12
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Predicting gait adaptations due to ankle plantarflexor muscle weakness and contracture using physics-based musculoskeletal simulations

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Deficits in the ankle plantarflexor muscles, such as weakness and contracture, occur commonly in conditions such as cerebral palsy, stroke, muscular dystrophy, Charcot-Marie-Tooth disease, and sarcopenia. While these deficits likely contribute to observed gait pathologies, determining cause-effect relationships is difficult due to the often co-occurring biomechanical and neural deficits. To elucidate the effects of weakness and contracture, we systematically introduced isolated deficits into a musculoskeletal model and generated simulations of walking to predict gait adaptations due to these deficits. We trained a planar model containing 9 degrees of freedom and 18 musculotendon actuators to walk using a custom optimization framework through which we imposed simple objectives, such as minimizing cost of transport while avoiding falling and injury, and maintaining head stability. We first generated gaits at prescribed speeds between 0.50 m/s and 2.00 m/s that reproduced experimentally observed kinematic, kinetic, and metabolic trends for walking. We then generated a gait at self-selected walking speed; quantitative comparisons between our simulation and experimental data for joint angles, joint moments, and ground reaction forces showed root-mean-squared errors of less than 1.6 standard deviations and normalized cross-correlations above 0.8 except for knee joint moment trajectories. Finally, we applied mild, moderate, and severe levels of muscle weakness or contracture to either the soleus (SOL) or gastrocnemius (GAS) or both of these major plantarflexors (PF) and retrained the model to walk at a self-selected speed. The model was robust to all deficits, finding a stable gait in all cases. Severe PF weakness caused the model to adopt a slower, "heel-walking" gait. Severe contracture of only SOL or both PF yielded similar results: the model adopted a "toe-walking" gait with excessive hip and knee flexion during stance. These results highlight how plantarflexor weakness and contracture may contribute to observed gait patterns.

          Author summary

          Deficits in the ankle plantarflexors, which are muscles that extend the ankle, are thought to contribute to abnormal walking patterns in conditions such as cerebral palsy, stroke, muscular dystrophy, Charcot-Marie-Tooth disease, and aging. To study how deficits in these muscles contribute to abnormal walking patterns, we used computer simulations to systematically introduce muscle deficits into a biomechanically accurate model. We first showed that our model could discover realistic walking patterns over a wide range of speeds when we posed a simple objective: walking while consuming a minimum amount of energy per distance, maintaining head stability, and avoiding injury. We then used the model to study the effect of two commonly observed problems: muscle weakness and muscle tightness. We found that severe weakness of the ankle plantarflexors caused the model to adopt a slower, “heel-walking” gait, and severe tightness caused the model to adopt a crouched, “toe-walking” gait. These results highlight how deficits in the ankle plantarflexors muscles may contribute to abnormal walking patterns commonly seen in pathological populations.

          Related collections

          Most cited references69

          • Record: found
          • Abstract: found
          • Article: not found

          Contributions of the individual ankle plantar flexors to support, forward progression and swing initiation during walking.

          Walking is a motor task requiring coordination of many muscles. Previous biomechanical studies, based primarily on analyses of the net ankle moment during stance, have concluded different functional roles for the plantar flexors. We hypothesize that some of the disparities in interpretation arise because of the effects of the uniarticular and biarticular muscles that comprise the plantar flexor group have not been separated. Furthermore, we believe that an accurate determination of muscle function requires quantification of the contributions of individual plantar flexor muscles to the energetics of individual body segments. In this study, we examined the individual contributions of the ankle plantar flexors (gastrocnemius (GAS); soleus (SOL)) to the body segment energetics using a musculoskeletal model and optimization framework to generate a forward dynamics simulation of normal walking at 1.5 m/s. At any instant in the gait cycle, the contribution of a muscle to support and forward progression was defined by its contribution to trunk vertical and horizontal acceleration, respectively, and its contribution to swing initiation by the mechanical energy it delivers to the leg in pre-swing (i.e., double-leg stance prior to toe-off). GAS and SOL were both found to provide trunk support during single-leg stance and pre-swing. In early single-leg stance, undergoing eccentric and isometric activity, they accelerate the trunk vertically but decelerate forward trunk progression. In mid single-leg stance, while isometric, GAS delivers energy to the leg while SOL decelerates it, and SOL delivers energy to the trunk while GAS decelerates it. In late single-leg stance through pre-swing, though GAS and SOL both undergo concentric activity and accelerate the trunk forward while decelerating the downward motion of the trunk (i.e., providing forward progression and support), they execute different energetic functions. The energy produced from SOL accelerates the trunk forward, whereas GAS delivers almost all its energy to accelerate the leg to initiate swing. Although GAS and SOL maintain or accelerate forward motion in mid single-leg stance through pre-swing, other muscles acting at the beginning of stance contribute comparably to forward progression. In summary, throughout single-leg stance both SOL and GAS provide vertical support, in mid single-leg stance SOL and GAS have opposite energetic effects on the leg and trunk to ensure support and forward progression of both the leg and trunk, and in pre-swing only GAS contributes to swing initiation.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities.

            While neuroscientists identify increasingly complex neural circuits that control animal and human gait, biomechanists find that locomotion requires little control if principles of legged mechanics are heeded that shape and exploit the dynamics of legged systems. Here, we show that muscle reflexes could be vital to link these two observations. We develop a model of human locomotion that is controlled by muscle reflexes which encode principles of legged mechanics. Equipped with this reflex control, we find this model to stabilize into a walking gait from its dynamic interplay with the ground, reproduce human walking dynamics and leg kinematics, tolerate ground disturbances, and adapt to slopes without parameter interventions. In addition, we find this model to predict some individual muscle activation patterns known from walking experiments. The results suggest not only that the interplay between mechanics and motor control is essential to human locomotion, but also that human motor output could for some muscles be dominated by neural circuits that encode principles of legged mechanics.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Flexing computational muscle: modeling and simulation of musculotendon dynamics.

              Muscle-driven simulations of human and animal motion are widely used to complement physical experiments for studying movement dynamics. Musculotendon models are an essential component of muscle-driven simulations, yet neither the computational speed nor the biological accuracy of the simulated forces has been adequately evaluated. Here we compare the speed and accuracy of three musculotendon models: two with an elastic tendon (an equilibrium model and a damped equilibrium model) and one with a rigid tendon. Our simulation benchmarks demonstrate that the equilibrium and damped equilibrium models produce similar force profiles but have different computational speeds. At low activation, the damped equilibrium model is 29 times faster than the equilibrium model when using an explicit integrator and 3 times faster when using an implicit integrator; at high activation, the two models have similar simulation speeds. In the special case of simulating a muscle with a short tendon, the rigid-tendon model produces forces that match those generated by the elastic-tendon models, but simulates 2-54 times faster when an explicit integrator is used and 6-31 times faster when an implicit integrator is used. The equilibrium, damped equilibrium, and rigid-tendon models reproduce forces generated by maximally-activated biological muscle with mean absolute errors less than 8.9%, 8.9%, and 20.9% of the maximum isometric muscle force, respectively. When compared to forces generated by submaximally-activated biological muscle, the forces produced by the equilibrium, damped equilibrium, and rigid-tendon models have mean absolute errors less than 16.2%, 16.4%, and 18.5%, respectively. To encourage further development of musculotendon models, we provide implementations of each of these models in OpenSim version 3.1 and benchmark data online, enabling others to reproduce our results and test their models of musculotendon dynamics.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: VisualizationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                7 October 2019
                October 2019
                : 15
                : 10
                : e1006993
                Affiliations
                [1 ] Department of Bioengineering, Stanford University, Stanford, California, United States of America
                [2 ] Department of Biomechatronics & Human-Machine Control, Delft University of Technology, Delft, The Netherlands
                [3 ] Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America
                [4 ] Department of Orthopaedic Surgery, Stanford University, Stanford, California, United States of America
                The Ohio State University, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-0589-5347
                http://orcid.org/0000-0002-2516-9334
                http://orcid.org/0000-0002-9643-7551
                Article
                PCOMPBIOL-D-19-00510
                10.1371/journal.pcbi.1006993
                6797212
                31589597
                ce461f8e-0258-4c26-8702-4d0a484c3803
                © 2019 Ong et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 29 March 2019
                : 5 September 2019
                Page count
                Figures: 11, Tables: 1, Pages: 27
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: U54 EB020405
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: P2C HD065690
                Funded by: funder-id http://dx.doi.org/10.13039/100011098, Stanford Bio-X;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100011225, Thomas and Stacey Siebel Foundation;
                Award Recipient :
                This work was supported by the National Institutes of Health ( https://www.nih.gov) through grants U54 EB020405 and P2C HD065690. CFO received support from the Stanford (University) Bio-X Graduate Fellowship ( https://biox.stanford.edu/fellowship-app-info) and from the Siebel Scholars Program ( http://www.siebelscholars.com). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Physiology
                Biological Locomotion
                Walking
                Medicine and Health Sciences
                Physiology
                Biological Locomotion
                Walking
                Biology and Life Sciences
                Physiology
                Biological Locomotion
                Gait Analysis
                Medicine and Health Sciences
                Physiology
                Biological Locomotion
                Gait Analysis
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Skeletal Joints
                Knee Joints
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Skeletal Joints
                Knee Joints
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Body Limbs
                Legs
                Ankles
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Body Limbs
                Legs
                Ankles
                Research and Analysis Methods
                Simulation and Modeling
                Physical Sciences
                Physics
                Classical Mechanics
                Kinematics
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Pelvis
                Hip
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Pelvis
                Hip
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Skeletal Joints
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Skeletal Joints
                Custom metadata
                vor-update-to-uncorrected-proof
                2019-10-17
                All simulation results files are available from https://simtk.org/projects/pfdeficitsgait.

                Quantitative & Systems biology
                Quantitative & Systems biology

                Comments

                Comment on this article