As the population ages, the number of stroke cases increases, creating serious economic and societal challenges. Poststroke aphasia, a condition affecting language, impacts up to 40% of stroke survivors, often causing depression and difficulty with daily life. Most patients do not recover despite treatment. Moreover, crucial factors influencing recovery remain unclear. To improve recovery predictions and personalize therapy, this study will (i) develop new machine learning models from large-scale data of 2000 stroke patients that can predict aphasia recovery based on neural and clinical factors; (ii) determine how a 10-week remote music-based rehabilitation program improves language, motor function, cognition, and emotional well-being in a sample of 100 stroke patients. This research aims to improve recovery predictions and offer better therapies which could revolutionise aphasia care, reduce long-term disability, and improve quality of life for stroke survivors.

2025

2029