EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks.

Area of Science:

• Neuroscience
• Exercise Physiology
• Network Science

Background:

• Central nervous system responses to exercise are gaining research interest.
• Mobile electroencephalography (EEG) and graph theory offer new ways to study brain networks.
• The reliability of brain graphs after exercise is not yet well understood.

Purpose of the Study:

• To investigate the reliability of brain graphs derived from resting-state EEG data.
• To assess reliability before and after submaximal exercise in male participants.
• To determine the influence of exercise on the reliability of graph measures.

Main Methods:

• Resting-state EEG data collected twice weekly from male participants.
• Calculation of graph measures: small-world index (SWI), clustering coefficient (CC), characteristic path length (PL).
• Analysis using weighted phase lag index (wPLI) and spectral coherence (Coh), with reliability assessed by Intraclass Correlation Coefficient (ICC) and Coefficient of Variation (CoV).

Main Results:

• Good to excellent Intraclass Correlation Coefficients (ICCs) were found after exercise across multiple frequency bands (theta, alpha, beta).
• Exercise positively impacted the reliability of wPLI-based graph measures.
• Exercise negatively affected the reliability of spectral coherence-based graph measures.

Conclusions:

• Brain graphs are a reliable tool for analyzing brain networks in exercise contexts.
• Exercise may enhance brain graph reliability through neuroregulatory effects on functional connectivity.
• These findings support the use of brain graph measures in both acute and longitudinal exercise studies.