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发作间期颅内脑电图不对称可使颞叶癫痫定位在一侧。

Interictal intracranial EEG asymmetry lateralizes temporal lobe epilepsy.

作者信息

Conrad Erin C, Lucas Alfredo, Ojemann William K S, Aguila Carlos A, Mojena Marissa, LaRocque Joshua J, Pattnaik Akash R, Gallagher Ryan, Greenblatt Adam, Tranquille Ashley, Parashos Alexandra, Gleichgerrcht Ezequiel, Bonilha Leonardo, Litt Brian, Sinha Saurabh, Ungar Lyle, Davis Kathryn A

机构信息

Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.

出版信息

medRxiv. 2023 Dec 14:2023.12.13.23299907. doi: 10.1101/2023.12.13.23299907.

Abstract

Patients with drug-resistant temporal lobe epilepsy often undergo intracranial EEG recording to capture multiple seizures in order to lateralize the seizure onset zone. This process is associated with morbidity and often ends in postoperative seizure recurrence. Abundant interictal (between-seizure) data is captured during this process, but these data currently play a small role in surgical planning. Our objective was to predict the laterality of the seizure onset zone using interictal (between-seizure) intracranial EEG data in patients with temporal lobe epilepsy. We performed a retrospective cohort study (single-center study for model development; two-center study for model validation). We studied patients with temporal lobe epilepsy undergoing intracranial EEG at the University of Pennsylvania (internal cohort) and the Medical University of South Carolina (external cohort) between 2015 and 2022. We developed a logistic regression model to predict seizure onset zone laterality using interictal EEG. We compared the concordance between the model-predicted seizure onset zone laterality and the side of surgery between patients with good and poor surgical outcomes. 47 patients (30 women; ages 20-69; 20 left-sided, 10 right-sided, and 17 bilateral seizure onsets) were analyzed for model development and internal validation. 19 patients (10 women; ages 23-73; 5 left-sided, 10 right-sided, 4 bilateral) were analyzed for external validation. The internal cohort cross-validated area under the curve for a model trained using spike rates was 0.83 for a model predicting left-sided seizure onset and 0.68 for a model predicting right-sided seizure onset. Balanced accuracies in the external cohort were 79.3% and 78.9% for the left- and right-sided predictions, respectively. The predicted concordance between the laterality of the seizure onset zone and the side of surgery was higher in patients with good surgical outcome. In conclusion, interictal EEG signatures are distinct across seizure onset zone lateralities. Left-sided seizure onsets are easier to distinguish than right-sided onsets. A model trained on spike rates accurately identifies patients with left-sided seizure onset zones and predicts surgical outcome.

摘要

耐药性颞叶癫痫患者常需进行颅内脑电图记录,以捕捉多次发作,从而确定癫痫发作起始区的位置。这一过程存在一定的发病风险,且术后癫痫复发的情况也较为常见。在此过程中会收集到大量发作间期(两次发作之间)的数据,但这些数据目前在手术规划中发挥的作用较小。我们的目标是利用颞叶癫痫患者发作间期的颅内脑电图数据来预测癫痫发作起始区的位置。我们进行了一项回顾性队列研究(单中心用于模型开发;双中心用于模型验证)。我们研究了2015年至2022年间在宾夕法尼亚大学(内部队列)和南卡罗来纳医科大学(外部队列)接受颅内脑电图检查的颞叶癫痫患者。我们开发了一个逻辑回归模型,利用发作间期脑电图来预测癫痫发作起始区的位置。我们比较了手术效果良好和不佳的患者中,模型预测的癫痫发作起始区位置与手术侧别之间的一致性。对47例患者(30名女性;年龄20 - 69岁;20例左侧发作、10例右侧发作、17例双侧发作)进行了模型开发和内部验证分析。对19例患者(10名女性;年龄23 - 73岁;5例左侧发作、10例右侧发作、4例双侧发作)进行了外部验证分析。使用尖波率训练的模型在内部队列交叉验证中,预测左侧癫痫发作起始的曲线下面积为0.83,预测右侧癫痫发作起始的曲线下面积为0.68。在外部队列中,左侧和右侧预测的平衡准确率分别为79.3%和78.9%。手术效果良好的患者中,癫痫发作起始区位置与手术侧别的预测一致性更高。总之,发作间期脑电图特征在癫痫发作起始区的不同位置上是不同的。左侧癫痫发作起始比右侧更容易区分。基于尖波率训练的模型能够准确识别左侧癫痫发作起始区的患者,并预测手术效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c53d/10760281/f596547a39b9/nihpp-2023.12.13.23299907v1-f0001.jpg

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