Effect of reinforcement history on hand choice in an unconstrained reaching task.

Choosing which hand to use for an action is one of the most frequent decisions people make in everyday behavior. We developed a simple reaching task in which we vary the lateral position of a target and the participant is free to reach to it with either the right or left hand. While people exhibit a strong preference to use the hand ipsilateral to the target, there is a region of uncertainty within which hand choice varies across trials. We manipulated the reinforcement rates for the two hands, either by increasing the likelihood that a reach with the non-dominant hand would successfully intersect the target or decreasing the likelihood that a reach with the dominant hand would be successful. While participants had minimal awareness of these manipulations, we observed an increase in the use of the non-dominant hand for targets presented in the region of uncertainty. We modeled the shift in hand use using a Q-learning model of reinforcement learning. The results provided a good fit of the data and indicate that the effects of increasing and decreasing the rate of positive reinforcement are additive. These experiments emphasize the role of decision processes for effector selection, and may point to a novel approach for physical rehabilitation based on intrinsic reinforcement.

Neuromechanical considerations for incorporating rhythmic arm movement in the rehabilitation of walking.

We have extensively used arm cycling to study the neural control of rhythmic movements such as arm swing during walking. Recently rhythmic movement of the arms has also been shown to enhance and shape muscle activity in the legs. However, restricted information is available concerning the conditions necessary to maximally alter lumbar spinal cord excitability. Knowledge on the neuromechanics of a task can assist in the determination of the type, level, and timing of neural signals, yet arm swing during walking and arm cycling have not received a detailed neuromechanical comparison. The purpose of this research was to provide a combined neural and mechanical measurement approach that could be used to assist in the determination of the necessary and sufficient conditions for arm movement to assist in lower limb rehabilitation after stroke and spinal cord injury. Subjects performed three rhythmic arm movement tasks: (1) cycling (cycle); (2) swinging while standing (swing); and (3) swinging while treadmill walking (walk). We hypothesized that any difference in neural control between tasks (i.e., pattern of muscle activity) would reflect changes in the mechanical constraints unique to each task. Three-dimensional kinematics were collected simultaneously with force measurement at the hand and electromyography from the arms and trunk. All data were appropriately segmented to allow a comparison between and across conditions and were normalized and averaged to 100% movement cycle based on shoulder excursion. Separate mathematical principal components analysis of kinematic and neural variables was performed to determine common task features and muscle synergies. The results highlight important neural and mechanical features that distinguish differences between tasks. For example, there are considerable differences in the anatomical positions of the arms during each task, which relate to the moments experienced about the elbow and shoulder. Also, there are differences between tasks in elbow flexion/extension kinematics alongside differential muscle activation profiles. As well, mechanical assistance and constraints during all tasks could affect muscle recruitment and the functional role of muscles. Overall, despite neural and mechanical differences, the results are consistent with conserved common central motor control mechanisms operational for cycle, walk, and swing but appropriately sculpted to demands unique to each task. However, changing the mechanical parameters could affect the role of afferent feedback altering neural control and the coupling to the lower limbs.

Neural regulation of rhythmic arm and leg movement is conserved across human locomotor tasks.

It has been proposed that different forms of rhythmic human limb movement have a common central neural control ('common core hypothesis'), just as in other animals. We compared the modulation patterns of background EMG and cutaneous reflexes during walking, arm and leg cycling, and arm-assisted recumbent stepping. We hypothesized that patterns of EMG and reflex modulation during cycling and stepping (deduced from mathematical principal components analysis) would be comparable to those during walking because they rely on similar neural substrates. Differences between the tasks were assessed by evoking cutaneous reflexes via stimulation of nerves in the foot and hand in separate trials. The EMG was recorded from flexor and extensor muscles of the arms and legs. Angular positions of the hip, knee and elbow joints were also recorded. Factor analysis revealed that across the three tasks, four principal components explained more than 93% of the variance in the background EMG and middle-latency reflex amplitude. Phase modulation of reflex amplitude was observed in most muscles across all tasks, suggesting activity in similar control networks. Significant correlations between EMG level and reflex amplitude were frequently observed only during static voluntary muscle activation and not during rhythmic movement. Results from a control experiment showed that strong correlation between EMG and reflex amplitudes was observed during discrete, voluntary leg extension but not during walking. There were task-dependent differences in reflex modulation between the three tasks which probably arise owing to specific constraints during each task. Overall, the results show strong correlation across tasks and support common neural patterning as the regulator of arm and leg movement during various rhythmic human movements.

Recumbent stepping has similar but simpler neural control compared to walking.

The purpose of this study was to compare muscle activation patterns and kinematics during recumbent stepping and walking to determine if recumbent stepping has a similar motor pattern as walking. We measured joint kinematics and electromyography in ten neurologically intact humans walking on a treadmill at 0 and 50% body weight support (BWS), and recumbent stepping using a commercially available exercise machine. Cross correlation of upper and lower limb electromyography patterns between conditions revealed high correlations for most muscles. A principal component analysis revealed that the first factor accounted for more muscle activation signal content during recumbent stepping (81%) than during walking (70%). This indicates that the motor pattern during walking is more complex than during stepping. Cross correlation analysis found a high correlation between factors for recumbent stepping and walking (R = 0.54), though not as high as the correlation between factors for walking at 0% BWS and walking at 50% BWS (R = 0.68). There were substantial differences in joint kinematics between walking and recumbent stepping, most notably in hip, elbow, and shoulder motions. These results suggest that although the two tasks have different kinematic patterns, recumbent stepping relies on similar neural networks as walking. Individuals with neurological impairments may be able to improve walking ability from recumbent stepping practice given similarities in neural control between the two tasks.