Pharamacological manipulation of brain networks to improve recovery after stroke
How do brain networks anticipate, endure, respond and adapt to limit the consequences of a stroke? Our lab is interested in investigating how changes in network architecture are clinically relevant acutely and during recovery. We are currently conducting a Randomized Clinical Trial to investigate whether Maraviroc support plasticity in the peri-infarct cortex to ultimately improve functional outcome. To address these issues, we use the most recent developments in structural, functional, and dynamic MRI connectivity analysis.
Intermanual transfer to promote motor recovery after stroke
Generalization refers to our ability to apply what has been learned in one context to other situations. For example, tennis players pick up on table tennis faster than people who have never played racket sports before. Intermanual transfer is an example of generalization that is observed when learning to perform a motor task with one hand results in improved performance of the untrained hand. We investigate, based on an innovative behavioral and imaging approach in human, whether the intermanual transfer could be of clinical importance to promote recovery after stroke.
Deep learning to improve outcome prediction and therapeutic strategies after stroke
Early after stroke, predicting long-term outcome is essential to select the most efficient therapy. Our objective is to improve outcome prediction using machine learning models combining imaging, physiological, biological and clinical variables. We currently develop new therapeutic strategies based on our experience in the development of deep learning models.
