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体感诱发电场可预测迷走神经刺激的反应。

Somatosensory evoked fields predict response to vagus nerve stimulation.

作者信息

Mithani Karim, Wong Simeon M, Mikhail Mirriam, Pourmotabbed Haatef, Pang Elizabeth, Sharma Roy, Yau Ivanna, Ochi Ayako, Otsubo Hiroshi, Snead O Carter, Donner Elizabeth, Go Cristina, Widjaja Elysa, Babajani-Feremi Abbas, Ibrahim George M

机构信息

Faculty of Medicine, University of Toronto, Toronto, Canada.

Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada.

出版信息

Neuroimage Clin. 2020;26:102205. doi: 10.1016/j.nicl.2020.102205. Epub 2020 Feb 4.

Abstract

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.

摘要

目前迫切需要开发强大的预测算法,以便在术前识别出对迷走神经刺激(VNS)有反应的小儿癫痫患者。鉴于迷走神经和正中神经向初级体感皮层的传入投射在神经回路方面具有相似性,本研究假设正中神经体感诱发电场(SEF)可用于预测对VNS的癫痫反应。本研究使用了来自两家不同机构接受VNS治疗的48例儿科患者的回顾性数据。36例患者(“发现队列”)在脑磁图(MEG)记录期间接受了术前正中神经电刺激,12例患者(“验证队列”)在MEG期间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连接性。在发现队列上训练支持向量机(SVM)分类器,以根据这些输入特征区分反应者和无反应者,并通过将模型预测的对VNS的反应与已知反应进行比较,在验证队列上进行测试。我们发现,对VNS有反应的患者在正中神经刺激时,SEF定位明显更广泛,边缘和感觉运动网络内的功能连接性更强。两个队列之间未观察到SEF幅度或潜伏期的差异。SVM分类器在交叉验证中的准确率为88.9%(受试者操作特征曲线下面积为0.93),在验证队列中降至67%。通过利用重叠的神经回路,我们发现正中神经SEF特征和功能连接性可以识别对VNS有反应的患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8ea/7026289/2652f12da824/gr1.jpg

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