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). These changes can be measured by electroencephalography (EEG), and various methods have been employed to investigate different aspects of modulation in regional brain activity related to the performance of a cycling task.

For instance, Maceri et al. (2019) focused on the variations of signal power (quantified by means of power spectral density—PSD) in the prefrontal and the motor cortex during an incremental cycling test and found a significant increase of PSD in the Alpha and Beta bands in association with an increase of exercise intensity, particularly during the last two stages of the incremental test. 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. On the other hand, Brümmer et al. (2011) used the low-resolution brain electromagnetic tomography (LORETA) technique to analyze low-resolution EEG data and to investigate the effect of increasing cycling intensity on the magnitude and the location of the changes in electrocortical current density, showing that current density in the primary motor cortex increases with increasing exercise intensity. With a similar approach, Schneider et al. (2013) studied how motor cortex activity varied during a moderate- to high-intensity cycling exercise.

Other studies go a step further and calculate functional connectivity patterns within the brain, with the purpose to identify the routes of information flow during the different phases of a cycling task. For instance, Comani et al. (2014) analyzed EEG data recorded during an exhaustive cycling test by calculating the coherence patterns at the sensor level and detected functional connectivity patterns related to three different attentional strategies. Similarly, di Fronso et al. (2018) performed a coherence analysis to detect cortical connectivity patterns in relation to the attentional strategies and the phases of an endurance cycling task. The Alpha and Beta bands were considered, and the results showed higher EEG coherence values at rest than during cycling periods for all electrode pairs and frequency bands, irrespective of the attentional strategy adopted. Hilty et al. (2011) also performed a functional connectivity study on EEG data recorded during a fatiguing cycling exercise to investigate the intracortical communication between the mid/anterior insular and the motor cortex. They evaluated the lagged phase synchronization (LPS) (Pascual-Marqui, 2007a) between these two specific brain areas and found that LPS significantly increased at the end of the cycling task and returned to baseline after the participants volitionally stopped the exercise, hence demonstrating that the mid/anterior insular cortex communicates with the motor cortex during a muscle-fatiguing activity.

Coherence and lagged phase synchronization permit to reconstruct networks of spatially distributed electrodes (sensor level) or brain areas (source level) that are functionally connected during the performance of a given task. These networks provide an overview of the functional interactions between neighboring and distant brain regions (Friston, 2011) but do not elucidate how information is exchanged within the functional network and do not identify its functional properties—such as the type of efficiency. A Graph Theory perspective can be beneficial in this regard (Latora and Marchiori, 2001). According to a graph theoretical approach, the brain is defined as a network containing nodes and edges, which respectively, represent brain regions and the connecting pathways between those regions (Bullmore and Sporns, 2009; Rubinov and Sporns, 2010). Very recently, Porter et al. (2019) used EEG electrodes space graph theoretical analysis to examine Theta band functional connectivity during a combined graded working memory and exercise task on a stationary bicycle. Several graph theoretical indices were computed, and the analysis revealed significant changes only for the clustering coefficient, which is a measure of the prevalence of local connectivity within a functional network. Specifically, Porter et al. (2019) found that local connectivity increased during the first three blocks of the exercise and decreased toward the end of the exercise, when the task became more difficult. However, these results are limited because the graph theory analysis was applied to the frontal region only: as a consequence, the overall properties of the complete functional brain network underpinning the cycling task remained unexplored. Further studies should examine the properties of functional networks over the whole brain.

Drawing on the aforementioned research results, we conducted a within-subject study to examine how functional connectivity and brain network efficiency varied during the performance of an endurance cycling task. For this purpose, we reconstructed the EEG signals at the source level by using the extended Loreta (eLoreta) algorithm (Pascual-Marqui et al., 2018b), calculated lagged coherence maps (Pascual-Marqui, 2007a, b; Pascual-Marqui et al., 2011, 2018a), and computed three graph theoretical indices—namely, local efficiency (LE), global efficiency (GE), and density (D)—to examine local and global functional properties. 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, and D is a global measure of connectivity that describes the extent of connectivity in a network (Sporns et al., 2004; Bullmore and Sporns, 2009; Rubinov and Sporns, 2010; Friston, 2011). All measures were computed for both Alpha (8–13 Hz) and Beta (13–20 Hz) frequency bands because Alpha activity is associated with alertness level (Wilson et al., 2011), whereas Beta activity provides information on motor binding and fatigue (Laufs et al., 2003; Cheron et al., 2016). Given the scant research that applies graph theoretical indices on endurance performance, the current investigation can be considered exploratory in nature.

As shown in Figures 3A,D, the within-subjects one-way ANOVA for LE values did not show significant differences among the six intervals (i.e., pre-cycling, cycling stage 1, cycling stage 2, cycling stage 3, and active and passive recovery) of the endurance cycling task for the two frequency bands [Alpha: F(5) = 0.498, p = 0.777, η_p² = 0.037, power = 0.175; Beta: F(5) = 1.901, p = 0.106, η_p² = 0.128, power = 0.609]. Therefore, we did not find any significant differences in the local efficiency across the various phases of the endurance cycling task for any of the analyzed frequency bands.

On the other hand, the within-subjects one-way ANOVA for GE yielded significant differences among the six intervals of the endurance cycling task for the Alpha band [F(5) = 3.749, p = 0.005, η_p² = 0.224, power = 0.914; see Figure 3B], but not for the Beta band [F(5,2.811) = 2.105, p = 0.120, η_p² = 0.139, power = 0.478; see Figure 3E]. As shown in Figure 3B, post hoc pairwise comparisons revealed significant differences between pre-cycling and cycling stage 1 (p = 0.005, Cohen d = 1.283) and between pre-cycling and cycling stage 2 (p = 0.005, Cohen d = 1.276), highlighting a significant increase of the global efficiency in the Alpha band during the initial phases of the endurance cycling task.

The within-subjects one-way ANOVA for D values also showed significant differences among the six intervals of the endurance cycling task for the Beta band [F(5) = 5.252, p < 0.001, η_p² = 0.288, power = 0.981; see Figure 3F], but not for the Alpha band [F(5) = 0.646, p = 0.665, η_p² = 0.047, power = 0.221; see Figure 3C]. Post hoc pairwise comparisons showed significant differences between pre-cycling and cycling stage 3 (p = 0.047, Cohen d = 0.966), between cycling stage 1 and cycling stage 3 (p = 0.031, Cohen d = 1.024), and between cycling stage 3 and passive recovery (p = 0.007, Cohen d = 1.239) (see Figure 3F), highlighting a significant decrease of the extent of functional connectivity in the Beta band during the initial phases of the endurance cycling task and a subsequent significant increase of the extent of functional connectivity in the Beta band during the final phases of the endurance cycling task. Table 2 provides the mean and the range for LE, GE, and D found for the six task periods in the Alpha and the Beta bands.

The aim of this exploratory study was to examine how functional connectivity at the source level (i.e., across brain areas) changed during the course of an endurance cycling task. 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 LE did not change significantly across the six defined intervals of the endurance cycling task, regardless of the frequency band considered. Given that LE remained essentially constant during the task in both bands (except for a small increase during cycling stage 1 in the Beta band), we can speculate that fatigue processes did not affect segregated neural processing (Rubinov and Sporns, 2010). It is noteworthy that we observed a sparse rather than a local connectivity pattern involving the fronto-parietal areas in each interval of the task (see Figure 2). The lack of significant LE changes during task execution and the corresponding sparse connectivity patterns could be justified by the fact that a cycling task is bilateral in nature, hence requiring sustained movement and effort (Comani et al., 2014). Accordingly, cortico-cortical communication, even during the stages of sustained movement, cannot be considered as locally distributed and efficient. Despite the perception that fatigue might lead to a local reorganization of the cortical networks (Berchicci et al., 2013), our results contradict the notion that an expert athlete (e.g., an athlete who practices at least twice a week) engages in less cortico-cortical communication in the Alpha and the Beta bands (Deeny et al., 2009). Of note is that the patterns of less cortico-cortical communication in the Alpha and the Beta bands were mainly observed in predominantly unilateral and precision sports such as shooting or archery, for which strong contro-lateral cortical patterns were also observed (Deeny et al., 2009). Notwithstanding the lack of significant differences, we observed an increased LE during cycling stage 1 (see Figures 3A,D). This result could be due to the transition from a period of global resting state (i.e., pre-cycling) that requires an extensive exchange of background information among all brain regions before task execution and movement (Petsche et al., 1997; Pfurtscheller and Andrew, 1999) to a period of sustained movement (di Fronso et al., 2018) such as cycling stage 1.

A similar result was obtained for GE. In particular, we observed significant differences on GE values between pre-cycling and cycling stage 1 and between pre-cycling and cycling stage 2, mainly apparent in the Alpha band and characterized by clearly increased values. The increase of GE in the Alpha band during cycling stages 1 and 2 could be due to greater alertness processes and preparedness (Gutmann et al., 2015) to stimuli during exercise (such as the increase of the power level of the cycle-ergometer). Since GE is a measure of functional integration in the brain, which reflects the ability to combine specialized information from distributed brain regions, the increased GE values could also be related to the tendency of central and temporal areas to overlay portions of the sensorimotor cortex that receive afferent feedback during exercise (Dishman et al., 2010). Moreover, the increased metabolic demands and arousal during exercise might have elicited diffuse increases in cortical EEG activity (Steriade et al., 1990).

The GE decrease in cycling stage 3 is partly in line with the findings obtained by di Fronso et al. (2018), who showed reduced coherence during the periods of an exhaustive cycling task characterized by high effort. Indeed during cycling stage 3 our participants were approaching exhaustion and, consequently, their highest perceived sustainable effort (see Table 3). Moreover, a high effort could likely make it harder to integrate information between different brain areas (Kong et al., 2015). In this perspective, it makes sense that the GE values clearly decrease during cycling stage 3 and subsequently increase during active and passive recovery, which are periods characterized by lower effort and absence of movement, respectively. Our results on GE during the pre-cycling period seem to disconfirm the aforementioned extensive exchange of background information among all brain regions before task execution and movement (Petsche et al., 1997; Pfurtscheller and Andrew, 1999; di Fronso et al., 2018). Indeed we noticed lower GE values during pre-cycling as compared to those during movement periods. This finding could be related to the easy nature of the task subsequently performed (i.e., pedaling), which does not involve complex demands (e.g., attentional demands).

During active and passive recovery, the restoring of values similar to those of the pre-cycling period is observed also for D, which reflects the extent of connectivity in a network. Indeed we observed—especially in the Beta band—significant differences between cycling stage 3 and passive recovery, with increased values during recovery. These results partly concur with the idea that, after a fatiguing cycling exercise, there is an increase in the communication between the mid/anterior insula and the motor cortex (Hilty et al., 2011). On the other hand, the significant difference obtained between pre-cycling and cycling stage 3 could be interpreted in terms of “efficiency,” that is, the cost of transmitting information within the network: an organization of brain networks, during high effort, may likely approach the maximum possible cost efficiency (Poldrack, 2015). The significant reduction of D during cycling can also be ascribed to the fact that task execution becomes more automated and less controlled during cycling stage 3, despite the high effort due to the increasing power level of the cycle-ergometer. The general reduction of functional connectivity during cycling stage 3 can be interpreted within the framework of the neural efficiency hypothesis (Callan and Naito, 2014), which posits a reduction of nonessential functional connectivity within the brain for expert/automated performances (Hatfield and Kerick, 2007). The decreased values of both GE and D indices during stage 3 may also have been influenced by a decision-making process (e.g., to endure or withdraw from a demanding task) typical of the last stages of an endurance task (Allen et al., 2013). In line with decision making tenets, during cycling stage 3, the connectivity could be more restricted to pre-frontal cortical areas (Kennerley and Walton, 2011).

Some limitations of our investigation should be considered for future research. First, the artifacts deriving from cycling affect low-frequency ranges: for this reason, we did not consider the Theta band in our study. Future studies examining also the Theta band within a graph theory framework could better address the neural efficiency hypothesis. Additionally, examining other graph theoretical indices, such as nodes (ROIs) and/or edges (functional connections between ROIs) betweenness centrality (He et al., 2009; Sporns, 2013, 2018), could enable a better understanding of which ROIs and which connections are most active during the different phases of an endurance cycling task. Also, to better understand how graph indices vary during active and passive recovery, longer periods of post-task recovery should be considered. Finally, to obtain more generalizable findings, the graph theory analysis should be extended to different endurance tasks and to a larger sample of experienced participants. Future studies should also envisage protocols including a control group to better examine patterns of cortico-cortical communications in the Alpha and the Beta bands.

To our knowledge, our study is one of the first ones attempting to estimate functional connectivity at the source level to typify endurance performance. Despite the mentioned limitations, we argue that the use of graph theoretical indices may represent a clear methodological advancement in investigating the brain patterns associated with the different phases of an endurance task and can provide new insights on the neurophysiological correlates of endurance performance.

The datasets generated for this study will be made available on request to the corresponding author.

The studies involving human participants were reviewed and approved by Ethics Committee of the University “G. d’Annunzio” of Chieti–Pescara (Italy) (Ethical approval Ref. no. 10-21/05/2015). The patients/participants provided their written informed consent to participate in this study.

GT and SdF conceived the study. GT, SdF, MB, and SC designed the study. GT and SC developed the methodology. GT and SdF ran the experiments. GT analyzed the data. GT, SdF, CR, MB, and SC wrote, revised, and edited the manuscript. All authors contributed to the article and approved the submitted version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.