Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology.
Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology.
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DOI：
10.1186/s13195-023-01181-1
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发表时间：
2023-02-10
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影响因子：
--
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脑电图（EEG）已成为一种检测与阿尔茨海默病（AD）不同阶段相关的异常神经元活动的非侵入性工具。然而，脑电图在AD及其临床前阶段——遗忘性轻度认知障碍（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，我们旨在确定关键的脑电图生物标志物，这些生物标志物可以有效地识别早期阿尔茨海默病患者并监测阿尔茨海默病的进展。共纳入890名参与者，包括189名轻度认知障碍患者、330名AD患者、125名其他痴呆症患者（额颞叶痴呆、路易体痴呆和血管性认知障碍）和246名健康对照组（HC）。从静息状态脑电图记录中提取生物标志物，对HC、MCI和AD进行三级分类。然后根据分类性能确定最佳EEG生物标志物。随机森林回归通过结合参与者的脑电图生物标志物、人口统计学信息（即性别、年龄）、脑脊液生物标志物和APOE表型来训练一系列模型，以评估疾病进展和个体认知功能。所鉴定的脑电图生物标志物在HC、MCI和AD的三级分类中准确率达到70%以上。在所有六组中，ad相关神经退行性变对脑电图指标的最显著影响局限于顶枕区。在交叉验证预测分析中，最佳EEG特征在预测发病年龄和病程方面比CSF + APOE生物标志物更有效，而EEG + CSF + APOE联合测量在所有预测目标上都取得了最好的效果。我们的研究表明脑电图可以作为MCI和AD的诊断和疾病进展评估的有用筛查工具。在线版本包含补充材料，可在10.1186/s13195-023-01181-1获得。
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. 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. 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. Our study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD. The online version contains supplementary material available at 10.1186/s13195-023-01181-1.
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DOI：
10.1016/j.jalz.2011.03.005
发表时间：
2011-05
期刊：
Alzheimer's & dementia : the journal of the Alzheimer's Association
影响因子：
--
作者：
McKhann GM;Knopman DS;Chertkow H;Hyman BT;Jack CR Jr;Kawas CH;Klunk WE;Koroshetz WJ;Manly JJ;Mayeux R;Mohs RC;Morris JC;Rossor MN;Scheltens P;Carrillo MC;Thies B;Weintraub S;Phelps CH
通讯作者：
Phelps CH
影响因子：
5.1
作者：
Cassani, Raymundo;Falk, Tiago H.;Anghinah, Renato
通讯作者：
Anghinah, Renato
影响因子：
1.7
作者：
Jelic, V;Blomberg, M;Wahlund, LO
通讯作者：
Wahlund, LO
影响因子：
4.2
作者：
Li, Rihui;Thinh Nguyen;Zhang, Yingchun
通讯作者：
Zhang, Yingchun
影响因子：
5.7
作者：
Farina, F. R.;Emek-Savas, D. D.;Whelan, R.
通讯作者：
Whelan, R.