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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Translational Neuroscience
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1492225

Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness

Provisionally accepted
Hui Li Hui Li 1Linghui Dong Linghui Dong 1Wenlong Su Wenlong Su 2Ying Liu Ying Liu 2Zhiqing Tang Zhiqing Tang 2Xingxing Liao Xingxing Liao 2Junzi Long Junzi Long 2Xiaonian Zhang Xiaonian Zhang 2Xinting Sun Xinting Sun 2Hao Zhang Hao Zhang 2,3*
  • 1 Shandong University, Jinan, Shandong Province, China
  • 2 China Rehabilitation Research Center, Beijing, China
  • 3 Other, Beijing, China

The final, formatted version of the article will be published soon.

    Prognostication in patients with prolonged disorders of consciousness (pDoC) remains a challenging task. Electroencephalography (EEG) is a neurophysiological method that provides objective information for evaluating overall brain function. In this study, we aim to investigate the multiple features of pDoC using EEG and evaluate the prognostic values of these indicators. We analyzed the EEG features: (i) spectral power; (ii) microstates; (iii) mismatch negativity (MMN) and P3a of healthy controls, patients in minimally conscious state (MCS), and unresponsive wakefulness syndrome (UWS). Patients were followed up for 6 months. A combination of machine learning and SHapley Additive exPlanations (SHAP) were used to develop predictive model and interpret the results. The results indicated significant abnormalities in low-frequency spectral power, microstate parameters, and amplitudes of MMN and P3a in MCS and UWS. A predictive model constructed using support vector machine achieved an area under the curve (AUC) of 0.95, with the top 10 SHAP values being associated with transition probability (TP) from state C to F, time coverage of state E, TP from state D to F and D to F, mean duration of state A, TP from state F to C, amplitude of MMN, time coverage of state F, TP from state C to D, and mean duration of state E. Predictive models constructed for each component using support vector machine revealed that microstates had the highest AUC (0.95), followed by MMN and P3a (0.65), and finally spectral power (0.05). This study provides preliminary evidence for the application of microstate-based multiple EEG features for prognosis prediction in pDoC. Clinical trial registration: chictr.org.cn, ChiCTR2200064099.

    Keywords: Prolonged Disorders of consciousness, EEG, Minimally Conscious State, vegetative state, Microstate

    Received: 23 Sep 2024; Accepted: 22 Jan 2025.

    Copyright: © 2025 Li, Dong, Su, Liu, Tang, Liao, Long, Zhang, Sun and Zhang. 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: Hao Zhang, Other, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.