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

基本信息

批准号：
10625963
负责人：
Danielle Smith Bassett
金额：
$ 63.82万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
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/10625963
关键词：
Ablation Acceleration Adoption Algorithms Anatomy Archives Biomedical Engineering Brain Mapping Brain region Caring Clinical Clinical Trials Code Collaborations Computer Models Computing Methodologies Creativeness 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 Regional Anatomy 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 clinical care clinical practice clinical translation computational neuroscience computer code cost data sharing implantable device improved individual patient innovation network models neuroimaging neuroregulation neurosurgery novel open source outcome prediction predictive tools prospective side effect success tool virtual

项目摘要

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，超过 1/3（美国约 330 万）的癫痫发作无法治愈。可以通过药物来控制。手术和植入装置是许多人的选择，但它们的成功取决于手动绘制癫痫网络，这仅适用于某些患者，而且标准化程度较差。当手术目标确定后，目前还没有严格的方法来选择最佳手术入路。该提案的总体目标是开发严格、标准化、定量的方法来：（1）绘制地图来自成像和立体脑电图 (SEEG) 的癫痫网络，(2) 选择切除、消融或的最佳区域根据患者的数据和临床假设对个体患者进行神经调节，以及（3）确定局部何时发生干预不太可能成功。这些方法将对临床护理产生巨大的积极影响。过去四年来，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）源自硬膜下颅内脑电图 (ECOG) 的稳健措施可预测癫痫手术的结果；（2）个性化方法，定位癫痫网络并预测不同干预措施对癫痫网络的影响癫痫发作控制； (3) 结合 MRI 和 IEEG 来预测癫痫发作扩散路径的工具。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，我们现在通过创新解决 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释稀疏采样和立体脑电图的不同原理，绘制了连接的大脑区域的网络并测试了临床关于癫痫发作在何处开始和传播的假设； (2)评估抽样偏差和缺失信息：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，以确保我们不会因信息缺失而错误定位； (3) 在更大人群中进行验证跨中心：在完善上述方法的同时，我们将跨中心验证和协调我们的分析中心在大量患者中进行硬化以供临床使用。在一个新颖的模型中，我们聘请了一组主要癫痫外科中心公开合作、标准化方法、汇总数据并共享所有内容算法、计算机代码、数据和结果请参见 http://ieeg.org。我们的中心假设是我们的定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、神经学和神经外科制造实用工具来改善和标准化患者护理。它还建立了一个 15 个主要癫痫中心之间合作以标准化和共享数据。最后，该项目利用神经病学、计算神经科学、神经外科、宾夕法尼亚大学的神经影像和生物工程专业，在临床转化方面拥有良好的记录。