Model Development for Prediction of Surgical Outcome in Temporal Lobe Epilepsy Patients: Incorporation of the Correlation between Post-Surgical Reorganization Phenotypes and Pre-Surgical Data

预测颞叶癫痫患者手术结果的模型开发：纳入术后重组表型与术前数据之间的相关性

• 批准号: 9803083
• 负责人: Joseph I. Tracy
• 金额: $ 49万
• 依托单位: THOMAS JEFFERSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9803083
• 关键词: Ablation; Algorithms; Area; Belief; Brain; Brain region; Clinical; Communication; Communities; Data; Decision Making; Epilepsy; Epileptogenesis; Excision; Failure; Freedom; Generalized Epilepsy; Health Personnel; Individual; Intervention; Knowledge; Lead; Lesion; Link; Literature; Machine Learning; Measures; Methodology; Methods; Modeling; Neuronal Plasticity; Neurons; Operative Surgical Procedures; Organizational Change; Outcome; Output; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Phenotype; Postoperative Period; Process; Recurrence; Relapse; Rest; Seizures; Structure; System; Techniques; Temporal Lobe; Temporal Lobe Epilepsy; Thalamic structure; Work; base; brain surgery; innovation; machine learning algorithm; model development; neuroimaging; novel; outcome prediction; predictive modeling; random forest; recruit; relating to nervous system; response; standard measure; stem; surgery outcome

Project Summary For epileptic patients who undergo brain resection or ablation interventions, it is the postoperative brain that will dictate seizure status, whether controlled or relapsed. Yet, it is data from the preoperative brain that drives the postoperative prediction process – a critical process for both patient and doctor, and one that is only clinically meaningful when seizure outcomes are predicted presurgically to optimize surgical-decision making. Accordingly, we propose to develop a multi-step model that will establish more accurate predictors of post- surgical seizure outcome in temporal lobe epilepsy (TLE) emphasizing post-surgical status, for it is the areas of the brain spared during surgery that form the neural substrates generating postoperative seizures. A second perspective motivating our project is the need to identify those changes in functional and structural brain network organization that support adaptive versus maladaptive seizure outcomes following brain surgery. These are the network changes (e.g., the new seizure generators) that dispose and place a potential surgical candidate on a specific outcome trajectory. Therefore, identifying the phenotypes of brain reorganization and change, and incorporating their status into presurgical predictive models of outcome will likely prove crucial to enhancing our ability to predict postoperative neuroplastic responses. While existing outcome prediction models in TLE have focused on clinical variables (e.g., lesional status), we choose instead to focus on structural and functional measures of network reorganization (communication dynamics, regional interactions, structural control). This stems from our belief that capturing network changes throughout the whole postsurgical brain offers a better practical method for identifying and predicting the latent seizure foci (epileptogenesis) that will emerge after surgery. Through machine learning techniques we will deliver an algorithm to be used with new, potential surgical patients, an algorithm that utilizes solely presurgical data, but incorporates our innovative prediction about postsurgical brain organization. Accordingly, our approach provides both a methodologic and conceptual (reorganization phenotypes) advance. The scientific premise leading to our hypotheses is that the failure in the literature to account for the impact of unresected/ablated brain regions, and the brain reorganizations these areas compel, has seriously impeded the predictive power of previous outcome models.

项目摘要 对于接受大脑切除或消融干预措施的癫痫患者，术后大脑将 决定癫痫发作状态，无论是控制还是继电器。然而，正是术前大脑的数据推动了 术后预测过程 - 患者和医生的关键过程，而仅在临床上是一个关键过程 当预测癫痫发作结果以优化外科手术检查时有意义。 彼此之间，我们建议开发一个多步模型，该模型将建立更准确的后预测指标 临时叶癫痫（TLE）强调术后状态的手术癫痫结局，因为它是 在手术过程中保留的大脑形成了神经底物产生术后癫痫发作。第二 激励我们项目的观点是需要确定功能和结构性大脑网络的变化 支持自适应与适应不良的癫痫发作结局后的组织。这些是 网络更改（例如，新的癫痫发电机）将潜在的外科候选者处置并放在 具体结果轨迹。因此，确定大脑重组和变化的表型，以及 将其地位纳入预性结果的预测模型中可能对增强我们的 能够预测术后神经塑性反应。而TLE中的现有结果预测模型具有 我们专注于临床变量（例如，病变状态），我们选择专注于结构和功能 网络重组的度量（通信动态，区域互动，结构控制）。这 我们的信念是，整个术后大脑中捕获网络变化提供了更好的 识别和预测潜在癫痫发作灶（EPLEPTECENES）的实用方法，该焦点将在 外科手术。通过机器学习技术，我们将提供一种算法，可与新的潜在手术一起使用 患者，一种仅利用前术数据的算法，但纳入了我们有关的创新预测 术后大脑组织。根据每种方法，都提供了一种方法论和概念性 （重组表型）提前。导致我们假设的科学前提是 文献来说明未分析/消融的大脑区域的影响，而大脑的重组 强迫的区域严重阻碍了先前结果模型的预测能力。