Virtual Resection to Treat Epilepsy

癫痫影响着全世界6500万人。虽然药物控制了许多超过2000万的患者， 尽管进行了最大限度的药物治疗，但仍继续癫痫发作。新的外科技术，激光热消融 对这些患者来说，响应设备是令人兴奋的选择，但它们的有效性受到我们无法 以准确地绘制出哪些大脑区域应该被切除或用电刺激治疗。目前这款 手工绘制，但颅内EEG（IEEG）上的癫痫发作模式往往不好 局部化，临床医生往往不同意癫痫发作的时间，位置和哪些地区应作为目标。 最后，大多数患者评价提供了许多可行的手术和器械放置选择。有 目前没有办法测试特定治疗方法（手术或器械放置）对 实际上，除了做手术之外，一种可以模拟这些干预措施的技术， 对个别病人来说，最好的方法将是临床护理的一个巨大进步。 在这项提案中，我们开发并验证了令人兴奋的新方法，以定位癫痫网络， 颅内EEG：（1）用自动化客观工具取代临床医生的手动标记，（2）去除 需要在评估过程中诱发急性癫痫发作，以定位它们;（3）允许临床医生模拟癫痫发作， 不同的脑部手术或设备放置对个体患者选择治疗的影响， 对他们最好。这项工作结合了新的图论计算方法来模拟大脑网络 从IEEG与最先进的神经成像技术，以精确定位植入的电极，设备和 大脑结构癫痫手术评价期间接受脑植入的成人和儿童患者 或NeuroPace响应神经刺激器（RNS）器械置入将在 宾夕法尼亚大学和费城儿童医院。我们将获得高分辨率的大脑 在电极植入之前和之后以及在手术或装置放置之后成像。我们的模特，最近 将应用于每个患者的数据，驱动癫痫发作的大脑区域将被定量 识别并映射到他们的大脑图像。患者将接受标准侵入性治疗， 或装置植入，和结果-癫痫发作频率的减少-将与 癫痫网络被植入的设备移除或刺激。最后，我们将测试我们的"虚拟 针对每个患者的数据使用“切除”技术来预测哪种治疗干预将是最有效的， 并将该预测与所执行的程序和患者结果进行比较。 这项工作不同于许多计算研究，因为它的重点是开发实用的工具， 指导药物抵抗性癫痫的侵入性治疗。它充分利用了 在成人和儿童癫痫方面经验丰富的临床医生，以及神经影像学、生物工程、功能 神经外科和麦克阿瑟奖获奖计算神经科学家在宾夕法尼亚大学。

Epilepsy affects 65 million people worldwide. While medications control many, over 20 million patients continue to have seizures despite maximal medical therapy. New surgical techniques, laser thermal ablation and responsive devices are exciting options for these patients, but their effectiveness is limited by our inability to accurately map which brain regions should be removed or treated with electrical stimulation. Currently, this mapping is done manually, but seizure onset patterns on intracranial EEG (IEEG) are frequently not well localized, and clinicians often disagree on seizure onset time, location, and what regions should be targeted. Finally, most patient evaluations present a number of viable options for surgery and device placement. There is currently no way to test the effects of a specific therapeutic approach- an operation or device placement- on outcome other than actually doing the procedure. A technique that could simulate these interventions and pick the best approach for individual patients would be a tremendous step forward in clinical care. In this proposal we develop and validate exciting new methods to localize epileptic networks from intracranial EEG that: (1) replace manual marking by clinicians with automated, objective tools, (2) remove the need for precipitating acute seizures during evaluation to localize them and (3) allow clinicians to simulate the effects of different brain surgeries or device placements for individual patients to select the treatment that will work best for them. This work marries new graph theoretical computational methods to model brain networks from IEEG with state of the art neuroimaging techniques to precisely localize implanted electrodes, devices and brain structure. Adult and pediatric patients undergoing brain implants during evaluation for epilepsy surgery or NeuroPace Responsive Neurostimulator (RNS) device placement will be enrolled at the Hospital of the University of Pennsylvania and Children's Hospital of Philadelphia. We will obtain high-resolution brain imaging before and after electrode implant and after surgery or device placement. Our models, recently published, will be applied to each patient's data and brain regions that drive seizures will be quantitatively identified and mapped to their brain images. Patients will undergo standard invasive therapy, either resection or device implant, and outcome- reduction in seizure frequency- will be compared to the amount of the epileptic network that is removed or stimulated by an implanted device. Finally, we will test our “virtual resection” technique against each patient's data to predict which therapeutic intervention will be most effective, and compare this prediction to the performed procedure and patient outcome. This work differs from many computational studies in that its focus is on developing practical tools to guide invasive treatment for medication resistant epilepsy. It leverages an established collaboration between experienced clinicians in adult and pediatric epilepsy with experts in neuroimaging, bioengineering, functional neurosurgery and a MacArthur-award-winning computational neuroscientist at the University of Pennsylvania.