Igor Sibon

This paper develops and validates a deep learning model to generate synthetic T-max perfusion maps from non-contrast MRI data in acute stroke patients.

This study investigates post-stroke fatigue by analyzing lesion characteristics and their associations with fatigue facets, revealing links to motor pathways and socio-demographic factors.

This study shows that MRI R2* changes reflect inflammation in disconnected brain regions after stroke, both in humans and mice, linking disconnection to iron accumulation and microglial activation.

This study benchmarks predictive models for long-term stroke outcomes using a global consortium, revealing mechanistic diversity and key predictors across motor, cognitive, and emotional domains.

This study identifies three infarct growth phenotypes in ischemic stroke patients based on MRI data and shows they predict 3-month clinical outcomes.