There is an unmet need to develop robust predictive algorithms to preoperatively identify pediatric epilepsy patients who will respond to vagus nerve stimulation (VNS). Given the similarity in the neural circuitry between vagus and median nerve afferent projections to the primary somatosensory cortex, the current study hypothesized that median nerve somatosensory evoked field(s) (SEFs) could be used to predict seizure response to VNS. Retrospective data from forty-eight pediatric patients who underwent VNS at two different institutions were used in this study. Thirty-six patients ("Discovery Cohort") underwent preoperative electrical median nerve stimulation during magnetoencephalography (MEG) recordings and 12 patients ("Validation Cohort") underwent preoperative pneumatic stimulation during MEG. SEFs and their spatial deviation, waveform amplitude and latency, and event-related connectivity were calculated for all patients. A support vector machine (SVM) classifier was trained on the Discovery Cohort to differentiate responders from non-responders based on these input features and tested on the Validation Cohort by comparing the model-predicted response to VNS to the known response. We found that responders to VNS had significantly more widespread SEF localization and greater functional connectivity within limbic and sensorimotor networks in response to median nerve stimulation. No difference in SEF amplitude or latencies was observed between the two cohorts. The SVM classifier demonstrated 88.9% accuracy (0.93 area under the receiver operator characteristics curve) on cross-validation, which decreased to 67% in the Validation cohort. By leveraging overlapping neural circuitry, we found that median nerve SEF characteristics and functional connectivity could identify responders to VNS.

中文翻译：

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体感诱发的场预测迷走神经刺激的反应。

亟需开发强大的预测算法，以在术前确定对迷走神经刺激（VNS）有反应的小儿癫痫患者。考虑到迷走神经和正中神经传入投射与初级体感皮层之间神经回路的相似性，目前的研究假设正中神经体感诱发场（SEF）可用于预测对VNS的癫痫发作反应。这项研究使用了来自两个不同机构接受VNS的48例儿科患者的回顾性数据。在磁脑电图（MEG）记录期间，有36例患者（“发现队列”）接受了术前正中神经电刺激，在MEG期间有12例患者（“验证队列”）接受了术前气动刺激。计算了所有患者的SEF及其空间偏差，波形幅度和潜伏期以及事件相关的连通性。在发现队列中训练了支持向量机（SVM）分类器，以基于这些输入特征将响应者与非响应者区分开，并通过将对VNS的模型预测响应与已知响应进行比较，在验证队列中进行了测试。我们发现，对中枢神经刺激反应，对VNS的反应者在边缘和感觉运动网络内的SEF定位明显更广泛，并且功能连接性更大。两个队列之间未观察到SEF振幅或潜伏期的差异。SVM分类器在交叉验证中显示出88.9％的准确性（在接收者操作员特征曲线下为0.93面积），在验证队列中下降到67％。