Virtual resection predicts surgical outcome for drug-resistant epilepsy
Virtual resection predicts surgical outcome for drug-resistant epilepsy
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DOI：
10.1093/brain/awz303
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发表时间：
2019-12-01
期刊：
影响因子：
14.5
通讯作者：
Litt, Brian
中科院分区：
文献类型：
--
作者：
Kini, Lohith G.;Bernabei, John M.;Litt, Brian
患有耐药性癫痫的患者通常需要手术才能摆脱癫痫。虽然激光消融和植入式刺激装置降低了这些手术的发病率，但无瘤率并未显著提高，特别是对于无局灶性病变的患者。部分原因是，在这些情况下，人们往往不清楚在哪里进行干预。为了满足这种临床需求，一些研究小组已经发表了绘制癫痫网络的方法，但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转化：（i）提出并共享一个强大的管道，以严格量化切除区的边界，并确定哪些颅内EEG电极位于其中;（ii）在手术切除前植入颅内电极的28名耐药癫痫患者的回顾性队列中验证大脑网络模型;以及（iii）共享所有神经成像、注释的电生理学和临床元数据以促进未来的合作。我们的网络方法准确地预测患者是否有可能受益于手术干预的基础上同步颅内脑电图（0.89的接收器工作特征曲线下的面积），并提供新的信息，传统的电图功能不。我们进一步报告说，删除同步的大脑区域与改善临床结果，并假设，节省去干扰区域可能会进一步有益。我们的研究结果表明，数据驱动的基于网络的方法可以识别可能从切除或消融治疗中受益的患者，并可能阻止那些不太可能这样做的侵入性干预。
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.