中文摘要：

目的 探讨遗忘型轻度认知障碍（amnestic mild cognitive impairment,aMCI）患者脑白质网络全局属性的特征,并评估脑网络分析方法对早期诊断阿尔茨海默病的作用.方法 收集2011年1月至2014年8月于首都医科大学宣武医院记忆门诊就诊的26例aMCI患者及同期社区招募的30名健康对照的弥散张量成像图像数据,基于纤维根数和部分各向异性构建56个脑网络矩阵,通过置换检验对不同阈值下两组人群的脑网络参数进行比较.结果 aMCI组和健康对照组的脑白质网络均表现出显著的小世界特征,与健康对照组相比,aMCI患者标准化聚集系数（如阈值取0.1,aMCI组为2.47,健康对照组为2.57,P=0.049）、局部效率（aMCI组为12.01,健康对照组为13.57,P=0.001）及小世界属性（aMCI组为2.02,健康对照组为2.11,P=0.013）明显下降,但其平均节点度（aMCI组为92.02,健康对照组为103.62,P=0.502）,全局效率（aMCI组为3.32,健康对照组为3.62,P=0.061）和标准化平均最短路径长度（aMCI组为1.23,健康对照组为1.23,P=0.199）与健康对照组相比差异无统计学意义.结论 aMCI患者的脑白质网络已经出现改变,基于弥散张量成像的脑网络分析有望成为新的早期诊断aMCI的影像学标志.

英文摘要：

Objective To investigate the characteristics of the topological architecture of structural brain networks using diffusion tensor imaging （DTI） in amnestic mild cognitive impairment （aMCI） patients and evaluate the value of quantitative complex network analysis in early diagnoses of Alzheimer＇s disease.Methods In this study,26 aMCI patients and 30 age-matched normal controls,collected in memory clinics at Xuanwu Hospital of Capital Medical University from January 2011 to August 2014,underwent DTI.Fiftysix network matrices were constructed thresholding fractional anisotropy and fiber number.Finally relevant network parameters were compared between the two groups utilizing permutation test.Results Both groups showed small-world architecture,whereas compared to normal controls,significant decrease in normalized clustering coefficient （for example,when threshold is 0.1,aMCI group was 2.47,normol control group was 2.57,P =0.049）,local efficiency （aMCI group was 12.01,normol control group was 13.57,P =0.001） and small-world （aMCI group was 2.02,normol control group was 2.11,P =0.013） were found in aMCI,but there was no significant difference in average degree （aMCI group was 92.02,normol control group was 103.62,P =0.502）,normalized characteristic path length （aMCI group was 3.32,normol control group 3.62,P =0.061） and global efficiency （aMCI group was 1.23,normol control group 1.23,P =0.199） between the two groups.Conclusion Our findings suggest that the structural network widely alters in aMCI patients and network analysis has the potential to be an imaging biomarker for aMCI diagnosis.