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

项目概要 对于接受脑切除或消融干预的癫痫患者来说，术后的大脑将 决定癫痫发作状态，无论是控制还是复发。然而，驱动手术的是来自术前大脑的数据。 术后预测过程——对于患者和医生来说都是一个关键过程，并且仅在临床上进行 当术前预测癫痫发作结果以优化手术决策时，这是有意义的。 因此，我们建议开发一个多步骤模型，该模型将建立更准确的后预测因子 颞叶癫痫 (TLE) 的手术癫痫结果强调手术后状态，因为它是 手术期间幸存的大脑形成神经基质，产生术后癫痫发作。一秒钟 推动我们项目的观点是需要识别大脑网络功能和结构的变化 支持脑部手术后适应性和适应不良癫痫结果的组织。这些是 网络变化（例如，新的癫痫发生器）将潜在的手术候选者处置并放置在 具体的结果轨迹。因此，识别大脑重组和变化的表型，以及 将他们的状态纳入术前结果预测模型可能对增强我们的能力至关重要 预测术后神经塑性反应的能力。虽然 TLE 中现有的结果预测模型已经 关注临床变量（例如病变状态），我们选择关注结构和功能 网络重组措施（通讯动态、区域互动、结构控制）。这个 源于我们的信念，捕获整个术后大脑的网络变化可以提供更好的结果 识别和预测潜在癫痫病灶（癫痫发生）的实用方法 手术。通过机器学习技术，我们将提供一种算法，用于新的、潜在的手术 患者，一种仅利用术前数据的算法，但结合了我们对 术后脑组织。因此，我们的方法提供了方法论和概念 （重组表型）前进。导致我们假设的科学前提是， 文献解释了未切除/消融的大脑区域的影响，以及这些区域的大脑重组 领域迫使，严重阻碍了以前结果模型的预测能力。

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.