Reliability and Individual Specificity of EEG Microstate Characteristics

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.

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.

Exploring the Association Between EEG Microstates During Resting-State and Error-Related Activity in Young Children

The error-related negativity (ERN) is a negative deflection in the electroencephalography (EEG) waveform at frontal-central scalp sites that occurs after error commission. The relationship between the ERN and broader patterns of brain activity measured across the entire scalp that support error processing during early childhood is unclear. We examined the relationship between the ERN and EEG microstates - whole-brain patterns of dynamically evolving scalp potential topographies that reflect periods of synchronized neural activity - during both a go/no-go task and resting-state in 90, 4-8-year-old children. The mean amplitude of the ERN was quantified during the -64 to 108 millisecond (ms) period of time relative to error commission, which was determined by data-driven microstate segmentation of error-related activity. We found that greater magnitude of the ERN associated with greater global explained variance (GEV; i.e., the percentage of total variance in the data explained by a given microstate) of an error-related microstate observed during the same -64 to 108 ms period (i.e., error-related microstate 3), and to greater anxiety risk as measured by parent-reported behavioral inhibition. During resting-state, six data-driven microstates were identified. Both greater magnitude of the ERN and greater GEV values of error-related microstate 3 associated with greater GEV values of resting-state microstate 4, which showed a frontal-central scalp topography. Source localization results revealed overlap between the underlying neural generators of error-related microstate 3 and resting-state microstate 4 and canonical brain networks (e.g., ventral attention) known to support the higher-order cognitive processes involved in error processing. Taken together, our results clarify how individual differences in error-related and intrinsic brain activity are related and enhance our understanding of developing brain network function and organization supporting error processing during early childhood.

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.

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.

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.

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.

Resting-state electroencephalography microstates as a marker of photosensitivity in juvenile myoclonic epilepsy

Juvenile myoclonic epilepsy is an idiopathic generalized epilepsy syndrome associated with photosensitivity in approximately 30-40% of cases. Microstates consist of a brief period of time during which the topography of the whole resting-state electroencephalography signal is characterized by a specific configuration. Previous neurophysiological and neuroimaging studies have suggested that Microstate B may represent activity within the visual network. In this case-control study, we aimed to investigate whether anatomical and functional alterations in the visual network observed in individuals with photosensitivity could lead to changes in Microstate B dynamics in photosensitive patients with juvenile myoclonic epilepsy. Resting-state electroencephalography microstate analysis was performed on 28 patients with juvenile myoclonic epilepsy. Of these, 15 patients exhibited photosensitivity, while the remaining 13 served as non-photosensitive controls. The two groups were carefully matched in terms of age, sex, seizure control and anti-seizure medications. Multivariate analysis of variance and repeated-measures analysis of variance were performed to assess significant differences in microstate metrics and syntax between the photosensitive and the non-photosensitive group. Post hoc false discovery rate adjusted unpaired t-tests were used to determine differences in specific microstate classes between the two groups. The four classical microstates (Classes A, B, C and D) accounted for 72.8% of the total electroencephalography signal variance in the photosensitive group and 75.64% in the non-photosensitive group. Multivariate analysis of variance revealed a statistically significant class-group interaction on microstate temporal metrics (P = 0.021). False discovery rate adjusted univariate analyses of variance indicated a significant class-group interaction for both mean occurrence (P = 0.002) and coverage (P = 0.03), but not for mean duration (P = 0.14). Post hoc false discovery rate adjusted unpaired t-tests showed significantly higher coverage (P = 0.02) and occurrence (P = 0.04) of Microstate B in photosensitive patients compared with non-photosensitive participants, along with an increased probability of transitioning from Microstates C (P = 0.04) and D (P = 0.02) to Microstate B. No significant differences were found concerning the other microstate classes between the two groups. Our study provides novel insights on resting-state electroencephalography microstate dynamics underlying photosensitivity in patients with juvenile myoclonic epilepsy. The increased representation of Microstate B in these patients might reflect the resting-state overactivation of the visual system underlying photosensitivity. Further research is warranted to investigate microstate dynamics in other photosensitive epilepsy syndromes.

EEG microstate D as psychosis-specific correlate in adolescents and young adults with clinical high risk for psychosis and first-episode psychosis

Resting-state electroencephalography (EEG) microstates are brief periods (60-120 ms) of quasi-stable scalp field potentials, indicating simultaneous activity of large-scale networks. Microstates are assumed to reflect basic neuronal information processing. A common finding in psychosis spectrum disorders is that microstates classes C and D are altered. Whereas evidence in adults with schizophrenia is substantial, little is known about effects in underage patients, particularly in those at clinical high risk for psychosis (CHR) and first-episode psychosis (FEP). The present study used 74-channel EEG to investigate microstate effects in a large sample of patients with CHR (n = 100) and FEP (n = 33), clinical controls (CC, n = 18), as well as age-matched healthy controls (HC, n = 68). Subjects span an age range from 9 to 35 years, thus, covering underage patients as well as the most vulnerable period for the emergence of psychosis and its prodrome. Four EEG microstates classes were analyzed (A-D). In class D, CHR and FEP patients showed a decrease compared to HC, and CHR patients also to CC. An increase in class C was found in CHR and FEP compared to HC but not to CC. Results were independent of age and no differences were found between the psychosis spectrum groups. The findings suggest an age-independent decrease of microstate class D to be specific to the psychosis spectrum, whereas the increase in class C seems to reflect unspecific psychopathology. Overall, present data strengthens the role of microstate D as potential biomarker for psychosis, as early as in adolescence and already in CHR status.

EEG-based assessment of temporal fine structure and envelope effect in mandarin syllable and tone perception

In recent years, speech perception research has benefited from low-frequency rhythm entrainment tracking of the speech envelope. However, speech perception is still controversial regarding the role of speech envelope and temporal fine structure, especially in Mandarin. This study aimed to discuss the dependence of Mandarin syllables and tones perception on the speech envelope and the temporal fine structure. We recorded the electroencephalogram (EEG) of the subjects under three acoustic conditions using the sound chimerism analysis, including (i) the original speech, (ii) the speech envelope and the sinusoidal modulation, and (iii) the fine structure of time and the modulation of the non-speech (white noise) sound envelope. We found that syllable perception mainly depended on the speech envelope, while tone perception depended on the temporal fine structure. The delta bands were prominent, and the parietal and prefrontal lobes were the main activated brain areas, regardless of whether syllable or tone perception was involved. Finally, we decoded the spatiotemporal features of Mandarin perception from the microstate sequence. The spatiotemporal feature sequence of the EEG caused by speech material was found to be specific, suggesting a new perspective for the subsequent auditory brain-computer interface. These results provided a new scheme for the coding strategy of new hearing aids for native Mandarin speakers.

HIGHLIGHTS

Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness

Introduction

Ischemic stroke patients commonly experience disorder of consciousness (DOC), leading to poorer discharge outcomes and higher mortality risks. Therefore, the identification of applicable electrophysiological biomarkers is crucial for the rapid diagnosis and evaluation of post-stroke disorder of consciousness (PS-DOC), while providing supportive evidence for cerebral neurology.

Methods

In our study, we conduct microstate analysis on resting-state electroencephalography (EEG) of 28 post-stroke patients with awake consciousness and 28 patients with PS-DOC, calculating the temporal features of microstates. Furthermore, we extract the Lempel-Ziv complexity of microstate sequences and the delta/alpha power ratio of EEG on spectral. Statistical analysis is performed to examine the distinctions in features between the two groups, followed by inputting the distinctive features into a support vector machine for the classification of PS-DOC.

Results

Both groups obtain four optimal topographies of EEG microstates, but notable distinctions are observed in microstate C. Within the PS-DOC group, there is a significant increase in the mean duration and coverage of microstates B and C, whereas microstate D displays a contrasting trend. Additionally, noteworthy variations are found in the delta/alpha ratio and Lempel-Ziv complexity between the two groups. The integration of the delta/alpha ratio with microstates' temporal and Lempel-Ziv complexity features demonstrates the highest performance in the classifier (Accuracy = 91.07%).

Discussion

Our results suggest that EEG microstates can provide insights into the abnormal brain network dynamics in DOC patients post-stroke. Integrating the temporal and Lempel-Ziv complexity microstate features with spectral features offers a deeper understanding of the neuro mechanisms underlying brain damage in patients with DOC, holding promise as effective electrophysiological biomarkers for diagnosing PS-DOC.

Neurophysiological biomarkers for depression classification: Utilizing microstate k-mers and a bag-of-words model

Microstates are analogous to characters in a language, and short fragments consisting of several microstates (k-mers) are analogous to words. We aimed to investigate whether microstate k-mers could be used as neurophysiological biomarkers to differentiate between depressed patients and normal controls. We utilized a bag-of-words model to process microstate sequences, using k-mers with a k range of 1-10 as terms, and the term frequency (TF) with or without inverse-document-frequency (IDF) as features. We performed nested cross-validation on Dataset 1 (27 patients and 26 controls) and Dataset 2 (34 patients and 30 controls) separately and then trained on one dataset and tested on the other. The best area under the curve (AUC) of 81.5% was achieved for the model with L1 regularization using the TF of 4-mers as features in Dataset 1, and the best AUC of 88.9% was achieved for the model with L1 regularization using the TF of 9-mers as features in Dataset 2. When Dataset 1 was used as the training set, the best AUC of predicting Dataset 2 was 74.1% for the model with L2 regularization using the TF-IDF of 9-mers as features, while the best AUC of predicting Dataset 1 was 70.2% for the model with L1 regularization using the TF of 8-mers as features. Our study provided novel insights into the potential of microstate k-mers as neurophysiological biomarkers for individual-level classification of depression. These may facilitate further exploration of microstate sequences using natural language processing techniques.

Exploring the association between EEG microstates during resting-state and error-related activity in young children

The error-related negativity (ERN) is a negative deflection in the electroencephalography (EEG) waveform at frontal-central scalp sites that occurs after error commission. The relationship between the ERN and broader patterns of brain activity measured across the entire scalp that support error processing during early childhood is unclear. We examined the relationship between the ERN and EEG microstates - whole-brain patterns of dynamically evolving scalp potential topographies that reflect periods of synchronized neural activity - during both a go/no-go task and resting-state in 90, 4-8-year-old children. The mean amplitude of the ERN was quantified during the - 64 to 108 millisecond (ms) period of time relative to error commission, which was determined by data-driven microstate segmentation of error-related activity. We found that greater magnitude of the ERN associated with greater global explained variance (GEV; i.e., the percentage of total variance in the data explained by a given microstate) of an error-related microstate observed during the same - 64 to 108 ms period (i.e., error-related microstate 3), and to greater parent-report-measured anxiety risk. During resting-state, six data-driven microstates were identified. Both greater magnitude of the ERN and greater GEV values of error-related microstate 3 associated with greater GEV values of resting-state microstate 4, which showed a frontal-central scalp topography. Source localization results revealed overlap between the underlying neural generators of error-related microstate 3 and resting-state microstate 4 and canonical brain networks (e.g., ventral attention) known to support the higher-order cognitive processes involved in error processing. Taken together, our results clarify how individual differences in error-related and intrinsic brain activity are related and enhance our understanding of developing brain network function and organization supporting error processing during early childhood.

Study on tinnitus-related electroencephalogram microstates in patients with vestibular schwannomas

Tinnitus is closely associated with cognition functioning. In order to clarify the central reorganization of tinnitus in patients with vestibular schwannoma (VS), this study explored the aberrant dynamics of electroencephalogram (EEG) microstates and their correlations with tinnitus features in VS patients. Clinical and EEG data were collected from 98 VS patients, including 76 with tinnitus and 22 without tinnitus. Microstates were clustered into four categories. Our EEG microstate analysis revealed that VS patients with tinnitus exhibited an increased frequency of microstate C compared to those without tinnitus. Furthermore, correlation analysis demonstrated that the Tinnitus Handicap Inventory (THI) score was negatively associated with the duration of microstate A and positively associated with the frequency of microstate C. These findings suggest that the time series and syntax characteristics of EEG microstates differ significantly between VS patients with and without tinnitus, potentially reflecting abnormal allocation of neural resources and transition of functional brain activity. Our results provide a foundation for developing diverse treatments for tinnitus in VS patients.

The EEG microstate representation of discrete emotions

Understanding how human emotions are represented in our brain is a central question in the field of affective neuroscience. While previous studies have mainly adopted a modular and static perspective on the neural representation of emotions, emerging research suggests that emotions may rely on a distributed and dynamic representation. The present study aimed to explore the EEG microstate representations for nine discrete emotions (Anger, Disgust, Fear, Sadness, Neutral, Amusement, Inspiration, Joy and Tenderness). Seventy-eight participants were recruited to watch emotion eliciting videos with their EEGs recorded. Multivariate analysis revealed that different emotions had distinct EEG microstate features. By using the EEG microstate features in the Neutral condition as the reference, the coverage of C, duration of C and occurrence of B were found to be the top-contributing microstate features for the discrete positive and negative emotions. The emotions of Disgust, Fear and Joy were found to be most effectively represented by EEG microstate. The present study provided the first piece of evidence of EEG microstate representation for discrete emotions, highlighting a whole-brain, dynamical representation of human emotions.

Multimodal Brain Signal Complexity Predicts Human Intelligence

Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven's Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.

Are the EEG microstates correlated with motor and non-motor parameters in patients with Parkinson's disease?

OBJECTIVES

This study compared electroencephalography microstates (EEG-MS) of patients with Parkinson's disease (PD) to healthy controls and correlated EEG-MS with motor and non-motor aspects of PD.

METHODS

This cross-sectional exploratory study was conducted with patients with PD (n = 10) and healthy controls (n = 10) matched by sex and age. We recorded EEG-MS using 32 channels during eyes-closed and eyes-open conditions and analyzed the four classic EEG-MS maps (A, B, C, D). Clinical information (e.g., disease duration, medications, levodopa equivalent daily dose), motor (Movement Disorder Society - Unified Parkinson Disease Rating Scale II and III, Timed Up and Go simple and dual-task, and Mini-Balance Evaluation Systems Test) and non-motor aspects (Mini-Mental State Exam [MMSE], verbal fluency, Hospital Anxiety and Depression Scale, and Parkinson's Disease Questionnaire-39 [PDQ-39]) were assessed in the PD group. Mann-Whitney U test was used to compare groups, and Spearman's correlation coefficient to analyze the correlations between coverage of EEG-MS and clinical aspects of PD.

RESULTS

The PD group showed a shorter duration of EEG-MS C in the eyes-closed condition than the control group. We observed correlations (rho = 0.64 to 0.82) between EEG-MS B, C, and D and non-motor aspects of PD (MMSE, verbal fluency, PDQ-39, and levodopa equivalent daily dose).

CONCLUSION

Alterations in EEG-MS and correlations between topographies and cognitive aspects, quality of life, and medication dose indicate that EEG could be used as a PD biomarker. Future studies should investigate these associations using a longitudinal design.

Transcranial Direct Current Stimulation Modulates EEG Microstates in Low-Functioning Autism: A Pilot Study

Autism spectrum disorder (ASD) is a heterogeneous disorder that affects several behavioral domains of neurodevelopment. Transcranial direct current stimulation (tDCS) is a new method that modulates motor and cognitive function and may have potential applications in ASD treatment. To identify its potential effects on ASD, differences in electroencephalogram (EEG) microstates were compared between children with typical development (n = 26) and those with ASD (n = 26). Furthermore, children with ASD were divided into a tDCS (experimental) and sham stimulation (control) group, and EEG microstates and Autism Behavior Checklist (ABC) scores before and after tDCS were compared. Microstates A, B, and D differed significantly between children with TD and those with ASD. In the experimental group, the scores of microstates A and C and ABC before tDCS differed from those after tDCS. Conversely, in the control group, neither the EEG microstates nor the ABC scores before the treatment period (sham stimulation) differed from those after the treatment period. This study indicates that tDCS may become a viable treatment for ASD.

Error-related brain state analysis using electroencephalography in conjunction with functional near-infrared spectroscopy during a complex surgical motor task

Error-based learning is one of the basic skill acquisition mechanisms that can be modeled as a perception-action system and investigated based on brain-behavior analysis during skill training. Here, the error-related chain of mental processes is postulated to depend on the skill level leading to a difference in the contextual switching of the brain states on error commission. Therefore, the objective of this paper was to compare error-related brain states, measured with multi-modal portable brain imaging, between experts and novices during the Fundamentals of Laparoscopic Surgery (FLS) "suturing and intracorporeal knot-tying" task (FLS complex task)-the most difficult among the five psychomotor FLS tasks. The multi-modal portable brain imaging combined functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for brain-behavior analysis in thirteen right-handed novice medical students and nine expert surgeons. The brain state changes were defined by quasi-stable EEG scalp topography (called microstates) changes using 32-channel EEG data acquired at 250 Hz. Six microstate prototypes were identified from the combined EEG data from experts and novices during the FLS complex task that explained 77.14% of the global variance. Analysis of variance (ANOVA) found that the proportion of the total time spent in different microstates during the 10-s error epoch was significantly affected by the skill level (p < 0.01), the microstate type (p < 0.01), and the interaction between the skill level and the microstate type (p < 0.01). Brain activation based on the slower oxyhemoglobin (HbO) changes corresponding to the EEG band power (1-40 Hz) changes were found using the regularized temporally embedded Canonical Correlation Analysis of the simultaneously acquired fNIRS-EEG signals. The HbO signal from the overlying the left inferior frontal gyrus-opercular part, left superior frontal gyrus-medial orbital, left postcentral gyrus, left superior temporal gyrus, right superior frontal gyrus-medial orbital cortical areas showed significant (p < 0.05) difference between experts and novices in the 10-s error epoch. We conclude that the difference in the error-related chain of mental processes was the activation of cognitive top-down attention-related brain areas, including left dorsolateral prefrontal/frontal eye field and left frontopolar brain regions, along with a 'focusing' effect of global suppression of hemodynamic activation in the experts, while the novices had a widespread stimulus(error)-driven hemodynamic activation without the 'focusing' effect.

Spatiotemporal dynamics of EEG microstates in four- to eight-year-old children: Age- and sex-related effects

The ultrafast spatiotemporal dynamics of large-scale neural networks can be examined using resting-state electroencephalography (EEG) microstates, representing transient periods of synchronized neural activity that evolve dynamically over time. In adults, four canonical microstates have been shown to explain most topographic variance in resting-state EEG. Their temporal structures are age-, sex- and state-dependent, and are susceptible to pathological brain states. However, no studies have assessed the spatial and temporal properties of EEG microstates exclusively during early childhood, a critical period of rapid brain development. Here we sought to investigate EEG microstates recorded with high-density EEG in a large sample of 103, 4-8-year-old children. Using data-driven k-means cluster analysis, we show that the four canonical microstates reported in adult populations already exist in early childhood. Using multiple linear regressions, we demonstrate that the temporal dynamics of two microstates are associated with age and sex. Source localization suggests that attention- and cognitive control-related networks govern the topographies of the age- and sex-dependent microstates. These novel findings provide unique insights into functional brain development in children captured with EEG microstates.

EEG microstate in first-episode drug-naive adolescents with depression

BACKGROUND

A growing number of studies have revealed significant abnormalities in EEG microstate in patients with depression, but these findings may be affected by medication. Therefore, how the EEG microstates abnormally change in patients with depression in the early stage and without the influence of medication has not been investigated so far.

METHODS

Resting-state EEG data and Hamilton Depression Rating Scale (HDRS) were collected from 34 first-episode drug-naïve adolescent with depression and 34 matched healthy controls. EEG microstate analysis was applied and nonlinear characteristics of EEG microstate sequences were studied by sample entropy and Lempel-Ziv complexity (LZC). The microstate temporal parameters and complexity were tried to train a SVM for classification of patients with depression.

RESULTS

Four typical EEG microstate topographies were obtained in both groups, but microstate C topography was significantly abnormal in depression patients. The duration of microstate B, C, D and the occurrence and coverage of microstate B significantly increased, the occurrence and coverage of microstate A, C reduced significantly in depression group. Sample entropy and LZC in the depression group were abnormally increased and were negatively correlated with HDRS. When the combination of EEG microstate temporal parameters and complexity of microstate sequence was used to classify patients with depression from healthy controls, a classification accuracy of 90.9% was obtained.

CONCLUSION

Abnormal EEG microstate has appeared in early depression, reflecting an underlying abnormality in configuring neural resources and transitions between distinct brain network states. EEG microstate can be used as a neurophysiological biomarker for early auxiliary diagnosis of depression.

Effects of modafinil on electroencephalographic microstates in healthy adults

RATIONALE

Modafinil has been proposed as a potentially effective clinical treatment for neuropsychiatric disorders characterized by cognitive control deficits. However, the precise effects of modafinil, particularly on brain network functions, are not completely understood.

OBJECTIVES

To address this gap, we examined the effects of modafinil on resting-state brain activity in 30 healthy adults using microstate analysis. Electroencephalographic (EEG) microstates are discrete voltage topographies generated from resting-state network activity.

METHODS

Using a placebo-controlled, within-subjects design, we examined changes to microstate parameters following placebo (0 mg), low (100 mg), and high (200 mg) modafinil doses. We also examined the functional significance of these microstates via associations between microstate parameters and event-related potential indexes of conflict monitoring and automatic error processing (N2 and error-related negativity) and behavioral responses (accuracy and RT) from a subsequent flanker interference task.

RESULTS

Five microstates emerged following each treatment condition, including four canonical microstates (A-D). Modafinil increased microstate C proportion and occurrence regardless of dose, relative to placebo. Modafinil also decreased microstate A proportion and microstate B proportion and occurrence relative to placebo. These modafinil-related changes in microstate parameters were not associated with similar changes in flanker ERPs or behavior. Finally, modafinil made transitions between microstates A and B less likely and transitions from A and B to C more likely.

CONCLUSIONS

Previous fMRI work has correlated microstates A and B with auditory and visual networks and microstate C with a salience network. Thus, our results suggest modafinil may deactivate large-scale sensory networks in favor of a higher order functional network during resting-state in healthy adults.

Neuroimaging Modalities in Alzheimer’s Disease: Diagnosis and Clinical Features

Alzheimer’s disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death. AD affects millions of individuals worldwide in the absence of effective treatment options, and its clinical causes are still uncertain. The onset of dementia symptoms indicates severe neurodegeneration has already taken place. Therefore, AD diagnosis at an early stage is essential as it results in more effective therapy to slow its progression. The current clinical diagnosis of AD relies on mental examinations and brain imaging to determine whether patients meet diagnostic criteria, and biomedical research focuses on finding associated biomarkers by using neuroimaging techniques. Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease. This review presents the advantages and limitations of brain imaging modalities for AD diagnosis and discusses their clinical value.

EEG Evidence Reveals Zolpidem-Related Alterations and Prognostic Value in Disorders of Consciousness

Effective treatment and accurate long-term prognostication of patients with disorders of consciousness (DOC) remain pivotal clinical issues and challenges in neuroscience. Previous studies have shown that zolpidem produces paradoxical recovery and induces similar change patterns in specific electrophysiological features in some DOC (∼6%). However, whether these specific features are neural markers of responders, and how neural features evolve over time remain unclear. Here, we capitalized on static and dynamic EEG analysis techniques to fully uncover zolpidem-induced alterations in eight patients with DOC and constructed machine-learning models to predict long-term outcomes at the single-subject level. We observed consistent patterns of change across all patients in several static features (e.g., decreased relative theta power and weakened alpha-band functional connectivity) after zolpidem administration, albeit none zolpidem responders. Based on the current evidence, previously published electrophysiological features are not neural markers for zolpidem responders. Moreover, we found that the temporal dynamics of the brain slowed down after zolpidem intake. Brain states before and after zolpidem administration could be completely characterized by the EEG features. Furthermore, long-term outcomes were accurately predicted using connectivity features. Our findings suggest that EEG neural signatures have huge potential to assess consciousness states and predict fine-grained outcomes. In summary, our results extend the understanding of the effects of zolpidem on the brain and open avenues for the application prospect of zolpidem and EEG in patients with DOC.

EEG Microstate-Specific Functional Connectivity and Stroke-Related Alterations in Brain Dynamics

The brain, as a complex dynamically distributed information processing system, involves the coordination of large-scale brain networks such as neural synchronization and fast brain state transitions, even at rest. However, the neural mechanisms underlying brain states and the impact of dysfunction following brain injury on brain dynamics remain poorly understood. To this end, we proposed a microstate-based method to explore the functional connectivity pattern associated with each microstate class. We capitalized on microstate features from eyes-closed resting-state EEG data to investigate whether microstate dynamics differ between subacute stroke patients (N = 31) and healthy populations (N = 23) and further examined the correlations between microstate features and behaviors. An important finding in this study was that each microstate class was associated with a distinct functional connectivity pattern, and it was highly consistent across different groups (including an independent dataset). Although the connectivity patterns were diminished in stroke patients, the skeleton of the patterns was retained to some extent. Nevertheless, stroke patients showed significant differences in most parameters of microstates A, B, and C compared to healthy controls. Notably, microstate C exhibited an opposite pattern of differences to microstates A and B. On the other hand, there were no significant differences in all microstate parameters for patients with left-sided vs. right-sided stroke, as well as patients before vs. after lower limb training. Moreover, support vector machine (SVM) models were developed using only microstate features and achieved moderate discrimination between patients and controls. Furthermore, significant negative correlations were observed between the microstate-wise functional connectivity and lower limb motor scores. Overall, these results suggest that the changes in microstate dynamics for stroke patients appear to be state-selective, compensatory, and related to brain dysfunction after stroke and subsequent functional reconfiguration. These findings offer new insights into understanding the neural mechanisms of microstates, uncovering stroke-related alterations in brain dynamics, and exploring new treatments for stroke patients.

Abnormalities in resting-state EEG microstates are a vulnerability marker of migraine

Background

Resting-state EEG microstates are thought to reflect brief activations of several interacting components of resting-state brain networks. Surprisingly, we still know little about the role of these microstates in migraine. In the present study, we attempted to address this issue by examining EEG microstates in patients with migraine without aura (MwoA) during the interictal period and comparing them with those of a group of healthy controls (HC).

Methods

Resting-state EEG was recorded in 61 MwoA patients (50 females) and 66 HC (50 females). Microstate parameters were compared between the two groups. We computed four widely identified canonical microstate classes A-D.

Results

Microstate classes B and D displayed higher time coverage and occurrence in the MwoA patient group than in the HC group, while microstate class C exhibited significantly lower time coverage and occurrence in the MwoA patient group. Meanwhile, the mean duration of microstate class C was significantly shorter in the MwoA patient group than in the HC group. Moreover, among the MwoA patient group, the duration of microstate class C correlated negatively with clinical measures of headache-related disability as assessed by the six-item Headache Impact Test (HIT-6). Finally, microstate syntax analysis showed significant differences in transition probabilities between the two groups, primarily involving microstate classes B, C, and D.

Conclusions

By exploring EEG microstate characteristics at baseline we were able to explore the neurobiological mechanisms underlying altered cortical excitability and aberrant sensory, affective, and cognitive processing, thus deepening our understanding of migraine pathophysiology.

Resting-state networks of believers and non-believers: An EEG microstate study

Atheism and agnosticism are becoming increasingly popular, yet the neural processes underpinning individual differences in religious belief and non-belief remain poorly understood. In the current study, we examined differences between Believers and Non-Believers with regard to fundamental neural resting networks using EEG microstate analysis. Results demonstrated that Non-Believers show increased contribution from a resting-state network associated with deliberative or analytic processing (Microstate D), and Believers show increased contribution from a network associated with intuitive or automatic processing (Microstate C). Further, analysis of resting-state network communication suggested that Non-Believers may process visual information in a more deliberative or top-down manner, and Believers may process visual information in a more intuitive or bottom-up manner. These results support dual process explanations of individual differences in religious belief and add to the representation of non-belief as more than merely a lack of belief.

Resting state electroencephalography microstates in autism spectrum disorder: A mini-review

Atypical spatial organization and temporal characteristics, found via resting state electroencephalography (EEG) microstate analysis, have been associated with psychiatric disorders but these temporal and spatial parameters are less known in autism spectrum disorder (ASD). EEG microstates reflect a short time period of stable scalp potential topography. These canonical microstates (i.e., A, B, C, and D) and more are identified by their unique topographic map, mean duration, fraction of time covered, frequency of occurrence and global explained variance percentage; a measure of how well topographical maps represent EEG data. We reviewed the current literature for resting state microstate analysis in ASD and identified eight publications. This current review indicates there is significant alterations in microstate parameters in ASD populations as compared to typically developing (TD) populations. Microstate parameters were also found to change in relation to specific cognitive processes. However, as microstate parameters are found to be changed by cognitive states, the differently acquired data (e.g., eyes closed or open) resting state EEG are likely to produce disparate results. We also review the current understanding of EEG sources of microstates and the underlying brain networks.

Relationship between Spatiotemporal Dynamics of the Brain at Rest and Self-Reported Spontaneous Thoughts: An EEG Microstate Approach

Rationale: The resting-state paradigm is frequently applied in electroencephalography (EEG) research; however, it is associated with the inability to control participants’ thoughts. To quantify subjects’ subjective experiences at rest, the Amsterdam Resting-State Questionnaire (ARSQ) was introduced covering ten dimensions of mind wandering. We aimed to estimate associations between subjective experiences and resting-state microstates of EEG. Methods: 5 min resting-state EEG data of 197 subjects was used to evaluate temporal properties of seven microstate classes. Bayesian correlation approach was implemented to assess associations between ARSQ domains assessed after resting and parameters of microstates. Results: Several associations between Comfort, Self and Somatic Awareness domains and temporal properties of neuroelectric microstates were revealed. The positive correlation between Comfort and duration of microstates E showed the strongest evidence (BF₁₀ > 10); remaining correlations showed substantial evidence (10 > BF₁₀ > 3). Conclusion: Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the intrinsic brain activity reflected in microstates.

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

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

Meditation training modulates brain electric microstates and felt states of awareness

Meditation practice is believed to foster states of mindful awareness and mental quiescence in everyday life. If so, then the cultivation of these qualities with training ought to leave its imprint on the activity of intrinsic functional brain networks. In an intensive longitudinal study, we investigated associations between meditation practitioners' experiences of felt mindful awareness and changes in the spontaneous electrophysiological dynamics of functional brain networks. Experienced meditators were randomly assigned to complete 3 months of full‐time training in focused‐attention meditation (during an initial intervention) or to serve as waiting‐list controls and receive training second (during a later intervention). We collected broadband electroencephalogram (EEG) during rest at the beginning, middle, and end of the two training periods. Using a data‐driven approach, we segmented the EEG into a time series of transient microstate intervals based on clustering of topographic voltage patterns. Participants also provided daily reports of felt mindful awareness and mental quiescence, and reported daily on four experiential qualities of their meditation practice during training. We found that meditation training led to increases in mindful qualities of awareness, which corroborate contemplative accounts of deepening mental calm and attentional focus. We also observed reductions in the strength and duration of EEG microstates across both interventions. Importantly, changes in the dynamic sequencing of microstates were associated with daily increases in felt attentiveness and serenity during training. Our results connect shifts in subjective qualities of meditative experience with the large‐scale dynamics of whole brain functional EEG networks at rest.

Associations between self-reported spontaneous thought and temporal sequences of EEG microstates

Thought dynamically evolves from one moment to the next even in the absence of external stimulation. The extent to which patterns of spontaneous thought covary with time-varying fluctuations in intrinsic brain activity is of great interest but remains unknown. We conducted novel analyses of data originally reported by Portnova et al. (2019) to examine associations between the intrinsic dynamics of EEG microstates and self-reported thought measured using the Amsterdam Resting-State Questionnaire (ARSQ). Accordingly, the millisecond fluctuations of microstates were associated with specific dimensions of thought. We evaluated the reliability of these findings by combining our results with those of another study using meta-analysis. Importantly, we extended this investigation using multivariate methods to evaluate multidimensional thought profiles of individuals and their links to sequences of successive microstates. Thought profiles were identified based on hierarchical clustering of ARSQ ratings and were distinguished in terms of the temporal ordering of successive microstates based on sequence analytic methods. These findings demonstrate the relevance of assessing spontaneous thought for understanding intrinsic brain activity and the novel use of sequence analysis for characterizing microstate dynamics. Integrating the phenomenological view from within remains crucial for understanding the functional significance of intrinsic large-scale neurodynamics.

Reconfiguration of Electroencephalography Microstate Networks after Breath-Focused, Digital Meditation Training

Sustained attention and working memory were improved in young adults after they engaged in a recently developed, closed-loop, digital meditation practice. Whether this type of meditation also has a sustained effect on dominant resting-state networks is currently unknown. In this study, we examined the resting brain states before and after a period of breath-focused, digital meditation training versus placebo using an electroencephalography (EEG) microstate approach. We found topographical changes in postmeditation rest, compared with baseline rest, selectively for participants who were actively involved in the meditation training and not in participants who engaged with an active, expectancy-match, placebo control paradigm. Our results suggest a reorganization of brain network connectivity after 6 weeks of intensive meditation training in brain areas, mainly including the right insula, the superior temporal gyrus, the superior parietal lobule, and the superior frontal gyrus bilaterally. These findings provide an opening for the development of a novel noninvasive treatment of neuropathological states by low-cost, breath-focused, digital meditation practice, which can be monitored by the EEG microstate approach.

Dynamic Changes of Brain Networks during Working Memory Tasks in Schizophrenia

Electroencephalograph (EEG) signals and graph theory measures have been widely used to characterize the brain functional networks of healthy individuals and patients by calculating the correlations between different electrodes over an entire time series. Although EEG signals have a high temporal resolution and can provide relatively stable results, the process of constructing and analyzing brain functional networks is inevitably complicated by high time complexity. Our goal in this research was to distinguish the brain function networks of schizophrenia patients from those of healthy participants during working memory tasks. Consequently, we utilized a method involving microstates, which are each characterized by a unique topography of electric potentials over an entire channel array, to reduce the dimension of the EEG signals during working memory tasks and then compared and analyzed the brain functional networks using the microstates time series (MTS) and original time series (OTS) of the schizophrenia patients and healthy individuals. We found that the right frontal and parietal-occipital regions neurons of the schizophrenia patients were less active than those of the healthy participants during working memory tasks. Notably, compared with OTS, the time needed to construct the brain functional networks was significantly reduced by using MTS. In conclusion, our results show that, like OTS, MTS can well distinguish the brain functional network of schizophrenia patients from those of healthy individuals during working memory tasks while greatly decreasing time complexity. MTS can thus provide a method for characterizing the original time series for the construction and analysis of EEG brain functional networks.