Coherence analysis of the human sleep electroencephalogram

doi:10.1016/S0306-4522(97)00692-1

Neuroscience

Volume 85, Issue 4, August 1998, Pages 1195-1208

https://doi.org/10.1016/S0306-4522(97)00692-1 Get rights and content

Abstract

Animal studies have shown that the sleep-related oscillations in the frequency range of spindles and slow-waves, and in the gamma band occur synchronously over large parts of the cerebral cortex. Coherence analysis was used to investigate these oscillations in the human sleep electroencephalogram. In all-night electroencephalogram recordings from eight young subjects power and coherence spectra within and between cerebral hemispheres were computed from bipolar derivations placed bilaterally along the antero-posterior axis. The 0.75–50 Hz range was examined with a resolution of 0.25 Hz. Distinct peaks in coherence were present in non-rapid eye movement sleep but not in rapid eye movement sleep. The most prominent and consistent peak was seen in the range of sleep spindles (13–14 Hz), and additional peaks were present in the alpha band (9–10 Hz) and low delta band (1–2 Hz). Whereas coherence in the spindle range was highest in stage 2, the alpha peak was most prominent in slow-wave sleep (stages 3 and 4). Interhemispheric coherence at 30 Hz was higher in rapid eye movement sleep than in non-rapid eye movement sleep. There were also marked sleep state-independent regional differences. Coherence between homologous interhemispheric derivations was high in the low frequency range and declined with increasing frequencies, whereas coherence of intrahemispheric and non-homologous interhemispheric derivations was at a low level throughout the spectra.

It is concluded that coherence analysis may provide insights into large-scale functional connectivities of brain regions during sleep. The high coherence of sleep spindles is an indication for their widespread and quasi-synchronous occurrence throughout the cortex and may point to their specific role in the sleep process.

Reference (59)

AchermannP. et al.
Low-frequency (<1H˜z) oscillations in the human sleep EEG
Neuroscience
(1997)
AchermannP. et al.
A model of human sleep homeostasis based on EEG slow-wave activity: quantitative comparison of data and simulations
Brain Res. Bull.
(1993)
AeschbachD. et al.
Dynamics of EEG spindle frequency activity during extended sleep in humans: relationship to slow-wave activity and time of day
Brain Res.
(1997)
BanquetJ.P.
Inter- and intrahemispheric relationships of the EEG activity during sleep in man
Electroenceph. clin. Neurophysiol.
(1983)
BarcaroU. et al.
Changes in the interhemispheric correlation during sleep in normal subjects
Electroenceph. clin. Neurophysiol.
(1986)
BeningtonJ.H. et al.
Restoration of brain energy metabolism as the function of sleep
Prog. Neurobiol
(1995)
BullockT.H. et al.
EEG coherence has structure in the millimeter domain: subdural and hippocampal recordings from epileptic patients
Electroenceph. clin. Neurophysiol.
(1995)
BuzsákiG. et al.
Temporal structure in spatially organized neuronal ensembles: a role for interneuronal networks
Curr. Opin. Neurobiol.
(1995)
DijkD.J. et al.
Dynamics of electroencephalographic sleep spindles and slow, wave activity in men: effect of sleep deprivation
Brain Res.
(1993)
FeinG. et al.
Common reference coherence data are confounded by power and phase effects
Electroenceph. clin. Neurophysiol.
(1988)

FrenchC.C. et al.

A critical review of EEG coherence studies of hemisphere function

Int. J. Psychophysiol.

(1984)

HirschJ.C. et al.

Sleep-related variations of membrane potential in the lateral geniculate body relay neurons of the cat

Brain Res.

(1983)

HjorthB.

An on-line transformation of EEG scalp potentials into orthogonal source derivations

Electroenceph. clin. Neurophysiol.

(1975)

KaminskiM. et al.

Topographic analysis of coherence and propagation of EEG activity during sleep and wakefulness

Electroenceph. clin. Neurophysiol.

(1997)

KoedaT. et al.

The EEG in acallosal children. Coherence values in the resting state: left hemisphere compensatory mechanism?

Electroenceph. clin. Neurophysiol.

(1995)

KuksJ.B.M. et al.

Coherence patterns of the infant sleep EEG in absence of the corpus callosum

Electroenceph. clin. Neurophysiol.

(1987)

LagerlundT.D. et al.

Interelectrode coherences from nearest-neighbor and spherical harmonic expansion computation of laplacian of scalp potential

Electroenceph. clin. Neurophysiol.

(1995)

NielsenT. et al.

Interhemispheric EEG coherence during sleep and wakefulness in left- and right-handed subjects

Brain Cogn.

(1990)

WallinG. et al.

Source derivation in clinical routine EEG

Electroenceph. clin. Neurophysiol.

(1980)

WerthE. et al.

Spindle frequency activity in the sleep EEG: individual differences and topographical distribution

Electroenceph. clin. Neurophysiol.

(1997)

AeschbachD. et al.

All-night dynamics of the human sleep EEG

J. Sleep Res.

(1993)

AmzicaF. et al.

Short- and long-range neuronal synchronization of the slow (<1Hz) cortical oscillation

J. Neurophysiol.

(1995)

AmzicaF. et al.

The K-complex: its slow (<1 Hz) rhythmicity and relation with delta waves

Neurology

(1997)

BorbélyA.A.

A two process model of sleep regulation

Hum. Neurobiol.

(1982)

BorbélyA.A. et al.

Concepts and models of sleep regulation: an overview

J. Sleep Res.

(1992)

BorbélyA.A. et al.

Sleep deprivation: effect on sleep stages and EEG power density in man

Electroenceph. clin. Neurophysiol.

(1981)

BullockT.H. et al.

Temporal fluctuations in coherence of brain waves

ContrerasD. et al.

Cellular basis of EEG slow rhythms: a study of dynamic corticothalamic relationships

J. Neurosci.

(1995)

ContrerasD. et al.

Control of spatiotemporal coherence of a thalamic oscillation by corticothalamic feedback

Science

(1996)

Cited by (136)

Interhemispheric differences of electroencephalography signal characteristics in different sleep stages
2024, Sleep Medicine
The current electroencephalography (EEG) measurement setup is complex, laborious to set up, and uncomfortable for patients. We hypothesize that differences in EEG signal characteristics for sleep staging between the left and right hemispheres are negligible; therefore, there is potential to simplify the current measurement setup. We aimed to investigate the technical hemispheric differences in EEG signal characteristics along with electrooculography (EOG) signals during different sleep stages.
Type II portable polysomnography (PSG) recordings of 50 patients were studied. Amplitudes and power spectral densities (PSDs) of the EEG and EOG signals were compared between the left (C3-M2, F3-M2, O1-M2, and E1-M2) and the right (C4-M1, F4-M1, O2-M1, and E2-M2) hemispheres. Regression analysis was performed to investigate the potential influence of sleep stages on the hemispheric differences in PSDs. Wilcoxon signed-rank tests were also employed to calculate the effect size of hemispheres across different frequency bands and sleep stages.
The results showed statistically significant differences in signal characteristics between hemispheres, but the absolute differences were minor. The median hemispheric differences in amplitudes were smaller than 3 μv with large interquartile ranges during all sleep stages. The absolute and relative PSD characteristics were highly similar between hemispheres in different sleep stages. Additionally, there were negligible differences in the effect size between hemispheres across all sleep stages.
Technical signal differences between hemispheres were minor across all sleep stages, indicating that both hemispheres contain similar information needed for sleep staging. A reduced measurement setup could be suitable for sleep staging without the loss of relevant information.
Sleepiness and the transition from wakefulness to sleep
2024, Neurophysiologie Clinique
The transition from wakefulness to sleep is a progressive process that is reflected in the gradual loss of responsiveness, an alteration of cognitive functions, and a drastic shift in brain dynamics. These changes do not occur all at once. The sleep onset period (SOP) refers here to this period of transition between wakefulness and sleep. For example, although transitions of brain activity at sleep onset can occur within seconds in a given brain region, these changes occur at different time points across the brain, resulting in a SOP that can last several minutes. Likewise, the transition to sleep impacts cognitive and behavioral levels in a graded and staged fashion. It is often accompanied and preceded by a sensation of drowsiness and the subjective feeling of a need for sleep, also associated with specific physiological and behavioral signatures. To better characterize fluctuations in vigilance and the SOP, a multidimensional approach is thus warranted. Such a multidimensional approach could mitigate important limitations in the current classification of sleep, leading ultimately to better diagnoses and treatments of individuals with sleep and/or vigilance disorders. These insights could also be translated in real-life settings to either facilitate sleep onset in individuals with sleep difficulties or, on the contrary, prevent or control inappropriate sleep onsets.
Quantitative sleep EEG synchronization analysis for automatic arousals detection
2020, Biomedical Signal Processing and Control
Citation Excerpt :
Some neurological disorders such as Alzheimer, insomnia, epilepsy, schizophrenia, and sleep apnea have been detected using these methods [29–35]. Moreover, sleep stages relationship with synchronization, and coherence of EEG is investigated [36,37]. In this study, we have demonstrated that the coherence spectrum between C3-A2 and C4-A1 channels, where EEG arousal is best detected [38], is directly related to EEG synchronization and desynchronization.
Electroencephalographic arousals are considered to be the main reason for the interruption of sleep and are visually examined by sleep physicians. Visual scoring of all-night recordings has inter-scorer variability which may lead to subjective results. Hence, we aimed to develop a novel automated method to detect arousals from two electroencephalographic channels in terms of the synchronic events of the right and left hemispheres.
In the context of the occurrence of arousal pattern, the relationship between two synchronic C3-A2 and C4-A1 channels were quantified using by coherence spectrum and mutual information. The power and the ratio values of the sub-bands of the coherence spectrum were selected as the five features. Furthermore, the mutual information value was determined as the sixth feature. The automatic detection performance was evaluated using six features and machine learning techniques, on five different patients' whole-night electroencephalography recordings. The presented method does not include any signal conditioning, pre-processing steps, any manual involvement, meta-rule-based approaches, and some empirical thresholds.
The significant increases were found in sub-bands of the coherence spectrum in case of arousal. Moreover, the mutual information of these channels was distinctive during the arousal state. Consequently, the overall accuracy, sensitivity, specificity, and PPV values were achieved as 99.5 %, 99.8 %, 99.6 %, and 99.3 %, respectively with using ensemble bagged tree.
The novelty of the present study is the practical determination of the relationship between electroencephalographic synchronization and the occurrence of the arousals between the central regions of the right and left hemispheres.
Sleep EEG functional connectivity varies with age and sex, but not general intelligence
2019, Neurobiology of Aging
Variations in the anatomical and functional connectivity between brain areas underlie both healthy and pathological variation in psychological measures. Largely independent from external stimuli, the sleep EEG is particularly well suited to measure individual variations in functional brain connectivity. In this study of 172 healthy individuals (17–69 years old), we show that functional connectivity between distant brain areas—reflected by the weighted phase lag index of the sleep EEG—is strongly affected by the age and sex of participants. Both NREM and REM connectivity in the theta and beta range increased with age, whereas a decrease was seen in the sigma range. Connectivity was substantially greater in females than in males in the high sigma frequency range, but an opposite pattern was seen in the alpha/low sigma and beta range. General intelligence was not significantly associated with connectivity in either sex. Our results confirm strong age effects on sleep spindle-frequency activity, which loses synchrony as a function of aging. Furthermore, we found support for a vigilance state–independent age-related increase in high beta power, previously demonstrated in waking EEG studies. The results highlight that future studies establishing sleep EEG connectivity measures as psychological or psychiatric biomarkers should take into account that sleep EEG synchronization is strongly affected by age and sex, and clinical thresholds must be adjusted accordingly.
Cross-subject network investigation of the EEG microstructure: A sleep spindles study
2019, Journal of Neuroscience Methods
The microstructural EEG elements and their functional networks relate to many neurophysiological functions of the brain and can reveal abnormalities. Despite the blooming variety of methods for estimating connectivity in the EEG of a single subject, a common pitfall is seen in relevant studies; grand averaging is used for estimating the characteristic connectivity patterns of a group of subjects. This averaging may distort results and fail to account for the internal variability of connectivity results across the subjects of a group.
In this study, we propose a novel methodology for the cross-subject network investigation of EEG graphoelements. We used dimensionality reduction techniques in order to reveal internal connectivity properties and to examine how consistent these are across a number of subjects. In addition, graph theoretical measures were utilized to prioritize regions according to their network attributes.
As proof of concept, we applied this method on fast sleep spindles across 10 healthy subjects. Neurophysiological findings revealed subnetworks of the spindle events across subjects, highlighting a predominance for occipito-parietal areas and their connectivity with frontal regions.
This is a new approach for the examination of within-group connectivities in EEG research. The results accounted for more than 85% of the overall data variance and the detected subnetworks were found to be meaningful down-projections of the grand average of the group, suggesting sufficient performance for the proposed methodology.
We conclude that the proposed methodology can serve as an observatory tool for the EEG connectivity patterns across subjects, providing a supplementary analysis of the existing topography techniques.
The relationship between coherence and the phase-locking value
2017, Journal of Theoretical Biology

View all citing articles on Scopus

View full text

Coherence analysis of the human sleep electroencephalogram

Abstract

Neuroscience

Brain Res. Bull.

Brain Res.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Prog. Neurobiol

Electroenceph. clin. Neurophysiol.

Curr. Opin. Neurobiol.

Brain Res.

Electroenceph. clin. Neurophysiol.

Int. J. Psychophysiol.

Brain Res.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

Brain Cogn.

Electroenceph. clin. Neurophysiol.

Electroenceph. clin. Neurophysiol.

All-night dynamics of the human sleep EEG

J. Sleep Res.

Short- and long-range neuronal synchronization of the slow (<1Hz) cortical oscillation

J. Neurophysiol.

The K-complex: its slow (<1 Hz) rhythmicity and relation with delta waves

Neurology

A two process model of sleep regulation

Hum. Neurobiol.

Concepts and models of sleep regulation: an overview

J. Sleep Res.

Sleep deprivation: effect on sleep stages and EEG power density in man

Electroenceph. clin. Neurophysiol.

Temporal fluctuations in coherence of brain waves

Cellular basis of EEG slow rhythms: a study of dynamic corticothalamic relationships

J. Neurosci.

Control of spatiotemporal coherence of a thalamic oscillation by corticothalamic feedback

Science