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基于脑电的复杂网络性别差异性研究
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基于脑电的复杂网络性别差异性研究
徐桂芝,付灵弟,尹宁,耿跃华
基金项目:教育部博士学科点专项科研基金(20121317110002);国家自然科学基金资助项目(51377045) 作者简介:徐桂芝(1962),女,教授,博士生导师,主要研究方向,脑电与认知工程,生物电磁.

(河北工业大学电气工程学院,天津 300130)

摘要:人脑的生理结构和功能因性别不同存在很大差异,为了考察不同性别在脑网络研究中的差异性表现,本文利用复杂网络理论对20例健康被试静息状态下的脑电数据在连续阈值区间内进行了脑网络构建和分析。结果发现:在所选阈值区间内女性组聚类系数、特征路径长度、网络平均度、效率都普遍高于男性组,与之前研究者所得结论具有一致性,而且不同阈值间脑网络拓扑属性差异极显著(P<0.01)。结果提示:性别差异在脑网络分析中是一个不容忽视的因素。在利用复杂网络手段诊断各类疾病时,将性别因素考虑在内,很可能可以提高基于脑网络分析手段对各类疾病诊断的准确率。
关键词:复杂网络;脑电;性别;静息状态
中图分类号:R444

The study of gender difference using complex network theory based EEG data
Xu Guizhi, Fu Lingdi, Yin Ning, Geng Yuehua
(Hebei University of Technology,Tianjin 300130)
Abstract: There is a big difference in physiological structure and function of the human brain of  different gender, and in order to understand the differences in performance of the brain network of different gender. In this paper, the EEG data of 20healthy subjects in the resting state were studied by using the complex network theory and be analyzed in successive threshold range. Results show clustering coefficient, the average path length, the average degree and efficiency of the brain functional network of female group are generally higher than the male group, which is consistent with the result concluded by previous researchers. We also find that topology attributes of brain network in different thresholds show significant difference (P <0.01). The results suggest that: Gender differences are a factor, which can not be ignored. Taking gender difference into account when we use complex network theory to diagnose diseases may improve the accuracy.
Key words: complex networks; EEG; gender;resting state