Application of Small-World Measures to Multichannel Event-Related Potential Activity during Generation of Global and Local Imagery

The Small world model has been successfully used to explore the abnormal pattern of brain information processing in some neuropsychiatric diseases, but not engaged in the study of cognitive functions. We apply the small-world measures： the clustering coefficient and average path length, to evaluate multi-channel event-related potential activity during the generation of global and local imagery. Results show that the brain functional networks of the global and local imagery generation are both small-world ones. In addition, the local imagery generation has a larger clustering coefficient, while the global imagery generation has a shorter average path length. These results support the global precedence in the global-local imagery generation, and reflect the different processing modes in which global imagery emphasizes particularly on global integration, while local imagery on local specialization. Our results indicate that small-world measures could be applied to quantify the difference of brain activities in different cognitive tasks, and further provide some explanations for cognitive behavior.     

探测静息状态大脑活动的关键区域

负激活作为一种定量分析静息状态脑活动的方法对脑功能成像研究具有重要意义．文中关注不同认知任务下的负激活信息,结合复杂网络理论,提取三个试验任务(符号方向判别、数字加和与汉字字形认知)的负激活点分别构建网络,发现三个网络均具有小世界特性．同时计算了网络的度值和介数值,推测出扣带回\楔前叶,额叶是静息状态下大脑活动的关键区域．提出了一种研究静息状态下大脑活动的新方法,通过这种方法获得的信息不同于体素逐点分析获得的局部激活信息．它反映了大脑任意两个体素点间的关联性以及在静息状态下大脑活动的整体协作关系．     

Modelling Human Cortical Network in Real Brain Space

Highly specific structural organization is of great significance in the topology of cortical networks. We introduce a human cortical network model, taking the specific cortical structure into account, in which nodes are brain sites placed in the actual positions of cerebral cortex and the establishment of edges depends on the spatial path length rather than the linear distance. The resulting network exhibits the essential features of cortical connectivity, properties of small-world networks and multiple clusters structure. Additionally, assortative mixing is also found in this model. All of these findings may be attributed to the specific cortical architecture.