ArticleScientific reports2021
EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Enhanced Brain Connectivity Following Six Weeks of Upper Extremity Offset Loading in Neurotypical Adults-A Preliminary Study.Sensors (Basel, Switzerland) · 2026Article
- Article
- Reliability and state-dependency of EEG connectivity, complexity and network characteristics.Scientific reports · 2025Article
- Impact of moderate aerobic exercise on small-world topology and characteristics of brain networks after sport-related concussion: an exploratory study.Scientific reports · 2024Article
- Reliable electrocortical dynamics of target-directed pass-kicks.Cognitive neurodynamics · 2024Article
- Unveiling the acute neurophysiological responses to strength training: An exploratory study on novices performing weightlifting bouts with different motor learning models.Biology of sport · 2024Article
- Stability, change, and reliable individual differences in electroencephalography measures: A lifespan perspective on progress and opportunities.NeuroImage · 2023Review
- Repeatability of Brain Activity as Measured by a 32-Channel EEG System during Resistance Exercise in Healthy Young Adults.International journal of environmental research and public health · 2023Article
- An ecological study protocol for the multimodal investigation of the neurophysiological underpinnings of dyadic joint action.Frontiers in human neuroscience · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The interaction of acute exercise and the central nervous system evokes increasing interest in interdisciplinary research fields of neuroscience. Novel approaches allow to monitor large-scale brain networks from mobile electroencephalography (EEG) applying graph theory, but it is yet uncertain whether brain graphs extracted after exercise are reliable. We therefore aimed to investigate brain graph reliability extracted from resting state EEG data before and after submaximal exercise twice within one week in male participants. To obtain graph measures, we extracted global small-world-index (SWI), clustering coefficient (CC) and characteristic path length (PL) based on weighted phase leg index (wPLI) and spectral coherence (Coh) calculation. For reliability analysis, Intraclass-Correlation-Coefficient (ICC) and Coefficient of Variation (CoV) were computed for graph measures before (REST) and after POST) exercise. Overall results revealed poor to excellent measures at PRE and good to excellent ICCs at POST in the theta, alpha-1 and alpha-2, beta-1 and beta-2 frequency band. Based on bootstrap-analysis, a positive effect of exercise on reliability of wPLI based measures was observed, while exercise induced a negative effect on reliability of Coh-based graph measures. Findings indicate that brain graphs are a reliable tool to analyze brain networks in exercise contexts, which might be related to the neuroregulating effect of exercise inducing functional connections within the connectome. Relative and absolute reliability demonstrated good to excellent reliability after exercise. Chosen graph measures may not only allow analysis of acute, but also longitudinal studies in exercise-scientific contexts.
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