Predicting epilepsy surgery outcomes from individualized resting state functional anomalies

Project Summary/Abstract: This proposal details a 5-year plan to provide the candidate, Dr. Taha Gholipour, with the knowledge and expertise to become an independent investigator. He is a board-certified neurologist and epileptologist with research training in neuroimaging. The candidate's training will be guided by established mentors with expertise in the field of epilepsy research, functional imaging, advanced statistics and machine learning, and an advisory committee of scientists with collective expertise in clinical neuroscience and image analysis across prominent institutions. Uncontrolled seizures from epilepsy are associated with high morbidity, mortality, and cost. Current clinical and imaging predictors of response to surgery are inadequate, and surgical treatment outcomes are mixed. Predicting treatment outcome is critical for clinical decision making. Functional MRI (fMRI) offers noninvasive and accessible means for assessment of brain networks and may complement current methods of surgical planning to guide treatment. Statistical constraints from abundance of variables and data heterogeneity in fMRI analysis can be addressed by application of novel statistical and machine learning methods. The candidate will conduct a study with retrospective analysis of large multicenter datasets of resting state fMRI studies from adult and pediatric focal epilepsy patients, and a prospective arm to identify preliminary predictors of treatment response to guide future multi-site studies. The candidate will use functional anomaly mapping method to identify associations of this method with commonly used functional connectivity analysis and treatment outcomes 12 months after surgery. Post-surgical resection masks, clinical outcomes of seizure control and cognitive decline from surgery are collected in prospective arm. The goal is to identify common features in patients who become seizure-free following surgery. This study will use innovative methods to improve non-invasive evaluation of patients with refractory epilepsy, which can expand surgical candidacy for patients with or without apparent lesions on MRI. This project aims to help overcome current barriers to personalized care for people with epilepsy. The innovative use of advances statistics for solving clinical challenges in epilepsy imaging will have a fundamental impact on designing future investigations focused on developing biomarkers, predicting response to treatment, and understanding the disease mechanisms in epilepsy, as advocated by the 2021 AES/NINDS Epilepsy Research Benchmarks.