Changes in the cortical network during sleep stage transitions

Sleep state transitions are closely related to insomnia, drowsiness, and sleep maintenance. However, how the cortical network varies during such a transition process remains unclear. Changes in the cortical interaction during the short-term process of sleep stage transitions were investigated. In all, 40 healthy young participants underwent overnight polysomnography. The phase transfer entropy of six frequency bands was obtained from 16 electroencephalography channels to assess the strength and direction of information flow between the cortical regions. Differences in the cortical network between the first and the last 10 s in a 40-s transition period across wakefulness, N1, N2, N3, and rapid eye movement were, respectively, studied. Various frequency bands exhibited different patterns during the sleep stage transitions. It was found that the mutual transitions between the sleep stages were not necessarily the opposite. More significant changes were observed in the sleep deepening process than in the process of sleep awakening. During sleep stage transitions, changes in the inflow and outflow strength of various cortical regions led to regional differences, but for the entire sleep progress, such an imbalance did not intensify, and a dynamic balance was instead observed. The detailed findings of variations in cortical interactions during sleep stage transition promote understanding of sleep mechanism, sleep process, and sleep function. Additionally, it is expected to provide helpful clues for sleep improvement, like reducing the time required to fall asleep and maintaining sleep depth.

Generalizability of Frequency Weighting Curve for Extraction of Spectral Drowsy Component From the EEG Signals Recorded in Eyes-Closed Condition

One of the critical barriers to reducing the threats of sleep loss to public health, safety, and productivity is a lack of practical tools for quick identification of objective level of sleepiness. We examined a novel sleepiness measure named "spectral drowsy component score" to provide evidence for generalizability of a frequency weighting curve required for calculation of this measure. Each spectral drowsy component score is a sum of 16 weighted ln-transformed single-Hz power densities (1-16 Hz) obtained by the fast Fourier transformation of an electroencephalographic signal recorded during the first minute after closing the eyes. A set of 16 weights (frequency weighting curve) is derived empirically. One type of such curve is a correlation spectrum. It consists of 16 coefficients of correlation of a group-averaged experimental time course of sleepiness with16 time courses of single-Hz power densities. Sleepiness is determined either subjectively (by self-scoring on the Karolinska Sleepiness Scale) or objectively (as sleep latency). Another type is a differential spectrum reflecting difference between 2 sets of 16 power densities obtained for either distant phases of sleep deprivation experiment or distinct alertness-sleepiness substates. Analysis of 3 datasets collected in sleep deprivation experiments with, in total, 160 participants showed that, despite differences in the protocols of these experiments and ages of their participants, the forms of frequency weighting curves always resembled one another. Such resemblance led to practical identity of scoring results. We concluded that spectral drowsy component scoring might be implemented into quick, simple, direct, transparent, and objective test of sleepiness.

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

Wakefulness is separated from a well-established sleep by an onset period. This is characterized by dramatic changes in scores on the first and second principal components of the electroencephalographic (EEG) spectrum, which reflects the kinetics of sleep- and wake-promoting processes. The present analysis examined whether significant buildups and declines of the first and second scores can occur throughout stage 1 sleep, or only on its boundaries with stage 2 and wakefulness. Twenty-seven adults participated in multiple 20-minute attempts to nap in the course of 24-hour wakefulness after either deprivation, restriction or ad lib night sleep. Power spectra were calculated on 1-minute intervals of 251 EEG records. Irrespective of accumulated sleep debt and duration of stage 1 sleep (from <2 to >5 minutes), the first principal component score was permanently attenuated across this stage as well as during preceding wakefulness. It showed rapid buildup only on the boundary with stage 2. The second principal component score always started its decline earlier, on the wake-sleep boundary. It did not show further decline throughout the following intervals of stages 1 and 2. It seems that stage 1 sleep occurs due to a delay of the buildup of the sleep-promoting process relative to the decline of the wake-promoting process which coincide, with initiation of stage 2 sleep and termination of wakefulness. Therefore, "drowsy" sleep can be regarded as occupying "no man's land", between the opponent driving forces for wake and sleep.

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.

Two distinct synchronization processes in the transition to sleep: a high-density electroencephalographic study

OBJECTIVES

To assess how the characteristics of slow waves and spindles change in the falling-asleep process.

DESIGN

Participants undergoing overnight high-density electroencephalographic recordings were awakened at 15- to 30-min intervals. One hundred forty-one falling-asleep periods were analyzed at the scalp and source level.

SETTING

Sleep laboratory.

PARTICIPANTS

Six healthy participants.

INTERVENTIONS

Serial awakenings.

RESULTS

The number and amplitude of slow waves followed two dissociated, intersecting courses during the transition to sleep: slow wave number increased slowly at the beginning and rapidly at the end of the falling-asleep period, whereas amplitude at first increased rapidly and then decreased linearly. Most slow waves occurring early in the transition to sleep had a large amplitude, a steep slope, involved broad regions of the cortex, predominated over frontomedial regions, and preferentially originated from the sensorimotor and the posteromedial parietal cortex. Most slow waves occurring later had a smaller amplitude and slope, involved more circumscribed parts of the cortex, and had more evenly distributed origins. Spindles were initially sparse, fast, and involved few cortical regions, then became more numerous and slower, and involved more areas.

CONCLUSIONS

Our results provide evidence for two types of slow waves, which follow dissociated temporal courses in the transition to sleep and have distinct cortical origins and distributions. We hypothesize that these two types of slow waves result from two distinct synchronization processes: (1) a "bottom-up," subcorticocortical, arousal system-dependent process that predominates in the early phase and leads to type I slow waves, and (2) a "horizontal," corticocortical synchronization process that predominates in the late phase and leads to type II slow waves. The dissociation between these two synchronization processes in time and space suggests that they may be differentially affected by experimental manipulations and sleep disorders.

Calibration of an objective alertness scale

In order to establish validity of an objective scale of alertness-sleepiness, one is forced to somehow calibrate it using subjective scales, such as the Karolinska Sleepiness Scale (KSS). We evaluated the effects of prolonged wakefulness on the extent of disagreement between objective and subjective KSS assessments, and tested whether calibration of an objective alertness-sleepiness scale can be established despite the limited reliability of subjective reports. Starting from 7p.m., the resting electroencephalogram (EEG) was recorded at 2-hour intervals over the last 32-50 h of 44-61-hour wakefulness of 15 healthy study participants. Frontal and occipital scores on the 2nd principal component of the EEG spectrum and occipital alpha-theta power difference were computed for 1-min intervals of 5-min eyes-closed EEG recordings. To obtain alertness scale scores in the range from 5 to 0, positive and negative values of these EEG indexes were assigned to 1 and 0, respectively, and then summed. Although correlation between time courses of objective and subjective (KSS) scores was very strong, evidence for systematic errors in both the mean and the calibration was also found. Correction of these errors resulted in strengthening of correlation (r = 0.99) and establishing one-to-one correspondence between the steps of objective and subjective scales. The results indicate that scores from 5 to 0 on the objective alertness scale can be anchored to minimal, mild, moderate, marked, severe, and disabling levels of sleepiness.

When does this cortical area drop off? Principal component structuring of the EEG spectrum yields yes-or-no criteria of local sleep onset

The traditional sleep scoring approach has been invented long before the recognition of strictly local nature of the sleep process. It considers sleep as a whole-organism behavior state, and, thus, it cannot be used for identification of sleep onset in a separate brain region. Therefore, this paper was aimed on testing whether the practically useful, simple and reliable yes-or-no criterion of sleep onset in a particular cortical region might be developed through applying principal component analysis to the electroencephalographic (EEG) spectra. The resting EEG was recorded with 2-hour intervals throughout 43-61-hour prolongation of wakefulness, and during 12 20-minute attempts to nap in the course of 24-hour wakefulness (15 and 18 adults, respectively). The EEG power spectra were averaged on 1-min intervals of each resting EEG record and on 1-min intervals of each napping attempt, respectively. Since we earlier demonstrated that scores on the first and second principal components of the EEG spectrum exhibit dramatic changes during the sleep onset period, a zero-crossing buildup of the first score and a zero-crossing decline of the second score were examined as possible yes-or-no markers of regional sleep onsets. The results suggest that, irrespective of electrode location, sleep onset criterion and duration of preceding wakefulness, a highly significant zero-crossing decline of the second principal component score always occurred within 1-minute interval of transition from wakefulness to sleep. Therefore, it was concluded that such zero-crossing decline can serve as a reliable, simple, and practically useful yes-or-no marker of drop off event in a given cortical area.