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Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology
Jiao B, Li R, Zhou H, Qing K, Liu H, Pan H, Lei Y, Fu W et al.. Alzheimer's research & therapy, 2023-02-10
Topic: Amyloid, tau and blood-test diagnosis
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.
Key findings
- Results The identified EEG biomarkers achieved over 70% accuracy in the three-level classification of HC, MCI, and AD.
- 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.
Why it matters
Using EEG recordings from 890 participants, this study identified biomarkers that achieved over 70% accuracy in distinguishing healthy controls, mild cognitive impairment, and Alzheimer's, and combined them with other data to assess progression. The authors conclude EEG can be a useful screening tool for diagnosis and progression, but it is a diagnostic study and no treatment was tested.
Study details
- Participants890
- Evidence strengthLow
How this study scored 57/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer's research & therapy) |
| Journal's citation record | 1 / 4 | Alzheimer s Research & Therapy: journal h-index 146 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2023 — within 6 year(s) of the newest study here |
| Sample size | 2 / 4 | 890 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 194 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 38.8 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 5 / 6 | Cited 34.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 8 other studies in this library |
| Still being cited | 3 / 4 | Cited 174 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Lu Hua Shen: h-index 49 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Stanford University: institution h-index 2,684 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 6 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 36765411) |
| Free to read | 2 / 2 | Free to read |
| Total | 57 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1186/s13195-023-01181-1
- PubMed: 36765411
- Original source →
- Free full text: Alzheimer s Research & Therapy →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Jiao, B., Li, R., Zhou, H., Qing, K., Liu, H., Pan, H., et al. (2023). Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology. Alzheimer's research & therapy. https://doi.org/10.1186/s13195-023-01181-1
Vancouver
Jiao B, Li R, Zhou H, Qing K, Liu H, Pan H, et al. Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology. Alzheimer's research & therapy. 2023. doi:10.1186/s13195-023-01181-1. PMID: 36765411.
BibTeX
@article{jiao2023-36765411,
author = {Jiao, B. and Li, R. and Zhou, H. and Qing, K. and Liu, H. and Pan, H. and Lei, Y. and Fu, W. and others},
title = {{Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology}},
journal = {Alzheimer's research \& therapy},
year = {2023},
doi = {10.1186/s13195-023-01181-1},
pmid = {36765411},
url = {https://doi.org/10.1186/s13195-023-01181-1}
}
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