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

Reward boosts reinforcement-based motor learning

Besides relying heavily on sensory and reinforcement feedback, motor skill learning may also depend on the level of motivation experienced during training. Yet, how motivation by reward modulates motor learning remains unclear. In 90 healthy subjects, we investigated the net effect of motivation by reward on motor learning while controlling for the sensory and reinforcement feedback received by the participants. Reward improved motor skill learning beyond performance-based reinforcement feedback. Importantly, the beneficial effect of reward involved a specific potentiation of reinforcement-related adjustments in motor commands, which concerned primarily the most relevant motor component for task success and persisted on the following day in the absence of reward. We propose that the long-lasting effects of motivation on motor learning may entail a form of associative learning resulting from the repetitive pairing of the reinforcement feedback and reward during training, a mechanism that may be exploited in future rehabilitation protocols.

Breaking Proportional Recovery After Stroke

People with hemiparesis after stroke appear to recover 70% to 80% of the difference between their baseline and the maximum upper extremity Fugl-Meyer (UEFM) score, a phenomenon called proportional recovery (PR). Two recent commentaries explained that PR should be expected because of mathematical coupling between the baseline and change score. Here we ask, If mathematical coupling encourages PR, why do a fraction of stroke patients (the "nonfitters") not exhibit PR? At the neuroanatomical level of analysis, this question was answered by Byblow et al-nonfitters lack corticospinal tract (CST) integrity at baseline-but here we address the mathematical and behavioral causes. We first derive a new interpretation of the slope of PR: It is the average probability of scoring across remaining scale items at follow-up. PR therefore breaks when enough test items are discretely more difficult for a patient at follow-up, flattening the slope of recovery. For the UEFM, we show that nonfitters are most unlikely to recover the ability to score on the test items related to wrist/hand dexterity, shoulder flexion without bending the elbow, and finger-to-nose movement, supporting the finding that nonfitters lack CST integrity. However, we also show that a subset of nonfitters respond better to robotic movement training in the chronic phase of stroke. These persons are just able to move the arm out of the flexion synergy and pick up small blocks, both markers of CST integrity. Nonfitters therefore raise interesting questions about CST function and the basis for response to intensive movement training.