Life Sciences, Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi 710071, PR China.
Neuroscience. 2012 Jan 27;202:276-82. doi: 10.1016/j.neuroscience.2011.11.048. Epub 2011 Dec 3.
Both anatomical and functional brain network studies have drawn great attention recently. Previous studies have suggested the significant impacts of brain network topology on cognitive function. However, the relationship between non-task-related resting-state functional brain network topology and overall efficiency of sensorimotor processing has not been well identified. In the present study, we investigated the relationship between non-task-related resting-state functional brain network topology and reaction time (RT) in a Go/Nogo task using an electroencephalogram (EEG). After estimating the functional connectivity between each pair of electrodes, graph analysis was applied to characterize the network topology. Two fundamental measures, clustering coefficient (functional segregation) and characteristic path length (functional integration), as well as "small-world-ness" (the ratio between the clustering coefficient and characteristic path length) were calculated in five frequency bands. Then, the correlations between the network measures and RT were evaluated in each band separately. The present results showed that increased overall functional connectivity in alpha and gamma frequency bands was correlated with a longer RT. Furthermore, shorter RT was correlated with a shorter characteristic path length in the gamma band. This result suggested that human RTs were likely to be related to the efficiency of the brain integrating information across distributed brain regions. The results also showed that a longer RT was related to an increased gamma clustering coefficient and decreased small-world-ness. These results provided further evidence of the association between the resting-state functional brain network and cognitive function.
最近,解剖和功能大脑网络研究引起了广泛关注。先前的研究表明,大脑网络拓扑结构对认知功能有重大影响。然而,非任务相关静息态功能大脑网络拓扑结构与感觉运动处理整体效率之间的关系尚未得到很好的确定。在本研究中,我们使用脑电图(EEG)研究了非任务相关静息态功能大脑网络拓扑结构与 Go/Nogo 任务中反应时间(RT)之间的关系。在估计每个电极对之间的功能连接后,我们应用图分析来描述网络拓扑结构。在五个频带中计算了两个基本度量标准,即聚类系数(功能分离)和特征路径长度(功能整合),以及“小世界特性”(聚类系数与特征路径长度之比)。然后,分别评估了网络测量值与 RT 之间的相关性。本研究结果表明,α 和γ 频带中整体功能连接的增加与 RT 延长有关。此外,γ 频带中特征路径长度较短与 RT 较短有关。这一结果表明,人类 RT 可能与大脑在分布式脑区之间整合信息的效率有关。结果还表明,较长的 RT 与γ 聚类系数增加和小世界特性降低有关。这些结果进一步提供了静息态功能大脑网络与认知功能之间关联的证据。