Characteristics of integrative brain activity during various stages of sleep and in transitional states

Electropolygraphic study of natural night sleep was performed in 16 adult subjects using correlation, coherent, cluster, and factor analyses. New evidence testifies to the active nature of sleep, which is especially manifest during falling asleep and transition from one stage of sleep to another. Falling asleep and deepening the sleep proved to be accompanied by intense reorganization of the cortico-subcortical relationships, which is reflected in the dynamics of cross-correlative and coherent interrelationships of the brain??s bioelectric potentials. Transition from wakefulness to sleep is a heterogeneous process, which is expressed in significant changes in the weights of factors I, II and III of the vector image of multichannel EEG at stage I (B) of sleep, which might reflect changes in the contribution of the main integrative brain system in the reorganization of the brain??s integrated activity. A considerable increase in the weight of factor I (this reflects generalized the modulating effect of the brainstem on the cortex) and a decrease in the weights of factors II and III (which are related to fronto-occipital and interhemispheric interactions) testify to the special synchronizing role of the brainstem in the development of this initial stage of sleep. Deeper sleep is accompanied by a decrease in interhemispheric EEG relationships of the anterior and inferior frontal areas of the cortex, which suggests that coordinated inactivation of the cortex in both hemispheres leads to reorganization of the activity in the frontal areas. Analysis of the average variance of cross-correlative (CC) EEG relationships demonstrates that stability of the spatial structure of interrelationships between various areas of the brain cortex increases with falling asleep at stage I (A); however, during transition to stage I (B), the CC EEG values become unstable and, with deepening sleep, the variance of these values decreases in the frontal brain cortex. With the onset of the paradoxical phase of sleep, the variance of the levels of interregional interactions increases to the maximum, especially with respect to the EEG relations of the posteriotemporal and inferiofrontal areas of both hemispheres.     

Mu-delta opioid interactions. II: Beta-FNA inhibits DPDPE-induced increases in morphine EEG and EEG spectral power.

In the present study, the effects of beta-FNA on DPDPE-induced increases in morphine EEG and EEG power spectra were assessed. Adult female Sprague-Dawley rats were implanted with cortical EEG electrodes and permanent indwelling ICV and IV cannulae. Rats were administered ICV beta-FNA at 20 nmol or ICV sterile water. Then 18-24 h later, rats were administered ICV DPDPE at 2.5 nmol or ICV sterile water followed, 10 min later, by IV morphine at 3 mg/kg. Morphine-induced changes in EEG global (1-50 Hz) spectral parameters, the duration of morphine-induced high voltage EEG bursts, the period of EEG and behavioral excitation, and the latency to onset of slow-wave sleep were statistically analyzed using a one-way analysis of variance. beta-FNA pretreatment significantly decreased morphine-induced total spectral power seen in the DPDPE + morphine group. beta-FNA pretreatment also significantly decreased the duration of morphine-induced EEG bursts, the period of EEG and behavioral excitation, and the latency to onset of slow-wave sleep in the DPDPE + morphine group. These data, therefore, suggest that DPDPE may be increasing the effects of morphine on EEG through delta opioid receptors associated with the mu-delta opioid receptor complex.     

Sleep EEG as a nonlinear dynamic process: a comparison of global correlation dimension of human EEG and measures of linear interdependence between channels

The goal of this work was to study (1) whether the estimation of correlation dimension (D2) using spatial embedding distinguishes between sleep stages and (2) whether information gained from the application of global D2 is redundant to measures of linear interdependence between channels. Twenty one-channel EEG segments of 12 healthy male subjects recorded during waking and sleep stages REM, I, II, and III-IV (according to the Rechtshaffen and Kales criteria) were analyzed with global (multichannel) D2, mean square correlation coefficients (MS) and proportion of variance accounted for by the first principal component (PC1). D2 was found to decrease progressively from stage I to stage III-IV with D2 values of waking and REM being close to those of stages I and II. MS and PC1 did not distinguish among sleep stages but yielded significant differences between waking and sleep. The results suggest that global D2 extracts information from human EEG. That sort of evidence cannot be obtained with measures of linear interdependence between channels.     

Sleep impairments in healthy seniors: roles of stress, cortisol, and interleukin-1 beta

Study Objectives: Increased stress responsivity and a longer-lasting glucocorticoid increase are common findings in aging studies. Increased cortisol levels at the circadian nadir also accompany aging. We used 24h free urine cortisol to assess these age changes in healthy seniors. We hypothesized that free cortisol levels would explain individual differences in age-related sleep impairments. Design: The study compared sleep, cortisol, and sleep-cortisol correlations under baseline and “stress” conditions in men and women. Setting: Subjects were studied in the General Clinical Research Center under baseline conditions and a mildly stressful procedure (24h indwelling intravenous catheter placement). Participants: Eighty-eight healthy, nonobese subjects (60 women and 28 men) from a large study of successful aging participated in the study. Mean ages were 70.6 (±6.2) and 72.3 (±5.7) years for women and men, respectively. Measurements: The 24h urines were collected for cortisol assay (radioimmunoassay [RIA]); blood was sampled at three diurnal time points for assay (enzyme-linked immunosorbent assay [ELISA]) of interleukin-1 (IL-1) beta; sleep architecture and sleep electroencephalograms (EEGs) were analyzed (after an adaptation and screening night) on baseline and stress nights via polysomnography and EEG power spectral analysis. Results: Healthy older women and men with higher levels of free cortisol (24h urine level) under a mild stress condition had impaired sleep (lower sleep efficiency; fewer minutes of stages 2, 3, and 4 sleep; more EEG beta activity during non-rapid eye movement sleep [NREM] sleep). Similar results were obtained when stress reactivity measures were used (cortisol and sleep values adjusted for baseline values), but not when baseline values alone were used. Gender differences were apparent: Men had higher levels of free urine cortisol in both baseline and mild stress conditions. Cortisol and sleep correlated most strongly in men; cortisol stress response levels explained 36% of the variance in NREM sleep stress responses. In women, but not men, higher cortisol was also associated with earlier time of arising and less REM sleep. Higher cortisol response to stress was associated with increased circulating levels of IL-1β, explaining 24% of the variance in a subset of women. Conclusion: These results indicate that free cortisol (as indexed by 24h urine values) can index responses to mild stress in healthy senior adults, revealing functional correlations (impaired sleep, earlier times of arising, more EEG beta activity during sleep, more IL-1β) and gender differences. (Chronobiology International, 17(3), 391-404, 2000)     

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.     

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.     

SLEEP IMPAIRMENTS IN HEALTHY SENIORS: ROLES OF STRESS,CORTISOL, AND INTERLEUKIN-1 BETA

Study Objectives: Increased stress responsivity and a longer-lasting glucocorticoid increase are common findings in aging studies. Increased cortisol levels at the circadian nadir also accompany aging. We used 24h free urine cortisol to assess these age changes in healthy seniors. We hypothesized that free cortisol levels would explain individual differences in age-related sleep impairments. Design: The study compared sleep, cortisol, and sleep-cortisol correlations under baseline and “stress” conditions in men and women. Setting: Subjects were studied in the General Clinical Research Center under baseline conditions and a mildly stressful procedure (24h indwelling intravenous catheter placement). Participants: Eighty-eight healthy, nonobese subjects (60 women and 28 men) from a large study of successful aging participated in the study. Mean ages were 70.6 (±6.2) and 72.3 (±5.7) years for women and men, respectively. Measurements: The 24h urines were collected for cortisol assay (radioimmunoassay [RIA]); blood was sampled at three diurnal time points for assay (enzyme-linked immunosorbent assay [ELISA]) of interleukin-1 (IL-1) beta; sleep architecture and sleep electroencephalograms (EEGs) were analyzed (after an adaptation and screening night) on baseline and stress nights via polysomnography and EEG power spectral analysis. Results: Healthy older women and men with higher levels of free cortisol (24h urine level) under a mild stress condition had impaired sleep (lower sleep efficiency; fewer minutes of stages 2, 3, and 4 sleep; more EEG beta activity during non–rapid eye movement sleep [NREM] sleep). Similar results were obtained when stress reactivity measures were used (cortisol and sleep values adjusted for baseline values), but not when baseline values alone were used. Gender differences were apparent: Men had higher levels of free urine cortisol in both baseline and mild stress conditions. Cortisol and sleep correlated most strongly in men; cortisol stress response levels explained 36% of the variance in NREM sleep stress responses. In women, but not men, higher cortisol was also associated with earlier time of arising and less REM sleep. Higher cortisol response to stress was associated with increased circulating levels of IL-1β, explaining 24% of the variance in a subset of women. Conclusion: These results indicate that free cortisol (as indexed by 24h urine values) can index responses to mild stress in healthy senior adults, revealing functional correlations (impaired sleep, earlier times of arising, more EEG beta activity during sleep, more IL-1β) and gender differences. (Chronobiology International, 17(3), 391–404, 2000)     

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-Stage Correlates of Hippocampal Electroencephalogram in Primates

It has been demonstrated in the rodent hippocampus that rhythmic slow activity (theta) predominantly occurs during rapid eye movement (REM) sleep, while sharp waves and associated ripples occur mainly during non-REM sleep. However, evidence is lacking for correlates of sleep stages with electroencephalogram (EEG) in the hippocampus of monkeys. In the present study, we recorded hippocampal EEG from the dentate gyrus in monkeys overnight under conditions of polysomnographical monitoring. As result, the hippocampal EEG changed in a manner similar to that of the surface EEG: during wakefulness, the hippocampal EEG showed fast, desynchronized waves, which were partly replaced with slower waves of intermediate amplitudes during the shallow stages of non-REM sleep. During the deep stages of non-REM sleep, continuous, slower oscillations (0.5–8 Hz) with high amplitudes were predominant. During REM sleep, the hippocampal EEG again showed fast, desynchronized waves similar to those found during wakefulness. These results indicate that in the monkey, hippocampal rhythmic slow activity rarely occurs during REM sleep, which is in clear contrast to that of rodents. In addition, the increase in the slower oscillations of hippocampal EEG during non-REM sleep, which resembled that of the surface EEG, may at least partly reflect cortical inputs to the dentate gyrus during this behavioral state.     

Early and Later Life Stress Alter Brain Activity and Sleep in Rats

Exposure to early life stress may profoundly influence the developing brain in lasting ways. Neuropsychiatric disorders associated with early life adversity may involve neural changes reflected in EEG power as a measure of brain activity and disturbed sleep. The main aim of the present study was for the first time to characterize possible changes in adult EEG power after postnatal maternal separation in rats. Furthermore, in the same animals, we investigated how EEG power and sleep architecture were affected after exposure to a chronic mild stress protocol. During postnatal day 2–14 male rats were exposed to either long maternal separation (180 min) or brief maternal separation (10 min). Long maternally separated offspring showed a sleep-wake nonspecific reduction in adult EEG power at the frontal EEG derivation compared to the brief maternally separated group. The quality of slow wave sleep differed as the long maternally separated group showed lower delta power in the frontal-frontal EEG and a slower reduction of the sleep pressure. Exposure to chronic mild stress led to a lower EEG power in both groups. Chronic exposure to mild stressors affected sleep differently in the two groups of maternal separation. Long maternally separated offspring showed more total sleep time, more episodes of rapid eye movement sleep and higher percentage of non-rapid eye movement episodes ending in rapid eye movement sleep compared to brief maternal separation. Chronic stress affected similarly other sleep parameters and flattened the sleep homeostasis curves in all offspring. The results confirm that early environmental conditions modulate the brain functioning in a long-lasting way.     

Interhemispheric EEG asymmetries during unilateral bright-light exposure and subsequent sleep in humans

We tested whether evening exposure to unilateral photic stimulation has repercussions on interhemispheric EEG asymmetries during wakefulness and later sleep. Because light exerts an alerting response in humans, which correlates with a decrease in waking EEG theta/alpha-activity and a reduction in sleep EEG delta activity, we hypothesized that EEG activity in these frequency bands show interhemispheric asymmetries after unilateral bright light (1,500 lux) exposure. A 2-h hemi-field light exposure acutely suppressed occipital EEG alpha activity in the ipsilateral hemisphere activated by light. Subjects felt more alert during bright light than dim light, an effect that was significantly more pronounced during activation of the right than the left visual cortex. During subsequent sleep, occipital EEG activity in the delta and theta range was significantly reduced after activation of the right visual cortex but not after stimulation of the left visual cortex. Furthermore, hemivisual field light exposure was able to shift the left predominance in occipital spindle EEG activity toward the stimulated hemisphere. Time course analysis revealed that this spindle shift remained significant during the first two sleep cycles. Our results reflect rather a hemispheric asymmetry in the alerting action of light than a use-dependent recovery function of sleep in response to the visual stimulation during prior waking. However, the observed shift in the spindle hemispheric dominance in the occipital cortex may still represent subtle local use-dependent recovery functions during sleep in a frequency range different from the delta range.     

miR-155 deletion modulates lipopolysaccharide-induced sleep in female mice

Immune signaling is known to regulate sleep. miR-155 is a microRNA that regulates immune responses. We hypothesized that miR-155 would alter sleep regulation. Thus, we investigated the potential effects of miR-155 deletion on sleep-wake behavior in adult female homozygous miR-155 knockout (miR-155^KO) mice and littermate controls (WT). Mice were implanted with biotelemetry units and EEG/EMG biopotentials were recorded continuously for three baseline days. miR-155^KO mice had decreased bouts of NREM and REM sleep compared with WT mice, but no differences were observed in the length of sleep bouts or total time spent in sleep-wake states. Locomotor activity and subcutaneous temperature did not differ between WT and miR-155^KO mice. Following baseline recordings, mice were sleep-deprived during the first six hours of the rest phase (light phase; ZT 0–6) followed by an 18 h recovery period. There were no differences between groups in sleep rebound (% sleep and NREM δ power) after sleep deprivation. Following recovery from sleep deprivation, mice were challenged with a somnogen (viz., lipopolysaccharide (LPS)) one hour prior to the initiation of the dark (active) phase. Biopotentials were continuously recorded for the following 24 h, and miR-155^KO mice displayed increased wakefulness and decreased NREM sleep during the dark phase following LPS injection. Additionally, miR-155^KO mice had reduced EEG slow-wave responses (0.5–4 Hz) compared to WT mice. Together, our findings indicate that miR-155 deletion attenuates the somnogenic and EEG delta-enhancing effects of LPS.

Abbreviations: ANOVA: analysis of variance; EEG: electroencephalogram; EMG: electromyogram; h: hour; IL-1: interleukin-1; IL-6: interleukin-6; IP: intra-peritoneal; LPS: lipopolysaccharide; miR/miRNA: microRNA; miR-155^KO: miR-155 knockout; NREM: non-rapid eye movement; REM: rapid eye movement; TNF: tumor necrosis factor; SWS: slow-wave sleep; WT: wild-type.     

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.     

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.     

Adenosine deaminase polymorphism affects sleep EEG spectral power in a large epidemiological sample

Slow wave oscillations in the electroencephalogram (EEG) during sleep may reflect both sleep need and intensity, which are implied in homeostatic regulation. Adenosine is strongly implicated in sleep homeostasis, and a single nucleotide polymorphism in the adenosine deaminase gene (ADA G22A) has been associated with deeper and more efficient sleep. The present study verified the association between the ADA G22A polymorphism and changes in sleep EEG spectral power (from C3-A2, C4-A1, O1-A2, and O2-A1 derivations) in the Epidemiologic Sleep Study (EPISONO) sample from São Paulo, Brazil. Eight-hundred individuals were subjected to full-night polysomnography and ADA G22A genotyping. Spectral analysis of the EEG was carried out in all individuals using fast Fourier transformation of the signals from each EEG electrode. The genotype groups were compared in the whole sample and in a subsample of 120 individuals matched according to ADA genotype for age, gender, body mass index, caffeine intake status, presence of sleep disturbance, and sleep-disturbing medication. When compared with homozygous GG genotype carriers, A allele carriers showed higher delta spectral power in Stage 1 and Stages 3+4 of sleep, and increased theta spectral power in Stages 1, 2 and REM sleep. These changes were seen both in the whole sample and in the matched subset. The higher EEG spectral power indicates that the sleep of individuals carrying the A allele may be more intense. Therefore, this polymorphism may be an important source of variation in sleep homeostasis in humans, through modulation of specific components of the sleep EEG.     

Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages

ABSTRACT

We compared performance in deriving sleep variables by both Fitbit Charge 2?, which couples body movement (accelerometry) and heart rate variability (HRV) in combination with its proprietary interpretative algorithm (IA), and standard actigraphy (Motionlogger® Micro Watch Actigraph: MMWA), which relies solely on accelerometry in combination with its best performing ‘Sadeh’ IA, to electroencephalography (EEG: Zmachine® Insight+ and its proprietary IA) used as reference. We conducted home sleep studies on 35 healthy adults, 33 of whom provided complete datasets of the three simultaneously assessed technologies. Relative to the Zmachine EEG method, Fitbit showed an overall Kappa agreement of 54% in distinguishing wake/sleep epochs and sensitivity of 95% and specificity of 57% in detecting sleep epochs. Fitbit, relative to EEG, underestimated sleep onset latency (SOL) by ~11 min and overestimated sleep efficiency (SE) by ~4%. There was no statistically significant difference between Fitbit and EEG methods in measuring wake after sleep onset (WASO) and total sleep time (TST). Fitbit showed substantial agreement with EEG in detecting rapid eye movement and deep sleep, but only moderate agreement in detecting light sleep. The MMWA method showed 51% overall Kappa agreement with the EEG one in detecting wake/sleep epochs, with sensitivity of 94% and specificity of 53% in detecting sleep epochs. MMWA, relative to EEG, underestimated SOL by ~10 min. There was no significant difference between Fitbit and MMWA methods in amount of bias in estimating SOL, WASO, TST, and SE; however, the minimum detectable change (MDC) per sleep variable with Fitbit was better (smaller) than with MMWA, respectively, by ~10 min, ~16 min, ~22 min, and ~8%. Overall, performance of Fitbit accelerometry and HRV technology in conjunction with its proprietary IA to detect sleep vs. wake episodes is slightly better than wrist actigraphy that relies solely on accelerometry and best performing Sadeh IA. Moreover, the smaller MDC of Fitbit technology in deriving sleep parameters in comparison to wrist actigraphy makes it a suitable option for assessing changes in sleep quality over time, longitudinally, and/or in response to interventions.     

Repeated characterization, analysis of variance limitation and discrimination analysis classification of EEG-activity patterns in human sleep]

1. To describe quantitatively and to deliminate nine EEG sleep patterns, mean values and standard deviations of abundances of the frequencies 0.8 ... 1.8 c/sec, 2...3.5 c/sec, 4...13c/sec, 14 to 17 c/sec, 18 to 22 c/sec, and 23 to 40 c/sec as well as of the average amplitudes in selected frequency ranges were calaculated and the distributions represented. 2. All nine EEG activity patterns could be separated by means of univariate and multivariate analyses of variance on the basis of all 28 as well as the 17 indispensable variables. 3. In the course of a stepwise reduction of variables within the framework of a linear discriminant analysis an optimal set of 17 variables was determined for the separation of the patterns, comprising: the percent quantity of the frequencies 0.8 ... 3.5 c/sec, 7 ... 9 c/sec and 18 to 40 c/sec as well as the average amplitudes in the frequency ranges 0.8 to 3.5 c/sec and 7.5 to 40 c/sec. 4. By linear regression analyses it could be shown that the sleep scording system used, can be reflected on an interval scale with the aid of discriminant functions; this can be achieved on the basis of the optimal set of variables as well as of the five most indispensable variables. 5. Finally the degree of the objectivity of the scoring procedures was demonstrated. Advantages and disadvantages of sleep scoring systems were discussed and possibilities of the utilization of results suggested, also in respect to the further development of the automatic recognition of EEG activity patterns.     

Regional slow waves and spindles in human sleep

The most prominent EEG events in sleep are slow waves, reflecting a slow (<1 Hz) oscillation between up and down states in cortical neurons. It is unknown whether slow oscillations are synchronous across the majority or the minority of brain regions--are they a global or local phenomenon? To examine this, we recorded simultaneously scalp EEG, intracerebral EEG, and unit firing in multiple brain regions of neurosurgical patients. We find that most sleep slow waves and the underlying active and inactive neuronal states occur locally. Thus, especially in late sleep, some regions can be active while others are silent. We also find that slow waves can propagate, usually from medial prefrontal cortex to the medial temporal lobe and hippocampus. Sleep spindles, the other hallmark of NREM sleep EEG, are likewise predominantly local. Thus, intracerebral communication during sleep is constrained because slow and spindle oscillations often occur out-of-phase in different brain regions.     

Modern non-drug approaches to human sleep regulation

Existing approaches to non-drug regulation and induction of human sleep are analyzed with an attempt of their classification. The main attention is paid to the methods that utilize modern computer technologies of registration and analysis of various characteristics of human organism functioning, basically to the electroencephalogram (EEG). Normal human sleep EEG correlates and their changes during different sleep stages are reviewed. Modern opportunities to regulate and induce human sleep via utilization of different EEG characteristics are analyzed.