Wu Xiaotian, Liu Yanli, Che Jiajun, Cheng Nan, Wen Dong, Liu Haining, Dong Xianling
Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.
Department of Psychology, Chengde Medical University, Chengde City, Hebei Province, China.
J Alzheimers Dis. 2025 Feb;103(3):735-748. doi: 10.1177/13872877241305961. Epub 2025 Jan 8.
Mild cognitive impairment (MCI) is recognized as a condition that may increase the risk of developing Alzheimer's disease (AD). Understanding the neural correlates of MCI is crucial for elucidating its pathophysiology and developing effective interventions. Electroencephalogram (EEG) microstates, reflecting brain activity changes, have shown promise in MCI research. However, current approaches often lack comprehensive characterization of the complex neural dynamics associated with MCI.
This study aims to investigate neurophysiological changes associated with MCI using a comprehensive set of microstate features, including traditional temporal features and entropy measures.
Resting-state EEG data were collected from 69 MCI patients and healthy controls (HC). Microstate analysis was performed to extract conventional features (duration, coverage) and entropy measures. Statistical analysis, principal component analysis (PCA), and machine learning (ML) techniques were employed to evaluate neurophysiological patterns associated with MCI.
MCI displayed altered microstate dynamics, with significantly longer coverage and duration in Microstate C but shorter in Microstates A, B, and D compared to HCs. PCA revealed two principal components, primarily composed of microstate dynamics and entropy measures, explaining over 75% of the variance. ML models achieved high accuracy in distinguishing MCI patterns.
Our comprehensive analysis of EEG microstate features provides new insights into neurophysiological changes associated with MCI, highlighting the potential of EEG microstates for investigating complex neural changes in cognitive decline.
轻度认知障碍（MCI）被认为是一种可能增加患阿尔茨海默病（AD）风险的病症。了解MCI的神经相关性对于阐明其病理生理学和开发有效的干预措施至关重要。反映大脑活动变化的脑电图（EEG）微状态在MCI研究中显示出前景。然而，目前的方法往往缺乏对与MCI相关的复杂神经动力学的全面表征。
本研究旨在使用包括传统时间特征和熵测量在内的一套全面的微状态特征来研究与MCI相关的神经生理变化。
从69名MCI患者和健康对照（HC）中收集静息态EEG数据。进行微状态分析以提取传统特征（持续时间、覆盖率）和熵测量。采用统计分析、主成分分析（PCA）和机器学习（ML）技术来评估与MCI相关的神经生理模式。
MCI表现出微状态动力学改变，与HC相比，微状态C的覆盖率和持续时间显著更长，而微状态A、B和D则更短。PCA揭示了两个主要成分，主要由微状态动力学和熵测量组成，解释了超过75%的方差。ML模型在区分MCI模式方面取得了高精度。
我们对EEG微状态特征的综合分析为与MCI相关的神经生理变化提供了新的见解，突出了EEG微状态在研究认知衰退中复杂神经变化方面 的潜力。
