基于持久同调的复杂脑网络动态演化分析

有研究表明阿尔茨海默病（Alzheimer's disease，AD）的认知状态与动态功能连接时间特性的改变有关，持久同调指标分析方法可为AD动态脑网络的研究提供更深的见解，但是目前研究主要集中在空间演化方面，尚未有针对时变方面的脑网络演化研究．本文基于静息态功能磁共振成像（resting state-functional magnetic resonanceimaging，rs-fMRI），对AD患者和正常被试（normal controls，NC）的静态脑网络和基于滑动窗口构建的动态脑网络进行功能连接性分析．对基于持久同调和基于图论的分析结果进行了比较，并采用k均值聚类进行了时间属性的分析．结果表明相对图论指标，持久同调的指标在AD患者和NC被试间具有更显著的差异性；而且相对于静态脑网络，基于持久同调的动态脑网络演化分析可为脑功能网络标志物的检测提供新思路．

Analysis of Dynamic Evolution of Complex Brain Networks Based on Persistent Homology

Some studies have shown that the cognitive state in Alzheimer's disease(AD)patients is related to the changes of the temporal characteristics of dynamic functional connection.Persistent homology index analysis method provides a deeper insight into the property of dynamic brain networks.However,current research mainly focused on spatial evolution,while there is little research on time-varying brain network evolution.Based on resting state-functional magnetic resonance imaging(rs-fMRI)data,this paper analyzed the static functional connectivity network in AD patients and normal controls(NC),as well as the dynamic brain networks constructed by sliding windows.The analysis method based on persistent homology was compared with the ones based on graph theory,and the temporal characteristics were analyzed with k-means clustering.The results demonstrated that there were more significant differences between the AD patients and NC subjects,when the index based on persistent homology was used.Compared with the analysis of static brain networks,the dynamic brain network evolution analysis based on persistent homology provides new ideas for the detection of functional brain network biomarkers.