Pang Xuerui, Ji Yi, Hu Chenyang, Dai Yulong, Hu Panpan, Wu Xingqi, Wang Kai
Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.
Front Aging Neurosci. 2025 Jul 23;17:1617191. doi: 10.3389/fnagi.2025.1617191. eCollection 2025.
Dynamic functional network connectivity (dFNC) assesses temporal fluctuations in functional connectivity (FC) during magnetic resonance imaging (MRI), capturing transient changes in neural activity. Investigating dFNC may provide valuable insights into the complex clinical manifestations of Alzheimer's disease (AD). However, research on dynamic FC alterations in AD remain limited. This study aimed to comprehensively characterize dFNC patterns in patients with AD.
A total of 100 patients diagnosed with AD and 69 with healthy controls (HC) were included. Resting-state functional magnetic resonance imaging (rs-fMRI) data were analyzed using a sliding-window approach and k-means clustering based on independent component analysis to examine dFNC alterations. Correlation analyses were conducted to assess associations between dFNC variations and clinical scores in individuals with AD. Additionally, an exploratory multivariate pattern analysis was performed to classify AD across different dFNC states.
Four recurrent connectivity states were identified. In state III, patients with AD exhibited a significantly longer mean dwell time and a higher fractional time compared to the HC group, whereas the opposite trend was observed in state IV. In state III, both fractional time and mean dwell time were negatively correlated with cognitive scores. Significant differences in FC strength were observed between states II and III. The highest classification accuracy for distinguishing AD was achieved in state II, which was characterized by intra- and inter-network dysfunction across multiple functional networks.
Distinct alterations in dFNC were identified, with significant associations observed between connectivity patterns and clinical symptoms. These findings provide new insights into the pathophysiology of AD.
动态功能网络连接性（dFNC）评估磁共振成像（MRI）期间功能连接性（FC）的时间波动，捕捉神经活动的瞬时变化。研究dFNC可能为阿尔茨海默病（AD）的复杂临床表现提供有价值的见解。然而，关于AD中动态FC改变的研究仍然有限。本研究旨在全面表征AD患者的dFNC模式。
共纳入100例诊断为AD的患者和69例健康对照（HC）。使用基于独立成分分析的滑动窗口方法和k均值聚类分析静息态功能磁共振成像（rs-fMRI）数据，以检查dFNC改变。进行相关性分析以评估AD患者dFNC变化与临床评分之间的关联。此外，进行探索性多变量模式分析以在不同dFNC状态下对AD进行分类。
识别出四种反复出现的连接状态。在状态III中，与HC组相比，AD患者的平均停留时间显著更长，分数时间更高，而在状态IV中观察到相反的趋势。在状态III中，分数时间和平均停留时间均与认知评分呈负相关。在状态II和III之间观察到FC强度的显著差异。在状态II中区分AD的分类准确率最高，其特征是多个功能网络内和网络间功能障碍。
识别出dFNC的明显改变，连接模式与临床症状之间存在显著关联。这些发现为AD的病理生理学提供了新的见解。
