Muscle mass in musculoskeletal models.
Ontology highlight
ABSTRACT:
SUBMITTER: Pai DK
PROVIDER: S-EPMC2957906 | biostudies-literature | 2010 Aug
REPOSITORIES: biostudies-literature
Publications
Most current models of musculoskeletal dynamics lump a muscle's mass with its body segment, and then simulate the dynamics of these body segments connected by joints. As shown here, this popular approach leads to errors in the system's inertia matrix and hence in all aspects of the dynamics. Two simplified mathematical models were created to capture the relevant features of monoarticular and biarticular muscles, and the errors were analyzed. The models were also applied to two physiological exam ...[more]
Similar Datasets
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.
| S-EPMC8944069 | biostudies-literature
Project description:Musculoskeletal modeling is a well-established method in spine biomechanics and generally employed for investigations concerning both the healthy and the pathological spine. It commonly involves inverse kinematics and optimization of muscle activity and provides detailed insight into joint loading. The aim of the present work was to develop and validate a procedure for the automatized generation of semi-subject-specific multi-rigid body models with an articulated lumbar spine. Individualization of the models was achieved with a novel approach incorporating information from annotated EOS images. The size and alignment of bony structures, as well as specific body weight distribution along the spine segments, were accurately reproduced in the 3D models. To ensure the pipeline's robustness, models based on 145 EOS images of subjects with various weight distributions and spinopelvic parameters were generated. For validation, we performed kinematics-dependent and segment-dependent comparisons of the average joint loads obtained for our cohort with the outcome of various published in vivo and in situ studies. Overall, our results agreed well with literature data. The here described method is a promising tool for studying a variety of clinical questions, ranging from the evaluation of the effects of alignment variation on joint loading to the assessment of possible pathomechanisms involved in adjacent segment disease.
| S-EPMC8438119 | biostudies-literature
Project description:The geometrical representation of muscles in computational models of the musculoskeletal system typically consists of a series of line segments. These muscle anatomies are based on measurements from a limited number of cadaveric studies that recently have been used as atlases for creating subject-specific models from medical images, so potentially restricting the options for personalisation and assessment of muscle geometrical models. To overcome this methodological limitation, we propose a novel, completely automated technique that, from a surface geometry of a skeletal muscle and its attachment areas, can generate an arbitrary number of lines of action (fibres) composed by a user-defined number of straight-line segments. These fibres can be included in standard musculoskeletal models and used in biomechanical simulations. This methodology was applied to the surfaces of four muscles surrounding the hip joint (iliacus, psoas, gluteus maximus and gluteus medius), segmented on magnetic resonance imaging scans from a cadaveric dataset, for which highly discretised muscle representations were created and used to simulate functional tasks. The fibres' moment arms were validated against measurements and models of the same muscles from the literature with promising outcomes. The proposed approach is expected to improve the anatomical representation of skeletal muscles in personalised biomechanical models and finite element applications.
| S-EPMC7280327 | biostudies-literature
Project description:Musculoskeletal modelling is an important platform on which to study the biomechanics of morphological structures in vertebrates and is widely used in clinical, zoological and palaeontological fields. The popularity of this approach stems from the potential to non-invasively quantify biologically important but difficult-to-measure functional parameters. However, while it is known that model predictions are highly sensitive to input values, it is standard practice to build models by combining musculoskeletal data from different sources resulting in 'generic' models for a given species. At present, there are little quantitative data on how merging disparate anatomical data in models impacts the accuracy of these functional predictions. This issue is addressed herein by quantifying the accuracy of both subject-specific human limb models containing individualised muscle force-generating properties and models built using generic properties from both elderly and young individuals, relative to experimental muscle torques obtained from an isokinetic dynamometer. The results show that subject-specific models predict isokinetic muscle torques to a greater degree of accuracy than generic models at the ankle (root-mean-squared error - 7.9% vs. 49.3% in elderly anatomy-based models), knee (13.2% vs. 57.3%) and hip (21.9% vs. 32.8%). These results have important implications for the choice of musculoskeletal properties in future modelling studies, and the relatively high level of accuracy achieved in the subject-specific models suggests that such models can potentially address questions about inter-subject variations of muscle functions. However, despite relatively high levels of overall accuracy, models built using averaged generic muscle architecture data from young, healthy individuals may lack the resolution and accuracy required to study such differences between individuals, at least in certain circumstances. The results do not wholly discourage the continued use of averaged generic data in musculoskeletal modelling studies but do emphasise the need for to maximise the accuracy of input values if studying intra-species form-function relationships in the musculoskeletal system.
| S-EPMC7542200 | biostudies-literature
Project description:Mdx mice were castrated and treated with selective androgen receptor modulator (SARM) GTx-026. Ten weeks after treatment, gastrocnemious muscle were isolated, RNA extracted, and sequenced using ion torrent sequencer.
2017-05-12 | GSE98473 | GEO
Project description:Maximum muscle stress (MMS) is a critical parameter in musculoskeletal modeling, defining the maximum force that a muscle of given size can produce. However, a wide range of MMS values have been reported in literature, and few studies have estimated MMS in trunk muscles. Due to widespread use of musculoskeletal models in studies of the spine and trunk, there is a need to determine reasonable magnitude and range of trunk MMS. We measured trunk extension strength in 49 participants over 65 years of age, surveyed participants about low back pain, and acquired quantitative computed tomography (QCT) scans of their lumbar spines. Trunk muscle morphology was assessed from QCT scans and used to create a subject-specific musculoskeletal model for each participant. Model-predicted extension strength was computed using a trunk muscle MMS of 100?N/cm2 . The MMS of each subject-specific model was then adjusted until the measured strength matched the model-predicted strength (±20?N). We found that measured trunk extension strength was significantly higher in men. With the initial constant MMS value, the musculoskeletal model generally over-predicted trunk extension strength. By adjusting MMS on a subject-specific basis, we found apparent MMS values ranging from 40 to 130?N/cm2 , with an average of 75.5?N/cm2 for both men and women. Subjects with low back pain had lower apparent MMS than subjects with no back pain. This work incorporates a unique approach to estimate subject-specific trunk MMS values via musculoskeletal modeling and provides a useful insight into MMS variation. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:498-505, 2018.
| S-EPMC5723244 | biostudies-literature
Project description:BackgroundHeritability explains 45-82% of muscle mass and strength variation, yet polygenic models for muscle phenotypes in older women are scarce. Therefore, the objective of the present study was to (1) assess if total genotype predisposition score (GPSTOTAL) for a set of polymorphisms differed between older women with low and high muscle mass, and (2) utilise a data-driven GPS (GPSDD) to predict the variance in muscle size and strength-related phenotypes.MethodsIn three-hundred 60- to 91-year-old Caucasian women (70.7 ± 5.7 years), skeletal muscle mass, biceps brachii thickness, vastus lateralis anatomical cross-sectional area (VLACSA), hand grip strength (HGS), and elbow flexion (MVCEF) and knee extension (MVCKE) maximum voluntary contraction were measured. Participants were classified as having low muscle mass if the skeletal muscle index (SMI) < 6.76 kg/m2 or relative skeletal muscle mass (%SMMr) < 22.1%. Genotyping was completed for 24 single-nucleotide polymorphisms (SNPs). GPSTOTAL was calculated from 23 SNPs and compared between the low and high muscle mass groups. A GPSDD was performed to identify the association of SNPs with other skeletal muscle phenotypes.ResultsThere was no significant difference in GPSTOTAL between low and high muscle mass groups, irrespective of classification based on SMI or %SMMr. The GPSDD model, using 23 selected SNPs, revealed that 13 SNPs were associated with at least one skeletal muscle phenotype: HIF1A rs11549465 was associated with four phenotypes and, in descending number of phenotype associations, ACE rs4341 with three; PTK2 rs7460 and CNTFR rs2070802 with two; and MTHFR rs17421511, ACVR1B rs10783485, CNTF rs1800169, MTHFR rs1801131, MTHFR rs1537516, TRHR rs7832552, MSTN rs1805086, COL1A1 rs1800012, and FTO rs9939609 with one phenotype. The GPSDD with age included as a predictor variable explained 1.7% variance of biceps brachii thickness, 12.5% of VLACSA, 19.0% of HGS, 8.2% of MVCEF, and 9.6% of MVCKE.ConclusionsIn older women, GPSTOTAL did not differ between low and high muscle mass groups. However, GPSDD was associated with muscle size and strength phenotypes. Further advancement of polygenic models to understand skeletal muscle function during ageing might become useful in targeting interventions towards older adults most likely to lose physical independence.
| S-EPMC9223182 | biostudies-literature
Project description:Skeletal muscle is a highly plastic organ that is necessary for homeostasis and health of the human body. The size of skeletal muscle changes in response to intrinsic and extrinsic stimuli. Although protein-coding RNAs including myostatin, NF-??, and insulin-like growth factor-1 (IGF-1), have pivotal roles in determining the skeletal muscle mass, the role of long non-coding RNAs (lncRNAs) in the regulation of skeletal muscle mass remains to be elucidated. Here, we performed expression profiling of nine skeletal muscle differentiation-related lncRNAs (DRR, DUM1, linc-MD1, linc-YY1, LncMyod, Neat1, Myoparr, Malat1, and SRA) and three genomic imprinting-related lncRNAs (Gtl2, H19, and IG-DMR) in mouse skeletal muscle. The expression levels of these lncRNAs were examined by quantitative RT-PCR in six skeletal muscle atrophy models (denervation, casting, tail suspension, dexamethasone-administration, cancer cachexia, and fasting) and two skeletal muscle hypertrophy models (mechanical overload and deficiency of the myostatin gene). Cluster analyses of these lncRNA expression levels were successfully used to categorize the muscle atrophy models into two sub-groups. In addition, the expression of Gtl2, IG-DMR, and DUM1 was altered along with changes in the skeletal muscle size. The overview of the expression levels of lncRNAs in multiple muscle atrophy and hypertrophy models provides a novel insight into the role of lncRNAs in determining the skeletal muscle mass.
| S-EPMC7084395 | biostudies-literature
Project description:Understanding the sources of error is critical before models of the musculoskeletal system can be usefully translated. Using in vivo measured tibiofemoral forces, the impact of uncertainty in muscle-tendon parameters on the accuracy of knee contact force estimates of a generic musculoskeletal model was investigated following a probabilistic approach. Population variability was introduced to the routine musculoskeletal modeling framework by perturbing input parameters of the lower limb muscles around their baseline values. Using ground reaction force and skin marker trajectory data collected from six subjects performing body-weight squat, the knee contact force was calculated for the perturbed models. The combined impact of input uncertainties resulted in a considerable variation in the knee contact force estimates (up to 2.1 BW change in the predicted force), especially at larger knee flexion angles, hence explaining up to 70% of the simulation error. Although individual muscle groups exhibited different contributions to the overall error, variation in the maximum isometric force and pathway of the muscles showed the highest impacts on the model outcomes. Importantly, this study highlights parameters that should be personalized in order to achieve the best possible predictions when using generic musculoskeletal models for activities involving deep knee flexion.
| S-EPMC9204520 | biostudies-literature
Project description:Mass transport represents the most fundamental process in living organisms. It includes delivery of nutrients, oxygen, drugs, and other substances from the vascular system to tissue and transport of waste and other products from cells back to vascular and lymphatic network and organs. Furthermore, movement is achieved by mechanical forces generated by muscles in coordination with the nervous system. The signals coming from the brain, which have the character of electrical waves, produce activation within muscle cells. Therefore, from a physics perspective, there exist a number of physical fields within the body, such as velocities of transport, pressures, concentrations of substances, and electrical potential, which is directly coupled to biochemical processes of transforming the chemical into mechanical energy and further internal forces for motion. The overall problems of mass transport and electrophysiology coupled to mechanics can be investigated theoretically by developing appropriate computational models. Due to the enormous complexity of the biological system, it would be almost impossible to establish a detailed computational model for the physical fields related to mass transport, electrophysiology, and coupled fields. To make computational models feasible for applications, we here summarize a concept of smeared physical fields, with coupling among them, and muscle mechanics, which includes dependence on the electrical potential. Accuracy of the smeared computational models, also with coupling to muscle mechanics, is illustrated with simple example, while their applicability is demonstrated on a liver model with tumors present. The last example shows that the introduced methodology is applicable to large biological systems.
| S-EPMC6914730 | biostudies-literature