Abstract：
OBJECTIVE: Microstates represent the global and topographical distribution of electrical brain activity from scalp-recorded EEG. This study aims to explore EEG microstates of patients with focal epilepsy prior to medication, and employ extracted microstate metrics for predicting treatment outcomes with Oxcarbazepine monotherapy.
METHODS: This study involved 25 newly-diagnosed focal epilepsy patients (13 females), aged 12 to 68, with various etiologies. Patients were categorized into Non-Seizure-Free (NSF) and Seizure-Free (SF) groups according to their first follow-up outcomes. From pre-medication EEGs, four representative microstates were identified by using clustering. The temporal parameters and transition probabilities of microstates were extracted and analyzed to discern group differences. With generating sample method, Support Vector Machine (SVM), Logistic Regression (LR), and Naïve Bayes (NB) classifiers were employed for predicting treatment outcomes.
RESULTS: In the NSF group, Microstate 1 (MS1) exhibited a significantly higher duration (mean±std. = 0.092±0.008 vs. 0.085±0.008, p = 0.047), occurrence (mean±std. = 2.587±0.334 vs. 2.260±0.278, p = 0.014), and coverage (mean±std. = 0.240±0.046 vs. 0.194±0.040, p = 0.014) compared to the SF group. Additionally, the transition probabilities from Microstate 2 (MS2) and Microstate 3 (MS3) to MS1 were increased. In MS2, the NSF group displayed a stronger correlation (mean±std. = 0.618±0.025 vs. 0.571±0.034, p < 0.001) and a higher global explained variance (mean±std. = 0.083±0.035 vs. 0.055±0.023, p = 0.027) than the SF group. Conversely, Microstate 4 (MS4) in the SF group demonstrated significantly greater coverage (mean±std. = 0.388±0.074 vs. 0.334±0.052, p = 0.046) and more frequent transitions from MS2 to MS4, indicating a distinct pattern. Temporal parameters contribute major predictive role in predicting treatment outcomes of Oxcarbazepine, with area under curves (AUCs) of 0.95, 0.70, and 0.86, achieved by LR, NB and SVM, respectively.
CONCLUSIONS: This study underscores the potential of EEG microstates as predictive biomarkers for Oxcarbazepine treatment responses in newly-diagnosed focal epilepsy patients.
METHODS: This study involved 25 newly-diagnosed focal epilepsy patients (13 females), aged 12 to 68, with various etiologies. Patients were categorized into Non-Seizure-Free (NSF) and Seizure-Free (SF) groups according to their first follow-up outcomes. From pre-medication EEGs, four representative microstates were identified by using clustering. The temporal parameters and transition probabilities of microstates were extracted and analyzed to discern group differences. With generating sample method, Support Vector Machine (SVM), Logistic Regression (LR), and Naïve Bayes (NB) classifiers were employed for predicting treatment outcomes.
RESULTS: In the NSF group, Microstate 1 (MS1) exhibited a significantly higher duration (mean±std. = 0.092±0.008 vs. 0.085±0.008, p = 0.047), occurrence (mean±std. = 2.587±0.334 vs. 2.260±0.278, p = 0.014), and coverage (mean±std. = 0.240±0.046 vs. 0.194±0.040, p = 0.014) compared to the SF group. Additionally, the transition probabilities from Microstate 2 (MS2) and Microstate 3 (MS3) to MS1 were increased. In MS2, the NSF group displayed a stronger correlation (mean±std. = 0.618±0.025 vs. 0.571±0.034, p < 0.001) and a higher global explained variance (mean±std. = 0.083±0.035 vs. 0.055±0.023, p = 0.027) than the SF group. Conversely, Microstate 4 (MS4) in the SF group demonstrated significantly greater coverage (mean±std. = 0.388±0.074 vs. 0.334±0.052, p = 0.046) and more frequent transitions from MS2 to MS4, indicating a distinct pattern. Temporal parameters contribute major predictive role in predicting treatment outcomes of Oxcarbazepine, with area under curves (AUCs) of 0.95, 0.70, and 0.86, achieved by LR, NB and SVM, respectively.
CONCLUSIONS: This study underscores the potential of EEG microstates as predictive biomarkers for Oxcarbazepine treatment responses in newly-diagnosed focal epilepsy patients.
摘要：
目的：微状态代表头皮记录的脑电图的脑电活动的整体和地形分布。本研究旨在探讨局灶性癫痫患者用药前的脑电图微观状态,并使用提取的微状态指标来预测奥卡西平单药治疗的结果。
方法：本研究纳入了25例新诊断的局灶性癫痫患者（13例女性），年龄12至68岁，病因各异。根据首次随访结果，将患者分为无癫痫（NSF）和无癫痫（SF）组。从用药前的脑电图，通过聚类鉴定了四种代表性的微状态.提取并分析了微观状态的时间参数和转移概率，以辨别群体差异。使用生成样本方法，支持向量机(SVM)逻辑回归(LR)，和朴素贝叶斯(NB)分类器用于预测治疗结果。
结果：在NSF组中，微状态1（MS1）表现出明显更长的持续时间（平均值±std。=0.092±0.008vs.0.085±0.008,p=0.047),发生率(平均值±std.=2.587±0.334vs.2.260±0.278，p=0.014），和覆盖率(平均值±标准。=0.240±0.046vs.与SF组相比，0.194±0.040，p=0.014）。此外,从微态2(MS2)和微态3(MS3)到MS1的转移概率增加。在MS2中，NSF组显示出较强的相关性（平均值±std。=0.618±0.025vs.0.571±0.034，p<0.001）和更高的全局解释方差（平均值±std。=0.083±0.035vs.0.055±0.023,p=0.027)比SF组。相反，SF组中的微状态4（MS4）表现出明显更大的覆盖率（平均值±std。=0.388±0.074vs.0.334±0.052，p=0.046）和从MS2到MS4的更频繁转换，表明不同的模式。时间参数在预测奥卡西平的治疗结果方面具有重要的预测作用，LR实现的曲线下面积(AUC)为0.95、0.70和0.86，NB和SVM，分别。
结论：本研究强调了脑电图微状态作为新诊断局灶性癫痫患者奥卡西平治疗反应的预测生物标志物的潜力。
方法：本研究纳入了25例新诊断的局灶性癫痫患者（13例女性），年龄12至68岁，病因各异。根据首次随访结果，将患者分为无癫痫（NSF）和无癫痫（SF）组。从用药前的脑电图，通过聚类鉴定了四种代表性的微状态.提取并分析了微观状态的时间参数和转移概率，以辨别群体差异。使用生成样本方法，支持向量机(SVM)逻辑回归(LR)，和朴素贝叶斯(NB)分类器用于预测治疗结果。
结果：在NSF组中，微状态1（MS1）表现出明显更长的持续时间（平均值±std。=0.092±0.008vs.0.085±0.008,p=0.047),发生率(平均值±std.=2.587±0.334vs.2.260±0.278，p=0.014），和覆盖率(平均值±标准。=0.240±0.046vs.与SF组相比，0.194±0.040，p=0.014）。此外,从微态2(MS2)和微态3(MS3)到MS1的转移概率增加。在MS2中，NSF组显示出较强的相关性（平均值±std。=0.618±0.025vs.0.571±0.034，p<0.001）和更高的全局解释方差（平均值±std。=0.083±0.035vs.0.055±0.023,p=0.027)比SF组。相反，SF组中的微状态4（MS4）表现出明显更大的覆盖率（平均值±std。=0.388±0.074vs.0.334±0.052，p=0.046）和从MS2到MS4的更频繁转换，表明不同的模式。时间参数在预测奥卡西平的治疗结果方面具有重要的预测作用，LR实现的曲线下面积(AUC)为0.95、0.70和0.86，NB和SVM，分别。
结论：本研究强调了脑电图微状态作为新诊断局灶性癫痫患者奥卡西平治疗反应的预测生物标志物的潜力。
