Evidence map›Paper›PMID 42037718›Full record

ArticleFrontiers in neurology

Exploring the neural mechanisms of mild cognitive impairment in elderly patients with coronary artery disease using machine learning and source-localized EEG.

Huiwei Wan, Yanyao Deng, Fangbo Lin, Jie Li, Hao Xiao, Le Xiao

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Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Huiwei WanDepartment of Rehabilitation, The First Hospital of Changsha, Changsha, Hunan, China.
Yanyao DengDepartment of Rehabilitation, The First Hospital of Changsha, Changsha, Hunan, China.
Fangbo LinDepartment of Rehabilitation, The First Hospital of Changsha, Changsha, Hunan, China.
Jie LiDepartment of Rehabilitation, The First Hospital of Changsha, Changsha, Hunan, China.
Hao XiaoDepartment of Rehabilitation, The First Hospital of Changsha, Changsha, Hunan, China.
Le XiaoDepartment of Rehabilitation, The First Hospital of Changsha, Changsha, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study seeks to investigate the electrophysiological mechanisms associated with mild cognitive impairment (MCI) in elderly patients with coronary artery disease (CAD) through the application of source-reconstructed EEG in conjunction with machine learning methodologies. Methods: We retrospectively analyzed clinical data and resting-state 64-channel EEG recorded during hospitalization at The First Hospital of Changsha. Participants included primary hypertension without CAD ( Results: Baseline demographics and vascular comorbidities were comparable across groups; MoCA scores were lower in the MCI subgroup. Relative to hypertensive controls without CAD, cognitively normal CAD patients showed reduced frontal connectivity, including decreased alpha-band LPS (BA8L-46R) and beta-band LPS (BA44L-44R). Compared with cognitively normal CAD, CAD with MCI exhibited broader multi-band dysconnectivity across alpha, beta, theta, and delta bands, with mixed delta-band changes. In the test set, the Gradient Boosting model achieved the best performance for identifying MCI within CAD (AUC = 0.895). SHAP highlighted the most influential features, led by decreased alpha-band BA8L-46R connectivity, alongside delta- and beta-band alterations. Conclusion: Coronary artery disease is associated with frontal network disruption, which becomes more extensive and frequency-diverse as MCI progresses. Interpretable machine learning further highlights a small set of connectivity abnormalities-particularly within premotor-prefrontal pathways-as candidate markers for MCI classification within a CAD cohort, supporting a vascular-relevant interpretation, which warrants further validation.

Indexed as

coronary artery diseaseelectroencephalographyfunctional connectivitylagged phase synchronizationmachine learningmild cognitive impairmentsource reconstruction

Identifiers

PMID42037718
PMCPMC13106079

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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.