Reliability of resting-state microstate features in electroencephalography

The role of testosterone in modulating positive and negative empathy in social interactions

Empathy encompasses both negative (e.g., distress) and positive (e.g., shared joy) dimensions, yet the effects of testosterone on positive empathy and its modulation of intrinsic neural dynamics remain underexplored. This double-blind, placebo-controlled study examined how testosterone influences neural sensitivity to empathy within social inclusion and exclusion contexts, as well as its impact on resting-state EEG microstates-millisecond-scale transient patterns of brain activity. Healthy male participants received either testosterone or placebo before completing resting-state EEG recordings and an empathy task featuring social scenarios. While self-reported empathy ratings remained unchanged, testosterone amplified neurophysiological responses: it enhanced anterior N2 amplitude (250-310 ms), associated with negative empathy toward social exclusion, and increased posterior α-event-related desynchronization (8.28-10 Hz; 1226-1901 ms), linked to positive empathy during social inclusion. These findings suggest that testosterone enhances neural responsiveness to both threatening and affiliative social cues, reinforcing its role in adaptive social vigilance. Resting-state EEG microstate analysis further revealed that testosterone prolonged the temporal dominance of microstate E-a centro-parietal activity pattern associated with interoceptive awareness and emotional processing. Notably, these microstate E changes predicted increased emotional empathy across both positive and negative contexts. Together, our findings suggest that testosterone indirectly enhances empathy-related responsiveness by amplifying baseline interoceptive sensitivity to socially salient stimuli. These dual effects-enhanced intrinsic interoceptive processing and heightened neural reactivity to social cues-position testosterone as a key neuromodulator of context-adaptive social perception.

Advancing flavor perception research with EEG microstate analysis: A dynamic approach to understanding brain responses to alcoholic stimuli

Understanding how our brain's perceptual system related to sensory evaluation of food can be affected by alcohol concentration is essential for both neuroscience and food science. This study applied EEG microstate analysis to characterize dynamic brain activity across seven alcohol levels (water, 5 %, 10 %, 20 %, 40 %, 53 % ABV, and Baijiu). Unlike traditional EEG analyses, microstate analysis provides a temporally resolved perspective on large-scale neural dynamics. Four microstates (A, B, C, D) were identified, with microstates B and C predominantly involved in sensory-emotional processing. Lower alcohol levels (≤20 % ABV) enhanced sensory focus, whereas higher concentrations (≥ 40 % ABV) induced frequent sensory re-evaluation and attentional shifts. These results reveal concentration-dependent neural adaptations, demonstrating that alcohol modulates both sensory and cognitive processing through dynamic brain state transitions. These findings enhance our understanding of alcohol-induced sensory and cognitive processing, providing insights for both neuro-flavor research and food science applications.

Facilitating cognitive neuroscience research with 80-sensor optically pumped magnetometer magnetoencephalography (OPM-MEG)

Recent advancements in optically pumped magnetometer magnetoencephalography (OPM-MEG) make it a promising alternative to conventional SQUID-MEG systems. Nonetheless, as reported in the literature, current OPM-MEG systems are often constrained by a limited number of sampling points, which restricts their capability to match the full-head coverage offered by SQUID-MEG systems. Additionally, whether OPM-MEG can deliver results comparable to SQUID-MEG in practical cognitive neuroscience applications remains largely unexplored. In this study, we introduce a high-density, full-head coverage OPM-MEG system with 80 sensors and systematically compare the performance of OPM-MEG and SQUID-MEG, from sensor- to source-level analysis, across various classic cognitive tasks. Our results demonstrate that visual and auditory evoked fields captured using OPM-MEG align closely with those obtained from SQUID-MEG. Furthermore, steady-state visual evoked field and finger-tapping-induced beta power change recorded with OPM-MEG are accurately localized to corresponding brain regions, with activation centers highly congruent to those observed with SQUID-MEG. For resting-state recordings, the two modalities exhibit similar power distributions, functional connectomes, and microstate clusters. These findings indicate that the 80-sensor OPM-MEG system provides spatial and temporal characteristics comparable to those of traditional SQUID-MEG. Thus, our study offers empirical evidence supporting the efficacy of high-density OPM-MEG and suggests that OPM-MEG, with dense sampling capability, represents a compelling alternative to conventional SQUID-MEG, facilitating further exploration of human cognition.

Exploring cortical excitability in children with cerebral palsy through lower limb robot training based on MI-BCI

This study aims to compare brain activity differences under the motor imagery-brain-computer interface (MI-BCI), motor imagery (MI), and resting (REST) paradigms through EEG microstate and functional connectivity (FC) analysis, providing a theoretical basis for applying MI-BCI in the rehabilitation of children with cerebral palsy (CP). This study included 30 subjects aged 4-6 years with GMFCS II-III grade, diagnosed with CP and classified as spastic diplegia. They sequentially completed EEG signal acquisition under REST, MI, and MI-BCI conditions. Clustering analysis was used to analyze EEG microstates and extract EEG microstate temporal parameters. Additionally, the strength of brain FC in different frequency bands was analyzed to compare the differences under various conditions. Four microstate classes (A-D) were identified to best explain the datasets of three groups. Compared to REST, the average duration and coverage rate of microstate D under MI and MI-BCI significantly increased (P < 0.05), while their frequency and the coverage rate and frequency of microstate A decreased. Compared to MI, the average duration of microstate C under MI-BCI significantly decreased (P < 0.05), while the frequency of microstate B significantly increased (P < 0.05). Additionally, the transition probability results showed that other microstates under REST had a higher transition probability to microstate A, while under MI and MI-BCI, other microstates had a higher transition probability to microstate D. The brain network results revealed significant differences in brain network connectivity among REST, MI, and MI-BCI across different frequency bands. No FC differences were found between REST, MI, and MI-BCI in the α2 frequency band. In the δ and γ frequency bands, MI and MI-BCI both had greater inter-electrode connectivity strength than REST. In the θ frequency band, REST had greater inter-electrode connectivity strength than MI-BCI, while MI-BCI had greater inter-electrode connectivity strength than both REST and MI. In the α1 frequency band, MI-BCI had greater inter-electrode connectivity strength than REST, and in the β frequency band, MI-BCI had greater inter-electrode connectivity strength than MI. MI-BCI can significantly alter the brain activity patterns of children with CP, particularly by enhancing the activity intensity of EEG microstates related to attention, motor planning, and execution, as well as the brain FC strength in different frequency bands. It holds high application value in the lower limb motor rehabilitation of children with CP.

Gamma Transcranial Alternating Current Stimulation Enhances Working Memory Ability in Healthy People: An EEG Microstate Study

BACKGROUND

Working memory (WM) is a core cognitive function closely linked to various cognitive processes including language, decision making, and reasoning. Transcranial alternating current stimulation (tACS), a non-invasive brain stimulation technique, has been shown to modulate cognitive abilities and treat psychiatric disorders. Although gamma tACS (γ-tACS) has demonstrated positive effects on WM, its underlying neural mechanisms remain unclear.

METHODS

In this study, we employed electroencephalogram (EEG) microstate analysis to investigate the spatiotemporal dynamics of γ-tACS effects on WM performance. Healthy participants (N = 104) participated in two-back and three-back WM tasks before and after two types (sine and triangular) of γ-tACS, with sham stimulation as a control.

RESULTS

Our results revealed that γ-tACS improved performance in both the two-back and three-back tasks, with triangular γ-tACS showing greater accuracy improvement in the three-back task than the sham group. Furthermore, γ-tACS significantly modulated EEG microstate dynamics, specifically downregulating microstate Class C and upregulating microstate Classes D and B. These changes were positively correlated with reduced reaction times in the three-back task.

CONCLUSIONS

Our findings establish microstate analysis as an effective approach for evaluating γ-tACS-induced changes in global brain activity and advance the understanding of how γ-tACS influences WM.

EEG microstate syntax analysis: A review of methodological challenges and advances

Electroencephalography (EEG) microstates are "quasi-stable" periods of electrical potential distribution in multichannel EEG derived from peaks in Global Field Power. Transitions between microstates form a temporal sequence that may reflect underlying neural dynamics. Mounting evidence indicates that EEG microstate sequences have long-range, non-Markovian dependencies, suggesting a complex underlying process that drives EEG microstate syntax (i.e., the transitional dynamics between microstates). Despite growing interest in EEG microstate syntax, the field remains fragmented, with inconsistent terminologies used between studies and a lack of defined methodological categories. To advance the understanding of functional significance of microstates and to facilitate methodological comparability and finding replicability across studies, we: i) derive categories of syntax analysis methods, reviewing how each may be utilised most readily; ii) define three "time-modes" for EEG microstate sequence construction; and iii) outline general issues concerning current microstate syntax analysis methods, suggesting that the microstate models derived using these methods are cross-referenced against models of continuous EEG. We advocate for these continuous approaches as they do not assume a winner-takes-all model inherent in the microstate derivation methods and contextualise the relationship between microstate models and EEG data. They may also allow for the development of more robust associative models between microstates and functional Magnetic Resonance Imaging data.

The Feasibility and Test-Retest Reliability of Wireless Dry-Electrode EEG During a Dynamic Psychomotor Virtual Reality Task

PURPOSE

Virtual reality (VR) offers immersive environments for studying psychomotor performance, but the reliability of dry-electrode electroencephalography (EEG) in assessing brain activity during dynamic VR exergames (VRex) remains unclear. The present study investigated the feasibility and reliability of dry-electrode EEG frequency band, with primary focus on alpha band activity.

METHODS

Ten amateur combat sports male participants (37 ± 11 years) volunteered for this study. The feasibility of dry-electrode EEG recording during motion and test-retest (24 h) reliability, was investigated. EEG measurements were obtained pre, post, and throughout a standardized boxing focus ball VRex session, comprising three 3-min rounds interspersed with 1-min rest intervals. EEG data were analyzed globally and at each electrode site, calculating average power spectral density values.

FINDINGS

ICCs data indicated poor-to-excellent (0.208-0.858) reliability across all measurements within the 4- to 30-Hz frequency range. Poor-to-good reliability (0.393-0.636) was found across the task-active VRex intervals. Electrode sites ranged in reliability from poor (electrode P3; 0.262) to excellent (electrode P4; 0.728), with higher reliability found in the alpha band across electrode sites compared to average spectral band values.

CONCLUSION

The present study demonstrates the feasibility, although variable reliability, in neuronal detection during a dynamic VR task, using novel dry-electrode EEG technology.

Spectral and Microstate EEG Analysis in Narcolepsy Type 1 and Type 2 Across Sleep Stages

BACKGROUND

The primary distinction between narcolepsy type 1 (NT1) and narcolepsy type 2 (NT2) is the presence or absence of cataplexy, which is commonly determined through clinical interviews, though it can be prone to error due to vague patients descriptions.

OBJECTIVE

This study aimed to investigate EEG microstate differences between NT1 and NT2 and their correlation with clinical assessments.

METHODS

Polysomnography (PSG) and the Multiple Sleep Latency Test (MSLT) were performed on 14 NT1 and 13 NT2 patients from three hospitals, with data from the ISRUC-SLEEP dataset serving as the comparison group. After EEG preprocessing, we performed the spectral analysis in NT1 and NT2, followed by microstate analysis. Grand mean maps were used for backfitting to obtain microstate parameters. Then, Spearman correlation was performed between the microstate parameters and the ESS and MSLT parameters.

RESULTS

We found that the relative delta power in N2 was lower in the NT1 group compared to the NT2 group. Four microstates were clustered in all groups, and no statistical differences were observed in the microstate parameters between NT1 and NT2 groups. In the NT1 group, microstate D during wakefulness showed a positive correlation with ESS, while in the NT2 group, microstate D during wakefulness showed a negative correlation with ESS.

CONCLUSIONS

There are spectral differences between the NT1 and NT2 groups, and the opposite correlation between microstate D and ESS during wakefulness in NT1 and NT2 suggest that the underlying mechanisms leading to excessive daytime sleepiness in the two groups may be different.

Infant EEG microstate dynamics relate to fine-grained patterns of infant attention during naturalistic play with caregivers

As infants grow, they develop greater attentional control during interactions with others, shifting from patterns of attention primarily driven by caregivers (exogenous) to those that are also self-directed (endogenous). The ability to endogenously control attention during infancy is thought to reflect ongoing brain development and is influenced by patterns of joint attention between infant and caregiver. However, whether measures of infant attentional control and caregiver behavior during infant-caregiver interactions relate to patterns of infant brain activity is unknown and key for informing developmental models of attentional control. Using data from 43 infant-caregiver dyads, we quantified patterns of visual attention with dyadic, head-mounted eye tracking during infant-caregiver play and associated them with the duration of infant EEG microstate D/4 measured during rest. Importantly, microstate D/4 is a scalp potential topography thought to reflect the organization and function of attention-related brain networks. We found that microstate D/4 associated positively with infant-led joint attention rate but did not associate with caregiver-led joint attention rate, suggesting that infant-led coordination of joint attention during play may be critical for the neurobiological development of attentional control, or vice versa. Further, we found that microstate D/4 associated negatively with infant attention shift rate and positively with infant sustained attention duration, suggesting that increased stability of microstate D/4 may reflect maturation of attentional control and its underlying neural substrates. Together, our findings provide insights into how infant attentional control abilities and infant-caregiver visual behavior during play are associated with the spatial and temporal dynamics of infant brain activity.

Diminished attention network activity and heightened salience-default mode transitions in generalized anxiety disorder: Evidence from resting-state EEG microstate analysis

Generalized anxiety disorder (GAD) is a common anxiety disorder characterized by excessive, uncontrollable worry and physical symptoms such as difficulty concentrating and sleep disturbances. Although functional magnetic resonance imaging (fMRI) studies have reported aberrant network-level activity related to cognition and emotion in GAD, its low temporal resolution restricts its ability to capture the rapid neural activity in mental processes. EEG microstate analysis offers millisecond-resolution for tracking the dynamic changes in brain electrical activity, thereby illuminating the neurophysiological mechanisms underlying the cognitive and emotional dysfunctions in GAD. This study collected 64-channel resting-state EEG data from 28 GAD patients and 28 healthy controls (HC), identifying five microstate classes (A-E) in both groups. Results showed that GAD patients exhibited significantly lower duration (p < 0.01), occurrence (p < 0.05), and coverage (p < 0.01) of microstate class D, potentially reflecting deficits in attention-related networks. Such alterations may contribute to the impairments in attention maintenance and cognitive control. Additionally, GAD patients displayed reduced transition probabilities in A → D, B → D, C → D, and E → D (all corrected p < 0.05), but increased in C → E (corrected p < 0.05) and E → C (corrected p < 0.01). These results highlight a significant reduction in the brain's ability to transition into microstate class D, alongside overactivity in switching between the default mode network and the salience network. Such neurophysiological changes may underlie cognitive control deficits, increased spontaneous rumination, and emotional regulation challenges observed in GAD. Together, these insights provide a new perspective for understanding the neurophysiological and pathological mechanisms underlying GAD.

EEG Microstate Dynamics during Different Physiological Developmental Stages and the Effects of Medication in Schizophrenia

BACKGROUND

Schizophrenia (SCZ) is associated with abnormal neural activities and brain connectivity. Electroencephalography (EEG) microstate is a voltage topographical representation of temporary brain network activations. Most research on EEG microstates in SCZ has focused on differences between patients and healthy controls (HC). However, changes in EEG microstates among SCZ patients across various stages of physiological and cognitive development have not been thoroughly assessed. Consequently, we stratified patients with SCZ into four age-specific cohorts (20-29 years (brain maturation), 30-39 years (stabilization), 40-49 years (early aging), and 50-59 years (advanced aging)) to evaluate EEG microstate alterations. Additionally, we assessed changes in EEG microstates in first-episode psychosis (FEP) before and after an 8-week treatment period.

METHODS

We acquired 19-channel resting-state EEG from 140 chronic SCZ patients, aged 20 to 59 years, as well as from 19 FEP and 20 healthy controls. FEP patients underwent an 8-week inpatient follow-up. After pre-processing, EEG data from different groups were subjected to microstate analysis, and the K-Means clustering algorithm was applied to classify the data into 4 microstates. Subsequently, templates of these microstates were used to fit EEG signals from each patient, and the collected microstate parameters were analyzed.

RESULTS

Patients with SCZ aged 20 to 29 years demonstrated an increased time coverage of microstate class D compared to other age cohorts. In individuals aged 30-39 years, the parameters of microstate class B-specifically time coverage and occurrence-exhibited significant reductions relative to those in the 40-49 and 50-59 years age groups. Compared to healthy controls, microstates class A parameters were significantly reduced in SCZ patients, while microstates class C parameters were prolonged; after 8 weeks of treatment, microstates class A parameters increased and microstates class C parameters decreased.

CONCLUSIONS

Alterations in microstate dynamics were observed among SCZ patients across developmental stages, suggesting potential changes in brain activity patterns. Changes in microstates A and C may serve as potential biomarkers for evaluating treatment efficacy, establishing a foundation for personalized therapeutic approaches.

Specific endophenotypes in EEG microstates for methamphetamine use disorder

Background

Electroencephalogram (EEG) microstates, which reflect large-scale resting-state networks of the brain, have been proposed as potential endophenotypes for methamphetamine use disorder (MUD). However, current endophenotypes lack refinement at the frequency band level, limiting their precision in identifying key frequency bands associated with MUD.

Methods

In this study, we investigated EEG microstate dynamics across various frequency bands and different tasks, utilizing machine learning to classify MUD and healthy controls.

Results

During the resting state, the highest classification accuracy for detecting MUD was 85.5%, achieved using microstate parameters in the alpha band. Among these, the coverage of microstate class A contributed the most, suggesting it as the most promising endophenotype for specifying MUD.

Discussion

We accurately categorize the endophenotype of MUD into different sub-frequency bands, thereby providing reliable biomarkers.

Unveiling neural activity changes in mild cognitive impairment using microstate analysis and machine learning

BACKGROUND

Mild cognitive impairment (MCI) is recognized as a condition that may increase the risk of developing Alzheimer's disease (AD). Understanding the neural correlates of MCI is crucial for elucidating its pathophysiology and developing effective interventions. Electroencephalogram (EEG) microstates, reflecting brain activity changes, have shown promise in MCI research. However, current approaches often lack comprehensive characterization of the complex neural dynamics associated with MCI.

OBJECTIVE

This study aims to investigate neurophysiological changes associated with MCI using a comprehensive set of microstate features, including traditional temporal features and entropy measures.

METHODS

Resting-state EEG data were collected from 69 MCI patients and healthy controls (HC). Microstate analysis was performed to extract conventional features (duration, coverage) and entropy measures. Statistical analysis, principal component analysis (PCA), and machine learning (ML) techniques were employed to evaluate neurophysiological patterns associated with MCI.

RESULTS

MCI displayed altered microstate dynamics, with significantly longer coverage and duration in Microstate C but shorter in Microstates A, B, and D compared to HCs. PCA revealed two principal components, primarily composed of microstate dynamics and entropy measures, explaining over 75% of the variance. ML models achieved high accuracy in distinguishing MCI patterns.

CONCLUSIONS

Our comprehensive analysis of EEG microstate features provides new insights into neurophysiological changes associated with MCI, highlighting the potential of EEG microstates for investigating complex neural changes in cognitive decline.

Relational Integration Training Modulated the Frontoparietal Network for Fluid Intelligence: An EEG Microstates Study

Relational integration is a key subcomponent of working memory and a strong predictor of fluid intelligence. Both relational integration and fluid intelligence share a common neural foundation, particularly involving the frontoparietal network. This study utilized a randomized controlled experiment to examine the effect of relational integration training on brain networks using electroencephalogram (EEG) and microstate analysis. Participants were randomly assigned to either a relational integration training group (n = 29) or an active control group (n = 28) for one month. The Sandia matrices task assessed fluid intelligence, while rest-EEG was recorded during pre- and post-tests. Microstate analysis revealed that, for microstate D, the training group demonstrated a significant increase in occurrence and contribution following the intervention compared to the control group. Additionally, microstate D occurrence was negatively correlated with reaction times (RTs). Post-training, the training group showed a lower occurrence and contribution of microstate C compared to the control group. Regarding transfer probability, the training group exhibited a decrease between microstates A and B, and an increase between microstates C and D. In contrast, the control group showed increased transfer probability between microstates A, B, and C, and a decrease between microstate D and other microstates (B and A). These findings indicate that relational integration training influences frontoparietal networks associated with fluid intelligence. The current study suggests that relational integration training is an effective intervention for enhancing fluid intelligence.

Spatiotemporal Microstate Dynamics of Spike-Free Scalp EEG Offer a Potential Biomarker for Refractory Temporal Lobe Epilepsy

Refractory temporal lobe epilepsy (TLE) is one of the most frequently observed subtypes of epilepsy and endangers more than 50 million people world-wide. Although electroencephalogram (EEG) had been widely recognized as a classic tool to screen and diagnose epilepsy, for many years it heavily relied on identifying epileptic discharges and epileptogenic zone localization, which however, limits the understanding of refractory epilepsy due to the network nature of this disease. This work hypothesizes that the microstate dynamics based on resting-state scalp EEG can offer an additional network depiction of the disease and provide potential complementary evaluation tool for the TLE even without detectable epileptic discharges on EEG. We propose a novel framework for EEG microstate spatial-temporal dynamics (EEG-MiSTD) analysis based on machine learning to comprehensively model millisecond-changing whole-brain network dynamics. With only 100 seconds of resting-state EEG even without epileptic discharges, this approach successfully distinguishes TLE patients from healthy controls and is related to the lateralization of epileptic focus. Besides, microstate temporal and spatial features are found to be widely related to clinical parameters, which further demonstrate that TLE is a network disease. A preliminary exploration suggests that the spatial topography is sensitive to the following surgical outcomes. From such a new perspective, our results suggest that spatiotemporal microstate dynamics is potentially a biomarker of the disease. The developed EEG-MiSTD framework can probably be considered as a general tool to examine dynamical brain network disruption in a user-friendly way for other types of epilepsy.

Common and differential EEG microstate of major depressive disorder patients with and without response to rTMS treatment

OBJECTIVE

Repetitive transcranial magnetic stimulation (rTMS) has recently emerged as a novel treatment option for patients with major depressive disorder (MDD), but clinical observations reveal variability in patient's responses to rTMS. Therefore, it is clinically significant to investigate the baseline neuroimaging differences between patients with (Responder) and without (NonResponder) response to rTMS treatment and predict rTMS treatment outcomes based on baseline neuroimaging data.

METHOD

Baseline resting-state EEG data and Beck Depression Inventory (BDI) were collected from 74 rTMS Responder, 43 NonResponder, and 47 matched healthy controls (HC). EEG microstate analysis was applied to analyze common and differential microstate characteristics of Responder and NonResponder. In addition, the microstate temporal parameters were sent to four machine learning models to classify Responder from NonResponder.

RESULT

There exists some common and differential EEG microstate characteristics for Responder and NonResponder. Specifically, compared to the HC group, both Responder and NonResponder exhibited a significant increase in the occurrence of microstate A. Only Responder showed an increase in the coverage of microstate A, occurrence of microstate D, transition probability (TP) from A to D, D to A, and C to A, and a decrease in the duration of microstates B and E, TP from A to B and C to B compared to HC. Only NonResponder exhibited a significant decrease in the duration of microstate D, TP from C to D, and an increase in the occurrence of microstate E, TP from C to E compared to HC. The primary differences between the Responder and NonResponder are that Responder had higher parameters for microstate D, TP from other microstates to D, and lower parameters for microstate E, TP from other microstates to E compared to NonResponder. Baseline parameters of microstate D showed significant correlation with Beck Depression Inventory (BDI) reduction rate. Additionally, these microstate features were sent to four machine learning models to predict rTMS treatment response and classification results indicate that an excellent predicting performance (accuracy = 97.35 %, precision = 96.31 %, recall = 100 %, F1 score = 98.06 %) was obtained when using AdaBoost model. These results suggest that baseline resting-state EEG microstate parameters could serve as robust indicators for predicting the effectiveness of rTMS treatment.

CONCLUSION

This study reveals significant baseline EEG microstate differences between rTMS Responder, NonResponder, and healthy controls. Microstates D and E in baseline EEG can serve as potential biomarkers for predicting rTMS treatment outcomes in MDD patients. These findings may aid in identifying patients likely to respond to rTMS, optimizing treatment plans and reducing trial-and-error approaches in therapy selection.

Resting-State EEG Microstates Dynamics Associated with Interindividual Vulnerability to Sleep Deprivation

Purpose

Sleep deprivation can induce severe deficits in vigilant maintenance and alternation in large-scale networks. However, differences in the dynamic brain networks after sleep deprivation across individuals have rarely been investigated. In the present study, we used EEG microstate analysis to investigate the effects of sleep deprivation and how it differentially affects resting-state brain activity in different individuals.

Participants and Methods

A total of 44 healthy adults participated in a within-participant design study involving baseline sleep and 24-hour sleep deprivation, with resting-state EEG recorded during wakefulness. The psychomotor vigilance task (PVT) was used to measure vigilant attention. Participants were median split as vulnerable or resilient according to their changes in the number of lapses between the baseline sleep and sleep deprivation conditions.

Results

Sleep deprivation caused decreases in microstates A, B, and D, and increases in microstate C. We also found increased transition probabilities of microstates C and D between each other, lower transition probabilities from microstates C and D to microstate B, and higher transition probabilities from microstates A and B to microstate C. Sleep-deprived vulnerable individuals showed decreased occurrence of microstate B and transition probability from microstate C to B after sleep deprivation, but not in resilient individuals.

Conclusion

The findings suggest that sleep deprivation critically affects dynamic brain-state properties and the differences in time parameters of microstates might be the underlying neural basis of interindividual vulnerability to sleep deprivation.

Resting-state EEG microstate analysis of internet gaming disorder and alcohol use disorder

INTRODUCTION

To investigate the neurophysiological aspects of addiction, the microstate characteristics of internet gaming disorder (IGD), alcohol use disorder (AUD), and healthy control (HC) groups were compared using resting-state electroencephalography (EEG).

METHODS

In total, 199 young adults (75 patients with IGD, 57 patients with AUD, and 67 HCs) participated in this study. We conducted EEG microstate analysis among the groups and also compared the obtained parameters with the results of psychological assessments.

RESULTS

The global explained variance, occurrence, and coverage of microstate C were significantly lower in the AUD group than in the IGD group. Additionally, rates of transition from microstates A, B, and D to C were significantly lower in the AUD group than in the IGD group, whereas rates of transition from microstate A to B were lower in the IGD group compared to HCs. Furthermore, the occurrence of microstate C and transition from microstate B to C were negatively correlated with the Alcohol Use Disorder Identification and Behavioural Inhibition Scale score.

CONCLUSION

There were significant differences in microstate characteristics among the groups, which correlated with the psychological scores. These findings suggest that microstate features can be used as neuromarkers in clinical settings to differentiate between addictive disorders and evaluate the pathophysiology of AUD and IGD.

Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms

Electroencephalography (EEG) microstates are canonical voltage topographies that reflect the temporal dynamics of brain networks on a millisecond time scale. Abnormalities in broadband microstate parameters have been observed in subjects with psychiatric symptoms, indicating their potential as clinical biomarkers. Considering distinct information provided by specific frequency bands of EEG, we hypothesized that microstates in decomposed frequency bands could provide a more detailed depiction of the underlying neuropathological mechanism. In this study, with a large open access resting-state dataset (n = 203), we examined the properties of frequency-specific microstates and their relationship with anxiety and depression symptoms. We conducted clustering on EEG topographies in decomposed frequency bands (delta, theta, alpha and beta), and determined the number of clusters with a meta-criterion. Microstate parameters, including global explained variance (GEV), duration, coverage, occurrence and transition probability, were calculated for eyes-open and eyes-closed states, respectively. Their ability to predict the severity of depression and anxiety symptoms were systematically identified by correlation, regression and classification analyses. Distinct microstate patterns were observed across different frequency bands. Microstate parameters in the alpha band held the best predictive power for emotional symptoms. Microstates B (GEV, coverage) and parieto-central maximum microstate E (coverage, occurrence, transitions from B to E) in the alpha band exhibited significant correlations with depression and anxiety, respectively. Microstate parameters of the alpha band achieved predictive R-square of 0.100 for anxiety scores, which is much higher than those of broadband (R-square = -0.026, p < 0.01). Similar results were found in classification of participants with high and low anxiety symptom scores (68% accuracy in alpha vs. 52% in broadband). These results suggested the value of frequency-specific microstates in predicting emotional symptoms.

Brain Evoked Response Qualification Using Multi-Set Consensus Clustering: Toward Single-Trial EEG Analysis

In event-related potential (ERP) analysis, it is commonly assumed that individual trials from a subject share similar properties and originate from comparable neural sources, allowing reliable interpretation of group-averages. Nevertheless, traditional group-level ERP analysis methods, including cluster analysis, often overlook critical information about individual subjects' neural processes due to using fixed measurement intervals derived from averaging. We developed a multi-set consensus clustering pipeline to examine cognitive processes at the individual subject level. Initially, consensus clustering from diverse methods was applied to single-trial EEG epochs of individual subjects. Subsequently, a second level of consensus clustering was performed across the trials of each subject. A newly modified time window determination method was then employed to identify individual subjects' ERP(s) of interest. We validated our method with simulated data for ERP components N2 and P3, and real data from a visual oddball task to confirm the P3 component. Our findings revealed that estimated time windows for individual subjects provide precise ERP identification compared to fixed time windows across all subjects. Additionally, Monte Carlo simulations with synthetic single-trial data demonstrated stable scores for the N2 and P3 components, confirming the reliability of our method. The proposed method enhances the examination of brain-evoked responses at the individual subject level by considering single-trial EEG data, thereby extracting mutual information relevant to the neural process. This approach offers a significant improvement over conventional ERP analysis, which relies on the averaging mechanism and fixed measurement interval.

EEG Evidence of Acute Stress Enhancing Inhibition Control by Increasing Attention

OBJECTIVE

Research about the impact of acute stress on inhibitory control remains a contentious topic, with no consensus reached thus far. This study aims to investigate the effects of acute stress on an individual's inhibitory control abilities and to elucidate the underlying neural mechanisms by analyzing resting state electroencephalogram (EEG) data.

METHODS

We recruited 32 male college students through participant recruitment information to undergo within-subject experiments under stress and non-stress conditions. Physiological indicators (cortisol and heart rate), self-report questionnaires, and behavioral data from the Stroop task were collected before, during, and after the experiment. Additionally, a five-minute eyes closed resting state EEG data collection was conducted during the Stroop task before.

RESULTS

(1) Acute stress led to a reduction in the conflict effect during the participants' Stroop task in individuals. (2) Stress resulted in an increase in the power of the beta in the resting state EEG. (3) Acute stress caused an increase in the duration of class D and an increase in the transition probabilities from classes C and B to class D in the microstates of the resting state EEG. (4) Acute stress leads to an increase in beta power values in individuals' resting state EEGs, which is significantly negatively correlated with the reduction of the conflict effect in the Stroop task under stress.

CONCLUSIONS

Acute stress can enhance individuals' attentional level, thereby promoting inhibitory control performance.

Deep-learning-optimized microstate network analysis for early Parkinson's disease with mild cognitive impairment

Graph-theory-based topological impairment of the whole-brain network has been verified to be one of the characteristics of mild cognitive impairment (MCI). However, two major challenges impede the further understanding of topological features for the personalized functional connectivity network of early Parkinson's disease (ePD) with MCI. The uncertain of characteristic frequency band reflecting the abnormality of ePD-MCI and the setting of fixed length of sliding window at a second level in the construction of conventional brain network both limit a deeper exploration of network characteristics for ePD-MCI. Thus, a convolutional neural network is constructed first and the gradient-weighted class activation mapping method is used to determine the characteristic frequency band of the ePD-MCI. It is found that 1-4 Hz is a characteristic frequency band for recognizing MCI in ePD. Then, we propose a microstate window construction method based on electroencephalography microstate sequences to build brain functional network. By exploring the graph-theory-based topological features and their clinical correlations with cognitive impairment, it is shown that the clustering coefficient, global efficiency, and local efficiency of the occipital lobe significantly decrease in ePD-MCI, which reflects the low degree of nodes interconnection, low efficiency of parallel information transmission and low communication efficiency among the nodes in the brain network of the occipital lobe may be the neural marker of ePD-MCI. The finding of personalized topological impairments of the brain network may be a potential characteristic of early PD-MCI.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-10016-6.

Resting state EEG microstate profiling and a machine-learning based classifier model in epilepsy

Electroencephalography-based (EEG) microstate analysis is a promising and widely studied method in which spontaneous cerebral activity is segmented into sub second level quasi-stable states and analyzed. Currently it is being widely explored due to increasing evidence of the association of microstates with cognitive functioning and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). In our study using the four archetypal microstates (A, B, C and D), we investigated the changes in resting state EEG microstate dynamics in persons with temporal lobe epilepsy (TLE) and idiopathic generalized epilepsy (IGE) compared to healthy controls (HC). Machine learning was applied to study its feasibility in differentiating between different groups using microstate statistics. We found significant differences in all parameters related to Microstate D (fronto-parietal network) in TLE patients and Microstate B (visual processing) in IGE patients compared to HCs. Occurrence, duration and time coverage of Microstate B was highest in IGE when compared to the other groups. We also found significant deviations in transition probabilities for both epilepsy groups, particularly into Microstate C (salience network) in IGE. Classification accuracy into clinical groups was found to exceed 70% using microstate parameters which improved on incorporating neuropsychological test differences. To the best of our knowledge, the current study is the first to compare and validate the use of microstate features to discriminate between two disparate epilepsy syndromes (TLE, IGE) and HCs using machine learning suggesting that resting state EEG microstates can be used for endophenotyping and to study resting state dysfunction in epilepsy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10095-z.

Microstate D as a Biomarker in Schizophrenia: Insights from Brain State Transitions

Objectives. There is a significant correlation between EEG microstate and the neurophysiological basis of mental illness, brain state, and cognitive function. Given that the unclear relationship between network dynamics and different microstates, this paper utilized microstate, brain network, and control theories to understand the microstate characteristics of short-term memory task, aiming to mechanistically explain the most influential microstates and brain regions driving the abnormal changes in brain state transitions in patients with schizophrenia. Methods. We identified each microstate and analyzed the microstate abnormalities in schizophrenia patients during short-term memory tasks. Subsequently, the network dynamics underlying the primary microstates were studied to reveal the relationships between network dynamics and microstates. Finally, using control theory, we confirmed that the abnormal changes in brain state transitions in schizophrenia patients are driven by specific microstates and brain regions. Results. The frontal-occipital lobes activity of microstate D decreased significantly, but the left frontal lobe of microstate B increased significantly in schizophrenia, when the brain was moving toward the easy-to-reach states. However, the frontal-occipital lobes activity of microstate D decreased significantly in schizophrenia, when the brain was moving toward the hard-to-reach states. Microstate D showed that the right-frontal activity had a higher priority than the left-frontal, but microstate B showed that the left-frontal priority decreased significantly in schizophrenia, when changes occur in the synchronization state of the brain. Conclusions. In conclusion, microstate D may be a biomarker candidate of brain abnormal activity during the states transitions in schizophrenia, and microstate B may represent a compensatory mechanism that maintains brain function and exchanges information with other brain regions. Microstate and brain network provide complementary perspectives on the neurodynamics, offering potential insights into brain function in health and disease.

Altered Resting-State Electroencephalogram Microstate Characteristics in Stroke Patients

BACKGROUND

Stroke remains a leading cause of disability globally and movement impairment is the most common complication in stroke patients. Resting-state electroencephalography (EEG) microstate analysis is a non-invasive approach of whole-brain imaging based on the spatiotemporal pattern of the entire cerebral cortex. The present study aims to investigate microstate alterations in stroke patients.

METHODS

Resting-state EEG data collected from 24 stroke patients and 19 healthy controls matched by age and gender were subjected to microstate analysis. For four classic microstates labeled as class A, B, C and D, their temporal characteristics (duration, occurrence and coverage) and transition probabilities (TP) were extracted and compared between the two groups. Furthermore, we explored their correlations with clinical outcomes including the Fugl-Meyer assessment (FMA) and the action research arm test (ARAT) scores in stroke patients. Finally, we analyzed the relationship between the temporal characteristics and spectral power in frequency bands. False discovery rate (FDR) method was applied for correction of multiple comparisons.

RESULTS

Microstate analysis revealed that the stroke group had lower occurrence of microstate A which was regarded as the sensorimotor network (SMN) compared with the control group (p = 0.003, adjusted p = 0.036, t = -2.959). The TP from microstate A to microstate D had a significant positive correlation with the Fugl-Meyer assessment of lower extremity (FMA-LE) scores (p = 0.049, r = 0.406), but this finding did not survive FDR adjustment (adjusted p = 0.432). Additionally, the occurrence and the coverage of microstate B were negatively correlated with the power of delta band in the stroke group, which did not pass adjustment (p = 0.033, adjusted p = 0.790, r = -0.436; p = 0.026, adjusted p = 0.790, r = -0.454, respectively).

CONCLUSIONS

Our results confirm the abnormal temporal dynamics of brain activity in stroke patients. The study provides further electrophysiological evidence for understanding the mechanism of brain motor functional reorganization after stroke.

A Study on the Effects of Repetitive Transcranial Magnetic Stimulation on EEG Microstate in Patients With Parkinson's Disease

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive neuromodulation technology that can modulate cerebral cortical excitability. Electroencephalography (EEG) microstate analysis is an important tool for studying dynamic changes in brain functional activity. This study explores the pathophysiological changes in Parkinson's disease (PD) patients by analyzing the EEG microstate of PD patients, and analyzes the impact of rTMS on the clinical symptoms of PD patients. In a trial, 25 patients with PD and 18 healthy subjects of the same age were included. The clinical scale (the third part of Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (UPDRS-III) and Montreal Cognitive Assessment (MoCA)) scores of each patient were evaluated and the microstate characteristic parameters of all subjects were calculated. 10 Hz rTMS was used to stimulate the bilateral primary motor cortex (M1) of PD patients. After two weeks of treatment (10 times), the clinical scale score of each patient was re-evaluated and the microstate characteristic parameters were calculated. At the baseline, the occurrence, duration and coverage of microstate C in PD patients were significantly higher than those in healthy controls (P <0.05),and were significantly negatively correlated with the MoCA score (P <0.05). The duration and coverage of microstate D in PD patients were significantly lower than those in healthy controls (P <0.05), and were significantly negatively correlated with UPDRS-III score (P <0.05). After rTMS treatment in the PD group, the scale score of UPDRS-III was significantly reduced (P <0.05) and the scale score of MoCA was significantly increased. Moreover, the occurrence and coverage of microstate B were significantly increased (p <0.05). The occurrence, duration and coverage of microstate C were significantly reduced (P <0.05). The occurrence, duration and coverage of microstate D were significantly increased (P <0.05). This study shows that abnormal brain functional activity of PD patients can change microstate characteristic parameters, and these changes are significantly related to the decline of motor and cognitive functions. Furthermore, rTMS can improve the motor and cognitive functions and adjust the microstate characteristic parameters of PD patients. EEG microstate analysis can reflect the therapeutic effect of rTMS on PD patients.

Investigating the relationship between resting-state EEG microstates and executive functions: A null finding

Recent advances in cognitive neurosciences suggest that intrinsic brain networks dynamics are associated with cognitive functioning. Despite this emerging perspective, limited research exists to validate this hypothesis. This Registered Report aimed to specifically test the relationship between intrinsic brain spatio-temporal dynamics and executive functions. Resting-state EEG microstates were used to assess brain spatio-temporal dynamics, while a comprehensive battery of nine cognitive function tasks was employed to evaluate executive functions in 140 participants. We hypothesized that microstates (class C and D) metrics would correlate with an executive functions composite score. Contrary to expectations, our hypotheses were not supported by the data. We however observed a small, non-significant trend with a negative correlation between microstate D occurrences and executive functions scores (r = -.18, 95% CI [-.33, -.01]) which however did not meet the adjusted threshold for significance. In light of the inconclusive or minor effect sizes observed, the assertion that intrinsic brain networks dynamics - as measured by resting-state EEG microstate metrics - are a reliable signature of executive functioning remains unsupported.

Shared and distinct electroencephalogram microstate abnormalities across schizophrenia, bipolar disorder, and depression

BACKGROUND

Microstates of an electroencephalogram (EEG) are canonical voltage topographies that remain quasi-stable for 90 ms, serving as the foundational elements of brain dynamics. Different changes in EEG microstates can be observed in psychiatric disorders like schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD). However, the similarities and disparatenesses in whole-brain dynamics on a subsecond timescale among individuals diagnosed with SCZ, BD, and MDD are unclear.

METHODS

This study included 1112 participants (380 individuals diagnosed with SCZ, 330 with BD, 212 with MDD, and 190 demographically matched healthy controls [HCs]). We assembled resting-state EEG data and completed a microstate analysis of all participants using a cross-sectional design.

RESULTS

Our research indicates that SCZ, BD, and MDD exhibit distinct patterns of transition among the four EEG microstate states (A, B, C, and D). The analysis of transition probabilities showed a higher frequency of switching from microstates A to B and from B to A in each patient group compared to the HC group, and less frequent transitions from microstates A to C and from C to A in the SCZ and MDD groups compared to the HC group. And the probability of the microstate switching from C to D and D to C in the SCZ group significantly increased compared to those in the patient and HC groups.

CONCLUSIONS

Our findings provide crucial insights into the abnormalities involved in distributing neural assets and enabling proper transitions between different microstates in patients with major psychiatric disorders.

EEG microstates are associated with music training experience

Background

Music training facilitates the development of individual cognitive functions and influences brain plasticity. A comprehensive understanding of the pathways and processes through which music affects the human brain, as well as the neurobiological mechanisms underlying human brain perception of music, is necessary to fully harness the plasticity that music offers for brain development.

Aims

To investigate the resting-state electroencephalogram (EEG) activity of individuals with and without music training experience, and explore the microstate patterns of EEG signals.

Method

In this study, an analysis of electroencephalogram (EEG) microstates from 57 participants yielded temporal parameters(mean duration, time coverage, occurrence, and transition probability)of four classic microstate categories (Categories A, B, C, and D) for two groups: those with music training experience and those without. Statistical analysis was conducted on these parameters between groups.

Results

The results indicate that compared to individuals without music training experience, participants with music training experience exhibit significantly longer mean durations of microstate A, which is associated with speech processing. Additionally, they show a greater time coverage of microstate B, which is associated with visual processing. Transition probabilities from microstate A to microstate B were greater in participants with music training experience compared to those without. Conversely, transition probabilities from microstate A to microstate C and from microstate C to microstate D were greater in participants without music training experience.

Conclusion

Our study found differences in characteristic parameters of certain microstates between individuals with and without music training experience. This suggests distinct brain activity patterns during tasks related to speech, vision, and attention regulation among individuals with varying levels of music training experience. These findings support an association between music training experience and specific neural activities. Furthermore, they endorse the hypothesis of music training experience influencing brain activity during resting states. Additionally, they imply a facilitative role of music training in tasks related to speech, vision, and attention regulation, providing initial evidence for further empirical investigation into the cognitive processes influenced by music training.

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability

Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy - a critical period of rapid brain development and plasticity - microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.

Personality Moderates Intra-Individual Variability in EEG Microstates and Spontaneous Thoughts

Variability in brain activity that persists after accounting for overt behavioral and physiological states is often considered noise and controlled as a covariate in research. However, studying intra-individual variability in brain function can provide valuable insights into the dynamic nature of the brain. To explore this, we conducted a study on 43 participants analyzing the EEG microstate dynamics and self-reported spontaneous mental activity during five-minute resting-state recordings on two separate days with a twenty days average delay between recordings. Our results showed that the associations between EEG microstates and spontaneous cognition significantly changed from one day to another. Moreover, microstate changes were associated with changes in spontaneous cognition. Specifically, inter-day changes in Verbal thoughts about Others and future Planning were positively related to bottom-up sensory network-related microstate changes and negatively associated with top-down, attention, and salience network-related microstates. In addition, we find that personality traits are related to inter-day changes in microstates and spontaneous thoughts. Specifically, extraversion, neuroticism, agreeableness, and openness to experience moderated the relationship between inter-day changes in EEG microstates and spontaneous thoughts. Our study provides valuable information on the dynamic changes in the EEG microstate-spontaneous cognition organization, which could be essential for developing interventions and treatments for neuropsychiatric disorders.

Trait Aggression is Reflected by a Lower Temporal Stability of EEG Resting Networks

Trait aggression can lead to catastrophic consequences for individuals and society. However, it remains unclear how aggressive people differ from others regarding basic, task-independent brain characteristics. We used EEG microstate analysis to investigate how the temporal organization of neural resting networks might help explain inter-individual differences in aggression. Microstates represent whole-brain networks, which are stable for short timeframes (40-120 ms) before quickly transitioning into other microstate types. Recent research demonstrates that the general temporal stability of microstates across types predicts higher levels of self-control and inhibitory control, and lower levels of risk-taking preferences. Given that these outcomes are inversely related to aggression, we investigated whether microstate stability at rest would predict lower levels of trait aggression. As males show higher levels of aggression than females, and males and females express aggression differently, we also tested for possible gender-differences. As hypothesized, people with higher levels of trait aggression showed lower microstate stability. This effect was moderated by gender, with men showing stronger associations compared to women. These findings support the notion that temporal dynamics of sub-second resting networks predict complex human traits. Furthermore, they provide initial indications of gender-differences in the functional significance of EEG microstates.

EEG Microstates in Social and Affective Neuroscience

Social interactions require both the rapid processing of multifaceted socio-affective signals (e.g., eye gaze, facial expressions, gestures) and their integration with evaluations, social knowledge, and expectations. Researchers interested in understanding complex social cognition and behavior face a "black box" problem: What are the underlying mental processes rapidly occurring between perception and action and why are there such vast individual differences? In this review, we promote electroencephalography (EEG) microstates as a powerful tool for both examining socio-affective states (e.g., processing whether someone is in need in a given situation) and identifying the sources of heterogeneity in socio-affective traits (e.g., general willingness to help others). EEG microstates are identified by analyzing scalp field maps (i.e., the distribution of the electrical field on the scalp) over time. This data-driven, reference-independent approach allows for identifying, timing, sequencing, and quantifying the activation of large-scale brain networks relevant to our socio-affective mind. In light of these benefits, EEG microstates should become an indispensable part of the methodological toolkit of laboratories working in the field of social and affective neuroscience.

Resting-State Functional Networks Correlate with Motor Performance in a Complex Visuomotor Task: An EEG Microstate Pilot Study on Healthy Individuals

Developing motor and cognitive skills is needed to achieve expert (motor) performance or functional recovery from a neurological condition, e.g., after stroke. While extensive practice plays an essential role in the acquisition of good motor performance, it is still unknown whether certain person-specific traits may predetermine the rate of motor learning. In particular, learners' functional brain organisation might play an important role in appropriately performing motor tasks. In this paper, we aimed to study how two critical cognitive brain networks-the Attention Network (AN) and the Default Mode Network (DMN)-affect the posterior motor performance in a complex visuomotor task: virtual surfing. We hypothesised that the preactivation of the AN would affect how participants divert their attention towards external stimuli, resulting in robust motor performance. Conversely, the excessive involvement of the DMN-linked to internally diverted attention and mind-wandering-would be detrimental for posterior motor performance. We extracted seven widely accepted microstates-representing participants mind states at rest-out of the Electroencephalography (EEG) resting-state recordings of 36 healthy volunteers, prior to execution of the virtual surfing task. By correlating neural biomarkers (microstates) and motor behavioural metrics, we confirmed that the preactivation of the posterior DMN was correlated with poor posterior performance in the motor task. However, we only found a non-significant association between AN preactivation and the posterior motor performance. In this EEG study, we propose the preactivation of the posterior DMN-imaged using EEG microstates-as a neural trait related to poor posterior motor performance. Our findings suggest that the role of the executive control system is to preserve an homeostasis between the AN and the DMN. Therefore, neurofeedback-based downregulation of DMN preactivation could help optimise motor training.

Changes in electroencephalographic microstates between evening and morning are associated with overnight sleep slow waves in healthy individuals

STUDY OBJECTIVES

Microstates are semi-stable voltage topographies that account for most of electroencephalogram (EEG) variance. However, the impact of time of the day and sleep on microstates has not been examined. To address this gap, we assessed whether microstates differed between the evening and morning and whether sleep slow waves correlated with microstate changes in healthy participants.

METHODS

Forty-five healthy participants were recruited. Each participant underwent 6 minutes of resting state EEG recordings in the evening and morning, interleaved by sleep EEGs. Evening-to-morning changes in microstate duration, coverage, and occurrence were assessed. Furthermore, correlation between microstate changes and sleep slow-wave activity (SWA) and slow-wave density (SWD) were performed.

RESULTS

Two-way ANOVAs with microstate class (A, B, C, and D) and time (evening and morning) revealed significant microstate class × time interaction for duration (F(44) = 5.571, p = 0.002), coverage (F(44) = 6.833, p = 0.001), and occurrence (F(44) = 5.715, p = 0.002). Post hoc comparisons showed significant effects for microstate C duration (padj = 0.048, Cohen's d = -0.389), coverage (padj = 0.002, Cohen's d = -0.580), and occurrence (padj = 0.002, Cohen's d = -0.606). Topographic analyses revealed inverse correlations between SWD, but not SWA, and evening-to-morning changes in microstate C duration (r = -0.51, padj = 0.002), coverage (r = -0.45, padj = 0.006), and occurrence (r = -0.38, padj = 0.033).

CONCLUSIONS

Microstate characteristics showed significant evening-to-morning changes associated with, and possibly regulated by, sleep slow waves. These findings suggest that future microstate studies should control for time of day and sleep effects.

Microstate Analysis Reflects Maturation of the Preterm Brain

Preterm neonates are at risk of long-term neurodevelopmental impairments due to disruption of natural brain development. Electroencephalography (EEG) analysis can provide insights into brain development of preterm neonates. This study aims to explore the use of microstate (MS) analysis to evaluate global brain dynamics changes during maturation in preterm neonates with normal neurodevelopmental outcome.The dataset included 135 EEGs obtained from 48 neonates at varying postmenstrual ages (26.4 to 47.7 weeks), divided into four age groups. For each recording we extracted a 5-minute epoch during quiet sleep (QS) and during non-quiet sleep (NQS), resulting in eight groups (4 age group x 2 sleep states). We compared MS maps and corresponding (map-specific) MS metrics across groups using group-level maps. Additionally, we investigated individual map metrics.Four group-level MS maps accounted for approximately 70% of the global variance and showed non-random syntax. MS topographies and transitions changed significantly when neonates reached 37 weeks. For both sleep states and all MS maps, MS duration decreased and occurrence increased with age. The same relationships were found using individual maps, showing strong correlations (Pearson coefficients up to 0.74) between individual map metrics and post-menstrual age. Moreover, the Hurst exponent of the individual MS sequence decreased with age.The observed changes in MS metrics with age might reflect the development of the preterm brain, which is characterized by formation of neural networks. Therefore, MS analysis is a promising tool for monitoring preterm neonatal brain maturation, while our study can serve as a valuable reference for investigating EEGs of neonates with abnormal neurodevelopmental outcomes.

Atypical alpha band microstates produced during eyes-closed resting state EEG in autism

Electroencephalogram (EEG) microstates, which represent quasi-stable patterns of scalp topography, are a promising tool that has the temporal resolution to study atypical spatial and temporal networks in autism spectrum disorder (ASD). While current literature suggests microstates are atypical in ASD, their clinical utility, i.e., relationship with the core behavioural characteristics of ASD, is not fully understood. The aim of this study was to examine microstate parameters in ASD, and examine the relationship between these parameters and core behavioural characteristics in ASD. We compared duration, occurrence, coverage, global explained variance percentage, global field power and spatial correlation of EEG microstates between autistic and neurotypical (NT) adults. Modified k-means cluster analysis was used on eyes-closed, resting state EEG from 30 ASD (10 females, 28.97 ± 9.34 years) and 30 age-equated NT (13 females, 29.33 ± 8.88 years) adults. Five optimal microstates, A to E, were selected to best represent the data. Five microstate maps explaining 80.44% of the NT and 78.44% of the ASD data were found. The ASD group was found to have atypical parameters of microstate A, C, D, and E. Of note, all parameters of microstate C in the ASD group were found to be significantly less than NT. While parameters of microstate D, and E were also found to significantly correlate with subscales of the Ritvo Autism Asperger Diagnostic Scale - Revised (RAADS-R), these findings did not survive a Bonferroni Correction. These findings, in combination with previous findings, highlight the potential clinical utility of EEG microstates and indicate their potential value as a neurophysiologic marker that can be further studied.

A GRU-CNN model for auditory attention detection using microstate and recurrence quantification analysis

Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dynamic features extracted from multichannel electroencephalography (EEG) signals when listeners attend to a target speaker in the presence of a competing talker. To this aim, microstate and recurrence quantification analysis are utilized to extract different types of features that reflect changes in the brain state during cognitive tasks. Then, an optimized feature set is determined by employing the processes of significant feature selection based on classification performance. The classifier model is developed by hybrid sequential learning that employs Gated Recurrent Units (GRU) and Convolutional Neural Network (CNN) into a unified framework for accurate attention detection. The proposed AAD method shows that the selected feature set achieves the most discriminative features for the classification process. Also, it yields the best performance as compared with state-of-the-art AAD approaches from the literature in terms of various measures. The current study is the first to validate the use of microstate and recurrence quantification parameters to differentiate auditory attention using reinforcement learning without access to stimuli.

Open access EEG dataset of repeated measurements from a single subject for microstate analysis

Electroencephalography (EEG) microstate analysis is a neuroimaging analytical method that has received considerable attention in recent years and is widely used for analysing EEG signals. EEG is easily influenced by internal and external factors, which can affect the repeatability and stability of EEG microstate analysis. However, there have been few reports and publicly available datasets on the repeatability of EEG microstate analysis. In the current study, a 39-year-old healthy male underwent a total of 60 simultaneous electroencephalography and electrocardiogram measurements over a period of three months. After the EEG recording was completed, magnetic resonance imaging (MRI) was also conducted. To date, this EEG dataset has the highest number of repeated measurements for one individual. The dataset can be used to assess the stability and repeatability of EEG microstates and other analytical methods, to decode resting EEG states among subjects with open eyes, and to explore the stability and repeatability of cortical spatiotemporal dynamics through source analysis with individual MRI.

EEG analysis in patients with schizophrenia based on microstate semantic modeling method

Introduction

Microstate analysis enables the characterization of quasi-stable scalp potential fields on a sub-second timescale, preserving the temporal dynamics of EEG and spatial information of scalp potential distributions. Owing to its capacity to provide comprehensive pathological insights, it has been widely applied in the investigation of schizophrenia (SCZ). Nevertheless, previous research has primarily concentrated on differences in individual microstate temporal characteristics, neglecting potential distinctions in microstate semantic sequences and not fully considering the issue of the universality of microstate templates between SCZ patients and healthy individuals.

Methods

This study introduced a microstate semantic modeling analysis method aimed at schizophrenia recognition. Firstly, microstate templates corresponding to both SCZ patients and healthy individuals were extracted from resting-state EEG data. The introduction of a dual-template strategy makes a difference in the quality of microstate sequences. Quality features of microstate sequences were then extracted from four dimensions: Correlation, Explanation, Residual, and Dispersion. Subsequently, the concept of microstate semantic features was proposed, decomposing the microstate sequence into continuous sub-sequences. Specific semantic sub-sequences were identified by comparing the time parameters of sub-sequences.

Results

The SCZ recognition test was performed on the public dataset for both the quality features and semantic features of microstate sequences, yielding an impressive accuracy of 97.2%. Furthermore, cross-subject experimental validation was conducted, demonstrating that the method proposed in this paper achieves a recognition rate of 96.4% between different subjects.

Discussion

This research offers valuable insights for the clinical diagnosis of schizophrenia. In the future, further studies will seek to augment the sample size to enhance the effectiveness and reliability of this method.

State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis

Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.

The Functional Aspects of Resting EEG Microstates: A Systematic Review

A growing body of clinical and cognitive neuroscience studies have adapted a broadband EEG microstate approach to evaluate the electrical activity of large-scale cortical networks. However, the functional aspects of these microstates have not yet been systematically reviewed. Here, we present an overview of the existing literature and systematize the results to provide hints on the functional role of electrical brain microstates. Studies that evaluated and manipulated the temporal properties of resting-state microstates and utilized questionnaires, task-initiated thoughts, specific tasks before or between EEG session(s), pharmacological interventions, neuromodulation approaches, or localized sources of the extracted microstates were selected. Fifty studies that met the inclusion criteria were included. A new microstate labeling system has been proposed for a comprehensible comparison between the studies, where four classical microstates are referred to as A-D, and the others are labeled by the frequency of their appearance. Microstate A was associated with both auditory and visual processing and links to subjects' arousal/arousability. Microstate B showed associations with visual processing related to self, self-visualization, and autobiographical memory. Microstate C was related to processing personally significant information, self-reflection, and self-referential internal mentation rather than autonomic information processing. In contrast, microstate E was related to processing interoceptive and emotional information and to the salience network. Microstate D was associated with executive functioning. Microstate F is suggested to be a part of the Default Mode Network and plays a role in personally significant information processing, mental simulations, and theory of mind. Microstate G is potentially linked to the somatosensory network.

EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings.

On the Reliability of the EEG Microstate Approach

EEG microstates represent functional brain networks observable in resting EEG recordings that remain stable for 40-120ms before rapidly switching into another network. It is assumed that microstate characteristics (i.e., durations, occurrences, percentage coverage, and transitions) may serve as neural markers of mental and neurological disorders and psychosocial traits. However, robust data on their retest-reliability are needed to provide the basis for this assumption. Furthermore, researchers currently use different methodological approaches that need to be compared regarding their consistency and suitability to produce reliable results. Based on an extensive dataset largely representative of western societies (2 days with two resting EEG measures each; day one: n = 583; day two: n = 542) we found good to excellent short-term retest-reliability of microstate durations, occurrences, and coverages (average ICCs = 0.874-0.920). There was good overall long-term retest-reliability of these microstate characteristics (average ICCs = 0.671-0.852), even when the interval between measures was longer than half a year, supporting the longstanding notion that microstate durations, occurrences, and coverages represent stable neural traits. Findings were robust across different EEG systems (64 vs. 30 electrodes), recording lengths (3 vs. 2 min), and cognitive states (before vs. after experiment). However, we found poor retest-reliability of transitions. There was good to excellent consistency of microstate characteristics across clustering procedures (except for transitions), and both procedures produced reliable results. Grand-mean fitting yielded more reliable results compared to individual fitting. Overall, these findings provide robust evidence for the reliability of the microstate approach.

Normative Intercorrelations Between EEG Microstate Characteristics

EEG microstates are brief, recurring periods of stable brain activity that reflect the activation of large-scale neural networks. The temporal characteristics of these microstates, including their average duration, number of occurrences, and percentage contribution have been shown to serve as biomarkers of mental and neurological disorders. However, little is known about how microstate characteristics of prototypical network types relate to each other. Normative intercorrelations among these parameters are necessary to help researchers better understand the functions and interactions of underlying networks, interpret and relate results, and generate new hypotheses. Here, we present a systematic analysis of intercorrelations between EEG microstate characteristics in a large sample representative of western working populations (n = 583). Notably, we find that microstate duration is a general characteristic that varies across microstate types. Further, microstate A and B show mutual reinforcement, indicating a relationship between auditory and visual sensory processing at rest. Microstate C appears to play a special role, as it is associated with longer durations of all other microstate types and increased global field power, suggesting a relationship of these parameters with the anterior default mode network. All findings could be confirmed using independent EEG recordings from a retest-session (n = 542).

Normative Temporal Dynamics of Resting EEG Microstates

The large-scale electrophysiological events known as electroencephalographic microstates provide an important window into the intrinsic activity of whole-brain neuronal networks. The spontaneous activity of coordinated brain networks, including the ongoing temporal dynamics expressed by microstates, are thought to reflect individuals' neurocognitive functioning, and predict development, disease progression, and psychological differences among varied populations. A comprehensive understanding of human brain function therefore requires characterizing typical and atypical patterns in the temporal dynamics of microstates. But population-level estimates of normative microstate temporal dynamics are still unknown. To address this gap, I conducted a systematic search of the literature and accompanying meta-analysis of the average dynamics of microstates obtained from studies investigating spontaneous brain activity in individuals during periods of eyes-closed and eyes-open rest. Meta-analyses provided estimates of the average temporal dynamics of microstates across 93 studies totaling 6583 unique individual participants drawn from diverse populations. Results quantified the expected range of plausible estimates of average microstate dynamics across study samples, as well as characterized heterogeneity resulting from sampling variability and systematic differences in development, clinical diagnoses, or other study methodological factors. Specifically, microstate dynamics significantly differed for samples with specific developmental differences or clinical diagnoses, relative to healthy, typically developing samples. This research supports the notion that microstates and their dynamics reflect functionally relevant properties of large-scale brain networks, encoding typical and atypical neurocognitive functioning.

Electroencephalographic microstates as a potential neurophysiological marker differentiating bilateral from unilateral temporal lobe epilepsy

OBJECTIVE

Electroencephalographic (EEG) microstate abnormalities have been documented in different neurological disorders. We aimed to assess whether EEG microstates are altered also in patients with temporal epilepsy (TLE) and whether they show different activations in patients with unilateral TLE (UTLE) and bilateral TLE (BTLE).

METHODS

Nineteen patients with UTLE, 12 with BTLE, and 15 healthy controls were enrolled. Resting state high-density electroencephalography (128 channels) was recorded for 15 min with closed eyes. We obtained a set of stable scalp maps representing the EEG activity, named microstates, from which we acquired the following variables: global explained variance (GEV), mean duration (MD), time coverage (TC), and frequency of occurrence (FO). Two-way repeated measures analysis of variance was used to compare groups, and Spearman correlation was performed to study the maps in association with the clinical and neuropsychological data.

RESULTS

Patients with BTLE and UTLE showed differences in most of the parameters (GEV, MD, TC, FO) of the four microstate maps (A-D) compared to controls. Patients with BTLE showed a significant increase in all parameters for the microstates in Map-A and a decrease in Map-D compared to UTLE and controls. We observed a correlation between Map-A, disease duration, and spatial short-term memory, whereas microstate Map-D was correlated with the global intelligence score and short-term memory performance.

SIGNIFICANCE

A global alteration of the neural dynamics was observed in patients with TLE compared to controls. A different pattern of EEG microstate abnormalities was identified in BTLE compared to UTLE, which might represent a distinctive biomarker.

Identifying ADHD and subtypes through microstates analysis and complex networks

The diagnosis of attention-deficit hyperactivity disorder (ADHD) is based on the health history and on the evaluation of questionnaires to identify symptoms. This evaluation can be subjective and lengthy, especially in children. Therefore, a biomarker would be of great value to assist mental health professionals in the process of diagnosing ADHD. Event-related potential (ERP) is one of the most informative and dynamic methods of monitoring cognitive processes. Previous works suggested that specific sets of ERP-microstates are selectively affected by ADHD. This paper proposes a new methodology for the ERP-microstate analysis and identification of ADHD patients based on complex networks to model the microstate topographic maps. The analysis of global and local features of ERP-microstate networks revealed topological differences between ADHD and healthy control. The classification using a neural network with a single hidden layer resulted in an average accuracy of 99.72% in binary classification and 99.31% in the classification of ADHD subtypes. The results were compared to the power band spectral densities and the energy of wavelet coefficients. The temporal features of ERP-microstates, such as frequency of occurrence, duration, coverage, and transition probabilities, were also evaluated for comparison proposes. Overall, the selected topological features of ERP-microstate networks derived from the proposed method performed significantly better classification results. The results suggest that topological features of ERP-microstate networks are promising to identify ADHD and its subtypes with a neural network model compared to power band spectrum density, wavelet transform, and temporal features of ERP-microstates.

Transient resting-state salience-limbic co-activation patterns in functional neurological disorders

BACKGROUND

Functional neurological disorders were historically regarded as the manifestation of a dynamic brain lesion which might be linked to trauma or stress, although this association has not yet been directly tested yet. Analysing large-scale brain network dynamics at rest in relation to stress biomarkers assessed by salivary cortisol and amylase could provide new insights into the pathophysiology of functional neurological symptoms.

METHODS

Case-control resting-state functional magnetic resonance imaging study of 79 patients with mixed functional neurological disorders (i.e., functional movement disorders, functional seizures, persistent perceptual-postural dizziness) and 74 age- and sex-matched healthy controls. Using a two-step hierarchical data-driven neuroimaging approach, static functional connectivity was first computed between 17 resting-state networks. Second, dynamic alterations in these networks were examined using co-activation pattern analysis. Using a partial least squares correlation analysis, the multivariate pattern of correlation between altered temporal characteristics and stress biomarkers as well as clinical scores were evaluated.

RESULTS

Compared to healthy controls, patients presented with functional aberrancies of the salience-limbic network connectivity. Thus, the insula and amygdala were selected as seed-regions for the subsequent analyses. Insular co-(de)activation patterns related to the salience network, the somatomotor network and the default mode network were detected, which patients entered more frequently than controls. Moreover, an insular co-(de)activation pattern with subcortical regions together with a wide-spread co-(de)activation with diverse cortical networks was detected, which patients entered less frequently than controls. In patients, dynamic alterations conjointly correlated with amylase measures and duration of symptoms.

CONCLUSION

The relationship between alterations in insular co-activation patterns, stress biomarkers and clinical data proposes inter-related mechanisms involved in stress regulation and functional (network) integration. In summary, altered functional brain network dynamics were identified in patients with functional neurological disorder supporting previously raised concepts of impaired attentional and interoceptive processing.

Metastability indexes global changes in the dynamic working point of the brain following brain stimulation

Several studies have shown that coordination among neural ensembles is a key to understand human cognition. A well charted path is to identify coordination states associated with cognitive functions from spectral changes in the oscillations of EEG or MEG. A growing number of studies suggest that the tendency to switch between coordination states, sculpts the dynamic repertoire of the brain and can be indexed by a measure known as metastability. In this article, we characterize perturbations in the metastability of global brain network dynamics following Transcranial Magnetic Stimulation that could quantify the duration for which information processing is altered. Thus allowing researchers to understand the network effects of brain stimulation, standardize stimulation protocols and design experimental tasks. We demonstrate the effect empirically using publicly available datasets and use a digital twin (a whole brain connectome model) to understand the dynamic principles that generate such observations. We observed a significant reduction in metastability, concurrent with an increase in coherence following single-pulse TMS reflecting the existence of a window where neural coordination is altered. The reduction in complexity was validated by an additional measure based on the Lempel-Ziv complexity of microstate labeled EEG data. Interestingly, higher frequencies in the EEG signal showed faster recovery in metastability than lower frequencies. The digital twin shed light on how the phase resetting introduced by the single-pulse TMS in local cortical networks can propagate globally, giving rise to changes in metastability and coherence.