Altered Resting-State EEG Microstate Parameters and Enhanced Spatial Complexity in Male Adolescent Patients with Mild Spastic Diplegia

Utilization of EEG microstates as a prospective biomarker for assessing the impact of ketogenic diet in GLUT1-DS

OBJECTIVE

The aim of the study is to analyze microstate patterns in GLUT1-DS, both before and after the ketogenic diet (KD).

METHODS

We conducted microstate analysis of a patient with GLUT-1 DS and 27 healthy controls. A systematic literature review and meta-analysis was done. We compared the parameters of the patients with those of healthy controls and the incorporating findings in literature.

RESULTS

The durations of the patient were notably shorter, and the occurrence rates were longer than those of healthy controls and incorporating findings from the review. After 10 months of KD, the patient's microstate durations exhibited an increase from 53.05 ms, 57.17 ms, 61.80 ms, and 49.49 ms to 60.53 ms, 63.27 ms, 71.11 ms, and 66.55 ms. The occurrence rates changed from 4.0774 Hz, 4.9462 Hz, 4.8006 Hz, and 4.0579 Hz to 3.3354 Hz, 3.7893 Hz, 3.5956 Hz, and 4.1672 Hz. In healthy controls, the durations of microstate class A, B, C, and D were 61.86 ms, 63.58 ms, 70.57 ms, and 72.00 ms, respectively.

CONCLUSIONS

Our findings suggest EEG microstates may be a promising biomarker for monitoring the effect of KD. Administration of KD may normalize the dysfunctional patterns of temporal parameters.

Dragon boat exercise reshapes the temporal-spatial dynamics of the brain

Although exercise training has been shown to enhance neurological function, there is a shortage of research on how exercise training affects the temporal-spatial synchronization properties of functional networks, which are crucial to the neurological system. This study recruited 23 professional and 24 amateur dragon boat racers to perform simulated paddling on ergometers while recording EEG. The spatiotemporal dynamics of the brain were analyzed using microstates and omega complexity. Temporal dynamics results showed that microstate D, which is associated with attentional networks, appeared significantly altered, with significantly higher duration, occurrence, and coverage in the professional group than in the amateur group. The transition probabilities of microstate D exhibited a similar pattern. The spatial dynamics results showed the professional group had lower brain complexity than the amateur group, with a significant decrease in omega complexity in the α (8-12 Hz) and β (13-30 Hz) bands. Dragon boat training may strengthen the attentive network and reduce the complexity of the brain. This study provides evidence that dragon boat exercise improves the efficiency of the cerebral functional networks on a spatiotemporal scale.

Normative Temporal Dynamics of Resting EEG Microstates

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

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.

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.

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 effect of repetitive transcranial magnetic stimulation on electroencephalography microstates of patients with heroin-addiction

The effects of transcranial magnetic stimulation in treating substance use disorders are gaining attention; however, most existing studies used subjective measures to examine the treatment effects. Objective electroencephalography (EEG)-based microstate analysis is important for measuring the efficacy of transcranial magnetic stimulation in patients with heroin addiction. We investigated dynamic brain activity changes in individuals with heroin addiction after transcranial magnetic stimulation using microstate indicators. Thirty-two patients received intermittent theta-burst stimulation (iTBS) over the left dorsolateral prefrontal cortex. Resting-state EEG data were collected pre-intervention and 10 days post-intervention. The feature values of the significantly different microstate classes were computed using a K-means clustering algorithm. Four EEG microstate classes (A-D) were noted. There were significant increases in the duration, occurrence, and contribution of microstate class A after the iTBS intervention. K-means classification accuracy reached 81.5%. The EEG microstate is an effective improvement indicator in patients with heroin addiction treated with iTBS. Microstates were examined using machine learning; this method effectively classified the pre- and post-intervention cohorts among patients with heroin addiction and healthy individuals. Using EEG microstate to measure heroin addiction and further exploring the effect of iTBS in patients with heroin addiction merit clinical investigation.

Cortical neuronal assemblies coordinate with EEG microstate dynamics during resting wakefulness

The disruption of cortical assembly activity has been associated with anesthesia-induced loss of consciousness. However, the relationship between cortical assembly activity and the variations in consciousness associated with natural vigilance states remains unclear. Here, we address this by performing vigilance state-specific clustering analysis on 2-photon calcium imaging data from the sensorimotor cortex in combination with global electroencephalogram (EEG) microstate analysis derived from multi-EEG signals obtained over widespread cortical locations. We report no difference in the structure of assembly activity during quiet wakefulness (QW), non-rapid eye movement sleep (NREMs), or REMs, despite the latter two vigilance states being associated with significantly reduced levels of consciousness relative to QW. However, we describe a significant coordination between global EEG microstate dynamics and general local cortical assembly activity during periods of QW, but not sleep. These results suggest that the coordination of cortical assembly activity with global brain dynamics could be a key factor of sustained conscious experience.

Brain structural and functional connectivity and network organization in cerebral palsy: A scoping review

AIM

To explore altered structural and functional connectivity and network organization in cerebral palsy (CP), by clinical CP subtype (unilateral spastic, bilateral spastic, dyskinetic, and ataxic CP).

METHOD

PubMed and Embase databases were systematically searched. Extracted data included clinical characteristics, analyses, outcome measures, and results.

RESULTS

Sixty-five studies were included, of which 50 investigated structural connectivity, and 20 investigated functional connectivity using functional magnetic resonance imaging (14 studies) or electroencephalography (six studies). Five of the 50 studies of structural connectivity and one of 14 of functional connectivity investigated whole-brain network organization. Most studies included patients with unilateral spastic CP; none included ataxic CP.

INTERPRETATION

Differences in structural and functional connectivity were observed between investigated clinical CP subtypes and typically developing individuals on a wide variety of measures, including efferent, afferent, interhemispheric, and intrahemispheric connections. Directions for future research include extending knowledge in underrepresented CP subtypes and methodologies, evaluating the prognostic potential of specific connectivity and network measures in neonates, and understanding therapeutic effects on brain connectivity.

EEG microstate temporal Dynamics Predict depressive symptoms in College Students

Previous studies on resting-state electroencephalographic responses in patients with depressive disorders have identified electroencephalogram (EEG) parameters as potential biomarkers for the early detection and diagnosis of depressive disorders. However, these studies did not investigate the relationship between resting-state EEG microstates and the early detection of depressive symptoms in preclinical individuals. To explore the possible association between resting-state EEG microstate temporal dynamics and depressive symptoms among college students, EEG microstate analysis was performed on eyes-closed resting-state EEG data for approximately 5 min from 34 undergraduates with high intensity of depressive symptoms and 34 age- and sex-matched controls with low intensity of depressive symptoms. Five microstate classes (A-E) were identified to best explain the datasets of both groups. Compared to controls, the mean duration, occurrence, and coverage of microstate class B increased significantly, whereas the occurrence and coverage of microstate classes D and E decreased significantly in individuals with high intensity of depressive symptoms. Additionally, the presence of microstate class B was positively correlated with participants' Beck Depression Inventory-II (BDI-II) scores, and the presence of microstate classes D and E were negatively correlated with their BDI-II scores. Further, individuals with high intensity of depressive symptoms had higher transition probabilities of A→B, B→A, B→C, B→D, and C→B, with lower transition probabilities of A→D, A→E, D→A, D→E, E→A, E→C, and E→D than controls. These results highlight resting-state EEG microstate temporal dynamics as potential biomarkers for the early detection and timely treatment of depression in college students.

Multidimensional Assessment of Electroencephalography in the Neuromodulation of Disorders of Consciousness

In the present study, we aimed to elucidate changes in electroencephalography (EEG) metrics during recovery of consciousness and to identify possible clinical markers thereof. More specifically, in order to assess changes in multidimensional EEG metrics during neuromodulation, we performed repeated stimulation using a high-density transcranial direct current stimulation (HD-tDCS) protocol in 42 patients with disorders of consciousness (DOC). Coma Recovery Scale-Revised (CRS-R) scores and EEG metrics [brain network indicators, spectral energy, and normalized spatial complexity (NSC)] were obtained before as well as fourteen days after undergoing HD-tDCS stimulation. CRS-R scores increased in the responders (R +) group after HD-tDCS stimulation. The R + group also showed increased spectral energy in the alpha2 and beta1 bands, mainly at the frontal and parietal electrodes. Increased graphical metrics in the alpha1, alpha2, and beta1 bands combined with increased NSC in the beta2 band in the R + group suggested that improved consciousness was associated with a tendency toward stronger integration in the alpha1 band and greater isolation in the beta2 band. Following this, using NSC as a feature to predict responsiveness through machine learning, which yielded a prediction accuracy of 0.929, demonstrated that the NSC of the alpha and gamma bands at baseline successfully predicted improvement in consciousness. According to our findings reported herein, we conclude that neuromodulation of the posterior lobe can lead to an EEG response related to consciousness in DOC, and that the posterior cortex may be one of the key brain areas involved in the formation or maintenance of consciousness.

Temporal and Spatial Dynamics of EEG Features in Female College Students with Subclinical Depression

Synchronization of the dynamic processes in structural networks connect the brain across a wide range of temporal and spatial scales, creating a dynamic and complex functional network. Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data. Few studies have focused on potential brain spatiotemporal dynamics in the early stages of depression to use as an early screening feature for depression. Thus, this study aimed to explore large-scale brain network dynamics of individuals both with and without subclinical depression, from the perspective of temporal and spatial dimensions and to input them as features into a machine learning framework for the automatic diagnosis of early-stage depression. To achieve this, spatio–temporal dynamics of rest-state EEG signals in female college students (n = 40) with and without (n = 38) subclinical depression were analyzed using EEG microstate and omega complexity analysis. Then, based on differential features of EEGs between the two groups, a support vector machine was utilized to compare performances of spatio–temporal features and single features in the classification of early depression. Microstate results showed that the occurrence rate of microstate class B was significantly higher in the group with subclinical depression when compared with the group without. Moreover, the duration and contribution of microstate class C in the subclinical group were both significantly lower than in the group without subclinical depression. Omega complexity results showed that the global omega complexity of β-2 and γ band was significantly lower for the subclinical depression group compared with the other group (p < 0.05). In addition, the anterior and posterior regional omega complexities were lower for the subclinical depression group compared to the comparison group in α-1, β-2 and γ bands. It was found that AUC of 81% for the differential indicators of EEG microstates and omega complexity was deemed better than a single index for predicting subclinical depression. Thus, since temporal and spatial complexity of EEG signals were manifestly altered in female college students with subclinical depression, it is possible that this characteristic could be adopted as an early auxiliary diagnostic indicator of depression.

Altered Microstate Dynamics and Spatial Complexity in Late-Life Schizophrenia

BACKGROUND

Resting-state EEG microstate and omega complexity analyses have been widely used to explore deviant brain function in various neuropsychiatric disorders. This study aimed to investigate the features of microstate dynamics and spatial complexity in patients with late-life schizophrenia (LLS).

METHOD

Microstate and omega complexity analyses were performed on resting-state EEG data from 39 in patients with LLS and compared with 40 elderly normal controls (NCs).

RESULT

The duration of microstate classes A and D were significantly higher in patients with LLS compared with NCs. The occurrence of microstate classes A, B, and C was significantly lower in patients with LLS compared with NCs. LLS patients have a lower time coverage of microstate class A and a higher time coverage of class D than NCs. Transition probabilities from microstate class A to B and from class A to C were significantly lower in patients with LLS compared with NCs. Transition probabilities between microstate class B and D were significantly higher in patients with LLS compared with NCs. Global omega complexity and anterior omega complexity were significantly higher in patients with LLS compared with NCs.

CONCLUSION

This study revealed an altered pattern of microstate dynamics and omega complexity in patients with LLS. This may reflect the disturbed neural basis underlying LLS and enhance the understanding of the pathophysiology of LLS.

Decreased Right Prefrontal Synchronization Strength and Asymmetry During Joint Attention in the Left-Behind Children: A Functional Near-Infrared Spectroscopy Study

Although there are millions of left-behind children in China, the researches on brain structure and functions in left-behind children are not sufficient at the brain imaging level. This study aimed to explore whether there is decreased prefrontal synchronization during joint attention in left-behind children. Sixty children (65.12 ± 6.54 months, 29 males) with 34 left-behind children were recruited. The functional near-infrared spectroscopy (fNIRS) imaging data from the prefrontal cortex during joint attention, as well as behavioral measures (associated with family income, intelligence, language, and social-emotional abilities), were collected. Results verified that brain imaging data and behavioral measures are correlative and support that left-behind children have deficits in social-emotional abilities. More importantly, left-behind children showed decreased synchronization strength and asymmetry in the right middle frontal gyrus during joint attention. The findings suggest that decreased right prefrontal synchronization strength and asymmetry during joint attention might be vulnerability factors in the development of left-behind children.

Action Video Gaming Experience Related to Altered Resting-State EEG Temporal and Spatial Complexity

Action video gaming (AVG) places sustained cognitive load on various behavioral systems, thus offering new insights into learning-related neural plasticity. This study aims to determine whether AVG experience is associated with resting-state electroencephalogram (rs-EEG) temporal and spatial complexity, and if so, whether this effect is observable across AVG subgenres. Two AVG games - League of Legends (LOL) and Player Unknown's Battle Grounds (PUBG) that represent two major AVG subgenres - were examined. We compared rs-EEG microstate and omega complexity between LOL experts and non-experts (Experiment 1) and between PUBG experts and non-experts (Experiment 2). We found that the experts and non-experts had different rs-EEG activities in both experiments, thus revealing the adaptive effect of AVG experience on brain development. Furthermore, we also found certain subgenre-specific complexity changes, supporting the recent proposal that AVG should be categorized based on the gaming mechanics of a specific game rather than a generic genre designation.

The Resting-State Electroencephalogram Microstate Correlations With Empathy and Their Moderating Effect on the Relationship Between Empathy and Disgust

Humans have a natural ability to understand the emotions and feelings of others, whether one actually witnesses the situation of another, perceives it from a photograph, reads about it in a fiction book, or merely imagines it. This is the phenomenon of empathy, which requires us to mentally represent external information to experience the emotions of others. Studies have shown that individuals with high empathy have high anterior insula and adjacent frontal operculum activation when they are aware of negative emotions in others. As a negative emotion, disgust processing involves insula coupling. What are the neurophysiological characteristics for regulating the levels of empathy and disgust? To answer this question, we collected electroencephalogram microstates (EEG-ms) of 196 college students at rest and used the Disgust Scale and Interpersonal Reactivity Index. The results showed that: (1) there was a significant positive correlation between empathy and disgust sensitivity; (2) the empathy score and the intensity of transition possibility between EEG-ms C and D were significantly positively correlated; and (3) the connection strength between the transition possibility of EEG-ms C and D could adjust the relationship between the disgust sensitivity score and the empathy score. This study provides new neurophysiological characteristics for an understanding of the regulate relationship between empathy and disgust and provides a new perspective on emotion and attention.

Treatment Effect of Exercise Intervention for Female College Students with Depression: Analysis of Electroencephalogram Microstates and Power Spectrum

This paper aims to assess the effect of exercise intervention on the improvement of college students with depression and to explore the change characteristics of microstates and the power spectrum in their resting-state electroencephalogram (EEG). Forty female college students with moderate depression were screened according to the Beck Depression Inventory-II (BDI-II) and Depression Self-Rating Scale (SDS) scores, and half of them received an exercise intervention for 18 weeks. The study utilized an EEG to define the resting-state networks, and the scores of all the participants were tracked during the intervention. Compared with those in the depression group, the power spectrum values in the θ and α bands were significantly decreased (p < 0.05), and the duration of microstate C increased significantly (p < 0.05), while the frequency of microstate B decreased significantly (p < 0.05) in the exercise intervention group. The transition probabilities showed that the exercise intervention group had a higher probability from B to D than those in the depression group (p < 0.01). In addition, the power of the δ and α bands were negatively correlated with the occurrence of microstate C (r = −0.842, p < 0.05 and r = −0.885, p < 0.01, respectively), and the power of the β band was positively correlated with the duration of microstate C (r = 0.900, p < 0.01) after exercise intervention. Our results suggest that the decreased duration of microstate C and the increased α power in depressed students are associated with reduced cognitive ability, emotional stability, and brain activity. Depression symptoms were notably improved after exercise intervention, thus providing a more scientific index for the research, rehabilitation mechanisms, and treatment of depression.

Altered peri-seizure EEG microstate dynamics in patients with absence epilepsy

OBJECTIVE

To investigate whether the parameters of EEG microstates changed before and after an absence seizure episode.

METHODS

AE patients with a current high frequency of seizures were included (n=21). Each included subject underwent a two-hour and 19-channel video EEG examination. Five epochs of 10-second EEG data in interictal, pre-seizure, and post-seizure states were collected from each AE patient. Five 10-second resting-state EEG epochs from sex- and age-matched HCs who reported no history of neurological or psychiatric disorders and visited the hospital for routine physical examinations were collected. Microstate analysis and source localization of microstates were performed using the LORETA KEY tool.

RESULTS

Compared with the resting-state EEGs of HCs, the interictal EEGs of AE patients showed a higher relative transition rate from microstates B to D (p<0.05). From interictal to pre-seizure EEG, the total time ratio of microstate C and the occurrence of microstate B decreased significantly, while the duration of microstate B increased significantly (p<0.05). Compared with pre-seizure EEGs, microstate C in post-seizure EEGs showed a significantly downregulated total time percentage and occurrence (p<0.05). The source localization of each microstate in each condition also varied and showed spatial recovery tends from pre- to post-seizure states.

CONCLUSION

Altered EEG microstate dynamics exist between inter-ictal EEGs of AE patients and resting-state EEGs of HCs and between pre- and post-seizure EEGs in AE patients. The EEG microstates of epileptic patients before and after absence seizures are characterized by a "slowdown" in transitions between microstates. Microstates might be used as an index to evaluate the temporal and spatial recovery process of absence seizures in AE.

Disrupted brain network dynamics and cognitive functions in methamphetamine use disorder: insights from EEG microstates

BACKGROUND

Dysfunction in brain network dynamics has been found to correlate with many psychiatric disorders. However, there is limited research regarding resting electroencephalogram (EEG) brain network and its association with cognitive process for patients with methamphetamine use disorder (MUD). This study aimed at using EEG microstate analysis to determine whether brain network dynamics in patients with MUD differ from those of healthy controls (HC).

METHODS

A total of 55 MUD patients and 27 matched healthy controls were included for analysis. The resting brain activity was recorded by 64-channel electroencephalography. EEG microstate parameters and intracerebral current sources of each EEG microstate were compared between the two groups. Generalized linear regression model was used to explore the correlation between significant microstates with drug history and cognitive functions.

RESULTS

MUD patients showed lower mean durations of the microstate classes A and B, and a higher global explained variance of the microstate class C. Besides, MUD patients presented with different current density power in microstates A, B, and C relative to the HC. The generalized linear model showed that MA use frequency is negatively correlated with the MMD of class A. Further, the generalized linear model showed that MA use frequency, scores of Two-back task, and the error rate of MA word are correlated with the MMD and GEV of class B, respectively.

CONCLUSIONS

Intracranial current source densities of resting EEG microstates are disrupted in MUD patients, hence causing temporal changes in microstate topographies, which are correlated with attention bias and history of drug use.

Aberrant Intrinsic Brain Activity in Patients with Autism Spectrum Disorder: Insights from EEG Microstates

Autism spectrum disorder (ASD) involves aberrant organization and functioning of large-scale brain networks. The aim of this study was to examine whether the resting-state EEG microstate analysis could provide novel insights into the abnormal temporal and spatial properties of intrinsic brain activities in patients with ASD. To achieve this goal, EEG microstate analysis was conducted on the resting-state EEG datasets of 15 patients with ASD and 18 healthy controls from the Healthy Brain Network. The parameters (i.e., duration, occurrence rate, time coverage and topographical configuration) of four classical microstate classes (i.e., class A, B, C and D) were statistically tested between two groups. The results showed that: (1) the occurrence rate and time coverage of microstate class B in ASD group were significantly larger than those in control group; (2) the duration of microstate class A, the duration and time coverage of microstate class C were significantly smaller than those in control group; (3) the map configuration and occurrence rate differed significantly between two groups for microstate class D. These results suggested that EEG microstate analysis could be used to detect the deviant functions of large-scale cortical activities in ASD, and may provide indices that could be used in clinical researches of ASD.

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.

The influence of vertical disparity gradient and cue conflict on EEG omega complexity in Panum's limiting case

Using behavioral measures and ERP technique, researchers discovered at least two factors could influence the final perception of depth in Panum's limiting case, which are the vertical disparity gradient and the degree of cue conflict between two- and three-dimensional shapes. Although certain event-related potential components have been proved to be sensitive to the different levels of these two factors, some methodological limitations existed in this technique. In this study, we proposed that the omega complexity of EEG signal may serve as an important supplement of the traditional event-related potential technique. We found that the trials with lower vertical gradient disparity have lower omega complexity (i.e., higher global functional connectivity) of the occipital region, especially that of the right-occipital hemisphere. Moreover, for occipital omega complexity, the trials with low-cue conflict have significantly larger omega complexity than those with medium- and high-cue conflict. It is also found that the electrodes located in the middle line of the occipital region (i.e., POz and Oz) are more crucial to the impact of different levels of cue conflict on omega complexity than the other electrodes located in the left- and right-occipital hemispheres. These evidences demonstrated that the EEG omega complexity could reflect distinct neural activities evoked by Panum's limiting case configurations, with different levels of vertical disparity gradient and cue conflict. Besides, the influence of vertical disparity gradient and cue conflict on omega complexity may be regional dependent. NEW & NOTEWORTHY The EEG omega complexity could reflect distinct neural activities evoked by Panum's limiting case configurations with different levels of vertical disparity gradient and cue conflict. The influence of vertical disparity gradient and cue conflict on omega complexity is regional dependent. The omega complexity of EEG signal can serve as an important supplement of the traditional ERP technique.

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

The present review discusses a well-established method for characterizing resting-state activity of the human brain using multichannel electroencephalography (EEG). This method involves the examination of electrical microstates in the brain, which are defined as successive short time periods during which the configuration of the scalp potential field remains semi-stable, suggesting quasi-simultaneity of activity among the nodes of large-scale networks. A few prototypic microstates, which occur in a repetitive sequence across time, can be reliably identified across participants. Researchers have proposed that these microstates represent the basic building blocks of the chain of spontaneous conscious mental processes, and that their occurrence and temporal dynamics determine the quality of mentation. Several studies have further demonstrated that disturbances of mental processes associated with neurological and psychiatric conditions manifest as changes in the temporal dynamics of specific microstates. Combined EEG-fMRI studies and EEG source imaging studies have indicated that EEG microstates are closely associated with resting-state networks as identified using fMRI. The scale-free properties of the time series of EEG microstates explain why similar networks can be observed at such different time scales. The present review will provide an overview of these EEG microstates, available methods for analysis, the functional interpretations of findings regarding these microstates, and their behavioral and clinical correlates.

Attenuation of temporal correlations of neuronal oscillations in patients with mild spastic diplegia

The aim of this study was to investigate the temporal correlations of neuronal oscillations in patients with mild spastic diplegia (MSD). Resting-state electroencephalography (EEG) was recorded from 15 male adolescent and young adult patients with MSD and 15 healthy controls. We characterized the temporal correlations of neuronal oscillations, both on long temporal scale (i.e., >1 second) and short-to-intermediate temporal scale (i.e., <≈1 second) using detrended fluctuation analysis (DFA) and an analysis of the life- and waiting-time statistics of oscillation bursts respectively. The DFA exponents at alpha and beta bands, the life-time biomarker of alpha oscillation, and the life- and waiting-time biomarkers of beta oscillation were significantly attenuated in the patients compared with controls. Moreover, altered scalp distributions of some temporal correlation measures were found at alpha and beta bands in these patients. All these findings suggest that MSD is associated with highly volatile neuronal states of alpha and beta oscillations on short-to-intermediate and much longer time scales, which may be related to cognitive dysfunction in patients with MSD.