Rapid Changes in Scores on Principal Components of the EEG Spectrum do not Occur in the Course of "Drowsy" Sleep of Varying Length

New bands in the sleep stages of spider monkeys (Ateles geoffroyi): Electroencephalographic correlations and spatial distribution

The study of electroencephalographic (EEG) signals in nonhuman primates has led to important discoveries in neurophysiology and sleep behavior. Several studies have analyzed digital EEG data from primate species with prehensile tails, like the spider monkey, and principal component analysis has led to the identification of new EEG bands and their spatial distribution during sleep and wakefulness in these monkeys. However, the spatial location of the EEG correlations of these new bands during the sleep-wake cycle in the spider monkey has not yet been explored. Thus, the objective of this study was to determine the spatial distribution of EEG correlations in the new bands during wakefulness, rapid eye movement (REM) sleep, and non-REM sleep in this species. EEG signals were obtained from the scalp of six monkeys housed in experimental conditions in a laboratory setting. Regarding the 1-21 Hz band, a significant correlation between left frontal and central regions was recorded during non-REM 2 sleep. In the REM sleep, a significant correlation between these cortical areas was seen in two bands: 1-3 and 3-13 Hz. This reflects a modification of the degree of coupling between the cortical areas studied, associated with the distinct stages of sleep. The intrahemispheric EEG correlation found between left perceptual and motor regions during sleep in the spider monkey could indicate activation of a neural circuit for the processing of environmental information that plays a critical role in monitoring the danger of nocturnal predation.

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.

Topographic distribution of the EEG ad hoc broad bands during sleep and wakefulness in the spider monkey (Ateles Geoffroyi)

There is evidence that research on sleep among New World monkeys may provide important knowledge related to the evolution of sleep more broadly in the primate order. Digital electroencephalographic (EEG) analyses provide essential knowledge on sleep in the spider monkey. Recently, specific EEG bands related to sleep in these animals have been obtained using principal component analysis, but the exact spatio-temporal distribution of these EEG bands in this species has not yet been analyzed. This study determined the topographic distribution of the EEG spectral power of ad hoc broad bands during rapid eye movement sleep, nonrapid eye movement sleep, and wakefulness. Superficial EEG activity was obtained from the occipital, frontal, and central areas of six young adult male monkeys housed in a laboratory. During wakefulness, occipital areas showed high absolute power in the 1-3, 3-12, and 11-30 Hz ranges, while during nonrapid eye movement 1 sleep the highest absolute power was in the 13-30 Hz range. During nonrapid eye movement 3 sleep, frontal and central areas showed a high absolute power in the 18-19 Hz range. Finally, the right central area showed a high absolute power in the 20-30 Hz range during rapid eye movement sleep. This topographic distribution of EEG bands could represent the brain organization required for arousal and mnemonic processing during sleep in the spider monkey.

Differential relationship of two measures of sleepiness with the drives for sleep and wake

PURPOSE

Since disagreement has been found between an objective sleep propensity measured by sleep onset latency (SOL) and subjective sleepiness assessment measured by the Epworth sleepiness scale (ESS) score, distinct underlying causes and consequences were suggested for these two sleepiness measures. We addressed the issue of validation of the ESS against objective sleepiness and sleep indexes by examining the hypothesis that these two sleepiness measures are disconnected due to their differential relationship with the antagonistic drives for sleep and wake.

METHODS

The polysomnographic records of 50-min napping attempts were collected from 27 university students on three occasions. Scores on the first and second principal components of the electroencephalographic (EEG) spectrum were calculated to measure the sleep and wake drives, respectively. Self-assessments of subjective sleepiness and sleep were additionally collected in online survey of 633 students at the same university.

RESULTS

An ESS score was disconnected with the polysomnographic and self-assessed SOL in the nap study and online survey, respectively. An ESS score but not SOL was significantly linked to the spectral EEG measure of the sleep drive, while SOL but not ESS showed a significant association with the spectral EEG measure of the opposing wake drive.

CONCLUSIONS

Each of two sleepiness measures was validated against objective indicators of the opposing sleep-wake regulating processes, but different underlying causes were identified for two distinct aspects of sleepiness. A stronger sleep drive and a weaker opposing drive for wake seem to contribute to a higher ESS score and to a shorter SOL, respectively.

Principal component analysis of electroencephalographic activity during sleep and wakefulness in the spider monkey (Ateles geoffroyi)

The study of electroencephalographic (EEG) activity during sleep in the spider monkey has provided new insights into primitive arboreal sleep physiology and behavior in anthropoids. Nevertheless, studies conducted to date have maintained the frequency ranges of the EEG bands commonly used with humans. The aim of the present work was to determine the EEG broad bands that characterize sleep and wakefulness in the spider monkey using principal component analysis (PCA). The EEG activity was recorded from the occipital, central, and frontal EEG derivations of six young-adult male spider monkeys housed in a laboratory setting. To determine which frequencies covaried and which were orthogonally independent during sleep and wakefulness, the power EEG spectra and interhemispheric and intrahemispheric EEG correlations from 1 to 30 Hz were subjected to PCA. Findings show that the EEG bands detection differed from those reported previously in both spider monkeys and humans, and that the 1-3 and 2-13 Hz frequency ranges concur with the oscillatory activity elucidated by cellular recordings of subcortical regions. Results show that applying PCA to the EEG spectrum during sleep and wakefulness in the spider monkey led to the identification of frequencies that covaried with, and were orthogonally independent of, other frequencies in each behavioral vigilance state. The new EEG bands differ from those used previously with both spider monkeys and humans. The 1-3 and 2-13 Hz frequency ranges are in accordance with the oscillatory activity elucidated by cellular recordings of subcortical regions in other mammals.

Owls, larks, swifts, woodcocks and they are not alone: A historical review of methodology for multidimensional self-assessment of individual differences in sleep-wake pattern

Differences between the so-called larks and owls representing the opposite poles of morningness-eveningness dimension are widely known. However, scientific consensus has not yet been reached on the methodology for ranking and typing people along other dimensions of individual variation in their sleep-wake pattern. This review focused on the history and state-of-the-art of the methodology for self-assessment of individual differences in more than one trait or adaptability of the human sleep-wake cycle. The differences between this and other methodologies for the self-assessment of trait- and state-like variation in the perceived characteristics of daily rhythms were discussed and the critical issues that remained to be addressed in future studies were highlighted. These issues include a) a failure to develop a unidimensional scale for scoring chronotypological differences, b) the inconclusive results of the long-lasting search for objective markers of chronotype, c) a disagreement on both number and content of scales required for multidimensional self-assessment of chronobiological differences, d) a lack of evidence for the reliability and/or external validity of most of the proposed scales and e) an insufficient development of conceptualizations, models and model-based quantitative simulations linking the differences between people in their sleep-wake pattern with the differences in the basic parameters of underlying chronoregulatory processes. It seems that, in the nearest future, the wide implementation of portable actigraphic and somnographic devices might lead to the development of objective methodologies for multidimensional assessment and classification of sleep-wake traits and adaptabilities.

Principal component analysis of the EEG spectrum can provide yes-or-no criteria for demarcation of boundaries between NREM sleep stages

Human sleep begins in stage 1 and progresses into stages 2 and 3 of Non-Rapid-Eye-Movement (NREM) sleep. These stages were defined using several arbitrarily-defined thresholds for subdivision of albeit continuous process of sleep deepening. Since recent studies indicate that stage 3 (slow wave sleep) has unique vital functions, more accurate measurement of this stage duration and continuity might be required for both research and practical purposes. However, the true neurophysiological boundary between stages 2 and 3 remains unknown. In a search for non-arbitrary threshold criteria for distinguishing the boundaries between NREM sleep stages, scores on the principal components of the electroencephalographic (EEG) spectrum were analyzed in relation to stage onsets. Eighteen young men made 12-20-minute attempts to nap during 24-hour wakefulness. Single-minute intervals of the nap EEG records were assigned relative to the minute of onsets of polysomnographically determined stages 1, 2, and 3. The analysis of within-nap time courses of principal components scores revealed that, unlike any conventional spectral EEG index, score on the 4th principal component exhibited a rather rapid rise on the boundary between stages 2 and 3. This was mostly a change from negative to positive score. Therefore, it might serve as yes-or-no criterion of stage 3 onset. Additionally, similarly rapid changes in sign of scores were exhibited by the 1st and 2nd principal components on the boundary of stages 2 and 1 and on the boundary between stage 1 and wakefulness, respectively.