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: 10359810
• 负责人: Kathryn Adamiak Davis
• 金额: $ 63.93万
• 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/10359810
• 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; Sampling; Sampling Errors; Seizures; South Carolina; Stereotyping; Structure; Temporal Lobe; Temporal Lobe Epilepsy; Testing; Translating; United States; Universities; Work; base; 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.

尽管最近在神经影像学方面取得了进展，但大约2/3的难治性癫痫患者接受 手术评估仍然需要颅内脑电图（IEEG），可以说是最具侵入性的诊断测试， 药我们目前缺乏方法来定量地映射结构的非侵入性成像测量， 功能IEEG。具体而言，迫切需要验证全脑非侵入性神经成像网络- 基于生物标志物来指导电极的精确放置，并将非侵入性网络神经成像转化为 改变临床护理的模式。该建议的长期目标是预测IEEG功能动力学 和手术结果使用非侵入性MRI为基础的结构和功能的措施。我们的总体目标， 这是实现我们长期目标的下一步，就是开发开源的非侵入性成像工具， 通过整合MRI和IEEG数据来绘制癫痫网络。我们的核心假设是， 结构和功能的测量与IEEG上捕获的复杂的功能动力学有关，并且可以预测IEEG上捕获的复杂的功能动力学。 中心假设将在接受以颞叶网络为目标的IEEG的患者中进行测试， 追求三个具体目标：1）将患者特异性结构连接体映射到IEEG癫痫发作， 传播，2）将IEEG上的癫痫发作和传播与来自静息的网络测量相关联 状态功能MRI（rsfMRI），和3）整合结构（目标1）和功能（目标2）连接体与 标准的定性临床数据来预测IEEG网络动力学和手术结果。在第一个目标下 患者将在立体定向IEEG之前接受弥散张量成像（DTI），IEEG方法固有地 长距离网络的示例。功能IEEG网络将映射到DTI，从而定义癫痫发作如何 在它们传播的过程中受到底层结构连接体的限制。在第二个目标下， 颞叶癫痫将在立体定向IEEG之前在7 T MRI上进行rsfMRI。功能网络测度 将rsfMRI和IEEG联合记录，并将rsfMRI用于预测功能性EEG发作和发作间期 网络.在第三个目标中，将建立两个预测IEEG网络动力学和癫痫手术结果的模型。 在目标1和2中开发的方法的基础上创建。这项研究是创新的，因为它 通过直接连接非侵入性多模态成像， 与IEEG中的功能网络动力学的措施。这项研究之所以重要，是因为 预期这些目标的成功完成将产生基于以下方面的IEEG针对性的个性化策略： 非侵入性神经成像