Limits...
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.

Show MeSH

Related in: MedlinePlus

Four-second segment of a perturbation experiment on the NMS model and the output of the lumped reflex gain fit for a single condition. Disturbance torque (top) and resulting arm motion (bottom). Simulation experiment with the NMS model (solid) and the fit of the lumped reflex gain model (dashed). In this case VAF of the fit was 0.95
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3108017&req=5

Fig3: Four-second segment of a perturbation experiment on the NMS model and the output of the lumped reflex gain fit for a single condition. Disturbance torque (top) and resulting arm motion (bottom). Simulation experiment with the NMS model (solid) and the fit of the lumped reflex gain model (dashed). In this case VAF of the fit was 0.95

Mentions: Figure 3 illustrates the results of a single model simulation. The top panel shows the multisine disturbance force d(t) acting on the joint. The resulting joint rotation x(t) of the NMS model is illustrated in the bottom panel, together with the reflex gain model fit . Of all reflex gain model fits, one fit with a VAF of 0.48 was rejected. This was the condition in which tonic descending excitation (TDE) was minimal, causing some of the neural populations to completely cease activity. The average VAF of the remaining 215 fits was 0.95 with a standard deviation of 0.014 and a minimum of 0.84.Fig. 3


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)

Four-second segment of a perturbation experiment on the NMS model and the output of the lumped reflex gain fit for a single condition. Disturbance torque (top) and resulting arm motion (bottom). Simulation experiment with the NMS model (solid) and the fit of the lumped reflex gain model (dashed). In this case VAF of the fit was 0.95
© Copyright Policy
Related In: Results  -  Collection

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

Fig3: Four-second segment of a perturbation experiment on the NMS model and the output of the lumped reflex gain fit for a single condition. Disturbance torque (top) and resulting arm motion (bottom). Simulation experiment with the NMS model (solid) and the fit of the lumped reflex gain model (dashed). In this case VAF of the fit was 0.95
Mentions: Figure 3 illustrates the results of a single model simulation. The top panel shows the multisine disturbance force d(t) acting on the joint. The resulting joint rotation x(t) of the NMS model is illustrated in the bottom panel, together with the reflex gain model fit . Of all reflex gain model fits, one fit with a VAF of 0.48 was rejected. This was the condition in which tonic descending excitation (TDE) was minimal, causing some of the neural populations to completely cease activity. The average VAF of the remaining 215 fits was 0.95 with a standard deviation of 0.014 and a minimum of 0.84.Fig. 3

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