A Dynamic Simulation of Musculoskeletal Function in the Mouse Hindlimb During Trotting Locomotion

Charles, James P.; Cappellari, Ornella; Hutchinson, John R.

doi:10.3389/fbioe.2018.00061

Cited by 37 publications

(42 citation statements)

References 66 publications

(133 reference statements)

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“…The nodes on the distal surface were restrained from any rotation, via kinematic coupling to the area centroid of the distal surface. Peak physiological walking load at the ankle joint was calculated by solving a free-body diagram, using the mass of the foot, and force plate data available in Charles et al (2018), which recorded an average peak vertical and horizontal ground reaction force of 120% and 10.9% of body mass, respectively. No muscle load was included.…”

Section: Micro-fea Modelsmentioning

confidence: 99%

A novel algorithm to predict bone changes in the mouse tibia properties under physiological conditions

Cheong

Marin

Lacroix

et al. 2019

Biomech Model Mechanobiol

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Understanding how bone adapts to mechanical stimuli is fundamental for optimising treatments against musculoskeletal diseases in preclinical studies, but the contribution of physiological loading to bone adaptation in mouse tibia has not been quantified so far. In this study, a novel mechanistic model to predict bone adaptation based on physiological loading was developed and its outputs were compared with longitudinal scans of the mouse tibia. Bone remodelling was driven by the mechanical stimuli estimated from micro-FEA models constructed from micro-CT scans of C57BL/6 female mice (N = 5) from weeks 14 and 20 of age, to predict bone changes in week 16 or 22. Parametric analysis was conducted to evaluate the sensitivity of the models to subject-specific or averaged parameters, parameters from week 14 or week 20, and to strain energy density (SED) or maximum principal strain (ε maxprinc ). The results at week 20 showed no significant difference in bone densitometric properties between experimental and predicted images across the tibia for both stimuli, and 59% and 47% of the predicted voxels matched with the experimental sites in apposition and resorption, respectively. The model was able to reproduce regions of bone apposition in both periosteal and endosteal surfaces (70% and 40% for SED and ε maxprinc , respectively), but it under-predicted the experimental sites of resorption by over 85%. This study shows for the first time the potential of a subject-specific mechanoregulation algorithm to predict bone changes in a mouse model under physiological loading. Nevertheless, the weak predictions of resorption suggest that a combined stimulus or biological stimuli should be accounted for in the model.

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Section: Micro-fea Modelsmentioning

confidence: 99%

A novel algorithm to predict bone changes in the mouse tibia properties under physiological conditions

Cheong

Marin

Lacroix

et al. 2019

Biomech Model Mechanobiol

View full text Add to dashboard Cite

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“…Realistic finite element (FE) models of the whole mouse joint would be helpful to study the effect of interventions and treatments on the mechanical properties of the cartilage (Silva et al, 2005 ; Das Neves Borges et al, 2014 ; Yang et al, 2014 ). However, creating realistic geometries is challenging due to the complex anatomy of the mouse knee (Charles et al, 2016 , 2018 ), that consists of bones (distal femur and proximal tibia) and soft tissues, including a meniscus with calcifications within its structure (example in Figure 1 ). Subject specific geometry of the bone in the knee joint can be acquired by imaging techniques such as in vivo micro computed tomography (microCT) (Dall'Ara et al, 2016 ), ex vivo microCT (Das Neves Borges et al, 2014 ) or Synchrotron radiation microCT (Madi et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Development of Subject Specific Finite Element Models of the Mouse Knee Joint for Preclinical Applications

Zanjani-Pour

Giorgi

Dall’Ara

2020

Front. Bioeng. Biotechnol.

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Osteoarthritis is the most common musculoskeletal disabling disease worldwide. Preclinical studies on mice are commonly performed to test new interventions. Finite element (FE) models can be used to study joint mechanics, but usually simplified geometries are used. The aim of this project was to create a realistic subject specific FE model of the mouse knee joint for the assessment of joint mechanical properties. Four different FE models of a C57Bl/6 female mouse knee joint were created based on micro-computed tomography images of specimens stained with phosphotungstic acid in order to include different features: individual cartilage layers with meniscus, individual cartilage layers without meniscus, homogeneous cartilage layers with two different thickness values, and homogeneous cartilage with same thickness for both condyles. They were all analyzed under compressive displacement and the cartilage contact pressure was compared at 0.3 N reaction force. Peak contact pressure in the femur cartilage was 25% lower in the model with subject specific cartilage compared to the simpler model with homogeneous cartilage. A much more homogeneous pressure distribution across the joint was observed in the model with meniscus, with cartilage peak pressure 5-34% lower in the two condyles compared to that with individual cartilage layers. In conclusion, modeling the meniscus and individual cartilage was found to affect the pressure distribution in the mouse knee joint under compressive load and should be included in realistic models for assessing the effect of interventions preclinically.

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“…However, when comparing the computed muscle activity with the envelopes of the recorded EMG signals, we found a weaker correspondence. However, quantitative discrepancies between simulated and recorded muscle activities are common in biomechanical models [46], [47]. Since we compute muscle activities from joint kinematics, our model cannot simulate the co-contraction of antagonist muscles (e.g.…”

Section: A a Realistic Primate Arm Modelmentioning

confidence: 99%

Spatiotemporal Maps of Proprioceptive Inputs to the Cervical Spinal Cord During Three-Dimensional Reaching and Grasping

Kibleur

Tata

Greiner

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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Proprioceptive feedback is a critical component of voluntary movement planning and execution. Neuroprosthetic technologies aiming at restoring movement must interact with it to restore accurate motor control. Optimization and design of such technologies depends on the availability of quantitative insights into the neural dynamics of proprioceptive afferents during functional movements. However, recording proprioceptive neural activity during unconstrained movements in clinically relevant animal models presents formidable challenges. In this work, we developed a computational framework to estimate the spatiotemporal patterns of proprioceptive inputs to the cervical spinal cord during three-dimensional arm movements in monkeys. We extended a biomechanical model of the monkey arm with ex-vivo measurements, and combined it with models of mammalian group-Ia, Ib and II afferent fibers. We then used experimental recordings Manuscript

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A Dynamic Simulation of Musculoskeletal Function in the Mouse Hindlimb During Trotting Locomotion

Cited by 37 publications

References 66 publications

A novel algorithm to predict bone changes in the mouse tibia properties under physiological conditions

A novel algorithm to predict bone changes in the mouse tibia properties under physiological conditions

Development of Subject Specific Finite Element Models of the Mouse Knee Joint for Preclinical Applications

Spatiotemporal Maps of Proprioceptive Inputs to the Cervical Spinal Cord During Three-Dimensional Reaching and Grasping

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