基于轻度认知障碍高阶动态功能连接的脑网络时变分析
收稿日期: 2024-03-22
网络出版日期: 2024-05-08
基金资助
云南省科技厅面上项目(202201AT070021);云南省教育厅科学研究基金项目(2022J0439)
Time-varying Analysis of Brain Networks Based on High-order Dynamic Functional Connections in Mild Cognitive Impairment
Received date: 2024-03-22
Online published: 2024-05-08
现有研究常用功能连接（FC）结合图论分析完成轻度认知障碍（MCI）疾病的辅助诊断．传统FC分析方法通常以低阶FC网络为对象，而高阶FC网络能够揭示脑网络中更高层次的交互关系，但在高阶FC网络中涉及图论研究尚少，且传统图论指标在高阶FC网络中具有局限性．本文通过高阶动态功能连接构建高阶FC网络，结合图论对MCI和正常认知（NC）的脑网络状态进行分析，定义了阻滞系数和平均转换时间两个新的图论指标，以表征脑网络的时间变异性．结果表明在高阶FC网络中应用图论能有效提取MCI组和NC组之间的差异性信息，所提出的阻滞系数和平均转换时间指标均能呈现显著性差异，为高阶脑网络的研究提供了一种新的分析方法．
王霞 , 王勇 , 兰青 . 基于轻度认知障碍高阶动态功能连接的脑网络时变分析[J]. 波谱学杂志, 2024 , 41(3) : 286 -303 . DOI: 10.11938/cjmr20243102
Existing research commonly uses functional connectivity (FC) combined with graph theory analysis to accomplish the auxiliary diagnosis of mild cognitive impairment (MCI). Traditional FC analysis methods usually target low-order FC networks, while high-order FC networks can reveal higher-level interactions in brain networks. However, there are few studies involving graph theory in high-order FC networks, and traditional graph theory indicators have limitations in high-order FC networks. This paper constructs a high-order FC network through high-order dynamic functional connections, combines graph theory to analyze the brain network status of MCI and normal cognition (NC), and defines two new graph theory indicators, blocking coefficient and average transition time, to characterize temporal variability in brain networks. The results show that the application of graph theory in high-order FC network can effectively extract the differential information between MCI group and NC group. The proposed blocking coefficient and average conversion time index can both show significant differences, providing a new analysis method for the study of high-order brain network.
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