Multivariate associations between temporal resting-state EEG microstate parameters and physical fitness: An exploratory study

Purpose Electroencephalography (EEG) microstate analysis provides a marker of the temporal dynamics of large-scale functional brain networks. While exercise is known to influence resting-state brain activity, its relationship with dynamic network organization remains unclear. We therefore investigated associations between physical fitness and EEG microstate dynamics at rest.
Methods: In 30 healthy subjects, VO2max and lower-limb strength was tested using spiroergometric testing on a cycling ergometer and isokinetic strength testing. A 32-channel resting EEG was recorded (2 min eyes open/closed) and temporal parameters for four representative microstate topographies (A – D) were extracted. Associations between physical fitness and temporal parameters of microstate dynamics were examined using partial least squares correlation (PLSC), complemented by FDR-corrected univariate regressions for features showing stable multivariate contributions (Bootstrap ratio ≥ 2.5).
Results: The PLSC analysis shows a significant multivariate association between the microstate parameters and strength (r = 0.71, p_perm = 0.028). The regression analyses on the stable features identified by the PLSC model showed that higher strength was associated with longer duration of microstates B (ß = 0.488, qFDR = 0.036, R2 = 0.36) and C (ß = 0.493, qFDR = 0.036, R2 = 0.34) and lower individual explained variance of microstate C (ß = − 0.536, qFDR = 0.036, R2 = 0.20). No such association was identified for VO2max.
Conclusion: Our results provide first evidence that interindividual differences in physical fitness are also reflected in the temporal organization of large-scale brain networks. Future studies should determine whether longitudinal exercise interventions can induce such neurobiological adaptations.