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

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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.