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.     

Young women with major depression live on higher homeostatic sleep pressure than healthy controls

There is mounting evidence for the involvement of the sleep-wake cycle and the circadian system in the pathogenesis of major depression. However, only a few studies so far focused on sleep and circadian rhythms under controlled experimental conditions. Thus, it remains unclear whether homeostatic sleep pressure or circadian rhythms, or both, are altered in depression. Here, the authors aimed at quantifying homeostatic and circadian sleep-wake regulatory mechanisms in young women suffering from major depressive disorder and healthy controls during a multiple nap paradigm under constant routine conditions. After an 8-h baseline night, 9 depressed women, 8 healthy young women, and 8 healthy older women underwent a 40-h multiple nap protocol (10 short sleep-wake cycles) followed by an 8-h recovery night. Polysomnographic recordings were done continuously, and subjective sleepiness was assessed. In order to measure circadian output, salivary melatonin samples were collected during scheduled wakefulness, and the circadian modulation of sleep spindles was analyzed with reference to the timing of melatonin secretion. Sleep parameters as well as non-rapid eye movement (NREM) sleep electroencephalographic (EEG) spectra were determined for collapsed left, central, and right frontal, central, parietal, and occipital derivations for the night and nap-sleep episodes in the frequency range .75-25 Hz. Young depressed women showed higher frontal EEG delta activity, as a marker of homeostatic sleep pressure, compared to healthy young and older women across both night sleep episodes together with significantly higher subjective sleepiness. Higher delta sleep EEG activity in the naps during the biological day were observed in young depressed women along with reduced nighttime melatonin secretion as compared to healthy young volunteers. The circadian modulation of sleep spindles between the biological night and day was virtually absent in healthy older women and partially impaired in young depressed women. These data provide strong evidence for higher homeostatic sleep pressure in young moderately depressed women, along with some indications for impairment of the strength of the endogenous circadian output signal involved in sleep-wake regulation. This finding may have important repercussions on the treatment of the illness as such that a selective suppression of EEG slow-wave activity could promote acute mood improvement.     

基于奇异值第一主成分的睡眠脑电分期方法研究

目的:脑电信号含多种噪声和伪迹,信噪比较低,特征提取前必须进行复杂的预处理,严重影响睡眠分期的速度。鉴于此,本文提出一种基于奇异值第一主成分的睡眠脑电分期方法,该方法抗噪性能较强,可省去预处理过程,减少计算量,提高睡眠分期的效率。方法:对未经过预处理的睡眠脑电进行奇异系统分析,研究奇异谱曲线,提取奇异值第一主成分,探索其随睡眠状态变化的规律。并通过支持向量机利用奇异值第一主成分对睡眠分期。结果:奇异值第一主成分不仅能表征脑电信号主体,而且可以抑制噪声、降低维数。随着睡眠的深入,奇异值第一主成分的值逐渐增大,但在REM期处于S1期和S2期之间。经MIT-BIH睡眠数据库中5例同导联位置的脑电数据测试(仅1导脑电数据),睡眠脑电分期的准确率达到86.4%。结论:在未对脑电信号进行预处理的情况下,提取的睡眠脑电的奇异值第一主成分能有效表征睡眠状态,是一种有效的睡眠分期依据。本文运用提出的方法仅采用1导脑电数据,就能得到较为满意的睡眠分期结果。该方法有较强的分类性能,且抗噪能力强,不需要对脑电作复杂的预处理,计算量小,方法简单,很大程度上提高了睡眠分期的效率。     

Sleep homeostasis and models of sleep regulation

According to the two-process model of sleep regulation, the timing and structure of sleep are determined by the interaction of a homeostatic and a circadian process. The original qualitative model was elaborated to quantitative versions that included the ultradian dynamics of sleep in relation to the non-REM-REM sleep cycle. The time course of EEG slow-wave activity, the major marker of non-REM sleep homeostasis, as well as daytime alertness were simulated successfully for a considerable number of experimental protocols. They include sleep after partial sleep deprivation and daytime napping, sleep in habitual short and long sleepers, and alertness in a forced desynchrony protocol or during an extended photoperiod. Simulations revealed that internal desynchronization can be obtained for different shapes of the thresholds. New developments include the analysis of the waking EEG to delineate homeostatic and circadian processes, studies of REM sleep homeostasis, and recent evidence for local, use-dependent sleep processes. Moreover, nonlinear interactions between homeostatic and circadian processes were identified. In the past two decades, models have contributed considerably to conceptualizing and analyzing the major processes underlying sleep regulation, and they are likely to play an important role in future advances in the field.     

Quantification of sleepiness through principal component analysis of the electroencephalographic spectrum

Although circadian and sleep research has made extraordinary progress in the recent years, one remaining challenge is the objective quantification of sleepiness in individuals suffering from sleep deprivation, sleep restriction, and excessive somnolence. The major goal of the present study was to apply principal component analysis to the wake electroencephalographic (EEG) spectrum in order to establish an objective measure of sleepiness. The present analysis was led by the hypothesis that in sleep-deprived individuals, the time course of self-rated sleepiness correlates with the time course score on the 2nd principal component of the EEG spectrum. The resting EEG of 15 young subjects was recorded at 2-h intervals for 32-50 h. Principal component analysis was performed on the sets of 16 single-Hz log-transformed EEG powers (1-16 Hz frequency range). The time course of self-perceived sleepiness correlated strongly with the time course of the 2nd principal component score, irrespective of derivation (frontal or occipital) and of analyzed section of the 7-min EEG record (2-min section with eyes open or any of the five 1-min sections with eyes closed). This result indicates the possibility of deriving an objective index of physiological sleepiness by applying principal component analysis to the wake EEG spectrum.     

EEG Correlates of Dream Recall in Depressed Outpatients and Healthy Controls

The present study explored EEG correlates of dream recall in 17 symptomatic, unmedicated depressed patients and in 19 healthy adults. EEG segments from the last 30 minutes of sleep, from the five minutes following morning awakening, and the absolute difference between sleep and waking EEG were contrasted between the two groups of participants during successful dream recall and during no recall. Period amplitude analysis was used to quantify EEG frequencies. Increased high-frequency beta incidence in the right hemisphere and amplitude in both hemispheres during sleep were associated with dream recall in both patient and control groups. Depressed patients also showed higher delta amplitude in both hemispheres during sleep associated with recall, but this effect did not reach significance. With regard to the changes between sleep and wakefiilness, a smaller change in right hemisphere beta and delta incidence characterized successful recall in healthy controls. By contrast, those with depression showed recall success when the sleep/wake shifts in right hemisphere beta and delta incidence were larger. Recall failure was characterized by small EEG shifts from sleep to wakefulness in the depressed group. The same effects were observed for beta and delta amplitude measures, except that healthy controls showed a large shift in delta amplitude in the sleep-wake transition during successful recall but not during recall failure. Recall in those with depression was associated with a dramatic shift in left hemisphere delta amplitude. These findings provide support for Koukkou and Lehmann's (1983, 1993) state-shift hypothesis of dream recall in healthy controls (except for left hemisphere delta amplitude) but not in the depressed. It appears that in order to recall a dream, depressed patients must undergo larger shifts in brain activity and perhaps a different pattern of reorganization of EEG frequencies than controls. This finding may account for the low rates of recall reported previously in this clinical group.     

Young Women With Major Depression Live on Higher Homeostatic Sleep Pressure Than Healthy Controls

There is mounting evidence for the involvement of the sleep-wake cycle and the circadian system in the pathogenesis of major depression. However, only a few studies so far focused on sleep and circadian rhythms under controlled experimental conditions. Thus, it remains unclear whether homeostatic sleep pressure or circadian rhythms, or both, are altered in depression. Here, the authors aimed at quantifying homeostatic and circadian sleep-wake regulatory mechanisms in young women suffering from major depressive disorder and healthy controls during a multiple nap paradigm under constant routine conditions. After an 8-h baseline night, 9 depressed women, 8 healthy young women, and 8 healthy older women underwent a 40-h multiple nap protocol (10 short sleep-wake cycles) followed by an 8-h recovery night. Polysomnographic recordings were done continuously, and subjective sleepiness was assessed. In order to measure circadian output, salivary melatonin samples were collected during scheduled wakefulness, and the circadian modulation of sleep spindles was analyzed with reference to the timing of melatonin secretion. Sleep parameters as well as non-rapid eye movement (NREM) sleep electroencephalographic (EEG) spectra were determined for collapsed left, central, and right frontal, central, parietal, and occipital derivations for the night and nap-sleep episodes in the frequency range .75–25?Hz. Young depressed women showed higher frontal EEG delta activity, as a marker of homeostatic sleep pressure, compared to healthy young and older women across both night sleep episodes together with significantly higher subjective sleepiness. Higher delta sleep EEG activity in the naps during the biological day were observed in young depressed women along with reduced nighttime melatonin secretion as compared to healthy young volunteers. The circadian modulation of sleep spindles between the biological night and day was virtually absent in healthy older women and partially impaired in young depressed women. These data provide strong evidence for higher homeostatic sleep pressure in young moderately depressed women, along with some indications for impairment of the strength of the endogenous circadian output signal involved in sleep-wake regulation. This finding may have important repercussions on the treatment of the illness as such that a selective suppression of EEG slow-wave activity could promote acute mood improvement. (Author correspondence: Christian.cajochen@upkbs.ch)     

Deterministic chaos and the first positive Lyapunov exponent: a nonlinear analysis of the human electroencephalogram during sleep

Under selected conditions, nonlinear dynamical systems, which can be described by deterministic models, are able to generate so-called deterministic chaos. In this case the dynamics show a sensitive dependence on initial conditions, which means that different states of a system, being arbitrarily close initially, will become macroscopically separated for sufficiently long times. In this sense, the unpredictability of the EEG might be a basic phenomenon of its chaotic character. Recent investigations of the dimensionality of EEG attractors in phase space have led to the assumption that the EEG can be regarded as a deterministic process which should not be mistaken for simple noise. The calculation of dimensionality estimates the degrees of freedom of a signal. Nevertheless, it is difficult to decide from this kind of analysis whether a process is quasiperiodic or chaotic. Therefore, we performed a new analysis by calculating the first positive Lyapunov exponent L ₁ from sleep EEG data. Lyapunov exponents measure the mean exponential expansion or contraction of a flow in phase space. L ₁ is zero for periodic as well as quasiperiodic processes, but positive in the case of chaotic processes expressing the sensitive dependence on initial conditions. We calculated L ₁ for sleep EEG segments of 15 healthy men corresponding to the sleep stages I, II, III, IV, and REM (according to Rechtschaffen and Kales). Our investigations support the assumption that EEG signals are neither quasiperiodic waves nor a simple noise. Moreover, we found statistically significant differences between the values of L ₁ for different sleep stages. All together, this kind of analysis yields a useful extension of the characterization of EEG signals in terms of nonlinear dynamical system theory.     

Cyclic and sleep-like spontaneous alternations of brain state under urethane anaesthesia

Background

Although the induction of behavioural unconsciousness during sleep and general anaesthesia has been shown to involve overlapping brain mechanisms, sleep involves cyclic fluctuations between different brain states known as active (paradoxical or rapid eye movement: REM) and quiet (slow-wave or non-REM: nREM) stages whereas commonly used general anaesthetics induce a unitary slow-wave brain state.

Methodology/Principal Findings

Long-duration, multi-site forebrain field recordings were performed in urethane-anaesthetized rats. A spontaneous and rhythmic alternation of brain state between activated and deactivated electroencephalographic (EEG) patterns was observed. Individual states and their transitions resembled the REM/nREM cycle of natural sleep in their EEG components, evolution, and time frame (∼11 minute period). Other physiological variables such as muscular tone, respiration rate, and cardiac frequency also covaried with forebrain state in a manner identical to sleep. The brain mechanisms of state alternations under urethane also closely overlapped those of natural sleep in their sensitivity to cholinergic pharmacological agents and dependence upon activity in the basal forebrain nuclei that are the major source of forebrain acetylcholine. Lastly, stimulation of brainstem regions thought to pace state alternations in sleep transiently disrupted state alternations under urethane.

Conclusions/Significance

Our results suggest that urethane promotes a condition of behavioural unconsciousness that closely mimics the full spectrum of natural sleep. The use of urethane anaesthesia as a model system will facilitate mechanistic studies into sleep-like brain states and their alternations. In addition, it could also be exploited as a tool for the discovery of new molecular targets that are designed to promote sleep without compromising state alternations.     

Sleep and EEG spectra in the Syrian hamster (Mesocricetus auratus) under baseline conditions and following sleep deprivation

Summary Sleep was studied by continuous 24-h recordings in adult male Syrian hamsters, chronically implanted with EEG and EMG electrodes. Three vigilance states were determined using visual scoring and EEG power spectra (0.25–25 Hz) computed for 4-s episodes.The effects of two methods of total sleep deprivation (SD) were examined on vigilance states and the EEG power spectrum. The animals were subjected to 24 h SD by: (1) forced locomotion in a slowly rotating drum, (2) gentle handling whenever the hamsters attempted a sleeping posture. In addition, the hamsters were subjected to SD by handling during the first 3 h of the L period.Sleep predominated in the L period (78.2% of 12 h) and the D period (51.2%). The power spectra of the 3 vigilance states were similar during the L and D period. In NREM sleep, power density values in the low frequency range (0.25–6.0 Hz) exceeded those of REM sleep and W by a maximum factor of 8.3 and 2.8, respectively. At frequencies above 16 Hz, NREM and REM sleep power density values were significantly lower than during W. A progressive decrease in power density for low EEG frequencies (0.25–7 Hz) during NREM sleep was seen in the course of the L period. Power density values of higher frequencies (8–25 Hz) increased at the end of the L period and remained high during the first hours of the D period.The effect of prolonged SD on vigilance states and EEG spectra was similar by both methods and strikingly small compared to similar results in rats. In contrast, 3 h SD induced a large and more prolonged effect. The similarities and differences of sleep and sleep regulation are summarized for the hamster, rat and man.Abbreviations EEG electroencephalogram - LD light dark - REM rapid eye movements - NREM sleep non REM sleep - W waking - SD sleep deprivation - TST total sleep time - L light - D dark     

Quantification of Sleepiness Through Principal Component Analysis of the Electroencephalographic Spectrum

Although circadian and sleep research has made extraordinary progress in the recent years, one remaining challenge is the objective quantification of sleepiness in individuals suffering from sleep deprivation, sleep restriction, and excessive somnolence. The major goal of the present study was to apply principal component analysis to the wake electroencephalographic (EEG) spectrum in order to establish an objective measure of sleepiness. The present analysis was led by the hypothesis that in sleep-deprived individuals, the time course of self-rated sleepiness correlates with the time course score on the 2nd principal component of the EEG spectrum. The resting EEG of 15 young subjects was recorded at 2-h intervals for 32–50?h. Principal component analysis was performed on the sets of 16 single-Hz log-transformed EEG powers (1–16?Hz frequency range). The time course of self-perceived sleepiness correlated strongly with the time course of the 2nd principal component score, irrespective of derivation (frontal or occipital) and of analyzed section of the 7-min EEG record (2-min section with eyes open or any of the five 1-min sections with eyes closed). This result indicates the possibility of deriving an objective index of physiological sleepiness by applying principal component analysis to the wake EEG spectrum. (Author correspondence: putilov@ngs.ru)     

Principal components of electroencephalographic spectrum as markers of opponent processes underlying ultradian sleep cycles

Sleep-wake regulation involves reciprocal interactions between sleep- and wake-promoting processes that inhibit one another. To uncover the signatures of the opponent processes underlying ultradian sleep cycles, principal component analysis was performed on the sets of 16 single-Hz log-transformed electroencephalographic (EEG) power densities (1-16?Hz frequency range). Data were collected during unrestricted night sleep followed by 9 20-min naps (14 women aged 17-55 yrs) and during 12 20-min naps after either restriction or deprivation of sleep (9 males and 9 males, respectively, aged 18-22 yrs). It was found that any subset of power spectra could be reduced to the invariant four-principal component structure. The time courses of scores on these four components might be interpreted as the spectral EEG markers of the kinetics of two pairs of opponent chronoregulatory processes. In a sequence of ultradian sleep cycles, the 1st and 2nd components represent the alternations between competing drives for sleep and wakefulness, respectively, whereas the 3rd and 4th components reflect the alternations between light and deep sleep, respectively. The results suggest that principal component structuring of EEG spectrum can be employed for derivation of the parameters of the quantitative models conceptualizing the three major aspects of sleep-wake regulation—homeostatic, circadian, and ultradian processes.     

The effect of the deprivation of the paradoxical sleep stage on the temporal dynamics of swimming in rats with antidepressive and depressant exposures]

After REM sleep deprivation the time-course of the forced swimming was reorganized. As shown, reduction of rhythmical index of depression, such effect has an antidepressive nature. In this model potentiation of specific activity of antidepressant imipramine and attenuation of depressive properties of clonidine were observed. These results suggest that shifts in sleep phase structure may be a source of restriction of circadian desynchronosis, upon which depression is based.     

Modeling the Dynamics of Disease States in Depression

Major depressive disorder (MDD) is a common and costly disorder associated with considerable morbidity, disability, and risk for suicide. The disorder is clinically and etiologically heterogeneous. Despite intense research efforts, the response rates of antidepressant treatments are relatively low and the etiology and progression of MDD remain poorly understood. Here we use computational modeling to advance our understanding of MDD. First, we propose a systematic and comprehensive definition of disease states, which is based on a type of mathematical model called a finite-state machine. Second, we propose a dynamical systems model for the progression, or dynamics, of MDD. The model is abstract and combines several major factors (mechanisms) that influence the dynamics of MDD. We study under what conditions the model can account for the occurrence and recurrence of depressive episodes and how we can model the effects of antidepressant treatments and cognitive behavioral therapy within the same dynamical systems model through changing a small subset of parameters. Our computational modeling suggests several predictions about MDD. Patients who suffer from depression can be divided into two sub-populations: a high-risk sub-population that has a high risk of developing chronic depression and a low-risk sub-population, in which patients develop depression stochastically with low probability. The success of antidepressant treatment is stochastic, leading to widely different times-to-remission in otherwise identical patients. While the specific details of our model might be subjected to criticism and revisions, our approach shows the potential power of computationally modeling depression and the need for different type of quantitative data for understanding depression.     

Depression in sleep disturbance: A review on a bidirectional relationship,mechanisms and treatment

Sleep disturbance is the most prominent symptom in depressive patients and was formerly regarded as a main secondary manifestation of depression. However, many longitudinal studies have identified insomnia as an independent risk factor for the development of emerging or recurrent depression among young, middle‐aged and older adults. This bidirectional association between sleep disturbance and depression has created a new perspective that sleep problems are no longer an epiphenomenon of depression but a predictive prodromal symptom. In this review, we highlight the treatment of sleep disturbance before, during and after depression, which probably plays an important role in improving outcomes and preventing the recurrence of depression. In clinical practice, pharmacological therapies, including hypnotics and antidepressants, and non‐pharmacological therapies are typically applied. A better understanding of the pathophysiological mechanisms between sleep disturbance and depression can help psychiatrists better manage this comorbidity.     

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.     

Effects of the Seasons and of Bright Light Administered at Different Times of Day on Sleep EEG and Mood in Patients with Seasonal Affective Disorder

The study examines objective characteristics of sleep in women (n=31) with and without seasonal affective disorder, winter type, before and after a week of light treatment (at either 0800-1000 h, 1600-1800 h or 1800-2000 h). Subsamples of 13 patients and 7 controls were studied additionally in summer, and, among these patients, 9 were also recorded in spring and fall. Ranking the results from the lowest to the largest degree of deviation of sleep structure in patients from the norm yields the sequence: spring -> summer -> winter after light treatment -> fall -> winter before light treatment. In winter before light treatment the total amounts and percentage of slow wave sleep were significantly lower in responders to light (n=13) compared to both nonresponders (n=8) and controls (n=10), while following light treatment the difference disappeared. The reduced amounts of slow wave sleep in the depressive state predicted higher reduction and low posttreatment scores on psychiatric scales. Light treatment and summer season showed similar effects on patients' sleep: they caused an increase of slow wave sleep and a decline of sleep stage 2. Our data do not suggest that time of light treatment is important to achieve an antidepressant effect. Moreover, phase shifting effects of light treatment and of changing season on sleep EEG were not considerable. At the same time, subjective ratings of arousal demonstrated an advance shift of the arousal rhythm after morning and a delay shift after afternoon LT. We did not find significant changes in total amounts and percentage of REM sleep over time. The data suggest that abnormally increased need for REM sleep results in the hypersomnia and may be considered as a trait marker of winter depression. An abnormal architecture of nonREM sleep appears to be a state marker of those patients who benefit from bright light administered during waking hours.     

Functional variability of the autocorrelation structure of the EEG

On an example of records EEG of 39 healthy subjects, the quantitative analysis of variability of the autocorrelation structure of one-second EEG segments was carried out on the basis of comparison of structural functions constructed for these segments. It was shown that more than 30% of cases, statistically significant sifferences were observed between the structural functions of successive one-second EEG segments shifted by 1-3 s, as compared to surrogate EEGs formed with the tangled random sequence of count points. On the basis of the obtained data, the index of nonstationarity of the EEG autocorrelation structure was proposed. This index can be used for the objective quantitative evaluation of the functional states of the human brain.     

Objective characterization and differentiation of sleep states in healthy newborns and newborns-at-risk by spectral analysis of heart rate and respiration rhythms

Best parameters of power and coherence spectra of heart rate fluctuations (HR) and respiration rhythms (RR) were established to differentiate sleep states in healthy newborns and newborns-at-risk by means of multivariate variance and discriminant analysis. Nine healthy newborns and 20 newborns with low risk features were examined polygraphically. Long-term-variability of both HR and RR and coherence between HR and RR in the frequency range of 0.26-0.97 Hz, corresponding to Respiratory Sinus Arrhythmia, allow the best differentiation of neonatal sleep states S 1 and S2. Differentiation of healthy newborns and newborns-at-risk by these parameters was not possible. Thus, in studies dealing with low risk features in newborns sleep states must be previously classified. State 1 and 2 represent different autonomic organisations. State 1 is a neuro-vegetative, relatively stable state. State 2 shows cyclic increases of coherence between HR and RR within the frequency range of Respiratory Sinus Arrhythmia. These properties are related to autonomic brain stem functions and were absent in 6 out of the 20 newborns-at-risk.