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

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

doi:10.1186/s13195-023-01181-1

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

Alzheimers Res Ther. 2023 Feb 10;15(1):32. doi: 10.1186/s13195-023-01181-1.

Authors

Bin Jiao^#^{1

2

3

4

5}, Rihui Li^#^{6

7}, Hui Zhou¹, Kunqiang Qing⁷, Hui Liu¹, Hefu Pan⁷, Yanqin Lei⁷, Wenjin Fu⁷, Xiaoan Wang⁷, Xuewen Xiao¹, Xixi Liu¹, Qijie Yang¹, Xinxin Liao⁸, Yafang Zhou⁸, Liangjuan Fang¹, Yanbin Dong⁷, Yuanhao Yang⁹, Haiyan Jiang¹, Sha Huang¹, Lu Shen^{10

11

12

13

14

15}

Affiliations

¹ Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.
² National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.
³ Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China.
⁴ Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China.
⁵ Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China.
⁶ Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
⁷ Brainup Institute of Science and Technology, Chongqing, China.
⁸ Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China.
⁹ Mater Research Institute, The University of Queensland, Woolloongabba, Queensland, 4102, Australia.
¹⁰ Department of Neurology, Xiangya Hospital, Central South University, Changsha, China. shenlu@csu.edu.cn.
¹¹ National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China. shenlu@csu.edu.cn.
¹² Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China. shenlu@csu.edu.cn.
¹³ Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China. shenlu@csu.edu.cn.
¹⁴ Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China. shenlu@csu.edu.cn.
¹⁵ Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, China. shenlu@csu.edu.cn.

^# Contributed equally.

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.

Keywords: Alzheimer’s disease; Biomarker; Diagnosis; Electroencephalography; Mild cognitive impairment; Prediction.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Alzheimer Disease* / psychology
Apolipoproteins E
Biomarkers
Cognitive Dysfunction* / psychology
Disease Progression
Electroencephalography
Humans

Substances

Biomarkers
Apolipoproteins E