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Relating reflex gain modulation in posture control to underlying neural network properties using a neuromusculoskeletal model.

Schuurmans J, van der Helm FC, Schouten AC - J Comput Neurosci (2010)

Bottom Line: The goal of this study was to investigate the effects of underlying neural and sensory mechanisms on mechanical joint behavior.A sensitivity analysis was then performed on the neuromusculoskeletal model, determining the influence of the neural, sensory and synaptic parameters on the joint dynamics.However, position feedback and force feedback gains show strong interactions with other neural and sensory properties.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, 2628 CD, Delft, The Netherlands. j.schuurmans@tudelft.nl

ABSTRACT
During posture control, reflexive feedback allows humans to efficiently compensate for unpredictable mechanical disturbances. Although reflexes are involuntary, humans can adapt their reflexive settings to the characteristics of the disturbances. Reflex modulation is commonly studied by determining reflex gains: a set of parameters that quantify the contributions of Ia, Ib and II afferents to mechanical joint behavior. Many mechanisms, like presynaptic inhibition and fusimotor drive, can account for reflex gain modulations. The goal of this study was to investigate the effects of underlying neural and sensory mechanisms on mechanical joint behavior. A neuromusculoskeletal model was built, in which a pair of muscles actuated a limb, while being controlled by a model of 2,298 spiking neurons in six pairs of spinal populations. Identical to experiments, the endpoint of the limb was disturbed with force perturbations. System identification was used to quantify the control behavior with reflex gains. A sensitivity analysis was then performed on the neuromusculoskeletal model, determining the influence of the neural, sensory and synaptic parameters on the joint dynamics. The results showed that the lumped reflex gains positively correlate to their most direct neural substrates: the velocity gain with Ia afferent velocity feedback, the positional gain with muscle stretch over II afferents and the force feedback gain with Ib afferent feedback. However, position feedback and force feedback gains show strong interactions with other neural and sensory properties. These results give important insights in the effects of neural properties on joint dynamics and in the identifiability of reflex gains in experiments.

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Lumped reflex gain model used to fit reflex gains onto the output of the perturbation experiments of the neuromusculoskeletal model. In this lumped model, the force disturbance d is applied to a single inertia m. Muscle viscoelasticity is represented by a stiffness k and viscosity b. Reflexive feedback is represented by a positional feedback gain kp, a velocity feedback gain kv and a force feedback gain kf. A single reflexive feedback neural time delay τdel is represented by Hdel. The first order muscle activation dynamics are Hact. Output of this lumped model is joint position
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Fig2: Lumped reflex gain model used to fit reflex gains onto the output of the perturbation experiments of the neuromusculoskeletal model. In this lumped model, the force disturbance d is applied to a single inertia m. Muscle viscoelasticity is represented by a stiffness k and viscosity b. Reflexive feedback is represented by a positional feedback gain kp, a velocity feedback gain kv and a force feedback gain kf. A single reflexive feedback neural time delay τdel is represented by Hdel. The first order muscle activation dynamics are Hact. Output of this lumped model is joint position

Mentions: After simulation of a perturbation experiment, reflex gains were determined by fitting a lumped reflex gain model onto the joint dynamics. The reflex gain model is illustrated in Fig. 2. The model input was disturbance force d and output was joint position . The intrinsic dynamics were parameterized by the inertia of the arm m and the muscle stiffness k and viscosity b. Reflexive feedback consisted of position feedback (with a gain kp), velocity feedback (gain kv) and force feedback (gain kf). A single reflexive feedback neural time delay τdel is represented by Hdel. Like in the simulated NMS model, the muscle activation dynamics Hact were represented by a first order system with time constant τact.Fig. 2


Relating reflex gain modulation in posture control to underlying neural network properties using a neuromusculoskeletal model.

Schuurmans J, van der Helm FC, Schouten AC - J Comput Neurosci (2010)

Lumped reflex gain model used to fit reflex gains onto the output of the perturbation experiments of the neuromusculoskeletal model. In this lumped model, the force disturbance d is applied to a single inertia m. Muscle viscoelasticity is represented by a stiffness k and viscosity b. Reflexive feedback is represented by a positional feedback gain kp, a velocity feedback gain kv and a force feedback gain kf. A single reflexive feedback neural time delay τdel is represented by Hdel. The first order muscle activation dynamics are Hact. Output of this lumped model is joint position
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3108017&req=5

Fig2: Lumped reflex gain model used to fit reflex gains onto the output of the perturbation experiments of the neuromusculoskeletal model. In this lumped model, the force disturbance d is applied to a single inertia m. Muscle viscoelasticity is represented by a stiffness k and viscosity b. Reflexive feedback is represented by a positional feedback gain kp, a velocity feedback gain kv and a force feedback gain kf. A single reflexive feedback neural time delay τdel is represented by Hdel. The first order muscle activation dynamics are Hact. Output of this lumped model is joint position
Mentions: After simulation of a perturbation experiment, reflex gains were determined by fitting a lumped reflex gain model onto the joint dynamics. The reflex gain model is illustrated in Fig. 2. The model input was disturbance force d and output was joint position . The intrinsic dynamics were parameterized by the inertia of the arm m and the muscle stiffness k and viscosity b. Reflexive feedback consisted of position feedback (with a gain kp), velocity feedback (gain kv) and force feedback (gain kf). A single reflexive feedback neural time delay τdel is represented by Hdel. Like in the simulated NMS model, the muscle activation dynamics Hact were represented by a first order system with time constant τact.Fig. 2

Bottom Line: The goal of this study was to investigate the effects of underlying neural and sensory mechanisms on mechanical joint behavior.A sensitivity analysis was then performed on the neuromusculoskeletal model, determining the influence of the neural, sensory and synaptic parameters on the joint dynamics.However, position feedback and force feedback gains show strong interactions with other neural and sensory properties.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, 2628 CD, Delft, The Netherlands. j.schuurmans@tudelft.nl

ABSTRACT
During posture control, reflexive feedback allows humans to efficiently compensate for unpredictable mechanical disturbances. Although reflexes are involuntary, humans can adapt their reflexive settings to the characteristics of the disturbances. Reflex modulation is commonly studied by determining reflex gains: a set of parameters that quantify the contributions of Ia, Ib and II afferents to mechanical joint behavior. Many mechanisms, like presynaptic inhibition and fusimotor drive, can account for reflex gain modulations. The goal of this study was to investigate the effects of underlying neural and sensory mechanisms on mechanical joint behavior. A neuromusculoskeletal model was built, in which a pair of muscles actuated a limb, while being controlled by a model of 2,298 spiking neurons in six pairs of spinal populations. Identical to experiments, the endpoint of the limb was disturbed with force perturbations. System identification was used to quantify the control behavior with reflex gains. A sensitivity analysis was then performed on the neuromusculoskeletal model, determining the influence of the neural, sensory and synaptic parameters on the joint dynamics. The results showed that the lumped reflex gains positively correlate to their most direct neural substrates: the velocity gain with Ia afferent velocity feedback, the positional gain with muscle stretch over II afferents and the force feedback gain with Ib afferent feedback. However, position feedback and force feedback gains show strong interactions with other neural and sensory properties. These results give important insights in the effects of neural properties on joint dynamics and in the identifiability of reflex gains in experiments.

Show MeSH
Related in: MedlinePlus