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Altered static and dynamic functional network connectivity in post-stroke cognitive impairment

默认模式网络 动态功能连接 楔前 静息状态功能磁共振成像 任务正网络 显著性(神经科学) 心理学 听力学 认知 神经科学 冲程(发动机) 医学 工程类 机械工程
作者
Xipeng Yue,Ziqiang Li,Ying Li,Jiahuan Gao,Hui Han,Ge Zhang,Xiaochen Li,Yu Shen,Wei Wei,Yan Bai,Jiapei Xie,Luo Zhi,Xianchang Zhang,Meiyun Wang
出处
期刊:Neuroscience Letters [Elsevier BV]
卷期号:799: 137097-137097 被引量:38
标识
DOI:10.1016/j.neulet.2023.137097
摘要

Post-stroke cognitive impairment (PSCI) is a common symptom following brain stroke, yet the mechanisms remain unknown. This study aimed to investigate alterations of static and dynamic functional network connectivity (sFNC and dFNC) in PSCI patients. We prospectively recruited 17 PSCI patients and 24 Healthy controls (HC). Restingstate fMRI (rs-fMRI) and Mini-Mental State Examination (MMSE) were performed. Independent component analysis combined with sliding-window and K-means clustering approach were applied to examine the FNC among 11 resting-state networks: auditory network (AUDN), left executive control network (lECN), language network (LN), precuneus network (PCUN), right executive control network (rECN), salience network (SN), visuospatial network (VN), dorsal default mode network (dDMN), higher visual network (hVIS), primary visual network (pVIS), and ventral mode network (vDMN). The FNC and dynamic indices (fraction time, mean dwell time, transition number) were calculated. Static and dynamic measures were compared between two groups and the correlation between clinical and imaging indicators was analyzed. For sFNC, PSCI group showed decreased interactions in dDMN-vDMN, vDMN-SN, dDMN-hVIS, AUDN-rECN, and AUDN-VN. For dFNC, we derived 3 states of FNC that occurred repeatedly. Significant group differences were found, including decreased interactions in the AUDN-VN, AUDN-pVIS in state 2 and dDMN-VN in state 3. The mean dwell time in PSCI group was longer in state 1, and negatively correlated with MMSE score. These results demonstrated that PSCI patients are characterized with altered sFNC and dFNC, which could help us explore the neural mechanisms of the PSCI from a new perspective.
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