中文摘要：

阿尔茨海默症（AD）是以进行性认知功能障碍为主要特征的神经系统变性疾病，如何识别其大脑认知障碍的早期改变并干预治疗，对延缓痴呆的发生具有重要意义。已有研究表明AD与脑连接的异常变化有关。本文选取AD组和正常对照组各15名志愿者，采集清醒闭目状态的16导联脑电图（EEG）数据，分别对全频段和alpha频段（8～13Hz）EEG进行同步似然分析，选择合适阈值构建脑网络并计算网络的全局效率和聚类系数。结果表明，当阈值0．05≤T≤0．07时，AD组和正常对照组全频段脑网络的聚类系数无显著差异，阈值为0．06和0．07时AD组全频段网络全局效率比正常对照组小（P（0．05）；阈值范围（0．05≤T≤0．07）内，alpha网络AD组聚类系数和全局效率均低于正常对照组（P〈0．05）。AD患者静息态脑电alpha网络可能存在功能连接减弱现象，为从脑网络角度定量评估AD患者脑功能状态提供支持。

英文摘要：

Alzheimer＇s disease fAD） is the most common type of dementia and a neurodegenerative disease with pro- gressive cognitive dysfunction as the main feature. How to identify the early changes of cognitive dysfunction and give appropriate treatments is of great significance to delay the onset of dementia. Some other researches have shown that AD is associated with abnormal changes of brain networks. To study human brain functional connectivity char- acteristics in AD, 16 channels electroencephalogram （EEG） were recorded under resting and eyes-closed condition in 15 AD patients and 15 subjects in the control group. The synchronization likelihood of the full-band and alpha-band （8 13 Hz） data were evaluated, which resulted in the synchronization likelihood coefficient matrices. Considering a threshold T, the matrices were converted into binary graphs. Then the graphs of two groups were measured by to- pological parameters including the clustering coefficient and global efficiency. The results showed that the global effi ciency of the network in full-band EEG was significantly smaller in AD group for the values of T： 0. 06 and T= 0. 07, but there was no statistically significant difference in the clustering coefficients between the two groups for the values of T （0.05-0.07）. However, the clustering coefficient and global efficiency were significantly lower in AD pa- tients at alpha-band for the same threshold range than those of subjects in the control group. It suggests that there may be decreases of the brain connectivity strength in AD patients at alpha-band of the resting-state EEG. This study provides a support for quantifying functional brain state of AD from the brain network perspective.