Computational Tools for Improving Stereo-EEG Implantation and Resection Surgery

在340万患有癫痫的美国人中，超过30%的人没有从药物治疗中受益。外科手术 切除癫痫发作所在的脑组织(致痫区域)是一种替代方案，可以消除 在这些患者中，1年癫痫发作的自由率为61%。然而，成功的切除需要 致痫区域的定位，这是具有挑战性的，因为致痫区域难以区分 临床图像(例如磁共振成像)上的健康组织。立体脑电(SEEG)是一种微创记录技术 其中100-200个电极触点通过小的经颅钻孔插入到广泛的 大脑中数百微米的活跃神经元，导致信号保真度大大高于 常规脑电检查。尽管sEEG具有巨大的潜力，但癫痫切除手术的结果并不是 在过去的20年里有了很大的改善。这在一定程度上是由于对最优的 SEEG植入策略和缺乏算法来开发sEEG的时空分辨率 致痫灶定位。这项提案的目标是开发和部署临床上有用的计算机 使用sEEG改进致痫区域定位的工具。 第一个目标是开发一套计算工具，使可以记录的脑组织可视化 通过一组sEEG电极和优化电极轨迹的优化算法 在最大化皮质覆盖率的同时，植入电极的数量。我将开发现实的计算方法 头部模型采用患者特定头部有限元建模。我会把头像模特儿和一个老牌的 对神经源强度的估计，以估计和可视化可由一组sEEG记录的组织 电极。然后我将把头部模型和震源强度估计耦合到蒙特卡罗树搜索 确定绘制感兴趣区域所需的最小电极轨迹集的算法。这个 结果将是一对可视化可记录脑组织和优化电极的计算工具 植入轨迹。 第二个目标是开发一种时空源重建算法来映射神经记录 进入大脑。我将开发一个贝叶斯信源重建算法来估计时间进程和 神经活动的空间范围。我将在癫痫样活动的记录上使用源重建算法 以描绘与致痫区域相关的大脑区域。结果将是一个贝叶斯 来源重建工具，即癫痫专家可以用来辅助手术切除决策。 这项工作的成功完成预计将改善致痫区域的定位，并导致 癫痫患者的癫痫自由率较高。

More than 30% of the 3.4 million Americans with epilepsy do not benefit from drug therapies. Surgical removal of the brain tissue where seizures originate (the epileptogenic zone) is an alternative that can eliminate seizures in these patients with 1 year seizure freedom rates of 61%. However, successful resection requires localization of the epileptogenic zone, which is challenging because the epileptogenic zone is indistinguishable from healthy tissue on clinical images (e.g. MRI). Stereo-EEG (sEEG) is a minimally invasive recording technique where 100-200 electrode contacts are inserted through small transcranial burr holes into widespread regions of the brain hundreds of microns from active neurons, resulting in substantially higher signal fidelity than conventional EEG. Despite the enormous potential of sEEG, outcomes of seizure resection surgeries have not improved substantially over the past 20 years. This is due, in part, to an incomplete understanding of an optimal sEEG implantation strategy and a lack of algorithms to exploit the spatiotemporal resolution of sEEG for epileptogenic zone localization. The goal of this proposal is to develop and deploy clinically useful computational tools to improve epileptogenic zone localization using sEEG. The first aim is to develop a set of computational tools that visualize the brain tissue that can be recorded by a set of sEEG electrodes and an optimization algorithm that optimizes the electrode trajectories to minimize the number of implanted electrodes while maximizing cortical coverage. I will develop realistic computational head models using patient specific head finite element modeling. I will couple the head models to an established estimate of the neural source strength to estimate and visualize the tissue that can be recorded by a set of sEEG electrodes. I will then couple the head models and source strength estimate to a Monte Carlo Tree Search algorithm to determine the minimum set of electrode trajectories necessary to map a region of interest. The outcome will be a pair of computational tools that visualize the recordable brain tissue and optimize electrode implantation trajectories. The second aim is to develop a spatiotemporal source reconstruction algorithm to map neural recordings into the brain. I will develop a Bayesian source reconstruction algorithm that estimates the time courses and spatial extent of neural activity. I will use the source reconstruction algorithm on recordings of epileptiform activity to delineate zone of the brain that are associated with the epileptogenic zone. The outcome will be a Bayesian source reconstruction tool that that epileptologists can use aid surgical resection decision making. Successful completion of this work is expected to improve epileptogenic zone localization and result in higher seizure freedom rates in patients with epilepsy.