The time course of high-frequency bands (15-45 Hz) in all-night spectral analysis of sleep EEG

Overnight dynamics in scale-free and oscillatory spectral parameters of NREM sleep EEG

Unfolding the overnight dynamics in human sleep features plays a pivotal role in understanding sleep regulation. Studies revealed the complex reorganization of the frequency composition of sleep electroencephalogram (EEG) during the course of sleep, however the scale-free and the oscillatory measures remained undistinguished and improperly characterized before. By focusing on the first four non-rapid eye movement (NREM) periods of night sleep records of 251 healthy human subjects (4-69 years), here we reveal the flattening of spectral slopes and decrease in several measures of the spectral intercepts during consecutive sleep cycles. Slopes and intercepts are significant predictors of slow wave activity (SWA), the gold standard measure of sleep intensity. The overnight increase in spectral peak sizes (amplitudes relative to scale-free spectra) in the broad sigma range is paralleled by a U-shaped time course of peak frequencies in frontopolar regions. Although, the set of spectral indices analyzed herein reproduce known age- and sex-effects, the interindividual variability in spectral slope steepness is lower as compared to the variability in SWA. Findings indicate that distinct scale-free and oscillatory measures of sleep EEG could provide composite measures of sleep dynamics with low redundancy, potentially affording new insights into sleep regulatory processes in future studies.

The Sleep Side of Aging and Alzheimer's Disease

As we age, sleep patterns undergo significant modifications in micro and macrostructure, worsening cognition and quality of life. These are associated with remarkable brain changes, like deterioration in synaptic plasticity, gray and white matter, and significant modifications in hormone levels. Sleep alterations are also a core component of mild cognitive impairment (MCI) and Alzheimer's Disease (AD). AD night time is characterized by a gradual decrease in slow-wave activity and a substantial reduction of REM sleep. Sleep abnormalities can accelerate AD pathophysiology, promoting the accumulation of amyloid-β (Aβ) and phosphorylated tau. Thus, interventions that target sleep disturbances in elderly people and MCI patients have been suggested as a possible strategy to prevent or decelerate conversion to dementia. Although cognitive-behavioral therapy and pharmacological medications are still first-line treatments, despite being scarcely effective, new interventions have been proposed, such as sensory stimulation and Noninvasive Brain Stimulation (NiBS). The present review outlines the current state of the art of the relationship between sleep modifications in healthy aging and the neurobiological mechanisms underlying age-related changes. Furthermore, we provide a critical analysis showing how sleep abnormalities influence the prognosis of AD pathology by intensifying Aβ and tau protein accumulation. We discuss potential therapeutic strategies to target sleep disruptions and conclude that there is an urgent need for testing new therapeutic sleep interventions.

Predictability decomposition detects the impairment of brain-heart dynamical networks during sleep disorders and their recovery with treatment

This work introduces a framework to study the network formed by the autonomic component of heart rate variability (cardiac processη) and the amplitude of the different electroencephalographic waves (brain processes δ, θ, α, σ, β) during sleep. The framework exploits multivariate linear models to decompose the predictability of any given target process into measures of self-, causal and interaction predictability reflecting respectively the information retained in the process and related to its physiological complexity, the information transferred from the other source processes, and the information modified during the transfer according to redundant or synergistic interaction between the sources. The framework is here applied to theη,δ,θ,α,σ,βtime series measured from the sleep recordings of eight severe sleep apnoea-hypopnoea syndrome (SAHS) patients studied before and after long-term treatment with continuous positive airway pressure (CPAP) therapy, and 14 healthy controls. Results show that the full and self-predictability of η, δ and θ decreased significantly in SAHS compared with controls, and were restored with CPAP forδandθbut not forη The causal predictability of η and δ occurred through significantly redundant source interaction during healthy sleep, which was lost in SAHS and recovered after CPAP. These results indicate that predictability analysis is a viable tool to assess the modifications of complexity and causality of the cerebral and cardiac processes induced by sleep disorders, and to monitor the restoration of the neuroautonomic control of these processes during long-term treatment.

EEG based evaluation of stereoscopic 3D displays for viewer discomfort

BACKGROUND

Consumer preference is rapidly changing from 2D to 3D movies due to the sensational effects of 3D scenes, like those in Avatar and The Hobbit. Two 3D viewing technologies are available: active shutter glasses and passive polarized glasses. However, there are consistent reports of discomfort while viewing in 3D mode where the discomfort may refer to dizziness, headaches, nausea or simply not being able to see in 3D continuously.

METHODS

In this paper, we propose a theory that 3D technology which projects the two images (required for 3D perception) alternatively, cannot provide true 3D visual experience while the 3D technology projecting the two images simultaneously is closest to the human visual system for depth perception. Then we validate our theory by conducting experiments with 40 subjects and analyzing the EEG results of viewing 3D movie clips with passive polarized glasses while the images are projected simultaneously compared to 2D viewing. In addition, subjective feedback of the subjects was also collected and analyzed.

RESULTS

A higher theta and alpha band absolute power is observed across various areas including the occipital lobe for 3D viewing. We also found that the complexity of the signal, e.g. variations in EEG samples over time, increases in 3D as compared to 2D. Various results conclude that working memory, as well as, attention is increased in 3D viewing because of the processing of more data in 3D as compared to 2D. From subjective feedback analysis, 75% of subjects felt comfortable with 3D passive polarized while 25% preferred 3D active shutter technology.

CONCLUSIONS

We conclude that 3D passive polarized technology provides more comfortable visualization than 3D active shutter technology. Overall, 3D viewing is more attractive than 2D due to stereopsis which may cause of high attention and involvement of working memory manipulations.

Long-term CPAP treatment partially improves the link between cardiac vagal influence and delta sleep

BACKGROUND

Continuous positive airway pressure (CPAP) treatment improves the risk of cardiovascular events in patients suffering from severe sleep apnea-hypopnea syndrome (SAHS) but its effect on the link between delta power band that is related to deep sleep and the relative cardiac vagal component of heart rate variability, HF(nu) of HRV, is unknown. Therefore, we tested the hypothesis that CPAP restores the link between cardiac autonomic activity and delta sleep across the night.

METHODS

Eight patients suffering from severe SAHS before and after 4 ± 3 years of nasal CPAP treatment were matched with fourteen healthy controls. Sleep EEG and ECG were analysed to obtain spectral sleep and HRV components. Coherence analysis was applied between HF(nu) and delta power bands across the first three sleep cycles.

RESULTS

Sleep characteristics and spectral HRV components were similar between untreated patients, treated patients and controls, with the exception of decreased Rapid Eye Movement duration in untreated patients. Coherence and gain values between HF(nu) and delta EEG variability were decreased in untreated patients while gain values normalized in treated patients. In patients before and during long-term CPAP treatment, phase shift and delay between modifications in HF(nu) and delta EEG variability did not differ from controls but were not different from zero. In healthy men, changes in cardiac vagal activity appeared 9 ± 7 minutes before modifications in delta sleep.

CONCLUSIONS

Long-term nasal CPAP restored, in severe SAHS, the information between cardiovascular and sleep brainstem structures by increasing gain, but did not improve its tightness or time shift.

Transcranial electrical currents to probe EEG brain rhythms and memory consolidation during sleep in humans

Previously the application of a weak electric anodal current oscillating with a frequency of the sleep slow oscillation (∼0.75 Hz) during non-rapid eye movement sleep (NonREM) sleep boosted endogenous slow oscillation activity and enhanced sleep-associated memory consolidation. The slow oscillations occurring during NonREM sleep and theta oscillations present during REM sleep have been considered of critical relevance for memory formation. Here transcranial direct current stimulation (tDCS) oscillating at 5 Hz, i.e., within the theta frequency range (theta-tDCS) is applied during NonREM and REM sleep. Theta-tDCS during NonREM sleep produced a global decrease in slow oscillatory activity conjoint with a local reduction of frontal slow EEG spindle power (8-12 Hz) and a decrement in consolidation of declarative memory, underlining the relevance of these cortical oscillations for sleep-dependent memory consolidation. In contrast, during REM sleep theta-tDCS appears to increase global gamma (25-45 Hz) activity, indicating a clear brain state-dependency of theta-tDCS. More generally, results demonstrate the suitability of oscillating-tDCS as a tool to analyze functions of endogenous EEG rhythms and underlying endogenous electric fields as well as the interactions between EEG rhythms of different frequencies.

Slow EEG amplitude oscillations during NREM sleep and reading disabilities in children with dyslexia

STUDY OBJECTIVES

To analyze non-rapid eye movement (NREM) sleep microstructure of children with dyslexia, by means of cyclic alternating pattern (CAP) analysis and to correlate CAP parameters with neuropsychological measures.

DESIGN

Cross-sectional study using polysomnographic recordings and neuropsychological assessments.

SETTING

Sleep laboratory in academic center.

PARTICIPANTS

Sixteen subjects with developmental dyslexia (mean age 10.8 years) and 11 normally reading children (mean age 10.1 years) underwent overnight polysomnographic recording.

INTERVENTION

N/A.

MEASUREMENTS AND RESULTS

Sleep architecture parameters only showed some statistically significant differences: number of sleep stage shifts per hour of sleep, percentage of N3, and number of R periods were significantly lower in dyslexic children versus controls. CAP analysis revealed a higher total CAP rate and A1 index in stage N3. A2% and A2 index in stage N2 and N3 were lower in dyslexic children while no differences were found for A3 CAP subtypes. The correlation analysis between CAP parameters and cognitive-behavioral measures showed a significant positive correlation between A1 index in N3 with Verbal IQ, full-scale IQ, and Memory and Learning Transfer reading test; while CAP rate in N3 was positively correlated with verbal IQ.

CONCLUSIONS

To overcome reading difficulties, dyslexic subjects overactivate thalamocortical and hippocampal circuitry to transfer information between cortical posterior and anterior areas. The overactivation of the ancillary frontal areas could account for the CAP rate modifications and mainly for the increase of CAP rate and of A1 index in N3 that seem to be correlated with IQ and reading abilities.

Cyclic alternating pattern: A window into pediatric sleep

Cyclic alternating pattern (CAP) has now been studied in different age groups of normal infants and children, and it is clear that it shows dramatic changes with age. In this review we first focus on the important age-related changes of CAP from birth to peripubertal age and, subsequently, we describe the numerous studies on CAP in developmental clinical conditions such as pediatric sleep disordered breathing, disorders of arousal (sleep walking and sleep terror), pediatric narcolepsy, learning disabilities with mental retardation (fragile-X syndrome, Down syndrome, autistic spectrum disorder, Prader-Willi syndrome) or without (dyslexia, Asperger syndrome, attention-deficit/hyperactivity disorder). CAP rate is almost always decreased in these conditions with the exception of the disorders of arousal and some cases of sleep apnea. Another constant result is the reduction of A1 subtypes, probably in relationship with the degree of cognitive impairment. The analysis of CAP in pediatric sleep allows a better understanding of the underlying neurophysiological mechanisms of sleep disturbance. CAP can be considered as a window into pediatric sleep, allowing a new vision on how the sleeping brain is influenced by a specific pathology or how sleep protecting mechanisms try to counteract internal or external disturbing events.

From alpha to gamma: electrophysiological correlates of meditation-related states of consciousness

Meditation practice is difficult to access because of its countless forms of appearances originating from the complexity of cultures it has to serve. This makes a suitable categorization for scientific use almost impossible. However, empirical data suggest that different forms of meditation show similar steps of development in terms of their neurophysiological correlates. Some electrophysiological alterations can be observed on the beginner/student level, which are closely related to non-meditative processes. Others seem to correspond to an advanced/expert level, and seem to be unique for meditation-related states of consciousness. Meditation is one possibility to specialize brain/mind functions using the brain's immanent neural plasticity. This plasticity is probably recruited by certain EEG patterns observed during or as a result of meditation, for instance, synchronized gamma oscillations. While meditation formerly has been understood to comprise mainly passive relaxation states, recent EEG findings suggest that meditation is associated with active states which involve cognitive restructuring and learning.

INFLUENCE OF AGE ON THE INTERRELATION BETWEEN EEG FREQUENCY BANDS DURING NREM AND REM SLEEP

The age-dependence of temporal interrelations between distinct frequency bands of sleep EEG was investigated in a group of 59 healthy young and middle-aged males via cross correlation analysis. Based on global evaluation throughout the entire night, a highly significant decline of the delta/theta correlation with increasing age was found. A separate analysis for non-rapid eye movement (NREM) and rapid eye movement (REM) sleep revealed different changes with aging. During NREM sleep, the correlation between the delta and theta frequency bands decreased with increasing age. In contrast, during REM sleep, a stronger correlation became obvious between the theta, alpha, and beta frequency bands with increasing age, whereas the lower frequency components were not affected. These findings indicate that aging processes seem to interact with sleep EEG rhythms in a complex manner, where most conspicuous is a disintegration of the activities in the lower frequency range, both concerning the successive sleep cycles across the night and the micro-structure of NREM sleep.

Automatic analysis of single-channel sleep EEG: validation in healthy individuals

STUDY OBJECTIVE

To assess the performance of automatic sleep scoring software (ASEEGA) based on a single EEG channel comparatively with manual scoring (2 experts) of conventional full polysomnograms.

DESIGN

Polysomnograms from 15 healthy individuals were scored by 2 independent experts using conventional R&K rules. The results were compared to those of ASEEGA scoring on an epoch-by-epoch basis.

SETTING

Sleep laboratory in the physiology department of a teaching hospital.

PARTICIPANTS

Fifteen healthy volunteers.

MEASUREMENTS AND RESULTS

The epoch-by-epoch comparison was based on classifying into 2 states (wake/sleep), 3 states (wake/REM/ NREM), 4 states (wake/REM/stages 1-2/SWS), or 5 states (wake/REM/ stage 1/stage 2/SWS). The obtained overall agreements, as quantified by the kappa coefficient, were 0.82, 0.81, 0.75, and 0.72, respectively. Furthermore, obtained agreements between ASEEGA and the expert consensual scoring were 96.0%, 92.1%, 84.9%, and 82.9%, respectively. Finally, when classifying into 5 states, the sensitivity and positive predictive value of ASEEGA regarding wakefulness were 82.5% and 89.7%, respectively. Similarly, sensitivity and positive predictive value regarding REM state were 83.0% and 89.1%.

CONCLUSIONS

Our results establish the face validity and convergent validity of ASEEGA for single-channel sleep analysis in healthy individuals. ASEEGA appears as a good candidate for diagnostic aid and automatic ambulant scoring.

Sleep architecture and NREM alterations in children and adolescents with Asperger syndrome

STUDY OBJECTIVES

To analyze sleep in children with Asperger syndrome (AS) by means of standard sleep questionnaires, to evaluate sleep architecture and NREM sleep alterations by means of cyclic alternating pattern (CAP) and to correlate objective sleep parameters with cognitive behavioral measures.

DESIGN

Cross-sectional study involving validated sleep questionnaires, neuropsychological scales, and PSG recording.

SETTING

Sleep medicine center.

PARTICIPANTS

Eight children with AS, 10 children with autism, and 12 healthy control children.

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

Children with AS had a higher prevalence of problems of initiating sleep and daytime sleepiness. Sleep architecture parameters showed minor differences between the 3 groups. CAP parameters showed an increased percentage of A1 and a decreased percentage of A2 subtypes in subjects with AS vs. controls. All A subtype indexes (number per hour of NREM sleep) were decreased, mostly in sleep stage 2 but not in SWS. With respect to children with autism, subjects with AS showed increased CAP rate in SWS and A1 percentage. In subjects with AS, verbal IQ had a significant positive correlation with total CAP rate and CAP rate in SWS and with global and SWS A1 index. The percentage of A2 negatively correlated with full scale IQ, verbal and performance IQ. CBCL total score correlated positively with CAP rate and A1 index while externalizing score correlated negatively with A3%.

CONCLUSIONS

This study shows peculiar CAP modifications in children with AS and represents an attempt to correlate the quantification of sleep EEG oscillations with the degree of mental ability/disability.

Correlation between EEG rhythms during sleep: surface versus mediotemporal EEG

We compared surface and intracranial electroencephalogram recordings of mediotemporal structures. These structures are critically involved in declarative memory formation and memory consolidation during sleep. As memory processing is suggested to involve the interplay between fast and slow oscillations, we hypothesized different correlations between frequency bands in surface versus mediotemporal electroencephalogram recordings. Polysomnographic recordings obtained in 10 patients with unilateral temporal lobe epilepsy were analyzed. In accordance with earlier studies, we observed that power density in surface electroencephalogram is organized reciprocally between delta/theta and fast frequencies above 16 Hz during non-rapid-eye-movement sleep (negative correlations). In contrast, we found that within the hippocampus delta/theta power alternated in parallel with fast oscillations above 16 Hz during non-rapid-eye-movement sleep (positive correlations).

Small-world network organization of functional connectivity of EEG slow-wave activity during sleep

OBJECTIVE

To analyze the functional connectivity patterns of the EEG slow-wave activity during the different sleep stages and Cyclic Alternating Pattern (CAP) conditions, using concepts derived from Graph Theory.

METHODS

We evaluated spatial patterns of EEG slow-wave synchronization between all possible pairs of electrodes (19) placed over the scalp of 10 sleeping healthy young normal subjects using two graph theoretical measures: the clustering coefficient (Cp) and the characteristic path length (Lp). The measures were obtained during the different sleep stages and CAP conditions from the real EEG connectivity networks and randomized control (surrogate) networks (Cp-s and Lp-s).

RESULTS

Cp and Cp/Cp-s increased significantly from wakefulness to sleep while Lp and Lp/Lp-s did not show changes. Cp/Cp-s was higher for A1 phases, compared to B phases of CAP.

CONCLUSIONS

The network organization of the EEG slow-wave synchronization during sleep shows features characteristic of small-world networks (high Cp combined with low Lp); this type of organization is slightly but significantly more evident during the CAP A1 subtypes.

SIGNIFICANCE

Our results show feasibility of using graph theoretical measures to characterize the complexity of brain networks during sleep and might indicate sleep, and the A1 phases of CAP in particular, as a period during which slow-wave synchronization shows optimal network organization for information processing.

All-night EEG power spectral analysis of the cyclic alternating pattern components in young adult subjects

OBJECTIVE

To analyze in detail the frequency content of the different EEG components of the Cyclic Alternating Pattern (CAP), taking into account the ongoing EEG background and the nonCAP (NCAP) periods in the whole night polysomnographic recordings of normal young adults.

METHODS

Sixteen normal healthy subjects were included in this study. Each subject underwent one polysomnographic night recording; sleep stages were scored following standard criteria. Subsequently, each CAP A phase was detected in all recordings, during NREM sleep, and classified into 3 subtypes (A1, A2, and A3). The same channel used for the detection of CAP A phases (C3/A2 or C4/A1) was subdivided into 2-s mini-epochs. For each mini-epoch, the corresponding CAP condition was determined and power spectra calculated in the frequency range 0.5-25 Hz. Average spectra were obtained for each CAP condition, separately in sleep stage 2 and SWS, for each subject. Finally, the first 6h of sleep were subdivided into 4 periods of 90 min each and the same spectral analysis was performed for each period.

RESULTS

During sleep stage 2, CAP A subtypes differed from NCAP periods for all frequency bins between 0.5 and 25 Hz; this difference was most evident for the lowest frequencies. The B phase following A1 subtypes had a power spectrum significantly higher than that of NCAP, for frequencies between 1 and 11 Hz. The B phase after A2 only differed from NCAP for a small but significant reduction in the sigma band power; this was evident also after A3 subtypes. During SWS, we found similar results. The comparison between the different CAP subtypes also disclosed significant differences related to the stage in which they occurred. Finally, a significant effect of the different sleep periods was found on the different CAP subtypes during sleep stage 2 and on NCAP in both sleep stage 2 and SWS.

CONCLUSIONS

CAP subtypes are characterized by clearly different spectra and also the same subtype shows a different power spectrum, during sleep stage 2 or SWS. This finding underlines a probable different functional meaning of the same CAP subtype during different sleep stages. We also found 3 clear peaks of difference between CAP subtypes and NCAP in the delta, alpha, and beta frequency ranges which might indicate the presence of 3 frequency components characterizing CAP subtypes, in different proportion in each of them. The B component of CAP differs from NCAP because of a decrease in power in the sigma frequency range.

SIGNIFICANCE

This study shows that A components of CAP might correspond to periods in which the very-slow delta activity of sleep groups a range of different EEG activities, including the sigma and beta bands, while the B phase of CAP might correspond to a period in which this activity is quiescent or inhibited.

High frequency EEG activity during sleep: characteristics in schizophrenia and depression

Previous studies indicate that high frequency power (>20Hz) in the electroencephalogram (EEG) are associated with feature binding and attention. It has been hypothesized that hallucinations and perceptual abnormalities might be linked to irregularities in fast frequency activity. This study examines the power and distribution of high frequency activity (HFA) during sleep in healthy control subjects and unmedicated patients with schizophrenia and depression. This is a post-hoc analysis of an archival database collected under identical conditions. Groups were compared using multivariate analyses of covariance (MANCOVA) using group frequency by stage analysis. A multiple regression analyzed the association between HFA power and clinical symptoms. Schizophrenic (SZ) and major depressive disorder (MDD) patients showed significantly greater high frequency (HF) power than healthy controls (HC) in all sleep stages (p<0.0001). SZs also exhibited significantly greater HF power than MDD patients in all sleep stages except wakefulness (W) (p<0.05). In all groups, gamma (35-45Hz) power was greater in W, decreased during slow wave sleep (SWS) and decreased further during rapid eye movement (REM). Beta 2 (20-35 Hz) power was greater in W and REM than in SWS. Only positive symptoms exhibited an association with HF power. Elevated HFA during sleep in unmedicated patients with SZ and MDD is associated with positive symptoms of illness. It is not clear how HFA would change in relation to clinical improvement, and further study is needed to clarify the association of HFA to the state/trait characteristics of SZ and MDD.

State transitions between wake and sleep, and within the ultradian cycle, with focus on the link to neuronal activity

The structure of sleep across the night as expressed by the hypnogram, is characterised by repeated transitions between the different states of vigilance: wake, light and deep non-rapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. This review is concerned with current knowledge on these state transitions, focusing primarily on those findings that allow the integration of data at cellular level with spectral time-course data at the encephalographic (EEG) level. At the cellular level it has been proposed that, under the influence of circadian and homeostatic factors, transitions between wake and sleep may be determined by mutually inhibitory interaction between sleep-active neurons in the hypothalamic preoptic area and wake-active neurons in multiple arousal centres. These two fundamentally different behavioural states are separated by the sleep onset and the sleep inertia periods each characterised by gradual changes in which neither true wake nor true sleep patterns are present. The results of sequential spectral analysis of EEG data on moves towards and away from deep sleep are related to findings at the cellular level on the generating mechanisms giving rise to the various NREM oscillatory modes under the neuromodulatory control of brainstem-thalamic activating systems. And there is substantial evidence at cellular level that transition to and from REM sleep is governed by the reciprocal interaction between cholinergic REM-on neurons and aminergic REM-off neurons located in the brainstem. Similarity between the time-course of the REM-on neuronal activity and that of EEG power in the high beta range (approximately 18-30 Hz) allows a tentative parallelism to be drawn between the two. This review emphasises the importance of the thalamically projecting brainstem activating systems in the orchestration of the transitions that give rise to state progression across the sleep-wake cycle.

EEG activities during elicited sleep onset REM and NREM periods reflect different mechanisms of dream generation. Electroencephalograms. Rapid eye movement

OBJECTIVE

To be the first to compare EEG power spectra during sleep onset REM periods (SOREMP) and sleep onset NREM periods (NREMP) in normal individuals and relate this to dream appearance processes underlying these different types of sleep periods.

METHODS

Eight healthy undergraduates spent 7 consecutive nights in the sleep lab including 4 nights for SOREMP elicitation using the Sleep Interruption Technique. This enabled us to control preceding sleep processes between SOREMP and NREMP. EEG power spectra when participants did and did not report 'dreams' were compared between both types of sleep. Sleep stages, subjective measurements including dream property scores, sleepiness, mood, and tiredness after awakenings were also examined to determine their consistency with EEG findings.

RESULTS

Increased alpha EEG activities (11.72-13.67 Hz) observed mainly in the central area were related to the absence of SOREMP dreams and appearance of NREMP dreams. Analyses of sleep stages combining two studies (16 participants) also supported the Fast Fourier Transform findings, showing that when dreams were reported there were decreased amounts of stage 2 and increased stage REM in SOREMP and increased stage W in NREMP. SOREMP dreams were more bizarre than NREMP dreams. Participants felt more tired after SOREMP with dreams than without dreams, while the opposite was observed after NREMP episodes.

CONCLUSIONS

EEG power spectra patterns reflected different physiological mechanisms underlying generation of SOREMP and NREMP dreams. The same relationships were also reflected by sleep stage analyses as well as subjective measurements including dream properties and tiredness obtained after awakenings. This study not only supports the hypothesized relationships between REM mechanisms and REM dreams as well as arousal processes and NREM dreams, it also provides a new perspective to dream research due to its unique techniques to awaken participants and collect REM dreams during experimentally induced SOREMP.

Non-linear EEG measures during sleep: effects of the different sleep stages and cyclic alternating pattern

The objective of this work was to study the non-linear aspects of sleep EEG, taking into account the different sleep stages and the peculiar organization of its phasic events in ordered sequences (CAP) by applying a series of new non-linear measures (non-linear cross prediction or NLCP), which appear more reliable for the detection and characterization of non-linear structures in experimental data than the commonly used correlation dimension. Eight healthy subjects aged 18-20 years participated in this study. Polysomnography was performed in all subjects; signals were sampled at 128 Hz and stored on hard disk. The C3 or C4 derivation was used for all the subsequent computational steps, which were performed on EEG epochs (4096 data points) selected from sleep stage 2 (S2) and slow-wave sleep (SWS), in both CAP and non-CAP (NCAP) conditions. Also, epochs from sleep stage 1 (S1), REM and wakefulness preceding sleep were recorded. The dynamic properties of the EEG were assessed by means of the non-linear cross-prediction test, which uses three different 'model' time series in order to predict non-linearly the original data set (Pred, Ama, and Tir). Pred is a measure of the predictability of the time series, and Ama and Tir are measures of asymmetry, indicating non-linear structure. The non-linear measures applied in this study indicate that sleep EEG tends to show non-linear structure only during CAP periods, both during S2 and SWS. Moreover, during CAP periods, non-linearity can only be detected during the phase A1 subtypes (and partially A2) of CAP. The A3 phases show characteristics of non-stationarity and bear some resemblance to wakefulness. Based on the results of this study, sleep might be considered as a dynamically evolving sequence of different states of the EEG, which we could track by detecting non-linearity, mostly in association with CAP. Our results clearly show that detectable non-linearity in the EEG is closely related to the occurrence of the phase A of CAP.

Scalp Topographic Distribution of Beta and Gamma Ratios During Sleep

Abstract The present study analyzes the topographic distribution of two newly introduced measures related to the beta and gamma EEG bands during REM sleep. For this purpose, power spectra of three EEG channels (F4, C4, and O2, all referred to A1) were obtained by means of the fast Fourier transform, and the power of the bands ranging from 0.75-4.50 Hz (delta) and 12.50-15.00 (sigma) was calculated for the whole period of analysis (7 h) in 10 healthy subjects. Also, two additional time series - the ratio between beta and gamma2 and between gamma1 and gamma2 - were calculated (beta and gamma ratios). The difference between the mean group values of the delta and sigma bands power, and of the beta and gamma ratios, during the different sleep stages, over the three different scalp locations recorded was evaluated by means of the nonparametric Friedman ANOVA. During non-REM slow-wave sleep, the delta band showed the highest values over the central and frontal regions, followed by those observed over the occipital lead. During sleep stage 2, the sigma band showed the highest values over the central regions, followed by those observed over the occipital areas and, lastly, those from the frontal lead. During REM sleep, the beta ratio showed its highest values over the central field, which were significantly higher that those obtained from both the frontal and the occipital regions. The gamma ratio showed a statistically nonsignificant tendency to show a similar topographic distribution pattern. Sleep can be considered a complex phenomenon with a differential involvement of multiple cortical and subcortical structures. The analysis of high-frequency EEG bands and of our beta and gamma ratios represent an additional important element to include in the study of sleep.

Relationship between Delta, Sigma, Beta, and Gamma EEG bands at REM sleep onset and REM sleep end

OBJECTIVE

The aim of the present study was to analyze in detail the relationship of two newly introduced measures, related to the Beta and Gamma EEG bands during REM sleep, with Delta and Sigma activity at REM sleep onset and REM sleep end, in order to understand their eventual role in the sleep modulation mechanism.

METHODS

For this purpose, power spectra of 1 EEG channel (C4, referred to A1) were obtained by means of the fast Fourier transform and the power of the bands ranging 0.75-4.50 Hz (Delta), 4.75-7.75 (Theta), 8.00-12.25 (Alpha), 12.50-15.00 (Sigma), 15.25-24.75 (Beta), 25.00-34.75 (Gamma 1), and 35.00-44.75 (Gamma 2) was calculated for the whole period of analysis (7 h), in 10 healthy subjects. Additionally, two other time series were calculated: the ratio between Beta and Gamma2, and between Gamma1 and Gamma2 (Beta and Gamma ratios). For each subject, we extracted 3 epochs of 30 min corresponding to the 15 min preceding and the 15 min following the onset of the first 3 REM episodes. Data were then averaged in order to obtain group mean values and standard deviation. The same process was applied to the 30-min epochs around REM sleep end.

RESULTS

The course of the Delta band around REM sleep onset was found to be characterized by a first phase of slow decline lasting from the beginning of our window up to a few seconds before REM onset; this phase was followed by a sudden, short decrease centered around REM onset, lasting for approximately 1.5-2 min. At the end of this phase, the Delta band reached its lowest values and remained stable up to the end of the time window. The Sigma band showed a similar course with stable values before and after REM sleep onset. The Beta and Gamma ratios also showed a 3-phase course; the first phase, in this case, was characterized by stable low values, from the beginning of our window up to approximately 5 min before REM onset. The following second phase was characterized by an increase which reached its maximum shortly after REM sleep onset (approximately 1 min). In the last phase, both Beta and Gamma ratios showed stable high values, up to the end of our time window. At REM sleep end, the Delta band only showed a very small gradual increase, the Sigma band presented a more evident gradual increase; on the contrary, both Beta and Gamma ratios showed a small gradual decrease.

CONCLUSIONS

The results of the present study show a different time synchronization of the changes in the Delta band and in Beta and Gamma ratios, at around REM sleep onset, and seem to suggest that the oscillations of these parameters might be modulated by mechanisms more complex than a simple reciprocity. All these considerations point to the fact that REM sleep can be considered as a complex phenomenon and the analysis of high-frequency EEG bands and of our Beta and Gamma ratios represent an additional important element to include in the study of this sleep stage.