Modern nondrug methods of human sleep regulation

Nondrug approaches for the regulation and induction of human sleep available at present have been analyzed; an attempt at classifying them has been made. The main attention is paid to the methods using modern computer technologies for the recording and analysis of various characteristics of the functioning of the human body, primarily, its electroencephalogram (EEG). Normal human sleep EEG correlates and their changes during different stages of the wakefulness-sleep cycle are reviewed and modern opportunities to use these characteristics for artificial regulation and induction of human sleep are analyzed.     

Modeling the effect of sleep regulation on a neural mass model

In mammals, sleep is categorized by two main sleep stages, rapid eye movement (REM) and non-REM (NREM) sleep that are known to fulfill different functional roles, the most notable being the consolidation of memory. While REM sleep is characterized by brain activity similar to wakefulness, the EEG activity changes drastically with the emergence of K-complexes, sleep spindles and slow oscillations during NREM sleep. These changes are regulated by circadian and ultradian rhythms, which emerge from an intricate interplay between multiple neuronal populations in the brainstem, forebrain and hypothalamus and the resulting varying levels of neuromodulators. Recently, there has been progress in the understanding of those rhythms both from a physiological as well as theoretical perspective. However, how these neuromodulators affect the generation of the different EEG patterns and their temporal dynamics is poorly understood. Here, we build upon previous work on a neural mass model of the sleeping cortex and investigate the effect of those neuromodulators on the dynamics of the cortex and the corresponding transition between wakefulness and the different sleep stages. We show that our simplified model is sufficient to generate the essential features of human EEG over a full day. This approach builds a bridge between sleep regulatory networks and EEG generating neural mass models and provides a valuable tool for model validation.     

Relationship of ethanol blood level to REM and non-REM sleep time and distribution in the rat.

Direct electroencephalographic (EEG) and integrated electromyographic (EMG) recordings were analyzed for possible changes in the REM and non-REM sleep time in chronically implanted rats given 0, 1, 2, and 4 g/kg ethanol. REM and non-REM sleep were found, respectively, to be lessened and elevated in a dose-related manner. The degree of disruption of normal sleep-awake patterns was also found to correspond with blood-ethanol concentrations for the different doses of ethanol. These findings are discussed in relation to the influence of ethanol on the sleep of the human subject and the suggestion that the rat with chronic EEG and EMG electrodes may serve as a model for studying the degree of disruption of sleep upon chronic exposure to ethanol.     

Evidence for age-associated disinhibition of the wake drive provided by scoring principal components of the resting EEG spectrum in sleep-provoking conditions

Age-associated changes in different bandwidths of the human electroencephalographic (EEG) spectrum are well documented, but their functional significance is poorly understood. This spectrum seems to represent summation of simultaneous influences of several sleep–wake regulatory processes. Scoring of its orthogonal (uncorrelated) principal components can help in separation of the brain signatures of these processes. In particular, the opposite age-associated changes were documented for scores on the two largest (1st and 2nd) principal components of the sleep EEG spectrum. A decrease of the first score and an increase of the second score can reflect, respectively, the weakening of the sleep drive and disinhibition of the opposing wake drive with age. In order to support the suggestion of age-associated disinhibition of the wake drive from the antagonistic influence of the sleep drive, we analyzed principal component scores of the resting EEG spectra obtained in sleep deprivation experiments with 81 healthy young adults aged between 19 and 26 and 40 healthy older adults aged between 45 and 66 years. At the second day of the sleep deprivation experiments, frontal scores on the 1st principal component of the EEG spectrum demonstrated an age-associated reduction of response to eyes closed relaxation. Scores on the 2nd principal component were either initially increased during wakefulness or less responsive to such sleep-provoking conditions (frontal and occipital scores, respectively). These results are in line with the suggestion of disinhibition of the wake drive with age. They provide an explanation of why older adults are less vulnerable to sleep deprivation than young adults.     

不同生理状态时脑电时间序列的三神复杂度计算比较

为了研究不同生理状态下的脑电复杂度变化特点,本文依照１９９４年徐京华等人应用的算法,对４种状态（安静睁眼、清醒闭目、浅度睡眠、深度睡眠）下的三种脑电复杂度（ＫＣ、Ｃ１、２）的变化规律进行了比较分析,ＫＣ与Ｃ１的变化相一致。从安静睁眼到清醒闭目到浅睡到深度睡眠,ＫＣ与Ｃ１值均依次下降。Ｃ２值的变化则与它们相反,尤其在深睡期显著升高。实验结果提示,复杂度可做为脑电时间序列的研究指标。     

不同生理状态时脑电时间序列的三神复杂度计算比较

为了研究不同生理状态下的脑电复杂度变化特点,本文依照１９９４年徐京等人应用的算法,对４种状态（安静睁眼,清醒闭目,浅度睡眠,深度睡眠）下的三种脑电复杂度（Ｋｃ,Ｃ１,Ｃ２）的变化规律进行了比较分析,Ｋｃ与Ｃ１的变化相一致,从安静睁眼剂的清醒闭目到浅睡到深度睡眠,Ｋｃ与Ｃ１值均依次下降,Ｃ２值的变化则与它们相反,尤其在深睡期显著升高,实验结果提示,复杂度可做为脑电时间序列的研究指标。     

Age-related changes in the circadian and homeostatic regulation of human sleep

The reduction of electroencephalographic (EEG) slow-wave activity (SWA) (EEG power density between 0.75-4.5 Hz) and spindle frequency activity, together with an increase in involuntary awakenings during sleep, represent the hallmarks of human sleep alterations with age. It has been assumed that this decrease in non-rapid eye movement (NREM) sleep consolidation reflects an age-related attenuation of the sleep homeostatic drive. To test this hypothesis, we measured sleep EEG characteristics (i.e., SWA, sleep spindles) in healthy older volunteers in response to high (sleep deprivation protocol) and low sleep pressure (nap protocol) conditions. Despite the fact that the older volunteers had impaired sleep consolidation and reduced SWA levels, their relative SWA response to both high and low sleep pressure conditions was similar to that of younger persons. Only in frontal brain regions did we find an age-related diminished SWA response to high sleep pressure. On the other hand, we have clear evidence that the circadian regulation of sleep during the 40 h nap protocol was changed such that the circadian arousal signal in the evening was weaker in the older study participants. More sleep occurred during the wake maintenance zone, and subjective sleepiness ratings in the late afternoon and evening were higher than in younger participants. In addition, we found a diminished melatonin secretion and a reduced circadian modulation of REM sleep and spindle frequency-the latter was phase-advanced relative to the circadian melatonin profile. Therefore, we favor the hypothesis that age-related changes in sleep are due to weaker circadian regulation of sleep and wakefulness. Our data suggest that manipulations of the circadian timing system, rather than the sleep homeostat, may offer a potential strategy to alleviate age-related decrements in sleep and daytime alertness levels.     

Divergent Cortical Generators of MEG and EEG during Human Sleep Spindles Suggested by Distributed Source Modeling

Background

Sleep spindles are ∼1-second bursts of 10–15 Hz activity, occurring during normal stage 2 sleep. In animals, sleep spindles can be synchronous across multiple cortical and thalamic locations, suggesting a distributed stable phase-locked generating system. The high synchrony of spindles across scalp EEG sites suggests that this may also be true in humans. However, prior MEG studies suggest multiple and varying generators.

Methodology/Principal Findings

We recorded 306 channels of MEG simultaneously with 60 channels of EEG during naturally occurring spindles of stage 2 sleep in 7 healthy subjects. High-resolution structural MRI was obtained in each subject, to define the shells for a boundary element forward solution and to reconstruct the cortex providing the solution space for a noise-normalized minimum norm source estimation procedure. Integrated across the entire duration of all spindles, sources estimated from EEG and MEG are similar, diffuse and widespread, including all lobes from both hemispheres. However, the locations, phase and amplitude of sources simultaneously estimated from MEG versus EEG are highly distinct during the same spindles. Specifically, the sources estimated from EEG are highly synchronous across the cortex, whereas those from MEG rapidly shift in phase, hemisphere, and the location within the hemisphere.

Conclusions/Significance

The heterogeneity of MEG sources implies that multiple generators are active during human sleep spindles. If the source modeling is correct, then EEG spindles are generated by a different, diffusely synchronous system. Animal studies have identified two thalamo-cortical systems, core and matrix, that produce focal or diffuse activation and thus could underlie MEG and EEG spindles, respectively. Alternatively, EEG spindles could reflect overlap at the sensors of the same sources as are seen from the MEG. Although our results generally match human intracranial recordings, additional improvements are possible and simultaneous intra- and extra-cranial measures are needed to test their accuracy.     

A model-based monitor of human sleep stages

Stochastic models are proposed for sleep and for the sleep related electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG). The evolution of sleep through its various stages is described as a Markov chain. The EEG is modelled using Wiener processes. The EOG and EMG are modelled as combinations of Poisson point processes and Gaussian processes, respectively. The EEG models contain a feedback structure that is based on physiological data. The maximum likelihood sleep stage monitor, that uses the sleep-related observations, has been derived and implemented. The agreement between automatic and human stage classifications of six sleep recordings was 70.6%, which was 4.5% worse than the average agreement between six human classifiers. Monitoring of simulated sleep suggests that the difficulty in separating wakefulness from stage 1 is due to poor modelling. If one ignores this difference, which, from a diagnostic point of view is fairly unimportant, the above mentioned agreement reaches 81.8%, which is 0.5% better than the corresponding average human vs human agreement.     

Inter-Hemispheric Oscillations in Human Sleep

Sleep is generally categorized into discrete stages based on characteristic electroencephalogram (EEG) patterns. This traditional approach represents sleep architecture in a static way, but it cannot reflect variations in sleep across time and across the cortex. To investigate these dynamic aspects of sleep, we analyzed sleep recordings in 14 healthy volunteers with a novel, frequency-based EEG analysis. This approach enabled comparison of sleep patterns with low inter-individual variability. We then implemented a new probability dependent, automatic classification of sleep states that agreed closely with conventional manual scoring during consolidated sleep. Furthermore, this analysis revealed a previously unrecognized, interhemispheric oscillation during rapid eye movement (REM) sleep. This quantitative approach provides a new way of examining the dynamic aspects of sleep, shedding new light on the physiology of human sleep.     

Comparison of Different Methods for the Evaluation of Treatment Effects from the Sleep EEG of Patients with Major Depression

In healthy subjects, sleep has a typical structure of three to five cyclic transitions between different sleep states. In major depression, this regular pattern is often destroyed but can be reestablished during successful treatment. The differences between healthy and abnormal sleep are generally assessed in a time-consuming process, which consists of determining the nightly variations of the sleep states (the hypnogram) based on visual inspection of the electroencephalogram (EEG), electrooculogram, and electromyogram. In this study, three different methods of sleep EEG analysis (spectrum, outlier, and recurrence analysis) have been examined with regard to their ability to extract information about treatment effects in patients with major depression. Our data suggest that improved sleep patterns during treatment with antidepressant medication can be identified with an appropriate analysis of the EEG. By comparing different methods, we have found that many treatment effects identified by spectrum analysis can be reproduced by the much simpler technique of outlier analysis. Finally, the cyclic structure of sleep and its modification by antidepressant treatment is best illustrated by a non-linear approach, the so-called recurrence method.     

Hemispheric asymmetries in cortical electrical activity during sleep. I

The hypothesis of a predominance of the right hemisphere in stage REM as compared to NREM has been tested through a spectral analysis of the EEG recorded from left (T3) and right (T4) temporal sites in 5 young healthy right-handed male subjects. Variations in the asymmetry coefficient R - L/R + L in different sleep stages have been analyzed by one way ANOVAs and Sheffé's tests. The hypothesis of a progressive increase in left hemisphere activity throughout different REM cycles as one approaches final awakenings have been investigated by comparing variations in the asymmetry coefficient for epochs of REM and stage 2 NREM sampled in different phases of the REM cycle. EEG results do not support either the hypothesized stage dependent or cycle dependent variation in EEG activity during sleep. We question whether variations in EEG amplitude and synchronization can be used as indices of hemispheric asymmetries during sleep.     

Age‐related Changes in the Circadian and Homeostatic Regulation of Human Sleep

The reduction of electroencephalographic (EEG) slow‐wave activity (SWA) (EEG power density between 0.75–4.5 Hz) and spindle frequency activity, together with an increase in involuntary awakenings during sleep, represent the hallmarks of human sleep alterations with age. It has been assumed that this decrease in non‐rapid eye movement (NREM) sleep consolidation reflects an age‐related attenuation of the sleep homeostatic drive. To test this hypothesis, we measured sleep EEG characteristics (i.e., SWA, sleep spindles) in healthy older volunteers in response to high (sleep deprivation protocol) and low sleep pressure (nap protocol) conditions. Despite the fact that the older volunteers had impaired sleep consolidation and reduced SWA levels, their relative SWA response to both high and low sleep pressure conditions was similar to that of younger persons. Only in frontal brain regions did we find an age‐related diminished SWA response to high sleep pressure. On the other hand, we have clear evidence that the circadian regulation of sleep during the 40 h nap protocol was changed such that the circadian arousal signal in the evening was weaker in the older study participants. More sleep occurred during the wake maintenance zone, and subjective sleepiness ratings in the late afternoon and evening were higher than in younger participants. In addition, we found a diminished melatonin secretion and a reduced circadian modulation of REM sleep and spindle frequency—the latter was phase‐advanced relative to the circadian melatonin profile. Therefore, we favor the hypothesis that age‐related changes in sleep are due to weaker circadian regulation of sleep and wakefulness. Our data suggest that manipulations of the circadian timing system, rather than the sleep homeostat, may offer a potential strategy to alleviate age‐related decrements in sleep and daytime alertness levels.     

Sleep characterization of a one-month-old freely moving stumptail macaque (Macaca arctoides): a pilot study

Electroencephalographic (EEG) activity during both human and adult primates' sleep has been proven to be similar and consequently, it could be assessed under similar parameters. However, there is no information regarding this EEG activity in the early stages of development in non-human primates. Therefore, the aim of this study was to describe EEG sleep patterns in a 1-month-old Macaca arctoides with a non-invasive and free-movement method. Sleep stages were initially scored using the criteria of quiet sleep and active sleep. This procedure allowed us to observe graphoelements to distinguish sleep phases as described in adult macaques. Afterwards, the final score was recorded following the Slow Wave Sleep and Rapid Eye Movement Sleep criteria. The present results suggest that sleep features of this monkey are similar to those of a 2-month-old human neonate.     

Automatic EEG arousal detection for sleep apnea syndrome

Electroencephalographic (EEG) arousals are seen in EEG recordings as an awakening response of the human brain. Sleep apnea is a serious sleep disorder. Severe sleep apnea brings about EEG arousals and sleep for patients with sleep apnea syndrome (SAS) is thus frequently interrupted. The number of respiratory-related arousals during the whole night on PSG recordings is directly related to the quality of sleep. Detecting EEG arousals in the PSG record is thus a significant task for clinical diagnosis in sleep medicine. In this paper, a method for automatic detection of EEG arousals in SAS patients was proposed. To effectively detect respiratory-related arousals, threshold values were determined according to pathological events as sleep apnea and electromyogram (EMG). If resumption of ventilation (end of the apnea interval) was detected, much lower thresholds were adopted for detecting EEG arousals, including relatively doubtful arousals. Conversely, threshold was maintained high when pathological events were undetected. The proposed method was applied to polysomnographic (PSG) records of eight patients with SAS and accuracy of EEG arousal detection was verified by comparative visual inspection. Effectiveness of the proposed method in clinical diagnosis was also investigated.     

Topographical EEG Mapping in a Case of Recurrent Sleep Terrors

Sleep terrors are characterized by marked CNS arousal and typically occur during stage 3-4 sleep within the first NREM cycle. Studies of the EEG during sleep terrors suggest that delta power and synchrony in the EEG may be important physiological markers of sleep terror presence and intensity. An EEG mapping study was undertaken with a single participant who experienced three sleep terror episodes in the laboratory. A one-minute section of EEG was sampled immediately prior to the onset of each of the three sleep terrors. Similar EEG sections were taken from 10 healthy sex- and age-matched controls. The sleep tenors and control (normative) data were then compared topographically with z-scores (z-mapping). The z-maps indicated that all three sleep terrors contained more total and delta power in central and frontal areas than the control EEG sections. Moreover, relative delta power in these areas for the three sleep terrors was proportional to the subjective intensity of the episode. Although this pre-arousal EEG pattern may be related to ongoing slow-wave sleep mentation that may sometimes trigger sleep terror episodes, its functional significance remains an open question. The results demonstrate the utility of EEG mapping for the quantification of brain activation during sleep terror attacks and suggest that discrete activity profiles are identifiable for different types of dreaming-related arousal.     

A probabilistic framework for a physiological representation of dynamically evolving sleep state

This work presents a probabilistic method for mapping human sleep electroencephalogram (EEG) signals onto a state space based on a biologically plausible mathematical model of the cortex. From a noninvasive EEG signal, this method produces physiologically meaningful pathways of the cortical state over a night of sleep. We propose ways in which these pathways offer insights into sleep-related conditions, functions, and complex pathologies. To address explicitly the noisiness of the EEG signal and the stochastic nature of the mathematical model, we use a probabilistic Bayesian framework to map each EEG epoch to a distribution of likelihoods over all model sleep states. We show that the mapping produced from human data robustly separates rapid eye movement sleep (REM) from slow wave sleep (SWS). A Hidden Markov Model (HMM) is incorporated to improve the path results using the prior knowledge that cortical physiology has temporal continuity.     

Iron-Deficiency Anemia is Associated with Altered Characteristics of Sleep Spindles in NREM Sleep in Infancy

Objective To determine the effects of iron-deficiency anemia on the development of non-rapid-eye-movement (NREM) sleep stages, as indexed by sleep spindles. Study design Patterns of sleep spindles during NREM sleep stages 2 and 3–4 (slow-wave-sleep, SWS) were compared in 26 otherwise healthy 6-month-old Chilean infants with iron-deficiency anemia and 18 non-anemic control infants. From polygraphic recordings, EEG activity was analyzed for sleep spindles to assess their number (density), duration, frequency, and inter-spindle interval. Results Iron-deficient anemic infants differed from the control group by having sleep spindles with reduced density, lower frequency, and longer inter-spindle intervals in NREM sleep stage 2 and SWS. Conclusions These results provide evidence of delayed sleep spindle patterns in iron-deficient anemic infants, suggesting that iron is an essential micronutrient for the normal progression of NREM sleep pattern development in the human. Special issue dedicated to Dr. Moussa Youdim.     

Segmentation of EEG during sleep using time-varying autoregressive modeling

Time-varying AR modeling is applied to sleep EEG signal, in order to perform parameter estimation and detect changes in the signal characteristics (segmentation). Several types of basis functions have been analyzed to determine how closely they can approximate parameter changes characteristics of the EEG signal. The TV-AR model was applied to a large number of simulated signal segments, in order to examine the behaviour of the estimation under various conditions such as variations in the EEG parameters and in the location of segment boundaries, and different orders of the basis functions. The set of functions that is the basis for the Discrete Cosine Transform (DCT), and the Walsh functions were found to be the most efficient in the estimation of the model parameters. A segmentation algorithm based on an “Identification function” calculated from the estimated model parameters is suggested.     

Graph Theoretical Analysis of BOLD Functional Connectivity during Human Sleep without EEG Monitoring

Background

Functional brain networks of human have been revealed to have small-world properties by both analyzing electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) time series.

Methods & Results

In our study, by using graph theoretical analysis, we attempted to investigate the changes of paralimbic-limbic cortex between wake and sleep states. Ten healthy young people were recruited to our experiment. Data from 2 subjects were excluded for the reason that they had not fallen asleep during the experiment. For each subject, blood oxygen level dependency (BOLD) images were acquired to analyze brain network, and peripheral pulse signals were obtained continuously to identify if the subject was in sleep periods. Results of fMRI showed that brain networks exhibited stronger small-world characteristics during sleep state as compared to wake state, which was in consistent with previous studies using EEG synchronization. Moreover, we observed that compared with wake state, paralimbic-limbic cortex had less connectivity with neocortical system and centrencephalic structure in sleep.

Conclusions

In conclusion, this is the first study, to our knowledge, has observed that small-world properties of brain functional networks altered when human sleeps without EEG synchronization. Moreover, we speculate that paralimbic-limbic cortex organization owns an efficient defense mechanism responsible for suppressing the external environment interference when humans sleep, which is consistent with the hypothesis that the paralimbic-limbic cortex may be functionally disconnected from brain regions which directly mediate their interactions with the external environment. Our findings also provide a reasonable explanation why stable sleep exhibits homeostasis which is far less susceptible to outside world.