Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) Rostock-Greifswald, Rostock, Germany.
Int J Geriatr Psychiatry. 2024 Sep;39(9):e6138. doi: 10.1002/gps.6138.
Predicting which individuals may convert to dementia from mild cognitive impairment (MCI) remains difficult in clinical practice. Electroencephalography (EEG) is a widely available investigation but there is limited research exploring EEG connectivity differences in patients with MCI who convert to dementia.
Participants with a diagnosis of MCI due to Alzheimer's disease (MCI-AD) or Lewy body disease (MCI-LB) underwent resting state EEG recording. They were followed up annually with a review of the clinical diagnosis (n = 66). Participants with a diagnosis of dementia at year 1 or year 2 follow up were classed as converters (n = 23) and those with a diagnosis of MCI at year 2 were classed as stable (n = 43). We used phase lag index (PLI) to estimate functional connectivity as well as analysing dominant frequency (DF) and relative band power. The Network-based statistic (NBS) toolbox was used to assess differences in network topology.
The converting group had reduced DF (U = 285.5, p = 0.005) and increased relative pre-alpha power (U = 702, p = 0.005) consistent with previous findings. PLI showed reduced average beta band synchrony in the converting group (U = 311, p = 0.014) as well as significant differences in alpha and beta network topology. Logistic regression models using regional beta PLI values revealed that right central to right lateral (Sens = 56.5%, Spec = 86.0%, -2LL = 72.48, p = 0.017) and left central to right lateral (Sens = 47.8%, Spec = 81.4%, -2LL = 71.37, p = 0.012) had the best classification accuracy and fit when adjusted for age and MMSE score.
Patients with MCI who convert to dementia have significant differences in EEG frequency, average connectivity and network topology prior to the onset of dementia. The MCI group is clinically heterogeneous and have underlying physiological differences that may be driving the progression of cognitive symptoms. EEG connectivity could be useful to predict which patients with MCI-AD and MCI-LB convert to dementia, regardless of the neurodegenerative aetiology.
在临床实践中，预测哪些轻度认知障碍（MCI）患者可能会发展为痴呆仍然具有挑战性。脑电图（EEG）是一种广泛应用的检查方法，但对于从 MCI 发展为痴呆的患者，EEG 连接差异的研究有限。
诊断为阿尔茨海默病（MCI-AD）或路易体病（MCI-LB）引起的 MCI 的参与者进行静息状态 EEG 记录。每年对他们进行一次临床诊断复查（n=66）。在第 1 年或第 2 年随访时被诊断为痴呆的患者被归类为转化者（n=23），在第 2 年被诊断为 MCI 的患者被归类为稳定者（n=43）。我们使用相位滞后指数（PLI）来估计功能连接，同时分析主导频率（DF）和相对频带功率。使用基于网络的统计（NBS）工具箱评估网络拓扑的差异。
转化组的 DF 降低（U=285.5，p=0.005），相对前阿尔法功率增加（U=702，p=0.005），与之前的发现一致。PLI 显示转化组的平均β波段同步性降低（U=311，p=0.014），以及α和β网络拓扑的显著差异。使用局部β PLI 值的逻辑回归模型显示，右侧中央到右侧外侧（灵敏度=56.5%，特异性=86.0%，-2LL=72.48，p=0.017）和左侧中央到右侧外侧（灵敏度=47.8%，特异性=81.4%，-2LL=71.37，p=0.012）在调整年龄和 MMSE 评分后具有最佳的分类准确性和拟合度。
在痴呆发生之前，从 MCI 转化为痴呆的患者的 EEG 频率、平均连接性和网络拓扑存在显著差异。MCI 组在临床上具有异质性，存在潜在的生理差异，这可能是导致认知症状进展的原因。无论神经退行性病因如何，EEG 连接性都可能有助于预测哪些 MCI-AD 和 MCI-LB 患者会转化为痴呆。
