Computational Models of Stroke Recovery

Computational models of neuromotor recovery following a stroke might help to unveil the underlying physiological mechanisms and might suggest how to make recovery faster and more effective. Here we review computational models of use-dependent neuromotor recovery and their implications for treatment. Some of these models focus on the "central" level (cortical and subcortical), in terms of synaptic changes and/or structural rewiring mechanisms such as axonal outgrowth. Other models address learning or re-learning of functional behaviors. Only few models address both levels of description. Most models provide qualitative predictions on the recovery process. Very few models provide quantitative predictions on an individual patient basis. We suggest that to properly account for the underlying mechanisms, the interplay of central and functional levels needs to be taken into account. Future models should also allow a direct comparison with empirical observations. In this way, computational models could be used to make predictions of the recovery process of individual patients and could contribute to the design of patient-specific "optimal" therapy.