EEG dynamical network analysis method reveals the neural signature of visual-motor coordination
EEG dynamical network analysis method reveals the neural signature of visual-motor coordination
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DOI：
10.1371/journal.pone.0231767
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发表时间：
2020-05-27
期刊：
影响因子：
3.7
通讯作者：
Hayashi, Yoshikatsu
中科院分区：
文献类型：
--
作者：
Li, Xinzhe;Mota, Bruno;Hayashi, Yoshikatsu
人类视觉-运动协调是运动控制的一项基本功能，需要多个脑区的相互作用。了解皮质-运动协调对于改善运动障碍的物理治疗是很重要的。然而，其潜在的瞬时神经动力学在很大程度上仍是未知的。在这项研究中，我们应用基于特征向量的动态网络分析方法来研究视觉-运动协调任务下从脑电信号计算的功能连通性，并识别其亚稳态动力学。我们首先在模拟网络上测试了这种信号处理方法，并与其他动力学方法进行了比较，证明了基于特征向量的动态网络分析能够正确地提取演化网络的动态特征。随后，对视觉-运动协调实验下采集的脑电数据进行了基于特征向量的分析。在有参与者参与的脑电研究中，拓扑分析和基于特征向量的动态分析的结果都能够区分视觉跟踪任务的不同实验条件。通过动力学分析，我们发现，通过研究功能连接性的亚稳态动力学，可以区分不同的视觉-运动协调状态。
Human visual-motor coordination is an essential function of movement control, which requires interactions of multiple brain regions. Understanding the cortical-motor coordination is important for improving physical therapy for motor disabilities. However, its underlying transient neural dynamics is still largely unknown. In this study, we applied an eigenvector-based dynamical network analysis method to investigate the functional connectivity calculated from electroencephalography (EEG) signals under visual-motor coordination task and to identify its meta-stable states dynamics. We first tested this signal processing on a simulated network to evaluate it in comparison with other dynamical methods, demonstrating that the eigenvector-based dynamical network analysis was able to correctly extract the dynamical features of the evolving networks. Subsequently, the eigenvector-based analysis was applied to EEG data collected under a visual-motor coordination experiment. In the EEG study with participants, the results of both topological analysis and the eigenvector-based dynamical analysis were able to distinguish different experimental conditions of visual tracking task. With the dynamical analysis, we showed that different visual-motor coordination states can be distinguished by investigating the meta-stable states dynamics of the functional connectivity.