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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 of the lumped reflex gains kp, kv, kf to the sensory parameters of the muscle spindle and Golgi tendon organs. The most closely related neural substrates of each reflex gain parameter are indicated with an asterisk (*), e.g: velocity feedback gain kv is expected to be closest related to the velocity components dIa and eIa. (See Eqs. 1–3 and Table 1 for a list of these parameters)
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Fig6: Sensitivity measure Sij of the lumped reflex gains kp, kv, kf to the sensory parameters of the muscle spindle and Golgi tendon organs. The most closely related neural substrates of each reflex gain parameter are indicated with an asterisk (*), e.g: velocity feedback gain kv is expected to be closest related to the velocity components dIa and eIa. (See Eqs. 1–3 and Table 1 for a list of these parameters)

Mentions: Figure 5 demonstrates that most lumped reflex gains were sensitive to a mixture of neural and sensory parameters. To elucidate the relation between the properties of the proprioceptors and the estimated reflex gains, Fig. 6 shows a subset of the data: the sensitivity of kp, kv and kf to only the sensory constants of the muscle spindle and GTO. The velocity components of the muscle spindle (constants dIa and eIa) positively correlated with velocity gain kv, with relatively low interaction with the other reflex gains. Position feedback gain kp and force feedback gain kf however did not show such a distinct sensitivity. There was positive sensitivity of kp to the stretch component of the muscle spindle cII, but kp decreased with velocity component dIa as well. The GTO constant cIb led to an increase of kf, but the spindle parameters aIa, cIa and dIa have a far stronger negative (decreasing) effect on kf. Summarizing, Fig. 6 demonstrates that kv and kf are mostly influenced by muscle spindle feedback, while kp is influenced by a mixture of afferent feedback pathways.Fig. 6


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 of the lumped reflex gains kp, kv, kf to the sensory parameters of the muscle spindle and Golgi tendon organs. The most closely related neural substrates of each reflex gain parameter are indicated with an asterisk (*), e.g: velocity feedback gain kv is expected to be closest related to the velocity components dIa and eIa. (See Eqs. 1–3 and Table 1 for a list of these parameters)
© Copyright Policy
Related In: Results  -  Collection

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

Fig6: Sensitivity measure Sij of the lumped reflex gains kp, kv, kf to the sensory parameters of the muscle spindle and Golgi tendon organs. The most closely related neural substrates of each reflex gain parameter are indicated with an asterisk (*), e.g: velocity feedback gain kv is expected to be closest related to the velocity components dIa and eIa. (See Eqs. 1–3 and Table 1 for a list of these parameters)
Mentions: Figure 5 demonstrates that most lumped reflex gains were sensitive to a mixture of neural and sensory parameters. To elucidate the relation between the properties of the proprioceptors and the estimated reflex gains, Fig. 6 shows a subset of the data: the sensitivity of kp, kv and kf to only the sensory constants of the muscle spindle and GTO. The velocity components of the muscle spindle (constants dIa and eIa) positively correlated with velocity gain kv, with relatively low interaction with the other reflex gains. Position feedback gain kp and force feedback gain kf however did not show such a distinct sensitivity. There was positive sensitivity of kp to the stretch component of the muscle spindle cII, but kp decreased with velocity component dIa as well. The GTO constant cIb led to an increase of kf, but the spindle parameters aIa, cIa and dIa have a far stronger negative (decreasing) effect on kf. Summarizing, Fig. 6 demonstrates that kv and kf are mostly influenced by muscle spindle feedback, while kp is influenced by a mixture of afferent feedback pathways.Fig. 6

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