Sex Difference in EEG Functional Connectivity during Sleep Stages and Resting Wake State Based on Weighted Phase Lag Index

Liao, Yuanyuan; Zhou, Guolin; Luo, Yuxi

doi:10.1109/embc.2019.8857296

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…There have been many previous exploratory studies using gender as a variable, for example, in a study in which the weighted phase lag index of sleep EEG reflected the functional brain connectivity of people of different genders, the results responded to significantly greater connectivity in female than in male in the high sigma frequency range, but the opposite pattern was observed in the alpha, low sigma, and beta frequency ranges ( 69 ). Another study using the same index calculation noted that synchronization strength showed significant gender differences in all stages and bands, being higher in females during the NREM stage and higher in males during the W and REM stages in the alpha and beta bands ( 70 ). All of these findings suggest differences in functional connectivity for sleep staging by gender.…”

Section: Discussionmentioning

confidence: 96%

“…In a previous EEG study exploring differences in brain function between adults and older adults, the SVM classifier was used with 93% accuracy when categorizing the brain by age group ( 52 ). Another study found gender differences in EEG functional connectivity between sleep stages and resting wake states based on weighted phase lag indices ( 70 ). This coincides with our findings.…”

Section: Discussionmentioning

confidence: 99%

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Effects of gender and age on sleep EEG functional connectivity differences in subjects with mild difficulty falling asleep

Luo,

Zhou,

Shi

et al. 2024

Front. Psychiatry

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IntroductionDifficulty falling asleep place an increasing burden on society. EEG-based sleep staging is fundamental to the diagnosis of sleep disorder, and the selection of features for each sleep stage is a key step in the sleep analysis. However, the differences of sleep EEG features in gender and age are not clear enough.MethodsThis study aimed to investigate the effects of age and gender on sleep EEG functional connectivity through statistical analysis of brain functional connectivity and machine learning validation. The two-overnight sleep EEG data of 78 subjects with mild difficulty falling asleep were categorized into five sleep stages using markers and segments from the "sleep-EDF" public database. First, the 78 subjects were finely grouped, and the mutual information of the six sleep EEG rhythms of δ, θ, α, β, spindle, and sawtooth wave was extracted as a functional connectivity measure. Then, one-way analysis of variance (ANOVA) was used to extract significant differences in functional connectivity of sleep rhythm waves across sleep stages with respect to age and gender. Finally, machine learning algorithms were used to investigate the effects of fine grouping of age and gender on sleep staging. Results and discussionThe results showed that: (1) The functional connectivity of each sleep rhythm wave differed significantly across sleep stages, with delta and beta functional connectivity differing significantly across sleep stages. (2) Significant differences in functional connections among young and middle-aged groups, and among young and elderly groups, but no significant difference between middle-aged and elderly groups. (3) Female functional connectivity strength is generally higher than male at the high-frequency band of EEG, but no significant difference in the low-frequency. (4) Finer group divisions based on gender and age can indeed improve the accuracy of sleep staging, with an increase of about 3.58% by using the random forest algorithm. Our results further reveal the electrophysiological neural mechanisms of each sleep stage, and find that sleep functional connectivity differs significantly in both gender and age, providing valuable theoretical guidance for the establishment of automated sleep stage models.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Effects of gender and age on sleep EEG functional connectivity differences in subjects with mild difficulty falling asleep

Luo,

Zhou,

Shi

et al. 2024

Front. Psychiatry

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“…Muñoz-Torres [32] adopted the average power spectral density as an indicator to compare the difference among subjects with obstructive sleep apnea (OSA), and found that OSA men displayed decreased power of a large frequency range (sigma, beta, and gamma) during sleep compared with women. Liao [33] compared the synchronization intensities during the resting wakeful stage EEG, which showed significant gender differences in all stages and bands: higher in women in non-rapid eye movement sleep, and higher in men in α and β bands in the wakeful stage and REM sleep. However, our result showed no difference by using the WLMF method.…”

Section: Discussionmentioning

confidence: 99%

Wavelet Leader Based Multifractal Analysis of Sleep Electroencephalogram

Li¹,

Tang²,

Liu³

et al. 2020

Preprint

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Background: Conventional manual sleep stage classiﬁcation is time-consuming and relies on the knowledge and experience of the specialists. The emergence of automatic sleep stage classiﬁcation greatly improves the classiﬁcation eﬃciency. The feature extraction in automatic sleep stage classiﬁcation is particularly important, which usually uses the linear methods based on techniques in the time domain, frequency domain, or time-frequency domain. Electroencephalograms (EEGs) contain a wealth of physiological information, have been widely used for the classiﬁcation of sleep stage. Due to the nonlinear, non-stationary, and multifractal characteristics of EEGs, some nonlinear methods have been used to extract features of sleep stages in recent years, such as complexity, multifractal theory, and chaos theory. The Wavelet Leader Multifractal Formalism (WLMF) of the multifractal theory is widely applied to diﬀerent physiological signals. The current researches focus on discussing the mean H¨older exponent (h0) and the width of the multifractal singularity spectrum (WD(h)) estimated by the WLMF method. However, in the ﬁeld of sleep staging, a number of researches focused on h0, but few studies on WD(h). Results: This paper aims to assess the multifractal characteristic for sleep EEG time series from the Sleep-EDF Expanded Database by the WLMF method. In the young group, the mean h0 increased from the Wake stage to the S3 stage (p<0.01). So did the elderly group (p<0.001). WD(h) of the Wake stage was less than that of the S3 stage for the young group, and this relationship was reversed for the elderly group(χ2=13.769, df=1, p<0.001). Gender did not aﬀect, with statistical signiﬁcance, WD(h) of the Wake stage and the S3 stage (χ2=0.085, df=1, p=0.608), nor did the brain region (χ2=3.137, df=1, p=0.078). Conclusions: The result shows that WD(h) was inﬂuenced by aging. The gender and location of brain regions did not show signiﬁcant inﬂuence on the multifractal characteristics of wakefulness and sleeping. This ﬁnding extends the application of the multifractal singularity spectrum on sleep staging, and raises a fundamental question on what might be the underlying mechanisms of the WD(h) reversion.

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“…Phase synchronization is a category that focuses on the phase coupling of oscillation systems. The phase locking value (PLV; Bajo et al, 2015; Bedo, Ribary, & Ward, 2020; Delgado‐Restituto, Romaine, & Rodríguez‐Vázquez, 2019; Mheich, Hassan, Khalil, Berrou, & Wendling, 2015; Sadaghiani & Kleinschmidt, 2016) and the phase lag index (PLI; Chaturvedi et al, 2019; Fraga González et al, 2018; Liao, Zhou, & Luo, 2019; Stam et al, 2007) are high‐frequently used to obtain the strength of phase synchronization. Information theory is regarded as another efficient method in the case of extracting nonlinear interactions among EEG signals.…”

Section: Estimationmentioning

confidence: 99%

Brain functional and effective connectivity based on electroencephalography recordings: A review

Cao

Zhao

Shan

et al. 2021

Human Brain Mapping

109

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Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG‐based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG‐based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time‐based, and frequency‐based or time‐frequency‐based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.

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Sex Difference in EEG Functional Connectivity during Sleep Stages and Resting Wake State Based on Weighted Phase Lag Index

Cited by 4 publications

References 10 publications

Effects of gender and age on sleep EEG functional connectivity differences in subjects with mild difficulty falling asleep

Effects of gender and age on sleep EEG functional connectivity differences in subjects with mild difficulty falling asleep

Wavelet Leader Based Multifractal Analysis of Sleep Electroencephalogram

Brain functional and effective connectivity based on electroencephalography recordings: A review

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