Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability

Brain Topography, Journal Year: 2024, Volume and Issue: 37(4), P. 496 - 513

Published: March 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.

Language: Английский

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A gender recognition method based on EEG microstates

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 173, P. 108366 - 108366

Published: March 22, 2024

Language: Английский

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Characterizing the temporal dynamics and maturation of resting-state activity: an EEG microstate study in preterm and full-term infants

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 19, 2024

Abstract By interfering with the normal sequence of mechanisms serving brain maturation, premature birth and related stress can alter perinatal experiences, potential long-term consequences on a child’s neurodevelopment. The early characterization functioning maturational changes is thus critical interest in infants who are at high risk atypical outcomes could benefit from diagnosis dedicated interventions. Using high-density electroencephalography (HD-EEG), we recorded activity extreme very preterm equivalent age pregnancy term (n=43), longitudinally 2-months later (n=33), compared full-term born (n=14). We characterized maturation by using microstate analysis to quantify spatio-temporal dynamics spontaneous transient network while controlling for vigilance states. comparison first showed slower as well altered properties infants. Maturation functional networks between term-equivalent 2 months preterms was linked emergence faster dynamics, manifested part shorter duration microstates, an evolution spatial organization dominant microstates. inter-individual differences temporal were further impacted sex (with boys) gestational some but not other considered factors. This study highlights approach reveal emerging

Language: Английский

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Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 17, 2024

Abstract Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine feature extraction power with efficiency SNNs has been introduced. This paper studies feasibility using a spiking network (CSNN) detect anticipatory slow cortical potentials (SCPs) related braking intention human participants an electroencephalogram (EEG). Data was collected during experiment wherein operated remote-controlled vehicle on testbed designed simulate urban environment. Participants were alerted incoming event via audio countdown elicit that measured EEG. The CSNN’s performance compared standard CNN, EEGNet three graph 10-fold cross-validation. CSNN outperformed all other networks, had predictive accuracy 99.06% true positive rate 98.50%, negative 99.20% F1-score 0.98. Performance comparable CNN ablation study subset EEG channels localized SCPs. Classification degraded only slightly when floating-point data converted into trains delta modulation connections.

Language: Английский

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Microsynt: Exploring the syntax of EEG microstates

NeuroImage, Journal Year: 2023, Volume and Issue: 277, P. 120196 - 120196

Published: June 5, 2023

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

Language: Английский

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Infant EEG microstate dynamics relate to fine-grained patterns of infant attention during naturalistic play with caregivers

Proceedings of the National Academy of Sciences, Journal Year: 2025, Volume and Issue: 122(11)

Published: March 13, 2025

As infants grow, they develop greater attentional control during interactions with others, shifting from patterns of attention primarily driven by caregivers (exogenous) to those that are also self-directed (endogenous). The ability endogenously infancy is thought reflect ongoing brain development and influenced joint between infant caregiver. However, whether measures caregiver behavior infant–caregiver relate activity unknown key for informing developmental models control. Using data 43 dyads, we quantified visual dyadic, head-mounted eye tracking play associated them the duration EEG microstate D/4 measured rest. Importantly, a scalp potential topography organization function attention-related networks. We found positively infant-led rate but did not associate caregiver-led rate, suggesting coordination may be critical neurobiological control, or vice versa. Further, negatively shift sustained duration, increased stability maturation its underlying neural substrates. Together, our findings provide insights into how abilities spatial temporal dynamics activity.

Language: Английский

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EEG microstates in early‐to‐middle childhood show associations with age, biological sex, and alpha power

Human Brain Mapping, Journal Year: 2023, Volume and Issue: 44(18), P. 6484 - 6498

Published: Oct. 24, 2023

Abstract Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large‐scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental features extracted from broadband EEG signal in large ( N = 139) cohort children spanning early‐to‐middle childhood (4–12 years age). Linear regression models were used to examine if participants' age and biological sex could predict parameters GEV , duration coverage occurrence for five microstate classes (A–E) both eyes‐closed eyes‐open resting‐state recordings. We further explored associations between these posterior alpha power after removal 1/ f ‐like aperiodic signal. The obtained our neurodevelopmental recordings broadly replicated four canonical (A D) frequently reported adults, with addition more recently established class E. Biological served as significant predictor (A, C, D, E). In addition, E found be positively associated recordings, while C exhibited band spectral power. Together, findings highlight influence on functional during childhood, extending understanding neural this important period development.

Language: Английский

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Lifespan music training experience changes duration and transition rates of EEG microstates related to working memory

Brain-Apparatus Communication A Journal of Bacomics, Journal Year: 2025, Volume and Issue: 4(1)

Published: Feb. 13, 2025

Language: Английский

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Distinct features of EEG microstates in autism spectrum disorder revealed by meta-analysis: the contribution of individual age to heterogeneity across studies

Frontiers in Psychiatry, Journal Year: 2025, Volume and Issue: 16

Published: April 22, 2025

Electroencephalographic (EEG) microstates, as quasi-stable scalp EEG spatial patterns, are characterized by their high temporal resolution, making them a potentially powerful approach for studying the function of large-scale brain networks. A substantial body research has demonstrated that abnormalities in or structure networks closely related to many characteristics autism spectrum disorder (ASD). Investigating microstate features individuals with can help reveal nature autism. To date, numerous studies have observed unique resting-state patterns However, results these not been consistent. Therefore, present study aims assess differences parameters between ASD and non-autistic groups through meta-analysis explore sources heterogeneity. This was preregistered PROSPERO (CRD42024599897) followed PRISMA guidelines. Studies English comparing Non-autistic were retrieved database search October 20, 2024. The then conducted using RevMan5.2. Pooled expressed standardized mean difference (SMD). Heterogeneity (I²) publication bias assessed Stata15.0. Seven enrolling 194 included, four deemed quality three moderate according risk assessment. Microstate B duration coverage significantly greater pooled group (duration SMD=0.83, 95%CI: 0.17-1.5; SMD=0.54, 0.18-0.90), but heterogeneity could be excluded. C occurrence frequency also (SMD= -0.61, -1.08 -0.15), significant. Sensitivity analysis revealed only robust. Subgroup suggested age main source coverage. Results affected Egger's test. Future on must control an important cofounding variable. PROSPERO, identifier CRD42024599897.

Language: Английский

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Resting state electroencephalography microstates in autism spectrum disorder: A mini-review

Frontiers in Psychiatry, Journal Year: 2022, Volume and Issue: 13

Published: Dec. 1, 2022

Atypical spatial organization and temporal characteristics, found via resting state electroencephalography (EEG) microstate analysis, have been associated with psychiatric disorders but these parameters are less known in autism spectrum disorder (ASD). EEG microstates reflect a short time period of stable scalp potential topography. These canonical (i.e., A, B, C, D) more identified by their unique topographic map, mean duration, fraction covered, frequency occurrence global explained variance percentage; measure how well topographical maps represent data. We reviewed the current literature for analysis ASD eight publications. This review indicates there is significant alterations populations as compared to typically developing (TD) populations. Microstate were also change relation specific cognitive processes. However, be changed states, differently acquired data (e.g., eyes closed or open) likely produce disparate results. understanding sources underlying brain networks.

Language: Английский

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