Interindividual differences in the dynamics of the homeostatic process are trait-like and distinct for sleep versus wakefulness

Sustained polyphasic sleep restriction abolishes human growth hormone release

STUDY OBJECTIVES

Voluntary sleep restriction is a common phenomenon in industrialized societies aiming to increase time spent awake and thus productivity. We explored how restricting sleep to a radically polyphasic schedule affects neural, cognitive, and endocrine characteristics.

METHODS

Ten young healthy participants were restricted to one 20-minute nap opportunity at the end of every 4 hours (i.e. six sleep episodes per 24 hours) without any extended core sleep window, which resulted in a cumulative sleep amount of just 2 hours per day (i.e. ~20 minutes per bout).

RESULTS

All but one participant terminated this schedule during the first month. The remaining participant (a 25-year-old male) succeeded in adhering to a polyphasic schedule for five out of the eight planned weeks. Cognitive and psychiatric measures showed modest changes during polyphasic as compared to monophasic sleep, while in-blood cortisol or melatonin release patterns and amounts were apparently unaltered. In contrast, growth hormone release was almost entirely abolished (>95% decrease), with the residual release showing a considerably changed polyphasic secretional pattern.

CONCLUSIONS

Even though the study was initiated by volunteers with exceptional intrinsic motivation and commitment, none of them could tolerate the intended 8 weeks of the polyphasic schedule. Considering the decreased vigilance, abolished growth hormone release, and neurophysiological sleep changes observed, it is doubtful that radically polyphasic sleep schedules can subserve the different functions of sleep to a sufficient degree.

Short-term and long-term phenotypic stability of actigraphic sleep metrics involving repeated sleep loss and recovery

For the first time, we determined whether actigraphic-assessed sleep measures show inter-individual differences and intra-individual stability during baseline (BL) and recovery (REC) phases surrounding repeated total sleep deprivation (TSD). We conducted a 5-day experiment at Months 2 and 4 in two separate studies (N = 11). During each experiment, sleep measures were collected via wrist actigraphy during two BL 8 h time-in-bed (TIB) nights (B1, B2) and during two REC 8-10 h TIB nights (R1, R2). Intraclass correlation coefficients (ICCs) assessed actigraphic measure long-term stability between 2 and 4 months for (1) the pre-experimental phase before BL; and (2) the BL (B1 + B2), REC (R1 + R2), and BL and REC average (BL + REC) phases; and short-term stability at Month 2 and at Month 4; and (3) between B1 versus B2 and R1 versus R2 in each 5-day experiment. Nearly all ICCs during the pre-experimental, BL, REC, and BL + REC phases were moderate to almost perfect (0.446-0.970) between Months 2 and 4. B1 versus B2 ICCs were more stable (0.440-0.899) than almost all R1 versus R2 ICCs (-0.696 to 0.588) at Month 2 and 4. Actigraphic sleep measures show phenotypic long-term stability during BL and REC surrounding repeated TSD between 2 and 4 months. Furthermore, within each 5-day experiment at Month 2 and 4, the two BL nights before TSD were more stable than the two REC nights following TSD, likely due to increased R1 homeostatic pressure. Given the consistency of actigraphic measures across the short-term and long-term, they can serve as biomarkers to predict physiological and neurobehavioral responses to sleep loss.

Cardiovascular measures display robust phenotypic stability across long-duration intervals involving repeated sleep deprivation and recovery

Introduction

We determined whether cardiovascular (CV) measures show trait-like responses after repeated total sleep deprivation (TSD), baseline (BL) and recovery (REC) exposures in two long-duration studies (total N = 11 adults).

Methods

A 5-day experiment was conducted twice at months 2 and 4 in a 4-month study (N = 6 healthy adults; 3 females; mean age ± SD, 34.3 ± 5.7 years; mean BMI ± SD, 22.5 ± 3.2 kg/m2), and three times at months 2, 4, and 8 in an 8-month study (N = 5 healthy adults; 2 females; mean age ± SD, 33.6 ± 5.17 years; mean BMI ± SD, 27.1 ± 4.9 kg/m2). Participants were not shift workers or exposed to TSD in their professions. During each experiment, various seated and standing CV measures were collected via echocardiography [stroke volume (SV), heart rate (HR), cardiac index (CI), left ventricular ejection time (LVET), and systemic vascular resistance index (SVRI)] or blood pressure monitor [systolic blood pressure (SBP)] after (1) two BL 8h time in bed (TIB) nights; (2) an acute TSD night; and (3) two REC 8-10 h TIB nights. Intraclass correlation coefficients (ICCs) assessed CV measure stability during BL, TSD, and REC and for the BL and REC average (BL + REC) across months 2, 4, and 8; Spearman's rho assessed the relative rank of individuals' CV responses across measures.

Results

Seated BL (0.693-0.944), TSD (0.643-0.962) and REC (0.735-0.960) CV ICCs showed substantial to almost perfect stability and seated BL + REC CV ICCs (0.552-0.965) showed moderate to almost perfect stability across months 2, 4, and 8. Individuals also exhibited significant, consistent responses within seated CV measures during BL, TSD, and REC. Standing CV measures showed similar ICCs for BL, TSD, and REC and similar response consistency.

Discussion

This is the first demonstration of remarkably robust phenotypic stability of a number of CV measures in healthy adults during repeated TSD, BL and REC exposures across 2, 4, and 8 months, with significant consistency of responses within CV measures. The cardiovascular measures examined in our studies, including SV, HR, CI, LVET, SVRI, and SBP, are useful biomarkers that effectively track physiology consistently across long durations and repeated sleep deprivation and recovery.

Phenotypic Interindividual Differences in the Dynamic Structure of Sleep in Healthy Young Adults

Introduction

Evaluating the dynamic structure of sleep may yield new insights into the mechanisms underlying human sleep physiology.

Methods

We analyzed data from a 12-day, 11-night, strictly controlled laboratory study with an adaptation night, 3 iterations of a baseline night followed by a recovery night after 36 h of total sleep deprivation, and a final recovery night. All sleep opportunities were 12 h in duration (22:00-10:00) and recorded with polysomnography (PSG). The PSG records were scored for the sleep stages: rapid eye movement (REM) sleep; non-REM (NREM) stage 1 sleep (S1), stage 2 sleep (S2), and slow wave sleep (SWS); and wake (W). Phenotypic interindividual differences were assessed using indices of dynamic sleep structure - specifically sleep stage transitions and sleep cycle characteristics - and intraclass correlation coefficients across nights.

Results

NREM/REM sleep cycles and sleep stage transitions exhibited substantial and stable interindividual differences that were robust across baseline and recovery nights, suggesting that mechanisms underlying the dynamic structure of sleep are phenotypic. In addition, the dynamics of sleep stage transitions were found to be associated with sleep cycle characteristics, with a significant relationship between the length of sleep cycles and the degree to which S2-to-W/S1 and S2-to-SWS transitions were in equilibrium.

Discussion

Our findings are consistent with a model for the underlying mechanisms that involves three subsystems - characterized by S2-to-W/S1, S2-to-SWS, and S2-to-REM transitions - with S2 playing a hub-like role. Furthermore, the balance between the two subsystems within NREM sleep (S2-to-W/S1 and S2-to-SWS) may serve as a basis for the dynamic regulation of sleep structure and may represent a novel target for interventions aiming to improve sleep.

Association between sleep slow-wave activity and in-vivo estimates of myelin in healthy young men

Sleep has been suggested to contribute to myelinogenesis and associated structural changes in the brain. As a principal hallmark of sleep, slow-wave activity (SWA) is homeostatically regulated but also differs between individuals. Besides its homeostatic function, SWA topography is suggested to reflect processes of brain maturation. Here, we assessed whether interindividual differences in sleep SWA and its homeostatic response to sleep manipulations are associated with in-vivo myelin estimates in a sample of healthy young men. Two hundred twenty-six participants (18-31 y.) underwent an in-lab protocol in which SWA was assessed at baseline (BAS), after sleep deprivation (high homeostatic sleep pressure, HSP) and after sleep saturation (low homeostatic sleep pressure, LSP). Early-night frontal SWA, the frontal-occipital SWA ratio, as well as the overnight exponential SWA decay were computed over sleep conditions. Semi-quantitative magnetization transfer saturation maps (MTsat), providing markers for myelin content, were acquired during a separate laboratory visit. Early-night frontal SWA was negatively associated with regionally decreased myelin estimates in the temporal portion of the inferior longitudinal fasciculus. By contrast, neither the responsiveness of SWA to sleep saturation or deprivation, its overnight dynamics, nor the frontal/occipital SWA ratio were associated with brain structural indices. Our results indicate that frontal SWA generation tracks inter-individual differences in continued structural brain re-organization during early adulthood. This stage of life is not only characterized by ongoing region-specific changes in myelin content, but also by a sharp decrease and a shift towards frontal predominance in SWA generation.

Timing is everything: Circadian rhythms and their role in the control of sleep

Sleep and the circadian clock are intertwined and have persisted throughout history. The suprachiasmatic nucleus (SCN) orchestrates sleep by controlling circadian (Process C) and homeostatic (Process S) activities. As a "hand" on the endogenous circadian clock, melatonin is critical for sleep regulation. Light serves as a cue for sleep/wake control by activating retino-recipient cells in the SCN and subsequently suppressing melatonin. Clock genes are the molecular timekeepers that keep the 24 h cycle in place. Two main sleep and behavioural disorder diagnostic manuals have now officially recognised the importance of these processes for human health and well-being. The body's ability to respond to daily demands with the least amount of effort is maximised by carefully timing and integrating all components of sleep and waking. In the brain, the organization of timing is essential for optimal brain physiology.

The 3-Minute Psychomotor Vigilance Test Demonstrates Inadequate Convergent Validity Relative to the 10-Minute Psychomotor Vigilance Test Across Sleep Loss and Recovery

The Psychomotor Vigilance Test (PVT) is a widely used behavioral attention measure, with the 10-min (PVT-10) and 3-min (PVT-3) as two commonly used versions. The PVT-3 may be comparable to the PVT-10, though its convergent validity relative to the PVT-10 has not been explicitly assessed. For the first time, we utilized repeated measures correlation (rmcorr) to evaluate intra-individual associations between PVT-10 and PVT-3 versions across total sleep deprivation (TSD), chronic sleep restriction (SR) and multiple consecutive days of recovery. Eighty-three healthy adults (mean ± SD, 34.7 ± 8.9 years; 36 females) received two baseline nights (B1-B2), five SR nights (SR1-SR5), 36 h TSD, and four recovery nights (R1-R4) between sleep loss conditions. The PVT-10 and PVT-3 were completed every 2 h during wakefulness. Rmcorr compared responses on two frequently used, sensitive PVT metrics: reaction time (RT) via response speed (1/RT) and lapses (RT > 500 ms on the PVT-10 and > 355 ms on the PVT-3) by day (e.g., B2), by study phase (e.g., SR1-SR5), and by time point (1000-2000 h). PVT 1/RT correlations were generally stronger than those for lapses. The majority of correlations (48/50 [96%] for PVT lapses and 38/50 [76%] for PVT 1/RT) were values below 0.70, indicating validity issues. Overall, the PVT-3 demonstrated inadequate convergent validity with the "gold standard" PVT-10 across two different types of sleep loss and across extended recovery. Thus, the PVT-3 is not interchangeable with the PVT-10 for assessing behavioral attention performance during sleep loss based on the design of our study and the metrics we evaluated. Our results have substantial implications for design and measure selection in laboratory and applied settings, including those involving sleep deprivation.

Working around the Clock: Is a Person’s Endogenous Circadian Timing for Optimal Neurobehavioral Functioning Inherently Task-Dependent?

Neurobehavioral task performance is modulated by the circadian and homeostatic processes of sleep/wake regulation. Biomathematical modeling of the temporal dynamics of these processes and their interaction allows for prospective prediction of performance impairment in shift-workers and provides a basis for fatigue risk management in 24/7 operations. It has been reported, however, that the impact of the circadian rhythm—and in particular its timing—is inherently task-dependent, which would have profound implications for our understanding of the temporal dynamics of neurobehavioral functioning and the accuracy of biomathematical model predictions. We investigated this issue in a laboratory study designed to unambiguously dissociate the influences of the circadian and homeostatic processes on neurobehavioral performance, as measured during a constant routine protocol preceded by three days on either a simulated night shift or a simulated day shift schedule. Neurobehavioral functions were measured every 3 h using three functionally distinct assays: a digit symbol substitution test, a psychomotor vigilance test, and the Karolinska Sleepiness Scale. After dissociating the circadian and homeostatic influences and accounting for inter-individual variability, peak circadian performance occurred in the late biological afternoon (in the “wake maintenance zone”) for all three neurobehavioral assays. Our results are incongruent with the idea of inherent task-dependent differences in the endogenous circadian impact on performance. Rather, our results suggest that neurobehavioral functions are under top-down circadian control, consistent with the way they are accounted for in extant biomathematical models.

Who is the hardest to wake up from sleep? An investigation of self-reported sleep inertia using a latent profile analysis

Few studies have assessed the overall nature and profiles of subjective sleep inertia (SI) within the general population. This study was designed to identify subjective SI profiles and examine the associations between profiles of subjective SI with sociodemographic and sleep-related characteristics. A total of 11 colleges and universities were surveyed from May 30 to June 17, 2021, by convenience sampling. A total of 1,240 participants provided usable data regarding sociodemographic information, Sleep Inertia Questionnaire, and sleep-related characteristics via an online platform. Latent profile analysis was utilised to identify profiles of SI. Multinomial logistic regression was further performed to examine the predisposing factors of profiles of SI. Four profiles of SI were identified: (1) "Low SI", 20%; (2) "Mild SI", 31%; (3) "Moderate SI", 33%; and (4) "Severe SI", 16%. Compared to a Low SI profile, younger, individuals with an evening chronotype, and individuals who had <6 h sleep/night, experienced poor sleep quality, and moderate-to-severe sleep disturbance were at increased risk of experiencing severe SI. Individuals with more languid types tended to show more severe SI, while individuals reporting greater flexibility experienced less SI. This study is the first effort to examine the profiles of subjective SI using latent profile analysis and identified four profiles of SI in the general population. This effort may contribute to a greater understanding of SI, including the development of a screening tool and interventions to reduce SI.

An Exploration of Self-Reported Sleep Inertia Symptoms Using Network Analysis

PURPOSE

Sleep inertia (SI) is the transitional state accompanied by compromised cognitive and physical performance and sleepiness. Network analysis offers a potential new framework to conceptualize a complex network of symptom-symptom interactions, and the network structure is analyzed to reveal the core characteristics. However, no previous study examined the network structure of SI symptoms. Thus, this study aimed to elucidate characteristics and compare sex differences of SI symptom networks in the general population.

MATERIALS AND METHODS

A total of 1491 participants from China were recruited from 30 May to 17 June, 2021. SI symptoms were assessed by using the Sleep Inertia Questionnaire (SIQ). The network structures were estimated and compared using network analytic methods in the R version 4.1.1.

RESULTS

Centrality properties analysis of the expected influence suggested that symptoms of "Feel sleepy", "Groggy, fuzzy or hazy mind", and "Dread starting your day" exerted greatest influences. The weighted adjacency matrix revealed that the "Dread starting your day" and "Anxious about the upcoming day" edge showed the strongest connection (edge weight value = 0.70). The network comparison test found no significant difference in network global strength (p=0.928), distribution of edge weights (p=0.194) and individual edge weights (all p values >0.05 after Holm-Bonferroni corrections) between males and females.

CONCLUSION

Symptoms of "Feel sleepy", "Groggy, fuzzy or hazy mind", and "Dread starting your day" were central in the SI symptom network. Intervention, such as the artificial dawn and change in body temperature, for symptoms of "Feel sleepy", "Groggy, fuzzy or hazy mind", and "Dread starting your day" might be crucial to hasten the dissipation of SI in the general population who may need to perform tasks upon waking.

A trait of mind: stability and robustness of sleep across sleep opportunity manipulations during simulated military operational stress

STUDY OBJECTIVES

Within-subject stability of certain sleep features across multiple nights is thought to reflect the trait-like behavior of sleep. However, to be considered a trait, a parameter must be both stable and robust. Here, we examined the stability (i.e., across the same sleep opportunity periods) and robustness (i.e., across sleep opportunity periods that varied in duration and timing) of different sleep parameters.

METHODS

Sixty-eight military personnel (14 W) spent 5 nights in the sleep laboratory during a simulated military operational stress protocol. After an adaptation night, participants had an 8-hour sleep opportunity (23:00-07:00) followed by 2 consecutive nights of sleep restriction and disruption which included two 2-hour sleep opportunities (01:00-03:00; 05:00-07:00) and, lastly, another 8-hour sleep opportunity (23:00-07:00). Intra-class correlation coefficients were calculated to examine differences in stability and robustness across different sleep parameters.

RESULTS

Sleep architecture parameters were less stable and robust than absolute and relative spectral activity parameters. Further, relative spectral activity parameters were less robust than absolute spectral activity. Absolute alpha and sigma activity demonstrated the highest levels of stability that were also robust across sleep opportunities of varying duration and timing.

CONCLUSIONS

Stability and robustness varied across different sleep parameters, but absolute NREM alpha and sigma activity demonstrated robust trait-like behavior across variable sleep opportunities. Reduced stability of other sleep architecture and spectral parameters during shorter sleep episodes as well as across different sleep opportunities has important implications for study design and interpretation.

Robust stability of trait-like vulnerability or resilience to common types of sleep deprivation in a large sample of adults

STUDY OBJECTIVES

Sleep loss produces large individual differences in neurobehavioral responses, with marked vulnerability or resilience among individuals. Such differences are stable with repeated exposures to acute total sleep deprivation (TSD) or chronic sleep restriction (SR) within short (weeks) and long (years) intervals. Whether trait-like responses are observed to commonly experienced types of sleep loss and across various demographically defined groups remains unknown.

METHODS

Eighty-three adults completed two baseline nights (10 h-12 h time-in-bed, TIB) followed by five 4 h TIB SR nights or 36 h TSD. Participants then received four 12-h TIB recovery nights followed by five SR nights or 36 h TSD, in counterbalanced order to the first sleep loss sequence. Neurobehavioral tests were completed every 2 h during wakefulness.

RESULTS

Participants who displayed neurobehavioral vulnerability to TSD displayed vulnerability to SR, evidenced by substantial to near perfect intraclass correlation coefficients (ICCs; 78%-91% across measures). Sex, race, age, body mass index (BMI), season, and sleep loss order did not impact ICCs significantly. Individuals exhibited significant consistency of responses within, but not between, performance and self-reported domains.

CONCLUSIONS

Using the largest, most diverse sample to date, we demonstrate for the first time the remarkable stability of phenotypic neurobehavioral responses to commonly experienced sleep loss types, across demographic variables and different performance and self-reported measures. Since sex, race, age, BMI, and season did not affect ICCs, these variables are not useful for determining stability of responses to sleep loss, underscoring the criticality of biological predictors. Our findings inform mathematical models and are relevant for the general population and military and health professions.

Optimizing computation of overnight decline in delta power: Evidence for slower rate of decline in delta power in insomnia patients

OBJECTIVE

To determine the best of commonly used methods for computing the rate of decline in non-rapid eye movement (NREM) sleep EEG delta power overnight (Delta Decline) in terms of vulnerability to missing data and to evaluate whether this rate is slower in insomnia patients than healthy controls (HC).

METHODS

Fifty-one insomnia patients and 53 HC underwent 6 nights of polysomnography. Four methods for estimating Delta Decline were compared (exponential and linear best-fit functions using NREM (1) episode mean, (2) peak, and (3) total delta power and (4) delta power for all available NREM epochs). The best method was applied to compare groups on linear and exponential rates of Delta Decline.

RESULTS

Best-fit models using all available NREM epochs were significantly less vulnerable to deviation due to missing data than other methods. Insomnia patients displayed significantly slower linear and exponential Delta Decline than HC.

CONCLUSIONS

Computing Delta Decline using all available NREM epochs was the best of the methods studied for minimizing the effects of missing data. Insomnia patients display slower Delta Decline, which is not explained by differences in total sleep time or wake after sleep onset.

SIGNIFICANCE

This study supports using all available NREM epochs in Delta Decline computation and suggests a slower rate in insomnia.

Changes in Subjective Motivation and Effort During Sleep Restriction Moderate Interindividual Differences in Attentional Performance in Healthy Young Men

PURPOSE

The effects of sleep restriction on subjective alertness, motivation, and effort vary among individuals and may explain interindividual differences in attention during sleep restriction. We investigated whether individuals with a greater decrease in subjective alertness or motivation, or a greater increase in subjective effort (versus other participants), demonstrated poorer attention when sleep restricted.

PARTICIPANTS AND METHODS

Fifteen healthy men (M±SD, 22.3±2.8 years) completed a study with three nights of 10-hour time in bed (baseline), five nights of 5-hour time in bed (sleep restriction), and two nights of 10-hour time in bed (recovery). Participants completed a 10-minute psychomotor vigilance task (PVT) of sustained attention and rated alertness, motivation, and effort every two hours during wake (range: 3-9 administrations on a given day). Analyses examined performance across the study (first two days excluded) moderated by per-participant change in subjective alertness, motivation, or effort from baseline to sleep restriction. For significant interactions, we investigated the effect of study day² (day*day) on the outcome at low (mean-1 SD) and high (mean+1 SD) levels of the moderator (N = 15, all analyses).

RESULTS

False starts increased across sleep restriction in participants who reported lower (mean-1 SD) but not preserved (mean+1 SD) motivation during sleep restriction. Lapses increased across sleep restriction regardless of change in subjective motivation, with a more pronounced increase in participants who reported lower versus preserved motivation. Lapses increased across sleep restriction in participants who reported higher (mean+1 SD) but not preserved (mean-1 SD) effort during sleep restriction. Change in subjective alertness did not moderate the effects of sleep restriction on attention.

CONCLUSION

Vigilance declines during sleep restriction regardless of change in subjective alertness or motivation, but individuals with reduced motivation exhibit poorer inhibition. Individuals with preserved subjective alertness still perform poorly during sleep restriction, while those reporting additional effort demonstrate impaired vigilance.

What are we measuring with the morningness-eveningness questionnaire? Exploratory factor analysis across four samples from two countries

Individual variability in diurnal preference or chronotype is commonly assessed with self-report scales such as the widely used morningness-eveningness questionnaire (MEQ). We sought to investigate the MEQ's internal consistency by applying exploratory factor analysis (EFA) to determine the number of underlying latent factors in four different adult samples, two each from the United Kingdom and Brazil (total N = 3,457). We focused on factors that were apparent in all samples, irrespective of particular sociocultural diversity and geographical characteristics, so as to show a common core reproducible structure across samples. Results showed a three-factor solution with acceptable to good model fit indexes in all studied populations. Twelve of the 19 MEQ items in the three-correlated factor solution loaded onto the same factors across the four samples. This shows that the scale measures three distinguishable, yet correlated constructs: (1) items related to how people feel in the morning, which we termed efficiency of dissipation of sleep pressure (recovery process) (items 1, 3, 4, 5, 7, 9, 13, and 19); (2) items related to how people feel before sleep, which we called sensitivity to buildup of sleep pressure (items 2, 10, and 12); and (3) peak time of cognitive arousal (item 11). Although the third factor was not regarded as consistent since only one item was common among all samples, it might represent subjective amplitude. These results suggested that the latent constructs of the MEQ reflect dissociable homeostatic processes in addition to a less consistent propensity for cognitive arousal at different times of the day. By analyzing answers to MEQ items that compose these latent factors, it may be possible to extract further knowledge of factors that affect morningness-eveningness.

Wearable Device Heart Rate and Activity Data in an Unsupervised Approach to Personalized Sleep Monitoring: Algorithm Validation

Background

The proliferation of wearable devices that collect activity and heart rate data has facilitated new ways to measure sleeping and waking durations unobtrusively and longitudinally. Most existing sleep/wake identification algorithms are based on activity only and are trained on expensive and laboriously annotated polysomnography (PSG). Heart rate can also be reflective of sleep/wake transitions, which has motivated its investigation herein in an unsupervised algorithm. Moreover, it is necessary to develop a personalized approach to deal with interindividual variance in sleep/wake patterns.

Objective

We aimed to develop an unsupervised personalized sleep/wake identification algorithm using multifaceted data to explore the benefits of incorporating both heart rate and activity level in these types of algorithms and to compare this approach’s output with that of an existing commercial wearable device’s algorithms.

Methods

In this study, a total of 14 community-dwelling older adults wore wearable devices (Fitbit Alta; Fitbit Inc) 24 hours a day and 7 days a week over period of 3 months during which their heart rate and activity data were collected. After preprocessing the data, a model was developed to distinguish sleep/wake states based on each individual’s data. We proposed the use of hidden Markov models and compared different modeling schemes. With the best model selected, sleep/wake patterns were characterized by estimated parameters in hidden Markov models, and sleep/wake states were identified.

Results

When applying our proposed algorithm on a daily basis, we found there were significant differences in estimated parameters between weekday models and weekend models for some participants.

Conclusions

Our unsupervised approach can be effectively implemented based on an individual’s multifaceted sleep-related data from a commercial wearable device. A personalized model is shown to be necessary given the interindividual variability in estimated parameters.

Interaction of Sleep and Cortical Structural Maintenance From an Individual Person Microlongitudinal Perspective and Implications for Precision Medicine Research

Sleep and maintenance of brain structure are essential for the continuity of a person's cognitive/mental health. Interestingly, whether normal structural maintenance of the brain and sleep continuously interact in some way over day-week-month times has never been assessed at an individual-person level. This study used unconventional microlongitudinal sampling, structural magnetic resonance imaging, and n-of-1 analyses to assess normal interactions between fluctuations in the structural maintenance of cerebral cortical thickness and sleep duration for day, week, and multi-week intervals over a 6-month period in a healthy adult man. Correlation and time series analyses provided indications of "if-then," i.e., "if" this preceded "then" this followed, sleep-to-thickness maintenance and thickness maintenance-to-sleep bidirectional inverse interactions. Inverse interaction patterns were characterized by concepts of graded influences across nights, bilaterally positive relationships, continuity across successive weeks, and longer delayed/prolonged effects in the thickness maintenance-to-sleep than sleep-to-thickness maintenance direction. These interactions are proposed to involve normal circadian/allostatic/homeostatic mechanisms that continuously influence, and are influenced by, cortical substrate remodeling/turnover and sleep/wake cycle. Understanding interactions of individual person "-omics" is becoming a central interest in precision medicine research. The present n-of-1 findings contribute to this interest and have implications for precision medicine research use of a person's cortical structural and sleep "-omics" to optimize the continuous maintenance of that individual's cortical structure, sleep, and cognitive/mental health.

Trait-like vulnerability of higher-order cognition and ability to maintain wakefulness during combined sleep restriction and circadian misalignment

STUDY OBJECTIVES

Determine stability of individual differences in executive function, cognitive processing speed, selective visual attention, and maintenance of wakefulness during simulated sustained operations with combined sleep restriction and circadian misalignment.

METHODS

Twenty healthy adults (eight female), aged 25.7 (±4.2 SD), body mass index (BMI) 22.3 (±2.1) kg/m2 completed an 18-day protocol twice. Participants maintained habitual self-selected 8-hour sleep schedules for 2 weeks at home prior to a 4-day laboratory visit that included one sleep opportunity per day: 8 hours on night 1, 3 hours on night 2, and 3 hours on mornings 3 and 4. After 3 days of unscheduled sleep at home, participants repeated the entire protocol. Stability and task dependency of individual differences in performance were quantified by intra-class correlation coefficients (ICC) and Kendall's Tau, respectively.

RESULTS

Performance on Stroop, Visual Search, and the Maintenance of Wakefulness Test were highly consistent within individuals during combined sleep restriction and circadian misalignment. Individual differences were trait-like as indicated by ICCs (0.54-0.96) classified according to standard criteria as moderate to almost perfect. Individual differences on other performance tasks commonly reported in sleep studies showed fair to almost perfect ICCs (0.22-0.94). Kendall's rank correlations showed that individual vulnerability to sleep restriction and circadian misalignment varied by task and by metric within a task.

CONCLUSIONS

Consistent vulnerability of higher-order cognition and maintenance of wakefulness to combined sleep restriction and circadian misalignment has implications for the development of precision countermeasure strategies for workers performing safety-critical tasks, e.g. military, police, health care workers and emergency responders.

Preparing for Mars: human sleep and performance during a 13 month stay in Antarctica

Study Objectives

Manned spaceflights from Earth to Mars will likely become reality within the next decades. Humans will be exposed to prolonged isolation, confinement, and altered photoperiods under artificial atmospheric conditions, with potential adverse effects on sleep and performance. On Earth, polar environments serve as space analogs to study human adaptation; yet, few studies include polysomnography due to operational constraints.

Methods

Polysomnography, self-reported sleepiness and fatigue, and psychomotor performance were measured every 6 weeks in 13 males ("Hivernauts") during a 13 month winter-over campaign at Concordia (Antarctica). Stability and robustness of interindividual differences were examined by means of intraclass correlations.

Results

Hivernauts present with high-altitude periodic breathing, increased sleep onset latencies, and reduced psychomotor speed. Except for obstructive apneas, all sleep, sleepiness, and psychomotor performance variables remain stable over time. Individual differences in respiratory variables show the highest degree of stability and robustness, followed by fatigue and situational sleepiness, sleep fragmentation, and psychomotor speed, suggesting moderate to substantial trait-like characteristics for these variables. Phase delays are suspected in Hivernauts, both in individuals with imposed and self-selected bedtimes. A significant decline in psychomotor speed over time is observed in the latter group.

Conclusions

Space analog conditions such as isolated confinement, extreme photoperiods, and altered atmospheric conditions affect human sleep and performance. However, individual responses to these extreme environmental challenges show large differences and remain relatively stable under prolonged exposure. Ad hoc polysomnographic, including respiratory function monitoring is therefore recommended for selecting eligible candidates for extraterrestrial sojourns.

Trait-like characteristics of sleep EEG power spectra in adolescents across sleep opportunity manipulations

The electroencephalographic power spectra of non-rapid eye movement sleep in adults demonstrate trait-like consistency within participants across multiple nights, even when prior sleep deprivation is present. Here, we examined the extent to which this finding applies to adolescents who are habitually sleep restricted on school-days and sleep longer on weekends. We evaluated 78 adolescents across three sleep restriction groups who underwent different permutations of adequate sleep (9 hr time-in-bed), sleep restriction (5 hr time-in-bed), afternoon naps (1 hr afternoon) and recovery sleep (9 hr time-in-bed) that simulate behaviour on school-days and weekends. The control group comprised a further 22 adolescents who had 9 hr of sleep opportunity each night. Intra-class correlation coefficients showed moderate to almost perfect within-subject stability in electroencephalographic power spectra across multiple nights in both sleep restriction and control groups, even when changes to sleep macrostructure were observed. While nocturnal intra-class correlation metrics were lower in the low-frequency and spindle frequency bins in the sleep restriction compared with the control group, hierarchical clustering measures could still identify multi-night electroencephalographic spectra as originating from the same individual. The trait-like characteristics of electroencephalographic spectra from an adolescent remain identifiable despite the disruptive effects of multi-night sleep restriction to sleep architecture.

Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach

BACKGROUND

The current gold standard for measuring sleep is polysomnography (PSG), but it can be obtrusive and costly. Actigraphy is a relatively low-cost and unobtrusive alternative to PSG. Of particular interest in measuring sleep from actigraphy is prediction of sleep-wake states. Current literature on prediction of sleep-wake states from actigraphy consists of methods that use population data, which we call generalized models. However, accounting for variability of sleep patterns across individuals calls for personalized models of sleep-wake states prediction that could be potentially better suited to individual-level data and yield more accurate estimation of sleep.

PURPOSE

To investigate the validity of developing personalized machine learning models, trained and tested on individual-level actigraphy data, for improved prediction of sleep-wake states and reliable estimation of nightly sleep parameters.

PARTICIPANTS AND METHODS

We used a dataset including 54 participants and systematically trained and tested 5 different personalized machine learning models as well as their generalized counterparts. We evaluated model performance compared to concurrent PSG through extensive machine learning experiments and statistical analyses.

RESULTS

Our experiments show the superiority of personalized models over their generalized counterparts in estimating PSG-derived sleep parameters. Personalized models of regularized logistic regression, random forest, adaptive boosting, and extreme gradient boosting achieve estimates of total sleep time, wake after sleep onset, sleep efficiency, and number of awakenings that are closer to those obtained by PSG, in absolute difference, than the same estimates from their generalized counterparts. We further show that the difference between estimates of sleep parameters obtained by personalized models and those of PSG is statistically non-significant.

CONCLUSION

Personalized machine learning models of sleep-wake states outperform their generalized counterparts in terms of estimating sleep parameters and are indistinguishable from PSG labeled sleep-wake states. Personalized machine learning models can be used in actigraphy studies of sleep health and potentially screening for some sleep disorders.

Intraindividual Increase of Homeostatic Sleep Pressure Across Acute and Chronic Sleep Loss: A High-Density EEG Study

Study Objectives

To compare intraindividually the effects of acute sleep deprivation (ASD) and chronic sleep restriction (CSR) on the homeostatic increase in slow wave activity (SWA) and to relate it to impairments in basic cognitive functioning, that is, vigilance.

Methods

The increase in SWA after ASD (40 hours of wakefulness) and after CSR (seven nights with time in bed restricted to 5 hours per night) relative to baseline sleep was assessed in nine healthy, male participants (age = 18-26 years) by high-density electroencephalography. The SWA increase during the initial part of sleep was compared between the two conditions of sleep loss. The increase in SWA was related to the increase in lapses of vigilance in the psychomotor vigilance task (PVT) during the preceding days.

Results

While ASD induced a stronger increase in initial SWA than CSR, the increase was globally correlated across the two conditions in most electrodes. The increase in initial SWA was positively associated with the increase in PVT lapses.

Conclusions

The individual homeostatic response in SWA is globally preserved across acute and chronic sleep loss, that is, individuals showing a larger increase after ASD also do so after CSR and vice versa. Furthermore, the increase in SWA is globally correlated to vigilance impairments after sleep loss over both conditions. Thus, the increase in SWA might therefore provide a physiological marker for individual differences in performance impairments after sleep loss.

Healthy Adults Display Long-Term Trait-Like Neurobehavioral Resilience and Vulnerability to Sleep Loss

Sleep loss produces well-characterized cognitive deficits, although there are large individual differences, with marked vulnerability or resilience among individuals. Such differences are stable with repeated exposures to acute total sleep deprivation (TSD) within a short-time interval (weeks). Whether such stability occurs with chronic sleep restriction (SR) and whether it endures across months to years in TSD, indicating a true trait, remains unknown. In 23 healthy adults, neurobehavioral vulnerability to TSD exposures, separated by 27–2,091 days (mean: 444 days; median: 210 days), showed trait-like stability in performance and subjective measures (82–95% across measures). Similarly, in 24 healthy adults, neurobehavioral vulnerability to SR exposures, separated by 78–3,058 days (mean: 935 days; median: 741 days), also showed stability (72–92% across measures). Cognitive performance outcomes and subjective ratings showed consistency across objective measures, and consistency across subjective measures, but not between objective and subjective domains. We demonstrate for the first time the stability of phenotypic neurobehavioral responses in the same individuals to SR and to TSD over long-time intervals. Across multiple measures, prior sleep loss responses are strong predictors of individual responses to subsequent sleep loss exposures chronically or intermittently, across months and years, thus validating the need for biomarkers and predictors.