Classes of Multichannel EEG Microstates in Light and Deep Hypnotic Conditions

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

STUDY OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Changes in high-order interaction measures of synergy and redundancy during non-ordinary states of consciousness induced by meditation, hypnosis, and auto-induced cognitive trance

High-order interactions are required across brain regions to accomplish specific cognitive functions. These functional interdependencies are reflected by synergistic information that can be obtained by combining the information from all the sources considered and redundant information (i.e., common information provided by all the sources). However, electroencephalogram (EEG) functional connectivity is limited to pairwise interactions thereby precluding the estimation of high-order interactions. In this multicentric study, we used measures of synergistic and redundant information to study in parallel the high-order interactions between five EEG electrodes during three non-ordinary states of consciousness (NSCs): Rajyoga meditation (RM), hypnosis, and auto-induced cognitive trance (AICT). We analyzed EEG data from 22 long-term Rajyoga meditators, nine volunteers undergoing hypnosis, and 21 practitioners of AICT. We here report the within-group changes in synergy and redundancy for each NSC in comparison with their respective baseline. During RM, synergy increased at the whole brain level in the delta and theta bands. Redundancy decreased in frontal, right central, and posterior electrodes in delta, and frontal, central, and posterior electrodes in beta1 and beta2 bands. During hypnosis, synergy decreased in mid-frontal, temporal, and mid-centro-parietal electrodes in the delta band. The decrease was also observed in the beta2 band in the left frontal and right parietal electrodes. During AICT, synergy decreased in delta and theta bands in left-frontal, right-frontocentral, and posterior electrodes. The decrease was also observed at the whole brain level in the alpha band. However, redundancy changes during hypnosis and AICT were not significant. The subjective reports of absorption and dissociation during hypnosis and AICT, as well as the mystical experience questionnaires during AICT, showed no correlation with the high-order measures. The proposed study is the first exploratory attempt to utilize the concepts of synergy and redundancy in NSCs. The differences in synergy and redundancy during different NSCs warrant further studies to relate the extracted measures with the phenomenology of the NSCs.

Valence-specific EEG microstate modulations during self-generated affective states

Introduction

This study aims to explore the temporal dynamics of brain networks involved in self-generated affective states, specifically focusing on modulating these states in both positive and negative valences. The overarching goal is to contribute to a deeper understanding of the neurodynamic patterns associated with affective regulation, potentially informing the development of biomarkers for therapeutic interventions in mood and anxiety disorders.

Methods

Utilizing EEG microstate analysis during self-generated affective states, we investigated the temporal dynamics of five distinct microstates across different conditions, including baseline resting state and self-generated states of positive valence (e.g., awe, contentment) and negative valence (e.g., anger, fear).

Results

The study revealed noteworthy modulations in microstate dynamics during affective states. Additionally, valence-specific mechanisms of spontaneous affective regulation were identified. Negative valence affective states were characterized by the heightened presence of attention-associated microstates and reduced occurrence of salience-related microstates during negative valence states. In contrast, positive valence affective states manifested a prevalence of microstates related to visual/autobiographical memory and a reduced presence of auditory/language-associated microstates compared to both baseline and negative valence states.

Discussion

This study contributes to the field by employing EEG microstate analysis to discern the temporal dynamics of brain networks involved in self-generated affective states. Insights from this research carry significant implications for understanding neurodynamic patterns in affective regulation. The identification of valence-specific modulations and mechanisms has potential applications in developing biomarkers for mood and anxiety disorders, offering novel avenues for therapeutic interventions.

EEG Microstates in Mood and Anxiety Disorders: A Meta-analysis

To reduce the psycho-social burden increasing attention has focused on brain abnormalities in the most prevalent and highly co-occurring neuropsychiatric disorders, such as mood and anxiety. However, high inter-study variability in these patients results in inconsistent and contradictory alterations in the fast temporal dynamics of large-scale networks as measured by EEG microstates. Thus, in this meta-analysis, we aim to investigate the consistency of these changes to better understand possible common neuro-dynamical mechanisms of these disorders.In the systematic search, twelve studies investigating EEG microstate changes in participants with mood and anxiety disorders and individuals with subclinical depression were included in this meta-analysis, adding up to 787 participants.The results suggest that EEG microstates consistently discriminate mood and anxiety impairments from the general population in patients and subclinical states. Specifically, we found a small significant effect size for B microstates in patients compared to healthy controls, with larger effect sizes for increased B presence in unmedicated patients with comorbidity. In a subgroup meta-analysis of ten mood disorder studies, microstate D showed a significant effect size for decreased presence. When investigating only the two anxiety disorder studies, we found a significantly small effect size for the increased microstate A and a medium effect size for decreased microstate E (one study). However, more studies are needed to elucidate whether these findings are diagnostic-specific markers.Results are discussed in relation to the functional meaning of microstates and possible contribution to an explanatory mechanism of overlapping symptomatology of mood and anxiety disorders.

EEG Spatial-temporal Dynamics of Resting-state Activity in Young Women with Anorexia Nervosa: Preliminary Evidence

The aim of this study was to provide preliminary evidence on temporal dynamics of resting-state brain networks in youth with anorexia nervosa (AN) using electroencephalography (EEG). Resting-state EEG data were collected in 18 young women with AN and 18 healthy controls (HC). Between-group differences in brain networks were assessed using microstates analyses. Five microstates were identified across all subjects (A, B, C, D, E). Using a single set of maps representative of the whole dataset, group differences were identified for microstates A, C, and E. A common-for-all template revealed a relatively high degree of consistency in results for reduced time coverage of microstate C, but also an increased presence of microstate class E. AN and HC had different microstate transition probabilities, largely involving microstate A. Using LORETA, for microstate D, we found that those with AN had augmented activations in the left frontal inferior operculum, left insula, and bilateral paracentral lobule, compared with HC. For microstate E, AN had augmented activations in the para-hippocampal gyrus, caudate, pallidum, cerebellum, and cerebellar vermis. Our findings suggest altered microstates in young women with AN associated with integration of sensory and bodily signals, monitoring of internal/external mental states, and self-referential processes. Future research should examine how EEG-derived microstates could be applied to develop diagnostic and prognostic models of AN.

Monitoring the after-effects of ischemic stroke through EEG microstates

BACKGROUND AND PURPOSE

Stroke may cause extensive after-effects such as motor function impairments and disorder of consciousness (DoC). Detecting these after-effects of stroke and monitoring their changes are challenging jobs currently undertaken via traditional clinical examinations. These behavioural examinations often take a great deal of manpower and time, thus consuming significant resources. Computer-aided examinations of the electroencephalogram (EEG) microstates derived from bedside EEG monitoring may provide an alternative way to assist medical practitioners in a quick assessment of the after-effects of stroke.

METHODS

In this study, we designed a framework to extract microstate maps and calculate their statistical parameters to input to classifiers to identify DoC in ischemic stroke patients automatically. As the dataset is imbalanced with the minority of patients being DoC, an ensemble of support vector machines (EOSVM) is designed to solve the problem that classifiers always tend to be the majority classes in the classification on an imbalanced dataset.

RESULTS

The experimental results show EOSVM get better performance (with accuracy and F1-Score both higher than 89%), improving sensitivity the most, from lower than 60% (SVM and AdaBoost) to higher than 80%. This highlighted the usefulness of the EOSVM-aided DoC detection based on microstates parameters.

CONCLUSION

Therefore, the classifier EOSVM classification based on features of EEG microstates is helpful to medical practitioners in DoC detection with saved resources that would otherwise be consumed in traditional clinic checks.

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.

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.

EEG microstate co-specificity in schizophrenia and obsessive-compulsive disorder

The past 20 years of research on EEG microstates has yielded the hypothesis that the imbalance pattern in the temporal dynamics of microstates C (increased) and D (decreased) is specific to schizophrenia. A similar microstate imbalance has been recently found in obsessive-compulsive disorder (OCD). The aim of the present high-density EEG study was to examine whether this pathological microstate pattern is co-specific to schizophrenia and OCD. We compared microstate temporal dynamics using Bayesian analyses, transition probabilities analyses and the Topographic Electrophysiological State Source-Imaging method for source reconstruction in 24 OCD patients and 28 schizophrenia patients, respectively, free of comorbid psychotic and OCD symptoms, and 27 healthy controls. OCD and schizophrenia patients exhibited the same increased contribution of microstate C, decreased duration and contribution of microstate D and greater D → C transition probabilities, compared with controls. A Bayes factor of 4.424 for the contribution of microstate C, 4.600 and 3.824, respectively, for the duration and contribution of microstate D demonstrated that there was no difference in microstate patterns between the two disorders. Source reconstruction further showed undistinguishable dysregulations between the Salience Network (SN), associated with microstate C, and the Executive Control Network (ECN), associated with microstate D, and between the ECN and cognitive cortico-striato-thalamo-cortical (CSTC) loop in the two disorders. The ECN/CSTC loop dysconnectivity was slightly worsened in schizophrenia. Our findings provide substantial evidence for a common aetiological pathway in schizophrenia and OCD, i.e. microstate co-specificity, and same anomalies in salience and external attention processing, leading to co-expression of symptoms.

Microstates of the cortical brain-heart axis

Electroencephalographic (EEG) microstates are brain states with quasi-stable scalp topography. Whether such states extend to the body level, that is, the peripheral autonomic nerves, remains unknown. We hypothesized that microstates extend at the brain-heart axis level as a functional state of the central autonomic network. Thus, we combined the EEG and heartbeat dynamics series to estimate the directional information transfer originating in the cortex targeting the sympathovagal and parasympathetic activity oscillations and vice versa for the afferent functional direction. Data were from two groups of participants: 36 healthy volunteers who were subjected to cognitive workload induced by mental arithmetic, and 26 participants who underwent physical stress induced by a cold pressure test. All participants were healthy at the time of the study. Based on statistical testing and goodness-of-fit evaluations, we demonstrated the existence of microstates of the functional brain-heart axis, with emphasis on the cerebral cortex, since the microstates are derived from EEG. Such nervous-system microstates are spatio-temporal quasi-stable states that exclusively refer to the efferent brain-to-heart direction. We demonstrated brain-heart microstates that could be associated with specific experimental conditions as well as brain-heart microstates that are non-specific to tasks.

Neurophysiological explorations across the spectrum of psychosis, autism, and depression, during wakefulness and sleep: protocol of a prospective case-control transdiagnostic multimodal study (DEMETER)

BACKGROUND

Quantitative electroencephalography (EEG) analysis offers the opportunity to study high-level cognitive processes across psychiatric disorders. In particular, EEG microstates translate the temporal dynamics of neuronal networks throughout the brain. Their alteration may reflect transdiagnostic anomalies in neurophysiological functions that are impaired in mood, psychosis, and autism spectrum disorders, such as sensorimotor integration, speech, sleep, and sense of self. The main questions this study aims to answer are as follows: 1) Are EEG microstate anomalies associated with clinical and functional prognosis, both in resting conditions and during sleep, across psychiatric disorders? 2) Are EEG microstate anomalies associated with differences in sensorimotor integration, speech, sense of self, and sleep? 3) Can the dynamic of EEG microstates be modulated by a non-drug intervention such as light hypnosis?

METHODS

This prospective cohort will include a population of adolescents and young adults, aged 15 to 30 years old, with ultra-high-risk of psychosis (UHR), first-episode psychosis (FEP), schizophrenia (SCZ), autism spectrum disorder (ASD), and major depressive disorder (MDD), as well as healthy controls (CTRL) (N = 21 × 6), who will be assessed at baseline and after one year of follow-up. Participants will undergo deep phenotyping based on psychopathology, neuropsychological assessments, 64-channel EEG recordings, and biological sampling at the two timepoints. At baseline, the EEG recording will also be coupled to a sensorimotor task and a recording of the characteristics of their speech (prosody and turn-taking), a one-night polysomnography, a self-reference effect task in virtual reality (only in UHR, FEP, and CTRL). An interventional ancillary study will involve only healthy controls, in order to assess whether light hypnosis can modify the EEG microstate architecture in a direction opposite to what is seen in disease.

DISCUSSION

This transdiagnostic longitudinal case-control study will provide a multimodal neurophysiological assessment of clinical dimensions (sensorimotor integration, speech, sleep, and sense of self) that are disrupted across mood, psychosis, and autism spectrum disorders. It will further test the relevance of EEG microstates as dimensional functional biomarkers.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier NCT06045897.

The effect of acupuncture at the Taiyang acupoint on visual function and EEG microstates in myopia

Objective

Acupuncture has certain effects to improve myopia visual function, but its neural mechanism is unclear. In this study, we acupunctured at the right Taiyang acupoint of myopic patients to analyze the effects of acupuncture on visual function and electroencephalographic activity and to investigate the correlation between improvements in visual function and changes in the brain.

Methods

In this study, a total of 21 myopic patients were recruited. The contrast sensitivity (CS) of the subjects was examined before and after acupuncture, and electroencephalography (EEG) data of the entire acupuncture process were recorded.

Results

The study found that compared with before acupuncture, the CS of both eyes in myopic patients at each spatial frequency was increased after acupuncture; compared with the resting state, the contribution of microstate C was decreased during the post-acupuncture state, and the transition probability between microstate A and microstate C was reduced; in addition, the contribution of microstate C was negatively correlated with CS at both 12 and 18 cpd.

Conclusion

The contrast sensitivity of myopic patients was improved after acupuncture at the Taiyang acupoint (20 min), which may be related to microstate C.

Microstates imbalance is associated with a functional dysregulation of the resting-state networks in obsessive-compulsive disorder: a high-density electrical neuroimaging study using the TESS method

The dysfunctional patterns of microstates dynamics in obsessive-compulsive disorder (OCD) remain uncertain. Using high-density electrical neuroimaging (EEG) at rest, we explored microstates deterioration in OCD and whether abnormal microstates patterns are associated with a dysregulation of the resting-state networks interplay. We used EEG microstates analyses, TESS method for sources reconstruction, and General Linear Models to test for the effect of disease severity on neural responses. OCD patients exhibited an increased contribution and decreased duration of microstates C and D, respectively. Activity was decreased in the Salience Network (SN), associated with microstate C, but increased in the Default Mode Network (DMN) and Executive Control Network (ECN), respectively, associated with microstates E and D. The hyperactivity of the right angular gyrus in the ECN correlated with the symptoms severity. The imbalance between microstates C and D invalidates the hypothesis that this electrophysiological pattern is specific to psychosis. Demonstrating that the SN-ECN dysregulation manifests as abnormalities in microstates C and D, we confirm that the SN deterioration in OCD is accompanied by a failure of the DMN to deactivate and aberrant compensatory activation mechanisms in the ECN. These abnormalities explain typical OCD clinical features but also detachment from reality, shared with psychosis.

EEG microstate changes according to mental fatigue induced by aircraft piloting simulation: An exploratory study

BACKGROUND

A continuous flight task load can induce fatigue and lead to changes in electroencephalography (EEG). EEG microstates can reflect the activities of large-scale neural networks during mental fatigue. This exploratory experiment explored the effects of mental fatigue induced by continuous simulated flight multitasking on EEG microstate indices.

METHODS

Twenty-four participants performed continuous 2-hour aircraft piloting simulation while EEG were recorded. The Stanford sleepiness scale (SSS) and critical flicker fusion frequency (CFF) were measured before and after the task. Microstate analysis was applied to EEG. Four microstate classes (A-D) were identified during the pre-task, post-task, beginning, and end phases. The effects of mental fatigue were analyzed.

RESULTS

Compared with the pre-task, the post-task had a higher global explained variance (GEV) and time parameters of class C but lower occurrence and coverage of class D. The end had a higher GEV but lower duration and coverage of class D than at the beginning. After 2 h of multitasking, the transition probability between A and D, and between B and D decreased but between A and C increased. Subjective fatigue scores were negatively correlated with occurrence and coverage of class D. Task performance was negatively correlated with duration and coverage of class C but positively correlated with duration and occurrence of class B.

CONCLUSION

Time parameters and transition probability of EEG microstates can detect mental fatigue induced by continuous aircraft piloting simulation. The global brain network activation of mental fatigue can be detected by EEG microstates that can evaluate flight fatigue.

Migraine Type-Dependent Patterns of Brain Activation After Facial and Intranasal Trigeminal Stimulation

In migraine, the trigeminal nerve is intimately involved in the pathophysiology of the disease. We hypothesized that alterations in the sensory trigeminal activation in migraine would be reflected by EEG-derived event-related potentials (ERP). We aimed to investigate differences in the temporal and spatial processing of trigeminal stimuli between interictal migraine patients and healthy subjects. ERP to trigeminal stimuli were recorded at 128-channels to allow localization of their cortical sources with high temporal resolution. Seventeen patients with episodic migraine without aura, 17 subjects with episodic migraine with aura, and 17 healthy subjects participated in the study. The first branch of the trigeminal nerve was stimulated using intranasal chemical (CO₂), cutaneous electrical, and cutaneous mechanical (air puff) stimuli. Analyses were performed with regard to micro-state segmentation, ERP source localization, and correlation with the patients' clinical characteristics. Topographical assessments of EEG configurations were associated with the pathological condition. The source analysis revealed altered trigeminal-sensory response patterns in the precuneus, temporal pole, and cerebellum for both migraine groups during the interictal phase. The estimated current source density was positively correlated with migraine disease duration, indicating brain functional and structural changes as a consequence of the disease. Hyperactivity of the cerebellar posterior lobe was observed as a specific trigeminal response of migraine patients with aura. In conclusion, our results suggest the presence of brain changes accompanying the advancement of migraine as an expression of dysfunctional central pain processing. Hence, we identified EEG patterns in response to mechano-/chemosensory stimuli that can serve as biomarkers of migraine.

Dynamic Changes of Brain Activity in Different Responsive Groups of Patients with Prolonged Disorders of Consciousness

As medical technology continues to improve, many patients diagnosed with brain injury survive after treatments but are still in a coma. Further, multiple clinical studies have demonstrated recovery of consciousness after transcranial direct current stimulation. To identify possible neurophysiological mechanisms underlying disorders of consciousness (DOCs) improvement, we examined the changes in multiple resting-state EEG microstate parameters after high-definition transcranial direct current stimulation (HD-tDCS). Because the left dorsolateral prefrontal cortex is closely related to consciousness, it is often chosen as a stimulation target for tDCS treatment of DOCs. A total of 21 patients diagnosed with prolonged DOCs were included in this study, and EEG microstate analysis of resting state EEG datasets was performed on all patients before and after interventions. Each of them underwent 10 anodal tDCS sessions of the left dorsolateral prefrontal cortex over 5 consecutive working days. According to whether the clinical manifestations improved, DOCs patients were divided into the responsive (RE) group and the non-responsive (N-RE) group. The dynamic changes of resting state EEG microstate parameters were also analyzed. After multiple HD-tDCS interventions, the duration and coverage of class C microstates in the RE group were significantly increased. This study also found that the transition between microstates A and C increased, while the transition between microstates B and D decreased in the responsive group. However, these changes in EEG microstate parameters in the N-RE group have not been reported. Our findings suggest that EEG neural signatures have the potential to assess consciousness states and that improvement in the dynamics of brain activity was associated with the recovery of DOCs. This study extends our understanding of the neural mechanism of DOCs patients in consciousness recovery.

Transient oscillation dynamics during sleep provide a robust basis for electroencephalographic phenotyping and biomarker identification

Transient oscillatory events in the sleep electroencephalogram represent short-term coordinated network activity. Of particular importance, sleep spindles are transient oscillatory events associated with memory consolidation, which are altered in aging and in several psychiatric and neurodegenerative disorders. Spindle identification, however, currently contains implicit assumptions derived from what waveforms were historically easiest to discern by eye, and has recently been shown to select only a high-amplitude subset of transient events. Moreover, spindle activity is typically averaged across a sleep stage, collapsing continuous dynamics into discrete states. What information can be gained by expanding our view of transient oscillatory events and their dynamics? In this paper, we develop a novel approach to electroencephalographic phenotyping, characterizing a generalized class of transient time-frequency events across a wide frequency range using continuous dynamics. We demonstrate that the complex temporal evolution of transient events during sleep is highly stereotyped when viewed as a function of slow oscillation power (an objective, continuous metric of depth-of-sleep) and phase (a correlate of cortical up/down states). This two-fold power-phase representation has large intersubject variability-even within healthy controls-yet strong night-to-night stability for individuals, suggesting a robust basis for phenotyping. As a clinical application, we then analyze patients with schizophrenia, confirming established spindle (12-15 Hz) deficits as well as identifying novel differences in transient non-rapid eye movement events in low-alpha (7-10 Hz) and theta (4-6 Hz) ranges. Overall, these results offer an expanded view of transient activity, describing a broad class of events with properties varying continuously across spatial, temporal, and phase-coupling dimensions.

Electroencephalogram microstates and functional connectivity of cybersickness

Virtual reality (VR) is a rapidly developing technology that simulates the real world. However, for some cybersickness-susceptible people, VR still has an unanswered problem-cybersickness-which becomes the main obstacle for users and content makers. Sensory conflict theory is a widely accepted theory for cybersickness. It proposes that conflict between afferent signals and internal models can cause cybersickness. This study analyzes the brain states that determine cybersickness occurrence and related uncomfortable feelings. Furthermore, we use the electroencephalogram (EEG) microstates and functional connectivity approach based on the sensory conflict theory. The microstate approach is a time-space analysis method that allows signals to be divided into several temporarily stable states, simultaneously allowing for the exploration of short- and long-range signals. These temporal dynamics can show the disturbances in mental processes associated with neurological and psychiatric conditions of cybersickness. Furthermore, the functional connectivity approach gives us in-depth insight and relationships between the sources related to cybersickness. We recruited 40 males (24.1 ± 2.3 years), and they watched a VR video on a curved computer monitor for 10 min to experience cybersickness. We recorded the 5-min resting state EEG (baseline condition) and 10-min EEG while watching the VR video (task condition). Then, we performed a microstate analysis, focusing on two temporal parameters: mean duration and global explained variance (GEV). Finally, we obtained the functional connectivity data using eLoreta and lagged phase synchronization (LPS). We discovered five sets of microstates (A-E), including four widely reported canonical microstates (A-D), during baseline and task conditions. The average duration increased in microstates A and B, which is related to the visual and auditory networks. The GEV and duration decreased in microstate C, whereas those in microstate D increased. Microstate C is related to the default mode network (DMN) and D to the attention network. The temporal dynamics of the microstate parameters are from cybersickness disturbing the sensory, DMN, and attention networks. In the functional connectivity part, the LPS between the left and right parietal operculum (OP) significantly decreased (p < 0.05) compared with the baseline condition. Furthermore, the connectivity between the right OP and V5 significantly decreased (p < 0.05). These results also support the disturbance of the sensory network because a conflict between the visual (V5) and vestibular system (OP) causes cybersickness. Changes in the microstates and functional connectivity support the sensory conflict theory. These results may provide additional information in understanding brain dynamics during cybersickness.

Electroencephalographic Microstates as Novel Functional Biomarkers for Adult Attention-Deficit/Hyperactivity Disorder

BACKGROUND

Research on the electroencephalographic (EEG) signatures of attention-deficit/hyperactivity disorder (ADHD) has historically concentrated on its frequency spectrum or event-related evoked potentials. In this work, we investigate EEG microstates (MSs), an alternative framework defined by the clustering of recurring topographical patterns, as a novel approach for examining large-scale cortical dynamics in ADHD.

METHODS

Using k-means clustering, we studied the spatiotemporal dynamics of ADHD during the rest condition by comparing the MS segmentations between adult patients with ADHD and neurotypical control subjects across two independent datasets: the first dataset consisted of 66 patients with ADHD and 66 control subjects, and the second dataset comprised 22 patients with ADHD and 22 control subjects and was used for out-of-sample validation.

RESULTS

Spatially, patients with ADHD and control subjects displayed equivalent MS topographies (canonical maps), indicating the preservation of prototypical EEG generators in patients with ADHD. However, this concordance was accompanied by significant differences in temporal dynamics. At the group level, and across both datasets, ADHD diagnosis was associated with longer mean durations of a frontocentral topography (MS D), indicating that its electrocortical generator(s) could be acting as pronounced attractors of global cortical dynamics. In addition, its spatiotemporal metrics were correlated with sleep disturbance, the latter being known to have a strong relationship with ADHD. Finally, in the first (larger) dataset, we also found evidence of decreased time coverage and mean duration of a left-right diagonal topography (MS A), which inversely correlated with ADHD scores.

CONCLUSIONS

Overall, our study underlines the value of EEG MSs as promising functional biomarkers for ADHD, offering an additional lens through which to examine its neurophysiological mechanisms.

EEG microstates as markers of major depressive disorder and predictors of response to SSRIs therapy

BACKGROUND

Major depressive disorder (MDD) is associated with abnormal neural activities and brain connectivity. EEG microstate is a voltage topology map that reflects transient activations of the brain network. A limited number of studies on EEG microstate in MDD have focused on differences between patients and healthy controls. However, EEG microstate changes in MDD patients before and after drug treatment have not been evaluated. We assessed EEG microstate characteristics and evaluated changes in brain network dynamics in MDD patients before and after drug treatment. Moreover, we evaluated the neuro-electrophysiological mechanisms of antidepressant therapies.

METHODS

64-channel resting EEG was obtained from 101 patients with first-episode untreated depression (0 week) and 45 healthy controls (HC) from January to December 2020. MDD patients were treated with selective serotonin reuptake inhibitors (SSRI). EEG data for 51 MDD patients who had completed an 8-week follow-up was collected. After pre-processing, EEG data from different groups were subjected to microstate analysis, and the atomize and agglomerate hierarchical clustering (AAHC) was into 4 microstates. Next, EEG signals from each patient were fitted using templates of 4 microstates. Finally, microstate indices were collected and analyzed.

RESULTS

Global clustering generated 4 microstates (A, B, C, D) in all subjects, which explained 65-84% of the global variance. Compared to HC, the duration of microstate D reduced while those of microstates A and B increased in MDD patients. After the 8-week treatment period, the duration and coverage of microstate D increased, the frequency of microstate A and transition probability of microstate D to A reduced, while transition probability of microstate B to D and D to B increased in MDD patients. There were no differences in microstate features between HC and MDD at 8 weeks. In patients with first-episode untreated depression, lower average durations of microstate D, relatively higher frequencies of microstate C and lower transition probabilities of microstate D to B correlated with better effects after 8 weeks. The higher occurrence and proportion of microstate C at 8 weeks was positively correlated with the HAMD score and reduction rate. The same observation was reached for the transition probability of microstate A to C. However, the transition probability of microstate D to B showed a negative correlation with the HAMD score at 8 weeks.

CONCLUSION

Microstate D is a potential electrophysiological trait of MDD and can predict treatment outcomes of SSRIs. Therefore, EEG microstate analysis may not only be an objective method for evaluating treatment outcomes of depression, but is also a potential new approach for exploring the neuro-electrophysiological mechanisms of antidepressant therapy. Public title: Multidimensional diagnosis, individualized treatment and management techniques based on clinic-pathological characteristics of depressive disorder; Registration number: ChiCTR1900026600; Date of registration: 2019-10-15; URL: http://www.chictr.org.cn/index.aspx.

EEG Microstates in Altered States of Consciousness

Conscious experiences unify distinct phenomenological experiences that seem to be continuously evolving. Yet, empirical evidence shows that conscious mental activity is discontinuous and can be parsed into a series of states of thoughts that manifest as discrete spatiotemporal patterns of global neuronal activity lasting for fractions of seconds. EEG measures the brain’s electrical activity with high temporal resolution on the scale of milliseconds and, therefore, might be used to investigate the fast spatiotemporal structure of conscious mental states. Such analyses revealed that the global scalp electric fields during spontaneous mental activity are parceled into blocks of stable topographies that last around 60–120 ms, the so-called EEG microstates. These brain states may be representing the basic building blocks of consciousness, the “atoms of thought.” Altered states of consciousness, such as sleep, anesthesia, meditation, or psychiatric diseases, influence the spatiotemporal dynamics of microstates. In this brief perspective, we suggest that it is possible to examine the underlying characteristics of self-consciousness using this EEG microstates approach. Specifically, we will summarize recent results on EEG microstate alterations in mind-wandering, meditation, sleep and anesthesia, and discuss the functional significance of microstates in altered states of consciousness.

EEG microstate dynamics indicate a U-shaped path to propofol-induced loss of consciousness

Evidence suggests that the stream of consciousness is parsed into transient brain states manifesting themselves as discrete spatiotemporal patterns of global neuronal activity. Electroencephalographical (EEG) microstates are proposed as the neurophysiological correlates of these transiently stable brain states that last for fractions of seconds. To further understand the link between EEG microstate dynamics and consciousness, we continuously recorded high-density EEG in 23 surgical patients from their awake state to unconsciousness, induced by step-wise increasing concentrations of the intravenous anesthetic propofol. Besides the conventional parameters of microstate dynamics, we introduce a new implementation of a method to estimate the complexity of microstate sequences. The brain activity under the surgical anesthesia showed a decreased sequence complexity of the stereotypical microstates, which became sparser and longer-lasting. However, we observed an initial increase in microstates' temporal dynamics and complexity with increasing depth of sedation leading to a distinctive "U-shape" that may be linked to the paradoxical excitation induced by moderate levels of propofol. Our results support the idea that the brain is in a metastable state under normal conditions, balancing between order and chaos in order to flexibly switch from one state to another. The temporal dynamics of EEG microstates indicate changes of this critical balance between stability and transition that lead to altered states of consciousness.

Dynamic brain functional network based on EEG microstate during sensory gating in schizophrenia

OBJECTIVE

Cognitive impairment is one of the core symptoms of schizophrenia, with an emphasis on dysfunctional information processing. Sensory gating deficits have consistently been reported in schizophrenia, but the underlying physiological mechanism is not well-understood. We report the discovery and characterization of P50 dynamic brain connections based on microstate analysis.

APPROACH

We identify five main microstates associated with the P50 response and the difference between the first and second click presentation (S1-S2-P50) in first-episode schizophrenia patients (FESZ), ultra-high-risk individuals (UHR) and healthy controls (HC). The we used the signal segments composed of consecutive time points with the same microstate label to construct brain functional networks.

MAIN RESULTS

The microstate with a prefrontal extreme location during the response to the S1 of P50 are statistically different in duration, occurrence and coverage among the FESZ, UHR and HC groups. In addition, a microstate with anterior-posterior orientation was found to be associated with S1-S2-P50 and its coverage was found to differ among the FESZ, UHR and HC groups. Source location of microstates showed that activated brain regions were mainly concentrated in the right temporal lobe. Furthermore, the connectivities between brain regions involved in P50 processing of HC were widely different from those of FESZ and UHR.

SIGNIFICANCE

Our results indicate that P50 suppression deficits in schizophrenia may be due to both aberrant baseline sensory perception and adaptation to repeated stimulus. Our findings provide new insight into the mechanisms of P50 suppression in the early stage of schizophrenia.

Spontaneous thought and microstate activity modulation by social imitation

The human mind wanders spontaneously and frequently, revisiting the past and imagining the future of self and of others. External and internal factors can influence wandering spontaneous thoughts, whose content predicts subsequent emotional states. We propose that social imitation, an action that increases well-being and closeness by poorly understood mechanisms, impacts behavioural states in part by modulating post-imitation mind-wandering. In 43 young subjects, we find that imitating the arm movements of an actor alters the dynamics and the content of subsequent resting-state spontaneous thoughts. Imitation-sensitive features of spontaneous thoughts correlate with behavioural states and personality traits. EEG microstate analysis reveals that global patterns of correlated neuronal activity predict imitation-induced changes in spontaneous thoughts. Exploratory analyses indicate a possible modulatory effect of social imitation via the endogenous release of oxytocin. Thus, social imitation can induce selective modulations of ongoing activity in specific neural networks to change spontaneous thought patterns as a function of personality traits, and to ultimately orchestrate behavioural states.

Altered EEG Microstates Dynamics During Cue-Induced Methamphetamine Craving in Virtual Reality Environments

BACKGROUND

Cue-induced craving is widely considered to be the most important risk factor for relapse during abstinence from methamphetamine (Meth). There is limited research regarding electroencephalography (EEG) microstates of Meth-dependent patients under exposure to drug-related cues. Our objective was to investigate whether EEG microstate temporal characteristics could capture neural correlates of cue-induced Meth craving in virtual reality (VR) environments.

METHODS

EEG recordings of 35 Meth-dependent patients and 30 healthy controls (HCs) were collected during eyes-open state and cue-induced state, respectively. Group differences and condition differences in temporal parameters of four microstate classes were compared.

RESULTS

The results demonstrated the greater presence of microstate B in both Meth-dependent patients and HCs during the cue-induced condition, compared to resting state. In addition, for Meth-dependent patients, microstate C occurred significantly less frequently, along with a tendency of increased occurrence for class D during the cue-induced condition, compared to resting state. However, the change direction of class C and class D in HCs was completely opposite to that of Meth-dependent patients. The cue-induced condition also elicited different changes in transition probability between Meth-dependent patients and HCs.

CONCLUSION

This study explored the features of EEG microstates in Meth-dependent patients during the cue-induced condition, which can improve our understanding of Meth addiction and contribute to the development of effective assessments and intervention tools.

Altered resting-state electroencephalography microstate characteristics in young male smokers

The development of nicotine addiction was associated with the abnormalities of intrinsic functional networks during the resting state in young adult smokers. As a whole-brain imaging approach, EEG microstate analysis treated multichannel EEG recordings as a series of quasi-steady microscopic states which were related to the resting-state networks (RSNs) found by fMRI. The aim of this study was to examine whether the resting-state EEG microstate analysis may provide novel insights into the abnormal temporal properties of intrinsic brain activities in young smokers. We used 64-channel resting-state EEG datasets to investigate alterations in microstate characteristics between twenty-five young smokers and 25 age- and gender-matched non-smoking controls. Four classic EEG microstates (microstate A, B, C, and D) were obtained, and the four temporal parameters of each microstate were extracted, i.e., duration, occurrence, coverage, and transition probabilities. Compared with non-smoking controls, young smokers showed decreased occurrence of microstate C and increased duration of microstate D. Furthermore, both the duration and coverage of microstate D were significantly negatively correlated with Fagerstrom Test of Nicotine Dependence (FTND) in young smoker group. The complex changes in the microstate time-domain parameters might correspond to the abnormalities of RSNs in analyses of FC measured with fMRI in the previous studies and indicate the altered specific brain functions in young smokers. Microstate D could be potentially represented as a selective biomarker for predicting the dependence degree of adolescent smokers on cigarettes. These results suggested that EEG microstate analysis might detect the deviant functions of large-scale cortical activities in young smokers and provide a new perspective for the study of brain networks of adolescent smokers.

Microstates in complex and dynamical environments: Unraveling situational awareness in critical helicopter landing maneuvers

Understanding decision-making in complex and dynamic environments is relevant for designing strategies targeting safety improvements and error rate reductions. However, studies evaluating brain dynamics in realistic situations are scarce in the literature. Given the evidence that specific microstates may be associated with perception and attention, in this work we explored for the first time the application of the microstate model in an ecological, dynamic and complex scenario. More specifically, we evaluated elite helicopter pilots during engine-failure missions in the vicinity of the so called "dead man's curve," which establishes the operational limits for a safe landing after the execution of a recovery maneuver (autorotation). Pilots from the Brazilian Air Force flew a AS-350 helicopter in a certified aerodrome and physiological sensor data were synchronized with the aircraft's flight test instrumentation. We assessed these neural correlates during maneuver execution, by comparing their modulations and source reconstructed activity with baseline epochs before and after flights. We show that the topographies of our microstate templates with 4, 5, and 6 classes resemble the literature, and that a distinct modulation characterizes decision-making intervals. Moreover, the source reconstruction result points to a differential activity in the medial prefrontal cortex, which is associated to emotional regulation circuits in the brain. Our results suggest that microstates are promising neural correlates to evaluate realistic situations, even in a challenging and intrinsically noisy environment. Furthermore, it strengthens their usage and expands their application for studying cognition under more realistic conditions.

Effective Brain State Estimation During Propofol-Induced Sedation Using Advanced EEG Microstate Spectral Analysis

Brain states are patterns of neuronal synchrony, and the electroencephalogram (EEG) microstate provides a promising tool to characterize and analyze the synchronous neural firing. However, the topographical spectral information for each predominate microstate is still unclear during the switch of consciousness, such as sedation, and the practical usage of the EEG microstate is worth probing. Also, the mechanism behind the anesthetic-induced alternations of brain states remains poorly understood. In this study, an advanced EEG microstate spectral analysis was utilized using multivariate empirical mode decomposition in Hilbert-Huang transform. The practicability was further investigated in scalp EEG recordings during the propofol-induced transition of consciousness. The process of transition from the awake baseline to moderate sedation was accompanied by apparent increases in microstate (A, B, and F) energy, especially in the whole-brain delta band, frontal alpha band and beta band. In comparison to other effective EEG-based parameters that commonly used to measure anesthetic depth, using the selected spectral features reached better performance (80% sensitivity, 90% accuracy) to estimate the brain states during sedation. The changes in microstate energy also exhibited high correlations with individual behavioral data during sedation. In a nutshell, the EEG microstate spectral analysis is an effective method to estimate brain states during propofol-induced sedation, giving great insights into the underlying mechanism. The generated spectral features can be promising markers to dynamically assess the consciousness level.

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.

Reliability of EEG microstate analysis at different electrode densities during propofol-induced transitions of brain states

Electroencephalogram (EEG) microstate analysis is a promising and effective spatio-temporal method that can segment signals into several quasi-stable classes, providing a great opportunity to investigate short-range and long-range neural dynamics. However, there are still many controversies in terms of reproducibility and reliability when selecting different parameters or datatypes. In this study, five electrode configurations (91, 64, 32, 19, and 8 channels) were used to measure the reliability of microstate analysis at different electrode densities during propofol-induced sedation. First, the microstate topography and parameters at five different electrode densities were compared in the baseline (BS) condition and the moderate sedation (MD) condition, respectively. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were introduced to quantify the consistency of the microstate parameters. Second, statistical analysis and classification between BS and MD were performed to determine whether the microstate differences between different conditions remained stable at different electrode densities, and ICC was also calculated between the different conditions to measure the consistency of the results in a single condition. The results showed that in both the BS or MD condition, respectively, there were few significant differences in the microstate parameters among the 91-, 64-, and 32-channel configurations, with most of the differences observed between the 19- or 8-channel configurations and the other configurations. The ICC and CV data also showed that the consistency among the 91-, 64-, and 32-channel configurations was better than that among all five electrode configurations after including the 19- and 8-channel configurations. Furthermore, the significant differences between the conditions in the 91-channel configuration remained stable at the 64- and 32-channel resolutions, but disappeared at the 19- and 8-channel resolutions. In addition, the classification and ICC results showed that the microstate analysis became unreliable with fewer than 20 electrodes. The findings of this study support the hypothesis that microstate analysis of different brain states is more reliable with higher electrode densities; the use of a small number of channels is not recommended.

Fundamentally altered global- and microstate EEG characteristics in Huntington's disease

OBJECTIVE

Huntington's disease (HD) is characterized by psychiatric, cognitive, and motor disturbances. The study aimed to determine electroencephalography (EEG) global state and microstate changes in HD and their relationship with cognitive and behavioral impairments.

METHODS

EEGs from 20 unmedicated HD patients and 20 controls were compared using global state properties (connectivity and dimensionality) and microstate properties (EEG microstate analysis). For four microstate classes (A, B, C, D), three parameters were computed: duration, occurrence, coverage. Global- and microstate properties were compared between groups and correlated with cognitive test scores for patients.

RESULTS

Global state analysis showed reduced connectivity in HD and an increasing dimensionality with increasing HD severity. Microstate analysis revealed parameter increases for classes A and B (coverage), decreases for C (occurrence) and D (coverage and occurrence). Disease severity and poorer test performances correlated with parameter increases for class A (coverage and occurrence), decreases for C (coverage and duration) and a dimensionality increase.

CONCLUSIONS

Global state changes may reflect higher functional dissociation between brain areas and the complex microstate changes possibly the widespread neuronal death and corresponding functional deficits in brain regions associated with HD symptomatology.

SIGNIFICANCE

Combining global- and microstate analyses can be useful for a better understanding of progressive brain deterioration in HD.

Evidence for modulation of EEG microstate sequence by vigilance level

The momentary global functional state of the brain is reflected in its electric field configuration and cluster analytical approaches have consistently shown four configurations, referred to as EEG microstate classes A to D. Changes in microstate parameters are associated with a number of neuropsychiatric disorders, task performance, and mental state establishing their relevance for cognition. However, the common practice to use eye-closed resting state data to assess the temporal dynamics of microstate parameters might induce systematic confounds related to vigilance levels. Here, we studied the dynamics of microstate parameters in two independent data sets and showed that the parameters of microstates are strongly associated with vigilance level assessed both by EEG power analysis and fMRI global signal. We found that the duration and contribution of microstate class C, as well as transition probabilities towards microstate class C were positively associated with vigilance, whereas the sign was reversed for microstate classes A and B. Furthermore, in looking for the origins of the correspondence between microstates and vigilance level, we found Granger-causal effects of vigilance levels on microstate sequence parameters. Collectively, our findings suggest that duration and occurrence of microstates have a different origin and possibly reflect different physiological processes. Finally, our findings indicate the need for taking vigilance levels into consideration in resting-sate EEG investigations.

EEG microstates as biomarker for psychosis in ultra-high-risk patients

Resting-state EEG microstates are brief (50-100 ms) periods, in which the spatial configuration of scalp global field power remains quasi-stable before rapidly shifting to another configuration. Changes in microstate parameters have been described in patients with psychotic disorders. These changes have also been observed in individuals with a clinical or genetic high risk, suggesting potential usefulness of EEG microstates as a biomarker for psychotic disorders. The present study aimed to investigate the potential of EEG microstates as biomarkers for psychotic disorders and future transition to psychosis in patients at ultra-high-risk (UHR). We used 19-channel clinical EEG recordings and orthogonal contrasts to compare temporal parameters of four normative microstate classes (A-D) between patients with first-episode psychosis (FEP; n = 29), UHR patients with (UHR-T; n = 20) and without (UHR-NT; n = 34) later transition to psychosis, and healthy controls (HC; n = 25). Microstate A was increased in patients (FEP & UHR-T & UHR-NT) compared to HC, suggesting an unspecific state biomarker of general psychopathology. Microstate B displayed a decrease in FEP compared to both UHR patient groups, and thus may represent a state biomarker specific to psychotic illness progression. Microstate D was significantly decreased in UHR-T compared to UHR-NT, suggesting its potential as a selective biomarker of future transition in UHR patients.

Temporal dynamics of resting EEG networks are associated with prosociality

As prosociality is key to facing many of our societies' global challenges (such as fighting a global pandemic), we need to better understand why some individuals are more prosocial than others. The present study takes a neural trait approach, examining whether the temporal dynamics of resting EEG networks are associated with inter-individual differences in prosociality. In two experimental sessions, we collected 55 healthy males' resting EEG, their self-reported prosocial concern and values, and their incentivized prosocial behavior across different reward domains (money, time) and social contexts (collective, individual). By means of EEG microstate analysis we identified the temporal coverage of four canonical resting networks (microstates A, B, C, and D) and their mutual communication in order to examine their association with an aggregated index of prosociality. Participants with a higher coverage of microstate A and more transitions from microstate C to A were more prosocial. Our study demonstrates that temporal dynamics of intrinsic brain networks can be linked to complex social behavior. On the basis of previous findings on links of microstate A with sensory processing, our findings suggest that participants with a tendency to engage in bottom-up processing during rest behave more prosocially than others.

Non-Canonical Microstate Becomes Salient in High Density EEG During Propofol-Induced Altered States of Consciousness

Dynamically assessing the level of consciousness is still challenging during anesthesia. With the help of Electroencephalography (EEG), the human brain electric activity can be noninvasively measured at high temporal resolution. Several typical quasi-stable states are introduced to represent the oscillation of the global scalp electric field. These so-called microstates reflect spatiotemporal dynamics of coherent neural activities and capture the switch of brain states within the millisecond range. In this study, the microstates of high-density EEG were extracted and investigated during propofol-induced transition of consciousness. To analyze microstates on the frequency domain, a novel microstate-wise spectral analysis was proposed by the means of multivariate empirical mode decomposition and Hilbert–Huang transform. During the transition of consciousness, a map with a posterior central maximum denoted as microstate F appeared and became salient. The current results indicated that the coverage, occurrence, and power of microstate F significantly increased in moderate sedation. The results also demonstrated that the transition of brain state from rest to sedation was accompanied by significant increase in mean energy of all frequency bands in microstate F. Combined with studies on the possible cortical sources of microstates, the findings reveal that non-canonical microstate F is highly associated with propofol-induced altered states of consciousness. The results may also support the inference that this distinct topography can be derived from canonical microstate C (anterior-posterior orientation). Finally, this study further develops pertinent methodology and extends possible applications of the EEG microstate during propofol-induced anesthesia.

EEG Microstate Differences in Medicated vs. Medication-Naïve First-Episode Psychosis Patients

There has been considerable interest in the role of synchronous brain activity abnormalities in the pathophysiology of psychotic disorders and their relevance for treatment; one index of such activity are EEG resting-state microstates. These reflect electric field configurations of the brain that persist over 60-120 ms time periods. A set of quasi-stable microstates classes A, B, C, and D have been repeatedly identified across healthy participants. Changes in microstate parameters coverage, duration and occurrence have been found in medication-naïve as well as medicated patients with psychotic disorders compared to healthy controls. However, to date, only two studies have directly compared antipsychotic medication effects on EEG microstates either pre- vs. post-treatment or between medicated and unmedicated chronic schizophrenia patients. The aim of this study was therefore to directly compare EEG resting-state microstates between medicated and medication-naïve (untreated) first-episode (FEP) psychosis patients (mFEP vs. uFEP). We used 19-channel clinical EEG recordings to compare temporal parameters of four prototypical microstate classes (A-D) within an overall sample of 47 patients (mFEP n = 17; uFEP n = 30). The results demonstrated significant decreases of microstate class A and significant increases of microstate class B in mFEP compared to uFEP. No significant differences between groups were found for microstate classes C and D. Further studies are needed to replicate these results in longitudinal designs that assess antipsychotic medication effects on neural networks at the onset of the disorder and over time during illness progression. As treatment response and compliance in FEP patients are relatively low, such studies could contribute to better understand treatment outcomes and ultimately improve treatment strategies.

EEG microstates distinguish between cognitive components of fluid reasoning

Fluid reasoning is considered central to general intelligence. How its psychometric structure relates to brain function remains poorly understood. For instance, what is the dynamic composition of ability-specific processes underlying fluid reasoning? We investigated whether distinct fluid reasoning abilities could be differentiated by electroencephalography (EEG) microstate profiles. EEG microstates specifically capture rapidly altering activity of distributed cortical networks with a high temporal resolution as scalp potential topographies that dynamically vary over time in an organized manner. EEG was recorded simultaneously with functional magnetic resonance imaging (fMRI) in twenty healthy adult participants during cognitively distinct fluid reasoning tasks: induction, spatial relationships and visualization. Microstate parameters successfully discriminated between fluid reasoning and visuomotor control tasks as well as between the fluid reasoning tasks. Mainly, microstate B coverage was significantly higher during spatial relationships and visualization, compared to induction, while microstate C coverage was significantly decreased during spatial relationships and visualization, compared to induction. Additionally, microstate D coverage was highest during spatial relationships and microstate A coverage was most strongly reduced during the same condition. Consistently, multivariate analysis with a leave-one-out cross-validation procedure accurately classified the fluid reasoning tasks based on the coverage parameter. These EEG data and their correlation with fMRI data suggest that especially the tasks most strongly relying on visuospatial processing modulated visual and default mode network activity. We propose that EEG microstates can provide valuable information about neural activity patterns with a dynamic and complex temporal structure during fluid reasoning, suggesting cognitive ability-specific interplays between multiple brain networks.

Transient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness

As we fall sleep, our brain traverses a series of gradual changes at physiological, behavioural and cognitive levels, which are not yet fully understood. The loss of responsiveness is a critical event in the transition from wakefulness to sleep. Here we seek to understand the electrophysiological signatures that reflect the loss of capacity to respond to external stimuli during drowsiness using two complementary methods: spectral connectivity and EEG microstates. Furthermore, we integrate these two methods for the first time by investigating the connectivity patterns captured during individual microstate lifetimes. While participants performed an auditory semantic classification task, we allowed them to become drowsy and unresponsive. As they stopped responding to the stimuli, we report the breakdown of alpha networks and the emergence of theta connectivity. Further, we show that the temporal dynamics of all canonical EEG microstates slow down during unresponsiveness. We identify a specific microstate (D) whose occurrence and duration are prominently increased during this period. Employing machine learning, we show that the temporal properties of microstate D, particularly its prolonged duration, predicts the response likelihood to individual stimuli. Finally, we find a novel relationship between microstates and brain networks as we show that microstate D uniquely indexes significantly stronger theta connectivity during unresponsiveness. Our findings demonstrate that the transition to unconsciousness is not linear, but rather consists of an interplay between transient brain networks reflecting different degrees of sleep depth.

Comparison of EEG microstates with resting state fMRI and FDG-PET measures in the default mode network via simultaneously recorded trimodal (PET/MR/EEG) data

Simultaneous trimodal positron emission tomography/magnetic resonance imaging/electroencephalography (PET/MRI/EEG) resting state (rs) brain data were acquired from 10 healthy male volunteers. The rs-functional MRI (fMRI) metrics, such as regional homogeneity (ReHo), degree centrality (DC) and fractional amplitude of low-frequency fluctuations (fALFFs), as well as 2-[18F]fluoro-2-desoxy-d-glucose (FDG)-PET standardised uptake value (SUV), were calculated and the measures were extracted from the default mode network (DMN) regions of the brain. Similarly, four microstates for each subject, showing the diverse functional states of the whole brain via topographical variations due to global field power (GFP), were estimated from artefact-corrected EEG signals. In this exploratory analysis, the GFP of microstates was nonparametrically compared to rs-fMRI metrics and FDG-PET SUV measured in the DMN of the brain. The rs-fMRI metrics (ReHO, fALFF) and FDG-PET SUV did not show any significant correlations with any of the microstates. The DC metric showed a significant positive correlation with microstate C (r_s  = 0.73, p = .01). FDG-PET SUVs indicate a trend for a negative correlation with microstates A, B and C. The positive correlation of microstate C with DC metrics suggests a functional relationship between cortical hubs in the frontal and occipital lobes. The results of this study suggest further exploration of this method in a larger sample and in patients with neuropsychiatric disorders. The aim of this exploratory pilot study is to lay the foundation for the development of such multimodal measures to be applied as biomarkers for diagnosis, disease staging, treatment response and monitoring of neuropsychiatric disorders.

Calculation and Analysis of Microstate Related to Variation in Executed and Imagined Movement of Force of Hand Clenching

Objective

In order to investigate electroencephalogram (EEG) instantaneous activity states related to executed and imagined movement of force of hand clenching (grip force: 4 kg, 10 kg, and 16 kg), we utilized a microstate analysis in which the spatial topographic map of EEG behaves in a certain number of discrete and stable global brain states.

Approach

Twenty subjects participated in EEG collection; the global field power of EEG and its local maximum were calculated and then clustered using cross validation and statistics; the 4 parameters of each microstate (duration, occurrence, time coverage, and amplitude) were calculated from the clustering results and statistically analyzed by analysis of variance (ANOVA); finally, the relationship between the microstate and frequency band was analyzed.

Main Results

The experimental results showed that all microstates related to executed and imagined grip force tasks were clustered into 3 microstate classes (A, B, and C); these microstates generally transitioned from A to B and then from B to C. With the increase of the target value of executed and imagined grip force, the duration and time coverage of microstate B gradually decreased, while these parameters of microstate C gradually increased. The occurrence times of microstate B and C related to executed grip force were significantly more than those related to imagined grip force; furthermore, the amplitudes of these 3 microstates related to executed grip force were significantly greater than those related to imagined grip force. The correlation coefficients between the microstates and the frequency bands indicated that the microstates were correlated to mu rhythm and beta frequency bands, which are consistent with event-related desynchronization/synchronization (ERD/ERS) phenomena of sensorimotor rhythm.

Significance

It is expected that this microstate analysis may be used as a new method for observing EEG instantaneous activity patterns related to variation in executed and imagined grip force and also for extracting EEG features related to these tasks. This study may lay a foundation for the application of executed and imagined grip force training for rehabilitation of hand movement disorders in patients with stroke in the future.

Neurophysiological correlates of Avolition-apathy in schizophrenia: A resting-EEG microstates study

Background

The “Avolition-apathy” domain of the negative symptoms was found to include different symptoms by factor analytic studies on ratings derived by different scales. In particular, the relationship of anhedonia with this domain is controversial. Recently introduced negative symptom rating scales provide a better assessment of anhedonia, allowing the distinction of anticipatory and consummatory aspects, which might be related to different psychopathological dimensions. The study of associations with external validators, such as electrophysiological, brain imaging or cognitive indices, might shed further light on the status of anhedonia within the Avolition-apathy domain.

Objectives

We used brain electrical microstates (MSs), which represent subsecond periods of quasi-stable scalp electrical field, associated with resting-state neural networks (and thus with global patterns of functional connectivity), to test whether the component symptoms of Avolition-apathy share the same correlates.

Method

We analyzed multichannel resting EEGs in 142 individuals with schizophrenia (SCZ) and in 64 healthy controls (HC), recruited within the add-on EEG study of the Italian Network for Research on Psychoses. Relative time contribution, duration and occurrence of four MS classes (MS-A/-B/-C/−D) were calculated. Group differences on MS parameters (contribution and duration) and their associations with negative symptom domains (assessed using the Brief Negative Symptoms Scale) were investigated.

Results

SCZ, in comparison to HC, showed increased contribution and duration of MS-C. The contribution of MS-A positively correlated with Avolition-apathy, but not with Expressive deficit. Within the Avolition-apathy domain, anticipatory anhedonia, avolition and asociality, but not consummatory anhedonia, showed the same correlations with MS-A contribution.

Conclusion

Our findings support the existence of distinct electrophysiological correlates of Avolition-apathy with respect to Expressive deficit, and lend support to the hypothesis that only the anticipatory component of anhedonia shares the same pathophysiological underpinnings of the Avolition-apathy domain.

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Microstate C contribution and duration were increased in SCZ compared to HC.

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Avolition-apathy was correlated with the contribution of microstate A.

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Avolition-apathy might be associated with sensory processing deficit.

From swing to cane: Sex differences of EEG resting-state temporal patterns during maturation and aging

While many insights on brain development and aging have been gained by studying resting-state networks with fMRI, relating these changes to cognitive functions is limited by the temporal resolution of fMRI. In order to better grasp short-lasting and dynamically changing mental activities, an increasing number of studies utilize EEG to define resting-state networks, thereby often using the concept of EEG microstates. These are brief (around 100 ms) periods of stable scalp potential fields that are influenced by cognitive states and are sensitive to neuropsychiatric diseases. Despite the rising popularity of the EEG microstate approach, information about age changes is sparse and nothing is known about sex differences. Here we investigated age and sex related changes of the temporal dynamics of EEG microstates in 179 healthy individuals (6-87 years old, 90 females, 204-channel EEG). We show strong sex-specific changes in microstate dynamics during adolescence as well as at older age. In addition, males and females differ in the duration and occurrence of specific microstates. These results are of relevance for the comparison of studies in populations of different age and sex and for the understanding of the changes in neuropsychiatric diseases.

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.

Electroencephalographic Resting-State Networks: Source Localization of Microstates

Using electroencephalography (EEG) to elucidate the spontaneous activation of brain resting-state networks (RSNs) is nontrivial as the signal of interest is of low amplitude and it is difficult to distinguish the underlying neural sources. Using the principles of electric field topographical analysis, it is possible to estimate the meta-stable states of the brain (i.e., the resting-state topographies, so-called microstates). We estimated seven resting-state topographies explaining the EEG data set with k-means clustering (N = 164, 256 electrodes). Using a method specifically designed to localize the sources of broadband EEG scalp topographies by matching sensor and source space temporal patterns, we demonstrated that we can estimate the EEG RSNs reliably by measuring the reproducibility of our findings. After subtracting their mean from the seven EEG RSNs, we identified seven state-specific networks. The mean map includes regions known to be densely anatomically and functionally connected (superior frontal, superior parietal, insula, and anterior cingulate cortices). While the mean map can be interpreted as a "router," crosslinking multiple functional networks, the seven state-specific RSNs partly resemble and extend previous functional magnetic resonance imaging-based networks estimated as the hemodynamic correlates of four canonical EEG microstates.

Exploring differences between left and right hand motor imagery via spatio-temporal EEG microstate

EEG-based motor imagery is very useful in brain-computer interface. How to identify the imaging movement is still being researched. Electroencephalography (EEG) microstates reflect the spatial configuration of quasi-stable electrical potential topographies. Different microstates represent different brain functions. In this paper, microstate method was used to process the EEG-based motor imagery to obtain microstate. The single-trial EEG microstate sequences differences between two motor imagery tasks - imagination of left and right hand movement were investigated. The microstate parameters - duration, time coverage and occurrence per second as well as the transition probability of the microstate sequences were obtained with spatio-temporal microstate analysis. The results were shown significant differences (P < 0.05) with paired t-test between the two tasks. Then these microstate parameters were used as features and a linear support vector machine (SVM) was utilized to classify the two tasks with mean accuracy 89.17%, superior performance compared to the other methods. These indicate that the microstate can be a promising feature to improve the performance of the brain-computer interface classification.

The EEG microstate topography is predominantly determined by intracortical sources in the alpha band

Human brain electric activity can be measured at high temporal and fairly good spatial resolution via electroencephalography (EEG). The EEG microstate analysis is an increasingly popular method used to investigate this activity at a millisecond resolution by segmenting it into quasi-stable states of approximately 100 ms duration. These so-called EEG microstates were postulated to represent atoms of thoughts and emotions and can be classified into four classes of topographies A through D, which explain up to 90% of the variance of continuous EEG. The present study investigated whether these topographies are primarily driven by alpha activity originating from the posterior cingulate cortex (all topographies), left and right posterior cortices, and the anterior cingulate cortex (topographies A, B, and C, respectively). We analyzed two 64-channel resting state EEG datasets (N = 61 and N = 78) of healthy participants. Sources of head-surface signals were determined via exact low resolution electromagnetic tomography (eLORETA). The Hilbert transformation was applied to identify instantaneous source strength of four EEG frequency bands (delta through beta). These source strength values were averaged for each participant across time periods belonging to a particular microstate. For each dataset, these averages of the different microstate classes were compared for each voxel. Consistent differences across datasets were identified via a conjunction analysis. The intracortical strength and spatial distribution of alpha band activity mainly determined whether a head-surface topography of EEG microstate class A, B, C, or D was induced. EEG microstate class C was characterized by stronger alpha activity compared to all other classes in large portions of the cortex. Class A was associated with stronger left posterior alpha activity than classes B and D, and class B was associated with stronger right posterior alpha activity than A and D. Previous results indicated that EEG microstate dynamics reflect a fundamental mechanism of the human brain that is altered in different mental states in health and disease. They are characterized by systematic transitions between four head-surface topographies, the EEG microstate classes. Our results show that intra-cortical alpha oscillations, which likely reflect decreased cortical excitability, primarily account for the emergence of these classes. We suggest that microstate class dynamics reflect transitions between four global attractor states that are characterized by selective inhibition of specific intra-cortical regions.

Age-related changes of gustatory function depend on alteration of neuronal circuits

Studies on age-related gustatory function report a reduction of the taste function, but the degeneration of the peripheral papillae alone cannot explain this reduction. In the present study, we apply psychophysics and gustatory event-related potentials (gERPs) to explore age-related differences in the processing of gustatory information as indicated by the cerebral sources of the gERP. A total of 96 subjects (47 female), subdivided into four groups with increasing age, participated in the study. After olfactory and gustatory screening for normal function, the subjects were invited to two sessions of gERP acquisition. They received a randomized combination of five isointense basic tastants that were presented at a medium level. At the same time, we recorded scalp electroencephalography (EEG) from 128 scalp locations. Psychophysical testing for smell and taste function exhibited a significant decrease with age. Topographical analyses of the EEG delineated four basic topographical maps that explained the processing of taste in the pre-decline age range, with sources inside the relevant gustatory areas. The age-related change of gustatory processing was associated with the absence of a specific map with sources inside the cerebellum and posterior insula, and the temporal broadening of a map with sources in the bilateral inferior frontal gyrus. These results confirm the hypothesis that the reduction of taste function with aging is not only due to degradation of gustatory peripheral tissues but is also related to different neural signatures in the central nervous system.

EEG microstates during different phases of Transcendental Meditation practice

Two phases of Transcendental Meditation (TM)-transcending and undirected mentation-were compared to each other and to task-free resting using multichannel EEG recorded from 20 TM practitioners. An EEG microstate analysis identified four classes of microstates which were labeled A, B, C and D, based on their similarity to previously published classes. For each class of microstates, mean duration, coverage and occurrence were computed. Resting and transcending differed from undirected mentation with decreased prominence of Class A and increased prominence of Class D microstates. In addition, transcending showed decreased prominence of Class C microstates compared to undirected mentation. Based on previous findings on the functional significance of the microstate classes, the results indicate an increased reference to reality and decreased visualization during resting and transcending compared to undirected mentation. Also, our results indicate decreased saliency of internally generated mentations during transcending compared to undirected mentation reflecting a more detached and less evaluative processing. It is proposed that the continuous cycling through these two phases of meditation during a TM session might facilitate and train the flexible modulation of the parameters of these microstates of these particular classes which are known to be altered in psychiatric disorders. This might promote beneficial stabilizing effects for the practitioner of TM.