No abstract

Robotic technology can provide a reliable quantitative means to assess deficits in limb position sense following stroke.

A basic difficulty for the nervous system is integrating locally ambiguous sensory information to form accurate perceptions about the outside world1–4. This local-to-global problem is also fundamental to motor control of the arm since complex mechanical interactions between the shoulder and elbow allow a particular amount of motion at one joint to arise from an infinite combination of shoulder and elbow torques5 (Fig. 1a). Here we show that a transcortical pathway through primary motor cortex (M1) resolves this ambiguity during fast feedback control. We demonstrate that single M1 neurons of behaving monkeys can integrate shoulder and elbow motion information into motor commands which appropriately counter the underlying torque within ~50 ms of a mechanical perturbation. Moreover, we reveal a causal link between M1 processing and multi-joint integration in humans by showing that shoulder muscle responses occurring ~50 ms after pure elbow displacement can be potentiated by transcranial magnetic stimulation. Our results show that M1 underlies multi-joint integration during fast feedback control, demonstrating that transcortical processing permits feedback responses to express a level of sophistication previously reserved for voluntary control and providing neurophysiological support for influential theories positing that voluntary movement is generated by the intelligent manipulation of sensory feedback6,7.

Robotic technology using a visually guided reaching task can provide reliable information with greater sensitivity about a patient's sensorimotor impairments following stroke than a standard clinical assessment scale.

Considerable research has established that rapid motor responses (traditionally called reflexes), can be modified by a subject's voluntary goals. Here, we expand on past observations using verbal instructions by defining the voluntary goal via visual target position. This approach allowed us to objectively enforce task adherence and explore a richer set of variables, such as target direction and distance, metrics that modify voluntary control and that--according to our hypothesis--will influence rapid motor responses. Our first experiment tested whether upper-limb responses are categorically modulated by target direction by placing targets such that the same perturbation could push the hand into one target and out of the other, a spatial analogue to "resist/yield" verbal instructions. Consistent with these classical results, we found that the short-latency rapid response (R1, 20-45 ms) was not modulated by target direction, whereas long-latency rapid responses (R2/R3, 45-105 ms) were modified in a manner approaching the voluntary response (VOL, 120-180 ms). Our second experiment tested whether upper-limb responses are continuously modulated by target distance by distributing five targets along one axis centered on the hand. Here, the long-latency and voluntary response mirrored the task demands by increasing activity in a graded fashion with increasing target distance. Our final experiment explored how upper-limb responses incorporate two-dimensional spatial information by placing targets radially around the hand. Notably, long-latency responses exhibited smooth tuning functions to target direction that were similar to those observed for the voluntary response. Taken together, these results illustrate the flexibility of long-latency rapid responses and emphasize their similarity to later voluntary responses.

This study shows that the discharge of many motor cortical cells is strongly influenced by attributes of movement related to the geometry and mechanics of the arm and not only by spatial attributes of the hand trajectory. The activity of 619 directionally tuned cells was recorded from the motor cortex of two monkeys during reaching movements with the use of similar hand paths but two different arm orientations, in the natural parasagittal plane and abducted into the horizontal plane. Nearly all cells (588 of 619, 95%) showed statistically significant changes in activity between the two arm orientations [analysis of variance (ANOVA). P < 0.01]. A majority of cells showed a significant change in their overall level of activity (ANOVA, main effect of task, P < 0.01) between arm orientations before, during, and after movement. Many cells (433 of 619, 70%) also showed a significant change in the relation of their discharge with movement direction (ANOVA, task x direction interaction term, P < 0.01) during movement, including changes in the dynamic range of discharge with movement and changes in the directional preference of cells that were directionally tuned in both arm orientations. Similar effects were seen for the discharge of cells while the monkey maintained constant arm postures over the different peripheral targets with the use of different arm orientations. Repeated data files from the same cell with the use of the same arm orientation showed only small changes in the level of discharge or in directional tuning, suggesting that changes in cell discharge between arm orientations cannot be explained by random temporal variations in cell activity. The distribution of movement-related preferred directions of the whole sample differed between arm orientations, and also differed strongly between cells receiving passive input predominantly from the shoulder or elbow. The electromyographic activity of most prime mover muscles at the shoulder and elbow was also strongly affected by arm orientation, resulting in changes in overall level of activity and/or directional tuning that often resembled those of the proximal arm-related motor cortical cells. A mathematical model that represented movements in terms of movement direction centered on the hand could not account for any of the arm-orientation-related response changes seen in this task, whereas models in intrinsic parameter spaces of joint kinematics and joint torques predicted many of the effects.

A key feature of successful motor control is the ability to counter unexpected perturbations. This process is complicated in multijoint systems, like the human arm, by the fact that loads applied at one joint will create motion at other joints [1-3]. Here, we test whether our most rapid corrections, i.e., reflexes, address this complexity through an internal model of the limb's mechanical properties. By selectively applying torque perturbations to the subject's shoulder and/or elbow, we revealed a qualitative difference between the arm's short-latency/spinal reflexes and long-latency/cortical reflexes. Short-latency reflexes of shoulder muscles were linked exclusively to shoulder motion, whereas its long-latency reflexes were sensitive to both shoulder and elbow motion, i.e., matching the underlying shoulder torque. In fact, a long-latency reflex could be evoked without even stretching or lengthening the shoulder muscle but by displacing just the elbow joint. Further, the shoulder's long-latency reflexes were appropriately modified across the workspace to account for limb-geometry changes that affect the transformation between joint torque and joint motion. These results provide clear evidence that long-latency reflexes possess an internal model of limb dynamics, a degree of motor intelligence previously reserved for voluntary motor control [3-5]. The use of internal models for both voluntary and reflex control is consistent with substantial overlap in their neural substrates and current notions of intelligent feedback control [6-8].

The motor system must consider a variety of environmental factors when executing voluntary motor actions, such as the shape of the goal or the possible presence of intervening obstacles. It remains unknown whether rapid feedback responses to mechanical perturbations also consider these factors. Our first experiment quantified how feedback corrections were altered by target shape, which was either a circular dot or a bar. Unperturbed movements to each target were qualitatively similar on average but with greater dispersion of end point positions when reaching to the bar. On random trials, multijoint torque perturbations deviated the hand left or right. When reaching to a circular target, perturbations elicited corrective movements that were directed straight to the location of the target. In contrast, corrective movements when reaching to a bar were redirected to other locations along the bar axis. Our second experiment quantified whether the presence of obstacles could interfere with feedback corrections. We found that hand trajectories after the perturbations were altered to avoid obstacles in the environment. Importantly, changes in muscle activity reflecting the different target shapes (bar vs. dot) or the presence of obstacles were observed in as little as 70 ms. Such changes in motor responses were qualitatively consistent with simulations based on optimal feedback control. Taken together, these results highlight that long-latency motor responses consider spatial properties of the goal and environment.