脑网络社团连接在轻度认知障碍分类中的应用

Application of Brain Network Community Bridges in the Classification of Mild Cognitive Impairment

轻度认知障碍(mild cognitive impairment,MCI)发展成为阿尔茨海默病(alzheimer’s disease,AD)的概率极高,因此对于MCI的早诊尤为重要。本研究首次选取模块桥梁连接数作为分类特征,精确直观地反映出各功能脑区连接的异常损失。首先利用“堆结构”的贪婪算法模块化MCI和正常人(normal control,NC)的静息态功能脑网络,之后根据连接介数中心性去除网络中冗余功能连接,选取模块间与模块内桥梁连接数作为分类特征。该研究利用支持向量机对NC和MCI进行识别,平均分类正确率达92.89%,且统计分析显示两组被试在模块内及模块间的桥梁连接数有明显差异,其中默认网络、边缘系统的差异最为显著,这与先前研究基本一致。

The mild cognitive impairment(MCI)is highly likely to develop into Alzheimer’s disease,so early diagnosis of MCI is especially important.In this study,the number of global bridges were selected as the classification feature for the first time.This research used the greedy algorithm of“heap structure”to modularize the resting state functional brain network of MCI and Normal control(NC),then,the redundant functional connections in the network were removed according to the centrality of connection mediators,and the number of global bridges between modules and within modules were selected as the classification features.Support vector machine(SVM)was used as classification model to recognize NC and MCI.Results show that the average classification accuracy rate reached 92.89%,and the statistical analysis shows that there were significant differences in the number of global bridges between and within the modules between the two groups,especially in the default network and the limbic system,which was basically consistent with the previous research.