Dataset Information

Musculoskeletal models with generic and subject-specific geometry estimate different joint biomechanics in dysplastic hips.

ABSTRACT: Optimizing the geometric complexity of musculoskeletal models is important for reliable yet feasible estimation of joint biomechanics. This study investigated the effects of subject-specific model geometry on hip joint reaction forces (JRFs) and muscle forces in patients with developmental dysplasia of the hip (DDH) and healthy controls. For nine DDH and nine control subjects, three models were created with increasingly subject-specific pelvis geometry, hip joint center locations and muscle attachments. Hip JRFs and muscle forces during a gait cycle were compared among the models. For DDH subjects, resultant JRFs from highly specific models including subject-specific pelvis geometry, joint locations and muscle attachments were not significantly different compared to models using generic geometry in early stance, but were significantly higher in late stance (p?=?0.03). Estimates from moderately specific models using CT-informed scaling of generic pelvis geometry were not significantly different from low specificity models using generic geometry scaled with skin markers. For controls, resultant JRFs in early stance from highly specific models were significantly lower than moderate and low specificity models (p???0.02) with no significant differences in late stance. Inter-model JRF differences were larger for DDH subjects than controls. Inter-model differences for JRF components and muscle forces were similar to resultant JRFs. Incorporating subject-specific pelvis geometry significantly affects JRF and muscle force estimates in both DDH and control groups, which may be especially important for reliable estimation of pathomechanics in dysplastic hips.

SUBMITTER: Song K

PROVIDER: S-EPMC6478547 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Musculoskeletal models with generic and subject-specific geometry estimate different joint biomechanics in dysplastic hips.

Song Ke K Anderson Andrew E AE Weiss Jeffrey A JA Harris Michael D MD

Computer methods in biomechanics and biomedical engineering 20190120 3

Optimizing the geometric complexity of musculoskeletal models is important for reliable yet feasible estimation of joint biomechanics. This study investigated the effects of subject-specific model geometry on hip joint reaction forces (JRFs) and muscle forces in patients with developmental dysplasia of the hip (DDH) and healthy controls. For nine DDH and nine control subjects, three models were created with increasingly subject-specific pelvis geometry, hip joint center locations and muscle atta ...[more]

PMID: 30663342

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Project description:The majority of musculoskeletal modelling studies investigating healthy populations use generic models linearly scaled to roughly match an individual's anthropometry. Generic models disregard the considerable variation in musculoskeletal geometry and tissue properties between individuals. This study investigated the physiological implications of personalizing musculoskeletal model geometry (body segment mass, inertia, joint center, and maximum isometric muscle force). Nine healthy athletes performed ten repetitions of 15 meter sprints at 75-95% of their maximum sprinting speed and ten repetitions of unanticipated sidestep cut trials with a 4.5-5.5 m/s approach running speed. Structural magnetic resonance imaging was collected on the lower extremities, from which subject-specific musculoskeletal models were developed. A one-dimensional statistical parametric mapping paired t-test was used to compare generic and subject-specific musculoskeletal models for: lower-limb kinematics, kinetics, torque matching, as well as hamstrings, adductors, and quadriceps muscle activations and fiber dynamics. Percentage change of geometric parameters between generic and subject-specific models were determined. Compared to generic models, subject-specific models showed significantly lower ankle dorsi/plantar flexion angle during sprinting and several significantly different net joint moments during sprint and cut tasks. Additionally, subject-specific models demonstrated better torque matching, more physiologically plausible fiber lengths, higher fiber velocities, lower muscle forces, and lower simulated activations in a subset of investigated muscles and motor tasks. Furthermore, subject-specific models identified between-limb differences that were not identified with generic models. Use of subject-specific modeling, even in healthy populations, may result in more physiologically plausible muscle fiber mechanics. Implementing subject-specific models may be especially beneficial when investigating populations with substantial geometric between-limb differences, or unilateral musculoskeletal pathologies, as these are not captured by a generic model.

Project description:Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312) across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force predictions could be affected by an uncertainty in the same order of magnitude of its value, although this condition has low probability to occur.

Project description:BackgroundMusculoskeletal modeling is currently a preferred method for estimating the muscle forces that underlie observed movements. However, these estimates are sensitive to a variety of assumptions and uncertainties, which creates difficulty when trying to interpret the muscle forces from musculoskeletal simulations. Here, we describe an approach that uses Bayesian inference to identify plausible ranges of muscle forces for a simple motion while representing uncertainty in the measurement of the motion and the objective function used to solve the muscle redundancy problem.MethodsWe generated a reference elbow flexion-extension motion and computed a set of reference forces that would produce the motion while minimizing muscle excitations cubed via OpenSim Moco. We then used a Markov Chain Monte Carlo (MCMC) algorithm to sample from a posterior probability distribution of muscle excitations that would result in the reference elbow motion. We constructed a prior over the excitation parameters which down-weighted regions of the parameter space with greater muscle excitations. We used muscle excitations to find the corresponding kinematics using OpenSim, where the error in position and velocity trajectories (likelihood function) was combined with the sum of the cubed muscle excitations integrated over time (prior function) to compute the posterior probability density.ResultsWe evaluated the muscle forces that resulted from the set of excitations that were visited in the MCMC chain (seven parallel chains, 500,000 iterations per chain). The estimated muscle forces compared favorably with the reference forces generated with OpenSim Moco, while the elbow angle and velocity from MCMC matched closely with the reference (average RMSE for elbow angle = 2°; and angular velocity = 32°/s). However, our rank plot analyses and potential scale reduction statistics, which we used to evaluate convergence of the algorithm, indicated that the chains did not fully mix.ConclusionsWhile the results from this process are a promising step towards characterizing uncertainty in muscle force estimation, the computational time required to search the solution space with, and the lack of MCMC convergence indicates that further developments in MCMC algorithms are necessary for this process to become feasible for larger-scale models.

Dataset Information

Musculoskeletal models with generic and subject-specific geometry estimate different joint biomechanics in dysplastic hips.

Publications

Musculoskeletal models with generic and subject-specific geometry estimate different joint biomechanics in dysplastic hips.

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