EEG microstates of wakefulness and NREM sleep

Spatio-temporal dynamics of large-scale electrophysiological networks during cognitive action control in healthy controls and Parkinson's disease patients

Among the cognitive symptoms that are associated with Parkinson's disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 10 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the task-related brain network states. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, we found three functional network states in the beta band that involved fronto-temporal, temporo-cingulate and fronto-frontal connections with typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). The presence of these networks did not differ between PD patients and HC when analyzing microstates metrics, and no robust correlations with behavior were found. In the gamma band, five networks were found, including one fronto-temporal network that was identical to the one found in the beta band. These networks also included CAC-related nodes previously identified in different neuroimaging modalities. Similarly to the beta networks, no subject-level differences were found between PD patients and HC. Interestingly, in both frequency bands, the dominant network at the subject-level was never the one that was the most durably modulated by the task. Altogether, this study identified the dynamic functional brain networks observed during CAC, but did not highlight PD-related changes in these networks that might explain behavioral changes. Although other new methods might be needed to investigate the presence of task-related networks at the subject-level, this study still highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond.

Neural network involvement for religious experiences in worship measured by EEG microstate analysis

To date, not much is known about large-scale brain activation patterns in religious states of mind and previous studies have not set an emphasis on experience. The present study investigated the phenomenon of religious experiences through microstate analysis, and it was the first neurocognitive research to tackle the dimension of experience directly. Hence, a total of 60 evangelical Christians participated in an experiment where they were asked to engage in worship and try to connect with God. With a bar slider, people were able to continuously rate how strongly they sensed God's presence at any given moment. A selection of songs was used to help in the induction of the desired experience. With 64 electroencephalography (EEG) electrodes, the brain activity was assessed and analyzed with five clusters of microstate classes. First, we hypothesized that the neural network for multisensory integration was involved in the religious experience. Second, we hypothesized that the same was true for the Default Mode Network (DMN). Our results suggested an association between the auditory network and the religious experience, and an association with the salience network as well as with the DMN. No associations with the network thought to be involved with multisensory integration was detected.

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.

MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale.

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.

Brain Responses to a Self-Compassion Induction in Trauma Survivors With and Without Post-traumatic Stress Disorder

Self-compassion (SC) is a mechanism of symptom improvement in post-traumatic stress disorder (PTSD), however, the underlying neurobiological processes are not well understood. High levels of self-compassion are associated with reduced activation of the threat response system. Physiological threat responses to trauma reminders and increased arousal are key symptoms which are maintained by negative appraisals of the self and self-blame. Moreover, PTSD has been consistently associated with functional changes implicated in the brain's saliency and the default mode networks. In this paper, we explore how trauma exposed individuals respond to a validated self-compassion exercise. We distinguish three groups using the PTSD checklist; those with full PTSD, those without PTSD, and those with subsyndromal PTSD. Subsyndromal PTSD is a clinically relevant subgroup in which individuals meet the criteria for reexperiencing along with one of either avoidance or hyperarousal. We use electroencephalography (EEG) alpha-asymmetry and EEG microstate analysis to characterize brain activity time series during the self-compassion exercise in the three groups. We contextualize our results with concurrently recorded autonomic measures of physiological arousal (heart rate and skin conductance), parasympathetic activation (heart rate variability) and self-reported changes in state mood and self-perception. We find that in all three groups directing self-compassion toward oneself activates the negative self and elicits a threat response during the SC exercise and that individuals with subsyndromal PTSD who have high levels of hyperarousal have the highest threat response. We find impaired activation of the EEG microstate associated with the saliency, attention and self-referential processing brain networks, distinguishes the three PTSD groups. Our findings provide evidence for potential neural biomarkers for quantitatively differentiating PTSD subgroups.

Slow waves form expanding, memory-rich mesostates steered by local excitability in fading anesthesia

In the arousal process, the brain restores its integrative activity from the synchronized state of slow wave activity (SWA). The mechanisms underpinning this state transition remain, however, to be elucidated. Here we simultaneously probed neuronal assemblies throughout the whole cortex with micro-electrocorticographic recordings in mice. We investigated the progressive shaping of propagating SWA at different levels of isoflurane. We found a form of memory of the wavefront shapes at deep anesthesia, tightly alternating posterior-anterior-posterior patterns. At low isoflurane, metastable patterns propagated in more directions, reflecting an increased complexity. The wandering across these mesostates progressively increased its randomness, as predicted by simulations of a network of spiking neurons, and confirmed in our experimental data. The complexity increase is explained by the elevated excitability of local assemblies with no modifications of the network connectivity. These results shed new light on the functional reorganization of the cortical network as anesthesia fades out.

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Complexity of isoflurane-induced slow waves reliably determines anesthesia level

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In deep anesthesia, the propagation strictly alternates between front-back-front patterns

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In light anesthesia, there is a continuum of directions and faster propagation

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Local excitability underpins the cortical reorganization in fading anesthesia

Vocal melody and musical background are simultaneously processed by the brain for musical predictions

Musical pleasure is related to the capacity to predict and anticipate the music. By recording early cerebral responses of 16 participants with electroencephalography during periods of silence inserted in known and unknown songs, we aimed to measure the contribution of different musical attributes to musical predictions. We investigated the mismatch between past encoded musical features and the current sensory inputs when listening to lyrics associated with vocal melody, only background instrumental material, or both attributes grouped together. When participants were listening to chords and lyrics for known songs, the brain responses related to musical violation produced event-related potential responses around 150-200 ms that were of a larger amplitude than for chords or lyrics only. Microstate analysis also revealed that for chords and lyrics, the global field power had an increased stability and a longer duration. The source localization identified that the right superior temporal and frontal gyri and the inferior and medial frontal gyri were activated for a longer time for chords and lyrics, likely caused by the increased complexity of the stimuli. We conclude that grouped together, a broader integration and retrieval of several musical attributes at the same time recruit larger neuronal networks that lead to more accurate predictions.

Real-Time Detection and Feedback of Canonical Electroencephalogram Microstates: Validating a Neurofeedback System as a Function of Delay

Recent neurotechnology has developed various methods for neurofeedback (NF), in which participants observe their own neural activity to be regulated in an ideal direction. EEG-microstates (EEGms) are spatially featured states that can be regulated through NF training, given that they have recently been indicated as biomarkers for some disorders. The current study was conducted to develop an EEG-NF system for detecting “canonical 4 EEGms” in real time. There are four representative EEG states, regardless of the number of channels, preprocessing procedures, or participants. Accordingly, our 10 Hz NF system was implemented to detect them (msA, B, C, and D) and audio-visually inform participants of its detection. To validate the real-time effect of this system on participants’ performance, the NF was intentionally delayed for participants to prevent their cognitive control in learning. Our results suggest that the feedback effect was observed only under the no-delay condition. The number of Hits increased significantly from the baseline period and increased from the 1- or 20-s delay conditions. In addition, when the Hits were compared among the msABCD, each cognitive or perceptual function could be characterized, though the correspondence between each microstate and psychological ability might not be that simple. For example, msD should be generally task-positive and less affected by the inserted delay, whereas msC is more delay-sensitive. In this study, we developed and validated a new EEGms-NF system as a function of delay. Although the participants were naive to the inserted delay, the real-time NF successfully increased their Hit performance, even within a single-day experiment, although target specificity remains unclear. Future research should examine long-term training effects using this NF system.

Microstate Detection in Naturalistic Electroencephalography Data: A Systematic Comparison of Topographical Clustering Strategies on an Emotional Database

Electroencephalography (EEG) microstate analysis is a powerful tool to study the spatial and temporal dynamics of human brain activity, through analyzing the quasi-stable states in EEG signals. However, current studies mainly focus on rest-state EEG recordings, microstate analysis for the recording of EEG signals during naturalistic tasks is limited. It remains an open question whether current topographical clustering strategies for rest-state microstate analysis could be directly applied to task-state EEG data under the natural and dynamic conditions and whether stable and reliable results could still be achieved. It is necessary to answer the question and explore whether the topographical clustering strategies would affect the performance of microstate detection in task-state EEG microstate analysis. If it exists differences in microstate detection performance when different topographical clustering strategies are adopted, then we want to know how the alternations of the topographical clustering strategies are associated with the naturalistic task. To answer these questions, we work on a public emotion database using naturalistic and dynamic music videos as the stimulation to evaluate the effects of different topographical clustering strategies for task-state EEG microstate analysis. The performance results are systematically examined and compared in terms of microstate quality, task efficacy, and computational efficiency, and the impact of topographical clustering strategies on microstate analysis for naturalistic task data is discussed. The results reveal that a single-trial-based bottom-up topographical clustering strategy (bottom-up) achieves comparable results with the task-driven-based top-down topographical clustering (top-down). It suggests that, when task information is unknown, the single-trial-based topographical clustering could be a good choice for microstate analysis and neural activity study on naturalistic EEG data.

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.

Dynamic reconfiguration of frequency-specific cortical coactivation patterns during psychedelic and anesthetized states induced by ketamine

Recent neuroimaging studies have demonstrated that spontaneous brain activity exhibits rich spatiotemporal structure that can be characterized as the exploration of a repertoire of spatially distributed patterns that recur over time. The repertoire of brain states may reflect the capacity for consciousness, since general anesthetics suppress and psychedelic drugs enhance such dynamics. However, the modulation of brain activity repertoire across varying states of consciousness has not yet been studied in a systematic and unified framework. As a unique drug that has both psychedelic and anesthetic properties depending on the dose, ketamine offers an opportunity to examine brain reconfiguration dynamics along a continuum of consciousness. Here we investigated the dynamic organization of cortical activity during wakefulness and during altered states of consciousness induced by different doses of ketamine. Through k-means clustering analysis of the envelope data of source-localized electroencephalographic (EEG) signals, we identified a set of recurring states that represent frequency-specific spatial coactivation patterns. We quantified the effect of ketamine on individual brain states in terms of fractional occupancy and transition probabilities and found that ketamine anesthesia tends to shift the configuration toward brain states with low spatial variability. Furthermore, by assessing the temporal dynamics of the occurrence and transitions of brain states, we showed that subanesthetic ketamine is associated with a richer repertoire, while anesthetic ketamine induces dynamic changes in brain state organization, with the repertoire richness evolving from a reduced level to one comparable to that of normal wakefulness before recovery of consciousness. These results provide a novel description of ketamine's modulation of the dynamic configuration of cortical activity and advance understanding of the neurophysiological mechanism of ketamine in terms of the spatial, temporal, and spectral structures of underlying whole-brain dynamics.

Spectral decomposition of EEG microstates in post-traumatic stress disorder

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EEG microstates reveal significant temporal differences in PTSD.

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Microstate E (with centro-posterior maximum) is temporally underrepresented in PTSD.

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In PTSD, microstate E has a reduced occurrence and a shorter mean duration.

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Spectral decomposition of EEG microstates improves microstate-based classification.

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Alpha band SVM features yield the highest classification accuracy of PTSD (76%).

Microstates offer a promising framework to study fast-scale brain dynamics in the resting-state electroencephalogram (EEG). However, microstate dynamics have yet to be investigated in post-traumatic stress disorder (PTSD), despite research demonstrating resting-state alterations in PTSD. We performed microstate-based segmentation of resting-state EEG in a clinical population of participants with PTSD (N = 61) and a non-traumatized, healthy control group (N = 61). Microstate-based measures (i.e., occurrence, mean duration, time coverage) were compared group-wise using broadband (1–30 Hz) and frequency-specific (i.e., delta, theta, alpha, beta bands) decompositions. In the broadband comparisons, the centro-posterior maximum microstate (map E) occurred significantly less frequently (d = -0.64, pFWE = 0.03) and had a significantly shorter mean duration in participants with PTSD as compared to controls (d = -0.71, pFWE < 0.01). These differences were reflected in the narrow frequency bands as well, with lower frequency bands like delta (d = -0.78, pFWE < 0.01), theta (d = -0.74, pFWE = 0.01), and alpha (d = -0.65, pFWE = 0.02) repeating these group-level trends, only with larger effect sizes. Interestingly, a support vector machine classification analysis comparing broadband and frequency-specific measures revealed that models containing only alpha band features significantly out-perform broadband models. When classifying PTSD, the classification accuracy was 76 % and 65 % for the alpha band and the broadband model, respectively (p = 0.03). Taken together, we provide original evidence supporting the clinical utility of microstates as diagnostic markers of PTSD and demonstrate that filtering EEG into distinct frequency bands significantly improves microstate-based classification of a psychiatric disorder.

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.

Network oscillations imply the highest cognitive workload and lowest cognitive control during idea generation in open-ended creation tasks

Design is a ubiquitous, complex, and open-ended creation behaviour that triggers creativity. The brain dynamics underlying design is unclear, since a design process consists of many basic cognitive behaviours, such as problem understanding, idea generation, idea analysis, idea evaluation, and idea evolution. In this present study, we simulated the design process in a loosely controlled setting, aiming to quantify the design-related cognitive workload and control, identify EEG-defined large-scale brain networks, and uncover their temporal dynamics. The effectiveness of this loosely controlled setting was tested through comparing the results with validated findings available in the literature. Task-related power (TRP) analysis of delta, theta, alpha and beta frequency bands revealed that idea generation was associated with the highest cognitive workload and lowest cognitive control, compared to other design activities in the experiment, including problem understanding, idea evaluation, and self-rating. EEG microstate analysis supported this finding as microstate class C, being negatively associated with the cognitive control network, was the most prevalent in idea generation. Furthermore, EEG microstate sequence analysis demonstrated that idea generation was consistently associated with the shortest temporal correlation times concerning finite entropy rate, autoinformation function, and Hurst exponent. This finding suggests that during idea generation the interplay of functional brain networks is less restricted and the brain has more degrees of freedom in choosing the next network configuration than during other design activities. Taken together, the TRP and EEG microstate results lead to the conclusion that idea generation is associated with the highest cognitive workload and lowest cognitive control during open-ended creation task.

The Temporal Dynamics of EEG Microstate Reveals the Neuromodulation Effect of Acupuncture With Deqi

The electroencephalography (EEG) microstate has recently emerged as a new whole-brain mapping tool for studying the temporal dynamics of the human brain. Meanwhile, the neuromodulation effect of external stimulation on the human brain is of increasing interest to neuroscientists. Acupuncture, which originated in ancient China, is recognized as an external neuromodulation method with therapeutic effects. Effective acupuncture could elicit the deqi effect, which is a combination of multiple sensations. However, whether the EEG microstate could be used to reveal the neuromodulation effect of acupuncture with deqi remains largely unclear. In this study, multichannel EEG data were recorded from 16 healthy subjects during acupuncture manipulation, as well as during pre- and post-manipulation tactile controls and pre- and post-acupuncture rest controls. As the basic acupuncture unit for regulating the central nervous system, the Hegu acupoint was used in this study, and each subject’s acupuncture deqi behavior scores were collected. To reveal the neuroimaging evidence of acupuncture with deqi, EEG microstate analysis was conducted to obtain the microstate maps and microstate parameters for different conditions. Furthermore, Pearson’s correlation was analyzed to investigate the correlation relationship between microstate parameters and deqi behavioral scores. Results showed that: (1) compared with tactile controls, acupuncture manipulation caused significantly increased deqi behavioral scores. (2) Acupuncture manipulation significantly increased the duration, occurrence, and contribution parameters of microstate C, whereas it decreased those parameters of microstate D. (3) Microstate C’s duration parameter showed a significantly positive correlation with acupuncture deqi behavior scores. (4) Acupuncture manipulation significantly increased the transition probabilities with microstate C as node, whereas it reduced the transition probabilities with microstate D as node. (5) Microstate B→C’s transition probability also showed a significantly positive correlation with acupuncture deqi behavior scores. Taken together, the temporal dynamic feature of EEG microstate could be used as objective neuroimaging evidence to reveal the neuromodulation effect of acupuncture with deqi.

RoWDI: rolling window detection of sleep intrusions in the awake brain using fMRI

Objective.Brief episodes of sleep can intrude into the awake human brain due to lack of sleep or fatigue-compromising the safety of critical daily tasks (i.e. driving). These intrusions can also introduce artefactual activity within functional magnetic resonance imaging (fMRI) experiments, prompting the need for an objective and effective method of removing them.Approach.We have developed a method to track sleep-like events in awake humans via rolling window detection of intrusions (RoWDI) of fMRI signal template. These events can then be used in voxel-wise event-related analysis of fMRI data. To test this approach, we generated a template of fMRI activity associated with transition to sleep via simultaneous fMRI and electroencephalogram (EEG) (N= 10). RoWDI was then used to identify sleep-like events in 20 individuals performing a cognitive task during fMRI after a night of partial sleep deprivation. This approach was further validated in an independent fMRI dataset (N= 56).Main results.Our method (RoWDI) was able to infer frequent sleep-like events during the cognitive task performed after sleep deprivation. The sleep-like events were associated with on average of 20% reduction in pupil size and prolonged response time. The blood-oxygen-level-dependent activity during the sleep-like events covered thalami-cortical regions, which although spatially distinct, co-existed with, task-related activity. These key findings were validated in the independent dataset.Significance.RoWDI can reliably detect spontaneous sleep-like events in the human brain. Thus, it may also be used as a tool to delineate and account for neural activity associated with wake-sleep transitions in both resting-state and task-related fMRI studies.

EEG microstate in obstructive sleep apnea patients

Obstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA_OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex.

Dysfunctional Brain Dynamics of Parkinson's Disease and the Effect of Acute Deep Brain Stimulation

Background: Parkinson's disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease, and deep brain stimulation (DBS) can effectively alleviate PD symptoms. Although previous studies have detected network features of PD and DBS, few studies have considered their dynamic characteristics.

Objective: We tested two hypotheses. (1) Reduced brain dynamics, as evidenced by slowed microstate dynamic change, is a characteristic of PD and is related to the movement disorders of patients with PD. (2) Therapeutic acute DBS can partially reverse slow brain dynamics in PD to healthy levels.

Methods: We used electroencephalography (EEG) microstate analysis based on high density (256-channel) EEG to detect the effects of PD and DBS on brain dynamic changes on a sub-second timescale. We compared 21 healthy controls (HCs) with 20 patients with PD who were in either DBS-OFF or DBS-ON states. Assessment of movement disorder using the Unified Parkinson's Disease Rating Scale III was correlated with microstate parameters.

Results: Compared with HCs, patients with PD displayed a longer mean microstate duration with reduced occurrence per second, which were significantly associated with movement disorders. In patients with PD, some parameters of microstate analysis were restored toward healthy levels after DBS.

Conclusions: Resting-state EEG microstate analysis is an important tool for investigating brain dynamic changes in PD and DBS. PD can slow down brain dynamic change, and therapeutic acute DBS can partially reverse this change toward a healthy level.

Characterization of the Functional Dynamics in the Neonatal Brain during REM and NREM Sleep States by means of Microstate Analysis

Neonates spend most of their life sleeping. During sleep, their brain experiences fast changes in its functional organization. Microstate analysis permits to capture the rapid dynamical changes occurring in the functional organization of the brain by representing the changing spatio-temporal features of the electroencephalogram (EEG) as a sequence of short-lasting scalp topographies—the microstates. In this study, we modeled the ongoing neonatal EEG into sequences of a limited number of microstates and investigated whether the extracted microstate features are altered in REM and NREM sleep (usually known as active and quiet sleep states—AS and QS—in the newborn) and depend on the EEG frequency band. 19-channel EEG recordings from 60 full-term healthy infants were analyzed using a modified version of the k-means clustering algorithm. The results show that ~ 70% of the variance in the datasets can be described using 7 dominant microstate templates. The mean duration and mean occurrence of the dominant microstates were significantly different in the two sleep states. Microstate syntax analysis demonstrated that the microstate sequences characterizing AS and QS had specific non-casual structures that differed in the two sleep states. Microstate analysis of the neonatal EEG in specific frequency bands showed a clear dependence of the explained variance on frequency. Overall, our findings demonstrate that (1) the spatio-temporal dynamics of the neonatal EEG can be described by non-casual sequences of a limited number of microstate templates; (2) the brain dynamics described by these microstate templates depends on frequency; (3) the features of the microstate sequences can well differentiate the physiological conditions characterizing AS and QS.

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.

EEG microstate features for schizophrenia classification

Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. However, previous studies have only reported differences in each microstate feature and have not determined whether microstate features are suitable for schizophrenia classification. Therefore, it is necessary to validate microstate features for schizophrenia classification. Nineteen microstate features, including duration, occurrence, and coverage as well as thirty-one conventional EEG features, including statistical, frequency, and temporal characteristics were obtained from resting-state EEG recordings of 14 patients diagnosed with schizophrenia and from 14 healthy (control) subjects. Machine-learning based multivariate analysis was used to evaluate classification performance. EEG recordings of patients and controls showed different microstate features. More importantly, when differentiating among patients and controls, EEG microstate features outperformed conventional EEG ones. The performance of the microstate features exceeded that of conventional EEG, even after optimization using recursive feature elimination. EEG microstate features applied with conventional EEG features also showed better classification performance than conventional EEG features alone. The current study is the first to validate the use of microstate features to discriminate schizophrenia, suggesting that EEG microstate features are useful for schizophrenia classification.

State-independent and state-dependent patterns in the rat default mode network

Resting-state studies have typically assumed constant functional connectivity (FC) between brain regions, and these parameters of interest provide meaningful descriptions of the functional organization of the brain. A number of studies have recently provided evidence pointing to dynamic FC fluctuations in the resting brain, especially in higher-order regions such as the default mode network (DMN). The neural activities underlying dynamic FC remain poorly understood. Here, we recorded electrophysiological signals from DMN regions in freely behaving rats. The dynamic FCs between signals within the DMN were estimated by the phase locking value (PLV) method with sliding time windows across vigilance states [quiet wakefulness (QW) and slow-wave and rapid eye movement sleep (SWS and REMS)]. Factor analysis was then performed to reveal the hidden patterns within the DMN. We identified distinct spatial FC patterns according to the similarities between their temporal dynamics. Interestingly, some of these patterns were vigilance state-dependent, while others were independent across states. The temporal contributions of these patterns fluctuated over time, and their interactive relationships were different across vigilance states. These spatial patterns with dynamic temporal contributions and combinations may offer a flexible framework for efficiently integrating information to support cognition and behavior. These findings provide novel insights into the dynamic functional organization of the rat DMN.

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.

Meditation training modulates brain electric microstates and felt states of awareness

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

Topography-Related EEG-fMRI in Surgically Confirmed Epileptic Foci: A Comparison to Spike-Related EEG-fMRI in Clinical Practice

EEG-fMRI has gained increasing importance in epilepsy pre-surgical diagnosis. However, 40-70% of EEG-fMRI recordings in patients lack interictal epileptiform discharges (IEDs) during the scan, which could be overcome by detecting matching topography maps. We tried to validate this method in clinical settings taking various electroclinical factors into consideration. Eleven patients who had undergone EEG-fMRI during pre-surgical evaluation for drug-resistant epilepsy and who had had clinical long-term video-EEG were studied. Spike-related blood oxygen level-dependent (BOLD) maps were created using IEDs occurring during the EEG-fMRI scan. Separate maps were then generated from IEDs marked on the clinical long-term EEG recordings, which were averaged to produce topographical IED maps and correlated with the EEGs recorded inside the scanner yielding a correlation coefficient time course. Epileptogenic zones were defined by an expert panel during pre-surgical evaluation and validated by an epilepsy surgery resulting in a good outcome. Both techniques' performance was evaluated according to factors including arousal during IED recording, IED topography and lateralization, lesion type, and localization. Topography-related EEG-fMRI yielded more specific results compared to the spike-related method. Superficial lesion location and ipsilateral IED seem to result in a higher concordance of BOLD maps. The polarity of BOLD responses may be lesion-dependent, and both positive and negative BOLD changes may be associated with the irritative zone. Topography-related EEG-fMRI may show improved specificity especially for superficial lesions producing ipsilateral spikes. This method can be used as an alternative either in the absence of spikes during the simultaneous EEG-fMRI acquisition or to sharpen a diffusely activated BOLD-map.

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.

EEG microstate features according to performance on a mental arithmetic task

In this study, we hypothesized that task performance could be evaluated applying EEG microstate to mental arithmetic task. This pilot study also aimed at evaluating the efficacy of microstates as novel features to discriminate task performance. Thirty-six subjects were divided into good and poor performers, depending on how well they performed the task. Microstate features were derived from EEG recordings during resting and task states. In the good performers, there was a decrease in type C and an increase in type D features during the task compared to the resting state. Mean duration and occurrence decreased and increased, respectively. In the poor performers, occurrence of type D feature, mean duration and occurrence showed greater changes. We investigated whether microstate features were suitable for task performance classification and eleven features including four archetypes were selected by recursive feature elimination (RFE). The model that implemented them showed the highest classification performance for differentiating between groups. Our pilot findings showed that the highest mean Area Under Curve (AUC) was 0.831. This study is the first to apply EEG microstate features to specific cognitive tasks in healthy subjects, suggesting that EEG microstate features can reflect task achievement.

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.

Children With Autism Produce a Unique Pattern of EEG Microstates During an Eyes Closed Resting-State Condition

Although fMRI studies have produced considerable evidence for differences in the spatial connectivity of resting-state brain networks in persons with autism spectrum disorder (ASD) relative to typically developing (TD) peers, little is known about the temporal dynamics of these brain networks in ASD. The aim of this study was to examine the EEG microstate architecture in children with ASD as compared to TD at rest in two separate conditions – eyes-closed (EC) and eyes-open (EO). EEG microstate analysis was performed on resting-state data of 13 ASD and 13 TD children matched on age, gender, and IQ. We found that children with ASD and TD peers produced topographically similar canonical microstates at rest. Group differences in the duration and frequency of these microstates were found primarily in the EC resting-state condition. In line with previous fMRI findings that have reported differences in spatial connectivity within the salience network (previously correlated with the activity of microstate C) in ASD, we found that the duration of activation of microstate C was increased, and the frequency of microstate C was decreased in ASD as compared to TD in EC resting-state. Functionally, these results may be reflective of alterations in interoceptive processes in ASD. These results suggest a unique pattern of EEG microstate architecture in ASD relative to TD during resting-states and also that EEG microstate parameters in ASD are susceptible to differences in resting-state conditions.

Abnormalities in electroencephalographic microstates are state and trait markers of major depressive disorder

Neuroimaging studies have shown that major depressive disorder (MDD) is characterized by abnormal neural activity and connectivity. However, hemodynamic imaging techniques lack the temporal resolution needed to resolve the dynamics of brain mechanisms underlying MDD. Moreover, it is unclear whether putative abnormalities persist after remission. To address these gaps, we used microstate analysis to study resting-state brain activity in major depressive disorder (MDD). Electroencephalographic (EEG) "microstates" are canonical voltage topographies that reflect brief activations of components of resting-state brain networks. We used polarity-insensitive k-means clustering to segment resting-state high-density (128-channel) EEG data into microstates. Data from 79 healthy controls (HC), 63 individuals with MDD, and 30 individuals with remitted MDD (rMDD) were included. The groups produced similar sets of five microstates, including four widely-reported canonical microstates (A-D). The proportion of microstate D was decreased in MDD and rMDD compared to the HC group (Cohen's d = 0.63 and 0.72, respectively) and the duration and occurrence of microstate D was reduced in the MDD group compared to the HC group (Cohen's d = 0.43 and 0.58, respectively). Among the MDD group, proportion and duration of microstate D were negatively correlated with symptom severity (Spearman's rho = -0.34 and -0.46, respectively). Finally, microstate transition probabilities were nonrandom and the MDD group, relative to the HC and the rMDD groups, exhibited multiple distinct transition probabilities, primarily involving microstates A and C. Our findings highlight both state and trait abnormalities in resting-state brain activity in MDD.

Budgerigars have complex sleep structure similar to that of mammals

Birds and mammals share specialized forms of sleep including slow wave sleep (SWS) and rapid eye movement sleep (REM), raising the question of why and how specialized sleep evolved. Extensive prior studies concluded that avian sleep lacked many features characteristic of mammalian sleep, and therefore that specialized sleep must have evolved independently in birds and mammals. This has been challenged by evidence of more complex sleep in multiple songbird species. To extend this analysis beyond songbirds, we examined a species of parrot, the sister taxon to songbirds. We implanted adult budgerigars (Melopsittacus undulatus) with electroencephalogram (EEG) and electrooculogram (EOG) electrodes to evaluate sleep architecture, and video monitored birds during sleep. Sleep was scored with manual and automated techniques, including automated detection of slow waves and eye movements. This can help define a new standard for how to score sleep in birds. Budgerigars exhibited consolidated sleep, a pattern also observed in songbirds, and many mammalian species, including humans. We found that REM constituted 26.5% of total sleep, comparable to humans and an order of magnitude greater than previously reported. Although we observed no spindles, we found a clear state of intermediate sleep (IS) similar to non-REM (NREM) stage 2. Across the night, SWS decreased and REM increased, as observed in mammals and songbirds. Slow wave activity (SWA) fluctuated with a 29-min ultradian rhythm, indicating a tendency to move systematically through sleep states as observed in other species with consolidated sleep. These results are at variance with numerous older sleep studies, including for budgerigars. Here, we demonstrated that lighting conditions used in the prior budgerigar study-and commonly used in older bird studies-dramatically disrupted budgerigar sleep structure, explaining the prior results. Thus, it is likely that more complex sleep has been overlooked in a broad range of bird species. The similarities in sleep architecture observed in mammals, songbirds, and now budgerigars, alongside recent work in reptiles and basal birds, provide support for the hypothesis that a common amniote ancestor possessed the precursors that gave rise to REM and SWS at one or more loci in the parallel evolution of sleep in higher vertebrates. We discuss this hypothesis in terms of the common plan of forebrain organization shared by reptiles, birds, and mammals.

Self-reported Mind Wandering and Response Time Variability Differentiate Prestimulus Electroencephalogram Microstate Dynamics during a Sustained Attention Task

Brain activity continuously and spontaneously fluctuates during tasks of sustained attention. This spontaneous activity reflects the intrinsic dynamics of neurocognitive networks, which have been suggested to differentiate moments of externally directed task focus from episodes of mind wandering. However, the contribution of specific electrophysiological brain states and their millisecond dynamics to the experience of mind wandering is still unclear. In this study, we investigated the association between electroencephalogram microstate temporal dynamics and self-reported mind wandering. Thirty-six participants completed a sustained attention to response task in which they were asked to respond to frequently occurring upright faces (nontargets) and withhold responses to rare inverted faces (targets). Intermittently, experience sampling probes assessed whether participants were focused on the task or whether they were mind wandering (i.e., off-task). Broadband electroencephalography was recorded and segmented into a time series of brain electric microstates based on data-driven clustering of topographic voltage patterns. The strength, prevalence, and rate of occurrence of specific microstates differentiated on- versus off-task moments in the prestimulus epochs of trials preceding probes. Similar associations were also evident between microstates and variability in response times. Together, these findings demonstrate that distinct microstates and their millisecond dynamics are sensitive to the experience of mind wandering.

EEG microstates of dreams

Why do people sometimes report that they remember dreams, while at other times they recall no experience? Despite the interest in dreams that may happen during the night, it has remained unclear which brain states determine whether these conscious experiences will occur and what prevents us from waking up during these episodes. Here we address this issue by comparing the EEG activity preceding awakenings with recalled vs. no recall of dreams using the EEG microstate approach. This approach characterizes transiently stable brain states of sub-second duration that involve neural networks with nearly synchronous dynamics. We found that two microstates (3 and 4) dominated during NREM sleep compared to resting wake. Further, within NREM sleep, microstate 3 was more expressed during periods followed by dream recall, whereas microstate 4 was less expressed. Source localization showed that microstate 3 encompassed the medial frontal lobe, whereas microstate 4 involved the occipital cortex, as well as thalamic and brainstem structures. Since NREM sleep is characterized by low-frequency synchronization, indicative of neuronal bistability, we interpret the increased presence of the “frontal” microstate 3 as a sign of deeper local deactivation, and the reduced presence of the “occipital” microstate 4 as a sign of local activation. The latter may account for the occurrence of dreaming with rich perceptual content, while the former may account for why the dreaming brain may undergo executive disconnection and remain asleep. This study demonstrates that NREM sleep consists of alternating brain states whose temporal dynamics determine whether conscious experience arises.

Comparison Between Facilitating and Suppressing Facial Emotional Expressions Using Frontal EEG Asymmetry

The prefrontal cortex plays a key role in emotional state. Electroencephalography (EEG) studies have reported relationships between frontal asymmetry in the alpha band, emotional state, and emotion-related motivation. The current study investigated whether the positive or negative valence of emotional stimulation or the behavioral intention to either facilitate or suppress one’s facial expression in response to these stimuli is reflected in relevant changes in frontal EEG asymmetry. EEG was recorded while participants either produced a facial expression that was in accord with positive or negative feelings corresponding to image stimuli, or suppressed their facial expressions. The laterality index of frontal alpha power indicated greater relative right frontal activity while participants suppressed facial expression compared with facilitating facial expression during emotional stimulation. However, there was no difference in frontal asymmetry between the presentation of image stimuli showing facial expressions corresponding to positive vs. negative emotions. These results suggested that frontal asymmetry was related to the control of facial emotional expressions rather than the perception of positive vs. negative emotions. Moreover, microstate analysis revealed that the appearance rate of microstate class B with polarity in the left frontal area increased during the suppression of facial expressions. The present results suggested that frontal asymmetry reflects the control of facial emotional expressions, which supports the motivational direction model.

Children With Autism Produce a Unique Pattern of EEG Microstates During an Eyes Closed Resting-State Condition

Although fMRI studies have produced considerable evidence for differences in the spatial connectivity of resting-state brain networks in persons with autism spectrum disorder (ASD) relative to typically developing (TD) peers, little is known about the temporal dynamics of these brain networks in ASD. The aim of this study was to examine the EEG microstate architecture in children with ASD as compared to TD at rest in two separate conditions - eyes-closed (EC) and eyes-open (EO). EEG microstate analysis was performed on resting-state data of 13 ASD and 13 TD children matched on age, gender, and IQ. We found that children with ASD and TD peers produced topographically similar canonical microstates at rest. Group differences in the duration and frequency of these microstates were found primarily in the EC resting-state condition. In line with previous fMRI findings that have reported differences in spatial connectivity within the salience network (previously correlated with the activity of microstate C) in ASD, we found that the duration of activation of microstate C was increased, and the frequency of microstate C was decreased in ASD as compared to TD in EC resting-state. Functionally, these results may be reflective of alterations in interoceptive processes in ASD. These results suggest a unique pattern of EEG microstate architecture in ASD relative to TD during resting-states and also that EEG microstate parameters in ASD are susceptible to differences in resting-state conditions.

EEG microstate periodicity explained by rotating phase patterns of resting-state alpha oscillations

Spatio-temporal patterns in electroencephalography (EEG) can be described by microstate analysis, a discrete approximation of the continuous electric field patterns produced by the cerebral cortex. Resting-state EEG microstates are largely determined by alpha frequencies (8-12 Hz) and we recently demonstrated that microstates occur periodically with twice the alpha frequency. To understand the origin of microstate periodicity, we analyzed the analytic amplitude and the analytic phase of resting-state alpha oscillations independently. In continuous EEG data we found rotating phase patterns organized around a small number of phase singularities which varied in number and location. The spatial rotation of phase patterns occurred with the underlying alpha frequency. Phase rotors coincided with periodic microstate motifs involving the four canonical microstate maps. The analytic amplitude showed no oscillatory behaviour and was almost static across time intervals of 1-2 alpha cycles, resulting in the global pattern of a standing wave. In n=23 healthy adults, time-lagged mutual information analysis of microstate sequences derived from amplitude and phase signals of awake eyes-closed EEG records showed that only the phase component contributed to the periodicity of microstate sequences. Phase sequences showed mutual information peaks at multiples of 50 ms and the group average had a main peak at 100 ms (10 Hz), whereas amplitude sequences had a slow and monotonous information decay. This result was confirmed by an independent approach combining temporal principal component analysis (tPCA) and autocorrelation analysis. We reproduced our observations in a generic model of EEG oscillations composed of coupled non-linear oscillators (Stuart-Landau model). Phase-amplitude dynamics similar to experimental EEG occurred when the oscillators underwent a supercritical Hopf bifurcation, a common feature of many computational models of the alpha rhythm. These findings explain our previous description of periodic microstate recurrence and its relation to the time scale of alpha oscillations. Moreover, our results corroborate the predictions of computational models and connect experimentally observed EEG patterns to properties of critical oscillator networks.

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.

Aberrant Awake Spontaneous Brain Activity in Obstructive Sleep Apnea: A Review Focused on Resting-State EEG and Resting-State fMRI

As one of the most common sleep-related respiratory disorders, obstructive sleep apnea (OSA) is characterized by excessive snoring, repetitive apnea, arousal, sleep fragmentation, and intermittent nocturnal hypoxemia. Focused on the resting-state brain imaging techniques, we reviewed the OSA-related resting-state electroencephalogram and resting-state functional magnetic resonance imaging (rsfMRI) studies. Compared with the healthy control group, patients with OSA presented increased frontal and central δ/θ powers during resting-state wakefulness, and their slow-wave activity showed a positive correlation with apnea–hypopnea index. For rsfMRI, the prefrontal cortex and insula may be the vital regions for OSA and are strongly related to the severity of the disease. Meanwhile, some large-scale brain networks, such as the default-mode network, salience network, and central executive network, play pivotal roles in the pathology of OSA. We then discussed the contribution of resting-state brain imaging as an evaluation approach for disease interventions. Finally, we briefly introduced the effects of OSA-related physiological and mental diseases and discussed some future research directions from the perspective of resting-state brain imaging.

Diagnosis of epilepsy after first seizure. Introducing the SWISS FIRST study

Diagnosis of epilepsy after a first unprovoked seizure is possible according to the guidelines by the International League Against Epilepsy, if the risk recurrence of a second unprovoked seizure is exceeding 60%. However, this cutoff constitutes only a proxy depending on the patients’ history, magnetic resonance imaging (MRI), and electroencephalography (EEG) findings but nevertheless also from the treating neurologists’ individual experience. In a Switzerland-wide observational study, we aim to recruit patients that were admitted to the emergency department with the referral diagnosis of a first and unprovoked seizure. We make use of optimized MRI protocols to identify potential structural epileptogenic lesions, introduce new imaging-based markers of epileptogenecity, and use most recent postprocessing methods as automatic morphometry, spike map analysis, and functional connectivity. With these diagnostic tools, we aim to segregate patients that present with epileptic seizures versus mimicks and non-epileptic seizures and stratify for every finding in MRI and EEG its predictive value for a second unprovoked seizure. These findings shall support neurologists to calculate and not only estimate the seizure recurrence rate in future.

Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide. Understanding the underlying gene mutations in HCC provides great prognostic value for treatment planning and targeted therapy. Radiogenomics has revealed an association between non-invasive imaging features and molecular genomics. However, imaging feature identification is laborious and error-prone. In this paper, we propose an end-to-end deep learning framework for mutation prediction in APOB, COL11A1 and ATRX genes using multiphasic CT scans. Considering intra-tumour heterogeneity (ITH) in HCC, multi-region sampling technology is implemented to generate the dataset for experiments. Experimental results demonstrate the effectiveness of the proposed model.

Euthymic bipolar disorder patients and EEG microstates: a neural signature of their abnormal self experience?

BACKGROUND

A growing number of neuroimaging studies have revealed spatial abnormalities of resting-state functional brain network activity in bipolar disorder (BD). Conversely, abnormalities of resting state temporal dynamics have been scarcely investigated so far. The aim of this study was to characterize the EEG microstates activity in BD patients with a history of manic predominant polarity. Patients were euthymic and pharmacologically stabilized.

METHODS

Nineteen BD patients (mean age 34.4 ± 11.0, 7 female) and 19 healthy controls (HC; mean age 38.2 ± 9.9, 7 female) were recruited. The psychometric evaluation included the Hamilton Depression Scale (HAMD), the Young Mania Rating Scale (YMRS), the Dissociative Experience Scale (DES), and the State-Trait Anxiety Inventory (STAI).  Two runs of 2 minutes of EEG activity by a 128-channel system were acquired at rest and analyzed through microstate analysis.

RESULTS

We found a reduced presence of microstate B in BD patients compared to HC, since BD patients have a tendency to transit from the microstate B to the microstates C and D significantly more than HC. Furthermore, microstate B features were correlated with DES, state STAI and trait STAI scores.

CONCLUSION

The reduced presence of microstate B might be associated with episodic autobiographic memory deficit, exaggerated self-focusing and states of dissociations characteristic of BD. Strong correlations of microstate B metrics and dynamics with symptoms of dissociation and anxiety across the two groups supported this interpretation.

EEG microstates are a candidate endophenotype for schizophrenia

Electroencephalogram microstates are recurrent scalp potential configurations that remain stable for around 90 ms. The dynamics of two of the four canonical classes of microstates, commonly labeled as C and D, have been suggested as a potential endophenotype for schizophrenia. For endophenotypes, unaffected relatives of patients must show abnormalities compared to controls. Here, we examined microstate dynamics in resting-state recordings of unaffected siblings of patients with schizophrenia, patients with schizophrenia, healthy controls, and patients with first episodes of psychosis (FEP). Patients with schizophrenia and their siblings showed increased presence of microstate class C and decreased presence of microstate class D compared to controls. No difference was found between FEP and chronic patients. Our findings suggest that the dynamics of microstate classes C and D are a candidate endophenotype for schizophrenia.