Overall Population Generalities, Sex Differences, and Individual Differences in Sleep Electroencephalography Functional Connectivity

Liao, Yuanyuan; Zhou, Guolin; Liang, Jiuxing; Zhang, Xiangmin; Guo, Xinwen; Luo, Yuxi

doi:10.1109/access.2019.2950066

Cited by 20 publications

(15 citation statements)

References 56 publications

(70 reference statements)

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“…WPLI can reflect the cortical oscillation relation between different sites in the same brain region or between different brain regions (Imperatori et al, 2020). An improved WPLI calculation method was used in this research (Liao et al, 2019), and its definition is as follows:…”

Section: Fc Analysismentioning

confidence: 99%

“…Previous studies have tried to explore different inter-regional features to exhibit differences in FC distribution, such as left-right asymmetry and anterior-posterior anisotropy (Liao et al, 2019;Pan et al, 2021;Zhou et al, 2020). It has been reported that the FC of the limbic system in the hemisphere of patients with depression is reduced (Cao et al, 2012), whereas Irwin et al found that the FC between the hemispheres of the amygdala was enhanced (Irwin et al, 2004).…”

Section: Inter-regional Fc Differencementioning

confidence: 99%

“…Previous researches applied WPLI to explore abnormalFC in patients with Alzheimer (Briels et al, 2020) and the ones with autism (Haartsen et al, 2019). Moreover, brain FC during sleep was evaluated by WPLI to tap potential sleep regulation mechanism (Liao et al, 2019). With the advantages of nice noise robustness, WPLI was applied in this study to investigate the FC in patients with depression during sleep.…”

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confidence: 99%

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Characterization of specific spatial functional connectivity difference in depression during sleep

Lian

Song

Zhang

et al. 2021

J of Neuroscience Research

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The connectivity of the brain network allows the brain to perform a variety of complex functions (Friston, 2011). Functional connectivity (FC) represents the temporal correlations that suggest direct or indirect interactions between brain regions (Friston et al., 1993;Song et al., 2008). Studying FC and dynamics in neural networks allows us to understand how the brain performs various complex functions (He et al., 2019). It has also been reported that the brain network connectivity is affected by mental illness (Leistedt et al., 2009;van den Heuvel et al., 2013).

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Section: Fc Analysismentioning

confidence: 99%

Section: Inter-regional Fc Differencementioning

confidence: 99%

mentioning

confidence: 99%

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Characterization of specific spatial functional connectivity difference in depression during sleep

Lian

Song

Zhang

et al. 2021

J of Neuroscience Research

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“…Developed from TE, the dSPTE has better noise robustness and can correctly identify the EC, and its calculation procedure contains phase information extraction, symbolic process, and true delay search. For phase information extraction, a combined Morlet wavelet was used to obtain the instantaneous phase ( Liao et al, 2019 ). The definition of a single Morlet complex wavelet is:…”

Section: Methodsmentioning

confidence: 99%

Sleep-Dependent Anomalous Cortical Information Interaction in Patients With Depression

et al. 2022

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Depression is a prevalent mental illness with high morbidity and is considered the main cause of disability worldwide. Brain activity while sleeping is reported to be affected by such mental illness. To explore the change of cortical information flow during sleep in depressed patients, a delay symbolic phase transfer entropy of scalp electroencephalography signals was used to measure effective connectivity between cortical regions in various frequency bands and sleep stages. The patient group and the control group shared similar patterns of information flow between channels during sleep. Obvious information flows to the left hemisphere and to the anterior cortex were found. Moreover, the occiput tended to be the information driver, whereas the frontal regions played the role of the receiver, and the right hemispheric regions showed a stronger information drive than the left ones. Compared with healthy controls, such directional tendencies in information flow and the definiteness of role division in cortical regions were both weakened in patients in most frequency bands and sleep stages, but the beta band during the N1 stage was an exception. The computable sleep-dependent cortical interaction may provide clues to characterize cortical abnormalities in depressed patients and should be helpful for the diagnosis of depression.

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“…The research found that the fluctuation in delta frequency is higher in females than in males. In this experiment, 10-20 instruments were used for EEG recordings [11,17]. According to Ravan et al [12], frequent use of the 10-20 system can cause brain related disorders and therefore such experiments should be avoided.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Sleep Time Slot for Bruxism and Insomnia Identification based on Frequency based EEG Patterns using Machine Learning Techniques

Tiwari¹

2020

IJATCSE

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Many science researchers believe that a healthy thinking depends on the quality of human sleep. Giving importance to the quality sleeps, several systems for the measurement of sleep quality were developed. All the systems can be divided into two sections -Wearable Devices & Automated devices. Exact estimation of the sleep quality by both the systems is quite difficult. Accurate assessment the quality of sleep depends on many physical and environmental factors such as dimension of bed, light effects, room temperature, any type of disease, oxygen level, etc. Accurately measurement these factors and establishing their relationship with sleep quality is a complex process. Measuring sleep quality through the systems currently available is not only complicated but also to a large extent expensive. This research attempts to summarize the systems available at present in measuring sleep quality. It divides the entire sleep-period into 4 parts and compares the accuracy of the predictions of the sleep related diseases at the end of each interval by applying machine-learning algorithms to the data of different time periods. In this research, diseases such as Insomnia and Bruxism have been included as a data set. With the results it is found that the slot 1 (12:00 -1:20) is the most important one in the processes of measuring sleep quality.

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Overall Population Generalities, Sex Differences, and Individual Differences in Sleep Electroencephalography Functional Connectivity

Cited by 20 publications

References 56 publications

Characterization of specific spatial functional connectivity difference in depression during sleep

Characterization of specific spatial functional connectivity difference in depression during sleep

Sleep-Dependent Anomalous Cortical Information Interaction in Patients With Depression

Optimizing Sleep Time Slot for Bruxism and Insomnia Identification based on Frequency based EEG Patterns using Machine Learning Techniques

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