The boundary between wakefulness and sleep: quantitative electroencephalographic changes during the sleep onset period

Spatiotemporal EEG dynamics of the sleep onset process in preadolescence

BACKGROUND

During preadolescence the sleep electroencephalography undergoes massive qualitative and quantitative modifications. Despite these relevant age-related peculiarities, the specific EEG pattern of the wake-sleep transition in preadolescence has not been exhaustively described.

METHODS

The aim of the present study is to characterize regional and temporal electrophysiological features of the sleep onset (SO) process in a group of 23 preadolescents (9-14 years) and to compare the topographical pattern of slow wave activity and delta/beta ratio of preadolescents with the EEG pattern of young adults.

RESULTS

Results showed in preadolescence the same dynamics known for adults, but with peculiarities in the delta and beta activity, likely associated with developmental cerebral modifications: the delta power showed a widespread increase during the SO with central maxima, and the lower bins of the beta activity showed a power increase after SO. Compared to adults, preadolescents during the SO exhibited higher delta power only in the slowest bins of the band: before SO slow delta activity was higher in prefrontal, frontal and occipital areas in preadolescents, and, after SO the younger group had higher slow delta activity in occipital areas. In preadolescents delta/beta ratio was higher in more posterior areas both before and after the wake-sleep transition and, after SO, preadolescents showed also a lower delta/beta ratio in frontal areas, compared to adults.

CONCLUSION

Results point to a general higher homeostatic drive for the developing areas, consistently with plastic-related maturational modifications, that physiologically occur during preadolescence.

Hyperarousal-state of Insomnia Disorder in Wake-resting State Quantitative Electroencephalography

Objective

: Insomnia is associated with elevated high-frequency electroencephalogram power in the waking state. Although affective symptoms (e.g., depression and anxiety) are commonly comorbid with insomnia, few reports distinguished objective sleep disturbance from affective symptoms. In this study, we investigated whether daytime electroencephalographic activity explains insomnia, even after controlling for the effects of affective symptoms.

Methods

: A total of 107 participants were divided into the insomnia disorder (n = 58) and healthy control (n = 49) groups using the Mini-International Neuropsychiatric Interview and diagnostic criteria for insomnia disorder. The participants underwent daytime resting-state electroencephalography sessions (64 channels, eye-closed).

Results

: The insomnia group showed higher levels of anxiety, depression, and insomnia than the healthy group, as well as increased beta [t(105) = -2.56, p = 0.012] and gamma [t(105) = -2.44, p = 0.016] spectra. Among all participants, insomnia symptoms positively correlated with the intensity of beta (r = 0.28, p ＜ 0.01) and gamma (r = 0.25, p ＜ 0.05) spectra. Through hierarchical multiple regression, the beta power showed the additional ability to predict insomnia symptoms beyond the effect of anxiety (ΔR2 = 0.041, p = 0.018).

Conclusion

: Our results showed a significant relationship between beta electroencephalographic activity and insomnia symptoms, after adjusting for other clinical correlates, and serve as further evidence for the hyperarousal theory of insomnia. Moreover, resting-state quantitative electroencephalography may be a supplementary tool to assess insomnia.

Unsupervised Multitaper Spectral Method for Identifying REM Sleep in Intracranial EEG Recordings Lacking EOG/EMG Data

A large number of human intracranial EEG (iEEG) recordings have been collected for clinical purposes, in institutions all over the world, but the vast majority of these are unaccompanied by EOG and EMG recordings which are required to separate Wake episodes from REM sleep using accepted methods. In order to make full use of this extremely valuable data, an accurate method of classifying sleep from iEEG recordings alone is required. Existing methods of sleep scoring using only iEEG recordings accurately classify all stages of sleep, with the exception that wake (W) and rapid-eye movement (REM) sleep are not well distinguished. A novel multitaper (Wake vs. REM) alpha-rhythm classifier is developed by generalizing K-means clustering for use with multitaper spectral eigencoefficients. The performance of this unsupervised method is assessed on eight subjects exhibiting normal sleep architecture in a hold-out analysis and is compared against a classical power detector. The proposed multitaper classifier correctly identifies 36±6 min of REM in one night of recorded sleep, while incorrectly labeling less than 10% of all labeled 30 s epochs for all but one subject (human rater reliability is estimated to be near 80%), and outperforms the equivalent statistical-power classical test. Hold-out analysis indicates that when using one night's worth of data, an accurate generalization of the method on new data is likely. For the purpose of studying sleep, the introduced multitaper alpha-rhythm classifier further paves the way to making available a large quantity of otherwise unusable IEEG data.

Postoperative Delirium Severity and Recovery Correlate With Electroencephalogram Spectral Features

BACKGROUND

Delirium is an acute syndrome characterized by inattention, disorganized thinking, and an altered level of consciousness. A reliable biomarker for tracking delirium does not exist, but oscillations in the electroencephalogram (EEG) could address this need. We evaluated whether the frequencies of EEG oscillations are associated with delirium onset, severity, and recovery in the postoperative period.

METHODS

Twenty-six adults enrolled in the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES; ClinicalTrials.gov NCT02241655) study underwent major surgery requiring general anesthesia, and provided longitudinal postoperative EEG recordings for this prespecified substudy. The presence and severity of delirium were evaluated with the confusion assessment method (CAM) or the CAM-intensive care unit. EEG data obtained during awake eyes-open and eyes-closed states yielded relative power in the delta (1-4 Hz), theta (4-8 Hz), and alpha (8-13 Hz) bands. Discriminability for delirium presence was evaluated with c-statistics. To account for correlation among repeated measures within patients, mixed-effects models were generated to assess relationships between: (1) delirium severity and EEG relative power (ordinal), and (2) EEG relative power and time (linear). Slopes of ordinal and linear mixed-effects models are reported as the change in delirium severity score/change in EEG relative power, and the change in EEG relative power/time (days), respectively. Bonferroni correction was applied to confidence intervals (CIs) to account for multiple comparisons.

RESULTS

Occipital alpha relative power during eyes-closed states offered moderate discriminability (c-statistic, 0.75; 98% CI, 0.58-0.87), varying inversely with delirium severity (slope, -0.67; 98% CI, -1.36 to -0.01; P = .01) and with severity of inattention (slope, -1.44; 98% CI, -2.30 to -0.58; P = .002). Occipital theta relative power during eyes-open states correlated directly with severity of delirium (slope, 1.28; 98% CI, 0.12-2.44; P = .007), inattention (slope, 2.00; 98% CI, 0.48-3.54; P = .01), and disorganized thinking (slope, 3.15; 98% CI, 0.66-5.65; P = .01). Corresponding frontal EEG measures recapitulated these relationships to varying degrees. Severity of altered level of consciousness correlated with frontal theta relative power during eyes-open states (slope, 11.52; 98% CI, 6.33-16.71; P < .001). Frontal theta relative power during eyes-open states correlated inversely with time (slope, -0.05; 98% CI, -0.12 to -0.04; P = .002).

CONCLUSIONS

Presence, severity, and core features of postoperative delirium covary with spectral features of the EEG. The cost and accessibility of EEG facilitate the translation of these findings to future mechanistic and interventional trials.

Infra-low frequency neurofeedback and insomnia as a model of CNS dysregulation

This paper will review what is conventionally known of sleep homeostasis and focus on insomnia as a primary manifestation of brain dysregulation, whether as a solitary symptom or as part of a larger syndrome such as post-traumatic stress disorder, PTSD. It will discuss in brief behavioral/mindfulness treatments that have been used to treat neurologic diseases, as this is germane to the phenomenology of neurofeedback (NF). It will explore how neurofeedback may work at the subconscious level and cover the current clinical experience of the effectiveness of this technique in the treatment of insomnia. It will conclude with a case presentation.

Time Course of Motor Activity Wake Inertia Dissipation According to Age

The time course of motor activity sleep inertia (maSI) dissipation was recently investigated through actigraphy in an everyday life condition from middle childhood to late adulthood. Motor activity sleep inertia was dissipated in 70 min, and the sleep inertia phenomenon was more evident in younger participants than in older participants. The aim of the current secondary analysis of previously published data was to examine, within the same sample, the time course of motor activity wake inertia (maWI) dissipation, i.e., the motor pattern in the transition phase from wakefulness to sleep, according to age. To this end, an overall sample of 374 participants (215 females), ranging in age between 9 and 70 years old, was examined. Each participant was asked to wear an actigraph around their non-dominant wrist for one week. The variation in the motor activity pattern of the wake–sleep transition according to age was examined through functional linear modeling (FLM). FLM showed that motor activity wake inertia dissipated around 20 min after bedtime. Moreover, a lower age was significantly associated with greater motor activity within the last two hours of wakefulness and the first twenty minutes after bedtime. Overall, this pattern of results seems to suggest that maWI dissipation is comparable to that of maSI.

Drowsiness detection using portable wireless EEG

BACKGROUND AND OBJECTIVE

The ever-increasing fatality rate due to traffic and workplace accidents, resulting from drowsiness have been a persistent concern during the past years. An efficient technology capable of monitoring and detecting drowsiness can help to alleviate this concern and has potential applications in driver vigilance monitoring, vigilance monitoring in air traffic control rooms and other safety critical work places. In this paper, we present the feasibility of a wearable light weight wireless consumer grade Electroencephalogram (EEG)-based drowsiness detection.

METHODS

A set of informative features were extracted from short daytime nap EEG signals and their applicability in discriminating between alert and drowsy state was studied. We derived an optimal set of EEG features, that give maximum detection rate for the drowsy state. In addition, heart rate was also recorded concurrently with EEG and correlation between heart rate and the EEG features corresponding to drowsiness was also studied.

RESULTS

Using the selected features, the EEG data is shown to be capable of classifying alert and drowsy states with an accuracy of 78.3% using Support Vector Machine classifier employing cross subject validation. The feature selection results also revealed that, the EEG features extracted from the temporal electrodes are more significant for drowsiness detection than the features from frontal electrodes. In addition, EEG features extracted from the temporal electrodes yielded higher correlation coefficient with heart rate, which was in concordance with the feature selection results.

CONCLUSIONS

The results reveal that using the proposed drowsiness detection algorithm, it is possible to perform drowsiness detection using a single EEG electrode placed behind the ear.

Pediatric Sleep Apnea: The Overnight Electroencephalogram as a Phenotypic Biomarker

Pediatric obstructive sleep apnea (OSA) is a prevalent disorder that disrupts sleep and is associated with neurocognitive and behavioral negative consequences, potentially hampering the development of children for years. However, its relationships with sleep electroencephalogram (EEG) have been scarcely investigated. Here, our main objective was to characterize the overnight EEG of OSA-affected children and its putative relationships with polysomnographic measures and cognitive functions. A two-step analysis involving 294 children (176 controls, 57% males, age range: 5-9 years) was conducted for this purpose. First, the activity and irregularity of overnight EEG spectrum were characterized in the typical frequency bands by means of relative spectral power and spectral entropy, respectively: δ1 (0.1-2 Hz), δ2 (2-4 Hz), θ (4-8 Hz), α (8-13 Hz), σ (10-16 Hz), β1 (13-19 Hz), β2 (19-30 Hz), and γ (30-70 Hz). Then, a correlation network analysis was conducted to evaluate relationships between them, six polysomnography variables (apnea-hypopnea index, respiratory arousal index, spontaneous arousal index, overnight minimum blood oxygen saturation, wake time after sleep onset, and sleep efficiency), and six cognitive scores (differential ability scales, Peabody picture vocabulary test, expressive vocabulary test, design copying, phonological processing, and tower test). We found that as the severity of the disease increases, OSA broadly affects sleep EEG to the point that the information from the different frequency bands becomes more similar, regardless of activity or irregularity. EEG activity and irregularity information from the most severely affected children were significantly associated with polysomnographic variables, which were coherent with both micro and macro sleep disruptions. We hypothesize that the EEG changes caused by OSA could be related to the occurrence of respiratory-related arousals, as well as thalamic inhibition in the slow oscillation generation due to increases in arousal levels aimed at recovery from respiratory events. Furthermore, relationships between sleep EEG and cognitive scores emerged regarding language, visual-spatial processing, and executive function with pronounced associations found with EEG irregularity in δ1 (Peabody picture vocabulary test and expressive vocabulary test maximum absolute correlations 0.61 and 0.54) and β2 (phonological processing, 0.74; design copying, 0.65; and Tow 0.52). Our results show that overnight EEG informs both sleep alterations and cognitive effects of pediatric OSA. Moreover, EEG irregularity provides new information that complements and expands the classic EEG activity analysis. These findings lay the foundation for the use of sleep EEG to assess cognitive changes in pediatric OSA.

The Regional EEG Pattern of the Sleep Onset Process in Older Adults

Healthy aging is characterized by macrostructural sleep changes and alterations of regional electroencephalographic (EEG) sleep features. However, the spatiotemporal EEG pattern of the wake-sleep transition has never been described in the elderly. The present study aimed to assess the topographical and temporal features of the EEG during the sleep onset (SO) in a group of 36 older participants (59–81 years). The topography of the 1 Hz bins’ EEG power and the time course of the EEG frequency bands were assessed. Moreover, we compared the delta activity and delta/beta ratio between the older participants and a group of young adults. The results point to several peculiarities in the elderly: (a) the generalized post-SO power increase in the slowest frequencies did not include the 7 Hz bin; (b) the alpha power revealed a frequency-specific pattern of post-SO modifications; (c) the sigma activity exhibited only a slight post-SO increase, and its highest bins showed a frontotemporal power decrease. Older adults showed a generalized reduction of delta power and delta/beta ratio in both pre- and post-SO intervals compared to young adults. From a clinical standpoint, the regional EEG activity may represent a target for brain stimulation techniques to reduce SO latency and sleep fragmentation.

Abnormally abrupt transitions from sleep-to-wake in Huntington's disease sheep (Ovis aries) are revealed by automated analysis of sleep/wake transition dynamics

Sleep disturbance is a common and disruptive symptom of neurodegenerative diseases such as Alzheimer’s and Huntington’s disease (HD). In HD patients, sleep fragmentation appears at an early stage of disease, although features of the earliest sleep abnormalities in presymptomatic HD are not fully established. Here we used novel automated analysis of quantitative electroencephalography to study transitions between wake and non-rapid eye movement sleep in a sheep model of presymptomatic HD. We found that while the number of transitions between sleep and wake were similar in normal and HD sheep, the dynamics of transitions from sleep-to-wake differed markedly between genotypes. Rather than the gradual changes in EEG power that occurs during transitioning from sleep-to-wake in normal sheep, transition into wake was abrupt in HD sheep. Furthermore, transitions to wake in normal sheep were preceded by a significant reduction in slow wave power, whereas in HD sheep this prior reduction in slow wave power was far less pronounced. This suggests an impaired ability to prepare for waking in HD sheep. The abruptness of awakenings may also have potential to disrupt sleep-dependent processes if they are interrupted in an untimely and disjointed manner. We propose that not only could these abnormal dynamics of sleep transitions be useful as an early biomarker of HD, but also that our novel methodology would be useful for studying transition dynamics in other sleep disorders.

The falling asleep process in adolescents

STUDY OBJECTIVES

To investigate the pre-sleep psychophysiological state and the arousal deactivation process across the sleep onset (SO) transition in adolescents.

METHODS

Data were collected from a laboratory overnight recording in 102 healthy adolescents (48 girls, 12-20 years old). Measures included pre-sleep self-reported cognitive/somatic arousal, and cortical electroencephalographic (EEG) and electrocardiographic activity across the SO transition.

RESULTS

Adolescent girls, compared with boys, reported higher pre-sleep cognitive activation (p = 0.025) and took longer to fall asleep (p < 0.05), as defined with polysomnography. Girls also showed a less smooth progression from wake-to-sleep compared with boys (p = 0.022). In both sexes, heart rate (HR) dropped at a rate of ~0.52 beats per minute in the 5 minutes preceding SO, and continued to drop, at a slower rate, during the 5 minutes following SO (p < 0.05). Older girls had a higher HR overall in the pre-sleep period and across SO, compared to younger girls and boys (p < 0.05). The EEG showed a progressive cortical synchronization, with increases in Delta relative power and reductions in Alpha, Sigma, Beta1, and Beta2 relative powers (p < 0.05) in the approach to sleep, in both sexes. Delta relative power was lower and Theta, Alpha, and Sigma relative powers were higher in older compared to younger adolescents at bedtime and across SO (p < 0.05).

CONCLUSIONS

Our findings show the dynamics of the cortical-cardiac de-arousing process across the SO transition in a non-clinical sample of healthy adolescents. Findings suggest a female-specific vulnerability to inefficient sleep initiation, which may contribute to their greater risk for developing insomnia.

Special Report on the Impact of the COVID-19 Pandemic on Clinical EEG and Research and Consensus Recommendations for the Safe Use of EEG

INTRODUCTION

The global COVID-19 pandemic has affected the economy, daily life, and mental/physical health. The latter includes the use of electroencephalography (EEG) in clinical practice and research. We report a survey of the impact of COVID-19 on the use of clinical EEG in practice and research in several countries, and the recommendations of an international panel of experts for the safe application of EEG during and after this pandemic.

METHODS

Fifteen clinicians from 8 different countries and 25 researchers from 13 different countries reported the impact of COVID-19 on their EEG activities, the procedures implemented in response to the COVID-19 pandemic, and precautions planned or already implemented during the reopening of EEG activities.

RESULTS

Of the 15 clinical centers responding, 11 reported a total stoppage of all EEG activities, while 4 reduced the number of tests per day. In research settings, all 25 laboratories reported a complete stoppage of activity, with 7 laboratories reopening to some extent since initial closure. In both settings, recommended precautions for restarting or continuing EEG recording included strict hygienic rules, social distance, and assessment for infection symptoms among staff and patients/participants.

CONCLUSIONS

The COVID-19 pandemic interfered with the use of EEG recordings in clinical practice and even more in clinical research. We suggest updated best practices to allow safe EEG recordings in both research and clinical settings. The continued use of EEG is important in those with psychiatric diseases, particularly in times of social alarm such as the COVID-19 pandemic.

The brain network organization during sleep onset after deprivation

OBJECTIVE

Aim of the present study is to investigate the alterations of brain networks derived from EEG analysis in pre- and post-sleep onset conditions after 40 h of sleep deprivation (SD) compared to sleep onset after normal sleep in 39 healthy subjects.

METHODS

Functional connectivity analysis was made on electroencelographic (EEG) cortical sources of current density and small world (SW) index was evaluated in the EEG frequency bands (delta, theta, alpha, sigma and beta).

RESULTS

Comparing pre- vs. post-sleep onset conditions after a night of SD a significant decrease of SW in delta and theta bands in post-sleep onset condition was found together with an increase of SW in sigma band. Comparing pre-sleep onset after sleep SD versus pre-sleep onset after a night of normal sleep a decreased of SW index in beta band in pre-sleep onset in SD compared to pre-sleep onset in normal sleep was evidenced.

CONCLUSIONS

Brain functional network architecture is influenced by the SD in different ways. Brain networks topology during wake resting state needs to be further explored to reveal SD-related changes in order to prevent possible negative effects of SD on behaviour and brain function during wakefulness.

SIGNIFICANCE

The SW modulations as revealed by the current study could be used as an index of an altered balance between brain integration and segregation processes after SD.

Correspondence between physiological and behavioural responses to vibratory stimuli during the sleep onset period: A quantitative electroencephalography analysis

Behavioural responses to auditory stimuli cease in late N1 or early N2 sleep. Yet, responsiveness to minimal intensity tactile stimuli and the correspondence with sleep microstructure during the sleep onset period is unknown. The aim of the present study was to investigate sleep microstructure using quantitative electroencephalography analysis when participants behaviourally responded to minimal intensity vibratory stimuli compared to when participants did not respond to stimuli during the sleep onset period. Eighteen participants wore a device that emitted vibratory stimuli to which individuals responded by tapping their index finger. A fast Fourier transform using multitaper-based estimation was applied to electroencephalography signals in 5-s epochs. Participants exhibited increases in higher frequencies 5 s before and immediately after the stimulus presentation when they responded to the stimulus compared to when they did not respond during all sleep stages. They also had greater delta power after stimulus onset when they did not respond to stimuli presented in N1 and N2 sleep compared to when they did respond. Participants responded to a significantly greater proportion of stimuli in wake than in N1 sleep (p < .001, d = 2.38), which was also significantly greater than the proportion of responses in N2 sleep (p < .001, d = 1.12). Participants showed wake-like sleep microstructure when they responded to vibratory stimuli and sleep-like microstructure when they did not respond during all sleep stages. The present study adds to the body of evidence characterising N1 sleep as a transitional period between sleep and wake containing rapid fluctuations between these two states.

Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal

BACKGROUND

Detecting human drowsiness during some critical works like vehicle driving, crane operating, mining blasting, etc. is one of the safeguards to prevent accidents. Among several drowsiness detection (DD) methods, a combination of neuroscience and computer science knowledge has a better ability to differentiate awake and sleep states. Most of the current models are implemented using multi-sensors electroencephalogram (EEG) signals, multi-domain features, predefined features selection algorithms. Therefore, there is great interest in the method of detecting drowsiness on embedded platforms with improved accuracy using generalized best features.

NEW-METHOD

Single-channel EEG based drowsiness detection (DD) model is proposed in this by utilizing wavelet packet transform (WPT) to extract the time-domain features from considered channel EEG. The dimension of the feature vector is reduced by the proposed novel feature selection method.

RESULTS

The proposed model on freely available real-time sleep analysis EEG and Simulated Virtual Driving Driver (SVDD) EEG achieves 94.45% and 85.3% accuracy, respectively.

COMPARISON-WITH-EXISTING-METHOD

The results show that the proposed DD method produces better accuracy compared to the state-of-the-art using the physiological dataset with the proposed time-domain sub-band-based features and feature selection method. This task of detecting drowsiness by analyzing the 5-seconds EEG signal with four features is an improvement to my previous work on detecting drowsiness using a 30-seconds EEG signal with 66 features.

CONCLUSIONS

Time-domain features obtained from EEG time-domain sub-bands collected using WPT achieving excellent accuracy rate by selecting unique optimization features for all subjects by the proposed feature selection algorithm.

Exploration of User's Mental State Changes during Performing Brain-Computer Interface

Substantial developments have been established in the past few years for enhancing the performance of brain–computer interface (BCI) based on steady-state visual evoked potential (SSVEP). The past SSVEP-BCI studies utilized different target frequencies with flashing stimuli in many different applications. However, it is not easy to recognize user’s mental state changes when performing the SSVEP-BCI task. What we could observe was the increasing EEG power of the target frequency from the user’s visual area. BCI user’s cognitive state changes, especially in mental focus state or lost-in-thought state, will affect the BCI performance in sustained usage of SSVEP. Therefore, how to differentiate BCI users’ physiological state through exploring their neural activities changes while performing SSVEP is a key technology for enhancing the BCI performance. In this study, we designed a new BCI experiment which combined working memory task into the flashing targets of SSVEP task using 12 Hz or 30 Hz frequencies. Through exploring the EEG activity changes corresponding to the working memory and SSVEP task performance, we can recognize if the user’s cognitive state is in mental focus or lost-in-thought. Experiment results show that the delta (1–4 Hz), theta (4–7 Hz), and beta (13–30 Hz) EEG activities increased more in mental focus than in lost-in-thought state at the frontal lobe. In addition, the powers of the delta (1–4 Hz), alpha (8–12 Hz), and beta (13–30 Hz) bands increased more in mental focus in comparison with the lost-in-thought state at the occipital lobe. In addition, the average classification performance across subjects for the KNN and the Bayesian network classifiers were observed as 77% to 80%. These results show how mental state changes affect the performance of BCI users. In this work, we developed a new scenario to recognize the user’s cognitive state during performing BCI tasks. These findings can be used as the novel neural markers in future BCI developments.

Cortical source localization of sleep-stage specific oscillatory activity

The oscillatory features of non-REM sleep states have been a subject of intense research over many decades. However, a systematic spatial characterization of the spectral features of cortical activity in each sleep state is not available yet. Here, we used magnetoencephalography (MEG) and electroencephalography (EEG) recordings during night sleep. We performed source reconstruction based on the individual subject’s anatomical magnetic resonance imaging (MRI) scans and spectral analysis on each non-REM sleep epoch in eight standard frequency bands, spanning the complete spectrum, and computed cortical source reconstructions of the spectral contrasts between each sleep state in comparison to the resting wakefulness. Despite not distinguishing periods of high and low activity within each sleep stage, our results provide new information about relative overall spectral changes in the non-REM sleep stages.

Melatonin effects on EEG activity during non-rapid eye movement sleep in mild-to-moderate Alzheimer´s disease: a pilot study

INTRODUCTION

There is evidence to suggest that melatonin diminishes non-rapid eye movement sleep (NREMS) latency in patients with Alzheimer´s disease (AD). However, melatonin's effects on cortical activity during NREMS in AD have not been studied. The objective of this research was to analyze the effects of melatonin on cortical activity during the stages of NREMS in 8 mild-to-moderate AD patients that received 5-mg of fast-release melatonin.

METHODS

During a single-blind, placebo-controlled crossover study, polysomnographic recordings were obtained from C3-A1, C4-A2, F7-T3, F8-T4, F3-F4 and O1-O2. Also, the relative power (RP) and EEG coherences of the delta, theta, alpha1, alpha2, beta1, beta2 and gamma bands were calculated during NREMS-1, NREMS-2 and NREMS-3. These sleep latencies and all EEG data were then compared between the placebo and melatonin conditions.

RESULTS

During NREMS-2, a significant RP increase was observed in the theta band of the left-central hemisphere. During NREMS-3, significant RP decreases in the beta bands were recorded in the right-central hemisphere, compared to the placebo group. After melatonin administration, significant decreases of EEG coherences in the beta2, beta1 and gamma bands were observed in the right hemisphere during NREMS-3.

DISCUSSION

We conclude that short NREMS onset related to melatonin intake in AD patients is associated with a significant RP increase in the theta band and a decrease in RP and EEG coherences in the beta and gamma bands during NREMS-3. These results suggest that the GABAergic pathways are preserved in mild-to-moderate AD.

Differences in electroencephalographic spectra during pre-sleep wakefulness, N1, and R sleep between comorbid insomnia and obstructive sleep apnea

PURPOSE

The neuropsychobiological effects of the comorbidity of insomnia and obstructive sleep apnea are not well studied. Our objective was to compare electroencephalographic spectra of patients with comorbid insomnia and sleep apnea syndrome to those of patients with sleep apnea syndrome alone during pre-sleep wakefulness and the N1 and R sleep periods.

METHOD

We performed electroencephalography and polysomnography on 10 patients with comorbid insomnia and sleep apnea and 10 with only sleep apnea. Electroencephalography spectra analysis was performed for absolute power in clinical bands in six derivations.

RESULTS

Compared to sleep apnea patients, comorbid patients had lower sleep efficiency and total sleep time, higher beta-1 power in the left frontal and central derivations during pre-sleep wakefulness, higher delta power in the left frontal and central derivations during the N1 stage, and higher beta-2 power in the left frontal and central, and right central derivations during the R stage.

CONCLUSIONS

Data suggest that patients with insomnia and sleep apnea, compared to patients with only sleep apnea, presented higher left high-frequency rhythms during pre-sleep wakefulness and R sleep stage, and may be for increased emotional and cognitive-related activity, while in stage N1, presented higher left delta power, which suggest some slowing after sleep deprivation.

Oscillatory patterns in the electroencephalogram at sleep onset

Falling asleep is a gradually unfolding process. We investigated the role of various oscillatory activities including sleep spindles and alpha and delta oscillations at sleep onset (SO) by automatically detecting oscillatory events. We used two datasets of healthy young males, eight with four baseline recordings, and eight with a baseline and recovery sleep after 40 h of sustained wakefulness. We analyzed the 2-min interval before SO (stage 2) and the five consecutive 2-min intervals after SO. The incidence of delta/theta events reached its maximum in the first 2-min episode after SO, while the frequency of them was continuously decreasing from stage 1 onwards, continuing over SO and further into deeper sleep. Interestingly, this decrease of the frequencies of the oscillations were not affected by increased sleep pressure, in contrast to the incidence which increased. We observed an increasing number of alpha events after SO, predominantly frontally, with their prevalence varying strongly across individuals. Sleep spindles started to occur after SO, with first an increasing then a decreasing incidence and a continuous decrease in their frequency. Again, the frequency of the spindles was not altered after sleep deprivation. Oscillatory events revealed derivation dependent aspects. However, these regional aspects were not specific of the process of SO but rather reflect a general sleep related phenomenon. No individual traits of SO features (incidence and frequency of oscillations) and their dynamics were observed. Delta/theta events are important features for the analysis of SO in addition to slow waves.

Brain dynamics during the sleep onset transition: An EEG source localization study

EEG source localization is an essential tool to reveal the cortical sources underlying brain oscillatory activity. We applied LORETA, a technique of EEG source localization, to identify the principal brain areas involved in the process of falling asleep (sleep onset, SO). We localized the contributing brain areas of activity in the classical frequency bands and tracked their temporal evolution (in 2-min intervals from 2 min prior to SO up to 10 min after SO) during a baseline night and subsequent recovery sleep after total sleep deprivation of 40 h.

Delta activity (0.5–5 Hz) gradually increased both in baseline and recovery sleep, starting in frontal areas and finally involving the entire cortex. This increase was steeper in the recovery condition. The evolution of sigma activity (12–16 Hz) resembled an inverted U-shape in both conditions and the activity was most salient in the parietal cortex. In recovery, sigma activity reached its maximum faster than in baseline, but attained lower levels. Theta activity (5–8 Hz) increased with time in large parts of the occipital lobe (baseline and recovery) and in recovery involved additionally frontal areas. Changes in alpha activity (8–12 Hz) at sleep onset involved large areas of the cortex, whereas activity in the beta range (16–24 Hz) was restricted to small cortical areas. The dynamics in recovery could be considered as a “fast-forward version” of the one in baseline.

Our results confirm that the process of falling asleep is neither spatially nor temporally a uniform process and that different brain areas might be falling asleep at a different speed potentially reflecting use dependent aspects of sleep regulation.

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LORETA is a valuable tool to reveal cortical sources of brain activity at sleep onset.

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Spectral bands had location dependent dynamics; brain areas fell asleep asynchronously

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BA 11 was the most relevant brain region associated with delta activity.

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Spindle dynamics resembled an inverted U-shape.

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During recovery from sleep deprivation capacity for spindle generation was reduced.

A Comprehensive Survey of Driving Monitoring and Assistance Systems

Improving a vehicle driver's performance decreases the damage caused by, and chances of, road accidents. In recent decades, engineers and researchers have proposed several strategies to model and improve driving monitoring and assistance systems (DMAS). This work presents a comprehensive survey of the literature related to driving processes, the main reasons for road accidents, the methods of their early detection, and state-of-the-art strategies developed to assist drivers for a safe and comfortable driving experience. The studies focused on the three main elements of the driving process, viz. driver, vehicle, and driving environment are analytically reviewed in this work, and a comprehensive framework of DMAS, major research areas, and their interaction is explored. A well-designed DMAS improves the driving experience by continuously monitoring the critical parameters associated with the driver, vehicle, and surroundings by acquiring and processing the data obtained from multiple sensors. A discussion on the challenges associated with the current and future DMAS and their potential solutions is also presented.

Inside the clinical evaluation of sleepiness: subjective and objective tools

PURPOSE

To critically review the available tools for evaluating excessive daytime sleepiness (EDS) in clinical practice.

METHODS

Objective tests and subjective scales were divided into three groups in accordance with the different dimensions of sleepiness they measure, namely physiological, manifest, and introspective. Strengths, weaknesses, and limitations of each test have been analysed and discussed along with the available recommendations for their use in clinical practice.

RESULTS

The majority of the tests developed for sleepiness evaluation do not have practical usefulness outside the research setting. The suboptimal correlation between different tests mainly depends on the different dimensions of sleepiness they analyse. Most importantly in-laboratory tests poorly correlate with sleepiness in real-life situations and, to date, none is able to predict the risk of injuries related to EDS, especially on an individual level.

CONCLUSIONS

There exists not the one best test to assess EDS, however, clinicians can choose a more specific test to address a specific diagnostic challenge on the individual level. The development of novel performance tests with low cost and easy to administer is advisable for both screening purposes and fitness for duty evaluations in populations at high risk of EDS-related injuries, for example professional drivers.

EEG-correlated fMRI of human alpha (de-)synchronization

OBJECTIVES

We investigated blood oxygenation level-dependent (BOLD) brain activity changes in wakefulness and light sleep and in relation to those associated with the posterior alpha rhythm, the most prominent feature of the clinical EEG. Studies have reported different sets of brain regions changing their oxygen consumption with waxing and waning alpha oscillations. Here, we hypothesize that these dissimilar activity patterns reflect different wakefulness-dependent brain states.

METHODS

We recorded BOLD signal changes and electroencephalography (EEG) simultaneously in 149 subjects at rest. Based on American Academy of Sleep Medicine criteria, we selected subjects exhibiting wakefulness or light sleep (N1). We identified brain regions in which BOLD signal changes correlated with (i) clinical sleep stages, (ii) alpha band power and (iii) a multispectral EEG index, respectively.

RESULTS

During light sleep, we found increased BOLD activity in parieto-occipital regions. In wakefulness compared to light sleep, we revealed BOLD signal increases in the thalamus. The multispectral EEG-index revealed hippocampal activity changes in light sleep not reported before.

CONCLUSION

Changes in alpha oscillations reflect different brain states associated with different levels of wakefulness and thalamic activity. We can link the previously described parieto-occipital pattern to drowsiness. Additionally, in that stage, we identify hippocampal activity fluctuations.

SIGNIFICANCE

Thalamic activity varies with early changes of wakefulness, which is important to consider in resting state experiments. The EEG-indexed activation of the hippocampus during light sleep suggests that memory encoding might already take place during this early stage of sleep.

The Spatiotemporal Pattern of the Human Electroencephalogram at Sleep Onset After a Period of Prolonged Wakefulness

During the sleep onset (SO) process, the human electroencephalogram (EEG) is characterized by an orchestrated pattern of spatiotemporal changes. Sleep deprivation (SD) strongly affects both wake and sleep EEG, but a description of the topographical EEG power spectra and oscillatory activity during the wake-sleep transition after a period of prolonged wakefulness is still missing. The increased homeostatic sleep pressure should induce an earlier onset of sleep-related EEG oscillations. The aim of the present study was to assess the spatiotemporal EEG pattern at SO following SD. A dataset of a previous study was analyzed. We assessed the spatiotemporal EEG changes (19 cortical derivations) during the SO (5 min before vs. 5 min after the first epoch of Stage 2) of a recovery night after 40 h of SD in 39 healthy subjects, analyzing the EEG power spectra (fast Fourier transform) and the oscillatory activity [better oscillation (BOSC) detection method]. The spatiotemporal pattern of the EEG power spectra mostly confirmed the changes previously observed during the wake-sleep transition at baseline. The comparison between baseline and recovery showed a wide increase of the post- vs. pre-SO ratio during the recovery night in the frequency bins ≤10 Hz. We found a predominant alpha oscillatory rhythm in the pre-SO period, while after SO the theta oscillatory activity was prevalent. The oscillatory peaks showed a generalized increase in all frequency bands from delta to sigma with different predominance, while beta activity increased only in the fronto-central midline derivations. Overall, the analysis of the EEG power replicated the topographical pattern observed during a baseline night of sleep but with a stronger intensity of the SO-induced changes in the frequencies ≤10 Hz, and the detection of the rhythmic activity showed the rise of several oscillations at SO after SD that was not observed during the wake-sleep transition at baseline (e.g., alpha and frontal theta in correspondence of their frequency peaks). Beyond confirming the local nature of the EEG pattern at SO, our results show that SD has an impact on the spatiotemporal modulation of cortical activity during the falling-asleep process, inducing the earlier emergence of sleep-related EEG oscillations.

EEG-vigilance regulation in Borderline Personality Disorder

BACKGROUND

Borderline Personality Disorder (BPD) is characterized by emotional instability, impulsivity, disturbed cognition, sleeplessness and states of high inner tension. Altered arousal regulation which is regarded as a higher domain of functioning according to the research domain criteria of the NIMH and which has previously been reported in several psychiatric disorders, such as mania or major depression could be involved in these features of BPD.

METHODS

40 unmedicated patients with BPD and 42 matched healthy volunteers participated in a twenty minute resting-state EEG measurement with closed eyes. EEG-vigilance regulation was assessed with VIGALL2.0 (Vigilance Algorithm Leipzig), which allows a classification of consecutive 1-s segments in different vigilance stages ranging from high alertness/relaxed wakefulness (stages 0, A1, A2, A3) to drowsiness (B1, B2/3) and sleep onset (C).

RESULTS

Across 20 min, both groups showed a similar decline from higher to lower vigilance stages, but patients with BPD remained in higher stages of vigilance compared to healthy volunteers across the whole measurement (p = .013). Contrary to this, pre-experimental ratings indicated enhanced subjective sleepiness but no differences in self-reported sleep quantity in the previous night in patients with BPD compared to healthy volunteers.

CONCLUSIONS

The results of an elevated arousal regulation (in combination with increased subjective sleepiness) might reflect several symptoms, such as aversive inner tension and impoverished sense of self in patients with BPD. As arousal is linked to the noradrenergic system, further investigations in this field seem to be promising.

Bilateral Theta Transcranial Alternating Current Stimulation (tACS) Modulates EEG Activity: When tACS Works Awake It Also Works Asleep

PURPOSE

Recent studies demonstrate that 5-Hz bilateral transcranial alternating current stimulation (θ-tACS) on fronto-temporal areas affects resting EEG enhancing cortical synchronization, but it does not affect subjective sleepiness. This dissociation raises questions on the resemblance of this effect to the physiological falling asleep process. The current study aimed to evaluate the ability of fronto-temporal θ-tACS to promote sleep.

SUBJECTS AND METHODS

Twenty subjects (10 F/10 M; mean age: 24.60 ± 2.9 y) participated in a single-blind study consisting of two within-subject sessions (active/sham), one week apart in counterbalanced order. Stimulation effects on EEG were assessed during wake and post-stimulation nap. The final sample included participants who fell asleep in both sessions (n=17).

RESULTS

Group analyses on the whole sample reported no θ-tACS effects on subjective sleepiness and sleep measures, while a different scenario came to light by analysing data of responders to the stimulation (ie, subjects actually showing the expected increase of theta activity in the wake EEG after the θ-tACS, n=7). Responders reported a significant increase in subjective sleepiness during wakefulness after the active stimulation as compared to the sham. Moreover, the sleep after the θ-tACS compared to sham in this sub-group showed: (1) greater slow-wave activity (SWA); (2) SWA time-course revealing increases much larger as closer to the sleep onset; (3) stimulation-induced changes in SWA during sleep topographically associated to those in theta activity during wake.

CONCLUSION

Subjects who show the expected changes during wake after the stimulation also had a consistent pattern of changes during sleep. The enhancement of cortical synchronization by θ-tACS during wakefulness actually corresponds to increased sleep pressure, but it occurs only in some individuals. Thus, θ-tACS can enhance sleep, although individual factors to be further investigated affect the actual responsiveness to this treatment.

Changes in brain arousal (EEG-vigilance) after therapeutic sleep deprivation in depressive patients and healthy controls

Depressed patients frequently exhibit a hyperstable brain arousal regulation. According to the arousal regulation model of affective disorders, the antidepressant effect of therapeutic sleep deprivation could be achieved by counter-acting this dysregulation. We investigated the impact of partial sleep deprivation (PSD) on EEG-vigilance (an indicator of brain arousal regulation) in depressed patients (n = 27) and healthy controls (n = 16). PSD was hypothesized to cause a more prominent destabilisation of brain arousal regulation in depressed patients (reflected by increased occurrence of lower EEG-vigilance stages). Furthermore, it was studied whether responders (n = 17) exhibit a more stable baseline brain arousal regulation and would show a more prominent arousal destabilisation after PSD than non-responders (n = 10). Before PSD, patients showed a more stable EEG-vigilance with less declines to lower vigilance stages compared to controls. Contrary to the hypothesis, a greater destabilisation of brain arousal after PSD was seen in controls. Within the patient sample, responders generally showed a less stable EEG-vigilance, especially after PSD when we found significant differences compared to non-responders. EEG-vigilance in non-responders showed only little change from baseline to after PSD. In summary, PSD had a destabilizing impact on brain arousal regulation in healthy controls whereas depressed patients reacted heterogeneously depending on the outcome of treatment.

Melatonin Effects on EEG Activity During Sleep Onset in Mild-to-Moderate Alzheimer's Disease: A Pilot Study

There is evidence demonstrating that 5-mg of fast-release melatonin significantly reduces nocturnal sleep onset in patients with mild-to-moderate Alzheimer's disease (AD). However, the physiological mechanism that could promote sleep installation by melatonin in patients with AD is still poorly understood. The present pilot study was designed to analyze the effects of melatonin on cortical activity during the sleep onset period (SOP) in eight mild-to-moderate AD patients treated with 5-mg of fast-release melatonin. Electroencephalographic recordings were obtained from C3-A1, C4-A2, F7-T3, F8-T4, F3-F4, and O1-O2. The relative power (RP), interhemispheric, intrahemispheric, and fronto-posterior correlations of six electroencephalographic bands were calculated and compared between two conditions: placebo and melatonin. Results show that at F7-T3, F3-F4, and C3-A1, melatonin induced an increase of the RP of the delta band. Likewise, in F7-T3, melatonin induced a decrease of the RP in the alpha1 band. Similarly, results show a lower interhemispheric correlation between the F7-T3 and F8-T4 derivations in the alpha1 band compared to the placebo condition. We conclude that the short sleep onset related to melatonin intake in AD patients was associated with a lower RP of the alpha1, a higher RP of the delta band (mainly in the left hemisphere) and a decreased interhemispheric EEG coupling in the alpha1 band. The possible role of the GABAergic neurotransmission as well as of the cascade of neurochemical events that melatonin triggers on sleep onset are discussed.

Challenges in sleep stage R scoring in patients with autosomal dominant spinocerebellar ataxias (SCA1, SCA2 and SCA3) and oculomotor abnormalities: a whole night polysomnographic evaluation

OBJECTIVES

Spinocerebellar ataxias are progressive neurodegenerative disorders characterized by progressive cerebellar features with additional neuro-axis involvement. Oculomotor abnormality is one of the most frequent manifestations. This study was done to assess the polysomnographic abnormalities in patients with Spinocerebellar ataxia (SCA1, SCA2 and SCA3) and also to evaluate whether oculomotor abnormalities interfere with sleep stage R scoring.

METHODS

The study was carried out using 36 genetically positive SCA patients. All patients underwent neurological examination with special focus on oculomotor function (optokinetic nystagmus-OKN and extraocular movement restriction-EOM). The sleep quality was measured with Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS). Disease severity was assessed with International Cooperative Ataxia Rating Scale (ICARS). All the patients underwent over-night video-polysomnography (VPSG).

RESULTS

Out of 36 patients studied, the data of 34 patients [SCA1 (n = 12), SCA2 (n = 13), SCA3 (n = 9)] were used for final analysis. Patients from SCA1, SCA2, and SCA3 category did not show significant differences in age and diseases severity (ICARS). All patients had vertical OKN impairment. Oculomotor impairment was higher in SCA2 patients. Sleep macro-architecture analysis showed absent stage R sleep, predominantly in SCA2 (69%) followed by SCA3 (44%) and SCA1 (8%). Patients showed a strong negative correlation of stage R sleep percentage with disease severity and oculomotor dysfunction.

CONCLUSION

Voluntary saccadic eye movement velocity and rapid eye movements (REMs) in sleep are strongly correlated. The more severe the saccadic velocity impairment, the less likely was it to generate REMs (rapid eye movements) during stage R. Accordingly 69% of SCA2 patients with severe occulomotor impairments showed absent stage R as per the AASM sleep scoring. We presume that the impaired REMs generation in sleep could be due to oculomotor abnormality and has resulted in spuriously low or absent stage R sleep percentage in SCA patients with conventional VPSG scoring rules. The present study recommends the modification of AASM scoring rules for stage R in patients with oculomotor abnormalities.

Bilateral 5 Hz transcranial alternating current stimulation on fronto-temporal areas modulates resting-state EEG

Rhythmic non-invasive brain stimulations are promising tools to modulate brain activity by entraining neural oscillations in specific cortical networks. The aim of the study was to assess the possibility to influence the neural circuits of the wake-sleep transition in awake subjects via a bilateral transcranial alternating current stimulation at 5 Hz (θ-tACS) on fronto-temporal areas. 25 healthy volunteers participated in two within-subject sessions (θ-tACS and sham), one week apart and in counterbalanced order. We assessed the stimulation effects on cortical EEG activity (28 derivations) and self-reported sleepiness (Karolinska Sleepiness Scale). θ-tACS induced significant increases of the theta activity in temporo-parieto-occipital areas and centro-frontal increases in the alpha activity compared to sham but failed to induce any online effect on sleepiness. Since the total energy delivered in the sham condition was much less than in the active θ-tACS, the current data are unable to isolate the specific effect of entrained theta oscillatory activity per se on sleepiness scores. On this basis, we concluded that θ-tACS modulated theta and alpha EEG activity with a topography consistent with high sleep pressure conditions. However, no causal relation can be traced on the basis of the current results between these rhythms and changes on sleepiness.

Developmental Changes in Sleep Oscillations during Early Childhood

Although quantitative analysis of the sleep electroencephalogram (EEG) has uncovered important aspects of brain activity during sleep in adolescents and adults, similar findings from preschool-age children remain scarce. This study utilized our time-frequency method to examine sleep oscillations as characteristic features of human sleep EEG. Data were collected from a longitudinal sample of young children (n = 8; 3 males) at ages 2, 3, and 5 years. Following sleep stage scoring, we detected and characterized oscillatory events across age and examined how their features corresponded to spectral changes in the sleep EEG. Results indicated a developmental decrease in the incidence of delta and theta oscillations. Spindle oscillations, however, were almost absent at 2 years but pronounced at 5 years. All oscillatory event changes were stronger during light sleep than slow-wave sleep. Large interindividual differences in sleep oscillations and their characteristics (e.g., "ultrafast" spindle-like oscillations, theta oscillation incidence/frequency) also existed. Changes in delta and spindle oscillations across early childhood may indicate early maturation of the thalamocortical system. Our analytic approach holds promise for revealing novel types of sleep oscillatory events that are specific to periods of rapid normal development across the lifespan and during other times of aberrant changes in neurobehavioral function.

Cortical connectivity modulation during sleep onset: A study via graph theory on EEG data

Sleep onset is characterized by a specific and orchestrated pattern of frequency and topographical EEG changes. Conventional power analyses of electroencephalographic (EEG) and computational assessments of network dynamics have described an earlier synchronization of the centrofrontal areas rhythms and a spread of synchronizing signals from associative prefrontal to posterior areas. Here, we assess how "small world" characteristics of the brain networks, as reflected in the EEG rhythms, are modified in the wakefulness-sleep transition comparing the pre- and post-sleep onset epochs. The results show that sleep onset is characterized by a less ordered brain network (as reflected by the higher value of small world) in the sigma band for the frontal lobes indicating stronger connectivity, and a more ordered brain network in the low frequency delta and theta bands indicating disconnection on the remaining brain areas. Our results depict the timing and topography of the specific mechanisms for the maintenance of functional connectivity of frontal brain regions at the sleep onset, also providing a possible explanation for the prevalence of the frontal-to-posterior information flow directionality previously observed after sleep onset. Hum Brain Mapp 38:5456-5464, 2017. © 2017 Wiley Periodicals, Inc.

Assessment of Wakefulness and Brain Arousal Regulation in Psychiatric Research

During the last few decades, much knowledge has been gained about sleep being a heterogeneous condition with several distinct sleep stages that represent fundamentally different physiological states. The same applies for the wake state which also comprises distinct global functional states (called vigilance stages). However, various terms and concepts have been introduced describing different aspects of wakefulness, and accordingly several methods of assessment exist, e.g. sleep laboratory assessments (Multiple Sleep Latency Test, Maintenance of Wakefulness Test), questionnaires (Epworth Sleepiness Scale, Karolinska Sleepiness Scale), behavioural tasks (Psychomotor Vigilance Test) or electroencephalography (EEG)-based assessments (Alpha Attenuation Test, Karolinska Drowsiness Test). Furthermore, several theoretical concepts about the regulation of sleep and wakefulness have been put forward, and physiological correlates have been identified. Most relevant for healthy functioning is the regulation of brain arousal and the adaption of wakefulness to the environmental and situational needs so that the optimal balance between energy conservation and responsiveness can be obtained. Since one approach to the assessment of brain arousal regulation is the classification of EEG vigilance stages, a computer-based algorithm (Vigilance Algorithm Leipzig) has been introduced, allowing classification of EEG vigilance stages in EEG recordings under resting conditions. The time course of EEG vigilance stages in EEGs of 15-20 min duration allows estimation of the individual arousal regulation (hyperstable, adaptive, or unstable vigilance pattern). The vigilance model of affective disorders and attention-deficit/hyperactivity disorder links a disturbed arousal regulation to the pathogenesis of psychiatric disorders and accordingly helps to explain and possibly also predict treatment effects of pharmacological and non-pharmacological interventions for these conditions.

Substantive nature of sleep in updating the temporal conditions necessary for inducing units of internal sensations

Unlike other organs that operate continuously, such as the heart and kidneys, many of the operations of the nervous system shut down during sleep. The evolutionarily conserved unconscious state of sleep that puts animals at risk from predators indicates that it is an indispensable integral part of systems operation. A reasonable expectation is that any hypothesis for the mechanism of the nervous system functions should be able to provide an explanation for sleep. In this regard, the semblance hypothesis is examined. Postsynaptic membranes are continuously being depolarized by the quantally-released neurotransmitter molecules arriving from their presynaptic terminals. In this context, an incidental lateral activation of the postsynaptic membrane is expected to induce a semblance (cellular hallucination of arrival of activity from its presynaptic terminal, which forms a unit for internal sensation) of the arrival of activity from its presynaptic terminal as a systems property. This restricts induction of semblance to a context of a very high ratio of the duration of the default state of neurotransmitter-induced postsynaptic depolarization to the total duration of incidental lateral activations of the postsynaptic membrane. This requirement spans within a time-bin of a few sleep-wake cycles. Since the duration of quantal release remains maximized, the above requirement can be achieved only by ceiling the total duration of incidental lateral activations of the postsynaptic membrane, which necessitates a state of sleep.

Objective Sleep Assessments in Patients with Postural Tachycardia Syndrome using Overnight Polysomnograms

STUDY OBJECTIVES

Patients with postural tachycardia syndrome (POTS) commonly complain of fatigue, unrefreshing sleep, daytime sleepiness, and diminished quality of life. The study objective was to assess objective sleep quality in POTS patients using overnight polysomnography.

METHODS

We studied 16 patients with POTS and 15 healthy control subjects performing daytime autonomic functions tests and overnight polysomnography at the Vanderbilt Clinical Research Center.

RESULTS

There were no significant differences in the objective sleep parameters including sleep efficiency, sleep onset latency, wake time after sleep onset, REM latency, percentage of time spent in N1, N2, N3, and REM sleep, arousal index, apnea-hypopnea index, or periodic leg movement index in POTS patients as compared with healthy control subjects. There were significant negative correlations between sleep efficiency and the change in HR from supine to stand (rs = -0.527; p = 0.036).

CONCLUSIONS

POTS patients do not have significant differences in objective sleep parameters as compared to control subjects based on overnight polysomnograms. Activation of the sympathetic nervous system may contribute significantly to the hyper arousal state and worsening of subjective estimates of sleep quality as previously reported in POTS patients.

A survey based study on sleep disturbance in postural tachycardia syndrome

BACKGROUND

Postural tachycardia syndrome (POTS) is an autonomic disturbance characterized by an excessive increase in heart rate when an individual moves from a sitting to an upright position. POTS patients often complain of fatigue, daytime sleepiness and insomnia, but there is limited evidence to elucidate the mechanism or the prevalence of sleep-related symptoms in POTS, as well as the effect on patient quality of life. Here, we investigated the prevalence of sleep disturbances in POTS patients, as well as the use of medication and effects on daily life.

METHODS

A survey was administered to 30 patients with POTS. The survey contained 22 questions on various characteristics of sleep disturbances in POTS. Answers were recorded on a five-point Likert rating scale.

RESULTS

The majority of the patients reported fatigue (96.7%) and low energy (93.3%) during the day. Most (83.3%) patients reported that they do not feel well rested when waking up in the morning. More than half of the patients reported trouble falling asleep at night (63.3%) and maintaining sleep through the night (62.1%). Despite the frequent complaint of sleep disturbance, a very low percentage of POTS patient actually report seeking treatment.

CONCLUSION

In this study, we explored the prevalence of sleep disturbance in patients with POTS. Almost all POTS patients reported trouble with sleep and fatigue; however, there is major discrepancy between the high percentage of symptoms and small percentage of patients seeking medical assistance for better sleep quality.

Neurofeedback Treatment and Posttraumatic Stress Disorder: Effectiveness of Neurofeedback on Posttraumatic Stress Disorder and the Optimal Choice of Protocol

Neurofeedback is an alternative, noninvasive approach used in the treatment of a wide range of neuropsychiatric disorders, including posttraumatic stress disorder (PTSD). Many different neurofeedback protocols and methods exist. Likewise, PTSD is a heterogeneous disorder. To review the evidence on effectiveness and preferred protocol when using neurofeedback treatment on PTSD, a systematic search of PubMed, PsychInfo, Embase, and Cochrane databases was undertaken. Five studies were included in this review. Neurofeedback had a statistically significant effect in three studies. Neurobiological changes were reported in three studies. Interpretation of results is, however, limited by differences between the studies and several issues regarding design. The optimistic results presented here qualify neurofeedback as probably efficacious for PTSD treatment.

Nocturnal Sleep Dynamics Identify Narcolepsy Type 1

STUDY OBJECTIVES

To evaluate the reliability of nocturnal sleep dynamics in the differential diagnosis of central disorders of hypersomnolence.

DESIGN

Cross-sectional.

SETTING

Sleep laboratory.

PATIENTS

One hundred seventy-five patients with hypocretin-deficient narcolepsy type 1 (NT1, n = 79), narcolepsy type 2 (NT2, n = 22), idiopathic hypersomnia (IH, n = 22), and "subjective" hypersomnolence (sHS, n = 52).

INTERVENTIONS

None.

METHODS

Polysomnographic (PSG) work-up included 48 h of continuous PSG recording. From nocturnal PSG conventional sleep macrostructure, occurrence of sleep onset rapid eye movement period (SOREMP), sleep stages distribution, and sleep stage transitions were calculated. Patient groups were compared, and receiver operating characteristic (ROC) curve analysis was used to test the diagnostic utility of nocturnal PSG data to identify NT1.

RESULTS

Sleep macrostructure was substantially stable in the 2 nights of each diagnostic group. NT1 and NT2 patients had lower latency to rapid eye movement (REM) sleep, and NT1 patients showed the highest number of awakenings, sleep stage transitions, and more time spent in N1 sleep, as well as most SOREMPs at daytime PSG and at multiple sleep latency test (MSLT) than all other groups. ROC curve analysis showed that nocturnal SOREMP (area under the curve of 0.724 ± 0.041, P < 0.0001), percent of total sleep time spent in N1 (0.896 ± 0.023, P < 0.0001), and the wakefulness-sleep transition index (0.796 ± 0.034, P < 0.0001) had a good sensitivity and specificity profile to identify NT1 sleep, especially when used in combination (0.903 ± 0.023, P < 0.0001), similarly to SOREMP number at continuous daytime PSG (0.899 ± 0.026, P < 0.0001) and at MSLT (0.956 ± 0.015, P < 0.0001).

CONCLUSIONS

Sleep macrostructure (i.e. SOREMP, N1 timing) including stage transitions reliably identifies hypocretin-deficient narcolepsy type 1 among central disorders of hypersomnolence.

Rapid Changes in Scores on Principal Components of the EEG Spectrum do not Occur in the Course of "Drowsy" Sleep of Varying Length

Wakefulness is separated from a well-established sleep by an onset period. This is characterized by dramatic changes in scores on the first and second principal components of the electroencephalographic (EEG) spectrum, which reflects the kinetics of sleep- and wake-promoting processes. The present analysis examined whether significant buildups and declines of the first and second scores can occur throughout stage 1 sleep, or only on its boundaries with stage 2 and wakefulness. Twenty-seven adults participated in multiple 20-minute attempts to nap in the course of 24-hour wakefulness after either deprivation, restriction or ad lib night sleep. Power spectra were calculated on 1-minute intervals of 251 EEG records. Irrespective of accumulated sleep debt and duration of stage 1 sleep (from <2 to >5 minutes), the first principal component score was permanently attenuated across this stage as well as during preceding wakefulness. It showed rapid buildup only on the boundary with stage 2. The second principal component score always started its decline earlier, on the wake-sleep boundary. It did not show further decline throughout the following intervals of stages 1 and 2. It seems that stage 1 sleep occurs due to a delay of the buildup of the sleep-promoting process relative to the decline of the wake-promoting process which coincide, with initiation of stage 2 sleep and termination of wakefulness. Therefore, "drowsy" sleep can be regarded as occupying "no man's land", between the opponent driving forces for wake and sleep.

Mutual information measures applied to EEG signals for sleepiness characterization

Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders with a great impact on the patient lives. While many studies have been carried out in order to assess daytime sleepiness, the automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on non-linear dynamical analysis of EEG signal was proposed. Multichannel EEG signals were recorded during five maintenance of wakefulness (MWT) and multiple sleep latency (MSLT) tests alternated throughout the day from patients suffering from sleep disordered breathing. A group of 20 patients with excessive daytime sleepiness (EDS) was compared with a group of 20 patients without daytime sleepiness (WDS), by analyzing 60-s EEG windows in waking state. Measures obtained from cross-mutual information function (CMIF) and auto-mutual-information function (AMIF) were calculated in the EEG. These functions permitted a quantification of the complexity properties of the EEG signal and the non-linear couplings between different zones of the scalp. Statistical differences between EDS and WDS groups were found in β band during MSLT events (p-value < 0.0001). WDS group presented more complexity than EDS in the occipital zone, while a stronger nonlinear coupling between occipital and frontal zones was detected in EDS patients than in WDS. The AMIF and CMIF measures yielded sensitivity and specificity above 80% and AUC of ROC above 0.85 in classifying EDS and WDS patients.