Graph Neural Networks Predict Anti-Epileptic Treatment Response in Non-Lesional Infantile Epileptic Spasm Syndrome
Karen K. Wurzel
Background: With an incidence rate of approximately three out of 10,000 live births, Infantile Epileptic Spasm Syndrome (IESS) is a rare form of epilepsy with about 2,000 to 2,500 new cases in the United States annually. Each year, thousands of infants and families face uncertainty, often requiring multiple medication regimens before finding an effective treatment. This thesis uses pretreatment structural brain connectivity data to train Graph Neural Networks (GNN) to predict antiepileptic drug