Summary
Dr. Edilberto Amorim's career development award focuses on identifying physiology-driven biomarkers for personalized treatment of hypoxic-ischemic brain injury and seizures post-cardiac arrest, utilizing deep learning, causal inference, and quantitative brain imaging.
What they want
The project aims to establish Dr. Amorim as a clinician-scientist with expertise in deep learning applied to physiology time-series, causal inference for observational data, and quantitative brain imaging. It 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.
Deliverables
- Identify early longitudinal epileptiform EEG phenotypes predictive of neurological recovery using interpretable and deep learning algorithms
- Establish quantitative EEG biomarkers of seizure treatment response to anesthetics
- Estimate the causal effect of rapid seizure treatment with anesthetics in preventing structural brain injury quantified with brain MRI
Technical requirements
- Deep learning applied to physiology time-series
- Causal inference for observational data
- Quantitative brain imaging
- EEG and neuroimaging dataset analysis
Key personnel
- Dr. Edilberto Amorim (Clinician-Scientist)
- Dr. Edward Chang (Primary Mentor, Neuroscientist)
- Dr. Brandon Westover (Co-mentor, Machine learning applied to critical care EEG)
- Dr. Donna Ferriero (Co-mentor, Translational and neuroimaging investigator)
- Dr. Srikantan Nagarajan (Mentor, Quantitative neuroimaging)
- Dr. Charles McCulloch (Mentor, Biostatistics)
