Diverse and complex muscle spindle afferent firing properties emerge from multiscale muscle mechanics

Blum, Kyle P.; Campbell, Kenneth S.; Horslen, Brian C.; Nardelli, Paul; Housley, Stephen N.; Cope, Timothy C.; Ting, Lena H.

doi:10.7554/elife.55177

Cited by 54 publications

(85 citation statements)

References 83 publications

(131 reference statements)

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“…Several possibilities were debated at the Neural Control of Movement conference in 2013 without definitive results. We have emphasized that the motor cortex communicates a coded kinematic plan together with stiffness settings that play an essential role in shaping the dynamics in muscle spindles (Blum et al, 2020).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Computational Modeling: Human Dynamic Model

2021

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Improvements in quantitative measurements of human physical activity are proving extraordinarily useful for studying the underlying musculoskeletal system. Dynamic models of human movement support clinical efforts to analyze, rehabilitate injuries. They are also used in biomechanics to understand and diagnose motor pathologies, find new motor strategies that decrease the risk of injury, and predict potential problems from a particular procedure. In addition, they provide valuable constraints for understanding neural circuits. This paper describes a physics-based movement analysis method for analyzing and simulating bipedal humanoid movements. The model includes the major body segments and joints to report human movements' energetic components. Its 48 degrees of freedom strike a balance between very detailed models that include muscle models and straightforward two-dimensional models. It has sufficient accuracy to analyze and synthesize movements captured in real-time interactive applications, such as psychophysics experiments using virtual reality or human-in-the-loop teleoperation of a simulated robotic system. The dynamic model is fast and robust while still providing results sufficiently accurate to be used to animate a humanoid character. It can also estimate internal joint forces used during a movement to create effort-contingent stimuli and support controlled experiments to measure the dynamics generating human behaviors systematically. The paper describes the innovative features that allow the model to integrate its dynamic equations accurately and illustrates its performance and accuracy with demonstrations. The model has a two-foot stance ability, capable of generating results comparable with an experiment done with subjects, and illustrates the uncontrolled manifold concept. Additionally, the model's facility to capture large energetic databases opens new possibilities for theorizing as to human movement function. The model is freely available.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Computational Modeling: Human Dynamic Model

2021

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“…Importantly, these signals could not have originated within TA, which is slack during the initial portion of this perturbation. Instead, they likely arise from muscle spindle proprioceptive signals [2,52] within the MG and within other muscles within the limbs-and potentially torso and other areas-that are stretched due to the perturbations and which exhibit similar feedback-mediated responses.…”

Section: Plos Onementioning

confidence: 99%

Abnormal center of mass feedback responses during balance: A potential biomarker of falls in Parkinson’s disease

et al. 2021

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Although Parkinson disease (PD) causes profound balance impairments, we know very little about how PD impacts the sensorimotor networks we rely on for automatically maintaining balance control. In young healthy people and animals, muscles are activated in a precise temporal and spatial organization when the center of body mass (CoM) is unexpectedly moved that is largely automatic and determined by feedback of CoM motion. Here, we show that PD alters the sensitivity of the sensorimotor feedback transformation. Importantly, sensorimotor feedback transformations for balance in PD remain temporally precise, but become spatially diffuse by recruiting additional muscle activity in antagonist muscles during balance responses. The abnormal antagonist muscle activity remains precisely time-locked to sensorimotor feedback signals encoding undesirable motion of the body in space. Further, among people with PD, the sensitivity of abnormal antagonist muscle activity to CoM motion varies directly with the number of recent falls. Our work shows that in people with PD, sensorimotor feedback transformations for balance are intact but disinhibited in antagonist muscles, likely contributing to balance deficits and falls.

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“…13393724) (16,17). Therefore, the muscle spindle model ( 26) was replaced with a multiscale muscle mechanics model in which this burst does emerge (19).…”

Section: Simulation Modelmentioning

confidence: 99%

“…Similarly, studies on muscle spindle firing dynamics are subject to the same interdependence. For example, Blum et al (19) observed a relationship of the Ia afferent response's dynamic index with stretch velocity and initial burst with stretch acceleration. However, the simulated stretch velocity and acceleration were varied with a perfect correlation, thus the observed relations cannot conclusively be linked to either velocity or acceleration.…”

Section: Introductionmentioning

confidence: 99%

“…To disentangle the perturbation parameters under investigation, 49 unique perturbation profiles are used. In addition, a biophysical simulation model of the muscle spindle Ia afferents (19) coupled to a motoneuron pool (20) was implemented. This simulation model was used to corroborate the experimental findings, investigate stretch reflex dependencies across an extended set of perturbation parameters and gain a physiological understanding of the observed dependencies.…”

Section: Introductionmentioning

confidence: 99%

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Disentangling acceleration-, velocity-, and duration-dependency of the short- and medium-latency stretch reflexes in the ankle plantarflexors

Veld

Asseldonk

Kooij

et al. 2021

Journal of Neurophysiology

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Motorized assessment of the stretch reflex is instrumental to gain understanding of the stretch reflex, its physiological origin and to differentiate effects of neurological disorders, like spasticity. Both short-latency (M1) and medium-latency (M2) stretch reflexes have been reported to depend on the velocity and acceleration of an applied ramp-and-hold perturbation. In the upper limb, M2 has also been reported to depend on stretch duration. However, wrong conclusions might have been drawn in previous studies as the interdependence of perturbation parameters (amplitude, duration, velocity, acceleration) possibly created uncontrolled, confounding effects. We disentangled the duration-, velocity- and acceleration-dependence and their interactions of the M1 and M2 stretch reflex in the ankle plantarflexors. To disentangle the parameter interdependence, forty-nine unique ramp-and-hold joint perturbations elicited reflexes in ten healthy volunteers during a torque control task. Linear mixed model analysis showed M1 depended on acceleration, not velocity or duration, whereas M2 depended on acceleration, velocity and duration. Simulations of the muscle spindle Ia afferents coupled to a motoneuron pool corroborated these experimental findings. Additionally, this simulation model did show a nonlinear M1 velocity- and duration-dependence for perturbation parameters outside the experimental scope. Concluding, motorized assessment of the stretch reflex or spasticity using ramp-and-hold perturbations should be systematically executed and reported. Our systematic motorized and simulation assessments showed that M1 and M2 depend on acceleration, velocity and duration of the applied perturbation. The simulation model suggested that these dependencies emerge from: muscle-tendon unit and muscle cross-bridge dynamics, Ia sensitivity to force and yank, and motoneuron synchronization.

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Diverse and complex muscle spindle afferent firing properties emerge from multiscale muscle mechanics

Cited by 54 publications

References 83 publications

Computational Modeling: Human Dynamic Model

Computational Modeling: Human Dynamic Model

Abnormal center of mass feedback responses during balance: A potential biomarker of falls in Parkinson’s disease

Disentangling acceleration-, velocity-, and duration-dependency of the short- and medium-latency stretch reflexes in the ankle plantarflexors

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