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

The diverse sleep structure of mid-day nap in college students

The aim of this study was to investigate the sleep structure of mid-day naps in students who frequently nap, and to determine whether this structure has any impact on their subjective feelings after nap. A total of 91 college students (male, mean age 20.47 ± 2.02 years) completed one-hour mid-day nap with polysomnographic recording in a sleep laboratory. Upon awakening, self-ratings of sleep quality and sleepiness were assessed using questionnaires. The sleep structure of mid-day naps varied among participants: 47.3% participants experienced stage-1, -2, and -3 non-rapid eye movement (NREM) sleep in sequence in the one-hour nap, while rapid eye movement (REM) sleep occurred between 40 and 60 min after lights off in 27.5% participants, and less than 15 min in 16.5% participants. After nap, participants who achieved stage-3 sleep reported better sleep quality, but the subjective sleepiness was not influenced by the sleep stage contained in nap or the sleep stage at awakening. These findings highlight the diversity in the sleep structure of mid-day naps, suggesting that this variability may need to be considered when studying the effects of napping.

[High individual stability of daytime sleep EEG characteristics in nighttime sleep restriction settings]

OBJECTIVE

To evaluate the stability of the electroencephalogram (EEG) spectral characteristics of daytime sleep with moderate nighttime sleep deprivation.

MATERIAL AND METHODS

The study included 44 students without sleep disorders. The participants limited the nighttime sleep the day before the experiment to 5 hours; then, polysomnograms of a 90-minute daytime sleep were recorded in three repeated sessions. The obtained records were visually staged by experts. Spectral analysis of the wave amplitudes in the delta, theta, alpha, and sigma ranges and averaging for N1, N2, and N3 sleep phases in each record were performed. Stability was assessed through intra-group correlation coefficient (ICC).

RESULTS

N1 phase showed moderate individual stability (ICC 0.42-0.52) for all wave amplitudes except the sigma waves (ICC=0.68). In N2 phase, stability was increased: the sigma wave amplitude reached ICC=0.91 and 0.7-0.72 for theta and alpha wave amplitudes. In N3 phase, the delta and theta waves showed high stability (ICC=0.92-0.95). Sigma waves in the N3 phase were less stable. The results were consistent with those obtained for nighttime sleep: the sigma and delta waves were highly stable. Sigma waves (associated with sleep spindles) are most stable in N2; the delta and theta activity in N3. These observations show that despite external impacts, daytime sleep maintains intact individual neurophysiological patterns.

CONCLUSION

Daytime sleep demonstrates significant stability in the EEG spectral characteristics, especially in the deeper phases. Single EEG recording can be used in assessing individual sleep patterns, which is important for developing personalized approaches to improve the effects of sleep deprivation.

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.

Does the brain sleep differently depending on intellectual abilities?

AIMS

To compare the children's sleep electroencephalogram according to their intellectual profile.

METHODS

Children were grouped according to their Wechsler Intelligence Scale for Children (WISC) scores (17 with normal intelligence quotient [IQ, NIQ] and 24 with high IQ [HIQ]). Comparisons of spectral power between groups and its relationship with WISC scores were assessed using analyses of variance and linear regression models, adjusted for age and sex.

RESULTS

Children with HIQ had more rapid eye movement (REM) sleep, especially late at night, and more power in slow-frequency bands during REM sleep than those with NIQ. There were also positive associations between the processing speed index and the spectral power in β bands in NREM sleep, and with the spectral power in α, σ, β, and γ bands in REM sleep, with different associations between groups.

CONCLUSION

The enhanced power in slow bands during REM sleep in children with HIQ overlaps with that of typical REM sleep oscillations thought to be involved in emotional memory consolidation. The dissimilar relationships between spectral power and WISC scores in NIQ and HIQ groups may underlie functional differences in brain activity related to cognitive efficiency, questioning the direction of the relationship between sleep and cognitive functioning.

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.

Local slow-wave activity over the right prefrontal cortex reveals individual risk preferences

In everyday life, we have to make decisions under varying degrees of risk. Even though previous research has shown that the manipulation of sleep affects risky decision-making, it remains unknown whether individual, temporally stable neural sleep characteristics relate to individual differences in risk preferences. Here, we collected sleep data under normal conditions in fifty-four healthy adults using a portable high-density EEG at participants' home. Whole-brain corrected for multiple testing, we found that lower slow-wave activity (SWA, an indicator of sleep depth) in a cluster of electrodes over the right prefrontal cortex (PFC) is associated with higher individual risk propensity. Importantly, the association between local sleep depth and risk preferences remained significant when controlling for total sleep time and for time spent in deep sleep, i.e., sleep stages N2 and N3. Moreover, the association between risk preferences and SWA over the right PFC was very similar in all sleep cycles. Because the right PFC plays a central role in cognitive control functions, we speculate that local sleep depth in this area, as reflected by SWA, might serve as a dispositional indicator of self-regulatory ability, which in turn reflects risk preferences.

The Fingerprint-Like Pattern of Nocturnal Brain Activity Demonstrated in Young Individuals is Also Present in Senior Adulthood

PURPOSE

The quantitative sleep EEG has been considered as electroencephalographic "fingerprint", ie, it is stable within but differs between individuals. So far, however, almost all studies addressing this aspect have been conducted in young men. It was therefore of interest to know whether the sleep EEG fingerprint concept holds true in older samples of both sexes.

PATIENTS AND METHODS

Data from three different subsamples of 30 healthy individuals each were reused for the present secondary analysis (young men (YM) = 25.6 ± 2.4 years, elderly men (EM) = 69.1 ± 5.5 years, elderly women (EW) = 67.8 ± 5.7 years). Individuals slept ten times in the sleep laboratory, resulting in a total of 900 study nights. However, to avoid misinterpretation due to intervention-related changes in sleep EEG power spectra, only the 3 sham nights without any intervention were included, reducing the datasets to 270. To determine stability of NREM sleep EEG power spectra between sham night pairs, within- and between-subject Manhattan distance measures were computed separately by sample.

RESULTS

Regardless of subsample and sham night pair, lowest distance measures, ie, largest similarity, were observed for within-subject power spectra comparisons (range of mean distance measures for EW from 3.82 to 4.06, for EM from 3.55 to 3.63, and for YM from 3.04 to 3.62). Moreover, intraindividual similarity did not differ substantially between samples. Between-subject power spectra distance measures were considerably larger (range of mean distance measures for EW from 12.95 to 13.15, for EM from 12.21 to 12.57, and for YM from 10.33 to 10.78) and varied significantly between young and elderly individuals.

CONCLUSION

The present results support the view that the sleep EEG power spectrum is an individual trait-like characteristic that remains unique up until old age. This finding may help to increase the sensitivity in measuring intervention effects.

Linking stages of non-rapid eye movement sleep to the spectral EEG markers of the drives for sleep and wake

The conventional staging classification reduces all patterns of sleep polysomnogram signals to a small number of yes-or-no variables labeled wake or a stage of sleep (e.g., W, N1, N2, N3, and R for wake, the first, second, and third stages of non-rapid eye movement sleep and rapid eye movement sleep, respectively). However, the neurobiological underpinnings of such stages remained to be elucidated. We tried to evaluate their link to scores on the first and second principal components of the EEG spectrum (1PCS and 2PCS), the markers of two major groups of promoters/inhibitors of sleep/wakefulness delineated as the drives for sleep and wake, respectively. On two occasions, polysomnographic records were obtained from 69 university students during 50-min afternoon naps and 30-s stage epochs were assigned to 1PCS and 2PCS. Results suggested two dimensionality of the structure of individual differences in amounts of stages. Amount of N1 loaded exclusively on one of two dimensions associated with 1PCS, amounts of W and N2 loaded exclusively on another dimension associated with 2PCS, and amount of N3 was equally loaded on both dimensions. Scores demonstrated stability within each stage, but a drastic change in just one of two scores occurred during transitions from one stage to another on the way from wakefulness to deeper sleep (e.g., 2PCS changed from >0 to <0 during transition W→N1, 1PCS changed from <0 to >0 during transition N1→N2). Therefore, the transitions between stages observed during short naps might be linked to rapid changes in the reciprocal interactions between the promoters/inhibitors of sleep/wakefulness.NEW & NOTEWORTHY In the present nap study, two dimensionality of the structure of individual differences in sleep stages was revealed. These results also suggested that individual variation in the sleep and wake drives associated with the first and second principal components of the EEG spectrum might underlie this structure. It seemed that each stage might be related to a certain, stage-specific combination of wake-sleep promoting/inhibiting influences associated with these drives for sleep and wake.

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.

Aerobic fitness and the sleeping brain of adolescents-a pilot study

STUDY OBJECTIVES

Aerobic fitness (AF) and sleep are major determinants of health in adolescents and impact neurocognitive and psychological development. However, little is known about the interactions between AF and sleep during the developmental transition experienced across adolescence. This study aimed to consider the relationships between AF and habitual sleep patterns and sleep neurophysiology in healthy adolescents.

METHODS

Subjects (mean age = 14.6 ± 2.3 years old, range 11-17, 11 females) were evaluated for AF (peak VO2 assessed by ramp-type progressive cycle ergometry in the laboratory), habitual sleep duration and efficiency (7-14 days actigraphy), and topographic patterns of spectral power in slow wave, theta, and sleep spindle frequencies in non-rapid eye movement (NREM) sleep using overnight polysomnography (PSG) with high-density electroencephalography (hdEEG, 128 channels).

RESULTS

Significant relationships were observed between peak VO2 and habitual bedtime (r = -0.650, p = .009) and wake-up time (r = -0.603, p = .017), with greater fitness associated with going to bed and waking up earlier. Peak VO2 significantly predicted slow oscillations (0.5-1 Hz, p = .018) and theta activity (4.5-7.5 Hz, p = .002) over anterior frontal and central derivations (p < .001 and p = .001, respectively) after adjusting for sex and pubertal development stage. Similar associations were detected for fast sleep spindle activity (13-16 Hz, p = .006), which was greater over temporo-parietal derivations.

CONCLUSIONS

Greater AF was associated with a more mature pattern of topographically-specific features of sleep EEG known to support neuroplasticity and cognitive processes and which are dependent on prefrontal cortex and hippocampal function in adolescents and adults. AF was also correlated with a smaller behavioral sleep phase delay commonly seen during adolescence.

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.