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44/100

✓ Peer-reviewed alzhub usefulness score 44 out of 100. Mild cognitive impairment

Topic: Risk, prevention and care in population studies

EEG/ERP analysis suggested differences in frontal alpha asymmetry, power ratio, and ERP components between the MCI and CN groups. MRI revealed atrophy of the hippocampus and accumbens along with cortical thinning in specific brain regions. Combining EEG/ERP and MRI features in a machine learning model yielded the highest classification accuracy of 80.90%. Prefrontal wearable EEG/ERP and MRI offered complementary candidate biomarkers of MCI in community-dwelling individuals using a machine learning approach. These findings support further validation of EEG/ERP and MRI biomarkers in larger and externally validated cohorts.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Song, Y. W., Park, Y. E., Kim, S., Kim, E., Kim, S., & Lee, K. H. (2026). Combining EEG, event-related potentials, and MRI biomarkers for detection of mild cognitive impairment: A machine learning approach. Clinical Neurophysiology. https://doi.org/10.1016/j.clinph.2026.2111917

Vancouver

Song YW, Park YE, Kim S, Kim E, Kim S, Lee KH. Combining EEG, event-related potentials, and MRI biomarkers for detection of mild cognitive impairment: A machine learning approach. Clinical Neurophysiology. 2026. doi:10.1016/j.clinph.2026.2111917. PMID: 42143838.

BibTeX

@article{song2026-42143838,
  author  = {Song, Young Wook and Park, Young Eun and Kim, Seunghu and Kim, Euijin and Kim, Sungkean and Lee, Kun Ho},
  title   = {{Combining EEG, event-related potentials, and MRI biomarkers for detection of mild cognitive impairment: A machine learning approach}},
  journal = {Clinical Neurophysiology},
  year    = {2026},
  doi     = {10.1016/j.clinph.2026.2111917},
  pmid    = {42143838},
  url     = {https://doi.org/10.1016/j.clinph.2026.2111917}
}

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Key findings

EEG/ERP analysis suggested differences in frontal alpha asymmetry, power ratio, and ERP components between the MCI and CN groups.
MRI revealed atrophy of the hippocampus and accumbens along with cortical thinning in specific brain regions.
Combining EEG/ERP and MRI features in a machine learning model yielded the highest classification accuracy of 80.90%.
Prefrontal wearable EEG/ERP and MRI offered complementary candidate biomarkers of MCI in community-dwelling individuals using a machine learning approach.
These findings support further validation of EEG/ERP and MRI biomarkers in larger and externally validated cohorts.

Why it matters

Combining EEG, event-related potentials, and MRI features in a machine learning model classified mild cognitive impairment with 80.90% accuracy. These are candidate biomarkers for detection, and the authors support further validation in larger cohorts, not a treatment or cure.

Study details

Participants70
Effect size80.90%
Evidence strengthLow

How this study scored 44/100

Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 42 of the 96 points we could judge it on, which is 44 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Clinical Neurophysiology)
Journal's citation record	2 / 4	Clinical Neurophysiology: journal h-index 247 (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	5 / 5	2026 — within 1 year(s) of the newest study here
Sample size	1 / 4	70 participants
Effect reported	2 / 3	Effect reported as a number, without an interval or test
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	1 / 7	Cited 1 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.5 citations a year over 2 year(s) since 2026
Impact for its field and year	3 / 6	Cited 4.7× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	0 / 4	Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	— / 4	OpenAlex's profile for last-listed author Kun Ho Lee holds only 1 work(s), too few to judge a track record — left out of this score
Institution's track record	1 / 3	Seoul National University: institution h-index 1,053 (OpenAlex)
Multi-centre collaboration	2 / 3	OpenAlex lists 5 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 42143838)
Free to read	2 / 2	Free to read
Points	42 / 96	Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 42 of the 96 points we could judge it on is 44 out of 100.
Total	44 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

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