Evidence map›Paper›PMID 36765411›Full record

ArticleAlzheimer's research & therapy2023

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 and 10 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Alzheimer's research & therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
92citing papers in PubMed, 4 pooled it
28.1field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

92 citing papers in PubMed, 4 syntheses or guidelines pooled it, 160 citations in OpenAlex.

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  19. Decoding the mechanisms of amyloid-β in synaptic toxicity.Journal of Alzheimer's disease : JAD · 2026
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32 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors at 3 institutions in 4 countries.

Bin Jiao *Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.
Rihui Li *Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Hui ZhouDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, China.
Kunqiang QingBrainup Institute of Science and Technology, Chongqing, China.
Hui LiuDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, China.
Hefu PanBrainup Institute of Science and Technology, Chongqing, China.
Yanqin LeiBrainup Institute of Science and Technology, Chongqing, China.
Wenjin FuBrainup Institute of Science and Technology, Chongqing, China.
Xiaoan WangBrainup Institute of Science and Technology, Chongqing, China.
Xuewen XiaoDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, China.
Xixi LiuDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, China.
Qijie YangDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, China.
Xinxin LiaoDepartment of Geriatrics, Xiangya Hospital, Central South University, Changsha, China.
Yafang ZhouDepartment of Geriatrics, Xiangya Hospital, Central South University, Changsha, China.
Liangjuan FangDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, China.
Yanbin DongBrainup Institute of Science and Technology, Chongqing, China.
Yuanhao YangMater Research Institute, The University of Queensland, Woolloongabba, Queensland, 4102, Australia.
Haiyan JiangDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, China.
Sha HuangDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, China.
Lu ShenDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, China. shenlu@csu.edu.cn.
Central South University · CNStanford University · USThe University of Queensland · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundElectroencephalogram (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.

methodsA 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.

resultsThe 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.

conclusionsOur study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD.

Indexed as

Alzheimer DiseaseCognitive DysfunctionApolipoproteins EBiomarkersDisease ProgressionElectroencephalographyHumansApolipoproteins EBiomarkersAlzheimer’s diseaseBiomarkerDiagnosisElectroencephalographyMild cognitive impairmentPrediction

Identifiers

PMID36765411
PMCPMC9912534
OpenAlexW4319982255

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.