Frédéric Crevecoeur

Influence of the behavioral goal and environmental obstacles on rapid feedback responses

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.

Optimal Integration of Gravity in Trajectory Planning of Vertical Pointing Movements

The planning and control of motor actions requires knowledge of the dynamics of the controlled limb to generate the appropriate muscular commands and achieve the desired goal. Such planning and control imply that the CNS must be able to deal with forces and constraints acting on the limb, such as the omnipresent force of gravity. The present study investigates the effect of hypergravity induced by parabolic flights on the trajectory of vertical pointing movements to test the hypothesis that motor commands are optimized with respect to the effect of gravity on the limb. Subjects performed vertical pointing movements in normal gravity and hypergravity. We use a model based on optimal control to identify the role played by gravity in the optimal arm trajectory with minimal motor costs. First, the simulations in normal gravity reproduce the asymmetry in the velocity profiles (the velocity reaches its maximum before half of the movement duration), which typically characterizes the vertical pointing movements performed on Earth, whereas the horizontal movements present symmetrical velocity profiles. Second, according to the simulations, the optimal trajectory in hypergravity should present an increase in the peak acceleration and peak velocity despite the increase in the arm weight. In agreement with these predictions, the subjects performed faster movements in hypergravity with significant increases in the peak acceleration and peak velocity, which were accompanied by a significant decrease in the movement duration. This suggests that movement kinematics change in response to an increase in gravity, which is consistent with the hypothesis that motor commands are optimized and the action of gravity on the limb is taken into account. The results provide evidence for an internal representation of gravity in the central planning process and further suggest that an adaptation to altered dynamics can be understood as a reoptimization process.

Priors Engaged in Long-Latency Responses to Mechanical Perturbations Suggest a Rapid Update in State Estimation

In every motor task, our brain must handle external forces acting on the body. For example, riding a bike on cobblestones or skating on irregular surface requires us to appropriately respond to external perturbations. In these situations, motor predictions cannot help anticipate the motion of the body induced by external factors, and direct use of delayed sensory feedback will tend to generate instability. Here, we show that to solve this problem the motor system uses a rapid sensory prediction to correct the estimated state of the limb. We used a postural task with mechanical perturbations to address whether sensory predictions were engaged in upper-limb corrective movements. Subjects altered their initial motor response in ∼60 ms, depending on the expected perturbation profile, suggesting the use of an internal model, or prior, in this corrective process. Further, we found trial-to-trial changes in corrective responses indicating a rapid update of these perturbation priors. We used a computational model based on Kalman filtering to show that the response modulation was compatible with a rapid correction of the estimated state engaged in the feedback response. Such a process may allow us to handle external disturbances encountered in virtually every physical activity, which is likely an important feature of skilled motor behaviour.

Feedback responses rapidly scale with the urgency to correct for external perturbations.

Healthy subjects can easily produce voluntary actions at different speeds and with varying accuracy requirements. It remains unknown whether rapid corrective responses to mechanical perturbations also possess this flexibility and, thereby, contribute to the capability expressed in voluntary control. Paralleling previous studies on self-initiated movements, we examined how muscle activity was impacted by either implicit or explicit criteria affecting the urgency to respond to the perturbation. Participants maintained their arm position against torque perturbations with unpredictable timing and direction. In the first experiment, the urgency to respond was explicitly altered by varying the time limit (300 ms vs. 700 ms) to return to a small target. A second experiment addresses implicit urgency criteria by varying the radius of the goal target, such that task accuracy could be achieved with less vigorous corrections for large targets than small target. We show that muscle responses at ∼60 ms scaled with the task demand. Moreover, in both experiments, we found a strong intertrial correlation between long-latency responses (∼50-100 ms) and the movement reversal times, which emphasizes that these rapid motor responses are directly linked to behavioral performance. The slopes of these linear regressions were sensitive to the experimental condition during the long-latency and early voluntary epochs. These findings suggest that feedback gains for very rapid responses are flexibly scaled according to task-related urgency.

Movement Stability Under Uncertain Internal Models of Dynamics

Sensory noise and feedback delay are potential sources of instability and variability for the on-line control of movement. It is commonly assumed that predictions based on internal models allow the CNS to anticipate the consequences of motor actions and protect the movements from uncertainty and instability. However, during motor learning and exposure to unknown dynamics, these predictions can be inaccurate. Therefore a distinct strategy is necessary to preserve movement stability. This study tests the hypothesis that in such situations, subjects adapt the speed and accuracy constraints on the movement, yielding a control policy that is less prone to undesirable variability in the outcome. This hypothesis was tested by asking subjects to hold a manipulandum in precision grip and to perform single-joint, discrete arm rotations during short-term exposure to weightlessness (0 g), where the internal models of the limb dynamics must be updated. Measurements of grip force adjustments indicated that the internal predictions were altered during early exposure to the 0 g condition. Indeed, the grip force/load force coupling reflected that the grip force was less finely tuned to the load-force variations at the beginning of the exposure to the novel gravitational condition. During this learning period, movements were slower with asymmetric velocity profiles and target undershooting. This effect was compared with theoretical results obtained in the context of optimal feedback control, where changing the movement objective can be directly tested by adjusting the cost parameters. The effect on the simulated movements quantitatively supported the hypothesis of a change in cost function during early exposure to a novel environment. The modified optimization criterion reduces the trial-to-trial variability in spite of the fact that noise affects the internal prediction. These observations support the idea that the CNS adjusts the movement objective to stabilize the movement when internal models are uncertain.

Fast corrective responses are evoked by perturbations approaching the natural variability of posture and movement tasks

A wealth of studies highlight the importance of rapid corrective responses during voluntary motor tasks. These studies used relatively large perturbations to evoke robust muscle activity. Thus it remains unknown whether these corrective responses (latency 20-100 ms) are evoked at perturbation levels approaching the inherent variability of voluntary control. To fill this gap, we examined responses for large to small perturbations applied while participants either performed postural or reaching tasks. To address multijoint corrective responses, we induced various amounts of single-joint elbow motion with scaled amounts of combined elbow and shoulder torques. Indeed, such perturbations are known to elicit a response at the unstretched shoulder muscle, which reflects an internal model of arm intersegmental dynamics. Significant muscle responses were observed during both postural control and reaching, even when perturbation-related joint angle, velocity, and acceleration overlapped in distribution with deviations encountered in unperturbed trials. The response onsets were consistent across the explored range of perturbation loads, with short-latency onset for the muscles spanning the elbow joints (20-40 ms), and long-latency for shoulder muscles (onset > 45 ms). In addition, the evoked activity was strongly modulated by perturbation magnitude. These results suggest that multijoint responses are not specifically engaged to counter motor errors that exceed a certain threshold. Instead, we suggest that these corrective processes operate continuously during voluntary motor control.

Towards a "gold-standard" approach to address the presence of long-range auto-correlation in physiological time series.

Series of motor outputs generated by cyclic movements are typically complex, suggesting that the correlation function of the time series spans over a large number of consecutive samples. Famous examples include inter-stride intervals, heartbeat variability, spontaneous neural firing patterns or motor synchronization with external pacing. Long-range correlations are potentially important for fundamental research, as the neural and biomechanical mechanisms generating these correlations remain unknown, and for clinical applications, given that the loss of long-range correlation may be a marker of disease. However, no systematic approach or robust analysis methods have yet been used to support the study of correlation functions in physiological series. This study investigates four selected methods (the Hurst exponent, the power spectral density analysis, the rate of moment convergence and the multiscale entropy methods). We present the result of each analysis performed on artificial computer-generated series in which the auto-correlation function is known, and then on time series extracted from gait and upper limb rhythmic movements. Our results suggest that combined analysis using the Hurst exponent and the power spectral density is suitable for rather short series (512 points). The rate of moment convergence directly supports the power spectral density analysis, and the multiscale entropy further confirms the presence of long-range correlation, although this method seems more appropriate for longer series. The proposed methodology increases the level of confidence in the hypothesis that physiological series are long-memory processes, which is of prime importance for future fundamental and clinical research.

FORWARD MODELS OF INERTIAL LOADS IN WEIGHTLESSNESS

In this experiment, we investigated whether the CNS uses internal forward models of inertial loads to maintain the stability of a precision grip when manipulating objects in the absence of gravity. The micro-gravity condition causes profound changes in the profile of tangential constraints at the finger-object interface. In order to assess the ability to predict the micro-gravity-specific variation of inertial loads, we analyzed the grip force adjustments that occurred when naive subjects held an object in a precision grip and performed point-to-point movements under the weightless condition induced by parabolic flight. Such movements typically presented static and dynamic phases, which permitted distinction between a static component of the grip force (measured before the movement) and a dynamic component of the grip force (measured during the movement). The static component tended to gradually decrease across the parabolas, whereas the dynamic component was rapidly modulated with the micro-gravity-specific inertial loads. In addition, the amplitude of the modulation significantly correlated with the amplitude of the tangential constraints for the dynamic component. These results strongly support the hypothesis that the internal representation of arm and object dynamics adapts to new gravitational contexts. In addition, the difference in time scales of adaptation of static and dynamic components suggests that they can be processed independently. The prediction of self-induced variation of inertial loads permits fine modulation of grip force, which ensures a stable grip during manipulation of an object in a new environment.

A perspective on multisensory integration and rapid perturbation responses.

In order to perform accurate movements, the nervous system must transform sensory feedback into motor commands that compensate for errors caused by motor variability and external disturbances. Recent studies focusing on the importance of sensory feedback in motor control have illustrated that the brain generates highly flexible responses to visual perturbations (hand-cursor or target jumps), or following mechanical loads applied to the limb. These parallel approaches have emphasized sophisticated, goal-directed feedback control, but also reveal that flexible perturbation responses are expressed at different latencies depending on what sensory system is engaged by the perturbation. Across studies, goal-directed visuomotor responses consistently emerge in muscle activity ∼100ms after a perturbation, while mechanical perturbations evoke goal-directed muscle responses in as little as ∼60ms (long-latency responses). We discuss the limitation of current models of multisensory integration in light of these asynchronous processing delays, and suggest that understanding how the brain performs real-time multisensory integration is an open question for future studies.

Brief paper: Improving the state estimation for optimal control of stochastic processes subject to multiplicative noise

Computational models for the neural control of movement must take into account the properties of sensorimotor systems, including the signal-dependent intensity of the noise and the transmission delay affecting the signal conduction. For this purpose, this paper presents an algorithm for model-based control and estimation of a class of linear stochastic systems subject to multiplicative noise affecting the control and feedback signals. The state estimator based on Kalman filtering is allowed to take into account the current feedback to compute the current state estimate. The optimal feedback control process is adapted accordingly. The resulting estimation error is smaller than the estimation error obtained when the current state must be predicted based on the last feedback signal, which reduces variability of the simulated trajectories. In particular, the performance of the present algorithm is good in a range of feedback delay that is compatible with the delay induced by the neural transmission of the sensory inflow.