Optimized Intracranial EEG Targeting in Focal Epilepsy based upon Neuroimaging Connectomics

基于神经影像连接组学的局灶性癫痫颅内脑电图优化靶向

• 批准号: 10617198
• 负责人: Kathryn Adamiak Davis
• 金额: $ 63.6万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10617198
• 关键词: Ablation; Anatomy; Biological Markers; Biomedical Engineering; Brain; Brain region; Clinical; Clinical Data; Collaborations; Coupling; Data; Development; Devices; Diagnostic tests; Diffusion Magnetic Resonance Imaging; Disease; Electrodes; Electroencephalography; Epilepsy; Evaluation; Functional Magnetic Resonance Imaging; Goals; Graph; Image; Imaging Device; Implant; Intervention; Intractable Epilepsy; Lasers; Lesion; Limbic System; Magnetic Resonance Imaging; Maps; Measures; Medical; Medicine; Methods; Morbidity - disease rate; Multimodal Imaging; Network-based; Neurology; Operative Surgical Procedures; Outcome; Partial Epilepsies; Patients; Pattern; Pennsylvania; Persons; Pharmaceutical Preparations; Procedures; Property; Public Health; Research; Resistance; Rest; Risk Reduction; Sampling; Sampling Errors; Seizures; South Carolina; Stereotyping; Structure; Temporal Lobe; Temporal Lobe Epilepsy; Testing; Translating; United States; Universities; Work; clinical care; computational neuroscience; connectome; imaging biomarker; implantation; improved; individual patient; innovation; multimodal data; neuroimaging; neuroimaging marker; neurosurgery; non-invasive imaging; novel; novel therapeutics; open source; personalized strategies; predictive modeling; professional atmosphere; spatiotemporal; surgery outcome

Despite recent advances in neuroimaging, approximately 2/3 of intractable epilepsy patients that undergo surgical evaluation continue to require intracranial EEG (IEEG), arguably the most invasive diagnostic test in medicine. We currently lack methods to quantitatively map noninvasive imaging measures of structure and function to IEEG. Specifically, there is a critical need to validate whole-brain noninvasive neuroimaging network- based biomarkers to guide precise placement of electrodes and translate noninvasive network neuroimaging to change the paradigms of clinical care. The long-term goal of this proposal is to predict IEEG functional dynamics and surgical outcomes using noninvasive MRI-based measures of structure and function. Our overall objective, which is the next step toward attaining our long-term goal, is to develop open-source noninvasive imaging tools that map epileptic networks by integrating MRI and IEEG data. Our central hypothesis is that noninvasive measures of structure and function relate to and can predict the intricate functional dynamics captured on IEEG. The central hypothesis will be tested in patients undergoing IEEG targeting the temporal lobe network by pursuing three specific aims: 1) To map the patient specific structural connectome to IEEG seizure onset and propagation, 2) To correlate seizure onset and propagation on IEEG with network measures derived from resting state functional MRI (rsfMRI), and 3) To integrate the structural (Aim 1) and functional (Aim 2) connectome with standard qualitative clinical data to predict IEEG network dynamics and surgical outcomes. Under the first aim patients will undergo diffusion tensor imaging (DTI) prior to stereotactic IEEG, an IEEG method that inherently samples long range networks. The functional IEEG network will be mapped to DTI thus defining how seizures are constrained by the underlying structural connectome as they propagate. Under the second aim patients with temporal lobe epilepsy will undergo rsfMRI on 7T MRI prior to stereotactic IEEG. Functional network measures from rsfMRI and IEEG will be coregistered and rsfMRI will be used to predict functional EEG ictal and interictal networks. In the third aim two models predicting IEEG network dynamics and epilepsy surgical outcomes will be created building off of methods developed in Aims 1 and 2. The proposed research is innovative because it represents a substantive departure from the status quo by directly connecting noninvasive multimodal imaging with measures of functional network dynamics in IEEG. The proposed research is significant because it is expected that successful completion of these aims will yield personalized strategies for IEEG targeting based on noninvasive neuroimaging.

尽管神经影像学的最新进展，但经历了大约2/3的顽固性癫痫患者 手术评估继续需要颅内脑电图（IEEG），可以说是最具侵入性的诊断测试 药品。目前，我们缺乏定量绘制结构和 对IEEG的功能。具体而言，迫切需要验证全脑无创神经影像网络 - 基于生物标志物，以指导电极的精确放置并将非侵入性网络神经影像转化为 改变临床护理的范例。该建议的长期目标是预测IEEG功能动态 以及使用基于非侵入性MRI的结构和功能测量的手术结局。我们的整体目标 这是实现我们的长期目标的下一步，就是开发开源无创成像工具 该通过集成MRI和IEEG数据来映射癫痫网络。我们的中心假设是无创的 结构和功能的度量与IEEG上捕获的复杂功能动力学有关，并且可以预测IEEG上的复杂功能动力学。 中央假设将在针对颞叶网络的患者中进行测试 追求三个具体目标：1）将特定的结构连接映射到IEEG癫痫发作和 传播，2）将癫痫发作和对IEEG的传播与静止的网络度量相关联 状态功能MRI（RSFMRI），3）将结构（AIM 1）和功能（AIM 2）连接组合在一起 标准定性临床数据可预测IEEG网络动态和手术结果。在第一个目标下 患者将在立体定向IEEG之前进行扩散张量成像（DTI），这是一种固有的IEEG方法 样本远程网络。功能性IEEG网络将映射到DTI，从而定义了癫痫发作 在传播时会受到潜在的结构连接组的限制。在第二个目标患者下 颞叶癫痫将在立体定向IEEG之前在7T MRI上进行RSFMRI。功能网络度量 来自RSFMRI和IEEG将是核心的，RSFMRI将用于预测功能性EEG ICTAL和PUSTICTAL 网络。在第三个目标中，两个模型预测IEEG网络动态和癫痫手术结果将是 根据目标1和2中开发的方法创建的建筑。拟议的研究具有创新性，因为它是创新的 通过直接连接非侵入性多模式成像，代表与现状的实质性偏离 IEEG中功能网络动力学的度量。拟议的研究很重要，因为它是 预计这些目标的成功完成将为IEEG定位提供个性化策略 无创神经影像学。