ORIGINAL RESEARCH article
Front. Aging Neurosci.
Sec. Neurocognitive Aging and Behavior
Volume 17 - 2025 | doi: 10.3389/fnagi.2025.1639970
This article is part of the Research TopicThe early detection of neurodegenerative diseases: an aging perspectiveView all 9 articles
Peak Alpha Frequency as an Objective Biomarker for Cognitive Assessment in Post-Stroke Cognitive Impairment
Provisionally accepted
- 1Fujian University of Traditional Chinese Medicine Affiliated Rehabilitative Hospital, Fuzhou, China
- 2Fujian University of Traditional Chinese Medicine, Fuzhou, China
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Objective To investigate regional associations between peak alpha frequency (PAF) and poststroke cognitive impairment (PSCI) and evaluate PAF as an objective biomarker for cognitive assessment in PSCI. Methods A cross-sectional study compared 103 participants (PSCI, poststroke non-impaired [PSN], and healthy controls). Cognitive function was assessed using MoCA scores. PAF characteristics were analyzed across brain regions via EEG, with logistic regression and Random Forest identifying key predictorsWe aimed to evaluate whether PAF can be an effective indicator of cognitive status in PSCI. Results The Kruskal-Wallis test with post-hoc Bonferroni correction revealed that PSCI exhibited significantly lower PAF compared to HC across all major brain regions (frontal, temporal, central, and parieto-occipital; all P < 0.05). Compared to PSN, the PSCI group showed significantly reduced PAF at specific electrodes (F3, F4, F7, T3, T6, Fz; P < 0.05). Spearman correlation analysis demonstrated that PAF at multiple leads was positively correlated with MoCA scores across all subjects. Notably, after FDR correction, only T3PAF and T4PAF remained significantly negatively correlated with MoCA in all subjects (q < 0.05). Binary logistic regression identified T4PAF as the most discriminative predictor for distinguishing PSCI from HC (OR = 2.525). Random Forest analysis corroborated these findings, identifying F7PAF, O2PAF, T3PAF, and T4PAF as the most important predictors. Both models demonstrated excellent discriminatory power, with AUCs of 0.761 (logistic regression) and 0.773 (Random Forest), indicating robust performance of EEG-based biomarkers for PSCI detection. Conclusions PAF serves as a robust electrophysiological biomarker for PSCI. Multi-region PAF analysis enhances diagnostic precision for poststroke cognitive decline.
Keywords: poststroke cognitive impairment, PAF, Montreal Cognitive Assessment, EEG, PSD
Received: 03 Jun 2025; Accepted: 09 Oct 2025.
Copyright: © 2025 Zhao, Shi, Kong, Wang, Wei, Zhan and Xue. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zengtu Zhan, 359908311@qq.com
Xiehua Xue, f110015@fjtcm.edu.cn
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