Modulation of Brain Functional Connectivity and Efficiency During an Endurance Cycling Task: A Source-Level EEG and Graph Theory Approach

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.

A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

“…A bilateral connectivity pattern of information flow during different exertion levels was observed by Comani et al [271]. A recent study addressed the functional brain patterns and network topology during a cycling task [281]. Three graph theory measures were computed in EEG source level for six different difficulties.…”

Alterations of Cerebral Hemodynamics and Network Properties Induced by Newsvendor Problem in the Human Prefrontal Cortex

“…However, there are a few investigations that explore dynamic network properties during a task phase. Tamburro et al explored connectivity and efficiency of the brain networks during an endurance cycling task (Tamburro et al, 2020 ). The study discussed several aspects of global network parameters that could affect behavior.…”

“…In this study, we observed Eloc and Cp increase during our decision phase with respect to the rest phase, indicating the transition from the resting state. Moreover, it was suggested that the reduction of global efficiency (Eg) is a probable sign of hard effort (Kong et al, 2015 ; Tamburro et al, 2020 ). In theory, Eg and Lp are highly anticorrelated, a reduction of Eg is equivalent to an increase of Lp.…”

“…Detailed definitions and explanations of these network metrics can be found in Cai et al ( 2018 ). These metrics were chosen primarily because they are network parameters popularly reported in literature (Iturria-Medina et al, 2008 ; Tamburro et al, 2020 ), Furthermore, these metrics focus on local network connections and may reflect more direct linkage or association with local brain activations.…”

EEG Correlates of Central Origin of Cancer-Related Fatigue

“…Future efforts should look at neural correlates of CRF by studying brain activity and connectivity within the wide motor network regions, in particular the sensory and frontal cortical regions given their potential roles in supraspinal fatigue. To this end, advanced processing techniques, like independent component analysis or source imaging, have been successfully used to study fatigue in endurance cycling [ 62 ] and air traffic control [ 63 ]. In addition, corticomuscular coherence can study changes in brain to muscle drive or muscle to brain afferent inputs in CRF compared to healthy individuals [ 60 ].…”