基于复杂网络的三种眼睛状态下脑功能连通性比较  

Brain functional connectivity investigation of three eyes status based on complex networks analysis

作　　者：陈东太郎 曾卫明[1] 王倪传[1] 石玉虎[1] 

机构地区：[1]上海海事大学信息工程学院,上海201306

出　　处：《安徽大学学报（自然科学版）》2016年第4期73-79,共7页Journal of Anhui University(Natural Science Edition)

基　　金：国家自然科学基金资助项目(31170952;31470954);上海海事大学研究生创新基金项目(2014ycx026)

摘　　要：尝试构建不同眼睛状态下的静息态脑功能网络,研究睁眼、闭眼和自由状态下的大脑网络拓扑结构差异.招募了13例被试者,用一台3T的西门子磁共振仪,获取了3种眼睛静息状态下各8min的功能磁共振数据,并同时采集了结构像.随后,构建3组被试者的功能连通矩阵,计算其复杂网络的全局特征系数、局部特征系数等测度参数.对测度进行方差分析与双样本t检验,以统计分析被试者之间的差异.我们发现,所有的状态在合适的稀疏度下,展现了小世界特性;相比于其他状态,闭眼状态下的全局效率较低.自由状态在复杂网络的各项测度上接近睁眼状态.综上所述,该项研究展现了睁眼、闭眼、自由状态对人脑功能连通网络的影响,并为眼睛状态如何影响功能磁共振实验提供了关键的证据.This study investigates how eyes status affects the key topological signatures of human brain networks in eyes closed （EC）, open （EO） and eyes free （EF） condition. 13 subjects participated and underwent 3 imaging sessions with three 8 min resting-state fMRI scans （EC, EF, EO）. Image acquisition was acquired using a 3T MR scanner and consisted of anatomical images for co-registration of fMRI data. Followed by graph theory, the global and local metrics of functional connectivity in a range of sparse degree with the three eyes status were calculated to detect the statistically significant difference between them. Based on graph-theoretic topology metrics, we found that all the subject networks had demonstrated a small-world properties with a proper sparse degree, and the networks in EC had reduced both local and global efficiency. To summarize, our results demonstrated that the topologic structure of human brain network is related to eyes status, and provide an empirical evidence on how eyes status affects fMRI experiments.

关 键 词：自由状态 静息态功能磁共振 睁闭眼 复杂网络 功能连通性 

分 类 号：R319[医药卫生—基础医学]