Exploring intensity-dependent modulations in EEG resting-state network efficiency induced by exercise

PURPOSE: Exhaustive cardiovascular load can affect neural processing and is associated with decreases in sensorimotor performance. The purpose of this study was to explore intensity-dependent modulations in brain network efficiency in response to treadmill running assessed from resting-state electroencephalography (EEG) measures. METHODS: Sixteen trained participants were tested for individual peak oxygen uptake (VO(2 peak)) and performed an incremental treadmill exercise at 50% (10 min), 70% (10 min) and 90% speed VO(2 peak) (all-out) followed by cool-down running and active recovery. Before the experiment and after each stage, borg scale (BS), blood lactate concentration (B(La)), resting heartrate (HR(rest)) and 64-channel EEG resting state were assessed. To analyze network efficiency, graph theory was applied to derive small world index (SWI) from EEG data in theta, alpha-1 and alpha-2 frequency bands. RESULTS: Analysis of variance for repeated measures revealed significant main effects for intensity on BS, B(La), HR(rest) and SWI. While BS, B(La) and HR(rest) indicated maxima after all-out, SWI showed a reduction in the theta network after all-out. CONCLUSION: Our explorative approach suggests intensity-dependent modulations of resting-state brain networks, since exhaustive exercise temporarily reduces brain network efficiency. Resting-state network assessment may prospectively play a role in training monitoring by displaying the readiness and efficiency of the central nervous system in different training situations.