highest in controls and lower in patient groups. N3 was the most reliably classified stage across models, whereas N1 remained consistently challenging. REM classification improved after fine-tuning in several architectures but remained model, and subgroup-dependent, particularly in RBD subjects. Conclusion: Fine-tuning substantially mitigates domain shift, updating model parameters to align with new data distributions, when applying automatic sleep staging algorithms to portable clinical recordings. Model architecture influences robustness, with feature-learning approaches demonstrating greater adaptability than fixed-feature models. Despite moderate agreement after adaptation, performance, especially for REM and N1 remains insufficient for fully automated diagnostic use in clinical populations.

This study aimed to explore night-to-night variability of multiscale sleep patterns by analyzing subcutaneous electroencephalography (sqEEG) from 20 healthy participants over one year (205-388 nights per participant, 6,429 nights in total). We utilized the time series of aperiodic slopes, sigma and slow-wave power as a new whole-night unit of sleep macrostructure. Using dynamic time warping, we calculated the distances (differences) between those time series to assess night-to-night sleep macrostructure dissimilarity. We found that the overall sleep macrostructural patterns were relatively similar across nights (20% dissimilarity), while their temporal alignment was quite variable (time series warped by ~60% for the best alignment). Lower variation in macrostructure dissimilarity was associated with better subjective sleep quality (r=-0.25). Then, we qualitatively compared yearlong variation in macroscale, microscale (sleep stage proportions, mean spectral power) and mesoscale (sleep cycle duration) metrics. We found that intra-individual night-to-night variation was '"low (coefficients of variation < 20%) for spectral power, sleep duration, N2 and REM sleep; ''medium'' (20-40%) - for N3 and macrostructure dissimilarity; and "high" (>40%) - for sleep cycle duration, wake and N1. In summary, different sleep metrics showed differential night-to-night variability, which was more metric-specific than scale-dependent. This might reflect a distinction between more trait-like versus more dynamically varying features of sleep, although this assumption needs further clarification.

Background: Accurate REM identification is critical for diagnosing REM sleep behaviour disorder (RBD), yet many automated sleep staging systems, especially single-channel EEG models trained on healthy cohorts, do not generalise well to real-life polysomnography (PSG) performed in patients. Objective: To compare a feature-based Random Forest (RF) model tuned for RBD with a state-of-the-art single-EEG deep architecture (AttnSleep), and to assess the impact of cohort adaptation and multimodal inputs (EEG, EOG, EMG, ECG). Methods: Experiments used 89 multi-site in-clinic PSGs (SleepWearables Phase-1) plus 53 MASS healthy controls (mean age 63, std 5 years), with 10-fold cross-validation and out-of-fold evaluation. Model performance was assessed using Cohen's kappa, and attention-based modality analysis was performed to quantify signal contributions. Results: When applied out-of-the-box after training on open-source healthy datasets, both models achieved moderate agreement overall (Cohen's kappa = 0.46), but performance declined in RBD, particularly for REM sleep (AttnSleep Cohen's kappa = 0.19 vs RF Cohen's kappa = 0.44), highlighting limited cross-cohort generalisation. The multimodal model improved overall agreement (Cohen's kappa 0.59 - 0.60) and performance in RBD (Cohen's kappa 0.45 - 0.46), with gains most pronounced in REM (Cohen's kappa 0.45 - 0.49). Attention-based modality analysis identified EEG as the dominant signal, increased EOG contribution during REM, and elevated ECG importance during N3. In RBD subjects, EOG weighting increased relative to non-RBD controls (Delta = +0.081). Guided by these weights, a reduced four-channel EEG model matched full multimodal performance in non-RBD subjects, and adding EOG achieved the best overall configuration (Cohen's kappa = 0.61 overall; Cohen's kappa = 0.48 in RBD) with improved REM classification (53% vs 45% recall). Inclusion of EOG also reduced inter-dataset variability in REM staging. Nonetheless, staging performance in RBD remained lower than in controls, particularly for REM. Conclusions: These results highlight the limited generalisability of minimal-sensor models trained on healthy cohorts, the value of mixed cohort-specific training, and the benefit of multimodal integration and attention-guided channel selection, rather than minimal-sensor approaches alone, for robust clinical sleep staging in pathological populations such as RBD.

Modeling the circadian impact of seasonal clock changing requires precise synchronization between solar and social time. This report critiques a recent study that associated disease prevalence in the United States with seasonal clock exposure. We identify a fundamental computational error in which a sign reversal of the longitudinal offset effectively inverted the US East-West axis, cross-correlating local health data with the circadian burden of hypothetical locations on the opposite side of a time zone. We outline the methodology for a correct modelization of the circadian process in the context of US geography.

Young adults frequently report cognitive complaints often attributed to sleep loss alone. However, subjective cognitive functioning is shaped by broader lifestyle and affective factors. Cross-sectional data were analyzed from 530 young adults (mean age 22.1 +/- 2.3 years) to examine the independent, interactive, and cumulative associations of short sleep duration, low physical activity, and psychological distress with everyday cognitive failures. Cognitive failures were strongly associated with sleep duration, physical activity, sleep quality, and distress in univariate analyses. However, hierarchical regression revealed that psychological distress, poor sleep quality, and short sleep duration were the dominant independent correlates of cognitive failures, collectively explaining a substantial proportion of variance in Cognitive Failures Questionnaire scores (R-squared = 0.585, p < 0.001). In contrast, the apparent protective effect of physical activity was not observed after adjustment for sleep and distress (p = 0.976), and no significant sleep-by-physical activity interaction was observed. Further, cumulative risk modeling demonstrated a robust dose-dependent relationship, with cognitive failures increasing progressively as behavioral and psychological risk factors accumulated (p < 0.001). Individuals exposed to all three risk factors exhibited more than double the cognitive failure burden observed in individuals with no risk factors. These results indicate that the cognitive burden in young adults can best be described by an additive increase of behavioral and psychological risk factors as a function of the co-occurrence, rather than by the presence of compensatory effects of lifestyle risk factors. Interventions aimed at preserving cognitive function may therefore benefit from simultaneously targeting sleep health and psychological well-being rather than relying on physical activity alone to offset cognitive burden.

The slow oscillation (SO) of non-rapid eye movement (NREM) sleep has been implicated in the restorative properties of sleep. Slow oscillatory transcranial direct current stimulation (SO-tDCS), involving a positive oscillatory current applied to the scalp at a peak frequency of 0.75 Hz, has been used to enhance SO power during NREM sleep. We examined whether enhancing SO power with SO-tDCS during a restricted nighttime sleep opportunity would accelerate the restorative properties of sleep during an otherwise insufficient sleep period and help sustain performance during subsequent extended wakefulness. A total of twenty-six healthy young adults (ages 18-39, n=16 females) completed a 15-day study. After 7 baseline nights at home and 3 baseline nights in the laboratory, participants entered the laboratory for 5 consecutive days including a baseline day, a 2-hour nighttime sleep period with participants randomized to the SO-tDCS (n=11) or SHAM (n=15) condition, 46 hours of sleep deprivation, and two recovery nights. In the SO-tDCS condition, stimulation was administered for one hour starting exactly 60 minutes after sleep onset, with intervals of five minutes of continuous stimulation followed by one minute of no stimulation. Polysomnographic recordings were conducted during each sleep period. Performance was assessed using the Psychomotor Vigilance Test (PVT) approximately every 75 minutes across baseline, sleep deprivation, and recovery. Prior to the two-hour sleep opportunity, a Paired Words Associate Task was administered. Participants listened to 54-word pairs and were asked to recall 46 of the word pairs, with up to three attempts to successfully recall at least 60% of word pairs (T0). Recall was also assessed 20- (T20) and 120-minutes (T120) after awakening from the two-hour sleep period. Data were analyzed using mixed-effects ANOVA. PVT performance (defined as mean response time and number of response times greater than 1,000 ms) significantly declined across sleep deprivation with performance degradations peaking in the early morning hours. Participants in the STIM condition demonstrated significantly better performance during sleep deprivation relative to the SHAM condition. On the PWAT, participants in the SHAM condition recalled fewer word-pairs upon awakening relative to T0. In sharp contrast, performance of participants in the SO-tDCS condition did not deteriorate at T20 and was actually improved at T120 relative to T0. We conclude that SO-tDCS can robustly accelerate the restorative properties of sleep and can additionally enhance sleep related memory consolidation when sleep opportunity is restricted.

Background: Bidirectional next-day associations between sleep disturbances and affective symptoms have been shown in previous research, yet the consecutive day effects between these factors remains poorly understood. Methods: We analysed longitudinal ecological momentary assessment (EMA) data obtained from a subsample of young persons in the Brazilian High-Risk Cohort (BHRC) study collected in 2020-2021. Participants reported sleep quality each morning and rated affective symptoms relating to mood, anxiety, and energy four times daily for 28 days. We selected 88 individuals (17.83{+/-}1.74 years, 56 [63.6%] female gender) with at least one instance where individuals were observed three-days in a row. Within-person bidirectional next-day effects between sleep quality and affective symptoms were estimated using mixed-effects regression analysis adjusting. We then applied g-estimation approaches to estimate the effect that lagged sleep quality and consecutive improvements in sleep quality had on affective symptoms. Results: Sleep quality and affective symptoms had bidirectional next-day effects, with sleep quality tending to have greater influence on affective symptoms than the reverse. Improved lagged sleep quality had positive effects on affective symptoms incrementally above the prior night's sleep quality. Also, improvement of sleep quality across consecutive days had incremental and approximately equal effects on affective symptoms. Conclusions: Sleep quality and affective symptoms exhibit a feedback loop, whereby poor sleep quality influences affective symptoms over consecutive days. Breaking these feedback loops, by improving sleep quality across several consecutive nights should improve affective symptoms. This supports interventions that target sustained improvement in sleep and possibly circadian regulation to improve affective symptoms.

Light is a fundamental regulator of human physiology and behaviour. Whether prior light exposure shapes subsequent higher-order cognition and mood beyond the period of exposure remains unknown. We tested this in a within-subject, randomised crossover experiment in which 24 healthy young adult males completed a multimodal cognitive battery following 2 x15 min of full-spectrum light (FL; median 1,029 melanopic equivalent daylight illuminance [mEDI]) or standard indoor light (SL; median 234 mEDI), with all testing conducted under identical dim illumination. FL improved Digit-Symbol Substitution Test accuracy and promoted digit-directed gaze reallocation, consistent with more efficient associative encoding. On the Balloon Analogue Risk Task, FL reduced reward-seeking behaviour and suppressed backward-referencing gaze transitions linking current and prior-trial reward information. Mood declined following SL but remained stable after FL. Sustained attention, vigilance, and subjective sleepiness were unaffected. Our findings identify pre-task FL exposure as a selective primer of higher-order cognition and mood, independent of alertness.

BackgroundChronic sleep restriction (CSR) impairs cognitive function, but its effects on the cortical dynamics underlying active motor performance remain poorly understood. High-density EEG provides a means to examine task-related oscillatory activity across sensorimotor and attentional networks during movement. MethodsFifteen healthy males completed a randomized crossover study involving a CSR condition (five hours sleep per night for four nights) and a control condition (normal sleep). Before and after each intervention, participants performed sustained isometric ankle contractions at 40% of their maximal force while EEG was recorded. Source-reconstructed event-related desynchronization (ERD) was computed across theta, alpha, beta, and gamma bands in the sensorimotor network and dorsal attention network. Sustained attention was assessed with the Psychomotor Vigilance Task (PVT) and perceived workload with the NASA Task Load Index. ResultsCSR successfully reduced sleep duration by 2.36 hours on average (p < .001). Following CSR, PVT reaction times increased significantly ({Delta} = +31 ms, p = .002) and attentional lapses increased ({Delta} = +9.87, p < .001). CSR produced a significant overall increase in ERD across bands, networks, and movement directions (F(1, 5713) = 14.20, p < .001). This effect was present in both the sensorimotor and dorsal attention networks. The ERD increase was specific to dorsiflexion and absent during plantarflexion (condition x session x movement direction: F(1, 5713) = 9.13, p = .003). Subjective mental demand increased following CSR (p = .027), while objective motor performance was largely unimpaired. ConclusionCSR increased broadband ERD during dorsiflexion across both sensorimotor and attentional networks, alongside impaired sustained attention and greater perceived mental demand. As motor performance was largely preserved, this increased ERD may reflect compensatory neural recruitment under sleep pressure.

Automated severity rating of rapid eye movement (REM) sleep behavior disorder (RBD) movements would enable multi-night monitoring to detect potentially injurious behaviors and provide objective endpoints for clinical trials. We compared a heuristic classifier using optical flow-derived features against V-JEPA2, a self-supervised video foundation model, for clip-level severity classification (3329 mild versus 284 moderate-to-severe) of in-laboratory video-polysomnography infrared recordings in 86 isolated RBD patients. V-JEPA2 with checkpoint fine-tuning and maximum optical flow-based frame sampling achieved the best performance across both evaluation conditions -- Macro F1 of 0.76 and 93% accuracy in the clip-level split, and 0.68 and 85% in the patient-level split -- outperforming heuristic and domain-specific pretrained models. Clip duration was the dominant heuristic predictor. Whole-night severity scores preserved patient-level ordering despite systematic overestimation, with V-JEPA2 achieving a mean absolute error of 25% versus 52% for the heuristic classifier. These findings establish a foundation for objective, home-deployable monitoring of RBD severity.