Brain Microstates Research Articles

Brain microstate spatio-temporal dynamics as a candidate endotype of consciousness

Consciousness can be defined as a phenomenological experience continuously evolving. Current research showed how conscious mental activity can be subdivided into a series of atomic brain states converging to a discrete spatiotemporal pattern of global neuronal firing. Using the high temporal resolution of EEG recordings in patients with a severe Acquired Brain Injury (sABI) admitted to an Intensive Rehabilitation Unit (IRU), we detected a novel endotype of consciousness from the spatiotemporal brain dynamics identified via microstate analysis. Also, we investigated whether microstate features were associated with common neurophysiological alterations. Finally, the prognostic information comprised in such descriptors was analysed in a sub-cohort of patients with prolonged Disorder of Consciousness (pDoC). Occurrence of frontally-oriented microstates (C microstate), likelihood of maintaining such brain state or transitioning to the C topography and complexity were found to be indicators of consciousness presence and levels. Features of left–right asymmetric microstates and transitions toward them were found to be negatively correlated with antero-posterior brain reorganization and EEG symmetry. Substantial differences in microstates’ sequence complexity and presence of C topography were found between groups of patients with alpha dominant background, cortical reactivity and antero-posterior gradient. Also, transitioning from left-right to antero-posterior microstates was found to be an independent predictor of consciousness recovery, stronger than consciousness levels at IRU’s admission. In conclusions, global brain dynamics measured with scale-free estimators can be considered an indicator of consciousness presence and a candidate marker of short-term recovery in patients with a pDoC.

Brain network mechanism of acupuncture for chronic spontaneous urticaria: a functional magnetic resonance imaging study protocol.

Chronic spontaneous urticaria (CSU) is a common skin condition that can significantly impact patients' quality of life. Although studies have demonstrated the efficacy of acupuncture in treating CSU, the underlying mechanisms remain unclear. Dysfunction within the brain's default mode network (DMN) represents a fundamental characteristic of central pathological changes associated with CSU. Therefore, it is hypothesized that improving brain network dysfunction could serve as a key mechanism through which acupuncture exerts its therapeutic effects. This study aims to provide evidence supporting this hypothesis. This study, a parallel, randomized, sham-controlled functional neuroimaging investigation will be conducted in China. We aim to enroll 50 patients with CSU and 25 healthy controls, distributing them evenly between the acupuncture and sham acupuncture groups in a 1:1 ratio. The total observation period will span 6 weeks, including 2 weeks designated for the baseline phase and 4 weeks allocated for the clinical treatment phase. Prior to treatment, all participants will undergo magnetic resonance scanning, clinical index detection, and microbiota collection. Following treatment, the patients with CSU will be retested for these indicators. Using resting-state functional connectivity (rsFC) analysis, dynamic Functional Connection (dFC) analysis, and brain microstate extraction technology combined with correlation analysis of microbiota and clinical indicators, the regulatory mechanism of acupuncture on the brain network of CSU will be evaluated from multiple dimensions. This trial was approved by the Biomedical Ethics Review Committee of the West China Hospital, Sichuan University (No. 2022-1255). Each participant will provide written informed consent to publish any potentially identifiable images or data.Clinical trial registrationhttps://www.chictr.org.cn/, identifier: ChiCTR2200064563.

Towards a dynamical understanding of microstate analysis of M/EEG data

One of the interesting aspects of EEG data is the presence of temporally stable and spatially coherent patterns of activity, known as microstates, which have been linked to various cognitive and clinical phenomena. However, there is still no general agreement on the interpretation of microstate analysis. Various clustering algorithms have been used for microstate computation, and multiple studies suggest that the microstate time series may provide insight into the neural activity of the brain in the resting state. This study addresses two gaps in the literature. Firstly, by applying several state-of-the-art microstate algorithms to a large dataset of EEG recordings, we aim to characterise and describe various microstate algorithms. We demonstrate and discuss why the three “classically” used algorithms ((T)AAHC and modified K-Means) yield virtually the same results, while HMM algorithm generates the most dissimilar results. Secondly, we aim to test the hypothesis that dynamical microstate properties might be, to a large extent, determined by the linear characteristics of the underlying EEG signal, in particular, by the cross-covariance and autocorrelation structure of the EEG data. To this end, we generated a Fourier transform surrogate of the EEG signal to compare microstate properties. Here, we found that these are largely similar, thus hinting that microstate properties depend to a very high degree on the linear covariance and autocorrelation structure of the underlying EEG data. Finally, we treated the EEG data as a vector autoregression process, estimated its parameters, and generated surrogate stationary and linear data from fitted VAR. We observed that such a linear model generates microstates highly comparable to those estimated from real EEG data, supporting the conclusion that a linear EEG model can help with the methodological and clinical interpretation of both static and dynamic human brain microstate properties.

Spontaneous brain microstates correlate with impaired inhibitory control in internet addiction disorder.

The prevalence of the Internet addiction disorder (IAD) has been on the rise, making it increasingly imperative to explore the neurophysiological markers of it. Using the whole-brain imaging approach of EEG microstate analysis, which treats multichannel EEG recordings as a series of quasi-steady states, similar as the resting-state networks found by fMRI, the present study aimed to investigate the specificity of the IAD in class C of the four canonical microstates. The existing EEG data of 40 participants (N=20 for each group) was used, and correlation between the time parameters of microstate C and the performance of the Go/NoGo task was analyzed. Results suggested that the duration and coverage of class C were significantly reduced in the IAD group as compared to the healthy control (HC) group. Furthermore, the duration of class C had a significant inverse correlation with Go RTs in the IAD group. These results implied that class C might serve as a neurophysiological marker of IAD, helping to understand the underlying neural mechanism of inhibitory control in IAD.

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

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

Electroencephalography Microstate Class D is a Brain Marker of Subjective Sleep Quality for College Students with High Habitual Sleep Efficiency.

Subjective sleep quality is an individual's subjective sleep feeling, and its effective evaluation is the premise of improving sleep quality. However, people with autism or mental disorders often experience difficulties in verbally expressing their subjective sleep quality. To solve the above problem, this study provides a non-verbal and convenient brain feature to assess subjective sleep quality. Reportedly, microstates are often used to characterize the patterns of functional brain activity in humans. The occurrence frequency of microstate class D is an important feature in the insomnia population. We therefore hypothesize that the occurrence frequency of microstate class D is a physiological indicator of subjective sleep quality. To test this hypothesis, we recruited college students from China as participants [N = 61, mean age = 20.84 years]. The Chinese version of the Pittsburgh Sleep Quality Index scale was used to measure subjective sleep quality and habitual sleep efficiency, and the state characteristics of the brain at this time were assessed using closed eyes resting-state brain microstate class D. The occurrence frequency of EEG microstate class D was positively associated with subjective sleep quality (r = 0.32, p < 0.05). Further analysis of the moderating effect showed that the occurrence frequency of microstate class D was significantly and positively correlated with subjective sleep quality in the high habitual sleep efficiency group. However, the relationship was not significant in the low sleep efficiency group (βsimple = 0.63, p < 0.001). This study shows that the occurrence frequency of microstate class D is a physiological indicator of assessing subjective sleep quality levels in the high sleep efficiency group. This study provides brain features for assessing subjective sleep quality of people with autism and mental disorders who cannot effectively describe their subjective feelings.

Event-related potential studies relating to brain microstates, intelligence, and rehabilitation

Event-related potential studies relating to brain microstates, intelligence, and rehabilitation

The Functional Aspects of Resting EEG Microstates: A Systematic Review.

A growing body of clinical and cognitive neuroscience studies have adapted a broadband EEG microstate approach to evaluate the electrical activity of large-scale cortical networks. However, the functional aspects of these microstates have not yet been systematically reviewed. Here, we present an overview of the existing literature and systematize the results to provide hints on the functional role of electrical brain microstates. Studies that evaluated and manipulated the temporal properties of resting-state microstates and utilized questionnaires, task-initiated thoughts, specific tasks before or between EEG session(s), pharmacological interventions, neuromodulation approaches, or localized sources of the extracted microstates were selected. Fifty studies that met the inclusion criteria were included. A new microstate labeling system has been proposed for a comprehensible comparison between the studies, where four classical microstates are referred to as A-D, and the others are labeled by the frequency of their appearance. Microstate A was associated with both auditory and visual processing and links to subjects' arousal/arousability. Microstate B showed associations with visual processing related to self, self-visualization, and autobiographical memory. Microstate C was related to processing personally significant information, self-reflection, and self-referential internal mentation rather than autonomic information processing. In contrast, microstate E was related to processing interoceptive and emotional information and to the salience network. Microstate D was associated with executive functioning. Microstate F is suggested to be a part of the Default Mode Network and plays a role in personally significant information processing, mental simulations, and theory of mind. Microstate G is potentially linked to the somatosensory network.

A Hidden Markov Model reveals magnetoencephalography spectral frequency-specific abnormalities of brain state power and phase-coupling in neuropathic pain

Neuronal populations in the brain are engaged in a temporally coordinated manner at rest. Here we show that spontaneous transitions between large-scale resting-state networks are altered in chronic neuropathic pain. We applied an approach based on the Hidden Markov Model to magnetoencephalography data to describe how the brain moves from one activity state to another. This identified 12 fast transient (~80 ms) brain states including the sensorimotor, ascending nociceptive pathway, salience, visual, and default mode networks. Compared to healthy controls, we found that people with neuropathic pain exhibited abnormal alpha power in the right ascending nociceptive pathway state, but higher power and coherence in the sensorimotor network state in the beta band, and shorter time intervals between visits of the sensorimotor network, indicating more active time in this state. Conversely, the neuropathic pain group showed lower coherence and spent less time in the frontal attentional state. Therefore, this study reveals a temporal imbalance and dysregulation of spectral frequency-specific brain microstates in patients with neuropathic pain. These findings can potentially impact the development of a mechanism-based therapeutic approach by identifying brain targets to stimulate using neuromodulation to modify abnormal activity and to restore effective neuronal synchrony between brain states.

Automated diagnosis of schizophrenia using EEG microstates and Deep Convolutional Neural Network

Schizophrenia is a chronic and debilitating illness that includes a wide range of emotional, social, and cognitive disorders associated with impaired performance. It affects more than 21 million people in the world, with a 2 to 3 times higher mortality rate and a 10 to 20 years shorter life expectancy than a healthy person. There is evidence that an early diagnosis substantially improves clinical outcomes, but this a complex task, since there is no single symptom that is exclusive to this severe mental illness. Currently, the accurate diagnosis of this type of disorder could take a 6-month minimum and is based mainly on interviews and the existence of some observable and repetitive behavior or symptom as an indicator.In this study, we propose a computer-assisted diagnosis for the detection of schizophrenia in 3 min. Which is obtained after the processing of brain micro-states, the brain signal to process is acquired by an electroencephalogram (EEG). The methodology relies heavily on the processing of brain micro-states through a trajectory of successive random steps in time (that is, treated through a random walk built on the basis of previously analyzed studies). Also, we used a Convolutional Neural Network (CNN) as a deep learning method that allowed the exploration and automatic extraction of the main characteristics of the random walk. The EEG data was taken from an open-source repository made up of 28 recordings from 14 healthy subjects and 14 patients with schizophrenia. The results provide an automated method and a substantial improvement for the classification of people with schizophrenia, obtaining a 93% success rate from 3 min of signal capture. Finally, three possible future challenges are open related to improve the classifier, the optimization of electrodes, and the development of a device for the schizophrenia diagnosis.

A Hybrid Classification to Detect Abstinent Heroin-Addicted Individuals Using EEG Microstates

Objective: Diagnosis of the severity of heroin addiction with electroencephalography (EEG) signals is a challenging problem. It has been shown that brain microstates are associated with brain status and healthy condition. However, there is no study on how heroin addiction affects brain microstates. Approach: We propose a hybrid classifier based on the microstate features, extracting from resting state EEGs, to objectively and effectively identify abstinent heroin-addicted individuals (AHAIs) and healthy controls (HCs). In addition to the commonly used features such as duration, occurrence, and transition, we calculated three new features. Main Results: The results showed that the support vector machine (SVM), which allows classification of the AHAIs and HCs with a 73% accuracy rate, was an optimal classifier. Moreover, the weight setting-based genetic algorithm (GA) further improved the accuracy rate to 81%. The hybrid classification not only provides direct evidence showing the differences in EEG microstate features between AHAIs and HCs, but also offers a method to distinguish the heroin brain states of people addicted to heroin and healthy individuals and demonstrates that microstate features could serve as potential bio-markers for identifying AHAIs. Significance: our methods and the selected features may provide electrophysiological insights for the assessment of the heroin withdrawal treatment effects.

+microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG

+microstate is a MATLAB toolbox for brain functional microstate analysis. It builds upon previous EEG microstate literature and toolboxes by including algorithms for source-space microstate analysis. +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 statistical testing for associated functional connectivity patterns, 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.

EEG-Microstates Reflect Auditory Distraction After Attentive Audiovisual Perception Recruitment of Cognitive Control Networks.

Processing of sensory information is embedded into ongoing neural processes which contribute to brain states. Electroencephalographic microstates are semi-stable short-lived power distributions which have been associated with subsystem activity such as auditory, visual and attention networks. Here we explore changes in electrical brain states in response to an audiovisual perception and memorization task under conditions of auditory distraction. We discovered changes in brain microstates reflecting a weakening of states representing activity of the auditory system and strengthening of salience networks, supporting the idea that salience networks are active after audiovisual encoding and during memorization to protect memories and concentrate on upcoming behavioural response.

Negative Effects of Mobile Phone Addiction Tendency on Spontaneous Brain Microstates: Evidence From Resting-State EEG.

The prevalence of mobile phone addiction (MPA) has increased rapidly in recent years, and it has had a certain negative impact on emotions (e.g., anxiety and depression) and cognitive capacities (e.g., executive control and working memory). At the level of neural circuits, the continued increase in activity in the brain regions associated with addiction leads to neural adaptations and structural changes. At present, the spontaneous brain microstates that could be negatively influenced by MPA are unclear. In this study, the temporal characteristics of four resting-state electroencephalogram (RS-EEG) microstates (MS1, MS2, MS3, and MS4) related to mobile phone addiction tendency (MPAT) were investigated using the Mobile Phone Addiction Tendency Scale (MPATS). We attempted to analyze the correlation between MPAT and corresponding microstates and provide evidence to explain the brain and behavioral changes caused by MPA. The results showed that the total score of the MPATS was positively correlated with the duration of MS1, related to phonological processing and negatively correlated with the duration of MS2, related to visual or imagery processing, and MS4, related to the attentional network; the score of the withdrawal symptoms subscale was additionally associated with duration of MS3, related to the cingulo-opercular emotional network. Based on these results, we believe that MPAT may have some negative effects on attentional networks and sensory brain networks; moreover, withdrawal symptoms may induce some negative emotions.

Shape facilitates number: brain potentials and microstates reveal the interplay between shape and numerosity in human vision

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.

Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance.

Dynamic functional connectivity (DFC) obtained from resting state functional magnetic resonance imaging (fMRI) data has been shown to provide novel insights into brain function which may be obscured by static functional connectivity (SFC). Further, DFC, and by implication how different brain regions may engage or disengage with each other over time, has been shown to be behaviorally relevant and more predictive than SFC of behavioral performance and/or diagnostic status. DFC is not a directional entity and may capture neural synchronization. However, directional interactions between different brain regions is another putative mechanism by which neural populations communicate. Accordingly, static effective connectivity (SEC) has been explored as a means of characterizing such directional interactions. But investigation of its dynamic counterpart, i.e., dynamic effective connectivity (DEC), is still in its infancy. Of particular note are methodological insufficiencies in identifying DEC configurations that are reproducible across time and subjects as well as a lack of understanding of the behavioral relevance of DEC obtained from resting state fMRI. In order to address these issues, we employed a dynamic multivariate autoregressive (MVAR) model to estimate DEC. The method was first validated using simulations and then applied to resting state fMRI data obtained in-house (N = 21), wherein we performed dynamic clustering of DEC matrices across multiple levels [using adaptive evolutionary clustering (AEC)] – spatial location, time, and subjects. We observed a small number of directional brain network configurations alternating between each other over time in a quasi-stable manner akin to brain microstates. The dominant and consistent DEC network patterns involved several regions including inferior and mid temporal cortex, motor and parietal cortex, occipital cortex, as well as part of frontal cortex. The functional relevance of these DEC states were determined using meta-analyses and pertained mainly to memory and emotion, but also involved execution and language. Finally, a larger cohort of resting-state fMRI and behavioral data from the Human Connectome Project (HCP) (N = 232, Q1–Q3 release) was used to demonstrate that metrics derived from DEC can explain larger variance in 70 behaviors across different domains (alertness, cognition, emotion, and personality traits) compared to SEC in healthy individuals.

Spatiotemporal Phase Synchronization in Adaptive Reconfiguration from Action Observation Network to Mentalizing Network for Understanding Other's Action Intention.

In action intention understanding, the mirror system is involved in perception-action matching process and the mentalizing system underlies higher-level intention inference. By analyzing the dynamic functional connectivity in α (8-12Hz) and β (12-30Hz) frequency bands over a "hand-cup interaction" observation task, this study investigates the topological transition from the action observation network (AON) to the mentalizing network (MZN), and estimates their functional relevance for intention identification from other's different action kinematics. Sequential brain microstates were extracted based on event-related potentials (ERPs), in which significantly differing neuronal responses were found in N170-P200 related to perceptually matching kinematic profiles and P400-700 involved in goal inference. Inter-electrode weighted phase lag index analysis on the ERP microstates revealed a shift of hub centrality salient in α frequency band, from the AON dominated by left-lateral frontal-premotor-temporal and temporal-parietooccipital synchronizations to the MZN consisting of more bilateral frontal-parietal and temporal-parietal synchronizations. As compared with usual actions, intention identification of unintelligible actions induces weaker synchronizations in the AON but dramatically increased connectivity in right frontal-temporal-parietal regions of the MZN, indicating a spatiotemporally complementary effect between the functional network configurations involved in mirror and mentalizing processes. Perceptual processing in observing usual/unintelligible actions decreases/increases requirements for intention inference, which would induce less/greater functional network reorganization on the way to mentalization. From the comparison, our study suggests that the adaptive topological changes from the AON to the MZN indicate implicit causal association between the mirror and mentalizing systems for decoding others' intentionality.

Functional brain microstate predicts the outcome in a visuospatial working memory task

Humans have limited capacity of processing just up to 4 integrated items of information in the working memory. Thus, it is inevitable to commit more errors when challenged with high memory loads. However, the neural mechanisms that determine the accuracy of response at high memory loads still remain unclear. High temporal resolution of Electroencephalography (EEG) technique makes it the best tool to resolve the temporal dynamics of brain networks. EEG-defined microstate is the quasi-stable scalp electrical potential topography that represents the momentary functional state of brain. Thus, it has been possible to assess the information processing currently performed by the brain using EEG microstate analysis. We hypothesize that the EEG microstate preceding the trial could determine its outcome in a visuospatial working memory (VSWM) task. Twenty-four healthy participants performed a high memory load VSWM task, while their brain activity was recorded using EEG. Four microstate maps were found to represent the functional brain state prior to the trials in the VSWM task. One pre-trial microstate map was found to determine the accuracy of subsequent behavioural response. The intracranial generators of the pre-trial microstate map that determined the response accuracy were localized to the visuospatial processing areas at bilateral occipital, right temporal and limbic cortices. Our results imply that the behavioural outcome in a VSWM task could be determined by the intensity of activation of memory representations in the visuospatial processing brain regions prior to the trial.

Altered Brain Microstate Dynamics in Adolescents with Narcolepsy.

Narcolepsy is a chronic sleep disorder caused by a loss of hypocretin-1 producing neurons in the hypothalamus. Previous neuroimaging studies have investigated brain function in narcolepsy during rest using positron emission tomography (PET) and single photon emission computed tomography (SPECT). In addition to hypothalamic and thalamic dysfunction they showed aberrant prefrontal perfusion and glucose metabolism in narcolepsy. Given these findings in brain structure and metabolism in narcolepsy, we anticipated that changes in functional magnetic resonance imaging (fMRI) resting state network (RSN) dynamics might also be apparent in patients with narcolepsy. The objective of this study was to investigate and describe brain microstate activity in adolescents with narcolepsy and correlate these to RSNs using simultaneous fMRI and electroencephalography (EEG). Sixteen adolescents (ages 13–20) with a confirmed diagnosis of narcolepsy were recruited and compared to age-matched healthy controls. Simultaneous EEG and fMRI data were collected during 10 min of wakeful rest. EEG data were analyzed for microstates, which are discrete epochs of stable global brain states obtained from topographical EEG analysis. Functional MRI data were analyzed for RSNs. Data showed that narcolepsy patients were less likely than controls to spend time in a microstate which we found to be related to the default mode network and may suggest a disruption of this network that is disease specific. We concluded that adolescents with narcolepsy have altered resting state brain dynamics.

Dynamic spatiotemporal brain analyses of the visual checkerboard task: Similarities and differences between passive and active viewing conditions.

We introduce a new analytic technique for the microsegmentation of high-density EEG to identify the discrete brain microstates evoked by the visual reversal checkerboard task. To test the sensitivity of the present analytic approach to differences in evoked brain microstates across experimental conditions, subjects were instructed to (a) passively view the reversals of the checkerboard (passive viewing condition), or (b) actively search for a target stimulus that may appear at the fixation point, and they were offered a monetary reward if they correctly detected the stimulus (active viewing condition). Results revealed that, within the first 168 ms of a checkerboard presentation, the same four brain microstates were evoked in the passive and active viewing conditions, whereas the brain microstates evoked after 168 ms differed between these two conditions, with more brain microstates elicited in the active than in the passive viewing condition. Additionally, distinctions were found in the active condition between a change in a scalp configuration that reflects a change in microstate and a change in scalp configuration that reflects a change in the level of activation of the same microstate. Finally, the bootstrapping procedure identified that two microstates lacked robustness even though statistical significance thresholds were met, suggesting these microstates should be replicated prior to placing weight on their generalizability across individuals. These results illustrate the utility of the analytic approach and provide new information about the spatiotemporal dynamics of the brain states underlying passive and active viewing in the visual checkerboard task.