MCI患者高阶动态功能连接的图论网络构建方法及分类

Graph theory network construction method and classification ofhigh-order dynamic functional connections in MCI patients

针对在低阶脑网络应用图论忽视了功能连接高阶动态性的问题,提出了一种基于高阶动态功能连接的图论网络构建方法(GNC-HodFC),提取高阶FC网络的图论特征以对轻度认知障碍患者和健康被试者进行差异性分析及分类。首先定义了表征高阶动态脑网络连接的图论节点和边;然后利用滑动窗相关技术提取低阶功能连接信息,提出平稳性判据,选取最优特征子集以构建图论的节点;最后提出自适应阈值策略对高阶动态功能连接信息进行选取以构建图论的边,最终完成高阶动态脑网络的图构建。实验结果表明,GNC-HodFC的平均分类准确率可以达到70.5%,优于其他三种对比方法,且患者组和健康组的图论特征中存在显著性差异,GNC-HodFC方法可以为轻度认知障碍的诊断提供新的辅助手段。

The application of graph theory to low-order brain networks ignores the high-order dynamics of functional connections.To address these issues,this paper proposed a graph-theoretic network construction method based on high-order dynamic functional connectivity(GNC-HodFC),to analyze and classify the difference between patients with mild cognitive impairment and healthy subjects by extracting the graph theory features of higher-order FC network.Firstly,the algorithm defined graph theoretic nodes and edges that represented high-order dynamic brain network connections.Then,the algorithm used sliding window technology to extract low-order functional connection information,and put forward the stability criterion for selecting the optimal feature subset to build graph nodes.Finally,the algorithm proposed adaptive threshold strategy selection of high order dynamic functional connection information so as to build the edge of graph theory,which completed the graph construction of the higher-order dynamic brain network.The experimental results show that the average classification accuracy of GNC-HodFC is 70.5%,which is better than the other three comparison methods,and there are significant differences in the graph theory characteristics between the patient group and the healthy group.GNC-HodFC method can provide a new auxiliary means for the diagnosis of mild cognitive impairment.