Quantitative analysis of sleep EEG microstructure in the time-frequency domain

Non-REM sleep in major depressive disorder

Disturbed sleep is a key symptom in major depressive disorder (MDD). REM sleep alterations are well described in the current literature, but little is known about non-REM sleep alterations. Additionally, sleep disturbances relate to a variety of cognitive symptoms in MDD, but which features of non-REM sleep EEG contribute to this, remains unknown. We comprehensively analyzed non-REM sleep EEG features in two central channels in three independently collected datasets (N = 284 recordings of 216 participants). This exploratory and descriptive study included MDD patients with a broad age range, varying duration and severity of depression, unmedicated or medicated, age- and gender-matched to healthy controls. We explored changes in sleep architecture including sleep stages and cycles, spectral power, sleep spindles, slow waves (SW), and SW-spindle coupling. Next, we analyzed the association of these sleep features with acute measures of depression severity and overnight consolidation of procedural memory. Overall, no major systematic alterations in non-REM sleep architecture were found in patients compared to controls. For the microstructure of non-REM sleep, we observed a higher spindle amplitude in unmedicated patients compared to controls, and after the start of antidepressant medication longer SWs with lower amplitude and a more dispersed SW-spindle coupling. In addition, long-term, but not short-term medication seemed to lower spindle density. Overnight procedural memory consolidation was impaired in medicated patients and associated with lower sleep spindle density. Our results suggest that alterations of non-REM sleep EEG in MDD might be more subtle than previously reported. We discuss these findings in the context of antidepressant medication intake and age.

Transient oscillation dynamics during sleep provide a robust basis for electroencephalographic phenotyping and biomarker identification

Transient oscillatory events in the sleep electroencephalogram represent short-term coordinated network activity. Of particular importance, sleep spindles are transient oscillatory events associated with memory consolidation, which are altered in aging and in several psychiatric and neurodegenerative disorders. Spindle identification, however, currently contains implicit assumptions derived from what waveforms were historically easiest to discern by eye, and has recently been shown to select only a high-amplitude subset of transient events. Moreover, spindle activity is typically averaged across a sleep stage, collapsing continuous dynamics into discrete states. What information can be gained by expanding our view of transient oscillatory events and their dynamics? In this paper, we develop a novel approach to electroencephalographic phenotyping, characterizing a generalized class of transient time-frequency events across a wide frequency range using continuous dynamics. We demonstrate that the complex temporal evolution of transient events during sleep is highly stereotyped when viewed as a function of slow oscillation power (an objective, continuous metric of depth-of-sleep) and phase (a correlate of cortical up/down states). This two-fold power-phase representation has large intersubject variability-even within healthy controls-yet strong night-to-night stability for individuals, suggesting a robust basis for phenotyping. As a clinical application, we then analyze patients with schizophrenia, confirming established spindle (12-15 Hz) deficits as well as identifying novel differences in transient non-rapid eye movement events in low-alpha (7-10 Hz) and theta (4-6 Hz) ranges. Overall, these results offer an expanded view of transient activity, describing a broad class of events with properties varying continuously across spatial, temporal, and phase-coupling dimensions.

Automatic Cyclic Alternating Pattern (CAP) analysis: Local and multi-trace approaches

The Cyclic Alternating Pattern (CAP) is composed of cycles of two different electroencephalographic features: an activation A-phase followed by a B-phase representing the background activity. CAP is considered a physiological marker of sleep instability. Despite its informative nature, the clinical applications remain limited as CAP analysis is a time-consuming activity. In order to overcome this limit, several automatic detection methods were recently developed. In this paper, two new dimensions were investigated in the attempt to optimize novel, efficient and automatic detection algorithms: 1) many electroencephalographic leads were compared to identify the best local performance, and 2) the global contribution of the concurrent detection across several derivations to CAP identification. The developed algorithms were tested on 41 polysomnographic recordings from normal (n = 8) and pathological (n = 33) subjects. In comparison with the visual CAP analysis as the gold standard, the performance of each algorithm was evaluated. Locally, the detection on the F4-C4 derivation showed the best performance in comparison with all other leads, providing practical suggestions of electrode montage when a lean and minimally invasive approach is preferable. A further improvement in the detection was achieved by a multi-trace method, the Global Analysis—Common Events, to be applied when several recording derivations are available. Moreover, CAP time and CAP rate obtained with these algorithms positively correlated with the ones identified by the scorer. These preliminary findings support efficient automated ways for the evaluation of the sleep instability, generalizable to both normal and pathological subjects affected by different sleep disorders.

Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings

Arousals during sleep are transient accelerations of the EEG signal, considered to reflect sleep perturbations associated with poorer sleep quality. They are typically detected by visual inspection, which is time consuming, subjective, and prevents good comparability across scorers, studies and research centres. We developed a fully automatic algorithm which aims at detecting artefact and arousal events in whole-night EEG recordings, based on time-frequency analysis with adapted thresholds derived from individual data. We ran an automated detection of arousals over 35 sleep EEG recordings in healthy young and older individuals and compared it against human visual detection from two research centres with the aim to evaluate the algorithm performance. Comparison across human scorers revealed a high variability in the number of detected arousals, which was always lower than the number detected automatically. Despite indexing more events, automatic detection showed high agreement with human detection as reflected by its correlation with human raters and very good Cohen's kappa values. Finally, the sex of participants and sleep stage did not influence performance, while age may impact automatic detection, depending on the human rater considered as gold standard. We propose our freely available algorithm as a reliable and time-sparing alternative to visual detection of arousals.

Difference in spectral power density of sleep EEG between patients with simple snoring and those with obstructive sleep apnoea

Patients with simple snoring (SS) often complain of poor sleep quality despite a normal apnoea-hypopnoea index (AHI). We aimed to identify the difference in power spectral density of electroencephalography (EEG) between patients with SS and those with obstructive sleep apnoea (OSA). We compared the absolute power spectral density values of standard EEG frequency bands between the SS (n = 42) and OSA (n = 129) groups during the non-rapid eye movement (NREM) sleep period, after controlling for age and sex. We also analysed partial correlation between AHI and the absolute values of the EEG frequency bands. The absolute power spectral density values in the beta and delta bands were higher in the OSA group than in the SS group. AHI also positively correlated with beta power in the OSA group as well as in the combined group (OSA + SS). In conclusion, higher delta and beta power during NREM sleep were found in the OSA group than in the SS group, and beta power was correlated with AHI. These findings are microstructural characteristics of sleep-related breathing disorders.

Identifying Phase-Amplitude Coupling in Cyclic Alternating Pattern using Masking Signals

Judiciously classifying phase-A subtypes in cyclic alternating pattern (CAP) is critical for investigating sleep dynamics. Phase-amplitude coupling (PAC), one of the representative forms of neural rhythmic interaction, is defined as the amplitude of high-frequency activities modulated by the phase of low-frequency oscillations. To examine PACs under more or less synchronized conditions, we propose a nonlinear approach, named the masking phase-amplitude coupling (MPAC), to quantify physiological interactions between high (α/lowβ) and low (δ) frequency bands. The results reveal that the coupling intensity is generally the highest in subtype A1 and lowest in A3. MPACs among various physiological conditions/disorders (p < 0.0001) and sleep stages (p < 0.0001 except S4) are tested. MPACs are found significantly stronger in light sleep than deep sleep (p < 0.0001). Physiological conditions/disorders show similar order in MPACs. Phase-amplitude dependence between δ and α/lowβ oscillations are examined as well. δ phase tent to phase-locked to α/lowβ amplitude in subtype A1 more than the rest. These results suggest that an elevated δ-α/lowβ MPACs can reflect some synchronization in CAP. Therefore, MPAC can be a potential tool to investigate neural interactions between different time scales, and δ-α/lowβ MPAC can serve as a feasible biomarker for sleep microstructure.

Automatic identification of Cyclic Alternating Pattern (CAP) sequences based on the Teager Energy Operator

The Cyclic Alternating Pattern (CAP) is a periodic cerebral activity prevalent during Non-Rapid Eye Movement (NREM) sleep-stages. The CAP is composed by A-phases that are related to a change in amplitude, frequency or both from the background activity epochs, called B-phases. Depending on the type of increase the A-phase could be classified as A1, A2 or A3 subtype. This paper proposes the usage of the Teager Energy Operator (TEO) to analyze the amplitude changes in the different frequency-bands to detect A-phases subtypes. The TEO classification performance is compared with the performance of a state-of-the art EEG feature, applied previously for CAP scoring and referred as the macro-micro structure descriptor (MMSD). In general, the TEO is the best feature and the improved results were obtained in the delta band for the A1 and A2 sub-types. More precisely, a sensitivity and specificity of 80.31% and 82.93% were obtained for the A1 subtype, respectively. A2 phases were detected with 76.96% of sensitivity and 73.22% of specificity. The two features detected A3 subtype with approximately the same sensitivity (approx. 70%) and specificity (approx. 75%), however the results were improved by considering the highest frequency band. These results are consistent with the frequency content of the different sub-phases.

Distinguishing cognitive state with multifractal complexity of hippocampal interspike interval sequences

Fractality, represented as self-similar repeating patterns, is ubiquitous in nature and the brain. Dynamic patterns of hippocampal spike trains are known to exhibit multifractal properties during working memory processing; however, it is unclear whether the multifractal properties inherent to hippocampal spike trains reflect active cognitive processing. To examine this possibility, hippocampal neuronal ensembles were recorded from rats before, during and after a spatial working memory task following administration of tetrahydrocannabinol (THC), a memory-impairing component of cannabis. Multifractal detrended fluctuation analysis was performed on hippocampal interspike interval sequences to determine characteristics of monofractal long-range temporal correlations (LRTCs), quantified by the Hurst exponent, and the degree/magnitude of multifractal complexity, quantified by the width of the singularity spectrum. Our results demonstrate that multifractal firing patterns of hippocampal spike trains are a marker of functional memory processing, as they are more complex during the working memory task and significantly reduced following administration of memory impairing THC doses. Conversely, LRTCs are largest during resting state recordings, therefore reflecting different information compared to multifractality. In order to deepen conceptual understanding of multifractal complexity and LRTCs, these measures were compared to classical methods using hippocampal frequency content and firing variability measures. These results showed that LRTCs, multifractality, and theta rhythm represent independent processes, while delta rhythm correlated with multifractality. Taken together, these results provide a novel perspective on memory function by demonstrating that the multifractal nature of spike trains reflects hippocampal microcircuit activity that can be used to detect and quantify cognitive, physiological, and pathological states.

The relevance of the cross-wavelet transform in the analysis of human interaction - a tutorial

This article sheds light on a quantitative method allowing psychologists and behavioral scientists to take into account the specific characteristics emerging from the interaction between two sets of data in general and two individuals in particular. The current article outlines the practical elements of the cross-wavelet transform (CWT) method, highlighting WHY such a method is important in the analysis of time-series in psychology. The idea is (1) to bridge the gap between physical measurements classically used in physiology - neuroscience and psychology; (2) and demonstrates how the CWT method can be applied in psychology. One of the aims is to answer three important questions WHO could use this method in psychology, WHEN it is appropriate to use it (suitable type of time-series) and HOW to use it. Throughout these explanations, an example with simulated data is used. Finally, data from real life application are analyzed. This data corresponds to a rating task where the participants had to rate in real time the emotional expression of a person. The objectives of this practical example are (i) to point out how to manipulate the properties of the CWT method on real data, (ii) to show how to extract meaningful information from the results, and (iii) to provide a new way to analyze psychological attributes.

Heart rate variability in normal and pathological sleep

Sleep is a physiological process involving different biological systems, from molecular to organ level; its integrity is essential for maintaining health and homeostasis in human beings. Although in the past sleep has been considered a state of quiet, experimental and clinical evidences suggest a noteworthy activation of different biological systems during sleep. A key role is played by the autonomic nervous system (ANS), whose modulation regulates cardiovascular functions during sleep onset and different sleep stages. Therefore, an interest on the evaluation of autonomic cardiovascular control in health and disease is growing by means of linear and non-linear heart rate variability (HRV) analyses. The application of classical tools for ANS analysis, such as HRV during physiological sleep, showed that the rapid eye movement (REM) stage is characterized by a likely sympathetic predominance associated with a vagal withdrawal, while the opposite trend is observed during non-REM sleep. More recently, the use of non-linear tools, such as entropy-derived indices, have provided new insight on the cardiac autonomic regulation, revealing for instance changes in the cardiovascular complexity during REM sleep, supporting the hypothesis of a reduced capability of the cardiovascular system to deal with stress challenges. Interestingly, different HRV tools have been applied to characterize autonomic cardiac control in different pathological conditions, from neurological sleep disorders to sleep disordered breathing (SDB). In summary, linear and non-linear analysis of HRV are reliable approaches to assess changes of autonomic cardiac modulation during sleep both in health and diseases. The use of these tools could provide important information of clinical and prognostic relevance.

Dissociated wake-like and sleep-like electro-cortical activity during sleep

Sleep is traditionally considered a global process involving the whole brain. However, recent studies have shown that sleep depth is not evenly distributed within the brain. Sleep disorders, such as sleepwalking, also suggest that EEG features of sleep and wakefulness might be simultaneously present in different cerebral regions. In order to probe the coexistence of dissociated (wake-like and sleep-like) electrophysiological behaviors within the sleeping brain, we analyzed intracerebral electroencephalographic activity drawn from sleep recordings of five patients with pharmacoresistant focal epilepsy without sleep disturbances, who underwent pre-surgical intracerebral electroencephalographic investigation. We applied spectral and wavelet transform analysis techniques to electroencephalographic data recorded from scalp and intracerebral electrodes localized within the Motor cortex (Mc) and the dorso-lateral Prefrontal cortex (dlPFc). The Mc showed frequent Local Activations (lasting from 5 to more than 60s) characterized by an abrupt interruption of the sleep electroencephalographic slow waves pattern and by the appearance of a wake-like electroencephalographic high frequency pattern (alpha and/or beta rhythm). Local activations in the Mc were paralleled by a deepening of sleep in other regions, as expressed by the concomitant increase of slow waves in the dlPFc and scalp electroencephalographic recordings. These results suggest that human sleep can be characterized by the coexistence of wake-like and sleep-like electroencephalographic patterns in different cortical areas, supporting the hypothesis that unusual phenomena, such as NREM parasomnias, could result from an imbalance of these two states.

Cyclic alternating pattern (CAP): the marker of sleep instability

Cyclic alternating pattern CAP is the EEG marker of unstable sleep, a concept which is poorly appreciated among the metrics of sleep physiology. Besides, duration, depth and continuity, sleep restorative properties depend on the capacity of the brain to create periods of sustained stable sleep. This issue is not confined only to the EEG activities but reverberates upon the ongoing autonomic activity and behavioral functions, which are mutually entrained in a synchronized oscillation. CAP can be identified both in adult and children sleep and therefore represents a sensitive tool for the investigation of sleep disorders across the lifespan. The present review illustrates the story of CAP in the last 25 years, the standardized scoring criteria, the basic physiological properties and how the dimension of sleep instability has provided new insight into pathophysiolology and management of sleep disorders.

Characterization of A phases during the cyclic alternating pattern of sleep

OBJECTIVE

This study aims to identify, starting from a single EEG trace, quantitative distinctive features characterizing the A phases of the Cyclic Alternating Pattern (CAP).

METHODS

The C3-A2 or C4-A1 EEG leads of the night recording of eight healthy adult subjects were used for this analysis. CAP was scored by an expert and the portions relative to NREM were selected. Nine descriptors were computed: band descriptors (low delta, high delta, theta, alpha, sigma and beta); Hjorth activity in the low delta and high delta bands; differential variance of the EEG signal. The information content of each descriptor in recognizing the A phases was evaluated through the computation of the ROC curves and the statistics sensitivity, specificity and accuracy.

RESULTS

The ROC curves show that all the descriptors have a certain significance in characterizing A phases. The average accuracy obtained by thresholding the descriptors ranges from 59.89 (sigma descriptor) to 72.44 (differential EEG variance).

CONCLUSIONS

The results show that it is possible to attribute a significant quantitative value to the information content of the descriptors.

SIGNIFICANCE

This study gives a mathematical confirm to the features of CAP generally described qualitatively, and puts the bases for the creation of automatic detection methods.

Progressive changes in cortical state before and after spontaneous arousals from sleep in elderly and middle-aged women

Arousals are often considered to be events which have an abrupt onset and offset, indicating abrupt changes in the state of the cortex. We hypothesized that cortical state, as reflected in electroencephalograph (EEG) signals, exhibits progressive systematic changes before and after a spontaneous, isolated arousal and that the time courses of the spectral components of the EEG before and after an arousal would differ between healthy middle-aged and elderly subjects. We analyzed the power spectrum and Sample Entropy of the C3A2 EEG before and after isolated arousals from 20 middle-aged (47.2±2.0 years) and 20 elderly (78.4±3.8 years) women using polysomnograms from the Sleep Heart Health Study database. In middle-aged women, all EEG spectral band powers <16 Hz exhibited a significant increase relative to baseline at some time in the 21 s before an arousal, but only low- (0.2-2.0 Hz) and high-frequency (2.0-4.0 Hz) delta increased in elderly and only during the last 7 s pre-arousal. Post-arousal, all frequency bands below 12 Hz transiently fell below pre-arousal baseline in both age groups. Consistent with these findings, Sample Entropy decreased steadily before an arousal, increased markedly during the arousal, and remained above pre-arousal baseline levels for ∼30 s after the arousal. In middle-aged, but not in elderly, women the presence of early pre-arousal low delta power was associated with shorter arousals. We propose that this attenuation of the effect of the arousing stimulus may be related to the slow (<1 Hz) cortical state oscillation, and that prolonged alterations of cortical state due to arousals may contribute to the poor correlation between indices of arousals and indices of sleepiness or impaired cognitive function.

Nocturnal epileptiform EEG discharges, nocturnal epileptic seizures, and language impairments in children: review of the literature

This review addresses the effect on language function of nocturnal epileptiform EEG discharges and nocturnal epileptic seizures in children. In clinical practice, language impairment is frequently reported in association with nocturnal epileptiform activity. Vice versa, nocturnal epileptiform EEG abnormalities are a common finding in children with specific language impairment. We suggest a spectrum that is characterized by nocturnal epileptiform activity and language impairment ranging from specific language impairment to rolandic epilepsy, nocturnal frontal lobe epilepsy, electrical status epilepticus of sleep, and Landau-Kleffner syndrome. In this spectrum, children with specific language impairment have the best outcome, and children with electrical status epilepticus of sleep or Landau-Kleffner syndrome, the worst. The exact nature of this relationship and the factors causing this spectrum are unknown. We suggest that nocturnal epileptiform EEG discharges and nocturnal epileptic seizures during development will cause or contribute to diseased neuronal networks involving language. The diseased neuronal networks are less efficient compared with normal neuronal networks. This disorganization may cause language impairments.

Borderline intellectual functioning and sleep: the role of cyclic alternating pattern

In the clinical literature there are few specific studies about the relationship between cognition processes and sleep during childhood. In addition, milder deficits in general intellectual capacity have received less attention relative to major cognitive dysfunctions (such as the genetic or environmental basis of mental retardation), especially concerning the low normal and borderline status. Sleep could play a key role in multiple intellectual abilities such as memory, executive functions, and school performances. Aim of our study is to assess the sleep macrostructure and NREM instability (cyclic alternating pattern) and their relationship with IQ in a sample of subjects with borderline intellectual functioning (BIF). The DSM-IV defines BIF as a total intelligence quotient (TIQ) ranging between 71 and 84. Intellective functioning was assessed using the Italian version of Wechsler Intelligence Scale for Children-Revised (WISC-R), a well validated test for the developmental age between 6 and 16. For this study, 12 BIF and 17 healthy children, matched for sex and age, underwent an overnight PSG recording. Macrostructural sleep and CAP analysis were also performed. To our knowledge, this study represents the first attempt to evaluate sleep architecture and NREM instability organization in children with BIF. Findings from this investigation evidence that BIF presents alterations in both macro- and microstructural sleep architecture, with an interesting statistical significant correlation with IQ.

Transient decoupling of cortical EEGs following arousals during NREM sleep in middle-aged and elderly women

Spontaneous cortical arousals in non-REM sleep increase with age and contribute to sleep fragmentation in the elderly. EEG spectral power in the faster frequencies exhibits well-described shifts during arousals. On the other hand, EEG activities also exhibit correlations, which are interpreted as an index of interdependence between distant cortical neural activities. The possibility of changes to the interdependence between cortical regions due to an arousal has not been considered. In this work, using previously recorded C3A2 and C4A1 EEG signals from two groups of adults, middle-aged (42-50 years) and elderly (71-86 years) women, we examined the effects of spontaneous arousals in NREM sleep on cortical interdependence. We quantified the auto- and cross-correlations in these signals using mutual information and characterized these correlations in periods before the onset and following the end of arousals. The pre-arousal period exhibited significantly higher interdependence between central regions than that following the arousal in both age groups (middle-aged: p=0.004, elderly: p<0.0001). Also, for both EEG signals the auto mutual information had a faster rate of decay, implying lower signal predictability, following the arousal than prior to it (both age groups, p<0.0001). These results indicate that the state of the cortex is different after, compared to before, the arousal even when the spectral power changes characteristic of an arousal are no longer visible. The findings suggest that the state following an arousal characterized by lower interdependence may resemble a more vigilant period during which the system may be vulnerable to more arousals.

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.

The effects of experimental sleep fragmentation on cognitive processing

OBJECTIVE

The primary objective of this study was to characterize the association between cyclic alternating pattern (CAP) and neurocognitive performance in a group of normal subjects before and after two nights of experimentally-induced sleep fragmentation.

SUBJECTS AND METHODS

Fifteen healthy subjects underwent one night of uninterrupted and two sequential nights of experimental sleep fragmentation achieved by auditory and mechanical stimuli. Eight subjects were re-examined using a similar paradigm with three nights of uninterrupted sleep. Sleep was polygraphically recorded and CAP analysis was performed for all recordings. A battery of neurocognitive tests was performed for spatial attention, inhibition of return, mental rotation, and Stroop color word test in the afternoon following the first and third night of sleep under fragmented and non-fragmented conditions.

RESULTS

With sleep fragmentation, the percentage of slow-wave sleep was dramatically reduced and there was a twofold increase in total CAP rate across all NREM sleep stages. Moreover, the number of all CAP A subtypes/hour of sleep (index) was significantly increased. Total CAP rate during the non-fragmented night correlated with reaction times. Similarly, the percentages of A1 and A3 subtypes were negatively and positively correlated with reaction times, respectively. Of the neurocognitive test battery, however, only values obtained from some subtests of the mental rotation test showed a significant improvement after sleep fragmentation.

CONCLUSIONS

The results of this study suggest that CAP A1 subtypes are associated with higher cognitive functioning, whereas CAP A3 subtypes are associated with lower cognitive functioning in young healthy subjects. The lack of cognitive functioning impairment after sleep fragmentation may be due to persistence and even enhancement of transient slow-wave activity contained in CAP A1 subtypes which also caused a significant enhancement of the EEG power spectrum in the lower frequencies.

Commentary from the Italian Association of Sleep Medicine on the AASM manual for the scoring of sleep and associated events: for debate and discussion

In 2007, the American Academy of Sleep Medicine (AASM) completed a new manual for the scoring of sleep and associated events. The AASM manual is divided into separate sections relative to the parameters reported for polysomnography. The present commentary, accomplished by a Task Force of the Italian Association of Sleep Medicine, focuses on sleep scoring data, arousal rules, movement and respiratory events. Comparisons with the previous Rechtschaffen and Kales system are detailed and a number of methodological weaknesses are pointed out. Major comments address the 30-s scoring epochs, the restrictive approach to arousals and EEG activating patterns, the incomplete quantification of motor events and the thresholds for the definition of hypopnea. Since the new AASM manual is an iterative process, proposals for discussion and re-examination of the agreed criteria with other national and international organizations are encouraged.

Periodic limb movements both in non-REM and REM sleep: relationships between cerebral and autonomic activities

OBJECTIVE

To investigate the temporal relationship between cerebral and autonomic activities before and during periodic limb movements in NREM and REM sleep (PLMS).

METHODS

Patterns of EEG, cardiac and muscle activities associated with PLMS were drawn from polysomnographic recordings of 14 outpatients selected for the presence of PLMS both in NREM and REM sleep. PLMS were scored during all sleep stages from tibial EMG. Data from a bipolar EEG channel were analyzed by wavelet transform. Heart rate (HR) was evaluated from the electrocardiogram. EEG, HR and EMG activations were detected as transient increase of signal parameters and examined by analysis of variance and correlation analysis independently in NREM and REM sleep. Homologous parameters in REM and NREM sleep were compared by paired t-test.

RESULTS

The autonomic component, expressed by HR increase, took place before the motor phenomenon both in REM and NREM sleep, but it was significantly earlier during NREM. In NREM sleep, PLM onset was heralded by a significant activation of delta-EEG, followed by a progressive increase of all the other bands. No significant activations of delta EEG were found in REM sleep. HR and EEG activations positively correlated with high frequency EEG activations and negatively (in NREM) with slow frequency ones.

CONCLUSIONS

Our findings suggested a heralding role for delta band only in NREM sleep and for HR during both NREM and REM sleep. Differences in EEG and HR activation between REM and NREM sleep and correlative data suggested a different modulation of the global arousal response.

SIGNIFICANCE

In this study, time-frequency analysis and advanced statistical methods enabled an accurate comparison between brain and autonomic changes associated to PLM in NREM and REM sleep providing indications about interaction between autonomic and slow and fast EEG components of arousal response.

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.

Heart rate and spectral EEG changes accompanying periodic and non-periodic leg movements during sleep

OBJECTIVE

To evaluate the changes in heart rate (HR) and EEG spectra accompanying periodic (PLM) and non-periodic leg movements (NPLM) during sleep in patients with restless legs syndrome (RLS).

METHODS

Sixteen patients with RLS underwent one polysomnographic night recording; leg movements (LMs) during sleep were detected and classified as PLM or NPLM; up to 10 PLM and NPLM were chosen from NREM and REM sleep, for each patient and for each type (mono- or bilateral). EEG spectral analysis and HR were evaluated for 20s preceding and 30s following the onset of each LM.

RESULTS

EEG activation preceded LMs, particularly in the delta band which increased before the other frequency bands, in NREM sleep but not in REM sleep for PLM, and in both stages for NPLM. A similar difference was seen between mono- and bilateral LMs.

CONCLUSIONS

Sleep EEG, HR, and leg motor activity seems to be modulated by a complex dynamically interacting system of cortical and subcortical mechanisms, which influence each other.

SIGNIFICANCE

Future studies on the clinical significance of leg motor events during sleep need to take into account events classifiable as "isolated" and to integrate the autonomic and EEG changes accompanying them.

Regional scalp EEG slow-wave synchronization during sleep cyclic alternating pattern A1 subtypes

The levels of EEG synchronization, in the 0.25-2.5 Hz band, during the A1 subtypes of the sleep "cyclic alternating pattern" (CAP) were measured in five healthy subjects by means of the synchronization likelihood (SL) algorithm. SL was measured for seven electrode pairs (F4-F3, C4-C3, P4-P3 for the analysis of interhemispheric SL and F4-C4, C4-P4, F3-C3, and C3-P3, for the analysis of intrahemispheric SL). During the A1 CAP subtypes, SL tended to be highest between pairs of electrodes situated over different hemispheres; in particular, SL obtained from F4-F3 was the highest, followed by that of P4-P3. These results indicate that the transient high level of synchronization in the slow-wave EEG range, during the sleep A1 CAP subtypes, is a phenomenon involving mostly the anterior parts of the brain and is probably based on interhemispheric interactions, possibly mediated by transcallosal connections.

CAP, epilepsy and motor events during sleep: the unifying role of arousal

Arousal systems play a topical neurophysiologic role in protecting and tailoring sleep duration and depth. When they appear in NREM sleep, arousal responses are not limited to a single EEG pattern but are part of a continuous spectrum of EEG modifications ranging from high-voltage slow rhythms to low amplitude fast activities. The hierarchic features of arousal responses are reflected in the phase A subtypes of CAP (cyclic alternating pattern) including both slow arousals (dominated by the <1Hz oscillation) and fast arousals (ASDA arousals). CAP is an infraslow oscillation with a periodicity of 20-40s that participates in the dynamic organization of sleep and in the activation of motor events. Physiologic, paraphysiologic and pathologic motor activities during NREM sleep are always associated with a stereotyped arousal pattern characterized by an initial increase in EEG delta power and heart rate, followed by a progressive activation of faster EEG frequencies. These findings suggest that motor patterns are already written in the brain codes (central pattern generators) embraced with an automatic sequence of EEG-vegetative events, but require a certain degree of activation (arousal) to become visibly apparent. Arousal can appear either spontaneously or be elicited by internal (epileptic burst) or external (noise, respiratory disturbance) stimuli. Whether the outcome is a physiologic movement, a muscle jerk or a major epileptic attack will depend on a number of ongoing factors (sleep stage, delta power, neuro-motor network) but all events share the common trait of arousal-activated phenomena.

A Practical Guide to Time—Frequency Analysis in the Study of Human Motor Behavior: The Contribution of Wavelet Transform

The authors present a practical guide for studying nonstationary data on human motor behavior in a time-frequency representation. They explain the limits of classical methods founded exclusively on the time or frequency basis and then answer those limits with the windowed Fourier transform and the wavelet transform (WT) methods, both of which are founded on time-frequency bases. The authors stress an interest in the WT method because it permits access to the whole complexity of a signal (in terms of time, frequency, amplitude, and phase). They then show that the WT method is well suited for the analysis of the interaction between two signals, particularly in human movement studies. Finally, to demonstrate its practical applications, the authors apply the method to real data.

CAP and arousals are involved in the homeostatic and ultradian sleep processes

There is growing evidence that cyclic alternating pattern (CAP) and arousals are woven into the basic mechanisms of sleep regulation. In the present study, the overnight sleep cycles (SC) of 20 normal subjects were analyzed according to their stage composition, CAP rate, phase A subtypes and arousals. Individual SC were then divided into 10 normalized temporal epochs. CAP parameters and arousals were measured in each epoch and averaged in relation to the SC order. Subtypes A2 and A3 of CAP in non-rapid eye movement (NREM) sleep, and arousals, both in REM and NREM sleep when not coincident with a A2 or A3 phases, were lumped together as fast electroencephalographic (EEG) activities (FA). Subtypes A1 of CAP, characterized by slow EEG activities (SA), were analyzed separately. The time distribution of SA and FA was compared to the mathematical model of normal sleep structure including functions representing the homeostatic process S, the circadian process C, the ultradian process generating NREM/REM cycles and the slow wave activity (SWA) resulting from the interaction between homeostatic and ultradian processes. The relationship between SA and FA and the sleep-model components was evaluated by multiple regression analysis in which SA and FA were considered as dependent variables while the covariates were the process S, process C, SWA, REM-on and REM-off activities and their squared values. Regression was highly significant (P < 0.0001) for both SA and FA. SA were prevalent in the first three SC, and exhibited single or multiple peaks immediately before and in the final part of deep sleep (stages 3 + 4). The peaks of FA were delayed and prevailed during the pre-REM periods of light sleep (stages 1 + 2) and during REM sleep. SA showed an exponential decline across the successive SC, according to the homeostatic process. In contrast, the distribution of FA was not influenced by the order of SC, with periodic peaks of FA occurring before the onset of REM sleep, in accordance with the REM-on switch. The dynamics of CAP and arousals during sleep can be viewed as an intermediate level between cellular activities and macroscale EEG phenomena as they reflect the decay of the homeostatic process and the interaction between REM-off and REM-on mechanisms while are slightly influenced by circadian rhythm.

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.

Reorganization of sleep patterns in severe OSAS under prolonged CPAP treatment

OBJECTIVE

To evaluate the immediate and long-term recovery processes of sleep and daytime vigilance in patients with sleep apnea syndrome (OSAS) after continuous CPAP treatment.

METHODS

Five consecutive polysomnographic (PSG) studies were carried out on 10 male patients with severe OSAS. The first recording (baseline) was accomplished without ventilatory support (N0). The other 4 recordings were carried out during the CPAP titration night (N1), during the second night of treatment (N2), during the third night of treatment (N3), and after 30 days of regular CPAP use (N30). Ten age-balanced healthy male subjects were selected from the Parma Sleep Center database as controls. Respiratory variables, conventional PSG variables, arousals, CAP (cyclic alternating pattern) variables, and daytime function (including MSLT) were quantified. ANOVA followed by post-hoc tests explored the differences between controls and OSAS patients in the different recording conditions (N0, N1, N2, N3, N30). The PSG measures that showed significant ANOVA values were correlated with the MSLT scores.

RESULTS

Values of control subjects were recovered by REM sleep, REM latency, subtypes A3 and arousal index during N1, by CAP rate and total arousals during N2, by deep sleep (stages 3 + 4) during N3, by light sleep (stages 1 + 2) during N30. The only measures which remained below control values even after 1 month of sustained treatment were the amount of CAP cycles and A1 subtypes. MSLT scores correlated significantly with CAP rate, deep sleep and arousals.

CONCLUSIONS

The changes induced by CPAP treatment do not restore immediately a normal sleep structure, which is re-established with different time scales

SIGNIFICANCE

The modifications of sleep patterns and the different adjustments of phase A subtypes allow us to monitor the reorganization of sleep in OSAS patients treated with CPAP and the hierarchy of the mechanisms involved in sleep regulation.

Facilitation of task performance and removal of the effects of sleep deprivation by an ampakine (CX717) in nonhuman primates

The deleterious effects of prolonged sleep deprivation on behavior and cognition are a concern in modern society. Persons at risk for impaired performance and health-related issues resulting from prolonged sleep loss would benefit from agents capable of reducing these detrimental effects at the time they are sleep deprived. Agents capable of improving cognition by enhancing brain activity under normal circumstances may also have the potential to reduce the harmful or unwanted effects of sleep deprivation. The significant prevalence of excitatory α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) glutamatergic receptors in the brain provides a basis for implementing a class of drugs that could act to alter or remove the effects of sleep deprivation. The ampakine CX717 (Cortex Pharmaceuticals), a positive allosteric modulator of AMPA receptors, was tested for its ability to enhance performance of a cognitive, delayed match-to-sample task under normal circumstances in well-trained monkeys, as well as alleviate the detrimental effects of 30–36 h of sleep deprivation. CX717 produced a dose-dependent enhancement of task performance under normal alert testing conditions. Concomitant measures of regional cerebral metabolic rates for glucose (CMR_glc) during the task, utilizing positron emission tomography, revealed increased activity in prefrontal cortex, dorsal striatum, and medial temporal lobe (including hippocampus) that was significantly enhanced over normal alert conditions following administration of CX717. A single night of sleep deprivation produced severe impairments in performance in the same monkeys, accompanied by significant alterations in task-related CMR_glc in these same brain regions. However, CX717 administered to sleep-deprived monkeys produced a striking removal of the behavioral impairment and returned performance to above-normal levels even though animals were sleep deprived. Consistent with this recovery, CMR_glc in all but one brain region affected by sleep deprivation was also returned to the normal alert pattern by the drug. The ampakine CX717, in addition to enhancing cognitive performance under normal alert conditions, also proved effective in alleviating impairment of performance due to sleep deprivation. Therefore, the ability to activate specific brain regions under normal alert conditions and alter the deleterious effects of sleep deprivation on activity in those same regions indicate a potential role for ampakines in sustaining performance under these types of adverse conditions.

Decline in cognitive performance and changes in neural activity associated with sleep deprivation can be reversed by an AMPA receptor agonist.

Is insomnia a neurophysiological disorder? The role of sleep EEG microstructure

Unlike other sleep disorders, such as sleep-related breathing disorders and periodic limb movement (PLM), the nature and severity of which are quantified by specific respiratory and motor indexes, no apparent organ dysfunction underlies several cases of insomnia (in particular primary insomnia), which can be objectively diagnosed only through the structural alterations of sleep. Polysomnography (PSG) investigation indicates that insomnia is the outcome of a neurophysiological disturbance that impairs the regulatory mechanisms of sleep control, including sleep duration, intensity, continuity and stability. In particular, analysis of sleep microstructure has permitted to establish that etiologic factors of different nature (including depressive disorders) exert a common destabilizing action on sleep, which is reflected in an increase of cyclic alternating pattern (CAP) rate. These premises allow us to attribute a more objective identity to insomnia, which risks otherwise to be considered as an unexplainable mental complaint. In conclusion, PSG remains the "gold standard" for measuring sleep, and especially insomnia.