How do strength and coordination recovery interact after stroke? A computational model for informing robotic training

Abstract

Robotic devices can train strength, coordination, or a combination of both. If a robotic device focuses on coordination, what happens to strength recovery, and vice versa? Understanding this interaction could help optimize robotic training. We developed a computational neurorehabilitation model to gain insight into the interaction between strength and coordination recovery after stroke. In the model, the motor system recovers by optimizing the activity of residual corticospinal cells (focally connected, excitatory and inhibitory) and reticulospinal cells (diffusely connected and excitatory) to achieve a motor task. To do this, the model employs a reinforcement learning algorithm that uses stochastic search based on a reward signal produced by task execution. We simulated two tasks that require strength and coordination: a finger movement task and a bilateral wheelchair propulsion task. We varied the reward signal to value strength versus coordination, determined by a weighting factor. The model predicted a nonlinear relationship between strength and coordination recovery consistent with clinical data obtained for each task. The model also predicted that stroke can cause a competition between strength and coordination recovery, due to a scarcity of focal and inhibitory cells. These results provide a rationale for implementing robotic movement therapy that can adaptively alter the combination of force and coordination training to target desired components of motor recovery.