Current State of EEG/ERP Microstate Research

Brain Topography, Год журнала: 2024, Номер 37(2), С. 169 - 180

Опубликована: Фев. 13, 2024

The analysis of EEG microstates for investigating rapid whole-brain network dynamics during rest and tasks has become a standard practice in the research community, leading to substantial increase publications across various affective, cognitive, social clinical neuroscience domains. Recognizing growing significance this analytical method, authors aim provide microstate community with comprehensive discussion on methodological standards, unresolved questions, functional relevance microstates. In August 2022, conference was hosted Bern, Switzerland, which brought together many researchers from 19 countries. During conference, gave scientific presentations engaged roundtable discussions aiming at establishing steps toward standardizing methods. Encouraged by conference's success, special issue launched Brain Topography compile current state-of-the-art research, encompassing advancements, experimental findings, applications. call submissions garnered 48 contributions worldwide, spanning reviews, meta-analyses, tutorials, studies. Following rigorous peer-review process, 33 papers were accepted whose findings we will comprehensively discuss Editorial.

Язык: Английский

---

Complexity Measures for EEG Microstate Sequences: Concepts and Algorithms

Brain Topography, Год журнала: 2023, Номер 37(2), С. 296 - 311

Опубликована: Сен. 26, 2023

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

Язык: Английский

---

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability

Brain Topography, Год журнала: 2024, Номер 37(4), С. 496 - 513

Опубликована: Март 2, 2024

Abstract Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns scalp potential topographies, or microstates, that reflect stable but transient periods synchronized neural activity evolving dynamically over time. During infancy – critical period rapid brain development and plasticity microstate offers opportunity characterizing the spatial temporal dynamics activity. However, whether measurements derived from this approach (e.g., properties, transition probabilities, sources) show strong psychometric properties (i.e., reliability) during unknown key information advancing our understanding how microstates are shaped by early life experiences they relate to individual differences in infant abilities. A lack methodological resources performing has further hindered adoption cutting-edge researchers. As result, current study, we systematically addressed these knowledge gaps report most microstate-based organization functioning except probabilities were with four minutes video-watching data highly internally consistent just one minute. In addition results, provide step-by-step tutorial, accompanying website, open-access using free, user-friendly software called Cartool. Taken together, study supports reliability feasibility increases accessibility field developmental neuroscience.

Язык: Английский

---

How does Independent Component Analysis Preprocessing Affect EEG Microstates?

Brain Topography, Год журнала: 2025, Номер 38(2)

Опубликована: Фев. 4, 2025

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

Язык: Английский

---

EEG Microstate Syntax Analysis: A Review of Methodological Challenges and Advances

NeuroImage, Год журнала: 2025, Номер unknown, С. 121090 - 121090

Опубликована: Фев. 1, 2025

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

Язык: Английский

---

Higher-order EEG microstate syntax and surrogate testing

Computers in Biology and Medicine, Год журнала: 2025, Номер 193, С. 110367 - 110367

Опубликована: Май 30, 2025

Язык: Английский

---

Electroencephalography microstate alterations reflect potential double‐edged cognitive adaptation in Ménière's disease

CNS Neuroscience & Therapeutics, Год журнала: 2024, Номер 30(8)

Опубликована: Авг. 1, 2024

Abstract Purpose To explore the microstate characteristics and underlying brain network activity of Ménière's disease (MD) patients based on high‐density electroencephalography (EEG), elucidate association between dynamics clinical manifestation, potential EEG features as future neurobiomarkers for MD. Methods Thirty‐two diagnosed with MD 29 healthy controls (HC) matched demographic were included in study. Dysfunction subjective symptom severity assessed by neuropsychological questionnaires, pure tone audiometry, vestibular function tests. Resting‐state recordings obtained using a 256‐channel system, electric field topographies clustered into four dominant classes (A, B, C, D). The dynamic parameters each analyzed utilized input support vector machine (SVM) classifier to identify significant signatures associated significance was further explored through Spearman correlation analysis. Results exhibited an increased presence class C decreased frequency transitions A well D. from also elevated. Further analysis revealed positive equilibrium scores under somatosensory challenging conditions. Conversely, B negatively correlated vertigo symptoms. No correlations detected these auditory test results or emotional scores. Utilizing identified via sequential backward selection, linear SVM achieved sensitivity 86.21% specificity 90.61% distinguishing HC. Conclusions We several that facilitate postural control yet exacerbate symptoms, effectively discriminate may offer new approach optimizing cognitive compensation strategies exploring neurobiological markers

Язык: Английский

---

EEG microstate in people with different degrees of fear of heights during virtual high-altitude exposure

Brain Research Bulletin, Год журнала: 2024, Номер unknown, С. 111112 - 111112

Опубликована: Окт. 1, 2024

Язык: Английский

---

Microstate Analysis of Infant EEG: Tutorial and Reliability

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Июль 17, 2023

Abstract Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns scalp potential topographies, or microstates, that reflect stable but transient periods synchronized neural activity evolving dynamically over time. During infancy – critical period rapid brain development and plasticity microstate offers opportunity characterizing the spatial temporal dynamics activity. However, whether measurements derived from this approach (e.g., properties, transition probabilities, sources) show strong psychometric properties (i.e., reliability) during unknown key information advancing our understanding how microstates are shaped by early life experiences they relate to individual differences in infant abilities. A lack methodological resources performing has further hindered adoption cutting-edge researchers. As result, current study, we systematically addressed these knowledge gaps report all microstate-based organization functioning except probabilities were highly reliable with as little 2–3 minutes video-watching data provide step-by-step tutorial, accompanying website, open-access using free, user-friendly software called Cartool. Taken together, study supports reliability feasibility increases accessibility field developmental neuroscience.

Язык: Английский

---

Assessing Brain Network Dynamics during Postural Control Task using EEG Microstates

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 27, 2024

The ability to maintain our body’s balance and stability in space is crucial for performing daily activities. Effective postural control (PC) strategies rely on integrating visual, vestibular, proprioceptive sensory inputs. While neuroimaging has revealed key areas involved PC—including brainstem, cerebellum, cortical networks—the rapid neural mechanisms underlying dynamic tasks remain less understood. Therefore, we used EEG microstate analysis within the BioVRSea experiment explore temporal brain dynamics that support PC. This complex paradigm simulates maintaining an upright posture a moving platform, integrated with virtual reality (VR), replicate sensation of balancing boat. Data were acquired from 266 healthy subjects using 64-channel system. Using modified k-means method, five maps identified best model paradigm. Differences each feature (occurrence, duration, coverage) between experimental phases analyzed linear mixed model, revealing significant differences microstates phases. parameters C showed significantly higher levels all compared other maps, whereas B displayed opposite pattern, consistently showing lower levels. study marks first attempt use during task, demonstrating decisive role and, conversely, differentiating PC These results demonstrate technique studying potential application early detection neurodegenerative diseases.

Язык: Английский

---