Predicting Epilepsy Surgery Outcomes Using Neural Network Architecture

使用神经网络架构预测癫痫手术结果

• 批准号: 10619937
• 负责人: Leonardo F Bonilha
• 金额: $ 82.07万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10619937
• 关键词: Ablation; Anterior Temporal Lobectomy; Area; Basic Science; Brain; Brain Mapping; Caring; Clinical; Clinical Data; Common Data Element; Contralateral; Counseling; Data; Data Collection; Detection; Diffusion; Diffusion Magnetic Resonance Imaging; Electrocoagulation; Electroencephalography; Ensure; Epilepsy; Evaluation; Excision; Failure; Fiber; Freedom; Functional Magnetic Resonance Imaging; Functional disorder; Future; Hippocampus (Brain); Image; Individual; Intractable Epilepsy; Knowledge; Lesion; Location; Machine Learning; Magnetic Resonance Imaging; Measures; Medial; Methods; Modality; Modeling; Modernization; Neurobiology; Neurons; Operative Surgical Procedures; Outcome; Pathologic; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacology; Phenotype; Predictive Value; Procedures; Prospective Studies; Prospective cohort; Publishing; Refractory; Reproducibility; Research; Resistance; Rest; Risk; Scalp structure; Seizures; Sensitivity and Specificity; Severities; Structure; Temporal Lobe Epilepsy; Testing; Thalamic structure; Tonic-Clonic Epilepsy; Treatment outcome; United States National Institutes of Health; Validation; base; computerized tools; connectome; experience; gray matter; hippocampal atrophy; imaging modality; improved; millimeter; multimodality; neural network; neural network architecture; neuroimaging; neuronal circuitry; novel strategies; outcome prediction; predictive modeling; prospective; recruit; response; standard of care; success; surgery outcome; tractography; white matter

Abstract Temporal lobe epilepsy (TLE) is one of the most common forms of pharmacologically resistant epilepsy. The resection or ablation of medial temporal structures can be curative for many patients. Unfortunately, approximately one third of patients who undergo TLE surgery continue to have disabling seizures post- procedurally. The reasons for suboptimal outcomes are not well understood and therefore constitute a very important knowledge gap in epilepsy care. A better understanding of this difference in surgical response phenotype could be used to improve surgical planning, treatment, outcome prediction and counseling. Promising preliminary studies suggest that TLE surgical outcomes can be inferred by neuroimaging computational tools assessing the cumulative degree of abnormalities in the topological organization of structural networks involving limbic and extra-limbic regions. Nonetheless, network abnormalities are not routinely or systematically used and quantified in the pre-surgical evaluation of epilepsies, and their assessment requires refinement and further validation. The purpose of this proposal is to perform a prospective study to test the hypothesis that the degree of limbic and extra-limbic network abnormalities in TLE, systematically assessed using a connectome approach based on optimized diffusion MRI (dMRI), can be used to predict and better understand epilepsy surgery outcomes. This hypothesis builds on the well-defined basic science and neurobiological premises that epilepsy is associated with pathological alterations in networks that are related to seizure onset and seizure propagation. Importantly, network abnormalities are not visible on routine MRI, but their detection using connectomes constitutes a modern approach to quantifying the location and magnitude of “lesional epilepsy,” where broad computational network abnormalities imply worse outcomes. We will prospectively gather clinical and imaging data at six epilepsy centers using the NIH epilepsy common data elements. This project will be fundamentally based on standard of care data, thus minimizing the burden of extra data collection and ensuring feasibility. Furthermore, this project will be embedded in the ENIGMA-Epilepsy framework, which is a collaborative platform for clinical and neuromaging multi-center research. Specific Aim 1 will define the accuracy, reproducibility, and predictive values of the pre-surgical dMRI tractography connectome model towards surgical results in TLE. We will perform hypothesis-driven tests of specific limbic and extra-limbic networks in relationship with clinical data and surgical outcomes. Specific Aim 2 will test if the neuroimaging-clinical outcome model can be further improved with advanced diffusion methods (multi-shell diffusional kurtosis imaging), resting state functional MRI networks, or a multimodal approach. We believe that this research will have an important impact on our understanding of the mechanisms related to TLE treatment.

抽象的 颞叶癫痫（TLE）是药物抗癫痫的最常见形式之一。这 媒体临时结构的切除或消融对于许多患者可以治愈。很遗憾， 大约三分之一接受手术的患者在 - 程序性。次优效果的原因尚不清楚，因此构成了一个非常 癫痫护理中的重要知识差距。更好地理解手术反应差异 表型可用于改善手术计划，治疗，结果预测和咨询。 有希望的初步研究表明，可以通过神经影像来推断出手术结果 评估拓扑组织中异常累积程度的计算工具 结构网络涉及边缘和边缘区域。但是，网络异常不是 在术前评估发作及其中，定期使用或系统地使用和量化 评估需要改进和进一步验证。该提议的目的是执行 前瞻性研究以检验以下假设： TLE，使用基于优化扩散MRI（DMRI）的连接组方法进行系统评估，可以是 用于预测和更好地了解癫痫手术结果。这个假设建立在定义明确的 癫痫与病理改变有关的基础科学和神经生物学前提 与癫痫发作和癫痫发作传播有关的网络。重要的是，网络异常不是 在常规MRI上可见，但是使用连接组的检测构成了一种现代方法来量化 广泛的计算网络异常的位置和大小 结果。我们可能会使用NIH癫痫在六个癫痫中心收集临床和成像数据 常见数据元素。该项目将从根本上基于护理数据的标准，从而最大程度地减少 收集额外数据的负担并确保可行性。此外，该项目将嵌入 Enigma-Epilepsy框架，它是用于临床和神经元件多中心的协作平台 研究。特定的目标1将定义手术前DMRI的准确性，可重复性和预测值 拖拉术连接模型朝着手术的结果。我们将进行假设驱动的测试 与临床数据和手术结果有关的特定边缘和外部网络。具体目标2 将测试是否可以通过先进的扩散方法进一步改善神经影像临床结果模型 （多壳扩散性峰度成像），静止状态功能性MRI网络或多模式方法。我们 认为这项研究将对我们对与之相关机制的理解产生重要影响 TLE处理。