Sleep Onset Process Research Articles

Spatiotemporal EEG dynamics of the sleep onset process in preadolescence

BackgroundDuring preadolescence the sleep electroencephalography undergoes massive qualitative and quantitative modifications. Despite these relevant age-related peculiarities, the specific EEG pattern of the wake-sleep transition in preadolescence has not been exhaustively described. MethodsThe aim of the present study is to characterize regional and temporal electrophysiological features of the sleep onset (SO) process in a group of 23 preadolescents (9–14 years) and to compare the topographical pattern of slow wave activity and delta/beta ratio of preadolescents with the EEG pattern of young adults. ResultsResults showed in preadolescence the same dynamics known for adults, but with peculiarities in the delta and beta activity, likely associated with developmental cerebral modifications: the delta power showed a widespread increase during the SO with central maxima, and the lower bins of the beta activity showed a power increase after SO. Compared to adults, preadolescents during the SO exhibited higher delta power only in the slowest bins of the band: before SO slow delta activity was higher in prefrontal, frontal and occipital areas in preadolescents, and, after SO the younger group had higher slow delta activity in occipital areas. In preadolescents delta/beta ratio was higher in more posterior areas both before and after the wake-sleep transition and, after SO, preadolescents showed also a lower delta/beta ratio in frontal areas, compared to adults. ConclusionResults point to a general higher homeostatic drive for the developing areas, consistently with plastic-related maturational modifications, that physiologically occur during preadolescence.

The neurophysiologic landscape of the sleep onset: a systematic review.

The sleep onset process is an ill-defined complex process of transition from wakefulness to sleep, characterized by progressive modifications at the subjective, behavioural, cognitive, and physiological levels. To this date, there is no international consensus which could aid a principled characterisation of this process for clinical research purposes. The current review aims to systemise the current knowledge about the underlying mechanisms of the natural heterogeneity of this process. In this systematic review, studies investigating the process of the sleep onset from 1970 to 2022 were identified using electronic database searches of PsychINFO, MEDLINE, and Embase. A total of 139 studies were included; 110 studies in healthy participants and 29 studies in participants with sleep disorders. Overall, there is a limited consensus across a body of research about what distinct biomarkers of the sleep onset constitute. Only sparse data exists on the physiology, neurophysiology and behavioural mechanisms of the sleep onset, with majority of studies concentrating on the non-rapid eye movement stage 2 (NREM 2) as a potentially better defined and a more reliable time point that separates sleep from the wake, on the sleep wake continuum. The neurophysiologic landscape of sleep onset bears a complex pattern associated with a multitude of behavioural and physiological markers and remains poorly understood. The methodological variation and a heterogenous definition of the wake-sleep transition in various studies to date is understandable, given that sleep onset is a process that has fluctuating and ill-defined boundaries. Nonetheless, the principled characterisation of the sleep onset process is needed which will allow for a greater conceptualisation of the mechanisms underlying this process, further influencing the efficacy of current treatments for sleep disorders.

Technology use as a sleep-onset aid: are adolescents using apps to distract themselves from negative thoughts?

The aim of this study was to; (1) explore whether adolescents use technology as distraction from negative thoughts before sleep, (2) assess whether adolescents who perceive having a sleep problem use technology as distraction more compared to adolescents without sleep complaints, and (3) collect qualitative information about which devices and apps adolescents use as a distraction. This study used a mixed-methods cross-sectional design, where 684 adolescents (M = 15.1, SD = 1.2, 46% female) answered both quantitative and qualitative questions about their sleep (perceived sleep problem, sleep onset time (SOT), and sleep onset latency [SOL]) and technology use as distraction from negative thoughts. The majority of adolescents answered "yes" or "sometimes" using technology as a distraction from negative thoughts (23.6% and 38.4%). Adolescents who answered "yes" to using technology as distraction were more likely to report having a sleep problem, longer SOL, and later SOT, compared to adolescents who answered "no". The most popular device to distract was the phone, because of its availability, and the most common apps used for distraction included YouTube, Snapchat, and music apps. This study shows that many adolescents use technology to distract themselves from negative thoughts, which may help them manage the sleep-onset process. Thus, distraction may be one mechanism explaining how sleep affects technology use, rather than vice versa.

Do people use methods or tricks to fall asleep? A comparison between people with and without chronic insomnia.

The present study estimated the frequency of using methods or tricks to fall asleep in the general Norwegian population. Further, people with chronic insomnia were compared with people without chronic insomnia. A representative sample of 1028 participants aged 18 years or older completed a web-based survey. The response rate was 33.5%. Insomnia symptoms were assessed with the validated Bergen Insomnia Scale, and chronic insomnia based on ICSD-3/DSM-5 criteria. Data were analysed with chi-square tests and logistic regression with adjustment for sex, age, education, and circadian preference. The results showed that 34.3% reported using a method or a trick to fall asleep, with relaxation exercises/breathing exercises being the most common. More females (39.5%) compared with males (29.1%) reported the use of a method/trick to fall asleep with an adjusted odds ratio (aOR) of 1.44. Chronic insomnia was reported by 24.9%, and clearly associated with higher use of such methods/tricks (53.7%; aOR=3.49). Among the participants without chronic insomnia, 28.1% reported using methods/tricks to fall asleep. In conclusion, most people do not use methods or tricks to fall asleep, but chronic insomnia was associated with a higher frequency of such use. Still, since methods/tricks were also used by some participants without chronic insomnia, this may suggest that, for some people, this strategy may be effective or at least does not seem to disrupt the sleep onset process.

Changes during the sleep onset process on EEG activity and the components of attention.

The sleep onset process (SOP) happens every time a person falls asleep, regardless of the time of day or if they are doing an activity. Basic cognitive processes, such as attention, differ between wakefulness and sleep. The components of attention - tonic alertness, phasic alertness, selective, and sustained attention - are known to decrease during sleep, however they have not been analyzed during the sleep onset process. This study analyses the state of three of the four components of attention during the sleep onset process through electroencephalographic (EEG) activity and task performance in young people. Nine undergraduate students (18.54±1.24 years old) underwent a control session which was compared to the average of four sleep-inducing sessions. During all sessions, the EEG activity of the subjects was recorded to assess the effect of the SOP on electroencephalographic activity while they answered a continuous performance task (CPT) to assess the effect of the SOP on the components of attention. Comparisons of the EEG recordings of the control and the sleep inducing sessions demonstrated that there is lower activity in fast beta, as well as a higher theta and delta activity right before the sleep onset. There was a decrease in tonic alertness, phasic alertness, and selective attention. This study shows that there is an increase in EEG slow activity and a decrease in fast activity, as well as in attentional capacity during the SOP. This decrease can become a safety hazard since it could happen while performing daily activities.

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.

Effects of the Effort to Fall Asleep on the Sleep Onset Process

Effects of the Effort to Fall Asleep on the Sleep Onset Process

Low Frequency Transcranial Alternating Current Stimulation Accelerates Sleep Onset Process.

The aim of this study is to find a kind of low frequency oscillation transcranial alternating current stimulation, which is directly applied to the scalp epidermal, to stimulate the cerebral cortex with a large spatial range of electric field oscillation across the brain hemisphere, and then trigger the start of the Top-Down processing of sleep homeostasis, in the daytime nap. Thirty healthy subjects, to take naps, underwent an intervention of electrical stimulation at 5 Hz, applied to the dorsal lateral prefrontal cortex. The subjects in the experiments were strictly controlled, and opened their eyes when stimulation was transmitted. Subsequently, after 15 min transcranial alternating current stimulation, subjects entered the experimental procedure of sleep. Electroencephalograph was taken at baseline and during sleep. Behavioral indicators were also added to the experiment. We found that the total power of Electroencephalograph activity in the theta band, as well as low-frequency power at 1-7 Hz, was significantly entrained and increased, and that alpha activity was attenuated faster and spindle activity active earlier. Even more, the transition from awake to Non-rapid eye movement stages occurs earlier. Alertness also decreased when the subjects woke up after brief sleep. The intervention of low frequency brain rhythmic transcranial alternating current stimulation may induce accelerated effect on sleep onset process, thereby possibly alleviating the problems related to sleep disorders such as difficulty to reach the real sleep state quickly after lying down.

The effects of sleep effort on sleep onset process

The effects of sleep effort on sleep onset process

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

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

The Assessment of ‘Sleepiness’ in Human Drug Trials: A New Perspective

The investigation of the efficacy and safety of drugs requires assessments of their effects on alertness/sleepiness. Unfortunately, there is confusion about the nature of ‘sleepiness’, the factors which influence it, and how it can be measured under different circumstances. This review aims to clarify these matters and to offer some suggestions about how current difficulties might be overcome. Different meanings of the word ‘sleepiness’ are examined initially. Methods that purport to measure ‘sleepiness’ are then examined, including their testretest reliability and the relationship between the results of different measurements within the same subjects. Some objective methods are found not to be as reliable as was initially reported. Information about the reliability of several other methods is either inadequate or nonexistent. One assumption which underlies two frequently used objective methods for measuring ‘sleepiness’ (the Multiple Sleep Latency Test and the Maintenance of Wakefulness Test) is that the ‘sleepier’ a person is, the quicker they will fall asleep. While this assumption has face validity, other assumptions about these tests are re-examined and are found wanting, at least sometimes. The difficulty arises in part because it is not always clear when the sleep onset process begins and ends. ‘Sleepiness’ is found to be influenced much more by short-term factors, such as the subject’s posture at the time and during the preceding few minutes, than has been acknowledged previously. Some possible solutions to these difficulties are suggested, including a new conceptual model of sleep-wake control, with implications for the design of drug trials.

Sparse Multi-task Inverse Covariance Estimation for Connectivity Analysis in EEG Source Space.

Understanding how different brain areas interact to generate complex behavior is a primary goal of neuroscience research. One approach, functional connectivity analysis, aims to characterize the connectivity patterns within brain networks. In this paper, we address the problem of discriminative connectivity, i.e. determining the differences in network structure under different experimental conditions. We introduce a novel model called Sparse Multi-task Inverse Covariance Estimation (SMICE) which is capable of estimating a common connectivity network as well as discriminative networks across different tasks. We apply the method to EEG signals after solving the inverse problem of source localization, yielding networks defined on the cortical surface. We propose an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve SMICE. We apply our newly developed framework to find common and discriminative connectivity patterns for α-oscillations during the Sleep Onset Process (SOP) and during Rapid Eye Movement (REM) sleep. Even though both stages exhibit a similar α-oscillations, we show that the underlying networks are distinct.

Tracking wakefulness as it fades: Micro-measures of alertness

A major problem in psychology and physiology experiments is drowsiness: around a third of participants show decreased wakefulness despite being instructed to stay alert. In some non-visual experiments participants keep their eyes closed throughout the task, thus promoting the occurrence of such periods of varying alertness. These wakefulness changes contribute to systematic noise in data and measures of interest. To account for this omnipresent problem in data acquisition we defined criteria and code to allow researchers to detect and control for varying alertness in electroencephalography (EEG) experiments under eyes-closed settings. We first revise a visual-scoring method developed for detection and characterization of the sleep-onset process, and adapt the same for detection of alertness levels. Furthermore, we show the major issues preventing the practical use of this method, and overcome these issues by developing an automated method (micro-measures algorithm) based on frequency and sleep graphoelements, which are capable of detecting micro variations in alertness. The validity of the micro-measures algorithm was verified by training and testing using a dataset where participants are known to fall asleep. In addition, we tested generalisability by independent validation on another dataset. The methods developed constitute a unique tool to assess micro variations in levels of alertness and control trial-by-trial retrospectively or prospectively in every experiment performed with EEG in cognitive neuroscience under eyes-closed settings.

Psychophysiological Study of Effect of Emotion in Sleep Onset Process by Using Hypnagogic Imagery

Psychophysiological Study of Effect of Emotion in Sleep Onset Process by Using Hypnagogic Imagery

Comparative analysis of the effect of stimulation with a binaural beat and similar kinds of sounds on the falling asleep process: A brief note

Beats are the physical phenomenon appearing when two oscillation processes of close frequencies are superimposed. In acoustics, there is also the concept of binaural beats, a subjective feeling of the listener when acoustic tones of slightly different frequencies are applied separately to each ear. Commercial products based on the effect of binaural beats enjoy steady popularity in the market of the modern technological tools for psycho- and physiotherapy. In particular, they are applied to improve sleep. But it is the objective evaluation of the physiologic effect of binaural beats on the sleep onset process that has very little evidence for support. This paper provides comparative analysis of the time to fall asleep determined by the onset of the second daytime sleep stage (sleep spindle appearance). The subjects listened to a monotonous sound of three similar kinds: a combination of binaural beats with pink noise, a similar sound with a combination of monaural beats, and a similar sound without any beat. Stimulation by the combination of binaural beats is shown to produce the least sleep onset time as compared to the similar sound containing monaural beats and to the similar sound without beats. Further investigation is required to obtain results that are more consistent.

Epileptic hypnagogic jerks mimicking repetitive sleep starts

According to the International Classification of Sleep Disorders – Third Edition (ICSD-3), sleep starts, also called hypnic jerks, are classified as Sleep RelatedMovement Disorders and are listed among Isolated Symptoms and Normal Variants [1]. They are characterized by sudden, brief, nonperiodic contractions of one or more body segments, usually involving the trunk and all extremities simultaneously, representing a physiological and almost universal component of the sleep-onset process [2]. Sleep starts usually occur in isolation, in a spontaneous form or evoked by sensory stimuli. Although the differential diagnosis between sleep-related movement disorders and epilepsy has been thoroughly discussed [2], semiology overlaps, scant electroencephalography (EEG) correlates, and possible coexistence still pose diagnostic challenges. This is more so in pediatric neurology, given the peak incidence of nonrapid eyemovement sleep disorders during childhood. The following video-report illustrates the clinical and neurophysiologic characteristics of repetitive brief epileptic hypnagogic jerks, mimicking nonepileptic repetitive sleep starts.

Dreaming and the default network: A review, synthesis, and counterintuitive research proposal.

This article argues that the default network, augmented by secondary visual and sensorimotor cortices, is the likely neural correlate of dreaming. This hypothesis is based on a synthesis of work on dream content, the findings on the contents and neural correlates of mind-wandering, and the results from EEG and neuroimaging studies of REM sleep. Relying on studies in the 1970s that serendipitously discovered episodes of dreaming during waking mind-wandering, this article presents the seemingly counterintuitive hypothesis that the neural correlates for dreaming could be further specified in the process of carrying out EEG/fMRI studies of mind-wandering and default network activity. This hypothesis could be tested by asking participants for experiential reports during moments of differentially high levels of default network activation, as indicated by mixed EEG/fMRI criteria. Evidence from earlier EEG/fMRI studies of mind-wandering and from laboratory studies of dreaming during the sleep-onset process is used to support the argument.

Tracking the sleep onset process: an empirical model of behavioral and physiological dynamics.

The sleep onset process (SOP) is a dynamic process correlated with a multitude of behavioral and physiological markers. A principled analysis of the SOP can serve as a foundation for answering questions of fundamental importance in basic neuroscience and sleep medicine. Unfortunately, current methods for analyzing the SOP fail to account for the overwhelming evidence that the wake/sleep transition is governed by continuous, dynamic physiological processes. Instead, current practices coarsely discretize sleep both in terms of state, where it is viewed as a binary (wake or sleep) process, and in time, where it is viewed as a single time point derived from subjectively scored stages in 30-second epochs, effectively eliminating SOP dynamics from the analysis. These methods also fail to integrate information from both behavioral and physiological data. It is thus imperative to resolve the mismatch between the physiological evidence and analysis methodologies. In this paper, we develop a statistically and physiologically principled dynamic framework and empirical SOP model, combining simultaneously-recorded physiological measurements with behavioral data from a novel breathing task requiring no arousing external sensory stimuli. We fit the model using data from healthy subjects, and estimate the instantaneous probability that a subject is awake during the SOP. The model successfully tracked physiological and behavioral dynamics for individual nights, and significantly outperformed the instantaneous transition models implicit in clinical definitions of sleep onset. Our framework also provides a principled means for cross-subject data alignment as a function of wake probability, allowing us to characterize and compare SOP dynamics across different populations. This analysis enabled us to quantitatively compare the EEG of subjects showing reduced alpha power with the remaining subjects at identical response probabilities. Thus, by incorporating both physiological and behavioral dynamics into our model framework, the dynamics of our analyses can finally match those observed during the SOP.

Hormones, hormonal agents, and neuropeptides involved in the neuroendocrine regulation of sleep in humans

Sleep is an essential ubiquitous biological process, a periodical state of quiescence in which there is minimal processing of sensory information and no interaction with conspecifics or the environment. Despite relevant research on sleep structure and testing of numerous endogenous sleep-affecting chemicals, questions as to the precise mechanisms and functions of sleep remain without satisfactory responses. The purpose of this review is to report on current evidence as regards the effect of several endogenous and exogenous hormones, hormonal agents, and neuropeptides on sleep onset or wake process, when administered in humans in specific doses and via different routes. The actions of several peptides are presented in detail. Some of them (growth hormone releasing hormone, ghrelin, galanin, neuropeptide Y) seem to promote sleep, whereas others (corticotropin, somatostatin) impair its continuity.

Laterality of the sleep onset process: Which hemisphere goes to sleep first?

This study aims to assess whether the hemispheric asymmetry inversion observed in the wake-sleep transition can also be revealed by the latency of inter tapping intervals ≥2.5 s for each hand and the latency of theta burst ≥2.5 s in symmetrical loci of the two hemispheres during the sleep onset process. Data collected from 16 right-handed subjects showed a hemispheric asymmetry in the sleep onset latency with both behavioural and EEG indices. For the first time, a hemispheric asymmetry in the sleep onset latency was found considering a visual analysis of EEG. Results suggest that the hemispheric pattern found during sleep onset can be considered a steady characteristic of the transition from wake to sleep, relatively independent of homeostatic and time of night effects. These results are interpreted as being consistent with the hypothesis concerning an advantage of the right hemisphere in sustaining vigilance.