Evidence for modulation of EEG microstate sequence by vigilance level

Krylova, Marina; Alizadeh, Sarah; Izyurov, I; Teckentrup, Vanessa; Chang, Catie; Meer, Johan N. van der; Erb, Michael; Kroemer, Nils B.; Koenig, Thomas; Walter, Martín; Jamalabadi, Hamidreza

doi:10.1016/j.neuroimage.2020.117393

Cited by 36 publications

(28 citation statements)

References 52 publications

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“…We could hardly see any significant changes in MS parameters for the control group after watching and memorising the video in comparison of the two RS recordings ( Figures 2A,D,G ). No significant results were also observed for MS transitions in the control condition ( Figure 3A , as we have already illustrated in Krylova et al, 2021 ).…”

Section: Resultssupporting

confidence: 70%

“…In accordance with Murray et al ( 2008 ), we calculated global field power peaks (GFP) for each epoch, which constitutes a reference-independent measure of response strength (Murray et al, 2008 ). Subsequently, we followed the methodology of Krylova et al ( 2021 ) for MS analysis, for more details see Supplementary Section 1.2 (Krylova et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…FIGURE 3 | (A)Network graphs of the transition rate (original/delta) of the four canonical MSs for the control condition comparing the two resting state measurements (modified fromKrylova et al, 2021). In the control condition, no significant results could be obtained for any of the transition rates between MSs (thin grey arrows).…”

mentioning

confidence: 99%

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EEG-Microstates Reflect Auditory Distraction After Attentive Audiovisual Perception Recruitment of Cognitive Control Networks

Korn

Krylova

Heck

et al. 2021

Front. Syst. Neurosci.

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Processing of sensory information is embedded into ongoing neural processes which contribute to brain states. Electroencephalographic microstates are semi-stable short-lived power distributions which have been associated with subsystem activity such as auditory, visual and attention networks. Here we explore changes in electrical brain states in response to an audiovisual perception and memorization task under conditions of auditory distraction. We discovered changes in brain microstates reflecting a weakening of states representing activity of the auditory system and strengthening of salience networks, supporting the idea that salience networks are active after audiovisual encoding and during memorization to protect memories and concentrate on upcoming behavioural response.

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Section: Resultssupporting

confidence: 70%

Section: Methodsmentioning

confidence: 99%

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EEG-Microstates Reflect Auditory Distraction After Attentive Audiovisual Perception Recruitment of Cognitive Control Networks

Korn

Krylova

Heck

et al. 2021

Front. Syst. Neurosci.

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“…Microstate activation is thus exclusive , i.e., two microstates cannot be simultaneously active, and complete , i.e., a microstate is active at any time. Importantly, this basic criterion is fairly close in spirit to the Viterbi algorithm used in the HMM (as explained below), but it is frequently altered in the microstate literature by using a temporally smoothed version of these binary time series ( da Cruz et al., 2020 ; D'Croz-Baron et al., 2019 ; Krylova et al., 2020 ; Pascual-Marqui et al., 1995 ; Sikka et al., 2020 ). In our direct comparison, we implemented the popular approach whereby microstate activation is determined as described above but at GFP peaks only and is then extended between these peaks by nearest-neighbor interpolation ( Krylova et al., 2020 ; Sikka et al., 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Microstates and power envelope hidden Markov modeling probe bursting brain activity at different timescales

et al. 2022

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State modeling of whole-brain electroencephalography (EEG) or magnetoencephalography (MEG) allows to investigate transient, recurring neurodynamical events. Two widely-used techniques are the microstate analysis of EEG signals and hidden Markov modeling (HMM) of MEG power envelopes. Both reportedly lead to similar state lifetimes on the 100 ms timescale, suggesting a common neural basis. To investigate whether microstates and power envelope HMM states describe the same neural dynamics, we used simultaneous MEG/EEG recordings at rest and compared the spatial signature and temporal activation dynamics of microstates and power envelope HMM states obtained separately from EEG and MEG. Results showed that microstates and power envelope HMM states differ both spatially and temporally. Microstates reflect sharp events of neural synchronization, whereas power envelope HMM states disclose network-level activity with 100–200 ms lifetimes. Further, MEG microstates do not correspond to the canonical EEG microstates but are better interpreted as split HMM states. On the other hand, both MEG and EEG HMM states involve the (de)activation of similar functional networks. Microstate analysis and power envelope HMM thus appear sensitive to neural events occurring over different spatial and temporal scales. As such, they represent complementary approaches to explore the fast, sub-second scale bursting electrophysiological dynamics in spontaneous human brain activity.

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“…As mentioned above, longer recording durations may lead to fatigue which might impact microstate dynamics (potentially differentially between young and older adults) and might therefore explain discrepancies between studies. Interestingly, it has been reported that vigilance level is negatively associated with occurrence of map A and B and positively associated with duration of map C and D in 39 adults (mean age 43.7 ± 9.8) and 20 young adults (mean age 26.8 ± 7.6) (Krylova et al 2020). Detailed comparisons of experimental procedures in various populations will be essential to provide a better understanding of these differences.…”

Section: Microstates a And Bmentioning

confidence: 99%

Resting‐State EEG Microstates Parallel Age‐Related Differences in Allocentric Spatial Working Memory Performance

et al. 2021

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Alterations of resting-state EEG microstates have been associated with various neurological disorders and behavioral states. Interestingly, age-related differences in EEG microstate organization have also been reported, and it has been suggested that resting-state EEG activity may predict cognitive capacities in healthy individuals across the lifespan. In this exploratory study, we performed a microstate analysis of resting-state brain activity and tested allocentric spatial working memory performance in healthy adult individuals: twenty 25–30-year-olds and twenty-five 64–75-year-olds. We found a lower spatial working memory performance in older adults, as well as age-related differences in the five EEG microstate maps A, B, C, C′ and D, but especially in microstate maps C and C′. These two maps have been linked to neuronal activity in the frontal and parietal brain regions which are associated with working memory and attention, cognitive functions that have been shown to be sensitive to aging. Older adults exhibited lower global explained variance and occurrence of maps C and C′. Moreover, although there was a higher probability to transition from any map towards maps C, C′ and D in young and older adults, this probability was lower in older adults. Finally, although age-related differences in resting-state EEG microstates paralleled differences in allocentric spatial working memory performance, we found no evidence that any individual or combination of resting-state EEG microstate parameter(s) could reliably predict individual spatial working memory performance. Whether the temporal dynamics of EEG microstates may be used to assess healthy cognitive aging from resting-state brain activity requires further investigation.

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Evidence for modulation of EEG microstate sequence by vigilance level

Cited by 36 publications

References 52 publications

EEG-Microstates Reflect Auditory Distraction After Attentive Audiovisual Perception Recruitment of Cognitive Control Networks

EEG-Microstates Reflect Auditory Distraction After Attentive Audiovisual Perception Recruitment of Cognitive Control Networks

Microstates and power envelope hidden Markov modeling probe bursting brain activity at different timescales

Resting‐State EEG Microstates Parallel Age‐Related Differences in Allocentric Spatial Working Memory Performance

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