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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.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.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.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.

In this paper, we extend the notion of microstate free entropy to the bi-free setting. In particular, using the bi-free analogue of random matrices, microstate bi-free entropy is defined. Properties essential to an entropy theory are developed, such as the behaviour of the entropy when transformations on the left variables or on the right variables are performed. In addition, the microstate bi-free entropy is demonstrated to be additive over bi-free collections provided additional regularity assumptions are included and is computed for all bi-free central limit distributions. Moreover, an orbital version of bi-free entropy is examined which provides a tighter upper bound for the subadditivity of microstate bi-free entropy and provides an alternate characterization of bi-freeness in certain settings., Comment: (new version contains corrections, orbital bi-free entropy, and a new characterization of bi-freenness)

Ionizable residues can release and take up protons and this has an influence on protein structure and function. The extent of protonation is linked to the overall pH of the solution and the local environments of ionizable residues. Binding or unbinding of a single proton generates a distinct charge microstate defined by a specific pattern of charges. Accordingly, the overall partition function is a sum over all charge microstates and Boltzmann weights of all conformations associated with each of the charge microstates. This ensemble-of-ensembles description recast as a q-canonical ensemble allows us to analyze and interpret potentiometric titrations that provide information regarding net charge as a function of pH. In the q-canonical ensemble, charge microstates are grouped into mesostates where each mesostate is a collection of microstates of the same net charge. Here, we show that leveraging the structure of the q-canonical ensemble allows us to decouple contributions of net proton binding and release from proton arrangement and conformational considerations. Through application of the q-canonical formalism to analyze potentiometric measurements of net charge in proteins with repetitive patterns of Lys and Glu residues, we are able to determine the underlying mesostate pKa values and, more importantly, we estimate relative mesostate populations as a function of pH. This is a strength of using the q-canonical approach and cannot be obtained using purely site-specific analyses. Overall, our work shows how measurements of charge equilibria, decoupled from measurements of conformational equilibria, and analyzed using the framework of the q-canonical ensemble, provide protein-specific quantitative descriptions of pH-dependent populations of mesostates. This method is of direct relevance for measuring and understanding how different charge states contribute to conformational, binding, and phase equilibria of proteins.STATEMENT OF SIGNIFICANCEThe net charge of a protein in solution is governed by the overall pH as well as context and conformational contexts. Measurements of net charge are accessible via techniques such as potentiometry that quantify the buffering capacity of a protein solution. Here, we use the formal structure of the q-canonical ensemble to identify charge states that are compatible with a measured net charge profile as a function of pH. Our approach highlights how measurements of charge, decoupled from measurements of conformation, can be used to identify the ensembles of charge states that contribute to the overall population for given solution conditions. The methods introduced will be useful for measuring charge states and interpreting these measurements in different contexts.

We construct a three-parameter family of non-extremal microstate geometries, or "microstrata," that are dual to states and deformations of the D1-D5 CFT. These families are non-extremal analogues of superstrata. We find these microstrata by using a Q-ball-inspired Ansatz that reduces the equations of motion to solving for eleven functions of one variable. We then solve this system both perturbatively and numerically and the results match extremely well. We find that the solutions have normal mode frequencies that depend upon the amplitudes of the excitations. We also show that, at higher order in perturbations, some of the solutions, having started with normalizable modes, develop a "non-normalizable" part, suggesting that the microstrata represent states in a perturbed form of the D1-D5 CFT. This paper is intended as a "Proof of Concept" for the Q-ball-inspired approach, and we will describe how it opens the way to many interesting follow-up calculations both in supergravity and in the dual holographic field theory., 72 pages, 22 figures, version submitted to journal

Continuous theta-burst stimulation (cTBS) induces long-lasting inhibitory effects on cortical excitability. Although cTBS has been reported to modulate neural oscillations and functional connectivity, it is still unclear how cTBS affects brain dynamics that could be captured by the resting-sate EEG microstate sequences. This study aims to investigate how cTBS over the left motor cortex affects brain dynamics. We applied 40 s-long cTBS over the left motor cortex of 28 healthy participants. Before and in multi-sessions up to 90 min after cTBS, their performance in a Nine-Hole Peg Test (NHPT), that measures the hand dexterity, and resting state EEG were recorded. Resting-sate EEG data were clustered into four microstates (namely A, B, C, and D) using k-means clustering algorithms. cTBS-induced changes in NHPT performance, microstate dynamics and functional connectivity networks were comprehensively assessed. As compared with baseline, the completion time of NHPT became shorter immediately after cTBS, suggesting cTBS-induced motor function improvement. After cTBS, the topography of microstate B revealed a greater change compared with other three topographies. Importantly, cTBS-induced decrease in completion time of NHPT correlated with cTBS-induced decrease of the mean occurrence of microstate B. Functional connectivity analysis further revealed that cTBS led to an increase of the node efficiency at C4 electrode in microstate B. These results indicated the specific modulation of cTBS over the motor cortex on the dynamics of microstate B. This work provided the evidence of the association between B and motor function, and it also implies the modulation of cTBS over the motor network. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-021-09726-6.

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.

In this paper, the differences between two motor imagery tasks are captured through microstate parameters (occurrence, duration and coverage, and mean spatial correlation (Mspatcorr)) derived from a novel method based on electroencephalogram microstate and Teager energy operator. The results show that the significance between microstate parameters for two tasks is different (P < 0.05) with paired t-test. Furthermore, these microstate parameters are utilized as features. Support vector machine is utilized to classify the two tasks with a mean accuracy of 93.93%, which yielded superior performance compared to the other methods.

Alterations of resting-state EEG microstates have been associated with various neurological disorders and behavioral states. Interestingly, age-related differences in EEG microstate organization have also been reported, and it has been suggested that resting-state EEG activity may predict cognitive capacities in healthy individuals across the lifespan. In this exploratory study, we performed a microstate analysis of resting-state brain activity and tested allocentric spatial working memory performance in healthy adult individuals: twenty 25–30-year-olds and twenty-five 64–75-year-olds. We found a lower spatial working memory performance in older adults, as well as age-related differences in the five EEG microstate maps A, B, C, C′ and D, but especially in microstate maps C and C′. These two maps have been linked to neuronal activity in the frontal and parietal brain regions which are associated with working memory and attention, cognitive functions that have been shown to be sensitive to aging. Older adults exhibited lower global explained variance and occurrence of maps C and C′. Moreover, although there was a higher probability to transition from any map towards maps C, C′ and D in young and older adults, this probability was lower in older adults. Finally, although age-related differences in resting-state EEG microstates paralleled differences in allocentric spatial working memory performance, we found no evidence that any individual or combination of resting-state EEG microstate parameter(s) could reliably predict individual spatial working memory performance. Whether the temporal dynamics of EEG microstates may be used to assess healthy cognitive aging from resting-state brain activity requires further investigation. Supplementary Information The online version contains supplementary material available at 10.1007/s10548-021-00835-3.

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. IMPACT STATEMENT: Breath-focused, digital meditation training leads to sustained changes of resting-state, brain network functional connectivity, as assessed with electroencephalography microstates.

EEG microstate analysis investigates the collection of distinct temporal blocks that characterize the electrical activity of the brain. Brain activity within each microstate is stable, but activity switches rapidly between different microstates in a nonrandom way. We propose a Bayesian nonparametric model that concurrently estimates the number of microstates and their underlying behaviour. We use a Markov switching vector autoregressive (VAR) framework, where a hidden Markov model (HMM) controls the nonrandom state switching dynamics of the EEG activity and a VAR model defines the behaviour of all time points within a given state. We analyze the resting-state EEG data from twin pairs collected through the Minnesota Twin Family Study, consisting of 70 epochs per participant, where each epoch corresponds to 2 s of EEG data. We fit our model at the twin pair level, sharing information within epochs from the same participant and within epochs from the same twin pair. We capture within twin-pair similarity, using an Indian buffet process, to consider an infinite library of microstates, allowing each participant to select a finite number of states from this library. The state spaces of highly similar twins may completely overlap while dissimilar twins could select distinct state spaces. In this way, our Bayesian nonparametric model defines a sparse set of states that describe the EEG data. All epochs from a single participant use the same set of states and are assumed to adhere to the same state switching dynamics in the HMM model, enforcing within-participant similarity.L’analyse des micro-états d’un électroencéphalogramme (EEG) porte sur une collection de différents blocs temporels caractérisant l’activité électrique du cerveau. L’activité cérébrale est stable à l’intérieur de chaque bloc, mais elle varie rapidement entre les différents micro-états de façon non aléatoire. Les auteurs proposent un modèle bayésien non paramétrique qui estime simultanément le nombre de micro-états et leur comportement sous-jacent. Ils utilisent le cadre de vecteurs autorégressifs (VAR) markoviens commutants où un modèle de Markov caché (MMC) contrôle les dynamiques de commutations non aléatoires de l’activité de l’EEG et le modèle de VAR définit le comportement à travers le temps pour un état donné. Ils analysent des données d’EEG au repos de paires de jumeaux collectées dans l’étude des jumeaux du Minnesota comportant 70 époques de deux secondes d’EEG chacune pour chaque participant. Les auteurs ajustent leur modèle au niveau des paires de jumeaux, partageant les informations d’un participant et de son jumeau pour une même époque. Ils capturent les similarités dans la paire de jumeaux avec un processus du buffet indien afin de constituer une bibliothèque infinie de micro-états et de permettre à chaque participant de choisir un ensemble fini d’états provenant de celle-ci. L’espace d’états de jumeaux très semblables peut se chevaucher entièrement alors que des jumeaux différents pourraient avoir des espaces distincts. Le modèle bayésien non paramétrique des auteurs définit ainsi un ensemble creux d’états qui décrivent les données d’EEG. Toutes les époques d’un même participant utilisent le même ensemble d’états, et elles doivent adhérer au même régime de changement d’état pour leur dynamique de commutation selon le MMC, forçant ainsi une similarité intra-participant.

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.

Excessive gaming can lead to gaming disorder, which affects the function and structure of the brain. In order to diagnose gaming disorders, it is of importance to understand how gaming behavior affects brain activity patterns and whether there exist differences in the effects on different individuals. Electroencephalogram (EEG) microstate reflects a transiently stable brain topological structure with spatiotemporal characteristics, and the spatial characteristic microstate classes and temporal parameters provide insight into the brain's functional activities in gaming disorders. In order to explore the feasibility of using EEG microstate parameters as markers of gaming addiction, in the present study, resting-state EEG data were acquired before and after playing video game for 30 minutes from 38 participants, containing gaming disorder individual (GD), recreational game users (RGU), and healthy controls (HC). Our study compared the microstate parameters before and after playing game among three groups. According to the results, the resting-state EEG microstate parameters exert no significant difference before playing game among three groups. However, we found significant difference in microstate B and D parameters among three groups after playing game. Additionally, compared with the pre-gaming, the post-gaming data showed that the variation trend of microstate B and D parameters of the three groups was similar. Moreover, these discoveries provide the first causal evidence of a microstate modification following gaming behavior interference, suggesting that microstates B and D are closely associated with gaming behavior, which can be used as EEG markers for distinguishing gaming addiction.

The use of electroencephalography (EEG) to study overt speech production has increased substantially in the past 15 years and the alignment of evoked potential (ERPs) on the response onset has become an extremely useful method to target “latest” stages of speech production. Yet, response-locked ERPs raise a methodological issue: on which event should the point of alignment be placed? Response-locked ERPs are usually aligned to the vocal (acoustic) onset, although it is well known that articulatory movements may start up to a hundred milliseconds prior to the acoustic onset and that this “articulatory onset to acoustic onset interval” (AAI) depends on the phoneme properties. Given the previously reported difficulties to measure the AAI, the purpose of this study was to determine if the AAI could be reliably detected with EEG-microstates. High-density EEG was recorded during delayed speech production of monosyllabic pseudowords with four different onset consonants. Whereas the acoustic response onsets varied depending on the onset consonant, the response-locked spatiotemporal EEG analysis revealed a clear asynchrony of the same sequence of microstates across onset consonants. A specific microstate, the latest observed in the ERPs locked to the vocal onset, presented longer duration for phonemes with longer acoustic response onsets. Converging evidences seemed to confirm that this microstate may be related to the articulatory onset of motor execution: its scalp topography corresponded to those previously associated with muscle activity and source localization highlighted the involvement of motor areas. Finally, the analyses on the duration of such microstate in single trials further fit with the AAI intervals for specific phonemes reported in previous studies. These results thus suggest that a particular ERP-microstate is a reliable index of articulation onset and of the AAI. Electronic supplementary material The online version of this article (10.1007/s10548-020-00803-3) contains supplementary material, which is available to authorized users.

Re-activations of task-dependent patterns of neural activity take place during post-encoding periods of wakeful rest and sleep. However, it is still unclear how the temporal dynamics of brain states change during these periods, which are shaped by prior conscious experiences. Here, we examined the very brief periods of wakeful rest immediately after encoding and recognition of auditory and visual stimuli, by applying the EEG microstate analysis, in which the global variance of the EEG is explained by only a few prototypical topographies. We identified the dominant brain states of sub-second duration during the tasks-dependent periods of rest, finding that the temporal dynamics-represented here by two temporal parameters: the frequency of occurrence and the fraction of time coverage-of three task-related microstate classes changed compared to wakeful rest. This study provides evidence of experience-dependent temporal changes in post-encoding periods of resting brain activity, which can be captured using the EEG microstates approach.

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.

Recognition of simple shapes and numerosity estimation for small quantities are often studied independently of each other, but we know that these processes are both rapid and accurate, suggesting that they may be mediated by common neural mechanisms. Here we address this issue by examining how spatial configuration, shape complexity, and luminance polarity of elements affect numerosity estimation. We directly compared the Event Related Potential (ERP) time-course for numerosity estimation under shape and random configurations and found a larger N2 component for shape over lateral-occipital electrodes (250–400 ms), which also increased with higher numbers. We identified a Left Mid Frontal (LMF; 400–650 ms) component over left-lateralised medial frontal sites that specifically separated low and high numbers of elements, irrespective of their spatial configuration. Different luminance-polarities increased N2 amplitude only, suggesting that shape but not numerosity is selective to polarity. Functional microstates confined numerosity to a strict topographic distribution occurring within the LMF time-window, while a microstate responding only to shape-configuration was evidenced earlier, in the N2 time-window. We conclude that shape-coding precedes numerosity estimation, which can be improved when the number of elements and shape vertices are matched. Thus, numerosity estimation around the subitizing range is facilitated by a shape-template matching process.

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.

Spontaneous broadband electroencephalography (EEG) demonstrates short moments of stability in the spatial distribution of the head-surface voltage topography. This phenomenon underlies the premise behind segmenting multichannel EEG into topographically defined brain states, known as EEG microstates. Microstate segmentation methods commonly identify representative topographical configurations based on clustering applied to a subset of voltage maps selected at the time series points of greatest strength in the neuroelectric field. These moments are well-reasoned to best represent periods of momentary stability in the voltage topography, and consequently, points of greatest signal relative to noise. Yet, more direct empirical evidence for these assumptions is warranted, and the consistency of this phenomenon across individuals has not been characterized. In the present investigation, the association between electric field strength and topographic dissimilarity of temporally adjacent samples of EEG were characterized in a large sample of healthy adults engaged in quiet rest. Samples of individuals' EEG time series high in electric field strength were found to be topographically similar relative to adjacent time series samples. The strong phase-synchronized actvity of neuronal populations therefore coincides with momentary stability in the topographic voltage configuration, providing robust empirical support for the basic premise underlying segmentation of broadband EEG into microstates.

Objective: We aimed to study the effects of EEG examination and ABA-therapy on resting-state EEG in children with low-functioning autism and tactile defensiveness. Methods: We have performed this study with three cohorts of preschoolers: children with autistic spectrum disorder (ASD) who needed applied behavior analysis (ABA) therapy due to their tactile defensiveness; children with ASD who didn’t need ABA therapy; and the control group of healthy children. Number of microstates was determined in the initial and final parts of the resting-state EEGs. Results and conclusions: Children with higher tactile defensiveness for the most part had specific EEG microstates associated with unpleasant emotions and senses. The EEG microstates of children with ASD who did not need ABA therapy, had more similarities with the EEG microstates of typically developing children except for temporary changes. Meanwhile, the children with tactile defensiveness demonstrated typical patterns of EEG microstates from start to finish of the procedure.

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., EEG microstate abnormalities have been reported in patients with schizophrenia. Here the authors demonstrate that patients and their siblings show similar microstate abnormalities compared to healthy controls.

Electroencephalography (EEG) microstates (MSs) are defined as quasi-stable topographies that represent global coherent activation. Alternations in EEG MSs have been reported in numerous neuropsychiatric disorders. Transferring the results of these studies into clinical practice requires not only high reliability but also sufficient individual specificity. Nevertheless, whether the amount of data used in microstate analysis influences reliability and how much individual information is provided by EEG MSs are unclear. In the current study, we aimed to assess the within-subject consistency and between-subject differences in the characteristics of EEG MSs. Two sets of eyes-closed resting-state EEG recordings were collected from 54 young, healthy participants on two consecutive days. The Raven Advanced Progressive Matrices test was conducted to assess general fluid intelligence (gF). We obtained four MSs (labeled A, B, C and D) through EEG microstate analysis. EEG MS characteristics including traditional features (the global explained variances, mean durations, coverages, occurrences and transition probabilities), the Hurst exponents and temporal dynamic features (the autocorrelation functions and the partial autocorrelation functions) were calculated and evaluated. The data with a duration greater than 2 min showed moderate to high reliability and individual specificity. The mean duration and coverage of MS C were significantly correlated with the gF score. The dynamic features showed a higher identification accuracy and were more significantly correlated with gF than the traditional MS features. These findings reveal that EEG microstate characteristics are reliably unique in single subjects and possess abundant inter-individual variability.

The pKa is the standard measure used to describe the aqueous proton affinity of a compound, indicating the proton concentration (pH) at which two protonation states (e.g. A− and AH) have equal free energy. However, compounds can have additional protonation states (e.g. AH2+), and may assume multiple tautomeric forms, with the protons in different positions (microstates). Macroscopic pKas give the pH where the molecule changes its total number of protons, while microscopic pKas identify the tautomeric states involved. As tautomers have the same number of protons, the free energy difference between them and their relative probability is pH independent so there is no pKa connecting them. The question arises: What is the best way to describe protonation equilibria of a complex molecule in any pH range? Knowing the number of protons and the relative free energy of all microstates at a single pH, ∆G°, provides all the information needed to determine the free energy, and thus the probability of each microstate at each pH. Microstate probabilities as a function of pH generate titration curves that highlight the low energy, observable microstates, which can then be compared with experiment. A network description connecting microstates as nodes makes it straightforward to test thermodynamic consistency of microstate free energies. The utility of this analysis is illustrated by a description of one molecule from the SAMPL6 Blind pKa Prediction Challenge. Analysis of microstate ∆G°s also makes a more compact way to archive and compare the pH dependent behavior of compounds with multiple protonatable sites.

This study aims to reveal dynamic brain networks during speech perception. All male subjects were presented five English vowel [a], [e], [i], [o], and [u] stimuli. Brain dynamics were decoded using multivariate Gaussian hidden Markov model (MGHMM), which trained on spatiotemporal patterns of broadband multivariate event-related potential amplitudes to identify distinct broadband EEG microstates (MS), microstate source imaging, and microstate functional connectivity (μFC). Obtained results showed fluctuated cortical generators and μFC in eight microstates throughout the perception. Microstate source imaging revealed involvements of bilateral (left-side dominance) posterior superior temporal cortex (TC), inferior frontal gyrus (IFG), and supramarginal regions in perception. Precentral cortex where primary motor cortex located was also significantly activated. These regions were early appeared at 96-151 ms (left-side dominance) and at 186-246 ms (left hemisphere only) after the stimuli onset. Results from μFC revealed significant increases in delta (2.5-4.5 Hz), theta (4.5-8.5 Hz), alpha (12.5-14.5 Hz), beta (22.5-24.5 Hz), low gamma (30.5-32.5, 38.5-40.5 Hz) but decreases in high gamma (42.5-46.5 Hz) bands in perception. Increased FC were observed mainly at; (1) microstate segments 34-95 ms (MS2) and 96-151 ms (MS3) in early stages, (2) microstate intervals 186-246 ms (MS5) and 297-449 ms (MS6) in subsequent stages of perception. We found that stronger statistical FC differences in perception at TCs, with respect to left IFG (Broca' area), left TC, and precentral areas. Furthermore, by conducting a comparative protocol measuring FC distinction degree, we showed performance improvements of 8.01% (p-value = 0.0162), 14.41% (pvalue = 0.006) when compared MGHMM to well established Lehmann-based modified K-means, Atomize and Agglomerative Hierarchical Clustering and 8.791% (p-value = 0.0097) over the combination of K-means and sliding window methods, respectively. This study indicates the usefulness of EEG microstates to investigate broadband brain dynamics in speech perception. The current findings based on male subjects would be generalized more by future studies with a larger appropriate sample size including female subjects.

In recent years, EEG microstate analysis has gained popularity as a tool to characterize spatio-temporal dynamics of large-scale electrophysiology data. It has been used in a wide range of EEG studies and the discovered microstates have been linked to cognitive function and brain diseases. EEG microstates are assumed to (1) be winner-take-all, meaning that the topography at any given time point is in one state; and (2) discretely transition from one state into another. In this study we investigated these assumptions by taking a geometric perspective of EEG data, treating microstate topographies as basis vectors for a subspace of the original channel space. We found that within- and across-microstate distance distributions were largely overlapping: for the low GFP (Global Field Power) range (lower 15%), individual time points labeled as one microstate are often equidistant to multiple microstate vectors, challenging the winner-take-all assumption. At high global field power, separability of microstates improved, but remained rather weak. Although many GFP peaks (which are the time points used for defining microstates) occur during high GFP ranges, low GFP ranges associated with poor separability also contain GFP peaks. Furthermore, the geometric analysis suggested that microstates and their transitions appear to be more continuous than discrete. The Analysis of rate of change of trajectory in sensor space suggests gradual microstate transitions as opposed to the classical binary view of EEG microstates. Taken together, our findings suggest that EEG microstates are better conceptualized as spatially and temporally continuous, rather than discrete activations of neural populations.

The present study aimed to characterize microstate dynamics induced by non-reactive attention underlying mindfulness. Electroencephalogram signals from eighteen trained meditators and a matched non-meditators group were recorded before, during, and after a non-reactive attention meditation or during three resting periods respectively, while they were passively exposed to auditory stimulation. In a multimodal approach, microstate cluster decompositions, personality trait questionnaires, phenomenological ratings, and microstates sources localization were analyzed. Our results revealed that temporal parameters of microstates A and C at rest were negatively correlated to mindfulness traits across all participants. After meditation, the frequency of microstate A and C was decreased while microstate B was of longer duration, in meditators. Source localization analysis revealed that the non-reactive trait effect on microstate C at rest was explained by a modified activity of the salience network (identified by the anterior cingulate cortex, thalamus, and insula), while the non-reactive attentional state effect relied on a contribution of (anterior and posterior) cerebellum during meditation. Our results suggest that decreased microstates A and C reflect decreased mental state reactivity, while the increased microstate B relies on attention stability. These findings strongly encourage more research to assess the use of the microstate temporal parameters as a biomarker of the salience network activity, as well as objectify the brain changes induced by non-reactive attention training.HIGHLIGHTThe present study aimed to characterize microstate dynamics induced by non-reactive attention meditation, by the use of multimodal analysis including EEG microstate clusters decompositions, personality trait questionnaires, phenomenological reports, and source localization analysis.The occurrence of microstate A, recognized to be related to phonological processing and depressive disorders, was negatively correlated to mindfulness trait and was decreased after non-reactive attention meditation.The duration of microstate B, generally associated with the visual system, increases after meditation, in particular in meditators with a high non-reactivity trait.Temporal parameters of microstate C, recognized to be related to default mode, were negatively correlated to the non-reactivity trait of meditators and were decreased after non-reactive attention meditation. Source analysis revealed that these trait and state effects reflect modified activities of the salient network.

In carefully designed experiments, cognitive scientists interpret the mean event-related potentials (ERP) in terms of cognitive operations. However, the huge signal variability from one trial to the next, questions the representability of such mean events. We explored here whether this variability is an unwanted noise, or an informative part of the neural response. We took advantage of the rapid changes in the visual system during human infancy and analyzed the variability of visual responses to central and lateralized faces in 2-to 6-month-old infants and adults using high-density electroencephalography (EEG). We observed that neural trajectories of individual trials always remain very far from ERP components, only moderately bending their direction with a substantial temporal jitter across trials. However, single trial trajectories displayed characteristic patterns of acceleration and deceleration when approaching ERP components, as if they were under the active influence of steering forces causing transient attraction and stabilization. These dynamic events could only partly be accounted for by induced microstate transitions or phase reset phenomena. Furthermore, these structured modulations of response variability, both between and within trials, had a rich sequential organization, which, in infants, was modulated by the task difficulty. Our approaches to characterize Event Related Variability (ERV) expand and reinterpret classic ERP analyses, making them compliant with pervasive neural variability and providing a more faithful description of neural events following stimulus presentation.

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

It is commonly believed that the stream of consciousness is not continuous but 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 method that estimates 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.HighlightsEEG microstates capture discrete spatiotemporal patterns of global neuronal activityWe studied their temporal dynamics in relation to different states of consciousnessWe introduce a new method to estimate the complexity of microstates sequencesWith moderate sedation complexity increases then decreases with full sedationComplexity of microstate sequences is sensitive to altered states of consciousness

We construct a family of non-supersymmetric extremal black holes and their horizonless microstate geometries in four dimensions. The black holes can have finite angular momentum and an arbitrary charge-to-mass ratio, unlike their supersymmetric cousins. These features make them and their microstate geometries astrophysically relevant. Thus, they provide interesting prototypes to study deviations from Kerr solutions caused by new horizon-scale physics. In this paper, we compute the gravitational multipole structure of these solutions and compare them to Kerr black holes. The multipoles of the black hole differ significantly from Kerr as they depend non-trivially on the charge-to-mass ratio. The horizonless microstate geometries have the same multipoles as their corresponding black hole, with small deviations set by the scale of their microstructure., Comment: v2: 26 pages + Appendices, 8 figures, revision of the solutions in section 4.4

ADHD have been associated with social cognitive impairments across the lifespan, but no studies have specifically addressed the presence of abnormalities in eye-gaze processing in the adult brain.This study investigated the neural basis of eye-gaze perception in adults with ADHD using event-related potentials (ERP). Twenty-three ADHD and 23 controls performed a delayed face-matching task with neutral faces that had either direct or averted gaze. ERPs were classified using microstate analyses.ADHD and controls displayed similar P100 and N170 microstates. ADHD was associated with cluster abnormalities in the attention-sensitive P200 to direct gaze, and in the N250 related to facial recognition. For direct gaze, source localization revealed reduced activity in ADHD for the P200 in the left/midline cerebellum, as well as in a cingulate-occipital network at the N250. These results suggest brain impairments involving eye-gaze decoding in adults with ADHD, suggesting that neural deficits persist across the lifespan.

Emotions dynamically change in response to ever-changing environments. It is of great importance, both clinically and scientifically, to investigate the neural representation and evoking mechanism of emotion dynamics. But, there are many unknown places in this stream of research, such as consistent and conclusive findings are still lacking. In this work, we perform an in-depth investigation of emotion dynamics under a video-watching task by gauging the dynamic associations among evoked emotions, electroencephalography (EEG) responses, and multimedia stimulation. Here, we introduce EEG microstate analysis to study emotional EEG signals, which provides a spatial-temporal neural representation of emotion dynamics. To investigate the temporal characteristics of evoking emotions during video watching with its neural mechanism, we conduct two studies from the perspective of EEG microstates. In Study 1, the dynamic microstate activities under different emotion states and emotion levels are explored to identify EEG spatial-temporal correlates of emotion dynamics. In Study 2, the stimulation effects of multimedia content (visual and audio) on EEG microstate activities are examined to learn about the involved affective information and investigate the emotion-evoking mechanism. The results show that emotion dynamics could be well reflected by four EEG microstates (MS1, MS2, MS3, and MS4). Specifically, emotion tasks lead to an increase in MS2 and MS4 coverage but a decrease in MS3 coverage, duration, and occurrence. Meanwhile, there exists a negative association between valence and MS4 occurrence as well as a positive association between arousal and MS3 coverage and occurrence. Further, we find that MS4 and MS3 activities are significantly affected by visual and audio content, respectively. In this work, we verify the possibility to reveal emotion dynamics through EEG microstate analysis from sensory and stimulation dimensions, where EEG microstate features are found to be highly correlated to different emotion states (emotion task effect and level effect) and different affective information involved in the multimedia content (visual and audio). Our work deepens the understanding of the neural representation and evoking mechanism of emotion dynamics, which can be beneficial for future development in the applications of emotion decoding and regulation.

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.

Schizophrenia is a complex and devastating disorder with unclear pathogenesis. Electroencephalogram (EEG) microstates have been suggested as a potential endophenotype for this disorder. However, no clear dynamic pattern of microstates has been found. This study aims to identify the dynamics of EEG microstates in schizophrenia and to test whether schizophrenia patients with altered clinical symptoms severity showed different microstates abnormalities compared with healthy controls. Resting-state EEG data in 46 individuals who met the ICD-10 diagnostic criteria for schizophrenia and 39 healthy controls was recorded. The patients with schizophrenia were divided into subgroups based on the level of their negative or positive symptoms assessed using the Positive and Negative Syndrome Scale. Microstate parameters (contribution, occurrence, and duration) of four prototypical microstate classes (A–D) were investigated. Compared with healthy controls, individuals with schizophrenia showed increased duration and contribution of microstate class C, decreased contribution and occurrence of microstate class B. Different microstate patterns were found between subgroups and healthy controls. Results in this study support the consistent observation of abnormal EEG microstates patterns in patients with schizophrenia and highlight the necessity to divide subjects into subgroups according to their clinical symptoms.

Objective: Rapid eye movement (REM) sleep behavior disorder (RBD) is a disease characterized by dream enacting behavior and is now commonly believed to be a harbinger to alpha-synucleinopathy diseases such as dementia with Lewy bodies, Parkinson's disease, and multiple system atrophy. The aim of this study was to explore the quasi-stable topological structure of the brain in RBD by analyzing resting-state electroencephalography (EEG) microstates.Methods: We enrolled 22 participants with RBD and 46 healthy controls (HCs) with age and gender-matched. After the resting-state EEG recordings were acquired, EEG microstate features were analyzed to assess the functional networks of all participants.Results: Significant differences in the brain topological structure and temporal characteristics of sub-second brain activity were identified between the RBD and HCs. The RBD group had a shorter average duration of microstate A and microstate D when compared with HCs, and microstate B contributed more, while microstate D contributed significantly less to the RBD group. Furthermore, the average duration and proportion of microstate D were negatively correlated with the RBD questionnaire Hong Kong (RBDQ-HK) score.Conclusion: The result of this study indicates that the microstate dynamics is disturbed in RBD, which might jeopardize the flexibility and adaptability of the brain. Microstates are potential biomarkers to explore the early electrophysiological abnormality of alpha-synucleinopathy diseases.

EEG microstate analysis is a useful approach for studying brain states - nicknamed ‘atoms of thought’ - 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 into the cortical mechanisms underpinning these states. In this study, 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 MEG microstates were associated with distinct functional connectivity patterns. Using a passive auditory paradigm, we further demonstrated that the occurrence probability of MEG microstates were altered by evoked auditory responses, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of MEG source-level microstates as a data-driven method for investigating brain dynamic activity and connectivity at the millisecond scale.

The present paper introduces a new approach to the dynamics of a particle system, split into two distinct microstates diffusing in a homogeneous medium. The particles belonging to the main microstate spread according to the classical Fick’s law and the complementary set moves excited by a new potential. Each set is associated with a particular energy level. The particles can move between the two sets, introducing a third flux which is internal to the system. The governing equation is a fourth-order PDE containing two new parameters, which can be time-dependent functions, in addition to the classical diffusion constant. It is shown that the solutions can avoid violations of the mass conservation requirements.

We study interpolation between two multi-center microstate geometries in 4d/5d that represent Lunin-Mathur geometries with circular profiles. The interpolating solution is a Lunin-Mathur geometry with a helical profile, and is represented by a 2-center solution with a codimension-2 source. The interpolating 2-center solution exhibits interesting features such as some of the charges being delocalized, and some of the charges getting transferred from the codimension-2 center to the other, codimension-3 center as the interpolation proceeds. We also discuss the spectral flow of this entire process and speculate on the relevance of such solutions to understanding general microstates of 3-charge black holes., 30 pages + appendices

Wason's selection task (WST) as a representative of the field of conditional proposition testing has been explored by multiple disciplines for more than 50 years, but the neural basis of its key falsification thinking remains unclear. Considering that the accuracy of individuals in WST has stability over time, we believe that falsification thinking has a specific brain structural basis and intrinsic neural characteristics. To test this hypothesis, we studied individuals who were able to complete the WST using T1-weighted MRI (using voxel-based morphology (VBM) analysis) and resting electroencephalogram (EEG) (using microstate analysis, which can reflect stable cognitive characteristics of individuals) techniques. First, VBM analysis found that, compared with the verification group, the gray matter volume (GMV) of the left inferior temporal gyrus and the right superior temporal region of the falsification group was larger, whereas the GMV in the cerebellum of the verification group was significantly larger than that of the falsification group. Subsequently, the results of the microstate analysis of the resting EEG data showed that the contribution of class A of the falsification group, which is closely related to the language network, is significantly higher than that of the verification group. Our structural MRI and resting EEG results consistently show that the structure and intrinsic activity pattern of the temporal lobe in individuals with falsification thinking are specific. Furthermore, the findings may provide potential insights into the role of the temporal lobe (which is also a brain region of language processing) in thought.

Background: The difficulty in timely evaluating patient response to antidepressants has brought great challenge to the treatment of major depressive disorder (MDD). Some studies found that the electroencephalogram (EEG) microstates might be a reliable marker to evaluate patient response to treatment. The present study aims to evaluate the relationship between EEG microstate parameters and MDD symptoms before and after treatment to identify predictive biological markers for patient response.Methods: Thirty drug-naïve MDD patients (20 females and 10 males) were enrolled in this study. All the patients received effective dosages of selective serotonin reuptake inhibitors, and EEG recordings were collected at baseline and 2 weeks of treatment. Brain activities during the eyes-closed state were recorded using 64-channel electroencephalography, and the patients' microstates were clustered into four maps according to their topography (labeled A, B, C, and D). The differences of EEG microstates before and after treatment were compared using paired t-test. Spearman correlation coefficients were calculated to identify the relationships between the improvement of depression and anxiety symptoms and microstate parameters.Results: The mean duration (69.67 ± 10.33 vs. 64.00 ± 7.70, p < 0.001) and occurrence (4.06 ± 0.69, vs. 3.69 ± 0.70, p = 0.002) of microstate B decreased significantly after treatment. The proportion of microstate B also decreased (27.53 ± 5.81, vs. 23.23 ± 4.61, p < 0.001), while the occurrence of microstate A increased after treatment. A significant negative correlation was found between the change of score of Hamilton Rating Scale for Anxiety and the increase of the occurrence of microstate A (r = −0.431, p < 0.05) after 2 weeks of treatment. The reduction of the duration of microstate B was found to be predictive of patient response to antidepressants after 3 months.Conclusion: This study explored the relationship between changes of EEG microstates and patient response to antidepressants. Depression symptoms might be associated with the duration of microstate B and anxiety symptoms related to the occurrence of microstate A. Therefore, the duration of microstate B and the occurrence of microstate A are potential biological markers for MDD patients' early response and further clinical outcomes.

Music is being studied related to either its impact on the psychological interaction or cognitive process behind it. These examinations bring out music's coordination to numerous disciplines including neuroscience. A few past examinations exhibited the contrast among musicians and non-musicians regarding brain structure and brain activity. The current investigation exhibited the diverse brain activation while musicians tuned in to music with regards to their musical experiences utilizing microstate classes method analysis. The investigation intended to determine electroencephalography microstate changes in Karawitan musicians' brain while tuning in to Gendhing Lancaran. Applying the electroencephalography microstate investigation of Karawitan musicians, the occurrence parameters was computed for four microstate classes (A, B, C, and D). Microstate properties were compared among subjects and correlated to Gendhing Lancaran perception. The present results revealed that Karawitan musicians' brain were characterized by microstate classes with the increased prominence of classes A, B, and D, but decreased prominence of classes C while tuning in to Gendhing Lancaran. Our finding is the first study to identify the typical microstate characteristics of the Karawitan musician’s brains while tuning in to Gendhing Lancaran by using the microstate segmentaion method.

Electroencephalogram (EEG) microstate analysis is an important emerging method that can classify continuous multichannel EEG signals into a limited number of microstates through clustering. Microstate analysis combines the time and space information of EEG, which can reflect important transformation process of high-level cognitive functions in the brain. In recent years, Microstate analysis has made great progress in the research of depth of anesthesia (DOA) monitoring. In this paper, a new DOA monitoring algorithm is designed by combining microstate sequence and hidden Markov model (HMM). The trained Hidden Markov Model shows the information of brain nerve activity hidden in the microstate sequence, which can effectively distinguish the mental states of different DOAs, thereby realizing the corresponding DOA classification. The experimental dataset was obtained from an open-access section of the University of Cambridge Data Repository, which contains EEG data from 20 healthy subjects. During propofol injection, the brain states of the subjects were divided into four conditions: baseline (BS), mild sedation (ML), moderate sedation (MD), and the recovery stage (RC). The algorithm classified BS and ML, BS and MD, ML and MD with the accuracy rates of 71.40%, 73.48%, 67.75% respectively. This shows that the microstate analysis has great application potential in the study of anesthesia. Hidden Markov model training for microstate sequences can become a new research direction for DOA monitoring.

+microstate is a MATLAB toolbox for brain functional microstate analysis. It builds upon previous EEG microstate literature and toolboxes by including algorithms for microstate analysis for other neuroimaging modalities such as sensor-space MEG and source-space data. +microstate includes codes for performing individual- and group-level brain microstate analysis in resting-state and task-based data including event-related potentials/fields. Functions are included to visualise and perform statistical analysis of microstate sequences, including novel advanced statistical approaches such as microstate-segmented functional connectivity analysis, cluster-permutation topographic ANOVAs, and χ2 analysis of microstate probabilities in response to stimuli. Additionally, codes for simulating microstate sequences and their associated M/EEG data are included in the toolbox, which can be used to generate artificial data with ground truth microstates and to validate the methodology. +microstate integrates with widely used toolboxes for M/EEG processing including Fieldtrip, SPM, LORETA/sLORETA, EEGLAB, and Brainstorm to aid with accessibility, and includes wrappers for pre-existing toolboxes for brain-state estimation such as Hidden Markov modelling (HMM-MAR) and independent component analysis (FastICA) to aid with direct comparison with these techniques. In this paper, we first introduce +microstate before subsequently performing example analyses using open access datasets to demonstrate and validate the methodology. MATLAB live scripts for each of these analyses are included in +microstate, to act as a tutorial and to aid with reproduction of the results presented in this manuscript.

Background Electrophysiological markers of prodromal dementia with Lewy bodies were described in the spectral domain. The sub-second temporal resolution may provide additional information. Objective To evaluate electroencephalography (EEG) microstates in patients with mild cognitive impairment with Lewy bodies and to assess the association between their temporal dynamics and the spectral marker. Methods Temporal parameters of microstates were compared between 21 patients with mild cognitive impairment with Lewy bodies and 21 healthy controls. The dominant alpha frequency was correlated with microstate parameters. Results Microstates A-D showed higher occurrence in the patient group. Microstate B additionally revealed shorter mean duration and increased time coverage; its occurrence correlated with the dominant alpha frequency in the patient group. Conclusions Temporal dynamics of all EEG microstates were altered in medication-naive subjects with prodromal dementia with Lewy bodies. Longitudinal follow-up may reveal how EEG microstates reflect progression of brain function deficits and effects of treatment manipulations. © 2021 International Parkinson and Movement Disorder Society.

Patients with schizophrenia present with various symptoms related to different domains. Abnormalities of auditory and visual perception are parts of a more general problem. Nevertheless, the relationship between the lifetime history of auditory verbal hallucination (AVH), one of the most prevalent symptoms in schizophrenia, and visuospatial deficits remains unclear. This study aimed to investigate differences in hemispheric involvement and visuospatial processing between healthy controls (HCs) and schizophrenia patients with and without AVHs. HCs (N = 20), schizophrenia patients with AVH (AVH group, N = 16), and schizophrenia patients without hallucinations (NH group, N = 10) participated in a 4-choice reaction task with lateralized stimuli. An event-related potential (ERP)-microstate approach was used to analyze ERP differences between the conditions and groups. The schizophrenia patients without hallucinations had slower responses than the HCs. An early visual N1 contralateral to stimulation side was prominent in all groups of participants but with decreased amplitude in the patients with schizophrenia, especially in the AVH group over the right hemisphere. The amplitude of P3b, a cognitive evaluation component, was also decreased in schizophrenia. Compared to AVH and HC groups, the patients in the NH group had altered microstate patterns: P3b was replaced by a novelty component, P3a. Although the difference between both patient groups was only based on the presence of AVHs, our findings indicated that patients had specific visuospatial deficits associated with a lifetime history of hallucinations: patients with AVHs showed early visual component alterations in the right hemisphere, and those without AVHs had more prominent visuospatial impairment.

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.

We study the BPS solutions of the asymptotically flat, stationary microstate geometries with bi-axisymmetry and reflection symmetry in the five-dimensional ungauged minimal supergravity. We show that the angular momenta of the microstate geometry with a small number of centers (at least, five centers) have lower bounds, which are slightly smaller than those of the maximally spinning BMPV black hole. Therefore, there exists a certain narrow parameter region such that the microstate geometry with a small number of centers admits the same angular momenta as the BMPV black hole. Moreover, we investigate the dependence of the topological structure of the evanescent ergosurfaces on the magnetic fluxes through the 2-circles between two centers., 27 pages, 12 figures, TTI-MATHPHYS-5, some errors are corrected, to be published in PRD

In electroencephalography (EEG), microstates are distributions of activity across the scalp that persist for several tens of milliseconds before changing into a different pattern. Microstate analysis is a way of utilizing EEG as both temporal and spatial imaging tool, but has rarely been applied to task-based data. This study aimed to conceptually replicate microstate findings of valence and emotional arousal processing and investigate the effects of emotion regulation on microstates, using data of an EEG paradigm with 107 healthy adults who actively viewed emotional pictures, cognitively detached from them, or suppressed facial reactions. Within the first 600 ms after stimulus onset only the comparison of viewing positive and negative pictures yielded significant results, caused by different electrodes depending on the microstate. Since the microstates associated with more and less emotionally arousing pictures did not differ, sequential processing could not be replicated. When extending the analysis to 2000 ms after stimulus onset, differences were exclusive to the comparison of viewing and detaching from negative pictures. Intriguingly, we observed the novel phenomenon of a microstate difference that could not be attributed to single electrodes. This suggests that microstate analysis can detect differences beyond those detected by event-related potential analysis.

Objective: Idiopathic generalized epilepsy (IGE) involves aberrant organization and functioning of large-scale brain networks. This study aims to investigate whether the resting-state EEG microstate analysis could provide novel insights into the abnormal temporal and spatial properties of intrinsic brain activities in patients with IGE.Methods: Three groups of participants were chosen for this study (namely IGE-Seizure, IGE-Seizure Free, and Healthy Controls). EEG microstate analysis on the resting-state EEG datasets was conducted for all participants. The average duration (“Duration”), the average number of microstates per second (“Frequency”), as well as the percentage of total analysis time occupied in that state (“Coverage”) of the EEG microstate were compared among the three groups.Results: For microstate classes B and D, the differences in Duration, Frequency, and Coverage among the three groups were not statistically significant. Both Frequency and Coverage of microstate class A were statistically significantly larger in the IGE-Seizure group than in the other two groups. The Duration and Coverage of microstate class C were statistically significantly smaller in the IGE-Seizure group than those in the other two groups.Conclusions: The Microstate class A was regarded as a sensorimotor network and Microstate class C was mainly related to the salience network, this study indicated an altered sensorimotor and salience network in patients with IGE, especially in those who had experienced seizures in the past 2 years, while the visual and attention networks seemed to be intact.Significance: The temporal dynamics of resting-state networks were studied through EEG microstate analysis in patients with IGE, which is expected to generate indices that could be utilized in clinical researches of epilepsy.

Recently an exact worldsheet description of strings propagating in certain black hole microstate geometries was constructed in terms of null-gauged WZW models. In this paper we consider a family of such coset models, in which the currents being gauged are specified by a set of parameters that a priori take arbitrary values. We show that consistency of the spectrum of the worldsheet CFT implies a set of quantisation conditions and parity restrictions on the gauging parameters. We also derive these constraints from an independent geometrical analysis of smoothness, absence of horizons and absence of closed timelike curves. This allows us to prove that the complete set of consistent backgrounds in this class of models is precisely the general family of (NS5-decoupled) non-BPS solutions known as the JMaRT solutions, together with their various (BPS and non-BPS) limits. We clarify several aspects of these backgrounds by expressing their six-dimensional solutions explicitly in terms of five non-negative integers and a single length-scale. Finally we study non-trivial two-charge limits, and exhibit a novel set of non-BPS supergravity solutions describing bound states of NS5 branes carrying momentum charge., 43 pages, v3: typos corrected