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.

中文翻译：

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在耐力自行车任务期间调节大脑功能连接和效率：源级脑电图和图论方法

已采用各种方法来研究与骑自行车任务的表现相关的大脑活动调节的不同方面。在我们的研究中，我们研究了在耐力骑行任务中功能连接和大脑网络效率如何变化。为此，我们在源级重建 EEG 信号：我们通过 eLORETA 算法计算了 28 个感兴趣解剖区域 (ROI) 的电流密度，然后我们计算了 28 个电流密度信号的滞后相干性以定义邻接矩阵。为了量化详尽循环任务期间功能网络效率的变化，我们计算了三个图理论指数：局部效率 (LE)、全局效率 (GE) 和密度 (D) 在两个不同的频段，Alpha 和 Beta 频段，表明警觉性过程和运动束缚/疲劳，分别。LE 是功能隔离的度量，用于量化网络在本地交换信息的能力。GE 是一种功能集成的度量，它量化了网络在全球范围内交换信息的能力。D 是连通性的全局度量，用于描述网络中的连通性程度。该分析针对六个不同的任务间隔进行：预循环；骑行的初始、中间和最后阶段；以及主动恢复和被动恢复。14 名参与者执行了一项增量骑行任务，同时进行 EEG 记录和评级感知劳累监测，以检测参与者的疲劳。在两个波段的耐力骑行任务期间，LE 保持不变。因此，我们推测疲劳过程不会影响分离的神经过程。我们仅在骑自行车期间观察到 Alpha 波段中的 GE 增加，这可能是由于在运动期间更加警觉性过程和对刺激的准备。相反，尽管 D 在 Alpha 波段中没有随时间发生显着变化，但它在骑行期间 Beta 波段的普遍减少可以在神经效率假设的框架内进行解释，该假设假设专家/自动化表现的神经活动减少。我们认为，图形理论指数的使用代表了研究耐力表现的明显方法论进步。其在骑行期间 Beta 波段的普遍减少可以在神经效率假设的框架内进行解释，该假设假设专家/自动化表现的神经活动减少。我们认为，图形理论指数的使用代表了研究耐力表现的明显方法论进步。其在骑行期间 Beta 波段的普遍减少可以在神经效率假设的框架内进行解释，该假设假设专家/自动化表现的神经活动减少。我们认为，图形理论指数的使用代表了研究耐力表现的明显方法论进步。