EEG microstates of wakefulness and NREM sleep

Brodbeck, Verena; Kuhn, Alena; Wegner, Frederic von; Morzelewski, Astrid; Tagliazucchi, Enzo; Borisov, S. V.; Michel, Christoph M.; Laufs, Helmut

doi:10.1016/j.neuroimage.2012.05.060

Cited by 233 publications

(270 citation statements)

References 60 publications

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“…Somatic Awareness was evaluated by questions related to the sensation of bodily signals ("I was conscious of my body"; "I thought about my heartbeat"; "I thought about my breathing"). The negative correlation between Class C and SA thus seems reasonable: it was previously proposed by Rieger et al (2016) that Class C becomes more dominant when connections to contextual information (including perception of bodily signals, as measured with SA) are loosened, similarly to what happens in reduced attention states (Brandeis and Lehmann 1989), during sleep (Brodbeck et al 2012), hypnosis (Katayama et al 2007), or psychosis (metaanalysis by Rieger et al 2016). A negative association between Class C and D measures (r values from -0.29 to -0.52, Supplement 5) and the positive association of SA and Class D measures (not surviving correction, GFP: r = 0.35, Contribution: r = 0.30, Table 1) is also worth mentioning as it fits to the notion of antagonistic functional roles of Class C and D (Rieger et al 2016).…”

Section: Resultssupporting

confidence: 57%

Association Between Resting-State Microstates and Ratings on the Amsterdam Resting-State Questionnaire

et al. 2016

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There is a gap in understanding on how physiologically observed activity is related to the subjective, internally oriented experience during resting state. Microstate analysis is a frequent approach to evaluate resting-state EEG. But the relationship of commonly observed resting-state microstates to psychological domains of resting is not clear. The Amsterdam Resting-State Questionnaire (ARSQ) was recently introduced, offering an effective way to quantify subjective states after a period of resting and associate these quantifiers to psychological and physiological variables. In a sample of 94 healthy volunteers who participated in closed-eyes 5 min. resting session with concurrent EEG recording and subsequent filling of the ARSQ we evaluated parameters of microstate Classes A, B, C, D. We showed a moderate negative association between contribution (r = -0.40) of Class C and experienced Somatic Awareness (SA). The negative correlation between Class C and SA seems reasonable as Class C becomes more dominant when connections to contextual information (and bodily sensations as assessed with SA) are loosened (in reduced attention states, during sleep, hypnosis, or psychosis). We suggest that the use of questionnaires such as the ARSQ is helpful in exploring the variation of resting-state EEG parameters and its relationship to variation in non-sensory experiences.

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Section: Resultssupporting

confidence: 57%

Association Between Resting-State Microstates and Ratings on the Amsterdam Resting-State Questionnaire

et al. 2016

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“…Several initial studies that used the k-mean clustering approach and determined the optimal number of clusters by the cross-validation criterion revealed that the optimal number of maps across subjects is four Brodbeck et al, 2012;Koenig et al, 1999). The amount of global variance that these four cluster maps explain varies between different reports, ranging from 65 to 84% (see figure 3).…”

Section: Defining the Number Of Clustersmentioning

confidence: 99%

“…Brodbeck and colleagues (Brodbeck et al, 2012) compared EEG microstates during different sleep stages with those observed in waking states. Using the M A N U S C R I P T…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

2018

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SummaryThe present review discusses a well-established method for characterizing resting-state activity of the human brain using multichannel electroencephalography (EEG). This method involves the examination of electrical microstates in the brain, which are defined as successive short time periods during which the configuration of the scalp potential field remains semi-stable, suggesting quasi-simultaneity of activity among the nodes of largescale networks. A few prototypic microstates, which occur in a repetitive sequence across time, can be reliably identified across participants. Researchers have proposed that these microstates represent the basic building blocks of the chain of spontaneous conscious mental processes, and that their occurrence and temporal dynamics determine the quality of mentation. Several studies have further demonstrated that disturbances of mental processes associated with neurological and psychiatric conditions manifest as changes in the temporal dynamics of specific microstates. Combined EEG-fMRI studies and EEG source imaging studies have indicated that EEG microstates are closely associated with resting-state networks as identified using fMRI. The scale-free properties of the time series of EEG microstates explain why similar networks can be observed at such different time scales.The present review will provide an overview of these EEG microstates, available methods for analysis, the functional interpretations of findings regarding these microstates, and their behavioral and clinical correlates.

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“…Of the 20 2-second segments all topographies at Global Field Power (GFP) peaks were selected and subjected to a modified k-means spatial cluster analysis (Pascual-Marqui et al, 1995). A number of 4 microstate classes were obtained per participant, which is an adaption from previous studies that recorded for example simultaneous functional MRI-RSN and EEG in normal controls, dementia or schizophrenia (Koenig et al, 1999, Kikuchi et al, 2007, Brodbeck et al, 2012, Nishida et al, 2013.…”

Section: Microstate Analysismentioning

confidence: 99%

“…Moreover, it has been shown that depending on the sleep stage, the topographies of the microstate classes differ slightly (Brodbeck et al, 2012). Alterations of resting state microstate duration as well as microstate transitions were found in patients with frontotemporal dementia, AD, and schizophrenia as compared to controls (Dierks et al, 1997, Kikuchi et al, 2007, Nishida et al, 2013.…”

Section: Introductionmentioning

confidence: 99%

Discovering EEG resting state alterations of semantic dementia

Grieder

Koenig

Kinoshita

et al. 2016

Clinical Neurophysiology

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HighlightsResults: Topographical differences between the groups were found in microstate classes B and C, while microstate classes A and D were comparable. The data showed that the semantic dementia group had a peculiar microstate E, but the commonly found microstate C was lacking. Furthermore, the presence of microstate E was significantly correlated with lower MMSE and language scores. Conclusion: Alterations in resting EEG can be found in semantic dementia. Topographical shifts in microstate C might be related to semantic memory deficits. Significance: This is the first study that discovered resting state EEG abnormality in semantic dementia. The notion that resting EEG in this dementia subtype is normal has to be revised.

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EEG microstates of wakefulness and NREM sleep

Cited by 233 publications

References 60 publications

Association Between Resting-State Microstates and Ratings on the Amsterdam Resting-State Questionnaire

Association Between Resting-State Microstates and Ratings on the Amsterdam Resting-State Questionnaire

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

Discovering EEG resting state alterations of semantic dementia

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