Biomarkers to Predict Outcome from Responsive Brain Stimulation for Epilepsy

摘要 ________________________________________________________________________________________ 目前FDA批准的响应性神经刺激（RNS）器械提供了一种有前途的替代方案， 超过600，000名患有难治性癫痫的美国人接受了手术，这些人不适合切除手术。 不幸的是，在放置这些装置之前，还没有经过验证的生物标志物来预测癫痫发作的结果， 约1/3的患者不能从RNS长期获益。迫切需要发展 基于临床和电生理数据的生物标志物，以确定对患者最有效的治疗 药物抵抗性癫痫发作，并为临床决策带来定量的严格性。远景目标 该提议的一个重要目的是发现和验证可用于RNS应答的预测性生物标志物特征， 在癫痫手术决策中被广泛采用。为达致这个目标，我们的整体目标是 使用应用于精心选择的一组特征的机器学习来开发这种预后生物标志物签名 以及根据术前评估期间获得的颅内EEG（IEEG）计算的模型， 定性临床特征。我们将通过一个新的联合平台， 方法，每个临床研究中心将以通用格式将数据发布到自己的私有云数据中 存储，这将是访问分析管道集中运行从我们的云平台。我们的中央 假设是从器械植入前评价期间收集的多模态数据中获得的生物标志物特征 植入物可用于预测患者对RNS治疗反应。我们的初步数据，分析10个RNS患者 每个来自加州大学旧金山分校，纽约大学和宾夕法尼亚大学，证明了我们的能力，执行拟议的研究。 在R61阶段，我们将在125例既往接受IEEG的患者中回顾性检验这一假设。 在宾夕法尼亚大学、加州大学旧金山分校和纽约大学癫痫中心放置RNS装置。我们在这一阶段的具体目标是： 1)为了使用术前评估神经成像构建用于生物标志物发现的联合处理管道， IEEG和临床元数据，2）从该数据中鉴定预测性生物标志物特征。我们的联合分析 框架将使我们能够：（a）加速跨多个地点和行业合作伙伴的生物标志物发现，（B） 满足患者和行业在共享专有数据方面的限制，（c）提供一个实用框架， 在全球临床中心采用。在R33阶段，生物标志物特征将在100 另外的患者在9个临床地点纵向随访。这项研究是创新的，因为它 通过严格分析多模态患者数据来预测， 对RNS的反应，并指导器械植入的决策。这项研究意义重大，因为它 通过更好的患者选择，有可能显著提高RNS治疗癫痫的成功率。 这项研究还提出了一个新颖的，通用的，联合的数据挖掘基础设施，多中心和行业 在转化神经科学中的合作。

Abstract ________________________________________________________________________________________ The current FDA-approved responsive neurostimulation (RNS) device offers a promising alternative to surgery for more than 600,000 Americans with intractable epilepsy who are not candidates for resective surgery. Unfortunately, there are no validated biomarkers to predict seizure outcomes before these devices are placed, and approximately 1/3 of patients do not benefit from RNS long-term. There is a critical need to develop biomarkers based upon clinical and electrophysiological data to determine the most effective therapy for patients with medication-resistant seizures, and to bring quantitative rigor to clinical decision making. The long-term goal of this proposal is to discover and validate a predictive biomarker signature for RNS response that can be used in epilepsy surgery decision making and broadly adopted. To achieve this goal, our overall objective is to develop this prognostic biomarker signature using machine learning applied to a carefully selected set of features and models calculated from intracranial EEG (IEEG) obtained during presurgical evaluation that incorporates qualitative clinical features. We will collaborate across centers and with industry partners via a novel federated approach, whereby each clinical site will post data in a common format to their own, private, cloud-based data store, which will be accessible to analysis pipelines run centrally from our cloud-based platform. Our central hypothesis is that biomarker signatures derived from multimodal data collected during evaluation prior to device implant can be used to predict patient response to RNS therapy. Our preliminary data, analyzing 10 RNS patients each from UCSF, NYU and UPenn, demonstrates our ability to perform the proposed research. In the R61 Phase, we will test this hypothesis retrospectively in 125 patients who underwent IEEG prior to RNS device placement at the UPenn, UCSF and NYU epilepsy centers. Our specific aims for this phase are: 1) To build a federated processing pipeline for biomarker discovery using presurgical evaluation neuroimaging, IEEG and clinical metadata, 2) To identify a predictive biomarker signature from this data. Our federated analysis framework will enable us to: (a) accelerate biomarker discovery across multiple sites and industry partners, (b) satisfy patient and industry limitations on sharing proprietary data, (c) provide a practical framework for rapid adoption across clinical centers worldwide. In the R33 phase, the biomarker signature will be validated in 100 additional patients followed longitudinally at 9 clinical sites. The proposed research is innovative because it represents a substantive departure from the status quo by rigorously analyzing multimodal patient data to predict response to RNS and guide decisions on device implantation. The proposed research is significant because it has the potential to dramatically improve the success rate of RNS for epilepsy through better patient selection. This study also puts into place a novel, versatile, federated data mining infrastructure for multi-center and industry collaboration in translational neuroscience.