Characteristics Of Microstates Research Articles

Increased occurrence of microstate class B as the independent risk factor for persistent psychiatric symptoms related to omicron infection

ObjectiveThis study aimed to investigate the EEG microstate characteristics in patients with persistent Omicron-related Psychiatric Symptoms (ORPS) as well as their correlations with the severity of ORPS. MethodsThis study included 31 patients with ORPS, and they were divided into remission group (n=19) and non-remission group (n=12) according to the decrease of Brief Psychiatric Rating Scale (BPRS) at discharge. Multivariate logistic models were applied to analyze the risk features associated with non-remission of ORPS at discharge, and the Spearman rank correlation was adopted to analyze the correlation between the occurrence of microstate class B and BPRS score at admission. ResultsThe analysis revealed that an increased occurrence of EEG microstate class B was significantly associated with a higher likelihood of non-remission of ORPS at discharge (p < 0.05). Furthermore, a moderate positive correlation was observed between the occurrence of microstate class B and BPRS scores at admission (r = 0.390, p = 0.030), indicating that patients with more frequent microstate class B tended to exhibit more severe psychiatric symptoms at onset. ConclusionsThe findings suggest that an increased occurrence of EEG microstate class B is an independent risk factor for non-remission of ORPS at discharge. Additionally, the positive correlation between microstate class B and BPRS scores underscores the potential of microstate class B as a biomarker for the severity of psychiatric symptoms in ORPS patients. SignificanceIdentifying the increased occurrence of microstate class B at admission could serve as a novel marker for early assessment of ORPS severity and prognostic evaluation.

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.

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. 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. 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). 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.

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

ObjectiveRepetitive 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. MethodBaseline 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. ResultThere 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. ConclusionThis 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.

Electroencephalography microstate alterations reflect potential double-edged cognitive adaptation in Ménière's disease.

To explore the microstate characteristics and underlying brain network activity of Ménière's disease (MD) patients based on high-density electroencephalography (EEG), elucidate the association between microstate dynamics and clinical manifestation, and explore the potential of EEG microstate features as future neurobiomarkers for MD. Thirty-two patients diagnosed with MD and 29 healthy controls (HC) matched for demographic characteristics were included in the study. Dysfunction and subjective symptom severity were assessed by neuropsychological questionnaires, pure tone audiometry, and vestibular function tests. Resting-state EEG recordings were obtained using a 256-channel EEG system, and the electric field topographies were clustered into four dominant microstate classes (A, B, C, and D). The dynamic parameters of each microstate were analyzed and utilized as input for a support vector machine (SVM) classifier to identify significant microstate signatures associated with MD. The clinical significance was further explored through Spearman correlation analysis. MD patients exhibited an increased presence of microstate class C and a decreased frequency of transitions between microstate class A and B, as well as between class A and D. The transitions from microstate class A to C were also elevated. Further analysis revealed a positive correlation between equilibrium scores and the transitions from microstate class A to C under somatosensory challenging conditions. Conversely, transitions between class A and B were negatively correlated with vertigo symptoms. No significant correlations were detected between these characteristics and auditory test results or emotional scores. Utilizing the microstate features identified via sequential backward selection, the linear SVM classifier achieved a sensitivity of 86.21% and a specificity of 90.61% in distinguishing MD patients from HC. We identified several EEG microstate characteristics in MD patients that facilitate postural control yet exacerbate subjective symptoms, and effectively discriminate MD from HC. The microstate features may offer a new approach for optimizing cognitive compensation strategies and exploring potential neurobiological markers in MD.

Resting-state electroencephalography (EEG) microstates of healthy individuals following mild sleep deprivation

Mild sleep deprivation is widespread in many societies worldwide. Electroencephalography (EEG) microstate analysis provides information on spatial and temporal characteristics of resting brain network, serving as an indicator of neurophysiological activities at rest. This study seeks to investigate potential neural markers in EEG following mild sleep deprivation of a single night using EEG microstate analysis. Six-minute resting EEG was conducted on thirty healthy adults within 6 hours of waking in the morning and after at least 18 h of sleep deprivation. Translated and validated Malay language Karolinska Sleepiness Scale was used to assess the participants’ degree of sleepiness. Microstate characteristics analysis was conducted on the final 24 subjects based on four standard microstate maps. Microstate C shows a significant increase in mean duration, coverage and occurrence, while microstate D has significantly higher occurrence after sleep deprivation. This study demonstrates notable changes in resting state EEG microstates following mild sleep deprivation. Present findings deepen our understanding of the brain's spatiotemporal dynamics under this condition and suggest the potential utility of neural markers in this domain as components of composite markers for sleep deprivation.

Decoding the Preparation Stage of Target Shooting under Audiovisual Restricted Conditions: Investigating Neural Mechanisms Using Microstate Analysis.

Shooting is a fine sport that is greatly influenced by mental state, and the neural activity of brain in the preparation stage of shooting has a direct influence on the level of shooting. In order to explore the brain neural mechanism in the preparation stage of pistol shooting under audiovisual restricted conditions, and to reveal the intrinsic relationship between brain activity and shooting behavior indicators, the electroencephalography (EEG) signals and seven shooting behaviors including shooting performance, gun holding stability, and firing stability, were experimentally captured from 30 shooters, these shooters performed pistol shooting under three conditions, normal, dim, and noisy. Using EEG microstates combined with standardized low-resolution brain electromagnetic tomography (sLORETA) traceability analysis method, we investigated the difference between the microstates characteristics under audiovisual restricted conditions and normal condition, the relationship between the microstates characteristics and the behavioral indicators during the shooting preparation stage under different conditions. The experimental results showed that microstate 1 corresponded to microstate A, microstate 2 corresponded to microstate B, and microstate 4 corresponded to microstate D; Microstate 3 was a unique template, which was localized in the occipital lobe, its function was to generate the "vision for action"; The dim condition significantly reduced the shooter's performance, whereas the noisy condition had less effect on the shooter's performance; In audiovisual restricted conditions, the microstate characteristics were significantly different from those in the normal condition. Microstate 4' parameters decreased significantly while microstate 3' parameters increased significantly under restricted visual and auditory conditions; Dim condition required more shooting skills from the shooter; There was a significant relationship between characteristics of microstates and indicators of shooting behavior; It was concluded that in order to obtain good shooting performance, shooters should improve attention and concentrate on the adjustment of collimator and target's center leveling relation, but the focus was slightly different in the three conditions; Microstates that are more important for accomplishing the task have less variation in their characteristics over time; Similar conclusions to previous studies were obtained at the same time, i.e., increased visual attention prior to shooting is detrimental to shooting performance, and there is a high positive correlation with microstate D for task completion. The experimental results further reveal the brain neural mechanism in the shooting preparation stage, and the extracted neural markers can be used as effective functional indicators for monitoring the brain state in the shooting preparation stage of pistols.

EEG Microstate Associated with Trait Nostalgia.

Nostalgia, a self-related emotion characterized by its bittersweet yet predominantly positive nature, plays a vital role in shaping individual psychology and behavior. This includes impacts on mental and physical health, behavioral patterns, and cognitive functions. However, higher levels of trait nostalgia may be linked to potential adverse outcomes, such as increased loneliness, heightened neuroticism, and more intense experiences of grief. The specific electroencephalography (EEG) feature associated with individuals exhibiting trait nostalgia, and how it differs from others, remains an area of uncertainty. To address this, our study employs microstate analysis to investigate the differences in resting-state EEG between individuals with varying levels of trait nostalgia. We assessed trait nostalgia in 63 participants using the Personal Inventory of Nostalgia and collected their resting-state EEG signals with eyes closed. The results of the regression analysis indicate a significant correlation between trait nostalgia and the temporal characteristics of microstates A, B, and C. Further, the occurrence of microstate B was significantly more frequent in the high trait nostalgia group than in the low trait nostalgia group. Independent samples t-test results showed that the transition probability between microstates A and B was significantly higher in the high trait nostalgia group. These results support the hypothesis that trait nostalgia is reflected in the resting state brain activity. Furthermore, they reveal a deeper sensory immersion in nostalgia experiences among individuals with high levels of trait nostalgia, and highlight the critical role of self-referential and autobiographical memory processes in nostalgia.

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

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. 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. 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). 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.

Abnormalities of electroencephalography microstates in patients with depression and their association with cognitive function.

A growing number of recent studies have explored underlying activity in the brain by measuring electroencephalography (EEG) in people with depression. However, the consistency of findings on EEG microstates in patients with depression is poor, and few studies have reported the relationship between EEG microstates, cognitive scales, and depression severity scales. To investigate the EEG microstate characteristics of patients with depression and their association with cognitive functions. A total of 24 patients diagnosed with depression and 32 healthy controls were included in this study using the Structured Clinical Interview for Disease for The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. We collected information relating to demographic and clinical characteristics, as well as data from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS; Chinese version) and EEG. Compared with the controls, the duration, occurrence, and contribution of microstate C were significantly higher [depression (DEP): Duration 84.58 ± 24.35, occurrence 3.72 ± 0.56, contribution 30.39 ± 8.59; CON: Duration 72.77 ± 10.23, occurrence 3.41 ± 0.36, contribution 24.46 ± 4.66; Duration F = 6.02, P = 0.049; Occurrence F = 6.19, P = 0.049; Contribution F = 10.82, P = 0.011] while the duration, occurrence, and contribution of microstate D were significantly lower (DEP: Duration 70.00 ± 15.92, occurrence 3.18 ± 0.71, contribution 22.48 ± 8.12; CON: Duration 85.46 ± 10.23, occurrence 3.54 ± 0.41, contribution 28.25 ± 5.85; Duration F = 19.18, P < 0.001; Occurrence F = 5.79, P = 0.050; Contribution F = 9.41, P = 0.013) in patients with depression. A positive correlation was observed between the visuospatial/constructional scores of the RBANS scale and the transition probability of microstate class C to B (r = 0.405, P = 0.049). EEG microstate, especially C and D, is a possible biomarker in depression. Patients with depression had a more frequent transition from microstate C to B, which may relate to more negative rumination and visual processing.

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

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). 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. 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. 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.

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

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

Electroencephalography microstates as novel functional biomarkers for insomnia disorder

BackgroundInsomnia disorder (ID) is one of the most common mental disorders. Research on ID focuses on exploring its mechanism of disease, novel treatments and treatment outcome prediction. An emerging technique...

Dynamic network characteristics of adolescents with major depressive disorder: Attention network mediates the association between anhedonia and attentional deficit.

Attention deficit is a critical symptom that impairs social functioning in adolescents with major depressive disorder (MDD). In this study, we aimed to explore the dynamic neural network activity associated with attention deficits and its relationship with clinical outcomes in adolescents with MDD. We included 188 adolescents with MDD and 94 healthy controls. By combining psychophysics, resting-state electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) techniques, we aimed to identify dynamic network features through the investigation of EEG microstate characteristics and related temporal network features in adolescents with MDD. At baseline, microstate analysis revealed that the occurrence of Microstate C in the patient group was lower than that in healthy controls, whereas the duration and coverage of Microstate D increased in the MDD group. Mediation analysis revealed that the probability of transition from Microstate C to D mediated anhedonia and attention deficits in the MDD group. fMRI results showed that the temporal variability of the dorsal attention network (DAN) was significantly weaker in patients with MDD than in healthy controls. Importantly, the temporal variability of DAN mediated the relationship between anhedonia and attention deficits in the patient group. After acute-stage treatment, the response prediction group (RP) showed improvement in Microstates C and D compared to the nonresponse prediction group (NRP). For resting-state fMRI data, the temporal variability of DAN was significantly higher in the RP group than in the NRP group. Overall, this study enriches our understanding of the neural mechanisms underlying attention deficits in patients with MDD and provides novel clinical biomarkers.

Preoperative resting-state microstate as a marker for chronic pain after breast cancer surgery.

Chronic postoperative pain poses challenges, emphasizing the importance of accurately predicting pain in advance. Generally, pain perception is associated with the temporal dynamics of the brain, which can be represented by microstates. Specifically, microstates are transient and patterned brain topographies formed by temporally overlapping and spatially synchronized oscillatory activities. Consequently, by characterizing brain activity, microstates offer valuable insights into pain perception. In this prospective study, 66 female patients undergoing breast cancer surgery were included. Their preoperative resting-state electroencephalography (EEG) was recorded. Preoperative resting-state EEG was recorded and four specific brain microstates (labeled as A, B, C, and D) were extracted. Temporal characteristics were then analyzed from these microstates. Patients were classified into two groups based on their Numerical Rating Scale (NRS) scores at three months postoperatively. Those with NRS scores ranging from 4 to 10 were classified as the high pain group, while patients with NRS ranging from 0 to 3 were classified as the lowpain group. Statistical analyses were performed to compare the microstate characteristics between these two groups. Twenty-one patients (32%) were classified as the high pain group and forty-five (68%) as the low-pain group. The occurrence and coverage of microstate C were significantly higher in the high pain group. Additionally, there were significant differences in the microstates transitions between the two groups. Furthermore, the study revealed a positive correlation between the coverage of microstate C and the NRS. Preoperative resting-state microstate features have shown correlations with postoperative pain. This study presents a novel and advanced perspective on the potential of microstates as a marker for postoperative pain.

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).

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 EEG Reveals Abnormal Microstate Characteristics of Depression with Insomnia.

Previous research revealed various aspects of resting-state EEG for depression and insomnia. However, the EEG characteristics of depressed subjects with insomnia are rarely studied, especially EEG microstates that capture the dynamic activities of the large-scale brain network. To fill these research gaps, the present study collected resting-state EEG data from 32 subclinical depression subjects with insomnia (SDI), 31 subclinical depression subjects without insomnia (SD), and 32 healthy controls (HCs). Four topographic maps were generated from clean EEG data after clustering and rearrangement. Temporal characteristics were obtained for statistical analysis, including cross-group variance analysis (ANOVA) and intra-group correlation analysis. In our study,the global clustering of all individuals in the EEG microstate analysis revealed the four previously discovered categories of microstates (A, B, C, and D). The occurrence of microstate B was lower in SDI than in SD and HC subjects. The correlation analysis showed that the total Pittsburgh Sleep Quality Index (PSQI) score negatively correlated with the occurrence of microstate C in SDI (r=- 0.415, p < 0.05). Conversely, there was a positive correlation between Self-rating Depression Scale (SDS) scores and the duration of microstate C in SD (r = 0.359, p < 0.05). These results indicate that microstates reflect altered large-scale brain network dynamics in subclinical populations. Abnormalities in the visual network corresponding to microstate B are an electrophysiological characteristic of subclinical individuals with symptoms of depressive insomnia. Further investigation is needed for microstate changes related to high arousal and emotional problems in people suffering from depression and insomnia.

An enhanced EEG microstate recognition framework based on deep neural networks: an application to Parkinson's disease.

Variations in brain activity patterns reveal impairments of motor and cognitive functions in the human brain. Electroencephalogram (EEG) microstates embody brain activity patterns at a microscopic time scale. However, current microstate analysis method can only recognize less than 90% of EEG signals per subject, which severely limits the characterization of dynamic brain activity. As an application to early Parkinson's disease (PD), we propose an enhanced EEG microstate recognition framework based on deep neural networks, which yields recognition rates from 90% to 99%, as accompanied by a strong anti-artifact property. Additionally, gradient-weighted class activation mapping, as a visualization technique, is employed to locate the activated functional brain regions of each microstate class. We find that each microstate class corresponds to a particular activated brain region. Finally, based on the improved identification of microstate sequences, we explore the EEG microstate characteristics and their clinical associations. We show that the decreased occurrences of a particular microstate class reflect the degree of cognitive decline in early PD, and reduced transitions between certain microstates suggest injury in motor-related brain regions. The novel EEG microstate recognition framework paves the way to revealing more effective biomarkers for early PD.

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.