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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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Sensitivity measure Sij for the eight lumped reflex gain model parameters (m, b, k, kp, kv, kf, τdel, τact) and RMS of the joint position. Low RMS indicates high task performance: the force disturbances result in small deviations. For each graph, only the eight parameters with the highest sensitivity values are shown
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Fig5: Sensitivity measure Sij for the eight lumped reflex gain model parameters (m, b, k, kp, kv, kf, τdel, τact) and RMS of the joint position. Low RMS indicates high task performance: the force disturbances result in small deviations. For each graph, only the eight parameters with the highest sensitivity values are shown

Mentions: The sensitivity of the lumped reflex gains to variations in the neural and sensory parameters of the NMS model is illustrated in Fig. 5. For each of the parameters of the lumped reflex gain model and the RMS of the joint deviation, the eight neural and sensory parameters with the largest effect (per parameter) are shown. The height of the bars shows the magnitude of the sensitivity metric Sij, plus and minus signs above the bars indicate the sign of Sij.Fig. 5


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)

Sensitivity measure Sij for the eight lumped reflex gain model parameters (m, b, k, kp, kv, kf, τdel, τact) and RMS of the joint position. Low RMS indicates high task performance: the force disturbances result in small deviations. For each graph, only the eight parameters with the highest sensitivity values are shown
© Copyright Policy
Related In: Results  -  Collection

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

Fig5: Sensitivity measure Sij for the eight lumped reflex gain model parameters (m, b, k, kp, kv, kf, τdel, τact) and RMS of the joint position. Low RMS indicates high task performance: the force disturbances result in small deviations. For each graph, only the eight parameters with the highest sensitivity values are shown
Mentions: The sensitivity of the lumped reflex gains to variations in the neural and sensory parameters of the NMS model is illustrated in Fig. 5. For each of the parameters of the lumped reflex gain model and the RMS of the joint deviation, the eight neural and sensory parameters with the largest effect (per parameter) are shown. The height of the bars shows the magnitude of the sensitivity metric Sij, plus and minus signs above the bars indicate the sign of Sij.Fig. 5

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