Relationship between the time courses of power in the frequency bands of human sleep EEG

Purkinje cell dysfunction causes disrupted sleep in ataxic mice

Purkinje cell dysfunction disrupts movement and causes disorders such as ataxia. Recent evidence suggests that Purkinje cell dysfunction may also alter sleep regulation. Here, we used an ataxic mouse model generated by silencing Purkinje cell neurotransmission (L7Cre;Vgatfx/fx) to better understand how cerebellar dysfunction impacts sleep physiology. We focused our analysis on sleep architecture and electrocorticography (ECoG) patterns based on their relevance to extracting physiological measurements during sleep. We found that circadian activity was unaltered in the mutant mice, although their sleep parameters and ECoG patterns were modified. The L7Cre;Vgatfx/fx mutant mice had decreased wakefulness and rapid eye movement (REM) sleep, whereas non-REM sleep was increased. The mutants had an extended latency to REM sleep, which is also observed in human patients with ataxia. Spectral analysis of ECoG signals revealed alterations in the power distribution across different frequency bands defining sleep. Therefore, Purkinje cell dysfunction may influence wakefulness and equilibrium of distinct sleep stages in ataxia. Our findings posit a connection between cerebellar dysfunction and disrupted sleep and underscore the importance of examining cerebellar circuit function in sleep disorders.

Purkinje cell dysfunction causes disrupted sleep in ataxic mice

Purkinje cell dysfunction causes movement disorders such as ataxia, however, recent evidence suggests that Purkinje cell dysfunction may also alter sleep regulation. Here, we used an ataxia mouse model generated by silencing Purkinje cell neurotransmission ( L7 Cre ;Vgat fx/fx ) to better understand how cerebellar dysfunction impacts sleep physiology. We focused our analysis on sleep architecture and electrocorticography (ECoG) patterns based on their relevance to extracting physiological measurements during sleep. We found that circadian activity is unaltered in the mutant mice, although their sleep parameters and ECoG patterns are modified. The L7 Cre ;Vgat fx/fx mutant mice have decreased wakefulness and rapid eye movement (REM) sleep, while non-rapid eye movement (NREM) sleep is increased. The mutant mice have an extended latency to REM sleep, which is also observed in human ataxia patients. Spectral analysis of ECoG signals revealed alterations in the power distribution across different frequency bands defining sleep. Therefore, Purkinje cell dysfunction may influence wakefulness and equilibrium of distinct sleep stages in ataxia. Our findings posit a connection between cerebellar dysfunction and disrupted sleep and underscore the importance of examining cerebellar circuit function in sleep disorders.

Summary Statement

Utilizing a precise genetic mouse model of ataxia, we provide insights into the cerebellum's role in sleep regulation, highlighting its potential as a therapeutic target for motor disorders-related sleep disruptions.

Effects of Sedatives on Sleep Architecture Measured With Odds Ratio Product in Critically Ill Patients

Supplemental Digital Content is available in the text.

OBJECTIVES:

Evaluation of sleep quality in critically ill patients is difficult using conventional scoring criteria. The aim of this study was to examine sleep in critically ill patients with and without light sedation using the odds ratio product, a validated continuous metric of sleep depth (0 = deep sleep; 2.5 = full wakefulness) that does not rely on the features needed for conventional staging.

DESIGN:

Retrospective study.

SETTINGS:

A 16-bed medical-surgical ICU.

PATIENTS:

Twenty-three mechanically ventilated patients who had previously undergone two nocturnal sleep studies, one without and one with sedation (propofol, n = 12; dexmedetomidine, n = 11).

INTERVENTIONS:

None.

MEASUREMENTS AND MAIN RESULTS:

Sleep architecture was evaluated with odds ratio product analysis by the distribution of 30-second epochs with different odds ratio product values. Electroencephalogram spectral patterns and frequency of wake intrusions (3-s odds ratio product > 1.75) were measured at different odds ratio product levels. Thirty-seven normal sleepers were used as controls. Compared with normal sleepers, unsedated critically ill patients spent little time in stable sleep (percent odds ratio product < 1.0: 31% vs 63%; p < 0.001), whereas most of the time were either in stage wake (odds ratio product > 1.75) or in a transitional state (odds ratio product 1.0–1.75), characterized by frequent wake intrusions. Propofol and dexmedetomidine had comparable effects on sleep. Sedation resulted in significant shift in odds ratio product distribution toward normal; percent odds ratio product less than 1.0 increased by 54% (p = 0.006), and percent odds ratio product greater than 1.75 decreased by 48% (p = 0.013). In six patients (26%), sedation failed to improve sleep.

CONCLUSIONS:

In stable critically ill unsedated patients, sleep quality is poor with frequent wake intrusions and little stable sleep. Light sedation with propofol or dexmedetomidine resulted in a shift in sleep architecture toward normal in most, but not all, patients.

REM Sleep EEG Activity and Clinical Correlates in Adults With Autism

We tested the hypothesis of an atypical scalp distribution of electroencephalography (EEG) activity during Rapid Eye Movement (REM) sleep in young autistic adults. EEG spectral activity and ratios along the anteroposterior axis and across hemispheres were compared in 16 neurotypical (NT) young adults and 17 individuals with autism spectrum disorder (ASD). EEG spectral power was lower in the ASD group over the bilateral central and right parietal (beta activity) as well as bilateral occipital (beta, theta, and total activity) recording sites. The NT group displayed a significant posterior polarity of intra-hemispheric EEG activity while EEG activity was more evenly or anteriorly distributed in ASD participants. No significant inter-hemispheric EEG lateralization was found. Correlations between EEG distribution and ASD symptoms using the Autism Diagnostic Interview-Revised (ADI-R) showed that a higher posterior ratio was associated with a better ADI-R score on communication skills, whereas a higher anterior ratio was related to more restricted interests and repetitive behaviors. EEG activity thus appears to be atypically distributed over the scalp surface in young adults with autism during REM sleep within cerebral hemispheres, and this correlates with some ASD symptoms. These suggests the existence in autism of a common substrate between some of the symptoms of ASD and an atypical organization and/or functioning of the thalamo-cortical loop during REM sleep.

Ultradian modulation of cortical arousals during sleep: effects of age and exposure to nighttime transportation noise

STUDY OBJECTIVES

The present study aimed at assessing the temporal non-rapid eye movement (NREM) EEG arousal distribution within and across sleep cycles and its modifications with aging and nighttime transportation noise exposure, factors that typically increase the incidence of EEG arousals.

METHODS

Twenty-six young (19-33 years, 12 women) and 16 older (52-70 years, 8 women) healthy volunteers underwent a 6-day polysomnographic laboratory study. Participants spent two noise-free nights and four transportation noise exposure nights, two with continuous and two characterized by eventful noise (average sound levels of 45 dB, maximum sound levels between 50 and 62 dB for eventful noise). Generalized mixed models were used to model the time course of EEG arousal rates during NREM sleep and included cycle, age, and noise as independent variables.

RESULTS

Arousal rate variation within NREM sleep cycles was best described by a u-shaped course with variations across cycles. Older participants had higher overall arousal rates than the younger individuals with differences for the first and the fourth cycle depending on the age group. During eventful noise nights, overall arousal rates were increased compared to noise-free nights. Additional analyses suggested that the arousal rate time course was partially mediated by slow wave sleep (SWS).

CONCLUSIONS

The characteristic u-shaped arousal rate time course indicates phases of reduced physiological sleep stability both at the beginning and end of NREM cycles. Small effects on the overall arousal rate by eventful noise exposure suggest a preserved physiological within- and across-cycle arousal evolution with noise exposure, while aging affected the shape depending on the cycle.

Thalamic and neocortical differences in the relationship between the time course of delta and sigma power during NREM sleep in humans

Sleep spindles and slow waves are the hallmarks of non-rapid eye movement (NREM) sleep and are produced by the dynamic interplay between thalamic and cortical regions. Several studies in both human and animal models have focused their attention on the relationship between electroencephalographic (EEG) spindles and slow waves during NREM, using the power in the sigma and delta bands as a surrogate for the production of spindles and slow waves. A typical report is an overall inverse relationship between the time course of sigma and delta power as measured by a single correlation coefficient both within and across NREM episodes. Here we analysed stereotactically implanted intracerebral electrode (Stereo-EEG [SEEG]) recordings during NREM simultaneously acquired from thalamic and from several neocortical sites in six neurosurgical patients. We investigated the relationship between the time course of delta and sigma power and found that, although at the cortical level it shows the expected inverse relationship, these two frequency bands follow a parallel time course at the thalamic level. Both these observations were consistent across patients and across different cortical as well as thalamic regions. These different temporal dynamics at the neocortical and thalamic level are discussed, considering classical as well as more recent interpretations of the neurophysiological determinants of sleep spindles and slow waves. These findings may also help understanding the regulatory mechanisms of these fundamental sleep EEG graphoelements across different brain compartments.

Comparing two measures of sleep depth/intensity

Study Objectives

To compare delta spectral power (delta) and odds ratio product (ORP) as measures of sleep depth during sleep restriction with placebo or a drug that increases delta.

Methods

This is a secondary analysis of data from a study of 41 healthy participants randomized to receive placebo or gaboxadol 15 mg during sleep restriction. Participants underwent in-laboratory sleep studies on two baseline, four sleep restriction (5-h), and two recovery nights. Relation between delta or ORP and sleep depth was operationally defined as the degree of association of each metric to the probability of arousal or awakening occurring during the next 30 s (arousability).

Results

ORP values in wake, N1, N2, N3, and REM were significantly different. Delta differed between both N2 and N3 and other sleep stages but not between wake and N1 or N1 and REM. Epoch-by-epoch and individual correlations between ORP and delta power were modest or insignificant. The relation between ORP and arousability was linear across the entire ORP range. Delta also changed with arousability but only when delta values were less than 300 μV2. Receiver-operating-characteristic analysis found the ability to predict imminent arousal to be significantly greater with ORP than with log delta power for all experimental conditions. Changes in ORP, but not log delta, across the night correlated with next-day physiologic sleep tendency.

Conclusions

Compared to delta power, ORP is more discriminating among sleep stages, more sensitive to sleep restriction, and more closely associated with arousability. This evidence supports ORP as a measure of sleep depth/intensity.

Electroencephalographic changes associated with subjective under- and overestimation of sleep duration

Feeling awake although sleep recordings indicate clear-cut sleep sometimes occurs in good sleepers and to an extreme degree in patients with so-called paradoxical insomnia. It is unknown what underlies sleep misperception, as standard polysomnographic (PSG) parameters are often normal in these cases. Here we asked whether regional changes in brain activity could account for the mismatch between objective and subjective total sleep times (TST). To set cutoffs and define the norm, we first evaluated sleep perception in a population-based sample, consisting of 2,092 individuals who underwent a full PSG at home and estimated TST the next day. We then compared participants with a low mismatch (normoestimators, n = 1,147, ±0.5 SD of mean) with those who severely underestimated (n = 52, &lt;2.5th percentile) or overestimated TST (n = 53, &gt;97.5th percentile). Compared with normoestimators, underestimators displayed higher electroencephalographic (EEG) activation (beta/delta power ratio) in both rapid eye movement (REM) and non-rapid eye movement (NREM) sleep, while overestimators showed lower EEG activation (significant in REM sleep). To spatially map these changes, we performed a second experiment, in which 24 healthy subjects and 10 insomnia patients underwent high-density sleep EEG recordings. Similarly to underestimators, patients displayed increased EEG activation during NREM sleep, which we localized to central-posterior brain areas. Our results indicate that a relative shift from low- to high-frequency spectral power in central-posterior brain regions, not readily apparent in conventional PSG parameters, is associated with underestimation of sleep duration. This challenges the concept of sleep misperception, and suggests that instead of misperceiving sleep, insomnia patients may correctly perceive subtle shifts toward wake-like brain activity.

Neural mechanisms of mental schema: a triplet of delta, low beta/spindle and ripple oscillations

Schemas are higher-level knowledge structures that integrate and organise lower-level representations. As internal templates, schemas are formed according to how events are perceived, interpreted and remembered. Although these higher-level units are assumed to play a fundamental role in our daily life from an early age, the neuronal basis and mechanisms of schema formation and use remain largely unknown. It is important to elucidate how the brain constructs and maintains these higher-level units. In order to examine the possible neural underpinnings of schema, we recapitulate previous work and discuss their findings related to schemas as the brain template. We specifically focused on low beta/spindle oscillations, which are assumed to be the key components of schemas, and propose that the brain template is implemented with a triplet of neural oscillations, that is delta, low beta/spindle and ripple oscillations.

Automatic characterization of sleep need dissipation dynamics using a single EEG signal

In the two-process model of sleep regulation, slow-wave activity (SWA, i.e. the EEG power in the 0.5-4 Hz frequency band) is considered a direct indicator of sleep need. SWA builds up during non-rapid eye movement (NREM) sleep, declines before the onset of rapid-eye-movement (REM) sleep, remains low during REM and the level of increase in successive NREM episodes gets progressively lower. Sleep need dissipates with a speed that is proportional to SWA and can be characterized in terms of the initial sleep need, and the decay rate. The goal in this paper is to automatically characterize sleep need from a single EEG signal acquired at a frontal location. To achieve this, a highly specific and reasonably sensitive NREM detection algorithm is proposed that leverages the concept of a single-class Kernel-based classifier. Using automatic NREM detection, we propose a method to estimate the decay rate and the initial sleep need. This method was tested on experimental data from 8 subjects who recorded EEG during three nights at home. We found that on average the estimates of the decay rate and the initial sleep need have higher values when automatic NREM detection was used as compared to manual NREM annotation. However, the average variability of these estimates across multiple nights of the same subject was lower when the automatic NREM detection classifier was used. While this method slightly over estimates the sleep need parameters, the reduced variability across subjects makes it more effective for within subject statistical comparisons of a given sleep intervention.

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.

Sleep alterations following exposure to stress predict fear-associated memory impairments in a rodent model of PTSD

Sleep abnormalities, such as insomnia, nightmares, hyper-arousal, and difficulty initiating or maintaining sleep, are diagnostic criteria of posttraumatic stress disorder (PTSD). The vivid dream state, rapid eye movement (REM) sleep, has been implicated in processing emotional memories. We have hypothesized that REM sleep is maladaptive in those suffering from PTSD. However, the precise neurobiological mechanisms regulating sleep disturbances following trauma exposure are poorly understood. Using single prolonged stress (SPS), a well-validated rodent model of PTSD, we measured sleep alterations in response to stressor exposure and over a subsequent 7-day isolation period during which the PTSD-like phenotype develops. SPS resulted in acute increases in REM sleep and transition to REM sleep, and decreased waking in addition to alterations in sleep architecture. The severity of the PTSD-like phenotype was later assessed by measuring freezing levels on a fear-associated memory test. Interestingly, the change in REM sleep following SPS was significantly correlated with freezing behavior during extinction recall assessed more than a week later. Reductions in theta (4-10 Hz) and sigma (10-15 Hz) band power during transition to REM sleep also correlated with impaired fear-associated memory processing. These data reveal that changes in REM sleep, transition to REM sleep, waking, and theta and sigma power may serve as sleep biomarkers to identify individuals with increased susceptibility to PTSD following trauma exposure.

Theta frequency activity during rapid eye movement (REM) sleep is greater in people with resilience versus PTSD

Emotional memory consolidation has been associated with rapid eye movement (REM) sleep, and recent evidence suggests that increased electroencephalogram spectral power in the theta (4-8 Hz) frequency range indexes this activity. REM sleep has been implicated in posttraumatic stress disorder (PTSD) as well as in emotional adaption. In this cross-sectional study, thirty young healthy African American adults with trauma exposure were assessed for PTSD status using the Clinician Administered PTSD Scale. Two consecutive night polysomnographic (PSG) recordings were performed and data scored for sleep stages. Quantitative electroencephalographic spectral analysis was used to measure theta frequency components sampled from REM sleep periods of the second-night PSG recordings. Our objective was to compare relative theta power between trauma-exposed participants who were either resilient or had developed PTSD. Results indicated higher right prefrontal theta power during the first and last REM periods in resilient participants compared with participants with PTSD. Right hemisphere prefrontal theta power during REM sleep may serve as a biomarker of the capacity for adaptive emotional memory processing among trauma-exposed individuals.

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.

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.

EEG spectral analysis in primary insomnia: NREM period effects and sex differences

STUDY OBJECTIVES

To compare NREM EEG power in primary insomnia (PI) and good sleeper controls (GSC), examining both sex and NREM period effects; to examine relationships between EEG power, clinical characteristics, and self-reports of sleep.

DESIGN

Overnight polysomnographic study.

SETTING

Sleep laboratory.

PARTICIPANTS

PI (n=48; 29 women) and GSC (n=25; 15 women).

INTERVENTIONS

None.

MEASUREMENTS

EEG power from 1-50 Hz was computed for artifact-free sleep epochs across four NREM periods. Repeated measures mixed effect models contrasted differences between groups, EEG frequency bands, and NREM periods. EEG power-frequency curves were modeled using regressions with fixed knot splines.

RESULTS

Mixed models showed no significant group (PI vs. GSC) differences; marginal sex differences (delta and theta bands); significant differences across NREM periods; and group*sex and group*NREM period interactions, particularly in beta and gamma bands. Modeled power-frequency curves showed no group difference in whole-night NREM, but PI had higher power than GSC from 18-40 Hz in the first NREM period. Among women, PI had higher 16 to 44-Hz power than GSC in the first 3 NREM periods, and higher 3 to 5-Hz power across all NREM periods. PI and GSC men showed no consistent differences in EEG power. High-frequency EEG power was not related to clinical or subjective sleep ratings in PI.

CONCLUSIONS

Women with PI, but not men, showed increased high-frequency and low-frequency EEG activity during NREM sleep compared to GSC, particularly in early NREM periods. Sex and NREM period may moderate quantitative EEG differences between PI and GSC.

REM sleep EEG spectral analysis in patients with first-episode schizophrenia

The pathophysiology of schizophrenia includes abnormalities in subcortical-cortical transfer of information that can be studied using REM sleep EEG spectral analysis, a measure that reflects spontaneous and endogenous thalamocortical activity. We recorded 10 patients with first-episode schizophrenia and 30 healthy controls for two consecutive nights in a sleep laboratory, using a 10-electrode EEG montage. Sixty seconds of REM sleep EEG without artifact were analyzed using FFT spectral analysis. Absolute and relative spectral amplitudes of five frequency bands (delta, theta, alpha, beta1 and beta2) were extracted and compared between the two groups. Frequency bands with significant differences were correlated with BPRS positive and negative symptoms scores. Patients with schizophrenia showed lower relative alpha and higher relative beta2 spectral amplitudes compared to healthy controls over the averaged total scalp. Analysis using cortical regions showed lower relative alpha over frontal, central and temporal regions and higher relative beta2 over the occipital region. Absolute spectral amplitude was not different between groups for any given EEG band. However, absolute alpha activity correlated negatively with BPRS positive symptoms scores and correlated positively with negative symptoms scores. Since similar results have been reported following EEG spectral analysis during the waking state, we conclude that abnormalities of subcortical-cortical transfer of information in schizophrenia could be generated by mechanisms common to REM sleep and waking.

EEG correlates of emotions in dream narratives from typical young adults and individuals with autistic spectrum disorders

The relationship between emotional dream content and Alpha and Beta REM sleep EEG activity was investigated in typical individuals and in Autistic Spectrum Disorders (ASD). Dream narratives of persons with ASD contained fewer emotional elements. In both groups, emotions correlated positively with slow Alpha (8.0-10.0 Hz) spectral power over parieto-occipital and left central regions, as well as with a right occipital EEG asymmetry. Slow Alpha activity in ASD individuals was lower over midline and parasagittal areas and higher over lateral areas compared to controls. Both groups displayed a right-biased slow Alpha activity for midparietal and occipital (significantly higher in control) sites. Results indicate that Alpha EEG activity may represent a neurophysiological substrate associated with emotional dream content. Distinctive Alpha EEG patterns and asymmetries suggest that dream generation implies different brain connectivity in ASD.

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).

Neurophysiological aspects of primary insomnia: Implications for its treatment

Insomnia has usually been studied from a behavioral perspective. Somatic and/or cognitive conditioned arousal was shown to play a central role in sleep complaints becoming chronic, and was used as a starting point for the development of treatment modalities. The introduction of the neurocognitive perspective, with its focus on cortical or CNS arousal, has given rise to a renewed interest in the neurophysiological characteristics of insomnia. Recent research, using quantitative EEG, neuroimaging techniques and the study of the microstructure of sleep, suggests a state of hyperarousal with a biological basis. Furthermore, insomnia might not be restricted to sleep complaints alone because it appears to be a 24-h disorder, affecting several aspects of daytime functioning as well. These new findings have implications for the treatments used and indicate that a focus on cortical or CNS arousal should be pursued. As such, the use of EEG neurofeedback, a self-regulation method based on the paradigm of operant conditioning, might be a promising treatment modality. Preliminary results for insomnia and successful applications for other disorders suggest that this treatment can have the necessary stabilizing effects on the EEG activity, possibly resulting in a normalizing effect on daytime as well as nighttime functioning.

QRS artifact elimination on full night sleep EEG

Spectral analysis is now a standard procedure for analyzing the electroencephalograms (EEG) obtained by polysomnographic recordings. These numerical methods assume an artifact-free EEG since artifacts create spurious spectral components. Our aim was the development of a QRS artifact removal technique that might be applied to full night EEG with a minimal human intervention. This technique should handle one EEG channel, with or without use of one ECG channel. Variance minimization, independent component analysis (ICA), morphological filters (MF) have been implemented. Careful attention has been given to define the MF structuring element. The tests on artifact-simulated and real data were checked on the residual ECG spectral components present in the cleaned EEG. The best results are obtained by the MF when the structuring element is an artifact template defined either directly on the EEG or on the ICA ECG component. Further developments are required to identify and subtract the T-wave artifacts.

Difference in sleep regulation between morning and evening circadian types as indexed by antero-posterior analyses of the sleep EEG

Circadian types classify individuals according to their preferred timing for activity and sleep, morning and evening types showing, respectively, early or late preferences. This characteristic has been associated with corresponding differences in circadian sleep propensity. In this study, quantitative analysis of the sleep EEG in antero-posterior derivations was used to test the hypothesis that morning and evening types differ not only in the circadian aspect of sleep regulation but also in the homeostatic aspect. Morning types and evening types (six men and six women per group, aged 19-34 years) were selected using the Morningness-Eveningness Questionnaire. They were studied by polysomnography according to their preferred sleep schedule. Spectral activity in four midline derivations (Fz, Cz, Pz, Oz) was calculated separately in nonrapid eye movement (NREM) sleep and in rapid eye movement (REM) sleep. In NREM sleep, morning types showed a steeper decrease of slow-wave activity (SWA; 1-5 Hz) per sleep cycle in the fronto-central derivations and a steeper increase in 13-14 Hz activity in the parieto-occipital derivations than did evening types. Nonlinear regression analysis revealed that the exponential decay rate of relative values of SWA in NREM sleep was faster in morning than evening types, in the frontal derivation. In REM sleep, morning types showed a steeper decrease of high sigma (14-16 Hz) and beta (16-24 Hz) activities across the night in centro-parietal derivations than did evening types. These results show for the first time a clear difference between morning types and evening types in homeostatic sleep regulation.

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.

Models of human sleep regulation

Non-REM sleep deprivation and REM sleep deprivation both lead to specific rebounds, suggesting that these states fulfil physiological needs. In view of impaired performance after sleep deprivation, a recovery function of sleep seems likely. The timing of this recovery is restricted to a narrow time interval within the 24 hour day, i.e. the night. Generally, nocturnal sleep in humans is considered a consequence of the impact of the circadian pacemaker in the hypothalamus on sleep propensity. The interaction between the homeostatic recovery process and the circadian pacemaker has been modelled in the two-process model of sleep regulation. This model is used as a starting point in the present review. A series of refinements and several alternative models are discussed, both with respect to the quality of fit of theory and data, as well as with respect to the concepts behind the models.

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.

Interdependency between heart rate variability and sleep EEG: linear/non-linear?

OBJECTIVE

To investigate whether the interdependency between heart rate variability (HRV) and sleep electroencephalogram (EEG) power spectra is linear or non-linear.

METHODS

Heart rate and sleep EEG signals were recorded in 8 healthy young men. Spectral analysis was applied to electrocardiogram and EEG sleep recordings. Synchronization likelihood was computed over the first 3 non-rapid eye movement-rapid eye movement sleep cycles between normalized high frequency of RR intervals (RRI) and all electroencephalographic frequency bands. Comparison to surrogate data of different types was used to attest statistical significance of the coupling between RRI and EEG power bands and its linear or non-linear character.

RESULTS

Synchronization likelihood values were statistically greater than univariate surrogate synchronization for all sleep bands both at the individual and the group levels. With reference to multivariate surrogates, synchronization values were statistically greater at the group level and, in a majority of cases, for individual comparison except for sigma and beta bands.

CONCLUSIONS

While all electroencephalographic power bands are linked to normalized high frequency RRI band, this interdependency is non-linear for delta, theta and alpha bands.

SIGNIFICANCE

Non-linear description is required to capture the full interdependent dynamics of HRV and sleep EEG power bands.

Spectral Power Time-courses of Human Sleep EEG Reveal a Striking Discontinuity at ∼18 Hz Marking the Division between NREM-specific and Wake/REM-specific Fast Frequency Activity

Spectral power time-courses over the ultradian cycle of the sleep electroencephalogram (EEG) provide a useful window for exploring the temporal correlation between cortical EEG and sub-cortical neuronal activities. Precision in the measurement of these time-courses is thus important, but it is hampered by lacunae in the definition of the frequency band limits that are in the main based on wake EEG conventions. A frequently seen discordance between the shape of the beta power time-course across the ultradian cycle and that reported for the sequential mean firing rate of brainstem-thalamic activating neurons invites a closer examination of these band limits, especially since the sleep EEG literature indicates in several studies an intriguing non-uniformity of time-course comportment across the traditional beta band frequencies. We ascribe this tentatively to the sharp reversal of slope we have seen at approximately 18 Hz in our data and that of others. Here, therefore, using data for the first four ultradian cycles from 18 healthy subjects, we apply several criteria based on changes in time-course comportment in order to examine this non-uniformity as we move in 1 Hz bins through the frequency range 14-30 Hz. The results confirm and describe in detail the striking discontinuity of shape at around 18 Hz, with only the upper range (18-30 Hz) displaying a time-course similar to that of the firing-rate changes measured in brainstem activating neurons and acknowledged to engender states of brain activation. Fast frequencies in the lower range (15-18 Hz), on the other hand, are shown to be specific to non-rapid-eye-movement sleep. Splitting the beta band at approximately 18 Hz therefore permits a significant improvement in EEG measurement and a more precise correlation with cellular activity.

EEG spectral analysis of wakefulness and REM sleep in high functioning autistic spectrum disorders

OBJECTIVE

The aim of this study was to investigate the involvement of temporo-occipital regions in the pathophysiology of autistic spectrum disorders (ASD) by using REM sleep and waking EEG.

METHODS

The EEG recordings of 9 persons with ASD and 8 control participants were recorded using a 12-electrode montage. Spectral analysis (0.75-19.75 Hz) was performed on EEG activity recorded upon two activated states: REM sleep and wakefulness.

RESULTS

During REM sleep, persons with ASD showed a selective, significantly lower absolute beta (13.0-19.75 Hz) spectral amplitude over the primary (O(1), O(2)) and associative (T(5), T(6)) cortical visual areas compared to controls. Persons with ASD showed significantly higher absolute theta (4.0-7.75 Hz) spectral amplitude over the left frontal pole region (Fp1) compared to controls during evening wakefulness, but not during morning wakefulness.

SIGNIFICANCE

The results of waking EEG are consistent with previously reported observations of neuropsychological signs of frontal atypicalities in ASD; results from REM sleep are the first EEG evidence to support the hypothesis of abnormal visuoperceptual functioning in ASD. Altogether, these results point toward atypical thalamo-cortical mechanisms subserving the neural processing of information in ASD.

Sleep spindles: an overview

Sleep spindles are a distinctive EEG phasic feature of NREM sleep and are prevalent during stage 2 as compared to slow wave sleep. While the neurophysiological mechanisms of spindle generation, that involves thalamic and corticothalamic networks, have been recently delineated and are briefly reviewed, their definitive functional meaning still remains to be elucidated. This review summarizes the present knowledge on visually scored and automatically detected spindles, as well as the literature on EEG power in the sigma band. Among the factors known to affect sleep spindles and sigma activity, their intra-cycle temporal dynamics, their time-course across sleep cycles, the reciprocal relationship with delta activity, the effects of sleep deprivation, of circadian factors and of ageing, and their role in information processing have been discussed. Moreover, specific attention has been paid to the existence of functionally and topographically distinct slow- and fast-spindles, also taking into account the presence of large inter-individual differences. Nevertheless, several fundamental issues remain to be elucidated: the physiological mechanisms controlling age-related changes in spindle parameters; the role of melatonin as a spindle-promoting agent; the relationships between plastic mechanisms (after stroke, or as a consequence of learning) and modifications in spindle activity; the possibility of using some spindle parameters as an index of the severity of developmental disorders in abnormal maturational processes.

Spectral analysis of all-night human sleep EEG in narcoleptic patients and normal subjects

To investigate the pathophysiology of narcoleptic patients' sleep in detail, we analysed and compared the whole-night polysomnograms of narcoleptic patients and normal human subjects. Eight drug-naive narcoleptic patients and eight age-matched normal volunteers underwent polysomnography (PSG) on two consecutive nights. In addition to conventional visual scoring of the polysomnograms, rapid eye movement (REM)-density and electroencephalograph (EEG) power spectra analyses were also performed. Sleep onset REM periods and fragmented nocturnal sleep were observed as expected in our narcoleptic patients. In the narcoleptic patients, REM period duration across the night did not show the significant increasing trend that is usually observed in normal subjects. In all narcoleptic patient REM periods, eye movement densities were significantly increased. The power spectra of narcoleptic REM sleep significantly increased between 0.3 and 0.9 Hz and decreased between 1.0 and 5.4 Hz. Further analysis revealed that non-rapid eye movement (NREM) period duration and the declining trend of delta power density in the narcoleptic patients were not significantly different from the normal subjects. Compared with normal subjects, the power spectra of narcoleptic NREM sleep increased in the 1.0-1.4 Hz and 11.0-11.9 Hz frequency bands, and decreased in a 24.0-26.9 Hz frequency band. Thus, increased EEG delta and decreased beta power densities were commonly observed in both the NREM and REM sleep of the narcoleptic patients, although the decrease in beta power during REM sleep was not statistically significant. Our visual analysis revealed fragmented nocturnal sleep and increased phasic REM components in the narcoleptic patients, which suggest the disturbance of sleep maintenance mechanism(s) and excessive effects of the mechanism(s) underlying eye movement activities during REM sleep in narcolepsy. Spectral analysis revealed significant increases in delta components and decreases in beta components, which suggest decreased activity in central arousal mechanisms. These characteristics lead us to hypothesize that two countervailing mechanisms underlie narcoleptic sleep pathology.

An automatic method for the recognition and classification of the A-phases of the cyclic alternating pattern

OBJECTIVES

The aim of this research has been to introduce an automatic method, simple from the mathematical and computational points of view, for the recognition and classification of the A-phases of the cyclic alternating pattern.

METHODS

The automatic method was based on the computation of 5 descriptors, which were derived from the EEG signal and were able to provide a meaningful data reduction. Each of them corresponded to a different frequency band.

RESULTS

The computation of these descriptors, followed by the introduction of two suitable thresholds and of simple criteria for logical discrimination, provided results which were in good agreement with those obtained with visual analysis. The method was versatile and could be applied to the study of other important microstructure phenomena by means of very small adaptations.

CONCLUSIONS

The simplicity of the method leads to a better understanding and a more precise definition of the visual criteria for the recognition and classification of the microstructure phenomena.

The sleep EEG's microstructure in depression: alterations of the phase relations between EEG rhythms during REM and NREM sleep

OBJECTIVE

We investigated the microstructure of sleep electroencephalograms (EEGs) of 13 unmedicated depressive inpatients and 13 healthy controls matched in sex and age, hypothesizing that depressives depict an alteration of certain EEG oscillations across the night.

METHODS

We digitized the sleep EEGs with a sampling rate of 100 Hz (bipolar derivation C(z)-P(z), 1440 single sweeps; 2048 data points each), calculated the time course of delta (1-3.5 Hz), theta (3.5-7.5 Hz), alpha (7.5-15 Hz), and beta (15-35 Hz) activity over the night, and determined the correlation coefficients of these different EEG rhythms separately for rapid eye movement (REM) and non-rapid eye movement (NREM) sleep.

RESULTS

For both groups we detected a clear difference between REM and NREM sleep cycles at certain frequency bands. The most impressive changes occurred for the delta/beta and theta/beta correlations, which change their signs between NREM (negatively correlated) and REM (positively correlated) sleep cycles. Following an analysis of variance model with repeated measurement design, a statistically significant group effect (P=0.024) between depressives and controls was observable during NREM sleep for the delta/beta (P=0.010) and theta/beta (P=0.018) interactions.

CONCLUSION

We detected alterations of certain sleep EEG oscillations during the NREM sleep cycle, where the delta/beta as well as the theta/beta activities were higher (negatively) compared to healthy controls. Together with previous investigations on the influence of antidepressants on the microstructure of sleep EEGs, this is another hint that the NREM sleep cycle plays a major role in depression.

Regional differences of the temporal EEG dynamics during the first 30 min of human sleep

The analysis of the time-course of different EEG bands during the first sleep cycle has up to now taken into account mostly central derivations. In the present study, we assessed the topographical differences of the time-course of different EEG bands during the first 30 min of sleep, by analysing the EEG power from four different scalp locations along the antero-posterior axis. Correlation of EEG bands time courses within and between each derivation has also been evaluated. Delta-theta (1-6.75 Hz) and alpha (7-10.75 Hz) activities exhibited an antero-posterior gradient with maximal power on the frontal lead and increased at all derivations in the first 15 min and 5 min periods, respectively. Then alpha power decreased at all derivations, but the frontal. Sigma EEG power (11-15.75 Hz) showed a coherent behavior over the four derivations, characterized by a steep increase in the first 3-5 min of sleep, followed by a stable decreasing trend. Beta power (16-25.75 Hz) linearly decreased only on the more posterior derivations, abruptly increasing on the frontal lead after a 15 min interval. Correlations between delta-theta and alpha band were higher on the frontal derivation. Moreover, frontal alpha was strongly related to delta-theta activity on all the four derivations, while occipital alpha was not. The negative correlations between delta-theta and beta time courses were very high on all derivations but the frontal one. This study shows the existence of topographical differences in the time-course of different EEG bands during the first 30 min of sleep. The peculiar behavior of the alpha and beta EEG bands over the frontal derivation indicates the need to re-consider the functional role of traditional EEG bands during sleep.

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.

Slow (0.7-2 Hz) and fast (2-4 Hz) delta components are differently correlated to theta, alpha and beta frequency bands during NREM sleep

OBJECTIVES

Spectral power of 40 all-night sleep EEGs (Cz-Pz bipolar lead) recorded in 20 healthy young subjects was calculated after normalization on 30-s consecutive epochs by means of an autocorrelation method based on a 15-order autoregressive model.

METHODS

The spectral parameters were calculated for the 7 main EEG bands: slow delta (0.7-2 Hz); fast delta (2-4 Hz); theta (4-8 Hz); alpha (8-12 Hz); sigma (12-16 Hz); beta1 (16-35 Hz); and beta 2 (>35 Hz).

RESULTS

Strong negative correlations were found between power in the fast delta and either the alpha or the beta bands and between slow delta and theta bands, whereas the two delta bands showed little correlation with each other.

CONCLUSION

The possibility that theses different relationships of slow and fast delta components with other frequency bands might reflect the neocortical or the thalamocortical origin of the delta waves is discussed.

Brain frequency magnetic fields alter cardiac autonomic control mechanisms

OBJECTIVE

Heart rate variability (HRV) is a noninvasive indicator of sympathetic and vagal cardiovascular control known to be tightly correlated with sleep stages. Recent studies indicate that HRV in humans is altered by nocturnal exposure to power-frequency (60 Hz) magnetic fields. Given the central origin of autonomic cardiac control, we determined if field exposure in the beta(1) EEG/MEG frequency range was a more effective stimulus for HRV alteration than 60 Hz fields, and explored the mechanisms involved.

METHODS

Healthy young men were exposed (n=9) overnight to an intermittent magnetic field (16 Hz, 28.3 microTesla, microT), or sham exposed (n=9), under blind test conditions in a laboratory exposure facility.

RESULTS

Field exposure was associated (P<0.05) with reduced power in the low band of the HRV frequency spectrum, and with decreases in mean heart rate. Analysis of the timing of the R waves surrounding each on-off transition of the intermittent field revealed no evidence for a direct effect on the cardiac pacemaker.

CONCLUSIONS

Magnetic field exposure in the EEG/MEG beta(1) frequency range alters HRV via a CNS effect. Phase-resetting experiments rule out a direct effect on the cardiac pacemaker. Biophysical calculations of the intensity of the electric fields induced in brain versus heart under the present exposure conditions are also consistent with and support a central rather than a peripheral site of action.

Effects of hypnotics on the sleep EEG of healthy young adults: new data and psychopharmacologic implications

Benzodiazepine hypnotics increase NREM sleep and alter its EEG by reducing delta (0.3-3 Hz) and increasing sigma (12-15 Hz) and beta (15-23 Hz) activity. We tested whether the nonbenzodiazepine hypnotic, zolpidem (10 mg), produced the same pattern of sleep and EEG changes as two "classical" benzodiazepines, triazolam (0.25 mg) and temazepam (30 mg). Sleep EEG of 16 subjects was analyzed with period amplitude analysis for 3 nights during drug administration or placebo. The effects of zolpidem were in the same direction but generally of smaller magnitude than those of the classical benzodiazepines. These differences are more likely the result of non-equivalent dosages than different pharmacologic actions. Period amplitude analysis showed that the decreased delta activity resulted mainly from a decrease in wave amplitude. In contrast, the increased sigma and beta activity were produced by increased wave incidence. Delta suppression increased with repeated drug administration but sigma and beta stimulation did not. While these findings have little relevance for the clinical choice of hypnotics they may hold important implications for the brain mechanisms involved in hypnotic tolerance and withdrawal delirium.

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

OBJECTIVE

The EEG spectral content of all-night sleep recordings obtained in 7 healthy young subjects, aged 18-20 years, including frequencies up to 45 Hz, was studied in order to detect eventual changes in the high-frequency range similar to those reported by magnetic field recording during REM sleep at 40 Hz.

METHODS

For this purpose, power spectra were calculated with a 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 (Gamma1), and 35.00-44.75 (Gamma 2) was calculated for-the whole period of analysis (7 h). Also two additional time series: the ratio between Beta and Gamma2, and between Gamma1 and Gamma2 were calculated (Beta and Gamma ratios).

RESULTS

Beta and Gamma1 showed small changes with a tendency to increase during REM sleep; Gamma2, on the contrary, showed small changes with a tendency to decrease during REM sleep. Beta and Gamma ratio peaks were clearly correlated with the occurrence of REM sleep. The small changes shown by Beta, Gamma1 and Gamma2 were not statistically significant; on the contrary, Beta ratio and Gamma ratio showed the most important statistical significance values being highest during REM sleep and lowest during slow-wave sleep. Finally, the calculation of the linear correlation coefficient and of the cross-correlation between the different bands showed a clear reciprocity between Delta and Beta and Gamma ratios.

CONCLUSIONS

Our study shows a new method for the analysis of high frequencies (up to 45 Hz) in the scalp-recorded sleep EEG which allowed us to better define, as compared to previous studies on the same topic, the changes in power characteristically associated with REM sleep and correlated with the REM/non-REM ultradian rhythm, and to propose it as a tool for future studies.

Alpha activity and cardiac correlates: three types of relationships during nocturnal sleep

OBJECTIVE

We examined simultaneously alpha activity and cardiac changes during nocturnal sleep, in order to differentiate non-rapid eye movement (NREM) sleep, REM sleep, and intra-sleep awakening.

METHODS

Ten male subjects displaying occasionally spontaneous intra-sleep awakenings underwent EEG and cardiac recordings during one experimental night. The heart rate and heart rate variability were calculated over 5 min periods. Heart rate variability was estimated: (1) by the ratio of low frequency (LF) to high frequency (HF) power calculated from spectral analysis of R-R intervals; and (2) by the interbeat autocorrelation coefficient of R-R intervals (rRR). EEG spectral analysis was performed using a fast Fourier transform algorithm.

RESULTS

Three types of relationships between alpha waves (8-13 Hz) and cardiac correlates could be distinguished. During NREM sleep, alpha activity and cardiac correlates showed opposite variations, with high levels of alpha power associated with decreased heart rate, rRR and LF/HF ratio, indicating low sympathetic activity. Conversely, during REM sleep, alpha activity was low whereas heart rate, rRR, and the LF/HF ratio peaked, indicating high sympathetic activity. During intra-sleep awakenings, alpha activity and cardiac correlates both increased. No difference in time-course between alpha 1 (8-10 Hz) and alpha 2 (10-13 Hz) activity could be shown. Alpha waves occurred in fronto-central areas during slow wave sleep (SWS), migrated to posterior areas during REM sleep, and were localized in occipital areas during intra-sleep awakenings.

CONCLUSIONS

These results suggest that alpha waves are not simply a sign of arousal, as is commonly thought. Fronto-central alpha waves, associated with decreased heart rate, possibly reflect sleep-maintaining processes.

Temporal EEG dynamics of non-REM sleep episodes in humans

The process of the human non-rapid eye movement (non-REM) sleep period has not been clarified. Time-based analysis on sleep EEG may provide an explanation. We focused on chronological aspects of initiation and termination of non-REM episodes, using spectral analysis of sleep EEG. The subjects were healthy male volunteers (n14 Hz) and longer in lower frequency ranges (<14 Hz). There were significant differences in the rise and decay latencies between low and high sigma ranges, indicating that the whole frequency ranges were clearly separated at the middle of the sigma range (14 Hz). The rise and decay latencies were significantly different in lower frequency ranges. The clock time of the night significantly affected only the rise latencies of the delta (0.78-3.9 Hz), alpha (8.2-11.7 Hz) and low sigma (12.1-13.7 Hz) ranges. In conclusion, initiation and termination of non-REM sleep was represented by higher frequency ranges, whereas further evolution and devolution of non-REM sleep was represented by lower frequency ranges, and only the evolution process was affected by the clock time of the night.

CAP components and EEG synchronization in the first 3 sleep cycles

OBJECTIVE

There is consolidated evidence that stage changes in sleep are closely related to spontaneous EEG fluctuations centered on the 20-40 periodicity of the cyclic alternating pattern (CAP). The present investigation aimed at assessing the involvement of the different components of CAP in the process of build-up, maintenance and demolition of deep non-REM (NREM) sleep.

METHODS

CAP parameters were quantified in the first 3 sleep cycles (SC1, SC2, SC3), selected from polysomnographic recordings of 25 healthy sound sleepers belonging to an extensive age range (10-49 years). Only ideal SCs were selected, i.e. the ones uninterrupted by intervening wakefulness and in which all stages were represented and linked in a regular succession of a descending branch, a trough and an ascending branch.

RESULTS

Among the first 3 SCs, a total amount of 45 (SC1, 16; SC2, 13; SC3, 16) met the inclusion requirements. SCI contained the highest amount of slow wave sleep (43.7 min) and the lowest values of CAP rate (31.6%). The number of phase A1 subtypes remained unmodified across the 3 SCs (SC1, 48; SC2, 48; SC3, 48), whereas both subtypes A2 (SC1, 9; SC2, 14; SC3, 14) and A3 (SC1, 2; SC2, 8; SC3, 10) increased significantly (P<0.028 and P<0.0001, respectively). The A1 subtypes composed more than 90% of all the A phases collected in the descending branches and in the troughs, while the A2 and A3 subtypes were the major representatives (64.3%) of the A phases occurring in the ascending branches.

CONCLUSIONS

Within the dynamic organization of sleep, the non-random distribution of CAP sequences, with their succession of slow (subtypes A1) and rapid (subtypes A2 and A3) EEG shifts, seem to be responsible for sculpturing EEG synchrony under the driving and alternating forces of NREM and REM sleep.

A comparison of period amplitude analysis and FFT power spectral analysis of all-night human sleep EEG

Zero-cross and zero-derivative period amplitude analysis (PAA) data were compared with power spectral analysis (PSA) data obtained with the fast Fourier transform in all-night sleep EEG from 10 subjects. Although PAA zero-cross-integrated amplitude showed good agreement with PSA power in 0.3-2 Hz, zero-cross analysis appears relatively ineffective in measuring 2-4 Hz and above waves. However, PAA zero-derivative measures of peak-trough amplitude correlated well with PSA power in 2-4 Hz. Thus, while PAA appears able to measure the entire EEG spectrum, the analytic technique should be changed from zero cross to zero derivative at about 2 Hz in human sleep EEG. PAA and PSA both demonstrate robust and interrelated across-night oscillations in three frequency bands: delta (0.3-4 Hz); sigma (12-16 Hz); and fast beta (20-10 Hz). The frequencies between delta and sigma, and between sigma and fast beta, did not show clear across-night oscillations using either method, and the two methods showed lower epoch-to-epoch agreement in these intermediate bands. The causes of this reduced agreement are not immediately clear, nor is it obvious which method gives more valid results. We believe that the three strongly oscillating frequency bands represent fundamental properties of the human sleep EEG that provide important clues to underlying physiological mechanisms. These mechanisms are more likely to be understood if their dynamic properties are preserved and measured naturalistically rather than being forced into arbitrary sleep stages or procrustean models. Both PAA and PSA can be employed for such naturalistic studies. PSA has the advantages of applying the same analytic method across the EEG spectrum and rests on more fully developed theory. Combined zero-cross and zero-derivative PAA demonstrates EEG oscillations that closely parallel those observed with spectral power, and the PAA measures do not rely on assumptions about the spectral composition of the signal. In addition, both PAA techniques can measure the relative contributions of wave amplitude and incidence to total power: These waveform characteristics represent different biological processes and respond differentially to a wide range of experimental conditions.