Zhang Yu, Ma Yue, Gao Yu-Lin, Fu Hai-Chao
School of Nursing, Southern Medical University, No. 1023 Shatai Road (South), Baiyun District, Guangzhou City, Guangdong Province, China.
The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.
Geroscience. 2024 Dec 26. doi: 10.1007/s11357-024-01475-8.
This study aims to analyze the characteristics of EEG microstates across different cognitive frailty (CF) subtypes, providing insights for the prevention and early diagnosis of CF. This study included 60 eligible older adults. Their resting-state EEG microstates were analyzed using agglomerative adaptive hierarchical clustering. Microstate temporal parameters were extracted through global field power peak-based backfitting. Spearman's partial correlation analysis and linear mixed-effects models were employed to investigate the relationship between microstate temporal parameters and CF. Statistical differences were observed in transition probabilities (TPs) from microstate B to A between healthy controls (HCs) and reversible cognitive frailty (RCF) group (t = -2.076, P = 0.042). Potentially reversible cognitive frailty (PRCF) and RCF group also exhibited statistical differences in the TPs from microstate B to A (t = 3.122, P = 0.003). In the RCF group, the occurrence of microstates A and B differed significantly from microstate C (t = 3.455, P = 0.002; t = 3.108, P = 0.004). In the PRCF group, the occurrence of microstates A, B, and C differed significantly from microstate D (t = -3.688, P = 0.001; t = -3.334, P = 0.002; t = -4.188, P < 0.001). The neural networks and processing modes engaged by microstate D during executive memory tasks differ between RCF and PRCF. A decreased occurrence of microstate C and higher TPs of microstates A and B may serve as early warning signals for RCF. Conversely, an increased occurrence of microstate D and decreased TPs of microstates C and D indicate the onset of PRCF.
本研究旨在分析不同认知衰弱（CF）亚型的脑电图微状态特征，为CF的预防和早期诊断提供见解。本研究纳入了60名符合条件的老年人。使用凝聚自适应层次聚类分析他们的静息态脑电图微状态。通过基于全局场功率峰值的反向拟合提取微状态时间参数。采用斯皮尔曼偏相关分析和线性混合效应模型研究微状态时间参数与CF之间的关系。在健康对照组（HCs）和可逆性认知衰弱（RCF）组之间，观察到从微状态B到A的转移概率（TPs）存在统计学差异（t = -2.076，P = 0.042）。潜在可逆性认知衰弱（PRCF）组和RCF组在从微状态B到A的TPs方面也表现出统计学差异（t = 3.122，P = 0.003）。在RCF组中，微状态A和B的出现与微状态C有显著差异（t = 3.455，P = 0.002；t = 3.108，P = 0.004）。在PRCF组中，微状态A、B和C的出现与微状态D有显著差异（t = -3.688，P = 0.001；t = -3.334，P = 0.002；t = -4.188，P < 0.001）。在执行记忆任务期间，RCF和PRCF中微状态D所涉及的神经网络和处理模式有所不同。微状态C出现减少以及微状态A和B的TPs升高可能是RCF的早期预警信号。相反，微状态D出现增加以及微状态C和D的TPs降低表明PRCF的开始。
