Project Summary Extracorporeal Membrane Oxygenation (ECMO) is a form of cardiopulmonary bypass used in critically ill children and adults to support the heart and lungs when conventional therapies fail. More than 79,762 children worldwide have been supported to date, and global use of this tool is expanding. Advances in ECMO and critical care have improved survival of otherwise fatal illnesses, thereby unmasking neurologic injury which itself reduces survival by 50-60% and leads to significant long-term neurologic morbidity. The mechanisms of ECMO-related cerebral injuries are poorly understood. Existing research focuses on evaluating discrete elements, such as underlying illness, coagulation abnormalities, anticoagulation management, or markers of end- organ perfusion as factors associated with brain injury. Prior studies have not considered the temporal and dynamic element of clinical events that may play a large role in the genesis of brain injury, and few have explored which variables could predict significant neurologic injury without the bias of pre-selecting variables of interest. Machine learning is a form of artificial intelligence that employs algorithms to discover patterns in an iterative manner directly from input data: in the context of ECMO, it can identify dynamic patterns and relationships between variables prior to neurologic injury. The long-term goal of this research is to identify modifiable bedside predictors of neurologic impairment and thereby drive the development of early interventions to improve neurologic outcomes of children undergoing ECMO. Towards this goal, we have assembled a multi- disciplinary team with clinical and computational expertise. Our central hypothesis is that a robust risk predictive model for SNI in ECMO patients can be developed based on the physiological and laboratory data routinely collected in real-world clinical settings and that this model can be used to identify parameters of SNI for potential intervention. This proposal will utilize advanced machine learning algorithms to build this prediction model in a large multicenter cohort (0-18 years, n=750). In Aim 1, we will use novel probabilistic machine learning algorithms to train and develop a model to predict SNI by neuroimaging. In Aim 2, we will validate and refine the model from Aim 1 using neuroimaging scores and explore a personalized anytime query algorithm that predicts the timing and type of SNI. This collaborative proposal between clinical and computational scientists will lay the groundwork for a neuroprotective interventional study by identifying modifiable risk factors to improve the tragically high neurologic morbidity and mortality in ECMO survivors.