Combining Partial Directed Coherence and Graph Theory to Analyse Effective Brain Networks of Different Mental Tasks.
Combining Partial Directed Coherence and Graph Theory to Analyse Effective Brain Networks of Different Mental Tasks.
复制标题
结合部分定向的连贯性和图形论，以分析不同心理任务的有效大脑网络。
DOI：
10.3389/fnhum.2016.00235
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发表时间：
2016
影响因子：
2.9
通讯作者：
Huang L
中科院分区：
文献类型：
--
作者：
Huang D;Ren A;Shang J;Lei Q;Zhang Y;Yin Z;Li J;von Deneen KM;Huang L
目的：本研究的目的是确定健康人群中两种不同心理任务（游戏任务或休息任务）之间大脑网络的网络特性。方法和材料：记录 19 名健康受试者在执行不同心理任务时的脑电图信号。基于格兰杰因果关系（GC）的部分定向一致性（PDC）分析用于评估不同心理任务期间的有效大脑网络。此外，还计算和分析了网络度量，包括 delta、theta、alpha 和 beta 节律的度、度分布、局部和全局效率。结果：在游戏任务中，局部效率的 beta 频率较高，theta 频率较低，而在休息任务中，全局效率的 theta 频率较高，beta 频率较低。意义：本研究揭示了不同心理状态下的网络测量和效率测量可以作为改善注意力表现的特征量。
Purpose: The aim of this study is to qualify the network properties of the brain networks between two different mental tasks (play task or rest task) in a healthy population. Methods and Materials: EEG signals were recorded from 19 healthy subjects when performing different mental tasks. Partial directed coherence (PDC) analysis, based on Granger causality (GC), was used to assess the effective brain networks during the different mental tasks. Moreover, the network measures, including degree, degree distribution, local and global efficiency in delta, theta, alpha, and beta rhythms were calculated and analyzed. Results: The local efficiency is higher in the beta frequency and lower in the theta frequency during play task whereas the global efficiency is higher in the theta frequency and lower in the beta frequency in the rest task. Significance: This study reveals the network measures during different mental states and efficiency measures may be used as characteristic quantities for improvement in attentional performance.