摘要
利用视觉空间注意事件相关电位(ERP)构建了功能性网络;计算并分析了该网络的聚类系数;提出了一个适用的复杂网络统计参数即成对区域连接边数百分比;研究了ERP网络的特性及注意、刺激视野区域对该网络的影响。该聚类系数显著大于相应的随机网络的聚类系数,验证了网络的小世界特性。成对区域连接边数百分比显示刺激对侧大脑前后部的连接显著比刺激同侧大脑前后部的连接强。发现注意和非注意条件下的两个复杂网络参数有明显的不同,说明这两个参数能反映不同实验条件的大脑动力学特性。新的复杂网络统计参数的提出是研究各种认知任务下大脑动力学特性的一种有效的方法。
Event-related potential （ERP） measurements are used to build functional network of spatial attention. The clustering coefficient is picked for analyzing this complex network. One new statistical parameter of existing edges percent between paired regions of interest （ROI） is proposed for analyzing ERP networks. Upon this, the properties of ERP functional network and the influences of locations of attention and stimulus are investigated. The fact that the clustering coefficient of ERP network is bigger than that of equivalent random network demonstrates the small world property of ERP network. Comparing existing edges percent between four ROI, the result shows that more edges exist between tile stimulus contralateral posterior and anterior brain regions than those in ipsilateral regions. The statistical parameters of ERP networks between attention and unattention are obviously different, which indicates these parameters might be important indices of reflecting the ongoing brain dynamics. Proposal of new statistical parameters of complex networks may be a useful approach to study detailedly the connectivity of brain in various cognitive tasks.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2012年第5期792-795,共4页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金(30800242
91120016)
关键词
功能性脑网络
聚类系数
事件相关电位
小世界特性
统计检验
brain functional network
clustering coefficient
event-related potential
small-world
statistical tests
