Modeling movement disorders - CRPS-related dystonia explained by abnormal proprioceptive reflexes

AbstractHumans control their movements using adaptive proprioceptive feedback from muscle afferents. The interaction between proprioceptive reflexes and biomechanical properties of the limb is essential in understanding the etiology of movement disorders. A non-linear neuromuscular model of the wrist incorporating muscle dynamics and neural control was developed to test hypotheses on fixed dystonia. Dystonia entails sustained muscle contractions resulting in abnormal postures. Lack of inhibition is often hypothesized to result in hyperreflexia (exaggerated reflexes), which may cause fixed dystonia. In this study the model-simulated behavior in case of several abnormal reflex settings was compared to the clinical features of dystonia: abnormal posture, sustained muscle contraction, increased stiffness, diminished voluntary control and activity-aggravation.The simulation results were rated to criteria based on characteristic features of dystonia. Three abnormal reflex scenarios were tested: (1) increased reflex sensitivity—increased sensitivity of both the agonistic and antagonistic reflex pathways; (2) imbalanced reflex offset—a static offset to the reflex pathways on the agonistic side only; and (3) imbalanced reflex sensitivity—increased sensitivity of only the agonistic reflex pathways.Increased reflex sensitivity did not fully account for the features of dystonia, despite distinct motor dysfunction, since no abnormal postures occurred. Although imbalanced reflex offset did result in an abnormal posture, it could not satisfy other criteria. Nevertheless, imbalanced reflex sensitivity with unstable force feedback in one of the antagonists closely resembled all features of dystonia. The developed neuromuscular model is an effective tool to test hypotheses on the underlying pathophysiology of movement disorders

A rigorous model of reflex function indicates that position and force feedback are flexibly tuned to position and force tasks

This study aims to quantify the separate contributions of muscle force feedback, muscle spindle activity and co-contraction to the performance of voluntary tasks (“reduce the influence of perturbations on maintained force or position”). Most human motion control studies either isolate only one contributor, or assume that relevant reflexive feedback pathways during voluntary disturbance rejection tasks originate mainly from the muscle spindle. Human ankle-control experiments were performed, using three task instructions and three perturbation characteristics to evoke a wide range of responses to force perturbations. During position tasks, subjects (n = 10) resisted the perturbations, becoming more stiff than when being relaxed (i.e., the relax task). During force tasks, subjects were instructed to minimize force changes and actively gave way to imposed forces, thus becoming more compliant than during relax tasks. Subsequently, linear physiological models were fitted to the experimental data. Inhibitory, as well as excitatory force feedback, was needed to account for the full range of measured experimental behaviors. In conclusion, force feedback plays an important role in the studied motion control tasks (excitatory during position tasks and inhibitory during force tasks), implying that spindle-mediated feedback is not the only significant adaptive system that contributes to the maintenance of posture or force

Fixed Dystonia in Complex Regional Pain Syndrome: a Descriptive and Computational Modeling Approach

Background: Complex regional pain syndrome (CRPS) may occur after trauma, usually to one limb, and is characterized by pain and disturbed blood flow, temperature regulation and motor control. Approximately 25% of cases develop fixed dystonia. Involvement of dysfunctional GABAergic interneurons has been suggested, however the mechanisms that underpin fixed dystonia are still unknown. We hypothesized that dystonia could be the result of aberrant proprioceptive reflex strengths of position, velocity or force feedback. Methods: We systematically characterized the pattern of dystonia in 85 CRPS-patients with dystonia according to the posture held at each joint of the affected limb. We compared the patterns with a neuromuscular computer model simulating aberrations of proprioceptive reflexes. The computer model consists of an antagonistic muscle pair with explicit contributions of the musculotendinous system and reflex pathways originating from muscle spindles and Golgi tendon organs, with time delays reflective of neural latencies. Three scenarios were simulated with the model: (i) increased reflex sensitivity (increased sensitivity of the agonistic and antagonistic reflex loops); (ii) imbalanced reflex sensitivity (increased sensitivity of the agonistic reflex loop); (iii) imbalanced reflex offset (an offset to the reflex output of the agonistic proprioceptors). Results: For the arm, fixed postures were present in 123 arms of 77 patients. The dominant pattern involved flexion of the fingers (116/123), the wrists (41/123) and elbows (38/123). For the leg, fixed postures were present in 114 legs of 77 patients. The dominant pattern was plantar flexion of the toes (55/114 legs), plantar flexion and inversion of the ankle (73/114) and flexion of the knee (55/114). Only the computer simulations of imbalanced reflex sensitivity to muscle force from Golgi tendon organs caused patterns that closely resembled the observed patient characteristics. In parallel experiments using robot manipulators we have shown that patients with dystonia were less able to adapt their force feedback strength. Conclusions: Findings derived from a neuromuscular model suggest that aberrant force feedback regulation from Golgi tendon organs involving an inhibitory interneuron may underpin the typical fixed flexion postures in CRPS patients with dystonia.Biomechanical EngineeringMechanical, Maritime and Materials Engineerin

Modeling intradriver steering variability based on sensorimotor control theories

The purpose of this study is to develop and validate a human-like steering model that can capture, not only the mean, but also the intradriver variability (IDV) of steering behavior, in both routine and emergency scenarios. The IDV model proposed in this study is based on the assumption that steering behavior, in both scenarios, is governed by the same principles as performing point-to-point reaching tasks. The optimal feedback control framework that models the reaching tasks, and the presence of signal-dependent noise in motor commands and sensory feedback, are the mainstays of the proposed model. The driver is assumed to have acquired an internal model of system (muscles, arms, and vehicle) dynamics, and has a preview of the upcoming road. The model is validated using simulator-based data from both routine (curve negotiation) and emergency (obstacle avoidance) scenarios. The IDV model could capture mean steering torque behavior in both routine (variance accounted for (VAF) = 92% and emergency (VAF = 74% scenarios, but more prominently, it could capture the standard deviation of the steering torque as well, in both routine (VAF = 83% and emergency (VAF = 65% scenarios. The promising results show that including signal-dependent noise and modeling steering as a reaching task are steps in the right direction in the field of driver modeling. The model, however, poorly captured the lateral deviation behavior, primarily suspected due to the satisficing behavior exhibited by humans. Developing a nonlinear-iterative version of the IDV model could address the limitations

Human force reproduction error depends upon force level

For optimal haptic tele-manipulation system design, it is important to understand the accuracy and limitations of human force perception. Previous research demonstrated that humans generate higher forces when asked to reproduce an externally applied force; these studies proposed that the nervous system attenuates feedback from self-generated forces. The goal of this study was to determine how accurately subjects reproduce self-generated forces with the same hand over a broad range of force levels. Subjects (n=10, all right handed) had to generate an onscreen target force with visual support and subsequently reproduce the same force without visual support with their right hand against a static handle equipped with a force sensor. Six force levels (10 to 160N) were each presented randomly for eight repetitions. Subjects generated too high forces for lower force levels (≤40N) and too low forces for higher force levels (≥ 130N). Our results support force-dependent sensory integration and demonstrate that attenuated feedback of self-generated forces is not the sole factor in force reproduction errors

Reliance on haptic assistance reflected in haptic cue weighting

\u3cp\u3eWhen using an automated system, user trust in the automation is an important factor influencing performance. Prior studies have analyzed trust during supervisory control of automation, and how trust influences reliance: the behavioral correlate of trust. Here, we investigated how reliance on haptic assistance affects performance during shared control with an automated system. Subjects made reaches towards a hidden target using a visual cue and haptic cue (assistance from the automation). We sought to influence reliance by changing the variability of trial-by-trial random errors in the haptic assistance. Reliance was quantified in terms of the subject's position at the end of the reach relative to the two cues. Our results show that subjects aimed more towards the visual cue when the variability of the haptic cue errors increased, resembling cue weighting behavior. Similar behavior was observed both when subjects had explicit knowledge about the haptic cue error variability, as well as when they had only implicit knowledge (from experience). However, the group with explicit knowledge was able to more quickly adapt their reliance on the haptic assistance. The method we introduce here provides a quantitative way to study user reliance on the information provided by automated systems with shared control.\u3c/p\u3

NMClab, a model to assess the contributions of muscle visco-elasticity and afferent feedback to joint dynamics

The dynamic behavior of a neuromusculoskeletal system results from the complex mechanical interaction between muscle visco-elasticity resulting from (co-)contraction and afferent feedback from muscle spindles and Golgi tendon organs. As a result of the multiple interactions the individual effect of each of the structures to the overall dynamics is hard to recognize, if not impossible. Here a neuromuscular control (NMC) model is developed to analyze the functional contribution of the various physiological structures on the mechanical behavior of a limb. The dynamics of a joint are presented in admittances, i.e. the dynamic relation between input force (or torque) and the output displacement, which can be represented by either frequency or impulse response functions. With the model it can be shown that afferent feedback reduces, while muscle visco-elasticity increases, the stability margins. This implicates that there is a delicate balance between muscle co-contraction and afferent feedback, which depends on the joint specific physiological properties. The main application of the model is educational; it is implemented in a graphical user interface allowing users to explore the role of the various physiological structures on joint dynamics. Other applications of the model are more experimental, e.g. to elucidate experimentally measured admittances and to compare the quantified parameter values with the theoretically optimal ones. It is concluded that the NMC model is a useful and intuitive tool to investigate human motor control, in a theoretical as well as an experimental way

Reliance on haptic assistance reflected in haptic cue weighting

\u3cp\u3eWhen using an automated system, user trust in the automation is an important factor influencing performance. Prior studies have analyzed trust during supervisory control of automation, and how trust influences reliance: the behavioral correlate of trust. Here, we investigated how reliance on haptic assistance affects performance during shared control with an automated system. Subjects made reaches towards a hidden target using a visual cue and haptic cue (assistance from the automation). We sought to influence reliance by changing the variability of trial-by-trial random errors in the haptic assistance. Reliance was quantified in terms of the subject's position at the end of the reach relative to the two cues. Our results show that subjects aimed more towards the visual cue when the variability of the haptic cue errors increased, resembling cue weighting behavior. Similar behavior was observed both when subjects had explicit knowledge about the haptic cue error variability, as well as when they had only implicit knowledge (from experience). However, the group with explicit knowledge was able to more quickly adapt their reliance on the haptic assistance. The method we introduce here provides a quantitative way to study user reliance on the information provided by automated systems with shared control.\u3c/p\u3

Haptic perception of force magnitude and its relation to postural arm dynamics in 3D

In a previous study, we found the perception of force magnitude to be anisotropic in the horizontal plane. In the current study, we investigated this anisotropy in three dimensional space. In addition, we tested our previous hypothesis that the perceptual anisotropy was directly related to anisotropies in arm dynamics. In experiment 1, static force magnitude perception was studied using a free magnitude estimation paradigm. This experiment revealed a significant and consistent anisotropy in force magnitude perception, with forces exerted perpendicular to the line between hand and shoulder being perceived as 50% larger than forces exerted along this line. In experiment 2, postural arm dynamics were measured using stochastic position perturbations exerted by a haptic device and quantified through system identification. By fitting a mass-damper-spring model to the data, the stiffness, damping and inertia parameters could be characterized in all the directions in which perception was also measured. These results show that none of the arm dynamics parameters were oriented either exactly perpendicular or parallel to the perceptual anisotropy. This means that endpoint stiffness, damping or inertia alone cannot explain the consistent anisotropy in force magnitude perception. In a previous study, we found the perception of force magnitude to be anisotropic in the horizontal plane. In the current study, we investigated this anisotropy in three dimensional space. In addition, we tested our previous hypothesis that the perceptual anisotropy was directly related to anisotropies in arm dynamics. In experiment 1, static force magnitude perception was studied using a free magnitude estimation paradigm. This experiment revealed a significant and consistent anisotropy in force magnitude perception, with forces exerted perpendicular to the line between hand and shoulder being perceived as 50% larger than forces exerted along this line. In experiment 2, postural arm dynamics were measured using stochastic position perturbations exerted by a haptic device and quantified through system identification. By fitting a mass-damper-spring model to the data, the stiffness, damping and inertia parameters could be characterized in all the directions in which perception was also measured. These results show that none of the arm dynamics parameters were oriented either exactly perpendicular or parallel to the perceptual anisotropy. This means that endpoint stiffness, damping or inertia alone cannot explain the consistent anisotropy in force magnitude perception