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: 10158551
• 负责人: Leonardo F Bonilha
• 金额: $ 63.89万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 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 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10158551
• 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; organizational structure; 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）是最常见的药物耐药性癫痫形式之一。这 内侧颞叶结构的切除或消融对于许多患者来说可以治愈。很遗憾， 大约三分之一接受 TLE 手术的患者在术后仍会出现致残性癫痫发作 程序上。次优结果的原因尚不清楚，因此构成了一个非常严重的问题 癫痫护理方面的重要知识差距。更好地理解手术反应的这种差异 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 TLE 手术结果可以通过神经影像学推断 评估拓扑组织异常累积程度的计算工具 涉及边缘和边缘外区域的结构网络。但网络异常并不 在癫痫术前评估中常规或系统地使用和量化，及其 评估需要细化和进一步验证。该提案的目的是执行 前瞻性研究检验边缘系统和边缘系统外网络异常程度的假设 使用基于优化扩散 MRI (dMRI) 的连接组方法系统地评估 TLE，可以 用于预测和更好地了解癫痫手术结果。这个假设建立在明确定义的基础上 基础科学和神经生物学前提表明癫痫与身体的病理改变有关 与癫痫发作和癫痫传播相关的网络。重要的是，网络异常不会 在常规 MRI 上可见，但使用连接组进行检测构成了一种现代方法来量化 “病变性癫痫”的位置和严重程度，广泛的计算网络异常意味着更严重 结果。我们将使用 NIH 癫痫研究所前瞻性地收集六个癫痫中心的临床和影像数据 公共数据元素。该项目将从根本上基于护理数据标准，从而最大限度地减少 额外数据收集和确保可行性的负担。此外，该项目将嵌入 ENIGMA-Epilepsy框架，这是一个临床和神经影像学多中心协作平台 研究。具体目标 1 将定义术前 dMRI 的准确性、再现性和预测值 针对 TLE 手术结果的纤维束成像连接组模型。我们将进行假设驱动的测试 与临床数据和手术结果相关的特定边缘和边缘外网络。具体目标2 将测试神经影像临床结果模型是否可以通过先进的扩散方法进一步改进 （多壳扩散峰度成像）、静息态功能 MRI 网络或多模态方法。我们 相信这项研究将对我们理解相关机制产生重要影响 TLE 治疗。