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

Suvorexant alters dynamics of the sleep-electroencephalography-power spectrum and depressive-symptom trajectories during inpatient opioid withdrawal

STUDY OBJECTIVES

Opioid withdrawal is an aversive experience that often exacerbates depressive symptoms and poor sleep. The aims of the present study were to examine the effects of suvorexant on oscillatory sleep-electroencephalography (EEG) band power during medically managed opioid withdrawal, and to examine their association with withdrawal severity and depressive symptoms.

METHODS

Participants with opioid use disorder (N = 38: age-range:21-63, 87% male, 45% white) underwent an 11-day buprenorphine taper, in which they were randomly assigned to suvorexant (20 mg [n = 14] or 40 mg [n = 12]), or placebo [n = 12], while ambulatory sleep-EEG data was collected. Linear mixed-effect models were used to explore: (1) main and interactive effects of drug group, and time on sleep-EEG band power, and (2) associations between sleep-EEG band power change, depressive symptoms, and withdrawal severity.

RESULTS

Oscillatory spectral power tended to be greater in the suvorexant groups. Over the course of the study, decreases in delta power were observed in all study groups (β = -189.082, d = -0.522, p = <0.005), increases in beta power (20 mg: β = 2.579, d = 0.413, p = 0.009 | 40 mg β = 5.265, d = 0.847, p < 0.001) alpha power (20 mg: β = 158.304, d = 0.397, p = 0.009 | 40 mg: β = 250.212, d = 0.601, p = 0.001) and sigma power (20 mg: β = 48.97, d = 0.410, p < 0.001 | 40 mg: β = 71.54, d = 0.568, p < 0.001) were observed in the two suvorexant groups. During the four-night taper, decreases in delta power were associated with decreases in depressive symptoms (20 mg: β = 190.90, d = 0.308, p = 0.99 | 40 mg: β = 433.33, d = 0.889 p = <0.001), and withdrawal severity (20 mg: β = 215.55, d = 0.034, p = 0.006 | 40 mg: β = 192.64, d = -0.854, p = <0.001), in both suvorexant groups and increases in sigma power were associated with decreases in withdrawal severity (20 mg: β = -357.84, d = -0.659, p = 0.004 | 40 mg: β = -906.35, d = -1.053, p = <0.001). Post-taper decreases in delta (20 mg: β = 740.58, d = 0.964 p = <0.001 | 40 mg: β = 662.23, d = 0.882, p = <0.001) and sigma power (20 mg only: β = 335.54, d = 0.560, p = 0.023) were associated with reduced depressive symptoms in the placebo group.

CONCLUSIONS

Results highlight a complex and nuanced relationship between sleep-EEG power and symptoms of depression and withdrawal. Changes in delta power may represent a mechanism influencing depressive symptoms and withdrawal.

Fundamentals of sleep regulation: Model and benchmark values for fractal and oscillatory neurodynamics

Homeostatic, circadian and ultradian mechanisms play crucial roles in the regulation of sleep. Evidence suggests that ratios of low-to-high frequency power in the electroencephalogram (EEG) spectrum indicate the instantaneous level of sleep pressure, influenced by factors such as individual sleep-wake history, current sleep stage, age-related differences and brain topography characteristics. These effects are well captured and reflected in the spectral exponent, a composite measure of the constant low-to-high frequency ratio in the periodogram, which is scale-free and exhibits lower interindividual variability compared to slow wave activity, potentially serving as a suitable standardization and reference measure. Here we propose an index of sleep homeostasis based on the spectral exponent, reflecting the level of membrane hyperpolarization and/or network bistability in the central nervous system in humans. In addition, we advance the idea that the U-shaped overnight deceleration of oscillatory slow and fast sleep spindle frequencies marks the biological night, providing somnologists with an EEG-index of circadian sleep regulation. Evidence supporting this assertion comes from studies based on sleep replacement, forced desynchrony protocols and high-resolution analyses of sleep spindles. Finally, ultradian sleep regulatory mechanisms are indicated by the recurrent, abrupt shifts in dominant oscillatory frequencies, with spindle ranges signifying non-rapid eye movement and non-spindle oscillations - rapid eye movement phases of the sleep cycles. Reconsidering the indicators of fundamental sleep regulatory processes in the framework of the new Fractal and Oscillatory Adjustment Model (FOAM) offers an appealing opportunity to bridge the gap between the two-process model of sleep regulation and clinical somnology.

Phenotypic Interindividual Differences in the Dynamic Structure of Sleep in Healthy Young Adults

Introduction

Evaluating the dynamic structure of sleep may yield new insights into the mechanisms underlying human sleep physiology.

Methods

We analyzed data from a 12-day, 11-night, strictly controlled laboratory study with an adaptation night, 3 iterations of a baseline night followed by a recovery night after 36 h of total sleep deprivation, and a final recovery night. All sleep opportunities were 12 h in duration (22:00-10:00) and recorded with polysomnography (PSG). The PSG records were scored for the sleep stages: rapid eye movement (REM) sleep; non-REM (NREM) stage 1 sleep (S1), stage 2 sleep (S2), and slow wave sleep (SWS); and wake (W). Phenotypic interindividual differences were assessed using indices of dynamic sleep structure - specifically sleep stage transitions and sleep cycle characteristics - and intraclass correlation coefficients across nights.

Results

NREM/REM sleep cycles and sleep stage transitions exhibited substantial and stable interindividual differences that were robust across baseline and recovery nights, suggesting that mechanisms underlying the dynamic structure of sleep are phenotypic. In addition, the dynamics of sleep stage transitions were found to be associated with sleep cycle characteristics, with a significant relationship between the length of sleep cycles and the degree to which S2-to-W/S1 and S2-to-SWS transitions were in equilibrium.

Discussion

Our findings are consistent with a model for the underlying mechanisms that involves three subsystems - characterized by S2-to-W/S1, S2-to-SWS, and S2-to-REM transitions - with S2 playing a hub-like role. Furthermore, the balance between the two subsystems within NREM sleep (S2-to-W/S1 and S2-to-SWS) may serve as a basis for the dynamic regulation of sleep structure and may represent a novel target for interventions aiming to improve sleep.

Validation of Deep Learning-based Sleep State Classification

Deep learning methods have been developed to classify sleep states of mouse electroencephalogram (EEG) and electromyogram (EMG) recordings with accuracy reported as high as 97%. However, when applied to independent datasets, with a variety of experimental and recording conditions, sleep state classification accuracy often drops due to distributional shift. Mixture z-scoring, a pre-processing standardization of EEG/EMG signals, has been suggested to account for these variations. This study sought to validate mixture z-scoring in combination with a deep learning method on an independent dataset. The open-source software Accusleep, which implements mixture z-scoring in combination with deep learning via a convolutional neural network, was used to classify sleep states in 12, three-hour EEG/EMG recordings from mice sleeping in a head-fixed position. Mixture z-scoring with deep learning classified sleep states on two independent recordings with 85-92% accuracy and a Cohen's κ of 0.66-0.71. These results validate mixture z-scoring in combination with deep learning to classify sleep states with the potential for widespread use.

Slow waves form expanding, memory-rich mesostates steered by local excitability in fading anesthesia

In the arousal process, the brain restores its integrative activity from the synchronized state of slow wave activity (SWA). The mechanisms underpinning this state transition remain, however, to be elucidated. Here we simultaneously probed neuronal assemblies throughout the whole cortex with micro-electrocorticographic recordings in mice. We investigated the progressive shaping of propagating SWA at different levels of isoflurane. We found a form of memory of the wavefront shapes at deep anesthesia, tightly alternating posterior-anterior-posterior patterns. At low isoflurane, metastable patterns propagated in more directions, reflecting an increased complexity. The wandering across these mesostates progressively increased its randomness, as predicted by simulations of a network of spiking neurons, and confirmed in our experimental data. The complexity increase is explained by the elevated excitability of local assemblies with no modifications of the network connectivity. These results shed new light on the functional reorganization of the cortical network as anesthesia fades out.

•

Complexity of isoflurane-induced slow waves reliably determines anesthesia level

•

In deep anesthesia, the propagation strictly alternates between front-back-front patterns

•

In light anesthesia, there is a continuum of directions and faster propagation

•

Local excitability underpins the cortical reorganization in fading anesthesia

Mapping the neural systems driving breathing at the transition to unconsciousness

After falling asleep, the brain needs to detach from waking activity and reorganize into a functionally distinct state. A functional MRI (fMRI) study has recently revealed that the transition to unconsciousness induced by propofol involves a global decline of brain activity followed by a transient reduction in cortico-subcortical coupling. We have analyzed the relationships between transitional brain activity and breathing changes as one example of a vital function that needs the brain to readapt. Thirty healthy participants were originally examined. The analysis involved the correlation between breathing and fMRI signal upon loss of consciousness. We proposed that a decrease in ventilation would be coupled to the initial decline in fMRI signal in brain areas relevant for modulating breathing in the awake state, and that the subsequent recovery would be coupled to fMRI signal in structures relevant for controlling breathing during the unconscious state. Results showed that a slight reduction in breathing from wakefulness to unconsciousness was distinctively associated with decreased activity in brain systems underlying different aspects of consciousness including the prefrontal cortex, the default mode network and somatosensory areas. Breathing recovery was distinctively coupled to activity in deep brain structures controlling basic behaviors such as the hypothalamus and amygdala. Activity in the brainstem, cerebellum and hippocampus was associated with breathing variations in both states. Therefore, our brain maps illustrate potential drives to breathe, unique to wakefulness, in the form of brain systems underlying cognitive awareness, self-awareness and sensory awareness, and to unconsciousness involving structures controlling instinctive and homeostatic behaviors.

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.

Chaos in memory function of sleep: A nonlinear dynamical analysis in thalamocortical study

Studying the dynamical behaviors of neuronal models may help in better understanding of real nervous system. In addition, it can help researchers to understand some specific phenomena in neuronal system. The thalamocortical network is made of neurons in the thalamus and cortex. In it, the memory function is consolidated in sleep by creating up and down state oscillations (1 Hz) and fast (13-17 Hz) - slow (8-12 Hz) spindles. Recently, a nonlinear biological model for up-down oscillations and fast-slow spindles of the thalamocortical network has been proposed. In this research, the power spectral for the fast-slow spindle of the model is extracted. Dynamical properties of the model, such as the bifurcation diagrams, and attractors are investigated. The results show that the variation of the synaptic power between the excitatory neurons of the cortex and the reticular neurons in the thalamus changes the spindles' activity. According to previous experimental findings, it is an essential rule for consolidating the memory function during sleep. It is also pointed out that when the fast-slow spindles of the brain increase, the dynamics of the thalamocortical system tend to chaos.

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.

Impact of number of sleep ultradian cycles on polysomnographic parameters related to REM sleep in major depression: Implications for future sleep research in psychiatry

Given the contradictory data on REMS alterations in major depression, the aim of this study was to empirically demonstrate that based on the number of sleep ultradian cycles, it was possible to highlight different subtypes of major depression characterized by specific patterns of REMS alterations. Demographic and polysomnographic data from 211 individuals (30 healthy controls and 181 untreated major depressed individuals) recruited from the sleep laboratory database were analyzed. Major depressed individuals with sleep ultradian cycles <4 showed alterations consistent with REMS deficiency (non-shortened REM latency as well as decrease in REMS percentage, REMS duration and REMS/NREMS ratio) whereas major depressed individuals with sleep ultradian cycles >4 showed alterations consistent with REMS disinhibition (shortened REM latency as well as increase in REMS percentage, REMS duration and REMS/NREMS ratio). Regarding major depressed individuals with 4 sleep ultradian cycles, their REMS alterations were intermediate to those present in major depressed individuals with sleep ultradian cycles <4 and >4. Thus, in major depressed individuals, the highlighting of this heterogeneity of REMS alterations based on the number of sleep ultradian cycles seems to suggest the involvement of distinct pathophysiological mechanisms and could open new perspectives for future sleep research in psychiatry.

On the Hierarchical Organization of Oscillatory Assemblies: Layered Superimposition and a Global Bioelectric Framework

Bioelectric oscillations occur throughout the nervous system of nearly all animals, revealed to play an important role in various aspects of cognitive activity such as information processing and feature binding. Modern research into this dynamic and intrinsic bioelectric activity of neural cells continues to raise questions regarding their role in consciousness and cognition. In this theoretical article, we assert a novel interpretation of the hierarchical nature of "brain waves" by identifying that the superposition of multiple oscillations varying in frequency corresponds to the superimposing of the contents of consciousness and cognition. In order to describe this isomorphism, we present a layered model of the global functional oscillations of various frequencies which act as a part of a unified metastable continuum described by the Operational Architectonics theory and suggested to be responsible for the emergence of the phenomenal mind. We detail the purposes, functions, and origins of each layer while proposing our main theory that the superimposition of these oscillatory layers mirrors the superimposition of the components of the integrated phenomenal experience as well as of cognition. In contrast to the traditional view that localizations of high and low-frequency activity are spatially distinct, many authors have suggested a hierarchical nature to oscillations. Our theoretical interpretation is founded in four layers which correlate not only in frequency but in evolutionary development. As other authors have done, we explore how these layers correlate to the phenomenology of human experience. Special importance is placed on the most basal layer of slow oscillations in coordinating and grouping all of the other layers. By detailing the isomorphism between the phenomenal and physiologic aspects of how lower frequency layers provide a foundation for higher frequency layers to be organized upon, we provide a further means to elucidate physiological and cognitive mechanisms of mind and for the well-researched outcomes of certain voluntary breathing patterns and meditative practices which modulate the mind and have therapeutic effects for psychiatric and other disorders.

Cortical hyperarousal in NREM sleep normalizes from pre- to post- REM periods in individuals with frequent nightmares

Study Objectives

Frequent nightmares have a high prevalence and constitute a risk factor for psychiatric conditions, but their pathophysiology is poorly understood. Our aim was to examine sleep architecture and electroencephalographic markers—with a specific focus on state transitions—related to sleep regulation and hyperarousal in participants with frequent nightmares (NM participants) versus healthy controls.

Methods

Healthy controls and NM participants spent two consecutive nights in the sleep laboratory. Second night spectral power during NREM to REM sleep (pre-REM) and REM to NREM (post-REM) transitions as well as during NREM and REM periods were evaluated for 22 NM participants compared to 22 healthy controls with a similar distribution of age, gender, and dream recall frequency.

Results

We found significant differences between the groups in the pre-REM to post-REM changes in low- and high-frequency domains. NM participants experienced a lower amount of slow-wave sleep and showed increased beta and gamma power during NREM and pre-REM periods. No difference was present during REM and post-REM phases. Furthermore, while increased pre-REM high-frequency power seems to be mainly driven by post-traumatic stress disorder (PTSD) symptom intensity, decreased low-frequency activity occurred regardless of PTSD symptom severity.

Conclusion

Our findings indicate that NM participants had increased high-frequency spectral power during NREM and pre-REM periods, as well as relatively reduced slow frequency and increased fast frequency spectral power across pre-and post-REM periods. This combination of reduced sleep-protective activity and increased hyperarousal suggests an imbalance between sleep regulatory and wake-promoting systems in NM participants.

Hip Arthroscopy for Femoroacetabular Impingement Improves Sleep Quality Postoperatively

PURPOSE

To describe the prevalence of abnormal sleep quality in patients with femoroacetabular impingement syndrome and to determine whether arthroscopic hip preservation surgery with cam/pincer correction, labral preservation, and capsular plication can improve sleep quality postoperatively.

METHODS

All patients undergoing primary hip arthroscopy for cam/pincer correction who failed nonoperative management between March 1, 2017, and July 1, 2017, were administered a validated sleep quality questionnaire-the Pittsburgh Sleep Quality Index (PSQI)-preoperatively and at 3, 6, 12, and 24 weeks postoperatively. Exclusion criteria included patients undergoing revision arthroscopy, gluteus medius repair, or a contralateral procedure during the follow-up period and those with known sleep disorders. A global (total) PSQI score >5 indicates poor sleep quality. The Hip Outcome Score-Activities of Daily Living, Hip Outcome Score-Sports Specific Subscale, modified Harris Hip Score, and International Hip Outcome Tool-12 were used to assess functional outcomes. A repeated measures analysis of variance with post hoc Greenhouse-Geisser and Bonferroni corrections was conducted to determine statistically significant changes in sleep patterns.

RESULTS

A total of 52 patients (94.6%) were included in the final analysis. The mean (± standard error) patient age was 37.8 ± 1.9 years, and body mass index was 27.6 ± 0.7. Preoperatively, 49 (94.2%) of patients experienced poor sleep quality, defined as a global PSQI score >5, with a mean PSQI score of 9.8 ± 0.6. At 24 weeks postoperatively, 10 (21.7%) of patients experienced poor sleep quality with a mean PSQI score of 2.2 ± 0.2. All patients had significant improvements in all hip outcome instruments at 24 weeks postoperatively (P < .001).

CONCLUSIONS

Preoperatively, patients with femoroacetabular impingement syndrome have a high prevalence of sleep disturbance. These patients experience subsequent improvement in sleep disturbance after arthroscopic hip surgery early in the postoperative period.

LEVEL OF EVIDENCE

Level IV, case series.

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

Introduction

Sleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques.

Methods

Sleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis.

Results

Neighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy, respectively.

Discussion and Conclusion

Similar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.

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.

Coupling and dynamics of cortical and autonomic signals are linked to central inhibition during the wake-sleep transition

Maintaining temporal coordination across physiological systems is crucial at the wake-sleep transition. As shown in recent studies, the degree of coordination between brain and autonomic arousal influences attention, which highlights a previously unrecognised point of potential failure in the attention system. To investigate how cortical and autonomic dynamics are linked to the attentive process we analysed electroencephalogram, electrocardiogram and skin conductance data of 39 healthy adults recorded during a 2-h resting-state oddball experiment. We related cross-correlations to fluctuation periods of cortical and autonomic signals and correlated obtained measures to event-related potentials N1 and P2, reflecting excitatory and inhibitory processes. Increasing alignment of cortical and autonomic signals and longer periods of vigilance fluctuations corresponded to a larger and earlier P2; no such relations were found for N1. We compared two groups, with (I) and without measurable (II) delay in cortico-autonomic correlations. Individuals in Group II had more stable vigilance fluctuations, larger and earlier P2 and fell asleep more frequently than individuals in Group I. Our results support the hypothesis of a link between cortico-autonomic coupling and dynamics and central inhibition. Quantifying this link could help refine classification in psychiatric disorders with attention and sleep-related symptoms, particularly in ADHD, depression, and insomnia.

A Neurophysiological Approach for Evaluating Noise-Induced Sleep Disturbance: Calculating the Time Constant of the Dynamic Characteristics in the Brainstem

Chronic sleep disturbance induced by traffic noise is considered to cause environmental sleep disorder, which increases the risk of cardiovascular disease, stroke, diabetes and other stress-related diseases. However, noise indices for the evaluation of sleep disturbance are not based on the neurophysiological process of awakening regulated by the brainstem. In this study, through the neurophysiological approach, we attempted (1) to investigate the thresholds of awakening due to external stimuli in the brainstem; (2) to evaluate the dynamic characteristics in the brainstem and (3) to verify the validity of existing noise indices. Using the mathematical Phillips-Robinson model, we obtained thresholds of awakening in the brainstem for different durations of external stimuli. The analysis revealed that the brainstem seemed insensitive to short stimuli and that the response to external stimuli in the brainstem could be approximated by a first-order lag system with a time constant of 10-100 s. These results suggest that the brainstem did not integrate sound energy as external stimuli, but neuroelectrical signals from auditory nerve. To understand the awakening risk accumulated in the brainstem, we introduced a new concept of "awakening potential" instead of sound energy.

Insomnia Characterization: From Hypnogram to Graph Spectral Theory

OBJECTIVE

To quantify and differentiate control and insomnia sleep onset patterns through biomedical signal processing of overnight polysomnograms.

METHODS

The approach consisted of three tandem modules: 1) biosignal processing module, which used state-space time-varying autoregressive moving average (TVARMA) processes with recursive particle filter, 2) hypnogram generation module that implemented a fuzzy inference system (FIS), and 3) insomnia characterization module that discriminated between control and subjects with insomnia using a logistic regression model trained with a set of similarity measures ( d1, d2 , d3, d4). The study employed sleep onset periods from 16 control and 16 subjects with insomnia.

RESULTS

State-spaced TVARMA processes with recursive particle filtering provided resilience to nonlinear, nonstationary, and non-Gaussian conditions of biosignals. FIS managed automated sleep scoring robust to intersubjects' and interraters' variability. The similarity distances quantified in a scalar measure the transitions amongst sleep onset stages, computed from expert and automated hypnograms. A statistical set of unpaired two-tailed t -tests suggested that distances d1 , d2, and d3 had larger statistical significance ( ) to characterize sleeping patterns. The logistic regression model classified control and subjects with insomnia with sensitivity 87 % , specificity 75 %, and accuracy 81 %.

CONCLUSION

Our approach can perform a supportive role in either biosignal processing, sleep staging, insomnia characterization, or all the previous, coping with time-consuming procedures and massive data volumes of standard protocols.

SIGNIFICANCE

The introduction of graph spectral theory and logistic regression for the diagnosis of insomnia represents a novel concept, attempting to describe complex sleep dynamics throughout transitions networks and scalar measures.

Low-cost EEG-based sleep detection

A real-time stage 1 sleep detection system using a low-cost single dry-sensor EEG headset is described. This device issues an auditory warning at the onset of stage 1 sleep using the "NeuroSky Mindset," an inexpensive commercial entertainment-based headset. The EEG signal is filtered into low/high alpha and low/high beta frequency bands which are analyzed to indicate the onset of sleep. Preliminary results indicate an 81% effective rate of detecting sleep with all failures being false positives of sleep onset. This device was able to predict and respond to the onset of drowsiness preceding stage 1 sleep allowing for earlier warnings with the result of fewer sleep-related accidents.

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.

Characterising insomnia: A graph spectral theory approach

This paper introduces a computational approach to characterise healthy controls and insomniacs based on graph spectral theory. Based upon expert-generated hypnograms of sleep onset periods, a network of sleep stages transitions is derived to compute four similarity distances amongst subjects' sleeping patterns. A subsequent statistical analysis is performed to differentiate the 16-subject healthy group from a 16-patient disordered cohort. Our findings demonstrated that the similarity distances based on eigenvalues determination, i.e. d1 and d4 were the most reliable and robust measures to characterise insomniacs, discriminating 93% and 87% of the affected population, respectively.

Principal component analysis of the EEG spectrum can provide yes-or-no criteria for demarcation of boundaries between NREM sleep stages

Human sleep begins in stage 1 and progresses into stages 2 and 3 of Non-Rapid-Eye-Movement (NREM) sleep. These stages were defined using several arbitrarily-defined thresholds for subdivision of albeit continuous process of sleep deepening. Since recent studies indicate that stage 3 (slow wave sleep) has unique vital functions, more accurate measurement of this stage duration and continuity might be required for both research and practical purposes. However, the true neurophysiological boundary between stages 2 and 3 remains unknown. In a search for non-arbitrary threshold criteria for distinguishing the boundaries between NREM sleep stages, scores on the principal components of the electroencephalographic (EEG) spectrum were analyzed in relation to stage onsets. Eighteen young men made 12-20-minute attempts to nap during 24-hour wakefulness. Single-minute intervals of the nap EEG records were assigned relative to the minute of onsets of polysomnographically determined stages 1, 2, and 3. The analysis of within-nap time courses of principal components scores revealed that, unlike any conventional spectral EEG index, score on the 4th principal component exhibited a rather rapid rise on the boundary between stages 2 and 3. This was mostly a change from negative to positive score. Therefore, it might serve as yes-or-no criterion of stage 3 onset. Additionally, similarly rapid changes in sign of scores were exhibited by the 1st and 2nd principal components on the boundary of stages 2 and 1 and on the boundary between stage 1 and wakefulness, respectively.

Two distinct synchronization processes in the transition to sleep: a high-density electroencephalographic study

OBJECTIVES

To assess how the characteristics of slow waves and spindles change in the falling-asleep process.

DESIGN

Participants undergoing overnight high-density electroencephalographic recordings were awakened at 15- to 30-min intervals. One hundred forty-one falling-asleep periods were analyzed at the scalp and source level.

SETTING

Sleep laboratory.

PARTICIPANTS

Six healthy participants.

INTERVENTIONS

Serial awakenings.

RESULTS

The number and amplitude of slow waves followed two dissociated, intersecting courses during the transition to sleep: slow wave number increased slowly at the beginning and rapidly at the end of the falling-asleep period, whereas amplitude at first increased rapidly and then decreased linearly. Most slow waves occurring early in the transition to sleep had a large amplitude, a steep slope, involved broad regions of the cortex, predominated over frontomedial regions, and preferentially originated from the sensorimotor and the posteromedial parietal cortex. Most slow waves occurring later had a smaller amplitude and slope, involved more circumscribed parts of the cortex, and had more evenly distributed origins. Spindles were initially sparse, fast, and involved few cortical regions, then became more numerous and slower, and involved more areas.

CONCLUSIONS

Our results provide evidence for two types of slow waves, which follow dissociated temporal courses in the transition to sleep and have distinct cortical origins and distributions. We hypothesize that these two types of slow waves result from two distinct synchronization processes: (1) a "bottom-up," subcorticocortical, arousal system-dependent process that predominates in the early phase and leads to type I slow waves, and (2) a "horizontal," corticocortical synchronization process that predominates in the late phase and leads to type II slow waves. The dissociation between these two synchronization processes in time and space suggests that they may be differentially affected by experimental manipulations and sleep disorders.

Cerebral perfusion differences between drowsy and nondrowsy individuals after acute sleep restriction

OBJECTIVES

To investigate changes in resting cerebral blood flow (CBF) after acute sleep restriction. To investigate the extent to which changes in CBF after sleep restriction are related to drowsiness as manifested in eye-video.

DESIGN

Participants were scanned for 5 min using arterial spin labeling (ASL) perfusion imaging after both sleep-restricted and rested nights. Participants were rated for visual signs of drowsiness in the eye-video recorded during the scan.

SETTING

Lying supine in a 3-Tesla magnetic resonance imaging scanner.

PARTICIPANTS

Twenty healthy adults (age 20-37 yr) with no history of neurologic, psychiatric, or sleep disorder, and with usual time in bed of 7.0-8.5 h.

INTERVENTIONS

In the night before the sleep-restricted session, participants were restricted to 4 h time in bed.

RESULTS

There was an overall reduction in CBF in the right-lateralized fronto-parietal attentional network after acute sleep restriction, although this was largely driven by participants who showed strong signs of drowsiness in the eye-video after sleep restriction. Change in CBF correlated with change in drowsiness in the basal forebrain-cingulate regions. In particular, there was a pronounced increase in CBF in the basal forebrain and anterior and posterior cingulate cortex of participants who remained alert after sleep restriction.

CONCLUSIONS

The pattern of cerebral activity after acute sleep restriction is highly dependent on level of drowsiness. Nondrowsy individuals are able to increase activity in the arousal-promoting brain regions and maintain activity in attentional regions. In contrast, drowsy individuals are unable to maintain arousal and show decreased activity in both arousal-promoting and attentional regions.

How well do truck drivers sleep in cabin sleeper berths?

The aim of this study was to evaluate the sleep obtained by livestock transport truck drivers while resting in truck sleeper berths during long-haul commercial operations. Operations were carried out in the very remote regions of Australia. The sample comprised of 32 drivers who wore wrist activity monitors and reported bed-times for a two-week period. Drivers had a mean (±standard deviation) age of 35.41 (± 9.78) years and had worked as truck drivers for 13.83 (± 9.11) years. On average, they obtained 6.07 (± 1.18) hours of sleep/24-h period. The majority of sleep occurred at night, but drivers occasionally supplemented their main sleep with a daytime nap. Consistent with operational demands, drivers were most likely to sleep in cabin sleeper berths (n = 394, 77%). Only a small proportion of sleeps were sampled at home (n = 63, 12%) or at truck depots (n = 56, 11%). Mixed-model ANOVA revealed that while earlier bed-times at home yielded more sleep, there were only marginal differences in sleep quality across location. No intrinsic safety concerns associated with the use of sleeper berths were identified across consecutive days of long-haul transport operations.

Seizure susceptibility and electroencephalogram power spectra alterations at various phases of the lipopolysaccharide-induced hypothermic response in biotelemetered rats

The neuronal excitability has been evaluated at various phases of lipopolysaccharide (LPS; E. coli O111:B4, 250 μg/kg, ip)-induced hypothermia including the initial phase, the plateau (including the nadir) and the end of the response in biotelemetered adult Wistar rats. The latency of pentylenetetrazole-induced seizures (60 mg/kg, ip) was lower at the initial phase, but a clear anticonvulsive activity was observed at the end of the hypothermic response. Seizure parameters did not change at the nadir. There was no electroencephalogram (EEG) spike-wave activity generation at either phase of the LPS-induced hypothermia. Meanwhile, the power of the 12-32 Hz beta band of the EEG spectra increased at the initial phase. This increment persisted at the plateau where there was also a decrease in the 1-4 Hz delta power. The data indicate that spike-wave activity is not facilitated during LPS-induced hypothermia but, proconvulsant and anticonvulsant activities occur sequentially depending on the phase of the response. The EEG power spectra also change. These effects may not be attributed merely to the reduction of body temperature. Thus, it is possible that pathophysiological mechanisms involved in the development of hypothermia may also be responsible for neuronal excitability changes in rats.

Electroencephalogram bands modulated by vigilance states in an anuran species: a factor analytic approach

Dramatic changes in neocortical electroencephalogram (EEG) rhythms are associated with the sleep-waking cycle in mammals. Although amphibians are thought to lack a neocortical homologue, changes in rest-activity states occur in these species. In the present study, EEG signals were recorded from the surface of the cerebral hemispheres and midbrain on both sides of the brain in an anuran species, Babina daunchina, using electrodes contacting the meninges in order to measure changes in mean EEG power across behavioral states. Functionally relevant frequency bands were identified using factor analysis. The results indicate that: (1) EEG power was concentrated in four frequency bands during the awake or active state and in three frequency bands during rest; (2) EEG bands in frogs differed substantially from humans, especially in the fast frequency band; (3) bursts similar to mammalian sleep spindles, which occur in non-rapid eye movement mammalian sleep, were observed when frogs were at rest suggesting sleep spindle-like EEG activity appeared prior to the evolution of mammals.

Electrocortical activity in the near-term ovine fetus: automated analysis using amplitude frequency components

We have designed an automated method for analyzing electrocortical (ECoG) activity in the near-term ovine fetus to process and quantitatively classify large amounts of data rapidly and objectively. Seven chronically catheterized fetal sheep were studied for 8h each at ~0.9 of gestation with continuous recording of ECoG activity using a computerized data acquisition system. Multiple ECoG amplitude and frequency parameters were scored from which we established animal specific parameter cut-off values as well as population based duration cut-off values to distinguish low-voltage/high frequency (LV/HF) and high-voltage/low frequency (HV/LF) state epochs, and indeterminate voltage/frequency (IV/F) and transition period activities. We have shown that the incidence of the predominant LV/HF and HV/LF activity states at 45% and 36% of the time, respectively, is comparable to that previously reported using semi-quantitative techniques with visual analysis. However, the duration of these state epochs is considerably shorter due to the detection of brief periods of IV/F activity which would be difficult to capture using visual analysis. Importantly, our findings in the healthy ovine fetus near-term using this automated ECoG scoring methodology now provide a framework from which to study maturational events in younger animals, and under adverse pregnancy conditions.

Sleep-wake disturbances in common neurodegenerative diseases: a closer look at selected aspects of the neural circuitry

There is a growing appreciation regarding the relationship between common neurodegenerative diseases, such as Alzheimer's and Parkinson's and sleep-wake disturbances. These clinical features often herald the onset of such conditions and certainly appear to influence disease phenotype and progression. This article reviews some of the pathophysiological processes underlying specific disruptions within the neural circuitry underlying sleep-wake disturbances and explores how clinicopathological relationships commonly manifest. It is proposed that a greater understanding of these relationships should allow insights in to the efficacy of currently available treatments and help in the development of future therapies targeting disruptions within the sleep-wake neural circuitry.

The cholinergic system, EEG and sleep

Acetylcholine is a potent excitatory neurotransmitter, crucial for cognition and the control of alertness and arousal. Vigilance-specific recordings of the electroencephalogram (EEG) potently reflect thalamo-cortical and brainstem-cortical cholinergic activity that drives theta rhythms and task-specific cortical (de-synchronisation. Additionally, cholinergic projections from the basal forebrain act as a relay centre for the brainstem-cortical arousal system, but also directly modulate cortical activity, and thus promote wakefulness or rapid-eye movement (REM) sleep. Disease states such as sleep disorders, dementia and certain types of epilepsy are a further reflection of the potent cholinergic impact on CNS physiology and function, and highlight the relevance and inter-dependence of sleep and EEG. With novel technologies and computational tools now becoming available, advanced mechanistic insights may be gained and new avenues explored for diagnostics and therapeutics.

Modulation of the brain's functional network architecture in the transition from wake to sleep

The transition from quiet wakeful rest to sleep represents a period over which attention to the external environment fades. Neuroimaging methodologies have provided much information on the shift in neural activity patterns in sleep, but the dynamic restructuring of human brain networks in the transitional period from wake to sleep remains poorly understood. Analysis of electrophysiological measures and functional network connectivity of these early transitional states shows subtle shifts in network architecture that are consistent with reduced external attentiveness and increased internal and self-referential processing. Further, descent to sleep is accompanied by the loss of connectivity in anterior and posterior portions of the default-mode network and more locally organized global network architecture. These data clarify the complex and dynamic nature of the transitional period between wake and sleep and suggest the need for more studies investigating the dynamics of these processes.

Visualization and clustering of sleep states in a frequency domain feature space

Sleep studies assess the recurrent manifestation of stereotype configurations of relevant biosignals. These configurations are known as states (Wake, REM sleep and NonREM sleep) and stages (N1-N3 within NREM sleep). These two fundamental descriptive domains, time course and variable configuration, can be readily rendered available through improved visualization techniques. Time course is summarized by EEG spectrograms, instantaneous frequency analysis of cardio-respiratory signals and other sleep dependent variables. State and stage configurations can be further evidenced as clusters in 2D or 3D spaces whose axis are sleep-relevant extracted variables. The latter techniques also allows for visualization of transition process as pathways from one cluster to another.

Tonic and phasic EEG and behavioral changes induced by arousing feedback

This study investigates brain dynamics and behavioral changes in response to arousing auditory signals presented to individuals experiencing momentary cognitive lapses during a sustained-attention task. Electroencephalographic (EEG) and behavioral data were simultaneously collected during virtual-reality (VR) based driving experiments, in which subjects were instructed to maintain their cruising position and compensate for randomly induced lane deviations using the steering wheel. 30-channel EEG data were analyzed by independent component analysis and the short-time Fourier transform. Across subjects and sessions, intermittent performance during drowsiness was accompanied by characteristic spectral augmentation or suppression in the alpha- and theta-band spectra of a bilateral occipital component, corresponding to brief periods of normal (wakeful) and hypnagogic (sleeping) awareness and behavior. Arousing auditory feedback was delivered to the subjects in half of the non-responded lane-deviation events, which immediately agitated subject's responses to the events. The improved behavioral performance was accompanied by concurrent spectral suppression in the theta- and alpha-bands of the bilateral occipital component. The effects of auditory feedback on spectral changes lasted 30s or longer. The results of this study demonstrate the amount of cognitive state information that can be extracted from noninvasively recorded EEG data and the feasibility of online assessment and rectification of brain networks exhibiting characteristic dynamic patterns in response to momentary cognitive challenges.

Sleep debt and depression in female college students

The objective of the study was to evaluate relationships between sleep habits and depressive symptoms. Pilot study data were collected about sleep schedules, related factors and depression in female college students to find whether their sleep schedules correlate with affective symptoms. In the subsequent main study, similar information was collected under more controlled conditions. Depression was measured using the CES-D (Center for Epidemiologic Studies Depression Scale) and HAM-D-3 (modified Hamilton Depression Rating Scale). Response rates were 31.3% of eligible students for the pilot survey and 71.6% for the main study. Both studies showed that about 20% of students reported weekday sleep debts of greater than 2 h and about 28% reported significantly greater sleep debt and had significantly higher depression scores (P<0.0001) than other students. Melancholic symptoms indicated by high CES-D scores (>24), were observed in 24% of students. Sleep problems explained 13% of the variance for both the CESD scale and the HAM-D-3 scale. Among female college students, those who report a sleep debt of at least 2 h or significant daytime sleepiness have a higher risk of reporting melancholic symptoms than others.

Sample entropy tracks changes in electroencephalogram power spectrum with sleep state and aging

The regularity of electroencephalogram signals was compared between middle-aged (47.2 +/- 2.0 years) and elderly (78.4 +/- 3.8 years) female subjects in wake, nonrapid eye movement stages 2 and 3 (S-2, S-3), and rapid eye movement sleep. Signals from C3A2 leads of healthy subjects, acquired from polysomnograms obtained from the Sleep Heart Health Study, were analyzed using both sample entropy (SaEn) and power spectral analysis (delta, theta, alpha, and beta frequency band powers). SaEn changed systematically and significantly (P < 0.001) with sleep state in both age groups, following the relationships wake > rapid eye movement > S-2 > S-3. SaEn was found to be negatively correlated with delta power and positively correlated with beta power. Small changes in SaEn seem to reflect changes in spectral content rather than changes in regularity of the signal. A better predictor of SaEn than the frequency band powers was the logarithm of the power ratio (alpha + beta)/(delta + theta). Thus, SaEn seems to reflect the balance between sleep-promoting and alertness-promoting mechanisms. SaEn of the elderly was larger than that of middle-aged subjects in S-2 (P = 0.029) and rapid eye movement (P = 0.001), suggesting that cortical state is shifted toward alertness in elderly subjects in these sleep states compared with the middle-aged subjects.

Approaches to measuring the effects of wake-promoting drugs: a focus on cognitive function

OBJECTIVES

In clinical drug development, wakefulness and wake-promotion may be assessed by a large number of scales and questionnaires. Objective assessment of wakefulness is most commonly made using sleep latency/maintenance of wakefulness tests, polysomnography and/or behavioral measures. The purpose of the present review is to highlight the degree of overlap in the assessment of wakefulness and cognition, with consideration of assessment techniques and the underlying neurobiology of both concepts.

DESIGN

Reviews of four key areas were conducted: commonly used techniques in the assessment of wakefulness; neurobiology of sleep/wake and cognition; targets of wake promoting and/or cognition enhancing drugs; and ongoing clinical trials investigating wake promoting effects.

RESULTS

There is clear overlap between the assessment of wakefulness and cognition. There are common techniques which may be used to assess both concepts; aspects of the neurobiology of both concepts may be closely related; and wake-promoting drugs may have nootropic properties (and vice versa). Clinical trials of wake-promoting drugs often, though not routinely, assess aspects of cognition.

CONCLUSIONS

Routine and broad assessment of cognition in the development of wake-promoting drugs may reveal important nootropic effects, which are not secondary to alertness/wakefulness, whilst existing cognitive enhancers may have underexplored or unknown wake promoting properties.

A mathematical model of the sleep/wake cycle

We present a biologically-based mathematical model that accounts for several features of the human sleep/wake cycle. These features include the timing of sleep and wakefulness under normal and sleep-deprived conditions, ultradian rhythms, more frequent switching between sleep and wakefulness due to the loss of orexin and the circadian dependence of several sleep measures. The model demonstrates how these features depend on interactions between a circadian pacemaker and a sleep homeostat and provides a biological basis for the two-process model for sleep regulation. The model is based on previous "flip-flop" conceptual models for sleep/wake and REM/NREM and we explore whether the neuronal components in these flip-flop models, with the inclusion of a sleep-homeostatic process and the circadian pacemaker, are sufficient to account for the features of the sleep/wake cycle listed above. The model is minimal in the sense that, besides the sleep homeostat and constant cortical drives, the model includes only those nuclei described in the flip-flop models. Each of the cell groups is modeled by at most two differential equations for the evolution of the total population activity, and the synaptic connections are consistent with those described in the flip-flop models. A detailed analysis of the model leads to an understanding of the mathematical mechanisms, as well as insights into the biological mechanisms, underlying sleep/wake dynamics.

The time course of the probability of transition into and out of REM sleep

STUDY OBJECTIVES

A model of rapid eye movement (REM) sleep expression is proposed that assumes underlying regulatory mechanisms operating as inhomogenous Poisson processes, the overt results of which are the transitions into and out of REM sleep.

DESIGN

Based on spontaneously occurring REM sleep episodes ("Episode") and intervals without REM sleep ("Interval"), 3 variables are defined and evaluated over discrete 15-second epochs using a nonlinear logistic regression method: "Propensity" is the instantaneous rate of into-REM transition occurrence throughout an Interval, "Volatility" is the instantaneous rate of out-of-REM transition occurrence throughout an Episode, and "Opportunity" is the probability of being in non-REM (NREM) sleep at a given time throughout an Interval, a requisite for transition.

SETTING

12:12 light:dark cycle, isolated boxes.

PARTICIPANTS

Sixteen male Sprague-Dawley rats.

INTERVENTIONS

None. Spontaneous sleep cycles.

MEASUREMENTS AND RESULTS

The highest levels of volatility and propensity occur, respectively, at the very beginning of Episodes and Intervals. The new condition stabilizes rapidly, and variables reach nadirs at minute 1.25 and 2.50, respectively. Afterward, volatility increases markedly, reaching values close to the initial level. Propensity increases moderately, the increment being stronger through NREM sleep bouts occurring at the end of long Intervals. Short-term homeostasis is evidenced by longer REM sleep episodes lowering propensity in the following Interval.

CONCLUSIONS

The stabilization after transitions into Episodes or Intervals and the destabilization after remaining for some time in either condition may be described as resulting from continuous processes building up during Episodes and intervals. These processes underlie the overt occurrence of transitions.

Quantitative study of the sleep onset period via detrended fluctuation analysis: normal vs. narcoleptic subjects

OBJECTIVE

To examine the process of the sleep onset quantitatively and explore differences between narcoleptics and controls during the sleep onset period (SOP).

METHOD

Dynamic detrended fluctuation analysis (DFA) was applied to electroencephalograms recorded during multiple sleep latency tests of 11 drug-free narcoleptic patients (19.3+/-4.4 yrs; 8 males) and 9 healthy controls (23.8+/-6.3 yrs; 6 males). The SOP of each group was estimated by fitting the time courses of the DFA scaling exponents to a parametric curve.

RESULTS

The sequence of DFA exponents showed that electrophysiological brain activity was changing rapidly across the SOP. This transition was also verified by a conventional method (i.e., dynamic spectral analysis). The SOP durations of narcoleptics and controls were estimated as 239+/-25 s and 145+/-20 s, respectively.

CONCLUSIONS

The significantly larger SOP of narcoleptics, compared to controls, is consistent with the wake state of narcolepsy being more susceptible to sleep due to a lower barrier to transitioning to sleep.

SIGNIFICANCE

Our results suggest that electrophysiological signatures of narcolepsy could be quantified by dynamic DFA, so the method may have promise as a potential tool to help the diagnosis of narcolepsy despite the present study's limited sample size.

A therapeutic dose of zolpidem reduces thalamic GABA in healthy volunteers: a proton MRS study at 4 T

BACKGROUND

Zolpidem is a nonbenzodiazepine sedative/hypnotic that acts at GABA(A) receptors to influence inhibitory neurotransmission throughout the central nervous system. A great deal is known about the behavioral effects of this drug in humans and laboratory animals, but little is known about zolpidem's specific effects on neurochemistry in vivo.

OBJECTIVES

We evaluated how acute administration of zolpidem affected levels of GABA, glutamate, glutamine, and other brain metabolites.

MATERIALS AND METHODS

Proton magnetic resonance spectroscopy ((1)H MRS) at 4 T was employed to measure the effects of zolpidem on brain chemistry in 19 healthy volunteers. Participants underwent scanning following acute oral administration of a therapeutic dose of zolpidem (10 mg) in a within-subject, single-blind, placebo-controlled, single-visit study. In addition to neurochemical measurements from single voxels within the anterior cingulate (ACC) and thalamus, a series of questionnaires were administered periodically throughout the experimental session to assess subjective mood states.

RESULTS

Zolpidem reduced GABA levels in the thalamus, but not the ACC. There were no treatment effects with respect to other metabolite levels. Self-reported ratings of "dizzy," "nauseous," "confused," and "bad effects" were increased relative to placebo, as were ratings on the sedation/intoxication (PCAG) and psychotomimetic/dysphoria (LSD) scales of the Addiction Research Center Inventory. Moreover, there was a significant correlation between the decrease in GABA and "dizzy."

CONCLUSIONS

Zolpidem engendered primarily dysphoric-like effects and the correlation between reduced thalamic GABA and "dizzy" may be a function of zolpidem's interaction with alpha1GABA(A) receptors in the cerebellum, projecting through the vestibular system to the thalamus.

Late evening brain activation patterns and their relation to the internal biological time, melatonin, and homeostatic sleep debt

Sleep propensity increases sharply at night. Some evidence implicates the pineal hormone melatonin in this process. Using functional magnetic resonance imaging, brain activation during a visual search task was examined at 22:00 h (when endogenous melatonin levels normally increase). The relationships between brain activation, endogenous melatonin (measured in saliva), and self-reported fatigue were assessed. Finally, the effects of exogenous melatonin administered at 22:00 h were studied in a double blind, placebo-controlled crossover manner. We show that brain activation patterns as well as the response to exogenous melatonin significantly differ at night from those seen in afternoon hours. Thus, activation in the rostro-medial and lateral aspects of the occipital cortex and the thalamus diminished at 22:00 h. Activation in the right parietal cortex increased at night and correlated with individual fatigue levels, whereas exogenous melatonin given at 22:00 h reduced activation in this area. The right dorsolateral prefrontal cortex, an area considered to reflect homeostatic sleep debt, demonstrated increased activation at 22:00 h. Surprisingly, this increase correlated with endogenous melatonin. These results demonstrate and partially differentiate circadian effects (whether mediated by melatonin or not) and homeostatic sleep debt modulation of human brain activity associated with everyday fatigue at night.