Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness
IntroductionPrognostication 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 multipl...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1492225/full |
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| author | Hui Li Hui Li Hui Li Linghui Dong Linghui Dong Linghui Dong Wenlong Su Wenlong Su Ying Liu Ying Liu Zhiqing Tang Zhiqing Tang Xingxing Liao Xingxing Liao Junzi Long Junzi Long Xiaonian Zhang Xinting Sun Hao Zhang Hao Zhang Hao Zhang Hao Zhang |
| author_facet | Hui Li Hui Li Hui Li Linghui Dong Linghui Dong Linghui Dong Wenlong Su Wenlong Su Ying Liu Ying Liu Zhiqing Tang Zhiqing Tang Xingxing Liao Xingxing Liao Junzi Long Junzi Long Xiaonian Zhang Xinting Sun Hao Zhang Hao Zhang Hao Zhang Hao Zhang |
| author_sort | Hui Li |
| collection | DOAJ |
| description | IntroductionPrognostication 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.MethodsWe analyzed the EEG features: (i) spectral power; (ii) microstates; and (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.ResultsThe 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).DiscussionThis study provides preliminary evidence for the application of microstate-based multiple EEG features for prognosis prediction in pDoC.Clinical trial registrationchictr.org.cn, identifier ChiCTR2200064099. |
| format | Article |
| id | doaj-art-ec9dcf99549d4f37a141697db30c4b9b |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroscience |
| spelling | doaj-art-ec9dcf99549d4f37a141697db30c4b9b2025-02-05T07:32:08ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-02-011910.3389/fnins.2025.14922251492225Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousnessHui Li0Hui Li1Hui Li2Linghui Dong3Linghui Dong4Linghui Dong5Wenlong Su6Wenlong Su7Ying Liu8Ying Liu9Zhiqing Tang10Zhiqing Tang11Xingxing Liao12Xingxing Liao13Junzi Long14Junzi Long15Xiaonian Zhang16Xinting Sun17Hao Zhang18Hao Zhang19Hao Zhang20Hao Zhang21Cheeloo College of Medicine, Shandong University, Jinan, Shandong, ChinaChina Rehabilitation Research Center, Beijing, ChinaUniversity of Health and Rehabilitation Sciences, Qingdao, Shandong, ChinaCheeloo College of Medicine, Shandong University, Jinan, Shandong, ChinaChina Rehabilitation Research Center, Beijing, ChinaUniversity of Health and Rehabilitation Sciences, Qingdao, Shandong, ChinaChina Rehabilitation Research Center, Beijing, ChinaCapital Medical University, Beijing, ChinaChina Rehabilitation Research Center, Beijing, ChinaCapital Medical University, Beijing, ChinaChina Rehabilitation Research Center, Beijing, ChinaCapital Medical University, Beijing, ChinaChina Rehabilitation Research Center, Beijing, ChinaCapital Medical University, Beijing, ChinaChina Rehabilitation Research Center, Beijing, ChinaCapital Medical University, Beijing, ChinaChina Rehabilitation Research Center, Beijing, ChinaChina Rehabilitation Research Center, Beijing, ChinaCheeloo College of Medicine, Shandong University, Jinan, Shandong, ChinaChina Rehabilitation Research Center, Beijing, ChinaUniversity of Health and Rehabilitation Sciences, Qingdao, Shandong, ChinaCapital Medical University, Beijing, ChinaIntroductionPrognostication 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.MethodsWe analyzed the EEG features: (i) spectral power; (ii) microstates; and (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.ResultsThe 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).DiscussionThis study provides preliminary evidence for the application of microstate-based multiple EEG features for prognosis prediction in pDoC.Clinical trial registrationchictr.org.cn, identifier ChiCTR2200064099.https://www.frontiersin.org/articles/10.3389/fnins.2025.1492225/fullprolonged disorders of consciousnessEEGminimally conscious stateunresponsive wakefulness syndromemicrostate |
| spellingShingle | Hui Li Hui Li Hui Li Linghui Dong Linghui Dong Linghui Dong Wenlong Su Wenlong Su Ying Liu Ying Liu Zhiqing Tang Zhiqing Tang Xingxing Liao Xingxing Liao Junzi Long Junzi Long Xiaonian Zhang Xinting Sun Hao Zhang Hao Zhang Hao Zhang Hao Zhang Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness Frontiers in Neuroscience prolonged disorders of consciousness EEG minimally conscious state unresponsive wakefulness syndrome microstate |
| title | Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness |
| title_full | Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness |
| title_fullStr | Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness |
| title_full_unstemmed | Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness |
| title_short | Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness |
| title_sort | multiple patterns of eeg parameters and their role in the prediction of patients with prolonged disorders of consciousness |
| topic | prolonged disorders of consciousness EEG minimally conscious state unresponsive wakefulness syndrome microstate |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1492225/full |
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