EEG microstates are correlated with brain functional networks during slow-wave sleep

Xu, Jing; Pan, Yu; Zhou, Shuqin; Zou, Guangyuan; Liu, Jiayi; Su, Zaiming; Zou, Qihong; Gao, Jia‐Hong

doi:10.1016/j.neuroimage.2020.116786

Cited by 32 publications

(28 citation statements)

References 53 publications

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“…The higher number of microstate templates required to reach high GEV in neonates as compared to adults could be due to the fact that newborn EEG is typically recorded and analyzed during sleep, whereas adult studies are typically performed during awake state. To our knowledge, only two microstate studies in adults reported data during non-REM sleep (no microstate studies on adult REM sleep exist), indicating lower GEV values in comparison with those obtained for newborns: Brodbeck et al ( 2012 ) reported GEV between 60 and 67%, whereas Xu et al ( 2020 ) reported GEV values around 65%.…”

Section: Discussionmentioning

confidence: 95%

“…Brodbeck et al ( 2012 ) analyzed the microstate templates during different phases of the non-rapid-eye-movement (NREM) sleep state and showed that they had a relatively high degree of spatial correlation with those extracted during wakefulness. In another study, Xu et al ( 2020 ) investigated the relationship between fMRI fluctuations and microstates during slow wave sleep, revealing a correlation between EEG microstates and brain functional networks. Recently, Bréchet et al ( 2020 ) compared microstates during NREM sleep with microstates in wakefulness, showing that two microstates dominated sleep, with a different spectral content with respect to the microstates dominating wakefulness.…”

Section: Introductionmentioning

confidence: 99%

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Characterization of the Functional Dynamics in the Neonatal Brain during REM and NREM Sleep States by means of Microstate Analysis

et al. 2021

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Neonates spend most of their life sleeping. During sleep, their brain experiences fast changes in its functional organization. Microstate analysis permits to capture the rapid dynamical changes occurring in the functional organization of the brain by representing the changing spatio-temporal features of the electroencephalogram (EEG) as a sequence of short-lasting scalp topographies—the microstates. In this study, we modeled the ongoing neonatal EEG into sequences of a limited number of microstates and investigated whether the extracted microstate features are altered in REM and NREM sleep (usually known as active and quiet sleep states—AS and QS—in the newborn) and depend on the EEG frequency band. 19-channel EEG recordings from 60 full-term healthy infants were analyzed using a modified version of the k-means clustering algorithm. The results show that ~ 70% of the variance in the datasets can be described using 7 dominant microstate templates. The mean duration and mean occurrence of the dominant microstates were significantly different in the two sleep states. Microstate syntax analysis demonstrated that the microstate sequences characterizing AS and QS had specific non-casual structures that differed in the two sleep states. Microstate analysis of the neonatal EEG in specific frequency bands showed a clear dependence of the explained variance on frequency. Overall, our findings demonstrate that (1) the spatio-temporal dynamics of the neonatal EEG can be described by non-casual sequences of a limited number of microstate templates; (2) the brain dynamics described by these microstate templates depends on frequency; (3) the features of the microstate sequences can well differentiate the physiological conditions characterizing AS and QS.

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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Characterization of the Functional Dynamics in the Neonatal Brain during REM and NREM Sleep States by means of Microstate Analysis

et al. 2021

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“…A complementary approach is to uncover associations between microstates and the well studied resting-state networks (RSNs) widely studied in fMRI, which have been associated with cognitive domains in large cohort studies (Smith et al, 2009). In sensor-space EEG, studies have demonstrated associations between microstates and fMRI-RSNS through convolution of the microstate time courses with a haemodynamic response function and general linear modelling (Britz et al, 2010; Musso et al, 2010; Van De Ville et al, 2010; Yuan et al, 2012; Abreu et al, 2020; Xu et al, 2020; Zoubi et al, 2020), or through correlations between RSNs and microstate statistics (Schumacher et al, 2019). An advantage of working in source space for this purpose is that spatial patterns of microstate activations can be associated with RSNs when directly comparable brain atlases are used for parcellation of the brain dynamics in the M/EEG and fMRI data.…”

Section: Discussionmentioning

confidence: 99%

“…Microstate analysis therefore does not require an arbitrarily chosen window length, and has minimal assumptions about the underlying generative process. Resting-state EEG microstates are robust and highly reproducible (Michel and Koenig, 2018), and have been associated with fMRI resting-state networks (Britz et al, 2010; Musso et al, 2010; Yuan et al, 2012; Schumacher et al, 2019; Abreu et al, 2020; Xu et al, 2020; Zoubi et al, 2020) and cognitive domains (Brodbeck et al, 2012; Britz et al, 2014; Milz et al, 2016; Seitzman et al, 2017; Zappasodi et al, 2019), earning EEG microstates the nickname the ‘atoms of thought’ (Lehmann, 1990). EEG microstates have also been demonstrated to be a potentially useful clinical tool for understanding and diagnosing neurological diseases such as Alzheimer’s disease and other dementias (Nishida et al, 2013; Musaeus et al, 2019; Smailovic et al, 2019; Schumacher et al, 2019; Tait et al, 2020b), schizophrenia (Lehmann et al, 2005; Andreou et al, 2014; Tomescu et al, 2014), and a range of other disorders (Khanna et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

Tait

Zhang

2021

Preprint

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EEG microstate analysis is a useful approach for studying brain states - nicknamed `atoms of thought' - and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight into the cortical mechanisms underpinning these states. In this study, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level MEG microstates were associated with distinct functional connectivity patterns. Using a passive auditory paradigm, we further demonstrated that the occurrence probability of MEG microstates were altered by evoked auditory responses, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of MEG source-level microstates as a data-driven method for investigating brain dynamic activity and connectivity at the millisecond scale.

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Section: Introductionmentioning

confidence: 99%

MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

Tait

Zhang

2021

Preprint

View full text Add to dashboard Cite

EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale.

show abstract

EEG microstates are correlated with brain functional networks during slow-wave sleep

Cited by 32 publications

References 53 publications

Characterization of the Functional Dynamics in the Neonatal Brain during REM and NREM Sleep States by means of Microstate Analysis

Characterization of the Functional Dynamics in the Neonatal Brain during REM and NREM Sleep States by means of Microstate Analysis

MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

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