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

The changes in neural complexity and connectivity in thalamocortical and cortico-cortical systems after propofol-induced unconsciousness in different temporal scales

Existing studies have indicated neural activity across diverse temporal and spatial scales. However, the alterations in complexity, functional connectivity, and directional connectivity within the thalamocortical and corticocortical systems across various scales during propofol-induced unconsciousness remain uncertain. We analyzed the stereo-electroencephalography (SEEG) from wakefulness to unconsciousness among the brain regions of the prefrontal cortex, temporal lobe, and anterior nucleus of the thalamus. The complexity (examined by permutation entropy (PE)), functional connectivity (permutation mutual information (PMI)), and directional connectivity (symbolic conditional mutual information (SCMI) and directionality index (DI)) were calculated across various scales. In the lower-band frequency (0.1-45 Hz) SEEG, after the loss of consciousness, PE significantly decreased (p < 0.001) in all regions and scales, except for the thalamus, which remained relatively unchanged at large scales (τ=32 ms). Following the loss of consciousness, inter-regional PMI either significantly increased or remained stable across different scales (τ=4 ms to 32 ms). During the unconscious state, SCMI between brain regions exhibited inconsistent changes across scales. In the late unconscious stage, the inter-regional DI across all scales indicated a shift from a balanced state of information flow between brain regions to a pattern where the prefrontal cortex and thalamus drive the temporal lobe. Our findings demonstrate that propofol-induced unconsciousness is associated with reduced cortical complexity, diverse functional connectivity, and a disrupted balance of information integration among thalamocortical and cortico-cortical systems. This study enhances the theoretical understanding of anesthetic-induced loss of consciousness by elucidating the scale- and region-specific effects of propofol on thalamocortical and cortico-cortical systems.

Frequency- and state-dependent dynamics of EEG microstates during propofol anesthesia

Electroencephalography microstate analysis has emerged as a powerful tool for investigating brain dynamics during anesthesia-induced unconsciousness. However, existing studies typically analyze EEG signals across broad frequency bands, leaving the frequency-specific temporal characteristics of microstates poorly understood. In this study, we investigated frequency-specific EEG microstate features in the delta (0.5-4 Hz) and EEG-without-delta (4-30 Hz) frequency bands during propofol anesthesia. Sixty-channel EEG recordings were collected from 18 healthy male participants during wakefulness and propofol-induced unconsciousness. Microstate analysis was conducted separately for delta and EEG-without-delta frequency bands and microstate features were compared across frequency bands and conscious states. Our results revealed eight consistent microstate classes (MS1-MS8) with high topographic similarity across frequency bands, while global explained variance (GEV), mean duration (MeanDur), occurrence (Occ), and coverage (Cov) exhibited significant frequency- and state-dependent variations during propofol anesthesia. In the delta band, propofol-induced unconsciousness was associated with significantly longer MeanDur for microstate classes of MS4, MS5, and MS6 (p < 0.05). In the EEG-without-delta band, GEV, Cov, and Occ significantly increased for MS1 and MS3 (p < 0.01) and decreased for MS2 and MS4 (p < 0.05) during unconsciousness. Notably, microstate features in the EEG-without-delta band showed better sensitivity for discriminating conscious states, achieving a classification accuracy of 0.944. These findings emphasize the importance of frequency-specific microstate analysis in unraveling the neural dynamics of anesthesia-induced unconsciousness and highlight its potential clinical applications for improving anesthesia depth monitoring.

EEG microstate syntax analysis: A review of methodological challenges and advances

Electroencephalography (EEG) microstates are "quasi-stable" periods of electrical potential distribution in multichannel EEG derived from peaks in Global Field Power. Transitions between microstates form a temporal sequence that may reflect underlying neural dynamics. Mounting evidence indicates that EEG microstate sequences have long-range, non-Markovian dependencies, suggesting a complex underlying process that drives EEG microstate syntax (i.e., the transitional dynamics between microstates). Despite growing interest in EEG microstate syntax, the field remains fragmented, with inconsistent terminologies used between studies and a lack of defined methodological categories. To advance the understanding of functional significance of microstates and to facilitate methodological comparability and finding replicability across studies, we: i) derive categories of syntax analysis methods, reviewing how each may be utilised most readily; ii) define three "time-modes" for EEG microstate sequence construction; and iii) outline general issues concerning current microstate syntax analysis methods, suggesting that the microstate models derived using these methods are cross-referenced against models of continuous EEG. We advocate for these continuous approaches as they do not assume a winner-takes-all model inherent in the microstate derivation methods and contextualise the relationship between microstate models and EEG data. They may also allow for the development of more robust associative models between microstates and functional Magnetic Resonance Imaging data.

Spatiospectral dynamics of electroencephalography patterns during propofol-induced alterations of consciousness states

Altered consciousness induced by anesthetics is characterized by distinct spatial and spectral neural dynamics that are readily apparent in the human electroencephalogram. Despite considerable study, we remain uncertain which brain regions and neural oscillations are involved, as well as how they are impacted when consciousness is disrupted. The experimental data was obtained from the open-access dataset, which contains pre-processed EEG data recorded from 20 healthy participants during propofol sedation. Using unsupervised machine learning methods (i.e., non-negative matrix factorization, NMF), we investigated the spatiospectral dynamic evolution of brain activity from awake to sedation and back induced by propofol in healthy research volunteers. Our methods yielded six dynamical patterns that continuously reflect the neural activity changes in specific brain regions and frequency bands under propofol sedation. Temporal dynamic analyses showed that differences in alpha oscillation patterns were less pronounced in response group than drowsy group, with hemispheric asymmetry in posterior occipital lobe over the course of the sedation procedure. We designed an index 'hemispheric lateralization modulation of alpha [HLM(α)]' to measure asymmetry during awake state and predicting individual variability in propofol-induced alterations of consciousness states, obtaining prediction AUC of 0.8462. We present an alpha modulation index which characterizes how these patterns track the transition from awake to sedation as a function of increasing dosage. Our study reveals dynamics indices that track the evolution of neurophysiological of propofol on brain circuits. Analyzing the spatiospectral dynamics influenced by propofol provides valuable understanding of the mechanisms of these agents and strategies for monitoring and precisely controlling the level of consciousness in patients under sedation and general anesthesia.

Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness

Introduction

Prognostication in patients with prolonged disorders of consciousness (pDoC) remains a challenging task. Electroencephalography (EEG) is a neurophysiological method that provides objective information for evaluating overall brain function. In this study, we aim to investigate the multiple features of pDoC using EEG and evaluate the prognostic values of these indicators.

Methods

We analyzed the EEG features: (i) spectral power; (ii) microstates; and (iii) mismatch negativity (MMN) and P3a of healthy controls, patients in minimally conscious state (MCS), and unresponsive wakefulness syndrome (UWS). Patients were followed up for 6 months. A combination of machine learning and SHapley Additive exPlanations (SHAP) were used to develop predictive model and interpret the results.

Results

The results indicated significant abnormalities in low-frequency spectral power, microstate parameters, and amplitudes of MMN and P3a in MCS and UWS. A predictive model constructed using support vector machine achieved an area under the curve (AUC) of 0.95, with the top 10 SHAP values being associated with transition probability (TP) from state C to F, time coverage of state E, TP from state D to F and D to F, mean duration of state A, TP from state F to C, amplitude of MMN, time coverage of state F, TP from state C to D, and mean duration of state E. Predictive models constructed for each component using support vector machine revealed that microstates had the highest AUC (0.95), followed by MMN and P3a (0.65), and finally spectral power (0.05).

Discussion

This study provides preliminary evidence for the application of microstate-based multiple EEG features for prognosis prediction in pDoC.

Clinical trial registration

chictr.org.cn, identifier ChiCTR2200064099.

How does Independent Component Analysis Preprocessing Affect EEG Microstates?

Over recent years, electroencephalographic (EEG) microstates have been increasingly used to investigate, at a millisecond scale, the temporal dynamics of large-scale brain networks. By studying their topography and chronological sequence, microstates research has contributed to the understanding of the brain's functional organization at rest and its alteration in neurological or mental disorders. Artifact removal strategies, which differ from study to study, may alter microstates topographies and features, possibly reducing the generalizability and comparability of results across research groups. The aim of this work was therefore to test the reliability of the microstate extraction process and the stability of microstate features against different strategies of EEG data preprocessing with Independent Component Analysis (ICA) to remove artifacts embedded in the data. A normative resting state EEG dataset was used where subjects alternate eyes-open (EO) and eyes-closed (EC) periods. Four strategies were tested: (i) avoiding ICA preprocessing altogether, (ii) removing ocular artifacts only, (iii) removing all reliably identified physiological/non physiological artifacts, (iv) retaining only reliably identified brain ICs. Results show that skipping the removal of ocular artifacts affects the stability of microstate evaluation criteria, microstate topographies and greatly reduces the statistical power of EO/EC microstate features comparisons, however differences are not as prominent with more aggressive preprocessing. Provided a good-quality dataset is recorded, and ocular artifacts are removed, microstates topographies and features can capture brain-related physiological data and are robust to artifacts, independently of the level of preprocessing, paving the way to automatized microstate extraction pipelines.

Resting-state EEG microstates predict mentalizing ability as assessed by the Reading the Mind in the Eyes test

Microstates analysis of electroencephalography (EEG) has gained increasing attention among researchers and clinicians as a valid tool for investigating temporal dynamics of large-scale brain networks with a millisecond time resolution. Although microstates analysis has been widely applied to elucidate the neurophysiological basis of various cognitive functions in both clinical and non-clinical samples, its application in relation to socio-affective processing has been relatively under-researched. Therefore, the main aim of the current study was to investigate the relationship between EEG microstates and mentalizing (i.e., the ability to understand the mental states of others). Eighty-two participants (thirty-six men; mean age: 24.28 ± 7.35 years; mean years of education: 15.82 ± 1.77) underwent a resting-state EEG recording and performed the Reading the Mind in the Eyes Test (RMET). The parameters of the microstates were then calculated using Cartool v. 4.09 software. Our results showed that the occurrence of microstate map C was independently and positively associated with the RMET total score and contributed to the prediction of mentalizing performance, even when controlling for potential confounding variables (i.e., age, sex, education level, tobacco and alcohol use). Since microstate C is involved in self-related processes, our findings may reflect the link between self-awareness of one's own thoughts/feelings and the enhanced ability to recognize the mental states of others at the neurophysiological level. This finding extends the functions traditionally attributed to microstate C, i.e. mind-wandering, self-related thoughts, prosociality, and emotional and interoceptive processing, to include mentalizing ability.

Electrophysiological (EEG) microstates during dream-like bizarre experiences in a naturalistic scenario using immersive virtual reality

Monitoring the reality status of conscious experience is essential for a human being to interact successfully with the external world. Despite its importance for everyday functioning, reality monitoring can systematically become erroneous, for example, while dreaming or during hallucinatory experiences. To investigate brain processes associated with reality monitoring occurring online during an experience, i.e., perceptual reality monitoring, we assessed EEG microstates in healthy, young participants. In a within-subjects design, we compared the experience of reality when being confronted with dream-like bizarre elements versus realistic elements in an otherwise highly naturalistic real-world scenario in immersive virtual reality. Dream-like bizarreness induced changes in the subjective experience of reality and bizarreness, and led to an increase in the contribution of a specific microstate labelled C'. Microstate C' was related to the suspension of disbelief, i.e. the suppression of bizarre mismatches. Together with the functional interpretation of microstate C' as reported by previous studies, the findings of this study point to the importance of prefrontal meta-conscious control processes in perceptual reality monitoring.

Unravelling consciousness and brain function through the lens of time, space, and information

Disentangling how cognitive functions emerge from the interplay of brain dynamics and network architecture is among the major challenges that neuroscientists face. Pharmacological and pathological perturbations of consciousness provide a lens to investigate these complex challenges. Here, we review how recent advances about consciousness and the brain's functional organisation have been driven by a common denominator: decomposing brain function into fundamental constituents of time, space, and information. Whereas unconsciousness increases structure-function coupling across scales, psychedelics may decouple brain function from structure. Convergent effects also emerge: anaesthetics, psychedelics, and disorders of consciousness can exhibit similar reconfigurations of the brain's unimodal-transmodal functional axis. Decomposition approaches reveal the potential to translate discoveries across species, with computational modelling providing a path towards mechanistic integration.

State-related Electroencephalography Microstate Complexity during Propofol- and Esketamine-induced Unconsciousness

BACKGROUND

Identifying the state-related "neural correlates of consciousness" for anesthetics-induced unconsciousness is challenging. Spatiotemporal complexity is a promising tool for investigating consciousness. The authors hypothesized that spatiotemporal complexity may serve as a state-related but not drug-related electroencephalography (EEG) indicator during an unconscious state induced by different anesthetic drugs (e.g., propofol and esketamine).

METHODS

The authors recorded EEG from patients with unconsciousness induced by propofol (n = 10) and esketamine (n = 10). Both conventional microstate parameters and microstate complexity were analyzed. Spatiotemporal complexity was constructed by microstate sequences and complexity measures. Two different EEG microstate complexities were proposed to quantify the randomness (type I) and complexity (type II) of the EEG microstate series during the time course of the general anesthesia.

RESULTS

The coverage and occurrence of microstate E (prefrontal pattern) and the duration of microstate B (right frontal pattern) could distinguish the states of preinduction wakefulness, unconsciousness, and recovery under both anesthetics. Type I EEG microstate complexity based on mean information gain significantly increased from awake to unconsciousness state (propofol: from mean ± SD, 1.562 ± 0.059 to 1.672 ± 0.023, P < 0.001; esketamine: 1.599 ± 0.051 to 1.687 ± 0.013, P < 0.001), and significantly decreased from unconsciousness to recovery state (propofol: 1.672 ± 0.023 to 1.537 ± 0.058, P < 0.001; esketamine: 1.687 ± 0.013 to 1.608 ± 0.028, P < 0.001) under both anesthetics. In contrast, type II EEG microstate fluctuation complexity significantly decreased in the unconscious state under both drugs (propofol: from 2.291 ± 0.771 to 0.782 ± 0.163, P < 0.001; esketamine: from 1.645 ± 0.417 to 0.647 ± 0.252, P < 0.001), and then increased in the recovery state (propofol: 0.782 ± 0.163 to 2.446 ± 0.723, P < 0.001; esketamine: 0.647 ± 0.252 to 1.459 ± 0.264, P < 0.001).

CONCLUSIONS

Both type I and type II EEG microstate complexities are drug independent. Thus, the EEG microstate complexity measures that the authors proposed are promising tools for building state-related neural correlates of consciousness to quantify anesthetic-induced unconsciousness.

EDITOR’S PERSPECTIVE

EEG Microstate Analysis in Patients with Disorders of Consciousness and Its Clinical Significance

Disorders of Consciousness are divided into two major categories such as vegetative and minimally conscious states. Objective measures that allow correct identification of patients with vegetative and minimally conscious state are needed. EEG microstate analysis is a promising approach that we believe has the potential to be effective in examining the resting state activities of the brain in different stages of consciousness by allowing the proper identification of vegetative and minimally conscious patients. As a result, we try to identify clinical evaluation scales and microstate characteristics with resting state EEGs from individuals with disorders of consciousness. Our prospective observational study included 28 individuals with a disorder of consciousness. Control group included 18 healthy subjects with proper EEG data. We made clinical evaluations using patient behavior scales. We also analyzed the EEGs using microstate analysis. In our study, microstate D coverage differed substantially between vegetative and minimally conscious state patients. Also, there was a strong connection between microstate D characteristics and clinical scale scores. Consequently, we have demonstrated that the most accurate parameter for representing consciousness level is microstate D. Microstate analysis appears to be a strong option for future use in the diagnosis, follow-up, and treatment response of patients with Disorders of Consciousness.

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

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

Complexity Measures for EEG Microstate Sequences: Concepts and Algorithms

EEG microstate sequence analysis quantifies properties of ongoing brain electrical activity which is known to exhibit complex dynamics across many time scales. In this report we review recent developments in quantifying microstate sequence complexity, we classify these approaches with regard to different complexity concepts, and we evaluate excess entropy as a yet unexplored quantity in microstate research. We determined the quantities entropy rate, excess entropy, Lempel-Ziv complexity (LZC), and Hurst exponents on Potts model data, a discrete statistical mechanics model with a temperature-controlled phase transition. We then applied the same techniques to EEG microstate sequences from wakefulness and non-REM sleep stages and used first-order Markov surrogate data to determine which time scales contributed to the different complexity measures. We demonstrate that entropy rate and LZC measure the Kolmogorov complexity (randomness) of microstate sequences, whereas excess entropy and Hurst exponents describe statistical complexity which attains its maximum at intermediate levels of randomness. We confirmed the equivalence of entropy rate and LZC when the LZ-76 algorithm is used, a result previously reported for neural spike train analysis (Amigó et al., Neural Comput 16:717-736, https://doi.org/10.1162/089976604322860677 , 2004). Surrogate data analyses prove that entropy-based quantities and LZC focus on short-range temporal correlations, whereas Hurst exponents include short and long time scales. Sleep data analysis reveals that deeper sleep stages are accompanied by a decrease in Kolmogorov complexity and an increase in statistical complexity. Microstate jump sequences, where duplicate states have been removed, show higher randomness, lower statistical complexity, and no long-range correlations. Regarding the practical use of these methods, we suggest that LZC can be used as an efficient entropy rate estimator that avoids the estimation of joint entropies, whereas entropy rate estimation via joint entropies has the advantage of providing excess entropy as the second parameter of the same linear fit. We conclude that metrics of statistical complexity are a useful addition to microstate analysis and address a complexity concept that is not yet covered by existing microstate algorithms while being actively explored in other areas of brain research.

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.

Propofol Reversibly Attenuates Short-Range Microstate Ordering and 20 Hz Microstate Oscillations

Microstate sequences summarize the changing voltage patterns measured by electroencephalography, using a clustering approach to reduce the high dimensionality of the underlying data. A common approach is to restrict the pattern matching step to local maxima of the global field power (GFP) and to interpolate the microstate fit in between. In this study, we investigate how the anesthetic propofol affects microstate sequence periodicity and predictability, and how these metrics are changed by interpolation. We performed two frequency analyses on microstate sequences, one based on time-lagged mutual information, the other based on Fourier transform methodology, and quantified the effects of interpolation. Resting-state microstate sequences had a 20 Hz frequency peak related to dominant 10 Hz (alpha) rhythms, and the Fourier approach demonstrated that all five microstate classes followed this frequency. The 20 Hz periodicity was reversibly attenuated under moderate propofol sedation, as shown by mutual information and Fourier analysis. Characteristic microstate frequencies could only be observed in non-interpolated microstate sequences and were masked by smoothing effects of interpolation. Information-theoretic analysis revealed faster microstate dynamics and larger entropy rates under propofol, whereas Shannon entropy did not change significantly. In moderate sedation, active information storage decreased for non-interpolated sequences. Signatures of non-equilibrium dynamics were observed in non-interpolated sequences, but no changes were observed between sedation levels. All changes occurred while subjects were able to perform an auditory perception task. In summary, we show that low dose propofol reversibly increases the randomness of microstate sequences and attenuates microstate oscillations without correlation to cognitive task performance. Microstate dynamics between GFP peaks reflect physiological processes that are not accessible in interpolated sequences.

EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings.

On the Reliability of the EEG Microstate Approach

EEG microstates represent functional brain networks observable in resting EEG recordings that remain stable for 40-120ms before rapidly switching into another network. It is assumed that microstate characteristics (i.e., durations, occurrences, percentage coverage, and transitions) may serve as neural markers of mental and neurological disorders and psychosocial traits. However, robust data on their retest-reliability are needed to provide the basis for this assumption. Furthermore, researchers currently use different methodological approaches that need to be compared regarding their consistency and suitability to produce reliable results. Based on an extensive dataset largely representative of western societies (2 days with two resting EEG measures each; day one: n = 583; day two: n = 542) we found good to excellent short-term retest-reliability of microstate durations, occurrences, and coverages (average ICCs = 0.874-0.920). There was good overall long-term retest-reliability of these microstate characteristics (average ICCs = 0.671-0.852), even when the interval between measures was longer than half a year, supporting the longstanding notion that microstate durations, occurrences, and coverages represent stable neural traits. Findings were robust across different EEG systems (64 vs. 30 electrodes), recording lengths (3 vs. 2 min), and cognitive states (before vs. after experiment). However, we found poor retest-reliability of transitions. There was good to excellent consistency of microstate characteristics across clustering procedures (except for transitions), and both procedures produced reliable results. Grand-mean fitting yielded more reliable results compared to individual fitting. Overall, these findings provide robust evidence for the reliability of the microstate approach.

Frequency Analysis of EEG Microstate Sequences in Wakefulness and NREM Sleep

The majority of EEG microstate analyses concern wakefulness, and the existing sleep studies have focused on changes in spatial microstate properties and on microstate transitions between adjacent time points, the shortest available time scale. We present a more extensive time series analysis of unsmoothed EEG microstate sequences in wakefulness and non-REM sleep stages across many time scales. Very short time scales are assessed with Markov tests, intermediate time scales by the entropy rate and long time scales by a spectral analysis which identifies characteristic microstate frequencies. During the descent from wakefulness to sleep stage N3, we find that the increasing mean microstate duration is a gradual phenomenon explained by a continuous slowing of microstate dynamics as described by the relaxation time of the transition probability matrix. The finite entropy rate, which considers longer microstate histories, shows that microstate sequences become more predictable (less random) with decreasing vigilance level. Accordingly, the Markov property is absent in wakefulness but in sleep stage N3, 10/19 subjects have microstate sequences compatible with a second-order Markov process. A spectral microstate analysis is performed by comparing the time-lagged mutual information coefficients of microstate sequences with the autocorrelation function of the underlying EEG. We find periodic microstate behavior in all vigilance states, linked to alpha frequencies in wakefulness, theta activity in N1, sleep spindle frequencies in N2, and in the delta frequency band in N3. In summary, we show that EEG microstates are a dynamic phenomenon with oscillatory properties that slow down in sleep and are coupled to specific EEG frequencies across several sleep stages.

EEG spectral slope: A reliable indicator for continuous evaluation of consciousness levels during propofol anesthesia

The level of consciousness undergoes continuous alterations during anesthesia. Prior to the onset of propofol-induced complete unconsciousness, degraded levels of behavioral responsiveness can be observed. However, a reliable index to monitor altered consciousness levels during anesthesia has not been sufficiently investigated. In this study, we obtained 60-channel EEG data from 24 healthy participants during an ultra-slow propofol infusion protocol starting with an initial concentration of 1 μg/ml and a stepwise increase of 0.2 μg/ml in concentration. Consecutive auditory stimuli were delivered every 5 to 6 s, and the response time to the stimuli was used to assess the responsiveness levels. We calculated the spectral slope in a time-resolved manner by extracting 5-second EEG segments at each auditory stimulus and estimated their correlation with the corresponding response time. Our results demonstrated that during slow propofol infusion, the response time to external stimuli increased, while the EEG spectral slope, fitted at 15-45 Hz, became steeper, and a significant negative correlation was observed between them. Moreover, the spectral slope further steepened at deeper anesthetic levels and became flatter during anesthesia recovery. We verified these findings using an external dataset. Additionally, we found that the spectral slope of frontal electrodes over the prefrontal lobe had the best performance in predicting the response time. Overall, this study used a time-resolved analysis to suggest that the EEG spectral slope could reliably track continuously altered consciousness levels during propofol anesthesia. Furthermore, the frontal spectral slope may be a promising index for clinical monitoring of anesthesia depth.

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.

EEG microstate quantifiers and state space descriptors during anaesthesia in patients with postoperative delirium: a descriptive analysis

Postoperative delirium is a serious sequela of surgery and surgery-related anaesthesia. One recommended method to prevent postoperative delirium is using bi-frontal EEG recording. The single, processed index of depth of anaesthesia allows the anaesthetist to avoid episodes of suppression EEG and excessively deep anaesthesia. The study data presented here were based on multichannel (19 channels) EEG recordings during anaesthesia. This enabled the analysis of various parameters of global electrical brain activity. These parameters were used to compare microstate topographies under anaesthesia with those in healthy volunteers and to analyse changes in microstate quantifiers and EEG global state space descriptors with increasing exposure to anaesthesia. Seventy-three patients from the Surgery Depth of Anaesthesia and Cognitive Outcome study (SRCTN 36437985) received intraoperative multichannel EEG recordings. Altogether, 720 min of artefact-free EEG data, including 210 min (29.2%) of suppression EEG, were analysed. EEG microstate topographies, microstate quantifiers (duration, frequency of occurrence and global field power) and the state space descriptors sigma (overall EEG power), phi (generalized frequency) and omega (number of uncorrelated brain processes) were evaluated as a function of duration of exposure to anaesthesia, suppression EEG and subsequent development of postoperative delirium. The major analyses involved covariate-adjusted linear mixed-effects models. The older (71 ± 7 years), predominantly male (60%) patients received a median exposure of 210 (range: 75-675) min of anaesthesia. During seven postoperative days, 21 patients (29%) developed postoperative delirium. Microstate topographies under anaesthesia resembled topographies from healthy and much younger awake persons. With increasing duration of exposure to anaesthesia, single microstate quantifiers progressed differently in suppression or non-suppression EEG and in patients with or without subsequent postoperative delirium. The most pronounced changes occurred during enduring suppression EEG in patients with subsequent postoperative delirium: duration and frequency of occurrence of microstates C and D progressed in opposite directions, and the state space descriptors showed a pattern of declining uncorrelated brain processes (omega) combined with increasing EEG variance (sigma). With increasing exposure to general anaesthesia, multiple changes in the dynamics of microstates and global EEG parameters occurred. These changes varied partly between suppression and non-suppression EEG and between patients with or without subsequent postoperative delirium. Ongoing suppression EEG in patients with subsequent postoperative delirium was associated with reduced network complexity in combination with increased overall EEG power. Additionally, marked changes in quantifiers in microstate C and in microstate D occurred. These putatively adverse intraoperative trajectories in global electrical brain activity may be seen as preceding and ultimately predicting postoperative delirium.

Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness

Introduction

Ischemic stroke patients commonly experience disorder of consciousness (DOC), leading to poorer discharge outcomes and higher mortality risks. Therefore, the identification of applicable electrophysiological biomarkers is crucial for the rapid diagnosis and evaluation of post-stroke disorder of consciousness (PS-DOC), while providing supportive evidence for cerebral neurology.

Methods

In our study, we conduct microstate analysis on resting-state electroencephalography (EEG) of 28 post-stroke patients with awake consciousness and 28 patients with PS-DOC, calculating the temporal features of microstates. Furthermore, we extract the Lempel-Ziv complexity of microstate sequences and the delta/alpha power ratio of EEG on spectral. Statistical analysis is performed to examine the distinctions in features between the two groups, followed by inputting the distinctive features into a support vector machine for the classification of PS-DOC.

Results

Both groups obtain four optimal topographies of EEG microstates, but notable distinctions are observed in microstate C. Within the PS-DOC group, there is a significant increase in the mean duration and coverage of microstates B and C, whereas microstate D displays a contrasting trend. Additionally, noteworthy variations are found in the delta/alpha ratio and Lempel-Ziv complexity between the two groups. The integration of the delta/alpha ratio with microstates' temporal and Lempel-Ziv complexity features demonstrates the highest performance in the classifier (Accuracy = 91.07%).

Discussion

Our results suggest that EEG microstates can provide insights into the abnormal brain network dynamics in DOC patients post-stroke. Integrating the temporal and Lempel-Ziv complexity microstate features with spectral features offers a deeper understanding of the neuro mechanisms underlying brain damage in patients with DOC, holding promise as effective electrophysiological biomarkers for diagnosing PS-DOC.

Cardiorespiratory fitness modulates prestimulus EEG microstates during a sustained attention task

Higher cardiorespiratory fitness is associated with an increased ability to perform sustained attention tasks and detect rare and unpredictable signals over prolonged periods. The electrocortical dynamics underlying this relationship were mainly investigated after visual stimulus onset in sustained attention tasks. Prestimulus electrocortical activity supporting differences in sustained attention performance according to the level of cardiorespiratory fitness have yet to be examined. Consequently, this study aimed to investigate EEG microstates 2 seconds before the stimulus onset in 65 healthy individuals aged 18-37, differing in cardiorespiratory fitness, while performing a psychomotor vigilance task. The analyses showed that a lower duration of the microstate A and a higher occurrence of the microstate D correlated with higher cardiorespiratory fitness in the prestimulus periods. In addition, increased global field power and occurrence of microstate A were associated with slower response times in the psychomotor vigilance task, while greater global explained variance, coverage, and occurrence of microstate D were linked to faster response times. Our collective findings showed that individuals with higher cardiorespiratory fitness exhibit typical electrocortical dynamics that allow them to allocate their attentional resources more efficiently when engaged in sustained attention tasks.

Microsynt: exploring the syntax of EEG microstates

Microstates represent electroencephalographic (EEG) activity as a sequence of switching, transient, metastable states. Growing evidence suggests the useful information on brain states is to be found in the higher-order temporal structure of these sequences. Instead of focusing on transition probabilities, here we propose "Microsynt", a method designed to highlight higher-order interactions that form a preliminary step towards understanding the syntax of microstate sequences of any length and complexity. Microsynt extracts an optimal vocabulary of "words" based on the length and complexity of the full sequence of microstates. Words are then sorted into classes of entropy and their representativeness within each class is statistically compared with surrogate and theoretical vocabularies. We applied the method on EEG data previously collected from healthy subjects undergoing propofol anaesthesia, and compared their "fully awake" (BASE) and "fully unconscious" (DEEP) conditions. Results show that microstate sequences, even at rest, are not random but tend to behave in a more predictable way, favoring simpler sub-sequences, or "words". Contrary to high-entropy words, lowest-entropy binary microstate loops are prominent and favored on average 10 times more than what is theoretically expected. Progressing from BASE to DEEP, the representation of low-entropy words increases while that of high-entropy words decreases. During the awake state, sequences of microstates tend to be attracted towards "A - B - C" microstate hubs, and most prominently A - B binary loops. Conversely, with full unconsciousness, sequences of microstates are attracted towards "C - D - E" hubs, and most prominently C - E binary loops, confirming the putative relation of microstates A and B to externally-oriented cognitive processes and microstate C and E to internally-generated mental activity. Microsynt can form a syntactic signature of microstate sequences that can be used to reliably differentiate two or more conditions.

Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer's Disease continuum?

Alzheimer's disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by mild cognitive impairment (MCI), the initial stage of clinical cognitive decline. However, the neural correlates of these prodromic stages are not completely clear yet. Recent studies suggest that EEG analysis tools characterizing the cortical activity as a whole, such as microstates and cortical regions connectivity, might support a characterization of SCD and MCI conditions. Here we test this approach by performing a broad set of analyses to identify the prominent EEG markers differentiating SCD (n = 57), MCI (n = 46) and healthy control subjects (HC, n = 19). We found that the salient differences were in the temporal structure of the microstates patterns, with MCI being associated with less complex sequences due to the altered transition probability, frequency and duration of canonic microstate C. Spectral content of EEG, network connectivity, and spatial arrangement of microstates were instead largely similar in the three groups. Interestingly, comparing properties of EEG microstates in different cerebrospinal fluid (CSF) biomarkers profiles, we found that canonic microstate C displayed significant differences in topography in AD-like profile. These results show that the progression of dementia might be associated with a degradation of the cortical organization captured by microstates analysis, and that this leads to altered transitions between cortical states. Overall, our approach paves the way for the use of non-invasive EEG recordings in the identification of possible biomarkers of progression to AD from its prodromal states.

Brain Complexities and Anesthesia: Their Meaning and Measurement

A complex system is often associated with emergence of new phenomena from the interactions between the system's components. General anesthesia reduces brain complexity and so inhibits the emergence of consciousness. An understanding of complexity is necessary for the interpretation of brain monitoring algorithms. Complexity indices capture the "difficulty" of understanding brain activity over time and/or space. Complexity-entropy plots reveal the types of complexity indices and their balance of randomness and structure. Lempel-Ziv complexity is a common index of temporal complexity for single-channel electroencephalogram containing both power spectral and nonlinear effects, revealed by phase-randomized surrogate data. Computing spatial complexities involves forming a connectivity matrix and calculating the complexity of connectivity patterns. Spatiotemporal complexity can be estimated in multiple ways including temporal or spatial concatenation, estimation of state switching, or integrated information. This article illustrates the concept and application of various complexities by providing working examples; a website with interactive demonstrations has also been created.