IEEE Trans Neural Syst Rehabil Eng. 2018 Feb;26(2):263-271. doi: 10.1109/TNSRE.2016.2581809. Epub 2016 Jun 16.
Training is a process to improve one's capacity or performance through the acquisition of knowledge or skills specific for the trained task. Although behavioral performance would be improved monotonically and reach a plateau as the learning progresses, neurophysiological signal shows different patterns like a U-shaped curve. One possible account for the phenomenon is that the brain first works hard to learn how to use task-relevant areas, followed by improvement in the efficiency derived from disuse of irrelevant brain areas for good task performance. Here, we hypothesize that topology of the brain network would show U-shaped changes during the training on a piloting task. To test this hypothesis, graph theoretical metrics quantifying global and local characteristics of the network were investigated. Our results demonstrated that global information transfer efficiency of the functional network in a high frequency band first decreased and then increased during the training while other measures such as local information transfer efficiency and small-worldness showed opposite patterns. Additionally, the centrality of nodes changed due to the training at frontal and temporal sites. Our results suggest network metrics can be used as biomarkers for quantifying the training progress, which can be differed depending on network efficiency of the brain.
训练是通过获取特定于训练任务的知识或技能来提高一个人能力或表现的过程。虽然行为表现会随着学习的进展单调地提高并达到一个平台，但神经生理信号显示出不同的模式，如 U 形曲线。一种可能的解释是，大脑首先努力学习如何使用与任务相关的区域，然后通过不使用与任务无关的大脑区域来提高效率，从而提高任务表现。在这里，我们假设在驾驶任务的训练过程中，大脑网络的拓扑结构会显示出 U 形变化。为了验证这一假设，我们研究了量化网络全局和局部特征的图论指标。我们的结果表明，在训练过程中，高频带功能网络的全局信息传递效率先降低后升高，而其他指标，如局部信息传递效率和小世界特性，则呈现相反的模式。此外，由于训练，节点的中心性在额叶和颞叶部位发生了变化。我们的研究结果表明，网络指标可作为量化训练进展的生物标志物，其取决于大脑网络效率的不同而有所差异。
