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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 of reflex gain parameters kp, kv, kf and RMS of joint deviation to the velocity component dIa of the muscle spindle. Lines indicate the linear regression fit; the normalized slope determined the sensitivity measure Sij
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Fig4: Sensitivity of reflex gain parameters kp, kv, kf and RMS of joint deviation to the velocity component dIa of the muscle spindle. Lines indicate the linear regression fit; the normalized slope determined the sensitivity measure Sij

Mentions: One by one each parameter was simulated at 0.5, 0.9, 1.0, 1.1, 1.5 and 2.0 times its nominal value, with all other parameters kept to their nominal value. For each value, a single set of reflex gains was fitted onto the data of the eight simulation repetitions. A sensitivity measure was defined by taking the slope of a linear regression through the six resulting reflex gain values (Fig. 4). To allow for comparisons between the different sensitivities the sensitivity measure was normalized with the reflex gain value when all neural parameters had their default, nominal value (relative sensitivity, see Frank 1978). So the sensitivity measure gave the relative amount of change in a fitted lumped reflex gain as the result of a changing neural or sensory parameter. Figure 4 illustrates this process for the three reflex gains kp, kv, kf and the RMS of joint deviation.


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 of reflex gain parameters kp, kv, kf and RMS of joint deviation to the velocity component dIa of the muscle spindle. Lines indicate the linear regression fit; the normalized slope determined the sensitivity measure Sij
© Copyright Policy
Related In: Results  -  Collection

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

Fig4: Sensitivity of reflex gain parameters kp, kv, kf and RMS of joint deviation to the velocity component dIa of the muscle spindle. Lines indicate the linear regression fit; the normalized slope determined the sensitivity measure Sij
Mentions: One by one each parameter was simulated at 0.5, 0.9, 1.0, 1.1, 1.5 and 2.0 times its nominal value, with all other parameters kept to their nominal value. For each value, a single set of reflex gains was fitted onto the data of the eight simulation repetitions. A sensitivity measure was defined by taking the slope of a linear regression through the six resulting reflex gain values (Fig. 4). To allow for comparisons between the different sensitivities the sensitivity measure was normalized with the reflex gain value when all neural parameters had their default, nominal value (relative sensitivity, see Frank 1978). So the sensitivity measure gave the relative amount of change in a fitted lumped reflex gain as the result of a changing neural or sensory parameter. Figure 4 illustrates this process for the three reflex gains kp, kv, kf and the RMS of joint deviation.

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