Strains at the myotendinous junction predicted by a micromechanical model

Sharafi, Bahar; Ames, Elizabeth G.; Holmes, Jeffrey W.; Blemker, Silvia S.

doi:10.1016/j.jbiomech.2011.08.025

Cited by 31 publications

(21 citation statements)

References 32 publications

(38 reference statements)

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“…For muscle and connective tissue, the relationship between passive along-fiber stretch and stress is characterized by a nonlinear toe region followed by a linear increase (51). Shear deformation along the fiber and across the fiber are represented by an exponential relationship between shear strain and stress (42). The materials are quasi-incompressible, which is enforced by highly penalizing changes in volume.…”

Section: Methodsmentioning

confidence: 99%

Computational Models Predict Larger Muscle Tissue Strains at Faster Sprinting Speeds

Fiorentino

Rehorn

Chumanov

et al. 2014

Medicine &Amp; Science in Sports &Amp; Exercise

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Introduction: Proximal biceps femoris musculotendon strain injury has been well established as a common injury among athletes participating in sports that require sprinting near or at maximum speed; however, little is known about the mechanisms that make this muscle tissue more susceptible to injury at faster speeds. Purpose: Quantify localized tissue strain during sprinting at a range of speeds. Methods: Biceps femoris long head (BFlh) musculotendon dimensions of 14 athletes were measured on magnetic resonance (MR) images and used to generate a finite element computational model. The model was first validated through comparison with previous dynamic MR experiments. After validation, muscle activation and muscle-tendon unit length change were derived from forward dynamic simulations of sprinting at 70%, 85% and 100% maximum speed and used as input to the computational model simulations. Simulations ran from mid-swing to foot contact. Results: The model predictions of local muscle tissue strain magnitude compared favorably with in vivo tissue strain measurements determined from dynamic MR experiments of the BFlh. For simulations of sprinting, local fiber strain was non-uniform at all speeds, with the highest muscle tissue strain where injury is often observed (proximal myotendinous junction). At faster sprinting speeds, increases were observed in fiber strain non-uniformity and peak local fiber strain (0.56, 0.67 and 0.72, for sprinting at 70%, 85% and 100% maximum speed). A histogram of local fiber strains showed that more of the BFlh reached larger local fiber strains at faster speeds. Conclusions: At faster sprinting speeds, peak local fiber strain, fiber strain non-uniformity and the amount of muscle undergoing larger strains are predicted to increase, likely contributing to the BFlh muscle’s higher injury susceptibility at faster speeds.

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Section: Methodsmentioning

confidence: 99%

Computational Models Predict Larger Muscle Tissue Strains at Faster Sprinting Speeds

Fiorentino

Rehorn

Chumanov

et al. 2014

Medicine &Amp; Science in Sports &Amp; Exercise

Self Cite

View full text Add to dashboard Cite

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“…These models differ from previous diffusion/pathway models as they typically rely on systems of PDEs that are then solved using numerical approaches. Broadly speaking, finite element methods (and related finite volume methods) are also uniquely suited for monitoring geometrically-constrained properties such as cell surface interfaces and mechanical properties of tissues across all scales (17, 38, 50-54). Aguado-Sierra et al (35) generated a patient-specific three-dimensional model of heart failure in which a finite element mesh was fitted to echocardiographs and mechanical parameters were directly estimated from a combination of MR and cardiac ultrasound.…”

Section: Current Multiscale Modeling Effortsmentioning

confidence: 99%

Multiscale Computational Models of Complex Biological Systems

Walpole

Papin²,

Peirce³

2013

Annu. Rev. Biomed. Eng.

195

188

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Integration of data across spatial, temporal, and functional scales is a primary focus of biomedical engineering efforts. The advent of powerful computing platforms, coupled with quantitative data from high-throughput experimental platforms, has allowed multiscale modeling to expand as a means to more comprehensively investigate biological phenomena in experimentally relevant ways. This review aims to highlight recently published multiscale models of biological systems while using their successes to propose the best practices for future model development. We demonstrate that coupling continuous and discrete systems best captures biological information across spatial scales by selecting modeling techniques that are suited to the task. Further, we suggest how to best leverage these multiscale models to gain insight into biological systems using quantitative, biomedical engineering methods to analyze data in non-intuitive ways. These topics are discussed with a focus on the future of the field, the current challenges encountered, and opportunities yet to be realized.

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“…λ *: stretch at which stress–strain relationship becomes linear. A o : exponential shear modulus (Sharafi et al, 2011). G af : along-fiber shear modulus.…”

Section: Figmentioning

confidence: 99%

Musculotendon variability influences tissue strains experienced by the biceps femoris long head muscle during high-speed running

Fiorentino

Blemker

2014

Journal of Biomechanics

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The hamstring muscles frequently suffer injury during high-speed running, though the factors that make an individual more susceptible to injury remain poorly understood. The goals of this study were to measure the musculotendon dimensions of the biceps femoris long head (BFlh) muscle, the hamstring muscle injured most often, and to use computational models to assess the influence of variability in the BFlh’s dimensions on internal tissue strains during high-speed running. High-resolution magnetic resonance (MR) images were acquired over the thigh in 12 collegiate athletes, and musculotendon dimensions were measured in the proximal free tendon/aponeurosis, muscle and distal free tendon/aponeurosis. Finite element meshes were generated based on the average, standard deviation and range of BFlh dimensions. Simulation boundary conditions were defined to match muscle activation and musculotendon length change in the BFlh during high-speed running. Muscle and connective tissue dimensions were found to vary between subjects, with a coefficient of variation (CV) of 17 ± 6% across all dimensions. For all simulations peak local strain was highest along the proximal myotendinous junction, which is where injury typically occurs. Model variations showed that peak local tissue strain increased as the proximal aponeurosis width narrowed and the muscle width widened. The aponeurosis width and muscle width variation models showed that the relative dimensions of these structures influence internal muscle tissue strains. The results of this study indicate that a musculotendon unit’s architecture influences its strain injury susceptibility during high-speed running.

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Strains at the myotendinous junction predicted by a micromechanical model

Cited by 31 publications

References 32 publications

Computational Models Predict Larger Muscle Tissue Strains at Faster Sprinting Speeds

Computational Models Predict Larger Muscle Tissue Strains at Faster Sprinting Speeds

Multiscale Computational Models of Complex Biological Systems

Musculotendon variability influences tissue strains experienced by the biceps femoris long head muscle during high-speed running

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