Multi-perspective characterization of seizure prediction based on microstate analysis DOI Creative Commons
Wei Shi, Yina Cao, Fangni Chen

et al.

Frontiers in Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: Nov. 19, 2024

Epilepsy is an irregular and recurrent cerebral dysfunction that significantly impacts the affected individual's social functionality quality of life. This study aims to integrate cognitive dynamic attributes brain into seizure prediction, evaluating effectiveness various characterization perspectives for while delving impact varying fragment lengths on performance each characterization. We adopted microstate analysis extract properties states, calculated EEG-based microstate-based features characterize nonlinear attributes, assessed power values across different frequency bands represent spectral information EEG. Based aforementioned characteristics, predictor achieved a sensitivity 93.82% private FH-ZJU dataset 93.22% Siena Scalp EEG dataset. The outperforms state-of-the-art works in terms metrics indicating it crucial incorporate prediction.

Language: Английский

Effect of animal behavior on EEG microstates in healthy children: An outdoor observation task DOI
Xiaoting Ding, Jiuchuan Jiang,

Mengting Wei

et al.

Journal of Intelligent & Fuzzy Systems, Journal Year: 2024, Volume and Issue: 46(4), P. 10757 - 10771

Published: March 12, 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Language: Английский

Citations

0

EEG microstates as an important marker of depression: A systematic review and meta-analysis DOI
Si Zhang, Aiping Chi,

Li-quan Gao

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 12, 2024

Abstract This study conducts a literature search through databases such as PubMed, Web of Science, CNKI (China National Knowledge Infrastructure), and the Cochrane Library to collect case-control studies on microstates in patients with depression. Conducting bias risk assessment using Review Manager 5.4, meta-analysis is performed Stata 18.0 14.0 software. has been registered Prospero, CRD42024543793. Our research results suggest that increased duration frequency microstate A may serve potential biomarker for An increase parameter B also observed when individuals experience anxiety. The coverage C are closely related rumination levels. Abnormalities D among some depression indicate presence comorbid conditions overlapping mental disorders or attention executive function deficits. provides important insights into identifying symptoms etiology by examining differences between healthy individuals.

Language: Английский

Citations

0

Neural Reward Processing in Young Children Associates with Current and Later Depressive Symptoms DOI Open Access
Nicolas L. Camacho, Michael S. Gaffrey

Published: July 24, 2024

Background: The emergence of depression during the preschool years is well established. However, neural correlates depressive symptoms this developmental period remain relatively understudied. Prior work has suggested a concurrent association between left amygdala salience reactivity (i.e., reward and loss versus neutral) in early childhood. Replication extension into predictive utility other relevant neurobiological are now needed. Methods: current longitudinal study conducted conceptual replication prior using functional Magnetic Resonance Imaging sample 4-8-year-old children. We investigated whether reward-related priori regions interest were associated with parent-reported childhood concurrently (N = 114; 54% female) approximately one year later 75; 53% female). Results: Left right caudate medial prefrontal cortex negatively symptom severity. Only severity later. Conclusions: Results replicate research suggesting an response to salient outcomes processing elevations young They also provide novel information supporting potential role as biomarker future symptoms. These findings insights our understanding emerging underscore important for depression.

Language: Английский

Citations

0

Longitudinal trajectories of aperiodic EEG activity in early to middle childhood DOI Creative Commons
Dashiell D. Sacks, Viviane Valdes, Carol L. Wilkinson

et al.

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

Published: Sept. 14, 2024

Abstract Background Emerging evidence suggests that aperiodic EEG activity may follow a nonlinear growth trajectory in childhood. However, existing studies are limited by small assessment windows and cross-sectional samples unable to fully capture these patterns. The current study aimed characterize the developmental trajectories of longitudinally from infancy middle We examined potential differences sex brain region. further investigated whether is associated with maternal anxiety symptoms, associations vary because differential development trajectories. Methods A community sample children their parents ( N =391) enrolled longitudinal emotion processing were assessed at infancy, ages 3 years, 5 7 years. Analyses included individual curve mixed effect models. Developmental slope offset across whole brain, frontal, central, temporal, posterior regions. Associations symptoms also examined. Results for both generally characterized relative increase early childhood subsequent decrease or stabilization age 7, variation Females showed relatively steeper slopes some ages, males greater certain ages. Maternal was negatively years positively Conclusions shows magnitude direction varied age, corresponding changes stage should be considered when interpreting findings related

Language: Английский

Citations

0

Multi-perspective characterization of seizure prediction based on microstate analysis DOI Creative Commons
Wei Shi, Yina Cao, Fangni Chen

et al.

Frontiers in Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: Nov. 19, 2024

Epilepsy is an irregular and recurrent cerebral dysfunction that significantly impacts the affected individual's social functionality quality of life. This study aims to integrate cognitive dynamic attributes brain into seizure prediction, evaluating effectiveness various characterization perspectives for while delving impact varying fragment lengths on performance each characterization. We adopted microstate analysis extract properties states, calculated EEG-based microstate-based features characterize nonlinear attributes, assessed power values across different frequency bands represent spectral information EEG. Based aforementioned characteristics, predictor achieved a sensitivity 93.82% private FH-ZJU dataset 93.22% Siena Scalp EEG dataset. The outperforms state-of-the-art works in terms metrics indicating it crucial incorporate prediction.

Language: Английский

Citations

0