Patients with drug-resistant epilepsy often require surgery to become seizure-free. While laser ablation and implantable stimulation devices have lowered the morbidity of these procedures, seizure-free rates have not dramatically improved, particularly for patients without focal lesions. This is in part because it is often unclear where to intervene in these cases. To address this clinical need, several research groups have published methods to map epileptic networks but applying them to improve patient care remains a challenge. In this study we advance clinical translation of these methods by: (i) presenting and sharing a robust pipeline to rigorously quantify the boundaries of the resection zone and determining which intracranial EEG electrodes lie within it; (ii) validating a brain network model on a retrospective cohort of 28 patients with drug-resistant epilepsy implanted with intracranial electrodes prior to surgical resection; and (iii) sharing all neuroimaging, annotated electrophysiology, and clinical metadata to facilitate future collaboration. Our network methods accurately forecast whether patients are likely to benefit from surgical intervention based on synchronizability of intracranial EEG (area under the receiver operating characteristic curve of 0.89) and provide novel information that traditional electrographic features do not. We further report that removing synchronizing brain regions is associated with improved clinical outcome, and postulate that sparing desynchronizing regions may further be beneficial. Our findings suggest that data-driven network-based methods can identify patients likely to benefit from resective or ablative therapy, and perhaps prevent invasive interventions in those unlikely to do so.

中文翻译：

---

虚拟切除术可预测耐药性癫痫的手术结局。

耐药性癫痫患者通常需要手术以免于癫痫发作。尽管激光消融和植入式刺激设备降低了这些手术的发病率，但无癫痫发作率并未得到显着提高，尤其是对于无局灶性病变的患者。部分原因是在这些情况下通常不清楚应在何处进行干预。为了满足这种临床需求，一些研究小组已经发布了绘制癫痫网络图谱的方法，但是将其应用于改善患者护理仍然是一个挑战。在这项研究中，我们通过以下方式推进这些方法的临床翻译：（i）提出并共享一个健壮的管道，以严格量化切除区域的边界，并确定其中包含哪些颅内EEG电极；（ii）在28例在手术切除之前植入颅内电极的耐药性癫痫患者的回顾性队列中验证了大脑网络模型；（iii）共享所有神经影像，带注释的电生理学和临床元数据，以促进未来的合作。我们的网络方法基于颅内EEG（在接收器工作特征曲线下的面积为0.89的区域）的可同步性，准确地预测患者是否可能从手术干预中受益，并提供传统的电子照相功能无法提供的新颖信息。我们进一步报告说，删除同步的大脑区域与改善临床效果有关，并假设保留不同步的区域可能进一步有益。