Modeling the impact of impulsive stimuli on sleep-wake dynamics

Recruiting neural field theory for data augmentation in a motor imagery brain-computer interface

We introduce a novel approach to training data augmentation in brain-computer interfaces (BCIs) using neural field theory (NFT) applied to EEG data from motor imagery tasks. BCIs often suffer from limited accuracy due to a limited amount of training data. To address this, we leveraged a corticothalamic NFT model to generate artificial EEG time series as supplemental training data. We employed the BCI competition IV '2a' dataset to evaluate this augmentation technique. For each individual, we fitted the model to common spatial patterns of each motor imagery class, jittered the fitted parameters, and generated time series for data augmentation. Our method led to significant accuracy improvements of over 2% in classifying the "total power" feature, but not in the case of the "Higuchi fractal dimension" feature. This suggests that the fit NFT model may more favorably represent one feature than the other. These findings pave the way for further exploration of NFT-based data augmentation, highlighting the benefits of biophysically accurate artificial data.

Longitudinal Analysis of Sleep-Wake States in Neonatal Rats Subjected to Hypoxia-Ischemia

OBJECTIVE

Sleep is necessary for brain maturation in infants. Perinatal hypoxic-ischemic encephalopathy (HIE) is a major cause of chronic neurological disease in infants. Although the developmental changes of electroencephalogram (EEG) in human newborns have been described, little is known about the EEG normal maturation characteristics in rodents and the changes in sleep-awake states caused by hypoxia-ischemia (HI). This study aimed to investigate the pathological response of sleep-wake states in neonatal rats with HIE.

METHODS

We constructed HIE and sham models on postnatal day (P) 3 rats and continuously monitored them using electroencephalography and electromyography for up to P12. The distribution of sleep-wake states was analyzed to estimate the effects of HIE.

RESULTS

Compared with the sham group, the HI group showed lower rapid eye movement (REM) sleep percentage, but wake percentage and frequency was higher during P4-P12. The frequency of REM and non-rapid eye movement (NREM) sleep increased and the duration of REM and NREM sleep decreased after HI induction. However, it gradually returned to the normal level with an increase in daytime.

CONCLUSION

HI damage alters the sleep-wake patterns during early neural development. The findings provide a comprehensive assessment of serial sleep-wake state recordings in neonatal rats from P4-P12.

Sleep-wake regulation in preterm and term infants

Study Objectives

In adults, wakefulness can be markedly prolonged at the expense of sleep, e.g. to stay vigilant in the presence of a stressor. These extra-long wake bouts result in a heavy-tailed distribution (highly right-skewed) of wake but not sleep durations. In infants, the relative importance of wakefulness and sleep are reversed, as sleep is necessary for brain maturation. Here, we tested whether these developmental pressures are associated with the unique regulation of sleep–wake states.

Methods

In 175 infants of 28–40 weeks postmenstrual age (PMA), we monitored sleep–wake states using electroencephalography and behavior. We constructed survival models of sleep–wake bout durations and the effect of PMA and other factors, including stress (salivary cortisol), and examined whether sleep is resilient to nociceptive perturbations (a clinically necessary heel lance).

Results

Wake durations followed a heavy-tailed distribution as in adults and lengthened with PMA and stress. However, differently from adults, active sleep durations also had a heavy-tailed distribution, and with PMA, these shortened and became vulnerable to nociception-associated awakenings.

Conclusions

Sleep bouts are differently regulated in infants, with especially long active sleep durations that could consolidate this state’s maturational functions. Curtailment of sleep by stress and nociception may be disadvantageous, especially for preterm infants given the limited value of wakefulness at this age. This could be addressed by environmental interventions in the future.

Sleep-Wake Survival Dynamics in People with Insomnia

INTRODUCTION

Assessing objective measures of sleep fragmentation could yield important features reflecting impaired sleep quality in people with insomnia. Survival analysis allows the specific examination of the stability of NREM sleep, REM sleep and wake. The objective of this study was to assess the differences between survival dynamics of NREM sleep, REM sleep and wake between people with insomnia and healthy controls.

METHODS

We analyzed retrospective polysomnography recordings from 86 people with insomnia and 94 healthy controls. For each participant, survival dynamics of REM sleep, NREM sleep and wake were represented using Weibull distributions. We used lasso penalized parameter selection in combination with linear regression to analyze the difference between participant groups with respect to the Weibull scale and shape parameters, while correcting for age, sex, total sleep time and relevant interaction effects.

RESULTS

Significant effects of group were found for the NREM scale parameter, and for the wake scale and shape parameters. Results indicated that people with insomnia had less stable NREM sleep and more stable wake after sleep onset compared to healthy controls. Additionally, the altered distribution of wake segment lengths indicated an increased difficulty to fall asleep after longer awakenings in the insomnia group. However, these differences were mainly observed in younger participants. Significant effects of group for the survival parameters of REM sleep were not found.

CONCLUSION

As illustrated by our results, survival analysis can be very useful for disentangling different types of sleep fragmentation in people with insomnia. For instance, the current findings suggest that people with insomnia have an increased fragmentation of NREM sleep, but not necessarily of REM sleep. Additional research into the underlying mechanisms of NREM sleep fragmentation could possibly lead to a better understanding of impaired sleep quality in people with insomnia, and consequently to improved treatment.

Numerical study of entrainment of the human circadian system and recovery by light treatment

Background

While the effects of light as a zeitgeber are well known, the way the effects are modulated by features of the sleep-wake system still remains to be studied in detail.

Methods

A mathematical model for disturbance and recovery of the human circadian system is presented. The model combines a circadian oscillator and a sleep-wake switch that includes the effects of orexin. By means of simulations, we characterize the period-locking zone of the model, where a stable 24-hour circadian rhythm exists, and the occurrence of circadian disruption due to both insufficient light and imbalance in orexin. We also investigate how daily bright light treatments of short duration can recover the normal circadian rhythm.

Results

It is found that the system exhibits continuous phase advance/delay at lower/higher orexin levels. Bright light treatment simulations disclose two optimal time windows, corresponding to morning and evening light treatments. Among the two, the morning light treatment is found effective in a wider range of parameter values, with shorter recovery time.

Conclusions

This approach offers a systematic way to determine the conditions under which circadian disruption occurs, and to evaluate the effects of light treatment. In particular, it could potentially offer a way to optimize light treatments for patients with circadian disruption, e.g., sleep and mood disorders, in clinical settings.

The psycho-sensory wake drive-a power source for power naps and other common sleep-wake phenomena: a hypothesis

Power naps are extensively practiced worldwide and there exists ample documentation of their efficacy in reversing daytime sleepiness. The source of their efficacy, however, as well as the cause and manifestation of many other common sleep-wake phenomena, cannot be entirely explained by the most commonly accepted model of sleep-wake regulation, the two-process model of Borbély, which considers the drives of the circadian and homeostatic sleep processes only. When considering the causes and manifestations of these unexplained phenomena, there appears to be evidence of a wake-promoting drive that is independent of the circadian oscillator indicated in the two-process model of sleep-wake regulation. Although this posited secondary wake drive, herein referred to as the psycho-sensory wake drive, is always active during the awake state, its strength unpredictably varies during a normal day and, therefore, cannot be incorporated into the prevalent two-process model by any current mathematical formula. However, a supplemental graphic model superimposing it on the drives of Process S and Process C can provides plausible and parsimonious explanations for many otherwise unexplainable sleep-wake phenomena and enables rational guidelines for their effective practical management.

The effects of self-selected light-dark cycles and social constraints on human sleep and circadian timing: a modeling approach

Why do we go to sleep late and struggle to wake up on time? Historically, light-dark cycles were dictated by the solar day, but now humans can extend light exposure by switching on artificial lights. We use a mathematical model incorporating effects of light, circadian rhythmicity and sleep homeostasis to provide a quantitative theoretical framework to understand effects of modern patterns of light consumption on the human circadian system. The model shows that without artificial light humans wakeup at dawn. Artificial light delays circadian rhythmicity and preferred sleep timing and compromises synchronisation to the solar day when wake-times are not enforced. When wake-times are enforced by social constraints, such as work or school, artificial light induces a mismatch between sleep timing and circadian rhythmicity ('social jet-lag'). The model implies that developmental changes in sleep homeostasis and circadian amplitude make adolescents particularly sensitive to effects of light consumption. The model predicts that ameliorating social jet-lag is more effectively achieved by reducing evening light consumption than by delaying social constraints, particularly in individuals with slow circadian clocks or when imposed wake-times occur after sunrise. These theory-informed predictions may aid design of interventions to prevent and treat circadian rhythm-sleep disorders and social jet-lag.

A mathematical model of ultradian sleep-wake regulation in rodents

Rodent models are widely used for the experimental analysis of sleep. While this is motivated by similarities in brain circuitry and electrophysiological rhythms, unlike the circadian sleep-wake cycle in humans, rodent sleep is polyphasic, containing multiple bouts of sleep and wake minutes to hours in duration over the course of a day. Each sleep bout is punctuated by several brief arousals several seconds to minutes long. Physiologically motivated mathematical models replicate the shorter timescale of arousal within sleep, but not the longer one representing prolonged wakefulness. Here, we adapt a previously published "flip-flop" model of human sleep to capture the ultradian alternation of sleep and wakefulness in mice on the longer timescale. The resulting model reproduces both the mean durations of alternating sleep and wake bouts as well as the circadian trends in their bout durations documented in our experiments on mice.

Wake-sleep transition as a noisy bifurcation

A recent physiologically based model of the ascending arousal system is used to analyze the dynamics near the transition from wake to sleep, which corresponds to a saddle-node bifurcation at a critical point. A normal form is derived by approximating the dynamics by those of a particle in a parabolic potential well with dissipation. This mechanical analog is used to calculate the power spectrum of fluctuations in response to a white noise drive, and the scalings of fluctuation variance and spectral width are derived versus distance from the critical point. The predicted scalings are quantitatively confirmed by numerical simulations, which show that the variance increases and the spectrum undergoes critical slowing, both in accord with theory. These signals can thus serve as potential precursors to indicate imminent wake-sleep transition, with potential application to safety-critical occupations in transport, air-traffic control, medicine, and heavy industry.

Unsupervised Estimation of Mouse Sleep Scores and Dynamics Using a Graphical Model of Electrophysiological Measurements

The proportion, number of bouts, and mean bout duration of different vigilance states (Wake, NREM, REM) are useful indices of dynamics in experimental sleep research. These metrics are estimated by first scoring state, sometimes using an algorithm, based on electrophysiological measurements such as the electroencephalogram (EEG) and electromyogram (EMG), and computing their values from the score sequence. Isolated errors in the scores can lead to large discrepancies in the estimated sleep metrics. But most algorithms score sleep by classifying the state from EEG/EMG features independently in each time epoch without considering the dynamics across epochs, which could provide contextual information. The objective here is to improve estimation of sleep metrics by fitting a probabilistic dynamical model to mouse EEG/EMG data and then predicting the metrics from the model parameters. Hidden Markov models (HMMs) with multivariate Gaussian observations and Markov state transitions were fitted to unlabeled 24-h EEG/EMG feature time series from 20 mice to model transitions between the latent vigilance states; a similar model with unbiased transition probabilities served as a reference. Sleep metrics predicted from the HMM parameters did not deviate significantly from manual estimates except for rapid eye movement sleep (REM) ([Formula: see text]; Wilcoxon signed-rank test). Changes in value from Light to Dark conditions correlated well with manually estimated differences (Spearman’s rho 0.43–0.84) except for REM. HMMs also scored vigilance state with over 90% accuracy. HMMs of EEG/EMG features can therefore characterize sleep dynamics from EEG/EMG measurements, a prerequisite for characterizing the effects of perturbation in sleep monitoring and control applications.

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.

Night-Time Noise Index Based on the Integration of Awakening Potential

Sleep disturbance induced by night-time noise is a serious environmental problem that can cause adverse health effects, such as hypertension and ischemic heart disease. Night-time noise indices are used to facilitate the enforcement of permitted noise levels during night-time. However, existing night-time noise indices, such as sound exposure level (SEL), maximum sound level (LA max) and night equivalent level (L night) are selected mainly because of practical reasons. Therefore, this study proposes a noise index based on neurophysiological determinants of the awakening process. These determinants have revealed that the potential on awakening is likely integrated into the brainstem that dominates wakefulness and sleep. From this evidence, a night-time noise index, N awake,year, was redefined based on the integration of the awakening potential unit (p unit) estimated from the existing dose-response relationships of awakening. The newly-defined index considers the total number of awakenings and covers a wide-range and number of noise events. We also presented examples of its applicability to traffic noise. Although further studies are needed, it may reveal a reasonable dose-response relationship between sleep disturbance and adverse health effects and provide a consistent explanation for the risks of different sound sources where the characteristics of noise exposure are quite different.

A review of the methods for neuronal response latency estimation

Neuronal response latency is usually vaguely defined as the delay between the stimulus onset and the beginning of the response. It contains important information for the understanding of the temporal code. For this reason, the detection of the response latency has been extensively studied in the last twenty years, yielding different estimation methods. They can be divided into two classes, one of them including methods based on detecting an intensity change in the firing rate profile after the stimulus onset and the other containing methods based on detection of spikes evoked by the stimulation using interspike intervals and spike times. The aim of this paper is to present a review of the main techniques proposed in both classes, highlighting their advantages and shortcomings.

Mathematical models for sleep-wake dynamics: comparison of the two-process model and a mutual inhibition neuronal model

Sleep is essential for the maintenance of the brain and the body, yet many features of sleep are poorly understood and mathematical models are an important tool for probing proposed biological mechanisms. The most well-known mathematical model of sleep regulation, the two-process model, models the sleep-wake cycle by two oscillators: a circadian oscillator and a homeostatic oscillator. An alternative, more recent, model considers the mutual inhibition of sleep promoting neurons and the ascending arousal system regulated by homeostatic and circadian processes. Here we show there are fundamental similarities between these two models. The implications are illustrated with two important sleep-wake phenomena. Firstly, we show that in the two-process model, transitions between different numbers of daily sleep episodes can be classified as grazing bifurcations. This provides the theoretical underpinning for numerical results showing that the sleep patterns of many mammals can be explained by the mutual inhibition model. Secondly, we show that when sleep deprivation disrupts the sleep-wake cycle, ostensibly different measures of sleepiness in the two models are closely related. The demonstration of the mathematical similarities of the two models is valuable because not only does it allow some features of the two-process model to be interpreted physiologically but it also means that knowledge gained from study of the two-process model can be used to inform understanding of the behaviour of the mutual inhibition model. This is important because the mutual inhibition model and its extensions are increasingly being used as a tool to understand a diverse range of sleep-wake phenomena such as the design of optimal shift-patterns, yet the values it uses for parameters associated with the circadian and homeostatic processes are very different from those that have been experimentally measured in the context of the two-process model.

A physiologically based model of orexinergic stabilization of sleep and wake

The orexinergic neurons of the lateral hypothalamus (Orx) are essential for regulating sleep-wake dynamics, and their loss causes narcolepsy, a disorder characterized by severe instability of sleep and wake states. However, the mechanisms through which Orx stabilize sleep and wake are not well understood. In this work, an explanation of the stabilizing effects of Orx is presented using a quantitative model of important physiological connections between Orx and the sleep-wake switch. In addition to Orx and the sleep-wake switch, which is composed of mutually inhibitory wake-active monoaminergic neurons in brainstem and hypothalamus (MA) and the sleep-active ventrolateral preoptic neurons of the hypothalamus (VLPO), the model also includes the circadian and homeostatic sleep drives. It is shown that Orx stabilizes prolonged waking episodes via its excitatory input to MA and by relaying a circadian input to MA, thus sustaining MA firing activity during the circadian day. During sleep, both Orx and MA are inhibited by the VLPO, and the subsequent reduction in Orx input to the MA indirectly stabilizes sustained sleep episodes. Simulating a loss of Orx, the model produces dynamics resembling narcolepsy, including frequent transitions between states, reduced waking arousal levels, and a normal daily amount of total sleep. The model predicts a change in sleep timing with differences in orexin levels, with higher orexin levels delaying the normal sleep episode, suggesting that individual differences in Orx signaling may contribute to chronotype. Dynamics resembling sleep inertia also emerge from the model as a gradual sleep-to-wake transition on a timescale that varies with that of Orx dynamics. The quantitative, physiologically based model developed in this work thus provides a new explanation of how Orx stabilizes prolonged episodes of sleep and wake, and makes a range of experimentally testable predictions, including a role for Orx in chronotype and sleep inertia.

Physiologically-based modeling of sleep-wake regulatory networks

Mathematical modeling has played a significant role in building our understanding of sleep-wake and circadian behavior. Over the past 40 years, phenomenological models, including the two-process model and oscillator models, helped frame experimental results and guide progress in understanding the interaction of homeostatic and circadian influences on sleep and understanding the generation of rapid eye movement sleep cycling. Recent advances in the clarification of the neural anatomy and physiology involved in the regulation of sleep and circadian rhythms have motivated the development of more detailed and physiologically-based mathematical models that extend the approach introduced by the classical reciprocal-interaction model. Using mathematical formalisms developed in the field of computational neuroscience to model neuronal population activity, these models investigate the dynamics of proposed conceptual models of sleep-wake regulatory networks with a focus on generating appropriate sleep and wake state transition patterns as well as simulating disease states and experimental protocols. In this review, we discuss several recent physiologically-based mathematical models of sleep-wake regulatory networks. We identify common features among these models in their network structures, model dynamics and approaches for model validation. We describe how the model analysis technique of fast-slow decomposition, which exploits the naturally occurring multiple timescales of sleep-wake behavior, can be applied to understand model dynamics in these networks. Our purpose in identifying commonalities among these models is to propel understanding of both the mathematical models and their underlying conceptual models, and focus directions for future experimental and theoretical work.

Identification of noisy response latency

In many physical systems there is a time delay before an applied input (stimulation) has an impact on the output (response), and the quantification of this delay is of paramount interest. If the response can only be observed on top of an indistinguishable background signal, the estimation can be highly unreliable, unless the background signal is accounted for in the analysis. In fact, if the background signal is ignored, however small it is compared to the response and however large the delay is, the estimate of the time delay will go to zero for any reasonable estimator when increasing the number of observations. Here we propose a unified concept of response latency identification in event data corrupted by a background signal. It is done in the context of information transfer within a neural system, more specifically on spike trains from single neurons. The estimators are compared on simulated data and the most suitable for specific situations are recommended.

Revisiting spontaneous internal desynchrony using a quantitative model of sleep physiology

Early attempts to characterize free-running human circadian rhythms generated three notable results: 1) observed circadian periods of 25 hours (considerably longer than the now established 24.1- to 24.2-hour average intrinsic circadian period) with sleep delayed to later circadian phases than during entrainment; 2) spontaneous internal desynchrony of circadian rhythms and sleep/wake cycles--the former with an approximately 24.9-hour period, and the latter with a longer (28-68 hour) or shorter (12-20 hour) period; and 3) bicircadian (48-50 hour) sleep/wake cycles. All three results are reproduced by Kronauer et al.'s (1982) coupled oscillator model, but the physiological basis for that phenomenological model is unclear. We use a physiologically based model of hypothalamic and brain stem nuclei to investigate alternative physiological mechanisms that could underlie internal desynchrony. We demonstrate that experimental observations can be reproduced by changes in two pathways: promotion of orexinergic (Orx) wake signals, and attenuation of the circadian signal reaching hypothalamic nuclei. We reason that delayed sleep is indicative of an additional wake-promoting drive, which may be of behavioral origin, associated with removal of daily schedules and instructions given to participants. We model this by increasing Orx tone during wake, which reproduces the observed period lengthening and delayed sleep. Weakening circadian input to the ventrolateral preoptic nucleus (possibly mediated by the dorsomedial hypothalamus) causes desynchrony, with observed sleep/wake cycle period determined by degree of Orx up-regulation. During desynchrony, sleep/wake cycles are driven by sleep homeostasis, yet sleep bout length maintains circadian phase dependence. The model predicts sleep episodes are shortest when started near the temperature minimum, consistent with experimental findings. The model also correctly predicts that it is possible to transition to bicircadian rhythms from either a synchronized or desynchronized state. Our findings suggest that feedback from behavioral choices to physiology could play an important role in spontaneous internal desynchrony.

Quantitative modelling of sleep dynamics

Arousal is largely controlled by the ascending arousal system of the hypothalamus and brainstem, which projects to the corticothalamic system responsible for electroencephalographic (EEG) signatures of sleep. Quantitative physiologically based modelling of brainstem dynamics theory is described here, using realistic parameters, and links to EEG are outlined. Verification against a wide range of experimental data is described, including arousal dynamics under normal conditions, sleep deprivation, stimuli, stimulants and jetlag, plus key features of wake and sleep EEGs.

Sleep state switching

We take for granted the ability to fall asleep or to snap out of sleep into wakefulness, but these changes in behavioral state require specific switching mechanisms in the brain that allow well-defined state transitions. In this review, we examine the basic circuitry underlying the regulation of sleep and wakefulness and discuss a theoretical framework wherein the interactions between reciprocal neuronal circuits enable relatively rapid and complete state transitions. We also review how homeostatic, circadian, and allostatic drives help regulate sleep state switching and discuss how breakdown of the switching mechanism may contribute to sleep disorders such as narcolepsy.

Loss of Goosecoid-like and DiGeorge syndrome critical region 14 in interpeduncular nucleus results in altered regulation of rapid eye movement sleep

Sleep and wakefulness are regulated primarily by inhibitory interactions between the hypothalamus and brainstem. The expression of the states of rapid eye movement (REM) sleep and non-REM (NREM) sleep also are correlated with the activity of groups of REM-off and REM-on neurons in the dorsal brainstem. However, the contribution of ventral brainstem nuclei to sleep regulation has been little characterized to date. Here we examined sleep and wakefulness in mice deficient in a homeobox transcription factor, Goosecoid-like (Gscl), which is one of the genes deleted in DiGeorge syndrome or 22q11 deletion syndrome. The expression of Gscl is restricted to the interpeduncular nucleus (IP) in the ventral region of the midbrain-hindbrain transition. The IP has reciprocal connections with several cell groups implicated in sleep/wakefulness regulation. Although Gscl(-/-) mice have apparently normal anatomy and connections of the IP, they exhibited a reduced total time spent in REM sleep and fewer REM sleep episodes. In addition, Gscl(-/-) mice showed reduced theta power during REM sleep and increased arousability during REM sleep. Gscl(-/-) mice also lacked the expression of DiGeorge syndrome critical region 14 (Dgcr14) in the IP. These results indicate that the absence of Gscl and Dgcr14 in the IP results in altered regulation of REM sleep.

Quantitative physiologically based modeling of subjective fatigue during sleep deprivation

A quantitative physiologically based model of the sleep-wake switch is used to predict variations in subjective fatigue-related measures during total sleep deprivation. The model includes the mutual inhibition of the sleep-active neurons in the hypothalamic ventrolateral preoptic area (VLPO) and the wake-active monoaminergic brainstem populations (MA), as well as circadian and homeostatic drives. We simulate sleep deprivation by introducing a drive to the MA, which we call wake effort, to maintain the system in a wakeful state. Physiologically this drive is proposed to be afferent from the cortex or the orexin group of the lateral hypothalamus. It is hypothesized that the need to exert this effort to maintain wakefulness at high homeostatic sleep pressure correlates with subjective fatigue levels. The model's output indeed exhibits good agreement with existing clinical time series of subjective fatigue-related measures, supporting this hypothesis. Subjective fatigue, adrenaline, and body temperature variations during two 72h sleep deprivation protocols are reproduced by the model. By distinguishing a motivation-dependent orexinergic contribution to the wake-effort drive, the model can be extended to interpret variation in performance levels during sleep deprivation in a way that is qualitatively consistent with existing, clinically derived results. The example of sleep deprivation thus demonstrates the ability of physiologically based sleep modeling to predict psychological measures from the underlying physiological interactions that produce them.

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.