Resting-State Subjective Experience and EEG Biomarkers Are Associated with Sleep-Onset Latency (original) (raw)

Considerations towards a neurobiologically-informed EEG measurement of sleepiness

2023

Sleep is a daily experience across humans and other species, yet our understanding of how and why we sleep is presently incomplete. This is particularly prevalent in research examining the online neurophysiological measurement of sleepiness in humans, where several electroencephalographic (EEG) phenomena have been linked with prolonged wakefulness. This leaves researchers without a solid basis for the measurement of sleep need in the individual and complicates our understanding of the nature of sleep. Recent theoretical and technical advances have allowed for a greater understanding of the neurobiological basis of sleep need: sleep need may result from increases in neuronal excitability and shifts in excitation/inhibition balance in neuronal circuits, and this can be directly measured via the aperiodic component of the EEG. Here, we review the literature on EEG-derived markers of sleepiness in humans and argue that changes in these may actually result from changes in aperiodic markers of neural activity. We argue for the use of aperiodic markers derived from the EEG in predicting sleepiness and suggest areas for future research based on these.

Recorded and Reported Sleepiness: The Association Between Brain Arousal in Resting State and Subjective Daytime Sleepiness

Sleep, 2017

Daytime sleepiness is a significant public health concern. Early evidence points toward the computerized VIGALL (Vigilance Algorithm Leipzig) as time-efficient tool to assess sleepiness objectively. In the present study, we investigated the association between VIGALL variables of EEG vigilance (indicating brain arousal in resting state) and subjective daytime sleepiness in the LIFE cohort study. Additionally, we validated VIGALL against the self-rated likelihood of having fallen asleep during the conducted resting EEG and against heart periods. Participants of the primary sample LIFE 60+ (N = 1927, 60-79 years) and replication sample LIFE 40+ (N = 293, 40-56 years) completed the Epworth Sleepiness Scale (ESS). After an average interval of 3 weeks (LIFE 60+) and 65 weeks (LIFE 40+), respectively, participants underwent a single 20-minute resting EEG, analyzed using VIGALL 2.1. Analyses revealed significant associations between ESS and EEG vigilance in LIFE 60+ (rho = -0.17, p = 1E-14...

Subjective sleepiness correlates negatively with global alpha (8–12 Hz) and positively with central frontal theta (4–8 Hz) frequencies in the human resting awake electroencephalogram

Neuroscience Letters, 2003

Subjective sleepiness is part of the system controlling the decision to go to sleep in humans. Extended periods of waking lead to increased sleepiness, as well as to changes in cortical electroencephalogram (EEG) during waking. We investigated the association of sleepiness and awake EEG spectra during 40 h of wakefulness using multi-electrode EEG recordings for full coverage of the scalp. We found: (1) strong negative correlations of alpha (8 -12 Hz) power with subjective sleepiness at all scalp locations, suggesting a negative association between sleepiness and general cortical activation; and (2) positive correlations of theta (4-8 Hz) power with subjective sleepiness with a focus on frontal locations, suggesting additional location specific associations between sleepiness and cortical activation. These findings support the notion that sleepiness is directly represented in the awake EEG. q

Can sleepiness be evaluated quickly, directly, objectively, and in absolute terms? Scoring of alert/drowsy components of the resting electroencephalogram spectrum

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The Neurophysiological Basis of the Discrepancy between Objective and Subjective Sleep during the Sleep Onset Period: an EEG-fMRI study

Sleep, 2018

Subjective perception of sleep is not necessarily consistent with EEG indications of sleep. The mismatch between subjective reports and objective measures is often referred to as "sleep state misperception" (SSM). Previous studies evince that this mismatch is found in both insomnia patients and in normal sleepers, but the neurophysiological mechanism remains unclear. The aim of the study is to explore the neurophysiological basis of this mechanism, from the perspective of both EEG power and fMRI fluctuations. Thirty-six healthy young adults participated in the study. Simultaneous EEG and fMRI recordings were conducted while the participants were trying to fall asleep in an MRI scanner at approximately 9:00 PM. They were awakened after achieving stable N1 or N2 sleep, or after 90 minutes without falling into stable sleep. Next they were asked to recall their conscious experiences from the moment immediately prior to awakening. Sixty-one instances of scheduled awakenings wer...

Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages

Sensors

Electroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outside a highly equipped clinical setting compared with multimodal physiological signal-based polysomnography. This study aims to quantify the neurological EEG-biomarkers and predict five-class sleep stages using sleep EEG data. We investigated the three-channel EEG sleep recordings of 154 individuals (mean age of 53.8 ± 15.4 years) from the Haaglanden Medisch Centrum (HMC, The Hague, The Netherlands) open-access public dataset of PhysioNet. The power of fast-wave alpha, beta, and gamma rhythms decreases; and the power of slow-wave delta and theta oscillations gradually increases as sleep becomes deeper. Delta wave power ratios (DAR, DTR, and DTABR) may be considered biomarkers for their cha...

The theta paradox: 4-8 Hz EEG oscillations reflect both local sleep and cognitive control

ABSTRACTHuman brain activity generates electroencephalographic (EEG) oscillations that characterize specific behavioral and vigilance states. The frequency of these oscillations is typically sufficient to distinguish a given state, however theta oscillations (4-8 Hz) have instead been found in near-opposite conditions of drowsiness during sleep deprivation and alert cognitive control. While the latter has been extensively studied and is often referred to as “frontal midline theta”, the former has been investigated far less but is considered to be a marker for local sleep during wake. In this study we investigated to what extent theta oscillations differed during cognitive tasks and sleep deprivation. We measured high-density EEG in 18 young healthy adults performing 6 tasks under 3 levels of sleep deprivation. We found both cognitive load and sleep deprivation increased theta power in medial prefrontal cortical areas, however sleep deprivation caused additional increases in theta in...

More Severe Insomnia Complaints in People with Stronger Long-Range Temporal Correlations in Wake Resting-State EEG

Frontiers in physiology, 2016

The complaints of people suffering from Insomnia Disorder (ID) concern both sleep and daytime functioning. However, little is known about wake brain temporal dynamics in people with ID. We therefore assessed possible alterations in Long-Range Temporal Correlations (LRTC) in the amplitude fluctuations of band-filtered oscillations in electroencephalography (EEG) recordings. We investigated whether LRTC differ between cases with ID and matched controls. Within both groups, we moreover investigated whether individual differences in subjective insomnia complaints are associated with LRTC. Resting-state high-density EEG (256-channel) was recorded in 52 participants with ID and 43 age- and sex-matched controls, during Eyes Open (EO) and Eyes Closed (EC). Detrended fluctuation analysis was applied to the amplitude envelope of band-filtered EEG oscillations (theta, alpha, sigma, beta-1, beta-2) to obtain the Hurst exponents (H), as measures of LRTC. Participants rated their subjective insom...

Sleep EEG characteristics associated with sleep onset misperception

Sleep Medicine, 2019

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