Guiding epilepsy surgery using network models and Stereo EEG

使用网络模型和立体脑电图指导癫痫手术

• 批准号: 10344259
• 负责人: Danielle Smith Bassett
• 金额: $ 64.48万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10344259
• 关键词: Ablation; Adoption; Algorithms; Anatomy; Archives; Biomedical Engineering; Brain Mapping; Brain region; Caring; Clinical; Clinical Trials; Code; Collaborations; Computer Models; Computing Methodologies; Data; Data Aggregation; Diffuse; Eastern Cooperative Oncology Group; Electrodes; Electroencephalography; Engineering; Ensure; Epilepsy; Excision; Fostering; Foundations; Generations; Goals; Grant; Health; Human; Image; Implant; Individual; Information Theory; Intervention; Letters; Machine Learning; Magnetic Resonance Imaging; Manuals; Maps; Measures; Metadata; Methods; Mission; Modeling; Morbidity - disease rate; Multi-Institutional Clinical Trial; Neurology; Neurosciences; Operating Rooms; Operative Surgical Procedures; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Pennsylvania; Persons; Pharmaceutical Preparations; Phase; Philosophy; Population; Probability; Procedures; Protocols documentation; Public Health; Quality of Care; Research; Resected; Sampling; Sampling Biases; Sampling Errors; Seizures; Standardization; Structure; Testing; Tissues; Tonic-Clonic Epilepsy; Translating; United States; United States National Institutes of Health; Universities; Validation; Work; base; clinical care; clinical practice; clinical translation; computational neuroscience; computer code; cost; data sharing; data standards; implantable device; improved; individual patient; innovation; network models; neuroimaging; neuroregulation; neurosurgery; novel; open source; outcome prediction; predictive tools; prospective; side effect; success; tool; virtual

More than 1/3 of the world's 65 million people with epilepsy (~3.3 million in the U.S.) have seizures that cannot be controlled by medications. Surgery and implanted devices are options for many, but their success depends upon manually mapping epileptic networks, which is only possible for some patients, and poorly standardized. When surgical targets are identified, there is currently no rigorous way to select the best surgical approach. The overall aim of this proposal is to develop rigorous, standardized, quantitative methods to: (1) map epileptic networks from imaging and Stereo EEG (SEEG), (2) pick the best region for resection, ablation or neuromodulation for individual patients from their data and clinical hypotheses, and (3) to determine when focal intervention is unlikely to succeed. These methods would have tremendous positive impact on clinical care. Over the past four years we have made substantial progress towards these goals. We have developed: (1) robust measures derived from subdural intracranial EEG (ECOG) that predict outcome from epilepsy surgery; (2) personalized methods that localize epileptic networks and predict the impact of different interventions on seizure control; (3) tools that predict the path of seizure spread from combined MRI and IEEG. We also have a track record of openly sharing our methods, data, results and code on http: //ieeg.org, to accelerate research. Based upon this work, we now innovate to solve 3 fundamental challenges to translating our work into practice: (1) Guiding SEEG: We must develop new methods that account for the sparser sampling and different philosophy of stereo EEG, which maps a network of connected brain regions and tests clinical hypotheses about where seizures initiate and propagate; (2) Assessing sampling bias and missing information: We will develop methods to determine if electrodes sample all key regions of the epileptic network, to ensure we do not falsely localize due to missing information; (3) Validating in a larger population across centers: In parallel to refining the above methods, we will validate and harmonize our analyses across centers in a large number of patients to harden it for clinical use. In a novel model, we have engaged a group of major surgical epilepsy centers to openly collaborate, standardize methods, aggregate data, and share all algorithms, computer code, data and results on http: //ieeg.org. Our central hypothesis is that our quantitative methods can be standardized across centers, predict outcome from personalized epilepsy surgery, and ultimately be translated to improve clinical care. This work is significant because it merges state of the art network neuroscience, engineering, neurology and neurosurgery to make practical tools to improve and standardize patient care. It also establishes a collaboration between 15 major epilepsy centers to standardize and share data. Finally, this project leverages a thriving collaboration between experts in neurology, computational neuroscience, neurosurgery, neuroimaging and bioengineering at Penn, with a strong track record of clinical translation.

全球6500万癫痫患者中有超过1/3（美国约330万）有癫痫发作， 通过药物控制。手术和植入设备是许多人的选择，但他们的成功取决于 在手动映射癫痫网络时，这仅对某些患者是可能的，并且标准化较差。 当确定手术目标时，目前没有严格的方法来选择最佳手术方法。 本提案的总体目标是制定严格的、标准化的定量方法，以：（1）绘制 癫痫网络从成像和立体脑电图（SEEG），（2）挑选最佳区域切除，消融或 根据他们的数据和临床假设，对个体患者进行神经调节，以及（3）确定何时发生局灶性神经调节， 干预不太可能成功。这些方法将对临床护理产生巨大的积极影响。 在过去的四年里，我们在实现这些目标方面取得了实质性进展。我们开发了：（1） 来自硬膜下颅内EEG（ECOG）的可靠指标，可预测癫痫手术的结局; (2)个性化的方法，定位癫痫网络，并预测不同干预措施的影响， 癫痫控制;（3）结合MRI和IEEG预测癫痫传播路径的工具。我们也有一个 在http：//www.example.com上公开分享我们的方法、数据、结果和代码，以加速研究。//ieeg.org 基于这项工作，我们现在创新，以解决将我们的工作转化为 实践：（1）指导SEEG：我们必须开发新的方法，解释稀疏采样， 立体脑电图的不同哲学，它映射了一个连接的大脑区域网络，并测试了临床 关于癫痫发作开始和传播的假设;（2）评估抽样偏倚和缺失 信息：我们将开发方法来确定电极是否对癫痫患者的所有关键区域进行采样。 网络，以确保我们不会由于缺少信息而错误地定位;（3）在更大的人群中验证 跨中心：在完善上述方法的同时，我们将验证和协调我们的分析， 集中在大量的患者中，使其硬化以供临床使用。在一个新颖的模式中，我们让一群 开放合作，标准化方法，汇总数据，并共享所有 算法、计算机代码、数据和结果，请访问http：//ieeg.org。 方法可以在不同中心标准化，预测个性化癫痫手术的结果， 最终转化为改善临床护理。 这项工作是重要的，因为它融合了最先进的网络神经科学，工程，神经学和 神经外科，使实用的工具，以改善和标准化的病人护理。它还建立了一个 15个主要癫痫中心之间的合作，以标准化和共享数据。最后，该项目利用 神经学、计算神经科学、神经外科、 神经成像和生物工程在宾夕法尼亚大学，具有良好的临床翻译记录。