Optimized Intracranial EEG Targeting in Focal Epilepsy based upon Neuroimaging Connectomics

尽管最近在神经成像方面取得了进展，但大约三分之二的顽固性癫痫患者接受了 外科评估仍然需要颅内脑电(IEEG)，可以说是最具侵入性的诊断测试 医药。我们目前缺乏定量绘制结构和组织的非侵入性成像测量的方法 对iEEG的作用。具体地说，迫切需要验证全脑无创神经成像网络- 基于生物标志物，指导电极的精确放置，并将非侵入性网络神经成像转换为 改变临床护理的范式。这项建议的长期目标是预测iEEG功能动力学 以及使用基于MRI的非侵入性结构和功能测量的手术结果。我们的总体目标是， 这是实现我们长期目标的下一步，是开发开源的非侵入性成像工具 通过整合核磁共振和iEEG数据绘制癫痫网络图。我们的中心假设是非侵入性 结构和功能的测量与iEEG捕捉到的复杂的功能动力学有关，并可以预测。 中心假说将在接受以颞叶网络为靶点的iEEG患者中进行测试 追求三个具体目标：1)将患者特定的结构连接组与iEEG癫痫发作和 传播，2)将iEEG上的癫痫发作和传播与来自休息的网络测量相关联 状态功能磁共振成像(RsfMRI)，以及3)将结构(目标1)和功能(目标2)连接体与 用于预测iEEG网络动力学和手术结果的标准定性临床数据。在第一个目标下 患者将在立体定向iEEG之前接受扩散张量成像(DTI)，iEEG是一种固有的iEEG方法 远程网络样本。功能性iEEG网络将被映射到DTI，从而定义癫痫发作 在它们传播时受到底层结构连接体的约束。在第二个目标下，患者有 在立体定向iEEG之前，将在7T MRI上对颞叶癫痫患者进行rsfMRI检查。功能网络衡量标准 RsfMRI和iEEG将被共同注册，rsfMRI将用于预测功能性脑电发作期和发作间歇期 网络。在第三个目标中，将有两个预测iEEG网络动力学和癫痫手术结果的模型 在目标1和目标2中开发的方法的基础上创建的。拟议的研究具有创新性，因为它 通过直接连接无创多模式成像，代表了对现状的实质性改变 IEEG中的功能网络动力学测量。这项拟议的研究具有重要意义，因为它 预计这些目标的成功完成将为iEEG目标产生个性化的策略，基于 非侵入性神经成像。

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.