在快速波纹网络的引导下,模拟切除术和响应性神经刺激器置入可优化术后癫痫发作结果。

IF 4.1 Q1 CLINICAL NEUROLOGY Brain communications Pub Date : 2024-10-14 eCollection Date: 2024-01-01 DOI:10.1093/braincomms/fcae367
Shennan Aibel Weiss, Michael R Sperling, Jerome Engel, Anli Liu, Itzhak Fried, Chengyuan Wu, Werner Doyle, Charles Mikell, Sima Mofakham, Noriko Salamon, Myung Shin Sim, Anatol Bragin, Richard Staba
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引用次数: 0

摘要

对于药物耐受性癫痫,癫痫手术的目标是通过尽可能小的切除/消融手术使患者摆脱癫痫发作,从而将发病率降至最低。癫痫手术边缘规划的标准包括癫痫发作区的电临床划定,并结合核磁共振成像、正电子发射计算机断层显像、单光子发射计算机断层显像和脑磁图等神经影像学检查结果。研究发现,切除产生高频振荡的皮层组织是针对癫痫发作起始区的一种更有效的替代方法。在这项研究中,我们使用了支持向量机(SVM),并将四个不同的快速波纹(FR:振荡为 350-600 Hz,尖峰为 200-600 Hz)指标作为因子。这些指标包括快速波纹切除率、一个空间快速波纹网络测量值和两个时间快速波纹网络测量值。SVM 根据这四个因子的值与 18 名医学难治性局灶性癫痫患者的实际切除边界和实际无发作标签进行训练。在这一训练集中,对训练好的 SVM 进行了留空交叉验证,准确率为 0.78。接下来,我们使用了模拟迭代虚拟切除术,目标是切除率最高、时间自主性最强的 FR 位点。训练好的 SVM 利用四个虚拟 FR 指标来预测虚拟癫痫发作自由度。我们发现,在九名术后无癫痫发作的患者中,除一名患者外,其他患者的虚拟切除体积都较大(P < 0.05),足以实现虚拟无癫痫发作。在 9 名未摆脱癫痫发作的患者中,较大的虚拟切除术使其中 5 人几乎摆脱了癫痫发作。我们还检查了 10 名植入反应性神经刺激器系统的药物难治性局灶性癫痫患者,并虚拟瞄准了反应性神经刺激器系统刺激接触点,这些接触点靠近癫痫发作率最高的部位,以确定刺激发作起始区和刺激癫痫发作率指标的模拟值是否会趋向于那些癫痫发作结果较好的患者。我们的结果表明(i) FR 指标可以准确预测按护理标准定义的切除术是否会导致癫痫发作;(ii) 仅利用 FR 指标来规划有效的手术可能与较大的切除术有关;(iii) 当 FR 指标预测护理标准的切除术将失败时,通过某些产生 FR 的部位来修改计划切除术的边界可能会改善结果;(iv) 还需要做更多的工作来确定将响应性神经刺激器系统刺激触点定位在产生 FR 的部位附近是否会改善癫痫发作结果。
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Simulated resections and responsive neurostimulator placement can optimize postoperative seizure outcomes when guided by fast ripple networks.

In medication-resistant epilepsy, the goal of epilepsy surgery is to make a patient seizure free with a resection/ablation that is as small as possible to minimize morbidity. The standard of care in planning the margins of epilepsy surgery involves electroclinical delineation of the seizure-onset zone and incorporation of neuroimaging findings from MRI, PET, single-photon emission CT and magnetoencephalography modalities. Resecting cortical tissue generating high-frequency oscillations has been investigated as a more efficacious alternative to targeting the seizure-onset zone. In this study, we used a support vector machine (SVM), with four distinct fast ripple (FR: 350-600 Hz on oscillations, 200-600 Hz on spikes) metrics as factors. These metrics included the FR resection ratio, a spatial FR network measure and two temporal FR network measures. The SVM was trained by the value of these four factors with respect to the actual resection boundaries and actual seizure-free labels of 18 patients with medically refractory focal epilepsy. Leave-one-out cross-validation of the trained SVM in this training set had an accuracy of 0.78. We next used a simulated iterative virtual resection targeting the FR sites that were of highest rate and showed most temporal autonomy. The trained SVM utilized the four virtual FR metrics to predict virtual seizure freedom. In all but one of the nine patients who were seizure free after surgery, we found that the virtual resections sufficient for virtual seizure freedom were larger in volume (P < 0.05). In nine patients who were not seizure free, a larger virtual resection made five virtually seizure free. We also examined 10 medically refractory focal epilepsy patients implanted with the responsive neurostimulator system and virtually targeted the responsive neurostimulator system stimulation contacts proximal to sites generating FR at highest rates to determine if the simulated value of the stimulated seizure-onset zone and stimulated FR metrics would trend towards those patients with a better seizure outcome. Our results suggest the following: (i) FR measures can accurately predict whether a resection, defined by the standard of care, will result in seizure freedom; (ii) utilizing FR alone for planning an efficacious surgery can be associated with larger resections; (iii) when FR metrics predict the standard-of-care resection will fail, amending the boundaries of the planned resection with certain FR-generating sites may improve outcome and (iv) more work is required to determine whether targeting responsive neurostimulator system stimulation contact proximal to FR generating sites will improve seizure outcome.

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