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：
10845904
负责人：
Danielle Smith Bassett
金额：
$ 8.56万
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/10845904
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

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

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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6500万人癫痫患者中有超过1/3（美国约330万）的癫痫发作不能由药物控制。手术和植入设备是许多人的选择，但它们的成功取决于在手动映射癫痫网络时，只有某些患者才有可能，并且标准化较差。当确定手术目标时，目前尚无严格的方法来选择最佳的手术方法。该提案的总体目的是开发严格，标准化的定量方法：（1）地图来自成像和立体声EEG的癫痫网络（SEEG），（2）选择切除，消融或通过其数据和临床假设为个别患者的神经调节，（3）确定焦点何时干预不太可能成功。这些方法将对临床护理产生巨大的积极影响。在过去的四年中，我们朝着这些目标取得了长足的进步。我们已经开发了：（1）源自颅内脑外（ECOG）的鲁棒测量，这些测量可以预测癫痫手术结果；（2）定位癫痫网络并预测不同干预措施对的个性化方法癫痫发作控制；（3）预测癫痫发作路径从联合MRI和IEEG传播的工具。我们也有一个在http：//ieeg.org上公开共享我们的方法，数据，结果和代码的记录，以加速研究。基于这项工作，我们现在创新了解决3种基本挑战，以将我们的工作转化为练习：（1）指导Seeg：我们必须开发新方法来说明稀疏采样和立体声脑电图的不同哲学，该哲学映射了连接的大脑区域网络并测试临床假设癫痫发起和传播的位置；（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采样癫痫的所有关键区域网络，以确保由于缺少信息而不会错误地本地化；（3）验证较大的人口整个中心：与完善上述方法平行，我们将验证和协调我们的分析以大量患者为中心将其硬化以供临床使用。在一个新颖的模型中，我们参与了一个小组主要的手术癫痫中心公开协作，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定量方法可以在各个中心进行标准化，可预测个性化癫痫手术的结果，以及最终被翻译成改善临床护理。这项工作很重要，因为它合并了最先进的网络神经科学，工程，神经病学和神经外科手术以改善和标准化患者护理的实用工具。它还建立了 15个主要癫痫中心之间的合作，以标准化和共享数据。最后，这个项目利用神经病学专家，计算神经科学，神经外科专家之间的繁荣合作，宾夕法尼亚州的神经影像学和生物工程，具有临床翻译的良好记录。