Sleep EEG Spectra Research Articles

The sleep EEG envelope is a novel, neuronal firing-based human biomarker

Sleep EEG reflects voltage differences relative to a reference, while its spectrum reflects its composition of various frequencies. In contrast, the envelope of the sleep EEG reflects the instantaneous amplitude of oscillations, while its spectrum reflects the rhythmicity of the occurrence of these oscillations. The sleep EEG spectrum is known to relate to demographic, psychological and clinical characteristics, but the envelope spectrum has been rarely studied. In study 1, we demonstrate in human invasive data from cortex-penetrating microelectrodes and subdural grids that the sleep EEG envelope spectrum reflects neuronal firing. In study 2, we demonstrate that the scalp EEG envelope spectrum is stable within individuals. A multivariate learning algorithm could predict age (r = 0.6) and sex (r = 0.5) from the EEG envelope spectrum. With age, oscillations shifted from a 4–5 s rhythm to faster rhythms. Our results demonstrate that the sleep envelope spectrum is a promising biomarker of demographic and disease-related phenotypes.

332 Non-REM EEG Spectral Power at Baseline and After Total Sleep Deprivation in Individuals with Sleep-Onset Insomnia

Abstract Introduction The baseline non-REM sleep EEG of individuals with insomnia has been found to display increased spectral power at frequencies >14Hz, which may reflect hyperarousal. There is some evidence in this population of reduced slow wave activity after total sleep deprivation (TSD), potentially indicating altered sleep homeostasis. We investigated non-REM sleep EEG spectra at baseline and after TSD in individuals with sleep-onset insomnia. Methods 10 individuals with sleep-onset insomnia and 5 healthy controls (ages 22-40y, 11 females) completed a 5-day laboratory study with an adaptation night, baseline night, assignment to 38h TSD (n=5 insomnia, n=5 control) or equivalent non-TSD control (n=5 insomnia), and recovery night. Sleep periods were 10h (22:00-08:00) with digital polysomnography (250Hz; Nihon Kohden). Following artifact rejection, 5s subepochs of the non-REM (stages N2, N3) sleep EEG (C3-M2 derivation) in baseline and recovery nights were subjected to spectral analysis. Spectra (0.2Hz bins) were averaged over subepochs in 30s epochs. Repeated-measures ANOVA compared baseline spectra between insomnia and controls, and baseline-recovery difference spectra between TSD insomnia, non-TSD insomnia, and TSD controls. Results Average non-REM sleep amount was 5.9 at baseline, increasing by 1.1h after TSD, with no differences between groups (p≥0.20). At baseline, the insomnia group showed increased power in theta/alpha (~4–12Hz), reaching significance in the lower spindle range, compared to controls (p<0.05). As anticipated, no differences emerged between baseline and recovery nights in the non-TSD insomnia group. However, the TSD insomnia group showed increased delta (~1–3Hz) and theta/alpha (~6–10Hz) power (p<0.05) during recovery. Healthy controls showed expected power increases in delta and lower spindle range, and decreases in upper spindle range (~14–15Hz), after TSD (p<0.05). Conclusion Compared to healthy controls, individuals with sleep-onset insomnia showed increased non-REM sleep EEG power in the theta/alpha bands and low spindle frequency range, with further significant increases in theta/alpha in addition to delta power following TSD, despite small sample size. The increase in delta power following TSD was equivalent to that in healthy controls, suggesting no sleep homeostasis abnormality. Whether the elevated theta/alpha power may be related to hyperarousal is unclear. Support (if any) ONR grant N00014-13-C-0063

Time-frequency analysis and fuzzy-based detection of heat-stressed sleep EEG spectra.

Nowadays, sleep disorders are contemplated as the major issue in the human lives. The current work aims at extraction of time-frequency information from recorded dataset and provides an efficient sleep stage detection method. Recordings of brain signal namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) were carried out under defined clinical condition for the classification of sleep EEG. Subsequent upon the extraction of various features from the raw EEG data, neuro-fuzzy system is trained to classify the sleep stages into three major classes namely awake, slow wave sleep (SWS), and rapid eye movement sleep (REM). This classification would enable medical professionals to diagnose sleep related disorders accurately. The results obtained clearly indicate that the mean performance for SWS stage is profound as compared to REM and awake stage. Specificity and sensitivity of the proposed method are obtained as 95.4% and 80%, respectively. The average accuracy of the system employing neuro-fuzzy approach is found to be 90.6% in which SWS stage was best detected among the other stages of sleep EEG.Graphical abstract.

Effects of an Orexin‐2 Receptor Antagonist on Sleep and Event‐Related Oscillations in Female Rats Exposed to Chronic Intermittent Ethanol During Adolescence

Alcohol use is on the rise among women in the United States which is especially concerning since women who drink have a higher risk of alcohol-related problems. Orexin (hypocretin) receptor antagonists may have some therapeutic value for alcohol-induced insomnia; however, the use of this class of drugs following female adolescent binge drinking is limited. The current study will address whether adolescent intermittent ethanol (AIE) in female rats can result in lasting changes in sleep pathology and whether orexin-targeted treatment can alleviate these deficits. Following a 5-week AIE vapor model, young adult rats were evaluated on waking event-related oscillations (EROs) and EEG sleep. Subsequently, AIE rats were treated with orexin receptor 2 (OX2 R) antagonist (MK-1064; 10, 20mg/kg) to test for modifications in sleep pathology and waking ERO. Female AIE rats exhibited lasting changes in sleep compared to controls. This was demonstrated by increased fragmentation of slow wave sleep (SWS) and rapid eye movement sleep, as well as reductions in delta and theta power during SWS. There was no impact of AIE on waking EROs. Acute MK-1064 hastened SWS onset and increased the number of SWS episodes, without increasing sleep fragmentation in AIE and controls. While treatment with MK-1064 did not impact sleep EEG spectra, waking ERO energy was increased in delta, theta, and beta frequency bands. These results demonstrate that AIE can produce lasting changes in sleep in female rats, highly similar to what we previously found in males. Additionally, while the OX2 R antagonist promoted sleep in both alcohol-exposed and unexposed rats, it did not reverse most of the alcohol-induced disruptions in sleep. Thus, OX2 R antagonism may serve as a potential therapeutic strategy for the treatment of insomnia, but not the specific signs of alcohol-induced insomnia.

Reliability of Family Dogs' Sleep Structure Scoring Based on Manual and Automated Sleep Stage Identification.

Simple SummarySleep alterations are known to be severe accompanying symptoms of many human psychiatric conditions, and validated clinical protocols are in place for their diagnosis and treatment. However, sleep monitoring is not yet part of standard veterinary practice, and the possible importance of sleep-related physiological alterations for certain behavioral problems in pets is seriously understudied. Recently, a non-invasive electroencephalography (EEG) method was developed for pet dogs that is well-suited for untrained individuals. This so called polysomnography protocol could easily be implemented in veterinary diagnosis. However, in order to make the procedure more effective and standardized, methodological questions about the validity and reliability of data processing need to be answered. As a first step, the present study tests the effect of several factors on the manual scoring of the different sleep stages (a standard procedure adopted from human studies). Scoring the same recordings but varying the number of EEG channels visible to the scorer (emulating the difference between single channel versus four channel recordings) resulted in significant differences in hypnograms. This finding suggests that using more recording electrodes may provide a more complete picture of dog brain electrophysiological activity. Visual sleep staging by three different expert raters also did not provide a full agreement, but despite this, there were no significant differences between raters in important output values such as sleep structure and the spectral features of the EEG. This suggests that the non-invasive canine polysomnography method is ready to be implemented for veterinary use, but there is room for further refinement of the data processing. Here, we describe which parts of the sleep recording yield the lowest agreement and present the first form of an automated method that can reliably distinguish awake from sleep stages and could thus accelerate the time-consuming manual data processing. The translation of the findings into clinical practice will open the door to the more effective diagnosis and treatment of disorders with sleep-related implications.Non-invasive polysomnography recording on dogs has been claimed to produce data comparable to those for humans regarding sleep macrostructure, EEG spectra and sleep spindles. While functional parallels have been described relating to both affective (e.g., emotion processing) and cognitive (e.g., memory consolidation) domains, methodologically relevant questions about the reliability of sleep stage scoring still need to be addressed. In Study 1, we analyzed the effects of different coders and different numbers of visible EEG channels on the visual scoring of the same polysomnography recordings. The lowest agreement was found between independent coders with different scoring experience using full (3 h-long) recordings of the whole dataset, and the highest agreement within-coder, using only a fraction of the original dataset (randomly selected 100 epochs (i.e., 100 × 20 s long segments)). The identification of drowsiness was found to be the least reliable, while that of non-REM (rapid eye movement, NREM) was the most reliable. Disagreements resulted in no or only moderate differences in macrostructural and spectral variables. Study 2 targeted the task of automated sleep EEG time series classification. Supervised machine learning (ML) models were used to help the manual annotation process by reliably predicting if the dog was sleeping or awake. Logistic regression models (LogREG), gradient boosted trees (GBT) and convolutional neural networks (CNN) were set up and trained for sleep state prediction from already collected and manually annotated EEG data. The evaluation of the individual models suggests that their combination results in the best performance: ~0.9 AUC test scores.

The sleep EEG spectrum is a sexually dimorphic marker of general intelligence

The shape of the EEG spectrum in sleep relies on genetic and anatomical factors and forms an individual “EEG fingerprint”. Spectral components of EEG were shown to be connected to mental ability both in sleep and wakefulness. EEG sleep spindle correlates of intelligence, however, exhibit a sexual dimorphism, with a more pronounced association to intelligence in females than males. In a sample of 151 healthy individuals, we investigated how intelligence is related to spectral components of full-night sleep EEG, while controlling for the effects of age. A positive linear association between intelligence and REM anterior beta power was found in females but not males. Transient, spindle-like “REM beta tufts” are described in the EEG of healthy subjects, which may reflect the functioning of a recently described cingular-prefrontal emotion and motor regulation network. REM sleep frontal high delta power was a negative correlate of intelligence. NREM alpha and sigma spectral power correlations with intelligence did not unequivocally remain significant after multiple comparisons correction, but exhibited a similar sexual dimorphism. These results suggest that the neural oscillatory correlates of intelligence in sleep are sexually dimorphic, and they are not restricted to either sleep spindles or NREM sleep.

The interrelated effect of sleep and learning in dogs (Canis familiaris); an EEG and behavioural study

The active role of sleep in memory consolidation is still debated, and due to a large between-species variation, the investigation of a wide range of different animal species (besides humans and laboratory rodents) is necessary. The present study applied a fully non-invasive methodology to study sleep and memory in domestic dogs, a species proven to be a good model of human awake behaviours. Polysomnography recordings performed following a command learning task provide evidence that learning has an effect on dogs’ sleep EEG spectrum. Furthermore, spectral features of the EEG were related to post-sleep performance improvement. Testing an additional group of dogs in the command learning task revealed that sleep or awake activity during the retention interval has both short- and long-term effects. This is the first evidence to show that dogs’ human-analogue social learning skills might be related to sleep-dependent memory consolidation.

Time course of a new spectral electroencephalographic marker of sleep homeostasis

Background and objective As compared to preceding wakefulness, stage 1 sleep is characterized by elevated slow-wave activity (SWA) that is regarded as the most popular electroencephalographic (EEG) indicator of sleep debt. However, some other indicators did not suggest such elevation until stage 2 sleep. In this paper, a more direct indicator of the signature left by sleep debt in the sleep EEG spectrum named “spectral sleep debt component score” (SDS) was introduced, and its changes throughout three stages of non-rapid eye movement (NREM) sleep were studied.

The spectrum of the non-rapid eye movement sleep electroencephalogram following total sleep deprivation is trait-like.

The sleep electroencephalogram (EEG) spectrum is unique to an individual and stable across multiple baseline recordings. The aim of this study was to examine whether the sleep EEG spectrum exhibits the same stable characteristics after acute total sleep deprivation. Polysomnography (PSG) was recorded in 20 healthy adults across consecutive sleep periods. Three nights of baseline sleep [12h time in bed (TIB)] following 12h of wakefulness were interleaved with three nights of recovery sleep (12h TIB) following 36h of sustained wakefulness. Spectral analysis of the non-rapid eye movement (NREM) sleep EEG (C3LM derivation) was used to calculate power in 0.25Hz frequency bins between 0.75 and 16.0Hz. Intraclass correlation coefficients (ICCs) were calculated to assess stable individual differences for baseline and recovery night spectra separately and combined. ICCs were high across all frequencies for baseline and recovery and for baseline and recovery combined. These results show that the spectrum of the NREM sleep EEG is substantially different among individuals, highly stable within individuals and robust to an experimental challenge (i.e. sleep deprivation) known to have considerable impact on the NREM sleep EEG. These findings indicate that the NREM sleep EEG represents a trait.

Principal component scoring of the resting EEG spectrum provides further evidence for age-associated disinhibition of the wake drive

Background: Aging is often accompanied by increasingly occurrence of profound sleep–wake disturbances. However, normal ‘sleep aging’ can also be advantageous to older people, for example, they have a better ability to tolerate sleep deprivation than to younger people. In previous work, we compared the principal component structure of the electroencephalographic (EEG) spectra of Non-Rapid-Eye-Movement (NREM) sleep in young and elderly people, and found that the decrease in slow-wave activity, which is the most earliest and most obvious age-related modification of the sleep EEG spectrum, can be viewed as a consequence of a reduced rise of the first principal component score combined with an insufficient fall of the second principal component score. It was suggested that such changes in principal component scores can be caused by disinhibition (i.e. strengthening) of the wake drive due to weakening of the sleep drive. It is possible that the second principal component score remains higher in older people compared to younger people not only during NREM sleep, but also during normal and extended wakefulness. Methods: To test this suggestion, principal component analysis was applied to the spectra of resting EEG signals obtained with a three-hour interval during the course of sustained wakefulness of 130 and 33 participants of two independent sleep deprivation experiments. Results: The second principal component score was positively linked to participants’ age. Conclusions: Elevation of score can reflect age-associated strengthening of the sleep drive, which predisposes older people to develop certain disturbances of their sleep–wake cycle and, on the other hand, to tolerate sleep deprivation better than younger people.

Principal Component Structuring of the Non-REM Sleep EEG Spectrum in Older Adults Yields Age-Related Changes in the Sleep and Wake Drives

Age-related disturbances of the sleep-wake cycle can reflect ontogenetic changes in regulatory mechanisms underlying normal and pathological aging, but the exact nature of these changes remains unclear. The present report is the first attempt to apply principal component analysis to the electroencephalographic (EEG) spectrum to examine of whether the observed age-related changes in the objective sleep measures can be linked to the opponent sleep-promoting and wake-promoting processes. The EEG indicators of these processes--scores on the 1st and 2nd principal components of the EEG spectrum, respectively--were compared in 15 older (57-74 years) and 16 younger (20-31 years) healthy volunteers. The scores were calculated for non-REM sleep episodes which occurred during ten 75-min naps scheduled every 150 min throughout a 40-h constant routine protocol. Both, a decrease of the 1st principal component score and an increase of the 2nd principal component score were found to contribute to such most obvious age-related modification of the sleep EEG spectrum as attenuation of EEG slow-wave activity in older people. Therefore, we concluded that the normal aging process can reflect both a weakening of the sleep-promoting process and a strengthening of the wake-promoting process, respectively. Such bidirectional changes in chronoregulatory processes may explain why sleep of older people is characterized by the few profitable and a number of detrimental features (i.e., a better ability to cope with daytime sleepiness and sleep loss vs. difficulty of falling asleep, decreased total nighttime sleep, "lightened" and fragmentized sleep, unwanted early morning awakenings, etc.).

Prospects of using electroencephalographic signatures of the chronoregulatory processes for meaningful, parsimonious and quantitative description of the sleep–wake sub-states

The two traditional terminological systems for description of sleep–wake phenomena are based on criteria for visual scoring of sleep and on measuring EEG power densities in conventional frequency bands. There are serious limitations concerning the use of these terminologies for distinguishing different sleep–wake sub-states and transitions between them. Polysomnographic recordings obtained throughout all-night sleep (n = 14) and during 24-hour multiple sleep latency test (n = 32) were analyzed to demonstrate the possibilities (1) to uncover the universal three principal component structure of wake and sleep EEG spectra, (2) to predict such wake–sleep parameters as sleepiness, sleep latency, amount of any sleep stage, etc. from the three principal component scores, and (3) to link the magnitudes and time courses of these scores with the levels and kinetics of sleep–wake regulatory processes. This demonstration could lead to the development of a theoretically based terminological system allowing meaningful, parsimonious and quantitative description of sub-states along the sleep–wake continuum.

Sleep bruxism and sleep arousal: an experimental challenge to assess the role of cyclic alternating pattern

Rhythmic masticatory muscle activity (RMMA) is the characteristic electromyographic pattern of sleep bruxism (SB), a sleep-related motor disorder associated with sleep arousal. Sleep arousals are generally organised in a clustered mode known as the cyclic alternating pattern (CAP). CAP is the expression of sleep instability between sleep maintaining processes (phase A1) and stronger arousal processes (phases A2 and A3). This study aimed to investigate the role of sleep instability on RMMA/SB occurrence by analysing CAP and electroencephalographic (EEG) activities. The analysis was performed on the sleep recordings of 8 SB subjects and 8 controls who received sensory stimulations during sleep. Baseline and experimental nights were compared for sleep variables, CAP, and EEG spectral analyses using repeated measure ANOVAs. Overall, no differences in sleep variables and EEG spectra were found between SB subjects and controls. However, SB subjects had higher sleep instability (more phase A3) than controls (P= 0·05). The frequency of phase A3 was higher in the pre-REM sleep periods (P < 0·001), where peaks in RMMA/SB activity were also observed (P = 0·05). When sleep instability was experimentally increased by sensory stimuli, both groups showed an enhancement in EEG theta and alpha power (P = 0·04 and 0·02, respectively) and significant increases in sleep arousal and all CAP variables. No change in RMMA/SB index was found within either groups (RMMA/SB occurred in all SB subjects and only one control during the experimental night). These findings suggest that CAP phase A3 may act as a permissive window rather than a generator of RMMA/SB activity in predisposed individuals.

Developmental Changes in the Human Sleep EEG During Early Adolescence

To use time-frequency analysis to characterize developmental changes in the human sleep electroencephalogram (EEG) across early adolescence. Sleep EEG was recorded when children were 9/10 years old and 1 to 3 years later after sleeping at home on a fixed schedule for at least one week. A 4-bed sleep laboratory. Fourteen (5 girls) healthy children ages 9/10 (mean = 10.13, SD = +/- 0.51) years at initial and 11 to 13 (mean = 12.28, SD = +/- 0.62) years at follow-up. N/A. All-night polysomnography was performed at each assessment and sleep stages were scored with Rechtschaffen and Kales criteria. Slow wave sleep minutes decreased from the initial to the follow-up session by 29%, while minutes of stage 2 increased by 17%. NREM and REM sleep EEG spectra from two central and two occipital leads were examined for developmental changes. All-night analyses showed a significant decrease of EEG power from the initial to follow-up session across a range of frequencies during NREM and REM sleep. This decline occurred across leads and states in the delta/theta bands (3.8 - 7 Hz). Time-frequency analyses indicated that this effect was consistent across the night. The decline in power with age was most pronounced in the left central and right occipital leads. The frequency of greatest power in the sigma band (11 - 16 Hz) was significantly higher at follow-up. This longitudinal analysis highlights asymmetrical frequency-specific declines in sleep EEG spectral power with early adolescent maturation, which may reflect early signs of the cortical synaptic pruning in the healthy adolescent.

1/f Function: Quantifying the individual fingerprints of sleep EEG by curve fitting to and parametrization of the NREM sleep EEG spectra

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Sleep Spindle Activity Is Correlated With Reading Abilities in Developmental Dyslexia

To analyze sleep architecture of children with dyslexia, by means of conventional parameters and EEG spectral analysis and to correlate sleep parameters and EEG spectra with neuropsychological measures. Cross-sectional study involving validated sleep questionnaires, neuropsychological scales, and polysomnographic recordings. Sleep laboratory in academic center. Sixteen subjects with developmental dyslexia (mean age 10.8 years) and 11 normally reading children (mean age 10.1 years). All the subjects underwent overnight polysomnographic recording; EEG power spectra were computed from the Cz derivation and spindle density was calculated during sleep stages N2. N/A. Dyslexic children showed an increase in power of frequency bands between 0.5-3 Hz and 11-12 Hz in stage N2 and between 0.5-1 Hz in stage N3; they also showed significantly increased spindle density during N2. The power of the sigma band in N2 was positively correlated with the Word reading and MT reading tests; similarly, spindle density was significantly correlated with the Word reading test. The increased spindle activity and EEG sigma power in dyslexic subjects were found to be correlated with the degree of dyslexic impairment. The correlation found between sleep spindle activity and reading abilities in developmental dyslexia supports the hypothesis of a role for NREM sleep and spindles in sleep-related neurocognitive processing.

Neural Network-Based Evaluation of Chronic Non-Thermal Effects of Modulated 2450 MHz Microwave Radiation on Electroencephalogram

The effects of chronic exposure (2 h daily for 21 days) of 1 kHz square wave-modulated 2450 MHz microwave radiation (non-thermal) on sleep-EEG, open field behavior, and thyroid hormones (T(3), T(4), and TSH) have been analyzed in an animal model. Results revealed significant changes in these pathophysiological parameters (p < 0.05 or better), except body temperature, grooming behavior, and TSH levels. The sleep-EEG power spectrum data for slow wave sleep (SWS), rapid eye movement (REM) sleep, and awake (AWA) states in two experimental groups of rats (microwave exposed and the control) were tested by an artificial neural network (ANN), containing 60 nodes in input layer, weighted from power spectrum data from 0 to 30 Hz, 18 nodes in hidden layer and an output node. The target output values for this network were determined with another five-layered neural network (with the structure of 6-14-1-14-6). The input and output of this network was assigned with the six confirmed pathophysiological changes. The most important feature for chronic exposure of 2450 MHz microwave exposure and for control subjects was extracted from the third layer single neuron and used as the target value for the three-layered ANN. The network was found effective in recognizing the EEG power spectra with an average of 71.93% for microwave exposure and 93.13% for control subjects, respectively. However, the lower percentage of pattern identification agreement in the microwave-exposed group in comparison to the control group suggest only mild effects of microwave exposure with this experimental setup.

The EEG effects of THIP (Gaboxadol) on sleep and waking are mediated by the GABAAδ‐subunit‐containing receptors

THIP (4,5,6,7-tetrahydroisoxazolo-[5,4-c]pyridine-3-ol, Gaboxadol) is a selective gamma-aminobutyric acid (GABA)(A) agonist, acting in vitro with high potency and efficacy at the extrasynaptic GABA(A)delta-containing receptors. THIP was suggested to be a potential hypnotic to treat insomnia, and it is currently in clinical trial. Here we assessed whether the GABA(A)delta-containing receptors mediate in vivo the effect of THIP on sleep and the sleep electroencephalogram (EEG). We performed EEG recordings in a mouse model deficient in the GABA(A)delta-subunit gene (delta(-/-) mice) and in wild-type littermate controls. THIP (4 and 6 mg/kg intraperitoneally) induced an abnormal EEG pattern, resulting in dramatic changes in the waking and non-rapid eye movement (NREM) sleep EEG spectra in wild-type mice. Indeed, a massive increase in EEG power lasting 2-3 h occurred in both the frontal and parietal derivation, especially in frequencies below 6 Hz. All effects were more prominent in the frontal EEG. Furthermore, the highest dose of THIP lengthened REM sleep latency and suppressed REM sleep. In contrast, vigilance states and sleep latencies were not affected in delta(-/-) mice. Moreover, only minor changes were observed in the NREM sleep EEG spectrum after THIP injection in the delta-subunit-deficient mice. The present findings do not indicate a sleep-promoting effect of THIP in mice, which is in accordance with a previous report in this species. Moreover, our results in vivo demonstrate that THIP acts preferentially at GABA(A) receptors containing the delta-subunit.

Comparison of the effects of modafinil and sleep deprivation on sleep and cortical EEG spectra in mice

Modafinil is a wakefulness-promoting substance whose profile differs from that of the classical psychostimulants. It is still unknown whether waking induced by modafinil and wakefulness induced by sleep deprivation differ in terms of their effect on subsequent sleep. To investigate this problem sleep was recorded in two groups of OF1 mice. One group received modafinil (200 mg/kg, i.p.) at light onset which induced a period of wakefulness of approx. 5 h, while animals of the subsequent control group were injected with vehicle and kept awake for an equivalent duration. The effect of the two treatments on sleep was similar. REM sleep was initially reduced and slow-wave activity (SWA; EEG power in the 0.75–4.0 Hz range) in nonREM sleep was enhanced for several hours. The SWA increase was more prominent over the frontal cortex than over the occipital cortex after both treatments. A minor difference was seen at the occipital site where the initial rise of power in the low-frequency range was larger after vehicle combined with enforced waking than after modafinil. The study shows that the homeostatic sleep response following the modafinil-induced wakefulness corresponds largely to the response following a non-pharmacologically induced extended waking episode.

Independent sleep EEG slow-wave and spindle band dynamics associated with 4 weeks of continuous application of short-half-life hypnotics in healthy subjects

Objectives: Habituation and adverse withdrawal reactions after prolonged medication with benzodiazepine (BZ) hypnotics are believed to play a role in dose escalation and the development of dependence.Methods: In the current sleep EEG study in 43 healthy male subjects, the known property of BZ- and similar hypnotics to change the NREM sleep EEG spectrum is utilized for a detailed quantitative analysis across 4 weeks of continuous medication and a subsequent two-week withdrawal period. The BZ hypnotic triazolam and the non-BZ hypnotics zopiclone and zolpidem, differing in pharmacological properties and reported adverse effects, were examined in parallel to a placebo group.Results: Reliably occurring spectral effects in the sleep stage 2 EEG were found in the 3 frequency bands 0.8–5 Hz, 5–10 Hz and 10–15 Hz. All 3 hypnotics showed the typical ‘benzodiazepine signature’, a 10–15 Hz increase and lower-frequency (<10 Hz) suppression relative to the preceding drug-free night. However, these effects developed differently across the first medication night, across the 4 medication weeks and after withdrawal: While the 5–10 Hz effect covaried with the blood presence of the drugs as estimated from the known plasma half-lifes, showed habituation and a rebound after withdrawal, the 10–15 Hz power increased across medication days and showed no rebound. Effects in the 0.8–5 Hz band in the first medication night correlated with the decrease of sleep efficiency at later withdrawal for triazolam and zolpidem.