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

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.

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.

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.

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.