基于格兰杰因果分析的MCI脑网络分类研究  

Classifications of MCI Brain Network Based on Granger Causality Analysis

作　　者：崔会芳 周梦妮 王彬[1] 相洁[1] 曹锐[1] 阎鹏飞 CUI Huifang;ZHOU Mengni;WANG Bin;XIANG Jie;CAO Rui;YAN Pengfei(College of Computer Science and Technology,Taiyuan University of Technology,Jinzhong 030600,China)

机构地区：[1]太原理工大学计算机科学与技术学院,山西晋中030600

出　　处：《太原理工大学学报》2018年第6期853-860,共8页Journal of Taiyuan University of Technology

基　　金：国家自然科学基金资助项目(61503272;61305142;61373101);山西省自然科学青年基金资助项目(2015021090;201601D202042);博士后基金资助项目(2016M601287);山西省回国留学人员科研资助项目(2016-037)

摘　　要：使用静息态fMRI脑数据,构建了基于格兰杰因果分析的有向功能脑网络,对正常对照组和轻度认知障碍的早期和晚期进行识别。在特征选择部分,有向脑网络的局部属性和全局属性被施以双样本t检验,三组间具有显著差异的属性被选作分类特征;接着,支持向量机算法被应用于后续的分类。最后,运用单因素方差分析探测三组间两两均具有显著差异的脑区信息。实验结果表明,本研究取得了较好的分类效果,颞中回、楔前叶、海马旁回等许多脑区在3个考察组中均体现出了显著性差异,这与已有研究的结果基本一致。In order to improve the early diagnosis of MCI,the directed functional brain network based on granger causality analysis(GCA)was constructed and used to identify subjects across normal subjects for control(NC),patients with early mild cognitive impairment(EMCI)and those with late mild cognitive impairment(LMCI),by examining resting-state functional magnetic resonance imaging(rs-fMRI)data.During the section of feature selection,the significantly different measures of local and global graph attributes,among the three groups,were selected as classification features by applying two-sample t-test.Subsequnetly,support vector machine(SVM)was used to classify the data,and one-way ANOVA was used to find out brain regions with significant difference on the designated network features between every pair of the three groups.The results show that the achieved accuracy was satisfactory for classification in the study.Moreover,some brain regions showed significant difference among the three groups,being consistent with previous findings to large extent.

关 键 词：格兰杰因果分析 轻度认知障碍 有向脑网络 支持向量机 分类 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]