Summary
This project aims to identify physiology-driven biomarkers for seizure management and neurological recovery in patients with hypoxic-ischemic brain injury post-cardiac arrest, utilizing deep learning and quantitative brain imaging on large EEG and neuroimaging datasets.
What they want
Dr. Edilberto Amorim, a neurologist, will conduct research to personalize treatment for patients with hypoxic-ischemic brain injury post-cardiac arrest. This career development award will establish his expertise in deep learning applied to physiology time-series, causal inference for observational data, and quantitative brain imaging. The project involves identifying early longitudinal epileptiform EEG phenotypes predictive of neurological recovery using interpretable and deep learning algorithms, establishing quantitative EEG biomarkers of seizure treatment response to anesthetics, and estimating the causal effect of rapid seizure treatment with anesthetics in preventing structural brain injury quantified with brain MRI. The research leverages a massive EEG and neuroimaging dataset with over 1,500 subjects.
Technical requirements
- Deep learning applied to physiology time-series
- Causal inference for observational data
- Quantitative brain imaging
- Interpretable and deep learning algorithms
- Quantitative EEG biomarkers
- Brain MRI
