Abstract：
Recent researches have noted many changes of short-term dynamic modalities in mild cognitive impairment (MCI) patients\' brain functional networks. In this study, the dynamic functional brain networks of 82 MCI patients and 85 individuals in the normal control (NC) group were constructed using the sliding window method and Pearson correlation. The window size was determined using single-scale time-dependent (SSTD) method. Subsequently, k-means was applied to cluster all window samples, identifying three dynamic functional connectivity (DFC) states. Collective sparse symmetric non-negative matrix factorization (cssNMF) was then used to perform community detection on these states and quantify differences in brain regions. Finally, metrics such as within-community connectivity strength, community strength, and node diversity were calculated for further analysis. The results indicated high similarity between the two groups in state 2, with no significant differences in optimal community quantity and functional segregation (p < 0.05). However, for state 1 and state 3, the optimal community quantity was smaller in MCI patients compared to the NC group. In state 1, MCI patients had lower within-community connectivity strength and overall strength than the NC group, whereas state 3 showed results opposite to state 1. Brain regions with statistical difference included MFG.L, ORBinf.R, STG.R, IFGtriang.L, CUN.L, CUN.R, LING.R, SOG.L, and PCUN.R. This study on DFC states explores changes in the brain functional networks of patients with MCI from the perspective of alterations in the community structures of DFC states. The findings could provide new insights into the pathological changes in the brains of MCI patients.
摘要：
最近的研究注意到轻度认知障碍（MCI）患者脑功能网络的短期动态模式的许多变化。在这项研究中,采用滑动窗口法和Pearson相关法构建82例MCI患者和85例正常对照(NC)组患者的动态功能脑网络。使用单尺度时间依赖性(SSTD)方法确定窗口大小。随后，应用k-均值对所有窗口样本进行聚类，识别三个动态功能连接(DFC)状态。然后使用集体稀疏对称非负矩阵分解（cssNMF）对这些状态进行社区检测，并量化大脑区域的差异。最后,社区内连接强度等指标，社区力量,并计算节点多样性进行进一步分析。结果表明，在状态2中，两组之间的相似性很高，最佳群落数量和功能隔离没有显着差异（p<0.05）。然而,对于状态1和状态3,MCI患者的最佳社区数量小于NC组.在状态1中，MCI患者的社区内连接强度和整体强度低于NC组，而状态3显示与状态1相反的结果。具有统计学差异的脑区包括MFG。L,奥尔宾夫。R,STG.R,IFGtriang.L,CUN.L,CUN.R,林。R,SOG。L,和PCUN.R.这项关于DFC状态的研究从DFC状态社区结构变化的角度探讨了MCI患者脑功能网络的变化。这些发现可以为MCI患者大脑的病理变化提供新的见解。
