Resilience of brain Networks 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 the architecture of brain networks before stroke limits its consequences, and how the changes in brain networks organization caused by the lesion are clinically relevant acutely and during recovery.  To address these issues, we use the most recent developments in structural and functional 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 mice and human, whether the intermanual transfer could be of clinical importance to promote recovery after stroke.


Deep learning to predict the fate of the ischemic penumbra after stroke 

In acute stroke, predicting long-term outcome is essential to select the most efficient therapy. Unfortunately, on an individual basis, an accurate estimation of outcome is challenging. Based on perfusion imaging, our objective is to improve the delineation of the thresholds 1) between healthy and penumbral tissues (i.e. the tissue at risk but that can be salvageable when reperfused early enough), 2) between the penumbral tissue and the irreversibly damaged tissue. In collaboration with the EPFL, we will apply the most recent advances in deep learning networks to predict, early after stroke, the fate of the acute hypoperfused tissue.