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Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer’s disease using EEG technology

Authors :
Bin Jiao
Rihui Li
Hui Zhou
Kunqiang Qing
Hui Liu
Hefu Pan
Yanqin Lei
Wenjin Fu
Xiaoan Wang
Xuewen Xiao
Xixi Liu
Qijie Yang
Xinxin Liao
Yafang Zhou
Liangjuan Fang
Yanbin Dong
Yuanhao Yang
Haiyan Jiang
Sha Huang
Lu Shen
Source :
Alzheimer’s Research & Therapy, Vol 15, Iss 1, Pp 1-14 (2023)
Publication Year :
2023
Publisher :
BMC, 2023.

Abstract

Abstract 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.

Details

Language :
English
ISSN :
17589193
Volume :
15
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Alzheimer’s Research & Therapy
Publication Type :
Academic Journal
Accession number :
edsdoj.2ffe54ead5bc44689ae0365a8aabaa06
Document Type :
article
Full Text :
https://doi.org/10.1186/s13195-023-01181-1