Electroencephalography (EEG) microstates are used to study cognitive processes and brain disease-related changes. However, dysfunctional patterns of microstate dynamics in Alzheimer's disease (AD) remain uncertain. To investigate microstate changes in AD using EEG and assess their association with cognitive function and pathological changes in cerebrospinal fluid (CSF). We enrolled 56 patients with AD and 38 age- and sex-matched healthy controls (HC). All participants underwent various neuropsychological assessments and resting-state EEG recordings. Patients with AD also underwent CSF examinations to assess biomarkers related to the disease. Stepwise regression was used to analyze the relationship between changes in microstate patterns and CSF biomarkers. Receiver operating characteristics analysis was used to assess the potential of these microstate patterns as diagnostic predictors for AD. Compared with HC, patients with AD exhibited longer durations of microstates C and D, along with a decreased occurrence of microstate B. These microstate pattern changes were associated with Stroop Color Word Test and Activities of Daily Living scale scores (all P < 0.05). Mean duration, occurrences of microstate B, and mean occurrence were correlated with CSF Aβ _1–42 levels, while duration of microstate C was correlated with CSF Aβ _1–40 levels in AD (all P < 0.05). EEG microstates are used to predict AD classification with moderate accuracy. Changes in EEG microstate patterns in patients with AD correlate with cognition and disease severity, relate to Aβ deposition, and may be useful predictors for disease classification.

脑电图 (EEG) 微状态用于研究认知过程和脑部疾病相关的变化。然而，阿尔茨海默病（AD）中微状态动力学的功能失调模式仍然不确定。使用脑电图研究 AD 的微观状态变化，并评估其与认知功能和脑脊液 (CSF) 病理变化的关系。我们招募了 56 名 AD 患者和 38 名年龄和性别匹配的健康对照 (HC)。所有参与者都接受了各种神经心理学评估和静息态脑电图记录。 AD 患者还接受了脑脊液检查，以评估与该疾病相关的生物标志物。采用逐步回归分析微状态模式变化与脑脊液生物标志物之间的关系。接受者操作特征分析用于评估这些微观状态模式作为 AD 诊断预测因子的潜力。与 HC 相比，AD 患者表现出微状态 C 和 D 的持续时间更长，同时微状态 B 的发生率减少。这些微状态模式变化与 Stroop 色词测试和日常生活活动量表评分相关（均P  < 0.05）。AD 中微状态 B 的平均持续时间、发生率和平均发生率与 CSF Aβ _1-42水平相关，而微状态 C 的持续时间与 CSF Aβ _1-40水平相关（所有P  < 0.05）。 EEG 微状态用于以中等准确度预测 AD 分类。 AD 患者脑电图微状态模式的变化与认知和疾病严重程度相关，与 Aβ 沉积相关，并且可能是疾病分类的有用预测因子。