Abstract
Various methods have been employed to investigate different aspects of brain activity modulation related to the performance of a cycling task. In our study, we examined how functional connectivity and brain network efficiency varied during an endurance cycling task. For this purpose, we reconstructed EEG signals at source level: we computed current densities in 28 anatomical regions of interest (ROIs) through the eLORETA algorithm, and then we calculated the lagged coherence of the 28 current density signals to define the adjacency matrix. To quantify changes of functional network efficiency during an exhaustive cycling task, we computed three graph theoretical indices: local efficiency (LE), global efficiency (GE), and density (D) in two different frequency bands, Alpha and Beta bands, that indicate alertness processes and motor binding/fatigue, respectively. LE is a measure of functional segregation that quantifies the ability of a network to exchange information locally. GE is a measure of functional integration that quantifies the ability of a network to exchange information globally. D is a global measure of connectivity that describes the extent of connectivity in a network. This analysis was conducted for six different task intervals: pre-cycling; initial, intermediate, and final stages of cycling; and active recovery and passive recovery. Fourteen participants performed an incremental cycling task with simultaneous EEG recording and rated perceived exertion monitoring to detect the participants’ exhaustion. LE remained constant during the endurance cycling task in both bands. Therefore, we speculate that fatigue processes did not affect the segregated neural processing. We observed an increase of GE in the Alpha band only during cycling, which could be due to greater alertness processes and preparedness to stimuli during exercise. Conversely, although D did not change significantly over time in the Alpha band, its general reduction in the Beta bands during cycling could be interpreted within the framework of the neural efficiency hypothesis, which posits a reduced neural activity for expert/automated performances. We argue that the use of graph theoretical indices represents a clear methodological advancement in studying endurance performance.
Highlights
Studies performed in the last decade have assessed that cycling induces specific changes in brain cortical activity (Schneider et al, 2009, 2013; Ludyga et al, 2016; Bao et al, 2019)
The connectivity maps were calculated for two meaningful frequency bands (Alpha and Beta), and the properties of the functional networks obtained were quantified using indices derived from the Graph Theory
We found that local efficiency (LE) did not change significantly across the six defined intervals of the endurance cycling task, regardless of the frequency band considered
Summary
Studies performed in the last decade have assessed that cycling induces specific changes in brain cortical activity (Schneider et al, 2009, 2013; Ludyga et al, 2016; Bao et al, 2019). Other studies employed independent component analysis (ICA) and models of signal sources to identify the brain areas maximally involved in a cycling task, such as in the study by Enders et al (2015), who applied ICA and an equivalent current dipole model to the EEG signals recorded during a time-to-exhaustion test. They identified four cortical areas (two in the parietal cortex and two in the frontal cortex) showing a significant increase in signal power, which is likely due to fatigue developed during the exercise. Schneider et al (2013) studied how motor cortex activity varied during a moderate- to high-intensity cycling exercise
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