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精神疲劳对大脑功能网络组织的影响。

Effects of Mental Fatigue on Brain Functional Network Organization.

机构信息

College of Engineering, Zhejiang Normal University, Jinhua 321004, China.

出版信息

Neural Plast. 2019 Dec 6;2019:1716074. doi: 10.1155/2019/1716074. eCollection 2019.

Abstract

Brain functional network has been widely applied to investigate brain function changes among different conditions and proved to be a -like network. But seldom researches explore the effects of mental fatigue on the brain functional network organization. In the present study, 20 healthy individuals were included to do a consecutive mental arithmetic task to induce mental fatigue, and scalp electroencephalogram (EEG) signals were recorded before and after the task. Correlations between all pairs of EEG channels were determined by mutual information (MI). The resulting adjacency matrices were converted into brain functional networks by applying a threshold, and then, the clustering coefficient (), characteristic path length (), and corresponding feature were calculated. Through performing analysis of variance (ANOVA) on the mean MI for every EEG rhythm, only the data of 1 rhythm during the task state were emerged for the further explorations of mental fatigue. For a wide range of thresholds, increased and and feature decreased with the deepening mental fatigue. The pattern of the characteristic still existed when computed with a constant degree. Our present findings indicated that more functional connectivities were activated at the mental fatigue stage for efficient information transmission and processing, and mental fatigue can be characterized by a reduced network characteristic. Our results provide a new perspective to understand the neural mechanisms of mental fatigue based on complex network theories.

摘要

脑功能网络已被广泛应用于研究不同状态下的大脑功能变化,并已被证明是一种类似网络。但很少有研究探讨心理疲劳对脑功能网络组织的影响。在本研究中,纳入了 20 名健康个体,进行连续的算术任务以诱导心理疲劳,并在任务前后记录头皮脑电图(EEG)信号。通过互信息(MI)确定所有 EEG 通道之间的相关性。将得到的邻接矩阵通过应用阈值转换为脑功能网络,然后计算聚类系数()、特征路径长度()和相应的特征。通过对每个 EEG 节律的平均 MI 进行方差分析(ANOVA),仅在任务状态下的 1 个节律的数据被用于进一步探讨心理疲劳。对于广泛的阈值,随着心理疲劳的加深,增加,和特征降低。当以恒定程度计算时,模式仍然存在。我们的研究结果表明,在心理疲劳阶段,更多的功能连接被激活,以实现有效的信息传输和处理,并且可以通过降低网络特征来表征心理疲劳。我们的结果为基于复杂网络理论理解心理疲劳的神经机制提供了新的视角。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a73/6918937/2fcbaf8c3d12/NP2019-1716074.001.jpg

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