基于 EEG 技术的轻度认知障碍和阿尔茨海默病的神经生物标志物诊断和预测。
Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology.
机构信息
Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.
National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.
出版信息
Alzheimers Res Ther. 2023 Feb 10;15(1):32. doi: 10.1186/s13195-023-01181-1.
BACKGROUND
Electroencephalogram (EEG) has emerged as a non-invasive tool to detect the aberrant neuronal activity related to different stages of Alzheimer's disease (AD). However, the effectiveness of EEG in the precise diagnosis and assessment of AD and its preclinical stage, amnestic mild cognitive impairment (MCI), has yet to be fully elucidated. In this study, we aimed to identify key EEG biomarkers that are effective in distinguishing patients at the early stage of AD and monitoring the progression of AD.
METHODS
A total of 890 participants, including 189 patients with MCI, 330 patients with AD, 125 patients with other dementias (frontotemporal dementia, dementia with Lewy bodies, and vascular cognitive impairment), and 246 healthy controls (HC) were enrolled. Biomarkers were extracted from resting-state EEG recordings for a three-level classification of HC, MCI, and AD. The optimal EEG biomarkers were then identified based on the classification performance. Random forest regression was used to train a series of models by combining participants' EEG biomarkers, demographic information (i.e., sex, age), CSF biomarkers, and APOE phenotype for assessing the disease progression and individual's cognitive function.
RESULTS
The identified EEG biomarkers achieved over 70% accuracy in the three-level classification of HC, MCI, and AD. Among all six groups, the most prominent effects of AD-linked neurodegeneration on EEG metrics were localized at parieto-occipital regions. In the cross-validation predictive analyses, the optimal EEG features were more effective than the CSF + APOE biomarkers in predicting the age of onset and disease course, whereas the combination of EEG + CSF + APOE measures achieved the best performance for all targets of prediction.
CONCLUSIONS
Our study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD.
背景
脑电图（EEG）已成为一种非侵入性工具，可用于检测与阿尔茨海默病（AD）不同阶段相关的异常神经元活动。然而，EEG 在 AD 及其临床前阶段——遗忘型轻度认知障碍（MCI）的精确诊断和评估中的有效性尚未得到充分阐明。在这项研究中，我们旨在确定有效的 EEG 生物标志物，以区分 AD 早期患者并监测 AD 的进展。
方法
共纳入 890 名参与者，包括 189 名 MCI 患者、330 名 AD 患者、125 名其他痴呆症（额颞叶痴呆、路易体痴呆和血管性认知障碍）患者和 246 名健康对照（HC）。从静息状态 EEG 记录中提取生物标志物，用于 HC、MCI 和 AD 的三级分类。然后根据分类性能确定最佳 EEG 生物标志物。随机森林回归用于通过结合参与者的 EEG 生物标志物、人口统计学信息（即性别、年龄）、CSF 生物标志物和 APOE 表型来训练一系列模型，以评估疾病进展和个体的认知功能。
结果
所确定的 EEG 生物标志物在 HC、MCI 和 AD 的三级分类中达到了 70%以上的准确率。在所有六个组中，AD 相关神经退行性变对 EEG 指标的影响最明显的部位是顶枕叶区域。在交叉验证预测分析中，最佳 EEG 特征比 CSF+APOE 生物标志物更有效地预测发病年龄和疾病进程，而 EEG+CSF+APOE 测量的组合在所有预测目标中表现最佳。
结论
我们的研究表明，EEG 可作为诊断 MCI 和 AD 以及评估疾病进展的有用筛查工具。
Zotero 插件