Quantitative analysis of visually reviewed normal scalp EEG predicts seizure freedom following anterior temporal lobectomy.
Quantitative analysis of visually reviewed normal scalp EEG predicts seizure freedom following anterior temporal lobectomy.
复制标题
目测正常头皮脑电图定量分析预测前颞叶切除术后癫痫发作自由。
DOI：
10.1111/epi.17257
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发表时间：
2022-07
期刊：
影响因子：
5.6
通讯作者：
Worrell G
中科院分区：
文献类型：
--
作者：
Varatharajah Y;Joseph B;Brinkmann B;Morita-Sherman M;Fitzgerald Z;Vegh D;Nair D;Burgess R;Cendes F;Jehi L;Worrell G
前颞叶切除术（ATL）是一种广泛实施且成功治疗耐药性颞叶癫痫（TLE）的干预措施。然而，高达三分之一的患者在ATL后1年内癫痫复发。尽管有大量关于ATL后无癫痫发作的术前脑电图（EEG）和磁共振成像（MRI）异常的文献，但目视检查正常发作间期EEG的定量分析在此类癫痫发作中的价值仍不清楚。在这项回顾性多中心研究中，我们调查了正常发作间期头皮EEG研究的机器学习分析是否可以预测接受ATL的患者术后无癫痫发作的结局。我们分析了41名马约诊所（MC）和23名克利夫兰诊所（CC）患者的正常术前头皮脑电图记录。我们使用无偏自动算法从头皮EEG研究中提取无任何癫痫样活动的闭眼清醒时期，然后提取频谱EEG特征，这些特征表示（a）频谱功率和（B）在几个大脑区域中频率在1和25 Hz之间的半球间频谱相干性。我们分析了无癫痫发作和非无癫痫发作患者之间的差异，并采用使用多个光谱特征的朴素贝叶斯分类器来预测手术结果。我们使用MC数据集内的留一患者交叉验证方案训练分类器，然后使用样本外CC数据集进行测试。最后，我们比较了正常头皮EEG衍生特征与MRI异常的预测性能。我们发现，几个频谱功率和相干性特征显示出与手术结果相关的显著差异，并且在10-25 Hz范围内最为明显。基于这些特征的朴素贝叶斯分类预测ATL后1年无癫痫发作，MC和CC数据集的曲线下面积（AUC）值分别为0.78和0.76。随后的分析显示，（a）10-25 Hz范围内的半球间频谱相干特征比其他组合提供了更好的预测性，（B）与MRI异常相比，正常头皮EEG衍生特征提供了上级和潜在独特的预测价值（F1评分高出>10%）。这些结果支持，即使是正常的术前头皮EEG的定量分析也可能有助于耐药TLE患者ATL后的癫痫发作自由。虽然该结果的机制尚不清楚，但预测癫痫发作自由度的头皮EEG频谱和相干特性可能代表了由新皮层或负责ATL边缘内外颞叶癫痫发作生成的网络引起的活动。
Anterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug‐resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL. We analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure‐free and non–seizure‐free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave‐one‐patient‐out cross‐validation scheme within the MC data set and then tested using the out‐of‐sample CC data set. Finally, we compared the predictive performance of normal scalp EEG‐derived features against MRI abnormalities. We found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10–25 Hz range. The Naïve Bayes classification based on those features predicted 1‐year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10–25 Hz range provided better predictability than other combinations and (b) normal scalp EEG‐derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score). These results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug‐resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.
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DOI：
10.1073/pnas.2011130118
发表时间：
2021-04-27
影响因子：
11.1
作者：
Cai Z;Sohrabpour A;Jiang H;Ye S;Joseph B;Brinkmann BH;Worrell GA;He B
通讯作者：
He B
影响因子：
4.3
作者：
Gramfort A;Luessi M;Larson E;Engemann DA;Strohmeier D;Brodbeck C;Goj R;Jas M;Brooks T;Parkkonen L;Hämäläinen M
通讯作者：
Hämäläinen M
影响因子：
4.6
作者：
Boran, Ece;Sarnthein, Johannes;Fedele, Tommaso
通讯作者：
Fedele, Tommaso
影响因子：
9.9
作者：
Jeha, L. E.;Najm, I. M.;Luders, H. O.
通讯作者：
Luders, H. O.
影响因子：
3.6
作者：
Jobst, Barbara C.;Bartolomei, Fabrice;Gotman, Jean
通讯作者：
Gotman, Jean