Prediction of glycosaminoglycan content in human cartilage by age, T1ρ and T2 MRI.

OBJECTIVE: A relationship between T1ρ relaxation time and glycosaminoglycan (GAG) content has been demonstrated in chemically degraded bovine cartilage, but has not been demonstrated with quantitative biochemistry in human cartilage. A relationship has also been established between T2 relaxation time in cartilage and osteoarthritis (OA) severity. We hypothesized that T1ρ relaxation time would be associated with GAG content in human cartilage with normal T2 relaxation times. METHODS: T2 relaxation time, T1ρ relaxation time, and glycosaminoglycan as a percentage of wet weight (sGAG) were measured for top and bottom regions at 7 anatomical locations in 21 human cadaver patellae. For our analysis, T2 relaxation time was classified as normal or elevated based on a threshold defined by the mean plus one standard deviation of the T2 relaxation time for all samples. RESULTS: In the normal T2 relaxation time subset, T1ρ relaxation time correlated with sGAG content in the full-thickness and bottom regions, but only marginally in the top region alone. sGAG content decreased significantly with age in all regions. CONCLUSION: In the subset of cartilage specimens with normal T2 relaxation time, T1ρ relaxation time was inversely associated with sGAG content, as hypothesized. A predictive model, which accounts for T2 relaxation time and the effects of age, might be able to determine longitudinal trends in GAG content in the same person based on T1ρ relaxation time maps.

Automated creation and tuning of personalised muscle paths for OpenSim musculoskeletal models of the knee joint.

Computational modelling is an invaluable tool for investigating features of human locomotion and motor control which cannot be measured except through invasive techniques. Recent research has focussed on creating personalised musculoskeletal models using population-based morphing or directly from medical imaging. Although progress has been made, robust definition of two critical model parameters remains challenging: (1) complete tibiofemoral (TF) and patellofemoral (PF) joint motions, and (2) muscle tendon unit (MTU) pathways and kinematics (i.e. lengths and moment arms). The aim of this study was to develop an automated framework, using population-based morphing approaches to create personalised musculoskeletal models, consisting of personalised bone geometries, TF and PF joint mechanisms, and MTU pathways and kinematics. Informed from medical imaging, personalised rigid body TF and PF joint mechanisms were created. Using atlas- and optimisation-based methods, personalised MTU pathways and kinematics were created with the aim of preventing MTU penetration into bones and achieving smooth MTU kinematics that follow patterns from existing literature. This framework was integrated into the Musculoskeletal Atlas Project Client software package to create and optimise models for 6 participants with incrementally increasing levels of personalisation with the aim of improving MTU kinematics and pathways. Three comparisons were made: (1) non-optimised (Model 1) and optimised models (Model 3) with generic joint mechanisms; (2) non-optimised (Model 2) and optimised models (Model 4) with personalised joint mechanisms; and (3) both optimised models (Model 3 and 4). Following optimisation, improvements were consistently shown in pattern similarity to cadaveric data in comparison (1) and (2). For comparison (3), a number of comparisons showed no significant difference between the two compared models. Importantly, optimisation did not produce statistically significantly worse results in any case.

Rapid muscle volume prediction using anthropometric measurements and population-derived statistical models.

Knowledge of subject-specific muscle volumes may be used as surrogates for evaluating muscle strength and power generated by 'fat-free' muscle mass. This study presents population-based statistical learning approaches for predicting 'fat-free' muscle volume from known anthropometric measurements. Using computed tomography (CT) imaging data to obtain lower-limb muscle volumes from 50 men and women, this study evaluated six statistical learning methods for predicting muscle volumes from anthropometric measurements: (i) stepwise regression, (ii) linear support vector machine (SVM), (iii) 2nd-order polynomial SVM, (iv) linear partial least squares regression (PLSR), (v) quadratic PLSR, and (vi) 3rd-order spline fit PLSR. These techniques have successfully been demonstrated in bioengineering applications and freely available in open-source toolkits. Analysis revealed that separating a general population into sexes and/or cohorts based on adipose level may improve prediction accuracies. The most important measures that statistically influence muscle volume predictions were shank girth, followed by sex and finally leg length, as identified using stepwise regression. SVM learning predicted muscle volume with an accuracy of 85 ± 4% when using linear interpolation, but performed poorly with an accuracy of 59 ± 6% using polynomial interpolation. The simpler linear PLSR exhibited muscle volume prediction accuracy of 87 ± 2%, while quadratic PLSR was slightly reduced at 82 ± 3%. For the spline fit PLSR, high accuracy was observed on the training data set (~ 99%) but over-fitting (a drawback of high-interpolation methods) resulted in erroneous predictions on testing data, and hence, the model was deemed unsuitable. In conclusion, use of linear PLSR models with variables of sex, leg length, and shank girth is a useful tool for predicting 'fat-free' muscle volume.

Machine learning methods to support personalized neuromusculoskeletal modelling.

Many biomedical, orthopaedic, and industrial applications are emerging that will benefit from personalized neuromusculoskeletal models. Applications include refined diagnostics, prediction of treatment trajectories for neuromusculoskeletal diseases, in silico design, development, and testing of medical implants, and human-machine interfaces to support assistive technologies. This review proposes how physics-based simulation, combined with machine learning approaches from big data, can be used to develop high-fidelity personalized representations of the human neuromusculoskeletal system. The core neuromusculoskeletal model features requiring personalization are identified, and big data/machine learning approaches for implementation are presented together with recommendations for further research.

Toward modeling locomotion using electromyography-informed 3D models: application to cerebral palsy.

This position paper proposes a modeling pipeline to develop clinically relevant neuromusculoskeletal models to understand and treat complex neurological disorders. Although applicable to a variety of neurological conditions, we provide direct pipeline applicative examples in the context of cerebral palsy (CP). This paper highlights technologies in: (1) patient-specific segmental rigid body models developed from magnetic resonance imaging for use in inverse kinematics and inverse dynamics pipelines; (2) efficient population-based approaches to derive skeletal models and muscle origins/insertions that are useful for population statistics and consistent creation of continuum models; (3) continuum muscle descriptions to account for complex muscle architecture including spatially varying material properties with muscle wrapping; (4) muscle and tendon properties specific to CP; and (5) neural-based electromyography-informed methods for muscle force prediction. This represents a novel modeling pipeline that couples for the first time electromyography extracted features of disrupted neuromuscular behavior with advanced numerical methods for modeling CP-specific musculoskeletal morphology and function. The translation of such pipeline to the clinical level will provide a new class of biomarkers that objectively describe the neuromusculoskeletal determinants of pathological locomotion and complement current clinical assessment techniques, which often rely on subjective judgment. WIREs Syst Biol Med 2017, 9:e1368. doi: 10.1002/wsbm.1368 For further resources related to this article, please visit the WIREs website.

Anticipatory effects on knee joint loading during running and cutting maneuvers.

PURPOSE: To determine how unanticipated performance of cutting maneuvers in sport affects the external loads applied to the knee joint and the potential risk for ligament injury. METHODS: A 50-Hz VICON motion analysis system was used to determine the lower limb kinematics of 11 healthy male subjects during running and cutting tasks performed under preplanned (PP) and unanticipated (UN) conditions. Subjects performed the UN tasks in response to a light stimulus on a target board. A kinematic model was then used in conjunction with force place data to calculate the three-dimensional loads at the knee joint. RESULTS: External flexion/extension moments at the knee joint were similar between PP and UN conditions; however, the varus/valgus and internal/external rotation moments during the UN cutting tasks were up to twice the magnitude of the moments measured during the PP condition. CONCLUSION: Cutting maneuvers performed without adequate planning may increase the risk of noncontact knee ligament injury due to the increased external varus/valgus and internal/external rotation moments applied to the knee. These results are probably due to the small amount of time to make appropriate postural adjustments before performance of the task, such as the position of the foot on the ground relative to the body center of mass. Subsequently, training for the game situation should involve drills that familiarize players with making unanticipated changes of direction. Practice sessions should also incorporate plyometrics and should focus on better interpretation of visual cues to increase the time available to preplan a movement.

External loading of the knee joint during running and cutting maneuvers.

PURPOSE: To investigate the external loads applied to the knee joint during dynamic cutting tasks and assess the potential for ligament loading. METHODS: A 50-Hz VICON motion analysis system was used to determine the lower limb kinematics of 11 healthy male subjects during running, sidestepping, and crossover cut. A kinematic model was used in conjunction with force place data to calculate the three-dimensional loads at the knee joint during stance phase. RESULTS: External flexion/extension loads at the knee joint were similar across tasks; however, the varus/valgus and internal/external rotation moments applied to the knee during sidestepping and crossover cutting were considerably larger than those measured during normal running (P < 0.05). Sidestepping tasks elicited combined loads of flexion, valgus, and internal rotation, whereas crossover cutting tasks elicited combined loads of flexion, varus, and external rotation. CONCLUSION: Compared with running, the potential for increased ligament loading during sidestepping and crossover cutting maneuvers is a result of the large increase in varus/valgus and internal/external rotation moments rather than any change in the external flexion moment. The combined external moments applied to the knee joint during stance phase of the cutting tasks are believed to place the ACL and collateral ligaments at risk of injury, particularly at knee flexion angles between 0 degrees and 40 degrees, if appropriate muscle activation strategies are not used to counter these moments.

Muscle and external load contribution to knee joint contact loads during normal gait.

Large knee adduction moments during gait have been implicated as a mechanical factor related to the progression and severity of tibiofemoral osteoarthritis and it has been proposed that these moments increase the load on the medial compartment of the knee joint. However, this mechanism cannot be validated without taking into account the internal forces and moments generated by the muscles and ligaments, which cannot be easily measured. Previous musculoskeletal models suggest that the medial compartment of the tibiofemoral joint bears the majority of the tibiofemoral load, with the lateral compartment unloaded at times during stance. Yet these models did not utilise explicitly measured muscle activation patterns and measurements from an instrumented prosthesis which do not portray lateral compartment unloading. This paper utilised an EMG-driven model to estimate muscle forces and knee joint contact forces during healthy gait. Results indicate that while the medial compartment does bear the majority of the load during stance, muscles provide sufficient stability to counter the tendency of the external adduction moment to unload the lateral compartment. This stability was predominantly provided by the quadriceps, hamstrings, and gastrocnemii muscles, although the contribution from the tensor fascia latae was also significant. Lateral compartment unloading was not predicted by the EMG-driven model, suggesting that muscle activity patterns provide useful input to estimate muscle and joint contact forces.

Is cartilage thickness different in young subjects with and without patellofemoral pain?

OBJECTIVE: To determine the differences in load-bearing patellofemoral joint cartilage thickness between genders. To determine the differences in load-bearing cartilage thickness between pain-free controls and individuals with patellofemoral pain. METHODS: The articular cartilage thickness of the patella and anterior femur was estimated from magnetic resonance images in 16 young, pain-free control subjects (eight males, eight females) and 34 young individuals with patellofemoral pain (12 males, 22 females). The average age of all subjects was 28+/-4 years. The cartilage surfaces were divided into regions approximating the location of patellofemoral joint contact during knee flexion. The mean and peak cartilage thicknesses of each region were computed and compared using a repeated-measures Analysis of Variance. RESULTS: On average, males had 22% and 23% thicker cartilage than females in the patella (P < 0.01) and femur (P < 0.05), respectively. Male control subjects had 18% greater peak patellar cartilage thickness than males with patellofemoral pain (P < 0.05); however, we did not detect differences in patellar cartilage thickness between female control subjects and females with patellofemoral pain (P = 0.45). We detected no significant differences in femoral cartilage thickness between the control and pain groups. CONCLUSIONS: Thin cartilage at the patella may be one mechanism of patellofemoral pain in male subjects, but is unlikely to be a dominant factor in the development of pain in the female population.