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: 10667100
• 负责人: Danielle Smith Bassett
• 金额: $ 16.25万
• 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/10667100
• 关键词: Ablation; Acceleration; Adoption; Algorithms; Anatomy; Archives; Biomedical Engineering; Brain Mapping; Brain region; Caring; Clinical; Clinical Trials; Code; Collaborations; Computing Methodologies; Data; Data Aggregation; Data Science; Diffuse; Eastern Cooperative Oncology Group; Education; Educational workshop; Effectiveness; Electrodes; Electroencephalography; Engineering; Ensure; Epilepsy; Excision; Foundations; Future; Generations; Goals; Grant; Image; Implant; Individual; Information Theory; Intervention; Laboratories; Letters; Machine Learning; Magnetic Resonance Imaging; Manuals; Maps; Measures; Mentors; Metadata; Methods; Modeling; Morbidity - disease rate; Multi-Institutional Clinical Trial; Neurology; Neurosciences; Operative Surgical Procedures; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Pennsylvania; Persons; Pharmaceutical Preparations; Phase; Philosophy; Population; Probability; Protocols documentation; Quality of Care; Regional Anatomy; Research; Research Personnel; Resected; Sampling; Sampling Biases; Sampling Errors; Seizures; Standardization; Structure; Testing; Tissues; Tonic-Clonic Epilepsy; Training; Translating; United States; Universities; Validation; Visit; Work; clinical care; clinical practice; clinical translation; computational neuroscience; computer code; cost; data sharing; implantable device; improved; individual patient; innovation; network models; neuroimaging; neuroregulation; neurosurgery; next generation; novel; open source; outcome prediction; predictive tools; prospective; research and development; skills; success; tool; virtual; visiting scholar

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6500万癫痫患者中有三分之一以上(美国约330万)有癫痫发作，不能 被药物控制。手术和植入设备是许多人的选择，但它们的成功取决于 人工绘制癫痫网络图，这只对一些患者是可能的，而且标准化程度很低。 当确定手术靶点时，目前还没有严格的方法来选择最佳的手术入路。 这项建议的总体目标是开发严格、标准化、量化的方法来：(1)地图 来自成像和立体脑电(SEEG)的癫痫网络，(2)选择最佳区域进行切除、消融或 根据他们的数据和临床假说对个别患者的神经调节，以及(3)确定何时局部 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。 在过去四年中，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) 硬膜下颅内脑电(ECOG)预测癫痫手术结果的稳健措施； (2)个性化的方法，定位癫痫网络并预测不同干预措施对 癫痫控制；(3)结合MRI和iEEG预测癫痫扩散路径的工具。我们还有一个 在http：//ieeg.org上公开分享我们的方法、数据、结果和代码以加速研究的记录。 基于这项工作，我们现在创新地解决3个基本挑战，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来解决稀疏采样和 立体脑电的不同原理，它绘制了一个连接的大脑区域网络，并测试临床 关于癫痫发作起始和传播地点的假设；(2)评估抽样偏差和缺失 信息：我们将开发确定电极是否对癫痫患者的所有关键区域进行采样的方法 网络，以确保我们不会因信息缺失而错误定位；(3)在更大的人口中验证 跨中心：在改进上述方法的同时，我们将验证和协调我们的分析 以大量患者为中心，使其硬化，供临床使用。在一个新的模式中，我们雇佣了一个团队 开放协作、标准化方法、汇总数据并共享所有 Http：//ieeg.org上的算法、计算机代码、数据和结果。我们的中心假设是我们的数量 方法可以跨中心标准化，预测个性化癫痫手术的结果，以及 最终转化为改善临床护理。 这项工作意义重大，因为它融合了最先进的网络神经科学、工程学、神经学和 神经外科，使实用的工具，以改善和标准化病人护理。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、 在宾夕法尼亚大学从事神经成像和生物工程，在临床翻译方面有着良好的记录。