The Functional Aspects of Resting EEG Microstates: A Systematic Review

Tarailis, Povilas; Koenig, Thomas; Michel, Christoph M.; Griškova-Bulanova, Inga

doi:10.1007/s10548-023-00958-9

Cited by 80 publications

(106 citation statements)

References 172 publications

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“…Specifically, while all four of the microstate parameters (GEV, duration, coverage, occurrence) were seen to increase as a function of age (Figure 6), age was only found to significantly contribute to the model for duration and occurrence after multiple comparison corrections were applied. We also note that, while we refer here to the topography with left-to-right configuration as microstate E, in line with other studies (e.g., Baldini et al, 2023;Brechet et al, 2019;Custo et al, 2017;Das et al, 2022;D'Croz-Baron et al, 2019;Ke et al, 2021;Takarae et al, 2022;Tarailis et al, 2021), inconsistent microstate labelling across studies has also seen this microstate labelled as microstate F (see Tarailis et al (2023) for a recent in-depth review of resting-state EEG microstates). As microstate E is relatively newly described, only limited information presently exists on its functional relevance (Tarailis et al, 2023).…”

Section: Age Effects On Microstate Featuressupporting

confidence: 87%

“…Four of the microstate maps obtained for both the eyes-closed and eyes-open conditions strongly resembled the canonical microstates A, B, C, and D previously reported in the literature (Khanna et al, 2015;Michel & Koenig, 2018;Tarailis et al, 2023), while the fifth map resembled the more recently described microstate E (Custo et al, 2017;Das et al, 2022;Ke et al, 2021) (sometimes also referred to as microstate F (Tarailis et al, 2023); Figure 2; scalp field potential data pertaining to each microstate template map is also provided in Table S2). Together, the five microstates explained 69.2% (eyesclosed) and 68.4% (eyes-open) of the total global variance, which is broadly in line with previous reports (Custo et al, 2017;Michel & Koenig, 2018)…”

Section: Resultssupporting

confidence: 71%

“…It is therefore likely that the spatio-temporal properties of large-scale cortical network activity, as reflected by microstates, might also change throughout early-to-middle childhood in line with known structural and functional network alterations that occur during neurodevelopment (Casey et al, 2005;Huang et al, 2015). We found that both the eyes-closed and eyes-open EEG recordings yielded microstate topographies in this cohort of children resembling the canonical microstate classes A to D frequently reported in adults (Khanna et al, 2015;Michel & Koenig, 2018;Tarailis et al, 2023), with the addition of the more recently described microstate E (e.g., Baldini et al, 2023;Brechet et al, 2019;Custo et al, 2017;Das et al, 2022).…”

Section: Discussionsupporting

confidence: 70%

“…It has been shown that spontaneous EEG recorded at rest can be characterised by a relatively small number of microstates, typically four (labelled by previous research as canonical classes A, B, C, and D; see Figure 1), which can explain between $65% and 85% of the total topographic variance on the EEG record (Khanna et al, 2015;Michel & Koenig, 2018). The temporal features at these microstate classes demonstrate high test-retest reliability (Khanna et al, 2014), and appear to be relatively consistent across the adult lifespan (Khanna et al, 2015;Koenig et al, 2002), with more recent studies also beginning to establish additional classes beyond the canonical four (Das et al, 2022;Tarailis et al, 2023). The topographical features of several EEG-derived microstates have also been shown to overlap with well-defined fMRI-derived resting-state networks (RSNs) (Britz et al, 2010;Custo et al, 2017;Musso et al, 2010).…”

Section: Introductionmentioning

confidence: 93%

See 3 more Smart Citations

EEG microstates in early‐to‐middle childhood show associations with age, biological sex, and alpha power

Hill,

Bailey,

Zomorrodi

et al. 2023

Human Brain Mapping

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Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large‐scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental temporal features of microstates extracted from the broadband EEG signal in a large (N = 139) cohort of children spanning early‐to‐middle childhood (4–12 years of age). Linear regression models were used to examine if participants' age and biological sex could predict the temporal parameters GEV, duration, coverage, and occurrence, for five microstate classes (A–E) across both eyes‐closed and eyes‐open resting‐state recordings. We further explored associations between these microstate parameters and posterior alpha power after removal of the 1/f‐like aperiodic signal. The microstates obtained from our neurodevelopmental EEG recordings broadly replicated the four canonical microstate classes (A to D) frequently reported in adults, with the addition of the more recently established microstate class E. Biological sex served as a significant predictor in the regression models for four of the five microstate classes (A, C, D, and E). In addition, duration and occurrence for microstate E were both found to be positively associated with age for the eyes‐open recordings, while the temporal parameters of microstates C and E both exhibited associations with alpha band spectral power. Together, these findings highlight the influence of age and sex on large‐scale functional brain networks during early‐to‐middle childhood, extending understanding of neural dynamics across this important period for brain development.

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Section: Age Effects On Microstate Featuressupporting

confidence: 87%

Section: Resultssupporting

confidence: 71%

Section: Discussionsupporting

confidence: 70%

Section: Introductionmentioning

confidence: 93%

See 2 more Smart Citations

EEG microstates in early‐to‐middle childhood show associations with age, biological sex, and alpha power

Hill,

Bailey,

Zomorrodi

et al. 2023

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…The median optimal number of microstate templates, determined using the Krzanowski–Lai criterion, was found to be

k = 7

across all subjects, within a range of 4 − 15 clusters. Figure 4 displays the topographical maps of each of these seven microstates, which exhibit a strong resemblance to the classical microstate maps (A–F and C

^{'}

) (Custo et al, 2017; Michel & Koenig, 2018; Tarailis et al, 2023). Additional information and detailed results regarding microstate topographies can be found in section 4 of the Supporting Information.…”

Section: Resultsmentioning

confidence: 87%

Time course of EEG complexity reflects attentional engagement during listening to speech in noise

Eqlimi,

Bockstael,

Schönwiesner

et al. 2023

Eur J of Neuroscience

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Auditory distractions are recognized to considerably challenge the quality of information encoding during speech comprehension. This study explores electroencephalography (EEG) microstate dynamics in ecologically valid, noisy settings, aiming to uncover how these auditory distractions influence the process of information encoding during speech comprehension. We examined three listening scenarios: (1) speech perception with background noise (LA), (2) focused attention on the background noise (BA), and (3) intentional disregard of the background noise (BUA). Our findings showed that microstate complexity and unpredictability increased when attention was directed towards speech compared with tasks without speech (LA > BA & BUA). Notably, the time elapsed between the recurrence of microstates increased significantly in LA compared with both BA and BUA. This suggests that coping with background noise during speech comprehension demands more sustained cognitive effort. Additionally, a two‐stage time course for both microstate complexity and alpha‐to‐theta power ratio was observed. Specifically, in the early epochs, a lower level was observed, which gradually increased and eventually reached a steady level in the later epochs. The findings suggest that the initial stage is primarily driven by sensory processes and information gathering, while the second stage involves higher level cognitive engagement, including mnemonic binding and memory encoding.

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Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis

Metzger,

Dukic,

McMackin

et al. 2023

Human Brain Mapping

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Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal‐based abnormality in ALS. High‐density resting‐state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24‐month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A‐D) using K‐means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late‐stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher‐order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.

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The Functional Aspects of Resting EEG Microstates: A Systematic Review

Cited by 80 publications

References 172 publications

EEG microstates in early‐to‐middle childhood show associations with age, biological sex, and alpha power

EEG microstates in early‐to‐middle childhood show associations with age, biological sex, and alpha power

Time course of EEG complexity reflects attentional engagement during listening to speech in noise

Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis

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