Information-theoretical analysis of EEG microstate sequences in wakefulness and sleep DOI Creative Commons

Milena Wiemers

Опубликована: Янв. 1, 2024

Microstates are short, recurring electric field topographies over the cortex. The majority of electroencephalogram (EEG) signal variance is explained by four representative topographies, canonically known as maps A-D. Similar have been found in wakefulness and sleep. examined thoroughly during wakeful rest, but our understanding microstates other vigilance states limited. Different usually distinguished EEG frequency spectrum graphoelements, while microstate approach focuses on spatial distribution at each time point. aim this study was to analyze temporal structure sequences Using information-theoretic methods, a direct comparison between classic frequency-based analysis made possible. We present an 32 healthy subjects sleep which we increase mean duration transition matrix relaxation with deepening stages, pointing towards slower dynamics Interestingly, more than half deep could not be from simple Markov models can interpreted decrease sequence complexity. entropy rate decreased stage, indicating less random, i.e. predictable structure. Furthermore, that occur periodically whenever underlying has dominant frequency. This shows oscillatory brain activity tracked level, making it possible distinguish different quantitatively. Interpreting correlates functional networks, conclude same or very similar networks activated wakefulness, their activation slowed down complex.

Язык: Английский

Current State of EEG/ERP Microstate Research DOI Creative Commons
Christoph M. Michel, Lucie Bréchet, Bastian Schiller

и другие.

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

Процитировано

9
Critical dynamics in spontaneous EEG predict anesthetic-induced loss of consciousness and perturbational complexity DOI Creative Commons
Charlotte Maschke, Jordan O’Byrne,

Michele Colombo

и другие.

Communications Biology, Год журнала: 2024, Номер 7(1)

Опубликована: Авг. 5, 2024

Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits adaptive computational properties, maximally complex and divergent sensitivity perturbation. Here, we investigate properties of the resting-state electroencephalogram (EEG) healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. Importantly, all participants were unresponsive under anesthesia, while consciousness was retained only during ketamine (in form vivid dreams), enabling an experimental dissociation between unresponsiveness unconsciousness. For each condition, measure (i) avalanche (ii) chaoticity, (iii) criticality-related metrics, revealing that states unconsciousness are characterized distancing from both criticality edge chaos. We then ask whether these same predictive perturbational complexity index (PCI), TMS-based shown remarkably high in detecting independently behavior. successfully predict individual subjects' PCI values considerably accuracy EEG alone. Our results establish firm link provide further evidence is necessary condition for emergence consciousness. An study demonstrates mark (un)consciousness able TMS-derived (PCI) adults.

Язык: Английский

Процитировано

9
Two-brain microstates: A novel hyperscanning-EEG method for quantifying task-driven inter-brain asymmetry DOI Creative Commons
Qianliang Li, Marius Zimmermann, Ivana Konvalinka

и другие.

Journal of Neuroscience Methods, Год журнала: 2025, Номер 416, С. 110355 - 110355

Опубликована: Янв. 22, 2025

The neural mechanisms underlying real-time social interaction remain poorly understood. While hyperscanning has emerged as a popular method to better understand inter-brain mechanisms, methods underdeveloped, and primarily focused on synchronization (IBS). We developed novel approach employing two-brain EEG microstates, investigate during symmetric asymmetric interactive tasks. Microstates are quasi-stable configurations of brain activity that have been proposed represent basic building blocks for mental processing. Expanding the microstate methodology dyads interacting participants enables us moments synchronous activity. Conventional microstates fitted individuals were not related different conditions. However, modulated in observer-actor condition, compared all other conditions where had more task demands, same trend was observed follower-leader condition. This indicates differences resting state default-mode network interactions with Hyperscanning studies estimated IBS based functional connectivity measures. localized connections often hard interpret larger scale when multiple across brains found be important. Two-brain offer an alternative evaluate from large-scale global perspective, by quantifying task-driven states between individuals. present using including open-source code, which expands current hyperscanning-EEG measure potentially identify both interaction.

Язык: Английский

Процитировано

1
Complexity Measures for EEG Microstate Sequences: Concepts and Algorithms DOI Creative Commons
Frederic von Wegner,

Milena Wiemers,

Gesine Hermann

и другие.

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

Процитировано

15
Unveiling neural activity changes in mild cognitive impairment using microstate analysis and machine learning DOI Creative Commons
Xiaotian Wu, Yanli Liu,

Jiajun Che

и другие.

Journal of Alzheimer s Disease, Год журнала: 2025, Номер unknown

Опубликована: Янв. 8, 2025

Background Mild cognitive impairment (MCI) is recognized as a condition that may increase the risk of developing Alzheimer's disease (AD). Understanding neural correlates MCI crucial for elucidating its pathophysiology and effective interventions. Electroencephalogram (EEG) microstates, reflecting brain activity changes, have shown promise in research. However, current approaches often lack comprehensive characterization complex dynamics associated with MCI. Objective This study aims to investigate neurophysiological changes using set microstate features, including traditional temporal features entropy measures. Methods Resting-state EEG data were collected from 69 patients healthy controls (HC). Microstate analysis was performed extract conventional (duration, coverage) Statistical analysis, principal component (PCA), machine learning (ML) techniques employed evaluate patterns Results displayed altered dynamics, significantly longer coverage duration C but shorter Microstates A, B, D compared HCs. PCA revealed two components, primarily composed measures, explaining over 75% variance. ML models achieved high accuracy distinguishing patterns. Conclusions Our provides new insights into MCI, highlighting potential microstates investigating decline.

Язык: Английский

Процитировано

0
A method for dyadic cardiac rhythmicity analysis: Preliminary evidence on bilateral interactions in fetal–maternal cardiac dynamics DOI Creative Commons
Diego Candia‐Rivera, Mario Chávez

Experimental Physiology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 21, 2025

Abstract Cardiac activity responds dynamically to metabolic demands and neural regulation. However, little is known about this process during pregnancy. Reports show occasional fetal–maternal heart rate couplings, but it has remained unclear whether these couplings extend more complex oscillatory patterns of the rhythm. We developed a framework time‐varying measures rhythm, test presence co‐varying in concurrent maternal fetal (late pregnancy dataset, n = 10, labour 12). These were derived from first second‐order Poincaré plots, with aim describe changes short‐ long‐term rhythmicity, also dynamic shifts acceleration deceleration rate. found episodes maternal–fetal cardiac rhythm all explored, both datasets (at least 90% dataset presented significant correlation each measure, P < 0.001), delays suggesting bilateral interactions at different time scales. that intensify (test between late vs. datasets, 0.0015 plot‐derived measures). While most literature suggests breathing or contractions, we propose possibility may have signalling function context co‐regulatory mechanisms inter‐organ interactions. Understanding visceral oscillations utero enhance assessment healthy development.

Язык: Английский

Процитировано

0
Spectral and Microstate EEG Analysis in Narcolepsy Type 1 and Type 2 Across Sleep Stages DOI

Shengpeng Liang,

Yihong Cheng,

Shixu Du

и другие.

Brain Topography, Год журнала: 2025, Номер 38(3)

Опубликована: Март 29, 2025

Язык: Английский

Процитировано

0
Higher-order EEG microstate syntax and surrogate testing DOI Creative Commons
Frederic von Wegner,

Gesine Hermann,

Inken Tödt

и другие.

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

Опубликована: Май 30, 2025

Язык: Английский

Процитировано

0
Mindfulness meditation styles differently modulate source-level MEG microstate dynamics and complexity DOI Creative Commons
Antea D’Andrea, Pierpaolo Croce, Jordan O’Byrne

и другие.

Frontiers in Neuroscience, Год журнала: 2024, Номер 18

Опубликована: Фев. 2, 2024

Background The investigation of mindfulness meditation practice, classically divided into focused attention (FAM), and open monitoring (OMM) styles, has seen a long tradition theoretical, affective, neurophysiological clinical studies. In particular, the high temporal resolution magnetoencephalography (MEG) or electroencephalography (EEG) been exploited to fill gap between personal experience practice its neural correlates. Mounting evidence, in fact, shows that human brain activity is highly dynamic, transiting different states (microstates). this study, we aimed at exploring MEG microstates source-level during FAM, OMM resting state, as well complexity criticality dynamic transitions microstates. Methods Ten right-handed Theravada Buddhist monks with meditative expertise minimum 2,265 h participated experiment. data were acquired randomized block design task (6 min 6 OMM, each preceded followed by 3 state). Source reconstruction was performed using eLORETA on individual cortical space, then parcellated according Human Connect Project atlas. Microstate analysis applied parcel level signals order derive microstate topographies indices. addition, from sequences, Hurst exponent Lempel-Ziv (LZC) computed. Results Our results show coverage occurrence specific are modulated either being state performing style. values both conditions reduced respect value observed rest, LZC significant differences REST, progressive increase REST FAM OMM. Discussion Importantly, report changes indices line state-like effect cognitive performance. previous reports, suggest change experienced paralleled shift critical points dynamics.

Язык: Английский

Процитировано

2
Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia DOI Creative Commons
Wang Wan,

Zhongze Gu,

Chung‐Kang Peng

и другие.

Brain Sciences, Год журнала: 2024, Номер 14(5), С. 487 - 487

Опубликована: Май 11, 2024

Exploring the spatiotemporal dynamic patterns of multi-channel electroencephalography (EEG) is crucial for interpreting dementia and related cognitive decline. Spatiotemporal EEG can be described through microstate analysis, which provides a discrete approximation continuous electric field generated by brain cortex. Here, we propose novel indicator, termed sequence non-randomness index (MSNRI). The essence method lies in initially generating transition state space compression data using analysis. Following this, assess these information-based similarity results suggest that this MSNRI metric potential marker distinguishing between health control (HC) frontotemporal (FTD) (HC vs. FTD: 6.958 5.756, p < 0.01), as well HC populations with Alzheimer’s disease (AD) AD: 5.462, 0.001). Healthy individuals exhibit more complex macroscopic structures non-random microstates, whereas disorders lead to random patterns. Additionally, extend proposed integrating Complementary Ensemble Empirical Mode Decomposition (CEEMD) explore microstates at specific frequency scales. Moreover, assessed effectiveness innovative predicting scores. demonstrate incorporation CEEMD-enhanced indicators significantly improved prediction accuracy Mini-Mental State Examination (MMSE) scores (R2 = 0.940). not only aids exploration large-scale neural changes but also offers robust tool characterizing dynamics transitions their impact on function.

Язык: Английский

Процитировано

2