Deep Learning disconnectomes to accelerate & improve long-term predictions for post-stroke symptoms

COEX 

White matter connections are recognized as fundamental building blocks of behavior and cognition, and their disconnections can be quantified to facilitate personalized prediction. This is particularly relevant in the context of stroke that is going to damage a specific brain region but also disconnect several remote areas. Being able to anticipate the risk of developing motor, cognitive, and emotional impairments following stroke could help to refer the patients to dedicated training to improve their outcomes. With the rise of Artificial Intelligence applications in healthcare, we explored and evaluated the potential of deep-learning models to accurately generate disconnectomes in a population of stroke survivors in order to speed up and accelerate the individualized prediction of neuropsychological scores one year post-stroke.