Fatigue-induced changes of impedance and performance in target tracking

Kinematic variability is caused, in part, by force fluctuations. It has been shown empirically and numerically that the effects of force fluctuations on kinematics can be suppressed by increasing joint impedance. Given that force variability increases with muscular fatigue, we hypothesized that joint impedance would increase with fatigue to retain a prescribed accuracy level. To test this hypothesis, subjects tracked a target by elbow flexion and extension both with fatigued and unfatigued elbow flexor and extensor muscles. Joint impedance was estimated from controlled perturbations to the elbow. Contrary to the hypothesis, elbow impedance decreased, whereas performance, expressed as the time-on-target, was unaffected by fatigue. Further analysis of the data revealed that subjects changed their control strategy with increasing fatigue. Although their overall kinematic variability increased, task performance was retained by staying closer to the center of the target when fatigued. In conclusion, the present study reveals a limitation of impedance modulation in the control of movement variability

Attention-dependent modulation of cortical taste circuits revealed by granger causality with signal-dependent noise

We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention

Effects of fatigue on trunk stability in elite gymnasts

The aim of the present study was to test the hypothesis that fatigue due to exercises performed in training leads to a decrement of trunk stability in elite, female gymnasts. Nine female gymnasts participated in the study. To fatigue trunk muscles, four series of five dump handstands on the uneven bar were performed. Before and after the fatigue protocol, participants performed three trials of a balancing task while sitting on a seat fixed over a hemisphere to create an unstable surface. A force plate tracked the location of the center of pressure (CoP). In addition, nine trials were performed in which the seat was backward inclined over a set angle and suddenly released after which the subject had to regain balance. Sway amplitude and frequency in unperturbed sitting were determined from the CoP time series and averaged over trials. The maximum displacement and rate of recovery of the CoP location after the sudden release were determined and averaged over trials. After the fatigue protocol, sway amplitude in the fore-aft direction was significantly increased (p = 0.03), while sway frequency was decreased (p = 0.005). In addition, the maximum displacement after the sudden release was increased (p = 0.009), while the rate of recovery after the perturbation was decreased (p = 0.05). Fatigue induced by series of exercises representing a realistic training load caused a measurable decrement in dynamic stability of the trunk in elite gymnasts

Non-linear stimulus-response behavior of the human stance control system is predicted by optimization of a system with sensory and motor noise

We developed a theory of human stance control that predicted (1) how subjects re-weight their utilization of proprioceptive and graviceptive orientation information in experiments where eyes closed stance was perturbed by surface-tilt stimuli with different amplitudes, (2) the experimentally observed increase in body sway variability (i.e. the “remnant” body sway that could not be attributed to the stimulus) with increasing surface-tilt amplitude, (3) neural controller feedback gains that determine the amount of corrective torque generated in relation to sensory cues signaling body orientation, and (4) the magnitude and structure of spontaneous body sway. Responses to surface-tilt perturbations with different amplitudes were interpreted using a feedback control model to determine control parameters and changes in these parameters with stimulus amplitude. Different combinations of internal sensory and/or motor noise sources were added to the model to identify the properties of noise sources that were able to account for the experimental remnant sway characteristics. Various behavioral criteria were investigated to determine if optimization of these criteria could predict the identified model parameters and amplitude-dependent parameter changes. Robust findings were that remnant sway characteristics were best predicted by models that included both sensory and motor noise, the graviceptive noise magnitude was about ten times larger than the proprioceptive noise, and noise sources with signal-dependent properties provided better explanations of remnant sway. Overall results indicate that humans dynamically weight sensory system contributions to stance control and tune their corrective responses to minimize the energetic effects of sensory noise and external stimuli

Impedance control reduces instability that arises from motor noise.

There is ample evidence that humans are able to control the endpoint impedance of their arms in response to active destabilizing force fields. However, such fields are uncommon in daily life. Here, we examine whether the CNS selectively controls the endpoint impedance of the arm in the absence of active force fields but in the presence of instability arising from task geometry and signal-dependent noise (SDN) in the neuromuscular system. Subjects were required to generate forces, in two orthogonal directions, onto four differently curved rigid objects simulated by a robotic manipulandum. The endpoint stiffness of the limb was estimated for each object curvature. With increasing curvature, the endpoint stiffness increased mainly parallel to the object surface and to a lesser extent in the orthogonal direction. Therefore, the orientation of the stiffness ellipses did not orient to the direction of instability. Simulations showed that the observed stiffness geometries and their pattern of change with instability are the result of a tradeoff between maximizing the mechanical stability and minimizing the destabilizing effects of SDN. Therefore, it would have been suboptimal to align the stiffness ellipse in the direction of instability. The time course of the changes in stiffness geometry suggests that modulation takes place both within and across trials. Our results show that an increase in stiffness relative to the increase in noise can be sufficient to reduce kinematic variability, thereby allowing stiffness control to improve stability in natural tasks

Deliberation in the motor system: reflex gains track evolving evidence leading to a decision.

Both decision making and sensorimotor control require real-time processing of noisy information streams. Historically these processes were thought to operate sequentially: cognitive processing leads to a decision, and the outcome is passed to the motor system to be converted into action. Recently, it has been suggested that the decision process may provide a continuous flow of information to the motor system, allowing it to prepare in a graded fashion for the probable outcome. Such continuous flow is supported by electrophysiology in nonhuman primates. Here we provide direct evidence for the continuous flow of an evolving decision variable to the motor system in humans. Subjects viewed a dynamic random dot display and were asked to indicate their decision about direction by moving a handle to one of two targets. We probed the state of the motor system by perturbing the arm at random times during decision formation. Reflex gains were modulated by the strength and duration of motion, reflecting the accumulated evidence in support of the evolving decision. The magnitude and variance of these gains tracked a decision variable that explained the subject's decision accuracy. The findings support a continuous process linking the evolving computations associated with decision making and sensorimotor control

Selection and control of limb posture for stability

Impedance control can be used to stabilize the limb against both instability and unpredictable perturbations. Limb posture influences motor noise, energy usage and limb impedance as well as their interaction. Here we examine whether subjects use limb posture as part of a mechanism to regulate limb stability. Subjects performed stabilization tasks while attached to a two dimensional robotic manipulandum which generated a virtual environment. Subjects were instructed that they could perform the stabilization task anywhere in the workspace, while the chosen postures were tracked as subjects repeated the task. In order to investigate the mechanisms behind the chosen limb postures, simulations of the neuro-mechanical system were performed. The results indicate that posture selection is performed to provide energy efficiency in the presence of force variability