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听觉运动任务的皮质动态因果网络

Cortical Dynamic Causality Network for Auditory-Motor Tasks.

作者信息

Liu Tiejun, Li Fali, Jiang Yi, Zhang Tao, Wang Fei, Gong Diankun, Li Peiyang, Ma Teng, Qiu Kan, Li He, Yao Dezhong, Xu Peng

出版信息

IEEE Trans Neural Syst Rehabil Eng. 2017 Aug;25(8):1092-1099. doi: 10.1109/TNSRE.2016.2608359. Epub 2016 Sep 12.

Abstract

Motor preparation and execution require the interactions of a large-scale brain network, while the study of the dynamic changes of their interactions could uncover the underlying neural mechanism of the corresponding information processing. This dynamic analysis requires high temporal resolution of the recorded signals. Electroencephalogram (EEG) with high temporal resolution has been widely used in related studies. However, studies based on scalp EEG always lead to distorted results, due to scalp volume conduction, compared with that of cortically recorded signals. In the current study, the dynamic networks of motor preparation and execution are investigated using Go/No-go tasks performed with the left/right hand. In the analysis, the EEG source localization and dynamic causal model are combined together to investigate the neural processes of motor preparation and execution. The results show that similar network patterns with nodes distributed in the bilateral occipital lobe, bilateral temporal lobe, bilateral dorsolateral prefrontal cortex, and contralateral supplementary motor area could be revealed for both the Go and No-go tasks. Statistical testing further indicates that stronger couplings with the supplementary motor area could be found in Go and right-hand response tasks compared with No-go and left-hand response tasks, respectively. The findings in the current study demonstrate that the information exchange within the motor related brain networks plays an important role for motor related functions, i.e., the different motor functions may have the different information exchange and processing network patterns.

摘要

运动准备和执行需要大规模脑网络的相互作用,而对其相互作用动态变化的研究可以揭示相应信息处理的潜在神经机制。这种动态分析需要记录信号具有高时间分辨率。具有高时间分辨率的脑电图(EEG)已在相关研究中广泛使用。然而,与皮质记录信号相比,基于头皮脑电图的研究由于头皮容积传导,总是会导致结果失真。在当前研究中,使用左右手执行的Go/No-go任务来研究运动准备和执行的动态网络。在分析中,将EEG源定位和动态因果模型结合起来,以研究运动准备和执行的神经过程。结果表明,Go任务和No-go任务都可以揭示出类似的网络模式,其节点分布在双侧枕叶、双侧颞叶、双侧背外侧前额叶皮质和对侧辅助运动区。统计检验进一步表明,与No-go任务和左手反应任务相比,在Go任务和右手反应任务中分别可以发现与辅助运动区更强的耦合。当前研究的结果表明,运动相关脑网络内的信息交换对运动相关功能起着重要作用,即不同的运动功能可能具有不同的信息交换和处理网络模式。

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